human/types/dist/tfjs.esm.d.ts

declare var Vs: string;
declare var Ni: string;
declare var Ti: string;
declare var xp: {
    new (e: any, t: any, n?: null): {
        learningRate: any;
        rho: any;
        epsilon: any;
        accumulatedGrads: any[];
        accumulatedUpdates: any[];
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            rho: any;
            epsilon: any;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
    className: string;
};
declare var yp: {
    new (e: any, t?: number): {
        learningRate: any;
        initialAccumulatorValue: number;
        accumulatedGrads: any[];
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            initialAccumulatorValue: number;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
    className: string;
};
declare var bp: {
    new (e: any, t: any, n: any, o?: null): {
        learningRate: any;
        beta1: any;
        beta2: any;
        epsilon: any;
        accumulatedFirstMoment: any[];
        accumulatedSecondMoment: any[];
        accBeta1: any;
        accBeta2: any;
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            beta1: any;
            beta2: any;
            epsilon: any;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
    className: string;
};
declare var wp: {
    new (e: any, t: any, n: any, o?: null, s?: number): {
        learningRate: any;
        beta1: any;
        beta2: any;
        epsilon: any;
        decay: number;
        accumulatedFirstMoment: any[];
        accumulatedWeightedInfNorm: any[];
        iteration: any;
        accBeta1: any;
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<void>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            beta1: any;
            beta2: any;
            epsilon: any;
            decay: number;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
    className: string;
};
declare var Wn: string;
declare var So: string;
declare var Ei: string;
declare var Ai: string;
declare var Io: string;
declare var Xa: string;
declare var $i: string;
declare var Di: string;
declare var Ri: string;
declare var Oi: string;
declare var Fi: string;
declare var No: string;
declare var Ya: string;
declare var xc: string;
declare var gc: string;
declare var My: {
    new (e: any): {
        wasm: any;
        dataIdNextNumber: number;
        dataIdMap: {
            backend: any;
            dataMover: any;
            data: WeakMap<object, any>;
            dataIdsCount: number;
            get(e: any): any;
            set(e: any, t: any): void;
            has(e: any): boolean;
            delete(e: any): boolean;
            numDataIds(): number;
        };
        write(e: any, t: any, n: any): {
            id: number;
        };
        numDataIds(): number;
        time(e: any): Promise<{
            kernelMs: number;
        }>;
        move(e: any, t: any, n: any, o: any, s: any): void;
        read(e: any): Promise<any>;
        readSync(e: any): any;
        disposeData(e: any, t?: boolean): boolean;
        refCount(e: any): any;
        incRef(e: any): void;
        floatPrecision(): number;
        getMemoryOffset(e: any): any;
        dispose(): void;
        memory(): {
            unreliable: boolean;
        };
        makeOutput(e: any, t: any, n: any): {
            dataId: {
                id: number;
            };
            shape: any;
            dtype: any;
        };
        typedArrayFromHeap({ shape: e, dtype: t, dataId: n }: {
            shape: any;
            dtype: any;
            dataId: any;
        }): Float32Array | Int32Array | Uint8Array;
        timerAvailable(): boolean;
        epsilon(): number;
    };
};
declare var To: string;
declare var Ws: string;
declare var yc: string;
declare var Zh: string;
declare var XI: string;
declare var mv: {
    new (...args: any[]): {
        model: {
            [x: string]: any;
            isTraining: boolean;
            summary(e: any, t: any, n?: {
                (...data: any[]): void;
                (...data: any[]): void;
                (message?: any, ...optionalParams: any[]): void;
            }): void;
            compile(e: any): void;
            loss: any;
            optimizer_: any;
            isOptimizerOwned: boolean | undefined;
            lossFunctions: any;
            feedOutputNames: any[] | undefined;
            feedOutputShapes: any[] | undefined;
            feedLossFns: any[] | undefined;
            metrics: any;
            metricsNames: string[] | undefined;
            metricsTensors: any[] | undefined;
            collectedTrainableWeights: any;
            checkTrainableWeightsConsistency(): void;
            evaluate(e: any, t: any, n?: {}): any;
            evaluateDataset(e: any, t: any): Promise<any>;
            checkNumSamples(e: any, t: any, n: any, o?: string): any;
            execute(e: any, t: any): any;
            retrieveSymbolicTensors(e: any): any[];
            predictLoop(e: any, t?: number, n?: boolean): any;
            predict(e: any, t?: {}): any;
            predictOnBatch(e: any): any;
            standardizeUserDataXY(e: any, t: any, n: boolean | undefined, o: any): any[];
            standardizeUserData(e: any, t: any, n: any, o: any, s: boolean | undefined, a: any): Promise<any[]>;
            testLoop(e: any, t: any, n: any, o: number | undefined, s: any): any;
            getDedupedMetricsNames(): string[];
            makeTrainFunction(): (e: any) => any[];
            makeTestFunction(): void;
            testFunction: ((e: any) => any) | undefined;
            fit(e: any, t: any, n?: {}): Promise<any>;
            fitDataset(e: any, t: any): Promise<any>;
            trainOnBatch(e: any, t: any): Promise<any>;
            getNamedWeights(e: any): {
                name: any;
                tensor: any;
            }[];
            stopTraining: any;
            stopTraining_: any;
            optimizer: any;
            dispose(): any;
            getLossIdentifiers(): any;
            getMetricIdentifiers(): {};
            getTrainingConfig(): {
                loss: any;
                metrics: {};
                optimizer_config: {
                    class_name: any;
                    config: any;
                };
            };
            loadTrainingConfig(e: any): void;
            save(e: any, t: any): Promise<any>;
            setUserDefinedMetadata(e: any): void;
            userDefinedMetadata: any;
            getUserDefinedMetadata(): any;
        } | null;
        setModel(e: any): void;
        validationData: any;
        setParams(e: any): void;
        params: any;
        onEpochBegin(e: any, t: any): Promise<void>;
        onEpochEnd(e: any, t: any): Promise<void>;
        onBatchBegin(e: any, t: any): Promise<void>;
        onBatchEnd(e: any, t: any): Promise<void>;
        onTrainBegin(e: any): Promise<void>;
        onTrainEnd(e: any): Promise<void>;
    };
};
declare var C_: {
    new (e: any, t?: number): {
        callbacks: any;
        queueLength: number;
        append(e: any): void;
        setParams(e: any): void;
        setModel(e: any): void;
        onEpochBegin(e: any, t: any): Promise<void>;
        onEpochEnd(e: any, t: any): Promise<void>;
        onBatchBegin(e: any, t: any): Promise<void>;
        onBatchEnd(e: any, t: any): Promise<void>;
        onTrainBegin(e: any): Promise<void>;
        onTrainEnd(e: any): Promise<void>;
    };
};
declare var Qn: string;
declare var Eo: string;
declare var eo: string;
declare var bc: string;
declare var Za: string;
declare var js: string;
declare var Ao: string;
declare var wc: string;
declare var $o: string;
declare var Ja: string;
declare var kc: string;
declare var _c: string;
declare var Do: string;
declare var Ro: string;
declare var Pi: string;
declare var Fo: string;
declare var I_: {
    new (e: any, t: any): {
        currentEpoch: number;
        yieldEvery: any;
        maybeWait(e: any, t: any, n: any): Promise<void>;
        trainBegin: any;
        trainEnd: any;
        epochBegin: any;
        epochEnd: any;
        batchBegin: any;
        batchEnd: any;
        yield: any;
        onEpochBegin(e: any, t: any): Promise<void>;
        onEpochEnd(e: any, t: any): Promise<void>;
        onBatchBegin(e: any, t: any): Promise<void>;
        onBatchEnd(e: any, t: any): Promise<void>;
        onTrainBegin(e: any): Promise<void>;
        onTrainEnd(e: any): Promise<void>;
        validationData: any;
        setParams(e: any): void;
        params: any;
        setModel(e: any): void;
    };
};
declare var Ka: {
    new (e: any, t: any): {
        backend: any;
        dataMover: any;
        data: WeakMap<object, any>;
        dataIdsCount: number;
        get(e: any): any;
        set(e: any, t: any): void;
        has(e: any): boolean;
        delete(e: any): boolean;
        numDataIds(): number;
    };
};
declare var vc: string;
declare var Mi: string;
declare var Oo: string;
declare var Cc: string;
declare var Sc: string;
declare var Ic: string;
declare var Qa: string;
declare var Dm: string;
declare var $m: string;
declare var gw: any;
declare var fv: {
    new (e: any): {
        monitor: any;
        minDelta: number;
        patience: any;
        verbose: any;
        mode: any;
        baseline: any;
        monitorFunc: typeof fx;
        onTrainBegin(e: any): Promise<void>;
        wait: number | undefined;
        stoppedEpoch: any;
        best: any;
        onEpochEnd(e: any, t: any): Promise<void>;
        onTrainEnd(e: any): Promise<void>;
        getMonitorValue(e: any): any;
        model: {
            [x: string]: any;
            isTraining: boolean;
            summary(e: any, t: any, n?: {
                (...data: any[]): void;
                (...data: any[]): void;
                (message?: any, ...optionalParams: any[]): void;
            }): void;
            compile(e: any): void;
            loss: any;
            optimizer_: any;
            isOptimizerOwned: boolean | undefined;
            lossFunctions: any;
            feedOutputNames: any[] | undefined;
            feedOutputShapes: any[] | undefined;
            feedLossFns: any[] | undefined;
            metrics: any;
            metricsNames: string[] | undefined;
            metricsTensors: any[] | undefined;
            collectedTrainableWeights: any;
            checkTrainableWeightsConsistency(): void;
            evaluate(e: any, t: any, n?: {}): any;
            evaluateDataset(e: any, t: any): Promise<any>;
            checkNumSamples(e: any, t: any, n: any, o?: string): any;
            execute(e: any, t: any): any;
            retrieveSymbolicTensors(e: any): any[];
            predictLoop(e: any, t?: number, n?: boolean): any;
            predict(e: any, t?: {}): any;
            predictOnBatch(e: any): any;
            standardizeUserDataXY(e: any, t: any, n: boolean | undefined, o: any): any[];
            standardizeUserData(e: any, t: any, n: any, o: any, s: boolean | undefined, a: any): Promise<any[]>;
            testLoop(e: any, t: any, n: any, o: number | undefined, s: any): any;
            getDedupedMetricsNames(): string[];
            makeTrainFunction(): (e: any) => any[];
            makeTestFunction(): void;
            testFunction: ((e: any) => any) | undefined;
            fit(e: any, t: any, n?: {}): Promise<any>;
            fitDataset(e: any, t: any): Promise<any>;
            trainOnBatch(e: any, t: any): Promise<any>;
            getNamedWeights(e: any): {
                name: any;
                tensor: any;
            }[];
            stopTraining: any;
            stopTraining_: any;
            optimizer: any;
            dispose(): any;
            getLossIdentifiers(): any;
            getMetricIdentifiers(): {};
            getTrainingConfig(): {
                loss: any;
                metrics: {};
                optimizer_config: {
                    class_name: any;
                    config: any;
                };
            };
            loadTrainingConfig(e: any): void;
            save(e: any, t: any): Promise<any>;
            setUserDefinedMetadata(e: any): void;
            userDefinedMetadata: any;
            getUserDefinedMetadata(): any;
        } | null;
        setModel(e: any): void;
        validationData: any;
        setParams(e: any): void;
        params: any;
        onEpochBegin(e: any, t: any): Promise<void>;
        onBatchBegin(e: any, t: any): Promise<void>;
        onBatchEnd(e: any, t: any): Promise<void>;
    };
};
declare var Nc: string;
declare var Mo: string;
declare var Tc: string;
declare var Yh: {
    new (e: any): {
        global: any;
        flags: {};
        flagRegistry: {};
        urlFlags: {};
        getQueryParams: typeof kj;
        setPlatform(e: any, t: any): void;
        platformName: any;
        platform: any;
        registerFlag(e: any, t: any, n: any): void;
        getAsync(e: any): Promise<any>;
        get(e: any): any;
        getNumber(e: any): any;
        getBool(e: any): any;
        getFlags(): {};
        readonly features: {};
        set(e: any, t: any): void;
        evaluateFlag(e: any): any;
        setFlags(e: any): void;
        reset(): void;
        populateURLFlags(): void;
    };
};
declare var zi: string;
declare var Li: string;
declare var Lo: string;
declare var Gs: string;
declare var Bi: string;
declare var Ec: string;
declare var el: string;
declare var Vi: string;
declare var zo: string;
declare var Bo: string;
declare var Rm: string;
declare var Vo: string;
declare var ni: string;
declare var oi: string;
declare var hy: {
    new (e: any): {
        outputTexture: any;
        program: any;
        disposed: boolean;
        vertexAttrsAreBound: boolean;
        itemsToPoll: any[];
        gl: any;
        textureFloatExtension: any;
        textureHalfFloatExtension: any;
        colorBufferFloatExtension: any;
        colorBufferHalfFloatExtension: any;
        vertexBuffer: any;
        indexBuffer: any;
        framebuffer: any;
        textureConfig: {
            internalFormatFloat: any;
            internalFormatHalfFloat: any;
            internalFormatPackedHalfFloat: any;
            internalFormatPackedFloat: any;
            textureFormatFloat: any;
            downloadTextureFormat: any;
            downloadUnpackNumChannels: number;
            defaultNumChannels: number;
            textureTypeHalfFloat: any;
            textureTypeFloat: any;
        };
        readonly debug: any;
        dispose(): void;
        createFloat32MatrixTexture(e: any, t: any): any;
        createFloat16MatrixTexture(e: any, t: any): any;
        createUnsignedBytesMatrixTexture(e: any, t: any): any;
        uploadPixelDataToTexture(e: any, t: any): void;
        uploadDenseMatrixToTexture(e: any, t: any, n: any, o: any): void;
        createFloat16PackedMatrixTexture(e: any, t: any): any;
        createPackedMatrixTexture(e: any, t: any): any;
        deleteMatrixTexture(e: any): void;
        downloadByteEncodedFloatMatrixFromOutputTexture(e: any, t: any, n: any): any;
        downloadPackedMatrixFromBuffer(e: any, t: any, n: any, o: any, s: any, a: any): Float32Array;
        downloadFloat32MatrixFromBuffer(e: any, t: any): Float32Array;
        createBufferFromTexture(e: any, t: any, n: any): any;
        createAndWaitForFence(): Promise<any>;
        createFence(e: any): {
            query: any;
            isFencePassed: () => any;
        };
        downloadMatrixFromPackedTexture(e: any, t: any, n: any): any;
        createProgram(e: any): any;
        vertexShader: any;
        deleteProgram(e: any): void;
        setProgram(e: any): void;
        getUniformLocation(e: any, t: any, n?: boolean): any;
        getAttributeLocation(e: any, t: any): any;
        getUniformLocationNoThrow(e: any, t: any): any;
        setInputMatrixTexture(e: any, t: any, n: any): void;
        setOutputMatrixTexture(e: any, t: any, n: any): void;
        setOutputPackedMatrixTexture(e: any, t: any, n: any): void;
        setOutputMatrixWriteRegion(e: any, t: any, n: any, o: any): void;
        setOutputPackedMatrixWriteRegion(e: any, t: any, n: any, o: any): void;
        debugValidate(): void;
        executeProgram(): void;
        blockUntilAllProgramsCompleted(): void;
        getQueryTimerExtension(): any;
        disjointQueryTimerExtension: any;
        getQueryTimerExtensionWebGL2(): any;
        getQueryTimerExtensionWebGL1(): any;
        beginQuery(): any;
        endQuery(): void;
        waitForQueryAndGetTime(e: any): Promise<number | null>;
        getQueryTime(e: any, t: any): number | null;
        isQueryAvailable(e: any, t: any): any;
        disjoint: any;
        pollFence(e: any): Promise<any>;
        pollItems(): void;
        addItemToPoll(e: any, t: any): void;
        bindTextureToFrameBuffer(e: any): void;
        unbindTextureToFrameBuffer(): void;
        downloadMatrixDriver(e: any, t: any): any;
        setOutputMatrixTextureDriver(e: any, t: any, n: any): void;
        setOutputMatrixWriteRegionDriver(e: any, t: any, n: any, o: any): void;
        throwIfDisposed(): void;
        throwIfNoProgram(): void;
    };
};
declare var Wi: string;
declare var Us: string;
declare var jv: {
    new (e: any, t?: {}): {
        modelUrl: any;
        loadOptions: {};
        version: string;
        resourceManager: {
            hashTableNameToHandle: {};
            hashTableMap: {};
            addHashTable(e: any, t: any): void;
            getHashTableHandleByName(e: any): any;
            getHashTableById(e: any): any;
            dispose(): void;
        };
        readonly modelVersion: string;
        readonly inputNodes: any;
        readonly outputNodes: any;
        readonly inputs: any;
        readonly outputs: any;
        readonly weights: any;
        readonly metadata: any;
        readonly modelSignature: any;
        findIOHandler(): void;
        handler: any;
        load(): Promise<boolean>;
        loadSync(e: any): boolean;
        artifacts: any;
        signature: any;
        executor: {
            graph: any;
            parent: any;
            compiledMap: Map<any, any>;
            _weightMap: {};
            SEPERATOR: string;
            _functions: any;
            _functionExecutorMap: {};
            _outputs: any;
            _inputs: any;
            _initNodes: any;
            _signature: any;
            readonly weightIds: any;
            readonly functionExecutorMap: any;
            weightMap: any;
            _weightIds: any[] | undefined;
            resourceManager: any;
            _resourceManager: any;
            readonly inputs: any;
            readonly outputs: any;
            readonly inputNodes: any;
            readonly outputNodes: any;
            readonly functions: {};
            getCompilationKey(e: any, t: any): string;
            compile(e: any, t: any): any[];
            execute(e: any, t: any): any;
            getFrozenTensorIds(e: any): Set<any>;
            checkTensorForDisposal(e: any, t: any, n: any, o: any, s: any, a: any, i: any): void;
            executeAsync(e: any, t: any): Promise<any>;
            _executeAsync(e: any, t: any, n?: boolean, o?: {}, s?: {}): Promise<any>;
            executeFunctionAsync(e: any, t: any, n: any): Promise<any>;
            executeWithControlFlow(e: any, t: any, n: any, o: any): Promise<any>;
            processStack(e: any, t: any, n: any, o: any, s: any, a: any, i: any, l: any, u: any): any[];
            processChildNodes(e: any, t: any, n: any, o: any, s: any, a: any): void;
            dispose(): void;
            checkInputShapeAndType(e: any): void;
            mapInputs(e: any): {};
            checkInputs(e: any): void;
            mapOutputs(e: any): any;
            checkOutputs(e: any): void;
        } | undefined;
        initializer: {
            graph: any;
            parent: any;
            compiledMap: Map<any, any>;
            _weightMap: {};
            SEPERATOR: string;
            _functions: any;
            _functionExecutorMap: {};
            _outputs: any;
            _inputs: any;
            _initNodes: any;
            _signature: any;
            readonly weightIds: any;
            readonly functionExecutorMap: any;
            weightMap: any;
            _weightIds: any[] | undefined;
            resourceManager: any;
            _resourceManager: any;
            readonly inputs: any;
            readonly outputs: any;
            readonly inputNodes: any;
            readonly outputNodes: any;
            readonly functions: {};
            getCompilationKey(e: any, t: any): string;
            compile(e: any, t: any): any[];
            execute(e: any, t: any): any;
            getFrozenTensorIds(e: any): Set<any>;
            checkTensorForDisposal(e: any, t: any, n: any, o: any, s: any, a: any, i: any): void;
            executeAsync(e: any, t: any): Promise<any>;
            _executeAsync(e: any, t: any, n?: boolean, o?: {}, s?: {}): Promise<any>;
            executeFunctionAsync(e: any, t: any, n: any): Promise<any>;
            executeWithControlFlow(e: any, t: any, n: any, o: any): Promise<any>;
            processStack(e: any, t: any, n: any, o: any, s: any, a: any, i: any, l: any, u: any): any[];
            processChildNodes(e: any, t: any, n: any, o: any, s: any, a: any): void;
            dispose(): void;
            checkInputShapeAndType(e: any): void;
            mapInputs(e: any): {};
            checkInputs(e: any): void;
            mapOutputs(e: any): any;
            checkOutputs(e: any): void;
        } | undefined;
        save(e: any, t: any): Promise<any>;
        predict(e: any, t: any): any;
        normalizeInputs(e: any): any;
        normalizeOutputs(e: any): any[];
        execute(e: any, t: any): any;
        executeAsync(e: any, t: any): Promise<any>;
        convertTensorMapToTensorsMap(e: any): {};
        dispose(): void;
    };
};
declare var ji: string;
declare var Wo: string;
declare var S_: {
    new (): {
        onTrainBegin(e: any): Promise<void>;
        epoch: any[] | undefined;
        history: {} | undefined;
        onEpochEnd(e: any, t: any): Promise<void>;
        syncData(): Promise<void>;
        validationData: any;
        setParams(e: any): void;
        params: any;
        onEpochBegin(e: any, t: any): Promise<void>;
        onBatchBegin(e: any, t: any): Promise<void>;
        onBatchEnd(e: any, t: any): Promise<void>;
        onTrainEnd(e: any): Promise<void>;
        setModel(e: any): void;
    };
};
declare var Ac: string;
declare var to: string;
declare var $c: string;
declare var _t: {
    new (e: any): {
        dtype: any;
        shape: any;
        ndim: any;
        maxNDim: any;
        minNDim: any;
        axes: any;
    };
};
declare var Gi: string;
declare var Ui: string;
declare var Hi: string;
declare var Ls: {
    new (): {
        refCount(e: any): void;
        incRef(e: any): void;
        timerAvailable(): boolean;
        time(e: any): void;
        read(e: any): void;
        readSync(e: any): void;
        numDataIds(): void;
        disposeData(e: any, t: any): void;
        write(e: any, t: any, n: any): void;
        move(e: any, t: any, n: any, o: any, s: any): void;
        memory(): void;
        floatPrecision(): void;
        epsilon(): number;
        dispose(): void;
    };
};
declare var tl: string;
declare var Rc: string;
declare var Yg: {
    new (e: any, t?: string, n?: string, o?: boolean, s?: null): {
        dtype: string;
        shape: any;
        id: number;
        originalName: string;
        name: any;
        trainable_: boolean;
        constraint: any;
        val: any;
        read(): any;
        write(e: any): any;
        dispose(): void;
        assertNotDisposed(): void;
        trainable: boolean;
    };
};
declare var Xn: {
    new (e: any): {
        [x: string]: any;
        isTraining: boolean;
        summary(e: any, t: any, n?: {
            (...data: any[]): void;
            (...data: any[]): void;
            (message?: any, ...optionalParams: any[]): void;
        }): void;
        compile(e: any): void;
        loss: any;
        optimizer_: any;
        isOptimizerOwned: boolean | undefined;
        lossFunctions: any;
        feedOutputNames: any[] | undefined;
        feedOutputShapes: any[] | undefined;
        feedLossFns: any[] | undefined;
        metrics: any;
        metricsNames: string[] | undefined;
        metricsTensors: any[] | undefined;
        collectedTrainableWeights: any;
        checkTrainableWeightsConsistency(): void;
        evaluate(e: any, t: any, n?: {}): any;
        evaluateDataset(e: any, t: any): Promise<any>;
        checkNumSamples(e: any, t: any, n: any, o?: string): any;
        execute(e: any, t: any): any;
        retrieveSymbolicTensors(e: any): any[];
        predictLoop(e: any, t?: number, n?: boolean): any;
        predict(e: any, t?: {}): any;
        predictOnBatch(e: any): any;
        standardizeUserDataXY(e: any, t: any, n: boolean | undefined, o: any): any[];
        standardizeUserData(e: any, t: any, n: any, o: any, s: boolean | undefined, a: any): Promise<any[]>;
        testLoop(e: any, t: any, n: any, o: number | undefined, s: any): any;
        getDedupedMetricsNames(): string[];
        makeTrainFunction(): (e: any) => any[];
        makeTestFunction(): void;
        testFunction: ((e: any) => any) | undefined;
        fit(e: any, t: any, n?: {}): Promise<any>;
        fitDataset(e: any, t: any): Promise<any>;
        trainOnBatch(e: any, t: any): Promise<any>;
        getNamedWeights(e: any): {
            name: any;
            tensor: any;
        }[];
        stopTraining: any;
        stopTraining_: any;
        optimizer: any;
        dispose(): any;
        getLossIdentifiers(): any;
        getMetricIdentifiers(): {};
        getTrainingConfig(): {
            loss: any;
            metrics: {};
            optimizer_config: {
                class_name: any;
                config: any;
            };
        };
        loadTrainingConfig(e: any): void;
        save(e: any, t: any): Promise<any>;
        setUserDefinedMetadata(e: any): void;
        userDefinedMetadata: any;
        getUserDefinedMetadata(): any;
    };
    [x: string]: any;
    className: string;
};
declare var jo: string;
declare var qi: string;
declare var Ki: string;
declare var Dc: string;
declare var Go: string;
declare var Xi: string;
declare var YI: string;
declare var Yi: string;
declare var jl: string;
declare var Gl: string;
declare var Pu: {
    new (): {
        blockSize: number;
        firstUse: boolean;
        data: {
            backend: any;
            dataMover: any;
            data: WeakMap<object, any>;
            dataIdsCount: number;
            get(e: any): any;
            set(e: any, t: any): void;
            has(e: any): boolean;
            delete(e: any): boolean;
            numDataIds(): number;
        };
        nextDataId(): number;
        write(e: any, t: any, n: any): {
            id: number;
        };
        makeTensorInfo(e: any, t: any, n: any): {
            dataId: {
                id: number;
            };
            shape: any;
            dtype: any;
        };
        refCount(e: any): any;
        incRef(e: any): void;
        decRef(e: any): void;
        move(e: any, t: any, n: any, o: any, s: any): void;
        numDataIds(): number;
        read(e: any): Promise<any>;
        readSync(e: any): any;
        bufferSync(e: any): {
            dtype: any;
            shape: any;
            size: any;
            values: any;
            strides: any[];
            set(e: any, ...t: any[]): void;
            get(...e: any[]): any;
            locToIndex(e: any): any;
            indexToLoc(e: any): any[];
            readonly rank: any;
            toTensor(): any;
        };
        makeOutput(e: any, t: any, n: any): any;
        disposeData(e: any, t?: boolean): boolean;
        disposeIntermediateTensorInfo(e: any): void;
        time(e: any): Promise<{
            kernelMs: number;
        }>;
        memory(): {
            unreliable: boolean;
            reasons: string[];
        };
        where(e: any): any;
        dispose(): void;
        floatPrecision(): number;
        epsilon(): number;
        timerAvailable(): boolean;
    };
    nextDataId: number;
};
declare var Hu: {
    new (e: any): {
        pendingRead: WeakMap<object, any>;
        pendingDisposal: WeakSet<object>;
        dataRefCount: WeakMap<object, any>;
        numBytesInGPU: number;
        uploadWaitMs: number;
        downloadWaitMs: number;
        lastGlFlushTime: number;
        warnedAboutMemory: boolean;
        pendingDeletes: number;
        disposed: boolean;
        binaryCache: any;
        gpgpu: any;
        canvas: any;
        gpgpuCreatedLocally: boolean;
        textureManager: {
            gpgpu: any;
            numUsedTextures: number;
            numFreeTextures: number;
            _numBytesAllocated: number;
            _numBytesFree: number;
            freeTextures: {};
            logEnabled: boolean;
            usedTextures: {};
            acquireTexture(e: any, t: any, n: any): any;
            releaseTexture(e: any, t: any, n: any, o: any): void;
            log(): void;
            readonly numBytesAllocated: number;
            readonly numBytesFree: number;
            getNumUsedTextures(): number;
            getNumFreeTextures(): number;
            dispose(): void;
        };
        numMBBeforeWarning: number;
        texData: {
            backend: any;
            dataMover: any;
            data: WeakMap<object, any>;
            dataIdsCount: number;
            get(e: any): any;
            set(e: any, t: any): void;
            has(e: any): boolean;
            delete(e: any): boolean;
            numDataIds(): number;
        };
        nextDataId(): number;
        numDataIds(): number;
        write(e: any, t: any, n: any): {
            id: number;
        };
        refCount(e: any): any;
        incRef(e: any): void;
        decRef(e: any): void;
        move(e: any, t: any, n: any, o: any, s: any): void;
        disposeIntermediateTensorInfo(e: any): void;
        readSync(e: any): any;
        read(e: any): any;
        bufferSync(e: any): {
            dtype: any;
            shape: any;
            size: any;
            values: any;
            strides: any[];
            set(e: any, ...t: any[]): void;
            get(...e: any[]): any;
            locToIndex(e: any): any;
            indexToLoc(e: any): any[];
            readonly rank: any;
            toTensor(): any;
        };
        checkNumericalProblems(e: any): void;
        getValuesFromTexture(e: any): any;
        timerAvailable(): boolean;
        time(e: any): Promise<{
            uploadWaitMs: number;
            downloadWaitMs: number;
            kernelMs: null;
            wallMs: null;
        }>;
        programTimersStack: any[] | null | undefined;
        activeTimers: any;
        memory(): {
            unreliable: boolean;
            numBytesInGPU: number;
            numBytesInGPUAllocated: number;
            numBytesInGPUFree: number;
        };
        startTimer(): any;
        endTimer(e: any): any;
        getQueryTime(e: any): Promise<any>;
        disposeData(e: any, t?: boolean): any;
        releaseGPUData(e: any): void;
        getTexture(e: any): any;
        getDataInfo(e: any): any;
        shouldExecuteOnCPU(e: any, t?: any): any;
        getGPGPUContext(): any;
        where(e: any): any;
        packedUnaryOp(e: any, t: any, n: any): any;
        abs(e: any): any;
        makeTensorInfo(e: any, t: any, n: any): {
            dataId: {
                id: number;
            };
            shape: any;
            dtype: any;
        };
        makeOutput(e: any, t: any, n: any): any;
        unpackTensor(e: any): any;
        packTensor(e: any): any;
        packedReshape(e: any, t: any): {
            dataId: any;
            shape: any;
            dtype: any;
        };
        decode(e: any): {
            dtype: any;
            shape: any;
            dataId: any;
        };
        runWebGLProgram(e: any, t: any, n: any, o: any, s?: boolean): any;
        compileAndRun(e: any, t: any, n: any, o: any, s?: boolean): any;
        getAndSaveBinary(e: any, t: any): any;
        getTextureManager(): {
            gpgpu: any;
            numUsedTextures: number;
            numFreeTextures: number;
            _numBytesAllocated: number;
            _numBytesFree: number;
            freeTextures: {};
            logEnabled: boolean;
            usedTextures: {};
            acquireTexture(e: any, t: any, n: any): any;
            releaseTexture(e: any, t: any, n: any, o: any): void;
            log(): void;
            readonly numBytesAllocated: number;
            readonly numBytesFree: number;
            getNumUsedTextures(): number;
            getNumFreeTextures(): number;
            dispose(): void;
        };
        dispose(): void;
        floatPrecision(): any;
        floatPrecisionValue: any;
        epsilon(): number;
        uploadToGPU(e: any): void;
        convertAndCacheOnCPU(e: any, t: any): any;
        acquireTexture(e: any, t: any, n: any, o: any): any;
        computeBytes(e: any, t: any): number;
    };
    nextDataId: number;
};
declare var Uo: string;
declare var qo: string;
declare var rl: string;
declare var Oc: string;
declare var Fc: string;
declare var Pc: string;
declare var Ho: string;
declare var Ko: string;
declare var Xo: string;
declare var Yo: string;
declare var Zo: string;
declare var Zi: string;
declare var kp: {
    new (e: any, t: any, n?: boolean): {
        learningRate: any;
        momentum: any;
        useNesterov: boolean;
        accumulations: any[];
        m: any;
        applyGradients(e: any): void;
        dispose(): void;
        setMomentum(e: any): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            momentum: any;
            useNesterov: boolean;
        };
        setLearningRate(e: any): void;
        c: any;
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
    className: string;
};
declare var Mc: string;
declare var Jo: string;
declare var Hs: string;
declare var Qi: string;
declare var ea: string;
declare var ta: string;
declare var Ji: string;
declare var S1: string;
declare var Qo: string;
declare var qs: string;
declare var Wr: {
    new (): {
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        dispose(): void;
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        getWeights(): Promise<void>;
        setWeights(e: any): Promise<void>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
};
declare var Ks: string;
declare var es: string;
declare var fse: string;
declare var ts: string;
declare var rs: string;
declare var ra: string;
declare var _p: {
    new (e: any, t?: number, n?: number, o?: null, s?: boolean): {
        learningRate: any;
        decay: number;
        momentum: number;
        epsilon: any;
        accumulatedMeanSquares: any[];
        accumulatedMoments: any[];
        accumulatedMeanGrads: any[];
        centered: boolean;
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            decay: number;
            momentum: number;
            epsilon: any;
            centered: boolean;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
    className: string;
};
declare var On: any;
declare var nl: string;
declare var Iw: any;
declare var Lc: string;
declare var Po: string;
declare var na: string;
declare var qt: any;
declare var ns: string;
declare var ss: string;
declare var Xs: string;
declare var os: string;
declare var Bc: string;
declare var ol: string;
declare var zc: string;
declare var is: string;
declare var ma: string;
declare var as: string;
declare var ls: string;
declare var dl: {
    new (e: any): {
        learningRate: any;
        applyGradients(e: any): void;
        setLearningRate(e: any): void;
        c: any;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    fromConfig(e: any, t: any): any;
    className: string;
};
declare var oa: string;
declare var Ys: string;
declare var sa: string;
declare var $a: any;
declare var cs: string;
declare var aa: string;
declare var us: string;
declare var ia: string;
declare var Zs: string;
declare var fs: string;
declare var la: string;
declare var Js: string;
declare var Vc: string;
declare var Wc: string;
declare var jc: string;
declare var Gc: string;
declare var Uc: string;
declare var Qs: string;
declare var ps: string;
declare var sl: string;
declare var ds: string;
declare var ro: string;
declare var ua: string;
declare var Hc: string;
declare var qc: string;
declare var Kc: string;
declare var hs: string;
declare var ms: string;
declare var on: {
    new (e: any, t: any, n: any, o: any, s: any, a: any, i: any): {
        dtype: any;
        shape: any;
        sourceLayer: any;
        inputs: any;
        callArgs: any;
        outputTensorIndex: any;
        id: number;
        originalName: string | undefined;
        name: any;
        rank: any;
    };
};
declare var gs: string;
declare var xs: string;
declare var je: {
    new (e: any, t: any, n: any, o: any): {
        kept: boolean;
        isDisposedInternal: boolean;
        shape: any;
        dtype: any;
        size: any;
        strides: any[];
        dataId: any;
        id: any;
        rankType: any;
        readonly rank: any;
        buffer(): Promise<any>;
        bufferSync(): any;
        array(): Promise<any>;
        arraySync(): any;
        data(): Promise<any>;
        dataSync(): any;
        bytes(): Promise<any>;
        dispose(): void;
        readonly isDisposed: boolean;
        throwIfDisposed(): void;
        print(e?: boolean): any;
        clone(): any;
        toString(e?: boolean): string;
        cast(e: any): any;
        variable(e: boolean | undefined, t: any, n: any): any;
    };
};
declare var ct: {
    new (e: any, t: any, n: any): {
        dtype: any;
        shape: any;
        size: any;
        values: any;
        strides: any[];
        set(e: any, ...t: any[]): void;
        get(...e: any[]): any;
        locToIndex(e: any): any;
        indexToLoc(e: any): any[];
        readonly rank: any;
        toTensor(): any;
    };
};
declare var jn: string;
declare var ca: string;
declare var pa: string;
declare var ys: string;
declare var Xc: string;
declare var ei: string;
declare var il: string;
declare var ul: {
    new (e: any, t: any, n: any, o: any): {
        trainable: any;
        name: any;
        assign(e: any): void;
        dataId: any;
        dispose(): void;
        isDisposedInternal: boolean;
        kept: boolean;
        shape: any;
        dtype: any;
        size: any;
        strides: any[];
        id: any;
        rankType: any;
        readonly rank: any;
        buffer(): Promise<any>;
        bufferSync(): any;
        array(): Promise<any>;
        arraySync(): any;
        data(): Promise<any>;
        dataSync(): any;
        bytes(): Promise<any>;
        readonly isDisposed: boolean;
        throwIfDisposed(): void;
        print(e?: boolean): any;
        clone(): any;
        toString(e?: boolean): string;
        cast(e: any): any;
        variable(e: boolean | undefined, t: any, n: any): any;
    };
};
declare var ti: string;
declare var ri: string;
declare function Tt(...s: any[]): any;
declare namespace Tt {
    const name: string;
}
declare function tk(...s: any[]): any;
declare namespace tk { }
declare function rk(...s: any[]): any;
declare namespace rk { }
declare function Y(...s: any[]): any;
declare namespace Y { }
declare function nk(...s: any[]): any;
declare namespace nk { }
declare function Gm(...s: any[]): any;
declare namespace Gm { }
declare function lp(...s: any[]): any;
declare namespace lp { }
declare function ba(...s: any[]): any;
declare namespace ba { }
declare function ok(...s: any[]): any;
declare namespace ok { }
declare function sk(...s: any[]): any;
declare namespace sk { }
declare function ik(...s: any[]): any;
declare namespace ik { }
declare function ak(...s: any[]): any;
declare namespace ak { }
declare function lk(...s: any[]): any;
declare namespace lk { }
declare function uk(...s: any[]): any;
declare namespace uk { }
declare function nu(...s: any[]): any;
declare namespace nu { }
declare function Um(...s: any[]): any;
declare namespace Um { }
declare function kN(): any;
declare var S: {};
declare function b4(...s: any[]): any;
declare namespace b4 { }
declare function ai(...s: any[]): any;
declare namespace ai { }
declare function fk(...s: any[]): any;
declare namespace fk { }
declare function dk(...s: any[]): any;
declare namespace dk { }
declare function hk(...s: any[]): any;
declare namespace hk { }
declare function ou(...s: any[]): any;
declare namespace ou { }
declare function Hm(...s: any[]): any;
declare namespace Hm { }
declare function XIe(r: any, e: any, t: any): Promise<any>;
declare function gk(...s: any[]): any;
declare namespace gk { }
declare function su(...s: any[]): any;
declare namespace su { }
declare var cg: {};
declare function Se(r: any, e: string | undefined, t: any): {
    dtype: any;
    shape: any;
    size: any;
    values: any;
    strides: any[];
    set(e: any, ...t: any[]): void;
    get(...e: any[]): any;
    locToIndex(e: any): any;
    indexToLoc(e: any): any[];
    readonly rank: any;
    toTensor(): any;
};
declare namespace PX {
    export { OX as earlyStopping };
}
declare function J(...s: any[]): any;
declare namespace J { }
declare function xk(...s: any[]): any;
declare namespace xk { }
declare function Sr(...s: any[]): any;
declare namespace Sr { }
declare function hn(...s: any[]): any;
declare namespace hn { }
declare function $n(...s: any[]): any;
declare namespace $n { }
declare function tt(...s: any[]): any;
declare namespace tt { }
declare function yk(...s: any[]): any;
declare namespace yk { }
declare function bk(...s: any[]): any;
declare namespace bk { }
declare function wk(...s: any[]): any;
declare namespace wk { }
declare function kk(...s: any[]): any;
declare namespace kk { }
declare var PE: {};
declare function qm(...s: any[]): any;
declare namespace qm { }
declare function Dn(...s: any[]): any;
declare namespace Dn { }
declare function Km(...s: any[]): any;
declare namespace Km { }
declare function Xm(...s: any[]): any;
declare namespace Xm { }
declare function _k(...s: any[]): any;
declare namespace _k { }
declare function wse(r: any, e: any): void;
declare function iu(...s: any[]): any;
declare namespace iu { }
declare function Ym(...s: any[]): any;
declare namespace Ym { }
declare function Tg(r: any, e: any, t: any): any;
declare function Zm(...s: any[]): any;
declare namespace Zm { }
declare function Qr(r: any): any;
declare var s0: {};
declare function vk(...s: any[]): any;
declare namespace vk { }
declare function ek(r: any): void;
declare function Ck(...s: any[]): any;
declare namespace Ck { }
declare function ka(...s: any[]): any;
declare namespace ka { }
declare function LX(r: any): void;
declare var Ql: {};
declare function K4(...s: any[]): any;
declare namespace K4 { }
declare function Sk(...s: any[]): any;
declare namespace Sk { }
declare function nue(): void;
declare function Ae(r: any): void;
declare function oue(): void;
declare function ue(...s: any[]): any;
declare namespace ue { }
declare function Ik(...s: any[]): any;
declare namespace Ik { }
declare function rU(...s: any[]): any;
declare namespace rU { }
declare function UN(...s: any[]): any;
declare namespace UN { }
declare function Nk(...s: any[]): any;
declare namespace Nk { }
declare function _a(...s: any[]): any;
declare namespace _a { }
declare function rue(): void;
declare function tue(): void;
declare function HN(r: any): number;
declare function ks(): any;
declare function j(): any;
declare function Dr(...s: any[]): any;
declare namespace Dr { }
declare function Tk(...s: any[]): any;
declare namespace Tk { }
declare function tr(...s: any[]): any;
declare namespace tr { }
declare function gr(...s: any[]): any;
declare namespace gr { }
declare function Ek(...s: any[]): any;
declare namespace Ek { }
declare function pp(...s: any[]): any;
declare namespace pp { }
declare function gu(...s: any[]): any;
declare namespace gu { }
declare function _s(r: any, e: any, t: any): any;
declare function cue(r: any): any;
declare function pue(r: any): any;
declare function va(...s: any[]): any;
declare namespace va { }
declare function jm(...s: any[]): any;
declare namespace jm { }
declare function BP(): void;
declare var lo: {};
declare function li(...s: any[]): any;
declare namespace li { }
declare function jN(...s: any[]): any;
declare namespace jN { }
declare var pg: {};
declare function lue(): any;
declare function bw(r: any): any;
declare function Om(r: any, e: any): any;
declare function Jh(r: any): any[];
declare var VO: {};
declare function NU(r: any): (e: any, t: any) => any;
declare function TU(r: any): (e: any, t: any) => any;
declare function Ht(...s: any[]): any;
declare namespace Ht { }
declare function Un(...s: any[]): any;
declare namespace Un { }
declare function fl(...s: any[]): any;
declare namespace fl { }
declare function au(...s: any[]): any;
declare namespace au { }
declare namespace bn {
    export { QN as flipLeftRight };
    export { eT as grayscaleToRGB };
    export { pT as resizeNearestNeighbor };
    export { cT as resizeBilinear };
    export { tT as rotateWithOffset };
    export { JN as cropAndResize };
    export { rT as nonMaxSuppression };
    export { sT as nonMaxSuppressionAsync };
    export { iT as nonMaxSuppressionWithScore };
    export { aT as nonMaxSuppressionWithScoreAsync };
    export { lT as nonMaxSuppressionPadded };
    export { uT as nonMaxSuppressionPaddedAsync };
    export { mT as threshold };
    export { fT as transform };
}
declare function sNe(r: any, e: any, t?: number): Promise<any>;
declare var rA: {};
declare function O_(r: any): any;
declare var $r: {};
declare function ff(...s: any[]): any;
declare namespace ff { }
declare function xU(...s: any[]): any;
declare namespace xU { }
declare function bU(...s: any[]): any;
declare namespace bU { }
declare function Ak(...s: any[]): any;
declare namespace Ak { }
declare function Dt(r: any): any;
declare var Mr: {};
declare var MA: {};
declare function lu(...s: any[]): any;
declare namespace lu { }
declare function Jm(...s: any[]): any;
declare namespace Jm { }
declare function Hn(...s: any[]): any;
declare namespace Hn { }
declare namespace RT {
    export { dT as bandPart };
    export { hT as gramSchmidt };
    export { xT as qr };
}
declare function $k(r: any, e: any, t: any): any;
declare function m7(r: any, e?: {}): Promise<{
    modelUrl: any;
    loadOptions: {};
    version: string;
    resourceManager: {
        hashTableNameToHandle: {};
        hashTableMap: {};
        addHashTable(e: any, t: any): void;
        getHashTableHandleByName(e: any): any;
        getHashTableById(e: any): any;
        dispose(): void;
    };
    readonly modelVersion: string;
    readonly inputNodes: any;
    readonly outputNodes: any;
    readonly inputs: any;
    readonly outputs: any;
    readonly weights: any;
    readonly metadata: any;
    readonly modelSignature: any;
    findIOHandler(): void;
    handler: any;
    load(): Promise<boolean>;
    loadSync(e: any): boolean;
    artifacts: any;
    signature: any;
    executor: {
        graph: any;
        parent: any;
        compiledMap: Map<any, any>;
        _weightMap: {};
        SEPERATOR: string;
        _functions: any;
        _functionExecutorMap: {};
        _outputs: any;
        _inputs: any;
        _initNodes: any;
        _signature: any;
        readonly weightIds: any;
        readonly functionExecutorMap: any;
        weightMap: any;
        _weightIds: any[] | undefined;
        resourceManager: any;
        _resourceManager: any;
        readonly inputs: any;
        readonly outputs: any;
        readonly inputNodes: any;
        readonly outputNodes: any;
        readonly functions: {};
        getCompilationKey(e: any, t: any): string;
        compile(e: any, t: any): any[];
        execute(e: any, t: any): any;
        getFrozenTensorIds(e: any): Set<any>;
        checkTensorForDisposal(e: any, t: any, n: any, o: any, s: any, a: any, i: any): void;
        executeAsync(e: any, t: any): Promise<any>;
        _executeAsync(e: any, t: any, n?: boolean, o?: {}, s?: {}): Promise<any>;
        executeFunctionAsync(e: any, t: any, n: any): Promise<any>;
        executeWithControlFlow(e: any, t: any, n: any, o: any): Promise<any>;
        processStack(e: any, t: any, n: any, o: any, s: any, a: any, i: any, l: any, u: any): any[];
        processChildNodes(e: any, t: any, n: any, o: any, s: any, a: any): void;
        dispose(): void;
        checkInputShapeAndType(e: any): void;
        mapInputs(e: any): {};
        checkInputs(e: any): void;
        mapOutputs(e: any): any;
        checkOutputs(e: any): void;
    } | undefined;
    initializer: {
        graph: any;
        parent: any;
        compiledMap: Map<any, any>;
        _weightMap: {};
        SEPERATOR: string;
        _functions: any;
        _functionExecutorMap: {};
        _outputs: any;
        _inputs: any;
        _initNodes: any;
        _signature: any;
        readonly weightIds: any;
        readonly functionExecutorMap: any;
        weightMap: any;
        _weightIds: any[] | undefined;
        resourceManager: any;
        _resourceManager: any;
        readonly inputs: any;
        readonly outputs: any;
        readonly inputNodes: any;
        readonly outputNodes: any;
        readonly functions: {};
        getCompilationKey(e: any, t: any): string;
        compile(e: any, t: any): any[];
        execute(e: any, t: any): any;
        getFrozenTensorIds(e: any): Set<any>;
        checkTensorForDisposal(e: any, t: any, n: any, o: any, s: any, a: any, i: any): void;
        executeAsync(e: any, t: any): Promise<any>;
        _executeAsync(e: any, t: any, n?: boolean, o?: {}, s?: {}): Promise<any>;
        executeFunctionAsync(e: any, t: any, n: any): Promise<any>;
        executeWithControlFlow(e: any, t: any, n: any, o: any): Promise<any>;
        processStack(e: any, t: any, n: any, o: any, s: any, a: any, i: any, l: any, u: any): any[];
        processChildNodes(e: any, t: any, n: any, o: any, s: any, a: any): void;
        dispose(): void;
        checkInputShapeAndType(e: any): void;
        mapInputs(e: any): {};
        checkInputs(e: any): void;
        mapOutputs(e: any): any;
        checkOutputs(e: any): void;
    } | undefined;
    save(e: any, t: any): Promise<any>;
    predict(e: any, t: any): any;
    normalizeInputs(e: any): any;
    normalizeOutputs(e: any): any[];
    execute(e: any, t: any): any;
    executeAsync(e: any, t: any): Promise<any>;
    convertTensorMapToTensorsMap(e: any): {};
    dispose(): void;
}>;
declare function K5(r: any, e: any): Promise<any>;
declare function Dk(...s: any[]): any;
declare namespace Dk { }
declare function Ir(...s: any[]): any;
declare namespace Ir { }
declare function uu(...s: any[]): any;
declare namespace uu { }
declare function FU(...s: any[]): any;
declare namespace FU { }
declare function Qm(...s: any[]): any;
declare namespace Qm { }
declare function Pk(...s: any[]): any;
declare namespace Pk { }
declare function Fr(...s: any[]): any;
declare namespace Fr { }
declare function cu(...s: any[]): any;
declare namespace cu { }
declare function tf(...s: any[]): any;
declare namespace tf { }
declare function HU(...s: any[]): any;
declare namespace HU { }
declare namespace tFe {
    export { yT as absoluteDifference };
    export { Pr as computeWeightedLoss };
    export { bT as cosineDistance };
    export { wT as hingeLoss };
    export { kT as huberLoss };
    export { _T as logLoss };
    export { vT as meanSquaredError };
    export { CT as sigmoidCrossEntropy };
    export { ST as softmaxCrossEntropy };
}
declare function Me(...s: any[]): any;
declare namespace Me { }
declare var oN: {};
declare function Vr(...s: any[]): any;
declare namespace Vr { }
declare function pu(...s: any[]): any;
declare namespace pu { }
declare function rf(...s: any[]): any;
declare namespace rf { }
declare function Mk(...s: any[]): any;
declare namespace Mk { }
declare function Rn(...s: any[]): any;
declare namespace Rn { }
declare function Ct(...s: any[]): any;
declare namespace Ct { }
declare function Wm(): any;
declare function JU(r: any, e: any, { indexing: t }?: {
    indexing?: string | undefined;
}): any[];
declare var LA: {};
declare function mp(...s: any[]): any;
declare namespace mp { }
declare function Ca(...s: any[]): any;
declare namespace Ca { }
declare function Lk(...s: any[]): any;
declare namespace Lk { }
declare function zk(...s: any[]): any;
declare namespace zk { }
declare function H5(r: any): {
    [x: string]: any;
    isTraining: boolean;
    summary(e: any, t: any, n?: {
        (...data: any[]): void;
        (...data: any[]): void;
        (message?: any, ...optionalParams: any[]): void;
    }): void;
    compile(e: any): void;
    loss: any;
    optimizer_: any;
    isOptimizerOwned: boolean | undefined;
    lossFunctions: any;
    feedOutputNames: any[] | undefined;
    feedOutputShapes: any[] | undefined;
    feedLossFns: any[] | undefined;
    metrics: any;
    metricsNames: string[] | undefined;
    metricsTensors: any[] | undefined;
    collectedTrainableWeights: any;
    checkTrainableWeightsConsistency(): void;
    evaluate(e: any, t: any, n?: {}): any;
    evaluateDataset(e: any, t: any): Promise<any>;
    checkNumSamples(e: any, t: any, n: any, o?: string): any;
    execute(e: any, t: any): any;
    retrieveSymbolicTensors(e: any): any[];
    predictLoop(e: any, t?: number, n?: boolean): any;
    predict(e: any, t?: {}): any;
    predictOnBatch(e: any): any;
    standardizeUserDataXY(e: any, t: any, n: boolean | undefined, o: any): any[];
    standardizeUserData(e: any, t: any, n: any, o: any, s: boolean | undefined, a: any): Promise<any[]>;
    testLoop(e: any, t: any, n: any, o: number | undefined, s: any): any;
    getDedupedMetricsNames(): string[];
    makeTrainFunction(): (e: any) => any[];
    makeTestFunction(): void;
    testFunction: ((e: any) => any) | undefined;
    fit(e: any, t: any, n?: {}): Promise<any>;
    fitDataset(e: any, t: any): Promise<any>;
    trainOnBatch(e: any, t: any): Promise<any>;
    getNamedWeights(e: any): {
        name: any;
        tensor: any;
    }[];
    stopTraining: any;
    stopTraining_: any;
    optimizer: any;
    dispose(): any;
    getLossIdentifiers(): any;
    getMetricIdentifiers(): {};
    getTrainingConfig(): {
        loss: any;
        metrics: {};
        optimizer_config: {
            class_name: any;
            config: any;
        };
    };
    loadTrainingConfig(e: any): void;
    save(e: any, t: any): Promise<any>;
    setUserDefinedMetadata(e: any): void;
    userDefinedMetadata: any;
    getUserDefinedMetadata(): any;
};
declare var zA: {};
declare function fp(...s: any[]): any;
declare namespace fp { }
declare function k1e(...s: any[]): any;
declare namespace k1e { }
declare function F(...s: any[]): any;
declare namespace F { }
declare function iH(...s: any[]): any;
declare namespace iH { }
declare function Bk(...s: any[]): any;
declare namespace Bk { }
declare function Ke(...s: any[]): any;
declare namespace Ke { }
declare function xf(): Promise<any>;
declare function Ig(...s: any[]): any;
declare namespace Ig { }
declare function ci(...s: any[]): any;
declare namespace ci { }
declare function xa(...s: any[]): any;
declare namespace xa { }
declare function rr(r: any, e?: string): any;
declare function xr(...s: any[]): any;
declare namespace xr { }
declare function I(r: any): {
    (...s: any[]): any;
    readonly name: string;
};
declare function pH(...s: any[]): any;
declare namespace pH { }
declare function xn(...s: any[]): any;
declare namespace xn { }
declare function dH(...s: any[]): any;
declare namespace dH { }
declare function gH(...s: any[]): any;
declare namespace gH { }
declare function yH(...s: any[]): any;
declare namespace yH { }
declare function wH(...s: any[]): any;
declare namespace wH { }
declare function SH(...s: any[]): any;
declare namespace SH { }
declare function yn(...s: any[]): any;
declare namespace yn { }
declare function fu(...s: any[]): any;
declare namespace fu { }
declare function Gw(r: any, e?: boolean): void;
declare function nf(...s: any[]): any;
declare namespace nf { }
declare function sue(r: any): any;
declare function AH(...s: any[]): any;
declare namespace AH { }
declare function LH(...s: any[]): any;
declare namespace LH { }
declare function _g(...s: any[]): any;
declare namespace _g { }
declare function vs(...s: any[]): any;
declare namespace vs { }
declare function Sa(r: any, e: any, t?: number, n?: string): any;
declare function aue(): any;
declare function ml(...s: any[]): any;
declare namespace ml { }
declare function Zk(...s: any[]): any;
declare namespace Zk { }
declare function ap(r: any, e: any, t?: number): any;
declare function X5(r: any, e: any): void;
declare function ZI(r: any): void;
declare function Ul(r: any): void;
declare function MX(r: any, e: any): void;
declare var BA: {};
declare function Or(...s: any[]): any;
declare namespace Or { }
declare function of(...s: any[]): any;
declare namespace of { }
declare function uue(r: any): void;
declare function O(...s: any[]): any;
declare namespace O { }
declare function lr(...s: any[]): any;
declare namespace lr { }
declare function qH(...s: any[]): any;
declare namespace qH { }
declare function XH(...s: any[]): any;
declare namespace XH { }
declare function ZH(...s: any[]): any;
declare namespace ZH { }
declare function QH(...s: any[]): any;
declare namespace QH { }
declare function xu(...s: any[]): any;
declare namespace xu { }
declare function sf(...s: any[]): any;
declare namespace sf { }
declare function af(...s: any[]): any;
declare namespace af { }
declare function ce(r: any, e: any): any;
declare function VN(...s: any[]): any;
declare namespace VN { }
declare var fg: {};
declare function lf(...s: any[]): any;
declare namespace lf { }
declare function Jk(...s: any[]): any;
declare namespace Jk { }
declare function q5(r: any): any;
declare var ee: {};
declare function VG(r: any): any;
declare function mue(r: any, e: any): void;
declare function Hoe(r: any, e?: boolean): void;
declare function qoe(r: any, e?: boolean): void;
declare function W0(r: any, e: any): void;
declare function Qk(r: any, e: any): Promise<any[]>;
declare var Yx: {};
declare function Jr(...s: any[]): any;
declare namespace Jr { }
declare function e_(...s: any[]): any;
declare namespace e_ { }
declare namespace TRe {
    export { YN as hammingWindow };
    export { $g as hannWindow };
    export { Dg as frame };
    export { ZN as stft };
}
declare function uf(...s: any[]): any;
declare namespace uf { }
declare function cf(...s: any[]): any;
declare namespace cf { }
declare function Oe(...s: any[]): any;
declare namespace Oe { }
declare function pf(...s: any[]): any;
declare namespace pf { }
declare function vg(...s: any[]): any;
declare namespace vg { }
declare function mf(...s: any[]): any;
declare namespace mf { }
declare function hp(...s: any[]): any;
declare namespace hp { }
declare var ar: {};
declare function hu(...s: any[]): any;
declare namespace hu { }
declare function ui(...s: any[]): any;
declare namespace ui { }
declare function mu(...s: any[]): any;
declare namespace mu { }
declare namespace gf {
    export { IT as sparseFillEmptyRows };
    export { NT as sparseReshape };
    export { TT as sparseSegmentMean };
    export { ET as sparseSegmentSum };
}
declare function Ng(...s: any[]): any;
declare namespace Ng { }
declare namespace vRe {
    export { gu as fft };
    export { fl as ifft };
    export { xu as rfft };
    export { ff as irfft };
}
declare function mr(...s: any[]): any;
declare namespace mr { }
declare function St(...s: any[]): any;
declare namespace St { }
declare function We(...s: any[]): any;
declare namespace We { }
declare function df(...s: any[]): any;
declare namespace df { }
declare function en(...s: any[]): any;
declare namespace en { }
declare function nr(...s: any[]): any;
declare namespace nr { }
declare function Ia(...s: any[]): any;
declare namespace Ia { }
declare function t_(...s: any[]): any;
declare namespace t_ { }
declare namespace Pg {
    export { AT as stringNGrams };
    export { $T as stringSplit };
    export { DT as stringToHashBucketFast };
}
declare function le(...s: any[]): any;
declare namespace le { }
declare function me(...s: any[]): any;
declare namespace me { }
declare function Zl(r: any): any;
declare function r_(...s: any[]): any;
declare namespace r_ { }
declare function wa(...s: any[]): any;
declare namespace wa { }
declare function vr(r: any, e: any, t: any): any;
declare function At(r: any, e: any): any;
declare function pi(r: any, e: any, t: any): any;
declare function Kw(r: any, e: any, t: any): any;
declare function Sq(r: any, e: any, t: any): any;
declare function Iq(r: any, e: any, t: any): any;
declare function Nq(r: any, e: any, t: any): any;
declare var io: {};
declare var wN: {};
declare function z(r: any, e: any): any;
declare function Rr(...s: any[]): any;
declare namespace Rr { }
declare function iue(r: any): any;
declare function n_(...s: any[]): any;
declare namespace n_ { }
declare namespace vu {
    import sgd = Na.sgd;
    export { sgd };
    import momentum = Na.momentum;
    export { momentum };
    import adadelta = Na.adadelta;
    export { adadelta };
    import adagrad = Na.adagrad;
    export { adagrad };
    import rmsprop = Na.rmsprop;
    export { rmsprop };
    import adamax = Na.adamax;
    export { adamax };
    import adam = Na.adam;
    export { adam };
}
declare function Ve(...s: any[]): any;
declare namespace Ve { }
declare function yu(...s: any[]): any;
declare namespace yu { }
declare function Cg(...s: any[]): any;
declare namespace Cg { }
declare function bse(r: any): void;
declare function yse(r: any, e: any): void;
declare function o_(...s: any[]): any;
declare namespace o_ { }
declare function Nr(...s: any[]): any;
declare namespace Nr { }
declare function pr(r: any, e: any): any;
declare var b: {};
declare function EU(r: any): (e: any, t: any) => {
    grad: any;
    value: any;
};
declare function AU(r: any): (e: any, t: any) => any;
declare function s_(r: any, e: boolean | undefined, t: any, n: any): any;
declare function yg(r: any, e: any): {
    value: any;
    grads: {};
};
declare var FWt: {
    tfjs: string;
    "tfjs-core": string;
    "tfjs-data": string;
    "tfjs-layers": string;
    "tfjs-converter": string;
    "tfjs-backend-cpu": string;
    "tfjs-backend-webgl": string;
    "tfjs-backend-wasm": string;
};
declare var f7: string;
declare var BG: string;
declare var q7: string;
declare var Vf: string;
declare var Koe: string;
declare var PJ: string;
declare namespace Cwt {
    export { BP as forceHalfFloat };
}
declare var $O: {};
declare function Et(...s: any[]): any;
declare namespace Et { }
declare function hf(r: any): Promise<any>;
declare function ht(r: any, e?: string): any;
declare function Ie(...s: any[]): any;
declare namespace Ie { }
declare function fx(r: any, e: any): boolean;
declare function kj(r: any): {};
declare function OX(r: any): {
    monitor: any;
    minDelta: number;
    patience: any;
    verbose: any;
    mode: any;
    baseline: any;
    monitorFunc: typeof fx;
    onTrainBegin(e: any): Promise<void>;
    wait: number | undefined;
    stoppedEpoch: any;
    best: any;
    onEpochEnd(e: any, t: any): Promise<void>;
    onTrainEnd(e: any): Promise<void>;
    getMonitorValue(e: any): any;
    model: {
        [x: string]: any;
        isTraining: boolean;
        summary(e: any, t: any, n?: {
            (...data: any[]): void;
            (...data: any[]): void;
            (message?: any, ...optionalParams: any[]): void;
        }): void;
        compile(e: any): void;
        loss: any;
        optimizer_: any;
        isOptimizerOwned: boolean | undefined;
        lossFunctions: any;
        feedOutputNames: any[] | undefined;
        feedOutputShapes: any[] | undefined;
        feedLossFns: any[] | undefined;
        metrics: any;
        metricsNames: string[] | undefined;
        metricsTensors: any[] | undefined;
        collectedTrainableWeights: any;
        checkTrainableWeightsConsistency(): void;
        evaluate(e: any, t: any, n?: {}): any;
        evaluateDataset(e: any, t: any): Promise<any>;
        checkNumSamples(e: any, t: any, n: any, o?: string): any;
        execute(e: any, t: any): any;
        retrieveSymbolicTensors(e: any): any[];
        predictLoop(e: any, t?: number, n?: boolean): any;
        predict(e: any, t?: {}): any;
        predictOnBatch(e: any): any;
        standardizeUserDataXY(e: any, t: any, n: boolean | undefined, o: any): any[];
        standardizeUserData(e: any, t: any, n: any, o: any, s: boolean | undefined, a: any): Promise<any[]>;
        testLoop(e: any, t: any, n: any, o: number | undefined, s: any): any;
        getDedupedMetricsNames(): string[];
        makeTrainFunction(): (e: any) => any[];
        makeTestFunction(): void;
        testFunction: ((e: any) => any) | undefined;
        fit(e: any, t: any, n?: {}): Promise<any>;
        fitDataset(e: any, t: any): Promise<any>;
        trainOnBatch(e: any, t: any): Promise<any>;
        getNamedWeights(e: any): {
            name: any;
            tensor: any;
        }[];
        stopTraining: any;
        stopTraining_: any;
        optimizer: any;
        dispose(): any;
        getLossIdentifiers(): any;
        getMetricIdentifiers(): {};
        getTrainingConfig(): {
            loss: any;
            metrics: {};
            optimizer_config: {
                class_name: any;
                config: any;
            };
        };
        loadTrainingConfig(e: any): void;
        save(e: any, t: any): Promise<any>;
        setUserDefinedMetadata(e: any): void;
        userDefinedMetadata: any;
        getUserDefinedMetadata(): any;
    } | null;
    setModel(e: any): void;
    validationData: any;
    setParams(e: any): void;
    params: any;
    onEpochBegin(e: any, t: any): Promise<void>;
    onBatchBegin(e: any, t: any): Promise<void>;
    onBatchEnd(e: any, t: any): Promise<void>;
};
declare function QN(...s: any[]): any;
declare namespace QN { }
declare function eT(...s: any[]): any;
declare namespace eT { }
declare function pT(...s: any[]): any;
declare namespace pT { }
declare function cT(...s: any[]): any;
declare namespace cT { }
declare function tT(...s: any[]): any;
declare namespace tT { }
declare function JN(...s: any[]): any;
declare namespace JN { }
declare function rT(...s: any[]): any;
declare namespace rT { }
declare function sT(r: any, e: any, t: any, n?: number, o?: number): Promise<any>;
declare function iT(...s: any[]): any;
declare namespace iT { }
declare function aT(r: any, e: any, t: any, n?: number, o?: number, s?: number): Promise<{
    selectedIndices: any;
    selectedScores: any;
}>;
declare function lT(...s: any[]): any;
declare namespace lT { }
declare function uT(r: any, e: any, t: any, n?: number, o?: number, s?: boolean): Promise<{
    selectedIndices: any;
    validOutputs: any;
}>;
declare function mT(...s: any[]): any;
declare namespace mT { }
declare function fT(...s: any[]): any;
declare namespace fT { }
declare function dT(...s: any[]): any;
declare namespace dT { }
declare function hT(...s: any[]): any;
declare namespace hT { }
declare function xT(...s: any[]): any;
declare namespace xT { }
declare function yT(...s: any[]): any;
declare namespace yT { }
declare function Pr(...s: any[]): any;
declare namespace Pr { }
declare function bT(...s: any[]): any;
declare namespace bT { }
declare function wT(...s: any[]): any;
declare namespace wT { }
declare function kT(...s: any[]): any;
declare namespace kT { }
declare function _T(...s: any[]): any;
declare namespace _T { }
declare function vT(...s: any[]): any;
declare namespace vT { }
declare function CT(...s: any[]): any;
declare namespace CT { }
declare function ST(...s: any[]): any;
declare namespace ST { }
declare function YN(...s: any[]): any;
declare namespace YN { }
declare function $g(...s: any[]): any;
declare namespace $g { }
declare function Dg(...s: any[]): any;
declare namespace Dg { }
declare function ZN(...s: any[]): any;
declare namespace ZN { }
declare function IT(...s: any[]): any;
declare namespace IT { }
declare function NT(...s: any[]): any;
declare namespace NT { }
declare function TT(...s: any[]): any;
declare namespace TT { }
declare function ET(...s: any[]): any;
declare namespace ET { }
declare function AT(...s: any[]): any;
declare namespace AT { }
declare function $T(...s: any[]): any;
declare namespace $T { }
declare function DT(...s: any[]): any;
declare namespace DT { }
declare var Na: {
    new (): {};
    sgd(e: any): {
        learningRate: any;
        applyGradients(e: any): void;
        setLearningRate(e: any): void;
        c: any;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    momentum(e: any, t: any, n?: boolean): {
        learningRate: any;
        momentum: any;
        useNesterov: boolean;
        accumulations: any[];
        m: any;
        applyGradients(e: any): void;
        dispose(): void;
        setMomentum(e: any): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            momentum: any;
            useNesterov: boolean;
        };
        setLearningRate(e: any): void;
        c: any;
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    rmsprop(e: any, t?: number, n?: number, o?: null, s?: boolean): {
        learningRate: any;
        decay: number;
        momentum: number;
        epsilon: any;
        accumulatedMeanSquares: any[];
        accumulatedMoments: any[];
        accumulatedMeanGrads: any[];
        centered: boolean;
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            decay: number;
            momentum: number;
            epsilon: any;
            centered: boolean;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    adam(e?: number, t?: number, n?: number, o?: null): {
        learningRate: any;
        beta1: any;
        beta2: any;
        epsilon: any;
        accumulatedFirstMoment: any[];
        accumulatedSecondMoment: any[];
        accBeta1: any;
        accBeta2: any;
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            beta1: any;
            beta2: any;
            epsilon: any;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    adadelta(e?: number, t?: number, n?: null): {
        learningRate: any;
        rho: any;
        epsilon: any;
        accumulatedGrads: any[];
        accumulatedUpdates: any[];
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            rho: any;
            epsilon: any;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    adamax(e?: number, t?: number, n?: number, o?: null, s?: number): {
        learningRate: any;
        beta1: any;
        beta2: any;
        epsilon: any;
        decay: number;
        accumulatedFirstMoment: any[];
        accumulatedWeightedInfNorm: any[];
        iteration: any;
        accBeta1: any;
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<void>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            beta1: any;
            beta2: any;
            epsilon: any;
            decay: number;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
    adagrad(e: any, t?: number): {
        learningRate: any;
        initialAccumulatorValue: number;
        accumulatedGrads: any[];
        applyGradients(e: any): void;
        dispose(): void;
        getWeights(): Promise<{
            name: string;
            tensor: any;
        }[]>;
        setWeights(e: any): Promise<void>;
        getConfig(): {
            learningRate: any;
            initialAccumulatorValue: number;
        };
        minimize(e: any, t: boolean | undefined, n: any): any;
        readonly iterations: any;
        iterations_: any;
        incrementIterations(): void;
        computeGradients(e: any, t: any): {
            value: any;
            grads: {};
        };
        saveIterations(): Promise<{
            name: string;
            tensor: any;
        }>;
        extractIterations(e: any): Promise<any>;
        getClassName(): any;
    };
};
export { Vs as Abs, Ni as Acos, Ti as Acosh, xp as AdadeltaOptimizer, yp as AdagradOptimizer, bp as AdamOptimizer, wp as AdamaxOptimizer, Wn as Add, So as AddN, Ei as All, Ai as Any, Io as ArgMax, Xa as ArgMin, $i as Asin, Di as Asinh, Ri as Atan, Oi as Atan2, Fi as Atanh, No as AvgPool, Ya as AvgPool3D, xc as AvgPool3DGrad, gc as AvgPoolGrad, My as BackendWasm, To as BatchMatMul, Ws as BatchToSpaceND, yc as Bincount, Zh as BroadcastArgs, XI as BroadcastTo, mv as Callback, C_ as CallbackList, Qn as Cast, Eo as Ceil, eo as ClipByValue, bc as Complex, Za as ComplexAbs, js as Concat, Ao as Conv2D, wc as Conv2DBackpropFilter, $o as Conv2DBackpropInput, Ja as Conv3D, kc as Conv3DBackpropFilterV2, _c as Conv3DBackpropInputV2, Do as Cos, Ro as Cosh, Pi as CropAndResize, Fo as Cumsum, I_ as CustomCallback, Ka as DataStorage, vc as DenseBincount, Mi as DepthToSpace, Oo as DepthwiseConv2dNative, Cc as DepthwiseConv2dNativeBackpropFilter, Sc as DepthwiseConv2dNativeBackpropInput, Ic as Diag, Qa as Dilation2D, Dm as Dilation2DBackpropFilter, $m as Dilation2DBackpropInput, gw as ENV, fv as EarlyStopping, Nc as Einsum, Mo as Elu, Tc as EluGrad, Yh as Environment, zi as Equal, Li as Erf, Lo as Exp, Gs as ExpandDims, Bi as Expm1, Ec as FFT, el as Fill, Vi as FlipLeftRight, zo as Floor, Bo as FloorDiv, Rm as FromPixels, Vo as FusedBatchNorm, ni as FusedConv2D, oi as FusedDepthwiseConv2D, hy as GPGPUContext, Wi as GatherNd, Us as GatherV2, jv as GraphModel, ji as Greater, Wo as GreaterEqual, S_ as History, Ac as IFFT, to as Identity, $c as Imag, _t as InputSpec, Gi as IsFinite, Ui as IsInf, Hi as IsNan, Ls as KernelBackend, tl as LRN, Rc as LRNGrad, Yg as LayerVariable, Xn as LayersModel, jo as LeakyRelu, qi as Less, Ki as LessEqual, Dc as LinSpace, Go as Log, Xi as Log1p, YI as LogSoftmax, Yi as LogicalAnd, jl as LogicalNot, Gl as LogicalOr, Pu as MathBackendCPU, Hu as MathBackendWebGL, Uo as Max, qo as MaxPool, rl as MaxPool3D, Oc as MaxPool3DGrad, Fc as MaxPoolGrad, Pc as MaxPoolWithArgmax, Ho as Maximum, Ko as Mean, Xo as Min, Yo as Minimum, Zo as MirrorPad, Zi as Mod, kp as MomentumOptimizer, Mc as Multinomial, Jo as Multiply, Hs as Neg, Qi as NonMaxSuppressionV3, ea as NonMaxSuppressionV4, ta as NonMaxSuppressionV5, Ji as NotEqual, S1 as OP_SCOPE_SUFFIX, Qo as OneHot, qs as OnesLike, Wr as Optimizer, Ks as Pack, es as PadV2, fse as Pool, ts as Pow, rs as Prelu, ra as Prod, _p as RMSPropOptimizer, On as RNN, nl as Range, Iw as Rank, Lc as Real, Po as RealDiv, na as Reciprocal, qt as Reduction, ns as Relu, ss as Relu6, Xs as Reshape, os as ResizeBilinear, Bc as ResizeBilinearGrad, ol as ResizeNearestNeighbor, zc as ResizeNearestNeighborGrad, is as Reverse, ma as RotateWithOffset, as as Round, ls as Rsqrt, dl as SGDOptimizer, oa as ScatterNd, Ys as Select, sa as Selu, $a as Sequential, cs as Sigmoid, aa as Sign, us as Sin, ia as Sinh, Zs as Slice, fs as Softmax, la as Softplus, Js as SpaceToBatchND, Vc as SparseFillEmptyRows, Wc as SparseReshape, jc as SparseSegmentMean, Gc as SparseSegmentSum, Uc as SparseToDense, Qs as SplitV, ps as Sqrt, sl as Square, ds as SquaredDifference, ro as Step, ua as StridedSlice, Hc as StringNGrams, qc as StringSplit, Kc as StringToHashBucketFast, hs as Sub, ms as Sum, on as SymbolicTensor, gs as Tan, xs as Tanh, je as Tensor, ct as TensorBuffer, jn as Tile, ca as TopK, pa as Transform, ys as Transpose, Xc as Unique, ei as Unpack, il as UnsortedSegmentSum, ul as Variable, ti as ZerosLike, ri as _FusedMatMul, Tt as abs, tk as acos, rk as acosh, Y as add, nk as addN, Gm as all, lp as any, ba as argMax, ok as argMin, sk as asin, ik as asinh, ak as atan, lk as atan2, uk as atanh, nu as avgPool, Um as avgPool3d, kN as backend, S as backend_util, b4 as basicLSTMCell, ai as batchNorm, fk as batchNorm2d, dk as batchNorm3d, hk as batchNorm4d, ou as batchToSpaceND, Hm as bincount, XIe as booleanMaskAsync, gk as broadcastArgs, su as broadcastTo, cg as browser, Se as buffer, PX as callbacks, J as cast, xk as ceil, Sr as clipByValue, hn as clone, $n as complex, tt as concat, yk as concat1d, bk as concat2d, wk as conc