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

declare var ti: string;
declare var Mi: string;
declare var Li: string;
declare var Hu: {
    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 qu: {
    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 Ku: {
    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 Xu: {
    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 jn: string;
declare var $o: string;
declare var zi: string;
declare var Bi: string;
declare var Ro: string;
declare var ml: string;
declare var Vi: string;
declare var Gi: string;
declare var Wi: string;
declare var ji: string;
declare var Ui: string;
declare var Fo: string;
declare var fl: string;
declare var Uc: string;
declare var Wc: string;
declare var xb: {
    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 Oo: string;
declare var ri: string;
declare var jc: string;
declare var Hc: string;
declare var rN: string;
declare var Sv: {
    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 Lk: {
    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 eo: string;
declare var Po: string;
declare var to: string;
declare var qc: string;
declare var dl: string;
declare var ni: string;
declare var Mo: string;
declare var Kc: string;
declare var Lo: string;
declare var hl: string;
declare var Xc: string;
declare var Yc: string;
declare var zo: string;
declare var Bo: string;
declare var Hi: string;
declare var Vo: string;
declare var Bk: {
    new (e: any, t: any): {
        currentEpoch: number;
        nowFunc: any;
        nextFrameFunc: any;
        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 pl: {
    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 Zc: string;
declare var qi: string;
declare var Go: string;
declare var Jc: string;
declare var Qc: string;
declare var ep: string;
declare var gl: string;
declare var rf: string;
declare var tf: string;
declare var Xw: any;
declare var Nv: {
    new (e: any): {
        monitor: any;
        minDelta: number;
        patience: any;
        verbose: any;
        mode: any;
        baseline: any;
        monitorFunc: typeof Hx;
        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 tp: string;
declare var Uo: string;
declare var rp: string;
declare var Eg: {
    new (e: any): {
        global: any;
        flags: {};
        flagRegistry: {};
        urlFlags: {};
        getQueryParams: typeof EW;
        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 Xi: string;
declare var Ki: string;
declare var jo: string;
declare var oi: string;
declare var Yi: string;
declare var np: string;
declare var xl: string;
declare var Zi: string;
declare var Ho: string;
declare var qo: string;
declare var nf: string;
declare var Ko: string;
declare var xi: string;
declare var yi: string;
declare var Xy: {
    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 Ji: string;
declare var si: string;
declare var ly: {
    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 Qi: string;
declare var Xo: string;
declare var zk: {
    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 op: string;
declare var ro: string;
declare var sp: string;
declare var Tt: {
    new (e: any): {
        dtype: any;
        shape: any;
        ndim: any;
        maxNDim: any;
        minNDim: any;
        axes: any;
    };
};
declare var ea: string;
declare var ta: string;
declare var ra: string;
declare var Js: {
    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 yl: string;
declare var ap: string;
declare var Ex: {
    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 Yn: {
    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 Yo: string;
declare var na: string;
declare var oa: string;
declare var ip: string;
declare var Zo: string;
declare var sa: string;
declare var nN: string;
declare var ia: string;
declare var au: string;
declare var lu: string;
declare var _c: {
    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 Jo: string;
declare var es: string;
declare var bl: string;
declare var up: string;
declare var lp: string;
declare var cp: string;
declare var Qo: string;
declare var ts: string;
declare var rs: string;
declare var ns: string;
declare var os: string;
declare var aa: string;
declare var Yu: {
    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 pp: string;
declare var ss: string;
declare var ii: string;
declare var ua: string;
declare var ca: string;
declare var pa: string;
declare var la: string;
declare var $N: string;
declare var is: string;
declare var ai: string;
declare var qr: {
    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 li: string;
declare var as: string;
declare var hse: string;
declare var ls: string;
declare var us: string;
declare var ma: string;
declare var Zu: {
    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 Ln: any;
declare var wl: string;
declare var s_: any;
declare var mp: string;
declare var Wo: string;
declare var fa: string;
declare var Yt: any;
declare var cs: string;
declare var ms: string;
declare var ui: string;
declare var ps: string;
declare var dp: string;
declare var _l: string;
declare var fp: string;
declare var fs: string;
declare var ka: string;
declare var ds: string;
declare var hs: string;
declare var Ga: {
    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 da: string;
declare var ci: string;
declare var ha: string;
declare var qa: any;
declare var xs: string;
declare var xa: string;
declare var gs: string;
declare var ga: string;
declare var pi: string;
declare var ws: string;
declare var ya: string;
declare var mi: string;
declare var hp: string;
declare var gp: string;
declare var xp: string;
declare var yp: string;
declare var bp: string;
declare var fi: string;
declare var ys: string;
declare var kl: string;
declare var _s: string;
declare var no: string;
declare var ba: string;
declare var wp: string;
declare var _p: string;
declare var kp: string;
declare var ks: string;
declare var bs: string;
declare var cn: {
    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 vs: string;
declare var Cs: string;
declare var Le: {
    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 mt: {
    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 Hn: string;
declare var wa: string;
declare var _a: string;
declare var Is: string;
declare var vp: string;
declare var di: string;
declare var vl: string;
declare var Sl: {
    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 hi: string;
declare var gi: string;
declare function Ct(...s: any[]): any;
declare namespace Ct {
    const name: string;
}
declare function df(...s: any[]): any;
declare namespace df { }
declare function hf(...s: any[]): any;
declare namespace hf { }
declare function Z(...s: any[]): any;
declare namespace Z { }
declare function F_(...s: any[]): any;
declare namespace F_ { }
declare function wu(...s: any[]): any;
declare namespace wu { }
declare function El(...s: any[]): any;
declare namespace El { }
declare function As(...s: any[]): any;
declare namespace As { }
declare function gf(...s: any[]): any;
declare namespace gf { }
declare function xf(...s: any[]): any;
declare namespace xf { }
declare function yf(...s: any[]): any;
declare namespace yf { }
declare function bf(...s: any[]): any;
declare namespace bf { }
declare function wf(...s: any[]): any;
declare namespace wf { }
declare function _f(...s: any[]): any;
declare namespace _f { }
declare function Ta(...s: any[]): any;
declare namespace Ta { }
declare function kf(...s: any[]): any;
declare namespace kf { }
declare function A1(): any;
declare var I: {};
declare function IU(...s: any[]): any;
declare namespace IU { }
declare function lo(...s: any[]): any;
declare namespace lo { }
declare function L_(...s: any[]): any;
declare namespace L_ { }
declare function z_(...s: any[]): any;
declare namespace z_ { }
declare function B_(...s: any[]): any;
declare namespace B_ { }
declare function Ea(...s: any[]): any;
declare namespace Ea { }
declare function vf(...s: any[]): any;
declare namespace vf { }
declare function JSe(r: any, e: any, t: any): Promise<any>;
declare function V_(...s: any[]): any;
declare namespace V_ { }
declare function Aa(...s: any[]): any;
declare namespace Aa { }
declare var Gg: {};
declare function Ie(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 Y(...s: any[]): any;
declare namespace Y { }
declare function Cf(...s: any[]): any;
declare namespace Cf { }
declare function gr(...s: any[]): any;
declare namespace gr { }
declare function wn(...s: any[]): any;
declare namespace wn { }
declare function Pn(...s: any[]): any;
declare namespace Pn { }
declare function tt(...s: any[]): any;
declare namespace tt { }
declare function G_(...s: any[]): any;
declare namespace G_ { }
declare function W_(...s: any[]): any;
declare namespace W_ { }
declare function U_(...s: any[]): any;
declare namespace U_ { }
declare function j_(...s: any[]): any;
declare namespace j_ { }
declare var W2: {};
declare function vu(...s: any[]): any;
declare namespace vu { }
declare function nn(...s: any[]): any;
declare namespace nn { }
declare function Cu(...s: any[]): any;
declare namespace Cu { }
declare function If(...s: any[]): any;
declare namespace If { }
declare function H_(...s: any[]): any;
declare namespace H_ { }
declare function kse(r: any, e: any): void;
declare function Da(...s: any[]): any;
declare namespace Da { }
declare function Iu(...s: any[]): any;
declare namespace Iu { }
declare function sx(r: any, e: any, t: any): any;
declare function Su(...s: any[]): any;
declare namespace Su { }
declare function on(r: any): any;
declare var v$: {};
declare function q_(...s: any[]): any;
declare namespace q_ { }
declare function R_(r: any): void;
declare function Sf(...s: any[]): any;
declare namespace Sf { }
declare function $s(...s: any[]): any;
declare namespace $s { }
declare function LX(r: any): void;
declare var xu: {};
declare function ej(...s: any[]): any;
declare namespace ej { }
declare function Nf(...s: any[]): any;
declare namespace Nf { }
declare function sue(): void;
declare function De(r: any): void;
declare function iue(): void;
declare function ce(...s: any[]): any;
declare namespace ce { }
declare function Tf(...s: any[]): any;
declare namespace Tf { }
declare function K_(...s: any[]): any;
declare namespace K_ { }
declare function eT(...s: any[]): any;
declare namespace eT { }
declare function X_(...s: any[]): any;
declare namespace X_ { }
declare function Rs(...s: any[]): any;
declare namespace Rs { }
declare function oue(): void;
declare function nue(): void;
declare function tT(r: any): number;
declare function Es(): any;
declare function U(): any;
declare function kr(...s: any[]): any;
declare namespace kr { }
declare function Ef(...s: any[]): any;
declare namespace Ef { }
declare function Kt(...s: any[]): any;
declare namespace Kt { }
declare function mr(...s: any[]): any;
declare namespace mr { }
declare function Af(...s: any[]): any;
declare namespace Af { }
declare function Lp(...s: any[]): any;
declare namespace Lp { }
declare function Ba(...s: any[]): any;
declare namespace Ba { }
declare function Fs(r: any, e: any, t: any): any;
declare function mue(r: any): any;
declare function fue(r: any): any;
declare function Os(...s: any[]): any;
declare namespace Os { }
declare function bu(...s: any[]): any;
declare namespace bu { }
declare function XP(): void;
declare var fo: {};
declare function uo(...s: any[]): any;
declare namespace uo { }
declare function J1(...s: any[]): any;
declare namespace J1 { }
declare var Wg: {};
declare function cue(): any;
declare function Jw(r: any): any;
declare function sf(r: any, e: any): any;
declare function Ag(r: any): any[];
declare function Koe(): number;
declare var XO: {};
declare function Aj(r: any): (e: any, t: any) => any;
declare function Dj(r: any): (e: any, t: any) => any;
declare function zt(...s: any[]): any;
declare namespace zt { }
declare function kn(...s: any[]): any;
declare namespace kn { }
declare function _i(...s: any[]): any;
declare namespace _i { }
declare function Nu(...s: any[]): any;
declare namespace Nu { }
declare namespace Cn {
    export { lT as flipLeftRight };
    export { uT as grayscaleToRGB };
    export { dx as resizeNearestNeighbor };
    export { fx as resizeBilinear };
    export { cT as rotateWithOffset };
    export { aT as cropAndResize };
    export { pT as nonMaxSuppression };
    export { dT as nonMaxSuppressionAsync };
    export { hT as nonMaxSuppressionWithScore };
    export { gT as nonMaxSuppressionWithScoreAsync };
    export { xT as nonMaxSuppressionPadded };
    export { yT as nonMaxSuppressionPaddedAsync };
    export { bT as threshold };
    export { wT as transform };
}
declare function l1e(r: any, e: any, t?: number): Promise<any>;
declare var uA: {};
declare function Xk(r: any): any;
declare var Lr: {};
declare function zu(...s: any[]): any;
declare namespace zu { }
declare function Y_(...s: any[]): any;
declare namespace Y_ { }
declare function Z_(...s: any[]): any;
declare namespace Z_ { }
declare function Df(...s: any[]): any;
declare namespace Df { }
declare function Ft(r: any): any;
declare var Gr: {};
declare var UA: {};
declare function $a(...s: any[]): any;
declare namespace $a { }
declare function Tu(...s: any[]): any;
declare namespace Tu { }
declare function vn(...s: any[]): any;
declare namespace vn { }
declare namespace BT {
    export { _T as bandPart };
    export { kT as gramSchmidt };
    export { CT as qr };
}
declare function J_(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 $f(...s: any[]): any;
declare namespace $f { }
declare function xr(...s: any[]): any;
declare namespace xr { }
declare function Ra(...s: any[]): any;
declare namespace Ra { }
declare function Q_(...s: any[]): any;
declare namespace Q_ { }
declare function Eu(...s: any[]): any;
declare namespace Eu { }
declare function Ff(...s: any[]): any;
declare namespace Ff { }
declare function Cr(...s: any[]): any;
declare namespace Cr { }
declare function Fa(...s: any[]): any;
declare namespace Fa { }
declare function Au(...s: any[]): any;
declare namespace Au { }
declare function nk(...s: any[]): any;
declare namespace nk { }
declare namespace oFe {
    export { IT as absoluteDifference };
    export { Vr as computeWeightedLoss };
    export { ST as cosineDistance };
    export { NT as hingeLoss };
    export { TT as huberLoss };
    export { ET as logLoss };
    export { AT as meanSquaredError };
    export { DT as sigmoidCrossEntropy };
    export { $T as softmaxCrossEntropy };
}
declare function ze(...s: any[]): any;
declare namespace ze { }
declare var p1: {};
declare function Rr(...s: any[]): any;
declare namespace Rr { }
declare function Oa(...s: any[]): any;
declare namespace Oa { }
declare function Of(...s: any[]): any;
declare namespace Of { }
declare function ok(...s: any[]): any;
declare namespace ok { }
declare function sn(...s: any[]): any;
declare namespace sn { }
declare function xt(...s: any[]): any;
declare namespace xt { }
declare function ff(): any;
declare function Qj(r: any, e: any, { indexing: t }?: {
    indexing?: string | undefined;
}): any[];
declare var jA: {};
declare function Al(...s: any[]): any;
declare namespace Al { }
declare function Ps(...s: any[]): any;
declare namespace Ps { }
declare function Pf(...s: any[]): any;
declare namespace Pf { }
declare function Mf(...s: any[]): any;
declare namespace Mf { }
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 HA: {};
declare function zp(...s: any[]): any;
declare namespace zp { }
declare function CNe(...s: any[]): any;
declare namespace CNe { }
declare function O(...s: any[]): any;
declare namespace O { }
declare function aH(...s: any[]): any;
declare namespace aH { }
declare function sk(...s: any[]): any;
declare namespace sk { }
declare function He(...s: any[]): any;
declare namespace He { }
declare function wk(): Promise<any>;
declare function Wp(...s: any[]): any;
declare namespace Wp { }
declare function mo(...s: any[]): any;
declare namespace mo { }
declare function Ts(...s: any[]): any;
declare namespace Ts { }
declare function or(r: any, e?: string): any;
declare function fr(...s: any[]): any;
declare namespace fr { }
declare function N(r: any): {
    (...s: any[]): any;
    readonly name: string;
};
declare function mH(...s: any[]): any;
declare namespace mH { }
declare function jr(...s: any[]): any;
declare namespace jr { }
declare function hH(...s: any[]): any;
declare namespace hH { }
declare function xH(...s: any[]): any;
declare namespace xH { }
declare function bH(...s: any[]): any;
declare namespace bH { }
declare function _H(...s: any[]): any;
declare namespace _H { }
declare function ik(...s: any[]): any;
declare namespace ik { }
declare function Hr(...s: any[]): any;
declare namespace Hr { }
declare function Ma(...s: any[]): any;
declare namespace Ma { }
declare function C_(r: any, e?: boolean): void;
declare function Du(...s: any[]): any;
declare namespace Du { }
declare function aue(r: any): any;
declare function AH(...s: any[]): any;
declare namespace AH { }
declare function LH(...s: any[]): any;
declare namespace LH { }
declare function tx(...s: any[]): any;
declare namespace tx { }
declare function Ms(...s: any[]): any;
declare namespace Ms { }
declare function La(r: any, e: any, t?: number, n?: string): any;
declare function uue(): any;
declare function Dl(...s: any[]): any;
declare namespace Dl { }
declare function Lf(...s: any[]): any;
declare namespace Lf { }
declare function Op(r: any, e: any, t?: number): any;
declare function X5(r: any, e: any): void;
declare function oN(r: any): void;
declare function uu(r: any): void;
declare function MX(r: any, e: any): void;
declare var qA: {};
declare function Ir(...s: any[]): any;
declare namespace Ir { }
declare function Ru(...s: any[]): any;
declare namespace Ru { }
declare function pue(r: any): void;
declare function F(...s: any[]): any;
declare namespace F { }
declare function er(...s: any[]): any;
declare namespace er { }
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 Va(...s: any[]): any;
declare namespace Va { }
declare function Fu(...s: any[]): any;
declare namespace Fu { }
declare function Ou(...s: any[]): any;
declare namespace Ou { }
declare function pe(r: any, e: any): any;
declare function Y1(...s: any[]): any;
declare namespace Y1 { }
declare var jg: {};
declare function Pu(...s: any[]): any;
declare namespace Pu { }
declare function zf(...s: any[]): any;
declare namespace zf { }
declare function q5(r: any): any;
declare var ee: {};
declare function q4(r: any): any;
declare function due(r: any, e: any): void;
declare function qoe(r: any): void;
declare function joe(r: any, e?: boolean): void;
declare function Hoe(r: any, e?: boolean): void;
declare function tC(r: any, e: any): void;
declare function xk(r: any, e: any): Promise<any[]>;
declare function zr(...s: any[]): any;
declare namespace zr { }
declare function Bf(...s: any[]): any;
declare namespace Bf { }
declare namespace DRe {
    export { sT as hammingWindow };
    export { lx as hannWindow };
    export { ux as frame };
    export { iT as stft };
}
declare function Mu(...s: any[]): any;
declare namespace Mu { }
declare function Lu(...s: any[]): any;
declare namespace Lu { }
declare function Oe(...s: any[]): any;
declare namespace Oe { }
declare function Vf(...s: any[]): any;
declare namespace Vf { }
declare function rx(...s: any[]): any;
declare namespace rx { }
declare function Gf(...s: any[]): any;
declare namespace Gf { }
declare function Vp(...s: any[]): any;
declare namespace Vp { }
declare var pr: {};
declare function za(...s: any[]): any;
declare namespace za { }
declare function co(...s: any[]): any;
declare namespace co { }
declare function Pa(...s: any[]): any;
declare namespace Pa { }
declare namespace Kf {
    export { RT as sparseFillEmptyRows };
    export { FT as sparseReshape };
    export { OT as sparseSegmentMean };
    export { PT as sparseSegmentSum };
}
declare function ox(...s: any[]): any;
declare namespace ox { }
declare namespace SRe {
    export { Ba as fft };
    export { _i as ifft };
    export { Va as rfft };
    export { zu as irfft };
}
declare function sr(...s: any[]): any;
declare namespace sr { }
declare function bt(...s: any[]): any;
declare namespace bt { }
declare function Ve(...s: any[]): any;
declare namespace Ve { }
declare function Bu(...s: any[]): any;
declare namespace Bu { }
declare function Br(...s: any[]): any;
declare namespace Br { }
declare function Xt(...s: any[]): any;
declare namespace Xt { }
declare function Ls(...s: any[]): any;
declare namespace Ls { }
declare function Wf(...s: any[]): any;
declare namespace Wf { }
declare namespace hx {
    export { MT as stringNGrams };
    export { LT as stringSplit };
    export { zT as stringToHashBucketFast };
}
declare function le(...s: any[]): any;
declare namespace le { }
declare function me(...s: any[]): any;
declare namespace me { }
declare function hu(r: any): any;
declare function Uf(...s: any[]): any;
declare namespace Uf { }
declare function Ds(...s: any[]): any;
declare namespace Ds { }
declare function Dr(r: any, e: any, t: any): any;
declare function $t(r: any, e: any): any;
declare function ki(r: any, e: any, t: any): any;
declare function T_(r: any, e: any, t: any): any;
declare function Iq(r: any, e: any, t: any): any;
declare function Sq(r: any, e: any, t: any): any;
declare function Nq(r: any, e: any, t: any): any;
declare var ao: {};
declare var T1: {};
declare function V(r: any, e: any): any;
declare function vr(...s: any[]): any;
declare namespace vr { }
declare function lue(r: any): any;
declare function jf(...s: any[]): any;
declare namespace jf { }
declare namespace Ju {
    import sgd = Wa.sgd;
    export { sgd };
    import momentum = Wa.momentum;
    export { momentum };
    import adadelta = Wa.adadelta;
    export { adadelta };
    import adagrad = Wa.adagrad;
    export { adagrad };
    import rmsprop = Wa.rmsprop;
    export { rmsprop };
    import adamax = Wa.adamax;
    export { adamax };
    import adam = Wa.adam;
    export { adam };
}
declare function Be(...s: any[]): any;
declare namespace Be { }
declare function Vu(...s: any[]): any;
declare namespace Vu { }
declare function Gp(...s: any[]): any;
declare namespace Gp { }
declare function _se(r: any): void;
declare function wse(r: any, e: any): void;
declare function Hf(...s: any[]): any;
declare namespace Hf { }
declare function yr(...s: any[]): any;
declare namespace yr { }
declare function hr(r: any, e: any): any;
declare var b: {};
declare function $j(r: any): (e: any, t: any) => {
    grad: any;
    value: any;
};
declare function Rj(r: any): (e: any, t: any) => any;
declare function yk(r: any, e: boolean | undefined, t: any, n: any): any;
declare function Zg(r: any, e: any): {
    value: any;
    grads: {};
};
declare var sse: {
    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 SD: string;
declare var E1: string;
declare var lm: string;
declare var Xoe: string;
declare var KP: string;
declare namespace LTt {
    export { XP as forceHalfFloat };
}
declare var zO: {};
declare function St(...s: any[]): any;
declare namespace St { }
declare function qf(r: any): Promise<any>;
declare function yt(r: any, e?: string): any;
declare function Se(...s: any[]): any;
declare namespace Se { }
declare function Hx(r: any, e: any): boolean;
declare function EW(r: any): {};
declare function OX(r: any): {
    monitor: any;
    minDelta: number;
    patience: any;
    verbose: any;
    mode: any;
    baseline: any;
    monitorFunc: typeof Hx;
    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 lT(...s: any[]): any;
declare namespace lT { }
declare function uT(...s: any[]): any;
declare namespace uT { }
declare function dx(...s: any[]): any;
declare namespace dx { }
declare function fx(...s: any[]): any;
declare namespace fx { }
declare function cT(...s: any[]): any;
declare namespace cT { }
declare function aT(...s: any[]): any;
declare namespace aT { }
declare function pT(...s: any[]): any;
declare namespace pT { }
declare function dT(r: any, e: any, t: any, n?: number, o?: number): Promise<any>;
declare function hT(...s: any[]): any;
declare namespace hT { }
declare function gT(r: any, e: any, t: any, n?: number, o?: number, s?: number): Promise<{
    selectedIndices: any;
    selectedScores: any;
}>;
declare function xT(...s: any[]): any;
declare namespace xT { }
declare function yT(r: any, e: any, t: any, n?: number, o?: number, s?: boolean): Promise<{
    selectedIndices: any;
    validOutputs: any;
}>;
declare function bT(...s: any[]): any;
declare namespace bT { }
declare function wT(...s: any[]): any;
declare namespace wT { }
declare function _T(...s: any[]): any;
declare namespace _T { }
declare function kT(...s: any[]): any;
declare namespace kT { }
declare function CT(...s: any[]): any;
declare namespace CT { }
declare function IT(...s: any[]): any;
declare namespace IT { }
declare function Vr(...s: any[]): any;
declare namespace Vr { }
declare function ST(...s: any[]): any;
declare namespace ST { }
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 DT(...s: any[]): any;
declare namespace DT { }
declare function $T(...s: any[]): any;
declare namespace $T { }
declare function sT(...s: any[]): any;
declare namespace sT { }
declare function lx(...s: any[]): any;
declare namespace lx { }
declare function ux(...s: any[]): any;
declare namespace ux { }
declare function iT(...s: any[]): any;
declare namespace iT { }
declare function RT(...s: any[]): any;
declare namespace RT { }
declare function FT(...s: any[]): any;
declare namespace FT { }
declare function OT(...s: any[]): any;
declare namespace OT { }
declare function PT(...s: any[]): any;
declare namespace PT { }
declare function MT(...s: any[]): any;
declare namespace MT { }
declare function LT(...s: any[]): any;
declare namespace LT { }
declare function zT(...s: any[]): any;
declare namespace zT { }
declare var Wa: {
    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 { ti as Abs, Mi as Acos, Li as Acosh, Hu as AdadeltaOptimizer, qu as AdagradOptimizer, Ku as AdamOptimizer, Xu as AdamaxOptimizer, jn as Add, $o as AddN, zi as All, Bi as Any, Ro as ArgMax, ml as ArgMin, Vi as Asin, Gi as Asinh, Wi as Atan, ji as Atan2, Ui as Atanh, Fo as AvgPool, fl as AvgPool3D, Uc as AvgPool3DGrad, Wc as AvgPoolGrad, xb as BackendWasm, Oo as BatchMatMul, ri as BatchToSpaceND, jc as Bincount, Hc as BroadcastArgs, rN as BroadcastTo, Sv as Callback, Lk as CallbackList, eo as Cast, Po as Ceil, to as ClipByValue, qc as Complex, dl as ComplexAbs, ni as Concat, Mo as Conv2D, Kc as Conv2DBackpropFilter, Lo as Conv2DBackpropInput, hl as Conv3D, Xc as Conv3DBackpropFilterV2, Yc as Conv3DBackpropInputV2, zo as Cos, Bo as Cosh, Hi as CropAndResize, Vo as Cumsum, Bk as CustomCallback, pl as DataStorage, Zc as DenseBincount, qi as DepthToSpace, Go as DepthwiseConv2dNative, Jc as DepthwiseConv2dNativeBackpropFilter, Qc as DepthwiseConv2dNativeBackpropInput, ep as Diag, gl as Dilation2D, rf as Dilation2DBackpropFilter, tf as Dilation2DBackpropInput, Xw as ENV, Nv as EarlyStopping, tp as Einsum, Uo as Elu, rp as EluGrad, Eg as Environment, Xi as Equal, Ki as Erf, jo as Exp, oi as ExpandDims, Yi as Expm1, np as FFT, xl as Fill, Zi as FlipLeftRight, Ho as Floor, qo as FloorDiv, nf as FromPixels, Ko as FusedBatchNorm, xi as FusedConv2D, yi as FusedDepthwiseConv2D, Xy as GPGPUContext, Ji as GatherNd, si as GatherV2, ly as GraphModel, Qi as Greater, Xo as GreaterEqual, zk as History, op as IFFT, ro as Identity, sp as Imag, Tt as InputSpec, ea as IsFinite, ta as IsInf, ra as IsNan, Js as KernelBackend, yl as LRN, ap as LRNGrad, Ex as LayerVariable, Yn as LayersModel, Yo as LeakyRelu, na as Less, oa as LessEqual, ip as LinSpace, Zo as Log, sa as Log1p, nN as LogSoftmax, ia as LogicalAnd, au as LogicalNot, lu as LogicalOr, _c as MathBackendWebGL, Jo as Max, es as MaxPool, bl as MaxPool3D, up as MaxPool3DGrad, lp as MaxPoolGrad, cp as MaxPoolWithArgmax, Qo as Maximum, ts as Mean, rs as Min, ns as Minimum, os as MirrorPad, aa as Mod, Yu as MomentumOptimizer, pp as Multinomial, ss as Multiply, ii as Neg, ua as NonMaxSuppressionV3, ca as NonMaxSuppressionV4, pa as NonMaxSuppressionV5, la as NotEqual, $N as OP_SCOPE_SUFFIX, is as OneHot, ai as OnesLike, qr as Optimizer, li as Pack, as as PadV2, hse as Pool, ls as Pow, us as Prelu, ma as Prod, Zu as RMSPropOptimizer, Ln as RNN, wl as Range, s_ as Rank, mp as Real, Wo as RealDiv, fa as Reciprocal, Yt as Reduction, cs as Relu, ms as Relu6, ui as Reshape, ps as ResizeBilinear, dp as ResizeBilinearGrad, _l as ResizeNearestNeighbor, fp as ResizeNearestNeighborGrad, fs as Reverse, ka as RotateWithOffset, ds as Round, hs as Rsqrt, Ga as SGDOptimizer, da as ScatterNd, ci as Select, ha as Selu, qa as Sequential, xs as Sigmoid, xa as Sign, gs as Sin, ga as Sinh, pi as Slice, ws as Softmax, ya as Softplus, mi as SpaceToBatchND, hp as SparseFillEmptyRows, gp as SparseReshape, xp as SparseSegmentMean, yp as SparseSegmentSum, bp as SparseToDense, fi as SplitV, ys as Sqrt, kl as Square, _s as SquaredDifference, no as Step, ba as StridedSlice, wp as StringNGrams, _p as StringSplit, kp as StringToHashBucketFast, ks as Sub, bs as Sum, cn as SymbolicTensor, vs as Tan, Cs as Tanh, Le as Tensor, mt as TensorBuffer, Hn as Tile, wa as TopK, _a as Transform, Is as Transpose, vp as Unique, di as Unpack, vl as UnsortedSegmentSum, Sl as Variable, hi as ZerosLike, gi as _FusedMatMul, Ct as abs, df as acos, hf as acosh, Z as add, F_ as addN, wu as all, El as any, As as argMax, gf as argMin, xf as asin, yf as asinh, bf as atan, wf as atan2, _f as atanh, Ta as avgPool, kf as avgPool3d, A1 as backend, I as backend_util, IU as basicLSTMCell, lo as batchNorm, L_ as batchNorm2d, z_ as batchNorm3d, B_ as batchNorm4d, Ea as batchToSpaceND, vf as bincount, JSe as booleanMaskAsync, V_ as broadcastArgs, Aa as broadcastTo, Gg as browser, Ie as buffer, PX as callbacks, Y as cast, Cf as ceil, gr as clipByValue, wn as clone, Pn as complex, tt as concat, G_ as concat1d, W_ as concat2d, U_ as concat3d, j_ as concat4d, 
//# sourceMappingURL=tfjs.esm.d.ts.map