/**
 * @license
 * Copyright 2018 Google LLC
 *
 * Use of this source code is governed by an MIT-style
 * license that can be found in the LICENSE file or at
 * https://opensource.org/licenses/MIT.
 * =============================================================================
 */
/**
 * TensorFlow.js Layers: Recurrent Neural Network Layers.
 */
import * as tfc from '@tensorflow/tfjs-core';
import { serialization, tidy, util } from '@tensorflow/tfjs-core';
import { getActivation, serializeActivation } from '../activations';
import * as K from '../backend/tfjs_backend';
import { nameScope } from '../common';
import { getConstraint, serializeConstraint } from '../constraints';
import { InputSpec, SymbolicTensor } from '../engine/topology';
import { Layer } from '../engine/topology';
import { AttributeError, NotImplementedError, ValueError } from '../errors';
import { getInitializer, Initializer, Ones, serializeInitializer } from '../initializers';
import { getRegularizer, serializeRegularizer } from '../regularizers';
import { assertPositiveInteger } from '../utils/generic_utils';
import * as math_utils from '../utils/math_utils';
import { getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes } from '../utils/types_utils';
import { batchGetValue, batchSetValue } from '../variables';
import { deserialize } from './serialization';
/**
 * Standardize `apply()` args to a single list of tensor inputs.
 *
 * When running a model loaded from file, the input tensors `initialState` and
 * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the
 * dedicated kwargs fields. `inputs` consists of
 * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this
 * case.
 * This method makes sure that arguments are
 * separated and that `initialState` and `constants` are `Array`s of tensors
 * (or None).
 *
 * @param inputs Tensor or `Array` of  tensors.
 * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.
 * @param constants Tensor or `Array` of tensors or `null`/`undefined`.
 * @returns An object consisting of
 *   inputs: A tensor.
 *   initialState: `Array` of tensors or `null`.
 *   constants: `Array` of tensors or `null`.
 * @throws ValueError, if `inputs` is an `Array` but either `initialState` or
 *   `constants` is provided.
 */
export function standardizeArgs(inputs, initialState, constants, numConstants) {
    if (Array.isArray(inputs)) {
        if (initialState != null || constants != null) {
            throw new ValueError('When inputs is an array, neither initialState or constants ' +
                'should be provided');
        }
        if (numConstants != null) {
            constants = inputs.slice(inputs.length - numConstants, inputs.length);
            inputs = inputs.slice(0, inputs.length - numConstants);
        }
        if (inputs.length > 1) {
            initialState = inputs.slice(1, inputs.length);
        }
        inputs = inputs[0];
    }
    function toListOrNull(x) {
        if (x == null || Array.isArray(x)) {
            return x;
        }
        else {
            return [x];
        }
    }
    initialState = toListOrNull(initialState);
    constants = toListOrNull(constants);
    return { inputs, initialState, constants };
}
/**
 * Iterates over the time dimension of a tensor.
 *
 * @param stepFunction RNN step function.
 *   Parameters:
 *     inputs: tensor with shape `[samples, ...]` (no time dimension),
 *       representing input for the batch of samples at a certain time step.
 *     states: an Array of tensors.
 *   Returns:
 *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).
 *     newStates: list of tensors, same length and shapes as `states`. The first
 *       state in the list must be the output tensor at the previous timestep.
 * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at
 *   least 3D).
 * @param initialStates Tensor with shape `[samples, outputDim]` (no time
 *   dimension), containing the initial values of the states used in the step
 *   function.
 * @param goBackwards If `true`, do the iteration over the time dimension in
 *   reverse order and return the reversed sequence.
 * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for
 *   every element that is masked.
 * @param constants An Array of constant values passed at each step.
 * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*
 *   applicable to this imperative deeplearn.js backend. Its value is ignored.
 * @param needPerStepOutputs Whether the per-step outputs are to be
 *   concatenated into a single tensor and returned (as the second return
 *   value). Default: `false`. This arg is included so that the relatively
 *   expensive concatenation of the stepwise outputs can be omitted unless
 *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which
 *   `returnSequence` is `true`.)
 * @returns An Array: `[lastOutput, outputs, newStates]`.
 *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.
 *   outputs: tensor with shape `[samples, time, ...]` where each entry
 *     `output[s, t]` is the output of the step function at time `t` for sample
 *     `s`. This return value is provided if and only if the
 *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this
 *     return value will be `undefined`.
 *   newStates: Array of tensors, latest states returned by the step function,
 *      of shape `(samples, ...)`.
 * @throws ValueError If input dimension is less than 3.
 *
 * TODO(nielsene): This needs to be tidy-ed.
 */
export function rnn(stepFunction, inputs, initialStates, goBackwards = false, mask, constants, unroll = false, needPerStepOutputs = false) {
    return tfc.tidy(() => {
        const ndim = inputs.shape.length;
        if (ndim < 3) {
            throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);
        }
        // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,
        // ...].
        const axes = [1, 0].concat(math_utils.range(2, ndim));
        inputs = tfc.transpose(inputs, axes);
        if (constants != null) {
            throw new NotImplementedError('The rnn() functoin of the deeplearn.js backend does not support ' +
                'constants yet.');
        }
        // Porting Note: the unroll option is ignored by the imperative backend.
        if (unroll) {
            console.warn('Backend rnn(): the unroll = true option is not applicable to the ' +
                'imperative deeplearn.js backend.');
        }
        if (mask != null) {
            mask = tfc.cast(tfc.cast(mask, 'bool'), 'float32');
            if (mask.rank === ndim - 1) {
                mask = tfc.expandDims(mask, -1);
            }
            mask = tfc.transpose(mask, axes);
        }
        if (goBackwards) {
            inputs = tfc.reverse(inputs, 0);
            if (mask != null) {
                mask = tfc.reverse(mask, 0);
            }
        }
        // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop
        //   (tf.while_loop). But for the imperative deeplearn.js backend, we just
        //   use the usual TypeScript control flow to iterate over the time steps in
        //   the inputs.
        // Porting Note: PyKeras patches a "_use_learning_phase" attribute to
        // outputs.
        //   This is not idiomatic in TypeScript. The info regarding whether we are
        //   in a learning (i.e., training) phase for RNN is passed in a different
        //   way.
        const perStepOutputs = [];
        let lastOutput;
        let states = initialStates;
        const timeSteps = inputs.shape[0];
        const perStepInputs = tfc.unstack(inputs);
        let perStepMasks;
        if (mask != null) {
            perStepMasks = tfc.unstack(mask);
        }
        for (let t = 0; t < timeSteps; ++t) {
            const currentInput = perStepInputs[t];
            const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));
            if (mask == null) {
                lastOutput = stepOutputs[0];
                states = stepOutputs[1];
            }
            else {
                const maskedOutputs = tfc.tidy(() => {
                    const stepMask = perStepMasks[t];
                    const negStepMask = tfc.sub(tfc.onesLike(stepMask), stepMask);
                    // TODO(cais): Would tfc.where() be better for performance?
                    const output = tfc.add(tfc.mul(stepOutputs[0], stepMask), tfc.mul(states[0], negStepMask));
                    const newStates = states.map((state, i) => {
                        return tfc.add(tfc.mul(stepOutputs[1][i], stepMask), tfc.mul(state, negStepMask));
                    });
                    return { output, newStates };
                });
                lastOutput = maskedOutputs.output;
                states = maskedOutputs.newStates;
            }
            if (needPerStepOutputs) {
                perStepOutputs.push(lastOutput);
            }
        }
        let outputs;
        if (needPerStepOutputs) {
            const axis = 1;
            outputs = tfc.stack(perStepOutputs, axis);
        }
        return [lastOutput, outputs, states];
    });
}
class RNN extends Layer {
    constructor(args) {
        super(args);
        let cell;
        if (args.cell == null) {
            throw new ValueError('cell property is missing for the constructor of RNN.');
        }
        else if (Array.isArray(args.cell)) {
            cell = new StackedRNNCells({ cells: args.cell });
        }
        else {
            cell = args.cell;
        }
        if (cell.stateSize == null) {
            throw new ValueError('The RNN cell should have an attribute `stateSize` (tuple of ' +
                'integers, one integer per RNN state).');
        }
        this.cell = cell;
        this.returnSequences =
            args.returnSequences == null ? false : args.returnSequences;
        this.returnState = args.returnState == null ? false : args.returnState;
        this.goBackwards = args.goBackwards == null ? false : args.goBackwards;
        this._stateful = args.stateful == null ? false : args.stateful;
        this.unroll = args.unroll == null ? false : args.unroll;
        this.supportsMasking = true;
        this.inputSpec = [new InputSpec({ ndim: 3 })];
        this.stateSpec = null;
        this.states_ = null;
        // TODO(cais): Add constantsSpec and numConstants.
        this.numConstants = null;
        // TODO(cais): Look into the use of initial_state in the kwargs of the
        //   constructor.
        this.keptStates = [];
    }
    // Porting Note: This is the equivalent of `RNN.states` property getter in
    //   PyKeras.
    getStates() {
        if (this.states_ == null) {
            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
            return math_utils.range(0, numStates).map(x => null);
        }
        else {
            return this.states_;
        }
    }
    // Porting Note: This is the equivalent of the `RNN.states` property setter in
    //   PyKeras.
    setStates(states) {
        this.states_ = states;
    }
    computeOutputShape(inputShape) {
        if (isArrayOfShapes(inputShape)) {
            inputShape = inputShape[0];
        }
        inputShape = inputShape;
        // TODO(cais): Remove the casting once stacked RNN cells become supported.
        let stateSize = this.cell.stateSize;
        if (!Array.isArray(stateSize)) {
            stateSize = [stateSize];
        }
        const outputDim = stateSize[0];
        let outputShape;
        if (this.returnSequences) {
            outputShape = [inputShape[0], inputShape[1], outputDim];
        }
        else {
            outputShape = [inputShape[0], outputDim];
        }
        if (this.returnState) {
            const stateShape = [];
            for (const dim of stateSize) {
                stateShape.push([inputShape[0], dim]);
            }
            return [outputShape].concat(stateShape);
        }
        else {
            return outputShape;
        }
    }
    computeMask(inputs, mask) {
        return tfc.tidy(() => {
            if (Array.isArray(mask)) {
                mask = mask[0];
            }
            const outputMask = this.returnSequences ? mask : null;
            if (this.returnState) {
                const stateMask = this.states.map(s => null);
                return [outputMask].concat(stateMask);
            }
            else {
                return outputMask;
            }
        });
    }
    /**
     * Get the current state tensors of the RNN.
     *
     * If the state hasn't been set, return an array of `null`s of the correct
     * length.
     */
    get states() {
        if (this.states_ == null) {
            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
            const output = [];
            for (let i = 0; i < numStates; ++i) {
                output.push(null);
            }
            return output;
        }
        else {
            return this.states_;
        }
    }
    set states(s) {
        this.states_ = s;
    }
    build(inputShape) {
        // Note inputShape will be an Array of Shapes of initial states and
        // constants if these are passed in apply().
        const constantShape = null;
        if (this.numConstants != null) {
            throw new NotImplementedError('Constants support is not implemented in RNN yet.');
        }
        if (isArrayOfShapes(inputShape)) {
            inputShape = inputShape[0];
        }
        inputShape = inputShape;
        const batchSize = this.stateful ? inputShape[0] : null;
        const inputDim = inputShape.slice(2);
        this.inputSpec[0] = new InputSpec({ shape: [batchSize, null, ...inputDim] });
        // Allow cell (if RNNCell Layer) to build before we set or validate
        // stateSpec.
        const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));
        if (constantShape != null) {
            throw new NotImplementedError('Constants support is not implemented in RNN yet.');
        }
        else {
            this.cell.build(stepInputShape);
        }
        // Set or validate stateSpec.
        let stateSize;
        if (Array.isArray(this.cell.stateSize)) {
            stateSize = this.cell.stateSize;
        }
        else {
            stateSize = [this.cell.stateSize];
        }
        if (this.stateSpec != null) {
            if (!util.arraysEqual(this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]), stateSize)) {
                throw new ValueError(`An initialState was passed that is not compatible with ` +
                    `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +
                    `However cell.stateSize is ${this.cell.stateSize}`);
            }
        }
        else {
            this.stateSpec =
                stateSize.map(dim => new InputSpec({ shape: [null, dim] }));
        }
        if (this.stateful) {
            this.resetStates();
        }
    }
    /**
     * Reset the state tensors of the RNN.
     *
     * If the `states` argument is `undefined` or `null`, will set the
     * state tensor(s) of the RNN to all-zero tensors of the appropriate
     * shape(s).
     *
     * If `states` is provided, will set the state tensors of the RNN to its
     * value.
     *
     * @param states Optional externally-provided initial states.
     * @param training Whether this call is done during training. For stateful
     *   RNNs, this affects whether the old states are kept or discarded. In
     *   particular, if `training` is `true`, the old states will be kept so
     *   that subsequent backpropgataion through time (BPTT) may work properly.
     *   Else, the old states will be discarded.
     */
    resetStates(states, training = false) {
        tidy(() => {
            if (!this.stateful) {
                throw new AttributeError('Cannot call resetStates() on an RNN Layer that is not stateful.');
            }
            const batchSize = this.inputSpec[0].shape[0];
            if (batchSize == null) {
                throw new ValueError('If an RNN is stateful, it needs to know its batch size. Specify ' +
                    'the batch size of your input tensors: \n' +
                    '- If using a Sequential model, specify the batch size by ' +
                    'passing a `batchInputShape` option to your first layer.\n' +
                    '- If using the functional API, specify the batch size by ' +
                    'passing a `batchShape` option to your Input layer.');
            }
            // Initialize state if null.
            if (this.states_ == null) {
                if (Array.isArray(this.cell.stateSize)) {
                    this.states_ =
                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));
                }
                else {
                    this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];
                }
            }
            else if (states == null) {
                // Dispose old state tensors.
                tfc.dispose(this.states_);
                // For stateful RNNs, fully dispose kept old states.
                if (this.keptStates != null) {
                    tfc.dispose(this.keptStates);
                    this.keptStates = [];
                }
                if (Array.isArray(this.cell.stateSize)) {
                    this.states_ =
                        this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));
                }
                else {
                    this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);
                }
            }
            else {
                if (!Array.isArray(states)) {
                    states = [states];
                }
                if (states.length !== this.states_.length) {
                    throw new ValueError(`Layer ${this.name} expects ${this.states_.length} state(s), ` +
                        `but it received ${states.length} state value(s). Input ` +
                        `received: ${states}`);
                }
                if (training === true) {
                    // Store old state tensors for complete disposal later, i.e., during
                    // the next no-arg call to this method. We do not dispose the old
                    // states immediately because that BPTT (among other things) require
                    // them.
                    this.keptStates.push(this.states_.slice());
                }
                else {
                    tfc.dispose(this.states_);
                }
                for (let index = 0; index < this.states_.length; ++index) {
                    const value = states[index];
                    const dim = Array.isArray(this.cell.stateSize) ?
                        this.cell.stateSize[index] :
                        this.cell.stateSize;
                    const expectedShape = [batchSize, dim];
                    if (!util.arraysEqual(value.shape, expectedShape)) {
                        throw new ValueError(`State ${index} is incompatible with layer ${this.name}: ` +
                            `expected shape=${expectedShape}, received shape=${value.shape}`);
                    }
                    this.states_[index] = value;
                }
            }
            this.states_ = this.states_.map(state => tfc.keep(state.clone()));
        });
    }
    apply(inputs, kwargs) {
        // TODO(cais): Figure out whether initialState is in kwargs or inputs.
        let initialState = kwargs == null ? null : kwargs['initialState'];
        let constants = kwargs == null ? null : kwargs['constants'];
        if (kwargs == null) {
            kwargs = {};
        }
        const standardized = standardizeArgs(inputs, initialState, constants, this.numConstants);
        inputs = standardized.inputs;
        initialState = standardized.initialState;
        constants = standardized.constants;
        // If any of `initial_state` or `constants` are specified and are
        // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify
        // the input_spec to include them.
        let additionalInputs = [];
        let additionalSpecs = [];
        if (initialState != null) {
            kwargs['initialState'] = initialState;
            additionalInputs = additionalInputs.concat(initialState);
            this.stateSpec = [];
            for (const state of initialState) {
                this.stateSpec.push(new InputSpec({ shape: state.shape }));
            }
            // TODO(cais): Use the following instead.
            // this.stateSpec = initialState.map(state => new InputSpec({shape:
            // state.shape}));
            additionalSpecs = additionalSpecs.concat(this.stateSpec);
        }
        if (constants != null) {
            kwargs['constants'] = constants;
            additionalInputs = additionalInputs.concat(constants);
            // TODO(cais): Add this.constantsSpec.
            this.numConstants = constants.length;
        }
        const isTensor = additionalInputs[0] instanceof SymbolicTensor;
        if (isTensor) {
            // Compute full input spec, including state and constants.
            const fullInput = [inputs].concat(additionalInputs);
            const fullInputSpec = this.inputSpec.concat(additionalSpecs);
            // Perform the call with temporarily replaced inputSpec.
            const originalInputSpec = this.inputSpec;
            this.inputSpec = fullInputSpec;
            const output = super.apply(fullInput, kwargs);
            this.inputSpec = originalInputSpec;
            return output;
        }
        else {
            return super.apply(inputs, kwargs);
        }
    }
    // tslint:disable-next-line:no-any
    call(inputs, kwargs) {
        // Input shape: `[samples, time (padded with zeros), input_dim]`.
        // Note that the .build() method of subclasses **must** define
        // this.inputSpec and this.stateSpec owith complete input shapes.
        return tidy(() => {
            const mask = kwargs == null ? null : kwargs['mask'];
            const training = kwargs == null ? null : kwargs['training'];
            let initialState = kwargs == null ? null : kwargs['initialState'];
            inputs = getExactlyOneTensor(inputs);
            if (initialState == null) {
                if (this.stateful) {
                    initialState = this.states_;
                }
                else {
                    initialState = this.getInitialState(inputs);
                }
            }
            const numStates = Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;
            if (initialState.length !== numStates) {
                throw new ValueError(`RNN Layer has ${numStates} state(s) but was passed ` +
                    `${initialState.length} initial state(s).`);
            }
            if (this.unroll) {
                console.warn('Ignoring unroll = true for RNN layer, due to imperative backend.');
            }
            const cellCallKwargs = { training };
            // TODO(cais): Add support for constants.
            const step = (inputs, states) => {
                // `inputs` and `states` are concatenated to form a single `Array` of
                // `tf.Tensor`s as the input to `cell.call()`.
                const outputs = this.cell.call([inputs].concat(states), cellCallKwargs);
                // Marshall the return value into output and new states.
                return [outputs[0], outputs.slice(1)];
            };
            // TODO(cais): Add support for constants.
            const rnnOutputs = rnn(step, inputs, initialState, this.goBackwards, mask, null, this.unroll, this.returnSequences);
            const lastOutput = rnnOutputs[0];
            const outputs = rnnOutputs[1];
            const states = rnnOutputs[2];
            if (this.stateful) {
                this.resetStates(states, training);
            }
            const output = this.returnSequences ? outputs : lastOutput;
            // TODO(cais): Property set learning phase flag.
            if (this.returnState) {
                return [output].concat(states);
            }
            else {
                return output;
            }
        });
    }
    getInitialState(inputs) {
        return tidy(() => {
            // Build an all-zero tensor of shape [samples, outputDim].
            // [Samples, timeSteps, inputDim].
            let initialState = tfc.zeros(inputs.shape);
            // [Samples].
            initialState = tfc.sum(initialState, [1, 2]);
            initialState = K.expandDims(initialState); // [Samples, 1].
            if (Array.isArray(this.cell.stateSize)) {
                return this.cell.stateSize.map(dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);
            }
            else {
                return this.cell.stateSize > 1 ?
                    [K.tile(initialState, [1, this.cell.stateSize])] :
                    [initialState];
            }
        });
    }
    get trainableWeights() {
        if (!this.trainable) {
            return [];
        }
        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.
        return this.cell.trainableWeights;
    }
    get nonTrainableWeights() {
        // Porting Note: In TypeScript, `this` is always an instance of `Layer`.
        if (!this.trainable) {
            return this.cell.weights;
        }
        return this.cell.nonTrainableWeights;
    }
    setFastWeightInitDuringBuild(value) {
        super.setFastWeightInitDuringBuild(value);
        if (this.cell != null) {
            this.cell.setFastWeightInitDuringBuild(value);
        }
    }
    getConfig() {
        const baseConfig = super.getConfig();
        const config = {
            returnSequences: this.returnSequences,
            returnState: this.returnState,
            goBackwards: this.goBackwards,
            stateful: this.stateful,
            unroll: this.unroll,
        };
        if (this.numConstants != null) {
            config['numConstants'] = this.numConstants;
        }
        const cellConfig = this.cell.getConfig();
        if (this.getClassName() === RNN.className) {
            config['cell'] = {
                'className': this.cell.getClassName(),
                'config': cellConfig,
            };
        }
        // this order is necessary, to prevent cell name from replacing layer name
        return Object.assign(Object.assign(Object.assign({}, cellConfig), baseConfig), config);
    }
    /** @nocollapse */
    static fromConfig(cls, config, customObjects = {}) {
        const cellConfig = config['cell'];
        const cell = deserialize(cellConfig, customObjects);
        return new cls(Object.assign(config, { cell }));
    }
}
/** @nocollapse */
RNN.className = 'RNN';
export { RNN };
serialization.registerClass(RNN);
// Porting Note: This is a common parent class for RNN cells. There is no
// equivalent of this in PyKeras. Having a common parent class forgoes the
//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.
/**
 * An RNNCell layer.
 *
 * @doc {heading: 'Layers', subheading: 'Classes'}
 */
export class RNNCell extends Layer {
}
class SimpleRNNCell extends RNNCell {
    constructor(args) {
        super(args);
        this.DEFAULT_ACTIVATION = 'tanh';
        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';
        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';
        this.DEFAULT_BIAS_INITIALIZER = 'zeros';
        this.units = args.units;
        assertPositiveInteger(this.units, `units`);
        this.activation = getActivation(args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);
        this.useBias = args.useBias == null ? true : args.useBias;
        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
        this.biasInitializer =
            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
        this.biasRegularizer = getRegularizer(args.biasRegularizer);
        this.kernelConstraint = getConstraint(args.kernelConstraint);
        this.recurrentConstraint = getConstraint(args.recurrentConstraint);
        this.biasConstraint = getConstraint(args.biasConstraint);
        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);
        this.recurrentDropout = math_utils.min([
            1,
            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
        ]);
        this.dropoutFunc = args.dropoutFunc;
        this.stateSize = this.units;
        this.dropoutMask = null;
        this.recurrentDropoutMask = null;
    }
    build(inputShape) {
        inputShape = getExactlyOneShape(inputShape);
        // TODO(cais): Use regularizer.
        this.kernel = this.addWeight('kernel', [inputShape[inputShape.length - 1], this.units], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
        if (this.useBias) {
            this.bias = this.addWeight('bias', [this.units], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
        }
        else {
            this.bias = null;
        }
        this.built = true;
    }
    // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:
    //   `inputs` and `states`. Here, the two tensors are combined into an
    //   `Tensor[]` Array as the first input argument.
    //   Similarly, PyKeras' equivalent of this method returns two values:
    //    `output` and `[output]`. Here the two are combined into one length-2
    //    `Tensor[]`, consisting of `output` repeated.
    call(inputs, kwargs) {
        return tidy(() => {
            inputs = inputs;
            if (inputs.length !== 2) {
                throw new ValueError(`SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);
            }
            let prevOutput = inputs[1];
            inputs = inputs[0];
            const training = kwargs['training'] == null ? false : kwargs['training'];
            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
                this.dropoutMask = generateDropoutMask({
                    ones: () => tfc.onesLike(inputs),
                    rate: this.dropout,
                    training,
                    dropoutFunc: this.dropoutFunc,
                });
            }
            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&
                this.recurrentDropoutMask == null) {
                this.recurrentDropoutMask = generateDropoutMask({
                    ones: () => tfc.onesLike(prevOutput),
                    rate: this.recurrentDropout,
                    training,
                    dropoutFunc: this.dropoutFunc,
                });
            }
            let h;
            const dpMask = this.dropoutMask;
            const recDpMask = this.recurrentDropoutMask;
            if (dpMask != null) {
                h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());
            }
            else {
                h = K.dot(inputs, this.kernel.read());
            }
            if (this.bias != null) {
                h = K.biasAdd(h, this.bias.read());
            }
            if (recDpMask != null) {
                prevOutput = tfc.mul(prevOutput, recDpMask);
            }
            let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));
            if (this.activation != null) {
                output = this.activation.apply(output);
            }
            // TODO(cais): Properly set learning phase on output tensor?
            return [output, output];
        });
    }
    getConfig() {
        const baseConfig = super.getConfig();
        const config = {
            units: this.units,
            activation: serializeActivation(this.activation),
            useBias: this.useBias,
            kernelInitializer: serializeInitializer(this.kernelInitializer),
            recurrentInitializer: serializeInitializer(this.recurrentInitializer),
            biasInitializer: serializeInitializer(this.biasInitializer),
            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
            biasRegularizer: serializeRegularizer(this.biasRegularizer),
            activityRegularizer: serializeRegularizer(this.activityRegularizer),
            kernelConstraint: serializeConstraint(this.kernelConstraint),
            recurrentConstraint: serializeConstraint(this.recurrentConstraint),
            biasConstraint: serializeConstraint(this.biasConstraint),
            dropout: this.dropout,
            recurrentDropout: this.recurrentDropout,
        };
        return Object.assign(Object.assign({}, baseConfig), config);
    }
}
/** @nocollapse */
SimpleRNNCell.className = 'SimpleRNNCell';
export { SimpleRNNCell };
serialization.registerClass(SimpleRNNCell);
class SimpleRNN extends RNN {
    constructor(args) {
        args.cell = new SimpleRNNCell(args);
        super(args);
        // TODO(cais): Add activityRegularizer.
    }
    call(inputs, kwargs) {
        return tidy(() => {
            if (this.cell.dropoutMask != null) {
                tfc.dispose(this.cell.dropoutMask);
                this.cell.dropoutMask = null;
            }
            if (this.cell.recurrentDropoutMask != null) {
                tfc.dispose(this.cell.recurrentDropoutMask);
                this.cell.recurrentDropoutMask = null;
            }
            const mask = kwargs == null ? null : kwargs['mask'];
            const training = kwargs == null ? null : kwargs['training'];
            const initialState = kwargs == null ? null : kwargs['initialState'];
            return super.call(inputs, { mask, training, initialState });
        });
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        return new cls(config);
    }
}
/** @nocollapse */
SimpleRNN.className = 'SimpleRNN';
export { SimpleRNN };
serialization.registerClass(SimpleRNN);
class GRUCell extends RNNCell {
    constructor(args) {
        super(args);
        this.DEFAULT_ACTIVATION = 'tanh';
        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';
        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';
        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';
        this.DEFAULT_BIAS_INITIALIZER = 'zeros';
        if (args.resetAfter) {
            throw new ValueError(`GRUCell does not support reset_after parameter set to true.`);
        }
        this.units = args.units;
        assertPositiveInteger(this.units, 'units');
        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :
            args.activation);
        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?
            this.DEFAULT_RECURRENT_ACTIVATION :
            args.recurrentActivation);
        this.useBias = args.useBias == null ? true : args.useBias;
        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
        this.biasInitializer =
            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
        this.biasRegularizer = getRegularizer(args.biasRegularizer);
        this.kernelConstraint = getConstraint(args.kernelConstraint);
        this.recurrentConstraint = getConstraint(args.recurrentConstraint);
        this.biasConstraint = getConstraint(args.biasConstraint);
        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);
        this.recurrentDropout = math_utils.min([
            1,
            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
        ]);
        this.dropoutFunc = args.dropoutFunc;
        this.implementation = args.implementation;
        this.stateSize = this.units;
        this.dropoutMask = null;
        this.recurrentDropoutMask = null;
    }
    build(inputShape) {
        inputShape = getExactlyOneShape(inputShape);
        const inputDim = inputShape[inputShape.length - 1];
        this.kernel = this.addWeight('kernel', [inputDim, this.units * 3], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 3], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
        if (this.useBias) {
            this.bias = this.addWeight('bias', [this.units * 3], null, this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
        }
        else {
            this.bias = null;
        }
        // Porting Notes: Unlike the PyKeras implementation, we perform slicing
        //   of the weights and bias in the call() method, at execution time.
        this.built = true;
    }
    call(inputs, kwargs) {
        return tidy(() => {
            inputs = inputs;
            if (inputs.length !== 2) {
                throw new ValueError(`GRUCell expects 2 input Tensors (inputs, h, c), got ` +
                    `${inputs.length}.`);
            }
            const training = kwargs['training'] == null ? false : kwargs['training'];
            let hTMinus1 = inputs[1]; // Previous memory state.
            inputs = inputs[0];
            // Note: For superior performance, TensorFlow.js always uses
            // implementation 2, regardless of the actual value of
            // config.implementation.
            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
                this.dropoutMask = generateDropoutMask({
                    ones: () => tfc.onesLike(inputs),
                    rate: this.dropout,
                    training,
                    count: 3,
                    dropoutFunc: this.dropoutFunc,
                });
            }
            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&
                this.recurrentDropoutMask == null) {
                this.recurrentDropoutMask = generateDropoutMask({
                    ones: () => tfc.onesLike(hTMinus1),
                    rate: this.recurrentDropout,
                    training,
                    count: 3,
                    dropoutFunc: this.dropoutFunc,
                });
            }
            const dpMask = this.dropoutMask;
            const recDpMask = this.recurrentDropoutMask;
            let z;
            let r;
            let hh;
            if (0 < this.dropout && this.dropout < 1) {
                inputs = tfc.mul(inputs, dpMask[0]);
            }
            let matrixX = K.dot(inputs, this.kernel.read());
            if (this.useBias) {
                matrixX = K.biasAdd(matrixX, this.bias.read());
            }
            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);
            }
            const recurrentKernelValue = this.recurrentKernel.read();
            const [rk1, rk2] = tfc.split(recurrentKernelValue, [2 * this.units, this.units], recurrentKernelValue.rank - 1);
            const matrixInner = K.dot(hTMinus1, rk1);
            const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);
            const [recurrentZ, recurrentR] = tfc.split(matrixInner, 2, matrixInner.rank - 1);
            z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));
            r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));
            const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);
            hh = this.activation.apply(tfc.add(xH, recurrentH));
            const h = tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));
            // TODO(cais): Add use_learning_phase flag properly.
            return [h, h];
        });
    }
    getConfig() {
        const baseConfig = super.getConfig();
        const config = {
            units: this.units,
            activation: serializeActivation(this.activation),
            recurrentActivation: serializeActivation(this.recurrentActivation),
            useBias: this.useBias,
            kernelInitializer: serializeInitializer(this.kernelInitializer),
            recurrentInitializer: serializeInitializer(this.recurrentInitializer),
            biasInitializer: serializeInitializer(this.biasInitializer),
            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
            biasRegularizer: serializeRegularizer(this.biasRegularizer),
            activityRegularizer: serializeRegularizer(this.activityRegularizer),
            kernelConstraint: serializeConstraint(this.kernelConstraint),
            recurrentConstraint: serializeConstraint(this.recurrentConstraint),
            biasConstraint: serializeConstraint(this.biasConstraint),
            dropout: this.dropout,
            recurrentDropout: this.recurrentDropout,
            implementation: this.implementation,
            resetAfter: false
        };
        return Object.assign(Object.assign({}, baseConfig), config);
    }
}
/** @nocollapse */
GRUCell.className = 'GRUCell';
export { GRUCell };
serialization.registerClass(GRUCell);
class GRU extends RNN {
    constructor(args) {
        if (args.implementation === 0) {
            console.warn('`implementation=0` has been deprecated, and now defaults to ' +
                '`implementation=1`. Please update your layer call.');
        }
        args.cell = new GRUCell(args);
        super(args);
        // TODO(cais): Add activityRegularizer.
    }
    call(inputs, kwargs) {
        return tidy(() => {
            if (this.cell.dropoutMask != null) {
                tfc.dispose(this.cell.dropoutMask);
                this.cell.dropoutMask = null;
            }
            if (this.cell.recurrentDropoutMask != null) {
                tfc.dispose(this.cell.recurrentDropoutMask);
                this.cell.recurrentDropoutMask = null;
            }
            const mask = kwargs == null ? null : kwargs['mask'];
            const training = kwargs == null ? null : kwargs['training'];
            const initialState = kwargs == null ? null : kwargs['initialState'];
            return super.call(inputs, { mask, training, initialState });
        });
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        if (config['implmentation'] === 0) {
            config['implementation'] = 1;
        }
        return new cls(config);
    }
}
/** @nocollapse */
GRU.className = 'GRU';
export { GRU };
serialization.registerClass(GRU);
class LSTMCell extends RNNCell {
    constructor(args) {
        super(args);
        this.DEFAULT_ACTIVATION = 'tanh';
        this.DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';
        this.DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';
        this.DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';
        this.DEFAULT_BIAS_INITIALIZER = 'zeros';
        this.units = args.units;
        assertPositiveInteger(this.units, 'units');
        this.activation = getActivation(args.activation === undefined ? this.DEFAULT_ACTIVATION :
            args.activation);
        this.recurrentActivation = getActivation(args.recurrentActivation === undefined ?
            this.DEFAULT_RECURRENT_ACTIVATION :
            args.recurrentActivation);
        this.useBias = args.useBias == null ? true : args.useBias;
        this.kernelInitializer = getInitializer(args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);
        this.recurrentInitializer = getInitializer(args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);
        this.biasInitializer =
            getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);
        this.unitForgetBias = args.unitForgetBias;
        this.kernelRegularizer = getRegularizer(args.kernelRegularizer);
        this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);
        this.biasRegularizer = getRegularizer(args.biasRegularizer);
        this.kernelConstraint = getConstraint(args.kernelConstraint);
        this.recurrentConstraint = getConstraint(args.recurrentConstraint);
        this.biasConstraint = getConstraint(args.biasConstraint);
        this.dropout = math_utils.min([1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);
        this.recurrentDropout = math_utils.min([
            1,
            math_utils.max([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
        ]);
        this.dropoutFunc = args.dropoutFunc;
        this.implementation = args.implementation;
        this.stateSize = [this.units, this.units];
        this.dropoutMask = null;
        this.recurrentDropoutMask = null;
    }
    build(inputShape) {
        var _a;
        inputShape = getExactlyOneShape(inputShape);
        const inputDim = inputShape[inputShape.length - 1];
        this.kernel = this.addWeight('kernel', [inputDim, this.units * 4], null, this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
        this.recurrentKernel = this.addWeight('recurrent_kernel', [this.units, this.units * 4], null, this.recurrentInitializer, this.recurrentRegularizer, true, this.recurrentConstraint);
        let biasInitializer;
        if (this.useBias) {
            if (this.unitForgetBias) {
                const capturedBiasInit = this.biasInitializer;
                const capturedUnits = this.units;
                biasInitializer = new (_a = class CustomInit extends Initializer {
                        apply(shape, dtype) {
                            // TODO(cais): More informative variable names?
                            const bI = capturedBiasInit.apply([capturedUnits]);
                            const bF = (new Ones()).apply([capturedUnits]);
                            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);
                            return K.concatAlongFirstAxis(K.concatAlongFirstAxis(bI, bF), bCAndH);
                        }
                    },
                    /** @nocollapse */
                    _a.className = 'CustomInit',
                    _a)();
            }
            else {
                biasInitializer = this.biasInitializer;
            }
            this.bias = this.addWeight('bias', [this.units * 4], null, biasInitializer, this.biasRegularizer, true, this.biasConstraint);
        }
        else {
            this.bias = null;
        }
        // Porting Notes: Unlike the PyKeras implementation, we perform slicing
        //   of the weights and bias in the call() method, at execution time.
        this.built = true;
    }
    call(inputs, kwargs) {
        return tidy(() => {
            const training = kwargs['training'] == null ? false : kwargs['training'];
            inputs = inputs;
            if (inputs.length !== 3) {
                throw new ValueError(`LSTMCell expects 3 input Tensors (inputs, h, c), got ` +
                    `${inputs.length}.`);
            }
            let hTMinus1 = inputs[1]; // Previous memory state.
            const cTMinus1 = inputs[2]; // Previous carry state.
            inputs = inputs[0];
            if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {
                this.dropoutMask = generateDropoutMask({
                    ones: () => tfc.onesLike(inputs),
                    rate: this.dropout,
                    training,
                    count: 4,
                    dropoutFunc: this.dropoutFunc
                });
            }
            if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&
                this.recurrentDropoutMask == null) {
                this.recurrentDropoutMask = generateDropoutMask({
                    ones: () => tfc.onesLike(hTMinus1),
                    rate: this.recurrentDropout,
                    training,
                    count: 4,
                    dropoutFunc: this.dropoutFunc
                });
            }
            const dpMask = this.dropoutMask;
            const recDpMask = this.recurrentDropoutMask;
            // Note: For superior performance, TensorFlow.js always uses
            // implementation 2 regardless of the actual value of
            // config.implementation.
            let i;
            let f;
            let c;
            let o;
            if (0 < this.dropout && this.dropout < 1) {
                inputs = tfc.mul(inputs, dpMask[0]);
            }
            let z = K.dot(inputs, this.kernel.read());
            if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
                hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);
            }
            z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));
            if (this.useBias) {
                z = K.biasAdd(z, this.bias.read());
            }
            const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);
            i = this.recurrentActivation.apply(z0);
            f = this.recurrentActivation.apply(z1);
            c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));
            o = this.recurrentActivation.apply(z3);
            const h = tfc.mul(o, this.activation.apply(c));
            // TODO(cais): Add use_learning_phase flag properly.
            return [h, h, c];
        });
    }
    getConfig() {
        const baseConfig = super.getConfig();
        const config = {
            units: this.units,
            activation: serializeActivation(this.activation),
            recurrentActivation: serializeActivation(this.recurrentActivation),
            useBias: this.useBias,
            kernelInitializer: serializeInitializer(this.kernelInitializer),
            recurrentInitializer: serializeInitializer(this.recurrentInitializer),
            biasInitializer: serializeInitializer(this.biasInitializer),
            unitForgetBias: this.unitForgetBias,
            kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
            recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),
            biasRegularizer: serializeRegularizer(this.biasRegularizer),
            activityRegularizer: serializeRegularizer(this.activityRegularizer),
            kernelConstraint: serializeConstraint(this.kernelConstraint),
            recurrentConstraint: serializeConstraint(this.recurrentConstraint),
            biasConstraint: serializeConstraint(this.biasConstraint),
            dropout: this.dropout,
            recurrentDropout: this.recurrentDropout,
            implementation: this.implementation,
        };
        return Object.assign(Object.assign({}, baseConfig), config);
    }
}
/** @nocollapse */
LSTMCell.className = 'LSTMCell';
export { LSTMCell };
serialization.registerClass(LSTMCell);
class LSTM extends RNN {
    constructor(args) {
        if (args.implementation === 0) {
            console.warn('`implementation=0` has been deprecated, and now defaults to ' +
                '`implementation=1`. Please update your layer call.');
        }
        args.cell = new LSTMCell(args);
        super(args);
        // TODO(cais): Add activityRegularizer.
    }
    call(inputs, kwargs) {
        return tidy(() => {
            if (this.cell.dropoutMask != null) {
                tfc.dispose(this.cell.dropoutMask);
                this.cell.dropoutMask = null;
            }
            if (this.cell.recurrentDropoutMask != null) {
                tfc.dispose(this.cell.recurrentDropoutMask);
                this.cell.recurrentDropoutMask = null;
            }
            const mask = kwargs == null ? null : kwargs['mask'];
            const training = kwargs == null ? null : kwargs['training'];
            const initialState = kwargs == null ? null : kwargs['initialState'];
            return super.call(inputs, { mask, training, initialState });
        });
    }
    /** @nocollapse */
    static fromConfig(cls, config) {
        if (config['implmentation'] === 0) {
            config['implementation'] = 1;
        }
        return new cls(config);
    }
}
/** @nocollapse */
LSTM.className = 'LSTM';
export { LSTM };
serialization.registerClass(LSTM);
class StackedRNNCells extends RNNCell {
    constructor(args) {
        super(args);
        this.cells = args.cells;
    }
    get stateSize() {
        // States are a flat list in reverse order of the cell stack.
        // This allows preserving the requirement `stack.statesize[0] ===
        // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,
        // assuming one LSTM has states `[h, c]`.
        const stateSize = [];
        for (const cell of this.cells.slice().reverse()) {
            if (Array.isArray(cell.stateSize)) {
                stateSize.push(...cell.stateSize);
            }
            else {
                stateSize.push(cell.stateSize);
            }
        }
        return stateSize;
    }
    call(inputs, kwargs) {
        return tidy(() => {
            inputs = inputs;
            let states = inputs.slice(1);
            // Recover per-cell states.
            const nestedStates = [];
            for (const cell of this.cells.slice().reverse()) {
                if (Array.isArray(cell.stateSize)) {
                    nestedStates.push(states.splice(0, cell.stateSize.length));
                }
                else {
                    nestedStates.push(states.splice(0, 1));
                }
            }
            nestedStates.reverse();
            // Call the cells in order and store the returned states.
            const newNestedStates = [];
            let callInputs;
            for (let i = 0; i < this.cells.length; ++i) {
                const cell = this.cells[i];
                states = nestedStates[i];
                // TODO(cais): Take care of constants.
                if (i === 0) {
                    callInputs = [inputs[0]].concat(states);
                }
                else {
                    callInputs = [callInputs[0]].concat(states);
                }
                callInputs = cell.call(callInputs, kwargs);
                newNestedStates.push(callInputs.slice(1));
            }
            // Format the new states as a flat list in reverse cell order.
            states = [];
            for (const cellStates of newNestedStates.slice().reverse()) {
                states.push(...cellStates);
            }
            return [callInputs[0]].concat(states);
        });
    }
    build(inputShape) {
        if (isArrayOfShapes(inputShape)) {
            // TODO(cais): Take care of input constants.
            // const constantShape = inputShape.slice(1);
            inputShape = inputShape[0];
        }
        inputShape = inputShape;
        let outputDim;
        this.cells.forEach((cell, i) => {
            nameScope(`RNNCell_${i}`, () => {
                // TODO(cais): Take care of input constants.
                cell.build(inputShape);
                if (Array.isArray(cell.stateSize)) {
                    outputDim = cell.stateSize[0];
                }
                else {
                    outputDim = cell.stateSize;
                }
                inputShape = [inputShape[0], outputDim];
            });
        });
        this.built = true;
    }
    getConfig() {
        const baseConfig = super.getConfig();
        const getCellConfig = (cell) => {
            return {
                'className': cell.getClassName(),
                'config': cell.getConfig(),
            };
        };
        const cellConfigs = this.cells.map(getCellConfig);
        const config = { 'cells': cellConfigs };
        return Object.assign(Object.assign({}, baseConfig), config);
    }
    /** @nocollapse */
    static fromConfig(cls, config, customObjects = {}) {
        const cells = [];
        for (const cellConfig of config['cells']) {
            cells.push(deserialize(cellConfig, customObjects));
        }
        return new cls({ cells });
    }
    get trainableWeights() {
        if (!this.trainable) {
            return [];
        }
        const weights = [];
        for (const cell of this.cells) {
            weights.push(...cell.trainableWeights);
        }
        return weights;
    }
    get nonTrainableWeights() {
        const weights = [];
        for (const cell of this.cells) {
            weights.push(...cell.nonTrainableWeights);
        }
        if (!this.trainable) {
            const trainableWeights = [];
            for (const cell of this.cells) {
                trainableWeights.push(...cell.trainableWeights);
            }
            return trainableWeights.concat(weights);
        }
        return weights;
    }
    /**
     * Retrieve the weights of a the model.
     *
     * @returns A flat `Array` of `tf.Tensor`s.
     */
    getWeights() {
        const weights = [];
        for (const cell of this.cells) {
            weights.push(...cell.weights);
        }
        return batchGetValue(weights);
    }
    /**
     * Set the weights of the model.
     *
     * @param weights An `Array` of `tf.Tensor`s with shapes and types matching
     *     the output of `getWeights()`.
     */
    setWeights(weights) {
        const tuples = [];
        for (const cell of this.cells) {
            const numParams = cell.weights.length;
            const inputWeights = weights.splice(numParams);
            for (let i = 0; i < cell.weights.length; ++i) {
                tuples.push([cell.weights[i], inputWeights[i]]);
            }
        }
        batchSetValue(tuples);
    }
}
/** @nocollapse */
StackedRNNCells.className = 'StackedRNNCells';
export { StackedRNNCells };
serialization.registerClass(StackedRNNCells);
export function generateDropoutMask(args) {
    const { ones, rate, training = false, count = 1, dropoutFunc } = args;
    const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones(), rate) : K.dropout(ones(), rate);
    const createMask = () => K.inTrainPhase(droppedInputs, ones, training);
    // just in case count is provided with null or undefined
    if (!count || count <= 1) {
        return tfc.keep(createMask().clone());
    }
    const masks = Array(count).fill(undefined).map(createMask);
    return masks.map(m => tfc.keep(m.clone()));
}
//# 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* @license\n * Copyright 2018 Google LLC\n *\n * Use of this source code is governed by an MIT-style\n * license that can be found in the LICENSE file or at\n * https://opensource.org/licenses/MIT.\n * =============================================================================\n */\n\n/**\n * TensorFlow.js Layers: Recurrent Neural Network Layers.\n */\n\nimport * as tfc from '@tensorflow/tfjs-core';\nimport {DataType, serialization, Tensor, tidy, util} from '@tensorflow/tfjs-core';\n\nimport {Activation, getActivation, serializeActivation} from '../activations';\nimport * as K from '../backend/tfjs_backend';\nimport {nameScope} from '../common';\nimport {Constraint, ConstraintIdentifier, getConstraint, serializeConstraint} from '../constraints';\nimport {InputSpec, SymbolicTensor} from '../engine/topology';\nimport {Layer, LayerArgs} from '../engine/topology';\nimport {AttributeError, NotImplementedError, ValueError} from '../errors';\nimport {getInitializer, Initializer, InitializerIdentifier, Ones, serializeInitializer} from '../initializers';\nimport {ActivationIdentifier} from '../keras_format/activation_config';\nimport {Shape} from '../keras_format/common';\nimport {getRegularizer, Regularizer, RegularizerIdentifier, serializeRegularizer} from '../regularizers';\nimport {Kwargs, RnnStepFunction} from '../types';\nimport {assertPositiveInteger} from '../utils/generic_utils';\nimport * as math_utils from '../utils/math_utils';\nimport {getExactlyOneShape, getExactlyOneTensor, isArrayOfShapes} from '../utils/types_utils';\nimport {batchGetValue, batchSetValue, LayerVariable} from '../variables';\n\nimport {deserialize} from './serialization';\n\n/**\n * Standardize `apply()` args to a single list of tensor inputs.\n *\n * When running a model loaded from file, the input tensors `initialState` and\n * `constants` are passed to `RNN.apply()` as part of `inputs` instead of the\n * dedicated kwargs fields. `inputs` consists of\n * `[inputs, initialState0, initialState1, ..., constant0, constant1]` in this\n * case.\n * This method makes sure that arguments are\n * separated and that `initialState` and `constants` are `Array`s of tensors\n * (or None).\n *\n * @param inputs Tensor or `Array` of  tensors.\n * @param initialState Tensor or `Array` of tensors or `null`/`undefined`.\n * @param constants Tensor or `Array` of tensors or `null`/`undefined`.\n * @returns An object consisting of\n *   inputs: A tensor.\n *   initialState: `Array` of tensors or `null`.\n *   constants: `Array` of tensors or `null`.\n * @throws ValueError, if `inputs` is an `Array` but either `initialState` or\n *   `constants` is provided.\n */\nexport function standardizeArgs(\n    inputs: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],\n    initialState: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],\n    constants: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],\n    numConstants?: number): {\n  inputs: Tensor|SymbolicTensor,\n  initialState: Tensor[]|SymbolicTensor[],\n  constants: Tensor[]|SymbolicTensor[]\n} {\n  if (Array.isArray(inputs)) {\n    if (initialState != null || constants != null) {\n      throw new ValueError(\n          'When inputs is an array, neither initialState or constants ' +\n          'should be provided');\n    }\n    if (numConstants != null) {\n      constants = inputs.slice(inputs.length - numConstants, inputs.length);\n      inputs = inputs.slice(0, inputs.length - numConstants);\n    }\n    if (inputs.length > 1) {\n      initialState = inputs.slice(1, inputs.length);\n    }\n    inputs = inputs[0];\n  }\n\n  function toListOrNull(x: Tensor|Tensor[]|SymbolicTensor|\n                        SymbolicTensor[]): Tensor[]|SymbolicTensor[] {\n    if (x == null || Array.isArray(x)) {\n      return x as Tensor[] | SymbolicTensor[];\n    } else {\n      return [x] as Tensor[] | SymbolicTensor[];\n    }\n  }\n\n  initialState = toListOrNull(initialState);\n  constants = toListOrNull(constants);\n\n  return {inputs, initialState, constants};\n}\n\n/**\n * Iterates over the time dimension of a tensor.\n *\n * @param stepFunction RNN step function.\n *   Parameters:\n *     inputs: tensor with shape `[samples, ...]` (no time dimension),\n *       representing input for the batch of samples at a certain time step.\n *     states: an Array of tensors.\n *   Returns:\n *     outputs: tensor with shape `[samples, outputDim]` (no time dimension).\n *     newStates: list of tensors, same length and shapes as `states`. The first\n *       state in the list must be the output tensor at the previous timestep.\n * @param inputs Tensor of temporal data of shape `[samples, time, ...]` (at\n *   least 3D).\n * @param initialStates Tensor with shape `[samples, outputDim]` (no time\n *   dimension), containing the initial values of the states used in the step\n *   function.\n * @param goBackwards If `true`, do the iteration over the time dimension in\n *   reverse order and return the reversed sequence.\n * @param mask Binary tensor with shape `[sample, time, 1]`, with a zero for\n *   every element that is masked.\n * @param constants An Array of constant values passed at each step.\n * @param unroll Whether to unroll the RNN or to use a symbolic loop. *Not*\n *   applicable to this imperative deeplearn.js backend. Its value is ignored.\n * @param needPerStepOutputs Whether the per-step outputs are to be\n *   concatenated into a single tensor and returned (as the second return\n *   value). Default: `false`. This arg is included so that the relatively\n *   expensive concatenation of the stepwise outputs can be omitted unless\n *   the stepwise outputs need to be kept (e.g., for an LSTM layer of which\n *   `returnSequence` is `true`.)\n * @returns An Array: `[lastOutput, outputs, newStates]`.\n *   lastOutput: the lastest output of the RNN, of shape `[samples, ...]`.\n *   outputs: tensor with shape `[samples, time, ...]` where each entry\n *     `output[s, t]` is the output of the step function at time `t` for sample\n *     `s`. This return value is provided if and only if the\n *     `needPerStepOutputs` is set as `true`. If it is set as `false`, this\n *     return value will be `undefined`.\n *   newStates: Array of tensors, latest states returned by the step function,\n *      of shape `(samples, ...)`.\n * @throws ValueError If input dimension is less than 3.\n *\n * TODO(nielsene): This needs to be tidy-ed.\n */\nexport function rnn(\n    stepFunction: RnnStepFunction, inputs: Tensor, initialStates: Tensor[],\n    goBackwards = false, mask?: Tensor, constants?: Tensor[], unroll = false,\n    needPerStepOutputs = false): [Tensor, Tensor, Tensor[]] {\n  return tfc.tidy(() => {\n    const ndim = inputs.shape.length;\n    if (ndim < 3) {\n      throw new ValueError(`Input should be at least 3D, but is ${ndim}D.`);\n    }\n\n    // Transpose to time-major, i.e., from [batch, time, ...] to [time, batch,\n    // ...].\n    const axes = [1, 0].concat(math_utils.range(2, ndim));\n    inputs = tfc.transpose(inputs, axes);\n\n    if (constants != null) {\n      throw new NotImplementedError(\n          'The rnn() functoin of the deeplearn.js backend does not support ' +\n          'constants yet.');\n    }\n\n    // Porting Note: the unroll option is ignored by the imperative backend.\n    if (unroll) {\n      console.warn(\n          'Backend rnn(): the unroll = true option is not applicable to the ' +\n          'imperative deeplearn.js backend.');\n    }\n\n    if (mask != null) {\n      mask = tfc.cast(tfc.cast(mask, 'bool'), 'float32');\n      if (mask.rank === ndim - 1) {\n        mask = tfc.expandDims(mask, -1);\n      }\n      mask = tfc.transpose(mask, axes);\n    }\n\n    if (goBackwards) {\n      inputs = tfc.reverse(inputs, 0);\n      if (mask != null) {\n        mask = tfc.reverse(mask, 0);\n      }\n    }\n\n    // Porting Note: PyKeras with TensorFlow backend uses a symbolic loop\n    //   (tf.while_loop). But for the imperative deeplearn.js backend, we just\n    //   use the usual TypeScript control flow to iterate over the time steps in\n    //   the inputs.\n    // Porting Note: PyKeras patches a \"_use_learning_phase\" attribute to\n    // outputs.\n    //   This is not idiomatic in TypeScript. The info regarding whether we are\n    //   in a learning (i.e., training) phase for RNN is passed in a different\n    //   way.\n\n    const perStepOutputs: Tensor[] = [];\n    let lastOutput: Tensor;\n    let states = initialStates;\n    const timeSteps = inputs.shape[0];\n    const perStepInputs = tfc.unstack(inputs);\n    let perStepMasks: Tensor[];\n    if (mask != null) {\n      perStepMasks = tfc.unstack(mask);\n    }\n\n    for (let t = 0; t < timeSteps; ++t) {\n      const currentInput = perStepInputs[t];\n      const stepOutputs = tfc.tidy(() => stepFunction(currentInput, states));\n\n      if (mask == null) {\n        lastOutput = stepOutputs[0];\n        states = stepOutputs[1];\n      } else {\n        const maskedOutputs = tfc.tidy(() => {\n          const stepMask = perStepMasks[t];\n          const negStepMask = tfc.sub(tfc.onesLike(stepMask), stepMask);\n          // TODO(cais): Would tfc.where() be better for performance?\n          const output = tfc.add(\n              tfc.mul(stepOutputs[0], stepMask),\n              tfc.mul(states[0], negStepMask));\n          const newStates = states.map((state, i) => {\n            return tfc.add(\n                tfc.mul(stepOutputs[1][i], stepMask),\n                tfc.mul(state, negStepMask));\n          });\n          return {output, newStates};\n        });\n        lastOutput = maskedOutputs.output;\n        states = maskedOutputs.newStates;\n      }\n\n      if (needPerStepOutputs) {\n        perStepOutputs.push(lastOutput);\n      }\n    }\n    let outputs: Tensor;\n    if (needPerStepOutputs) {\n      const axis = 1;\n      outputs = tfc.stack(perStepOutputs, axis);\n    }\n    return [lastOutput, outputs, states] as [Tensor, Tensor, Tensor[]];\n  });\n}\n\nexport declare interface BaseRNNLayerArgs extends LayerArgs {\n  /**\n   * A RNN cell instance. A RNN cell is a class that has:\n   *   - a `call()` method, which takes `[Tensor, Tensor]` as the\n   *     first input argument. The first item is the input at time t, and\n   *     second item is the cell state at time t.\n   *     The `call()` method returns `[outputAtT, statesAtTPlus1]`.\n   *     The `call()` method of the cell can also take the argument `constants`,\n   *     see section \"Note on passing external constants\" below.\n   *     Porting Node: PyKeras overrides the `call()` signature of RNN cells,\n   *       which are Layer subtypes, to accept two arguments. tfjs-layers does\n   *       not do such overriding. Instead we preserve the `call()` signature,\n   *       which due to its `Tensor|Tensor[]` argument and return value is\n   *       flexible enough to handle the inputs and states.\n   *   - a `stateSize` attribute. This can be a single integer (single state)\n   *     in which case it is the size of the recurrent state (which should be\n   *     the same as the size of the cell output). This can also be an Array of\n   *     integers (one size per state). In this case, the first entry\n   *     (`stateSize[0]`) should be the same as the size of the cell output.\n   * It is also possible for `cell` to be a list of RNN cell instances, in which\n   * case the cells get stacked on after the other in the RNN, implementing an\n   * efficient stacked RNN.\n   */\n  cell?: RNNCell|RNNCell[];\n\n  /**\n   * Whether to return the last output in the output sequence, or the full\n   * sequence.\n   */\n  returnSequences?: boolean;\n\n  /**\n   * Whether to return the last state in addition to the output.\n   */\n  returnState?: boolean;\n\n  /**\n   * If `true`, process the input sequence backwards and return the reversed\n   * sequence (default: `false`).\n   */\n  goBackwards?: boolean;\n\n  /**\n   * If `true`, the last state for each sample at index i in a batch will be\n   * used as initial state of the sample of index i in the following batch\n   * (default: `false`).\n   *\n   * You can set RNN layers to be \"stateful\", which means that the states\n   * computed for the samples in one batch will be reused as initial states\n   * for the samples in the next batch. This assumes a one-to-one mapping\n   * between samples in different successive batches.\n   *\n   * To enable \"statefulness\":\n   *   - specify `stateful: true` in the layer constructor.\n   *   - specify a fixed batch size for your model, by passing\n   *     - if sequential model:\n   *       `batchInputShape: [...]` to the first layer in your model.\n   *     - else for functional model with 1 or more Input layers:\n   *       `batchShape: [...]` to all the first layers in your model.\n   *     This is the expected shape of your inputs\n   *     *including the batch size*.\n   *     It should be a tuple of integers, e.g., `[32, 10, 100]`.\n   *   - specify `shuffle: false` when calling `LayersModel.fit()`.\n   *\n   * To reset the state of your model, call `resetStates()` on either the\n   * specific layer or on the entire model.\n   */\n  stateful?: boolean;\n  // TODO(cais): Explore whether we can warn users when they fail to set\n  //   `shuffle: false` when training a model consisting of stateful RNNs\n  //   and any stateful Layers in general.\n\n  /**\n   * If `true`, the network will be unrolled, else a symbolic loop will be\n   * used. Unrolling can speed up a RNN, although it tends to be more\n   * memory-intensive. Unrolling is only suitable for short sequences (default:\n   * `false`).\n   * Porting Note: tfjs-layers has an imperative backend. RNNs are executed with\n   *   normal TypeScript control flow. Hence this property is inapplicable and\n   *   ignored in tfjs-layers.\n   */\n  unroll?: boolean;\n\n  /**\n   * Dimensionality of the input (integer).\n   *   This option (or alternatively, the option `inputShape`) is required when\n   *   this layer is used as the first layer in a model.\n   */\n  inputDim?: number;\n\n  /**\n   * Length of the input sequences, to be specified when it is constant.\n   * This argument is required if you are going to connect `Flatten` then\n   * `Dense` layers upstream (without it, the shape of the dense outputs cannot\n   * be computed). Note that if the recurrent layer is not the first layer in\n   * your model, you would need to specify the input length at the level of the\n   * first layer (e.g., via the `inputShape` option).\n   */\n  inputLength?: number;\n}\n\nexport class RNN extends Layer {\n  /** @nocollapse */\n  static className = 'RNN';\n  public readonly cell: RNNCell;\n  public readonly returnSequences: boolean;\n  public readonly returnState: boolean;\n  public readonly goBackwards: boolean;\n  public readonly unroll: boolean;\n\n  public stateSpec: InputSpec[];\n  protected states_: Tensor[];\n\n  // NOTE(cais): For stateful RNNs, the old states cannot be disposed right\n  // away when new states are set, because the old states may need to be used\n  // later for backpropagation through time (BPTT) and other purposes. So we\n  // keep them here for final disposal when the state is reset completely\n  // (i.e., through no-arg call to `resetStates()`).\n  protected keptStates: Tensor[][];\n\n  private numConstants: number;\n\n  constructor(args: RNNLayerArgs) {\n    super(args);\n    let cell: RNNCell;\n    if (args.cell == null) {\n      throw new ValueError(\n          'cell property is missing for the constructor of RNN.');\n    } else if (Array.isArray(args.cell)) {\n      cell = new StackedRNNCells({cells: args.cell});\n    } else {\n      cell = args.cell;\n    }\n    if (cell.stateSize == null) {\n      throw new ValueError(\n          'The RNN cell should have an attribute `stateSize` (tuple of ' +\n          'integers, one integer per RNN state).');\n    }\n    this.cell = cell;\n    this.returnSequences =\n        args.returnSequences == null ? false : args.returnSequences;\n    this.returnState = args.returnState == null ? false : args.returnState;\n    this.goBackwards = args.goBackwards == null ? false : args.goBackwards;\n    this._stateful = args.stateful == null ? false : args.stateful;\n    this.unroll = args.unroll == null ? false : args.unroll;\n\n    this.supportsMasking = true;\n    this.inputSpec = [new InputSpec({ndim: 3})];\n    this.stateSpec = null;\n    this.states_ = null;\n    // TODO(cais): Add constantsSpec and numConstants.\n    this.numConstants = null;\n    // TODO(cais): Look into the use of initial_state in the kwargs of the\n    //   constructor.\n\n    this.keptStates = [];\n  }\n\n  // Porting Note: This is the equivalent of `RNN.states` property getter in\n  //   PyKeras.\n  getStates(): Tensor[] {\n    if (this.states_ == null) {\n      const numStates =\n          Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      return math_utils.range(0, numStates).map(x => null);\n    } else {\n      return this.states_;\n    }\n  }\n\n  // Porting Note: This is the equivalent of the `RNN.states` property setter in\n  //   PyKeras.\n  setStates(states: Tensor[]): void {\n    this.states_ = states;\n  }\n\n  override computeOutputShape(inputShape: Shape|Shape[]): Shape|Shape[] {\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = (inputShape as Shape[])[0];\n    }\n    inputShape = inputShape as Shape;\n\n    // TODO(cais): Remove the casting once stacked RNN cells become supported.\n    let stateSize = this.cell.stateSize;\n    if (!Array.isArray(stateSize)) {\n      stateSize = [stateSize];\n    }\n    const outputDim = stateSize[0];\n    let outputShape: Shape|Shape[];\n    if (this.returnSequences) {\n      outputShape = [inputShape[0], inputShape[1], outputDim];\n    } else {\n      outputShape = [inputShape[0], outputDim];\n    }\n\n    if (this.returnState) {\n      const stateShape: Shape[] = [];\n      for (const dim of stateSize) {\n        stateShape.push([inputShape[0], dim]);\n      }\n      return [outputShape].concat(stateShape);\n    } else {\n      return outputShape;\n    }\n  }\n\n  override computeMask(inputs: Tensor|Tensor[], mask?: Tensor|Tensor[]): Tensor\n      |Tensor[] {\n    return tfc.tidy(() => {\n      if (Array.isArray(mask)) {\n        mask = mask[0];\n      }\n      const outputMask = this.returnSequences ? mask : null;\n\n      if (this.returnState) {\n        const stateMask = this.states.map(s => null);\n        return [outputMask].concat(stateMask);\n      } else {\n        return outputMask;\n      }\n    });\n  }\n\n  /**\n   * Get the current state tensors of the RNN.\n   *\n   * If the state hasn't been set, return an array of `null`s of the correct\n   * length.\n   */\n  get states(): Tensor[] {\n    if (this.states_ == null) {\n      const numStates =\n          Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      const output: Tensor[] = [];\n      for (let i = 0; i < numStates; ++i) {\n        output.push(null);\n      }\n      return output;\n    } else {\n      return this.states_;\n    }\n  }\n\n  set states(s: Tensor[]) {\n    this.states_ = s;\n  }\n\n  public override build(inputShape: Shape|Shape[]): void {\n    // Note inputShape will be an Array of Shapes of initial states and\n    // constants if these are passed in apply().\n    const constantShape: Shape[] = null;\n    if (this.numConstants != null) {\n      throw new NotImplementedError(\n          'Constants support is not implemented in RNN yet.');\n    }\n\n    if (isArrayOfShapes(inputShape)) {\n      inputShape = (inputShape as Shape[])[0];\n    }\n    inputShape = inputShape as Shape;\n\n    const batchSize: number = this.stateful ? inputShape[0] : null;\n    const inputDim = inputShape.slice(2);\n    this.inputSpec[0] = new InputSpec({shape: [batchSize, null, ...inputDim]});\n\n    // Allow cell (if RNNCell Layer) to build before we set or validate\n    // stateSpec.\n    const stepInputShape = [inputShape[0]].concat(inputShape.slice(2));\n    if (constantShape != null) {\n      throw new NotImplementedError(\n          'Constants support is not implemented in RNN yet.');\n    } else {\n      this.cell.build(stepInputShape);\n    }\n\n    // Set or validate stateSpec.\n    let stateSize: number[];\n    if (Array.isArray(this.cell.stateSize)) {\n      stateSize = this.cell.stateSize;\n    } else {\n      stateSize = [this.cell.stateSize];\n    }\n\n    if (this.stateSpec != null) {\n      if (!util.arraysEqual(\n              this.stateSpec.map(spec => spec.shape[spec.shape.length - 1]),\n              stateSize)) {\n        throw new ValueError(\n            `An initialState was passed that is not compatible with ` +\n            `cell.stateSize. Received stateSpec=${this.stateSpec}; ` +\n            `However cell.stateSize is ${this.cell.stateSize}`);\n      }\n    } else {\n      this.stateSpec =\n          stateSize.map(dim => new InputSpec({shape: [null, dim]}));\n    }\n    if (this.stateful) {\n      this.resetStates();\n    }\n  }\n\n  /**\n   * Reset the state tensors of the RNN.\n   *\n   * If the `states` argument is `undefined` or `null`, will set the\n   * state tensor(s) of the RNN to all-zero tensors of the appropriate\n   * shape(s).\n   *\n   * If `states` is provided, will set the state tensors of the RNN to its\n   * value.\n   *\n   * @param states Optional externally-provided initial states.\n   * @param training Whether this call is done during training. For stateful\n   *   RNNs, this affects whether the old states are kept or discarded. In\n   *   particular, if `training` is `true`, the old states will be kept so\n   *   that subsequent backpropgataion through time (BPTT) may work properly.\n   *   Else, the old states will be discarded.\n   */\n  override resetStates(states?: Tensor|Tensor[], training = false): void {\n    tidy(() => {\n      if (!this.stateful) {\n        throw new AttributeError(\n            'Cannot call resetStates() on an RNN Layer that is not stateful.');\n      }\n      const batchSize = this.inputSpec[0].shape[0];\n      if (batchSize == null) {\n        throw new ValueError(\n            'If an RNN is stateful, it needs to know its batch size. Specify ' +\n            'the batch size of your input tensors: \\n' +\n            '- If using a Sequential model, specify the batch size by ' +\n            'passing a `batchInputShape` option to your first layer.\\n' +\n            '- If using the functional API, specify the batch size by ' +\n            'passing a `batchShape` option to your Input layer.');\n      }\n      // Initialize state if null.\n      if (this.states_ == null) {\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ =\n              this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n        } else {\n          this.states_ = [tfc.zeros([batchSize, this.cell.stateSize])];\n        }\n      } else if (states == null) {\n        // Dispose old state tensors.\n        tfc.dispose(this.states_);\n        // For stateful RNNs, fully dispose kept old states.\n        if (this.keptStates != null) {\n          tfc.dispose(this.keptStates);\n          this.keptStates = [];\n        }\n\n        if (Array.isArray(this.cell.stateSize)) {\n          this.states_ =\n              this.cell.stateSize.map(dim => tfc.zeros([batchSize, dim]));\n        } else {\n          this.states_[0] = tfc.zeros([batchSize, this.cell.stateSize]);\n        }\n      } else {\n        if (!Array.isArray(states)) {\n          states = [states];\n        }\n        if (states.length !== this.states_.length) {\n          throw new ValueError(\n              `Layer ${this.name} expects ${this.states_.length} state(s), ` +\n              `but it received ${states.length} state value(s). Input ` +\n              `received: ${states}`);\n        }\n\n        if (training === true) {\n          // Store old state tensors for complete disposal later, i.e., during\n          // the next no-arg call to this method. We do not dispose the old\n          // states immediately because that BPTT (among other things) require\n          // them.\n          this.keptStates.push(this.states_.slice());\n        } else {\n          tfc.dispose(this.states_);\n        }\n\n        for (let index = 0; index < this.states_.length; ++index) {\n          const value = states[index];\n          const dim = Array.isArray(this.cell.stateSize) ?\n              this.cell.stateSize[index] :\n              this.cell.stateSize;\n          const expectedShape = [batchSize, dim];\n          if (!util.arraysEqual(value.shape, expectedShape)) {\n            throw new ValueError(\n                `State ${index} is incompatible with layer ${this.name}: ` +\n                `expected shape=${expectedShape}, received shape=${\n                    value.shape}`);\n          }\n          this.states_[index] = value;\n        }\n      }\n      this.states_ = this.states_.map(state => tfc.keep(state.clone()));\n    });\n  }\n\n  override apply(\n      inputs: Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[],\n      kwargs?: Kwargs): Tensor|Tensor[]|SymbolicTensor|SymbolicTensor[] {\n    // TODO(cais): Figure out whether initialState is in kwargs or inputs.\n    let initialState: Tensor[]|SymbolicTensor[] =\n        kwargs == null ? null : kwargs['initialState'];\n    let constants: Tensor[]|SymbolicTensor[] =\n        kwargs == null ? null : kwargs['constants'];\n    if (kwargs == null) {\n      kwargs = {};\n    }\n\n    const standardized =\n        standardizeArgs(inputs, initialState, constants, this.numConstants);\n    inputs = standardized.inputs;\n    initialState = standardized.initialState;\n    constants = standardized.constants;\n\n    // If any of `initial_state` or `constants` are specified and are\n    // `tf.SymbolicTensor`s, then add them to the inputs and temporarily modify\n    // the input_spec to include them.\n\n    let additionalInputs: Array<Tensor|SymbolicTensor> = [];\n    let additionalSpecs: InputSpec[] = [];\n    if (initialState != null) {\n      kwargs['initialState'] = initialState;\n      additionalInputs = additionalInputs.concat(initialState);\n      this.stateSpec = [];\n      for (const state of initialState) {\n        this.stateSpec.push(new InputSpec({shape: state.shape}));\n      }\n      // TODO(cais): Use the following instead.\n      // this.stateSpec = initialState.map(state => new InputSpec({shape:\n      // state.shape}));\n      additionalSpecs = additionalSpecs.concat(this.stateSpec);\n    }\n    if (constants != null) {\n      kwargs['constants'] = constants;\n      additionalInputs = additionalInputs.concat(constants);\n      // TODO(cais): Add this.constantsSpec.\n      this.numConstants = constants.length;\n    }\n\n    const isTensor = additionalInputs[0] instanceof SymbolicTensor;\n    if (isTensor) {\n      // Compute full input spec, including state and constants.\n      const fullInput =\n          [inputs].concat(additionalInputs) as Tensor[] | SymbolicTensor[];\n      const fullInputSpec = this.inputSpec.concat(additionalSpecs);\n      // Perform the call with temporarily replaced inputSpec.\n      const originalInputSpec = this.inputSpec;\n      this.inputSpec = fullInputSpec;\n      const output = super.apply(fullInput, kwargs);\n      this.inputSpec = originalInputSpec;\n      return output;\n    } else {\n      return super.apply(inputs, kwargs);\n    }\n  }\n\n  // tslint:disable-next-line:no-any\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    // Input shape: `[samples, time (padded with zeros), input_dim]`.\n    // Note that the .build() method of subclasses **must** define\n    // this.inputSpec and this.stateSpec owith complete input shapes.\n    return tidy(() => {\n      const mask = kwargs == null ? null : kwargs['mask'] as Tensor;\n      const training = kwargs == null ? null : kwargs['training'];\n      let initialState: Tensor[] =\n          kwargs == null ? null : kwargs['initialState'];\n\n      inputs = getExactlyOneTensor(inputs);\n      if (initialState == null) {\n        if (this.stateful) {\n          initialState = this.states_;\n        } else {\n          initialState = this.getInitialState(inputs);\n        }\n      }\n\n      const numStates =\n          Array.isArray(this.cell.stateSize) ? this.cell.stateSize.length : 1;\n      if (initialState.length !== numStates) {\n        throw new ValueError(\n            `RNN Layer has ${numStates} state(s) but was passed ` +\n            `${initialState.length} initial state(s).`);\n      }\n      if (this.unroll) {\n        console.warn(\n            'Ignoring unroll = true for RNN layer, due to imperative backend.');\n      }\n\n      const cellCallKwargs: Kwargs = {training};\n\n      // TODO(cais): Add support for constants.\n      const step = (inputs: Tensor, states: Tensor[]) => {\n        // `inputs` and `states` are concatenated to form a single `Array` of\n        // `tf.Tensor`s as the input to `cell.call()`.\n        const outputs =\n            this.cell.call([inputs].concat(states), cellCallKwargs) as Tensor[];\n        // Marshall the return value into output and new states.\n        return [outputs[0], outputs.slice(1)] as [Tensor, Tensor[]];\n      };\n\n      // TODO(cais): Add support for constants.\n\n      const rnnOutputs =\n          rnn(step, inputs, initialState, this.goBackwards, mask, null,\n              this.unroll, this.returnSequences);\n      const lastOutput = rnnOutputs[0];\n      const outputs = rnnOutputs[1];\n      const states = rnnOutputs[2];\n\n      if (this.stateful) {\n        this.resetStates(states, training);\n      }\n\n      const output = this.returnSequences ? outputs : lastOutput;\n\n      // TODO(cais): Property set learning phase flag.\n\n      if (this.returnState) {\n        return [output].concat(states);\n      } else {\n        return output;\n      }\n    });\n  }\n\n  getInitialState(inputs: Tensor): Tensor[] {\n    return tidy(() => {\n      // Build an all-zero tensor of shape [samples, outputDim].\n      // [Samples, timeSteps, inputDim].\n      let initialState = tfc.zeros(inputs.shape);\n      // [Samples].\n      initialState = tfc.sum(initialState, [1, 2]);\n      initialState = K.expandDims(initialState);  // [Samples, 1].\n\n      if (Array.isArray(this.cell.stateSize)) {\n        return this.cell.stateSize.map(\n            dim => dim > 1 ? K.tile(initialState, [1, dim]) : initialState);\n      } else {\n        return this.cell.stateSize > 1 ?\n            [K.tile(initialState, [1, this.cell.stateSize])] :\n            [initialState];\n      }\n    });\n  }\n\n  override get trainableWeights(): LayerVariable[] {\n    if (!this.trainable) {\n      return [];\n    }\n    // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n    return this.cell.trainableWeights;\n  }\n\n  override get nonTrainableWeights(): LayerVariable[] {\n    // Porting Note: In TypeScript, `this` is always an instance of `Layer`.\n    if (!this.trainable) {\n      return this.cell.weights;\n    }\n    return this.cell.nonTrainableWeights;\n  }\n\n  override setFastWeightInitDuringBuild(value: boolean) {\n    super.setFastWeightInitDuringBuild(value);\n    if (this.cell != null) {\n      this.cell.setFastWeightInitDuringBuild(value);\n    }\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const baseConfig = super.getConfig();\n\n    const config: serialization.ConfigDict = {\n      returnSequences: this.returnSequences,\n      returnState: this.returnState,\n      goBackwards: this.goBackwards,\n      stateful: this.stateful,\n      unroll: this.unroll,\n    };\n\n    if (this.numConstants != null) {\n      config['numConstants'] = this.numConstants;\n    }\n\n    const cellConfig = this.cell.getConfig();\n\n    if (this.getClassName() === RNN.className) {\n      config['cell'] = {\n        'className': this.cell.getClassName(),\n        'config': cellConfig,\n      } as serialization.ConfigDictValue;\n    }\n\n    // this order is necessary, to prevent cell name from replacing layer name\n    return {...cellConfig, ...baseConfig, ...config};\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends serialization.Serializable>(\n      cls: serialization.SerializableConstructor<T>,\n      config: serialization.ConfigDict,\n      customObjects = {} as serialization.ConfigDict): T {\n    const cellConfig = config['cell'] as serialization.ConfigDict;\n    const cell = deserialize(cellConfig, customObjects) as RNNCell;\n    return new cls(Object.assign(config, {cell}));\n  }\n}\nserialization.registerClass(RNN);\n\n// Porting Note: This is a common parent class for RNN cells. There is no\n// equivalent of this in PyKeras. Having a common parent class forgoes the\n//  need for `has_attr(cell, ...)` checks or its TypeScript equivalent.\n/**\n * An RNNCell layer.\n *\n * @doc {heading: 'Layers', subheading: 'Classes'}\n */\nexport abstract class RNNCell extends Layer {\n  /**\n   * Size(s) of the states.\n   * For RNN cells with only a single state, this is a single integer.\n   */\n  // See\n  // https://www.typescriptlang.org/docs/handbook/release-notes/typescript-4-0.html#properties-overriding-accessors-and-vice-versa-is-an-error\n  public abstract stateSize: number|number[];\n  public dropoutMask: Tensor|Tensor[];\n  public recurrentDropoutMask: Tensor|Tensor[];\n}\n\nexport declare interface SimpleRNNCellLayerArgs extends LayerArgs {\n  /**\n   * units: Positive integer, dimensionality of the output space.\n   */\n  units: number;\n\n  /**\n   * Activation function to use.\n   * Default: hyperbolic tangent ('tanh').\n   * If you pass `null`,  'linear' activation will be applied.\n   */\n  activation?: ActivationIdentifier;\n\n  /**\n   * Whether the layer uses a bias vector.\n   */\n  useBias?: boolean;\n\n  /**\n   * Initializer for the `kernel` weights matrix, used for the linear\n   * transformation of the inputs.\n   */\n  kernelInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the `recurrentKernel` weights matrix, used for\n   * linear transformation of the recurrent state.\n   */\n  recurrentInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the bias vector.\n   */\n  biasInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Regularizer function applied to the `kernel` weights matrix.\n   */\n  kernelRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /**\n   * Regularizer function applied to the `recurrent_kernel` weights matrix.\n   */\n  recurrentRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /**\n   * Regularizer function applied to the bias vector.\n   */\n  biasRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /**\n   * Constraint function applied to the `kernel` weights matrix.\n   */\n  kernelConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Constraint function applied to the `recurrentKernel` weights matrix.\n   */\n  recurrentConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Constraint function applied to the bias vector.\n   */\n  biasConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Float number between 0 and 1. Fraction of the units to drop for the linear\n   * transformation of the inputs.\n   */\n  dropout?: number;\n\n  /**\n   * Float number between 0 and 1. Fraction of the units to drop for the linear\n   * transformation of the recurrent state.\n   */\n  recurrentDropout?: number;\n\n  /**\n   * This is added for test DI purpose.\n   */\n  dropoutFunc?: Function;\n}\n\nexport class SimpleRNNCell extends RNNCell {\n  /** @nocollapse */\n  static className = 'SimpleRNNCell';\n  readonly units: number;\n  readonly activation: Activation;\n  readonly useBias: boolean;\n\n  readonly kernelInitializer: Initializer;\n  readonly recurrentInitializer: Initializer;\n  readonly biasInitializer: Initializer;\n\n  readonly kernelConstraint: Constraint;\n  readonly recurrentConstraint: Constraint;\n  readonly biasConstraint: Constraint;\n\n  readonly kernelRegularizer: Regularizer;\n  readonly recurrentRegularizer: Regularizer;\n  readonly biasRegularizer: Regularizer;\n\n  readonly dropout: number;\n  readonly recurrentDropout: number;\n  readonly dropoutFunc: Function;\n\n  readonly stateSize: number;\n\n  kernel: LayerVariable;\n  recurrentKernel: LayerVariable;\n  bias: LayerVariable;\n\n  readonly DEFAULT_ACTIVATION = 'tanh';\n  readonly DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n  readonly DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n  readonly DEFAULT_BIAS_INITIALIZER: InitializerIdentifier = 'zeros';\n\n  constructor(args: SimpleRNNCellLayerArgs) {\n    super(args);\n    this.units = args.units;\n    assertPositiveInteger(this.units, `units`);\n    this.activation = getActivation(\n        args.activation == null ? this.DEFAULT_ACTIVATION : args.activation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n\n    this.kernelInitializer = getInitializer(\n        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(\n        args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n\n    this.biasInitializer =\n        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n\n    this.dropout = math_utils.min(\n        [1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = math_utils.min([\n      1,\n      math_utils.max(\n          [0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n\n  override build(inputShape: Shape|Shape[]): void {\n    inputShape = getExactlyOneShape(inputShape);\n    // TODO(cais): Use regularizer.\n    this.kernel = this.addWeight(\n        'kernel', [inputShape[inputShape.length - 1], this.units], null,\n        this.kernelInitializer, this.kernelRegularizer, true,\n        this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\n        'recurrent_kernel', [this.units, this.units], null,\n        this.recurrentInitializer, this.recurrentRegularizer, true,\n        this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\n          'bias', [this.units], null, this.biasInitializer,\n          this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    this.built = true;\n  }\n\n  // Porting Note: PyKeras' equivalent of this method takes two tensor inputs:\n  //   `inputs` and `states`. Here, the two tensors are combined into an\n  //   `Tensor[]` Array as the first input argument.\n  //   Similarly, PyKeras' equivalent of this method returns two values:\n  //    `output` and `[output]`. Here the two are combined into one length-2\n  //    `Tensor[]`, consisting of `output` repeated.\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      inputs = inputs as Tensor[];\n      if (inputs.length !== 2) {\n        throw new ValueError(\n            `SimpleRNNCell expects 2 input Tensors, got ${inputs.length}.`);\n      }\n      let prevOutput = inputs[1];\n      inputs = inputs[0];\n      const training = kwargs['training'] == null ? false : kwargs['training'];\n\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n                             ones: () => tfc.onesLike(inputs as Tensor),\n                             rate: this.dropout,\n                             training,\n                             dropoutFunc: this.dropoutFunc,\n                           }) as Tensor;\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n          this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n                                      ones: () => tfc.onesLike(prevOutput),\n                                      rate: this.recurrentDropout,\n                                      training,\n                                      dropoutFunc: this.dropoutFunc,\n                                    }) as Tensor;\n      }\n      let h: Tensor;\n      const dpMask: Tensor = this.dropoutMask as Tensor;\n      const recDpMask: Tensor = this.recurrentDropoutMask as Tensor;\n      if (dpMask != null) {\n        h = K.dot(tfc.mul(inputs, dpMask), this.kernel.read());\n      } else {\n        h = K.dot(inputs, this.kernel.read());\n      }\n      if (this.bias != null) {\n        h = K.biasAdd(h, this.bias.read());\n      }\n      if (recDpMask != null) {\n        prevOutput = tfc.mul(prevOutput, recDpMask);\n      }\n      let output = tfc.add(h, K.dot(prevOutput, this.recurrentKernel.read()));\n      if (this.activation != null) {\n        output = this.activation.apply(output);\n      }\n\n      // TODO(cais): Properly set learning phase on output tensor?\n      return [output, output];\n    });\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const baseConfig = super.getConfig();\n\n    const config: serialization.ConfigDict = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n    };\n\n    return {...baseConfig, ...config};\n  }\n}\nserialization.registerClass(SimpleRNNCell);\n\nexport declare interface SimpleRNNLayerArgs extends BaseRNNLayerArgs {\n  /**\n   * Positive integer, dimensionality of the output space.\n   */\n  units: number;\n\n  /**\n   * Activation function to use.\n   *\n   * Defaults to  hyperbolic tangent (`tanh`)\n   *\n   * If you pass `null`, no activation will be applied.\n   */\n  activation?: ActivationIdentifier;\n\n  /**\n   * Whether the layer uses a bias vector.\n   */\n  useBias?: boolean;\n\n  /**\n   * Initializer for the `kernel` weights matrix, used for the linear\n   * transformation of the inputs.\n   */\n  kernelInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the `recurrentKernel` weights matrix, used for\n   * linear transformation of the recurrent state.\n   */\n  recurrentInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Initializer for the bias vector.\n   */\n  biasInitializer?: InitializerIdentifier|Initializer;\n\n  /**\n   * Regularizer function applied to the kernel weights matrix.\n   */\n  kernelRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /**\n   * Regularizer function applied to the recurrentKernel weights matrix.\n   */\n  recurrentRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /**\n   * Regularizer function applied to the bias vector.\n   */\n  biasRegularizer?: RegularizerIdentifier|Regularizer;\n\n  /**\n   * Constraint function applied to the kernel weights matrix.\n   */\n  kernelConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Constraint function applied to the recurrentKernel weights matrix.\n   */\n  recurrentConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Constraint function applied to the bias vector.\n   */\n  biasConstraint?: ConstraintIdentifier|Constraint;\n\n  /**\n   * Number between 0 and 1. Fraction of the units to drop for the linear\n   * transformation of the inputs.\n   */\n  dropout?: number;\n\n  /**\n   * Number between 0 and 1. Fraction of the units to drop for the linear\n   * transformation of the recurrent state.\n   */\n  recurrentDropout?: number;\n\n  /**\n   * This is added for test DI purpose.\n   */\n  dropoutFunc?: Function;\n}\n\n/**\n * RNNLayerConfig is identical to BaseRNNLayerConfig, except it makes the\n * `cell` property required. This interface is to be used with constructors\n * of concrete RNN layer subtypes.\n */\nexport declare interface RNNLayerArgs extends BaseRNNLayerArgs {\n  cell: RNNCell|RNNCell[];\n}\n\nexport class SimpleRNN extends RNN {\n  /** @nocollapse */\n  static override className = 'SimpleRNN';\n  constructor(args: SimpleRNNLayerArgs) {\n    args.cell = new SimpleRNNCell(args);\n    super(args as RNNLayerArgs);\n    // TODO(cais): Add activityRegularizer.\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        tfc.dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        tfc.dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs['mask'];\n      const training = kwargs == null ? null : kwargs['training'];\n      const initialState: Tensor[] =\n          kwargs == null ? null : kwargs['initialState'];\n      return super.call(inputs, {mask, training, initialState});\n    });\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends serialization.Serializable>(\n      cls: serialization.SerializableConstructor<T>,\n      config: serialization.ConfigDict): T {\n    return new cls(config);\n  }\n}\nserialization.registerClass(SimpleRNN);\n\n// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we extend\n//   that interface instead of repeating the fields.\nexport declare interface GRUCellLayerArgs extends SimpleRNNCellLayerArgs {\n  /**\n   * Activation function to use for the recurrent step.\n   *\n   * Defaults to hard sigmoid (`hardSigmoid`).\n   *\n   * If `null`, no activation is applied.\n   */\n  recurrentActivation?: ActivationIdentifier;\n\n  /**\n   * Implementation mode, either 1 or 2.\n   *\n   * Mode 1 will structure its operations as a larger number of\n   *   smaller dot products and additions.\n   *\n   * Mode 2 will batch them into fewer, larger operations. These modes will\n   * have different performance profiles on different hardware and\n   * for different applications.\n   *\n   * Note: For superior performance, TensorFlow.js always uses implementation\n   * 2, regardless of the actual value of this configuration field.\n   */\n  implementation?: number;\n\n  /**\n   * GRU convention (whether to apply reset gate after or before matrix\n   * multiplication). false = \"before\", true = \"after\" (only false is\n   * supported).\n   */\n  resetAfter?: boolean;\n}\n\nexport class GRUCell extends RNNCell {\n  /** @nocollapse */\n  static className = 'GRUCell';\n  readonly units: number;\n  readonly activation: Activation;\n  readonly recurrentActivation: Activation;\n  readonly useBias: boolean;\n\n  readonly kernelInitializer: Initializer;\n  readonly recurrentInitializer: Initializer;\n  readonly biasInitializer: Initializer;\n\n  readonly kernelRegularizer: Regularizer;\n  readonly recurrentRegularizer: Regularizer;\n  readonly biasRegularizer: Regularizer;\n\n  readonly kernelConstraint: Constraint;\n  readonly recurrentConstraint: Constraint;\n  readonly biasConstraint: Constraint;\n\n  readonly dropout: number;\n  readonly recurrentDropout: number;\n  readonly dropoutFunc: Function;\n\n  readonly stateSize: number;\n  readonly implementation: number;\n\n  readonly DEFAULT_ACTIVATION = 'tanh';\n  readonly DEFAULT_RECURRENT_ACTIVATION: ActivationIdentifier = 'hardSigmoid';\n\n  readonly DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n  readonly DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n  readonly DEFAULT_BIAS_INITIALIZER: InitializerIdentifier = 'zeros';\n\n  kernel: LayerVariable;\n  recurrentKernel: LayerVariable;\n  bias: LayerVariable;\n\n  constructor(args: GRUCellLayerArgs) {\n    super(args);\n    if (args.resetAfter) {\n      throw new ValueError(\n          `GRUCell does not support reset_after parameter set to true.`);\n    }\n    this.units = args.units;\n    assertPositiveInteger(this.units, 'units');\n    this.activation = getActivation(\n        args.activation === undefined ? this.DEFAULT_ACTIVATION :\n                                        args.activation);\n    this.recurrentActivation = getActivation(\n        args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n\n    this.kernelInitializer = getInitializer(\n        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(\n        args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n\n    this.biasInitializer =\n        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n\n    this.dropout = math_utils.min(\n        [1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = math_utils.min([\n      1,\n      math_utils.max(\n          [0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = this.units;\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n\n  public override build(inputShape: Shape|Shape[]): void {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\n        'kernel', [inputDim, this.units * 3], null, this.kernelInitializer,\n        this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\n        'recurrent_kernel', [this.units, this.units * 3], null,\n        this.recurrentInitializer, this.recurrentRegularizer, true,\n        this.recurrentConstraint);\n    if (this.useBias) {\n      this.bias = this.addWeight(\n          'bias', [this.units * 3], null, this.biasInitializer,\n          this.biasRegularizer, true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n    //   of the weights and bias in the call() method, at execution time.\n    this.built = true;\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      inputs = inputs as Tensor[];\n      if (inputs.length !== 2) {\n        throw new ValueError(\n            `GRUCell expects 2 input Tensors (inputs, h, c), got ` +\n            `${inputs.length}.`);\n      }\n\n      const training = kwargs['training'] == null ? false : kwargs['training'];\n      let hTMinus1 = inputs[1];  // Previous memory state.\n      inputs = inputs[0];\n\n      // Note: For superior performance, TensorFlow.js always uses\n      // implementation 2, regardless of the actual value of\n      // config.implementation.\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n                             ones: () => tfc.onesLike(inputs as Tensor),\n                             rate: this.dropout,\n                             training,\n                             count: 3,\n                             dropoutFunc: this.dropoutFunc,\n                           }) as Tensor[];\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n          this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n                                      ones: () => tfc.onesLike(hTMinus1),\n                                      rate: this.recurrentDropout,\n                                      training,\n                                      count: 3,\n                                      dropoutFunc: this.dropoutFunc,\n                                    }) as Tensor[];\n      }\n      const dpMask = this.dropoutMask as [Tensor, Tensor, Tensor];\n      const recDpMask = this.recurrentDropoutMask as [Tensor, Tensor, Tensor];\n      let z: Tensor;\n      let r: Tensor;\n      let hh: Tensor;\n\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = tfc.mul(inputs, dpMask[0]);\n      }\n      let matrixX = K.dot(inputs, this.kernel.read());\n      if (this.useBias) {\n        matrixX = K.biasAdd(matrixX, this.bias.read());\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n      }\n\n      const recurrentKernelValue = this.recurrentKernel.read();\n      const [rk1, rk2] = tfc.split(\n          recurrentKernelValue, [2 * this.units, this.units],\n          recurrentKernelValue.rank - 1);\n      const matrixInner = K.dot(hTMinus1, rk1);\n\n      const [xZ, xR, xH] = tfc.split(matrixX, 3, matrixX.rank - 1);\n      const [recurrentZ, recurrentR] =\n          tfc.split(matrixInner, 2, matrixInner.rank - 1);\n      z = this.recurrentActivation.apply(tfc.add(xZ, recurrentZ));\n      r = this.recurrentActivation.apply(tfc.add(xR, recurrentR));\n\n      const recurrentH = K.dot(tfc.mul(r, hTMinus1), rk2);\n      hh = this.activation.apply(tfc.add(xH, recurrentH));\n\n      const h =\n          tfc.add(tfc.mul(z, hTMinus1), tfc.mul(tfc.add(1, tfc.neg(z)), hh));\n      // TODO(cais): Add use_learning_phase flag properly.\n      return [h, h];\n    });\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const baseConfig = super.getConfig();\n\n    const config: serialization.ConfigDict = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation,\n      resetAfter: false\n    };\n\n    return {...baseConfig, ...config};\n  }\n}\nserialization.registerClass(GRUCell);\n\n// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we inherit\n//   from that interface instead of repeating the fields here.\nexport declare interface GRULayerArgs extends SimpleRNNLayerArgs {\n  /**\n   * Activation function to use for the recurrent step.\n   *\n   * Defaults to hard sigmoid (`hardSigmoid`).\n   *\n   * If `null`, no activation is applied.\n   */\n  recurrentActivation?: ActivationIdentifier;\n\n  /**\n   * Implementation mode, either 1 or 2.\n   *\n   * Mode 1 will structure its operations as a larger number of\n   * smaller dot products and additions.\n   *\n   * Mode 2 will batch them into fewer, larger operations. These modes will\n   * have different performance profiles on different hardware and\n   * for different applications.\n   *\n   * Note: For superior performance, TensorFlow.js always uses implementation\n   * 2, regardless of the actual value of this configuration field.\n   */\n  implementation?: number;\n}\n\nexport class GRU extends RNN {\n  /** @nocollapse */\n  static override className = 'GRU';\n  constructor(args: GRULayerArgs) {\n    if (args.implementation === 0) {\n      console.warn(\n          '`implementation=0` has been deprecated, and now defaults to ' +\n          '`implementation=1`. Please update your layer call.');\n    }\n    args.cell = new GRUCell(args);\n    super(args as RNNLayerArgs);\n    // TODO(cais): Add activityRegularizer.\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        tfc.dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        tfc.dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs['mask'];\n      const training = kwargs == null ? null : kwargs['training'];\n      const initialState: Tensor[] =\n          kwargs == null ? null : kwargs['initialState'];\n      return super.call(inputs, {mask, training, initialState});\n    });\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends serialization.Serializable>(\n      cls: serialization.SerializableConstructor<T>,\n      config: serialization.ConfigDict): T {\n    if (config['implmentation'] === 0) {\n      config['implementation'] = 1;\n    }\n    return new cls(config);\n  }\n}\nserialization.registerClass(GRU);\n\n// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we extend\n//   that interface instead of repeating the fields.\nexport declare interface LSTMCellLayerArgs extends SimpleRNNCellLayerArgs {\n  /**\n   * Activation function to use for the recurrent step.\n   *\n   * Defaults to hard sigmoid (`hardSigmoid`).\n   *\n   * If `null`, no activation is applied.\n   */\n  recurrentActivation?: ActivationIdentifier;\n\n  /**\n   * If `true`, add 1 to the bias of the forget gate at initialization.\n   * Setting it to `true` will also force `biasInitializer = 'zeros'`.\n   * This is recommended in\n   * [Jozefowicz et\n   * al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)\n   */\n  unitForgetBias?: boolean;\n\n  /**\n   * Implementation mode, either 1 or 2.\n   *\n   * Mode 1 will structure its operations as a larger number of\n   *   smaller dot products and additions.\n   *\n   * Mode 2 will batch them into fewer, larger operations. These modes will\n   * have different performance profiles on different hardware and\n   * for different applications.\n   *\n   * Note: For superior performance, TensorFlow.js always uses implementation\n   * 2, regardless of the actual value of this configuration field.\n   */\n  implementation?: number;\n}\n\nexport class LSTMCell extends RNNCell {\n  /** @nocollapse */\n  static className = 'LSTMCell';\n  readonly units: number;\n  readonly activation: Activation;\n  readonly recurrentActivation: Activation;\n  readonly useBias: boolean;\n\n  readonly kernelInitializer: Initializer;\n  readonly recurrentInitializer: Initializer;\n  readonly biasInitializer: Initializer;\n  readonly unitForgetBias: boolean;\n\n  readonly kernelConstraint: Constraint;\n  readonly recurrentConstraint: Constraint;\n  readonly biasConstraint: Constraint;\n\n  readonly kernelRegularizer: Regularizer;\n  readonly recurrentRegularizer: Regularizer;\n  readonly biasRegularizer: Regularizer;\n\n  readonly dropout: number;\n  readonly recurrentDropout: number;\n  readonly dropoutFunc: Function;\n\n  readonly stateSize: number[];\n  readonly implementation: number;\n\n  readonly DEFAULT_ACTIVATION = 'tanh';\n  readonly DEFAULT_RECURRENT_ACTIVATION = 'hardSigmoid';\n  readonly DEFAULT_KERNEL_INITIALIZER = 'glorotNormal';\n  readonly DEFAULT_RECURRENT_INITIALIZER = 'orthogonal';\n\n  readonly DEFAULT_BIAS_INITIALIZER = 'zeros';\n\n  kernel: LayerVariable;\n  recurrentKernel: LayerVariable;\n  bias: LayerVariable;\n\n  constructor(args: LSTMCellLayerArgs) {\n    super(args);\n\n    this.units = args.units;\n    assertPositiveInteger(this.units, 'units');\n    this.activation = getActivation(\n        args.activation === undefined ? this.DEFAULT_ACTIVATION :\n                                        args.activation);\n    this.recurrentActivation = getActivation(\n        args.recurrentActivation === undefined ?\n            this.DEFAULT_RECURRENT_ACTIVATION :\n            args.recurrentActivation);\n    this.useBias = args.useBias == null ? true : args.useBias;\n\n    this.kernelInitializer = getInitializer(\n        args.kernelInitializer || this.DEFAULT_KERNEL_INITIALIZER);\n    this.recurrentInitializer = getInitializer(\n        args.recurrentInitializer || this.DEFAULT_RECURRENT_INITIALIZER);\n\n    this.biasInitializer =\n        getInitializer(args.biasInitializer || this.DEFAULT_BIAS_INITIALIZER);\n    this.unitForgetBias = args.unitForgetBias;\n\n    this.kernelRegularizer = getRegularizer(args.kernelRegularizer);\n    this.recurrentRegularizer = getRegularizer(args.recurrentRegularizer);\n    this.biasRegularizer = getRegularizer(args.biasRegularizer);\n\n    this.kernelConstraint = getConstraint(args.kernelConstraint);\n    this.recurrentConstraint = getConstraint(args.recurrentConstraint);\n    this.biasConstraint = getConstraint(args.biasConstraint);\n\n    this.dropout = math_utils.min(\n        [1, math_utils.max([0, args.dropout == null ? 0 : args.dropout])]);\n    this.recurrentDropout = math_utils.min([\n      1,\n      math_utils.max(\n          [0, args.recurrentDropout == null ? 0 : args.recurrentDropout])\n    ]);\n    this.dropoutFunc = args.dropoutFunc;\n    this.implementation = args.implementation;\n    this.stateSize = [this.units, this.units];\n    this.dropoutMask = null;\n    this.recurrentDropoutMask = null;\n  }\n\n  public override build(inputShape: Shape|Shape[]): void {\n    inputShape = getExactlyOneShape(inputShape);\n    const inputDim = inputShape[inputShape.length - 1];\n    this.kernel = this.addWeight(\n        'kernel', [inputDim, this.units * 4], null, this.kernelInitializer,\n        this.kernelRegularizer, true, this.kernelConstraint);\n    this.recurrentKernel = this.addWeight(\n        'recurrent_kernel', [this.units, this.units * 4], null,\n        this.recurrentInitializer, this.recurrentRegularizer, true,\n        this.recurrentConstraint);\n    let biasInitializer: Initializer;\n    if (this.useBias) {\n      if (this.unitForgetBias) {\n        const capturedBiasInit = this.biasInitializer;\n        const capturedUnits = this.units;\n        biasInitializer = new (class CustomInit extends Initializer {\n          /** @nocollapse */\n          static className = 'CustomInit';\n\n          apply(shape: Shape, dtype?: DataType): Tensor {\n            // TODO(cais): More informative variable names?\n            const bI = capturedBiasInit.apply([capturedUnits]);\n            const bF = (new Ones()).apply([capturedUnits]);\n            const bCAndH = capturedBiasInit.apply([capturedUnits * 2]);\n            return K.concatAlongFirstAxis(\n                K.concatAlongFirstAxis(bI, bF), bCAndH);\n          }\n        })();\n      } else {\n        biasInitializer = this.biasInitializer;\n      }\n      this.bias = this.addWeight(\n          'bias', [this.units * 4], null, biasInitializer, this.biasRegularizer,\n          true, this.biasConstraint);\n    } else {\n      this.bias = null;\n    }\n    // Porting Notes: Unlike the PyKeras implementation, we perform slicing\n    //   of the weights and bias in the call() method, at execution time.\n    this.built = true;\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      const training = kwargs['training'] == null ? false : kwargs['training'];\n      inputs = inputs as Tensor[];\n      if (inputs.length !== 3) {\n        throw new ValueError(\n            `LSTMCell expects 3 input Tensors (inputs, h, c), got ` +\n            `${inputs.length}.`);\n      }\n      let hTMinus1 = inputs[1];    // Previous memory state.\n      const cTMinus1 = inputs[2];  // Previous carry state.\n      inputs = inputs[0];\n      if (0 < this.dropout && this.dropout < 1 && this.dropoutMask == null) {\n        this.dropoutMask = generateDropoutMask({\n                             ones: () => tfc.onesLike(inputs as Tensor),\n                             rate: this.dropout,\n                             training,\n                             count: 4,\n                             dropoutFunc: this.dropoutFunc\n                           }) as Tensor[];\n      }\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1 &&\n          this.recurrentDropoutMask == null) {\n        this.recurrentDropoutMask = generateDropoutMask({\n                                      ones: () => tfc.onesLike(hTMinus1),\n                                      rate: this.recurrentDropout,\n                                      training,\n                                      count: 4,\n                                      dropoutFunc: this.dropoutFunc\n                                    }) as Tensor[];\n      }\n      const dpMask = this.dropoutMask as [Tensor, Tensor, Tensor, Tensor];\n      const recDpMask =\n          this.recurrentDropoutMask as [Tensor, Tensor, Tensor, Tensor];\n\n      // Note: For superior performance, TensorFlow.js always uses\n      // implementation 2 regardless of the actual value of\n      // config.implementation.\n      let i: Tensor;\n      let f: Tensor;\n      let c: Tensor;\n      let o: Tensor;\n      if (0 < this.dropout && this.dropout < 1) {\n        inputs = tfc.mul(inputs, dpMask[0]);\n      }\n      let z = K.dot(inputs, this.kernel.read());\n      if (0 < this.recurrentDropout && this.recurrentDropout < 1) {\n        hTMinus1 = tfc.mul(hTMinus1, recDpMask[0]);\n      }\n      z = tfc.add(z, K.dot(hTMinus1, this.recurrentKernel.read()));\n      if (this.useBias) {\n        z = K.biasAdd(z, this.bias.read());\n      }\n\n      const [z0, z1, z2, z3] = tfc.split(z, 4, z.rank - 1);\n\n      i = this.recurrentActivation.apply(z0);\n      f = this.recurrentActivation.apply(z1);\n      c = tfc.add(tfc.mul(f, cTMinus1), tfc.mul(i, this.activation.apply(z2)));\n      o = this.recurrentActivation.apply(z3);\n\n      const h = tfc.mul(o, this.activation.apply(c));\n      // TODO(cais): Add use_learning_phase flag properly.\n      return [h, h, c];\n    });\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const baseConfig = super.getConfig();\n\n    const config: serialization.ConfigDict = {\n      units: this.units,\n      activation: serializeActivation(this.activation),\n      recurrentActivation: serializeActivation(this.recurrentActivation),\n      useBias: this.useBias,\n      kernelInitializer: serializeInitializer(this.kernelInitializer),\n      recurrentInitializer: serializeInitializer(this.recurrentInitializer),\n      biasInitializer: serializeInitializer(this.biasInitializer),\n      unitForgetBias: this.unitForgetBias,\n      kernelRegularizer: serializeRegularizer(this.kernelRegularizer),\n      recurrentRegularizer: serializeRegularizer(this.recurrentRegularizer),\n      biasRegularizer: serializeRegularizer(this.biasRegularizer),\n      activityRegularizer: serializeRegularizer(this.activityRegularizer),\n      kernelConstraint: serializeConstraint(this.kernelConstraint),\n      recurrentConstraint: serializeConstraint(this.recurrentConstraint),\n      biasConstraint: serializeConstraint(this.biasConstraint),\n      dropout: this.dropout,\n      recurrentDropout: this.recurrentDropout,\n      implementation: this.implementation,\n    };\n\n    return {...baseConfig, ...config};\n  }\n}\nserialization.registerClass(LSTMCell);\n\n// Porting Note: Since this is a superset of SimpleRNNLayerConfig, we inherit\n//   from that interface instead of repeating the fields here.\nexport declare interface LSTMLayerArgs extends SimpleRNNLayerArgs {\n  /**\n   * Activation function to use for the recurrent step.\n   *\n   * Defaults to hard sigmoid (`hardSigmoid`).\n   *\n   * If `null`, no activation is applied.\n   */\n  recurrentActivation?: ActivationIdentifier;\n\n  /**\n   * If `true`, add 1 to the bias of the forget gate at initialization.\n   * Setting it to `true` will also force `biasInitializer = 'zeros'`.\n   * This is recommended in\n   * [Jozefowicz et\n   * al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)\n   */\n  unitForgetBias?: boolean;\n\n  /**\n   * Implementation mode, either 1 or 2.\n   *   Mode 1 will structure its operations as a larger number of\n   *   smaller dot products and additions, whereas mode 2 will\n   *   batch them into fewer, larger operations. These modes will\n   *   have different performance profiles on different hardware and\n   *   for different applications.\n   *\n   * Note: For superior performance, TensorFlow.js always uses implementation\n   * 2, regardless of the actual value of this config field.\n   */\n  implementation?: number;\n}\n\nexport class LSTM extends RNN {\n  /** @nocollapse */\n  static override className = 'LSTM';\n  constructor(args: LSTMLayerArgs) {\n    if (args.implementation === 0) {\n      console.warn(\n          '`implementation=0` has been deprecated, and now defaults to ' +\n          '`implementation=1`. Please update your layer call.');\n    }\n    args.cell = new LSTMCell(args);\n    super(args as RNNLayerArgs);\n    // TODO(cais): Add activityRegularizer.\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      if (this.cell.dropoutMask != null) {\n        tfc.dispose(this.cell.dropoutMask);\n        this.cell.dropoutMask = null;\n      }\n      if (this.cell.recurrentDropoutMask != null) {\n        tfc.dispose(this.cell.recurrentDropoutMask);\n        this.cell.recurrentDropoutMask = null;\n      }\n      const mask = kwargs == null ? null : kwargs['mask'];\n      const training = kwargs == null ? null : kwargs['training'];\n      const initialState: Tensor[] =\n          kwargs == null ? null : kwargs['initialState'];\n      return super.call(inputs, {mask, training, initialState});\n    });\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends serialization.Serializable>(\n      cls: serialization.SerializableConstructor<T>,\n      config: serialization.ConfigDict): T {\n    if (config['implmentation'] === 0) {\n      config['implementation'] = 1;\n    }\n    return new cls(config);\n  }\n}\nserialization.registerClass(LSTM);\n\nexport declare interface StackedRNNCellsArgs extends LayerArgs {\n  /**\n   * An `Array` of `RNNCell` instances.\n   */\n  cells: RNNCell[];\n}\n\nexport class StackedRNNCells extends RNNCell {\n  /** @nocollapse */\n  static className = 'StackedRNNCells';\n  protected cells: RNNCell[];\n\n  constructor(args: StackedRNNCellsArgs) {\n    super(args);\n    this.cells = args.cells;\n  }\n\n  get stateSize(): number[] {\n    // States are a flat list in reverse order of the cell stack.\n    // This allows preserving the requirement `stack.statesize[0] ===\n    // outputDim`. E.g., states of a 2-layer LSTM would be `[h2, c2, h1, c1]`,\n    // assuming one LSTM has states `[h, c]`.\n    const stateSize: number[] = [];\n    for (const cell of this.cells.slice().reverse()) {\n      if (Array.isArray(cell.stateSize)) {\n        stateSize.push(...cell.stateSize);\n      } else {\n        stateSize.push(cell.stateSize);\n      }\n    }\n    return stateSize;\n  }\n\n  override call(inputs: Tensor|Tensor[], kwargs: Kwargs): Tensor|Tensor[] {\n    return tidy(() => {\n      inputs = inputs as Tensor[];\n      let states = inputs.slice(1);\n\n      // Recover per-cell states.\n      const nestedStates: Tensor[][] = [];\n      for (const cell of this.cells.slice().reverse()) {\n        if (Array.isArray(cell.stateSize)) {\n          nestedStates.push(states.splice(0, cell.stateSize.length));\n        } else {\n          nestedStates.push(states.splice(0, 1));\n        }\n      }\n      nestedStates.reverse();\n\n      // Call the cells in order and store the returned states.\n      const newNestedStates: Tensor[][] = [];\n      let callInputs: Tensor[];\n      for (let i = 0; i < this.cells.length; ++i) {\n        const cell = this.cells[i];\n        states = nestedStates[i];\n        // TODO(cais): Take care of constants.\n        if (i === 0) {\n          callInputs = [inputs[0]].concat(states);\n        } else {\n          callInputs = [callInputs[0]].concat(states);\n        }\n        callInputs = cell.call(callInputs, kwargs) as Tensor[];\n        newNestedStates.push(callInputs.slice(1));\n      }\n\n      // Format the new states as a flat list in reverse cell order.\n      states = [];\n      for (const cellStates of newNestedStates.slice().reverse()) {\n        states.push(...cellStates);\n      }\n      return [callInputs[0]].concat(states);\n    });\n  }\n\n  public override build(inputShape: Shape|Shape[]): void {\n    if (isArrayOfShapes(inputShape)) {\n      // TODO(cais): Take care of input constants.\n      // const constantShape = inputShape.slice(1);\n      inputShape = (inputShape as Shape[])[0];\n    }\n    inputShape = inputShape as Shape;\n    let outputDim: number;\n    this.cells.forEach((cell, i) => {\n      nameScope(`RNNCell_${i}`, () => {\n        // TODO(cais): Take care of input constants.\n\n        cell.build(inputShape);\n        if (Array.isArray(cell.stateSize)) {\n          outputDim = cell.stateSize[0];\n        } else {\n          outputDim = cell.stateSize;\n        }\n        inputShape = [inputShape[0], outputDim] as Shape;\n      });\n    });\n    this.built = true;\n  }\n\n  override getConfig(): serialization.ConfigDict {\n    const baseConfig = super.getConfig();\n\n    const getCellConfig = (cell: RNNCell) => {\n      return {\n        'className': cell.getClassName(),\n        'config': cell.getConfig(),\n      };\n    };\n\n    const cellConfigs = this.cells.map(getCellConfig);\n\n    const config = {'cells': cellConfigs};\n\n    return {...baseConfig, ...config};\n  }\n\n  /** @nocollapse */\n  static override fromConfig<T extends serialization.Serializable>(\n      cls: serialization.SerializableConstructor<T>,\n      config: serialization.ConfigDict,\n      customObjects = {} as serialization.ConfigDict): T {\n    const cells: RNNCell[] = [];\n    for (const cellConfig of (config['cells'] as serialization.ConfigDict[])) {\n      cells.push(deserialize(cellConfig, customObjects) as RNNCell);\n    }\n    return new cls({cells});\n  }\n\n  override get trainableWeights(): LayerVariable[] {\n    if (!this.trainable) {\n      return [];\n    }\n    const weights: LayerVariable[] = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.trainableWeights);\n    }\n    return weights;\n  }\n\n  override get nonTrainableWeights(): LayerVariable[] {\n    const weights: LayerVariable[] = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.nonTrainableWeights);\n    }\n    if (!this.trainable) {\n      const trainableWeights: LayerVariable[] = [];\n      for (const cell of this.cells) {\n        trainableWeights.push(...cell.trainableWeights);\n      }\n      return trainableWeights.concat(weights);\n    }\n    return weights;\n  }\n\n  /**\n   * Retrieve the weights of a the model.\n   *\n   * @returns A flat `Array` of `tf.Tensor`s.\n   */\n  override getWeights(): Tensor[] {\n    const weights: LayerVariable[] = [];\n    for (const cell of this.cells) {\n      weights.push(...cell.weights);\n    }\n    return batchGetValue(weights);\n  }\n\n  /**\n   * Set the weights of the model.\n   *\n   * @param weights An `Array` of `tf.Tensor`s with shapes and types matching\n   *     the output of `getWeights()`.\n   */\n  override setWeights(weights: Tensor[]): void {\n    const tuples: Array<[LayerVariable, Tensor]> = [];\n    for (const cell of this.cells) {\n      const numParams = cell.weights.length;\n      const inputWeights = weights.splice(numParams);\n      for (let i = 0; i < cell.weights.length; ++i) {\n        tuples.push([cell.weights[i], inputWeights[i]]);\n      }\n    }\n    batchSetValue(tuples);\n  }\n\n  // TODO(cais): Maybe implement `losses` and `getLossesFor`.\n}\nserialization.registerClass(StackedRNNCells);\n\nexport function generateDropoutMask(args: {\n  ones: () => tfc.Tensor,\n  rate: number,\n  training?: boolean,\n  count?: number,\n  dropoutFunc?: Function,\n}): tfc.Tensor|tfc.Tensor[] {\n  const {ones, rate, training = false, count = 1, dropoutFunc} = args;\n\n  const droppedInputs = () =>\n      dropoutFunc != null ? dropoutFunc(ones(), rate) : K.dropout(ones(), rate);\n\n  const createMask = () => K.inTrainPhase(droppedInputs, ones, training);\n\n  // just in case count is provided with null or undefined\n  if (!count || count <= 1) {\n    return tfc.keep(createMask().clone());\n  }\n\n  const masks = Array(count).fill(undefined).map(createMask);\n\n  return masks.map(m => tfc.keep(m.clone()));\n}\n"]}