/**
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* @license
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* Copyright 2018 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/// <amd-module name="@tensorflow/tfjs-layers/dist/variables" />
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import * as tfc from '@tensorflow/tfjs-core';
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import { DataType, Tensor } from '@tensorflow/tfjs-core';
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import { Constraint } from './constraints';
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import { Shape } from './keras_format/common';
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/**
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* A `tf.layers.LayerVariable` is similar to a `tf.Tensor` in that it has a
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* dtype and shape, but its value is mutable. The value is itself represented
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* as a`tf.Tensor`, and can be read with the `read()` method and updated with
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* the `write()` method.
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*/
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export declare class LayerVariable {
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readonly dtype: DataType;
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readonly shape: Shape;
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readonly id: number;
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readonly name: string;
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readonly originalName: string;
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private trainable_;
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protected readonly val: tfc.Variable;
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readonly constraint: Constraint;
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/**
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* Construct Variable from a `tf.Tensor`.
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*
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* If not explicitly named, the Variable will be given a name with the
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* prefix 'Variable'. Variable names are unique. In the case of name
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* collision, suffixies '_<num>' will be added to the name.
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*
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* @param val Initial value of the Variable.
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* @param name Name of the variable. If `null` or `undefined` is provided, it
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* will default a name with the prefix 'Variable'.
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* @param constraint Optional, projection function to be applied to the
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* variable after optimize updates
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* @throws ValueError if `name` is `null` or `undefined`.
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*/
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constructor(val: Tensor, dtype?: DataType, name?: string, trainable?: boolean, constraint?: Constraint);
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/**
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* Get a snapshot of the Variable's value.
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*
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* The returned value is a snapshot of the Variable's value at the time of
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* the invocation. Future mutations in the value of the tensor will only
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* be reflected by future calls to this method.
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*/
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read(): Tensor;
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/**
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* Update the value of the Variable.
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*
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* @param newVal: The new value to update to. Must be consistent with the
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* dtype and shape of the Variable.
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* @return This Variable.
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*/
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write(newVal: Tensor): this;
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/**
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* Dispose this LayersVariable instance from memory.
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*/
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dispose(): void;
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protected assertNotDisposed(): void;
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get trainable(): boolean;
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set trainable(trainable: boolean);
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}
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/**
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* Create a Variable.
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* @param x The initial value of the `Variable`.
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* @param dtype optional, the type of the variable.
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* @param name optional, the name of the variable, default provided by
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* Variable.
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* @param constraint optional, a constraint to be applied after every update.
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* @return The newly instantiated `Variable`.
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*/
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export declare function variable(x: Tensor, dtype?: DataType, name?: string, constraint?: Constraint): LayerVariable;
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/**
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* Instantiates an all-zeros Variable and returns it.
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*
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* @param shape Shape of the tensor.
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* @param dtype DType of the tensor.
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* @param name Name of the tensor.
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* @return An all-zero Variable.
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*/
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export declare function zerosVariable(shape: Shape, dtype?: DataType, name?: string): LayerVariable;
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/**
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* Instantiates an all-zeros tensor of the same shape as another tensor.
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*
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* @param x The other tensor.
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* @param dtype DType of the tensor.
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* @param name Name of the tensor.
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* @return A newly instantiated Variable.
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*/
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export declare function zerosLike(x: Tensor, dtype?: DataType, name?: string): LayerVariable;
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/**
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* Instantiates an all-ones tensor and returns it.
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*
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* @param shape Shape of the tensor.
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* @param dtype DType of the tensor.
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* @param name Name of the tensor.
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* @return An all-ones Variable.
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*/
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export declare function onesVariable(shape: Shape, dtype?: DataType, name?: string): LayerVariable;
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/**
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* Instantiates an all-ones tensor of the same shape as another tensor.
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*
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* @param x The other tensor.
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* @param dtype DType of the tensor.
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* @param name Name of the tensor.
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* @return A newly instantiated Variable.
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*/
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export declare function onesLike(x: Tensor, dtype?: DataType, name?: string): LayerVariable;
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/**
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* Instantiate an identity matrix and returns it, as a Variable
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*
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* @param size Number of rows/columns.
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* @param dtype Data type of returned Variable.
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* @param name Name of returned Variable.
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* @return A Variable, an identity matrix.
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*/
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export declare function eyeVariable(size: number, dtype?: DataType, name?: string): LayerVariable;
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/**
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* Get a Variable with uniform distribution of values.
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* @param shape Shape of the tensor.
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* @param minval Lower bound of the uniform distribution.
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* @param maxval Upper bound of the uniform distribution.
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* @param dtype
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* @param seed
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* @param name Optional name.
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* @return The uniform-random Variable.
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*/
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export declare function randomUniformVariable(shape: Shape, minval: number, maxval: number, dtype?: DataType, seed?: number, name?: string): LayerVariable;
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/**
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* Get a Variable with truncated-normal distribution of values.
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* @param shape Shape of the tensor.
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* @param mean mean value of the normal distribution.
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* @param stddev standard deviation of the normal distribution.
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* @param dtype
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* @param seed
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* @param name Optional name.
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* @return The truncated-normal-random Variable.
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*/
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export declare function truncatedNormalVariable(shape: Shape, mean?: number, stddev?: number, dtype?: DataType, seed?: number, name?: string): LayerVariable;
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/**
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* Get a Variable with normal distribution of values.
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* @param shape Shape of the tensor.
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* @param mean mean value of the normal distribution.
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* @param stddev standard deviation of the normal distribution.
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* @param dtype
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* @param seed
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* @param name Optional name.
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* @return The truncated-normal-random Variable.
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*/
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export declare function randomNormalVariable(shape: Shape, mean?: number, stddev?: number, dtype?: DataType, seed?: number, name?: string): LayerVariable;
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/**
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* Update the value of a Variable.
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* @param x The Variable to be updated.
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* @param xNew The new value to update to.
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* @return The Variable updated.
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*/
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export declare function update(x: LayerVariable, xNew: Tensor): LayerVariable;
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/**
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* Update the value of a Variable by adding an increment.
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* @param x The Variable to be updated.
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* @param increment The incrment to add to `x`.
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* @return The Variable updated.
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*/
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export declare function updateAdd(x: LayerVariable, increment: Tensor): LayerVariable;
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/**
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* Update the value of a Variable by subtracting a decrement.
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* @param x The Variable to be updated.
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* @param decrement The decrement to subtract from `x`.
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* @return The Variable updated.
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*/
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export declare function updateSub(x: LayerVariable, decrement: Tensor): LayerVariable;
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/**
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* Get the values of an array of Variables.
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*
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* @param tensors An `Array` of `Variable`s to get the values of.
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* @return The values of the inputs, as an `Array` of`tf.Tensor`s.
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*/
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export declare function batchGetValue(xs: LayerVariable[]): Tensor[];
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/**
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* Update the value of multiple Variables at once.
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*
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* @param variablesAndValues An `Array`, each element is of type
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* [Variable, Tensor]. The first item is the
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* `Variable` of which the value is to be updated. The second item
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* carries the new value.
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*/
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export declare function batchSetValue(variablesAndValues: Array<[LayerVariable, Tensor]>): void;
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/**
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* Returns the gradients of `variables` w.r.t. the return value of `lossFn`.
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* @param lossFn A function which returns a Scalar to be used as the function
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* value (i.e., numerator) for differentiation.
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* @param variables List of variables to be used as the independent variables
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* (i.e., denominator) for differentiation.
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* @returns An Array of gradients tensors.
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*/
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export declare function gradients(lossFn: () => tfc.Scalar, variables: LayerVariable[]): Tensor[];
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