/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import { Tensor } from '../tensor';
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import { TensorLike } from '../types';
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/**
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* Computes the log(sum(exp(elements across the reduction dimensions)).
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*
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* Reduces the input along the dimensions given in `axis`. Unless `keepDims`
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* is true, the rank of the array is reduced by 1 for each entry in `axis`.
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* If `keepDims` is true, the reduced dimensions are retained with length 1.
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* If `axis` has no entries, all dimensions are reduced, and an array with a
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* single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.logSumExp().print(); // or tf.logSumExp(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.logSumExp(axis).print(); // or tf.logSumExp(a, axis)
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* ```
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* @param x The input tensor.
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* @param axis The dimension(s) to reduce. If null (the default),
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* reduces all dimensions.
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* @param keepDims If true, retains reduced dimensions with length
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* of 1. Defaults to false.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function logSumExp_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Computes the sum of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If axes has no entries, all dimensions are reduced, and a
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.sum().print(); // or tf.sum(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.sum(axis).print(); // or tf.sum(x, axis)
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* ```
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*
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* @param x The input tensor to compute the sum over. If the dtype is `bool`
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* it will be converted to `int32` and the output dtype will be `int32`.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function sum_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Computes the product of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If `axes` has no entries, all dimensions are reduced, and a
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.prod().print(); // or tf.prod(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.prod(axis).print(); // or tf.prod(x, axis)
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* ```
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*
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* @param x The input tensor to compute the product over. If the dtype is `bool`
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* it will be converted to `int32` and the output dtype will be `int32`.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function prod_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Computes the mean of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is
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* true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.
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* If `keepDims` is true, the reduced dimensions are retained with length 1.
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* If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with
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* a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.mean().print(); // or tf.mean(a)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.mean(axis).print(); // or tf.mean(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function mean_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Computes the minimum value from the input.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the array is reduced by 1 for each entry in `axes`.
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* If `keepDims` is true, the reduced dimensions are retained with length 1.
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* If `axes` has no entries, all dimensions are reduced, and an array with a
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* single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.min().print(); // or tf.min(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.min(axis).print(); // or tf.min(x, axis)
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* ```
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*
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* @param x The input Tensor.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function min_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Computes the maximum of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If `axes` has no entries, all dimensions are reduced, and an
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.max().print(); // or tf.max(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
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*
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* const axis = 1;
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* x.max(axis).print(); // or tf.max(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function max_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Returns the indices of the minimum values along an `axis`.
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*
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* The result has the same shape as `input` with the dimension along `axis`
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* removed.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.argMin().print(); // or tf.argMin(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
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*
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* const axis = 1;
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* x.argMin(axis).print(); // or tf.argMin(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
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*
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function argMin_<T extends Tensor>(x: Tensor | TensorLike, axis?: number): T;
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/**
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* Returns the indices of the maximum values along an `axis`.
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*
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* The result has the same shape as `input` with the dimension along `axis`
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* removed.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3]);
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*
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* x.argMax().print(); // or tf.argMax(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);
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*
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* const axis = 1;
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* x.argMax(axis).print(); // or tf.argMax(x, axis)
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* ```
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*
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* @param x The input tensor.
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* @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function argMax_<T extends Tensor>(x: Tensor | TensorLike, axis?: number): T;
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/**
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* Computes the logical and of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If `axes` has no entries, all dimensions are reduced, and an
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 1, 1], 'bool');
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*
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* x.all().print(); // or tf.all(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
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*
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* const axis = 1;
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* x.all(axis).print(); // or tf.all(x, axis)
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* ```
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*
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* @param x The input tensor. Must be of dtype bool.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function all_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Computes the logical or of elements across dimensions of a `tf.Tensor`.
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*
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* Reduces the input along the dimensions given in `axes`. Unless `keepDims`
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* is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in
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* `axes`. If `keepDims` is true, the reduced dimensions are retained with
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* length 1. If `axes` has no entries, all dimensions are reduced, and an
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* `tf.Tensor` with a single element is returned.
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*
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* ```js
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* const x = tf.tensor1d([1, 1, 1], 'bool');
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*
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* x.any().print(); // or tf.any(x)
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* ```
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*
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* ```js
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* const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
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*
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* const axis = 1;
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* x.any(axis).print(); // or tf.any(x, axis)
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* ```
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*
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* @param x The input tensor. Must be of dtype bool.
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* @param axis The dimension(s) to reduce. By default it reduces
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* all dimensions.
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* @param keepDims If true, retains reduced dimensions with size 1.
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*/
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/** @doc {heading: 'Operations', subheading: 'Reduction'} */
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declare function any_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
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/**
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* Calculates the mean and variance of `x`. The mean and variance are
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* calculated by aggregating the contents of `x` across `axes`. If `x` is
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* 1-D and `axes = [0]` this is just the mean and variance of a vector.
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*
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* @param x The input tensor.
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* @param axis The dimension(s) along with to compute mean and
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* variance. By default it reduces all dimensions.
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* @param keepDims If true, the moments have the same dimensionality as the
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* input.
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* @return An object with two keys: `mean` and `variance`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Normalization'} */
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declare function moments_(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): {
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mean: Tensor;
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variance: Tensor;
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};
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export declare const all: typeof all_;
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export declare const any: typeof any_;
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export declare const argMax: typeof argMax_;
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export declare const argMin: typeof argMin_;
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export declare const logSumExp: typeof logSumExp_;
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export declare const max: typeof max_;
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export declare const mean: typeof mean_;
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export declare const min: typeof min_;
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export declare const moments: typeof moments_;
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export declare const sum: typeof sum_;
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export declare const prod: typeof prod_;
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export {};
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