/**
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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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* Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
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*
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* We also expose `tf.addStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 2, 3, 4]);
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* const b = tf.tensor1d([10, 20, 30, 40]);
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*
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* a.add(b).print(); // or tf.add(a, b)
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* ```
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*
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* ```js
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* // Broadcast add a with b.
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* const a = tf.scalar(5);
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* const b = tf.tensor1d([10, 20, 30, 40]);
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*
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* a.add(b).print(); // or tf.add(a, b)
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* ```
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* @param a The first `tf.Tensor` to add.
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* @param b The second `tf.Tensor` to add. Must have the same type as `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function add_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
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*
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* ```js
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* const a = tf.tensor1d([1, 2]);
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* const b = tf.tensor1d([3, 4]);
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* const c = tf.tensor1d([5, 6]);
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*
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* tf.addN([a, b, c]).print();
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* ```
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* @param tensors A list of tensors with the same shape and dtype.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function addN_<T extends Tensor>(tensors: Array<T | TensorLike>): T;
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/**
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* Adds two `tf.Tensor`s element-wise, A + B.
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*
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* Inputs must be the same shape. For broadcasting support, use add() instead.
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*
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* @param a The first Tensor to add element-wise.
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* @param b The second Tensor to add element-wise.
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*/
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declare function addStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
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*
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* We also expose `tf.subStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([10, 20, 30, 40]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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*
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* a.sub(b).print(); // or tf.sub(a, b)
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* ```
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*
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* ```js
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* // Broadcast subtract a with b.
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* const a = tf.tensor1d([10, 20, 30, 40]);
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* const b = tf.scalar(5);
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*
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* a.sub(b).print(); // or tf.sub(a, b)
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* ```
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* @param a The first `tf.Tensor` to subtract from.
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* @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function sub_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must
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* be the same shape.
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*
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* For broadcasting support, use `tf.sub` instead.
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*
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* @param a The first Tensor to subtract element-wise.
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* @param b The second Tensor to subtract element-wise.
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*/
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declare function subStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Computes the power of one `tf.Tensor` to another. Supports broadcasting.
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*
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* Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
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* corresponding elements in x and y. The result's dtype will be the upcasted
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* type of the `base` and `exp` dtypes.
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*
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* ```js
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* const a = tf.tensor([[2, 3], [4, 5]])
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* const b = tf.tensor([[1, 2], [3, 0]]).toInt();
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*
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* a.pow(b).print(); // or tf.pow(a, b)
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* ```
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*
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* ```js
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* const a = tf.tensor([[1, 2], [3, 4]])
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* const b = tf.tensor(2).toInt();
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*
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* a.pow(b).print(); // or tf.pow(a, b)
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* ```
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* We also expose `powStrict` which has the same signature as this op and
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* asserts that `base` and `exp` are the same shape (does not broadcast).
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*
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* @param base The base `tf.Tensor` to pow element-wise.
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* @param exp The exponent `tf.Tensor` to pow element-wise.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function pow_<T extends Tensor>(base: Tensor | TensorLike, exp: Tensor | TensorLike): T;
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/**
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* Computes the power of one `tf.Tensor` to another. Inputs must
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* be the same shape.
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*
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* For broadcasting support, use `tf.pow` instead.
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*
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* @param base The base tensor to pow element-wise.
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* @param exp The exponent tensor to pow element-wise.
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*/
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declare function powStrict_<T extends Tensor>(base: T, exp: Tensor): T;
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/**
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* Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
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*
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* We also expose `tf.mulStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 2, 3, 4]);
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* const b = tf.tensor1d([2, 3, 4, 5]);
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*
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* a.mul(b).print(); // or tf.mul(a, b)
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* ```
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*
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* ```js
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* // Broadcast mul a with b.
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* const a = tf.tensor1d([1, 2, 3, 4]);
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* const b = tf.scalar(5);
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*
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* a.mul(b).print(); // or tf.mul(a, b)
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* ```
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* @param a The first tensor to multiply.
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* @param b The second tensor to multiply. Must have the same dtype as `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function mul_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Multiplies two `tf.Tensor`s element-wise, A * B.
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*
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* Inputs must be the same shape. For broadcasting support, use `tf.mul`.
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*
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* @param a The first tensor to multiply.
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* @param b The first tensor to multiply. Must have the same
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* dtype as `a`.
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*/
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declare function mulStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
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*
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* We also expose `tf.divStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 9, 16]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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*
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* a.div(b).print(); // or tf.div(a, b)
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* ```
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*
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* ```js
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* // Broadcast div a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(2);
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*
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* a.div(b).print(); // or tf.div(a, b)
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* ```
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*
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* @param a The first tensor as the numerator.
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* @param b The second tensor as the denominator. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function div_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
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* if denominator is 0.
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*
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* We also expose `tf.divStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 9, 16]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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* const c = tf.tensor1d([0, 0, 0, 0]);
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*
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* a.divNoNan(b).print(); // or tf.divNoNan(a, b)
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* a.divNoNan(c).print(); // or tf.divNoNan(a, c)
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* ```
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*
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* ```js
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* // Broadcast div a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(2);
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* const c = tf.scalar(0);
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*
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* a.divNoNan(b).print(); // or tf.divNoNan(a, b)
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* a.divNoNan(c).print(); // or tf.divNoNan(a, c)
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* ```
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*
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* @param a The first tensor as the numerator.
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* @param b The second tensor as the denominator. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function divNoNan_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
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* The result is rounded with floor function.
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*
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 9, 16]);
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* const b = tf.tensor1d([1, 2, 3, 4]);
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*
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* a.floorDiv(b).print(); // or tf.div(a, b)
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* ```
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*
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* ```js
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* // Broadcast div a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(2);
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*
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* a.floorDiv(b).print(); // or tf.floorDiv(a, b)
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* ```
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*
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* @param a The first tensor as the numerator.
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* @param b The second tensor as the denominator. Must have the same dtype as
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* `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function floorDiv_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Divides two `tf.Tensor`s element-wise, A / B. Inputs must
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* be the same shape.
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*
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* @param a The first tensor as the numerator for element-wise division.
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* @param b The second tensor as the denominator for element-wise division.
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*/
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declare function divStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Returns the mod of a and b element-wise.
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* `floor(x / y) * y + mod(x, y) = x`
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* Supports broadcasting.
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*
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* We also expose `tf.modStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 3, 16]);
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* const b = tf.tensor1d([1, 2, 9, 4]);
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*
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* a.mod(b).print(); // or tf.mod(a, b)
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* ```
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*
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* ```js
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* // Broadcast a mod b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(5);
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*
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* a.mod(b).print(); // or tf.mod(a, b)
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* ```
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same type as `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function mod_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must
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* be the same shape. For broadcasting support, use mod().
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same dtype as `a`.
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*/
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declare function modStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Returns the min of a and b (`a < b ? a : b`) element-wise.
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* Supports broadcasting.
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*
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* We also expose `minimumStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 3, 16]);
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* const b = tf.tensor1d([1, 2, 9, 4]);
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*
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* a.minimum(b).print(); // or tf.minimum(a, b)
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* ```
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*
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* ```js
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* // Broadcast minimum a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(5);
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*
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* a.minimum(b).print(); // or tf.minimum(a, b)
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* ```
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same type as `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function minimum_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must
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* be the same shape. For broadcasting support, use minimum().
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same dtype as `a`.
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*/
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declare function minimumStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Returns the max of a and b (`a > b ? a : b`) element-wise.
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* Supports broadcasting.
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*
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* We also expose `tf.maximumStrict` which has the same signature as this op and
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* asserts that `a` and `b` are the same shape (does not broadcast).
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*
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* ```js
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* const a = tf.tensor1d([1, 4, 3, 16]);
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* const b = tf.tensor1d([1, 2, 9, 4]);
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*
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* a.maximum(b).print(); // or tf.maximum(a, b)
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* ```
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*
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* ```js
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* // Broadcast maximum a with b.
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* const a = tf.tensor1d([2, 4, 6, 8]);
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* const b = tf.scalar(5);
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*
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* a.maximum(b).print(); // or tf.maximum(a, b)
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* ```
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same type as `a`.
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*/
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/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
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declare function maximum_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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/**
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* Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must
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* be the same shape. For broadcasting support, use maximum().
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same dtype as `a`.
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*/
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declare function maximumStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Returns (a - b) * (a - b) element-wise.
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*
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* Inputs must be the same shape. For broadcasting support, use
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* `tf.squaredDifference` instead.
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same type as `a`.
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*/
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declare function squaredDifferenceStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
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/**
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* Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
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* Supports broadcasting.
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*
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* ```js
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* const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
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* const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
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*
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* tf.atan2(a, b).print()
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* ```
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*
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* @param a The first tensor.
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* @param b The second tensor. Must have the same dtype as `a`.
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*
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*/
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/** @doc {heading: 'Operations', subheading: 'Basic math'} */
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declare function atan2_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
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export declare const add: typeof add_;
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export declare const addN: typeof addN_;
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export declare const addStrict: typeof addStrict_;
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export declare const atan2: typeof atan2_;
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export declare const div: typeof div_;
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export declare const divNoNan: typeof divNoNan_;
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export declare const divStrict: typeof divStrict_;
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export declare const floorDiv: typeof floorDiv_;
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export declare const maximum: typeof maximum_;
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export declare const maximumStrict: typeof maximumStrict_;
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export declare const minimum: typeof minimum_;
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export declare const minimumStrict: typeof minimumStrict_;
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export declare const mod: typeof mod_;
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export declare const modStrict: typeof modStrict_;
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export declare const mul: typeof mul_;
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export declare const mulStrict: typeof mulStrict_;
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export declare const pow: typeof pow_;
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export declare const powStrict: typeof powStrict_;
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export declare const squaredDifferenceStrict: typeof squaredDifferenceStrict_;
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export declare const sub: typeof sub_;
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export declare const subStrict: typeof subStrict_;
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export {};
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