/**
 * @license
 * Copyright 2018 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
import { Tensor } from '../tensor';
import { TensorLike } from '../types';
/**
 * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.
 *
 * We also expose `tf.addStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3, 4]);
 * const b = tf.tensor1d([10, 20, 30, 40]);
 *
 * a.add(b).print();  // or tf.add(a, b)
 * ```
 *
 * ```js
 * // Broadcast add a with b.
 * const a = tf.scalar(5);
 * const b = tf.tensor1d([10, 20, 30, 40]);
 *
 * a.add(b).print();  // or tf.add(a, b)
 * ```
 * @param a The first `tf.Tensor` to add.
 * @param b The second `tf.Tensor` to add. Must have the same type as `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function add_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.
 *
 * ```js
 * const a = tf.tensor1d([1, 2]);
 * const b = tf.tensor1d([3, 4]);
 * const c = tf.tensor1d([5, 6]);
 *
 * tf.addN([a, b, c]).print();
 * ```
 * @param tensors A list of tensors with the same shape and dtype.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function addN_<T extends Tensor>(tensors: Array<T | TensorLike>): T;
/**
 * Adds two `tf.Tensor`s element-wise, A + B.
 *
 * Inputs must be the same shape. For broadcasting support, use add() instead.
 *
 * @param a The first Tensor to add element-wise.
 * @param b The second Tensor to add element-wise.
 */
declare function addStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.
 *
 * We also expose `tf.subStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([10, 20, 30, 40]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 *
 * a.sub(b).print();  // or tf.sub(a, b)
 * ```
 *
 * ```js
 * // Broadcast subtract a with b.
 * const a = tf.tensor1d([10, 20, 30, 40]);
 * const b = tf.scalar(5);
 *
 * a.sub(b).print();  // or tf.sub(a, b)
 * ```
 * @param a The first `tf.Tensor` to subtract from.
 * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as
 * `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function sub_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must
 * be the same shape.
 *
 * For broadcasting support, use `tf.sub` instead.
 *
 * @param a The first Tensor to subtract element-wise.
 * @param b The second Tensor to subtract element-wise.
 */
declare function subStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Computes the power of one `tf.Tensor` to another. Supports broadcasting.
 *
 * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for
 * corresponding elements in x and y. The result's dtype will be the upcasted
 * type of the `base` and `exp` dtypes.
 *
 * ```js
 * const a = tf.tensor([[2, 3], [4, 5]])
 * const b = tf.tensor([[1, 2], [3, 0]]).toInt();
 *
 * a.pow(b).print();  // or tf.pow(a, b)
 * ```
 *
 * ```js
 * const a = tf.tensor([[1, 2], [3, 4]])
 * const b = tf.tensor(2).toInt();
 *
 * a.pow(b).print();  // or tf.pow(a, b)
 * ```
 * We also expose `powStrict` which has the same signature as this op and
 * asserts that `base` and `exp` are the same shape (does not broadcast).
 *
 * @param base The base `tf.Tensor` to pow element-wise.
 * @param exp The exponent `tf.Tensor` to pow element-wise.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function pow_<T extends Tensor>(base: Tensor | TensorLike, exp: Tensor | TensorLike): T;
/**
 * Computes the power of one `tf.Tensor` to another. Inputs must
 * be the same shape.
 *
 * For broadcasting support, use `tf.pow` instead.
 *
 * @param base The base tensor to pow element-wise.
 * @param exp The exponent tensor to pow element-wise.
 */
declare function powStrict_<T extends Tensor>(base: T, exp: Tensor): T;
/**
 * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.
 *
 * We also expose `tf.mulStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 2, 3, 4]);
 * const b = tf.tensor1d([2, 3, 4, 5]);
 *
 * a.mul(b).print();  // or tf.mul(a, b)
 * ```
 *
 * ```js
 * // Broadcast mul a with b.
 * const a = tf.tensor1d([1, 2, 3, 4]);
 * const b = tf.scalar(5);
 *
 * a.mul(b).print();  // or tf.mul(a, b)
 * ```
 * @param a The first tensor to multiply.
 * @param b The second tensor to multiply. Must have the same dtype as `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function mul_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Multiplies two `tf.Tensor`s element-wise, A * B.
 *
 * Inputs must be the same shape. For broadcasting support, use `tf.mul`.
 *
 * @param a The first tensor to multiply.
 * @param b The first tensor to multiply. Must have the same
 *    dtype as `a`.
 */
declare function mulStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
 *
 * We also expose `tf.divStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 9, 16]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 *
 * a.div(b).print();  // or tf.div(a, b)
 * ```
 *
 * ```js
 * // Broadcast div a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(2);
 *
 * a.div(b).print();  // or tf.div(a, b)
 * ```
 *
 * @param a The first tensor as the numerator.
 * @param b The second tensor as the denominator. Must have the same dtype as
 * `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function div_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0
 * if denominator is 0.
 *
 * We also expose `tf.divStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 9, 16]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 * const c = tf.tensor1d([0, 0, 0, 0]);
 *
 * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
 * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
 * ```
 *
 * ```js
 * // Broadcast div a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(2);
 * const c = tf.scalar(0);
 *
 * a.divNoNan(b).print();  // or tf.divNoNan(a, b)
 * a.divNoNan(c).print();  // or tf.divNoNan(a, c)
 * ```
 *
 * @param a The first tensor as the numerator.
 * @param b The second tensor as the denominator. Must have the same dtype as
 * `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function divNoNan_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.
 * The result is rounded with floor function.
 *
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 9, 16]);
 * const b = tf.tensor1d([1, 2, 3, 4]);
 *
 * a.floorDiv(b).print();  // or tf.div(a, b)
 * ```
 *
 * ```js
 * // Broadcast div a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(2);
 *
 * a.floorDiv(b).print();  // or tf.floorDiv(a, b)
 * ```
 *
 * @param a The first tensor as the numerator.
 * @param b The second tensor as the denominator. Must have the same dtype as
 * `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function floorDiv_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Divides two `tf.Tensor`s element-wise, A / B. Inputs must
 * be the same shape.
 *
 * @param a The first tensor as the numerator for element-wise division.
 * @param b The second tensor as the denominator for element-wise division.
 */
declare function divStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Returns the mod of a and b element-wise.
 * `floor(x / y) * y + mod(x, y) = x`
 * Supports broadcasting.
 *
 * We also expose `tf.modStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.mod(b).print();  // or tf.mod(a, b)
 * ```
 *
 * ```js
 * // Broadcast a mod b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.mod(b).print();  // or tf.mod(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function mod_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must
 * be the same shape. For broadcasting support, use mod().
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same dtype as `a`.
 */
declare function modStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Returns the min of a and b (`a < b ? a : b`) element-wise.
 * Supports broadcasting.
 *
 * We also expose `minimumStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.minimum(b).print();  // or tf.minimum(a, b)
 * ```
 *
 * ```js
 * // Broadcast minimum a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.minimum(b).print();  // or tf.minimum(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function minimum_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must
 * be the same shape. For broadcasting support, use minimum().
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same dtype as `a`.
 */
declare function minimumStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Returns the max of a and b (`a > b ? a : b`) element-wise.
 * Supports broadcasting.
 *
 * We also expose `tf.maximumStrict` which has the same signature as this op and
 * asserts that `a` and `b` are the same shape (does not broadcast).
 *
 * ```js
 * const a = tf.tensor1d([1, 4, 3, 16]);
 * const b = tf.tensor1d([1, 2, 9, 4]);
 *
 * a.maximum(b).print();  // or tf.maximum(a, b)
 * ```
 *
 * ```js
 * // Broadcast maximum a with b.
 * const a = tf.tensor1d([2, 4, 6, 8]);
 * const b = tf.scalar(5);
 *
 * a.maximum(b).print();  // or tf.maximum(a, b)
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 */
/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */
declare function maximum_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
/**
 * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must
 * be the same shape. For broadcasting support, use maximum().
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same dtype as `a`.
 */
declare function maximumStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Returns (a - b) * (a - b) element-wise.
 *
 * Inputs must be the same shape. For broadcasting support, use
 * `tf.squaredDifference` instead.
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same type as `a`.
 */
declare function squaredDifferenceStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
/**
 * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.
 * Supports broadcasting.
 *
 * ```js
 * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);
 * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);
 *
 * tf.atan2(a, b).print()
 * ```
 *
 * @param a The first tensor.
 * @param b The second tensor. Must have the same dtype as `a`.
 *
 */
/** @doc {heading: 'Operations', subheading: 'Basic math'} */
declare function atan2_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
export declare const add: typeof add_;
export declare const addN: typeof addN_;
export declare const addStrict: typeof addStrict_;
export declare const atan2: typeof atan2_;
export declare const div: typeof div_;
export declare const divNoNan: typeof divNoNan_;
export declare const divStrict: typeof divStrict_;
export declare const floorDiv: typeof floorDiv_;
export declare const maximum: typeof maximum_;
export declare const maximumStrict: typeof maximumStrict_;
export declare const minimum: typeof minimum_;
export declare const minimumStrict: typeof minimumStrict_;
export declare const mod: typeof mod_;
export declare const modStrict: typeof modStrict_;
export declare const mul: typeof mul_;
export declare const mulStrict: typeof mulStrict_;
export declare const pow: typeof pow_;
export declare const powStrict: typeof powStrict_;
export declare const squaredDifferenceStrict: typeof squaredDifferenceStrict_;
export declare const sub: typeof sub_;
export declare const subStrict: typeof subStrict_;
export {};
