/**

* @license

* Copyright 2020 Google LLC. 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 {ENGINE} from '../engine';

import {Multiply, MultiplyInputs} from '../kernel_names';

import {Tensor} from '../tensor';

import {NamedTensorMap} from '../tensor_types';

import {makeTypesMatch} from '../tensor_util';

import {convertToTensor} from '../tensor_util_env';

import {TensorLike} from '../types';

import {op} from './operation';

/**

* 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'}

*/

function mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {

}

export const mul = /* @__PURE__ */ op({mul_});