/**

* @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 {Tensor} from '../tensor';

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

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

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

import {div} from './div';

import {equal} from './equal';

import {op} from './operation';

import {where} from './where';

import {zerosLike} from './zeros_like';

/**

* Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0

* if denominator is 0.

*

*

* ```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'}

*/

function divNoNan_<T extends Tensor>(

}

export const divNoNan = /* @__PURE__ */ op({divNoNan_});