/**

* @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 {Multinomial, MultinomialAttrs, MultinomialInputs} from '../kernel_names';

import {NamedAttrMap} from '../kernel_registry';

import {Tensor1D, Tensor2D} from '../tensor';

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

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

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

import {op} from './operation';

import {reshape} from './reshape';

/**

* Creates a `tf.Tensor` with values drawn from a multinomial distribution.

*

* ```js

* const probs = tf.tensor([.75, .25]);

* tf.multinomial(probs, 3).print();

* ```

*

* @param logits 1D array with unnormalized log-probabilities, or

* 2D array of shape `[batchSize, numOutcomes]`. See the `normalized`

* parameter.

* @param numSamples Number of samples to draw for each row slice.

* @param seed The seed number.

* @param normalized Whether the provided `logits` are normalized true

* probabilities (sum to 1). Defaults to false.

* @return 1D array of shape `[numSamples]`, or 2D array of shape

* `[batchSize, numSamples]`, depending on the rank of the input.

*

* @doc {heading: 'Tensors', subheading: 'Random'}

*/

function multinomial_(

}

export const multinomial = /* @__PURE__ */ op({multinomial_});