/**
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* @license
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* Copyright 2020 Google LLC. 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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/// <amd-module name="@tensorflow/tfjs-core/dist/ops/multinomial" />
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import { Tensor1D, Tensor2D } from '../tensor';
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import { TensorLike } from '../types';
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/**
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* Creates a `tf.Tensor` with values drawn from a multinomial distribution.
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*
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* ```js
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* const probs = tf.tensor([.75, .25]);
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* tf.multinomial(probs, 3).print();
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* ```
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*
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* @param logits 1D array with unnormalized log-probabilities, or
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* 2D array of shape `[batchSize, numOutcomes]`. See the `normalized`
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* parameter.
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* @param numSamples Number of samples to draw for each row slice.
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* @param seed The seed number.
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* @param normalized Whether the provided `logits` are normalized true
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* probabilities (sum to 1). Defaults to false.
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* @return 1D array of shape `[numSamples]`, or 2D array of shape
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* `[batchSize, numSamples]`, depending on the rank of the input.
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*
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* @doc {heading: 'Tensors', subheading: 'Random'}
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*/
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declare function multinomial_(logits: Tensor1D | Tensor2D | TensorLike, numSamples: number, seed?: number, normalized?: boolean): Tensor1D | Tensor2D;
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export declare const multinomial: typeof multinomial_;
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export {};
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