/**
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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/batchnorm" />
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import { Tensor, Tensor1D } from '../tensor';
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import { Rank, TensorLike } from '../types';
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/**
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* Batch normalization.
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*
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* As described in
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* [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).
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*
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* Mean, variance, scale, and offset can be of two shapes:
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* - The same shape as the input.
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* - In the common case, the depth dimension is the last dimension of x, so
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* the values would be a `tf.Tensor1D` of shape [depth].
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*
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* Also available are stricter rank-specific methods with the same signature
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* as this method that assert that parameters passed are of given rank
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* - `tf.batchNorm2d`
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* - `tf.batchNorm3d`
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* - `tf.batchNorm4d`
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*
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* @param x The input Tensor.
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* @param mean A mean Tensor.
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* @param variance A variance Tensor.
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* @param offset An offset Tensor.
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* @param scale A scale Tensor.
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* @param varianceEpsilon A small float number to avoid dividing by 0.
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*
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* @doc {heading: 'Operations', subheading: 'Normalization'}
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*/
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declare function batchNorm_<R extends Rank>(x: Tensor<R> | TensorLike, mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike, scale?: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number): Tensor<R>;
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export declare const batchNorm: typeof batchNorm_;
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export {};
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