/**
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* @license
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* Copyright 2018 Google Inc. 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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import { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
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import { Rank, TensorLike } from '../types';
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/**
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* Batch normalization, strictly for 2D. For the more relaxed version, see
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* `tf.batchNorm`.
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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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declare function batchNorm2d_(x: Tensor2D | TensorLike, mean: Tensor2D | Tensor1D | TensorLike, variance: Tensor2D | Tensor1D | TensorLike, offset?: Tensor2D | Tensor1D | TensorLike, scale?: Tensor2D | Tensor1D | TensorLike, varianceEpsilon?: number): Tensor2D;
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/**
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* Batch normalization, strictly for 3D. For the more relaxed version, see
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* `tf.batchNorm`.
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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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declare function batchNorm3d_(x: Tensor3D | TensorLike, mean: Tensor3D | Tensor1D | TensorLike, variance: Tensor3D | Tensor1D | TensorLike, offset?: Tensor3D | Tensor1D | TensorLike, scale?: Tensor3D | Tensor1D | TensorLike, varianceEpsilon?: number): Tensor3D;
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/**
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* Batch normalization, strictly for 4D. For the more relaxed version, see
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* `tf.batchNorm`.
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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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declare function batchNorm4d_(x: Tensor4D | TensorLike, mean: Tensor4D | Tensor1D | TensorLike, variance: Tensor4D | Tensor1D | TensorLike, offset?: Tensor4D | Tensor1D | TensorLike, scale?: Tensor4D | Tensor1D | TensorLike, varianceEpsilon?: number): Tensor4D;
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/**
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* @deprecated Please use `tf.batchNorm` instead and note the positional
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* argument change of scale, offset, and varianceEpsilon.
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*/
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declare function batchNormalization_<R extends Rank>(x: Tensor<R> | TensorLike, mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike): Tensor<R>;
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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 an `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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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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/**
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* @deprecated Please use `tf.batchNorm2d` instead and note the positional
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* argument change of scale, offset, and varianceEpsilon.
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*/
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declare function batchNormalization2d_(x: Tensor2D | TensorLike, mean: Tensor2D | Tensor1D | TensorLike, variance: Tensor2D | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor2D | Tensor1D | TensorLike, offset?: Tensor2D | Tensor1D | TensorLike): Tensor2D;
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/**
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* @deprecated Please use `tf.batchNorm3d` instead and note the positional
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* argument change of scale, offset, and varianceEpsilon.
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*/
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declare function batchNormalization3d_(x: Tensor3D | TensorLike, mean: Tensor3D | Tensor1D | TensorLike, variance: Tensor3D | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor3D | Tensor1D | TensorLike, offset?: Tensor3D | Tensor1D | TensorLike): Tensor3D;
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/**
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* @deprecated Please use `tf.batchNorm4d` instead and note the positional
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* argument change of scale, offset, and varianceEpsilon.
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*/
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declare function batchNormalization4d_(x: Tensor4D | TensorLike, mean: Tensor4D | Tensor1D | TensorLike, variance: Tensor4D | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor4D | Tensor1D | TensorLike, offset?: Tensor4D | Tensor1D | TensorLike): Tensor4D;
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export declare const batchNormalization2d: typeof batchNormalization2d_;
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export declare const batchNormalization3d: typeof batchNormalization3d_;
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export declare const batchNormalization4d: typeof batchNormalization4d_;
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export declare const batchNormalization: typeof batchNormalization_;
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export declare const batchNorm: typeof batchNorm_;
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export declare const batchNorm2d: typeof batchNorm2d_;
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export declare const batchNorm3d: typeof batchNorm3d_;
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export declare const batchNorm4d: typeof batchNorm4d_;
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export {};
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