/**
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* @license
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* Copyright 2022 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/euclidean_norm" />
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import { Tensor } from '../tensor';
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import { TensorLike } from '../types';
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/**
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* Computes the Euclidean norm of scalar, vectors, and matrices.
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*
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* ```js
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* const x = tf.tensor1d([1, 2, 3, 4]);
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*
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* x.euclideanNorm().print(); // or tf.euclideanNorm(x)
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* ```
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*
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* @param x The input array.
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* @param axis Optional. If axis is null (the default), the input is
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* considered a vector and a single vector norm is computed over the entire
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* set of values in the Tensor, i.e. euclideanNorm(x) is equivalent
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* to euclideanNorm(x.reshape([-1])). If axis is an integer, the input
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* is considered a batch of vectors, and axis determines the axis in x
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* over which to compute vector norms. If axis is a 2-tuple of integer it is
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* considered a batch of matrices and axis determines the axes in NDArray
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* over which to compute a matrix norm.
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* @param keepDims Optional. If true, the norm has the same dimensionality
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* as the input.
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*
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* @doc {heading: 'Operations', subheading: 'Matrices'}
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*/
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declare function euclideanNorm_(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): Tensor;
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export declare const euclideanNorm: typeof euclideanNorm_;
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export {};
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