/**
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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/broadcast_to" />
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import { Tensor } from '../tensor';
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import { Rank, ShapeMap, TensorLike } from '../types';
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/**
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* Broadcast an array to a compatible shape NumPy-style.
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*
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* The tensor's shape is compared to the broadcast shape from end to beginning.
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* Ones are prepended to the tensor's shape until it has the same length as
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* the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is
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* already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then
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* the input tensor is tiled N times along that axis (using tf.tile).
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*
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* @param input The tensor that is to be broadcasted.
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* @param shape The input is to be broadcast to this shape.
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*
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* @doc {heading: 'Tensors', subheading: 'Transformations'}
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*/
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declare function broadcastTo_<R extends Rank>(x: Tensor | TensorLike, shape: ShapeMap[R]): Tensor<R>;
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export declare const broadcastTo: typeof broadcastTo_;
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export {};
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