/// <amd-module name="@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input" />
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import { Tensor3D, Tensor4D } from '../tensor';
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import * as conv_util from './conv_util';
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/**
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* Computes the derivative of the input of a 2D convolution.
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*
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* @param xShape The shape of the input: [batch, height, width, inDepth].
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* If length of 3, batch of 1 is assumed.
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* @param dy The derivative of the output, of rank 4 or rank 3 of shape
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* `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is
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* assumed.
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* @param filter The filter, rank 4, of shape
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* `[filterHeight, filterWidth, inDepth, outDepth]`.
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* @param strides The strides of the convolution: `[strideHeight,
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* strideWidth]`.
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* @param pad The type of padding algorithm used:
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid`: output will be smaller than input if filter is larger
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* than 1x1.
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* @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
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* "NHWC". Specify the data format of the input and output data. With the
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* default format "NHWC", the data is stored in the order of: [batch,
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* height, width, channels].
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* @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
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* provided, it will default to truncate.
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*/
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declare function conv2DBackpropInput_<T extends Tensor3D | Tensor4D>(xShape: [number, number, number, number] | [number, number, number], dy: T, filter: Tensor4D, strides: [number, number] | number, pad: 'valid' | 'same' | number | conv_util.ExplicitPadding, dataFormat?: 'NHWC' | 'NCHW', dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
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export declare const conv2DBackpropInput: typeof conv2DBackpropInput_;
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export {};
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