/// <amd-module name="@tensorflow/tfjs-core/dist/ops/conv3d" />
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import { Tensor4D, Tensor5D } from '../tensor';
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import { TensorLike } from '../types';
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/**
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* Computes a 3D convolution over the input x.
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*
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* @param x The input tensor, of rank 5 or rank 4, of shape
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* `[batch, depth, height, width, channels]`. If rank 4,
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* batch of 1 is assumed.
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* @param filter The filter, rank 5, of shape
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* `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.
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* inChannels must match between input and filter.
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* @param strides The strides of the convolution: `[strideDepth, strideHeight,
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* strideWidth]`.
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* @param pad The type of padding algorithm.
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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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* - For more info, see this guide:
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* [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
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* https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
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* @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to
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* "NDHWC". Specify the data format of the input and output data. With the
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* default format "NDHWC", the data is stored in the order of: [batch,
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* depth, height, width, channels]. Only "NDHWC" is currently supported.
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* @param dilations The dilation rates: `[dilationDepth, dilationHeight,
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* dilationWidth]` in which we sample input values across the height
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* and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.
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* If `dilations` is a single number, then
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* `dilationDepth == dilationHeight == dilationWidth`. If it is greater
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* than 1, then all values of `strides` must be 1.
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*
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* @doc {heading: 'Operations', subheading: 'Convolution'}
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*/
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declare function conv3d_<T extends Tensor4D | Tensor5D>(x: T | TensorLike, filter: Tensor5D | TensorLike, strides: [number, number, number] | number, pad: 'valid' | 'same', dataFormat?: 'NDHWC' | 'NCDHW', dilations?: [number, number, number] | number): T;
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export declare const conv3d: typeof conv3d_;
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export {};
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