#pragma once
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#include <torch/expanding_array.h>
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#include <torch/nn/cloneable.h>
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#include <torch/nn/options/fold.h>
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#include <torch/nn/pimpl.h>
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#include <torch/types.h>
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namespace torch {
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namespace nn {
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/// Applies fold over a 3-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.Fold to learn about
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/// the exact behavior of this module.
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class TORCH_API FoldImpl : public torch::nn::Cloneable<FoldImpl> {
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public:
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FoldImpl(ExpandingArray<2> output_size, ExpandingArray<2> kernel_size)
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: FoldImpl(FoldOptions(output_size, kernel_size)) {}
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explicit FoldImpl(const FoldOptions& options_);
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void reset() override {}
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/// Pretty prints the `Fold` module into the given `stream`.
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void pretty_print(std::ostream& stream) const override {
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stream << "torch::nn::Fold";
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}
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Tensor forward(const Tensor& input);
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/// The options with which this `Module` was constructed.
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FoldOptions options;
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};
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/// A `ModuleHolder` subclass for `FoldImpl`.
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/// See the documentation for `FoldImpl` class to learn what methods it
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/// provides, or the documentation for `ModuleHolder` to learn about PyTorch's
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/// module storage semantics.
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TORCH_MODULE(Fold);
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} // namespace nn
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} // namespace torch
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