#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/pooling.h>
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#include <torch/nn/functional/pooling.h>
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#include <torch/csrc/WindowsTorchApiMacro.h>
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namespace torch {
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namespace nn {
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/// Base class for all (dimension-specialized) avgpool modules.
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template <size_t D, typename Derived>
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class TORCH_API AvgPoolImpl : public torch::nn::Cloneable<Derived> {
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public:
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AvgPoolImpl(ExpandingArray<D> kernel_size)
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: AvgPoolImpl(AvgPoolOptions<D>(kernel_size)) {}
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explicit AvgPoolImpl(const AvgPoolOptions<D>& options_);
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void reset() override;
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/// Pretty prints the `AvgPool{1,2,3}d` module into the given `stream`.
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void pretty_print(std::ostream& stream) const override;
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/// The options with which this `Module` was constructed.
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AvgPoolOptions<D> options;
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};
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AvgPool1d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies avgpool over a 1-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.AvgPool1d to learn
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/// about the exact behavior of this module.
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class TORCH_API AvgPool1dImpl : public AvgPoolImpl<1, AvgPool1dImpl> {
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public:
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using AvgPoolImpl<1, AvgPool1dImpl>::AvgPoolImpl;
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Tensor forward(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `AvgPool1dImpl`.
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/// See the documentation for `AvgPool1dImpl` 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(AvgPool1d);
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AvgPool2d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies avgpool over a 2-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.AvgPool2d to learn
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/// about the exact behavior of this module.
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class TORCH_API AvgPool2dImpl : public AvgPoolImpl<2, AvgPool2dImpl> {
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public:
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using AvgPoolImpl<2, AvgPool2dImpl>::AvgPoolImpl;
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Tensor forward(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `AvgPool2dImpl`.
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/// See the documentation for `AvgPool2dImpl` 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(AvgPool2d);
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AvgPool3d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies avgpool over a 3-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.AvgPool3d to learn
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/// about the exact behavior of this module.
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class TORCH_API AvgPool3dImpl : public AvgPoolImpl<3, AvgPool3dImpl> {
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public:
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using AvgPoolImpl<3, AvgPool3dImpl>::AvgPoolImpl;
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Tensor forward(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `AvgPool2dImpl`.
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/// See the documentation for `AvgPool2dImpl` 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(AvgPool3d);
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// ============================================================================
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/// Base class for all (dimension-specialized) maxpool modules.
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template <size_t D, typename Derived>
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class TORCH_API MaxPoolImpl : public torch::nn::Cloneable<Derived> {
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public:
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MaxPoolImpl(ExpandingArray<D> kernel_size)
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: MaxPoolImpl(MaxPoolOptions<D>(kernel_size)) {}
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explicit MaxPoolImpl(const MaxPoolOptions<D>& options_);
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void reset() override;
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/// Pretty prints the `MaxPool{1,2,3}d` module into the given `stream`.
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void pretty_print(std::ostream& stream) const override;
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/// The options with which this `Module` was constructed.
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MaxPoolOptions<D> options;
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};
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MaxPool1d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies maxpool over a 1-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.MaxPool1d to learn
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/// about the exact behavior of this module.
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class TORCH_API MaxPool1dImpl : public MaxPoolImpl<1, MaxPool1dImpl> {
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public:
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using MaxPoolImpl<1, MaxPool1dImpl>::MaxPoolImpl;
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Tensor forward(const Tensor& input);
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/// Returns the outputs and the indices of the max values.
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/// Useful for `torch::nn::MaxUnpool1d` later.
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std::tuple<Tensor, Tensor> forward_with_indices(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `MaxPool1dImpl`.
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/// See the documentation for `MaxPool1dImpl` 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(MaxPool1d);
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MaxPool2d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies maxpool over a 2-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.MaxPool2d to learn
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/// about the exact behavior of this module.
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class TORCH_API MaxPool2dImpl : public MaxPoolImpl<2, MaxPool2dImpl> {
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public:
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using MaxPoolImpl<2, MaxPool2dImpl>::MaxPoolImpl;
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Tensor forward(const Tensor& input);
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/// Returns the outputs and the indices of the max values.
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/// Useful for `torch::nn::MaxUnpool2d` later.
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std::tuple<Tensor, Tensor> forward_with_indices(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `MaxPool2dImpl`.
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/// See the documentation for `MaxPool2dImpl` 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(MaxPool2d);
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MaxPool3d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies maxpool over a 3-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.MaxPool3d to learn
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/// about the exact behavior of this module.
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class TORCH_API MaxPool3dImpl : public MaxPoolImpl<3, MaxPool3dImpl> {
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public:
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using MaxPoolImpl<3, MaxPool3dImpl>::MaxPoolImpl;
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Tensor forward(const Tensor& input);
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/// Returns the outputs and the indices of the max values.
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/// Useful for `torch::nn::MaxUnpool3d` later.
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std::tuple<Tensor, Tensor> forward_with_indices(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `MaxPool3dImpl`.
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/// See the documentation for `MaxPool3dImpl` 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(MaxPool3d);
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// ============================================================================
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/// Base class for all (dimension-specialized) adaptive maxpool modules.
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template <size_t D, typename Derived>
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class TORCH_API AdaptiveMaxPoolImpl : public torch::nn::Cloneable<Derived> {
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public:
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AdaptiveMaxPoolImpl(ExpandingArray<D> output_size)
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: AdaptiveMaxPoolImpl(AdaptiveMaxPoolOptions<D>(output_size)) {}
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explicit AdaptiveMaxPoolImpl(const AdaptiveMaxPoolOptions<D>& options_);
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void reset() override;
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/// Pretty prints the `AdaptiveMaxPool{1,2,3}d` module into the given `stream`.
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void pretty_print(std::ostream& stream) const override;
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/// The options with which this `Module` was constructed.
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AdaptiveMaxPoolOptions<D> options;
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};
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~ AdaptiveMaxPool1d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies adaptive maxpool over a 1-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.AdaptiveMaxPool1d
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/// to learn about the exact behavior of this module.
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class TORCH_API AdaptiveMaxPool1dImpl :
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public AdaptiveMaxPoolImpl<1, AdaptiveMaxPool1dImpl> {
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public:
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using AdaptiveMaxPoolImpl<1, AdaptiveMaxPool1dImpl>::AdaptiveMaxPoolImpl;
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Tensor forward(const Tensor& input);
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/// Returns the indices along with the outputs.
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/// Useful to pass to nn.MaxUnpool1d.
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std::tuple<Tensor, Tensor> forward_with_indices(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `AdaptiveMaxPool1dImpl`.
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/// See the documentation for `AdaptiveMaxPool1dImpl` 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(AdaptiveMaxPool1d);
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AdaptiveMaxPool2d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies adaptive maxpool over a 2-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.AdaptiveMaxPool2d
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/// to learn about the exact behavior of this module.
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class TORCH_API AdaptiveMaxPool2dImpl :
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public AdaptiveMaxPoolImpl<2, AdaptiveMaxPool2dImpl> {
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public:
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using AdaptiveMaxPoolImpl<2, AdaptiveMaxPool2dImpl>::AdaptiveMaxPoolImpl;
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Tensor forward(const Tensor& input);
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/// Returns the indices along with the outputs.
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/// Useful to pass to nn.MaxUnpool2d.
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std::tuple<Tensor, Tensor> forward_with_indices(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `AdaptiveMaxPool2dImpl`.
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/// See the documentation for `AdaptiveMaxPool2dImpl` 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(AdaptiveMaxPool2d);
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// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AdaptiveMaxPool3d ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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/// Applies adaptive maxpool over a 3-D input.
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/// See https://pytorch.org/docs/master/nn.html#torch.nn.AdaptiveMaxPool3d
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/// to learn about the exact behavior of this module.
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class TORCH_API AdaptiveMaxPool3dImpl :
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public AdaptiveMaxPoolImpl<3, AdaptiveMaxPool3dImpl> {
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public:
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using AdaptiveMaxPoolImpl<3, AdaptiveMaxPool3dImpl>::AdaptiveMaxPoolImpl;
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Tensor forward(const Tensor& input);
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/// Returns the indices along with the outputs.
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/// Useful to pass to nn.MaxUnpool3d.
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std::tuple<Tensor, Tensor> forward_with_indices(const Tensor& input);
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};
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/// A `ModuleHolder` subclass for `AdaptiveMaxPool3dImpl`.
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/// See the documentation for `AdaptiveMaxPool3dImpl` 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(AdaptiveMaxPool3d);
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} // namespace nn
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} // namespace torch
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