#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/loss.h>
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#include <torch/nn/pimpl.h>
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#include <torch/types.h>
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#include <torch/csrc/WindowsTorchApiMacro.h>
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#include <cstddef>
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#include <vector>
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namespace torch {
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namespace nn {
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/// Creates a criterion that measures the mean absolute error (MAE) between each
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/// element in the input : math :`x` and target : `y`.
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struct TORCH_API L1LossImpl : Module {
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explicit L1LossImpl(const L1LossOptions& options_ = {});
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/// Pretty prints the `L1Loss` module into the given `stream`.
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void pretty_print(std::ostream& stream) const override;
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Tensor forward(const Tensor& input, const Tensor& target);
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/// The options with which this `Module` was constructed.
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L1LossOptions options;
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};
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/// A `ModuleHolder` subclass for `L1LossImpl`.
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/// See the documentation for `L1LossImpl` 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(L1Loss);
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} // namespace nn
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} // namespace torch
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