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#pragma once
// ${generated_comment}
#include <ATen/ATen.h>
#include <ATen/core/functional.h>
#include <ATen/TensorGeometry.h>
#include "torch/csrc/autograd/function.h"
#include "torch/csrc/autograd/variable.h"
#include "torch/csrc/autograd/saved_variable.h"
#include <torch/csrc/Export.h>
#include <c10/core/SymIntArrayRef.h>
namespace torch { namespace autograd { namespace generated {
using at::Scalar;
using at::Tensor;
using at::IntArrayRef;
using at::ArrayRef;
using at::Type;
using at::TensorGeometry;
using at::ScalarType;
using std::optional;
using c10::fmap;
inline std::vector<Tensor> unpack_list(at::ArrayRef<SavedVariable> xs, std::shared_ptr<Node> saved_for = nullptr) {
// NB: we must explicitly do the conversion in the lambda, otherwise template
// deduction will give a Tensor of Variable which is not convertible
return fmap(xs, [&saved_for](const SavedVariable& x) {
// TODO(crcrpar): Use `std::move(saved_for)` to avoid incrementing refcount, which would need refactoring.
return static_cast<Tensor>(x.unpack(saved_for));
});
}
inline c10::List<std::optional<Tensor>> unpack_opt_list(at::ArrayRef<SavedVariable> xs, std::shared_ptr<Node> saved_for = nullptr) {
torch::List<std::optional<Tensor>> result;
result.reserve(xs.size());
for (const SavedVariable& v : xs) {
auto var = v.unpack(saved_for);
result.push_back(var.defined() ? std::optional<Tensor>(var) : ::std::nullopt);
}
return result;
}
using torch::autograd::TypeAndSize;
${autograd_function_declarations}
}}} // namespace torch::autograd::generated
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