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#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
// ${generated_comment}
#include "torch/csrc/Device.h"
#include "torch/csrc/DynamicTypes.h"
#include "torch/csrc/Exceptions.h"
#include "torch/csrc/autograd/python_fft_functions.h"
#include "torch/csrc/autograd/python_return_types.h"
#include "torch/csrc/autograd/python_variable.h"
#include "torch/csrc/autograd/utils/wrap_outputs.h"
#include "torch/csrc/autograd/utils/python_arg_parsing.h"
#include "torch/csrc/autograd/generated/variable_factories.h"
#include "torch/csrc/utils/out_types.h"
#include "torch/csrc/utils/pycfunction_helpers.h"
#include "torch/csrc/utils/python_arg_parser.h"
#include "torch/csrc/utils/structseq.h"
#include "torch/csrc/utils/cuda_lazy_init.h"
#include <ATen/core/Tensor.h>
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#else
$ops_headers
#endif
using at::Tensor;
using at::Device;
using at::Layout;
using at::Scalar;
using at::ScalarType;
using at::Backend;
using at::OptionalDeviceGuard;
using at::DeviceGuard;
using at::TensorOptions;
using at::IntArrayRef;
using at::Generator;
using at::TensorList;
using at::Dimname;
using at::DimnameList;
using torch::utils::check_out_type_matches;
using namespace torch::autograd::utils;
namespace torch { namespace autograd {
// generated forward declarations start here
${py_forwards}
static PyMethodDef fft_functions[] = {
${py_method_defs}
{NULL}
};
static PyObject* THPFFTVariableFunctionsModule = NULL;
void initFFTFunctions(PyObject* module) {
static struct PyModuleDef def = {
PyModuleDef_HEAD_INIT,
"torch._C._fft",
NULL,
-1,
fft_functions
};
PyObject* fft = PyModule_Create(&def);
THPFFTVariableFunctionsModule = fft;
if (!fft) {
throw python_error();
}
// steals a reference to fft
if (PyModule_AddObject(module, "_fft", fft) != 0) {
throw python_error();
}
}
// generated methods start here
${py_methods}
}} // namespace torch::autograd
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