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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_sparse_functions.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/utils/pycfunction_helpers.h"
#include "torch/csrc/utils/python_arg_parser.h"
#include "torch/csrc/utils/structseq.h"
#ifndef AT_PER_OPERATOR_HEADERS
#include <ATen/Functions.h>
#else
$ops_headers
#endif
using at::Tensor;
using at::Scalar;
using at::ScalarType;
using at::MemoryFormat;
using at::Generator;
using at::IntArrayRef;
using at::TensorList;
using namespace torch::autograd::utils;
namespace torch { namespace autograd {
// generated forward declarations start here
${py_forwards}
static PyMethodDef sparse_functions[] = {
${py_method_defs}
{NULL}
};
static PyObject* THPSparseVariableFunctionsModule = NULL;
void initSparseFunctions(PyObject* module) {
static struct PyModuleDef def = {
PyModuleDef_HEAD_INIT,
"torch._C._sparse",
NULL,
-1,
sparse_functions
};
PyObject* sparse = PyModule_Create(&def);
THPSparseVariableFunctionsModule = sparse;
if (!sparse) {
throw python_error();
}
// steals a reference to sparse
if (PyModule_AddObject(module, "_sparse", sparse) != 0) {
throw python_error();
}
}
// generated methods start here
${py_methods}
}} // namespace torch::autograd
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