code: def process_function(
      info: DifferentiabilityInfo, template: CodeTemplate
  ) -> str:
      saved_variables: List[str] = []
      release_variables: List[str] = []
      saved_list_sizes: List[str] = []
      unpack: List[str] = []
      asserts: List[str] = []
      compute_index_ranges: List[str] = []
      getter_definitions: List[str] = []
      py_getsetdef_structs: List[str] = []
      compiled_args: List[str] = []
      apply_with_saved_before: List[str] = []
      apply_with_saved_after: List[str] = []
  
      for arg in info.args_with_derivatives:
          if arg.type in TENSOR_LIST_LIKE_CTYPES:
              size = f"{arg.name}_size_"
              saved_list_sizes.append(f"size_t {arg.name}_size_;")
          else:
              size = "1"
          compute_index_ranges.append(
              f"auto {arg.name}_ix = gen.range({size});"
          )
  
      def save_var(var: SavedAttribute, is_output: bool) -> None:
          name = var.nctype.name
          type = var.nctype.type
          should_append_getsetdef = True
          should_append_raw_getsetdef = False
          visit_name = name
  
          if (
              type == BaseCType(tensorT)
              or type == OptionalCType(BaseCType(tensorT))
              or type == MutRefCType(OptionalCType(BaseCType(tensorT)))
              or (type == BaseCType(scalarT) and is_output)
          ):
              saved_variables.append(f"SavedVariable {name}_;")
              release_variables.append(f"{name}_.reset_data();")
              ptr = "shared_from_this()" if is_output else ""
              unpack.append(f"auto {name} = {name}_.unpack({ptr});")
              getter_definitions.append(
                  GETTER_DEFINITION_SAVEDVAR.substitute(
                      op=info.op, name=name, body=GETTER_BODY_SAVEDVAR
                  )
              )
              getter_definitions.append(
                  GETTER_DEFINITION_RAW_SAVEDVAR.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_RAW_SAVEDVAR,
                  )
              )
              should_append_raw_getsetdef = True
              visit_name = f"{name}_"
          elif (
              type == BaseCType(tensorListT)
              or type == BaseCType(iTensorListRefT)
              or type == VectorCType(BaseCType(tensorT))
          ):
              # note(crcrpar): [nuanced return type of out-of-place foreach functions]
              # When an out-of-place foreach function whose return signature is `Tensor[]`
              # spells out its backward definitions in `derivatives.yaml`, and some of them depend on
              # `result`, `result`'s type is interpreted and treated as `std::vector<Tensor>`.
              # An out-of-place foreach whose backwards rely on their output doesn't suffer from this
              # difference if the definitions are codegen'ed.
              # This special case is needed for `_foreach_pow.List` and `_foreach_pow.ScalarAndTensor`
              # as of https://github.com/pytorch/pytorch/pull/105504.
              if type == VectorCType(BaseCType(tensorT)):
                  assert (
                      info.func.func.name.name.base.startswith(
                          "_foreach"
                      )
                      and is_output
                  )
              saved_variables.append(
                  f"std::vector<SavedVariable> {name}_;"
              )
              saved_variables.append(f"bool {name}_released_ = false;")
              # Just clear() is sufficient, we don't need to loop and clear each variable.
              # Because the SavedVariable owns a tensor and a grad_fn, removing the SavedVariable makes them go away as well.
              release_variables.append(f"{name}_.clear();")
              release_variables.append(f"{name}_released_ = true;")
              ptr = "shared_from_this()" if is_output else "nullptr"
              unpack.append(
                  f"auto {name} = unpack_list({name}_, {ptr});"
              )
              asserts.append(
                  f"TORCH_CHECK(!{name}_released_, ERR_BACKWARD_TWICE);"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_VEC_SAVEDVAR.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_VEC_SAVEDVAR,
                  )
              )
              getter_definitions.append(
                  GETTER_DEFINITION_RAW_VEC_SAVEDVAR.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_RAW_VEC_SAVEDVAR,
                  )
              )
              should_append_raw_getsetdef = True
              visit_name = f"{name}_"
          elif type == ListCType(OptionalCType(BaseCType(tensorT))):
              saved_variables.append(
                  f"std::vector<SavedVariable> {name}_;"
              )
              saved_variables.append(f"bool {name}_released_ = false;")
              # Just clear() is sufficient, we don't need to loop and clear each variable.
              # Because the SavedVariable owns a tensor and a grad_fn, removing the SavedVariable makes them go away as well.
              release_variables.append(f"{name}_.clear();")
              release_variables.append(f"{name}_released_ = true;")
              unpack.append(f"auto {name} = unpack_opt_list({name}_);")
              asserts.append(
                  f"TORCH_CHECK(!{name}_released_, ERR_BACKWARD_TWICE);"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_VEC_SAVEDVAR.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_VEC_SAVEDVAR,
                  )
              )
              getter_definitions.append(
                  GETTER_DEFINITION_RAW_VEC_SAVEDVAR.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_RAW_VEC_SAVEDVAR,
                  )
              )
              should_append_raw_getsetdef = True
              visit_name = f"{name}_"
          elif type == BaseCType(intArrayRefT):
              saved_variables.append(f"std::vector<int64_t> {name};")
              getter_definitions.append(
                  GETTER_DEFINITION.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_ARRAYREF_LONG,
                  )
              )
          elif type == BaseCType(symIntArrayRefT):
              saved_variables.append(
                  f"std::vector<c10::SymInt> {name};"
              )
              getter_definitions.append(
                  GETTER_DEFINITION.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_ARRAYREF_SYMINT,
                  )
              )
          elif type == BaseCType(optionalIntArrayRefT):
              saved_variables.append(
                  f"c10::OptionalArray<int64_t> {name};"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_OPT_ARRAYREF.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_ARRAYREF_LONG,
                  )
              )
          elif type == BaseCType(optionalSymIntArrayRefT):
              saved_variables.append(
                  f"c10::OptionalArray<c10::SymInt> {name};"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_OPT_ARRAYREF.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_ARRAYREF_SYMINT,
                  )
              )
          elif type == OptionalCType(BaseCType(intArrayRefT)):
              saved_variables.append(
                  f"c10::OptionalArray<int64_t> {name};"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_OPT_ARRAYREF.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_ARRAYREF_LONG,
                  )
              )
          elif type == OptionalCType(BaseCType(symIntArrayRefT)):
              saved_variables.append(
                  f"c10::OptionalArray<c10::SymInt> {name};"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_OPT_ARRAYREF.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_ARRAYREF_SYMINT,
                  )
              )
          elif type == OptionalCType(ArrayRefCType(BaseCType(doubleT))):
              saved_variables.append(
                  f"c10::OptionalArray<double> {name};"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_OPT_ARRAYREF.substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_ARRAYREF_DOUBLE,
                  )
              )
          elif type == BaseCType(longT):
              saved_variables.append(f"{type.cpp_type()} {name} = 0;")
              getter_definitions.append(
                  GETTER_DEFINITION.substitute(
                      op=info.op, name=name, body=GETTER_BODY_INT64_T
                  )
              )
          elif type == BaseCType(SymIntT):
              saved_variables.append(f"c10::SymInt {name};")
              getter_definitions.append(
                  GETTER_DEFINITION.substitute(
                      op=info.op, name=name, body=GETTER_BODY_SYMINT
                  )
              )
          elif type == BaseCType(stringT):
              saved_variables.append(f"std::string {name};")
              getter_definitions.append(
                  GETTER_DEFINITION.substitute(
                      op=info.op, name=name, body=GETTER_BODY_STRING
                  )
              )
          elif type == OptionalCType(BaseCType(stringT)):
              saved_variables.append(
                  f"c10::optional<std::string> {name};"
              )
              getter_definitions.append(
                  GETTER_DEFINITION_OPT.substitute(
                      op=info.op, name=name, body=GETTER_BODY_STRING
                  )
              )
          elif type == ArrayRefCType(
              elem=BaseCType(type=BaseCppType(ns="at", name="Scalar"))
          ):
              saved_variables.append(f"std::vector<at::Scalar> {name};")
              saved_variables.append(f"bool {name}_released_ = false;")
              # Just clear() is sufficient, we don't need to loop and clear each variable.
              # Because the SavedVariable owns a tensor and a grad_fn, removing the SavedVariable makes them go away as well.
              release_variables.append(f"{name}.clear();")
              # release_variables.append(f"{name}_released_ = true;")
              # unpack.append(f"auto {name} = unpack_list({name}_);")
              # asserts.append(f"TORCH_CHECK(!{name}_released_, ERR_BACKWARD_TWICE);")
              getter_definitions.append(
                  CodeTemplate(
                      """\
  PyObject* THP${op}_${name}_getter(THPCppFunction *self, void *_unused) {
    HANDLE_TH_ERRORS
    const auto *node = static_cast<${op}*>(self->cdata.get());
    const auto& prop = node->${name};
    if (node->${name}_released_) {
      PyErr_SetString(PyExc_RuntimeError, ERR_BACKWARD_TWICE);
      return nullptr;
    }
    ${body}
    END_HANDLE_TH_ERRORS
  }
                              """
                  ).substitute(
                      op=info.op,
                      name=name,
                      body=GETTER_BODY_VEC_SCALAR,
                  )
              )
          else:
              # Check for indicators that you're putting a non-owning reference
              # into the saved variable field.  If this is spuriously firing,
              # edit this field.  Otherwise, you probably need to add a case
              # above.
              assert (
                  "ref" not in type.cpp_type().lower()
                  and "view" not in type.cpp_type().lower()
                  and "*" not in type.cpp_type()
                  and "&" not in type.cpp_type()
              ), f"{type.cpp_type()} looks like it contains a non-owning reference"
              saved_variables.append(f"{type.cpp_type()} {name};")
  
              if type in MISC_GETTER_DEFS:
                  getter_def, body = MISC_GETTER_DEFS[type]
                  getter_definitions.append(
                      getter_def.substitute(
                          op=info.op, name=name, body=body
                      )
                  )
              else:
                  # Types we don't expose python bindings to yet:
                  #   TypeAndSize, at::ScalarType, TensorOptions, TensorGeometry,
                  #   std::vector<std::vector<int64_t>>, std::vector<at::ScalarType>
                  should_append_getsetdef = False
  
          if should_append_getsetdef:
              py_getsetdef_structs.append(
                  PY_GETSETDEF_STRUCT.substitute(op=info.op, name=name)
              )
          if should_append_raw_getsetdef:
              py_getsetdef_structs.append(
                  PY_RAW_GETSETDEF_STRUCT.substitute(
                      op=info.op, name=name
                  )
              )
  
          compiled_args.append(f"args.collect({visit_name});")
          apply_with_saved_before.append(f"saved.before({visit_name});")
          apply_with_saved_after.append(f"saved.after({visit_name});")
  
      for var in sorted(
          info.all_saved_inputs, key=lambda sa: str(sa.nctype.name)
      ):
          save_var(var, is_output=False)
      for var in sorted(
          info.all_saved_outputs, key=lambda sa: str(sa.nctype.name)
      ):
          save_var(var, is_output=True)
  
      # lock the mutex when we release variables and in Node::apply to protect thread safety
      # see Note [Thread Safety on Autograd Node]
      if len(release_variables) > 0:
          thread_lock = "std::lock_guard<std::mutex> lock(mutex_);"
      else:
          thread_lock = ""
  
      if uses_retain_variables(info):
          will_release_variables = WILL_RELEASE_VARIABLES.substitute()
      else:
          will_release_variables = ""
  
      body: List[str] = []
  
      if uses_single_grad(info):
          body.append("const auto& grad = grads[0];")
      else:
          # Generate aliases for gradients named for returned values.
          body.extend(
              f"const auto& {name} = grads[{info.available_named_gradients.index(name)}];"
              for name in sorted(info.used_named_gradients)
          )
  
      def emit_derivative(
          derivative: Derivative,
          args_with_derivatives: Sequence[Binding],
      ) -> Tuple[bool, str]:
          formula = derivative.formula
          var_names = derivative.var_names
          if len(var_names) == 1:
              checks_any_grad_defined = False
              if "not_implemented" not in formula:
                  matching_args = [
                      arg
                      for arg in args_with_derivatives
                      if arg.name == var_names[0]
                  ]
                  if len(matching_args) == 1:
                      # We can add undefined grad support if the input variable is a Tensor
                      arg = matching_args[0]
                      if isinstance(arg.argument, Argument) and str(
                          arg.argument.type
                      ) in ("Tensor", "Tensor?"):
                          formula = (
                              "any_grad_defined ? ("
                              + formula
                              + ") : Tensor()"
                          )
                          checks_any_grad_defined = True
              if info.name.startswith("_foreach_"):
                  derivative_template = DERIVATIVE_SINGLE_FOREACH
              else:
                  derivative_template = DERIVATIVE_SINGLE
              return (
                  checks_any_grad_defined,
                  derivative_template.substitute(
                      name=var_names[0], derivative=formula
                  ),
              )
          else:
              if "grad_input_mask" in formula:
                  masks = [
                      f"task_should_compute_output({{ {n}_ix }}),"
                      for n in var_names
                  ]
                  grad_input_mask = GRAD_INPUT_MASK.substitute(
                      masks=masks, n=len(var_names)
                  )
              else:
                  grad_input_mask = ""
              idx_ranges = ", ".join(f"{n}_ix" for n in var_names)
              copy_ranges: List[str] = []
              for i, n in enumerate(var_names):
                  copy_ranges.append(
                      DERIVATIVE_MULTI_COPY_RANGE.substitute(
                          name=n, i=i
                      )
                  )
              return False, DERIVATIVE_MULTI.substitute(
                  idx_ranges=idx_ranges,
                  copy_ranges=copy_ranges,
                  derivative=formula,
                  grad_input_mask=grad_input_mask,
              )
  
      body.extend(unpack)
      need_any_grad_defined_var = False
      for derivative in info.derivatives:
          checks_any_grad_defined, derivative_text = emit_derivative(
              derivative, info.args_with_derivatives
          )
          body.append(derivative_text)
          need_any_grad_defined_var |= checks_any_grad_defined
      # Since single-output derivative formulas need to check if grads are
      # defined, only perform the check once, before all the formulas
      if need_any_grad_defined_var:
          body.insert(
              -len(info.derivatives),
              "bool any_grad_defined = any_variable_defined(grads);",
          )
  
      if info.name in UNTRACEABLE_FUNCTIONS:
          superclass = "Node"
      else:
          superclass = "TraceableFunction"
  
      all_getsetdef_structs = (
          ",\n".join(py_getsetdef_structs) + ","
          if len(py_getsetdef_structs) != 0
          else ""
      )
      all_getter_definitions = "\n".join(getter_definitions)
  
      return template.substitute(
          op=info.op,
          compute_index_ranges=compute_index_ranges,
          saved_variables=saved_variables,
          release_variables=release_variables,
          saved_list_sizes=saved_list_sizes,
          asserts=asserts,
          thread_lock=thread_lock,
          will_release_variables=will_release_variables,
          body=body,
          superclass=superclass,
          all_getter_definitions=all_getter_definitions,
          all_getsetdef_structs=all_getsetdef_structs,
          compiled_args=compiled_args,
          apply_with_saved_before=apply_with_saved_before,
          apply_with_saved_after=apply_with_saved_after,
      )
  
file_path: pytorch/tools/autograd/gen_autograd_functions.py
imports: from typing import Dict, List, Sequence, Tuple
  from torchgen.api.autograd import Derivative, DifferentiabilityInfo, SavedAttribute, uses_retain_variables, uses_single_grad
  from torchgen.api.types import ArrayRefCType, BaseCppType, BaseCType, Binding, boolT, doubleT, intArrayRefT, iTensorListRefT, ListCType, longT, MutRefCType, OptionalCType, optionalIntArrayRefT, optionalSymIntArrayRefT, scalarT, stringT, symIntArrayRefT, SymIntT, TENSOR_LIST_LIKE_CTYPES, tensorListT, tensorT, VectorCType
  from torchgen.code_template import CodeTemplate
  from torchgen.model import Argument, FunctionSchema
  from torchgen.utils import FileManager
  from .gen_inplace_or_view_type import VIEW_FUNCTIONS
label_desc: Use image node_id 4 for calling a global function with example usage: process_function(info, template) and returns: template, checks_any_grad_defined,  derivative_template, False,  DERIVATIVE_MULTI