code: def argumenttype_type(
      t: Type,
      *,
      mutable: bool,
      binds: ArgName,
      remove_non_owning_ref_types: bool = False,
  ) -> NamedCType:
      # If it's a value type, do the value type translation
      r = valuetype_type(
          t,
          binds=binds,
          remove_non_owning_ref_types=remove_non_owning_ref_types,
      )
      if r is not None:
          return r
      if isinstance(t, BaseType):
          if t.name == BaseTy.Tensor:
              if (
                  mutable
                  and not local.use_const_ref_for_mutable_tensors()
              ):
                  return NamedCType(
                      binds, MutRefCType(BaseCType(tensorT))
                  )
              else:
                  return NamedCType(
                      binds, ConstRefCType(BaseCType(tensorT))
                  )
          elif t.name == BaseTy.Scalar:
              return NamedCType(
                  binds, ConstRefCType(BaseCType(scalarT))
              )
          else:
              raise AssertionError(
                  f"base type should have been value type {t}"
              )
      elif isinstance(t, OptionalType):
          if str(t.elem) == "Tensor":
              if (
                  mutable
                  and not local.use_const_ref_for_mutable_tensors()
              ):
                  return NamedCType(
                      binds, MutRefCType(BaseCType(tensorT))
                  )  # TODO: fix this discrepancy
              else:
                  return NamedCType(
                      binds,
                      ConstRefCType(OptionalCType(BaseCType(tensorT))),
                  )
          elif str(t.elem) == "Scalar":
              return NamedCType(
                  binds,
                  ConstRefCType(OptionalCType(BaseCType(scalarT))),
              )
          elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
          return NamedCType(binds, OptionalCType(elem.type))
      elif isinstance(t, ListType):
          # TODO: keeping these special cases for Tensor[] and Tensor?[] so that we can hookup with ATen kernels.
          if str(t.elem) == "Tensor":
              return NamedCType(binds, BaseCType(tensorListT))
          elif str(t.elem) == "Dimname":
              raise NotImplementedError(
                  "Executorch doesn't support Dimname"
              )
          elif str(t.elem) == "Tensor?":
              return NamedCType(
                  binds,
                  ArrayRefCType(OptionalCType(BaseCType(tensorT))),
              )
          elem = argumenttype_type(t.elem, mutable=mutable, binds=binds)
          return NamedCType(binds, ArrayRefCType(elem.type))
      else:
          raise AssertionError(f"unrecognized type {repr(t)}")
  
file_path: pytorch/torchgen/executorch/api/et_cpp.py
imports: from typing import List, Optional, Sequence, Set, Union
  from torchgen import local
  from torchgen.api.types import ArgName, ArrayCType, BaseCType, Binding, ConstRefCType, CType, MutRefCType, NamedCType, SpecialArgName, TupleCType, VectorCType, voidT
  from torchgen.model import Argument, Arguments, BaseTy, BaseType, ListType, NativeFunction, OptionalType, Return, SelfArgument, TensorOptionsArguments, Type
  from torchgen.utils import assert_never
  from .types import ArrayRefCType, BaseTypeToCppMapping, OptionalCType, scalarT, tensorListT, tensorT
label_desc: Use image node_id 2 for calling a global function with example usage: argumenttype_type(t) and returns: r, NamedCType, NamedCType, NamedCType, NamedCType, NamedCType, NamedCType, NamedCType, NamedCType, NamedCType, NamedCType