code: def argument_type_str_pyi(t: Type) -> str:
      add_optional = False
      if isinstance(t, OptionalType):
          t = t.elem
          add_optional = True
  
      if isinstance(t, BaseType):
          if t.name in [BaseTy.int, BaseTy.DeviceIndex]:
              ret = "_int"
          if t.name == BaseTy.SymInt:
              ret = "Union[_int, SymInt]"
          elif t.name == BaseTy.float:
              ret = "_float"
          elif t.name == BaseTy.str:
              ret = "str"
          elif t.name == BaseTy.Scalar:
              ret = "Union[Number, _complex]"
          elif t.name == BaseTy.ScalarType:
              ret = "_dtype"
          elif t.name == BaseTy.bool:
              ret = "_bool"
          elif t.name == BaseTy.QScheme:
              ret = "_qscheme"
          elif t.name == BaseTy.Layout:
              ret = "_layout"
          elif t.name == BaseTy.Device:
              ret = "Optional[DeviceLikeType]"
          elif t.name == BaseTy.MemoryFormat:
              ret = "memory_format"
          elif t.name == BaseTy.Dimname:
              ret = "Union[str, ellipsis, None]"
          elif t.name == BaseTy.Storage:
              ret = "Union[Storage, UntypedStorage]"
          elif t.name in [
              BaseTy.Tensor,
              BaseTy.Generator,
              BaseTy.Stream,
          ]:
              # These python schema type names line up with their function schema names
              ret = t.name.name
  
      elif isinstance(t, ListType):
          if str(t.elem) == "int":
              ret = (
                  "Union[_int, _size]"
                  if t.size is not None
                  else "_size"
              )
          elif t.is_tensor_like():
              # TODO: this doesn't seem right...
              # Tensor?[] currently translates to Optional[Union[Tuple[Tensor, ...], List[Tensor]]]
              # It should probably translate to   Union[Tuple[Optional[Tensor], ...], List[Optional[Tensor]]]
              if isinstance(t.elem, OptionalType):
                  add_optional = True
              ret = (
                  "Union[Tensor, Tuple[Tensor, ...], List[Tensor]]"
                  if t.size is not None
                  else "Union[Tuple[Tensor, ...], List[Tensor]]"
              )
          elif str(t.elem) == "float":
              ret = "Sequence[_float]"
          elif str(t.elem) == "SymInt" and t.size is not None:
              elem = argument_type_str_pyi(t.elem)
              ret = f"Union[{elem}, Sequence[{elem}]]"
          else:
              elem = argument_type_str_pyi(t.elem)
              ret = f"Sequence[{elem}]"
  
      else:
          raise RuntimeError(f"unrecognized type {repr(t)}")
  
      if add_optional:
          ret = "Optional[" + ret + "]"
  
      return ret
  
imports: from dataclasses import dataclass
  from typing import Dict, List, Optional, Sequence, Set, Tuple, Union
  from torchgen.api import cpp
  from torchgen.api.types import Binding, CppSignature, CppSignatureGroup
  from torchgen.gen import pythonify_default
  from torchgen.model import Argument, BaseTy, BaseType, FunctionSchema, ListType, NativeFunction, OptionalType, Return, Type, Variant
label_desc: Use image node_id 9 for calling a global function with example usage: argument_type_str_pyi(t) and returns: ret