code: def arg_parser_unpack_method(
      t: Type,
      default: Optional[str],
      default_init: Optional[str],
      *,
      symint: bool = True,
  ) -> str:
      has_default_init = default_init is not None
      if has_default_init and str(t) not in (
          "ScalarType?",
          "ScalarType",
          "Device",
          "Device?",
          "Layout",
          "Layout?",
          "bool",
          "bool?",
      ):
          raise RuntimeError(
              f"type '{t}' does not supported unpacking with default"
          )
  
      if isinstance(t, BaseType):
          if t.name in [
              BaseTy.Tensor,
              BaseTy.Stream,
              BaseTy.Storage,
              BaseTy.Scalar,
              BaseTy.Dimname,
          ]:
              # These unpack methods line up with their schema names
              return t.name.name.lower()
          elif t.name == BaseTy.ScalarType:
              return (
                  "scalartypeWithDefault"
                  if has_default_init
                  else "scalartype"
              )
          elif t.name == BaseTy.Device:
              return (
                  "deviceWithDefault" if has_default_init else "device"
              )
          elif t.name == BaseTy.DeviceIndex:
              return "toInt64"
          elif t.name == BaseTy.int:
              return "toInt64"
          elif t.name == BaseTy.SymInt:
              return "toSymInt" if symint else "toInt64"
          elif t.name == BaseTy.bool:
              return (
                  "toBoolWithDefault" if has_default_init else "toBool"
              )
          elif t.name == BaseTy.float:
              return "toDouble"
          elif t.name == BaseTy.str:
              return "stringView"
          elif t.name == BaseTy.Layout:
              return (
                  "layoutWithDefault" if has_default_init else "layout"
              )
          elif t.name == BaseTy.MemoryFormat:
              return "memoryformat"
  
      elif isinstance(t, OptionalType):
          if str(t.elem) == "Tensor":
              return "optionalTensor"
          elif str(t.elem) == "Generator":
              return "generator"
          elif str(t.elem) == "Dimname[]":
              return "toDimnameListOptional"
          elif not has_default_init and default in (
              None,
              "None",
              "c10::nullopt",
          ):
              # If default is None: append 'Optional' to elem's unpacking method
              return (
                  arg_parser_unpack_method(
                      t.elem, None, None, symint=symint
                  )
                  + "Optional"
              )
          else:
              # Otherwise, load as underlying type with default
              return arg_parser_unpack_method(
                  t.elem, default, default_init, symint=symint
              )
  
      elif isinstance(t, ListType):
          if str(t.elem) == "Tensor":
              # accept and use definite size
              return (
                  f"tensorlist_n<{t.size}>"
                  if t.size is not None
                  else "tensorlist"
              )
          elif str(t.elem) == "Tensor?":
              return "list_of_optional_tensors"
          elif str(t.elem) == "Dimname":
              # accept definite size
              return "dimnamelist"
          elif str(t.elem) == "int":
              # accept definite size
              return "intlist"
          elif str(t.elem) == "float":
              return "doublelist"
          elif str(t.elem) == "SymInt":
              # accept definite size
              return "symintlist" if symint else "intlist"
          elif str(t.elem) == "Scalar":
              return "scalarlist"
      raise RuntimeError(
          f"type '{t}' is not supported by PythonArgParser"
      )
  
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 17 for calling a global function with example usage: arg_parser_unpack_method(t, default, default_init) and returns: t, unknown, str, unknown, str, unknown, str, str, str, str, unknown, arg_parser_unpack_method, str, unknown, str, unknown, str, str, unknown, str, str, unknown, str