code: def node_ctor_arg_rvalue_string(arg: LazyArgument) -> str:
      """
      Given a LazyArgument,
      generate a c++ string for materializing an rvalue of that arg for passing into
      a lazy Node constructor.
      """
  
      # TODO: Matching on CType seems wrong; should be matching on Type
      if isValueType(arg.lazy_type):
          if isinstance(arg.lazy_type, BaseCType):
              if arg.is_wrapped_scalar:
                  return f"node_{arg.name}"
              elif arg.lazy_type.type is tensorListValueT:
                  return f"lazy_{arg.name}_tensorlist"
              elif arg.is_symint_or_list:
                  return f"GetSymIntValue({arg.name})"
              return f"lazy_{arg.name}->GetIrValue()"
          elif isinstance(arg.lazy_type, OptionalCType):
              if arg.is_symint_or_list:
                  # TODO: I don't understand when you should put lazy_ in the name
                  # or not
                  return f"{arg.name} ? c10::make_optional(GetSymIntValue(*{arg.name})) : c10::nullopt"
              elif arg.is_wrapped_scalar:
                  return f"node_{arg.name}"
              return (
                  f"lazy_{arg.name} ? "
                  f"c10::make_optional(lazy_{arg.name}->GetIrValue()) : "
                  "c10::nullopt"
              )
          else:
              raise AssertionError(
                  f"TODO not sure if there are other valid types to handle here ({arg.lazy_type})"
              )
      else:
          # NB: this is here because right now we aren't treating SymInt[] as a
          # value type; when we do this needs to move above
          # NB: we cannot test arg.lazy_type as we've already specified it is an
          # int64_t and so we cannot distinguish between SymInt and int64_t
          if isinstance(
              arg.orig_type, ListType
          ) and arg.orig_type.elem == BaseType(BaseTy.SymInt):
              if arg.symint:
                  return f"GetSymIntArrayRefValue({arg.name})"
              else:
                  return f"std::vector<int64_t>({arg.name}.begin(), {arg.name}.end())"
          elif isinstance(arg.lazy_type, VectorCType) and isinstance(
              arg.lazy_type.elem, BaseCType
          ):
              return f"std::vector<{arg.lazy_type.elem.type}>({arg.name}.begin(), {arg.name}.end())"
          elif (
              isinstance(arg.lazy_type, OptionalCType)
              and isinstance(arg.lazy_type.elem, VectorCType)
              and isinstance(arg.lazy_type.elem.elem, BaseCType)
          ):
              return f"torch::lazy::ToOptionalVector<{arg.lazy_type.elem.elem.type}>({arg.name})"
          else:
              return f"{arg.name}"
  
imports: import itertools
  from abc import ABC
  from dataclasses import dataclass
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torchgen.api.dispatcher
  from torchgen.api.lazy import getValueT, isValueType, LazyArgument, LazyIrProperties, LazyIrSchema, tensorListValueT
  from torchgen.api.translate import translate
  from torchgen.api.types import BaseCType, Binding, deviceT, DispatcherSignature, kernel_signature, NativeSignature, OptionalCType, VectorCType
  from torchgen.context import method_with_native_function
  from torchgen.dest.lazy_ts_lowering import ts_lowering_body
  from torchgen.model import Argument, BackendIndex, BackendMetadata, BaseTy, BaseType, FunctionSchema, ListType, NativeFunction, NativeFunctionsGroup
label_desc: Use image node_id 1 for calling a global function with example usage: node_ctor_arg_rvalue_string(arg) and returns: str, arg, str, str, arg, str, arg, str, arg, str, str, arg, str, str, arg, str, arg, str, str, arg, str, arg, str, arg, str, str, arg, str, arg, str, arg, str, str, arg, str, arg, str, arg, str, arg, str, str, arg