docstr: A method that lowers the objects seen as arguments during symbolic evaluation
  into Argument types that can be stored in IR.
  
  Can be override to support more trace-specific types.
docstr_tok: ["A","method","that","lowers","the","objects","seen","as","arguments","during","symbolic","evaluation","into","Argument","types","that","can","be","stored","in","IR",".","Can","be","override","to","support","more","trace-specific","types","."]
code: def create_arg(self, a: Any) -> Argument:
      """
      A method that lowers the objects seen as arguments during symbolic evaluation
      into Argument types that can be stored in IR.
  
      Can be override to support more trace-specific types.
      """
      if not isinstance(a, Proxy) and hasattr(a, "__fx_create_arg__"):
          return a.__fx_create_arg__(self)
      # aggregates
      elif isinstance(a, tuple) and hasattr(a, "_fields"):
          # NamedTuple constructors don't seem to like getting a generator
          # expression as an argument to their constructor, so build this
          # intermediate tuple and unpack it into the NamedTuple constructor
          args = tuple(self.create_arg(elem) for elem in a)
          return type(a)(*args)  # type: ignore[arg-type]
      elif isinstance(a, (tuple, list)):
          return type(a)(self.create_arg(elem) for elem in a)
      elif isinstance(a, dict):
          r = {}
          for k, v in a.items():
              # Check for invalid dict keys. We do not want a Proxy to appear
              # anywhere within the key. Since keys can be collection types,
              # we iterate through the key with map_aggregate
              k = self.create_arg(k)
  
              def no_node(arg):
                  if isinstance(arg, Node):
                      raise RuntimeError(
                          "Keys for dictionaries used as an argument cannot contain a "
                          f"Node. Got key: {k}"
                      )
  
              map_aggregate(k, no_node)
  
              r[k] = self.create_arg(v)
          return r
      elif isinstance(a, slice):
          return slice(
              self.create_arg(a.start),
              self.create_arg(a.stop),
              self.create_arg(a.step),
          )
  
      elif isinstance(a, range):
          return range(
              self.create_arg(a.start),
              self.create_arg(a.stop),
              self.create_arg(a.step),
          )
  
      elif isinstance(a, torch._ops.OpOverload):
          return a
  
      if isinstance(a, Proxy):
          # base case: we unwrap the Proxy object
          return a.node
  
      if is_dataclass(a):
          kwargs = {
              field.name: self.create_arg(getattr(a, field.name))
              for field in fields(a)
          }
          return self.create_node(
              "call_function", a.__class__, (), kwargs
          )
  
      elif (
          isinstance(a, (*base_types, enum.Enum))
          or a is None
          or a is ...
      ):
          return a
      raise NotImplementedError(f"argument of type: {type(a)}")
  
imports: import enum
  import dis
  import copy
  import sys
  import torch
  import inspect
  import operator
  import traceback
  import collections
  from dataclasses import is_dataclass, fields
  from .graph import magic_methods, reflectable_magic_methods, Graph
  from typing import Tuple, Dict, OrderedDict, Optional, Any, Iterator, Callable
  from .node import Target, Node, Argument, base_types, map_aggregate
  from ._compatibility import compatibility
  from .operator_schemas import check_for_mutable_operation
  import torch.fx.traceback
label_desc: Use image node_id 5 for calling the TracerBase obj's underlying member method code with example usage: obj.create_arg(a) and returns: a, a, self, type, a, type, r, slice, range, a