code: def trace(
      self,
      root: torch.nn.Module,
      concrete_args: Optional[Dict[str, torch.Tensor]] = None,
      meta_args: Optional[Dict[str, torch.Tensor]] = None,
  ) -> Graph:
      if meta_args is None:
          meta_args = {}
  
      if concrete_args is None:
          concrete_args = {}
  
      # check concrete and meta args have valid names
      sig = inspect.signature(root.forward)
      sig_names = set(sig.parameters.keys())
      meta_arg_names = set(meta_args.keys())
      concrete_arg_names = set(concrete_args.keys())
      non_concrete_arg_names = sig_names - concrete_arg_names
      # update concrete args with default values
      for k, v in sig.parameters.items():
          if (
              k in sig_names - meta_arg_names
              and k not in concrete_args
              and v.default is not inspect.Parameter.empty
          ):
              concrete_args[k] = v.default
  
      def _check_arg_name_valid(names: Iterable[str]):
          for name in names:
              if name not in sig_names:
                  raise ValueError(
                      f"Argument {name} is not in the signature of {root.__class__.__name__}.forward"
                  )
  
      _check_arg_name_valid(meta_arg_names)
      _check_arg_name_valid(concrete_arg_names)
  
      self.concrete_args = concrete_args
      self.meta_args = meta_args
  
      with self._torch_factory_override(), self._tracer_override(), torch.no_grad():
          self.mod_dir = "self"
          self.graph = super().trace(root, concrete_args=concrete_args)
          self.mod_dir = ""
      self.graph.lint()
  
      for node in self.graph.nodes:
          if node.op == "placeholder":
              # Removing default values for inputs as the forward pass will fail with them.
              if node.target in non_concrete_arg_names:
                  node.args = ()
                  # Without this, torch.jit.script fails because the inputs type is Optional[torch.Tensor].
                  # It cannot infer on the attributes and methods the input should have, and fails.
                  node.type = torch.Tensor
              # It is a concrete arg so it is not used and should be removed.
              else:
                  if hasattr(
                      torch.fx._symbolic_trace, "_assert_is_none"
                  ):
                      # Newer versions of torch.fx emit an assert statement
                      # for concrete arguments; delete those before we delete
                      # the concrete arg.
                      to_delete = []
                      for user in node.users:
                          if (
                              user.target
                              == torch.fx._symbolic_trace._assert_is_none
                          ):
                              to_delete.append(user)
                      for user in to_delete:
                          self.graph.erase_node(user)
  
                  self.graph.erase_node(node)
  
          # TODO: solves GraphModule creation.
          # Without this, return type annotation "Tuple" is causing code execution failure.
          if node.op == "output":
              node.type = None
      return self.graph
  
imports: import functools
  import inspect
  from contextlib import contextmanager
  from typing import Any, Callable, Dict, Iterable, Optional, Set, Tuple, Type, Union
  import torch
  import torch.nn
  from torch.fx import Graph, Node, Proxy, Tracer
  from torch.utils._pytree import tree_map
  from colossalai._analyzer._subclasses import _TensorPropertyMethod, _TorchFactoryMethod
  from ..node_util import MetaInfo
  from .proxy import ColoProxy
label_desc: Use image node_id 7 for calling the ColoTracer obj's underlying member method code with example usage: obj.trace(root, concrete_args, meta_args) and returns: self