code: def create_proxy(
      self,
      kind,
      target,
      args,
      kwargs,
      name=None,
      type_expr=None,
      proxy_factory_fn=None,
  ):
      rv = super().create_proxy(
          kind, target, args, kwargs, name, type_expr, proxy_factory_fn
      )
  
      if kind == "placeholder" and target in self.meta_args:
          rv.install_tensor_meta(self.meta_args[target])
          return rv
  
      if target in self.orig_fns:
          # NOTE: tensor constructors in PyTorch define the `device` argument as
          # *kwargs-only*. That is why this works. If you add methods to
          # _TORCH_METHODS_TO_PATCH that do not define `device` as kwarg-only,
          # this will break and you will likely see issues where we cannot infer
          # the size of the output.
          if "device" in kwargs:
              kwargs["device"] = "meta"
  
      try:
          args_metas = torch.fx.node.map_aggregate(
              args, proxys_to_metas
          )
          kwargs_metas = torch.fx.node.map_aggregate(
              kwargs, proxys_to_metas
          )
  
          if kind == "call_function":
              meta_target = manual_meta_overrides.get(target, target)
              meta_out = meta_target(*args_metas, **kwargs_metas)
          elif kind == "call_method":
              meta_out = getattr(args_metas[0], target)(
                  *args_metas[1:], **kwargs_metas
              )
          elif kind == "call_module":
              assert hasattr(self, "orig_forward")
              self._disable_module_getattr = True
              try:
                  mod = self.root.get_submodule(target)
                  mod_type = type(mod)
                  if mod_type in manual_meta_overrides:
                      meta_out = manual_meta_overrides[mod_type](
                          mod, *args_metas, **kwargs_metas
                      )
                  else:
                      meta_out = self.orig_forward(
                          *args_metas, **kwargs_metas
                      )
              finally:
                  self._disable_module_getattr = False
          elif kind == "get_attr":
              self._disable_module_getattr = True
              try:
                  attr_itr = self.root
                  atoms = target.split(".")
                  for atom in atoms:
                      attr_itr = getattr(attr_itr, atom)
                  assert isinstance(attr_itr, torch.Tensor)
                  meta_out = attr_itr.to(device="meta")
              finally:
                  self._disable_module_getattr = False
          else:
              return rv
  
          # TODO
          assert isinstance(
              rv, torch.fx.Proxy
          ), "Dont support composite output yet"
          rv.install_tensor_meta(meta_out)
      except Exception as e:
          warnings.warn(
              f"Could not compute metadata for {kind} target {target}: {e}"
          )
  
      return rv
  
imports: import torch
  import torch.fx
  import warnings
  import functools
  import builtins
  from typing import Any, Callable, Dict, Optional, Union
label_desc: Use image node_id 1 for calling the MetaTracer obj's underlying member method code with example usage: obj.create_proxy(kind, target, args, kwargs, name, type_expr, proxy_factory_fn) and returns: rv, rv, rv