class_docstr: A ComptimeVar represents a Python value, at some particular point
  in time, in the Python code we are symbolically evaluating with
  torchdynamo.  This must be distinguished from a runtime value, as
  at compile-time there are some properties of the variable we
  do not know (for example, if the ComptimeVar represents a Tensor,
  we only know metadata about the tensor; we do NOT know what the
  actual data in the Tensor is.)
docstr: Returns an fx.Proxy (or tuple/list of fx.Proxy) representing
  this variable in the FX graph we are assembling to pass
  to the user compiler.
  
  This method only works for variables we actually track in
  the FX graph, aka Tensors (and ints, if you are compiling
  with dynamic shapes).  In particular, if you have a list
  or tuple of tensors, you will get a list/tuple of proxies
  (not a single proxy representing the entire list/tuple).
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code: def as_proxy(self):
      """
      Returns an fx.Proxy (or tuple/list of fx.Proxy) representing
      this variable in the FX graph we are assembling to pass
      to the user compiler.
  
      This method only works for variables we actually track in
      the FX graph, aka Tensors (and ints, if you are compiling
      with dynamic shapes).  In particular, if you have a list
      or tuple of tensors, you will get a list/tuple of proxies
      (not a single proxy representing the entire list/tuple).
      """
      return self.__variable.as_proxy()
  
imports: import builtins
  import dis
  import traceback
  from typing import Optional, Union
  import torch
  from .exc import unimplemented
label_desc: Use image node_id 2 for calling the ComptimeVar obj's underlying member method code with example usage: obj.as_proxy() and returns: self