code: def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
      erased_tensors = [
          e
          for e in pytree.arg_tree_leaves(*args, **kwargs)
          if isinstance(e, ErasedTensor)
      ]
      assert len(erased_tensors) > 0
      e = erased_tensors[0]
  
      raise RuntimeError(
          f"Trying to run Pytorch Eager Module after Dynamo Freezing. "
          "The original parameters have been discarded for memory efficiency. "
          f"Found in op {func} for erased parameter {e.erased_name} of {e.owning_mod_ref()}"
      )
  
imports: from __future__ import annotations
  import itertools
  import logging
  import weakref
  from typing import Any, List, Optional, Tuple
  import torch
  import torch.utils._pytree
  from torch._dynamo.utils import dynamo_timed, lazy_format_graph_code
  from torch._functorch.aot_autograd import MutationType
  from torch._functorch.compile_utils import fx_graph_cse
  from torch._inductor.constant_folding import constant_fold, replace_node_with_constant
  from torch._inductor.fx_passes.freezing_patterns import freezing_passes
  from torch._inductor.fx_passes.post_grad import view_to_reshape
  from .None import config
label_desc: Use image node_id 3 for calling the ErasedTensor obj's underlying member method code with example usage: obj.__torch_dispatch__(cls, func, types, args, kwargs) without return types