code: def convert(gm):
      from torch.nn.modules.module import _addindent
  
      tab = " " * 4
  
      model_str = textwrap.dedent(
          """
              from torch.nn import *
              class Repro(torch.nn.Module):
                  def __init__(self):
                      super().__init__()
              """
      )
  
      for module_name, module in gm.named_children():
          module_str = f"{module.__repr__()}"
          # module should be a core torch.nn.Module, so all parameters
          # should be on the same device.
          example_param = next(module.parameters(), None)
          if example_param is not None and example_param.is_cuda:
              module_str = f"{module_str}.cuda()"
          model_str += f"{tab*2}self.{module_name} = {module_str}\n"
  
      for buffer_name, buffer in gm._buffers.items():
          if buffer is None:
              continue
          # Serialize full data for small buffers
          if buffer.numel() <= MAX_CONSTANT_NUMEL_INLINE:
              from torch._tensor_str import PRINT_OPTS
  
              assert PRINT_OPTS.threshold >= MAX_CONSTANT_NUMEL_INLINE
              tensor_str = repr(buffer)
          elif torch.is_floating_point(buffer):
              tensor_str = f"torch.randn({list(buffer.shape)}, dtype={buffer.dtype})"
          else:
              tensor_str = f"torch.randint(1, size={list(buffer.shape)}, dtype={buffer.dtype})"
          if buffer.is_cuda:
              tensor_str = f"{tensor_str}.cuda()"
          model_str += f"{tab*2}self.register_buffer('{buffer_name}', {tensor_str})\n"
  
      for param_name, param in gm._parameters.items():
          if param is None:
              continue
          maybe_device = ""
          if param.is_cuda:
              maybe_device = ', device="cuda"'
          tensor_str = f"torch.nn.Parameter(torch.randn({list(param.shape)}, dtype={param.dtype}{maybe_device}))"
          model_str += f"{tab*2}self.{param_name} = {tensor_str}\n"
  
      # TODO - Keep this code for now. But, I don't think we will need this.
      # attrs = dir(gm)
      # for attr in attrs:
      #     if "_tensor_constant" in attr:
      #         val = getattr(gm, attr)
      #         model_str += f"    {attr} = {val!r}\n"
  
      model_str += f"{_addindent(gm.code, 4)}\n"
      return model_str
  
imports: import copy
  import functools
  import getpass
  import itertools
  import logging
  import os
  import subprocess
  import tempfile
  import textwrap
  from collections import Counter
  from importlib import import_module
  from typing import Callable, Optional, TypeVar
  import torch
  import torch._prims_common
  import torch._subclasses.meta_utils
  from torch._dynamo.testing import rand_strided
  from torch._prims_common import is_float_dtype
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._content_store import ContentStoreReader, ContentStoreWriter
  from .None import config
  from .utils import clone_inputs, get_debug_dir
label_desc: Use image node_id 2 for calling the NNModuleToString obj's underlying member method code with example usage: obj.convert(gm) and returns: model_str