code: def __init__(self, wrapped_type, module_config, debug, save=False):
      pt_fn = module_config.pt_fn
      self.module = wrapped_type(pt_fn)
      self.tensor_inputs = []
      self.module_name = wrapped_type.__name__
      for _ in range(module_config.num_params):
          self.tensor_inputs.append(torch.randn(1))
      if module_config.graph_mode:
          self.module = torch.jit.trace(self.module, self.tensor_inputs)
          if save:
              file_name = (
                  self.module_name + "_" + pt_fn.__name__ + ".pt"
              )
              torch.jit.save(self.module, file_name)
              print(f"Generated graph is saved in {file_name}")
      print(
          f"Benchmarking module {self.module_name} with fn {pt_fn.__name__}: Graph mode:{module_config.graph_mode}"
      )
      if debug and isinstance(self.module, torch.jit.ScriptModule):
          print(self.module.graph)
          print(self.module.code)
  
label_desc: Use image node_id 1 to create a new WrapperModule object  with example: obj = WrapperModule(wrapped_type, module_config, debug, save)