code: def update_init_info(module, init_info):
      """Update the `_params_init_info` in the module if the value of parameters
      are changed.
  
      Args:
          module (obj:`nn.Module`): The module of PyTorch with a user-defined
              attribute `_params_init_info` which records the initialization
              information.
          init_info (str): The string that describes the initialization.
      """
      assert hasattr(
          module, "_params_init_info"
      ), f"Can not find `_params_init_info` in {module}"
      for name, param in module.named_parameters():
          assert param in module._params_init_info, (
              f"Find a new :obj:`Parameter` "
              f"named `{name}` during executing the "
              f"`init_weights` of "
              f"`{module.__class__.__name__}`. "
              f"Please do not add or "
              f"replace parameters during executing "
              f"the `init_weights`. "
          )
  
          # The parameter has been changed during executing the
          # `init_weights` of module
          mean_value = param.data.mean()
          if (
              module._params_init_info[param]["tmp_mean_value"]
              != mean_value
          ):
              module._params_init_info[param]["init_info"] = init_info
              module._params_init_info[param][
                  "tmp_mean_value"
              ] = mean_value
  
imports: import copy
  import math
  import warnings
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
label_desc: Use image node_id 1 for calling a global function with example usage: update_init_info(module, init_info) without return types