code: def torch_parameter_transfer(obj, path):
      """Non-strict Torch Module state_dict load.
  
      Loads a set of parameters from path to obj. If obj has layers for which
      parameters can't be found, only a warning is logged. Same thing
      if the path has parameters for layers which don't find a counterpart
      in obj.
  
      Arguments
      ---------
      obj : torch.nn.Module
          Instance for which to load the parameters.
      path : str
          Path where to load from.
  
      Returns
      -------
      None
          The object is modified in place.
      """
      device = "cpu"
      incompatible_keys = obj.load_state_dict(
          torch.load(path, map_location=device), strict=False
      )
      for missing_key in incompatible_keys.missing_keys:
          logger.warning(
              f"During parameter transfer to {obj} loading from "
              + f"{path}, the transferred parameters did not have "
              + f"parameters for the key: {missing_key}"
          )
      for unexpected_key in incompatible_keys.unexpected_keys:
          logger.warning(
              f"During parameter transfer to {obj} loading from "
              + f"{path}, the object could not use the parameters loaded "
              + f"with the key: {unexpected_key}"
          )
  
imports: import torch
  import collections
  import collections.abc
  import os
  import time
  import yaml
  import pathlib
  import inspect
  import shutil
  import logging
  import warnings
  from packaging import version
  import speechbrain.utils._workarounds
  from speechbrain.utils.distributed import main_process_only, if_main_process, ddp_barrier
label_desc: Use image node_id 3 for calling a global function with example usage: torch_parameter_transfer(obj, path) without return types