code: def consume_prefix_in_state_dict_if_present(
      state_dict: Dict[str, Any], prefix: str
  ) -> None:
      r"""Strip the prefix in state_dict in place, if any.
  
      ..note::
          Given a `state_dict` from a DP/DDP model, a local model can load it by applying
          `consume_prefix_in_state_dict_if_present(state_dict, "module.")` before calling
          :meth:`torch.nn.Module.load_state_dict`.
  
      Args:
          state_dict (OrderedDict): a state-dict to be loaded to the model.
          prefix (str): prefix.
      """
      keys = sorted(state_dict.keys())
      for key in keys:
          if key.startswith(prefix):
              newkey = key[len(prefix) :]
              state_dict[newkey] = state_dict.pop(key)
  
      # also strip the prefix in metadata if any.
      if "_metadata" in state_dict:
          metadata = state_dict["_metadata"]
          for key in list(metadata.keys()):
              # for the metadata dict, the key can be:
              # '': for the DDP module, which we want to remove.
              # 'module': for the actual model.
              # 'module.xx.xx': for the rest.
  
              if len(key) == 0:
                  continue
              newkey = key[len(prefix) :]
              metadata[newkey] = metadata.pop(key)
  
imports: import collections
  from itertools import repeat
  from typing import List, Dict, Any
label_desc: Use image node_id 4 for calling a global function with example usage: consume_prefix_in_state_dict_if_present(state_dict, prefix) without return types