code: def load_pretrained_model(
      init_param: str,
      model: torch.nn.Module,
      ignore_init_mismatch: bool,
      map_location: str = "cpu",
  ):
      """Load a model state and set it to the model.
  
      Args:
          init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>
  
      Examples:
          >>> load_pretrained_model("somewhere/model.pth", model)
          >>> load_pretrained_model("somewhere/model.pth:decoder:decoder", model)
          >>> load_pretrained_model("somewhere/model.pth:decoder:decoder:", model)
          >>> load_pretrained_model(
          ...     "somewhere/model.pth:decoder:decoder:decoder.embed", model
          ... )
          >>> load_pretrained_model("somewhere/decoder.pth::decoder", model)
      """
      sps = init_param.split(":", 4)
      if len(sps) == 4:
          path, src_key, dst_key, excludes = sps
      elif len(sps) == 3:
          path, src_key, dst_key = sps
          excludes = None
      elif len(sps) == 2:
          path, src_key = sps
          dst_key, excludes = None, None
      else:
          (path,) = sps
          src_key, dst_key, excludes = None, None, None
      if src_key == "":
          src_key = None
      if dst_key == "":
          dst_key = None
  
      if dst_key is None:
          obj = model
      else:
  
          def get_attr(obj: Any, key: str):
              """Get an nested attribute.
  
              >>> class A(torch.nn.Module):
              ...     def __init__(self):
              ...         super().__init__()
              ...         self.linear = torch.nn.Linear(10, 10)
              >>> a = A()
              >>> assert A.linear.weight is get_attr(A, 'linear.weight')
  
              """
              if key.strip() == "":
                  return obj
              for k in key.split("."):
                  obj = getattr(obj, k)
              return obj
  
          obj = get_attr(model, dst_key)
  
      src_state = torch.load(path, map_location=map_location)
      if excludes is not None:
          for e in excludes.split(","):
              src_state = {
                  k: v
                  for k, v in src_state.items()
                  if not k.startswith(e)
              }
  
      if src_key is not None:
          src_state = {
              k[len(src_key) + 1 :]: v
              for k, v in src_state.items()
              if k.startswith(src_key)
          }
  
      dst_state = obj.state_dict()
      if ignore_init_mismatch:
          src_state = filter_state_dict(dst_state, src_state)
      dst_state.update(src_state)
      obj.load_state_dict(dst_state)
  
imports: import logging
  from typing import Any, Dict, Union
  import torch
  import torch.nn
  import torch.optim
label_desc: Use image node_id 2 for calling a global function with example usage: load_pretrained_model(init_param, model, ignore_init_mismatch, map_location) and returns: obj, obj