code: def create_agent_from_opt_file(opt: Opt):
      """
      Load agent options and module from file if opt file exists.
  
      Checks to see if file exists opt['model_file'] + ".opt"; if so, load up the
      options from the file and use that to create an agent, loading the model
      type from that file and overriding any options specified in that file when
      instantiating the agent.
  
      If that file does not exist, return None.
      """
      model_file = opt["model_file"]
      optfile = model_file + ".opt"
  
      if not PathManager.exists(optfile):
          return None
  
      opt_from_file = Opt.load(optfile)
  
      # delete args that we do not want to copy over when loading the model
      for arg in NOCOPY_ARGS:
          if arg in opt_from_file:
              del opt_from_file[arg]
  
      # only override opts specified in 'override' dict
      if opt.get("override"):
          for k, v in opt["override"].items():
              if k in opt_from_file and str(v) != str(
                  opt_from_file.get(k)
              ):
                  logging.warning(
                      f'Overriding opt["{k}"] to {v} (previously: {opt_from_file.get(k)})'
                  )
              opt_from_file[k] = v
  
      model_class = load_agent_module(opt_from_file["model"])
  
      if hasattr(model_class, "upgrade_opt"):
          opt_from_file = model_class.upgrade_opt(opt_from_file)
  
      # add model arguments to opt_from_file if they aren't in opt_from_file already
      for k, v in opt.items():
          if k not in opt_from_file:
              opt_from_file[k] = v
  
      # update model file path to the one set by opt
      opt_from_file["model_file"] = model_file
      # update init model path to the one set by opt
      # NOTE: this step is necessary when for example the 'init_model' is
      # set by the Train Loop (as is the case when loading from checkpoint)
      if opt.get("init_model") is not None:
          opt_from_file["init_model"] = opt["init_model"]
  
      # update dict file path
      if not opt_from_file.get("dict_file"):
          old_dict_file = None
          opt_from_file["dict_file"] = model_file + ".dict"
      elif opt_from_file.get("dict_file") and not PathManager.exists(
          opt_from_file["dict_file"]
      ):
          old_dict_file = opt_from_file["dict_file"]
          opt_from_file["dict_file"] = model_file + ".dict"
      if not PathManager.exists(opt_from_file["dict_file"]):
          warn_once(
              "WARNING: Neither the specified dict file ({}) nor the "
              "`model_file`.dict file ({}) exists, check to make sure either "
              "is correct. This may manifest as a shape mismatch later "
              "on.".format(old_dict_file, opt_from_file["dict_file"])
          )
  
      # if we want to load weights from --init-model, compare opts with
      # loaded ones
      compare_init_model_opts(opt, opt_from_file)
      return model_class(opt_from_file)
  
imports: import copy
  from typing import List, Union
  from parlai.core.build_data import modelzoo_path
  from parlai.core.loader import load_agent_module
  from parlai.core.loader import register_agent
  from parlai.core.message import Message
  from parlai.core.opt import Opt
  from parlai.utils.misc import warn_once
  import parlai.utils.logging
  from parlai.utils.io import PathManager
label_desc: Use image node_id 3 for calling a global function with example usage: create_agent_from_opt_file(opt) and returns: model_class, None