code: def fine_tune_model_from_file_paths(
      model_archive_path,
      config_file,
      serialization_dir,
      overrides="",
      extend_vocab=False,
      file_friendly_logging=False,
  ):
      """
      A wrapper around :func:`fine_tune_model` which loads the model archive from a file.
  
      Parameters
      ----------
      model_archive_path : ``str``
          Path to a saved model archive that is the result of running the ``train`` command.
      config_file : ``str``
          A configuration file specifying how to continue training.  The format is identical to the
          configuration file for the ``train`` command, but any contents in the ``model`` section is
          ignored (as we are using the provided model archive instead).
      serialization_dir : ``str``
          The directory in which to save results and logs. We just pass this along to
          :func:`fine_tune_model`.
      overrides : ``str``
          A JSON string that we will use to override values in the input parameter file.
      file_friendly_logging : ``bool``, optional (default=False)
          If ``True``, we make our output more friendly to saved model files.  We just pass this
          along to :func:`fine_tune_model`.
      """
      # We don't need to pass in `cuda_device` here, because the trainer will call `model.cuda()` if
      # necessary.
      archive = load_archive(model_archive_path)
      params = Params.from_file(config_file, overrides)
      return fine_tune_model(
          model=archive.model,
          params=params,
          serialization_dir=serialization_dir,
          extend_vocab=extend_vocab,
          file_friendly_logging=file_friendly_logging,
      )
  
imports: from __future__ import with_statement
  from __future__ import absolute_import
  import argparse
  import json
  import logging
  import os
  from copy import deepcopy
  import re
  from allennlp.commands.evaluate import evaluate
  from allennlp.commands.subcommand import Subcommand
  from allennlp.commands.train import datasets_from_params
  from allennlp.common import Params
  from allennlp.common.util import prepare_environment, prepare_global_logging, get_frozen_and_tunable_parameter_names
  from allennlp.data.iterators.data_iterator import DataIterator
  from allennlp.models import load_archive, archive_model
  from allennlp.models.archival import CONFIG_NAME
  from allennlp.models.model import Model, _DEFAULT_WEIGHTS
  from allennlp.training.trainer import Trainer
  from allennlp.common.checks import ConfigurationError
  from io import open
label_desc: Use image node_id 2 for calling a global function with example usage: fine_tune_model_from_file_paths(model_archive_path, config_file, serialization_dir, overrides, extend_vocab, file_friendly_logging) and returns: fine_tune_model