code: def _load_model_from_config(
      config_path, hparam_overrides, vocab_file, mode
  ):
      """Loads model from a configuration file"""
      with gfile.GFile(config_path) as config_file:
          config = yaml.load(config_file)
      model_cls = locate(config["model"]) or getattr(
          models, config["model"]
      )
      model_params = config["model_params"]
      if hparam_overrides:
          model_params.update(hparam_overrides)
      # Change the max decode length to make the test run faster
      model_params["decoder.params"]["max_decode_length"] = 5
      model_params["vocab_source"] = vocab_file
      model_params["vocab_target"] = vocab_file
      return model_cls(params=model_params, mode=mode)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import os
  from pydoc import locate
  import yaml
  import tensorflow
  from tensorflow import gfile
  from seq2seq.test.models_test import EncoderDecoderTests
  from seq2seq import models
label_desc: Use image node_id 1 for calling a global function with example usage: _load_model_from_config(config_path, hparam_overrides, vocab_file, mode) and returns: model_cls