code: def predict(config_path: str, restore_from: Optional[str], cl_arguments: Iterable[str], output_root: str) -> None:
      
      config = None
  
      try:
          config_path = find_config(config_path)
          restore_from = restore_from or path.dirname(config_path)
          config = load_config(config_file=config_path, additional_args=cl_arguments)
  
          if 'predict' in config:
              for section in ['dataset', 'model', 'main_loop']:
                  if section in config['predict']:
                      config[section].update(config['predict'][section])
              if 'hooks' in config['predict']:
                  config['hooks'] = config['predict']['hooks']
              else:
                  logging.warning('Config does not contain `predict.hooks` section. '
                                  'No hook will be employed during the prediction.')
                  config['hooks'] = []
  
          validate_config(config)
  
          logging.debug('\tLoaded config: %s', config)
      except Exception as ex:  # pylint: disable=broad-except
          fallback('Loading config failed', ex)
  
      run(config=config, output_root=output_root, restore_from=restore_from, eval='predict')
docs: Run prediction from the specified config path.
  
      If the config contains a ``predict`` section:
          - override hooks with ``predict.hooks`` if present
          - update dataset, model and main loop sections if the respective sections are present
  
      :param config_path: path to the config file or the directory in which it is stored
      :param restore_from: backend-specific path to the already trained model to be restored from.
                           If ``None`` is passed, it is inferred from the configuration file location as the directory
                           it is located in.
      :param cl_arguments: additional command line arguments which will update the configuration
      :param output_root: output root in which the training directory will be created