code: def _get_operator_definition(task_id, task_details, parameters):
      
      operator_type = task_details['type']
      full_param_string = 'task_id=\'{0}_id\''.format(task_id)
      operator_class_name, module = PipelineGenerator._get_operator_class_name(operator_type)
      operator_class_instance = getattr(sys.modules[module], operator_class_name, None)
      templated_fields = operator_class_instance.template_fields if operator_class_instance else ()
  
      operator_param_values = PipelineGenerator._get_operator_param_name_and_values(
          operator_class_name, task_details)
  
      # This loop resolves all the macros and builds up the final string
      merged_parameters = google.datalab.bigquery.Query.merge_parameters(
        parameters, date_time=datetime.datetime.now(), macros=True, types_and_values=False)
      for (operator_param_name, operator_param_value) in sorted(operator_param_values.items()):
        # We replace modifiers in the parameter values with either the user-defined values, or with
        # with the airflow macros, as applicable.
        # An important assumption that this makes is that the operators parameters have the same names
        # as the templated_fields. TODO(rajivpb): There may be a better way to do this.
        if operator_param_name in templated_fields:
          operator_param_value = google.datalab.bigquery.Query._resolve_parameters(
            operator_param_value, merged_parameters)
        param_format_string = PipelineGenerator._get_param_format_string(operator_param_value)
        param_string = param_format_string.format(operator_param_name, operator_param_value)
        full_param_string = full_param_string + param_string
  
      return '{0} = {1}({2}, dag=dag)\n'.format(task_id, operator_class_name, full_param_string)
docs: Internal helper that gets the definition of the airflow operator for the task with the
        python parameters. All the parameters are also expanded with the airflow macros.
        :param parameters: