def _get_operator_param_name_and_values(operator_class_name, task_details):
    """ Internal helper gets the name of the python parameter for the Airflow operator class. In
      some cases, we do not expose the airflow parameter name in its native form, but choose to
      expose a name that's more standard for Datalab, or one that's more friendly. For example,
      Airflow's BigQueryOperator uses 'bql' for the query string, but we want %%bq users in Datalab
      to use 'query'. Hence, a few substitutions that are specific to the Airflow operator need to
      be made.

      Similarly, we the parameter value could come from the notebook's context. All that happens
      here.

      Returns:
        Dict containing _only_ the keys and values that are required in Airflow operator definition.
      This requires a substituting existing keys in the dictionary with their Airflow equivalents (
      i.e. by adding new keys, and removing the existing ones).
    """

    # We make a clone and then remove 'type' and 'up_stream' since these aren't needed for the
    # the operator's parameters.
    operator_task_details = task_details.copy()
    if 'type' in operator_task_details.keys():
      del operator_task_details['type']
    if 'up_stream' in operator_task_details.keys():
      del operator_task_details['up_stream']

    # We special-case certain operators if we do some translation of the parameter names. This is
    # usually the case when we use syntactic sugar to expose the functionality.
    # TODO(rajivpb): It should be possible to make this a lookup from the modules mapping via
    # getattr() or equivalent. Avoid hard-coding these class-names here.
    if (operator_class_name == 'BigQueryOperator'):
      return PipelineGenerator._get_bq_execute_params(operator_task_details)
    if (operator_class_name == 'BigQueryToCloudStorageOperator'):
      return PipelineGenerator._get_bq_extract_params(operator_task_details)
    if (operator_class_name == 'GoogleCloudStorageToBigQueryOperator'):
      return PipelineGenerator._get_bq_load_params(operator_task_details)
    return operator_task_details
Internal helper gets the name of the python parameter for the Airflow operator class. In
      some cases, we do not expose the airflow parameter name in its native form, but choose to
      expose a name that's more standard for Datalab, or one that's more friendly. For example,
      Airflow's BigQueryOperator uses 'bql' for the query string, but we want %%bq users in Datalab
      to use 'query'. Hence, a few substitutions that are specific to the Airflow operator need to
      be made.

      Similarly, we the parameter value could come from the notebook's context. All that happens
      here.

      Returns:
        Dict containing _only_ the keys and values that are required in Airflow operator definition.
      This requires a substituting existing keys in the dictionary with their Airflow equivalents (
      i.e. by adding new keys, and removing the existing ones).