def collect_as_cf(self: 'DataFrame',
                  type: str = 'json') -> tuple[list[Result], int, bool]:
    """Collects DataFrame execution plan as presigned URLs.

    This method is a wrapper around the `to_cf` method of SparkConnectClient. It takes the
    execution plan of the current DataFrame, converts it to a protocol buffer format, and then
    uses the `to_cf` method to execute the plan and fetch results as presigned URLs.

    Args:
        self (pd.DataFrame): The dataframe we are processing.
        type (str): The output format of the result, supported formats are 'json', 'csv', and 'arrow'.

    Returns:
        Tuple[List[Result], int, bool]: A tuple containing:
            - A list of Result namedtuples, each containing a URL, row count, compressed size,
              and uncompressed size of the part of the result.
            - Total row count of all parts of the result.
            - A boolean indicating whether the result is truncated or overflowed.
    """
    query = self._plan.to_proto(self._session.client)  # pyright: ignore
    return self._session.client.to_cf(query, type)  # pyright: ignore
Collects DataFrame execution plan as presigned URLs.

    This method is a wrapper around the `to_cf` method of SparkConnectClient. It takes the
    execution plan of the current DataFrame, converts it to a protocol buffer format, and then
    uses the `to_cf` method to execute the plan and fetch results as presigned URLs.

    Args:
        self (pd.DataFrame): The dataframe we are processing.
        type (str): The output format of the result, supported formats are 'json', 'csv', and 'arrow'.

    Returns:
        Tuple[List[Result], int, bool]: A tuple containing:
            - A list of Result namedtuples, each containing a URL, row count, compressed size,
              and uncompressed size of the part of the result.
            - Total row count of all parts of the result.
            - A boolean indicating whether the result is truncated or overflowed.
    
llmfoundry/command_utils/data_prep/convert_delta_to_json.py