How to use the modin.data_management.utils.split_result_of_axis_func_pandas function in modin

(see `split_result_of_axis_func_pandas`).
        len_of_left: The number of values in `partitions` that belong to the
            left data set.
        kwargs: A dictionary of keyword arguments.
        partitions: All partitions that make up the full axis (row or column)
            for both data sets.

    Returns:
        A list of Pandas DataFrames.
    """
    lt_frame = pandas.concat(list(partitions[:len_of_left]), axis=axis, copy=False)
    rt_frame = pandas.concat(list(partitions[len_of_left:]), axis=axis, copy=False)

    result = func(lt_frame, rt_frame, **kwargs)
    return [
        df.copy() for df in split_result_of_axis_func_pandas(axis, num_splits, result)
    ]

lengths = list(lengths)
        # We set lengths to None so we don't use the old lengths for the resulting partition
        # layout. This is done if the number of splits is changing or we are told not to
        # keep the old partitioning.
        elif num_splits != len(partitions) or not maintain_partitioning:
            lengths = None
        else:
            if axis == 0:
                lengths = [len(part) for part in partitions]
                if sum(lengths) != len(result):
                    lengths = None
            else:
                lengths = [len(part.columns) for part in partitions]
                if sum(lengths) != len(result.columns):
                    lengths = None
        return split_result_of_axis_func_pandas(axis, num_splits, result, lengths)

#    if num_splits != len(partitions) or isinstance(result, pandas.Series):
    #        import pdb; pdb.set_trace()
    #        lengths = None
    else:
        if axis == 0:
            lengths = [len(part) for part in partitions]
            if sum(lengths) != len(result):
                lengths = None
        else:
            lengths = [len(part.columns) for part in partitions]
            if sum(lengths) != len(result.columns):
                lengths = None
    return [
        df.copy()
        for df in split_result_of_axis_func_pandas(axis, num_splits, result, lengths)
    ]

def _split_result_for_readers(axis, num_splits, df):  # pragma: no cover
    """Splits the DataFrame read into smaller DataFrames and handles all edge cases.

    Args:
        axis: Which axis to split over.
        num_splits: The number of splits to create.
        df: The DataFrame after it has been read.

    Returns:
        A list of pandas DataFrames.
    """
    splits = split_result_of_axis_func_pandas(axis, num_splits, df)
    if not isinstance(splits, list):
        splits = [splits]
    return splits

func: The function to perform.
            num_splits: The number of splits to return
                (see `split_result_of_axis_func_pandas`).
            len_of_left: The number of values in `partitions` that belong to the
                left data set.
            kwargs: A dictionary of keyword arguments.
            partitions: All partitions that make up the full axis (row or column)
                for both data sets.

        Returns:
            A list of Pandas DataFrames.
        """
        lt_frame = pandas.concat(partitions[:len_of_left], axis=axis, copy=False)
        rt_frame = pandas.concat(partitions[len_of_left:], axis=axis, copy=False)
        result = func(lt_frame, rt_frame, **kwargs)
        return split_result_of_axis_func_pandas(axis, num_splits, result)