Load a h5 file from the file path or buffer, returning a DataFrame.

        Args:
            path_or_buf: string, buffer or path object
                Path to the file to open, or an open :class:`pandas.HDFStore` object.
            kwargs: Pass into pandas.read_hdf function.

        Returns:
            DataFrame constructed from the h5 file.

def read_hdf(cls, path_or_buf, **kwargs):
        """Load a h5 file from the file path or buffer, returning a DataFrame.

        Args:
            path_or_buf: string, buffer or path object
                Path to the file to open, or an open :class:`pandas.HDFStore` object.
            kwargs: Pass into pandas.read_hdf function.

        Returns:
            DataFrame constructed from the h5 file.
        """
        if cls.read_hdf_remote_task is None:
            return super(RayIO, cls).read_hdf(path_or_buf, **kwargs)

        format = cls._validate_hdf_format(path_or_buf=path_or_buf)

        if format is None:
            ErrorMessage.default_to_pandas(
                "File format seems to be `fixed`. For better distribution consider saving the file in `table` format. "
                "df.to_hdf(format=`table`)."
            )
            return cls.from_pandas(pandas.read_hdf(path_or_buf=path_or_buf, **kwargs))

        columns = kwargs.get("columns", None)
        if not columns:
            empty_pd_df = pandas.read_hdf(path_or_buf, start=0, stop=0)
            columns = empty_pd_df.columns

        num_partitions = cls.frame_mgr_cls._compute_num_partitions()
        num_splits = min(len(columns), num_partitions)
        # Each item in this list will be a list of column names of the original df
        column_splits = (
            len(columns) // num_partitions
            if len(columns) % num_partitions == 0
            else len(columns) // num_partitions + 1
        )
        col_partitions = [
            columns[i : i + column_splits]
            for i in range(0, len(columns), column_splits)
        ]
        blk_partitions = np.array(
            [
                cls.read_hdf_remote_task._remote(
                    args=(path_or_buf, cols, num_splits, kwargs),
                    num_return_vals=num_splits + 1,
                )
                for cols in col_partitions
            ]
        ).T
        remote_partitions = np.array(
            [
                [cls.frame_partition_cls(obj) for obj in row]
                for row in blk_partitions[:-1]
            ]
        )
        index_len = ray.get(blk_partitions[-1][0])
        index = pandas.RangeIndex(index_len)
        new_query_compiler = cls.query_compiler_cls(
            cls.frame_mgr_cls(remote_partitions), index, columns
        )
        return new_query_compiler