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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _sparse_series_to_coo(ss, row_levels=(0, ), column_levels=(1, ), sort_labels=False):
 """ Convert a SparseSeries to a scipy.sparse.coo_matrix using index levels row_levels, column_levels as the row and column labels respectively. Returns the sparse_matrix, row and column labels. """


    import scipy.sparse

    if ss.index.nlevels < 2:
        raise ValueError('to_coo requires MultiIndex with nlevels > 2')
    if not ss.index.is_unique:
        raise ValueError('Duplicate index entries are not allowed in to_coo '
                         'transformation.')

    # to keep things simple, only rely on integer indexing (not labels)
    row_levels = [ss.index._get_level_number(x) for x in row_levels]
    column_levels = [ss.index._get_level_number(x) for x in column_levels]

    v, i, j, rows, columns = _to_ijv(ss, row_levels=row_levels,
                                     column_levels=column_levels,
                                     sort_labels=sort_labels)
    sparse_matrix = scipy.sparse.coo_matrix(
        (v, (i, j)), shape=(len(rows), len(columns)))
    return sparse_matrix, rows, columns