How to use the elephant.spade_src.fim.fpgrowth function in elephant

if max_c is None:
        max_c = len(transactions)

    # Initializing outputs
    concepts = []
    if report == '#':
        spec_matrix = np.zeros((max_z + 1, max_c + 1))
    if report == '3d#':
        spec_matrix = np.zeros((max_z + 1, max_c + 1, winlen))
    spectrum = []
    # Mining the data with fpgrowth algorithm
    if np.unique(transactions, return_counts=True)[1][0] == len(
            transactions):
        fpgrowth_output = [(tuple(transactions[0]), len(transactions))]
    else:
        fpgrowth_output = fim.fpgrowth(
            tracts=transactions,
            target=target,
            supp=-min_c,
            zmin=min_z,
            zmax=max_z,
            report='a',
            algo='s')
    # Applying min/max conditions and computing extent (window positions)
    fpgrowth_output = list(filter(
        lambda c: _fpgrowth_filter(
            c, winlen, max_c, min_neu), fpgrowth_output))
    # filter out subsets of patterns that are found as a side-effect
    # of using the moving window strategy
    fpgrowth_output = _filter_for_moving_window_subsets(
        fpgrowth_output, winlen)
    for (intent, supp) in fpgrowth_output: