How to use the elephant.spade_src.fast_fca.FormalConcepts function in elephant

# Check parameters
    if min_neu < 1:
        raise AttributeError('min_neu must be an integer >=1')
    # By default set maximum number of attributes
    if max_z is None:
        max_z = len(context)
    # By default set maximum number of data to number of bins
    if max_c is None:
        max_c = len(context)
    if report == '#':
        spec_matrix = np.zeros((max_z, max_c))
    if report == '3d#':
        spec_matrix = np.zeros((max_z, max_c, winlen))
    spectrum = []
    # Mining the data with fast fca algorithm
    fca_out = fast_fca.FormalConcepts(context)
    fca_out.computeLattice()
    fca_concepts = fca_out.concepts
    fca_concepts = list(filter(
        lambda c: _fca_filter(
            c, winlen, min_c, min_z, max_c, max_z, min_neu), fca_concepts))
    fca_concepts = _filter_for_moving_window_subsets(fca_concepts, winlen)
    # Applying min/max conditions
    for fca_concept in fca_concepts:
        intent = tuple(fca_concept.intent)
        extent = tuple(fca_concept.extent)
        concepts.append((intent, extent))
        # computing spectrum
        if report == '#':
            spec_matrix[len(intent) - 1, len(extent) - 1] += 1
        if report == '3d#':
            spec_matrix[len(intent) - 1, len(extent) - 1, max(