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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 connected_sets(C, directed=True):
 r"""Compute connected components for a directed graph with weights represented by the given count matrix. Parameters C : scipy.sparse matrix or numpy ndarray square matrix specifying edge weights. directed : bool, optional Whether to compute connected components for a directed or undirected graph. Default is True. Returns ------- cc : list of arrays of integers Each entry is an array containing all vertices (states) in the corresponding connected component. """

    M = C.shape[0]
    """ Compute connected components of C. nc is the number of
    components, indices contain the component labels of the states
    """
    nc, indices = csgraph.connected_components(C, directed=directed, connection='strong')

    states = np.arange(M)  # Discrete states

    """Order indices"""
    ind = np.argsort(indices)
    indices = indices[ind]

    """Order states"""
    states = states[ind]
    """ The state index tuple is now of the following form (states,
    indices)=([s_23, s_17,...,s_3, s_2, ...], [0, 0, ..., 1, 1, ...])
    """

    """Find number of states per component"""
    count = np.bincount(indices)

    """Cumulative sum of count gives start and end indices of
    components"""
    csum = np.zeros(len(count) + 1, dtype=int)
    csum[1:] = np.cumsum(count)

    """Generate list containing components, sort each component by
    increasing state label"""
    cc = []
    for i in range(nc):
        cc.append(np.sort(states[csum[i]:csum[i + 1]]))

    """Sort by size of component - largest component first"""
    cc = sorted(cc, key=lambda x: -len(x))

    return cc