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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 compute_csets_dTRAM(connectivity, count_matrices, nn=None, callback=None):
 r""" Computes the largest connected sets for dTRAM data. Parameters connectivity : string one 'reversible_pathways', 'neighbors', 'summed_count_matrix' or None. Selects the algorithm for measuring overlap between thermodynamic and Markov states. * 'reversible_pathways' : requires that every state in the connected set can be reached by following a pathway of reversible transitions. A reversible transition between two Markov states (within the same thermodynamic state k) is a pair of Markov states that belong to the same strongly connected component of the count matrix (from thermodynamic state k). A pathway of reversible transitions is a list of (i_(N-1), i_N)]. The thermodynamic state where the reversible transitions happen, is ignored in constructing the reversible pathways. This is equivalent to assuming that two ensembles overlap at some Markov state whenever there exist frames from both ensembles in that Markov state. * 'largest' : alias for reversible_pathways * 'neighbors' : similar to 'reversible_pathways' but with a more strict requirement for the overlap between thermodynamic states. It is required that every state in the connected set can be reached by following a pathway of reversible transitions or jumping between overlapping thermodynamic states while staying in the same Markov state. A reversible transition between two Markov states (within the same thermodynamic state k) is a pair of Markov states that belong to the same strongly connected component of the count matrix (from thermodynamic state k). It is assumed that the data comes from an Umbrella sampling simulation and the number of the thermodynamic state matches the position of the Umbrella along the order parameter. The overlap of thermodynamic states k and l within Markov state n is set according to the value of nn; if there are samples in both product-space states (k,n) and (l,n) and |l-n|<=nn, the states are overlapping. * 'summed_count_matrix' : all thermodynamic states are assumed to overlap. The connected set is then computed by summing the count matrices over all thermodynamic states and taking it's largest strongly connected set. Not recommended! * None : assume that everything is connected. For debugging. count_matrices : numpy.ndarray((T, M, M)) Count matrices for all T thermodynamic states. nn : int or None, optional Number of neighbors that are assumed to overlap when connectivity='neighbors' Returns ------- csets, projected_cset csets : list of numpy.ndarray((M_prime_k,), dtype=int) List indexed by thermodynamic state. Every element csets[k] is the largest connected set at thermodynamic state k. projected_cset : numpy.ndarray(M_prime, dtype=int) The overall connected set. This is the union of the individual connected sets of the thermodynamic states. """

    if connectivity=='post_hoc_RE' or connectivity=='BAR_variance':
        raise Exception('Connectivity type %s not supported for dTRAM data.'%connectivity)
    state_counts =  _np.maximum(count_matrices.sum(axis=1), count_matrices.sum(axis=2))
    return _compute_csets(
        connectivity, state_counts, count_matrices, None, None, None, nn=nn, callback=callback)