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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 score_ll(self, sequences):
 r"""log of the likelihood of sequences with respect to the model Parameters sequences : list of array-like List of sequences, or a single sequence. Each sequence should be a 1D iterable of state labels. Labels can be integers, strings, or other orderable objects. Returns ------- loglikelihood : float The natural log of the likelihood, computed as :math:`\sum_{ij} C_{ij} \log(P_{ij})` where C is a matrix of counts computed from the input sequences. """

        counts, mapping = _transition_counts(sequences)
        if not set(self.mapping_.keys()).issuperset(mapping.keys()):
            return -np.inf
        inverse_mapping = {v: k for k, v in mapping.items()}

        # maps indices in counts to indices in transmat
        m2 = _dict_compose(inverse_mapping, self.mapping_)
        indices = [e[1] for e in sorted(m2.items())]

        transmat_slice = self.transmat_[np.ix_(indices, indices)]
        return np.nansum(np.log(transmat_slice) * counts)