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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 count_matrix_coo2_mult(dtrajs, lag, sliding=True, sparse=True, nstates=None):
 r"""Generate a count matrix from a given list discrete trajectories. The generated count matrix is a sparse matrix in compressed sparse row (CSR) or numpy ndarray format. Parameters dtraj : list of ndarrays discrete trajectories lag : int Lagtime in trajectory steps sliding : bool, optional If true the sliding window approach is used for transition counting sparse : bool (optional) Whether to return a dense or a sparse matrix nstates : int, optional Enforce a count-matrix with shape=(nstates, nstates). If there are more states in the data, this will lead to an exception. Returns ------- C : scipy.sparse.csr_matrix or numpy.ndarray The countmatrix at given lag in scipy compressed sparse row or numpy ndarray format. """

    # Determine number of states
    if nstates is None:
        from msmtools.dtraj import number_of_states
        nstates = number_of_states(dtrajs)
    rows = []
    cols = []
    # collect transition index pairs
    for dtraj in dtrajs:
        if dtraj.size > lag:
            if (sliding):
                rows.append(dtraj[0:-lag])
                cols.append(dtraj[lag:])
            else:
                rows.append(dtraj[0:-lag:lag])
                cols.append(dtraj[lag::lag])
    # is there anything?
    if len(rows) == 0:
        raise ValueError('No counts found - lag ' + str(lag) + ' may exceed all trajectory lengths.')
    # feed into one COO matrix
    row = np.concatenate(rows)
    col = np.concatenate(cols)
    data = np.ones(row.size)
    C = scipy.sparse.coo_matrix((data, (row, col)), shape=(nstates, nstates))
    # export to output format
    if sparse:
        return C.tocsr()
    else:
        return C.toarray()