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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 sum_distances(self, indices, distance_matrix):
 """Calculate combinatorial distance between a select group of trajectories, indicated by indices Arguments --------- indices : tuple distance_matrix : numpy.ndarray (M,M) Returns ------- numpy.ndarray Notes ----- This function can perhaps be quickened by calculating the sum of the distances. The calculated distances, as they are right now, are only used in a relative way. Purely summing distances would lead to the same result, at a perhaps quicker rate. """

        combs_tup = np.array(tuple(combinations(indices, 2)))

        # Put indices from tuples into two-dimensional array.
        combs = np.array([[i[0] for i in combs_tup],
                          [i[1] for i in combs_tup]])

        # Calculate distance (vectorized)
        dist = np.sqrt(
            np.sum(np.square(distance_matrix[combs[0], combs[1]]), axis=0))

        return dist