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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 min_row_dist_sum_idx(dists):
 """Find the index of the row with the minimum row distance sum This should return the index of the row index with the least distance overall to all other rows. Args: dists (np.array):
 must be square distance matrix Returns: int: index of row with min dist row sum """

    row_sums = np.apply_along_axis(arr=dists, axis=0, func1d=np.sum)
    return row_sums.argmin()