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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_of_squares(obs, pred):
 """ Sum of squares between observed and predicted data Parameters obs : iterable Observed data pred : iterable Predicted data Returns ------- float Sum of squares Notes ----- The length of observed and predicted data must match. """


    return np.sum((np.array(obs) - np.array(pred)) ** 2)