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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 most_uncertain_by_mask(self, mask, y):
 """ Extracts the first 4 most uncertain indexes from the ordered list of probabilities Arguments: mask (numpy.ndarray):
 the mask of probabilities specific to the selected class; a boolean array with shape (num_of_samples,) which contains True where class==selected_class, and False everywhere else y (int):
 the selected class Returns: idxs (ndarray):
 An array of indexes of length 4 """

        idxs = np.where(mask)[0]
        # the most uncertain samples will have abs(probs-1/num_classes) close to 0;
        return idxs[np.argsort(np.abs(self.probs[idxs,y]-(1/self.num_classes)))[:4]]