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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_confused(self, min_val:int=1, slice_size:int=1)->Collection[Tuple[str,str,int]]:
        "Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences."
        cm = self.confusion_matrix(slice_size=slice_size)
        np.fill_diagonal(cm, 0)
        res = [(self.data.classes[i],self.data.classes[j],cm[i,j])
                for i,j in zip(*np.where(cm>=min_val))]
        return sorted(res, key=itemgetter(2), reverse=True)