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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 inverse_transform(self, y):
 """Transform labels back to original encoding. Parameters y : numpy array of shape [n_samples] Target values. Returns ------- y : numpy array of shape [n_samples] """

        check_is_fitted(self, 'classes_')

        diff = np.setdiff1d(y, np.arange(len(self.classes_)))
        if diff:
            raise ValueError("y contains new labels: %s" % str(diff))
        y = np.asarray(y)
        return self.classes_[y]