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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 transform(self, y):
 """Transform labels to normalized encoding. Parameters y : array-like of shape [n_samples] Target values. Returns ------- y : array-like of shape [n_samples] """

        check_is_fitted(self, 'classes_')
        y = column_or_1d(y, warn=True)

        classes = np.unique(y)
        _check_numpy_unicode_bug(classes)
        if len(np.intersect1d(classes, self.classes_)) < len(classes):
            diff = np.setdiff1d(classes, self.classes_)
            raise ValueError("y contains new labels: %s" % str(diff))
        return np.searchsorted(self.classes_, y)