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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, lengths=None):
 """Return label strings. Args: y: label id matrix. lengths: sentences length. Returns: list: list of list of strings. """

        y = np.argmax(y, -1)
        inverse_y = [self._label_vocab.id2doc(ids) for ids in y]
        if lengths is not None:
            inverse_y = [iy[:l] for iy, l in zip(inverse_y, lengths)]

        return inverse_y