code: def inverse_transform(self, sequences):
          
          sequences = list_of_1d(sequences)
          inverse_mapping = {v: k for k, v in self.mapping_.items()}
          f = np.vectorize(inverse_mapping.get)
  
          result = []
          for y in sequences:
              uq = np.unique(y)
              if not np.all(np.logical_and(0 <= uq, uq < self.n_states_)):
                  raise ValueError('sequence must be between 0 and n_states-1')
  
              result.append(f(y))
          return result
docs: Transform a list of sequences from internal indexing into
          labels
  
          Parameters
          ----------
          sequences : list
              List of sequences, each of which is one-dimensional array of
              integers in ``0, ..., n_states_ - 1``.
  
          Returns
          -------
          sequences : list
              List of sequences, each of which is one-dimensional array
              of labels.