code: def fit_predict(self, sequences, y=None):
          
          if hasattr(super(MultiSequenceClusterMixin, self), 'fit_predict'):
              check_iter_of_sequences(sequences, allow_trajectory=self._allow_trajectory)
              labels = super(MultiSequenceClusterMixin, self).fit_predict(sequences)
          else:
              self.fit(sequences)
              labels = self.predict(sequences)
  
          if not isinstance(labels, list):
              labels = self._split(labels)
          return labels
docs: Performs clustering on X and returns cluster labels.
  
          Parameters
          ----------
          sequences : list of array-like, each of shape [sequence_length, n_features]
              A list of multivariate timeseries. Each sequence may have
              a different length, but they all must have the same number
              of features.
  
          Returns
          -------
          Y : list of ndarray, each of shape [sequence_length, ]
              Cluster labels