<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def fit_predict(self, sequences, y=None):
 """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 """

        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