def fit(self, X, y):
        """Fit each classifier.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features, n_timestamps)
            Multivariate time series.

        y : None or array-like, shape = (n_samples,)
            Class labels.

        Returns
        -------
        self : object

        """
        X = check_3d_array(X)
        _, n_features, _ = X.shape
        self._check_params(n_features)

        if self.weights is None:
            self._weights = None
        else:
            self._weights = np.asarray(self.weights)

        self._le = LabelEncoder().fit(y)
        self.classes_ = self._le.classes_
        y_ind = self._le.transform(y)
        for i, clf in enumerate(self.estimators_):
            clf.fit(X[:, i, :], y_ind)
        return self
Fit each classifier.

        Parameters
        ----------
        X : array-like, shape = (n_samples, n_features, n_timestamps)
            Multivariate time series.

        y : None or array-like, shape = (n_samples,)
            Class labels.

        Returns
        -------
        self : object

        
pyts/multivariate/classification/multivariate.py