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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 transform(self, X, y=None, sample_weight=None):
        '''
        Transforms the time series data into segments
        Note this transformation changes the number of samples in the data
        If y is provided, it is segmented and transformed to align to the new samples as per
        ``y_func``
        Currently sample weights always returned as None

        Parameters
        ----------
        X : array-like, shape [n_series, ...]
           Time series data and (optionally) contextual data
        y : array-like shape [n_series], default = None
            target vector
        sample_weight : array-like shape [n_series], default = None
            sample weights

        Returns
        -------
        Xt : array-like, shape [n_segments, ]
            transformed time series data
        yt : array-like, shape [n_segments]
            expanded target vector
        sample_weight_new : None

        '''
        check_ts_data(X, y)
        Xt, Xc = get_ts_data_parts(X)
        yt = y

        N = len(Xt)  # number of time series

        if Xt[0].ndim > 1:
            Xt = np.array([sliding_tensor(Xt[i], self.width, self._step, self.order)
                           for i in np.arange(N)])
        else:
            Xt = np.array([sliding_window(Xt[i], self.width, self._step, self.order)
                           for i in np.arange(N)])

        Nt = [len(Xt[i]) for i in np.arange(len(Xt))]
        Xt = np.concatenate(Xt)

        if Xc is not None:
            Xc = expand_variables_to_segments(Xc, Nt)
            Xt = TS_Data(Xt, Xc)

        if yt is not None:
            yt = np.array([sliding_window(yt[i], self.width, self._step, self.order)
                           for i in np.arange(N)])
            yt = np.concatenate(yt)
            yt = self.y_func(yt)

        if self.shuffle is True:
            check_random_state(self.random_state)
            Xt, yt, _ = shuffle_data(Xt, yt)

        return Xt, yt, None