code: def sliding_tensor(mv_time_series, width, step, order='F'):
      '''
      segments multivariate time series with sliding window
  
      Parameters
      ----------
      mv_time_series : array like shape [n_samples, n_variables]
          multivariate time series or sequence
      width : int > 0
          segment width in samples
      step : int > 0
          stepsize for sliding in samples
  
      Returns
      -------
      data : array like shape [n_segments, width, n_variables]
          segmented multivariate time series data
      '''
      D = mv_time_series.shape[1]
      data = [sliding_window(mv_time_series[:, j], width, step, order) for j in range(D)]
      return np.stack(data, axis=2f sliding_tensor(mv_time_series, width, step, order='F'):
      '''
      segments multivariate time series with sliding window
  
      Parameters
      ----------
      mv_time_series : array like shape [n_samples, n_variables]
          multivariate time series or sequence
      width : int > 0
          segment width in samples
      step : int > 0
          stepsize for sliding in samples
  
      Returns
      -------
      data : array like shape [n_segments, width, n_variables]
          segmented multivariate time series data
      '''
      D = mv_time_series.shape[1]
      data = [sliding_window(mv_time_series[:, j], width, step, order) for j in range(D)]
      return np.stack(data, axis=2)
docs: segments multivariate time series with sliding window
  
      Parameters
      ----------
      mv_time_series : array like shape [n_samples, n_variables]
          multivariate time series or sequence
      width : int > 0
          segment width in samples
      step : int > 0
          stepsize for sliding in samples
  
      Returns
      -------
      data : array like shape [n_segments, width, n_variables]
          segmented multivariate time series data