code: def dtw_distance(rep_one, rep_two, norm=True):
      
      if not isinstance(rep_one, np.ndarray):
          rep_one = rep_one.to_array()
      if not isinstance(rep_two, np.ndarray):
          rep_two = rep_two.to_array()
      assert (rep_one.shape[1] == rep_two.shape[1])
      distMat = generate_distance_matrix(rep_one, rep_two)
      return regularDTW(distMat, norm=norm)
docs: Computes the distance between two representations with the same
      number of filters using Dynamic Time Warping.
  
      Parameters
      ----------
      rep_one : 2D array
          First representation to compare. First dimension is time in frames
          or samples and second dimension is the features.
      rep_two : 2D array
          Second representation to compare. First dimension is time in frames
          or samples and second dimension is the features.
  
      Returns
      -------
      float
          Distance of dynamically time warping `rep_one` to `rep_two`.