code: def get_mr_filters(data_shape, opt='', coarse=False):  # pragma: no cover
      
  
      # Adjust the shape of the input data.
      data_shape = np.array(data_shape)
      data_shape += data_shape % 2 - 1
  
      # Create fake data.
      fake_data = np.zeros(data_shape)
      fake_data[tuple(zip(data_shape // 2))] = 1
  
      # Call mr_transform.
      mr_filters = call_mr_transform(fake_data, opt=opt)
  
      # Return filters
      if coarse:
          return mr_filters
      else:
          return mr_filters[:-1]
docs: Get mr_transform filters
  
      This method obtains wavelet filters by calling mr_transform
  
      Parameters
      ----------
      data_shape : tuple
          2D data shape
      opt : list, optional
          List of additonal mr_transform options
      coarse : bool, optional
          Option to keep coarse scale (default is 'False')
  
      Returns
      -------
      np.ndarray 3D array of wavelet filters