code: def encodeLocPrediction(
      self, num_matches, matches, loc_data, gt_boxes, priors
  ):
      # output regression gt data
      loc_gt_data = np.zeros((num_matches, 4))
      loc_pred_data = np.zeros((num_matches, 4))
      cnt = 0
  
      for batch in range(self.be.bsz):
          match_inds = matches[batch]["inds"]
  
          inds = np.where(match_inds > -1)[0]
          for ind in inds:
              gt_ind = match_inds[ind]
              gt_box = gt_boxes[batch][gt_ind, :]
  
              loc_gt_data[cnt, :] = self.encodeBBox(priors[ind], gt_box)
              loc_pred_data[cnt, :] = loc_data[batch][ind, :]
              cnt += 1
  
      return (loc_gt_data, loc_pred_data)
  
imports: from neon import NervanaObject
  import numpy
  from util import util
  from neon.transforms import Softmax
label_desc: Use image node_id 7 for calling the MBoxLoss obj's underlying member method code with example usage: obj.encodeLocPrediction(num_matches, matches, loc_data, gt_boxes, priors) and returns: loc_gt_data,  loc_pred_data