code: def bbox_head_loss_pre(rois, roi_indices, std, bboxes, labels):
      """Loss function for Head (pre).
  
      This function processes RoIs for :func:`bbox_head_loss_post`.
  
      Args:
          rois (iterable of arrays): An iterable of arrays of
              shape :math:`(R_l, 4)`, where :math:`R_l` is the number
              of RoIs in the :math:`l`-th feature map.
          roi_indices (iterable of arrays): An iterable of arrays of
              shape :math:`(R_l,)`.
          std (tuple of floats): Two coefficients used for encoding
              bounding boxes.
          bboxes (list of arrays): A list of arrays whose shape is
              :math:`(R_n, 4)`, where :math:`R_n` is the number of
              ground truth bounding boxes.
          labels (list of arrays): A list of arrays whose shape is
              :math:`(R_n,)`.
  
       Returns:
           tuple of four lists:
           :obj:`rois`, :obj:`roi_indices`, :obj:`gt_locs`, and :obj:`gt_labels`.
  
            * **rois**: A list of arrays of shape :math:`(R'_l, 4)`, \
                where :math:`R'_l` is the number of RoIs in the :math:`l`-th \
                feature map.
            * **roi_indices**: A list of arrays of shape :math:`(R'_l,)`.
            * **gt_locs**: A list of arrays of shape :math:`(R'_l, 4) \
                indicating the bounding boxes of ground truth.
            * **roi_indices**: A list of arrays of shape :math:`(R'_l,)` \
                indicating the classes of ground truth.
      """
  
      thresh = 0.5
      batchsize_per_image = 512
      fg_ratio = 0.25
  
      xp = cuda.get_array_module(*rois)
  
      n_level = len(rois)
      roi_levels = xp.hstack(
          xp.array((l,) * len(rois[l])) for l in range(n_level)
      ).astype(np.int32)
      rois = xp.vstack(rois).astype(np.float32)
      roi_indices = xp.hstack(roi_indices).astype(np.int32)
  
      rois_yx = (rois[:, 2:] + rois[:, :2]) / 2
      rois_hw = rois[:, 2:] - rois[:, :2]
      indices = np.unique(cuda.to_cpu(roi_indices))
  
      gt_locs = xp.empty_like(rois)
      gt_labels = xp.empty_like(roi_indices)
      for i in indices:
          mask = roi_indices == i
  
          if len(bboxes[i]) > 0:
              iou = utils.bbox_iou(rois[mask], bboxes[i])
              gt_index = iou.argmax(axis=1)
  
              gt_loc = bboxes[i][gt_index].copy()
          else:
              gt_loc = xp.empty_like(rois[mask])
          # tlbr -> yxhw
          gt_loc[:, 2:] -= gt_loc[:, :2]
          gt_loc[:, :2] += gt_loc[:, 2:] / 2
          # offset
          gt_loc[:, :2] = (
              (gt_loc[:, :2] - rois_yx[mask]) / rois_hw[mask] / std[0]
          )
          gt_loc[:, 2:] = xp.log(gt_loc[:, 2:] / rois_hw[mask]) / std[1]
  
          if len(bboxes[i]) > 0:
              gt_label = labels[i][gt_index] + 1
              gt_label[iou.max(axis=1) < thresh] = 0
          else:
              gt_label = xp.zeros(int(mask.sum()), dtype=np.int32)
  
          fg_index = xp.where(gt_label > 0)[0]
          n_fg = int(batchsize_per_image * fg_ratio)
          if len(fg_index) > n_fg:
              gt_label[choice(fg_index, size=len(fg_index) - n_fg)] = -1
  
          bg_index = xp.where(gt_label == 0)[0]
          n_bg = batchsize_per_image - int((gt_label > 0).sum())
          if len(bg_index) > n_bg:
              gt_label[choice(bg_index, size=len(bg_index) - n_bg)] = -1
  
          gt_locs[mask] = gt_loc
          gt_labels[mask] = gt_label
  
      mask = gt_labels >= 0
      rois = rois[mask]
      roi_indices = roi_indices[mask]
      roi_levels = roi_levels[mask]
      gt_locs = gt_locs[mask]
      gt_labels = gt_labels[mask]
  
      masks = [roi_levels == l for l in range(n_level)]
      rois = [rois[m] for m in masks]
      roi_indices = [roi_indices[m] for m in masks]
      gt_locs = [gt_locs[m] for m in masks]
      gt_labels = [gt_labels[m] for m in masks]
  
      return rois, roi_indices, gt_locs, gt_labels
  
imports: from __future__ import division
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer import initializers
  import chainer.links
  from chainercv.links.model.fpn.misc import argsort
  from chainercv.links.model.fpn.misc import choice
  from chainercv.links.model.fpn.misc import exp_clip
  from chainercv.links.model.fpn.misc import smooth_l1
  from chainercv import utils
label_desc: Use image node_id 1 for calling a global function with example usage: bbox_head_loss_pre(rois, roi_indices, std, bboxes, labels) and returns: rois,  roi_indices,  gt_locs,  gt_labels