code: def forward(self, imgs, bboxes, labels, masks=None):
      B = len(imgs)
      pad_size = np.array([im.shape[1:] for im in imgs]).max(axis=0)
      pad_size = (
          np.ceil(pad_size / self.model.stride) * self.model.stride
      ).astype(int)
      x = np.zeros(
          (len(imgs), 3, pad_size[0], pad_size[1]), dtype=np.float32
      )
      for i, img in enumerate(imgs):
          _, H, W = img.shape
          x[i, :, :H, :W] = img
      x = self.xp.array(x)
  
      bboxes = [self.xp.array(bbox) for bbox in bboxes]
      labels = [self.xp.array(label) for label in labels]
      sizes = [img.shape[1:] for img in imgs]
  
      with chainer.using_config("train", False):
          hs = self.model.extractor(x)
  
      rpn_locs, rpn_confs = self.model.rpn(hs)
      anchors = self.model.rpn.anchors(h.shape[2:] for h in hs)
      rpn_loc_loss, rpn_conf_loss = rpn_loss(
          rpn_locs, rpn_confs, anchors, sizes, bboxes
      )
  
      rois, roi_indices = self.model.rpn.decode(
          rpn_locs, rpn_confs, anchors, x.shape
      )
      rois = self.xp.vstack([rois] + bboxes)
      roi_indices = self.xp.hstack(
          [roi_indices]
          + [
              self.xp.array((i,) * len(bbox))
              for i, bbox in enumerate(bboxes)
          ]
      )
      rois, roi_indices = self.model.bbox_head.distribute(
          rois, roi_indices
      )
      (
          rois,
          roi_indices,
          head_gt_locs,
          head_gt_labels,
      ) = bbox_head_loss_pre(
          rois, roi_indices, self.model.bbox_head.std, bboxes, labels
      )
      head_locs, head_confs = self.model.bbox_head(
          hs, rois, roi_indices
      )
      head_loc_loss, head_conf_loss = bbox_head_loss_post(
          head_locs,
          head_confs,
          roi_indices,
          head_gt_locs,
          head_gt_labels,
          B,
      )
  
      mask_loss = 0
      if masks is not None:
          # For reducing unnecessary CPU/GPU copy, `masks` is kept in CPU.
          pad_masks = [
              np.zeros(
                  (mask.shape[0], pad_size[0], pad_size[1]),
                  dtype=np.bool,
              )
              for mask in masks
          ]
          for i, mask in enumerate(masks):
              _, H, W = mask.shape
              pad_masks[i][:, :H, :W] = mask
          masks = pad_masks
  
          (
              mask_rois,
              mask_roi_indices,
              gt_segms,
              gt_mask_labels,
          ) = mask_head_loss_pre(
              rois,
              roi_indices,
              masks,
              bboxes,
              head_gt_labels,
              self.model.mask_head.segm_size,
          )
          n_roi = sum([len(roi) for roi in mask_rois])
          if n_roi > 0:
              segms = self.model.mask_head(
                  hs, mask_rois, mask_roi_indices
              )
              mask_loss = mask_head_loss_post(
                  segms, mask_roi_indices, gt_segms, gt_mask_labels, B
              )
          else:
              # Compute dummy variables to complete the computational graph
              mask_rois[0] = self.xp.array(
                  [[0, 0, 1, 1]], dtype=np.float32
              )
              mask_roi_indices[0] = self.xp.array([0], dtype=np.int32)
              segms = self.model.mask_head(
                  hs, mask_rois, mask_roi_indices
              )
              mask_loss = 0 * F.sum(segms)
      loss = (
          rpn_loc_loss
          + rpn_conf_loss
          + head_loc_loss
          + head_conf_loss
          + mask_loss
      )
      chainer.reporter.report(
          {
              "loss": loss,
              "loss/rpn/loc": rpn_loc_loss,
              "loss/rpn/conf": rpn_conf_loss,
              "loss/bbox_head/loc": head_loc_loss,
              "loss/bbox_head/conf": head_conf_loss,
              "loss/mask_head": mask_loss,
          },
          self,
      )
      return loss
  
imports: from __future__ import division
  import argparse
  import multiprocessing
  import numpy
  import PIL
  import chainer
  import chainer.functions
  import chainer.links
  from chainer.optimizer_hooks import WeightDecay
  from chainer import serializers
  from chainer import training
  from chainer.training import extensions
  import chainermn
  from chainercv.chainer_experimental.datasets.sliceable import TransformDataset
  from chainercv.chainer_experimental.training.extensions import make_shift
  from chainercv.links.model.fpn.misc import scale_img
  from chainercv import transforms
  from chainercv.datasets import coco_instance_segmentation_label_names
  from chainercv.datasets import COCOInstanceSegmentationDataset
  from chainercv.links import MaskRCNNFPNResNet101
  from chainercv.links import MaskRCNNFPNResNet50
  from chainercv.datasets import coco_bbox_label_names
  from chainercv.datasets import COCOBboxDataset
  from chainercv.links import FasterRCNNFPNResNet101
  from chainercv.links import FasterRCNNFPNResNet50
  from chainercv.links.model.fpn import bbox_head_loss_post
  from chainercv.links.model.fpn import bbox_head_loss_pre
  from chainercv.links.model.fpn import mask_head_loss_post
  from chainercv.links.model.fpn import mask_head_loss_pre
  from chainercv.links.model.fpn import rpn_loss
label_desc: Use image node_id 2 for calling the TrainChain obj's underlying member method code with example usage: obj.forward(imgs, bboxes, labels, masks) and returns: loss