code: def forward(self, imgs, gt_mb_locs, gt_mb_labels):
      mb_locs, mb_confs = self.model(imgs)
      loc_loss, conf_loss = multibox_loss(
          mb_locs, mb_confs, gt_mb_locs, gt_mb_labels, self.k, self.comm
      )
      loss = loc_loss * self.alpha + conf_loss
  
      chainer.reporter.report(
          {"loss": loss, "loss/loc": loc_loss, "loss/conf": conf_loss},
          self,
      )
  
      return loss
  
imports: import argparse
  import multiprocessing
  import numpy
  import chainer
  from chainer.optimizer_hooks import WeightDecay
  from chainer import serializers
  from chainer import training
  from chainer.training import extensions
  from chainer.training import triggers
  import chainermn
  from chainercv.chainer_experimental.datasets.sliceable import ConcatenatedDataset
  from chainercv.chainer_experimental.datasets.sliceable import TransformDataset
  from chainercv.datasets import voc_bbox_label_names
  from chainercv.datasets import VOCBboxDataset
  from chainercv.extensions import DetectionVOCEvaluator
  from chainercv.links.model.ssd import GradientScaling
  from chainercv.links.model.ssd import multibox_loss
  from chainercv.links import SSD300
  from chainercv.links import SSD512
  from train import Transform
  import cv2
label_desc: Use image node_id 2 for calling the MultiboxTrainChain obj's underlying member method code with example usage: obj.forward(imgs, gt_mb_locs, gt_mb_labels) and returns: loss