code: def __init__(self, model, alpha=1, k=3, comm=None):
      super(MultiboxTrainChain, self).__init__()
      with self.init_scope():
          self.model = model
      self.alpha = alpha
      self.k = k
      self.comm = comm
  
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 1 to create a new MultiboxTrainChain object from inherited base classes: chainer with example: obj = MultiboxTrainChain(model, alpha, k, comm)