code: def predict(self, imgs):
      n_img = len(imgs)
      masks = self.masks[self.count : self.count + n_img]
      labels = self.labels[self.count : self.count + n_img]
      scores = [np.ones_like(l) for l in labels]
  
      self.count += n_img
  
      return masks, labels, scores
  
imports: import numpy
  import unittest
  import chainer
  from chainer.datasets import TupleDataset
  from chainer.iterators import SerialIterator
  from chainer import testing
  from chainercv.extensions import InstanceSegmentationVOCEvaluator
  from chainercv.utils.testing import attr
  from chainermn import create_communicator
label_desc: Use image node_id 2 for calling the _InstanceSegmentationStubLink obj's underlying member method code with example usage: obj.predict(imgs) and returns: masks,  labels,  scores