code: def check_call(self):
      xp = self.link.xp
  
      feat_size = (12, 16)
      x = chainer.Variable(
          xp.random.uniform(
              low=-1.0,
              high=1.0,
              size=(self.B, 3, feat_size[0] * 16, feat_size[1] * 16),
          ).astype(np.float32)
      )
      roi_cls_locs, roi_scores, rois, roi_indices = self.link(x)
  
      n_roi = roi_scores.shape[0]
      if self.train:
          self.assertGreaterEqual(self.B * self.n_train_post_nms, n_roi)
      else:
          self.assertGreaterEqual(
              self.B * self.n_test_post_nms * 2, n_roi
          )
  
      self.assertIsInstance(roi_cls_locs, chainer.Variable)
      self.assertIsInstance(roi_cls_locs.array, xp.ndarray)
      self.assertEqual(roi_cls_locs.shape, (n_roi, self.n_class * 4))
  
      self.assertIsInstance(roi_scores, chainer.Variable)
      self.assertIsInstance(roi_scores.array, xp.ndarray)
      self.assertEqual(roi_scores.shape, (n_roi, self.n_class))
  
      self.assertIsInstance(rois, xp.ndarray)
      self.assertEqual(rois.shape, (n_roi, 4))
  
      self.assertIsInstance(roi_indices, xp.ndarray)
      self.assertEqual(roi_indices.shape, (n_roi,))
  
imports: import numpy
  import unittest
  import chainer
  from chainer.backends import cuda
  from chainer import testing
  from chainer.testing import attr
  from chainercv.links import LightHeadRCNNResNet101
  from chainercv.links.model.light_head_rcnn import LightHeadRCNNTrainChain
  from chainercv.utils import generate_random_bbox
label_desc: Use image node_id 2 for calling the TestLightHeadRCNNResNet101 obj's underlying member method code with example usage: obj.check_call() without return types