code: def testCorrectBatchSize(self):
      config = EasyDict(
          {
              "foreground_threshold": 0.5,
              "background_threshold_high": 0.5,
              # Use zero to get all non matching as backgrounds.
              "background_threshold_low": 0.0,
              "foreground_fraction": 0.5,
              # We change the minibatch_size the catch all our foregrounds
              "minibatch_size": 64,
          }
      )
  
      gt_boxes = tf.constant([[10, 10, 20, 20, 0]], dtype=tf.float32)
  
      proposed_boxes_backgrounds = [[21, 21, 30, 30]] * 100
      proposed_boxes_foreground = [[11, 11, 19, 19]] * 100
  
      proposed_boxes = tf.constant(
          proposed_boxes_backgrounds + proposed_boxes_foreground,
          dtype=tf.float32,
      )
  
      model = RCNNTarget(self._num_classes, config, seed=0)
  
      (proposals_label, bbox_targets) = self._run_rcnn_target(
          model, gt_boxes, proposed_boxes
      )
  
      self.assertEqual(
          proposals_label[proposals_label >= 0].shape[0],
          config.minibatch_size,
      )
  
file_path: luminoth/luminoth/models/fasterrcnn/rcnn_target_test.py
imports: import numpy
  import tensorflow
  from easydict import EasyDict
  from luminoth.models.fasterrcnn.rcnn_target import RCNNTarget
  from luminoth.utils.test.gt_boxes import generate_gt_boxes
label_desc: Use image node_id 11 for calling the RCNNTargetTest obj's underlying member method code with example usage: obj.testCorrectBatchSize() without return types