code: def predict(self, imgs):
      bboxes = []
      labels = []
      scores = []
  
      for img in imgs:
          n_bbox = np.random.randint(1, 10)
          bboxes.append(
              generate_random_bbox(n_bbox, img.shape[1:], 4, 12)
          )
          labels.append(
              np.random.randint(0, 20, size=n_bbox).astype(np.int32)
          )
          scores.append(
              np.random.uniform(0, 1, size=n_bbox).astype(np.float32)
          )
  
      return bboxes, labels, scores
  
imports: import numpy
  import unittest
  import chainer
  from chainercv.utils import assert_is_detection_link
  from chainercv.utils import generate_random_bbox
  from chainercv.utils import testing
label_desc: Use image node_id 1 for calling the DetectionLink obj's underlying member method code with example usage: obj.predict(imgs) and returns: bboxes,  labels,  scores