code: def test_random_bbox_crop_no_labels():
      batch_size = 3
      pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=0)
      test_box_shape = [200, 4]
  
      def get_boxes():
          out = [
              (
                  np.random.randint(
                      0, 255, size=test_box_shape, dtype=np.uint8
                  )
                  / 255
              ).astype(dtype=np.float32)
              for _ in range(batch_size)
          ]
          return out
  
      boxes = fn.external_source(source=get_boxes)
      processed = fn.random_bbox_crop(
          boxes,
          aspect_ratio=[0.5, 2.0],
          thresholds=[0.1, 0.3, 0.5],
          scaling=[0.8, 1.0],
          bbox_layout="xyXY",
      )
      pipe.set_outputs(*processed)
      pipe.build()
      for _ in range(3):
          pipe.run()
  
imports: from nvidia.dali.pipeline import Pipeline
  from nvidia.dali import pipeline_def
  import nvidia.dali.ops
  import nvidia.dali.fn
  import nvidia.dali.types
  import numpy
  import random
label_desc: Use image node_id 13 for calling a global function with example usage: test_random_bbox_crop_no_labels() and returns: out