code: def _get_rand_example(self):
      num_annotations = random.randint(1, 3)
      bboxes, classes, is_crowds = [], [], []
      image_size = random.randint(10, self.MAX_IMAGE_SIZE + 1)
      rgb = [random.uniform(0, 255) for _ in range(3)]
      image = test_utils.fill_image(rgb, image_size)
      for _ in range(num_annotations):
          x1, x2 = random.uniform(0, image_size), random.uniform(
              0, image_size
          )
          y1, y2 = random.uniform(0, image_size), random.uniform(
              0, image_size
          )
          bbox = [min(x1, x2), min(y1, y2), abs(x1 - x2), abs(y1 - y2)]
          bboxes.append(bbox)
          classes.append(random.randint(0, self.NUM_CLASSES - 1))
          is_crowds.append(0)
      return {
          "image": tf.cast(image, dtype=tf.float32),
          "groundtruth_boxes": tf.cast(bboxes, dtype=tf.float32),
          "groundtruth_classes": tf.cast(classes, dtype=tf.int64),
          "groundtruth_is_crowd": tf.cast(is_crowds, dtype=tf.bool),
          "groundtruth_area": tf.cast(is_crowds, dtype=tf.float32),
          "source_id": tf.cast(1, dtype=tf.int64),
          "height": tf.cast(image_size, dtype=tf.int64),
          "width": tf.cast(image_size, dtype=tf.int64),
      }
  
file_path: mediapipe/mediapipe/model_maker/python/vision/object_detector/preprocessor_test.py
imports: import random
  from absl.testing import parameterized
  import tensorflow
  from mediapipe.model_maker.python.vision.core import test_utils
  from mediapipe.model_maker.python.vision.object_detector import model_spec
  from mediapipe.model_maker.python.vision.object_detector import preprocessor
label_desc: Use image node_id 1 for calling the DatasetTest obj's underlying member method code with example usage: obj._get_rand_example() and returns: dict