code: def test_empty_detection_outputs(self):
      options = _HolisticLandmarkerOptions(
          base_options=_BaseOptions(model_asset_path=self.model_path)
      )
      with _HolisticLandmarker.create_from_options(
          options
      ) as landmarker:
          # Load the cat image.
          cat_test_image = _Image.create_from_file(
              test_utils.get_test_data_path(_CAT_IMAGE)
          )
          # Performs holistic landmarks detection on the input.
          detection_result = landmarker.detect(cat_test_image)
          self.assertEmpty(detection_result.face_landmarks)
          self.assertEmpty(detection_result.pose_landmarks)
          self.assertEmpty(detection_result.pose_world_landmarks)
          self.assertEmpty(detection_result.left_hand_landmarks)
          self.assertEmpty(detection_result.left_hand_world_landmarks)
          self.assertEmpty(detection_result.right_hand_landmarks)
          self.assertEmpty(detection_result.right_hand_world_landmarks)
          self.assertIsNone(detection_result.face_blendshapes)
          self.assertIsNone(detection_result.segmentation_mask)
  
imports: import enum
  from unittest import mock
  from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  from google.protobuf import text_format
  from mediapipe.python._framework_bindings import image
  from mediapipe.tasks.cc.vision.holistic_landmarker.proto import holistic_result_pb2
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.test import test_utils
  from mediapipe.tasks.python.vision import holistic_landmarker
  from mediapipe.tasks.python.vision.core import image_processing_options
  from mediapipe.tasks.python.vision.core import vision_task_running_mode
label_desc: Use image node_id 11 for calling the HolisticLandmarkerTest obj's underlying member method code with example usage: obj.test_empty_detection_outputs() without return types