code: def test_empty_detection_outputs(self):
      options = _FaceLandmarkerOptions(
          base_options=_BaseOptions(model_asset_path=self.model_path)
      )
      with _FaceLandmarker.create_from_options(options) as landmarker:
          # Load the image with no faces.
          no_faces_test_image = _Image.create_from_file(
              test_utils.get_test_data_path(_CAT_IMAGE)
          )
          # Performs face landmarks detection on the input.
          detection_result = landmarker.detect(no_faces_test_image)
          self.assertEmpty(detection_result.face_landmarks)
          self.assertEmpty(detection_result.face_blendshapes)
          self.assertEmpty(
              detection_result.facial_transformation_matrixes
          )
  
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.framework.formats import classification_pb2
  from mediapipe.framework.formats import landmark_pb2
  from mediapipe.python._framework_bindings import image
  from mediapipe.tasks.python.components.containers import category
  from mediapipe.tasks.python.components.containers import landmark
  from mediapipe.tasks.python.components.containers import rect
  from mediapipe.tasks.python.core import base_options
  from mediapipe.tasks.python.test import test_utils
  from mediapipe.tasks.python.vision import face_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 FaceLandmarkerTest obj's underlying member method code with example usage: obj.test_empty_detection_outputs() without return types