code: def test_detect_succeeds_with_num_hands(self):
      # Creates hand landmarker.
      base_options = _BaseOptions(model_asset_path=self.model_path)
      options = _HandLandmarkerOptions(
          base_options=base_options, num_hands=2
      )
      with _HandLandmarker.create_from_options(options) as landmarker:
          # Load the two hands image.
          test_image = _Image.create_from_file(
              test_utils.get_test_data_path(_TWO_HANDS_IMAGE)
          )
          # Performs hand landmarks detection on the input.
          detection_result = landmarker.detect(test_image)
          # Comparing results.
          self.assertLen(detection_result.handedness, 2)
  
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.components.containers.proto import landmarks_detection_result_pb2
  from mediapipe.tasks.python.components.containers import landmark
  from mediapipe.tasks.python.components.containers import landmark_detection_result
  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 hand_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 HandLandmarkerTest obj's underlying member method code with example usage: obj.test_detect_succeeds_with_num_hands() without return types