code: def test_represent_for_skipped_detector_backend_with_image_path():
      face_img = "dataset/img5.jpg"
      img_objs = DeepFace.represent(
          img_path=face_img, detector_backend="skip"
      )
      assert len(img_objs) >= 1
      img_obj = img_objs[0]
      assert "embedding" in img_obj.keys()
      assert "facial_area" in img_obj.keys()
      assert isinstance(img_obj["facial_area"], dict)
      assert "x" in img_obj["facial_area"].keys()
      assert "y" in img_obj["facial_area"].keys()
      assert "w" in img_obj["facial_area"].keys()
      assert "h" in img_obj["facial_area"].keys()
      assert "face_confidence" in img_obj.keys()
      logger.info(
          "✅ test represent function for skipped detector and image path input backend done"
      )
  
imports: import cv2
  from deepface import DeepFace
  from deepface.commons.logger import Logger
label_desc: Use image node_id 2 for calling a global function with example usage: test_represent_for_skipped_detector_backend_with_image_path() without return types