code: def face_encodings(face_image, known_face_locations=None, num_jitters=1):
      
      raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model="small")
      return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks]
docs: Given an image, return the 128-dimension face encoding for each face in the image.
  
      :param face_image: The image that contains one or more faces
      :param known_face_locations: Optional - the bounding boxes of each face if you already know them.
      :param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower)
      :return: A list of 128-dimensional face encodings (one for each face in the image)