code: def face_encodings(
      face_image,
      known_face_locations=None,
      num_jitters=1,
      model="small",
  ):
      """
      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)
      :param model: Optional - which model to use. "large" or "small" (default) which only returns 5 points but is faster.
      :return: A list of 128-dimensional face encodings (one for each face in the image)
      """
      raw_landmarks = _raw_face_landmarks(
          face_image, known_face_locations, model
      )
      return [
          np.array(
              face_encoder.compute_face_descriptor(
                  face_image, raw_landmark_set, num_jitters
              )
          )
          for raw_landmark_set in raw_landmarks
      ]
  
file_path: face_recognition/face_recognition/api.py
imports: import PIL.Image
  import dlib
  import numpy
  from PIL import ImageFile
label_desc: Use image node_id 12 for calling a global function with example usage: face_encodings(face_image, known_face_locations, num_jitters, model) and returns: unknown