code: def _raw_face_locations(
      img, number_of_times_to_upsample=1, model="hog"
  ):
      """
      Returns an array of bounding boxes of human faces in a image
  
      :param img: An image (as a numpy array)
      :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
      :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate
                    deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog".
      :return: A list of dlib 'rect' objects of found face locations
      """
      if model == "cnn":
          return cnn_face_detector(img, number_of_times_to_upsample)
      else:
          return face_detector(img, number_of_times_to_upsample)
  
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 6 for calling a global function with example usage: _raw_face_locations(img, number_of_times_to_upsample, model) and returns: cnn_face_detector, face_detector