                      input_image_bgr=cv2.imread(x),
                      detector='dlib-cnn',
                      verbose=True,
                      input_is_predetected_face=True).landmarks
                  temp_list.append([x, np.array(d[0][1])
                                    if len(d) > 0
                                    else np.zeros((68, 2))])
          elif group_method == 'group_face_yaw':
              temp_list = []
              for x in tqdm(self.find_images(input_dir),
                            desc="Reloading",
 
</s> remove             for x in tqdm(self.find_images(input_dir),
                          desc="Reloading",
                          file=sys.stdout):
                d = face_alignment.Extract(
                    input_image_bgr=cv2.imread(x),
 </s> add             for img in tqdm(self.find_images(input_dir),
                            desc="Reloading",
                            file=sys.stdout):
                landmarks = face_alignment.Extract(
                    input_image_bgr=cv2.imread(img), </s> remove             for x in tqdm(self.find_images(input_dir),
                          desc="Reloading",
                          file=sys.stdout):
                d = face_alignment.Extract(
                    input_image_bgr=cv2.imread(x),
 </s> add             for img in tqdm(self.find_images(input_dir),
                            desc="Reloading",
                            file=sys.stdout):
                landmarks = face_alignment.Extract(
                    input_image_bgr=cv2.imread(img), </s> remove             img_list.append([x, np.array(d[0][1])
                             if len(d) > 0
 </s> add             img_list.append([img, np.array(landmarks[0][1])
                             if landmarks </s> remove             img_list.append([x, np.array(d[0][1])
                             if len(d) > 0
 </s> add             img_list.append([img, np.array(landmarks[0][1])
                             if landmarks </s> remove             temp_list = [[x, face_recognition.face_encodings(cv2.imread(x))]
                         for x in
                         tqdm(self.find_images(input_dir),
                              desc="Reloading",
                              file=sys.stdout)]
 </s> add             temp_list = [
                [img, face_recognition.face_encodings(cv2.imread(img))]
                for img in tqdm(self.find_images(input_dir),
                                desc="Reloading",
                                file=sys.stdout)]
                temp_list.append([img, np.array(landmarks[0][1])
                                  if landmarks
                      input_image_bgr=cv2.imread(x),
                      detector='dlib-cnn',
                      verbose=True,
                      input_is_predetected_face=True).landmarks
                temp_list.append([img, np.array(landmarks[0][1])
                                  if landmarks
                temp_list.append([img, np.array(landmarks[0][1])
                                  if landmarks
                                    else np.zeros((68, 2))])
          elif group_method == 'group_face_yaw':
              temp_list = []
              for x in tqdm(self.find_images(input_dir),
                            desc="Reloading",
keep keep keep keep replace replace keep keep keep keep keep
 Sort tool bugfixes and linting 
tools/sort.py