diff --git a/mediapipe/model_maker/python/core/utils/model_util.py b/mediapipe/model_maker/python/core/utils/model_util.py
index 58262ed9..2b1eebf9 100644
--- a/mediapipe/model_maker/python/core/utils/model_util.py
+++ b/mediapipe/model_maker/python/core/utils/model_util.py
@@ -34,7 +34,8 @@ ESTIMITED_STEPS_PER_EPOCH = 1000
 
 
 def get_default_callbacks(
-    export_dir: str) -> Sequence[tf.keras.callbacks.Callback]:
+    export_dir: str,
+) -> Sequence[tf.keras.callbacks.Callback]:
   """Gets default callbacks."""
   summary_dir = os.path.join(export_dir, 'summaries')
   summary_callback = tf.keras.callbacks.TensorBoard(summary_dir)
@@ -43,12 +44,14 @@ def get_default_callbacks(
   checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
       os.path.join(checkpoint_path, 'model-{epoch:04d}'),
       save_weights_only=True,
-      period=5)
+      period=5,
+  )
   return [summary_callback, checkpoint_callback]
 
 
-def load_keras_model(model_path: str,
-                     compile_on_load: bool = False) -> tf.keras.Model:
+def load_keras_model(
+    model_path: str, compile_on_load: bool = False
+) -> tf.keras.Model:
   """Loads a tensorflow Keras model from file and returns the Keras model.
 
   Args:
@@ -82,9 +85,11 @@ def load_tflite_model_buffer(model_path: str) -> bytearray:
   return tflite_model_buffer
 
 
-def get_steps_per_epoch(steps_per_epoch: Optional[int] = None,
-                        batch_size: Optional[int] = None,
-                        train_data: Optional[dataset.Dataset] = None) -> int:
+def get_steps_per_epoch(
+    steps_per_epoch: Optional[int] = None,
+    batch_size: Optional[int] = None,
+    train_data: Optional[dataset.Dataset] = None,
+) -> int:
   """Gets the estimated training steps per epoch.
 
   1. If `steps_per_epoch` is set, returns `steps_per_epoch` directly.
@@ -201,17 +206,20 @@ def save_tflite(tflite_model: bytearray, tflite_file: str) -> None:
   with tf.io.gfile.GFile(tflite_file, 'wb') as f:
     f.write(tflite_model)
   tf.compat.v1.logging.info(
-      'TensorFlow Lite model exported successfully to: %s' % tflite_file)
+      'TensorFlow Lite model exported successfully to: %s' % tflite_file
+  )
 
 
 class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
   """Applies a warmup schedule on a given learning rate decay schedule."""
 
-  def __init__(self,
-               initial_learning_rate: float,
-               decay_schedule_fn: Callable[[Any], Any],
-               warmup_steps: int,
-               name: Optional[str] = None):
+  def __init__(
+      self,
+      initial_learning_rate: float,
+      decay_schedule_fn: Callable[[Any], Any],
+      warmup_steps: int,
+      name: Optional[str] = None,
+  ):
     """Initializes a new instance of the `WarmUp` class.
 
     Args:
@@ -239,14 +247,15 @@ class WarmUp(tf.keras.optimizers.schedules.LearningRateSchedule):
           global_step_float < warmup_steps_float,
           lambda: warmup_learning_rate,
           lambda: self.decay_schedule_fn(step),
-          name=name)
+          name=name,
+      )
 
   def get_config(self) -> Dict[str, Any]:
     return {
         'initial_learning_rate': self.initial_learning_rate,
         'decay_schedule_fn': self.decay_schedule_fn,
         'warmup_steps': self.warmup_steps,
-        'name': self.name
+        'name': self.name,
     }
 
 
@@ -280,7 +289,8 @@ class LiteRunner(object):
     """
 
     if not isinstance(input_tensors, list) and not isinstance(
-        input_tensors, dict):
+        input_tensors, dict
+    ):
       input_tensors = [input_tensors]
 
     interpreter = self.interpreter
@@ -288,19 +298,18 @@ class LiteRunner(object):
     # Reshape inputs
     for i, input_detail in enumerate(self.input_details):
       input_tensor = _get_input_tensor(
-          input_tensors=input_tensors,
-          input_details=self.input_details,
-          index=i)
+          input_tensors=input_tensors, input_details=self.input_details, index=i
+      )
       interpreter.resize_tensor_input(
-          input_index=input_detail['index'], tensor_size=input_tensor.shape)
+          input_index=input_detail['index'], tensor_size=input_tensor.shape
+      )
     interpreter.allocate_tensors()
 
     # Feed input to the interpreter
     for i, input_detail in enumerate(self.input_details):
       input_tensor = _get_input_tensor(
-          input_tensors=input_tensors,
-          input_details=self.input_details,
-          index=i)
+          input_tensors=input_tensors, input_details=self.input_details, index=i
+      )
       if input_detail['quantization'] != (DEFAULT_SCALE, DEFAULT_ZERO_POINT):
         # Quantize the input
         scale, zero_point = input_detail['quantization']
@@ -331,9 +340,11 @@ def get_lite_runner(tflite_buffer: bytearray) -> 'LiteRunner':
   return lite_runner
 
 
-def _get_input_tensor(input_tensors: Union[List[tf.Tensor], Dict[str,
-                                                                 tf.Tensor]],
-                      input_details: Dict[str, Any], index: int) -> tf.Tensor:
+def _get_input_tensor(
+    input_tensors: Union[List[tf.Tensor], Dict[str, tf.Tensor]],
+    input_details: Dict[str, Any],
+    index: int,
+) -> tf.Tensor:
   """Returns input tensor in `input_tensors` that maps `input_detail[i]`."""
   if isinstance(input_tensors, dict):
     # Gets the mapped input tensor.
@@ -341,7 +352,9 @@ def _get_input_tensor(input_tensors: Union[List[tf.Tensor], Dict[str,
     for input_tensor_name, input_tensor in input_tensors.items():
       if input_tensor_name in input_detail['name']:
         return input_tensor
-    raise ValueError('Input tensors don\'t contains a tensor that mapped the '
-                     'input detail %s' % str(input_detail))
+    raise ValueError(
+        "Input tensors don't contains a tensor that mapped the input detail %s"
+        % str(input_detail)
+    )
   else:
     return input_tensors[index]