diff --git a/examples/benchmarks/TFT/libs/tft_model.py b/examples/benchmarks/TFT/libs/tft_model.py
index 658bae60f..3e6e4346e 100644
--- a/examples/benchmarks/TFT/libs/tft_model.py
+++ b/examples/benchmarks/TFT/libs/tft_model.py
@@ -721,12 +721,7 @@ class TemporalFusionTransformer(object):
         encoder_steps = self.num_encoder_steps
 
         # Inputs.
-        all_inputs = tf.keras.layers.Input(
-            shape=(
-                time_steps,
-                combined_input_size,
-            )
-        )
+        all_inputs = tf.keras.layers.Input(shape=(time_steps, combined_input_size,))
 
         unknown_inputs, known_combined_layer, obs_inputs, static_inputs = self.get_tft_embeddings(all_inputs)
 
@@ -866,10 +861,7 @@ class TemporalFusionTransformer(object):
             """Returns LSTM cell initialized with default parameters."""
             if self.use_cudnn:
                 lstm = tf.keras.layers.CuDNNLSTM(
-                    self.hidden_layer_size,
-                    return_sequences=True,
-                    return_state=return_state,
-                    stateful=False,
+                    self.hidden_layer_size, return_sequences=True, return_state=return_state, stateful=False,
                 )
             else:
                 lstm = tf.keras.layers.LSTM(
diff --git a/examples/benchmarks/TFT/tft.py b/examples/benchmarks/TFT/tft.py
index b5398e7f2..388ec7f14 100644
--- a/examples/benchmarks/TFT/tft.py
+++ b/examples/benchmarks/TFT/tft.py
@@ -234,7 +234,7 @@ class TFTModel(ModelFT):
 
         predict50 = format_score(p50_forecast, "pred", 1)
         predict90 = format_score(p90_forecast, "pred", 1)
-        predict = (predict50 + predict90)/2  # self.label_shift
+        predict = (predict50 + predict90) / 2  # self.label_shift
         # ===========================Predicting Process===========================
         return predict