Apply style

src/transformers/modeling_tf_albert.py

@@ -475,12 +475,16 @@ class TFAlbertMainLayer(tf.keras.layers.Layer):
 
         self.embeddings = TFAlbertEmbeddings(config, name="embeddings")
         self.encoder = TFAlbertTransformer(config, name="encoder")
-        self.pooler = tf.keras.layers.Dense(
-            config.hidden_size,
-            kernel_initializer=get_initializer(config.initializer_range),
-            activation="tanh",
-            name="pooler",
-        ) if add_pooling_layer else None
+        self.pooler = (
+            tf.keras.layers.Dense(
+                config.hidden_size,
+                kernel_initializer=get_initializer(config.initializer_range),
+                activation="tanh",
+                name="pooler",
+            )
+            if add_pooling_layer
+            else None
+        )
 
     def get_input_embeddings(self):
         return self.embeddings

src/transformers/modeling_tf_bert.py

@@ -1025,7 +1025,7 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
     BERT_START_DOCSTRING,
 )
 class TFBertForNextSentencePrediction(TFBertPreTrainedModel):
-   def __init__(self, config, *inputs, **kwargs):
+    def __init__(self, config, *inputs, **kwargs):
         super().__init__(config, *inputs, **kwargs)
 
         self.bert = TFBertMainLayer(config, name="bert")

src/transformers/modeling_tf_utils.py

@@ -23,8 +23,8 @@ from typing import Dict, List, Optional, Union
 import h5py
 import numpy as np
 import tensorflow as tf
-from tensorflow.python.keras.saving import hdf5_format
 from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.saving import hdf5_format
 
 from .configuration_utils import PretrainedConfig
 from .file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url