Reformat (#9482)

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -777,6 +777,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
 
         return outputs
     
+    # Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
     def serving_output(self, output):
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
@@ -885,15 +886,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelca
             attentions=outputs.attentions,
         )
     
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
     def serving_output(self, output):
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
 
-        return TFMaskedLMOutput(
-            logits=output.logits,
-            hidden_states=hs,
-            attentions=attns,
-        )
+        return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
 
 @add_start_docstrings(
     """{{cookiecutter.modelname}} Model with a `language modeling` head on top for CLM fine-tuning. """, {{cookiecutter.uppercase_modelname}}_START_DOCSTRING
@@ -993,15 +991,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelca
             attentions=outputs.attentions,
         )
     
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output
     def serving_output(self, output):
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
 
-        return TFCausalLMOutput(
-            logits=output.logits,
-            hidden_states=hs,
-            attentions=attns,
-        )
+        return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
 
 class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -1114,15 +1109,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookie
             attentions=outputs.attentions,
         )
     
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
     def serving_output(self, output):
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
 
-        return TFSequenceClassifierOutput(
-            logits=output.logits,
-            hidden_states=hs,
-            attentions=attns,
-        )
+        return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
 
 
 @add_start_docstrings(
@@ -1258,15 +1250,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.c
         
         return self.serving_output(output)
     
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output
     def serving_output(self, output):
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
 
-        return TFMultipleChoiceModelOutput(
-            logits=output.logits,
-            hidden_states=hs,
-            attentions=attns,
-        )
+        return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
 
 
 @add_start_docstrings(
@@ -1357,15 +1346,12 @@ class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecut
             attentions=outputs.attentions,
         )
     
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output
     def serving_output(self, output):
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
 
-        return TFTokenClassifierOutput(
-            logits=output.logits,
-            hidden_states=hs,
-            attentions=attns,
-        )
+        return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
 
 
 @add_start_docstrings(
@@ -1470,15 +1456,13 @@ class TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutte
             attentions=outputs.attentions,
         )
     
+    # Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
     def serving_output(self, output):
         hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
         attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
         
         return TFQuestionAnsweringModelOutput(
-            start_logits=output.start_logits,
-            end_logits=output.end_logits,
-            hidden_states=hs,
-            attentions=attns,
+            start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
         )
 
 {% else %}
@@ -2454,6 +2438,7 @@ class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_mod
             encoder_attentions=inputs["encoder_outputs"].attentions,
         )
     
+    # Copied from transformers.models.bart.modeling_tf_bart.TFBartModel.serving_output
     def serving_output(self, output):
         pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
@@ -2616,6 +2601,7 @@ class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiec
             encoder_attentions=outputs.encoder_attentions,  # 2 of e out
         )
     
+    # Copied from transformers.models.bart.modeling_tf_bart.TFBartForConditionalGeneration.serving_output
     def serving_output(self, output):
         pkv = tf.tuple(output.past_key_values)[1] if self.config.use_cache else None,
         dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None