Use google style to document properties (#6130)

* Use google style to document properties

* Update src/transformers/configuration_utils.py

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

src/transformers/configuration_utils.py

@@ -194,12 +194,18 @@ class PretrainedConfig(object):
                 raise err
 
     @property
-    def use_return_tuple(self):
+    def use_return_tuple(self) -> bool:
+        """
+        :obj:`bool`: Whether or not the model should return a tuple.
+        """
         # If torchscript is set, force return_tuple to avoid jit errors
         return self.return_tuple or self.torchscript
 
     @property
     def num_labels(self) -> int:
+        """
+        :obj:`int`: The number of labels for classification models.
+        """
         return len(self.id2label)
 
     @num_labels.setter

src/transformers/modeling_tf_utils.py

@@ -238,10 +238,7 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin):
     @property
     def dummy_inputs(self) -> Dict[str, tf.Tensor]:
         """
-        Dummy inputs to build the network.
+        :obj:`Dict[str, tf.Tensor]`: Dummy inputs to build the network.
-
-        Returns:
-            :obj:`Dict[str, tf.Tensor]`: The dummy inputs.
         """
         return {"input_ids": tf.constant(DUMMY_INPUTS)}
 

src/transformers/modeling_utils.py

@@ -157,10 +157,8 @@ class ModuleUtilsMixin:
     @property
     def device(self) -> device:
         """
-        The device on which the module is (assuming that all the module parameters are on the same device).
+        :obj:`torch.device`: The device on which the module is (assuming that all the module parameters are on the same
-
+        device).
-        Returns:
-            :obj:`torch.device` The device of the module.
         """
         try:
             return next(self.parameters()).device
@@ -178,10 +176,7 @@ class ModuleUtilsMixin:
     @property
     def dtype(self) -> dtype:
         """
-        The dtype of the module (assuming that all the module parameters have the same dtype).
+        :obj:`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
-
-        Returns:
-            :obj:`torch.dtype` The dtype of the module.
         """
         try:
             return next(self.parameters()).dtype
@@ -350,10 +345,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
 
     @property
     def dummy_inputs(self) -> Dict[str, torch.Tensor]:
-        """ Dummy inputs to do a forward pass in the network.
+        """
-
+        :obj:`Dict[str, torch.Tensor]`: Dummy inputs to do a forward pass in the network.
-        Returns:
-            :obj:`Dict[str, torch.Tensor]`: The dummy inputs.
         """
         return {"input_ids": torch.tensor(DUMMY_INPUTS)}
 
@@ -371,7 +364,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin):
         self.config = config
 
     @property
-    def base_model(self):
+    def base_model(self) -> nn.Module:
+        """
+        :obj:`torch.nn.Module`: The main body of the model.
+        """
         return getattr(self, self.base_model_prefix, self)
 
     def get_input_embeddings(self) -> nn.Module: