Fix all sphynx warnings (#5068)

src/transformers/modeling_utils.py

@@ -530,6 +530,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin):
             config: (`optional`) one of:
                 - an instance of a class derived from :class:`~transformers.PretrainedConfig`, or
                 - a string valid as input to :func:`~transformers.PretrainedConfig.from_pretrained()`
+
                 Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
                     - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                     - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.

src/transformers/pipelines.py

@@ -323,6 +323,7 @@ class Pipeline(_ScikitCompat):
 
     Base class implementing pipelined operations.
     Pipeline workflow is defined as a sequence of the following operations:
+
         Input -> Tokenization -> Model Inference -> Post-Processing (Task dependent) -> Output
 
     Pipeline supports running on CPU or GPU through the device argument. Users can specify

src/transformers/tokenization_auto.py

@@ -103,6 +103,7 @@ class AutoTokenizer:
         The `from_pretrained()` method takes care of returning the correct tokenizer class instance
         based on the `model_type` property of the config object, or when it's missing,
         falling back to using pattern matching on the `pretrained_model_name_or_path` string:
+
             - `t5`: T5Tokenizer (T5 model)
             - `distilbert`: DistilBertTokenizer (DistilBert model)
             - `albert`: AlbertTokenizer (ALBERT model)
@@ -136,6 +137,7 @@ class AutoTokenizer:
         The tokenizer class to instantiate is selected
         based on the `model_type` property of the config object, or when it's missing,
         falling back to using pattern matching on the `pretrained_model_name_or_path` string:
+
             - `t5`: T5Tokenizer (T5 model)
             - `distilbert`: DistilBertTokenizer (DistilBert model)
             - `albert`: AlbertTokenizer (ALBERT model)