Mark "code in the Hub" API as experimental

docs/source/custom_models.mdx

@@ -213,6 +213,12 @@ code of the model is saved.
 
 ## Sending the code to the Hub
 
+<Tip warning={true}>
+
+This API is experimental and may have some slight breaking changes in the next releases.
+
+</Tip>
+
 First, make sure your model is fully defined in a `.py` file. It can rely on relative imports to some other files as
 long as all the files are in the same directory (we don't support submodules for this feature yet). For our example,
 we'll define a `modeling_resnet.py` file and a `configuration_resnet.py` file in a folder of the current working

src/transformers/configuration_utils.py

@@ -867,6 +867,12 @@ def register_for_auto_class(cls, auto_class="AutoConfig"):
         Register this class with a given auto class. This should only be used for custom configurations as the ones in
         the library are already mapped with `AutoConfig`.
 
+        <Tip warning={true}>
+
+        This API is experimental and may have some slight breaking changes in the next releases.
+
+        </Tip>
+
         Args:
             auto_class (`str` or `type`, *optional*, defaults to `"AutoConfig"`):
                 The auto class to register this new configuration with.

src/transformers/modeling_flax_utils.py

@@ -725,6 +725,12 @@ def register_for_auto_class(cls, auto_class="FlaxAutoModel"):
         Register this class with a given auto class. This should only be used for custom models as the ones in the
         library are already mapped with an auto class.
 
+        <Tip warning={true}>
+
+        This API is experimental and may have some slight breaking changes in the next releases.
+
+        </Tip>
+
         Args:
             auto_class (`str` or `type`, *optional*, defaults to `"FlaxAutoModel"`):
                 The auto class to register this new model with.

src/transformers/modeling_tf_utils.py

@@ -2021,6 +2021,12 @@ def register_for_auto_class(cls, auto_class="TFAutoModel"):
         Register this class with a given auto class. This should only be used for custom models as the ones in the
         library are already mapped with an auto class.
 
+        <Tip warning={true}>
+
+        This API is experimental and may have some slight breaking changes in the next releases.
+
+        </Tip>
+
         Args:
             auto_class (`str` or `type`, *optional*, defaults to `"TFAutoModel"`):
                 The auto class to register this new model with.

src/transformers/modeling_utils.py

@@ -1821,6 +1821,12 @@ def register_for_auto_class(cls, auto_class="AutoModel"):
         Register this class with a given auto class. This should only be used for custom models as the ones in the
         library are already mapped with an auto class.
 
+        <Tip warning={true}>
+
+        This API is experimental and may have some slight breaking changes in the next releases.
+
+        </Tip>
+
         Args:
             auto_class (`str` or `type`, *optional*, defaults to `"AutoModel"`):
                 The auto class to register this new model with.

src/transformers/tokenization_utils_base.py

@@ -3417,6 +3417,12 @@ def register_for_auto_class(cls, auto_class="AutoTokenizer"):
         Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the
         library are already mapped with `AutoTokenizer`.
 
+        <Tip warning={true}>
+
+        This API is experimental and may have some slight breaking changes in the next releases.
+
+        </Tip>
+
         Args:
             auto_class (`str` or `type`, *optional*, defaults to `"AutoTokenizer"`):
                 The auto class to register this new tokenizer with.