fix bug: register_for_auto_class should be defined on TFPreTrainedModel...

fix bug: register_for_auto_class should be defined on TFPreTrainedModel instead of TFSequenceSummary (#18607)

src/transformers/modeling_tf_utils.py

@@ -2541,6 +2541,32 @@ class TFPreTrainedModel(tf.keras.Model, TFModelUtilsMixin, TFGenerationMixin, Pu
                 work_dir, repo_id, files_timestamps, commit_message=commit_message, token=token
             )
 
+    @classmethod
+    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.
+        """
+        if not isinstance(auto_class, str):
+            auto_class = auto_class.__name__
+
+        import transformers.models.auto as auto_module
+
+        if not hasattr(auto_module, auto_class):
+            raise ValueError(f"{auto_class} is not a valid auto class.")
+
+        cls._auto_class = auto_class
+
 
 class TFConv1D(tf.keras.layers.Layer):
     """
@@ -2795,32 +2821,6 @@ class TFSequenceSummary(tf.keras.layers.Layer):
 
         return output
 
-    @classmethod
-    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.
-        """
-        if not isinstance(auto_class, str):
-            auto_class = auto_class.__name__
-
-        import transformers.models.auto as auto_module
-
-        if not hasattr(auto_module, auto_class):
-            raise ValueError(f"{auto_class} is not a valid auto class.")
-
-        cls._auto_class = auto_class
-
 
 def get_initializer(initializer_range: float = 0.02) -> tf.initializers.TruncatedNormal:
     """