Use class decorator instead of superclass

When supplied by Keras deserialization, the config parameter to initializers
will be a dict. So intercept it and convert to PretrainedConfig object (and
store in instance attribute for get_config to get at it) before passing to the
actual initializer. To accomplish this, and repeat as little code as possible,
use a class decorator on TF*MainLayer classes.

src/transformers/modeling_tf_bert.py

@@ -23,7 +23,7 @@ import tensorflow as tf
 
 from .configuration_bert import BertConfig
 from .file_utils import MULTIPLE_CHOICE_DUMMY_INPUTS, add_start_docstrings, add_start_docstrings_to_callable
-from .modeling_tf_utils import TFMainLayer, TFPreTrainedModel, get_initializer, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, get_initializer, keras_serializable, shape_list
 
 
 logger = logging.getLogger(__name__)
@@ -471,9 +471,10 @@ class TFBertNSPHead(tf.keras.layers.Layer):
         return seq_relationship_score
 
 
-class TFBertMainLayer(TFMainLayer):
+@keras_serializable
+class TFBertMainLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
-        super().__init__(config, **kwargs)
+        super().__init__(**kwargs)
         self.num_hidden_layers = config.num_hidden_layers
 
         self.embeddings = TFBertEmbeddings(config, name="embeddings")