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_ctrl.py

@@ -23,7 +23,7 @@ import tensorflow as tf
 
 from .configuration_ctrl import CTRLConfig
 from .file_utils import add_start_docstrings, add_start_docstrings_to_callable
-from .modeling_tf_utils import TFMainLayer, TFPreTrainedModel, TFSharedEmbeddings, shape_list
+from .modeling_tf_utils import TFPreTrainedModel, TFSharedEmbeddings, keras_serializable, shape_list
 
 
 logger = logging.getLogger(__name__)
@@ -164,9 +164,10 @@ class TFEncoderLayer(tf.keras.layers.Layer):
         return outputs
 
 
-class TFCTRLMainLayer(TFMainLayer):
+@keras_serializable
+class TFCTRLMainLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
-        super().__init__(config, **kwargs)
+        super().__init__(**kwargs)
         self.output_hidden_states = config.output_hidden_states
         self.output_attentions = config.output_attentions
         self.output_past = config.output_past