Make dummy inputs a property of TFPreTrainedModel.

transformers/modeling_tf_utils.py

@@ -52,6 +52,15 @@ class TFPreTrainedModel(tf.keras.Model):
     pretrained_model_archive_map = {}
     base_model_prefix = ""
 
+    @property
+    def dummy_inputs(self):
+        """ Dummy inputs to build the network.
+
+        Returns:
+            tf.Tensor with dummy inputs
+        """
+        return tf.constant(DUMMY_INPUTS)
+
     def __init__(self, config, *inputs, **kwargs):
         super(TFPreTrainedModel, self).__init__(*inputs, **kwargs)
         if not isinstance(config, PretrainedConfig):
@@ -265,15 +274,14 @@ class TFPreTrainedModel(tf.keras.Model):
             # Load from a PyTorch checkpoint
             return load_pytorch_checkpoint_in_tf2_model(model, resolved_archive_file)
 
-        dummy_inputs = tf.constant(DUMMY_INPUTS)  # dummy inputs to build the network
-        ret = model(dummy_inputs, training=False)  # build the network with dummy inputs
+        ret = model(model.dummy_inputs, training=False)  # build the network with dummy inputs
 
         assert os.path.isfile(resolved_archive_file), "Error retrieving file {}".format(resolved_archive_file)
         # 'by_name' allow us to do transfer learning by skipping/adding layers
         # see https://github.com/tensorflow/tensorflow/blob/00fad90125b18b80fe054de1055770cfb8fe4ba3/tensorflow/python/keras/engine/network.py#L1339-L1357
         model.load_weights(resolved_archive_file, by_name=True)
 
-        ret = model(dummy_inputs, training=False)  # Make sure restore ops are run
+        ret = model(model.dummy_inputs, training=False)  # Make sure restore ops are run
 
         return model