optimum/graphcore/models/bart/modeling_bart.py
def deparallelize(self):
        """
        Undo the changes to the model done by `parallelize`.
        You should call this before doing `save_pretrained` so that the `model.state_dict` is
        fully compatible with `transformers.BartForConditionalGeneration`.
        """
        super().deparallelize()
        self.model.encoder_and_decoder_embeddings_computation(False)
        self.model.change_bart_encoder_and_decoder_classes(True)
        self.model.change_bart_attention_class(True)
        self.model.__class__ = BartModel
        return self

        Undo the changes to the model done by `parallelize`.
        You should call this before doing `save_pretrained` so that the `model.state_dict` is
        fully compatible with `transformers.BartForConditionalGeneration`.
        
Undo the changes to the model done by `parallelize`.
Undo the changes to the model done by ` parallelize ` .
def deparallelize(self):
        super().deparallelize()
        self.model.encoder_and_decoder_embeddings_computation(False)
        self.model.change_bart_encoder_and_decoder_classes(True)
        self.model.change_bart_attention_class(True)
        self.model.__class__ = BartModel
        return self
def deparallelize ( self ) : super ( ) . deparallelize ( ) self . model . encoder_and_decoder_embeddings_computation ( False ) self . model . change_bart_encoder_and_decoder_classes ( True ) self . model . change_bart_attention_class ( True ) self . model . __class__ = BartModel return self
Undo the changes to the model done by `parallelize`.
Undo the changes to the model done by ` parallelize ` .
"""
        Undo the changes to the model done by `parallelize`.
        You should call this before doing `save_pretrained` so that the `model.state_dict` is
        fully compatible with `transformers.BartForConditionalGeneration`.
        """
self