src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py/RobertaPreLayerNormForCausalLM/__init__
class RobertaPreLayerNormForCausalLM:    def __init__(self, config):
        super().__init__(config)

        if not config.is_decoder:
            logger.warning(
                "If you want to use `RobertaPreLayerNormLMHeadModel` as a standalone, add `is_decoder=True.`"
            )

        self.roberta_prelayernorm = RobertaPreLayerNormModel(config, add_pooling_layer=False)
        self.lm_head = RobertaPreLayerNormLMHead(config)

        # The LM head weights require special treatment only when they are tied with the word embeddings
        self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])

        # Initialize weights and apply final processing
        self.post_init()