src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py/TFDebertaV2ForMaskedLM/__init__
class TFDebertaV2ForMaskedLM:    def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
        super().__init__(config, *inputs, **kwargs)

        if config.is_decoder:
            logger.warning(
                "If you want to use `TFDebertaV2ForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.deberta = TFDebertaV2MainLayer(config, name="deberta")
        self.mlm = TFDebertaV2OnlyMLMHead(config, input_embeddings=self.deberta.embeddings, name="cls")