src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py/TFDebertaV2Embeddings/__init__
class TFDebertaV2Embeddings:    def __init__(self, config, **kwargs):
        super().__init__(**kwargs)

        self.vocab_size = config.vocab_size
        self.type_vocab_size = config.type_vocab_size
        self.embedding_size = getattr(config, "embedding_size", config.hidden_size)
        self.hidden_size = config.hidden_size
        self.max_position_embeddings = config.max_position_embeddings
        self.position_biased_input = getattr(config, "position_biased_input", True)
        self.initializer_range = config.initializer_range
        self.embeddings_sum = tf.keras.layers.Add()
        if self.embedding_size != config.hidden_size:
            self.embed_proj = tf.keras.layers.Dense(config.hidden_size, bias=False)
        self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
        self.dropout = TFDebertaV2StableDropout(config.hidden_dropout_prob, name="dropout")