src/transformers/models/deberta/modeling_deberta.py/DebertaEmbeddings/forward
class DebertaEmbeddings:    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]

        if position_ids is None:
            position_ids = self.position_ids[:, :seq_length]

        if token_type_ids is None:
            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)

        if inputs_embeds is None:
            inputs_embeds = self.word_embeddings(input_ids)

        if self.position_embeddings is not None:
            position_embeddings = self.position_embeddings(position_ids.long())
        else:
            position_embeddings = torch.zeros_like(inputs_embeds)

        embeddings = inputs_embeds
        if self.position_biased_input:
            embeddings += position_embeddings
        if self.config.type_vocab_size > 0:
            token_type_embeddings = self.token_type_embeddings(token_type_ids)
            embeddings += token_type_embeddings

        if self.embedding_size != self.config.hidden_size:
            embeddings = self.embed_proj(embeddings)

        embeddings = self.LayerNorm(embeddings)

        if mask is not None:
            if mask.dim() != embeddings.dim():
                if mask.dim() == 4:
                    mask = mask.squeeze(1).squeeze(1)
                mask = mask.unsqueeze(2)
            mask = mask.to(embeddings.dtype)

            embeddings = embeddings * mask

        embeddings = self.dropout(embeddings)
        return embeddings