src/transformers/models/deit/modeling_deit.py/DeiTEmbeddings/forward
class DeiTEmbeddings:    def forward(self, pixel_values):
        batch_size = pixel_values.shape[0]
        embeddings = self.patch_embeddings(pixel_values)

        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        distillation_tokens = self.distillation_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, distillation_tokens, embeddings), dim=1)
        embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings