code: def forward(self, x):
      batchsize, seq_len = paddle.shape(x)[:2]
      input_ids = paddle.zeros((batchsize, seq_len), dtype="int64")
      position_ids = (
          paddle.arange(seq_len, dtype="int64")
          .unsqueeze(0)
          .expand_as(input_ids)
      )
  
      attention_mask = (
          paddle.ones_like(input_ids).unsqueeze(1).unsqueeze(2)
      )
      head_mask = [None] * self.num_hidden_layers
  
      position_embs = self.position_embeddings(position_ids)
      attention_mask = (1.0 - attention_mask) * -10000.0
  
      img_features = self.img_embedding(x)
  
      # We empirically observe that adding an additional learnable position embedding leads to more stable training
      embeddings = position_embs + img_features
      if self.use_img_layernorm:
          embeddings = self.layernorm(embeddings)
      embeddings = self.dropout(embeddings)
  
      encoder_outputs = self.encoder(
          embeddings, attention_mask, head_mask=head_mask
      )
  
      pred_score = self.cls_head(encoder_outputs[0])
      res_img_feats = self.residual(x)
      pred_score = pred_score + res_img_feats
  
      if self.output_attentions and self.output_hidden_feats:
          return pred_score, encoder_outputs[1], encoder_outputs[-1]
      else:
          return pred_score
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import ReLU, Swish, GELU
  import math
  from ppdet.core.workspace import register
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 3 for calling the METROEncoder obj's underlying member method code with example usage: obj.forward(x) and returns: pred_score,  encoder_outputs,  encoder_outputs, pred_score