code: def forward(
      self,
      src_ids,
      src_sids,
      src_pids,
      tgt_ids,
      tgt_sids,
      tgt_pids,
      attn_ids,
      mask_src_2_src,
      mask_tgt_2_srctgt,
      mask_attn_2_srctgtattn,
      tgt_labels,
      tgt_pos,
  ):
      _, __, info = self.model(
          src_ids,
          sent_ids=src_sids,
          pos_ids=src_pids,
          attn_bias=mask_src_2_src,
          encode_only=True,
      )
      cached_k, cached_v = info["caches"]
      _, __, info = self.model(
          tgt_ids,
          sent_ids=tgt_sids,
          pos_ids=tgt_pids,
          attn_bias=mask_tgt_2_srctgt,
          past_cache=(cached_k, cached_v),
          encode_only=True,
      )
      cached_k2, cached_v2 = info["caches"]
      past_cache_k = [
          paddle.concat([k, k2], 1)
          for k, k2 in zip(cached_k, cached_k2)
      ]
      past_cache_v = [
          paddle.concat([v, v2], 1)
          for v, v2 in zip(cached_v, cached_v2)
      ]
      loss, _, __ = self.model(
          attn_ids,
          sent_ids=tgt_sids,
          pos_ids=tgt_pids,
          attn_bias=mask_attn_2_srctgtattn,
          past_cache=(past_cache_k, past_cache_v),
          tgt_labels=tgt_labels,
          tgt_pos=tgt_pos,
      )
      loss = loss.mean()
      return loss
  
imports: import paddle
  import paddle.nn
  import numpy
label_desc: Use image node_id 2 for calling the StackModel obj's underlying member method code with example usage: obj.forward(src_ids, src_sids, src_pids, tgt_ids, tgt_sids, tgt_pids, attn_ids, mask_src_2_src, mask_tgt_2_srctgt, mask_attn_2_srctgtattn, tgt_labels, tgt_pos) and returns: loss