code: def forward(
      self,
      img_src: Optional[Tensor] = None,
      img_mask: Optional[Tensor] = None,
      img_pos: Optional[Tensor] = None,
      text_src: Optional[Tensor] = None,
      text_mask: Optional[Tensor] = None,
      text_pos: Optional[Tensor] = None,
      query_embed: Optional[Tensor] = None,
      task_type: Optional[str] = None,
      dataset_name: Optional[str] = None,
      task_idx: Optional[int] = None,
  ):
      # flatten NxCxHxW to HWxNxC
      memories = []
      pos_embeds = []
      masks = []
  
      if img_src is not None:
          bs, c, h, w = img_src.shape
          img_src = img_src.flatten(2).permute(2, 0, 1)
          img_pos = img_pos.flatten(2).permute(2, 0, 1)
          img_mask = img_mask.flatten(1)
          if text_src is None:
              query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
              if self.pass_pos_and_query:
                  tgt = torch.zeros_like(query_embed)
              else:
                  img_src, tgt, query_embed, img_pos = (
                      img_src + 0.1 * img_pos,
                      query_embed,
                      None,
                      None,
                  )
          (
              img_src,
              img_mask,
              img_pos,
          ) = self._prefix_task_embedding_to_encoder_inputs(
              img_src, img_mask, img_pos, task_idx
          )
          memory = self.encoder(
              img_src, src_key_padding_mask=img_mask, pos=img_pos
          )
  
          if self.mem_out_begin_idx != 0:
              img_src = img_src[self.mem_out_begin_idx :]
              img_pos = img_pos[self.mem_out_begin_idx :]
              img_mask = img_mask[:, self.mem_out_begin_idx :]
              memory = memory[self.mem_out_begin_idx :]
  
          if self.args.residual_in_encoder:
              memory = img_src + memory
  
          memory = self.enc2dec_proj(memory)
          img_pos = self.pos_embed_proj(img_pos)
          memories.append(memory)
          pos_embeds.append(img_pos)
          masks.append(img_mask)
  
      if text_src is not None:
          text_src = text_src.permute(1, 0, 2)
          memories.append(text_src)
          text_pos = text_pos.unsqueeze(1).repeat(
              1, text_src.size(1), 1
          )
          pos_embeds.append(text_pos)
          masks.append(text_mask != 1)
  
          query_embed = query_embed.unsqueeze(1).repeat(
              1, text_src.size(1), 1
          )
          if self.pass_pos_and_query:
              tgt = torch.zeros_like(query_embed)
          else:
              raise NotImplementedError()
  
      decoder = self.decoders[task_type][dataset_name]
  
      memories = torch.cat(memories)
      masks = torch.cat(masks, dim=-1)
      pos_embeds = torch.cat(pos_embeds)
  
      hs = decoder(
          tgt,
          memories,
          memory_key_padding_mask=masks,
          pos=pos_embeds,
          query_pos=query_embed,
      )
      hs = hs.transpose(1, 2)
      # hs is num_layer x batch_size x seq_length x hidden_dim
  
      return hs, memories.permute(1, 2, 0)
  
imports: import copy
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
label_desc: Use image node_id 3 for calling the UniTTransformer obj's underlying member method code with example usage: obj.forward(img_src, img_mask, img_pos, text_src, text_mask, text_pos, query_embed, task_type, dataset_name, task_idx) and returns: hs,  memories