code: def _prefix_task_embedding_to_encoder_inputs(
      self, img_src, img_mask, img_pos, task_idx
  ):
      if not self.args.use_task_embedding_in_img_encoder:
          return img_src, img_mask, img_pos
  
      bs = img_src.size(1)
      task_embed = self.task_embeddings_enc.weight[task_idx]
      task_embed = task_embed.unsqueeze(0).unsqueeze(0).repeat(1, bs, 1)
      img_src = torch.cat([task_embed, img_src], dim=0)
  
      # 0 for non-padding in img_mask
      img_mask_pad = torch.zeros_like(img_mask[:, :1])
      img_mask = torch.cat([img_mask_pad, img_mask], dim=1)
      img_pos_pad = torch.zeros_like(img_pos[:1])
      img_pos = torch.cat([img_pos_pad, img_pos], dim=0)
  
      return img_src, img_mask, img_pos
  
imports: import copy
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
label_desc: Use image node_id 4 for calling the UniTTransformer obj's underlying member method code with example usage: obj._prefix_task_embedding_to_encoder_inputs(img_src, img_mask, img_pos, task_idx) and returns: img_src,  img_mask,  img_pos, img_src,  img_mask,  img_pos