code: def __init__(self, args):
      super().__init__(args=args)
  
      num_queries = self.args.num_queries
      self.decoders = nn.ModuleDict()
      for task in num_queries:
          task_dict = nn.ModuleDict()
          for dataset in num_queries[task]:
              if self.share_decoders:
                  task_dict[dataset] = self.decoder
              else:
                  task_dict[dataset] = self.build_decoder_layer(
                      d_model_dec=self.d_model_dec,
                      nhead=self.nhead,
                      dim_feedforward=self.dim_feedforward,
                      dropout=self.dropout,
                      activation=self.activation,
                      normalize_before=self.normalize_before,
                      num_decoder_layers=self.num_decoder_layers,
                      return_intermediate_dec=self.return_intermediate_dec,
                  )
          self.decoders[task] = task_dict
          # A separate decoder for VQA
  
      MAX_TASK_NUM = 256
      if args.use_task_embedding_in_img_encoder:
          self.task_embeddings_enc = nn.Embedding(
              MAX_TASK_NUM, self.d_model_enc
          )
      # when adding the task embedding to the beginning of the decoder, we'll strip
      # it from the hidden state outputs to make it compatible with previous models
      self.mem_out_begin_idx = (
          1 if args.use_task_embedding_in_img_encoder else 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 1 to create a new UniTTransformer object from inherited base classes: Transformer with example: obj = UniTTransformer(args)