code: def __init__(self, args):
      super().__init__()
  
      self.args = args
      self.d_model_enc = args.encoder_hidden_dim
      self.d_model_dec = args.decoder_hidden_dim
      self.dropout = args.dropout
      self.nhead = args.nheads
      self.dim_feedforward = args.dim_feedforward
      self.num_encoder_layers = args.enc_layers
      self.num_decoder_layers = args.dec_layers
      self.normalize_before = args.pre_norm
      self.return_intermediate_dec = True
      self.pass_pos_and_query = args.pass_pos_and_query
      self.share_decoders = args.share_decoders
      self.activation = "relu"
  
      self.pass_pos_and_query = self.pass_pos_and_query
      encoder_layer = TransformerEncoderLayer(
          self.d_model_enc,
          self.nhead,
          self.dim_feedforward,
          self.dropout,
          self.activation,
          self.normalize_before,
      )
      encoder_norm = (
          nn.LayerNorm(self.d_model_enc)
          if self.normalize_before
          else None
      )
      self.encoder = TransformerEncoder(
          encoder_layer, self.num_encoder_layers, encoder_norm
      )
  
      if self.d_model_dec != self.d_model_enc:
          self.enc2dec_proj = nn.Linear(
              self.d_model_enc, self.d_model_dec
          )
          self.pos_embed_proj = nn.Linear(
              self.d_model_enc, self.d_model_dec
          )
      else:
          self.enc2dec_proj = nn.Identity()
          self.pos_embed_proj = nn.Identity()
  
      if self.share_decoders:
          decoder_layer = TransformerDecoderLayer(
              self.d_model_dec,
              self.nhead,
              self.dim_feedforward,
              self.dropout,
              self.activation,
              self.normalize_before,
          )
          decoder_norm = nn.LayerNorm(self.d_model_dec)
          self.decoder = TransformerDecoder(
              decoder_layer,
              self.num_decoder_layers,
              decoder_norm,
              return_intermediate=self.return_intermediate_dec,
          )
  
      self._reset_parameters()
  
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 Transformer object from inherited base classes: nn with example: obj = Transformer(args)