code: def __init__(
      self,
      vocab_size=50265,
      max_position_embeddings=1024,
      encoder_layers=12,
      encoder_ffn_dim=4096,
      encoder_attention_heads=16,
      decoder_layers=12,
      decoder_ffn_dim=4096,
      decoder_attention_heads=16,
      encoder_layerdrop=0.0,
      decoder_layerdrop=0.0,
      activation_function="gelu",
      d_model=1024,
      dropout=0.1,
      attention_dropout=0.0,
      activation_dropout=0.0,
      init_std=0.02,
      classifier_dropout=0.0,
      scale_embedding=False,
      use_cache=True,
      num_labels=3,
      pad_token_id=1,
      bos_token_id=0,
      eos_token_id=2,
      is_encoder_decoder=True,
      decoder_start_token_id=2,
      forced_eos_token_id=2,
      **kwargs,
  ):
      self.vocab_size = vocab_size
      self.max_position_embeddings = max_position_embeddings
      self.d_model = d_model
      self.encoder_ffn_dim = encoder_ffn_dim
      self.encoder_layers = encoder_layers
      self.encoder_attention_heads = encoder_attention_heads
      self.decoder_ffn_dim = decoder_ffn_dim
      self.decoder_layers = decoder_layers
      self.decoder_attention_heads = decoder_attention_heads
      self.dropout = dropout
      self.attention_dropout = attention_dropout
      self.activation_dropout = activation_dropout
      self.activation_function = activation_function
      self.init_std = init_std
      self.encoder_layerdrop = encoder_layerdrop
      self.decoder_layerdrop = decoder_layerdrop
      self.classifier_dropout = classifier_dropout
      self.use_cache = use_cache
      self.num_hidden_layers = encoder_layers
      self.scale_embedding = (
          scale_embedding  # scale factor will be sqrt(d_model) if True
      )
  
      super().__init__(
          num_labels=num_labels,
          pad_token_id=pad_token_id,
          bos_token_id=bos_token_id,
          eos_token_id=eos_token_id,
          is_encoder_decoder=is_encoder_decoder,
          decoder_start_token_id=decoder_start_token_id,
          forced_eos_token_id=forced_eos_token_id,
          **kwargs,
      )
  
      # ensure backward compatibility for BART CNN models
      if self.forced_bos_token_id is None and kwargs.get(
          "force_bos_token_to_be_generated", False
      ):
          self.forced_bos_token_id = self.bos_token_id
          logger.rank_zero_warning(
              f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions."
              "The config can simply be saved and uploaded again to be fixed."
          )
  
imports: from fastNLP.transformers.torch.configuration_utils import PretrainedConfig
  from fastNLP.core.log import logger
label_desc: Use image node_id 1 to create a new BartConfig object from inherited base classes: PretrainedConfig with example: obj = BartConfig(vocab_size, max_position_embeddings, encoder_layers, encoder_ffn_dim, encoder_attention_heads, decoder_layers, decoder_ffn_dim, decoder_attention_heads, encoder_layerdrop, decoder_layerdrop, activation_function, d_model, dropout, attention_dropout, activation_dropout, init_std, classifier_dropout, scale_embedding, use_cache, num_labels, pad_token_id, bos_token_id, eos_token_id, is_encoder_decoder, decoder_start_token_id, forced_eos_token_id)