code: def __init__(self, args, encoder, decoder):
      super().__init__(args, encoder, decoder)
  
      self.classification_heads = nn.ModuleDict()
      if hasattr(self.encoder, "dictionary"):
          self.eos: int = self.encoder.dictionary.eos()
  
imports: import logging
  from typing import Optional
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.models import register_model, register_model_architecture
  from fairseq.models.transformer import TransformerModel
  from fairseq.models.bart import BARTHubInterface, BARTModel
  from .ls_transformer import LSTransformerModel
label_desc: Use image node_id 1 to create a new LSBARTModel object from inherited base classes: LSTransformerModel with example: obj = LSBARTModel(args, encoder, decoder)