code: def from_pretrained(
      cls,
      model_name_or_path,
      checkpoint_file="model.pt",
      data_name_or_path=".",
      bpe="gpt2",
      sample_break_mode="eos",
      **kwargs,
  ):
      from fairseq import hub_utils
  
      if "arch" in kwargs and not kwargs["arch"].startswith("ls_"):
          kwargs["args"] = "ls_" + kwargs["args"]
      x = hub_utils.from_pretrained(
          model_name_or_path,
          checkpoint_file,
          data_name_or_path,
          archive_map=cls.hub_models(),
          bpe=bpe,
          load_checkpoint_heads=True,
          sample_break_mode=sample_break_mode,
          **kwargs,
      )
      return BARTHubInterface(x["args"], x["task"], x["models"][0])
  
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 5 for calling the LSBARTModel obj's underlying member method code with example usage: obj.from_pretrained(cls, model_name_or_path, checkpoint_file, data_name_or_path, bpe, sample_break_mode) and returns: BARTHubInterface