code: def from_pretrained(
      cls,
      model_name_or_path,
      checkpoint_file="model.pt",
      data_name_or_path=".",
      bpe="gpt2",
      **kwargs,
  ):
      from fairseq import hub_utils
  
      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,
          **kwargs,
      )
  
      logger.info(x["args"])
      return RobertaHubInterface(x["args"], x["task"], x["models"][0])
  
imports: import logging
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderModel, register_model, register_model_architecture
  from fairseq.models.transformer import DEFAULT_MIN_PARAMS_TO_WRAP, TransformerEncoder
  from fairseq.modules import LayerNorm
  from fairseq.modules.quant_noise import quant_noise
  from fairseq.modules.transformer_sentence_encoder import init_bert_params
  from fairseq.utils import safe_getattr, safe_hasattr
  from .hub_interface import RobertaHubInterface
label_desc: Use image node_id 11 for calling the RobertaModel obj's underlying member method code with example usage: obj.from_pretrained(cls, model_name_or_path, checkpoint_file, data_name_or_path, bpe) and returns: RobertaHubInterface