code: def original_forward(self, *args, **kwargs):
      return RoBERTa.forward(self, *args, **kwargs)
  
imports: import logging
  from typing import Dict, List, Optional, Tuple
  import torch
  from pytext import resources
  from pytext.common.constants import Stage, TORCH_VERSION
  from pytext.config import ConfigBase
  from pytext.data.roberta_tensorizer import RoBERTaTensorizer, RoBERTaTokenLevelTensorizer
  from pytext.data.tensorizers import FloatListTensorizer, LabelTensorizer, NumericLabelTensorizer, Tensorizer
  from pytext.models.bert_classification_models import NewBertModel
  from pytext.models.bert_regression_model import NewBertRegressionModel
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.model import BaseModel
  from pytext.models.module import create_module, Module
  from pytext.models.output_layers import WordTaggingOutputLayer
  from pytext.models.representations.transformer import MultiheadLinearAttention, PassthroughEncoder, PassthroughTransformer, PostEncoder, QuantizedMultiheadLinearAttention, SELFIETransformer, SentenceEncoder
  from pytext.models.representations.transformer_sentence_encoder_base import PoolingMethod, TransformerSentenceEncoderBase
  from pytext.models.utils import normalize_embeddings
  from pytext.torchscript.module import ScriptPyTextEmbeddingModuleIndex, ScriptPyTextModule, ScriptPyTextModuleWithDense
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from pytorch.text.fb.nn.modules.multihead_attention import MultiheadSelfAttention
  from pytorch.text.fb.nn.modules.transformer import DEFAULT_MAX_SEQUENCE_LENGTH, Transformer, TransformerLayer
  from torch import nn
  from torch.serialization import default_restore_location
  from .r3f_models import R3FConfigOptions, R3FPyTextMixin
label_desc: Use image node_id 2 for calling the RoBERTaR3F obj's underlying member method code with example usage: obj.original_forward() and returns: RoBERTa