code: def forward(
      self,
      tokens: torch.Tensor,
      segment_labels: torch.Tensor = None,
      positions: torch.Tensor = None,
      token_embeddings: torch.Tensor = None,
      attn_mask: torch.Tensor = None,
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      return self.eager_encoder(
          tokens,
          segment_labels,
          positions=positions,
          token_embeddings=token_embeddings,
          attn_mask=attn_mask,
      )
  
file_path: pytext/pytext/models/representations/traced_transformer_encoder.py
imports: from typing import Tuple
  import torch
  import torch.cuda
  import torch.nn
  from fairseq.modules import TransformerSentenceEncoder
  from pytext.utils.usage import log_class_usage
label_desc: Use image node_id 2 for calling the TraceableTransformerWrapper obj's underlying member method code with example usage: obj.forward(tokens, segment_labels, positions, token_embeddings, attn_mask) and returns: self