code: def forward(self, src_tokens, src_lengths):
      segment_labels = self.get_segment_labels(src_tokens)
      positions = self.get_positions(src_tokens, segment_labels)
  
      inner_states, _ = self.model(
          tokens=src_tokens,
          segment_labels=segment_labels,
          last_state_only=True,
          positions=positions,
      )
  
      return inner_states[-1].transpose(0, 1)
  
imports: from dataclasses import dataclass, field
  import os
  import torch
  import torch.nn
  from fairseq import utils
  from fairseq.dataclass import ChoiceEnum, FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from fairseq.models.roberta.model import RobertaClassificationHead
  from fairseq.modules import LayerNorm, TransformerSentenceEncoder, TransformerSentenceEncoderLayer
label_desc: Use image node_id 2 for calling the BertRanker obj's underlying member method code with example usage: obj.forward(src_tokens, src_lengths) and returns: inner_states