code: def get_positions(self, src_tokens, segment_labels):
      segment_positions = (
          torch.arange(src_tokens.shape[1])
          .to(src_tokens.device)
          .repeat(src_tokens.shape[0], 1)
      )
      segment_boundary = (src_tokens == self.separator_token).long()
      _, col_idx = (segment_positions * segment_boundary).nonzero(
          as_tuple=True
      )
      col_idx = torch.cat([torch.zeros(1).type_as(col_idx), col_idx])
      offset = torch.cat(
          [
              torch.zeros(1).type_as(segment_boundary),
              segment_boundary.sum(dim=1).cumsum(dim=0)[:-1],
          ]
      )
      segment_positions -= col_idx[
          segment_labels + offset.unsqueeze(1)
      ] * (segment_labels != 0)
  
      padding_mask = src_tokens.ne(self.padding_idx)
      segment_positions = (
          segment_positions + 1
      ) * padding_mask.type_as(segment_positions) + self.padding_idx
  
      return segment_positions
  
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 4 for calling the BaseRanker obj's underlying member method code with example usage: obj.get_positions(src_tokens, segment_labels) and returns: segment_positions