class_docstr: Task to finetune RoBERTa for Winograd Schemas.
class_docstr_tok: ["Task","to","finetune","RoBERTa","for","Winograd","Schemas","."]
code: def binarize(self, s: str, append_eos: bool = False):
      if self.tokenizer is not None:
          s = self.tokenizer.encode(s)
      if self.bpe is not None:
          s = self.bpe.encode(s)
      tokens = self.vocab.encode_line(
          s,
          append_eos=append_eos,
          add_if_not_exist=False,
      ).long()
      if self.args.init_token is not None:
          tokens = torch.cat(
              [tokens.new([self.args.init_token]), tokens]
          )
      return tokens
  
imports: import json
  import os
  import tempfile
  import numpy
  import torch
  import torch.nn.functional
  from fairseq import utils
  from fairseq.data import Dictionary, IdDataset, ListDataset, NestedDictionaryDataset, NumelDataset, NumSamplesDataset, PadDataset, SortDataset, data_utils, encoders
  from fairseq.tasks import LegacyFairseqTask, register_task
  from .None import wsc_utils
label_desc: Use image node_id 5 for calling the WSCTask obj's underlying member method code with example usage: obj.binarize(s, append_eos) and returns: tokens