code: def create_training_instance(self, instance):
      is_next = 1
      raw_text_list = self.get_new_segment(instance)
      tokens_a = raw_text_list
      assert len(tokens_a) == len(instance)
      # tokens_a, tokens_b, is_next = instance.get_values()
      # print(f'is_next label:{is_next}')
      # Create mapper
      tokens = []
      original_tokens = []
      segment_ids = []
      tokens.append("[CLS]")
      original_tokens.append("[CLS]")
      segment_ids.append(0)
      for index, token in enumerate(tokens_a):
          tokens.append(token)
          original_tokens.append(instance[index])
          segment_ids.append(0)
  
      tokens.append("[SEP]")
      original_tokens.append("[SEP]")
      segment_ids.append(0)
  
      # for token in tokens_b:
      #     tokens.append(token)
      #     segment_ids.append(1)
  
      # tokens.append("[SEP]")
      # segment_ids.append(1)
  
      # Get Masked LM predictions
      if self.backend == "c++":
          (
              output_tokens,
              masked_lm_output,
          ) = mask.create_whole_masked_lm_predictions(
              tokens,
              original_tokens,
              self.vocab_words,
              self.tokenizer.vocab,
              self.max_predictions_per_seq,
              self.masked_lm_prob,
          )
      elif self.backend == "python":
          (
              output_tokens,
              masked_lm_output,
          ) = self.create_whole_masked_lm_predictions(tokens)
  
      # Convert to Ids
      input_ids = self.tokenizer.convert_tokens_to_ids(output_tokens)
      input_mask = [1] * len(input_ids)
  
      while len(input_ids) < self.max_seq_length:
          input_ids.append(PAD)
          segment_ids.append(PAD)
          input_mask.append(PAD)
          masked_lm_output.append(-1)
      return [
          map_to_numpy(input_ids),
          map_to_numpy(input_mask),
          map_to_numpy(segment_ids),
          map_to_numpy(masked_lm_output),
          map_to_numpy([is_next]),
      ]
  
file_path: colossalai/examples/community/roberta/preprocessing/get_mask.py
imports: import collections
  import logging
  import random
  import jieba
  import re
  import mask
  import numpy
label_desc: Use image node_id 3 for calling the PreTrainingDataset obj's underlying member method code with example usage: obj.create_training_instance(instance) and returns: list