code: def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id):
      """Merge segments A and B, add [CLS] and [SEP] and build tokentypes."""
  
      tokens = []
      tokentypes = []
      # [CLS].
      tokens.append(cls_id)
      tokentypes.append(0)
      # Segment A.
      for token in tokens_a:
          tokens.append(token)
          tokentypes.append(0)
      # [SEP].
      tokens.append(sep_id)
      tokentypes.append(0)
      # Segment B.
      for token in tokens_b:
          tokens.append(token)
          tokentypes.append(1)
      if tokens_b:
          # [SEP].
          tokens.append(sep_id)
          tokentypes.append(1)
  
      return tokens, tokentypes
  
imports: import collections
  import os
  import subprocess
  import time
  from typing import Any
  import numpy
  import torch
  from omegaconf import OmegaConf
  from omegaconf.dictconfig import DictConfig
  from nemo.collections.nlp.data.language_modeling.megatron.base_dataset_utils import get_datasets_weights_and_num_samples, get_train_valid_test_split_
  from nemo.collections.nlp.data.language_modeling.megatron.blendable_dataset import BlendableDataset
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import deallocate_indexed_dataset_memory
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_dataset
  from nemo.collections.nlp.data.language_modeling.megatron.indexed_dataset import make_indexed_dataset_compatibility
  from nemo.collections.nlp.data.language_modeling.megatron.length_distribution_type import LengthDistribution
  from nemo.collections.nlp.data.language_modeling.megatron.lm_adapted_t5_dataset import T5LMAdaptedDataset
  from nemo.utils import logging
  from nemo.utils.get_rank import is_global_rank_zero
label_desc: Use image node_id 4 for calling a global function with example usage: create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id) and returns: tokens,  tokentypes