code: def build_tokens_types_paddings_from_text(
      src_text, tokenizer, max_seq_length
  ):
      """Build token types and paddings, trim if needed, and pad if needed."""
  
      src_text_ids = tokenizer.tokenize(src_text)
  
      return build_tokens_types_paddings_from_ids(
          src_text_ids,
          max_seq_length,
          tokenizer.cls,
          tokenizer.sep,
          tokenizer.pad,
      )
  
imports: from abc import ABC
  import csv
  from collections import OrderedDict
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from torch.utils.data import Dataset, BatchSampler
  from megatron import print_rank_0, get_args, get_tokenizer
  from megatron.data.biencoder_dataset_utils import make_attention_mask
label_desc: Use image node_id 4 for calling a global function with example usage: build_tokens_types_paddings_from_text(src_text, tokenizer, max_seq_length) and returns: build_tokens_types_paddings_from_ids