code: def main(args):
      dataset = datasets.load_dataset(
          args.dataset_name, args.dataset_config, split="train"
      )
  
      if args.limit is not None:
          max_train_samples = min(len(dataset), args.limit)
          dataset = dataset.select(range(max_train_samples))
          logger.info(f"Limiting the dataset to {args.limit} entries.")
  
      def batch_iterator():
          for i in range(0, len(dataset), args.batch_size):
              yield dataset[i : i + args.batch_size]["text"]
  
      # Prepare the tokenizer.
      tokenizer = Tokenizer(Unigram())
      tokenizer.normalizer = normalizers.Sequence(
          [
              normalizers.Replace("``", '"'),
              normalizers.Replace("''", '"'),
          ]
      )
      tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()
  
      # Prepare the trainer.
      trainer = UnigramTrainer(
          unk_token="<unk>",
          special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"],
          vocab_size=args.vocab_size,
      )
  
      logger.info("Training the tokenizer.")
      tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
      logger.info("Tokenizer training complete!")
  
      cls_token_id = tokenizer.token_to_id("[CLS]")
      sep_token_id = tokenizer.token_to_id("[SEP]")
      tokenizer.post_processor = processors.TemplateProcessing(
          single="[CLS]:0 $A:0 [SEP]:0",
          pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
          special_tokens=[
              ("[CLS]", cls_token_id),
              ("[SEP]", sep_token_id),
          ],
      )
      tokenizer.decoder = decoders.Metaspace()
  
      if args.export_to_hub:
          logger.info("Exporting the trained tokenzier to Hub.")
          new_tokenizer = AlbertTokenizerFast(
              tokenizer_object=tokenizer
          )
          new_tokenizer.push_to_hub("unigram-tokenizer-dataset")
  
file_path: transformers/examples/tensorflow/language-modeling-tpu/train_unigram.py
imports: import argparse
  import logging
  import datasets
  from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
  from tokenizers.models import Unigram
  from tokenizers.trainers import UnigramTrainer
  from transformers import AlbertTokenizerFast
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types