code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default="wikitext",
          help="Name of the training. Explore datasets at: hf.co/datasets.",
      )
      parser.add_argument(
          "--dataset_config",
          type=str,
          default="wikitext-103-raw-v1",
          help="Configuration name of the dataset.",
      )
      parser.add_argument(
          "--tokenizer_name_or_path",
          type=str,
          default="sayakpaul/unigram-tokenizer-wikitext",
          help="Tokenizer identifier. Can be a local filepath or a Hub identifier.",
      )
      parser.add_argument(
          "--shard_size",
          type=int,
          default=1000,
          help="Number of entries to go in a single shard.",
      )
      parser.add_argument(
          "--split",
          type=str,
          default="train",
          choices=["train", "test", "validation"],
      )
      parser.add_argument(
          "--limit",
          default=None,
          type=int,
          help="Limit the number of shards (used for debugging).",
      )
      parser.add_argument(
          "--max_length",
          type=int,
          default=512,
          help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
          " sequence length that is a multiple of 8.",
      )
      parser.add_argument(
          "--output_dir",
          default="tf-tpu",
          type=str,
          help="Output directory where the TFRecord shards will be saved. If the"
          " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
          " shards will be directly saved to a Google Cloud Storage bucket.",
      )
  
      args = parser.parse_args()
      return args
  
file_path: transformers/examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py
imports: import argparse
  import logging
  import os
  import datasets
  import tensorflow
  from transformers import AutoTokenizer
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args