code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Train a masked language model on TPU."
      )
      parser.add_argument(
          "--pretrained_model_config",
          type=str,
          default="roberta-base",
          help="The model config to use. Note that we don't copy the model's weights, only the config!",
      )
      parser.add_argument(
          "--tokenizer",
          type=str,
          default="unigram-tokenizer-wikitext",
          help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size.",
      )
  
      parser.add_argument(
          "--per_replica_batch_size",
          type=int,
          default=8,
          help="Batch size per TPU core.",
      )
  
      parser.add_argument(
          "--no_tpu",
          action="store_true",
          help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances.",
      )
  
      parser.add_argument(
          "--tpu_name",
          type=str,
          help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs.",
          default="local",
      )
  
      parser.add_argument(
          "--tpu_zone",
          type=str,
          help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.",
      )
  
      parser.add_argument(
          "--gcp_project",
          type=str,
          help="Google cloud project name. Only used for non-Colab TPU nodes.",
      )
  
      parser.add_argument(
          "--bfloat16",
          action="store_true",
          help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.",
      )
  
      parser.add_argument(
          "--train_dataset",
          type=str,
          help="Path to training dataset to load. If the path begins with `gs://`"
          " then the dataset will be loaded from a Google Cloud Storage bucket.",
      )
  
      parser.add_argument(
          "--shuffle_buffer_size",
          type=int,
          default=2
          ** 18,  # Default corresponds to a 1GB buffer for seq_len 512
          help="Size of the shuffle buffer (in samples)",
      )
  
      parser.add_argument(
          "--eval_dataset",
          type=str,
          help="Path to evaluation dataset to load. If the path begins with `gs://`"
          " then the dataset will be loaded from a Google Cloud Storage bucket.",
      )
  
      parser.add_argument(
          "--num_epochs",
          type=int,
          default=1,
          help="Number of epochs to train for.",
      )
  
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=1e-4,
          help="Learning rate to use for training.",
      )
  
      parser.add_argument(
          "--weight_decay_rate",
          type=float,
          default=1e-3,
          help="Weight decay rate to use for training.",
      )
  
      parser.add_argument(
          "--max_length",
          type=int,
          default=512,
          help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py",
      )
  
      parser.add_argument(
          "--mlm_probability",
          type=float,
          default=0.15,
          help="Fraction of tokens to mask during training.",
      )
  
      parser.add_argument(
          "--output_dir",
          type=str,
          required=True,
          help="Path to save model checkpoints to.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          help="Model ID to upload to on the Hugging Face Hub.",
      )
  
      args = parser.parse_args()
      return args
  
file_path: transformers/examples/tensorflow/language-modeling-tpu/run_mlm.py
imports: import argparse
  import logging
  import os
  import re
  import tensorflow
  from transformers import AutoConfig, AutoTokenizer, DataCollatorForLanguageModeling, PushToHubCallback, TFAutoModelForMaskedLM, create_optimizer
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args