code: def main():
      """Runs the command-line tool"""
      # Parse command-line arguments
      parser = ArgumentParser(
          description="Command-line Grapheme-to-Phoneme conversion tool"
      )
      parser.add_argument(
          "--model",
          required=True,
          help="The path to the pretrained model",
      )
      parser.add_argument(
          "--hparams",
          help="The name of the hyperparameter file",
          default="hyperparams.yaml",
      )
      parser.add_argument("--ckpt", help="The checkpoint to use")
      parser.add_argument(
          "--ckpt-best",
          help="Use the checkpoint with the best performance",
          action="store_true",
      )
      parser.add_argument(
          "--ckpt-train-step",
          help="The train step to use when searching for checkpoints",
      )
      parser.add_argument("--text", help="The text to transcribe")
      parser.add_argument(
          "--text-file", help="the text file to transcribe"
      )
      parser.add_argument(
          "--output-file",
          help="the file to which results will be output",
      )
      parser.add_argument(
          "-i",
          "--interactive",
          help="Launches an interactive shell",
          default=False,
          action="store_true",
      )
  
      arguments, override_arguments = parser.parse_known_args()
      _, run_opts, overrides = sb.parse_arguments(
          [arguments.hparams] + override_arguments
      )
  
      # Ensure the model directory exists
      if not os.path.isdir(arguments.model):
          raise ValueError(MSG_MODEL_NOT_FOUND)
  
      # Determine the path to the hyperparameters file
      hparams_file_name = os.path.join(
          arguments.model, arguments.hparams
      )
      if not os.path.isfile(hparams_file_name):
          raise ValueError(MSG_HPARAMS_NOT_FILE)
  
      # Initialize the pretrained grapheme-to-phoneme model
      if arguments.ckpt or arguments.ckpt_best:
          g2p = load_g2p_checkpoint(
              hparams_file_name=hparams_file_name,
              path=arguments.ckpt,
              best=arguments.ckpt_best,
              train_step=arguments.ckpt_train_step,
              run_opts=run_opts,
              overrides=overrides,
          )
      else:
          g2p = GraphemeToPhoneme.from_hparams(
              hparams_file=hparams_file_name,
              source=arguments.model,
              overrides=overrides,
              run_opts=run_opts,
              savedir=arguments.model,
          )
  
      # Language model adjustments
      if getattr(g2p.hparams, "use_language_model", False):
          g2p.hparams.beam_searcher = g2p.hparams.beam_searcher_lm
  
      # Launch an interactive model
      if arguments.interactive:
          shell = InteractiveG2P(model=g2p)
          shell.cmdloop()
      # Transcribe a single line of text
      elif arguments.text:
          transcribe_text(g2p, arguments.text)
      # Transcribe a file
      elif arguments.text_file:
          transcribe_file(
              g2p=g2p,
              text_file_name=arguments.text_file,
              output_file_name=arguments.output_file,
              batch_size=g2p.hparams.eval_batch_size,
          )
      else:
          print(
              "ERROR: Either --text or --text-file is required "
              "in non-interactive mode",
              file=sys.stderr,
          )
  
imports: import itertools
  import math
  import os
  import speechbrain
  import sys
  import traceback
  from cmd import Cmd
  from argparse import ArgumentParser
  from hyperpyyaml import load_hyperpyyaml
  from speechbrain.inference.text import GraphemeToPhoneme
  from tqdm.auto import tqdm
label_desc: Use image node_id 7 for calling a global function with example usage: main() without return types