code: def load_g2p_checkpoint(
      hparams_file_name,
      path=None,
      best=False,
      train_step=None,
      run_opts=None,
      overrides=None,
  ):
      """Loads a G2P model from a checkpoint using the specified
      criteria
  
      Arguments
      ---------
      hparams_file_name: str
          the path to the hyperparameters file
      run_opts: dict
          the run options
      path: str
          the path to the specified checkpoint
      best: bool
          whether to use the best checkpoint
      train_step: str
          whether or not to use checkpoints only from a specific
          training step
      run_opts: dict
          the run options
      overrides: dict
          command-line parameters to overrde the ones supplied
          in hparams
  
      Returns
      -------
      g2p: speechbrain.inference.text.GraphemeToPhoneme
          a pretrained G2P model, initialized from a checkpoint
      """
      with open(hparams_file_name) as hparams_file:
          hparams = load_hyperpyyaml(hparams_file, overrides)
      checkpointer = hparams.get("checkpointer")
      if checkpointer is None:
          raise ValueError(
              f"Unable to use hparams - {hparams_file_name} - no checkpointer found"
          )
      if best:
          ckpt = checkpointer.recover_if_possible(
              min_key="PER",
              ckpt_predicate=(
                  (lambda ckpt: ckpt.meta.get("step") == train_step)
                  if train_step is not None
                  else None
              ),
          )
          if not ckpt and train_step == "homograph":
              ckpt = checkpointer.recover_if_possible(
                  min_key="PER_homograph",
                  ckpt_predicate=(
                      (lambda ckpt: ckpt.meta.get("step") == train_step)
                      if train_step is not None
                      else None
                  ),
              )
      elif path:
          ckpt = checkpointer.recover_if_possible(
              ckpt_predicate=lambda ckpt: ckpt.path == path
          )
      else:
          ckpt = checkpointer.recover_if_possible()
  
      if ckpt is None:
          raise ValueError("Checkpoint not found")
  
      print(f"Using checkpoint {ckpt.path} with metadata {ckpt.meta}")
  
      return GraphemeToPhoneme(
          hparams=hparams, modules=hparams["modules"], run_opts=run_opts
      )
  
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 6 for calling a global function with example usage: load_g2p_checkpoint(hparams_file_name, path, best, train_step, run_opts, overrides) and returns: GraphemeToPhoneme