code: def process_sentence(
      self,
      sample: Dict[str, Any],
      hypo: Dict[str, Any],
      sid: int,
      batch_id: int,
  ) -> Tuple[int, int]:
      speaker = None  # Speaker can't be parsed from dataset.
  
      if "target_label" in sample:
          toks = sample["target_label"]
      else:
          toks = sample["target"]
      toks = toks[batch_id, :]
  
      # Processes hypothesis.
      hyp_pieces = self.tgt_dict.string(hypo["tokens"].int().cpu())
      if "words" in hypo:
          hyp_words = " ".join(hypo["words"])
      else:
          hyp_words = post_process(
              hyp_pieces, self.cfg.common_eval.post_process
          )
  
      # Processes target.
      target_tokens = utils.strip_pad(toks, self.tgt_dict.pad())
      tgt_pieces = self.tgt_dict.string(target_tokens.int().cpu())
      tgt_words = post_process(
          tgt_pieces, self.cfg.common_eval.post_process
      )
  
      if self.cfg.decoding.results_path is not None:
          print(
              f"{hyp_pieces} ({speaker}-{sid})",
              file=self.hypo_units_file,
          )
          print(
              f"{hyp_words} ({speaker}-{sid})",
              file=self.hypo_words_file,
          )
          print(
              f"{tgt_pieces} ({speaker}-{sid})",
              file=self.ref_units_file,
          )
          print(
              f"{tgt_words} ({speaker}-{sid})", file=self.ref_words_file
          )
  
      if not self.cfg.common_eval.quiet:
          logger.info(f"HYPO: {hyp_words}")
          logger.info(f"REF: {tgt_words}")
          logger.info("---------------------")
  
      hyp_words, tgt_words = hyp_words.split(), tgt_words.split()
  
      return editdistance.eval(hyp_words, tgt_words), len(tgt_words)
  
imports: import ast
  import hashlib
  import logging
  import os
  import shutil
  import sys
  import re
  from dataclasses import dataclass, field, is_dataclass
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple, Union
  import editdistance
  import torch
  import torch.distributed
  from examples.speech_recognition.new.decoders.decoder_config import DecoderConfig, FlashlightDecoderConfig
  from examples.speech_recognition.new.decoders.decoder import Decoder
  from fairseq import checkpoint_utils, distributed_utils, progress_bar, tasks, utils
  from fairseq.data.data_utils import post_process
  from fairseq.dataclass.configs import CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, FairseqDataclass
  from fairseq.logging.meters import StopwatchMeter, TimeMeter
  from fairseq.logging.progress_bar import BaseProgressBar
  from fairseq.models.fairseq_model import FairseqModel
  from omegaconf import OmegaConf
  import hydra
  from hydra.core.config_store import ConfigStore
label_desc: Use image node_id 15 for calling the InferenceProcessor obj's underlying member method code with example usage: obj.process_sentence(sample, hypo, sid, batch_id) and returns: editdistance,  len