code: def main(cfg: InferConfig) -> float:
      """Entry point for main processing logic.
  
      Args:
          cfg: The inferance configuration to use.
          wer: Optional shared memory pointer for returning the WER. If not None,
              the final WER value will be written here instead of being returned.
  
      Returns:
          The final WER if `wer` is None, otherwise None.
      """
  
      yaml_str, wer_file = OmegaConf.to_yaml(
          cfg.decoding
      ), get_wer_file(cfg)
  
      # Validates the provided configuration.
      if (
          cfg.dataset.max_tokens is None
          and cfg.dataset.batch_size is None
      ):
          cfg.dataset.max_tokens = 4000000
      if not cfg.common.cpu and not torch.cuda.is_available():
          raise ValueError(
              "CUDA not found; set `cpu=True` to run without CUDA"
          )
  
      logger.info(cfg.common_eval.path)
  
      with InferenceProcessor(cfg) as processor:
          for sample in processor:
              processor.process_sample(sample)
  
          processor.log_generation_time()
  
          if cfg.decoding.results_path is not None:
              processor.merge_shards()
  
          errs_t, leng_t = (
              processor.total_errors,
              processor.total_length,
          )
  
          if cfg.common.cpu:
              logger.warning("Merging WER requires CUDA.")
          elif processor.data_parallel_world_size > 1:
              stats = torch.LongTensor([errs_t, leng_t]).cuda()
              dist.all_reduce(stats, op=dist.ReduceOp.SUM)
              errs_t, leng_t = stats[0].item(), stats[1].item()
  
          wer = errs_t * 100.0 / leng_t
  
          if distributed_utils.is_master(cfg.distributed_training):
              with open(wer_file, "w") as f:
                  f.write(
                      (
                          f"WER: {wer}\n"
                          f"err / num_ref_words = {errs_t} / {leng_t}\n\n"
                          f"{yaml_str}"
                      )
                  )
  
          return wer
  
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 4 for calling a global function with example usage: main(cfg) and returns: wer