code: def get_wer_file(cfg: InferConfig) -> Path:
      """Hashes the decoding parameters to a unique file ID."""
      base_path = "wer"
      if cfg.decoding.results_path is not None:
          base_path = os.path.join(cfg.decoding.results_path, base_path)
  
      if cfg.decoding.unique_wer_file:
          yaml_str = OmegaConf.to_yaml(cfg.decoding)
          fid = int(
              hashlib.md5(yaml_str.encode("utf-8")).hexdigest(), 16
          )
          return Path(f"{base_path}.{fid % 1000000}")
      else:
          return Path(base_path)
  
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 3 for calling a global function with example usage: get_wer_file(cfg) and returns: Path, Path