code: def log_results(result: Dataset, args: Dict[str, str]):
      """DO NOT CHANGE. This function computes and logs the result metrics."""
  
      log_outputs = args.log_outputs
      dataset_id = "_".join(
          args.dataset.split("/") + [args.config, args.split]
      )
  
      # load metric
      wer = load_metric("wer")
      cer = load_metric("cer")
  
      # compute metrics
      wer_result = wer.compute(
          references=result["target"], predictions=result["prediction"]
      )
      cer_result = cer.compute(
          references=result["target"], predictions=result["prediction"]
      )
  
      # print & log results
      result_str = f"WER: {wer_result}\nCER: {cer_result}"
      print(result_str)
  
      with open(f"{dataset_id}_eval_results.txt", "w") as f:
          f.write(result_str)
  
      # log all results in text file. Possibly interesting for analysis
      if log_outputs is not None:
          pred_file = f"log_{dataset_id}_predictions.txt"
          target_file = f"log_{dataset_id}_targets.txt"
  
          with open(pred_file, "w") as p, open(target_file, "w") as t:
              # mapping function to write output
              def write_to_file(batch, i):
                  p.write(f"{i}" + "\n")
                  p.write(batch["prediction"] + "\n")
                  t.write(f"{i}" + "\n")
                  t.write(batch["target"] + "\n")
  
              result.map(write_to_file, with_indices=True)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/robust-speech-event/eval.py
imports: import argparse
  import re
  from typing import Dict
  import torch
  from datasets import Audio, Dataset, load_dataset, load_metric
  from transformers import AutoFeatureExtractor, pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: log_results(result, args) without return types