code: def configure_logger(
      model_args: ModelArguments, training_args: TrainingArguments
  ):
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
          datefmt="%m/%d/%Y %H:%M:%S",
          handlers=[logging.StreamHandler(sys.stdout)],
      )
      logging_level = logging.WARNING
      if model_args.verbose_logging:
          logging_level = logging.DEBUG
      elif trainer_utils.is_main_process(training_args.local_rank):
          logging_level = logging.INFO
      logger.setLevel(logging_level)
  
imports: import logging
  import pathlib
  import re
  import sys
  from dataclasses import dataclass, field
  from typing import Any, Callable, Dict, List, Optional, Set, Union
  import datasets
  import librosa
  import numpy
  import torch
  from lang_trans import arabic
  from packaging import version
  from torch import nn
  from transformers import HfArgumentParser, Trainer, TrainingArguments, Wav2Vec2CTCTokenizer, Wav2Vec2FeatureExtractor, Wav2Vec2ForCTC, Wav2Vec2Processor, is_apex_available, trainer_utils
label_desc: Use image node_id 1 for calling a global function with example usage: configure_logger(model_args, training_args) without return types