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
      logger.setLevel(logging_level)
  
imports: import logging
  import sys
  import time
  from dataclasses import field
  from pathlib import Path
  from typing import Dict, List, Optional, Union
  import numpy
  from datasets import DatasetDict, load_dataset
  from tqdm import tqdm
  import flax
  import jax
  import jax.numpy
  import librosa
  import optax
  from flax import jax_utils, traverse_util
  from flax.training import train_state
  from flax.training.common_utils import get_metrics, onehot, shard
  from transformers import FlaxWav2Vec2ForPreTraining, HfArgumentParser, TrainingArguments, Wav2Vec2Config, Wav2Vec2FeatureExtractor, is_tensorboard_available
  from transformers.models.wav2vec2.modeling_flax_wav2vec2 import _compute_mask_indices, _sample_negative_indices
label_desc: Use image node_id 1 for calling a global function with example usage: configure_logger(model_args, training_args) without return types