code: def _create_audio_dataset(
      self,
      root_name: str,
      total_num_examples: int,
      durations: tp.Union[float, tp.Tuple[float, float]] = (0.1, 1.0),
      sample_rate: int = 16_000,
      channels: int = 1,
      segment_duration: tp.Optional[float] = None,
      num_examples: int = 10,
      shuffle: bool = True,
      return_info: bool = False,
  ):
      root_dir = self._create_audio_files(
          root_name,
          total_num_examples,
          durations,
          sample_rate,
          channels,
      )
      dataset = AudioDataset.from_path(
          root_dir,
          minimal_meta=True,
          segment_duration=segment_duration,
          num_samples=num_examples,
          sample_rate=sample_rate,
          channels=channels,
          shuffle=shuffle,
          return_info=return_info,
      )
      return dataset
  
imports: from functools import partial
  from itertools import product
  import json
  import math
  import os
  import random
  import typing
  import pytest
  import torch
  from torch.utils.data import DataLoader
  from audiocraft.data.audio_dataset import AudioDataset, AudioMeta, _get_audio_meta, load_audio_meta, save_audio_meta
  from audiocraft.data.zip import PathInZip
  from ..common_utils import TempDirMixin, get_white_noise, save_wav
label_desc: Use image node_id 2 for calling the TestAudioDataset obj's underlying member method code with example usage: obj._create_audio_dataset(root_name, total_num_examples, durations, sample_rate, channels, segment_duration, num_examples, shuffle, return_info) and returns: dataset