code: def test_dataset_samples(self):
      total_examples = 1
      num_samples = 2
      audio_duration = 1.0
      segment_duration = 1.0
      sample_rate = 16_000
      channels = 1
  
      create_dataset = partial(
          self._create_audio_dataset,
          "dset",
          total_examples,
          durations=audio_duration,
          sample_rate=sample_rate,
          channels=channels,
          segment_duration=segment_duration,
          num_examples=num_samples,
      )
  
      dataset = create_dataset(shuffle=True)
      # when shuffle = True, we have different inputs for the same index across epoch
      sample_1 = dataset[0]
      sample_2 = dataset[0]
      assert not torch.allclose(sample_1, sample_2)
  
      dataset_noshuffle = create_dataset(shuffle=False)
      # when shuffle = False, we have same inputs for the same index across epoch
      sample_1 = dataset_noshuffle[0]
      sample_2 = dataset_noshuffle[0]
      assert torch.allclose(sample_1, sample_2)
  
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 6 for calling the TestAudioDataset obj's underlying member method code with example usage: obj.test_dataset_samples() without return types