code: def gan_dataset_case(
      batch_size,
      seq_len,
      hop_len,
      conv_pad,
      return_segments,
      use_noise_augment,
      use_cache,
      num_workers,
  ):
      """run dataloader with given parameters and check conditions"""
      ap = AudioProcessor(**C.audio)
      _, train_items = load_wav_data(test_data_path, 10)
      dataset = GANDataset(
          ap,
          train_items,
          seq_len=seq_len,
          hop_len=hop_len,
          pad_short=2000,
          conv_pad=conv_pad,
          return_segments=return_segments,
          use_noise_augment=use_noise_augment,
          use_cache=use_cache,
      )
      loader = DataLoader(
          dataset=dataset,
          batch_size=batch_size,
          shuffle=True,
          num_workers=num_workers,
          pin_memory=True,
          drop_last=True,
      )
  
      max_iter = 10
      count_iter = 0
  
      # return random segments or return the whole audio
      if return_segments:
          for item1, _ in loader:
              feat1, wav1 = item1
              # feat2, wav2 = item2
              expected_feat_shape = (
                  batch_size,
                  ap.num_mels,
                  seq_len // hop_len + conv_pad * 2,
              )
  
              # check shapes
              assert np.all(
                  feat1.shape == expected_feat_shape
              ), f" [!] {feat1.shape} vs {expected_feat_shape}"
              assert (
                  feat1.shape[2] - conv_pad * 2
              ) * hop_len == wav1.shape[2]
  
              # check feature vs audio match
              if not use_noise_augment:
                  for idx in range(batch_size):
                      audio = wav1[idx].squeeze()
                      feat = feat1[idx]
                      mel = ap.melspectrogram(audio)
                      # the first 2 and the last 2 frames are skipped due to the padding
                      # differences in stft
                      assert (feat - mel[:, : feat1.shape[-1]])[
                          :, 2:-2
                      ].sum() <= 0, f" [!] {(feat - mel[:, :feat1.shape[-1]])[:, 2:-2].sum()}"
  
              count_iter += 1
              # if count_iter == max_iter:
              #     break
      else:
          for item in loader:
              feat, wav = item
              expected_feat_shape = (
                  batch_size,
                  ap.num_mels,
                  (wav.shape[-1] // hop_len) + (conv_pad * 2),
              )
              assert np.all(
                  feat.shape == expected_feat_shape
              ), f" [!] {feat.shape} vs {expected_feat_shape}"
              assert (
                  feat.shape[2] - conv_pad * 2
              ) * hop_len == wav.shape[2]
              count_iter += 1
              if count_iter == max_iter:
                  break
  
file_path: TTS/tests/test_vocoder_gan_datasets.py
imports: import os
  import numpy
  from tests import get_tests_path, get_tests_input_path, get_tests_output_path
  from torch.utils.data import DataLoader
  from TTS.utils.audio import AudioProcessor
  from TTS.utils.io import load_config
  from TTS.vocoder.datasets.gan_dataset import GANDataset
  from TTS.vocoder.datasets.preprocess import load_wav_data
label_desc: Use image node_id 1 for calling a global function with example usage: gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers) without return types