code: def wavernn_dataset_case(
      batch_size, seq_len, hop_len, pad, mode, mulaw, num_workers
  ):
      """run dataloader with given parameters and check conditions"""
      ap = AudioProcessor(**C.audio)
  
      C.batch_size = batch_size
      C.mode = mode
      C.seq_len = seq_len
      C.data_path = test_data_path
  
      preprocess_wav_files(test_data_path, C, ap)
      _, train_items = load_wav_feat_data(
          test_data_path, test_mel_feat_path, 5
      )
  
      dataset = WaveRNNDataset(
          ap=ap,
          items=train_items,
          seq_len=seq_len,
          hop_len=hop_len,
          pad=pad,
          mode=mode,
          mulaw=mulaw,
      )
      # sampler = DistributedSampler(dataset) if num_gpus > 1 else None
      loader = DataLoader(
          dataset,
          shuffle=True,
          collate_fn=dataset.collate,
          batch_size=batch_size,
          num_workers=num_workers,
          pin_memory=True,
      )
  
      max_iter = 10
      count_iter = 0
  
      try:
          for data in loader:
              x_input, mels, _ = data
              expected_feat_shape = (
                  ap.num_mels,
                  (x_input.shape[-1] // hop_len) + (pad * 2),
              )
              assert np.all(
                  mels.shape[1:] == expected_feat_shape
              ), f" [!] {mels.shape} vs {expected_feat_shape}"
  
              assert (
                  mels.shape[2] - pad * 2
              ) * hop_len == x_input.shape[1]
              count_iter += 1
              if count_iter == max_iter:
                  break
      # except AssertionError:
      #     shutil.rmtree(test_mel_feat_path)
      #     shutil.rmtree(test_quant_feat_path)
      finally:
          shutil.rmtree(test_mel_feat_path)
          shutil.rmtree(test_quant_feat_path)
  
file_path: TTS/tests/vocoder_tests/test_vocoder_wavernn_datasets.py
imports: import os
  import shutil
  import numpy
  from torch.utils.data import DataLoader
  from tests import get_tests_output_path, get_tests_path
  from TTS.utils.audio import AudioProcessor
  from TTS.vocoder.configs import WavernnConfig
  from TTS.vocoder.datasets.preprocess import load_wav_feat_data, preprocess_wav_files
  from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset
label_desc: Use image node_id 1 for calling a global function with example usage: wavernn_dataset_case(batch_size, seq_len, hop_len, pad, mode, mulaw, num_workers) without return types