def METHOD_NAME(self, config, batch_size=2):
    input_dummy = torch.rand(batch_size, 30 * config.audio["hop_length"]).to(device)
    input_lengths = torch.randint(100, 30 * config.audio["hop_length"], (batch_size,)).long().to(device)
    input_lengths[-1] = 30 * config.audio["hop_length"]
    spec = torch.rand(batch_size, 30, config.audio["filter_length"] // 2 + 1).to(device)
    mel = torch.rand(batch_size, 30, config.audio["n_mel_channels"]).to(device)
    spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
    spec_lengths[-1] = spec.size(2)
    waveform = torch.rand(batch_size, spec.size(2) * config.audio["hop_length"]).to(device)
    return input_dummy, input_lengths, mel, spec, spec_lengths, waveform