def METHOD_NAME(self):
    """Validate the output dimensions of a WaveRNN model's infer method."""
    upsample_scales = [5, 5, 8]
    n_rnn = 128
    n_fc = 128
    n_classes = 128
    hop_length = 200
    n_batch = 2
    n_time = 50
    n_freq = 25
    n_output = 64
    n_res_block = 2
    n_hidden = 32
    kernel_size = 5
    model = WaveRNN(
        upsample_scales, n_classes, hop_length, n_res_block, n_rnn, n_fc, kernel_size, n_freq, n_hidden, n_output
    )
    x = torch.rand(n_batch, n_freq, n_time)
    lengths = torch.tensor([n_time, n_time // 2])
    out, waveform_lengths = model.infer(x, lengths)
    assert out.size() == (n_batch, 1, hop_length * n_time)
    assert waveform_lengths[0] == hop_length * n_time
    assert waveform_lengths[1] == hop_length * n_time // 2