def METHOD_NAME(tmpdir, compute_on_cpu):
    """Check that the hole pipeline give the same result as torch-fidelity."""
    from torch_fidelity import calculate_metrics
    metric = InceptionScore(splits=1, compute_on_cpu=compute_on_cpu).cuda()
    # Generate some synthetic data
    img1 = torch.randint(0, 255, (100, 3, 299, 299), dtype=torch.uint8)
    batch_size = 10
    for i in range(img1.shape[0] // batch_size):
        metric.update(img1[batch_size * i : batch_size * (i + 1)].cuda())
    torch_fid = calculate_metrics(
        input1=_ImgDataset(img1), isc=True, isc_splits=1, batch_size=batch_size, save_cpu_ram=True
    )
    tm_mean, _ = metric.compute()
    assert torch.allclose(tm_mean.cpu(), torch.tensor([torch_fid["inception_score_mean"]]), atol=1e-3)