def METHOD_NAME(self, x):
    # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
    # NOTE: we expect that the correct transform has been called during dataloading.
    with torch.no_grad():
        prediction = self.model(x)
        prediction = torch.nn.functional.interpolate(
            prediction.unsqueeze(1),
            size=x.shape[2:],
            mode="bicubic",
            align_corners=False,
        )
    assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
    return prediction