super().__init__()

          self.register_modules(unet=unet, scheduler=scheduler)

+     def __call__(self):
+         image = torch.randn(
+             (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),
+         )
+         timestep = 1

+         model_output = self.unet(image, timestep).sample
+         scheduler_output = self.scheduler.step(model_output, timestep, image).prev_sample

+        