>>> less_noisy_sample.shape
torch.Size([1, 3, 256, 256])
```  
The `less_noisy_sample` can be passed to the next `timestep` where it'll get even less noisy! Let's bring it all together now and visualize the entire denoising process.  
First, create a function that postprocesses and displays the denoised image as a `PIL.Image`:  
```py
>>> import PIL.Image
>>> import numpy as np