sample
>>> 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:   Copied >>> import PIL.Image
>>> import numpy as np


>>> def display_sample(sample, i):
...     image_processed = sample.cpu