# Codeblock 24
def inference():

    denoised_images = []  #(1)
    
    with torch.no_grad():  #(2)
        current_prediction = torch.randn((64, NUM_CHANNELS, IMAGE_SIZE, IMAGE_SIZE)).to(DEVICE)  #(3)
        
        for i in tqdm(reversed(range(NUM_TIMESTEPS))):  #(4)
            predicted_noise = model(current_prediction, torch.as_tensor(i).unsqueeze(0))  #(5)
            current_prediction, denoised_image = noise_scheduler.backward_diffusion(current_prediction, predicted_noise, torch.as_tensor(i))  #(6)

            if i%100 == 0:  #(7)
                denoised_images.append(denoised_image)
            
        return denoised_images