# Codeblock 16c
    def backward_diffusion(self, current_image, predicted_noise, t):  #(1)
        denoised_image = (current_image - (self.sqrt_one_minus_alphas_cum_prod[t] * predicted_noise)) / self.sqrt_alphas_cum_prod[t]  #(2)
        denoised_image = 2 * (denoised_image - denoised_image.min()) / (denoised_image.max() - denoised_image.min()) - 1  #(3)
        
        current_prediction = current_image - ((self.betas[t] * predicted_noise) / (self.sqrt_one_minus_alphas_cum_prod[t]))  #(4)
        current_prediction = current_prediction / torch.sqrt(self.alphas[t])  #(5)
        
        if t == 0:  #(6)
            return current_prediction, denoised_image
        
        else:
            variance = (1 - self.alphas_cum_prod[t-1]) / (1. - self.alphas_cum_prod[t])  #(7)
            variance = variance * self.betas[t]  #(8)
            sigma = variance ** 0.5
            z = torch.randn(current_image.shape).to(DEVICE)
            current_prediction = current_prediction + sigma*z
            
            return current_prediction, denoised_image