code: def single_sample_to_image(sample, approximation=None):
      x_sample = (
          samples_to_images_tensor(sample.unsqueeze(0), approximation)[
              0
          ]
          * 0.5
          + 0.5
      )
  
      x_sample = torch.clamp(x_sample, min=0.0, max=1.0)
      x_sample = 255.0 * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
      x_sample = x_sample.astype(np.uint8)
  
      return Image.fromarray(x_sample)
  
imports: import inspect
  from collections import namedtuple
  import numpy
  import torch
  from PIL import Image
  from modules import devices, images, sd_vae_approx, sd_samplers, sd_vae_taesd, shared, sd_models
  from modules.shared import opts, state
  import k_diffusion.sampling
label_desc: Use image node_id 3 for calling a global function with example usage: single_sample_to_image(sample, approximation) and returns: Image