code: def samples_to_image_grid(samples, approximation=None):
      return images.image_grid(
          [
              single_sample_to_image(sample, approximation)
              for sample in samples
          ]
      )
  
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 6 for calling a global function with example usage: samples_to_image_grid(samples, approximation) and returns: images