code: def torch_to_numpy(images):
      numpy_images = [
          x.float().cpu().permute(0, 2, 3, 1).numpy() for x in images
      ]
      return numpy_images
  
file_path: NeMo/nemo/collections/multimodal/parts/stable_diffusion/pipeline.py
imports: import os
  import pickle
  import time
  import torch
  from PIL import Image
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.ddim import DDIMSampler
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.para_ddim import ParaDDIMSampler
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.plms import PLMSSampler
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.samplers.sampler_dpm import DPMSolverSampler
  from nemo.collections.multimodal.parts.stable_diffusion.utils import DataParallelWrapper
label_desc: Use image node_id 5 for calling a global function with example usage: torch_to_numpy(images) and returns: numpy_images