code: def decode_images(model, samples):
      images = model.decode_first_stage(samples)
  
      images = torch.clamp((images + 1.0) / 2.0, min=0.0, max=1.0)
  
      return 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 3 for calling a global function with example usage: decode_images(model, samples) and returns: images