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/examples/multimodal/text_to_image/controlnet/controlnet_infer.py
imports: import os
  import time
  import cv2
  import einops
  import torch
  from PIL import Image
  from nemo.collections.multimodal.models.text_to_image.controlnet import get_preprocessing_function
  from nemo.collections.multimodal.models.text_to_image.controlnet.controlnet import MegatronControlNet
  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.plms import PLMSSampler
  from nemo.collections.multimodal.parts.utils import setup_trainer_and_model_for_inference
  from nemo.core.config import hydra_runner
label_desc: Use image node_id 4 for calling a global function with example usage: decode_images(model, samples) and returns: images