code: def encode_prompt(
      cond_stage_model, prompt, unconditional_guidance_scale, batch_size
  ):
      c = cond_stage_model.encode(batch_size * [prompt])
      if unconditional_guidance_scale != 1.0:
          uc = cond_stage_model.encode(batch_size * [""])
      else:
          uc = None
      return c, uc
  
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 2 for calling a global function with example usage: encode_prompt(cond_stage_model, prompt, unconditional_guidance_scale, batch_size) and returns: c,  uc