code: def main(inference_config):
      inference_config: ImagenPipelineConfig = OmegaConf.merge(
          ImagenPipelineConfig(), inference_config
      )
      captions = pickle.load(open("coco_captions.pkl", "rb"))
      ntasks = 8
      if os.environ.get("CUDA_VISIBLE_DEVICES"):
          # Multi-GPU
          task_id = int(os.environ.get("CUDA_VISIBLE_DEVICES", 0))
      else:
          # Single GPU
          task_id = 0
      chuncksize = int(len(captions) // ntasks)
      if task_id != ntasks - 1:
          input = captions[
              task_id * chuncksize : (task_id + 1) * chuncksize
          ]
      else:
          input = captions[task_id * chuncksize :]
      captions = input
  
      trainer = Trainer()
      pipeline = ImagenPipeline.from_pretrained(
          cfg=inference_config, trainer=trainer
      )
      batch_size = 16
      batch_idx = 0
  
      possible_res = [64, 256]  # [64, 256]
      outpaths = []
      for res in possible_res:
          outpath = f"{inference_config.output_path}_RES{res}"
          os.makedirs(outpath, exist_ok=True)
          outpaths.append(outpath)
      while True:
          if batch_idx * batch_size >= len(captions):
              break
          batch_captions = captions[
              batch_idx * batch_size : (batch_idx + 1) * batch_size
          ]
  
          # Different seed for every image
          seeds = [
              task_id * chuncksize + batch_idx * batch_size + idx
              for idx in range(len(batch_captions))
          ]
          seed = batch_idx + chuncksize
  
          with torch.no_grad():
              images, all_res_images, throughput = pipeline(
                  prompts=batch_captions,
                  seed=seeds,
                  single_batch_mode=True,
              )
  
          for outpath, one_res in zip(outpaths, all_res_images):
              for idx, (caption, image) in enumerate(
                  zip(batch_captions, one_res[0])
              ):
                  image.save(
                      os.path.join(
                          outpath,
                          f"image_{task_id*chuncksize+batch_idx*batch_size+idx}.png",
                      )
                  )
                  with open(
                      os.path.join(
                          outpath,
                          f"image_{task_id*chuncksize+batch_idx*batch_size+idx}.txt",
                      ),
                      "w",
                  ) as f:
                      f.writelines(caption)
          batch_idx += 1
  
imports: import os
  import pickle
  import torch
  from omegaconf import OmegaConf
  from pytorch_lightning import Trainer
  from nemo.collections.multimodal.models.text_to_image.imagen.imagen_pipeline import ImagenPipeline, ImagenPipelineConfig
  from nemo.core.config import hydra_runner
label_desc: Use image node_id 1 for calling a global function with example usage: main(inference_config) without return types