code: def main(cfg):
      def model_cfg_modifier(model_cfg):
          model_cfg.precision = cfg.trainer.precision
          model_cfg.ckpt_path = None
          model_cfg.inductor = False
          model_cfg.unet_config.use_flash_attention = False
          model_cfg.unet_config.from_pretrained = None
          model_cfg.first_stage_config.from_pretrained = None
          model_cfg.target = "nemo.collections.multimodal.models.stable_diffusion.ldm.ddpm.MegatronLatentDiffusion"
  
      (
          trainer,
          megatron_diffusion_model,
      ) = setup_trainer_and_model_for_inference(
          model_provider=MegatronLatentDiffusion,
          cfg=cfg,
          model_cfg_modifier=model_cfg_modifier,
      )
      model = megatron_diffusion_model.model
      model.cuda().eval()
  
      rng = torch.Generator().manual_seed(cfg.infer.seed)
      pipeline(model, cfg, rng=rng)
  
imports: import torch
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion
  from nemo.collections.multimodal.parts.stable_diffusion.pipeline import pipeline
  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 1 for calling a global function with example usage: main(cfg) without return types