code: def main(cfg) -> None:
      logging.info(
          "\n\n************** Experiment configuration ***********"
      )
      logging.info(f"\n{OmegaConf.to_yaml(cfg)}")
  
      torch.backends.cuda.matmul.allow_tf32 = True
  
      if cfg.model.capture_cudagraph_iters >= 0:
          # Required by CUDA graph with DDP
          os.environ["NCCL_ASYNC_ERROR_HANDLING"] = "0"
  
          # Hack to avoid CUDA graph issue with AMP, PyTorch Lightning doesn't support
          # changing autocast arguments for now.
          # https://github.com/pytorch/pytorch/blob/v1.13.1/torch/cuda/graphs.py#L234
          def amp_autocast_init(self, *args, **kwargs):
              if "cache_enabled" not in kwargs:
                  kwargs["cache_enabled"] = False
              return self.__orig_init__(*args, **kwargs)
  
          torch.cuda.amp.autocast.__orig_init__ = (
              torch.cuda.amp.autocast.__init__
          )
          torch.cuda.amp.autocast.__init__ = amp_autocast_init
          torch.autocast.__orig_init__ = torch.autocast.__init__
          torch.autocast.__init__ = amp_autocast_init
  
      trainer = MegatronStableDiffusionTrainerBuilder(
          cfg
      ).create_trainer()
  
      exp_manager(trainer, cfg.exp_manager)
  
      model = MegatronLatentDiffusion(cfg.model, trainer)
  
      trainer.fit(model)
  
file_path: NeMo/examples/multimodal/text_to_image/stable_diffusion/sd_train.py
imports: import os
  import torch
  from omegaconf.omegaconf import OmegaConf
  from nemo.collections.multimodal.models.text_to_image.stable_diffusion.ldm.ddpm import MegatronLatentDiffusion
  from nemo.collections.nlp.parts.megatron_trainer_builder import MegatronTrainerBuilder
  from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy
  from nemo.core.config import hydra_runner
  from nemo.utils import logging
  from nemo.utils.exp_manager import exp_manager
label_desc: Use image node_id 1 for calling a global function with example usage: main(cfg) and returns: self