code: def main(cfg):
      # Read configuration parameters
      nnodes_per_cfg = cfg.fid.nnodes_per_cfg
      ntasks_per_node = cfg.fid.ntasks_per_node
      local_task_id = cfg.fid.local_task_id
      num_images_to_eval = cfg.fid.num_images_to_eval
      path = cfg.fid.coco_captions_path
  
      node_id = int(os.environ.get("SLURM_ARRAY_TASK_ID", 0))
      node_id_per_cfg = node_id % nnodes_per_cfg
  
      current_node_cfg = cfg.fid.classifier_free_guidance[
          node_id // nnodes_per_cfg
      ]
      with open_dict(cfg):
          cfg.infer.unconditional_guidance_scale = current_node_cfg
      save_path = os.path.join(cfg.fid.save_path, str(current_node_cfg))
  
      # Read and store captions
      captions = []
      caption_files = sorted(os.listdir(path))
      assert len(caption_files) >= num_images_to_eval
      for file in caption_files[:num_images_to_eval]:
          with open(os.path.join(path, file), "r") as f:
              captions += f.readlines()
  
      # Calculate partition sizes and select the partition for the current node
      partition_size_per_node = num_images_to_eval // nnodes_per_cfg
      start_idx = node_id_per_cfg * partition_size_per_node
      end_idx = (
          (node_id_per_cfg + 1) * partition_size_per_node
          if node_id_per_cfg != nnodes_per_cfg - 1
          else None
      )
      captions = captions[start_idx:end_idx]
  
      local_task_id = (
          int(local_task_id)
          if local_task_id is not None
          else int(os.environ.get("SLURM_LOCALID", 0))
      )
      partition_size_per_task = int(len(captions) // ntasks_per_node)
  
      # Select the partition for the current task
      start_idx = local_task_id * partition_size_per_task
      end_idx = (
          (local_task_id + 1) * partition_size_per_task
          if local_task_id != ntasks_per_node - 1
          else None
      )
      input = captions[start_idx:end_idx]
  
      print(
          f"Current worker {node_id}:{local_task_id} will generate {len(input)} images"
      )
  
      os.makedirs(save_path, exist_ok=True)
  
      # Modify the model configuration
      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.global_batch_size = (
              model_cfg.micro_batch_size * ntasks_per_node
          )
  
      # Set up the trainer and model for inference
      (
          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()
  
      # Generate images using the model and save them
      for i, prompt in enumerate(input):
          cfg.infer.prompts = [prompt]
          rng = torch.Generator().manual_seed(
              cfg.infer.seed
              + local_task_id * 10
              + node_id_per_cfg * 100
              + i * 1000
          )
          output = pipeline(model, cfg, rng=rng)
          for image in output[0]:
              image_num = (
                  i
                  + partition_size_per_node * node_id_per_cfg
                  + partition_size_per_task * local_task_id
              )
              image.save(
                  os.path.join(save_path, f"image{image_num:06d}.png")
              )
  
imports: import os
  import torch
  from omegaconf.omegaconf import open_dict
  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