code: def main(cfg: DictConfig):
      """
      Encode data using a diffusion model.
  
      Args:
          cfg: Configuration for encoding.
      """
      model, diffusion, dataloader = setup(cfg, log_ranks=True)
  
      codec = hydra.utils.instantiate(
          cfg.codec,
          model={
              "model": model,
              "diffusion": diffusion,
              "clip_denoised": cfg.evaluator.clip_denoised,
          },
      )
  
      # load checkpoint
      update_checkpoint(
          cfg, "encode_checkpoint", prefix="encode-ckpt", suffix="npz"
      )
      if cfg.get("encode_checkpoint"):
          logger.log(
              f"load encode checkpoint {cfg.encode_checkpoint}..."
          )
          codec.load_state_dict(np.load(cfg.encode_checkpoint))
  
      if (
          codec.data_shape[0] * codec.data_count * dist.get_world_size()
          < cfg.evaluator.num_samples
      ):
          logger.log(
              f"encode with model checkpoint {cfg.evaluator.model_path}..."
          )
          encode(
              codec,
              islice(dataloader, codec.data_count, None),
              cfg.evaluator.num_samples,
              cfg.save_interval,
          )
  
      if cfg.check:
          update_checkpoint(
              cfg,
              "decode_checkpoint",
              prefix="decode-ckpt",
              suffix="npz",
          )
          if cfg.get("decode_checkpoint"):
              logger.log(
                  f"load decode checkpoint {cfg.decode_checkpoint}..."
              )
              codec.load_state_dict(np.load(cfg.decode_checkpoint))
  
          save_dir = Path(logger.get_dir())
          logger.log(
              f"decode with model checkpoint {cfg.evaluator.model_path}..."
          )
          decode(codec, save_dir, cfg.save_interval)
  
          # check batches
          dataloader = hydra.utils.instantiate(cfg.data.val)
          data_paths = list(save_dir.glob("batch*.npz"))
          data_paths.sort(key=lambda x: int(x.stem[len("batch") :]))
          for path in data_paths:
              batch, _ = next(dataloader)
              assert (
                  np.load(path)["batch"]
                  == codec.quantize(batch.numpy())
              ).all()
  
      dist.barrier()
      logger.log("completed")
  
file_path: NeuralCompression/projects/bits_back_diffusion/encode.py
imports: from itertools import islice
  from pathlib import Path
  from typing import Dict, Iterator, List, Tuple, Union
  import hydra
  import numpy
  import torch
  import torch.distributed
  import wandb
  from bits_back_diffusion.codec import BitsBackCodec
  from bits_back_diffusion.script_util import plot_evolution, setup, update_checkpoint
  from improved_diffusion import logger
  from omegaconf import DictConfig
label_desc: Use image node_id 1 for calling a global function with example usage: main(cfg) without return types