code: def main(cfg: DictConfig):
      """
      Evluate the variational lower bound.
  
      Args:
          cfg: Configuration for the evaluation.
      """
      model, diffusion, dataloader = setup(cfg)
  
      logger.log(f"evaluating checkpoint {cfg.evaluator.model_path}")
      num_complete = 0
      bpds = []
      metrics: Dict[str, List[np.ndarray]] = {
          "vb": [],
          "mse": [],
          "xstart_mse": [],
      }
  
      while num_complete < cfg.evaluator.num_samples:
          batch, model_kwargs = next(dataloader)
          batch = batch.to(dist_util.dev())
          model_kwargs = {
              k: v.to(dist_util.dev()) for k, v in model_kwargs.items()
          }
  
          # compute vlb
          batch_metrics = diffusion.calc_bpd_loop(
              model,
              batch,
              clip_denoised=cfg.evaluator.clip_denoised,
              model_kwargs=model_kwargs,
          )
  
          for key, terms in metrics.items():
              batch_terms = batch_metrics[key]
              if key == "vb":
                  batch_terms = torch.cat(
                      [
                          batch_metrics["prior_bpd"].unsqueeze(-1),
                          batch_terms,
                      ],
                      dim=1,
                  )
              gathered_terms = [
                  torch.zeros_like(batch_terms)
                  for _ in range(dist.get_world_size())
              ]
              dist.all_gather(gathered_terms, batch_terms)
              terms.extend(
                  [term.cpu().numpy() for term in gathered_terms]
              )
  
          # the logger does (per default) not average across processes
          total_bpd = (
              batch_metrics["total_bpd"].mean() / dist.get_world_size()
          )
          dist.all_reduce(total_bpd)
          bpds.append(total_bpd.item())
          num_complete += batch.shape[0] * dist.get_world_size()
          logger.logkvs(
              {"total_bpd": np.mean(bpds), "samples": num_complete}
          )
          logger.dumpkvs()
  
      if dist.get_rank() == 0:
          # log terms
          plots = {}
          for name, terms in metrics.items():
              out_path = Path(logger.get_dir()) / f"{name}-terms.npz"
              logger.log(f"saving {name} terms to {out_path}")
              terms_arr = np.concatenate(terms)[:, ::-1]
              assert terms_arr.shape == (
                  num_complete,
                  diffusion.num_timesteps + (name == "vb"),
              )
              np.savez(
                  out_path,
                  mean=terms_arr.mean(axis=0),
                  stdd=terms_arr.std(axis=0),
                  min=terms_arr.min(axis=0),
                  max=terms_arr.max(axis=0),
              )
              plots[name] = plot_evolution(
                  terms_arr, xaxis_title="t", yaxis_title=name
              )
          wandb.log(plots)
  
      dist.barrier()
      logger.log("evaluation complete")
  
file_path: NeuralCompression/projects/bits_back_diffusion/compute_vlb.py
imports: from pathlib import Path
  from typing import Dict, List
  import hydra
  import numpy
  import torch
  import torch.distributed
  import wandb
  from bits_back_diffusion.script_util import plot_evolution, setup
  from improved_diffusion import dist_util, 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