code: def setup(
      cfg: DictConfig,
      train: bool = False,
      log_dir: str = "logging",
      log_ranks: bool = False,
  ):
      """
      Set up an experiment.
  
      Args:
          cfg: Configuration to use for setting up the experiment.
          train: Whether to use training or evaluation configurations.
          log_dir: Directory for logging.
              If `log_ranks` is True, the rank will be added as suffix.
          log_ranks: Whether to log each process separately.
  
      Returns:
          The model, the diffusion, and an iterator yielding the data.
      """
      # distributed and logging setup
      dist_util.setup_dist()
      if log_ranks:
          log_dir += f"_rank{dist.get_rank():03d}"
      logger.configure(
          dir=log_dir, format_strs=["stdout", "log", "csv"]
      )
  
      # update configs
      data = cfg.data.train if train else cfg.data.val
      data.data_dir = to_absolute_path(data.data_dir)
      if train:
          update_checkpoint(cfg, "trainer.resume_checkpoint")
      else:
          update_checkpoint(cfg, "evaluator.model_path", prefix="ema")
  
      # wandb setup
      if log_ranks or (dist.get_rank() == 0):
          current_logger = logger.get_current()
          if current_logger:
              wandb_output_format = WandbOutputFormat(
                  current_logger.dir
              )
              current_logger.output_formats.append(wandb_output_format)
              wandb.config.update(
                  OmegaConf.to_container(cfg, resolve=True),
                  allow_val_change=True,
              )
  
      logger.log("creating model and diffusion...")
      model = instantiate(cfg.model)
      if not train:
          model.load_state_dict(
              dist_util.load_state_dict(
                  cfg.evaluator.model_path, map_location="cpu"
              )
          )
          eff_batch_size = data.batch_size * dist.get_world_size()
          if cfg.evaluator.num_samples % eff_batch_size:
              logger.log(
                  f"dropping samples as number of samples {cfg.evaluator.num_samples}"
                  f" is not divisible by effective batchsize {eff_batch_size}."
              )
  
      model.to(dist_util.dev())
      model.train(train)
      diffusion = instantiate(cfg.diffusion)
  
      logger.log(f"creating data loader from {data.data_dir}...")
      dataloader = instantiate(data)
  
      return model, diffusion, dataloader
  
imports: import copy
  import hashlib
  from pathlib import Path
  from typing import List, Set, Union
  import numpy
  import plotly.graph_objs
  import torch
  import torch.distributed
  import wandb
  from hydra.core.hydra_config import HydraConfig
  from hydra.utils import instantiate, to_absolute_path
  from improved_diffusion import dist_util
  from improved_diffusion import gaussian_diffusion
  from improved_diffusion import logger, respace, train_util
  from omegaconf import DictConfig, OmegaConf
label_desc: Use image node_id 4 for calling a global function with example usage: setup(cfg, train, log_dir, log_ranks) and returns: model,  diffusion,  dataloader