class_docstr: Weights & Biases logger.
  
  Args:
      dir: Directory for saving the logs.
class_docstr_tok: ["Weights","&","Biases","logger",".","Args",":","dir",":","Directory","for","saving","the","logs","."]
code: def __init__(self, dir: str):
      save_dir = Path(dir).absolute()
      save_dir.mkdir(parents=True, exist_ok=True)
      self.dir = str(save_dir)
  
      # construct SHA256 hash of the log dir for the wandb id
      sha = hashlib.sha256()
      sha.update(self.dir.encode())
  
      run_dir = Path(HydraConfig.get().run.dir)
      wandb.require(experiment="service")
      self.run = wandb.init(
          dir=self.dir,
          name="/".join(
              [
                  run_dir.parent.name,
                  run_dir.name,
              ]
          ),
          project="bits-back-diffusion",
          group=HydraConfig.get().job.name,
          tags=[t[-64:] for t in HydraConfig.get().overrides.task],
          id=sha.hexdigest(),
          resume="allow",
      )
  
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 1 to create a new WandbOutputFormat object from inherited base classes: logger with example: obj = WandbOutputFormat(dir)