code: def setup(self, stage=None):
      self.datasets = dict(
          (k, instantiate_from_config(self.dataset_configs[k]))
          for k in self.dataset_configs
      )
      if self.wrap:
          for k in self.datasets:
              self.datasets[k] = WrappedDataset(self.datasets[k])
  
imports: import argparse, os, sys, datetime, glob, importlib, csv
  import numpy
  import time
  import torch
  import torchvision
  import pytorch_lightning
  from packaging import version
  from omegaconf import OmegaConf
  from torch.utils.data import random_split, DataLoader, Dataset, Subset
  from functools import partial
  from PIL import Image
  from pytorch_lightning import seed_everything
  from pytorch_lightning.trainer import Trainer
  from pytorch_lightning.callbacks import ModelCheckpoint, Callback, LearningRateMonitor
  from pytorch_lightning.utilities.distributed import rank_zero_only
  from pytorch_lightning.utilities import rank_zero_info
  from ldm.data.base import Txt2ImgIterableBaseDataset
  from ldm.util import instantiate_from_config
label_desc: Use image node_id 3 for calling the DataModuleFromConfig obj's underlying member method code with example usage: obj.setup(stage) without return types