code: def _predict_dataloader(self, shuffle=False):
      if (
          isinstance(
              self.datasets["predict"], Txt2ImgIterableBaseDataset
          )
          or self.use_worker_init_fn
      ):
          init_fn = worker_init_fn
      else:
          init_fn = None
      return DataLoader(
          self.datasets["predict"],
          batch_size=self.batch_size,
          num_workers=self.num_workers,
          worker_init_fn=init_fn,
      )
  
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 7 for calling the DataModuleFromConfig obj's underlying member method code with example usage: obj._predict_dataloader(shuffle) and returns: DataLoader