code: def get_dls(size, woof, pct_noise, bs, sh=0.0, workers=None):
      assert pct_noise in [0, 5, 50], "`pct_noise` must be 0,5 or 50."
      if size <= 224:
          path = URLs.IMAGEWOOF_320 if woof else URLs.IMAGENETTE_320
      else:
          path = URLs.IMAGEWOOF if woof else URLs.IMAGENETTE
      source = untar_data(path)
      workers = ifnone(workers, min(8, num_cpus()))
      blocks = (ImageBlock, CategoryBlock)
      tfms = [RandomResizedCrop(size, min_scale=0.35), FlipItem(0.5)]
      batch_tfms = [Normalize.from_stats(*imagenet_stats)]
      if sh:
          batch_tfms.append(RandomErasing(p=0.3, max_count=3, sh=sh))
  
      csv_file = (
          "noisy_imagewoof.csv" if woof else "noisy_imagenette.csv"
      )
      inp = pd.read_csv(source / csv_file)
      dblock = DataBlock(
          blocks=blocks,
          splitter=ColSplitter(),
          get_x=ColReader("path", pref=source),
          get_y=ColReader(f"noisy_labels_{pct_noise}"),
          item_tfms=tfms,
          batch_tfms=batch_tfms,
      )
  
      return dblock.dataloaders(
          inp, path=source, bs=bs, num_workers=workers
      )
  
file_path: fastai/nbs/examples/train_imagenette.py
imports: from fastai.basics import *
  from fastai.vision.all import *
  from fastai.callback.all import *
  from fastai.distributed import *
  from fastprogress import fastprogress
  from torchvision.models import *
  from fastai.vision.models.xresnet import *
  from fastai.callback.mixup import *
  from fastcore.script import *
label_desc: Use image node_id 2 for calling a global function with example usage: get_dls(size, woof, pct_noise, bs, sh, workers) and returns: dblock