code: def get_pytorch_val_loader(
      data_path,
      image_size,
      batch_size,
      num_classes,
      one_hot,
      interpolation="bilinear",
      workers=5,
      _worker_init_fn=None,
      crop_padding=32,
      memory_format=torch.contiguous_format,
      prefetch_factor=2,
  ):
      interpolation = {
          "bicubic": Image.BICUBIC,
          "bilinear": Image.BILINEAR,
      }[interpolation]
      valdir = os.path.join(data_path, "val")
      val_dataset = datasets.ImageFolder(
          valdir,
          transforms.Compose(
              [
                  transforms.Resize(
                      image_size + crop_padding,
                      interpolation=interpolation,
                  ),
                  transforms.CenterCrop(image_size),
              ]
          ),
      )
  
      if torch.distributed.is_initialized():
          val_sampler = torch.utils.data.distributed.DistributedSampler(
              val_dataset, shuffle=False
          )
      else:
          val_sampler = None
  
      val_loader = torch.utils.data.DataLoader(
          val_dataset,
          sampler=val_sampler,
          batch_size=batch_size,
          shuffle=(val_sampler is None),
          num_workers=workers,
          worker_init_fn=_worker_init_fn,
          pin_memory=True,
          collate_fn=partial(fast_collate, memory_format),
          drop_last=False,
          persistent_workers=True,
          prefetch_factor=prefetch_factor,
      )
  
      return PrefetchedWrapper(
          val_loader, 0, num_classes, one_hot
      ), len(val_loader)
  
imports: import os
  import torch
  import numpy
  from PIL import Image
  from functools import partial
  from image_classification.autoaugment import AutoaugmentImageNetPolicy
  import torchvision.datasets
  import torchvision.transforms
label_desc: Use image node_id 7 for calling a global function with example usage: get_pytorch_val_loader(data_path, image_size, batch_size, num_classes, one_hot, interpolation, workers, _worker_init_fn, crop_padding, memory_format, prefetch_factor) and returns: PrefetchedWrapper,  len