text: import torch
  from torchvision import transforms
  from torch.utils.data import DataLoader
  
  from pytorch_classifier.image import Rescale, normalize, RandomColorDistort
  from pytorch_classifier.dataset import FolderDataset, FolderAugmentedDataset, FolderAugmentedTenCropDataset
  
  
  def get_simple_dataloader(data_path, shuffle=False, batch_size=16):
      trans = transforms.Compose([
          # transforms.Resize((224,224)),
          # transforms.CenterCrop((224,224)),
          Rescale((224, 224)),
          transforms.ToTensor(),
          normalize
      ])
  
      return DataLoader(FolderDataset(data_path, trans), shuffle=shuffle, batch_size=batch_size, num_workers=16)
  
  
  def get_ensemble_of_augmentations_dataloader(data_path, shuffle=False, batch_size=16):
      trans = transforms.Compose([
          Rescale((264, 264)),
          # transforms.RandomResizedCrop(size=224, scale=(0.8, 1), ratio=(1, 1)),
          transforms.RandomResizedCrop(size=224),
          RandomColorDistort(),
          # Rescale((224,224)),
          transforms.RandomHorizontalFlip(),
          transforms.ToTensor(),
          normalize,
      ])
  
      return DataLoader(FolderAugmentedDataset(data_path, trans, 7), shuffle=shuffle, batch_size=batch_size, num_workers=16)
  
  
  def get_ten_crop_dataloader(data_path, shuffle=False, batch_size=16):
      trans = transforms.Compose([
          Rescale((240, 240)),
          # ResizeForTenCrop(224, 226),
          transforms.TenCrop((224, 224)),
          lambda crops: torch.stack([transforms.Compose([transforms.ToTensor(), normalize])(crop) for crop in crops])
      ])
  
      return DataLoader(FolderAugmentedTenCropDataset(data_path, trans), shuffle=shuffle, batch_size=batch_size, num_workers=16)
  