text: import torch
  from torchvision import datasets, transforms
  import pathlib
  
  from torchvision.datasets import ImageFolder
  
  flip_and_jitter = transforms.Compose(
              [
                  transforms.RandomHorizontalFlip(p=0.5),
                  transforms.RandomApply(
                      [
                          transforms.ColorJitter(
                              brightness=0.4,
                              contrast=0.4,
                              saturation=0.2,
                              hue=0.1,
                          ),
                      ]
                  ),
                  transforms.RandomGrayscale(p=0.2),
              ])
  
  
  normalize = transforms.Compose([
              transforms.ToTensor(),
              transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
          ])
  
  transform_plain = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Resize((224, 224)),
              flip_and_jitter,
  
              transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
  
          ])
  
  data_path = pathlib.Path('/home/black0017/data/imagenette2-320/train')
  
  
  dataset = ImageFolder(data_path,transform=transform_plain)
  
  data = dataset[0]
  image , label = data
  print(image.shape)
  print(type(image))
  
  data_loader = torch.utils.data.DataLoader(dataset ,batch_size =20)
  
  data = next(iter(data_loader))
  
  im, _ = data
  print(im.shape)