text: import pickle
  import os
  import numpy as np
  import matplotlib.pyplot as plt
  from PIL import Image
  from torch.utils.data import Dataset
  
  class CIFAR10(Dataset):
      
      def __init__(self,root,train=True,transforms=None):
          self.root = root
          self.transforms = transforms
          self.split = train
          
          self.data = []
          self.targets = []
          self.train_data = [file for file in os.listdir(root) if "data_batch" in file]
          self.test_data = [file for file in os.listdir(root) if "test_batch" in file]
                  
          data_split = self.train_data if self.split else self.test_data
          
          for files in data_split:
              entry = self.extract(os.path.join(root,files))
              self.data.append(entry["data"])
              self.targets.extend(entry["labels"])
                  
          self.data = np.vstack(self.data).reshape(-1, 3, 32, 32)
          self.data = self.data.transpose((0, 2, 3, 1))
          self.load_meta()
          
      def extract(self,filename):
          with open(filename,"rb") as f:
              batch_data = pickle.load(f,encoding="latin1")
          return batch_data  
      
      def load_meta(self):
          path = os.path.join(self.root,"batches.meta")
          with open(path,"rb") as infile:
              data = pickle.load(infile,encoding="latin1")
              self.classes = data["label_names"]
              self.classes_to_idx = {_class:i for i,_class in enumerate(self.classes)}
              
      def plot(self,image,target=None):
          if target is not None:
              print(f"Target :{target} class :{self.classes[target]}")
          plt.figure(figsize=(2,2))
          plt.imshow(image.permute(1,2,0))
          plt.show()
          
      def __len__(self):
          return len(self.data)
      
      def __getitem__(self,idx):
          image,target = self.data[idx],self.targets[idx]
          image = Image.fromarray(image)
          
          if self.transforms:
              image = self.transforms(image)
              
          return image,target