text: import torch
  import torchvision
  import torchvision.transforms as transforms
  
  import matplotlib.pyplot as plt
  import numpy as np
  
  #download dataset start
  transform = transforms.Compose(
      [transforms.ToTensor(),
       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
  
  trainset = torchvision.datasets.CIFAR10(root='./PyTorch/data', train=True,
                                          download=True, transform=transform)
  trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                            shuffle=True, num_workers=0)
  
  testset = torchvision.datasets.CIFAR10(root='./PyTorch/data', train=False,
                                         download=True, transform=transform)
  testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                           shuffle=False, num_workers=0)
  
  classes = ('plane', 'car', 'bird', 'cat',
             'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
  
  #download dataset end
  
  def imshow(img):
      img = img / 2 + 0.5     # unnormalize
      npimg = img.numpy()
      plt.imshow(np.transpose(npimg, (1, 2, 0)))
      plt.show()
  
  
  # get some random training images
  dataiter = iter(trainloader)
  images, labels = dataiter.next()
  
  # show images
  imshow(torchvision.utils.make_grid(images))
  # print labels
  print(' '.join('%5s' % classes[labels[j]] for j in range(4)))