text: import torch.optim as optim
  import torch.nn.functional as F
  import torch.nn as nn
  import numpy as np
  import matplotlib.pyplot as plt
  import torch
  import torchvision
  import torchvision.transforms as transforms
  from datetime import datetime
  
  # check if cuda is enabled
  USE_GPU=1
  # Device configuration
  device = torch.device('cuda' if (torch.cuda.is_available() and USE_GPU) else 'cpu')
  
  
  # Assume that we are on a CUDA machine, then this should print a CUDA device:
  print(f'Using {device} device')
  
  num_classes = 10
  num_epochs = 5
  batch_size = 4
  
  
  transform = transforms.Compose(
      [transforms.ToTensor(),
       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
  
  trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                          download=True, transform=transform)
  trainloader = torch.utils.data.DataLoader(trainset,
                                            batch_size=batch_size,
                                            shuffle=True)
  
  testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                         download=True, transform=transform)
  testloader = torch.utils.data.DataLoader(testset,
                                           batch_size=batch_size,
                                           shuffle=False)
  
  classes = ('plane', 'car', 'bird', 'cat',
             'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
  
  
  # functions to show an image
  
  
  def imshow(img):
      img = img / 2 + 0.5     # unnormalize
      npimg = img.numpy()
      plt.imshow(np.transpose(npimg, (1, 2, 0)))
  
  
  # # get some random training images
  # dataiter = iter(trainloader)
  # images, labels = dataiter.next()
  
  # # show images
  # imshow(torchvision.utils.make_grid(images))
  # plt.show()
  
  # # print labels
  # print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))
  
  
  class ConvNet(nn.Module):
      def __init__(self):
          super(ConvNet, self).__init__()
  
          self.pool = nn.MaxPool2d(2, 2)
  
          # channel_in=3 channels_out=8
          self.conv1 = nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3, stride=1, padding=1)
          
          self.conv2 = nn.Conv2d(8, 16, kernel_size=3, stride=1, padding=1)
  
          self.conv3 = nn.Conv2d(16, 24, kernel_size=3, stride=1, padding=1)
  
          # 24 chaneels by 8x8 pixesl
          self.fc1 = nn.Linear(24 * 8 * 8, 100)
  
          self.output = nn.Linear(100, 10)
  
      def forward(self, x):
  
          x = F.relu(self.conv1(x))
  
          # max_pooling will resize input from 32 to 16
          x = self.pool(F.relu(self.conv2(x)))
          # max_pooling will resize input from 16 to 8
          x = self.pool(F.relu(self.conv3(x)))
  
          x = x.view(-1, 24 * 8 * 8)
          x = F.relu(self.fc1(x))
          x = self.output(x)
          return x
  
  
  net = ConvNet()
  net.to(device)
  
  criterion = nn.CrossEntropyLoss()
  #optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
  optimizer = optim.Adam(net.parameters(), lr=0.001)
  
  
  for epoch in range(num_epochs):  # loop over the dataset multiple times
  
      start_time = datetime.now()
      net.train()
      running_loss = 0.0
      epoch_loss = 0
      for i, data in enumerate(trainloader, 0):
          # get the inputs
          inputs, labels = data
          # move data to device (GPU if enabled, else CPU do nothing)
          inputs, labels = inputs.to(device), labels.to(device)
  
          # zero the parameter gradients
          optimizer.zero_grad()
  
          # forward + backward + optimize
          outputs = net(inputs)
          loss = criterion(outputs, labels)
          loss.backward()
          optimizer.step()
  
          # print statistics
          epoch_loss += loss.item()
  
      epoch_loss = epoch_loss / len(trainloader)
  
      time_elapsed = datetime.now() - start_time
  
      # Test the model
  
      # set our model in the training mode
      net.eval()
      # In test phase, we don't need to compute gradients (for memory efficiency)
      correct = 0
      total = 0
      with torch.no_grad():
          for data in testloader:
              inputs, labels = data
              inputs, labels = inputs.to(device), labels.to(device)
              outputs = net(inputs)
              _, predicted = torch.max(outputs.data, 1)
              total += labels.size(0)
              correct += (predicted == labels).sum().item()
  
      # Accuracy of the network on the 10000 test images
      acc = correct/total
      print(
          f'Epoch [{epoch+1}/{num_epochs}], Loss: {epoch_loss:.4f} Test acc: {acc} time={time_elapsed}')
  
  
  print('Finished Training')
  
  # Detailed accuracy per class
  class_correct = list(0. for i in range(10))
  class_total = list(0. for i in range(10))
  with torch.no_grad():
      for data in testloader:
          inputs, labels = data
          inputs, labels = inputs.to(device), labels.to(device)
          outputs = net(inputs)
          _, predicted = torch.max(outputs, 1)
          c = (predicted == labels).squeeze()
          for i in range(4):
                  label = labels[i]
                  class_correct[label] += c[i].item()
                  class_total[label] += 1
  
  
  for i in range(10):
      print('Accuracy of %5s : %2d %%' % (
          classes[i], 100 * class_correct[i] / class_total[i]))
  