code: def lenet5(images, labels):
    
    images = pt.wrap(images)
    with pt.defaults_scope(activation_fn=tf.nn.relu, l2loss=0.00001):
      return (images.conv2d(5, 20).max_pool(2, 2).conv2d(5, 50).max_pool(2, 2)
              .flatten().fully_connected(500).softmax_classifier(10, labels))
docs: Creates a multi layer convolutional network.
  
    The architecture is similar to that defined in LeNet 5.
    Please change this to experiment with architectures.
  
    Args:
      images: The input images.
      labels: The labels as dense one-hot vectors.
    Returns:
      A softmax result.