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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def lenet5(images, labels):
 """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. """

  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))