Why Tensorflow's non-standard VGG implementation?

Question

Tensorflow .slim library implements vgg, but replaces the fully connected layers with convolutional ones. The code is at:

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/slim/python/slim/nets/vgg.py

So for example, fc8 layer is implemented as:

net = layers.conv2d(
      net,
      num_classes, [1, 1],
      activation_fn=None,
      normalizer_fn=None,
      scope='fc8')

which looks like a very different operation, with much fewer weights. What is the reason for this?