Update adversary attack generation example (#12918)

* Fix adversary example generation

* Update README.md

* Fix test_utils.list_gpus()

* fix unused variable

example/adversary/README.md

@@ -1,7 +1,7 @@
 # Adversarial examples
 
 This demonstrates the concept of "adversarial examples" from [1] showing how to fool a well-trained CNN.
-The surprising idea is that one can easily generate examples which the CNN will consistently 
-make the wrong prediction for that a human can easily tell are correct.
+Adversarial examples are samples where the input has been manipulated to confuse a model (i.e. confident in an incorrect prediction) but where the correct answer still appears obvious to a human.
+This method for generating adversarial examples uses the gradient of the loss with respect to the input to craft the adversarial examples.
 
 [1] Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." [arXiv preprint arXiv:1412.6572 (2014)](https://arxiv.org/abs/1412.6572)