Implements elastic-net attack

[rsokl]

Derived from: https://arxiv.org/pdf/1709.04114.pdf

Here is a trivial scenario where we are merely perturbing the "logits" themselves so that the specified targets will be optimized for. Let's see that the longer we run the optimizer, the more the learned perturbation shrinks (while still amounting to a successful attack).

>>> from rai_toolbox.perturbations.solvers import elastic_net_attack
>>> logits = [[0.497, 0.503]]
>>> target = [0]

>>> for num_steps in [1, 10, 100]:
...     _, x_adv, _ = elastic_net_attack(
...         model=lambda x: x,
...         data=logits,
...         target=target,
...         beta=1e-3,
...         c=2,
...         steps=num_steps,
...         confidence=.01,
...         lr=0.5,
...     )
...     print(f"num-steps: {num_steps}\n{x_adv}")
num-steps: 1
tensor([[ 1.4960, -0.4960]])
num-steps: 10
tensor([[0.5062, 0.4938]])
num-steps: 100
tensor([[0.5018, 0.4982]])