Torch anderson

The list of changes:

• reimplemented utils/anderson_acceleration.py
• removed modules/AccelerationModule.py:
  • in my opinion, it is unnecessary
  • moved all logic to modules/optimizers.py
• updated modules/optimizers.py:
  • removed abstract Optimizers class
  • the training loop is now implemented only in FixedPointIteration class
  • DeterministicAcceleration now inherits from FixedPointIteration and just reimplements its accelerate method
  • acceleration is now performed every time parameters are updated and not just on every epoch (not sure about this)
  • added with torch.no_grad() logic to avoid memory leaking
  • history of updates is now collections.deque instead of list