class_docstr: Online Newton Step (ONS) optimizer.
  
  This optimizer uses second-order information (i.e. the Hessian of the cost function) in
  addition to first-order information (i.e. the gradient of the cost function).
  
  Parameters
  ----------
  lr
  eps
  
  References
  ----------
  [^1]: [Hazan, E., Agarwal, A. and Kale, S., 2007. Logarithmic regret algorithms for online convex optimization. Machine Learning, 69(2-3), pp.169-192](https://www.cs.princeton.edu/~ehazan/papers/log-journal.pdf)
code: def __init__(self, lr=0.1, eps=1e-5):
      super().__init__(lr)
      self.eps = eps
      self.H_inv = {}
  
imports: from __future__ import annotations
  from river import optim, utils
label_desc: Use image node_id 1 to create a new Newton object from inherited base classes: optim.base with example: obj = Newton(lr, eps)