class_docstr: AMSGrad optimizer.
  
  Parameters
  ----------
  lr
      The learning rate.
  beta_1
  beta_2
  eps
  correct_bias
  
  Attributes
  ----------
  m : collections.defaultdict
  v : collections.defaultdict
  v_hat : collections.defaultdict
  
  Examples
  --------
  
  >>> from river import datasets
  >>> from river import evaluate
  >>> from river import linear_model
  >>> from river import metrics
  >>> from river import optim
  >>> from river import preprocessing
  
  >>> dataset = datasets.Phishing()
  >>> optimizer = optim.AMSGrad()
  >>> model = (
  ...     preprocessing.StandardScaler() |
  ...     linear_model.LogisticRegression(optimizer)
  ... )
  >>> metric = metrics.F1()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  F1: 86.60%
  
  References
  ----------
  [^1]: [Reddi, S.J., Kale, S. and Kumar, S., 2019. On the convergence of adam and beyond. arXiv preprint arXiv:1904.09237](https://arxiv.org/pdf/1904.09237.pdf)
code: def __init__(
      self,
      lr: int | float | optim.base.Scheduler = 0.1,
      beta_1=0.9,
      beta_2=0.999,
      eps=1e-8,
      correct_bias=True,
  ):
      super().__init__(lr)
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.eps = eps
      self.correct_bias = correct_bias
      self.m: dict[str, float] = collections.defaultdict(float)
      self.v: dict[str, float] = collections.defaultdict(float)
      self.v_hat: dict[str, float] = collections.defaultdict(float)
  
imports: from __future__ import annotations
  import collections
  from river import optim
label_desc: Use image node_id 1 to create a new AMSGrad object from inherited base classes: optim.base with example: obj = AMSGrad(lr, beta_1, beta_2, eps, correct_bias)