class_docstr: Adam optimizer.
  
  Parameters
  ----------
  lr
  beta_1
  beta_2
  eps
  
  Attributes
  ----------
  m : collections.defaultdict
  v : 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.Adam()
  >>> model = (
  ...     preprocessing.StandardScaler() |
  ...     linear_model.LogisticRegression(optimizer)
  ... )
  >>> metric = metrics.F1()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  F1: 86.52%
  
  References
  ----------
  [^1]: [Kingma, D.P. and Ba, J., 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.](https://arxiv.org/pdf/1412.6980.pdf)
code: def __init__(self, lr=0.1, beta_1=0.9, beta_2=0.999, eps=1e-8):
      super().__init__(lr)
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.eps = eps
      self.m = None
      self.v = None
  
file_path: river/river/optim/adam.py
imports: from __future__ import annotations
  import collections
  import numpy
  from river import optim, utils
label_desc: Use image node_id 1 to create a new Adam object from inherited base classes: optim.base with example: obj = Adam(lr, beta_1, beta_2, eps)