class_docstr: Nadam optimizer.
  
  Parameters
  ----------
  lr
  beta_1
  beta_2
  eps
  
  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.Nadam()
  >>> model = (
  ...     preprocessing.StandardScaler() |
  ...     linear_model.LogisticRegression(optimizer)
  ... )
  >>> metric = metrics.F1()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  F1: 86.60%
  
  References
  ----------
  [^1]: [Nadam: A combination of adam and nesterov](https://ruder.io/optimizing-gradient-descent/index.html#nadam)
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 = collections.defaultdict(float)
      self.v = collections.defaultdict(float)
  
file_path: river/river/optim/nadam.py
imports: from __future__ import annotations
  import collections
  import math
  from river import optim
label_desc: Use image node_id 1 to create a new Nadam object from inherited base classes: optim.base with example: obj = Nadam(lr, beta_1, beta_2, eps)