class_docstr: RMSProp optimizer.
  
  Parameters
  ----------
  lr
  rho
  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.RMSProp()
  >>> model = (
  ...     preprocessing.StandardScaler() |
  ...     linear_model.LogisticRegression(optimizer)
  ... )
  >>> metric = metrics.F1()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  F1: 87.24%
  
  References
  ----------
  [^1]: [Divide the gradient by a running average of itsrecent magnitude](https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
code: def __init__(self, lr=0.1, rho=0.9, eps=1e-8):
      super().__init__(lr)
      self.rho = rho
      self.eps = eps
      self.g2 = None
  
file_path: river/river/optim/rms_prop.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 RMSProp object from inherited base classes: optim.base with example: obj = RMSProp(lr, rho, eps)