class_docstr: Reduces the learning rate using a power schedule.
  
  Assuming an initial learning rate $\eta$, the learning rate at step $t$ is:
  
  $$\\frac{eta}{(t + 1) ^ p}$$
  
  where $p$ is a user-defined parameter.
  
  Parameters
  ----------
  learning_rate
  power
class_docstr_tok: ["Reduces","the","learning","rate","using","a","power","schedule",".","Assuming","an","initial","learning","rate","$","\\eta","$",",","the","learning","rate","at","step","$","t","$","is",":","$","$","\\\\frac","{","eta","}","{","(","t","+","1",")","^","p","}","$","$","where","$","p","$","is","a","user-defined","parameter",".","Parameters","--","--","--","--","--","learning_rate","power"]
code: def __init__(self, learning_rate: float, power=0.5):
      self.learning_rate = learning_rate
      self.power = power
  
file_path: river/river/optim/schedulers.py
imports: from __future__ import annotations
  import math
  from river import optim
label_desc: Use image node_id 1 to create a new InverseScaling object from inherited base classes: optim.base with example: obj = InverseScaling(learning_rate, power)