class_docstr: Abstract Base Class of Scheduler. To modify your new scheduler, simply inherit
  this class and implement `__init__()` and `lr()` with ``@property`` decorator.
class_docstr_tok: ["Abstract","Base","Class","of","Scheduler",".","To","modify","your","new","scheduler",",","simply","inherit","this","class","and","implement","`","__init__","(",")","`","and","`","lr","(",")","`","with","``","@","property","``","decorator","."]
docstr: Update learning rate. One just need to implement `lr` property.
docstr_tok: ["Update","learning","rate",".","One","just","need","to","implement","`","lr","`","property","."]
code: def _update_learning_rate(self):
      r"""Update learning rate. One just need to implement `lr` property."""
      self.n_steps += 1
      lr = self.lr
  
      for param_group in self.optimizer.param_groups:
          param_group["lr"] = lr
  
imports: import numpy
  from torch.optim import Optimizer
  from typing import Tuple
label_desc: Use image node_id 3 for calling the AbstractScheduler obj's underlying member method code with example usage: obj._update_learning_rate() without return types