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: Get learning rate for current step.
docstr_tok: ["Get","learning","rate","for","current","step","."]
code: def lr(self):
      r"""Get learning rate for current step."""
      raise NotImplementedError
  
imports: import numpy
  from torch.optim import Optimizer
  from typing import Tuple
label_desc: Use image node_id 4 for calling the AbstractScheduler obj's underlying member method code with example usage: obj.lr() without return types