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","."]
code: def __init__(self, base_optimizer: Optimizer, init_lr: float):
      self.optimizer = base_optimizer
      self.init_lr = init_lr
      self.n_steps: int = 0
  
imports: import numpy
  from torch.optim import Optimizer
  from typing import Tuple
label_desc: Use image node_id 1 to create a new AbstractScheduler object  with example: obj = AbstractScheduler(base_optimizer, init_lr)