class_docstr: A learning rate decay policy, where the effective learning rate is
  calculated as base_lr * gamma ^ (floor(iter / step_size))
  
  
  :param step_size:
  :param gamma:
  
  
  >>> step = Step(2, 0.3)
  creating: createStep
code: def get_scheduler(self) -> "optimizer.Step":
      return self.scheduler
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, TYPE_CHECKING
label_desc: Use image node_id 2 for calling the Step obj's underlying member method code with example usage: obj.get_scheduler() and returns: self