code: def get_piecewise_constant_schedule(
      optimizer: Optimizer, step_rules: str, last_epoch: int = -1
  ):
      """
      Create a schedule with a constant learning rate, using the learning rate set in optimizer.
  
      Args:
          optimizer ([`~torch.optim.Optimizer`]):
              The optimizer for which to schedule the learning rate.
          step_rules (`string`):
              The rules for the learning rate. ex: rule_steps="1:10,0.1:20,0.01:30,0.005" it means that the learning rate
              if multiple 1 for the first 10 steps, mutiple 0.1 for the next 20 steps, multiple 0.01 for the next 30
              steps and multiple 0.005 for the other steps.
          last_epoch (`int`, *optional*, defaults to -1):
              The index of the last epoch when resuming training.
  
      Return:
          `torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
      """
  
      rules_dict = {}
      rule_list = step_rules.split(",")
      for rule_str in rule_list[:-1]:
          value_str, steps_str = rule_str.split(":")
          steps = int(steps_str)
          value = float(value_str)
          rules_dict[steps] = value
      last_lr_multiple = float(rule_list[-1])
  
      def create_rules_function(rules_dict, last_lr_multiple):
          def rule_func(steps: int) -> float:
              sorted_steps = sorted(rules_dict.keys())
              for i, sorted_step in enumerate(sorted_steps):
                  if steps < sorted_step:
                      return rules_dict[sorted_steps[i]]
              return last_lr_multiple
  
          return rule_func
  
      rules_func = create_rules_function(rules_dict, last_lr_multiple)
  
      return LambdaLR(optimizer, rules_func, last_epoch=last_epoch)
  
file_path: llmga/llmga/diffusers/src/diffusers/optimization.py
imports: import math
  from enum import Enum
  from typing import Optional, Union
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR
  from .utils import logging
label_desc: Use image node_id 3 for calling a global function with example usage: get_piecewise_constant_schedule(optimizer, step_rules, last_epoch) and returns: LambdaLR, rule_func, last_lr_multiple, rules_dict