code: def get_constant_schedule_with_warmup(
      optimizer: Optimizer, num_warmup_steps: int, last_epoch: int = -1
  ):
      """
      Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate
      increases linearly between 0 and the initial lr set in the optimizer.
  
      Args:
          optimizer ([`~torch.optim.Optimizer`]):
              The optimizer for which to schedule the learning rate.
          num_warmup_steps (`int`):
              The number of steps for the warmup phase.
          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.
      """
  
      def lr_lambda(current_step: int):
          if current_step < num_warmup_steps:
              return float(current_step) / float(
                  max(1.0, num_warmup_steps)
              )
          return 1.0
  
      return LambdaLR(optimizer, lr_lambda, 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 2 for calling a global function with example usage: get_constant_schedule_with_warmup(optimizer, num_warmup_steps, last_epoch) and returns: LambdaLR, int, unknown