code: def get_cosine_schedule_with_warmup(
      optimizer: Optimizer,
      num_warmup_steps: int,
      num_training_steps: int,
      num_cycles: float = 0.5,
      last_epoch: int = -1,
  ):
      """
      Create a schedule with a learning rate that decreases following the values of the cosine function between the
      initial lr set in the optimizer to 0, after a warmup period during which it increases linearly between 0 and the
      initial lr set in the optimizer.
  
      Args:
          optimizer (:class:`~torch.optim.Optimizer`):
              The optimizer for which to schedule the learning rate.
          num_warmup_steps (:obj:`int`):
              The number of steps for the warmup phase.
          num_training_steps (:obj:`int`):
              The total number of training steps.
          num_cycles (:obj:`float`, `optional`, defaults to 0.5):
              The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0
              following a half-cosine).
          last_epoch (:obj:`int`, `optional`, defaults to -1):
              The index of the last epoch when resuming training.
  
      Return:
          :obj:`torch.optim.lr_scheduler.LambdaLR` with the appropriate schedule.
      """
  
      def lr_lambda(current_step):
          if current_step < num_warmup_steps:
              return float(current_step) / float(
                  max(1, num_warmup_steps)
              )
          progress = float(current_step - num_warmup_steps) / float(
              max(1, num_training_steps - num_warmup_steps)
          )
          return max(
              0.0,
              0.5
              * (
                  1.0
                  + math.cos(
                      math.pi * float(num_cycles) * 2.0 * progress
                  )
              ),
          )
  
      return LambdaLR(optimizer, lr_lambda, last_epoch)
  
file_path: towhee/towhee/trainer/optimization/optimization.py
imports: import math
  from typing import Optional, Union
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR
  from towhee.trainer.utils.trainer_utils import SchedulerType
label_desc: Use image node_id 4 for calling a global function with example usage: get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles, last_epoch) and returns: LambdaLR, max, unknown