code: def warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor):
      def f(x):
          """根据step数返回一个学习率倍率因子"""
          if x >= warmup_iters:  # 当迭代数大于给定的warmup_iters时，倍率因子为1
              return 1
          alpha = float(x) / warmup_iters
          # 迭代过程中倍率因子从warmup_factor -> 1
          return warmup_factor * (1 - alpha) + alpha
  
      return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=f)
  
imports: from collections import defaultdict, deque
  import datetime
  import pickle
  import time
  import errno
  import os
  from contextlib import contextmanager
  import torch
  import torch.distributed
label_desc: Use image node_id 3 for calling a global function with example usage: warmup_lr_scheduler(optimizer, warmup_iters, warmup_factor) and returns: torch, unknown, int