code: def adjust_learning_rate(optimizer, epoch, alpha_plan, beta1_plan):
      """Scheduler to adjust learning rate and betas for Adam Optimizer"""
      for param_group in optimizer.param_groups:
          param_group["lr"] = alpha_plan[epoch]
          param_group["betas"] = (beta1_plan[epoch], 0.999)
  
file_path: cleanlab/cleanlab/experimental/coteaching.py
imports: import torch
  import torch.nn.functional
  from torch.autograd import Variable
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: adjust_learning_rate(optimizer, epoch, alpha_plan, beta1_plan) without return types