code: def initialize_lr_scheduler(
      lr=0.001, epochs=250, epoch_decay_start=80
  ):
      """Scheduler to adjust learning rate and betas for Adam Optimizer"""
      mom1 = 0.9
      mom2 = 0.9  # Original author had this set to 0.1
      alpha_plan = [lr] * epochs
      beta1_plan = [mom1] * epochs
      for i in range(epoch_decay_start, epochs):
          alpha_plan[i] = (
              float(epochs - i) / (epochs - epoch_decay_start) * lr
          )
          beta1_plan[i] = mom2
      return alpha_plan, beta1_plan
  
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 2 for calling a global function with example usage: initialize_lr_scheduler(lr, epochs, epoch_decay_start) and returns: alpha_plan,  beta1_plan