class_docstr: Exponentially anneal the lr of each parameter group from the initial lr to end_lr over a number of iterations.
  
  Args:
  ----
      optimizer (Optimizer): Wrapped optimizer.
      end_lr (float): The final learning rate.
      iterations (int): The number of iterations over which to increase the
          learning rate.
      last_epoch (int): The index of the last iteration. Default: -1.
class_docstr_tok: ["Exponentially","anneal","the","lr","of","each","parameter","group","from","the","initial","lr","to","end_lr","over","a","number","of","iterations",".","Args",":","--","--","optimizer","(","Optimizer",")",":","Wrapped","optimizer",".","end_lr","(","float",")",":","The","final","learning","rate",".","iterations","(","int",")",":","The","number","of","iterations","over","which","to","increase","the","learning","rate",".","last_epoch","(","int",")",":","The","index","of","the","last","iteration",".","Default",":","-1","."]
code: def get_lr(self):
      iteration = self.last_epoch + 1
      pct = iteration / self.iterations
      return [
          base_lr * (self.end_lr / base_lr) ** pct
          for base_lr in self.base_lrs
      ]
  
imports: import logging
  import torch
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR, ReduceLROnPlateau, _LRScheduler
  from torch.optim.optimizer import required
label_desc: Use image node_id 2 for calling the ExpAnnealLR obj's underlying member method code with example usage: obj.get_lr() and returns: unknown