class_docstr: Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
  Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps.
code: def get_lr_(self, progress):
      if progress < self.warmup:
          return progress / self.warmup
      return max((progress - 1.0) / (self.warmup - 1.0), 0.0)
  
imports: import math
  import torch
  from torch.optim.optimizer import Optimizer
  from torch.nn.utils import clip_grad_norm_
  import logging
  import abc
  import sys
label_desc: Use image node_id 1 for calling the WarmupLinearSchedule obj's underlying member method code with example usage: obj.get_lr_(progress) and returns: max, unknown