class_docstr: Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps.
  Keeps learning rate equal to 1. after warmup.
code: def get_lr_(self, progress):
      if progress < self.warmup:
          return progress / self.warmup
      return 1.0
  
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 WarmupConstantSchedule obj's underlying member method code with example usage: obj.get_lr_(progress) and returns: int, unknown