class_docstr: Creates a LR scheduler with a constant learning rate preceded by a
  warmup period during which the learning rate increases linearly between
  :obj:`0` and the initial LR set in the optimizer.
  
  Args:
      optimizer (Optimizer): The optimizer to be scheduled.
      num_warmup_steps (int): The number of steps for the warmup phase.
      last_epoch (int, optional): The index of the last epoch when resuming
          training. (default: :obj:`-1`)
class_docstr_tok: ["Creates","a","LR","scheduler","with","a","constant","learning","rate","preceded","by","a","warmup","period","during","which","the","learning","rate","increases","linearly","between",":","obj",":","`","0","`","and","the","initial","LR","set","in","the","optimizer",".","Args",":","optimizer","(","Optimizer",")",":","The","optimizer","to","be","scheduled",".","num_warmup_steps","(","int",")",":","The","number","of","steps","for","the","warmup","phase",".","last_epoch","(","int",",","optional",")",":","The","index","of","the","last","epoch","when","resuming","training",".","(","default",":",":","obj",":","`","-1","`",")"]
code: def _lr_lambda(
      current_step: int,
      num_warmup_steps: int,
  ) -> float:
      if current_step < num_warmup_steps:
          return float(current_step) / float(max(1.0, num_warmup_steps))
      return 1.0
  
imports: import functools
  import math
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR
label_desc: Use image node_id 2 for calling the ConstantWithWarmupLR obj's underlying member method code with example usage: obj._lr_lambda(current_step, num_warmup_steps) and returns: int, unknown