class_docstr: Warm up the parameter value of each parameter group by quadratic
  formula:
  
  .. math::
  
      X_{t} = X_{t-1} + \frac{2t+1}{{(end-begin)}^{2}} \times X_{base}
  
  Args:
      optimizer (Optimizer): Wrapped optimizer.
      param_name (str): Name of the parameter to be adjusted, such as
          ``lr``, ``momentum``.
      begin (int): Step at which to start updating the parameters.
          Defaults to 0.
      end (int): Step at which to stop updating the parameters.
          Defaults to INF.
      last_step (int): The index of last step. Used for resume without
          state dict. Defaults to -1.
      by_epoch (bool): Whether the scheduled parameters are updated by
          epochs. Defaults to True.
      verbose (bool): Whether to print the value for each update.
          Defaults to False.
code: def __init__(
      self,
      optimizer: Optimizer,
      param_name: str,
      begin: int = 0,
      end: int = INF,
      last_step: int = -1,
      by_epoch: bool = True,
      verbose: bool = False,
  ):
      if end >= INF:
          raise ValueError(
              "``end`` must be less than infinity,"
              "Please set ``end`` parameter of "
              "``QuadraticWarmupScheduler`` as the "
              "number of warmup end."
          )
      self.total_iters = end - begin
      super().__init__(
          optimizer=optimizer,
          param_name=param_name,
          begin=begin,
          end=end,
          last_step=last_step,
          by_epoch=by_epoch,
          verbose=verbose,
      )
  
file_path: mmdetection/mmdet/engine/schedulers/quadratic_warmup.py
imports: from mmengine.optim.scheduler.lr_scheduler import LRSchedulerMixin
  from mmengine.optim.scheduler.momentum_scheduler import MomentumSchedulerMixin
  from mmengine.optim.scheduler.param_scheduler import INF, _ParamScheduler
  from torch.optim import Optimizer
  from mmdet.registry import PARAM_SCHEDULERS
label_desc: Use image node_id 1 to create a new QuadraticWarmupParamScheduler object from inherited base classes: _ParamScheduler with example: obj = QuadraticWarmupParamScheduler(optimizer, param_name, begin, end, last_step, by_epoch, verbose)