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.
docstr: Build an iter-based instance of this scheduler from an epoch-based
  config.
docstr_tok: ["Build","an","iter-based","instance","of","this","scheduler","from","an","epoch-based","config","."]
code: def build_iter_from_epoch(
      cls,
      *args,
      begin=0,
      end=INF,
      by_epoch=True,
      epoch_length=None,
      **kwargs,
  ):
      """Build an iter-based instance of this scheduler from an epoch-based
      config."""
      assert by_epoch, (
          "Only epoch-based kwargs whose `by_epoch=True` can "
          "be converted to iter-based."
      )
      assert epoch_length is not None and epoch_length > 0, (
          f"`epoch_length` must be a positive integer, "
          f"but got {epoch_length}."
      )
      by_epoch = False
      begin = begin * epoch_length
      if end != INF:
          end = end * epoch_length
      return cls(
          *args, begin=begin, end=end, by_epoch=by_epoch, **kwargs
      )
  
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 2 for calling the QuadraticWarmupParamScheduler obj's underlying member method code with example usage: obj.build_iter_from_epoch(cls) and returns: cls