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: Compute value using chainable form of the scheduler.
docstr_tok: ["Compute","value","using","chainable","form","of","the","scheduler","."]
code: def _get_value(self):
      """Compute value using chainable form of the scheduler."""
      if self.last_step == 0:
          return [
              base_value
              * (2 * self.last_step + 1)
              / self.total_iters**2
              for base_value in self.base_values
          ]
  
      return [
          group[self.param_name]
          + base_value
          * (2 * self.last_step + 1)
          / self.total_iters**2
          for base_value, group in zip(
              self.base_values, self.optimizer.param_groups
          )
      ]
  
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 3 for calling the QuadraticWarmupParamScheduler obj's underlying member method code with example usage: obj._get_value() and returns: unknown, unknown