class_docstr: Encourage higher PAs, but penalize stacking all the amounts within a very short time.
  Formally, for each time step, the reward is :math:`(PA_t * vol_t / target - vol_t^2 * penalty)`.
  
  Parameters
  ----------
  penalty
      The penalty for large volume in a short time.
  scale
      The weight used to scale up or down the reward.
code: def __init__(
      self, penalty: float = 100.0, scale: float = 1.0
  ) -> None:
      self.penalty = penalty
      self.scale = scale
  
file_path: qlib/qlib/rl/order_execution/reward.py
imports: from __future__ import annotations
  from typing import cast
  import numpy
  from qlib.backtest.decision import OrderDir
  from qlib.rl.order_execution.state import SAOEMetrics, SAOEState
  from qlib.rl.reward import Reward
label_desc: Use image node_id 1 to create a new PAPenaltyReward object from inherited base classes: Reward with example: obj = PAPenaltyReward(penalty, scale)