code: def __init__(self, action_low: np.ndarray, action_high: np.ndarray):
      self.action_low = torch.tensor(action_low)
      self.action_high = torch.tensor(action_high)
      (train_low, train_high) = CONTINUOUS_TRAINING_ACTION_RANGE
      self.train_low = torch.tensor(train_low)
      self.train_high = torch.tensor(train_high)
  
imports: import inspect
  import logging
  from typing import Dict, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.types import Trajectory
  from reagent.preprocessing.types import InputColumn
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import rescale_actions
label_desc: Use image node_id 1 to create a new PolicyNetworkInputMaker object  with example: obj = PolicyNetworkInputMaker(action_low, action_high)