code: def process_reward(self, reward):
      # The magnitude of the reward can be important. Since each step yields a relatively
      # high reward, we reduce the magnitude by two orders.
      return reward / 100.0
  
file_path: keras-rl/examples/naf_pendulum.py
imports: import numpy
  import gym
  from keras.models import Sequential, Model
  from keras.layers import Dense, Activation, Flatten, Input, Concatenate
  from keras.optimizers import Adam
  from rl.agents import NAFAgent
  from rl.memory import SequentialMemory
  from rl.random import OrnsteinUhlenbeckProcess
  from rl.core import Processor
label_desc: Use image node_id 1 for calling the PendulumProcessor obj's underlying member method code with example usage: obj.process_reward(reward) and returns: unknown