class_docstr: Abstract base class for implementing processors.
  
  A processor acts as a coupling mechanism between an `Agent` and its `Env`. This can
  be necessary if your agent has different requirements with respect to the form of the
  observations, actions, and rewards of the environment. By implementing a custom processor,
  you can effectively translate between the two without having to change the underlaying
  implementation of the agent or environment.
  
  Do not use this abstract base class directly but instead use one of the concrete implementations
  or write your own.
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docstr: Processes the reward as obtained from the environment for use in an agent and
  returns it.
  
  # Arguments
      reward (float): A reward as obtained by the environment
  
  # Returns
      Reward obtained by the environment processed
docstr_tok: ["Processes","the","reward","as","obtained","from","the","environment","for","use","in","an","agent","and","returns","it",".","#","Arguments","reward","(","float",")",":","A","reward","as","obtained","by","the","environment","#","Returns","Reward","obtained","by","the","environment","processed"]
code: def process_reward(self, reward):
      """Processes the reward as obtained from the environment for use in an agent and
      returns it.
  
      # Arguments
          reward (float): A reward as obtained by the environment
  
      # Returns
          Reward obtained by the environment processed
      """
      return reward
  
imports: import warnings
  from copy import deepcopy
  import numpy
  from keras.callbacks import History
  from rl.callbacks import CallbackList, TestLogger, TrainEpisodeLogger, TrainIntervalLogger, Visualizer, WandbLogger
label_desc: Use image node_id 3 for calling the Processor obj's underlying member method code with example usage: obj.process_reward(reward) and returns: reward