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 an action predicted by an agent but before execution in an environment.
  
  # Arguments
      action (int): Action given to the environment
  
  # Returns
      Processed action given to the environment
docstr_tok: ["Processes","an","action","predicted","by","an","agent","but","before","execution","in","an","environment",".","#","Arguments","action","(","int",")",":","Action","given","to","the","environment","#","Returns","Processed","action","given","to","the","environment"]
code: def process_action(self, action):
      """Processes an action predicted by an agent but before execution in an environment.
  
      # Arguments
          action (int): Action given to the environment
  
      # Returns
          Processed action given to the environment
      """
      return action
  
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 5 for calling the Processor obj's underlying member method code with example usage: obj.process_action(action) and returns: action