code: def eval_genome(genome, config):
      
  
      net = neat.nn.FeedForwardNetwork.create(genome, config)
      error = 4.0
      for xi, xo in zip(xor_inputs, xor_outputs):
          output = net.activate(xi)
          error -= (output[0] - xo[0]) ** 2
      return error
docs: This function will be run in parallel by ParallelEvaluator.  It takes two
      arguments (a single genome and the genome class configuration data) and
      should return one float (that genome's fitness).
  
      Note that this function needs to be in module scope for multiprocessing.Pool
      (which is what ParallelEvaluator uses) to find it.  Because of this, make
      sure you check for __main__ before executing any code (as we do here in the
      last few lines in the file), otherwise you'll have made a fork bomb
      instead of a neuroevolution demo. :)