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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def run(config_file):
 	"""load the config, create a population, evolve and show the result"""

	# Load configuration.
	config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
						 neat.DefaultSpeciesSet, neat.DefaultStagnation,
						 config_file)

	# Create the population, which is the top-level object for a NEAT run.
	p = neat.Population(config)

	# Add a stdout reporter to show progress in the terminal.
	p.add_reporter(neat.StdOutReporter(True))
	stats = neat.StatisticsReporter()
	p.add_reporter(stats)

	# Run for up to 300 generations.
	pe = neat.ThreadedEvaluator(4, eval_genome)
	winner = p.run(pe.evaluate, 300)
	pe.stop()

	# Display the winning genome.
	print('\nBest genome:\n{!s}'.format(winner))

	# Show output of the most fit genome against training data.
	print('\nOutput:')
	winner_net = neat.nn.FeedForwardNetwork.create(winner, config)
	for xi, xo in zip(xor_inputs, xor_outputs):
		output = winner_net.activate(xi)
		print(
			"input {!r}, expected output {!r}, got {!r}".format(xi, xo, output)
			)

	if visualize is not None:
		node_names = {-1: 'A', -2: 'B', 0: 'A XOR B'}
		visualize.draw_net(config, winner, True, node_names=node_names)
		visualize.plot_stats(stats, ylog=False, view=True)
		visualize.plot_species(stats, view=True)