class_docstr: Defines the configuration and handles the conversion and mutation of
  individual genomes. Should be created and passed to a `DEvol` instance.
  
  ---
  Genomes are represented as fixed-with lists of integers corresponding
  to sequential layers and properties. A model with 2 convolutional layers
  and 1 dense layer would look like:
  
  [<conv layer><conv layer><dense layer><optimizer>]
  
  The makeup of the convolutional layers and dense layers is defined in the
  GenomeHandler below under self.convolutional_layer_shape and
  self.dense_layer_shape. <optimizer> consists of just one property.
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code: def decode_best(self, csv_path, metric="accuracy"):
      return self.decode(self.best_genome(csv_path, metric, False))
  
file_path: devol/devol/genome_handler.py
imports: import numpy
  import random
  import math
  from keras.models import Sequential
  from keras.layers import Activation, Dense, Dropout, Flatten
  from keras.layers.convolutional import Convolution2D, MaxPooling2D
  from keras.layers.normalization import BatchNormalization
label_desc: Use image node_id 10 for calling the GenomeHandler obj's underlying member method code with example usage: obj.decode_best(csv_path, metric) and returns: self