code: def select(self):
      dart = rand.uniform(0, self.s_fit)
      sum_fits = 0
      for i in range(len(self.members)):
          sum_fits += self.scores[i]
          if sum_fits >= dart:
              return self.members[i]
  
imports: from __future__ import print_function
  import random
  import csv
  import operator
  import gc
  import os
  from datetime import datetime
  from keras.callbacks import EarlyStopping
  from keras.models import load_model
  import keras.backend
  from sklearn.metrics import log_loss
  import numpy
label_desc: Use image node_id 4 for calling the _Population obj's underlying member method code with example usage: obj.select() and returns: self