code: def readme():
      data = sklearn.datasets.load_digits()
      X = data["data"]
      y = data["target"]
  
      paramgrid = {
          "kernel": ["rbf"],
          "C": np.logspace(-9, 9, num=25, base=10),
          "gamma": np.logspace(-9, 9, num=25, base=10),
      }
  
      random.seed(1)
  
      cv = EvolutionaryAlgorithmSearchCV(
          estimator=SVC(),
          params=paramgrid,
          scoring="accuracy",
          cv=StratifiedKFold(n_splits=4),
          verbose=1,
          population_size=10,
          gene_mutation_prob=0.10,
          gene_crossover_prob=0.5,
          tournament_size=3,
          generations_number=5,
      )
      cv.fit(X, y)
      return cv
  
file_path: sklearn-deap/test.py
imports: from evolutionary_search import EvolutionaryAlgorithmSearchCV, maximize
  import sklearn.datasets
  import numpy
  from sklearn.model_selection import StratifiedKFold
  from sklearn.svm import SVC
  import unittest
  import random
label_desc: Use image node_id 2 for calling a global function with example usage: readme() and returns: cv