code: def _cxIndividual(ind1, ind2, indpb, gene_type):
      for i, gt, rn in zip(
          range(len(ind1)),
          gene_type,
          [random.random() for _ in range(len(ind1))],
      ):
          if rn > indpb:
              continue
          if gt is param_types.Categorical:
              ind1[i], ind2[i] = ind2[i], ind1[i]
          else:
              # Case when parameters are numerical
              if ind1[i] <= ind2[i]:
                  ind1[i] = random.randint(ind1[i], ind2[i])
                  ind2[i] = random.randint(ind1[i], ind2[i])
              else:
                  ind1[i] = random.randint(ind2[i], ind1[i])
                  ind2[i] = random.randint(ind2[i], ind1[i])
  
      return ind1, ind2
  
imports: from __future__ import division
  import os
  import warnings
  import numpy
  import random
  from deap import base, creator, tools, algorithms
  from collections import defaultdict
  from sklearn.base import clone, is_classifier
  from sklearn.model_selection._validation import _fit_and_score
  from sklearn.model_selection._search import BaseSearchCV, check_cv, _check_param_grid
  from sklearn.metrics import check_scoring
  from sklearn.utils.validation import _num_samples, indexable
label_desc: Use image node_id 5 for calling a global function with example usage: _cxIndividual(ind1, ind2, indpb, gene_type) and returns: ind1,  ind2