docstr: Perform k fold cross validation and evaluate the results.
code: def cross_validation_training(self, verbose=False):
      """Perform k fold cross validation and evaluate the results."""
      final_report = {}
      (
          x,
          y,
          y_vector,
          terms,
          _,
      ) = self.load_terms_and_y_labels_and_generate_features(
          self.train_rerank_data_path
      )
  
      for seed in self.seeds:
          np.random.seed(seed)
  
          for epochs, batch_size in self.epochs_and_batch_size:
              self.print_params(batch_size, epochs, seed)
              k_fold = StratifiedKFold(
                  n_splits=5, shuffle=True, random_state=seed
              )
              f1_scores = []
              precision_scores = []
              recall_scores = []
  
              try:
                  for i, (train, test) in enumerate(k_fold.split(x, y)):
                      model = self.generate_model(x.shape[1])
                      model.fit(
                          x[train],
                          y_vector[train],
                          epochs=epochs,
                          batch_size=batch_size,
                          verbose=0,
                      )
  
                      measures, report = self.evaluate(
                          model, x[test], y_vector[test], terms[test]
                      )
                      final_report.update(report)
  
                      precision, recall, f1 = measures
                      f1_scores.append(f1)
                      precision_scores.append(precision)
                      recall_scores.append(recall)
  
                      if verbose:
                          print("Fold " + str(i + 1) + ":")
                          self.print_evaluation_results(
                              precision, recall, f1
                          )
  
                  print("\nSummary:")
                  self.print_evaluation_results(
                      precision_scores, recall_scores, f1_scores
                  )
  
              except ZeroDivisionError:
                  print("Division by zero, skipping test")
  
      self.write_evaluation_report(final_report)
  
imports: import csv
  import pickle
  import numpy
  import tensorflow
  from os import PathLike
  from pathlib import Path
  from nlp_architect.models.absa.utils import _read_generic_lex_for_similarity
  from nlp_architect.models.absa import TRAIN_OUT, TRAIN_LEXICONS, GENERIC_OP_LEX, LEXICONS_OUT
  from scipy.spatial.distance import cosine
  from sklearn.model_selection import StratifiedKFold
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.models import Sequential, load_model
  from nlp_architect.utils.embedding import load_word_embeddings
label_desc: Use image node_id 16 for calling the RerankTerms obj's underlying member method code with example usage: obj.cross_validation_training(verbose) without return types