docstr: Class for training a reranking model.
code: def rerank_train(self):
      """Class for training a reranking model."""
      (
          x,
          y,
          _,
          _,
          _,
      ) = self.load_terms_and_y_labels_and_generate_features(
          self.train_rerank_data_path
      )
  
      try:
          print("\nModel training...")
          model = self.generate_model(x.shape[1])
          e = self.epochs_and_batch_size[0][0]
          b = self.epochs_and_batch_size[0][1]
  
          model.fit(x, y, epochs=e, batch_size=b, verbose=0)
          RerankTerms.model_dir.mkdir(parents=True, exist_ok=True)
  
          model.save(str(RerankTerms.model_dir) + "/rerank_model.h5")
          print(
              "\nSaved model to: "
              + str(RerankTerms.model_dir)
              + "/rerank_model.h5"
          )
  
      except ZeroDivisionError:
          print("Division by zero, skipping test")
  
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 15 for calling the RerankTerms obj's underlying member method code with example usage: obj.rerank_train() without return types