docstr: Predict classification class according to model.
  
  Args:
     input_table_file: feature(X) and labels(Y) table file
     generic_opinion_terms: generic opinion terms file name
  
  Returns:
      final_concat_opinion_lex: reranked_lex conctenated with generic lex
code: def predict(self, input_table_file, generic_opinion_terms):
      """Predict classification class according to model.
  
      Args:
         input_table_file: feature(X) and labels(Y) table file
         generic_opinion_terms: generic opinion terms file name
  
      Returns:
          final_concat_opinion_lex: reranked_lex conctenated with generic lex
      """
      x, terms, polarities = self.load_terms_and_generate_features(
          input_table_file
      )
  
      model = load_model(self.rerank_model_path)
      reranked_lexicon = model.predict(x, verbose=0)
  
      reranked_lex = {}
      for i, prediction in enumerate(reranked_lexicon):
          if (
              not np.isnan(prediction[0])
              and prediction[0] > self.PREDICTION_THRESHOLD
          ):
              reranked_lex[terms[i]] = (
                  prediction[0],
                  polarities[terms[i]],
              )
  
      final_concat_opinion_lex = self._generate_concat_reranked_lex(
          reranked_lex, generic_opinion_terms
      )
      return final_concat_opinion_lex
  
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 14 for calling the RerankTerms obj's underlying member method code with example usage: obj.predict(input_table_file, generic_opinion_terms) and returns: final_concat_opinion_lex