code: def generate_features(self, terms, polarities):
      generic_terms = _read_generic_lex_for_similarity(
          self.generic_lexicon
      )
      # generate unified list of candidate terms and generic terms
      terms_list = [term for term in terms]
      for term in generic_terms.keys():
          terms_list.append(term.strip("'\""))
  
      print("\nLoading embedding model...\n")
      embedding_dict, _ = load_word_embeddings(
          self.emb_model_path, terms_list
      )
      x = self.generate_embbeding_features(terms, embedding_dict)
      neg, pos = self.calc_similarity_scores_for_all_terms(
          terms, generic_terms, embedding_dict
      )
      x = self.concat_sim_scores_and_features(x, neg, pos)
      polarities = self._determine_unk_polarities(
          terms, polarities, neg, pos
      )
      print("\nDimensions of X: " + str(x.shape))
      return x, terms, polarities
  
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 11 for calling the RerankTerms obj's underlying member method code with example usage: obj.generate_features(terms, polarities) and returns: x,  terms,  polarities