code: def _generate_concat_reranked_lex(
      acquired_opinion_lex, generic_opinion_lex_file
  ):
      print(
          "Loading generic sentiment terms from {}...".format(
              generic_opinion_lex_file
          )
      )
      generics_table = np.genfromtxt(
          generic_opinion_lex_file,
          delimiter=",",
          skip_header=1,
          dtype=str,
      )
      print(
          str(generics_table.shape[0])
          + " generic sentiment terms loaded"
      )
  
      concat_opinion_dict = {}
  
      for key, value in acquired_opinion_lex.items():
          concat_opinion_dict[key] = (value[0], value[1], "Y")
      for row in generics_table:
          concat_opinion_dict[row[0]] = (row[2], row[1], "N")
  
      return concat_opinion_dict
  
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 20 for calling the RerankTerms obj's underlying member method code with example usage: obj._generate_concat_reranked_lex(acquired_opinion_lex, generic_opinion_lex_file) and returns: concat_opinion_dict