code: def __init__(
      self,
      vector_cache=True,
      rerank_model: PathLike = None,
      emb_model_path: PathLike = None,
  ):
      # model and training params
      self.embeddings_len = 300
      self.activation_1 = "relu"
      self.activation_2 = "relu"
      self.activation_3 = "sigmoid"
      self.loss = "binary_crossentropy"
      self.optimizer = "rmsprop"
  
      self.epochs_and_batch_size = [(10, 2)]
      self.seeds = [3]
      self.threshold = 0.5
  
      self.sim_lexicon = TRAIN_LEXICONS / "RerankSentSimLex.csv"
      self.generic_lexicon = GENERIC_OP_LEX
  
      self.vector_cache = vector_cache
      self.word_vectors_dict = {}
      self.vectors_sim_dict = {}
  
      self.rerank_model_path = rerank_model
      self.emb_model_path = emb_model_path
  
      LEXICONS_OUT.mkdir(parents=True, exist_ok=True)
  
      tensorflow.logging.set_verbosity(tensorflow.logging.ERROR)
  
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 1 to create a new RerankTerms object from inherited base classes: object with example: obj = RerankTerms(vector_cache, rerank_model, emb_model_path)