docstr: Print training params.
  
  Args:
      batch_size(int): batch size
      epochs(int): num of epochs
      seed(int): seed
code: def print_params(self, batch_size, epochs, seed):
      """Print training params.
  
      Args:
          batch_size(int): batch size
          epochs(int): num of epochs
          seed(int): seed
      """
      print(
          "\nModel Parameters: act_1= "
          + self.activation_1
          + ", act_2= "
          + self.activation_2
          + ", act_3= "
          + self.activation_3
          + ", loss= "
          + self.loss
          + ", optimizer= "
          + self.optimizer
          + "\nseed= "
          + str(seed)
          + ", epochs= "
          + str(epochs)
          + ", batch_size= "
          + str(batch_size)
          + ", threshold= "
          + str(self.threshold)
          + ", use_complete_w2v= "
          + ", sim_lexicon= "
          + str(self.sim_lexicon)
          + "\n"
      )
  
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 17 for calling the RerankTerms obj's underlying member method code with example usage: obj.print_params(batch_size, epochs, seed) without return types