text: import numpy as np
  
  
  
  def  get_estimator(scorer_type, save_folder=None):
      if scorer_type == 'esim':
          # submitted model, glove + fasttext, with attention
          from os import path
          from te.deep_models.ESIM_for_ensemble import ESIM
          from utils.config import Config
          pos_weight = np.asarray(Config.esim_hyper_param['pos_weight'], np.float32)
          clf = ESIM(random_state=Config.seed, tensorboard_logdir="logdir/", learning_rate=Config.esim_hyper_param['lr'],
                     max_check_without_progress=Config.esim_hyper_param['max_checks_no_progress'],
                     activation=Config.esim_hyper_param['activation'],
                     initializer=Config.esim_hyper_param['initializer'],
                     num_epoch=Config.esim_hyper_param['num_epoch'],
                     lstm_layers=Config.esim_hyper_param['lstm_layers'],
                     optimizer=Config.esim_hyper_param['optimizer'],
                     trainable=Config.esim_hyper_param['trainable'],
                     batch_size=Config.esim_hyper_param['batch_size'],
                     dropout_rate=Config.esim_hyper_param['dropout'],
                     num_neurons=Config.esim_hyper_param['num_neurons'], pos_weight=pos_weight,
                     ckpt_path=path.join(save_folder, Config.name + '.ckpt'), name=Config.name)
      return clf
  