old_contents: 
  import basic_config
  
  def infer_sent_v1():
      """
      set configs
      """
      config = basic_config.config()
      config.learning_rate = 0.1
      config.lr_decay = 0.99
      config.optimizer_type = 'sgd'
      config.save_dirname = "model_dir"
      config.use_pretrained_word_embedding = False
      config.dict_dim = 40000 # approx_vocab_size
      config.class_dim = 2
  
      # net config
      config.emb_dim = 300 
      config.droprate_lstm = 0.0
      config.droprate_fc = 0.0
      config.word_embedding_trainable = False
      config.rnn_hid_dim = 2048
      config.mlp_non_linear = False
  
      return config
  
  def infer_sent_v2():
      """
      use our own config
      """
      config = basic_config.config()
      config.learning_rate = 0.0002
      config.lr_decay = 0.99
      config.optimizer_type = 'adam'
      config.save_dirname = "model_dir"
      config.use_pretrained_word_embedding = True
      config.dict_dim = 40000 # approx_vocab_size
      config.class_dim = 2
  
      # net config
      config.emb_dim = 300
      config.droprate_lstm = 0.0
      config.droprate_fc = 0.2
      config.word_embedding_trainable = False
      config.rnn_hid_dim = 2048
      config.mlp_non_linear = True
  
      return config
  
new_contents: 
  import basic_config
  
  def infer_sent_v1():
      """
      set configs
      """
      config = basic_config.config()
      config.learning_rate = 0.1
      config.lr_decay = 0.99
      config.optimizer_type = 'sgd'
      config.save_dirname = "model_dir"
      config.use_pretrained_word_embedding = True
      config.dict_dim = 40000 # approx_vocab_size
      config.class_dim = 2
  
      # net config
      config.emb_dim = 300 
      config.droprate_lstm = 0.0
      config.droprate_fc = 0.0
      config.word_embedding_trainable = False
      config.rnn_hid_dim = 2048
      config.mlp_non_linear = False
  
      return config
  
  def infer_sent_v2():
      """
      use our own config
      """
      config = basic_config.config()
      config.learning_rate = 0.0002
      config.lr_decay = 0.99
      config.optimizer_type = 'adam'
      config.save_dirname = "model_dir"
      config.use_pretrained_word_embedding = True
      config.dict_dim = 40000 # approx_vocab_size
      config.class_dim = 2
  
      # net config
      config.emb_dim = 300
      config.droprate_lstm = 0.0
      config.droprate_fc = 0.2
      config.word_embedding_trainable = False
      config.rnn_hid_dim = 2048
      config.mlp_non_linear = True
  
      return config
  
subject: change infer-sent config in text_matching_on_quora model