text: import tensorflow as tf
  from keras.models import Model
  from keras.layers import LSTM, BatchNormalization, Dropout, Dense, Bidirectional, concatenate, Input
  from keras import regularizers
  
  def get_big_parallel_model(embeddings):
      
      dropout_rate = 0.25
  
      x1 = LSTM(64, activation='tanh', recurrent_activation='sigmoid', recurrent_dropout=0, unroll=False, use_bias=True)(embeddings)
      x1 = BatchNormalization()(x1)
      x1 = Dropout(dropout_rate)(x1)
      x1 = Dense(128, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x1)
      x1 = Dropout(dropout_rate)(x1)
      x1 = Dense(128, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x1)
      x1 = Dropout(dropout_rate)(x1)
      x1 = Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x1)
      x1 = Dropout(dropout_rate)(x1)
      x1 = Dense(32, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x1)
      x1 = Dropout(dropout_rate)(x1)
      x1 = Dense(16, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x1)
      x1 = Dropout(dropout_rate)(x1)
  
      x2 = Bidirectional(LSTM(32, activation='tanh', recurrent_activation='sigmoid', recurrent_dropout=0, unroll=False, use_bias=True))(embeddings)
      x2 = BatchNormalization()(x2)
      x2 = Dropout(dropout_rate)(x2)
      x2 = Dense(128, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x2)
      x2 = Dropout(dropout_rate)(x2)
      x2 = Dense(128, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x2)
      x2 = Dropout(dropout_rate)(x2)
      x2 = Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x2)
      x2 = Dropout(dropout_rate)(x2)
      x2 = Dense(64, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x2)
      x2 = Dropout(dropout_rate)(x2)
      x2 = Dense(32, activation='relu', kernel_regularizer=regularizers.l2(1e-4))(x2)
      x2 = Dropout(dropout_rate)(x2)
      combined = concatenate([x1, x2])
  
      x3 = Dense(48, activation='relu')(combined)
      x3 = Dense(32, activation='relu')(x3)
      x3 = Dense(1, activation='relu')(x3)
      return x3
  
  inputs = Input(shape=(None,300), name='embedding_input')
  # embeddings = Word2Vec(max_sentence_len)(inputs)
  x1 = get_big_parallel_model(inputs)
  x2 = get_big_parallel_model(inputs)
  x3 = get_big_parallel_model(inputs)
  x4 = get_big_parallel_model(inputs)
  shared = concatenate([x1, x2, x3, x4])
  model = Model(inputs=inputs, outputs=shared)
  model.load_weights("./models/20210224-102401/Uni+Bi-LSTM.hdf5", by_name=True)
  model.summary()
  
  file = open("./models/20210224-102401/no_embedding/Uni+Bi-LSTM.json", "w+")
  model.save_weights("./models/20210224-102401/no_embedding/Uni+Bi-LSTM.hdf5")
  file.write(model.to_json())
  