Help required to implement the below model using Bi-GRU
from keras.layers import LSTM
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense,BatchNormalization
model = Sequential()
model.add(LSTM(800, return_sequences=True,
               input_shape=(1, 64)))  # returns a sequence of vectors of dimension 32
model.add(BatchNormalization())  # returns a sequence of vectors of dimension 32
model.add(LSTM(800))  # return a single vector of dimension 32
model.add(BatchNormalization())
model.add(Dense(204, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='Adam',metrics=['accuracy'])


model.summary()


I figured it out on my own.