# How can we add preprocessing steps, in the keras sequential model itself?

Is there a way to add a layer which includes my preprocessing steps in this sequential model.For example

model.add(LabelEncoder.transform(X_train['gender'],X_train['grade']),scaler(X_train))


How to include this step in the create model definition?

def create_model(optimizer='adagrad',
kernel_initializer='glorot_uniform',
dropout=0.2):
model = Sequential()