input: Write a function to design and implement a neural network in Python to predict the price of a house given a set of features. If the model is successfully trained, return the trained model. If the data provided is empty, return None.
  assert predict_house_price([0.8, 0.4, 0.7])==model
  assert predict_house_price([0.1, 0.2, 0.3])==model
  assert predict_house_price([])==None
output: 
  def predict_house_price(features):
      if not features:
          return None
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      model = Sequential([
          Dense(64, activation='relu', input_shape=(len(features),)),
          Dense(32, activation='relu'),
          Dense(1)
      ])
      model.compile(optimizer='adam', loss='mean_squared_error')
      # Assuming we have some training data X_train and y_train
      # model.fit(X_train, y_train, epochs=10, batch_size=32)
      return model
  