input: 
  from typing import List, Tuple
  from keras.models import Sequential
  from keras.layers import Dense
  import numpy as np
  
  def predict_house_price(features: List[List[float]]) -> Tuple[float]:
      """Design and implement a neural network to predict the house prices
      given a set of features. The features are input as a list of lists, where
      each sublist contains the features for a house. The network is designed to
      have 2 hidden layers with 64 and 32 units respectively, and an output layer
      with 1 unit for the predicted price. The network is trained using the Adam
      optimizer and the mean squared error as the loss function.
      
      Example:
      >>> predict_house_price([[0.8, 0.4, 0.7]])
      array([[0.12345]])  # Example output, not actual
      """
      features_array = np.array(features)
      
      model = Sequential()
      model.add(Dense(64, input_dim=3, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
      
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
      
      model.fit(features_array, batch_size=3, epochs=200)
      
      predictions = model.predict(features_array)
      return tuple(predictions[0])
  
output:     features_array = np.array(features)
      
      model = Sequential()
      model.add(Dense(64, input_dim=features_array.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
      
      model.compile(loss='mse', optimizer='adam', metrics=['mse'])
      
      model.fit(features_array, np.zeros((features_array.shape[0], 1)), batch_size=3, epochs=200, verbose=0)
      
      predictions = model.predict(features_array)
      return tuple(predictions.flatten())