input: 
  def predict_house_price(features: np.ndarray) -> float:
      """ Predict the price of a house given a set of features using a neural network.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      0.0
      """
  
output:     model = tf.keras.models.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(features.shape[1],)),
          tf.keras.layers.Dense(32, activation='relu'),
          tf.keras.layers.Dense(1, activation='linear')
      ])
      
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      # Dummy data for training
      X_train = np.random.rand(100, features.shape[1])
      y_train = np.random.rand(100, 1)
      
      model.fit(X_train, y_train, epochs=10, verbose=0)
      
      prediction = model.predict(features)
      return prediction[0][0]