input: 
  def predict_house_price(features: np.ndarray) -> float:
      """ Predict the price of a house given a set of features using a neural network.
      The neural network is designed with three layers: an input layer, a hidden layer, 
      and an output layer. The input layer has 64 neurons, the hidden layer has 32 neurons,
      and the output layer has 1 neuron. The activation function used is ReLU for the 
      hidden layers and linear for the output layer. The model is compiled with mean 
      squared error (MSE) as the loss function and Adam as the optimizer.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      1.234  # Example output, actual values will vary based on training data
      """
      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, batch_size=3, epochs=200)
      return model.predict(features)
  
output:     model = Sequential()
      model.add(Dense(64, input_dim=features.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, np.zeros((features.shape[0], 1)), batch_size=3, epochs=200, verbose=0)
      return model.predict(features)[0][0]