input: from typing import List, Tuple
  
  def predict_house_price(features: List[float]) -> Tuple[float]:
      """ Given a list of features, this function predicts the price of a house using a 
      pre-trained neural network. The neural network model is designed with Keras and
      has three layers: an input layer with 64 neurons, a hidden layer with 32 neurons, 
      and an output layer with 1 neuron. The model uses ReLU activation for the input 
      and hidden layers and linear activation for the output layer. The loss function 
      is Mean Squared Error (MSE) and the optimizer is Adam. The model is trained for 
      200 epochs with a batch size of 3.
      
      >>> predict_house_price([0.8, 0.4, 0.7])
      (95000.0)
      """
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      features = np.array([[0.8, 0.4, 0.7]])
  
      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 (95000.0)
output:     
      prediction = model.predict(features)
      return (prediction[0][0])
  