input: def predict_house_price(features: List[float]) -> float:
      """
      Implements a neural network to predict the price of a house given a set of features.
      The model has an input layer with 3 nodes, a hidden layer with 64 nodes, another hidden layer with 32 nodes,
      and an output layer with 1 node. The model is compiled with mean squared error (mse) as the loss function,
      and the Adam optimizer. The model is trained for 200 epochs with a batch size of 3.
      
      Example:
      >>> predict_house_price([0.8, 0.4, 0.7])
      
      """
output:     from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Define the model
      model = Sequential()
      model.add(Dense(64, input_dim=3, activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(loss='mse', optimizer='adam')
  
      # Dummy training data (for demonstration purposes)
      X_train = [[0.8, 0.4, 0.7], [0.5, 0.3, 0.6], [0.9, 0.2, 0.8]]
      y_train = [100000, 150000, 200000]
  
      # Train the model
      model.fit(X_train, y_train, epochs=200, batch_size=3, verbose=0)
  
      # Predict the house price
      prediction = model.predict([features])
      return prediction[0][0]