input: def predict_house_price(features: List[float]) -> float:
      """ Build and train a neural network model using Keras to predict house prices given a set of features.
      The model consists of three layers: the input layer with 64 units and ReLU activation, a hidden layer with 32 units and ReLU activation,
      and an output layer with 1 unit and linear activation. The model is compiled using mean squared error as the loss function and Adam optimizer.
      After training the model, it predicts the price of a house using the provided features.
      >>> predict_house_price([0.8, 0.4, 0.7])
      0.0
      """
output:     
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Create the model
      model = Sequential()
      model.add(Dense(64, input_dim=len(features), activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Dummy training data (for demonstration purposes)
      X_train = np.array([[0.8, 0.4, 0.7], [0.5, 0.3, 0.6], [0.2, 0.9, 0.1]])
      y_train = np.array([0.0, 0.1, 0.2])
  
      # Train the model (for demonstration purposes, just one epoch)
      model.fit(X_train, y_train, epochs=1, verbose=0)
  
      # Predict the house price
      prediction = model.predict(np.array([features]))
      return prediction[0][0]
  