input: def predict_house_price(features: List[float]) -> float:
      """ This function implements a neural network using Keras to predict the price of a house given a set of features.
      The neural network has an input layer, two hidden layers with ReLU activation functions, and an output layer with a linear activation function. 
      The model is compiled with mean squared error (MSE) as the loss function and the Adam optimizer.
      The model is then fit on the given features.
      
      >>> predict_house_price([0.8, 0.4, 0.7])
      0.0
      """
output:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      # Define the model
      model = Sequential()
      model.add(Dense(10, input_dim=len(features), activation='relu'))
      model.add(Dense(10, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Fit the model (dummy data for demonstration)
      dummy_data = np.array([features])
      model.fit(dummy_data, np.array([0.0]), epochs=1, verbose=0)
  
      # Predict the price
      prediction = model.predict(dummy_data)
      return prediction[0][0]