input: 
  from keras.models import Sequential
  from keras.layers import Dense
  import numpy as np
  
  def predict_house_price(features: np.ndarray) -> float:
      """
      Design and implement a neural network to predict the price of a house given a set of features.
      The neural network is composed of two hidden layers with 64 and 32 units respectively, and uses ReLU activation.
      The output layer consists of a single unit with a linear activation function.
      The model is trained using mean squared error (MSE) as the loss function and the Adam optimizer.
      
      >>> predict_house_price(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'])
      
      # Assuming the model has already been trained with appropriate data
      # For illustration purposes, this function omits the training step
      
      return model.predict(features)[0][0]
  
output:     model.fit(np.random.rand(100, 3), np.random.rand(100, 1), epochs=10, verbose=0)