input: def predict_house_price(features: np.array) -> float:
      """Design and implement a neural network to predict the price of a house given a set of features.
      The network consists of:
      - An input layer with 64 units and ReLU activation.
      - A hidden layer with 32 units and ReLU activation.
      - An output layer with 1 unit and linear activation.
      The model is compiled with mean squared error (mse) loss and the Adam optimizer.
      Training is done for 200 epochs with a batch size of 3.
      >>> predict_house_price(np.array([[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
  
      # Define the model
      model = Sequential()
      model.add(Dense(64, input_shape=(features.shape[1],), 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
      X_train = np.random.rand(100, features.shape[1])
      y_train = np.random.rand(100, 1)
  
      # 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]
  