input: def predict_house_price(features: np.array) -> float:
      """ Predict the price of a house given a set of features using a neural network implemented in Keras.
      >>> predict_house_price(np.array([[0.8, 0.4, 0.7]]))
      
      """
output:     from keras.models import Sequential
      from keras.layers import Dense
      import numpy as np
  
      # Define the model
      model = Sequential()
      model.add(Dense(10, input_dim=features.shape[1], activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      # Compile the model
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      # Dummy weights for the model
      model.set_weights([np.array([[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
                                   [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
                                   [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]]),
                        np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]),
                        np.array([[0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]]),
                        np.array([0.1])])
  
      # Predict the price
      prediction = model.predict(features)
      return float(prediction[0][0])