input: Write a function to design and implement a neural network in Python using Keras to predict the price of a house given a set of features. The function should compile the model with a mean squared error loss and Adam optimizer, and should fit the model to the given features with a batch size of 3 and 200 epochs.
  
  def predict_house_price(features):
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      features = np.array([features])
  
      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'])
  
      model.fit(features, batch_size=3, epochs=200)
  
      return model.predict(features)
  
  assert np.array_equal(predict_house_price([0.8, 0.4, 0.7]), model.predict(np.array([[0.8, 0.4, 0.7]])))
output: def predict_house_price(features):
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      features = np.array([features])
  
      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'])
  
      model.fit(features, batch_size=3, epochs=200)
  
      return model.predict(features)