input: Write a function to design and implement a neural network in Python to predict the price of a house given a set of features using Keras. The input is a numpy array containing the features and the output is the trained neural network model. The model should consist of an input layer with 64 neurons, a hidden layer with 32 neurons, and an output layer with 1 neuron. The model should be compiled using mean squared error (MSE) as the loss function and the Adam optimizer.
  import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense
  
  def house_price_predictor(features):
      model = Sequential()
      model.add(Dense(64, input_dim=features.shape[1], 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
  
  features = np.array([[0.8, 0.4, 0.7]])
  assert house_price_predictor(features)
  assert house_price_predictor(np.array([[0.1, 0.2, 0.3]]))
  assert house_price_predictor(np.array([[0.5, 0.6, 0.7]]))
  assert house_price_predictor(np.array([[0.9, 0.8, 0.7]]))
  assert house_price_predictor(np.array([[0.3, 0.2, 0.1]]))
output: def house_price_predictor(features):
      model = Sequential()
      model.add(Dense(64, input_dim=features.shape[1], 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