input: Write a function to create and train a neural network in Python using Keras to predict the price of a house given a set of features. The function should return the trained model, and it should be able to handle input features as a numpy array with three dimensions.
  assert model_predict_house_price(np.array([[0.8, 0.4, 0.7]]))!=None
  assert model_predict_house_price(np.array([[0.6, 0.3, 0.8]]))!=None
  assert model_predict_house_price(np.array([[0.5, 0.5, 0.5]]))!=None
  assert model_predict_house_price(np.array([[0.9, 0.1, 0.2]]))!=None
output: def model_predict_house_price(features):
      import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, input_shape=(features.shape[1],), activation='relu'))
      model.add(Dense(1, activation='linear'))
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Dummy training data
      X_train = np.random.rand(100, features.shape[1])
      y_train = np.random.rand(100, 1)
  
      model.fit(X_train, y_train, epochs=10, verbose=0)
      return model