input: Write a function to design and implement a neural network in Python to predict the house price given a set of input features. The neural network should be implemented using Keras with a Sequential model, consisting of dense layers with ReLU activation functions, and finally a linear output layer. The model should be compiled using mean squared error loss and the Adam optimizer, and trained for 200 epochs with a batch size of 3.
  
  assert train_model_for_house_price([0.8, 0.4, 0.7]) == None
  assert train_model_for_house_price([1.0, 0.9, 0.8]) == None
  assert train_model_for_house_price([0.5, 0.6, 0.7]) == None
output: def train_model_for_house_price(input_features):
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Dummy data for training
      X_train = np.array([[0.8, 0.4, 0.7], [1.0, 0.9, 0.8], [0.5, 0.6, 0.7]])
      y_train = np.array([100000, 200000, 150000])
  
      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='mean_squared_error', optimizer='adam')
      model.fit(X_train, y_train, epochs=200, batch_size=3, verbose=0)
  
      return None