input: Write a function to create a neural network model using TensorFlow that takes in a trajectory of stock prices and predicts the stock price for the next day. The function should define the model architecture, compile it, and return the compiled model. To test the function, create an input array representing a trajectory of stock prices and print the predicted price for the next day.
  assert create_model() is not None
  assert np.isscalar(model.predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]]))[0][0])
output: import tensorflow as tf
  
  def create_model():
      model = tf.keras.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(7,)),
          tf.keras.layers.Dense(32, activation='relu'),
          tf.keras.layers.Dense(1)
      ])
      model.compile(optimizer='adam', loss='mean_squared_error')
      return model