input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices up to seven days and predicts the stock price for the next day. The function should initialize, compile, and return the model. The model will be evaluated on a given input using TensorFlow and should print out the predicted stock price for the next day.
  
  assert create_model().predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]]))[0][0] > 11.0
  assert create_model().predict(np.array([[9, 9.2, 9.3, 9.4, 9.5, 9.6, 9.8]]))[0][0] > 9.8
  assert create_model().predict(np.array([[12, 12.1, 12.2, 12.3, 12.4, 12.5, 12.6]]))[0][0] > 12.6
output: def create_model():
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential([
          Dense(10, input_shape=(7,), activation='relu'),
          Dense(1, activation='linear')
      ])
      
      model.compile(optimizer='adam', loss='mean_squared_error')
      return model