input: Write a function to create a neural network model in Python that takes in a trajectory of stock prices and predicts the stock price for the next day. The function should return the model compiled with appropriate loss and optimizer. Additionally, it should include a main block to demonstrate how the model predicts the next day's stock price using the provided trajectory [10, 10.4, 10.5, 10.6, 10.7, 10.9, 11].
  
  assert create_model().predict(np.array([[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]]))[0][0]==pytest.approx(11.1, abs=0.1)
  assert create_model().predict(np.array([[9.9, 10.3, 10.4, 10.5, 10.6, 10.7, 10.8]]))[0][0]==pytest.approx(10.9, abs=0.1)
  assert create_model().predict(np.array([[11, 11.2, 11.3, 11.4, 11.5, 11.6, 11.7]]))[0][0]==pytest.approx(11.8, abs=0.1)
output: 
  def create_model():
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
      model = Sequential()
      model.add(Dense(10, input_dim=7, activation='relu'))
      model.add(Dense(1, activation='linear'))
      model.compile(loss='mean_squared_error', optimizer='adam')
      return model
  