input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day. The function should define the architecture of the model and compile it using appropriate loss and optimizer. Finally, it should return the model and predict the next day's stock price using the provided trajectory.
  
  assert create_predict_model([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) == "Predicted stock price for the next day is: "
output: def create_predict_model(trajectory):
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
      model.compile(loss='mean_squared_error', optimizer='adam')
  
      trajectory = np.array(trajectory).reshape(-1, 1)
      X = trajectory[:-1]
      y = trajectory[1:]
  
      model.fit(X, y, epochs=100, verbose=0)
  
      next_day_price = model.predict(np.array([trajectory[-1]]))
      return f"Predicted stock price for the next day is: {next_day_price[0][0]}"