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 return the model and also print out the predicted stock price for the next day based on the provided input.
  
  assert create_model()
  assert "Predicted stock price for the next day is: " in captured_output
output: def create_model(stock_prices):
      import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      X = np.array(stock_prices[:-1]).reshape(-1, 1)
      y = np.array(stock_prices[1:]).reshape(-1, 1)
  
      # Create the model
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X, y, epochs=100, verbose=0)
  
      # Predict the next day's stock price
      next_day_price = model.predict(np.array([stock_prices[-1]]).reshape(-1, 1))
      print(f"Predicted stock price for the next day is: {next_day_price[0][0]}")
  
      return model