input: Write a function to create a neural network model using TensorFlow that takes in a given array representing a trajectory of stock prices and predicts the stock price for the next day. The function should return the model and use it to predict the next day's stock price from the input array. 
  
  If successful, the prediction should be returned via a print statement indicating the predicted stock price for the next day.
  
  assert create_model()[10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]==11.1
output: def create_model(stock_prices):
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential([
          Dense(10, input_shape=(len(stock_prices)-1,), activation='relu'),
          Dense(1)
      ])
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      X = tf.constant([stock_prices[:-1]])
      y = tf.constant([stock_prices[-1]])
  
      model.fit(X, y, epochs=100, verbose=0)
  
      next_day_price = model.predict(tf.constant([stock_prices[-6:]]))
      print(f"Predicted stock price for the next day: {next_day_price[0][0]}")
      return model