input: 
  def predict_next_day_stock_price(trajectory: List[float]) -> float:
      """ Given a trajectory of stock prices, this function uses a pre-trained neural network 
      model to predict the stock price for the next day.
      >>> predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.1
      """
  
output:     import numpy as np
      from tensorflow.keras.models import load_model
  
      # Load the pre-trained model
      model = load_model('stock_prediction_model.h5')
  
      # Prepare the input data
      input_data = np.array(trajectory).reshape(1, -1, 1)
  
      # Predict the next day's stock price
      next_day_price = model.predict(input_data)
  
      return float(next_day_price[0][0])