input: def predict_next_day_stock_price(stock_prices: List[float]) -> float:
      """ Given a list of stock prices representing the trajectory of stock prices over consecutive days, 
      predict the stock price for the next day using a pre-defined neural network model.
      
      >>> predict_next_day_stock_price([10.0, 10.4, 10.5, 10.6, 10.7, 10.9, 11.0])
      11.15
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Prepare the data
      X = np.array(stock_prices[:-1]).reshape(-1, 1)
      y = np.array(stock_prices[1:])
  
      # Train the model
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=1000, random_state=42)
      model.fit(X, y)
  
      # Predict the next day's stock price
      last_price = stock_prices[-1]
      next_day_price = model.predict(np.array([[last_price]]))
  
      return next_day_price[0]