input: from typing import List
  
  
  def predict_stock_price(trajectory: List[float]) -> float:
      """Given a trajectory of stock prices, predict the stock price for the next day using a neural network model.
      >>> predict_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.098
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Prepare the data
      X = np.array(trajectory[:-1]).reshape(-1, 1)
      y = np.array(trajectory[1:])
  
      # Train the neural network model
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=1000)
      model.fit(X, y)
  
      # Predict the next stock price
      next_price = model.predict(np.array([trajectory[-1]]).reshape(1, -1))
      return round(next_price[0], 3)