input: from typing import List
  
  
  def predict_next_day_stock_price(trajectory: List[float]) -> float:
      """ Create a neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day.
      >>> predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.1542
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      X = np.array(trajectory[:-1]).reshape(-1, 1)
      y = np.array(trajectory[1:])
  
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=1000, random_state=42)
      model.fit(X, y)
  
      last_price = trajectory[-1]
      next_day_price = model.predict(np.array([[last_price]]))
  
      return round(next_day_price[0], 4)