input: from typing import List
  
  def predict_next_stock_price(prices: List[float]) -> float:
      """ Given a list of historical stock prices, predict the stock price for the next day using a neural network model.
      Example:
      >>> predict_next_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.1
      """
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Prepare the data
      X = np.array(prices[:-1]).reshape(-1, 1)
      y = np.array(prices[1:]).reshape(-1, 1)
  
      # Train the neural network model
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=1000)
      model.fit(X, y.ravel())
  
      # Predict the next stock price
      last_price = prices[-1]
      next_price = model.predict(np.array([[last_price]]))
  
      return next_price[0]