input: 
  def predict_next_day_stock_price(input_prices: list) -> float:
      """
      Create a Python 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])
      11.2
      """
  
output:     import numpy as np
      from sklearn.neural_network import MLPRegressor
  
      # Prepare the data
      X = np.array(input_prices[:-1]).reshape(-1, 1)
      y = np.array(input_prices[1:])
  
      # Create and train the neural network model
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=1000)
      model.fit(X, y)
  
      # Predict the next day's stock price
      next_day_price = model.predict(np.array([input_prices[-1]]).reshape(-1, 1))
  
      return next_day_price[0]