input: def predict_next_stock_price(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.
      
      Example:
      >>> predict_next_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      Predicted stock price for the next day is: 11.11
      """
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:])
  
      # Create and train the model
      model = MLPRegressor(hidden_layer_sizes=(10,), max_iter=1000, random_state=42)
      model.fit(X, y)
  
      # Predict the next stock price
      last_price = prices[-1]
      next_price = model.predict(np.array([[last_price]]))[0]
  
      return next_price