input: from typing import List
  
  def get_predicted_stock_price(stock_prices: List[float]) -> float:
      """
      This function takes in a list of stock prices, creates a neural network model using Keras,
      and predicts the stock price for the next day.
      >>> get_predicted_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.05
      """
output:     import numpy as np
      from keras.models import Sequential
      from keras.layers import Dense
  
      # Prepare the data
      X = np.array(stock_prices[:-1]).reshape(-1, 1)
      y = np.array(stock_prices[1:]).reshape(-1, 1)
  
      # Create the model
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X, y, epochs=100, verbose=0)
  
      # Predict the next stock price
      last_price = stock_prices[-1]
      predicted_price = model.predict(np.array([[last_price]]))
  
      return float(predicted_price[0][0])