input: def predict_next_stock_price(stock_prices: 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_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      Predicted stock price for the next day is: 11.25
      """
output:     import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.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)
  
      # Build 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=50, verbose=0)
  
      # Predict the next stock price
      next_price = model.predict(np.array([stock_prices[-1]]).reshape(-1, 1))
  
      return next_price[0][0]