input: def predict_stock_price(traj: List[float]) -> float:
      """
      Given a trajectory of stock prices, this function creates a neural network model using TensorFlow to predict
      the stock price for the next day.
  
      >>> predict_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.16818
      """
output:     import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      X = [[traj[i]] for i in range(len(traj) - 1)]
      y = [[traj[i + 1]] for i in range(len(traj) - 1)]
  
      model.fit(X, y, epochs=100, verbose=0)
  
      next_day_price = model.predict([[traj[-1]]])
  
      return round(next_day_price[0][0], 5)