input: def predict_next_day_stock_price(trajectory: List[float]) -> float:
      """Given a trajectory of stock prices, this function predicts the stock price for the next day using a neural network model implemented in TensorFlow.
      >>> predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.07832
      """
output:     import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Create a simple neural network model
      model = Sequential([
          Dense(10, activation='relu', input_shape=(len(trajectory),)),
          Dense(1)
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model on the given trajectory
      model.fit(tf.expand_dims(trajectory, axis=0), tf.expand_dims([trajectory[-1]], axis=0), epochs=100, verbose=0)
  
      # Predict the next day's stock price
      next_day_price = model.predict(tf.expand_dims(trajectory, axis=0))
  
      return next_day_price[0][0]