input: def predict_next_day_stock_price(trajectory: List[float]) -> float:
      """Given a list of stock prices over consecutive days, create and train a neural network model 
      to predict the stock price for the next day. The model should be created using TensorFlow's Keras API.
      >>> predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.1
      """
output:     import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      X = []
      y = []
      for i in range(len(trajectory) - 1):
          X.append([trajectory[i]])
          y.append(trajectory[i + 1])
  
      X = tf.convert_to_tensor(X, dtype=tf.float32)
      y = tf.convert_to_tensor(y, dtype=tf.float32)
  
      # Create the model
      model = Sequential([
          Dense(10, input_shape=(1,), activation='relu'),
          Dense(1)
      ])
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X, y, epochs=100, verbose=0)
  
      # Predict the next day's stock price
      last_price = tf.convert_to_tensor([[trajectory[-1]]], dtype=tf.float32)
      next_day_price = model.predict(last_price, verbose=0)
  
      return next_day_price[0][0].item()