input: def predict_next_stock_price(stock_trajectory: List[float]) -> float:
      """This function creates a simple neural network model using TensorFlow to predict the stock price for the next day based on a given trajectory of stock prices.
      >>> predict_next_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      Predicted stock price for the next day is: 11.1
      """
output:     
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      X = tf.constant(stock_trajectory[:-1], dtype=tf.float32)
      y = tf.constant(stock_trajectory[1:], dtype=tf.float32)
  
      # Create the model
      model = Sequential([
          Dense(10, input_shape=(1,), activation='relu'),
          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(tf.constant([stock_trajectory[-1]], dtype=tf.float32))
  
      return f"Predicted stock price for the next day is: {next_price[0][0]}"
  