input: 
  from typing import List
  
  def predict_next_day_stock_price(prices: List[float]) -> float:
      """
      Create a simple neural network model using TensorFlow/Keras that takes in a list of
      stock prices and predicts the stock price for the next day. The model consists of a
      single dense layer with a ReLU activation function followed by an output layer.
      The model is compiled with mean squared error loss and Adam optimizer.
      
      Example:
      >>> 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 = tf.constant(prices[:-1], dtype=tf.float32)
      y = tf.constant(prices[1:], dtype=tf.float32)
  
      # Create the model
      model = Sequential([
          Dense(10, activation='relu', input_shape=(1,)),
          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 day's stock price
      next_day_price = model.predict(tf.constant([prices[-1]], dtype=tf.float32))
  
      return next_day_price[0][0]
  