input: 
  def predict_stock_price(trajectory: List[float]) -> float:
      """
      Create a neural network model using TensorFlow to predict the stock price 
      for the next day given a trajectory of stock prices.
  
      >>> predict_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.0987654321  # Example output, actual value may vary based on the model's training
      """
  
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)
  
      # Reshape X to match the input shape expected by the model
      X = tf.reshape(X, (-1, 1))
  
      # 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.reshape([trajectory[-1]], (1, 1)))
  
      return next_price[0][0].numpy()