input: from typing import List
  
  def predict_next_day_stock_price(trajectory: List[float]) -> float:
      """
      Given a trajectory of stock prices, creates a simple neural network model using TensorFlow 
      to predict the stock price for the next day. The model is a simple feedforward neural network 
      with a single hidden layer.
      
      >>> predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.2
      """
output:     import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      X = tf.constant(trajectory[:-1], dtype=tf.float32)
      y = tf.constant(trajectory[1:], dtype=tf.float32)
  
      # Build the model
      model = Sequential([
          Dense(10, activation='relu', input_shape=(1,)),
          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
      next_day_price = model.predict(tf.constant([trajectory[-1]], dtype=tf.float32))
  
      return next_day_price[0][0].numpy()