input: from typing import List
  
  def predict_next_stock_price(trajectory: List[float]) -> float:
      """ Given a trajectory of stock prices, create and train a neural network model
      using TensorFlow and Keras to predict the stock price for the next day.
      
      Example:
      for predict_next_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) == 11.1
      """
output:     import numpy as np
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      trajectory = np.array(trajectory)
      X = trajectory[:-1].reshape(-1, 1)
      y = trajectory[1:].reshape(-1, 1)
  
      # Build the model
      model = Sequential()
      model.add(Dense(10, input_dim=1, activation='relu'))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X, y, epochs=100, verbose=0)
  
      # Predict the next stock price
      next_price = model.predict(np.array([trajectory[-1]]).reshape(-1, 1))
  
      return next_price[0][0]