input: 
  def predict_next_stock_price(trajectory: List[float]) -> float:
      """ This function takes in a trajectory of stock prices and predicts the stock price for the next day.
      The model is a simple neural network implemented using TensorFlow's Keras API.
      
      >>> predict_next_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.05
      """
  
output:     import numpy as np
      import tensorflow as tf
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Prepare the data
      X = np.array(trajectory[:-1]).reshape(-1, 1)
      y = np.array(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=50, verbose=0)
  
      # Predict the next stock price
      next_price = model.predict(np.array([trajectory[-1]]).reshape(-1, 1))
      return float(next_price[0][0])