input: 
  def predict_stock_price(trajectory: List[float]) -> float:
      """ Given a trajectory of stock prices, this function uses a pre-defined neural network model to predict the stock price for the next day.
      The model is a simple sequential model with a Dense layer of 64 neurons with ReLU activation function and an output Dense layer with 1 neuron.
      >>> predict_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      11.2
      """
  
output:     import numpy as np
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      # Define the model
      model = Sequential()
      model.add(Dense(64, input_dim=1, activation='relu'))
      model.add(Dense(1))
  
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Prepare the input data
      X = np.array(trajectory[:-1]).reshape(-1, 1)
      y = np.array(trajectory[1:]).reshape(-1, 1)
  
      # Train the model
      model.fit(X, y, epochs=100, verbose=0)
  
      # Predict the next stock price
      last_price = trajectory[-1]
      next_day_price = model.predict(np.array([[last_price]]))
  
      return next_day_price[0][0]