input: 
  def predict_next_day_price(trajectory: List[float]) -> float:
      """ Constructs a neural network model using TensorFlow's Keras API to predict the stock price for the next day based on a given trajectory of stock prices. The model is trained on random data and not on the provided trajectory. Returns the predicted stock price.
      >>> predict_next_day_price([10.0, 10.4, 10.5, 10.6, 10.7, 10.9, 11.0])
      11.1
      """
      import numpy as np
      import tensorflow as tf
  
      def create_model():
          model = tf.keras.Sequential([
              tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
              tf.keras.layers.Dense(1)
          ])
  
          model.compile(loss='mean_squared_error', 
                        optimizer='adam',
                        metrics=['accuracy'])
  
          return model
      
      model = create_model()
  
      input_data = np.array([trajectory])
      predictions = model.predict(input_data)
  
      return predictions[0][0]
  
output:     import numpy as np
      import tensorflow as tf
  
      def create_model():
          model = tf.keras.Sequential([
              tf.keras.layers.Dense(64, activation='relu', input_shape=(7, )),
              tf.keras.layers.Dense(1)
          ])
  
          model.compile(loss='mean_squared_error', 
                        optimizer='adam',
                        metrics=['accuracy'])
  
          return model
      
      model = create_model()
  
      input_data = np.array([trajectory])
      predictions = model.predict(input_data)
  
      return predictions[0][0]