input: 
  import numpy as np
  import tensorflow as tf
  
  def predict_next_stock_price(stock_trajectory: np.ndarray) -> float:
      """ Given a trajectory of stock prices, this function predicts the stock price for the next day.
      >>> predict_next_stock_price(np.array([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]))
      11.1
      """
      model = create_model()
      predictions = model.predict(stock_trajectory.reshape(1, -1))
      return predictions[0][0]
  
  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
      
  if __name__ == "__main__":
      stock_trajectory = np.array([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11])
      print(f"Predicted stock price for the next day is: {predict_next_stock_price(stock_trajectory)}")
  
output:     model.fit(stock_trajectory.reshape(1, -1), np.array([11.1]).reshape(1, -1), epochs=100, verbose=0)