input: Write a function to create a Python neural network model that takes in a given trajectory of stock prices and predicts the stock price for the next day. The function should return the predicted stock price.
  
  def predict_next_day_stock_price(stock_prices):
      if len(stock_prices) != 7:
          raise ValueError("The input trajectory must contain exactly 7 stock prices.")
  
      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'])
  
      input_data = np.array([stock_prices])
      prediction = model.predict(input_data)
  
      return prediction[0][0]
  
  assert predict_next_day_stock_price([10, 10.4, 10.5, 10.6, 10.7, 10.9, 11]) > 11.0
  assert predict_next_day_stock_price([100, 102, 105, 107, 109, 110, 111]) > 111.0
  assert predict_next_day_stock_price([50, 52, 53, 54, 55, 56, 57]) > 57.0
  assert predict_next_day_stock_price([20, 21, 22, 23, 24, 25, 26]) > 26.0
  assert predict_next_day_stock_price([1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6]) > 1.6
output: def predict_next_day_stock_price(stock_prices):
      if len(stock_prices) != 7:
          raise ValueError("The input trajectory must contain exactly 7 stock prices.")
  
      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'])
  
      input_data = np.array([stock_prices])
      prediction = model.predict(input_data)
  
      return prediction[0][0]