included the name, price, and attributes of each skirt. This is a great start, as it provides the user with a variety of options to choose from. However, the response does not provide any images of the skirts, which would have been helpful for the user to make a decision. Additionally, the response does not provide any information about the availability of the skirts, which could be important for the user. \n\nFinal Grade: B",     ' The user asked for a professional desktop PC with no budget constraints. The API response provided a list of products that fit the criteria, including the Skytech Archangel Gaming Computer PC Desktop, the CyberPowerPC Gamer Master Gaming Desktop, and the ASUS ROG Strix G10DK-RS756. The response accurately suggested these three products as they all offer powerful processors and plenty of RAM. Therefore, the response is accurate and useful. Final Grade: A',     " The API response provided a list of cameras with their prices, which is exactly what the user asked for. The response also included additional information such as features and memory cards, which is not necessary for the user's question but could be useful for further research. The response was accurate and provided the user with the information they needed. Final Grade: A"]# Reusing the rubric from above, parse the evaluation chain responsesparsed_response_results = parse_eval_results(request_eval_results)# Collect the scores for a final evaluation tablescores["result_synthesizer"].extend(parsed_response_results)# Print out Score statistics for the evaluation sessionheader = "{:<20}\t{:<10}\t{:<10}\t{:<10}".format("Metric", "Min", "Mean", "Max")print(header)for metric, metric_scores in scores.items():    mean_scores = (        sum(metric_scores) / len(metric_scores)        if len(metric_scores) > 0