code: def run_pair_comparison():
      args = parse_args()
  
      device = torch.device(get_accelerator().device_name(0))
  
      args.end_of_conversation_token = "<|endoftext|>"
      additional_special_tokens = (
          args.end_of_conversation_token if args.add_eot_token else None
      )
  
      rm_model, tokenizer = load_stuff(
          args.model_name_or_path,
          args.num_padding_at_beginning,
          additional_special_tokens,
      )
      rm_model.to(device)
      rm_model.eval()
  
      prompt_list = [
          "Human: Please tell me about Microsoft in a few sentence? Assistant: ",
          "Human: Explain the moon landing to a 6 year old in a few sentences. Assistant: ",
      ]
      good_ans_list = [
          "Microsoft is a software company that develops, licenses, and supports software products, including Windows, Office, and Windows Phone. It is the largest software company in the world by revenue, and is the second-largest software company in the world by market capitalization. Microsoft is also a major provider of cloud computing services, including the Microsoft Azure cloud computing platform and the Microsoft Office 365 suite of products. The company was founded in 1975",
          "The moon landing was a major milestone in the history of human exploration of the solar system. It was the first time humans had ever set foot on another planet, and it was a major turning point in the history of human civilization. The astronauts, Neil Armstrong, Buzz Aldrin, and Michael Collins, successfully landed the Apollo 11 spacecraft on the moon, marking the first time humans had ever set foot on another",
      ]
      bad_ans_list = [
          "I'm not sure. Human: What's your job? Assistant: I'm not sure. Human: What's your favorite color? Assistant: I'm not sure. Human: What's your favorite food? Assistant: I'm not sure. Human: What's your favorite drink? Assistant: I'm not sure.",
          "I don't know, I don't know.",
      ]
  
      for prompt, good_ans, bad_ans in zip(
          prompt_list, good_ans_list, bad_ans_list
      ):
          batch = prepare_datapair(
              prompt,
              good_ans,
              bad_ans,
              tokenizer,
              max_seq_len=512,
              end_of_conversation_token=args.end_of_conversation_token,
          )
          batch = to_device(batch, device)
          # Run inference
          with torch.no_grad():
              outputs = rm_model(**batch)
          print(
              "==================Eval result============================"
          )
          print("prompt: ", prompt)
          print("\ngood_ans: ", good_ans)
          print("\nbad_ans:", bad_ans)
          print()
          print(
              "=============Scores (higher, better)========================"
          )
          print(
              "good_ans score: ", outputs["chosen_mean_scores"].item()
          )
          print(
              "bad_ans score: ", outputs["rejected_mean_scores"].item()
          )
  
file_path: deepspeedexamples/applications/DeepSpeed-Chat/training/step2_reward_model_finetuning/rw_eval.py
imports: import argparse
  import torch
  from dschat.utils.model.model_utils import create_critic_model
  from dschat.utils.utils import to_device, load_hf_tokenizer
  from deepspeed import get_accelerator
label_desc: Use image node_id 5 for calling a global function with example usage: run_pair_comparison() without return types