code: def run_single_sample():
      args = parse_args()
      device = torch.device(get_accelerator().device_name())
  
      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)
  
      prompt = "Human: Explain the moon landing to a 6 year old in a few sentences."
      my_ans = "Assistant: 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"
  
      batch = prepare_singlesample(
          prompt,
          my_ans,
          tokenizer,
          max_seq_len=512,
          end_of_conversation_token=args.end_of_conversation_token,
      )
      batch = to_device(batch, device)
  
      rm_model.eval()
      # Run inference
      with torch.no_grad():
          outputs = rm_model.forward_value(
              **batch,
              prompt_length=max(2, args.num_padding_at_beginning),
          )  # we just need to skip the number of padding tokens at the beginning
      print("==================Eval result============================")
      print("prompt: ", prompt)
      print("my_ans: ", my_ans)
      print()
      print("=============Scores========================")
      print("my_ans score: ", outputs["chosen_end_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 6 for calling a global function with example usage: run_single_sample() without return types