code: def eval_subject(
      self,
      subject_name,
      test_df,
      eval_type="validation",
      dev_df=None,
      few_shot=False,
      cot=False,
      with_prompt=False,
      constrained_decoding=False,
  ):
      all_answers = {}
      if constrained_decoding is True:
          self.generation_config.output_scores = True
          self.generation_config.return_dict_in_generate = True
          self.generation_config.max_new_tokens = 1
          self.generation_config.top_p = 1.0
          self.generation_config.top_k = 0
  
      correct_num = 0
      if few_shot:
          if with_prompt:
              history = self.generate_alpaca2_few_shot_prompt(
                  subject_name, dev_df, cot=cot
              )
          else:
              history = self.generate_llama2_few_shot_prompt(
                  subject_name, dev_df, cot=cot
              )
      else:
          history = ""
      answers = (
          ["NA"] * len(test_df)
          if (eval_type == "test") is True
          else list(test_df["answer"])
      )
      for row_index, row in tqdm(
          test_df.iterrows(), total=len(test_df)
      ):
          question = self.format_example(
              row,
              include_answer=False,
              cot=cot,
              with_prompt=with_prompt,
          )
          instruction = question
          if with_prompt:
              prompt_template = (
                  "[INST] <<SYS>>\n"
                  "{system_prompt}\n"
                  "<</SYS>>\n\n"
                  "{instruction} [/INST]"
              )
  
              instruction = prompt_template.format_map(
                  {
                      "instruction": instruction,
                      "system_prompt": DEFAULT_SYSTEM_PROMPT,
                  }
              )
          instruction = history + instruction
          inputs = self.tokenizer(instruction, return_tensors="pt")
          generation_output = self.model.generate(
              input_ids=inputs["input_ids"].to(self.device),
              attention_mask=inputs["attention_mask"].to(self.device),
              eos_token_id=self.tokenizer.eos_token_id,
              pad_token_id=self.tokenizer.pad_token_id,
              generation_config=self.generation_config,
          )
  
          _, length = inputs.input_ids.shape
          if constrained_decoding is True:
              logits = generation_output.scores[0][0]
  
              logits = logits.float().cpu().detach()
              choices1_logits = logits[
                  [self.sA_id, self.sB_id, self.sC_id, self.sD_id]
              ]
              choices2_logits = logits[
                  [self.A_id, self.B_id, self.C_id, self.D_id]
              ]
              choicesAll_logits = (
                  choices1_logits + choices2_logits
              ).numpy()
              assert not (
                  np.any(np.isinf(choicesAll_logits))
                  or np.any(np.isnan(choicesAll_logits))
              )
              ans = {0: "A", 1: "B", 2: "C", 3: "D"}[
                  np.argmax(choicesAll_logits)
              ]
              response = self.tokenizer.decode(
                  [logits.argmax(-1).item()]
              )
          else:
              response = self.tokenizer.decode(
                  generation_output[0, length:],
                  skip_special_tokens=True,
              )
              ans, _ = self.extract_answer(response, row)
          if ans == answers[row_index]:
              correct_num += 1
  
          all_answers[str(row_index)] = ans
  
      correct_ratio = 100 * correct_num / len(answers)
  
      return correct_ratio, all_answers
  
file_path: BigDL/python/llm/dev/benchmark/ceval/evaluators/llama.py
imports: import re
  import random
  from tqdm import tqdm
  import numpy
  import torch
  from transformers import LlamaTokenizer, GenerationConfig
  from bigdl.llm.transformers import AutoModelForCausalLM
  from evaluators.evaluator import Evaluator
label_desc: Use image node_id 2 for calling the LlamaEvaluator obj's underlying member method code with example usage: obj.eval_subject(subject_name, test_df, eval_type, dev_df, few_shot, cot, with_prompt, constrained_decoding) and returns: correct_ratio,  all_answers