code: def eval(
      model: LLaMA,
      tokenizer,
      tasks: list = ["hellaswag"],
      limit: Optional[int] = None,
      max_seq_length: Optional[int] = None,
  ) -> dict:
      """
      Evaluates a language model on a specified task using the lm-evaluation-harness library.
  
      Args:
          model (LLaMA): The pre-trained language model to evaluate.
          tokenizer: The tokenizer to use for encoding/decoding text.
          task (str): The name of the evaluation task to perform.
          limit (Optional[int]): The maximum number of samples to evaluate (None for all available).
          max_seq_length (Optional[int]): The maximum sequence length allowed for input text.
  
      Returns:
          eval_results (dict): A dictionary of evaluation results for the specified task(s).
      """
      model_eval_wrapper = SimpleGPTEvalWrapper(
          model,
          tokenizer,
          max_seq_length,
      )
  
      if "hendrycks_test" in tasks:
          tasks.remove("hendrycks_test")
          tasks += [
              x
              for x in lm_eval.tasks.hendrycks_test.create_all_tasks().keys()
          ]
      task_dict = lm_eval.tasks.get_task_dict(tasks)
  
      eval_results = lm_eval.evaluator.evaluate(
          model_eval_wrapper,
          task_dict,
          limit=limit,
      )
      return eval_results
  
imports: import sys
  import time
  from pathlib import Path
  from typing import Optional
  import torch
  import torch._dynamo.config
  import torch._inductor.config
  import os
  import sys
  from sentencepiece import SentencePieceProcessor
  from model import LLaMA
  import lm_eval
  import main
  from generate import _load_model, encode_tokens, model_forward
label_desc: Use image node_id 2 for calling a global function with example usage: eval(model, tokenizer, tasks, limit, max_seq_length) and returns: eval_results