code: def load_solutions(sample_path: PathLike) -> Iterable[Dict]:
      """We accept two formats of inputs.
      + `sample.jsonl` which is the format from HumanEval, i.e., {task_id, completion}.
      + A folder which contains sub-folders named after the task_id. Each sub-folder
      contains samples named in `[?].py` where `?` is the solution id starting with 0.
      Different from `sample.jsonl`, the solutions must be complete (with prompt prefix).
      """
  
      # if it is a file
      if os.path.isfile(sample_path):
          for i, sample in enumerate(stream_jsonl(sample_path)):
              sample["_identifier"] = sample["task_id"] + "_" + str(i)
              yield sample
      else:
          # if it is a folder
          for task_id in os.listdir(sample_path):
              task_path = os.path.join(sample_path, task_id)
              if not os.path.isdir(task_path):
                  continue
  
              for solution_id in os.listdir(task_path):
                  solution_path = os.path.join(task_path, solution_id)
                  if os.path.isfile(
                      solution_path
                  ) and solution_path.endswith(".py"):
                      with open(solution_path, "r") as f:
                          completion = f.read()
                      yield {
                          "_identifier": solution_path,
                          "_path": solution_path,
                          "task_id": task_id.replace("_", "/"),
                          "solution": completion,
                      }
  
file_path: evalplus/evalplus/data/utils.py
imports: import gzip
  import json
  import os
  from os import PathLike
  from typing import Dict, Iterable
  import tempdir
  import wget
  from appdirs import user_cache_dir
label_desc: Use image node_id 5 for calling a global function with example usage: load_solutions(sample_path) without return types