code: def test_prep_instruct_vicuna():
      from datasets import load_dataset
  
      filename = "ShareGPT_unfiltered_cleaned_split.json"
      if not os.path.exists(filename):
          os.system(
              "wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/%s"
              % filename
          )
      data = load_dataset("json", data_files={"train": filename})[
          "train"
      ]
      training_rows = []
      for i in range(data.num_rows):
          conversations = data[i]["conversations"]
          assert isinstance(conversations, list), conversations
          convo = ""
          for j, conv in enumerate(conversations):
              # Get ready for generate.py prompt_type=human_bot
              # But train with prompt_type=plain
              if conv["from"] == "human":
                  FROM = "<human>: "
              elif conv["from"] == "gpt":
                  FROM = "<bot>: "
              convo += f"{FROM}" + conv["value"] + "\n"
          if convo:
              training_rows.append(dict(input=convo))
      with open(
          filename + ".generate_human_bot.train_plain.json", "wt"
      ) as f:
          f.write(json.dumps(training_rows, indent=2))
  
imports: import ast
  import concurrent.futures
  import contextlib
  import hashlib
  import json
  import os
  import shutil
  import signal
  import sys
  import traceback
  from concurrent.futures import ProcessPoolExecutor
  import psutil
  import pytest
  import pandas
  import numpy
  from tqdm import tqdm
  from utils import flatten_list, remove
  from joblib import Parallel, delayed, effective_n_jobs
  from sklearn.utils import gen_even_slices
  from sklearn.utils.validation import _num_samples
label_desc: Use image node_id 13 for calling a global function with example usage: test_prep_instruct_vicuna() without return types