code: def setUp(self):
      dataset = datasets.Dataset.from_dict(
          {
              "sentence": [
                  f"this is test number {i}" for i in range(10)
              ],
              "label": [
                  "POS" if (i % 2) == 0 else "NEG" for i in range(10)
              ],
          },
          features=datasets.Features(
              {
                  "sentence": datasets.Value("string"),
                  "label": datasets.ClassLabel(2, names=["NEG", "POS"]),
              }
          ),
      )
      tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
  
      def preprocess(example):
          results = tokenizer(
              example["sentence"],
              max_length=50,
              truncation=True,
              padding="max_length",
          )
          return results
  
      tokenized_dataset = dataset.map(preprocess, batched=True)
      self.active_dataset = ActiveLearningDataset(tokenized_dataset)
  
imports: import unittest
  import pytest
  from torch.utils.data import Dataset
  from transformers import AutoTokenizer
  from baal.active.dataset import ActiveLearningDataset
  from baal.active.dataset.nlp_datasets import HuggingFaceDatasets
  import datasets
label_desc: Use image node_id 1 for calling the ActiveArrowDatasetTest obj's underlying member method code with example usage: obj.setUp() and returns: results