code: def test_prepare_for_training_with_spark_nlp(self):
      ner_dataset = datasets.load_dataset(
          "argilla/gutenberg_spacy-ner",
          use_auth_token=_HF_HUB_ACCESS_TOKEN,
          split="train",
      )
      rb_dataset: DatasetForTokenClassification = read_datasets(
          ner_dataset, task="TokenClassification"
      )
      for r in rb_dataset:
          r.annotation = [
              (label, start, end)
              for label, start, end, _ in r.prediction
          ]
  
      train = rb_dataset.prepare_for_training(
          framework="spark-nlp", seed=42
      )
      assert isinstance(train, pd.DataFrame)
      assert len(train) == 100
  
      train, test = rb_dataset.prepare_for_training(
          framework="spark-nlp", train_size=0.8, seed=42
      )
      assert isinstance(train, pd.DataFrame)
      assert isinstance(test, pd.DataFrame)
      assert len(train) == 80
      assert len(test) == 20
  
imports: import copy
  import os
  import sys
  from time import sleep
  import datasets
  import pandas
  import pytest
  import spacy
  from argilla.client.datasets import DatasetBase, DatasetForText2Text, DatasetForTextClassification, DatasetForTokenClassification, WrongRecordTypeError, read_datasets, read_pandas
  from argilla.client.models import Text2TextRecord, TextClassificationRecord, TokenClassificationRecord
  from argilla.datasets import TextClassificationSettings
label_desc: Use image node_id 10 for calling the TestDatasetForTokenClassification obj's underlying member method code with example usage: obj.test_prepare_for_training_with_spark_nlp() without return types