code: def test_prepare_for_training(self, request, records):
      records = request.getfixturevalue(records)
  
      ds = DatasetForTextClassification(records)
      train = ds.prepare_for_training(seed=42)
  
      if not ds[0].multi_label:
          column_names = ["id", "text", "label"]
      else:
          column_names = ["id", "text", "label", "binarized_label"]
  
      assert isinstance(train, datasets.Dataset)
      assert train.column_names == column_names
      assert len(train) == 2
      assert train[1]["text"] == "mock3"
      assert train.features["text"] == datasets.Value("string")
      if records[0].multi_label:
          assert train.features["label"] == [
              datasets.ClassLabel(names=["a", "b"])
          ]
      else:
          assert train.features["label"] == datasets.ClassLabel(
              names=["a"]
          )
  
      train_test = ds.prepare_for_training(train_size=0.5, seed=42)
      assert len(train_test["train"]) == 1
      assert len(train_test["test"]) == 1
      for split in ["train", "test"]:
          assert set(train_test[split].column_names) == set(
              column_names
          )
  
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 7 for calling the TestDatasetForTextClassification obj's underlying member method code with example usage: obj.test_prepare_for_training(request, records) without return types