code: def test_train_textcat_multi_label(
      dataset_text_classification_multi_label,
  ):
      trainer = ArgillaTrainer(
          name=dataset_text_classification_multi_label,
          model=MODEL,
          train_size=0.5,
          framework=FRAMEWORK,
      )
      trainer.update_config(max_steps=1, num_train_epochs=1)
      output_dir = f"tmp_{FRAMEWORK}_train_multi_label"
      train_with_cleanup(trainer, output_dir)
      record = trainer.predict(
          "This is a text", as_argilla_records=True
      )
      assert isinstance(record, TextClassificationRecord)
      assert record.multi_label is True
      not_record = trainer.predict(
          "This is a text", as_argilla_records=False
      )
      assert not isinstance(not_record, TextClassificationRecord)
      train_with_cleanup(trainer, output_dir, train=False)
  
file_path: argilla/tests/integration/training/test_transformers.py
imports: from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.training import ArgillaTrainer
  from .helpers import train_with_cleanup
label_desc: Use image node_id 3 for calling a global function with example usage: test_train_textcat_multi_label(dataset_text_classification_multi_label) without return types