code: def test_prepare_for_training_text_classification_with_defaults(
      framework: Union[Framework, str],
      feedback_dataset_guidelines: str,
      feedback_dataset_fields: List["AllowedFieldTypes"],
      feedback_dataset_questions: List["AllowedQuestionTypes"],
      feedback_dataset_records: List[FeedbackRecord],
  ) -> None:
      dataset = FeedbackDataset(
          guidelines=feedback_dataset_guidelines,
          fields=feedback_dataset_fields,
          questions=feedback_dataset_questions,
      )
      dataset.add_records(records=feedback_dataset_records * 5)
  
      questions = [
          question
          for question in dataset.questions
          if isinstance(question, (LabelQuestion, MultiLabelQuestion))
      ]
      label = LabelQuestionUnification(question=questions[0])
      task = TrainingTask.for_text_classification(
          text=dataset.fields[0], label=label
      )
  
      if framework == Framework("span_marker"):
          with pytest.raises(
              NotImplementedError,
              match=f"Framework {framework} is not supported for this {TrainingTaskForTextClassification}.",
          ):
              trainer = ArgillaTrainer(
                  dataset=dataset, task=task, framework=framework
              )
      elif framework == Framework("spark-nlp"):
          with pytest.raises(
              NotImplementedError,
              match=f"{framework} is not a valid framework.",
          ):
              trainer = ArgillaTrainer(
                  dataset=dataset, task=task, framework=framework
              )
      else:
          if framework in [Framework("peft")] and sys.version_info < (
              3,
              9,
          ):
              pass
          else:
              trainer = ArgillaTrainer(
                  dataset=dataset, task=task, framework=framework
              )
              if framework in [
                  Framework("spacy"),
                  Framework("spacy-transformers"),
              ]:
                  trainer.update_config(max_steps=1)
              elif framework in [
                  Framework("transformers"),
                  Framework("setfit"),
              ]:
                  trainer.update_config(num_iterations=1)
              train_with_cleanup(trainer, __OUTPUT_DIR__)
  
      if Path(__OUTPUT_DIR__).exists():
          shutil.rmtree(__OUTPUT_DIR__)
  
imports: import random
  from collections import Counter
  from typing import TYPE_CHECKING, Callable, List, Union
  import pytest
  import re
  import shutil
  import sys
  from pathlib import Path
  from argilla.client.feedback.dataset import FeedbackDataset
  from argilla.client.feedback.schemas import FeedbackRecord, LabelQuestion, MultiLabelQuestion
  from argilla.client.feedback.schemas.enums import ResponseStatusFilter
  from argilla.client.feedback.schemas.records import SortBy
  from argilla.client.feedback.training import ArgillaTrainer
  from argilla.client.feedback.training.schemas.base import TrainingTask, TrainingTaskForTextClassification, TrainingTaskMapping, TrainingTaskMappingForTextClassification, TrainingTaskTypes
  from argilla.client.feedback.training.schemas.return_types import QuestionAnsweringReturnTypes, TextClassificationReturnTypes
  from argilla.client.feedback.unification import LabelQuestionUnification
  from argilla.client.models import Framework
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from tests.integration.training.helpers import train_with_cleanup
label_desc: Use image node_id 1 for calling a global function with example usage: test_prepare_for_training_text_classification_with_defaults(framework, feedback_dataset_guidelines, feedback_dataset_fields, feedback_dataset_questions, feedback_dataset_records) without return types