code: def test_push_to_huggingface(
      framework: Union[Framework, str],
      feedback_dataset_guidelines: str,
      feedback_dataset_fields: List["AllowedFieldTypes"],
      feedback_dataset_questions: List["AllowedQuestionTypes"],
      feedback_dataset_records: List[FeedbackRecord],
      mocked_trainer_push_to_huggingface,
  ) -> None:
      dataset = FeedbackDataset(
          guidelines=feedback_dataset_guidelines,
          fields=feedback_dataset_fields,
          questions=feedback_dataset_questions,
      )
      dataset.add_records(records=feedback_dataset_records * 2)
  
      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}.",
          ):
              ArgillaTrainer(
                  dataset=dataset, task=task, framework=framework
              )
          return
  
      else:
          if framework == Framework("spacy"):
              model = "en_core_web_sm"
          elif framework == Framework("setfit"):
              model = "all-MiniLM-L6-v2"
          else:
              model = "prajjwal1/bert-tiny"
  
          trainer = ArgillaTrainer(
              dataset=dataset,
              task=task,
              framework=framework,
              model=model,
          )
  
      # We need to initialize the model (is faster than calling the whole training process) before calling push_to_huggingface.
      # The remaining models need to call the train method first.
      repo_id = "mocked"
      if framework in (Framework("transformers"), Framework("peft")):
          trainer.update_config(num_iterations=1)
          trainer._trainer.init_model(new=True)
      elif framework in (
          Framework("setfit"),
          Framework("spacy"),
          Framework("spacy-transformers"),
      ):
          if framework in (
              Framework("spacy"),
              Framework("spacy-transformers"),
          ):
              trainer.update_config(max_steps=1)
              repo_id = __OUTPUT_DIR__
          else:
              trainer.update_config(num_iterations=1)
      else:
          trainer._trainer.init_model()
  
      # We have to train the model and push it with spacy before removing the
      # generated folder, as it needs to be packaged.
      if framework in (
          Framework("spacy"),
          Framework("spacy-transformers"),
      ):
          trainer.train(__OUTPUT_DIR__)
      else:
          trainer.train(__OUTPUT_DIR__)
  
      # This functionality is mocked, no need to check the generated card too.
      trainer.push_to_huggingface(repo_id, generate_card=False)
      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 11 for calling a global function with example usage: test_push_to_huggingface(framework, feedback_dataset_guidelines, feedback_dataset_fields, feedback_dataset_questions, feedback_dataset_records, mocked_trainer_push_to_huggingface) without return types