code: def test_push_to_huggingface(
      formatting_func: Callable,
      training_task: Callable,
      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)
  
      task = training_task(formatting_func)
      model = "sshleifer/tiny-gpt2"
  
      trainer = ArgillaTrainer(
          dataset=dataset, task=task, framework="trl", model=model
      )
  
      if training_task == TrainingTask.for_proximal_policy_optimization:
          from transformers import pipeline
          from trl import PPOConfig
  
          reward_model = pipeline(
              "sentiment-analysis", model="lvwerra/distilbert-imdb"
          )
          trainer.update_config(
              config=PPOConfig(batch_size=1, ppo_epochs=2),
              reward_model=reward_model,
          )
      else:
          trainer.update_config(max_steps=1)
  
      train_with_cleanup(trainer, OUTPUT_DIR)
  
      trainer.push_to_huggingface("mocked", generate_card=False)
  
imports: import os
  import re
  from collections import Counter
  from typing import TYPE_CHECKING, Any, Callable, Dict, Iterator, List
  import pytest
  from argilla.client.feedback.dataset import FeedbackDataset
  from argilla.client.feedback.schemas.records import FeedbackRecord
  from argilla.client.feedback.training.base import ArgillaTrainer
  from argilla.client.feedback.training.schemas.base import TrainingTask
  from argilla.client.feedback.training.schemas.return_types import DPOReturnTypes, PPOReturnTypes, RMReturnTypes, SFTReturnTypes
  from datasets import Dataset, DatasetDict
  from peft import LoraConfig, TaskType
  from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
  from trl import AutoModelForCausalLMWithValueHead
  from tests.integration.client.feedback.helpers import formatting_func_dpo, formatting_func_ppo, formatting_func_rm, formatting_func_sft
  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(formatting_func, training_task, feedback_dataset_guidelines, feedback_dataset_fields, feedback_dataset_questions, feedback_dataset_records, mocked_trainer_push_to_huggingface) without return types