code: def test_ppo_trainer_no_peft(self):
      with patch.dict(sys.modules, {"peft": None}):
          from trl import (
              AutoModelForCausalLMWithValueHead,
              PPOConfig,
              PPOTrainer,
          )
  
          ppo_model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
  
          trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(
              ppo_model_id
          )
          tokenizer = AutoTokenizer.from_pretrained(ppo_model_id)
          tokenizer.pad_token_id = tokenizer.eos_token_id
  
          ppo_config = PPOConfig(
              batch_size=2, mini_batch_size=1, log_with=None
          )
  
          dummy_dataset = DummyDataset(
              [
                  torch.LongTensor([0, 1, 0, 1, 0, 1]),
                  torch.LongTensor([0, 1, 0, 1, 0, 1]),
              ],
              [
                  torch.LongTensor([1, 0, 1, 0, 1, 0]),
                  torch.LongTensor([0, 1, 0, 1, 0, 1]),
              ],
          )
  
          ppo_trainer = PPOTrainer(
              config=ppo_config,
              model=trl_model,
              ref_model=None,
              tokenizer=tokenizer,
              dataset=dummy_dataset,
          )
          dummy_dataloader = ppo_trainer.dataloader
  
          for query_tensor, response_tensor in dummy_dataloader:
              # define a reward for response
              # (this could be any reward such as human feedback or output from another model)
              reward = [torch.tensor(1.0), torch.tensor(0.0)]
              # train model
              train_stats = ppo_trainer.step(
                  [q for q in query_tensor],
                  [r for r in response_tensor],
                  reward,
              )
              break
  
          # check gradients are not None
          for _, param in trl_model.named_parameters():
              if param.requires_grad:
                  self.assertIsNotNone(param.grad)
  
          # check expected stats
          for stat in EXPECTED_STATS:
              self.assertIn(stat, train_stats)
  
file_path: trl/tests/test_no_peft.py
imports: import sys
  import unittest
  from unittest.mock import patch
  import torch
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from .testing_utils import is_peft_available, require_peft
label_desc: Use image node_id 4 for calling the TestPeftDependancy obj's underlying member method code with example usage: obj.test_ppo_trainer_no_peft() without return types