class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
code: def _init_dummy_dataset(self):
      # encode a query
      query_txt = "This morning I went to the "
      query_tensor = self.gpt2_tokenizer.encode(
          query_txt, return_tensors="pt"
      )
      assert query_tensor.shape == (1, 7)
      # get model response
      response_tensor = respond_to_batch(self.gpt2_model, query_tensor)
      assert response_tensor.shape == (1, 20)
  
      # create a dummy dataset
      min_length = min(len(query_tensor[0]), len(response_tensor[0]))
      dummy_dataset = DummyDataset(
          [query_tensor[:, :min_length].squeeze(0) for _ in range(2)],
          [
              response_tensor[:, :min_length].squeeze(0)
              for _ in range(2)
          ],
      )
  
      return dummy_dataset
  
imports: import copy
  import fnmatch
  import gc
  import re
  import tempfile
  import unittest
  import pytest
  import torch
  from huggingface_hub import HfApi, HfFolder, delete_repo
  from parameterized import parameterized
  from pytest import mark
  from requests.exceptions import HTTPError
  from transformers import AutoTokenizer
  from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer, set_seed
  from trl.core import respond_to_batch
  from .testing_constants import CI_HUB_ENDPOINT, CI_HUB_USER, CI_HUB_USER_TOKEN
  from .testing_utils import require_peft, require_torch_multi_gpu
label_desc: Use image node_id 5 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj._init_dummy_dataset() and returns: dummy_dataset