class_docstr: Full testing suite of the `BetterTransformers` integration into Hugging Face
  `transformers` ecosystem. Check the docstring of each test to understand the
  purpose of each test. Basically we test:
  - if the conversion dictionnary is consistent, ie if the converted model exists
  in HuggingFace `transformers` library.
  - if the converted model produces the same logits as the original model.
  - if the converted model is faster than the original model.
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code: def test_logits(
      self, test_name: str, model_type: str, batch_size: int
  ):
      # TODO: enable those tests
      if model_type in [
          "rocbert",
          "splinter",
          "markuplm",
          "bert-generation",
      ]:
          self.skipTest(
              f"tiny tokenizers are broken on the Hub {model_type}"
          )
      if model_type in ["tapas"]:
          self.skipTest(f"{model_type} requires dataframe")
  
      model_id = MODELS_DICT[model_type]
      self._test_logits(
          model_id=model_id,
          model_type=model_type,
          batch_size=batch_size,
      )
  
imports: import gc
  import timeit
  import pytest
  import torch
  import transformers
  from parameterized import parameterized
  from testing_utils import MODELS_DICT, BetterTransformersTestMixin
  from transformers import AutoModel, AutoProcessor, AutoTokenizer
  from optimum.bettertransformer import BetterTransformer
  from optimum.pipelines import pipeline
  from optimum.utils.testing_utils import grid_parameters, require_accelerate, require_torch_gpu
label_desc: Use image node_id 12 for calling the BetterTransformersEncoderTest obj's underlying member method code with example usage: obj.test_logits(test_name, model_type, batch_size) without return types