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 tearDown(self):
      gc.collect()
  
imports: import gc
  import unittest
  import pytest
  import torch
  from parameterized import parameterized
  from testing_utils import MODELS_DICT, BetterTransformersTestMixin
  from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  from optimum.bettertransformer import BetterTransformer
  from optimum.utils.testing_utils import grid_parameters, require_torch_gpu
label_desc: Use image node_id 1 for calling the BetterTransformersEncoderDecoderTest obj's underlying member method code with example usage: obj.tearDown() without return types