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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docstr: The converted models should be at least slightly faster than the native
  model. This test aims to check this.
  Let's test the inference speed on bert-base-uncased only. If it works for this
  model, it should be applicable to all other models, see the test above.
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code: def test_inference_speed(self):
      r"""
      The converted models should be at least slightly faster than the native
      model. This test aims to check this.
      Let's test the inference speed on bert-base-uncased only. If it works for this
      model, it should be applicable to all other models, see the test above.
      """
      model_name = "bert-base-uncased"
  
      hf_model = AutoModel.from_pretrained(model_name).eval()
      bt_model = BetterTransformer.transform(
          hf_model, keep_original_model=True
      )
  
      BATCH_SIZE = 8
      SEQ_LEN = 16
      MAX_SEQ_LEN = 32
      STD_SEQ_LEN = (
          10  # let's take a large sequence length standard deviation
      )
      VOCAB_SIZE = 50
      N_REPEAT = 10
  
      input_ids, _, attention_mask = get_batch(
          BATCH_SIZE, SEQ_LEN, MAX_SEQ_LEN, STD_SEQ_LEN, VOCAB_SIZE
      )
      for i in range(1, BATCH_SIZE):
          attention_mask[i, SEQ_LEN // 4 :] = 0
  
      mean_hf_time = 0
      mean_bt_time = 0
  
      # warmup hf_model
      _ = hf_model(input_ids, attention_mask=attention_mask)
      # warmup bt_model
      _ = bt_model(input_ids, attention_mask=attention_mask)
  
      for _ in range(N_REPEAT):
          mean_hf_time += timeit.timeit(
              lambda: hf_model(
                  input_ids, attention_mask=attention_mask
              ),
              number=1,
          )
          mean_bt_time += timeit.timeit(
              lambda: bt_model(
                  input_ids, attention_mask=attention_mask
              ),
              number=1,
          )
  
      mean_hf_time /= N_REPEAT
      mean_bt_time /= N_REPEAT
  
      self.assertLess(
          mean_bt_time,
          mean_hf_time,
          "The converted model is slower than the original model.",
      )
      gc.collect()
  
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 4 for calling the BetterTransformersEncoderTest obj's underlying member method code with example usage: obj.test_inference_speed() without return types