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.
class_docstr_tok: ["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","."]
code: def test_generation(
      self,
      test_name: str,
      model_type: str,
      batch_size: int,
      padding: str,
  ):
      if batch_size == 1 and padding == "max_length":
          self.skipTest(
              "batch_size=1 + padding='max_length' is unsupported"
          )
  
      model_id = MODELS_DICT[model_type]
      tokenizer = AutoTokenizer.from_pretrained(model_id)
  
      model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
  
      if (
          not hasattr(tokenizer, "pad_token")
          or tokenizer.pad_token is None
      ):
          tokenizer.pad_token = tokenizer.eos_token
  
      text = ["This is me and me"]
      if batch_size > 1:
          text = text + ["Please"] * (batch_size - 1)
      inp = tokenizer(
          text, return_tensors="pt", padding=padding, max_length=25
      )
  
      length = 50
      result_vanilla = model.generate(
          **inp, num_beams=1, min_length=length, max_length=length
      )
  
      model = BetterTransformer.transform(model)
  
      result_bettertransformer = model.generate(
          **inp, num_beams=1, min_length=length, max_length=length
      )
  
      self.assertTrue(
          torch.allclose(result_vanilla, result_bettertransformer),
          f" Maxdiff: {(result_vanilla - result_bettertransformer).abs().max()}",
      )
  
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 9 for calling the BetterTransformersEncoderDecoderTest obj's underlying member method code with example usage: obj.test_generation(test_name, model_type, batch_size, padding) without return types