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 prepare_inputs_for_class(
      self,
      model_id: str,
      model_type: str,
      no_padding: bool = False,
      batch_size: int = 2,
      **preprocessor_kwargs,
  ):
      tokenizer = AutoTokenizer.from_pretrained(model_id)
  
      padding = preprocessor_kwargs.pop("padding", True)
      if padding == "max_length":
          max_length = 25
      else:
          max_length = None
  
      if batch_size == 1:
          texts = ["a dummy input yeah!"]
      elif no_padding:
          texts = ["a dummy input yeah!"] * batch_size
      else:
          texts = ["a dummy input yeah!"] + ["and two"] * (
              batch_size - 1
          )
  
      inputs = tokenizer(
          texts,
          return_tensors="pt",
          padding=padding,
          max_length=max_length,
          **preprocessor_kwargs,
      )
      inputs["decoder_input_ids"] = inputs[
          "input_ids"
      ]  # just a hack for m2m100
      return inputs
  
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 2 for calling the BetterTransformersEncoderDecoderTest obj's underlying member method code with example usage: obj.prepare_inputs_for_class(model_id, model_type, no_padding, batch_size) and returns: inputs