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,
  ):
      # TODO: remove the need for tokenizer
      if model_type == "markuplm":
          preprocessor = AutoProcessor.from_pretrained(model_id)
      else:
          preprocessor = AutoTokenizer.from_pretrained(model_id)
      if batch_size == 1:
          texts = ["a dummy input yeah yeah!"]
      elif no_padding:
          texts = ["a dummy input yeah yeah!"] * batch_size
      else:
          texts = ["a dummy input yeah yeah!"] + ["and two"] * (
              batch_size - 1
          )
  
      padding = preprocessor_kwargs.pop("padding", True)
      if padding == "max_length":
          max_length = 25
      else:
          max_length = None
  
      inputs = preprocessor(
          texts,
          return_tensors="pt",
          padding=padding,
          max_length=max_length,
          **preprocessor_kwargs,
      )
      return inputs
  
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 2 for calling the BetterTransformersEncoderTest 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