code: def prepare_inputs_for_class(
      self,
      model_id: str,
      model_type: str,
      batch_size: int = 2,
      no_padding: bool = False,
      **preprocessor_kwargs,
  ):
      tokenizer = AutoTokenizer.from_pretrained(model_id)
      if (
          not hasattr(tokenizer, "pad_token")
          or tokenizer.pad_token is None
      ):
          if tokenizer.eos_token != "":
              tokenizer.pad_token = tokenizer.eos_token
          else:
              tokenizer.pad_token = "w"
  
      padding = preprocessor_kwargs.pop("padding", True)
      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=20,
          **preprocessor_kwargs,
      )
  
      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 AutoModelForCausalLM, AutoTokenizer
  from optimum.bettertransformer import BetterTransformer
  from optimum.utils import BloomDummyPastKeyValuesGenerator, DummyPastKeyValuesGenerator, NormalizedConfigManager
  from optimum.utils.testing_utils import grid_parameters, require_accelerate, require_torch_gpu
label_desc: Use image node_id 1 for calling the BetterTransformersDecoderTest obj's underlying member method code with example usage: obj.prepare_inputs_for_class(model_id, model_type, batch_size, no_padding) and returns: inputs