code: def _check_original_and_transformed_outputs_match(
      self,
      transformation,
      model_class,
      model_name,
      model_input_names,
      inputs,
      disable_check,
  ):
      model = model_class.from_pretrained(model_name)
      model.eval()
      traced = symbolic_trace(
          model,
          input_names=model_input_names,
          disable_check=disable_check,
      )
  
      if inputs is None:
          tokenizer = AutoTokenizer.from_pretrained(_MODEL_NAME)
          inputs = tokenizer("This is a test.", return_tensors="pt")
  
      model_output = model(**inputs)
      transformed = transformation(traced)
      transformed_output = transformed(**inputs)
      model_output = self.flatten_output(model_output)
      transformed_output = self.flatten_output(transformed_output)
  
      if transformation.preserves_computation:
          num_outputs = len(model_output)
          for i in range(num_outputs):
              self.assertTrue(
                  torch.allclose(
                      model_output[i], transformed_output[i]
                  ),
                  f"transformed {i}th output doesn't match model {i}th output for {transformation.__class__.__name__}",
              )
      return inputs, model, transformed, model_output
  
imports: import operator
  import unittest
  import torch
  from parameterized import parameterized
  from transformers import AutoModel, AutoModelForImageClassification, AutoTokenizer, BertModel
  from transformers.models.bert.modeling_bert import BertSelfAttention
  from transformers.utils.fx import symbolic_trace
  from optimum.fx.optimization import ChangeTrueDivToMulByInverse, FuseBatchNorm1dInLinear, FuseBatchNorm2dInConv2d, FuseBiasInLinear, MergeLinears, ReversibleTransformation, Transformation, compose
  from optimum.fx.utils import are_fx_features_available
label_desc: Use image node_id 2 for calling the TransformationTester obj's underlying member method code with example usage: obj._check_original_and_transformed_outputs_match(transformation, model_class, model_name, model_input_names, inputs, disable_check) and returns: inputs,  model,  transformed,  model_output