code: def test_reversible_transformation(
      self,
      transformation,
      model_class,
      model_name,
      model_input_names,
      dummy_inputs,
      disable_check,
  ):
      (
          inputs,
          model,
          transformed,
          model_output,
      ) = self._check_original_and_transformed_outputs_match(
          transformation,
          model_class,
          model_name,
          model_input_names,
          dummy_inputs,
          disable_check,
      )
  
      restored = transformation(transformed, reverse=True)
      restored_output = restored(**inputs)
      restored_output = self.flatten_output(restored_output)
  
      num_outputs = len(model_output)
      for i in range(num_outputs):
          self.assertTrue(
              torch.allclose(model_output[i], restored_output[i]),
              f"restored {i}th output doesn't match model {i}th output for {transformation.__class__.__name__}",
          )
  
      orig_named_parameters = dict(model.named_parameters())
      restored_named_parameters = dict(restored.named_parameters())
  
      self.assertSetEqual(
          set(orig_named_parameters.keys()),
          set(restored_named_parameters.keys()),
      )
      for name in orig_named_parameters:
          self.assertTrue(
              torch.allclose(
                  orig_named_parameters[name],
                  restored_named_parameters[name],
              ),
              f"the {name} parameter does not match between the original and the restored models",
          )
  
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 4 for calling the TransformationTester obj's underlying member method code with example usage: obj.test_reversible_transformation(transformation, model_class, model_name, model_input_names, dummy_inputs, disable_check) without return types