code: def test_transformation_signature(self):
      t1 = DummyTransformation()
      t2 = DummyTransformation(some_argument=1)
  
      # Same transformation
      self.assertEqual(t1.signature, DummyTransformation().signature)
      # Same transformation class, but different attributes
      self.assertNotEqual(t1.signature, t2.signature)
      # Different transformation class, but same attributes
      DifferentTransformation = type(
          "DifferentTransformation", (DummyTransformation,), {}
      )
      t3 = DifferentTransformation(some_argument=1)
      self.assertNotEqual(t2.signature, t3.signature)
  
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 9 for calling the TransformationTester obj's underlying member method code with example usage: obj.test_transformation_signature() without return types