code: def test_concrete_function_with(self, transform_fn, mlir_pipeline):
      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec((), dtype=dtypes.float32),
              tensor_spec.TensorSpec((), dtype=dtypes.float32),
          ]
      )
      def f(x, y):
          return math_ops.add(x, y, name="x_plus_y")
  
      # transfrom f(x, y): x + y -> f(x, y): x * y
      f = transform.transform_function(
          f, transform_fn=transform_fn, mlir_pipeline=mlir_pipeline
      )
      one = constant_op.constant(1.0)
      self.assertEqual(f(one, one), 1.0)
  
      @def_function.function
      def f2(x, y):
          z = f(x, y)
          return math_ops.add(z, 10.0)
  
      self.assertEqual(f2(one, one), 11.0)
  
      @def_function.function
      def f_g(x, y):
          z = f(x, y)
          dz_dx, dz_dy = gradients_impl.gradients(z, [x, y])
          return math_ops.add(dz_dx, dz_dy)
  
      self.assertEqual(
          f_g(constant_op.constant(2.0), constant_op.constant(3.0)), 5.0
      )
  
imports: import collections
  from absl.testing import parameterized
  from tensorflow.core.function.testing import test_pass
  from tensorflow.core.function.transform import transform
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_spec
  from tensorflow.python.framework import test_util
  from tensorflow.python.module import module
  from tensorflow.python.ops import custom_gradient
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import while_loop
  from tensorflow.python.platform import test
  from tensorflow.python.saved_model import load
  from tensorflow.python.saved_model import save
label_desc: Use image node_id 1 for calling the TransformTest obj's underlying member method code with example usage: obj.test_concrete_function_with(transform_fn, mlir_pipeline) and returns: math_ops, math_ops, math_ops