code: def test_tranform_randomized(self, input):
      # Compose deterministic and randomized transforms
      transforms = Compose(
          [
              Range("flip")(Flip()),
              Rotate90(),
              Range()(RandAdjustContrast(prob=0.0)),
              Range("random flip")(RandFlip(prob=1.0)),
              ToTensor(),
          ]
      )
      # Apply transforms
      output = transforms(input)
  
      # Decorate with NVTX Range
      transforms1 = Range()(transforms)
      transforms2 = Range("Transforms2")(transforms)
      transforms3 = Range(name="Transforms3", methods="__call__")(
          transforms
      )
  
      # Apply transforms with Range
      output1 = transforms1(input)
      output2 = transforms2(input)
      output3 = transforms3(input)
  
      # Check if the outputs are equal
      self.assertIsInstance(output, torch.Tensor)
      self.assertIsInstance(output1, torch.Tensor)
      self.assertIsInstance(output2, torch.Tensor)
      self.assertIsInstance(output3, torch.Tensor)
      np.testing.assert_equal(output.numpy(), output1.numpy())
      np.testing.assert_equal(output.numpy(), output2.numpy())
      np.testing.assert_equal(output.numpy(), output3.numpy())
  
      # Check if the first randomized is RandAdjustContrast
      for tran in transforms.transforms:
          if isinstance(tran, RandomizableTrait):
              self.assertIsInstance(tran, RandAdjustContrast)
              break
  
file_path: MONAI/tests/test_nvtx_decorator.py
imports: from __future__ import annotations
  import unittest
  import numpy
  import torch
  from parameterized import parameterized
  from monai.transforms import Compose, CuCIM, Flip, Flipd, OneOf, RandAdjustContrast, RandCuCIM, RandFlip, RandomizableTrait, Rotate90, ToCupy, ToNumpy, TorchVision, ToTensor, ToTensord
  from monai.utils import Range, optional_import
  from tests.utils import HAS_CUPY
label_desc: Use image node_id 6 for calling the TestNVTXRangeDecorator obj's underlying member method code with example usage: obj.test_tranform_randomized(input) without return types