code: def test_random_mixup_lam0(self, device, dtype):
      torch.manual_seed(0)
      f = RandomMixUpV2(
          lambda_val=(0.0, 0.0), p=1.0, data_keys=["input", "class"]
      )
  
      input = torch.stack(
          [
              torch.ones(1, 3, 4, device=device, dtype=dtype),
              torch.zeros(1, 3, 4, device=device, dtype=dtype),
          ]
      )
      label = torch.tensor([1, 0], device=device, dtype=dtype)
      lam = torch.tensor([0.0, 0.0], device=device, dtype=dtype)
  
      expected = input.clone()
  
      out_image, out_label = f(input, label)
  
      assert_close(out_image, expected, rtol=1e-4, atol=1e-4)
      assert_close(out_label[:, 0], label)
      assert_close(
          out_label[:, 1],
          torch.tensor([0, 1], device=device, dtype=dtype),
      )
      assert_close(out_label[:, 2], lam, rtol=1e-4, atol=1e-4)
  
imports: import copy
  import pytest
  import torch
  from kornia.augmentation import AugmentationSequential, RandomCutMixV2, RandomJigsaw, RandomMixUpV2, RandomMosaic, RandomTransplantation, RandomTransplantation3D
  from kornia.testing import BaseTester, assert_close, tensor_to_gradcheck_var
label_desc: Use image node_id 4 for calling the TestRandomMixUpV2 obj's underlying member method code with example usage: obj.test_random_mixup_lam0(device, dtype) without return types