code: def test_fill_padding_channels(self, device, dtype, num_channels):
      offset = 1.0
      h, w = 3, 4
      aff_ab = torch.eye(2, 3, device=device, dtype=dtype)[None]
      aff_ab[..., -1] += offset
  
      img_b = torch.arange(
          float(num_channels * h * w), device=device, dtype=dtype
      ).view(1, num_channels, h, w)
  
      fill_value = torch.zeros(num_channels, device=device, dtype=dtype)
  
      img_a = kornia.geometry.warp_affine(
          img_b,
          aff_ab,
          (h, w),
          padding_mode="fill",
          fill_value=fill_value,
      )
  
      assert_close(img_a[:, :, :1, :1].squeeze(), fill_value.squeeze())
  
imports: import sys
  import pytest
  import torch
  from torch.autograd import gradcheck
  import kornia
  import kornia.testing
  from kornia.testing import assert_close
  from kornia.utils._compat import torch_version, torch_version_lt
  from kornia.utils.helpers import _torch_inverse_cast
label_desc: Use image node_id 9 for calling the TestWarpAffine obj's underlying member method code with example usage: obj.test_fill_padding_channels(device, dtype, num_channels) without return types