code: def test_shift_batch(self, device, dtype):
      height, width = 3, 4
      inp = torch.tensor(
          [
              [
                  [
                      [1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0],
                  ]
              ]
          ],
          device=device,
          dtype=dtype,
      ).repeat(2, 1, 1, 1)
  
      expected = torch.tensor(
          [
              [
                  [
                      [1.0, 1.0, 1.0, 0.0],
                      [1.0, 1.0, 1.0, 0.0],
                      [1.0, 1.0, 1.0, 0.0],
                  ]
              ],
              [
                  [
                      [1.0, 1.0, 1.0, 1.0],
                      [1.0, 1.0, 1.0, 1.0],
                      [0.0, 0.0, 0.0, 0.0],
                  ]
              ],
          ],
          device=device,
          dtype=dtype,
      )
  
      # generate a batch of grids
      grid = kornia.utils.create_meshgrid(
          height, width, normalized_coordinates=False, device=device
      ).to(dtype)
      grid = grid.repeat(2, 1, 1, 1)
      grid[0, ..., 0] += 1.0  # apply shift in the x direction
      grid[1, ..., 1] += 1.0  # apply shift in the y direction
  
      input_warped = kornia.geometry.remap(
          inp, grid[..., 0], grid[..., 1], align_corners=True
      )
      assert_close(input_warped, expected, rtol=1e-4, atol=1e-4)
  
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 4 for calling the TestRemap obj's underlying member method code with example usage: obj.test_shift_batch(device, dtype) without return types