code: def test_untile_overlapping_patches(
      image_size,
      kernel_size,
      stride,
      remove_border_count,
      tile_size,
      mode,
  ):
      """Overlapping Tiling/Untiling should return the same image size."""
      tiler = Tiler(
          tile_size=kernel_size,
          stride=stride,
          remove_border_count=remove_border_count,
          mode=mode,
      )
  
      image = torch.rand(image_size)
      tiles = tiler.tile(image)
      reconstructed_image = tiler.untile(tiles)
      image = image[
          :,
          :,
          remove_border_count:-remove_border_count,
          remove_border_count:-remove_border_count,
      ]
      reconstructed_image = reconstructed_image[
          :,
          :,
          remove_border_count:-remove_border_count,
          remove_border_count:-remove_border_count,
      ]
      assert torch.equal(image, reconstructed_image)
  
file_path: anomalib/tests/pre_merge/datasets/test_tiler.py
imports: import pytest
  import torch
  from omegaconf import ListConfig
  from anomalib.pre_processing.tiler import StrideSizeError, Tiler
label_desc: Use image node_id 7 for calling a global function with example usage: test_untile_overlapping_patches(image_size, kernel_size, stride, remove_border_count, tile_size, mode) without return types