code: def test_clear_border():
      image = np.array(
          [
              [0, 0, 0, 0, 0, 0, 0, 1, 0],
              [1, 1, 0, 0, 1, 0, 0, 1, 0],
              [1, 1, 0, 1, 0, 1, 0, 0, 0],
              [0, 0, 0, 1, 1, 1, 1, 0, 0],
              [0, 1, 1, 1, 1, 1, 1, 1, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0],
          ]
      )
  
      # test default case
      result = clear_border(image.copy())
      ref = image.copy()
      ref[1:3, 0:2] = 0
      ref[0:2, -2] = 0
      assert_array_equal(result, ref)
  
      # test buffer
      result = clear_border(image.copy(), 1)
      assert_array_equal(result, np.zeros(result.shape))
  
      # test background value
      result = clear_border(image.copy(), buffer_size=1, bgval=2)
      assert_array_equal(result, 2 * np.ones_like(image))
  
      # test mask
      mask = np.array(
          [
              [0, 0, 1, 1, 1, 1, 1, 1, 1],
              [0, 0, 1, 1, 1, 1, 1, 1, 1],
              [1, 1, 1, 1, 1, 1, 1, 1, 1],
              [1, 1, 1, 1, 1, 1, 1, 1, 1],
              [1, 1, 1, 1, 1, 1, 1, 1, 1],
              [1, 1, 1, 1, 1, 1, 1, 1, 1],
          ]
      ).astype(bool)
      result = clear_border(image.copy(), mask=mask)
      ref = image.copy()
      ref[1:3, 0:2] = 0
      assert_array_equal(result, ref)
  
file_path: scikit-image/skimage/segmentation/tests/test_clear_border.py
imports: import numpy
  from skimage.segmentation import clear_border
  from skimage._shared.testing import assert_array_equal, assert_
label_desc: Use image node_id 1 for calling a global function with example usage: test_clear_border() without return types