code: def test_var(self):
      # Test idea: mean of variance is estimate of overall variance.
  
      # Fix random seed for test stability.
      np.random.seed(13141516)
  
      # Create random image with known variance.
      image = np.random.rand(500, 500)
      target_std = 0.3
      image = image / image.std() * target_std
  
      # Use P=4 to avoid interpolation effects
      P, R = 4, 1
      msg = "Applying `local_binary_pattern` to floating-point images"
      with expected_warnings([msg]):
          lbp = local_binary_pattern(image, P, R, "var")
  
      # Take central part to avoid border effect.
      lbp = lbp[5:-5, 5:-5]
  
      # The LBP variance is biased (ddof=0), correct for that.
      expected = target_std**2 * (P - 1) / P
  
      np.testing.assert_almost_equal(lbp.mean(), expected, 4)
  
imports: import numpy
  import pytest
  from skimage._shared.testing import expected_warnings, run_in_parallel
  from skimage.feature import graycomatrix, graycoprops, local_binary_pattern, multiblock_lbp
  from skimage.transform import integral_image
label_desc: Use image node_id 6 for calling the TestLBP obj's underlying member method code with example usage: obj.test_var() without return types