docstr: Tests when the mask has only zero values.
  
  If the mask consists only of zeros, only the image corner pixels are used
  as the data points for the interpolation.
docstr_tok: ["Tests","when","the","mask","has","only","zero","values",".","If","the","mask","consists","only","of","zeros",",","only","the","image","corner","pixels","are","used","as","the","data","points","for","the","interpolation","."]
code: def test_create_blurred_image_empty_mask(self):
      """Tests when the mask has only zero values.
  
      If the mask consists only of zeros, only the image corner pixels are used
      as the data points for the interpolation.
      """
      img = np.asarray(
          [[0.0, 0.0, 0.0], [1.0, 8.0, 1.0], [0.0, 0.0, 0.0]]
      )
      mask = np.asarray(
          [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
      )
      blurred_img = pic.create_blurred_image(
          full_img=img, pixel_mask=mask
      )
      self.assertIsNotNone(blurred_img)
      # The corners are always included in the mask.
      expected = [[0.0, 0.0, 0.0], [1.0, 0.5, 0.0], [0.0, 0.0, 0.0]]
      np.testing.assert_array_equal(blurred_img, expected)
  
file_path: saliency/saliency/metrics/pic_test.py
imports: import numpy
  import os
  import unittest
  from .None import pic
  from pathlib import Path
  from PIL import Image
label_desc: Use image node_id 4 for calling the PicTest obj's underlying member method code with example usage: obj.test_create_blurred_image_empty_mask() without return types