docstr: Tests an arbitrary mask by comparing with manually computed result.
docstr_tok: ["Tests","an","arbitrary","mask","by","comparing","with","manually","computed","result","."]
code: def test_create_blurred_image(self):
      """Tests an arbitrary mask by comparing with manually computed result."""
      img = np.asarray(
          [[1.0, 0.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, 1.0]]
      )
      mask = np.zeros_like(img)
      blurred_img = pic.create_blurred_image(
          full_img=img, pixel_mask=mask
      )
      self.assertIsNotNone(blurred_img)
      # Since only corners are used and their values are 1.0, the entire blurred
      # image should contain only 1.0 values.
      np.testing.assert_array_equal(blurred_img, np.ones_like(img))
  
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 5 for calling the PicTest obj's underlying member method code with example usage: obj.test_create_blurred_image() without return types