class_docstr: To run: "python -m saliency.core.blur_ig_test" top-level saliency directory.
code: def setUp(self):
      super().setUp()
      self.max_sigma = 10
      # All black except 2 pixels near the center.
      self.x_input_val = np.array(
          [
              [0.0, 0.0, 0.0, 0.0, 0.0],
              [0.0, 0.7, 0.0, 0.0, 0.0],
              [0.0, 0.0, 1.0, 0.0, 0.0],
              [0.0, 0.0, 0.0, 0.0, 0.0],
              [0.0, 0.0, 0.0, 0.0, 0.0],
          ],
          dtype=float,
      )
      self.x_input_val = self.x_input_val.reshape((5, 5, 1))
      # Calculate the value of `y` at the input.
      y_input_val = np.sin(self.x_input_val)
  
      # Baseline is the fully blurred version of the input.
      self.x_baseline_val = blur_ig.gaussian_blur(
          self.x_input_val, sigma=self.max_sigma
      )
      y_baseline_val = np.sin(self.x_baseline_val)
  
      # The expected BlurIG value is equal to the difference between
      # the `y` value at the input and the `y` value at the baseline. Because each
      # value is independent, we can calculate the expected blur_ig value of each.
      #
      # Expected: [[-0, -0, -0, -0, -0],
      #            [-0, 0.641, -0, -0, -0],
      #            [-0, -0, 0.838, -0, -0],
      #            [-0, -0, -0, -0, -0],
      #            [-0, -0, -0, -0, -0]
      self.expected_val = y_input_val - y_baseline_val
      self.blur_ig_instance = blur_ig.BlurIG()
  
file_path: saliency/saliency/core/blur_ig_test.py
imports: import unittest
  import unittest.mock
  from .None import blur_ig
  from .base import INPUT_OUTPUT_GRADIENTS
  from .base import SHAPE_ERROR_MESSAGE
  import numpy
label_desc: Use image node_id 1 for calling the BlurIgTest obj's underlying member method code with example usage: obj.setUp() without return types