message: Updated tests to use double quotes for error messages
diff: @@ -208,7 +208,7 @@ class ImageSegmenterTest(parameterized.TestCase, tf.test.TestCase):
  base_options = _BaseOptions(file_content=model_content)
  else:
  # Should never happen
  - raise ValueError('model_file_type is invalid.')
  + raise ValueError("model_file_type is invalid.")
  segmenter = _create_segmenter_from_options(base_options)
  @@ -222,7 +222,7 @@ class ImageSegmenterTest(parameterized.TestCase, tf.test.TestCase):
  # Check if the sizes of the result and expected colored labels are the same.
  self.assertEqual(
  len(colored_labels), len(expected_colored_labels),
  - 'Number of colored labels do not match.')
  + "Number of colored labels do not match.")
  # Comparing results.
  for index in range(len(expected_colored_labels)):
  @@ -297,9 +297,9 @@ class ImageSegmenterTest(parameterized.TestCase, tf.test.TestCase):
  colored_labels = segmentation.colored_labels
  # Check if confidence mask shape is correct.
  - self.assertEqual(len(confidence_masks), len(colored_labels),
  - 'Number of confidence masks must match with number of '
  - 'categories.')
  + self.assertEqual(
  + len(confidence_masks), len(colored_labels),
  + "Number of confidence masks must match with number of categories.")
  # Gather the confidence masks in a single array `confidence_mask_array`.
  confidence_mask_array = np.array([
  @@ -307,8 +307,9 @@ class ImageSegmenterTest(parameterized.TestCase, tf.test.TestCase):
  # Compute the category mask from the created confidence mask.
  calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
  - self.assertListEqual(calculated_category_mask.tolist(),
  - category_mask.tolist())
  + self.assertListEqual(
  + calculated_category_mask.tolist(), category_mask.tolist(),
  + "Confidence mask does not match with the category mask.")
  if __name__ == '__main__':
  