code: def test_certification_function(
      art_warning, fix_get_mnist_data, fix_get_cifar10_data
  ):
      """
      Check that based on a given set of synthetic class predictions the certification gives the expected results.
      """
      from art.estimators.certification.derandomized_smoothing.ablators.pytorch import (
          ColumnAblatorPyTorch,
      )
      import torch
  
      try:
          col_ablator = ColumnAblatorPyTorch(
              ablation_size=4,
              channels_first=True,
              mode="ViT",
              to_reshape=True,  # do not upsample initially
              original_shape=(3, 32, 32),
              output_shape=(3, 224, 224),
          )
          pred_counts = torch.from_numpy(
              np.asarray([[20, 5, 1], [10, 5, 1], [1, 16, 1]])
          )
          (
              cert,
              cert_and_correct,
              top_predicted_class,
          ) = col_ablator.certify(
              pred_counts=pred_counts,
              size_to_certify=4,
              label=0,
          )
          assert torch.equal(cert, torch.tensor([True, False, True]))
          assert torch.equal(
              cert_and_correct, torch.tensor([True, False, False])
          )
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/estimators/certification/test_vision_transformers.py
imports: import pytest
  import os
  import numpy
  from art.utils import load_dataset
  from tests.utils import ARTTestException
label_desc: Use image node_id 6 for calling a global function with example usage: test_certification_function(art_warning, fix_get_mnist_data, fix_get_cifar10_data) without return types