code: def test_mnist_certification_conversion(
      art_warning, fix_get_mnist_data
  ):
      """
      Assert that the re-convert method does not throw errors
      """
  
      class TestModel(torch.nn.Module):
          def __init__(self):
              super().__init__()
  
              self.device = torch.device(
                  "cuda:0" if torch.cuda.is_available() else "cpu"
              )
              self.conv1 = torch.nn.Conv2d(
                  in_channels=1,
                  out_channels=32,
                  kernel_size=5,
                  bias=True,
                  stride=1,
              ).to(self.device)
  
              self.conv2 = torch.nn.Conv2d(
                  in_channels=32,
                  out_channels=32,
                  kernel_size=5,
                  bias=True,
                  stride=1,
              ).to(self.device)
  
              self.fc1 = torch.nn.Linear(
                  in_features=12800, out_features=100
              )
              self.fc2 = torch.nn.Linear(
                  in_features=100, out_features=10
              )
              self.relu = torch.nn.ReLU()
  
          def forward(self, x):
              x = self.relu(self.conv1(x))
              x = self.relu(self.conv2(x))
              x = x.reshape(x.shape[0], -1)
              x = self.relu(self.fc1(x))
  
              return self.fc2(x)
  
      loss_fn = torch.nn.CrossEntropyLoss()
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
  
      model = TestModel().to(device)
  
      box_model = PyTorchIBPClassifier(
          model=model,
          clip_values=(0, 1),
          loss=torch.nn.CrossEntropyLoss(),
          input_shape=(1, 28, 28),
          nb_classes=10,
      )
  
      mnist_data = fix_get_mnist_data[0]
      mnist_labels = torch.tensor(fix_get_mnist_data[1]).to(device)
      box_model.model.set_forward_mode("concrete")
      for _ in range(5):
          preds = box_model.model.forward(mnist_data)
          loss = loss_fn(preds, mnist_labels)
          loss.backward()
          box_model.model.zero_grad()
          box_model.model.re_convert()
  
imports: import pytest
  import torch
  import numpy
  from art.estimators.certification.interval import PyTorchIBPClassifier
  from art.estimators.certification.interval import PyTorchIntervalConv2D
  from art.utils import load_dataset
  from tests.utils import ARTTestException
  from tests.utils import get_image_classifier_pt
label_desc: Use image node_id 13 for calling a global function with example usage: test_mnist_certification_conversion(art_warning, fix_get_mnist_data) and returns: self