code: def test_conv_train_loop():
      """
      Assert that training the interval conv layer gives the same results as a regular layer
      """
      output_channels = 12
      input_channels = 3
      loss_fn = torch.nn.MSELoss()
      synth_model = get_synthetic_model()
  
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
      target = torch.rand(size=(32, 12, 21, 21)).to(device)
  
      synthetic_data = torch.rand(32, input_channels, 25, 25).to(device)
      model = synth_model(
          input_shape=synthetic_data.shape,
          output_channels=output_channels,
          kernel_size=5,
          bias=True,
          stride=1,
          to_debug=False,
      ).to(device)
  
      class TestModel(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.conv = torch.nn.Conv2d(
                  in_channels=3,
                  out_channels=12,
                  kernel_size=5,
                  bias=True,
                  stride=1,
              )
  
          def forward(self, x):
              return self.conv(x)
  
      test_model = TestModel()
      # Get the weights we will transfer over
      # Set the weights in the normal model
      test_model.conv.weight = torch.nn.Parameter(
          torch.reshape(
              torch.tensor(
                  model.conv1.conv.weight.data.cpu().detach().numpy()
              ),
              shape=(output_channels, input_channels, 5, 5),
          )
      )
      test_model.conv.bias = torch.nn.Parameter(
          torch.tensor(
              model.conv1.bias_to_grad.data.cpu().detach().numpy()
          )
      )
      test_model.to(device)
  
      optimizer = torch.optim.Adam(model.parameters(), lr=0.0001)
      test_opt = torch.optim.Adam(test_model.parameters(), lr=0.0001)
  
      for _ in range(5):
          model.zero_grad()
  
          # Test model is for ground truth comparison
          test_model.zero_grad()
          output_from_equivalent = model.forward(synthetic_data)
          loss = loss_fn(output_from_equivalent, target)
          loss.backward()
  
          equivalent_grads = model.conv1.conv.weight.grad
          equivalent_grads = (
              torch.reshape(
                  equivalent_grads,
                  shape=(output_channels, input_channels, 5, 5),
              )
              .detach()
              .clone()
              .to(device)
          )
          equivalent_bias = model.conv1.bias_to_grad.grad.clone().to(
              device
          )
  
          output_from_conv = test_model.forward(synthetic_data)
          loss = loss_fn(output_from_conv, target)
          loss.backward()
  
          assert torch.allclose(
              equivalent_grads, test_model.conv.weight.grad, atol=1e-05
          )
          assert torch.allclose(
              equivalent_bias, test_model.conv.bias.grad, atol=1e-05
          )
  
          optimizer.step()
          test_opt.step()
  
          reshaped_weights = torch.reshape(
              model.conv1.conv.weight.data.clone().detach(),
              shape=(output_channels, input_channels, 5, 5),
          ).to(device)
  
          assert torch.allclose(
              reshaped_weights, test_model.conv.weight.data, atol=1e-05
          )
  
          # function to re-pop the dense layer
          model.conv1.re_convert(device)
  
          # Sanity check! Are the grads still present and the same after the conversion?
          equivalent_grads = model.conv1.conv.weight.grad
          equivalent_grads = (
              torch.reshape(
                  equivalent_grads,
                  shape=(output_channels, input_channels, 5, 5),
              )
              .detach()
              .clone()
              .to(device)
          )
          equivalent_bias = model.conv1.bias_to_grad.grad
  
          assert torch.allclose(
              equivalent_grads, test_model.conv.weight.grad, atol=1e-05
          )
          assert torch.allclose(
              equivalent_bias, test_model.conv.bias.grad, atol=1e-05
          )
  
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 11 for calling a global function with example usage: test_conv_train_loop() and returns: self