code: def test_conv_layer_dilation():
      """
      Check that the conversion works for multiple input/output channels with strided convolution, bias,
      padding, and dilation
      """
      synth_model = get_synthetic_model()
      device = torch.device(
          "cuda:0" if torch.cuda.is_available() else "cpu"
      )
      synthetic_data = torch.rand(32, 3, 25, 25).to(device)
      model = synth_model(
          input_shape=synthetic_data.shape,
          output_channels=12,
          kernel_size=5,
          bias=True,
          padding=2,
          stride=2,
          dilation=3,
      )
      output_from_equivalent = model.forward(synthetic_data)
      output_from_conv = model.conv1.conv_debug(synthetic_data)
  
      assert torch.allclose(
          output_from_equivalent, output_from_conv, 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 9 for calling a global function with example usage: test_conv_layer_dilation() without return types