def test_conv_layer_multi_channel_in_multi_out_with_stride_and_bias():
    """
    Check that the conversion works for multiple input/output channels with strided convolution and bias
    """
    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, stride=2)
    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)

    Check that the conversion works for multiple input/output channels with strided convolution and bias
    
tests/estimators/certification/test_interval.py