You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: record_type_tests.rs
    path of file: ./repos/burn/crates/burn-import/onnx-tests/tests
    
    the code of the file until where you have to start completion: // This test suite verifies that the exported models are compatible with the
// different record types. It uses an existing model (conv1d.onnx) and exports
// it with different record types. Then it loads the exported model and runs it
// with the same input to verify that the output is the same.
// For half precision, we use a different tolerance because the output is
// different.

macro_rules! test_model {
    ($mod_name:ident) 
    
        fn $mod_name() {
            // Initialize the model with weights (loaded from the exported file)
            let model: $mod_name::Model<Backend> = $mod_name::Model::default();

            // Run the model with pi as input for easier testing
            let input = Tensor::<Backend, 3>::full([6, 4, 10], consts::PI, &Default::default());

            let output = model.forward(input);

            // test the output shape
            let expected_shape: Shape<3> = Shape::from([6, 2, 7]);
            assert_eq!(output.shape(), expected_shape);

            // We are using the sum of the output tensor to test the correctness of the conv1d node
            // because the output tensor is too large to compare with the expected tensor.
            let output_sum = output.sum().into_scalar();
            let expected_sum = -54.549_243; // from pytorch
            assert!(expected_sum.approx_eq(output_sum, ($tolerance, 2)));
        }