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: maxpool1d.rs
    path of file: ./repos/burn/crates/burn-autodiff/src/tests
    
    the code of the file until where you have to start completion: #[burn_tensor_testgen::testgen(ad_max_pool1d)]
mod tests {
    use super::*;
    use burn_tensor::{module::max_pool1d, Data};

    #[test]
    fn test_max_pool1d_simple() {
        let kernel_size = 4;
        let padding = 0;
        let stride = 1;
        let dilation = 1;

        let device = Default::default();
        let x = TestAutodiffTensor::from_floats(
            [[[0.9861, 0.5474, 0.4477, 0.0732, 0.3548, 0.8221]]],
            &device,
        )
        .require_grad();
        let x_grad_expected =
            TestAutodiffTensor::from_floats([[[1., 1., 0., 0., 0., 1.]]], &device);

        let output = max_pool1d(x.clone(), kernel_size, stride, padding, dilation);
        let grads = output.backward();

        // Asserts
        let x_grad_actual = x.grad(&grads).unwrap();
        x_grad_expected
            .to_data()
            .assert_approx_eq(&x_grad_actual.to_data(), 3);
    }

    #[test]
    fn test_max_pool1d_with_dil
    
    fn test_max_pool1d_with_dilation() {
        let kernel_size = 4;
        let padding = 0;
        let stride = 1;
        let dilation = 2;

        let device = Default::default();
        let x = TestAutodiffTensor::from_floats(
            [[[
                0.5388, 0.0676, 0.7122, 0.8316, 0.0653, 0.9154, 0.1536, 0.9089, 0.8016, 0.7518,
                0.2073, 0.0501, 0.8811, 0.5604, 0.5075, 0.4384, 0.9963, 0.9698, 0.4988, 0.2609,
                0.3391, 0.2230, 0.4610, 0.5365, 0.6880,
            ]]],
            &device,
        )
        .require_grad();
        let x_grad_expected = TestAutodiffTensor::from_floats(
            [[[
                0., 0., 1., 0., 0., 3., 0., 1., 2., 1., 0., 0., 2., 0., 0., 0., 4., 4., 0., 0., 0.,
                0., 0., 0., 1.,
            ]]],
            &device,
        );

        let output = max_pool1d(x.clone(), kernel_size, stride, padding, dilation);
        let grads = output.backward();

        // Asserts
        let x_grad_actual = x.grad(&grads).unwrap();
        x_grad_expected
            .to_data()
            .assert_approx_eq(&x_grad_actual.to_data(), 3);
    }