burn-tensor 0.16.1

Tensor library with user-friendly APIs and automatic differentiation support
Documentation 
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#[burn_tensor_testgen::testgen(module_avg_pool1d)]
mod tests {
    use super::*;
    use burn_tensor::module::avg_pool1d;
    use burn_tensor::{Shape, Tensor};

    #[test]
    fn test_avg_pool1d_simple() {
        let test = AvgPool1dTestCase {
            batch_size: 1,
            channels: 1,
            kernel_size: 3,
            padding: 0,
            stride: 1,
            length: 6,
            count_include_pad: true,
        };

        test.assert_output(TestTensor::from([[[1., 2., 3., 4.]]]));
    }

    #[test]
    fn test_avg_pool1d_complex() {
        let test = AvgPool1dTestCase {
            batch_size: 1,
            channels: 2,
            kernel_size: 3,
            padding: 1,
            stride: 2,
            length: 6,
            count_include_pad: true,
        };

        test.assert_output(TestTensor::from([[
            [0.3333, 2.0000, 4.0000],
            [4.3333, 8.0000, 10.0000],
        ]]));
    }

    #[test]
    fn test_avg_pool1d_complex_dont_count_pad() {
        let test = AvgPool1dTestCase {
            batch_size: 1,
            channels: 2,
            kernel_size: 3,
            padding: 1,
            stride: 2,
            length: 6,
            count_include_pad: false,
        };

        test.assert_output(TestTensor::from([[
            [0.5000, 2.0000, 4.0000],
            [6.5000, 8.0000, 10.0000],
        ]]));
    }

    struct AvgPool1dTestCase {
        batch_size: usize,
        channels: usize,
        kernel_size: usize,
        padding: usize,
        stride: usize,
        length: usize,
        count_include_pad: bool,
    }

    impl AvgPool1dTestCase {
        fn assert_output(self, y: TestTensor<3>) {
            let shape_x = Shape::new([self.batch_size, self.channels, self.length]);
            let x = TestTensor::from(
                TestTensorInt::arange(0..shape_x.num_elements() as i64, &y.device())
                    .reshape::<3, _>(shape_x)
                    .into_data(),
            );
            let output = avg_pool1d(
                x,
                self.kernel_size,
                self.stride,
                self.padding,
                self.count_include_pad,
            );

            y.to_data().assert_approx_eq(&output.into_data(), 3);
        }
    }
}