burn-tensor 0.16.1

Tensor library with user-friendly APIs and automatic differentiation support
Documentation 
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#[burn_tensor_testgen::testgen(q_transpose)]
mod tests {
    use super::*;
    use burn_tensor::TensorData;

    // NOTE: we use affine quantization to reduce quantization errors for range of input values
    #[test]
    fn should_support_transpose_ops() {
        let tensor = QTensor::<TestBackend, 1>::int8_affine([
            0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0,
        ])
        .reshape([2, 2, 3]);

        let output = tensor.transpose();
        let expected = TensorData::from([
            [[0.0, 3.0], [1.0, 4.0], [2.0, 5.0]],
            [[6.0, 9.0], [7.0, 10.0], [8.0, 11.0]],
        ]);

        // Precision 1 to approximate de/quantization errors
        output
            .dequantize()
            .into_data()
            .assert_approx_eq(&expected, 1);
    }

    #[test]
    fn should_support_swap_dims() {
        let tensor = QTensor::<TestBackend, 1>::int8_affine([
            0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0,
        ])
        .reshape([2, 2, 3]);

        let output = tensor.swap_dims(0, 2);
        let expected = TensorData::from([
            [[0.0, 6.0], [3.0, 9.0]],
            [[1.0, 7.0], [4.0, 10.0]],
            [[2.0, 8.0], [5.0, 11.0]],
        ]);

        // Precision 1 to approximate de/quantization errors
        output
            .dequantize()
            .into_data()
            .assert_approx_eq(&expected, 1);
    }
}