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_powf)]
mod tests {
    use super::*;
    use burn_tensor::TensorData;

    #[test]
    fn should_support_powf_ops() {
        // NOTE: we use affine quantization to reduce quantization errors
        let tensor = QTensor::<TestBackend, 2>::int8_affine([[1.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
        let tensor_pow = QTensor::<TestBackend, 2>::int8_affine([[1.0, 1.0, 2.0], [3.0, 4.0, 2.0]]);

        let output = tensor.powf(tensor_pow);
        let expected = TensorData::from([[1.0, 1.0, 4.0], [27.0, 256.0, 25.0]]);

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

    #[test]
    fn should_support_neg_power() {
        let tensor = QTensor::<TestBackend, 2>::int8([[1.0, 1.0, 2.0], [3.0, 4.0, 5.0]]);
        let tensor_pow =
            QTensor::<TestBackend, 2>::int8([[-0.95, -0.67, -0.45], [-0.24, -0.5, -0.6]]);

        let output = tensor.powf(tensor_pow);
        let expected = TensorData::from([[1., 1., 0.73204285], [0.76822936, 0.5, 0.38073079]]);

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

    #[test]
    fn should_support_neg_values_with_even_power() {
        let tensor = QTensor::<TestBackend, 2>::int8([[0.0, -1.0, -2.0], [-3.0, -4.0, -5.0]]);
        let tensor_pow = QTensor::<TestBackend, 2>::int8([[2.0, 2.0, 2.0], [2.0, 2.0, 2.0]]);

        let output = tensor.powf(tensor_pow);
        let expected = TensorData::from([[0.0, 1.0, 4.0], [9.0, 16.0, 25.0]]);

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

    #[test]
    fn should_support_neg_values_with_odd_power() {
        let tensor = QTensor::<TestBackend, 2>::int8([[0.0, -1.0, -2.0], [-3.0, -4.0, -4.0]]);
        let tensor_pow = QTensor::<TestBackend, 2>::int8([[3.0, 3.0, 3.0], [3.0, 3.0, 3.0]]);

        let output = tensor.powf(tensor_pow);
        let expected = TensorData::from([[0.0, -1.0, -8.0], [-27.0, -64.0, -64.0]]);

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