burn-tensor 0.16.1

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

    #[test]
    fn cast_float_to_int() {
        let tensor = TestTensor::<2>::from([[1.0, 2.0, 3.0], [4.4, 5.5, 6.6]]).int();
        let expected = TensorData::from([[1, 2, 3], [4, 5, 6]]);

        tensor.into_data().assert_eq(&expected, false);
    }

    #[test]
    fn cast_int_to_float_tensor() {
        let tensor = TestTensorInt::<2>::from([[1, 2, 3], [4, 5, 6]]).float();

        let expected = TensorData::from([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]);

        tensor.into_data().assert_eq(&expected, false);
    }

    #[test]
    fn cast_bool_to_int_tensor() {
        let tensor = TestTensorBool::<2>::from([[true, false, true], [false, false, true]]).int();

        let expected = TensorData::from([[1, 0, 1], [0, 0, 1]]);

        tensor.into_data().assert_eq(&expected, false);
    }

    #[test]
    fn cast_bool_to_float_tensor() {
        let tensor = TestTensorBool::<2>::from([[true, false, true], [false, false, true]]).float();

        let expected = TensorData::from([[1., 0., 1.], [0., 0., 1.]]);

        tensor.into_data().assert_eq(&expected, false);
    }

    #[test]
    fn cast_float_precision() {
        let data = TensorData::from([[1.0, 2.0, 3.0], [4.4, 5.5, 6.6]]);
        let tensor = TestTensor::<2>::from(data.clone());

        let output = tensor.cast(DType::F32);

        assert_eq!(output.dtype(), DType::F32);
        // Use precision 2 for parametrized tests in f16 and bf16
        output.into_data().assert_approx_eq(&data, 2);
    }
}