Nils Lindemann <******@****.***> 1711829912 +0100

jaystone776 <******@****.***> 1711838642 +0800

LeeeeT <******@****.***> 1680341164 +0300

# ADR-0002 - Avoid using Java serialization

## Date

2012-12-01

## Context

In Gradle we often need to serialize in-memory objects for caching, or to transmit them across process barriers, etc.
Java serialization is one way to implement this, however, despite its simplicity of implementation, it has several drawbacks:

- **Performance:**
Java's built-in serialization mechanism is often slower compared to other serialization solutions.

Michael Weiner <******@****.***> 1697542898 -0500

// and returns the old value.
// Consider using the more ergonomic and less error-prone [Int32.And] instead.
func AndInt32(addr *int32, mask int32) (old int32)

// AndUint32 atomically performs a bitwise AND operation on *addr using the bitmask provided as mask
// and returns the old value.

        }

        then:
        stdout.toString().contains('this is stdout')
        stdout.toString().contains('A warning using SLF4j')
        stdout.toString().contains('A warning using JCL')
        stdout.toString().contains('A warning using Log4j')
        stdout.toString().contains('A warning using JUL')
        if (targetVersion.baseVersion >= GradleVersion.version('4.7')) {
            // Changed handling of error log messages

Nothing to minify - using producer.jar unchanged

> Task :resolveRuntimeClasspath
Minifying guava-27.1-jre.jar
Nothing to minify - using listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar unchanged
Nothing to minify - using jsr305-3.0.2.jar unchanged
Nothing to minify - using checker-qual-2.5.2.jar unchanged
Nothing to minify - using error_prone_annotations-2.2.0.jar unchanged

namespace internal {

namespace {

using llvm::dyn_cast;
using mlir::Attribute;
using mlir::Block;
using mlir::BlockArgument;
using mlir::DenseIntElementsAttr;
using mlir::failure;
using mlir::Location;
using mlir::LogicalResult;
using mlir::OpBuilder;
using mlir::Operation;
using mlir::OperationPass;
using mlir::OpOperand;
using mlir::RankedTensorType;
using mlir::StringAttr;
using mlir::success;

namespace tf2xla {
namespace internal {

namespace {

using mlir::BlockArgument;
using mlir::failure;
using mlir::LogicalResult;
using mlir::Operation;
using mlir::OperationPass;
using mlir::OpOperand;
using mlir::StringAttr;
using mlir::success;
using mlir::Value;
using mlir::WalkResult;
using mlir::func::FuncOp;
using mlir::TF::ReadVariableOp;
using mlir::tf_device::ReplicateOp;