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

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

# 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.

// 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.

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;

namespace mlir::quant::stablehlo {
namespace {

using ::stablehlo::quantization::RepresentativeDatasetConfig;
using ::tensorflow::quantization::RepresentativeDatasetFile;
using ::testing::Contains;
using ::testing::HasSubstr;
using ::testing::Key;
using ::testing::SizeIs;
using ::testing::StrEq;
using ::tsl::testing::IsOk;
using ::tsl::testing::StatusIs;

TEST(CreateRepresentativeDatasetFileMapTest,

Binaries
    Shared library 'hello:sharedLibrary'
        build using task: :helloSharedLibrary
        build type: build type 'debug'
        flavor: flavor 'default'
        target platform: platform 'current'
        tool chain: Tool chain 'clang' (Clang)
        shared library file: build/libs/hello/shared/libhello.dylib
    Static library 'hello:staticLibrary'
        build using task: :helloStaticLibrary
        build type: build type 'debug'

using ::stablehlo::quantization::io::GetLocalTmpFileName;
using ::stablehlo::quantization::io::ListDirectory;
using ::tensorflow::AssetFileDef;
using ::tensorflow::SignatureDef;
using ::tensorflow::calibrator::CalibrationStatistics;
using ::tensorflow::quantization::ExportedModel;
using ::tensorflow::quantization::PyFunctionLibrary;
using ::tensorflow::quantization::RunPasses;
using CalibrationStatisticsFlatMap =

root credentials. You can use the Browser to create buckets, upload objects, and browse the contents of the MinIO server.

You can also connect using any S3-compatible tool, such as the MinIO Client `mc` commandline tool. See
[Test using MinIO Client `mc`](#test-using-minio-client-mc) for more information on using the `mc` commandline tool. For application developers,