// 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;

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,

using tensorflow::AbstractFunction;
using tensorflow::AbstractTensorHandle;
using tensorflow::DataType;
using tensorflow::dyn_cast;
using tensorflow::OutputList;
using tensorflow::Status;
using tensorflow::unwrap;
using tensorflow::wrap;
using tensorflow::tracing::CreateTracingExecutionContext;
using tensorflow::tracing::SetDefaultTracingEngine;

include::sample[dir="snippets/providers/services/groovy",files="build.gradle[tags=object-factory]"]
====

TIP: It is preferable to let Gradle create objects automatically by using <<properties_providers.adoc#managed_properties,managed properties>>.

Using `ObjectFactory` to create these objects ensures that they are properly managed by Gradle, especially in terms of configuration avoidance and lazy evaluation.

#include "tsl/platform/statusor.h"

namespace mlir {
namespace mhlo {
namespace test {

using ::mlir::DialectRegistry;
using ::mlir::MLIRContext;
using ::mlir::ModuleOp;
using ::mlir::OwningOpRef;
using ::tsl::StatusOr;

absl::StatusOr<OwningOpRef<ModuleOp>> GetMlirModuleFromString(
    absl::string_view module_string, MLIRContext* context) {

namespace mlir {
namespace tf_saved_model {

using llvm::SmallSet;
using ::tensorflow::Device;
using ::tensorflow::DeviceMgr;
using ::tensorflow::mutex_lock;
using ::tensorflow::ResourceHandle;
using ::tensorflow::Session;
using ::tensorflow::Status;
using ::tensorflow::StatusOr;
using ::tensorflow::Tensor;
using ::tensorflow::Var;

namespace {

using ::mlir::quant::stablehlo::PreCalibrationComponent;
using ::mlir::quant::stablehlo::RunCalibrationPasses;
using ::mlir::quant::stablehlo::UpdateFunctionAliases;
using ::mlir::quant::stablehlo::WeightOnlyPtqComponent;
using ::stablehlo::quantization::AddCalibrationStatistics;
using ::stablehlo::quantization::ChangeToQuantizedFilename;
using ::stablehlo::quantization::DebuggerConfig;