#include "absl/strings/ascii.h"
#include "absl/strings/str_cat.h"
#include "absl/strings/string_view.h"
#include "absl/types/span.h"
#include "tensorflow/compiler/jit/compilability_check_util.h"
#include "tensorflow/compiler/jit/device_compiler.h"
#include "tensorflow/compiler/jit/variable_info.h"
#include "tensorflow/compiler/jit/variable_info_util.h"
#include "tensorflow/compiler/jit/xla_compiler_options_util.h"
#include "tensorflow/compiler/jit/xla_launch_util.h"

    device_allocator_ = device_->GetAllocator(device_alloc_attr);

    // Create the DeviceCompiler to help with compiling executables.
    auto pjrt_client_or = GetOrCreatePjRtClient(device_type_);
    TF_CHECK_OK(pjrt_client_or.status());
    pjrt_client_ = pjrt_client_or.value();
    device_compiler_ = new PjRtDeviceCompiler(
        std::make_unique<PjRtDeviceExecutablePersistor>(

    }                                                       \
  } while (0)

namespace tensorflow {

namespace {
using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;

auto* xla_launch_counter = monitoring::Counter<1>::New(
    "/tensorflow/core/xla_launch_counter",

  void Compute(OpKernelContext* ctx) override;

 private:
  Status Compile(const std::vector<XlaCompiler::Argument>& args,
                 OpKernelContext* ctx,
                 DeviceCompiler<xla::LocalExecutable, xla::LocalClient>**
                     xla_device_compiler,
                 DeviceCompilationProfiler** profiler,
                 const XlaCompiler::CompilationResult** result,

#include "tensorflow/core/framework/function.h"
#include "tsl/framework/device_id_utils.h"

namespace tensorflow {
namespace {
using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;

inline void LogOptions(const XlaCompiler::Options& options) {
  VLOG(2) << "XlaCompiler::Options[device_type=" << options.device_type

// `device_type` in either the XlaLaunch op or the XlaCompileOnDemand op.
bool UsePjRtForSingleDeviceCompilation(const DeviceType& device_type);

// Gets the resource name of the PjRt DeviceCompiler for `device_type`.
std::string GetPjRtDeviceCompilerResourceName(const DeviceType& device_type);

// Gets the resource name of the DeviceCompilationProfiler for `device_type`

#include "tensorflow/core/tpu/tpu_defs.h"
#include "tsl/framework/device_type.h"

namespace tensorflow {
namespace {
using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;
using XlaDeviceExecutablePersistor =
    DeviceExecutablePersistor<xla::LocalExecutable, xla::LocalClient>;

#include "tensorflow/core/tfrt/common/pjrt_util.h"
#include "tensorflow/core/tpu/tpu_defs.h"

namespace tensorflow {
namespace {
using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;

class XlaPlatformInfoTest : public ::testing::Test {
 protected:
  void SetUp() override {

#include "tensorflow/core/tpu/tpu_defs.h"

namespace tensorflow {
namespace {
using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using XlaDeviceExecutablePersistor =
    DeviceExecutablePersistor<xla::LocalExecutable, xla::LocalClient>;
using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;
using PjRtDeviceExecutablePersistor =

// Instead, it takes the following arguments that are obtained from
// OpKernelContext in the above function.
// - `device`: the device used to compile the function.
// - `rm`: the resource manager for DeviceCompiler to store JIT-compiled XLA
// computation.
// - `flr`: the FunctionLibraryRuntime for the `function`.
Status CompileToPjRtLoadedExecutable(
    const DeviceBase* device, const XlaPlatformInfo& platform_info,