absl::string_view visible_device_list);

// Returns the device type for building a DeviceCompiler from the given platform
// type.
absl::StatusOr<DeviceType> GetCompilationDeviceType(
    const DeviceType& platform_device_type);

// Builds a DeviceCompiler that uses xla::LocalClient using `platform_info` and
// `compilation_device_type` (in non-TPU case) and sets *xla_device_compiler to

// to disk.
//
// Since XLA computations must have static shapes, DeviceCompiler generates a
// new XLA computation for each new set of input shapes.
// TODO(b/255826209): De-templatize once we've moved to Device API completely.
template <typename ExecutableType, typename ClientType>
class DeviceCompiler : public ResourceBase {
 public:
  DeviceCompiler(
      std::unique_ptr<DeviceExecutablePersistor<ExecutableType, ClientType>>

#include "xla/client/local_client.h"
#include "xla/pjrt/pjrt_client.h"

namespace tensorflow {

// Returns created options for the XLA compiler.
XlaCompiler::Options GenerateCompilerOptions(
    const DeviceCompiler<xla::LocalExecutable, xla::LocalClient>&
        xla_device_compiler,
    const FunctionLibraryRuntime& function_library, DeviceBase* device,
    se::Stream* stream, const XlaPlatformInfo& platform_info,

  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 "tsl/lib/core/status_test_util.h"
#include "tsl/platform/status.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {
namespace {

using PjRtDeviceCompiler =
    DeviceCompiler<xla::PjRtLoadedExecutable, xla::PjRtClient>;
using PjRtDeviceExecutablePersistor =
    DeviceExecutablePersistor<xla::PjRtLoadedExecutable, xla::PjRtClient>;

absl::flat_hash_map<int, const Tensor*> GetVariableSnapshots(

#include "tensorflow/core/platform/status_matchers.h"
#include "tensorflow/core/platform/statusor.h"

namespace tensorflow {
namespace {
using ::testing::_;
using ::testing::Return;

using XlaDeviceCompiler =
    DeviceCompiler<xla::LocalExecutable, xla::LocalClient>;
using XlaDeviceExecutablePersistor =
    DeviceExecutablePersistor<xla::LocalExecutable, xla::LocalClient>;
using Signature = DeviceCompilationClusterSignature;

#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 {