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

#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`

absl::StatusOr<ResourceMgr*> GetResourceMgrForDeviceCompiler(
    const OpKernelContext& ctx, const DeviceType& device_type) {
  // We store information about the JIT-compiled XLA computation in the
  // ResourceMgr. The DeviceCompiler (which contains the DeviceCompilationCache)
  // is stored in the tfrt_global ResourceMgr for TPU and the Device ResourceMgr
  // for CPU/GPU. This is to make sure the DeviceCompiler's lifecycle is