OpKernelContext* ctx,
    const XlaCompiler::CompilationResult& compilation_result,
    int missing_ctx_input_prefix) {
  std::vector<VariableInfo> out;
  out.reserve(compilation_result.resource_updates.size());
  for (int i = 0; i < compilation_result.resource_updates.size(); ++i) {
    const XlaCompiler::ResourceUpdate& write =
        compilation_result.resource_updates[i];

    const std::vector<XlaCompiler::Argument>& args,
    DeviceCompileMode compile_mode, bool has_ref_vars,
    bool may_alias_resource_update, FunctionLibraryRuntime* flr,
    ResourceMgr* rm, const XlaCompiler::CompilationResult** compilation_result,
    xla::PjRtClient** client, xla::PjRtLoadedExecutable** executable) {
  PjRtDeviceCompiler* pjrt_device_compiler;
  DeviceCompilationProfiler* profiler;

      DeviceCompileMode::kStrict, /*has_ref_vars=*/true,
      /*may_alias_resource_update=*/true, device_setup.flr(), &resource_mgr,
      &compilation_result, &pjrt_client, &pjrt_executable));

  EXPECT_TRUE(compilation_result != nullptr);
  EXPECT_TRUE(pjrt_executable != nullptr);
  EXPECT_TRUE(pjrt_client != nullptr);
}

TEST(PjrtCompileUtilTest, CompileToPjRtLoadedExecutableWithOpKernelContext) {
  DeviceSetup device_setup;

    uint64 signature_hash, const XlaCompiler::Options& options,
    const XlaCompiler::CompilationResult& compilation_result,
    const ExecutableType& executable,
    DeviceCompilerClient<ExecutableType, ClientType>* compiler_client) const {
  XlaSerializedCacheEntry serialized_entry;
  const xla::HloModuleProto& hlo_module =
      compilation_result.computation->proto();
  *serialized_entry.mutable_key() =

    const NameAttrList& function,
    const std::vector<XlaCompiler::Argument>& args,
    DeviceCompileMode compile_mode, bool has_ref_vars,
    bool may_alias_resource_update,
    const XlaCompiler::CompilationResult** compilation_result,
    xla::PjRtClient** client, xla::PjRtLoadedExecutable** executable);

// Similar to the above function but it does not take a OpKernelContext.
// Instead, it takes the following arguments that are obtained from

// in the compilation_result.
absl::StatusOr<std::string> CompileFromMlirToXlaHlo(
    bool lower_to_xla_hlo, const tpu::MlirToHloArgs& computation,
    const tpu::TPUCompileMetadataProto& metadata, llvm::StringRef device_type,
    const XlaShapeLayoutHelpers::ShapeDeterminationFns& shape_determination_fns,
    bool use_tuple_args, XlaCompiler::CompilationResult* compilation_result,

  GuaranteedConsts consts;

  *compilation_result = {};

  TF_RETURN_IF_ERROR(CompileTFFunctionToHlo(
      *flib_def, versions.producer(), shape_determination_funcs, arg_shapes,
      consts, func, metadata, client, arg_core_mapping, per_core_arg_shapes,
      use_tuple_args, compilation_result));

  return PopulateInputOutputAliasing(main_fn, compilation_result,
                                     use_tuple_args);

    XlaCompiler compiler(DefaultXlaOptions());

    XlaCompiler::CompilationResult compilation_result;
    TF_RETURN_IF_ERROR(compiler.CompileGraph(XlaCompiler::CompileOptions(),
                                             "graph", std::move(graph), args,
                                             &compilation_result));
    return compilation_result;
  }

  XlaCompiler::Options DefaultXlaOptions() {

    std::vector<tpu::ShardingAndIndex>* arg_core_mapping,
    std::vector<std::vector<xla::Shape>>* per_core_arg_shapes,
    xla::CompileOnlyClient* client,
    XlaCompiler::CompilationResult* compilation_result);

}  // namespace v1
}  // namespace tf2xla
};  // namespace tensorflow

    const XlaShapeLayoutHelpers::ShapeDeterminationFns shape_determination_fns,
    XlaCompilationResult* compilation_result);

// Runs MLIR Bridge on a MLIR module.
//
// If lower_to_xla_hlo is true then compiles down into XLA HLO, generates all
// accompanying metadata and stores them in CompilationResult.
//
// If enable_op_fallback is set to false, graph is legalized only if the graph