#include "absl/strings/string_view.h"
#include "absl/types/span.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/core/platform/statusor.h"

namespace tensorflow {

class ProcessFunctionLibraryRuntime;
class Device;
class Tensor;
class TensorHandle;
class EagerContext;

enum class IrExportStage {
  HLO,
  HLO_NO_METADATA,
  HLO_SERIALIZED,
  OPTIMIZED_HLO,
  OPTIMIZED_HLO_SERIALIZED,

  Graph* graph = options.graph->get();

  OptimizerOptions opts;
  auto pflr = std::make_unique<ProcessFunctionLibraryRuntime>(
      nullptr, options.session_options->env, /*config=*/nullptr,
      TF_GRAPH_DEF_VERSION, options.flib_def, opts);
  FunctionLibraryRuntime* flr =
      pflr->GetFLR(ProcessFunctionLibraryRuntime::kDefaultFLRDevice);

  for (Node* node : graph->nodes()) {
    if (CanCreateXlaKernel(node->def())) {

  TF_RETURN_IF_ERROR(scope.ToGraph(graph.get()));

  std::unique_ptr<ProcessFunctionLibraryRuntime> pflr(
      new ProcessFunctionLibraryRuntime(
          nullptr, Env::Default(), /*config=*/nullptr, TF_GRAPH_DEF_VERSION,
          flib_def, OptimizerOptions{}));
  FunctionLibraryRuntime* lib_runtime =
      pflr->GetFLR(ProcessFunctionLibraryRuntime::kDefaultFLRDevice);

                                                         FunctionDefLibrary());
  if (fdef.has_value()) {
    TF_CHECK_OK(lib_def_->AddFunctionDef(*fdef));
  }
  pflr_ = std::make_unique<ProcessFunctionLibraryRuntime>(
      device_mgr_.get(), Env::Default(), /*config=*/nullptr,
      TF_GRAPH_DEF_VERSION, lib_def_.get(), opts,
      /*default_thread_pool=*/nullptr, /*cluster_flr=*/nullptr);

  FunctionLibraryRuntime* flr() { return flr_; }

 private:
  FunctionLibraryRuntime* flr_;
  std::unique_ptr<DeviceMgr> device_mgr_;
  std::unique_ptr<FunctionLibraryDefinition> lib_def_;
  std::unique_ptr<ProcessFunctionLibraryRuntime> pflr_;
};

}  // namespace tensorflow

  std::unique_ptr<tensorflow::ScopedStepContainer> step_container_;
  std::unique_ptr<tensorflow::FunctionLibraryDefinition> flib_def_;
  std::unique_ptr<tensorflow::ProcessFunctionLibraryRuntime> pflr_;
  tensorflow::OpKernelContext::Params params_;

  xla::XlaBuilder xla_builder_;
};

}  // namespace mhlo
}  // namespace mlir

  std::vector<bool> compile_time_const_nodes(graph->num_node_ids());
  OptimizerOptions opts;
  auto pflr = std::make_unique<ProcessFunctionLibraryRuntime>(
      nullptr, env, /*config=*/nullptr, TF_GRAPH_DEF_VERSION, flib_def, opts);
  FunctionLibraryRuntime* lib_runtime =
      pflr->GetFLR(ProcessFunctionLibraryRuntime::kDefaultFLRDevice);
  TF_RETURN_IF_ERROR(BackwardsConstAnalysis(*graph, nullptr,

 *   - `input_handles`: Contains all concrete_fn inputs tensors, including
 * captured inputs.
 */
absl::StatusOr<std::string> GetCompilerIr(
    IrExportStage stage, ProcessFunctionLibraryRuntime* pflr,
    absl::string_view func_name, Device* dev, EagerContext* context,
    absl::Span<const ArgShapeAndDType> input_arg_shape_and_dtype,
    absl::Span<const TensorHandle* const> input_handles,

      FunctionLibraryDefinition *fld,
      std::vector<string> *shape_inference_graphs,
      bool *has_outside_compilation) {
    OptimizerOptions opts;
    pflr_ = std::make_unique<ProcessFunctionLibraryRuntime>(
        device_mgr_.get(), Env::Default(), /*config=*/nullptr,
        TF_GRAPH_DEF_VERSION, fld, opts,
        /*default_thread_pool=*/nullptr);

  }

  flib_def_ = std::make_unique<tensorflow::FunctionLibraryDefinition>(
      tensorflow::OpRegistry::Global(), tensorflow::FunctionDefLibrary());
  pflr_ = std::make_unique<tensorflow::ProcessFunctionLibraryRuntime>(
      device_mgr_.get(), tensorflow::Env::Default(), /*config=*/nullptr,
      version_or.value(), flib_def_.get(), tensorflow::OptimizerOptions());
  params_.function_library = pflr_->GetFLR(device_->name());