// the results of side effect analysis.
  pm.addPass(
      mlir::tf_executor::CreateTFExecutorUpdateControlDependenciesPass());

  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUDevicePropagationPass());
  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUColocateSplitsPass());
  pm.addPass(mlir::createSymbolDCEPass());
  pm.addNestedPass<FuncOp>(
      mlir::tf_executor::CreateTFExecutorGraphPruningPass());
  if (tensorflow::GetMlirCommonFlags()

  auto add_pass = [&](std::unique_ptr<Pass> pass) {
    pm.addNestedPass<FuncOp>(std::move(pass));
    pm.addPass(mlir::CreateBreakUpIslandsPass());
  };

  pm.addPass(mlir::tf_executor::CreateTFExecutorTPUV1IslandInliningPass());
  // There are cases where we don't consume all compilation and
  // replication attributes like we do for the V2 pipeline, so we need to

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/attribute_utils.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"

namespace mlir {
namespace tf_executor {

namespace {

// Options when running passes for exporting an MLIR ModuleOp.
struct ExportOptions {
  // If set to `true`, it runs `DuplicateShapeDeterminingConstantsPass` before
  // lowering to tf_executor dialect.
  bool duplicate_shape_determining_constants = true;

  // If set to `true`, unfreezes constants into variables and saves them to a
  // checkpoint file. Setting this to `true` is an experimental feature that has

#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"  // IWYU pragma: keep
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"  // IWYU pragma: keep
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"  // IWYU pragma: keep

namespace mlir::quant::stablehlo::testing {

#define GEN_PASS_DEF_TESTPRECALIBRATIONCOMPONENTPASS

      /*flib_def=*/nullptr, &file));

  string expected_substr("tfg.graph");
  ExpectHasSubstr(actual, expected_substr);

  string not_expected_substr("tf_executor.island");
  ExpectHasNoSubstr(actual, not_expected_substr);
}

}  // namespace

#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"

namespace mlir {

  // - remove dead islands.
  // - fuse islands as much as possible.
  // - materialize the eventual "pass-through" ops by inlining their content.
  func_pm.addPass(tf_executor::CreateTFExecutorGraphPruningPass());
  func_pm.addPass(tf_executor::CreateTFExecutorIslandCoarseningPass());
  func_pm.addPass(CreateMaterializePassthroughOpPass());
  if (options.form_clusters) pm.addPass(TFDevice::CreateClusterFormationPass());

    configuration.
  }];
  let dependentDialects = [
    "mlir::stablehlo::StablehloDialect", "mlir::TF::TensorFlowDialect",
    "mlir::func::FuncDialect", "mlir::tf_executor::TensorFlowExecutorDialect",
    "mlir::mhlo::MhloDialect", "mlir::vhlo::VhloDialect",
  ];
}

def TestPostCalibrationComponentPass : Pass<"stablehlo-test-post-calibration-component", "mlir::ModuleOp"> {

  ::tensorflow::applyTensorflowAndCLOptions(pm);

  // Run island coarsening before shape inference to allow more exact shape
  // inference using constant folding within islands.
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorIslandCoarseningPass());
  pm.addPass(CreateTFShapeInferencePass());

  // Assign optimal data layout to layout sensitive operations and delete
  // redundant transposes from the IR.