// on TPU.

  // Optimizes TF graph via cleanups, merges, rewrites, constant folding,
  // and edge case handling where possible.
  pm.addNestedPass<func::FuncOp>(TF::CreateDropWhileShapeInvariantPass());
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorGraphPruningPass());
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorIslandCoarseningPass());
  pm.addPass(TF::CreateTFFunctionalControlFlowToRegions());

  pm.addNestedPass<func::FuncOp>(mhlo::createMhloQuantLegalizeToIntPass());
  pm.addNestedPass<func::FuncOp>(createCanonicalizerPass());
  // Integer graph optimization relies on chlo broadcast ops for easier handling
  // of dynamic shapes. Therefore we lower chlo ops after optimization.
  pm.addNestedPass<func::FuncOp>(CreateOptimizeIntGraphPass());
  pm.addNestedPass<func::FuncOp>(mhlo::createChloLegalizeToHloPass());

  pm.addNestedPass<mlir::func::FuncOp>(CreateConvertTFQuantOpsToMHLOPass());
  pm.addNestedPass<mlir::func::FuncOp>(mhlo::createChloLegalizeToHloPass());
  pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mhlo::createMhloQuantLegalizeToIntPass());
  pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());

  // First, we need to convert from functional, to executor dialect.
  pm.addNestedPass<FuncOp>(
      mlir::CreateFunctionalToExecutorDialectConversionPass());

  // Do a single pass to split the graph's single island op into an island per
  // op as expected by the following passes.
  pm.addNestedPass<FuncOp>(mlir::TF::CreateSplitIntoIslandPerOpPass());

  pm.addNestedPass<FuncOp>(mlir::TFDevice::CreateReplicateToIslandPass(

      /*legacy_graph_export=*/true));
  add_pass(mlir::TFDevice::CreateLaunchToDeviceAttributePass(
      /*legacy_graph_export=*/true));
  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUDevicePropagationPass());
  pm.addNestedPass<FuncOp>(mlir::TFTPU::CreateTPUColocateSplitsPass());
  pm.addPass(mlir::createSymbolDCEPass());
  if (tensorflow::GetMlirCommonFlags()

  if (enable_inliner) pass_manager->addPass(mlir::createInlinerPass());
  pass_manager->addPass(mlir::createSymbolDCEPass());
  pass_manager->addNestedPass<mlir::func::FuncOp>(
      mlir::createCanonicalizerPass());
  pass_manager->addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());
}
}  // namespace

// TODO(b/177376459): We should make this configureable.

  pm.addPass(mlir::createSymbolDCEPass());
  pm.addNestedPass<FuncOp>(
      mlir::TFDevice::CreateReplicateInvariantOpHoistingPass());
  pm.addNestedPass<FuncOp>(mlir::TFDevice::CreateEmbeddingProgramKeyPass());
  pm.addPass(mlir::TFTPU::CreateTPUMergeVariablesWithExecutePass());
  pm.addNestedPass<FuncOp>(
      mlir::TFTPU::CreateExtractTPUCopyWithDynamicShapeOpPass());
  pm.addNestedPass<FuncOp>(

  pm.addPass(mlir::tf_saved_model::CreateOptimizeGlobalTensorsPass());

  pm.addPass(
      mlir::tf_saved_model::CreateConvertSessionInitializerToFunctionPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::TFDevice::CreateDecomposeResourceOpsPass());
  pm.addPass(mlir::TF::CreateNameAnonymousIteratorsPass());

  // This will add regions to IfOp/WhileOp (turning them into IfRegionOp

      case QuantizationComponentSpec::COMPONENT_WEIGHT:
        weight_component = component;
        break;
      default:
        break;
    }
  }
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::stablehlo::CreateQuantizeWeightPass(weight_component));
}

}  // namespace quantization

  // Change this to user-given bit width once we have custom configuration.
  options.bit_width_ = 8;

  // Insert quantization parameters for weights for ops with `weight_only_ptq`
  // attribute.
  pm.addNestedPass<func::FuncOp>(createInsertWeightParamPass());

  // PrepareQuantizePass uses SymbolTable to fetch relevant GEMM ops for
  // determining quantization attributes. This requires module-level context.