LOG(ERROR) << "Couldn't import flatbuffer to MLIR.";
    return absl::InternalError("Couldn't import flatbuffer to MLIR.");
  }

  PassManager pm((*module)->getName(), OpPassManager::Nesting::Implicit);
  pm.addPass(TFL::CreateDenseToSparsePass());

  if (failed(pm.run(module.get()))) {
    LOG(ERROR) << "Failed to sparsify: "
               << statusHandler.ConsumeStatus().message();
    return absl::InternalError(absl::StrCat(

      ParseTextProto(GetQuantizationSpecsTextProto(quantization_specs_));
  if (!quantization_specs.ok()) {
    signalPassFailure();
    return;
  }

  pass_manager.addPass(
      CreateLiftQuantizableSpotsAsFunctionsPass(*quantization_specs));

  if (failed(pass_manager.run(getOperation()))) {
    signalPassFailure();
  }
}

}  // namespace

      // For the normal case, this should be always FALSE.
      // This `allow_tf` logic should be removed after the backend supports tf.
      optional_accepted_dialects.push_back("tf");
    }
    pm.addPass(
        mlir::odml::createCheckAcceptedOpsPass(optional_accepted_dialects));
  }

  mlir::odml::AddStablehloOptimizationPasses(pm);

  if (verbose) {
    // Print out a detailed report of non-converted stats.

  pm.addPass(std::make_unique<LayoutAssignmentPass>(options.force_data_format));

  // Move transposes to the beginning of the block and try to fold them.
  pm.addPass(std::make_unique<MoveTransposesPass>(
      MoveTransposeDirection::kBegin, !options.skip_fold_transpose_in_ops));

  // Move transposes to the end of the block and try to fold them.
  pm.addPass(std::make_unique<MoveTransposesPass>(

  // inference pass is also required to resolve unknown shapes in the TF dialect
  // after variable freezing.
  mlir::PassManager pm(module_op.getContext());
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());
  mlir::odml::AddLegalizeTFToStablehloPasses(pm, /*skip_quantization_ops=*/true,
                                             /*skip_resize=*/false,

        addPluginSource(pluginId, className, """
            ${packageName ? "package $packageName" : ""}

            class $className extends $RuleSource.name {
                @$Mutate.name
                void addTask($ModelMap.name<$Task.name> tasks) {
                    tasks.create("fromModelRule") {
                        it.doLast {
                            println "Model rule provided task executed"
                        }

    });
  }

  return RunPasses(
      /*name=*/
      CalibrationComponent::kName,
      /*add_passes_func=*/
      [calibration_data_dir, &skipping_aggregator_ops](PassManager& pm) {
        pm.addPass(CreateInsertCalibrationStatisticsSaverPass(
            calibration_data_dir, skipping_aggregator_ops));
      },
      ctx, module_op);
}

CalibrationComponent::CalibrationComponent(

  // TF2-only passes should go here. However, this should be very rare and
  // new passes generally should go into the internal
  // AddReplicatedBridgeClusteringPipelinePasses.
  pm.addPass(mlir::TFTPU::CreateTPUValidateInputsPass());
  pm.addNestedPass<FuncOp>(
      mlir::TF::CreateCanonicalizeCompileAndReplicateAttributesPass());
  tensorflow::tf2xla::internal::AddReplicatedBridgeClusteringPipelinePasses(

        for (String group : model.getGroups()) {
            renderer.startTaskGroup(group);
            for (TaskDetails task : model.getTasksForGroup(group)) {
                renderer.addTask(task);
            }
        }
        renderer.completeTasks();

        for (RuleDetails rule : reportModel.rules) {
            renderer.addRule(rule);
        }
    }

  pipeline_opts.sink_in_invariant_ops = false;
  pipeline_opts.cost_threshold = 1024;
  pipeline_opts.merge_inter_dependent_streams = true;

  mlir::PassManager pm(module->getContext());
  pm.addPass(CreateTfToTfrtConversionPass(pipeline_opts));

  CHECK(mlir::succeeded(pm.run(*module)))
      << "Failed to lower module to TFRT: " << os.str();

  // Convert module to BEF.
  bef_buffer_ =