}

// TODO(b/305211853): Unify the CPU/TPU/GPU Execution Ops and thus these two
// passes should merge together.
void RegisterTPULowerClusterToRuntimeOpsPassPipeline() {
  static mlir::PassPipelineRegistration<StandardPipelineOptions> pipeline(
      "tfrt-lower-cluster-to-runtime-ops-tpu",
      "Run all the passes involved after the clustering transformations from "

  return diag_handler.ConsumeStatus();
}

// Registers a pipeline builder function for TF Graph export. Should be
// the same as ExportFromTensorflowDialectToExecutor just in PassRegistration
// form.
mlir::PassPipelineRegistration<> tf_dialect_to_executor_pipeline(
    "tf-dialect-to-executor-v1",
    "Run passes to convert from TF Dialect to Executor in preparation for "
    "exporting module back to TF Graph.",

                            "Dialect. Read LLVM Pipeline Error"));
  }

  tf_dialect_to_executor_dialect_status->GetCell(kExportSuccess)
      ->IncrementBy(1);
  return absl::OkStatus();
}

mlir::PassPipelineRegistration<> tf_dialect_to_executor_pipeline(
    "tf-dialect-to-executor-v2",
    "Run passes to convert from TF Dialect to Executor in preparation for "
    "exporting module back to TF Graph.",

      options.skip_partitioned_calls));
  pm.addPass(mlir::createCanonicalizerPass());
  pm.addPass(mhlo::createHloLegalizeToStablehloPass());
}

static PassPipelineRegistration<TFToStablehloOptions>
    legalize_tf_to_stablehlo_pipeline("tf-stablehlo",
                                      "Legalize TF ops to StableHLO ops",

  // NCHW->NHWC convolution conversion).
  pm.addNestedPass<func::FuncOp>(createFoldConstantTransposePass());
}

void RegisterPassPipelines() {
  static PassPipelineRegistration<> nchw_tensor_format_processing_pipeline(
      /*arg=*/"stablehlo-process-nchw-tensor",
      /*description=*/"Optimizes tensors with NCHW format.",
      AddProcessNchwTensorPasses);
}

      /*n_fallback_enabled*/ is_in_fallback_enabled_mode,
      /*result=*/"success");

  return absl::OkStatus();
}

// Registers a pipeline builder function for TF TPU V1 bridge.
mlir::PassPipelineRegistration<> replicated_clustering_bridge_v1(
    "tf-replicated-clustering-bridge-v1",
    "Run all the passes involved in transforming a TensorFlow V1 graph before "
    "execution so that it is suitable for targeting devices.",

  return std::make_unique<TensorFlowOptimizePass>();
}

void RegisterTFOptimizePassPipeline() {
  // Registers a pipeline builder function for the default
  // canonicalize/optimizer.
  static mlir::PassPipelineRegistration<StandardPipelineOptions> pipeline(
      "tf-standard-pipeline",
      "Run all the passes involved in transforming/optimizing the graph after "
      "importing into MLIR, without any target specialization.",

      bridge_type, /*bridge_version=*/"v2", device_type,
      /*fallback_enabled=*/is_in_fallback_enabled_mode,
      /*result=*/"success");

  return absl::OkStatus();
}

mlir::PassPipelineRegistration<> replicated_clustering_bridge_v2(
    "tf-replicated-clustering-bridge-v2",
    "Run all the passes involved in transforming a TensorFlow 2 graph before "
    "execution so that it is suitable for targeting devices.",

  // This static is kind of hack, it hooks the pipeline registration for the
  // command line and piggy-back to the TableGen generated registration code.
  static mlir::PassPipelineRegistration<LayoutOptimizationPipelineOptions>
      pipeline("tf-layout-optimization",
               "Assigns optimal data layout to all layout sensitive operations "
               "and cancel redundant transpose operations.",

                            device_type, custom_legalization_passes);
}

void RegisterConvertMlirToXlaHloPipelineWithDefaults() {
  static mlir::PassPipelineRegistration<> pipeline(
      "tf-to-hlo-pipeline",
      "Convert TF dialect to HLO dialect (used for compilation in bridge).",
      [](mlir::OpPassManager& pm) {
        tensorflow::CreateConvertMlirToXlaHloPipeline(