)
    print(mlir)
    filename = '%s/result.mlirbc' % save_model_path
    pywrap_mlir.experimental_write_bytecode(filename, mlir)
    if not file_io.file_exists(filename):
      raise app.UsageError('Failed to create bytecode output.')

  RegisterPasses();
  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialects(registry);
  mlir::stablehlo::registerAllDialects(registry);
  registry.insert<mlir::shape::ShapeDialect>();
  mlir::MLIRContext context(registry);
  mlir::OwningOpRef<mlir::ModuleOp> module;
  {
    mlir::StatusScopedDiagnosticHandler diagnostic_handler(&context);

// Migrated from previous SWIG file (mlir.i) authored by aminim@.
#ifndef TENSORFLOW_COMPILER_MLIR_PYTHON_MLIR_H_
#define TENSORFLOW_COMPILER_MLIR_PYTHON_MLIR_H_

#include <string>
#include <vector>

#include "absl/strings/string_view.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/tf_status.h"

namespace tensorflow {

// Simple wrapper to support tf.mlir.experimental.convert_graph_def.

// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=: -tf-xla-emit-return-tuple | FileCheck %s
// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=: -tf-xla-emit-use-tuple-args -tf-xla-emit-return-tuple | FileCheck -check-prefix=TUPLE-ARGS %s
// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=: | FileCheck -check-prefix=NO_RET_TUPLE %s

// RUN: tf-mlir-translate -mlir-to-graphdef %s | tf-mlir-translate -graphdef-to-mlir | tf-mlir-translate -mlir-to-graphdef | FileCheck %s

// Verifies that optional symbol ref attributes that aren't optional in TensorFlow are handled by setting the value to an empty string.
module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 458 : i32}} {
  func.func @main() {
    tf_executor.graph {

// RUN: tf-mlir-translate -mlir-to-graphdef %s | tf-mlir-translate -graphdef-to-mlir

// Test graphdef export is producing valid GraphDef. The test imports the
// generated GraphDef to make sure the export is valid.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 890 : i32}}  {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s
// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate -tflite-flatbuffer-to-mlir - -o - | FileCheck --check-prefix=IMPORT %s

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
// CHECK: {
// CHECK-NEXT:   version: 3,
// CHECK-NEXT:     operator_codes: [ {
// CHECK-NEXT:       deprecated_builtin_code: 80,

// RUN: tf-mlir-translate -mlir-to-graphdef %s -tf-export-original-func-name | tf-mlir-translate -graphdef-to-mlir | tf-mlir-translate -mlir-to-graphdef -tf-export-original-func-name | FileCheck %s

// Tests #tf_type.func attributes are exported as AttrValue.NameAttrList attributes
// with its attr field populated with nested attributes.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 458 : i32}} {
  func.func @main() {

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() {
  tf_executor.graph {
    tf_executor.island wraps "tf.NoOp"() {} : () -> () loc("noop")
    tf_executor.fetch
  }
  func.return
}

// CHECK: node {
// CHECK-NEXT:  name: "noop"
// CHECK-NEXT:  op: "NoOp"
// CHECK-NEXT:  experimental_debug_info {
// CHECK-NEXT:  }

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

module attributes {tf.versions = {producer = 946 : i32}, tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32}  {