<< ", FallbackEnabled: " << num_passes_fallback_enabled
                         << ", Total: " << registry_->passes().size();
  }

  GraphDebugInfo debug_info;
  mlir::DialectRegistry registry;
  RegisterDialects(registry);
  mlir::MLIRContext context(registry);
  GraphImportConfig import_config;
  import_config.graph_as_function = true;
  import_config.control_outputs = *control_ret_node_names;

// shape assignment in shape_inference.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateReplicateTensorListInitOpsPass();

// Performs Shape Inference on the TensorFlow dialect using the global registry.
std::unique_ptr<OperationPass<ModuleOp>> CreateTFShapeInferencePass(
    ArrayRef<ArrayRef<int64_t>> input_shapes = {});

// Performs TF.data optimizations.

          PyExc_ValueError,
          "Failed to parse opdefs at index %d of custom op defs argument: %s",
          i, tf_opdefs);
      return nullptr;
    }

    // Register extra opdefs to TensorFlow global op registry.
    tensorflow::OpRegistry::Global()->Register(
        [opdef](
            tensorflow::OpRegistrationData* op_reg_data) -> tensorflow::Status {
          *op_reg_data = tensorflow::OpRegistrationData(opdef);

    xla::CompileOnlyClient* client,
    XlaCompiler::CompilationResult* compilation_result) {
  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialects(registry);
  mlir::mhlo::registerAllMhloDialects(registry);
  mlir::MLIRContext context(registry);

  mlir::OwningOpRef<mlir::ModuleOp> mlir_module;
  TF_RETURN_IF_ERROR(DeserializeMlirModule(mlir_computation.mlir_module,