mlir::TF::TensorFlowDialect,
                  mlir::shape::ShapeDialect,
                  mlir::tf_device::TensorFlowDeviceDialect,
                  mlir::tf_executor::TensorFlowExecutorDialect>();
  mlir::func::registerAllExtensions(registry);
  // clang-format on
}

Status MlirFunctionOptimizationPass::Run(
    const std::string& function_name, const DeviceSet& device_set,

                           BreakUpIslands, TF::SideEffectAnalysis> {
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<tf_executor::TensorFlowExecutorDialect>();
  }

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(BreakUpIslands)

  StringRef getArgument() const final { return "tf-executor-break-up-islands"; }

              .getValue());
    }
  }

  ConversionTarget target(context);
  target.addLegalDialect<TF::TensorFlowDialect>();
  target.addLegalDialect<tf_executor::TensorFlowExecutorDialect>();
  target.addLegalOp<ModuleOp>();
  target.addLegalOp<func::FuncOp>();
  target.addLegalOp<func::ReturnOp>();

  RewritePatternSet patterns(&context);
  patterns.add<ConvertGraphOp>(&context);

  let dependentDialects = ["mlir::tf_executor::TensorFlowExecutorDialect"];
}

def FunctionalToExecutorDialectConversionPass : Pass<"tf-functional-to-executor-conversion", "mlir::func::FuncOp"> {
  let summary = "Transform from func op to TF executor dialect.";
  let constructor = "CreateFunctionalToExecutorDialectConversionPass()";
  let dependentDialects = ["mlir::tf_executor::TensorFlowExecutorDialect"];
}

        module.getContext()->getOrLoadDialect<mlir::vhlo::VhloDialect>();
    // Right now the TF executor dialect is still needed to build NodeDef.
    module.getContext()
        ->getOrLoadDialect<mlir::tf_executor::TensorFlowExecutorDialect>();
  }

  std::optional<std::string> TranslateInternal();

  // Returns TFLite buffer populated with constant value if the operation is