opt<std::string> exported_model_signatures(
    "exported-model-signatures",
    llvm::cl::desc(
        "Comma-separated list of exported model signatures to convert"),
    llvm::cl::Optional, llvm::cl::init("serving_default"));

// NOLINTNEXTLINE
opt<std::string> tag_names(
    "tags",
    llvm::cl::desc("Comma-separated list of tags for loading SavedModel. "
                   "Ignored for MLIR input"),

// CHECK-NEXT:    "tf_mlrt.tf_promise"(%arg4, %5) : (!mlrt.promise, tensor<3x3xf32>) -> ()
// CHECK-NEXT:    return

//CHECK-LABEL: func.func @serving_default
func.func @serving_default(%max_iterations: tensor<i32>, %array_handle: tensor<?x!tf_type.resource>, %array_flow: tensor<*xf32>, %matrix: tensor<3x3xf32>) -> (tensor<3x3xf32>, tensor<*xf32>) {

                tags.end());
    EXPECT_NE(meta_graph_def.meta_info_def().tensorflow_version(), "");
    EXPECT_EQ(
        meta_graph_def.signature_def().at("serving_default").method_name(),
        "tensorflow/serving/predict");
  }
};

TEST_F(ReaderTest, TagMatch) {
  MetaGraphDef meta_graph_def;

  const string export_dir = GetDataDependencyFilepath(TestDataSharded());

    %cst = "tf.Const"() {value = dense<2> : tensor<960x960x3x512xi8>} : () -> tensor<960x960x3x512xi8>
    %cst_0 = "tf.Const"() {value = dense<0.00117647066> : tensor<f32>} : () -> tensor<f32>

    %cst = "tf.Const"() {device = "", value = dense<[[[[-0.315365672, 0.27481091], [0.0901821703, -0.382271349], [-0.105572946, -0.354302853]], [[-0.47703138, -0.307006568], [0.306320101, -0.209111646], [0.252869487, 0.449634969]], [[0.167675957,...

    %cst = "tf.Const"() {device = "", value = dense<[7.72826624, 8.8264122, 3.64885974]> : tensor<3xf32>} : () -> tensor<3xf32>

    %cst = "tf.Const"() {value = dense<2> : tensor<32x32x3x512xi8>} : () -> tensor<32x32x3x512xi8>
    %cst_0 = "tf.Const"() {value = dense<0.00117647066> : tensor<f32>} : () -> tensor<f32>

  // within the SavedModel to analyze. If not specified, ["serve"] is used.
  repeated string tags = 5;

  // Sequence of keys identifying SignatureDef containing inputs and outputs.
  // If not specified, ["serving_default"] is used.
  repeated string signature_keys = 6;

  // A map from signature keys to the corresponding representative dataset.
  map<string, RepresentativeDatasetFile> representative_datasets = 7;

// NOLINTNEXTLINE
opt<std::string> exported_model_signatures(
    "exported_model_signatures", llvm::cl::desc("model signature names"),
    llvm::cl::Optional, llvm::cl::init("serving_default"));

namespace mlir {
namespace odml {

absl::StatusOr<OwningOpRef<mlir::ModuleOp>> ImportSavedModelOrMLIR(
    const std::string& input_path, MLIRContext* context,
    llvm::SourceMgr* source_mgr,

    func.return
  }

  // CHECK-LABEL: func @main()
  func.func @main() -> (tensor<0xf32> {tf_saved_model.index_path = ["output"]}) attributes {tf_saved_model.exported_names = ["serving_default"]} {
    %handle = "tf.VarHandleOp"() {container="", shared_name="var1", device = "/job:worker/replica:0/task:1/device:CPU:0"} : () -> tensor<!tf_type.resource<tensor<0xf32>>>