// 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>) {

    %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,...

// 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>>>

            dim {
              size: 1
            }
          }
        }
      }
      method_name: "tensorflow/serving/regress"
    }
  }
  signature_def {
    key: "serving_default"
    value {
      inputs {
        key: "x"
        value {
          name: "x:0"
          dtype: DT_FLOAT
          tensor_shape {
            dim {
              size: -1