diff --git a/onnxruntime/core/optimizer/constant_folding.cc b/onnxruntime/core/optimizer/constant_folding.cc
index e6735c2e9ad0d..96fdacb12e23e 100644
--- a/onnxruntime/core/optimizer/constant_folding.cc
+++ b/onnxruntime/core/optimizer/constant_folding.cc
@@ -196,10 +196,10 @@ Status ConstantFolding::ApplyImpl(Graph& graph, bool& modified, int graph_level,
       ORT_ENFORCE(fetches.size() == node->OutputDefs().size());
       converted_to_constant = true;
       for (size_t fetch_idx = 0; fetch_idx < fetches.size(); ++fetch_idx) {
-        OrtValue& ort_value = fetches[fetch_idx];
+        auto* constant_arg_out = node->MutableOutputDefs()[fetch_idx];
         // XXX: Add support for SparseTensors outputs when we have sparse outputs
-        if (!ort_value.IsTensor()) {
-          LOGS(logger, WARNING) << "Unsupported output type of " << ort_value.Type()
+        if (!utils::HasTensorType(*constant_arg_out->TypeAsProto())) {
+          LOGS(logger, INFO) << "Unsupported output type of " << constant_arg_out->Type()
                                 << ". Can't constant fold " << node->OpType() << " node '" << node->Name() << "'";
           converted_to_constant = false;
           break;
diff --git a/onnxruntime/core/providers/cpu/optional/optional_ops.cc b/onnxruntime/core/providers/cpu/optional/optional_ops.cc
index 1d3ead1980c2d..25f69bcc9d29e 100644
--- a/onnxruntime/core/providers/cpu/optional/optional_ops.cc
+++ b/onnxruntime/core/providers/cpu/optional/optional_ops.cc
@@ -118,7 +118,11 @@ Status Optional::Compute(OpKernelContext* ctx) const {
 
   } else {  // No input was provided - we use the type proto to construct the output OrtValue
 
-    CheckValidTypeProto(*type_proto_);
+    if (!CheckValidTypeProto(*type_proto_)) {
+      return ORT_MAKE_STATUS(ONNXRUNTIME, INVALID_ARGUMENT,
+                             "The TypeProto attribute in the Optional op ",
+                             "can only be of type(tensor) or (seq(tensor))");
+    }
 
     // type is either Tensor or TensorSeq (we have validated this already in CheckValidTypeProto())
     if (type_proto_->has_tensor_type()) {