diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
index 547170cd5..0c7955b1e 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
@@ -2733,4 +2733,41 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
                       binder.op, tensorListResultType, input);
                   return success();
                 });
+  patterns.onOp(
+      "OptionalHasElement", 15,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ValueTensorType resultType;
+        if (binder.tensorResultType(resultType))
+          return rewriter.notifyMatchFailure(binder.op,
+                                             "result type bind failed");
+
+        Value input;
+        bool output;
+        if (!binder.tensorListOperand(input) || !binder.tensorOperand(input) ||
+            !binder.optionalTensorListOperand(input) ||
+            !binder.optionalTensorOperand(input))
+          output = true;
+        else
+          output = false;
+
+        Value cstOutput = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getI64IntegerAttr((int64_t)output));
+        Value cstDtype = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(),
+            rewriter.getI64IntegerAttr((int)torch_upstream::ScalarType::Bool));
+        Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(
+            binder.getLoc(), rewriter.getBoolAttr(false));
+        Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+
+        Value dataList = rewriter.create<Torch::PrimListConstructOp>(
+            binder.getLoc(),
+            rewriter.getType<Torch::ListType>(
+                rewriter.getType<Torch::IntType>()),
+            SmallVector<Value>{cstOutput});
+
+        rewriter.replaceOpWithNewOp<Torch::AtenTensorOp>(
+            binder.op, resultType, dataList, /*dtype=*/cstDtype,
+            /*layout=*/cstNone, /*requires_grad=*/cstFalse);
+        return success();
+      });
 }
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
index be07ac634..c60ac654f 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
@@ -1545,3 +1545,110 @@ func.func @test_optional_get_element_tensor(%arg0: !torch.vtensor<[4],f32>) -> !
   %0 = torch.operator "onnx.OptionalGetElement"(%arg0) : (!torch.vtensor<[4],f32>) -> !torch.vtensor<[4],f32>
   return %0 : !torch.vtensor<[4],f32>
 }
+
+// -----
+
+// CHECK-LABEL: @test_optional_has_element_empty_none_input
+func.func @test_optional_has_element_empty_none_input() -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[DTYPE:.*]] = torch.constant.int 11
+  // CHECK: %[[FALSE:.*]] = torch.constant.bool false
+  // CHECK: %[[NONE_0:.*]] = torch.constant.none
+  // CHECK: %[[DATA_LIST:.*]] = torch.prim.ListConstruct %[[INT0]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[RESULT:.*]] = torch.aten.tensor %[[DATA_LIST]], %[[DTYPE]], %[[NONE_0]], %[[FALSE]] : !torch.list<int>, !torch.int, !torch.none, !torch.bool -> !torch.vtensor<[],i1>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[],i1>
+  %none = torch.constant.none
+  %0 = torch.operator "onnx.OptionalHasElement"(%none) : (!torch.none) -> !torch.vtensor<[],i1>
+  return %0 : !torch.vtensor<[],i1>
+}
+
+// -----
+
+// CHECK-LABEL: @test_optional_has_element_empty_no_input
+func.func @test_optional_has_element_empty_no_input() -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[DTYPE:.*]] = torch.constant.int 11
+  // CHECK: %[[FALSE:.*]] = torch.constant.bool false
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[DATA_LIST:.*]] = torch.prim.ListConstruct %[[INT0]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[RESULT:.*]] = torch.aten.tensor %[[DATA_LIST]], %[[DTYPE]], %[[NONE]], %[[FALSE]] : !torch.list<int>, !torch.int, !torch.none, !torch.bool -> !torch.vtensor<[],i1>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[],i1>
+  %0 = torch.operator "onnx.OptionalHasElement"() : () -> !torch.vtensor<[],i1>
+  return %0 : !torch.vtensor<[],i1>
+}
+
+// -----
+
+// CHECK-LABEL: @test_optional_has_element_empty_optional_input
+func.func @test_optional_has_element_empty_optional_input(%arg0: !torch.optional<vtensor<[],si32>>) -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[DTYPE:.*]] = torch.constant.int 11
+  // CHECK: %[[FALSE:.*]] = torch.constant.bool false
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[DATA_LIST:.*]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[RESULT:.*]] = torch.aten.tensor %[[DATA_LIST]], %[[DTYPE]], %[[NONE]], %[[FALSE]] : !torch.list<int>, !torch.int, !torch.none, !torch.bool -> !torch.vtensor<[],i1>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[],i1>
+  %0 = torch.operator "onnx.OptionalHasElement"(%arg0) : (!torch.optional<vtensor<[],si32>>) -> !torch.vtensor<[],i1>
+  return %0 : !torch.vtensor<[],i1>
+}
+
+// -----
+
+// CHECK-LABEL: @test_optional_has_element_optional_tensor_input
+func.func @test_optional_has_element_optional_tensor_input(%arg0: !torch.optional<vtensor<[4],f32>>) -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[DTYPE:.*]] = torch.constant.int 11
+  // CHECK: %[[FALSE:.*]] = torch.constant.bool false
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[DATA_LIST:.*]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[RESULT:.*]] = torch.aten.tensor %[[DATA_LIST]], %[[DTYPE]], %[[NONE]], %[[FALSE]] : !torch.list<int>, !torch.int, !torch.none, !torch.bool -> !torch.vtensor<[],i1>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[],i1>
+  %0 = torch.operator "onnx.OptionalHasElement"(%arg0) : (!torch.optional<vtensor<[4],f32>>) -> !torch.vtensor<[],i1>
+  return %0 : !torch.vtensor<[],i1>
+}
+
+// -----
+
+// CHECK-LABEL: @test_optional_has_element_optional_list_tensor_input
+func.func @test_optional_has_element_optional_list_tensor_input(%arg0: !torch.optional<list<vtensor<[4],f32>>>) -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[DTYPE:.*]] = torch.constant.int 11
+  // CHECK: %[[FALSE:.*]] = torch.constant.bool false
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[DATA_LIST:.*]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[RESULT:.*]] = torch.aten.tensor %[[DATA_LIST]], %[[DTYPE]], %[[NONE]], %[[FALSE]] : !torch.list<int>, !torch.int, !torch.none, !torch.bool -> !torch.vtensor<[],i1>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[],i1>
+  %0 = torch.operator "onnx.OptionalHasElement"(%arg0) : (!torch.optional<list<vtensor<[4],f32>>>) -> !torch.vtensor<[],i1>
+  return %0 : !torch.vtensor<[],i1>
+}
+
+// -----
+
+// CHECK-LABEL: @test_optional_has_element_tensor_input
+func.func @test_optional_has_element_tensor_input(%arg0: !torch.vtensor<[4],f32>) -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[DTYPE:.*]] = torch.constant.int 11
+  // CHECK: %[[FALSE:.*]] = torch.constant.bool false
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[DATA_LIST:.*]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[RESULT:.*]] = torch.aten.tensor %[[DATA_LIST]], %[[DTYPE]], %[[NONE]], %[[FALSE]] : !torch.list<int>, !torch.int, !torch.none, !torch.bool -> !torch.vtensor<[],i1>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[],i1>
+  %0 = torch.operator "onnx.OptionalHasElement"(%arg0) : (!torch.vtensor<[4],f32>) -> !torch.vtensor<[],i1>
+  return %0 : !torch.vtensor<[],i1>
+}
+
+// -----
+
+// CHECK-LABEL: @test_optional_has_element_list_tensor_input
+func.func @test_optional_has_element_list_tensor_input(%arg0: !torch.list<vtensor<[4],f32>>) -> !torch.vtensor<[],i1> attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 18 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[DTYPE:.*]] = torch.constant.int 11
+  // CHECK: %[[FALSE:.*]] = torch.constant.bool false
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[DATA_LIST:.*]] = torch.prim.ListConstruct %[[INT1]] : (!torch.int) -> !torch.list<int>
+  // CHECK: %[[RESULT:.*]] = torch.aten.tensor %[[DATA_LIST]], %[[DTYPE]], %[[NONE]], %[[FALSE]] : !torch.list<int>, !torch.int, !torch.none, !torch.bool -> !torch.vtensor<[],i1>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[],i1>
+  %0 = torch.operator "onnx.OptionalHasElement"(%arg0) : (!torch.list<vtensor<[4],f32>>) -> !torch.vtensor<[],i1>
+  return %0 : !torch.vtensor<[],i1>
+}