diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 997be2430..1996aa91d 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -2120,20 +2120,20 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
         return success();
       });
   patterns.onOp(
-      "Topk", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+      "TopK", 11, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
         Torch::ValueTensorType Values_type, Indices_type;
-        Value X, K;
+        Value input, kValue;
         int64_t axis;
         bool largest, sorted;
-        if (binder.tensorOperandAtIndex(X, 0) ||
-            binder.tensorOperandAtIndex(K, 1) ||
+        if (binder.tensorOperandAtIndex(input, 0) ||
+            binder.tensorOperandAtIndex(kValue, 1) ||
             binder.s64IntegerAttr(axis, "axis", -1) ||
             binder.s64BoolAttr(largest, "largest", true) ||
             binder.s64BoolAttr(sorted, "sorted", true) ||
             binder.tensorResultTypeAtIndex(Values_type, 0) ||
             binder.tensorResultTypeAtIndex(Indices_type, 1))
           return failure();
-        std::optional<unsigned> maybeRank = Torch::getTensorRank(X);
+        std::optional<unsigned> maybeRank = Torch::getTensorRank(input);
         if (!maybeRank)
           return rewriter.notifyMatchFailure(binder.op,
                                              "Unimplemented: unranked tensor");
@@ -2145,9 +2145,11 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
             rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), largest);
         Value cstSorted =
             rewriter.create<Torch::ConstantBoolOp>(binder.getLoc(), sorted);
+        Value kValueInt = rewriter.create<Torch::AtenItemOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(), kValue);
         rewriter.replaceOpWithNewOp<Torch::AtenTopkOp>(
-            binder.op, Values_type, Indices_type, X, K, cstAxis, cstLargest,
-            cstSorted);
+            binder.op, Values_type, Indices_type, input, kValueInt, cstAxis,
+            cstLargest, cstSorted);
         return success();
       });
   patterns.onOp("Sign", 9,
diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py
index 2407ce2b7..44594e9b6 100644
--- a/projects/pt1/e2e_testing/xfail_sets.py
+++ b/projects/pt1/e2e_testing/xfail_sets.py
@@ -2042,13 +2042,6 @@ ONNX_XFAIL_SET = {
     "SqueezeModule_broadcast",
     "SqueezeModule_static",
 
-    # Failure - onnx_lowering: onnx.TopK
-    "SortTensorDescending_basic",
-    "SortTensorInteger_basic",
-    "SortTensorNegativeDimension_basic",
-    "SortTensorSpecificDimension_basic",
-    "SortTensor_basic",
-
     # Failure - incorrect dtype
     "ReduceMaxAlongDimUnsignedInt_basic",
     "ElementwiseToDtypeI64ToUI8Module_basic",
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index ee89cd09e..a46654e45 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -1589,32 +1589,41 @@ func.func @test_reshape_zero_and_negative_dim(%arg0: !torch.vtensor<[2,3,4],f32>
 // -----
 
 // CHECK-LABEL : func.func @test_top_k
-  func.func @test_top_k(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>) attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64} {
-    // CHECK: %[[RESULTS:.*]]:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
-    // CHECK: return %[[RESULTS]]#0, %[[RESULTS]]#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
-    %0:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
-    return %0#0, %0#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
-  }
+func.func @test_top_k(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>) attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64} {
+  // CHECK: %[[AXIS:.*]] = torch.constant.int 1
+  // CHECK: %[[LARGEST:.*]] = torch.constant.bool true
+  // CHECK: %[[SORTED:.*]] = torch.constant.bool true
+  // CHECK: %[[K:.*]] = torch.aten.item %arg1 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.topk %arg0, %[[K]], %[[AXIS]], %[[LARGEST]], %[[SORTED]] : !torch.vtensor<[3,4],f32>, !torch.int, !torch.int, !torch.bool, !torch.bool -> !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
+  %0:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = 1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
+  return %0#0, %0#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
+}
 
 // -----
 
 // CHECK-LABEL: func.func @test_top_k_smallest
-  func.func @test_top_k_smallest(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>) attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
-    // CHECK: %[[RESULTS:.*]]:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 0 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
-    // CHECK: return %[[RESULTS]]#0, %[[RESULTS]]#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
-    %0:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 0 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
-    return %0#0, %0#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
-  }
+func.func @test_top_k_smallest(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>) attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[AXIS:.*]] = torch.constant.int 1
+  // CHECK: %[[LARGEST:.*]] = torch.constant.bool false
+  // CHECK: %[[SORTED:.*]] = torch.constant.bool true
+  // CHECK: %[[K:.*]] = torch.aten.item %arg1 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.topk %arg0, %[[K]], %[[AXIS]], %[[LARGEST]], %[[SORTED]] : !torch.vtensor<[3,4],f32>, !torch.int, !torch.int, !torch.bool, !torch.bool -> !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
+  %0:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = 1 : si64, torch.onnx.largest = 0 : si64, torch.onnx.sorted = 1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
+  return %0#0, %0#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
+}
 
 // -----
 
 // CHECK-LABEL: func.func @test_top_k_negative_axis
-  func.func @test_top_k_negative_axis(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>) attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64} {
-    // CHECK: %[[RESULTS:.*]]:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = -1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
-    // CHECK: return %[[RESULTS]]#0, %[[RESULTS]]#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
-    %0:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = -1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
-    return %0#0, %0#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
-  }
+func.func @test_top_k_negative_axis(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>) attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64} {
+  // CHECK: %[[AXIS:.*]] = torch.constant.int 1
+  // CHECK: %[[LARGEST:.*]] = torch.constant.bool true
+  // CHECK: %[[SORTED:.*]] = torch.constant.bool true
+  // CHECK: %[[K:.*]] = torch.aten.item %arg1 : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: torch.aten.topk %arg0, %[[K]], %[[AXIS]], %[[LARGEST]], %[[SORTED]] : !torch.vtensor<[3,4],f32>, !torch.int, !torch.int, !torch.bool, !torch.bool -> !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
+  %0:2 = torch.operator "onnx.TopK"(%arg0, %arg1) {torch.onnx.axis = -1 : si64} : (!torch.vtensor<[3,4],f32>, !torch.vtensor<[1],si64>) -> (!torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>)
+  return %0#0, %0#1 : !torch.vtensor<[3,3],f32>, !torch.vtensor<[3,3],si64>
+}
 
 // -----