diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
index 40aaa6ac4..7fa859ff9 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
@@ -2315,12 +2315,15 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
         }
 
         // The torch.pad op expects a different arrangement of padding pairs for
-        // each dimension as compared to the onnx.pad op. So, rearranging pad
-        // tensor to satisfy torch.pad op semantics.
+        // each dimension as compared to the onnx.pad op. Rearrange the pad
+        // tensor as shown below:
+        //
+        // [x1_begin, x2_begin, ..., x1_end, x2_end,...] ->
+        // [xn_begin, xn_end, ...., x2_begin, x2_end, x1_begin, x1_end]
         SmallVector<Value> padsRearrange;
-        for (uint32_t i = 0; i < padsSize / 2; i++) {
+        for (uint32_t i = padsSize - 1; i >= padsSize / 2; i--) {
+          padsRearrange.emplace_back(padsTensorValue[i - padsSize / 2]);
           padsRearrange.emplace_back(padsTensorValue[i]);
-          padsRearrange.emplace_back(padsTensorValue[(padsSize / 2) + i]);
         }
 
         Value padsSizeList =
diff --git a/lib/Dialect/Torch/IR/TorchOps.cpp b/lib/Dialect/Torch/IR/TorchOps.cpp
index b10111f78..53372006d 100644
--- a/lib/Dialect/Torch/IR/TorchOps.cpp
+++ b/lib/Dialect/Torch/IR/TorchOps.cpp
@@ -3664,7 +3664,9 @@ OpFoldResult AtenSliceTensorOp::fold(FoldAdaptor adaptor) {
     return DenseElementsAttr::get(outType.toBuiltinTensor(), values);
   }
 
-  // If the input and output shapes are the same we can just fold:
+  // If the input and output shapes are the same & step == 1 we can fold:
+  if (!step || step.getValue().getSExtValue() != 1)
+    return nullptr;
   for (size_t i = 0; i < inType.getSizes().size(); ++i) {
     if (inType.getSizes()[i] != outType.getSizes()[i])
       return nullptr;
diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py
index 7bbd82a0d..b379b665b 100644
--- a/projects/pt1/e2e_testing/xfail_sets.py
+++ b/projects/pt1/e2e_testing/xfail_sets.py
@@ -2216,8 +2216,6 @@ ONNX_XFAIL_SET = {
     "ElementwiseLog2IntModule_basic",
     "ElementwiseFminModule_basic",
     "ElementwiseFmaxModule_basic",
-    "FlipModuleStaticShape_basic",
-    "FlipNegativeIndexModule_basic",
     "PixelShuffleModuleStaticRank4Float32_basic",
     "ReflectionPad1dModule2dInput_Right",
     "ReflectionPad1dModule2dInput_basic",
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
index 77991912c..74713552b 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
@@ -854,7 +854,7 @@ func.func @test_pad(%arg0: !torch.vtensor<[3,4],f32>, %arg1: !torch.vtensor<[4],
   // CHECK: %[[INT3:.+]] = torch.constant.int 3
   // CHECK: %[[SELECT_3:.+]] = torch.aten.select.int %arg1, %[[INT0]], %[[INT3]] : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
   // CHECK: %[[ITEM_3:.+]] = torch.aten.item %[[SELECT_3]] : !torch.vtensor<[],si64> -> !torch.int
-  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[ITEM_0]], %[[ITEM_2]], %[[ITEM_1]], %[[ITEM_3]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
+  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[ITEM_1]], %[[ITEM_3]], %[[ITEM_0]], %[[ITEM_2]] : (!torch.int, !torch.int, !torch.int, !torch.int) -> !torch.list<int>
   // CHECK: %[[STR:.+]] = torch.constant.str "constant"
   // CHECK: %[[PAD:.+]] = torch.aten.pad %arg0, %[[LIST]], %[[STR]], %[[VAL]] : !torch.vtensor<[3,4],f32>, !torch.list<int>, !torch.str, !torch.float -> !torch.vtensor<[5,4],f32>
   // CHECK: return %[[PAD]] : !torch.vtensor<[5,4],f32>