diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
index fcd4c5991cbc..e9f7dbd5c465 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp
@@ -1809,10 +1809,16 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
           flattenedIndices = rewriter.create<Torch::AtenUnsqueezeOp>(
               loc, flattenIndicesTy, reshapedIndices, constZero);
         } else if (indicesRank > 1) {
-          Value endDim = rewriter.create<Torch::ConstantIntOp>(
-              loc, rewriter.getI64IntegerAttr(indicesRank - 2));
-          flattenedIndices = rewriter.create<Torch::AtenFlattenUsingIntsOp>(
-              loc, flattenIndicesTy, reshapedIndices, batchDimCountVal, endDim);
+          if (batchDimCount > indicesRank - 2) {
+            flattenedIndices = rewriter.create<Torch::AtenUnsqueezeOp>(
+                loc, flattenIndicesTy, reshapedIndices, batchDimCountVal);
+          } else {
+            Value endDim = rewriter.create<Torch::ConstantIntOp>(
+                loc, rewriter.getI64IntegerAttr(indicesRank - 2));
+            flattenedIndices = rewriter.create<Torch::AtenFlattenUsingIntsOp>(
+                loc, flattenIndicesTy, reshapedIndices, batchDimCountVal,
+                endDim);
+          }
         }
 
         // step 8. Expand `r-b-indices_shape[-1]` dims of flattened indices.
@@ -1834,8 +1840,12 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
         Value endDim = rewriter.create<Torch::ConstantIntOp>(
             loc,
             rewriter.getI64IntegerAttr(batchDimCount + indicesLastDim - 1));
-        Value flattenedData = rewriter.create<Torch::AtenFlattenUsingIntsOp>(
-            loc, flattenDataTy, data, batchDimCountVal, endDim);
+        Value flattenedData = data;
+
+        if (indicesLastDim != 1) {
+          flattenedData = rewriter.create<Torch::AtenFlattenUsingIntsOp>(
+              loc, flattenDataTy, data, batchDimCountVal, endDim);
+        }
 
         // step 10. Now we have flattenedData and expandedIndices of same rank
         // to perform gather operation.
@@ -1851,6 +1861,13 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
               binder.op, resultType, gather, /*dim=*/constZero);
           return success();
         }
+
+        if (unflattenIndicesDims.empty()) {
+          rewriter.replaceOpWithNewOp<Torch::AtenSqueezeDimOp>(
+              binder.op, resultType, gather, /*dim=*/batchDimCountVal);
+          return success();
+        }
+
         Value unflattenSizeList = rewriter.create<Torch::PrimListConstructOp>(
             loc, intListTy, unflattenIndicesDims);
         rewriter.replaceOpWithNewOp<Torch::AtenUnflattenIntOp>(
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
index b4ba9b93861d..59f82964a02b 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_g_to_p.mlir
@@ -180,6 +180,41 @@ func.func @test_gather_nd_1D_indices(%arg0: !torch.vtensor<[2,6,8,5],f32>, %arg1
 
 // -----
 
+// CHECK-LABEL: func.func @test_gathernd_example_int32_batch_dim1
+func.func @test_gathernd_example_int32_batch_dim1(%arg0: !torch.vtensor<[2,2,2],si32>, %arg1: !torch.vtensor<[2,1],si64>) -> !torch.vtensor<[2,2],si32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 17 : si64} {
+  // CHECK: %[[INT0:.+]] = torch.constant.int 0
+  // CHECK: %[[DIM0:.+]] = torch.aten.size.int %arg0, %[[INT0]]
+  // CHECK: %[[INT1:.+]] = torch.constant.int 1
+  // CHECK: %[[DIM1:.+]] = torch.aten.size.int %arg0, %[[INT1]]
+  // CHECK: %[[INT2:.+]] = torch.constant.int 2
+  // CHECK: %[[DIM2:.+]] = torch.aten.size.int %arg0, %[[INT2]]
+  // CHECK: %[[INT0_0:.+]] = torch.constant.int 0
+  // CHECK: %[[INT1_1:.+]] = torch.constant.int 1
+  // CHECK: %[[INT0_2:.+]] = torch.constant.int 0
+  // CHECK: %[[B0:.+]] = torch.aten.size.int %arg1, %[[INT0_2]]
+  // CHECK: %[[INT1_3:.+]] = torch.constant.int 1
+  // CHECK: %[[INT1_4:.+]] = torch.constant.int 1
+  // CHECK: %[[SLICE:.+]] = torch.aten.slice.Tensor %arg1, %[[INT1_3]], %[[INT0_0]], %[[INT1_4]], %[[INT1_1]]
+  // CHECK: %[[LT:.+]] = torch.aten.lt.Scalar %[[SLICE]], %[[INT0_0]]
+  // CHECK: %[[ADD:.+]] = torch.aten.add.Scalar %[[SLICE]], %[[DIM1]], %[[INT1_1]]
+  // CHECK: %[[WHERE:.+]] = torch.aten.where.self %[[LT]], %[[ADD]], %[[SLICE]]
+  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[B0]], %[[INT1_1]]
+  // CHECK: %[[VIEW:.+]] = torch.aten.view %[[WHERE]], %[[LIST]]
+  // CHECK: %[[INT1_5:.+]] = torch.constant.int 1
+  // CHECK: %[[UNSQ:.+]] = torch.aten.unsqueeze %[[VIEW]], %[[INT1_5]]
+  // CHECK: %[[LIST:.+]] = torch.prim.ListConstruct %[[DIM0]], %[[INT1_1]], %[[DIM2]]
+  // CHECK: %[[FALSE:.+]] = torch.constant.bool false
+  // CHECK: %[[EXPAND:.+]] = torch.aten.expand %[[UNSQ]], %[[LIST]], %[[FALSE]]
+  // CHECK: %[[INT1_6:.+]] = torch.constant.int 1
+  // CHECK: %[[GATHER:.+]] = torch.aten.gather %arg0, %[[INT1_5]], %[[EXPAND]], %[[FALSE]]
+  // CHECK: %[[SQ:.+]] = torch.aten.squeeze.dim %[[GATHER]], %[[INT1_5]]
+  %none = torch.constant.none
+  %0 = torch.operator "onnx.GatherND"(%arg0, %arg1) {torch.onnx.batch_dims = 1 : si64} : (!torch.vtensor<[2,2,2],si32>, !torch.vtensor<[2,1],si64>) -> !torch.vtensor<[2,2],si32>
+  return %0 : !torch.vtensor<[2,2],si32>
+}
+
+// -----
+
 // CHECK-LABEL: func.func @test_gather_elements
 func.func @test_gather_elements(%arg0: !torch.vtensor<[3,4,5],f32>, %arg1: !torch.vtensor<[3,4,5], si64>) -> !torch.vtensor<[3,4,5],f32> attributes {torch.onnx_meta.opset_version = 13 : si64} {
   // CHECK-DAG: %[[INT0:.+]] = torch.constant.int 0