diff --git a/include/torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h b/include/torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h
index 0de85f4ee..f296b6dfe 100644
--- a/include/torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h
+++ b/include/torch-mlir/Conversion/TorchOnnxToTorch/Patterns.h
@@ -110,6 +110,18 @@ struct OpBinder {
     return success();
   }
 
+  ParseResult tensorListOperandAtIndex(Value &valueIdx, int64_t idx) {
+    if (idx >= op->getNumOperands())
+      return failure();
+    valueIdx = op->getOperand(idx);
+    auto tt = dyn_cast<Torch::ListType>(valueIdx.getType());
+    if (!tt)
+      return failure();
+    if (!toValidTensorType(tt.getContainedType()))
+      return failure();
+    return success();
+  }
+
   ParseResult tensorListResultType(Torch::ListType &type0) {
     if (op->getNumResults() != 1)
       return failure();
diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 1eb5bcc1c..18399aa2d 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -3120,7 +3120,145 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
         rewriter.replaceOpWithNewOp<Torch::AtenWhereSelfOp>(
             binder.op, resultType, inputLTNegLambd, inputPlusBias,
             inputSubBiasOrZero);
+        return success();
+      });
+  patterns.onOp("SequenceAt", 11,
+                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+                  Torch::ValueTensorType resultType;
+                  Value inputSequence, position;
+                  if (binder.tensorListOperandAtIndex(inputSequence, 0) ||
+                      binder.tensorOperandAtIndex(position, 1) ||
+                      binder.tensorResultType(resultType))
+                    return failure();
 
+                  Value index = rewriter.create<Torch::AtenItemOp>(
+                      binder.getLoc(), rewriter.getType<Torch::IntType>(),
+                      position);
+                  rewriter.replaceOpWithNewOp<Torch::Aten__Getitem__TOp>(
+                      binder.op, resultType, inputSequence, index);
+                  return success();
+                });
+  patterns.onOp(
+      "SequenceEmpty", 11,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ListType resultType;
+        int64_t dtypeIntOnnx;
+        if (binder.s64IntegerAttr(dtypeIntOnnx, "dtype", 1) ||
+            binder.tensorListResultType(resultType))
+          return failure();
+
+        std::optional<int64_t> dtypeIntTorch =
+            onnxDtypeIntToTorchDtypeInt(dtypeIntOnnx);
+        if (!dtypeIntTorch.has_value()) {
+          return rewriter.notifyMatchFailure(
+              binder.op,
+              "unimplemented support for the given dtype conversion");
+        }
+        Value constDtype = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getI64IntegerAttr(dtypeIntTorch.value()));
+
+        Value shapeList = createConstantIntList(binder, rewriter, {});
+        Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+
+        Value self = rewriter.create<Torch::AtenEmptyMemoryFormatOp>(
+            binder.op->getLoc(), resultType.getContainedType(), shapeList,
+            /*dtype=*/constDtype,
+            /*layout=*/cstNone,
+            /*device=*/cstNone, /*pinMemory=*/cstNone,
+            /*memoryFormat=*/cstNone);
+
+        rewriter.replaceOpWithNewOp<Torch::PrimListConstructOp>(
+            binder.op, resultType, llvm::SmallVector<Value>{self});
+        return success();
+      });
+  patterns.onOp(
+      "SequenceErase", 11,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ListType resultType;
+        Value inputSequence, position;
+        if (binder.tensorListOperandAtIndex(inputSequence, 0) ||
+            binder.tensorListResultType(resultType))
+          return failure();
+
+        Value length = rewriter.create<Torch::AtenLenTOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(), inputSequence);
+
+        Value cstNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());
+        Value cstOne = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getI64IntegerAttr(1));
+        if (binder.op->getNumOperands() == 1) {
+          // If True, it means that the `position` arg is missing and
+          // the last tensor from the list has to be erased.
+          Value lengthMinusOne = rewriter.create<Torch::AtenSubIntOp>(
+              binder.getLoc(), length, cstOne);
+          rewriter.replaceOpWithNewOp<Torch::AtenSliceTOp>(
+              binder.op, resultType, inputSequence, /*start=*/cstNone,
+              /*end=*/lengthMinusOne, /*step=*/cstOne);
+          return success();
+        }
+
+        if (binder.tensorOperandAtIndex(position, 1))
+          return failure();
+
+        Value positionInt = rewriter.create<Torch::AtenItemOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(), position);
+        // Handling negative position value.
+        Value cstZero = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getI64IntegerAttr(0));
+        Value isPositionNegative = rewriter.create<Torch::AtenLtIntOp>(
+            binder.getLoc(), positionInt, cstZero);
+        isPositionNegative = rewriter.create<Torch::AtenIntBoolOp>(
+            binder.getLoc(), isPositionNegative);
+        Value finalOffset = rewriter.create<Torch::AtenMulIntOp>(
+            binder.getLoc(), isPositionNegative, length);
+        positionInt = rewriter.create<Torch::AtenAddIntOp>(
+            binder.getLoc(), positionInt, finalOffset);
+
+        Value listBeforePosition = rewriter.create<Torch::AtenSliceTOp>(
+            binder.getLoc(), resultType, inputSequence, /*start=*/cstNone,
+            /*end=*/positionInt, /*step=*/cstOne);
+        Value positionPlusOne = rewriter.create<Torch::AtenAddIntOp>(
+            binder.getLoc(), positionInt, cstOne);
+        Value listAfterPosition = rewriter.create<Torch::AtenSliceTOp>(
+            binder.getLoc(), resultType, inputSequence,
+            /*start=*/positionPlusOne,
+            /*end=*/length, /*step=*/cstOne);
+
+        rewriter.replaceOpWithNewOp<Torch::AtenAddTOp>(
+            binder.op, resultType, listBeforePosition, listAfterPosition);
+        return success();
+      });
+  patterns.onOp(
+      "SequenceInsert", 11,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ListType resultType;
+        Value inputSequence, position, insertValue;
+        if (binder.tensorListOperandAtIndex(inputSequence, 0) ||
+            binder.tensorOperandAtIndex(insertValue, 1) ||
+            binder.tensorListResultType(resultType))
+          return failure();
+
+        if (binder.op->getNumOperands() == 1) {
+          // If True, it means that the `position` arg is missing and
+          // the tensor has to be inserted at the end of the list.
+          Value length = rewriter.create<Torch::AtenLenTOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(),
+              inputSequence);
+          rewriter.replaceOpWithNewOp<Torch::AtenInsertTOp>(
+              binder.op, inputSequence, /*idx=*/length,
+              /*el=*/insertValue);
+          return success();
+        }
+
+        if (binder.tensorOperandAtIndex(position, 2))
+          return failure();
+
+        Value positionInt = rewriter.create<Torch::AtenItemOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(), position);
+        rewriter.create<Torch::AtenInsertTOp>(binder.getLoc(), inputSequence,
+                                              /*idx=*/positionInt,
+                                              /*el=*/insertValue);
+        rewriter.replaceOp(binder.op, inputSequence);
         return success();
       });
 }
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index eb5a9f7ca..317a3aeb1 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -2339,3 +2339,192 @@ func.func @test_shrink_hard(%arg0: !torch.vtensor<[5],f32>) -> !torch.vtensor<[5
   %0 = torch.operator "onnx.Shrink"(%arg0) {torch.onnx.lambd = 1.500000e+00 : f32} : (!torch.vtensor<[5],f32>) -> !torch.vtensor<[5],f32>
   return %0 : !torch.vtensor<[5],f32>
 }
+
+// -----
+
+// CHECK-LABEL: func.func @test_sequence_at
+func.func @test_sequence_at(%arg0: !torch.vtensor<[2,3,4],f32>, %arg1: !torch.vtensor<[2,3,4],f32>, %arg2: !torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,3,4],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[VTENSOR_1:.*]] = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[SEQUENCE:.*]] = torch.prim.ListConstruct %arg0, %arg1, %arg2 : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[LENGTH:.*]] = torch.aten.len.t %[[SEQUENCE]] : !torch.list<vtensor<[2,3,4],f32>> -> !torch.int
+  // CHECK: %[[NONE:.+]] = torch.constant.none
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[ITEM:.*]] = torch.aten.item %[[VTENSOR]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[CMP:.*]] = torch.aten.lt.int %[[ITEM]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
+  // CHECK: %[[COND:.*]] = torch.aten.Int.bool %[[CMP]] : !torch.bool -> !torch.int
+  // CHECK: %[[OFFSET:.*]] = torch.aten.mul.int %[[COND]], %[[LENGTH]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[POSITION:.*]] = torch.aten.add.int %[[ITEM]], %[[OFFSET]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[NONE]], %[[POSITION]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.none, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[ADD:.*]] = torch.aten.add.int %[[POSITION]], %[[INT1]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE_1:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[ADD]], %[[LENGTH]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.int, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[CONCAT_LIST:.*]] = torch.aten.add.t %[[SLICE]], %[[SLICE_1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.list<vtensor<[2,3,4],f32>> -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[ITEM_0:.*]] = torch.aten.item %[[VTENSOR_1]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[RESULT:.*]] = torch.aten.__getitem__.t %[[CONCAT_LIST]], %[[ITEM_0]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.int -> !torch.vtensor<[2,3,4],f32>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[2,3,4],f32>
+  %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<1> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<1> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %2 = torch.operator "onnx.SequenceConstruct"(%arg0, %arg1, %arg2) : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %3 = torch.operator "onnx.SequenceErase"(%2, %0) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[],si64>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %4 = torch.operator "onnx.SequenceAt"(%3, %1) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[],si64>) -> !torch.vtensor<[2,3,4],f32>
+  return %4 : !torch.vtensor<[2,3,4],f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_sequence_insert
+func.func @test_sequence_insert(%arg0: !torch.vtensor<[2,3,4],f32>, %arg1: !torch.vtensor<[2,3,4],f32>, %arg2: !torch.vtensor<[2,3,4],f32>) -> !torch.vtensor<[2,3,4],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<-3> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[VTENSOR_1:.*]] = torch.vtensor.literal(dense<-1> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[VTENSOR_2:.*]] = torch.vtensor.literal(dense<-1> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[SEQUENCE:.*]] = torch.prim.ListConstruct %arg0, %arg1, %arg2 : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[LENGTH:.*]] = torch.aten.len.t %[[SEQUENCE]] : !torch.list<vtensor<[2,3,4],f32>> -> !torch.int
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[ITEM:.*]] = torch.aten.item %[[VTENSOR]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[CMP:.*]] = torch.aten.lt.int %[[ITEM]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
+  // CHECK: %[[COND:.*]] = torch.aten.Int.bool %[[CMP]] : !torch.bool -> !torch.int
+  // CHECK: %[[OFFSET:.*]] = torch.aten.mul.int %[[COND]], %[[LENGTH]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[POSITION:.*]] = torch.aten.add.int %[[ITEM]], %[[OFFSET]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[NONE]], %[[POSITION]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.none, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[ADD:.*]] = torch.aten.add.int %[[POSITION]], %[[INT1]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE_1:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[ADD]], %[[LENGTH]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.int, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[CONCAT_LIST:.*]] = torch.aten.add.t %[[SLICE]], %[[SLICE_1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.list<vtensor<[2,3,4],f32>> -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[ITEM_0:.*]] = torch.aten.item %[[VTENSOR_1]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: torch.aten.insert.t %[[CONCAT_LIST]], %[[ITEM_0]], %arg0 : !torch.list<vtensor<[2,3,4],f32>>, !torch.int, !torch.vtensor<[2,3,4],f32>
+  // CHECK: %[[ITEM_1:.*]] = torch.aten.item %[[VTENSOR_2]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[RESULT:.*]] = torch.aten.__getitem__.t %[[CONCAT_LIST]], %[[ITEM_1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.int -> !torch.vtensor<[2,3,4],f32>
+  // CHECK: return %[[RESULT]] : !torch.vtensor<[2,3,4],f32>
+  %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<-3> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %1 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<-1> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %2 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<-1> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %3 = torch.operator "onnx.SequenceConstruct"(%arg0, %arg1, %arg2) : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %4 = torch.operator "onnx.SequenceErase"(%3, %0) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[],si64>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %5 = torch.operator "onnx.SequenceInsert"(%4, %arg0, %1) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[],si64>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %6 = torch.operator "onnx.SequenceAt"(%5, %2) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[],si64>) -> !torch.vtensor<[2,3,4],f32>
+  return %6 : !torch.vtensor<[2,3,4],f32>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_sequence_erase_at_beginning
+func.func @test_sequence_erase_at_beginning(%arg0: !torch.vtensor<[2,3,4],f32>, %arg1: !torch.vtensor<[2,3,4],f32>, %arg2: !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[SEQUENCE:.*]] = torch.prim.ListConstruct %arg0, %arg1, %arg2 : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[LENGTH:.*]] = torch.aten.len.t %[[SEQUENCE]] : !torch.list<vtensor<[2,3,4],f32>> -> !torch.int
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[ITEM:.*]] = torch.aten.item %[[VTENSOR]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[CMP:.*]] = torch.aten.lt.int %[[ITEM]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
+  // CHECK: %[[COND:.*]] = torch.aten.Int.bool %[[CMP]] : !torch.bool -> !torch.int
+  // CHECK: %[[OFFSET:.*]] = torch.aten.mul.int %[[COND]], %[[LENGTH]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[POSITION:.*]] = torch.aten.add.int %[[ITEM]], %[[OFFSET]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[NONE]], %[[POSITION]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.none, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[ADD:.*]] = torch.aten.add.int %[[POSITION]], %[[INT1]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE_1:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[ADD]], %[[LENGTH]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.int, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[RESULT:.*]] = torch.aten.add.t %[[SLICE]], %[[SLICE_1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.list<vtensor<[2,3,4],f32>> -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: return %[[RESULT]] : !torch.list<vtensor<[2,3,4],f32>>
+  %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %3 = torch.operator "onnx.SequenceConstruct"(%arg0, %arg1, %arg2) : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %4 = torch.operator "onnx.SequenceErase"(%3, %0) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[],si64>) -> !torch.list<vtensor<[2,3,4],f32>>
+  return %4 : !torch.list<vtensor<[2,3,4],f32>>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_sequence_erase_at_end
+func.func @test_sequence_erase_at_end(%arg0: !torch.vtensor<[2,3,4],f32>, %arg1: !torch.vtensor<[2,3,4],f32>, %arg2: !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<2> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[SEQUENCE:.*]] = torch.prim.ListConstruct %arg0, %arg1, %arg2 : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[LENGTH:.*]] = torch.aten.len.t %[[SEQUENCE]] : !torch.list<vtensor<[2,3,4],f32>> -> !torch.int
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[ITEM:.*]] = torch.aten.item %[[VTENSOR]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[CMP:.*]] = torch.aten.lt.int %[[ITEM]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
+  // CHECK: %[[COND:.*]] = torch.aten.Int.bool %[[CMP]] : !torch.bool -> !torch.int
+  // CHECK: %[[OFFSET:.*]] = torch.aten.mul.int %[[COND]], %[[LENGTH]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[POSITION:.*]] = torch.aten.add.int %[[ITEM]], %[[OFFSET]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[NONE]], %[[POSITION]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.none, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[ADD:.*]] = torch.aten.add.int %[[POSITION]], %[[INT1]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE_1:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[ADD]], %[[LENGTH]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.int, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[RESULT:.*]] = torch.aten.add.t %[[SLICE]], %[[SLICE_1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.list<vtensor<[2,3,4],f32>> -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: return %[[RESULT]] : !torch.list<vtensor<[2,3,4],f32>>
+  %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<2> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %3 = torch.operator "onnx.SequenceConstruct"(%arg0, %arg1, %arg2) : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %4 = torch.operator "onnx.SequenceErase"(%3, %0) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[],si64>) -> !torch.list<vtensor<[2,3,4],f32>>
+  return %4 : !torch.list<vtensor<[2,3,4],f32>>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_sequence_erase_negative_idx
+func.func @test_sequence_erase_negative_idx(%arg0: !torch.vtensor<[2,3,4],f32>, %arg1: !torch.vtensor<[2,3,4],f32>, %arg2: !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<-2> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[SEQUENCE:.*]] = torch.prim.ListConstruct %arg0, %arg1, %arg2 : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[LENGTH:.*]] = torch.aten.len.t %[[SEQUENCE]] : !torch.list<vtensor<[2,3,4],f32>> -> !torch.int
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[ITEM:.*]] = torch.aten.item %[[VTENSOR]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[CMP:.*]] = torch.aten.lt.int %[[ITEM]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
+  // CHECK: %[[COND:.*]] = torch.aten.Int.bool %[[CMP]] : !torch.bool -> !torch.int
+  // CHECK: %[[OFFSET:.*]] = torch.aten.mul.int %[[COND]], %[[LENGTH]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[POSITION:.*]] = torch.aten.add.int %[[ITEM]], %[[OFFSET]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[NONE]], %[[POSITION]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.none, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[ADD:.*]] = torch.aten.add.int %[[POSITION]], %[[INT1]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE_1:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[ADD]], %[[LENGTH]], %[[INT1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.int, !torch.int, !torch.int -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: %[[RESULT:.*]] = torch.aten.add.t %[[SLICE]], %[[SLICE_1]] : !torch.list<vtensor<[2,3,4],f32>>, !torch.list<vtensor<[2,3,4],f32>> -> !torch.list<vtensor<[2,3,4],f32>>
+  // CHECK: return %[[RESULT]] : !torch.list<vtensor<[2,3,4],f32>>
+  %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<-2> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %3 = torch.operator "onnx.SequenceConstruct"(%arg0, %arg1, %arg2) : (!torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>, !torch.vtensor<[2,3,4],f32>) -> !torch.list<vtensor<[2,3,4],f32>>
+  %4 = torch.operator "onnx.SequenceErase"(%3, %0) : (!torch.list<vtensor<[2,3,4],f32>>, !torch.vtensor<[],si64>) -> !torch.list<vtensor<[2,3,4],f32>>
+  return %4 : !torch.list<vtensor<[2,3,4],f32>>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_sequence_erase_empty
+func.func @test_sequence_erase_empty() -> !torch.list<vtensor<[],f32>> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
+  // CHECK: %[[INT6:.*]] = torch.constant.int 6
+  // CHECK: %[[SHAPE_LIST:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[EMPTY_TENSOR:.*]] = torch.aten.empty.memory_format %[[SHAPE_LIST]], %[[INT6]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.list<int>, !torch.int, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
+  // CHECK: %[[SEQUENCE:.*]] = torch.prim.ListConstruct %[[EMPTY_TENSOR]] : (!torch.vtensor<[],f32>) -> !torch.list<vtensor<[],f32>>
+  // CHECK: %[[LENGTH:.*]] = torch.aten.len.t %[[SEQUENCE]] : !torch.list<vtensor<[],f32>> -> !torch.int
+  // CHECK: %[[NONE_0:.*]] = torch.constant.none
+  // CHECK: %[[INT1:.*]] = torch.constant.int 1
+  // CHECK: %[[ITEM:.*]] = torch.aten.item %[[VTENSOR]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[INT0:.*]] = torch.constant.int 0
+  // CHECK: %[[CMP:.*]] = torch.aten.lt.int %[[ITEM]], %[[INT0]] : !torch.int, !torch.int -> !torch.bool
+  // CHECK: %[[COND:.*]] = torch.aten.Int.bool %[[CMP]] : !torch.bool -> !torch.int
+  // CHECK: %[[OFFSET:.*]] = torch.aten.mul.int %[[COND]], %[[LENGTH]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[POSITION:.*]] = torch.aten.add.int %[[ITEM]], %[[OFFSET]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[NONE_0]], %[[POSITION]], %[[INT1]] : !torch.list<vtensor<[],f32>>, !torch.none, !torch.int, !torch.int -> !torch.list<vtensor<[],f32>>
+  // CHECK: %[[ADD:.*]] = torch.aten.add.int %[[POSITION]], %[[INT1]] : !torch.int, !torch.int -> !torch.int
+  // CHECK: %[[SLICE_1:.*]] = torch.aten.slice.t %[[SEQUENCE]], %[[ADD]], %[[LENGTH]], %[[INT1]] : !torch.list<vtensor<[],f32>>, !torch.int, !torch.int, !torch.int -> !torch.list<vtensor<[],f32>>
+  // CHECK: %[[RESULT:.*]] = torch.aten.add.t %[[SLICE]], %[[SLICE_1]] : !torch.list<vtensor<[],f32>>, !torch.list<vtensor<[],f32>> -> !torch.list<vtensor<[],f32>>
+  // CHECK: return %[[RESULT]] : !torch.list<vtensor<[],f32>>
+  %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<0> : tensor<si64>} : () -> !torch.vtensor<[],si64>
+  %1 = torch.operator "onnx.SequenceEmpty"() : () -> !torch.list<vtensor<[],f32>>
+  %4 = torch.operator "onnx.SequenceErase"(%1, %0) : (!torch.list<vtensor<[],f32>>, !torch.vtensor<[],si64>) -> !torch.list<vtensor<[],f32>>
+  return %4 : !torch.list<vtensor<[],f32>>
+}
+
+// -----
+
+// CHECK-LABEL: func.func @test_sequence_empty
+func.func @test_sequence_empty() -> !torch.list<vtensor<[],f32>> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 12 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[INT6:.*]] = torch.constant.int 6
+  // CHECK: %[[SHAPE_LIST:.*]] = torch.prim.ListConstruct  : () -> !torch.list<int>
+  // CHECK: %[[NONE:.*]] = torch.constant.none
+  // CHECK: %[[EMPTY_TENSOR:.*]] = torch.aten.empty.memory_format %[[SHAPE_LIST]], %[[INT6]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.list<int>, !torch.int, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[],f32>
+  // CHECK: %[[RESULT:.*]] = torch.prim.ListConstruct %[[EMPTY_TENSOR]] : (!torch.vtensor<[],f32>) -> !torch.list<vtensor<[],f32>>
+  // CHECK: return %[[RESULT]] : !torch.list<vtensor<[],f32>>
+  %0 = torch.operator "onnx.SequenceEmpty"() : () -> !torch.list<vtensor<[],f32>>
+  return %0 : !torch.list<vtensor<[],f32>>
+}