AtenStackOp does not see tensors appended after list construction

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Found this issue while working on #2969

Using AtenStackOp on list constructed using ListConstruct with a ValueRange does NOT fail:

```mlir
module {
func.func @test_stack() -> !torch.vtensor<[3,2,3],f32> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%int6 = torch.constant.int 6
%float0 = torch.constant.float 0.0
%none = torch.constant.none
%shape = torch.prim.ListConstruct %int2, %int3 : (!torch.int, !torch.int) -> !torch.list

%1 = torch.aten.full %shape, %float0, %int6, %none, %none, %none : !torch.list, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3],f32>
%2 = torch.aten.full %shape, %float0, %int6, %none, %none, %none : !torch.list, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3],f32>
%3 = torch.aten.full %shape, %float0, %int6, %none, %none, %none : !torch.list, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3],f32>
%4 = torch.prim.ListConstruct %1, %2, %3 : (!torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,3],f32>) -> !torch.list>
%5 = torch.aten.stack %4, %int0 : !torch.list>, !torch.int -> !torch.vtensor<[3,2,3],f32>
return %5 : !torch.vtensor<[3,2,3],f32>
}
}

```

Minimal replicating example:
```mlir
module {
func.func @test_stack() -> !torch.vtensor<[3,2,3],f32> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%int6 = torch.constant.int 6
%float0 = torch.constant.float 0.0
%none = torch.constant.none
%shape = torch.prim.ListConstruct %int2, %int3 : (!torch.int, !torch.int) -> !torch.list

%1 = torch.aten.full %shape, %float0, %int6, %none, %none, %none : !torch.list, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3],f32>
%2 = torch.aten.full %shape, %float0, %int6, %none, %none, %none : !torch.list, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3],f32>
%3 = torch.aten.full %shape, %float0, %int6, %none, %none, %none : !torch.list, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3],f32>

%list_of_tensors = torch.prim.ListConstruct : () -> !torch.list>

torch.aten.append.t %list_of_tensors, %1 : !torch.list>, !torch.vtensor<[2,3],f32> -> !torch.list>
torch.aten.append.t %list_of_tensors, %2 : !torch.list>, !torch.vtensor<[2,3],f32> -> !torch.list>
torch.aten.append.t %list_of_tensors, %3 : !torch.list>, !torch.vtensor<[2,3],f32> -> !torch.list>

%5 = torch.aten.stack %list_of_tensors, %int0 : !torch.list>, !torch.int -> !torch.vtensor<[3,2,3],f32>
return %5 : !torch.vtensor<[3,2,3],f32>
}
}
```

Replicating example with loop:
```mlir
module {
func.func @test_stack() -> !torch.vtensor<[3,2,3],f32> {
%int0 = torch.constant.int 0
%int2 = torch.constant.int 2
%int3 = torch.constant.int 3
%int6 = torch.constant.int 6
%float0 = torch.constant.float 0.0
%none = torch.constant.none
%shape = torch.prim.ListConstruct %int2, %int3 : (!torch.int, !torch.int) -> !torch.list
%true = torch.constant.bool true

%list_of_tensors = torch.prim.ListConstruct : () -> !torch.list>

%loop_iters_max = torch.constant.int 3

torch.prim.Loop %loop_iters_max, %true, init() {
^bb0(%iter_index: !torch.int):
%tensor = torch.aten.full %shape, %float0, %int6, %none, %none, %none : !torch.list, !torch.float, !torch.int, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[2,3],f32>
%discard_append_result = torch.aten.append.t %list_of_tensors, %tensor : !torch.list>, !torch.vtensor<[2,3],f32> -> !torch.list>
%continue = torch.constant.bool true
torch.prim.Loop.condition %true, iter()
} : (!torch.int, !torch.bool) -> ()

%5 = torch.aten.stack %list_of_tensors, %int0 : !torch.list>, !torch.int -> !torch.vtensor<[3,2,3],f32>
return %5 : !torch.vtensor<[3,2,3],f32>
}
}

```

Start by running the minimal MLIR reproducer and the loop variant to confirm how AtenStackOp handles tensors appended after list construction. Trace the AtenStackOp implementation and its list-handling logic; the work is done when both examples correctly account for the appended tensors and produce the declared stacked result.

Written by the indexing model from the issue text.