Fix var_mean when input dim is a list | fix(torchlib) (#1127)

Previously `var_mean.dim` handles dim only when it is a tuple. It can be
a list as well in practice.

onnxscript/function_libs/torch_lib/ops/core.py

@@ -8015,16 +8015,15 @@ def aten_var_mean(self: TReal, unbiased: bool = True) -> Tuple[TReal, TReal]:
 
 @torch_op("aten::var_mean.dim", trace_only=True)
 def aten_var_mean_dim(
-    self: TReal, dim: Optional[int], unbiased: bool = True, keepdim: bool = False
+    self: TReal, dim: int, unbiased: bool = True, keepdim: bool = False
 ) -> Tuple[TReal, TReal]:
     """var_mean.dim(Tensor self, int[1]? dim, bool unbiased=True, bool keepdim=False) -> (Tensor, Tensor)"""
 
-    # Although dim is Optional in signature, but we assume it must has value for this overload
+    # Although dim is Optional in signature, but we assume it must have value for this overload
     # Assert(dim is not None)
-    if isinstance(dim, Tuple):
-        dim_tensor = op.Constant(value_ints=dim)
-    else:
-        dim_tensor = op.Constant(value_int=dim)
+    if isinstance(dim, int):
+        dim = (dim,)
+    dim_tensor = op.Constant(value_ints=dim)
     return _aten_var_mean_dim_onnx(
         self, dim_tensor, correction=float(unbiased), keepdim=keepdim
     )
@@ -8045,10 +8044,9 @@ def aten_var_mean_correction(
     if dim is None:
         var, mean = _aten_var_mean_onnx(self, correction, keepdim)
     else:
-        if isinstance(dim, Tuple):
-            dim_tensor = op.Constant(value_ints=dim)
-        else:
-            dim_tensor = op.Constant(value_int=dim)
+        if isinstance(dim, int):
+            dim = (dim,)
+        dim_tensor = op.Constant(value_ints=dim)
         var, mean = _aten_var_mean_dim_onnx(self, dim_tensor, correction, keepdim)
     return var, mean