Update on "Implement the experimental evaluator for folding branches and castlikes | feat(torchlib)"

As an effort described in #1095, this PR

- Implements the experimental evaluator for folding branches and castlikes so that they are eagerly evaluated when possible.
- Updates implementation for `addr` for it to be traceable.
- Conditionally enabled previously xfailed tests.

Set `TORCHLIB_EXPERIMENTAL_PREFER_TRACING=1` and tested in CI.

E.g. clamp_min now becomes

```
<
   ir_version: 8,
   opset_import: ["" : 18, "pkg.onnxscript.torch_lib.common" : 1],
   producer_name: "pytorch",
   producer_version: "2.2.0"
>
main_graph (int32[5,10,5] input_0, int32[10,5] input_1) => (int32[5,10,5] _val_9) 
   <int64 _val_2, int64[2] _val_3, int64 _val_4, int64 _val_5, bool _val_6, int64 _val_7, bool _val_8>
{
   _val_2 = Size (input_0)
   _val_3 = Shape <start: int = 0> (input_1)
   _val_4 = Size (_val_3)
   _val_5 = Constant <value: tensor = int64 {0}> ()
   _val_6 = Equal (_val_2, _val_5)
   _val_7 = Constant <value: tensor = int64 {0}> ()
   _val_8 = Equal (_val_4, _val_7)
   _val_9 = Max (input_0, input_1)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
Rank (input) => (return_val)
{
   tmp = Shape (input)
   return_val = Size (tmp)
}
<
  domain: "pkg.onnxscript.torch_lib.common",
  opset_import: ["" : 18]
>
IsScalar (input) => (return_val)
{
   tmp = Shape (input)
   tmp_0 = Size (tmp)
   tmp_1 = Constant <value_int: int = 0> ()
   return_val = Equal (tmp_0, tmp_1)
}
```

[ghstack-poisoned]

Author: justinchuby

onnxscript/tests/function_libs/torch_lib/ops_test_data.py

@@ -557,7 +557,7 @@ def _where_input_wrangler(
     TorchLibOpInfo(
         "addr",
         core_ops.aten_addr,
-        tolerance={torch.float16: (1e-3, 3e-3)},
+        tolerance={torch.float16: (3e-3, 4e-3)},
     ),
     TorchLibOpInfo(
         "amax",