Nj2SSKrSSKr\RRSS5r\R S5 \RR \SS5\RR \SS5S\RS \S \R4S j55r S r g) NexportFRAGMENTzLaccess_subclass_inner_tensor(Tensor src_subclass_tensor, str attr) -> Tensoraccess_subclass_inner_tensorAutogradPythonsrc_subclass_tensorattrreturncSSKJn U"U5(d[S[U535e[ XS5nUb[ U[ R5(d[SUSU35eU$)Nr)is_traceable_wrapper_subclasszIExpected src_subclass_tensor to be a traceable wrapper subclass, but got z Attribute z% is not a tensor or doesn't exist in ) torch.utils._python_dispatchr AssertionErrortypegetattr isinstancetorchTensor RuntimeError)rr r vals h/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/torch/export/custom_ops.py_access_subclass_inner_tensorrsK ()< = =/01 3   %T 2C {*S%,,77CDWCX Y   JcURSS5up4[R"U5n[XT5n[ US5(d[ SUS35eUR "U0UD6$)aA Import a custom autograd function by string name and call it. This is pretty bad because: 1) There is no schema Ideally we should automatically wrap custom autograd functions with a custom op, but that is too much work because we need to schematize custom autograd functions. For now, we just hackily put it in the IR. .applyzExpected function class z to have 'apply' method)rsplit importlib import_modulerhasattrrr)function_cls_nameargskwargs module_name class_namemodule function_clss r._call_custom_autograd_function_in_pre_dispatchr($sv066sA>K $ $[ 1F6.L < ) )&'8&99P Q     t .v ..r) rrlibraryLibrarylibdefineimplrstrrr(rrr0s  mmHj1 R C7DC7B-0 \\CE$/r