Mj0ddlZddlZejjddZej dejj eddejj edddejd ed ejfd Z d Z y) NexportFRAGMENTzLaccess_subclass_inner_tensor(Tensor src_subclass_tensor, str attr) -> Tensoraccess_subclass_inner_tensorAutogradPythonsrc_subclass_tensorattrreturncddlm}||stdt|t ||d}|t |t jstd|d||S)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 V/var/www/html/ia-enhanced/venv/lib/python3.12/site-packages/torch/export/custom_ops.py_access_subclass_inner_tensorrs~K ()< =/01 3   %tT 2C {*S%,,7CDWCX Y   Jc|jdd\}}tj|}t||}t |dst d|d|j |i|S)a] 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($sy066sA>K $ $[ 1F6:.L < )&'8&99P Q   <  t .v ..r) rrlibraryLibrarylibdefineimplrstrrr(rrr0s  mmHj1 R C7DC7B-0 \\CE$/r