Nj: VSSKJr SSKJrJr SSKJr SrSrSr Sr Sr S r S r g ) )Refine)unifyVar) TensorTypec[U5nUR5 [UR5n[ UR U5 g)z% Calls our symbolic inferencer once. N)rrefineunify_eq constraintssubstitute_all_typesgraphtracedrmgus x/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/torch/fx/experimental/unify_refinements.py infer_symbolic_types_single_passrs2 vAHHJ 1== !Cs+c<[U5nUR5 [UR5n[ UR U5 [U5nUR5 [UR5n[ UR U5 UR 5 g)z Calls our symbolic inferencer twice. This is useful when one pass is not enough to infer all the information such as the case for broadcasting. N)rrr r r r symbolic_relationsr s rinfer_symbolic_typesrsl vAHHJ 1== !Cs+vAHHJ 1== !Cs+rc/n/nUH9nURUR5 URUR5 M; [U5[U54$)zU Convert equality constraints in the right format to be used by unification library. )appendlhsrhstuple) list_of_eqrreqs r convert_eqr%sM C C 266 266 :uSz !!rc2[U5up[X5$)z4 Apply unification to a set of equality constraints )rr)rrrs rr r 2s *%HC ?rc[U[5(a X;aX$U$[U[5(aT/nURH.nX0;aUR X5 MUR U5 M0 [[ U55$[U[ 5(a'/nUHnUR [X55 M U$[U[ 5(a0/nUHnUR [X55 M [ U5$U$)z* Apply the most general unifier to a type ) isinstancerr__args__rrlistsubstitute_solution_one_type)mappingtnew_typetyps rr$r$;s!S <: H Az " "::C~ -$  %/** At  C OO8F G Au  C OO8F GXrcSnU(a6SnUH%nXnXU;a XnXX'XAU:wdM#SnM' U(aM6URHn[XR5UlM g)z Apply the most general unifier to all types in a graph till reaching a fixed point. If the input and output graph are the same, we converge. TFN)nodesr$type)r r%flagkold_mapping_valnew_keyns rr r ^ss D A%jOzW$!*$- !*,  $[[-gvv>rc[URUR5H!up#URUR:wdM! g g)zf A check equality to be used in fixed points. We do not use graph equality but instead type equality. FT)zipr*r+)g1g2r0ms rcheck_for_type_equalityr6ss7 BHHbhh' 66QVV ( rN)/torch.fx.experimental.graph_gradual_typecheckerr!torch.fx.experimental.unificationrrtorch.fx.tensor_typerrrrr r$r r6rrr;s2B8+,( " F?* r