lib/torch/ext.bundle in torch-rb-0.1.5 vs lib/torch/ext.bundle in torch-rb-0.1.6

- old
+ new

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H躐f.UH]&fDUHSPHHH[]@UHAWAVSHH$HHHGHuHGHHĘ[A^A_]ÿp袌IHJ#fHnH#fHnflfpE]E]ffEHEPFHHE(")EH5#LH膌CLHEH$H5#H#H #L #H}LEHEHD$HpHxHL$H$HuL證1H5r$H$Lp袋IHJ#fHnH#fHnflfME`E`ffEHE`IHHE( ")EH5u#QH艋CQHEH$H5##H#H #L #H}LELHEHD$HEHMHL$H$HuL趈1H5{$H$L ILϊL5IƳEt8BIL诊LIEu IEt H}TEu uLH}9tLcLɍfUHAWAVAUATSH(LMLHMIIH}H LH$HHHH@H L萉HEEL-$IEHEHI@L LL5$ILHDžHDžHDžHDžDžL֊HLHJLeL踊HLH,L}L蚊HLH3H^H]HnHHHHut -HVHvHHH躜LHHEHL Lt HYHH5B$HH謇H LH([A\A]A^A_]HBHTHHEHL Lt HڇHhH5Ý$HH-H 荇H DUHAWAVAUATSH(LMLHMIIH}H LH$HHHH@H LHEEL-n$IEHEHI@L L蜆L55$ILHDžHDžHDžHDžDžLVHLHʚLeL8HLH謚L}LHLH莚3HޅH]HHHHbHut -HVHvHHH:LHkHEHL Lt HمHgH5›$HH,H 茅LH([A\A]A^A_]HBHTHHEHL Lt HZHH5C$HH譄H H菂 DUHAWAVSHxHHtHHHt Hx[A^A_]ÿpIH#fHnH#fHnflfMEEffEHE@蛄HEfo="fEHed zero.HH6H reachedHH0Hafter itHH(Hefcount HH Hcrease rHHHannot inHHHe_ptr: CHHHintrusivH@>HEH$H5#H#H #L #H}LEHEHD$HEHMHL$H$HuL讁1H5s$H$L IƳEt1I9IEuEuu&L݀ H}fEtH}WtL聃L诀 DUHSPHHHHVH)HfELEHHHH[]UHAWAVAUATSHxMIH}HxHUQH8BL(H@HtH@HEW)EHEEHaten::emHEEptyH}~Et H}kI}1҉؀IH߀HxHMH5#LˀH5n -#LLŀH5#LL迀I}L]L}MLW)`H`LmkLhMt#HIFHuILPLVILL}E11E1HxHEL}LuH}HuvHEHEL5o$LuAG A1҃AH}HuйHEL}ILuHEL9t+HHu$H}HPHEHHuH}HtHPLuHtKH]LmH}vH]Ht#HHCHuHHPHdLLuE1HEH;$t+HHu$H}HPHEHHuH}HtHPMt$HIEHuIELPLLHx[A\A]A^A_]H߃H׃IHEHH;$IHhHHHCHHHPH}H}IH]Ht#HHCHuHHPH>HEHH;$t4HHu-HEH8HPHEHHHuHEH8HtHPHEL01HEHEH;>$HӂIEH}H}LmIHEH;$t+HHu$H}HPHEHHuH}HtHPLuHEH;$t+HHu$H}HPHEHHuH}HtHPLuH}u9bHHLmIH}uBIL{ IH}t+HHMHAHuH]HHPH}L{ fUHAWAVSHHI{Aƿ{HH0HPHCLx H H @H HH HH!LEHEHCfE8t -@EEHMLELHAO{LH[A^A_]HHA-{Hz Hۀf.UHAWAVAUATSH(MHHUIIzI)EH}tɿH1E%HH=HEL޹$tDH5$fEA}t AEEELMLLHMIH([A\A]A^A_]H=Y$|tyH5 #HH-$H=.$}|H5$fEA}uHH= $L|Hty HfUHAWAVAUATSH8LMIIIILEA)EH}jLtɾH1E%II=LE}yHL!t + HĨ[A\A]A^A_]HHu*HEHEfoEfEffEHE1E1,HEHEHEHMHMHEffEHEAAHpHD$H`HhHL$H$HuLZ E1H51%Hy%L,\  IAEt,fIAHt$gI"IEu+Eu4Eu<HuEEtL[ L +Y  H}u[ EtHh[ EtH}Y[ HtHXD[ Euf.DUHAWAVAUATSHHIH}H0LH5%HHHH@H0LZ H]HEEL5U%M~LI@L0L9Z L% %ILHDž HDžHDžHDžDž(L[ HLH%H]HE0HXX HH?LL0Lt HY HY H5*%HHFY H0Y HH[A\A]A^A_]H>HPHLL0Lt HxY HY H5%HHX H0+Y HV  UHAWAVAUATSHHIH}H0LHE%HHHH@H0LX H]HEEL5e%M~LI@L0LIX L%0%ILHDž HDžHDžHDžDž(LZ HLH5H]HE0HhV HHOLL0Lt HX HW H5:%HHVW H0W HH[A\A]A^A_]H>HPHLL0Lt HW HW H5%HHV H0;W HT  UHAWAVAUATSHHIH}H0LHU%HHHH@H0LV H]HEEL5u%M~LI@L0LYV L%@%ILHDž HDžHDžHDžDž(LX HLHEH]HE0HHH_LL0Lt HV HU H5J%HHfU H0U HH[A\A]A^A_]H>HPHLL0Lt HU H&U H5ρ%HHT H0KU HR  UHAWAVAUATSH(HHUIH}H(LHa%HHHH@H(LT H]HEEL%%Ml$LI@L(LdT L=K%ILHDžHDžHDžHDžDž LV HLHPLuHEHHX8HU HHH'H]HU HHH LH:LL(Lt HS H|S H5%%HHAS H(S LH([A\A]A^A_]H>HPHLL(Lt HsS HS H5%HHR H(&S HP  f.fUHAWAVAUATSH@t"@t@u9H5#H5#H5#HĨ[A\A]A^A_]鲳pR IH#fHnH#fHnflfPDž`%HDžxƅhHn layoutHoHUnknown HiƅwH}HhQ ffEHE5R HHEfo#fEH5#HsR ƃEt HEHt +2HHu*HEHEfoEfEffEHE1E1,HEHEHEHMHMHEffEHEAAH`HD$HPHXHL$H$HuLqO E1H5|%HNo%LQ  IAEt'aIAhtbIEu+Eu4Eu<huEEtL/Q L{N  H}P EtHP EtH}P htHxP EuUHAWAVAUATSHHIH}H8LH{%HHHH@H8L-P HEEL5}%M~LI@L8LO L%}%ILHDž(HDž HDžHDžDž0LwQ HLH詰LmHHVQ HHH舰LH蹳LL8Lt H mO HN H5{%HHN H8 O LH[A\A]A^A_]H>HPHLL8Lt H N HN H5){%HHEN H8N HEL  f.UHAWAVAUATSHIILft +IwIWHLH}tMuM\HMVML9'IIEJH/HH#EA*MOMgMHu%E1HCH9ugKHHHtHSL9u^H{HtH{ uZM1f.DT A: u'HH9uyL9r1IHL9uvHHrhMHLLMMM MME1Huȅu!MI!IEJH1HIIIEJHE1LH[A\A]A^A_]IMHuE1HCH9t HL!L9uH9KHHHtHSL9uxH{HtH{ u+Mtr1f.DT A: uEHH9uLMtGLLMMLUL LUMMHuȅt f.DHHDIHf.DUHAWAVAUATSH w^H'H ,H\HHHH1Hi-8ߝHHH/H1H1HHH/H1HH1H@rHFLvLLII'1˗\ȥIHN3I IIH^HH9LHII!LIHDLLtI H HHHDHH9LIH!HHHHHO@/;jHMMILH/L1ILHH/H1HHsoHLFHLH'1˗\ȥH|HO@/;jHL)HHHHH)HH<AHH HWePIL1H,HHH1Hi-8ߝHHH/H1H1HHH/H1H[IsoIi-8ߝHDLDLTLLIH|H1IHH/H1H1III/I1ML\IJLH+O$K MMIMIH\HLLLL<MHLILIII+IIMIH6HuHHHHUA@fK9HUI@MLHOt9K\9LLKT9HUHHIHsoLKT9ILLILLUOl9LLMIMMIMHHIM1ILHLH+IIHOD9O\9LLLHUHLHIH+IL9}%I1Hi-8ߝLLH/I1I1LLH/L1HHH/H1HsoHLHM1LLH/M1I1LLH/L1HHH1HHH/H1H1HHH/H1H[A\A]A^A_]Hr>DHH1Hi-8ߝHHH/H1H1HHH/H1HHO@/;jHtHH<>tHH ύ HHWePIHH1HH/H1H^fDUHAWAVATSH IHIF IH3AW)EEHMHE M&H]Ht#HHCHuHHPH9F LH [A\A^A_]IL}Mt#HIGHuILPLE LRF LC  f.UHAWAVSHxHH;d%tHHHt Hx[A^A_]ÿpF IH#fHnH#fHnflfMEEffEHE@E HEfo#fEHed zero.HH6H reachedHH0Hafter itHH(Hefcount HH Hcrease rHHHannot inHHHe_ptr: CHHHintrusivH@>HEH$H5#H#H #L #H}LEHEHD$HEHMHL$H$HuLB 1H5p%Hb%LE  IƳEt1I9IEuEuu&LB  H}D EtH}uD tLD LA  UHAWAVAUATSH(LMLHMIIH}H LHZo%HHHH@H LC HEEL-~q%IEHEHI@L L^C L5Eq%ILHDžHDžHDžHDžDžLE HLHJLeLD HLH,L}LD HLH3HB H]HD HHHHut +HVHvHHH躣LHHEHL Lt HB H)B H5n%HHA H NB LH([A\A]A^A_]HBHTHHEHL Lt HB HA H5Sn%HHoA H A Ho?  DUHSPHHuH_%HEHHuHEH;_%t+HHu$H}HPHEHHuH}HtHPH[]H`HHEH;R_%t+HHu$H}HPHEHHuH}HtHPH>  HUHAWAVATSHHL%^%L&H?t [A\A^A_]H@ IH5-#H@ H5Pl%Hyk%LA  IHH;^%u?IL@ HH;^%t(HHu!H;HPHHHuH;HtHPL#L=  HJf.UH]&fDUHSPHHH[]"@ @UHAWAVSHHql%HHHGHuHGHHĘ[A^A_]ÿp? IH#fHnH5#fHnflfpE]E]ffEHEP? HHE(#)EH5)#LH? CLHEH$H5#H#H #L #H}LEHEHD$HpHxHL$H$HuL = 1H5:j%H\%L5? p> IH#fHnH5#fHnflfME`E`ffEHE`> HHE(#)EH5#QH> CQHEH$H5Y#H#H #L #H}LELHEHD$HEHMHL$H$HuL< 1H5Ci%H[%L>>  IL> LŎIƳEt8BIL= L襎IEu IEt H}= Eu uLvH}{= tL= LYfUHAWAVAUATSH(LMLHMIIH}H LHZh%HHHH@H L< HEEL-~j%IEHEHI@L L^< L5Ej%ILHDžHDžHDžHDžDžL> HLHJLeL= HLH,L}L= HLH3H; H]H= HHHHut +HVHvHHH躜LHHEHL Lt H; H); H5g%HH: H N; LH([A\A]A^A_]HBHTHHEHL Lt H; H: H5Sg%HHo: H : Ho8  DUHAWAVAUATSH(LMLHMIIH}H LHe%HHHH@H LR: HEEL-g%IEHEHI@L L9 L5g%ILHDžHDžHDžHDžDžL; HLHʚLeLz; HLH謚L}L\; HLH莚3H 9 H]H0; HHHbHut +HVHvHHH:LHkHEHL Lt H9 H8 H5Re%HHn8 H 8 LH([A\A]A^A_]HBHTHHEHL Lt H8 H*8 H5d%HH7 H O8 H5  DUHAWAVSHxHHtHHHt Hx[A^A_]ÿp68 IH#fHnH#fHnflfMEEffEHE@7 HEfo#fEHed zero.HH6H reachedHH0Hafter itHH(Hefcount HH Hcrease rHHHannot inHHHe_ptr: CHHHintrusivH@>HEH$H5(#H#H #L #H}LEHEHD$HEHMHL$H$HuL5 1H5;b%HT%L67  IƳEt1I9IEuEuu&L=4  H}6 EtH}6 tL6 L4  DUHSPHHHHVH)HfELEHHHH[]UHAWAVAUATSHxMIH}HxHU4 H84 L(H@HtH@HEW)EHEEHaten::emHEEptyH}3 Et H}5 I}1҉84 IH?4 HxHMH5 #L+4 H5#LL%4 H5#LL4 I}L]L}MLW)`H`Lm3 LhMt#HIFHuILPL4 ILL}E11E1HxHEL}LuH}HuvHEHEL5R%LuAG A1҃AH}HuйHEL}ILuHEL9t+HHu$H}HPHEHHuH}HtHPLuHtKH]LmH}2 H]Ht#HHCHuHHPH3 LL2 E1HEH;Q%t+HHu$H}HPHEHHuH}HtHPMt$HIEHuIELPL73 LHx[A\A]A^A_]HoHgIHEHH;VQ%IHhHHHCHHHPH2 H}IH]Ht#HHCHuHHPH2 HEHH;P%t4HHu-HEH8HPHEHHHuHEH8HtHPHEL01HEHEH;vP%HcIEH}[2 H}LmIHEH;+P%t+HHu$H}HPHEHHuH}HtHPLuHEH;O%t+HHu$H}HPHEHHuH}HtHPLuH}u9bH譂H襂LmIH}uBIL"/  IH}t+HHMHAHuH]HHPH1 L.  fUHAWAVSHHI2/ Aƿ+/ HH0HPHCLx H H @H HH HH!LEHEHCfE8t +@EEHMLELHA. 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C#f!! -,1>W- ssC6y!! -,1>W- yy- ss- yyC_~!! -,1>W- yyC c!! -,1>W- yy- yyE]"!! -,1>W   ;      /    +zEL{cc   ;      /    /  +  q   "M Hl- ss"M Hl"M @@ -4561,14 +4625,18 @@ Hl"M Hl"M Hl"M HlPz, GN -RDIPzPzPz4E=++ :S z}5Tl,)Xw +RDIBWv!! +,1>WTqf+ f0 5  +4H 5@T1&L ]& <:Y\[yy +,1>WT}} +1>_]] i->WTPzPzPz4E=++ :S z}5Tl,)Xw O>7UK// F\ tZ6yy "gl  AH  9ncR\    ,) [[8NN Z5*.3e -Q?R55,0#$G1&0u^$UaW- t$%++[^L,%++[^L,%!!%adL,%!! xGpJL,%!! xGpJL,%!! xGpJL,%!! xGpJL,%&&QzTL,%&&QzTL,%++'mpN,) - m#$?%++'mpN,%&&QzTL,%++[^L,%.. UXi,cQF::Bn ,m*>>h V'W*50 -XA^J1'^f j! h2T5,af  " " " O}}}}}}} TH$0L*>>h VRj { +Q?R55,0#$G1&0u^$UaW- t$%++[^L,%++[^L,%!!%adL,%!! xGpJL,%!! xGpJL,%!! xGpJL,%!! xGpJL,%&&QzTL,%&&QzTL,%++'mpN,) - m#$?%++'mpN,%&&QzTL,%++[^L,%.. UXi,QF::Bn ,m*>>h V'W*5c0 +XA^J1'^f j! h2T5,af  " " " O}}}}}}}|*>>h VRj { B [+0@j f}r  < P  @@ -4597,16 +4665,16 @@   QI f  f 18 6I0 XAIe 2 |w -  " " " O}}}}}}}<0 4e0 +  " " " O}}}}}}}dhlxe0 XAIe -2 |!y   " " " O}}}}}}}0 +2 |!y   " " " O}}}}}}}0$ (0 XAIe :, ,     -     q  q 0!  " " " O}}}}}}}YQx    ,&XX )/       +0@j f +     q  q 0!  " " " O}}}}}}} D8 <YQx    ,&XX )/       +0@j f [}r  < P   @@ -4624,223 +4692,114 @@  C   d     -   n   O   !      "  " " " O}}}}}}}Ⱦ̾оܾp!$.0 +   n   O   !      "  " " " O}}}}}}}(,`T0<Xp!$.0 XAIe  ;            v     u   -'   !            "   "   "   O}}}}}}}$0 +'   !            "   "   "   O}}}}}}}LPxT`|0 XAIe ;" "   j ;   -   j; j  }O!"  " " " O}}}}}}}H<$@0 +   j; j  }O!"  " " " O}}}}}}}ptx0 XAIe :, ,     -   P  P 0!  " " " O}}}}}}}<@thDPl0 +   P  P 0!  " " " O}}}}}}}0 XAIe >8 8     -   T  T 0!  " " " O}}}}}}}\`dp0 +   T  T 0!  " " " O}}}}}}}0 XAIe ;- -     7  E  -    C     d  \*!"  " " " O}}}}}}}ĵ0 +    C     d  \*!"  " " " O}}}}}}}$0 XAIe ;    lu '   !          "  -"  "    O  }}}}}}}8,00 +"  "    O  }}}}}}}`dht0 XAIe -2     Zu'! - - - - - - - -" - "  "    - O - -}}}}}}}YQx    ,&0 -XAIe - -;          g  u  - ' - - -! - - - - - -    "  "  "    O  }}}}}}}аİȰ0 -XAIe - ;   -  -        -               " " " O}}}}}}} TH$0L0 -XAIe -5             "  "  "    O  }}}}}}}̭0 -XAIe -;      gu'!         - - -" - - -" -  "    O  }}}}}}},0dX4@\0 -XAIe -5      du'!        " - - -" - - -" -    O  }}}}}}}ت̪Ъ0 -XAIe -5      _u'!        - -" - - -" - - -"    O  }}}}}}}L@(D0 -XAIe - ;  -   - -  - - - -  ,  - -, - -   v - -   u   '   !            " " " O}}}}}}}ptx0 -XAIe -;Q Q      -  - - -   x  u9= xu9=       Y3!    "    "  "  "    O!!}}}}}}}48l`<Hd0 -XAIe -;'  '          -             '!  " " " O}}}}}}}ȣ0 -XAIe - -; -  - -    -       k   - u -  -' -  !            "   "   "     O}}}}}}}0$ (0 -XAIe - ;  -   -  -7  -?   @ d -     - - -   *   !      "      "   " " O}}}}}}}@DxlHTp0 -XAIe ;3 3      -  l'!  " " " O}}}}}}}Ȟ0 +  l'!  " " " O}}}}}}} YQx    ,&0 XAIe -;     " " " O}}}}}}}\`dp0 -XAIe ; "   @  E"       C      -d     *!"  " " " O}}}}}}}Лěț0 +d     *!"  " " " O}}}}}}}0 XAIe ; "  6   E"    9      d  -      wO!"  " " " O}}}}}}} 7 +      wO!"  " " " O}}}}}}}4(,7 bAMd -2 Cg R  " " " O}}}}}}}Ԙؘ7 +2 Cg R  " " " O}}}}}}}7 bAMd -2 Cg  R  " " " O}}}}}}}ptx7 +2 Cg  R  " " " O}}}}}}}7 bAMd -2 Cg ,V  " " " O}}}}}}}8<pd@Lh7 -bAMd -;]~    Zw'!  " " " O}}}}}}} 0 +2 Cg ,V  " " " O}}}}}}}TX\h0 XAIe ;- -      -3    / 0!  " " " O}}}}}}}$(\P,8T0 +3    / 0!  " " " O}}}}}}}0 XAIe 5    Zu'!        "  "  "  -  O  }}}}}}}ܑБԑ0 +  O  }}}}}}}PD ,H0 XAIe -;             "  "  "    O  }}}}}}}LPxT`|0 +;             "  "  "    O  }}}}}}}0 XAIe -2 - 0   " " " O}}}}}}}, $0 +2     Zu'! + + + + + + + +" + "  "    + O + +}}}}}}}@DxlHTp0 XAIe +2 - 0   " " " O}}}}}}} 0 +XAIe 5   & _u'!         "  "  " -    O  }}}}}}}hlp|0 +    O  }}}}}}}\`dp0 XAIe ;   '!        "  "  "    O - }}}}}}}<0 40 + }}}}}}}0$ (0 XAIe - ;  -   - - -  -  - -   -    k - - -  -u   '   !            "   " " O}}}}}}}lpt0 +2      " " " O}}}}}}}0 XAIe -2      " " " O}}}}}}}L@(D0 -XAIe -;  *  *                    v     '!  " " " O}}}}}}}ptxYQx    ,&XX )/       +0@j f +;  *  *                    v     '!  " " " O}}}}}}}4(,YQx    ,&XX )/       +0@j f [}r  < P   @@ -4858,72 +4817,72 @@  "  -  O  }}}}}}}ăȃ̃؃QF::Bn ,m*>>h V'W*5 ,,H1,0 +  O  }}}}}}}PT|XdQF::Bn ,m*>>h V'W*5 ,,H1,0 XAIe -;   " " " O}}}}}}}Ёԁ؁0 +;   " " " O}}}}}}}\`dp0 XAIe ;    o!        "  "  "    O  -}}}}}}}lptI> ,o*>>h V0 +}}}}}}}0$ (I> ,o*>>h V0 XAIe ;    !    !w!         "  "  -"    O  }}}}}}}h~l~~~p~|~~0 +"    O  }}}}}}}, $0 XAIe -;  "k!        "  "  "    O  }}}}}}}}}H}<}}$}@}0 +;  "k!        "  "  "    O  }}}}}}}Լȼ̼0 XAIe  ;         <      -5     F   !            " " " O}}}}}}}{{{{{{{0 +5     F   !            " " " O}}}}}}} TH$0L0 XAIe ;"    < =  "         -  <   5      ZeZZ7z!  " " " O}}}}}}}yyyyyyy0 +  <   5      ZeZZ7z!  " " " O}}}}}}}PD ,H0 XAIe ;          .  <   =    . -    <   5   ZeZZ4k!  " " " O}}}}}}}|wwwwwww0 +    <   5   ZeZZ4k!  " " " O}}}}}}} @480 XAIe  5    /    <=     <5zZZ    Z   -Z   4   k   !          " " " O}}}}}}}uuuuuuu0 +Z   4   k   !          " " " O}}}}}}} $XL(4P0 XAIe ;       < =         < 5      Z   -e     Z   Z4k!  " " " O}}}}}}}sssssss0 +e     Z   Z4k!  " " " O}}}}}}}(,`T0<X0 XAIe ";"""-"! -!! !   -     !&qx !&qxlj ! b ! I!#!####&#&##&#&## &#&#$ &$&$"$ &$&$"$ &%&%"% %%%%%%&%O&&}}}}}}}DqHq|qpqLqXqtq} +     !&qx !&qxlj ! b ! I!#!####&#&##&#&## &#&#$ &$&$"$ &$&$"$ &%&%"% %%%%%%&%O&&}}}}}}}а԰ذ} i  < P   @@ -4949,33 +4908,34 @@    QI f  f 18 6IQI f  f 18 6I0 XAIe -2      " " " O}}}}}}}mmmmmmmYQx    ,&XX )/         ;((0 +2      " " " O}}}}}}}@DxlHTpYQx    ,&XX )/         ;((0 XAIe ;    '!        "  "  "  -  O  }}}}}}}4k8klk`k<kHkdk7 +  O  }}}}}}}ĪȪԪ7 bAMd -2 Cg N  " " " O}}}}}}}ij4j(jjj,jYQx    ,&7 +2 Cg N  " " " O}}}}}}}YQx    ,&7 bAMd -2 Cg R  " " " O}}}}}}}hhlhhhph|hh0 +2 Cg R  " " " O}}}}}}}, $0 XAIe ;   '!        "  "  "    O - }}}}}}}gg<g0g gg4g0 -XA1'^f t! r2*\.  " " " O}}}}}}}eefeeee "6 q?x3 + }}}}}}}Ȧ0 +XA1'^f t! r2*\.  " " " O}}}}}}}TX\h "6 q?x3  js 2+73 - js73 - jsF#i"!! -,4AZ;3 - js;=2cc6,=2gg6,/ J"!! + js7qf <@ + p<4F#i"!! +,4AZYN<Uf<,qf <@ + p<4YN<Uf<,. J"!! +,4J]. =2cc6,=2gg6,/ J"!! ,4A]I- msM%!! ,4Ab5 ut <   *  @@ -4984,15 +4944,18 @@ A ',  7 G     -*    *    *    y  SO- msJ"!! -,4AZ14 - sJK"!! -,4A]@4 - s@- js- jsFe"!! +*    *    *    y  SOA89 a   } 4) Q 34E}  /\ ) Q 3E;== cfg -?7I}LYY}} +,1>W- msJ"!! +,4AZ1qg<G + <4JK"!! +,4A]YN<^m<,qg<G + <4YN<^m<,- jsE]"!! +,1>Wqf0 k0 5# +4H 5@- jsFe"!! ,4AZ5 9 $ 5, 4H  $  5  @1- psJk"!! ,4Ab5 uI < $ $ ;', 4G @@ -5011,29 +4974,40 @@ 4G    $    -$    ;   P   D     ]9- js- js1=3G6,- js3 - jsE]"!! -,1>W115 - J"!! -,4A]A5 - A- jsF'm"!! +$    ;   P   D     ]9- jsF5{"!! +,4Ab2 d9 $ 5, +4H + +$ + +7  + , +  + +L- jsC2u!! +,1>W1=3G6,- jsmd~~ <> + j<4E]"!! +,1>WYN<U_<,1qg<] +    <   4J"!! +,4A]YN<to<,qg<] +    <   4YN<to<,- jsF'm"!! ,4AZ<. sJ`"!! ,4A]==3G6,4  yJs"!! ,4A]- js9=4=J,=4@G,3 - ps3 - psF#i"!! -,4AZ19=2gg6,=2gg6,=2gg6,=2gg8,=2gg6,=2gg6,=2gg8,=2gg6,- js- js7- js73 + psqf <@ + p<4F#i"!! +,4AZYN<U_<,9=2gg6,=2gg6,mdgg<55<[,}ugg<55<bZ,=2gg6,mdgg<55<[,}ugg<55<bZ,=2gg6,- js- js7- js73  js73  js73  js7- vyJ"!! ,4A]3  vyG!! -,4A]1=4<E,=4<E,=4<],=4<b,=4<],=4<b,- js9- js9- ps1- js1=3z54,=3z54,=4=X,=465,=3z54,=3z54,=4=X,=465,=4<E,=4<E,=4<G,=4<G,3 +,4A]1=4<E,=4<E,=4<],=4<b,=4<],=4<b,- js9- js9- ps1- js1=3z54,A9}847%%(<dg38E:<[7)!00IWsvW7=465,=3z54,A9}847E:<[7=465,=4<E,=4<E,=4<G,=4<G,3  psF+q"!! ,4AZ43  ps43  ps43  js73 @@ -5050,60 +5024,70 @@   @   d      -     *   !      "      " " " O}}}}}}}FFFFFFF0 +     *   !      "      " " " O}}}}}}}|||||||0 XAIe ; "   @   E"      C      d  -       *!"  " " " O}}}}}}}DDEDDDD0 +       *!"  " " " O}}}}}}}zz{zzz{0 XAIe -;     7   -< -8    -@  - -<   ]  - -  -<      <   *!"  " " " O}}}}}}}BB CCBBCYQ::QVn ,z7*>>h V-0:jm=0 + ;   7 @     *   !   + + + +" + + + +  "  "  "    O  }}}}}}}LyPyyxyTy`y|y0 XAIe -;  -  -      C/   -  -  -@ /   *!"  " " " O}}}}}}}@@@@@@@I> ,o*>>h V0 +;-  -      C    +  +  +@     *!"  " " " O}}}}}}}wwwwwww0 XAIe - ;   '  '             l       '!  " " " O}}}}}}}>>>>>>>0 +;E E {u  9 1  + +   {u91       Y!"  " " " O}}}}}}}uuuuuuu0 XAIe +;Q Q  +  {  u  97 {u97       Y!"  " " " O}}}}}}}tsxsss|sss0 +XAIe + ;   '  '             l       '!  " " " O}}}}}}}qq rrqqr0 +XAIe ;3 3       -     v'!  " " " O}}}}}}}==8=,===0=0 +     v'!  " " " O}}}}}}}PpTpp|pXpdpp0 XAIe ;E E qqkk  9 8 -       qqkk98          \!""""" " "  " " " " "!"! "!"!"! "!"!""""O""}}}}}}}:::::::0 +       qqkk98          \!""""" " "  " " " " "!"! "!"!"! "!"!""""O""}}}}}}}mmnnmm n0 XAIe ";"!!Q! Q! !!!    qq  n  -k  9 A   !!!   qqnk9A          \ " """!"%"%""%"%""# %#%## %#%#"$ %$%$"$ %$%$"$ %$%%%%%%O&&}}}}}}} 88D8888 8<80 +k  9 A   !!!   qqnk9A          \ " """!"%"%""%"%""# %#%## %#%#"$ %$%$"$ %$%$"$ %$%%%%%%O&&}}}}}}}\k`kkkdkpkk0 XAIe :, ,      -     x x 0!  " " " O}}}}}}} 6$6X6L6(646P60 +     x x 0!  " " " O}}}}}}}pitiiixiii0 XAIe :, ,      -     x x 0!  " " " O}}}}}}}4484l4`4<4H4d40 +     x x 0!  " " " O}}}}}}}ggggggg0 XAIe :8 8     -       x  x 0!!!!! !! !!"  "  "   ! O!!}}}}}}}<2@2t2h2D2P2l20 +       x  x 0!!!!! !! !!"  "  "   ! O!!}}}}}}}eeeeeee0 XAIe -;- -  x9# <t     x  9 # <    <a<|!"  " " " O}}}}}}}0 0T0H0$000L00 +;- -  u 9# +  + +u  9 #     `!"  " " " O}}}}}}}ccdcccc0 XAIe -;E E    {  9 -?     {9?    *!"  " " " O}}}}}}}D.H.|.p.L.X.t.0 +;E E    {  9 +2     {92    |!"  " " " O}}}}}}}aa,b bab$b0 XAIe ;     ]u'!    @@ -5121,56 +5105,65 @@  O  -}}}}}}},,,,,,,0 +}}}}}}}t`x```|```0 XAIe +;      gu'!         + + +" + + +" +  "    O  }}}}}}}^^ __^^_0 +XAIe  ;             l     u   -'   !            "   "   "   O}}}}}}},+0+d+X+4+@+\+0 +'   !            "   "   "   O}}}}}}}P]T]]|]X]d]]0 XAIe ;" "      -T     P ~0!  " " " O}}}}}}}d)h)))l)x))0 +T     P ~0!  " " " O}}}}}}}[[[[[[[0 XAIe ;" "      -T     P ~0!  " " " O}}}}}}}'''''''0 +T     P ~0!  " " " O}}}}}}}YYYYYYY0 XAIe :, ,      -    T  T 0!  " " " O}}}}}}}%%&%%%%0 +    T  T 0!  " " " O}}}}}}}WW$XXWXX0 XAIe ;9 9 qkk9-    -     q  kk9-        \!"  " " " O}}}}}}}#######0 +     q  kk9-        \!"  " " " O}}}}}}}UUUUUUU0 XAIe ;E E  qqk  9 5   -     qqk95         \!    "    "  "  "    O  }}}}}}}4!8!l!`!<!H!d!0 +     qqk95         \!    "    "  "  "    O  }}}}}}}XS\SSS`SlSS0 XAIe  ;               b     -u   '   !            "   "   " O}}}}}}}0 +u   '   !            "   "   " O}}}}}}}QQRQQQQ0 XAIe  ;   '  '          -     v       '!  " " " O}}}}}}}H<$@0 +     v       '!  " " " O}}}}}}}4P8PlP`P<PHPdP0 XAIe  ;         @@ -5182,176 +5175,171 @@  ,     v  -   u   '   !            " " " O}}}}}}}lpt0 +   u   '   !            " " " O}}}}}}}NNNNNNN0 XAIe + ;  +   + +  + + + +  ,  + +, + +   v + +   u   '   !            " " " O}}}}}}}LL$MMLMM0 +XAIe ;"   "      0  -   ,      u'!  " " " O}}}}}}}0 +   ,      u'!  " " " O}}}}}}}<K@KtKhKDKPKlK0 XAIe  ;               b     -u   '   !            "   "   " O}}}}}}}04h\8D`0 +u   '   !            "   "   " O}}}}}}}IIIIIII0 XAIe - ;    D  <mD<v   '   !      - - - - - - -"  "  "    O  }}}}}}}0 +;      " " " O}}}}}}}XH\HHH`HlHH0 XAIe -;   "    "s    !       - - - - - -" - - "  "    O  }}}}}}}L@(D0 +;                   + + +" + + +"  "    O  }}}}}}}FF GGFFG0 XAIe ;      bu '   !          "  -"  "    O  }}}}}}}0 +"  "    O  }}}}}}}\E`EEEdEpEE0 XAIe -;E E qqk  9 7 -  - -<      qqk97 <        <a<!!!!!" !! !!" ! ! " ! ! " ! ! !!!!O!!}}}}}}}PD ,H0 +;E E qnk  9 7      qnk97        !"  " " " O}}}}}}}LCPCCxCTC`C|C0 XAIe -;] ]     q - -q  q  9 Z  qqq9Z        *! # #  # # " # # ! #!#!"! #!#!"! "!"!"" """"""#"O##}}}}}}}( 0 +;] ]     q + +q  q  9 I  qqq9I        !""""" " "  " " " "!"!"! "!"!"! "!""""""O#"}}}}}}}$A(A\APA,A8ATA0 XAIe ; "   @   E"       C      -d        *!"  " " " O}}}}}}},0dX4@\0 +d        *!"  " " " O}}}}}}}`?d???h?t??0 XAIe ;- -     @  c  -    C     d  \*!"  " " " O}}}}}}}\ `   d p  0 +    C     d  \*!"  " " " O}}}}}}}=======0 XAIe ;9 9   uu  9 )   -     uu9)      Y!"  " " " O}}}}}}}8 -< -p -d -@ -L -h -0 +     uu9)      Y!"  " " " O}}}}}}}l;p;;;t;;;0 XAIe ;E E     {  u - 9 4       {u94       Y!"  " " " O}}}}}}} @480 + 9 4       {u94       Y!"  " " " O}}}}}}}<9@9t9h9D9P9l90 XAIe ;9 9 {u  9 ) -     {  u9)       Y!"  " " " O}}}}}}}0 +     {  u9)       Y!"  " " " O}}}}}}}77P7D7 7,7H70 XAIe ?E E      -    e e 0!  " " " O }}}}}}}H<$@0 +    e e 0!  " " " O }}}}}}}D5H5|5p5L5X5t50 XAIe ;Q Q       -      i  i 0!!!!! !! !!"  "  "   ! O!!}}}}}}}04h\8D`0 +      i  i 0!!!!! !! !!"  "  "   ! O!!}}}}}}}d3h333l3x330 XAIe - ;         -D -   -   -  - < - - -r -  -D     <   v  '!  " " " O}}}}}}}x|0 + ;                 + + + + + +"  "  "    O  }}}}}}}2282,222020 XAIe -;6  6       '   ' o'!  " " " O}}}}}}}4(,0 + ;   3  3                " " " O}}}}}}}00000000 XAIe -;3 3           v'!  " " " O}}}}}}}dhlx0 +;3 3           v'!  " " " O}}}}}}}..$//.//0 XAIe -;6 6         D   <D<'!  " " " O}}}}}}}0 + ;   3  3               " " " O}}}}}}}|-------0 XAIe -;Q Q       6  6s'!  " " " O}}}}}}}PD ,H0 +;N N       '  '   " " " O}}}}}}}++,, ,+,$,0 XAIe -;? ?       !  ! '!  " " " O}}}}}}}tx|0 +;? ?       !  ! '!  " " " O}}}}}}}P*T**|*X*d**0 XAIe :8 8        -    W W 0!  " " " O}}}}}}}0 +    W W 0!  " " " O}}}}}}}|(((((((0 XAIe :8 8        -    W W 0!  " " " O}}}}}}}0 +    W W 0!  " " " O}}}}}}}&&&&&&&0 XAIe ?E E       -      _  _ 0!  " " " O}}}}}}}$0 +      _  _ 0!  " " " O}}}}}}}$$%$$$$0 XAIe BQ Q       -k  q  9 D   kq9D        \! # #  # # " # # ! #!#!"! #!#!"! #"#""" """"""#"O##}}}}}}}0 +k  q  9 D   kq9D        \! # #  # # " # # ! #!#!"! #!#!"! #"#""" """"""#"O##}}}}}}}t"x"""|"""0 XAIe $B$$$]$ ]$ $  $## # #  -  k  q  q9P $###"kqq9P          \"%"$%%!%(%(%%(%(%"% (%(&& (&(&"& (&(&"' ('('"' ('('''((O((}}}}}}}PD ,H0 +  k  q  q9P $###"kqq9P          \"%"$%%!%(%(%%(%(%"% (%(&& (&(&"& (&(&"' ('('"' ('('''((O((}}}}}}},   $ 0 XAIe '?'''i' !i' ' ! !''& & & - !      k   qq9\#"'&&&%   kqq9\"!          !"!\"""%(%(((!(+(+((+(+("( +)+)) +)+)") +)+)"* +*+*"* +*+*+*++O++}}}}}}}0 + !      k   qq9\#"'&&&%   kqq9\"!          !"!\"""%(%(((!(+(+((+(+("( +)+)) +)+)") +)+)"* +*+*"* +*+*+*++O++}}}}}}}hlp|0 XAIe BE E      -u  9 4    u94      Y!"  " " " O}}}}}}}hlp|0 +u  9 4    u94      Y!"  " " " O}}}}}}}DH|pLXt0 XAIe -BE E      - -u  9 7  u97      !"  " " " O}}}}}}}lptQF44 ?n ,m*>>h V)!**@NjmW7%%(<dg380 +BE E     + +u  9 7 u97      3!"  " " " O}}}}}}}TX\h0 XAIe -!;!i i        {9i   {9i      !"!3"""!"%"%""%"%""# %#%## %#%#"# %#%#"$ %$%$"$ %$%$%%%%O%%}}}}}}}I> ,o*>>h V0 + ;   i  i        +     u9\    u9\      !!!!!!$!$!!$!$!"" $"$"" $"$""" $"$#"# $#$#"# $#$#$$$$O$$}}}}}}}@DxlHTp0 XAIe ?Q Q      -  u  9 =    u9=       Y!!!!!" !! !!" !! " ! ! " ! ! !!!!O!!}}}}}}} 0 +  u  9 =    u9=       Y!!!!!" !! !!" !! " ! ! " ! ! !!!!O!!}}}}}}}H<$@0 XAIe "?"""]" ]" "  ""!!   -  k  q  9P "!!! kq9P         \!#!###!#&#&##&#&#"# &$&$$ &$&$"$ &$&$"% &%&%"% &%&%&%&&O&&}}}}}}}tx|0 +  k  q  9P "!!! kq9P         \!#!###!#&#&##&#&#"# &$&$$ &$&$"$ &$&$"% &%&%"% &%&%&%&&O&&}}}}}}}0 XAIe  ;   "    7   ?"    C      d  -        *  !"  " " " O}}}}}}}0 +        *  !"  " " " O}}}}}}}0$ (0 XAIe ;- -   @  c    -=     d  \*!"  " " " O}}}}}}}0$ ( "6 q?x- yy 2+- ss- yy- ss- yy- ss- yy- yyF9"!! +=     d  \*!"  " " " O}}}}}}}48l`<Hd "6 q?x(  Z4   nA    M     A   A   "6 q?x- yy 2+- ssBWv!! +,1>W- yy- ss- yy- ssC6y!! +,1>W- yy- yyF9"!! ,4Ab8 u  <   $  $  ; D ',  4G   @@ -5365,11 +5353,12 @@ 7 G -   *   * * DMHNw- yy- |- ||- |- ||- |- ||- yy- yyFb"!! +   *   * * DMHNw- yy- |- ||- |- ||- |- ||- yyE]"!! +,1>W- yyFb"!! ,1>W- yy- ||F'm"!! ,4AZ8 < $ $ 5%, 4H  $ @@ -5383,47 +5372,57 @@ ,4A]H  <   *  *  *  *  A D ')  7 G -   *   *   * * APQi- yy- ||. FA"!! +   *   *   * * APQi\\ h->WT- yy- ||. FA"!! ,4Ge8 < $ $ 5%, 4H    $    $    5   G. - ||- ||Jh"!! ,4J]- ||CO!! -,4G]- yy- ss- yy- yy- yy- ss)XXK,- |- ss- yyFe"!! +,4G]- yy- ss- yy- yyFb"!! +,4AZ5 f < $ 55, +4H  $    5   ; +  +, +    T- yyC\{!! +,1>W- ss)XXK,- |- ss- yyFe"!! ,1>W- yyC$g!! ,1>W- yyC c!! ,1>W- ||F'm"!! -,1>W)YYN,4- yy- ss- yy- yy- yy- ss- yyFe"!! +,1>W)YYN,4- yy- ss- yy- yyC,o!! +,4AZ- yy- ss- yyFe"!! ,4AZ5 9 $ 5, 4H  $  5 @- yy- yyF$j"!! ,4AZ? | <   $  $  ; D'/  4G    $    $    ;   P   D     ]- ||F&l"!! -,4AZ- yy- ss- yy- ss- yy- yy- ss- yy- ss- ss- yy- yy- yy- ||F-s"!! +,4AZ- yy- ss- yy- ss- yy- yy- yyC c!! +,1>W- yy- ss- yy- ssXXg}$ +SgXXg}$ +Sg- yy- ss- yy- yy- yy- ss- yy- ||F-s"!! ,4AZ; <   *  *  *  A .,  7H    *    *    *    A   X- yy- ||F-s"!! -,4AZ- yy- ss- yy- ss- ss- yy- yy- yyFe"!! +,4AZ- yy- ss- yy- ss- yy- ss- yy- yy=2 @C<T<4- yyFe"!! ,1>W- ss- yy- ss- yy- yyC\{!! ,1>W2 c < $ 55) 4H  $    5   ;   , -    T- yy- ss- yy- ss- yy- ss- yy- ss/ - yy- yy- ss- yy- ||- yy- yyGQ!! +    T- yy- ss- yy- ss- yy- ss- yy- ss/ - yy- yy- ss- yy- ||- ss- yy- yyGQ!! ,1>W. F&l"!! ,4AZ8 < $ $ 5%, 4H  $ @@ -5443,11 +5442,11 @@ *  A D '/  - 7 G    *   *   *   APRo- ss- yy- ss- yy- yy- yy- yy- ss- yy- ss- yy- yy- ||Jr"!! + 7 G    *   *   *   APRo- ss- yy- ss- yy- yy=2 @C<T<4- yy- yy- ss- yy- ss- yy- yy- ||Jr"!! ,4Ab8 J <   *  *  *  A .,  7 G     @@ -5477,20 +5476,23 @@  $   $   ;   J  9    $    T- yy- yy- yy- ||FC"!! -,4A]- ss4- ss- ss- yy- yy- ||F4z"!! +,4A]- ssBWv!! +,1>Wqf+ f0 5  +4H 5@- ss- ss3 + ss4- ss- ss- yy- yy- ss- ||F4z"!! ,4A]O  <   *  *  *  *  A D '/  7 G    *   *   * * APUi- yyE]"!! ,1>Ww0 k0 5C -4H 5@^^ j->WT- ||F.t"!! +4H 5@^^ j->WT=2 @C<T<4- ||F.t"!! ,4AZ- ss- yy- ss- yy- ss- yyC`!! ,1>W- yy- yyF f"!! ,4AZ? |  @@ -5501,30 +5503,37 @@  $  ; D D '/  - 4 G    $   $   ;  K  MPe- yy- ss- yy- yy- ss- yy- ss- yy- ss- ss- yy- yy- yy- ss- yy- yy/ - yyFb"!! -,1>W- ss- yy- ss- yy- yy- yy- yy- yy- yy- yy- yy- yy- yy- yy- yy- ||F'm"!! -,4AZ- yy/ - yy- ||- yy- ||- yy/ - yy- ||- ss- yy- ss- yy- ||F.t"!! -,4AZ- yy- yy- yy- ||- ||- ss- yy- ss- yy- yy- ss- yy- yyE]"!! -,1>W- ss- yy- yy- ||F2x"!! + 4 G    $   $   ;  K  MPe- yy- ss- yy- yy- ss- yy- ss- yy- ss- ss- yyXXg}$ +Sg- yy- yy- ss- yy- yy/ =2 @C<T<4- yyFb"!! +,1>W- ss- yy- ss- yy- yy- yy- yy- yy- yy- yy- yy- yy- yy- yy- yy=2 @C<T<4- ||F'm"!! +,4AZ- yy/ XXg}$ +Ocss$ +]q- yy=2 @C<T<4- ||- yy=2 @C<T<4- ||- yy/ - yy=2 @C<T<4- ||- ss- yy- ss- yy- ||F.t"!! +,4AZ- yy- yy- yyC_~!! +,1>W- ||- ||- ss- yy- ss- yy- yy- ss- yy- yyE]"!! +,1>W- ss- yy- yy- yy- ||F2x"!! ,4AZ? |  <   *  *  *  *  A D D '/  7 G    * * * * APHZq4- yy- yy- yyF f"!! ,4AZ8 9 $ 5, 4H  $  5   @- yyFe"!! ,4AZ? }9 $ 5, -4H  $  5 @- yy- ss- yy- yy- yy- yy- yyC%h!! +4H  $  5 @- yy- ss- yy- yy- yy- yyXXg}$ +Oc[[j$ +Xl- yyC%h!! ,4AZ43  ss3  ss- ssBWv!! ,1>Wqf+ f0 5( -4H 5@- ss- yy4- yyC_~!! +4H 5@- ss=2 @C<T<4- yy4- yyC_~!! ,1>W- ||C*m!! -,4AZ- yy- ss- yy- ss- yy- ss- yy- ||Jr"!! +,4AZ. C#f!! +,1>W- yy- ss- yy- ss- ss- yy- ss- yy- ||Jr"!! ,4Ab8 yY <   *  *  *  A .,  7 G     @@ -5544,11 +5553,11 @@    $    $    ;   P   D   ]- yy- yyC c!! -,1>W- ||F/u"!! +,1>W- yy- ||F/u"!! ,4AZ; <   *  *  *  A .,  7H    *  @@ -5557,18 +5566,21 @@ ,4AZ- ||F*p"!! ,4AZ- yy- ss- yy- ss- ss- ss- yy- yy- yy. - ||F&l"!! ,4AZ- ss- yy4- yy- ss- yy- ss- yy- ss- yy- ss- yyC_~!! ,1>W0 }9 $ 5) 4H  $  5 @. F8~"!! -,4AZ- yy- yy- yy- ||- ss- yy- ||C.q!! +,4AZ=2 @C<T<4- yy- yyXXg}$ +Sg=2 @C<T<4- yy=2 @C<T<4- ||- ss- yy- ||C.q!! ,4AZ3  ss. . F*p"!! -,4AZ- yy- yy- ss- yy- yyFe"!! +,4AZ- yy- ss- yy- ss- yy- yyFe"!! ,1>W- vs- yy. F5{"!! -,4AZ- yy- yy- yy- yy- yy- ||F'm"!! -,4AZ- ||F'm"!! -,4AZ- yy- ss- yy- yy- ss- yy- ss- yy- yy. F,r"!! +,4AZ- yy- yy- ss- yy- yyXXg}$ +Ocss$ +]q- yy  :%+- ||F'm"!! +,4AZ=2 @C<T<4- ||F'm"!! +,4AZ- yy- ss- yy- yy- ss- yy- ss- yy 9#+ 9#+- yy. F,r"!! ,4AZ? | <   *  @@ -5576,46 +5588,47 @@ *  A D '/  - 7 G    *   *   *   APSo- yy- yy- ||- yy- ||C#f!! -,1>W- ||- ||F6|"!! + 7 G    *   *   *   APSo- yy- yy=2 @C<T<4- ||F8~"!! +,4AZ- yy- ||C#f!! +,1>W- ||=2 @C<T<4- ||F6|"!! ,4AZ- yy- yy- yy- yy- yy- ss- yy- yyFe"!! -,1>W- ||- yy- yy- yy- yy. - yy- yy0 +,1>W- ||- yy- yy- yy- yy- yy. - yy- yy- yy- ss0 XAIe ; "     "        /  -  /         ~0!  " " " O}}}}}}}hhhhhhh0 +  /         ~0!  " " " O}}}}}}} $XL(4P0 XAIe ; "     "         /  -  /         ~0!  " " " O}}}}}}}ff0g$gg g(g0 +  /         ~0!  " " " O}}}}}}}ptx0 XAIe ;9 9       -       ^ ^ 0!  " " " O}}}}}}}eePeDe e,eHe0 +       ^ ^ 0!  " " " O}}}}}}}Ȉ0 XAIe ;9 9      -       ^ ^ 0!  " " " O}}}}}}}8c<cpcdc@cLchc0 +       ^ ^ 0!  " " " O}}}}}}}܆Ć0 XAIe ;- -       -     2  2 0!  " " " O}}}}}}}|aaaaaaa0 +     2  2 0!  " " " O}}}}}}}, $0 XAIe ;- -        -   2  2 0!  " " " O}}}}}}}_______0 +   2  2 0!  " " " O}}}}}}}8<pd@Lh0 XAIe  ;       bu  '   !          " -  "  "    O  }}}}}}}4^8^l^`^<^H^d^0 +  "  "    O  }}}}}}}؁܁0 XAIe  ;           g   u @@ -5624,32 +5637,32 @@ '   !  -         "  "  "    O}}}}}}}\\\\\\\0 +         "  "  "    O}}}}}}} $XL(4P0 XAIe -;9 9                 " " " O}}}}}}},[0[d[X[4[@[\[0 +;9 9                 " " " O}}}}}}}~~~~~~~0 XAIe ;" r6 ]"        9       -       O!"  " " " O}}}}}}}DYHY|YpYLYXYtY0 +       O!"  " " " O}}}}}}}|||||||0 XAIe ;- - r6"    -u  9 "        Y|!"  " " " O}}}}}}}PWTWW|WXWdWW0 +u  9 "        Y|!"  " " " O}}}}}}}zz{zzzz0 XAIe ;      bu '   !          "  -"  "    O  }}}}}}}UUUUUUU0 +"  "    O  }}}}}}}<y@ytyhyDyPyly0 XAIe  ;   @@ -5660,71 +5673,85 @@      -        "  "  "    O}}}}}}}lTpTTTtTTT0 +        "  "  "    O}}}}}}}wwxxwwx0 XAIe ;    bu '   !          "  -"  "    O  }}}}}}}RRS SRRS0 +"  "    O  }}}}}}}Xv\vvv`vlvv0 XAIe  ;      bu   '   !          -"  "  "    O  }}}}}}}TQXQQQ\QhQQ0 +"  "  "    O  }}}}}}}ttutttt0 XAIe  ;           g     u   -'   !            "   "   "    O}}}}}}}OOPOOOO0 +'   !            "   "   "    O}}}}}}}@sDsxslsHsTsps0 XAIe + ;  +   + + +  +  + +   +    k + + +  +u   '   !            "   " " O}}}}}}}qqqqqqq0 +XAIe ;    ]u'!         "  "  -"    O  }}}}}}}HNLNNtNPN\NxN0 +"    O  }}}}}}}(p,p`pTp0p<pXp0 XAIe  ;             b     u   -'   !            "   "   "    O}}}}}}}LLLLLLL0 +'   !            "   "   "    O}}}}}}}nnnnnnn0 XAIe  ;         <         -   u   '   !            " " " O"}}}}}}}KKPKDK K,KHKQF44 ?n ,m*>>h V GJe%DvV0 +   u   '   !            " " " O"}}}}}}}ll0m$mm m(mQF44 ?n ,m*>>h V GJe%DvV0 XAIe  ;    @@ -5733,39 +5760,42 @@   '   -!            "   "  "    O}}}}}}}HHIHHHH0 +!            "   "  "    O}}}}}}}jjjjjjj0 XAIe + +; +  + +   +      g    + u +  +'   !            "   "   "   O}}}}}}} i$iXiLi(i4iPi0 +XAIe +;     " " " O}}}}}}}gghgggg0 +XAIe  ;   $  $                    -        '   !   " " " O}}}}}}}(G,G`GTG0G<GXG0 +        '   !   " " " O}}}}}}}$f(f\fPf,f8fTf0 XAIe  ;   !  !                    -        '   !        " " " O}}}}}}}EEEEEEE0 +        '   !        " " " O}}}}}}}ddddddd0 XAIe - -; - - -  - D  <mD  - <   v -  -' -  -!            "   "   "    O}}}}}}}CCDDCCD0 +;           "  "  "    O + }}}}}}}(c,c`cTc0c<cXc0 XAIe ;         @@ -5784,23 +5814,23 @@   O  -}}}}}}}tBxBBB|BBB0 +}}}}}}}aaaaaaa0 XAIe  ;   "    7   ?"    C     d     -      *   !   "  " " " O}}}}}}}@@@@@@@0 +      *   !   "  " " " O}}}}}}} ``D`8`` `<`0 XAIe  ;             /     -/     v     u  '!  " " " O}}}}}}}??P?D? ?,?H?0 +/     v     u  '!  " " " O}}}}}}}h^l^^^p^|^^0 XAIe  ;    @@ -5808,71 +5838,84 @@      q     u   '   -!            "   "   "    O}}}}}}}=======0 +!            "   "   "    O}}}}}}}\\]\\\]0 XAIe  ;   '  '               -v        '   !     " " " O}}}}}}};; <<;;<0 +v        '   !     " " " O}}}}}}}8[<[p[d[@[L[h[0 XAIe ;- -      -    W W 0!  " " " O}}}}}}}::L:@::(:D:0 +    W W 0!  " " " O}}}}}}}dYhYYYlYxYY0 XAIe ;  '  '           -            '!  " " " O}}}}}}}p8t888x8880 +            '!  " " " O}}}}}}}WWWWWWW0 XAIe  ;   '  '              -           '!  " " " O}}}}}}}66766660 +           '!  " " " O}}}}}}}V VTVHV$V0VLV0 XAIe ;- -      -   2   2 0!  " " " O}}}}}}}55H5<55$5@50 +   2   2 0!  " " " O}}}}}}}`TdTTThTtTT0 XAIe ;- -     -     2  2 0!  " " " O}}}}}}}T3X333\3h330 +     2  2 0!  " " " O}}}}}}}RRRRRRR0 XAIe  ;                 l     -u   '   !            "   "   " O}}}}}}}11111110 +u   '   !            "   "   " O}}}}}}} QQDQ8QQ Q<Q0 XAIe ;9 9   7    -   @  d  \*!"  " " " O}}}}}}}//00$00 0(00 +   @  d  \*!"  " " " O}}}}}}}HOLOOtOPO\OxO0 XAIe ;   -   " " " O}}}}}}}.......0 +   " " " O}}}}}}}MM(NNMN N0 XAIe ;9 9      -    '!  " " " O}}}}}}},-4-(---,-0 +    '!  " " " O}}}}}}}LLPLLxLTL`L|L0 XAIe -;  k!  " " " O}}}}}}}+++++++I> ,o*>>h V0 +;  k!  " " " O}}}}}}}JJ,K KJK$KI> ,o*>>h V0 XAIe -;$ $          D -  -    <      D    <'!  " " " O}}}}}}}d)h)))l)x))0 + +; + + +$ + +$ + + + + + +  +  + +                 "   "   "  O}}}}}}}I I@I4III8I0 XAIe  ;                 k     -u   '   !            "   "   " O}}}}}}}''(''''0 +u   '   !            "   "   " O}}}}}}}pGtGGGxGGG0 XAIe  ;       @@ -5880,58 +5923,109 @@           k     -u   '   !            "   " " O}}}}}}}4&8&l&`&<&H&d&0 +u   '   !            "   " " O}}}}}}}EEFFEEF0 XAIe - ;    -   - D     <r D    - < - - -v   '   !            "   "   " O}}}}}}}|$$$$$$$0 +;      + + + + + + + +" + +" +  + " +  +  +  + +O + +}}}}}}}tDxDDD|DDD0 XAIe - ;  -   -    D  - -  -  <   w  - D - -   <   v   '   !          " " " O}}}}}}}"""""""0 +;             " + +" + + +" + +   O  }}}}}}}CCHC<CC$C@C0 XAIe +;     ]u '   !       + + + + +" + + +"  "    O  }}}}}}}AAAAAAA0 +XAIe +?- -  V  c +  +S     d   \*!"  " " " O}}}}}}}??@@??@0 +XAIe +5      du'!        " + + +" + + +" +    O  }}}}}}}L>P>>x>T>`>|>0 +XAIe ;    <   u '   !          "  -"  "    O  "}}}}}}}8!<!p!d!@!L!h!0 +"  "    O  "}}}}}}}<<<<<<<0 XAIe -;3 3              v  '!  " " " O}}}}}}}0 +;3 3              v  '!  " " " O}}}}}}}(;,;`;T;0;<;X;0 XAIe + ;  +   +  +7  +?   @ d +     + + +   *   !      "      "   " " O}}}}}}}p9t999x9990 +XAIe ;    lu '   !          "  -"  "    O  }}}}}}}L@(D0 +"  "    O  }}}}}}}77887780 XAIe + ;   $  $        +  +  +  +l        '   !        " " " O}}}}}}}X6\666`6l660 +XAIe  ;   '  '               -v        '   !     " " " O}}}}}}}|0 +v        '   !     " " " O}}}}}}}44444440 XAIe -; / 2      " " "  O  }}}}}}}$(\P,8T0 +; / 2      " " "  O  }}}}}}}h3l333p3|330 XAIe -;  k!  " " " O}}}}}}}I> ,o*>>h V0 +;  k!  " " " O}}}}}}}22H2<22$2@2I> ,o*>>h V0 XAIe  ;        q   u @@ -5941,51 +6035,51 @@   !   -        "  "  "    O}}}}}}}0 +        "  "  "    O}}}}}}}/040(000,00 XAIe ;- -       -    S S 0!  " " " O}}}}}}}0 +    S S 0!  " " " O}}}}}}}(.,.`.T.0.<.X.0 XAIe ;- -      -    S S 0!  " " " O}}}}}}}H<$@0 +    S S 0!  " " " O}}}}}}}T,X,,,\,h,,0 XAIe  ;                l     -u   '   !            "   "   "  O}}}}}}}x|0 +u   '   !            "   "   "  O}}}}}}}*******0 XAIe  ;   $  $             -l        '   !          " " " O}}}}}}}$0 +l        '   !          " " " O}}}}}}}0)4)h)\)8)D)`)0 XAIe ;" r6 ]"     9       -       O!"  " " " O}}}}}}}<0 40 +       O!"  " " " O}}}}}}}H'L''t'P'\'x'0 XAIe ;  "     4   ?"     =       d  -       *!"  " " " O}}}}}}}@ D x l H T p 0 +       *!"  " " " O}}}}}}}%%%%%%%0 XAIe ;  +     @@ -5993,85 +6087,74 @@    "  "  "    O  -}}}}}}}       0 +}}}}}}},$0$d$X$4$@$\$0 XAIe ;           "  "  "    O  -}}}}}}} - - - - - - -0 +}}}}}}}""#""""0 XAIe ;* *          -            '!  " " " O}}}}}}}   0 +            '!  " " " O}}}}}}}!!P!D! !,!H!0 XAIe ;- -       4     -  @     d  \*!"  " " " O}}}}}}}<0 40 +  @     d  \*!"  " " " O}}}}}}}HLtP\x0 XAIe ; "  4@"       @      -     *!"  " " " O"}}}}}}}<@thDPl0 +     *!"  " " " O"}}}}}}}0 XAIe ;- -  @  c   -4     ^     \ *!"  " " " O}}}}}}}0 +4     ^     \ *!"  " " " O}}}}}}}0 XAIe -;    '!         " - - -" - - -" -    O  }}}}}}} $XL(4P0 +;           "  " + + +" + + + + + +O  }}}}}}}ptx0 XAIe - ;  -   -  - D  - - -  < m  - D - - -  -<   v   '   !            " " " O}}}}}}}tx|0 +;- -           l     '!  " " " O}}}}}}}0 XAIe + ;   $  +$     +   + + + +b        '   !          " " " O}}}}}}}dhlx0 +XAIe  ;   '  '             -l        '   !         " " " O}}}}}}} 0 +l        '   !         " " " O}}}}}}}0 XAIe -;- -  - -  - -D     <  D<'!  " " " O}}}}}}}$(\P,8T0 + ;   -  -       +                 " " " O}}}}}}}hlp|0 XAIe -;6 6      D      <   |D<'!  " " " O}}}}}}}lpt0 + ;   3  3                       " " " O}}}}}}}<0 40 XAIe  ;       <      u  @@ -6083,13 +6166,13 @@      -    "  "  "    O "}}}}}}}  "6 q?x- js 2+- js1- js- js13 - js3 - js4- ps- js- ps<- js<- js<- ps<- ps- ps<- ps<3 +    "  "  "    O "}}}}}}}lpt "6 q?x- js 2+- js1- js- js13 + jsqf <@ + p<44- ps- js- ps<- js<- js<- ps<- ps- ps<- ps<3  ps- js. sFe"!! ,1>W- js- ps1- ps- psF'm"!! ,4AZ= < $ $ 5, 4H  @@ -6106,13 +6189,13 @@ @   @   @   -D=3s6,=3y6$,YNe rc56@4YNl|p56@4YNm zy560@4- jsF f"!! -,1>WUM^ kxM57@4- js1- js=3S6 ,YNa nC56@4=3@5,- js1- js44 - s. sFe"!! +D=3s6,E:5#71(%(<dg53@YNe rc56@4h up<555<[ @ 4s y<5550<[ @   4i` <> j<4F f"!! +,1>W] j|M<556<[@4- js1- js=3S6 ,YNa nC56@4=3@5,- js1- js44 + s 9#+. sFe"!! ,1>W. sF&l"!! ,4AZ1- jsF$j"!! ,4AZ@ } <   $  $  ; D ',  4G    @@ -6162,27 +6245,27 @@ *  A .,  7 H  -   *   *   *   *   AXujp } t556(@@<=4<J,3 +   *   *   *   *   AXypo | t555( @ @ <=4<J,3  js5  M%!! ,4G]5  =4<X,3  js5  J"!! ,4A]5  3  js3 - js- js- js- js- js- js- ps1- js- ps- js1- js4- js- js- js4- ps- ps- js1- psF5{"!! + js!==!==!==!==- js- js- js- js- js- ps1- js- ps- js1- js4- js- js- js4- ps- psi` <@ p<4YN<U_<,- psF5{"!! ,4AZ? | <   *  *  *  *  A D '/  7 G -   *   *   * * APSi=3g6%,3 +   *   *   * * APSiA9n5$73  psC)l!! ,4AZ}rw 2 B 6 ,@A8CC# 3   2 ;)! H- js1- js4- js- js- js1- js- js1- js1- js- js1- js- js11193  psF5{"!! @@ -6207,26 +6290,34 @@ D  D  ' /  :J * * * * 0 0 AHHHMi5  J"!! -,4A]3 - jsE]"!! +,4A]md~~ <> + j<4E]"!! ,1>W3  js3  js3  ps3  ps3  ps- ps- ps- ps- js- js- ps- js- js- js1- js1- js4- js- js1- js- vy4}rx E C + , 9A8^^# F   -/ 84- ps4- js1- js4- js1- js4- psF4z"!! +/ 84- ps4- js1- js4i^~~ <> j<4YN<U_<,- js4- psF4z"!! ,4AZ? | <   *  *  *  *  A D '/  7 G -   *   *   * * APQi- ps- psYNd qV56@4YNe rm56@4/ Jx"!! -,4A]/ Jh"!! +   *   *   * * APQi- ps- psYNd qV56@4YNe rm56@4ia  < C   + < + + +4Jx"!! +,4A]i`  < C  }  + < + + +4Jh"!! ,4A]- js1- js4- js3  psF+q"!! ,4AZG  <   @@ -6242,35 +6333,44 @@  @   @   D- js- js1- ps- js- js3 - js- js- js- js- ps- js- js- ps1UMp }g56@4- js1- js4- js4- js<- js11- js- js- js1- js- js1- js- js1- js1- js- js1- js41- ps4YNn~o56"@4/ -    L$!! -,4A].  |L$!! -,4A]- js4- js1- js4- js<3 - ps- js- js- js- js4- js4- js- js- ps- js- ps1- js4. |5 - . |. |- ps- js- ps1. s- js- ps1- js4- psujp } t556(@@<=4<J,3 + js- js- js- js- ps- js- jsi`<D p<4YN<V_<,UMp }g56@4- js1- js4- js4- js<- js11- js- js- js1- js- js1- js- js1- js1!==- js- js1- js41- ps4k xr <555&   <   [  + @ + + +45 + + +    L$!! +,4A]4    | L$!! +,4A]i` <@ j<4YN<[b<,- js1- js4- js<3 + ps- js- js- js- js4- js4- js- js- ps- jsi` <@ p<4YN<U_<,- js4. |J"!! +,4A]qg<] +    <   4. |. |- ps!==!C=!@=- jsi` <@ p<4YN<U_<,. s- jsi` <@ p<4YN<U_<,- js4- psypo | t555( @ @ <=4<J,3  js5  5  =4<X,5  5  =4<J,5  5 - MC m5C@43 + QIm5C@4=3%(<dg553H3  js=49<,5  J"!! -,4G]5 - J"!! +,4G]qg  < [  +    + < + + +4J"!! ,4G]5 - - js4- ps14- js- js- ps1- js4- js- psp }  t5556, -@  - @  - @ - - -DYNd qU56@4- js- js1- js4- js- js1- js- js1- ps- js- js- vy41- js}rw 2 B 6 ,@A8CC# )   + - js4i` <@ p<4YN<U_<,4- js- js- ps1- js4- js- pso |  t 5555,    @   @ +  +@ +  +D]Td qU55@4- js- js1- js4- js- js1- js- js1- ps- js- js- vy41- js}rw 2 B 6 ,@A8CC# )   . 74- js4- ps43  ps- js- js- js- js- vyF?"!! ,4A]G  ?   *  *  *  *  0  A D '/  : @@ -6281,88 +6381,115 @@  $    5   -@1- js1- js4- js13 +@1- js1- js4- js!==!==!== 9#+13  js3 - js- js- js- js14 + js- js- jsi` <@ p<4YN<U_<, 9#+ 9#+4  }R,%! ,4Ge?          AMD  D  D  D  D  D  D  D  D  '/ -3JrHHHHHHHHHX/ / 4 - - - - |   Q,&! +3JrHHHHHHHHHXia  < C    + < + + +4i`  < C  }  + < + + +4' NSqf  < C  +  + | + + + + +   <   4Q,&! ,4G]5  4   }4   }4}rw 2 B 6 ,@A8CC# )  44}s E  M 6 ,@A8NN# -   3 <}s  X  U 9 , @ A8UU# 1   7 @4444}rw 2 B 6 ,@A8CC# )  4111- js- js1- js- js- js1- js4- ps13  ps3  ps3 - js- js- js1;11- psYNe rm56@4/ / YNe rm56@4/ / -  ws- ps- js- js1- psJ"!! + js- js- js1;11- psYNe rm56@4ia  < C    + < + + +4i`  < C  }  + < + + +4YNe rm56@4ia  < C    + < + + +4i`  < C  }  + < + + +4-  ws- ps- js- js1- psJ"!! ,4A]- psG!! -,4AZ- js- js1- ps- js- js- js- ps- ps- js- js- js- js1- js1- js- js1- js- js1- js- ms- js- js- js4- ps1- ms- ps- js- js1- js- js- ps<. s1- js- vy- js- vyF5{"!! -,4AZ7- js4- ps- js- ps9- ps1- js- js4- js- js1- js- js1. yF;"!! -,4A]- ps- ps1- ps1- js- ps- psF'm"!! +,4AZ- js- js1- ps- js- js- js- ps- ps- js- js- js- js1- js1- js- js1- js- js1- js- msi^~~ <> j<4- js!==i` <@ j<4YN<[b<,i` <@ p<4YN<U_<,- ms- ps- js- js1- js- js- ps<. s1- js- vyi` <@ p<4i`<D s<4F5{"!! +,4AZ7- js4- ps- js!==!C=!@=4  pE= DeZ,i` <@ p<4YN<U_<,- js- js4- js- js1- js- js1. yF;"!! +,4A]- ps- ps1i`<G p<4YN<U_<,- js- ps- psF'm"!! ,4AZ= < $ $ 5, 4H  $    $    5   -@- js- ps1- js14 +@- jsi`<D p<4YN<U_<,- js14  |JK"!! ,4G]W   ? * * * * 0 0 0 A  D  D  ' /  - : J * * * * 0 0 0 APHVq- js1- js- js1- js3 + : J * * * * 0 0 0 APHVq- js1- js- js1- js && F1+3  psJM"!! -,4A]- psF9"!! -,4AZ- js- js- vy- js- vy7- js- js- js}rw 2 B 6 ,@A8CC# )  40 +,4A] && F1+4  pF9"!! +,4AZ- js- js- vyi` <@ p<4i`<D s<47- js- js- js}rw 2 B 6 ,@A8CC# )  40 XAIe ;- -       -   3   3 0!  " " " O}}}}}}}m mTmHm$m0mLm0 +   3   3 0!  " " " O}}}}}}}ttttttt0 XAIe ;E E       -        b  b 0!  " " " O}}}}}}}0k4khk\k8kDk`k0 +        b  b 0!  " " " O}}}}}}}rrrrrrr0 XAIe -?9 9           6  6 0!  " " " O}}}}}}}hiliiipi|ii0 +?9 9           6  6 0!  " " " O}}}}}}}pp,q qpq$q0 XAIe ; -   }}<     }}     -      a>!  " " " OB}}}}}}}tgxggg|gggE;  Wt0 +      a>!  " " " OB}}}}}}}oo8o,ooo0oE;  Wt0 XAIe  ;   $  $               -v        '   !        " " " O}}}}}}}xe|eeeeee0 +v        '   !        " " " O}}}}}}}mm<m0m mm4m0 XAIe  ;              b     u   -'   !            "   "   "   O}}}}}}}cc$ddcdd0 +'   !            "   "   "   O}}}}}}}xk|kkkkkk0 XAIe  ;    @@ -6370,11 +6497,11 @@      b   u   '   -!            "   "   "    O}}}}}}}`bdbbbhbtbb0 +!            "   "   "    O}}}}}}}ii$jjijj0 XAIe  ;          @@ -6384,74 +6511,100 @@  )     v  -   u   '   !          " " " O}}}}}}}```````0 +   u   '   !          " " " O}}}}}}}HhLhhthPh\hxh0 XAIe :, ,     -     q  q 0!  " " " O}}}}}}}^^_^^^_0 +     q  q 0!  " " " O}}}}}}}\f`fffdfpff0 XAIe  ;                 k     -u   '   !            "   "   " O}}}}}}}8]<]p]d]@]L]h]0 +u   '   !            "   "   " O}}}}}}}ddddddd0 XAIe -;E E {u  9 1  - -   {u91       Y!"  " " " O}}}}}}} [$[X[L[([4[P[0 +;i i   3 3 v0!  " " " O}}}}}}},c0cdcXc4c@c\c0 XAIe -;i i   3 3 v0!  " " " O}}}}}}}YYYYYYY0 +;Q Q        '      ' '!  " " " O}}}}}}}pataaaxaaa0 XAIe -;Q Q        '      ' '!  " " " O}}}}}}}WWXWWWW0 +;W W      +  +   " " " O}}}}}}}__,` `_`$`0 XAIe -;] ]      +   + '!  " " " O}}}}}}}DVHV|VpVLVXVtVQF44 ?n ,m*>>h V=3++?SgWW71(%(<dg53@0 -XAIe ;E E    -      0!  " " " O}}}}}}}SSSSSSS0 +      0!  " " " O}}}}}}}P^T^^|^X^d^^0 XAIe -;] ]        ) D) <D<'!  " " " O}}}}}}}QQQQQQQ0 +;] ]        ))   " " " O}}}}}}}\\\\\\\0 XAIe -;] ]         C@ < C@ <]  <a<3!""""" "" " ! " ! ! " ! ! "! !!!!!!"!O""}}}}}}}OOOOOOO0 +;] ]         C@ C@  *!"  " " " O}}}}}}}[ [@[4[[[8[0 XAIe - ;        D    <r D   - - -< - - -v   '   !            "   "  " O}}}}}}}MM,N NMN$N0 +;    + + + + + + + +" +  + " +  + " +  + + + + + +O + +}}}}}}}YYYYYYY0 XAIe -;- -    D         <   |  D<'!  " " " O}}}}}}}<L@LtLhLDLPLlL0 + ;   -  -     +  +                   " " " O}}}}}}}@XDXxXlXHXTXpX0 XAIe ;  '  '         -       v     '!  " " " O}}}}}}}JJJJJJJ0 +       v     '!  " " " O}}}}}}}VVVVVVV0 XAIe +;  + + + + + + + +"  "  "    + O + +}}}}}}}\U`UUUdUpUU0 +XAIe ; -    T}<      W}   {   -   n   n   a>!  " " " O2}}}}}}}HHHHHHH0 +   n   n   a>!  " " " O2}}}}}}}lSpSSStSSS0 XAIe ;      X@  `    ]  }  -   {     nnHW!  " " " O2}}}}}}}FFFFFFF0 +   {     nnHW!  " " " O2}}}}}}}hQlQQQpQ|QQ0 XAIe ;      ]C         -a  }  {{HW!  " " " O2}}}}}}}DDDDDDD0 +a  }  {{HW!  " " " O2}}}}}}}XO\OOO`OlOO0 XAIe  ;   "    @@ -6461,11 +6614,11 @@        k     -u   '   !            "   " " O}}}}}}}C C@C4CCC8C0 +u   '   !            "   " " O}}}}}}}MMMMMMM0 XAIe  ;    @@ -6474,308 +6627,337 @@   '   -!            "   "  "    O}}}}}}}|AAAAAAA0 +!            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" " " O}}}}}}}0 XAIe -6B6665 /5 55. 5 -. - 5 /- /-----/43.Z1/ : ".$3%2&.&Z'0(/):)*:*,:,`, ., .010222444n555Q565t666!6:6:77:7:7"7 :7:77 :8:8"8 9898"8 9898"9 999999:9O::2}}}}}}}|QF44 ?n ,m*>>h VMC++?Sg{ZWT4=3%(<dg553H0 +B9 9  Y&   c    Y& d  \*!"  " " " O}}}}}}}0 XAIe +4B4444 .3 33,3 +, + 3 . , -,,,++-21-Z/8!-#1$1%-%Z&.(8()8)*8*`+ -+ -///010222n333Q343t444!5858558585"5 85855 8686"6 8676"6 7676"7 77777787O882}}}}}}}0 +XAIe ";"""]""  ""!]" "   " ! -!   u  {{9E u{{9E         \!#!###!#&#&##&#&#"$ &$&$$ &$&$"$ &%&%"% &%&%"% &%&&&&&&O&&}}}}}}} D8 <0 +!   u  {{9E u{{9E         \!#!###!#&#&##&#&#"$ &$&$$ &$&$"$ &%&%"% &%&%"% &%&&&&&&O&&}}}}}}}@DxlHTp0 XAIe -%;%$$]$$  $$$]$ $  -  -$ -$ -#   u{{9E" <u{{9E! <         < a ! < ! #%#%%%!%)%)%&)&)&"& (&(&& (&(&"' ('('"' ('('"( (((((()(O))}}}}}}}0 + ; ]  ]     + +   u  {{9Eu{{9E            ! $ $ !$!$!"! #!#!! #!#!"" #"#""" #"#""# ######$#O$$}}}}}}}PD ,H0 XAIe -;] ]    , , 0!  " " " O}}}}}}} @480 +;] ]    ,,   " " " O}}}}}}}0 XAIe -;E E  -     '!  " " " O}}}}}}}0 +;E E  +      " " " O}}}}}}},0dX4@\0 XAIe  ;       7   ?    C   d    -    n   O   !      "    " " " O}}}}}}}0 +    n   O   !      "    " " " O}}}}}}}tx|0 XAIe ;E E       -     '!  " " " O}}}}}}}$(\P,8T0 +     '!  " " " O}}}}}}}0 XAIe ;E E     -   v'!  " " " O}}}}}}}0 +   v'!  " " " O}}}}}}}8<pd@Lh0 XAIe  ;              q     u   -'   !            "   "   "   O}}}}}}}, $0 +'   !            "   "   "   O}}}}}}}0 XAIe  ;            )     )     -v     u   '   !        " " " O}}}}}}}PT|Xd0 +v     u   '   !        " " " O}}}}}}}4(,0 XAIe ; "   "      ,    -   ,       u'!  " " " O}}}}}}}0 +   ,       u'!  " " " O}}}}}}}LPxT`|0 XAIe +;0 0              v  '!  " " " O}}}}}}}0 +XAIe +;  g! + + + + + + + +" + +"  "    + O + +}}}}}}}hlp|I> ,o*>>h V0 +XAIe +;3 3     +  +     '!  " " " O}}}}}}}HLtP\x0 +XAIe +;* *      + +  +       v     '!  " " " O}}}}}}}0 +XAIe >;>>>= <= = < < - < < < < < < ; ; ;; < < < < < < ; ;" ;";#;#;% ;%;&;' ;(:(;* ;*:+;- ;-:-;/ ;/:0;1 ;1:2;4=6<6;8=:<:;:<<===>>>0>>>!?B?B??B?B?? B?B?@ B@B@"@ B@B@"@ A@AA"A AAAAAABAOBB}}}}}}} $XL(4P0 + < < < < < < ; ; ;; < < < < < < ; ;" ;";#;#;% ;%;&;' ;(:(;* ;*:+;- ;-:-;/ ;/:0;1 ;1:2;4=6<6;8=:<:;:<<===>>>0>>>!?B?B??B?B?? B?B?@ B@B@"@ B@B@"@ A@AA"A AAAAAABAOBB}}}}}}}04h\8D`0 XAIe -QBQMMM EM MM E E - D D C C C B B?M E E D D D C C C B? M A!@# B#?$@% B&>&@( A(>)@* A*>+@, A->-@/ A/>/?0K1I3 G4?4<4?4S5Q56Q6L7J8H: E;?;<;?;V<Q<}=Q=FGF<FGFaGHG<GHGHIHJJJKKKjLML_LMMLMMMMQMQMNQNQNN PNPNN PNPN"O POPO"O POPO"O PPPPPPQPOQQ[}}}}}}}4(,0 +.E.... $. ..r+$ + . + , . , $ .$*(Y&3$)'Y$332 2 !2!"2"}#2#%&%&'&'(')))***+++,,,n-.-Q./.t./.!/2/2//2/2// 2/2/0 2020"0 2020"1 2121"1 21211121O22R}}}}}}}0 XAIe +LBLHIH CH HH C C + B B B A A @ @=H C C C B B A A A @= H ?!?# @#=$>% @&=&>( @(=)>* ?+<+>- ?-<->/ ?/<0=0G2E4M45M5L6F8C:M:;M;DEDEFEFGFjGHG_HIHLIIIILILIILILII LJLJJ LJLJ"J LJLJ"K LKLK"K LKLKLKLLOLL[}}}}}}}<0 40 +XAIe ;  "     7   ?"     @      d  -      n  O!"  " " " O}}}}}}}8<pd@Lh0 +      n  O!"  " " " O}}}}}}}@DxlHTp0 XAIe ;- -     7  E    -C     d  \*!"  " " " O}}}}}}}hlp|0 +C     d  \*!"  " " " O}}}}}}}ptx0 XAIe :8 8      -    W  W 0!  " " " O}}}}}}}0 +    W  W 0!  " " " O}}}}}}}0 XAIe  ;             8 8  w   @@ -6786,15 +6968,15 @@      -    "  "  "    O  }}}}}}} D8 <I> ,o*>>h V0 +    "  "  "    O  }}}}}}}L@(D0 XAIe  ;              Y     -  Y        !     " " " O}}}}}}}4(,0 +  Y        !     " " " O}}}}}}}0 XAIe  ;   @@ -6806,11 +6988,11 @@   5  w      -!            "   "   "    O}}}}}}}0 +!            "   "   "    O}}}}}}}<0 40 XAIe ;    o!    @@ -6828,68 +7010,70 @@  O  -}}}}}}} TH$0L0 +}}}}}}}0 XAIe :, ,     -    P P 0!  " " " O}}}}}}}TX\h0 +    P P 0!  " " " O}}}}}}}0 XAIe :, ,      -   P P 0!  " " " O}}}}}}}Ŀ0 +   P P 0!  " " " O}}}}}}}H<$@0 XAIe -;- -  @  < \ - =     <   ]  <a<*!"  " " " O}}}}}}}Խؽ0 + ;   -  -    @     +4 +       *   !      "  " " " O}}}}}}}0 XAIe -;9 9  @$ c    = $   d  \*!"  " " " O}}}}}}}0 +;9 9  @$ c    = $   d  \*!"  " " " O}}}}}}}0 XAIe -;B B     )   )o'!  " " " O}}}}}}}hlp|0 +;? ?           " " " O}}}}}}}LPxT`|0 XAIe - ;   3  3    +  -   +  k   '   !        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(Could this error message be improved? If so, please report an enhancement request to PyTorch.)allocator<T>::allocate(size_t n) 'n' exceeds maximum supported sizevalidate/usr/local/include/c10/core/Device.hDevice index must be -1 or non-negative, got Expected index_ == -1 || index_ >= 0 to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)CPU device index must be -1 or zero, got Expected !is_cpu() || index_ <= 0 to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)aten::arangestartendstepoptionsinsertNode/usr/local/include/torch/csrc/jit/ir.hinsert_before_->inBlockList() && "insert point node is no longer in a block list" INTERNAL ASSERT FAILED at :, please report a bug to PyTorch. inBlockListprev() == nullptr INTERNAL ASSERT FAILED at aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> TensortensorTypeIdToBackend/usr/local/include/c10/core/Backend.hUnrecognized tensor type ID: backendToDeviceTypeUndefined backend is not a valid device typeUnknown backendcomputeTensorTypeId/usr/local/include/c10/core/TensorOptions.hUnsupported device type for dense layout: Unsupported device type for sparse layout: Unsupported device type for mkldnn layout: Unsupported layout: typeMetaToScalarType/usr/local/include/c10/core/ScalarType.hUnsupported TypeMeta in ATen: (please report this error)StridedSparseMkldnnoperator<</usr/local/include/c10/core/Layout.hUnknown layoutgetOpTable/usr/local/include/ATen/core/ATenDispatch.hNo functions are registered for schema Expected iter != op_tables_.end() to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)make_variable/usr/local/include/torch/csrc/autograd/variable.hMust not create a new variable from a variable, use its .tensor_data()retain_/usr/local/include/c10/util/intrusive_ptr.hnew_refcount != 1 INTERNAL ASSERT FAILED at intrusive_ptr: Cannot increase refcount after it reached zero.TensorImpl with nullptr is not supported~intrusive_ptr_targetrefcount_.load() == 0 INTERNAL ASSERT FAILED at Tried to destruct an intrusive_ptr_target that still has intrusive_ptr to itweakcount_.load() == 0 INTERNAL ASSERT FAILED at Tried to destruct an intrusive_ptr_target that still has weak_intrusive_ptr to itaten::emptysizememory_formataten::empty.memory_format(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensoraten::eyenmaten::eye.m(int n, int m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::fullfill_valueaten::full(int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::linspacestepsaten::linspace(Scalar start, Scalar end, int steps=100, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::logspacebaseaten::logspace(Scalar start, Scalar end, int steps=100, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::onesaten::ones(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randaten::rand(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randintlowhighaten::randint.low(int low, int high, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randnaten::randn(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randpermaten::randperm(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::zerosaten::zeros(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::_max.max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_indices) -> (Tensor(a!), Tensor(b!))aten::topkbad optional accessIssued LeftRight::read() after the destructor started runninglookup_/usr/local/include/ATen/core/dispatch/DispatchTable.hThere were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema . This usually means that this function requires a non-empty list of Tensors. Available functions are Didn't find kernel to dispatch to for operator ''. Tried to look up kernel for dispatch key ''. Registered dispatch keys are: , CATCH-ALL[, ]callUnboxedOnly/usr/local/include/ATen/core/boxing/KernelFunction.hfalse INTERNAL ASSERT FAILED at Tried to call KernelFunction::callUnboxedOnly() for a kernel that doesn't have an unboxed version.aten::softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensoraten::log_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensoraten::relucallUnboxedboxed_kernel_func_ != nullptr INTERNAL ASSERT FAILED at Tried to call KernelFunction::callUnboxed() on an uninitialized KernelFunction.callstack.size() == 1 INTERNAL ASSERT FAILED at A boxed kernel should only push one return to the stackreclaimowning_ptr == NullType::singleton() || owning_ptr->refcount_.load() > 0 INTERNAL ASSERT FAILED at intrusive_ptr: Can only intrusive_ptr::reclaim() owning pointers that were created using intrusive_ptr::release().toTensor/usr/local/include/ATen/core/ivalue_inl.hisTensor() INTERNAL ASSERT FAILED at Expected Tensor but got DoubleIntBoolTupleIntListDoubleListStringTensorListGenericListGenericDictFutureObjectUninitializedCapsuleInvalidTag()aten::preluaten::leaky_reludoublevalue cannot be converted to type without overflow: int64_taten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensoraten::bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias) -> Tensoraten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensoraten::max_pool2daten::avg_pool2daten::binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> TensorUnable to convert to Class is not registered/bound in RiceData type is not bound is already bound to a different typebad cast. No caster found for meansumUnsupported reduction: aten::numeltoInt/usr/local/include/ATen/core/ivalue.hisInt() INTERNAL ASSERT FAILED at from_blob/usr/local/include/ATen/Functions.hSpecified device does not match device of data Expected options.device() == device to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)getDeviceFromPtrStorageImpl/usr/local/include/c10/core/StorageImpl.hallocator_ INTERNAL ASSERT FAILED at For resizable storage, allocator must be providedConstructing a storage with meta of unknown type and non-zero numelaten::set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!)Cannot create bool from tensor method yetaten::reshapeaten::is_distributedtoBoolisBool() INTERNAL ASSERT FAILED at aten::is_complexaten::is_floating_pointaten::is_signeditemsize/usr/local/include/c10/core/TensorImpl.hCannot report itemsize of Tensor that doesn't have initialized dtype (e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)autograd_meta() INTERNAL ASSERT FAILED at requires_grad is not implemented for Tensoraten::view_asaten::addcmul_aten::addcdiv_aten::zero_aten::detachaten::detach_set_requires_gradset_requires_grad is not implemented for Tensoraten::backward(Tensor self, Tensor? gradient=None, bool keep_graph=False, bool create_graph=False) -> voidaten::to.dtype_layout(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False, bool non_blocking=False, bool copy=False) -> Tensortensor does not have a devicescalarTypeToTypeMetaUnrecognized Scalartype ByteCharShortLongHalfFloatComplexHalfComplexFloatComplexDoubleQInt8QUInt8QInt32BFloat16UNKNOWN_SCALARaten::resize_as_aten::fill_Scalaraten::relu_aten::normal_aten::random_aten::sub_aten::div_aten::sqrt_aten::unsqueeze_aten::copy_aten::cloneaten::dataUnsupported typedata_ptr/usr/local/include/ATen/core/TensorMethods.hexpected scalar type but found Expected scalar_type() == ScalarType::Byte to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Cannot access data pointer of Tensor that doesn't have storageCannot access data pointer of Tensor that doesn't have initialized dtype (e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)Expected scalar_type() == ScalarType::Char to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Short to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Int to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Long to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Float to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Double to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Bool to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)aten::to.device(Tensor self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False) -> Tensoras_variable_refAttempted to cast a Tensor to a Variable, but the dynamic type of the value is not Variable.VariableTensor that was converted to Variable was not actually a VariablestridedsparseSparse layout not supported yetcpucudaUnsupported device: conv1dconv3dconv_transpose1dconv_transpose2dconv_transpose3dsigmoidtanhUnsupported nonlinearity type: fan_infan_out is unboundunknown C++ exception thrown0initialize_adaptive_avg_pool2d_adaptive_avg_pool2d_out_adaptive_avg_pool3d_adaptive_avg_pool3d_out_adaptive_max_pool2d_adaptive_max_pool2d_out_adaptive_max_pool3d_adaptive_max_pool3d_out_binary_cross_entropy_binary_cross_entropy_out_col2im_col2im_out_elu_elu__elu_out_fractional_max_pool2d_fractional_max_pool2d_output_fractional_max_pool3d_fractional_max_pool3d_output_gelu_glu_glu_out_hardtanh_hardtanh__hardtanh_out_im2col_im2col_out_l1_loss_l1_loss_out_leaky_relu_leaky_relu__leaky_relu_out_linear_log_sigmoid_log_sigmoid_forward_log_sigmoid_forward_output_log_sigmoid_out_max_pool2d_with_indices_max_pool2d_with_indices_out_max_pool3d_with_indices_max_pool3d_with_indices_out_max_unpool2d_max_unpool2d_out_max_unpool3d_max_unpool3d_out_mkldnn_linear_mkldnn_reorder_conv2d_weight_mse_loss_mse_loss_out_multi_margin_loss_multi_margin_loss_out_multilabel_margin_loss_multilabel_margin_loss_forward_multilabel_margin_loss_forward_output_multilabel_margin_loss_out_nll_loss_nll_loss2d_nll_loss2d_forward_nll_loss2d_forward_output_nll_loss2d_out_nll_loss_forward_nll_loss_forward_output_nll_loss_out_one_hot_reflection_pad1d_reflection_pad1d_out_reflection_pad2d_reflection_pad2d_out_replication_pad1d_replication_pad1d_out_replication_pad2d_replication_pad2d_out_replication_pad3d_replication_pad3d_out_rrelu_with_noise_rrelu_with_noise__rrelu_with_noise_out_slow_conv_dilated2d_slow_conv_dilated3d_slow_conv_transpose2d_slow_conv_transpose2d_out_slow_conv_transpose3d_slow_conv_transpose3d_out_smooth_l1_loss_smooth_l1_loss_out_soft_margin_loss_soft_margin_loss_out_softplus_softplus_out_softshrink_softshrink_out_thnn_conv2d_thnn_conv2d_forward_thnn_conv2d_forward_output_thnn_conv2d_out_thnn_conv3d_thnn_conv3d_forward_thnn_conv3d_forward_output_thnn_conv3d_out_thnn_conv_depthwise2d_thnn_conv_depthwise2d_forward_thnn_conv_depthwise2d_forward_out_thnn_conv_depthwise2d_out_upsample_bicubic2d_upsample_bicubic2d_out_upsample_bilinear2d_upsample_bilinear2d_out_upsample_linear1d_upsample_linear1d_out_upsample_nearest1d_upsample_nearest1d_out_upsample_nearest2d_upsample_nearest2d_out_upsample_nearest3d_upsample_nearest3d_out_upsample_trilinear3d_upsample_trilinear3d_outaten::adaptive_avg_pool2daten::adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_avg_pool3daten::adaptive_avg_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_max_pool2daten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::adaptive_max_pool3daten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensoraten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::col2imaten::col2im.out(Tensor self, int[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)aten::eluaten::elu_aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)aten::fractional_max_pool2daten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::fractional_max_pool3daten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::geluaten::gluaten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)aten::hardtanhaten::hardtanh_aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)aten::im2colaten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)aten::l1_lossaten::l1_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::leaky_relu_aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)aten::log_sigmoidaten::log_sigmoid_forwardaten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::max_pool2d_with_indicesaten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::max_pool3d_with_indicesaten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::max_unpool2daten::max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::max_unpool3daten::max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)aten::mkldnn_linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensoraten::mkldnn_reorder_conv2d_weightaten::mse_lossaten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensoraten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::multilabel_margin_lossaten::multilabel_margin_loss_forwardaten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensoraten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensoraten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)aten::one_hotaten::reflection_pad1daten::reflection_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)aten::reflection_pad2daten::reflection_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad1daten::replication_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad2daten::replication_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad3daten::replication_pad3d.out(Tensor self, int[6] padding, *, Tensor(a!) out) -> Tensor(a!)aten::rrelu_with_noiseaten::rrelu_with_noise_aten::rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)aten::slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensoraten::slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensoraten::slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1) -> Tensoraten::slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::slow_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1) -> Tensoraten::slow_conv_transpose3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::smooth_l1_lossaten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::soft_margin_lossaten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::softplusaten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)aten::softshrinkaten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0) -> Tensoraten::thnn_conv2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)aten::thnn_conv2d_forward.output(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))aten::thnn_conv2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0) -> Tensoraten::thnn_conv3d_forward(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)aten::thnn_conv3d_forward.output(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))aten::thnn_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensoraten::thnn_conv_depthwise2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation) -> Tensoraten::thnn_conv_depthwise2d_forward.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_bicubic2daten::upsample_bicubic2d.out(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_bilinear2daten::upsample_bilinear2d.out(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_linear1daten::upsample_linear1d.out(Tensor self, int[1] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest1daten::upsample_nearest1d.out(Tensor self, int[1] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest2daten::upsample_nearest2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest3daten::upsample_nearest3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_trilinear3daten::upsample_trilinear3d.out(Tensor self, int[3] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)_abs_abs__acos_acos__add__scalar_add__tensor_add_scalar_add_tensor_addbmm_addbmm__addcdiv_addcdiv__addcmul_addcmul__addmm_addmm__addmv_addmv__addr_addr__alias_align_as_all_all_dim_allclose_any_any_dim_argmax_argmax_dim_argmin_argmin_dim_argsort_as_strided_as_strided__as_strided__storage_offset_as_strided_storage_offset_asin_asin__atan_atan2_atan2__atan__baddbmm_baddbmm__bernoulli_bernoulli__float_bernoulli__tensor_bernoulli_p_bincount_bitwise_not_bitwise_not__bmm_cauchy__ceil_ceil__cholesky_cholesky_inverse_cholesky_solve_chunk_clamp_max_clamp_max__clamp_min_clamp_min__clone_coalesce_copy__cos_cos__cosh_cosh__data_dense_dim_dequantize_det_detach_detach__diag_diag_embed_diagflat_diagonal_digamma_digamma__dist_div__scalar_div__tensor_div_scalar_div_tensor_dot_eig_eq__scalar_eq__tensor_eq_scalar_eq_tensor_equal_erf_erf__erfc_erfc__erfinv_erfinv__exp_exp__expand_expand_as_expm1_expm1__exponential__fft_fill__scalar_fill__tensor_fill_diagonal__flatten_using_ints_flip_floor_floor__fmod__scalar_fmod__tensor_fmod_scalar_fmod_tensor_frac_frac__gather_ge__scalar_ge__tensor_ge_scalar_ge_tensor_geometric__geqrf_ger_gt__scalar_gt__tensor_gt_scalar_gt_tensor_hardshrink_histc_ifft_index_add_index_add__index_copy_index_copy__index_fill__scalar_index_fill__tensor_index_fill_scalar_index_fill_tensor_index_select_indices_int_repr_inverse_irfft_is_coalesced_is_complex_is_distributed_is_floating_point_is_leaf_is_nonzero_is_pinned_is_same_size_is_set_to_is_signed_isclose_item_kthvalue_le__scalar_le__tensor_le_scalar_le_tensor_lerp__scalar_lerp__tensor_lerp_scalar_lerp_tensor_lgamma_lgamma__log_log10_log10__log1p_log1p__log2_log2__log__log_normal__logdet_logical_not_logical_not__logical_xor_logical_xor__logsumexp_lstsq_lt__scalar_lt__tensor_lt_scalar_lt_tensor_lu_solve_masked_fill__scalar_masked_fill__tensor_masked_fill_scalar_masked_fill_tensor_masked_scatter_masked_scatter__masked_select_matmul_matrix_power_max_max_dim_max_other_max_values_median_median_dim_min_min_dim_min_other_min_values_mm_mode_mul__scalar_mul__tensor_mul_scalar_mul_tensor_multinomial_mv_mvlgamma_mvlgamma__narrow_narrow_copy_ne__scalar_ne__tensor_ne_scalar_ne_tensor_neg_neg__nonzero_nonzero_numpy_norm_scalar_normal__numel_numpy_t_orgqr_ormqr_output_nr_permute_pin_memory_pinverse_polygamma_polygamma__pow__scalar_pow__tensor_pow_tensor_scalar_pow_tensor_tensor_prelu_put__q_per_channel_axis_q_per_channel_scales_q_per_channel_zero_points_q_scale_q_zero_point_qr_qscheme_random__random__from_random__to_reciprocal_reciprocal__relu_relu__remainder__scalar_remainder__tensor_remainder_scalar_remainder_tensor_renorm_renorm__repeat_repeat_interleave_self_int_repeat_interleave_self_int_dim_repeat_interleave_self_tensor_repeat_interleave_self_tensor_dim_reshape_reshape_as_resize__resize_as__rfft_roll_rot90_round_round__rsqrt_rsqrt__scatter__src_scatter__value_scatter_add_scatter_add__scatter_src_scatter_value_select_int_set__set__source_tensor_set_data_sigmoid_sigmoid__sign_sign__sin_sin__sinh_sinh__size_int_slice_tensor_slogdet_smm_solve_sort_sparse_dim_sparse_mask_sparse_resize__sparse_resize_and_clear__split_tensor_split_with_sizes_sqrt_sqrt__squeeze_squeeze__squeeze__dim_squeeze_dim_sspaddmm_std_std_dim_stride_int_sub__scalar_sub__tensor_sub_scalar_sub_tensor_sum_to_size_svd_symeig_t_t__take_tan_tan__tanh_tanh__to_dense_to_mkldnn_to_other_to_sparse_to_sparse_sparse_dim_trace_transpose__transpose_int_triangular_solve_tril_tril__triu_triu__trunc_trunc__type_as_unbind_int_unfold_uniform__unsqueeze_unsqueeze__values_var_var_dim_view_view_as_where_self_zero_aten::absaten::abs_aten::acosaten::acos_aten::add_aten::addaten::addbmmaten::addbmm_aten::addcdivaten::addcmulaten::addmmaten::addmm_aten::addmvaten::addmv_aten::addraten::addr_aten::aliasaten::align_asaten::allaten::allcloseaten::anyaten::argmaxaten::argminaten::argsortaten::as_stridedaten::as_strided_aten::asinaten::asin_aten::atanaten::atan2aten::atan2_aten::atan_aten::baddbmmaten::baddbmm_aten::bernoulliaten::bernoulli_floataten::bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensoraten::bitwise_notaten::bitwise_not_aten::bmmaten::cauchy_aten::ceilaten::ceil_aten::choleskyaten::cholesky_inverseaten::cholesky_solveaten::chunkaten::clamp_maxaten::clamp_max_aten::clamp_minaten::clamp_min_aten::coalesceaten::cosaten::cos_aten::coshaten::cosh_aten::dense_dimaten::dequantizeaten::detaten::diagaten::diag_embedaten::diagflataten::diagonalaten::digammaaten::digamma_aten::distaten::divaten::dotaten::eigaten::eq_aten::equalaten::erfaten::erf_aten::erfcaten::erfc_aten::erfinvaten::erfinv_aten::expaten::exp_aten::expandaten::expand_asaten::expm1aten::expm1_aten::exponential_aten::fftaten::fill_diagonal_aten::flattenusing_intsaten::flipaten::flooraten::floor_aten::fmod_aten::fmodaten::fracaten::frac_aten::gatheraten::ge_aten::geometric_aten::geqrfaten::geraten::gt_aten::hardshrinkaten::histcaten::ifftaten::index_addaten::index_add_aten::index_copyaten::index_copy_aten::index_fill_aten::index_fillaten::index_selectaten::indicesaten::int_repraten::inverseaten::irfftaten::is_coalescedaten::is_leaf(Tensor self) -> boolaten::is_nonzeroaten::is_pinnedaten::is_same_sizeaten::is_set_toaten::iscloseaten::itemIValue is not a ScalartoDoubleisDouble() INTERNAL ASSERT FAILED at aten::kthvalueaten::le_aten::lerp_aten::lerpaten::lgammaaten::lgamma_aten::logaten::log10aten::log10_aten::log1paten::log1p_aten::log2aten::log2_aten::log_aten::log_normal_aten::logdetaten::logical_notaten::logical_not_aten::logical_xoraten::logical_xor_aten::logsumexpaten::lstsqaten::lt_aten::lu_solveaten::masked_fill_aten::masked_fillaten::masked_scatteraten::masked_scatter_aten::masked_selectaten::matmulaten::matrix_poweraten::maxotheraten::max_valuesaten::medianaten::minaten::min_valuesaten::modeaten::mul_aten::mulaten::multinomialaten::mvlgammaaten::mvlgamma_aten::narrowaten::narrow_copyaten::ne_aten::negaten::neg_aten::nonzeroaten::nonzero_numpyaten::normaten::numpy_Taten::orgqraten::ormqraten::output_nr(Tensor self) -> intaten::permuteaten::pin_memoryaten::pinverseaten::polygammaaten::polygamma_aten::pow_aten::powTensor_ScalarTensor_Tensoraten::put_aten::q_per_channel_axis(Tensor self) -> intaten::q_per_channel_scalesaten::q_per_channel_zero_pointsaten::q_scaleaten::q_zero_pointaten::qscheme(Tensor self) -> QSchemeaten::reciprocalaten::reciprocal_aten::remainder_aten::remainderaten::renormaten::renorm_aten::repeataten::repeat_interleaveself_Tensoraten::reshape_asaten::resize_aten::rfftaten::rollaten::rot90aten::roundaten::round_aten::rsqrtaten::rsqrt_aten::scatter_srcvalueaten::scatter_addaten::scatter_add_aten::scatteraten::selectintaten::set_source_Tensoraten::set_data(Tensor(a!) self, Tensor new_data) -> voidaten::sigmoidaten::sigmoid_aten::signaten::sign_aten::sinaten::sin_aten::sinhaten::sinh_aten::sizeaten::sliceaten::slogdetaten::smmaten::solveaten::sortaten::sparse_dimaten::sparse_maskaten::sparse_resize_aten::sparse_resize_and_clear_aten::splitaten::split_with_sizesaten::sqrtaten::squeezeaten::squeeze_aten::sspaddmmaten::stdaten::strideaten::subaten::sum_to_sizeaten::svdaten::symeigaten::taten::takeaten::tanaten::tan_aten::tanhaten::tanh_aten::to_denseaten::to_mkldnnaten::to_sparsesparse_dimaten::traceaten::transpose_aten::transposeaten::triangular_solveaten::trilaten::tril_aten::triuaten::triu_aten::truncaten::trunc_aten::type_asaten::unbindaten::unfoldaten::uniform_aten::unsqueezeaten::valuesaten::varaten::viewaten::where_abs_out_acos_out_adaptive_avg_pool1d_adaptive_max_pool1d_add_out_addbmm_out_addcdiv_out_addcmul_out_addmm_out_addmv_out_addr_out_affine_grid_generator_align_tensors_all_out_alpha_dropout_alpha_dropout__any_out_arange_out_arange_start_out_asin_out_atan2_out_atan_out_avg_pool1d_baddbmm_out_batch_norm_batch_norm_backward_elemt_batch_norm_backward_reduce_batch_norm_elemt_batch_norm_gather_stats_batch_norm_gather_stats_with_counts_batch_norm_stats_batch_norm_update_stats_bernoulli_out_bilinear_bitwise_not_out_bmm_out_broadcast_tensors_cartesian_prod_cat_cat_out_cdist_ceil_out_celu_celu__chain_matmul_cholesky_inverse_out_cholesky_out_cholesky_solve_out_clamp_max_out_clamp_min_out_combinations_constant_pad_nd_conv1d_conv2d_conv3d_conv_tbc_conv_transpose1d_conv_transpose2d_input_conv_transpose3d_input_convolution_convolution_overrideable_copy_sparse_to_sparse__cos_out_cosh_out_cosine_embedding_loss_cosine_similarity_ctc_loss_intlist_ctc_loss_tensor_cudnn_affine_grid_generator_cudnn_batch_norm_cudnn_convolution_cudnn_convolution_backward_bias_cudnn_convolution_backward_input_cudnn_convolution_backward_weight_cudnn_convolution_transpose_cudnn_convolution_transpose_backward_bias_cudnn_convolution_transpose_backward_input_cudnn_convolution_transpose_backward_weight_cudnn_grid_sampler_cudnn_is_acceptable_diag_out_digamma_out_div_out_dot_out_dropout_dropout__eig_e_embedding_embedding_bag_embedding_renorm__empty_like_eq_scalar_out_eq_tensor_out_erf_out_erfc_out_erfinv_out_exp_out_expm1_out_eye_m_out_eye_out_fake_quantize_per_channel_affine_fake_quantize_per_tensor_affine_fbgemm_linear_fp16_weight_fbgemm_linear_fp16_weight_fp32_activation_fbgemm_linear_int8_weight_fbgemm_linear_int8_weight_fp32_activation_fbgemm_linear_quantize_weight_fbgemm_pack_gemm_matrix_fp16_fbgemm_pack_quantized_matrix_fbgemm_pack_quantized_matrix_kn_feature_alpha_dropout_feature_alpha_dropout__feature_dropout_feature_dropout__floor_out_fmod_scalar_out_fmod_tensor_out_frac_out_frobenius_norm_frobenius_norm_dim_frobenius_norm_out_full_like_full_out_gather_out_ge_scalar_out_ge_tensor_out_geqrf_a_ger_out_grid_sampler_grid_sampler_2d_grid_sampler_3d_group_norm_gru_cell_gru_data_gru_input_gt_scalar_out_gt_tensor_out_hinge_embedding_loss_histc_out_hspmm_hspmm_out_index_select_out_instance_norm_inverse_out_isnan_kl_div_kthvalue_values_layer_norm_le_scalar_out_le_tensor_out_lerp_scalar_out_lerp_tensor_out_lgamma_out_linspace_out_log10_out_log1p_out_log2_out_log_out_logical_not_out_logical_xor_out_logspace_out_logsumexp_out_lstm_cell_lstm_data_lstm_input_lstsq_x_lt_scalar_out_lt_tensor_out_lu_solve_out_margin_ranking_loss_masked_select_out_matmul_out_matrix_rank_matrix_rank_tol_max_dim_max_max_pool1d_max_pool1d_with_indices_max_pool2d_max_pool3d_median_dim_values_meshgrid_min_dim_min_min_out_miopen_batch_norm_miopen_convolution_miopen_convolution_backward_bias_miopen_convolution_backward_input_miopen_convolution_backward_weight_miopen_convolution_transpose_miopen_convolution_transpose_backward_input_miopen_convolution_transpose_backward_weight_miopen_depthwise_convolution_miopen_depthwise_convolution_backward_input_miopen_depthwise_convolution_backward_weight_miopen_rnn_mkldnn_adaptive_avg_pool2d_mkldnn_convolution_mkldnn_convolution_backward_input_mkldnn_convolution_backward_weights_mkldnn_max_pool2d_mm_out_mode_values_mul_out_multinomial_out_mv_out_native_batch_norm_native_layer_norm_native_norm_ne_scalar_out_ne_tensor_out_neg_out_nonzero_out_norm_except_dim_nuclear_norm_nuclear_norm_dim_nuclear_norm_dim_out_nuclear_norm_out_ones_like_ones_out_orgqr_out_ormqr_out_pairwise_distance_pdist_pixel_shuffle_poisson_poisson_nll_loss_polygamma_out_pow_scalar_pow_scalar_out_pow_tensor_scalar_out_pow_tensor_tensor_out_qr_q_quantized_gru_cell_quantized_gru_data_quantized_gru_input_quantized_lstm_cell_quantized_max_pool2d_quantized_rnn_relu_cell_quantized_rnn_tanh_cell_rand_generator_out_rand_like_rand_out_randint_generator_out_randint_like_randint_like_low_randint_low_generator_out_randint_low_out_randint_out_randn_generator_out_randn_like_randn_out_randperm_generator_out_randperm_out_range_out_reciprocal_out_remainder_scalar_out_remainder_tensor_out_renorm_out_repeat_interleave_tensor_result_type_scalar_result_type_scalar_scalar_result_type_scalar_tensor_result_type_tensor_rnn_relu_cell_rnn_relu_data_rnn_relu_input_rnn_tanh_cell_rnn_tanh_data_rnn_tanh_input_round_out_rrelu_rrelu__rsqrt_out_rsub_scalar_rsub_tensor_selu_selu__sigmoid_out_sign_out_sin_out_sinh_out_solve_solution_sort_values_sqrt_out_sspaddmm_out_stack_stack_out_std_mean_std_mean_dim_std_out_sub_out_svd_u_symeig_e_take_out_tan_out_tanh_out_tensordot_threshold_threshold__threshold_out_topk_values_trapz_dx_trapz_x_triangular_solve_x_tril_out_triplet_margin_loss_triu_out_trunc_out_unique_consecutive_unique_consecutive_dim_unique_dim_var_mean_var_mean_dim_var_out_where_zeros_like_zeros_outaten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_avg_pool1daten::adaptive_max_pool1daten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::affine_grid_generatoraten::align_tensorsaten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::alpha_dropoutaten::alpha_dropout_aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::avg_pool1daten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensoraten::batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu) -> Tensoraten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor)aten::batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensoraten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor)aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int[] counts) -> (Tensor, Tensor)aten::batch_norm_statsaten::batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor)aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)aten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::broadcast_tensorsaten::cartesian_prodaten::cataten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)aten::cdistaten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::celuaten::celu_aten::chain_matmulaten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)aten::combinationsaten::constant_pad_ndaten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensoraten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensoraten::conv_tbcaten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] output_padding=0, int groups=1, int[1] dilation=1) -> Tensoraten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> Tensoraten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensoraten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensoraten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensoraten::copy_sparse_to_sparse_aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::cosine_embedding_lossaten::cosine_similarityaten::ctc_lossaten::cudnn_affine_grid_generatoraten::cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)aten::cudnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::cudnn_convolution_backward_biasaten::cudnn_convolution_backward_inputaten::cudnn_convolution_backward_weightaten::cudnn_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::cudnn_convolution_transpose_backward_biasaten::cudnn_convolution_transpose_backward_inputaten::cudnn_convolution_transpose_backward_weightaten::cudnn_grid_sampleraten::cudnn_is_acceptableaten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)aten::dropoutaten::dropout_aten::eig.e(Tensor self, bool eigenvectors=False, *, Tensor(a!) e, Tensor(b!) v) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)aten::embeddingaten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None) -> (Tensor, Tensor, Tensor, Tensor)aten::embedding_renorm_aten::empty_likeselfaten::empty_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False, MemoryFormat? memory_format=contiguous_format) -> Tensoraten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::eye.m_out(int n, int m, *, Tensor(a!) out) -> Tensor(a!)aten::eye.out(int n, *, Tensor(a!) out) -> Tensor(a!)aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensoraten::fake_quantize_per_tensor_affineaten::fbgemm_linear_fp16_weightaten::fbgemm_linear_fp16_weight_fp32_activationaten::fbgemm_linear_int8_weightaten::fbgemm_linear_int8_weight_fp32_activationaten::fbgemm_linear_quantize_weightaten::fbgemm_pack_gemm_matrix_fp16aten::fbgemm_pack_quantized_matrixaten::feature_alpha_dropoutaten::feature_alpha_dropout_aten::feature_dropoutaten::feature_dropout_aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::frobenius_normaten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::full_likeaten::full_like.dtype(Tensor self, Scalar fill_value, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::full.out(int[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)aten::grid_sampleraten::grid_sampler_2daten::grid_sampler_3daten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensoraten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::gruinputaten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::hinge_embedding_lossaten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)aten::hspmmaten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensoraten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::isnanaten::kl_divaten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensoraten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::linspace.out(Scalar start, Scalar end, int steps=100, *, Tensor(a!) out) -> Tensor(a!)aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::logspace.out(Scalar start, Scalar end, int steps=100, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)aten::lstmaten::lstsq.X(Tensor self, Tensor A, *, Tensor(a!) X, Tensor(b!) qr) -> (Tensor(a!) solution, Tensor(b!) QR)aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)aten::margin_ranking_lossaten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::matrix_ranktolaten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::max_pool1daten::max_pool1d_with_indicesaten::max_pool3daten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::meshgridaten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_convolution_backward_biasaten::miopen_convolution_backward_inputaten::miopen_convolution_backward_weightaten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_convolution_transpose_backward_inputaten::miopen_convolution_transpose_backward_weightaten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_depthwise_convolution_backward_inputaten::miopen_depthwise_convolution_backward_weightaten::miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)aten::mkldnn_adaptive_avg_pool2daten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensoraten::mkldnn_convolution_backward_inputaten::mkldnn_convolution_backward_weightsaten::mkldnn_max_pool2daten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)aten::native_layer_norm(Tensor input, Tensor? weight, Tensor? bias, int M, int N, float eps) -> (Tensor, Tensor, Tensor)aten::native_normaten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::norm_except_dimaten::nuclear_normaten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::ones_likeaten::ones_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::ones.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)aten::pairwise_distanceaten::pdistaten::pixel_shuffleaten::poissonaten::poisson_nll_lossaten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)aten::quantized_gru_cellaten::quantized_gruaten::quantized_lstm_cellaten::quantized_max_pool2daten::quantized_rnn_relu_cellaten::quantized_rnn_tanh_cellaten::rand.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::rand_likeaten::rand_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint.out(int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randint_likeaten::randint_like.dtype(Tensor self, int high, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint.low_out(int low, int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randn.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randn_likeaten::randn_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randperm.out(int n, *, Tensor(a!) out) -> Tensor(a!)aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)aten::result_type.Scalar(Tensor tensor, Scalar other) -> ScalarTypeaten::result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarTypeaten::result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarTypeaten::result_type.Tensor(Tensor tensor, Tensor other) -> ScalarTypeaten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::rnn_reluaten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::rnn_tanhaten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::rreluaten::rrelu_aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::rsubaten::seluaten::selu_aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::solve.solution(Tensor self, Tensor A, *, Tensor(a!) solution, Tensor(b!) lu) -> (Tensor(a!) solution, Tensor(b!) LU)aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::stackaten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)aten::std_meanaten::std.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)aten::symeig.e(Tensor self, bool eigenvectors=False, bool upper=True, *, Tensor(a!) e, Tensor(b!) V) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::tensordotaten::thresholdaten::threshold_aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)aten::topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) ->(Tensor(a!) values, Tensor(b!) indices)aten::trapzaten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::triplet_margin_lossaten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::unique_consecutiveaten::unique_dimaten::var_meanaten::var.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::zeros_likeaten::zeros_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::zeros.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)Expected a Class but got a %snew__rice_casters__messageExpected a Module but got a %s<=>to_sinspect==<>wrong argument type %s (expected %s)Cannot get method id and class for function__rice__ HLXQ Q!QaQQ!QXay%`0SY0 P, i"\|;S 0S$^ P^ `D,h D<h Ehh Gh 0Jh Li N<i Qpi Ti Ui Vi `X(j X8j XHj ZXj PZhj Zxj Zj P[j p\j _j ak 0bk Ph4k iLk 0kk lk qk uk wl z,l l Pl p m m n $n P@n В\n @n n Po Po p 8p Tp pp p @p @p p p q 0q Tq |q q q r r r r r s s 0s `s @Tt t Pt @(u |u u Pv v w  w w w Xx 0x @x ,y dy y z 4z 0#z $z %{ &{ '({ (<{ +{ P/| @0$| 2L| p8| :| =| @} A$} D@} ET} Fh} H} I} @K} M~ Qt~ @T~ U~ @V~ W @Z  \@ ^ a c g @i `k4 0lT n| s@ 0vt w 0y ~h @ ܂  pD @X pl  P ԃ  0 P @x pԄ P h  0 D Ć ؆ X   ̇  0 @ ``   @ 8 L p  |  ̊  , `   @   P  , L 0t   0 P Pl  @  Ď   @ t  ȏ P $ pX 0x  P А ! " @$( `'\ )x 0/ p1 3 4 64 9h : ; @> A A8 C` F G Iܓ 0L M0 NX Q 0T V W PY< [p \ p^ a pc @d@ eh m( @pP r t wԗ py {$ ~L `t    Њ ` < P  ę  `̚   D @| @ ` @< d  Ъ ܜ p @, T | Я Н   H p   @ @$ нD d  |  Ġ ؠ `  ܡ P `  ( X آ , < pL l    `Ф 0   P@ p H  - $ p    8 X Ы ! $ `&4 0'T . @0̭ 1 = 0? @ȯ L\ `N| 0O ]< 0_\ `| j l, lL @s t uض z @}t ~   @ @ Б| ` 0 0   l p @ `  8   ` p( H пh pd    ( `H  P  X x p  0 $ `< \ |  0 @ P    @" #0 %( 'H `(h +` P- . 2 4 4 @t A `B `H I J4 OL PQl R V X X _0 `P pap f @h i q, rL `sl w y y ( p 0 pL p`  4 `D T t   ( 0X Pl | p @ P   в, 0x `   0 P Pp P    P( H ` , L @ < \  p$ @D  ` 0< T 0 0  p!$ ' ) `) ) +l ,| - @/ 0 @4 6@ = > ? `@( `Fd H H K!N!O!P8!`S0!TP!Up!Z!`\!^!c!!0!,!@!0x!`!!!@ !p !X!l!!P!` !@!Цh!|!Ы!0!!p!` !< !d !x ! ! ! ! -!D -!X -! -! -! -!P ! !  ! ! !00 ! !P4 !pT !@| ! !` ! ! !` !T!t!P!P!!!@!! P! ! !!0!0!0,!h!@!@! !! ! #H!$h!%!'!P*!p+!`- !/4! 1t!4!5!7!9(!0;<!>|!B!pD!F,!GL!It!J!K! N!PP!R\!T!@W!pY![\!0^!P`!b!c!fL!0g`!i!j!m!0n!p0!qD!tx!v!x! {!`|!P~@! 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+QF1,A>QLaQ!QCA1qb1+1/!QOA7ra\QAN4Rice6detail17Exception_HandlerEN4Rice6detail12Not_CopyableEN4Rice6detail25Default_Exception_HandlerEN4Rice11Module_implINS_11Module_baseENS_6ModuleEEEN4Rice11Module_baseEN4Rice11Module_implINS_6ModuleENS_5ClassEEEN3c105ErrorENSt3__119basic_ostringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEENSt3__115basic_stringbufIcNS_11char_traitsIcEENS_9allocatorIcEEEEN3c1015VariableVersion14VersionCounterEN3c1020intrusive_ptr_targetEN2at6TensorEN4Rice9Data_TypeIN2at6TensorEEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeIN2at6TensorEEEEEN4Rice14Data_Type_BaseEN4Rice11Module_implINS_5ClassENS_14Data_Type_BaseEEEN4Rice9Data_TypeIvEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeIvEEEEN4Rice6detail6CasterIvvEEN4Rice6detail15Abstract_CasterEN4Rice6detail6CasterIN2at6TensorEvEEN3c1011StorageImplENSt3__110__function6__funcIZN5torch9from_blobEPvN3c108ArrayRefIxEERKNS4_13TensorOptionsEEUlS3_E_NS_9allocatorISA_EEFvS3_EEENSt3__110__function6__baseIFvPvEEEZN5torch9from_blobEPvN3c108ArrayRefIxEERKNS1_13TensorOptionsEEUlS0_E_N4Rice14Builtin_ObjectILi7EEEN3c1019bad_optional_accessENSt3__120__shared_ptr_pointerIPN3c1014OperatorKernelENS_14default_deleteIS2_EENS_9allocatorIS2_EEEENSt3__114default_deleteIN3c1014OperatorKernelEEENSt3__118basic_stringstreamIcNS_11char_traitsIcEENS_9allocatorIcEEEEPN2at6TensorEN4Rice14Exception_BaseEN4Rice8Jump_TagEN4Rice11Data_ObjectINS_6detail16Wrapped_FunctionEEEN4Rice9Data_TypeINS_6detail16Wrapped_FunctionEEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeINS_6detail16Wrapped_FunctionEEEEEN4Rice6detail16Wrapped_FunctionEN4Rice6detail6CasterINS0_16Wrapped_FunctionEvEEPN4Rice6detail17Exception_HandlerEN4Rice11Data_ObjectINS_6detail17Exception_HandlerEEEN4Rice11Data_ObjectINS_6detail25Default_Exception_HandlerEEEN4Rice9Data_TypeINS_6detail17Exception_HandlerEEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeINS_6detail17Exception_HandlerEEEEEN4Rice6detail6CasterINS0_17Exception_HandlerEvEEPN3c1013TensorOptionsEN3c1013TensorOptionsEN4Rice9Data_TypeIN3c1013TensorOptionsEEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeIN3c1013TensorOptionsEEEEEN4Rice6detail6CasterIN3c1013TensorOptionsEvEEN4Rice14Builtin_ObjectILi5EEEN3c106DeviceEN4Rice9Data_TypeIN3c106DeviceEEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeIN3c106DeviceEEEEEN4Rice6detail6CasterIN3c106DeviceEvEEPN5torch8autograd8VariableEN5torch8autograd8VariableEN4Rice9Data_TypeIN5torch8autograd8VariableEEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeIN5torch8autograd8VariableEEEEEN4Rice6detail6CasterIN5torch8autograd8VariableEvEEN4Rice6detail6CasterIN5torch8autograd8VariableEN2at6TensorEEEPN3c106DeviceE!!1"!1$!!!CN4Rice5ArrayE!PNSt3__16vectorIN2at6TensorENS_9allocatorIS2_EEEEPN3c106ScalarEPN3c107QSchemeE1!1%1&1'A01#1(A21 1)PN3c1010ScalarTypeEN4Rice5ClassEN4Rice11Data_ObjectINSt3__13mapImPNS_6detail15Abstract_CasterENS1_4lessImEENS1_9allocatorINS1_4pairIKmS5_EEEEEEEEN4Rice9Data_TypeINSt3__13mapImPNS_6detail15Abstract_CasterENS1_4lessImEENS1_9allocatorINS1_4pairIKmS5_EEEEEEEEN4Rice11Module_implINS_14Data_Type_BaseENS_9Data_TypeINSt3__13mapImPNS_6detail15Abstract_CasterENS3_4lessImEENS3_9allocatorINS3_4pairIKmS7_EEEEEEEEEENSt3__13mapImPN4Rice6detail15Abstract_CasterENS_4lessImEENS_9allocatorINS_4pairIKmS4_EEEEEEN4Rice6detail6CasterINSt3__13mapImPNS0_15Abstract_CasterENS2_4lessImEENS2_9allocatorINS2_4pairIKmS5_EEEEEEvEEN4Rice9ExceptionEN4Rice6ObjectEN4Rice6ModuleEN4Rice6StringETorchTensorNNgrad_enabled?_set_grad_enabledmanual_seed_arange_empty_eye_full_linspace_logspace_ones_rand_randint_randn_randperm_zeros_binary_cross_entropy_with_logits_from_blob_tensorcuda?sparse?quantized?dimelement_sizerequires_gradaddcmul!addcdiv!_requires_grad!_backwardgrad_dtype_type_layoutdevice_flat_data_to_make_subclassTensorOptionsdtypelayoutInit_calculate_gain_uniform!_normal!_constant!_ones!_zeros!_eye!_dirac!_xavier_uniform!_xavier_normal!_kaiming_uniform!_kaiming_normal!_orthogonal!_sparse!ParameterDeviceindexindex?typeCUDAavailable?device_countdimname::defaultGenerator/usr/local/include/ATen/Context.h device type not enabled.Expected false to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)allocator<T>::allocate(size_t n) 'n' exceeds maximum supported sizevalidate/usr/local/include/c10/core/Device.hDevice index must be -1 or non-negative, got Expected index_ == -1 || index_ >= 0 to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)CPU device index must be -1 or zero, got Expected !is_cpu() || index_ <= 0 to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)aten::arangestartendstepoptionsinsertNode/usr/local/include/torch/csrc/jit/ir.hinsert_before_->inBlockList() && "insert point node is no longer in a block list" INTERNAL ASSERT FAILED at :, please report a bug to PyTorch. inBlockListprev() == nullptr INTERNAL ASSERT FAILED at aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> TensortensorTypeIdToBackend/usr/local/include/c10/core/Backend.hUnrecognized tensor type ID: backendToDeviceTypeUndefined backend is not a valid device typeUnknown backendcomputeTensorTypeId/usr/local/include/c10/core/TensorOptions.hUnsupported device type for dense layout: Unsupported device type for sparse layout: Unsupported device type for mkldnn layout: Unsupported layout: typeMetaToScalarType/usr/local/include/c10/core/ScalarType.hUnsupported TypeMeta in ATen: (please report this error)StridedSparseMkldnnoperator<</usr/local/include/c10/core/Layout.hUnknown layoutgetOpTable/usr/local/include/ATen/core/ATenDispatch.hNo functions are registered for schema Expected iter != op_tables_.end() to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)make_variable/usr/local/include/torch/csrc/autograd/variable.hMust not create a new variable from a variable, use its .tensor_data()retain_/usr/local/include/c10/util/intrusive_ptr.hnew_refcount != 1 INTERNAL ASSERT FAILED at intrusive_ptr: Cannot increase refcount after it reached zero.TensorImpl with nullptr is not supported~intrusive_ptr_targetrefcount_.load() == 0 INTERNAL ASSERT FAILED at Tried to destruct an intrusive_ptr_target that still has intrusive_ptr to itweakcount_.load() == 0 INTERNAL ASSERT FAILED at Tried to destruct an intrusive_ptr_target that still has weak_intrusive_ptr to itaten::emptysizememory_formataten::empty.memory_format(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensoraten::eyenmaten::eye.m(int n, int m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::fullfill_valueaten::full(int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::linspacestepsaten::linspace(Scalar start, Scalar end, int steps=100, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::logspacebaseaten::logspace(Scalar start, Scalar end, int steps=100, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::onesaten::ones(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randaten::rand(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randintlowhighaten::randint.low(int low, int high, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randnaten::randn(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randpermaten::randperm(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::zerosaten::zeros(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> TensorUnable to convert to Class is not registered/bound in RiceData type is not bound is already bound to a different typebad cast. No caster found for meansumUnsupported reduction: from_blob/usr/local/include/ATen/Functions.hSpecified device does not match device of data Expected options.device() == device to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)getDeviceFromPtrStorageImpl/usr/local/include/c10/core/StorageImpl.hallocator_ INTERNAL ASSERT FAILED at For resizable storage, allocator must be providedConstructing a storage with meta of unknown type and non-zero numelaten::set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!)Cannot create bool from tensor method yetaten::reshapebad optional accessIssued LeftRight::read() after the destructor started runninglookup_/usr/local/include/ATen/core/dispatch/DispatchTable.hThere were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema . This usually means that this function requires a non-empty list of Tensors. Available functions are Didn't find kernel to dispatch to for operator ''. Tried to look up kernel for dispatch key ''. Registered dispatch keys are: , CATCH-ALL[, ]callUnboxedOnly/usr/local/include/ATen/core/boxing/KernelFunction.hfalse INTERNAL ASSERT FAILED at Tried to call KernelFunction::callUnboxedOnly() for a kernel that doesn't have an unboxed version.itemsize/usr/local/include/c10/core/TensorImpl.hCannot report itemsize of Tensor that doesn't have initialized dtype (e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)autograd_meta() INTERNAL ASSERT FAILED at requires_grad is not implemented for Tensoraten::addcmul_aten::addcdiv_set_requires_gradset_requires_grad is not implemented for Tensoraten::backward(Tensor self, Tensor? gradient=None, bool keep_graph=False, bool create_graph=False) -> voidaten::to.dtype_layout(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False, bool non_blocking=False, bool copy=False) -> Tensortensor does not have a devicescalarTypeToTypeMetaUnrecognized Scalartype ByteCharShortIntLongHalfFloatDoubleComplexHalfComplexFloatComplexDoubleBoolQInt8QUInt8QInt32BFloat16UNKNOWN_SCALARUnsupported typedata_ptr/usr/local/include/ATen/core/TensorMethods.hexpected scalar type but found Expected scalar_type() == ScalarType::Byte to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)dataCannot access data pointer of Tensor that doesn't have storageCannot access data pointer of Tensor that doesn't have initialized dtype (e.g., caffe2::Tensor x(CPU), prior to calling mutable_data<T>() on x)aten::numelcallUnboxedboxed_kernel_func_ != nullptr INTERNAL ASSERT FAILED at Tried to call KernelFunction::callUnboxed() on an uninitialized KernelFunction.callstack.size() == 1 INTERNAL ASSERT FAILED at A boxed kernel should only push one return to the stackreclaimowning_ptr == NullType::singleton() || owning_ptr->refcount_.load() > 0 INTERNAL ASSERT FAILED at intrusive_ptr: Can only intrusive_ptr::reclaim() owning pointers that were created using intrusive_ptr::release().toInt/usr/local/include/ATen/core/ivalue.hisInt() INTERNAL ASSERT FAILED at Expected scalar_type() == ScalarType::Char to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Short to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Int to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Long to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Float to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Double to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Bool to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)aten::to.device(Tensor self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False) -> Tensoras_variable_refAttempted to cast a Tensor to a Variable, but the dynamic type of the value is not Variable.VariableTensor that was converted to Variable was not actually a VariablestridedsparseSparse layout not supported yetcpucudaUnsupported device: linearconv1dconv2dconv3dconv_transpose1dconv_transpose2dconv_transpose3dsigmoidtanhreluleaky_reluUnsupported nonlinearity type: fan_infan_out is unboundunknown C++ exception thrown0initialize_adaptive_avg_pool2d_adaptive_avg_pool2d_out_adaptive_avg_pool3d_adaptive_avg_pool3d_out_adaptive_max_pool2d_adaptive_max_pool2d_out_adaptive_max_pool3d_adaptive_max_pool3d_out_avg_pool2d_avg_pool2d_divisor_override_avg_pool3d_avg_pool3d_divisor_override_binary_cross_entropy_binary_cross_entropy_out_col2im_col2im_out_elu_elu__elu_out_fractional_max_pool2d_fractional_max_pool2d_output_fractional_max_pool3d_fractional_max_pool3d_output_gelu_glu_glu_out_hardtanh_hardtanh__hardtanh_out_im2col_im2col_out_l1_loss_l1_loss_out_leaky_relu_leaky_relu__leaky_relu_out_linear_log_sigmoid_log_sigmoid_forward_log_sigmoid_forward_output_log_sigmoid_out_max_pool2d_with_indices_max_pool2d_with_indices_out_max_pool3d_with_indices_max_pool3d_with_indices_out_max_unpool2d_max_unpool2d_out_max_unpool3d_max_unpool3d_out_mkldnn_linear_mkldnn_reorder_conv2d_weight_mse_loss_mse_loss_out_multi_margin_loss_multi_margin_loss_out_multilabel_margin_loss_multilabel_margin_loss_forward_multilabel_margin_loss_forward_output_multilabel_margin_loss_out_nll_loss_nll_loss2d_nll_loss2d_forward_nll_loss2d_forward_output_nll_loss2d_out_nll_loss_forward_nll_loss_forward_output_nll_loss_out_one_hot_reflection_pad1d_reflection_pad1d_out_reflection_pad2d_reflection_pad2d_out_replication_pad1d_replication_pad1d_out_replication_pad2d_replication_pad2d_out_replication_pad3d_replication_pad3d_out_rrelu_with_noise_rrelu_with_noise__rrelu_with_noise_out_slow_conv_dilated2d_slow_conv_dilated3d_slow_conv_transpose2d_slow_conv_transpose2d_out_slow_conv_transpose3d_slow_conv_transpose3d_out_smooth_l1_loss_smooth_l1_loss_out_soft_margin_loss_soft_margin_loss_out_softplus_softplus_out_softshrink_softshrink_out_thnn_conv2d_thnn_conv2d_forward_thnn_conv2d_forward_output_thnn_conv2d_out_thnn_conv3d_thnn_conv3d_forward_thnn_conv3d_forward_output_thnn_conv3d_out_thnn_conv_depthwise2d_thnn_conv_depthwise2d_forward_thnn_conv_depthwise2d_forward_out_thnn_conv_depthwise2d_out_upsample_bicubic2d_upsample_bicubic2d_out_upsample_bilinear2d_upsample_bilinear2d_out_upsample_linear1d_upsample_linear1d_out_upsample_nearest1d_upsample_nearest1d_out_upsample_nearest2d_upsample_nearest2d_out_upsample_nearest3d_upsample_nearest3d_out_upsample_trilinear3d_upsample_trilinear3d_outaten::adaptive_avg_pool2daten::adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_avg_pool3daten::adaptive_avg_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_max_pool2daten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::adaptive_max_pool3daten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::avg_pool2daten::avg_pool3daten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensoraten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::col2imaten::col2im.out(Tensor self, int[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)aten::eludoublevalue cannot be converted to type without overflow: int64_ttoTensor/usr/local/include/ATen/core/ivalue_inl.hisTensor() INTERNAL ASSERT FAILED at Expected Tensor but got TupleIntListDoubleListStringTensorListGenericListGenericDictFutureObjectUninitializedCapsuleInvalidTag()aten::elu_aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)aten::fractional_max_pool2daten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::fractional_max_pool3daten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::geluaten::gluaten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)aten::hardtanhaten::hardtanh_aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)aten::im2colaten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)aten::l1_lossaten::l1_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::leaky_reluaten::leaky_relu_aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensoraten::log_sigmoidaten::log_sigmoid_forwardaten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::max_pool2d_with_indicesaten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::max_pool3d_with_indicesaten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::max_unpool2daten::max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::max_unpool3daten::max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)aten::mkldnn_linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensoraten::mkldnn_reorder_conv2d_weightaten::mse_lossaten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensoraten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::multilabel_margin_lossaten::multilabel_margin_loss_forwardaten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensoraten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensoraten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)aten::one_hotaten::reflection_pad1daten::reflection_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)aten::reflection_pad2daten::reflection_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad1daten::replication_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad2daten::replication_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad3daten::replication_pad3d.out(Tensor self, int[6] padding, *, Tensor(a!) out) -> Tensor(a!)aten::rrelu_with_noiseaten::rrelu_with_noise_aten::rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)aten::slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensoraten::slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensoraten::slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1) -> Tensoraten::slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::slow_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1) -> Tensoraten::slow_conv_transpose3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::smooth_l1_lossaten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::soft_margin_lossaten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::softplusaten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)aten::softshrinkaten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0) -> Tensoraten::thnn_conv2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)aten::thnn_conv2d_forward.output(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))aten::thnn_conv2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0) -> Tensoraten::thnn_conv3d_forward(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)aten::thnn_conv3d_forward.output(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))aten::thnn_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensoraten::thnn_conv_depthwise2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation) -> Tensoraten::thnn_conv_depthwise2d_forward.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_bicubic2daten::upsample_bicubic2d.out(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_bilinear2daten::upsample_bilinear2d.out(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_linear1daten::upsample_linear1d.out(Tensor self, int[1] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest1daten::upsample_nearest1d.out(Tensor self, int[1] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest2daten::upsample_nearest2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest3daten::upsample_nearest3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_trilinear3daten::upsample_trilinear3d.out(Tensor self, int[3] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)_abs_abs__acos_acos__add__scalar_add__tensor_add_scalar_add_tensor_addbmm_addbmm__addcdiv_addcdiv__addcmul_addcmul__addmm_addmm__addmv_addmv__addr_addr__alias_align_as_all_all_dim_allclose_any_any_dim_argmax_argmax_dim_argmin_argmin_dim_argsort_as_strided_as_strided__as_strided__storage_offset_as_strided_storage_offset_asin_asin__atan_atan2_atan2__atan__baddbmm_baddbmm__bernoulli_bernoulli__float_bernoulli__tensor_bernoulli_p_bincount_bitwise_not_bitwise_not__bmm_cauchy__ceil_ceil__cholesky_cholesky_inverse_cholesky_solve_chunk_clamp_max_clamp_max__clamp_min_clamp_min__clone_coalesce_copy__cos_cos__cosh_cosh__cumprod_cumsum_data_dense_dim_dequantize_det_detach_detach__diag_diag_embed_diagflat_diagonal_digamma_digamma__dist_div__scalar_div__tensor_div_scalar_div_tensor_dot_eig_eq__scalar_eq__tensor_eq_scalar_eq_tensor_equal_erf_erf__erfc_erfc__erfinv_erfinv__exp_exp__expand_expand_as_expm1_expm1__exponential__fft_fill__scalar_fill__tensor_fill_diagonal__flatten_using_ints_flip_floor_floor__fmod__scalar_fmod__tensor_fmod_scalar_fmod_tensor_frac_frac__gather_ge__scalar_ge__tensor_ge_scalar_ge_tensor_geometric__geqrf_ger_gt__scalar_gt__tensor_gt_scalar_gt_tensor_hardshrink_histc_ifft_index_add_index_add__index_copy_index_copy__index_fill__scalar_index_fill__tensor_index_fill_scalar_index_fill_tensor_index_select_indices_int_repr_inverse_irfft_is_coalesced_is_complex_is_distributed_is_floating_point_is_leaf_is_nonzero_is_pinned_is_same_size_is_set_to_is_signed_isclose_item_kthvalue_le__scalar_le__tensor_le_scalar_le_tensor_lerp__scalar_lerp__tensor_lerp_scalar_lerp_tensor_lgamma_lgamma__log_log10_log10__log1p_log1p__log2_log2__log__log_normal__log_softmax_logdet_logical_not_logical_not__logical_xor_logical_xor__logsumexp_lstsq_lt__scalar_lt__tensor_lt_scalar_lt_tensor_lu_solve_masked_fill__scalar_masked_fill__tensor_masked_fill_scalar_masked_fill_tensor_masked_scatter_masked_scatter__masked_select_matmul_matrix_power_max_max_dim_max_other_max_values_mean_mean_dim_median_median_dim_min_min_dim_min_other_min_values_mm_mode_mul__scalar_mul__tensor_mul_scalar_mul_tensor_multinomial_mv_mvlgamma_mvlgamma__narrow_narrow_copy_ne__scalar_ne__tensor_ne_scalar_ne_tensor_neg_neg__nonzero_nonzero_numpy_norm_scalar_normal__numel_numpy_t_orgqr_ormqr_output_nr_permute_pin_memory_pinverse_polygamma_polygamma__pow__scalar_pow__tensor_pow_tensor_scalar_pow_tensor_tensor_prelu_prod_prod_dim_int_put__q_per_channel_axis_q_per_channel_scales_q_per_channel_zero_points_q_scale_q_zero_point_qr_qscheme_random__random__from_random__to_reciprocal_reciprocal__relu_relu__remainder__scalar_remainder__tensor_remainder_scalar_remainder_tensor_renorm_renorm__repeat_repeat_interleave_self_int_repeat_interleave_self_int_dim_repeat_interleave_self_tensor_repeat_interleave_self_tensor_dim_reshape_reshape_as_resize__resize_as__rfft_roll_rot90_round_round__rsqrt_rsqrt__scatter__src_scatter__value_scatter_add_scatter_add__scatter_src_scatter_value_select_int_set__set__source_tensor_set_data_sigmoid_sigmoid__sign_sign__sin_sin__sinh_sinh__size_int_slice_tensor_slogdet_smm_softmax_solve_sort_sparse_dim_sparse_mask_sparse_resize__sparse_resize_and_clear__split_tensor_split_with_sizes_sqrt_sqrt__squeeze_squeeze__squeeze__dim_squeeze_dim_sspaddmm_std_std_dim_stride_int_sub__scalar_sub__tensor_sub_scalar_sub_tensor_sum_sum_dim_intlist_sum_to_size_svd_symeig_t_t__take_tan_tan__tanh_tanh__to_dense_to_device_to_dtype_to_mkldnn_to_other_to_sparse_to_sparse_sparse_dim_topk_trace_transpose__transpose_int_triangular_solve_tril_tril__triu_triu__trunc_trunc__type_as_unbind_int_unfold_uniform__unsqueeze_unsqueeze__values_var_var_dim_view_view_as_where_self_zero_aten::absaten::abs_aten::acosaten::acos_aten::add_Scalaraten::addaten::addbmmaten::addbmm_aten::addcdivaten::addcmulaten::addmmaten::addmm_aten::addmvaten::addmv_aten::addraten::addr_aten::aliasaten::align_asaten::allaten::allclosetoBoolisBool() INTERNAL ASSERT FAILED at aten::anyaten::argmaxaten::argminaten::argsortaten::as_stridedaten::as_strided_aten::asinaten::asin_aten::atanaten::atan2aten::atan2_aten::atan_aten::baddbmmaten::baddbmm_aten::bernoulliaten::bernoulli_floataten::bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensoraten::bitwise_notaten::bitwise_not_aten::bmmaten::cauchy_aten::ceilaten::ceil_aten::choleskyaten::cholesky_inverseaten::cholesky_solveaten::chunkaten::clamp_maxaten::clamp_max_aten::clamp_minaten::clamp_min_aten::cloneaten::coalesceaten::copy_aten::cosaten::cos_aten::coshaten::cosh_aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> TensorScalarType arguments not implemented yetaten::cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensoraten::dataaten::dense_dimaten::dequantizeaten::detaten::detachaten::detach_aten::diagaten::diag_embedaten::diagflataten::diagonalaten::digammaaten::digamma_aten::distaten::div_aten::divaten::dotaten::eigaten::eq_aten::equalaten::erfaten::erf_aten::erfcaten::erfc_aten::erfinvaten::erfinv_aten::expaten::exp_aten::expandaten::expand_asaten::expm1aten::expm1_aten::exponential_aten::fftaten::fill_aten::fill_diagonal_aten::flattenusing_intsaten::flipaten::flooraten::floor_aten::fmod_aten::fmodaten::fracaten::frac_aten::gatheraten::ge_aten::geometric_aten::geqrfaten::geraten::gt_aten::hardshrinkaten::histcaten::ifftaten::index_addaten::index_add_aten::index_copyaten::index_copy_aten::index_fill_aten::index_fillaten::index_selectaten::indicesaten::int_repraten::inverseaten::irfftaten::is_coalescedaten::is_complexaten::is_distributedaten::is_floating_pointaten::is_leaf(Tensor self) -> boolaten::is_nonzeroaten::is_pinnedaten::is_same_sizeaten::is_set_toaten::is_signedaten::iscloseaten::itemIValue is not a ScalartoDoubleisDouble() INTERNAL ASSERT FAILED at aten::kthvalueaten::le_aten::lerp_aten::lerpaten::lgammaaten::lgamma_aten::logaten::log10aten::log10_aten::log1paten::log1p_aten::log2aten::log2_aten::log_aten::log_normal_aten::log_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensoraten::logdetaten::logical_notaten::logical_not_aten::logical_xoraten::logical_xor_aten::logsumexpaten::lstsqaten::lt_aten::lu_solveaten::masked_fill_aten::masked_fillaten::masked_scatteraten::masked_scatter_aten::masked_selectaten::matmulaten::matrix_poweraten::maxotheraten::max_valuesaten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::medianaten::minaten::min_valuesaten::modeaten::mul_aten::mulaten::multinomialaten::mvlgammaaten::mvlgamma_aten::narrowaten::narrow_copyaten::ne_aten::negaten::neg_aten::nonzeroaten::nonzero_numpyaten::normaten::normal_aten::numpy_Taten::orgqraten::ormqraten::output_nr(Tensor self) -> intaten::permuteaten::pin_memoryaten::pinverseaten::polygammaaten::polygamma_aten::pow_aten::powTensor_ScalarTensor_Tensoraten::preluaten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::put_aten::q_per_channel_axis(Tensor self) -> intaten::q_per_channel_scalesaten::q_per_channel_zero_pointsaten::q_scaleaten::q_zero_pointaten::qscheme(Tensor self) -> QSchemeaten::random_aten::reciprocalaten::reciprocal_aten::reluaten::relu_aten::remainder_aten::remainderaten::renormaten::renorm_aten::repeataten::repeat_interleaveself_Tensoraten::reshape_asaten::resize_aten::resize_as_aten::rfftaten::rollaten::rot90aten::roundaten::round_aten::rsqrtaten::rsqrt_aten::scatter_srcvalueaten::scatter_addaten::scatter_add_aten::scatteraten::selectintaten::set_source_Tensoraten::set_data(Tensor(a!) self, Tensor new_data) -> voidaten::sigmoidaten::sigmoid_aten::signaten::sign_aten::sinaten::sin_aten::sinhaten::sinh_aten::sizeaten::sliceaten::slogdetaten::smmaten::softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensoraten::solveaten::sortaten::sparse_dimaten::sparse_maskaten::sparse_resize_aten::sparse_resize_and_clear_aten::splitaten::split_with_sizesaten::sqrtaten::sqrt_aten::squeezeaten::squeeze_aten::sspaddmmaten::stdaten::strideaten::sub_aten::subaten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::sum_to_sizeaten::svdaten::symeigaten::taten::takeaten::tanaten::tan_aten::tanhaten::tanh_aten::to_denseaten::to_mkldnnaten::to_sparsesparse_dimaten::topkaten::traceaten::transpose_aten::transposeaten::triangular_solveaten::trilaten::tril_aten::triuaten::triu_aten::truncaten::trunc_aten::type_asaten::unbindaten::unfoldaten::uniform_aten::unsqueezeaten::unsqueeze_aten::valuesaten::varaten::viewaten::view_asaten::whereaten::zero__abs_out_acos_out_adaptive_avg_pool1d_adaptive_max_pool1d_add_out_addbmm_out_addcdiv_out_addcmul_out_addmm_out_addmv_out_addr_out_affine_grid_generator_align_tensors_all_out_alpha_dropout_alpha_dropout__any_out_arange_out_arange_start_out_asin_out_atan2_out_atan_out_avg_pool1d_baddbmm_out_batch_norm_batch_norm_backward_elemt_batch_norm_backward_reduce_batch_norm_elemt_batch_norm_gather_stats_batch_norm_gather_stats_with_counts_batch_norm_stats_batch_norm_update_stats_bernoulli_out_bilinear_bitwise_not_out_bmm_out_broadcast_tensors_can_cast_cartesian_prod_cat_cat_out_cdist_ceil_out_celu_celu__chain_matmul_cholesky_inverse_out_cholesky_out_cholesky_solve_out_clamp_max_out_clamp_min_out_combinations_constant_pad_nd_conv1d_conv2d_conv3d_conv_tbc_conv_transpose1d_conv_transpose2d_input_conv_transpose3d_input_convolution_convolution_overrideable_copy_sparse_to_sparse__cos_out_cosh_out_cosine_embedding_loss_cosine_similarity_ctc_loss_intlist_ctc_loss_tensor_cudnn_affine_grid_generator_cudnn_batch_norm_cudnn_convolution_cudnn_convolution_backward_bias_cudnn_convolution_backward_input_cudnn_convolution_backward_weight_cudnn_convolution_transpose_cudnn_convolution_transpose_backward_bias_cudnn_convolution_transpose_backward_input_cudnn_convolution_transpose_backward_weight_cudnn_grid_sampler_cudnn_is_acceptable_cumprod_out_cumsum_out_diag_out_digamma_out_div_out_dot_out_dropout_dropout__eig_e_embedding_embedding_bag_embedding_renorm__empty_like_eq_scalar_out_eq_tensor_out_erf_out_erfc_out_erfinv_out_exp_out_expm1_out_eye_m_out_eye_out_fake_quantize_per_channel_affine_fake_quantize_per_tensor_affine_fbgemm_linear_fp16_weight_fbgemm_linear_fp16_weight_fp32_activation_fbgemm_linear_int8_weight_fbgemm_linear_int8_weight_fp32_activation_fbgemm_linear_quantize_weight_fbgemm_pack_gemm_matrix_fp16_fbgemm_pack_quantized_matrix_fbgemm_pack_quantized_matrix_kn_feature_alpha_dropout_feature_alpha_dropout__feature_dropout_feature_dropout__floor_out_fmod_scalar_out_fmod_tensor_out_frac_out_frobenius_norm_frobenius_norm_dim_frobenius_norm_out_full_like_full_out_gather_out_ge_scalar_out_ge_tensor_out_geqrf_a_ger_out_grid_sampler_grid_sampler_2d_grid_sampler_3d_group_norm_gru_cell_gru_data_gru_input_gt_scalar_out_gt_tensor_out_hinge_embedding_loss_histc_out_hspmm_hspmm_out_index_select_out_instance_norm_inverse_out_isnan_kl_div_kthvalue_values_layer_norm_le_scalar_out_le_tensor_out_lerp_scalar_out_lerp_tensor_out_lgamma_out_linspace_out_log10_out_log1p_out_log2_out_log_out_logical_not_out_logical_xor_out_logspace_out_logsumexp_out_lstm_cell_lstm_data_lstm_input_lstsq_x_lt_scalar_out_lt_tensor_out_lu_solve_out_margin_ranking_loss_masked_select_out_matmul_out_matrix_rank_matrix_rank_tol_max_dim_max_max_out_max_pool1d_max_pool1d_with_indices_max_pool2d_max_pool3d_mean_out_median_dim_values_meshgrid_min_dim_min_min_out_miopen_batch_norm_miopen_convolution_miopen_convolution_backward_bias_miopen_convolution_backward_input_miopen_convolution_backward_weight_miopen_convolution_transpose_miopen_convolution_transpose_backward_input_miopen_convolution_transpose_backward_weight_miopen_depthwise_convolution_miopen_depthwise_convolution_backward_input_miopen_depthwise_convolution_backward_weight_miopen_rnn_mkldnn_adaptive_avg_pool2d_mkldnn_convolution_mkldnn_convolution_backward_input_mkldnn_convolution_backward_weights_mkldnn_max_pool2d_mm_out_mode_values_mul_out_multinomial_out_mv_out_native_batch_norm_native_layer_norm_native_norm_ne_scalar_out_ne_tensor_out_neg_out_nonzero_out_norm_except_dim_nuclear_norm_nuclear_norm_dim_nuclear_norm_dim_out_nuclear_norm_out_ones_like_ones_out_orgqr_out_ormqr_out_pairwise_distance_pdist_pixel_shuffle_poisson_poisson_nll_loss_polygamma_out_pow_scalar_pow_scalar_out_pow_tensor_scalar_out_pow_tensor_tensor_out_prod_int_out_promote_types_qr_q_quantize_per_channel_quantize_per_tensor_quantized_gru_cell_quantized_gru_data_quantized_gru_input_quantized_lstm_quantized_lstm_cell_quantized_max_pool2d_quantized_rnn_relu_cell_quantized_rnn_tanh_cell_rand_generator_out_rand_like_rand_out_randint_generator_out_randint_like_randint_like_low_randint_low_generator_out_randint_low_out_randint_out_randn_generator_out_randn_like_randn_out_randperm_generator_out_randperm_out_range_out_reciprocal_out_remainder_scalar_out_remainder_tensor_out_renorm_out_repeat_interleave_tensor_result_type_scalar_result_type_scalar_scalar_result_type_scalar_tensor_result_type_tensor_rnn_relu_cell_rnn_relu_data_rnn_relu_input_rnn_tanh_cell_rnn_tanh_data_rnn_tanh_input_round_out_rrelu_rrelu__rsqrt_out_rsub_scalar_rsub_tensor_selu_selu__sigmoid_out_sign_out_sin_out_sinh_out_solve_solution_sort_values_sqrt_out_sspaddmm_out_stack_stack_out_std_mean_std_mean_dim_std_out_sub_out_sum_intlist_out_svd_u_symeig_e_take_out_tan_out_tanh_out_tensordot_threshold_threshold__threshold_out_topk_values_trapz_dx_trapz_x_triangular_solve_x_tril_out_triplet_margin_loss_triu_out_trunc_out_unique_consecutive_unique_consecutive_dim_unique_dim_var_mean_var_mean_dim_var_out_where_zeros_like_zeros_outaten::abs.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_avg_pool1daten::adaptive_max_pool1daten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::affine_grid_generatoraten::align_tensorsaten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::alpha_dropoutaten::alpha_dropout_aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::avg_pool1daten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensoraten::batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu) -> Tensoraten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor)aten::batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensoraten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor)aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int[] counts) -> (Tensor, Tensor)aten::batch_norm_statsaten::batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor)aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)aten::bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias) -> Tensoraten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::broadcast_tensorsaten::cartesian_prodaten::cataten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)aten::cdistaten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::celuaten::celu_aten::chain_matmulaten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)aten::combinationsaten::constant_pad_ndaten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensoraten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensoraten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensoraten::conv_tbcaten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] output_padding=0, int groups=1, int[1] dilation=1) -> Tensoraten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> Tensoraten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensoraten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensoraten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensoraten::copy_sparse_to_sparse_aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::cosine_embedding_lossaten::cosine_similarityaten::ctc_lossaten::cudnn_affine_grid_generatoraten::cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)aten::cudnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::cudnn_convolution_backward_biasaten::cudnn_convolution_backward_inputaten::cudnn_convolution_backward_weightaten::cudnn_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::cudnn_convolution_transpose_backward_biasaten::cudnn_convolution_transpose_backward_inputaten::cudnn_convolution_transpose_backward_weightaten::cudnn_grid_sampleraten::cudnn_is_acceptableaten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)aten::dropoutaten::dropout_aten::eig.e(Tensor self, bool eigenvectors=False, *, Tensor(a!) e, Tensor(b!) v) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)aten::embeddingaten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None) -> (Tensor, Tensor, Tensor, Tensor)aten::embedding_renorm_aten::empty_likeselfaten::empty_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False, MemoryFormat? memory_format=contiguous_format) -> Tensoraten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::eye.m_out(int n, int m, *, Tensor(a!) out) -> Tensor(a!)aten::eye.out(int n, *, Tensor(a!) out) -> Tensor(a!)aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensoraten::fake_quantize_per_tensor_affineaten::fbgemm_linear_fp16_weightaten::fbgemm_linear_fp16_weight_fp32_activationaten::fbgemm_linear_int8_weightaten::fbgemm_linear_int8_weight_fp32_activationaten::fbgemm_linear_quantize_weightaten::fbgemm_pack_gemm_matrix_fp16aten::fbgemm_pack_quantized_matrixaten::feature_alpha_dropoutaten::feature_alpha_dropout_aten::feature_dropoutaten::feature_dropout_aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::frobenius_normaten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::full_likeaten::full_like.dtype(Tensor self, Scalar fill_value, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::full.out(int[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)aten::grid_sampleraten::grid_sampler_2daten::grid_sampler_3daten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensoraten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::gruinputaten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::hinge_embedding_lossaten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)aten::hspmmaten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensoraten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::isnanaten::kl_divaten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensoraten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::linspace.out(Scalar start, Scalar end, int steps=100, *, Tensor(a!) out) -> Tensor(a!)aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::logspace.out(Scalar start, Scalar end, int steps=100, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)aten::lstmaten::lstsq.X(Tensor self, Tensor A, *, Tensor(a!) X, Tensor(b!) qr) -> (Tensor(a!) solution, Tensor(b!) QR)aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)aten::margin_ranking_lossaten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::matrix_ranktolaten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::max_pool1daten::max_pool1d_with_indicesaten::max_pool2daten::max_pool3daten::mean.out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::meshgridaten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_convolution_backward_biasaten::miopen_convolution_backward_inputaten::miopen_convolution_backward_weightaten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_convolution_transpose_backward_inputaten::miopen_convolution_transpose_backward_weightaten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_depthwise_convolution_backward_inputaten::miopen_depthwise_convolution_backward_weightaten::miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)aten::mkldnn_adaptive_avg_pool2daten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensoraten::mkldnn_convolution_backward_inputaten::mkldnn_convolution_backward_weightsaten::mkldnn_max_pool2daten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)aten::native_layer_norm(Tensor input, Tensor? weight, Tensor? bias, int M, int N, float eps) -> (Tensor, Tensor, Tensor)aten::native_normaten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::norm_except_dimaten::nuclear_normaten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::ones_likeaten::ones_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::ones.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)aten::pairwise_distanceaten::pdistaten::pixel_shuffleaten::poissonaten::poisson_nll_lossaten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)aten::quantized_gru_cellaten::quantized_gruaten::quantized_lstm(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor)aten::quantized_lstm_cellaten::quantized_max_pool2daten::quantized_rnn_relu_cellaten::quantized_rnn_tanh_cellaten::rand.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::rand_likeaten::rand_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint.out(int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randint_likeaten::randint_like.dtype(Tensor self, int high, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint.low_out(int low, int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randn.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randn_likeaten::randn_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randperm.out(int n, *, Tensor(a!) out) -> Tensor(a!)aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)aten::result_type.Scalar(Tensor tensor, Scalar other) -> ScalarTypeaten::result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarTypeaten::result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarTypeaten::result_type.Tensor(Tensor tensor, Tensor other) -> ScalarTypeaten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::rnn_reluaten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::rnn_tanhaten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::rreluaten::rrelu_aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::rsubaten::seluaten::selu_aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::solve.solution(Tensor self, Tensor A, *, Tensor(a!) solution, Tensor(b!) lu) -> (Tensor(a!) solution, Tensor(b!) LU)aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::stackaten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)aten::std_meanaten::std.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::sum.IntList_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)aten::symeig.e(Tensor self, bool eigenvectors=False, bool upper=True, *, Tensor(a!) e, Tensor(b!) V) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::tensordotaten::thresholdaten::threshold_aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)aten::topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) ->(Tensor(a!) values, Tensor(b!) indices)aten::trapzaten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::triplet_margin_lossaten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::unique_consecutiveaten::unique_dimaten::var_meanaten::var.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::zeros_likeaten::zeros_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::zeros.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)Expected a Class but got a %snew__rice_casters__messageExpected a Module but got a %s<=>to_sinspect==<>wrong argument type %s (expected %s)Cannot get method id and class for function__rice__ HLXQ Q!QaQQ!QXa(&T:,[ +E ,k"~=Y T0H 8 9 : <8 =d > ? P? ? @ `@ @ 0A PB pEP Gl H 0N O Q pR !W![\!]x!_!d!0f4!Pj!pP!rp!t!0v!x! 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