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AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#H7ML@MIG8HEHpH8DAAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#XHנML@MIG8HEHpH8`CAAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1CHwML@MIG8HEHpH8BAAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cHML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ18HML@MIG8HEHpH8@?AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#4HWML@MIG8HEHpH8=AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1CExHML@MIG8HEHpH8<AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cVHML@MIG8HEHpH8 ;AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1gH7ML@MIG8HEHpH89AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1yXHוML@MIG8HEHpH8`8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HwML@MIG8HEHpH87AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HML@MIG8HEHpH85AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1蓬8HML@MIG8HEHpH8@4AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HWML@MIG8HEHpH82AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ13xHML@MIG8HEHpH81AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1SHML@MIG8HEHpH8 0AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1H7ML@MIG8HEHpH8.AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1SXH׊ML@MIG8HEHpH8`-AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HwML@MIG8HEHpH8,AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1&HML@MIG8HEHpH8*AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ178HML@MIG8HEHpH8@)AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HHWML@MIG8HEHpH8'AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1YxHML@MIG8HEHpH8&AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1jHML@MIG8HEHpH8 %AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1|H7ML@MIG8HEHpH8#AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#XHML@MIG8HEHpH8`"AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1CHw~ML@MIG8HEHpH8!AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cH}ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ18H{ML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1ؽHWzML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#xHxML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1CHwML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1鸹H7vML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1XHtML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1c(HwsML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ19阵HrML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1CK8HpML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1\زHWoML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#nxHmML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HlML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#鸮H7kML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1ӢXHiML@MIG8HEHpH8` AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HwhML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1s阪HgML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ18HeML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1 اHWdML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ11xHbML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1SVHaML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1h鸣H7`ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#yXH^ML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1CHw]ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1c队H\ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ18HZML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1؜HWYML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1xHWML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1+HVML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1<鸘H7UML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1MXHSML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1^HwRML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1Cp阔HQML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1裁8HOML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1ÒؑHWNML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1xHLML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1CHKML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1鸍H7JML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1XHHML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1HwGML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1 阉HFML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ18HDML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1U؆HWCML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1zxHAML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cH@ML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1鸂H7?ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ13XH=ML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1Hw<ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1~H;ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1S8}H9ML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1{HW8ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ13xzH6ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1)yH5ML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1;wH74ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1LXvH2ML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1^tHw1ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1osH0ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ18rH.ML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cpHW-ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1胣xoH+ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1nH*ML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1lH7)ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1C#XkH'ML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ14iHw&ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1sFhH%ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#X8gH#ML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1ieHW"ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1S{xdH ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cHML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1賞aH7ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cX`HML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1^HwML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1]HML@MIG8HEHpH8蠼AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1s8\HML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1裝ZHWML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1賙xYHML@MIG8HEHpH8耸AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ13XHML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1SVH7ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1XUHML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1bSHwML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1sRHML@MIG8HEHpH8蠱AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1胗8QH ML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1裻OHW ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1xNH ML@MIG8HEHpH8耭AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cMH ML@MIG8HEHpH8 AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1KH7ML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1,XJHML@MIG8HEHpH8`AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1cPHHwML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1胎GHML@MIG8HEHpH8蠦AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ18FHML@MIG8HEHpH8@AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1#DHWML@MIG8HEHpH8AAO)E)MAG AO0AO@MHhHhH`HpHMHxHMHHDžHXHHLMԉHPHHHHLHxHHDžHHLDž(U(UM8)M(E(LhH}ȉƺ1xCIEHHh:HHHhHHHheHHHhIHHHh-HHHhHHHhHuHHhvHYHHhZH=HHh>H!HHh"HHGH?H7H/H'HHHHHHHH}HHH}H{HH}HbHH}fHIHH}MH0HH}4lHHH}VHHH}@HHH}*HHH}HHH}LhHDžhMtAt I~ELEHEHxHHhyH_K@@ACJDEFG"IXJKLJVn.NnNU>^>^~RTNn.NVS~޴>^~^ZR.Nnέ.^Q^~~ޟ>>bPNn.nΘfO>^~ފ>jNqNstvnwx.z{|N~nMbc^efh~ij>lmn^prLTVnWX.Z[\NN^_afff.UHAWAVAUATSHxLxIAAHHUMHHEH=NEAHpD}DžlE~lHEH]HE2WEEEHEADH HcHIHcNHDDCEHH}HuHluH}uH}H5KHǺH}H5XKH0H5ڼKHHuHVHvH= NDDEML4$H= NH bIH NHDDfEHHpLEHLH=T NDD7EML4$H=5 NHNHpLEHHHKH5KH7H}H]HEH$t H{EHE H= NH@H= NH@HpLELx1dEH}u5HEH}EHMHH;EuHx[A\A]A^A_]FEIH]HuHEH5E H1HH}jELTEH}YEHEHH}H}:RHrEHfk}HUHAWAVAUATSH8L2Mt{LIHELuHEE7HHEH=] NHMLELMЉuĺ"jjP3EH HEL9LBLuHuL9v{H= NuEH8[A\A]A^A_]ffff.1AщHULMMB LILALMMC CTHHEHH9sLLsHKHS0LkHH{PLCxLLLHH LILLHHUUUUUUUUH!HULH)HH33333333H!HH!HHHHHUHH!HHH8HHI!@HUȍQ%UUUU)%33333333%i!fHH H LEtHI1HILLEI1ALLEmfffff.UHAWAVAUATSHHLLMBIHELEHEED7H$L= NALD#@1EHUHMLELMLDuD&fff.H=NuHUHMLELMiEHuG1AՉHULMMB LMMCLILA ATHHEHH9sL#LsHKHS0LSHL[PL{xLHLHH LiLLHHUUUUUUUUH!HULH)HH33333333H!HH!HHHHHUHH!HHH8HHI!@HUQ%UUUU)%33333333%i!fHH H L}tHI1HILL}I1ALL}mHH[A\A]A^A_]ffffff.UHAWAVAUATSHHLLM2IHELEHEED7L $L=NALD#@1EHUHMLELMLDuDH=YNuHUHMLELMEHuG1AՉHULMMB LMMCLILA ATHHEHH9sL#LsHKHS0LSHL[PL{xLHLHH LiLLHHUUUUUUUUH!HULH)HH33333333H!HH!HHHHHUHH!HHH8HHI!@HUQ%UUUU)%33333333%i!fHH H L}tHI1HILL}I1ALL}mHH[A\A]A^A_]ffffff.UHAWAVAUATSH8L2Mt{LIHELuHEE7HHEH= NHMLELMЉuĺ"jjPEH HEL9LBLuHuL9v{H=NuEH8[A\A]A^A_]ffff.1AщHULMMB LILALMMC CTHHEHH9sLLsHKHS0LkHH{PLCxLLLHH LILLHHUUUUUUUUH!HULH)HH33333333H!HH!HHHHHUHH!HHH8HHI!@HUȍQ%UUUU)%33333333%i!fHH H LEtHI1HILLEI1ALLEmfffff.UHAWAVAUATSH8L2Ir{LLHELuHEE7HLUH=NHMLELMЉu!PjAR EH HEL9ICHEHuH9vH=kNu? EH8[A\A]A^A_]Lu0fDHuLuHMHHL9ICHuHEHHH9sH9vfffffff.1AщHULMMB LILALMMC CTHHEHHH9kLL{HKHS0LsHH{PLCxLLLHH LILLHHUUUUUUUUH!HULH)HH33333333H!HH!HHHHHUHH!HHH8HHI!ffffff.HUQ%UUUU)%33333333%i!fHH H LEtHI1HILLEuI1ALLE]fffff.UHAWAVAUATSHHLLMBIHELEHEED7H$L=MALD$@1 EHUHMLELMLDuD&fff.H=MuHUHMLELMi EHuG1AՉHULMMB LMMCLILA ATHHEHH9sL#LsHKHS0LSHL[PL{xLHLHH LiLLHHUUUUUUUUH!HULH)HH33333333H!HH!HHHHHUHH!HHH8HHI!@HUQ%UUUU)%33333333%i!fHH H L}tHI1HILL}I1ALL}mHH[A\A]A^A_]ffffff.UHAWAVAUATSHxLxIAAHH%MHHEH=MEAHpD}DžlE~lHEH]HE2WEEEHEADH HcHIH3MHDDEHH}HuHlEH}uH}H5KHǺH}H5(KHH5KHHuHVHvH=MDDkEML4$H=iMH bIHVMHDD6EHQHpLEHLH=$MDDEML4$H=MHnHpLEHHHH5ĘKHH}H]HEH$t H{EHE H=MHpH=wMHPHpLELx14EH}u5HEH}EHMHH;EuHx[A\A]A^A_]EIH]HuHOEHE H1HH}:EL$EH})EH_DHH}H} @HBDHfk}HUHAWAVAUATSH8L2MtLIHELuHEE7HHEH=-MHMLELMЉuĺ"jjPEH HEL9LBLuHuL9H=MuEH8[A\A]A^A_]f1AщHULMMB LILAZLMMC CDHHEHH9}LLsHKHS0LkHH{PLCxLLLHH LILLHHUUUUUUUUH!HULH)HH33333333H!HH!HHHHHUHH!HHH8HHI!HUȍQ%UUUU)%33333333%i!fHH H 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HIF(HEL0Pfffff.HHPHHMH9~HHPHUw;HHPR>HH5Aj>HP>HH59@<HH5A:L}MtHIGHtHEHtHGILPL$>:HEHuIHLPLuHEMtLL?:LuMt#HIFHuILPL=:LuMt#HIFHuILPL=:HHh[A\A]A^A_]>:HH}HH}YH}PH}GHH}9H6:UHSPHHHN9HFșLcHFHHIHV A9uH H)Ht H<H;:v wH H)roHRHtHtaHHF(HHPHF8HHtH9wAHPHuHHPHtHt%HF0HHPHF@H0HHPHH[]~=:fffff.UHAWAVAUATSHLIIIH}HHvuAD$E؉]3э3șAI$HxLPI$HLLDPM/CHIW)EHEI)uE1E13L<:IHEIMLeHǾL>:LeM)LhLpELeLmH]LHHPffffff.W))HDžLPLXH`LhLpHxHxH} LLHLL HH(HH0HhH8HH@HEHtHE)fff.H}HHEHfHEHELuHEMtLL2;:HUHH"uLPL::L1H@8:HHH)WH@HHH@e-0::IHH HRIVINHW)HDžHUHHEAINAF8:IF HIF(HEL0Pfffff.HHPHHMH9~HHPHUHHPRmHH5@jiHPMHH5@HH56@HHVHvDH09:H9:PH`8:HHH}PLuHEMLL8:kHHHHH5u@HPLHDžMtAt I~j8:Lb8:fD(H(XHHx HHHHHXP0LuHEMtLL7:Hu.EEԋM9LHHP~HEHH}Ht7:LMt#HIFHuILPLb6:LMt#HIFHuILPL36:HEHĘ[A\A]A^A_]謚 H}葚 HH}HH}HHH}b H*HH}JHHPHqHHuu.PuHWH6:t HtHc6:PuHPtH`@6:HH}H}Ht$6:H Hx Hx.:HHYH}Hufffff.UHAWAVAUATSHhHIL"MFELcLL H tL1IHYMuD1HYMt HHEIA~7șэ7AIH}LPIH}LDPLxH]HE(E)EHtH@HE(E)EHLtH@HEH$H}HxLMLHME1L}Mt#HIGHuILPL3:L}MtHIGHtHEHtHGILPLQ3:HEHuIHLPLuHEMtL'L;4:LuMt#HIFHuILPL2:LuMt#HIFHuILPL2:HHh[A\A]A^A_]3:HH}$HH} H}} H}t HH}f H+:fffff.UHAWAVAUATSHIHHHN HFЙHcHVL:ML9u IF L8M)DHEEAH}HuHU2 H}H}H5@HǺ\H}H5@HH5@HHut HVHvHHHH5N@HH}X LeHEMtA$t I|$B2:L:2:IF ItK /H;v L8M)rzH@MtHtlLIN(L9HAIF8HItI9wLHPMuIHPMtHt0IF0L8HPIF@H0HHLPHH[A\A]A^A_]1:H HH}~ H}lH):HUDUHAWAVAUATSHHHHN HV+IHcHVL:ML9u HC L8M)DHEEAH}HuHU& H}H}H5@HǺPH}H5@H H5@H Hut HVHvHHH H5B@H H}L LeHEMtA$t I|$60:L.0:HC It K /H;v =L8M)r5HPMtHt'LHC(H0HLLLH[A\A]A^A_]/:H HH} H}jH)(:HUHAWAVAUATSHLIIIH}HHvuAD$E؉]3э3șAI$HxLPI$HLLDPM/CHIW)EHEI)uE1E13L.:IHEIMLeHǾL0:LeM)LhLpELeLmH]LHHPffffff.W))HDžLPLXH`LhLpHxHxH} LLHLL HH(HH0HhH8HH@HEHtHE)fff.H}HHEHfHEHELuHEMtLNLb-:HUHH"uLPL,:L1H@*:HHH)WH@HHH@e]0,:IHH HRIVINHW)HDžHUHHEAINAF +:IF HIF(HEL0Pfffff.HHPHHMH9~HHPHU'HHPR HH5@j HP} HH5@HH5f@HHVHvDH`+:HG+:PH`.+:HHH}PLuHEML߷L*:kHHHHH5@HPLHDžMtAt I~*:L*:fD(H(XHHx HHHHHXP0LuHEMtLL'*:Hu.EEԋM9LHHP~HEHH}Ht):LMt#HIFHuILPL(:LMt#HIFHuILPLc(:HEHĘ[A\A]A^A_]܌ H} HH}HH}HHH}蒵 H*HH}zHHPH dHHuu.PuHWH(:t HtH(:PuHPtH`p(:HH}ƴH}HtT(:HHxH :HH艴H}Hufffff.UHAWAVAUATSHhHIL"IFELcLL H tL1IHYMuD1HYMt HHEIA~7șэ7AIH}LPIH}LDPLxH]HE(E)EHtH@HE(E)EHLtH@HEH$H}HxLMLHME1L}Mt#HIGHuILPL%:L}MtHIGHtHEHtHGILPL%:HEHuIHLPLuHEMtLVLj&:LuMt#HIFHuILPL%:LuMt#HIFHuILPL$:HHh[A\A]A^A_]%:HH}SHH}H}H}HH}H-:ffff.UHAWAVAUATSHIHHHN HFЙHcHVL:ML9u IF L8M)LEHHEH}HuHUeH}H}H5@HǺ\H}H58@HH5@HHut HVHvHHHH5@HH}LeHEMtA$t I|$u$:Lm$:IF ItK /H;v L8M)rzH@MtHtlLIN(L9HAIF8HItI9wLHPMuIHPMtHt0IF0L8HPIF@H0HHLPHH[A\A]A^A_]#:H HH}H}^H :HUHAWAVAUATSHHHHN HV+IHcHVL:ML9u HC L8M)LEHHEH}HuHUYH}H}H5@HǺPH}H5,@HH5@HHut HVHvHHHH5u@HH}LeHEMtA$t I|$i":La":HC It K /H;v =L8M)r5HPMtHt'LHC(H0HLLLH[A\A]A^A_]!:H HH}H}$]H\:H@UHAWAVAUATSHLIIIH}HHvuAD$E؉]3э3șAI$HxLPI$HLLDPM/CHIW)EHEI)uE1E13L'!:IHEIMLeHǾL.#:LeM)LhLpELeLmH]LHHPffffff.W))HDžLPLXH`LhLpHxHxH} LLHLL HH(HH0HhH8HH@HEHtHE)fff.H}HHEHfHEHELuHEMtL~L:HUHH"uLPL:L1H@:HHH)WH@HHH@e荇0:IHH HRIVINHW)HDžHUHHEAINAFP:IF HIF(HEL0Pfffff.HHPHHMH9~HHPHUWHHPRHH5@jHPHH5@HH5@HHVHvDH:Hw:PH`^:HHH}PLuHEMLL#:kHHH4HH5կ@ HPLHDžMtAt I~:L:fD(H(XHHx HHHHHXP0LuHEMtLCLW:Hu.EEԋM9LHHP~HEHH}Ht:LMt#HIFHuILPL:LMt#HIFHuILPL:HEHĘ[A\A]A^A_]  H}~ HH}HH}HHH}§ H*HH}誧HHPHPVHHuu.PuHWH:t HtH:PuHPtH`:HH}H}Ht:HLHx@H:HH蹦H}Hufffff.UHAWAVAUATSHhHIL"IFELcLL H tL1IHYMuD1HYMt HHEIA~7șэ7AIH}LPIH}LDPLxH]HE(E)EHtH@HE(E)EHLtH@HEH$H}HxLMLHME1*L}Mt#HIGHuILPL:L}MtHIGHtHEHtHGILPL:HEHuIHLPLuHEMtL膥L:LuMt#HIFHuILPLN:LuMt#HIFHuILPL":HHh[A\A]A^A_]:HH}胤HH}H}H}HH}H]:ffff.UHAWAVAUATSHIHHHN HFЙHcHVL:ML9u IF L8M)LEHHEH}HuHUH}H}H5&@HǺ\ H}H5h@H@H5@H,Hut HVHvHHHH5@HH}LeHEMtA$t I|$:L:IF ItK /H;v L8M)rzH@MtHtlLIN(L9HAIF8HItI9wLHPMuIHPMtHt0IF0L8HPIF@H0HHLPHH[A\A]A^A_]:H HH}H}QHP:HUHAWAVAUATSHHHHN HV+IHcHVL:ML9u HC L8M)LEHHEH}HuHUH}H}H5@HǺPH}H5\@H4H5@H Hut HVHvHHHH5@HH}LeHEMtA$t I|$:L:HC It K /H;v =L8M)r5HPMtHt'LHC(H0HLLLH[A\A]A^A_].:H HH}H}TOH :H@UHAWAVAUATSHhHUIVHuFEHM)ؙA*:f: Xޛ?D,E]9EAT$Eփ1f.@@AAAAB@AAAAB9utAB‰EEH}HuHUH}trH}H5@HǺH}H5@HH56@ HHu<HVHv5EEЅt uHMDșHEHuHH}PMHuHEHHPHH}PL}MtHIGHt2HEHtCHEHEM1ILPL:HEHuHuHH}PMHuHH}PH]Ht#HHCHuHHPHa:H]HEHtHLH`:H]HtH3HG:H}ƅp"f7@fqfE.H4@H}HpDwy0:HEHMHKHMHKHMfWf)EHELuHEHKCS:HC Ls(IEpH]HEH]Ey :H5[BHH :H8# :LH[A\A]A^A_]ILc :6IH5BHHs :IH8 :L:IHL:ff.UHHHt] :]ff.UHSPHHHt :HH[] :ff.UHSPH]HnH5e@HǺjHRH5@*HH}e1H[]HH}PH:H0UHAWAVSHEIHD}DMHEHEHUHMHEHEP :HEHEQHEC@@0@@ f@@K@@?ons.@CHEHEHEHEHEHEHEHD$E$H}DHEHtH-IHLPLuHEMtL;LO :Et H}@ :HHĈ[A^A_]HH}臕HEt H} :H{:ff.UHSHHHHEHAHEHEHHDHH[]fUHSHHHHEHAHEHEHH薓HH[]fff.UHAWAVAUATSHMMIIHIH I@H(IH0IAH8H H0PIcFI9H]H]IOHIUHtH4HH;3vH H)H{Ht HLuDuHIuH9t :AttEI$H@ID$HHIHPIGHXHH`HCHhH@HPL`H]HHuI$HpID$HxHHEHCHEHpHMH]HHu0:HEHE1HE!"@@@f@ vHo@H}Huo0:H]IEHMINHMINHMW)EHEAINAF:IF IF(L3Et H}1:Et H}":HHĸ[A\A]A^A_]:HEuEu"He9H}:Et HEtH}:H:9f.UHAVSH`HHBHHE w[HEHHc HLuL=H5@HǺ\9L!H5K@+HmH}4HH HtHHc HH@HpHHHHUHIHMH gBHMLuLuH@DHUHL_H HHHc HH@HpHHHHUHIHMH BHMLuLuH@DHUHL+H HHHc HH@HpHHHHUHIHMH BHMLuLuH@DHUHLH HH@HHc HHpHHHHUHIHMH CHMLuLuH@DHUHLY$H HH@HHc HHpHHHHUHIHMH SCHMLuLuH@DHUHLH H' H@H Hc HHpHHHHUHIHMH CHMLuLuH@DHUHLRH Ha H@HHc HHpHHHHUHIHMH CHMLuLuH@DHUHLH HH@H Hc HHpHHHHUHIHMH  CHMLuLuH@DHUHLH H H@HHc HHpHHHHUHIHMH CHMLuLuH@DHUHLLH HH@H Hc HHpHHHHUHIHMH FCHMLuLuH@DHUHLH HH@HHc HHpHHHHUHIHMH CHMLuLuH@DHUHLzEEHInvalid HEE op.EHO@H}Hudh0}:H@HpHHHHUHIHMH NBHMLuLuH@DHUHLH@HpHHHHUHIHMH BHMLuLuH@DHUHLlHpLuL|9H@H}LP$h09HpLuLE9HL@H}LWg0{9HpLuL9H@H}L^g0D9KEHInvalid HEE op.EHѵ@H}Hudqg09H@HpHHHHUHIHMH PBHMLuLuH@DHUHLm8H@HpHHHHUHIHMH BHMLuLuH@DHUHL#HpLuL9H@H}LPf049;HpLuL9Hδ@H}LWof09HpLuL9H@H}L^8f09EHInvalid HEE op.EHS@H}Hude09H@HpHHHHUHIHMH RBHMLuLuH@DHUHL H@HpHHHHUHIHMH BHMLuLuH@DHUHLp HpLuL9H@H}LP(e09 HpLuLI9HP@H}LWd09 HpLuL9H@H}L^d0H9O EHInvalid HEE op.EHղ@H}Hudud09 HpHHHHUHIHMH XBHMLuLuH@DHUHLu@ HpHHHHUHIHMH BHMLuLuH@DHUHL/ HpHHHHUHIHMH BHMLuLuH@DHUHL HpHHHHUHIHMH BHMLuLuH@DHUHLn HpHHHHUHIHMH @BHMLuLuH@DHUHL]( EHInvalid HEE op.EH2@H}Hudb0`9g HpHHHHUHIHMH BHMLuLuH@DHUHL HpHHHHUHIHMH BHMLuLuH@DHUHLW HpHHHHUHIHMH )BHMLuLuH@DHUHLF HpHHHHUHIHMH cBHMLuLuH@DHUHL HpHHHHUHIHMH BHMLuLuH@DHUHL EHInvalid HEE op.EH@H}Hud/a09 HpHHHHUHIHMH BHMLuLuH@DHUHL/HpHHHHUHIHMH LBHMLuLuH@DHUHLHpHHHHUHIHMH BHMLuLuH@DHUHLnHpHHHHUHIHMH BHMLuLuH@DHUHL](HpHHHHUHIHMH BHMLuLuH@DHUHLEHInvalid HEE op.EH@H}Hud_09!HpHHHHUHIHMH oBHMLuLuH@DHUHLWHpHHHHUHIHMH BHMLuLuH@DHUHLFHpHHHHUHIHMH BHMLuLuH@DHUHLHpHHHHUHIHMH BHMLuLuH@DHUHLHpHHHHUHIHMH WBHMLuLuH@DHUHLt?HpHHHHUHIHMH BHMLuLuH@DHUHL.HpHHHHUHIHMH KBHMLuLuH@DHUHLHpHHHHUHIHMH BHMLuLuH@DHUHLmHpHHHHUHIHMH BHMLuLuH@DHUHL\'HpHHHHUHIHMH BHMLuLuH@DHUHLEHInvalid HEE op.EH@H}Hud\09 HpHHHHUHIHMH nCHMLuLuH@DHUHLVHpHHHHUHIHMH CHMLuLuH@DHUHLEHpHHHHUHIHMH CHMLuLuH@DHUHLHpHHHHUHIHMH CHMLuLuH@DHUHLHpHHHHUHIHMH VCHMLuLuH@DHUHLs>EHInvalid HEE op.EHH@H}HudZ0v9}HpHHHHUHIHMH CHMLuLuH@DHUHLHpHHHHUHIHMH CHMLuLuH@DHUHLmHpHHHHUHIHMH ?CHMLuLuH@DHUHL\'HpHHHHUHIHMH yCHMLuLuH@DHUHLHpHHHHUHIHMH CHMLuLuH@DHUHLEHInvalid HEE op.EH@H}HudEY09HpHHHHUHIHMH (CHMLuLuH@DHUHLEHpHHHHUHIHMH 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pointer argument: outBUILTIN_PREFETCH_PRESENTMM_PREFETCH_PRESENTUSE_OPENMPCLANG_VERSIONDEBUGUSE_FEDERATED[] :: %02d:%02d:%02d Stack trace: [bt] () vector_ZDMLC_LOG_STACK_TRACE_DEPTH%zujson_strtruefalseCheck failed: ec == std::errc()Unknown global parameters: { , }/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json.hInvalid cast, from to __Cannot find argument '', Possible Arguments: ---------------- : Check failed: eCheck failed: ( vs. fnameurisilentconfigXGDMatrixCreateFromURIdata_split_mode/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/json_utils.hArgument `` is required for ``.Invalid type for: ``, expecting one of the: {`}, got: ``/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/adapter.hCheck failed: at_first_Cannot reset IteratorAdapter/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/common.hXGBoost version not compiled with GPU support.cache_prefixXGDMatrixCreateFromCallbacknthreadnextresetmissing/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/c_api_utils.hArgument `missing` is required.`, XGDeviceQuantileDMatrixCreateFromCallback1.7.0XGQuantileDMatrixCreateFromCallbackCheck failed: _refmax_bin/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/proxy_dmatrix.hSlicing DMatrix is not supported for Proxy DMatrix.Slicing DMatrix columns is not supported for Proxy DMatrix.Proxy DMatrix cannot return data batch.c_interface_strCheck failed: p_mCheck failed: mCurrent DMatrix type does not support set data.DMatrix/Booster has not been initialized or has already been disposed.Check failed: c_interfaceindptrindicesdataXGDMatrixCreateFromCSREx2.0.0XGDMatrixCreateFromCSRc_json_configCheck failed: IsA(jarray)Check failed: consistentSize of columns should be the same.typestr/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../data/array_interface.hptr % alignment == 0Input pointer misalignment.maskCheck failed: mask_it == array.cend() || IsA(mask_it->second)Masked array is not yet supported.streamversionMissing `version' field for array interfaceMissing `typestr' field for array interfaceCheck failed: typestr.size() == 3 || typestr.size() == 4shapeMissing `shape' field for array interfaceMissing `data' field for array interfaceOnly version <= 3 of `__cuda_array_interface__' and `__array_interface__' are supported.`typestr' should be of format .map::at: key not foundHalf type is not supported.- is not supported.Bit fieldBooleanIntegerUnsigned integerFloating pointComplex floating pointTimedeltaDatetimeObjectStringUnicodeOtherInvalid type code: in `typestr' of input array. Please verify the `__cuda_array_interface__/__array_interface__' of your input data complies to: https://docs.scipy.org/doc/numpy/reference/arrays.interface.html Or open an issue.i < DCheck failed: m == 1 || n == 1Only dimensional array is valid.stridesj_strides.size() == j_shape.size()stride and shape don't match.size == 0Empty data with non-zero shape.Empty data passed in.get(array).size() == 1Column: n_bits == nShape of bit mask doesn't match data shape. XGBoost doesn't support internal broadcasting.j_shape.size() == 1type_length == 1mask with bitfield type should be of 1 byte per bitfield.mask with integer type should be of 1 byte per integer.mask must be of integer type or bit field type.strides.size() == 1get(strides.at(0)) == type_lengthMemory should be contiguous.XGDMatrixCreateFromCSCExXGDMatrixCreateFromCSCfloat32float64bool8int32int8int16int64Unknown data table type.static_cast*>(handle) ->get() ->Info() .group_ptr_.size() == 0Uslice does not support group structurebinary saving only supported by SimpleDMatrixfield<XGDMatrixSetUIntInfo2.1.0XGDMatrixSetInfoFromInterfaceout_featureslenXGDMatrixSetDenseInfoCheck failed: type >= 1 && type <= 4Unknown data typeCheck failed: ArrayInterface<1>{iface}.is_contiguousout_lenout_dptrCheck failed: pp_mout_indptrout_indicesout_datap_m->Info().num_col_ <= std::numeric_limits::max()Check failed: page.page/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/data.hCheck failed: impl_ != nullptr/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/threading_utils.hn_threads >= 1;unknown;unknown;0;0;;The quantile cut hasn't been generated yet. Unless this is a `QuantileDMatrix`, quantile cut is generated during training./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/context.hUnknown device.cpucudasyclsycl:cpusycl:gpui < data.size()end <= data.size()n_numeric == n_features - n_categoriesindptr.back() == vals.size() + mins.size() - n_categoriesdmatsjson_parametersout_strdtrainCheck failed: dtrXGBoosterBoostOneIterXGBoosterTrainOneItergradhessi_grad.Shape(0) == i_hess.Shape(0)i_grad.Shape(1) == i_hess.Shape(1)i_grad.Shape(0) == p_fmat->Info().num_row_Mismatched size between the gradient and training data.grad_is_cuda == hess_is_cudagradient and hessian should be on the same device.array.valid.Capacity() == 0half type is only supported for CUDA input.evnamesout_resultlearnergradient_boosternamegblineardartgbtreegbtree_model_paramnum_parallel_treeUnknown booster:Booster has not been initialized or has already been disposed.DMatrix has not been initialized or has already been disposed.typeXGBoosterPredictFromDMatrixiteration_beginiteration_endntree_limitCheck failed: iteration_end == 0Only one of the `ntree_limit` or `iteration_range` can be specified.`ntree_limit` is deprecated, use `iteration_range` instead.trainingstrict_shapeout_dimout_shapechunksize == groupsUnknown prediction type:std::accumulate(shape.cbegin(), shape.cend(), static_cast(1), std::multiplies<>{}) == chunksize * rowsInplacePredictImplCheck failed: p_predtp_predt->Size() >= n_samplesCheck failed: proxyInvalid input type for inplace predict.array_interfacejsonubjrstr.size() >= 3str[0] == '{'wdeprecatedSaving model in the UBJSON format as default. You can use file extension: `json`, `ubj` or `deprecated` to choose between formats.Saving into deprecated binary model format, please consider using `json` or `ubj`. Model format is default to UBJSON in XGBoost 2.1 if not specified.buf/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../common/io.hcurr_ptr_ + size <= buffer_size_json_configformatXGBoosterSaveModelToBufferUnknown format: `curr_ptr_ <= p_buffer_->length()read can not have position excceed buffer lengthCheck failed: p_outtextfmap/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/feature_map.hfid == static_cast(names_.size())iqintfloatcunknown feature type, use i for indicator and q for quantityout_modelscustom_feature_names.size() == n_featuresIncorrect number of feature names.feature_names.size() == n_featuresfeature_types.size() == n_featuresIncorrect number of feature types.ffeature_map.Size() == n_featuresftypesuccesskeyfeaturesfeature_namefeature_typeUnknown field for Booster feature info:importance_typeXGBoosterFeatureScorefeature_mapfeature_namestree_idxout_n_featuresfeatures.size() <= scores.size()scores.size() % features.size() == 0ulfeatures.size() == scores.size()out_scoresidx < names_.size()FeatureMap feature index exceed bound/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../collective/../data/array_interface.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../collective/allreduce.hCheck failed: data.Contiguous()/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../collective/broadcast.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/../collective/allgather.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/c_api/coll_c_api.ccdmlc_communicatorXGTrackerCreatefederatedrabitUnknown communicator:handleXGBoost is not compiled with federated learning support.argsCheck failed: ptrCheck failed: !ptr->second.valid()Tracker is already running.timeoutCheck failed: res != std::future_status::deferredTimeout waiting for the tracker.Time out seconds reached for TrackerFree, killing the tracker.name_str/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/allgather.ccworker_off < worldRing allgather failed, current iteration:recv_seg.size() != 0send_seg.size() != 0Broadcast AllgatherV failed, current iteration:Ring AllgatherV failed, current iterataion:/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/allgather.h*offset.cbegin() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/../data/array_interface.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/allreduce.ccdata.size_bytes() % n_bytes_elem == 0Ring Allreduce failed.Ring allreduce small failed.first.size() == data.size()Ring scatter reduce failed, current iteration:seg.size() == recv_seg.size()seg_nbytes % sizeof(T) == 0broadcast failed./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/broadcast.ccpeer != rootmask != 0Floating point is not supported for bit wise collective operations./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/coll.ccInvalid op.lhs.size() == out.size()Invalid input for reduction.Unknown algorithm for allgather-vNCCL is required for device communication./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/comm.ccCheck failed: !info.host.empty()Task connected to the trackerworld_sizeranktask_idFailed to send init command from worker./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/protocol.hFailed to verify.Invalid verification number.recv/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/collective/socket.hsendFailed to set timeout on recv.Failed to get socket flag.Failed to set socket to non-blocking.Failed to connect to the tracker.Got an invalid host name.Failed to get host names from peers.Failed to get the port from peers.peers[comm.Rank()].port == lportp.port != -1Failed to send rank.Failed to recv rank.Check failed: workers[r]Failed to close the socket.Failed to accept.Bootstrap failed to connect to ring next./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/comm.hCheck failed: sock_.get()Check failed: loop_socketFailed to call ``: Failed to bootstrap the communication group.Rank got rank /Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/../common/common.hXGBoost version not compiled with NCCL support.Bootstrap failed.Failed to receive the rank for the next worker.Failed to connect to other workers.Check failed: this->channels_.empty()Failed to listen.bind failed.getsockname failed.thread constructor failedAnother worker is running into error.Failed to receive error signal.Failed to recv init command from tracker.Invalid world size.cmdporterror_porthostFailed to set TCP keeaplive.Failed to set TCP no delay.The communicator is being destroyed without a call to shutdown first. This can lead to undefined behaviour.Failed to send shutdown signalFailed to connect to the error socket.inet_ntopFailed to send shutdown command from worker.msgFailed to send print command from worker.codeFailed to send error command from worker./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/comm_group.ccCheck failed: ctx->IsCUDA()libnccl.so.2dmlc_retrydmlc_timeoutdmlc_task_iddmlc_tracker_uridmlc_tracker_portdmlc_nccl_pathInvalid communicator type/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/../common/json_utils.hDuplicated parameter:/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/comm_group.hCheck failed: comm_/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/in_memory_handler.ccCheck failed: world_size_ < world_sizeIn memory handler already initialized.unique_lock::unlock: not lockedCheck failed: world_size_ > 0In memory handler already shutdown. rank : waiting for current sequence number: handling request: all requests received: waiting for all clients: sending reply: all replies sentAllgatherAllgatherVInvalid data typeInvalid reduce operationFloating point types do not support bitwise operations.AllreduceBroadcastProcessQueueInvalid socket operation./Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/loop.ccpollCheck failed: !qcopy.empty()Check failed: op.code == Op::kSleepCheck failed: op.sock->NonBlocking()Encountered EOF. The other end is likely closed.Invalid socket output.op.off <= op.noperator()Poll timeout: seconds./Users/runner/work/ml-builds/ml-builds/xgboost/rabit/include/rabit/internal/socket.hPoll failed, nfds:Poll error condition: code:Invalid polling request.Failed to retrieve socket error.Socket error.Check failed: qcopy.empty() || stop_Unknown exception inside the event loop.Exception inside the event loop:this->Block().OK() == this->rc_.OK()Worker has stopped.op.n != 0Loop - system error: |]: ]/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/result.cc/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/socket.ccFailed to get addr info for: , error: 127.0.0.10.0.0.0::1::Check failed: !this->IsClosed()str.size() < std::numeric_limits::max()Failed to send string.Failed to send string length.Failed to recv string.Failed to recv string length.static_cast(conn.Domain()) == static_cast(addr.Domain())Failed to set socket option.Retrying connection to for the time.connect failed.poll failed.Failed to connect to (): Failed to connect to: Error:Failed to get host name.sortbyn_workersTrackerFailed to start tracker, timeout:/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/tracker.ccInvalid port.Invalid initialization sequence.Failed to send init command from tracker.Invalid print command.Invalid error command.Connect protocol failed.Failed to bind socket.getsocknameFailed to accept connection.Failed to initialize worker proxy.Failed to handle abort.Invalid command received.pending.size() <= n_workersn_shutdown <= n_workersRecieved error from [Failed to inform worker: for error.Failed to send error signalpending.size() < n_workersCheck failed: !runningpending.size() == n_workersn_shutdown >= 0n_shutdown < n_workersFailed to shutdown socket.0Failed to get address info:127.Failed to create socket.10.255.255.255Failed to find IP address.Failed to get sock name.Failed to close socket.NaN0E0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/column_matrix.ccgmat.cut.Ptrs()[fid + 1] - gmat.cut.Ptrs()[fid] <= max_val/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.hbad_malloc: Failed to allocate bytes./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../data/../common/ref_resource_view.hmem_->Size() >= nreinterpret_cast(ptr) % std::alignment_of_v == 0w_n_bytes == n_bytesw_n_bytes == remaining\\\\"\b\f\n\r\t\u%04x` is deprecated since, use `` instead.The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead. E.g. tree_method = "hist", device = "cuda" /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/error_msg.ccYou have manually specified the `updater` parameter. The `tree_method` parameter will be ignored. Incorrect sequence of updaters will produce undefined behavior. For common uses, we recommend using `tree_method` parameter instead.gpu_iddevice E.g. device=cpu/cuda/cuda:0Empty dataset at worker: Falling back to prediction using DMatrix due to mismatched devices. This might lead to higher memory usage and slower performance. XGBoost is running on: , while the input data is on: . Potential solutions: - Use a data structure that matches the device ordinal in the booster. - Set the device for booster before call to inplace_predict. This warning will only be shown once. /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.hUnreachable/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/hist_util.ccCheck failed: !offsetsrow_indices.Size() != 0Check failed: offsetsentries_per_column.size() == info.num_col_/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.hentries_per_columns.size() == static_cast(n_columns)column_sizes_tloc[i].size() == static_cast(n_columns)/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/threading_utils.hSortedSketchContainerinvalid init parameterCheck failed: nlevel <= std::max(static_cast(1), static_cast(limit_size * eps))/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/host_device_vector.ccSize() == other.Size()Size() == other.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/io.ccpointer_ <= buffer_.size()Check failed: outfile://Check failed: (parsed.protocol == "file://" || parsed.protocol.length() == 0)Only local file is supported.://ar+w+a+wbabrbr+bw+ba+bOpening failed: fd >= 0Failed to open:. offset <= std::numeric_limits::max()File size has exceeded the limit on the current system.ptr != ((void *)-1)Failed to map: munmap(handle_->base_ptr, handle_->base_size) != -1Faled to call munmap: close(handle_->fd) != -1Faled to close: Not implemented/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/json.ccCheck failed: ret.ec == std::errc()NumberArrayNullF32ArrayF64ArrayU8ArrayI32ArrayI64ArrayObject of type can not be indexed by string. can not be indexed by Integer.Unknown construct, around character position: , " "\0/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/string_view.hbeg <= size_ "Unknown escapeExpecting: "", got: "EOF"\0"nullExpecting null value "null"ch != -1cursor_.Pos(): raw_str_.size():Expecting digit` is not supported for typed array.High precision number is not supported./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/json_io.hOnly UBJSON format can handle f64 array./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/common.hPesudoHuberParamhuber_slopeThe delta term in Pseudo-Huber loss.Invalid Parameter format for expect but value='Required parameter of is not presentedOut of range value for , value='/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/parameter.hpos <= value.length()Some trailing characters could not be parsed: ''Out of range valueNo conversion could be performed/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./strtonum.h*p == ')'Invalid NAN literalvalue for Parameter exceed bound [ should be greater equal to should be smaller equal to optional, default=, requiredkey has already been registered in /Users/runner/work/ml-builds/ml-builds/xgboost/src/common/quantile.cch_weights.size() == batch.Size()Invalid size of sample weight.Check failed: !this->columns_size_.empty()group_ptr.size() >= 2info.weights_.Size() == n_groupsgroup_ptr.back() == n_samples the number of rows from the data.Size of weight must equal to the number of query groups when ranking group is used.Invalid query group structure. The number of rows obtained from group doesn't equal to count <= total_entriesmingap=, maxgap=, wgap=Check failed: size <= sa.size + sb.sizebug in combinesrc.size == 0this->size == 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../data/adapter.hSketchContainerImplcolumns_size_.size() != 0n_threads_ >= 1base_rowid < group_ptr.back()Row: is not found in any group.Failed to get sketch scan.worker_segments.size() >= 1Failed to get sketch./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../collective/allreduce.hAllReducen_columns == sketches_.size()Number of columns differs across workersnum_cuts.size() == 0feature_ptr.front() == 0rank_size == totalCheck failed: reduced[i].dataCheck failed: worker_feature.data()PushRowPagesketches_.size() == n_columnsweights.size() == info.num_row_hessian.size() == info.num_row_group_ptr.back() == hessian.size()MakeCutsp_cuts->cut_values_.HostVector().size() <= std::numeric_limits::max()cut_size > p_cuts->cut_ptrs_.HostVector().back()Check failed: a.dataCheck failed: a.data && reduced[fidx].data check quantile stats, nbig=, n= srcsize=, maxsize=, range=, chunk=Check failed: nbig < nquantile: too many large chunk] rmin=, rmax=, wmin=, v=/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/categorical.hInvalid categorical value detected. Categorical value should be non-negative, less than total number of categories in training data and less than max_cat + 1 >= n_categoriesMaximum cateogry should not be lesser than the total number of categories.HostSketchContainerPushColPagesketch->temp.size < max_sizeinvalid maximum size max_size=, stemp.sizeINFO: rmax=, sum_total=, naxt_goal=, size=sketch->temp.size <= max_sizeFinalize: invalid maximum size, max_size=, stemp.size=alpha/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/../common/json_utils.hQuantileLossParamquantile_alphaList of quantiles for quantile loss./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.ccfeatures.size() > 0Check failed: ctx_/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/random.harray.size() == weights.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/algorithm.hCheck failed: ctx->IsCPU()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/ranking_utils.ccrank.size() == predt.size()g_rank.size() == sorted_idx.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/ranking_utils.hCheck failed: label_is_integerWhen using relevance degree as target, label must be either 0 or positive integer.Check failed: label_is_validRelevance degress must be lesser than or equal to when the exponential NDCG gain function is used. Set `ndcg_exp_gain` to false to use custom DCG gain.Unreachable.LambdaRankParamlambdarank_pair_methodmeantopkMethod for constructing pairs.lambdarank_num_pair_per_sampleNumber of pairs for each sample in the list.lambdarank_unbiasedUnbiased lambda mart. Use extended IPW to debias click positionlambdarank_normalizationWhether to normalize the leaf value for lambda rank.lambdarank_bias_normLp regularization for unbiased lambdarank.ndcg_exp_gainWhen set to true, the label gain is 2^rel - 1, otherwise it's rel.Invalid Input: '', valid values are: optional, default=enum_back_map_.count(value) != 0UValue not found in enum declaredEnum exisit!) Enums: ), booleandoubleCheck failed: is_binary can only be used with binary labels.%u[-]?@/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/stats.cct_v.Shape(1) != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/stats.hCheck failed: alpha >= 0 && alpha <= 1k >= 0AFTParamaft_loss_distributionnormallogisticextremeChoice of distribution for the noise term in Accelerated Failure Time modelaft_loss_distribution_scaleScaling factor used to scale the distribution in Accelerated Failure Time model/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/./parameter.h/sys/fs/cgroup/cpu.max/sys/fs/cgroup/cpu/cpu.cfs_quota_us/sys/fs/cgroup/cpu/cpu.cfs_period_uslimit >= 1Invalid thread limit for OpenMP./Users/runner/work/ml-builds/ml-builds/xgboost/src/common/timer.ccTimer for did not get stopped properly.s, calls @ us======== Monitor (): ========/Users/runner/work/ml-builds/ml-builds/xgboost/src/common/version.ccInvaid version format in loaded JSON object: Incorrect version format found in binary file. Binary file from XGBoost < 1.0.0 is no longer supported. Please generate it again.version:fi->Read(&read[0], verstr.size()) == verstr.size()Check failed: fi->Read(&major)Check failed: fi->Read(&minor)Check failed: fi->Read(&patch).ContextseedRandom number seed during training.random_stateseed_per_iterationSeed PRNG determnisticly via iterator number.Device ordinal.Number of threads to use.n_jobsfail_on_invalid_gpu_idFail with error when gpu_id is invalid.validate_parametersEnable checking whether parameters are used or not.long has not been registered in Alias string[Internal Error] Unknown parameters passed to the Context {} /Users/runner/work/ml-builds/ml-builds/xgboost/src/context.ccBoth `device` and `gpu_id` are specified. Use `device` instead.Check failed: opt_id.has_value()Invalid value for `gpu_id`. Got:new_d.ordinal == this->device_.ordinalthis->device_.ordinal == DeviceOrd::CPUOrdinal()this->device_.ordinal > DeviceOrd::CPUOrdinal()Invalid argument for `device`. Expected to be one of the following: - cpu - cuda - cuda: # e.g. cuda:0 - gpu - gpu: # e.g. gpu:0 gpu(:[0-9]+)?|cuda(:[0-9]+)?|cpu|sycl(:cpu|:gpu)?(:-1|:[0-9]+)?sycl(:cpu|:gpu)?(:-1|:[0-9]+)?gpuopt_id.value() <= std::numeric_limits::max()Ordinal value too large.XGBoost is not compiled with CUDA support.Got: `/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/adapter.hCheck failed: columns_ == data::kAdapterUnknownSize || columns_ == batch.columnsNumber of columns between batches changed from /Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../collective/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/data.ccCheck failed: validkey >= builder_base_row_offsetInput data contains `inf` or a value too large, while `missing` is not set to `inf`/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../data/adapter.hnum_rownum_colnum_nonzerolabelsgroup_ptrweightsbase_marginlabels_lower_boundlabels_upper_boundfeature_typesfeature_weightsAll feature_types must be one of {int, float, i, q, c}.Binary DMatrix generated by XGBoost: is no longer supported. Please process and save your data in current version: again.major >= 1minor >= 6Check failed: fi->Read(&num_field)MetaInfo: invalid formatnum_field >= expectedMetaInfo: insufficient number of fields (expected at least fields, but the binary file only contains fields.)MetaInfo: the given binary file contains extra fields which will be ignored.MetaInfo: Invalid format for Check failed: strm->Read(&name)name == expected_name Expected field: , got: Check failed: strm->Read(&type_val)Check failed: type == expected_typeExpected field of type: got field type: Check failed: strm->Read(&is_scalar)Check failed: is_scalarExpected field to be a scalar; got a vectorCheck failed: strm->Read(field)Check failed: !is_scalar to be a tensor; got a scalarCheck failed: strm->Read(&(shape[i]))Check failed: strm->Read(&field) to be a vector; got a scalarCheck failed: strm->Read(&shape.first)Check failed: strm->Read(&shape.second)shape.second == 1Number of columns is expected to be 1.this->base_margin_.Size() % this->num_row_ == 0Incorrect size of base margin vector./Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/linalg.hthis->Data()->Size() == detail::CalcSize(this->shape_)Inconsistent size after modification./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../collective/../data/array_interface.hCheck failed: this->base_margin_.Size() % this->num_row_ == 0Incorrect size for base margin.labelthis->labels.Size() % this->num_row_ == 0Incorrect size for labels.Label contains NaN, infinity or a value too large.qidCheck failed: non_dec`qid` must be sorted in non-decreasing order along with data.weightWeights must be positive values.label_lower_boundlabel_upper_boundFeature weight must be greater than 0.Unknown key for MetaInfo: Meta info like label or weight can not have missing value.Check failed: t_out.CContiguous()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../collective/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/validation.hCheck failed: valid_query_groupInvalid group structure.Unknown float field name: Unknown uint32 field name: Unknown data type for getting meta info.size == this->num_col_Length of must be equal to number of columns.Check failed: infoUnknown feature info name: result.size() == n_columnsUnknown feature info: this->num_col_ == that.num_col_Number of columns must be consistent across batches.that.group_ptr_.size() != 0shape[i] == r.Shape(i)/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../collective/allreduce.hgroup_ptr_.size() == weights_.Size() + 1group_ptr_.back() == num_row_the actual number of rows given by data.weights_.Size() == num_row_Size of weights must equal to number of rows.labels.Shape(0) == num_row_Size of labels must equal to number of rows.labels_lower_bound_.Size() == num_row_Size of label_lower_bound must equal to number of rows.feature_weights.Size() == num_col_Size of feature_weights must equal to number of columns.labels_upper_bound_.Size() == num_row_Size of label_upper_bound must equal to number of rows.num_nonzero_ <= num_col_ * num_row_base_margin_.Size() % num_row_ == 0Size of base margin must be a multiple of number of rows.Invalid device ordinal. Data is associated with a different device ordinal than the booster. The device ordinal of the data is: ; the device ordinal of the Booster is: cache_file.find('#') == std::string::nposOnly one `#` is allowed in file path for cache file specification.autoCheck failed: data_split_mode != DataSplitMode::kColColumn-wise data split is not supported for external memory. matrix with entries loaded from transpose.offset.Size() == num_columns + 1self_offset.size() == other_offset.size()self_data.size(): other_data.size(): beg <= data.size()ptr < offset.size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/ellpack_page.ccInternal Error: XGBoost is not compiled with CUDA but EllpackPage is required/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/file_iterator.ccname_args_cache.size() <= 2Only one `#` is allowed in file path for cachefile specificationURI parameter `format` is required for loading text data: filename?format=csvname_args.size() == 2Check failed: std::getline(is, kv.first, '=')Invalid uri argument format for key in arg Check failed: std::getline(is, kv.second) for value in arg ?Check failed: parser_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.ccCheck failed: columns_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/hist_util.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/column_matrix.hCheck failed: storage.Resource()->Type() == ResourceHandler::kMalloc[Internal Error]: Cannot grow the vector when external memory is used.m_size >= storage.size()Check failed: resource/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/io.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/ref_resource_view.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/threading_utils.hnum_nonzeros_.size() == n_featuresCheck failed: p_fmat->SingleColBlock()Check failed: !sorted_sketch/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index.hhit_count.size() == n_bins_totalrow_ptr.size() == 0n_bytes >= this->data.size()Check failed: this->data.empty()Check failed: resource->Type() == common::ResourceHandler::kMallocCheck failed: malloc_resourcerawRaw GHistIndex binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_format.ccCheck failed: fiCheck failed: binTypeSize == kUint8BinsTypeSize || binTypeSize == kUint16BinsTypeSize || binTypeSize == kUint32BinsTypeSize/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/gradient_index_page_source.cccount_ == source_->Iter()cuts_.Values().size() != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_source.hCheck failed: !at_end_n_prefetch_batches > 0total batches:n_prefetch_batches <= kPrefetchesfetch_it < cache_info_->offset.size()std::count_if(ring_->cbegin(), ring_->cend(), [](auto const& f) { return f.valid(); }) == n_prefetch_batchesSparse DMatrix assumes forward iteration.WaitCheck failed: !(*ring_)[count_].valid()Check failed: curr_exce_Check failed: fmt->Read(page.get(), fi.get())/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_writer.hUnknown format type format.front() == '.'cache_info_->offset.size() == n_batches_ + 1count_ == n_batches_count_ >= 1Check failed: lock_.try_lock()Multiple threads attempting to use Sparse DMatrix.Check failed: proxy_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.hUnknown type: Check failed: !cache_info_->written MB written in Check failed: page_this->count_ >= 1source_->Iter() == this->count_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.ccIterative DMatrix must have at least 1 batch.Check failed: proxy_handleInvalid proxy handle.Check failed: typedCheck failed: refCheck failed: p_cutsref->Info().num_col_ == n_featuresInvalid ref DMatrix, different number of features.n_features == num_cols()Inconsistent number of columns.Check failed: std::none_of(column_sizes.cbegin(), column_sizes.cend(), [&](auto f) { return f > accumulated_rows; })Something went wrong during iteration.n_features >= 1Data must has at least 1 column.accumulated_rows == Info().num_row_Check failed: p_sketchh_ft.size() == n_featuresrbegin == Info().num_row_this->ghist_->Features() == Info().num_col_proxy->Info().labels.Size() == 0h_ft.size() == n_ft/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/../data/gradient_index.hCheck failed: !std::isnan(sparse_thresh)Check failed: !detail::RegenGHist(param, batch_)`QuantileDMatrix` not initialized.`sparse_threshold` can not be changed when `QuantileDMatrix` is used instead of `DMatrix`./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/iterative_dmatrix.hparam.max_bin == batch_.max_binCheck failed: !param.regen && param.hess.empty()Only `hist` and `gpu_hist` tree method can use `QuantileDMatrix`.Inconsistent `max_bin`. `max_bin` should be the same across different QuantileDMatrix, and consistent with the Booster being trained./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_batch_iterator.hCheck failed: page_ != nullptrpage.row_ptr.size() == h_offset.size()Slicing DMatrix is not supported for Quantile DMatrix.Slicing DMatrix columns is not supported for Quantile DMatrix.Not implemented for `QuantileDMatrix`./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/array_interface.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/proxy_dmatrix.ccCheck failed: on_hostNot implemented on device.Check failed: p_fmatFailed to fallback./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/simple_dmatrix.ccCheck failed: (std::is_same::value || std::is_same::value)Expecting CSCAdapterThe total number of elements should be multiple of the number of workers./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../collective/allgather.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./simple_batch_iterator.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/../common/error_msg.hSample size too large for the current updater. Maximum number of samples:. Consider using a different updater or tree_method.batch_param_.max_bin == param.max_binCheck failed: !detail::RegenGHist(batch_param_, param)Generating new Ellpack page.param.max_bin >= 2/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/batch_utils.hCheck failed: r.Initialized()Batch parameter is not initialized.Inconsistent sparse threshold.Generating new Gradient Index.batch_param_.hess.data() == param.hess.data()Check failed: in_stream->Read(&tmagic)invalid input file formattmagic == kMagicinvalid format, magic number mismatchDMatrix-r/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_dmatrix.ccinfo_.num_col_ != 0.row.pageCheck failed: sparse_page_source_/Users/runner/work/ml-builds/ml-builds/xgboost/src/data/./sparse_page_dmatrix.hMake cache:Check failed: iter_.Next()Must have at least 1 batch..col.pagethis->Info().num_col_ != 0.sorted.col.pagethis->page_->Size() == n_features_this->page_->data.Size() == csr->data.Size().gradient_index.pagecuts.Values().size() != 0Check failed: ghist_index_source_Check failed: kv.secondCouldn't remove external memory cache file ; you may want to remove it manuallySlicing DMatrix is not supported for external memory.Slicing DMatrix columns is not supported for external memory.Can not obtain a single CSR page for external memory DMatrixRaw binary data format./Users/runner/work/ml-builds/ml-builds/xgboost/src/data/sparse_page_raw_format.ccpage->offset.Size() != 0UInvalid SparsePage fileCheck failed: page.offset.Size() != 0 && offset_vec[0] == 0offset_vec.back() == page.data.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear.cclayer_begin == 0Linear booster does not support prediction range.GBLinearTrainParamupdatershotgunUpdate algorithm for linear model. One of shotgun/coord_descenttoleranceStop if largest weight update is smaller than this number.max_row_perbatchMaximum rows per batch.Linear booster, implement generalized linear model.get(in["name"]) == "gblinear"modelgpu_coord_descentdevice="cuda", updater="coord_descent"coord_descentGBLinearLoading configuration on a CPU only machine. Changing updater to `coord_descent`./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gblinear_model.hfi->Read(¶m_, sizeof(param_)) == sizeof(param_)/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/gbm.hSlice is not supported by the current booster.DoBoostCheck failed: !p_fmat->Info().HasCategorical()`gblinear` doesn't support categorical features.PredictBatchPredictBatchInternalbase_margin.Size() == nsize * ngroup/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/threading_utils.hInplace predict is not supported by the current booster.gblinear does not support prediction of leaf index { "bias": [, ], "weight": [ ] }bias: weight: Check failed: !model_.weight.empty()Model is not initializedCheck failed: trees.empty()gblinear doesn't support number of trees for feature importance.importance_type == "weight"gblinear only has `weight` defined for feature importance.gblinear_train_paramCheck failed: this->updater_boosted_roundsDeprecatedGBLinearModelParamdeprecated_num_featuredeprecated_num_output_groupint (non-negative)/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbm.ccUnknown gbm type cpu_predictor/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.ccUsing tree method: The `exact` tree method is not supported on GPU.grow_colmaker,prunegrow_gpu_histUnknown tree_method: `unreachableThe requested feature doesn't have SYCL specific implementation yet. CPU implementation is usedUnknown device type:grow_quantile_histmakergrow_histmakergrow_gpu_approxh_tmp.Size() == h_in.Size()Check failed: !updaters_.empty()model_.param.num_parallel_tree == trees.size()model_.param.num_parallel_tree == 1Boosting random forest is not supported for current objective.Check failed: !trees.front()->IsMultiTarget()Update tree leaftrees.size() == model_.param.num_parallel_treeCheck failed: tparam_.tree_method == TreeMethod::kHist || tparam_.tree_method == TreeMethod::kAutoOnly the hist tree method is supported for building multi-target trees with vector leaf.Check failed: ctx_->IsCPU()GPU is not yet supported for vector leaf.BoostNewTreesn_groups != 0Current objective doesn't support external memory.in_gpair->Size() % n_groups == 0Umust have exactly ngroup * nrow gpairsCheck failed: !updaters_.front()->CanModifyTree()Updater: `` can not be used to create new trees. Set `process_type` to `update` if you want to update existing trees.Check failed: up->CanModifyTree()can not be used to modify existing trees. Set `process_type` to `default` if you want to build new trees.model_.trees.size() < model_.trees_to_update.size()No more tree left for updating. For updating existing trees, boosting rounds can not exceed previous training roundsMismatching size between number of rows from input data and size of gradient vector.n_out % gpair->Size() == 0gpair->Size() == n_outCommitModelget(in["name"]) == "gbtree"gbtree_train_paramtree_train_param Loading from a raw memory buffer (like pickle in Python, RDS in R) on a CPU-only machine. Consider using `save_model/load_model` instead. See: https://xgboost.readthedocs.io/en/latest/tutorials/saving_model.html for more details about differences between saving model and serializing.tree_methodhist Changing `tree_method` to `hist`.Changing updater from `grow_gpu_hist` to `grow_quantile_histmaker`.specified_updater/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/../common/error_msg.hIf you are loading a serialized model (like pickle in Python, RDS in R) or configuration generated by an older version of XGBoost, please export the model by calling `Booster.save_model` from that version first, then load it back in current version. See: https://xgboost.readthedocs.io/en/stable/tutorials/saving_model.html for more details about differences between saving model and serializing. defaultprocess_typeCheck failed: p_gbtreeCheck failed: this->model_.learner_model_param->Initialized()step >= 1end != beginEmpty slice is not allowed.this->model_.trees_to_update.size() == this->model_.trees.size()Not all trees are updated, trees remain. Slice the model before making update if you only want to update a portion of trees.out_model.iteration_indptr.front() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.hiteration_indptr.size() == 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree.hout_l == n_layersCheck failed: !model.iteration_indptr.empty()end <= model.BoostedRounds()Out of range for tree layers.tree_begin <= tree_endout_preds->version == 0tree_end <= model_.trees.size()Invalid number of trees.Unknown data type for inplace prediction:Check failed: gpu_predictor_Check failed: cpu_predictor_GBTreeModelParamnum_treesNumber of features used for training and prediction.Number of parallel trees constructed during each iteration. This option is used to support boosted random forest.GBTreeTrainParamupdater_seqTree updater sequence.updateWhether to run the normal boosting process that creates new trees, or to update the trees in an existing model.approxexactgpu_histChoice of tree construction method.DartTrainParamsample_typeuniformweightedDifferent types of sampling algorithm.normalize_typetreeforestDifferent types of normalization algorithm.rate_dropFraction of trees to drop during the dropout.one_dropWhether at least one tree should always be dropped during the dropout.skip_dropProbability of skipping the dropout during a boosting iteration.Tree booster, gradient boosted trees.GBTreeTree booster, dart.get(in["name"]) == "dart"weight_dropCheck failed: p_dartCheck failed: p_dart->weight_drop_.empty()Check failed: !this->model_.learner_model_param->IsVectorLeaf()Check failed: predictori >= p_out_preds->versionp_out_preds->predictions.Size() == predts.predictions.Size()Unsupported data type for inplace predict.predts.predictions.Size() == p_out_preds->predictions.Size()Check failed: successdart_train_paramdrop trees, weight = Check failed: tree_param_.GetInitialised()tree_begin == 0Predict leaf supports only iteration end: (0, n_iteration), use model slicing instead.Predict contribution supports only iteration end: (0, n_iteration), using model slicing instead.Predict interaction contribution supports only iteration end: (0, n_iteration), using model slicing instead.gaintotal_gaincovertotal_coverUnknown feature importance type, expected one of: {"weight", "total_gain", "total_cover", "gain", "cover"}, got: idx <= total_n_treesInvalid tree index./Users/runner/work/ml-builds/ml-builds/xgboost/src/gbm/gbtree_model.ccparam.num_trees == static_cast(trees.size())fi->Read(¶m, sizeof(param)) == sizeof(param)GBTree: invalid model filefi->Read(dmlc::BeginPtr(tree_info), sizeof(int32_t) * param.num_trees) == sizeof(int32_t) * param.num_treeslayer_trees != 0model.trees.size() == model.param.num_treesmodel.tree_info.size() == model.param.num_treesmodel.iteration_indptr.back() == model.param.num_treesparam.num_trees == static_cast(trees.size())treestree_infoiteration_indptridtrees_json.size() == param.num_treestree_info_json.size() == param.num_treesCheck failed: !iteration_indptr.empty()iteration_indptr.back() == param.num_treesGlobalConfigurationverbosityFlag to print out detailed breakdown of runtime.use_rmmWhether to use RAPIDS Memory Manager to allocate GPU memory in XGBoost/Users/runner/work/ml-builds/ml-builds/xgboost/src/learner.ccmulti-target-multi-class is not yet supported. Output classes:, output targets:Check failed: std::as_const(base_score_).Data()->HostCanRead()base_score_.Size() == 1Check failed: base_score_.Data()->HostCanRead()Check failed: base_score_.Data()->DeviceCanRead()base_score_.Data()->DeviceCanRead() == that.base_score_.Data()->DeviceCanRead()LearnerModelParamLegacybase_scoreGlobal bias of the model.num_featureNumber of features in training data, this parameter will be automatically detected by learner.num_classNumber of class option for multi-class classifier. By default equals 0 and corresponds to binary classifier.num_targetNumber of output targets. Can be set automatically if not specified.boost_from_averageWhether we should calculate the base score from training data.LearnerTrainParamdisable_default_eval_metricFlag to disable default metric. Set to >0 to disableboosterGradient booster used for training.objectivereg:squarederrorObjective function used for obtaining gradient.multi_strategyone_output_per_treemulti_output_treeStrategy used for training multi-target models. `multi_output_tree` means building one single tree for all targets.eval_metricCONFIG-offset:Learner/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/cache.hcontainer_.size() < max_size_Check failed: it != container_.cend()remained.size() + expired.size() == container_.size()queue_.size() == container_.size()unordered_map::at: key not foundCheck failed: IsA(in)Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.learner_model_paramattributesCheck failed: that.lock_.try_lock()Check failed: !this->need_configuration_Call Configure before saving model.Check failed: learner_model_param_.Initialized()learner_model_param_.BaseScore(this->Ctx()).Size() != 0Check failed: ret.ec == std::errc{}ConfigureCheck failed: matrix.first.ptrCheck failed: !matrix.second.ref.expired()mparam_.num_feature != 00 feature is supplied. Are you using raw Booster interface?/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/../data/../common/error_msg.hn_features <= max_n_featuresUnfortunately, XGBoost does not support data matrices with features or greater/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/allreduce.hmulti:softprobnum_output_groupmulti:softmaxmax_delta_stepcount:poisson0.7Check failed: tparam_.GetInitialised()learner_model_param_.BaseScore(Ctx()).Size() != 0Check failed: this->obj_Check failed: n_targets == t || 1 == tInconsistent labels.Check failed: n_targets == 1 || n_targets == mparam_.num_targetInconsistent configuration of num_target. Configuration result from input data:, configuration from parameter:_paramInvalid parameter "" contains whitespace. Parameters: { ", } are not used. UpdateOneIterPredictRawPredictionsGetGradientGradientsbase_score.Size() == 1Check failed: !std::isnan(mparam_.base_score)Check failed: !std::isinf(mparam_.base_score)/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/aggregator.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/collective/broadcast.hCheck failed: std::equal(data.cbegin(), data.cend(), sync.cbegin())Different model parameter across workers.learner_model_param_.num_feature == p_fmat->Info().num_col_Number of columns does not match number of features in booster.learner_model_param_.num_feature >= p_fmat->Info().num_col_Check failed: gbm_ != nullptrPredict must happen after Load or configurationBoostOneIterthis->learner_model_param_.OutputLength() == in_gpair->Shape(1)The number of columns in gradient should be equal to the number of targets/classes in the model.EvalOneItermultiple_predictions <= 1Perform one kind of prediction at a time.Unsupported prediction type:bs64header != "bs64"Base64 format is no longer supported in brick.binffp.Read(&header[0], 4) == 4UInvalid model formatfi->Read(&mparam_, sizeof(mparam_)) == sizeof(mparam_)BoostLearner: wrong model formatCheck failed: fi->Read(&tparam_.objective)Check failed: fi->Read(&tparam_.booster)count_poisson_max_delta_stepmulti:metricsLoading model from XGBoost < 1.0.0, consider saving it again for improved compatibilityfixed_size->Read(&buffer[0], size) == sizedata_.Size() == detail::CalcSize(shape_)Check failed: !this->learner_model_param_.IsVectorLeaf()Please use JSON/UBJ format for model serialization with multi-output models.feature names and feature types are being disregarded, use JSON/UBJSON format instead.;this->learner_model_param_.num_feature != 0begin >= 0out_impl->learner_model_param_.num_feature == this->learner_model_param_.num_featureout_impl->learner_model_param_.num_feature != 0best_iterationbest_scoreInvalid version string in configlearner_train_paramget(objective_fn["name"]) == tparam_.objectivegeneric_paramModelConfigInvalid serialization file.fp.Read(&header[0], header.size()) == serialisation_header_.size()Check failed: header == serialisation_header_fp.Read(&sz, sizeof(sz)) == sizeof(sz)sz > 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/linear_updater.ccUnknown linear updater LinearTrainParamlearning_rateLearning rate of each update.reg_lambdaL2 regularization on weights.reg_alphaL1 regularization on weights.feature_selectorcyclicshufflethriftygreedyrandomFeature selection or ordering method.etalambdaCoordinateParamtop_kThe number of top features to select in 'thrifty' feature_selector. The value of zero means using all the features.Update linear model according to coordinate descent algorithm.linear_train_paramcoordinate_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_coordinate.ccSave config for CPU updater.CoordinateUpdater/Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/coordinate_common.hunknown coordinate selector: /Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/../common/threading_utils.hUpdateFeatureUpdate linear model according to shotgun coordinate descent algorithm./Users/runner/work/ml-builds/ml-builds/xgboost/src/linear/updater_shotgun.ccUnsupported feature selector for shotgun updater. Supported options are: {cyclic, shuffle}WARNING: DEBUG: INFO: /Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/algorithm.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.cclabels.Size() != 0labels.Size() == predts.size()auc <= 1.0 + kRtEpsaucReceiver Operating Characteristic Area Under the Curve./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../collective/broadcast.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../collective/aggregator.hinfo.weights_.Size() == info.group_ptr_.size() - 1info.group_ptr_.back() == info.labels.Size()Total AUC across groups: , valid groups: n_classes != 0Dataset is empty, or contains only positive or negative samples./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../collective/allreduce.hinfo.group_ptr_.size() >= 2/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/auc.hInvalid group with less than 3 samples is found on worker . Calculating AUC value requires at least 2 pairs of samples.labels.Shape(1) == 1AUC doesn't support multi-target model./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/common.haucprArea under PR curve for both classification and rank.PR-AUC supports only binary relevance for learning to rank.rmseRooted mean square error./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/elementwise_metric.cupreds.Size() == info.labels.Size()label and prediction size not match, hint: use merror or mlogloss for multi-class classificationinfo.labels.Shape(1) != 0rmsleRooted mean square log error.maeMean absolute error.mapeMean absolute percentage error.loglossNegative loglikelihood for logistic regression.mpheMean Pseudo-huber error.pseudo_huber_paraminfo.labels.Shape(0) == info.num_row_slope != 0.0slope for pseudo huber cannot be 0.poisson-nloglikNegative loglikelihood for poisson regression.gamma-devianceResidual deviance for gamma regression.gamma-nloglikNegative log-likelihood for gamma regression.errorBinary classification error.%fsscanf(param, "%f", &threshold_) == 1unable to parse the threshold value for the error metrictweedie-nlogliktweedie-nloglik@rho for tweedie regression.Check failed: param != nullptrtweedie-nloglik must be in format tweedie-nloglik@rhoCheck failed: rho_ < 2 && rho_ >= 1tweedie variance power must be in interval [1, 2)tweedie-nloglik@quantileQuantile regression error.quantile_loss_paramname == "quantile"/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/quantile_loss_utils.hCheck failed: GetInitialised()Check failed: !quantile_alpha.Get().empty()quantile alpha must be in the range [0.0, 1.0].Check failed: !alpha_.Empty()dat[1] > 0n_targets != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/metric.ccUnknown metric function merrorMulticlass classification error./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/multiclass_metric.cupreds.Size() == 0Check failed: preds.Size() % info.labels.Size() == 0label and prediction size not matchnclass >= 1Umlogloss and merror are only used for multi-class classification, use logloss for binary classificationCheck failed: label_error >= 0 && label_error < static_cast(n_class)MultiClassEvaluation: label must be in [0, num_class), num_class= but found in labelmloglossMulticlass negative loglikelihood.amsAMS metric for higgs./Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/rank_metric.ccAMS must be in format ams@kams@Check failed: !collective::IsDistributed()metric AMS do not support distributed evaluationbest-ams-ratio=cox-nloglikNegative log partial likelihood of Cox proportional hazards model.Cox metric does not support distributed evaluationpreprecision@k for rank.lambdarank_paramCheck failed: p_cache->Param() == param_/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/../common/ranking_utils.hCheck failed: param_.GetInitialised()the size of label.Groups() == info.weights_.Size()score <= 1.0 + kRtEpsInvalid output score, might be caused by invalid query group weight.mapmap@k for ranking.weight.weights.size() == p_cache->Groups()ndcgndcg@k for ranking.aft-nloglikNegative log likelihood of Accelerated Failure Time model.aft_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/metric/survival_metric.cuUnknown probability distributionpreds.Size() == info.labels_lower_bound_.Size()preds.Size() == info.labels_upper_bound_.Size()ndata == labels_upper_bound.Size()Check failed: metric_AFT metric must be configured first, with distribution type and scaleinterval-regression-accuracy/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.ccbegin_pos <= sorted_pos.size()nptr.size() > 0n_unique == n_leaf/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/algorithm.hCheck failed: !position.empty()h_node_ptr.back() <= info.num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/adaptive.hCheck failed: quantiles.empty() || quantiles.size() == n_leafCheck failed: tree[nidx].IsLeaf()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../collective/allreduce.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../collective/broadcast.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../collective/aggregator.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../collective/../common/threading_utils.hk + 1 < h_node_ptr.size()Check failed: h_row_set.empty()y_idx <= info.labels.Shape(1)/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/stats.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../collective/../common/common.hsurvival:aftAFT loss function/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/aft_obj.cuinfo.labels_lower_bound_.Size() == ndatainfo.labels_upper_bound_.Size() == ndatainfo.weights_.Size() == ndataNumber of weights should be equal to number of data points.Unrecognized distribution/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/transform.hNot part of device code. WITH_CUDA: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/objective.hmultioutput is not supported by the current objective functionbinary:hingeHinge loss. Expects labels to be in [0,1f]/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/hinge.cuinfo.labels.Size() == preds.Size()Invalid shape of labels.info.weights_.Size() == info.num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/init_estimation.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/ranking_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.cch_n_rel.size() == gptr.back()h_n_rel.size() == label.Size()LambdaRank with NDCG loss as objectiverank:ndcgti+tj-info.labels.Size() == predt.Size()iter == 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/lambdarank_obj.hg_rank.size() == g_label.Size()info.labels.Shape(1) <= 1multi-output for LTR is not yet supported.LambdaRank with RankNet loss as objectiverank:pairwiseLambdaRank with MAP loss as objective.rank:mapinfo.labels.Shape(1) == 1multi-target for learning to rank is not yet supported.SoftmaxMultiClassParamNumber of output class in the multi-class classification.Softmax for multi-class classification, output class index.softmax_multiclass_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/multiclass_obj.cuCheck failed: preds.Size() == (static_cast(param_.num_class) * info.labels.Size())SoftmaxMultiClassObj: label size and pred size does not match. label.Size() * num_class: num_class: preds.Size(): SoftmaxMultiClassObj: label must be in [0, num_class).Softmax for multi-class classification, output probability distribution.Objective candidate: /Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/objective.ccUnknown objective function: `` _syclCheck failed: base_scoreRegression with quantile loss.reg:quantileerror/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/quantile_obj.cuget(in["name"]) == Name()/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/../common/quantile_loss_utils.hparam_.quantile_alpha.Get().size() == alpha_.Size()n_alphas != 0n_targets >= n_alphaspreds.Size() == info.num_row_ * n_targetsMulti-target for quantile regression is not yet supported.h_weights.size() == h_labels.Size()alpha.size() == alpha_.Size()The objective is not yet configured.Multi-target is not yet supported by the quantile loss.Check failed: !alpha.empty()RegLossParamscale_pos_weightScale the weight of positive examples by this factorRegression with squared error.reg_loss_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/regression_obj.cuRegression with root mean squared logarithmic error.reg:squaredlogerrorlabel must be greater than -1 for rmsle so that log(label + 1) can be valid.Logistic regression for probability regression task.reg:logisticlabel must be in [0,1] for logistic regression/Users/runner/work/ml-builds/ml-builds/xgboost/src/objective/./regression_loss.hCheck failed: base_score > 0.0f && base_score < 1.0fbase_score must be in (0,1) for logistic loss, got: Logistic regression for binary classification task.binary:logisticLogistic regression for classification, output score before logistic transformation.binary:logitrawGamma regression using the gamma deviance loss with log link.reg:gammalabel must be positive for gamma regression.reg:linearreg:linear is now deprecated in favor of reg:squarederror.reg:pseudohubererrorRegression Pseudo Huber error.PoissonRegressionParamMaximum delta step we allow each weight estimation to be. This parameter is required for possion regression.Poisson regression for count data.poisson_regression_paraminfo.labels.Size() != 0Ulabel set cannot be emptylabels are not correctly providedPoissonRegression: label must be nonnegativesurvival:coxCox regression for censored survival data (negative labels are considered censored).Check failed: last_abs_y <= abs_yCoxRegression: labels must be in sorted order, MetaInfo::LabelArgsort failed!TweedieRegressionParamtweedie_variance_powerTweedie variance power. Must be between in range [1, 2).reg:tweedieTweedie regression for insurance data.tweedie_regression_paramTweedieRegression: label must be nonnegativereg:absoluteerrorMean absoluate error.StringView{get(in["name"])} == StringView{"reg:absoluteerror"}base_margin->Size() == 1Sum of weights is close to 0.0, skipping base score estimation.Make predictions using CPU./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_predictor.ccCheck failed: !model.learner_model_param->IsVectorLeaf()Predict DMatrix with column splitout_preds->size() == n_samples * n_groupsCheck failed: xgboost::collective::IsDistributed()column-split prediction is only supported for distributed trainingout_preds->size() == p_fmat->Info().num_row_ * model_.learner_model_param->num_output_group/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/../collective/allreduce.h/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/tree_model.hCheck failed: IsMultiTarget()Check failed: !p_m->Info().IsColumnSplit()Inplace predict support for column-wise data split is not yet implemented.m->NumColumns() == model.learner_model_param->num_featureNumber of columns in data must equal to trained model.predict instancePredict instance with column splitPredict leaf with column splitout_preds->size() == p_fmat->Info().num_row_ * (tree_end_ - tree_begin_)Predict contributionCheck failed: !p_fmat->Info().IsColumnSplit()Predict contribution support for column-wise data split is not yet implemented.ngroup != 0ncolumns != 0base_margin.Shape(1) == ngroupPredict interaction contributionPredict interaction contribution support for column-wise data split is not yet implemented./Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/cpu_treeshap.ccunique_path[i].pweight == 0Unique path must have zero weight/Users/runner/work/ml-builds/ml-builds/xgboost/src/predictor/predictor.ccUnknown predictor type model.learner_model_param->num_output_group != 0Invalid shape of base_margin. Expected: ()margin.Shape(0) == n_samplesmargin.Shape(1) == n_groups/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/constraints.ccFailed to parse feature interaction constraint: With error: newsize != 0/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/fit_stump.ccn_targets == gpair.Shape(1)Check failed: h_sum.CContiguous()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/threading_utils.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/allreduce.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.ccnodes_to_build.size() == valid_candidates.size()HistMakerTrainParamdebug_synchronizeCheck if all distributed tree are identical after tree construction.max_cached_hist_nodeMaximum number of nodes in CPU histogram cache. Only for internal usage./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/param.ccCheck failed: *local_tree == ref_tree/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/../../collective/broadcast.hloss_changessum_hessianbase_weightssplit_indicessplit_conditionsdefault_leftparentsleft_childrenright_children/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/multi_target_tree_model.ccparam_->size_leaf_vector > 1nidx < left_.size()nidx < right_.size()nidx < parent_.size()nidx < split_index_.size()Check failed: this->IsLeaf(nidx)Collapsing a split node to leaf weight.Size() == this->NumTarget()weights_.size() >= next_nidx * weight.Size()parent_.size() >= 1parent_.size() == left_.size()left_.size() == right_.size()split_idx < this->param_->num_featureparent_[nidx] != InvalidNodeId()p_weight.Size() == base_weight.Size()l_weight.Size() == left_weight.Size()r_weight.Size() == right_weight.Size()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/param.ccstd::floor(d) == dFound floating point number in interaction constraintsUnknown value type for interaction constraint:TreeParamnum_nodesNumber of features used in tree construction.num_deletedsize_leaf_vectorSize of leaf vector, reserved for vector treeTrainParamLearning rate(step size) of update.min_split_lossMinimum loss reduction required to make a further partition.max_depthMaximum depth of the tree; 0 indicates no limit; a limit is required for depthwise policymax_leavesMaximum number of leaves; 0 indicates no limit.if using histogram-based algorithm, maximum number of bins per featuregrow_policydepthwiselossguideTree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change. (cf. LightGBM)max_cat_to_onehotMaximum number of categories to use one-hot encoding based split.max_cat_thresholdMaximum number of categories considered for split. Used only by partition-basedsplits.min_child_weightMinimum sum of instance weight(hessian) needed in a child.L2 regularization on leaf weightL1 regularization on leaf weightMaximum delta step we allow each tree's weight estimate to be. If the value is set to 0, it means there is no constraintsubsampleRow subsample ratio of training instance.sampling_methodgradient_basedSampling method. 0: select random training instances uniformly. 1: select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)colsample_bynodeSubsample ratio of columns, resample on each node (split).colsample_bylevelSubsample ratio of columns, resample on each level.colsample_bytreeSubsample ratio of columns, resample on each tree construction.sketch_ratioEXP Param: Sketch accuracy related parameter of approximate algorithm.cache_optEXP Param: Cache aware optimization.refresh_leafWhether the refresh updater needs to update leaf values.monotone_constraintsConstraint of variable monotonicityinteraction_constraintsConstraints for interaction representing permitted interactions.The constraints must be specified in the form of a nest list,e.g. [[0, 1], [2, 3, 4]], where each inner list is a group ofindices of features that are allowed to interact with each other.See tutorial for more informationsparse_thresholdpercentage threshold for treating a feature as sparsegammacategories_segmentscategories_sizescategories_nodescategoriessplit_type/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_model.ccj_end - j_begin > 0std::numeric_limits::min() != max_catUnknown Model Builder:Dump text representation of tree{nid}:[{fname}] yes={yes},no={no}{nid}{fname}{yes}{no}pos != std::string::nposfmap.Size() <= std::numeric_limits::max(){tabs}{nid}:[{fname}:{cond}] yes={right},no={left},missing={missing}{}{tabs}{cond}{left}{right}{missing}{tabs}{nid}:[{fname}<{cond}] yes={left},no={right},missing={missing},gain={loss_chg},cover={sum_hess}{loss_chg}{sum_hess}Unknown feature map type.Check failed: is_categorical in feature map is numerical but tree node is categorical.Check failed: is_numerical in feature map is categorical but tree node is numerical.{tabs}{nid}:leaf={leaf}{stats},cover={cover}{leaf}{stats}{cover}{parent}{stat} {left} {right}{parent}{stat}{nodes} {nodes}Dump json representation of tree "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "yes": {yes}, "no": {no}{depth} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {right}, "no": {left}, "missing": {missing} "nodeid": {nid}, "depth": {depth}, "split": "{fname}", "split_condition": {cond}, "yes": {left}, "no": {right}, "missing": {missing}, "gain": {loss_chg}, "cover": {sum_hess}{{properties} {stat}, "children": [{left}, {right} {indent}]}{properties}{indent} { "nodeid": {nid}, "leaf": {leaf} {stat}}, "cover": {sum_hess} {newline}{indent}{nodes}{newline}GraphvizParamyes_color#0000FFEdge color when meets the node condition.no_color#FF0000Edge color when doesn't meet the node condition.rankdirTBPassed to graphiz via graph_attr.condition_node_paramsConditional node configurationleaf_node_paramsLeaf node configurationgraph_attrsAny other extra attributes for graphviz `graph_attr`.dotDump graphviz representation of treeFailed to parse graphviz parameters: " edge graph [ {key}="{value}" ] {key}{value}The following parameters for graphviz are not recognized: /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/json.hch == '{'Error at, Expect '{' but get ' Line , around ^`ch == ',', JSON object expect '}' or ',' 'ch == ':', Expect ':' but get 'ch == '\"', Expect '"' but get 'unknown string escape \, Expect '"' but reach end of line {nid} [ label="{fname}:{cond}" {params}] {params} {nid} -> {child} [label="{branch}" color="{color}"] noyes, missing{child}{color}{branch} {nid} [ label="{fname}{<}{cond}" {params}] {<} {nid} [ label="leaf={leaf-value}" {params}] {leaf-value}/Users/runner/work/ml-builds/ml-builds/xgboost/include/xgboost/multi_target_tree_model.hCheck failed: IsLeaf(nidx)limit >= 2..., {parent} {left} {right}digraph { graph [ rankdir={rankdir} ] {graph_attrs} {nodes}}{rankdir}{graph_attrs} tree dump Check failed: !IsMultiTarget()Check failed: node.IsLeaf()param_.num_nodes < std::numeric_limits::max()number of nodes in the tree exceed 2^31split_index < this->param_.num_featureCheck failed: this->p_mt_tree_param_.size_leaf_vector > 1fi->Read(¶m_, sizeof(TreeParam)) == sizeof(TreeParam)param_.num_nodes != 0fi->Read(dmlc::BeginPtr(nodes_), sizeof(Node) * nodes_.size()) == sizeof(Node) * nodes_.size()fi->Read(dmlc::BeginPtr(stats_), sizeof(RTreeNodeStat) * stats_.size()) == sizeof(RTreeNodeStat) * stats_.size()static_cast(deleted_nodes_.size()) == param_.num_deletedparam_.num_nodes == static_cast(nodes_.size())param_.num_nodes == static_cast(stats_.size())param_.deprecated_num_roots == 1Please use JSON/UBJSON for saving models with multi-target trees.Check failed: !HasCategoricalSplit()Please use JSON/UBJSON for saving models with categorical splits.this->split_types_.size() == this->Size()this->GetSplitCategoriesPtr().size() == this->Size()size != 0tree_paramstatic_cast(deleted_nodes_.size()) == param_.num_deletedthis->split_categories_segments_.size() == param_.num_nodesn_nodes != 0loss_changes.size() == n_nodessum_hessian.size() == n_nodesbase_weights.size() == n_nodeslefts.size() == n_nodesrights.size() == n_nodesparents.size() == n_nodesindices.size() == n_nodesconds.size() == n_nodesdefault_left.size() == n_nodesget(out["tree_param"]["num_nodes"]) == std::to_string(param_.num_nodes)this->split_types_.size() == param_.num_nodesmean_values->size() > 0U/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/tree_updater.ccUnknown tree updater Tree constructor that uses approximate histogram construction for each node.GlobalApproxUpdaterhist_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_approx.ccCheck failed: hist_param_.GetInitialised()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/broadcast.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/../common/../tree/hist/../split_evaluator.hp.monotone_constraints.size() <= n_featuresThe size of monotone constraint should be less or equal to the number of features./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/../common/common.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/sampler.hCheck failed: out.Contiguous()param.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist.exponent <= modUpdatePositionEvaluateSplitsInitDatan_total_bins == page.cut.TotalBins()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/../common/row_set.helem_of_each_node_.size() == 0U/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/histogram.hn_targets >= 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/../common/io.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/../data/../common/ref_resource_view.hInitRootgpair.Shape(1) == n_targetsp_fmat->Info().num_row_ == gpair.Shape(0)target_builders_.size() == n_targetsCheck failed: dummy_sub.empty()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/../common/threading_utils.hbegin < endCheck failed: p_nodes_to_buildCheck failed: p_nodes_to_subCheck failed: !this->hist_.HasExceeded()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../collective/../common/../tree/hist/hist_cache.hcurrent_size_ == new_sizeCheck failed: gpair.Contiguous()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/hist_util.hnodes == targeted_hists.size()i < first_dimension_.size()row_ptr_[nid] == kMaxnid < nodes_tid < nthreads_id != kMaxi < ranges_.size()Check failed: !nodes_to_build.empty()end > beginnidx != kRoot/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/transform.h/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/common_row_partitioner.hbase_rowid == gmat.base_rowidupper_bound < static_cast(std::numeric_limits::max())decision_storage_.size() == decision.NumValues()/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/partition_builder.hany_missing == truegidx > -1p_tree->LeftChild(nidx) + 1 == p_tree->RightChild(nidx)n_left == 0n_right == 0n_left + n_right == e.Size()begin + n_left <= e.endbegin + n_left + n_right == e.endBuildHistogramnodes_to_build.size() >= nodes_to_sub.size()nodes_to_sub.size() + nodes_to_build.size() == valid_candidates.size() * 2gpair.Shape(1) == p_tree->NumTargets()t_gpair.Shape(0) == p_fmat->Info().num_row_/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/hist/evaluate_splits.hCheck failed: !features.empty()partition > 0cut_ptr[fidx] <= static_cast(std::numeric_limits::max())cut_ptr[fidx + 1] <= static_cast(std::numeric_limits::max())all_serialized.size() >= local_entries.size()niddepthsplitloss_chgsindexsplit_valuecat_bitsis_catleft_sumright_sumLeafPartitionCheck failed: tree.IsLeaf(node.node_id)ptr_offset <= row_set.Data()->size()UpdatePredictionCacheout_preds.Size() == data->Info().num_row_out_preds.Size() > 0UCheck failed: p_last_treeout_preds.Shape(1) == n_targetsCheck failed: out_preds.Device().IsCPU()part.Size() == n_nodesColMakerTrainParamopt_dense_colEXP Param: speed optimization for dense column.default_directionlearnleftrightDefault direction choice when encountering a missing valuegrow_colmakerGrow tree with parallelization over columns.colmaker_train_param/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_colmaker.ccUpdater `grow_colmaker` or `exact` tree method doesn't support distributed training.Updater `grow_colmaker` or `exact` tree method doesn't support external memory training.Updater `grow_colmaker` or `exact` tree methodcolumn sample by node is not yet supported by the exact tree methodgpair->Shape(1) == 1/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/split_evaluator.hCheck failed: this->ctx_param_.max_depth > 0exact tree method doesn't support unlimited depth.fmat.Info().num_row_ == position_.size()param_.sampling_method == TrainParam::kUniformOnly uniform sampling is supported, gradient-based sampling is only support by GPU Hist./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/../collective/broadcast.hCheck failed: param_.cache_optSupport for `cache_opt' is removed in 1.0.0ridx < position_.size()ridx exceed bound ridx= pos=prunePruner that prune the tree according to statistics.syncTreePrunerPrunerUpdate/Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_prune.cctree pruning end, extra nodes, pruned nodes, max_depth=Check failed: tree[nid].IsLeaf()Check failed: !tree[pid].IsLeaf()Check failed: nodes_[nodes_[rid].LeftChild() ].IsLeaf()Check failed: nodes_[nodes_[rid].RightChild()].IsLeaf()nid >= 1Grow tree using quantized histogram./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_quantile_hist.ccCheck failed: param->monotone_constraints.empty()monotone constraintMultiTargetHistBuilderHistUpdaterUpdateTreep_tree->LeftChild(candidate.nid) > candidate.nidroot_sum_tloc.Shape(1) == gpair.Shape(1)Check failed: root_sum.CContiguous()Check failed: p_tree->IsMultiTarget()left_child > candidate.nidright_child > candidate.nidstats_.Shape(1) == n_targetsrow_ptr.size() >= 2Check failed: p_mtimpl_Check failed: p_impl_out_preds.Size() == data->Info().num_row_ * p_last_tree_->NumTargets()refreshRefresher that refreshes the weight and statistics according to data./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/updater_refresh.ccbatch.Size() < std::numeric_limits::max()Syncher that synchronize the tree in all distributed nodes./Users/runner/work/ml-builds/ml-builds/xgboost/src/tree/../common/io.hLibSVMParserParamlibsvmFile formatindexing_modeIf >0, treat all feature indices as 1-based. If =0, treat all feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.LibFMParserParamlibfmIf >0, treat all field and feature indices as 1-based. If =0, treat all field and feature indices as 0-based. If <0, use heuristic to automatically detect mode of indexing. See https://en.wikipedia.org/wiki/Array_data_type#Index_origin for more details on indexing modes.CSVParserParamcsvFile format.label_columnColumn index (0-based) that will put into label.delimiterDelimiter used in the csv file.weight_columnColumn index that will put into instance weights..split.part/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/uri_spec.hname_cache.size() == 1Uonly one `#` is allowed in file path for cachefile specificationname_args.size() == 1U/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data.ccUnknown data type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/disk_row_iter.hCheck failed: TryLoadCache()failed to build cache file /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/threadediter.hCheck failed: !producer_sig_processed_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) == kProduceMake sure you call BeforeFirst not inconcurrent with Next!Check failed: produce_end_.load(std::memory_order_acquire)Check failed: out_data_ != NULLCalling Value at beginning or end?Check failed: producer_sig_.load(std::memory_order_acquire) == kDestroyCheck failed: cell != __null || produce_end_.load(std::memory_order_acquire)Check failed: producer_sig_.load(std::memory_order_acquire) != kDestroyNo thread/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./row_block.hCheck failed: fi->Read(&label)Bad RowBlock formatCheck failed: fi->Read(&weight)Check failed: fi->Read(&qid)Check failed: fi->Read(&field)Check failed: fi->Read(&index)Check failed: fi->Read(&value)Check failed: fi->Read(&max_field, sizeof(IndexType))Check failed: fi->Read(&max_index, sizeof(IndexType))MB read, MB/secfinish reading at %g MB/seclabel.size() + 1 == offset.size()offset.back() == index.size()Check failed: offset.back() == value.size() || value.size() == 0/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/basic_row_iter.hfinish reading at /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./libsvm_parser.hparam_.format == "libsvm"/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/./text_parser.hchunk.size != 0Uqid:Check failed: out->label.size() + 1 == out->offset.size()Check failed: base <= 10 && base >= 2sign == true/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/parser.hcannot call ParseNext/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/libfm_parser.hparam_.format == "libfm"Check failed: out->field.size() == out->index.size()/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/data/csv_parser.hparam_.format == "csv"Check failed: param_.label_column != param_.weight_column || param_.label_column < 0Must have distinct columns for labels and instance weightsDelimiter '' is not found in the line. Expected '' as the delimiter to separate fields.Check failed: out->label.size() == 0 || out->label.size() + 1 == out->offset.size()Check failed: out->weight.size() == 0 || out->weight.size() + 1 == out->offset.size()hdfs://viewfs:///Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io.ccPlease compile with DMLC_USE_HDFS=1 to use hdfss3://http://https://Please compile with DMLC_USE_S3=1 to use S3azure://Please compile with DMLC_USE_AZURE=1 to use Azureunknown filesystem protocol stdinCheck failed: part < nsplitinvalid input parameter for InputSplit::Createindexed_recordioneed to pass index file to use IndexedRecordIOrecordiounknown input split type /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/single_file_split.hCheck failed: fp_ != NULLSingleFileSplit: fail to open Check failed: part_index == 0 && num_parts == 1InputSplit do not support write/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/cached_input_split.hCheck failed: nread == sizeof(size) has invalid cache file formatCheck failed: fi_->Read(p->begin, size) == sizeBeforeFirst is not supportedCheck failed: this->InitCachedIter()Failed to initialize CachedIterResetPartition is not supported in CachedInputSplit/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/line_split.ccCheck failed: begin != end/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/recordio_split.ccCheck failed: fi->Read(&lrec, sizeof(lrec)) != 0invalid record io format(reinterpret_cast(begin) & 3UL) == 0U(reinterpret_cast(end) & 3UL) == 0UCheck failed: p >= pbegin + 2Check failed: chunk->begin + 2 * sizeof(uint32_t) <= chunk->endInvalid RecordIO Format(reinterpret_cast(chunk->begin) & 3UL) == 0U(reinterpret_cast(chunk->end) & 3UL) == 0UCheck failed: chunk->begin <= chunk->endCheck failed: cflag == 1UCheck failed: p[0] == RecordIOWriter::kMagic/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/indexed_recordio_split.ccexpanded_list.size() == 1ulIndexedRecordIOSplitter does not support multiple index files/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/input_split_base.ccCheck failed: files_[i].size % align_bytes == 0file do not align by bytesCheck failed: offset_end_ >file_offset_[file_ptr_end_]Check failed: file_ptr_end_ < files_.size() bad regex This could due to compiler version, g++-4.9 is neededfiles_.size() != 0UCannot find any files that matches the URI pattern curr=,begin=,end=,fileptr=,fileoffset=offset[]=file offset not calculated correctly/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/filesys.ccCheck failed: !IsSymlink(info.path.name)Symlink not supported in TemporaryDirectoryCouldn't remove file Successfully deleted temporary directory ~TemporaryDirectory(): Could not remove temporary directory /Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/include/dmlc/filesystem.hlstat(path.c_str(), &sb) == 0dmlc::TemporaryDirectory::IsSymlink(): Unable to read file attributes/Users/runner/work/ml-builds/ml-builds/xgboost/dmlc-core/src/io/local_filesys.ccLocalFileSystem.GetPathInfo: detected symlink error: LocalFileSystem.GetPathInfo: LocalFileSystem.ListDirectory ..stdoutCheck failed: allow_null LocalFileSystem::Open "": Check failed: std::fwrite(ptr, 1, size, fp_) == sizeFileStream.Write incompleteCheck failed: !std::fseek(fp_, static_cast(pos), SEEK_SET) LPXQXaQ!Q Q!aQ QNQ7ЂВp\Тx%"в70+I5^dBvaHЂ7vHp:vH@;vHpEwHF,xH H@xHIPxH0KdxHPxxH@SxHTxHXxHXyH_pyHdyHgyH`hyHprzH@uzH@x,{H {t{H}{H|H|H`|H||H|H@|H}H0}HL}HD~H`~HХt~Hp~H ~H`~HHpHH,HhHPH̀HPH`,HtH H|HP؂H`HЃH`H0`HHH H`tHHH0H܆H H00H`THxHHpH`H`HPHP`H`Hp HpH`H HpHpHQH0d8HvxH0HH8H0xHHpH08H xHHP1HC8HPVxHiH{HP8HРxHHPH8HxHpHP H# 8H6 xHI HP\ Ho 8H xHД H H 8H@ xH H H 8H` xH + H= HP 8Hc xHv H H 8H xHp HP H0 8H xH H Hp2 8HPE xH`X H@k HP~ 8H0 xH@ HP H` 8Hp xH H H 8H* xHp0 @H2 `H6 H< HF HPH HI XHP HR HS H`V `H\ HPa H f Hk Hl Hn xHs `Hv Hx H H Hp H0 H0 H H H H HP H H H Hp H $Hp HH` H0 H@ H H `H H@ H0 PH H H HP H H 0H hH H H H HH` H  H H` (H `H0 |H H H H H pH H Hp Hp H H`# DHp% H& H@( `HP* HP+ HP, hHP- H@0 8H`8 H0; 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