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'60VCSr.iR54bOKxqPLKrRlg0X.GkNLS_2b0_TMGjQ_xhNLtiPLtb4V.L.0g..4N.59Zv6sg7.........5LRTKnKqMM.BwtBOXO8BwrL8aonHSBqPOhaPOi_PQdZQOeaIOeaPOdomSs_LxDaLtcqGJ.0JOFS3O0SF.GZJJ.4G4_3G3M0MKG.3JF3.GKG5mQrytpErzjfrMSzSnbrb6EEESixrrfG3lwNzSYYMrdGrxWYAsi8qMBWvga9IvvwlsMhElJ9goy5kiPbFq663u5kxBOF9Ev4J5WtBxpU5Fcbu6L7MZTT1cLuhsI1d_T5qMCyDSIFPNA1941RT2kJoZsz4ZfvEleuhzpTe5_x8WFPMWc9OlWcjvDMonzWvoAW.RisFvUsKP6k_uk1D2NKw5bm6L6uYbw61snInWG82AXKFiyjWoACdHS2zxsiadUx9P2xJXXBgDkG.AY4H5SU6VdvLEM0_RPAfFZjiRabrIx2slfaa8hzKOYrL07Fr4naPx5bFi6cQsI1PeYKmB70NCzEgJeFj8Ra1F1kygLWg7KEwHnZ9YLr9LGFD39WZeMpjH7J2fk3WV2ZpsG4jnzwM_uON5SDwfCMV1wKX1wkvcLU.5JJhY2IPpZAUAiU8HG0XyjJx2oLiPXEWSxz2QxlnEX8w2jCgA5Bhigb5GNLbPQI_pV.krUGDsS1ree75LjsJgF3WqV1JyKIJFtXN8Bi8WdwUL1CvhS0QGQxiu.DRDU0kYM5EUxolAVAI2Is0ZI6fsG1wey4kYQPA22I54lJl6YMLKBXexk246z3OVP7DCdaoziQsUR9UMQHxWWAX5MR4afiE0Ll_V3eqdaVb56.pK3epf.JF9X0nHsnUYphdRc2NCV9CQsGUejdS87jAkizg638VmHl1q8cehlYFvBxEgda.pYZLIKSXlLO46suzCs20xw5ZzGOltUYVEGi_gJVVlGigq93NkTYnC93YUMKwAiQ2zgNzfpWzuybvrcV58Tch3qZxk0fIbgmgj4q5fwnz6v_GvxtGzey8izSdzC4aYP2qxMEWJ7iBlq3aRw0pWperWtypn9B95htE.95Gkm6b4P9ph6oeX7CGfp5dloJE4NALBgVjNdNp07wakfEiZgi77GrBLZiYNROR7ePDdNtvyZBxll2LwYtQW.rpGxZxvASqAHc16RNhzkrcGY2bF6o9QO41S1g0jGlLF.9pQ15fPke6Q0ZgXA59L.lnAZ8I7smOn9SLg2zUacqJeYuqK0N00nEbrma5a9WPWEbL4w6JjFLjIcdrExtw.GV5k42VKX9b2fzG0.HqOluu2g8YARTDinpj6dMc4LLqWyFveCC5RiuwGduUHvoDVVfMoEIEEZeoDosnCvxu_2cUqUcUj5hB06JDgSRlL2i99Ic0jWeWHno5q5i.Q0vaB77sxFnTygMJCKy9xGRWc2z5MsAFURWR42lCbPSqWYYdIE2tPYYdV47TpwEbYVMLm0z67KhnDLiPuAlTGVo6VegVT3jeFultwyyCt9Z.u.bxpS2cNRCqBKmxZJTIyw7jqjWkLAaAb_4G8EQJ5Qlkr.k8ZGCv1cJunABZrb3UkhQwk7fjV5jA42IzETVyMS1NIgRCVjbpv7dJiaKHgr8OoCW6KVdGPcfUTiVF28RL.Q19e28PJOLZzl1yEpnd.0CdtXv3mIUzGVRlt67PFrzznu1.qNUW1sA26i9_Pk4i1asHpon6m8uCbN2AfnktU0vY0Ne_Dd_BSGoJhCCnPtKZIElikoZEeG8Et2U0h6Fes79nbzhiFsuXzWwqbdFxwAcgodq5hxkjp6sbz2AOaJQjmUwra6J1bfR08QIo37ftQdXFQLJvfaoDFuwPmagXemcKe_bvvsWRjZEPQh7FLC07r7rYGjKHaXYKMqPle0eXcdBpj7iafAifsMU5FQ0gcu4l.JJuhKy92Hd5xCvL1t_lXoe3vHO.60g8DrmU.QqNCW8qlx.SBo7k58MjL1tJtW0jjmFCJ51eHXhy2zN0KFBVP3Ik694IlaFUlopLk6nDVM_nGcK0pKm7CSubdLIPPnEO2J4nOhXPaYvVi_DnIFsYuUaf3RQmp79PrLeS9c14C9xZ0cC.6_q8mEz3rnDT6AnEAojeQhIRtI5QCo9g6FHaUYQsQS6TRqBmlIqJoWXi2wBjohBW1uPFxBXjbklzDuWse052cHPdiNw2CF.a5uyvFVFY5nKHw4goqoXioH84d3q1.ojeYeXi2k1XW6w2lhdU8700m4a8QDnVtGOQLWlDmbw.piKYMBHtQ8HWOVdHxwLYfaxy1NNdGD.1s6nCdoFULEFwWIFvFB0Sl6sbY5eOHKG0ecBCR5bSaJo39jXh_3nI5ErHTx6c.BFlj3dVOfpcAP5VZnFn4aMcQqF_fuX.9oNhMBUy_6KYNLJKuU3elpl40t5ESZElaBOwllsmEg90UluguVP2Gy6zitW39h2AcNdA_pXyfvbQTVKZ9OG3PhltKm6mTuWl2P.hhhky.CeSf6PislIzjZklxfEo.hhhWpuCiaBNx4nT72LR69dTBzHzG5xhVgE8Bd13Mq7obc3T.v._7o.vq6IhFAEwkG9Um7.rPXKSkY2UWIpKlZkyancCzzdJ6EozHk7OQJ56K_fRHDuHCxU.DkA7XGB6sBvttYyNP1Tdb39QMAGwPvz.NGXfUexRZ0k4qklFDDyT5U4OrFCCQtU9ueAGhockASNPnDCeZ6u41HjGMaxlYeVQv5AOlbO26l5GmNaX1PdzdRNhL97D7KiUN65wHnp77KTNWsIbky4RMQHbVwVWq0DxQOtOoT9CGxuBIPCge2mDs2ESbZ3nWSMq6E4FXywyOAFfv12FRycOwlV_pNX8EC0u4uc6viDyI9z2xPGL.wy1qKk.8HW3UK_j2Y7c8VHLAlIzMkRpPwS1K51aQ_GhcjHDWjsC9Ob1hNgoFnIpJpJctyCgNo2OS8F37orE0NtBjfW9HfuZNbpQkzALNPqbae8V3C.GezV5IoQZOusIAugcvC51nQRGdfgcC23i1SdLotCjxt4a93Lh0a8o74hTDLEkqwQkpFmw66SmfFpSg2q9J0muc7UOmrOIEZWt0JKDeL8kcolvDUN2YFxfoRqHjVQHj_fP9hpm0NwXqzk14u36RWp837Y3qlYgRewQTf8Y9UMCcKTH3ESWbe4Lo6xUdWFHu3FF.bleC1f5Rnfba5wwvFdOTs31Vi.hJHC5rRZm_UKIT4EkXywgYe9qdnriZAI91W5wXXgcrRBWTB.7xpjNH.rUlN_2dZnIOj2YlCfd7NV.8If0XESh1WFxWb.oYREU4_wPe2.4hh2Zkw6eKMkK0.UZynv0dfoi4lCB4obYsKTnpjdqouZjLsLIBIGL7R28wgKNtB.f5Ou_3vsmXNO2YxDA3UMDRubmeRYyVkkoKONYVbY5Tq1C0K2hAZ9tpNWeaidDFkpWa1LPunVTrF1TsRNAsTquANKkAi44TWcKjYLgwJL55yJZn569IScaQ1eNTNwkWGQuStEfqEj2emLkfZgGyaAoiTVEzVVWPi5UOYSyBSDnKMkUkbl_FNWahwMOax5H0rIEO1ZHQqOYngtkMIVnuDkC6914qCybixt8VmTKiLtXtdJdY38YVv7pUwBMo9E1uWFTx2JBtbzVENzXAX4_YS.srQod.qsNwRGXzJPcxIBRU.qkLv_rkfAHDHFiVblWc0Ftz7R4bMQ1DdY0pJYe3bdx3TeOBBLanYHJy2DnVf5aL2uDp2j8hA2sC7k5jh47BiekE98kMxQ5bJuYB2TQH.jxtB3AjsqpDHYnTDIo4xn5DVbrVjFc2s8Wo1qYYwV6CDDWGMe13dZvL6CYUwNecsISsxb78oORP1p3roV7twpOt0TcDDy0U5zMSLCkXL1kFCODj.nLTFTkbOMSFaLoufsnt.qjw1yNff_vs66fwCd18TAih5dHJIn95MLHMWjaD0.9Y8j9lhi2TWUXssnVJqN2Y_IaPgLfgu1xcKcn2R7DxGjOa52LVG67S7xC_kUfEe.ZIffHBkOC1uJ2fb5VNZpKdjOl7YfqUX6cDWLp.DhQXrGkmlUrLFIiubsHtVRy6CEoIVSiL53yV1UOBDBYv0lkHSbbKZrWk26d7cNQDNQaH1j.8iZ.LOVCJSVcKbaU5HPZQ434MPFoN943exz4AoMlgoka.hRyaqpDMO6dLacUTsmF3SYB8.9CDcT9c_93nq5PqXgvrCr_57GmmTR4JJBQ5VzbnLVLejAQz13d8CiGYJg6UPSHExQp4FXmUI0QDAILSpQlbNcAA156hoGx38ieHoyo84DkS0rCnIfkLDGHdiAfEkWBacMCQBRgzUsK.dRdI.ktblqK61qGBoGUYE7OnPTg4UcG.FvYNXR0WfEkC5TkqOh5LpIWjM.9HBFKEyReHtuZizmXUCf5xWiBb9auVHZMsz3XFiTcNaui8dI4imeSKUBehdGRsumE7Cbzl8y_lVEpFFndO2GoW_U.lQ95uGqIqei5iZeOlVn_SHWQWdacORee2NHeZ9XY0p_TQVbrJFOR7S0jodeeV3OJRXGKJlIMOI8p11JBc4rqGlSl8HgfmZMXv9JYSVvV5vH0GiDV_ZBY5JZsm39LspBFo3SpqnLOSPab4aYAZqbUsCFOWkVDcnyaTa_iuBJvpFnU4a3_v3BAh5BoDvQniIMY4xdyOg90stQYLIIet44b9EF2Qce4G_oGyQTYBmRBm9RVh1w6qM2nm0juXWRGcLjzkF93Me6TJAx73t4vj_hgL9e8V14GbgBrY_I1jTWCRPN.fUsIS9cSOvp3iPOVFyarlBPfRCUbF0uPsoRFshSL1sOu6fST.tnR88gdn5umbU2voacrhiBwhaf9_0hwXZABZ2LW3lhGMaykGH8MoFG.OqrFOJqNd758UEfVxqCj5_19qq9qeBlO.UZRIvhplt2jqhuQH1JTIU8s62gUJXVTWOxaYWP3Xggd8gqk1kD_rKsSDD7cmM26di_1dESGLfos9iE0Ub_bsNM16JTtR1BVojphQhEoJFUNhuacu.CZHdVJdkd3hUNKii.Ni2QaAaNYVr7fV9VB3Wvbrok28umxP2y77lIWPvU.axJkwwATZMoVeo5YWv3nGmcBySy.N1HJUOQzmcJPd8VodEU1_KQPSo9hqPuPM1xY.aqqPIIu5__oA1TXl8K9s1gW.EBuceCdUwYYKYsSFB1etrO8Y5k4WMYgygOs8ubm1lZLy1ZiFAOCWapeCKQaBr_3QMQBdEb.R3gVCBasRRMVQ44rKObNPcqcqYYB5ReMcRBuCdxVe07GeyTMz7ACtg6K3TseNR3VHkZxw0ec2aX7REXmxHweXIHKjQ4k4uqtRppQw1q61LqBtn6P1p.Mk3CSZXl3d1P2_jqJ5W5p6FWg0BkDBhF5S7tO7sGViX19rNYYifNOJWo3k6mDO_FROfO3gbg4frUJt0n_B7uvruVsWLQ1pQfaPmRIER5kksxcJoTCsgs4mfOVYhNv28NesKD5Rmw5o4S_u35xaLZpdGbWwuzx0FSRbWTCYCuVKZff6oCY2gDLYGMQif9V6u5_.kSoqbLh.E.DB3bSoFdocK.jBeqTHld71NDZF10gxVkBokk4uKfIvg3KfUTC2rNuukl.DQR0FNuqCuu2lwGjSt1amLuzmzgOKmSAmS8FIX4wiNOhG3.Ss.9ZyHxFQ5LpL09tvAVBOecsfS2nW8kRG3cJ_H.96GW7LJ1WUvP2TNLB2ZwSkgg7TAC1n3loQgo0FnMIBqZ9LdBPUeK.MrhtLoBP3lAxvCd_mDGTLlSJJd2Xz4sh.UKZ6kkN.vZUIYQLB_2F1tZbEQ7Dow5dJkJpB6H60JFSnDPbDXK8ZDga20iVnNuCJ9NOVIt7QPm8agMj5_FGlvrBWZnKK4cWqrz1uspdeCb0DnE7Nbmg9kicre1qIYP8WiuOPOu_g0lsw59oal7qBHR7m.X2scKkd0QJEneaUgGGoX5XH0wZiJUJb1z2FED8E.StzHwvPoEpH4Yx5y1B1F_2G54qJxNT7c06w1gZk0aoEwXZKqnZt9YnA1_8SH5OGH_XzfFYBdLtQwJpzToIaVJiq_.DFrRWYQuSUOZgs92wJFlopXSdr7lUiK0WEUt4xlFAoDKGoSgV_5BppLYelKd5IE6LdCfB_.u2e.Tt310NV73qy5P61wodAoix5z_U2p.cxh95UBW9RmlbRh0ohtpi9EQFD0aE2kN.u_N0p_9iroR5npcO.jc1ZklnuFl0iQchAI31Qt3IiVJLvk4firnMXFBg1ItvYwKyZzXrRYJr44KoULDppaINIUg_VdA6Z2JTxrjgfNb.nOnlhrw9fFbMHVSVqJDrEM2RxS.o1pIQQDy3UfENJmBcseU.LaQ.E5ypuPOv.eOJSiT8jR9aqf8_CplVoDcIUoNRDPrrFA5I6WNUzJsmgtx5DJLTJkdvE2x98Uv7UpB_6w_kSB1FYKzX_1ipuxCbqN6TR6i2d9p0hLYccq4xPdX9hwBQnOnK8cmhLa1pgah_7f.m.LLS7eh_kju1woleyy3uIe9Z0aSlV2ic6NDGMbBmc2WE9nF96LeL4Tu5VgRd9zk6YVoWQLhWjpI1_CoBPplWPecODpSLYcOrkzxRx_zyTmLcr1VpylCp2wK_SxeGfF9ub3nR7otgksLO_uy8C40trWkXkwB5D_OO78TfpN2gMvWv9VoPeqxA2vGavE7Y54jNXZ0nDmP.uJe5qZku1MchkyWvwrx_4LGwDbHEVZJlxWjvS.WpPGZPhGtj71XalefZUyBtgM29ZQIQY00P_wJXi6xOTz.JBWGzdiy04.YmDpGtVGx5K5d5gWuRxFiTdgKWBnGTk2kn9feW24p1kwktIZ9GlZk4xGBRkNzw6V4HniOx.3uXrbs9FncUM.D1jPmhzmqVL8ntCJ2u7j33sqi78ht2_6YlI8x3KsNJP5CstDEC6VCFnsrxwMAJ4vdfuMvbHWs0V0OohxY6CxaFEUIqwy6DOqGs6C7vmPY65N1gdvS8OX.Te2BLn8VyCzW_zERvBkoMQOuim_RBSz7lN8nDi6CkR58kryOfdXoHUI34rsr9f0rlO1EoYSevu1CTxr6Hp8xMiL0YywWP0GdR5ztZm3OQWv5baGEvjJTZRZku39DXxsZ2ggoPokLJWKHZqBwwlCeo9Iqk.uvSKKFw3FYwUNkRx7O6RE3.lHQE3I5TL08X3dhN3UXI_uY2lgTLsoSpT0_5gLW_xaVkILLpgxKoMLBZlhpEqgHLqCXK6pugut.aiSgsClj7rVIWv2CNcLuOWP.hezKIBDrR342nzN5h36IawvcM2M_u9oG94cqfyRqeIT8dZ2_3QMdCSGZmAHnV0KoaYarVsExEfmo.H6O8kFyJx10JQKdXr1Hs2s8DaDlhQEOXqbJd5al7bw9UJR56PmS.JS.NPnM5dhlstsqhYKCjho198nWW2pYWWXqHmBDsJMJrl37thQoXO.hNh1vfb2b34vFkwl_f6q24o9OUGR36CIYxcYZnRqNgWtGHqNzyIc2ZXibkqT0oe4Z9K7BwgJ.ZTUW7.HhmYMd_tHrGy1s.x0m.LFY9vc9Y0KmnIuqSVxoI.M77bOTA8XxPeIWWq6uUZG43VWS2xHh843_cCj4uVBn0csRQ7.kEMWJebG_B51KPISOMsEsb.rMuOPAVENyiYfbuQy0SoQ0TOeey2_71ldIK5Tx1f8Xdup7y5NL_7vkMqhPbe4q1WZ6n8Id7J8voDHE3yHlAH9tveE0_d.XphNA97BKq2sdtdEgwH8X8c5bNSNkG3t.0PlsOPydKUVdDYXjQeInQNZ0fbDonrZ4EjvBRyGcYgE33tffMJGzOYGe5Lgv8wJ51dhWBBqA5KzuJ8L3SopixBPtCpi_BBXRBaRhuVu31SITEz1wEgEZaMIMujNKbFlNqDSun5MqPNmnScTJiGvWx0dL5FTjv3DnARkbJDfbJND.COx2WTjnZGWF_nb72fgEIbGd818lGa61K1HDQbdMSqhKtBVarJQSInO4kq57Qg1Nt_DiwSc9KD1JptaHFZ5MhlRaAZTdlwRFBIuqKq2RVlbB0jIWhwioDLam_z_EYCm_ngPZvbtI86BdyAW96WYhVVthx4d39XTW4wlur345IFBj7OLNKU51Cst8uMJPBEFxxC2a2Bj0Qt_8Lz7rjAuym61Q3A9pvD.3W9nDoXOet__T2SqxFatiYveHxr6JqpaJQl0cHiIFeFLlmBJuZfVB_6Z4Y3R2VBbQtgU0WkUuO8IZFNjl_VzdDJLGSbuiYwww4Ia7eJ.5oAk7uSH5rtfCkD0ICzXKtMoMW0CN04p__Iu7ZE85AjgX_Fw24vQkLTQDs.hGYo6h0ddg_CVeXdny1cJvgA.d4sQAD5bbuAqBfT8nGKsdvxC8mjuOWjSXkU3a4WL2nmT0nLzFNkPonIemaT1gtdf8lXdTrZR128paOdHUcUg2VAzEORcox7FOkc6hYCC7skh.T._sqV1p4lLr3.4GWs07wTOaqH4xy1HFTz4j2Aay0RCi_ou60wGH3YYX6u5jtJ5DiaU2o2UN.c_3Vkty0_J5fSMwQjME9qVNuR2U1qHrP6kIthc70t3OfDU8K8heb9I6efMUwClbRxGTOXyP9IUZEFUojp7gmI_gWokPNkxqk.8TS8z29tslXvyzSJ7x3dz4EQYWQ_UJiocdg3kf.aF9mPLroiUKx0UhVtRCE3mXtXGxB3lGOkqx1C_LkaSA3CMRimsJz_fUO.E_meVasE8lVYPDfoUc1XU.6PPOigpiPD35fm6eR84ZECwGik4jv3Dk8NEANpsMFyxE06NB7ZESDXPikK1tgBNaY7oHTi_lp3EY1pLyW43CrFSGO7x2lyi7TtzjHNxub5VNv6FIbPbzR4Y3TLndLdzQVrmuaON0xFe41y0SEvGM2SPXtPzpQITLM9a5NsYAy2_fxmuc5H8rjNCTxG.lBzP16pr4x.9cwFtX8KnQXklgp5KlaenaEIFu1VYQZUA8D2Eu7BVH0sciHrBWIA7z2uZrVuH2yAXiSf5.TWzdA4fOaHi1iVkPQzRG1MriZ1FL6YO8olkiQqauzKOfgUZyAYi8GBsYF55ZPDv_G9Cw6pSz8crpVJepeHWKWhuxC2334JECAYQ6Ui2I_3JqIR9J61g4.5D.k_hhdmFQbrICMI5TFw4kZsx7yFeYYlRg.M3jv8cEIqe77gSwzuaOLEkkiAv92Au5W6a9uxeZ6gseMxBmqgFGcy4YQe4J_XdYJLpJN81Drd6Us6hQvCPvWbo0kYR6nPAlTeoRPhQQFldz7G7P6rxQmw0vN59nH5iXAbDpFhKgAHTxbK5rlFixl5LTaKaFCBQJB0LC1mcyhJg4ybcwqnUxnTVQ17NaW_RhCuu_OLMT3tuGZLRXBS8BkQgqt7Qso8djb5paiDhu3ttutgR4w38IvKdUSU74eTpoKiK5WK7kmMj9TXLRxpE.xkBa37bat6A5Si8ruaUj9DcWXIbUD5Wx6hl75A0oGDcNDYJBJY5exlToQ.NYwwsBZ9r6fSH7Wf5I8JvFlgVvmQWTlJZrHwzsq8BGL22qPfsSLepXWsESW1W_gpPG2xdk6.qkZpOnc7t6dLLPZTSxStrTi6p5aO9ZD5EkEXvhqvsudfoBeRpkH5J6V5B0Pp2AZPZPP7BrlVHB1QPyQc8l7wrJhA8khOOIm4ooQhx1paQrvJcCeRFr0QfUFgJRt.A98TXl_OhRrXXwO5uzXHXtg5sfrlsmqIO.rF4uU08Y2fNvX7pq4PgdFxSw0sUX9Lw5VJBjnDrJHNz5owjOiW3hA76xdyEij4Zws5tyGHbuUqxgaxzjqrET4nr6dAZ52R1x8zhx4_QeVudoKvyNB6y1.UBpJ06LmDGmagIdqkAW4NZ5Bn.jZl_bCRjdN9LdZ2REYo2ClvaDnCOBVa1yTQu4kqXbKsxwnysVosxlPAS.Y1Ordw4ucBt.2KoEdWt0tWbUBs5KYf6jWl0IVn0Mz4aK_G6iIBnY62qDDpE6ROEELsE61bIweTRXdnJEQRWZUI0n0NFgFz4Z_bXNZMoaLwPvN2cSXhx2xBNG1DmfN8cYyWepT1Ql16GXt5hRcu28.fU0MOuj7Afovl2pt9XyGqDIVi5H7F0Ccr.2y7fBrn6qWGW4T6d_wHktMz5ha.QxMcWOzSnz.KI.m760OF..4f7h3F.'
UNWRAP
// Apply the natural log on values
[ SWAP e mapper.log 0 0 0 ] MAP
0 GET
// Store the GTS
'series' STORE
// Convert the GTS of the stack to prophet.py input
{
'ticks' $series TICKLIST // Must be in microseconds for prophet.py
'values' $series VALUES
'ticks_forecast' [ $series DUP 52 w TIMESHIFT ] TICKS
}
->PICKLE
->B64
'prophet.py' CALL
B64->
PICKLE->
// On top of the stack the output of prophet.py with timestamps in microseconds
// It's a map with keys 'ticks_forecast' 'values_forecast' 'values_forecast_upper' 'values_forecast_lower'
'prophet_forecast' STORE
// Extract forecast ticks from the output and store it.
$prophet_forecast 'ticks_forecast' REMOVE 'ticks_forecast' STORE DROP
// For each remaining entry, build a GTS using ticks_forecast.
$prophet_forecast
<%
[ 'key' 'value' ] STORE
// Make the prediction GTS
$ticks_forecast [] [] [] $value MAKEGTS
// Rename and relabel
[ $series NAME $key 7 SUBSTRING ] '.' JOIN RENAME
$series LABELS RELABEL
%>
FOREACH
$series
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