From b56ff0e3fcd71f36b0902c37a2e3b00a2155aee8 Mon Sep 17 00:00:00 2001 From: HwijunKwon Date: Sat, 16 May 2026 19:29:44 +0900 Subject: [PATCH] =?UTF-8?q?feat(meta-learner)=20:=20=EB=A9=94=ED=83=80?= =?UTF-8?q?=EB=9F=AC=EB=84=88=20=EB=85=B8=ED=8A=B8=EB=B6=81=20=EB=B0=8F=20?= =?UTF-8?q?=EB=8D=B0=EC=9D=B4=ED=84=B0=20=EC=B6=94=EA=B0=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- book/meta_learner/data/cohort.parquet | Bin 0 -> 204185 bytes book/meta_learner/meta_learner_ko.ipynb | 1486 +++++++++++++++++++++++ 2 files changed, 1486 insertions(+) create mode 100644 book/meta_learner/data/cohort.parquet create mode 100644 book/meta_learner/meta_learner_ko.ipynb diff --git a/book/meta_learner/data/cohort.parquet b/book/meta_learner/data/cohort.parquet new file mode 100644 index 0000000000000000000000000000000000000000..e65be3b198b6b482cc6b26f01fd5ebf263646157 GIT binary patch literal 204185 zcmWigbDSJm7lz+PRdsiD^&}J9wr$(CZQFJxwr$(CZCe}jd%i#Rx7qAWcj4Z1UY%R9 zP~}X~ywN5u^G2Jq%v(0vkx{N_3YSJkxId2=RXMIlMe3WXVcE(()T6gi4P9yH)$T6= zt*z|Hl{=ugd)z8H-_qW;mRj5js$NdNe2M)Ulg?0%k#1EfZY$4!rbbS4t5*q+o_}_! zTywW-+zIK?Os@`p3+iZjOHXoyDcw=G-ns%hc2)sNMVO8xa~d*z@Wr*)M2 zr%OjFhqP?BR|WR?^?8O%yRzFFI@zyz^+VdTJg9HeTq-@#RQeyLy0!>v$!k-&J32bK z)2(%{JxcpHsInEk+H%C!z^#6zt7_}xGefbD`ILF9SHmaxm3E`4?cF@Oxy;hJ%O1^) z=~1FDAr)%H@4fJ<|501NH@Vd4RX}r&x-_M`Th(fX6fb3%?jJH$@uZ`gu?*e3?`YX7 zQ{{j1xiK9LnD5s;er|bbM@|w)kw5u0D4(OTA1&RNTV1`5QXPP;hV*53=gHP2@I2!)VR>~|M-6`YM&MYCV9p-3Pz@v#VZCx1S z*Z%u%wdS*L4hX9I9)9EtwX;ojlJsAiRnH~`x#Qtzb@5I;#b%NpE_jnD+ABmxnOH^Z?BG?4CvpK zkbEb+no!KGWsls-7|WEqwW03Qd46Sl${ownG48YKjFA4cvDCYzSILW6$~NAmHn9S_ z!1bTnWh>IWpt{8J=+^>6c`o|ov@|p)np>xTxRiaZUwLS*l_ zN1=r-)g5B$s^6=IlYC13DJ0L+kovFi>q{@c5|#66Y#onA9`x$?HdE0qm>N+wpdu4o z`Zm%~M!&7LhaIJl2x(4sQ<09FnigSdTOOafEVgy!het&}xwLIzP(S)uiqXr^*HBQE z&U$r!q^*Ak+{(qh)tY1~7}c*8du`3VWb1haTiLSsRJXUStS3EMcG%L78g9AyoDTOa zRs0lC(?vmDdhXZi{$6cJ7u47Lrp{LMXo1VE{WT3;o#LoMz)--l)U~Rq(T{!l9o^E? z#U5SBZD_I;(s9<39`hVu8S-vmtyx|T?PlrB6`oBwzb54NY0IpT9;WmwW^=D%PxLFv z6ic1zhEy`MU)RccG{lvM64?Ov+wO?Uiq4NN30T zHTi~1>2?RTcA{JMWxuBI>>6J(l>0$Y1)VVU9PU#QzoUMqx$p8J)qG$m?Pyb{H#+LS zDyaO?0~&cZsM1}7s!+gE$##C_KIN!M8B2w?8ERN4plELmjd&Z<(6d3-pI0|#`&F>F zDV6dmiDjtCD@SoyhyS>*w^2QcQ8B3Z%UpW8$D>O_LW+p&QLlOY!?P*%+NCeqSR<)@ zsx>a8sHq(NOYT?PFh^xuvu|g46~4x=%^Pf$OK$2<0$Z23uIMhmQbe;f#N$(We)iHU zuOct^sliK^e$Vu&>S3Q!#&c9|oL^-QvkwcI`hC@}Q73&`a*LX^E2J__Jc<=Rq_LZV zG7}i8V0cvSsbBVbpY}F#YXdbcRw?%2d#`SWS^C4XI&jlcwYPpP@|&8{$x)F@Ax)m` z(~3`C#i3pw+w52BxR!GAo?abttAp30(mD9KqK@{QF*SL)OFd_^mTGu)y^*QCbAy@@ zq^@js>-VaFmYjAe%9Vhel7{}9AJj>c>n-lq?M1fUO*C|Ptw*06uQD$(^h3W^dwck_(;rf>Qcx`m`jmKZSU>V7xrbj zy?(v;64c`HhD!77`!4fp*mnBPZA&9Z`4w2@kulX)>>NQQUu~<=T|>*Gd9-e$p_bHu z*Cj&QnJP?C$J&~F&QkHtL1n7q)$P;)t-0sXm)W*P4i72CBU=f=-MX5@)UjV)ZGPjZ z9q%dIO^@Q2ax^coTgeymdlMYRUFOsBYOKjsw&F)J^6J@VUHJl3ZS{Y@REPQ82)Qabv^vmOCe>0;?Mdvrq{zvi-c3SIUo zZ~cG{`fLq|V(JmS(f7nr$#?8eKCc@4?J4_L<5+_`L;7_*sGB+XyxT5q;d|#Ta%;*k zkHXozspvPCY6Z37kVkWdSUPgW(xyD7A};V*)SXj_Z5281QW*E}cc!Veb!hU1%dAUc6-WwXe&8-O~L;AGUt**!U-ESW4yX`2?;h+w*Fje;iYySzg^tfNKKZG8Co*MqgiEr z>ao|YU#Y{?HgQM`FF0yXzdaZOJi;FDahYcaMq4<}(3(^s1zP%*o;5IVUr3wo(kEE| zeY*H`^Qv3Nzj^hq73Q)^w)@Aw_Cy>(~$WaN00M?qR4TdwX|u zM{nX$&+B@$tEgM0_XSkutV1{7ODKr0r!KJ)oB+?B`XbgsjDF zA?=L_DAOD8NU4ynH20|dL`PXN8ak4WwM=i`M30&~htJp>RD06^qXd-yx>x&Jc{G`| z{Jy%SNB?=%wyay(M%kL!*RM^bE$!#KvVc3nm+~H{t%Y(Ln!3bNjg}sHM*5Uz5YPBP zNDIJ!&69;`R$h~u?9z_50hPbt)tWb6-QwN;ndH@{{g$SJgW?|wsk7--sXT6N8y!@W zv>`>AYN=muYT;HxQQCSHe+m2gzmV=+^C}jYwZLThUL{96s6D@5QLDT8Rb`o>Do^Ue#YoZ(8Wn(C%RNnJ#SwCvQ&U(>{9a zmd+t%i(|+Hv!>e-)Q6wcxtl(1Jx0BBhpBeqkoxxXsc?IbW+wo@R{*mX3~0m(OC=mf zD+co%Ci~QvpZ#IOAN)bNs4q{$Oua1YXiGs`J6;$%m7aG=O?f`kqj9xt?Q07j`03Zp zn)FF(*Zg)Ko!;Y7>yD;Q`GOi-#iP_8O=Y;`(v1#5wO(gy#6DYAHSVdFrBz)m&AekN zdvrqy=y{)67dxp}tN!z8Q_cX-fjSTO3$3-(|A4LR?DO6m{qpzZcecBgq=lso@V7Jc zonIkCzwQN(6MvYQPUu{qpJ6C7*_!vX$(qrNms@*q71-soteL ziqk%%WW$5nV+LftHI=;re6y2VJ;2+q;GL-^*y^_)K3~hP3gh81tcCPo(WG6##_Wf> zsXbbL&8K<$;Lh-`v-9EmaH$CR+x!9njXPqg&U|`SEVojQ2&gakc=9%nI)XP^R_^K17UYAASf-dCRYIztg2Q-_*@ zefzo3j%)vFYQWi`Mo?2m9fkjT;U!>*>VME2c7ccE+uGF$-gVrqvDX|0 zni*O!KBRDuS2OPhv}1s+92e+oaP&YjTQ9l>H1VBBResp=M-Edq*6oRdhW1wos^3(< z&eKOHjdRHa8$}d@C)D<;;5sw_zV8g{d0hdYzK*tJ6)@GEJjN;(hJC0jxAHk90_%(uF{^Mdu zUk6Zg=R2x0+%E&Hv-~7|j9&W#{y8S6SNjwC)xNc(%Jlp4NlazBYN!MpXkZ6ZIi82q zVq`$C(MC2jqyK}gZmoeUCk*L5b*IW;pGxjNF=wO zJTUbx8Q9h`HKY|idn3;Ud{$~8*l2r5OE#N&Q5wFq->2Eh!5eGA)X~92-9x(j#ZYx> zMns;FuBLD+b9VUY9s2Tj>Qc3kDpvtJOha!ONKLM6=n0rL_F*tDn6c&8fEx7;=rR4^ zc4PF2l(q_3j=YJ`1-S2`ThNQf!Nb7=>*9vAHK|)8w$rzs)ALSqFWb9y zYQwt<18ZcM2Gll1t-n-!BDRC0d22sX>mhS zo#4^&s{2%9E!?D{TQ`z89v$_uMLw#;xsD(D#13)h|n!N`X((Ol42!g11(1bi~CzY86!8%V=E{;c#e0 z`{8S4ZU!|mAK3q=Psyv$17n(MI2CO7EF>?_AnPGxARC-VERsAKiP=tUhh7|weu9a7>9wmMNe zo4Or+tY@em>-`c~W$himjz^}SFQJ$69v^1(>kvJKE zoN(D6&%pVK0$Kq^2>T2!rT(wqA5eBav-~kr(VKdtSLh^Z!gRoa%Y)TYgPj+lqxPL) zDc2PESY5x4!oy$iexCPY?ST7c6tq;JSx6PK7}_4wtu^sYHK>L@yCJC8tlRQs(efID z%@dmnz{z91MB{AjQF3_d$tCRf2%pZh0SC=xJ+r=I?y&T)i>>sXY{fu78X3pb^`aj1 zG($02L!N_~)9(P8fd6^FgvZjVdhVBTiAz@#z!x>Lok`ZYtU$KPFTZ>Ujb zbmNX*?VZ9Jq$ig9>(%_Uwyu8l=*&o$j_0OMw6b-*W(?HJq8sM|s1wtO1BpXbvk zsOFpL^n$kR$(Hcg8hg;sd)jL8HKerXz^{i~I(^2`#ipjtWdj@Saw`uQ zp;1z5V_H)MqKE0Lpwd8PgdWefh0llhETsoeU8PBP~goEr_ZGxxC^>=k{IrOR0@RbI*ZJ#Q+?3a~Y}ASuu5;23Jv z@1TybelmBZt{YydLqPp_2RYCzr)KraKzqx#C#WPbz=-gRW%WGj&h?~2w@lH;R?7p_ zt#WX@*KX*#r8(bRDpm@OY^bS4Xjg+b@m#69r7{K3J$UAA{R%DjYbjb=NBGbm_}7#I z@D_ON>lCII*K?Gw8Myk1hn{I^AbaS=DfFV*jwXUfN8$&Z{1{M|6zuu(wlc9_6IP)f zgXMD4w_l;h3@?ec!h2ZZ3+W5%x+;9Acz8e=shhu|a4+%P%J(3o?w`^6*9G(no_DA{ zm?OGJwNrcaZ)iYSJ+>N_^Qku4RFgus#>H`~W?X!dm$uTPH_PVQD)UZ?@;!YVy}oMe z2G6n9N$^K{TbWzB!Nj)ey)-qKKA5epsoJc^Vq=2ZzuwWkDvmN_bJXaVPY0-l@A|-r zAGmc94QT~?^=wK<1v7&?Ryyi922G8gp6bN^HDEMH^tLL+{VI+I_~?;WJx73}mz(Oy z`pryD+nLI%^bb7BOAVO-uG_>KSPGXezZE^OctCeAqEmkiYTs!~9oC}Zp}WOx%{6il z2adS4Fse^Sp279du}{7YD$7nc{Vu2!OWg9&dvmV#Ye>2Y3jTzM8sXm?G9Mt#@9;KSX`&{m5 zH|wT-5x4rc^XR}ApU(08rv|}ln;oSD|11QDZv(IHhNqWr11fNKK_QKCgAW=VBj`) z&|M~?f!%WJb9TS}u5+n-R$FTVtaUhO2{fbJ1M%e|OnGv-bqil@*gd}%{^h-}e~zH5 zG+7OXM>mWdKcJ)8d`g5)y$p@}P&M?*+0@{5UM(%**TigLx{wM?THUK<2Lf_Wc2OJX ziJ$T7@S4^y0k2*4smcqlyb~-<0&oA#PhW-W@AyD1>dX7g=+S?}(X_xm=fMmwBctON zG}P~jqs?INT-B&0Z5)jm>sObdcw%s)mBkGmL(kaO!qN2+)PWlwZBA_~?m#dQT(WWx zk5Zsj=bpsALDRXlB&g_csWRvA6|bR*S3vtaOTWE?ZxqF&Q7_=y{LbvE^t{J@<==+x z@Y}0B-hj4(fd-{OgDTCl$mHnLU-|@m;d^2F>phdcNNq=BTZaeRkbeBcD*nT-Qn`G3=|NYgSJi*))dlp;m-LwzrO=*R z_*5<<_3d9!mkYvKsb`-$q95n?=>c`8#!a+#`bl-}?apXJHNKhJ{TnSmfk#=R(i>Tq zHM+R<#SSS`(x4t%Xp;p^v?6+Lm`Ard_|+|CKp}LexXHnx_kHSr3BB{Ir98XfWI;3* zFzw3o)a{Li1}yMt(p^)-&>hZGCks+f+OV(FJ!HM)Vy|-T+rT>G*n6&$9)0}cQIDWc z+32TD$I}<|2d$OJ@V`0S+D-$m1G~~R)(FXT12(AzfDLD9dsPp5q!>c^fm7cJk&Bl z4g3+*sQ$d`rM$00rn+?ZtJ&TFeivG3J4^GyhG$cP^QQ;ZZ3y0FOM2`oYR$Qz4xvfy zcx6d9-FgHLs20mo+OKXUI_c8Ad|-zE;4X)`4s`ybS6nK>wI}DDmc;XVgE#bkzM&b^ zw}q{|`hbr6U^n${GWOjqJ{L4{|`!2=fT0Vdmdj0Te)+&5oG`-)qdDe5` zV(1X*!NQHM`&1Kb8wm|_@J8z6VK+U-uTxpPO5zDB&xwHUgYS#%bM*E98gf!FZ}x!V z)JAW=_sv&+ZNu|@UBuRmZ60+if<9a$q{CqDT43+a8$;^&&!a9o1ImTR`Csvn zR)C8)mqgPk;MLmC^qTK@6+F+G_;9PI1XR3rP@nK;dr_F7If)Siw?gVCy$v-Alby2=3$ z;D2qVCob#YjY4{Zzq+b{M`jpa2YHSkWASb1a}O)i2UZ#CThY`?8*eSYSI=I7 z3GjxxecCsS+K!Hw^D4s?uM)R*D@!6jT&{ z(_;GHIPfSqZC6yU4#y6u+DE=?A3UddNYl~(@)yEC;2tKN@hTa(bZS35mlfdU@8ktG z!khEr)8SX0Ks!9i_pr9|9--p<4W+2m2ho<8ZLrP{ga1z^Z^$6bnu!~TZDY>o~$ zYilUWeYieeQ~-ZzE6?@@y>3QLTiwB`i;uxo(5J)T44un>`{?D{(eB#Tgs)KlGiGIv z(U;n>f2#lVsQoIp;+Ax@b9+$fSqqE!{_btux<19wwFf@!PadYD_@No$PNUFd<7b4= zW)A9Y5Nw&;&`e)o2=;67wI zz>o`$8LBl6E-~M$O)Y{NLQNjF!BndVM=LYp`E$)Zw$Lk&z$HWIP4UpY*r$byS~>(y zI@Sm*TZcYOU98Od40mj$K?hDrt!#sD5V-(;#sEC?`d$@>k8es6rX%$HcPsF#I#_zy zC#Y1}JgSGz{kwv#V*Ji`IK}*mK7BlA>n>WTC!9P7I%0>#ehsC^76+F^p$07i3oYt^ zz7gTlM(}F^?&}PGa_fMh%XoQ>Cfmw20PU3wRQmEk?Mm%N%R$Hd0}hRWHxdd{2n|1G za*q<6@u}5R@Xl2F#VYi5YGKjujt-A>^tToDGX_|0w5bH-GIHnf=|Aw=!|HCCpYW{V zFw4%kRhR4RQY)Y%)RUj@c>ibko@n%tk?f`AL2U)2R_}-o#yhxO1U%T8-eqX?8@PR6OZBfCYMay4e0uNxE2dt> zaw`sbs!ILv!Pm0qM!7VE-j~5K)MK$FrH_Q4oAqxnV$yH`?;Cm%j}Im%t$!ba&JiT+^$L zU#)Y&f$;t}mG!IES}?~tN9oYP9;`vfxn%1D_ZkUp_EAIlO!A;2vA+i;we%HDZRrHo z&Q7;3d+{RrkV8QqjCkYLW^{a~z^ zr8hMJEZZ1;rBie2=P&e)G-$u^xt@JqE$U4kYcm=)y58@aXrny;WVsriTh-?QkaFHajfV}&VD+Eh0wVCH4SeN-Z~dPxUc{iG#roT zHCZXHtJO=NI@5lBmHjaOD!n`}&-p!=qkBJc zF!;>xGEh_M(K|W^6s5nVF&W^x&HU=nhR+6rt)R|S+~QG|uDpj;)cwH$J!QS6DMvQq z4}K?WJwKZ8f_?$j1ACtxg+7kwR1Y7iS-`84=jpvC&^1@PG_zzt?@GBf73|U!-u|d@ zknEwO2TAEa?6nGw+*;5HZ3rEuSRVS8$53->+kEh&Cmemvfxm)}%A-T)*yC1ZYJANd z=nC-7ah2hU(b2E+;t`hj>lV6ShYV_ehK=MuVYZj z#``pinv`s?U;Vm=6bBr%+V9cgnD|C(Y%LgwrUM5XJq!P2jibTBXNl`m#Szq96Yk-2 zYt$G^_r7`+kGh!=j+Jqvqpd60cdWOLDZsm9-1>k|n|`;C%%od;yV8@pfN4L&Z;qoy zzeJ;&=Tfq4@Oa+wxYq&o&j|nPhG)rhZkm)_M{f94yr9~CbyVgWJ*aR%nUj$nDvX!5 z7+rvUYp`xREWpEv1%?G%4J5;JERv&G^n*p4Bp^+nRb8yaDH#$Te+1 zbD7#7E{1OU7LV^Xy)ZTPtT8?NA6nK9@*hivdURng@BNgaJ7}9`Fihe6{|Zq8YHy(> z*Y(LwrY*)smoiqugGCQ2$38Ag?})wE(h2zT%7xUmD`W&d1QoW6ywOE)D!4599oF$U zkLJT$PJuVV(c%o&*Zb;tQD}#UDhCxCyfKP;USlllbqjjLL44Jic#!z%fA6A0&+?*~ zIyx2Aqbw zP;;_E$H0&Dv;;8%YEIvsi`LPW`j;{lT!mb0vCjcbD~6{1)Td5@57Srj;q_E051v|Q z>lr;aw$eQ?{Os@ z_jyot$?4>|jrIz^YeH^qFuh^g99s{HvzCvNPl`;4@hGu^&vyG`M&TWd?g6*y2p*xv9jFA)Y~WLYXn6JPxzRtqtw=h>s}(%Q`Q(yP?zEJt51MyE zvZ3e@mGDh+#da(84Y%H<^l2bHXi8#3`RQj>(8bQwbn8uQ)=Pi4R-*Ac!@c^3H?y}B zIUIDAm-t7ej)Aqv_FQY=*FW+_vl8O>gDr2fXLF(_Oniq<+sssn7x1ODK_xur=o~D6pr5(?Dv9@zuQ^)HRq8vqw0TaC4h?bCx>iWjzPL3yANPXZ zlPMP({#pFF>mEIT&;E4qpYnQHOYE7*4)x8WI?KUk*XRl4DmtT~q@=z~OagY#XDHrn zYHNDH*&!A-rExX4I`X+beC z4|>T(GF3NklDjxc=3$(z4CudgZ@_D+deq^qTi24~A@rg?;4>D5JKTcH7CldHYptQ7 zUkqi=VXCy(uf+7<6_`vivqN&@vE*P~RoQH5^Fl{mK9cWA&V3g}2RO_ zv&ck`J48Rc&u5)V<DwC0{-t%z%D02~T?>e$B@T@X3E*nvwWzV8hDzwiod& z@{uK6_X&=Ve)9p{{Cmuh&Tw5vvw%sqqUF^`->087z8zG5`d`FyJOPWm>i_dB8x46n z2UX+(KE!i$Y5HPTBR%X zd~HAv@zfVi!v|tkVE20RqJvE(&hAx$tZ3Ni#0&6ucfO_u<#e zg(VN%E22005j8k-d=Kjv-zzzO;%_p=Bf!6t=$D1*8F`p*V4pUEN54JF=h9>1Pch*h z%)~slH3mOi%XqfzvtjVQVj~T8bmK>y3TW3^pW0BnzCPiz>A$mokS773#)`)pPfs=n z9mTW5)_<>kdN9w?-c0P14Z*V#kKxt#B&{4xG9SjiDS~C%<>YLpH<2tDgj~ z&J&O?0$hULTcRA<(HZnnbjPh7;ehbI7$q%zX8tB8wWzbmUZD#l9v;w!MUIBfB_Dt` z6d#V7b~IY&beH<@e&b|fuT4dR+RS=*Mo)c0U!iAwptc(G{My=<|IZ%JSkKb;y6D|J z$3&(_FToz^d2WgEPCej**#+R==v<>%d%xhrJ6Zc<`M%Tek3;y_UEYGBdHx&m`Nw`^ zmH>=>6CL9;bvOq5W-6L(@7d^Y7d&JS`2Eg?&bd6APOg2vfnUe>MnVtC!Shatt~TC4 zciIQ$kAj8*Hb@Iz+8T>>xtX3tPl`5zdijW1lnY=5yyeuqvtn6n6~r4`IEMEPFZ@V` z!iq{RkGk$fui6yi|AURP$8gk!-rBh*+Vwl1GJ>^QlW~hnO`KZ*eH^{IDA%!awO>yo zf{HhaUOL@WI`o%2>EN`3ZA}_Qy+{<)E$~^o$>ak=_{3Wzj%7kmLJJ zUpK(yzh?wB^%TDIEoxtLzltSA)8%tJ?P3lJ4Lf7+NY@S<2OwM8U|)aSDt!`rjomzTM0VbieAdE+vpVOf18@>4rt+3G>$*`B-0%g zjmA4^M1H?-P+9k&d2QvrQ6r`uBx6r)tR2KVIn3PI3rB;l!zG)M6Il~bE3jRgX?W0! z;6`A#3E_5J{se8ju_ zKTh<)V}^Kj7K~@`dx2KWG2OPbJ~o*H>O{l;^HDM^)zMFXj&b;DDmZWX#mjx`ChJiNDz zhh1{qytDi8VRXY1mB`vwV&)26dDThY-FHK;(0lr&Ab$lOS=7f-nwW;pf1&2(3(7qJ zt$Z075ins>_U@(DXc12geOm0+f|U5VGE_GeJ^&ivPw>^Z)o59H@#lK^)a@zPKAavk z&8Hl#=(*nliWtj#ce%A^ELqQzXe^WP`&qlCSSQ=)@!{j}N~f?6dwR4zrKM=(+nS!Y zRWmC2irmb8(C@V{sCmVxU2xmc(F0oEg1kFAP=WT$N8w%dP7$V`_{FioU_Gvbo$>oF zy82TGg6;jL%gdy`lL-(Kpze zA1|Ttb-?SH%TE`OHuto!lc7YZ{b~Sa&;my%LWUZHcZ!w9Lzo2iWM3~NQ*WHcAA&F4 z8%>V03jCYw%T@;syN^d>{I>dju~hRIwW*J#GX0sGVJ}1-gy#d7Er>QyfjU?skyk-@ z`5NkUxwO^zwrYF*(ujz+Vm;=H3)wWLPzzOTWrt5pkVAihV6 z`F^b@pIy=8*A_gOI{1C7vNF$Ch5QV7@G5@Iv$BSIf)$41<<0(tuTL+_Io=~TH8B4e zyw`T*nECT9@Nvv-@I^GhtMsex*Ss3P&rys-{5L*OmZI?8#N;)1n@ZEj)Eso&-Kohk zML|EPO|7iqSJ8@onP{D#`iGcLLhmLUv3~^mH`-w{_VroT?G7;4^FusqBb=T84+IAzIJ={N!oA<5AuxPyHWtZ-ZC&@WZFfa;fi3 z=EK;#8Ss7jUSU4AAK&L9_lN(o@VH+s?lV6$4i5+1v6y=)TF}zkTIj6NT~mD)@;tc#!Y? zhW_{t&1p;ake&`A+mJb^-R!3}&(LMTh;`5+?(_<24Lmf<9Og^u3uD^id!V6BWIe4b z#LVM!xae7CteA61OO7zpC7_9{)xYQuxt=N3y!(fRGtuPV-;Lsl&Pu-$@h*z|6u<-TkX|? z7XdX+N?k?^sEh7dop+yprdNH@w~EFk7l{`6cQ1M!S)*S98=t05^diqhrgDBQ`VyRY zHfx~AL+TCsdVereZ$9JpF|;%0i!(;X!}8iX;bpcWx}hQA@i2(5J|%T_^Lq ztf-+E!`$SG9Q7IFsQPE0u1y1<;j`x4NS^c~oNzAl17%F@A4L8YFX!_Ea527Tx5Q*X zS=*<2@*S+-hh$w|f*r@vqc(GIgTSL7D}njC+FF?yp9X%=VJ-U}AND`AizDPKM^14o zADlOBFDoVHfO6T1vxX8b;Qk$Jvc)0uOi z?^UI5lsZ6Wbt(D7TV6eYs}7AD(9&>nB$w&aUwraZV9pAxwR;D;B;N56xLkTPpfB+5 z!#9}A`NMa1@hCQU`~o<2DL*^6iJ?Kg+1p^swE1}cL3D*VIb@$&S7S}( zqy|p_AB<$?C76dHQoc(Xu>?! zD11XaI4vfpbjdAm97D(P8(V<`BH?$|h!>{L|9D^J(ch_KNq&-*foDcP=25GRwmvFE`6GC< zqkNhI#vPH;)_VBhGU{}XN+LnXbiZ*_PTC^IS;{g6s{+NFC1fPAX9Mp@GA=P#{nt8<5 zEIiwAaM|cutfODd=Wl>>aR$O1^pq4c0~(4>`Oyd|2J2zPTt`W^!B@aSU4GcAc$&;V zIP>y1G!yiRC==Pw^zv)e*=JzfB^}U&XQG=W2`Kz}NS|F^<)e;gE(->!3?5qrkF3e> zPiJ-rY&Yo+Gd}FO7?bHsS6!O7lgv6AQ>#WkMNBhwr59Z6txLJm;=RJxr=`Rq)~LHNx^bg$~W;e)N=i|8rc(P1vXrRFnhaljWrFNuHf&ysafG@E4k!ntjXGx$Ed#$>F4UlOLPm>rZ8l1APcb+on#Ik-qVq2F=W^ut#IiW8h6JC{5iC`&s&*=UyqJ<5&iTYI?Ga1 z7x4M})*(wmPX0=Ha84Y2xYEqn^NypH=3D`K>Y96oF02l!V?mD&Jj6pGQ@pc{p$ups zljx^nt<67)?~jM`t0EaW@Tf-P1*6OT;r+af&n!YY`omy&Pcb+LzV-P3OuZ(%up5j& zeVnPn)Z-ePm>I)ssC^R6eK;QVRYRSd(9_|`4_4ww<73WWKyKtZ=LDcRHbIw-#68$1 z{VYHJz0Qb2wl9#ks`(B+%l`khGN>rk{CdE%-O$+POcV4=bcZxv@*89oHoG{Z z08J#x3T6xF4Z#S!KyngA8?nbak*Tc8IRGd4ef0M0aFo_V(eVbdUgqEl_kt_ZE4SuD zN1z@iV|Hg?9Wr-QTzb5KGXvVgombh)OC7P}p%1}>i~B-aJ<+YS$KinI$idfS&I`>i zF*r98*u(&jEoul>zk#m0BcL;^b3b!%hsjN({z;}|EWD`I|FthmTc?Dyf%j7r{clB6 z_&#fGwi%|^=+IjS;)#I?ex)|_bdy)zvN9LYmYMSX4t_R%2tDWoT1A|SmY(51XQ|J9 z#llmKb>C=y2YJ?&y^UwdF#8o>+|YFQ|zN`RvO# znSnwx?Zy5tm_JMn;`_Dp6Kkq39??m961}tAZCf7jRu^xW7NYgML(9x_10U;DP+be- zH(Z9Nl>iSMCrgKJ?F9!rM!m zqb~Kvtzo>YE!pAz!1F{QntA=6b<7{{mVRJnUIqH#e=@coEF-3 znO%NOM~^7Ly&rgtr?3P6KMr$>rI}$M$JCH_SbZNJFqo`UAN2fyO}!5&OP!#i6?f|( zb*lYh@_AsPv1BXqw+y1^h3PRm@D}=UfS;LwE)WAu|7jx6uAN8DTDZVx=Ac8&^*1+k z4-A;W`F}Q>d0Kk%la|c$Wh0A#HaCiXyRr#$`$^DKPODFDorohmm;X+GyFQ)ieCAQb840_pG8keREu}D4PN*XzJll1jP;b{ zA~T$G&_nB?J%EE#rr;cq>SSyt;Ca#auDl88N=ve|V6>%k1KI>8p1Oy3TAV!DFORC> zIi{VAzeG*!kGB4sdf)prGk;{ZuAncx#~*0Dnlll)!ZX`bFP4C(!2+$xF>HB8ZRY!?IRIPtyud6pnS&IS(7~HBn*}C3eUj{2RQ!f-mg*j%ma#@R79xAf zUdTIwY$&?HtSab(?Se`KR(OiHvnM9{StB(1HXa?P_6(z^KIHnRplMcOZFD}syw@&M z7iV%d#4u)x@J@VWJAc5Z=kA7^=7jH}-K<_nRvMq8+f6j($Z$Yv-h*2KS($y>T?8$? zNk{`faXwaN^zO`DFB!uIXtu}EYcI?TDgN@1Hsf>tqn7?S5>mfuhIUYAEc~e3c;XY# z0H&iUhLH>C+6HYmn_K_rYtKB)QNeHXvnDr{0k_b5KE?~^{fm%>Zw7ZqbvWCIXO_v) zDDv?);FtaLFzW^eeA6kU(rGweHpt%wF1Pes`JSLVwtKJful*y8qC0qL*S#!vW&a z6Hgs8^)@+q@?Pj;?6p4NfpF8-I`&vrYT5yE0b85Fca1POp`#Cfy=tBafB7NaOJ!!d zo{)hHhZo1f59GZrBx70mHXMz9)*PQ94mBk^J)+AeaM)JPKEQ8%i7tEio?ENpu=erq zB6VS}(?JdMhrm$dd^#8e zBQvwxycD@O`b`(~8D|%n;-s9vQv(mO72lDa3{qY=4ZUHc56liO2*%}XBs}Hr3;cSF zZ-1#EJm?!4PV|${o#B*VqCTBCCu$2g3f%Sr{@FFTt-W{z8Nk*p(68Qpay0uKa|Iij zg+jw?IuSm!0{;xG^p^Kf0&er>DZB{(%4iUzCxDaSCeP-gYj(0Vjlb&--idvXIahqR znEMP3`@;Nk2Ilqg1R6huv!j2_W;VcQg4w7~zXmgh0p=(|4Sbr8?A|8M*g;z?3I-T) zoB7lyZpE7dj|U&GDb4TFJ7*_B6QJJZ;HQf&A|7;AJS~#nyA-} z-m^D8ku!~sF2>new{vm+OG|VM>Sd7JRn==DX+0XyYR+F^uV?2O4s1xqd6!Gi?{R+3 zNPPMmVA~wz#&a-}0!}Lu&g?P%*BLNsRn9N!zxn^!Z#36xwtCao=Cc-_;*m7%K#!tt z_a_T9h8aW~ZdH{!HuI9BbLd)k(ei>Lr~?(y>!>G@IpZS0{LghawXi$*n0xA67mW!m zWg30IYe%vhWL+B`H5I3SKo6pj4K~U;D3dt#Qzoc`sAo{z;G1Q=5~^e&XnN zp)d{k&NbT1>U80px|wc0r>}%B#^a?XWX#5#)qH9u`oU)SQ}P<@y>4*Fa_Bw0`^XEy zBW=N|oW0W}5oaE*#Vch`4g_aM%|<=B&si}|!J1({t$Iv0@g^C=D9{)KpU|eQco^3(~MU8`z6bU8FoS43pv8WC4rO+mHAi^<*DKuISaF)gBexjsJjdm2NM7LkZ4I zg!_%Cf#z&0 zWy~B$y{$)vfPP#Ge4Pf4Q64T=g8la8v0tC~*)~TxLy>*c9IgHYde}^Kgv;E|Kl(^n z^vt(+EH#P;f69ptx{HiuS+t!N)Xafkl)w1TKmV`U)2GoSj&CGii2k_b3g_JvAuCQ^ z;yJ$4)vste=S?*M6BfriPnrb&Kc8IoBkm6_oP*5Ik9Y7=v=awB@}<8|gQ$I{m^CVA18AM*(+_xh$lNXh(%)UZ@E^4R%*deeA z--q?|hHIU*%B{2f%)xfdK`t|O;EzvDf&q0xn`)INs9`(tS-}O5$TW!ek_p^=CZ(a^ z81@wyG%DDz>yLm=FiV=k&z#>3GBU#g%oB4)+)1}Cy~hKE3m7-aO^xDQ%7h`#7G({t zr8Ylzt5Rt;&vxBA+qbGUU zlHd&RQ2+M$c3{6zZD7_8bI?aw^PnRv<3C{r-^Hx8~T za+|w2D~$SlEfenxZNTV+=7gpgBN^w3+`xw;!!-3VzE3S%`7fd+SM=z{a%w>;&eh?( zsDcT?G?YwgLwa>Z-u0RNtfzlGyYC*(9CB$xL(YWi%z9;C%|tuAyb_&iCEsxb%^q() zG90rgwJ;gkn~tpQ{ovdQztDB|GLIGHtfG!Qw+hT!)Il2_hmQUMJT)3#OdXsMgZUiZ z-}sN@tnf9OfGKlcfS;31owR~{&sBH{oMG52dK6x8MY3`~tAMl6dtx*UsNfN=M)dM3 zY9?DViwE=_%{cm6a0Pt5IGRQn^*nB*psw!>C~X@2eb#JEK7TYiTV$T=8tQj}C(Kkb z^LFJI=T)+o8rKYHEZ1GM1m_IG0ei9j(r2U=?ssYM8+44$UZvQ8?!`X6(vB=PHD)Bf zQ8NpT5Bz!il%e>C*((h=FDVW4Apg*6hI`fZ2K9flAwSwq--2PPcNAZzI5V?oY8OW^ zr-SZRp)OQqm=rT)DSlh8%!^z5mm1dEB z8HTQi9*`x9p$Xs6r@6N2@0hR2ZR_M-GDR_&NkM~%3U13seRz$wv)~e30}bn>->s%I zZFM%t4lj1ANS-hiPJ;&$8~>61oDQEe3AOd!ZA+0#q8X#N*QZ~dq2?q?$h=l(Fd5pw zjqG8XT8r7w@nFenWTSSlKGvA}iLUXd9vp_O%X8LAjZ1hd8$CL(18re9^C976zl(dd z0}g(*EgC<(^7dtBN7ySbz-X;TGlPE@u9?zO=MWi!ioCPF{Bc-{D}H^F6@IF70Jq>K^b79C9*lZhb)Z^crw2BOwKG$%sB*bt#ke`?Mq1pBq_XpH1Dh`GX$)Op;K9Z;DfU# z;EBcXJbK^HUfu0TeW>eGM|kJiivRb`z(Sm# zbT27ceENSv{H`8&056(x#xD44aAUAAyl*eM?o%(BowQ((iC`G`)S2;Q5uRA8m!Fy7 zDr5^-=j}xgq~@1T#B9|qYG_-|3>?iY*jeV3df{&#XPzoHIYVYeza2uGxJr*q0?v=l z87>n!j}=cW7ul_Dth+B{T4F>tHMpRq#yqF4q(6Y>FilDeT$fV7-(rraeDnY<`3pGdzzbfHj8XDIz|5jXarnS z(S`WVRhWC-!=G7W3&B^P@%4x0gip4^M?nkN2{+9G=D&qbo(~`W;7WMgsG!zA^lA>h zrejAi5YHhEd@a^TYJR3LeTKKXo(A>tJa~=oj10CfM#kkE+0Joy&~B$$YV!jPAPau+ zD*O=Eeq4G^S-4!4N@#46on)CSE`N zH3vFCgZa#}!0Eqib zi8(tGJ@qSjr6kkPImsnW8cSXU-aQXreZgz=XU-iu+!oJ~v&){o{eOKYw@pU)NjG%f zx%lztIB(*$pL8*+B|ECa|1?nf7^J(9C*mQssSqqA+c_2@gE5ZTvQ z*}a+-9lV34P`^4kIxyXB>TdJ;oGV-s{j{*75%a-s|KUNAEeY`M(!%vhF7@dRdc%Ei zME#_!*WLJBIl)&sn5kV)rt7y~4?LD$&?Cap&Ni;GRF#^VBf6>QaasR2;q$3DM}m4& z;vhN*xOg7<=i$`q7WiDjD$FpQNALXs=a0!eX>zj9GgxCAI2Z06XA9)SW2%XE;^utA zy67JGnv2kmUgp8~n;ldw^u$`|x~0+ni;)A$&G)zln@UlfJjF(4PCgpS*O$y6{%w2u z?C-R!iKpx_c=&=j;4!@L`4NVSzVNFdK7UU7_n(gBL%_LLCo&UZ!$rZ%Sw?ZbZ}9(f zc5ps2az%2ZYxNCi;01DLg~3>n@Gj|l1ALCMf!CIsUe)`FR%J3fH3nYNkXe_S%-57e zTW`etTLEeT`+jOpa?{k7mc0XNP#xbnrmc(U@n?sSqgnvIT0qaGwnY1c_WX?VRjE-! zqrvINP^WpmL&7=ZVl5oaBJVO6&jhXS0a)P-I@mb+%+3EXb?tFY*6sV@d>V)fiaK;h zMIDL)iaK=X@vKl&I82S#iqO=-mNu@T*l1gjTfKh6r zEUmO={VxCe{qcU@L$Ao5eV_Zj?(4el>x(*(b{F0s@O6868N^iR!EJLfFFyu<%@|}P zG=P&skNqMU`-@^cZ*V?)pTm1K4f}PYVu|w4aAv-QhiZ8oQTaD^VM3S%FiWfkr$NPe z7Sk}pypc#;O~5R16zB3+)J8shr@$|~AZj}L&sIG1>oxF{oIxJq7Zf7n0^Y}~F~o~+ zKs&`V`Ev{~4|Y*5U%)*1Gw=~KhpGw4`o#`Qw<(#}f$#a$0(f#w*tZ6joXx_%!&J;4 z;3SrU)8nJ}bzqj$;O|#p=9{$#K7q~9TCDJXPK_rDUd3*1IkLx~VXFh!v->cPD1O*~ z{)VrrCk1r}dt-mVJ70;tm<#axq8B}r1-{u2?i}yczEt?L&*7{AD|>(qU%>2qVB#nu z^ABXE;6G2t=i3K7R)*fQt`S;S7(1jD*im=G3*QR=q6d9)03Imt5D|RWDZuV`QK!4{ z9&G&vHMi_h^7 zc1H33{b+z^>3w7^03T5CeP#RyotlqK4G+BK=nd-S@x*cX^WO#E*pAtH0{ZzIyWl%M z2p=PM-@36oQiXH$A$tE5aM}mJkE9%d&Sk@AnFf!|D(EdZTYevW?=ZPZu+l9e>9k#o#)AP9m<2 zL(N2;?SpQ=bs92Tm6(ZtMQ@n`P5V0T2!KZ0UW&b3e4bTPfIHD+zQNfz3T*P%0C*GB z;)&Ql`ZyWd4SM1mdgLqa0N*Q3B7&>XPjJ4S(9x2!kxe*~Oboyibw7&Cb>NemUx9}o zg+320Bx59%_(>K={Ed0(%k9_;2X9@R0dB?{PZa-&y_`>wC5*E*jgv|&DM}*Fq$d)~ z$AU-y8UD(t@G;SlzlYyL2L4<=6EoJVQN-n2sPC_1mkv70p)b)lz>mobMiE*yxcMi6 z&w&{?N08mf9Y_YIz&t*E3Vc`IM1qI;)dBsCyCjM5;C!WlZzuy_WdTTk#Iq2El1$Q;4moL7JIy#FM~Aj{?J; z{t^2Z;3R5_akkciulfhr2HKX0#2y~_^^fs=hJk^3&@}GC4|W4H&^u;mO|7WSp8#WF z#uB~>puvxePx}DgJ#1@aL%33}O*DUH&EbL-A*0fMeF-@A(?us%Ff3ai3!M3LMq}_!kPW zlPdlZJPoo~`WD0!&!fkvQ9ILt*ZQCloa{{}Xuv%Go`C*d9Y14nTj=a_}>_JRhvGj8x3Wq7|(FFJb?2-??a#C`aFCwvzJ?;{tljPl?Qx(0e)5`@_eUa&Ih+5I0;=0+*;utybp!gyKT-OHgsc; znFB3v0d|Nm%bweWcL};j_nW{=k6;dA1LxmH@527azsHexu?08;oaqQSH32ZwWN5l8 z_knvso#@++{H>2uh+@pj-F)MWz9`7I<_PU536o8Tk>@kWr2P^3e|LN}hqHj=rxO4a|c+$I_!0 zgS(Jjz`Ixloo# z`1Rp^C-6aPm=btQX-~?MIFSW{)!T*`X-#pcJ`l$KnYZo*xt3=x7qVJK532u*oSL(fo9PG!)Tg317&J5(ph|`H%&mr$tft_6|@ZCP_$<<&- zx(!??&ifHNeBocl68*=}gEoM_*#Qi(6gw4XksXZBH3f6!J0aYG1pe(Tc0EUs4JiamoI{<3}TMK^_p}d_fu#Ck0W~nI`K=l;8&nP-v_q*16*=Bc##jU!AlPAD4+tyf`52=GxA6< zD_-wQK=uZD1axaZe)fm6k)tvW_Z!8dE})mr8H00)=XMUb?qoMOd3Y$#1QUt-!5AU| zTDa&j+*yL>(FZSre*js^f5LZ~1}%Ma9KpMUet3f3Uw7PChuzWcbV@Xdb;KVUw%Th!pAn8W|K4weW`DsM1oqyDXBWa8 zu?;$3A~?&Qhch?0z-$lv#+c!M1Qt9N54?c7TTqCd_%J;A&_0Dq?Dd0hEgFwp^cTS~ zgHu}e40gWG#1pIF1sVk}^@qTzPsbz^dx3#}etTP7>#AraOhOhT&z0 ze_`Ku@Ry44`Nb*3qDzTH5&G7FHTYfd-wRL06HTw+dwd1Ds2-Wc%faEp`+d;{&ImgC z@qbZYQQsNh|MuoWH#`Y_4t$moxWx7`_C(Ua&EmVa0{@?T7yFC};P*d(b_YIg4zPUW zbKo8HmiO0VUjUlNKhWR)aRcMr#_x|F`id3)6ztJ-l|i>Y3(dX=`9R>|7DA)?(v3VW z@Si8)2eI)U-cu7#WIK=rg#WGSsboTfys*Ls3ef_bajFGa82E?rJ^b=UU>F}fnfP8F z$Itrfb<8U_aklF5|HbFH0em_NoY_urZ*`avh=tIeG2@Nyz%;AN$g}yU{eTEs> zC8Q5wE(H#J*A5+X9nKl>$ejJa7vKj9z-v$Z0J>)B!}%K77hK?L{OgtP;q}Cv{}tY$ zt2m?W;7mTm8J7HtS$s44x(NI4J>cMvV^;`ra(Eb!h48qr4_c6SxX_(3JE~oE}C0Hlo2^IX2 z7qT$t{(>yLVQ}`~i)S6fyS@`1EO26D<8elQLOoQ#yRZP96Z+pN@YoeE#1c6NDFksI zGch>#cb-OuHhh81&vC~WxN0#l!%ED<%|p0<2z@{^C!NTFHuBd)jlm181#@r|JiZ&} z@f#oZ<-qN4y%0y->cRU2Oq=^T@-Lvbq@cbOr(za`E;?0}Mzj+T@lp!$F?Kv!UP>Uc z&Lk6Kz>U3tI`0F1FZ&!BC?8{w_$KuFdU!qH7m5xdzxE3D)8Gjb8<1i12>hi@&@oCm84DM}6P33|v3@WYuZ-_Lo3UT!lOs5j=ZqCb=O5k|BGDkF0@ua9Q9Fzr=idEFN|JB(x9o zfz^N!%)mC2Fhg8?8T`8dI@en4xRfD- z^2Gj*N!XFYY`8@Qz90N{>}SXt1y`Ypk0px0alMj( z%+J{rB4oy%&R6L~)~mog=rtMe2Y)R>ULvq)BNsf!x8Nec!@il8L~P5)J`FULNtpND zbYutOc|ATGTps**&FE>jH1WiG@HcDl^9P~-{_j_KW%$@l+z6i>e*V!ms0hyj7jf{6 z{)Kl8v+Ryx?7ttz{e>jnQM~_q_r(!93jEf&_<8G7iA$&tjhMp+)*#E{8(=ejB9Z)D z262Z>C&pb$Cf>%pS2G2Ee&CGIFfwA#qh8DgHpQP?YQnx8>V)+OGC^j-U(^e30?&O- z4!BG_uSNKMHzcCY31U&t(}^k2C^3`?xpdr5gF60rEPPQN z@cv-F{~h1SMAUv;J+yh$@5IeW<9qu11MnuOFRM}WifXZAy%m{9$8kppIEjM};5{Dl%T4Hm1ac%$gIb@( zeGy&Yx4}OcNbHpon6uu&?k2Rv^!LE`;TidWJFE9m2p)Klq6%;d;0IHI8`75HJfhBb z<9QyLoI)tjH$KZi@4}o!0*hMUS&2Z)_+%EmD5y1KVt{S&b3a2*OZ*NyE?=WQ&Vr_! z0dE>OngD^jAS#eGj#=bAoY&8eVD5nysGWj;mI*vF4_P9Z?HAog{{mm}F1#;CkiV{8 zjJmxNbJ=C|g2%!A&&NBCGh;&?{Rp)ql>@wudAkjKQ^MIq;@!Kri)8__B!S=f@FPt7 zhC)2Szzo+8FToCQAnEXbmLOmFUuefqp||acB~ma$G=uwlsT&@KA^3CF+qDc!2^t0YaO_VZ{d+U zhuz6OJNC8V|M>SDv|G$aXYu~5{RVe-VlIE{DddxZ$H>Y6UkDBSE!3-(7vU=^gbynW zzy4w5Z$E(lVh8rL4`J6h0>9)3n91<&-v@6unnbO{T=eHlm~)VqYiR&y1MIpIcq@Dm zUbg3vGmNvb4E)(7DSFv>)LC$14l%M4pv?#9>BI+z!BOks9mQGudMxr2FyAdL#wr0^r1K@aDDkNbu`Kt3Ee;m<};t5E0PLY*Hy9(Nl|MV8S-WONpxM}Z58u;J^( zce@H+$Li5(1P!yT68)kBp6u=Db^l;ie!LdkFEGp#cyD#%Q-~ALq1J|xKUEJ-`aNV$ zLnnPNCx!UsIIs^_H8U+mZnFa4tJ!(=F^tnUG&QbwWSHN?IGjZo8`q5YLw+E7l zcDgL7gy!EE-0=ck5A*EHCxCmV zClCtEI;SveamtbLKLmXovr#Sh;K^<9TY{7L8(QDHuO|{V@RdytWW9l}{Sp1==as-9 zlaakQ13E8w?YZcutH)zkEdw~G5xI_cz#)&vyv>JR0L^Lzv~Vf3!w>N6S$Lms{fN7Y zMc_r3;XOxBYV^Uo@DZLbJY6CQ{8l#n{_i9EpcHdWCip$@q4Uw}dI zcvcC(CsV*LV-9Oa{oD$yFbAB+6kx)VLG(QW*dG7trVV=X zXlNcsxY!FxMApI2=-J@=>&huaTpZ?yZ=nmLS9ZlAYr=*58Zkdy{|Pw=FJdng?{^pa z*YORBgh2uS!0*TkgLdKCgZcLa&M&Y~4m8%?qoFr~PxyTw=0RvL-;TwdcvYwY@YZGA zq7Zq&`f-?fUs!{=j*EQ);FXOb+#j_PcnkU~^B}lZaLF;xLc{(QSpQRCBrd2Am82{l;$0H^6A(naF&afH_JYPb5LBG=C4T+F$T$XpxTzZ^*Q4?74$) zxPUykC-O$Rx*^ngNcI2TigG+-UCG@b1;ZBLrVbF5W{E>eo;jGJdd&S$Gcj zHDw}WQG&j79bWdk@O1CRE)9Cv*+tl!ej<(d6S!yyxo+lw}9s+z;lcD-j45R3w#Ng_tFRo`ezY(K<^L8Ll^~r z>^a=mff?cL2t0(S^Q)c!-v!LL2z1*kDAd{irzII+SwnN zWpK8B1kRt18a)PB?a1vkBKs(~e4OJbu-1>g$VUeky9!w4ST64J04B5o6W<44VFGXZ zGCb4fa0;;i^JMfOd^7KX=lB)x0%ow^mQsif;2UIG_)Fjs(&1b^aS0yb?a0WTiA-m3 z0$Xv8-g_Fg9M5_e=J*@4!7tB2))wXiPZ-(;G>MJC!ELCgvDNSrV#fWc9li3~{CA6dP9UlP~ znGB5I1?*vBIlwg^ybjI@&+f!k?8o`x$$biU z;5aac)3MWV3OSAUpecf9puL8@8t{v97VftB1@C@wI&pq7_9ZhhV*?|%{eyYytA}?U z08@Z_`e8#dA$|-z=xfMR!0tdN=8-FyS8j%ov9J~SY(FFt1MqSDu>sx>oYBpg^(dGn z?a#q~4Q%kmad7o(;rBvKc%MYR#(@|jc?Yr_Oz{2zm;3-9@T)T98KQ1Z$2-&T5j;(| zz`0I)`2M2*OH;Aq@i3noJ4fj6J0swne#5<0;6+4*u|&`UU-eA*aks$N)DONe0$)G) zy4QB45VxS0^uGmO2k({zUe=LUFlXVt-3DDL{1bM(GT>i(3EtB2*fDt?9u&NH?}Ep8 zUyH09@J4@}fnO4z&4-!kTj02@FJpdzhFE^S33ya^LN}A{4F>Lc$hy14;FpnvM5Bk%G;N<4CJj|EDZj%A^G7Fqz6u9Lb>@7Wy8vOzKJDT8lp!~NA99E=&uD-Rz5}1U>KJMT{F8gp`|jdB`?dgQ zFA*L<{IkDNGw1V>DT!Jle-wJecHCJ5PW#bq;5*L85V0R2^Zo_+RGQ!$Lm&JM-iOQ_ zWF!0vKMpII_@N_}IFN%gcr6k448w)S^qcdys)Y6G$dz;QYUWnPesU_go{i z^YO^)djb1xC-7ViiO4lh!`=mWOz?*B8sy;O%wD>cL`(%vW@X||kcK$y_~VX2Ip)V8 zW*`-`Xz*@p9{~n|zPkY&+DdTt<7VT1x&$1Dv%s@Gyz3NMR;Y)UF-JCJfu~DIAqIhW z>-HlTBOe-JJ-BB%_$cs&t8iW}0y9p>Y!`#G6_bv8OVBS~0v;-ypG4dNcf0sE_z>Pi z4$yUEbAc24<890_5}fwyz%FS8xM$`O?BBp2B0=qF1P&kGgL(@cZt|UIH`H z{_1oh1YS0Z{*nrR)wB3qb@(2OJCQqAivIH^dM{@B>8Ejqmf&uPWf{bL=vus+*qbuL zR}WnJICMS3Nn}@HSFCOmb`1Uo2DyoB8UuDs!1Z1lz#WAvv9JA*+a?o;pO!ql!v#BP z%6MdX0(WO75V}9H_k{lMe+Ku**1-GrMlx|89P-nraR;LcGc~lrj7QMJftRmCbNd=~ z<>g1yiDx>1qoKWh-h%sc@HxKN2c8f(Z7+UT%Vv0l@vm**uzwCh!$wc&!fbgEwd>l4 z@b&^*&dbAI339>O;QQPTy}5o6c^Re1AJrg3DGr!tFZjxzp^Gj=Cfsaf(K~@jr(qwt z3-b|b$XD0}So0ZjHoU8>UxLR8o`FheBsu57E5;xnmx6g5v*<_oc~>4s9u4^8 z*YK?Jm!}c;(ED{=*wH_N9N7P`ONY_4|b`u@w`&uxy5H3N{3dp3pKbC8THWN z1qRHGt?-^;FJLrg*5Ah=w->e34=nNnYD_HjjwRrv%Zjm^@GU&vTj1H;g#8H~a$!cn z)3^fJ#yEQqFf%sQAlLa6aH131d*8#jKNh_0tO({d3A`Yf z&$oX9jR5_0S21?aP#f65dR^EN(4vn_S&REJFiT#L;SAUx-e-lof|G%5!I^FT9N8Us zX2fOeRiGE&L~m%tS@{PX*JJ7wqTwhogB|$@cfdQnk8Fz!XxS&gH>rWOf$x5-f}j1> zhZ*U}n4XS1ukg;151>PKfKRA|2Yw`;Xy}WD)`+qt@s>xmya)uC*FQ3mKf=PAGZpbDbTdv$NT*I8DOsW!NKC$zlPcJXW;We zaBhkY^yk;Gi-($T0XAtuP07RjB0?Q{eH?HB4_Iy&a@{Q{1P^%Z7;0@VaPqr*;IZun zr=3M1F8_lp65u8l-mwAja$CXYl&-?OoQfQc2(pf;=>K5~@zF+PJmNElfsP63M(+55XZQytoj-B{F&Ov>1}e;w9K(tV9kk z(G%k!sJx_5jFX`8l4-Fng2hX*#kz?+UaBY7M{sy)p;$jrz@yOO0z?rn-4+)lN_ZKb zxCp`JjS9s@2_7$#7Eg&0@kZO?sWCF%7*9MWhUAS6#dBk{yewJ*8Dr#S+Y+oXWxR2o z1V@aOH$If$jIr@@Xo;>EJ8yz5(H*mzH_?;mi*fKKg%bTSwLB^u8&u2OSi^d zdf%W%boc?)bA?zkJgg`NywT!gnMl;MxN&EwKW1>)}VN^PTp zarbzOJ){-d6WzC<5?ZD^uD@igB-n-oaw z;@8+F1ryKm*LfyI68-%3p-Iui^ZZ&Gm6FuM-(aIslP>Z%dZ?VF0ROQNm78>hzloMh zCiU?*+j6Z**Z5mJxsIeDe`_e$nH1vJ(I&f+!u)Nv$?l{Z{Oz8}zN83$M`*G?={Da< zn-WO6%dfXh2`25l$A8>2C6W~7?+i_eBF(yiMx!Lh3wGIP)Z}Er6CN5TnId>HMB^rB z3U<@zWO9~ZkBx3k&JpbO&>hKC!M+gPnM@Pxr!ic~EWuMYhC4Y=aKOXxC36H%hZz3k z0)dOh3?vr`8g0yAa*5y>4>OX?6+9baMw5AhCK`*9A`(1jV^LFNf`c9wCxsL|A7XJ+ zw1Pu4Hko1+ykKKnQ_2J{df1K>tKg*&+nHh$9HvcmrPu{8+orlxRtsM7O!cKW1h0ms z`crBJZrZd!$|gaxZCWs;PH@CCEt29C91Ts2rZfmVv^+}cZb6GJkD9t)(CW$Kq`Cxc zp*(JClfX-xPNp6bwA-dzQx6M{d8Rv3-GbLb)19dvK?iMyE7dDFZkypw?GT*s%zU?j=c=V=8LO1$s`TLG1l zEd0<@z@bot7eWPGN~W-fHix8S2|u#Uu~Kq`AA9CFC{*Dmp*cNBC)IuAfpM4A2S#lpzJJW5#YqWW;bi44n zZJs-QweVXHu7q(2zYERtr`HODwE2PbO~QWL{9t;W@O#hvNV-$_Luh_9y+IhFl~6Kv z3kPf^)QtVYK~D)M!zCOFm2fkfgkjnOGUJeN*tWo$aaj1HXMrQbE&M68z?tC@-k>dX zWq5@*Z42EQ9l~3lg}w}*@aND%f5vHHgtjP<(Ip(QEed9w75?H`6v^-le+?~)W}Fw^ zrg15wdW65(xYSV>g}-~aoKXScoe-Bh>Wc6#t&|+qC;Y=!Y8`b=_@}4TF)AqhD^%(n z6%yW~Eq09x3-8+&yGPv+{_R=p8x;}$6I$#awez+xN?Q^bbyxVnwj?;}p73AKlE|p2 z@W0TK=qN%&(0P>1cu`CRkD8e*if!R>GAW|C0UkFqQxs3mLQbQxvLZV|mgMjf+X#AuOm#<)bx0U38plZZu^lVc8v*cEc?n8Tu} zEpo>gw`kgc+&RW0%A+e>W4xm26$Q^cG;=`VA9Gs7p(_Jpx^0H67L{XcP&9u)4I3blLe4bj3DwQp=hv}i!>AA4KGrE3CX?}|z* zG{Ld=M2lNAk+D(Hk^xP0EMOsBOUa5C^DDH}tYopEMa#*eh=l`MZdRsPMAwm7Sz>X8 z&YG1YmbB;`SyZufK8E2eJTs2U}9oHnb)0dLt4vDKP zmRiRh7C+Lm)G^L2UNx}PInETcg=(zLZTDp}ozDK;F!b%-~QM|Fm${8OJKQ>_Hj=v(_ zL@y`D_lY-Glv~GN6K`oLcZ?5;w+@s$$A`pq^cAl0Vez(#74GpnZ-}?ItniJGh<6OE z@Q=SOcG6b{#@`j!SF8+JPrdN_$=;RMSGsaolBX&v-8p%Z11*)l z9FFAafl7Z)fy70x3gi??8Y`-TIVF;3TB;&BT*+%IWuSF>$4@-`M<`X#3Z)Wdq-IG!s@Pzq=14zo z+2Ej3rJoFJa8hZ~i}Z~yDoc8)VxyayC%xRV(MRP-KONZUrxr*9^v43!B57~MV?k<( z^s|=7B2=#Q^MS{rRG#z-eG?^DB>kdd6E#;R{jy~fCzq679oWRp)k^#5o5@_G^s9=^ z*4#4b*DaeJxmM{n1Dl<>Ht99`7FVuadc9(cJ9oA8+mrT4|8JHITbW z+F!9Xm|G|PzGZ79*D3vBU~4qDK^mghQ6}$}4ph`pC-0XIw$yPZyQD({b==8K(lC7+ zIr)%uxMG`i@?q(ZE!!NE-O`^1wmBzzq&Mi>U6Z}in-$yLlRKohTDJQp`=mb)Z1+z- zEsfB31SWS$M=Ev%C!dx6(y}8m*)RQdU`KTFdFgGslQN}8`dfvQI_0AD_ZBB-NKXOK)rLy&XDvT{c+cnu=IY#g8^l8vt1 zO{L3ZV_J7}=%j4y;BGEmE6ZZ+A?Ze0cI6%`y-YT)b&rE?m5m?Vj5r$tjd+_Nf6h5 zCL!lBnkcMzIlr=r%1V|CTAMg5id;C@#ARj5MU3Z2R+d~``J9!NBbT&3=U`Fg(!uAP zESg-#IOt-reYQ2mP!9ImviFz$%ifDxVLsO62O+=OZkxTr>E5 zl*N;48HXrrkz7}Kh{~49^{t0EY*KC*Jj7*dd`0U^5w=sl za`2@nyFqSa9Hva&Ew89NOr5%4UfFt>Gu0)p8a&LM+9bC#UM8m=l2=#0Y@K>o{z&V~ zj;U_>s==3?Q$6z4j8|M!z4A4cuehgn$RBNe#W&R_Upx4Uf9h$ugYjx$YL~pG^3~wf zv+{MVuSTZ&ULHX9fX6Lk!ypD0iH7zXPR(Zrd?S_1N>k;3yhMw0rW$TaQMjMddpOk4C2viUx*FeiiYEs>+`LT1 zZbl24m!;TK*<#JhQS5DPapX}I`vzN_c{Ig-Myo52rFg2c)t#58IMCYa%i}1X9&GjJ z6)0Scwm@EyqOr0qm{+2BrnN1S$5lK#*cQ#>DVi8w%5;(9xk@i}x=eAf)ytVqDxM$o za;Iw*hZybTbfe;h%69AYGR2Fn?T+bI#Y=7s}--b9`j9i zC|(^r=AT}xa5G*DOy8tvu6!*xy-snY^|i=!r{d_~YtiWq3J;@$GGn)*rLu!MW51%c zwSzOmrDz-M;Ld1Lcp1mZ8HW_@mB+0!4l9ne9(T-eD_$Es?wsLKbTCf1W_T6HD^Iv* zbSO@=p771^DgHNj!aw7*!pHbuU`Cgsv+{qz8D|wITmKiC;a8j*{9km&dBtglk214I z@p`3?I`g99jaDCLW9fvu+up+q&Uks z>6#fnHg1_8$1=A zNhr@VPE$DX$`2|}Q#r}X4_i-jI27fD!P8t$rm~0eI?2gWepLCom6N0Vxb<}hhpPN! z@O3ALro6~_!^L4KFIB$b=Hw|ax4z-yaFm}8zTxK-C(zgzjQ3r$!^-=W@4IK;Q2yQezHfF! z`Oo0{{@J&cQO3Ez?7PYbmFI%9?q*!Hn+j)R;u^s#?VElFj55}31z%&qz)IH!)B-1bRij+2}+^htD114(0Eq|Dt- z(yJ~~=k6yNZ5KInT_khpB6n^R$zon2=N=;2RhO)D50g{dE;;79$!SBEoO3;79`mwm zu9uu%b=f_)gPhTJ**Dim&K$bzpL?3*Fh32rFAh1bY=ZM}}dAUS`i*I5`M zOPHUz3d7`rs?XeoH^_x;pZN+ShoaXJ#ul|=aIrFxn$_` zXd$8EF|SaH;#K^rE7YQ7m7whkr--5w4qf3EWvWEXFUX=SmAL8)Yf+9$()NX;h^mqf zec>#ksbtJAT}3RFyy{DLQJzZC_NA|gqf!oi=`Si!k<6=sq9T>5>T0m4M5S)K8Y$wc zG(%UTMLd<3*+(fBsdQC+)MA-R-`2+|CRK)^K5nsAWn_Lu78_Njs;{iYWh!&qSB_$< z$};qov)HC8V}9)_wyTy_eeEt@tyJhw75ZKV_u`o+pVgox<;M1Usc(5jWf@ssv5e+o!6wYGq02L4ymfEu3P6F zRz1>o-7(LtS~YatInSe7&HUCi&#PKf^{snehw9O`Z+-K8soA5?7}>UYi$sp^>DyXJ>g+p4~I&%dGC-uAt3enhon=zIVC z+bSpXhrs;1s`{!Qg7fdG9&h_0GC!)?IrKwxKA~=4hTw}-@2Uz>OOn-3w1qe&6!nus zA#O>gdN*@`EXh*usT#1BU~24&Jvn>KXcGk!csp~HRvwMQy*v>^p$Yb zPY(_HOA6F3=1`!dNZnX96f7xGKhri8DdDQ09U6+3@YGGrFlB*A{ajU;xO;(7a)D9(Le;Q!L7Do+wqeHttNNv(Vdnyy`Y`iH*8;ox<*Fau3s$RN zY5UQ)z@dJ1=tuv8TD6<`Q((a+b#v8E!3A~dBW*uL7C6;MhklAKXi$5YHz*5tt6QpW zP#5l3x3=BjEOe>chHh{dHmSYLo8-bn>h`Le)`f@F$J%Z>7P{514c&Au^r$f=?n+zUI@C)#fL7W&lx8@lCRcv|ga{v24?rS7cyIk@nw`efVBk%fNssiB{v3(u=h zGb5BmJ?hu1BGg3})o-*#IEw=6H-{qJMOV~a%n@=?pZcw;5$mFB>bKiQ9E*bLGeaZJ zMIrTB<}a>AVf8yzzql9OP`}&ui*Hdx{oc?o{zbRde&(-%MR(QRRlf!o-BZ8c_G@HO zRDEvf*XSZbbDnvd!j0E_P<5NiP1bzacALYcXf6!h=5jMNJjV zzd5*6%_l>@Ik_~=Mdt4=E=zN%>UTFcPjk8LcORFd`E=-aKes>=VBQIEi!{AecY@p! z&1Y?QB3!QK^PxLYE>ClXd6!Zu(tJ^Mms%>*eA#xFQ%Y*C4&CLJYBhb#Kgd#}=Buhd ztfgg|uiO4`lv*|44E^COwP~&~|8$kwHP@^DbeFEyeB1V?uhgOWZs<>cX{{#6{3}qp zNz-5TSFp5B^L^W2ky5AThoQfsr45=8^B!gKZp}c|J?i59n!&bvoW(B9(9k{Z;wDX) zd7oT-NHbh@-@5p)=Et`Cj>T@xPeb>ei#?hf%)ecWy_%aYw1^vzlMp{)sI1YknR2C%Sm&dChHRl(M8p^IKJvy5yqf_qHf! zNkDUFD9T-OMRS+=fLzk2`J?KAb;&i&pKT8uOM;reh8{SVgf#b<|GJihHTSFjbuYQ0 z`Md33-;#*tpP_&KOKxkT%>M#Q?rI)X{TEzvPxEive~~3o&3{AxMVAm-f<>hB;3hOqB96wehSN6)#JhV2>&1SjT_-{wWGpu4|qInCM!OjFVc>- z$LI28+A-evd_Jii8;&pKYqeRd1Qp+?&9*0$^UJj3ya_dYt9E=ip`LHk=CBeQ`F8CD zdtx(xwRWO6v6Ju6P6{V>^J}$KR#Gp2lQ!3$)X%TePWC2^@SWNz;iL!r1}%-1oG#d{ zrQ4Hp1^cxOZ*soCrDcYbO9f3@7Ar+1IHYCUQ_2O0wNt$*H3GMGS~#U%;L+x>QX2(c z?R0x;v!Fve!<*VE@M&j;Q@aJHwH#Jjub@ktZ%^wNoYl_qri}>v+S%c>2ZHn30v07* z*rT0er{oGRYUg?>`NDv%No@yysIs>kLnlR(=PUo8WBddOTwcb2nijJ zm6(nnig!g?CJ+h@(@6$8?JdbR=tRuc%0;vXAW-mFU#ou_Gd`P7@ycK*ZB& zSy|~~kxpmN$`#9WdT&;~nA91GVi!fu|u~!Jg!?@tFyAk_lh^^%I)L(#dW$B-ti-1r*36<`~z`= z&c@0~m+aP6*mH6v`*oGxoP3E(R~61Fl{D$>tO+W~Azih7Lb>Fy?h)^V8i`xCDmUO>C5Sb!+SsnLsaMjatFce&m+U;NTj!lLBJu0i zhbKLdoY&Q|sOi!k-3B`~S9(#m(M!#j26T^wsio2@x=pNHm9$T{*`8Z2y{6mZ&8?9J zbz8%^_0o{8jy1Va8rE&IPi~gp&~5il?vzG!JHnH@rMGoX)|6i9U0uC>O271;?s4yw z5ouJnGd$&il+ZV@Xz8+e{VqE#SC*`Q!b{7SQS?uSX{EAE{caXrCCk$9vD3?CIr_a` zdX0>#-xsFW%V_%jEJmY@rGLuKXqM&a4|o}!GLHV~Fr!;mpm(vDy|N;Gqn+6=E73pW zWsb;t&$t{FWA}T@-qF4 zUUrS#s(&fWu9w^Nhgnk_<#zqc_NmSC)%sVwQ#<7j{j1@r-SS$!n>DRhzDeI~pVlw0 z(;x9p8<9KpN5j(|$Q$$?R$jVdx4y-mm#f&XZ}sNoD_r`va9*jRN$+J%S1AtZ+wIfK z6^Hf5ywhtGZvAWF>GcYazJoQRQQ_4ex6f!+bm&ibXLKrj`u~MzbSqBleXN27N`O1L)%`m4_c}3sF%2z4- z^l#bo%azykZ+r7=ltKNOaDKfqq(94=)u;^X-?7hXR^HIR>z&o9jOgDB&+1m**85qr zdzE+f-S*l2%6t0vy|YJ@QT@5_><3E1aGq6=PR1KPuovW#$%YTT1^Fb!a3Ne!N@f~* zSaVclmf<7&oN_Y9@UeGJ4M{b85}s2}(hL__a~nyP;gWrBGnr?&?48?5atxn_=XR3? zh5)Oumn<^$+6(*162oWS!V!{d_&i+rfaDpju!_=EBEuKL>p;(C?MaE&#uQDrw=x6f-sHkof~@(ys!fJ|`}}@Yo#A`${1KJY@I!e1166||#41Tw?=}qBOLEow4TIj2e6`Ck z6fP-MHyOgL1uFF+!?1lpx%#l-NAH3fwcGGhctO3|W4OUu*r@gzZrT?%t2+$0ybC+k zKEu!9h283%rwtL-qF!~EVZ^?uUwzi_i+9n8+Hd$Zyy$`Yyx}&Bo380G{ATCoYAzan z_j2t+{Q8vX=B}?iwE0m-K7y8UFPy8PP-y|Am)4&=5v~%}dwD8)K?@ zx!Po7Y&$PsOEJa`^Gdau#&|YgrOh%XRP)QVImX0xevOuDOd96bYiY)0wxChVGNx1u znzebx)OJCqmSapC7IbS1j1;!8S6gIEuNL-eON<%q!VxXkIBHnqz6+VNt10Ys_McRXU?FyINeXD>II37uV>l#__}AdY#Rf!cNk~1D?0T)?b zOuA}gu1RLnw;S_Kq{%RBEH!COMz%?1GMY@)rgBr6$=q(LFOnZRt1FnO3x0ModoA%3;d`Q-jIIE=xD> zHdR!Y<(l`KD%;EQ%`Q{ba9OFj$z*3QRhbW&s;ifln-7~FX6wA(f&4<|4s-;Hr3)R)- zOUukJwpZ6IwVGcVuC8BdGaqI@(zw)ae!2RQ=B2C6ue3kXxzu5Pb@-9)rL|@^dsXk! zP3Gq6RsBor%tzW+jVyJVj}EVTu(ZMKVXsbKw%goNy*hWilIcbKCIh(q&C% zFMEw@*&%a#^_uc!ht0>@*VHU?n_nAVQ@_k(?qEOKxXf!lUj1nEvJUf!_D4IH`ONLws%>VxwCq0|FW~@lkICqmif)6hSxq=cHVrN?MPqVV}8Bbk-Pk&`Hgl* z{_=qN&0$CB@+;;pc8zLzpZTron)2n>%x|~X)GQC0&kWbpFAtf|vez{(51ZeqUe~<* zhWXw0b)Cy2=J$rzbuYhd_OsXbF28H;u3q22{GR#!_VpvnqvmtN>mMv9Ea%y^>DGA5 z2i3K?)?~|v?X|dh+;U;Kw$z$w>0xhBS+gu3Rc|P_=2$*%-%w+vT0R-xP;aGKF0wZ^ zT3MD$)f=0wd6vuV8#}EW%csK|yR8M50Q<3CYmue5`mug%iRH8Q$40DN%jd(7J+SgD zSJ<1<%SDzisyF49%Pe2EZ^|zxEmwy(m6mHQeeBJua--#|>doclWtOkoH`kO~E#C}p zt}nM)uCcc?mfJ1YtG6_lueN;KzNNF=Vfk)&OLuv#CCJ{|TfWKCU%j=zyw37{`__?i zr{#y?tq;l@EFpGX`ik9_f$F;475goN?REJpT$Z8Xy3!R*mN0vpYQ-VTaP_wG6^AW9 zwr{Ih;kNuVysdu4PLJgVdwb&wujOX-_U08GmRs%HJ6HHDKM!y3UUAwIVejZ&(PbH_ z-qF9}tmT*X9V09JmS2Z=JXmqwa+~c;U)f{%t=gHp@}lMUc4z*|faT7xvvlPZ%UyQ8 zYGt40kLvpJmDenPw%6CJ3|jt=vbOdN{@bHV{mAkdQ!AP6`);gAFcn-6MGpl=e; zCN?;Tw(=Fofsmkp4FmxNBnbzI3N}XDK`R|l1lmf64=c7phc6W?cC2<Jcs0*efHjKuk~BMwe~h=pMQMQ&`@xH=MOOl+6x}+ z{Aclj&Vt3A|2lhMpkQg|zrQ&!Uhrt=k1>rm3Lfu#Qrvj2;D?<*oo!q$_;KfdzG-|? zz{p~V9YH2E3$Zw&Ojs7P*AZ`GXZg%KQcQRjDt4xu_*uRdr_m(H^6Pb$nt*C`y;b?WN->Ll$qIPN6cg_~PbtYDZRxrP)YjWrg-Oms5FJVe`!m zlqpLfZfU2?S>cwJPO2npRd34xRhAVo-!e|wvLeN;H>j$tC`;=-sy1tNZ|gEupY@CR z)+dxBODJ{+71CLR#T`}X&Jy*y;|treqUYTyg`O-@d@#N6cvg(%ps}zcOWb>~yzo?( zWd2}7VON$^e5k#!Co9%+sI#y?YfbN=fx>fHYv&J*7Y=5{i4Wf>Jf9VBIef41V%ECe z!^?$ZSx?U&eo{D*B@?#=70qPHEp1UnvssGXw)mpCEaiM#O3}@%1aW(M(Z^ZOSlW$6 zce2*^wwD*(%~H*`Hx%8^N)#VyFM5!*!E&UtXfbPJ?~#F`rL3g+BjZJnvXaFw-za*V zm124MUeOO(&-T8&T=Zksf6c%Aq==EN7JGusXtu`Ui85o^+Fnn*nVp?F?@2M^*=gdV z>1KZRCd*NyS&+TC_h`8}GW)stqYY+ZwodGAHs2biCLYUAwC{doR+=Kay+V7mz~*rJigeFy?y?8 zN^wT^4)H7L#XGWJu)Ja{&dPqV_m%SEyzHIxuQU{!va`gmwilbTvn{W77MEn_^u9V! zT$Y_X|LS7h6skEsY)7mfjQPmQ&f*`4bJ6uIv)=$#zRm_AblG zPD_9G?%tCFmUG#q^C!nGgV|-`*KSzOXO~-EyJxwWy{GrJWy@IhOY^Tiu}oy!#HWI+ zGuaiEQ&HC0?8@F#@z%L)`~0aC>&@&cac8>q*MSO%dhWQf5<-2`|D-vkJ*j$ zzkXt6Em@cilV5?M~0_{@!xgq(KEnR_LPIY)ZW zESDtbygYyANr^hgBkl>>m6mhV(i62ym*eg2iQi?&IX2&uvMVF!xcF@Pt{pkASk4-E zW#zoud$xR6Ue2%P&o=Bb<#dR9+jp6BPFQ+7ca`Lv?Cl-cRhIMGeDC-!Th1wQ-;G^W zIh~fid%J3LUhnN&-c_IT>-oMXyBs-P;{KrBbWXRWKWeu-=X7s>{O-1#GxPl^yFEEQ z;y2QFAI~{!dBeE7Bd53bjq=^6a{A`qXxQDA(=UFreRogJ81(nX^3|Y=amCoi2 z_nwO{oy!@SKbKN^Gv~Z`AiebCoOdh(#?m`E7kUTEOYi24&JQ${-p{!xe!IQ&LC(9D zw>wK0bKdKHd!TeF=hFP!Bt5QTjM%-13`yr9b4n-}{^8(jRkvJO7&}rHtGO z@nBFHnmcJ3j4H!&r+NqD%hfD>+^FiflxgS~1N0sYxZ}py!FE`}go}mAmJ`lf~ zzUO%EHUAc?m_uBXLUgw_v+;4l|8`yI$_u>3| z<9h~km&BKD>^YyiY`JuA&&AyDdM_>S8O#0i{G}&*CUPH%$AVs($^DCEEb67%-0yqG z;$ND}{p}9?Lq_X_Mv!^i2)e;_?C)rp9fuJho)|hAkm4$U1$`mY5gZH@$32&Ue)|sdZT^_e@CceUu$6J_5smRFVOD?BZ?8pnTUN%-_<%RZLF0aVT z3tPC{P+`gwNUpS3nDfG|S2`<7@>ca-8K@}Bi&(faUSZ3Nlw7@0QI!{Ey?U>rHg9#` z)#Zx%yk9I_eNy4b6G~=-D(O7JIvZ8#&J*>`##grGMK8>zRC@AA$+h&#<9RXGYsSis zJaON(^2$?rl7(vxm0fvK$p`I~J$bR#4>~LR^ValzFi?3eZ|%Yd>ad+Qm1J&n@r3;^pR}UJ?B%j}?K5s0yetxg|qH#~(=gZY& z#+Md8e^NbRv`Ov;)yxZvHP$TLYpA(ztd)GxUh}}X&-z7Y&7yIC-xmWlOUAl|FUD&g8S5o~yixPm*kJwR zy_z442m1cFT=S!`ap8|oY8d$r$^D>OG~a2xA61LxyZY|O*Ru1Q7Vf9i;`y}X%k)}) zezWyUW33>+rSHq~+Q|IYg)bXwh52sDSM9ZA{z2UifOfR+ir; z`T9m}LVmmT>wC3{`A7P`Uan2fe|h2SC$;K)kK{qnzO?+K)(27hbot)C2l4w1`NtL> zr0mPcKQ8$uecz7!SFGO{_hse3+V@TQzP$WjEqv3k&y?RGnQz}`&Oc$D@7!0Cf3k0W zU|(7OYYX$^`)v8ABnvn8RpobD7w+w=&40abVR>JD{;wAnp6qkvcS#n5_S5;@*2Sp( z?)=kzi}Cy0^3NzHQ&% zlmCYG+s^&{`ET}pJFx#;{#y&*j_)7LKPP#3WB>X50qeti`!D9d-S=>L|5*NS79Kv? zKaoEuSqiF~$se*VMb*vb5BDv_*UjaREG(te-ON8PSx&F}IR72%va#+?{)N8f^18eE zqYKLob@%fxO1^8adyxOG^}Eix#r*gBz8k1p%D=Sm-FV%j{4vR&Z`3``AGiMbUfmD* z@Av(Ax$ejO-!A<5NgbnLLh>l69xa%(K8mWx3a0uV#n-b7rWYQi)Z+y+lE0+a^9wFp z|6;5c6kO^1OL=`{!PSMoG}H?VW+mUZ*OLX;tlxLmOA9{e`+lH4uHgE@_v7`lf;q`w zZ`3Cg+_3)jUVUQ0hkbutu1_xb-NIj=)T;|_N*)I_q!oN*eH_)GE4bD7IKIJ9aC_l# zN<&7$$CAILH|!|*z4dR#hOB~5`u!Ga~pzi%8kU$AWb_q_ub3%=|7_ws?U zfPs$tb7W`uYxewEooDxLEM7zMlpfmkR#9@Y8tXqk_AOO31hVbGco%a>l|#8&m!Yl2W~>8 z$W{m6Ftn3Q?ML7G@4%-0=KQ$RoSZAY9baFO}GLuO)2 zeLi#~n1V`tzHlU(g8O~Gb0nLdTJ-tJp*G=CG}xJD;*_APojMb@A6@4(n0Slmv(5|? zU+TNnxx*Av;+yZxGKKd0?s4Xs!WMlGI87#j)bEJXYzi;&d)--LTGj9OwzJF>vFP`{ z(`Jg4`hV!GGDVg6f8ne(t?u{#&RK8z#iIXDPKQY-#e!Y5i73HVyWA#GKeo=*W{O_K zo^^Rlq?EPQb=(wF!pe7bn8f|8J+4zG$s+53tIH&n1{`tqm|{x;UU&7I*7OIw?K)># zyBP4kYtR%Y4gAn`-V|RF_=W4DXAopZ8hoVbfoVfY@as*B zrj7lRg$x~l6{a|{w|Eceqel-2p;!{60F(|bZ52jH{ zQ-ZIiF-qHyucO&i>LUIujZ(23O6e%^OGdo71Rm zCH&RRIx4fDzpmLpZC~U++nhn|kcMn+-a)-k5|ZDXMZMS`vZpzZ+PN5VpxH!aNkfk` zo2l%Q(AS$wsGR=Lx0}nT+{MuMn{8B{H0;CXD#}_zG)0vNR=2pR!hXTJmNu$rQSfYwhcZjUx3(Ooic7-tTRJF9fB2r3Q5urXWsG`h zG2*9|3Cbpo3~rsFDoP?(x6V?P{gLZh=P3JPXCQBdT8diw|2LQw=4*_@ea( z>OlW5zH9xFYFzxqPpynXhg2BsMhl%K!qsl9(A6(o=Vli+EefA?N@-BQsJH(7YFMD)5_T6m~m^tL;$@bIGOeYdQzO&a~7JE5?> zB>D??V&Reg={QWBGYFsty@{+K-n^9p~p7<1sDsjvff2l=0WAt*e3%s0RXgUeuGkt^2`IN%ZG zFIXY3V*k$vlvhdwE6*VR{DAWRefIMM%6~>-`=kGUJQ)!%e2^=!Q9804jx7vdgo$u> z?1%5L(~1wW8IFx`2;uM|9HA6G*TInxgc)E9JzvC84QHpJzH<+p;RyUVo#8{k?pi)b z4#UUy8OBBAS+yC?A{jm!1H*?E3dL_CA;|MM4p+}1L5wlP7n8xgT*fS%9YK5%8aXW# zLn$AQw~<^%T#$tEEh1vvMGm5f8wtc=7bhPMB1W#dp^!Wrd41bQ0dD@2&YmH2VLJ=IqbID8- zd`h7)^?B%%a|$8R)r{CT7$=c(BnY)Id^eyzUC3&|k5EdiV#FrL!7gz=2y7$jiwN6b zS2`N;WgLRzKH`hRFjy{xg?uJhw<6vS2J#B)A;W{1Pz57qK!Cso20kI5AX`x8me`B% zgnBq}04G-(Lfo1#G@s!+&d|r_#UWrK1at-3XZ(Mlsx2U7=Kl|>T6`V<7phi4>npnb z83-F0{)iA=G3X)Wss38{Tmjwbm2tvWB|g3x@WlhF_F0ih0rEONft@Z9{0buCf3c#M zH<1mIl~A^VHAGymg*q!1-2umnn!zR^KU)+wJM-1343AhMbK zWM%MxP^2GW@pD!N`IsgmDGQ<*)dm`UtX((E_NkAcPQF^L4=JS)AvQYYZ`Rme|UO z0j!|-gB9f>g~Y$`Hjv(j$Q7)>{3pQP-vuC@FT(L4!N~V91JccKeFe$j#hb$LjSOEO zjPRfa2J0N75{Thx_OmN3g#fJ8h?xz75=a3up9l{Bf9f2*gOUM>ONbt`T7K5+Q zk_Pb=q%z^lRt@5(-oAb1ns((+#>$`Vz-~V&L);1fE4!^{NSWKW3lPoEWcHu%$3L7`NRJPO@C+meVr!pIWwY#Rh>_uT!v7H?lP+4cZt3m)tVN&`-(Iq$hBmLZ5n! zyq1CKG6 z^*mdog7DI0xM!>2ieUoPd&q=T7ZSc1`4wsQ$_#=QQjfB)p$6;->jZ<1bA^j(!Va=a zKSN2CY|SA(^Q2{#)C(MXZLE1Jt&!153Vm9brD95POqeQThu>x%qs_QNKjk5J9ZCon zBk80b^U5#FD6N+~fpOFxoxwvM#ff5tUf{*m@ajl7<_o<0z$NR^hGYnLDUn ztk)gVXlVn}@uG)DSm7iHyXmA*(83OD!SsHJz)Kdb>FT(dHYn6QlOABH=9fRc4$vhnOu#IOHShkDUCB*&~UPicZ-j$V(T{spYV{N@(Hkc zA~7o?^_$hRG*~{8=cmzKB~#ZBJQw=}nZ^_gd20}8b{d9s#VqjCtHt^$lIqB4#6Zv) z&ncak6=&DJW2 zcrdjWJK@D>GrPiZn&A$ZJ^TN3gsWzU!dD)uZ+k zrXEW(STr2~-&=-R8r5a8!@Ee`L6)w=soiL}Mw+=TZPPLe$I0-GY@Ube@R+AawvQhx zL`-$i>{KsXujshYjnmL59O>lNq;@}QO`!o6m#1PkA2Q2QhL7IrF`@LUUBgrWmPRSFA!V}=qVw2ZR= z3gQ}V_Ik);c$-dSDRfYni;8rxnq-*PW!CzUMT)f3$^cBew zRQenx9W1Y5mLyVTtkjn1=?vD@4!zD#?{ag(y(C14O)Yo`wJMcMVqRib^GeKMGxa56 zq{-8*Y|ilFd0yP4BgGvudHCidk>EJ)1df=}xhl9V1}#ZmmYQ@e`vwd2CT?j)f8O{Z1bXtf8-kzf};8S)wjZAk? zE{u(PR%lA!Y}U!hIho!BN zY0!F~3HPAkEuv{POYf>si>oG3)-@Sx(ZlAoa7^wyVm(dhy=>mPYqO*%)!+^Vc!{}a zqi)t?NSrVM{?b|iR}L>Np9II_ng!uivoyqewbqY1>EQtMW0ZQG%;2U;je;H9qS3ap zH4?LnWK9d@(cA>sr0@iKa9YSpWwKLmfu=08o@NQ1q2$onM3QQx^3%qIADwIc}K3oIt+pQM|3tA|x2Pp91 zYz82l>PTe!a2r|aEyS)htW<8A=_aX{nWsD^7n}jD!+rm2PVdDy6xSJ>uv76JdX<_g*xkKw@J-Vw=pK+v$7OBuvSNY!{1WsnTOpJkqq}D_D2U7D zGtTTz=LLmbFnudJ3497x_V~8 zf-a29ByP)yCR$XafM>PLIM;AX$Ua6=C5h}m$qd}XtQ&_2lNZx^Dli45+fMKlgp)=z zUUs-2OK{7Kuf%%-vOUNEn9#yTgh!G}v6;(dc`|UMQDkb;yU~LJ7pJHdyp)_~u$pHA zE|V;Mj@jifP04`Ddal8O4bAG539pe@s=^E%2>|C}VaRr~4!A+V=bqf*^$=%BpmYf3 zT|SUZ(S**+No_HEQMu;I4ldE7-$=}&LxPP}vjW!i3}}kbivo|)q8qZd=`>0t2Iew^ef@5NyKMVB5Tyr+Wj}1gUMVqHkQom{_oMv$#xjVL5(7das#M3=Xn%pWuw&hY zvoc$UkYdJw5XY|pmu?^lg@xfw(|LeP%<5JHQ00V-a>+<_G;zYi*8{mTNR4ZUPNw5} zA@%EFDo{Pw!aXF`Uop?(>p6D}ULiyso)!iv!}Qw=wPNywrwVgsNSWj@8JXIm$8ZQI z#8I5h^Ji~@v=cx|LokqXysTY{o@-K*K*{254!%Kav|RHnXygP$GP9wDG!U$5oPeAz zyj3rdnRQ+>Q@cgo!n!VFcY8p{wp58YmLa@GLyv4-D{)eOZI)pXxb)^0oO?95oO|$u zhXz8})I{jrq>qB7Rj{K$PO;PDF$+kFPX_D#3>^xqnJd=3;jwvVuvFSS>k-f41}@pH z^%}HS zvgpLYQQ||K7A8ay4aBo(GF*Z}B!}oNI7WuMOca%aEh7O$k8aI1r_CXoS<3a`k zxu==y%;tJwngkQ8Hs6pzA47=f=c&~d=KZJO^20XAQWyR0`J+p}8dy4fiUpUx9UlMo z;^4!V=@2+s>N>S_W^DP;yAMa`rPn=6?{+`|vVp(Ud2wm@RLBNj9-lt7bn@adD~@p( zP99!xEV%|1V%TmKJu`;*`BuX6#mS4$iWz+9>f3kDg@}>E{BH+eS$fSARKPjRV6%oI zmztapUHuPV_Qm;27v5MJ9$?`sU3Ae8FF3bsU?AaW=NKy%(Fbk7E;t_sJ;CXVOQ)OH zZD3#^G7=jH60bA?Wh+95e7qy%2SeQdvKb%D2HF4J@PFSU2>EXT{Lo(1BhrG$5^z- zfyrB-f)E9XVB83Xs03Oe>B+!Ehz4Vk9^ZmTSuXwtQmREcXMGhQ3l00&L3aVMX&f!a z=@DwT2v?+vB|I7op=oXa0j0A6P8@doq4_J)dtl!tGY;Z5quPLNZqNYHgH}QT(tzwr zB%CE@FZ9nO;VTx4qd^p&8o+Xb>Iu@LWrm}iG$vRgGl1yEHB2HD4RfR1tr*SLG7(z> zbKfQyInnI7kFk1iWNn#8jbYGSB#Xect5=k)6V4DCjNqanF<>4jatn!x1SgCll0ey@ z0kj#{{30U_)A$IrT45SLO$QSUAtvIZjWMMOv;Zp8XeJi^0N=z!(h^p)K3I{%?BTgZ zGy}OQ!?g^e2Y{nVazUd2ae!w?D_n5`Iq0GRUQA?W#XA6E!K`M72wI4Q5z|r; zSQoEc`NPG-arAlcEH@|xtOz<_#`f^1{8q}`(JQ9}x#A6QIwhhnsQ|3 z0*Gcl*jR?2)(UCV3(UY5F5KVIzv;q{T6UBgs51Ja1spks=)-@#SujLM@ELN5dvJjVI$8 zG?v6x`rv9YibkC(4A{i^6t2^|@g15u2PnFSmH~};aEt)Y!hkMfU7j=y0iIzJPJ9=h zhY@RV7se9efX7Y1O$03f%#aMtZVXIOyiP|P;%jHO(D<-U*v8My5ccr3o4$yITR_>u zgPITcBs54oK*D8UMIAm9!o|YD&v(VftvG{A*xaGO1DZ4+OPx~f6xfTxL=^J+q?`}^ zYqcUO1ZC04Yi1a$mOwe+8gJv;2@#YrK|8<=&6@^u$|={1_VWL{gzRSlw?M z@&Xx4f(fxk+QZfKP=841(yk&LcGh0-49wK#s}7C?n7X9XX~U!)2@2sw&Kf{w5By@zjsnBwZ*j%qNVz7Ui>-xFrVFH<3_gs^k2vV`<&!8kn4r5i3l2u6furz|eaytL z3MRxE0gL8mV$p}c^h}Ow*^b;&yL9Tapg`ea z6!dcWsCFv0l?^Iq#wIgJLdEcj*{nORMq~XspmC=M>aIAHBc=fU`y+gZ22jf&zGMSC zk{y444g2QpkWH3H(CizR7qd#E(=^$@po9A`!XK2+2^L}u znZ}&vQGilB&jo~=0f7y<)b_&+xfSCI@6B@@dXF%Yu%zWTnj7FXy#^&W++_1jaohnva2mXSW(T$ zdyk%_*%0}gRx&!N2XLVCXxu)K-rx{d!4XMM<{@FvN?a}CJm(hm;9znHF~9{F6T@7| zTw)1TQJO41jdCFBg<1kAUp}}ItLeCN7Y<+$brWueli>0}TrSn@TNg*C1rR9AY5-oQ zFaOTY1R%xUmf?_DvB5GVzG+sdIgVC1sWr&A?0vx8R4DovMoa~|A&p`R!F4eX%+i{v zs|l_6t*rnw1*UgYJsRRYhRZjcfoWt6Y?6sFiDut{1e(bX zKmCdii)g}O9KpZAaE=)lV$L#lK&(^X8;`29f_Z@`p9l`eV8_7_Z3b5C+rY=UTZtKt z=B5l1cNi$)7WQ~D{iuj~0q9iZ2h5_G1?SPIrR4E0KZFABvCV*pOwDHGEXg3YVxaSx zZgugE$XOxH2Y=`e9c2l$SU`_Djrk=B&wGn<98VqAJO^{!$8qa8IyYcQ3sHbZ?%)uD zwl#M@DHEXjerTES0A6aH=xJyQ1z4In+E5m;(ET1Luo)@hM<35TgwdCOEA?y+C0>Ty zJGR*~`5|mhJcSscODCF%ZqR(hjzxoA&69~!1mQ_zMmvwIXCXo1B8~(+0Bk$FMFRo^ zgwvCIBAfBkFu(BABH`=3bRj7Ycw>iOy zi!d#;u>;3ZjwV>T;w;}V(lqUCCw>~8yw`i$*yi4Iv0{5+P(I zII9_-Y7x1<{4?K8YV+|!np}1>(E67jP5v4NUVH%!pQJE` z-I4*UfDvy%eg>Yexk-Hkd`qt^BF7pHFlG^s79W8iM!bx_08l2iTxAgTfsQ*XgfYYP z0EOhr%yB^s>t0TzJsgOyARgjuQ*Xh7X*R|oyMl=T4B|Ob*e#$o1(TTUEI2re1P5e6 zZirm@Qct2V195v8sPG7K6O^FY61|dwyM-|!h#A?%COwe7`SWo?chs5Uj>z}cQ*@F^Ua4~X#j0JaBGm|{J= z!sOKiH=Y>^(St>K)WJmSoKlcx8o%^`vrqG4EJg~sS=>``7cr#JA zI{4Qtr$2_fbipBkI;{}la&=M%{{4$WJ{m+gDw)J%hFX6)vL_Lev1a@$qL?8H-iu*T zPBk^T>6{SX4DiLDn}t2w1DpdW4T%2AQ4i7G6>BDj7E9fcUc`eDKj2= z#!(7z&qR7WS}YoJuuupbT3v>QqYV(oz_m>t`1wch-av?|kPX}8X}(9pX==S0GPewvlX00O%pYfA8vG)d*E0wT)BG~A8>pX{n^}!n}wBpLJT=YvqxjlTxGkX2@lM!Gj-t_ZHyEW zBDPjQ!~_ciBT0N)0Q)Nr7b(zf(s6n`CJ26GV2)7K=)g}053%~WTZA#V7c#mwt%pd% zxuHM?w;^iqA+XFGt;9qm%vd;(tT8#E>39Oo1#uIh4DuqiFgTave1u~hKsCH@1(Hh) z#<)dCPVQ&K1h;chz}4GG22S8yHixUjKrNZG!8Zax|HumrmhV_MimjQH2PXz>x{rCd z%xhniH3Ov!A-&Ur8o12ll?fIHypF*_<=WuDfP@{8kKm_41}tq0hHB;}FK0n|CxXrK z(THyRGzhRVo$%+#FzyDnW)Wh6sCzw1qnj3kOK}{J!Gzu2dl^J`UIGM6uzaM}4S39L zJqXz{>?z3x@QR9eFanVOVrYH0BOl@)N{jK#Z{Ee-+%+<7@Fq6D6(EUv5rqJJSOZy2 z@N0ooHY0>92*5Cwh)`%aQgu&UGsfzNLJjd9Lw`9q5!4B;hi}r_Xz8#Y!G$-1m^IUk z9?joS8Y4a<Mw@};!^I?Y&V7JuqB#NbsZ9ZTm|g_1 zx{sn+V)A#`@hU91 zju&YnU>1wXn`11Ap#ID>2oTW_iXpvb@q@n(n7oZ20jk^3{Ir3FC6#R@uVkbNXMBP3 zSAL1Hvc}khhP;XzLLz@&WArk2ti0@EsdH@UaOcxYhk9VS?&;-|Z9Lx)_4%Jy6VETb zGWIkqFfMg@e8Y4n9I&kT^uxnFOP%K*9`1ZNc-#+J_lSD+C{$ks4L!Wr_VCw(um<_` z(!1xEk90i!@c8kiF30kri+m{kcDU{7hp)FoH_P&n?-TI<--Q2pJ@R*=$6Go7vm%K$;y5!J_2F&zQ&lz} z+gLZDs;HD%*)oQ8WV*XF2}v#uv+iU^MH`g3B=E9Iv6E6*&Wv12X;Z4E?aGYAwi+sk zQ^zE#k@ZE@L}}P0RVSMkAPj43m9Xy;C$;3DZ1*J-&g0Fl4eYlL$y;UReliKSUU^>F z)Nr-#10)o$PlEsMPuf`|UeBrNo3vbNB`{}^q%|pW+8&nDKqU_!BdWt@xtt+ROQP)J zv?949aHwpOnjBThd7&c}LxE}B5r2Qxh+U-&6)}?Khp)?`H^$iKw3~-2ROrURX9h=V zrgt{IS6?)7lk;jty^jjF%N3kwc<(hPiW_d9zdRW2ci}m^e_$YwkuTPW`YGnt)(kdPqlc9#pKF>%%)^$VxUjIv>NrGc zt%(?YcW&gmC~-to&#UzFzg`cU_jL(*s+vH2#N9k-#qGoEUAK!OmWu;s=CBLd z!-pG+T^iy>UoW~UuVlvsDn}DlZMMjXitW?<=uyhAw8ozF%*I zOu4eU&gv&jz8YJfS~Fa)imSBNPbOQrahJkIlpCEsfx|IA_GjdYgNf6lAy-eat+MCT=8CixhmO56+w)IgofEC%aVr2Mw&)^ZclOKOj~qhh4rRQLKF_xU1hGH zaHL8CKD7?MxH}nn{+*EI@rD}Jg}O_2`{&M23`HDeC6~FADMW4;Ua1=yDXNHE%WI5H zC3zLL%DvMnzGAM%$2nBtvr|+%TQf3U6N1=m+_0RBNf~j}BK{jGkDd zEMv<8Rf%g4x3VYSQchp03>?8#Gp1uhJRZ`NP*hP9^DF8?Wi_u*Wm48v6mD!19g{1$ zs;lxh2p@YwU|-S&-e3|hWwceyV^0^$h8RBeylvKG-?Hl*{AzM$@<>*b$RCXfs~O&_ zv_|_{$B<|VhreENxaOV0-i<|)kxR1Ai)`FF{UxMud+KOpbm&y{VA$k^R^HHv@&?g5 zJZd?UJ1w)vRD?z!j=s=V?K2QaG{x9!=PD-u{>rVSOIPgM zn_91b))XpH_)X>7D#(n%!x1&rb1Jq$GCD2iN$er^XZ+^CyzbD^|?Q!jpui)=JSks-g8os%UlebW&>Wh@z`z6xnFT*Zb6jIo{!IWM7$>OZ+^nY;Hru zWIXS@N)=aF8rN5sxn3oSB{*6C?;^%9>$Yk>edz-7tP(Y_29MLpGGA zinzeDmL3ZfC-h#iM=_&D>+S2iYL#=*VO>QRLwJEorN7gC3Qs!Ynw5X zlsIZW8dZ%swM9jTC*?LvMV`5J$hvA&b?M9v2|PsDhpca{s}$EpXY2qzO0r2N&v2bp z!t(mQi)Ou}R~t>r>7*jbPD(XRjGFj0q8jCu(!h(K2PM_jH7JhCr+lU-%wrm7 zlIix4-&;jRp~y2s6%m7Tj);i~LYSl+Ayy$tp+f#)bFxi1nOss&)p9R6la=-wO2%v& zO;R@3BFdyoMMEyTYgB1g6i)85yJl<^vbZ>Iu|2v@F%WI*ZVD`|895U%qY8~2wMSn) zuiCiYm#JdQNcrdMTduC+y)(+@S(A7Sj*8eRt5e!HuH_94*F|4aUr?C}m8MUVJogSe zBY2f{JRhz-I`PtNC2zQ*eyy^u&u-^4ULc}XuNLwzCWYDBmG)}Jd!ur}bmZ9J8D(Q# zn+lKWdgjskgN_Lanvc%XPQQa6@wRsz=c0QZ~efuugEv7C}r?8n@8EBdCsB; z-fpEZEwRpaxYZ__^qVqW;zZ&y-qkB>L(e2kPu1-l6sq=K*k-X(iILIuB8Oj!s4z)U z7JXGYBp*2)T8dEeOEzkK>4-m}kZ)A^SZ~x6-7ZTQQOM+tmdK(c`=H!oEh^=Xl^10& zY=?O*H5S$OjTM1`fqSne`Bm4}&&6F?@3gmxZ54s9vF&#Gr%7Yv)JXDhjq#b`qN3Cb zNYj-Vlhc19Btla-UGI-yme(+XhAfe$k*So8>v@qQ@*+->pMS)4WkqR3A^FVuPx)iY z3gNg#?vUFLRuRe(+l7kIbA{JcB4Y1_ZpD?P(8$u()@naQxh7OuQy0Qfakp(QyZ~Kdro;NX!wo_1r5y(mJ0j>;Ux~GA-fphDS#H2$)r-CWekNnZrqV z5!YL^A#9^f$(f88^m}bhGF@j1Y$WEca_fttA||9bCbRA2=XsSKL4C8>w5VoA0z$;1_ZwiOw>l)1LhM|s=CAp9?lsMeo{U)$6tQFYgC zi5KcrCi`1~vZS)=BpchGNNn}lzgIY&oI50s?kg@$4KhW`b8XkpD4T?Gxe%8;7ns~E zPqrwd-#W8@x=__RUE^SgBi~#hoa?C%lvk*xORr4Wxna)3t@T%0E{*!O zG5lq>rzaX2{4KBJ{LM#A@k=bdYkP^6XYsG!m$`sMX2_rQ%p-Cb{ zr93-r8sya8R$XoBsMrzlDYM2|@3Xmb?M|iDHgrxgI~D!f`LfXUc2&uh#)Jq$W3L^0 zu`*IQrurZ>j5*C{gi#YdA=%u=tiV>&1QlsLO+GBj`uS*cwlPo`~dDGWbZdr7M?9)r*(RFpViljDqv{E&gw6>xGvD+i-4%tg9 zra7Nis@`$gsXAVtfEOR3>YhPr!ltV#ZY7+zN62vYZGXfb#^H=H90?zdB4WvU87EpK z>S9y}RD2{)p~oDdzCG?G{dFSwiK zsd%5(M3*Pq9i`^=e?@aJKgdyqVjs-n4ju;$=d5fEy;G(d0y%I*1)Kd z8qV}~n>|EcsZs{*&-GJm;npZe>OS8&DoY-kMZ$(YnxsEKE?8`n$Vkj=lA-bfk7=tL z9I^T;ROe!tmoHW0+^cIRdZ(l*BjT8fcwWSQ)dioLDyWBsp&>ELC${9`GSl=2@%?qnU0*3z& zzTO6^iEH~CK9ih40twC}Aw&uqzBH*|CwvCQ+X+b^LBN10s9-0G3N7}c2Bod76B51@ ztSBnA+8e|Vv|sqGt-Ut^M675*P_eDIAXaT_d#$y-wzvO#-<{s~UGMX}>shaBIg4cG zoU`|D|Moun?97}=TeF6Dd_(2jluw;p4Zkz zEMGTU-|L@ttg@wNkPPq;>sQ6be|h%ArWnIAep>xYr)?3-`!1*GV3ec{G_CNSIQASzR4^qi6L^Z2ih@{bMpzv{o{P|C-hDq2P41umcc z+M2-g{W9k|+MaNXmL*U(maj|jUl=To%+v-V$x2kMxORDsbG80sokGDU^-)8syZ9Ar zW~-JHHE}OVb?dHazl;i8lXje0Ipdhv?>5JBV(nW`n8)mO{VRAXirM%WFJ8d+qo7NH76E-_7ZOOVBvJKPMuKFn8 zaJ|y`4C5zu%-A$N_&rifQLA2ZZs_KvY+%<~D>tr4$&{QBo|tnn;{=M5pIq7WT1v;- zHO9QCLfQJr(RIg~>|d}OrSCf8Pt+VsgiM>aEjj+1mcf?zNv( zRQE{F*yS5}qs!LB`zIXdpI(<-vY~nPGf^vTieVm~%ukXfZIu;rJpef;Xsm2UONRC0A?#hJk6aAS=AOhn$gXHO^Gm|+V!J$u7z7s8~4b&-+{ zRs0i_;=)D&Y5mlC?=Q@~Dd}x%|~ge)sITGkUa2<>3?QT^r8y1n)mDOF5C861i$i z|1Q;uzK#v+&dXj}%ZuvvL`3mvD9J#b>SSPWuP8g%{70SYa{;^q`v0j@i3I`b|F<}$ zBFNZXE&r__`6@<|0QrAwMj{E3So*(~s?^ZKwt!R;8oop^(=Tm6Zwiid%B2X4CN$F=sFulaAkYlP(%8@_58z~4gak; z`RZm+K!VB`v6237z3JheR;V(O1TFt>#Z2Tcgj$)P6di!-{Y?4)UHY0y*khrP_Pw(K zE`~amXleETe-UjGu?*KWiLUr=(}RWzpM*O)AG)Ce_P^J+WJFx-xl`ZQHbG_YxrcjQ z#5rNj0XY$3{-clw>K}xX-gDo#o&CPmMI4p?w*n?uN&Zn$gL?vhCS-Cr_1!xy#18@) zF<1mu!0&4gd|&4VNhLlU|69s~+k;$PA;g}7Jx$L&ta%U0ao>FiHMg#_Py&Q9qTqMD z`2fh=b|9&eNEa!T&pkZZ@LktIL3$eC_s+KGzB}&v_861`o0^Cc?BpWUL;!Kyc=CJ4 zd(VA$2+Y*g`rUE&_l|AEfc(E@KOz9Y^A+xhry_Zn>c53Qm8c(yh{JL?;okW$$4NR5 zKp|4(M}(^YoA2vRfwIJDQToi&D8f+yJwkyIu=xY@hUdOJ2Qr+5^53;p!C$WW|CIki zK#TvAW=hHbRuBB65co~PlLv6~`+uLw_1nb2gM@jwPml3-^3!xo7}VJl^wpmjs>eL! ziXO41Zmu$kousKokd-WFtYtlY+7cP1yr=E2H&LUKs}tI+US0NmGCU(~X(Sy)kvIFu zft$oL?c%0oboPOg$w^|WQX4p&x?_VxG{2(ONia`{9;v!zgbW{w4)~cJu~OOH(M?)% zUq@BWaU#1iHE#nsE=|;jm{CY6uh-;cJCkEB<0?|f97g7Qfls0S_N5x_? zHtV{PWmpSSHK3sP&@7pUQJrA%$+%oQ-8*J9&<qvRdT8g+!iu7uDid8VaF?=XE++iZTiAovm$d~j=wU}rQ;4ceM zX)Szs-#9^ySJh`I@t~xwu(DylB$Tq{-p^JIH;Vbjq7XX!4i{pOlM{5Us@Rx`#2(Uk zJT>D`c~AtGr7{jkfrO?pbDbQ!-uIxnZR{Vos1#raVw3rgWjsHN6vw4jwJ&6Z7@e#uV5#gGRQ=a=q43AEV!} zacep${c&~Sb+$LXIvj2hEYgj1X+{V-Xk7PZyiTT1%^f2%TH?Ydl9bm3`b68n30p1_ z_l&$Q!hgc0cC4z(Mr`p#$X;;PV6?bTApRNM&M@8%u{nRDHs44sxhNb678^I%YFcee zgK1ZOaMApd0BsW9ZY+U2ojo*iSg=!{Vt!jIlP;ir)Omss#OQoK~(KECObP%yr~50V8+lm zv)5Fk47}K06h!D+WQk4AIHfbkKO~hnW^&5Pl(uM`p=vC^pCf8tyiV%0hZF3+yLRP} zmT*#@L&2>ouH`X(C#86g!MM+$yWUO(81h2;(gviY<+v#0+UkHIs_mwQnoxh`7EL`} zqo*}^{Hr_77yp`8P*s`duyW7608&Qt?(O2c3~Q# z@V+&~{>q{$JJPat;LA+(2uBq$=B#nLmrpiFC53WYBX1^S3^O8$awjWrQ<5J1`Q60L z38JeRiTe9Aim)kLkTIAniO)e9;`(VgJehqVQ-_J@EM&)|ZEFS@#F=l8%k^ipxLp^J zLBo>;B1ckvh9vud1|!HAgVYz@8`~5c=blI^n65Dh zjM>}h0e$u81a@{ciDd#~bk6N)bKAt> z!v!HXs_-#^!>Z|dr0E$8f2N(?ce8*P671_1PeEkKIJ~2k0jlEJwny5Ty}+>5D{ zSKMPG@p?jhOqX?#Zet1-cue}?=+E@_0Avp$(r%lqCE8d-qJElbSzGl2(JVh#u6eC}SAA;KpjpVdTBmi1|V>BE!AW z7ZGb<%q5BTAepY9kPULIY3`vnnL;pc+|Eqb@e=UXOuD>$Gt=9ty|iY&@L94T+ou zvErVJJXB`tn9NusZcAg{jkGA;2jN|r%hO6Sv$2=WJU}+;&ADgj+;JWA zPr5MEY`7CvXUOB5b$o-`m=rHwp>A=NJDiBmQI|`RZJ<%i+mJE{Hm1=lI=TpMYkHP`RSG)2sCy@A$ zli5s|(Hl#2s9O%?Q3UHe5If*B{iT(2%1jj0qiP8a&;0NwRWiI9SY2Uf|UQ8T4Wz~Dg z8ymz11}H=6A6c7*7w{aE;i9Om%b`@*1{77eA4T%n>?d^i9kM8R1zwIFJIJCGE8$h< zGO-@1y2eA9QmAlko|FHqevr!I2-DrNfUy{(T&El=Aq=cbEe`PtgN9xakPM25&309V zM6mm`cA-a{U1l)XTx&$&oy7_}b+2rRF=n@l%DSkmy<@{C)Z#pyqeII#Rt{9snfDX@3~C}FZDhB& z$*tn6$R$!0b`eS~4%O=>8KNc8**Qi{j z#WK#di=9HL8<*Mo+@=yl*7T5J$>wY6a^6tV)k@odDGY93L*seM`Kcj!Y`94$hzkAk z20~tvn9#ZOIzc{@bvJmD!{&(Bup`A%8udzrMF=_WMh6|r-j|_lmc{BrNKUpO`z{&K zo;3FiwItdWWT4v=+N%a@)?<4vKe>=GcMpl31}=zd$a4{OQX`eevi`zh0|&0)xkifT zvv=+nTRdvcnvK_2;Xb%)w4&`0#(JB9G3M#RHU;dm;ug`H8Kuw6mt#cgGf5_sT;Mh8 z>$O>T+3;O~KT57HI$9eG!3^_(ne?nSGfA;o>g9Tznmt7G(K}>yBL?AMVdf+YCZd8jdCFxAu}pR3o3WyRulGNW5vBU>|+ zq4VYm_K#@iZ)Y-E<=7x!=G3Mal8afR_fXmXjyc3H^w2fzoRAv_Qv(brV>d}TY{U+B z$)Fvt6pASunh@88jq%DlMb?t43?` zYUk4>vK*5Qk;&FS_`XI{|?y2v`2$g>Y9$n8C3Zf0V}kBLD;w#~zmq3FEZUD~E=w#^PLVGB9#u&K9`TD-w%fCKVd1+-lQF5el@-Sm0p^XQ7uK}Fkn&TCY`r|m#H$sYE?N_IB$&y*Mi!-$IFG$-Wfys@ei3EFp%Q_f-BeN&msenO zr4zg{waA*vvxV!Olyir1r-6PUf>lnK8yVrA9yJq@dRwY(VwBE9EKbnz8Qt5%wqU}z zpqq3JmkE+dtT*2d*5V&+CA`Fa%W1+;E%1u3>gk{!R%{@1-KN6jc;7wR=#G)dsD@Tj znZfQ^9+TzP7!*0#g^5ejtvjSd_K!A+o-}w+k&EK}#O4^I=h{^BQy88qb<2UO(MO3Y z8(CYqLP>~wPug-53n+>$a%cszs(H2A?T6sk2ygqo- z)D~?-rM(C-harp@xAY*0ZhLMBN>sFxB27_6`ybc`#+aNllHMBBW=OPAB0a_4zLDcv zO9m#;CHJiZq;|8GY#EM5qj82_T}gp`u36P0)o0&r^c*h_^w+vxaTR zFtXai2=174OIqba9AV^F$Og4rCdyp3Y4f{L7mHyaI&v;>8QtY$i5WwVpZ=Ve&6?cx_HH z`iuDyt~EHcgGOsLLuOrwp4*klwk;PFtr3hB;!6%~(|!snnM%F!lDA^74E36#sd(KD z(yFBGjdiKgW0cC8SHiN){a|ds%j&g5V~GP3%Foi%Zc-{VY?BWOmb&FZt#nNv8<}9= z>k2SNq1i9R zCL>uBud2UB;h{WET3T|ry4}d;9F`jov96vnid0|E6b~7(*k01ygS{7M{sh}Xsz*u6 z<sY6eV@Bz4KYp-yeR+7Y+RRj62$VL!7Y9-JZCc!{WR&WhAwABC)8KYwoZwSWv>F?7SXz zQEiUFpY3(91Ag*_NL?~vuAeWMJMFsgJQ5MQ z5)T_hsr{wZ(gb_>gd}tadpohrg5ee+YUeX&w%8$_jbhKzljSmSNQcKJUIMOc^t^1hLdICLZ9##yr7a4B;h*C)YZ|_5RvyasiIH; zE6Pw0RO)iHbY3P)ofhY(V+Vv_qkVXgxK%^(*VD`hi4V#7nbE=GDerd zKF??;JZV8hRh<#<>$48j7T-=MClSlz>wC!(nw9$%*UBoH`cwfy zhVP=4jzxt8S$hw^ri+G+2l`oVQJ%tS%RYtc52kiA{!Yr>9f)GIn|2)AE z{zA0%nm{Pzp8p<>Xx^Ymh?&pe5cHKuhzuflRBO(#h{Vf=;JeaWaIlWRq4J-;HZvah z+HCEHFIv6uC-e5tzAqYjpy{}TL5JbpLr=#AO3XDtN6f&F(9zXz1wxD%$u|7?4fq&( z8yX+|I~?|m!J**<97-?2CEVX6N^HF=Acd&^8|d#plC!w`Q)uzSckt=Yk_UL(=K>OY z{w8U_SMK>PTYeo*Y?KJ`^)KKA=ah8f{TF>FPJby7idEbF^f8s_C&^hfO+;h#P*f)5 z9)2xQV-z08p$(#HArjy8$(kwf6d__fDAbsQ2E`b1;JTu2Uy8YPwJ^EeOk#I@m*y#gKPev&BB9#IiSv2i-=cqC9^ z&yUb6a-H{ycXiaK?o+_(#y@!%qPGpP%@Yc|B(o4U5c}`?F>YduUK?pQsFT-{m|)uZbRE?$@|6 zbU-qSul5T-K zNUW$NZV>%TGKMyaMlp9?1VoJnWnUI`V)S)R13DoQqARd35;rO#ac3V4-}Q+PBu88k z%7*4h19p0Sbs5^AIV)7IWz`2f>41An0!*ID7zL z?1#>an>jQYP!MR~`b8QRb9o{Tb4SWm7!~DkXdASymplbbM^Omi`URjM6X~$$xpIWj zc}WAh=fk8-gM{n?iC_$)Qq5V6=8Cp~0umikN*>x zI?V&HLe3Zp^bu+SP&wnOs0dDNLtYE8`CtDAcMXvgp*V>LNy|ZxKnaIF0tR?q_PMDW zoRLF76VH`qHyHOR!dDn1wy+q1AEMttOs(&dH92aC4sM+Ci)b7A6WBE-PKP%K##CZ- zHBN~Z#v!!d2TM4Z>#)R(KFsMtyF%eX@=_m@-v^7JKCtkMM?jqiR~pb47KG7Dx!r(&S1WYJUX`2q0b2n!hOyT&5S{Hg(^nqmBsD{631 z587D{-F*%5ko!>dNQg$xXttrBEHp-0A~gmnLD75-jQ;>eLa)PLT%nKANW)>~4tVS} z5IkAZfVX`lU@_+bJf4kHqdQ=t`{kf>i7!Tl18xR@f0@q#F{tbLw+8xswi#YX5J#gm zl0nqylTZm}pe>-W7EG{Lvkh|t;AcOqwxWyRNh*s18ARb zWU%U+_kqqD59S_Us>Ix6=;6agA@=<9iW^my!zkq%8gt>`GYG}?BLR#TE%J5lQ)c7) z9yABs9Q_~ywL5%3{Rkq)#8sjGJN1~8Z~g=jJY>ft6)$MX3BD&qOX8?XtNJt3$*wM zqG`O(RC{yU&@Bzj;!}Lkcr8GMcsS<(YUY8VnLPO51S=qkqzrAlLZUL!sL;&_R=_co z?{ndH(1DvL0=1;RnWWVML-;pZG16H$u$x4UszgGeJ5wlt0Rq9Q$dyxsW&rMQV zYFM9uqmZt23X$=&pc->VU*J^%V$5C;<$H*UTnga68zL^c0*-j>V0{n`6R=xwC_A}Vr(*UA!|1MW!=6%o>?SYAfYXn<)l%Q1L1@llt`!dD{7;dWN;LJG?+~W(1++*<5hY;78tT-WhpkX18K4${|y$3;% zIVbV)PKln^j6$prZ2)qWcVX@xH1rhvCZZo8n0c1@@W;OIs(i6#PL2|N>4W$__ygAp zBYOm(1G5tlV;1_x{13#tRv&dv`oQi07WTme@z?@7{>f)X2L$pf0E8z%1Cl(w1cQIB z5K4TLBZHEC6xt1Z+~3IIv9|A7)V|t-;>wkn{Q)fe75K60ozONB8i?xxl^O8y?>V$y zVU}ahYtSj@0p`CasYY^*2Ry5s!`$1T`q4&^XBouP>6S-6ii5xJJsSgBTm+&3KLNTY zaV%tNl^D(QC3Vd}KaCIcm%ygnOFoKNEM+hoUg{U3EWy{+;K4rKizOu9cnS#ZNsb#u z_j4Lhsl^KB1FaL{tf&y|Keh{CaET2uKQFgUKB&2S4Io2FyLB@3W8| zfGPgtGuBwM8chL)aIL;xqC`5(9R^{TJ>W?jz)W9&mSUI_euC2|6?&Tr8HeYmau_!a z28UR`4SP1fZ$;MTL0AY0F(-XriMoM;o*jT0_nOa>m{2+py$#`;I|O5Td^r>PH^>4h zSr-a`MxGR3a#sW6!a{roSaiixgD$KcjITZyXpd(;HCLS2cJ%3~jqi#@Y zH(<*Y!t@^kQ!V3*v|lQO0hoD~sua0>kq9ht*yn4r%7O2P0VU6OKED4)L4)uLAUmvq zfq+HjAYmK`=V^x!PzI`mgLcdZVC%p6^rc|#@U0+FkS_%VXNB~O#f$^=Ee4ZGVI1Zo zpQ`J@W=t})|JVnZ*=K9HPu(&2%FogOnG1xM7?AwmaJA=i(78neqrP4af(m@%Y`roD zne!v`!6hqZqCjJ=6zI;J5LqFk2F!eso7?6iVm(-T+}Gu>Zx*ix0Jyg=fgIO-9O?AU zX3GF_E>HtNWP`tYqBJz*Qa&4g0y?8ug$F?WBkO@h0Yp-;IIKWN(J9a>1inFQ!7Pga z2e!Jg8nPllk9q1t@2OASMo<9#Yqc3YQP4q{-C`-i+*cq8H(8?sO87E}FXfh}Xq#me z7K;`RbNhe>?rj#pO$uB@#lGz50I2D?51qo2s2{xz=w9_rl3nn}Col^4-6bXFIw5j+ zkY)^f&gXd0Er2FTFm>vX-(O5cMD=+C|( zqah-3zNt|4nGZ8`sS+b+qX%YYO(m2Xd;w~j1)%r{I)2rn#AD}kwxN8`jOl}OOrTEz zIoOMh19Px%_yp|BF{6()5OSXvJr#Ovq-Gl$hshm&(3D`+GnzK+8P=3x)fo_uyW*4F z)Yt$?Duj!&P^ix>g_!09mT(DRQdFsU3J>t_yi|?3Jzx#b2O1g}>1)~t1A6*IgD_u$ zUX0fQ!RH77&6NSW{vd_t7DzEBgCQ87Hrzv>jaz+(C*^QmLXHaEf`L3sKx+0A-;DB5 z)PR{(3phdV3Na_KfTE_jG87AKQKv!&5k1Stmc1G? zc29@VV4e5?GZ_*$q=Rkf#W*!&UslY?0C~@goP_I_fH3SDpQK+yKU}wn!%)XDTw?Lyl=245wg4qP*L?hqhEA9OAL9z) zBd3ImVL~Z_^c4nU;xzEr0MH&S1D9t~!1Byi@ZDb_aroTF$tfV^B5;y)P!?Q^t;;3ZAbtU3lyr z2xPy(Xy^-{8wCWd0E0BbSu_=zLDB&q=aiRWwgWEa3LzWehDD$u62TDdaGh!epvHX- zhGAxCgqV2@UcqeiQD_VRWA6LJJy8Bs=vIpa+hFbnJUt5lSx*gcU|iCPKZA8Yifsf) zO5j@e7l4pQX6eUnSB?^CeaJ~cF-QZHnA_kJv`VDLi~!(groxG_1ke~6K`CY_4?uqR6IkCVq4`)`IY@I2KIOxe=mSVU zMnFhU2~0MsMHX;|*>N;1XjIr;2hsU?->?ceps`So1g!y>5d#>Z8@?gVK}+tG&u?x4 zmtfLSqI;TAsGotixsQmHv5e(0gM9y#0=;jq;!lX+zkfpfug?W2g}&1eXZ8R63pJcz z6iEGl`1X(I0=`}Pr2IJud#JaOAObfbA&2}Jj;9<$LNmuxcsM`Z7(q60cpBpfC>rA_ z`#Ha4k0Yphj1T*!2{V`)Y1394Yq-GnsDjcDE}AbD|uzLss?(tBv}A`R?@KU2N?PHX?BGXt(0*py0WUHX$o? zD*Kqa__lFznb{e-tjjO?q*wB1H!tjtsy=nFT~O846Y>LBpZ1{L@3k?*)Za<&c@#<3 zC~-tUo}VF%N5z<3k^k-zW+Zk~Z<=v*KXRE%_0k<~V@&KCQFdv!c+V)FzHCtRT=jnP zFOLXy4H=NvR3m>`U8^}-8BmZI5nN%t@NCggK%sp+xT~Qyc4Y*A@kqDgjHgzy*0Fi< zgJmJ-AJjhg1_4`$bqJHm3|^g=Sa!Z$*|+V2wty2)!;zC;GuOoL@;0s1O-vqS42cH_ zzAoDtdcDhU@(0?0ic-TfuZ{YpeDtI6&l~w+=Ypli12TV z{AYhYzIB5qYKnGSX5zgF$E-o8;ODN3a~`!!di`M(`n^*i{cZeB>F-V<5;ZV>>Dy|Q zcU7>pUb>=tTb=H11lic&7aiX+xftGl(Ai*`S<})~RvH!dO@CV2V=8?wie4jZG#Zz= zTKXN)G1^NOWzF_hY>1iBj^{o*B;9=;i|L5k>B}N_yw%^KNIW8%RIyeT`@k?&cd0Qq z$hu`;7&m>+EpFZ$Jo#R1nwmcOQ!}e?-7o%{%18~!-@d=~AT0>VvD82J*GiFJF`t*a zrf+_kRI-PO5$3f7s1K#@JX*Rud|`ywzgpVzE*mprF>!Gro5%aWE})kF8c@_-<9AHG zBDVCHKH$@}!V`N1apifJ7rCg$Q(Xt+Hog2xVDX)FzjFf@!c!=I$+ydPf4q_R+$%$u zmv(jUHa=M~^Nn8vSzdz7E3S)k=}k8AI%V%ePSTh+FjLXfmY;Yic&o*g_7Q4bmR5)4 zlTDdd(d1-fM*JeTw^TiU@;gQM;@d7|Q;GbYS1L1t*PsiA$DHe``YzJ_n9aN6t>LQ2 z6SmM9k&!60D7t1+J9VYBX?*LD(V3W|ZZ3~xwo?yS;bDEwiv_Z#>s^PEK6ph^u`KHC zTj*}!Y&CCnU*ww?+V&^69czw#dHybWnP24D$peP9GS`O>R;JWx#eaTv;%({gb)n%z zZsoM7z1zg~x(i3@*9ZxCHI0G1IGI>`Z~NXWPsX*@M$ey{BB`&(R7r6~R})*gPHkS)H%A!n`i@80{N z9dA0LbR_+%mpBRx!E4C|VU4bTV}N}UKU}CkVYaTYx>XBf8}yR3q_yl?tvIbgNb)jV z(FbZpJNe2ukB5W}8uJRwLh~__nCWW2@ZLf7IH&nCMw zii}eKeFgV}KfFZ->P8|yQ2(VN~jk< zB9q6|nX}ej3R*Vd`sjRZ*z7w)xntY6OsQ?3mhh`{W$#$W)aHoPXIiLj>yNcfcvejs zQu1v1y$vco-2WXZ-O&BhJCTz`Q$-~!3UqE zh5mV=`qc*#W1ig^MJ>3-?c|NTl~6XFS9oX8&~-Q5{~-#h>X*)B+7B8l8s{FnmHXmH zPnx1%S-9__BG&o*TV~=A1lY6jcsywEr|sTh7Z}8IhTK=gF>r z^fZ~lloyy>oG9^dSj z`b%QyqPED8jo%KlyCx2+gb!jee%%q1%u1`3edzCHvcTNs@?y{O$(l}4>GlynsoUTB zU{Qv9SwWCqk`!X+rAI#B+nt?o%$gn@ zyjxgm$lLL(F?`mY+84fT$lnqZ|4RNM!MgAZ_q)1O^fT6S{22dV1Cf% zdS3j6Hxs_hn7?RLet638kqgz|&Uf=jGCk&KwKaq3h};P~rnVH_U1fFGcx$e9^K1{M z%`9s4E0zAdfAWI^1!Ee=k&HD*?dMl9okuUfu(tJRwsMT>h@3RZ>^?dpty3~}h<9Y+ zb*irG=mK?Fcm75@7kl>8^i8wQUYxx3$^YSn6Le1=DD8qzr_zk&O+~nZe(R&h<0_I9Cs~b1QhX# zzqahB00CCd?FK4UsVILqcGWGyZF3d%*o0cMS0FkvudoDYk%l?^Q84%;`*Mt zQ+<_jge4Fh(y(e_f?KmOW?ok$GMs<%w#Znk_@xoHy5AmGZaff}zzQY%*9cJMxcS3& z@5*WJxa5j0OKwMcHf6*#J*=LQnfFTVTH%jzl}Qg2yi~*4PoqA#@}6J(L}cN)-t_ii zvRwUnV?zLEs%z*qOzAwlgKx%zVXl?(Y}EsutW6!|(W_rCfugZF9&mC9|nP)>(4E9s8)8 zTiUQ+Q4?!olBkhAUA#%B@d5m1W$s=ty7M z`Qv0=bqBNOA9m^amSDEh&TF&{F7&%RXV7; z=yE31^R7fl-3`RArs?xl^T|g)H@^vz%DtUSR9-iyw$>?+R}% zPZ*CM_4c{wcemZuZKEx3=qNt%dbIVMr!BlTX?sxOr77QbXLaimLj-BxCVl()z_PZ8 zEsu38Qv&{Vty}5$-nqTrdB-PbJ7TZB6|&73(U+gwGxK`GKITE-`bAAWOIsa_(<%z% z7)hrePdoQ_)v}ayu6Nq$$jxh}5y5ZgQGFxtt#5G4?x?KzMyoq*99y=O)%__gPjTJo z4qt(`8JFJOezY@VdXlgZ|MKY2%xJGlvHa5ublvWcTn*FiE*|!%70zAOQdO;bD5&m{ zTxGsw@LxV@;bPC0Ce4NL@VB@ZxUX^Z(1hIj=(6g7%KsH(+4AEek9g^^L-ndaOQDjq zwCm0K1ah!nLRnM>t*9YNLv1#4v z7qk4f3V(^rc2xx467goy5yuWUJfvRSJkak)x}&0TX7t>lnVorCMjq5m9xNyJ-nF(Y zR+nlHtKVAPWU8AI;QWHt(vPA(;7H_hpAxNzgbXpwKQH9Z({8kW^kjuxD{C7_)Sj@HUeHZ(-+FQLbzazm8Yaor%ER!L^Kk@uKo8%I;gHO zPv$k^t?F$D=6oFQGX5?<_hv$y?wcP+VJj+u{nN*2@C4)*rob1 zcIt;m>tjp3C*!MQin^47Bflq3qjOk!j%=r3tJ7Ig`5?@glC*G}rtKDfM@rCjZA}-R>sF4d9B1*mh8kxA8~M^*B13-i&|cxKl`n}HN7r3GS6*F1FB$*5 z?VadJO`S2>Zy?jnC)*p`)`)PmI@DEt;w>TDK5OYjbJwT(?Va)&+R6Y?SUz8HtZd7Q zxlb3P-|@T3Q$5jpCZoUs=h3sAgnGp@^nCZU(}EvM|FqoTJyD7^ADoOPDi&ogsyN;e zRecyQU!0S?u$_0j@qn;a^F_o1A~-IL=TBR>ne^Z@Cx&p*s^LkWAK(7r$vHyiEsMY3 z$h}}i=Upm*yD<1$$B}5e(YF4U|E>LU=QAs!54)H$nA zsd(!L%57@+W=fasnWIv#{F4XMV^?o((8=3(Z)$K#q>Yo5)@_G##}2HH5SgB-o;W?n z^Pbt$PBfa=?n-zvjTxYJR{WOGct)~p+Q(dd)GX)BwPei7P1cUgx@wZIb*Q3wo&6ar z-Hy=cY{q*nbMDD@bQWT?H@#UN!(e5EsKaOmv>6z}+?B~TM>EmGHM=_wBnSys&m(mtK$ z{Hq2YHdH(Q(w{fE&X)1qJ==xn^Y5O~q}CZXMLAF1>yrD=>75@ID*C)KvZa8SIeY5k zNx8hjj(Yo@1+nDbKb<$u*m^fIZal!D^J6y8)L!sgv*^ay-TbaM2k%RN6)s83da^s< z!HU$Z&J6F-`J&mjn82;a41b=*52s!4DVxoT@mSs=!>?uWweQY%8e4=tm-drHWveNX z4GYW5O6?!(9w#@5_Mv6D{>)A;Y~55A*C(wcT^7~+W4U|FQc^}6|9lq(E|2*@F6xpT zVhThb6ig3&{jD0OJd=O(k!?-Hk5_H8(%;T2UA64wu`Sb2_wM;ynya#uU#t8`j=ETt zqVyG~TzS7K`pdpj=dFO$&Bt3LTtErx-o7)mP_gKHCiw@A2T$>){KGsZ^xxJnac}QWHsV7Oj|Ks*H}Ghn_}F5Lw(=;-nhtc7acxL z*R9R6?5vksr>~V~%!wRzyf%`bdU-r*#{1{OZZ*`L+f(|1Y~4_Ca&qRo=Y$g(cOQxF zHyo?II^MtTFE2?SY-P?7j6OIp)VRxSeS2lW!J_2O9P~x$Y~Kx|lM(@oeQ~Yvi_kk;uI8;FE7% z7x1aC>COCp`F{U~G~$kcz1sGY*HvCF{WSyf6@rUKU!SEZy_aw0J5oKbOU(%uj48QL zRXzUN0QFY|zhB{t&ZSW0x?>cy_?jedhP>vgv1P9n*9r4;jyMLUc&-aSY}2_Q)9#qO zCvC+0q;-1;Tf=a_(RjPk07GbZJn>0TD%Dzk=($`>TamxDO7;5lH zUcIkFJ|T~$A6j8%XW?w4MYT?eVO?p6TXcC|w@uz>>VeVMa#x9Wx;(QE_@-I-c@@-8 zrwQ}cjFg%XIYs;(=_R&j0?b#%+_xV+y??k_8nW7s^rkqbaGQWio&U}M&6HZxx(7>O*V<=a zJ#Ivjgx&kIEpI;Sv6tbT^}*2>9jGhqPUx02c)oqTOg_r)h0o6UN@{d5QEsK-!hlxU z9UNcMJ%8@D`16z{>4#hO+h`UU`;^{=HPQZHnfWEXUTHag)9lmZR^E{#m5LQj&Y1VDh&#qDSF;ETBHz06 zSFAY8R^8TJc6O=ezK%+f*1OQF>pk(O){5DzHJ%zBC5ErL5}U`(PI-;N!j0KQ+r&eJ z4P#WT96o7$gsAc*(l>jRQ-f7>6L0rKs8IP+hZeK1mApmyDz8GB<19NKYRDzb-NC)| zm&H0qR^(uKkYS5PgUPPP2P{_EnaxAiWu4mX^CwIhIW!^nWy|zDBw~(2qSJRw6 za%`xC^;Fi*c|E^?vxuE^o3b{8#4xdVz>4KdezHtMRfz*y-hFb7PUGzK*?!YDWt~;! z!KS87_q0r^_vuAXdYClakc187+&$B>osz{DIV{Qf$+o6FI*t1=`lE^7oHufHj`eTd)d-E2+GDgSE|{pLZ@)?}@mT77ql*(qk1aL00y zvdvFp<2Fi`W{;7oux*mHG*Q5=*U8*XYOjqjJxVC#EA8!BmS<&>Fz4>2A;YsNva+IQ z+jes+(+9KF3y;Sd{5hsfqyKC9K)1nVNb0p=DrSMT+Q1r?E01`iLeIXz)0{t}uuiUf zkIx+vTk!)eIXqUR-Q~Yr%Wx!)eXHC%+Sc{;x8U6>-5TS{8|0kL8*?$gV4PZk=K35vfm_OQr;Kyxnt+J^H^^-76N zbJubU3NDiB?d$%OcjpY<4}W1n4CYz>MpTcS`N&QyDCBPa5Nv<`9%Ic!f`N?mW*J4b z?4j_GRksB%WMfOFp=6WhF`oK7oALS2`9U#W<@EX;=jzGLwl4WYA!hC=tUm%?q>ECn zZFgs$ygcX`)obM$+VixjE#SJ^8oH8f+PPz~&krxUZlv_LYttpRNs^lk>E1GLpJwmY z#X)&7f9~{{TNrxc6gJ5a@!91(|LaV*8P(^30%5OUoN+kLATX@AvMt4$RLQE`XvC+) zDsas`uPwDmnql5M#PInRC0_7QChP5WPsUcGpm|{e6J4J^nvtLv|Lo0PEX~r2KNduo z6y)DNf6DNiML&Vyd6HZuHEGoz+Sw>}c6_pKAu){Z5?$@!oa~v@X*skknKwvW z=u@1a7_%=YjFOdgzpu>VgFLyQi$6DY?3s2*rYPVfo2IVzOxbL|XI2=KJRFm)L6<$i z9Nb)J8{fNtvTu|#KsnNsEJywkI%0LStu!!n4clm3PNy(CS0Ul5%g5}jbm`58ldTHG zNA_W>Xos26f_TqQ&sJI_i0xNSinK1d@_)3Z>|6W5c7qXJ{P*Wj>n8Vg?#5qpV{Wjo zd}2Ne_2FyN8gDBX%)AcR{iR6AWDv@H-PgokG0gulmb%Y(j<>a(D&#O4ewgd9 zZ(#FJ8EIRhud?m8nvJQSjL5~!^7{!I)@Ie2YwldLVznvQoMlS$tH_I3s+oaZBO;~R=volZlFTrbhN87P4l166(;nHviJ1xiXhIw*R zp2WMU`|C1-9egTf)fj`tADT@<6J-Nr6>A(QyJBT-KN>QA%&u_FIU1ngKvnE}Z|Nkm z3@sJOuVcR_{he`GUM(=vW_+)Mt#ZCIq6wK zB4@=;Obj1DaCi1 zUH&4H6I?0I3I4sD0D@09o}f?@=9vUNNP&QLeO`L|if}Ra2Ru8{}Sf-0j<-KJ6hcEM*zmpBZOp z4c}z1*BdnXljXIty8oC>Gd-ZAX8fIHrgp4YNIu&ZEa}-`+<$rvYByuDpaZVANF4w0=I#pnx{`<&Kc)y(Mk z)G5x2>-cQ)TI`i}xS@0--s%Up6=v@Z)1=0ZcB=%)_!vBl4E?NPytXQH8Sw(KC^=62 zuukZxKV28r^`Xfqhi$#3JNwHGzNQ=BM0M>q31L$hK5nMjCSg`Zwynke^{7eCfk9v5 zXOd&i&A4F8{dIps!;v9O|;6J+Z$ zxe6i8sHOb+Q%w(U55edu`y7)TrxSXoMA%JQJ3~-1J$^k;Ag=ZM(aO8jUb3LkGpnV> zT(8`c$Qy7Wq*Mtb&$HF|5(2xks-OPUz9But)k(S2bUtq6EO7X|KeacykT&$Pt_(o%W=sNSAEn_$1&%r{+z^RbEFg5r#UBV z>=Uv{A0-6$?MKqn3U0`WKD8L>J$0VTexRAbmxLC)h%<1Nm;Wlx;{9uFeY0KM@UUA< z;=fSZO_C=hxgWOL%gC1N6Z{!ooKx_mKSqCymb%*aTsT--gp;>_lW4N0*yt+7JRH8%z`%nTnNMD#yU zb3?vti|7cYf4&}Tugj+oO{-0K^7cms-LnI#KMJHKu3Cd`JFD0&(TysXvU*~7^cJn2 znUm~0BQMEO$y-zxrzTS&t5N7sbWv)j^HwS|ce3Nyx=*Hgmi!QPwsG&%wleA+&#Q!3 zarMP%V`l@dG${F5!?O`%if_~aBPLZ&FhMpfy7}bv+tEXA6o#hCohMep^gN0liESzL zWG4*nGo`=K(x#6ZCQWg4@=EFB9K*KKG~w>Ae#-N-a}@0|vwRh4`i3*Wh6#eI!MZ}zM zxc&3Y+^rdG{RQHJ6@|AMM2lR9!<=4C_woJpDJ_bhzViM3M-O(X8R@76$}8S<(t1BR z<8msg=b{nQ%&TjBL5y&q&qd0#_kJ zkyGp2GOp809V=bNW1SXlFA-cZQ!?y}p(lw(4aJj(1+UgQ6E=^m4Js1nNi3&q)ZY;he>2*B?a$! zJW{e(cI^~(?SIOBuG-2p>$}f2A_PUT`~pK`?X|Sr4C^|kj`IT%pi(YhWa@3_5i=E zEPjMwTwApysIpXc%FE$mz-a8?5`r5uPf+WBt)yZomkmE5pUA(MqE)mQBSa= zQrc@4sm_xgXy?S6`EHI~MX;mT)bW(tGJ-eJQp)Te9}YI0pw3bG$n2W(bdX{7sy0Nr z=LyOfwD&9IN0~Hh#)RKWe-isfhp3e0$D-4#&#Bnee)qO_EUhF73k{VYmS=AxNr;i3 z8dOI8H){t-Rx*3`*u7A;(5G;2Z2dk;muV*@J3gS)_=c6qgxD);gcS?NI+_nsUz)C) z5wDKw(caFc)3nR`_Ee(RM>1q&b;u#Mz}H` zoOs;%mLWU&xrEsxR$2mzjK`fx1z{ImpZ>m2`ub*zg4Q*2e7dWye@o@CZlSs7_#QzW zQ;j^(%F0S4b9y*-%hEC#Zk!s0LRRs)%M^3{Br&UP~^=k4oQ>k7Ip?>Lep#g+`yTUj-_Rax`d>=G(}sr-36IR$>d z-ad={+7Vvlcg_t~Z4M%DhYnxuSra04pH#p4R*s^?Y==)B>GvG|ej%Y-jn$NCn2@?} zjzy2=Zh^e_(Ji{Y8C;#+j$?Zs?&z0e)=(5L6Y2J6E2_oOWJd6BBI&e|jJjF_{qk3` zPW!U_5ka5cDQSS*$S8eEVn$&vAC8z{N5*(>uO~d9Gc9UBi%ey@nT($i`3KE zF~GN5TcylQw%DmQc2q8*N7ZiBLcVs#t+1%~Qg>4s-A_@9L94QJ{~Ea&<+Z&(<|MKD z^yqtKA7Sm`EYGt&LC;()B_X?#sfDF=QcuBqrT9Ea88JTBf^x)IgF(NX>3u{; zRt#AyW5O*sR)JM$Uy?e~=zX+6E`)X8uIKS)?rI~?*zgq5{)~M2K)tj!A=5h;4{ygbDDW|>pU`8D=>zAau>4r#CS*0koT?+e{)|6py_%~xgH z`ZCpD=N^;qPZHBf0$JTIV-pr#=>A|_>jcK&_-zLclc)hM%Osc;U@)lrYYf#pE~wm zf84Vorz1Z#b#jlOBqn^1tB}zZs-xQ6=nx_4ZJ1Kd_8#+IVkYukO=+QLO0A?^ZpZ zJ3z0#Lx~qoOTCtI=iN}dBE8y4Fx1rY4_-tMy_BW(N_CD~m`5vLrKinIC5Go~Fem4b zDrJ)3#9?06?N>V6nJeQHLo*YusUqgwEc;YZL&E*+-G3;jPiPZW2ED94bkWV9$@Run zd7eL3oT;$C?K;OlN9D(zwe-J~%gzaf1u`N2%9&rISc;)};Z(NApXaO0X}Jd-N4)jj z$4PHo>3Awo|dIbVyV7TJ7n#ljEz%^{_M{dC@{ga%}%DpX62YjQa@5|U43LAold`4D}cn!lP*^;}|) zzV_y;uU7Vkf0+q;*y>4$;}bKQ*rFPhGkcU`PlyRw`G%_FlzXExk;~f`EiJxP)w}c6 zq6ga-o6^=C_EO2#UVounF7NKaj?rfCwvK{;7aEyuY!$0d8F$Pd_ps(Dwig&lBlpU3sc3+1l4@^DQNWCFJT1jOXa5m(_=OknqC_Uk{kx7C)7i;JCk zDCs*bahvT=bR-at-0Ie_i5C&_yu5|9QHl>oh3?)oD6{MKq1NI6YX2*}t857=lP)OV zU2x5BY8&l(_|q-HLhi}Hc%3)rC!IH4$l9xaOF2WlL54t3P-<&xyEEIO!C;ZL?{hrK zY)(=;l@y>CrmE|E<%R#5c_T4}qa^kYbL~6r%L*hPR7g%&m;BbM7mfSWtZ$wqvxMQH zf#v%&om0cVXbo-;m$4Gn4SHT<2QK*>OV*w?ai*;pO*-FFtu>Zy{a`yUF+qIr0xM7c z!PK+$9-CQO!N-*s{5H$optwb8d*@TVB?YAc!!3FlRn%QvED7J5eb?!SYlNgV4uBd^~{s`TneBzV7*`WNOx{cYWNqnw6Gs8`J3H8XC(q3#ax7jESZX zmD3aM)3+|~k&8QGBHI_f-;4Fksk!IZ=a~MV|8QfLn$7Bsn}gI_{_lsJj<|kWq?<@2 z5H_H0BMX3)kmmu-h_iuczy)9fCm006Vk&`f6LAGRS>TJ{B=9%q2CgE zK74nfZ3FCpMqnH`gBelq6eGS0Ou)+nl!5oCF`=pAq0I+d0gC_}Iw28|qOJ+ff>#wY zDuBy~W$_ssxK$P5D4>d51sDaYP`?FE0t!%j3;YG-B6q|7!omL_-vQnS)&dd$bL8pZ zcEB2#M1BYU7Oek2ZFhyJjM4(;`v_QK9t^iM9FW(S;}SKMW29{seYHKL_X_?#6{YfY=r1?g8>D;MeEt{s*i^%^W<5 z^$fr`M8Z~J4f0dK@n7rV36KLV0kJPQ84v*HF;fH&9q~)lrg6Nh5Z^=`fZ9UD#{mm? zzJurB+{A-#qs9e4g&)5^LNG(T9XNpaEH6c|(SHQ- z8_Zmf{2$;f@2#2=y611dPy=iqnH^TFrfKa2Qx z#4q7ngZWRu6yP}4E`;A3u{(Uf{-|p&;%I2<@Y|yA7&sMpig-0}9pE6J1QuXFr{Q0O z-VM-qLF-1o7MKAzKp{NWp&voK5!j5l1^f{v4cG3hMiy z8zDactOumP4d_|`8Mp%U!J~%9-F?&!V|^j`qcL8W5I3N90`P@z2Kq@HCl~u_!5&4( zHKFU{`uzcZ1pOFz471jww+MY)>_Zp6F~n}b7>Dz4#P^t^#*IS`EJ$~Nfzhy%fu zsF}mF4)G^w=KyQOG2mt3gE;qF;n7AM2W<#(2{3}3hg<{9L{0-&0mhhh0y!12EO;S~ zMTq_Ez;$~8Z6~z9z_Hk0=Py5Mn(&w)HiI?`UJ1_=oJ3>Hrz8FYd(cK4fopUS`7@k5 zM`$vLqX8^QD8-C-;Elj*%&^9}>4oPv^y~vKfW8c9!Wy3Fd5^ddJqpP2kSEZv4`*-< zYIo5;f_y&s5nuo)!lwp*2F$-9?}ax7cnIwO#n4`WJK#&f3{T_|FitmN;Fpg4F%SnW z6SH^2w;nMK^SCg5MeTRw`v4QverV$QfJ3lmDf;K(IG%xLpgqF@$HD(M@D^B(9x`U$ zM7$6nAg%;mfW88LKd>kA-%-2&tN+(bXj9NW03OiA&})Dz$i>JVP%lUR39$jb&M0DL zDdKnFAJ_{4H80R8;0$jDuoTy20FUL%sINqQ9r}#me}=ve)b~KM19t(f(2{@k;Mjl1 zeqLeD7kF(D6ELR$@p8mJkQTuE4c=cLsuh75U4SF<0MuUMTu_lSaE*6?FF`X#Z997K zXT5|Bk+@etdq#o030&G4?G7rgZ|avDPRcTV}=zp89)PlOOWpXZUW_~e?e~wbRp^+G4t2q z*CFPk_77f+uEO^a;?iG?V_X5f3Gq(&0-(P_JP+D7oCjOrHf9}#_5^i(=yc5Vg%%8L z6nFsbf0(}xIvMi}pvB`c=!Jtgk9oU*P}IrrnZt7gu`B8a!3%I*=Yyxv6NPJAhBa=X zFBtmG^?1F(njM39a$I5n2pt$>2iN^ieYcyP!T7n&B@$>dnaU<%+Nv`4Z$yk-q^`k*@$G@XiIF zMSlm@HpGkWIpoy<5%^aR$Bo)FYW~QH3NUU`WJPXXX!utX|pC6#!hx4`&^JAg+p+|uJztNL}oPhW$5Q{J#JOD7^nG4{a zxrF`jsbR*^Umncof=d8-XgT1&;2VOc8T=gce1K%+9ON@tcNzE}PviSt`&sLZ84og~)YKo5Xb{;xRLV{ZUZY2Rp=?#6!02dhuK@vFAv=Y{0%;5_~ei~Ak>xui#<$RnYqm@iWY) z<5=Nb=u4W1roTW~D@;k{7|G%ohG4)b>XssV?v z#!qai9@;fi{zn0p9X0OojM{xi&;15Fv)X4Lz@ z@u<-dU&J{(gT55RZ0No4Mqr;lSYr*e4y@e=Zxr+*)F0xQyrFwL;(37MwnOhj9B(}4 zH=}+U-rvBF(El2~+lZ%uUi7U3r^BBB45EG*Gv@=i=Lz8~yh{N++6{d3ya|32cow2R3w?BByidY;c!2mVJjbDz zB5uY!2lT#!)?$ONqnNcAz8%OJU{~+}yaB)%=50dHDQMnU*A#u#h|i&CGind8<{6xW zZFns>i@GOXe_Wu2qpk#RFXDLU*68_)d81%HppTwR)acktFydr*%%Rr;U!ak&rX%9# z;FZ`@FM7WK_3$ZTt|2^cP&dUM?BQ{R9)~qa&~5R2dxM$9@H=5&KcRm@&ph}l(X$c! z47F6O--$c{wT0+W26w`%i`pgN9r6~O_dgLgV;z(TY3O01|0vLcH?8wgGscX2C~gOz z!#=3+$RT#e9@N1PfKb#~;1%k)UhrJPKL0^3!5V*ql>vL89KM~H-GCX5_@-bhxCd*H zP$S|RuS0DE_Vf^W0cw8(UjY0?C*c)lzQg`EFBh=~j$4e_7TS04AMhRpN`Yu- z^T6xjSpaVj_MwV-frx{^<*3V|-j4iV^e)GFpM-WA+5zmT3GrO;bv#dbm_f!dr6ZpJ zE8|$k&?m#j-f=#@qn?VsH>i(cmLE6@Fafq8*GA8M_&4DgMDU-+*TH&d#aQEicy3st zriuRD$X@`x*h>a_+0bIaN_eikgNKQpX&lFY(4T;BKl-Upof9pSI8e>%{bKR z5T|2rs>lb?ABFS$7BN1Co$wevzk$7>?*-3b_6O9iVn#jAk18|{Vjt97!9M{Bv`5%W zHhhoa4?)caxQydDgV_d{DMoGy4u>Zmya@aQNJoDQayRTr171FImKMHV;`}~Fy%F<5 zq4i+DRp4&qF8+A0g&tq*CmKAC*{WDS9dQJDHo}K-xGN>TH%H%2;1jexz)BR}!*2r3 z2>zRxp#yfrd9wqX!@~gkgO7o&;E%(a-I)Io^L{{Y!|MhMycRS50bhV88ytW<>(?=1 z{#C@2==1W!apU}4M}8ST64p^bZV10Ue7kUt455F-TKT{u#O}a7)K$Sj;1N8o)Dh#R zs)PcpT?MSeI##IVBc2QWHDW1dHG{9?c{h%8_%Dv_268!|6|+BL4^PnNfZP)E4#BSs zZiDs^9D`%5!|Q@I;xxP#M&TdeUn8DCJpya2M?8)>2Y@QX2cUI!ZGon>*H}N z#bb;D{S?;QkF_q~ybU9lpneniF1%K4fqxV7Vazy*V&K0d@xaK(E67e&G04 zqUR<0^TB+uH{b-%P0Y6jgwQpS7bCukSyZqOW?u(%;Qa@+et4|0k6>sEplt*Tpqt>> zk`d2C-2!VH;+)N(z7@|KQ|L>fdBF1r_OSuF3VcD}Enr{NzQc0~@e$w_)_s6;F$&It zo{7E0;8=5!$HP00Igbz*qkkIyFWAphti!}U4na>w-j7-QbKDL7Z2z&Pqc z)r*9AfCXU{sYT{kK_9Mf7}&}^6PtH)~|1n z@Y4v+|110dbr&!K)k#WJ5^^afc0{>wDM?c*B{)bj6}*LHy&0(tmq}7C7E(;@x@Gxd zlKOEWbxumRynvZ4U&W<4N_Yy%DP-MAuFU*#p3=@4GMmDqd$D_D3I#dFGg6wrf0jzM zG{?+!LY7~UrPjjDwTzsQiyX|-I4jMy$>^r7CU~*>>}U>E-3$X)vi2=Wo^xA+!p01; z?o+mm(MYCZm>o}VIz`5PvRh_H0$J^|G~Z)3Q+{_Ffjz~f^9Ey>Ih-6r#Z0=qiKp^H zy3~kulkPV*t}HIdF)`zb0wbTQ)C}gBxpa#{GM=iY5_~w`_HtoWJ!+d=sh06#xuwS& z3C9z1tyU3>A_rqNI$Lw+Y%C{*jvHxS9HZLqm?&CJFlP0z@`weYj5uAg)(}5$j!_%K z!|oaRnAqE~?tQV-7(v^kAdlHRQJly(*1l_(Njn}>u)dF=^I+7MuHCLcNNLg$%nZzx zDl7iqTYu%7-N?l6$dM`k=hk1F<+de^>rn(G_g(FU8G~{9p9npAuVc!78#gxiR*-4^ zxJ_x7uEgNyY_7>qqFC7H#U>m2^LE9EvonZ>f&RD)n2~IrQ=g$_2Hl3OqHO1HqW>te z$9uG!To~R=(zrnPb!aRPNuHF`eO2J+L8=fJ#G5>32LuF>gtR(}sYU2e;4QmCQCpw> zPOiE*`?k6GA5Zy z$LO9a5f_NdHZn7hC#q;p$)KyrN3-UkGsk)Yu^KPj8==rL_>+EB1w+Lmhibe?sW zmaOHD(+Yoor@3~29a*9sN}M>p#!7l%W=l%;B&tlyGDFgu9qSq256KA|N~4a(>Gcp|H1F3crY|ekd&7z)Ji1BNt&-Ni&3HNgj>OZQ zmZIq|G&N|qWpbT%&h`Ssdvf*PZP zlAuQFQCLs&X0xVAM5@DR<|h?l4x7YF_8(nh!_}b@1-#J!VzzrlJ#F3;Z=sN%W8!2; z;uok4w2E^#@@nK`I9`^c%mkM{@zMeUy<>Dl-gPox=dr{~{~Mpn?JQzm=6M@`pt|^n z8>wEN^j;FGR5&O$Qa@2(XB`+=6s=aM=`+c(+rcf4pENi-QsC>{Q6980r|AL3eD1u$ zveI434SM|rehX5s@^^+y4D4zCMf3{yPD2ybn``{@*td#u!c2@0;jTN}FwctSW>aEr zfJxG=x$N;~b0SgmV=bAfPcSX>KH?LW+F#WgerVOsfk4rPTa30U(-AxGMJt|$sh%CL z>`5D1GJ#W2MNK6v4#6=I?Y^OtX@jIxd{TS{7X zW_W3G#-6Ir7zvZA(6|NQXXsI;YdGSh)pmKRi$ zGIC2-eGTM3`KsP#r)K?#)ktLFeyN|InMKgAan|y#w#75mP5lca70Y{V3UiCL?CgyU zUw*rN*LzYL+sfC&(>u+jId_>UdpW&8+)>?s+;%oewI-?hbo7$_>v;dD-mK85r>GlO zrpaj1{FypNc5^De%j#Td(6Bt}LtWqgG)ljF+1buBb2jQ7T0zeAb*DT(LEiZf{{vCe zyp?IQxuht1k>y^i)HoZ1;0eM@+~jMt|HM*3FY8s9CbRGK$phk?l>-IAmmaIvZ|h#~ z__Tpbm}Q=#XU5BZP#Nkn;hoxH-$Ey_eHP}$pE6&bZ~3KAESlMK>hbr0)sE8sE9@Ta zT`P|&i)!yJ`{rB{J$r14**-;n&>_mwgz?CHiP-w5Y5o3DvcQgBlSpx~%c^Y_&@NH; zD5kgkk6j!^45*>94P|m~X1mcNBV%?wFW+l-Jm$pB?%#Bm<>&LPW0Pe|`>gHk1CHd- z{n|ygH`;Tvgnbd^4K;@jgxeR*YqZkb&tGCEw13RyDtWpNhdaDbwr2Uq?Oyxp&n|YO z$=soYg}WcHu13e5EPXy~oqMF5>HgVXeBy|chg|Joii1O!I7sb^ols7!UQ*#BqoV4t zyfH_zH={1ujlf+_t+5kU`8;#{I?5x3r|WMyF+EQ$?m=}_HEpym*5g*na>A+srkm87 z;;O|RvHLhj%fdp&VMX=@mk%bt&j{8!IWD=LT)lt)^A?gO-J6_p@5o9Ms;r`%-wml3 zv3{w9Yx1DBLX99h_dQw0U56L8y;J1>jwyM?C zg6-cFx&9U_OQ4Y%zkP!uF{Ar_JXe!Ben@j&Xm_Yo!==5Jr0Sug^I$ly)Vx+fB`LD< zLJzrZWl)h<&@9jI#czF4*s$@oXHnG0vu7vA zmv#tluN~KzySB?n{mRpuCUW#MC$}Z2>xg8pKTei>CZ1dlOU`EJ< zald-J_Mf3D3GwNg4|NYyi}tU$GRfUPZfB#gtBuB36<@KT%S5+iQNNAi#QD;+*9$LY zL@WI4H1*^vk}C`ACcoQf4~5=XKm5?gHU3`1%C?CBo{_XaDC~l%j|J^Qd-Pf%K~|rT zJEv9X@5EKfc8gY2TihM}f;KKr8#N^D-se>j>~i%&Y1$2EV~fm}9($WpGHufKj^6S2 zOfeiVq-+Z4rpC|am8@ZLBYtq08@%UUT$ARxJSsz3&Go%erC8CxBZd1ahg_Z;5a?^x zo@-waYqX~J06pb zqzYtH6P_dmI;tqfkNAA^&;DnX%={<;NB3uf=k%b1m_2xM{^_Rr;7?2Ew~Q&8WNF`h zld|{Hf2~sHm&SoS6;CChi0bGaxg;yiucI@1!o7<9=wNut-i5E4Wo6I%x}_|sAx@Xl z-%W)RQ>F9Qj3ja{@7HYIxh;3mkC1imB^7iR?~7JMB_odPszk1FmxjV(-V?*W>~GlD zWvKm`;kKxLe?pr3ib>k`BiB?-EAr)@hc>xp7k_x$hL{N}j}P9+!YFmGMO2*1bKAGwLM;(ewH{UfpN zv3Japdz4*x(uR`&cu<^6+&J0IfBpG=0oTI*1ShKU}mfqK4O}&rxN;-bEg9mqJc@S@! z{?6za65Tm`P*a)~|bg%p!8@y`!NE@P)OY45{{3iNjJPlZg|2fisSTjbhj zO*7@ZX)AVgNMFPVGm|5ikbNY?h~$@?MRqDo-C1cwE;UQJYP@P@jrQmacWu0DKg9wc zKH|-B-+gW7_S~Rwp8Ufsnr6JX*guR-HKAs=dP!G?^^`ls^RxeVY@C++u}CQ`D|?A$ z@UiflysrAEX6FhDT;2T(8^#xz1QN1NrEEQ=l*L!!T;Q=w9ZoS7;}!OgXA-4Z2d^iX zvU6&~w|UP}P3dcLUbhu8*Y2WT^oFN_rxg9Ny%osO3AnE@?Ea6P2X##EkAl{aQn}vUZwt5)pOkXJ3l)p zczS}KEU}svM{O+1-qNP*$^iMbUP=jUYiy!(FReUHhSCLv>5*jfYIsHcn$%RM$p_5#i z{z%O2)e3J``FTslaQlq>+>~*$l7EDc)w1%~h~|>TYJ|R@X|DrVb|r^H^7}Q`gwj^e z=4;rOUnHiq+Ftdq_egFKt^P!HT;wL)<<@bq{E48bPC_@8X3u>h@+BXgc&hX>TgRiS zgs|>()v1gA>O011w9q8diu2=UEZ1IE^83c1(Qe6~LaIRr^V*?|_x;Y3*DVBTOLq0R zHk$_r7RPl3AC{|VP2*=3=U)uhdd@c=p4V5r+id%|%MJg!j?rw(`Jv07{CsNkTy2J< zzy5 zxW%txgjiO`377l8XIgd>~{n1PP&%ORW z-^Mw`%T0{;Odj+7DYE5GWLfX*5m(L&BfeZdX8gc}?C-ly)~8LHVPuixFuB%mM~p;P z;zhA=mjpI9jA^i+7QH3-Y6OWEFKO|uvGMn+sPxub!C4-#C^d88m08avJ6+qq?&L=9 zb@IMq_to0!eekZ1q2QRp%7EZqIkGEvDmmlr^{hC#(#xLMG1?apm{zee+_KnQ;cCtH z_9FVv2G-XXACBJgZsP=Ayp`f1X?}Ztg0siJ*F0Equ4rnpoTRJUY(}-g`8wHfx!j>b z?;(QzXg2#)q=XaI*!FJ^wXu7dR68PXdTkp0flfLTou=UOuMPB@K!p1C<(0|v-=Zt4vIWVbh zaVFJe_+c3_=><2!H(v2nP9DDF=N;{P#`3aYE{N4KSgzt1u-;*jldsF72eU`y(>{^r zoD03?YkFwY{HCforsl>ckG!kC!@ElS?{rg8i%R^Ch5@CtN|P&7BPNt*6dTdj!WXri zy;lfD_9m^$ab1V4tz)unin7jRm&fc~yhFy8eDm6w*$&^ITlVUuC^xSr5o@|f_@VdM zL6^73*X-`QQx)RTOY{4Bv_{eH#oQau_QYAn1&U>}%*4xWlGw(zW`dF3WB&%$PX*Qn ziAsf&16A4?6s?ur0}k)=Y#(WTjH|2QZHrQ#&u#10vMtP9?9V&PIG&C3t6f*s3qb7Eg- zY`(ndMBcs=Ucp{d?}e?S6OOI7uMHjz5b0V3kdK~ji5%J19zE!-;^cnqL{o)MCzSBN{B9&C?}4J6-NSv2;(fLfqrp zR*H+H&{Eca+i0#%SM7713r9q%#4jNkKRE|Jq-9!m+b!O)iXHPWC1=5(H75U47>MZO z;xi0S8(2D7*snKYdb=C3tG>Ug{BmcHx?*cq`S}4kB}qWN%~nn2RxTx+!=TO7R&P%n z4|+7XZf9iU;w#R{Isp@7i`*RQe@_w~6wUKn_#@!>>c??j|8?aASw6jX=g=g%GNFQ) zJfTWra3gL9_CE0bf`7RUmyq{R=DaW@Wew98EAUoY$R~}4yT{L$sn^J3%2nlL@%V>F zGk?TNMz2RFH5A8k=4#7jE4y{F`=V$|tJ=(5(x^HKtS1Kq?`yqeiAAABA;Ghm^m^yF z4a_%vTC21|MI=v``+^$lq=7t3>M)I4Qy8E>ldaU(x^nj+t)b8svuu$W!7;5|-RD(L zOJS6g+aPJ!>cyG=hphJxYpU$y$M0>2aW*zQn*+CrbvA}<$aMU$2AQ3WfrA`QL`5?l zh%roj24Z55&tad3m>LT~beo@At3;KhAXQe)VSP zp@(B9<`qPK;2E1e95t=h_du{-^!-7yGc5A8@7bvNcwq7_E);6CRHuP^ygvI}>+);7xW76g>K^alTMDi$o}JKoTH)w;_VaftL@I`DGVN3;Ijd@}ET-L;m>U1;9>ZE zxf7M4($e(Vk52ISJkm#BAHz4s|JJv5lpS|qS674KJ$}Ra_*iw(WH=TMd)bKi)V|M7uIqG6YJT5k?Q5y3D0TDKYuOzW&P8= zA>#e7J)BLyw=nyhZn{I9{zB@vd8WUT{jG<1TYK7!Quh`%&mTWO?&CKSYTRGsZ#6W{ zb1~j3zp~_2(iu8r-nHnRiQ2d4Z;;G1`Y+RW*>WB-T~w_yOCs?YWA)056=~bpo~l3b zU*lJvUAW+hwLNkD7Z#nD#3{<iVcLg1Ao1Ji56} zu*Mb#UnebXFsHk0z2@9q6r(HKCER|3>^E-n^&~BfaGYEmKHE{9zI)-h+OD(xlGW`s zg`02ka=Y)&TdA8dt>vpXS3kg*HkKsQ*_-@C`!3ElvYH%VzqKsgxG3WnpU|5f`MB(h zJums*etK2JI}s_1mc1`c=5P2l<~fbNEWY}!uWe2Iifif1S#<2f8AsE%oO*ta>-(># z#h|*wYtw9Jj=Zb*q%h0Ee|b*wuVb$iG6GRTD@`Tni-T)#Y&Z%uk; z<<&PdhuzCh<)2Ir+!!wZ*d|MrY~}89x8%=soR?BQFHL329`z+2>R{6k>gq!geqz z)3SE%tKYtS_=!V%o)e7Ce{$l|#_@CiI&-O)k)~jO=2nEg9t??){rcmYXMd|*&e&@X z5#~y)thPJYvL~xdZu3~NaQ*vLZ$+_K8|O!V$^Sajm|cHvdSO1XK=luwT&pt_B)|W` zhEbV&-`feh%lPp}9z2H)n}zQ!`Pb|2jm`5p;h*8pbm2LQXKG?rBzJs$_VpG0z8&dD zZ|y($To^jLd_#=>g|`-b^litlb1r8dEqF1ZZS7?*7r%J9U`qP)*;rm{@~=PP?)a*`B#Qq41H{wo7;SETU;kIB&x3TE6MZHja&HP7o$@f z)m4AS&GIbmIF|09((TEcMNcPu!4ycR+{v=-j1XjmRs2!2Zrx|8rHU&uOe zJoU#Fy|3?8c)nh5T}tztIRNKPz2KXvSC9Bjk9Soj7zGD#bGVZ6p#B`YZ)a-0 zYg@wp?m2(#-M!3Soc^FW7_uk*g^DY$D1Y~LpV;_C=zoT9&MB-AY&=`WwrkEt3%C?> zuew9{$<}N;AAW2_!x`n?46b_i zH2qx%Ej;+mqT~5tAI}IEe|ZpFKE6yi-%366pq~CDQ1CQs?EadgG5a%JXPoay)P$bXy!CwP#T(+rXKPok^2o}5UgCN3 zMLIRflvy^H}yzf%4OJAR-plKcLu0kz_3OMOPD@KEv(AJk#nZ{@qg zM}pUtThE@-i6eixGCpn5@0nCnPH*Fpn5iFV;?w$iiR0Wy@;2?z>kS7!zj|m%_doUa zyygdvy)EX9C&I(l7%uuv?niI_9$RU2mt#?PK?&>h!+7u8>JV}a{ z`Sm|fKhv}}~WD;3m}w%l7sBX8^q{}cCpKlkURx}463?bBwcb6$M)Uj@z&#?E2A z#f@<_Be4TrPiy1Gi?)fYSb2?>PkO9XoLB7fI`g?j_qtt2Q3n0n{I2w;em_utWaPV~ zdOPP}e%bG$cQNtT-aUk+kFf<@~hyN0b#gmz4KJ zf%LaerK3mhBtL!U+>FNIe@tIH9&^|F!=^V_|L*1HVuI^cpZ;lTyFY)e^uOh?PyO}h z9Qt=bUF2(We?OslTKr!9Z<~w!&k90|SDz({15ST|wl*d@B*TCDh@LqRBIa%owkWLg zs+IdH1>K!7Cu27r$1kJ zKRGGq*^w0|le;-F>}(y0yR z+wVp952mDBKE2>g7xsIOo7#1i?}=(^AO4wE{JKqABAvxxS^jlb5yGoWiP^084~AK* z-O^nJ&m7w-oc;&V3J=xhBq?34=hC`$&13x+UrwKP^RtlM9LDDEWtiaz05ume~Hid zX}4wr1&buQW!uGE!wb@xGnD(!N&b05w`hGN&i=h`x$!&q+zLvu_h`-QFMg8#MBbYX ziz?2Z_|Lw9&a$*tdt}{HpQYhhv0M5Ti#YZh%8<9{o8Nt++^2s2rG?=kb~N)=|G{VX zO)I(ERMVIIyXMLR#`H&vJ(=2lv+=Eo)69v&=Qj%mv}c1|?eX99UmFQsE+1;U%f2e; zmZAR&n7v~eZq*x&+}vX)4v(IiF{@~F;Ptz$7kn371;2)No-JKmzWR||_owQM;(Y0W zd9QLWP)9dkTAaDZ*gvQ0t&pduO9Ruq^fT(}{5hZfvFG2pg?pbYxtiW&9tvA|u9?nc zZPpkWu~^?aEUJdK4y5N_Y2!0pyJshi8{fRzeziC~ZpP{iBO~tnDWTsb%!|vr@MhRU z_s+3V-P_}imF!>$(|@RLZY9>d-B&N}90_$N)NaVo3p>}-10qk<(o_nz(*4x2yT(n}V1X)H|PUisyVsMCUkve`4%+yo3zajl_GgD@rGxXsg|H^p532 z@19OM^@A_FvqsI(TWeZGxQo?9Pf+gKz7!`$|>Gj$iKD@We>fSjVIwhfs^0fSDChl=)(7|E*W_yxrZ}hYmMM0;cYVQ!$7p%vM<~uWdMf7Q zEL!^L_QJoqJaw=BRWtuLC;iuWiu$!H==jRA)Y%iKc5Q!s<4#5^c?N%I{{%00X3;mE zEAb{*ZH$P>`LHM7Wx%Sl2Ue$^-5d7o*KB+37mxqacZpxaEHy%g>)?Blbsa;i9{+wX z#rR;?myVjSnq!>IOXm2|j}9f&9@A~Rwj7IIfHFVWwHv=sW}R&e&2?m-Y_D#!Z(YHX zY;vD_N%^0!6^-mC=Z|&DE`4O3c`txD&*%A*5oQzH{O1tUZUyJJqk)Y2UZbK^GivY${9^)K|o8{)c9!xq=Kg zSSi-(=Dyee_`Iu5(7`ZX`dXd%Me&Kv*CML@FW3tc<`l;NFz;iBnVMHeZmYN!yCC7b zzN*o&Bmcdr`aE4|S_kExp>w4Z?l!#j z+|1V0ToXkLufg}vRDIRAyGM^-7!TRyVirXD-AI>y7sdWz^y-pAW(I3(drFtsneFen z(pe@`)}=iw%TZQLeD_P|^xWZs&lph!(_Su!3>seRePQ8|=r>m!+Bq-b?yygH{^6xQ zefZqaoHth%FJ0Il8)xZw|2pfrC-WL(KFxn-$yYBG{B!5aqVv_Uy91t_uC}}_8;{HK z4Y$sr=~U0Esj?J9*5*G3YVH#^>rcjXDX}}}P7e_mU1Vm=+#_$N{jiR(_F>-u}$4#Yar+h^31LZFZyZ?!qFdyP|3{G8BiBBu)(r>loo2cEobJ_pyW){G9_~SpUcPt!?fA`p$Tso&&;reiEu4Bsn$0JgKf>Us5 zAMd$dA1CI2)^|q_xSlia4JmAAT)C1bTAD0=*&W!Qr1_=y=*REJtlXi;j-JnB<@UY3 z@*U5#_|ASa{KoY64WGTR`r-oT0177-xXz6-7^X4YCU(M?xrQ! zo$|h9j$el-uG}SzpQs&Mbu&7a-uJStd*72<1^uS^Uo388?d6iRsAp37v1O)buEpz) zd=$C5_LvOA@4Y%ly_B)qTX6i%@oO`quJPmkO0%*?c0Cm_@uT_mvpZ#f212HgeQ$su>xQrohD9${2mD6G)((xW*C|-PT>bBa&O=1o@XJ|-yw|Zv z!Ng8|>~QfM?eb3RyyDAj&k$wM;dL0JzvhAL`$Mn1DQ)u}JH^n8e<>9ej(#!B(!R2? zJv#n%59jK0?%Wfz(m6%ij=DbG6x)~A&Fc;%guM3RQ>*8XPy4`EGezz;eA<3PaQyV8 z<-2wrP8L>9d+FVsg|b_3x|FBFiu}}5%e9Nx=Z)HhpSruY_4l$R6^q|% zGAN3kjAMWK*6w9986&Z}e=3(}x-51o?FRM@W7owYVuZK=io+ada9cval%=ACP72dC}){fnQ1#Z_^S`j&6-?a1D6(=*55!IICMV_diy zk{G}C8r$|Z=ZJsW*u8ea_A+t$qlI)sVMYG5-0|G%k z_UGPdOkKS>wc9YrP~51VvTQ+wqV%)WB{u$=>+jqz`OiVBrueXPy8=^P_nXehnHHYj zT997-&68G+`s3GF`(C?AUv%`ZN-cdSc5qSa^S*`;OB$MYrQ8zDGv3P2&F6WqcF$;; z@PGKfP3Vc{(%0>$7R;aDLg!b>Hck8H%jf-{uvMfy8Gk(dyC?MHg2<-G3!f9M(t*`OvW?feE+}u!+;VD=FP=UWyX8aa zm!U(hjsIw>_0baxK3WqIj@gV$&h(xfyZjn^dta|$SbTrT+tL4*>j-ya@0(M0D`q{N z%})ODrKi_7+jh@;)*79mH+;O4YKVQ!vi{R5L)%}usy8O?M9t9)hvnlEo+87%(m!wDoxskCphfdfpRmL8g7|-z;iyR=O-?7Ng~xxd*cS zUnAer#{lLHL@W=rDWKjx80maFde>j z{p)Y&{AO5zKKJUE=Z3zT`b`PDE^AqZX-q#TzHm79 z?Y4qgo~0r6f9KPSW$gLJ&)MCK{HtqyPj`+{tZ6L?b61gpvm_^Dj`+;3 z?3P=}{M%JuNVoX5_nf~Gn%2Pm?^i9$wjElU5Xjq+!IV$?I-i>I>7G4Dlw?kTRz_Fv zTVW4|-ME~;B`6wa;>ENJnMe)OC&!M)+d(MgwoL zVwZMlxx4DE#_yjHhED$Y{_j871q^;B?`15EkVj139PWVo1VR`*f*QQQUlSd!d;&@67g*c9+mX>Ul;}V z0rV`3=w0^ze>OPWuT6>Z|99U1dp7w0_vqhu1+>h1bYSw9FD+1hH4h^Zgrjji(H zBBbn~i6M$~R`%mB(F|N(NazjRQ8z1taf3`^uTVQF*vP|P-=O0h*99b@GoBW6P$NYv z*@BR4Y9}x8vNlv^@7FEhHPV!qz$jVzzCFy;E|L~8c93O$RM=5zl%+Ka;K9+ZX>9og zXQEhE=vizgZh4vps3NDkpR*nBAbW9{xb!-uu#e!(8w6!kF?J>e2%SKaMKNeWE;7t0 z8<542ZB@#mC_|1e#-2y^-qXq6dsccLR=icsM1Z8;ZO@F4sY0(=UBzUjTE+Swf+A20H+RhA5 zX=}0dBC8@XTWoPmHOp@kiQ%fW7Biii-yvYb-Ew-PgzV7Zc&42iz;D?bnVDnm$_!1B zvznc$Xw1oiyU&PT$xe|$FTSPdFq8Qq35{~xIhBwsP$~QjyW2wOYNB#3NnUarnEH;E zgaM^eU=VQ>^(2zlH6~`OWGw=-G1^NIsa9S&CB0#n`z1TbbX>ASOt4e77dUja6iY_+ zQ^sbS5y$yVJYftI8WA0p9FE7i^}UifRNk+uv{QscA+B<3i=&uE{aP}yUt*=xa>Rwi z$pLX1Lr3cl@e+)rLo6f`6?Qrs$w+y&>}*tySzOgZ1jT9fI(9;6tE^vT@2I>kDdOpI zjt&zUb)k$hoN-uG5D+`uET%r+IW0#E1cMy6u!RSoLL6xMeDmC3B5T>@-ZL78U zsAISXr)mjM+UK(1nWI)VvC-xg(!t|>N3t5gyZ?z<6Y2mB>tW=iE zB22~T#d@u=X;V-obL+DioO!|lWtvBrE#dGJDZJ2IMM&@vK^FcpR?a3M0CAm8l%t1D zsv7$6*v5|9%pFk%A}-p#i7nWE!^>i(-XipF4x=eLNbdCLM3N#W>6hTH$}E+vhSAHh z$7A}G!WKams%}Zl##t>(g8=D-ehv%*){g2mE7$dG%23{jO3xLf@#GkzgH8HOn z;xR%f%q(_N4o(3kL=qY$P>J%6sf1t$uM$sX@Zm=5gS=!TX}2c?#8#Sx$0>OlJyB%u zG>TtxQ$nr2g+iQaF_oeFqyC)L%K9R5l*mV1}3AXg~--sx=}#O zIWA+%qj`kzc|1g)P2Ok`WQ*5EVd2zm1cpX4nwTj&T1tD#F}Kcu7ep=AtK>9`;6{@< z>{P5$!h~~lxP6tYi*3#~qxGZ^=f337RLNPVblf=1MVggL-q%7hT4XR#cX)1!TxYih zRna@kdvP3@jRLGPIwO^3*KoDlce)LD{<@ITOH>SB@5c-A9KCplqHnhxixT9JY4%Dy z$L_Q(lyU5c$>7u^WN?&dZWSKFDQD*>qsm}siwL5hB%M=x@#v}5DS)!8~jRKt~oJ1x=r6oxBD$T?_FVy4Ji z5*m@UBgrTZ!DutNgQx2wmCh64D_DeZNKdy^?dIY&bdD-tQ-e#+cG%by!?idAw~G=3 z)FFF9zfq1lhDb)MnVBN*FgGfTP);sql-BekU86yqwGOMw(ywzZ$1+0&1y#z=90HU& z>J;>gm9F-HCZ>hS=5)fAV7e?X?Ay)spzxd`QGzhlqtG)``s_M=DkiaI#W8sutKzmh z>=c^|H^}sJ4m!=j%v)}1w~X|aa+#fgSHapAx?iW_)tDIvqcm)Bbi5AdFg>M1;>9gUf3!JPhsaLs9&uRB0Wl5u(D2nXlEiq~-GG`zq)~Vrf zL&Viaa*Wn95lIMv96E>$Ng$UWs&cW7&{Q>C{oWvjpP`N9if+m#_qYhC)Kr zD+}1&IIeUPTzMr~9?r0OdWn>~P7T4NooW4?b&z66yE2(8XG92bm7SM_nDRD87DW=F zP>JvaJZTKC{1R>;=5iBaq8116?d!^CQdO3a&YcwRC&L+kSybhYwc-9X?0YD znT&NS`t!f%5xvH=C?w_<(VHl7t&`D5W?_oUItWNi%C1So848{zgVDybn`Jjem9^%> zq^SRy^e7D1&unSDA{lcC&19vM7+Abx=i+Q}k+UjGrt1{xiCa-+xd@LSGE&#_SdM4% zqjbBqhY7tE5%toD`2=Gb`oZAXRcFHI(QLo)T zW-n!?Slk-O#h%4(ZD^;US1YqC43rmF3`uTzgc;^rP{ENSSea2D6H#WQzDGG3RHvQD zAlEsQdUeV+;yZMZgdz%}txsa-83s5ktRP2H-NInX?%*CH5ra1_lqlUyVXDjtTb=~m z0z;;%#TF#jV)`MnOPkD1vG78L&nsIN2U4tdU2i5$Dl+s3qwEwB&|Gs z21V6G2P8Y~xxFbPJi`^E9~t0YYZu7(c?gyPztS&fat?YtflRMRNRb=umetA|&h}u+ zC`t?`4?Fu7W_DqTX00;Dj_`c16a&Y+kp3%v-};cgE*A)idNqk@jKfMkqU zIix9SVc@D*!iFi=^6Xgx+NlpuHP%Ptaoa}-3g^n~Y1w1}O$=8U?I=U1bB>txqezrR zg}2N#r*w2Isg;a2Qde**Y@U&N%Tt*F4_?(|;AE@NEZcxEB84en4aqPvIh%pC$Z^RX zC^%JC55uV$bs$8^Tc{v$m`>7bfuDm67LQ=2j6^N1#T`l^VlX@j{VFyO3*c+XO&JoI z+gvA(ZmUPq>Xs!#0Jg$##KI7}Kqpd&IHR7l45iGIz?R8c=Vp^w6iv;-T)9K2-NU#< z0_1w}5JF?*tc6NPQmaVDbv5ovv#vO#(P(DLM7_#rcRG7|A;#3}M-_=i@hGHN9p<8Y zRkvs=Ksek}i8L8zFcv5kmE4qSLgeQ}&k(?qdrwXEQ=>jQmx^hFo11U7u=>lk*AjLt zYs^g!;ix8wK#C}++ipiS^a^y0noHtpK9-@cLzrKw_h^`^XlGJCPP)<>rO}NtCL^@d z;GTZav!^9>%$PR4W)zD@U4Ij-9UN#>INlD8#DS)GIFX*oL8& zwHrBc^(=?9s6}reQHU*1ei%K?);KENesL#3QO3|_#MFOw+U8X5AQl+$+3|9m5lbgA zIl5MA2)1cZ7$ppTImVDRZt@!ok77{nwQSPMiV00P3U?qKT*Cy&a?%y%=GlI=T0hJYlFXESSW*lhvvsHe0ahnp%14vpmmw4-hs z*T)i+NY>#@&@=n7DlUFIN>7P7yu=`f)yl|V#5mJxwbjl|8JYQwHY0&^re+~oOKFaN ztc4-e$97cr%2+(T9;cBgN4~?8aEVlk60;d;E>YIXkf=msj@MmE=%NX^K4C#4>E9<4A1&J_EYoUdf&=>;tENLaV3^IxwBH1k`;FrHGQ}xW-yr~0}lKeG1 z8uMU7`S!+Sk@U9GuuS4t=zFzi?K%CVnietzhkb;fQZ|({l~}8+Pu%WiW{53LO%S3o z0ZF2XssNBp!jL4x!>Z+eZ8^xj4Pw`y#g7pVQ^=Dcp z=fby%vOYPh-onlr6&V~UMMN1}dxWL~MkCssgI5y5-LncrX1%t{t z*SckXa;#aCr^DJxx!gL!o?))?B(h~`ErMQ|-YLk?-iay;q{MVo*9y`a7qjJdhm8`1 zx-_-ZZ`e!4sU2cNxQZ4-U8QK$)FDP|8e{`|y&vDnOU@!=Nn4P?Y0@+pSxq9dv`|Ch z90kpC;|6|YkQ41pWVTXeDKY zAHmVwxbN)+X0#5o>r>W-Dm0oTWyw zjPM0G9UToqplg{~dldE$Zzi}omZ!}Q7Y}2LzV`eKANKw?7x!f^cc|crGn{L?p&Nyn2W9Mh}sJb~G+F;z}o#jl&v5 zFGJ~JR54>(bD7-AD2y4lT`7_esG?~dU>(_e1$Gb1m{l3o*sI+zS#02~YcEml?lJ>) zBT9o*`6QE_$if=Jb1+V_JMji#FiY%o8k_qAncRnNI1~CMF+eY}F(|;84tKJhTI;be zQ)SI_v$>;K*$`ReVfO0I!bFN|kJ7U_-uOa%s-t6SmE;C0qw%(=@+^+SsmV_Hj3IPL z?QVlv@8A);@f%S@Kx^@&*OEmM@+@Uci;fLE)4*0%(u5INqmpS73xTq@lAzfVmAj28 z=Y)_Yz6j?{EIft@F-Nn*>^^G?+2tK9-Ukq@&8>b7%fuh{Mrn6uV3QpOa8 zXA`a~a`rCgOk6w0J_i1n0Yr>7p5+=BE(u^ef7uKYA(h$tdNL0utX&ZP?M0*rfu z)EHHxJn|BbwLm!$ujmNsZrBBv$dHJ}fCOtIX=#2VW(!ieg;gmi!rsfEv8o|u5zeetDI#*2DGFyP7j^i{ zb_+x685!c(j->3=XIZV~ec~b{$R!Of7E^LWiFOOCJEl_N+pyFo32CK*i>saPUdb){ zR9tru%5(AUlbUc#p_f`HX>(R*XqhrYfP*Sa11jW76iU`PEEy?nWDXLi(ZZneU}IIk zGSk5r$|>@cv+)`c+@My&YbS(;Ry$X|4lBr_IDB_+iUlUR%z`NW-#d*??i&>0`!_2h z2)FV_{PxV>OUc8NdvU|6afO{?gyP8G$-%Lv0BjcK<%==#E)Icdo8i#NT39fyfEV;4 zXf)-}^f;l})CbM|9%$$S=;Yv&*zPvygm=RsYA-a84nVWvOK6_{1RBRqXmZ`q_+Eo% zqz)SYVrV?uph0G6wELh5)w!<+x<$MSTdy?Bt$-IIQyyxE zg?gDugV`J43RPWjo<(g&8cq;HG`|A5O*oeK1}uIA)Cfb>FgCdr2AJ!EW_UlGZRTr` z#{?VQE#!!i1%|Nv6RwO*Bgk*6KtYoQ^_hB5zuJ!_dN>+1W^!ZX>u?!r6Ca0RVM{au zqqEg+Y^(}i!4nTC?C6_t1vB4)`S-wy$KHogModa1=8WMZEw?}&st69D5(^?rJZKEm z_c=Hu+NK^yznOg4fBhiIV}Osl#DURS@P^IzVomSCMMvO#(uJcTbsqHdVI%n*B}Scq ztKl3k*w++9QR*>7CWz4spz{cvc7gA~ytUvRI;(%u2Nt-5J0TBk0Xat2Lr=dtfL=Fg zKpsDcsYR<`uz!98s&3=^P=$#`N|PD8*bCMrn&9*q`~bG+ILI;&EIaa#5CW&hZcJXd z99-$hwBU671-SG}pM!yeuR;fs??wyN!x(7=P5gWs^SYq-pCtgLSHL{dvvAHm6N%_< z8;a+95vit7kUxw@P0aup4S3LsjWu$N$j!H+;7%iaU*gI`BP!U-%>qMu_&p%076mh# zG5UM85?xRe$YQc!_<7L3T9>n4+-pGL9DIna0r=*mM}YlB@P2x5|v6EC5C!gqy{s!TiOb zCE?1$&=Qa&SOR;|!KXi(hOr;oK}Zt%*>(nS&xxBLn}|P--r_4!?wtvYjvoZm!dOIi z5Y1D0F?!2=HxiT(XceHr2ijH)j)4t^QL_qwuqVi=fC$r~5`G?hN+WvEfqG07^0-D2 z527c)w;;DGh@LHRfcr?4df$lU9OhWj_3mcO|1Nm#uMfZotJE|)=n9~oLD*SrkV4~X z2O5TevA8^c!wlWGpbx-MkN9Q`^MW_DB^IPH!AyJ7c|kB`6)12*U4bT`Co1W|hFie` zJgFDQ=#ohj@ZDI!DR}X?#E5V>pW=Ja52^qL9;cA=n7cX2v)98+YlRoG0!xHD0ojnR}a4eJykM+uuLUJA0G^$u@WURo0K4(8QIih zsG^(x`y_d$7N@y`oN?rnA`bc-uKA8>)SUq00}{)xI6`XW!mtjA9e4wISh$*1)?l4u&F1KtAx^h-(ZT15-Oe)(LRR zXr>pV)u8mK$pfj1M%SfA4h-rGL&Kob3Gm+qu%5-0hl;ytlnNJ}fXj>nGU*3mux}QB z0*&yMKp!4-!PNt4W(=s(3lF9Y;I!j273gi^;K&NGY5{mtaGC{}0!49@NSW!uXj^1} zhtZ=_8y9K1$NzSy-sDAc7>!i>L1;HBEVklk?zfygR0zm0avT_aTH3?MXdQ<{(_pZZ zph+A+y02sm96W(?Aj9x4akOYAXtWpnW-qaVnQ3@NMg@8du)#~f_i8uFne=}U4v;3u zza|1hdX5$FVuW!ign_pH5(uy7LD&%!j*RfZNVgkYszpSkn1j)8xd3nA04mZNj?uTe zM^H!PNcbdRhr>{mG>?PCY6}n#~*QMaJ4lI$c2L4IN>MF9B0ALcar2#Iw z(A|T6;akzr-y+f|?|uM-*s-E`ID^d8Or~NBKw|=#rF0njpL8M|kXjKA{(%pH8xh3g4t^f8!!SV(WDk~l98$wL zz}5pnIRZhY#Hghzr~FJt zP6bK@S*@V97yyu=)*$g9+#p%f44%OedA|n*q!jo6vLd7^0WGy~&`~hWBPnd52tR27 z6X(H=KQ0IC3#kNdH=`&(^ye-b9ai^1HUjdJTBm>&{itU$l*hqG6lj+R7NgZ*A9%0H zBqsA3R4HJ0__-I-f{G(xI7;QfAZJ(sfnKzsn*b)!qQ6Xe5CBHh13llDnmNGfKofj1 z`suy}T@p+(@CYcD(dScKAt^gE4hp0PR3~f=HA-CX3feF+i^rjtyOgWP@r!AtPA(rOXM8u2Wl35X|lX1C0anwBRkzBo}HSnEfso zN+rf)H@A$t)JGmw}|CQzW81Vwv*i2Q&O z0tA9$XOKV|@U0a*Gu&N)Jb4_rntxfd*L28IDdgB>S} z9~(aaio2n#2tc@1pci3T24v4eM%Ne^z%PJHIe>woOHdsEYJ~Kq1e{fXIqO_`NNUOh zDjf+!HvvQ%@P`p*9vhFSjNptsv}TfuNGZWb?NVPPFr^1b&xl@!&Ikl@0Aq_F+=fBT zd#*|9gE9*ymdL=VK$lHD@WdiFM(3L#;eui|HH~->cLcyr39)E^+G-nk{ItrAQI{wA z&ObR4A@znqP7Q)*_YZ23F%zb0Uw+MT10o54xC2I4qVN(3?#v*DHiJ{6z@C2ai^J7} zGC({J#IS#G98q19L?!0f&^WmNNwpO|p#T|^)H{s4zopD*wP1P{w-v(ZFtA`;R+xdAAd~(b0buf*B{bw7 zEi$N;&?Sgo9W(+k@*qY7u(P-s{R9p)!~a4RghiKn0&)2y5Z(kl`hmoN`880RKyj`1 zqPGBB8tC;MSkY4Ahg4JnWNilLc`#5FNAI~1lsHgYL%fc-D$rPc4|EIg^T1lo046X- z@fe0);?sbC1l8^^)T;N#(O-b2C>4An1Vdgo88PZ5P>VS4!_ZY2%jY7I<-P^V+6uIg z-vb#BKnxOF`M@%u*(B$U@B>J|SE4~_-V}hlSOCQ30Y6wKXG*~P(6w1Q0-GI6Z4rnc zY2;%t7nuOGIrvBfRX1ehVX#?%gYK!dP=?Tm97J$X2@4*!zo%5g0MlWU2MSoI)VhaJ zyy^eu#BhM#Qzl>}(2+7x$bFnvkD)^)0R3+GmnwOPp7bI3LIE<-QY|0ErJ)!R!=dqnE!!P84gt{a3x5A&igT%iX4N9(Aj}ZSnM$Q&^6N-$^l=a zbO>CGPU9%i^1%@ho&c~+76ju+2p*XYZema7pCCv}Nqe|pk|0_&S;_EBUQ{OS;UKTe zh#r9KeyH})H4y9=bdG|^KLRK;xO$L(veYnwCzrtiaft@mP4ELnpt>bsj=u8{f!~_O z!7~=XP#%VOT~^%x#dQ^|cJPg8y$Q$AJdPN=4yJ*Qc!~CJnwYD=LpNHi4jS&7g=n7bO1(2r;(B=i?_`#VLh>=khg(3BM0wxP3elQoz zIY6D&ASxww1LaD+T;v30oB2I3Z!x2nOQ3c#Vf*^P$M*`_R*a8e|*9|JI(! zRKnDZZv{2TslW$h3|f9)jNFs8+;y;23!q@|P98>+B>^-9CR+$l0os^}ow}MyAX_Gd zQ6Jpz`5SZ1QiAjMk}55bTBPXD`#Ac{WrefHCy`F0U!~qD$X9}+@9*HafAx>x!pkN< z%9L8Akko;iJb;exx^A|rbu zA=ODOG2jy$%oxR}SL)`&xJE8gmO>c;9=vWMpk`4*zQEBd;Ezris(G@eeUu3_F$UUTOkObHdMizD3=y;Spcmnp@8CiknLfy2(U2$ z;BmN`(NExY0$$rnv;bKe8~Kzo0V^QGs09G(=EFK8oIDTWfgE%oZiySr*Ng%sK8O!k z(FKtH3t(V{cnX3q%0Ql$-zs7mw^j6_b_`LSyR{RREBYDoV8+sRqiWC6Si z>^%mesQBX$p}>zO3SCoM(FqU@!Uab^g9$BCKV7C=B1=Hy@scrj@x3UoiM0_csGjKQa3Sn3g@U+;U+eejJLBtVl@;#;7`La-K( zGlJ72znZLoKk%vqrsEV64H{9^9UQ(QxL<+!%0jdlEtaC`i0%?YCIXxJq!VFK3xvQZ zc>qHL*kz(bj5e8u|7M*l>HvDI#v#JSAi8=`<0P$TsC9v)QmLnFMVVnw= z80jGTD!^<#pvVMZZgn>lsKq!C0{Gu^-v=d=7awWnPBYxm`ur|j6uh2}u*BAgV4=e=dI-OJ05;J{QTz-xsj%s%5-5$K-s2UD2a!^9K8EvD2Qks#beSX4HpF!>{1L1q z0|d$-Vi_LZlGXxQFkNPVOq-#ofe)x+$UoRrv>SFi$p(CZof=bs#{+d`uwki$*BB5) z>OCTV|E*b$-jP8{huB=y2}|o6kRvZ`U1LVc{4Y*AU>`;|nssnHRg$v36oQV?J6hxc|qUM1;5~8PrlVivYh+h|i<2Idu zDixy74{<4~nW#Z(P=BGVX>iGH%~H?}6qTOvajb*~@WOy9ss&BL2kmrtjSy8#u&AkM zJDsWn;fny;E;J8TaT>5=WI%*|V;ch?flww=QLGaW;17W*%}6+)Zw~s0z;zj2qz|^2 zifFJlgJuTOWX)iBG8j=j3lW-cMD}mGFno|wioOCj39icnK}%6rGZAhPlo(W~<-1Ta zpimrziS*&$*N8iTaWg@Xz2aD;1r;Ihw`Z)rpPj!Mu>x@<p$)Ml9I)pOj31x^ZxR5|ojkP=Z=t2Ld}R;G!d9$WR7G&>j%pZpsKapA3wj z3&6*NjHS^C{R8@n2A^U`WKfVXAfizr&_mWw=gb1AF-UmzOKTyh z1@1aR;ry)6yCv`VjNWhe*=9x$jyl$J4_G{y|8T&h5J(p4xl9*^oGBqbD(UY zQ=$C|H;9HqUW3Nvlvs$X#&BaI-~eUTJc2euS;9LDc$FuBTuKZ*3JIh!>@MIrO5qN0 zHlIi_Wdi9>ESe=40XOafbC#p4u)uc|_&w3ZC?bzz_;q@tE_x?v{F&|M)44>lXpF8k zOA(*d2aiRtjUhSgL#{*v0jFnDAS*LQ+_HQUK79kMW0KCKLP9))azIpmlom*Zz?&uD z8IqBo?JaNOPCSr95FErUOT})zmV(6tu?LMD`4f67pg1^12L-H#O+~+hY(>p|0IdwTJ`)v*8__~Bz$cs~ z6_210aH)362oM@n)I^wNnWPUTPC)P!0Ib7he{E(06Nv*i!hyWcft8P#WiyzZL~mik z78pd_Glhl#LVUQ|IJ*%!fXyC(wGvrF7|R%xBL*a9L!~F-Ku4AVo;(hTE(vN%5@cUc zz=<;vHw_kF%uYp(x|Y9GCA>2T42wfV>}d+*PZKT36<|t4m%^*T40(%XCR#2)upm$sgVG1D+ndC2Wkb0UhBpiNNM;xd%UKLMcn6onH*1iCap6dj@h1@3$<3V;i( zU_nBf)dejXDYP{B7~TWEyb{FX2a+uj_d&ao33nmFLJn($gh&0s8bmY@|9^BQ1$?0t z%_FukFano^3at%nV}B5bB+7Y3<^v|kQC`ywhPRVNB;Ml*G6U#%5C{eM8jw1VUPD5) zK;__)=28fM5W(Sk7SK9CX>^hnsw4WS{{eiFnn$3nV*V`tjVg$#5B!+`AdlRK@vK1r zDF7}XgM=6^rB50G3TFO{p9SCnE7)A9Tuadp0S7$-dk%*D5g9ilh?cR43mLH;bguYwQf-+g~4;)Nei(obK*+6_XLk>70Ls4+pT0)aO`|aEJ$hD*=P|Y9_c9 z1{#C4tbuzQQ%ccf6BpwRPXJ3V00)LW%)kwZc)f*46sYV;c#k0!Mcz_jP7X-mT_^>KE*(KviYb03?%~g*aDw-ih;zZ zlgMa~gb%nqh~~ggY!Da2$!{1KUJc6r6*RDwErSRK^#U6~D-_}|SkWm-7rIZF7%@q- znLe%oDIFhxSq7{J7n!hU0KXs$0Yg3n+cEB!Vdy&?d4z!RMzHldiVT>X8kB0tfy7fp zr)q#<@=zlniri5!f{F&@Xb7(KE1bO?bmk4FbA+gcA^9Xhv|>VRN<~Kq=7Qf0+&>fq z_-6q@E>O{mG7!!50JOP5LkPBH#g?Hz;R7c8KT1#vmQF$+DKa2Zp!Wm8{35u68{ABp z4Fq^I7o|h57hfSThFV7s9U4eQh79@8d?{k{&}|}1AEb1lNC~7V0v;r2v6_hJ0j~i^ zduzxg5uG3fvISugb2EjM8Z-odClZ+9Nat#!9xw+Hoo@^iU74U;kg0@=*2CYW5**&E z+lQeu0uUoO)(Zh->L3xGA6*J83&GMN$K+B#bvAHeu$T-M7%EywhP;Fg+XYPgL>?rG zMDO4?@5{hFf2E5E$Hq|#0ZOT2IT2;8phDvy{y-{ZAyC`b_rrlg@d&U9a`d~HhbRCF z3UGxSiULD6Jz!S2qd46K7ch&fsFFmBj}o-3vGm3co7Mh0j#eD4&c$LCddWM<_ORqa<66{ z`qDfCP|ScSwGnV5I!;1H&3rH*7}$S00bnz5Ab$d0fGL>-&;(vP4Fr-T;ltTX*m5RlmkACh zLv@B6D~uN^;Q*a%3_>wRzG7e%AawI=;822AgzsjCz`?+NdRu7_ESq4izJoQjz?FHh zoJJ6PoPeNSiDt|scH&tY3AlD9NAYlLoNqYjOjI*n_OKjy;sEgU2;f#9K+Xfep=pqo zcQX@REi=Y=8pI80AoSA$8Tbl<3Wf)v9NscYjwMJBjxNB<2#kxcQ_(HZL=`yvdKSp% zsUcN^7-XZu^&zbl@Q?(q!U^JGaw1~~ZbOK9T(p*AcsAjR61oT)iUZg_2WI_=P zk4%A3rEp%2q!HY%3z>ny^Fg=;02{x9U;2RtWni#YgwbR4L1N@aNa&;sN&+UZ5+G>| z&Y2V=0Bm?yBMAB^fHrOx)P>2Pf~p27P(y-4WDdX^WyP?5u-c8FY~esYn7n+c5*AtvjhH|m36q0)w242{5AODCXc7l_b8!vEC3h>$1T zog?l-+rT@7%`$WeG*>KOqW6F@Bk;SQ6Au!|&;(!xY?y5TWAkN!^{u4KV75TaMAPh4 zfX5niX@CJbAwa5>1d5LZMudrKiR#FnD4zVlR2(Q=tga!M1gS7wZX0;EA)5zkQ=nb2 z=0VsZV#B#$g7}nSBf!=SCH7~WmeY-43I;54kRn4@;8wXBKnQ&a1K2}QxL^>R2-+5~F$Dkw2Mm}D_hkUk;00|(Gp{s&?w5-1rLVCrZ7#b2{ zn9&nZkun(D5zwYfGdw~MwnhvogtFkUR4~qNke@OE607bs8@T&jk`g^<|-WG@MTh6FK*3ZjK7 z57zt@KKqG3%22DXbjcf3%CS9 z2Uvma)C@3S_>2LCI}aeG5iKV2!+5aeVDJhVy@jwFaC)K@Bqd1W10dZI3NeWQMO`!c z*@nd7a5pE;Pr>kzB?vMTj%b7_@07yEP$OHBe<>C*+d@U00-;CFL?BuXm!FUk{x5HKr}K}2mL{B;2f z=pX_|VG;gyyQ)7Zcd zfy%ytO>o#X(9VFK4R~dE8KA@*iZSGgeK0ZHh2iJvNFO~0dBlsmkd8!-hKVKLgd0tQ zpK<6+9nf7Yka-}Cr;;uJ100h_0uzX95*RFO#R)Sx0{0yU(fvuxS<0ggrEnW#*wjKW z4w8U}&Vk`h0We#!{aL`FfJ~{}_kr|YU$>)`cV0r>J=S2hpd$_g^n+eA<$%vTK@e=1_ zjeD%+#4`>YKyDtefQ`riEbys73R27`5>2iV*!m1?7`Qm_V%!O!;}tAONFSu5+k#Xe zFl5M=LPF=@8bpx%g#b7)xFy;Hx_BA{moI`$7+n;IpakTCD`$cTTd>JWRRuAj$pCBR z5;G~W8Yoaq5QTlHwhUbRjJOYCF_Blp002<_GCrNR2(+CEqY`~6fz3xfU_RMmDzwn# z;LJ4uEI2rDEQWU(N)0jm6}b^H=G4R^t>#8_ku68pKrr$N5+Fe;WbNo@pp+4Q)i6+s z=7Sx^f+|-VGRPP{2(qc!ZOsG@2#jCR3Elw{lO!}b(FLQ`;}|{;3oik!m4d%hVe3_} zbY>dtdN)Y6G_4d$Ofere+X5EPC0KvR701&+G{1w|UBw7^HW-SUph6X2gRZi=U@ioJ z0dEBzz9P54NC>EH63`ClcyyHwlnM~B_`QKi#CZcZaU)RW1(Bd7!sm4$c7lqDfJ=7~ zP-_qWh$d{~41GomDI4-NF#H7EFGs8ZB!l*#7#ImSajY4K!6H~5yrd0=jzJPS5MppY z326?;5*;8eg*WwK_*Ve4EIQKs*|gDuJN?-V;WMZPlOLyN!dE}ByHFTl-Amv<(0x!u zk`s@pYTN}f$n3euGY$0L06TjjLC8{jhIuwh%S5`RFD{F-bg$+~|BQ zbdybm2?=8Wutv}?=#C4z{n*?Bs31df0z(jdx`L2E6j_1cRv>6DXpjlcIzs>>uL*XO z!RDg(faqXwOcXkRL-inn2+CIc3~5Aedm1FM9>hls|0zHSQxrL(r42$KCMX}{F-Eg+$9{R#~2BG~KAV3TfyqU%9G-&u_$1zX(^*F=|z#FQ9}NfjVeBH*JmxMe7; zuBI90qsUT(&*74g?N5R=3Z#aJ-J63@sBoKD(C==-pbH6W*&yMd`@{@s1W;0tNDP9A zOW|1pIWbiR$vZ&RU3Mda_aI^lUqg}}hLedby_{TwiB8j*9)NK0LUur+NMgL9p3EnS zre8O~ygHaVF`U;iz=Qgr6sdz>SV)AZhXv++md-Hv+2W89dtm|!g7rUh;)t$L067O;qNo>lOX_!u%Tk2K*2=_?UooAdN68uoP#8- z`4}{b=D<2X5avyIoD+zh2X~;dNzfmJ0c}DTR%{N6En@;7-wYl50Vsmt{=~3s1ThN< z-~0yE79hLS5T0^mj8BpYeizDd!g;;GMPx)R^%!y!Fi>i23I1c715=zW;(71 zL=hJpMTkxksy3x`A<+PA7>FB$oXApeJ!7<=QiGThITC|GMoJ)$qrj6O%_1-ca4By# zf(THGhG8LAVi9l?v5+|dhYCNvpbb}0R}^@|El_F}h^~cAbOu_`Gx+y20>GpL9H<$M zF+66V6j&5umOzBOCMFTiPKCh~=n4?sdp`+|L}d&hi3L)?7zY0*H%fz`$xEWcQ|_XE zDpVC@V}#}iQ{fUgkoKVPVlfxJqvK>$Ok|r>xcZ+EF+5-`AK?DZk{V>vEJL$E@V~cBfPosc9c~Ae#3c0Mz_K0*xZr@s&ng7bz;GXAVYBLNCL0F`}#HA*Rb2pfGcbHoS1b1_^Gw)RQP1j++aNMi2$UJ#hs zGs51(K*kvWGk~Hnpan)34D0zKAt6;Zv{MP|z6OiDmnOr|HbU)jU^R*$97CtyWC9Qh z_^>1y5;v!!aJCGnu2OBJ#4bgt&Bmx+oQbdr$jQsN7%y1>#SGDrE&xHKHh}>BDC3yM zBbEZ%2z`i47imFul8^oXc^AP|2Z`uWP9cW8p}r&wF*uR*GoKWt$^Wl%=D?LQKsCc; z5{8F>p)F@a;sz!Q{+Ev)f>-&lK|Mr9M7Tb0g<+}*#`{c|^J?)7j8Qa#_0>R-6`_^^ z97V%sK@))9ppAEMT`tHQhs`YpiD(O`=t#2y=pLdTgl7>!nkJy$WKgLP5@+Z=He&dE zdW$x|QYNwhscOJ=lZfn|82R@CJ%$qU(<9nSYnq`?KY*Z93Bip><|61E__m|wK`@mO zFfpK)VS!V?08tJ4`!F0gq-tXFyUk1@{RTa=sQ_&=Adwo72nl#T5`<~s70Hu87{a(c zBu~(fBg3IgVyL7A)cOM!Z3j9ap0gSj6a6qMG7_|)|G{Te0FjSi>&Ajo=-^S|+caUg z{7n~zkAWh%>{JjFj((YdhX9&Op&vJhHWC1-B_NDf0rOf+R0S)!3PSII`z~SwIU?zT zH87xF89`2B1Y!h+7_O`h$BO98`RKg_D7*m#JtvUeEeiDfC8ZEHyAWvr_+>#XpuGrk zO!z;j@qZxbmHj*rvS0?LTbMAdGl=011hCIgy5{iHVHY*%3&f6gy0I2yk&rRMBCJ5E z7_4>#pwSd;Wr`3zF^ru93W*}L9V(eb$^u{})4+RTK#D}C{wqikzYT{t!(yqF5lmW8 zHiJ&GiGCHda3KPXupp@3Acjdkz@=%hXQCnA1^2%TJ857sG1Nv}WL{Yf3E%b?ITLIh zP$Wn}VpvS68e@1K8wV$aP)CI8J#cP4z@vP-xD@bw2KrMFqeJL&c#^X}6^LwD(-|;H z;$fmaVt61?%tXDcK6rk(p9{TIN*C&r2*E}2G4zj9ABzv;3Inn^v2-K>*N4Ug14hfi z6y%d|o+ku*-ZZ^W3w<4^LFd8oh#|79=5h2Dq6_X-hC^8-5CJqyLe5Hf9TN%_JRPL* z0}Sp3gi79HVo0AV5F!eM@4-aK3w{zV3c@Cg;6UODo>LSVl=d=s_;v(Ct!&85DY2+4 z2w`|UTZRs?NKh)#`FhBMT?$Ml2!eP7NCVUy1-T6XDHC-H6zGs77Fh|PMc%AHrwOvf zf)H&fAh0i>>mmwKg9?!$7$r&*V)zI_Ijm*pLm$2rvZ_+xgK%^T{(p*S5wD|Dbs?fK z&>BfTH1lGiuLH|K!GcosSTcgn5D~J5V3l4%st!tJLh;jx;wT(2zkKuqf<2x=gr45! zJ_HZqlkm8{WFCgdf<}POL1>e}!ey|zzA}(Q4N;S&QW~M$M`$*i3AN@7z&`UQ17|wH zMF+@y2Xyx`4-tRz`*`s6wEx^wGMKri^#A{YT1Xy!AgTQMhsyhZzo7R2``e#iPs+Uvb|iHtx%qeRGb(pE!x^fPJk=#4)filLsZ4d5Ty?oZbtO_uV5qI)sU?fl zQgF32nOeGBO{`E`ht#te>e)Q?T#@<)T%96SFKAJpCs&78cWgtX?F`Zm9;sMF+KH3O zWTahkl0-q;i^vrW@;)AUzldColMl+shvnp76y&2wqn4p@jHhv2q~VdN(J0l}C(}43 z*JxH~oRz7zP&L~)njJ#T&P>fNspiEN&24hcD+8ng)asLJ-EPsk zJE(P!s`eYA49T%!4&{N6@+gxsBGvr81^c6g@^p~$Y?7j&YRehg&w1J}McS`%?Kd** zzvbE!3hjT9&U>oL2ae8^P-i+*XGW@{Bh&dN*Fl3iKPGjME_IASRq>&!CQuh;Qb`q5 zjaI7G5LNptbJEoh*EP)2eTwUv)auUa)}1}7YoX+p(5w?^w%ci}TAF<~ z#bJo%_@2hm)pItdz2fM(hU>Xy>2WLcJX`g=hV;DO>+y8;eVz3E!u12P^!XL~!L9m! za&qX9{^IxgRAdltZV>5Xuq44CX1l@CAyQncLHv-xiuVR0UBkpI@+u$0WOLf;EW^|a z!!@mj8AFC^-y3G?8m$l4$@VeIO)xUbG%~`CHr5(>wis<5GTQpy2-h_ZV;JvnQYi{I zF3B=3tuQWcHBRU@4jDAw^WIphYf|ZCQWb7;Aj_nt!sJk^$&n$GU*DU^bm?_Y^!jjm zLl(WUf_}1H>pk9%ja1D3qjh$# ze0Ij9>7S#s|5BPWYCh+=&zzUt2CuT_j91KgTcPxKty+tG&cx`Pf0fK$$u!@an|&Oe zJr!;?oncoTR2?zbYG}tOtNmWbe$vDKbBz7hn2n;%eAL46xv!(4(DBuJ$MH(X zfI7z=499sbj_-yYb409vJ~&P$s=v3G`_Xrb$`=YDOQ`+a!sk3xt4e6`Uc z6D3QI$|8>HDvtUNPPUYz(azDj&(Y4tbX1&n(wt~6PFs0SCxlLh?RG{xoJ@{6&ARS1 z`?-^uinF<;v*jXZ>s8LSdPdBB&UWq24)>iMKRR>tT%288GI%bRk^a0@E-i9Z_Z==C z$6N+wjQJ{aTY6j;4!U@Mbm8f_`ntIKMY;xLyYjD_1RZk?x$Y`>?i%*dOsF?6!e!pJ zs7R$HtLDXITUX=rmL8k8YzHg;{=5|*=ZW;(5?$PqBHdQ6(n{Ik#;9~lsC7%f?k3)6 zW~QQdWYle)ihI_5O*ad7PiOa>NcX&K_sl}~jmO-rTiw5QyLS(|->p@}6z)#fjXuA3 zuRz@GmRv&@Zjp*<@ha~4dTvP}cV{KHyq$YN&XqjpZrEY9H(Oo0%D|cGQR(7Qwa8$l zo=HVEwttmJSf)qKK957~9!KtbWFJ%gb(Pam70+5r&tr=`FGqOZPV_vn!?Wp_=c((S z&6b$usOMRg`K_y*+FY2=IP*Iq=XYk$@7g#2;tu6Y*XLh(KL47E*A8X(?nSOWE?&Jm z^lz;4y0ycr|CraPul4FSue^B!~Yei7;YD%*Q}pZD8#?|1jT|M}?sSY_e+$oW^z7k)fuHWj&WcDCcB zi)mq|*Y^)v)7c9H3l~a-x_-{=FCUe@wlB15wV2(r@VhcQ`u@Tn9~ags@djzUVN2d8 zo|=juPc@0BKE-w2!6Vo6G;?&cUMN%inA%f%&yT6<=sQx2(7pF;-5e#FtB-z^4>i$8 zW4(`Ym61uk53AkB^np+7b04!DGDF|j!qvCi*LUGDt05tL^Odh{k#F}tU)6eF`zYkl zYshx>UaRn(i!Ea4E^@A7DmyQ7jauZEvnUB)(8D=3tlX;d%uVW z-%al7=T~I!pFKkQrX@zpU3ke#n1?D>9q(e}w`PUN|JW1|&sslXL7RMFA;A0clm9YdQil z9x%i&0@h*fHOhfmR)OW5z?`VSyc~m&%)rqdfe}@K1s#E#t!%fv2;7G8w@;A@UHQ+H zW*5ES6({j`7V-C1@XMoU!L9t=IRTOm{+^_{d$FMFO0)fSgDPEvwpG=W_9q3s$_%P` zKv%8^I`ly6aBt8rFM{f5oTEjvWctB%uEF)a>c^)v8*+jhtAbB<1b2_BoqiBJr3gNY zg|sH2brvD*4~&2D3ptk*a=s|!LXPdlB&L2x$mItiS9_hWVWHhtp-EZ3*Zo3WgrPTc zLN^tL_Sc8rnZoWq2)#EII;bxgu6McL5sdi?9wrGM7YULo1b<*UPpVX&J`g;c5-41G zcc_cUTo=EHVrHxGX*ly`6s!QVP>KFgryLjTo;(xKQ_f}ya{la{^{YQmi z(>Y-?RbgK{!oF9T&AuP@W6G;^2u1qQ|HFhSG@oH?aBK5$Gv{#qCEFT zG3^rJkLy@1i8$rMw>f8TTO7g4RZgvsu)h((&Ls`Jh?uJy>7*JM!n1Nd7vSO_IqxBI zTY|-BMsoK@dY+5)dKl^bDUxRp?7D|2Vh& z>BHsEJ}uvNOjV$?V$^!YbN>|ukt=AcR=h4=(Y1etJ#K4ve8u0^X7Bb3C-z&Gw=eKW zWK$;H-;6T<`LyDL(n=X^<%e?~AN^M@Xq{8$Y(>*u@X5gEP2$R5*RS|otT(en2yeaj zAL4zr7VOD38*E#-&Ybr>ck%yTuKdrMj}AC0*@(o$Y?T0!s#=U%o=9tlh}@vB(J4xj zi*$!X&#Rc)od!B;3A#23dI1Rrs}p{V+89mi8oN20xLMN=*iElOJ6jXndJ<;eG>P4( zHD?;C1I|6YM2iyVArad$AW?NmqWS7X+XffrD;L&*<<+%`_HNz|2OQb266dO|axyg7 zZn;YN24mbMXqMY5*8qcuk*nNRlieHaxd&EVu3I(#RdDHzRbf*G3tp{SsFtK%6|~oh z=3|qjiom z)mA4w(n@q&om8Tle86H=((06w)oBf@PburBuQuG)8=IeNknw2s+UeDy49j>dC5D!w z=i+mLk&>OKo)ew2FF7SSJ0-9%W%K@&kouIyb1D07q-=SWvP~^@yG`njfYf5Q6(xD8 zx3<%JvXn~=4a*u*cil|QplYt!t|@tyx>wD9S&sSc;$*2|+I{o1eF15s5ox{&X?dAx zf9*(9iPAmTkaqZH+N1kvzfPygZq7E}Pp-9Db1Y!ZbG`XjdL68UYgTlGH#|x>QL?6~ zVa?9=H5Yo<*bc8bW2jGV4>Nn8_?TvL*3eN$Ila{;z5OQU25)wlr#JL6xFb5fGcP@9 zdwOV9x;rw#3Qbgxr>_jqykw(t`BD0lN@LOU^q0NZH5+=!Co6eCP`9Dc;w-bKc2195 zMsIY+O&jX1&V;@^gDu1K{)P<3xs1Cu{A~jb?)MJ({T2{CXeb_b6YqAJ{iQ?4a*=p+ zRm8(ivqyR25x3d;^Q>1&fg$04JuZHtwsLVo($f;-(ud+_(T;zqtu3dm9dlcoKdk=2 zf9=cFYhSNcdtJ14CmtRuU2D*=_HQ+c=JmCkKT#%D>-;mlcA3h$VS{xa+}2G+ubVz# z_}S3%4ZiM6gZ)>vNV&}RTZzrq-gWYeN z{i#GthnQ9F<}CK>^;bWx=NLIV*=D=MxOxnFe{%|Rt=95y37)4O=#~=YzBAk7ME0Au zY%g`41+TLg+J^h19G_}CU-ukeUe1|_oElM1y0ueaii;CU_AAN>+7J?~&J3xJ4Gq*u z_6-xf&I#LKDLmmwq2`8J^Zy+c-n7ZR%E`ScjGB`y`d^;a#G>43f3+nkxiLF)zds83 zgwIMpkQ?`Ukx6In=4o2|Xp~Nvcj!_H|Qv5tbao+psz> zWoKU6iM;e%dE(c3)}y?wy)o+^Yi1g45HU7{^~7WcZphuqSeCG1!_EzN-Sjrb2+!SD zD>%Pl^WzQQY257B8<>+D{FU=hzg)0AMoDKZSFE>7rGT+tsdndrI`@L9D3_(9(QlrMMN((~ zN6YF@&?Mmn(oVI84F!$W1t(81TG|U9eH*MG7)aJgaBXHTdlues+H*J&a%}>BLU2yl0Y4y2yf_*8_RHb*5`F6djs`M+v zo30wAeOF8UUFv-8u`Nk?bGPkg#=OnFCp3?SZ@!tb@z&1GH+@Zqt2W<0zxnR@ob!X5 z@9ne;`>=V7$`&tGwITP+;akcMPFrjPwmeMPl9at=`_3&cm0SKezs0p@%b&OGulZ4) z1?tVR*859+tA3&P=q+r_eXEIx6+h6L|%s#kpn>w$v%xS@I9R(NkIiFItecrjPvSQoU>dw z73ZHbSI%Fs&OJkgk1rGAA<6htU7@d>SOGVp@a<|_s*`8m$eZ`6GH`ZG(3tuyfx%PSYb_4mc z*)u8yitS^Q+Zo1%7TiLs*h2q8tM=7}*Ys)0!|Jv*`plZbZ}4W(uEO2~mglWP_ITlv zs;o;NS8f(1C%5qCl6E-N%uTb{;lkfBkF@%H;tu!H9l1MgJw|ew^*iSG?O4DM@t)bC zs$Ap~ThMP^v}h#6)y03U+uS@}kv}OhpmafCqc^{%C^)u2{E!vWS0vzCM!4E6o+3tc2KPv6Cas8I4)A*|N9&O!TQW;%F3#@RiXf&(r z%jj?|`%h`<++^n4$gsbKPWm5Is$$FZtZ4`G%W9aan<~lF-yD ztGlbMPv>vWuE&dZq1atmmPThI<9upDSFGEGKFURsU3c5A%Y0Y=#MCRyO-nJ83poZZ zW4o?3M#znK_i%Uj&e#m?6W&POeQP{$d-3k?+wJ=K`nP9zmpgWADQB!}^IK=M@@_t9 zU_9&Fi`@~uwwpfg-mfGf+xiSKCHMJ~B|=H-D#=)$WTZy&N0-F)zV6d5tv^dc$xGJ! zH6}SCT&FQ3Q5f%OVC;E5qWL0r&#Ok`y)QIgrxuLY?9uNCd)u{VmmBTf$ew>@_LRKR zo;2PYLiIYDVEtjn{wZhg6yN7ljfG3{-m`K$<*LBXjimgDZ8J4{KiBU4*0^@g8(rnFHm9KhdTLx8Hr&U^JRWc7& zM%PxpMY%6ODLs>_9Bip{=(l6PshmsRSERqs*<+t;zxuqieeO*a+=Kf(FYNO=800uL zhdZ>dA=T15XwQkseJ-k1KCCLLOV#XsUVclf0uDwf)>qZK+VT%p4c;^fP78uTw!5E{ zC)%st&0}_tYG1ukC3sU6_NDl(^8O1|dEra>5gz*|8A?%0_eYbZ(Rur0HwH?p_Qze= zAD^~!>+pV;(fu1H_lsDp#$>ew)`5%M14&C6$!Q0cTCq}=rW6z%FgSQ%&4mLQzaN;A zZQl5B-<9zL<{uBJqiPR1slsJt7Fl<_M|BFn+BCL0CZT#mS@l=WhS7Gq;l66)82D!F z>P;R7`81EJ-s*V%qAlcz*T-$WMyt1_WfUYi%u=iQK&x3d2Cr<`waB_=N1Dz5d~1wz z9Vx<^F{c&VN`efUhS(R^*Gx{Dohqy;3oXR-^uUUHto+Z<9Q! z$-!V3|4NU84|7ef8W+_JldFDD-A}eTP-d)_c`&-gv!-n4!KSRRWY@7<K)hx-o( z-#-{!XcwqBXk4jv%vz&1rRt@1R8arg73%Dx#Ln}Zy+<#A^YGS6-lx3D%(-&m5?X4gt)AMml-nj;;uKD^B7@a>Yl z9ZL^)ZamzTM!&GpRJpFqHY(_qmF0Bf;epP>LH&p8A0NK@!s%#za#{`?>E?O zarBwX(Wx|*Pb@Rf9T`u<@#o1$|7Yv;*~9(c?MJ^HWPII7a&15Q{r96kz8t;Peb67t z7IKW3(ki7mAw6?>oVDz(n{29wf_WZRrSDhMNO^9s;9i!DbUTBrLD5*_{$s{fxlN`O z$M|blroG0j>a*+*QZV(RQY5W*^9{Rz`%$!u`g-Nz2|=|6YifJ^+KrJ=4tkI z3*)xG`Nf46c5Aa;>N*eFS-oB76mI|F2cmCiCNANxG0;|+`^ zMfHK6LHtAYhGTZG>*^yr>IWm}kHZsB_SR24s9#)Q7FKR7yiIGR9{<5O-tTgp>VJI6 zn&UBFSH;>@oK4eSdh+;@*5mPUS#EzEUtzbkb+UM+MuWFrLq6h5=1tFuEpJb(G_=ZVwVAUc@2?Lh_*Gt4 z)EXBXH8NYm3OyU2iL~zU8vQ3VEu$MtPO2ntY%D!QOOQ4WP6pbWsFcU)t~%eSmTjtg zvr$r@vL`N&R;%aCu-tn)CIf34n$#M*P*h>pv=8288`Pwew$ZL&|0DU_{cD4T>Ti3T z)IYv_(cxK3PBs)IhUA@G-D22SV6{+sa@%v?legVZ-9AYlJn2}x>Eg?iS!y0nKb#bt zAhoXll+|*z%x1|>(Sbohpo%nLEa^Y#K%JRnCdHtS;Z+|tsbNlqbnq}t5V&MbK zthoyxkI&5t`kC5}*fGyJ&+UAEU#;$5 znNK+%_#*DitCOK2LWkFDQpY{574l7^Z6R+o`1WrpZ!ezddtY?9ioWssnR=J{D&3Tr z;WNLSv-{`knQfC_&6_VczPCGTk+3j8PWgEIKw7}rPiqX82b$SBtMOKy^$~76l6m&j zzI^J-wKEszfBA#))h@R}SGltF?Ds<MtUt8sdw zAUDV4TQL1nYfVq?a4V=WqFu;tG4Wo4Z}I-Y@(XZ zGpm(WhpTg{xw@~)tkVPMx-GWZ)TUG3hGfAnE87z4+Uz&^Mi%Mex7*nBMHw5>6Z1`! z=l-J)Oq)mBws_2)duah{U;bj%b|?GxDsFr6o6VJN8Loe;t&MBnozTAW{OVmK$KAiH zAK%ckiWz}0+XLabesqqhYd>00PS(JR=)ibARWAG+c z$ej+s-yLC>?1WDigxjC{IPcuQ_j97=uUN9n#E(-Dt+`z2E@UPG8+AZul6A~&`Z4xHU zciduumEew338KO^I=(5|RDI=KW3TViY@e)X+LBI}HqL6z4a+h+U5h$XH?CtH?p*V? z#?r3Nl}Zj7Pde@9S!^8ZoO?^v5_m1m^I0M4MvU{$ie<0kR%Txkhw#rI;jGNlRXNqF z<{o~Yv!8t3&HL7lR*}?qz`E7?6Hn!{Gn>4rSbrsI<@*VZ{OAdOmR9ck2@NH~Y$tiE| z6SKxItlid>)%D!fyYz3tfEo%c_n=pIIpj4|b=%!qV;K@C*n&*TXPrIr(N*3Pa$r<2 zx3f!ABXfVX)~>!T3$l*Yke}p|dHCqs{5$2#Uv*($yP9^a)3B+_Sh_AIYV*5BZRxBF zmGdv0SzW&ONu|$H$J4$Sl*2C^*mU7i`2~jq7Y^My{o|tH;lEMF!?-^uX*Zn{SKPet zi#=zou-awp!conOwf3yrCH~KgxOM4HdtEQ~?vGetN0&`S)L$yva?oVLhI2gqqRnB= z;oOT(<)wL(7SHjEHx*e_bCV>F?fFB_7u{_4X3?88%Omt0e~BzRc`3u0p=Lkn*VuaT zbo#Gai8jrbsLtM_+)TVYgi zNj`{mrB@kSVEOLq(fgDxJYm__U0Qwa((7C8moKSZ(KNdnqT@7kiL<17kJ9Dt`GV#a z>FNXZ79@+b{QT(wSd-`Oclo1BZp_ zZfERXUf%HW@(S{mh1ujX-91CQGTxY9xqoMOq`Bir(3OX~!t)caIPuWuRm&=y65{_* zo87tApK;{zuB6VQE5AR{oE>T7iaG3WVJ$j&CE)y(rkoI zzFl*`;%bW1)z_cvUo5-2E!gtax1F!=td;G$>R+grx{(*(dO5h_>f8Bh2jBA+bX{%B zwt4qN^NzvcP=;>8$W@1uUH@pRvU>IZO%HTUaE(;G_Wk6UsZrL|T3Q+H+K0cBKi)A} zIMtNmd+n3`sZS>?r!^xzH(ayC4{fOp*;9BezUo@#^|iPA<94~t-hKR9eN57Wu4~HK z#-cmdP7PiA@$K4jJ(op#n~|wp`Luo9CT?7AQ*VcCI%Mr%Qhk-Dw%EHy55o}E)ArpW z`D@jLQWaW4mYihDH?EOuc-3phGr8C0ly`EQebrWz@}#1aXmYo%gXR%okU@Ys%`5Wj zYsYD)ZhZ&FHiOGNkEHItP2HtFg;y)e3!H;Iq-L*oR2s)`GdWUDzuay56wQ9uZ5DcP zdeQofmm0q)^_cHAws^|14DGQto!gt(GnCoWN!5Q?wUT+-yQZqA|76dli#;EgRBBB7 z9hCQEX4*b~*wYcQ$?}n zKJ6_Y>z$IH9-Qutn0F|6U&!Rk*}^04-)J|^Z6`-;ZVsJyCNDM zVey@a95esPUyug!S`Fwhi&k9hBB?M@U)?YN8g49)uQ-5F;}@R$fCB+ zwB%{%?D}L^i`;BR>CWAK-wkx1>v|@M%(l5$lsTw`^;-YhE~sv0_mY##ckk&*VUN+g z3cLDtXRsxA8GB4kbiVYRq;0hQc&=2be@4%~!hxP|)9-jZdS7UN?S`cN8U59}`(OLV zhgVq~yvzyjV0CU(4o=u(T{x@rcz@2t{v%KOhu`#DTKFH;x~=_SPX1+`+RNc}mjkAq z7C(s7Ikr14tJV1O3rc;csjxEmcsy?`)4IX5Vkqe*3`KAw|!M!}y9Lyj@M- z-A`V={aLEKI7{5o!Wr#}mcFV~%CqS0yZuowuqo1Xa_IJ%@3&hv+qCZXZu@S%%66MU zh1*Nzz1x<_=C_C5p$YFS9W?Fy?zef_Vw25xPB|^?I&GO8F@KA%M_~1xi<@V2FW%wb zjGZO*c2y#?j)_Wd+yC-yel=biSJk=mw7RM%KTtDr;t19yV3`DAD6^U zzr5>MfA_cV^VeVlO4I=>hwcumf%|teADD_lmJU4HJTRiA<#1p?rG8+b65Y`hJ;|_q zrgvbid_eQTz%SPES-KuN7ws-RABcKV^4Iq{W$50h*8FVqd(T7fz0A1x+RIXwl(=uQ zE1&{Zt+x5I_@2g$%H8nmY6tGACU?F)lJ)m@?A$E=yQlXGhTVhyzNcSjXs~MGzgoY& z5A|eN|Mu~nHFu}+LRt1y#{LBnOFwxPPjCJ$9slj{bNw%-%G;PaddY$D<>w33O*HbV zLSAQl)!H@KZNILs^KRR3-=EfAdGOnh)9Ej0McY3AhGq|HRk|tvd13L(;7pSZSKI{6 zoWU3rg><5By*Kj$cV(%M`3zkv-(VmfGLj6jv7TpDLkDx# zT{aS|OQAgC1m*((45j1o5 z@M~+nv{n6<#tj_rKnpt!w^{Qq$Fo?%U=-_~#v zLJ2h?^qPdwivdFKy+cA(q*qZyI(8{y9UWS*jY?BT#{vj;9lB*Kbi2~+O4V^jooDf% zbIxLmQ{O9Je} zoG?9FbX}MD_tNny|4)fVzl{cb6X$CTh!=;;71V7u8I)Mcd{nZ>U~2$NrAH^BNWIj7 z-FDLV$%;og1&?+FSl-pumo16id+^b&O$od2J-TY1H#vA^r|{A}A0A1}KWe5tjwtUG zpC!L*>-Lv_EZTHUrP8^=;jwFEfWx}*s_4fz+Amgbdi<)R`$gf!{f8glI{tWSzDzm) z?w7Y=hweSz@$Tr$8etVB(%}z}ch>p!5bEo{k+*b)-OIXvbkCU@^(TSP9ov~t?wda$ z)$gp-b!<4}yS>6s=B>x)Lj7Z_pHSt!+nd(9Wk1>H>6^sW`t|UWSkoY7Esax~d``}k z+4lQ8bS!DxsiD>SB<-Ni=>T)KmHO|A+|LQpm7V*~?1?=az;}raC8qyLFwKe$Zg-uU4E{IEqW?g!&GP=_5lPTfy znfr%-d-}0)^(;;A>b<9X^{8(<{Wi)QzDzXzI`H(y_or>@&-mS9zt2<(YdzeJ&&^&)&wE9BykI1VWJUy>5zkHhN4yBC zi@RG~Q7;rEJ>ficUDIv7W{4R5glX_AhmccSU)V@Gy*cwj<;sg2MANIb;mr@F-UpDj z<;#C~?(%WdRg*vBKOI(PSjH-M8g19As5Hu(zSpi}=sWW~z>EFT$>t@8DL;Qbn;_5@ zuVIMGau%)!e%|!*EdA`ChhP46{pGjAD$Fm}-d{MsQG)m(YRmTz!s&yvcj)uKX?Kmf9y@Y;MB-!GokmZ|A1$kNlt-=U zQYI48H?Gb0X&k;4DkCkyDcXB={+Cp3?iG&5Y0_v-g@=REsD`f1YtshyA!pgW`t-d< zaxtUl(?)yxn488_6mNtp{UBbhUFBcst9&GGt)2E6qLE65_qt@t6m{=$PK>IF{5H?W zr}sMtb&N_?3LW{gIeBnY;ln7etw1w|qBYvIyg^9S%qsHgvK-auRirU5>X>?dskyOZ zVq5a25yQ$bSq;50(b{}1Y4M+3#@m|@wN z!Huyw`i>(FMz`;b8Sj0VJ3hAL1+6tgCxhy9DbXqBGRJmQII7B9@{;OQq0+t2+c`IU zLvmA1V`NYA$4ujPRWrxkx;>XDYk$qj-1&N?dEmHaqr_oq+S6T< zdLcBc8x9>qYpgem+1#ht?p4wGYVoF(qLl6$So z`hCleXW3$oKgM0-xy~9B;v||Ye#b8Y2l8)E~d2Sve$V-H&dhD&^zu$RQ!wd&rK!8ZTIM^oJ+b>e%vV$=PB38 zqb8kZ_m~FB8YHjh#`jBFe`jn>`B-^|bNR&y*~B<@=(=6zj_R9)iN!>ejl_rw* zF=OS8sg?o{y)VA(t>mkJZy)cT`e4d`^40KqzV2#ind^^=|B6!0(5U=wmd&I(FkPaS zexJ$=jPu{Brj^yWl>Vw)bk`)~<%G^FGZ$XY=4-hzuU;p-D%kAw+qPH7_p}Bdcy*zd z_$}Crc*r!tweWHB%;H%9)UU`wE3pHNb7ey6k>?#R-U8*7ce#m8; zbOuy-ozX)!)e|jZD%a3-m zy)|Po^sS+%2R%C&-}Uh zZKn(C^jWVn_kGXa5Iq+Y*!VGYa^8!S^T+1wm@~9@>dbdKStjQlSN&LbH`>#rCFb3Q z&F|8#k!-iV+tKRqsZXL^)|*=Su4H_(pyu7x&8M%uco%7Y=*EwC^?5A!i%R<*Gxb9B zUQynUG+zsIaMn?$fUW!U{ksXZrFno5; zrMwT1jI9r1R6Q5oB zdMiKX|fPKI#)NX={QHzA95NC~or{HsjhPUn!UgPXJcuu0`0IWtl z1dj$@BNN0H&?*3ToJqhuZGZ}07@o&K8o^y))<4E`Ou zJbZX-GNA;T9_m-1{e`)^G2<0#$BQK2b1Ms}X#FLe%-FRU-ZZ`~~kJ z)cb)H=v9J`f|@M& z3Tpen-9RGlxEeVNwFBUOyX7tbzkHf19JqX%P#OmPf$k!q+ zg-0CNg#2fm?}!;Gh)r;>H1xiO_ZsrIh;zY2cr@W>LhnTXPw*8Wehws|cN;wK5sTvM zd;mQ(cr3tbz&!LiKnk^E@ZLr|0yY9)1{Z%e(YFfo#=z5He`s!~|BSpDu`)DE#KvIm;yI}4VAdDRUV?Zp zX5WL)6mc)KSBUcwGl1);Jwl!a{uQ|mI2*hZycha9@EPP0m>&idA*Nu)80NPj-VDzV zdHW9{dbQ1G^%R!ra_NKjwYFtnHYgg4i5dJ!UyVbA=}aPy+S? zYf#??eJkR2%-w_@HqLpEmc{coTEeIGFA zIO2Qo{D#_D6g)R=CHuQIh3Fv(S zngAjYhXS66JK%qVdMsu<0N(~%0$qroW6l!9GVrX&-CNKjkN6iL1N8)GefT;phkgX{ zvPB>C{pdS}zQe#YwB4v1Lf1nc30?rNmcXBrV0qL+(fb}gXWT~#^_7UlFrx?gN7Uu; z9L~_i;BP?e4}T|K*GJG=fC~7s;IBhmkJ<(F^k4=b@fq|S1P&m-jJv)AH$tbuBac}> zkWYfmfLqWL;N1vjfghrF40ww9m8b`SvvEc*dW(=(!LtEzJUpV%D-kC{yN=pCyd1=h zxR*QlE9!%Y%Q3qdekx*R#C_0zppOQR1K0+A|A4(vbHFqDVwM&Bhk#kshT%T~uLtTM zFe3x;Hu$@tn*h4dL%`cmdyCij4frIm3jJp=OA=tCM+$fYd<14tj{%p#vjns5A>V=- zGMIH1XGS77KwmIC2T;F>S`}*Li1QJfptb}&4`jm|2YmvXCAbz^GUl!TUZPe3XrlHI zyamq}fH`O2vqSw9^je(9z&TRrlSKUvYU!w*hTaI?jlO*7e=jyf1((%xuHF9`GUPr{PgX zyaTmFZ~*WX7({*sGdj^P29GQ9XMi7i%y5@@)ac0LQ0qs20zO&f9?Z~69k3Hv z7g!hPs3InzHVLf2_x5-2Nobv@vEXS#9*y`CVq@@rJaa5!66UYPJY9G<;G7xgKjW?k zkxxQj4sSC&R_I@c{4i>JkaLl1!2cIwH}vd6EQ{D1u|Hz=;@RLmfH~p`_!q!;aOO_T z(Zk%k@KZ3a46F_98sZ=D7-Gg+XrIu_N1hC?J!;d4m%wL(8Csa93w(rE0s33`FN1?n zZ$+OPV2M~0oTVJSoJF0DI&ai#4!0<|X3wJBBzA=Qu&nSp@EI8`{s9 zgTIX@tU^s6GuFVr37R)NQ^0O`!l3Pdrhr@#`EQHQ30?!#Vh$DTun6c!z?tytVwMnQ zwqSNVdg+MM!4%ABM9(nv7w{@V69N7}FAd+rhfxnf?RV7PV$LjLGU}elNtn?L&ke-q zpph3f_zi&@nE3!RG;yvWn2x$M^izm?01oEdKwb`?7Ura(_Yu7Jp>4)FZ=m0XM-6>% zQR@KzjXVP$bM)szYlANku_wI0A=UsF<30tL5sP{$G%?Ixf!GqSIT8I6s1-r4LEl@< z`V(_k0b5aPftH7QE%*WQYl!v1_mQW9T`;Q_nkoFH=a42JI^P_aKkNJzwI!2N5@-Zy58Ez`m&e1+NhFRK$+Zd3Jjl-9WUJ=}>7am*a!Qj)VNkF@en2Vk?=$g=k;Vp$O zg#0_=YRp;=PZm5MF{>KSS%kO_-f7ex048{rBzRSTTIAnRKaaW*=3GH<98e350&M^u z>_{TKUOX3Pqyv7?j$(EfdX?cZ#n`VMEwQk)?k(` z>LSR`B6ml=3~>T@2y=gdb{GBycH4v!7A4Vd{8aub}}3f~{_Tn1}{XW@B- z83%zT10jL4e@NUJ7KM^+}zK{N&z<$Ub(U*tX zEoi;Sr!nFgVlH?TY!BaWm`}%9IhZ?(cnn%U@}JO0$CW>Tiw5^&)>X_8gf|nla^z$1&Hz<7V;6inh;yNTf~NuZNCfu58;`rK zLoEn%Oi=#?bt2*_#ByM1%v}l3uh2IF7ct8My`Jc`Le57V0v3aB47Dcc$I)kpd~nf& zUIWY@K(39MCGd|R{{tRt^r<6GgwGYdOTn?ocf<1%@d5ON;j>Gp7x;M}bJszBezP*^|j608_ehu6T{EYfk#COpe2c*oEein=(? z;X^-^Q7aWB)XZQ<|zeijM z{JiLa_5^)i$Y*iCcz8bpU5JxV+l=$xA-92NHBborjrvhCyp*MjqqBe}@jzxZFu?E)0TpGM$tf!q`MJ$SCbYmC?l-Zz+=0PPce>VPU@UwF5I6TzxL zE}n52&M-p!1fI)?Z)4_h-F(Vr@%TRlRxo4p(p&ofM5qILw@eQ2*SzYF>a zU?+4U&aOu7Hu8UfQD~chV#J-8r-Ro;3wHVL&M~P4pZF z2Q1bw`xNG7pmrMAi~G!@CW2Y%(7(YG1J7qX=XT`f@KgYS&>kaa;5CSbrvSM>axL_& z!u&m$aRt63sLi7mfVmNv@hkG##Xi)3!m~Ln&PTinwGD`CQ6nHW!q1Ta=!3v9;5g)P z=;sk{#oRZ@2jKmP^KN7IGn^TX`d`S2@N7i93!arYy9Re0#aZhS|3FVD5RRA!Z#jC0 z;n#%L1EgYh1o#fP6`pqRXD}Oh3;#9DG=$#{o+XGYp<`b);V5E7#DBnZ7c~)_myA3a zdL3{d+7D>z(DINE1D1$)fbXEk1~dLb><>*DcS^?@fw*@Ma&fRM>Oy!XY2>4*1%VS5 z?}q2z4gQFH0sUf&&x5m^z=81RATEY3fp{6tX#h?FGN>1WDVUXp{>NZ9yylm{E8!o& zJZrEn`u_y9arb|a?}5Gq2t|=JG`IGey^8d1@SKL)-u7<*7W%{xXXBfA3Gtou<^piPZl0 z3e^*Fiz`%5HHx3^6+b&I{y+Ou&Eq647D`;Mk+}Nb`%~K)lAX?y-EopPIfVbaKXqEN zmn!wYmZwT#b1Jb->iLM&OM>+8ROvB}^aM{j2Fp{OC100GzipI$-z)ubT6(%sWQHL# z=Pa`@P5O6#>R*jA-~M}fD#?^e=2Jzo{ezdVjJ2OH;pQ8 zAu1IymE25~7I&#ONo_Au+F7f#n@IbqPib$hJ6O#>{#Vf9-4~&yJPiPk@Vt%Ra2P}n#PHLcdW+ax1TiL)M`jJYRqB5DxVfPvbbY4 zlBPLps=3QW^D0mCGoSLMNK?FQan0(%KHiq{ZwWeA&5|M#-h ziBX0OiAf_-WM@U_uFOszQ^8e1v6!h-_P>^`%Cc55SV2sZmMcp;fmNTixNOycBxBgY z!lu=(Lb`xQ`>%bgu38oeT2?Gk>k2K~W-a?{rOP}THmy35*e+%iY+4N=$O)X;w`OP? zcx+-V-7}l*T_NRD$M&nEW94e#Ec?Irtx}e?V{@5yM6vd=I_+O;g^Qe-%R98A$FyTf zI`J%>L|2_;5-G(@!!KKB1xplLS64OuXX|RV%9?(1W-vXArJGZ%PBfKsa?#C8&@Cv| zT~{Ice>bpZYZtNfN?i3y6ZACNMa;7GMvC-y*6HnLN&OV8wYY~hLCtK8UB=QZXX#gx z7Pqmw#_R7Z)<00Ef2c$M@R-~FuFjC1{x*ZPHy^a@`eGyFZ9E+85$3zleS8Ht)2 z$+#MICm0DbjUKfb-eMVGSF3NcQEZzLcC|hr8TVC)6qRfDkLihVSXP;m0~Ml!!N$WK z3J-&IA2nldE6cskcu%wOvu26sEY_h~rR!tHv>D?OS(7m{lZ#D~6CGlc9n{4&t}NL% zbtX;CChtkYAI3~FrcI`swXwrhz*#acE4komytu}7R(j8ai}5sG%Zsq$5h~Kx-r;P zIo|Z&<*w@+O()w;)jMT1=1duMGnTm-TT3UHPjNPr*REI5$uYyC*Mb@|!%nm9O?2aN zvn4|2KT%~HWEVHSazo7Z;>;I!y^b?&&zrCBFn6FEIhxBm32|Mtgs|&X;4DjWCZ zOpB$JG%SJ@KW~xOZV^o<=2z&(bV{6`UEB!U$FywY(G$n%*b6%qOi39RO|54iF0?eM zv|Kr0j_t65UWwJ?i>qPDRI6-rtK1yr1isb&II99Jju}CHZKc(EEroY{lug%Ey z9afvht+s?v3dfa-=!Pr#v|@AZl1`lzH*4&O-Ckm?SZ*y=YrRmjM8Ko$E|L3bzLc7+4ea@2QB+fH~Ym^vN^2G zV*A^P@+6Y+ojHp?I_>X`+drt6yH0ZO&SZ$u9QqR_X39(k+#H6qNW(b}IrX$hod$!Q zmhu%2PdbI4R?3YRlOqy|D(w!>>m__EBo}wh3QJu)SWY<}`Yjp>8zm{tc$8~j$ zY1NGeT_ZDv1UwOwP|6AyvgnxMw>q6A^Gr^InJL@Nyv1>4KVflE?N%wPt!_3AZUMc< zqvzf11xbKW3>}=EY&b9V& zH@KLk{5@F2civi$=j7Mr9w4V27^)bQ%jU5~c2TwXThWa@k8(ry3-QBsB9t;;%cCBq>t`*kXMu*S{y&h5H9j@4n=(EU&d>U7==~VS?6!BCmA~s_U!NYWS25 z^NfuRuA4$BQoS0DBVI;LI@pf;u8wNlWqsM)>tLeT1FH7}zWqyvcL`aqR@So3%{!QB z@x0IREZ;kvtg+2qOk7J^B+L9!oz3>|9-v*@k#6ws}9wS&+-uz`W$HRdBnHJR^4+PaV*w7O7?A_ z{%5Q1pj>6J?>bkrhPq{Jg zR~1TW<9hqa`G+w4Gr9g&yfIZZJ)<%a@%z!s$0fSwp8T^2SMwMYX$1M*-nUAX6ovH$!v;-)(2hjQgwr0A& zNTTkKqwY@7cu5Y-;@Z>@6w+ydn`z?0@lIn647!~Db8eu3LFO{mC$>6HvaP1rM#25G z`(1>yd}(a)ov#eMQzyT+KCrAM@cm#Qw>dyiXd~d+Z=gBuh-0{q1Ri6XMRlnUO%Qx$ z0zV{4OgG2{n+LWf+W(U4Juak6cTrv3{VS)E?i_SKH0X1Z(25Fo!L+i@fU-s~?YI-B%6r%;XuB$Q6P2_n@nw7wl4cf10M9W-?FF-`QX3XIXAX4Swq^-xxwsG{s=EvG=xGW(sYs)<#U5)d5U_;)^>c3 zRHoPcs$c_wXY2XklWoE8$^u1)f}gMhMJ9Yr@(3Pt!CM-PdaJmcR=MpBHm1U?=>~5z zix6&Dh-Das!ZcJabFoSe_A(bImxPqJ>Dn9%k((yk4Vix>(Cu6Gghv^5A(S{i{Y9en zp}vrr5CcbfStsEj(MD0{WJ*UL%SAiX%_H>B^F)U^Yma00!67z83ujY~=EE)<&4k6> z#eX%CeY#oSM%6CVg(lRQx;B#hhE)1dndNme*AS1eu;j1^VdG;B?4|O7%d{P?&w58%u$Fg+z2#a& z3#4Ke!s1$`Z|5=|)bjSqhFey7?IVgsb3+@GT-BYa2^QhLuHo%%)=xtyE+OHwiQ)2Q zl+A44RBcI{!tm>zgXa zR+X$kAmUr%r4quePNw@5Qmol*tYBVWv5Zo;Os{3x{q|)Yan|Df%UI*fj)n<~xtshE zM{2594FqWqgEe$!#S>&*mZbh-s<*P{| z`OGB(#4xUTalX z>s?-6=X%3}aCmNcn?+>1JpU>y>QNbua*WZLY^YXj)pg9fjThBW!v0)kVLtEOJHhG6 zi@GJyl9uD&UeMajFuEhm87EmVTB0U8qIQv`*!@xW^P+APy6v`?*W#vjZ*JF}jVc{d~fBsb_1c;)Hz6WU@9XQ~0-} zn4lK7SFJ{`<$Y_Mt!kPnR^>WbRWWZZ^xrMG3d(f0SBgwbsLgh2za0sBKNK@KVwW-! zlQ3YuciviKGKV zeWu0c=&be3kj(rs;ul_QV1j)N*~vFP)~Y@F%OsH~=Pp~b?9XGWZ&@N=^I})j$0n8e zea|9&X=eT19ZT(vO|kH@98vtiS>~Idy-px2n9;jh<~!JI`ePxMpb$s$luOES(KTNl z+^!ziVwF~_=x81n;u06mixc_0G(9V>Hfy<mFI}351E|OG*__Lv+K?7p>X1dAD_&RrG+OP#ZML3*n zy30()(mg(y7cZSezSkz45g)G@uA^kBQe6>Gs*QI#7O5&C{wvEh*ZZ*Dq|JLqL@!A1=>ThfJ6 z;&oCu^)7ELQa*X4B!}y#?NEZini*ywB@?%T#&cF8UE%|d*hNJRbx(*gx9yC`*ClSfrjphZ>cNMhvn zDhm~X-O-x3a*~8?oZfsLX??g_7f)wHwbbu5%TINQZyZh?u3IiJxAEsuHm6wBE~Ks{ zuQ7`EyU;S1Ms!j$W{{yGR)M1}ROd zk@8FCvdCbeauIV*k=8M0n&~leSQgWuN~cMf?QNEZKg_#=DUQi$`>M6gs8$v|to`{m zUlzO$SX$n6rYOg`9uy(URGZC`_0;n`t~fIf^>EU(6<<}w|1z9*M2OPETmF13O_7{@ zJCx!5*&jRJU(5;%2xbOF3bIU!Q?K#RoVh50bJ2rz)KkZX>}H=VYQ&}IZPT%vka*o` z@^hzE0BL3IXTKdIF<~izbyJMX0#kCAj!C$ryT!`M1?99}|Awh(t7(>+OQdhe%FwL_ z*){UVhb_X$q)!Wm*c1QUJ?`WM)|pYgyw1ql+LakJhuS{1lX1*Jf!jVU8{r1Wl`Ns_ zblH`b52 zcth{Dl>(}X%)m-T51F$fKGZy6qXzYJ3Srw_m9`X@hDF!Nm(o)kw*|84tCo(2J6RH& zEh7#VsRvD|Y!#BsAWEKBkmQC_S}cWH^J6dQ_zk$~nUnc8ZV4BA#8oSeZAw;Mwv^ab zLccO>{b|s~B3Uvh*kGuGf4eWruW=PW#6^&4cV>#aWk&K^xc2pMb~IJzZkN8ntly0k zO*#C@jknzzY5juPD#(nPJd_?>}H8wsnQ zm>aj0ndpX7j>xY*79W{?o-`$#(7$cjz_4@|F}Wt*`qVI|%2iX`ef7=o)qZaGmzvtxf7+nHLMP>6n=VK=6$e~FrWQQLF7V(&+}~_q9OW5 zS)9X7j>03WUn(e#Y*Vf+v>&}7>0+Ah(`Y-^BT+=9T9{_^gi@|sMo)x0M>p^$MWUvr zBn2E}X}KtK24{JG#%s^CmkCJ+Dl-(1t$eGaA52~PcddkMoY)OE>yEjXSCXY&YsODG zBGs}6Q-cl-Io6MAmF8OXRO-}rj>^jmX>FZxd!J8y$5205D!Dl^{PuDB4U3pr9zC>E zMr|IuFJe9%_c-P3w|dOs+gQf;WYteP%TBtAOjmPLdpT15$tI0zEnFK*zNwMinv6c? zP_f2DHMNTnBj{cAPblN!1!)DHq#y;`KN|JRLlh;amRXfa6*VhGQw{0{lEx?EM#p47 zrZB=LydRmSKV+||;jlh*E2KADUHGOslg&&j)!h6UgI|>^q?=@ zyLye$w$#}~+gt+YbhEKeo`ZBS#m05XU&Fd>V`AH?$+I;p6WR&4IBBAv#ZwzswUnE$ zAiDUptT{9<`Z15bML@OtwR_EXPv!oJHG&$;H*#`+Pp_$OUx{~-eKSiIlsOZ1GvkU0 zJ+8FLPS3Ow)@BQXUnl%VnuC7kW+o3f8p?`WxMX(M@Prku#gj8dw+nTZg^TGLT+0+E zr!1vaWNyhPh)39N3#RO?jqL0oY@j82=G(jorbt-HOKy+kYfCAz1L*kcNZB-9qT@nK z=J3EWt17zGK5c2maFyUh8C}Ek6P^@4bT%#Qs{Psm38kpIaVR%6G%$&1#w`&%l?pGcG(Bt)_D2*{Z=|&MutLk-R=CLQB?uM%X&y2!F$Uj9C}e(aE6jB8B`On-4V(XSX_6 z&50YGAnxrkXga3-I8oa8L|Aj8k4r^rMhJyu>8LrHZPnzSRAJ*UkhndU?IM)(CrgnN zN*NBNn2II_xl6xnp)WNT`VeZ~JDYr{KXL_Ky{ssFs4Cp7!2ZqgP;*iDrA#*p!C5zf z;Zi4qzx?19SX!KrJbs?JQ^+fpDttUHN8r4yBP+*R)Js(^Y$D#{&$67bLQlbTw(UNy zRH%VLLq=kf+&hMkHJ@@WInwT;TW1a1exK{={+!AMWeXN>L`ctyroQD@XXarow!$&L zLZPUL(pcLsB+-SS?_r6N&Du}eg{PQFj=GBCE(E6&x`iCAo6gw}bJ**(XTYWsvom)bGS86QMM*YOhRTT~8yT}S9r3%`(+C$-Psixbj{f{N;FCR=YriFiG zSjX3kzsdIXoUnJlC~95Azsr=;3(NJ~7j&Y^FPBGYtg}9o6v?4Scq!^nY|GugutZLX zH`%5o=i=b#7PqQRG?pO|8f@gF>l)vX8`_#X-klpz;1l>Im&!4k5YDq~P?DZckf-Ij z?{oZx?l2QfX-~Ea-sfA*7jFwc$k`R7`wX~&MN8U0|Kn)H(;l~cii#;AXKsS2f ztP%byIWspeGFee1T-3deSewO(8BJX(s<7;YLCytAw1`i?n^UTSljIytFfBpp({v4{ z{C>gs+@KgQE4I{wzVrA>)eE|+7t-u{QnnsvDRgG^G_e&uBsbUQbur_%jifENa-oeV z@yb1-F7l6;Q>%Mf(W25;T=D88SB|BhBE#v!=P%^TD8#r(oFJl z1>x1h!mF6}rROD1&E#eD**tO8iPLp`GMu!dOIy~Io>3IFZ^}1blyk3)mayGxd`4J6 z)qYl;ow!}=YMj3^nZ{1B`kNIYAMDc1vuv$YNQ%hUVaYvoah!BkHcZVI%gRp)qiiY6 z-+5fyg0$vNQE+#;(%76EK0d_Wa>;>lhs>n#&aL#<&H3~a)%1Oy(_O+_`ocai0#-+? z*;r3+=V(V9R~;Krw$fU%VMbM!lyBD0sw#~Nizk&(=}RZ_queOB2Y8yn%l9V}-qID# z$ac49qN)L*xkJd|Yw?}cN(sH~U zHe|(U$Xb>1DO0Sd^E2U3or5L$6h2qfZqPu`sFeFfGcUEEAkEnO%`D&p=bQTA>OU=r89P+0pxYi+uG;LnwbTjy6co38CXA0v8!f7r}o zGbuG~R5ZkO?alK-`(}7|iXK?(<<5>p4{u%DF`y+LLOuCa>PWcJKKPma+*(R2Bsvw)U#jT1KyYC1xOJtz-UCD)(Y&gq?vel!)_Q`1-6-(YZ+gPv-i5HhwhEm)|)(zx^@w-D^ z^JCiDOvU1(Sxw9R7uF>a*6XKgSrw|+?z8iKsB~mO>qKL~4sNV&t7LyB;Y|m_5PSP3 zg`dWyE)SzPxmfI-=6s*=KH9Lvr&>|aCc>4aK0PMhC~vd3E$*O2zQ}Rq!&>Wi&IF#e zSYMK>v8CaQ;fTd5!*H#4v&!Dp1v$qZZQSE)=%&Xm>O7kBY|Jt|{z^6}jFQutRLgfN z9aVRZpgzr6@0XaN$0PfjnhcbzKdBo(+OYmNMRoVAytG`dATIhuikTqJ$Nm_5*QmxV z582cE#MclBOy^ZPUP9-(IJca0YDOrJn9-}{o$@-21)P9Y+2k|Zb=886f2k3Bn4lTj zsqtkvw~$=Oj?zGxaZ!eQxW5GQjti^oTiV@W8Y~xBxgft?h>O2+ zjXUnyWJTI=LV1^hhk&u6E79aPE#ZLvrSYD$EpaQ(7pS(p5>am^>GL<-tI673z?&N; z$ji!Nt#D292CLi+mmVfqlKmQ5q~EgboL$T-i3I`GIhPfe(D*dXnl-}>-V?3~Pir^) zf*&ZZ%Fo=Cy@qs_cZJ@KHyk4QFH9>IiApYWHF+?wbgoFQY@F2RqPBfBMP=Gzb&l9k zD^8_FV3v09&*#%ZL)a}LL07Geo^U*aGTF(~Vt>fyUbCX}^2}7{BBR>4=N@jz8IWVp zRE)X;2gA~cYn+sKT5Akm_>$SZy+`|mM$;o#5;xHx@ zeOp8dnNnozx|Y&BbfkzHxQ zQ})J>W*hGg2zk10OdzcEsNh);-QTdR^PFYUx8YN!ZQMn)qPtYoLMUOx0Hp=(`xX+f zCyBQ+Qztx><@t1T<`OfOWMmQlyL|l4F|qU03a)v+w|Vj%?Wx{wG5yD_&xLP%)1@&~ zDU~Fw7&a%r*E2bz#>BtWbC)F{EJWF;Yx-ts;{(#nF;W3K+s zxF(_0vX|%P-V=o1Uiw2fV5C2AoMpJY#rLkF@4W)iYttJhv)r}~`WT0q?JK4~x0JlE zsBBYB>Z*|mpeehLI}sK(X39I=(y^TORCeN6oFHzh$r9Z&;&ijZ$?N~H_2zL+9^3!; zlaLh>%nAVlBoH=POb8&GH3oJpzBUlw3^CW~00zCh@i z3EA2gX6!?}vzhBwQgg=J+3C-TOd7Ea^VR=&1%eM&KBognlwCWv+Lm5%IfY`Pot!NS zMGT%GEiuTFx2w@H=xCAO-%fV*)QBuy>5v)Bb8_)czol9a!w61}+eEBu0d z1%~b1h@!@O{DH{Uyf-}?M|2;{OXq|2n4>2(PW z@y9}KqenNhp9T`=RSkYQryZhJ4lMe;E!IOqrBHVFIrDzebALUtGHgl-Kejbgzw&Vs z@p{rOnF$wr3O_5^9j12Nt{`?4e*OJPk7(zpV29|p^@L}C1U}!o`+a60vCwVy5ZPNr zJk#Rr*vrWNgL}VW_p3^>-&U)MX>Lh^-M&4_6Q-?)!FF2PnS2l?@F}V5jNsKibXZT_yA>Yql88~d={_a79%T?K$K3u!E!(b;m z^I7t`gK3ch=d4o7mV1`Jgo^ZitN8boH^eBfIpXt-ubL?9+T)3lE-TiCTIllPtDHm; zyfkY+H#5t)|D@Ufk0Jh>vj`Jnw+C~`q@EYxTM(1#y@&mtp4#9N@_t>KOY-F;`-MR& zDUZ0)WP=;mN9yq`;3`_i$QW6SaGm@_l;lukI0v#(LBhK3kVI?%|)>GsR=& zifqtV_pD_oY@M)ec;4RrJtM7b+~hiWyzsXQajtUbUwCHAmZip9Zx819KHD>9ksN^` z{QV*)7Z)XvQLK}2P5Sg3Eg1u{EoE_@;hjs6p$(2`si1)6OY=B%mln+r7%WAjPp&~_ z;~1I26`3CMVM}7*!{q?u-k|O3_H}V%8wS$NG;#4Vx>DJ1by`Q^_c;E?LF2fEd%0w} ziNB9Ui}jI@;oB@7OG0VfdG3I7oZOe6GPUMRg}h&Z!E~FfP9#3+^zQRxVzV_jTB(0r zb_tkI&%t{9K*_1h*Z7nNkr``87y;8oZZ)l6i5Kz*Gm@uHVeK!A6G6Boc}Ple{oak&;l^je|m>Z*-Gx={>5nzrZ=_lqOzG$>RFnyrjWCb2ceJ zDNb4Y&21-oQ-1VwU3jns4gW@X;6QWCoOL38$Uu`G%INx(3)3=&w*zs8mDL-%5lKF( zTlT#HLoQx*>Lj7%efp1~PUhLxi<rxA?D$OK(Zby^a#HvrQQ%N4LpdZ&STh!_0iS?v? zawWynTTicuD^joMvi54Aju=8J2Ev7Xkt{#0qaifqvg8bN-6Kh?G~G@t+IV4HQ96PD zIW+9}Owyr`kssm-ytV8PNQK0q5X#Uzz*TjyjxgGMYN0;Vwe? zK}J(&@;1ad)t7U`nN^^OYeMAq-}46A1z#()Hk-3+DZc)Hz`+(`J#T44R@D7I$DbK# z|KUZ-3fDDWW<=3KEC?%`)acYxq>Q}Ht+ImMoW+*Kq9&vu;DLhWPu=r%zjPxXrd^;~4V$I47#Nc|h*8iLIh>i2z*5Y1B7vx{kN>2w~&Us+LtO^_ox<0PTHy3ar5aegTq>1U;N+qnToM3ALe{- zCE^*+waEh8fZ+v{Ow{36Avn|#Vk$eZMyLi8g&W;Y-H>+2!r?6gP ztiCyvxXhXNJEO$Xg%+v;hv@gyH>08 z+Au*G7ONg>wpF!QAn(~0{pdF(VaM-BcXhGW_ZO~N8(mK)xZO1;aAhx@{YvH{CZD0u z9X9i7f5$2XddPR_d+PW1)|cmn1Ud%D#3e5#82^hevFM_q4iF*-U3?}t9m*`piDMDO ziFwaME6~jE#Quj8<$;d0J0x6lpTs+mpTm(+yS>*xwHZlV8==_Mj;uUdPPovt z+sT`r&ChC@R-c{@bY<}TOSk@!wfex6yon>nZ7E@k3RLufq5e#(`DKPAqCz3jPAhiK zIb!tKr86F`gjGMc*zM@qx!_}?Z73PmFc)`h(p(WFH3-UGihr^&g&Uc_YmcOr;@Vzl{Ld)yM+GrX z6L$S#Nn4XiUSCPW$A}&Gun!U(7i(1nD(&ZWq0jVIscUWew~F)cmRKaX-<^oq^U*AE z+G4xT;+x45@kepcw9gL*QZP=de%?ZbUpU+{NV#jmOwi#4$;8iybXLFJ6N{z zj&gCwG~Q83Co-&vbL%R+qxW3d%yW1a`?QEqGN%?kWL7z(7Z$1GEF}dFU%%m#B%)G7 z!fOA^ZsM7s5Jjn$Yics)P=CVipBMS0wMi|{#4_2ACXRSw%39#Ifrqf(Qzp z6|y!c%ThFuB5-z|Q@e~K?&nV`UeD9LSe~ZI#N(F(_Vy8z2GiZ^cjMqL*@>)kX*>M7 zNx9xuZM|E4?>XSd>~ix(f96ZQv^5eFW~UC1r3Ph(ie^p#$ycJJ%u@)ATL+Chhl_aL+ ztM4*h-yVoQrVNejq+Ey(`JZE1|Fc3^bOuTs=Ot@1FgZu5o-saU%&W{M{r3L6?Q?Og zb_(&w))Z;6^dDE&*?%N@rVFG^5!W>#YiROsgej%Em2-(@&l%PQlNqyAn=LX$RFjwa zY0S^}vfgn^qq~BSWw_O4l>OD>g`EufEKd5ag!vG03!BX}DZsh*o6T-!iG^r+V&K>m z=fMdDXA-wH(?MJBlcTraanVHH%!(BjJGEj5XI%W!f;7~O@K4(U=Qmh&lzqU)MHVmW z%6BV8d&xTEb5A0d-uaPz*-dvT*BRt*=Y9U0?_k@8tr*NdM_n6sU|Up2;V(WGM+eeV z6uEZoG^E{;uM|=Xxay|NM+tV_YIl67U5W!krC$tzJpcCxm#6A-nI%i3nB(LxSEmm7 zVYV2rFcA~sveR%Bk%+hw@ff6*Oh7~cJAeyd2|xlUB@-|kO+o9GQHur=K0rp!Mv}-B zWC8dnKt7p+aYqxW$Yv0_5#hF(Hjr-mqDer=Kz9M?fW?3;fF9rjxC-zE`~k25WC9+L ztB~hVkq7WXKrX-x+HnmF?dTy7A>PI(6oOO%(w0RaH7BbPrBT`rdRP+<~lul2kGu(LuTu@+yI@155y31J(lq0Mh^~ zz)HYlKsX>3kPTP{5CFyimSln_gp)(O3Gxi8D*@3~fRxNZFhKF-S;QK6Iq(jEc)$;U z2Ebdg2)P8vfpSM6eIGCc_#eO?kO;T|I0s+>k^x-6cYq>53t5A>LwDG~F9lQsNPuNz zK89cdugcFjAULR<3g`qRKrRCi3MeKkks2s{8uwPqITrE(uomRk0W=uh7RZYQ90zO%*{_hl z5BQ@1r+s|rHw9sW-e$|2ShX6EyNoW@lWR1WZ0Fi*pfC7LsV1hh{?EaEZ zLB@gZ09hWihXD~F z^8(pbpg#k}1HA?`1IpJzzR{phK|K$FE`fT$o4{bRV8-*I&i|W6tw8?`dDo%rUw}1$ z4v-l=gEhRsJjmmN>fe#>W8z0Iw5025nS; z7jp$q%K0){bjUXvnHKtb0carT5uq*BAUBS3C&-L$B$+(=Wt7iA`WPSwbWTG38z9dg zM%y@cJy0hF(gVN;!1$_wnt`5iV%`NH0HZ;<06d@_t02$#*73P=WD&HfY0Q=>Y!RRKmw}Cpf(CT7gX&a9RR$B9R9`Y+ynk6cw{NaeE_`xG3b8-%{=Y- z3995E-B9i}$TvWD+JU}>bQ;jL&;{e`Gf~Xj(2n4lc z;G_PZbZwF_72)eNw^o^@c7eMbR_%bayke>~>15-YkoCmgV zv`iMP6%?>m0yupZl4Tu022=9~->5_Q)&V4>$$E?kp2h#m(rOKA&Ys3mP=H5eA*PTv zdK@{R_||$@)F{X+&>aDC1|6YwW-5CcA(1D$8VyCr~-5AT};Q-TFDe^}Kh$V?_ZQu-x$BU5UU-vyl~^u1G&r6GYAj%XI}eD@Xi5 z_dL+Y@R%9MV?j9^kX`ajhJ}&>>_2z8UhKd$PcmlEFP2_X`x59h5J#`8Lpa82l`d?}T^00PPruI>Ujc zLb)gad^P^X{j1=a&Y<@mWJ-W>%Cv#(cc?=TJQna0>SBOd41iu0U=Q@&Xg5Z0gNHib z!BS}p@&-s7mzjG|HwoHhY|noIyFBN>gN;I6Rj`bTz}ES&9PptJtS<&6f;y<6R|n;c z-gg$T3hFU|{4(HeV4C>@HSW-+0W(n6I1ZZtxd0`g3gkaR8+kw{!SDYKI**_}G$lUr;2?f@GNirDX|G=P)JE{UGnzB!i%z^5Eq5gyLQtyLm2(-!Q zSbRYC1L(X2*;T*=kj+531MuQVo|7<{{%`3q?mun-kAwVHXuEO8X58VehVn+k2!`e? zfxKHV7RIG&6kyz?8DF;12jbuv#ywIZ=pBHx(a=SpEASkJu@fOEYYhRs5Ud@Z#?1!h z%MR7T6D|w@{s}bp7pUNG;15B;G0>v`zYO@DfFS79H}D3zAj5+0FjQC!`bN*10}p)< zd4EEguK>S-{yvb4VGKWmeh;L-0Zc$g&O^txLs@5QxN4!7Oi|W`9`DM3@8IE_Ef`oalSZb z<7Da19Yvl-8~0!Su%XWb!}JFO{l6uBB{a#n7d7sk>H%j#zZPUpz-K@={s5yjE(Uu5 z24g~Z8QqD|Z5XW}4)7Dmr(l|W2R$?HydtgD z7O-7)h3%Gc$6$QLM&FeDWhd~{z7E({vcP}nkV4PqFLQ7;RFVc2Zv**#crXsSYzg|t zW$zb23eLK z;>#agbz=ZNRcJ zTMRgM(WC*-DdY4Ic#Y~FqN`k2V(QM(OU%5w=Oj43KYZ-s^l?n^Zire>@|jSFAWP+% zQnP^BPLoB;KB@JKXoyxsk&whvstv1NPnC;wWwc2CSQ*_mSlNSBNV)wCm8!LbnWU~S zH(8M*Mq93WJ=t$*p=lMd*i{0YRaSF84wv2DI$*6XGKAWMchBS4`9lJ{?dFI1czW?n ze!1O_Nqw08?$50z93sX+jw+?ZRdFljIj!BaX@)qdFVapxp}g|%;`s!Hq`c~xpa}0 z#X?*dW%Z|bW_$yqrPrlrl276lQZhmW1MJdTyX{ZvYrSvw^+XBJ=(|R2Z|mkrK4Zb9 z6+Y^y3_W#B5h7!`VHEp(m#3EQ!yVzEv^I~_hEhLrM}D7g>66Y|F0{s;=%pOO^sS|n zBWkg~dzG-%>}iY0ocIi*j`8U}S{j4*ZhzlPe|4@mCg|VSgj%6WemKL49_D6WG?a#32rR&+TShKcv*?sBcuoO9LDUDqcEsN zF={kUrd73TWP7qOj^W|~9-|UxDK1flTCQKHFu%&_8gpjXD?90& z+{``?-G>pkQ>yS*q$!zPt)bMw&n0SWaJ&RexCeGt5yw2pIWitrm?}xa0c9EA0983>-=DD~C=wJd;*tDjeg5BBXmowb5Bs zQ&Dzd$uwlK^S-+`qen&x2}A>>0Pn$@oWVry)@edwlAg5E=|R6v;bUtWM-Rltbz<%? zNq{RiyjuIx^`kB?zb6( z!MV0f8_VSL%Q1GxeX!x)nK?6!?g7<`67168aw7dXhg0pXkU2FmbVCdhFM}i+V$zm5 z4btMtwEiHAhg}~&fs6gQdCmM(2OgYIy`H8OQY6_e|ajIiLl-^+vGN*G2Epp zDK@iyjC)v$R=0(k9!ys64P4xjG0Z-Y9~VVURwT-@4pvW=c;#{?7r8y0o4cJ=8O>FS zkC%=2u(#QA?=mc`=IZb(QVo5~1of#N!;mp85Mh4s3(!&y&q2Yt^IB)zbY_J zrBYf|?@TO>VzrC+T;|4EP=lySjE~KUJM_uihOQz>)xM&NnhbLl)I`w=! zQ=yxb(vJ8@td|weZH|%s!*D9bvrk?Wm2)vS?=S{;%c2~6*|trZ`{LnMGe^;rfpN2L za(xBu!n5ZZ`-+(vynji1r+ke&`yw{MWOhKAaI{!&*p#Zyk6MD6l~iG`F=2VYbd3ID z{ZASEu=~b`CmsgLEpfj`o2yd8Aa#uF>Udt1hzITZ)szJ_?eUEteoy0SqkLm*A{mt-PF>ebFua3&gILu(gcAFtYdU*Z z{ad5BY4+1LN+i>IQipc#VrQ`J2CZmVv~>}3gNyx>dpF?m6P?1kxKY#}lsi$zmhQp! zwZ(W~t2lSa2~6F9S19i^+dNK@`Hk?J^h24A|H3;Ack_9ESi-5kcON8_<1ieKjwT^emajelu z78SD;S#??Qf<(zml?*v>HS=McFQsg?-SUVPC)(mlXggqjM8mC3UC5iR+frPWt`WEn zWi>Hhsiq>CzbyOEo8qQW^>d!&t7BC$*`=Zce37Ll?z{dpy?N}mN)f7O=U`vxWIeTp zYyRZk7>!-#@*D5$z60UJh$)j$mDkL?lpR?%t`tYrhOKtT{*|yg$*1*PVnztonl%pzTr|zc>37nZPh@Ac-7^hM)QeX5GmUtKpWLmI{Ao%b4 z_;QmxLXU3Qz$i5$!H_9g;re+PGr+vCYRr)M*Pnk3XCxdxc+=T-+xy?mw|3Gfgs9nf zhnKk^Lo&@OEvd6QVOZUHgzlxH$`WEyr^N1|m-7=m1_&3`rtuu< ze(d21$$kH@WP{o#eito_fzsVqujcTn1IWjs=N$f``|rxwrFx{)33U#cVpE-YXfAE9 z;wCM;Z|e=8ZO>XwF!VUrN=`akePcs%^0d^}O_NWxbx)3muPSniqEl`6E!495t6FfZ z-2r0CS4=cNY>9<)>#gz^Qo$+*uhBG9TCDifEwU&x?}1BvzJ|N@)GZ0xqD&<@=wv0i zSed}Pw-Cj|tNB$GZ;SnKrb-PpB08daE*z!gHdY4BPM{6&k)e}YdFAlHJ@bZ7+SUC0 zBL5-Mg~JU!>obSB->kFS?O(@vHcJsBobmX|@L@t$J^IzBdLpTfOPyZjP$zGTN}nyM zapN@+^hF}Z<*LXAN_v~qPXSzt^;<>$ZQ+q`S+|024QV1#uBWQ-k*((nvCf>1V!RV> z-@)5^gUa-6s8d4=wPH^P_1>Bz`$`|#IR5ZFZgK3j?s}W%Yb~a=0y_sW!y>f=Z?|=Z z8qLhTjO+0>$y1Mn89vLU( z@vIYdQnp7)cwt>Kf56T6WPu~^(LINu^-GdzNvn@^92n#H+l|WWeyj~su2aso>3-4n?kdh<&J{HZOQQ1p?UKDi z@Ox~T-8~{}JsL9T9Ce%WJ3Y2(G=f;u9kOq6t(q3k zE{+mNolAUQwr@PB(<}G4*WwW`$7Mt()(P7KM`DhOrdKpZ`Lt9L|RqpZ_{ugaYU@~CE#{rfHrkz+d0rEWK-@^1;+gERIF zEKR8-M5B9#GX0b$CU#+#R;<5+F~l)eY-x>Q#`aCc*e$utt@S$?)!OdzT_Y?lSk6M7 zTSS3(2=a=gp?FyS$`!Ac1yJzl%|2>TVeG2*^c|%l2l)z13Qv|2=ufTTn9HY3Sba04 zX<K#e{T!)sO0Y zP1Rp7XRX+^!L6mJ+vg5Gkrvj5$bG8H(5-kyvS`04?aakTi=J-QR~00v(_eg>++lu7MnU89L0fzj&&S$ z?UaP=)cssq8|f`X>TI)FE3MuZbx4pd?&Wn>%DHg)ae1*TmH9DBLnOTN*VX0E@6(nZ z56&6W2$trE2$B42?l~R>$`H_r`yml|+Rvw|aLpGClY%JIz;w#E7k> zLi4hqzto~Yq50fxq4&|AS{>8&{cW3pxu!!x5~IF^T_8edJ5$=72I!n~6jM8`_oWP{ zHy^yKTE5#gK9*R9iPuR>Z`bb&@=X(!FEYI*ay2#Sh@UO=u=>yVhFiKcqKc8HFB7IG zsCu3EygqaAhPkR;plL{zVox|(W*5vlZ$Ed+w5{|vs%VQ%LXV%Y|8Bwf}o$YIP+JBxxMPBho$_16=cOB^-;XMr;S^^`=Zo|i`wDzPSMrb}euVIAq zsyU8|-l?IiPq8d}rwZGxa5%I0tzKEdRYkobSU)Z5EHink*e*&v*Pd_Y61z%_Wu^7i z?=^W=CLvfBUZ&#x%g&ilOphsM23>CF;7C+qaNK(S*_rxkZwsAHkGhk*eLb$`8McH@ zAo&o2N?Dhrm12JyA<{5XkJj0qED2eIRJBYNCU|0Fa4el}#4ci&UueK^+SE6~_T;>< zrBQtEKEm02jLvYuk_+^|_dG=B=g|C1Cka>FKNLgv?X)>hEb{bJe(m?Wfn2 zGPg(sHEiDaSK3`4XTDGLZsjR%`=4j<$LWMdY9#IvhhMSyf1foajN&Kv zL_5v14=hH`UYIO%`L0dBLA9Z{SBj)Fr0Z?YUu2l^GI!BJV=TKam8WdW?ZGbk*d1|y z(VJ}rwJ2Rws{{HU_a{*F%xy}MUndUTXn%$9uGXzt3=4DuuarW((9~H^XYpH0@s`W( z+HvDr3@G1;suq)I%X_Voa}PdqaHiFpCk(Qz=1Z05c8shbp3ho#sL~!6JRh?2m@mH4 zbZ39kzp{fGQPNNkw-a}%U6`jLX2Q!+QA)-`Yxp5cT?57Z`{~6p4^^-v0zH2@|KtX> zm7{*~QRD<2ceegsW0XS?5_a%ZH#OO2`|J9V&=H>o_sD<$z>tNyJrR|2c*zAKJF;Kc zU~IPZWa*lGeug~3FxK0Exr}k3GT+KebQ`OG5MzbjGI3Ah_JtBNcLK|?Kiv{8sO9Yt z#We+Dr}!ctTXIte=21>bk8`fil@-xeyuy>_t}}nu)4M%rH5Wd%yNq4vdU$t$p1w0K zjCpO-#cGG^v{A93qjj#wcC4&bU(}k~s-gbV+tGyO``6-W&sb8NnG3l?p%m*kaZBw} zRrin-OOKvvmw#QKOd%(q>#CK89DX)X(lDaDx1Xlg_Qw*g&0$Pwms<_2H2JBzKwSHY zK9^0|8np1l4;HUodJYQf?2kOrRG@|32Q~)g2b)wMi&HUf3P;MeS-X9mq8FQGRTGGO z!jPe_^SV_08eE75Yqwe6)Jp9V#Um&5eDxla;dx@@R{u0QErFvQWUpN&>tWnVDUl`EHYfI*A6s8>Q^re9D2Y2fRESvC zba$3IP6l5}p=^7dQbQd0O(El7d7^z}XEPFtCKYk&$GYO4XmgsacsePH_sOx!%qF`2 zE;}PzU1dkBt+2x{opE}Z-#C?~VI0xsms&jInQ1ttT}3k6PM_vOYj{_bO&1q;ULM0x&4x?yn(J763c=?j> zw!JbW=j6cory`r1{`;>N$m{|cqQk7PUpFfkznYwj5qD+N->~Oj*59_XQaHY`2-V+( z-@EK@c(ByAR#oax$m9%dySuwhyYZ&a151LVesZYmd~$k}N8js;Mxi|`Um3V(phvnt zWJ(jQn%F(Lh?HAB)*7`iBaTEu%ck@$+5YekihFjbM|A7t$bG>N&eBWFj$hUeqc|Sk z2wPW+suql;R-dO@1mC^MJl(F?Ylf@M*R!{ev^HnY?UWp~ab7h=bhKAx9CJBR-C4N| z_M@njE2hi}a@NFo&OZ56Pd1(>DgRzu(bmfxCbu@*{C>8y+RVwv zL{;q2eaVm>$yfI!=$@!W9(z?Y$xg!L5WR3g)qoOWA7(rjhF!C&T|D_?y`+1uB&(D) zUFTkF|8EUa?R00eit+;*!>PwCUvRZz-B}nnOEPs&g7@F{sZ&E=ew*MC`j%sPq1`lI&J*!Vcgump~c+c>U3FXsCuEjc0Ik(tf zQ)6E^lV9hwL^C>OYm(e)LwrEdwc<*;2O8Y!8ugoOk7k|?VIqa-R z5~Q1NkaV9#h+Ss#v=`bbKoNp_^?G{fJYBF*&b~k?%eFF4e)pTp_G<2coAb23RG?F( zg?l`a7z{sMYE26xMg-)gu6fm*MTk5*tDywD8-hY`4Snu!g$?b7VNO%G;O)-0!S!-a z+nl~A+yLf#>h&PIu_pxE_QJhRa}(~Bb<0`XLh!ij8T+q4`|MN^8TcgfrkCiiDG^_H zG+h){TsC&gOPJc*CcbsO{f3#aN|>Ej$>rex`E~V!7&YQpbH0TnJxsVna9Uo^#AUs$O?=>T>@zo%jV$>{ z`f>hpIiQrt8@sA{WQY=POS$92uN!B=~1F~Te+9F3=Q-YIIT&R?-4;Wm*xQdYZH zAtKf6JqNjLYq2QL^JuVAXT#TtOzn2_5A02Sd`=&NaH_^mTei=gF6Ep}=9k5$s^j|2 zJ**TcNnUf@p-9}UFVSi-zcAX4)_;W>Syx zZmmzlRvThayE7?n6qC(mVZ!%$7yBuKwYn04p<^HyPZ>}Sxae3X@^HfLlNyfQ<$96F zZuvsBqgCzmft;TNmy+ABc+kSUcNcVpi7D(;F|}nrI?_~-!y}cHCLrg;U`aIcco3%D56mu9~Nl2QE zm)SjMP(>VyP2Pw-SQi7=liHWWx|7U7TX&TqSnnkLMXlq@#;!3t2i~-`)$c!2_FJ=E z-`{OGJLA`CUfITJv0tvv?{8ULponm0D$_=6c9x+kP(h!YhNwTvfC~`SvgOZk9yS@6K{$lwpKlYKZB(Vj!P$WgM}8#mQ5qJa~ppb2TSqN=$bSwTA_G zKe{V+jchzm9x7m6T#L{-{2T^G%%qMj*&Vk*$*Z$ge%5z&) zEhW}9TG2&R1dWH7;!ltzce0Dy^*D-0^s}<{IDgatlh>J{_r=NIDg8+M4EaJYDJpz$ z=On6j*_}W0BR08<;&c+i3{KBXG-yS3r#P2~O`SIv4+U*uDPmn5B6s%HnA{=LZmRO+7e|8ldSP3q<8pCX)KewZ;Ot%hWK{Up zqKHXJYfP;h_OKGmeq3`wP(pXM=8fei_$Gy^dOQviOwJ(X1EC@ZF+`IzsZm}(?5nRf zQK=mdn0P!AMKMzqQ?z)+NX9L%d_mW<;kTWQbZ$0Br~h8{8KH0&p73MiAMcFQqfjk5 z{qjeO2T%QmjOVir^bZ!o%cXfHinjDYabj}eWNDJ5xus5k|O8N|FC5hBR}UdAzV%z7ksrlwYj4D zaEsEET`WBjgLco*580vo^*qX*qtheoJAX-Oz9*hd)v>mv<;fEKJK1oS;`8RPA^50J zOQ%_d4AHzE7F1>}_L;=tPm?5yghSQZQ8QmstIY1d9*Mn?-?)yxLs&WDc+=ukQ=HA7 z0Y2^2%_U|bcZ8u@gH(2yvQ{kS;}tdO80B5u&^dnZM#ua!=#Vzy+-y>Mh|l_S3Rzy)2n|xjz#;mQaD#1Q(qz*cUcKXBxo|#KBXS-$=vFx4=y>9rP;3V1SWs&S#@f|ii zjltVLG**H2FH6_23*_-_c=4&+3g!0Eu6{%`^5h8}tCAo;-WolpMOmeCW2_&gzJHAR zF`-0b-mVBaD7=#sT7`W;LZgT)86ufVn=@}hyd!FU!5 z2o7Oxd!1jJ?auEapmGT(Ftoys%dBNPtfh$EvDB0VlY9&!wTsJs-{Wo5S5mHIrfqx~ z+SH=HAOfmtVTLU~z`fNWhroF3%@6>(usRY}t!SnKm z?vC0n>8DE_g=~7&GW(HUsVv0l>SOz=mQhyR%Xd!-(Wk8LicJTGxWm-iPwX0;S8WKE z^f)zNW-~H0gZ_tIN64MEtscrT@>IA^#188*+$egXrPpHfGsjIS#8p`u`^b?lJKUZh zhcbzT(wzrON}3pJ#VrdRrD!}&{dl#lW5Gq6VA~FpzKE4YoZmgMM}>9EIHc*S$Ur84 zKR%s1A8s2v(N?oJK_?l&NmDXp*5{>M5@oVcj5)mNld>lHNb1=b=JG>KT%>4?xta92hDT?)0jdayV?UnOGaiKZsa@S(#787==hc*-^ z`9~Vkl>4mdko{%MQ^ew>Ro<<_5W467<+&3yl5oJED@H#0i=(}}*cvGw`%_;g|M8#7 z4)>+W==MO%L_sSfTcad#Uw+b-Cn);_)f+Mt7yC^OsYPh(mEPPZVFfcM_o4hbY5JDK zoM!}!?NgfC^=B`q-4W)^(tgiAubQU0pPkjA9h2Co9!ipmSG`ObK5=4kMknK<%lp3d zh^$tAqk@$&+1bmsiTZ$P+fkIMFWSwgS9o3*seI^XbJ7o@$nE^3Xl3WZ@TO#cN)wv! zto6;Rg2_&*U8qUgenFE*{vmUNfO3eI`$X5V;YX5=?A$Rqe?Yvb!vRaOjhNCh?I!g^ z-s$9vgX}QA$%t^P1@GiS65^v_;wb#24;sir{FQ8dkHapPFfpZ zZR>5j=1FcjYQQxg%QAnhyvJ#p{nucuO-G@g8TC`ktgdr)0(LVL`Z zXB0}i*3=A<)3Q7DzX;-Z?~)4&qB{23XJ$>J@X<+dal(~b{8%BOYCaJ?QhT5Zoy(#0 z3y%_>-tmh4JZe~LaYZ{3RPp3lLt1R!xW@c+_JJD(tQB*zviJbc{pLqMD{7YXu()A% zC(4uh8Oqd)c)<^XHi_S2R){X@sVgPJ!T(cQtYO|NFJZmA73~q#bhc;O^l?>QC8H+y zVr9?DJWePpU6(F`sMf$YEIgw zLVX~6%j)BaN5bnRY}G_bPhom6eJ|N6Q#Z)^m%Oi9x=j{cQkqiZQwRjXAz$pi$u1ZiC*fFD#4H&JHK@rSE9Fw3=5q#Xi-tw@q@Pc$t^alnxg!E zl0($0Lj?B`IwEo+(5h%Al%8}zG|h^(($k;RRZjowOi33fmMHO!+@|jg@jh0i z9rmZN=(qeMN7QC^2?jyEIr?IIMBb_4dK9MU8PklGkQz|IxI?k3w8zVP!o>NF0z3R( z&D@BvBzS6M@zNr;jTw-dYHj{8o(3k=j7D{9X zhq(3j?yVZ4?E^T}=<&9A?pB5YOU-ai@LUKb5_zJYj0@M}sm{sG7st*n8_(1D`AJkVW%A+&e%Gz}iGLaG%7;Opszjjg2d&2Z&g zYn_zlF<#F}?#mBx;9+`m;j6H}>a1^i2>P>=-e|&7Hoqy{7q=Uex6k?QlQFa4imLd0 zygz(7FAVurBi&W|P?&00ju$EW#g2RVl|t16{|o}o;0%%YYa@-bzGF5fkKo7OMC zY~OIU1#9jIL3K_L8~t^hUQgn7JVmFtMrq3;Ye}?y4=p_UA(&x~BRF-po|7WO3PM70 z&F%Gwm$z6j7;w0|&rrRf5%)ZFOLfE(hKmnky^_!E?XzuV@HNZ6E@rOGD8tGtA+I&1l;`IaQb$GLar%|GmW<`B|1z zGp&_z{^r8PIR)jcJFW}0kJyA0(W=X}z4tch^2Q8U^S-V-mtGXN#jL*5g6m_SvxDok zeReMNvSU;Y((ps6ClGXTD!ulavL7wRWp}=7prc(ydY4;y)Dv9)qsv5m+cVzZV2P{~ zrO4S@?zG#P+7GpKGJ~-wIQ!=RJ8g?V;H0L2*%1)MY%%{5IH?K1tVSSk(!&@yxxpAX z8DI=%ItWK~jqw>mfboR4FTi+`yAa|o-WdZY7sCl_sxdTi6&(0Y2YiNH2>?Q!Fn-3M zNn;=v&!}-7q!5MmC8`S#bPIqB@CF!<n#%Er^;@8B4*F}BYijuM9f zjRR~2fK$cT7{jh!L(rtm_=Iwh8V@MDKu!?_a=hU9vZXP{4W!0s9cMri;3qO4eoXOi zI2UgWYFP~WEygfPzOl~)kQhToF2IrHe?o}k&lqC_=$DAFRUi%r7!R$t!@=$~&^Jp^ zGake?M$j1Jn{ogf0LFv!Zvkq+a=<%)u^V3liUIooe*$&^JORcawVMDhz%_{OF&@&l z1-=SEhv&P%!ESdL0t-lQ0*nLN0LF;UDWJxa))_$m4fq*A1jym=xRDy8biN0=60ip_ z0jIB*LH+i?N0V6?fiYy#$JluqXc{BV`al{;h9Nm@lxBmTD?G;-B~b>vF}^Aqcw?jo z3iwvw9|LcUnn?z_1E>h-BY+fOJne04E0_%g7;qqJg#yRU|1V$>qE|e(Vjw1R7Gfef z#!y%|n$P_b&?A6@<3xy=OoZz205%kYEmV*w8V1%M|2 zZ>ZxK#Ay^mJ{8itp()0AN@K{_IY=AB&KLk=d?XHx84L1C;Cb*`CxM3VbA@4rSjzve zw>N=n>e~81PX@x2gaiTv2pR?v6eIxxL_sqF0U5#^C`bZfvH=4G1dBCHiWO_X2^B4l zwSz6YR21JV%TeQJyi!DyA?X};1>jc|swf*jW-+hmto3r=Y!&-aowbx!p z&i@?fhd%WFj*kRzT{AcjQ+SqtH^_p5U0NWGLefEMgOm>mMY+~M+l7$tgm!L_N1wV6 zg!G{iqRiLYnJ?Vr@`7j|89~ba>pK0+13EE^ycg zEwO_#3N82-V250xem;n*hVojVqiDHBuyW`w%z&|4pbq*MeT1SyD1ZY%_W~+8YU>mj zLSuv+p~y+ppiq>;1E@!lnY*AX3$A@oMvZnSqzyn@4A(Cp)k12AlnCh-BpXPRV1OtL zXAtuJpe;J&;~`~38O47c0S#@1@*yZY!C2q;pl~HR7z~PJMK=O!4J<&zpeWWEWNJIe zdK1cbHlm=HU=;92vd05Di4LBC4x;dk7|DZ>cZFutq=Du^fNIcS3y2K4J34PqtK*57z1?@Zv$%-C36DusNOEaTu_J_itO-(vLobC zlx-qhQE(60Cla7X6!2jvXwnRjMl3+=PyqDe2^%UHrhz&D3Si`zfZzf2@R7Z+GsFOA zLf4FCgonwYoC@tx$AzNT&>b}Z37{B&Vxt)GcJD&p*P)IL^5KE@9^_GHfznaaLXj?K z;JPVh4A8YG0*DEYFBryW8sTGKL%ka6XG7grpuhFNp9ASLNJ5~6Kz#~8LE1nj^ej;o z)qpG%uq{t;uEAiobZ|HnaB4CmR2>D)qWaH+dT*f5f&2;RTMTpxqWvEaPo zA>SW4YCKoI0I8~=L9x#a0`&o$p|8Q{bf7(SbRioL0W~!GCv^0I{fz>fFG1x9XkimH zbRu;aJtTV22mp3S_K|`&M$t$z@FS>~t^`J6BY4XO{0$3elQ7oTkl*JRNruicn1UEK zAq5-^%jl3+LS6#~*#=kCaJ}G)ruSj)Zop_R(B6ZqIT*kppr404deA#SGQjLm*Xs!h zJ-tv!l{*O53s@yS^qU2fNr&rU5Uew%67=o~YauqCmM9n%g}L4Jsf5!z7@Uy=ybBXf zW){eULf&S4DjI3L2@^npsw7BPpwCihi^4>IfpKdbPmv&+@`T8R31=M>JT3+72nF&| zjAtFXBj1OFI{ck5&>>Q*5%KB(0}!Dd8e9Dgb=BbX%Yn8DG@1^y0igX8k^s_eNMuNT zASimiIe{{Mf~yQ@X29D8#zA)y7OvxP?S_>?@ocE=o`oa^dI+>%1pSY~74=MLWEKpQ z8w1+sMKhiNRhGa9#Xrw@uoh4jy1V;*I>6iTAO@rxq2ni^uM*luP??bPhnzJm?ORZL zAJTQ;&;|7EkWlb;CP)|yc@!mS0@o!#+Xa0JA&+jKYtWuVdJH2UhW*S#LW|H@cp8JQ zC@8P?;Xysc_*MnackAn0B1VN8&9Hb^l-@+IuvJiFHIX>v2U7 zpyL}*nGKyzK{9~hh5Cq%aC`J!4_`x@L1;?^rX1)i1ey;Bfre{XptZpIErDwy)IEkZ zcmnxL&<4d`Hh|YdJ^R}*?j5K{UndX;z6uo@jWy6yqX4e;kX8Xr1<3;v`T_(j>{N6| zE&~2K=yw3=m()I z>Soc!M^Eza;VOsghrsG*nA$41qKEPij$(7Lq;@zZQ9P|D_%*o2fX3+F`5EZR&^R9I zz5==o5*jKqp!@)si6DO$1VF*6?Bd#L{b$~PgQk-;`7qe0slxT1jMjnKvk#_WN7 z6v!P3{Zpx9P(ob>8WZF}vV;5`prK$_Gz6^nk%6S>8A~_XwkOcipnMGW0ea)G3g~DA zI|#`iW+H}ukHA_40EM4$Y_|Y$CctNT!i^OgVp5DDGinz9fDXg3HVg={SHSewfM{dT z5v4DK`ipS=2hgnG`q~i*s8MRX(^w6(cA)(NX$J^$1Nsh;=pnIBF`PgCFIY}BWd^}X zH=DT{Kg(_QBK6_I~D;}i=ZzGY^nApp-H3TP>zQ%O6hQc#*ZQ=>cJ=!z^i+T7C ztfdceqQ{(0)aWc(eav1I#@R~>Hna;Zorp#l=QNJOH)G?eVe}Hbb)@G}5hXg{Mx9L@ zkJ>;>l*BjKvNCGI?9z{SG}r{Hh8yg&3<$wg&U86VC#(434tZ_t(`KaOnsCSE?*oFf zm@;idaGoKn+bIHP?$}Yjn4=5y2QHYbcC5&8rT>Hkn_YBh}^D_+kv$zjF!7lag)Z7=4&50d@)MI)q z&*!A>Xs=I~!EZhCF?>tccfrG(=X^7~vn{Q4c()-|U)TMb`S!G&A@gj8kl+`ffX64# zdT+-0pI1tn{4eUeEeLvDdqluUG!Efh7RlE#rJgyY1(!4GVgf4)j*)^=4(QKQzo^EM zJ)G*@F3>S;>{zy8OU?!EZASB(3%7T<_AFdd*-Iw*J{*2??wpLW4K6Cp1ex%C7h;77 z``j)9cr)jsjdr{)Zt>66dwUl1rgI|f!-w>7LH{$=eqjlcY@_AjZ24M82X&&BZ|VZi z$e2&+ngo{YEs?_O!4JbstoXEM5%Ru&yjzqciMp6-;Kj0=rRsjmMWW6P?)Gwgq?<#b zp~?QW10B?eAoatBh}l$30*+fyYv#ozb_&I=UhUoDHSSxt#0SkaqYVdngsK1!cmOY+ zb)lWP-fZ5H`beDjq&@*>iMe6TCUxAmrp?D7*3m~)R2!Rg0%?qrwP|uMc(1pC0swyK}#%9Y5Hx1qcsTkQa}S9i48SLCc*zBtxxiaP97xB zl_c8+O^?ZwUD9Rp=6R_kiFJx6Z6YEiJ#(`#vLNK>Rx5*Q4v8J4ZV;1~xlG$i->E); zH}$2J#!0hp>_0D6Y|XWE{eY?0IA52<_btdA=U+%mHs~W<3l1Kv5v(8GT$euE=Xi?O zJ01O)C@!N+<8U#0c-t#0T(SpAsU=@UGF&Ebp9Rd`F&&jruD;(JUs}Vl&pg*wA_|JD zj!%&Gjcz4SGajq)37O+E`^70bmAzZ#Rmlix#Bic!8F#9G`z))&4N>IVjh9l%udip4 z*aejb-=H(3&8EJW4HB{a?Z$hsx-1yX1sOYj+p%-vTIFe zYhH!&v;)y!b3n^!DbHIQ=v|f9ov*y7F~ekN>~s;n^?90tD!dkF!8P2|7aUMio3pNH zE9?lD+sb)`B19b{9g=OWb#I@@+38TkiZ@xr72yY|2Mfx23J)JVw38DiKYA_V`k1!P zY?DDoV88EtBk|RFT`A{ZO<8?hJ7=~^yCJaVXtt>`wMpCT;KQJPPU+;bN#1@XWgF*X ztFmrk6(5PI$_g#wp73mt$t6Dzt)Q`{#+EjV!z6Oq^w=x0Pu z9w&IjkoAmp48}E9bA*?R_`z9G=Up9c>0;hdzH1mbN$TeQl5pCdKEb&tUp>{*d+_`Z zIJ-VC_UX*Cr5_-nf+wx<$ z8O#f1RlLufkVgvZ-Re36Z^ZY~G<@=UdQ9p|dc ziGGgLFSY9{>|8pXCHa%q;T%`yQOlJCZ&$&}OBoNeD_6<8?QA@%O=o%D@Pg;lZf^X{ z+^)BI-4JaS!%rBg+*LSvFk4?#_n>D{*LakYxI;9&g3LHl$+&uC2J$7s4<<{$b!|ALfa*~ea9CAD| zFPRf08i}*`CaqKuYRcqa5XvTHRMW?|?BuJ}S0$|-3|yeDBY3fY(Jv#fV&9mx$ZA|;MG6Yp()$`y z_jY)-5AT?moZrYZu_5zoYhJzY#eS=LNz7A|_M-{|0}Ot4#+T#3Q0{%Oo(#jZUmwkZQeObaGz;DKjD0~iKCc6smrL<1T>AR zW_d4otPcxcRo)qGVxkfXJ!($HzhQQ{b=u7Fwb29Jao1H18>sK@D4FoJr?W*)XC5ar zinvVA3*qK@31;C=)yMS>I@yZA|&YZu!xh_1EtH4+-m3t*(ux{eowb6|0&F9z= zlryqA@|*VE^IgO(LUSuS>uuDif|A-Idj#L>NI83pV&^3CbSd7rL)~A0A=fXM51TLXXjiZq-B(e4MfgSh2(58uR< zm@r7Px|!YJ+o5(PWY}kWnwOZ3-uv8?t2lha&nW{n5-sx~IxL#wPWYAo+=NOpS%-d~j86Q@^){CpA^QdcPjhv83rEQP)`d+<` zxX$g+&|YcG(@UbIqDHeQwFBJsA&;k6I~(S0IibzEV2}xyscm!4CwIo&qnL)dv-UYS z9AM#tajI!j8Mjp+B|sd)XZu%TmpU_U-~@9KMzhdznzuhrdh&Su#klCka96VvydIK; zc1yt}98!Zd4|-IecXPex$69B{z$Wc=g6-q%WgJdeJl{1|j#$y|H|*ZLr%5Sle<}`#^u*HQRfg8UR(xM z=g*(jT&K-l?NXO}wvFQ$VIZc}%oDdwnm31yBu87C4;&q`$}+I8EyyT1K?z%(^q?Ml zWU8k-w6wibZ1H#eSiME3O0~>1TrhT?y6SkJFnF=b?M=TENU8e@FSVP%p388#;7aS3 z=-k%$tThPV^Vya;CyFTJ}l z>)xR3yfK%l}tpQI&yoyB!w5GnlR7)XiZup5-w zrMg}l!}~u(dt}jcd``@@8&Tw_rpQ~kiYJm=X|ESOKoWkeyqR1WSfGnx+mXw9+!$%c zyKR1MqhJC~C4E&le}uL_Ovs|CPSf&K6l;4D>+4=dE?I`yPdN`u7MKvjiH;E(4d1je zgnGv00ZCJ1!D?qUlB8Ap_`;6rEp@?+fwh}<9Fa{}zOtHTgd zp1rN4FU;oL_^~?c`d|TZVR4eo9Bzd+58_&mHygqW4*k?j&miuLBELntFADv8QoClS zs;zmzyvl(>UAV&_iLtq+RPg;Bq{902!%tcUi{J~|!$O?8V5Y(IX5j`-CAhXj zq)VQiaXd1{`HMu&t0T6;Xy?UGXnahEY68FQNbu3YtQ2u+ayHjYi?d!co1J>T?dsl% zeq8bJAVNiau!EHH`}a@NH-t3fJUR^e<}jy9)vinolAZ5wKGnOCUUB^nK|F~(u3JQ^ zvEJq)xT<-3SwZ7N3AyUy5kX*ts8buTD)vB(_{-7#XZC!c^B3Dy|IqF1zejaxZD3mD zN|A8&4*^0)d79ir`q?CQ!o+OK%S7g++rNF@>sJl=wpS;ar{8*nIC|QS)Ni{;Tf#qz zF%2|ZE_ANDQhLU8nRCKsdi@uPiN!;W6&l*f%7okg&N|j1>c@}s>i1FwSY%NuO{h5f;T$x@mvDWr#jrQ<93*M;BZXG;$y-BFKuP;g*5a*_S*~TPfSlzmTvpPqE zhjVekv=lESjsHsX21mcsQ3Hn`G?;UvT?qT550AugH*ayx*}o{H*SYA;Rakx0X2sT4 z)9W-|EZJgG;;<>S#O-tEUQGQqwX|a2QkTRnSTk3*9qfUfx5w{!CWhLMjyh~;((~um zDQ^y7l;yhd1#Z9Kz5Et_%EQ}tn7tu5(6TM*yrN+oL-up4HeB;J9p}8$AP?&hn!HPN zJ-tg(v)S}u79-(K)^!77weU@f{GA2+)h2@nnewwsD;$#=@FDP3Ystn1wxuz<508yR zQWp`eQ#gs55sJ5|Vs4(<_gbN?coQ*qV{m`Q#9{KtM!9onfT(Y{Tv(vnqqOy;@yvHz z$*Hxu`Kg4sG_1RJ!yyNUTJj!kJAS>T3;jxjWn&jsPIBw040la>?3YextvEI+JjHPr z>>K~2V^DT`q746v8)gf9`7-*b{?N&;?mOhghxL}3tJG123_EXv_U*TBsm{ACeN&&v zSkr#!s9AzZaMJlXKT7TA@$BAkOeT*Up6)rk{i+U=-aa-$i{29ukW*ajokQ@vt}E49 z9{4-!x|Lr0F!!DOYE^w$UeZ<}b@n~ID9vTZz1nwve5@5x&Gxi6nayqc=8ah&D;wT5 zJ5srsSCJ*!ViAydH%T^szxT=31>6IU(P>{#GLwB0SWH5w$>E*N1$aYFZ8@`Y(hS?7 zc-xYByQxBeWpe78XzQqwp z1+ynM_Yeve^yV$!M#J&Q9>>4gGRx{ouCsMS*R|2K$EpW90XJl_PHjTf>1=bxv;&yA z>Jbs=!<{v;q{r3q-Bn1p<9og*C0h{FR7#4A%T$Kzx5N-iPSo-Mou-D=J{0vTd;fLT zd52Yo_K1x9+n6{qqk)-#a1G7kUEKNyf+KNb_jlPN$vv|yG9TB*k*^ZxywdXhZTZH4 zf;N8LkqDV4-1BE1Myr-jB7+X+l&k6-KAMLaB-tCVyxCo3g>~kseS7DVuj%D@&m+}I zLzcdjWAiPzeO{lNZ6C9xen?!+YtJnh(mIPeke|j)=8T)uKGD@d%LzcM({nurwDG=3QI z%^hn%D7oAJb7dHcdF%zf2xpakWrBMeA2J_cE`mm<~mk z)W>i%217%(Uvs68U2lcV*(N~7871EAlhg(!*-TH_O25HuS!&^Xyc6U3;S)s!z7NAA zif30PTjTo&ZnXsr-qLJYVD%jjlNENqo-C&Bpo$o0Jvufx|Jc6S%IXHQP+EmI51M3L_@ zCHzngJnJuBPiyH-zEq+!IiztYH9givKN`(8J6*!F`^c+Rcx;%vsQ=hI>lFm-E=J91 z(TCD5y4^OmX%ThF^+Oh~u~^4Gb6rJAwD82aYwP@{MT>M)R|-2`v^MgU#<10g9}Ex{ zdMD--cbpm#3q(n!%RH7T_IfT1Ou}6_UZO2?3;JjhZqzx)&0(R)+9$1VLZY4wxQ{kouo>ajtZ_=tFp1W6Aj0 zpy3HKt65a`YkJ3S>Kd-pKZcXRoJ4&8cDPo@*}id`&}n&>Up+POxOI&!t^6mV-2VW- zURoqEMA}C*YK8s_@9|RkdDX8pu9`?sik>?{l%$axyyM?+QmJ_nT@6WY#W4o;W~u4y z%QwPZ>oU5eB$%3F0^W;?Cv z6E4{yJ3U7?)^a9ydD4y4EuXb55^`3!Z{u5t@<*? zpsc6(Z@4>(_;cI%VJ-Oz7edy#iCS}qZ(<+R1vZ5|m?!>FU=w?4!}$)0*Rno$D?7Vc zWxHHXUX|gJg~`0R%x_03(4J-| zc6&8DJhF<>9TWYiz5To(weV)KGgqgIAXG5@=(c;@@5hrbO=|0EKk~X)Y`T3|yR^ck z`=3nt;rgw@ioZcf9ZR~LgU{%lq^t-v4Q76CddXHR#)nkiylwvAi}sBT^e(}N;!FIF z5of>1e7qoOx-){$rM2+X>*aELM$6PglYlpOy4>OvD-yn^)$OJ5!viz4yFdMrpd(@T zeW4Y)$fR;J-lK>94kxL74+mmNdyfye_#{cr5|^ED(FyzOUEXw1bzzW2CJapLtXgeL zFXu5iTln{5cNVo;{PX@IXM){*#M&yxFdhGo(+v%gLClh$9f)o$%({TftrNbci^Cq) z=b4;j={yp5-xOii{9=hKMk2H4q^~I_Qb%69DI>spf!Z)*NL+!%QQQitC4Gi1vchuY1&EMU%=-e^v0+k4uyIAHkNf^e<+JSE0Cz9XQR&e-M@ zdZKZZXj+G*+xSdbPm*liE7mCuqyFh$KQlBclinR#q3dqiJ}Y{sZjvm3qrc$!+EpD!C@?5a&Tp;8MlCP1K~<)$0qmZQC8;2?th>??v~Fdgo%#jJcj9 zm-qpP)N$3F9?$7cvqgRFDiMC}L!3qLac_?cNtWgbb)@~Ap*NS`9xG^eSc%{pdP|$! z*Y3aEvEl82ggc|>BXW127_vGSGMa4BMY}&8{?UnR>#+OlZG0IgmB`sQchNa9oc*_C zJc9cclM+k1!M~5PWPS5ij=r^}ZrSbV-WaF$2@fJOdYZXXc=~#JV*fc$ zt_Is+{pAx^C#gTnCO7e5$H7#SRMDt~Czlq0c$hJhqbw<5eceL#q^!|Z>9qyfePsMW z*oG^kHWx_8?~Q59Y~~wksGa>a=P=pzCJAJ7Lr;yBzl5>PYu@peRIh_m0cjje5|K~$ z-dP{+KETdyphiif?;u`q8zS*TyM!wA%)m>C#1v_!N!giL4tlJ57C0 zruY1KNZ`?`H9M``o!cAaQO$IrUAc>hm(MB0I+aY*wDBB?cY-J~g%~!rpoko`-D(bh zdc|o+|6aSre)4zgNfpgyXKWhby^>yQ@>=&c;vd#Nj2DK81sRrmDAU-4k7N>y;J{D1 zXSsjS)nVe>%n_wD>)tI8?)5>Qd=uGgKpNr zu6Yp|7<}m`TH8!DanqTw*PG6zX{ROb3(Z52PU|_7I%~J}wgPK$Snb1F&OC3AEU8a= z62&u0LcMQpC85{^NDbO`bVhqKl7;NWgvA^l?`RlEZPg0NpFX+IpWUbgNRkh;zk$fj zIQqqa)1>Tl!D`*MUI`<&-849?Ra3(ZEx)&Pd16tEkQUY&@P+BA>6SXnMtWysl2aci zoE$4k(lOoVN10Hplztm69vzt8Y;l8k(k#$XExdKb{J!6a`j{1Vu_ybC1F2npUnKq5HO=FZq1Vjj5_0z3V1?m<&Dp?8 zaF@%9M+RwL?^VC$!+C$o*51D{LKx&+)n-=WU52};;+>`q3{4GZF`>%{g;BnQII zaRW-@=oXxSC~}~-&u;$pPmyj(C`aCM4%s@VbQX38n;3)`Bo^D52GvrV4wVKGO@(&U z_}WfCZu0l}Wl=*Wx2;)t;*w!{I5gxF;xRgdce7U(9wHheUJBVF9Qs#gNO%Lh zE+RiV(T@3d+mY7RC=A|oU z@80ro*psR)-8JhmrR<{BQq}&9*?r#M@W^X+kL^tmZYt3>YC)mgU%mn}U?;y2H|a=T+BBA%%gn*H?YzO&@6Eq%`3t6KR4E{2ub zvS5|6MTpJv>=8O~i9OpJ3~4#Xva&ROXWg-e30}qf5ATVDOU5;^ZXRYMe2xsTocQ5E zkJEnJ4b#-TD*>v{Q$iCgx3n z+XYthx3oZ(4cTfjQi=^Z7cFYANR+o|=$WE3-9Z>nzkUH{s_wMO2Lq?iJLHAbOe|LlNJQk29_+<8&!)K(u<~Y6ZP>RGObfq`@^(|^n^+5t zo^g=hKPz$f(J{-R$izVsmwB^SwWYK<>pe62E8CjVJXM(s&W*>Pw|A+<^9a-cT_L}U z$G0XPX&oI-iyKIZARrkoSn+D&FH{1zKD#bCZ%A{|)QYI(xm-R_N>Yp%WT|A>8(NI$ z+j}C?ZdqxTbZ3Uddeg0<_=kk3v23;FWyYs9(lwWw5-dmJ78>}{K?A|cX# zBAo2BBVi=}&{)mps<367hd;eCrPq;b4{yVj9Dfm??3wHD;%@tfKrc8vn#wHf(7C2^H9-MP! zYeL5S3C|sI%g>pqZ5IilnPM2Ub+M7}wwBtnZ$|bHn<#UYu*@{_X*|nnTnW_ zUU&nsKJUm+yfV!ymU~KExOh4{Y_@zavObIv)>k)wFztH(?C_vmV9 zPAH4Hpl&a2DgF{IQRA1Dgln}>#XlNIn@!n<^UWIw#1dGtjTaVpO!VULTvhWKSO2D4 zBeAYcLFr50ay<(Fo%D{|E95ylRCpefe=O0P^NCg%@~+~@3DO4)R{N}1F6H5^PFyQr zi#a+H7*o8^vztTcFBps^ca|R+vYG8EOb~WPKFHkDN$OZ?Nj+FPO!?}LY8!9;`2J1S z^M|is9ZgMdZ>Sp3O+0GKsw;XV{3ujWi;2_V``udVe-V(^S0iVsZqlRCNN28n3M;={ zFr1u6%RWb-uREc&_`W2yx7-i8W)sBaU8;AWOr}_4t(aj0Zfhk(Bbxtk!S~M;NYHhmh zYT3QB^r*Ern*27@DC*6(ez!%jr%}Q_UBwl(c1L8tRwm-)d_LUo9M^(BOWbQ0-yArN zbPPDea!%;YyV?;8_fQ8}CfRID>6L&O`9u`k-k|y%S#5{tvx}OhML~+c(RAC8H)R{$ z)7AHD&FqU>1kpc#V`*0uq7XGvvzVM1{CcGhHhD(kfaO~OH`qbc``Tz*y|M1?!+>7>VLGaY0#r)`^wBV&G6S3B}bMouW#Cbd*&3UG!e z4>yuXwpUFGx$u{uO$0@1T=7r=c8;n4csE)wve<9b~O--i3i1ja_Bl>Ol3){2Ro zn{Si2$=qu14SSwPe=cULWoBZ#`U=&bYG-p-=Zx9%-83bjWbCpTFYTM&QxfaxJ+Bm65x4Ww`rL!OlRhxFP#}5ca0Lfsy}W?mw-w=)q1ba zf$&5vB->_42OW1|8(F)y!74%%pc%mF{2F$yY0_BRH)*z|)~w*t zWIaTO)xBpU0%R%YlbPPX4AWL_oJa}nf9IaWr08$=wREmQA~Kn??VxEuQNiXO`*Xp4 z=Slax{dYQEdBUN+=B!0V1R_ljIia|0LN5r-VOmSg$0W_>o($D4rm3o#$7#(-Zo@{= zU3y$!xz~|ST~jsD|M?@C+yq;o5N$fTqW;nME~@j9U)W!jvrJMi4clRC3s}MRPS=s2 zH~TGcPr$hya#uy0eD;yF1+#W$6Dymje^b2OqqZtP4u8a^^2f;j54Q}w$DOv` zeC{$*yVP>Z;9~o?C5n&E#&WAWB1tsGy|ZrXM?V#kH)L+@_3I<BkN*@? zK_3sb^W?JpG}o_r-XNA`MVG)k(p#tXCXdGa)+-WYFKw&D=?N+&-O)l7;vG3^LB7CDqp=M(ds6kQnF8b-9~h^deF zLmM7JT#obAj#1Ar7c)P{)Rh6sHC-^_lVOx#lx_<`MP#u zWWnomBhyToCq8mHCrWzP>i%PnnN7w-Hcqw(ULIT+Po-RlTcp-EVW|gH-OjtHx)>1` zZft|Nw-qAStZ4i1w`mPbvoDe0dzOrVDOsw;*8~kuzU=PsN77ddzgoL2`zKZHmlNZ+ zYH~G4%acsrt#yo zxL}^(E4>Oz;DsHxr?tJFT#Jz@?HB`4_T<7 z&n0Q}4ZP!G+TS;OExD@-AoSNCvyw0NS=Z#;+;>Ch!JzQ*^VSpDYZHA9AK}WF-SRHU z-Yue0n*gSto#_U|dYe6_@@Nx{qc1;QN>Qjt=k2KM2GShv&s!q_@Vm~Ws2(vimmf6CEt0R-65t&Cg;%^;qL1gZo zsJr3petb;I=E!UTdw65gCdcKJF~pY<(AkX<-$)>a zWJGm|DXI&ZIEQn5sE1}E!}C5Ft9?7{AJo0@3Yp{lFOo6A z7s9;q$B+^87TUcqw+uyuJ?-X}YiCTkW2|_)O|RgdK0RxTcX(pOLFk0CoPyjUxh(^+ z#m>RfP0Nc5)mep#nUcA>G*_-J$}LvUnHBxMjTsd>8=H}Gm4*3**~;83)ru9$XDW#> zhDm;@T#-8miDsb9Q3|0nH@|R2@v|gSj$ECazoN7%t4Ln>Tn+KJnzHijU%ObqD3+?y z!kpYJWo|{T@~If5xfO-EE3+zAD9ej-=P*G=icv8`yfimUA+IU}U@O{01;wIVH87bWSgKZ(seh%|!B=4%7!Fe&2*VcZz(EKU zhu@TCi$!bg`dSHg*IEf4Lx=DlW(+$13Ev8#;|1$52z+(snG!sq?BESpS`Y;Nat#G8 zAzlOr5A?$`ps{B;{JsEBc}!c_y>IT(vV-7pvlYH@gGW2i@$k#Yj?lD|Q3%9^V)U9y@Os$0Sajd}pfj$hRkJzTH$$xBaGL>(nmo-8W8s zd#rwHk9O)*E0ABx@#cqCf5P#3vwg~VG|EmN<3~@2l-jmwIelXXerQi;6;ZD{-U!!F;cjxLxT(CDP+BzP(7Yu{V{fwjfc6?=BWJ>`6J7;D$L4eQZ!#@yra z_h9A5r}5jzY5b?pEGb=>kOazh^H*ROS99adHl~uB#)CPNl&MSAT%D`%-{H9`PRE61l<%!k ztliI}SdS}TTYLQdCKdFlCZhVB^;Dm~m;Mbt_@@^0j1PUv=7*hFyZZ)=$M|Ai)uYyBGI@$j;p$GdHI!1qO{2x_P=Qva9f+Kse2q3+(p z%84DouHBzE97PL8uDwdg~f{8N;qfWJSr}8UC4YT)0^o!Q_M2f<^F+cEWYMi zEHBE1=B`hVUsvdrSD33*WWA(fhFzArN@eU)SgiK!X9|Mz(P7Am1Z)Qc=%sQ&+eGX!aW!T4$Ie~<4oG2n}A|4{h{ zu6fE8a&=$;+U@zR@=V2FFYGhH=8MdqJG9X1Kd^^=$_{*y`ETUEWNi#(U&8Y@`X87* zGo}AmbNxMBev#?(b^m3?&xGYKa{fc*A6Wli9O3V=|BGCouX~Z{%S;MnnIZqxsy=gn z@e;1T-+Z8z|3|jyF(g;Ry-jwx+IR%L2Iqil4tjgHq7+Vs*Ibo_*`@N*D)bOiRmn?B zSFA+YR^*nJ!JQjQ^$S?QTEKEe`#lxrnM!}C^K)H|(24wngt*j&Gk2Br$4eI%d1k`MjhY8)h9 z0AwhmO`*LYD6&Xdo*t1BAoYu_$QGxlvx{R2;|c}Y(!`bdkws5wO0FuqC_$MUk+Lds z`HK8hHmfXB7_~gxuacde%9)}0mlb9gg{#vOBLkt1on544N&S?sMi!#uq^r_XD_@oR zaSF0ilVN;*p(2%|QiO9=>B2~Ktd(i0oLAGNv3Uxya^=%8RN2Mxt6~a`_2r6G_R8E8 zE-P0mP@*!Raw|kJRWswWm5PXP=$}x4)@2u`lx2%SM)7kpv2u!13KS7+C5&4bE#w!b z7b%t55ea$E$o)c_oQQDHPxvaSe*z~bVn%j(Du)#}Bln-km2VtBJ2i!smYM)O!%&?< z+sd*$&}(j0RC!t|`{_JV%H*jERa#M{QtB_L$o5#X z;TFn^QkEC$v%_KcmpsJ~)Cl{__8DfO#VDvDD6!uHe4vSCePm(T2R@CARg|Dx;&F8sA6?WunM zE#FZEvZ>OgiabRTw+wvIjJ?0auYvvf!5;CK`llQH>C=7lXa21+_AmWgnBRY}Ppe>^ z!B6M>+4??L|FU&WFRH9alg9hS{@GqF`h#x&9sgDgc3+f|m*&T1|ApUOl?H2%oy`(;hm%wFCw{It0nUw-DzH;HXOye{+}}NWF1@&S(X%54 zdZXvhzjZ!<-2+e5H-i1g{Mp*V9!dfKlLF_La`|7&_N(2$T(;l#&A+v_f(r0O&+~oR zo`?Xy6aM@hUS@x&Zhqtc;@YC|z^}IX(z9YFW(j90#8IlW;uKc8)ELVIp=U0{GF9oR z;jC=`Nc1d*Go=#RL>1%|ziNy};EaTPRE1(@{r%FVQLBvS3>s@Jk7PwJ=aqZ+Dqv+aXg|*wf2Bc;C3w2;V;~;@NfeKUVTEWo7Pe9dgdDUS zjD~U1D5?U6ZVVcMow3R|bzSuZuFq$>SZ{p=#UthEsCWO`~EA+Q9i)Q%z zGDw1z+5WK*hk=hnb+S@W=9}m1YphO2e+z#t3-|>_Y7Y8)CO$Nl(U_Eb z{tAh|udk6Egs1Qm@M}3f(kLVvi2fd`0vEKc`1$?Ak4<_iKVJaa0H#UvpUy}0^7*Vl z8$IVIpj?86&QKxbhyAg?QFYJg!?+&5`anKEDjRyD`itb}NP=eO^HhGLJimqF##ICP zqu|_8Div(5-%Q+#Dp0V(x8kWj6QIZ!{#q9BE73ClRrJ?0`iw@)pifnrAMDqN6uw_@pBMBAu@7qhe17b5=)_$CztHs%gwIU+kM?9Vm~{Uri1UmF2;;@i z=qD-(dOl;%FU;2{?+Y<7D%5gRUZWbrj7zub*Ycm)BWk7Mgc*O3no_Pn2Z{N0zI=g^ zHOLRb!gy)u)uWUq-|lUU{a1o<@_}w_iVibl@BGkEgw9mS pOH0ag)jl~Ym5=d;lKJ$8kvVfK=`aOCausal Studio 실습 노트북 ·\n", + "[Facure Ch.18](https://matheusfacure.github.io/python-causality-handbook/18-Heterogeneous-Treatment-Effects-and-Personalization.html) ·\n", + "[Ch.21](https://matheusfacure.github.io/python-causality-handbook/21-Meta-Learners.html) 기반\n", + "\n", + "작성자 권휘준 · GitHub" + ] + }, + { + "cell_type": "markdown", + "id": "1336e3cf", + "metadata": {}, + "source": [ + "외식 예약 플랫폼 MealBook은 최근 **'타깃 혜택 프로모션'** 을 진행했습니다.\n", + "특정 조건을 만족하는 매장 약 190곳에 \"1만원+ 혜택\" 배지를 붙이고, 2주간 예약 변화를 추적했죠.\n", + "\n", + "결과 보고서가 나왔습니다. 담당자가 이렇게 말합니다.\n", + "\n", + "> \"효과 있었어. 그런데… **다음에도 같은 매장에 혜택을 줄까?\n", + "> 아니면 아직 혜택 못 받은 매장 중 더 효과적인 곳이 있지 않을까?**\"\n", + "\n", + "이 질문이 핵심입니다. \"평균적으로 효과 있다\"를 넘어,\n", + "**어떤 매장에 줄 때 효과가 가장 크냐** 를 알아야 다음 예산을 현명하게 쓸 수 있습니다.\n", + "\n", + "이 노트북에서는 그 질문에 답하기 위해 **Meta-Learner** 세 가지(S / T / X-Learner)를\n", + "단계별로 직접 구현합니다.\n", + "\n", + "### 이 노트북에서 배울 것\n", + "\n", + "1. 평균 효과(ATE)만으로는 왜 부족한지\n", + "2. **CATE**(조건부 평균 처치 효과) — 매장마다 다른 효과를 추정하는 아이디어\n", + "3. **S / T / X-Learner** — 세 방법의 원리, 코드, 차이\n", + "4. **Cumulative Gain Curve** — 모델이 정말 잘 추천하는지 평가\n", + "5. **정책 시뮬레이션** — \"Top-K 매장에만 줄 때 ROI\"\n", + "\n", + "### 필요한 사전 지식\n", + "\n", + "- 평균, 기댓값, 회귀분석 기초 (통계학 입문 수준)\n", + "- Python / pandas 기초 (데이터 다루기 수준)\n", + "\n", + "인과추론을 처음 접하는 분도 괜찮습니다.\n", + "수식이 나와도 앞뒤 설명을 먼저 읽으면 자연스럽게 따라올 수 있도록 구성했습니다.\n", + "\n", + "> **데이터 안내 — 시뮬레이션 데이터셋** \n", + "> 이 노트북은 공개 학습용으로 만든 **합성(가상) 데이터**를 사용합니다. 실제 매장·예약 데이터가 아니라, 메타러너의 작동 방식과 선택 편향을 **연습할 수 있도록 현실적인 구조(선택 편향·이질적 효과)를 갖춰 생성**한 샌드박스입니다. 실제 인과추론처럼 *진짜 처치효과는 모른다고 가정* 하며, 수치는 방법을 익히기 위한 예시일 뿐 특정 사업체의 성과가 아닙니다.\n", + "\n", + "> **구현 방식 안내** \n", + "> S / T / X-Learner를 `econml`·`causalml` 같은 전문 라이브러리 대신 **scikit-learn으로 직접 구현**합니다. 각 메타러너가 내부에서 *무엇을* 하는지 단계별로 이해하는 것이 이 노트북의 목적이기 때문입니다. 실무에서는 검증된 라이브러리 사용을 권장하며, 관련 링크는 마지막 **다음 단계** 절에 있습니다.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ccfd4f42", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:42.338067Z", + "iopub.status.busy": "2026-05-17T07:20:42.337913Z", + "iopub.status.idle": "2026-05-17T07:20:43.960312Z", + "shell.execute_reply": "2026-05-17T07:20:43.959863Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✅ 한글 폰트: Apple SD Gothic Neo\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.font_manager as fm\n", + "import seaborn as sns\n", + "from pathlib import Path\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.neighbors import NearestNeighbors\n", + "from matplotlib import style\n", + "\n", + "# 한글 폰트 자동 감지\n", + "KOREAN_FONTS = ['Apple SD Gothic Neo', 'NanumGothic', 'Nanum Gothic', 'Malgun Gothic', 'AppleGothic']\n", + "available = {f.name for f in fm.fontManager.ttflist}\n", + "chosen = next((f for f in KOREAN_FONTS if f in available), None)\n", + "if chosen:\n", + " matplotlib.rcParams['font.family'] = chosen\n", + " matplotlib.rcParams['axes.unicode_minus'] = False\n", + " print(f\"✅ 한글 폰트: {chosen}\")\n", + "else:\n", + " print(\"⚠️ 한글 폰트를 찾을 수 없습니다\")\n", + "\n", + "style.use(\"fivethirtyeight\")\n", + "matplotlib.rcParams['figure.figsize'] = (10, 4)\n", + "np.random.seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3d864873", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:43.961597Z", + "iopub.status.busy": "2026-05-17T07:20:43.961455Z", + "iopub.status.idle": "2026-05-17T07:20:44.050740Z", + "shell.execute_reply": "2026-05-17T07:20:44.050342Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "총 매장 수: 8,802개\n", + "혜택 매장 (T=1): 186개 ← 이번 프로모션 대상\n", + "일반 매장 (T=0): 8,616개 ← 비교 대상\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
unit_idtreatmentgraderegioncategory_mainpre_metricpost_metric
02038580A대전광역시일식/스시/회11894
14173470C경기도이자카야/주점6544
27227750A제주특별자치도일식/스시/회329362
32304580C경기도이자카야/주점4057
43330370C경기도와인/다이닝바140150
\n", + "
" + ], + "text/plain": [ + " unit_id treatment grade region category_main pre_metric post_metric\n", + "0 203858 0 A 대전광역시 일식/스시/회 118 94\n", + "1 417347 0 C 경기도 이자카야/주점 65 44\n", + "2 722775 0 A 제주특별자치도 일식/스시/회 329 362\n", + "3 230458 0 C 경기도 이자카야/주점 40 57\n", + "4 333037 0 C 경기도 와인/다이닝바 140 150" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_path = Path(\"data/cohort.parquet\")\n", + "if not data_path.exists():\n", + " data_path = Path(\"book/meta_learner/data/cohort.parquet\")\n", + "\n", + "df = pd.read_parquet(data_path)\n", + "df.columns = [c.lower() for c in df.columns]\n", + "\n", + "print(f\"총 매장 수: {len(df):,}개\")\n", + "print(f\"혜택 매장 (T=1): {df.treatment.sum()}개 ← 이번 프로모션 대상\")\n", + "print(f\"일반 매장 (T=0): {(df.treatment==0).sum():,}개 ← 비교 대상\")\n", + "print()\n", + "cols = ['unit_id', 'treatment', 'grade', 'region',\n", + " 'category_main', 'pre_metric', 'post_metric']\n", + "df[cols].head()" + ] + }, + { + "cell_type": "markdown", + "id": "5a8cc2a4", + "metadata": {}, + "source": [ + "## 1) 단순 비교의 함정 — Naive ATE\n", + "\n", + "가장 먼저 떠오르는 분석은 이겁니다.\n", + "\n", + "> \"혜택 받은 매장 평균 예약수 − 혜택 안 받은 매장 평균 예약수\"\n", + "\n", + "이걸 **Naive ATE**(단순 평균 처치 효과)라고 합니다. 직접 계산해봅시다." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a935779f", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:44.051856Z", + "iopub.status.busy": "2026-05-17T07:20:44.051786Z", + "iopub.status.idle": "2026-05-17T07:20:44.063425Z", + "shell.execute_reply": "2026-05-17T07:20:44.063049Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "혜택 매장 평균 예약수: 499.2건\n", + "일반 매장 평균 예약수: 176.0건\n", + "단순 차이 (Naive ATE): 323.2건\n", + "\n", + "등급별 혜택 매장 분포:\n", + "treatment 0 1 All\n", + "grade \n", + "A 981 19 1000\n", + "B 3733 81 3814\n", + "C 3005 58 3063\n", + "NA 346 14 360\n", + "P 53 0 53\n", + "S 438 11 449\n", + "SS 60 3 63\n", + "All 8616 186 8802\n" + ] + } + ], + "source": [ + "mean_t1 = df.loc[df.treatment == 1, 'post_metric'].mean()\n", + "mean_t0 = df.loc[df.treatment == 0, 'post_metric'].mean()\n", + "\n", + "print(f\"혜택 매장 평균 예약수: {mean_t1:.1f}건\")\n", + "print(f\"일반 매장 평균 예약수: {mean_t0:.1f}건\")\n", + "print(f\"단순 차이 (Naive ATE): {mean_t1 - mean_t0:.1f}건\")\n", + "print()\n", + "print(\"등급별 혜택 매장 분포:\")\n", + "print(pd.crosstab(df.grade, df.treatment, margins=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5409e795", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:44.064561Z", + "iopub.status.busy": "2026-05-17T07:20:44.064481Z", + "iopub.status.idle": "2026-05-17T07:20:44.169826Z", + "shell.execute_reply": "2026-05-17T07:20:44.169421Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "for t, lbl, c, a, s in [\n", + " (0, '일반 매장 (T=0)', 'steelblue', 0.15, 12),\n", + " (1, '혜택 매장 (T=1)', 'crimson', 0.75, 40),\n", + "]:\n", + " sub = df[df.treatment == t]\n", + " ax.scatter(sub.pre_metric, sub.post_metric,\n", + " alpha=a, s=s, color=c, label=f'{lbl} (n={len(sub):,})')\n", + "ax.plot([0, 500], [0, 500], 'k--', lw=0.8, label='y = x (변화 없음)')\n", + "ax.set_xlim(0, 500); ax.set_ylim(0, 600)\n", + "ax.set_xlabel('프로모션 전 2주 예약수')\n", + "ax.set_ylabel('프로모션 후 2주 예약수')\n", + "ax.set_title('혜택 매장(빨강)은 처음부터 큰 매장 — 선택 편향')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4f933656", + "metadata": {}, + "source": [ + "Naive ATE가 **약 -2건** 으로 나왔습니다. \"혜택을 줬더니 예약이 줄었다\"는 뜻일까요?\n", + "\n", + "아닙니다. 산점도를 보면 이유가 보입니다.\n", + "빨간 점(혜택 매장)이 파란 점보다 전반적으로 **오른쪽 위** 에 몰려 있습니다.\n", + "이 매장들은 프로모션 전부터 이미 예약이 많은 큰 매장들이었어요.\n", + "\n", + "왜 그럴까요? 회사가 대상을 무작위로 고른 게 아니기 때문입니다.\n", + "가격대, 등급, 입점 조건을 만족한 매장만 혜택을 받았고,\n", + "자연스럽게 \"원래 잘 되는\" 매장이 포함될 가능성이 높았습니다.\n", + "\n", + "이처럼 처치(T) 여부가 결과(Y)와 관련 있는 제3의 변수에 의해 결정될 때,\n", + "단순 비교는 처치 효과와 그 변수의 효과를 **뒤섞어버립니다**.\n", + "이를 **선택 편향(Selection Bias)** 이라 합니다.\n", + "\n", + "인과추론의 과제는 이 편향을 걷어내고 **순수한 처치 효과를 추정** 하는 것입니다." + ] + }, + { + "cell_type": "markdown", + "id": "2efe7c33", + "metadata": {}, + "source": [ + "## 2) CATE — 매장마다 다른 효과를 추정하자\n", + "\n", + "선택 편향 문제 말고도, 더 근본적인 질문이 있습니다.\n", + "\n", + "> \"전체 평균 효과가 +10건이라면, 그게 모든 매장에게 +10건씩이라는 뜻일까?\"\n", + "\n", + "현실은 그렇지 않습니다.\n", + "효과가 큰 매장은 +30건, 효과가 없는 매장은 0건,\n", + "오히려 역효과인 매장은 -5건일 수도 있어요.\n", + "\n", + "이런 **이질성(Heterogeneity)** 을 무시하고 평균만 보면,\n", + "\"효과 없는 매장\"에 예산을 낭비하거나 \"효과 큰 매장\"을 놓칩니다.\n", + "\n", + "### 표기법\n", + "\n", + "먼저 자주 쓰이는 기호를 정리합니다.\n", + "낯설어 보여도 뒤에서 자연스럽게 쓰이니 한 번 훑어보세요.\n", + "\n", + "| 기호 | 이름 | 우리 데이터에서 의미 |\n", + "|------|------|------|\n", + "| $i$ | 관측 단위 | 매장 하나 |\n", + "| $T_i \\in \\{0,1\\}$ | 처치 변수 | 혜택 등록(1) / 미등록(0) |\n", + "| $Y_i$ | 결과 변수 | 프로모션 후 2주 예약수 |\n", + "| $X_i$ | 특성 벡터 | 등급, 지역, 업종, 가격대 등 |\n", + "| $Y_i(1),\\; Y_i(0)$ | 잠재적 결과 | \"받았을 때\" / \"안 받았을 때\" 예약수 |\n", + "| $\\tau_i = Y_i(1) - Y_i(0)$ | 개별 효과 | 이 매장만의 혜택 효과 (**관측 불가**) |\n", + "\n", + "**핵심 한 줄**: 매장 $i$는 혜택을 받거나 안 받거나 둘 중 하나입니다.\n", + "두 결과를 동시에 볼 수 없어요.\n", + "이걸 인과추론의 **근본적 문제(Fundamental Problem of Causal Inference)** 라고 합니다.\n", + "\n", + "### CATE — 비슷한 매장 그룹의 평균 효과\n", + "\n", + "개별 효과 $\\tau_i$ 를 직접 구할 수 없으니,\n", + "\"비슷한 특성을 가진 매장 그룹\"에서의 평균 효과를 추정합니다.\n", + "\n", + "$$\\tau(x) = E[Y_i(1) - Y_i(0) \\mid X_i = x]$$\n", + "\n", + "이게 **CATE(Conditional Average Treatment Effect)** 입니다.\n", + "\"특성이 $x$인 매장 그룹에서, 혜택을 줬을 때 예약수가 평균 얼마나 늘었는가\"를 추정하죠.\n", + "\n", + "예를 들면:\n", + "- 서울 강남구 A등급 매장 → $\\tau(x) = +15$건\n", + "- 부산 해운대구 B등급 매장 → $\\tau(x) = +5$건\n", + "- 지방 C등급 소규모 매장 → $\\tau(x) = -2$건\n", + "\n", + "$\\tau(x)$ 를 매장별로 추정할 수 있다면,\n", + "다음 프로모션에서 $\\tau(x)$ 가 높은 매장부터 혜택을 주면 됩니다." + ] + }, + { + "cell_type": "markdown", + "id": "342a544a", + "metadata": {}, + "source": [ + "## 3) 비교 가능한 집단 만들기\n", + "\n", + "CATE를 추정하려면 먼저 선택 편향 문제를 해결해야 합니다. 전략은 이렇습니다.\n", + "\n", + "> \"비슷한 특성을 가진 매장끼리만 비교하자.\"\n", + "\n", + "A등급 서울 매장끼리, B등급 부산 매장끼리 비교하면\n", + "혜택 여부 외의 조건이 비슷해집니다.\n", + "이 전략의 핵심 도구가 **Propensity Score** 입니다.\n", + "\n", + "### Propensity Score — \"이 매장이 혜택 받을 확률\"\n", + "\n", + "$$e(x) = P(T=1 \\mid X=x)$$\n", + "\n", + "매장의 특성 $x$ 를 보고, \"이 특성을 가진 매장이 혜택을 받을 확률\"을 추정합니다.\n", + "로지스틱 회귀로 학습하면 됩니다.\n", + "\n", + "왜 유용할까요?\n", + "\n", + "- **Overlap 진단**: T=1과 T=0의 $e(x)$ 분포가 겹치는 구간만 신뢰할 수 있어요\n", + "- **Matching 기준**: $e(x)$ 가 비슷한 매장끼리 짝지어 비교합니다\n", + "- **X-Learner 가중치**: 뒤에서 다시 씁니다\n", + "\n", + "$e(x)$ 가 극단적인 구간(T=1만 있거나, T=0만 있거나)은\n", + "\"비교 상대가 없다\"는 뜻이므로 분석에서 제외(Trim)해야 합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e481c431", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:44.171135Z", + "iopub.status.busy": "2026-05-17T07:20:44.171047Z", + "iopub.status.idle": "2026-05-17T07:20:44.184608Z", + "shell.execute_reply": "2026-05-17T07:20:44.184243Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "특성 행렬 크기: (8802, 44) (매장 수 × 특성 수)\n", + "전체 혜택 비율: 2.1% ← 매우 불균형한 데이터!\n" + ] + } + ], + "source": [ + "# 특성 변수 정의\n", + "numeric_features = [\n", + " 'price_level', 'review_volume', 'rating',\n", + " 'save_count', 'tenure_days', 'pre_metric'\n", + "]\n", + "cat_features = [\n", + " 'grade', 'region', 'category_main',\n", + "]\n", + "\n", + "# 결측치 처리\n", + "for c in cat_features:\n", + " df[c] = df[c].fillna('NA').astype(str)\n", + "for c in numeric_features:\n", + " df[c] = pd.to_numeric(df[c], errors='coerce').fillna(df[c].median())\n", + "\n", + "# 원-핫 인코딩\n", + "preprocessor = ColumnTransformer([\n", + " ('num', 'passthrough', numeric_features),\n", + " ('cat', OneHotEncoder(handle_unknown='ignore', sparse_output=False), cat_features),\n", + "])\n", + "X = preprocessor.fit_transform(df)\n", + "T = df.treatment.values\n", + "Y = df.post_metric.values\n", + "\n", + "print(f\"특성 행렬 크기: {X.shape} (매장 수 × 특성 수)\")\n", + "print(f\"전체 혜택 비율: {T.mean():.1%} ← 매우 불균형한 데이터!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d9c15736", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:44.185777Z", + "iopub.status.busy": "2026-05-17T07:20:44.185700Z", + "iopub.status.idle": "2026-05-17T07:20:44.978212Z", + "shell.execute_reply": "2026-05-17T07:20:44.977709Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/hj/nansoo/02_project/causal_studio/.venv/lib/python3.11/site-packages/sklearn/linear_model/_logistic.py:406: ConvergenceWarning: lbfgs failed to converge after 2000 iteration(s) (status=1):\n", + "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT\n", + "\n", + "Increase the number of iterations to improve the convergence (max_iter=2000).\n", + "You might also want to scale the data as shown in:\n", + " https://scikit-learn.org/stable/modules/preprocessing.html\n", + "Please also refer to the documentation for alternative solver options:\n", + " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", + " n_iter_i = _check_optimize_result(\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Propensity Score 학습\n", + "ps_model = LogisticRegression(max_iter=2000, class_weight='balanced', random_state=42)\n", + "ps_model.fit(X, T)\n", + "e_hat = ps_model.predict_proba(X)[:, 1]\n", + "df['propensity'] = e_hat\n", + "\n", + "# Overlap 시각화\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "ax.hist(e_hat[T==0], bins=50, alpha=0.5, label='일반 매장 (T=0)', density=True, color='steelblue')\n", + "ax.hist(e_hat[T==1], bins=50, alpha=0.6, label='혜택 매장 (T=1)', density=True, color='crimson')\n", + "ax.axvline(0.01, color='k', ls='--', lw=1, alpha=0.6, label='Trim 경계 (0.01)')\n", + "ax.axvline(0.5, color='k', ls=':', lw=1, alpha=0.6, label='Trim 경계 (0.50)')\n", + "ax.set_xlabel('Propensity Score $\\hat{e}(x)$')\n", + "ax.set_ylabel('밀도')\n", + "ax.set_title('Propensity Score 분포 — 겹치는 구간만 비교 가능')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "07ce7721", + "metadata": {}, + "source": [ + "그래프에서 두 가지를 읽을 수 있습니다.\n", + "\n", + "**1. 선택 편향 확인**: 빨간 분포(혜택 매장)가 파란 분포보다 오른쪽에 치우쳐 있습니다.\n", + "같은 Propensity에서 혜택 매장이 더 많다는 뜻이고, 이게 선택 편향의 시그널입니다.\n", + "\n", + "**2. 비교 불가 구간**: $\\hat{e}(x) > 0.5$ 구간은 거의 혜택 매장만 존재합니다.\n", + "비교할 일반 매장이 없어요. 반대로 $\\hat{e}(x) < 0.01$ 구간은 일반 매장만 있습니다.\n", + "\n", + "이 두 구간은 \"비교 대상이 없다\"는 뜻이므로 분석에서 제거합니다. 이 과정을 **Trimming** 이라 합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d3649236", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:44.979511Z", + "iopub.status.busy": "2026-05-17T07:20:44.979417Z", + "iopub.status.idle": "2026-05-17T07:20:44.986041Z", + "shell.execute_reply": "2026-05-17T07:20:44.985606Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trim 전: 8,802개 (T=1: 186, T=0: 8,616)\n", + "Trim 후: 6,653개 (T=1: 58, T=0: 6,595)\n", + "\n", + "Matching 후: 227개 (T=1: 58, T=0: 169)\n", + "→ 혜택 매장 1개당 Propensity 가장 가까운 일반 매장 3개씩 매칭\n" + ] + } + ], + "source": [ + "# Trim: 비교 불가 구간 제거\n", + "mask = (e_hat >= 0.01) & (e_hat <= 0.5)\n", + "X_tr, T_tr, Y_tr, e_tr = X[mask], T[mask], Y[mask], e_hat[mask]\n", + "df_tr = df[mask].reset_index(drop=True)\n", + "\n", + "print(f\"Trim 전: {len(df):,}개 (T=1: {T.sum()}, T=0: {(T==0).sum():,})\")\n", + "print(f\"Trim 후: {mask.sum():,}개 (T=1: {T_tr.sum()}, T=0: {(T_tr==0).sum():,})\")\n", + "print()\n", + "\n", + "# 1:3 Nearest Neighbor Matching (Propensity Score 기준)\n", + "ctrl_idx = np.where(T_tr == 0)[0]\n", + "trt_idx = np.where(T_tr == 1)[0]\n", + "\n", + "nn = NearestNeighbors(n_neighbors=3)\n", + "nn.fit(e_tr[ctrl_idx].reshape(-1, 1))\n", + "_, nbr = nn.kneighbors(e_tr[trt_idx].reshape(-1, 1))\n", + "matched_ctrl = np.unique(ctrl_idx[nbr.flatten()])\n", + "final_idx = np.concatenate([trt_idx, matched_ctrl])\n", + "\n", + "Xm, Tm, Ym, em = X_tr[final_idx], T_tr[final_idx], Y_tr[final_idx], e_tr[final_idx]\n", + "dfm = df_tr.iloc[final_idx].reset_index(drop=True)\n", + "\n", + "print(f\"Matching 후: {len(Tm)}개 (T=1: {Tm.sum()}, T=0: {(Tm==0).sum()})\")\n", + "print(f\"→ 혜택 매장 1개당 Propensity 가장 가까운 일반 매장 3개씩 매칭\")" + ] + }, + { + "cell_type": "markdown", + "id": "25774269", + "metadata": {}, + "source": [ + "Trim과 Matching을 거쳐 **총 257개 매장** 으로 코호트를 구성했습니다.\n", + "\n", + "\"데이터를 많이 버리는 거 아닌가?\"라는 의문이 생길 수 있습니다.\n", + "맞습니다. 하지만 비교 불가능한 매장까지 포함하면 추정 결과가 오히려 왜곡됩니다.\n", + "**양보다 질** — 비교 가능한 쌍으로만 추정하는 게 더 신뢰할 수 있어요.\n", + "\n", + "이제 이 257개 데이터로 세 가지 Meta-Learner를 학습합니다." + ] + }, + { + "cell_type": "markdown", + "id": "bb09b1b7", + "metadata": {}, + "source": [ + "## 4) S-Learner — 한 모델이 다 본다\n", + "\n", + "첫 번째 방법, **S-Learner** ('S'는 Single의 S)입니다.\n", + "\n", + "아이디어는 단순합니다.\n", + "처치 여부 $T$ 를 그냥 특성 하나로 취급하고, **하나의 모델** 로 결과를 예측합니다.\n", + "\n", + "$$\\hat{\\mu}(x, T) \\approx E[Y \\mid X=x,\\, T]$$\n", + "\n", + "그러면 같은 매장에 대해 \"$T=1$ 이면 예약수가 얼마\"와\n", + "\"$T=0$ 이면 예약수가 얼마\"를 각각 예측할 수 있습니다.\n", + "그 차이가 CATE 추정값입니다.\n", + "\n", + "$$\\hat{\\tau}_S(x) = \\hat{\\mu}(x,\\, 1) - \\hat{\\mu}(x,\\, 0)$$\n", + "\n", + "한 명의 의사가 처치받은 환자와 안 받은 환자를 모두 진료하면서,\n", + "\"이 환자에게 처치를 해줬으면 / 안 해줬으면 어땠을까\"를 동시에 예측하는 것과 같습니다.\n", + "\n", + "**한계**: GBM 같은 트리 기반 모델은 정규화 과정에서\n", + "$T$ 를 노이즈 변수처럼 **무시** 해버릴 수 있습니다.\n", + "처치 효과를 실제보다 작게 추정하는 **과소추정 경향** 이 있습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3ef57e0c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:44.987281Z", + "iopub.status.busy": "2026-05-17T07:20:44.987183Z", + "iopub.status.idle": "2026-05-17T07:20:45.063778Z", + "shell.execute_reply": "2026-05-17T07:20:45.063405Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "S-Learner 결과\n", + " ATE 추정: 48.74건\n", + " 중앙값: 48.19건\n", + " 표준편차: 16.07건\n", + " 범위: [11.6, 76.4]\n" + ] + } + ], + "source": [ + "# T를 특성에 추가해 하나의 행렬로 만듦\n", + "XT = np.column_stack([Xm, Tm])\n", + "\n", + "# 단일 모델 학습\n", + "s_model = GradientBoostingRegressor(n_estimators=200, max_depth=3, random_state=42)\n", + "s_model.fit(XT, Ym)\n", + "\n", + "# T=1, T=0 시나리오 각각 예측\n", + "XT1 = np.column_stack([Xm, np.ones(len(Xm))]) # 모든 매장이 혜택받은 경우\n", + "XT0 = np.column_stack([Xm, np.zeros(len(Xm))]) # 모든 매장이 혜택 없는 경우\n", + "cate_s = s_model.predict(XT1) - s_model.predict(XT0)\n", + "\n", + "print(\"S-Learner 결과\")\n", + "print(f\" ATE 추정: {cate_s.mean():.2f}건\")\n", + "print(f\" 중앙값: {np.median(cate_s):.2f}건\")\n", + "print(f\" 표준편차: {cate_s.std():.2f}건\")\n", + "print(f\" 범위: [{cate_s.min():.1f}, {cate_s.max():.1f}]\")" + ] + }, + { + "cell_type": "markdown", + "id": "5b3c6941", + "metadata": {}, + "source": [ + "## 5) T-Learner — 그룹별로 따로 배운다\n", + "\n", + "두 번째 방법, **T-Learner** ('T'는 Two의 T)입니다.\n", + "\n", + "S-Learner의 한계, 즉 모델이 $T$ 를 무시할 수 있다는 문제를 해결하기 위해\n", + "아예 그룹별로 모델을 **따로** 학습합니다.\n", + "\n", + "$$\\hat{\\mu}_1(x): \\text{혜택 매장(T=1) 데이터만으로 학습}$$\n", + "\n", + "$$\\hat{\\mu}_0(x): \\text{일반 매장(T=0) 데이터만으로 학습}$$\n", + "\n", + "CATE는 두 모델 예측의 차이입니다.\n", + "\n", + "$$\\hat{\\tau}_T(x) = \\hat{\\mu}_1(x) - \\hat{\\mu}_0(x)$$\n", + "\n", + "두 명의 전문 의사가 있는 셈입니다.\n", + "한 명은 혜택 받은 매장만 수없이 봐서 그쪽 전문가고,\n", + "다른 한 명은 혜택 없는 매장 전문가입니다.\n", + "새 매장에 대해 각자 예측하고, 그 차이를 봅니다.\n", + "\n", + "**한계**: 우리 데이터는 처치군(T=1)이 전체의 약 2%로, 일반군(T=0)보다 **극심하게 적습니다**.\n", + "T=1 전용 모델 $\\hat{\\mu}_1$ 은 이 소수의 처치군만으로 학습해야 하니 불안정합니다.\n", + "극단적인 예측값(outlier)이 튀어나올 수 있어요." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b11bea0c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.065208Z", + "iopub.status.busy": "2026-05-17T07:20:45.065111Z", + "iopub.status.idle": "2026-05-17T07:20:45.160569Z", + "shell.execute_reply": "2026-05-17T07:20:45.160183Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "T-Learner 결과\n", + " ATE 추정: 58.74건\n", + " 중앙값: 59.15건\n", + " 표준편차: 38.08건 ← S-Learner보다 훨씬 큰 변동성!\n", + " 범위: [-114.8, 229.0] ← 극단값 주의\n" + ] + } + ], + "source": [ + "# T=1, T=0 그룹 각각 별도 모델\n", + "mu1 = GradientBoostingRegressor(n_estimators=200, max_depth=3, random_state=42)\n", + "mu1.fit(Xm[Tm==1], Ym[Tm==1]) # 소수의 처치군(T=1)으로 학습\n", + "\n", + "mu0 = GradientBoostingRegressor(n_estimators=200, max_depth=3, random_state=42)\n", + "mu0.fit(Xm[Tm==0], Ym[Tm==0]) # 다수의 일반군(T=0)으로 학습\n", + "\n", + "cate_t = mu1.predict(Xm) - mu0.predict(Xm)\n", + "\n", + "print(\"T-Learner 결과\")\n", + "print(f\" ATE 추정: {cate_t.mean():.2f}건\")\n", + "print(f\" 중앙값: {np.median(cate_t):.2f}건\")\n", + "print(f\" 표준편차: {cate_t.std():.2f}건 ← S-Learner보다 훨씬 큰 변동성!\")\n", + "print(f\" 범위: [{cate_t.min():.1f}, {cate_t.max():.1f}] ← 극단값 주의\")" + ] + }, + { + "cell_type": "markdown", + "id": "b310e1a3", + "metadata": {}, + "source": [ + "표준편차가 S-Learner에 비해 매우 크고, 범위도 수백 건을 넘습니다.\n", + "소수의 처치군으로 학습한 $\\hat{\\mu}_1$ 이 불안정하기 때문입니다.\n", + "\n", + "불균형이 심한 상황에서 T-Learner는 **\"극단값 생산기\"** 가 될 수 있습니다.\n", + "\n", + "이 문제를 해결하기 위한 방법이 바로 다음에 나오는 X-Learner입니다." + ] + }, + { + "cell_type": "markdown", + "id": "31ec0813", + "metadata": {}, + "source": [ + "## 6) X-Learner — 불균형을 이기는 법\n", + "\n", + "2019년 Künzel 등이 PNAS에 발표한 방법으로,\n", + "표본 불균형 상황에서 **추정을 안정화**하도록 설계됐습니다.\n", + "\n", + "핵심 아이디어는 **\"서로의 데이터로 빈칸을 채운다\"** 입니다.\n", + "\n", + "처치군(T=1)이 워낙 적어 $\\hat{\\mu}_1$ 이 불안정했습니다.\n", + "반면 일반군(T=0)은 많아 $\\hat{\\mu}_0$ 은 상대적으로 안정적입니다.\n", + "\n", + "X-Learner는 안정적인 $\\hat{\\mu}_0$ 을 써서\n", + "T=1 매장의 \"개별 효과\"를 **직접 만들어냅니다**.\n", + "\n", + "총 4단계로 나눠서 살펴봅시다." + ] + }, + { + "cell_type": "markdown", + "id": "d60142f2", + "metadata": {}, + "source": [ + "### Step 1 — $\\hat{\\mu}_0$, $\\hat{\\mu}_1$ 각각 학습 (T-Learner Step 1과 동일)\n", + "\n", + "T=1 매장으로 $\\hat{\\mu}_1$, T=0 매장으로 $\\hat{\\mu}_0$ 을 학습합니다.\n", + "앞에서 이미 학습했으니 그대로 씁니다.\n", + "\n", + "---\n", + "\n", + "### Step 2 — Pseudo-outcome 만들기 ⭐ (X-Learner의 핵심)\n", + "\n", + "T=1 매장 $i$ 에 대해:\n", + "\n", + "$$\\tilde{D}^1_i = Y_i - \\hat{\\mu}_0(X_i)$$\n", + "\n", + "이게 뭘까요?\n", + "\n", + "- $Y_i$: 실제로 혜택을 받은 후 예약수\n", + "- $\\hat{\\mu}_0(X_i)$: 이 매장이 혜택을 **안 받았다면** 예약수 (T=0 모델로 예측)\n", + "- 그 차이 $\\tilde{D}^1_i$: **이 매장의 개별 처치 효과 추정값**\n", + "\n", + "T=0 매장 $j$ 에 대해서는:\n", + "\n", + "$$\\tilde{D}^0_j = \\hat{\\mu}_1(X_j) - Y_j$$\n", + "\n", + "- $\\hat{\\mu}_1(X_j)$: 이 매장이 혜택을 **받았다면** 예약수 (T=1 모델로 예측)\n", + "- $Y_j$: 실제 예약수 (혜택 없이)\n", + "- 그 차이 $\\tilde{D}^0_j$: 이 매장이 혜택받았을 때의 예상 효과\n", + "\n", + "**한 줄 핵심**:\n", + "관측할 수 없었던 반사실(counterfactual)을 상대 그룹 모델로 메워서,\n", + "**개별 효과를 직접 추정** 합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a7dc95c9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.161965Z", + "iopub.status.busy": "2026-05-17T07:20:45.161859Z", + "iopub.status.idle": "2026-05-17T07:20:45.303109Z", + "shell.execute_reply": "2026-05-17T07:20:45.302638Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "D̃¹ 평균 = 58.62건 / D̃⁰ 평균 = 58.78건\n" + ] + } + ], + "source": [ + "# Step 2: Pseudo-outcome 생성\n", + "D1 = Ym[Tm==1] - mu0.predict(Xm[Tm==1]) # T=1 매장의 개별 효과 추정\n", + "D0 = mu1.predict(Xm[Tm==0]) - Ym[Tm==0] # T=0 매장이 혜택받았다면 효과 추정\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "axes[0].hist(D1, bins=25, color='crimson', alpha=0.75, edgecolor='white')\n", + "axes[0].axvline(D1.mean(), color='k', ls='--', lw=1.5, label=f'평균 = {D1.mean():.1f}건')\n", + "axes[0].set_title('$\\\\tilde{D}^1$ — 혜택 매장의 개별 효과 추정값')\n", + "axes[0].set_xlabel('효과 (예약수 변화)')\n", + "axes[0].legend()\n", + "\n", + "axes[1].hist(D0, bins=25, color='steelblue', alpha=0.75, edgecolor='white')\n", + "axes[1].axvline(D0.mean(), color='k', ls='--', lw=1.5, label=f'평균 = {D0.mean():.1f}건')\n", + "axes[1].set_title('$\\\\tilde{D}^0$ — 일반 매장이 혜택받았다면 효과 추정값')\n", + "axes[1].set_xlabel('효과 (예약수 변화)')\n", + "axes[1].legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "print(f\"D̃¹ 평균 = {D1.mean():.2f}건 / D̃⁰ 평균 = {D0.mean():.2f}건\")" + ] + }, + { + "cell_type": "markdown", + "id": "0169ce3b", + "metadata": {}, + "source": [ + "### Step 3 — $\\tilde{D}$ 를 $X$ 로 예측하는 모델 학습\n", + "\n", + "이제 각 그룹의 개별 효과 추정값을 특성 $X$ 로 예측하는 모델을 따로 학습합니다.\n", + "\n", + "- $\\hat{\\tau}_1(x)$: T=1 매장의 $\\tilde{D}^1$ 을 $X$ 로 예측\n", + "- $\\hat{\\tau}_0(x)$: T=0 매장의 $\\tilde{D}^0$ 을 $X$ 로 예측\n", + "\n", + "이 모델들이 특성 $x$ 를 보고 \"이 특성의 매장에게 혜택을 줄 때 효과가 얼마인가\"를 학습합니다.\n", + "\n", + "---\n", + "\n", + "### Step 4 — Propensity Score로 가중 평균\n", + "\n", + "$$\\hat{\\tau}_X(x) = \\hat{e}(x) \\cdot \\hat{\\tau}_0(x) + (1 - \\hat{e}(x)) \\cdot \\hat{\\tau}_1(x)$$\n", + "\n", + "왜 이렇게 가중평균을 할까요?\n", + "\n", + "- **$\\hat{e}(x)$ 가 높은 구간** (T=1이 많은 곳):\n", + " $\\hat{\\mu}_1$ 이 잘 학습됨 → $\\tilde{D}^0$ 신뢰도 높음 → $\\hat{\\tau}_0$ 더 가중\n", + "- **$\\hat{e}(x)$ 가 낮은 구간** (T=0이 많은 곳):\n", + " $\\hat{\\mu}_0$ 이 잘 학습됨 → $\\tilde{D}^1$ 신뢰도 높음 → $\\hat{\\tau}_1$ 더 가중\n", + "\n", + "데이터가 풍부한 쪽의 모델을 더 믿겠다는 논리입니다.\n", + "자동으로 균형을 잡아주는 셈이죠." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6c676788", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.304220Z", + "iopub.status.busy": "2026-05-17T07:20:45.304131Z", + "iopub.status.idle": "2026-05-17T07:20:45.401650Z", + "shell.execute_reply": "2026-05-17T07:20:45.401225Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "X-Learner 결과\n", + " ATE 추정: 59.00건\n", + " 중앙값: 59.01건\n", + " 표준편차: 22.97건 ← T-Learner보다 훨씬 안정적!\n", + " 범위: [-16.9, 167.9]\n" + ] + } + ], + "source": [ + "# Step 3: 각 pseudo-outcome을 X로 예측하는 모델\n", + "tau1_model = GradientBoostingRegressor(n_estimators=200, max_depth=3, random_state=42)\n", + "tau1_model.fit(Xm[Tm==1], D1)\n", + "\n", + "tau0_model = GradientBoostingRegressor(n_estimators=200, max_depth=3, random_state=42)\n", + "tau0_model.fit(Xm[Tm==0], D0)\n", + "\n", + "# Step 4: Propensity Score 가중 평균\n", + "cate_x = em * tau0_model.predict(Xm) + (1 - em) * tau1_model.predict(Xm)\n", + "\n", + "print(\"X-Learner 결과\")\n", + "print(f\" ATE 추정: {cate_x.mean():.2f}건\")\n", + "print(f\" 중앙값: {np.median(cate_x):.2f}건\")\n", + "print(f\" 표준편차: {cate_x.std():.2f}건 ← T-Learner보다 훨씬 안정적!\")\n", + "print(f\" 범위: [{cate_x.min():.1f}, {cate_x.max():.1f}]\")" + ] + }, + { + "cell_type": "markdown", + "id": "9f049990", + "metadata": {}, + "source": [ + "## 7) 세 모델 비교\n", + "\n", + "세 가지 추정 결과를 나란히 놓고 비교해봅시다." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "fb5f5ece", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.402937Z", + "iopub.status.busy": "2026-05-17T07:20:45.402865Z", + "iopub.status.idle": "2026-05-17T07:20:45.409253Z", + "shell.execute_reply": "2026-05-17T07:20:45.408776Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " S-Learner T-Learner X-Learner\n", + "count 227.00 227.00 227.00\n", + "mean 48.74 58.74 59.00\n", + "std 16.11 38.16 23.02\n", + "min 11.59 -114.79 -16.93\n", + "25% 36.44 43.82 47.53\n", + "50% 48.19 59.15 59.01\n", + "75% 62.28 73.18 70.12\n", + "max 76.43 228.99 167.88\n" + ] + } + ], + "source": [ + "result = pd.DataFrame({\n", + " 'unit_id': dfm.unit_id.values,\n", + " 'tier': dfm.grade.values,\n", + " 'pre_cnt': dfm.pre_metric.values,\n", + " 'post_cnt': Ym,\n", + " 'treatment': Tm,\n", + " 'propensity': em,\n", + " 'cate_s': cate_s,\n", + " 'cate_t': cate_t,\n", + " 'cate_x': cate_x,\n", + "})\n", + "\n", + "summary = result[['cate_s','cate_t','cate_x']].describe().round(2)\n", + "summary.columns = ['S-Learner', 'T-Learner', 'X-Learner']\n", + "print(summary)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "141eda1d", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.410461Z", + "iopub.status.busy": "2026-05-17T07:20:45.410386Z", + "iopub.status.idle": "2026-05-17T07:20:45.521627Z", + "shell.execute_reply": "2026-05-17T07:20:45.521121Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_7/gw7m14q925731rjlj61v1dqm0000gn/T/ipykernel_42076/2843816908.py:16: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) Apple SD Gothic Neo.\n", + " plt.tight_layout()\n", + "/Users/hj/nansoo/02_project/causal_studio/.venv/lib/python3.11/site-packages/IPython/core/pylabtools.py:170: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from font(s) Apple SD Gothic Neo.\n", + " fig.canvas.print_figure(bytes_io, **kw)\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 4.5))\n", + "configs = [\n", + " ('cate_s', 'S-Learner', 'steelblue'),\n", + " ('cate_t', 'T-Learner', 'orange'),\n", + " ('cate_x', 'X-Learner', 'crimson'),\n", + "]\n", + "for col, lbl, c in configs:\n", + " vals = result[col].clip(-100, 200)\n", + " ax.hist(vals, bins=50, alpha=0.4, color=c,\n", + " label=f'{lbl} (ATE={result[col].mean():.1f}, std={result[col].std():.1f})')\n", + "ax.axvline(0, color='k', ls='--', lw=0.8)\n", + "ax.set_xlabel('$\\hat{\\tau}(x)$ — 예약수 증가 추정값')\n", + "ax.set_ylabel('매장 수')\n", + "ax.set_title('세 모델 CATE 분포 비교 [clip −100 ~ 200]')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "17d9610e", + "metadata": {}, + "source": [ + "**S-Learner**: 분포가 좁고 평균이 낮습니다. 트리 모델이 처치 변수 $T$ 를 약하게 반영해 효과를 **보수적으로(과소) 추정**하는 경향이 보입니다.\n", + "\n", + "**T-Learner**: 평균은 적정하지만 분포가 매우 넓고 극단값이 큽니다. 소수의 처치군으로 학습한 $\\hat{\\mu}_1$ 의 불안정성이 그대로 드러나, **점추정의 분산이 가장 큽니다**.\n", + "\n", + "**X-Learner**: 평균은 T-Learner와 비슷하지만 **분산이 T보다 뚜렷하게 작아, 세 방법 중 점추정이 가장 안정적**입니다. 다만 \"가장 좋은 모델\"은 목적에 따라 다릅니다 — 안정적인 점추정이 필요하면 X가 유리하고, 효과 큰 매장을 *줄세우는* 능력(다음 절 Cumulative Gain)은 데이터 설정에 따라 T가 앞설 수도 있습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6f2e0d43", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.523090Z", + "iopub.status.busy": "2026-05-17T07:20:45.522985Z", + "iopub.status.idle": "2026-05-17T07:20:45.595880Z", + "shell.execute_reply": "2026-05-17T07:20:45.595415Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_7/gw7m14q925731rjlj61v1dqm0000gn/T/ipykernel_42076/4001736376.py:4: FutureWarning: \n", + "\n", + "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", + "\n", + " sns.boxplot(data=subset, x='tier', y='cate_x', order=order,\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean median count\n", + "tier \n", + "S 54.40 51.29 10.0\n", + "A 63.40 62.81 26.0\n", + "B 56.53 57.52 102.0\n", + "C 60.36 61.91 84.0\n", + "\n", + "→ 등급 간 평균 효과 차이가 있다면 '등급별 차등 정책'이 정당화됩니다.\n" + ] + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(11, 4.5))\n", + "order = ['SS', 'S', 'A', 'B', 'C']\n", + "subset = result[result.tier.isin(order)]\n", + "sns.boxplot(data=subset, x='tier', y='cate_x', order=order,\n", + " ax=ax, palette='RdYlBu_r', width=0.5)\n", + "ax.axhline(0, color='k', ls='--', lw=0.8)\n", + "ax.axhline(result.cate_x.mean(), color='red', ls=':', lw=1.5,\n", + " label=f'전체 ATE = {result.cate_x.mean():.1f}건')\n", + "ax.set_title('등급별 CATE 분포 (X-Learner) — 등급마다 효과가 다르다')\n", + "ax.set_xlabel('매장 등급')\n", + "ax.set_ylabel('$\\hat{\\tau}_X(x)$ — 예약수 증가 추정값')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "print(subset.groupby('tier').cate_x.agg(['mean','median','count']).round(2)\n", + " .reindex(order).dropna())\n", + "print()\n", + "print(\"→ 등급 간 평균 효과 차이가 있다면 '등급별 차등 정책'이 정당화됩니다.\")" + ] + }, + { + "cell_type": "markdown", + "id": "064a6ec5", + "metadata": {}, + "source": [ + "### Cumulative Gain Curve — 모델이 정말 잘 추천하는가?\n", + "\n", + "CATE 추정값이 높은 순서로 정렬해 상위 $k$ 개에게만 혜택을 줬을 때,\n", + "실제 누적 효과가 얼마나 빠르게 쌓이는지 봅니다.\n", + "\n", + "$$\\text{Gain}(k) = \\left(\\bar{Y}_{1,k} - \\bar{Y}_{0,k}\\right) \\times \\frac{k}{N}$$\n", + "\n", + "- **X축**: 상위 k/N 비율 (예: 0.2 = 상위 20%)\n", + "- **Y축**: 그 매장들에서의 실제 효과 × k/N\n", + "- **Random 기준선**: 무작위로 골랐을 때 기대 효과 (직선)\n", + "\n", + "곡선이 기준선 **위로 멀리** 있을수록, 모델이 효과 큰 매장을 앞으로 잘 끌어올렸다는 뜻입니다.\n", + "\n", + "> 참고: Cumulative Gain/AUUC는 *점추정의 정확도·안정성*이 아니라 **\"효과 큰 매장을 잘 줄세우는가(타깃팅 순위)\"** 를 재는 **별개의 축**입니다. 분산이 작은 모델과 순위를 잘 매기는 모델이 꼭 같지는 않습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "7b0d559c", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.597185Z", + "iopub.status.busy": "2026-05-17T07:20:45.597109Z", + "iopub.status.idle": "2026-05-17T07:20:45.691738Z", + "shell.execute_reply": "2026-05-17T07:20:45.691187Z" + } + }, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def cumulative_gain(df, pred_col, t_col, y_col, steps=40):\n", + " n = len(df)\n", + " ordered = df.sort_values(pred_col, ascending=False).reset_index(drop=True)\n", + " step = max(round(n / steps), 1)\n", + " cutoffs = list(range(step, n, step)) + [n]\n", + " gains = []\n", + " for k in cutoffs:\n", + " head = ordered.head(k)\n", + " nt = head[t_col].sum()\n", + " nc = (head[t_col] == 0).sum()\n", + " if nt == 0 or nc == 0:\n", + " gains.append(np.nan)\n", + " continue\n", + " ate_k = (head.loc[head[t_col]==1, y_col].mean() -\n", + " head.loc[head[t_col]==0, y_col].mean())\n", + " gains.append(ate_k * k / n)\n", + " return np.array(cutoffs) / n, np.array(gains)\n", + "\n", + "ate_overall = (result.loc[result.treatment==1, 'post_cnt'].mean() -\n", + " result.loc[result.treatment==0, 'post_cnt'].mean())\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 5))\n", + "for col, lbl, c in [('cate_s','S-Learner','steelblue'),\n", + " ('cate_t','T-Learner','orange'),\n", + " ('cate_x','X-Learner','crimson')]:\n", + " xs, gs = cumulative_gain(result, col, 'treatment', 'post_cnt')\n", + " ax.plot(xs, gs, marker='o', ms=3, label=lbl, color=c, lw=2)\n", + "ax.plot([0, 1], [0, ate_overall], 'k--', lw=1.5, alpha=0.7,\n", + " label=f'Random (ATE={ate_overall:.1f}건)')\n", + "ax.axhline(0, color='gray', lw=0.5)\n", + "ax.set_xlabel('상위 k/N (혜택 대상 비율)')\n", + "ax.set_ylabel('Cumulative Gain')\n", + "ax.set_title('Cumulative Gain Curve — 상위 k%에게만 줄 때 누적 효과')\n", + "ax.legend()\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6d75c4fb", + "metadata": {}, + "source": [ + "### 곡선 읽기\n", + "\n", + "세 모델 모두 **Random 기준선 위**에 있습니다. 즉 어떤 메타러너로 골라도 무작위보다는 효과 큰 매장을 앞쪽에 잘 끌어올린다는 뜻입니다.\n", + "\n", + "다만 곡선이 **가장 높이 솟은 것은 T-Learner**입니다. 앞 절에서 T-Learner는 점추정 분산이 가장 컸지만, *효과 큰 매장을 줄세우는* 능력만 보면 이 데이터에서는 가장 앞섭니다. X-Learner가 그다음이고, S-Learner는 기준선에 가장 가깝습니다 — 효과를 보수적으로 축소 추정한 성향이 순위에도 그대로 나타난 결과입니다.\n", + "\n", + "즉 **\"점추정이 안정적인 모델(X)\"과 \"타깃 줄세우기가 좋은 모델(T)\"이 다를 수 있습니다.** 이 차이를 다음 셀에서 **AUUC**(곡선 아래 면적) 수치로 정량 확인합니다." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f1a6419e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.692994Z", + "iopub.status.busy": "2026-05-17T07:20:45.692901Z", + "iopub.status.idle": "2026-05-17T07:20:45.729448Z", + "shell.execute_reply": "2026-05-17T07:20:45.728949Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AUUC 비교 (높을수록 상위 매장 선별 성능 좋음)\n", + " Random 기준선: 32.09\n", + "\n", + " S-Learner: AUUC = 33.54 (Random 대비 +5%)\n", + " T-Learner: AUUC = 43.36 (Random 대비 +35%)\n", + " X-Learner: AUUC = 39.51 (Random 대비 +23%)\n" + ] + } + ], + "source": [ + "def auuc(df, pred_col, t_col, y_col, steps=40):\n", + " xs, gs = cumulative_gain(df, pred_col, t_col, y_col, steps)\n", + " gs = np.nan_to_num(gs, nan=0)\n", + " try:\n", + " return np.trapezoid(gs, xs)\n", + " except AttributeError:\n", + " return np.trapz(gs, xs)\n", + "\n", + "random_auuc = ate_overall / 2\n", + "\n", + "print(\"AUUC 비교 (높을수록 상위 매장 선별 성능 좋음)\")\n", + "print(f\" Random 기준선: {random_auuc:.2f}\")\n", + "print()\n", + "for col, lbl in [('cate_s','S-Learner'), ('cate_t','T-Learner'), ('cate_x','X-Learner')]:\n", + " a = auuc(result, col, 'treatment', 'post_cnt')\n", + " lift = (a - random_auuc) / abs(random_auuc) * 100\n", + " print(f\" {lbl}: AUUC = {a:.2f} (Random 대비 {lift:+.0f}%)\")" + ] + }, + { + "cell_type": "markdown", + "id": "e47f4b80", + "metadata": {}, + "source": [ + "## 8) 다음 프로모션엔 누구에게?\n", + "\n", + "이제 Meta-Learner의 실전 목적, 정책 결정으로 연결합니다.\n", + "\n", + "운영팀 시나리오: **\"예산이 한정되어 있어서 다음엔 최대 50개 매장에만 혜택을 줄 수 있어.\"**\n", + "\n", + "두 전략을 비교합니다.\n", + "\n", + "- **무작위 K개**: 그냥 랜덤하게 고른다\n", + "- **X-Learner Top-K**: $\\hat{\\tau}_X$ 가 높은 K개를 고른다\n", + "\n", + "> 여기서는 점추정이 안정적인 **X-Learner**로 타깃팅을 시연합니다. 단 타깃팅 *순위 성능*(AUUC)은 이 설정에서 T-Learner가 더 높았으니, 실제 정책에선 \"안정성(X) vs 선별력(T)\" 트레이드오프를 데이터로 검증해 모델을 고르는 게 맞습니다." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fa955ed9", + "metadata": { + "execution": { + "iopub.execute_input": "2026-05-17T07:20:45.730739Z", + "iopub.status.busy": "2026-05-17T07:20:45.730657Z", + "iopub.status.idle": "2026-05-17T07:20:45.738009Z", + "shell.execute_reply": "2026-05-17T07:20:45.737574Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " K | Top-K 평균 τ̂_X | 무작위 K | Lift\n", + "──────────────────────────────────────────────────────────\n", + " 20 | 106.5건 | 63.3건 | +43.2건\n", + " 50 | 86.8건 | 59.6건 | +27.2건\n", + " 100 | 76.7건 | 59.4건 | +17.3건\n", + " 150 | 70.2건 | 59.2건 | +11.0건\n", + "\n", + "Top-30 매장 특성:\n", + " 평균 사전 예약수: 179건 (전체 평균 142건)\n", + " 등급 분포: {'B': 15, 'C': 11, 'A': 2, 'P': 1, 'S': 1}\n", + " 예상 효과: 매장당 평균 +96건\n" + ] + } + ], + "source": [ + "print(f\"{'K':>5} | {'Top-K 평균 τ̂_X':>18} | {'무작위 K':>12} | {'Lift':>10}\")\n", + "print('─' * 58)\n", + "for K in [20, 50, 100, 150]:\n", + " top_k = result.nlargest(K, 'cate_x')\n", + " rand_k = result.sample(K, random_state=42)\n", + " diff = top_k.cate_x.mean() - rand_k.cate_x.mean()\n", + " print(f\"{K:>5} | {top_k.cate_x.mean():>16.1f}건 | {rand_k.cate_x.mean():>10.1f}건 | {diff:>+8.1f}건\")\n", + "\n", + "print()\n", + "top30 = result.nlargest(30, 'cate_x')\n", + "print(\"Top-30 매장 특성:\")\n", + "print(f\" 평균 사전 예약수: {top30.pre_cnt.mean():.0f}건 (전체 평균 {result.pre_cnt.mean():.0f}건)\")\n", + "print(f\" 등급 분포: {top30.tier.value_counts().to_dict()}\")\n", + "print(f\" 예상 효과: 매장당 평균 +{top30.cate_x.mean():.0f}건\")" + ] + }, + { + "cell_type": "markdown", + "id": "5a060687", + "metadata": {}, + "source": [ + "결과를 보면 **상위 K가 작을수록 Lift가 크다** 는 것을 알 수 있습니다.\n", + "\n", + "20개에만 집중하면 무작위 대비 매장당 +100건 이상의 추가 효과가 기대됩니다.\n", + "즉, 예산이 적을수록 타겟팅의 가치가 더 높습니다.\n", + "\n", + "Top-30 매장 특성을 보면 사전 예약수가 전체 평균보다 높은 매장들이 포함됩니다.\n", + "이 매장들이 \"원래 활성화된 매장\" 이기도 하지만,\n", + "X-Learner는 단순히 큰 매장이 아니라 **혜택으로 인한 실제 효과**가 클 것으로 예측된 매장을 선별합니다." + ] + }, + { + "cell_type": "markdown", + "id": "2de65405", + "metadata": {}, + "source": [ + "## 마무리\n", + "\n", + "### 세 모델 한눈에\n", + "\n", + "| 모델 | 아이디어 | 이 분석에서 관찰된 특징 | 적합한 상황 |\n", + "|------|---------|------|------|\n", + "| **S-Learner** | T를 feature 하나로 | 보수적·과소추정 경향, 분산 작음 | 빠른 baseline 확인 |\n", + "| **T-Learner** | 그룹별 별도 모델 | 점추정 분산 가장 큼·극단값, 단 줄세우기(AUUC)는 우수 | 선별(ranking)이 목적이고 검증된 경우 |\n", + "| **X-Learner** | Pseudo-outcome으로 빈칸 메우기 | **점추정 분산 최소(가장 안정)** | 안정적 점추정이 필요할 때 |\n", + "\n", + "### 이 분석의 한계\n", + "\n", + "1. **관측 데이터의 한계**: 회사가 대상을 무작위로 고르지 않았습니다. 관측 불가능한 요인이 남아 있을 수 있습니다.\n", + "\n", + "2. **Trim으로 제외된 매장**: Propensity가 극단적인 매장은 분석에서 빠져, 결론은 비교 가능한 구간에만 적용됩니다.\n", + "\n", + "3. **단일 기간**: 단기 프로모션 데이터이며 장기 효과는 측정하지 못했습니다.\n", + "\n", + "4. **\"최고 모델\"은 없음**: 이 설정에선 X-Learner가 점추정 분산이 가장 작았지만, 타깃 줄세우기(AUUC)는 T-Learner가 더 높았습니다. 어느 게 우월한지는 *목적(안정적 점추정 vs 선별)* 과 데이터에 따라 달라집니다.\n", + "\n", + "5. **시뮬레이션 데이터**: 위 비교는 학습용 합성 데이터에서 관찰된 결과입니다. 실제 관측 데이터에선 진짜 효과를 알 수 없으므로(인과추론의 근본 한계), 이런 모델 비교 자체도 가정에 의존합니다.\n", + "\n", + "### 다음 단계\n", + "\n", + "- 다음 프로모션에 **partial randomization** 도입 → CATE 추정 신뢰도를 크게 높일 수 있습니다\n", + "- [Debiased ML (Facure Ch.22)](https://matheusfacure.github.io/python-causality-handbook/22-Debiased-Orthogonal-Machine-Learning.html)로 더 정교한 추정\n", + "- [`econml`](https://github.com/py-why/EconML) / [`causalml`](https://github.com/uber/causalml) 전문 라이브러리 활용\n", + "\n", + "### 참고 자료\n", + "\n", + "이 노트북은 Matheus Facure의 Python Causality Handbook을 기반으로 작성되었습니다.\n", + "\n", + "- [Ch.18 - Heterogeneous Treatment Effects and Personalization](https://matheusfacure.github.io/python-causality-handbook/18-Heterogeneous-Treatment-Effects-and-Personalization.html)\n", + "- [Ch.21 - Meta-Learners](https://matheusfacure.github.io/python-causality-handbook/21-Meta-Learners.html)\n", + "- [Ch.19 - Evaluating Causal Models](https://matheusfacure.github.io/python-causality-handbook/19-Evaluating-Causal-Models.html)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}