From e0961727d3fa2fab11175371aa256535b497f04e Mon Sep 17 00:00:00 2001 From: Divya Tiwari Date: Tue, 24 Sep 2024 18:08:00 +0530 Subject: [PATCH 1/8] AB overview nb --- .../anomaly_detection/anomaly_detection.ipynb | 109 ++++++++++++++++-- 1 file changed, 102 insertions(+), 7 deletions(-) diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index a49aaa71d4..17a1907c1d 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -1,35 +1,130 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", + "id": "36c7dcfec1245abe", + "metadata": {}, "source": [ - "# Anomaly Detection\n", + "# Anomaly Detection in Time Series with aeon\n", "\n", "This notebook is currently under construction! Please check back later.\n", "\n", "If you have any questions about the `aeon` anomaly detection module in the mean time, please ask us on Slack!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "88276e2b-149a-4d32-aa4e-e15400c1086b", + "metadata": {}, + "outputs": [ + { + "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", + "
nameestimator
0DWT_MLEAD<class 'aeon.anomaly_detection._dwt_mlead.DWT_...
1KMeansAD<class 'aeon.anomaly_detection._kmeans.KMeansAD'>
2MERLIN<class 'aeon.anomaly_detection._merlin.MERLIN'>
3PyODAdapter<class 'aeon.anomaly_detection._pyodadapter.Py...
4STOMP<class 'aeon.anomaly_detection._stomp.STOMP'>
5STRAY<class 'aeon.anomaly_detection._stray.STRAY'>
\n", + "
" + ], + "text/plain": [ + " name estimator\n", + "0 DWT_MLEAD \n", + "2 MERLIN \n", + "3 PyODAdapter \n", + "5 STRAY " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } ], - "id": "36c7dcfec1245abe" + "source": [ + "from aeon.registry import all_estimators\n", + "\n", + "all_estimators(\"anomaly-detector\", as_dataframe=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8da0e977-dd22-407c-ac62-d376bc12e31c", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.12.5" } }, "nbformat": 4, From 972cd73b875fbd0d2e17ef7e92c3490cf08d8df2 Mon Sep 17 00:00:00 2001 From: Divya Tiwari Date: Sun, 29 Sep 2024 10:38:58 +0530 Subject: [PATCH 2/8] Overview NB --- .../anomaly_detection/anomaly_detection.ipynb | 28 +- .../img/anomaly_detection.png | Bin 65807 -> 73962 bytes .../img/detector_classification.png | Bin 0 -> 106020 bytes examples/classification/classification.ipynb | 353 +++++++++++++----- 4 files changed, 284 insertions(+), 97 deletions(-) create mode 100644 examples/anomaly_detection/img/detector_classification.png diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index 17a1907c1d..3242d3d1d8 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -5,11 +5,31 @@ "id": "36c7dcfec1245abe", "metadata": {}, "source": [ - "# Anomaly Detection in Time Series with aeon\n", + "# Time Series Anomaly Detection with aeon\n", "\n", - "This notebook is currently under construction! Please check back later.\n", + "The aim of Time Series Anomaly Detection is to discover regions of a time series that in some way are not representative of the underlying generative process. An anomaly in time series can be a single point or a subsequence that deviates from the regular patterns of the sequence with respect to some measure, model or embedding. This notebook gives an overview of anomaly detection module and the available detectors.\n", "\n", - "If you have any questions about the `aeon` anomaly detection module in the mean time, please ask us on Slack!" + "\"time" + ] + }, + { + "cell_type": "markdown", + "id": "9673b594-aad1-46f3-bba9-158def0578fa", + "metadata": {}, + "source": [ + "## Data Storage and Problem types\n", + "\n", + "The anomaly detectors implemented in `aeon.anomaly_detection` module have different capabilities and can be grouped according to the following categories.\n", + "\n", + "\"taxonomy" + ] + }, + { + "cell_type": "markdown", + "id": "d6701c2c-fcbd-46f0-90dd-3077337123a3", + "metadata": {}, + "source": [ + "## Anomaly Detection in aeon" ] }, { @@ -102,7 +122,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8da0e977-dd22-407c-ac62-d376bc12e31c", + "id": "d5333c9a-6acb-4c8a-8145-72a949c89bc1", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/anomaly_detection/img/anomaly_detection.png b/examples/anomaly_detection/img/anomaly_detection.png index 3e8d78bcfade85fc4c2ba1cfc21366df22402548..9e950da16f6175881a76717ee15b817b8080d754 100644 GIT binary patch literal 73962 zcmeEt^_xv=d+@`B<_8(`?qf0!hI_x_U_g#4Axt> zZku9Z0Kec%PPPL7Zacn{6uDJAK(P*dxoh@D?#-=RWngTi0Xp#go}HA2kiz>Sox zw`~TUg>K!t&VDQQM#W8cD-ARCxoXulHmEGhq(%I(7(O~0H2gLi-Q$jMtdM(-9-+Io z9#9-p_p@{<#b5MKAJH;|Vo5*l;p+-CnbZNn)8wwt)kf2VoHx{5p|ZOJvg5mm^CKtc z#GU-<({#_Bx2_eqjn74}r~~l+`;Tbf>UrZoz5`d=56l4D42c4#68*Okqs_NbAtIbabZjV@zYjN1S^O@A66txz0|F z4bZH*3@rI!Qg&yo3zJvO2I3h`3^Vtkv{P&})GAKibU7-@v(<1oK<+{8K%RK8WnI5r zJzWYFLRj%GhCBnY==^+nU4rpY@Mthd@N~wpP1kd~YQcyKp?+oMc-F8dTn4@C`M}Qz zEa=i^Q@^d|W)18KxzJh5Lz~*`E5xBIw^`}9<%09vX?a3p;gN>dK(5en9xj*JKp$*2 zna^?c`*qv(6m(JB7r8`btKR70e9?4y3TrO8Jj)=_LrjeikGsR1$8~B9E-#MHne7Jv zA(~F9+?S)yD@L6*rrMnCsA|s^!$!JJxX!=0lytNrEZ8qT3qw)USitxW`?wHO)uHm& zW81$b7Wvn~slI1IO&MBi2p*W1;}uo^WTJJWp}4}7E%I!+(|uS-TY*_#@c4JrA+o;& zER%t~WC)3641})KpKS|NVp^NRsER+2Yw8eqFCY)03e7RQd(AxHbxcVrZ>L@#v<7x0 zdxpimZD@b<7|w;!8Ec(n@jdDdxk$_z%vaxd&?7UD83`RTk)P?)oZejxe3%;LHXP@S zJP$p8<`o~eB0!v8b6PX&qIH#VnLFYbELifI{y0k>nU2S=u<9Vx=0gW_+-t$KbwloV z9lDwn>Qu0eIuAN;Bi3_+DLu@4tEXZeL3X$c$rgt$-IHPh>X^Ik634F6q0jxQ*Sg_bM#Vr4;)+laZEN8aakic;pG6=h9491o^mw-{|K} z+|t(v1L(fkaKiACetl}l9ab0J1wJC3O5$<6^b*dy(vOn^@Pu zOA)X@djnyx)|u=Vdl8JaE-$dJZ|y#rQ4y)ZRshu2MRc{wp;IM-HC%?5Dl&MX{%m&o zw&?R+`1OmAfSX2g&AiIyxh-L`Y|oX{Z806&L+QCy_T7~up<$7*L<&YVe+(Boty{O>HGC?!Y10v}Ws}6BQT|*|x6)3j zeC0Rmh*7U;igi2{?c3eVr+oYE1Tk~yE(cAgjqL7AV)Vl2Jqp81uFZ&9?+o`5 zIUd^F3?C1@jBiwXGsX2g$wDWJQ}e90lgxF?(J}{S(Z=vJS9uTOYKxSEq*#&KlPSv> zZ1#p1mosw5S~X!*?@N3yH)G5MH#K_3v~1H@wx4jh!hH1{4tnTMkI{I6MaC$2EfSn( zagt{h)$JrS9cA}qiiaJTl|zlfIR~zfOMGc_(_A!PrQbPXy-*LQx?GdI31CNV*O#XX zzEqt&*01YrEBZk_v#v0mGuf%jWXs~WlmN;s>+E{Gzo1w$-fcXU?;7ssfz9yve1z@! zA*eziPq2)FPaAifOJsBpq7$Agcnrr3pDggRH=Qjum7yOMk=r(KB)znFcO@I8X0*9P zb?xRbW8anCrH5Q3o}NlQSz-5epQ^SynKYx{^@8({7*UIPuUF1J=3R~yjn246!q2m0 zSGVhTn|i{4M~qLlCVS9{wFl`5&KuHPrWosXrZzJ4YDIRXUcX*DnknO0wOU)=y>}vTF6Vn(pqCAoXr2aQatycS*mK|WUcKRS zfzokZ&+N+`?h<#oR|!L=M;Zer>%-aWsS%WqeM&?h3cft=f`spU&ALwCN(pGb{@l9Flkr?7u)lOo zM=^)-{0jLIzQ!y@*GV(O(|KHtZN1mQ?!z%f-0FJDmN>%Vzt@;*S+R?|yEK+pZMwCu zF;lyvo_BYu??lokCO`ZRc?*e9Leu&in+ba;$2gR)ora{2j(kO`u8N64@{I-d^2gw6 zco0jub^Eo-&){EANsDm zvR*5HF#c*4dZ0LyoV|5OTJ5E<h0Q&@TpRcRHC#d7pxLTBPBA&*1KDVDb=B@iq^i%66;}VaE;@O&f2+%H|he8mTDW% z8iKXxr;>e4BFK;mguLp7BN5dR25CeyFIZHn>bd8FwJMsvI5IUn%#tpERS=F%enjSF zrj?oChV=W0{_1`_7giyQMg6op(2l%t?yefhz|ESDhl3*c)FF&77jv<4t=$WKdo4ZX zlMDRI9;Hp%2Y>nNbbVNx`{Lt*3vH*7uVIdsAYw<{Zxl>5E>4rCP7VX6R)MFLnuxFC z!KVk)IG`WmSk#8b=T|7ZBp#|)Tw}K2dR%!p+mP!TMZh{s@RT)0<@%Sc&tVGkN4I{7 z>s*7og+}tN^#`(>YfK4~qGpqDfEIba3c*eXkK(>1JEUa`ugB zi)d75uYF?U?pOx5RC7->$>d=|I8``YwkDQ6VF=wThPTu4G z#pCbXoyS}-w#M-9ucx)K} zE;A2`C7?iX_E2cf3ZUHvQt32Pb2AW79b~ozc;l%HPyiKzMZ~pSgiftWi>LZ><((y` zTmH{G1!d@qPlsjL<%s=1`c+Qe;oP&mKC$g_nYK>wVLx`ibPq&-UebKpxGM)vDYMlj zAkLpEnWS<03LwF0Ddrol+F~I__YykSkATC%Q;|w-cmjB!j^hWh>FMde=c#O_ zZJWKXq^pS~_;IR>{6Ke#69m;Y{X|>HREv@DxOIEN5})IOk>ua~ZmV1wNmR5wTCj_; zoCo|k=p#9DGPszjhsg88!}n9cu$PsH=nk3toxz?v7B&o)7zk%On^fY1f(_S7PsfG@ zT-%Txvu8j-(;X$hCP%E}q~sGjx?uLjvf~ZrlUOR+K<{h}QTkP8?Cpv?(yf`H&!CCN z&G2d1)d`Z}3vM4NkB#;Lp>NMOrtOo~XGo%%cql5L54 z?l2;p(sfFVrH?SY27+p*;n6^2~kVxRk!G8Wf7S$k&np+_PH>ek5> zt&}B^4dZYw$n&m&3+}p~*T*=a^}4KyX%Xa+V5M~O_*;b$H|+(UXN_T?g)?2AUsn4r zy0F584O#<^YTlf%=rsxWR^yCAgQ~1LdveCDm#+cx57+xcc!m;tx_t8kk4$)rV4 zad3a9f3cXU1pm@IENYBT|IzmPNN(@?l+;VF%fj}gyHK6SiRFWs;6X&L*xna1yG;}iSb7x2VrRB3HJTS)Ca`_uVcX;t8! zen>U;0Eza}*}lG7-Kj5ZW z|Gc_H!LU8}p|AZyG+e&sI3{C8xBZ$RZm-o(TTOgP&ScC}nK^m<(*FdNV5SR8M-#~NQft&X$Xzj@`Qw-{d~CYRX`D-? z#HJ3&CGdzDRT})J*#cj{y7!a^lZk*8C`tvkH_|Unj0T>hnfuG_dsaJ$;JM+G2>W>X zyyOis9Ket+jG}$(YbfV8uv+Gav2FbQK6U%+s6QpQU%B^E1WyiauvGwWq^7*sF#p{C z7vpiW?P%mV)K@-Z_c>t0RhoCJdi9GF0wcJMUGG@_egGeNAQmR7ig0d~(w`(57gCg} zDrx1vhcRUv)OK8cW>ONWxOf}w_Z4C{gTlTYKd&O9iOdF7yIrKqk7QJLCV}uSEUn7V zd?!Qqy!6HeB#RRI+rg-G)co*SR8hMEZhNEC?zrHQruX%?tjR-;2 z`5#$dAmHfbtlev4&2%8n7&?jAYF2$v^#B)_jjCy7cNOggt(lTqr+uIFTCsS4y7f?$ z&-JcmRg2Vx;Izq)X~9^}Ou0)RmAlCS(VD5_l0wKRlrMuYGzkG-ZS9rf7~@-~PExby z*u-pdY(SE!GGvzZs{V)QevW`#?h|4_A!Q8fcm3;huf|PzOZ`?d1H&%7E4YJ;vNDCo zNocwOup|i6>+P&P@f+^LTRIgcF>fZ_*raWpi@!2>7#~dK!+Uzk2|T?K(1rk@_o ze)?8FVij6ViQ~TwFDrbUNke-5+MNlKt?fLj+Vjx&tZf-Zs(hQRn~eB1UdU)Ffpho7 z;9fZvnv`ANU=xY<$8q5+cT(X1yiq3+y5Kb*r-(po;v7H`w62;aSWcl=ycScVf`~Rn z=##;GT#4b`NjA~hokB*tfWe$pOmc@{cf{EeSYh{eV?-Ygb-;xBpIWwH(lBhOXH)m| zr^?WEm+(~-i*5sN#Jl^)UzEBrE6VI~HW|z%>OJKrPOF$AUPNwISsYZDLFI{`UN&lo zZYrlA#3C6GO)el(SssFy7Q026y%tL@(im0J*}F$o5ARZG>>6-XG}F(rj;UIK_b zoM6ymct#F8lNf<6B45cBRL>a%=dMVSZdX^aW^gAS=nQU9q$tsPPOQDM15Abpbn(Ri z*S`paYl|tZ9nd8CIZRPBwJ+s`#N=Q@TEs#i-f=d8hw_U(8up?sn@#|wBY}8u$qAyn zOvC;!jcix8NS&jUQ^P+|b%)#`t0d~U)yMdDy+sYcvf(UO0{rXHs9P4pkct>%S)JWS z@h>{RKFU3@?OGeSQ>U8q5q{9i4h84e0r|j%Py$YnTFUJQ%O0r00xtR1p@Z-}4#fPv zSO-2S(N|M%=HljWXX#AAViWuyK{j1Kyb;?qa7lZhq(l1_NAU5)Yrrvm#4PeDtM6Ah zU+lOgVkSlRa4a6?J!z7#BAJ$@s-^q@a-st%u6m^qjy|sK2;jt9-Mvr5IQcqSX+~W6 z#^6~BBvA6GjvN~8l4mlprmfMB-FmcPFg>hj^^z$7uOCQA+P&^~_sXz0z7(|S;7*v0 zq^g<|_^DjUPmH(h=QCKmGf5TXv16W#D*@RBKDMRGtxra#k%N|3h3;mUquEfm=e(8& z012-uK)f)b=`e4xfe1oT_SuNJtHUCfd!w?3e7h54s_1De(_Zp;Lylhd9hUy$(|%hI zM@|z+>`@{vE)-yxj@(`}T#S|s0N(sPMjisrKcC(8Nuc7k6|T=ats2)&q3UQ!q}aIq zzKo-|vAMFM7AJ+K73zC+Xngt8k5wx8z+>_&*_k(SNjmH|$(xi3sW$l`d97w3Bj^T~ zH%&v#9R~Q|N0*>V))dnRo%=7;jYL^wKKH*w6D)ZDQm~l|`YH>`fKSqQe0QRdtNZte zg0P_T_bJ@k(>c#w6eQ#WnmStLX|7Ws;$WZr`h{V0jILR*Na<0qU@HiHdjOpzzK!DO ztf6ZJAr1_>AMS|84b_qjgf>n_Y+U|g3Jhs# zK@A5$dQNXjL;GMF(+ur$ksO9BIjU{OSvd|em-@QPvxIOCMI>Y=hrolN<)N+D61lR@9q*;)tIg(Qn-gSA+cp2J@8?q^ z3gNw9XqemDHo-0?nYdE-vQJ-i05J7mPHC4|VSxYxtmS#uoC~5y;Xb*YFDl%F0gBb= zrZ*B&y!edjT9!=a$3CaGk{w+A@lo4#=QL-oH|Oc#Anz|Ncp7{%$M#LcN)Z$uoFkq%b$+rhtRF@2mopp}zRp{du{Ps-_vmr$t_6PLy zHJy7--PFO~!K@@?B$m9XQ|H?kGW^#47_s5Kzkq$Zdf7VIMq+YJG8Ne*Q(54wj}z-M zP!`HGDOzcNOEQiB3>H%|P5CHoV)p{VT2&V&A8j)r$yDri+-z6bAC{IdSzXMw2f>7i&$Y_F3M%k^>RY zht?3dz0HDm22lMSC@_qyTh?TZJUDR`xJDixn)TY@o>cHXoEl1BQU>m58ZT)$fZ#S_ zA0nH{oi0441EE#0p1OnSLnM&S?MwCpF;_PnI#^Bm+sFo%b&pwH3~Mi&J1i0z${Xm9xCN zuY~$L;{%~@f`YD>I;#L0h)_-Usc;(wxKPB$eiIDPTI=6qF6N*SP+SL&K1narGi_s4 zSt618gBJjMz+cHlJmY(P>23YaS(KOri`-}jpdKuPUEpmfnpU`ga7|^QvCqE9d{i%} zHx(D2?r8&a4 zT$7JgJ^UAO^I1m|+>j^yXMgy&w5GW(DqjJenT{)Am!)J41~tFiqvVWrfz?ZiyIS?8 zIvERb@Dx7QU$34S@-)T3iiN`}<#p@5^_bPqs_GenpMBxY%=V+1Nj^?PH5p1hjyIeZ zo*5-m7> z3eW#(fPC#T>C|OpYW@s7c=P?ULMksuk$0~yVJurFQknl4&IoqgDr=UNSq`|k_9Ds- zWoQxQBmGLD^doD!{%NQWC~w`b#A}6lT@a3FzD6`#oPS^GO~{3Am-n*uG$D3UD7^OQ zfvz0M>`Ujyk#X!;4Sn{|F!m@gpm`m#vS)rF3{Nlpscf-pIk0{R`4=B7^`XGE_x$hhXL{3 zN8D+)-lgFeJh9rUHq}nI?ksTEK2m|H7e>BwbzIQT+=9{wdl(9Cu5G~gEcwvUNi;K{p@ID%fyGR6@qGWumsHP|fOQ}5ejuvaFl7S&M8^UYUzGkR@ z9HgyCPE5Ks(K`64?Y_@h8&y0ApHKuVERMB1d7=CDia{+z+@S6I*L35p9C zHo7Ba_uX6=8%Wed$n8;?n&Cv5%k~uVM{+)Tf_KwK?eyBJB{|!Vh%g*uD||)|fE>^5 zO0jKnzv)nP0m4L+h6v1%n5UDG;)wf_uDMeBoGk$5K84%7p988~V*JxnHdOcHP{tba z_S)2fN%yw3)B^#o5(iin7IhF+tSz$N)+YuiYS{sL>^us{r^$>7QPT zgflikvuqiaW;iTpc5RyEzD0@pU4pkjfZKwTi7or02|ZVp{D_; zE(y<@a}b8=gD^ore{JOaIcQb~KB(A<&;UXavlSzG5?jE88--&ZM^CQsHv~Qu{S;U5Q7$r z+_ZR986-Gn>MIiNwUy|zNRW}(uw}v?vmlB!#14>JdMiMD2TYnq_>qWH@=Zs3tGsu3 z=^g>y_m!`@R|g5U<^8RJc&*R=(Q3cxiU*-HJ*2()^k08M4E+AtT?aH5KtlBA0Kk7A z0s(G1K~*W#?Sthu);>=!+JgEeEgZ#eG)NUL&ttm)Sq>I%crJL=EvXwVaH=u=y-P_V zV&EY#O$g3^tk0>mR`4nS>9Jb(?3_!+VWt@rmJO%DH{vi;HdghPxYb3lZcU=3|-xjA0#Q?g%qSnlkMcJYV z&J)mx;lDX#nh2O=aP7wJ0^F&4f$qUfPVw5=fuplc-m>R?# zHMjRojrwQY06er#Q%!;LUZS4l^iH}NU;$#jH)PMBO#zfol44QTHd}CzRVWZkf$AV# zg#FXT41Z0?VoSf#Fbh1XLW!;SKSsahsd?87eISf5Vi~oanY4e2-@@hX!RgsEWxN7O zN8Y<}u{Gg;-QjBEA(?}Y0w_Srux_rkzy6Oc{h1u4 zncK|oPrjc{g=hiZ^gplKLw~X!W=Xg+fZ!<8m=gb|=p6c1IUNCkjw6UjlW$h2rCt8( zIx?Mzm#Oj|z$=9K{dKTw4GV0Q82gGP762PO!>@lYxwV#1=6({<4Eyd708_xMlK-4_ z2tWS05q1(0r1pPq))Rx#4VD6t8=xd%_-hZolsX(w9SIq$ci)&$^S`D9Y)6;qE4 z`YS$Ivrcp68=$1hzYdBS4x)Bh7@}Bx19E>2yv7Tf{$IBDYm!!!MOlvI0BXJ4e?`wV ztm@|~-Ia=+|BI9_WunWxAB4^~3^t+M=U-?ySg1M6@X(}!W%pW&WD5V~P8b6}S=w6TKb3z0g!o@n z3_QNWe7#(O+TxI_zkq>+*F8u4X}oorTXrf5YI8*YnphuXC6qrO`$vZ@f9a6qMT&K` zM!D5-5E96{usC@CwXimf8z*&o@Rvz_Rc|o#_IWIsbTips@CE2x1PA-CQTz?xLQPR= z-M@$THynaE_pB*p9iYsv{ny_CD6t>ZTh208td%g=WnKkG`56A+KpaX~Q0{30U~m8l z%U`hNS7BrA5!{xkfwCH`p$C6myj@{%sMG^^5-LFrSi}1JVp}@kEFc~Z*7cr~n`5?` z{|c<+AS)>~tLkc7VeD;7V6}!X{@z77hKJdlO2B&l<-v-84`s|T0~{Z9yZ+xR4b2uL zA4cS}go!i9c>_ac3jU=()DxwDm{i~+pkFKw1|k+HMATh{4M+_B6}YV)S2ChP$G8mTK<)tOqQPiFy7f1S}$`} zLD83gqG5>cPQ9(}L9d=}5{2tbb2Ki%2}AGSNRC<^VM>u+LZP-!Uv(m`$R z9TZFhF*x%C=*kJ5ALT-&fwo`kGQdpA93Wt@#AYCw|Fim#_;5%$%Tr#a^p`$qoT!=z z2aAtYtcbc`kt$&(Va6Grdc*-4jgr(&qQo9z1o$2ohx3((&w&!2!B^ss0;zw7I9tZV zF?$Y+@&RPG2o4cy1XSpWo~%eXVN{(d(RhX~{X-`KF)U0UuN9+#Nv2Q;r`)<*-xyx%fF(p4bM#i|>z9j#h0mUH?>I8BJn6GtFdpA8W0(x9D}wZXvsc%$tRa6 z^i>?wre${Fdk(XmawLO)^)o}6_7;l;#5g4G1N#tZapOtUORd8If~z>MQ-0zcusCcP;l^RDBZ1j<`p8j10;st=yM zeIOc76kyNbYD^fIIQ*2;6v}3%@i8;5;C!^CSibkgFR5eLWp=7r{->#G`3RU&LD%#5Qcof6#>13QE@%VawsKxY)#=r8ObJvE zpX2IK_&SwEBbn3^zyLLeow|riQ!!FbZdczPXw0k&LgS#w_L>`Y2)yjb>IGOc(i=}Vcip|L4>E{ZqOMRcx^DoPj4|lJ9T&8r_@aeBkrYbA*08lcEJdVZl>DBrolYZLJHgfPe z^Y-?Ve?*SLj6f2v%VE>U}B;v=0UmgtFf*zBIAQ9aF1P6 z9+rIAuVikfUX2dH(>|GamLVac?Q*A{eB;%=z;$~PSS*N81P9kx=!xf+YLD=hz1~%L z$eUlw<4rC;4oeZ>?;3Dk?a3}F<3S;-IxcEI60USf`faz-oBhct&+<3%N1~Mgza(NmrESiM z>S}K{1~}UJX*r8ZkaadgQpK@{_7pCev~@AwY`P4-Tc|aTmHK(zdUype*P~$k@?3Fu z*llrSM~gbQk;Kpsmi*6g!S_*$O-?_~mfIKSp8LV_%*)YGzFoCo`E|G-ekVUpy*)Lr zr-nVUaD9km>al+Nqix4FTrL22?;okanj>`#4%L)!O)SJLyNP(WO0h?z+-MekFDhgX z-iw6;+^3$m{D-4ZBk-k}2MeSWYC18l(7xZIF+yjPZpD?SL@8}aedF#E{H!l12)s9y z+V!|&1r9oyNv|M-HLFm)C}F0|zF1yxpSFr3Xx%J~*vxYvgcB@To~|NakgU%5S{&Ay zM4FcXo!A0t_>Rx8yr%B6(A=^i+i9v-C%dn5^&^?whQUmeMUSnqAQY2|MZ&>SyJoeI z%l56y)OzXM^yzWoS8F%=SrV)={l{5Gr+vgsa zDccr@^%KLr!cCoL(W;UXv%bhKD&^cvyi5eii99w=SJ|2tKXoXEprD||-H`W>)JoJ$ zLxY=ux2l3eP!a1s!<=W;iq9Y-D+7!Bf`FjV6Xku%Kky>2ZBd5CTKhh z$(EEmBMRBM)e;EeD(Dl}KE`84^zoPa*4yKJFs@YaAXTZwcsOWy79U90GjKVYPPT}8GXCTrNS)k28E!(ex6^bp z(UDl;1q-UpsAyK2?za*KUeRjk^=EN$CXo4 zY?29|cv_A_erTi1huOruC&x}D*h&)d3ymlW zvcyQ)EO3SD+I))#h}hpsyy5)BbinCys%Xc7I#ypb9Q0>~oljf4GXv$hg&t9wvon4* z!4oU{`Mq-dr*mlX=l#95l`Gm{bqY{BxQB`KYO$SD`T1r+5?@8C66WmVA}@IAvG+k| z?~^k7{ivF!8dSL-gt2=rJ^(LC7+A=bs8hE&;Ls$LH2pMB#2?jmZ9GX>e^@hnqVD9Q zN~e&TI3m)*4J_x|xRSN7vilF-1YHkbJ>SgB1|{9+Jy2gC_k-D8ZIUOh!^-0H=TqYY z3SWeG^m_Qwomt4j1pH{d`! zxd9pVgJ$dVBV(czI|WkypTD7ENiu9oIj^~1kWo4I2{R+2tseDL6GSx%GYEDzrjCpb zs+pzQJ5G&9n)HSFzPkQ`R4vOR1w4m{M^m8Yp+z9a@-icrr^_TMk)o+IAarla^#`<{ zyd1gc1R0O>c`MAixRh0hh2%$l1ZGvHcKpyg0!t=dwwJTKrd3;AkC&E_TwkZ(5JmcLJINXwn03_%&Gn@s_F_Xyc6$>gwqo!?qS;+2DdCD7) z;tXzjUq{E#4*)nd01joS&B1ca7=_bYMNuucZ#|}Ydvy*CKFFde@T`1rzp~t{XO0k4 zXF&_r@YMIYCWr0Z@ba(>kN|Tl+Vu}|6s8g3yvVdQ>K-8s#;PWPP%!u_@tNYqa1!R` zdx1l^EY%uRQgD3jSNoKR6zcm@HMa3K=>j*J+#>{8?{sf&6|RL z^2uMD=2evs>j)#fg}-#MNfoi7OA=qT`#Q?9*qv9HNt7ta{1;iuJL@A>So7l|3V6KM zqC7VG_~Z4SieFn~{Jd;#Y}F@3Zx2qJVZiC0uSIGfsA_~_4R~JwhfKzne{KTBi#PL@ z@UzfV$ARtjM&r>v19g5@#m)k0ef>nG0Hvh-t=j-HO`O$0uu3$4n;0TA?yA^U^eutk z?r}kM_b~o7-d<)x@(57UM1a0ify?yZ4-9N+H=8sI(M9C{GObp|M;Y| zGyPT5kg34?s*_@_?n8A8{wmcdlipAVFH{$RBX8rO$(ok1z*Y(EWOkEke_h>3YfaQA z%b+g?dXyhpJ`rZW=`?!xkkH`w$3U41x!yQ3oq&^2>sXfjJcp;Dyj$!9qG2t(KsH=~ zS;-sTMmM7hp}Vij(-#3|frQ&%y~;MW)uwbejyehfy#d!AkJaLb!9OiXJa#`@I7=s* z>Z``_xYUVrJ{q@tvK(SY+}>MJy-L1P0e(uHLjl zOj2;1i(WurZ`;GJ-k}iAO5&hqrg-**I~x|Om5Po1G%Pmxu?j$#m|xztXud_^wO|ds}Cr zs`p!u5BSE!DHCd>Vk=@SjzDUiwVnNQ4_MZA8B(h9PVV~(ccnw4Br_$DonUIcv!VevU5(PEF>BgL#X1ER+4EZFrfm6@xXZ|^l_Xg07xmBFz8fBV4Fb; zHTJ7TYT0!Q4+PV@s7i{lO9Wp-@Sc$RUuAowh_8y3W3ar#gG5^3Yjt<`CZ9*)7mZij*t|PmbsnI?!U9n#G zg`BF~vya4og2uNXv~7kW$Y)tG0b*sA4-*U!)A4L=hjl@`rwoPvMh&xdy&PqAk|l)e?@ zLwHp6cBx#-rl6T|B(o*%z0hx!8r8aLj5QrK8@c3{Y{HPQPgF0z`9Wp(rCbQlr(rui z;Kgn_4ksMTT=c4#f=pY$HCBftwufgvD%P!Q)sA@w3j zbFOKcyW@SSlPp}~n_q8PAJ6;uo4i+D_0h2~4Zs`?wEZZUIXUy=@*P>BrfMp&L2tv$ z*rQrrVm&Xtk!p=bO~2gRR4T8mH91B^ybH0C7>QEwaiVp^lZ#3^204LvflVC8vp_To zASqa_)9t5e1f2jjLPO=4b;`2m5i163KPVn!bed(Dg*osln<0`$712AW7OKYf$v$VA ztA$cWMNp5USgre{Qc1rgR?Xy%%KcKmQG!TS9}WfxaNG!qlzNGVxhTiO4Lf^pRMvt# zFBs+^nLm3wEf1mkOj2~h{!Zr%VnsATlwQCjrzTlkw{Zo$ ztnsOa%ni{8nttpr{(5K+7bz4RL!WSZ3m;c={f{qHjbv&Z715YN@a|D5r@uIeq@*M3 zOH$r`Tf_+(PfB7M8DmEkQUE4gGbwXZS}-$iic`9Of0gy%=bLXH#!=6>wK|3?MR8clbJ+EgNn6NF z+L4{P+ggbWN2ez*^tRbnsVe0rY%wh!d+@mjJ#3q@P$KkZUVIt0LnO`s8^K}42zxm# zK(Op?^D*4Um-;Y2#BRzs zPp*fXE@=zyPOXuPPb`PGhy69p(6528b#CWq?1 z>>~1oU+$d*vDU=mH<8|jPH^yedpt8Dn9QC}{$>OO^^h z=-?C6NWrCav-6pfS9Q!^ouUyc?IbIIZ6Zsqo|fhlfQDb48kv+T0@KU_$&82fCM1mW z3X%Ej8y5NBS<`Xxeqo;paR9Yp#tALl6Q9iHcEO)&J1JpQ4t*eY6IZ4zhFHjS@2ld+ z?XFWf0s-QPn(AJr`7ljC2zr+lgB(xpmFpZNN1g+xcS8@C1l&I*^D>dI!ktLrW_|Gy zjNg3inhn=W)pR7DlL%if2CZ?E7qK)Jgm6C}WT#hPV^0SFwdhkvbZT zXrzmfEA^9Xj1WF2D`fkt`Pm1Ouy4DA2@>ErfuxsZ6S`zz+y-4B_N?H182UKR%AC@G zW`Fq#JrqdsJyu{$WV-lPxSHE8>?;-Bsgp9m^p6#%$A5ZfO<#Rdsw36J0VSp+R9WUk zmgp3OypySj*k8e8=5#dW&?F5mh|`(HEApEDB+Gqoyu*8qYNljD)Kxj~#MrSx3Ej%Jkp6;+-|;kM`|iWU-ms#4t#QU36oq3&@xg%qx2ECutiC{H(F3 zED=v?2B-*?=oCOaY}&P%PqPdiv%1YygIK#?_8<#wJGQv)hEJSC&5iylzPBzDLl+?j za@Bd8eT&>pYkj!~*h~nwxL4=-m=^KzMK)QOXlJR5=9#%0m}Y_uC?u;!K7CdQYNn}( zz)wG!w5ey5Luk$>aby92P2`UY@SG6Vi-eMzyc4DafYdt7;&gaQw4Yt+GV44o;!D0i z1hE{a!zMBzpLEm-&q%B3QF~6|NI)LE(i;{=P7~+7(I|Zz^!0hj6Hgy|QN7VS^k_?P z|A%bdR3;PdQ7*rOB5~s-q(Nup4}2QNNMWvY@?`HJU)8Q5`YadK`YaxTs?H1KUxPI@ znu~g6mF}%#2yN!wpH6oUEL-b6eDR=4SdE)jLs%*MR?5P7fP$MyS$N)sMpU*;80l}2 zy4WW4n+f2Un%3=&KPB&b&JRh3;alMpzNp2M%CFHdzCs2?#hy&M!;7$6rH-G4^*Id9 zQUbO-(S$xyovtpeqtm1v#3EB5%Q5t8*JA%WJvZdBlfMY}W>c5+;dt?gWGd_9|vo)>bQ4A8{-DY2(D~6?Y1sEMb*_$-Jic5gc`TdxJ;D)GGZwKOs zrPzu_MToO+0+>rN7EsOZ-j-oLpEZ-Kz>=J7#XmN?OGX~%L&Xt^o7)ol&ND*3H!-Ms zQ5Q%w+z_&|V+9U!X!U0IUm;^M1h@q;D0}C)op2m;#hs5>pD?LmmKFn4Q`?&4w179< z`aZTPDH&Nih&5M0#k}Ncq1ca_x*YZcz8BseW$6Xx^A+h6rO@2s(D1=#O%vtzO!>`F zp4gqrkxJwb{>S-z1Q{`NTJ8ewTc&~MR_@@;OCF4-UD?DJuOc|htxG!e(dujcfSkku zNQ1KZ$j{7|pSW6evkd}!OA@Ar;m|gLcR_wypq6)6;+I5ga5WiEv632|iedH}26U1O z0uARm_570$`d!c3rK@`O68yC9UqU!7W=z))tDlIwU!$jvDCZbI*cenR8k2H1%ytZw zOMC%xRt!cd1Bc9w9(uHH5?sJEZhTkEKTe6aqkT{R2906E-+q2`am`KSuTk0wsowW| z8G=#ffD6)0I#E^;#ZY?M^nm;F13ayU@ra$(1{F+WwZg@zs4YfvH*qdE%es@TNAe znfRoi=x4$L$qNiv=g9W0psk}KQ%_biCW3O+M-&2^@KO*O%>m4L85wR#6%;?U4 zrpfo~5P*kY{3=EdK9F8FaSANF-5CESs37>4^0e**CO1)Vkill9{+kI%Md6ko4hGZ+OvuFwIIPDT3&p!y z7}kVgUPo0wMr+9Wl2utGMdQ(y#=sTFmByz@fAemUdnV8Q}BS{CL1Vd<-*qI{pX6(polx{(i3OG`IMNH+^eF5QB3 zDF`Ad(kY!w>;j9DQcHI)4I;Qpck282eSd#(Jco1ezVBydu9>;!8VDIp_*0}9(a=LF zK1`xmy)b`}3|6~F(961I?^E07m^g2Z(CYW3Z;RU(=!X&HNl^lsva_JQ~* zFauq5&GhDRV4wF3vgch72-4atOdH{hPDFfY_nV6+rsjcoBtu=|5E^WIvlmB|)zb*3 zuVpLdjjBr6)hq&34KbZXf~)f(HNxV&ic1rgI?J`MgEUJY2;0V%`hTmnkc02^?t56~ zUVNP3C`&TW(OmdE=$PLyRJ;pXWql7%j5JF<7Wxz>5jaG}-t%>c;>C|b7yEm^B@xkv zZ5YxIyUrlF#<>CQ#FOUY`h#xZf;avs)fa#K)3a6aNb*(Hfa-92-HXBDvW0vqp*#C} z+^R3zVbXl*9#-BgxF4%IpJFXQp&Z~3qr>iN`sW{sIvpDlnU`jLRj5m&FMV+ul`U@0c z>AM%2QwO|4BQL=467jj1?84E#07G6h?&&XHo2gx=i&Vu$Gr zc1T!xew0G>8!576DgCA#(n|B)?PFKOh@xVY_`AKenan<}CQf26sOiNR&YDFQsT zt)6gE_IDp(I*k_K9*bV<;f9h#sV5U>6|Y9Htz{4&vxli*<q){EtJ&YhdrQ@pt5ImSdTw~JLkB^lDL4ITz(GLB`BCC z(HPLhg+Wdwx0WIhwxndksHSWwtYM44&%Qaty(uO!Ptq;4 zk2K$F*e!hNey7!a$zKB#zM~<;xXOvC9}dMB`O-8if2Uw^O0bw~wY^XPQaSVTsHBcO zN#Bq`akj!#a+kZs3;jptBKU8XjPco*n!TLjU-qmaZGTS{DG_bIYYdJ)Sh(i`UMrFSbWzrOY6|$wY9c{SmjFNF#e@UyTnP#F3P0w+T}$~Ly)1Q zV2@+2p?f};;+cEk*r{Z@lQnVafDB+>{qzlOu=k>djo#SiLoo=}YL8RBDVXim7uMZV z2-3r{>JN)hH@L)V(s)Ue4pCNS^WIAC_FTm2Z&AE{pr;8+s$e}kej!)&!G}6N#a5Su zgw>|9`Z1z3A!*rnx^8sQ`-T1q*d*cVX>rkoDI&_FVj8R!CduSNZ%V$T^h-scWBGpz1fqKl3s2yyp2lKd0)`-$ zC6@y>wXWHNji23C1J2Iud@J0oY>)RS{Uj=ctDeEzU^7XJlHIoA9n3n5&g49fx~fp} z{5Q0ORC;#)*ybpb0r*kyJ5ICj?|ow@MeSw_{TD?Iy|kh|KYljaNzTtEN5zFTl{s2i zO+5bahw>n!#`vv2w4{EVVd9Sv?V~s_Ev1lodp@`6*NuI0!=VQ_i@i3MFFkOkBn08* zeP%`w+9!aE>sDRUKX8Hzr@IJdc6IlW!UZ9s{L&nBS0fMOhd3W!NF-)RCWTpasoCE? zaqk=5Osj2Cd|7GrZK5;8JwTa9jMK3U$i=zU<91%#F#m?H9ylNW*hEI8W3r|F8tfGL zJb*$@Q@L`#DNGupRLDO$N3*V)WPD~s6pwH0h!4>NT7IPUqoOGQVg?XeKZ1H+v3@}O zsnL)RHw5(#QGZ%6&kDcLZ}fhr|Bi~mwsBEyo?R5zhy?xWX&i>-cO`9nAIq?NrH*-x z);;w7p_QzY2E0Q7s*eknNxRSoZfkU`++tv?UwdOmbqx;_>xp-fVM9trxKAVcfjTE0 zhm(gN3$$Vn7v}p?DZnUfT%+KideDo{m0zQ5)sd$O~Og;48{JBi$o9`4= zxUwt$L#gm%owzO{&#RmTo>p7rlzlBJi9f&$%>U%@mtk-_;bYQ=@*1w;K?ektyF*SF zxbgmtn}_a&eF#NpjvnQ)W1$R0Bol)#sVHza$+v4Edi8#X;%?#xhTP6{!V*DDS+mSy zOfZ2rU-8HX`GvjD4?kM}scZ5x8Ihh^n1tme{L^n(Ok^V2Pm0eX0v@mR4rj9kd+>Ph zwxoip($1enUcmQ-Ji4hW^ST|)pynKlI316K;bEyfIhhHn=deQS1xen!GMAYsM?oi? zR_RFE4#qo`&(=#d2fag#3CwGmN8Rnxm9&WPNzt$*-27>F=#W5SxQ76q47Ao4tM zuxQ#SZYN%UW~2nJ*muh2e(Wa9YfQ8CeEylJtZ6hju!HT!IA;+F3pSh3iEM}fnG9!( zm=P&D%jHX}FG}&x)fjvyV*&TlSq*rcPbSB9xYu|8CZE}V4*su^6=Jxb_8xnZ{z72v zYMqXDL)h(=&5hoK&V4{CP!*V8nD$)Yll!6|5zU+w-^Yv4^|FrlW{=dAQ`Tv6uRxR1 z8f9@=&zoe^3y0`ZUBpakkGWb%{?C_L$vaLC^?gw_Teb^`eB;(#yh72H-euA@{lz9|%*bXG&q*;+>Z5%tihgdxGv1%@nphcpM}>zensUHy7)MEZR7Elj5yIs_c$jnfS#hXm94`41z-EB#l*=j=ox z(A|!@`r`6(D3|Kv$~{}gpG$gn+n(;N@i|x4VTNNfR&9!*BjbUB7K*pj_E#)qMqy5g z^Yf8R3S=Mhga;Mle?xqW-E|s3Mi*8jH_naG`k4)Tj?jBnHD>q(utQ5&^NexrMCzvXb&&`@PFF z14!HBP~ScT_P+l1Pu2-usuDvnTZCi5e(!`Z2xtrCg3oHi{y4u$>0)Uha3WI=`z-i- zb|po9&Fot}xa`5W&qxY4J1b=JnaNzCKPX5kl1l8~HBgOP>ghImww^ii-itvu+7)--dI@KdJ<>=dEc7Yz*^P<( z7F=NfRigEc_v^)SuV<9~t<}nL&Sui^Wk11$F{+cZVeRUPTSp0_6G*fG+0V*u4?Z3>n|)f3I&@h($EKT3oezK-9`vw&IqZt4v^|0@ZvN(8E0W?4;xZ5$VGs zL^ar0ANQkF$gVV?hv?o7zGP@-2VB4n@%}c(|CT7SWiyW)81bbCXVT-hC38t@Q@Y`R zfIHoh7x;Vwl}y%4Q%TIHLrr2}zR!(qaSW+Dh2tW)amdBiI?68O=TsC;WDnsjYKb!b4DHm_l@LA1*17e-=Q>^jWQ8egK zoI*LIQdIQABTw)_^dCy9w$x~px1y5KMj(Gm@48Kc9?x_&gRLx9TkXONZxjy*Yw%nB zy+M!*?sg%xmh2f@sBs?P#sVy`+GT$=(hQpV=0!j`cv_J=+eQJtmm4O$jo8hFVlJi+ZJIglY6e_{@MLrAoqNpHLfI-+ny(Ep&-8%wb{i!jcjb9Cf6*5osKmVrB`p^p9Sex1~+8|hQ zAGdi${nXSI!Lox$y8o>%n7ku;#u&#lVt(y zA3F>92ec-1&?QZ9^hi>%o^VJ(M*80eAQwR6RcNS?NmQyUa)i6Z(!0-6OibC8Zd2^w zwuYwavEndotl<3N%s+H_wW|h{PHmj3-|RhGj$2DoogAW-y>&gA`SiP;;)A$GMlE-| zR4$eHa7b{%Sx{|!o>yKfRsA+j_h}eJRZtxKbFssS@;kyI;g|A}N7lFRIJFVEN3u!LI5!JRbg@VHBQr<4}Hgx9Gv98?;o=fuM8d!29&ifnkxa(QhXLcD8ZKF?=&;IUZrW?d}P>y?TJ1A(IvDsKC zJdcunnL}1dH1&H+mtf)vvS?JaoUydrp}qqqH01HcEjfP(94`fo67y2cz7AUy5rrgf z!65x*Nc*Lom(iq>$`02*cu{(@%tArN)WQZ4@ys$U3(X-}*OHr&%Hky2OgQ=^r2nwJ zUoLNR_Gh4>w141dbxG&87)G_-XANEt(5VuA&Yje^?t4+%2tc3>;&9d}&zXwNRZh%& zWqUzex$;~m_~lYq`4gUPeKRsU83H4PMKdWO;_zW($E|L@%^#9{-9BAithh;Qn8%8A z-&4=Ut4%0xt;fRhX?sO?N-Lkz2J%v+DtLul>=&H$QFd-jhFkU-XE8);BwYe* zG9o0C&4}*NWKhv#)g*yIsR8`Z`de)bFZ0W6Pj0pB_CM_E}W;^HyMyk2p1sf5pf z4a9jVNl`a}pN${SR~l^RvTA8L#}nWRIJn~F;5)j?j=mVsR}V`QGWGN-v#!+wGcou* zXecF2t?!I5H`(P1^PencP_lRLG}e0fNQldhA3Ay075bf>8Q*9Bs8`QkCp?XEQ`= zo)P)zVM$K#e;!wmc>V0@h}_bW}l5zcwX?A}W2a{+)J0jjgY%@zM9GQ*@S>{D$#wYp_M9PXS{tkxw;)FD#&}ZT>4gUQ8!r|VOdR& zMG4f?Y?Aigu(Eo4#*3%5MWjUv6cCDtG6E?(9_clKTx^9b$ldIVg%%3%cra;EnY6PR ze7vguAOG0<5_!I>WfOD^bK>F0WPp+jDP+1YSRsqI)+x_dF4We+kKlACHam{rv%h;a3d-E9Jg4oK%Y9l|5kB>?J3ILS-;(iE ztB`RE{0PWRfirK5Ac(18Y2aP||9jwwihY4X)(;W7&w`Ky>;RD7^SIPa8~cw=kLCSi zq#CLQ2wm^2Ng(9iMM%feA8@tN`<4v@P|qfEexHgKaIDG&N!I4A#w3EC1913o27vvTQ$KgO=H>#Hv#IeUjd*PG3+?|_*b9py77Sg z2an|Qyx#dJ)r|flF;o+!dyCrSQEALnm34B967z|Q{f%)<$9YAsx**x$cyf{E_i+0l zlZprA$V!w@58Z}yO2O9~5yV+7-~tN06G*W#$Yq&8Pb#>p*+qGKrkrgIK;(+kW%jh# zTS!W==O^^Hv&jzR;i4_{ZCxFShNZR=WX)Vr`b9%Po@}9rYOf<)n`Xm#*jD)B zjGwvdxv5pU1;8*V@@!WW(YL?fV^@ZF#SBHR8@NX=mF?Y``hQ(XSEeQvk8kWn{4VmOvJ!{3mY4g zZ=}lnFF~k9sjS_|i=~0N&}Z>t)jeIy+Ze)7=z*aT)x%q9_ex*gNqn805AD6S?^A6; z4NcNj-~!GZsJ`^=N59YC+*Ly>NgMYqQ# zkx+GsE5!MOzTRWfA<6&Q@)cc;f&xp`4F7`+uC%t)tSPlteiiuO#OIB)N6L3bTlUmL zj%+uG3f%-d)A1{3M8&zc>Ra4SRi&7LlcqEv-qXHvXWka4QFCW`G+sRx_#KJ^p(*DH zD9;qC5H45VwK83EIpYGlL{PtNX0)0FM7UOUB30wxO$KMnp~-uL56X!ys{TbpSs^kHwlga_&eq zXK2viEz8t%K(YF~X5qPw3qPuQH#CF1LqZlXVSL&Q7D4j!uT}2emZJ>+f&lkMF8Mls!B^^jH)BLUYAgYfgw#-6$ zy6lO%GDOh*$+HF9rU=So6Vb5`noL$@KU78LDd~^#49Xq$l^^A#xfbPK8@(8Lf$kQj zQiVgCJ8yr&i2EPqc;l7og%&ql#cSaQzj)_)2u20w)i(_r$aI4;Ly-sq=u@a*%Otf}sOH z51$_go$AT*Y@heXBA;HW$Swt~|8nlNUQWJO`^q3zSm@6MFicz0##0GP`vZ85ZKEU# z#;oP*;bZ8s4mqwK5#ce4UkRT~S3G_kIa(P6r(t7r=rCm@_R(NmmMty)1fMsfs&-#A zUd4Zvd?JMB#DmNumVPM2U(zpPJxcTQ>Avmd=~9U1n>>q4c=xKPT5H1KJihGbCulxQ zd+3A%$-P@N&-J3I%NK(@agEa0=XT47(cE20OcO8bak zgo>)mD@tRtvo_*f`Vj>^UCbXK?pXq0Kw=--u>0lgLVprj*`P!K3T^id8&%&Wm8 z$89MBBPXSs6}9y93rUcj8JJ3$Xy4X4z&`H}1y$sF_|vatR${jF7dRBJ4Um_04#`IY z-?_0}Eb6nQt%0INC=Y#T!Ar(M;)r-GhL44$HdhUGv!N?A!(Rxu1dX%H4x+CdT;2Hl z8<}{g?)#SP7Uag2QTxgY3-7aluhl1y>H~zZ>(3U<*q~^AYl-T z%(DhkvwGD>6}`6omH+o4K)tYQTnt2LU*+)UE7QgwDHLwX?oB@MK1n@(nsq*QtJU3R z(wPXdFoRMuFvlUF1O#dO{%mRW1>0ud65XE@YfzSAj^IlN z%ul8cS zDe-)VR@G9g8)o>+-1d(&${knYLkRAjx}$BaS+b}QNw6f-yiaLLq^7oZtS-_H>mM%0 z?i^GC{&&WJm8XESH@7S&%L<(kvWckdnBu_zE!TV-XL7J@o5kBFZ5Bk0pY=(k>vosm z9JA%ZI8OY3S0xqcKj1K58j%Pl!vZ#9?>Lp7TKFi+%)dLIS=v!P1i#fI7O+=;-U&UW zT->Hk^tIdQ-rRIRHM*T@$```#T}QK%T{AZ>N#=#@-ISbj^F4P5F!hQSMUboZ{O2@1 z@uT0!Xy=?2f=w+nQoFJ|v%nh_1-$L#j?D1iLUK`WUYXCz@I;9|ZgJdIefm5j@5zP; z3VINr#5K#Tm`+1tBE=N?*=6PE_9k%AW0+ZC#lC&*CnE>gh^m^Ds*cSv1Ad{9ShM_P zRpbRx>ntaExUrg#=A$-drwbWhq^g~1p}Q+ib-U0W6R~x%Ln93+5_=MnVL_yw4Ia@v zcAoKNOZU@>#?hr-HMs0!nLniFeR?Wu&$jSit5C>`Hlv01Q{NJgF-iaHo9;t*1i`M- zec@?64GARq;yg}4PTSP!V@!>tZ9nzCy)ap5Rs^NCYBifi2G!CJM8>`=-ID3c-`>T+ zQ_ubCB)>}N`hQlEeOJQ`c`9LinK0aLljo}gwz?H6qXU{c6Jy~D^F^b3U_liqg;JN^d16z*R#UR z2CSLRAxN^QLh=c@TS3cF-}ht$+;>cd&1jDQRV=csa;v%@mjXN%Pex22}q1}PC)!fGdUo0A7c@m#k=fSOvt&%7adeC79cxmR zU4}@v<7tsy`#gID-`CLu;z;RzBY(CK5V-eX@`2x2OpPGsWdl&IrC_;_YdKQbsbdSP zZGdZRdkDn%eMzR}DO`w=Ws%?+Il_5cVsVPT=;I1rwDdk4ECFk+>F< z@FTeFLaVK-+alg@%ljtMpczXb?)jKqagu$ws94V2!?;J7fHBd`;&WYWLji>^tN5`P zfYBGY-K$9q5u!chadJz~a~tEXM(0aAvW5AA10*f8h#h7(cy2f`Z7!;L>rHhHU9j0( zLf0TVdU0<#!1`_MuvL$QUJ)@_b@X5I0#rE|z2cNkO-g^dKR&ePJCro10awu?pgztK zrElu{s9g!Y!n4l^zQo- zn;jP69d)zhogY(pmwJ|+mc0KvG@5^`?y6#fJ3+sR8H=R^KE!t)S%&-LxpKniXI^JG zIWVWTy9NmCM%)U907JnQzF9Ls-`{!4NAb{V4pjD+B944d|F_NgtufPu0bMh8C{HYd=sGUr5gp zHHsZ~OT?XD*aM>m!k$K-cW8R7TyAkWl2GJb?^Yt#X4`MDTi!E5SlMWhs=1#!?u=KC zE)+!jNUywa;pHU%Xa?_(U`QG(&!=b)PIm-N;|l{8G*)c0)jDfxH&)Z*KNoN==!AOG}kkuPF01KqlaX33lpHYg2b~kiXCP7dH9xK z+rgTNDwL@YOn)epH`5s#u(*A$Px}7-Xs-Oq*wE^CzFqowF@~6jL?eEnp$!xd<`i&s zeGbFjy|AoeRd}2TWD#k7xdkhI;M~yS$a*vK&@=KVi_5nkSESshcod(>+)b*&$4d(& zAYfSA<+pqJ|iw<50cL&dy{I*iw(iFz?UAfdSg|#hPxfzytN_};= z5Ca-Lsa8|sg8A?G}&-E@&4d#`o)ZY zLgUnKn|!e0e!o)AYD<_kHd3p@Noek(um z5Z&<}!l6L5C`dKD(AZ*N7=BSr=;;~|_ie*^v)6?~X9ZS@{;X%I zQzIlaPg#^!12wrdeCopjZ+7^Buvqb(dFOx9i{J7xZ;42zyY9VH(jd}VO@jHn8rRUL zet(i%s`Fr$xh>x#GHKTl!CMn%rP>xs1`VA^7sRojB_{5faMo-8_Rz|+;UL)Tg*nm9 zza!F~MPmb}eL~|4xGU!O?6FsphoU|YpxyFo=FPRM@Md(z)IzJ2tsLLU>==_U+l z#!)UF(x*Dsy)-a>&=`Y}wtRRw*$g6?J)jjY3N@VT{gfahpGy83iFq=|_3OO0#c?)J ziOPFI(yz@@?{fX)c3G`;)%2~r73NQ6D;oZle;g@;77r?0_fCR7c-Da{TO~0TvcjcCD)sX+=$Ct_n$U@6K(!+}+GeK_ z=$~}5Vs=O4o?5SneQ*OTJ9$O-5^*xr@^u|>?fPR3oZR>w|7opEAJp`uT^x~^5Z2AF z7_7M+Gx%8dPKjY6n9a?zp%h01vudg58bR>yGBo|=;#=}8ptqRU#sw+$6&Rgsrq6|q zoN*IPZpL|HNEQqxmwZpSq6T`Y9VaQ(Zwr~kb6H%+TN_8mFvd0ZgUkEPd#LK_EX+OI zhv^+dxa22)pxAb$&Pw$h{t7VISz6ZAR6!~b0GXjf`ripB zz8My7FFkIJs^O)Lo_Xhdo;vPEyv1Vy?bSw0Gb{O$hQODM6Aj`?1uuhjDr-!JZqfOE z%;rR*ry#N5nx$ic?*B5p0TMU4C+B&zp6FvjNYlX($3g`zsz>Uz9*tXY;k(%^<2Xkt z(wV%4Fs_xcvp3t{^JG~~r%k~#X_ro%2mM>rk159P#ylzj4vJt6cAs0T5E zkl0)5hfO~*_>f6VrEE{yeLmJecMG1}!bOn`0_75715FUg(rN4Pb8iRO;{3=wew^LN z6f^_K7Ta}KYPCYz#c+|OrKLO{qhd#$#pRcP?Pmq2Axxhai$pEaw2`k4@l^zWXS?ts zz9(o~-DaL=WDd|!@e)S2ea&Q!TV<*cD(i^^-u=gC$`4Ocn4_WGI$Vd<4c&>h2h@?k zilip$y6Zj?tj;Q4%sVS0?fj|bBa_R%0xe~GkiOM)grYrVOn&x!=A|BSymFFzT}y-4 zKm6Y#8E8xHHth0Rv?Psnn8$G7CizjY8OS^5Nl?rLAxjdvZDId~#)?CS9iy2=muk-v z@gFpPPJ&rBuhVXIs$fm^!FED^7T=^3fBccP8mlu|D4$!eU z2GTxE&EDpDOC78o)+G&Q3 z7Nvc!^JzR0GO@(zmalH`DI%cwFya40l_F5ddWAYtHexh`=vd{trFs4fyG+!d?P9u+ z@YCqs7KLywgh_EIfb*6@$(_78P&w&cCRq*JxNQ3}r;;4mQ}mg4MBLOkbG5Isj+R zGe>okL|S{C^=JYr)5>_*cpP-UJJ|;VJK{BsdMSHo<)P|T>yZ~@E~{I4cD~&XSS9Wo zKZD|($z|&Jz{NuQ+xa;6&l1%9kfvaSyPL`igvXwNRaKwZv~d!FTrj$YQBYziQ_ zHoX7=w^N+OR2a8vGYnuoSsoHV4E8N8#g(dKIwc_u z-{v&^Cd+*Jx%MvkPvEX+yV0+A&RaZRdz=TMdN3AYE8rdP+m4wIPagPqjlyF#>XRQ(|dzNdV3}A{XX-VAFh?sqaB~E4e9*w`=|o#q+DQt|0|)OA~0%&Nn*u zjfRIlR{(tGeFrwp4RjBv=_WhG?XdKN&gxnusj2z-_&h?^VrQcql?IY~+w3uf^JH;9 zs&5$(f-zjHMk-fFw=)v^vc_e6kK!JIiBKJTJJFJQsPze{-et(|N@df-gaN8Sr5h7L z$n;?XC1G@h&*v}mtpdo#qS}A&EA|B|+MzCZw!J;#HR_FekjcCCr$_F_sv!bj)-*1q752`RXj z1S`H9Q&K_z;04T+082~sSQIS89%Fkt>Akh{*|qYEuWZk5SmyO|;^%3l=rZp0bifE3 z*v^P3PVwizp+O;z2q@!wHJO5mQ~Hhn&W*SN^t-+SoA+(YsnJ-dIr@!ArOpFM>Rv2+ z+c`N zjR(|c5dDWv#+1MBg0Gqo(a3nYeIM{&D=3U#FhV)|_hVm@up>l=Vc7RSKda+rO#N_7 znUTfJae`>eY#&aVXzjRYj;ygQO725isPLTH$j`dRwGuS+ZfnOmmVq3k?q;5>t096F zz^GKCi4*BfqVKaJaNn~9G(lx`$6n$q_ zfq4WdrCZ@$kD6iJ23#EFnD|@Kdj%JeH^aQcK^fx=)u-8$%_bwPIS(?ae4RR1sMKBWZN`!s zqIyJNBq7q$LC~6nb!^I2MQGK_pWm)aw@)?J_&ZzV+^FIX`Epek-EZrrV8{{DuC-+8 zEAqo|!@$G_jLT!{(#=>)6DWBV+uQXSW=z_YJ zXYC=dT{#U4yQA3A*R?4Al3$byIov^pidvw)*(BE8T$x0HK!KA(*p2CjR=i7-Oh{g9DCKHl! zZIGFv@$>tL33_Y{lm%q=QB8Y{OG0v!u?%w7RvJJBi5IC@Hw9$rxHXi*rR+yvQC z`r6lXiy`n{s)L38h)lCs1Vay{U4>se$AQx`xfx#IZx`;}ft95WfBSu2@sqv*0W=<` zI>}+aA;v|-W+s}3>ad_z*!W}7AwZ><@trk*mEQ=Zgn=UFF@Q4y*fjpZK6%AE=XCWr^s||Xx z-H6`%c`W%*GLkf)zDwk+Qxb1}foOgKOaulZgOzUt=p`xwu^DETa?c&0vtoMSCN3K{ zrWibH?nmh!dd6?1)r+S;_x&caiFC@}mbR;Ah-eW$ilI?zt0Ft>UKm7_LeVp!j2xC2YX}kn~XB%t%*yNC< zNBr!tYAwMUF6TfY1eK>cf1sucpd8GUjyiao8*u$|oxUPO) zVt{04>J~q!gf?KLoyUd+)s{s8GL;%z8 z;CCQ5AL$Qa4P{7Vx%TFcavc=%*#s2|43QM`Bhgq(F>G@`P@#;d}7y#jZV*mb?F~YLHfd;Ot(S{DJx_ep^LXs9gnVJ%s@u?(8??oOSA<3MEtsrymL2WU!!>+!vb^g)es_~9qbqo6p<~TAV z`4u0wnszV64{JHWRwORNkF#17J2%c-coy>>Kq_Vno#wyUlsSDc0hA#^1?DWNfIE4@ zv#aX|MHZ<7Z1V>)vk07Mnz~>d2~&UDo5`zVV$-OQQ@{jX+xBG`7}u-$40ttNbx;(K z1;1O62ieW^imG?a1$qj(raY+AXl&JSW;|M~f)zOE$@Rt${)0E1%yg~e_jn-rwhab80R2ub9oybk)^4y;#y zo!vQQ5rAg`4QKsZb6i#%*9ERbfIp=rYwFbHZZ{)3{k`;i>kpq|<3TD$tA>B|!C8k7 ztWy7Ii)go6!HPIPx)EPJaHp}8x%6H2|1qBM2G18pjG+Tz8S9iD1}IUHxbpu^pM7C% zcYWfma`fMHXIldvTAHeTmC_t?`{$RiDLP`@D+(OkC$x_yaLpDtWGzNy&7gV>cYt|a zOc$P3y36joxWMPV;fZFJGF37)+EvJ!xvHDM69`bYMP>}Cp@u{nO=`}A#1OV5N)p#V zmk7AAoCaj-L(KhVyRWvzIoFwB)DS5g9BjiGdb^aQbFp;TJP26%t)VGK^iE~)EDszU z$X-~hXWHS}_n~QxXhAh$=CP;UC*}7OmU9ba^l|f+w_Yfno?BqCvefjg2UcI@j3uwS z|lF;(-OILd~I2tn}iiM5Y^Rm~PcIh~!ZCv%T zqPEo*WNzCAaEtF_q5l2zU~?^&wvV?h#^Kgsr=f9f&WF9tX|7InE$K`Be%7~ClFB?e zj}9w+Xr~Kpfq)b8DXpr{`rYN6j33D#~;k0%qm(0QQkY5i% zIm+IBakg2iuGNM)bE(RZ3m50_W9nH_UosJm8!@j}o zoJC3Y?mF#Li0Suld)Es}BHhQqS+8_Jw|HTft@Kl{s}YN^Oa~7?wlMx@R&a{8y(%Er z9kD_17BxkzV8Lo>XJFn`tGlu&T34D@Utp`*e0+Q>*e~iUHKqm7GYQIAYaLVBoBOoD zu)}COaS*0+xgy5Tdp{&uVb03apV-6&$#K++HwL+ z8^RT==?eRm)zC}YoLvea2;FAP8Z_ZD(g6;wC{Mv54fNsr<@346b%uQux?k0qgzrZ4 z+e;2*mgN8yy0dRNFtN+OWYhENQ zOtpvU=rT{M7;FcGmn@CWUvoXY8eofyPK)F?!)uj~TZp*DKQwQH2mU3i0hoLzKfV9L zqiH*1NJes+U+}u)we4tFN%G4ah@!ISxo9bowno9TIJs2Dd`%E=g=_u5Gco=t{}3-? zd%}>fRjv-PgJsdRTs$H$EO;s7Krslg2FDa^h5^K+4NqPAp z*TXxdn1brs>}0?Z)gQkJN1EmJmu8%knTy?snl)ABi}Kg`H>I$bB@EN|o*7zwFXIOP zZ0>C51Lo*GIi%eFwy*kLY+X03^n798ZaV7yw7o8Gg=`!Pe)K;SxiKwhzCWq!uRNn_ z)BkKi)ch$ikU5!(@K>>cdY)3jE{G|I`aDqRE z>EvIVi?!veuYF2TB@LY#_CAA}v~5xk`sAN){lt^|#u<)it{}xnAIfVwNX5Ua@d)Gz#kqoABJ7l{cp*K$)8MSU(zt* zmvYr;4Fu_&@7K{nyMHbP?E-o>tdcbq81mxf`ZquiS49+9?`eN6yz=q<*)&yH(nBDG zw|^lO&CVG&tuJULA_o0x>u)#xlxAEo61G9OQ*w_%xi)Yfdh|Guc&&M$O8NixesGME z9tnavWgg<**$VvnNh?;%=>e(C^l`{oa!{bA`{PsxJP{R+E!)Bmeq7r5xvrs|(ZlYM zRPPUZK;y%dSvWQ9Fqry$s2eJm7yf4-*xkOI#zZQzK^F^HE!qk~_%~un~^8f>MXGB0djjwSrAe*SKju`Bl5(HUanpcmA8~M{RWR57OH| z%7bjA{F+oCvt)+Q8M#({lO`Xw`{$xm{qA~ha#DP}cCq`Ne+Pfp1fSo`MT7P+8NmEu zO1bt(QtW4Tx9uD}+AFU^@y4tE6|DPzAku&>F`t*iZc8w}M>o4gdxE2_E9D}FR^5GV zQ^wc#8yt9_jros_l^GVkYu}ciG?3m{C)4<^$u#RQ7vFn+3v-xHyS{jMYJ|~&2fHy- zz^mORc;!cm7 zbWi?gn(pTBt=>Le+`pZNn#P@~u0)Pw#hljxBtGpAu)g)Zn2mA%{FaBz$&@^=p7tQP zwot=WrvKAgjPxJrXRx(S7;SVO(GA+kYarYY!_*{|_A6<5mEoBlmmF{VB&EffApoI# z<-b2B$Zj6(uZ^iJ!*-W4r?IZr+*A7@Asdc6?JabFY=iN1vDnHq9=Wy764&|n4Ken` zDEnK5p7(^h58hLnNM|znKlMx8qJwN3ArpDWGTQj5w(q`==jHr^OurKf{$o&piFB(0 z$}gX<1(nkMb?dAQNNOvwJ2btao;clikHf;`bwYn8Z%T`|`djWHI&kg>hhXqZj|mQx zVCszsgmH;mmv7$KjUF{4#fx6HlszhD#8bw1Y6y@}%NYx8YpsF>+-ezF7jQT*Q_yDs zc*Yvy%ws2T#~K6bKzKtnyRsZt^=azM^HHJn8pXQ7@0URd(rxoRlrW&m->S({$Z$iH z0$z*oL+^flgB-oqGgkeJu6F#D)PfC|dNQSJow2*}e{Dv)5(g?ubFq}Jg!6+A(Xa~_ zAYd*eEMmX(|JczJ3OXI+Ddnu`&XbEj1LBWQCaTtT2|K4xp0L!#4A8N z&`m-T<_w!mo*3JFo({VP{})+Huc2keL3#-j!a)!R>R1Tl+3FS2sJAl!O}(py`BhSu zrN@oGMQNGqQz7goDw1K%!7=-O%($jxl3~a33NwTy9wWe1 zEE*;`Gf|ZohOqW%WObi+kETVr+8p{uhjoQQr74nCI}QWba0u?+c$Ig`{`EIC#wpOrQSHsD~@9le5!F-g2p_6&aXfW+lxykdCy!} z=e_2_3c2-NiP>it4bogB+tXO8u`u@#W}pl{yvNuzuY_f<`WJ6uIcm`;o#ps3%m@l} z-&VY07F;9-10x0h<|x@0ab#l`9UqMce3K>}%bW~l4+&}XuGV+=jecU|C%Y7S$p|P+ zLh@`RjCVrjpheip9jF9ARnMpU|8aC4j!^%994|s;Rm#XtMMhTkZcr4-o@Yxo+1w?e zBC^Ril2PRB8E0?K$UN@s5$Dc2&aL0)`}-H}-k;a|^?tpc&*vkDLs!dW`8Zm6nYl<^ zPhbgoG1mQgZ-9!t`2|vA)P9nmQ@~>50<5a<3C|g&$$a1+5U`r|c6P3&Ma;+p_2>Hw zQF+N;8cv6fTMnx-Gm`0(1PI^(1FXHl>GUlmYnQFpTib(metpa`h0RAMh(b$lJ0U#SQMRfBicq`2YB_0dd7+TrjW}ubz+&_TVs5Vfrnthqx!e zMXr9-2b5a&sCT|uANK$hUxvn{&1q}5NYHgF65(7?<}wyYAJmn#+ICzxY((1G);g9B zmhScy&UiZ#V**@b+YA);7S$08gjxG4@rcUz;B@9wnl;#aa& z8qDYMcO*k}!N%R`gv65(;C4n4=um`7fEAz+BkstiLgh#x}`L~6l8&T#D zexQN%)x2@q`d0nx^&-92YQDsTqlW`x`O;6=x}G;2OERtho)jTdfiQNNIJ}R~i_# z{S#q}Bf&jdiF~8KT#D8uzkxdA19n< zbHl=cPjY8DhMP~K763B`fdGwsfC;W^wyN;5=kh16<+;=rr2N=X*3Y&dPVE^$y4E!U zzMXbmJaze%a#c#ce~c14kkxK-70tJ%7Bc~&91K=4`Z;QF4%Pl-OU@1a%w0`t6L6ot zKBHL^iX5m9$wy9W$$wX~#{TeiZWUCWc@CaB-vZ;un3#||I*E=k6aS?(4@iQ2Lz-tc zIMgA5unKfUD95M~($&FPspCWp)KPRFu+71HT~IaEd4X}5XI<(^Z7=R|O}$tM0*$-v z54)-hYKT6RO7@J)%oCNr-!<%J7{B%L2^XO>X4c_~5Y*(F;$6{(?AJjJVQ=VS>v8WH zHV~3t-MqM>C)&z@|3$_4-bFx8Ci$5!qE@_IdiZV143{bF^{$d;SkJ9$hhzB&{>7D~^*)mta_}!)(qg2SY$_^bGlwLyV}Y3A{#HG?>U~O|Z3z&~IOYZPAx)bol$j0`K?I(=-+*3V`XWNk z^ht`PjyPtW8S+jLRUf91j@@6UcL?RM^QiP(j>;S}A_vnkl`ei@?R=xqHN`NaO7dxv z9En#1Yb)ywH%m|M3{?XzPFyt*5=!3M{#kHT*>(Rpn6wI6gS};#v9)?@`6oOPAeH~F z%I9Xo^ubq{zw zn%5K?K1_H2;@qge=P3p}rPHekr|xueAno=R8p{)( ztb6H7ot~is9l8ZC`Px%00YC(N974Jy)I?m-S6E2=B8HxYmnooQea-W+MEm6>V~)}TaQqc&o9_D0QP4? z4VXBW!+etZ2dzH9kG3w5*z)^<@D7(s%r9Nxv%sTO-`;pXQ0QsW-ainhf+uuIv7W_2 z)=Hzn(t>ry=Hw|rH2BiA3a~w10+@I`NJ!Tc*%pTdze}dY@DZcLb)=2wroPY%4=A6AAj+q zXEO!~gj#f0cf6iluI6wMZs%La7`N=osA-5g3}w#m-<9X>>UFz%IKj3*qko~rB)yao zEBuJhF>|~4^=V*WORVV~Q}+Af+-Yu-@(z1<0cnY#k%`!>2E3aSd!Dt1!*tOUop_A~ z#bv&J{g6xjd3u4wuL-DOrGx%pQan1SQ|D>*(!S?wWadm6&&Nf1cE|1J6wx&^SqI|& zxwsB5uEZ1NM|Z13N=iI)HAGi-I*FlZ{{w(57>@Fp&7Z=$nnWXN&1)wHR-)$|iCnDW zB7-vHOJ_T->pB+MYS<71AagHkUPbb(%BAMtFDJDQ)K&s%~Z1GfR z71lxu5;Bv9-FH4$_WfCzuG$5u`mo#&(Jmgo>dE_De=#>*!d-MtZaYmW^8P$k({!+K zkS;e8-?&2ip04?sflbvP@kECm?VQT`7fHS-i?GE2Ss*?I#g8ApxsA(4J6BsRM@ufW z`SCgO-$2@;LJXvmBh;iy;^bFcn0j~)_3ym6G{;f@g1sfAj^%WBMKl&%=I03@#d(1` zLKNbv72=iLDXYHHns604xuSw5yC39Yt-rc_irDkt49rE9=fUVFI|!(gm+honra8-FnOotA$hikJe@h@{qUgU;SZuCi2r@M2^xr_(M5Ai}U68Y?t;!2ERQHxL?r*yv2l_if^F2-nVv( zn`j3!LD;=d%LcD-{+kHQ?LCpieq=H4Vu~nTOnx9O)crJOr{1yj7evmA|2L>Xa1q

l;kAqZ8++e1R z!kJRVpq7M8<+3@DQ2&7e4oPA*@7eR|pbJ`0ytx2@oH%=)sR=j&^Kl$-M3RMbaIiM{ zqQ{%e3hc6F+R7Mudcb*s4K5$7wSz8sE$_8#vagXRp=~cj5e}z=a?x`lE_izJ6i(0ewPjZE}!Jc$hQe(<#~^dghWY_AA~&{zlR|1 zeX~kXzLw>u=I=6|fAF2d6Uu)BXkAyrx#l&-Jove)K`rNdGpCH)Xv-wc9YTLGIHp4o zyZ1l0(RnZA$p6-zRselA=CY4zuC);!IQsj2xI%Qu?bC6M=%a0){$D${+r)L#Y_Y@) z8?>=!>ahRfU-)c^U_VBn#eu~vG2_?=Q`b}deoVqb)y(GjB{5O zVNtUp!`lT?coIw$!Hj3Ks&-v-Bq8xi>i?e$VuSlG*>GH9T^FxvWbVU)oSKfRiLba` z7a&$XVCvy4#~t=Uxk#@tag8_qXU{6G3!KIDxz?C& zUmcThuG?#E$aLG^b?qCHj0zIy#oMYZe{fG>;2%CRIh!?z*#;Z*#{vkMPA4kaKp((A z$B^ch5tY6G;Gfp!P`sr_)Og*L*-WXSneEe7o zY$fymI}pufh9`N%P1Q{x9jQcAru$lo%~3}I3Vw0@q#*Y5;QoipR)c^=b29MbfJeYxztQES&l@_Hxj!Yo^>W#aP% zDrwgE+~DjFR5l-pzt*CpaOFbYad|NH;IIgx_)#J+iFUDg2JNR;)mo2Iu9yiUPmTWE z8Xu0VYO1pOzD!#+oI0!go}A}^IX}Xq1iWw5^z-0kOkP_^3UQ`&k88aCAR%60rz>pS z=w9wRvSyA)D!GwzrbDdw;ttu?Ln72K@Xy(-pRUxjxH*QygW5mH>mp5j*Cb(2D_Yct zXj(VEaScfK*`@(ap8C7mK`l(wq6hW+gH-<5BQdYV)H{JEi-{%l0Tp7nZee|Zt6r!^5F$u3L5Rp!f?==P2|F!omInH?*D)nG@CboR+WRiAB^%gu;mF^FzO z^BZl7F1>pyKWXXvUwM_B!AcgOtR(J2mFw!R@|Cw27gk+zX9DegyttWUUH^`m*tt}| z9BwQ##s~IYYiBcq>(0-X=kvP4>Z;_?QH@+q6o#q& z+h3dKB4;xypFi||SAXmK+th5TI1nFZylrDA3w;@HpiVe@tIzl8RHhId$uu0-#!{DI zbb_*ykC|v&tlc~(GwXVwN+nD=o6^)qNg7>|<(n+_P+XK)csp7Cp63Uz?culFLmkYc zbIhKrM4s*(jV?E>c~JPAS+9-9;(Mdv`dH@VY-u4c<6?65lW$&3UwKJX}~O z`&Z`fBr^7iVO=_tF`W>w z1iUxbG^b>Q zFF0RC_wl&tg&m7Le643PZ(!y(pg#+cg!>X+D5UXT4{BEP&{m!Sojff79rf)u5AT+l z)Gf=Chm;+&O2#l{PK!Pp>SiN$88&$@l_clI`R3bqep*vK8*b1VTr{u7fAj_)zCTh^ zT)wEz`}x3dixW;IGjGeCi&#Hhh3O|o&RAqnS2!|C>gUKO8uxf(yCUsZYjO1HzxutP zD~}yxUvn#4rb++3dEuF71il86thzI&p$jGqi5FQQz6tK&prlv-Q`&I;5W-*oTUf|C6G{fOTBK40-921sx@md)aGTAG5%pf zy(KXZPLKWwU%qvLVaU(y=HQQF$g7c zEw@U2<)ZOvd9%xLF>U=K&8`hVZ1~f_{aw@NSC}J3HqVfkZUUHFQ5|IiX=?dpyhL(T zj|e9ih~Kk;uw#9z+mdrt%?8E;*9F*w?1;nVccvWO9odL`DHRrJq%G{wL_HAAZg8-N zzCXLR8GJm|Ll(D`@VKie^kHi5n^M8%$j}+@xE~fm9T>=fhua?Hosd9h!BYO`$P9gY zK3iUwrNohxlhhKAG=90#ZycC5Gie<`xK{ZGgb8b0N25WnDZh7eAx-Fy0!1zhhr49xMD=&NS9%Nf(2K zH|B#T*uyZ#@c>(+_m`(+3t#Q1m!1jNkECvi26a>O!;Uym?IZACzUidih3kxOY0okO zUKREUC2GHE|FURx-O^KFF&^MzEY470mv{|nI+3mJk8ojK+rqs>p85Bru^Q8_jkzqo zx_l+#Une7Wl;8OezHVwX9Msw8znQ!Kj*I7MeGYrC>&D}wPf7G=ObO0NdSF`eXLsjx ze+2o)CFOZslfP^$U(@kMYr+c@d5&NRWypq3b^@Goy!Y02Wr5~`+=sfqz_GJ1&tU84L8`claLVg5x_^ijh55Tb za)81*P#cOaeovP!SyjEBzAr6Iyjkm=AqJ0B+1%F-h8z`^2-onri!fMY88nWPow7YwenyYzPCP)y7FhiF zxy}Yv6uOa|!*KF{+*z@;SeGn)qCaKK#|K9Z)@|z52VjT|H!jSndqebL*Is&Rb}xAl zzsfnu4b^H|c*~_L$ceL9^vh0XnU^R1k>vYw2#`@4_IVfyf zZ#5>ye7;rgA21gS=g;yQ)a>qX?Axkn@j^aG=>yh_V?kw1L$WVw`MFeKp;M9*J%a^$@oyjEW&6GR&>uYb#(^QJf{Myr99T>fV9>2xtBPX4Uk;*tmwJ2nnKo6;y6 zEz)ne0p~swW?^&}*VG5Ei$R`)_?b9tj2BA08X#e>)^A??HZ=}kPd`;|IsmK@>Gx!#6e}Z`Ncfcr~^e4qk8FVfHQ! zYz&=qmVF2K2X%O^4o9*Aa0aFZPV~utDjeG|-t2TK{%03j#cI!Ej#oJPj{p9rWuE_h za(}Q4mG4T-SJLbEBhMl3KTBW3TjBjqw+$p;B+|~lvOP$K{(t|!b^~_$A1sOV<_59T()dhkVBFRL9T^;D{_dHS$S9L|i#hygf(S(P z1C40F&s$pGm`=pgiL`l^>=zzavYPHQeQjGeihz>Vf`8>KYNm8JaYUsme`J@G>jZYozgvE%UakpA*8)7JNrLv;{^Jx0z{P4cq0=wd0 z_62{0(Eo{+Z_5iScdK_;Cojp@Z|6r(1lA{-c#30cy6py%cNKmoTWJzk>~e^~<2arK z13>LyTydV!r-E)%Pnh|FX4CskH7w4!fDt_ddTBjSTJ2Fc^#OpwCH}3hX0+gA%9m~y z#Gt%7N4&{Lxeab_jt;BKwlqCZ@OcTnM=gcHz8YhMNGiEsM}J4`5y#nnN_}oBX)oVx zfU&+z{?>~jfQvY6HNCYe9!L;Z0eWJ|y^704OjKiS z^?8Oq%yDm|Kt|0|asu@H@8ltR4fmJP2m_52Bp6HfgbNk9ohaJqe=b~Tri7l?$J)+R6n8ge}#;BUZ5cZO?VTFF9DEjf0x4buX z*EIt#(J$@b@tHCiPsBn&6RoJz-ryLqN}VWY%=~lq)eGT`(FLg@Xb7do5KQ+PjV4!E zQhAt$2`~s*+RcSXWK}M5jQK1E^YF-I;-utJj(#8N_;xPXIdfR3Rw>bT#?%jo5O@8< z{RXjKPg&GY|GuTXQ7A3q6^T8>H}Hc#x6yA5uo>_yc&`ejWgko6xHj!2`8P;b-U+Uj zZy$w-fMQBT&lXpZZ=x+^>n%60>}qExTd*Bj$T)}$bY$+&nRRhNwJ?|m7{ARN546n1 zliHEu%g=H7R#o<%!VpG~1|?b&tm}L(mK8C8G_Jz7YbM9AZ3k%@p2k+@ zC4?FHXV3Vk&^U>egsE|_FP=yje9pgoql?X=#Pqon8=sEF2u$=S>8C-?xB_gvB7?Mw z`rCW3Z2(PD#S&smofI3v{*i`_~8 z%(A__8P~Sb%v7(aCDx2K%c;!(e@8g4ssazqN$i)&6%$x!D9k4~0)R!sMF~p`pos_M zGK+b8**oXymQf2sZy4s#{@CQ$fq&f8<4uyGd}Ot`*NkuEJ1{0sr>H1gol2-lMuczd zlG5loshLcU50kLMm!V&NMP+*OWF{^=^-U1c7@`p+fdlJ3-nEq+AEbYd6>eINVv-!4V@ znLJsuS!<3F%YP2{;_GL;!Tv9f$b6|h*I_n+1jB=bpq!Vl_)eK1;94^iOZ)cwd2X<; z&7ZFpWM!z=GBQ3qQ`n65N9Z`>GG!bdH`d9d9$F}ZDXSWk%+P(L8^_Y&Pl+vvckk>I zj>WV;L>u@s4cO)C?oRJ6S7T4{MGqMd6I&(*$27sbHvhllX2qMQF$z4nwv3}0WYkQP z@`rHzfADVd3s_UA_5lH+Aq**sP|QF?>@B>42P4ArviWat$^BB)_bh%=Gkg~uNKGSvG(a_BNq`6J5B`;27SxMUld?TK+@)gV{740?zLoVjD=EXKml9)(vSEdIJR+pF@39m{;x zqxOl(TqH&D@YATn-paHagWP7H^}IgRq3RRM5D^pZzZ%d(B{x9gGS)6&*DPFg@0&VD zju#K#=}!&Yv-mn1m2PhLdrvR<<5lCW`x>ywjPXWYEM;WE^0G|38j^=c{4MQ>BgLV^}%uOt3Eg(IssAn;XQRgAzA{pxI9>yEGP_bQa# zqP>vyKwx{4*IdNOv_2zZ{@4PV?+t))4$<*)>nl&JgM7U%^fIx?fQieOSJMOUB%k5B zBnO})uj^-GgI^YWrJSV!4n zC#w2-hfMa*UZz$b0AuBzAyAv6U%wzsA0U{czmiV-h_|uft_xr3)T;$A-bu|fFS%sL z`DX^RHp<%3YsI9_NPqMCTVhv?BM-;#>zCN20gg0bn~LLYkC`<6YX2DJ*#Iq0Sw7D| zs>sLflR0m_Ot5%~X~90*cD)rYWgP3j42CwSHONFyiLun9iqXasok|EOznSLMZ_nS= zp4Y#@bR-hV(~z?9cE9H&J$36G{kY3#Nm$){`_0IN%?V22V>op~fhcUH|0U!5C4tmV zY!4NQ|0Y|aY^jvmuDL~7fxw>99oqzrJ{vOfRbLoYCr%%!MdH-X^Q}fwAXKaYT~V$( zWe2os$S2Zb|FFpBrLKd8nhxdQv+=%z{o^(E@g@@Z=w3ckx(58D?Ia{E#io5vM>8z? z?^KrTDv|kuB5TDCaNX$l(IxusMXYswk)ac}Mb*yiyIoSKz9h1_lza0$yNfr_mH%Ab zOf{;|Ydz(so^loWj<_D!f`zYp&A&r1AVC7E8x%kS+feOIEi@Zr2=<=pozTpl4ZJjt z`<=rc7fLiTc`N`xq8FyN zb$7iXL|?Z-ZxzGW{j~;=jbY7dP4Ve6248kAfHudl8cN2H(eFZBks^ zc<>E95!hqsP@^mSV<0aFc=>dNqXAV46E(k}gaRtyq8PH8PM@HbVLf5%awjHaLz|k- z5KQS8f&suOr%jVoD!Xk7dSwkf&Irl#Xjp78dSsf(rUopzt(mHSzMVq30~}v9?+r`# z(B+1jQ{T^qG3Qt}js@JgbU44Fac6~-O*=Xoe-sHg9eq8}&V`&VB7O+Zr)7Z4wzHHv z!ZvM6{{(91BRiJEqjIGuy!mr?7e-`^0DR>|&$IsFCq?Bfr;|`G0|-PzM;eQiiEDQy zuW?Ts)56dfiV7TvHokHHXiFHE~e^QrFfFKW9+A4>~!>K{s<80??w3qhdMttNQp< zHgtYps=oLrQlXRR`)PmUv)CY}Cm;gjzPXu5KMEkJEtVS?2CvN>YHGO9A|i1O@Ed)S zELEn>k4@6c(35?ga>jr-lIcw1fZQWV2>FH~b#n0KdBD4ZCd+M){MX)!1sjy3rPx7- zzX~UY#g15xIKW}^DFF;J*Rv2jID7*y=pUnG$g`c0@eBoK&nNuMcZ_M;Fy;!VP%HmF zwL_Gq(_k+>8G8AAIh{FzNLb`XI(DDEN~>_$jHF`HruqwLSyg`uIbLb~lwBt=cJewb zEP+Z!XRx0M9Wbk0txpW(r6I9aPlLT`Mo@j)Oh*~Emhq|J<+yqST8PA)clBLf4t|W9 z&cQraePvwW*+qc*4ZiUF9WYL3OX{TRx1ILXq#z)Btey;t4YdAu$-7(#l0DS33(AIx zR-1ff()nPiPXN=iNuO=XG*73tU(vFW4t8v}J$HeXh=9GP=n{?s*IJ_kU)Q&J(rMZO<<5EdRF4rC`jRG?724za{m%ykb2u(aaxn3%?@!1SrYETP zpRtf4h#KROq|5Dt5(%xnaZg7iQoPjSROq?SMCFwZEw(qb6nw*oQ7+79{QmhtRQ@ag z5;vHvhh6jHd>GEXd=crKAzb^QQz*65d&SN4Ve}KJ_jy!_gp0F_!99vI;hd zzp~tNN;dbHoM}6lI%M2rAnp;p35Hbr^0~nHqRTby`vEzh&fUG1-LrPSb;jXDXqgn= z`uL2C{N-<`6=PTm`e9OE zEPZR^N5FM%Lf^{-`|Be#LW%gOrAp!C|6M@XljIo)u5^DmQEWFFBGATvsc~c zxPA^y&P+7n`*Ox{~mZ~zn%-2*)S9s z5lEXk@z?YDin9`Ob>l$Jz=?}-jH^TVN4l@2`1AA4>ID7yWWGqdb=dF6`m^W<&NVeB zl*aB{M8g+2Gs~*)1ykeYPijxmW!$SDsl)?lhBi=PowUAY`3{@tF>cZu-A&ZvFvK^V zLy$?iMw4Iq1WD?`HgMIE{?oybxCt2}hFsN#rQLIs83>2Wxjs62&g+vdgGCDO|7<-m z7)<;HSPe^WEa+l8JD%PQWa6HR5qG`sAU;ngHFJ$;i2W>KJzmypuf;_@_V1yq`fBV? z!;X5oKF(jWY*FB$aPAE*CJPO+MR-BT@zfXjLJY__$@#oGChsKnqWrA}!lFY2jhg5% za!ZfXx$P`X1K?Ar;T2_C@8fap?|3NCFeV-K>cl2DfpIGxz7S`76nVOVa+7yvt1b01 z6L$Tkn}!qlg@SYM`Oz<%S=2J7qVRfV@VW2xK>Zj~rKRaw$_+{f1WeCEgOj|r_3dn7B0_he_;;H!Ob15 z{>m>fUvwBOx13{jnyZ^M?eMuVpN-wjRGw|0z1ttV`4uFdsX_QDE5;jpp62Nvqg^@J zy_*$N1+o$0E#dytE!;6Zu`7a3>X4?D0_&h%g_iF+z{RWJ`EAb0h5 zo6GN0J|g?muR+hZIgXY`yHTrge>QPlaXWd-=gu@{Ztk#JKT-*M&`mj+&)V0Fl46z8 zpqwmc9M!SO6fW9*;XE?HYpvklZ}KMLt-OxZDmS0p zpT(;E*ZrI5ohIQJ)7mu-=vTE4uZSf1Q4w__L9>_?X^1yViY-wlr2waS+(u6a54p4c*aYGYt zlwrX=`rD4Ip#x@Z^QB&sO!H~tl4kx8Y>{CWANLH*w53#H%Y-TZ^O&~`&x!Xk?X$+NW~=qT5_vE+*sN~oJeO`k72X(jGP zc!k=p5qQ*{l9wyYfG)xmoA??q6@Or@b03@j3t6()ol$TT^ydEpF) zr@u~#>WRmj+zqHmaaTY7%RM1~Zj{TlM>rIb{e*Rs{Aks<3a8P}H6LFt5KLXV zHno2^+*tNE9CNgA5cer{9crxK>{H4K?2R3aL)J}MTxIK%$B$J(=^k&*xz`TcpcG5O z_(0j|?`}nPbUK#d4lRy|Cs1p$%IcWnX{xv^?QT=6f5W8#IVe(8DFprO=P=7A_nxom zpG|cA(}duezefQ?xZ*u9Wb)TrC?g}xSH?Rol52v~|+{ zeNyMF&ZwnDbh7irp4il`)N?ECyrZX2Lc~&3$q)alj!AS<>a!8fV%ma3p&d*8!eQ@Iwa zWnTPWy#~0UlwA)lAa0wV(?hj?09YNh!DulbP+pg2K1F>DiGERxsiR#zUP_dIALw8W zCXlz~qN&cyi_byFbZ#RhPwXC7b6no7?o)qqXsXStbX3_ zuzi4UQ9XKgYv=qT&7xT9u2u=_nZf~e2c!b`mPkNxr=(%tt^Uh&#Ed5t!YY;kjsgB_8VJ#Xw$4>hRPW0Od_ zkY_!#9*pr4#HNK=>iN0HEw9|ZU!La*8w0dfZ9flJydh$HJ%i6bGtsR-HBxv7v zr5gu^cj==fCkEzRqzkc*u#-}NEY*UICT?W0 zYl+m&cYhu6!ouN(UE=Mm*KSeFKLoFCFxpl=@;l?=e-8FtYthrV5AA6eG)ZcudD;DJ z)mf8>y=2TqzPjdQRf*exkg11R)Bcwj_({nm5&fE4qRSoQQx?4v<~?1*qSfv8p!`1$;Y^G?OrJ!I@G5|SC&To9rkbrt^ceu= zw7XF8rSzu!odaB$YzJMiWKjkU1K(-qni(AI-Qm-tM*iHx%_W}R#o}ma^w#>cR5l?| zY2(*hzw-IDK5)in1U3u|ejpWY+HMpoY;awSkz1ZXXC=VNN^aWE+`5-~;Z~)kgzjsG zvjWd-&Of84>Rb8Z?w{yz5b6n1mrWF4oJ|YXMl5|C} z;LLxDp7~SUc)F(K1uo#OmUvp&TlKC(;Bf{ox=gW(O1IACW7o|@w_9>83QZHc0Kq4` zU}8QGo_zU>{?5rFmI8lU9)nKyCz0TA89heB{!oa!Qq~FV* z&D%!y6#kK-8o}TiO@6NHLDM1%~mu%oon5N~Inkz@71MpO< zvFO&6;T$P%?$o_MgZq#y-s$Rp9}%G+@B`HD?K3OlY4*3pk7i}jp2v{`B5Qkk2b zO1=i5TU(KMm6v<*UEq?LW3F%OULOKtsskh=oPc zIe1T;m11ZomP?>9rcJ+zi)#69GfG_348z)Qz7f7Q9tn&h4G?R2ujl|#NSU}%p&9DW zKNI*ij_dPW> zA_pOl_iu3%cuzg#HJE%@Fn@RPK+bQ(C;tOD|EOFTxXQndbT*UK)^B|r#s5GiR6IwR zej6JO*q&vbecmqPK6kjvG|WmnYn?dnC2R||yCM%mkITwx#7}>J^_2$v@uB}%RIVQj zgLK8&v-+Evbi~|~C{7ACZw-Bazpql@%n~6G7lBo<8Vk;r$;Ihu&3y;e*nFm(uYjt1 zZx*2gY|;{^?z3!GcK`f!wJj&%>q7|q4ns{!`*R*5EDr$Oz51p40i zdx=wa&vV>lBoq}uUY|F=vf>k^JF~>mxQfz`ArRNZggSN8Uu61#vXXlr7 zEo6{w1s@QLCr(B2yCH-S!Hs;@E9dsfsKeD9i7loJ+ol6|kixGGrggTiZrPS+TNuz_ z23-%8+{U_E44&3p>|bCyY@urk@q$%2+uN=+(?~)>0_V=Do1Y22uWKmk(?;(iO<$7>^0S>E1VoUp*0$SwhTdAWgS!mS#FUIn}^ zykt&pp33~^BtmlQ#!}BW{PS?FIn8}LjmTLl{cBidyV8EQ!#7FzRG9U*0I^-U58{04 zNCO^xC_(?#I*F#_b3@CQ$rBXOV!*8=qS9b28c~<$v=oY8JZWm}EDnExkKhp!K%byu zdK4QwV?xjw?o<%Irs6##i`vY*i|xHzQM=?ax%C3S_#Z& zIgO~KO@kk+?46rP?DF9*=iQ0*?i3brOkr21#`1@gBKRkAu7ze5=k1yeSOxu2Wb4B= zn_Z=lJAXx?m6bsqeKGatDM8SPyeTp7o7u3d1DXR>fPC`H_Nvb)Cx_HDL({dkj2Vp5 zl_q?3MnSmTcRybiG8xFom{)4|KQgEWiHG0SekgC#)z}kU{}OVn(N^n95n*Bb4r64^ zVg0IP7cyE?^FpsH z8Kxqdo*Zm1=TK0z|9Vb;YIT5ZYo|8k^^Gs=0On!J}^tY7AWi=e1PdRz|6Th2W3WBypoxMyu5 zQg>7cm(;7d6O4hKIEP-qvQmfC6m&I~m+bt#PzFjOc-Te81mL-rt`Yv)@|%`@$OfBs zNkW`KYq;Mx-VSL5oq8paePG5`;U~6vw~1EfqE5et`r(I8>T;vvHb>Zrb9D5H)fODP zVpGu3XCeIjm#s0S_QZQ(N8r z3396)D=2mApkT?FCrv_alMQac;Tu_B+TYK$`j;ZU*F$^0Oz4QmOKYa=e>yK1@2?34 zzD3ARu7P(csr(#V(855`*TM=1TusUL-Pm?lD;;Sx_IR1ou1H+gMZy1=uEeg0Gb(+#Vgt*-GKD26uAZyjN78QAcW-RsLL;n&y8Y>#V6rA*C_ zn`&*?#XD8q5x#xAC3g)X{?&Fi&Cf(Kds|@5JHy7RPzTeix>^@aPDLE4$ikFc$Oxo# zy6;wuxZ8UfpNDOJiJ;;$?);RO|3(genPH5jc#au#6K5&yN(6F$XYXXt!h9(sYkX8; z!u>qP4qWaOCl{Xo({VjR3X}o>`bplwhqw*pkOue`79FZ)kWuOR~H@6>QPiOmV z(@8rZY91w_|K_0xik+h&wQ^dzB3gFfq5yXIRBN#QJaa%gX&^k{5dADsVSo|IJ% z88>Z115cCfFvU9}Y0<-ioR@T1ii`j;4gc-1-F=OpTB(|MMEP;HPDBQc;5P@04}9*a z@H_Fj^G;t-lzqSV<*=V&B7$-l_9Klp?=Z(RCHL@}xjpk$$Gv}zq~_zQ7?zC?fuI$1 zsJqfu3LdD2!{^^SDeM?JL&&wYm$mNf9`YHs<* zO7)JX30k>IaheP~=KVou7grGsI+hQuc=l`H!miLoDas)U8R|R>6qL5oRhridyDnRj zPSdZNvnZc!M!{%qbfX^FnAsv=8Pdo~p38{2#n=I>>5}VSvNjhAJLP}7`zvsn+KHtO z7*GnN&<)?`2<>k7Unf_&QC&|Ki4DCgkSMjOQF~QtZOIF6Jxg-ipo8LPPxCE^V|zzdjEPzP<2`fNkrSggkyz06QzZK4 z+5OGOmwI!dgbxp)cEHopy%`2Ru8TgAg7sC`eO*=Wm*MeGkpmlY0K`Z#?&&I!Z=MM5J(h0XPR;I zQbkOg5fOv6nMOE8e(XIc|8AMDC zQ_n6HA==73c7Rv8o@DguNftb(qCYVdcizG)g>R}8olyT0!*A!=uM$5?N_SynUg?im z>w4gj7Ydn)?)lx&AXF^Us|HAR8&vyQ2IPqufDclgke+c#3Mi4)nw#%-2ty!s!_Quf zT{lcHp$8?gxeg`FotoM~%wiHrVnu!aCkqo!nG-HfmJuWNA=2i=^L2 z`qv8luWg6^P$e-yCs)qy|E`U~vKfD19xDcSEoJ1Fp`}6ijSmJN@yjO}lCXfPScyP6 zInn0`7S5@JG%qH$MLO_qLS|?Mt=6s#RHg`Tq6nUKs-RoQRBxom(?PX%v(iZ^Vzm z=OZI1+h83RC<#PBK6kgDq>Cq%3UA=D=SBt_nQXz}aK|0RQWLghMGWRYUl%^euGrid zTRi*oi-7@!%rM|LWbq%e#4Ntu={MXy_`%FD(5$3kGq#zOm51WS=PfX0pF&vy3-1Z% z^5~+q$<+@Pvm$_vuW@FC_6naJ-V^TG2%F8hUv&8FzH(3j{%6;1$0k>NW?GtA5d&w# zx8FP^&A_mZhrnlFIVmmqbl$;FNWSsLEmP;lD|Q@{eCcIdrDdkPBgAs_(eLinOTLh1 zAt1}GQx-7b;6P+PB^uvef3wm(7W*5AHU;0e$f)yKbyaX47!F!3YXLMf++J4@pt*<#Y~Y_hTvw*n6W7R z{un%uYzrkY0{or?zc3#%uW{}dU;L@wIjB)u66nLt#3b=s#Ujivj4@D$_ww23epT%WXPj|MpRtN) zw*db`dt$5%@Em=Sg)xgnucHJoUSQy}U~EJOjs=8ZY{kGFKCh~c`TjTcE_hopZd`Hx z#E=Dk<6rddou9sS{-_M!hYn%o1?Ps-%bz0wN~fRx!M=>K0PV!aqZqqh_@X0HBOS)D zzy7u8$p6MS-&na0pv{QX4*F~OZUQ2Fv@~_j{qOS1S2mwdstn6M>xoQ@r#)@!%HIdr zvyAl)QTz6K!v8S-*pJOdDaYWt{8)`Std2VBUCEdJW6#2Q2K;#j{SBuR0d)5B`Tuqt zV&Lad;E3;hBxl>Yd~+1^3neaPQ$eB%vigg(v? zuGxM@Y<{MU68f)~KNxE5|Dt!I7hBjX;4`0jMVb}xx4(Td9S5z}(=jGRqEuJ+tVw3i z$@VU?ue39dN+EmDa^SN%xUhleQnhgg6s0I8&mUGJrTG$lk>%@#Qa{ zmzEI0z>ZUkHB)Fq;M{ON@Lo#j`U(ZWWBL@qJ4BOXP-A}$`##R`{qKKKdfcwFXZpB! z3DDbSHGX3Yzcup!^Fgdc8LzYU$I$xGkDlJ=u;O{f0P>uD3=cTqcYTIE%KgwDd{^jC ztbdV393zX+ZE+m_$A7%0a8CaG0{mYvI?~to+UvO9wbUO|*~ss7wac<#oHcXk(+~Vu zgmR`u)^DHjjD|xWd}R&`GW7MY|5dvAJ?j(NEVKo1O#2{%=_5XPi!pcl=^skRpM|wd zcrWmIluX8^@Vk=_OiM7wveg3N^ZgnhS=&zV1LHdUPq>HMZ!hXC&wJjJQ@zBtmxZwy zvo%!_zKhTE`ZsG))`#d8Ro_>Iy#{Y1VO@_b037gkz0LRd%&r)*@^-6Qb2s&+7 zulLZ7&4Sc0n%n(df&R(Z#U{%wj8zXdEHb=KIpqVTeq}R+aa`{A-ruHK8aQJA@Q0#v zpq(|gg*G7w9GPS7#ytP}*YBKuo;_b5K#5a>>t-Ice7jfHT89zxB(|M^hjTdOOXro& zlYU1@H=hZ__zin_yzOnzPVX()8iO71v!8uMdJkL^Sxc}9ws8hEcC5%OjZKiiYnC--(bSDue%j<*SNmY}BY^Q)$0A`Q#_} z=v{bzF8zFOL}5bk@BipWch8RohZQ0)ZR?52lb%#mKK*9JRgjWZ1%jQjDKSNbYYdxX z)7Cy}0i({PmwzTr#SCrC|5(2G#jF$;!iW+Z{5BYEjyrC@)Un25tUomnM~^P{TT)lS zXIgB({nS!>dA{_eSEov2ICqrBlTSG)RpP0;R|N{fDZhVzL7~CXm^sy6pY}8QBK-z~ zYpc==9-`B}=8R>?5)?fPPQ*!*i+g-{i1_or4T}&jo9to`X7$Q8TQiaW4T~RS3(nNA z;9@*|A%Z<1{`99i3$Ih-@Kc{^Okft?*UWf@QRj@)KiI$i z<<2cY=80veowhD1isv|`BnF>vzk@N%qK5QGFH2Au$x^~$?Z1oPBRDRIdYvpS}Za@@S5L`XPa#%r3RWRxV~UO3NRnWHct0hVlm#- zJC9y^+2@iU|9GDP83sJG4Q)1UVm6lTX4#?L!h6E;Ba1flZO9#-=bpVIPQS6Z2*g?N zgEwc6}Cy^5wlJq|Y~H-P%+E#UYI>C$PdA zPq_vb07SES<^QEG-Jzu(0e*3VA&dV*?+Wbz+ip8C*}8XrYzNMR^RfSrK7~=9=fd+i z`v3QT-Wl!Z%xU)j1K=$@km;wM`hnyFA1E$3Eo4@;+Tbvzr!MVnuNe;)b$Y(rmOS9R!Y$wpGqc0QXd#CGK47F!hc!;n?y zOSNHSKu@od&0+rxf_s|v5p+$mBk6BH`N`YzXUkwT!ASR}Hyl&wOSJES2fQHp@|VjQ zmb`Z4k?&3&+3a!T$HU==XXPye;kOLXzgy`Pek_K08NJ^AF7(swx8Jg|EillANOE*> zqV>@SyuOZO?|=hNNp%UF6GXccEsAkDI6?``ve0h{;PN2z4msr2l@2jWxE7yj1+4Gh zvUl?h-UBg1){Qqd3^RNWGVsYy&Pp5t&(WSl!pGW=Hb3&ntYqy$2mP+lKX^a#&2BpU z@1K8V)aeZLTe72R7xdpFj(BI`H?Y4c{O5nZzBG66I5wYjf|V;V>=Xmt7=y^{W@V?I z``qpN&Wkze-@ow6{FMxhlsoNoSZZjd)GN*e)*)mL)8}IH+qZ=!yvF}EdmkW2iNN<+ zbev-_ZnaBQro|m|*7Wrq+lRurF&}t`KJYr%K(sN&?(lwpO`JP>mSO{8@5=!1!w$px zm-+RMJHC?IchGHtzTRwY6X0{sKmU$YULAVq#p!Pn;CK4gw~Co#S@2MG+hzTrIF=SJ zTv1wAY`JA7GszOO*X*y~k4wL%wD$r37}(?Won6io39PE27To1oz3}438!|!_7VQ=nNf8c%Ci%vXo|5WcH@btqUF6v!m z2;)2?02;?-XnXV&GS@7ny%imtqYaqnv$%OV&)oE~mp--B_Tjq&yY4zY{Y;$Y_L{K; z9dgKd=`s6h`91xBfQeuOwXd$n3e%78qE+{p~lV*Uaaiciu-^8p@h&&KDzATbYH2k6=St#}FUj{7{-~pYr{I z*SPKT*0NXA!teUq-yTqyv;bqy%U;&-oReWmu?ZG@8trO1y3`W%ZIoH6vp zR%4|ZL}d9ABA)&Ce?iOdU@>Ta8#cY7)RFBs9Q(9YGG_d5x_y)OeA_~JiUN9^K7}sZ>@!?WC@x6RL2E_gt ztbOz?pR*`eK)n+YBdK2uT1OE7%Qy*RipR7Mh5#Em${7ChU+zj5G`t76&!Ra#nJS|= ztT76ECp71Z<07rM=^~*5d=`os$`;Wfv4s$ZK@_TR?fg$W%4bY=E$i(!C@+37OP0vo5&slj(IKr%~ups!+t))E+~6JJ--Ip>0B@#iO}1 z0(gx!WB-Q$&&}$6PuPC~!tcv#_IcRv1ryVfO@5>6K=@z!&XMogI(04#)?v{v{AT5& zit{}4z?o-$pmGrr!01fm@IYlF|A(Gn`|6Yae|u*FXJ=L4{YSErWim-7Gn2`LVX{s( z2uWDNA|ZsZ1PDvm2~?s41OyD5EycJ2MG!6e(!N&VZC^oY-?k!JD^Q_+z*aw6tSz8* zLns(TL@J1^?|h$oPwsinbIx<0JL|pk{Jx*heC~6XnYs7ebN=Ul{%gmSOj*yK?HZQN z?*k>DFv=sBh_+=Oeg4kPy<_cHSCRb$ci5m`p(6luleKFy>q2u8qp6AZGVcdt5Dk@W zBLSbWT4Xo4#)dcEc(ZpMzLqK>>B<0{KtKFpGD4aN$`F@dey~>_^WEQh=b6#hzkYsn z^T&^i_fH-}K9iZpcW>Z-xTRX${ln;FW^9n9WO`s|+^}I$N?$Q7>(#@nG1@Vbn)hKx z+4C5N*Q~h(M|<|ZJMJizQE>ZNpbxQc_?!#|J{NKj*=8_fU~U;xlRbvc%lu`GL;5K) zlDTG&8K2KSNals#Gj+S&I`yU`XNRS$+K^?%y1MYf)u}ZJ{B7Rnr+d#TCOZ;iJ%$zY z9l7whTSkO{4)%`IdUYgMCSg#<=m6Zhfkp(A*-^4?jrIc$2$zP+0>h;pO$zLZI4x|Q zz{U-(;=4v4c<{jsQlE>+#{&=gRPP+SC60FIek@N;xII2oxb@mG{$pHQuzg$w`B&9dOJ87-wX zvT$K*d_M%)Znm!A8)FCs;_?`FjXUnB$Pyehl(`9E0xfM=+w{%+#y?*W{q5hqBhJ3y z%zzvV?U}x4a38i!!h8k-PyX}KO7=RBWA9p$(aR0p;FMGL@QwpAmoxM~er-!ae`Bw_ z{PIJ3pI@FBTXZMBd;77E^q!Y-)DlREodfxA&X=5(AAkJvIQx)*%YS?1()hg2oIK@} zJrjS2J_YP$-}+XizbEe*d71~e8VeXhym=g%%bB91V{+=X1{|)GtajnO_~w8MpN%sn z0ZJkGTkLztCgu(HB74s`qjxNgPQ`0^ubkztyzJw4f(79m_Yk1Yb7D(k$< z|1&y0dlSvBZODKl$YI;IQfYj9%+W&`PtG@A>pgS3WoXIhz2Sy`@@7{6>mU0X^Uc8f zwJVBn=3-9TTzl2e5PHZmg6>@OCBKGE%8x@Sv#Yc@q9ciclaV01T8JwySTLj@E5y6ZN?*LmsTqzPXEx@%+wrlHO^W@tDI)U%cV8)C}G^v?)_Wa{>+J_0ATXV?#T!vdVOcZn8i~0HB2Ma#YPzeYxmAOal$1(do|qXTYC(C z^Xp_2E_$Y9rQO3PKe;hBF0dhAcm0Og0DufR;D9-azeQOXTfyWOr9%oy`S;tzp0do;(3}__OvDq`S(1Q90zxt zf#2olh()CX8QBgvC2(F){t0-V-{mpNL1mR~8Z>e=E9d)TUHfDfpMgE%FaPqC-0O1E zq;c`@8B9+syF3)*fDqmij6PB09H6Xp-{*xWLw*fWrW$Tr(? zVc>sWb=BJPy$X!+egBjU2*BV|GDMsZ2UC!V%mI?Wd)w`&CJvHWOqx_Vs4^}l)54AV zGbK53Uf65bV2q^9p}+s)AH+I24-`{vi}c8%`2|_N~&DMx^>kJ>#P&To@nszJaT!wFPk#}#w|O`8qiB|p56VW zb25!6E^g11mtTH8QH!6?vvTF^-p`$kyhUfEOs)@pC)Oz)qcd|SWb{OSed3nmcN$u` zbsZLlv(8$Xa^CPi-Dc(kje8#Ee=@&tAlhSN^i#LdyMf{Pj&$B4ZFrbVfu|R7U1s1U;T$%w!a2|l|kPuxA7^JlelB$35#AR^~^N@Tk_R-SS$kf%{HdY6H5A?x1#K8$?sa00h% z_?;6^TpI8D?(Z``j1zm)V~<`Hef!&&MBnQD;e2C{G2=wGyuBYFFFDKjoLuIAhG6Y5 z?(7eo8IWWD{{72iI~xBRd5TQqz2)5-Z5S`g$N%WZzlvp#fybBc*%2-EWVRWQ_3VqB zUv2oVukSrS0?#9Z*()*VvR5)5oRxe5X$JHvw_KeI>xw`KY>T$O$=S??J?5M(Ux@oF z0%OkS%VUp#F=LYQi_8?)5N~8x))|}3fn$$Nf7hXM=sp-X_EE-@xyasyoI*!J7NL)@ z?;)Ff>lK@b(L;?4x%uV?Qw}2+p7WiPTz1_xxo#M1#>!q#rc~RUIpM4k{-5nI+uo~x z+F3h1PmqKA`{?%;DGG2fqj>Y6yP%hkz%@n)S5Hx22u zu!T5qct%jzB{2B1@uY?(4IFu7Npbc;v|nG0^L+6LUpBJ5#sIkZ;Zu zWwd;T3l`DmK6ge+DM7i&J4QCX+RDFu=~eGxuv*05k|pi2!fX^TZfjAXQOv*JyMYrW zNrPpRs+xyuuRSs`CMd6c+4QSn(ozkSz}T@P-4wH9=H%YBHYN4AAy18PgeEmc8Al?= zxUOYP`U{F0o24(R6(b2Us4qPCTyibqsmG`sg0))BKo2N87-ASNHY=Q{S!)8PgBx*b ze_w3s#&C^M5QTz>6BJCl)_m-^xb=is@%Y9!N=CB29Bh2iw>1AHs)Q^G`@Kz_Iw5vk zgg~~S_@O+pA)_Q4`Hb>t|1Ed356Kd~Z&F%ar-KcO{CB^*Ev}o40oL9087mZBj5n_Q zHy{I9JJkruu2gBeUb(<$_mykoJ#a{^#lSJtn9s<2B{C0w@#5<{ALn-7#Mh(AW0ULA zmu2DJ^1DYb9+xP`gi`|N1x>JjboH$0{_|VnTOJ$I?20~s(T8Yz6T!xJ%YMqfLTNzz zFq8*(et}zh4Efd87M4%*=1q@lzPj)o;2f+)Z!)kJQRazo_ZhVEGNpNZ>;TC!hU76c zfSYOY(o3&JKlw@0Xpa8rt~E+4{>Uj0g>xW_cW&7?mLYt{$Q*(>s;yzh$#<~kd*JV^ zJ9J?pMcf9Nc;N-fAc#;jook2-Vb&mUjUgVr%bs&{=T7gPUrEEP4X;J-I^l$6sqgU0 zEAA;@m0({qB>|W}>

t3yax=U zwt;~$LH3ZL?mHawKi}PZ{<0xMA#MQt&`;yfVLl6gf8QGW%b2<4X7MoR&F2keA+A{y z&g$TEQ6>>1TQzW^+203qm5jIkfWG&@KVKA=#lt|2p@&FjbmUreQv%v(W@}(<$o_KY z4@N5X4>v2tb;g+Uj}P>C_NOeF&s_Z1f4Ml;Hy?RqTTs698E?KhSuY=B=TI107$X<< zMSGr_Ad#%P@O$r_K3|{W$)w@$G0)h~jdPXp!ElO0ffkK4Duoh-q~bCd+KvHPk3`x<#62F z|Ceh5gKO0>`3%_6$SlBlf{~i{mIq{)ZIj?VyxMzS{QmcoeNg!9$a;QU8z8@9>t)wp zf8i|k#@g9Fs2E!okie-apI6}HXm+LF8YES-OYGV!spp% z)0q}-V`&?HkMU$PBXR}9?mhQxscJ}YK|w%ZXBnG)pqT#4zg`>*GmIH`-kEHcHJFe& zV8aH1P;PJY*F)s70}sq+2x1fa?sqPYqeh3)VPU{Hfl?4h8PYoY*u;X#`f)p$D6wEds@FfX;fkSiSk3i^sP_ke#n@U z2UjuVb7O3H&T+Uxd;7#*c^}@4*Q^O=)sa0C8f(ak+U9dp<__nL4Sx@Z5eA(+N&?0k z4u&Wf?$}c*ERTX4l3mm}xBuphvfT2?BF1?0=9T4p^~Z#=&KiHV-uG&F?zfdp3+S3U zD(ae6iWap$ySe6f?Z08}!=^pMMg^Q1G|{fwW1@~={$aeJ9|8sl_S4Y8GmECCPo9~r`TjLgYvsJra4bkrql;pk(R#D=Zok6)H@()0Sb0r;4tygBf|?vw!l z^6HFsnmg~IWLQo*Z@Rmu#@~_KywJdVTd-hSJm=kVF=nkBFr?>^t1dX`G4SCWLdKJ& zQ4RLqzWzTSsbGOTX<5%$)oL`$g2VFz51f|@e8BkRBP%HEoOAYx z&tW$2eCJ)gXN12l-z&k~%45iIGgBz1W$Rr$HpeQ->B~aoJc7e43-;7IzVHXBeVqHg zU>5^MF}t5bUtuhe0~n||H}oa&jyY<-7sdt$kNbJdYh3x|uCwc}KPvIN28`|h?{7Ef z(vR4$&^NQ38^GUs_@Vd2Mpn*Td5xX@#VyhC;)}0DPdrhHh-XZU1Ccf98xLfs&%lcu zrW7!r2ZJfWKtKGi_r=Cs_H*N0rT->!zuFkt@LglrXN__jnlI@1U;grz`2X|0@k9AZ z-dlfQT+CQJzT?fE*W6-!$3|n1l{>dMV{duqzNzuVv233Yk-axsJ*#Y(%j|P`V0`J@ ziF1fr`sVw}a#|oa$aZnxBTj@ovTLHVFS<;1bU7TZzSo(IG`@2JsK~nH9F0vWG-r4% zYYLr_*JXX*ob~K7H@t_fThmVI!NlEPc@OlwrH_t{T?&2AJ(Iz*{Lb376~`#wF&Xg> zJ#=xbN9VQSZ0yyceCJAfr`S&XzF?P~H{Z4WowGix^=L4-8aSIU0Kh$!?hy-@`->D_Oj4>q~910HT%68_zo!b911FGH2SyW3=?*V z6}}|WnRYlvW$5gj)T{W0l}tQcY{u>vjq$vejenp(*(4>8^i4LEfxvCp2b83-OF96; z6;Gor_-q&>UNdiQ*n8MtP@1Tv$)N{B`={R#riOV9sLna--+?pTprK za>0=Gt6wDzdN8#)JG%R@!xomW((swruJzZ1#&ELHlk#fA+WJ`Ufrw}~b3mciq$!D! z7i0u|AvmlV827x&Mo4y%F=xwl6A9vrCgt;PI4WQ5*x`iFW3L$+INIQ_Xm2VRRm^AS z_4YViwYC(oZT=3=vB&MV`_S+GjR?*PSMOphS&KA!4}l>9dC6L_A-Z}v^e!J9D!=%p zEnXvh-a6Ejq_O84dY9W^yQ{0YcYjRk5%%u@bCOJ}!Pvu*Ildagn>Lk#cKANbwc-4C z?;q<|?)%~X@y91+CGsTi-spnE^{FR*RlZk(Mxzk=o9JC8s}Dm92euG=_P)U9L|0{> zM8*%s_tF#u!T?gsv z{c8*6F`!4dhM7)lKg2oaLhp<`-!*3zWCzA6<{uG`L^4yuJOrho>SZ4Ja2uA>pTWG} zto4oTB4Z1gVPIaCBDas}QwDA|IA>5Qs;}#Nps+J7z^uiPQQ0+~+OQt@Jm}!eFW){n z*zlMLxE^z8~(;gr|jh&&k)R6cmLyky84WPTJV&%w)a{7?_q~!_n|QmWSWK1=M7{d znz6MxF_+lGj;b zU@tuU@Z^~pd3en=>&o}~4n4$t!6f_;s7i)Rf8+rUR~w^$hGaeemw5-sDeRaJfAc+Y zsp#Rb*AVpJgC^<7Prgg4ACQ&Cd(FF7@SWQ7h-=EZ+EDcY(8t`~q=ry1=LfbQo{%GC z)KFrSfRHR0<3GFg#GOtOz7L!o1lORa_D5FN8x85S7(M!JI|Y)NK+is#jPB+OV{^e^ zYF=x@^L&w{``S?wJbuVt7!wru903m_B= z4m}v!`=ZdIO!Q}g`ji(AIMk7}!C^6TX1GU#+thrd;?}->{q;AZ9XtLfu|Dmr0He6u zaCoLHM$Vb2*N{)mLPH^S-DOPd}MG`{)8YqPQn`U@NChd-Q*j$|`j zzC3)W8Z2DJjGET2l7P%cE``Q~=c~QHSO?@)dh5t{q8*_qm{Jv(GAp1?TP14UP zJ(9ORe@J#wN8ijNZcQUdUn6&#ytxt7^+dB(Q`BBFl!>y}3J#0irj))n^SF7vxqsNg z@zH6^OG>DD?Cj;q(omv;Pd7;EUA=k)Yr!?d+brrSWewbL!_hncCdzr}pfOyqhVQ=n zocIu7<_C_+{^(~v`&lK^g1<>KzSbGxaz~Ii)2gqy{TRr!TeYeZfq(VY>vonGqm0Pq zm!%CthRvH-#f}Ptn?0Mwq~5h{j{{|2-Gl~mNtG-z__BQ(1y|O; z{L-t@Z(evM(JuhGX37fs9MpMAa`qMUa3XVS%@DRZ5E*#N%aA{;2357DV_zwMOxK4%(w zVYp=mL%4x~E^ej;>?h})m+Z5+Vr6G^-~H#;T$hzfT3}8lAT- z5Cl%3o&7(J9W&3_c`(4KgT89Z4)g_g&GDZ5^L_C5Dd%ecAJglWJ;;}_$!n2YKFBEi zYVY|USw>0GzxkW|0kQn<{r8_2I}6Dwd+6c!#KAQqjp3P5{e*+AEs$um!^E*MB{0ae za0NKbD>pX8z)kH#BI=mblo=Z6=%eArUQpnRR;GCjLfrM*+w8U1;PkVocgP;F%QY~b zI4ux(l(cZ+`DM!nzm94>U^j8%bJKwCnP)1ErAuQ~+30+A^f^2a^&w-RsMU#wf?&&| zI9k~;%$NfqYtbjWfX3#72?mGl+mDEqU5r?_-F9l-d`E7nHY4lZG6jaWubE%~Nc$nryzw?VH zrt{|I%Qo#R7*#0u#32t^&*!vd0N;x#FGd-Wtb-^ARZ5m{IOOm|kCMFXGG$G65eezSsC-zrh9`@C-JlvqCyBV8|#Zyk1Nj@_UF>nvh zJlEe66@v})Zrf$)jC0l%#(x^k=M7LAKR!9PUU}uU=!qwinHF{=Elo6S_@Aa}dw-xl zy6x2CdmtdB)+pZLrlX@W?pXmelngOoKtX;s>blFqm0e_;yN({?-0}6~;r?Lti>yaX zFETLWW-_15eIlZX-nPfphA|@m!od8h7Er)mi(dVge|37Cb@5O4pEn>zWH{C;2gG~h z+C-GG7%DLJy%61~Up|B2C(b=l9&R7*R8JJw!e? zMpC=RACK9G8X7{{F!%EIvlvO(8<|_a#~78}2pXHLNalfp2!9l8nr!B+k2%nc-ldXN z2N)YdvOW~ScKG4dqZrxjsR?Xz{P6>)ybcw%>|)%I^+dzlV@QUW!88`cXo_;(D8>`} z^Stz0$@yo4!-6K+_Uml+o>|f%$g23l+0&yZuW64PZ7a*7Hpr^8F>8^CJ2%n^T!R$` z1ER6(`9O_c#9^@3@_4SXfy@DdAIz9=YmB453`E0F$X=Y4I3g!+R_8nlnOi%^;8&SRn4CRMfb-U{o zf+ui(k?Dh@0^`j09@!RWFw`{uYoM?VGd}D9k*olkMT-X4;aV@avg^qwE7`5Uwb9vGzpPFBc__;H8YmBC z^ONUa-KoQwaiNbBYtDA^*^r@MYtWd1vPA?04P<~Sx{L{c<@>FrY;YE%Muk5k@}sRS zdG6VL_jGpJaF!5$cr2O69p zrXNi$x|R*)_H9{2Ad-8YvhAqo_^#|CE2`c=|3r6SAOG9Gdq=;rK5cvfBR00(20g-k z9*}LKZ}d>M^6G@WT-7MZX1I}0wP@b-1#HyXC#ue9+^%FMK2%K$!JoP zC{w!1H(rbt>G4Hn$1jpbQu|P28%%+@H&CgVjezOLV>UAS@wk}-rVo!BL1SQSLsqZ`PM>$YR_u%D9FZdK}s?M<~(2Pu{*#dBFL3_m{TB#uOU4tVPBFBetzWvo`IuYWF}a3by>BCz-j=xDb3}=9te25A|4r znYB#-EI$L4$>x^tadGuB93|!)12`bpudjdIey||R!ptph|^l(oLj9o?L*S}7dwzFlH8^N}0kNoosr^i2j^<8mYTx!|pEj>|BxC#{| zX~^WU$HeEzZzS9`@#3qMHO1PX%#&f>#7c8V1Ft!7ZgFJJVByodP}ouI&9zHbTJ%T* zV}m_c?Z|E(M^7M96k`_UCVenh=g%)@w6a^G!0t0JYgThg4&aa}TLO6i03gmuL_t&t z$$AVClvBW%gz>x4^)mvuY#j^S%#@Cfq>jMY&@ZmmEQLnL4Y+W}4KR|r`#p1n5)FgV zYkNh*@GY+l;k+o^Re#MGn^|Nq! zS| zot-5i?JIYLAa`q)77Yx4wvs^OwbJ3)-xP9LLCPJ0Tc7mlrz_=-7#rId zYt44Db~xG72BN)-qNj3xwq-pISNpzehuF65?K_QGlu_KeHGl7)k%%qp88_qP!6}jD zv?zk{x!PG|`t)@FFe1w-?fc|Y*VJ6ra->rv2TmT`a{G4wR$wjK>&(^bsXWq%0e*wU zjF#kj!~tv0o-n_s7};l7(iJlQlv5wZ1x!Z_P4Lb z(YFSagqbr-Ihxs2Hem-sj~MO znKZAZ)bB#n#+WgsrsoEq-eqSY`kWR;N{eo4N=N+Jbahp9hfo92x$|23T(^6qfz!f7 z){;44H!o*wixTY@!i|s&8T*PzR^|Zv!(cbNZ#3MVDK@ON&T>ybz%k+W0fc%j88Q^m zoYlt7q@(W-Jr8Kk`-Q(gyH{5&meY)260;xL=O1%z#tU6Mv^N&DUy6(GoLFQ+Wt9yC zr71(FLe}00ZpNnDA7#y&eC3x%3ItNxGRua?T}M5-g{}J+xkT={ZEz6zL#H^sTEPJpZd?c_<_3eW`}LZrQRT8RC=12Df~Vo$6~FX@IdR zlCsW*XA5K(j4r1Chk?iuly;QSB1FoBVBFk@EK)I%O(<2q2pT9-R3Npe?eTf}1}i0v9qiZGFM6Q_NVc zUR|8tSd%8F>zun0lHJ{v#?nQ{mziQMqB$@94~u>d0%W+kw#TchD}CN6QgnBg(}FRpW~3KM(6x%Z-dx;r`!>ukH)E4C zHvO0g3fyO(uIT#fHL%P(KE)2WZ#y70o)@wMn14QaV|6{?><`9h+p z8WYNnY_JXSMLP(Bz3DMFG_(En3oj)Wp2|U@BkfHUr$mR9bzxq=v^`s92|uph_4wmE;`L|GFFS*E z!-mCi=?ma>XP&)pd|ec(8R6}^@S)yoMKY!$yX?8-GIws$2!H+cN5+RhIQ@(jsR+Q4 zOdg{P5v3v@-QCJc$qRDJ)#>dLu!d#Jilh5ii-vbIEkp?;@402aGuQUs*xjA(U$kgZ zaRLK51BLcQ#>_28Q*^Ry5{#ot0p^GKhF+Ke7MbQ_(curLt&`4u)rb9n%o52oo z_uX5fyY4zWy62uPy=U;^*lz}d5s_|S8?g*CtvZ{w*PcaKqzDkO>$xJdpL_15l(E!A zE1QPg242s>ut;?Pv+Cbsh&bSY?(#hvNw-IbAJJ337e!mQ9#EKQQVt~5ly!_QifX7} zXFd6_ONOu}eah;rS;bk4HjKrN9WSQFFH{e{h`6%r$tSaGD>pLcbD0QZvsQMSQc-JW zH&0y#gy>1|h}5-VBN3>XV#uU4pLlW^yj z$j+(*jaQOgGD;_gf&vM2Ca9+9)kOw^hICrE3Y9C+NyQmIz7lPD(M1QAuS$m>>YNuP z0>p)}`sFWQ+G*f1w3HVcJwR-DJ!z~(9WR%)Yv(8C77+1w{q@I0+qWMP{m~z;D?d8& zLHK6XzAY)2sugTgt+w*~`IW;nYtpBzc61a+iLh%WGZz2--^p?`Ag4u`XPojq7#tc| zmQUHWv~QXH%ihPBAKYKuQPB@(HII(V5*dj6x1SgJ!KiGHx6AIkrz5gx0*OKS4}15; zA}Z@y^&R346$RN9_1yUC2{5%kZ zddd`)(WArXAUEh+3_K0#w5W!wu&-OUAiDYHjnUOtua9oO{nXfVUnGUzZIbrXQ!gZp zp)9~D%_VRB)aLXiShR>4UsE~5+O_jiW5Ikn`|MTG)~&1K29ZT#+_QI zx{Mzm&f;Ob?BkTey{F{XGexdO~$8dYgVED6N&z$b=o>B5X zx21GAbIbhcaGX3J{Lm3Q=hwjp&yO~4ToPXw9lM{ODB+eAcImLHu!wXIR~KbkBysy} z8jw%ZrfEG6HVtWE&1xJ2n>OuHzN&EQsVkxjE;u0CYcKU?_~kDvhCi^q^+Y6f%&Z)E zScoX3qBIuk*UvBC69%J9x^PJB?$+VC7Hs1T#;PVy!_6W^iV)px^VnlM%2%aBj_Q*p zsTO6l@4k(lHD??SM&_7X<^tDy&rGW{ht3}pef`}t;-3XG!p0bpO_m`GWLVCRPi_&7 zYwxAAdtc)=yd9~q*SQ(2Hkw;$CIq6pk%eym4-wuQRT?TyR&b3k(}L0$^pzpQVs7tv z-u$s+%g+>pY9)o`6iegka0bpiqu%yr`CbX^HeY|>-O;`Go*#Yse{U*;Bj1o!+HNHE zs2eD#X5h{#1HmF%i%GJoKOoN)6qv?0ztFo>^JX>{O$#fqA7$IC!7a1ivJa+!y$ zs0P+#*KSGU%oI<$VUZ$5+>#;BKAS9eB!fc*oDm_}Wk80Fgf(eoj&W9)>uD1kQl)7K zvJ6?EX0}PP)?!yz+G#-#0yD3f({txawiz_H^26NGc%=ztrpO_dCd-f&ioB%^;D0`L zW%R%U?=GBSaP;V~PUV))5+#i8X7)V8(rIrJJz2h20bPH|lD5Llt*p{^%CtyBIW2ci z8C0e%O-N5=ZFY33G?WXa07LU@#tb)+A$vO-YS?iV;n?5b+v?yZKmTxT@ zC|xH?Ag8nH!gqE~9T7@9^TD1IW-sHhrS01LhYOz}zQ|;+#q?ot|NVC_UzO(ch7AkL z_lm)kDdBxhb1Mx25Wc;Uxie>u4Dl|$%oN5?W0fW=y4&qHAkgc>I;sq%X(pD>Ikp|b zK;X$6uO~At3`Grs3zfE`1f|H2$|j}>WgxOPrFYfF^y%tpG1xSm(}HNy&?3VqbNXp3 z%J+nUTEVtqhih}#;kLCksf3~!knYP*(Rwt1Jzh5SMiEJ}4mXPysR_4DRwO*@&{r~~ z*~+xwHARcsYHA8c&43!c=V#Tb-O5*u4Sg%zIbrs-_NK6*So9raS5p;d={%LBNf4^7 z%v|+F!k>Kdp1nVNMN5}z9RC%zUXe6^LkyIZ1#3uPd6$x<#CE^IAd z3CNONtV3TNY8pEk#h%v`v4?~2U}Km+Uu)6^Cr@a+_S zQ@Af3A_;N`!;DXlXxF@|nYK0=vV1VVjNWF`GD$WZ7hh>R#!fvgYD1;k(qxr=?d;Te zxljtww>N^ywGBQExjXnG*f1)ecIsZ`dm=9^zQflSs|-P3Sg$b(5;8U@(;`2tLtoSK z#f#f^D)zjlr>9tsAm*0uU;{j-2;4NG@mjgEv#RmA5$M>p^7-tw0kfLrv=}Tx4p*3? z{tGoRd$uCjY;dNI9qXT8>g|yqP?d#6$_-SaozXjR+lLpb4FUU}k}O z(A5I-%jj*uH9yLhP2($V2hGhglKSwi$9Qj)-c=K!{zXM5%O=qxC*q*fkecnTyC!Q| z1HCMysQbyP6}->BU5a9J@Qv4@ht4Zs2^XI+i^y9wEl(4wLCPlRGtQjzj(y{DA%HPr z+k4+0Ul#>RwS{23b{VAuv<(WCyEg)lFB%~ZuV;T>IntBLCIxUH4a0%ePliL22Pq z3`MvBQ91&G(n1LV=|m|K1f&E+1TIY}0YNZ?CLq1v5RuRfL_mswinLHeD1v}!=)FTI z`VzkHt$Wvcf5BVpogZe+>@_pzbN1e6ubFf9p3QK@xZ~=lfX+0P{TtJ(VRGB+A`-?) z?BIYr}vif$h$in?< zP!ZHWj;(#Atk{9k42G%7WlxRT9y+yKD2bj8BH}g)OK(FF zF;t!sNYC2TC-&pj+gDRE&lslHq~aq_w-A_@yuXLIrmw{dJmzJ!Ras_}NIt{p+V-qh zfcN+Pt0vp&et&<7>0tCVL?JXNZPb}ueD~}1{D|qP4Fyl6e!}%&sR|wo$~ZgQ2*3w! z-ah|VKk16PjI8w>R+!R-)K05@uOB)(6~?BR=F)6FtC`iGbyp)6gK?0qRB%;-y5`l% z2hFMOC2Q4P#0(BK?q-$qJrkq_^;r+T!%0t0^T<4o1s`EVh<)e}Qr5x|1ZEg()BNq( z%tf=2^G-^xFNvty2V&K%tRIgX_a@7cUeh17Mv#`C4R4BhHva-7gru_+%77V#m*0Zb z?2x3_-Q^QOd%8(3-N5D#$;s^rrhD)T&5#jf++`bhY>ixIHEvU)8k@&6Bo}m_JYBI> z)M1n{C3~oXh0b7*0fv^Q`8bx5Q%!ZEjglI1`Gh>w`SPhV02MW%Ctsg$rdE79M6Na3 zVD*F=g%1WM=5SX`xsh!s>Xtvy??T~_WY!H9(rfUG7&)7IKNo6VMI|dM%0g1L%PQV< z=?gK`<=Jqa(&Yd~PPSg~KHF@t!8h*9%F>TSQbcPGS5?%a)>q;$>TFWFj9^m`;8V{^ zI^RukNcO@Pu^FIT#^6x7_qARI$PzDuKMtZt>t(Ln5sddY&MgvuEJpo;u8yDS4AKB+ zpQRKT#bUHfV$T3d)}9W+Vu@Ct883#XJRj5gdrvBb{8>+o+jn(*VMfvca--N3BkXUn zqfg!^a&_m9$UDwtzyvcz-ep_32VcK{c&@0pQgXwGF-`__Eg_B=iqu6+t62{T8{Z}0 z!-EWIIUQ>=#p6;1&<OO@fUUz2s0!89(|3rDqyM#pT zAu{<*gt5u)#M9|i6d{-U*Jjf}+i4!-!miZ9iMHoamTz;hdfZ zOtncePx6`Ns=D*aTOKey%_|v)0rMl^q$G#&?$h&&0|7HoUejt&orS6od_NO#`wP3e zogeLgZmJtcTa7-@Ig*|7lKbI@N7!nMOdd$M$$^LfPZEFm@YonvIb3nGz^irruZ>p5 zm2EYJ_ex%N-1f0Iu<9JvV)tU2b~p_h>?<@2SCF*GjA(NRM>AG(t6CJO@*3N4w{s&86;AtU#82qe%?R0q(6CB+4oiH?1UKEaXpFb z7}+8Wudh;9S*=y491iLp#Wvrvjx}TfmRdc9zMl-bQx$T^LHCYF2@~YM`j1q3b3@XG zvH50WA*Km5@v^D=h2lS~q4BUWeC=ZYe$W-Uz{f%5xU!Gy7&JnosjPnVouVb^EB^{w zV>+F9^+3xWsH)U>bG0U0URqnguRrJE(lA3o$nHXnSAwdi8PFNhT%%Ux_VgXGLZX(V`@irRzOvsIleH&^PlZ{JXwmW%Kehk}~qMLpn zcNNgi#+MxnUK^d!djSOAI^{W0uks`@`lw>GGm*Q^YEGcNl@4ZLI=Wtxh-$KtUbGPvTa#2FUo-TuLDWrGd z&!$G$mLUt5m3^b<+%JPCME&fv_7P#$9&1~mDpc~&ZBlV1=b;B|R+Fb@@?1|3A0FV@ zd@U7K!g}r;>Vh(^NU1CO7T5f1*+>8c%?~S4nWJhcXWTS3$rDzn+|zU%X5R`Ig3hzD zzOoI83Y5V50CVhVIpWQzc3dGx6n-o$kD*|lJgPjjB*s}Fo2Yo4iqGMn@E;i^xq^LP z4LgRF&6cis$p4x_(4q>utUe=%DH|6WnctY^n>GC*v)bh4+F{sR3w_MQt%;yO1c28#u31`#X zKJGS2Tz;G;n{Ux{4S7G{Q#c92ZY!Wp3w9!L=xwJ6af65P_K6*1YQOdiGtZlY9wG3J zF0GB4DaMyj@)FCjH74xk<;Eo7wVyVT3y)!esc^GE+~egPF%hKy@x^RIx6@d$ndWn3(PyYtiZ#ieHfP zZpOM6(U|Z`tK6-}ZyR@UIGU~hRkz{PF7Xq85lGFr%Y3QV%y>rPdO9r|ETn2SkxKGz zU*WY49fikd0$L*ZFMJ;>3FQgYOqeQi8_b^3i;T5ZB3(zs5FszVR=M?k33@&Eh6$qY zpkPQobx5?3U`u+WS< zlXDL!v{RKT92afZ?2Rorj*2N99&?gjeU~8c*7^bR**u0B8zs~vrn1=qJ$ctmlo5g3 zR?qz5eoN)o#7bZXLaBE z;c|EL!Qu`H6xA)MA%gty`*`W8N*kL-fnNQi^E5kB43CANs_xT4<3a)s79xGI^-H3Qx=j~_sf~z$YtVurYjgQ;YXIEu?wJ$c9b6zce?1|v8Zgu%2|0dR_dig zo|THlL$fw0$w|uO!9H!4>&e+|?Y+$J6JFoew#?0T0~NLQgNo>*>(3!#w(ag%a#u@5 z0%#Z(Pv>LsSV+&aQU~F*I@|SG%0@NrC6UA=rI$Fwn4x5B5{uEMOP@Y~R-i-s zC&9`=OXUV@=V1=RgLk$(I4g}hJELtcZ&crY_hjWm?OYfyKAwN*9vuDSV^2v-IBB5Z zP8g*1ThL95l@j_aK*7lX^%(Hh=zzbw`+}wO*7Cc;Z^@=F3ub2rs_FMZrL?deZp~}_ zqAcw5F>FP!+X2#T#3eG)CU+N1)7N$!m;AAMdP4il6jFZ3*0Z0L2cO;U7Bn`L>5k9k z*)#!Av~27Rq>#O1F&U`uir&kZV3CU;RNiQ=kdd>^INhdbG4_am=ZuKX;W+^Weq8hd z$Er*du{PR)lKmYitU`VLo0I=tjhBY-SiQeLHPi8x5BwI*m4xef_Ni!#eaX#C+E}zo zt3P}^c#TRLC|sEdcD$ZcYI(vYdQouR62BUgM+;V)uhhfG^r}B?rfI>KFLI);P@)5^ zgMaseYZ=;0e59riY)g72>ah_bw=YK5jz~a@fk5V-i)awke6HM9w^+IHL|`$ACYP3^ z8xwpFXZgLL4RHpG*9Y7p0XszI=3lnk820a%?Ct;hbefc-O#bOEx8LD*BC`6AxGO5P z7wL8S)UD8e$@2v5;JL+ACCUFw$Mm8 z=IN?`TJ)NZ?VogY{P$doXHIzkFB0t1f587k`hQ6OH@L*8L;B4Kmy_l!!F$%8qFwq> L6P>r(51#)Q{;Z5< literal 65807 zcmdpdWl&pDyDp@mxI@sk5ZsH0;_mJaEfgpgtVN0hcemp16nAOR;x5IlK(XRp;D+za znS1`;KX>Nd%#biUd%gO+?^^p=D`9FXve+2p7)VG+*z$5x8c0Ylp-4!8?`SU&SBj|e zln@{2P&s`UB&65f&wt1c6K*1iJ6P0|w4|S&o^Ee%wY0P>EG&GGe4YxCR#sL>NqwHa zeM5{pSv`4te59m+J=N7+UEQIgq6P)E<>U+#5D=W7pHou@R#i*MR8q0O+cu$`ULU%&Q0d?@{Q|DdF#^i);V*0vBG-Nnux$;QT}qLR6} zd3mQP{P${lde_slk&22+Oib+a=emZ51`Umzj*bpz=h~;^ zV|RD=l$4aG3IbYMT4u)3lj9RE&Y0i7e?MKUj*N`-_pi^)%s4ny$;+od{kw-kp-=bc zAt51pdV2Z!`DA2dPjz&CeSJ?uR!_-%cC{b@ex zsRXF=E|`lWJSHaQY5a4dan!-Vffz^N($Yb@YvR+@Zh86SRQ}X<5GfB&%tqJxLSWL< zG%TGjy-2a>sfp{U-zYu(=gP{_)9GAxdPWjcQhxr3wRC7<;b?_zT!T%+VV-vE>)5cc zj>N>or@gKp-anR>mgeW@-4w$XoaweBlKZ3j^FG8)PEIZ?ENta^4S6Pf`}QqdEoN_T zucoG^(xCGF`}aFzJ9(@|N{Si9#iPzDQQ`RhRaI4s^^5oYt~K&%W652RJP$$@EmImINDkZd@s z6xyZXm?m#AVAAat+U;v%TL&vS;5(fCKL?gRWACCP+P3~4e*Jnqe((K5bymQ;_1<-= zxcuFoN|wKCwn*`QX>q5U2fLeqc#b%}8juKCTI~P%4;YFxRhvWrI0uaf0C>P$Jm=+> zHp|5y=)61JCP#RfBaYW>*(ZUEoR$r)Hbbkhj&&%qV+4fziLcM196Y4Ofvc<0F7m_H zY8&ytq7OX_s?fV<_WZ4kDTrq65Du51h7ND1G_h%!q{Se<9ElJEvHv}6ui^0rPXwaD z;p+Bf=m*OBCf#V(#YWvpZ6gtlS7*a+`XgM9FAV<4NUkpoJKFFhC%%z}qXPJieOhq2 z6eJO0Z>K)Vzfy42W6lkhK;lls?u+5dM`Hw(z5GO>mxopY*Uuv^@B3r|tYwC#HJv4% z3*f84xEX}*)bRCyK&^FFz@M~j391}`6}8%&5D|*l*VPW?c@%ZYOmF(n08@UCms`h$pjsQ#AIE96)ojh?t(TvgxF7|cMGcqwA-|(iE|L$f8x($hxyE)$ zI{ZF2*V6aTzNE~#v|)21OS`gidU|@vBs?>;w>ju!zF)F1Bmej2!}bti<+R^%@7+OF zk02nP!Vb@ru7@YL+DDVY5Y*`FYJi9MtiTnosW)#yAtI=gguHJDFgsaCg^uQ zN%P6wu=U_MX(9-2VXhBB3t4fPS5R6K?YN1)G=2LDo+B;(}Z|h#3*Cam6g?)A0CGR2DVjgl6Lt& zq!JJN@-Bun=r5YU>u+GvHj8u*^S-6YOnt>>*8fP8g((v6KXqG)t3|>Rd)>duU%lPGwzP`){xE$s$X!_x-(b3; z(goGWWP?r@z233q=*kfMnY?|D`UQJsyfh26c09Su65ULXl@po0Yv9Hk z_D}h`{`;a;#;0v3%t`W#vW)E|Ds8ZnLAP5e+{V5GnP5kiZ%($dl0%yaf^=8a)w#Fl zPplb#NsaX2>g4k-L^Cqv(cm2#YbX46c-l*Z-~F$Lrz)Nh&{z5(?1x6t#gBU2dE2b; zu!sf@@wUJhTl)pyQqmv2-n?mCb=g+5?ICYH4&!a+?|4*$tes3`6^mjxv=DK(R5qb@ zt$1ZcEBON#=gnscx-S|9H*Mo37O}58DjP0;41a5V+!JmJDvW`HU@o_WtgDTpA*x$8 zoU9;2$oQtrAIXH!jEuKE*80nk-2QfUt6Kvi26VKu7+EJ=3gA6<=<7wvgW_V}3P$Ke zU{$`bhrCo9&jEwj!~LI9cp*sTLnHb25E-nG1i~*>J~MH8mn);_ev-Vd9sWAFm?q4H z&1jpTNY`6S=c4L$2FRla?fP4>?Q!E|aq+xiT`0|w8(4-B`r0lNr(w}~ap6EW2pEw} zmbAUh$UjF?nTgTTtw$P_jQ=Ze!{9rzAH;q|GxZ6F5QP*J!A5y=P(-saVr`V}R@loC zL7{H*E5bQ><67av#>Lw7T}~W9imIw9nHC;I|92e+8dU}6ucVI;tmOJ}w7CA#dr8=a zx9&(T&te3{^N9yZqe!VlVmS>k#0PBr?|ayU4|a zW@>GwPe}pjE?4n;%UwPLuD#FZYZj;&Afk~^^kQ;lS>SyyNM=z{r{R0ceW~DFk7(eY z5et;S`srKgvFD$`@v61y)?z&rw!t=W9~0q~*8M9k85Z;mMIB|wrGLM%)#eT7uTzqc zdfwLry_k0P=CmKF&5jkcbQ&(2ypGHUiy)8Wr@^(``xX{#a!-JfZHKEidMgVp}EtyhYhPDU0 zXK&HiMJ+hU?;<1@!VVOdxfc`w3`X<+@Op0)Ew=%c?=evg!By9HM%<*k|PxUOO*oOIzDP6)>DonPi@xMT z*F7IzsTxcUHxr|%K(sr^rcL7JxeB7qYX-XYBi86{d}0je@k#rmt~QLwHJ~?-nb{Mj z--4z#sdtVx#WGhv4=PS3>ZZQlX5rCW2yuDQ?dhtRrGrA&ECb&7Iq#v5()`cvmYjK0mFLVvF7t1F=Q3C#QYai%; zK(Yll1!;cckL3K2ae50D8hk?p37Ujt^vLIRfZ9~@e!ph#Y#vc;_Qlbug<~Y67hIMJ zYZ&xaT5l-%S~69_NA%3`lRCnleYvGU3!$}79CkXus}cwdrtW;P*01<<`B#BsBYa38 z1r)=1yj4Vser;rG4gTt%?gXO&serIk2yPs{iUlip?7^h7H#~Vh*ga*L26Y zS79CRHG!*vc%l49UCVixluG4tV#P+p%qtuVJ#*{ag!YrY&Bbm8te|u%G?VU^ZBS&t zmX>km^)t+#fRPc2_1+?|1SUh=6}sFz++^i}4FDP?+9pHuqe{0YOD2mx!@lxCu{ zOcBUfr(X!K196+nW!P=}Ll0D933}z4H%gRk-4}=by)WuLEFYxOz$U%LO9pU)O&>_F zS{Sirs11DOi15Nn#v|tPKnmC%I>rBz*%Y zHoKB?frQoDup6Dvc=tJ1GLCDBf~3~hQ(;RYKgh#*u@b$xDzxjNKpdtn>`FZ=Z32bM z6GJie0!8ZA!PEY5CUOZ(r%!DLl$asyk2fg?9C3xQ)BRkW|MGL3%vnIuzxYg1-Z+UA z`v)aR*R?Kzkqqx*TUe#}{2DcvlihHB`WKlh;vnk81__g)m*md%Hzy{_nCT=(ghY)F zsSpr3fVb0@{Z{cu?1sA@k%d|iyrt8uF%YBzK58(cMvXtn=CZ|;*!lF&1P}JXKLJo>`SAbX&n8j$4teMfk*-Jfdhd(LcVYf6+4i1{qpbzB|Ka$B2;Z9g- zahVW4tj`J%h?PKPVT{ZbigS2HuYd=cnTe1LgAe+|T#gZ3p}S2m;>5wxUBa$5v~oH& z3+;W^^pwE_k6EFj^RsoNhjVJ=TJ_Lw?+E0t?>0fqK8DA1dg*n#elW z4{SQ3B6Vt^-=h^y`MCu@U((SJyuga+#$sd`jrCVvx7iNV!bzmv_KcPYB2DUCX(>dCZ<@@zLtc4G_z|(34-NF;-~=k5 zjQJB)l|1RNLEqz?Di}G(>fhtLp4z%2cb)&NO+~bnbW&X@+3bX?YgNAu$snmwr^}9{ zHatjBKs#T^&v(nj*^M4dof^x@I6dztnH&7BMy^luTuQ6H;jlzd#LT76pVweb%#lDi zK2@V&?#48)dE!zSTWk0K;wynd!S)l zE`KVoBu(Q>+43}87{gy?C4_A5riSqSqyEBgSh$QfLIaO%;)WQfi;;h+Yl$*nH~qwXBF#c|uVKT8EJ)K)grY<(<+ zb6S;-O5Ur*1r##B1_=|e5#Sg3ZDi!1k;ND~33oqZ<*24> zZ96HJ@-82H=aPb9!g?t^Y0izk;d|$s@L# z=t?CqS9P8bTNDUa@{)E z)@LVrhl@(yvL@@Xu|PrB%#)My8~SpWC&KL8P84W~7CJ-vok#m`Fx8(Z3VlmX2!CvX zR3IUmp?H-V5i70OrH`cd5Bx~|o{U3xjm7x3-iEB?9JAd_$Wp%S#K1JfvOKVZ|60hV zf2J|ys<-I99)+%nMq>9*Tlkvcn~+l0kq`TB!e0 zuJ)Eae~O7Gx`{YJ_PtdmJfic{xG6`+g#uPkc|{Z2Zh7v!sM8*ys>{EmG=y*UL3l$p zE@r>J3>KygQT)9ovO^{OfiH@Ttg5kee&LChtX2=K9pCCrN)hTPyA*Hk1H;bec_BRa zQw&snM;&?Knv0qfsy99jF<-8Z7HB9ERir+=-Rl2H7ygImPt4ioH$lebEL(TJ31O!# z;C2K?1VGva64aeEzELmU*{&7pSNH}9@^oP5LsF*tWk5ersC z0O>0KQWud1-BfeZHX;!5+dAqznUo~r;Ujgc(^{80?qSOJPLD*TKP@a&iFq3keAfvB zEClcI`bN8N9b)!04m+X+VVd8F0C%H-HU=W#fMw;Ba9YCmx(4!5OQB1VplG6=uVPuN z09d3mDbw3mEUz#^QJr9fqZ?9aDTa`R5U~-1c|P)#{FT=jlt$bYzGW62e!a$tCS=4i z97M2I>S{)1uk)QzBk7Ob*XjIAVaUrkj-9O@F0S=*)PO9X%b6^1+rJACh<30qf%iee8%Vw7_f4wCQD@t$!|Dnf8Z>OT8BJ%)S zNtE8(gx{peqCcG1gl(^cABbee7~eWzo&WK9VMeJiRq4+5`2`JP3VER$UAyD&he1hl!BYuV!v)ss z&b`nsTtGqybMRA80};W%2w_bAlD@33p82TE)|q%#d<4N{6{b}H%Ev+wOqQ{Ku%*r7 z@YOJ_;#kD$sz?CL(EXr_DQ(~ocBF|ZW6Shk9>9UoSt4J3kn!XFUvy4_x+E0;w1sRbz=Wvd4z!- z?gySgL#yZmj&1Yv&Qs+rGXu0~ssVS2FA7MRZIA*O>dDTl~+P|Mn9QtVuEE1 zJf=0Wc<-gUC4?BT@F)YNXT%MM*UILYS~+T98|d5@pVIIz2ad%ncy?w)HBysmE4Qr^RMiaHV=~r zUPbf66>*`IW6Q%mLD`^9kE^JU?^4W$+o2%rLfN;qqdrFoI()6(rSos^o?rm+-dMHC zS_Tg@&v4gYP4hpjTYIWu9PI;qv~_NQt@CD;*4sPN z#gGGD)gaz|NbvF~(1<@84))Jof)U306xUTBv3t(k#+jXkaKZ{DI+u^|<`*!S&+9f(cUD*&NY@siSyCmWktRlEgG) zRk@)=@2Pz{V*1i>OC&Tq3>11Xaqy#;!1uq2#>!s#yw_ae$!Agn(i6U_2d=*)^3pwX z(O5qK?>JlkH&Zul&w`P`V4y4P^k?E#Jz-=8qbkKe6q?hta$Mny84;{eTrwlapB;o> zDBl5Jn2!fkR=S{P6#V|PH;q-vNWW&?SD5ee@tTCE;~L*AYtVL`x62DkNTrl7x_*ee752jJ_cj4`nNoX+ zkof8}9?{@SgxM%t_&lECm{U_!+&G4c3KAyBc?vH(Y!d5ZzOM9Z!xU1AIB7vIH1&NS z^`~-N0=8mT>%O1sV`5Ra79cCg9`s@2T1?_ZB5-NlE-{sU!TG^LkkMXw;Q)HBF=uo2 zu7uH-*iHgVdOeXZzz#HeRKzNY9xFc^r?n7|cqIVkHqgvN-+|8Z0Gh5_26qj_cF|Yb^iBTC$hbpD&sz;$B-;G9f9c8uGjW z^ykS)k<#-8ARRGrF?3@vKC)|}$|X1C_X^RK>tZwLc7LzYUdCOzS$w}K4)g5&mdHHenfw@#;{@pfK(yOwusOC!hxb49}DO|!VAkijCG z`0KAtq(asc7&jHLQ_iST3LpGi^TD#&nhFiaoDH^N#+IxdR;b-OadwZqJY+EaMxA0U zM9r?wUx55)LYg=djC)NaL&A+#`}6y((+V7FZ3w1|TP}659SD(lRU}x-`V!-7fFdF#-2y~_&zSv~??{HGx?kW&_qk(*5PlHn#A;&|R&ddSgbgk{ zIAXd46R>8I1^8I3r&>MFEtA*b=?<-X_j0JpVC?hJF=2D2EA;XiiFG3~1<;LrMGYZP z4_m#U`#Xw<-e=#qe|enNeQ9%hOWSkVxxKROtXr5!zKpCsztWKAJ5NmT_IdDw!$!?S zFpEpx#WM?Xs(vI^5c$PYE}^_t7+-f9UP40pAT(p8lf$GxNtmRFcy!s0LZTNi)Lrpr zq<29e%VMM6H>CK_N!M=m4PZC_twjAF z_JP&Um!6>vgps}B{W+|u+`}&$le{b?G=}#n-b!DRG}7x;`eKW(FAt$VhQ2KzVyr9| zV9f~JmjlP9GSp!Nja+jw>8;bjJU2F!8H%E4u;0a|-k9fJ7$R1q{vqpy{b{SbDgO%B za@4_443Nq%^Z>K;SNw|8kHiJxfQ!2-stJs@Ro{lY!Qbog!7GG{80l+ZAyh;W(LDV?z zi?QjjWP(pD)NBtrh*dk){R7=wXVNQU7HIFiUYC6IE=qNyK1?${`K6qb9~`-bFeous z9&eHPtoQR=@9#bU>Kw{9;3z47fjV_%Y)Xuz+}ZjHhw#Pk1XXYw5SAd91_&8EX2uGZ z&JeZ6Zfv-x(wR4X!Y>hR5xE%b>!-pK1#-oPKPfv8ixADO@oAR+TB#KP?CAIAh-<>% zX7$c#OJg$Byo*Pp-4V!!%NLlKB_^cY!|&cq((dR_;t}g2@M9S$w&XT{{7uG}XUX=t zHFa+wnRWBAKu^jUj|ic!%t85hIR$xZ${~AFbiAu=a`Jt3)phiA93TkaY&P5uMv5eWeUJ2YAtluhYh>;6cPQT+k?)#*FdieQUo_k5ac_MFhR^?JAnLgVVj(RIBGq zIo4Hwu;;F1>NQZi#o%;Thfl1=7ON9y$TdO<%4x#H-e;YoXGra%$h=_pZsQpCMpi{@ zy30jOBTm3SZF=|kMG(tR`BLjdJ5aOlzx;e&kClN6rQfs(MiTl|iOI&uETreXnP8*& zIrH&;GqvEiG=QO=T}Mm^)Et))KOL+D^bKRPf=fYL;OO#AupWR7)~oDBthm zdWCigAz?F^k4py762RJ2lvGOkfQe5C-pg zfyEvvtOQPE%_(K$|JYb;m?fFT&oloQI>;QIntswhN}3RqhTsm;Sd5nGPEs{VV;uw` znq(r_Z!)3vAzT~f=lI8)9x$vAX-6AK z@?y4KMd|hNW89~EA|t&AYV}(c6$)t>GsSC;gq37kD0oWuCq~HWfZY#RWSQ9p=>c|t zKIFtaN4^?bo#TjIVBZ3Xh8XFPz)LE4%^9>^PkxD4K);5C67@ULqEmU7-qb6lZ-F4KVVS^x%b0LGj3ns00xa zYsLv)wJ3NqAyO?1l*iavE(=~p<=)_vJoYj&z}#Kjn^oixM-ZYU&mBLyy=o) zM8>ko64FR+@D8k!oLn77>SjjF;&G3!V+vgv-}IK?YjVN}rD>g{Vg)FhX`ViEc@axm z-cm+ae37J$C=Y9m6osjy>CL184);wR=fB(cca@4xS+GWK$>Ae|^dY3PG(YVw5(VKB z`!)M=w6Td+?R|9rh5s(p3}9$|nME3Be~t+=V3-KTQq?||iebxv?v3=;R%h_UV?`K3 z(PFltRMnbHP48fa$exX-XL86a{!2id%YYIxUf`>%#|_G^}Ttom?_qw zwUt-|qXz2V`@04P*08UG#x5kFmlxwfGrcoNBx+K#7mN139c} zY*4A<^ymeu^u8Djc7c2epsnmVyk3%fq!x+5PImVTqk(%y2uBme( zjt-!enE}mj`iXR(yD7!oXc7E0AQxp*R`XeWv2KG18g3vyL|nreFY*(*K==ga?;wz%UC3Ya3Zc)@n!cN63DVro_@(tSJ?Y<<*RwH@UPsr(KPa@t!gMKk}~o+ z5}IhwF8dMwEA)6-lPZtc6+`%K@uHl-e2${89G+~#1Q>pej1^<<{`RmTMN)!FNmZ11 zEu>B%+Ics?_V8^+;+6IvUvr4l0Nhq3te?@DA4e>5j36L2uKMUq&q`S?TF#t}Sj#Y@ zoG_X-{4(35?nIgu|n_W zK>#cz`;X|xAWOF-xONUm`&iqb_scVcS096PQ$AF?;`{eP{{TEbV+y*`cX%u?F^T5} zO%>(|Qy&B#eKkx?_hA8qiv_~%IgD$4*226Rg-_MYkH0J_9vrt=tibth6t%$CXLlbqeY|w&6;nG~-eR*KZ0fS9_crxIBRpfZQ0q3+>-ld2jAE`!L?x zxG8JNC49W~8Co}8OhZDAd1G+Ces>UsVjO55)H<|!C6HyueyL5bC5r;s_DE}VVBVb_ zO&7%|FICG%pTWeXd#7Z))ZaaGSBaRXFr$%xDm;9t8zk8~zHIF?_P}fL=4QROU;maB zhAMI?k1VFPeeENq@4Hmjly@CI9vxIX?Gf!hvg3_&e&;ERSOURUI5VWOf8{0-`F`Y@ifsk|# zK7Q!<1`pWRZ38{1zqTSis?2S$R6ms692T(s3GMTfPTfq%St%N;GBCL?ND@YvSy9Jx zz-w8GBsWB)OlS-oYlI4WPghq)G6NLxVIhzIcDI{6QG*^M*hxeAcSWCW$4|2r_wLFO z|HUqZwI?bRTB`q_oeG7&0HAgR2MNyfN=_#AzNhx%y+^;3;Sc1EdS?!1HY!(y4aA9$U^tM+74)7ls7HNmAkvM^RBYNwL33PIg>ZCrlxq$+W}_02$%y zIUvl?ci>$`+71djzsAvnREdwP!={ml^H>o83iQLsKmi!kg;9%Pq9SBq5q4++laUd0 zcCt7fzX86@1~e%RBSWT~Y+Dw;gyWg!j;PX5mB=aFwr_yO`%QX44#P{yVZJ58*w zSTp9|h9ul-?Lx_GeG-|Zb4;FtKvJvF#06kud$YZ+p$XF@1AFJ8u}VKv>wO0HCtQ== z8~v)p5Ns3mp)BzF^ZCszWNl|{s)?yHrt1yFcmPWjVgU8?fZQRwL z=>RkE)L>h{Z2Ipg$cUi64ax@;gHCv&z!SX)i)}WCY<1?C5P@#bbAgN+1PwKT)d&&J zfAUu*6re^Jc3cnDT8HG8gb~AvBoRd7w6^{^Hx}mZn#U9(qB~WJY%p-_ON4TpiK%8E zJU{rxMkX`#AG}nH2=-ANF`#Z{hG3HrG$u_7`L>4GT0oyI14-kjKHGc#p@8!%4MGV3 zDI3zp$>#~lvB_QY=0K!HjHNK%dNCU`BlLIK`;=3zQK@(edEeq0Q zfF@OdC*aR+tPkmzRfw-=?9~3La>KDI)u~BV#Fu+!SHY~Fbyg8fWkBa)siC|r!@c|hE! z58qAzupXw2Wd_84i_#~fh*C6CM6RqA^`Jm1D5E160=<-;qln|ZTF)tD{x{5hZa2)G z$d3S-)unA|3gzQ7D=*s;LRft#yg%BBZ`tJ@LCsB7PEqgF___fSSr zk(>$SLNITv2qQrt$5Ctq#zc}VCqHdiyQl$^pLc$eb-nqeY?z>=DhIWHf=h5MCGhna zpDiSsN>dFKYupyBL7+t{_D>MFn)O*aYgtPd#==>+0R&rzZYUaDjrUxnXok-R@*xZOr1+PtpP!!2M{%)mF(0vP5vK{Vg`?k_FdR zt91?$`9mcdtIf0Ky?y(M?uM_GAe8`}sM`~t)fWi{QQJn;In3Xw@I18r(EAVR)2tT2uUM)%xd=(N6#Y9!hX+tfgv z_cd&jnKea-kvR}Pp91m;7I{klb#jm@pMqT)G7 zND%DMD{CfjTe^1!3{1hNfcZcmP{JTpiO2UHiETO8|)L+I|J< zLNq6lN*fkh>+b#9gG>PI=di!5$|)RJ6-T4=5bfee0G{YTcenseO9D|;bQTvrD|iDw zAJ`~~A#Oc-=q!M!P{a_KiloA^=_LeEJc(^@iD0yd4AJK1UGImB>7b|>pK5Q0i{&Ho zP^@nI5&b;rq00c`ctANu@ckoO8d&W)-du7QG98Expy?Qx)CrzQeJ<`1PyHz)OH)lu zD|y{&kP3c@AL2#~_@ARY>ez>w=YXadJc#KGg^Rs-mW|9}Iaqg5+5q*L5Xhi2oi`AZ zrg|1lO6u7nhW0hYI~xlDa5U(s48AoM!ayDwVOAD)93Ly=PYA=cJ8aE~5m8xu38I7@ zvg~UZLMM17rE`!GaGpj=sy@`a(Pe>}AX>7e?zO?XWfVfPrSU_A=FOp_v#S+-KETR} zS2d%{6`58Mb=i8DyPBCj`a83`I|&N?EKJW_6qgyC?1MC-EZ%9)D<162$$cLg(h!ro3|N}G{A|L%ZH%eja6 zv|e~o21TVuJBq8|f0hqajg=DZ72?_X=DB=IpUbCo&hAADi=e~vWM#8%V31Xjx$rjK zIE&6SRihlPPnyA-LJB|?d>b;2I7~#0P}P-}5)mWz6s@%LZ?mq(RRH0Q>p`QF;v6z& zhyyP%B+5{BIKJ*s8?s$hhab^t>s*b$pfH+%kuei%1JDKHILD=7Q0kKMcK8CvaxFXe zvMadz>WsgQC5&m2KcnDfN#J(B1O*9V9@KZ3UB%jdB{sag=ojfQV&SaVJzI=GgWTQbXgcwh;s*~^q!m$An45WOae-q~g;(Q zlcEzoBP3f4vU7!+bu3m&ieSy#)Az!ARxvi$iGqQ>I;!rOD26SKJYS#Z?d1MTe>l5q zcC_kAF5o9-)~MWXclzm-no7`69ENkY!1ihIU z3dsJYhj@IMnnF^CbLX<)^nctS8AxH+;FUDswsEea{E}F&A?}DWw)E$L12UAJJOr>3 z{B;J@ZN#kqS}EsC$Wo{=xH~k#5x}s}_`=ZpCaz9P-t-20<&+RnwJ(q8OG6W^0pCpH zvkHDRrPtrp?hz&zLsXxgat3d_iuTOkz->Byuo#|3YeoLx?a)9WYQC>Bp@7;!49Nx? z%RT^cf_jr<<(nMUqlC~i%&WIYXLoff?h3OYKD{JPJoiS-ZbcIPyMFhWKXNO};H7>O z@jtwYl&Q9?W`^H?bEMz;`9W0dS>2F!pBw%Xi>S(Zpu;QExhbNEKd5(pn5$C=?{H>E z@6ajrXLbjh6O$LSifWnrN5g;4%}d0GFU*{n^~}BK1j%IV4Y_7i!h#BD55+|cn1!YU zFenjgVKo!bbuadg4K+TFyNbZRcHn<`7hbO-A$LOnS$TF1L)Dbtr5(Iv^BUS^sF1XR zLFsTmHapt=jn*`4eJDZ4i(HHOXQA!Sc|aGHb$N#R5Q8i{1$T|OZZsgdc%h#<_6!Vt zyZBqg1Em+SW4*wYtZ-3LvXDhdY$#Bxdgs?qs`w0&+bk5_mcQXKn!=>0Y^Hx(ItsQ? zJ?r|x#1}ermT?yig1hsvN$GS>HO1 z%YQkYj#>(2Q>7^xX}f`CmHTRx9qO!`F%HhEt`BSqh(ZU&dpfnQ9w^0y*^>4~x4AM+ z=-C7OBxY3=u>mpDR=+6!%JOb{PF4wHWW+^RmeyAcZ7q{ULynTN4+vG!)JDf{k<*f; z==nE;YSM$|@Hy&zs4G=C3VyRPnlcMk(y302J0mF<*HXaFv8OGwoOqm=ziZ?tXD)1@ z+@Z%0MSA4YE&gDDB@mm79tKsJ>*N=wIAam8tFDeb6l_cKE@B_;@cXhih_{a)`QiSq ztK&l7FD|?@D-oj+Y?w^=sFl-_s+(!2cw_=eO;jJpe7BPWbEUEF<1#v#k75W-F2<>Gz8U^L;n|q~TKH%$&e&wD)WD)|;6;VL=<0}%`qL82N9~7zC^bhD^Yn8X zXo0d;wpU-Ope518_DgjCjhf02K2qLqM^c=#%LhEBh2I*F$H3$0|5|b?n$dSnN97RL z6wgmgx4HFxI~o*j3bXS_ON3C>Ycwd*Kz$@##w_;fpwVcCK3}CaEiQl4Z07cT8?oyH zCG3d*z|sk@iPwres409&p)Vy0D2^#;ugJ;|^n(xH;zRWHySKWUc~CBPMzKmZqqQow zwQ!>eJXOoo%OVem^mmMfUWvq~I2@u-a%*@j2CogiJ2(TZ6Ulp=)kcid>{@BOFF#4b z@T7s7#0EOf4|a2&EkwXM`{Nm4G}G)NBxrX6Wyb}21g}gCK1~d!U2-!~f9@ceEkTv+ z{Zb8eF7~d_5TuWq4}f#FW;f|#VVe3R-k2p?UgVSC+UQ1WVa@nZ5({fQ{d^@5|9dwm zG28y;v&aCdp^W_nEmTL5tV8UFgj}I*ip+Wjh%VOzV5M|(20n<295}qxi25ipFdlw! z?w^x#&O3sXcB;|KSCM(n%QVS8`$v*D5B-`6sM3!xcJ_}s+j#-eM2b#YhnzD4L$T1E z;zCb^eyI@x3e}Jp*ZUV%VxBc`G^h6aqvgK+UMdVVk(m&qyz=?j6_uH$V#yDXg^oR_ zF<{DiX1&>(!txXl6#>(rEp_mqOWLO38uU|*WL$_1XB4gv-m(}prX+jfuhy%I_6dA8 z0Y5KyW{=(h&W`QbKx#wE1qoXG6%aWwMOxS`?hS$Udl{(^5MM6mP5`rTG^%4u->O=x7vxKV}HaOvM;;`T+& z!croS5T03Oo17{sg&nwXN8%QV`EUe3x4ZiK8K?i}fbx0wui3b;Y5Lc4Z&2B%tbTP# z=#+iqxnBC$MGTa&v{g;;{qQr1u1DZabhsS_GgcQW+K&2<_5dTcxJp^w<;o0I~zBCFhzRkQ(0f-f23z_NaeZyiEUy6 z>LO1X9bO#C3}$;%HknR3>w80_cY2 zzDL9UI3Y<^sWf_Xk_|lk>I3@zF={c^VWSK!qe?m8djh=1g+L^7*d=%}gXB$IWj`k_ z8@Sr?f*P7*#qDlW+SNRPn3N!tG&j%ySJ=!_m3VcumtG+~xO>>9Yev4QF7oxizuW}@ z>(U8_d=tBfF4v!9t`z3*X5`o7CJLqg`-DNI+w}-XA~DDTY_34NFwoBLRVlg&)9 zE9M8O^>(xB+j$RLwU|W_rKiOZNIdwY<-t3!`g)F0@vt)I^i5D+Tv}msLZgb3L>^J~F@7S-s z%xpuGaE-?!PHMwrV;ufPYz+TIBmc$axxh4ObE7cJF1-%b<`LR=BY?=H4_bJxr1uit zSTJ=cDU6Uew7W2jQmbdAnEWX36KD zh`SX(N6;hvrO_Wb_xI6+BQ9l2{2%_L9cjmUjI_=Nhf`3c8HJR( zA}X4sl~9CH#<+{b=jGQ21arUj70sP#!TvLof^)nNo>&coy63MdYqg8?fxh_6u zgyI`FXG5WC+e>Z^+l8L6N9ETyLyDJP>VW{wexqX4#_n8Z?Y08A$5JB0xv{a)r-0;E zTt|AD6iSLz64(df*w;W6zU8i(#SZd&XU44v#{8J%Pb+33cBdkmQ6egMfh4Rc9y_Sy zvmKbgwEsQ}@Ye+r7;fJC%5&&D^3<7`kp^_q@weo9^{nQ%iwTVt@iU38cjz6iTPm)k zs_TK@%e82%QN--FfOXOIkK3b7b{Lnn zqz!J4&Pl#2@JB+6lK@-G`l0;AaotS8}?USKK!H*J~xF`_Sk$&wh z=a&5Xg&&)Nl1QSv!)*SB8%IJY6=U_Av2SJ%se%`2C0005qb?JPC@ zPo7}5`QnHVHQsJz(i1K@ETCJAYh%kQ99j(!MHo+Dp(44C#G{*%wh*Y=EQCgBfPdid zk_~rN2?K4&8W%b!kN9;OM{Cpfm$`Up1onUb1x^=>%-xy*2Qmm2|BmT2t9Q%t z;$GaLxVsdmxU@KhQfT|$eD^;0U;gEsd1kL!Gi&WRtmCZuEXTIbNiXXhzTN39YCbu( z=okb80k6O?O0UAB_0*WiqfULQ1{?K^_@YjY1QD!G$E ze7WIiASeJg_Nt2yM~)B>+w4$6O3!@7n}z?ey$`Ds5E$gGz13T#bj5lPfhEVkn<%1u zFzGIo*ij`u)1YhBMRLG9TF2i++dW>g81CG>o`eahnQ5U0){?XC@G;7Vr8ae7p{KD` z@!n?{fa9C>{DXZEvolTxD2F#fJ}CtwLYlz`)Q6}$FH~=eLpU}XckT;8Y zSLrPvR^-eXT{=POIl`-2dim}pJ6rfBb4)sd$>W&27E7(T?7vxo1Inp$+fkZmisQbN z4W({+m1%i(O+qybU0)KaRzN^s+BMs>PIm~3BYP0Fiev;EK;L-g;1f;DRlrkI?{G^S zGM=Dh&TBmgA?!Y&^>lkp<;f2TdMkhUwaaf`a}G9$0d-~k)lyko4e4b{giu~%eew19 zVDm%WFQeu{$>s!N?}=q;yCCl^)0<~~4*DmRgs6qB;4b*c&Y0{ag{wHy(O*_;YbDJP ztSB4BVTl2wDT40zL`Yt!5h|t)!u+Q(9@8l*mx_k0{P$b4sUG1zunxbB9$_ZjXP#&jJOj1{``0u zx^gxr(a+bMO}J&<96zKlD5oRK|0sn9I#u@|*>;W20*0v|z-W)9Sa`2sH;H-qmsL81XVX2ybw%`vt32S=(w?+-juGS}v2ENsY{@TXRv> zXUoF4?Z0EyjrG+CV5NdTn$j$yIn$p0o0Jx)DQUvxs___lMFq=3`>!*zd(-87WDj!{`BYoaM!6Lc*Qg@25I{f);3;v)mq7HU@P) zUX+O30+cl-o^+xjOd5TW=Z1`Nu~I>$lGCQW9OJX)Oko&?RIfHg4t2KRsZy}^*ddbD zB&ZjNtubB)=BWgKyrMJXdPtp_^gSoBvE48@31^4Nmx*4C#m2t!{OQn8;c_Iy$ryR6 zmFn$TZIan=*pclhJ7ckDj*`av2U;vcgPxVt3tO|;1J)P0*}TWTWZkJ()eh3zmOft+ z@o=lzC?W%2?SPS*7)~)(pU%3PbOhV56fQjY@z9p*cTj$hG=tEd>Ylb3vl%kL3c{5h zIgQYM`7eQ}0Cqikq!u+rO15;hldHH*{gjsp#x`{rEqEy#5l`Lhp{CzjZY@3PTxwkB z**19GW16TtjP3CX!qgXJU+j`jLf9ezF!_Wz@;S1&i^{ryVj0_S=X*7kNiWzSn~~lomj8`%L)1&53u?z1q>zvW67R`63QU!UwU5o@udu|))%dgy<*H@ASifQ<~Kq04<@LgY`XoN zcC~vzgW}@D5zy`zM#NHN>_9da{|ic$n^XcJ;i;=Hpn>=eS5Mptb_124XLSb0jv+Dl zink35|4OoZ#XGOV;uKmn*{b)o=WVejph^s_T|WScW=1)C*L+ap?nTScKx3QBZHzu5 zM1IlrSZs(`#nd|Ed-2(-9PV zueEe?adbwPQDBF*qg&vFfI2MtD`XYx7eW|*-_>OVNZxSa=~p0$-Z|!LmQy9}J(Iug z-N5chsUOYH3a@M;46hXVHXfjejCIeYDiC#AaF}ath zKAtilz(>1Smwu0|=>(a?IEC@T)g*__7Vw19DzV$3J56IS8TiZlgcUzxurAC!m;LcI zu9k(TCj+^Ix3liHhy(}FVXjOi3nW5_UOAIm2x1(ySd?*qELhkR%Ch>z)TAL(T}ON4 zokj&&v?{!twpHei1=`th^~BF`b~Rg;XstZp!@7e3pLvt$a&#qf(Af5EIXB;&5%+x# zzV#zYxXfwcJ&Q`8Dpcw$IVg3KaPh`J=R$hV$sdKDu3I1`s&FQvLvol^xmxo%Pe#6H z#$6R(ng>+ql2KbQAkb6Yd?9?b^VF%&P*D5A1Af>j=+jh_on?a%N{%hy3-$b|x^QR6 zI)5moo&`MjEt&5qFL*RQn5g4+`S@4UqNMqdy}AON_G-UV_X~)m{Jot#1@fX}Q82N! z2&U94%Da^dcX{Lw>%Il5_!W=DV)dnM)mO%-PVbo7oWqweZy2Cg{ClnaVpG$9MkbNl=t_F@wZ}FbcRm{)b1i;VA8L`nnC-z&KCe2! z5G`$)=6BKkxW$ndFk&H!w>s&qg&KY{~D{0`S}qX_nw(6d{6Fr$F()bT(yBG zsB!N)md8JJy0Nk#ztrAR&qR}N_ zFiFVleOdZ3@Oxb|78%S;`gfdYgkcX5^ebCU^>BLVlhxHay3BUve1$A4ecf1P4^33- z<1+dDtb|y6B0?mi+g8%i>Io%y}yZCjEh5g7-u2nG(ET=E=3x^++|yj z=!!K?>mDa_B(04!m?oxVK%1bESC?8~$boA!uT zljb%C0j-au-SdpaS5M`A!|9)SEdyye8hd@NhYaUj&=?4_SRh{U)viXVH$4U~GNqhu zmo4@te8Y%RZS|M+7`{on1s+&0Tw#Nn+>tOHFB`8xO9#R7y&hB69-)xNF2w$% z*tghc;h}iW##ydceSsA;0+a%I-Y=_ex3;q2fP!0oVIjw*9g$yk-e!FyMJkrtz2$8mRRP z)4BS}Bb9D}`ZbnOd3DoBC)rHFAMgj#bN(mI?^xn*chB)k171jel$bl)9MXeZ*oW{lA6xM^I_i?)T{}Gw#dmZFCCXKZ zACvA1R!05l@&=A`J{DQ6yr7_PMsrSZ5fpnf(eMdw(`Y^N49(k>vRa3kX)G`=vlQ=ZTg4Icg=7opqqV6>~C@7_tC85 zQpj|V#pRvU^ycsaC;&l|I{^tl77YF%Ym^1im*;qPzlqep} zVT83VCGNTUS+h|t-J}w&!6TJ6)B<-yJHV7r`DVR9Qz0fOTmq0r3*hXvY@{i6R$3Tw zV~31@xwd()R}pPu;Mn=Z_@MGGRnyWif~p;CB+|md*f4Ohj`5fC96!GEo;m7{Vu9KE z>#2(QuhDp7rjUL_Cy=~JqReUG+*3>9k~!zWfWKJYm=JBy07pl9m$u!olx;tna$!AM zcsJXJP?M)0Omzc|!Ihs5I&liI$(p#&Mk`-_=20!BglG1A+etF53lAkhcfOhggD=L( z-)4CJJ(xowpW!W0?V9H$IU|14vbS_@i0r^fy$&uk0|x4Yu7VFS$(qJ9YI9oK?fQ6n zsL`v<&0Nk1+y8_|(K@hI5&+mKG0a+E#U9%jhACd;+w$s{=vl-jHkVS!>r*}G3}^oy zNtc=I&@bP*O>ugh3@&6AXS=Mr>AJlggxIWL1M|Iz!R!AWJMB}3C}h^}kId+93)`-- zkuC1v%Ml*MRVlsbK~pIW=60{&?lh4)i;T^hgtx3MZJ4U`J|ZV z0(y1TU|GXUvyqG(a;mz1=?pIVcCFI!0Ut{17KD#I&IuLV{&z`|&I1a1IhX|p-6PJnlj|K~{PSFB(}=;9!=+>vN1AtB zB8xQkyj?B3>oLvmvh5=ED_ljqlSld9jIk;&%(=-X%xx9Tz8e*ckf^;Vq}kqc)IH#t z%08#a+UY8Xhm&E<;XeJFTO1N6q{eE#n`1>*ZMa1Nz_md_lz*9g#N4lpg@ow424Sa< zVX}G9Z3dm%tGO=IbW>--wR*6kezmL-argXN%n zxs|+uWwxb}$DReT`*8b!8(f(pD`O`=?FOfSFpSuy?7u*M|4f0F+))!x;b=)f!h$4d zm|S~f&yl%uDs&gYC5NP4=7)yJlE9G=KG}@&xnZl`$v_b5Dzd;;sYWomh%`WTR0cHCWA$#YphCdU>4mKdsKj@J82xJ2JD^k<LMA@{e43!%$143WzJQ|QT9mIdd}Lv6W0ZLK45dSw$;%GZ)Of&$3ur47FR=kS_!)!9NY}+$rmH5L$gl@j_aA(bp|C0!; z^3>)9U4iVW{I_$i>{%$gVTor)T3zd6_jY8O0C6+N6(TtXP~mM!8E#Spp~wp#PKUIy_l*Lf!4HvTS7H}%3hLENWF`BO{fWjpmr?=?d z%i>dMP6=!$^^2vI6QG&R@oKNhN0u>VR#?y-(NqY1iB{(9rUb{=t_*o3X%w!xuAl4i z*DMm>ME-q$!0c_XagT`B`AlP#`pV-Ijcl^rv77Z8v3~vwl@RXk^;p&(Ix`%A%swPRkhO`dM_n&4{}n(8Ge^~KjuZ$4mNY?U^erv6JMbsh;^1|cWw96 z@C=f6$Ei;IyCH+*V)4cPdFDMQw_nLOsjija0g8%ViQ8lF`)p}UY9bP{ z0=q(ldg)EoO~LsWKuY$ze7{G1bs#evBGNtOO=f#3)gD+xlZ~v zUw5z%)tdPnYT;#b@X~w!Ktd$Egb-V1R-cU_wO*CzT6T+`rZa?mShmF_h8g7*I1BBY z@+v9->)}7zj{W1OW0G`oeA&tArg>r!BbX4bX(_Duff}JGcH)w%5b1}rp+l6H)wk!? z=z&@Qaf(`Iq4z4aBe0vE+SEAJb|NCX@Zixdrr3fYK&AV#e1(_ntqfKT_auc)=LyP_O z_+r1Xuld`jHZ4Pb)9O`QV>F8f-c@*{W#N~G-ayrFxcZGBLQ<{ZTp^t$5#CteOU)2> z5lTn$&1QMu?JLpuhYXpgXdU~~?{ zb3v)caaxDX^YxI0_0d0>Irc_^YtgFMNrK|1mvcKDTPm`%MKacv0}iA0HXz6+8E`cA z!n#W@(s4=BT0D=mw^(l|?4j@*;W~0V(F#Ce(v>&3Doy3-EWM0cu%&k^t@@|VIFbZ% zFUJo&`FP}QVwHL~=Xn%8sSw9`i%eRDj0<6rH&_fhb`ho1DUY8G`hG-35 z6_Wqjc41d@h$;R`W5)Go5*aSxy`*;PbW%wdtGTV8kqXp^P5|+l^(Oi1pnfUhvJ7C{f37rh$l@z;< zUAMf1=$0K$4a!bHsCa;_HGLlv;hQp2xV5d(wYopMr-bR%ha zgt8x6^djnAQFbW%Hh#Z;rdwFI&Nh~;y*R4mpXSw`lN=k$cG>W8A^UX2XoylXz$#ne zeGA$x%Jy-ue$D7jMGSuOCj-07eype4)j;w;Q&#=tZk?hAk5nnAW3)Gw~^nh4Q z7Y~m2Jr^G^v47XhzrA_9_gsvbuwFfts?%m_!5ISmndr2V6P4ZzhPkd z5Vtf*59N(G*ksf+LdqBC)6`tH#J&P2u#*D8f8&G6jXy+hy?>*@stglC*Y%y2eKm;> zCQmb*q;xXA{zNKr7il=3{k?`do-Ig;Ex2f0zfv$rJ(LV#jxG{`COyk(ymk-#KG#O0 zZvQX^{BEc2H~9a(0C>I}EbXOnmY`SRld%Kh1Yt^wp1zj;tY2Sn@-kJ9nI)d^pOsFi z0#^prY0(Mh{Uc|&!^n^z5*^iwhj*hjNWfj7?P8Q=QMj1;Abe8A)Dy*w82lJXJRKy% zvE8-*PM0aO`n?+u-?6PJ&gTwJsO$x4hd8J`xNa|v6n%^K_f#bBcU%w)TwVs$>6Q6ta2={biJ6fu}n%ga}fD=4C`=z>}Mj)VZ^)rHDUNpnC1dG{?#f4cWc{}*GP(9ksr zyidefMyNjWF**~(TBPjc?5wW}?c!5|9W|7AjLf#RMoFH{1xpOR;XC7n8@^qwPG_DA zA5)Z?I;2TFD+EM;CF39xRX(#{k;lzCZxPjp$vdG*=6NQm&#n%=t3B;Llug(?< zl0}dI)I_2#AVOmX_WLO+tz*&8=d^9i0mt6#z_RFnd#);AJ^+^EQ+HfJHVdwD9SIzH z*iOxH;#xCrg}FXaNq$~yOngS}6J4c_wI=-d*kbD@V*1K}*;nlp1L!*)y{n-NLz90! z4zu8)?a0o@NG=2x8VwW|M3Yt)z5<%x=|< zT7>~><|z0zO8Xt^oE?$m_MuOgvbc{zIVE;mvP*ut2Y4!W9LSrH3kT*W&=Nf$s!@&5 zQU(GX-n_{5X~6kyg7)7&9%<>;(chY{CK(Za*gxMhWL4No+L@e|ZI>tNq;U28xYaB{ zd3eslC`L8I8ptL@a5`;5xKpl?$tdT<+VuXT4`WoT`3e?w)NC6~tmSYkl=crnp58+4 z)isl6y15GzsFSQ5s}%XZ>x5eN`!WX1%4X&(7ot6IV9P$=KF7K^N^KP2v;>#2?0D0> zUEA2GM|I-(l>uBr8BK5oK`B>W<7$q;-Jg@9gH?)cOqtM4Kq43QMe^P z*m|$alylw)7+)<{%AKI-FG|skfu?dsBjZQG5MIB{ce5IgJDx3pOX6nnDIsc_jj++p z?vhYxq3{qmUJUSDI&@;~_n4 z9>?G=qs6Mn4sBsS-f>c`eS=Fa*xtQ}V@6^2rUNAM0kIK)K6-;_ZlMW)tq>`puXQQn z#QsGb%kM!$h?2#SYiml%S$8OfEdX0jY(xV&wY#14ESr#oOTpQblgKZOGCA_{dq-N5 zDl{y-6yOa;?Hmum7~H#4qLJzaLck8QfO zq^4ooAoTr{52Nr~3!?H`3e0z3<;6Uhe`Zdn{%!y`@~c40)NqadW9LXA26ssSK{(i( zy)vLz1+s}Auj$*L_z|pV5}yL}H-1HqkgeyBblfP@x#H~uM|FaflZD*`+tF_wn%Tvt zg2{=0{F>zf|J5Sc2RX&FU+SG*F$KA>ll#jJ96y^f!xD?qBBKfhIAyzV3L z%@tm`9csUl^#ESPWohJR5JaW^L{V7?oKJVKdT1r+4}bbRFcgH3UqB;EE7zsYn~U;5 zF^T^$*7SWtErH5}h_3tAZEh|ib~P{aUDmH5xIBzwsBOwub#VyWpvt4+`aIs@{tlri zG{D#xXEV3_-_<+vZ5)tejwxXc^7*kk&s6v2f{<3#b&?VC>%r*K0mh#^B}vu7c=^&l zOJtktCf}paHehm6EvO2OR^>#!`>DkmV9Kr<;ivdd;j`vc<5UQlB@eYQ;hMCxqin?I z^LTPX{Yg39R;|?Zv~XcP*>C>=e$HGo*fT)CTkp%N}jlSFsn69HgjC8)|F0aIT?w&yY6BG)eZfRm|mVa@5D*1rO-q z{a`Op^Q8$De=a8)?aW?Xd1a=?u~hhkVgI1)4^MxHT5-jTG@RC!WeRG+U(F@M0S*J`7zDNF@^4imZEsVk-Ds z6L-ds97OuJ`R|Dyk)Ln11$z8yIJaO}ZFr{~+1HOrk+oiorasYe2HSL~*wuZ>olsJ6 zv=39a?DXPqGmIY(DRO-pWnOgQl5oU+0uFr!$x6ABaVX%Dtydw)8I7|$xJR|+kzpuN z5(71-b|4QuzTPg{-tS^8RjB#y4X-(tmBQ}-lu+%FmR}Cc7E#CbyigXR7hhaHP|=s5 z)LbT-sNF?nJJ0L=?und9fo3LVM%$y9XY&E0=SlAIfFy`#er7ivZX` z_;aqEP%`klaYpn0qkEl&bO%F8%f``JFdFPIIMCL3WFp5((kwac4(Ll>UAu!~n%Tek zn+ZG3M0>@YowX`hjV6EsT-Y_|#e(R+oms z$bSsHOw7;)^UR8W4tHAT}?wD-|QSEPI5 z$hOnza}j}_5*wJGZQDsi!KGc@gz)ek`c4WXkyTA-NACBku>9pV>jx;M~B#;KMgr{LLKn+C$_R9U9f>l%Y2z@qX zeNBN5@@1e^Sw97ga0*uq?5zYyR-?ahI9j6dD&w+8edH=H4TXTY+;x{s@bVzPqAlW|zY&S!H+5Zk1Wh z%|FV<-MSHjKfnnJqW8~c=9x3O3dRnsg361s*KXmVnfT$%jayPE$^7@)7EAVC z^eZ(TBcm%NQ@U1KGxp9F@CJ^q6Y z#BZt52`op>tiQ2zuNVtb%`3}VqCaRWvl*FvV5>*Xrrue9xMVkjxRN?6F5D|?!gyB; zYk(}vZZNfpwM#subOwg+#TK}pvzfVetgyHw^Zee>mw^r3lGD7P zqpDnM8IeH|9Icd$uNc~?&qpbn0I`Hio26s~t8SgBgbHi2nm#j5p@lGdjO6&1Yl#>( zQdNuf+7C<+*-WQ;VEEyB|657XnmI@(Oi}X@lxwX9C7p-E7V??uCxpW20%rJ%TU)Cv zuK6PpX{*|K2SOJ2nKl-*N_^?zmG<$s%;GBaX`ognPiWt3e%_idm7{56Hqb89OU8GL zvh4XI#DWl36SBC&8Aor+ei9~M=1<@z6JurzQX2!mL^OQ|cH$JgKQ&9NiuvfAdX8jZ z(j+-8tn`DqfRy()B??2)#M6|$5?d3_o8eIvjE&q>FD$(AumACIP_{FuB~#>nPr7i& zJ=Fi(_X_!4r@>dOZ&y{-nYF^hlg8M+_MM1MAdK^PbLoc8TIXS&SkbE*a46V(g?CO6Qd z;)@$Et7Ikn_Cd5$@6$28IWB&ixdS@u{y@925q`gO!*J85k{aXbp zReA7aaS)VQ{ab`71yJd4MI%#Dx+BjN&*-Rl<_x$-n^%yYrKE|Cl%4v4=}hmnE+~t3 zA~Uv)oi}U6r*Cwz=4BPb{wJ30et1mA0B6z|Lcd@GbjyQ;iC3FSKY1<{SDI2Hc!|Lv zQI{pP(sxSm%1V-N7oTWUk{XG0E_H&sjMbZBQWxr~fZwh=m1Q<;}#uZ9%cT?%J^C=3xVO&vxcjoSZam{fMPh|fd{ z)1T`4{LPCLzBJgpVEDbhcW}nhxklyz| z?Qc4qHj;5yrv{h)(zrj{UxC+ME^PLp!dGPd>s8y9t|OO_%Q6uK%-Cp`dio*+!$Jl` zr+~BhxNpi?b&jcs3Tvb3^p|m2`Inp9Memv3X%-xo23TKb)hL(1qXtbP5j?n6&qZ&; zF|9#)0qCr{R;xNfk5cbbHku}D-dEaLrHOSMC@yRIX<&cdp1m$mQgK3w`HYOCkw)F@ z^ZB2IzX)qnTi_bQ1_}FDa~@h@$6vY=3V_qRw=&h5H!MJqAOzbgP;tU?yZW-|)=t~^ zZ)xZ2uh%yXLOxjy2SV_{F?{l51X?HSD>DI|Xe^A|HEa@)W0`6$tWhpstz&XTDM&>% zc_^XWY75NFewCW-Tc{>(=wp??WD*}VD7c>Dp*>6XPW*R9)jyRzYsUV>0dYdp3PvKn zDMt;y%g^osc7z-gXG4&Zb^~6WcN)y}{FQZB_uz*zEQRWifE#l z?kNaOt|+y!`GtsU=`BRZhMQ!RIr}n`mOj6QBbL;`-ZnK>@Z+RoK3H?43Q0KWQNqGW4Y96rCBKxU0foO;9 z*vI#Hs;hRq@lVWRix5PH+z+s4Io?XOLWR;*7x5?7qf=IR`xK!`E=lAr*)svk9VeO= zAeh>hG<|*B4N=3EIuYLdDdta9`f|TO*DEJ`n55nZT7$O>{tRW7<`KWc(y+J_H$H_B zeE@uGBPP{(NFoL^?6W}SJd#2`qF8ZvYhSG1zv4I-Zxs0a2YbLkGs_5}=B5z;CIVL? zaWk;8qR$poJ+mJD@q4g_7E)~QN%!FS;r9&0p-hu>cmq*um*daqy;sE>+Pix&K{vIfkYgPoZb-RnN*2CiWD@9FK}$ zbJQXLg4Sp9frGH-A0?AjN$)``_u>8VZhTN*j4BlfgOmBE+*yko%b}{*X^&WR)Yy3w zEzr9$vN>wtmfw9n1wb{=ar_OKo|3N5Yi~Xnt4fs$%L z1R$2wsIBd}MZ*b$AP$TT0%*?5?xRBl_>w2GUsgXvoj6~t=Ch}Gral@<6O-y~cFN8} zBC^;Y-@g*7u@`?JsfTpU4tayz?ig@lg@f-9wAB!E@q*jF<<=Uj{zxeSsq-V-^ampjIRo2J@y zkezAHrtgq33Uemo^9^H6q&!wK5G(P@&WEWM6Y>`26RhVdSP9pf*_^uK{S%~eSPlI~ zCRFhfo3Ow#SbdJZl1=z;62OtE zFrfOJGQ9u-ktm*UY#d$XZR+96N^ zcCF3Jc>W!&+_FdE8uGFoUfU<fQIe|Ex)~i}n974ZHwJf%7e9}7YD5-QDF~j-#MSx}`IFi8S zkHlEx+niZ1>nSfiG6r$$@STbTozGG<8$KVjt_Z`q{Ia#uYaoz}&AB#g-r6zHE8Q;U zrF8Yy=(fb?@|rmh0?cVa7>mrdMw(jZrR%uX zd(@bmd70}8iPU)f>oaL2Bi|*h6BD*X&i>0lytd?lYFW;>eTJo{Aua(FePOBd2RhTM zLxuyxaD|La3g~S@;a6dL;UT1g7rlUQQ&eZ;fYW2};b?F*jI`j?RfWFt`oPA3?732D z-sqJVDl;wWucCDNE!Oe-!#@P-c?lV3;F1aD70y;sl0PF*yF=M%N37JPnTS|srGI4#pR7w~!_s`Z*$=lWVt(*QU)x6ppQsSs`I4pm>a> zw`ZeIH_ZThwwsn+HxSp1X6ISMbS4^tbx)Rg=DPL<=jifnoRiG==QH=r%kl{81xywk)K4Te%)_glAQyQ%s6w zIqxN(eQHjq{C9_f`ju2x5I`NbBualrugNBOhn#11Q2LGd%$RFONPqahDG(6 z4*JJ4=E8(g{>Cj#6@;vQc%A+f_#WgU%D9yurNCdQb}``U{IE|jbPFu7ILy1vJIisSD(3pbW4>5O)lq^J zrfgO1#?qVlSTwq+nFzgGvw#DT78m+oa|sWbF5;|Mq{|=W7l-?Tf#;Yuja?blbGb{# zoy~GwV!!Sl-=#s3dzG>&tXXyT3fY=|&n0L7q703d@@^8N&cp^0pk0!W@tF?`d6-gA z8(Jw>)cSfp-3a*3slkFvovW9~v|>uVLdwyzuJ11lU4wM?Ph^?XjY5wc*-9LmeAfO{ z=C(zKKQy=e9oA7MjzSrqyM@EWm7@d+JY(v@==EWW7FzH8-i*jM5=zLxvKOTc4O z)n)9N_9-jaF6SVsrgEMA(?ve26TnE@LpW7fau9cDvWS zykvO7t!8X;N*-vRdTPx$TNZGqK^^QaPW1XgCW^a3^8gytC_xFD_pKOkjjKksS_C;; zgRwl_mDpo+B1XfnuYSPytz-0+mdHNAy;346fDp{)=&pRtzTX)sX7f zw`B&iy#tuU!d-_j1Vp3wqs09dM5*obmOqHi2?0SSGU5DDoyWQ-<}VH0_me`r-w>mx z7hPbIalkT)DrVQ^pON3kBPFz>0&&)| z$DIGe-dldv@q}I4!QI{6;oxv^cXuba%fVd^?(Q7i5(or$clY2BT!K46@_Tt^-uWwL zKJ{9?`a`Ymz3Q&&+V{2P3a)3rU=wSwn})2Z7&J0mUKsP-!1frO?2lPd%if1}%!W(u zkfVoA4Ky(ZTck@rv=)3E89z~HieM*%{S7NP$sXm*p-^zxUKg$YdxU)9vehh;k%}~* z%w_SK8iXI)DP_uV3jOAO)sU%?32YU{miUHW6C|N^k9iOO8R$_yCJ^eeJ#@CPFxop;F5>PqbRUlF4uG28 z1WfIrUpC@=MRXPtAU42>u7#ZuL$+<5mUb}n^vHqLol>i5-1Ig$TML8w9_`M8e6qYZ zVY*4fR(o&7jGd&Be+{&~qrftxe~zIsMZ{ptinuHe1&(fOpUOVjFRrq3U!Ec%T}bko zV6#sl1P#FUVJ3~6rJk)3K?+0)IkI58+OE)kNv9_(7dd4(*6_CyX`bmJHu?{8mJs?2 zVT>AYz$-03wrU(dQxjBj1%{QH^EO}nhkoKUot`%5YHHTG#8yt6Z zj^#yN(T4G?9RpBVbq~jEJmJrzS`xzF^?6h>bAi!!+Mj35nX+Y!5(+sIfkI&$ONiEF zYjhBkx?HNNKwN};5m^^f?}vjH;o>dZKNdt>bdkg+4& zBWJ*p0Jdn+e*rLQiTFs6)nedm+ZxE7nF?iD4Je>Q&mSX~c;|B+=+s=MR>p}B9iY;V zog5DPd5X=f2lEox6xhx}3+S*2?E0$i|6tbs00YCeTdG#&Oe!N=BIG}t-4;gJVK9v3 zEY=uv&X?5aNje{u_)D2Jv*)6s0xn|qj@zO9=obHJc33=MM|n0nE`>?`_{SHQY8PzC zi3PSTbYBI-6%RKgy^JI}jMb^fcowzVdpSQNO;@m0+_+w$_COf?2Q}JuzBKzItOSlI z#5_>Wm51odX$2dho{G;hKvmVt*)~0m>APJK)q~^9yPke)XV||)6C)rsy7PGS`ePA^ z5rM|QAB4NGFoOj8n*$jwsn2eXy3Wbzm=mb2A27iID6q=L25)*7RasuXD`Ek#<*ge{ zEvW;M92zaZ{mL=qWKl#Es3Vk+#Jh!-8h$60OFmm+&~!6aCB*bgI3YqPd{@^5b>{>1 z0-HCgTEP$-o?>o*YKyHYmHNoX3u&;N(iIT1-?J%}eNuMn1-Z#Py|+{=4X)AuK-JWu zHMOUW(q7|RFB-`{HEYE6bcY>?h+H7L(+_JRDX+@uXr6NVG;IokOC?}an0mC8n-cd| z)PPGIbg8Z{i)#4|UI+=9?lYHf)5i%`2qDh&c=(u^+Lgn}hqA~T+9=u2SaqF-6C@zN zk$d-xUpti6^AZ1+Y2>u24fVlRd3f;|9sT){-i)ugtE#Kak%p194AN*8dEz)u$~NI_ zuEWtxaAbNrW;U1a^u;KlfA0vRFWWk`^v{#4VA!MrGV8}}BSJxB*Km_b-Z*QWI+c(d z{nWl3|M1c;ylYhDz7f&iNsiwdYHlOvRkPIqq6UlJ%job}A|4B_xfC+Y+6AzhKpeow z_$9CIQiPlPEs?K?O&NXAQL5KkgB)m^YgT?`053Z0oq8ZyBgoXrs9qGd5{d4M|Fh{_lxcCZ)KbSAJs9yx$M%Efvh2X5 z%$%RYk?CjCGB};tx+9roqGCIu)Ua$&03;oCo z@ow2ux`db#uNl)O0rJu>D)SsK_Xf7zj0Ig}azbgIZ`O|z*L9Ya`AW;^LwrV}4h7)X zp$5<|9h;RO^m7e3N7Q0zu@h?5W&(a>mbN5K*GPhXWg2*lkUZJVn-mgf6p9Z7*D7W~%8HvBhx#&LF`}9QQHZ=#&<7+dyi3~@t(1j)$EKAY|4RSJ# z2%v$~`}FnVkT0kYIgGbz%XK2_jKJ`?9Yh}*>I4axwe8v*GeaNlD z2%|d_6(o==@NVqosniA`H?%%;hPMWL#y&CHcrJJ!z60LP+7Jc?)ql_36}Bz-gL(vs zL9*70^z`R*{em5s8VLY+IiXhhrZa|8nkUhz&FoCs$QspFJ_lms7lJ5$nv2xR+Esd< zubWux4__KAePo@yHZMms`q&j2#MTZiN?NlOGSVM?s^qaIf8=A^Bp0K36SsQge%R9T z6R~(4NZpB;ajx}iXl3D0Cy_ncs7RKPqk2#MGy1 zM>n`wx)Yi*g?{5`g-y{B%V}!szcC0!v$}-BF#ObNw)T)iMnVhk--;v>(?gZhUuCxu zMEc!BL@1IQorD1)p?|H{WAJb+Y9UrEFiimKt5y{L=C8_6K^}+M4`bt1x~>eziY8Je zEN}$;3I)M5#MX|r)?x`J_|KcuCdf|6-eqPb%4zrx3?{7!;R8^6v7g|bn`p*u+WsO5 z{i^4aW~6jQ$A?AN6!#`nqO;Y*7SAe%bR>c7`?-t8#kpq(X#yu_gmRa@_L&7M7Lr+V z_e5o@ZWXjR#R@h8Aa|3VDJr?JA|2*mEc+AQtpo)9!_mk(*eDmeG)~vSFglq{<84&L ztjQ7zLpU2R8;i4O2rov;3;(EFFl6S4NS>#4jct;sT$|LOe=Ushq<%nhA=stdhZ{HA zR_@m8u^up7j7kGh{T8Qh82m6P7#XxT$>g4d3!Ga``JRO9 zR%ah6{5%&_=}yr>nP)%sH5I}Qd+9^(f#0}*z8J(SC$Z43A8bOn$p?$D-u-q6oE_4i z1*kPID{nK`xvM?|wu}lP{%#*>UwVHxaPkBFB|_EbV5N?b{}Iy_(VQs02AmH zHjq01KIJ67hgCTpkjzbRq?eRV)T66Lb$4nwG^)2I@gnHVNq_T&hB4G8MZ#<~{un{j zT$iQ}A{mNqi97r&@b#E>iTfbTRhf{OzbDEqDnWB>^jBiRoRfY>VS3q%f}y~~QJ9E- zI0ja$+-_r8vl9Qc6%3I%Hi&t_EIL=msN0(S2ASa%jVsqTzT)UW8PH+Pz@L=t znt-b^B(Yg(+Ohl1_9Ty!ES4hFk>3S8w3W~DBpFVIWKDDxBe4e1nQvi10b8Dcf`_<2 z36POUSMPyuLWM_v^WI0*;JJg+-wtyr=Hc7Tc1gZOvT&tLeFzPwA_X8c84E>krs7Zh z>r!P-PvzFa##&*^lW=h&V>(QF#(w$^Re26{(H}-L(53cZ*>gayyqwBQ4W(-fN^awJ zZG{Wk&UNXOnIkQ74#ytI%eq8N;hz=~3&p^K5&wy?nrnA!d@MXKnNnrxZhykQL2qAJ zhXb8^*{VA;`!@efflPWl#y8zfNU3H!^N214T!V3RZ~c%kPwUqoxf19^6kLD3=!cpW zuf^o3a|dlQ$#i3)i+H!F@<0}$^BUs8c9JupyP=pw%_g~hZ@C!IhSnu5<7EEz&cG>> z#(H^SUIFj@_b;N7I@}3_Z{&i|k}iefyYnOVKAC7R%6W^NR#8 zgl5ns1;wjLxbQFl(?i$hr)*`8oty-xxTgaNs%zmzQ((VUD`CsQza)+mHs5Y2oDaUN z$fK8u_!u7V#rN{{g+52`DPNoOkwh3A=luEAc{_u717pvqX5WBxR=m>zozcZV5}fTI z?~?@imNLGlRJT!yzJg}{sBy`)GbY@Tz9B}t$30A5OX}yQ2`?99ZaxomSK>aUV`?-V z%QgaP)>k~Z@y>bOab>SYeZu!)cJJbu#=`Ya4y-6e9tVeYFVgG}HrN4he}hRD{2yt8 zqOq`dQi^`Er-l^&UN+3=)edOltgK~5LP7jT5dqoZ2ZOKz0d}3x8sq3w5)GRZ8jBw{ z-b9452EX3oeThXn_?0bQzl6lr<3=MkD;Y%l^lQRU+$?h-8EispyW<9QkdizlIguwK z|L39QoIugqENtni3^Avo9unTt=L*ed9cowaY5$I}O62A++pFL}m|K>Le)YgP+_Q%A z8L@`%4L~7aQCQ10??gq&O7e!;-bvhqn>I$svmF=&_wU8>u~X91vENT)9FYgf-5&f58?F$EcFFP%BGD4(wqt3J7r13~>g?|Y0Osl~cHd0kE$!#Qf zb}0wySL2u-7G)M@OI?b2f1r#nd&cFu7Aom&r^3lY{9LkQ(fbDz z5U{Zy^Hwlmq?hf2A4Y0zybmhbzx<(He4N1FS*X3_Cl$G%tbxkKXH#5e1Bw`5t=D+Kkg|<<4wN39l ztS-Zc(biAjw2?&hF*av5zv!c&7Q9Stvtm_@9VTCeJ#3A4y);qT%2K%fiw;CmFKsv) z!WWwgkVmi^nvW%cc{lG#ruDz?G>dp{i%2bJ`(dLt)ewDwVQSUvEi9@E@WwD15hYsT zCSRaVqXBL3k*+le(f?*;9AY3UVZUe!vI!1aJkGu>0JfPs{0AjGo2YtmVZODsvYx)R zI#xX`iAV(IPWt`x6Tv= zVsa4k1RwzvbV*#>l2_wmba|gkuJQFA>XxQZ{iTT^8 z?QYx~ChP_m!XaF&XuS*yZV>xnr{i>0t0q-hX72+sJ2ej9I^uPr>d|(4DbtJ(2rgH5 zG_HD=6XD^u`5w#MR0_YTvaNod z;plQJe6DzjKA?O@RC!OU7d5e@9$#0OfT`1YmqS+krtgxwrLyDBc(=e1KB$Q?;SDb@d^e=Mw^iL{sjJ+($irs-nq@Hxo!ymD_`hZ^4O?hUg@o~))Lsff;5@fCig3p?yJbv(+n0rT_42RgyK(kO;lly?r1oR$jzD+? z%wHb9S*t?2ZTDF8SjqC1`z=V9KibjECE)6u#B=JKWF!gBCQ1-)o61Rp-D?4`LQke)*`O#^&w`ELh8$}8RIk-7T9S_=(C=zREKV{^D~p21A~dOQAP+GHq+Z~ zIAn@EAsBJ5uK8c&m|Dr7{;>5#9?Ob~TTt6gaZBRP*EmyxT@HA0WndE@4>PP`PqsFH zlvvQ1j@<}op043tSP(I~Alz(|EgB|LY+82{r>=k-h?6%qB8$j|Y%9GVP~(nB&@*(= zsdJH9dlh~n_*gwo2me_rkV93{>H;<&t=V=|qJTF^8uqoJ0qJ32M`(?N-vk5!Sjk(u zc)NTu_0Z8Ow=KW>%%mk{LHz~ts0oD^!)mDQjdq+rTRDhY`pl4-zPiM z#724ds53$=z?7rz|1GG(z+GPu%)T^`^xX}}1dPW|!qm(%*vJzPYoF!XJbFhUZ4UR3a$xSvGjl2J*I!eep*ASN4|SiN{v>Z#DXLPX-;`Vwtmn} zUj(3)HxE>-u6P%=0>^wZIz4n-dmP|3&;KPk9*K4THbvlX)*7o_^TU`g_vSd+cNd8S z2pM}6?7{5L#q`K58x6uZcd3CgH(Ag+A9rz5ltFXq4JENuU3x*TIe(dJ#s&vHQLy(# zTY+)jag2Qxi9kGlT|2x)3AMXre7h)+5MzQclZ0f{16><&aPePpAI@r1dk~nvq^3S0 z6|0R{tTq1JL2sqrNfEdfi;xma`FMO|YMdlPXMY{+_V!OhRJNTk59B)y?*lc%%O8NM zM^;HI;GZCc)0_>*wRx7ynP)5Ca@bXbfna;nF*K_*_!bdKl$v4oha+d-rj?uy278C+ zUQu_qJ~!y~ZHW~n3{Jmp1_3G-e`TI;fRscGg)_Gec5AXEV$0I}$U|0}Uv`d=O+d~z z{m0u7+ve~qe?dO6_48CN;FT+5Dmh}oFs((@cDWSu$z(Bs);JtGXRV7dH9#jevUgETXcTFL5 zCJiVu0XMjja^jdKcfCE#;7KJXLFSjarhm?XH1gqeCTp~`2IZ=mV?^NwKbznDv{pq%CEC>k50SF{H?b8qkb774Xe* zgxf@1XYcA*_pUi>!e|_vB!$Y&(WZpWc{(5QN4*=0!#SR!$nxNxk+AH2i=L7k7@>~JC+R`b3jW=m;j}&>E|@zFTKjP~ET@3>Rk4n@8GKg*r!1_pRqj1V~Bb9llrLUMp;AE?x3+RX`))v%zZ=r z+}I%}xg{~yJ5UTggAyPkA>#_3G z0s!qEa$H!8I5V+b=Hh8a8sbA@pVaF?6~Ai9p$7Oj$8q8|i0FWAEew)0>=+~OBc+-L~hy6E(ppA^~3mS^9NJT%=Gdle9>M$~J0tjLIxs>mt4KiYxJ^IF)soevQ=2@JmqxkdZ&NbV z!_P+T8crUiDCwG%u586lR2rjxc9T}{f17zYxA-{3Wx|6W)~8LvZ2kU_zezpG=k^1U zUxLK(`y1j_LbOYoNzQg3cMI|g-ExPdV>;@3uYs^|Tk1~i&33sURVOH*6l)PkBdG5{4euw%MP zEL1uvu`ol9eP7Bd=Lbjs(O*W}7u1q|)EhrSWKf9$XAiKc^T+|=xJ8h?kbl}LKrdRW z@nK^8`xMq*8VWgS!6p#K;&hJmi-*1}%92ep$1cQD7w&+Cww~ZQBC+=-$#+7pDOhfX zTDbe4zV>VB0LPy$dZHP1_*}G2Vja3M?R6Cn;=&nv3mj+Z!HihD%#KozL>d?33&|*0 zqMx%aypcI(Qj8iBO1XuJwF$|ENWdQ39RF->F#vK4rp!U)H}^0&5}R8$ z@lH;F?2w$yZ|6#wW`b?jzu@8AUejRob$pM3;d*k)CTzI2;UKa?3I+}O?`wFt61f?n zOECOhwP$32K<0_1cUDBwI()&zaM=2fdpvKFdy21HiCQ`_NRYekJ4#`?TE`eoX!90a zUtvDc%&IgY?;U%3e`RUga*`&GeT0jlVdSC=Tfe4@LDB~^bnQQhc3xDs4VN!Dctc2s zy3$(M^;?!yT`nkdBi~8a>N}+ozS_#;ehQ{^PxvlI+e6gO(q~iA0K^$5ce~^_yS0({ zbIo*Lbj7#*(c4os3AIBw4X5$;2OD*iCzOqoOvcxX{lh;KYd_PJ23zcLzw^UJMQsC@ z`2-djcnxBUF2>jN8M$e|;dDjO0;GlRg_-&HYc1h?BnvV>H@+6zcsxr) z9!ATZGQHnv{3CwjJPf`7LUAI*FejZ(qTv)F?Qf3U7u6w={=ky)-kPwQzCl zT}9XXa^F_WlQ)V((1=i$riOU%-|u=$%Z8v~98wFLFz`7L`;vF+3E4i~Jqd->5Xers z$5ON(&ikJtdJ)+@5nn1tp5Ctf`Q)Q3w0#6p?rBby7t&m#Gt*XhvQP#_&HBO}brVRV z{w5BkgJABocRy#_g}mip(NpIy8_j8ayX})U(YmubyAW1Wj`uoyR!#hgs^z{!AW0-t z1LIZQJ&sIYxB+d(Jo&cmEB^#@REvRVejGWREuTv+lAhQ!zKdS>B#l5kk~b_yzZZ5n zORq~EhiO(iZu|Uxz2Vml7P+J40c28Eg7LuT>z6ekbQod>QD;7X-hyu&zePoBM^1@7 z&hxYk$vU#uKme8_GOndZZN*kC;if3RU6b9L$fg9eiq>*pswVb+dBR!YQ^ybbnWKGf zbP-yqU!MKsjZoGCr(_A89z@nVpKg#VIxzF%)Nj5U`t?E6z%;ZEcOmfj&JKrWeG`uE zEkw`;*QfylZa`YF@lWp ziiw55H@6I&(Q&9nHlc7EmpHePas(^*r*K)*onkNwUurpsd=A+|Oo8)@hs_dJV1f}l zYla4nvL30tBJPf(<8|z_lKRBMWzan3}htaroS}DY6IP{<~B9I1^q22l0YpgS77)8 z{)= z>4*C7_pR|LS_z32!3%M-Mu*qGnVnM4cQ=U40M_>*k}iJ^-%T^Rte8HJgazK`9P&z~ z*JH|sdumY*EZNRTuWOA9%Z}0~!W|0ni_4%r_F$=C8-Tj#+?o_~*MS;~aoa*f;w5?W zf;ipSBZj!Klc_H+-NVm=!V1Rb8S-tS54+QNrVP9PGy*-Rh8gOmj6#}l5-Eee0CG)E zZW@LPk;qo(HPc`+r#auvy@W=Rw#6Z7F|kP{Vr?)-ho$sq^}o=OkUARJT9!h|5I+e` zNDYT8w{=&gHv0H#{`KI(vaHh<`ZPP0g{Y0j2qgZTK!V(G+%Of>HJlVo{@&YrURom8 zlBV5g*Bfx?61?enLET0_=NkyyV?ZUzf1gOq>}fwfb3sVss6FM9Bo>Ptt`^M+MbF?M z&0F04#k%{=X(FBb(k+^$=YixVTgd(dA&L7HmgHt7E#m&2$EaBP&%>0gwLpbMq4Y7Q zPMWe@yLQ1dBU9J6ng1-d108(Wfkg=D3p(O+)4`OT?Y*k? z;EeQiLqQ-(D>Og;-Au_Vx%Xx6k1|An4f4%PV$Q}bp5PB|rr8dKi2N*01eG#Iibm5v zryZ+UEWuQN)R3I`;$e(uSCf7nL+3-(#X}&`nbvHG)W9xx$S11{er@7p`q#yKEc5UwEtwTWD!j5&g+Sxa=OU1+4-5;bz$`?> zGfo+KklRTRYQ1ctN!tMu`TO;B0yXl!LUby@Fu8H$JbXG8su?*Wgp?K-m16BUt8Sa0 z8=4M*E+O-T1K>D^MVs5A&ek$G$O&bG&!&R$uw$k2SRBNM#CGlqky|Kap;;vEr@&RM zZf$9CONZLje3sD`PDni{KQaq+&zNi~Gqy$>EceNt)u~(K{*$PuvIm2VnQuKEH&&Rn zmJ8d!lL>%(RmD^wESp@yg`LHaUj!!`S60Zp<_n0`3L4Nf9OD9Us}?(pZG8>|A3@{? zJqQzjv(MW$%Sdq%?j&2xUDpy-(t!dumD}G)r+f;hBy`E96rOKq1Gmg_8LZbW4VB7m z@WCx);f*HbNtd$Rb<`pbW;N-AGI}UI19q`Ijb9D{j*mTAmeVt7HIS-k@I3Y8 zb;i%|CLhzhlcM-M{Mz{f3viHBul~+Y9+To4UMZf!xdRxbz@QuSz4Rp=b6QBi+3Nby77VBb~G zWW+){h^d-4Us@+Py<gQ%nm2|>sr4yq1MSpaRI9R)_^t&+fCCH_ja~uh ziXZ#VNCh&2q$f6(kBUR3uLQE)=%KBV>yr z-93dwZDycx5z2-y;G*Tnu%?X1b?hfVI*6EdpFE8xo8=NtEW`2!1QvaKk&-h9$|d&acmXPf82hG3bI> zr+k>yI~9E}b-3!x#XZF=bmpA&RNkgI@R+xohc{Y%SKX+eIuE&v!-2l<25BA8)}(zN zM&*4Hhmu=kR*M6Bhzt2TZD;HTr^nl@ukjN86hi)WuK`!5oHd?nz@5tMC!8*zKw+m_ zBa8Vj{DxZ6D>?iplckwkz?9cVL2h@@P8jKOTLL4k7Jpy*`=p6JE$C)aEd!Q)ZrxmIxLA?^h0`tds-A7TYMZS1ICzHk}gc@>N4NZb*aq?!ud20H^q;ycQ^&=w`fLazu#@K6SrW&4SX8~KJutW}T(GZ!k0I#iYq!CmgBg8(HcC~z9&R_&0)ovH~61ZJTRL|-jtk+GiY>aqZ z3j3-|cq@=4t4b0I0($hb(#eiM-4m!tjr(P?E>d8aU-~QOLf=M9j+|!)f2LA=+AK{X zq1;D#G!QN${mWRr9*Rq_08Zv96`J?i^`9NFxy0B9p|h(%O!1p?6pz`xdanr~J@p`A zmPKpY0%$h)AA!O&1n4$MB^JBtp_!u`c@7v+X{-v>1u+Ul$dC;JAADdvv|Q4^KUB$j zzpSzmB@{5Qx#8~4eo}*B`q&JKCm8{F2#L<~E2Tqgn0Q0z*uKAh27JQLrjU>0EB%NV z9DkRm2aNVuku%ntDE5?GyTN)AVW)^Pm=&e|Erx{OVGD{_F+h8&BPcCiWPn%=V?5Wg zI6m9K)<|VV2I*Fw&E$KYTy@DZsgy@_u|EICXqLe3!a2I$Pj=g}MNa4H?WR;WN2I=Q zZEBpD8ymY9!_^F3ktpsZYS@fL?HVj;fH6na zlYOAn!HGt|saX3?oR+C0I;?HnrG3?U3acwzb!r}*p*(7>XmFz}m$J=+dDSJq#?_d? z+bMn1@gubATm4$%b2{;()0lXB=ZzlOAsqM~)e6lqAzDi8o*I8uyYFBLxk3OA-6xR) z^t#NOFW?OhJscU*ud{eJ z@Q{`73EIe*W0l$zO1ah=|2=Cwx>0uc0Vk{Zi|96B=ahDdrBu$I{h~UkYJH1w&W-xC zd7h2hS}o#^D@+yH$fjY2Hp(}e7lzA7#eE1)b?K`C_`YRNDT|PN%DP%0x>?gych?u| zagTCOTYBm=fvFTjX0cZYAUt1*jjiGtG8pEu1v5t~@!jD*^f`Y4!GsmG2pvm>;bfrq z6QfAZvz#N7fXJSdhkEk3_T_%T9M#wzTN^J14^}n40rZl0Mmz+;pQVi(+b#8vIBM8Y zUP$=%?DapB&n$|b=~~*%7fQW?Z$;61#NUInj-!6s^1(|7cW+wLlHs&g8>ec1k16Tr zqIy_X8klGry;~RL5aR3re@5sB1oYk| ze-I1oBTkYfkV@E^!Ao8(lO|+;Sj?PRBGpoRQhL`ME8)h217c>rU!#-Jduq3z7Pe9@ z)F=ehFdG=}*7NYPs>Tm5L7>N)0StW#+QkOYu1{egp@ONM+#8t$^eadV8w-fJaL_nL zez%|1XzE0&{_6N1(&b&L;?G0jc^X_Fm+t;QJbL;Yn73&zp&ve>XH6Yum z%-<$<#EIP4f6TiDEOBJCvom;A-wQ9fob6jMDm##JYh#(^yxr5n-DkkfR+m;YxTO)F z-{@>q*YA@YfYBT;G>yBE{OY=Cohfu|3C+L&JkexVRbl`^gj-sv^_nE~mG&6{!LiK1 zk4Tfy-SApa)v%R6U{Y(GPb_<>0-5#=ws!5D3^`fz>m^_FmNF6xD44rh^h|9=#t*v5 zbVzBeHC$TFkN)ghU#^ir!cOQ(-XB2B9cfZxKRNEI%-IwQn?UwYo2bEvRukRWK;>Qeq7flN8mP1<3jhj(;oE^3 zLKGd(Tgc^`apRj05RF@R_aY%i$k;RAfz*={i;nkgQm*uO%UAu=AW}4&=Znr+V>xI! zZOCs2c0;NPU&;w(PK)z*`&KPG%Ir}NSn2VPLqBG3EE|r{=5x!) zk!hNZqwWbYqG~7B3i8-%@2DL^p2|)VR+O0#?-uWklVDk_T~kyRg$~Thn;v{1VMCCBk8h2VR#&{b3_TgdNBWO z0n3|-FZ9^{c7BWYglKB>n9%RW<%I%=@2EhJi1O^v zotAWIE#mL%U2rf?j!eo7?AWT}#jjz^tW|R+|&Zi)n^X7V+PcBQIV^hg*Gint9qM1i)1}R zb2Y$Hd3Szrq9XX*P?>dx3FL3>8QT*j$}1lUE4!=R!CIQadh5XZ_wMlhI+ZIfotN_` z55LUrOR5PM%9RzEfUpgE%$X+%iFEotSgY9dEt4=JP_8PvUZ;A&(}~}GZ@MCzNRnIL zcRlw=m)Z4uz}$>K@#-au)CX%qE;y|9w-%KQ)*Ux>j!aN6=~F9->y5@F%AyHCTcKKh zR~={6V4)>S04~Yo?O-IVFooLXXJd?q9L{Hkc-=y83#se#4VJ8dtQeaY9Gjk+6Hz;< zkLnmKdPGn>MtefPzZs{`y`d8@Ie?KuODC3?b;{%%~q07akH zd5earu)qbHF+k3NAdX73wE8#e=Mt2IL*PeTVF3CVA_S7n%Qwn>C5M!|*FOuhC<5Cg zt`7MzyX-%{>R`(yDIN#hkIcdYsy+p7RYcue^dOQ7#B5!@ha(oL?ziy1p1GToP6S6p zpknKE0gT&S2AjtWY=c`}%N~Y?dMPnw#;1yHvrYcG`A=)YJ-SbV%w~fobmH}uce};$ z-R-~L6LCi$H-j$XJlSXAn$q3+mC86!F#UVxxi-Jg`j*gb7BKXrAeVbx-bpGQHZ9pZ znb9x=j$}t|uS)Gs67nhcX|+sNvJW1CYUFE?JXqUT=H&09p=r zHfJR5$z2tNAtfC39k)Sk_s;U$BsA<86pz!1-`&x7Kew(X3bf(R0nuv7=}T9M*nX*z z-+e!p*?mZ1Q~Sj@IF(HAm*#XU112BzdzzaTUxE2vAt#ATCFXUEN7_tRmELACP3X9u zyfk#0NX|g_8H8ML&jkRj6{=Nbay7hopg;g=zkv+gzeIUPtzBCdP7-_~oc!*zlv7tu zA?rFH92lCWNxPCskdBA)k6gI0B(@g$-M|yI-HTe{Z2_9N{@em?#a|fU?xK5`@P1V6 zV4kh&zphy^&`A_{%Z{mXC3M{5E${VrmLR_TX2X(V02Q0YVUIR6_bAW2H?MeM5O(d) zx33j;fftXy%r9ulC`$4D^XJJG2}=Vp4@I9g_@3 z&B9z3d6~^4{ZZO$Vd`VBG4pqB*B4k4Zj<1{&Xcq{p)1=K^fim72cAEXKi0|Y zvzA6-XIEM0ee5fgS%dMQ$JFu(>8nIc=aIcN5FVG~t@r-KQ1pgNn+?a)TR@e2N8XWF0-QtS{^y5QP8>OqIQ$sf@TC&AqEv+Iytm0(Mgp(2xf zODg4ed6r@!%WUuGe6^XjQt%UKi(Epx>$PGID6CqrMm*{8eyoD2)pRnTT+=-}+ux23 z59q3*&ihmt^ae6yRX>s49~)k#U93Fpu9Z_P zan&0YtF8>oWH16i^@?K+6GM5%BnN&D3h9v|KkPV1k@B2czw}I z=c9#Ym;9c0Geoqi?<;u0Oee(&zW)haKlZ%bx9Qb!Fn0Q5Poz;#P-XaB$!cG5$}sn2 z%_5@Y*GC}h+Qn|cbZ=UG+9(dHv9xgm-(Qo%B~``#6BcY`prT?2KQ^;wjaF2ZHItsJ zrL!YLgNh$sh)kqir~UFeRHJ1+t8AvjReC2fsaiA_g2Y$b6ZuYefbvC|2nku{f027l zPsUa#8R!3sKXWXP>RB7TU67d&IEaPFnTWB<`Ud9WGz8{;WwQZxl)q-_Y3ujc5yQUD z6>Xly8Z3|mVJVGhDepEzW)oNcJ@Lv{cbGVIEhLKk6|P_8*P-HVzGdqAE!2pt#t|1SR*zku!P*HLqQ0S#DOC>e1qI~&-#A`kQv0B=ZMhkK^a=Ph+u zVkB?}8>GuKtSXgR$?tzXS)7X%+MjFr`Jl<*%5-K;H_QKwWL_TeTjY{_OG)?t5^#M4 z*1Q0O&bR}`sQLFt;K-oOw+=Q2SoGdrh+>!3RG!`558qRT`)ikiA%jNV>lQPy{~n^X zSo8{C&x(39bu8koB=s^$Bk2_ zF63F6OIEPhY!JrktD)H9`FKUdK&H~;s-mCm`E(*iAb+h18^R^H;o{b8@V7Z4#+ z$1=Nw!2cPFmV`W*eZXxi z!u}<jK6J0wmH%*t6qoKxHs-EsfYL4Qnuw!URA* zF94G!DzEt{hwcswdJ2}x>2sFB^dcxhV=rwvfiBg#sB~F+VNUo{YJAf7na#s1Gwi=cEEp~+%vlh?^`ks&*!^whO zb*EbFt6yfJwit(po~St#^qCdX?PMmrhQ7;C9Lyupq0kOe+mO3mf0tiyi;3o$tH$IX z!72@PlKb&6^QU(@zC+gN{aK5$#Z{qgQkI)L(Qk#dJtN0B9pMC!(`-dl$my18MLWIF ze-AS|T}q(Q7?~Qa!owFknB_IJE7hVjv&_8Zr0TEV2M}Vf!34hD@}!F!@PyRsoRz0? za%8Y)O6g#}L~r!y?RbpTzah_YY;pZuDny8adYRy%aTS2gRUIw>CBHWk3a>)>LC|0z z-73H+gOrR%;o0cwQ5vJ6O5TX$I!D;23dL6|9Y-UjsUSW1tPnx<9oQe8{+P07Q%bMU zqZ}sQxU}(d3Arf0l2bYOFs^N)J+oiVGv8<}*paUHw{?Tx_#?Y_n{le2!yh6v(LZ%} zZfIu0MB#OBpfg=YvGoV0#h_-=&)ezqW!I{Q2I55_f_6vgBw^_eYbg*P&UV!Rqp%t( z+;UHm;ruk6G~U2SD#kOTENl2#`Q&@C>!~q;L4gww1-eK2?a}TcB8k4%7T3HtX-wxP zoIob6gb=(*=HX|r$H{&Jwe(?*M4~O`m3&GMzp%rO9{=MQ4>j~6ly==7cK?Dm_X%`( zx+5>M>z37ca?tC4BdI`ID4nL!5z#Cdd5Aw!VUF31r!LKa zRM61s-O@gAK5#|=4KJf8RwVvAMz)@pSHkvV%j`m77gf_)*kN-HgG+&SqW!4eQ6)2Z zDDJwYDak*u_pLeyNt@)Q{EWLzd0fUQ9+0_zB2l5 zLGP-c8&GbWBpn(Q`=efm7FnU7K1#`rAhgjcZvyro|0&(L=SL132jcLDP17glYH6;HHTA!tC`9(s$F#bVJi zKnZDFW2q8b3#fnKDb-C!NB0E3%y`__W<_3bM2xL-j0b=<57?!OKOUF-BCFC<`Wf`d znP2BoRAr#jr+E6}OSi81W5wZ%G+Rn7JUfS3Czqmw^xH*<_%0q!Ss@S~?g-kKnm8ab zajn_OF1in|6QOO&To}fmVrB)8tqaI9agxWG5(OU+h7_Cod_SB4?nIDQ>}5@MG4PxQ z0%}pyOSn1gvP>jjY2uaS?g_m;2JCADuKCV@@`QZv*9Yc6 zK9vQf(V^^N`pPi{f+kO)No9w={EY@$oe$0n^(*||lVv?f|6l4{5Ht0Hk+H6sux6~% z6jC3rWWRhu5$&u`aO4!JOXg3E`P1UKE%c_d;;+32=GMem5~>B6@+>|i8K!zF#E6b6 zck%T**}EWro(?uzN~vVgLJNFRxlZ@q#!*uIc@BcqWJm0Y}qJ0<+N1Pykpsp zbYhpJ=F$8aa|8~}GwfD&5riyMGUx?QcwztYOdf0r3l%V^__K4 z9x#=b{>>8C!Us1`dSi^5ZTBvfN9x4)^F3T&2J&5O0_!4kv||W<&Js(eYdO`)2S*Xq z`lGNUFW+zs`*@wKZJKq_hys&MeKUW;+n}LrojA9oy7?4O$8B1#=Bz+TA+ksle|x*_ zr&wcOM5mcySS7V-En=Ia8cnGP_Vs1hke!T>OQ%G!{qKR_6irkh+Zx+8rM-c1KHuv z{4~QYprl^qOZfr~Wl=_%A+|zDF(Y181y=hp;H$fN!xp5qI#Cyc}5(&$h% zzJl_%BtG32yltp#XYusM5&_~st(IEx@d)qW8GWFnw2_E~9xbUx2Zw2RXEoo-n zSCSSrWjQdQ(jysrSR3LnUSrf;YiS;l)*HA==DaIQCg!d5$du!F+AtGVo8(M~Inr8S;k&-)o+Z-a+VQ;Wg%YZ`;%nk~+vd}% zK{izg*0t$(btySem0tGnx%7oLk|}$c{m!yvxjJzX-wmh~+b#!qzEO-Vm0!LbB^rx6Ad? zai#P1O?JQbU`eo7;ryKO)dSwkiYTjjiQ2+n zf9X-U6@z5GYcRT=2?7Xp^A>*D34-Y-KdW>eOX%=4eGLcB>Dgd4CtUeIRpzsV2i;va zih5hqJI77`9JTwCod#~3QzCPfvPBs)W0{Cw-a(^w9&GJjJKO{6)%lFtT8W-OM8wMN zMN{jElmrKChm)-PTIYI-S{$f&kiNVMu(?9BiMV|mlK-I8=BM9sEr$|=2Qeob6KQpW z`a*~E*7HXuwJcaHgpf%fgQvS=+bhrDZx?+?ik+5!Y6;P+ueX#@HN&73`uBt1n(wH% z@h#G9#V}9hS2o0D$ay%yJHy2v7=Dz)tZ3(73-%OIsahGunH+5kt39sM;Y;^CD6tjE zup1Pl*LcpiJK@Jb%90#Ri{5{@&?w^FR>k;4fv=Y2>0MUx=FhO?#-m10s|i#1cEbP~ zV`O?DVP^E5U!yH&wm!E4V0;TQ%`DW~SnC4vf_me>I5eHoh^Kd0^@SeGbA~#Hg9HQ^ z(Sy=EE~~)z6`F3VyJWfp3V=ri8BVIzXfCd{)i#yZwasJ|+51BzX}=jiWUl8@D{hgB zl>fGSDg4qShoYbz=E++=OApBDc7FywPeRl=1A{(099eeWJ4(lx;G4vHy)QYZSX`o- zF?cudc6$6#EF$0}X-_8+6@|@LBl0ZQhoKX6cipeW5Px%hJP!x~0{u>;q-{6?FEO~g z+nbNvmjC^1Jd!|KF7L6gKRgMY>rQXp-yXTtmN9K*_X*M&byzFGPP{xb5>9eoryj;1 z{YUNiuxM5pvw&eMl~_;i!kFpJn@3g}DZx&(FQw#5mSJA;A{d zJiRon!kKShjYR}IXEkG*lJNT3xzE%2EzXlO3j+WpvB2++AvhyJvdU+MT^P_t^Idh? z;QJB_%|lf+6m(y@Ee~3R3G?$yRLiwFJb$(tU?4Y!0U&YR*JEV(Z=3z^fxEq(gXQ%$ zs3=+h2B>$VXph~oJ;SG|h(}7B)m#aeS=-RBliFP{LEo zsTj9i|15aq4kk_&g4k_buoKgsi*UnQ%aep{NIq`vSuB?O4!y*Za zN8IJz&i^7E8&-e;00*iZeUks+@Y-h(BMUoYy|>vCNk)|rnaJO#F)nzz(c38r#v-G} zi#K%HsiB6pCAYW3s0pU&3;wvH3onQ78XkGcdJ4af5KaMGpz(NZlX*{aBtm~g!nyKp zw*B?xFv(?)sYjy08UD+X6|)DYcUg5i=EazXGAwgeBY-d}zfmJb{N8$F5qv%B89w56 z;87du!SErGkcjn?$iMmJmBa~(m?N24SY!k*>*Ora_)VJ%ID3Jp@fePi;6*X#eWh?r zYBD@W)jT-!P7}cO9vu%k`}HWCv&&rfbNZ(p)cp`MwGBi23f1n5KiiM`wV-WaHXys| zAWf<}YJV-jUv}6*XcN+&=kI7+|1_MbXJ|CsuMj?-ZXKS?CJjfVMK5hTtVNOwtoiDb zCM*Z;cGccIk2Hl7{x$mdfpcc*Ng8Isb1HD9>OD$n@XTCXVDE)iOvDm(I6vbNYc%x2 ztSm3yZ`F!4lU~e~A3WVcJE$>+W!x}14bNwj>UTBPZhF5xmchka8CXr^imjygnYk3^ zsy3S$xSr^MphQ7Os?Jargyg$QczT^MC#L)u2Rx!3Q=q31t)1-9zV65pwzPL|@$8bPT%E>8xFOQA*MdmEKxf#m4B{>$-Gm};t=K<3}?YX8x; zSs%wTuQ#nYH>?ZFh%lLyxF6tq)nAdJ1j^t@#;C~P_?0UoJ<2Z$x|AgTg9@jH`T%3h zB-j3!Jo|5PM_69Y!8IZkkZuUA1rQ=Q_Xb5r$>O)&hkJnGLEngC;_pb;Nn<9o^3N4)5-)E8tt{RI{6pLeH5 zMdULCHVEt8mK?Wd`=#?-^~{>iSE4HClNE^ToTl^-7XH+h@qwQO%Phy+3~;~_dVL&Y zKA!%J5nsgS z_HCTaXhNdy7bsndl`PHjl%?M5%`XMx;p4-FZK4 z=t;A$HG(?%lu#OW_IL^nJ#7Mt_M~Y-cM<(7U}rq}?Us=&NwDHQ^tvXR^14_)I>oHwzuTI& z5?7(D2Vpd*$aQBL;}9LWr&<^+n<+vc_+YocsO3~Y2F z6MEQ|9{&wT9iCctd;IxweufxM6j5i^(rt5r5$gF zT`pgs$6H%oGI5TBm8o8?AkVGS!LFZ^7a<(OSq3A~Wr#cJ_-C(hE*^4xJ+4UM(KIZS z);o+4+LieBn)!?MFL56gb^@jScB?IrQ#nzmp-}q?=xszdSkc+9O=i95u8{_@E*%fK z2Zyo^Eg)DwkIc+0&0G1T(!e%yqE&1wc#g_>yOa~b8uWG@U{=LEz_9Y*tZlJ zHSB?hYG21s2ckcn`|b;PqWze`TfkRtS>2-K>Jen$JAR?P=08N0pezyHxmhV5vCuoe z7#^NxX(R$`9Nrz(?!C)s_(;XQm_REL;>3!blMj}od%WydSb{u~>c3q=c0O+Jn7$qo z?wkEA&F!(PpP?2-i?_MrxB2)dK35zz@Y-v*v;2E1hJ}8(?&%zHn7qWFx<7{aJ)hW| z9!!XS_jYvxg%`Daa+6mQ!-pH?C0z5K+d8*;99~(Xz&YC9+LeBqTmbK7!fn^+ z<(;Ld_{+jfj*+qq=p#mc=q_2}Du~0Ux`6I_TAj2`PQL5A6Nt?AbNhx$CYnX_MIHMG z2OUhU8}FL;yG#FwiA7Vntcn87#{w>W#_V4;;@xc;WQnkcnsrbKvPX1~Ia|khqZAO6 z^(q$sICr$IVOBkYAiO@*C6y<5yPRZ=wb9Krcnj|iCM|Gz`EMS%#~4MMe#}@S8>?3F z&XD&~-b>D=O*ISm`TFr3t^Wz)OI;5i4(%eNJTW$LSNOAMG(yU9vbvP1F;uX#^V<6H zL>kk)t_p06fXkC{5??-2(}3F26urxk-xv_&#U{73zUyp{&}iDU@p{ShNrHskl_-az z(i@*tS*LF-p}S@6Scv9VhDQ1lRMvs5HA5?Uq1IB?$stXMdwT*L>t~ksHywosU z77khSr#2Ve%ssV+zVoBCSkJ6T%Ek)o4IH&&u$oU7_2GO-%adD4gN?S*yP7-VJCp@Q$eY5z4jAN%@i-p5GF>__imVoKYWuc&Sf)Y8Y# z3m+MW>41)}Fb)qbB+UQhadCL3x;F3924X$Cv$UcAonbYz;>7TS3_Ueq*PUIj^_CL* zN__qh5Y)D^t^cr%y@WNq!|m~SqgOdao2uB6b0G@!z$>BiZ~Av6Ix_a3E=GM#Pty5L zCoVf~(~o1kMds6>E4OC*03;%mqk`ucNy4q#$I(-odJ2a;8_@o5gK+qt+a8)m*l(|~ zJ#;FQ^j^z=J7i0pn^UsYj8~~~?}=t2R-(IC#;#jQz?Mnoj`YAJJQZ}996;};>~uJl zc5_`>`S`j{x!SD@2W^%TU5MffH?t3gR8qi&ByWEtG;_3E zI~Fm~Vsw9EGC3OAoVUd*N?qY~(3y}Zd1Cd`ze0j<{=1cte?hK5?LRExXN*hiXpdiQ zSgPk=j?DeOP;w1qy&d*o7506RJ=z(CRBqsIgZGjF!y!{fUwg@ChjFn5HuezYCR1c2 zhNIssuM zicI{wu>T>DM)3JZ5sNT$M)o9&YkN2G;XE(myf+H3PCkfpB+JRg5)@f;8LZtoyzAy% z!O>u)eJsIRR-H+y#4y2eTT4sR7Zl+9S)~PXUgJypua+LQB)8L!D-XsbU3tX`?5sH| z?!0*}d(fACg+W(!aS^!o7=SZe}jmhj+J$g7!2euxUH@^d*Qu`3x4GqebbpUqbqo2idqR z6@QP@`BK}u>3^%QtE|5+Iuk>fxvqcHIQH0y)V=DiIK0W}t;*Tjo=P8Ne_=gjYpD{{ z3-+PEDov$@y7TpZ32O;^_pNSzju2`Jx!Sn*1!AR{(1Nq8dCf+s{EYt-8KmZgP!2KP zz?&4D`fx7w8Lecrh>I(}tTCw9#S>$3YOy3o@M-Jz%T8`>B{NnGEZba^E{oC+PJLGifp(J*?$CMrKAEwX`~C`1_Wx#Cm) z?AU(^#reeZ33ujWWT#LV;VI46(aNdC0-rO~uldB2iT~v9snn0wUqey`@hXV!*5DsJ zThoW>ujBZEog2KrVypY?I*3%H!Nq?w}-`7rO)155Y+h-nhkLUdZ8*RR;kNa()kcXqX z@pjnJFgH}*Z}SuYd*}m0+piDDu~S^$nC*SUS_Q|~NyQK~-P%Cewdk$5V){~$ePZ={ zibn;tk;HM+6Y2AxaQ{l&0=h&;k@p~^{~ew~@ul3&?Hy<}0K;R)%s(d1h^fQe|Ddgr z)ObzLtW=~$Aj`gUAD>rE43aXp6H^?7gjc`Wd%t$1D)9Y_mX%zmfN#ZuUsP0WY}+$I zi}1-ot4|_kbJ)7w2{Z&#n;|VTm4!KJ%1hi~Q^BqRCAo-2D8!(KtJk#M_p+6}pU;OF%pb>j?Du@cJ* z0g6|TjLy9=Aw66}J8reBqz7u?gKsj*2BOzf<9N>Iy~Z7|bfb!!z};YgmB1|BuN54c z%#bFfuegnL;BCe`haX|T)o1o&IE>|$yML=Tl|?ga!;&$}KRIsbQ3|RHr4`eCC8xJx zEzlYAUZ=0Zl`b3Wa!X4&n=ZvtkI7-CEm3Fcjj8RllFQ)#^rmqZPt)!NGqGiqZ)~`P z;HxfE1V(Q)giIQnao`pz4|hapeMXQk7Mp+*zer9m1TZ|j!W6DounayD;)VhvIvHHk z|0X_X_vBzSy_mx|4Zd$pdT&1fN|ZX1-CC0og6ascemZ0Yhb{VE>20KXpkNNjI%fFZ-`aXy=0{5RGCtt2;>hjqW~|g z{`#q;=$gfH_eTrY-~-gnw}|x)n0AlBV$T!trC4+2ai3p2?qWWDH7h0suqwmj$p&ca zoqv<5{z~zBaEk)hT3)s$Tg=iQjkWE177Pfxq0Ff21mdGeKH~DNt;YcVJ$j(EfW9qT zIA>IE{J->d3vCZd!5${q+ecE|-fxT#HTLJjJ^#+JwItuH#=~PTscW6vl;=`iX^@RY_EdKGDiR7l8GXI@UXSc( zJq^A&rUbw}|Ah1CbK?hRwq4SS2n>vbkcD3K`cH0HRUvkdUK)5<;*hO62Hc%@`&D*_ zMTW}RouqlbD z+J@{Y^^J~)TJc0dIyzk6J@5JIJo3K&!@YP#R|Hnk`*Sv5+(b8$<;>OwCS&(EV)C2$ zcWlAM9{nM`qt^a1_w~tN;ur{f?@2sxW#ve49lwzbsX55|KWCLnizJYj`a>SRz8xxY z_6$K5X+40aUD37M6TIkxNN}9E}Xya6$vUS^i;CExGhF^ss8)|B0pQFi8 z??6{XE2X80U*e45usIJIPvg79lh=dbdqN+*LDMB$Jf1oGMM1NaFCX*WaQn@j(;{}f ziCJonnTYm3hx|U|74o&_8k9FK?te$r392-;Bj(X^4p8_eErmrZ3p#%_Lh?`zTP;eC zzRs_5YYtwi(tb2wwJLf*i2ZsHW7H>pnAKYEw0po~`6h@>g&$((OR{gSMcY$tQ2Kt2e6+ znbhL^yNExxZd61k%3m>+zG9{+u9d>z0zn2YDxV(IkDWaj(O%zq;+P_usyQ+T=)k4D0SRNY}zRyf`p5bw;7%hllU|(v;olr#(FXnzNaa zR;s4&Zw`fSXy^Qnygk3_w%9+MY@Kmfd)bHn*cPLGB8;QJdKQt=XVuy!Sa&@=8vm}K zTRg~|p26Er_qcQ&_3>i?p-?4-zQZF^wXN?*V@iR_wkN5YJ1 zVF<77%nFVOCQ9HIej4*?3vDQJa{#HnH#F4T@@;>;;>R&S`v;{Y{usdZ^gz8oK`L&3 zmV0sSUE@b9K$R3fnH-^=_xLP8yEy8SQl?y-+P7R0wMTFhp0b%MWzy2AoRof^c)q9O z$p`HAhsIW(ao~^PuD62(@W&p5^!i>AllL|{Xjk5CubH;T& z_*b$;&K(}IhSYud#eqAASrsCrCI`KB^Mmu~^vVwvR7)irv;ATz?5+dQDi_4;F}0y~ zYQ`=r8DUIHQ_)C`2X`Bx8m515=0CbEDViSRGP#qDn7wLwry^&0A1<>#XAWIO?(@xI zg?M(on-@RX2ifnfo|4w)<@5gcSb5d@ior&UTr=3{-f+UnEh?Kc)#mp`;&X+cwX7T) zm^eA68LO0o-%S{^L*MO{wD)DpOm0P|$XgPvF;8`05aYpJl8JEw-mJTcX}!(5?sS>o zK^A5d1zI)j8rfL5|AxSXRWlrSeiHPEm&0B>vR16{io4GEgo4t&IKuO z&r45Dd>)+SYC(F%_>s_?5Dd9d)PGc(xP@$pOWtwL{;K|B%gM_Mw;BXM{X|| z`~g-aE-|=t3~trbZk(zTXV)f;tg@%SiDKGz$1`r2{82P}ig7n{?-wK>*YTK!@@SLX z7WMRMQj}*n!E{Hwglzwa7-oT6j2H*-&6~sZ+;mA`S5T|&sg`s+R!~FXbW!q)<-*Yw zkd6$hU%LfWdUWqWYeI5iQp+Q?v&ot5${@2s7Su(Zm?S~-coeOjzAs~c?e}R2CsE-& z6o{OW@jWJdc?wwp7B*V*YQP?JAwmfi1b!>y>o#G`%0OmR(@X5tXIZHU?u*9vT_SMP#zoK``NEhtm-!o_r4Xexb^tWHKIZ;jN^)y~<`A@D1 zyn-8eNfX$i#iF9@JaSD_LhhR}R^hEwAZtl zmJt6jTYV-L3b}4E9gB_^Zl-)9@Go@u%S4Bwv0Y5>YwWeL-iyyAnhbUi?zGH@T4s#G zr3gNQxU=T@HpA{9QH-cQ4C`)RI>ZcZqTFIG4RB>GV{K6~dPC~|;agv37G%tZ3g_X& zseu=4iT^1)@~~?9m++T4FMj4J_CpSJr7kMlP+xCpy1v!;BzeG9e(5%*NwP%v)upM} z^v&#es}F8#@XV^vi+)jvb`(Y%+u?5}oIXM`Wv5+p7PQh)Qnt7*F;Q;%s$Y<$QCGT$ zk5>gdJLhsJi*W7^-tiKNtJX{hSX?N+EHso>Lj^Gy_1uZ$hE;KD2H8ZcNnbY*-{K3D z)AHF};P7s_`l;H^w^pu$OxZs57-4E}TFTK+CTG4n#RZ-OsC*HoOxBhLCe59F-_4@* z^+*W(f0q}0|K5g)4GidpL`}(DnS8o$&*cW9YL?)G2^JKk!a@P~EUzh+dr=GjECH}d?Ef4(hpiAJ9ntTmYX zM(N2*R~=}6nXU-7EaOCZjLgcD04g%M8`?ZFjVX z0Ny-6CwoYz2RZz;>0(0wDnSjO*W481rPfaczCl~u`ZB&vixW`dT3f`oxH90PLOdC` z4gDIt^4Up?W6x4CptuAc-vVmALp9tRHWx;*-A6Mgyoh9(oG+h9bW+5{2(=7_FBi_o z>y{?$BXqL5c{LajNB<(tvfb&G9PLx|B=iK5GYz5+K}q9Ql$6}gF~ zA;W^Gn!5OJkxJ-jPU3UPK3Zl&vf#EFcM%!c6X*H_jJCkOzr0P4^rwasfycZIdd7Pc zgb(TTjxQT17Y85GZQEGhJ~Oz>3S7DiPVNzZ(f?tg@hUv zyDd}diuS2t`f9t0(LJN!D1w*{~pr2tm&hq}uQ z3SVVhJb(AUrE=MEHuA#8KY5Ze;xc`P6kuOZ<7J2n-SD<)=$*Eu{tQ91^IBv~s}V_7 z{L20Vb!2t-GEodF_2X!L2wCbl9VhIs9LPXraI6#9K5>9i+tLQAX675NY2f8Yz=aeB zyTeM%MdEoxYzY+Kgor_l6hxdX9|SbCD5)qpY-)B*SAV__^?a^TTCY>&6!D^lj}gfn zkHXZ8nZ~4MgLV!Vz6XN|*Jidhmv|@2%_rvE_NbB$!aC+QZLnW=HT`#-jp|o))fcFr zIf{`ddI?*QFKA_5WhfG8#`k-;W`djC54)b2jctICC(xSs{aY&Xvw}r0O9OCBR}pKC z8-t55%ys7&laQrZC)2zwLu!p)aM>`KtYT^|lHW{Ot`by=_f!L4OOk$?X19o0{(Kdf zXLh9-I8^ah6?Nij?Xh~=J97aK^>f_vg9ps)(WbPC!QyMwy_nG%=Su2&Fv2s@8iTJ8}U(|Tr1UWTygH&Q^Nn_OgZOI zXzuG)Sp;g6`p2vLcdHOJUu!!BjGY}%LsD}1IAK*+PDsYQ1zCuu)fGk18=n-_M7MFKSi&}R5g?v-rS*H6?XetiFO>Sv(g?)d)P zR`$Z0?jy6kc&I)6N&K-M@5)P;VB7+tHPAO?z)d2N_nz$B>O?~AVXUyAAH(tP`>CK0 z!z-U#1hWGc@d;d09YypD8|}DX22}(G?HJIZLcmkqS9H{N!IbF_V5d!Yp?fI#+nAk1 zU6lK0=ZPHIb{`1ysx!U*iXJBZ9dnqtGe$0yRh_>=6~X4(7esj<(sw{*xB}#D4at!P zF%HjlVxGckJn^6?ePo)Tj`PoSF*sfjBaqo!%TRiC``5FOOHYwvZVzWZnZgXP{HG-R zc$)x#ooMr%x()77vj!UqrS<$~`sk4t^05gDp!R~$4FxgKqkFGQ z4vl`~W~aq_*m})K+Z?9xnJda)6LmR^=``&$dyd*em?2?uJZeGND`S)RVxS33!5{A9 zj~b|ux8&|P?uJ^)5@M_@j_Rpv9kTd~kU!LR&PTW!v@H6TVdyJs8NQ(9Gre&05em(8 zm9!-N$9{ApqGceR2F4cJp)V(Fa zv=Dr>5dHLou2?DMn@QuK8L{|t60rG-hhhYcqeVK^2vrfP)y^CiESum{;iW>N;lye| z8R11(;PgX~gKg52NKeX7$5+3Ici*UJ6SeQ8*tpA*UbXSqJx%jIkFX1Q*ll_LAO@{R=S~D8> zWVdisi^yAsX?No<(s$bSbWhR`SRZA*Z6yn~_&j+6USVePH9&=KjsTLheo`DPrni`| z+Y=!*nM`LJsoSjj1os~#bU^~*$X^CIO_h`Q>PcUILYHeRUB=Z>?SfIyMKBkmu5<1z zt{2X|@qLIW`Lv1BCqMm<1!%-`g8s58tB2%j1PUjIJh~jU|H@`TLRMQ_4%+tfyA+(c z6Vs2^$MgUJ7JAB0_#yqMD@ActEz+78ff#dJD22TNJyEwDxQ?bRv*k#UcBAxK`C?+< z>*$$BOabap8pCt!AKYR$iyT~+(T?zaxR;;z3@sJ>IAtyn1ptozGD@G1V%}4hFpbwr zqgWEsZ%f(I6)S^u=(zF}vqB-sgvaPd2lDh60Wk9H|Lc+L|M}4P|Hlc}G?>q%gMqX8 Uu|DcC&rmOAd3Cv}51+&T2WdAUYybcN diff --git a/examples/anomaly_detection/img/detector_classification.png b/examples/anomaly_detection/img/detector_classification.png new file mode 100644 index 0000000000000000000000000000000000000000..0a4070cb0cca8e416c5cb80b1dd2526658368ea5 GIT binary patch literal 106020 zcmeFZXINF)(lxqK5d{GQh>{J6fPj)S*n%KPkQ@Xgi3Hi?oCFb-BqAUg1w?Yr!fvtz z$w*F0&XU81{nbL>Zco2`p65RI-k;w&9>Usd%{6Dus!^jxP0kAiIZ0wdN7^@ODPQ-6pEw<`5#^=3ken6VQZqSZmTYPSHJ-Mm`%?R zt#8EU_}CKeMxlg69WC_?%#3X5^^J^8EQA@C$}o)dCWgX{YTUBdWi4+RJu;DUwl-37 zmQyxxHZ$NiWE2%46mk@R0UjIK>d`wsHn*@5a1>@d7*_y(MlQ26!cEqO4+Rv(Z~wXl zeiLSVWNT|Fz|QXA;K1g<$%eKzX1~GD&(D6HgPnte74BfQak8-0b7Zx!xdbB+(jSZ= zZe(L%ZDMI_g0`SX#?;eC+t~^;G7{4N8v3TKt+k2%U!z;tu<0FKu|pnVgGICJ8QQSl zV7rb?i=JNS=ludV4Q-HLf4)S?$m-yipTC)#9K6X=&)UXF*~!vKm{HNl25o0;V0177 z^3bnAZdn`Y*%}#&a9ro$V!h7K%E2qd{*RB4cl`Z;grn^pLlONO2Hb`>I3BWc8Srzl za&hwVu<~(p8nWth8F2D)ap`gKJ>>supr4QY=PdE;-}^8b9R3=RI; zCQCbO^8*(!G+;L}H+pPjVT)|ijbEE&Xdqx~Vry>n_ZHnWxBbs80^bub*RwDdW^`mV zGa;HJA|lZ}0j4urABryc_YqgCIjZ z4?ciJasFBq{3dYI&h`=7T14E$-bs&M*~CH5+C&Js?SDQ1uRpN;|10}KSpH?;|CooP zp5<>50JjMocrMyn8I3j%loinex zFKA?`l%2hgU(lu{qL1HrQFs^E<9C2=VyC-^T<{Nm{1`*lcJRYxpF@<$51xq%Vuz6* zUXc+#LcXJ3|A9x0d>2!^8jpwkAVrAJg?vZddFOc)`A*ODe+T)$Hu=AP^8aJ(aQBH8 z-R8-7@u~1?F1GS>E9336wKhIYNM;sOxH&yiF%^Y1|aXjO)A{ZPuLb=`T z9c<@!F-j+3zL#=Cixnk?g}4-x&0C21+;4oTRraNjqr{d^@bk}l$QTkulKV?j!I*NY z4Xx{_Ki}PhxaY38P-?=A!>#+?o_a8sAOg!cJYP02doZmmtBh^(UA)N6 z6?C7*X(}{(q!ABAq6pLMuEx2rtaPG%M~0q1@A49IQyO22s&>~RKyAGN7&|r)s~v2# z=`+#;pXOqn<$9NvP$-x>{e7n;iAgt3Y~*e6MAb<~gNr!q^?6=rRJ@!L3_IrZ_}82;T^kP^d?GdC<#!mYPn(Y^Ebh&B_nb1S*bA5a~r!NC_vDX$z)1Suvd&8-BFZhsRV#3^=(1i zDW`d@F}3R#FWT`0=~DHrw`P+bMTyb8hj)^Md&LCTXA)yvPn}Bneth_)HUT`t2J0Bj z?k(Hx6x|~!w?B0%I3g~fHmAq24fW?(Jo1>IM3Ikf@$=_W*D41%P+}{{B*$G$Zf~jh zJ%3Kt)I|E!%@Kt%6$LxLGW58A+P&f8#fwP~_hj_wE~7j(bUra+^(Dl`nVZ>- zpSk@BFJeVrL>=|@nQjV9ieYsuu3E$#Mhihk3zWLnmY#fKf-KR!WFg}y5;o+z!){0A!jB{F?x4j0RLW3-EEVdi_H{=z`O2eLA2X;2-S5I*%>{2!D@DCc&F)VbyKxSDXi%UvID(!Dq|E3u+uo9^FHf zXQV9Rg%4Zn$2Kory!blNHJWZi4+iKvxZQq<5JLwWblGT1v`VBIh2oAv-qySr^Xqo( zTtYS}Cra$g2iWM-O3O>jyQfZ_x}#W#~9j#l-t*LJwO>xoMV5iZ_2B{Iuzc`zxg-PmEl}W#m@p0+X}a zV=fhIK5c5Z6TN<)7eg1!|25U`uYO92pD$&dfUNn~moS9{Pd3J^J^3KU%vsx2hCVHf zhsm^-_V$t1nGUnom>bzcmvn_W*FsMrf(~{EO zU6I>){9TqmGL0j@Oo&XQ>E$8K)GNOmOBN=j;|Mc6Y*81K71479H4kTwgIH zl!{{%9X)UqS;7vVH5iXWNd^UU$M3>NpTT8c2S;nr;w; z9SkN85@cQ{s;9FvN8$I+d zpI)s@Alt1Q8F8%qoSD&IbXT?-8@s*34-R%#HoLXSxlK02S$4r=Cn=exr_@AqIlhueu=nL? z-w7VpLUQs+Iwkff;Vpmm7%Nx%yepMwDCYx|g3LJL-TYh#+~18SK1YvO-!rc*%@?^+ z+W$C`m#L&*-Xp<=pOkV>Z{A9}v)MpoPfuun`nGXV<1u$HW~|cQ2;Cb6G=`_{&kpOnINDgEX`kcF$b#>jB7 zUgIs@8HKM!;ksfY!^SvTi`dxGDX};mxqXfw-=7L!9- z?19Rqo+LWj6mgovX>(09fwT5mp?1?@P!m)y&$U-GuMeADJ1fbGsW9VDE7!lqgJm!NX zcFQ>Bq;QVTBZ^D!9^vib7K_)O`q#C4eBhDdFEQyi`4ZG9wVOqlrCT;Rk={Bv;8I{R zoH)I*KIhTT7e0?i;9y>|JIpcA+pAw`>aiEy+{=tVK%TDn{TTu8_H>=3IJ?o_tOnz4 z%xQCnwAQ493I4I&{*Fe*E!|_d5BwB2JLpYdI>g1=8&DZZv9}}t%#L}T7D7O@c<*(u z(vMYitoE1m>pDHV%`d4r}oGepl++E zg?UhzMHG!V^?7B*buVtM^&YxZR>*ADsoyjR(Ouq}NfRrWSdAe~SF9{|^H>Yz)Gyar z^wFrySQjai+ujg)tS_iH;5fQ>PzhgBZ=J3pl2jg4rT7Bg;I{brSx%Tu zFMmW@MJKDh&ywI7cEZ@XD{z-C6`TKF7Hw*tF}O! z2@*ud_CLTWSyoO(oh?sv>k)SIW@PysSms>6(Toe2qutYQCo4NC;wT^xosC;rc%ZVh zuRj1zBWOPtdw$QW`?+22m&n900WRs~wOV_}@>aGTk_r~}km(HETjxG(mYu(_^yt^7 z4P6?#`Ly1bLtS%krZc=-%r&eFywx7$5V);Q5=BuzD)Js$iHX%fl3wrE$B|uTJK+^= z>7SQ4-FMbIJR(l9#0$ncuB{KrXKres_lNvMaD%+9`xDcvkP~jG$Bh!;Ug?}GxbSy6 zvn%k_S&y-3ci+zK3~JiFDVNgZy3{fMD9&ye0Lt!(Ivx%Aph}9z5H8MT$B@Rw&^99L z%?(y9^CSKbk|ytK?0AyLHBUQq_c7MdW~R5B`(<`C$aYExu2UP<{b`@$OVj~yl!b9~ zZ>ZI&*dH~@z+iITTnJ4tGSaL*YD7FwH9yd6G%AzDn&g_5?Wx1}ZGJkVa#dyDvLmNJ zc=4ljK+iocFvI!1;b+9NqBeFj11l{xe)`=u$|?A^V|#n8g4a*WK*kz&H~w*x=lyo= zN1HjiE%MKR(M;SIh(is;kwZI_U=8@63l2j?6@;SU({+S`Z{WJxwIX zX4g9rl)PE~{`>wA`6ug_UkBT%J1#%1Tzt7lnJ^Pf+(cQjn;>YdU+b{^a!1QVG%x3n zz-<1cWcM2r){wth3KC5O5pQi~C6Z~iR-BvF-B%Y@4FYshh{#yuxa>B2k>#(^o5%cH?xu zUW)B7_plRjSw7Ez-kT1LKEpv-Kl$Jqccon5@U0YYMrC!erHJ+{3Fn0AL!#4alg>w9 z6s(SvfBfX8&T)|PXs&GXXGAmTVp1flj}kcCPr+aN7Ca@6Cf-<%yYfbjnsnilhmQ;m z#kW@gJ-l^wAxv`==h}r_jeL94c0zEOGdLUhxDNzuhcWYO-y2Ipz+%D~+xO~eLN#ZV zJf!f~Y(|6x3nN;?Rcq>VO%$_weLC9j_4p+{!gCXs-fPU?u)Fx1-xYhZeZt^|JJ^=} zjr!~evT&?+4e#;SnwwhC#%d5+i~Ui7GS%~2*c%m!=3o$v(@T1c8TnJ$f3A8{_AS1Q~1Yn~gd?~6!;zHhB8F>f4_ zLEDNqQ1k*<*(4dnoldnZtOFEc{Q55I{6P7%Y>?}E^K_5WY>$iaH;D<+Z@O1>?$2I( z9(?Os{BF?zrp&A7jt^1&zCt|g%j>MCu)Ruo1Ooj^d^Kz z1miJ`$%=8)_aVY93Jx_g#cwuGQ1u!QdkWX` z#entus=rqP3@b2f>$zSwo)~Rqu=t(XhoT*dB#?&2KrZpZm2$G?6EzmcMyv(`K; z`mr|KhC^nU(j|U{@tYs<#1GL4o~G)udG>fxlly*PIJcQn8{cTVKiZ#&d}E9EzOTM` zgA^; zzqcSp-Gz30>CjHAuOqqZWLpZI-o)DO{g1;8WxE2j;_S95N*LI@+ z`@;AZ>LxXxSh zUE^(h+mw*cq^zF{Z0;X9>Y${{kX*i&?Li^)vPrOtQij%^lF}%AOlHf-kbw6i$e?R9qxkuE%f`abRCGtKT zCTnm%hbWY+#0H2* z<}{%8eBVSJd&qg0V=lKqj9YLa|~IY!!EkG-cP3T_q$U#=5V2MjN;)-BbgEnS_Y(V z<_|gV_iX4^L@Hv2TQM4wPknpr&9oWtZC}}FE@1ivHmlNmDKPC5iPIE*vMFi}M_SFj z4=XY38>%O5c=)Z|HX?76@V$^}X~TR!NBV4AaAn7)sS?Stz-$P0=2WwQ>c!lIr$dC>_s{Rdg8Ho#D(~T@;bz-pS_?zL>Nx z=`dci>HIuk)JiE!iTQrf!i%R&(>Z#QyN&j0&7$X8W82yZJ6_7iR)dT*AOHH5(S5qdZY2^jVHBUKK$jVGBuKyhp zgRT{A;i1y%FJXi!)(k!>hav-FxnJ z$7jgdu+=R6;YwlzCTfp&Hv_*fRy=8;~d*nkqz2}H?VS(~kbexmt@O4ok#?S?}FJPK$i zAH(w0p(r4g$Jg+)lW{CXQx%1jd#BxTQ0}Wkzo^S9d->OV18vamvEYj#J??Oc>4K* zgO2L{E!~m5@gL>64q-gU?gBj}^-sH3sVVUiE0@H<{;s~_tCeMa*wVU&OJ@!7_M{gpgN;YZ3mj>vzku$Qp(d_KG%XeEv><0bSg^cBl_smgaQFHp4=ZDy;X)uH;#Z5svb%H0OkpiroeYUGk)}ZB!LKoEy}{K%T5s z=OO#O5PpB0ABOt`n2pA3VvFT4M7`#WLsS?uy|IBi5JE_H+5;VRl;FMebu zM;Q05U@Gjs*gs99ZHXSaLLiXEB2YM}_I}kFc2510hMd`bAN)16Og$LKk=gR$sgAXZZ<~d4EYU}?hd;n_9`XC*xNH*BBB`fuv{$T zS5q}zEZbn+B#odMFyPfybf(bV9`tZ3>vfLpoPE2_OOaJoB-?Nva^&dO1WNAb<&M&< z@!qbxe@e7LrK`GGey28zdmkh^->mtQQLfg~=fkN8MyRyu=X!BF1vMUvGppmvc`sQ% zS=Lng#mmEnHd0BKJlf$M5y2tShY=fp#+#-PUfJ2uV0&@fCl5I3FsVv8xB0fFnwth{ zzhYg&!*Zt#Fqf~E`04CfZ1UFWs+(^M5>T6mA$(w^V;*Q;ozB{JFNe`yF;l!u&|jM7 z-I#0f-h)tws>j(u)CRd_dN~aR_;~_XXk0K{Ux^`C`eg z-(z(7eAno9fGV!?$|u)7ryLnf6LUP#;)2#%$I@sSP>{YYoxaNQq1QI1*)kBEFNd6= z_7Ag*ux!t{8JV1{1A$JrCg^6^qskhzzcwJF+AN{fF9w2I#XHHbF^A>iAe7C@3ihjh z5D<_aSB*DprIKB~%p-7-q0kW2C46?w=$(ju+}fyzoKTxCLCa&e=K4;V^GB}qMt~~$ zg7vHrrc%D~OSs{b>xMW%+56rVpUq7S=pMZ*m1Z-qJ0tO%Bpuu>m6pANhA7P?G!92y z{g3U_W{nq(9WL5slAQ~E3`#QD@Cc??j9T&o&#{VgWg)5Hhs`+6di5l&FwjR=G)ul% zn>QFzwB3x|o$PDRtx%2=%pFTza&=~M=m`;3Tbl#DS!+#Br$Lx6Q%ZR`%gaT^gePIU z9yCIpL}57-O$PQCqx`c&8^z8>X3mDy(^8$}XWMSt4-X|jO3@69?hIlvt_ebbtYDRa zc(FHkylAj}GyjreqwDSW09A$!GN1e&LiVKC^N~#Piczqp{8L@ke@3%~=({{aon{{- z_d%k(S+(qMarS*4>^Htiwi5)feem4R`VIBF4+*UFojVo6W_E^#v`AfevzmpZC6#xo zk>pF~CTV^9#qxXYr_gDiJV{o18o~$LF3lAuD9(kp71rJZ|6;NGR{8z~s|S*o&Fbti z*LQpM(<1xnCqNAyv3)T8;z7cW`#Yz6ofG@Jd$@{MLMwgCrzF@3Gu7H1+mNi<`x@fp z)4FIPSQ{6d6{}$zS@{v@vDaSq#GlkJK=ez{a9$`WAE2e6%6|VbkAu^To1GsklS4K* z;4m){7eris+GfV2J~Z7b;dsS<02^cN?T)LnfJ#ujvwCPU+0dSq$!_l2Pc{ZEra^GV z@mu>U=4;M0T3`ALOUaZ&WAoLG@=BuJOuWZU{j;L}k@=e!tYOyIE6+auyey}jMmk~R{m%dZq}hpjgL|96)=Fky#R_M@>bbpvBjxM z*<(flB^N?p{)U0<@kv)EqoxX8o^4BLLDTFsK3AUm+ufkw^T7zTK!fixSg_u^tRSg^ z-)lfkQ%%~6+*{_?zacHIQc0xocl+lTR&M6)&K*PAPCiXBoS}MhTaV;FZs_KtbH{a5 z?UKr2w-BlOy;4v>VXJZUE1ih*3*xiCu>i6*#ehyo(2O)8UpI-qV4T0cpm<0(P^>4N z?`B~u-k3JBK)W;hqRSI&VDno)u&n@&{k2U&MyDr^%xuDXoCzcAi(I2~7{aceU z&qZx(@41wT)0_*%y2*f0hMu?jx@*S`lLU4?r>1e4KXiG6DMLKm$bRc%Zn#oI+f&h^ z5JX_mUiV;2-(|g!`qI?m@zm`t9?Fyl;q!0h?R`grpo#oCcTV^B?&t{PuXg8rANk*- zR{gOg37zOWc)wbZz2AF$bc2x|^i(qMQ#a_;n+hSrX5(T`yURe+RV7yUF3~RPFsjQD z(Vq0Onu#zB&|PJ2osIkbeqs#auj{(*RDZW~tM>DRe>IAs!z=xOVuLzzZ%pZ3Gs0x z7#5gNY9lFo(EJe1-#u`0r~TbA-Yd|&X3%h!|GmMUG4gceEwX6C&NMwbW$5iA$JmsE z-6UrAvgl1@Gm{eQeMRt@HRoL_4^%j8Uq}x+g9^{wfy>?-hb2NFiZy(_Lpv_9Yw# z5D}$3&D48-qw+B7qYpxNt+8V%%e!y@BE|442K)@JBggy1I)r$bvC;=;GFt0={yvkz zLx7?p2KOB6=y-4frDmnK;NTbxEet3R15%$I zC=#DML5#742kA*kch}UsQK*Jw@ClM|IG*IR{Bqfh;#9CeY@hB6?A~5B>2Z{pHgfim zB!Lxuo0gi)eH#{FeCE?}kFDh2&d+2$_IQDPB(+5jM|x|CL zDCDZLB>|tsG!AA-?w4@S!@);|DFnH$!vTWN*x<3A8*nh^a!|m_aWv6x1|907A7W1_ z5vJ$Z*~(>^^U(W`BNu(?nkzWZ>xPOCM9%!YbM|;_xR|{>fP5N3M;e4(56B*w#`6?F z&NMvDcwlTev$kwT37>|A8%w}hhFw-#)WFRqO3WRHBs zLx~`VkWvGL=_K8S8L{8sG|}y;P^a6jaFG;(S>cQMu8d)W_82d6UW!d-DFExH)QkRx zLg5aMw8(4^T*Nr>Aty|H0-sB9w+20Pb?#mTnAxj?Ls`h-MTLNcefjYqcJZ_0&qkKO zy{fV1_s73bT#6%my7v%nx`Nzv2GG%P;!?1$uaEBL^XE@v;pkVov(QRbP?ftGoXhu#t?3dKi#Fv1^#_VZgqOYWPW zF9o@7mW?b76rojBRWA}-FE3QaknJ^6V76*`9`|3ij?2(2AY9~BFQ>z8)u*Xu^jEmK z^zNQOp;8gh^`vBHXGhn*B+oN$rR98hH%q5nv(Ws!1N&gMUUlK)0Uh`4iK0R4CV7vY z&K@0?$32T4`z4omLe19)TIcV-$w7)j4_BpTEQnwXdv4cYi;=09qO0p~Ky z7M~E%ZcVUzlo&OIEpJ>!p_GZiD^T3cVXRv4kH==R`Ch{9_38Emjl73e4i&PITr-0u zt14>R+;+2FQ?V|K8AZeWdB*Y@L0Y+?Jg!^IN2ZRO z_vf1wSqx>COh&pam2cKk=(^(eHi?Kx^$Lp`$=Z?{o0?oELe*&9x5jb|>NjROr^Z9% zGj%cUu2HWdBl|P8%V77uebqv1XFsScwVOM^NDQYLkyAwRrM=}Yn>*`saZZyF)6dVs zo<15Zwi>jvX$oU?S)b*G6%f$5T7vQE+}7@SrBOKGX?MVY`kWWtCs=g(zx#7ia+{Kk zJp7QD7;W9ibol6r;+YRov~FwdBe?xd@Bk^xbrdQ-6#xiztR9~HnyI~8b4LE|+v|Fx ze?EB(OKXe2Imlpc8~Z0XmGH{vOPJlErPueWRQ2$*Qyqq#rnaZz;=oP1)pUmn%z6w0 z`L9%zpW@r!`p#ZD{r*nK72)mi5D)Bkx{#bgfV%UL?olMZG=lc&I_1v8pPate1%Lvp z3!Wz}E$zBs%D;N#ib^ig^lNaPjX7Q1&O2Y)#xGyW=X0Ah3rxvJdTA;%TUuIZuRr*6 z>&N%0);P?=FOgU*wmR=PgoPC{eCjYftD3HU!)0UshC$uhYuxjQ{_vKB23mbolEO6ak$wo)V&{MqmB|&)~G2x;|9xi4<0I2R^hgkUMr#Hdem0);ta(# z^=ylwQZFyBY+e#r+Qt+4-a}b;clTZ@0+bk^=~N0Q3RSDDQl!3=XZ562aOu;jOSfz4 z@H0h_Rc5fq%k)DSf=^6+iQ=^wEOy`9F)}hrN=&TT%x!3HZfa=o2}={ zo&k;tuw>Bn@g9tJztFr7;ObzwRGlGVJIGu#8;e7`8v|%|K)czllBNKM5FW*Forlgn_6@S(SnOeyFaQSWk z@37-+O0q@SLSdyh0c@rSH#c`hZkB4xnYYS>)6)I_w!gx{!WC*J=8iv@ls;U$Uu+4} zD6n?40`AugrW9K*u$gKBe-imobLPyIyXW{UA4KsyrsR6`>Ortt8Td9czU@U=TrT{B z&z|xf!RY~nFzM;(Z{EDwa=*&K$!TM4ZEa&S)f|y$J<<5-(l zcxCJ3;{!-P{F*`LgM6Hbe$8{(numslHEL<0(TtXmB=pd_z!pWY|J6H01;Ijd?FscC z?VXbm!yI+W);KmQ3 z){6!#X$ohL1GfQ~g~ZSYP`b5~0?D<=v^y1SjhLgFR1`Zfvj0`+NrCsF(y)fsR^5O4 zM=>t`0w*UYBO@a(uR}{TKZ{ysb5j$M%Xi2-L@;YHZDn=!EkImWja)+r)l0Yir~3=c zZr{G`TC3FHbjV?G@chO`Fd5;rCK{*^OiK&WKA<82F(n!H5rOFFk8(~fv#;q8LJ#j#6l%#1_6oIDYIyuq_;eodAA-0UCGu zns@UVsco{hH;}1V;a?Q244mJ2ZMq#oc5B$@{jHr704v{WUJ#wVrj9Tw?J@)|J)a&- z_BFU{kFLjF17ln?mr2KHrT0LAS$Je5rDT&qtk}1B&krl`AJxqJ{NNCOy9@ISL^Q-T zz^s1iL(|KTl=y1!^TPcWzJRc=~ygu6v{)X5F7LnmUr~)9E9-0~@ z7J;O>ZV4ToVYK^dlrOFG^fM}p;7fGk|E;4S}eDaCUS8T-Q3YuB#v$Uhjo^ADmEg@$&i?RgQ`Er{>4>^h%--~k@{l)HfwkHa2Hm4ZW5tbf&N72X_#$FfRi zQ(IyLc}+SlS4h{M(0(d~_hn2?T^$HlVXne^gOS$s6)Y7Ss~^*zNE2LGsH* z-j!Nz%g^XOEwsWifNTHSPlXV~FQvf%Tl>39n5}Pqn-KQgJ@-3-z5xfS)8;k)hbQb1 znZo+GHE-TTh(37vb-=xw-bV|7H0V`7`%#{>OMDTf;3OA==PAF?mzSIhyywoHhJWF^ z*+rzOo`)heuQ*PPK%oWk2dK}SC!?B^^}B9?FTn~Cnt z$fHXp!kXAILcw#R(k*%_3JNDTHg3Y7Tc)fN5)ttN`!j5KJ=2*SbneD`v!2ZJ=gp(<(HdY>oi5n{7{!%zOCdo1oVltk1-+_@|!%-xhnU(6!$GTl@!dFflP{XlOv5L_8C6y((0wKroSf{!t$+ z9e*^f-|3JnGya5<*e#8f)zzMK4NhPfKfc!k$D|h&6kHT7FoE0!&(-;#=Y-@z&Z25Q zP{=wTa2R#^8(7xVU)W^>J0tJm3^m14@%KRRYWw-#8;gDK4@E>oz#rC^l$1cBs!?KX z1WBuRfS^0(5G8X~eEb6-JOFoZ`K-PHQ92!A9&gh50j^Q;TaUxL!q~J+omMB2x$+%B z<{Ft=<#b3%Q870!kKn|)>+aYsNVwpwS42fcAq*lx%6`!c+y^XtmhC>DQR5qADzjZo zi@FIPl9PeY8*MF*K>@4{yk8*Fx@nE8?~TxeN-_ z@$X#JJcN&q)xsLp{+T5L-+}G`MJcs_?KF+U;E(yd)_vH{6&5WN-wDM1RQk(Z9LQVF z^a5=F6bPghcK({K0gPgkp!OsiK9};D?d@&I+7ZA_p)Q5Jc@+~wGg7S5elv{?ETd&n zGLZ~%XO>l72z(mZP*WWZAoXSu zW-p`q2I}%>Kt5&F?E+5V82HC8uXb`0$Rt>znC>2>f;^6U^A(fv3~-g1Z$Jh3T+h*ShzrC z0vsyz*{D}MR0@%aImjppJYOwdu${Fg#yKy3J4X+tsC#;Fdx>RAR z>;JV@6TAf}8X*Z4Yf0>~u!lc@CP0{cu{6y_`3qgRpeS5)p1+*&5(LcqH z;+X;YhrP`Fw9+^LV#!Q$ocrn*USu1H0i-==Kf*SccO>3{XMLe|fs_PrRKB+{@XMDF z@VtDHuN)yPoUKBfPAlWUVlG=dD_K9gEaTHfAin)?1-YJ{UT@SfrMCKZc5>K43i*ZL2M$W1H*MEchm|4n?74}mJ{E3i0FNQXRd zd&u^JU4 zt6RYlRUR%4m#gr}Klqm-9poX-dD$bgT=-lHvQXq3bf>BS00P#U!$U~T4%XgkDVA=r zeDv@kDm4s(^c$HgpVQuYPn_#Y11N|{JpUB8t$MyxZo9kisBe?7g^Jh( zv3dYGuyVS4z(u8(G3L3owEPew1@T&|?way;oObxef0X75G< zWCb9x5XzgdV-PQ8fd1gnzU41a4e;Qs7eGsfdX7FMZG$@hneH?-sOY|nsNI#71%ZDE ziZe#p$TiTpEB1Hhu56<|dIQ{aSp(72fKs+Of>SbJ2R9!>;7BGxS7%eMDE^Y{3B4lO zP4OI0`uET2;s1x^q{N$x?~i%erg+82lihs9{G8s4j46zsuH1V~?)0IjNB%f=A>gum zUP*cRm!VZp+d(xBVj|u32V$_6goK2>VWJ~~)PI~j!ldv>;MU0_5`n-C zTVn)~Sb1@V5z-d-pLTm|&Th~i0bxxPS_x3v((qef78H!yV1)XsfRcar$txpOCfiPVe!*lWdu*Z8D?@FW*2R06US6jp#zDe%H8 zAq`9Rn8#l#fDrnFz5-QCvV2@!q}$0OH|4zWh;N=8mjjT7!bmGezZPHu(YDI$A1Aa= zJ9OrYzc>vVizBop80BM;ICBy=L5Ald*o zx@K(g3TL28BLXxM+8wpEweMs};Y}*n<1ZZZs$pSgUxJ7O9F76{3PS6d(Q%>w7?vzF zEDX`He26GWD7ij@)Cd38UEY<$+91WPB?O5e00rfqzP^6u{gOX+_vw#$RVf~fe@OZM z{Xro3AMQqU0EB@`^ycclDzr?$oo}_j|B3!hf}1Cg96u&&3?|-@mXjNXk_sp$(xL%D zfsmYetA|i6MLCpN#T2T3-2NWo#{fNDUCRBu@GKesf!!{bK%KGCDvE24=FbB)4;BhF zN%}}wzGg;XV4y|&`&v*PfnNF6T|aVD;rY)OmV~}~wLBH)0o7Ikl-L23+{_xei5{%x z0k@SvG6KXTfo=^@2m%snDS7#1HyhZf;A`jF;2X!)fAh9jA}M8v4OlSJA}U*|82IF5 zqpyE)Vb`1fm|}FB?KApggzv!xp;AXgYv}%!0({Dg?%BeQW6cES)_1_K3LtNee+~v_ zD(bxU$ljjk=ks9?#K48`-MfAJ*?8Hz2fnw!XnjvVa2~n&$`INs!jS7gX{!er6kq}j z8F}{#F0yvqcYF#G!j)vC^4{9_)WrBWHDEZp+LcvH9pz;AYl8-x$}}*eC$u0(Cb)R3AVfj6bvPjEnpv@scbB zT4NB403i9`_z;o?WJkg9#|V#d!MD%M|Ma?BnmvgD3q=eWoinCi)3^K0vLLz7L-H4J zT&B0@drE(7Me884UMOm6_5*lRKw$)w4KP6^L$iA+X<8cObdaK;Atf6lu#Onkb(>=$ z3heGBe*ECAj(1YOsJ+jwz5p{o9fXXO7|XBm6qx&j|Ql44**(9^}uL~9{?4BXcp?%z66l~X%_$h-%e;hz=K;S44=?n&GkY2IXD5- zzDtsUh^hdA027A6!*sGXv+h0Oh?xDFF#tRaAP`U+pj8P9^Plkh{%3H$k_CAOkU|TV z0jlhX(^L%bBMK(P8nldNlA=nwdJYuikpH7V%7!pkR8)L4auEq5WZ&W~Rh*r7{55l{4)Wfyl{?Vx`llEBh+Q-3p&^0h*UyzX9K7i2EcDE7+_(YC z%M$Pcn^s}94|mUFv`of}pyhsHDgyeRfRYUnCL`3a=VY`1$+SG?=VqqrYis-VYr8!w zpl1NYTo~HsAjP!FFd?_`NXy6!z?wnr6xp56gP@dBx~{r15H zc-4yo*GGJE8iNCQHaI4RwYA{C`SyoU&5TR{H>meO--lA*Jrt&Ts|YKQVh;AK#nJae z>4(L-x}Up<>r1?IW>K{14U_lYVVE-goi`yNHY?)|Q27T^@n%972poqy;_kj(qmL9>AUJRh8T z00E9#)Ln>?QSx;eq$4l-;Kt@=*Xeh@#l4B$rrYmcu6-=Q=|Yp;`uf5eFtMABihul3 z73ZQ6M${=?DGb2kELT4l9s;^kRYkX&}>l zCoLd5@$flEDai4`{E zi7LN+!}{F98)PCU6W|m9WQG4>8%->X!!$evK-Kdz;Cku zj=X+6O)6`^=Ke0UWlWMGCm-_~WCjEQb__uXr#6Cg9mjlto^AI8DW!F{x3|Mwsrf8N zAwKPwhGW9QCdW#FTUu(C?sUqFhAw1YI7Y~X_+2d%C+8|uWZ#MogL)AB=1mO^v;^rW zD2kx40o}$H$~};}vD$B4RUnl`LJtS1<$2DB=J3~^04-!oG{|hQZE#p&ime&c!Vvpe zWNSb3T7_uruFq|D$gm?tth>V|v+~C_;CLW^8A1gDbGPZYeuedGe>6@f`mp(&rUUR{ z31m`6OoD>DJ`_5mMJSk54ix%Os({Y+4ssPx=sfjniUpzv5Pkz}a|E9`xi+3(Zw8^1 zS2>`gcH7zKXHD=YXyw$OcV05cgqZ^%ffS4oHOQ8r<_tD?$BMWKEDZS2OIaXq#h-!7 z37lf8-u>1SteN`9?pdUywit2-r1Cq<3e`f~)OmGeJIE@mwNFsWhyppgc`AKv&9_x!Amqqn`=+oqqdGaIh5|?SY03a^ecuE<(u%USHXPLepdf zrm=!h9wrHh#WN$6*C_*__YeH;JM>E6OpO&BxrmOY?sq^T1mI0A>(QYiv~Ir%P(v?z zpmmJ`fbIY!zNB>fplgEGuASEXa^4!+oZ7mmG_zaDSG%pxkvHn;dBfBx+o|Mi}w zbm`K(dCi^oX+<8z(V*t~57}o#aVEx*9<&j#>E3Y>pxRHtZ|HIl_hE>@_{i7fXQX(J% z0)j{*-Mx{JP6266Gq9y1To!bffn3P7`ILylc-aG_ zIJgBZV&tNQ%nBJq;-f85DQ+BTj^wD+qO(^IngauT_B?KODA=;%5B4sCyeYxXD8ejz zYiIIIjc!9~za?7R&$LX`OVrS-2_zdn7jsW2qLN}^$>QR6DUJy&I=@}_;$R8S*&;LC z8Y}r6OOFSwpkZN1>pGFgRnX$L&sjU?+=~l_>kHED`$ZKBxl~apGPt$nKl}BiFY7#2 zG9^qi$&Ty59#Ir3gk@%>HZ^TiU~w<)Zf(SSmiY`0PB3S#v~o-obGI{NY{Cg&+ojFC%hP03!RatU*{JJQ;*jQD9XN(dxde(@~7vg*)sV8 zcU;(g!GaGwU0{j+mdrOv3(swuv+w9|rxg}+xO1TIj`DQAUgACK*$W@BoZXSj3mJ;L zIQKxuSTz4~1-sK0*}AjqpItXK-3kn2pns?T$^1GuVROxjgqj+CfT)ls4Hu==T#kD2 zeY2V^?Hcvdm)9*>O5_qK_B_QRXMBjN_nhK<{*cv=OPJFe&SRrNJNPbN^K73Y+CLXT zIhGBoRVNHMLVN#g+($Z*G7Z}pefuo;NLfMl^;dJ6gRcbE=RK?Qc%4tK(nC3z8wguP zoBuq5gKW#$nwi&Pizg{En@mIHI8BrkTpQm8O-M3)b+%7&MRs6EZwTXixA#m@_BEXQ zfMxK3-SzdMx@wi#_ETK6?euMpp2ZfUlJ6ZC`p@U$b4%q^N)IMzpw0L6XMXVha$edU z3$!LxwB}UVvB}XK=WD|>pY%al?}DO*%57KOL`>t12fS^O-;V{sEz6kh+czY*g$f3! z4$N$?EP^?PS`Twm8R2;4j7D=*k!ufiAokXe5!624*|A`!#Rxj<60!*?y-749&|xwr zVTpC>3Jq^|AzC!dFOym7-*KQxaJ_mA=WU%aF*W09RHEJ2B-wZ$@Lm#Wtb)n^X;&%+`ELWVpzf<<}gy?gKdHYE;!a5Xsz!#UXds2ZP;G6gW?vATA@k%bZ*- zgw8(v32cNHgGO7HI$V-px6OMr-3LJT*47 z7?KQ2GE?#3ZnG{5fNO{r9VNw>PX63v)qy{tbN~+SITcttU4v_qW6m>qMK#O&bUTjx z??P~JPn{$zL+{qshNYxPl?$WnP8frq!ofYtTd0}A|8DuSWa6zbV>$wk$-KN5Ifzt8 z8P`3W2%5|1^U}4rHlBoW?skKkR?>jo#b{y5A{@{&cDZ2W3*1~p!! zNN{kN{rRZWRYh07U!oZcRI^{n;JbeUtrT@mb|bq8W|A4l9DX}Go@pLWpn~q+!i;AZ znF>#y^{6P})$%*>w9_0?O z5n6(hw>=-y`8z#*!6%03@+Kv+!og#+iQt#uCEv({YE7o^Rw?nI{M*Wwp3%OaKB9u} zz43^3uU9@YpS=f6Mah0peTPgW?~0Q|1qT|MgEK6`^T;nj2NFDa*61R8D4DL+1rG-) zC+YbR!l(?Je=-t8HfIAD1=(@A8ZO%)frEqUb-9>JZu^2sW5Zxln+IsqJ0}&=xv*NM z5}0M31`p@WZ1uWPl;;%$o|=W>;mOc2c=y~N9$y!c&rcBM8pZ6b+*g>-K7+=;-etdO zrq-V7o%IX=vOa$mfs)`T<$bH7{-X(W1I}nC3yH)6TUzqp272#JcN#|-rS2zF_kHPI zf50=i-tXlcCLa*{h?||5gIrg^l(u8L&)f%n&k+IjMDU8hi!*wf)%7lN41R`TL|Cag zpRu1sfg{_7{3oGPMvr05j2;foj}Y(e1_*WWnJ9PA?fT<^b7XfA zq#5)9gnRyY*}hY3uVgtxO9y7fG&`4_HLqdbf~@F%pVc zOv&S<~G>uY@oK2oKB5`O zK;K|#1xu=HB%m`%R5@<>^ABGOciL$uf??c=tm1W#bzJcFQ*x4hbhZYT1tB9&>3L81 z2r%bI#uigODCF%xLz_bpEUpXKfgK^kAFW|Qop16AzFW#O06xIEfA&&iKiI~!^?}zE zi+=}7(N~`$TKFf9*)pX7<7VrYb}o-NL`9ZXokhKVr|wpnU4eSi6 z-difqkPcpPy9uW}hl1`w^j)Hv5q$og#PxmL_yg8g`G?Bs0$f;oYtK_2N97e@hZR-g zUpB$ECmonIZH$Dy-5Sz#E5Wmt|KBjECE8qO>91toU2+&9NqF5>m=AQY-uu80qZFBe zL;KkJMCRFnbEf@peX?jXjCM~WHm1vL$qhogEWtjW-Roe(kl@k7V5a8;q%Y#1Ffio5jc#*Nmn#Ha^78tT3 zthYy2$n8QI=nAwi90~HYt*yxioDCWZa$i@lKDg(c>d+WWE9@2j0dO~Pm>mOTKTNxyBL49bpIHLWA^jRIqn$_e zDW=G@ajgIsJQyy@yExhy=#|zX_J4EU|0P&l#B;Rm2SdF^z}v4R16I8>g!WZi$}i9I zz@-6s56;_I7@fofFc0$uT)|_oY-w?etlPZrk6OceOXL}X!FOc8AVAn=GKD?x-J-9v zfDDtoIW5Wl2_}AAW|=PdH?iVa6!Bo@y*6X|18pu4^Ysm1Re``^T) zWU)*l^_ct*7aE!O+h8wZeGzsX#=S4E6&m0@ego<90RjwB)8dN~<7uLjpGe~YOE9S? zE>i2KH~%XI%#JT=~GGmMEsKwLbxmP z9BhLzA1Ex?80Hfw==tjXm7I?u%`sX28?Z<61uXx2M8-Q20x0ECn>TEPKWzAzEDzCPg0dO`j<09+ix^iHvNd@A7ik$=p0H(-($kxr!lf=mh@Ongeg#Pc2Y=rUm- zl){Yo1F7=a#t7F?3p@ZMcwYXCio0d7P@NIRBio?iCP|(VDv@Ilf&iaCze%B(vkAxr z+dYyT%+$IE3wsic~}BS`KJ+mzjTmG`yT;tUo}cLs9eQ?(efvl1V(B& z1U2?{*v_ABI&-5LCx8GVpe7z3bN~m;h5M=l@@5o&x6t0^9Vh*Fy*^*yi#7gDLPV@G z2f+()n$bXD`v0fNu^s5fg95lik`G4r7Q|;URL@V>@W%Jq9-kIAPhbmCBE?y-{CRkP zF%>vBmEu6uuy+&2K0FWMQzGf`hNnyF=iYEBG?5ZoxeXkQO?hq!dGM3z)XHl7LwIk$!DN+B{6}G zU6GFY)cwgn0(CsHH~g9KL_pHf_&irp|e;zvVXwoaR& zKAowmU4$#?zvlxgzX42?ulExr`HcyDOK^KUMTI7a7`?niLNnfw=tr#5o#D7G96Pyb zorun+dO=V*>9AXAAX7B%u&SNkk@eA+ z(tn&4{Nz3Ij=Qo=KnFsrBEgrmP%$;Vg4c=x+ecjkp=9>x&uMW2hN z%jcS_t>T{Y<-O%StHg#7S7nxZJ}ZRjMYfRm-9IXWX?gFGMipiC<3Fp~wQ-t6-gmAB zj8Z7bN^SoWx=j+L1e0rqp$cy+FX6j557LSE0e}}Th5XnabzbD4zWuk%ljlryM)rmV z!xr|2zM@@CQCcwZqn`Dq_;k;0k=W9~>h<<4MsDy+e5J-yriaVgl%%jmA%!1(Ad`{*ztf7*%~DH}o)&|gO$OMHVztg_9qMtDVHIEaR;0$Pctr^Y`O z;$oVo{I_Pp)jfZIwZ7jh54w;*x&|kHcC@6m-gD{In=yaPfA4kq8WP9CUCHb_@&nC< zV5&454@P#zbew0OYlgNPJv(~#1BUY{DLUk2hR%`yasI=lt8T_i=G!4JJm_kWOTS@_ zpdqvR=aH*F^S?cII-dR=RmCeTI*6a5njmPz?kwCxOO5#NF#G$))3F4!YtN5mm0UkR z>k`Hr2%6;w0twFl=2LBkcYyq9R~QJ~yi91fQ3sO(SIMQV=*!>fL;f9?K_UIBM>S8+y+ zW)RJ1l=7Y6);#k-Y@I_AHDxs`vSE7Jz<4?~cy{i)mfk~Tph2g+0Q1zz=#zCnm$}W- zqO771IJANdLc2)BQ#JcKx4R!sDCe0-^=imBlWlG>e%0=pR`^vfoHU};q2I5n#9n^F}B%BKho z=3;y5e=QkDs-caFE}JbLZ_NF$?2+Ec65pa-yf-7Hnb%VIahA#qQz=MccelE_c!^a; zW5uBespN7;;C1)r?&fKaja}dPG^@M(slDQ2krhGfeYxCg&$GAcwk^q{_xkHLoo&d8 zy#&nNvA_B~aQZON-{x{_rVxrse9zVD^s$fXM3Hb<{uw7Alw%+g{n? z(M^`-=@h4F05JyrBN+4Y382}Iqz*B;x-397)x>9d=9iS%S&j4d?!8^|HeBkpv8!UJ zRZ%Gx--S>tDSe%qu##F!S)mZfrdiSAkj~mP7cpYJ`uG!5s?zgqX;XtTU+y`5zUf3b zuVA5^(_}TtX4?JF*7d9R1fmt$ohc0{dqn2?lqfPV zQR5cchT~s*3DUR>D@wDDwq(2Q37vKbh%B{~B32pPVRm{W|0;+ah<1Tq#C%lol)G`K zrT7(;-%t$ySlXlm+r(B%O$( zfs?7IPXOICnE%6vXp-t4R>0VArf|FDgLVH+_qR$m&cRh}?iInTN?os!R!PNY%9{Yk z>vU)<64Z9HQ24a8(c+=QukcMY8@iU<2Nk^~Wre$`!d`R4OTCb-Uy%~w#r@6IqteUt zdPV79urD9NMt*F?A=jHl>5)AzqERB>F-n!0y-7g1x`SU>KMiW*sr(Y-UL+?>A+0(# zKx#y?MJD@=YZREsE@D|MW*gW0;k@m_E@MUvssXGrRdh?%(?%+WMlb*ArbPQUGi5)G z{8r#Kcq?yR229oMwe!NUVrC^8qz>uzug{In8O<=w1jWWLUT>~`%nmz^FsJCj?PE0m z_MO1}+T|P(l>^|J3$zThs;8$l*oNoZ>>n+D{rF=u=b`dj@GO}vBW6c)RjGZ|tw+y} z%0jPDDYHBO@J?;q@&ozeB31R=qO2Gdv$XcAS!%=CUzKY1FWWR{hl^LJzfo zi!15*TAt^&yji>9fRuWdxGSH)`Cj9B^&Lyl-jvT`j`$y(CT?myjWWt&smDM%D@qX=@?{ZFF%Knned*MeO_wCYH4^!o(05X|AXGPKbs=SzK zD=8W+jDE*L+tBEjQT*ZK;$kZ=)$xSEL1M^$u&&#yCY)8u%x-Kn9DV)?ipI+u8CG?@ z+PkpH8zgsFj5Pb|i#hUxL0}%1R+Y1$XTcv2e%!0{JLes#W%~8|$On@FA!OZ_wHJK_ zWMcCbv4s#s9H6_Y6@cAy{WhEHRnE@_50&7>&PxqPuW_8?NuHpSeXg_X_eM|CH}>=S zHcM*f5)a8GkRLu3Pt_dmuOg6PbDFD|%(kC~MtA~UDSan;>-E8CisH-2y8^MBux}N0 zM{c|5WXV^Flwa%(5g4wfIxuHXdd0^m?D^2Cm%^gnx*64^k1j!lHY47fX_XJw`%zV} zpaN^f2j0Rht@{t(pQCfTk8s_GZ#>90EAd(>CXJ<7RFG493ld5!juA^~6pFqr6XZ;C zDKRyiAJt1bQV~FVtl~uRrTt|xell|8b67t>UeRu*)i{UEl0CAK#{M80OC@rca+TZ{ zZPwX+6hG{j>?VoJ-J!3%NIZ-tv9=sDySsW5F+w-tw zY94O5_7? z7y6^7cqjdi1d)s(1bMf@h048xh*{U|fxylxDw>bJD_h2T5w8moSbZ_(UjAH7mK{G+XbJ(OL)r4zX!m@OpOpe}RApo@r)TDP(P2@WMuT-5IrFnd#Y`@TprP)}rG>C<(AN&h<--U5#~VY$zUKZDBw4!`jL+3Bsi(5&~fy^$Xq7hdAk zs@qQ~IO8Z6IdaR6y*T#?(ki--Mt+cyFRO8qMhARpt1cF~RBlO5QT5T?);}r1k&ykl zHsn8`^lsu+sY2%t+Q9x$2e7hrAoEK`r5+d*OGT4pw-Fp^0_al}=g-HlsyHep+4i2M z_WqRONCYRg@0QrMFpfx!tyd{#D7%ou?Zk}U>E3n)v1%hc8d%MEo)6y}38V4m1x}7H zsmGA9bTYrasGNqz>wQu_0a}UQg=HhkDo&*+{ zO}sf^w+x7M*?4WpO8UK{qjHPPd5USPZb46rSUr)T^<6}*Wg;pj22Ak{db4{T3&pIX zGwm&1#V1`$ED4VfDa~yjm=_!6Hd|VWHnOCPe_EBvb@@Y``)o@DUg3bzs501(<=(1( zdYDs}o&f2PdZ#*5Po{C3p@cY-#;sNN7n&zVSH=BPWZi{6iCiJt7x6=_2kQZ*SSCqT ztkUY1ZpY&MjAp|2#C?|?`vxBu81Wz8BPMD3wyu?kx#nGC1V+F6EPFHh1WPHQpgkGA z_p+=*&!~<@Wqy?C$)UZP88sAc5HZ_=sLTE>68L2wOS9#XoYkq9c|I~i^*b;&?X1f{nLG4w|h1#jJkfAQT z+u7i|j&Y|K4|+*FUR=NnNKu`RLoV3u_b2TBVS~CJlXf}CAHkhanJ3P8QegK>+Xy5; zMj1S_c~m3vOb<{k`bPWtq8zho9E`@CsXp53?+--YzJ-ZIjauWOQ}95V5g&hZ%!oQG6nk&b+AKc$R(L;Tf4`>y)9_GV1tYuZRy zov5u55{2eG2*fl^!e4Wby~pAW5Vp*_V(?)~B2df)nTqOBE4uA@;39Q+4j~;zk$Em7 zj)hivgek`sZ958W<^r5j+Ji)W=tzOk+$Bg-(==F4^Dpw7X3W z%+|L5b&_ZhSxrK0ONxNS-43T;oiO2_C;jvlG@E~Q)7&(v)~>dN*G1_-$yd-?CsOt% zgNdPTQH8V`e<$?ma!1#bWH)-_F|Mh$$z3!dO4L+8`*-zW&+GH|;Q1}igs#aJ&~SjH zp(?jdJz|MbS7=*gekG$szr%_+HvD{DjiIvLsMmbK6~l)a-K3-5Q`KJKbjzmItK-KVMq8-i z384N88+wCWg=LT3kfwaL(@XBZW?9BJE3b@!-)`OQCfWuLhnRP2(b3uQ7K?GsRqeb9 z2KSf0^B$+D7TJ?8?|!_MGQ_AHn7RF4&SI8$Tr{k0I7epo?5UCefn;&gIi*|3XYNYI z^jHw=F^?t7JjwueOXF#T7B|N;v9iVe2`ML)hdIWugbhwx@3s#$P}cO}U_D-ySC_n^nr6x(jN%X=P=8+Y>>dk#(Uo`*Dc+7p4^;Y`gE93z+;CEsO`5;yuhF;7@FRHZr!b_OG@69L9aCqa+ zoF2M<@V0K!bo6H5MBBJ?3c-I`zl=HSt(RTbi}3N&1e-=z-v%|hcK=&<6nvP&$Y4Uf zHp@o}!8@*-~wNYA|_a-(0Cr6?zhrVAk1 zRzu3)&t5&fWg|m3He`>Jjw97_3Yx5O9k&~*$r&ERsmBPa-fJ8d*Z$0ICm$}G(CSmT zLU*FK<)9r;j^T)UTh}-)xE60@X;981GYnN7)6+)q+LeGkBOm1&j+@$vL z;{1c>uc*tBAAaP^1L1u+hjylqQM5HTNA?A9dGiK-ekI=+9F82=`OikG)a_JX7MgFU z+)}g=6tmtT^<>d}xQ8o6UjIy|sNEKxob>ss4t(6R!!)^+9-`O5#tQleBI;ko7=y%KyBVh>h{tby6D<45d)C`5*iY-rw6f#( z<|uA0qXIQwimXj$tVsCmwxDQ@_Mkr-W4GgRoj$N?elqOc5nWrH6VIkl_ttCiz||Yv z@WWZM{;p9m;ryQ=HrdohcdrRj0qZZQzN7q5thje$uJX7(k!u|mXk~0;t#Sp}& z{}@%Zc^qJ#fpZPF^LU)CvUKhj_c5t3nsMoJQ?ZOk^bTz@15p%W6&818k9csM3t2YY zr41snsG_*aN%8I1g_w-mWvb-h2O zy&AS>lH>qpeh<-6!;zfvE9io&f>z*&Dr$}LiaU1NXZFgxsr_mx%XyWhm^X&%Q-rK& z4^!ng(qDW`AuRJ4wogQjcUZSx+BldFHXp${oVz;sMSG` zU;o~m8p;|m+uj8Ng+K^H+EAKK-ZdhODFXtY-NeTAnp+zEK^ zf&Jaz>HWBO*sqcOw7d}QyL^upoLUvFkH684ffkGAotPgM5 z$Jc>$dMp0`-bH6fEjqNhKD4PNu#HmDlO@G|sEHy(z`U2!C1_k*21$(2z4(x@?CYYPQ(o-jgL5-T z-@akuOQy$@9ym>#T(zUcfyf=6NW+kh=s@eX+` zyYM~OH6^|kP50}45^lLs83Q#vXTaQz(8W)0pwLEUpO^6PGtr(esC>EG8;6QoX9r;eSB@xlziw8>3tbQ)an{Udu*hVv`__+uVB?KpG+gHl;d@P0 zrK^F32vTG{b`$sQrTu3lo&V>hw0myYA>FAFVt@(4*v242*DwSJ% z%lUK}&d-j%xV(ft-R4RKH@r9)m`pss|MyCYz6Gh1e2+zLOoB*t3)B-^oa$hBY3?tN z&QhP&Yl$`CZszZ~zwqi6=`6FF@lq;iDyiN6#~2Kr`4DFfIUm>!h8P6f7gW`Mhncs64@r_HT1zoS8h*|M|$3oO5rkr<^NC?*j z6#M9i^WK}q!TO2Rg-2ZnDdbHy)vcxmMo)VK9BhTXEMy;B|t+$A#%;0P4u$u_zZGBvISzkX)d zwSP0d;ac> z>Gcg^;?Xm~QH3F``xWbCYMS1vyT0%iqx4RipSOm#MAWDKy+xGkNslcd%SLG^h2z1a zj<$m`Tgbe|hzz`{Ic(_&W{lf(tvA;s0^urjy&!k^in zxZwz~%I1%h+mp=NAADU8*eXqDM;^O}TUa&bIW+%;Sf~&ZrxDqQHOMz9HGGwo%D;`L zffcrD4>zx9G5o58r#s}q$5qfo>b=x~Ttp3ehkYISaW%cT8~B>eT^xs+j_mVpBL?g6 zvhjv=9dZdgok@S~HCE(o)LT8HDO2^n5q`;0EyCj6K;rS5L-R}b27`5*`{!=36ZY54 zpNSaureF)Jm3BbRrfefR;zky0f*(H6d2Nr7$Wb}(F#LSAO3CA^bducA<{Y{F6P^wN zL@xsn?%TmL(W?OoCCfd8v81u)rO?Nc)A;So_|UgT&gk+Z$VDA85|hoxEvF(v^WJg0 zs_dtP4KEIz2|R3s zmtNwW zD~=ma>jDaxTA*A7y$6dB8}}>M(oc?^na-=ROmySLdE#;h&ocDw-$z4H&xd~0=zh-y zZo8jL$pL8snz6WK3tJVs@jBIqQB9@7A1%EA!J4v(0g_}0f5V}}0qFSX#tL38((x{< zXG6oW`}p>4wDSr0^S$<4GO-mXdiAy(ikRfBJj?vC8lNI04{)V)FWNKfbW^AbkeP zJ)I-F4Y1!^Dl%IwsvRL?w<5n0;QUs!_gJQ5K4|GyPl|;4LKnNk^3G$Rq$A;oaLRx^ zy}l=WI<*yv3_0;zfml%4|I`97rx!PMr>7V&?=4+JkE|G#ThWXuW{KQpJabCaqHjKc zVAU+)dEH(v4i_Yo}8-l^GUeQ65NXmb-r_iP4M?$IYLk87oKv zQVT72AtB6p>O-f!pFNDcQ*n!yBmg$%A){rRp!KwOT-7gip&4&OM`;i&Wi zLWDV1BhW)(L_v7G&i_1T8if8RkfJHDU6}4(U+z0^d*VYkyP3k=k}F42>14~prHt!M zzZl_K4=P7JqDFu@j%4^y)sRL?T?2Tw)&`)sS zbM*d|>=IFw8h8uzhmjv3oMm3Zzh%NkDcxzfZ-F@OT_d4(QvAjo61}}c^`+^_n#PHR z?>ZV|x-NuPnK6B~>`fMYvB$AeDe^eGTWqLkEV2DvQG+NElg(FwZ_0=WY9I^8KCEQw z%ondp(eE)dRlPTWh!&oNXl?ca@pUEL-ZP%mGmO?oo)BU;V9u($Wc5^8ESCl}Y zPl%j5^VXl#N}uXZQfN)jG1>-92Xl8cM08VB!XJEMQ_p(CvDQnTjgpAMl%62{GXeYAo}UXDdnL3q-GAI0-jE{zc!!@lzZGX-(jSxG2d#+{xwxWAiQ$2 zDg@C!W{G45b4{nAnu3J@kNe`KSdE97;548ARm8pbdg3A4wimrMLa)@g7cvMt5o`hIGjQOW^m-=LFeL#W>ku1_)-Ju zSZL2p9m2kibbHsFG|=yUNd4`ud7>PJ8*0z+#3(i~mtKz{YaI2u$-PcSEoRnK)oEt1 zy|kqmL>L|F^0SEZ6%ukwe<$0{Kc@uIxJG&nT4w#SSp%m-F~|BO%|XNa0X}(AY&XLU z>vxs#!#{?M8PK$D(l$lB3e2XRQ1%p_Nc-bK7qH)`pxs`ftc^?d_A=U6++n^Tw?e`d z*Sv_fzS+-wWC082Zh(Re^KXm@iLF|3(}%u?XO>|qW8`97H7`FF@Cr+v@Y}jQb7M^R zu;BV|{LA3gA+RNm6~HZuT~`I@eSa-bf`{nYulchWK6K;wvOFIBHCQV{N>pKWJP zCx3S}^_)GOXg=Wu-Qg-4fGF4{w}-+PC;xBEMS9Q#g8K)e!LeJe8^ra&RQ$$9?R4w` zStEF!wC1C#(-xEGT~^k{DoC~;mVZK?XA1DtL+F>vpFfGp8ExF7q82OD#|py2GJX)? zzd8GzcX-Lw%loZT^h*>CRzKh@rLk>63MAq%>+Q0z{uTk?L2twSfF4>P5?$iS)BO1a z-569DG$qcubc^yY%#gS14(2}bvfg51xE?&Ei}$)sQg@IaFG~n)7y%Z&I)<={Q++$A zmN7E(mQbQAx@eTH=YuOSn*xEGzKHhRUM+a$*-5wf`Jr&GK@WCADaVW|adSv`Qm0`z zpJiZQWX-qcg>9+jV*8}aRvA0J(#!R=9F#-}ru6!Qpl>goh>)p}`J3(iZFj<>7nrF7 zbeHo7R6fhDdA=?`t*qn=Yg4O%e<|kjH?%qg1k$nSZE1Yq({6Y13M668JgrBia4xzB z+GY!MUR{rgZYpq|;3T^t%=N~xJj7BScXzHGW!v=Zov(7%_)5jVU=sUT6P|H!QJTSQNkw(Z1DDb?b}7ID1tA%dI>dnGXQCx94x& zdu&#;Mk`kHK}?mXw=SskpAP6hbx1C>&R`zaTGtKH(g#)! zyF8#rQ}yl4*Rsv@arj&p0`qe3c{<90Pc!?$+Oj)n2VV~k__hvkZEEY zHSrTnXZcGLc;6W^HU^)`hRF+@Ja)@nyWmDI{|*evE^kBScjYg1N0ol~0ieva)6I?> z2Fo7;GrZesklO-=-me0e)Pr9eh92%aDCDVj3j05m@8mR(x~0bAELNTcR_v{~;$@ke=Kteoa@b%aK(tO(EF$(Ddc}6-P)o;XbCv`Qp6R&CaoSQE z`2i4XI1RnEi6WrJQ7YSwSxH7S{B8AfZJ$ZWtidyZ#iMnurkud(?Q1k+d&Akc%Qq8R zle^-G+*?=LdG$FF<|j>NCG(%xC5@CjqDIMiCeVy?8SRd1ZBVBljdD($V-T>{C$VJz zukyJG7&L?=y&HPYGC!jqE1_8W1+e#w4{4A4!Y~o_{8(6OsObJ{V>y}f60Xc9>jup( zw+BSVZ2RHK4xHWIU33`wmFzj!Y{DR-DTUGXQj=9}Ol#G$oy`y%R3354fP4=YffOoxWa2cVJu}aq zY8IPEW5}r@tjgzTYPmYHT^NZZ$>$>{%~&vtw{$kgMysu7)KfJXZQl4wUnL^kir2|cWzJF1IUKnUWj84@ z7$VCoRZwOR?$S4Av!y4rMDTG(v=H_SEvgeoz4RH^9V+zqcWVt{nk#N?xq*PB{}3Fv z>KPN$|JtCFjs<_6)>~I+$huTo<-7V(C0Kwa# zJ~iR_29|<5v^437)(dKEKGefVzmMzL{u(C>DIlhI-nV?tmB`J_N38?mv0@tR(hnjJ ztJWX@R8u%9?`Br@i#NMulRX2+R~D8O;dlC&j}LvEJKMRa6ZT+;)7 z@VV+tiv7W|o&tG?x8bAWyTzdHs%eNhmKUURQ=)sRNB{2y~#MruFRjVyK zvD*2jxZw`eUWaPRZh$+0)E4>vIf11@{cpCcBGoBE>qJE<{lp#K>o$az9`h*PG3J%h zTvDo&$C`nB4YZ!0BUlW9JCeY&G0P`&!kxiM3bVtELCQ$-j~*I_jn*Ywp+aX3OSlTc zoLZS0Yb{uXzD?A<3O%}!GzX2G2?Jt)3Zf!+i`B!Ec^DZ23{8~!cZgJORAN1RN){e9EU z>Hr4&0N{sx9d!??>Tia(I4j@ScShYR;qa&(Gm*=)V?Lxks^8RqxwSf#hZWV}=rP9r zTo01PGDy^KKfE9yhZSaV+BtzC(DPvA5iYoP+B-e+<0E+*5A$P?t0ECOxPE;o1Kbe9 zF4t9mⅆsY271~|IL|9mNX$&RqHe%W6x{s;*RO+QM~?!cMD37cU&N`GQUlcA_Yd5}@RBEB~MBd3)0J9F2U6R%#`+A{IH z0CHjO7@5yui&)88>_JQx7(!tY9jDd%Qrr>}O~%ACyNiAsMdJP|5kab-T2X67RsCB; z;D&CjtXZ3&BB($|woXK3D3i^Qav4LNY@St|-a!C?vuIIFuZ-)=;(2&%YA%KecYOr%{LCO4)t&BMXI{5$eUMVSU4<60*KIgV^>m zs(+~IZPB@!g`)1vtF~w$O*tF;AIa5}kBlg^J_U0=)Cg0bymY%)Gw;N*lUX+VS|lm? zzN|&vYfw8cfd@wyqW&{sLjZcD!t+vRHGAAF)g!1#K%7f|!RuxSXBt^66(vMFWws6I z5O+{^Fy(_I6Yq%Yh3I`ywLv@E!dfonSo?v~IFHBipu^7`-G!Dilx*6;tA0xUd?;8iB+KX( zqNn(X_;s3w_C)PR3r^GICO`4Xi2fng%#HacVF0FkZx9%mf^Oa$Z&=Fa&L5f`m_5)L z!f4i>`>E&W@@yC@I7d@(w*0HtZ-2SC#7og|kufG2-YZ+9Vxx8D!Z`G>XDwW4WI>15 z(I3~0sVH4JEqu=YEET3vwqHTn#pI1>xUn$B=#H;g$1z%wWH{dWpxhZn#3IroORmxBcI^_R!K~jbxZ_)v^3N z)R?C7B33Ua6MeHK`wemXA5>fW@!vikt2}?clUPx^Qv5~t-Xs{*U4zPVrrz+x_7|@+ zW+-pxBhV5v;HVvvzYn;h*b;}ld(a>m<~r1wy!+{?Jpsf9KY!%xK*U|E0I# zzZAt&_H>H~=#bQCZS_YaukJr9p0lgHE`2v5OU#=Fl$b`>3@wP;@AI&J+P<`hqw`8* z8rJB(KR?~cttW7|aAvGtZbFxO{wB6(<_N*0Hj8CX>}r5d3P)m~=WBr!$#c_3kza?p z3@{b7vfmd*5mzvlcK%qNdCGZ@8xvTsjJ)uM~pY4DJ9IPOL)Gf~h0hChk zU)leL;Fn>0I0)7M;Bh$H_u)U0fGB%6p;cmYk1oVbok{h*IRT*v&VCfx|2~K@T?iY7 z&Jl+d=V_Wyj&nKLdi}wOLuY@^-UW{rl?1l6r&B z*1U4s;^_Xqmz(tqfCNQ6{)anok<(!zf_skg4`c!lC&QR@lkmjj=z-xKL3rb9 z!>iL6-OJj-lh~QJY_G}ehKXiXS1KQt-b7QC3L@CUN20|}&n zQ9BAy3lyl*VW61jBEY?e*)c#n!L{KSdI?k~vRGzTaXXxSU=39_R&Nr7vYM87=3B;S z9dSZ(nEw#tW_K}~DX0e<@3KK#prWV0?Bqp)?Y>W55N(XZUc_gEgjglz15cC)gH(yV zSC9cbF81z(!@WHpxzMcwGA-2@~cDHvWM;L^V zp-M`?3Au8mc~WCOst@QOZUcZ{LIBPK7+2?si&&*_b_8%@wmON8f{LZ^LY=q;l;D4Y z2rE_v26L(w+;_tX;GQQk2AhY(7lL$X~3s@ zGkjr^U{kMJ%@fa(h>a9=2G2ilYR=h+QcLs=3hxGSRp7 z-C;Psr#bESgD?o8@LdqVcR$6ddK=;s6NE1Ukaiiobui4|Ur--jIXGaE13Z*bU=;>C z8H?ZZ1?<7gqE5F{CBdBknUBao+#O(~y!Rdsh$JXxv}?CBq-E z(Zu|L5$9b4Ot3h=At|}*N9rFZ@CTIvqx65S>odbpwYo{9fcuQ1nqnD5>f5LQI&7)y>a2*X(Vm6KtjLKLB5?+^fpFtU3yMry9yzeNY~P)kFd;;IT;9nPd< z_xQ7SD3ecqn8Nre^4XCd_6iHx;s3z4A_*4d3E#Bgt_AR*!uT#%0FJD{5DD9c?u^Tj zRc5Xz0I4#7&v0ZJ8>>ZCS>J4Nni1w{0EE*IO0=hn^AwRa3-mmZ|2Jt>%$5BhKT~74 z&OZ1rz?lSDK&$(jV1gCEr+2{;;JnFhQ=$ZU3jO?aAS{20iv?O)N*$2#)LH-Nt{mG}?M zT^pvIMI;+jGUrAw&j16DKhj^BC8$~#&j)KQlAkdHpzn?}01=-%7T!@yaSwC_jV9op zlRPvgi1VaUX;xyD6@o4TetbqOhht&?7h7)~R#n%n57S-Jf|RthAV`;lA|WXtARr|O zh;%m!(g-NE1!)x!1eMqdA}y(eQqtXB-&j7+d(Q8C=UkV6ZP{zjHRk9!=6!R&yhz{k z8oBkFrU8Ez@)t+o3y#$`58eZ~PJXrj?wQR=@yAVsVJ6uV9z2HyM|KtMPaMwYu zucv9jw*p}_qq~FcWwVis9&)1_JQ})lb$WYf3}+D+v^Q8=Ox8QXb32%5LCgFUJ6#_XYf=@^W2&&#TM6;1 zDjS+Sn?Qbs`orgQQZuxreHFfs`0&N}4#UzWs0u}evv{Vr9d)I45X=V!nY{-wnw{VC znPd^@0o-s!Q~0jx>kpA_jcg@*+Mr0x@V#r7m;H%WK420qMT8j@(ZP*jjEe_?0L}2> zTr@;2R+14|zUw3)J~-f$w!Q&`5-UqSaA(9Nt53DNo7{6Lek{OMW=>dZFn#v#sCxeh z1o$Dxx(4oo6Zo{d#MeG;(|q~n3fyx~qk^ZK5Bm`NaYj^?>u|u<%k4SG%KG5P7!x9Z z6<6e3pZgAj@=k=vyWH!Eqe<`zaGRCd$%PI2j7>GA>wrL2m`f+A*B8N~)<6F#t#N3H z?tyE-H4r%j(6Vv98}M~jg47ZcI-A)U`M54nFjoC9R)&+g0CiEa5?%NgDubYJ#+!0Q z;aneR&-JA;>p#Mx&N-^9kTHO9Nf0m2`}QGQtXab`S#_ zX2WbW0A*Gz`d)nETLBgtQ7MVrE4zGUr%zxJ7j&HZ`yO&>9-BvpRK_pNR}thK5mxr` z51^vp2LwNa*st)U&(zv$tUZ?rpVgs?UV;J_3TNe4ykTS8nZ zN$khBhb8^(;D!*UGOu6VO5)C}$IqFY8Br4W}nFqV^e7v%|M*Z|hw1Oofo7U?-PvbTBC$TE%qsDQ%KR2F??4A5d_mY+ zu%PGd!b{YWr}L>=VVRzg6WCO7%5MhgDaz{g9e`lK?u&L@psBXzflX4s8PjC6VQGA8 z(VxI2MIK zt*+tGRJRCx{cM+i|Emv41cKs8^1L}yzriR>UrQ47`-)XiPWn%H$vgAjD-FS|$XmXgbj{Fd%w*B-_ zV(lp0lyuKp0R&kN9@=K?Srz2FBUi+zwE*ZPi!#T=MW`B_D@Ll(Y5n0ND_>jYKMtY-gVDzwIj2;OTt!BPpSvLb>zGfnB0#27jkX=+;PJ&s2*I~)j& zKUOVS=M&&n{l3xNaWHppNbm%kn>3#wr`ke{9TwZlSIn9YpB$@ThL z?>^riRhWETN8Sg2D;naB;Hk>NhxBBYZI;?&7d` zMdukWOTMlOjXE6mFE(u6S`I2|&4F&y;)rMO-g(Pow}yklAbt_M;&H6U^x*y-{U+b5@sF0|=fZm;M2v6E?AP$YU=_d>rY?wZS^R8ZKyY6gb zaOyay-RTcA^3A%D{czzYAX_m3dYzkniQ@X5_V)Uutc>)mjLhUTHwhsD2^N+_woC2b?W(aF z9!kVhtLU-{+4ToWE6h`6Bvzgb40EDgE^KuTc*!5nf3y`cDA^2F&3m%R^KA4fZHi%O z?Ayb;{b{CkXB*rWH!~{Sr*dk$&7XGEX`I+9&OaJv*1m4w$tNL?Um3#2(BAH~t$j{g zQP}&9o*kuNsr zARW3b0d6cH3IoI8_s=2Nzp5OzdX&k}li+omSL$ER=mJ<#gs}h+OFse1EwCFPsL$WY z0ThuS$rOl2-Z-Xk^gj3e1SqX=q_3>Q0oc&Sp`#PPd~%T+zx1L4g8x|h>eVawD$rxz z1qMj~`3&0bRTH4q2fLxl_%R+U zm5W(gg;Y<=H#Rn^^zapRC~%Q?G6Z^(KWcjbt(nF$1whLh(joAJ6dWPW;|=vdE(*Bs z2yr9eNdu4PwT)2DLabW=shbT13<%)-dC^(GI+t^u!1xH9Bg|gGCU2_EUK}H&NYlb@ z>p;+<5=cLh53r*MJ1&4*DUSsO7k>6>aUt-W2dICbonPPi z=mvB#K$GMMasn_>EHVGj~_2bwZ6Y(^!7JEhynojO^^)`83C1ScUQA-B*;Z)=<(McKqB7AV+``HBy+X*ZRV`5mrs1pXR8!$Ejj&8d4osN!<6K*}L)GtIedGtsE(xz)_ zdbBfWk@oBvLRpLuMFIjh(BU+BojE3fIss7psPEDnfRUX?pY8~i{_q7-l~(2Go?x;z zAUiCF{|883NqPCR*$kIeVC(|r$djavRqY2{<SK{ewGIa7jx=MdcV<2gx!OWF!H07_jeC`He3F zHakKc45S^vh0y!qEs!>>z`Yn!iQk_Ms}QD2051nX+m?8ic%SKtD=1|*KqLoRq1iBU zxpd%w0`5dF@CSQ9NPrLu$Vn0s5`qZ@8XEpg;nmBAWg^tWAa6kFFQWefIR*x4@$1*q zOEwijM*8~eyGocY8p-xMU+lGd;XkQcl)JSsR ze^66a!bl;7{!K~~sL}KEbRPa+{59^iqiifstRSVru+s4Gct7eak)e3T& zoeIcsSy7)pjocMpcMR{cjhTJ|-iP?UdaLs}X%h^E3&EJA!OMgtsn38)?X;bX`L7f` z{&>~`k>71y-OVBE_*kF>062FbQ4!f-0sgECfLI6q?%O`6c~&Iz2^VC2-Qk&hhOfP6 zpWAV1XUhOr9Z$@Snm>UdcUeStEv;E3jnA;4N@P_6f`R=RR8bMn=3MHtXB&eiJ0OHl zpG@ii*lYt6oZBW>k%h3vX=zExiyflBvTC0Nrf>0#l`g>72P&#nV2tN0t4l(2RO1rR z&;vrWxXYMKHD&QDtgv^a^9kkT+MRwH@8VgG-hGjaUdC>dOI>bi8;dwpfCYSgnv_Ht zI3mFi+Z7Oj5-{}o{V<9i{OTV&~&&tVa#;*rqbO3FC zU0nQ9PKQAscH!&P2Es@W{0hLN2)t&Q=4ER@&X59ct7yYwwENPA#cUVTmc@G4vOrD; zG^Oj?r0e>iKDgj=gFM`_u*h%>9unv{w>~Q=DFH-kIVwRRA(xHPmq^5EB^GJl_XILW zfRF}_1g_1vVW;X}D<9^c;p4f}15GMI?F}f|fN!20P&^OL9%4uCf!I58(IfRra`7YY zN^;4gqDpd^qai&O9taMqmKKbFxE}sohL^W((zR+o-*<1dOQ?2fuq^&T6-KZ(7Wh8n zMz@LOWhXi9m`#bv$YvC{r1PcrAhv5wh`K_q1UU9UGouB>ZNMlC6fE}v7F=u<$p}dC znt+1@SX%0VgcxACYn;N8O5lC~|IorAcvN6cXaW+&aex*FJnsM?`Go9_s%tJaV0kzy zhZIwsV#xJwSSTrJ#4`qHryN|iY{Uud6XfN6GW!E;q}gKaSH1?F7zd6*17c9AKp(oL zQQLMe`t(XzwBp`EKR)9)=+~*fp<&==8+TdVCQ-&BLca;TCkU4nLwB5PRy~0q?{4U_ z&2UU*4NC5YF&4IzufNclaZ zjST~kra(iRoW$X`f$YM!Uc|>nqyfnh?4JS!n+v3B@DDKFndJlPzTBM$IDav%3%gO# za8EJuu7x$AlLPC?CgK-?A}tJ$wD11=UXpObm%ICwld1tI3=Gt5Cvisf^Cw-OE8L=y zpk#vq8}r<<<`)$eRRnIjjlEzRUt}4JK8aL&M*;jW&bn_su#Fgy2vjRX{6?s#fn5b~ z=Y{zAjt-Z~Zm}8Z-&JTyMpidg2j~XfRZ<6Uo{-9lV+;%53wsEh2^EKgEEpIAPg|yX z7u*}~Wh5o(-nw=&8obVZif$)wui>ccuV*r}wZ;@-m) ze{Cay)ajuZL+tTD`)uD(11=JJFd+1>GS-=wnU~F_?E;E=%!cY;UvRE5=`GL{eXH&= z0U8g4Fc`R9A(Etb+18E%CC|AD$BEy+e-Ehpf_!||<8}o=UIfHs zkgluw&|onxJGEAZbVr^6UFMMfXD%FAFT`(tTHMx)}8y*mPrT*mNI?0Y5fr zY18Gbt6rlXbZ2ph1axSazs7sgl_zs?=oMen%T$Dx>d=nQG z{4%PpF7vEH0;mN`N^UGmyn&boB(>`We;07c=~Lw|xEZ?hGgI;r!krBH9K55-)b%TM zfoDKH(!6s-1ZWPMSKX!`ciH*@J7A9YpC`av^$7UNfzPLx!Phso+Ul)s(#Kz158ZZP{TS0Idv zOM~%eQUjNV^qaUcfk>geElN|->7rHtxt$O=Na<+n=$Nwm2&|g>$O4sORS^%~iUpdZ z{^DN`J}LkSlpO~D(I50}6u5B()&F)z?d|L3if6FZGXE`M(o9VCIAAo#P8tfTZ1rP$` z>grTPvr!IUr3ZwuUC|qLeZ-`MmxC%KF zuEiVm!v1FTu`HN7t2DCBW=OS=`SsrA#PASgdDp*iN5LuBBvZ7m^wxVd%;*M|G zpau;I;uO@cLN@^NXoR&L*dsiF8mBkS$<{&F*Y{JeeI^t$z+5W_1RubDXYIxIn1a3s zWm8_}3K_6jgZ}}P@K+!$27(TU_apWNmsS6dxXB{tpN^y|?cDQ=w?~+Rfu<={OBl{o zkgyj$w6gp4>orSW8{_DXnOsuBW)#$~m7wVF-d#?W0p`!3^OA1&AV16#vj=krF22h^ zI%b#c!=+>K?2sa)4n_g2S|LinuGK-HhAcRqMPBZ(QSpsdp!EeY`yODr&Gua^p6N() z8U6Ik^$3*T2PZ!q=xI=~g(W3Kh*}XNYIOkuk$^y~h1wfKJ`WuaMB|C_3JPX{q&Ds> zA{mOdvZA8fkRt%8Nr84wU=yjtHZYJtjn_LQ*l7D1YzdhB9t^+3<;+m*gmryYwa zetA%U+~!Y(WJ+ecM;Uk)E&J7xWNJe%+^r}cs9zx0w0duHi;U(gK79}CY39|3R-nvC zX9*}opmv2A$8;j(3B*wg!&}rsYa*BbUF#)u4i|rW6D(RI2adD#t0=pa7L!_8@~gy0rE0Au3jjDiPqeJ zMYS;uPazICU_)|5UU>@eBPJ4nmvi!or+EM34lu{Tto7ObwJ>h=r zUOw{m&KX?VafU_&I6q!~{!AGkH;}@1L1Yi_63E;f%oqr=Js`e*$il%efw2l!X&^MR zsvI8k-|9j-ja>4ymk}mqh>PILzdsk90}`2czg8y8qO3J;CWzpWGhm$mGD{Jw%Kp?M z>edt(Q18mPXL$*4Gu=iy9 zt-`U$olw0?@*3$1YggdHiSQViEqDcO)5GPdr^DkBEKP=CkKsyLbddnG>nwqie zacr{o)g0&G`wAT1GR#?8+)sa}q@+Yg6D^_`a1pz|40ewKClFl|ep`$0zH)gw5=dI( zB3HMdlHY^o2cq}T45$S{QebdLcoywR2&(XL+VSwjbS^;z7(xe~Avu(QiJ0{_lDyv5 zpTvRP*2H(MUx6I~IsGT7*XO!2j0_FMorZ)@F_#69eY6=WX|cr)97;`1)y4mTgV@SF zWT;8R$qWf?gFB6lN{gtASl|v5&hfXQCqjvHD<~QIz!DgNvpNKynMY9YbUrWsjv(F6 zEi6_+VN}S-J~ZQ?|G4ufMhFoo(outuWmOwj9zn<~hLOZs6zm5iXdc(joud|ir;ovb zxRl41#FA(QAZ}cI`~nj~0xNMGi~JaHa?vv4*pm||U_eFygxo;L`v@cpt=_E5HtkRu zf=e0svy=a$E&JC%3l0WBNVC;s4W*rpO@&H=RMF$R5Al8+^9l+UkijWC72qt~Jgv&Spl_WkI=;iWjBH*D&ijnQ}{V`LDC&kbr4PwOS?&-kqU z)Z9`N>|(j<*vL8c?vursXnt0KN7J+Uy6;cMHh;C|J*yu|280V}9C2)C*F?A6)@|q!89I10~gQ**nA1XHrG9p;kStmjcID$G2S0 z<|puU=Tok=3Ad_F*swMM%0W_+$wsryLR^Z%!bWVqD8NPT=UEeJ()S} zNh%N=5+L;}x{N(uhwyM4^K3Q$wCOC5*;;}6u0knpx8T{}S;^6ZC;m4Q(Q-%VMC?9* znd>R2vewXDxliR2eksXDcl7%SHp@Z$iK^_kV0T^tftje1Ui(*ZJeQD~Vgb=BEZ~eo zQ_RlJ>Z(x-KsdbEJhi@d2V?xX4*J+@d7R7K&Gx8T(Hx_;d83T6wMj1y_UoiwHzrm* z8n&{rSoY7j(d_s(>j_RS^#ecN+DW&5wu%Q|%mRW0l!r;BjJXb&Vm3^9?bZM3iwT=} zNM*IKuPF&4ChHF2O`#5)>U7%Y&d!@T-D*twB%svhjXr5f+-jwy?;)B@uX)?TdZ3S? zNC}s_KaJZ4x4T}w;LqC(B)}@Ey3o3uOMZyEZHc8|j>wR?Jmm@yFk7rcySONfl9)Qo zP`aV%lu4-1PKScS=0RQeS-;VMHH{ctPq=~F)YFd$MR8Rv<$oG?2$s70+(cCs4;|Dx z>8gXzNd&5a0b<2`jIStP^VhR}RoNJH4N;Y>!+#=+qf#R4q8op^M+T;PncW$}Q3UTd z0)uwE2H0u8h5^ax`jcNvWoRv?#K$NoV8f9h>W+BNq9sV&jSl>XbVy8MKN$ATX;5$o z1#G-R?-K0v;3i^Wn|wR=Y&{!p+E!)`y~ojEb{dv*i`B`QYm_s)r(Eitm2@wCj;J^7$uP^WTT! znudl3l${*%3D#3&-TePFQRu{JJSE}rGkSUvF)!G7#19o!&^qnZ^gWx_XZYL7jG@#h zf%pT*iuh}i(0BLHd58r5$MUc}6vWAG;W-8;Ja})M0v{bojH%@SWw=$9 z7w%3G-Xy%Y=(_SE`A}-E-RAg!6YR?^Ru$!( zH*bG`_wL=$qAC+LDTLHNe5#%ceKWv>KA?z+x(yM4!Vj|g0~J{Tfvy!#J$x0yXhpsR zYx6TZ(FH0t0^L_+uk)EFSrHjxGhUmHi)&=Xa}hNUCY*2SmnnxE%8I*`5bK)| zMgc7#e5d^ca!Uf;@}V>ZR_%YHm4$nwRfP_|3OQ^)1q<>t4d@&|W_N(w=_Ia)&q)KC zD(Q5@9k7Y5*q#a-J~=v|%!C6sK=HgUY){dJQkSg=>5zN$P$F^0{dG~%W2uYRgP{?D zlqMKoNvhp#?CiEQjF7BFhceevWFB}Wb?|XtXgA+Dtilf+U|q^NX|O25m$^zx-=kYt z0D9_t_=Q;Ueow>YMNIM+YOv=yMGUd3i;p>QkY(Z{%M=Y29Bcdj-SB~Y)Hi}lc%x9~ z$G0dmQQMOlXdQ;bW7&_!i(CGAW{aVMyc3(ctW;f;Z)cc#aZ!=__3P(Sg|oDx`1uKt zH21F@8EJ*gJ8QeaF&P-xQ!<63ta_mIdKpJ>?dD3Ehw0_EN+tr`XP3tEm=7kXf#R&I zregu0AE)>RW5G+dm;CDo{&-;_XtE!+%B4>z5F^cn{&g_@-S;L}7-Gu}!Ke!RuQR2% z6JElLU9^5@xm3AbOomgeW59zaKm(L)X*phfW%m{Pr)Ot3+vub=!ZAVJ zDab*Z*JA#r5{;Mgd+mXz)I%qUtB77q+8Q2!eMzE^Tax0H)&t)J-{UZ7QfZ{c`fMxH zgbwiT%?(VkgtO0Y_5*<iL#Bo(4?C6s0n52bh}A zT{cscS7%dyKO!9KON0^o$Mx!dCLcR{-ltD5q@$-sQ=QS*g$&Aut9ml~U%XSl0D67!2WSa=I zSS0}Uhcp4wkFjHhPl>eF)!+*@`9@wo$mc?Fwp%@T` zR!ylao>yD}ySiYSlh2Y4A8j-4{!z#M$F1Ua+!emN=S-{sVb+Qm#PmWFf}XFh6q@>Z zm+d0z9`tTBUNCJfY@H_v#bK-G1{RR2&)TmjICv#33bLscn3&_$3i}C54d8qfdQ%=c zZV{ZPFxxOEG;{*hK~Wf%!dq~rD4+6aQHXjHH@CL7;AJ6}3fu~mzLwLeK=IXaP-9># zv;^+<2vm3Hu_RI8Y-}7(O;xA6=q&udSd{;QN*A^QW%Kv3(=#(0 zh7Ld@Z#w&*$7YuG*j5q1aN9tNyo>Ex@io8UiwX{)kh1%Av1ow-&?ex0I z7D0~TL+NVr^lSw8dAbD`hZXa$ULXsUh}SACef|0s*$qiVReGUe82s4v;~kSXsoK&o zT~QpS%L<}nt41s2G$~L}>#7C4%4XFEr(e9@QM27d5sFGs;!`3jDJ>O+BP*I4dA&nU z;)|#hJY;d4usC~$zsBTaT~%J5AjM+?dA{!Ckjpk9ylbq(TJ6B2@EfobG@fWxc@h|W zDc;d5;wHEfswuO982Dr0;J^@zZM*l!Xd6y_qNFZzLKh$j<;EU>g{`P#J+eu^bK#{f zNDFsK|Im^EK00(@gpUM6N2t#u-Z6y)meON|=q?V7W*jplKY#w@imH`8xR8*>vT}G|TtzC2%+Kp99xTWHJiHQtnvaj< z_>HUo2CjS}SElnK_NzFGl!Gt3;agWAVWGUR|2PmsiGo_yK9x~Oh8g5cMO%=PLY)#A zhrQhK`O4s(rnwSMPELr1tk}rnc(e*dB_)k-8&OhGL9yteb05@#_h5x0U=a&w%~#BC z$m;!F;@u;eLri#4Z>FPI-vS+6)x1QDXV6YAX4fUgCug2!q~zk{d6L%Y~DY{A}J7ZJn2`kPs|B z9ze9L=MF580xQ+h7Gf5kZ=jrR02YW3Zw@`bMN1I+g8DHykA*K+q$oIuR|3*}-|1YX zjfoJVehvrghCGWXeZ+nA@FNci1%DDK03qsIv=+EBs=GnaaxNjPMb}R}AH(;+fEErM z7*Be_;G>m0>axZ>&*89sU_jqe*6Pf*Mh8D%7b>LSR>)2igkkvQ_)=1Kwz+|U3R8+N z#CI(5-x@hJ3KRpcN-_tMBNl@(HM>uLY%7yPP8%T?p8Lfx_w{RjIY}T89#AYs4k=mP zU|FsVP=vnu(|DMenYrwKv56b688t=f(+7g*C^=5~&%zE34qw6uS+a@5bNGl~(bV~& z50500Eg`w=1hrtAM(K~uk2atl9y+KvI6K%JuYyeBS{zHhwh1di^tGe*qB&yZkS$w? zcp-`zM=^08hlu`Lc3+>a9QS30*nx8)Qpo$MBlVZ8d6`k!APC&jlFdVBf?6@+ zYCAC65ZHAWN%5|dR)>?*3oGnnLsoZtfca(jZDe^C=o_*YMP$HnLI1hBpPvQo3 zy6K`r^OYcqmu=mx90nI#c&^GK`b5a%8iqPC(nPs+`*v#?t#AF<(vB`ZZs;(yQqL;o zqZMO9*XRzPf`O?|-@f+mgKro1{j=1HaS(+{Vn#+sVd=a(w<2U-aGV7F$1o`#3=SlO z9?#6o@W)dBBz$AI9=-c=adGZHOa5hn`d0`)PZIxKvZaJ}Cl4?0 zq$uidWLL09L@EqhbiZZ6or{~BIOZA@2tV%UlM#hON!gtLO*y9myxWs%Ipu$snGi1x z^3-1`8`z3aLdvVK+NV#SE-v0*L@DGUt}+(7o<;wjCyjN9WpoWFhHydJ{!sEzG~gSe zIPcD~d>j61G#iT->{rnUJ3NdM2xPvuwo;^KgEK(-$<+2(h#GQ0pUc;>LjghyS{#nd z_3&|#CYcIs98MdM5l)f8KDV|o!B`{_o;!cKuq9T`TcpXRhI0`M@k{{QiZTzClDubU zSNSzbiid(195*e()Gi3K#9nt5`jzEYVUyvuWnMv15dl}hO{DX|08XMP<2gg4!SSFafogI^9C^H+zTENPJqkjVUR54VFDeZ{1mWsh6 z1$9^)1i1g>4NQql9g$G|8?{~O4-c9w8-}9sc9X>`%-e znk)l8UkXwl)mkF9^EevyQ?F=-R3Gve)WzFl=~-D_-KSj1PeA4{IVY9+IXLn~o=e(! zzf3T zfp=VQo{B~VDiQ$F5 zq3GSAS44V6&7wXfVAt4gApiOUQ}j;F7eruiiX-!n_9a}}aU6bdBKUfIKEbP_`Jeyn z!%@Md(Qb+iFDk>*hYlXf|F^T3i3x-JUQ@X3QE*6w`F5% zYn5t5$b-iOVO#kUu+jcU!~{ub5Nj-S;Tuv>T7}92iuo|KpwNYUk$Ya^8_et6nag!z zpbb-9i<<@ip!-u~N#+HJRb?+XbfYxBe?%=k=VcNbN%0I2Qg3)*Khi;NegK;gE4%bjeu<^- zjYL0P%v<8K!!j9nn~6lnGoJ^o_&BRJQtO5R2UF}e)@ifkbq+lTY1`_n0~=4@W>Fb? z^Q+I5=CRAawJz1<%ek=GvLsaZ+U`bLis!P2uZQ%~A*NYQ{n5L%@l)k=?p2?YZwfL( zV~zyB-Tb>&mh)H92Y*Duy3r3OeD+RX4laIdI_8$fC;Gx7$i#SE|JmAaewmrt8ZExP z-|I4B`pp>BVZ@4ptcU<2Sy#74xzA^ndJXBFL8J;^1jz@k+TN`ZHS3)qxOW^!A89nFK4XEtlz&V{>dQx2n2lhD8|8Fv|=rQ7}@YAoBm)HC$(y?kDm zwb!=fZf)K!5uPu8^0V1-C!Damc4&~BpKp`Q@#~%m zY4Pgks?eA3^dabb67PKW>#Ih1jib8mkvKn}EjiUf6^?%Gh?t+%__xNpW$W?3J5GdA zihAHc)T%H!zb&r+DtWjZM5oE(s>+_+ZG8QKk(}hK3W;dj)2~)^`@FsizJ;F!yvPPs zHbsWs-A!4)s2F(I)A0$%S?EyXETP*>&d=4_j_Y#@qi^I{uaQFx6Ulr5#O-78q5q28 z$B+r3#KDws|Lpkg0B2SP9UUFS**}_K0gz%sv>|9??12M~+uwPQZKSh{KOW~+sxf@8 zf9EwfEvjv{-BaMvc%)7KVBbB_Z8QG>n~GAaOEOC;J8_^vQ|C{bPqBFCPbYc1O}zQv z8ziZcTlE zeZO@*c53~!IP56<(Wzu%#9r(EL|W_*2TO<*pTMdV3v-pNOm}sU zEglpgx5WRWuNTkvXiwj1*S2sXTQ1PN|7&(&pvkZlj&CCUv+QTJ`GFxxsI&H>tA;eNITI#Hpe)jVTC^6M z2d=k&K7AK_-zeksXwT8tt8AR`N6)35nB-*Q+E`VF?*c*>E^&;F*I%(c4ZsVOAINzp zkD$K#r0L0P8=*>h=`kc-vsN_uqC0mxRE;kO-}TA+D{7+_Tyelg|LMG z(%SOlSG>ao9!jgRbSwTdc$1j&^?n_qUKRxorfAKb_OUF+2KAIJZra+6Ga70ecswfdrTbv4%%vdpf%EOCe8iMn)<)x z-fQpFe9Oe-2GcoK3{t`nnAs#vQB_pLM5n?G{Gq?xA_@b#I-^b;E((>`WY-7pqey~@g!42S#B8|m&l#p*S*uLGKb;YLQ+qm>q-@AN4P37k>Jnd2wTT0Q%Y9wBOE%T$f?b341sUqaNl-n;ad8>#{{ zex0lR#M>&t>uN?3V-v@)=Yp%AhD|IFtvyU$A?oRznVWm@l>7@N?c!sM^A`RaxZ@v7 zVC)9^Oh~Ow1j$pba7Nd$N%p^;Fsu~FK`ImLFh>veMz8;}`0o6rU!@&@2pDJc@0>L} zneh%$8#`A*l3(sv-15;*Jhk>v+j-+>e)f6q=}IqrwdT3$2fsL$FF9CQY+0<;ajZ{l zbzdjZ(@PpZx7)7FMx;vYGSU;=u(PK-y0s=+H4#sf+~26>J@NKY(yx>K9M_WuXGY(W z@~i(tHrY8PF3TQqWgpFM_nJGGkBhe-P{m}St~`n&j3+;vV0>F5PeY})YwztskNx5# z7R##wZ9;r{JK7AARf*6p+bL2Qo)13Jy;S(&Lwb$_oY=>9c!mH%7HLWtl7h(?;Kjk1 zo)Qx^3cneZ5j9O}|KLXL=NvZ!%g2=6nm3+i9+PM;v7}1$W|*rBC6Ck)-Vlm^BTh)R zM%=GdBTmV&Y-Z)-)??eWejIPNGx6i@!>as-nlzb5I&yc^z$k$6=JGqdB& zzRJGz&C^x-=%5VOjUBz@qlloylUb1mf#Y&`*bLzi=7n+adeJ_u@eBRa4@El^3MtxZ~oTZTF(M_%V@eqX|D;1V#7T0{vXJhpxg{chd?V zTZ)>W6q${+OXvLOfCbYRQsyE6I8k8xFuGb*B^LNo94Xc?i(w$5OJNglTfOOH2%233 zSJ{x1Oi0AdjaD~#?dYIU&n88plkv;=&O!`(M6FV43qU%b>fh?i5yQy9hgnKls`9Dz)V3w9KUk6=K2 z1V%;~a`)nZ0ewD{6_R;fa1g|dZs(0&d6{f@7u3jHJLvevp{jM-Eid64@t$=&sz6k1 z=cFLD^XeP&zOzw_pati`I3O~WvJiahqVWu(9kTOd<(c;C&t&`!CN@^Y-UONZI5y$0 zOE>&$0BO=xVsguk zf9uah?tE&6kJw%GpBfHkYVmCM#jC%QjN>Js?^!$o(!wZ;?339J=z z_UCsS=mzBLPtHGJ7Ps*q)%O}7%BFoHhzNvY;5ESk!32^;dm~vig?sZ2sIhlz{!0=^ zA0w5N-nH~K8dC`x%X-~zHNRQ+wY6=WxAiHV2J?wv1Nav??rBwhE~Kj=L9SzsR#pP+RU8l_1@CY)NWgpwh1$TbrybH2E%@ zcW=~x14bAmmS0kD^l_hc1I$dXbyV5tJP$Um>0$~hO=~v3j$};tf5kM1PE&Kw>JS66o z+`BZ@ksIZVm1WBe1-TXUk_U&mJ*Ht{?kbRjO>H@UOh=z+57UXuB&ZYk2C#9p6meZkP8NKyOcU;{#L#n^obgky+j(3#Fm-}ai( z1m3-XN&T7j6(PGVuLrZ9;VX`H!ujjl-vEtt+%VQ}XR`EE_=9PSsCL&7)`{TFhbPke zJJxO{tA{5lZ&`Y-Bvn@I?mZ&-AAV7|(n>)SBuU$qbCD_THC^rEo(q-M#PJcyv6-4t ztn|_AL-F7;*~{e>M)rNdyE(6$Hzq5MlPiUg6mY*qiu>cJ+8MzZb=fpj8QW|7{&FzP z=1+Nh_x|7Nybn%}Lv$fF+zZ7dlTp?=pvy(K@P8qHPzs4w)Ap^^dUj*TdZT= zqsB<`I2mpbm3U)jT}5b;;-~FBtVB`G^T#Ve;y+#Ltgics&Aqgd{FqaF=kUuf->n*A z>Bh)C@|wIGSIIR^1WuD{2W$B0uk2-a7$=~VH76JPu>Q+lheS+)zb6RUt9NSVH1OP_x z&ny7eqL2ByT$ycgv*ba4&?23;)#C7qqtT#6SJxVWDedkZ!Z|idpuC7b`#2LnnDc4d zdjq@heJO;B=du;2DuPn-xpc;mzR9?4-X_Fz{~dy<8E=n$r_7QA=tEXH4hll<_?Q-=mhAV~Cl4&*hGzV+ zKbgm$O|3_&vLqt7AaRt+F5XCua$MoLVDyM|J{XdJQkj75&s$c3CaJUJ)aI|+ z@Sfe&bDlpRpDAPbZ}ajW$3W2?OjcC!*Bc&LwG7ny1z3mSe=ERuJ~&;GC*Yfad9iRG z=NNeCsock$@{-LH>}Pe56z(1|k>TgK&$~7!`gbhFkDp1X&K2aHv(7yeiCNHPS}+Y;1!GY^h`Z2}4#NDK2nBD1u? zFu^k4qdpcqW^nni%*&keR76SpV4h5rY4r}V-~Jv#5h8M-WiZw?>lkI6#l&Y!DJy3$`tZozp0I;#Jv z4faGFud><_RHGX`Vl zvVK=LU9SJethe>$s@dZ65c#E;J%-TLlclUQoYh6}qC@X_Q9m9VAmr8PSW%5sGJSp85SSE1%P$~$` zk#{C_Gc}~Ob%JK-$&#(1C5c^k%i2Q$& z|HbBJtx-I8kcqq}-0Li2hqnixp9Ios8-LHxq_l2gNG9tx5RsM)L6%b{8ybsc1^o%!2(U}byxY$swu z_t~x4>Ai9PhZ75PImzB*Yjkt(!mHc3i7fGr1!j%I!fUtbvPOX}u`twTq|tiJ*>&OJ zO|6bHo0rdFFFbbbY(} z0p&ItKRLi=kSk{>9rI+=)H>YkhZC=5+4%aV^YvP$4%Ya9V*7?}c^Gb#!)4qcNw5s3~CDfaz%!={(ZM+!HKp=&4Lu`-Ev` z_f!*Ulxb^y+K!u)3bg2cTJdE^0*hfx-oU#v!c1BQ`mWGV`)Gd{eEdsPaN$-^NONNy z-_RM}>F7PJ@65j!_Ewsp7i-IV(eDOi&Mg+DRL*wgLk)o*?$%M&FbP?EJSJW7qm4@i z8X&thPtwYjGaTAK*r?Zh^?VTSXWU6@!PcI63{AiqDW=pf@2JM6sUlkNjXC(S?Shz2 zaY^aCKJAg$XP@fHdj>*1@;J+X@-3gA*IP5D%u5iQh2Naay?;?%NFjKWP(Ig@)K8Qybu~C&?3&l3>UN4iPrt1U}?4 zB5h@zlRW6Azh{hKRYlQnJE;E(wk`4AD)|?#SVX`Bzn?;aDJ3K(p}w&adv_Khx{F>pNEZ)Kc4}5B^wf))2)%9y1{`kb5UCbBvzqy#XpJ67nG zVm=UKICy0Kx3{B%1&3ay}XsJ z@JpP~IL*W7Y~kgk#bnFb@5?&{G5aUiFa?*MZ_Zla<1+W(iTP6hXBG}_sNYu5T&>YN zf+~k_T>VDEnsWaid_0`Dr(np!#g>q#H`BIp~&p$%K! z{f@-9NdfaNUhOPcc{G zn?VkOud3@)y3p25zU^!@0C%2q9FlpWIP7EZtpirR|9NOT$7qbDXhfJh@Pjf9jmNW-E_x?4e{yFp;nB_Ps$=YqY@Ipf}O_Za@!F28Sn zbI$jjZ#>U4HIHfmCfE56mzFGWDb-Kvn#)2b7t~Mt`z%)R+_ob>vWtFr9P%eRW8tpJ zVboaJ7`;=*)xH$H@QjU!6R&W+N2qf-;cg`b6S>9dT9w>BM!7fvw#mR2e^gq+7wp?W zP3bkdMogh(FA=hq^`zgBrN`^q6IaJn&!cw7&&Ese{D(}Ru;3l7-=pu~I;_NEX1t_0 zVrk(*j0lR!j(o4{t40iPOWzH8SG5BPJDpZ*xwhaz;^Y|-aAlnOhgxKVm3I=NC6zD! zO&UQHE?-IWH-h-ZTXYA&U^3s#Z{7Mme*-avW|sk0HE1Uw8KV(ANKk?J*iw=Tl4x~b znUxV;7XxQK&_9`imKbNf76wav_}4a9>`j@kHX|?DprvDHclRO*=tP2Akup8ln#5*)5(a^uU|g{gh3(%Oa|>O<|bYqFt%?n zH}Ge9^`s7=0b)jxK@riCKmNCKKFYxAq>Qb8&T{BZBP5nKV8DTCB$YXYIN%N>2Tj80 z=x9Izm!$v^wB5gmo1?TTOF$CI!&WEpEk?o>jA@sb3CfyX!~TT@Xz?d_uII}mm#v^W z(Y2W%1tH3Eo0m8u=L!km9`?z0nwpxrcpBy^?Tva!?0ag=Zfjt0l-Oo^T)6GOkgHl*0CK#eNl9PYW6x2cJ zmEry`LL?@xUfd!eC_?@Ns3>r+-vU%7RW?tbN>Sj+05WeW?SCP(GLxX7;N&+9V<^}S zG)Ro7Qi^NS!?*~D_NXS#S$v$B(MM`VLJ!@i{~{_ZT|WOGM2Vq_u5LMDkgDKI z3I#h`ZxVwLICvzpF3V@kdXJr}GQDn1I3vei8QQ~^C$ISi7)G~)Oi(Y{gCr>i89nr-NKufek&V~65 zw^*E#>u2HzNc_v4$9c=vii&su*)ob;fitiI2OpaBn)C+Pm%;tYcz3+j@`_0-9hp6l z*q~^k1GGcqbRkf;Skk_Egt;pDZf+{8)OVALy@&eJ($bEOypGC{QRKcwQrZrX*;PjG zO?KsNFshDo7Q(P`gP|IYytCQ$6cFNb);fxA0ZWH(X%3C=y4`}uUVZ!zF9ONE`;tm} z;W*b+!pY*Pbn z1Ar6VUAe)DLQEf2-S^cK_ZM>nyX^B(Nlq&L?W1;E4gksw!Eq*~Lg@jJl4@#drybFikUNMlko|<60;Toe_}Whp0A?!+S7N9GAa_84DnW%)jH6kdR4p`KlJxfxBhtm6b6A zIu+&Rr51NOvB9TC0R^f+7N-VLPEM70C@6RV9x_GfFMfZcLt=>~^Mvm)^q z4ntx(GgCZC0ccSbV0Tlg4JCmy0+Kl#Yg_8VBhGa}#O%UH1h z02xdNBnDR}Wp;qM_1k}Y_8mYXBUqhJ!&%aqbwLL*t%jJ0NJRz}{_>Y?2jzv#gRx#H z7N{~N*8)D~2~#-RCaC`q!0zkeoE3WAB6v3@E=SdF$0^eAFZ8y~+toY}3V@pKbLKad z6KZXwrGSP4^+O~(qVNM?M`XwgA*^FwX)Xh0zL z3~X{*)mv1R(tDGN1aN#xNp!U%BIf{l++iJia|3>Z_y)LAB;woSt6!R|@8`N3@EwG} zAMwf0-@m-Y3EBxTWpGx_s4oC9rDO#JJdh1J+F&+xkN5XHUcn>=*DCVXPyon2$~{p= zT!aQQ%Eg~CsqbeTeP?GUz|A2kmnxu+i`*d8#NgjIlD*?f(3}Po69{d_vf*Gg%GnQe zpO?jt&hr>XIRPty=3Ib}ZgeZhyx>P_vun`xib2RC( zgPj-{W*8!&L4_EwqL%HmYkn$<4Tw;1xfrW~5qwol)grLTEJQ{t;5~2dH4K?9t4#p( zCa?{H6d(n)k46XLfFH>1arCbOz>^X4sv95`WYG^i_}Yfd;}c9`bM#F7)WRz}73C$8 zHGFk8(W00tA{Y_t&x7y+QWMF^$v}&t%qs%D&@k*uR$LKDIbhfgIVRGP#P`l&1k^n7 z3t)^tz)2X$X0Ui5e zf_rBK4bVQp#Wi(W0*V%twQrX@BS&cp?Q_D>KI7jH?2 zUIKAQtQw#JnI>TCBV%r$qD$;W z0!mNGc|JztG*Wp_RaMn5p%E~ITU+iC68h{I;^%~E99}f0crHJUc(JT0FZxd=El?Cu z(!~ZGyGJPApFiPZ72Z?`?ttSa5cgG~dgc(O{j2}X8)6uvg)@B|;|nAj?-3fqRD+M_pQjOvRf6+guYaC1BLzbQMOaw2fOZp@bNF+BmKENq z^%bH;zyXYm^dAQy;Nm%S{^Mmp5He-$h^E-9L+23&ae^!ZkA`H|WM4HPM8(|*O+lV~ zkN$bjnnTqJ{?4j)LSOBfXupmkAXRu>2LHo@aI5T54 z=>faj<<^yK8j3^&ZIwOr5ECO;MMp>2bP)0L*BX#bslZU~z*Ky9j(vb+Z{;6)70d)u z>wwY;E)enfXmKOr0Zcav*{DQ{uc_Exoa`Z0t#BnTSs^~kmYnO%3?9U&65S(llQy)^ ze1avg0pQa?q5}mpxDJ>h{%-oi85N{X%D}>H;7m;ew6EfllUZ>HzzJ9eGM>QTiwk#8 zEdB&};P0uQfr(GtTZ%iVBS2+(ek6Fc#5)Qj_seN)Y)pstS!ab|^R$*_Ua_ZUQH>I0 z(On)89exW7MkB|0l%7s34Br{ z2!XW&Z;_HFkN-nI!|TD6^DM>L3`G<8WQ>K8&afITNaiM0Xtw1*= zB$Z*3#k06S0EP@4X#@s168Z#I4|iX~)95848V|Ze9jiu-`XfC&HR|TeI&E0; z+_3+)`61}r#qt0LKCJ!flIp~3mg-!>b{+fmSC~W}v5rhwSMv`;Adhu@D9peBh$9eV zIDm@&CI-cCq_#?;0tgaX{3@GRg@S{gC$}vb_1UT@3s!Ycp z=nzOkc*4O~%?X3*;7dhZC}hzmDntUqfb6PzZQ~vTW>oq3{C3GNf~337zch|j^~Dst z04q1Dy8p0&zr&=Fqq*i}LLo<-5&Y2ULZ8)(D%;m%Ykb4uqQ*ML6CI%i;)g&Q5Y=!a zYb5xc0JCddlLS_IIRLx{yO#gF$SY?6K`rZ3AotrV^gH9ZW6S($@H|)0RUkDZo;Lr~ z^ws>U40M7q?NFr&iwX$Y6}X*&X~AUUrNOTtl1 z<5~Xo4x^ww_ISfWPq3}5EJN#3BN@{LqnRiTZ}$VV#m4f{Au~T9f?OUnuP86 z7nw_OO(Tv#&(rai70$D^kJQhB!olSjh9O6DbIqvW^l|_U{H*@@)vVlQ3uW+k>L@}` zX}EB~_(LXxU@6b4u1kPvZN$bBE?V2S+`&%Iq1$te8%r(crm33=4}Cvhlb zI4|!?L>O1OnOSb4UmU3G202~4x|1)V^KgInTKPE|KkB5jneX>+aoD9u`U(_*3MNbn zoX3NxASg?Fd*IqMQTWY<8KRs%eCE*dyJ+jNe_<-kvu?56V&$KEiIX^A(whI4q&-D$ zFx&2t>Ib(U|>w}2z5gNO0d3&|FYs9TdhjsoWl*JiJfW)FAx3XWG zbqxbO{hj@gSA~vsuaQGwdN9zg!3RpZmlPee*FVqK@o{~$S$pWNAM&M}5hk3b`N5(^EZc2P@1@e~9C z=UrZ2{-d825*!R74R{xD0H#pj05Fk(%oH66WRf_c1msmDKw*``b1uwvo!~w>5PK+0 z9+i9mA;5p};>AhH;7vUU5(D_nKC1u~jtVdlfQ`!$o|S#u9)u{PAr+VkPKOkCrz9d; ze|#c>zyYw=Te| z6=;bg_n8nrdOr5!PbEcl?M+H4x{os`PeeM?Ub9Fi#_5eoe5|fNqzcBcZ`+UdwomU$^dY_io3Q&m$ar11W%f2rL zK4a`%R1~v99WU;g`jO)$$hVBiIo~>uNT)T7kMMGb{Z)m7kz*hg)?mMecW$p)LXOI% zkgpr@2WWEw9mVs704md10x1mxxK6OiKx(KAR7q(7AR8C(cV`>o0OhCIGt=0i2Mgun z-uB~|L^JXyq6OAU)m@zaL-nb0{QUF@$P)Mxch=X z$z&$%jD7a051hJL;5)*|(C|<6@=`~5G(J#Lf6)+6O(1(OWlnzn6ATiv^2=#j5`u}U znh8{DBdESfNJyNt0WBaG7new?Z%`<7{a*bF>PI+*EioNXs(x_gzVoYYxee%8#ah!y zyabDz%_u0CaBu`P*Eqil4ru~j{+|OI54a8p_&;yPy04npT5wxEK(7tNU=5cJU*ep831MAlUx!)dGF- zESqmoZ~y(7j(=Yz7xyHRs=m3oc|97L4|w6U0J*8k9!!*vevfOZ7k~txz`X+a7jP#X z+q3la^sE{$pc8&|b+z2m-`|gcM}g3VgAqUWSXj9EfL!0`jdN>Yww|Zn|2})?m*8K?iTU~Y_ICdZejad- z)#-jEHW>eC;5}(Bpw@peo3F+&oefg~0)k&?-@SXsLZgr@5a~1or;f-2Yqt*+5P`(% zBGA*r*=1I|PerBFkOg+cC+pN4n|dhyz#)5Z*b{SS8+)e1D8{rI0&hH2E|nBP`%f2x zRTUMFyk=%*fbKHYW1#LR4wR|T_7M`)(L|f6$!J(XKwQE!4)5mM{2q$B(oJs5$;shU ziHQKq2kPq0Af*I~ryu16FpX7#0yXpL3aGgw1p(>SQF9=qV<q* zK9B;kW~AA#xU@6bm!F?4Ma#aZ6EEF0_ zsR`DDfCwsM?i!!zpf~spb%fAmb4>FDdb~(v)vwOUD^(TI%@q|fFLy;ZQ^-d+ije0; zZ7=yGs7g%xgOM>4k&yIkR0jnGRm$DT0191fPl2*+6!)D<^RdkXGfe^tR4z6){pLm> z6Ml4bM3}3g7OkJM+^N0|zl;38*-e3vt zy7_yaTZw^5fcXO`k%k)@8Fk@W7HL<3D-f!HY_-J%wY4y&F<(7E?SnFsp4a6ekjB8k zz!=U1YV}eLrqp-1L}6#E`n~ODF+kv#dZYB zl!ix1qPgTk+WQ>V1hWAL5*9IPcA##?@W0 z*dMX1p4hDpHuPTc$BSmh+vpoQ9cFsj^+l7DH?X?!dhEx%!moD9)+60V=WVqKc*!d} z%8K9AMxp;Rc}ko2EfKFafh_yl-vv_Wma8`t8-UxpO$l(7Vys3o`c__Te2qRW2y(Y+<&*T&TIRdm{4+b{|p+ zJ9HmiW2ZED_$y~NQ(pc$#i;)!m$*XV8J6ND57e9Z_j=z2PpqJB*M;*JCufv|q9@lc zS{}n7)ZY~}QPHx}pMcm(v+FmNT+1Pfw-Z>GQ>(l7wp8}l;?uKTF%IXn_D2@0argzV zO*YpsA(aMhN~{0eUo8pk4HY+YW++}K8^pKP=wm-$P&muQfE-|-J$7~Fw!fyY>VJEu z{z%x~0s6Wc^tB7A>>b4Hw@J70(7E_iG!$6>++6d7K$7TX)l^_mVad@4+RP zTW^~f^=t(lC4a2Vt+#Glz2vE?O&J8Ce-v7r*y^<_v@IsBbe;0XQy*voA+E+U zHmiSr7vyu~&^$mtXc1R$x-2UazKn)K&{x(rdb6pR95{pP-C_l+nm0w+H)HW3=xDrl zqocce>9Z?vlZ;E7L7U3e-pw*IUXNtDan;>fzQyTJv_TYMZ2dL~q|}j;60U!;tNac1 z8M>@BVnp&-J?ipO4d{y%ost;dEtH7-Qay^2<)Xu~cbRcxvwfhX;kLivm3jOx?M=oedC18v z3}>ZL>)!r2tufWi)C&}z8MaDQUfRha{7%vAT9e%nLiv2-w`mgTMhe+370IgEyK}m= zQuu^@p%B*UH$Fte%BO`E%HLM4uvul!WD3jYDEJy~$z9xR>LKEptrNuygp`{f`O2^x z-%H*5?g79$0hCQ<`1h#Q#->UP%+U;Ne{X6Kv1w1)9L$O6Ru$YkEC8dle{ZLb$@GFN z80xWRH?}z}>^f9p0YS7MttA+RjLv^xn@ZipcGa#q76irkhme z>kl5;8;D$jOErUkleJ$ojhv(md_zC*6;>bTKI}$=j4aeV>(*LZ=zbGVhyqc9&q5vV z5Cmq9B(3u+R=i#Mu{s%wo4;U}`}Bj){@M~(hRh9^w^5;-FnJT3Z`; zWB}#D6>aQDey#hqNyR1_Vn9m)a`T1sk~HI^<`*qu&~H+cqVcC4c{I*AX7IEbmBStM zLm@PXn$bJ`)!(mRPNI+2kHmY^kJl&YAo0}V3Pw8@at$Fq^3Pk?vgTR)zTl(twOU)Q z^%Af(H@|VG=kyn>$Q5+xdiAWgQx0p{<}LZ4a1PdFci@)({W3{^`E?@p>XJZe{^ZrR z@1dtU-@S8w=HpgkU`JrBw>tOoUuVugfPNdDoQpoSc(e_;nmc;_-sBMK-j93IZbRQW zz}>Cy3ddEl^}-q@!4}5>K$ZnF1j6dhl}h|;G?H6kB$kh`RCb5 z`k`iR_a=`K?USt(0RB*WrKqAH@8n$OrmWRH^}=#x*;R}%!4Iu|(O40$T4Mrxpj!HV z7RHjXjf2g%dFD58D*dSI_7iz5&x<_VLp7716bW)}r4W^|?T}u4n_-&(FcSA;F8gig zX%BV#OZD%TTZ%15kZ&=9&A%``h9Kw%b=!kYP&n~SeZths^#+Z252N)7a!5Sdx)`w| z8G-3B{H$oma!T}3Kzs{uAZ7=%Zb@>wDKCf8NP@ne$kbjvyE`=$^mtoPrz>Zo0Za3z z-^qLSWY!v6g&<>NvbHym4^MR-vNcfj}}5N|KRQKpC4G@LV82eayap=H!2t&$X-7z(0-Ka z>In5FE307N-&Lw?coJBhS0ib^Iy7F6{wCE}doOT!eV(`~`gB&GxbkuT?}7dO(VSz4 zs*a%J{_Yh^+Ee!<>3S(Vth>xG*AHfuQsp~i=m&#_90-LVn=2XQ3=R*4Z}5vlIN0$N z4R_`dvyNjM)0`$6UT2PI5R)HA4|~PWaVMA#-@)h48kl`}56@5__QuEX)fR5otk-F$Y$ZVrnd{?h0dDjJ3Fhd0pc!B%Y~-jZ3*@G#cu$&r`9Fb~c6zS?vka%W=aJ z7YXpZgm`Pg(x0(`^m^-~ul)?&*dcN9J~{Ea&{^GNNE*?fma660oZ)@{_R4Kh71-Ig zN-J|(Obn(F#7GIlkAW+kW<>7J!=`Uw5tX;;LE0X&HjqonE85cv;%zv|9b5+#iYZ_6!_KB{O?3FrVo@M>+9GV{c?ZMqZD^oD7(at^ zduNh*2`Fhv+T~^#Plr6mJK~!x^T=6Illya*fn?(#IIR9RGTaA9Q0yV#NQkvE=H?*q zHy2GIPV*-5`tOEHFd)jL^Z4%`{uwD>PoX)*DJ@<*Iu{GSI#-;(_MH9k9fMcRU)u0}A(GrJAy zzBydmH0U?@x(#}4HTpqH&-*=e2&}vb!+xLLAf7zU)>{`b9E;lfww2kFimR3Z>flym8=xh~$3s-=VQ2M|`j z9&z#pU*FFFYvO1$%FX%e)vGFt+GH)J2R~f;BaDAfo_!E_&qt{>*+2gCfz5Ud@dZNF zfvNRx4Yw`Q{ah5?&M%A3dVhi|ISeBA&-p@co7>N=>6$B(YR6o!QHTLn@_dnz*Vr%b zPc`I_Z}AlRIYtZvnLu zZO$SKVcPtiD88CBysuqrLO*1>`7(Ks&$*mS)`th)Zj^)`gx`m-Mu>~?f7nG=0sCBt z+XEgI5?vIVbb%btPL>hG4#F#O9!3e);1D5>adfy;dddd(H0$5TeaY3tn2<^aal;Iaq+{k;^-d2udH_<_d!kY z4j^c`uAD?&u>XAban%~Z$h z)Kl`PNDK&p9<#`-h(e1l)cdhfEu2DTbx2$YQmT3Md9-+`n-6gup6wL@G3avVf7zf| ztQY5*<>de|Sa|*GT^=4@2Dt46`t3D9=D1ArCwexZK;%}FpuKIvXS>rAN`vA&kka@4 zmSY*a-Q0-lxo6jZAdr)a*CF6J>v7OSNf-nh!%;GV7I|Y$JhlOxMV{n%$mS&klB}73 zJ$HGDdDejjA?^>;Q;&Sl6C(n11*d8N2ILz!RDE?G=%;5NYM@;%p+Np&@fCf8GDJi= z2r+m9K8R!6!{0L#+?r;)n<&-UCw78a_>~O0{fYDs>p=*E#(gL z0d=IJPVg9_%^|QPc~pow!IqqVmWPiQ&CMF3&2;+5zhZvpL0p%^s~#L0IHDi4iyq)Vmjp*p-}5`A0MSxb zmO9Gt(beUk^V41e$m!MxK>;sw&)?NW{vH}Xgh<=pjbwc1BY3F%$vdlb^(w`ZL8V>c zx=Ifak~2!;Yn+299O3!d)mVN)(_*Wj-h94typ8ScXpa#e;t7^&Jm^P2>;cBN9WzH9 z=ACbA-sI`Z?>ByT?a&(cPm_5^rM8eswz~}4APMlP_`BV^-#2kgxnB!^0GZw5y{TzU z7BllV@_877m630Uz*7?$YoU3QqO!R?e34{gdKrNablXvq2Szl_>+$J`KBrkWtj= z-u)4Ueo%ycfG=(TrFnQXI2#-bYV1+XfnQgo!?u87{yPAXg-x4!y_QJ4Cb?^PFNP%P zVAG6rgvh-|P5D%r%>otWj~eicy;yu*BYT4+ch}RwhLQPi!*DqqGhbGNo8{BwFdVjL zk#=G9Aa@y2#Tey7{_URUx#q;y@nzCMcAD>st!+^GBWG@!mVyW#{_9M~1UcvyGWfM@<>F3Gl+E10shQrx z!>IKXwH_f$w~}m$GzqJOVU%!Q=$ztcxJCml6LKAgbigpDivAns^egs~tt16$WMC@ZWW-O4qyLBjJXKyHOr_*YdCzz;bO#FPE z%Et_hEwKz0Y^z7*akTgO5pNFB<$$1E<+Jx2h@%M`bk7uv&mVD2;UxDh;Mae06hTy= z`kzG+Q3tj_AvwLLv(v{rX}KfpEoPbez}k5JnW5o2FRO)Ko~xb(KnK5}Qu5qmCG-gE z9lJWiZRzI)Qi}Ja%FuZh=Xke;1v;|;cI(z(!3n%z5c!0(9J8q^s<}-457FSK-t^Zg zu_LkVxa*>s>r^l}?4HA*e5vfgsZ;5f37f3_hK;!SUl7G({|L>8xEGrIg@4~c?P~M97^^j}7rs6Df_HpPQVG&x4$xIEpD_`nj zN38f~#c%Z_T1KabnVyf`LZ#tNR!v&25u(KYsh`IDMIf;0h`EUXU-G4|PKrGyUd>Lg zg|6IHsd%#=|4xncZQ(i-TzK4G?4`nEWBS5&KVn@O|D`MyegG({jCXysIsPo(psMg% zBbnRB@A>TUo1M<`_2IA6E=(T>YVdRfGP-6Jl*V_3fP1Rv@Dq=7gug>2f)V}f>1+NJ zXVgUC{Ic^@Qtj%zJU;De;Y&H#`|+)x4Mgnf1g4_!vF^FH%l&p9`1&g@WRo}K5c{vA z^m&om2(M^ARmvXBUfW{Y$B`*Y6Bdzo3bK#`3#uu`Zj7Mc$Pf&D)fUE$>}^+h~$3Vxc^l0;&ijj zj)dXD_$BjIcSlW@%axibAzwe~_HG4_ zJyaU@j7|KV(nP^XF#1KR%K|wApN!i=?zNSQuxc7Ga$74Iw1#~Px7ZJ38+8m1P-aOU zpd|$U=bh04U0F~d@I-Pi^N}jxd-Uz1Wd^;k93^Hn>VIVM{wY)&8Jn4f&dNByz?SAW zHP;A=b9#AF6A&o5{}kQpA$?V{%%d1(lFUDhfB3y@aCR!FKWZyL{ennL0vKG6x0$&2g)~ScQ4WJ!p;^SCNyY?^5NFljmBiFWe|I-2~E#n(o<8S^WHzu zAMH&1d7>4Rr)y4NjxanoPgRC7Bpn$Np6`RWg+Y))Cz*W>0yu1~8);@_-BSne<)$!T z&&ZKW zWbv~q9F-u6{%yK7?H8CiTZvPU3t}kP%4>IY^(^#Yn*jGH!$y`{nuKAstc+#cj@F{? zQtJ}upZNi79iFc=Kn|@XmZrL4QYaN{aE-(lBsihW0@?p8@nFHU$BaSMbGU=?ba@Xvs9cysF6-D1!@AE`C|{vNNB1{>c8*EkW@ua z#&L#LecfEzj3H*teYJ+G@O`7*odfRQcuXz@qG7Qwb>uLzL9ddX`=EG!y=7pdmYc&O zG@q(e)9I8iVR%ls#fHx;_q@8q>27LZA39N6^>q0{?`}hRvwg*6?uG5k{z1q@yfXg0 zIXR|9e(iSq4g>Hj7m7Z3373tsMNp6T)=A$sVL$8~%R}xs$yBW^8q0|}IF|{R|H-F; z%fQYGopmxqo%brO47;V9hep~bh5Cutv{Al#q*Ug zcm=x#e@}=}L6JQFJK>k7>-S4NRAZ0@B@6Q4LHY}<2XL?v(qD!4lQeWuUey-;v(r23 zEgc}qXNyiH@k$-PJ&(D#c(->h>#&siL-&)CEs6sNYfV#BBd=#g(95p8monpIQXo%) zheCNFM~II+A z#=F=aA67PWUiw6a^2T6Y{g~@t1p!x8RpK~YozH>f`Q`m2A+9+a0#3wn=$ATt5E-#M z<%Q_HpEWpM$xJGHH|8OnsczTAKxqal!9H5g55A}j{P;)V`_<+e5n-VZH`ZPYdkA?& zuu2Ts-?0X^DJ;*#I2l(J@0t;-ND>NJypy^+4^Zlj1D?1Qk#$?G#8-kH84x@AE;o)3 z5zcBUQ_omnd@!p|B2u=jyr=AVmnwMo&MYH)t2?LIy5pBcWFx*8D&H z_$piiD%#u5BoCLIhMB~^FFy4oUf?`(LG>^f+x`A;&JLk{r4Apr=GMGoapab>r+ z-c=ZMdCgf*cFr=4&htY4AvdB)X*;d6`683kYZW2AOhR}qNSi-!cWnQM3iPC^&#R(W zS#${g`b;GW508r%z*`}!;Ks|k_emoY+(8+ z*IOM+ncqu}j^#6A>H-Fw64TyS$NR0?p6b%79}qL&>t1tXt7(b449@jSvwjMaF3c01 z)|R#`KY3r$v*nj&VU;}jtNzSork?*<=HtNhjaKWwxTaa4(t28!dROh7VG-5z;wfTC z>kq1x$6A+rFX-$oGQcH?5?De%8E{6s8YgtWkQ5#^a2R=-J8q$Uedf<x*_%1(Du z5H!R5@Xg5(DM6l1)I&Ofw_^h6JQ5?-Fa9o*4^lP7$S3|#k7W2sdIZ*(;MA3jSRnVS zUSNY1u0a;=I@n|HKMo08hszqLYBc0MB+j@iZOQ%sw9_Y3O))Fop4~Mz|1Xs7({>qilC2|WGcE``&Y;uOE)TVhe;rI*U3O1&J+pTb@$y`t~YqpL7)QdV&M5SDXT zqCXkOBCaG;*7?th&AW1?ksY$uyTM8&>$1a`(hD5=pwRk>mXKCbXr0JK1I9~p<*0l7 z=A%dVZ_A=~TE|iqavzv({}3kA&?DuJAA+?&Yan~5)0@vyi3hb5FP-au56?V)8gP^S z`!)T(`lG#-gC0w~(n}t2*v!+lJ;C3&Fm`15o+NyVURe}+sJDl9iPvu>srpx!_tU4c zqmf3DDtg%dhl0m-EEk6E^oRQv=$)DY%AMZL_F7kBIL7c6arh+vkBgI`XlYgfIqmwI z-jc9KJIe+v#6jqLh8XGu-n}2ZzN4CJ?9;Jhf8Jnjd`c-8tnJxvw;0;3<)>W#vWDeF zr+gP^2{6S=Kf);TP#fb>ufq55WR-I!Sx6`#B<=TBEM9vQiJxG>E{Yb5TIZGN2J5GH z$TBvo-H0kme4}t`IU~i>EEuQbW#M#bvR<@gSToIUb*I zp}q-kX^}WJ44d{I;XsKnTT2aV>>uj_tffi`2lm1~+OP+@L)jf4U%dMIt}F(amM=0G zSwam3!3gSqiDy#P&b3Sn78F7qu*JZ;H7l-=t#4xQy`788R?MDq^0&Ugcd&>`F$2zSyAE?Dx+V@uCZ=YX1tyu z8}(dXTxJwBWuvk5hI@hYN>tY{EM$#x<{k9TG#hBmqa>cpwVbl(TVBo|_XfAJJbT{r z15!S6E<`?vz8mz(;28e@noCpma$L|4w?Gxps~FTI&7f-GGTxPYpqzs?4jFzszo zadX-%e=Nl%jV`K`Y+c)Hr<)xDgbgx_l(^vht$;=tdLdhCV#t=7wqeGTHgm-bhEQ${ z-qs{{5sFdbB>8P;+wxc)!Rv>^zt8#xy~HCu>dubs7f#4~mk!@Q=i1P)%hC_!w1Ay~ zo;Vk-{`*<C^5 zkPuXPhlS2NZXH8XCs=5r`h=8TnfWMd%qYaaO$UYoP}0c#SJcIo`H2HD@<6u9dO*Yq zj*Tu1X3sh;QNfPj^Xp&LLEXWdL(&e4VhbQzQIXtDAPK7{*5e3$RCOt}Z5K#-3VQC{ z!{9KWKYY&ERX|Df+uEv+JqAdm5<;do_b3)XI6W*Nn1?bQ{Op>b?LB&>Cd3qn6OnsF zg2(3<$$@x2spU)}Ty-dENKO+}6!CLqQLL|c-c)KZYZVu2_H$jEHuAF-oQfbwBTS_f z3JUZM<_7oOj{`1?e}ZcC@cFQ-ronYpFZ!c{>~cwfd84 zN)5jQzR(^342@FR%>Yg8{f`~~f{B_T}P^=k|VqN_G1fB9Aa z>1?8Ii$a(8QG|O~D<7hrc&wdq##^lFE6g_dlvDaum`dokohME{(7oqxH@R3N)K;mv zT=yf5;8a?Z#d1!06`5=;FHHlm@yi(xDWeK3 zv_-sY{-ZlA5tUip0^XCrDKi}mcD#=0OB)K*55l&;Lc2T&M<0$(#Zo3oi63s(S2?P? zzC6KdTD0w@G(_ZWGZ@a@!>B?(=r@~^P*S3)tf>gXKX&C4zVEmJ&@-H@YU<7~*M&fO zV;PVSb4mD5NzmdzPWeq2oz9ec!w zob1XC_8Z zkrD1ak{qr(0#FSlkX66)2Bn3h?@iBi9m)E8K6m4 z7_U555m)1i`4yvE#B)34fHo_mHv-s4$=}omWK-T^)dFd(9GkyQg|qCNcEwEFz4Z0M z(GTu))$knzo<0}^U$>}W4(l=P+IRs>(f}R;1oDi>Mse1a^wpcp|1q$!4?0}(28IYq zzdrM0J*5a|u*Op|caEGBMi~}5Tb+`X-=6XQA8$5@dSJo4wkIibKu#1I3Tny1%R*JV zl6~XX2ylY`}NHnRfN>e zuc_mtEPDOU@GMX5KAaIw#Pb*6V|#TGy5;9^8f zjtP9en30D5@8@e?+O%kAUQmw<@z7vz^YO%82er#qnEKs_!GT9*>UskuOw_TN|JRzl z=}XfFK>NU=60h0}2pM$B);lHN`k#(Ci}8khl1X{$W8uguPwKx|v^%}U?FQ>5G-X&Zpk*F;Vd2GymErd3$9y%mA9~v{Uf$#4^ z)pHXj|2r&kjaTrK?XFNRfZqrx*xK)q?^z0Eqixls$sXG~lZd+yL_I^I*Yqr z!6FMtz z5S=IJb@8~BSwh|&BXc3p!Vf02amGltJM*UNXXw$h05>+CypLrUtn$i`x^gtH-vpm} zu-~(lTI)!Ny>+=~<`yLnw&oibFVIboowwv^2Mg`wU-B?1=eipRTBJZzUMo3%PZE{t z{x~bhmh`(H71#8y3Hoq)M`Y>bu6barj6QO?P5)NM9WclICfKjP32x9)vLHxNkJi4w zhJOLp?#=zfzw6-MA?io}HGBAQmalqV%niF3rd)YV14NB{s^NSE zRa<3}n5Li7aZSrB5mi-VD~=iATAtT*^V)~!BYrrhsGF7oZ`(S;Jzj-9e?+;#$1czO zv9Y+{lQI=H!@=bP?eo;7lrbmqYpk$&Yz^Pdb6TYMZS~NaDf-DU0!3ZmAvj!v(BbB8 z0D)d#7iC|n4P^gd5n9MEz_EC#5UzIbGZt^%?<{Y$HdTS|@3TR7GzF^gg*c`;+UrR! z-c-sH^vWD7^($&Pt>6E{)>p<=-L2b#2q>vYr_xACcXvydba!{Egmi;+gLH$m64Ko* zz36Th+{wGoK6jsc?gu~WZ#nwualB!i-PGoMqWY9s#zmKPPNseNrC6kQFJQYYZzJ~vsHJSfVi3$Pm7a}Rg z$M2ybLsDy?MZ13aU$FpjWvoslV1fk*JZ`(-pr+T$=&)>5h&Dmk!GX$ASy=0uPkS|M zNz;v1@^{!)zstC!--<>Qi4yIXN~?cr;#6uHo9m0ozQfe5n^yUH9#LUH3013&Twp%K zcRp9c5xZ|~dXv0j1J3NJ@so18HX6g(V3NSDCu_h4k~NB;#((J^QrZWy6k|46VUpP@ zaNzJGYK+~EF5^6_Ia*lpsV4Nff#)uu3Yn$YXIBE-uZasBrIAkq2Bo|F+xp)}iIPp$ zmi3c7|G55}2HHwEhAVk{8akG9WC^j-7_ES9wem!pTMD7*Fms*AqoYmiLV+ZI#b136 z&=*6C7ytJJHNbNZ|MS?)^8gQNNb?A7skE)+k;m2?kh7nTcR(@M`h!p4d=%9lwrhOS zt8(AqEcr?hQ{?GeXW(0Ud$}t%jg;@tQZ~MawfGH+9ZRW&gOp2aUxo3*=SsX47Gf%= z%7#@$%J$}>=NBZd+kWgkDj!JX?n0aeMv4ww&0h%_Aw7zci+k3{bRRu_ze~t3moNTV z1rF@U#@BE!O>-{h(c4=8g512MCyq&hVUt^$y0ejyDh{T3y?cEsG}|IZw%_0Pqvm|H z>KXM=9qJ-m;3yTN>qogi4fXZ&Y7n}8A@_BRz^79)yVa{nNsXR*6xytid7!4UBU zfwk$Hi>G*+`WljNT-9MdueOQ)3@AvW3w;2&a6+~)Jvc51IYqUaK*nGM1K?DJ=H7u@1`3SuE!rJMA*Oh z!MWSp60AACE%VTE-w{~H9jy&#V+Yu=v02G&nXqrIbqe_hTEdS}uTdaG6V-hiLe3rB zWg!%H?9S!6@r`8bdycgk~ z5kWl%HM6;3TlOIH+xBtrr2V@k47Mk=Y=v{k&FapF(v-_LJHBl(7x;ldF#~9vu_B@T zd4RNi8#m|eV-jF9zlE7PnLc*(2J^Ct`vd--`43Xf?m!Q~%n;A;m6wXym`P~YTFKw? zU{UNBDnazMDG|cy!ZOAiR;@<HT}tXFN~=4{k3eGTpj>T%|Eh$*TPvq=NqS*cPz-E(g|7PFq34q%qK7J2Gm{d z<61jBs=4T%@l+?fa~lG-XzLW2{Bbzog4yt&kItH3)aveMnTD3@2Ejw|N58$EQVw^( zq9;xc^|im}6|jS9b1@+7(gMb+Q?_){|Ec6NLt9TX05+@%$(qm%Z)}7eU+1QId1Rn=@U^zFJC#>y9lqmC(Lf+>-glU6ZSwbiPfg5hl66BDtY$eAGRB1 zG?N^~7}+6zalkVvY|YC^uF2s6Gu%W2!A#9LCf-rGoO!+X#i7Ne(?B^VtUV3CuzT{% z|Ndn!ew}Kv-0PZ$G8B1R$HQ$G66|wZ!Q25M@w9f)A}|U2*4Bl;`PSLpmq-$immp_I z{on%2ffSnU`A_ZMG7`)scmP_cdP|iWfcixRf2|0HyIoqL zxN}r&1&JA?I$hgKP_qkqZlXLX#SLB5P#Dm^@bXCHGT@U)*I4E@H*aePXJ;Y;iYH4? zs8d>nW@<}HaGm_2KXP{+s|1lBl5`B@0+2%WuOAdtSRh%JGxkD=fuZl(VYJkdR{@Z} zjV>qDVDnnY7DD=^KN^2M%oZ8FN>}i|Vf{cnGr=_@RvV3O4?n;*hu$0ug_k; z;>!Jt5SU^R1$feb@DVZSv8}9U5GF?o8Vw2I@T(4oP%dSC>C~y3mu<)%bam=d^7=pw zcZ5?Z@Q$LJeA2JmPfu7#!&<-}_`O{4mhbzuNuj#4^#b{ekyjFrZ8!JIEZ3|DRF1bd zg%#{33NO2a-D02ez|~7m)TaCpWM#Q~X8g zHYrnNv(E}QL;|dS_nJ`3H%4~UUwm=l)9si{U)ncB4f8>bY>^#l^lG=Yt=MV`{tpTADD%|6P)m3;Jz$M7ro?|zO z$yxnva4>Y*SqwK7WMK_wLj#ff6{;f%l}ttV#{^6q6U0=1DHV?d-LBf(xkhKgsW#)# zQanw2$^uX?&b^@jyu9$Dhab{(UipH5a={p`P5{~nk*g+o`l%Thp3-aN7?#5LLb!d7N+^J*k9h0@g0Mium` z#ElJ&&LgFtRN|0>^0Pk2zo|?1Z$h~pde<6%296l=b4%b*`Hp`u;mxi@CZJsW(jR1**2T|pNGuwY( zSCG4Dz@JYHW%N&ZXXROLsxmZ%#WSG1>QdO3oY>DX_NO8tEb519z#sW5g1-lYi6Z%u z`9kpu%qLw;gg{PfAP4KU_nE6KSj@k#h|QHMrp1S4!Id z=&W}RXhX#yFhZlzDz@GP*v6$1PBN;537JqH`5l|VN#Tw{!i*1{d@Wy5^^XD>EFzYu zK_|!PjHZ3iPu=1`BJ}>LK3kA$j4`f&j%~$fwAjlQHzPLyAKe2FRJLdX@D%b59FVF5 z15ka>zwQ%~0}>p$y0|RB4hrTY^OMZ$3oMOL0sn5Sm=K`E!t(JnvD4!){mI_je+Yn$ zm};}2|INq0s1$T57TY?*7?b(L2?JX1I`Uu4Qz5xs+HBfvE|8-9rP5F80YB$XWh`s( zo0(S7TFt2p&@2AyjevObnQ_%BOKW`joi*bDzY}4Aw(Ru>P1?V9S{5KldX2U3Yh5K6 zz}>1ca1e@7>(;{7JEQpl4eM++k1~=M3`<7f|8oYENHRd5_p9sLRh*Xh+P#?v_dr$O z1dPlu)K9M!3;yH%EF~rp>*Z(fufri`4dVjs}h^o5}{%INnCK)CtwZtzYp6mI+G71X>itO|KqGhR#385 z61!+@%bBwz62+`eT)@q->dBYc7H?6>)0t>Q4z9P;LbiL7B%~{t)LHEx8up;GD!3{R zjA(MB@SVB)GHGI}=P*C&E`msP}?&LbFufvaXb87o$B9c3r5-7ITa0KMeTtD~gGCC!MjvE9ndbk1}FJLTf!76UEJ> z8K)erjF>`R(bp&Pr2;5#sL!AO9PQxuv%IgM$ol>Aug3(x4dP&GB5~<=HDXJ&Ux7q7 zUExE1v6jsD6uzl#_&p4KsqZ?!_wdb__vY166~A28{1hC&akl#Rk7KB_qlFg+pT`s9ZvpP}+HWBxM7?o1=R`r1>5`TvWt^C^{Pd)~Dq(is z`rwZGG3CZGd(@KwCbVdJwB5+Tv!;HQR_P=>YAo{0~W>LjM>cb_(Mz}=Bn8CrCubP_zcDz5@f_`bM5P=@352&JFOJ6?t=cVQO>-*uv(}87$geQ;^ zBPM1xOUTIG!r3#;4@k0DW1rt}bA+EC0svoJ%(si29RnvGZ1z#G$*L73AgI2Uf808A zNU0A1LP^=3g;%PWmN1aT8&A$Ge5*LZZz+g9tr+XeTjOjMg>D0=VaR7dsBsRIoKv&R zOn;{sm;{ED1)dSC?XOTXrm&DV+WCquPzu5C{Qg0XQT6@dx)vIsd-4O}W7SysSdlFF z88iIV_Xo1SB??o3H;!NksO8zO5uow!2VXGs6-RL6+{^_9LHG3RlQv;k|K~OHO9UFw z*eo6;+C*#X(l?|nV5TU;Pj&T;z-O6%g>e4JJ(Zwr?TiyKQP^`+BVZ=6-D~ryM#nOm z)wrysVtEsx>+KDg*xZ_!s=C8ckpnE2d1!I$#5i~%m%2?)$pBEFkzgdd^PVc&nWyKl z2F1XFMT>E&8nk<{AzA`xpXZ~v@J7w*$BF3#xh27N%5IcrT3T2!a{KLReNTxjJx z8an~qUq}%p8!oVsi#q$y)%iC4gVKRM=?j;BofH~xql!E3@0Mk`3wZ=IBxikjD-6nS zWRshHUQ|GGTBOrDN-Y6e*75a0P_Mb9U@=VOt^QIO{FVOn5imM2*><-upQ~ady-ir;EP08Q9e$IHU|AJ>pMI8(LcPXW9;&xv1* zwzf7~`8JE0 zRJlqg>VyY2*Jp%xZr{4J!SMNQ^R0~_OVmMjUeofI;8*{wX=(iC+V?ENc66?1pLVod zmlu24*Mour8EF64c_YH%Ulf7a%eBVsA8}O$&f4LBsjK2N^y0~zbrioKFrDh@p$9`g zMuAIFpBwBs>P8|N@jmXB+}<;%Cf5d)Q6iEavTE7xV_C03X^nhZ=;5)UanDNxW^&G8 z-j>?_f_nq-MM@A&bldAC%>7H)tTG{Q_AAeX*5P^@E`Z#L2MOKT2#1GZP?-*K2U+^4 zzBoAYWL4KNSoUdokW2SrfgS^e?i;C?OJ#%5HUkgM+p^Dk^zDVkR$%kSkSm-zVk?uU z?IsNWyT&YSYbF%*mHP+J=`KKW;9R-2@<4PBm3juMm?e=HoQjjhl+If`=?_4G$&|+XNO!diAW|UvvC&ftd3;2?| z)zA3pVaFle>+6$U769M?fGqA4;9GV(ezXGLZX05gkv7G`nD(5(cwl70VULHNQ*TVB z&YSmBLP*@#vb1@@P4ejkP_EDqNB_Ao+Hki3=V&7?j@&V~V=2w!Zyo}xT=k#{Uz>83 zeS`W+^mYbpf&-l!hycb7iRzkLpqP>J!T5&$T}jWGM$y&o;>s~?ULdA4Ebw*`-AN0m zQb})s9D)cdIYE}L_sh9{3#s|`V6;f-H9BNgn^gss6YQl?CDr%z#qQmBLv>{^7}f#Z z4kiT2nCI*56}U^`gCr7Df&Jc}FP;qzovr~K?)8L(wS8P$)_#%iSJ7d2=625vqfg%p zou%0kxA{YuS*z)8J0^}`h`vv7Gg7>=8w$T_XT*eL5?%A>1r2yXF-FuzuEo`Z`)TNV z4J$d{Jjhyt&-_3{`i86|LsNX(F?E(_b>QWYVq6CQTN)mkzq4!Jm+Bv1j@4y+q8wGd z>%XO%s6Db_(BYz_|Ix&lg*>F`F(&!^>GLDuFAi7G0D*t*JeI^iZ@9z61LG~2EPcVy zv5c7W)HrKUS`O0_!VY;C$PufzWdYpK(wSOMx#CYep}L7v!j>h$oAIp>n^}RrF$4YX z3@bNvb)>AUtc%iiAb_yh8xDW0Fx@gmH}^*&QjqOZh{@a6?ZT0;LtC?J_W;fV#rDFO zkIUYndb5S50=)gJoBlF3>!xEBWWrNavi^Mn5wGtsJCru45I+INt~hN_{JNeYtH@h? zPG!Q!=>8I*RkdMmr~KerpkF;Ff*)rbEP(y(mzS+774sA;)3`Pr^aZWmu}|xT$FVnl}EzjhshknoI4z4e2iSZO|j- zqDi}!58}B4sG;GTk(=Ns1|>uFRzM&fUpaR51v!^fwcBPsdup)X|7U7s;;#Sh>okt! z{J5Aa$JzK53cyMEXEz9}Qa};m+vf5;ts091{rAq9^4HnS8k{zsR^f#k&8T-b=Ej14 zJh@mmq+V4t%r50_3ok+{tSklJ9IC`;RYX_TjMREt+K7Xmbf#2xqqNKAdYy`vB&7+~ z#~c2@;8|X(^NZhACemRb$A!RTUD3)&jobB(QKT+ev2JL$<&ZYvJQNy0w0r}_Wc8}V0e{?NbpQt1G?|4g8 z$iNx1myv1h4I3!o$l__zBbc%&xO zM$07`NoDo0k5?v49z4>y*YgVC`oqK{J>7NVr?Z2HQwl#^$y-TthFNM})0S&v<88T( zSW@b{^LB2R)}>Xm5+uv!@z@~+WON3(E*pD94I5|??eIsd${7&lU9*c|`5HZ6(md|+ zc$`Y9R5qWxxv*wMf$i5&pNAco0#JuBP~7x|;^(`>rY2sN$*ur*jHJsCu;X$~c%$Rd zv#%w!3+&-ajU=DdjlH|wD#b${o7`_gab_wtr_qkS5-1&Z@!%m$&1tx&!h!JsUd z*%eT{C91GrW*Q=90e4$Q>|khM$yBJ$o4hmVf%u7WYP`9upXcnt{_hNLs!`b498;GZ zkW`r<9Kb%ys{#06#V2{9;R1AoVqsbLpYzDW%v+T{ zmh5v!ArywY@|vR%&Py1Ik_r`w)F1r+lL6g8Df0Jd4pxLbCsl%bkq$h}BV zJ65C!!3?@iB@KN)X@<>TtJQoU`(9&#yqB<> zx$UG!{Re<$A*Gte6eIri^@*Ma_n;sARzm|(Ce)mbY++oXq1RX|JN_5nCX9ee6IFn~ zN-Dti!VhQZ$5C=Z*rRy95&^enhyRnGh^s0&^Sg;DA6`NfMfwem=%&q3`S@l<-AVFW znuTGYOa)u>zz)heksrl;k_K#&0xd!|d5Nn^B~QqYUx`|9T6D0VaYsi?gB{shKdGSG ztN$&SP}0k_z1$v={C||Mgn_)*DhaGPm-Rm zt7JL6v?E%m`;2YlkCLqcT9uctb%9D%EkbU+MY-c$V}_;DG4hRN!LKlbtQyAp4YE{V z#t~CGy1bB&ASHrbPB_7)miiU`f8FL-;$@S1m)w;f8K&J3(~S%krtws}8i3PW+PZv_ zq|MgH0Ue!pG0vp0+YXNV)izkW2|{ejE*moG@&8lZP?OB{nyZ?{E3eX?EZ5VzHKJ#I11nzySjxrtvJba51Ni11`TvVxZA=0 zNMp4+fW7r+3N+dc#A9bnC7c-|e&!_bEJ~EO_)Q*HUbcHYbIr}lh~Az>^!GqiDWx2? zHJ9UH3^osa$MnCnO{{l{crj%WE+Z|+Dg4EM07bE!%kaKD0CtI1p$<~gmGarzHW4Qd zTJFJ@UX!2^4N7}#ZvgIS-F+HGY#-B;mVSnUXL4>VQ7^*WoRoH76a3Q(#gXO#NP`~H zK}cJ>Dp-eo^YhY%s!|rC36XrRud{gxX3mmX$TdHRIvUe+q*1cEpy>W4%dTX9f8lIE3`6RD+qG@2q3Rn;&iSQusl6 zQ-}+6nzH+&pun?+U66$wSeGdph$o6E>?|DDnHhn)Sp^L|=k)t6LiV!uLl)@%Hd8gd z5%mB_#gf(cpIhp1&L2g>_G-)s@7y_Gq%eZ70@TgD=N#mKi~!#UxHyR=gBe47rP_mc z1V8_26*t+*rU~JE48ArPA(}t$D{QNimh0%m8<&Fv>$T3-j3G3ry}c2k0LQxyWV@%_ zL&QLi0y&(jaqIpgh4v#j7Rs<45#04<|2yS?KN_lif@Hz8e+CFQ&3CT`mb%L=!bzH_ zv#M@QJx&rX()%cNP|R7M#}qoGxtV~4(JulcW9(=MKpxtc1J(k1#WUQh1Lyzf4e;2v z2|rV4r$1kmM^hQ*f85~ana@$wp*#x)_Yej~<3ER*z@5_(pd3PXPF8`Fa^VEM);#o-B;P~2L>>SX1(@*)2pF}z?$<`=Ykl4>H{X8z)eu8YZmh2vSuUL z>amU^>_xLA(e8vg+0egx;=OR# zXz;~K;bpajl3MD9AcpdnGBB3|a!I;WNleMoOz@GFF{-s0mI&U^@JAUUKVVaFwLWF) zEvQ9N5w)wRC>%Wd%B_aH1?0ydYYt>?>U0jAfDLO~b#0acAhh88e3=C^9#_kjm1$R& z;)?(Gz}dPA@=K|`-jYc1EB+{ee^UWVzJd7w#HM||02LgXvt<28IeY3UZQ9bV;g427 zP?QS`-F!X;-@QB>RQl$d^tYA96W64RS_Ngm2<(q-^_|T}=}Md04cUBp`B@wF4) z6}{0*;OD}_%4l~nTML9TA1R->lln5QZ+I1e99j6l|7J7fFci2nY)}6&q(?gt_ZU0G z;{wzWyPbJIn3Xx?`a3G7QrUWvHvE|Qolbfu#mG{;xw5&6sfWEq$W)NIyYI{z{&3Ne zEbRkJ8q0L6iM7)TU|boUkyi$LaG2wH%x?A32$&Bg)(W9Qrl!umfoY9gSLiKWMdZ4j zk3YXKz;M|4b{}emK|FPtladc3ow54etvE~z2q+* zeo)Na1lH%ab~I<_TWd(;xI|)4ba!Vi_5I|RPpe|9UTwAx<%4?4S#kQ2>+U(mXYTsQ z!c|L!Cy**jFL z#VpjrR{2)i^YR32MXnLCg?n2{08LzMKAjW6O#Jd|*D9g|8-TG0U+ur?Rel~KF0aAM zT=&zXmqu;kD8}L2+bon4v1Gm=oJS8A2Wrby z8wV=drk6U_dNoA69ZSv^DU!6eGI9tDQcZfNQsut$Ob8JFe|XkP{k{p}Fgp5^pjeY9 zb^?SkPYf&?r5(RcPl;AwS0||P`ybfvKd>MpJe}E$FG!cQ>yeN zoY7aY)yewj|6go*y*;*JJIFnxM96*p>~)*K!7t&fa0oF^Hr zbH%^JIc~B8{MD69i8S=$@#5>r#WzJl6No=38~&xw`3mZVgYQVRERc;qYHDjRcBLCb zfXid7su!UBK>7zX$!PQ%A9FYj2z13eKuyULGPVS9Ah?@~WA%0p64;Co*A}sz4E_OA|uZ5Sw_V(8%LJK}5TR=Nq zjp;9blk3M=cA*g=%|-d~eae?OnZT82RZp}~m*5hyz!_MH z&X|;V>x`)nG7=Qme6Rq$33sMQ^TISUxCA=wHW`ozr{a%U>NS&mzz1nyQU=h>FG+6} zf8c|9`3h#d!P&jWm~()4leRu`8-z2E=jtP_3Euje(z;S^I4z-?cLlP+KW$e_2l}0( z(H4zl+RAA2&Vv~XVHe=HhbU#^>r*}4wk9^A zph2iTw*qC<1Q*D_kaM#}1MaM0@rM;69E&Jo=ehP8m)%CUP-2yzIM znreGpfU%WPSqtJvY~VB)_4$wWZOd#n1DAwcj!#ii4=)Uj_O{!Eol(Jazyrqo^o$3_ z;CO;ff$X=SkSYFFC`dQFapwdY&Lg0SaYzBdzuuvO5C=8^sBl?t5_M6`w@#XUXnXSV z*^bF%4qgMJ1F%N_0C4afBD@KpD_K%uuD62AEEKWPuW{VUqJoVP@T5tW)kZsZT^qz| zt!`sUrih>*LcfLNxRRT=XQiHZ_%J^uQqYP}2#L)Kqv`|2?(fDCYoJfnzLlPczipf5 zCF7V~flp5YjoUaE?!i9t>E4%mV9;>pT(>kkYJJ&doAJ+fs zKAcs;74&SH0JJB-7RC7)t;W#sERZZEYhF+@8=;^`O#WnlrGk!+vjKJY^^ar7tLfI{ zlZ71D+de`Rb0?84pHHSOmWS=F=|jik5b&85D}`Xo~Vh}xDxSyK3ge2uiqHI4ugnD|w*nF&o^#PoeGZc*6gTQ?$Pg&TA-3JZeTSw(N zdgQb{_DMFd0kAZ>=yUwAKmV--IAVJ0KgVH!&DSGviA87ruJ528wo$Q3bX+Q;O|S>f zK#39kE%Jw~Aylp_2Wr$Ry`j9!n20SX2zo`C6%jnNWre+4je z8S&?_(u3bg{ zp52pcT?8r&=13&};8IB~4{xtNaK0x^9;GE-q1(uRuEdD)76<&AS9Mnoz`bU@Gpcm6 zz{{3WSPM?T`(sToYhv63=%kv>Hhx5m=l%n^{hLh>gk(a`2?$`QQ*D-tQKX~407b^; zNmg~OD=Ln6P357SjL+&4AyAixg!?jJllTUk;s+CPXtJ_p{nLMrJvCWK2*$ve7YBNY z0E4tOE;OG`Xsi#^iXO6FOfOexMB$s`8t!0Ofae>l)qfvMTpVmbBA)tT@mm5i>1rJdQ=BTTjMErc|9S$yEu)C-2N z<5doo{-!U0IM4zPQZZb0rJeC@sM>7Bnv7jbJ)OU2I^R_%YNu}e0@LqsX*|~YmXOI# z69F!b*Kb&VBV`2?MT4+ZOjBdI+c$X7yASzMrT)59xOXHVj}j zAM^oPky}Tb-&JM^>W-CLu*%yr=ZY1YUrahHx{1@Qceai;Y&2+>dDRkL4)=@syt?SG z6jYxq8&%A51nS*n0ahT%tV$y+x1mnCb96Kiw^y;BiVl>T5Nk2VF8R<}cA)8Q7D zvxnh9o{5Q-IdZKIEYlf=bawov$#SDaWSr#HCXOA-fUwy~9EFrb`??8o`nG^a=z4&W zo1W8|%N^=|vpvLszSa1OP5qO1L)F0JNiXWac~mi@V6g4JM{xKHo%(kJjP;XisC?;_ zGIp)LkLy>sYE?<`$?6-84>+j-{4?BNJ?R(BR=d4i4_2BbAmfK@zMBM51ki4x%?t!{ zSIq(y0S0HW8lSUD?zyR>={jYD5oiEkuqsPKPO|4MHlq9|m#^i_yF8sczS90`xbUz+ z{zlw!FT_ZVxKgr+%lT;f=0^p3y0+oRGad!J)O0%5-oX}i*B~;jWx|fN&BIXx#G!}# z+M^0>)1nM$XTa!&G`;Gpa=7Kj`i%^qsWGxn#pc1xV6qNG4%zoLFiOl7%VV`?ZY7L@ zRC+9G=eMeQG@@#Y$#DW{J_8KduLEo3$L22gmnqVCtNk#B4-u2T(_Qays8{^tuB(Nd zdK@wG1j+<6FHn7wC8t$lH8|*aO_LTRGxRPk@A1=ZWNfBi^9fT4872D5$E!p=TP@C# zV+(BPiwEf4+V|Oh@6#rJ_KD)*3U1_{N~Tr08VT9f@vr0 zY6t&CcU-~d4JC21w*B04qic_yU7cZ}JdunG#qHIh!w^$$=z|R#JY@8d`^AqG{h1H+?aDWCcBQ&Q`0yTln|W?6&|b$} z{b2h0)|X$KoEaLUE_6#5G!fAlS9Ihkhlyc2`6tRmAKk_Rn`^-%BttZU?T^L-^m+vS zuv{G0K~a9ar(rg2w(T)ibajJDe2!QCWNrjnmS|K5HLlAU4G(`QI$x7lV6*ssg%t~9 zY=nngJya{7y2zEc;5=7X_J6qRGwRe$9n9_#PVanYcV5YFRsPf6wnnqcZzRW!QWyB& zEt0uwBvp#1mMw7*es#p5qw7o4*5PC2tB|vpztM*ETsGkTq+$HX!#b64%ZPFL$G%#D z^2N;axpa?)stBBB0}|cxqmFFFZ5@XAN6(r+*gBLXk1pGzgHAZ^2STm&gl*^FraZTG zpF1B;GBxsXF=jW)5!P8B8q%#)m~sBLr7EacH6y>?|?F1B+u zVH|77X;`2i#nraod#2#Z02%tWkIZx9GOp;Y?W2e&e7_e1Q3M#he>D85mSq+S!^*1Q zb5&WE6Vu^#Ln&D~DM?t!SZbD~afi7L7n7|47VrWz~o1-L>(~PM44As^O8_Y|nx0*36H`QzLUEbV{zn;eE0Q zXSO_#QP?6QU&m|Xs~d`%zdtH)iZaOIA@(ryes@8a*l38s`z`Y>J+_W_QmH!5*9bfj z#NRoD)w)xb;&37r@H<1HUb-hWF^xlizY z-RBgwC*rV1bUj4iQ*ONHw$uTAldSwo`$;}+*g3@U;T@Ty9rQ(T5=ic?^NwK&nP(a6 zYSIrEK9r@r&{!KMKLUA00yG;s`{1~>GAiuGIBu@)!-l1-{j1}ziJIhuuWb1ZCfXky zCn^I{D;5}!_O3Sc=v;~v*TqYqoQu1{&70`~q-+IvIFfXu{Jvz+R(VpYa}m8Z?>qa? zGc7%c`E_|kMv}`n>DU^@m3pXyMf^w1rj5-C3%m<`NK36aj0=aLu3V(AAfiBX7uvaN zEee!iZ*x=KbnT8~`@n8L@%2?)&z37W3D40$v(MeduI~Bt1CcoUvH+|5-y8#2M;>Vw zyENx5*iJ9{`H#Ja^Z65UJyFBZ&iMi6{9A__Tg&~H6|X;ub1yr*k-QEKE=rQMV=GVM zVm(I=bS*J*Y?32MB)Sf#An zZW-L#pvjf|){Z>FK1~YSD>pkLQxdX(U9O_*R&5B&ZC#ShC4y<)JjU#H?^%=Aa=EPM ziZqbCFGjSrl%-?oCRfmCxqO9Wkm_gGk+m3gv9#yP>u^*TE5~YBw@g3!4ga)z`=4S~ z`>r-6qtlHjmate_&IyFHcIiR|B$^@nH@TbIVcS$_zOe!m(J=$;yPp7jiie*@xQ!xwwk#Q`;l-_WlQ6!XW{g6Zc6F-qpL+VTqpd zqVG4#5K~+qhHEdMImZ^X1ytTXbFS2=n}vvcG{ zsw9_k>n?x~zwKzG_dOj}mNu$a`LBvs}_q6nAD=d1I?T?7eR6ukj^cI;sCR2Psm9$Mc$LI)! zhZtLr$ft8ZJp5gQD+|bnZei1sFKpL;IpR8H*AMo{-QV(Ai6*CWW!5b6DH!haR%~O# zp%onQY@|pmwJ|BY`C66X@$1<1-h-L&07>SG&PB9$aDgc4Xy&>nP5CX~qPANo{>q3uiW zj~YOQtYqsiHC>hXc&?Ot8KJiB@RKdb^{_@Wxkp~kPXJTX|FjDjI0w6$4~L(-3A`?% zl=BhZ%na_<&GXBsGZ|N)VY}C7&(@xA)!ued-@)RR_GmBG@b_Kc4T1B#qGl5`WCx{m zHlLnwlkEJix*dt{cR{)Sfl2Zy^rx&2@~G4er?XgLdX-r6%_oq0=NN33N2fAZ_&p}- zZWH}_Iq@0|7b2u1zbp1UJ5N@CrRhm5T!~BEq!&@?4-z@M!O+ z7gSg--9Ytkq|vKEmSehGAQD$|Z#=f4Vq1RI1nJOmBni1a_`H{3$E9Rt`cMIm{_pS1 z{PtN@a$Q(Y=x>?CWQ9|$1A7~$_1iT!XfQb(HA&4KJ~#(;@BxfbqP2ZB50MJYiTTa; zjFsa(Z|q_>gev1MHF=KI!TFya^;6%!Kw@Outsb-FLTmUFP-62!Siq)VcjqXYw2U`9 zxdr-hekkGGo1GS#{!z5J&r5-|%tgS{abN6qI+50x=QS<5M&4Rj(c$BPnpk1c7DYf{ zMazK9_|5HH!>OLzVlmAx$sM0$BluRBr67xP{ElZ6yaaWd%mv>~hXU z!|~ij<01b1f+8c>+YKLr%-Tb6$CT_mWgVf6>{9HCV?07}D^FGGlt)$L`+j`zVYSv7 z|1IUlq0jsJEkk%nW2UHHHG2}6JrbdxlC-?DbB@+%mbZ?j@RRJ?MPNjlU3mgvo$CB zbs(?N%6sBGeApnk zfrl*K%C6cC6kb-#Ci{M3zj!aW-8M<33r};-fp~7~O|S~)qW5f?L?GlJeYWLjR|1H+ zuVv`QIh!QM2-4!@YLDu__K#R*z65VvCE+PH*?K^}hn6*Jz;f683ddORJU!i9pgMx= zuQc~0Bck~2rC3Th^X>imgGicKx@HiXXTZ4F>a<+ph&-wqd8|}mdewDrH_NSZ_1%y8 z(-<6A)UKaH&jy>CHSa4oFb^H;1Y`1-kg{!tjXWK0MmqzvyPWg{X>^8=rFhUg7Eu?C zmdW3dy>Hm`mb>I#LDvXJHC~Ys90Nq$jQxqLWkP!iMVOn#1QMmnwTd)b3Uc$>4X1T zf;sg4raL01;WHJo-|uWJglWk`s=F=h1Usq^loKzpQ2g@6((Iy z$)ABGMXA6Vwn>-1$=j8n+1s`T3H5{Z=S*Lxiv{`l(t8%oz4CEdix4av#zvBsWQjA^N*d))6Mj%+R0KyQ=>D)(_g7i0Gec88Ypz zWf}P_b3sqc4I1)|{J=)7ESPj2{a+D1Sbhfjy0JV-VGO#=4GS_Y`5&Z|{2%^oZR$R# zWVYCgbWeZEfhqHvDE$ORGT+uFoWlts9`?n2ckoRZx%me`OZ)yl&T?ajibl|FZ@#D> zoK;tqdX+~?vP_TMZG9kh=c-TWoxaDYfYI3% zYnlUdz*&FfgkHfvA)tyLEk_VCa z=w77Q6EWTL6x@T`sT02}(D^+YRvjjrFD3mLv7{)JFNbB@Nm;g`wW*|KgJB%`?M|VR zH@x;LajBPPB;q=G0z(yE0mH(|J?w*WPZRi8p1Z7z)6m`!fRYf@nP%?$CE**dA6U69 znhZLMJ+{o`EVbR$C1g;u8{4zj#hb*^7~y1H;jd`8gNIbjx86Geu!CK@*{&y&2nkio zG^WCC*_}rn88hptE#Js&Yu+ESmqJ_0ud;9X6B?E@=YO@0BJh!wUd!KiI|gJKQP|r$ znlYU62-8dY=1<;W1PcFD4{4z7RMT3^7uZI(OHrv7);^ePl}XSndL;EYlR_E|yV}>< z98WBoGg+ZdQH3f6`ZYnQcR-f3}7ix)f>eO5&^%()n+tJqwa3xk`gfM-d|jut`KMD&6y;J>g<3F z6sNnI*jdurSM(usseaF`UYE)4;Pzs-t-A@@;flO>eRB|3!Bc-C=12e0-}hnMCa(ME z(I9<^W?&_paUr88m3wy-nN%2d*0!%w6%EKkBfhk^9d#Fjtu`}=zRgooccsv}i@WXV zppMTpcHZGW4MZvqGZmB<^n!X16A^KphCX(<-k0cahM{A(dMn;H-A8?c zZU4yHv0?$Vh9+K5X%=mcgVFFp7KQY^Aajd>d#@gqEs zw(@(vTWjuN&Z@%$E>FX5&9-tI3{ewtr#iGcmEGd}S861Q&h?0@roa5@G~1gBUeArI z25pMQo*#qE8~IZIKFzC@HixvmI>ZahOS70ka@;DdqtOb#&Cr9h0*k(XHG_$TYgu@6 zgdeIL6g1otNEfo#dSdc6kw(#pb>x&86#dZ(h;ZF*=9QnouOr`s0mQ+lBGK3`cR87W z$hwW)OAgL%JCYiDx_L8H$M8~O_4vp&RUqh>twZv`D?LQsNK;tUfaC+}cdEi{Er1s01W0DxR!>EQw+m9LWxLCrTaiYo_POZp zO7fHTms`-+{QTzGJ<`{sC{fY03n0dQs7WB?&@#oUJxaVKOqfUe15goJ*ViS-9pNE6 z>M9fQGB{b8yi|v$b?w$%-aGqd?L+EU*)mb{)Lgi&dYx{z;GEh$yOY)@7e6iu?hFk$ zc(D>2V(-n>NiiB&9eHf14{>>z`J8Lr4*Yj?B?kC4j9?&};E)}?D{XDCAXG1bG!#u0 zw!uTN7tHekh{;f+YwHUajIP();tZbH(laX{ihdZT8_!$x`nXj!!OZwLWDvde-Ph#y zuJT)azliD*nWpHVL6#W^Dy53jO|*nlc!&*$7UkW-6L@4w39=SDkLFp1YU?(KHaSr_ z;z;=iz*Y7qnY?$+-&6H(JNY@*XP0{Q?H-_BanQ4eR73-0&{v_d6YkP2DC*e3f{WStmhP9z7@*ohe<+l+07jCI!M@+|+s^W&LcW?u8U=U%S+v%Ht< zzQ($ii*#Z6J+o&H8)cz|l;*9th3czg>L2&b1DtwH2VM6CX}vXpy3OuQb*Hp-T+z^a zT!q}z+@a@OKI6aCZOj`TYW`1e&QWZVYZp08g+rf_Od`fgwCJz&w3swZCHf=>Yd5&7 zM)2}u<+a!EdR?Nf4sVz03?j~|Z<|D%7o>g!BPKDbPYQuXJZjlzsh}XQUx4X|uNLl< zO)9oTxyLs~?0Nj{Wp8X1t2lzk)!O(M^q-w~ zhY?T%LPew@DR__J$0foc*&4sO_G}gTa{?E9%9MseBg68&a-{+&n&NK>I8mdMt;> z(XYpprn4Hb1IAmIRdc!RmMAA$uXtUa&?H-X|3XgNFM_Yz40BA#;)iL99JSx#r{f@Bzo5rk3(r5W7@d;)n)`wRW-Y4`XkmC@oV26%Q#jfg-x~*QkGn!s{#s# zqmCoYcDBDd%NaoGZ}r0SglHF2an5Rut}ZJRc{DdDdxEk%C36OcaI5!FoR;iz!+XVm~q08s0i|oUGbKCCP z!uwvL$F$#^GL8??y6he3N<8`qSbidb5A~@8bPfXf{fmX^l{+m5+rT75#rL?M%cE!t z?I~_Cg|G}tf<`H*%JA^zvbF>E2&hlpY;mPK%T{z+HnRooX3boAcOWW7_#(~v+gvU0 z3y{7U3Wd;SG!rU&$#$qhyf4J3zc?kL97xheO5$<-ndq6KBJ#1SKU_6FQo^QcSw|N7 zm5r_drB%})*mP>tve&JdRi?{3#lo-lFq>uH`u>l3VX0tHA@yDkGF~d}W-hv;sT5Nl z0O|UkoI0&R*X?|Dv-uzpH9W?T?!fE%sZ-CY;(GPKX!h8_55fz}Vp>v!2B4TF10 zUlnuFl=p-P-yRvm+0*K>QclDl3X7V#MtL8YANo#GvhQ(~sZ~vX&cMH23;KdCvvewn zfNI)}iUq4Mo%m85wx+o8V9UW>+b;{Kg+pXN4IX{>?cL`I|MHppe8U=LF1MYs9os_@i-`^I_YG>|h@JHD6i%uVG zlQtb_(H71Hkcl)v9*?|z8ctO7Jwv_SmY8y@Lrc6c7jJpFnvdi@@gEbSFrS1*Ce0s}Eh=VI>nJHFB=K0%8|_ zIfZBDVi1@BFP%J;8uzT2+okU?8CfX<1oS*U3h0K{5q7@_Gs7TISDl^`Y9L_=ZW1p!dwQ$&g zP>N#|5}l_apkD8ZT^UUG+U!Iv?X^1-5`DUe`sU+tFf-TYrvHwL_q?by;h2l*n(YiE z<&)fCf#%eCLUl6r&0VDbHOFbk@am9dwlBM7;qTeaT&aC0k9(yaRx=`p58j){M**o_ zpDA5(TzC?D4PTYpb{t_GK(>?h)7eJ{;^BB_mzNE3(|&nhESYG<$F*7q@(0GRhTq?b}B zT0(Q1(Y8HV1`o&j4r#V@uwr@|Ert12aKlIt&TXB?OfnvIoZwrqKM$`b_HKV#rAhyp zd!@B4&wrZk8{@>fh$wz?F_OQ!la5aC=#b1TQWo#)6*O_LyOcu&ZIU(VwSJy}&n=(F zd>+;wQ5ahKIhbTt95)1aC?>*C1c);`7Ueo>+Z<|TX#H>+{Emytf z_D)UutQwkKn?<3;H!0%R0_YN!FPhfqCurm1eb_8mF!ouHySBmgqm#5hW7<~mskC7J zyvOTQ+sCLC+r>B0=f(f`uG&%HlU?LoIBvfbgf+NiHwevtX^yB+P-^_EoM#57)JZ#Q zZAE1iTwSStAiV?}l4U67c#ceV8hE4`Iv*i@sx^f^cNhd?k})1=#7 z&;|`)>@kbQR@JDdS`BLNnjG06dJaE4 zDf70jY2)+x{41y>=h^2??6a*x|Ie|Wi!NF&jqqArDVNn-+{HMACd!{T!%)fp#*)_I_1(!4)ft&2|>le5L+9MXRNnhJLWPuhy zc8){>%kG5^nl~PBd?Q zU+-N|O7YmFZupFA33N9LV%fb>RLSnhf(vTvO~l41(ORL3Nx-M=A}#D2eTDVCz%E#* zS7{DmT?iPewZJdU_23mrpQ4us%cd|KM4e!@#G-VWK@88YzA)d)3dpQmNMxqAoI4Mz(cono@+Ga-w>A&>xzlr zR;SzbKkhuU2D*Stc|WSiBaiq2>%CN9A~+$?tx&2%jPEFxzLF%kJh0Q3hjJ4f!g&uD zKKu6X8c!d8ZC;yCW-4k=R}|i`x#-97iYWYToB>=P;{2*)n^oI8VF+c!lRksL{Ajl@ z+L_CfT*abMOB;kiXz5FHy<0g>_!7ZR4mqvW`5qQZ@EeWm9pkRy^oESDI!;sUzEDnw zowVtX0Gu%{3cmqP_uQ%AdoNMtu7tq2K8|>9THmLGgNKQ}syZuO6sFb$ijknqGeN?2 zel(`)N%WvT<)0L;VY2z0cN&!|L=AsWED(VXKmIF&A6B>;-~#M$>AY6^H)?Wv-{nb& zd7IcbtsvITcu(`G+XFbf8pq-99b zc_-TbEFzX?=OnoyZ6&G-Ag$49Wu{;u?+sSKG*)8J?aZNrXvVZ59szlPFImP4v2fF? zUac;@B?7+gLUr=n%^0cNyZ07L!m) ztV{X0pCLm%%%I?VX10{vNyLCecg4mcbihqF8I|X)<#V0-z6v7W5`g6To*u~i&8Gf` zmCK}v(vdADB`UGbiA?gx z-Z!2B2Yq8S;08M}>cO$<`8Mw?r}vCYO|C2mJs zeK0~F1}?6cRHDQ>3*^lR!#c31c(A7$y8$=vOl-3oml>J7`T9(_H-r43X@mtYRMJRaa2)>Tn(Z1bb4S_ZD1vRw=o?uTSC62b>bKmX!Wi550r zfZ>hGOqJ&H6o-6Wa*u()?ZH@LV9)DYtw;r$UkNHxzXYvZSwM;>>!p|T8szXX3)ia}VnneGs6 zDsBwvT~dDK<&c)$*4K#jhvSkUCh(4;WTEmCb67X<046G!JHcKI;mLu?V@iH1p{)MZ z{W09~CU7|@Z!XoW$Zxg@I+|}W(hp<^o|HAF?s@$aE29&@s`t_Nf&6_hMQNkQj#)C1 z7ep2qaK85rOsXjPod^XBvu@c87eaCa#AVBG06olvRYNq%euMsAwyvM}k612G>ipV% zNWIbu-$>B2H+i2ipmQio%~ep&NQsu1p6BX)P2M8%GJcV6xO694ijF=NG#WZEX{99b zz?D#kNT^{mD5xW{K2Zvnnx|eKhrG4N{s?ZLPcL^E z+qHw}N&T8jj?q#kQ97Ky{sp9Z@;}uvueWXHIypH}O*6 zlg&r%Zl}v!kZ}I~ve;nX{JU5bF`P^Nqe#Y!D|0B@Rbo{+)x4aB@nC?;fhd4n9&8N^ zXH*#vkkWtgi9v5BNstqIW4uT#IhEw;rmgSf0~*2J9%QlL>qfF?{t?{JyMv_0W(XGp z%#yW&n0UGU`C^bGE%{MTR>vh33@!PQK#TaP}A|OUdNZ3FIn`^j~;D; zTsCeIyylgeYHukFt=_#1 z)AqjJZZy)8b9)x*jpyekYuH9pvVH_JK#iH~R?@&>MA{toeT~r(XS#QtwiaJmCxo=F z-(W7Gy1d!zHrfaNE2ZH;8_#c&_2z9>b*>eYp{b{F~#~u2O|7IBmt&@qzd1W=JbeUqOAwGq~~cD&#VC zv&#r8f)B288-}WlP5<=n&ck|ZB5qXcpqD<83sp-=V`k1Lq~%l5EK2Z=K;#dx;OU7@ z?OQo>U6DT7z-VsdCj^+`TC&koTyiAzrs(M}<>73jBKIwu1^AF6I_w# z)AON}15q@8630kHAq!5;c?~~~Sa)W;p&sl0VYg``+WV^yicMGLY%fS&`yK;726@1w zhZjuc(+?!sh<-IIO6PM{$#s|q9|In;JAL#kM%*`HSVvL+xmC(g zG@yZX(kh-PBPy}Pf~$H>x?6Y;7#-Q#w{WQ9g?5B4%SJjvU& z`67oLgT=_>Ks{L3x^H^ZbCC#g=^06R4awdJ1XWgBL^uoT{V_x$ zF=%TYdL`L*hv_a^P0NllV>A%7bxKPo!frma-Zwu@eDIGfuD8b}d0iW+N@l+v>Xf}G zd1<59aU5+T3XJ;+^1vkH" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Classification Notebooks\n", "\n", @@ -36,13 +45,16 @@ "- [Shapelet based](shapelet_based.ipynb)\n", "- [Hybrid](hybrid.ipynb)\n", "- [Early classification](early_classification.ipynb)\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Data Storage and Problem Types\n", "\n", @@ -56,14 +68,17 @@ " where possible our single problem loaders will return a\n", " 3D numpy. Unequal length classification problems are stored in a list of 2D numpy\n", " arrays." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "name": "stdout", @@ -88,13 +103,16 @@ "motions, motions_labels = load_basic_motions(split=\"train\")\n", "print(f\"ArrowHead series of type {type(arrow)} and shape {arrow.shape}\")\n", "print(f\"Motions type {type(motions)} of shape {motions_labels.shape}\")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "We tend to use 3D numpy even if the data is univariate, although all classifiers work\n", " with shape (instance, time point), currently some transformers do not work correctly\n", @@ -128,21 +146,23 @@ " involved a subject performing one of four tasks (walking, resting, running and\n", " badminton) for ten seconds. Time series in this data set have six dimensions or\n", " channels." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 2, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, "execution_count": 2, "metadata": {}, @@ -150,8 +170,10 @@ }, { "data": { - "text/plain": "

", - "image/png": "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" + "image/png": "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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -170,12 +192,17 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "text/plain": "[]" + "text/plain": [ + "[]" + ] }, "execution_count": 3, "metadata": {}, @@ -183,8 +210,10 @@ }, { "data": { - "text/plain": "
", - "image/png": "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" + "image/png": "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", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -215,10 +244,18 @@ { "cell_type": "code", "execution_count": 4, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "0.7028571428571428" + "text/plain": [ + "0.7028571428571428" + ] }, "execution_count": 4, "metadata": {}, @@ -235,15 +272,15 @@ "rand_forest.fit(arrow2d, arrow_labels)\n", "y_pred = rand_forest.predict(arrow_test)\n", "accuracy_score(arrow_test_labels, y_pred)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "source": [ "## Time Series Classifiers in aeon\n", @@ -265,12 +302,17 @@ "cell_type": "code", "execution_count": 5, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "text/plain": "0.7885714285714286" + "text/plain": [ + "0.7885714285714286" + ] }, "execution_count": 5, "metadata": {}, @@ -290,7 +332,10 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "source": [ "Another accurate classifier for time series classification is version 2 of the\n", @@ -307,12 +352,17 @@ "cell_type": "code", "execution_count": 6, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "text/plain": "0.8685714285714285" + "text/plain": [ + "0.8685714285714285" + ] }, "execution_count": 6, "metadata": {}, @@ -331,30 +381,44 @@ }, { "cell_type": "markdown", - "source": [], "metadata": { - "collapsed": false - } + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## Multivariate Classification\n", "To use ``sklearn`` classifiers directly on multivariate data, one option is to flatten\n", "the data so that the 3D array `(n_cases, n_channels, n_timepoints)` becomes a 2D array\n", "of shape `(n_cases, n_channels*n_timepoints)`." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 7, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "0.925" + "text/plain": [ + "0.925" + ] }, "execution_count": 7, "metadata": {}, @@ -370,28 +434,36 @@ "rand_forest.fit(motions2d, motions_labels)\n", "y_pred = rand_forest.predict(motions2d_test)\n", "accuracy_score(motions_test_labels, y_pred)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "However, many ``aeon`` classifiers, including ROCKET and HC2, are configured to\n", "work with multivariate input. This works exactly like univariate classification. For example:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 8, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "1.0" + "text/plain": [ + "1.0" + ] }, "execution_count": 8, "metadata": {}, @@ -402,28 +474,97 @@ "rocket.fit(motions, motions_labels)\n", "y_pred = rocket.predict(motions_test)\n", "accuracy_score(motions_test_labels, y_pred)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "A list of classifiers capable of handling multivariate classification can be obtained\n", " with this code" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 9, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "outputs": [ { "data": { - "text/plain": "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n ('CNNClassifier', aeon.classification.deep_learning.cnn.CNNClassifier),\n ('CanonicalIntervalForestClassifier',\n aeon.classification.interval_based._cif.CanonicalIntervalForestClassifier),\n ('Catch22Classifier',\n aeon.classification.feature_based._catch22.Catch22Classifier),\n ('ChannelEnsembleClassifier',\n aeon.classification.compose._channel_ensemble.ChannelEnsembleClassifier),\n ('DrCIFClassifier',\n aeon.classification.interval_based._drcif.DrCIFClassifier),\n ('DummyClassifier', aeon.classification._dummy.DummyClassifier),\n ('ElasticEnsemble',\n aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\n ('EncoderClassifier',\n aeon.classification.deep_learning.encoder.EncoderClassifier),\n ('FCNClassifier', aeon.classification.deep_learning.fcn.FCNClassifier),\n ('FreshPRINCEClassifier',\n aeon.classification.feature_based._fresh_prince.FreshPRINCEClassifier),\n ('HIVECOTEV2', aeon.classification.hybrid._hivecote_v2.HIVECOTEV2),\n ('InceptionTimeClassifier',\n aeon.classification.deep_learning.inception_time.InceptionTimeClassifier),\n ('IndividualInceptionClassifier',\n aeon.classification.deep_learning.inception_time.IndividualInceptionClassifier),\n ('IndividualOrdinalTDE',\n aeon.classification.ordinal_classification._ordinal_tde.IndividualOrdinalTDE),\n ('IndividualTDE', aeon.classification.dictionary_based._tde.IndividualTDE),\n ('IntervalForestClassifier',\n aeon.classification.interval_based._interval_forest.IntervalForestClassifier),\n ('KNeighborsTimeSeriesClassifier',\n aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\n ('MLPClassifier', aeon.classification.deep_learning.mlp.MLPClassifier),\n ('MUSE', aeon.classification.dictionary_based._muse.MUSE),\n ('OrdinalTDE',\n aeon.classification.ordinal_classification._ordinal_tde.OrdinalTDE),\n ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\n ('RSTSF', aeon.classification.interval_based._rstsf.RSTSF),\n ('RandomIntervalClassifier',\n aeon.classification.interval_based._interval_pipelines.RandomIntervalClassifier),\n ('RandomIntervalSpectralEnsembleClassifier',\n aeon.classification.interval_based._rise.RandomIntervalSpectralEnsembleClassifier),\n ('ResNetClassifier',\n aeon.classification.deep_learning.resnet.ResNetClassifier),\n ('RocketClassifier',\n aeon.classification.convolution_based._rocket_classifier.RocketClassifier),\n ('ShapeletTransformClassifier',\n aeon.classification.shapelet_based._stc.ShapeletTransformClassifier),\n ('SignatureClassifier',\n aeon.classification.feature_based._signature_classifier.SignatureClassifier),\n ('SummaryClassifier',\n aeon.classification.feature_based._summary_classifier.SummaryClassifier),\n ('SupervisedIntervalClassifier',\n aeon.classification.interval_based._interval_pipelines.SupervisedIntervalClassifier),\n ('SupervisedTimeSeriesForest',\n aeon.classification.interval_based._stsf.SupervisedTimeSeriesForest),\n ('TSFreshClassifier',\n aeon.classification.feature_based._tsfresh_classifier.TSFreshClassifier),\n ('TapNetClassifier',\n aeon.classification.deep_learning.tapnet.TapNetClassifier),\n ('TemporalDictionaryEnsemble',\n aeon.classification.dictionary_based._tde.TemporalDictionaryEnsemble),\n ('TimeSeriesForestClassifier',\n aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" + "text/plain": [ + "[('Arsenal', aeon.classification.convolution_based._arsenal.Arsenal),\n", + " ('CNNClassifier', aeon.classification.deep_learning.cnn.CNNClassifier),\n", + " ('CanonicalIntervalForestClassifier',\n", + " aeon.classification.interval_based._cif.CanonicalIntervalForestClassifier),\n", + " ('Catch22Classifier',\n", + " aeon.classification.feature_based._catch22.Catch22Classifier),\n", + " ('ChannelEnsembleClassifier',\n", + " aeon.classification.compose._channel_ensemble.ChannelEnsembleClassifier),\n", + " ('DrCIFClassifier',\n", + " aeon.classification.interval_based._drcif.DrCIFClassifier),\n", + " ('DummyClassifier', aeon.classification._dummy.DummyClassifier),\n", + " ('ElasticEnsemble',\n", + " aeon.classification.distance_based._elastic_ensemble.ElasticEnsemble),\n", + " ('EncoderClassifier',\n", + " aeon.classification.deep_learning.encoder.EncoderClassifier),\n", + " ('FCNClassifier', aeon.classification.deep_learning.fcn.FCNClassifier),\n", + " ('FreshPRINCEClassifier',\n", + " aeon.classification.feature_based._fresh_prince.FreshPRINCEClassifier),\n", + " ('HIVECOTEV2', aeon.classification.hybrid._hivecote_v2.HIVECOTEV2),\n", + " ('InceptionTimeClassifier',\n", + " aeon.classification.deep_learning.inception_time.InceptionTimeClassifier),\n", + " ('IndividualInceptionClassifier',\n", + " aeon.classification.deep_learning.inception_time.IndividualInceptionClassifier),\n", + " ('IndividualOrdinalTDE',\n", + " aeon.classification.ordinal_classification._ordinal_tde.IndividualOrdinalTDE),\n", + " ('IndividualTDE', aeon.classification.dictionary_based._tde.IndividualTDE),\n", + " ('IntervalForestClassifier',\n", + " aeon.classification.interval_based._interval_forest.IntervalForestClassifier),\n", + " ('KNeighborsTimeSeriesClassifier',\n", + " aeon.classification.distance_based._time_series_neighbors.KNeighborsTimeSeriesClassifier),\n", + " ('MLPClassifier', aeon.classification.deep_learning.mlp.MLPClassifier),\n", + " ('MUSE', aeon.classification.dictionary_based._muse.MUSE),\n", + " ('OrdinalTDE',\n", + " aeon.classification.ordinal_classification._ordinal_tde.OrdinalTDE),\n", + " ('RDSTClassifier', aeon.classification.shapelet_based._rdst.RDSTClassifier),\n", + " ('RSTSF', aeon.classification.interval_based._rstsf.RSTSF),\n", + " ('RandomIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.RandomIntervalClassifier),\n", + " ('RandomIntervalSpectralEnsembleClassifier',\n", + " aeon.classification.interval_based._rise.RandomIntervalSpectralEnsembleClassifier),\n", + " ('ResNetClassifier',\n", + " aeon.classification.deep_learning.resnet.ResNetClassifier),\n", + " ('RocketClassifier',\n", + " aeon.classification.convolution_based._rocket_classifier.RocketClassifier),\n", + " ('ShapeletTransformClassifier',\n", + " aeon.classification.shapelet_based._stc.ShapeletTransformClassifier),\n", + " ('SignatureClassifier',\n", + " aeon.classification.feature_based._signature_classifier.SignatureClassifier),\n", + " ('SummaryClassifier',\n", + " aeon.classification.feature_based._summary_classifier.SummaryClassifier),\n", + " ('SupervisedIntervalClassifier',\n", + " aeon.classification.interval_based._interval_pipelines.SupervisedIntervalClassifier),\n", + " ('SupervisedTimeSeriesForest',\n", + " aeon.classification.interval_based._stsf.SupervisedTimeSeriesForest),\n", + " ('TSFreshClassifier',\n", + " aeon.classification.feature_based._tsfresh_classifier.TSFreshClassifier),\n", + " ('TapNetClassifier',\n", + " aeon.classification.deep_learning.tapnet.TapNetClassifier),\n", + " ('TemporalDictionaryEnsemble',\n", + " aeon.classification.dictionary_based._tde.TemporalDictionaryEnsemble),\n", + " ('TimeSeriesForestClassifier',\n", + " aeon.classification.interval_based._tsf.TimeSeriesForestClassifier)]" + ] }, "execution_count": 9, "metadata": {}, @@ -438,15 +579,15 @@ " estimator_types=\"classifier\",\n", " as_dataframe=True,\n", ")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "source": [ "An alternative for MTSC is to build a univariate classifier on each dimension, then\n", @@ -462,12 +603,17 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "text/plain": "0.925" + "text/plain": [ + "0.925" + ] }, "execution_count": 10, "metadata": {}, @@ -493,16 +639,19 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "## sklearn Compatibility\n", "\n", "`aeon` classifiers are compatible with `sklearn` model selection and\n", "composition tools using `aeon` data formats. For example, cross-validation can\n", "be performed using the `sklearn` `cross_val_score` and `KFold` functionality:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", @@ -511,7 +660,9 @@ "outputs": [ { "data": { - "text/plain": "array([0.88888889, 0.66666667, 0.88888889, 0.77777778])" + "text/plain": [ + "array([0.88888889, 0.66666667, 0.88888889, 0.77777778])" + ] }, "execution_count": 11, "metadata": {}, @@ -526,13 +677,16 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "Parameter tuning can be done using `sklearn` `GridSearchCV`. For example, we can tune\n", " the _k_ and distance measure for a K-NN classifier:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", @@ -541,7 +695,9 @@ "outputs": [ { "data": { - "text/plain": "0.8" + "text/plain": [ + "0.8" + ] }, "execution_count": 12, "metadata": {}, @@ -565,23 +721,31 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, "source": [ "Probability calibration is possible with the `sklearn` `CalibratedClassifierCV`:" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 13, "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [ { "data": { - "text/plain": "0.7714285714285715" + "text/plain": [ + "0.7714285714285715" + ] }, "execution_count": 13, "metadata": {}, @@ -606,7 +770,10 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } }, "source": [ "### Background info and references for classifiers used here\n", @@ -652,9 +819,9 @@ "hash": "9d800c14abb2bd109b7479fe8830174a66f0a4a77373f77c2c7334932e1a4922" }, "kernelspec": { - "name": "python3", + "display_name": "Python 3 (ipykernel)", "language": "python", - "display_name": "Python 3 (ipykernel)" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -666,9 +833,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } From 78be55465bf51d1b084fb74d9e2a14428fd33322 Mon Sep 17 00:00:00 2001 From: Divya Tiwari Date: Thu, 12 Dec 2024 17:16:49 +0530 Subject: [PATCH 3/8] Anomaly Detection Overview --- .../anomaly_detection/anomaly_detection.ipynb | 70 +++++++++++++++++-- 1 file changed, 63 insertions(+), 7 deletions(-) diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index 3242d3d1d8..d134dc143c 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -19,9 +19,63 @@ "source": [ "## Data Storage and Problem types\n", "\n", - "The anomaly detectors implemented in `aeon.anomaly_detection` module have different capabilities and can be grouped according to the following categories.\n", + "The anomaly detectors implemented in the :py:mod:`aeon.anomaly_detection` module have different capabilities. They can be grouped according to the following categories based on the input data format, output data format and the learning type.\n", "\n", - "\"taxonomy" + "\"taxonomy\n", + "\n", + "The anomaly detectors in aeon take time series input as either `np.ndarray` or `pd.DataFrame` of shape (m, d) or (d, m) where `m` is the number of time points and `d` is the number of channels for a time series (d = 1 for univariate time series or the input becomes (m,)). The anomaly detectors can either return an anomaly score for each time point, indicating the degree of anomalousness, or they can return binary output indicating whether a time point is anomalous (`True`/`1`) or not (`False`/`0`).\n", + "\n", + "aeon provides a couple of functions to load anomaly detection datasets in the :py:mod:`datasets` module. For this example, we'll load the KDD-TSAD 135 UCR_Anomaly_Internal_Bleeding16 univariate dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6583bc79-e76f-40a5-b2af-b4f9a4a90f74", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import warnings\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "from aeon.datasets import load_kdd_tsad_135\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "X, y = load_kdd_tsad_135()\n", + "\n", + "# Create a time axis\n", + "time = np.arange(len(X))\n", + "\n", + "# Separate normal and anomaly points\n", + "normal_idx = y == 0\n", + "anomaly_idx = y == 1\n", + "\n", + "# Plot the time series\n", + "plt.figure(figsize=(12, 6))\n", + "plt.plot(time, X, label=\"Time Series\", color=\"blue\", linewidth=1.5)\n", + "\n", + "# Highlight anomalies\n", + "plt.scatter(time[anomaly_idx], np.array(X)[anomaly_idx], color=\"red\", label=\"Anomalies\")\n", + "\n", + "plt.title(\"Univariate Time Series with Anomalies\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Value\")\n", + "plt.legend()\n", + "plt.show()" ] }, { @@ -29,12 +83,14 @@ "id": "d6701c2c-fcbd-46f0-90dd-3077337123a3", "metadata": {}, "source": [ - "## Anomaly Detection in aeon" + "## Anomaly Detection in aeon\n", + "\n", + "All detectors inherit from the :py:class:`BaseAnomalyDetector` class. We currently have __ anomaly detectors, including Un-Supervised, Semi-Supervised and Supervised algorithms with different methods. Following is the list of all the anomaly detectors available in aeon." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "88276e2b-149a-4d32-aa4e-e15400c1086b", "metadata": {}, "outputs": [ @@ -108,7 +164,7 @@ "5 STRAY " ] }, - "execution_count": 2, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -122,7 +178,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d5333c9a-6acb-4c8a-8145-72a949c89bc1", + "id": "c63615c5-c2a6-43b4-af0a-adde55eb25cd", "metadata": {}, "outputs": [], "source": [] @@ -144,7 +200,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.5" + "version": "3.11.10" } }, "nbformat": 4, From 71922f659231c7d70878bab062401035d3106418 Mon Sep 17 00:00:00 2001 From: Divya Tiwari Date: Thu, 12 Dec 2024 18:43:32 +0530 Subject: [PATCH 4/8] First draft --- .../anomaly_detection/anomaly_detection.ipynb | 166 ++++++++++-------- 1 file changed, 94 insertions(+), 72 deletions(-) diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index d134dc143c..c2db7dbc4b 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "id": "6583bc79-e76f-40a5-b2af-b4f9a4a90f74", "metadata": {}, "outputs": [ @@ -85,100 +85,122 @@ "source": [ "## Anomaly Detection in aeon\n", "\n", - "All detectors inherit from the :py:class:`BaseAnomalyDetector` class. We currently have __ anomaly detectors, including Un-Supervised, Semi-Supervised and Supervised algorithms with different methods. Following is the list of all the anomaly detectors available in aeon." + "All detectors inherit from the :py:class:`BaseAnomalyDetector` class. Various anomaly detectors are available, including Un-Supervised, Semi-Supervised and Supervised algorithms with different methods. Following is the list of all the anomaly detectors available in aeon." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 2, "id": "88276e2b-149a-4d32-aa4e-e15400c1086b", "metadata": {}, "outputs": [ { "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", - "
nameestimator
0DWT_MLEAD<class 'aeon.anomaly_detection._dwt_mlead.DWT_...
1KMeansAD<class 'aeon.anomaly_detection._kmeans.KMeansAD'>
2MERLIN<class 'aeon.anomaly_detection._merlin.MERLIN'>
3PyODAdapter<class 'aeon.anomaly_detection._pyodadapter.Py...
4STOMP<class 'aeon.anomaly_detection._stomp.STOMP'>
5STRAY<class 'aeon.anomaly_detection._stray.STRAY'>
\n", - "
" - ], "text/plain": [ - " name estimator\n", - "0 DWT_MLEAD \n", - "2 MERLIN \n", - "3 PyODAdapter \n", - "5 STRAY " + "[('CBLOF', aeon.anomaly_detection._cblof.CBLOF),\n", + " ('COPOD', aeon.anomaly_detection._copod.COPOD),\n", + " ('DWT_MLEAD', aeon.anomaly_detection._dwt_mlead.DWT_MLEAD),\n", + " ('IsolationForest', aeon.anomaly_detection._iforest.IsolationForest),\n", + " ('KMeansAD', aeon.anomaly_detection._kmeans.KMeansAD),\n", + " ('LOF', aeon.anomaly_detection._lof.LOF),\n", + " ('LeftSTAMPi', aeon.anomaly_detection._left_stampi.LeftSTAMPi),\n", + " ('MERLIN', aeon.anomaly_detection._merlin.MERLIN),\n", + " ('OneClassSVM', aeon.anomaly_detection._one_class_svm.OneClassSVM),\n", + " ('PyODAdapter', aeon.anomaly_detection._pyodadapter.PyODAdapter),\n", + " ('STOMP', aeon.anomaly_detection._stomp.STOMP),\n", + " ('STRAY', aeon.anomaly_detection._stray.STRAY)]" ] }, - "execution_count": 9, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from aeon.registry import all_estimators\n", + "from aeon.utils.discovery import all_estimators\n", "\n", - "all_estimators(\"anomaly-detector\", as_dataframe=True)" + "detectors = all_estimators(\"anomaly-detector\")\n", + "detectors" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "efd5f400-41e8-40bb-8a8e-6317386c7532", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9984002133048927" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.metrics import accuracy_score\n", + "\n", + "from aeon.anomaly_detection import MERLIN\n", + "\n", + "detector = MERLIN(min_length=4, max_length=5)\n", + "y_pred = detector.fit_predict(X)\n", + "accuracy_score(y, y_pred)" + ] + }, + { + "cell_type": "markdown", + "id": "bcfcbfc5-8d6e-4a5d-ba3c-b952c32dc8f3", + "metadata": {}, + "source": [ + "Another example is an adapter for PyOD anomaly detection models to be used in the aeon framework." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "743fbbaa-a7d0-4f56-993a-07453f6a9442", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1.39885498, 1.27698668, 1.19722694, ..., 1.05130507, 1.02170587,\n", + " 0.95507392])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyod.models.lof import LOF\n", + "\n", + "from aeon.anomaly_detection import PyODAdapter\n", + "\n", + "detector = PyODAdapter(LOF(), window_size=3)\n", + "detector.fit_predict(X, axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "bb927d1a-ded3-4b94-8c03-604eb40f104c", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] Sebastian Schmidl, Phillip Wenig, and Thorsten Papenbrock. Anomaly\n", + " Detection in Time Series: A Comprehensive Evaluation. PVLDB, 15(9): 1779- 1797, 2022.\n", + " doi:10.14778/3538598.3538602" ] }, { "cell_type": "code", "execution_count": null, - "id": "c63615c5-c2a6-43b4-af0a-adde55eb25cd", + "id": "6083369f-fb43-42de-a2c2-58308a3977cf", "metadata": {}, "outputs": [], "source": [] From b24d4dcbb9ded62ad11e4746a2128b3c8eb45f46 Mon Sep 17 00:00:00 2001 From: Divya Tiwari Date: Thu, 12 Dec 2024 21:45:48 +0530 Subject: [PATCH 5/8] git status --- .../anomaly_detection/anomaly_detection.ipynb | 67 +++++++++++++++--- .../img/detector_classification.png | Bin 106020 -> 0 bytes 2 files changed, 57 insertions(+), 10 deletions(-) delete mode 100644 examples/anomaly_detection/img/detector_classification.png diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index c2db7dbc4b..49e072249f 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -7,7 +7,7 @@ "source": [ "# Time Series Anomaly Detection with aeon\n", "\n", - "The aim of Time Series Anomaly Detection is to discover regions of a time series that in some way are not representative of the underlying generative process. An anomaly in time series can be a single point or a subsequence that deviates from the regular patterns of the sequence with respect to some measure, model or embedding. This notebook gives an overview of anomaly detection module and the available detectors.\n", + "The aim of Time Series Anomaly Detection is to discover regions of a time series that, in some way, are not representative of the underlying generative process. An anomaly in a time series can be a single point or a subsequence that deviates from the regular patterns of the sequence with respect to some measure, model or embedding. This notebook gives an overview of the anomaly detection module and the available detectors.[1]\n", "\n", "\"time" ] @@ -19,13 +19,43 @@ "source": [ "## Data Storage and Problem types\n", "\n", - "The anomaly detectors implemented in the :py:mod:`aeon.anomaly_detection` module have different capabilities. They can be grouped according to the following categories based on the input data format, output data format and the learning type.\n", + "The anomaly detectors in the :py:mod:`aeon.anomaly_detection` module are designed with diverse capabilities. They are categorized based on their input data format, output format, and learning type.\n", "\n", - "\"taxonomy\n", + "### Input data format\n", + "The anomaly detectors in aeon accept time series input in either `np.ndarray` or `pd.DataFrame` formats. The shape of the input can vary depending on the number of time points (`m`) and the number of channels (`d`).\n", "\n", - "The anomaly detectors in aeon take time series input as either `np.ndarray` or `pd.DataFrame` of shape (m, d) or (d, m) where `m` is the number of time points and `d` is the number of channels for a time series (d = 1 for univariate time series or the input becomes (m,)). The anomaly detectors can either return an anomaly score for each time point, indicating the degree of anomalousness, or they can return binary output indicating whether a time point is anomalous (`True`/`1`) or not (`False`/`0`).\n", + "**Univariate series (default):**\n", + "* Numpy Array: shape `(m,)`, `(m, 1)` or `(1, m)` depending on the axis.\n", + "* Pandas DataFrame: shape `(m, 1)` or `(1, m)` depending on the axis.\n", + "* Pandas Series: shape `(m,)`.\n", "\n", - "aeon provides a couple of functions to load anomaly detection datasets in the :py:mod:`datasets` module. For this example, we'll load the KDD-TSAD 135 UCR_Anomaly_Internal_Bleeding16 univariate dataset." + "Example: :py:class:`MERLIN`.\n", + "\n", + "**Multivariate series:**\n", + "\n", + "* Numpy Array: shape `(m, d)` or `(d, m)` depending on axis.\n", + "* Pandas DataFrame: shape `(m, d)` or `(d, m)` depending on axis.\n", + "\n", + "Example: :py:class:`KMeansAD`.\n", + "\n", + "### Output format\n", + "The anomaly detectors would return one of the following as output:\n", + "\n", + "**Anomaly scores (default):**\n", + "\n", + "np.ndarray, shape (m,) of type float. For each point of the input time series, the anomaly score is a float value indicating the degree of anomalousness. The higher the score, the more anomalous the point. The detectors return raw anomaly scores that are not normalized. \n", + "\n", + "Example: :py:class:`PyODAdapter`.\n", + "\n", + "**Binary classification:**\n", + "\n", + "np.ndarray, shape (m,) of type bool or int. For each point of the input time series, the output is a boolean or integer value indicating whether the point is anomalous (`True`/`1`) or not (`False`/`0`). \n", + "\n", + "Example: :py:class:`STRAY`.\n", + "\n", + "Each detector in the module specifies its supported input data format, output format, and learning type as an overview table in its documentation. Some detectors support multiple learning types as well.\n", + "\n", + "There are a couple of functions in the :py:mod:`aeon.datasets` module to load anomaly detection datasets. Here, we'll load the KDD-TSAD 135 UCR_Anomaly_Internal_Bleeding16 univariate dataset." ] }, { @@ -85,7 +115,24 @@ "source": [ "## Anomaly Detection in aeon\n", "\n", - "All detectors inherit from the :py:class:`BaseAnomalyDetector` class. Various anomaly detectors are available, including Un-Supervised, Semi-Supervised and Supervised algorithms with different methods. Following is the list of all the anomaly detectors available in aeon." + "All the anomaly detectors inherit from the :py:class:`BaseAnomalyDetector` class and can be categorized into one of the three categories: \n", + "\n", + "**Unsupervised (default):**\n", + "Unsupervised detectors do not require training data and can be used directly on the target time series. You would usually call the `fit_predict` method on these detectors. \n", + "\n", + "Example: :py:class:`DWT_MLEAD`.\n", + "\n", + "**Semi-supervised:**\n", + "Semi-supervised detectors require a training step on a time series without anomalies (normal behaving time series). The target value `y` would consist of only zeros. You would usually first call the `fit` method on the training time series and then the `predict` method on your target time series. \n", + "\n", + "Example: :py:class:`KMeansAD`.\n", + "\n", + "**Supervised:**\n", + "Supervised detectors require a training step on a time series with known anomalies (anomalies should be present and must be annotated). The detector implements the `fit` method, and the target value y consists of zeros and ones, ones indicating points of an anomaly. You would usually first call the `fit` method on the training data and then the `predict` method on your target time series.\n", + "\n", + "We currently don't have any supervised detectors. Still, the problem can be treated as an imbalanced binary classification problem and time series classifiers can be used from the :py:mod:`aeon.classification` module. \n", + "\n", + "Following is the list of all the anomaly detectors available in aeon." ] }, { @@ -167,8 +214,8 @@ { "data": { "text/plain": [ - "array([1.39885498, 1.27698668, 1.19722694, ..., 1.05130507, 1.02170587,\n", - " 0.95507392])" + "array([ 3.89696881, 3.02367572, 2.09615194, ..., 0.42186597,\n", + " 0.33378197, -0.02893244])" ] }, "execution_count": 4, @@ -177,11 +224,11 @@ } ], "source": [ - "from pyod.models.lof import LOF\n", + "from pyod.models.ocsvm import OCSVM\n", "\n", "from aeon.anomaly_detection import PyODAdapter\n", "\n", - "detector = PyODAdapter(LOF(), window_size=3)\n", + "detector = PyODAdapter(OCSVM(), window_size=3)\n", "detector.fit_predict(X, axis=0)" ] }, diff --git a/examples/anomaly_detection/img/detector_classification.png b/examples/anomaly_detection/img/detector_classification.png deleted file mode 100644 index 0a4070cb0cca8e416c5cb80b1dd2526658368ea5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 106020 zcmeFZXINF)(lxqK5d{GQh>{J6fPj)S*n%KPkQ@Xgi3Hi?oCFb-BqAUg1w?Yr!fvtz z$w*F0&XU81{nbL>Zco2`p65RI-k;w&9>Usd%{6Dus!^jxP0kAiIZ0wdN7^@ODPQ-6pEw<`5#^=3ken6VQZqSZmTYPSHJ-Mm`%?R zt#8EU_}CKeMxlg69WC_?%#3X5^^J^8EQA@C$}o)dCWgX{YTUBdWi4+RJu;DUwl-37 zmQyxxHZ$NiWE2%46mk@R0UjIK>d`wsHn*@5a1>@d7*_y(MlQ26!cEqO4+Rv(Z~wXl zeiLSVWNT|Fz|QXA;K1g<$%eKzX1~GD&(D6HgPnte74BfQak8-0b7Zx!xdbB+(jSZ= zZe(L%ZDMI_g0`SX#?;eC+t~^;G7{4N8v3TKt+k2%U!z;tu<0FKu|pnVgGICJ8QQSl zV7rb?i=JNS=ludV4Q-HLf4)S?$m-yipTC)#9K6X=&)UXF*~!vKm{HNl25o0;V0177 z^3bnAZdn`Y*%}#&a9ro$V!h7K%E2qd{*RB4cl`Z;grn^pLlONO2Hb`>I3BWc8Srzl za&hwVu<~(p8nWth8F2D)ap`gKJ>>supr4QY=PdE;-}^8b9R3=RI; zCQCbO^8*(!G+;L}H+pPjVT)|ijbEE&Xdqx~Vry>n_ZHnWxBbs80^bub*RwDdW^`mV zGa;HJA|lZ}0j4urABryc_YqgCIjZ z4?ciJasFBq{3dYI&h`=7T14E$-bs&M*~CH5+C&Js?SDQ1uRpN;|10}KSpH?;|CooP zp5<>50JjMocrMyn8I3j%loinex zFKA?`l%2hgU(lu{qL1HrQFs^E<9C2=VyC-^T<{Nm{1`*lcJRYxpF@<$51xq%Vuz6* zUXc+#LcXJ3|A9x0d>2!^8jpwkAVrAJg?vZddFOc)`A*ODe+T)$Hu=AP^8aJ(aQBH8 z-R8-7@u~1?F1GS>E9336wKhIYNM;sOxH&yiF%^Y1|aXjO)A{ZPuLb=`T z9c<@!F-j+3zL#=Cixnk?g}4-x&0C21+;4oTRraNjqr{d^@bk}l$QTkulKV?j!I*NY z4Xx{_Ki}PhxaY38P-?=A!>#+?o_a8sAOg!cJYP02doZmmtBh^(UA)N6 z6?C7*X(}{(q!ABAq6pLMuEx2rtaPG%M~0q1@A49IQyO22s&>~RKyAGN7&|r)s~v2# z=`+#;pXOqn<$9NvP$-x>{e7n;iAgt3Y~*e6MAb<~gNr!q^?6=rRJ@!L3_IrZ_}82;T^kP^d?GdC<#!mYPn(Y^Ebh&B_nb1S*bA5a~r!NC_vDX$z)1Suvd&8-BFZhsRV#3^=(1i zDW`d@F}3R#FWT`0=~DHrw`P+bMTyb8hj)^Md&LCTXA)yvPn}Bneth_)HUT`t2J0Bj z?k(Hx6x|~!w?B0%I3g~fHmAq24fW?(Jo1>IM3Ikf@$=_W*D41%P+}{{B*$G$Zf~jh zJ%3Kt)I|E!%@Kt%6$LxLGW58A+P&f8#fwP~_hj_wE~7j(bUra+^(Dl`nVZ>- zpSk@BFJeVrL>=|@nQjV9ieYsuu3E$#Mhihk3zWLnmY#fKf-KR!WFg}y5;o+z!){0A!jB{F?x4j0RLW3-EEVdi_H{=z`O2eLA2X;2-S5I*%>{2!D@DCc&F)VbyKxSDXi%UvID(!Dq|E3u+uo9^FHf zXQV9Rg%4Zn$2Kory!blNHJWZi4+iKvxZQq<5JLwWblGT1v`VBIh2oAv-qySr^Xqo( zTtYS}Cra$g2iWM-O3O>jyQfZ_x}#W#~9j#l-t*LJwO>xoMV5iZ_2B{Iuzc`zxg-PmEl}W#m@p0+X}a zV=fhIK5c5Z6TN<)7eg1!|25U`uYO92pD$&dfUNn~moS9{Pd3J^J^3KU%vsx2hCVHf zhsm^-_V$t1nGUnom>bzcmvn_W*FsMrf(~{EO zU6I>){9TqmGL0j@Oo&XQ>E$8K)GNOmOBN=j;|Mc6Y*81K71479H4kTwgIH zl!{{%9X)UqS;7vVH5iXWNd^UU$M3>NpTT8c2S;nr;w; z9SkN85@cQ{s;9FvN8$I+d zpI)s@Alt1Q8F8%qoSD&IbXT?-8@s*34-R%#HoLXSxlK02S$4r=Cn=exr_@AqIlhueu=nL? z-w7VpLUQs+Iwkff;Vpmm7%Nx%yepMwDCYx|g3LJL-TYh#+~18SK1YvO-!rc*%@?^+ z+W$C`m#L&*-Xp<=pOkV>Z{A9}v)MpoPfuun`nGXV<1u$HW~|cQ2;Cb6G=`_{&kpOnINDgEX`kcF$b#>jB7 zUgIs@8HKM!;ksfY!^SvTi`dxGDX};mxqXfw-=7L!9- z?19Rqo+LWj6mgovX>(09fwT5mp?1?@P!m)y&$U-GuMeADJ1fbGsW9VDE7!lqgJm!NX zcFQ>Bq;QVTBZ^D!9^vib7K_)O`q#C4eBhDdFEQyi`4ZG9wVOqlrCT;Rk={Bv;8I{R zoH)I*KIhTT7e0?i;9y>|JIpcA+pAw`>aiEy+{=tVK%TDn{TTu8_H>=3IJ?o_tOnz4 z%xQCnwAQ493I4I&{*Fe*E!|_d5BwB2JLpYdI>g1=8&DZZv9}}t%#L}T7D7O@c<*(u z(vMYitoE1m>pDHV%`d4r}oGepl++E zg?UhzMHG!V^?7B*buVtM^&YxZR>*ADsoyjR(Ouq}NfRrWSdAe~SF9{|^H>Yz)Gyar z^wFrySQjai+ujg)tS_iH;5fQ>PzhgBZ=J3pl2jg4rT7Bg;I{brSx%Tu zFMmW@MJKDh&ywI7cEZ@XD{z-C6`TKF7Hw*tF}O! z2@*ud_CLTWSyoO(oh?sv>k)SIW@PysSms>6(Toe2qutYQCo4NC;wT^xosC;rc%ZVh zuRj1zBWOPtdw$QW`?+22m&n900WRs~wOV_}@>aGTk_r~}km(HETjxG(mYu(_^yt^7 z4P6?#`Ly1bLtS%krZc=-%r&eFywx7$5V);Q5=BuzD)Js$iHX%fl3wrE$B|uTJK+^= z>7SQ4-FMbIJR(l9#0$ncuB{KrXKres_lNvMaD%+9`xDcvkP~jG$Bh!;Ug?}GxbSy6 zvn%k_S&y-3ci+zK3~JiFDVNgZy3{fMD9&ye0Lt!(Ivx%Aph}9z5H8MT$B@Rw&^99L z%?(y9^CSKbk|ytK?0AyLHBUQq_c7MdW~R5B`(<`C$aYExu2UP<{b`@$OVj~yl!b9~ zZ>ZI&*dH~@z+iITTnJ4tGSaL*YD7FwH9yd6G%AzDn&g_5?Wx1}ZGJkVa#dyDvLmNJ zc=4ljK+iocFvI!1;b+9NqBeFj11l{xe)`=u$|?A^V|#n8g4a*WK*kz&H~w*x=lyo= zN1HjiE%MKR(M;SIh(is;kwZI_U=8@63l2j?6@;SU({+S`Z{WJxwIX zX4g9rl)PE~{`>wA`6ug_UkBT%J1#%1Tzt7lnJ^Pf+(cQjn;>YdU+b{^a!1QVG%x3n zz-<1cWcM2r){wth3KC5O5pQi~C6Z~iR-BvF-B%Y@4FYshh{#yuxa>B2k>#(^o5%cH?xu zUW)B7_plRjSw7Ez-kT1LKEpv-Kl$Jqccon5@U0YYMrC!erHJ+{3Fn0AL!#4alg>w9 z6s(SvfBfX8&T)|PXs&GXXGAmTVp1flj}kcCPr+aN7Ca@6Cf-<%yYfbjnsnilhmQ;m z#kW@gJ-l^wAxv`==h}r_jeL94c0zEOGdLUhxDNzuhcWYO-y2Ipz+%D~+xO~eLN#ZV zJf!f~Y(|6x3nN;?Rcq>VO%$_weLC9j_4p+{!gCXs-fPU?u)Fx1-xYhZeZt^|JJ^=} zjr!~evT&?+4e#;SnwwhC#%d5+i~Ui7GS%~2*c%m!=3o$v(@T1c8TnJ$f3A8{_AS1Q~1Yn~gd?~6!;zHhB8F>f4_ zLEDNqQ1k*<*(4dnoldnZtOFEc{Q55I{6P7%Y>?}E^K_5WY>$iaH;D<+Z@O1>?$2I( z9(?Os{BF?zrp&A7jt^1&zCt|g%j>MCu)Ruo1Ooj^d^Kz z1miJ`$%=8)_aVY93Jx_g#cwuGQ1u!QdkWX` z#entus=rqP3@b2f>$zSwo)~Rqu=t(XhoT*dB#?&2KrZpZm2$G?6EzmcMyv(`K; z`mr|KhC^nU(j|U{@tYs<#1GL4o~G)udG>fxlly*PIJcQn8{cTVKiZ#&d}E9EzOTM` zgA^; zzqcSp-Gz30>CjHAuOqqZWLpZI-o)DO{g1;8WxE2j;_S95N*LI@+ z`@;AZ>LxXxSh zUE^(h+mw*cq^zF{Z0;X9>Y${{kX*i&?Li^)vPrOtQij%^lF}%AOlHf-kbw6i$e?R9qxkuE%f`abRCGtKT zCTnm%hbWY+#0H2* z<}{%8eBVSJd&qg0V=lKqj9YLa|~IY!!EkG-cP3T_q$U#=5V2MjN;)-BbgEnS_Y(V z<_|gV_iX4^L@Hv2TQM4wPknpr&9oWtZC}}FE@1ivHmlNmDKPC5iPIE*vMFi}M_SFj z4=XY38>%O5c=)Z|HX?76@V$^}X~TR!NBV4AaAn7)sS?Stz-$P0=2WwQ>c!lIr$dC>_s{Rdg8Ho#D(~T@;bz-pS_?zL>Nx z=`dci>HIuk)JiE!iTQrf!i%R&(>Z#QyN&j0&7$X8W82yZJ6_7iR)dT*AOHH5(S5qdZY2^jVHBUKK$jVGBuKyhp zgRT{A;i1y%FJXi!)(k!>hav-FxnJ z$7jgdu+=R6;YwlzCTfp&Hv_*fRy=8;~d*nkqz2}H?VS(~kbexmt@O4ok#?S?}FJPK$i zAH(w0p(r4g$Jg+)lW{CXQx%1jd#BxTQ0}Wkzo^S9d->OV18vamvEYj#J??Oc>4K* zgO2L{E!~m5@gL>64q-gU?gBj}^-sH3sVVUiE0@H<{;s~_tCeMa*wVU&OJ@!7_M{gpgN;YZ3mj>vzku$Qp(d_KG%XeEv><0bSg^cBl_smgaQFHp4=ZDy;X)uH;#Z5svb%H0OkpiroeYUGk)}ZB!LKoEy}{K%T5s z=OO#O5PpB0ABOt`n2pA3VvFT4M7`#WLsS?uy|IBi5JE_H+5;VRl;FMebu zM;Q05U@Gjs*gs99ZHXSaLLiXEB2YM}_I}kFc2510hMd`bAN)16Og$LKk=gR$sgAXZZ<~d4EYU}?hd;n_9`XC*xNH*BBB`fuv{$T zS5q}zEZbn+B#odMFyPfybf(bV9`tZ3>vfLpoPE2_OOaJoB-?Nva^&dO1WNAb<&M&< z@!qbxe@e7LrK`GGey28zdmkh^->mtQQLfg~=fkN8MyRyu=X!BF1vMUvGppmvc`sQ% zS=Lng#mmEnHd0BKJlf$M5y2tShY=fp#+#-PUfJ2uV0&@fCl5I3FsVv8xB0fFnwth{ zzhYg&!*Zt#Fqf~E`04CfZ1UFWs+(^M5>T6mA$(w^V;*Q;ozB{JFNe`yF;l!u&|jM7 z-I#0f-h)tws>j(u)CRd_dN~aR_;~_XXk0K{Ux^`C`eg z-(z(7eAno9fGV!?$|u)7ryLnf6LUP#;)2#%$I@sSP>{YYoxaNQq1QI1*)kBEFNd6= z_7Ag*ux!t{8JV1{1A$JrCg^6^qskhzzcwJF+AN{fF9w2I#XHHbF^A>iAe7C@3ihjh z5D<_aSB*DprIKB~%p-7-q0kW2C46?w=$(ju+}fyzoKTxCLCa&e=K4;V^GB}qMt~~$ zg7vHrrc%D~OSs{b>xMW%+56rVpUq7S=pMZ*m1Z-qJ0tO%Bpuu>m6pANhA7P?G!92y z{g3U_W{nq(9WL5slAQ~E3`#QD@Cc??j9T&o&#{VgWg)5Hhs`+6di5l&FwjR=G)ul% zn>QFzwB3x|o$PDRtx%2=%pFTza&=~M=m`;3Tbl#DS!+#Br$Lx6Q%ZR`%gaT^gePIU z9yCIpL}57-O$PQCqx`c&8^z8>X3mDy(^8$}XWMSt4-X|jO3@69?hIlvt_ebbtYDRa zc(FHkylAj}GyjreqwDSW09A$!GN1e&LiVKC^N~#Piczqp{8L@ke@3%~=({{aon{{- z_d%k(S+(qMarS*4>^Htiwi5)feem4R`VIBF4+*UFojVo6W_E^#v`AfevzmpZC6#xo zk>pF~CTV^9#qxXYr_gDiJV{o18o~$LF3lAuD9(kp71rJZ|6;NGR{8z~s|S*o&Fbti z*LQpM(<1xnCqNAyv3)T8;z7cW`#Yz6ofG@Jd$@{MLMwgCrzF@3Gu7H1+mNi<`x@fp z)4FIPSQ{6d6{}$zS@{v@vDaSq#GlkJK=ez{a9$`WAE2e6%6|VbkAu^To1GsklS4K* z;4m){7eris+GfV2J~Z7b;dsS<02^cN?T)LnfJ#ujvwCPU+0dSq$!_l2Pc{ZEra^GV z@mu>U=4;M0T3`ALOUaZ&WAoLG@=BuJOuWZU{j;L}k@=e!tYOyIE6+auyey}jMmk~R{m%dZq}hpjgL|96)=Fky#R_M@>bbpvBjxM z*<(flB^N?p{)U0<@kv)EqoxX8o^4BLLDTFsK3AUm+ufkw^T7zTK!fixSg_u^tRSg^ z-)lfkQ%%~6+*{_?zacHIQc0xocl+lTR&M6)&K*PAPCiXBoS}MhTaV;FZs_KtbH{a5 z?UKr2w-BlOy;4v>VXJZUE1ih*3*xiCu>i6*#ehyo(2O)8UpI-qV4T0cpm<0(P^>4N z?`B~u-k3JBK)W;hqRSI&VDno)u&n@&{k2U&MyDr^%xuDXoCzcAi(I2~7{aceU z&qZx(@41wT)0_*%y2*f0hMu?jx@*S`lLU4?r>1e4KXiG6DMLKm$bRc%Zn#oI+f&h^ z5JX_mUiV;2-(|g!`qI?m@zm`t9?Fyl;q!0h?R`grpo#oCcTV^B?&t{PuXg8rANk*- zR{gOg37zOWc)wbZz2AF$bc2x|^i(qMQ#a_;n+hSrX5(T`yURe+RV7yUF3~RPFsjQD z(Vq0Onu#zB&|PJ2osIkbeqs#auj{(*RDZW~tM>DRe>IAs!z=xOVuLzzZ%pZ3Gs0x z7#5gNY9lFo(EJe1-#u`0r~TbA-Yd|&X3%h!|GmMUG4gceEwX6C&NMwbW$5iA$JmsE z-6UrAvgl1@Gm{eQeMRt@HRoL_4^%j8Uq}x+g9^{wfy>?-hb2NFiZy(_Lpv_9Yw# z5D}$3&D48-qw+B7qYpxNt+8V%%e!y@BE|442K)@JBggy1I)r$bvC;=;GFt0={yvkz zLx7?p2KOB6=y-4frDmnK;NTbxEet3R15%$I zC=#DML5#742kA*kch}UsQK*Jw@ClM|IG*IR{Bqfh;#9CeY@hB6?A~5B>2Z{pHgfim zB!Lxuo0gi)eH#{FeCE?}kFDh2&d+2$_IQDPB(+5jM|x|CL zDCDZLB>|tsG!AA-?w4@S!@);|DFnH$!vTWN*x<3A8*nh^a!|m_aWv6x1|907A7W1_ z5vJ$Z*~(>^^U(W`BNu(?nkzWZ>xPOCM9%!YbM|;_xR|{>fP5N3M;e4(56B*w#`6?F z&NMvDcwlTev$kwT37>|A8%w}hhFw-#)WFRqO3WRHBs zLx~`VkWvGL=_K8S8L{8sG|}y;P^a6jaFG;(S>cQMu8d)W_82d6UW!d-DFExH)QkRx zLg5aMw8(4^T*Nr>Aty|H0-sB9w+20Pb?#mTnAxj?Ls`h-MTLNcefjYqcJZ_0&qkKO zy{fV1_s73bT#6%my7v%nx`Nzv2GG%P;!?1$uaEBL^XE@v;pkVov(QRbP?ftGoXhu#t?3dKi#Fv1^#_VZgqOYWPW zF9o@7mW?b76rojBRWA}-FE3QaknJ^6V76*`9`|3ij?2(2AY9~BFQ>z8)u*Xu^jEmK z^zNQOp;8gh^`vBHXGhn*B+oN$rR98hH%q5nv(Ws!1N&gMUUlK)0Uh`4iK0R4CV7vY z&K@0?$32T4`z4omLe19)TIcV-$w7)j4_BpTEQnwXdv4cYi;=09qO0p~Ky z7M~E%ZcVUzlo&OIEpJ>!p_GZiD^T3cVXRv4kH==R`Ch{9_38Emjl73e4i&PITr-0u zt14>R+;+2FQ?V|K8AZeWdB*Y@L0Y+?Jg!^IN2ZRO z_vf1wSqx>COh&pam2cKk=(^(eHi?Kx^$Lp`$=Z?{o0?oELe*&9x5jb|>NjROr^Z9% zGj%cUu2HWdBl|P8%V77uebqv1XFsScwVOM^NDQYLkyAwRrM=}Yn>*`saZZyF)6dVs zo<15Zwi>jvX$oU?S)b*G6%f$5T7vQE+}7@SrBOKGX?MVY`kWWtCs=g(zx#7ia+{Kk zJp7QD7;W9ibol6r;+YRov~FwdBe?xd@Bk^xbrdQ-6#xiztR9~HnyI~8b4LE|+v|Fx ze?EB(OKXe2Imlpc8~Z0XmGH{vOPJlErPueWRQ2$*Qyqq#rnaZz;=oP1)pUmn%z6w0 z`L9%zpW@r!`p#ZD{r*nK72)mi5D)Bkx{#bgfV%UL?olMZG=lc&I_1v8pPate1%Lvp z3!Wz}E$zBs%D;N#ib^ig^lNaPjX7Q1&O2Y)#xGyW=X0Ah3rxvJdTA;%TUuIZuRr*6 z>&N%0);P?=FOgU*wmR=PgoPC{eCjYftD3HU!)0UshC$uhYuxjQ{_vKB23mbolEO6ak$wo)V&{MqmB|&)~G2x;|9xi4<0I2R^hgkUMr#Hdem0);ta(# z^=ylwQZFyBY+e#r+Qt+4-a}b;clTZ@0+bk^=~N0Q3RSDDQl!3=XZ562aOu;jOSfz4 z@H0h_Rc5fq%k)DSf=^6+iQ=^wEOy`9F)}hrN=&TT%x!3HZfa=o2}={ zo&k;tuw>Bn@g9tJztFr7;ObzwRGlGVJIGu#8;e7`8v|%|K)czllBNKM5FW*Forlgn_6@S(SnOeyFaQSWk z@37-+O0q@SLSdyh0c@rSH#c`hZkB4xnYYS>)6)I_w!gx{!WC*J=8iv@ls;U$Uu+4} zD6n?40`AugrW9K*u$gKBe-imobLPyIyXW{UA4KsyrsR6`>Ortt8Td9czU@U=TrT{B z&z|xf!RY~nFzM;(Z{EDwa=*&K$!TM4ZEa&S)f|y$J<<5-(l zcxCJ3;{!-P{F*`LgM6Hbe$8{(numslHEL<0(TtXmB=pd_z!pWY|J6H01;Ijd?FscC z?VXbm!yI+W);KmQ3 z){6!#X$ohL1GfQ~g~ZSYP`b5~0?D<=v^y1SjhLgFR1`Zfvj0`+NrCsF(y)fsR^5O4 zM=>t`0w*UYBO@a(uR}{TKZ{ysb5j$M%Xi2-L@;YHZDn=!EkImWja)+r)l0Yir~3=c zZr{G`TC3FHbjV?G@chO`Fd5;rCK{*^OiK&WKA<82F(n!H5rOFFk8(~fv#;q8LJ#j#6l%#1_6oIDYIyuq_;eodAA-0UCGu zns@UVsco{hH;}1V;a?Q244mJ2ZMq#oc5B$@{jHr704v{WUJ#wVrj9Tw?J@)|J)a&- z_BFU{kFLjF17ln?mr2KHrT0LAS$Je5rDT&qtk}1B&krl`AJxqJ{NNCOy9@ISL^Q-T zz^s1iL(|KTl=y1!^TPcWzJRc=~ygu6v{)X5F7LnmUr~)9E9-0~@ z7J;O>ZV4ToVYK^dlrOFG^fM}p;7fGk|E;4S}eDaCUS8T-Q3YuB#v$Uhjo^ADmEg@$&i?RgQ`Er{>4>^h%--~k@{l)HfwkHa2Hm4ZW5tbf&N72X_#$FfRi zQ(IyLc}+SlS4h{M(0(d~_hn2?T^$HlVXne^gOS$s6)Y7Ss~^*zNE2LGsH* z-j!Nz%g^XOEwsWifNTHSPlXV~FQvf%Tl>39n5}Pqn-KQgJ@-3-z5xfS)8;k)hbQb1 znZo+GHE-TTh(37vb-=xw-bV|7H0V`7`%#{>OMDTf;3OA==PAF?mzSIhyywoHhJWF^ z*+rzOo`)heuQ*PPK%oWk2dK}SC!?B^^}B9?FTn~Cnt z$fHXp!kXAILcw#R(k*%_3JNDTHg3Y7Tc)fN5)ttN`!j5KJ=2*SbneD`v!2ZJ=gp(<(HdY>oi5n{7{!%zOCdo1oVltk1-+_@|!%-xhnU(6!$GTl@!dFflP{XlOv5L_8C6y((0wKroSf{!t$+ z9e*^f-|3JnGya5<*e#8f)zzMK4NhPfKfc!k$D|h&6kHT7FoE0!&(-;#=Y-@z&Z25Q zP{=wTa2R#^8(7xVU)W^>J0tJm3^m14@%KRRYWw-#8;gDK4@E>oz#rC^l$1cBs!?KX z1WBuRfS^0(5G8X~eEb6-JOFoZ`K-PHQ92!A9&gh50j^Q;TaUxL!q~J+omMB2x$+%B z<{Ft=<#b3%Q870!kKn|)>+aYsNVwpwS42fcAq*lx%6`!c+y^XtmhC>DQR5qADzjZo zi@FIPl9PeY8*MF*K>@4{yk8*Fx@nE8?~TxeN-_ z@$X#JJcN&q)xsLp{+T5L-+}G`MJcs_?KF+U;E(yd)_vH{6&5WN-wDM1RQk(Z9LQVF z^a5=F6bPghcK({K0gPgkp!OsiK9};D?d@&I+7ZA_p)Q5Jc@+~wGg7S5elv{?ETd&n zGLZ~%XO>l72z(mZP*WWZAoXSu zW-p`q2I}%>Kt5&F?E+5V82HC8uXb`0$Rt>znC>2>f;^6U^A(fv3~-g1Z$Jh3T+h*ShzrC z0vsyz*{D}MR0@%aImjppJYOwdu${Fg#yKy3J4X+tsC#;Fdx>RAR z>;JV@6TAf}8X*Z4Yf0>~u!lc@CP0{cu{6y_`3qgRpeS5)p1+*&5(LcqH z;+X;YhrP`Fw9+^LV#!Q$ocrn*USu1H0i-==Kf*SccO>3{XMLe|fs_PrRKB+{@XMDF z@VtDHuN)yPoUKBfPAlWUVlG=dD_K9gEaTHfAin)?1-YJ{UT@SfrMCKZc5>K43i*ZL2M$W1H*MEchm|4n?74}mJ{E3i0FNQXRd zd&u^JU4 zt6RYlRUR%4m#gr}Klqm-9poX-dD$bgT=-lHvQXq3bf>BS00P#U!$U~T4%XgkDVA=r zeDv@kDm4s(^c$HgpVQuYPn_#Y11N|{JpUB8t$MyxZo9kisBe?7g^Jh( zv3dYGuyVS4z(u8(G3L3owEPew1@T&|?way;oObxef0X75G< zWCb9x5XzgdV-PQ8fd1gnzU41a4e;Qs7eGsfdX7FMZG$@hneH?-sOY|nsNI#71%ZDE ziZe#p$TiTpEB1Hhu56<|dIQ{aSp(72fKs+Of>SbJ2R9!>;7BGxS7%eMDE^Y{3B4lO zP4OI0`uET2;s1x^q{N$x?~i%erg+82lihs9{G8s4j46zsuH1V~?)0IjNB%f=A>gum zUP*cRm!VZp+d(xBVj|u32V$_6goK2>VWJ~~)PI~j!ldv>;MU0_5`n-C zTVn)~Sb1@V5z-d-pLTm|&Th~i0bxxPS_x3v((qef78H!yV1)XsfRcar$txpOCfiPVe!*lWdu*Z8D?@FW*2R06US6jp#zDe%H8 zAq`9Rn8#l#fDrnFz5-QCvV2@!q}$0OH|4zWh;N=8mjjT7!bmGezZPHu(YDI$A1Aa= zJ9OrYzc>vVizBop80BM;ICBy=L5Ald*o zx@K(g3TL28BLXxM+8wpEweMs};Y}*n<1ZZZs$pSgUxJ7O9F76{3PS6d(Q%>w7?vzF zEDX`He26GWD7ij@)Cd38UEY<$+91WPB?O5e00rfqzP^6u{gOX+_vw#$RVf~fe@OZM z{Xro3AMQqU0EB@`^ycclDzr?$oo}_j|B3!hf}1Cg96u&&3?|-@mXjNXk_sp$(xL%D zfsmYetA|i6MLCpN#T2T3-2NWo#{fNDUCRBu@GKesf!!{bK%KGCDvE24=FbB)4;BhF zN%}}wzGg;XV4y|&`&v*PfnNF6T|aVD;rY)OmV~}~wLBH)0o7Ikl-L23+{_xei5{%x z0k@SvG6KXTfo=^@2m%snDS7#1HyhZf;A`jF;2X!)fAh9jA}M8v4OlSJA}U*|82IF5 zqpyE)Vb`1fm|}FB?KApggzv!xp;AXgYv}%!0({Dg?%BeQW6cES)_1_K3LtNee+~v_ zD(bxU$ljjk=ks9?#K48`-MfAJ*?8Hz2fnw!XnjvVa2~n&$`INs!jS7gX{!er6kq}j z8F}{#F0yvqcYF#G!j)vC^4{9_)WrBWHDEZp+LcvH9pz;AYl8-x$}}*eC$u0(Cb)R3AVfj6bvPjEnpv@scbB zT4NB403i9`_z;o?WJkg9#|V#d!MD%M|Ma?BnmvgD3q=eWoinCi)3^K0vLLz7L-H4J zT&B0@drE(7Me884UMOm6_5*lRKw$)w4KP6^L$iA+X<8cObdaK;Atf6lu#Onkb(>=$ z3heGBe*ECAj(1YOsJ+jwz5p{o9fXXO7|XBm6qx&j|Ql44**(9^}uL~9{?4BXcp?%z66l~X%_$h-%e;hz=K;S44=?n&GkY2IXD5- zzDtsUh^hdA027A6!*sGXv+h0Oh?xDFF#tRaAP`U+pj8P9^Plkh{%3H$k_CAOkU|TV z0jlhX(^L%bBMK(P8nldNlA=nwdJYuikpH7V%7!pkR8)L4auEq5WZ&W~Rh*r7{55l{4)Wfyl{?Vx`llEBh+Q-3p&^0h*UyzX9K7i2EcDE7+_(YC z%M$Pcn^s}94|mUFv`of}pyhsHDgyeRfRYUnCL`3a=VY`1$+SG?=VqqrYis-VYr8!w zpl1NYTo~HsAjP!FFd?_`NXy6!z?wnr6xp56gP@dBx~{r15H zc-4yo*GGJE8iNCQHaI4RwYA{C`SyoU&5TR{H>meO--lA*Jrt&Ts|YKQVh;AK#nJae z>4(L-x}Up<>r1?IW>K{14U_lYVVE-goi`yNHY?)|Q27T^@n%972poqy;_kj(qmL9>AUJRh8T z00E9#)Ln>?QSx;eq$4l-;Kt@=*Xeh@#l4B$rrYmcu6-=Q=|Yp;`uf5eFtMABihul3 z73ZQ6M${=?DGb2kELT4l9s;^kRYkX&}>l zCoLd5@$flEDai4`{E zi7LN+!}{F98)PCU6W|m9WQG4>8%->X!!$evK-Kdz;Cku zj=X+6O)6`^=Ke0UWlWMGCm-_~WCjEQb__uXr#6Cg9mjlto^AI8DW!F{x3|Mwsrf8N zAwKPwhGW9QCdW#FTUu(C?sUqFhAw1YI7Y~X_+2d%C+8|uWZ#MogL)AB=1mO^v;^rW zD2kx40o}$H$~};}vD$B4RUnl`LJtS1<$2DB=J3~^04-!oG{|hQZE#p&ime&c!Vvpe zWNSb3T7_uruFq|D$gm?tth>V|v+~C_;CLW^8A1gDbGPZYeuedGe>6@f`mp(&rUUR{ z31m`6OoD>DJ`_5mMJSk54ix%Os({Y+4ssPx=sfjniUpzv5Pkz}a|E9`xi+3(Zw8^1 zS2>`gcH7zKXHD=YXyw$OcV05cgqZ^%ffS4oHOQ8r<_tD?$BMWKEDZS2OIaXq#h-!7 z37lf8-u>1SteN`9?pdUywit2-r1Cq<3e`f~)OmGeJIE@mwNFsWhyppgc`AKv&9_x!Amqqn`=+oqqdGaIh5|?SY03a^ecuE<(u%USHXPLepdf zrm=!h9wrHh#WN$6*C_*__YeH;JM>E6OpO&BxrmOY?sq^T1mI0A>(QYiv~Ir%P(v?z zpmmJ`fbIY!zNB>fplgEGuASEXa^4!+oZ7mmG_zaDSG%pxkvHn;dBfBx+o|Mi}w zbm`K(dCi^oX+<8z(V*t~57}o#aVEx*9<&j#>E3Y>pxRHtZ|HIl_hE>@_{i7fXQX(J% z0)j{*-Mx{JP6266Gq9y1To!bffn3P7`ILylc-aG_ zIJgBZV&tNQ%nBJq;-f85DQ+BTj^wD+qO(^IngauT_B?KODA=;%5B4sCyeYxXD8ejz zYiIIIjc!9~za?7R&$LX`OVrS-2_zdn7jsW2qLN}^$>QR6DUJy&I=@}_;$R8S*&;LC z8Y}r6OOFSwpkZN1>pGFgRnX$L&sjU?+=~l_>kHED`$ZKBxl~apGPt$nKl}BiFY7#2 zG9^qi$&Ty59#Ir3gk@%>HZ^TiU~w<)Zf(SSmiY`0PB3S#v~o-obGI{NY{Cg&+ojFC%hP03!RatU*{JJQ;*jQD9XN(dxde(@~7vg*)sV8 zcU;(g!GaGwU0{j+mdrOv3(swuv+w9|rxg}+xO1TIj`DQAUgACK*$W@BoZXSj3mJ;L zIQKxuSTz4~1-sK0*}AjqpItXK-3kn2pns?T$^1GuVROxjgqj+CfT)ls4Hu==T#kD2 zeY2V^?Hcvdm)9*>O5_qK_B_QRXMBjN_nhK<{*cv=OPJFe&SRrNJNPbN^K73Y+CLXT zIhGBoRVNHMLVN#g+($Z*G7Z}pefuo;NLfMl^;dJ6gRcbE=RK?Qc%4tK(nC3z8wguP zoBuq5gKW#$nwi&Pizg{En@mIHI8BrkTpQm8O-M3)b+%7&MRs6EZwTXixA#m@_BEXQ zfMxK3-SzdMx@wi#_ETK6?euMpp2ZfUlJ6ZC`p@U$b4%q^N)IMzpw0L6XMXVha$edU z3$!LxwB}UVvB}XK=WD|>pY%al?}DO*%57KOL`>t12fS^O-;V{sEz6kh+czY*g$f3! z4$N$?EP^?PS`Twm8R2;4j7D=*k!ufiAokXe5!624*|A`!#Rxj<60!*?y-749&|xwr zVTpC>3Jq^|AzC!dFOym7-*KQxaJ_mA=WU%aF*W09RHEJ2B-wZ$@Lm#Wtb)n^X;&%+`ELWVpzf<<}gy?gKdHYE;!a5Xsz!#UXds2ZP;G6gW?vATA@k%bZ*- zgw8(v32cNHgGO7HI$V-px6OMr-3LJT*47 z7?KQ2GE?#3ZnG{5fNO{r9VNw>PX63v)qy{tbN~+SITcttU4v_qW6m>qMK#O&bUTjx z??P~JPn{$zL+{qshNYxPl?$WnP8frq!ofYtTd0}A|8DuSWa6zbV>$wk$-KN5Ifzt8 z8P`3W2%5|1^U}4rHlBoW?skKkR?>jo#b{y5A{@{&cDZ2W3*1~p!! zNN{kN{rRZWRYh07U!oZcRI^{n;JbeUtrT@mb|bq8W|A4l9DX}Go@pLWpn~q+!i;AZ znF>#y^{6P})$%*>w9_0?O z5n6(hw>=-y`8z#*!6%03@+Kv+!og#+iQt#uCEv({YE7o^Rw?nI{M*Wwp3%OaKB9u} zz43^3uU9@YpS=f6Mah0peTPgW?~0Q|1qT|MgEK6`^T;nj2NFDa*61R8D4DL+1rG-) zC+YbR!l(?Je=-t8HfIAD1=(@A8ZO%)frEqUb-9>JZu^2sW5Zxln+IsqJ0}&=xv*NM z5}0M31`p@WZ1uWPl;;%$o|=W>;mOc2c=y~N9$y!c&rcBM8pZ6b+*g>-K7+=;-etdO zrq-V7o%IX=vOa$mfs)`T<$bH7{-X(W1I}nC3yH)6TUzqp272#JcN#|-rS2zF_kHPI zf50=i-tXlcCLa*{h?||5gIrg^l(u8L&)f%n&k+IjMDU8hi!*wf)%7lN41R`TL|Cag zpRu1sfg{_7{3oGPMvr05j2;foj}Y(e1_*WWnJ9PA?fT<^b7XfA zq#5)9gnRyY*}hY3uVgtxO9y7fG&`4_HLqdbf~@F%pVc zOv&S<~G>uY@oK2oKB5`O zK;K|#1xu=HB%m`%R5@<>^ABGOciL$uf??c=tm1W#bzJcFQ*x4hbhZYT1tB9&>3L81 z2r%bI#uigODCF%xLz_bpEUpXKfgK^kAFW|Qop16AzFW#O06xIEfA&&iKiI~!^?}zE zi+=}7(N~`$TKFf9*)pX7<7VrYb}o-NL`9ZXokhKVr|wpnU4eSi6 z-difqkPcpPy9uW}hl1`w^j)Hv5q$og#PxmL_yg8g`G?Bs0$f;oYtK_2N97e@hZR-g zUpB$ECmonIZH$Dy-5Sz#E5Wmt|KBjECE8qO>91toU2+&9NqF5>m=AQY-uu80qZFBe zL;KkJMCRFnbEf@peX?jXjCM~WHm1vL$qhogEWtjW-Roe(kl@k7V5a8;q%Y#1Ffio5jc#*Nmn#Ha^78tT3 zthYy2$n8QI=nAwi90~HYt*yxioDCWZa$i@lKDg(c>d+WWE9@2j0dO~Pm>mOTKTNxyBL49bpIHLWA^jRIqn$_e zDW=G@ajgIsJQyy@yExhy=#|zX_J4EU|0P&l#B;Rm2SdF^z}v4R16I8>g!WZi$}i9I zz@-6s56;_I7@fofFc0$uT)|_oY-w?etlPZrk6OceOXL}X!FOc8AVAn=GKD?x-J-9v zfDDtoIW5Wl2_}AAW|=PdH?iVa6!Bo@y*6X|18pu4^Ysm1Re``^T) zWU)*l^_ct*7aE!O+h8wZeGzsX#=S4E6&m0@ego<90RjwB)8dN~<7uLjpGe~YOE9S? zE>i2KH~%XI%#JT=~GGmMEsKwLbxmP z9BhLzA1Ex?80Hfw==tjXm7I?u%`sX28?Z<61uXx2M8-Q20x0ECn>TEPKWzAzEDzCPg0dO`j<09+ix^iHvNd@A7ik$=p0H(-($kxr!lf=mh@Ongeg#Pc2Y=rUm- zl){Yo1F7=a#t7F?3p@ZMcwYXCio0d7P@NIRBio?iCP|(VDv@Ilf&iaCze%B(vkAxr z+dYyT%+$IE3wsic~}BS`KJ+mzjTmG`yT;tUo}cLs9eQ?(efvl1V(B& z1U2?{*v_ABI&-5LCx8GVpe7z3bN~m;h5M=l@@5o&x6t0^9Vh*Fy*^*yi#7gDLPV@G z2f+()n$bXD`v0fNu^s5fg95lik`G4r7Q|;URL@V>@W%Jq9-kIAPhbmCBE?y-{CRkP zF%>vBmEu6uuy+&2K0FWMQzGf`hNnyF=iYEBG?5ZoxeXkQO?hq!dGM3z)XHl7LwIk$!DN+B{6}G zU6GFY)cwgn0(CsHH~g9KL_pHf_&irp|e;zvVXwoaR& zKAowmU4$#?zvlxgzX42?ulExr`HcyDOK^KUMTI7a7`?niLNnfw=tr#5o#D7G96Pyb zorun+dO=V*>9AXAAX7B%u&SNkk@eA+ z(tn&4{Nz3Ij=Qo=KnFsrBEgrmP%$;Vg4c=x+ecjkp=9>x&uMW2hN z%jcS_t>T{Y<-O%StHg#7S7nxZJ}ZRjMYfRm-9IXWX?gFGMipiC<3Fp~wQ-t6-gmAB zj8Z7bN^SoWx=j+L1e0rqp$cy+FX6j557LSE0e}}Th5XnabzbD4zWuk%ljlryM)rmV z!xr|2zM@@CQCcwZqn`Dq_;k;0k=W9~>h<<4MsDy+e5J-yriaVgl%%jmA%!1(Ad`{*ztf7*%~DH}o)&|gO$OMHVztg_9qMtDVHIEaR;0$Pctr^Y`O z;$oVo{I_Pp)jfZIwZ7jh54w;*x&|kHcC@6m-gD{In=yaPfA4kq8WP9CUCHb_@&nC< zV5&454@P#zbew0OYlgNPJv(~#1BUY{DLUk2hR%`yasI=lt8T_i=G!4JJm_kWOTS@_ zpdqvR=aH*F^S?cII-dR=RmCeTI*6a5njmPz?kwCxOO5#NF#G$))3F4!YtN5mm0UkR z>k`Hr2%6;w0twFl=2LBkcYyq9R~QJ~yi91fQ3sO(SIMQV=*!>fL;f9?K_UIBM>S8+y+ zW)RJ1l=7Y6);#k-Y@I_AHDxs`vSE7Jz<4?~cy{i)mfk~Tph2g+0Q1zz=#zCnm$}W- zqO771IJANdLc2)BQ#JcKx4R!sDCe0-^=imBlWlG>e%0=pR`^vfoHU};q2I5n#9n^F}B%BKho z=3;y5e=QkDs-caFE}JbLZ_NF$?2+Ec65pa-yf-7Hnb%VIahA#qQz=MccelE_c!^a; zW5uBespN7;;C1)r?&fKaja}dPG^@M(slDQ2krhGfeYxCg&$GAcwk^q{_xkHLoo&d8 zy#&nNvA_B~aQZON-{x{_rVxrse9zVD^s$fXM3Hb<{uw7Alw%+g{n? z(M^`-=@h4F05JyrBN+4Y382}Iqz*B;x-397)x>9d=9iS%S&j4d?!8^|HeBkpv8!UJ zRZ%Gx--S>tDSe%qu##F!S)mZfrdiSAkj~mP7cpYJ`uG!5s?zgqX;XtTU+y`5zUf3b zuVA5^(_}TtX4?JF*7d9R1fmt$ohc0{dqn2?lqfPV zQR5cchT~s*3DUR>D@wDDwq(2Q37vKbh%B{~B32pPVRm{W|0;+ah<1Tq#C%lol)G`K zrT7(;-%t$ySlXlm+r(B%O$( zfs?7IPXOICnE%6vXp-t4R>0VArf|FDgLVH+_qR$m&cRh}?iInTN?os!R!PNY%9{Yk z>vU)<64Z9HQ24a8(c+=QukcMY8@iU<2Nk^~Wre$`!d`R4OTCb-Uy%~w#r@6IqteUt zdPV79urD9NMt*F?A=jHl>5)AzqERB>F-n!0y-7g1x`SU>KMiW*sr(Y-UL+?>A+0(# zKx#y?MJD@=YZREsE@D|MW*gW0;k@m_E@MUvssXGrRdh?%(?%+WMlb*ArbPQUGi5)G z{8r#Kcq?yR229oMwe!NUVrC^8qz>uzug{In8O<=w1jWWLUT>~`%nmz^FsJCj?PE0m z_MO1}+T|P(l>^|J3$zThs;8$l*oNoZ>>n+D{rF=u=b`dj@GO}vBW6c)RjGZ|tw+y} z%0jPDDYHBO@J?;q@&ozeB31R=qO2Gdv$XcAS!%=CUzKY1FWWR{hl^LJzfo zi!15*TAt^&yji>9fRuWdxGSH)`Cj9B^&Lyl-jvT`j`$y(CT?myjWWt&smDM%D@qX=@?{ZFF%Knned*MeO_wCYH4^!o(05X|AXGPKbs=SzK zD=8W+jDE*L+tBEjQT*ZK;$kZ=)$xSEL1M^$u&&#yCY)8u%x-Kn9DV)?ipI+u8CG?@ z+PkpH8zgsFj5Pb|i#hUxL0}%1R+Y1$XTcv2e%!0{JLes#W%~8|$On@FA!OZ_wHJK_ zWMcCbv4s#s9H6_Y6@cAy{WhEHRnE@_50&7>&PxqPuW_8?NuHpSeXg_X_eM|CH}>=S zHcM*f5)a8GkRLu3Pt_dmuOg6PbDFD|%(kC~MtA~UDSan;>-E8CisH-2y8^MBux}N0 zM{c|5WXV^Flwa%(5g4wfIxuHXdd0^m?D^2Cm%^gnx*64^k1j!lHY47fX_XJw`%zV} zpaN^f2j0Rht@{t(pQCfTk8s_GZ#>90EAd(>CXJ<7RFG493ld5!juA^~6pFqr6XZ;C zDKRyiAJt1bQV~FVtl~uRrTt|xell|8b67t>UeRu*)i{UEl0CAK#{M80OC@rca+TZ{ zZPwX+6hG{j>?VoJ-J!3%NIZ-tv9=sDySsW5F+w-tw zY94O5_7? z7y6^7cqjdi1d)s(1bMf@h048xh*{U|fxylxDw>bJD_h2T5w8moSbZ_(UjAH7mK{G+XbJ(OL)r4zX!m@OpOpe}RApo@r)TDP(P2@WMuT-5IrFnd#Y`@TprP)}rG>C<(AN&h<--U5#~VY$zUKZDBw4!`jL+3Bsi(5&~fy^$Xq7hdAk zs@qQ~IO8Z6IdaR6y*T#?(ki--Mt+cyFRO8qMhARpt1cF~RBlO5QT5T?);}r1k&ykl zHsn8`^lsu+sY2%t+Q9x$2e7hrAoEK`r5+d*OGT4pw-Fp^0_al}=g-HlsyHep+4i2M z_WqRONCYRg@0QrMFpfx!tyd{#D7%ou?Zk}U>E3n)v1%hc8d%MEo)6y}38V4m1x}7H zsmGA9bTYrasGNqz>wQu_0a}UQg=HhkDo&*+{ zO}sf^w+x7M*?4WpO8UK{qjHPPd5USPZb46rSUr)T^<6}*Wg;pj22Ak{db4{T3&pIX zGwm&1#V1`$ED4VfDa~yjm=_!6Hd|VWHnOCPe_EBvb@@Y``)o@DUg3bzs501(<=(1( zdYDs}o&f2PdZ#*5Po{C3p@cY-#;sNN7n&zVSH=BPWZi{6iCiJt7x6=_2kQZ*SSCqT ztkUY1ZpY&MjAp|2#C?|?`vxBu81Wz8BPMD3wyu?kx#nGC1V+F6EPFHh1WPHQpgkGA z_p+=*&!~<@Wqy?C$)UZP88sAc5HZ_=sLTE>68L2wOS9#XoYkq9c|I~i^*b;&?X1f{nLG4w|h1#jJkfAQT z+u7i|j&Y|K4|+*FUR=NnNKu`RLoV3u_b2TBVS~CJlXf}CAHkhanJ3P8QegK>+Xy5; zMj1S_c~m3vOb<{k`bPWtq8zho9E`@CsXp53?+--YzJ-ZIjauWOQ}95V5g&hZ%!oQG6nk&b+AKc$R(L;Tf4`>y)9_GV1tYuZRy zov5u55{2eG2*fl^!e4Wby~pAW5Vp*_V(?)~B2df)nTqOBE4uA@;39Q+4j~;zk$Em7 zj)hivgek`sZ958W<^r5j+Ji)W=tzOk+$Bg-(==F4^Dpw7X3W z%+|L5b&_ZhSxrK0ONxNS-43T;oiO2_C;jvlG@E~Q)7&(v)~>dN*G1_-$yd-?CsOt% zgNdPTQH8V`e<$?ma!1#bWH)-_F|Mh$$z3!dO4L+8`*-zW&+GH|;Q1}igs#aJ&~SjH zp(?jdJz|MbS7=*gekG$szr%_+HvD{DjiIvLsMmbK6~l)a-K3-5Q`KJKbjzmItK-KVMq8-i z384N88+wCWg=LT3kfwaL(@XBZW?9BJE3b@!-)`OQCfWuLhnRP2(b3uQ7K?GsRqeb9 z2KSf0^B$+D7TJ?8?|!_MGQ_AHn7RF4&SI8$Tr{k0I7epo?5UCefn;&gIi*|3XYNYI z^jHw=F^?t7JjwueOXF#T7B|N;v9iVe2`ML)hdIWugbhwx@3s#$P}cO}U_D-ySC_n^nr6x(jN%X=P=8+Y>>dk#(Uo`*Dc+7p4^;Y`gE93z+;CEsO`5;yuhF;7@FRHZr!b_OG@69L9aCqa+ zoF2M<@V0K!bo6H5MBBJ?3c-I`zl=HSt(RTbi}3N&1e-=z-v%|hcK=&<6nvP&$Y4Uf zHp@o}!8@*-~wNYA|_a-(0Cr6?zhrVAk1 zRzu3)&t5&fWg|m3He`>Jjw97_3Yx5O9k&~*$r&ERsmBPa-fJ8d*Z$0ICm$}G(CSmT zLU*FK<)9r;j^T)UTh}-)xE60@X;981GYnN7)6+)q+LeGkBOm1&j+@$vL z;{1c>uc*tBAAaP^1L1u+hjylqQM5HTNA?A9dGiK-ekI=+9F82=`OikG)a_JX7MgFU z+)}g=6tmtT^<>d}xQ8o6UjIy|sNEKxob>ss4t(6R!!)^+9-`O5#tQleBI;ko7=y%KyBVh>h{tby6D<45d)C`5*iY-rw6f#( z<|uA0qXIQwimXj$tVsCmwxDQ@_Mkr-W4GgRoj$N?elqOc5nWrH6VIkl_ttCiz||Yv z@WWZM{;p9m;ryQ=HrdohcdrRj0qZZQzN7q5thje$uJX7(k!u|mXk~0;t#Sp}& z{}@%Zc^qJ#fpZPF^LU)CvUKhj_c5t3nsMoJQ?ZOk^bTz@15p%W6&818k9csM3t2YY zr41snsG_*aN%8I1g_w-mWvb-h2O zy&AS>lH>qpeh<-6!;zfvE9io&f>z*&Dr$}LiaU1NXZFgxsr_mx%XyWhm^X&%Q-rK& z4^!ng(qDW`AuRJ4wogQjcUZSx+BldFHXp${oVz;sMSG` zU;o~m8p;|m+uj8Ng+K^H+EAKK-ZdhODFXtY-NeTAnp+zEK^ zf&Jaz>HWBO*sqcOw7d}QyL^upoLUvFkH684ffkGAotPgM5 z$Jc>$dMp0`-bH6fEjqNhKD4PNu#HmDlO@G|sEHy(z`U2!C1_k*21$(2z4(x@?CYYPQ(o-jgL5-T z-@akuOQy$@9ym>#T(zUcfyf=6NW+kh=s@eX+` zyYM~OH6^|kP50}45^lLs83Q#vXTaQz(8W)0pwLEUpO^6PGtr(esC>EG8;6QoX9r;eSB@xlziw8>3tbQ)an{Udu*hVv`__+uVB?KpG+gHl;d@P0 zrK^F32vTG{b`$sQrTu3lo&V>hw0myYA>FAFVt@(4*v242*DwSJ% z%lUK}&d-j%xV(ft-R4RKH@r9)m`pss|MyCYz6Gh1e2+zLOoB*t3)B-^oa$hBY3?tN z&QhP&Yl$`CZszZ~zwqi6=`6FF@lq;iDyiN6#~2Kr`4DFfIUm>!h8P6f7gW`Mhncs64@r_HT1zoS8h*|M|$3oO5rkr<^NC?*j z6#M9i^WK}q!TO2Rg-2ZnDdbHy)vcxmMo)VK9BhTXEMy;B|t+$A#%;0P4u$u_zZGBvISzkX)d zwSP0d;ac> z>Gcg^;?Xm~QH3F``xWbCYMS1vyT0%iqx4RipSOm#MAWDKy+xGkNslcd%SLG^h2z1a zj<$m`Tgbe|hzz`{Ic(_&W{lf(tvA;s0^urjy&!k^in zxZwz~%I1%h+mp=NAADU8*eXqDM;^O}TUa&bIW+%;Sf~&ZrxDqQHOMz9HGGwo%D;`L zffcrD4>zx9G5o58r#s}q$5qfo>b=x~Ttp3ehkYISaW%cT8~B>eT^xs+j_mVpBL?g6 zvhjv=9dZdgok@S~HCE(o)LT8HDO2^n5q`;0EyCj6K;rS5L-R}b27`5*`{!=36ZY54 zpNSaureF)Jm3BbRrfefR;zky0f*(H6d2Nr7$Wb}(F#LSAO3CA^bducA<{Y{F6P^wN zL@xsn?%TmL(W?OoCCfd8v81u)rO?Nc)A;So_|UgT&gk+Z$VDA85|hoxEvF(v^WJg0 zs_dtP4KEIz2|R3s zmtNwW zD~=ma>jDaxTA*A7y$6dB8}}>M(oc?^na-=ROmySLdE#;h&ocDw-$z4H&xd~0=zh-y zZo8jL$pL8snz6WK3tJVs@jBIqQB9@7A1%EA!J4v(0g_}0f5V}}0qFSX#tL38((x{< zXG6oW`}p>4wDSr0^S$<4GO-mXdiAy(ikRfBJj?vC8lNI04{)V)FWNKfbW^AbkeP zJ)I-F4Y1!^Dl%IwsvRL?w<5n0;QUs!_gJQ5K4|GyPl|;4LKnNk^3G$Rq$A;oaLRx^ zy}l=WI<*yv3_0;zfml%4|I`97rx!PMr>7V&?=4+JkE|G#ThWXuW{KQpJabCaqHjKc zVAU+)dEH(v4i_Yo}8-l^GUeQ65NXmb-r_iP4M?$IYLk87oKv zQVT72AtB6p>O-f!pFNDcQ*n!yBmg$%A){rRp!KwOT-7gip&4&OM`;i&Wi zLWDV1BhW)(L_v7G&i_1T8if8RkfJHDU6}4(U+z0^d*VYkyP3k=k}F42>14~prHt!M zzZl_K4=P7JqDFu@j%4^y)sRL?T?2Tw)&`)sS zbM*d|>=IFw8h8uzhmjv3oMm3Zzh%NkDcxzfZ-F@OT_d4(QvAjo61}}c^`+^_n#PHR z?>ZV|x-NuPnK6B~>`fMYvB$AeDe^eGTWqLkEV2DvQG+NElg(FwZ_0=WY9I^8KCEQw z%ondp(eE)dRlPTWh!&oNXl?ca@pUEL-ZP%mGmO?oo)BU;V9u($Wc5^8ESCl}Y zPl%j5^VXl#N}uXZQfN)jG1>-92Xl8cM08VB!XJEMQ_p(CvDQnTjgpAMl%62{GXeYAo}UXDdnL3q-GAI0-jE{zc!!@lzZGX-(jSxG2d#+{xwxWAiQ$2 zDg@C!W{G45b4{nAnu3J@kNe`KSdE97;548ARm8pbdg3A4wimrMLa)@g7cvMt5o`hIGjQOW^m-=LFeL#W>ku1_)-Ju zSZL2p9m2kibbHsFG|=yUNd4`ud7>PJ8*0z+#3(i~mtKz{YaI2u$-PcSEoRnK)oEt1 zy|kqmL>L|F^0SEZ6%ukwe<$0{Kc@uIxJG&nT4w#SSp%m-F~|BO%|XNa0X}(AY&XLU z>vxs#!#{?M8PK$D(l$lB3e2XRQ1%p_Nc-bK7qH)`pxs`ftc^?d_A=U6++n^Tw?e`d z*Sv_fzS+-wWC082Zh(Re^KXm@iLF|3(}%u?XO>|qW8`97H7`FF@Cr+v@Y}jQb7M^R zu;BV|{LA3gA+RNm6~HZuT~`I@eSa-bf`{nYulchWK6K;wvOFIBHCQV{N>pKWJP zCx3S}^_)GOXg=Wu-Qg-4fGF4{w}-+PC;xBEMS9Q#g8K)e!LeJe8^ra&RQ$$9?R4w` zStEF!wC1C#(-xEGT~^k{DoC~;mVZK?XA1DtL+F>vpFfGp8ExF7q82OD#|py2GJX)? zzd8GzcX-Lw%loZT^h*>CRzKh@rLk>63MAq%>+Q0z{uTk?L2twSfF4>P5?$iS)BO1a z-569DG$qcubc^yY%#gS14(2}bvfg51xE?&Ei}$)sQg@IaFG~n)7y%Z&I)<={Q++$A zmN7E(mQbQAx@eTH=YuOSn*xEGzKHhRUM+a$*-5wf`Jr&GK@WCADaVW|adSv`Qm0`z zpJiZQWX-qcg>9+jV*8}aRvA0J(#!R=9F#-}ru6!Qpl>goh>)p}`J3(iZFj<>7nrF7 zbeHo7R6fhDdA=?`t*qn=Yg4O%e<|kjH?%qg1k$nSZE1Yq({6Y13M668JgrBia4xzB z+GY!MUR{rgZYpq|;3T^t%=N~xJj7BScXzHGW!v=Zov(7%_)5jVU=sUT6P|H!QJTSQNkw(Z1DDb?b}7ID1tA%dI>dnGXQCx94x& zdu&#;Mk`kHK}?mXw=SskpAP6hbx1C>&R`zaTGtKH(g#)! zyF8#rQ}yl4*Rsv@arj&p0`qe3c{<90Pc!?$+Oj)n2VV~k__hvkZEEY zHSrTnXZcGLc;6W^HU^)`hRF+@Ja)@nyWmDI{|*evE^kBScjYg1N0ol~0ieva)6I?> z2Fo7;GrZesklO-=-me0e)Pr9eh92%aDCDVj3j05m@8mR(x~0bAELNTcR_v{~;$@ke=Kteoa@b%aK(tO(EF$(Ddc}6-P)o;XbCv`Qp6R&CaoSQE z`2i4XI1RnEi6WrJQ7YSwSxH7S{B8AfZJ$ZWtidyZ#iMnurkud(?Q1k+d&Akc%Qq8R zle^-G+*?=LdG$FF<|j>NCG(%xC5@CjqDIMiCeVy?8SRd1ZBVBljdD($V-T>{C$VJz zukyJG7&L?=y&HPYGC!jqE1_8W1+e#w4{4A4!Y~o_{8(6OsObJ{V>y}f60Xc9>jup( zw+BSVZ2RHK4xHWIU33`wmFzj!Y{DR-DTUGXQj=9}Ol#G$oy`y%R3354fP4=YffOoxWa2cVJu}aq zY8IPEW5}r@tjgzTYPmYHT^NZZ$>$>{%~&vtw{$kgMysu7)KfJXZQl4wUnL^kir2|cWzJF1IUKnUWj84@ z7$VCoRZwOR?$S4Av!y4rMDTG(v=H_SEvgeoz4RH^9V+zqcWVt{nk#N?xq*PB{}3Fv z>KPN$|JtCFjs<_6)>~I+$huTo<-7V(C0Kwa# zJ~iR_29|<5v^437)(dKEKGefVzmMzL{u(C>DIlhI-nV?tmB`J_N38?mv0@tR(hnjJ ztJWX@R8u%9?`Br@i#NMulRX2+R~D8O;dlC&j}LvEJKMRa6ZT+;)7 z@VV+tiv7W|o&tG?x8bAWyTzdHs%eNhmKUURQ=)sRNB{2y~#MruFRjVyK zvD*2jxZw`eUWaPRZh$+0)E4>vIf11@{cpCcBGoBE>qJE<{lp#K>o$az9`h*PG3J%h zTvDo&$C`nB4YZ!0BUlW9JCeY&G0P`&!kxiM3bVtELCQ$-j~*I_jn*Ywp+aX3OSlTc zoLZS0Yb{uXzD?A<3O%}!GzX2G2?Jt)3Zf!+i`B!Ec^DZ23{8~!cZgJORAN1RN){e9EU z>Hr4&0N{sx9d!??>Tia(I4j@ScShYR;qa&(Gm*=)V?Lxks^8RqxwSf#hZWV}=rP9r zTo01PGDy^KKfE9yhZSaV+BtzC(DPvA5iYoP+B-e+<0E+*5A$P?t0ECOxPE;o1Kbe9 zF4t9mⅆsY271~|IL|9mNX$&RqHe%W6x{s;*RO+QM~?!cMD37cU&N`GQUlcA_Yd5}@RBEB~MBd3)0J9F2U6R%#`+A{IH z0CHjO7@5yui&)88>_JQx7(!tY9jDd%Qrr>}O~%ACyNiAsMdJP|5kab-T2X67RsCB; z;D&CjtXZ3&BB($|woXK3D3i^Qav4LNY@St|-a!C?vuIIFuZ-)=;(2&%YA%KecYOr%{LCO4)t&BMXI{5$eUMVSU4<60*KIgV^>m zs(+~IZPB@!g`)1vtF~w$O*tF;AIa5}kBlg^J_U0=)Cg0bymY%)Gw;N*lUX+VS|lm? zzN|&vYfw8cfd@wyqW&{sLjZcD!t+vRHGAAF)g!1#K%7f|!RuxSXBt^66(vMFWws6I z5O+{^Fy(_I6Yq%Yh3I`ywLv@E!dfonSo?v~IFHBipu^7`-G!Dilx*6;tA0xUd?;8iB+KX( zqNn(X_;s3w_C)PR3r^GICO`4Xi2fng%#HacVF0FkZx9%mf^Oa$Z&=Fa&L5f`m_5)L z!f4i>`>E&W@@yC@I7d@(w*0HtZ-2SC#7og|kufG2-YZ+9Vxx8D!Z`G>XDwW4WI>15 z(I3~0sVH4JEqu=YEET3vwqHTn#pI1>xUn$B=#H;g$1z%wWH{dWpxhZn#3IroORmxBcI^_R!K~jbxZ_)v^3N z)R?C7B33Ua6MeHK`wemXA5>fW@!vikt2}?clUPx^Qv5~t-Xs{*U4zPVrrz+x_7|@+ zW+-pxBhV5v;HVvvzYn;h*b;}ld(a>m<~r1wy!+{?Jpsf9KY!%xK*U|E0I# zzZAt&_H>H~=#bQCZS_YaukJr9p0lgHE`2v5OU#=Fl$b`>3@wP;@AI&J+P<`hqw`8* z8rJB(KR?~cttW7|aAvGtZbFxO{wB6(<_N*0Hj8CX>}r5d3P)m~=WBr!$#c_3kza?p z3@{b7vfmd*5mzvlcK%qNdCGZ@8xvTsjJ)uM~pY4DJ9IPOL)Gf~h0hChk zU)leL;Fn>0I0)7M;Bh$H_u)U0fGB%6p;cmYk1oVbok{h*IRT*v&VCfx|2~K@T?iY7 z&Jl+d=V_Wyj&nKLdi}wOLuY@^-UW{rl?1l6r&B z*1U4s;^_Xqmz(tqfCNQ6{)anok<(!zf_skg4`c!lC&QR@lkmjj=z-xKL3rb9 z!>iL6-OJj-lh~QJY_G}ehKXiXS1KQt-b7QC3L@CUN20|}&n zQ9BAy3lyl*VW61jBEY?e*)c#n!L{KSdI?k~vRGzTaXXxSU=39_R&Nr7vYM87=3B;S z9dSZ(nEw#tW_K}~DX0e<@3KK#prWV0?Bqp)?Y>W55N(XZUc_gEgjglz15cC)gH(yV zSC9cbF81z(!@WHpxzMcwGA-2@~cDHvWM;L^V zp-M`?3Au8mc~WCOst@QOZUcZ{LIBPK7+2?si&&*_b_8%@wmON8f{LZ^LY=q;l;D4Y z2rE_v26L(w+;_tX;GQQk2AhY(7lL$X~3s@ zGkjr^U{kMJ%@fa(h>a9=2G2ilYR=h+QcLs=3hxGSRp7 z-C;Psr#bESgD?o8@LdqVcR$6ddK=;s6NE1Ukaiiobui4|Ur--jIXGaE13Z*bU=;>C z8H?ZZ1?<7gqE5F{CBdBknUBao+#O(~y!Rdsh$JXxv}?CBq-E z(Zu|L5$9b4Ot3h=At|}*N9rFZ@CTIvqx65S>odbpwYo{9fcuQ1nqnD5>f5LQI&7)y>a2*X(Vm6KtjLKLB5?+^fpFtU3yMry9yzeNY~P)kFd;;IT;9nPd< z_xQ7SD3ecqn8Nre^4XCd_6iHx;s3z4A_*4d3E#Bgt_AR*!uT#%0FJD{5DD9c?u^Tj zRc5Xz0I4#7&v0ZJ8>>ZCS>J4Nni1w{0EE*IO0=hn^AwRa3-mmZ|2Jt>%$5BhKT~74 z&OZ1rz?lSDK&$(jV1gCEr+2{;;JnFhQ=$ZU3jO?aAS{20iv?O)N*$2#)LH-Nt{mG}?M zT^pvIMI;+jGUrAw&j16DKhj^BC8$~#&j)KQlAkdHpzn?}01=-%7T!@yaSwC_jV9op zlRPvgi1VaUX;xyD6@o4TetbqOhht&?7h7)~R#n%n57S-Jf|RthAV`;lA|WXtARr|O zh;%m!(g-NE1!)x!1eMqdA}y(eQqtXB-&j7+d(Q8C=UkV6ZP{zjHRk9!=6!R&yhz{k z8oBkFrU8Ez@)t+o3y#$`58eZ~PJXrj?wQR=@yAVsVJ6uV9z2HyM|KtMPaMwYu zucv9jw*p}_qq~FcWwVis9&)1_JQ})lb$WYf3}+D+v^Q8=Ox8QXb32%5LCgFUJ6#_XYf=@^W2&&#TM6;1 zDjS+Sn?Qbs`orgQQZuxreHFfs`0&N}4#UzWs0u}evv{Vr9d)I45X=V!nY{-wnw{VC znPd^@0o-s!Q~0jx>kpA_jcg@*+Mr0x@V#r7m;H%WK420qMT8j@(ZP*jjEe_?0L}2> zTr@;2R+14|zUw3)J~-f$w!Q&`5-UqSaA(9Nt53DNo7{6Lek{OMW=>dZFn#v#sCxeh z1o$Dxx(4oo6Zo{d#MeG;(|q~n3fyx~qk^ZK5Bm`NaYj^?>u|u<%k4SG%KG5P7!x9Z z6<6e3pZgAj@=k=vyWH!Eqe<`zaGRCd$%PI2j7>GA>wrL2m`f+A*B8N~)<6F#t#N3H z?tyE-H4r%j(6Vv98}M~jg47ZcI-A)U`M54nFjoC9R)&+g0CiEa5?%NgDubYJ#+!0Q z;aneR&-JA;>p#Mx&N-^9kTHO9Nf0m2`}QGQtXab`S#_ zX2WbW0A*Gz`d)nETLBgtQ7MVrE4zGUr%zxJ7j&HZ`yO&>9-BvpRK_pNR}thK5mxr` z51^vp2LwNa*st)U&(zv$tUZ?rpVgs?UV;J_3TNe4ykTS8nZ zN$khBhb8^(;D!*UGOu6VO5)C}$IqFY8Br4W}nFqV^e7v%|M*Z|hw1Oofo7U?-PvbTBC$TE%qsDQ%KR2F??4A5d_mY+ zu%PGd!b{YWr}L>=VVRzg6WCO7%5MhgDaz{g9e`lK?u&L@psBXzflX4s8PjC6VQGA8 z(VxI2MIK zt*+tGRJRCx{cM+i|Emv41cKs8^1L}yzriR>UrQ47`-)XiPWn%H$vgAjD-FS|$XmXgbj{Fd%w*B-_ zV(lp0lyuKp0R&kN9@=K?Srz2FBUi+zwE*ZPi!#T=MW`B_D@Ll(Y5n0ND_>jYKMtY-gVDzwIj2;OTt!BPpSvLb>zGfnB0#27jkX=+;PJ&s2*I~)j& zKUOVS=M&&n{l3xNaWHppNbm%kn>3#wr`ke{9TwZlSIn9YpB$@ThL z?>^riRhWETN8Sg2D;naB;Hk>NhxBBYZI;?&7d` zMdukWOTMlOjXE6mFE(u6S`I2|&4F&y;)rMO-g(Pow}yklAbt_M;&H6U^x*y-{U+b5@sF0|=fZm;M2v6E?AP$YU=_d>rY?wZS^R8ZKyY6gb zaOyay-RTcA^3A%D{czzYAX_m3dYzkniQ@X5_V)Uutc>)mjLhUTHwhsD2^N+_woC2b?W(aF z9!kVhtLU-{+4ToWE6h`6Bvzgb40EDgE^KuTc*!5nf3y`cDA^2F&3m%R^KA4fZHi%O z?Ayb;{b{CkXB*rWH!~{Sr*dk$&7XGEX`I+9&OaJv*1m4w$tNL?Um3#2(BAH~t$j{g zQP}&9o*kuNsr zARW3b0d6cH3IoI8_s=2Nzp5OzdX&k}li+omSL$ER=mJ<#gs}h+OFse1EwCFPsL$WY z0ThuS$rOl2-Z-Xk^gj3e1SqX=q_3>Q0oc&Sp`#PPd~%T+zx1L4g8x|h>eVawD$rxz z1qMj~`3&0bRTH4q2fLxl_%R+U zm5W(gg;Y<=H#Rn^^zapRC~%Q?G6Z^(KWcjbt(nF$1whLh(joAJ6dWPW;|=vdE(*Bs z2yr9eNdu4PwT)2DLabW=shbT13<%)-dC^(GI+t^u!1xH9Bg|gGCU2_EUK}H&NYlb@ z>p;+<5=cLh53r*MJ1&4*DUSsO7k>6>aUt-W2dICbonPPi z=mvB#K$GMMasn_>EHVGj~_2bwZ6Y(^!7JEhynojO^^)`83C1ScUQA-B*;Z)=<(McKqB7AV+``HBy+X*ZRV`5mrs1pXR8!$Ejj&8d4osN!<6K*}L)GtIedGtsE(xz)_ zdbBfWk@oBvLRpLuMFIjh(BU+BojE3fIss7psPEDnfRUX?pY8~i{_q7-l~(2Go?x;z zAUiCF{|883NqPCR*$kIeVC(|r$djavRqY2{<SK{ewGIa7jx=MdcV<2gx!OWF!H07_jeC`He3F zHakKc45S^vh0y!qEs!>>z`Yn!iQk_Ms}QD2051nX+m?8ic%SKtD=1|*KqLoRq1iBU zxpd%w0`5dF@CSQ9NPrLu$Vn0s5`qZ@8XEpg;nmBAWg^tWAa6kFFQWefIR*x4@$1*q zOEwijM*8~eyGocY8p-xMU+lGd;XkQcl)JSsR ze^66a!bl;7{!K~~sL}KEbRPa+{59^iqiifstRSVru+s4Gct7eak)e3T& zoeIcsSy7)pjocMpcMR{cjhTJ|-iP?UdaLs}X%h^E3&EJA!OMgtsn38)?X;bX`L7f` z{&>~`k>71y-OVBE_*kF>062FbQ4!f-0sgECfLI6q?%O`6c~&Iz2^VC2-Qk&hhOfP6 zpWAV1XUhOr9Z$@Snm>UdcUeStEv;E3jnA;4N@P_6f`R=RR8bMn=3MHtXB&eiJ0OHl zpG@ii*lYt6oZBW>k%h3vX=zExiyflBvTC0Nrf>0#l`g>72P&#nV2tN0t4l(2RO1rR z&;vrWxXYMKHD&QDtgv^a^9kkT+MRwH@8VgG-hGjaUdC>dOI>bi8;dwpfCYSgnv_Ht zI3mFi+Z7Oj5-{}o{V<9i{OTV&~&&tVa#;*rqbO3FC zU0nQ9PKQAscH!&P2Es@W{0hLN2)t&Q=4ER@&X59ct7yYwwENPA#cUVTmc@G4vOrD; zG^Oj?r0e>iKDgj=gFM`_u*h%>9unv{w>~Q=DFH-kIVwRRA(xHPmq^5EB^GJl_XILW zfRF}_1g_1vVW;X}D<9^c;p4f}15GMI?F}f|fN!20P&^OL9%4uCf!I58(IfRra`7YY zN^;4gqDpd^qai&O9taMqmKKbFxE}sohL^W((zR+o-*<1dOQ?2fuq^&T6-KZ(7Wh8n zMz@LOWhXi9m`#bv$YvC{r1PcrAhv5wh`K_q1UU9UGouB>ZNMlC6fE}v7F=u<$p}dC znt+1@SX%0VgcxACYn;N8O5lC~|IorAcvN6cXaW+&aex*FJnsM?`Go9_s%tJaV0kzy zhZIwsV#xJwSSTrJ#4`qHryN|iY{Uud6XfN6GW!E;q}gKaSH1?F7zd6*17c9AKp(oL zQQLMe`t(XzwBp`EKR)9)=+~*fp<&==8+TdVCQ-&BLca;TCkU4nLwB5PRy~0q?{4U_ z&2UU*4NC5YF&4IzufNclaZ zjST~kra(iRoW$X`f$YM!Uc|>nqyfnh?4JS!n+v3B@DDKFndJlPzTBM$IDav%3%gO# za8EJuu7x$AlLPC?CgK-?A}tJ$wD11=UXpObm%ICwld1tI3=Gt5Cvisf^Cw-OE8L=y zpk#vq8}r<<<`)$eRRnIjjlEzRUt}4JK8aL&M*;jW&bn_su#Fgy2vjRX{6?s#fn5b~ z=Y{zAjt-Z~Zm}8Z-&JTyMpidg2j~XfRZ<6Uo{-9lV+;%53wsEh2^EKgEEpIAPg|yX z7u*}~Wh5o(-nw=&8obVZif$)wui>ccuV*r}wZ;@-m) ze{Cay)ajuZL+tTD`)uD(11=JJFd+1>GS-=wnU~F_?E;E=%!cY;UvRE5=`GL{eXH&= z0U8g4Fc`R9A(Etb+18E%CC|AD$BEy+e-Ehpf_!||<8}o=UIfHs zkgluw&|onxJGEAZbVr^6UFMMfXD%FAFT`(tTHMx)}8y*mPrT*mNI?0Y5fr zY18Gbt6rlXbZ2ph1axSazs7sgl_zs?=oMen%T$Dx>d=nQG z{4%PpF7vEH0;mN`N^UGmyn&boB(>`We;07c=~Lw|xEZ?hGgI;r!krBH9K55-)b%TM zfoDKH(!6s-1ZWPMSKX!`ciH*@J7A9YpC`av^$7UNfzPLx!Phso+Ul)s(#Kz158ZZP{TS0Idv zOM~%eQUjNV^qaUcfk>geElN|->7rHtxt$O=Na<+n=$Nwm2&|g>$O4sORS^%~iUpdZ z{^DN`J}LkSlpO~D(I50}6u5B()&F)z?d|L3if6FZGXE`M(o9VCIAAo#P8tfTZ1rP$` z>grTPvr!IUr3ZwuUC|qLeZ-`MmxC%KF zuEiVm!v1FTu`HN7t2DCBW=OS=`SsrA#PASgdDp*iN5LuBBvZ7m^wxVd%;*M|G zpau;I;uO@cLN@^NXoR&L*dsiF8mBkS$<{&F*Y{JeeI^t$z+5W_1RubDXYIxIn1a3s zWm8_}3K_6jgZ}}P@K+!$27(TU_apWNmsS6dxXB{tpN^y|?cDQ=w?~+Rfu<={OBl{o zkgyj$w6gp4>orSW8{_DXnOsuBW)#$~m7wVF-d#?W0p`!3^OA1&AV16#vj=krF22h^ zI%b#c!=+>K?2sa)4n_g2S|LinuGK-HhAcRqMPBZ(QSpsdp!EeY`yODr&Gua^p6N() z8U6Ik^$3*T2PZ!q=xI=~g(W3Kh*}XNYIOkuk$^y~h1wfKJ`WuaMB|C_3JPX{q&Ds> zA{mOdvZA8fkRt%8Nr84wU=yjtHZYJtjn_LQ*l7D1YzdhB9t^+3<;+m*gmryYwa zetA%U+~!Y(WJ+ecM;Uk)E&J7xWNJe%+^r}cs9zx0w0duHi;U(gK79}CY39|3R-nvC zX9*}opmv2A$8;j(3B*wg!&}rsYa*BbUF#)u4i|rW6D(RI2adD#t0=pa7L!_8@~gy0rE0Au3jjDiPqeJ zMYS;uPazICU_)|5UU>@eBPJ4nmvi!or+EM34lu{Tto7ObwJ>h=r zUOw{m&KX?VafU_&I6q!~{!AGkH;}@1L1Yi_63E;f%oqr=Js`e*$il%efw2l!X&^MR zsvI8k-|9j-ja>4ymk}mqh>PILzdsk90}`2czg8y8qO3J;CWzpWGhm$mGD{Jw%Kp?M z>edt(Q18mPXL$*4Gu=iy9 zt-`U$olw0?@*3$1YggdHiSQViEqDcO)5GPdr^DkBEKP=CkKsyLbddnG>nwqie zacr{o)g0&G`wAT1GR#?8+)sa}q@+Yg6D^_`a1pz|40ewKClFl|ep`$0zH)gw5=dI( zB3HMdlHY^o2cq}T45$S{QebdLcoywR2&(XL+VSwjbS^;z7(xe~Avu(QiJ0{_lDyv5 zpTvRP*2H(MUx6I~IsGT7*XO!2j0_FMorZ)@F_#69eY6=WX|cr)97;`1)y4mTgV@SF zWT;8R$qWf?gFB6lN{gtASl|v5&hfXQCqjvHD<~QIz!DgNvpNKynMY9YbUrWsjv(F6 zEi6_+VN}S-J~ZQ?|G4ufMhFoo(outuWmOwj9zn<~hLOZs6zm5iXdc(joud|ir;ovb zxRl41#FA(QAZ}cI`~nj~0xNMGi~JaHa?vv4*pm||U_eFygxo;L`v@cpt=_E5HtkRu zf=e0svy=a$E&JC%3l0WBNVC;s4W*rpO@&H=RMF$R5Al8+^9l+UkijWC72qt~Jgv&Spl_WkI=;iWjBH*D&ijnQ}{V`LDC&kbr4PwOS?&-kqU z)Z9`N>|(j<*vL8c?vursXnt0KN7J+Uy6;cMHh;C|J*yu|280V}9C2)C*F?A6)@|q!89I10~gQ**nA1XHrG9p;kStmjcID$G2S0 z<|puU=Tok=3Ad_F*swMM%0W_+$wsryLR^Z%!bWVqD8NPT=UEeJ()S} zNh%N=5+L;}x{N(uhwyM4^K3Q$wCOC5*;;}6u0knpx8T{}S;^6ZC;m4Q(Q-%VMC?9* znd>R2vewXDxliR2eksXDcl7%SHp@Z$iK^_kV0T^tftje1Ui(*ZJeQD~Vgb=BEZ~eo zQ_RlJ>Z(x-KsdbEJhi@d2V?xX4*J+@d7R7K&Gx8T(Hx_;d83T6wMj1y_UoiwHzrm* z8n&{rSoY7j(d_s(>j_RS^#ecN+DW&5wu%Q|%mRW0l!r;BjJXb&Vm3^9?bZM3iwT=} zNM*IKuPF&4ChHF2O`#5)>U7%Y&d!@T-D*twB%svhjXr5f+-jwy?;)B@uX)?TdZ3S? zNC}s_KaJZ4x4T}w;LqC(B)}@Ey3o3uOMZyEZHc8|j>wR?Jmm@yFk7rcySONfl9)Qo zP`aV%lu4-1PKScS=0RQeS-;VMHH{ctPq=~F)YFd$MR8Rv<$oG?2$s70+(cCs4;|Dx z>8gXzNd&5a0b<2`jIStP^VhR}RoNJH4N;Y>!+#=+qf#R4q8op^M+T;PncW$}Q3UTd z0)uwE2H0u8h5^ax`jcNvWoRv?#K$NoV8f9h>W+BNq9sV&jSl>XbVy8MKN$ATX;5$o z1#G-R?-K0v;3i^Wn|wR=Y&{!p+E!)`y~ojEb{dv*i`B`QYm_s)r(Eitm2@wCj;J^7$uP^WTT! znudl3l${*%3D#3&-TePFQRu{JJSE}rGkSUvF)!G7#19o!&^qnZ^gWx_XZYL7jG@#h zf%pT*iuh}i(0BLHd58r5$MUc}6vWAG;W-8;Ja})M0v{bojH%@SWw=$9 z7w%3G-Xy%Y=(_SE`A}-E-RAg!6YR?^Ru$!( zH*bG`_wL=$qAC+LDTLHNe5#%ceKWv>KA?z+x(yM4!Vj|g0~J{Tfvy!#J$x0yXhpsR zYx6TZ(FH0t0^L_+uk)EFSrHjxGhUmHi)&=Xa}hNUCY*2SmnnxE%8I*`5bK)| zMgc7#e5d^ca!Uf;@}V>ZR_%YHm4$nwRfP_|3OQ^)1q<>t4d@&|W_N(w=_Ia)&q)KC zD(Q5@9k7Y5*q#a-J~=v|%!C6sK=HgUY){dJQkSg=>5zN$P$F^0{dG~%W2uYRgP{?D zlqMKoNvhp#?CiEQjF7BFhceevWFB}Wb?|XtXgA+Dtilf+U|q^NX|O25m$^zx-=kYt z0D9_t_=Q;Ueow>YMNIM+YOv=yMGUd3i;p>QkY(Z{%M=Y29Bcdj-SB~Y)Hi}lc%x9~ z$G0dmQQMOlXdQ;bW7&_!i(CGAW{aVMyc3(ctW;f;Z)cc#aZ!=__3P(Sg|oDx`1uKt zH21F@8EJ*gJ8QeaF&P-xQ!<63ta_mIdKpJ>?dD3Ehw0_EN+tr`XP3tEm=7kXf#R&I zregu0AE)>RW5G+dm;CDo{&-;_XtE!+%B4>z5F^cn{&g_@-S;L}7-Gu}!Ke!RuQR2% z6JElLU9^5@xm3AbOomgeW59zaKm(L)X*phfW%m{Pr)Ot3+vub=!ZAVJ zDab*Z*JA#r5{;Mgd+mXz)I%qUtB77q+8Q2!eMzE^Tax0H)&t)J-{UZ7QfZ{c`fMxH zgbwiT%?(VkgtO0Y_5*<iL#Bo(4?C6s0n52bh}A zT{cscS7%dyKO!9KON0^o$Mx!dCLcR{-ltD5q@$-sQ=QS*g$&Aut9ml~U%XSl0D67!2WSa=I zSS0}Uhcp4wkFjHhPl>eF)!+*@`9@wo$mc?Fwp%@T` zR!ylao>yD}ySiYSlh2Y4A8j-4{!z#M$F1Ua+!emN=S-{sVb+Qm#PmWFf}XFh6q@>Z zm+d0z9`tTBUNCJfY@H_v#bK-G1{RR2&)TmjICv#33bLscn3&_$3i}C54d8qfdQ%=c zZV{ZPFxxOEG;{*hK~Wf%!dq~rD4+6aQHXjHH@CL7;AJ6}3fu~mzLwLeK=IXaP-9># zv;^+<2vm3Hu_RI8Y-}7(O;xA6=q&udSd{;QN*A^QW%Kv3(=#(0 zh7Ld@Z#w&*$7YuG*j5q1aN9tNyo>Ex@io8UiwX{)kh1%Av1ow-&?ex0I z7D0~TL+NVr^lSw8dAbD`hZXa$ULXsUh}SACef|0s*$qiVReGUe82s4v;~kSXsoK&o zT~QpS%L<}nt41s2G$~L}>#7C4%4XFEr(e9@QM27d5sFGs;!`3jDJ>O+BP*I4dA&nU z;)|#hJY;d4usC~$zsBTaT~%J5AjM+?dA{!Ckjpk9ylbq(TJ6B2@EfobG@fWxc@h|W zDc;d5;wHEfswuO982Dr0;J^@zZM*l!Xd6y_qNFZzLKh$j<;EU>g{`P#J+eu^bK#{f zNDFsK|Im^EK00(@gpUM6N2t#u-Z6y)meON|=q?V7W*jplKY#w@imH`8xR8*>vT}G|TtzC2%+Kp99xTWHJiHQtnvaj< z_>HUo2CjS}SElnK_NzFGl!Gt3;agWAVWGUR|2PmsiGo_yK9x~Oh8g5cMO%=PLY)#A zhrQhK`O4s(rnwSMPELr1tk}rnc(e*dB_)k-8&OhGL9yteb05@#_h5x0U=a&w%~#BC z$m;!F;@u;eLri#4Z>FPI-vS+6)x1QDXV6YAX4fUgCug2!q~zk{d6L%Y~DY{A}J7ZJn2`kPs|B z9ze9L=MF580xQ+h7Gf5kZ=jrR02YW3Zw@`bMN1I+g8DHykA*K+q$oIuR|3*}-|1YX zjfoJVehvrghCGWXeZ+nA@FNci1%DDK03qsIv=+EBs=GnaaxNjPMb}R}AH(;+fEErM z7*Be_;G>m0>axZ>&*89sU_jqe*6Pf*Mh8D%7b>LSR>)2igkkvQ_)=1Kwz+|U3R8+N z#CI(5-x@hJ3KRpcN-_tMBNl@(HM>uLY%7yPP8%T?p8Lfx_w{RjIY}T89#AYs4k=mP zU|FsVP=vnu(|DMenYrwKv56b688t=f(+7g*C^=5~&%zE34qw6uS+a@5bNGl~(bV~& z50500Eg`w=1hrtAM(K~uk2atl9y+KvI6K%JuYyeBS{zHhwh1di^tGe*qB&yZkS$w? zcp-`zM=^08hlu`Lc3+>a9QS30*nx8)Qpo$MBlVZ8d6`k!APC&jlFdVBf?6@+ zYCAC65ZHAWN%5|dR)>?*3oGnnLsoZtfca(jZDe^C=o_*YMP$HnLI1hBpPvQo3 zy6K`r^OYcqmu=mx90nI#c&^GK`b5a%8iqPC(nPs+`*v#?t#AF<(vB`ZZs;(yQqL;o zqZMO9*XRzPf`O?|-@f+mgKro1{j=1HaS(+{Vn#+sVd=a(w<2U-aGV7F$1o`#3=SlO z9?#6o@W)dBBz$AI9=-c=adGZHOa5hn`d0`)PZIxKvZaJ}Cl4?0 zq$uidWLL09L@EqhbiZZ6or{~BIOZA@2tV%UlM#hON!gtLO*y9myxWs%Ipu$snGi1x z^3-1`8`z3aLdvVK+NV#SE-v0*L@DGUt}+(7o<;wjCyjN9WpoWFhHydJ{!sEzG~gSe zIPcD~d>j61G#iT->{rnUJ3NdM2xPvuwo;^KgEK(-$<+2(h#GQ0pUc;>LjghyS{#nd z_3&|#CYcIs98MdM5l)f8KDV|o!B`{_o;!cKuq9T`TcpXRhI0`M@k{{QiZTzClDubU zSNSzbiid(195*e()Gi3K#9nt5`jzEYVUyvuWnMv15dl}hO{DX|08XMP<2gg4!SSFafogI^9C^H+zTENPJqkjVUR54VFDeZ{1mWsh6 z1$9^)1i1g>4NQql9g$G|8?{~O4-c9w8-}9sc9X>`%-e znk)l8UkXwl)mkF9^EevyQ?F=-R3Gve)WzFl=~-D_-KSj1PeA4{IVY9+IXLn~o=e(! zzf3T zfp=VQo{B~VDiQ$F5 zq3GSAS44V6&7wXfVAt4gApiOUQ}j;F7eruiiX-!n_9a}}aU6bdBKUfIKEbP_`Jeyn z!%@Md(Qb+iFDk>*hYlXf|F^T3i3x-JUQ@X3QE*6w`F5% zYn5t5$b-iOVO#kUu+jcU!~{ub5Nj-S;Tuv>T7}92iuo|KpwNYUk$Ya^8_et6nag!z zpbb-9i<<@ip!-u~N#+HJRb?+XbfYxBe?%=k=VcNbN%0I2Qg3)*Khi;NegK;gE4%bjeu<^- zjYL0P%v<8K!!j9nn~6lnGoJ^o_&BRJQtO5R2UF}e)@ifkbq+lTY1`_n0~=4@W>Fb? z^Q+I5=CRAawJz1<%ek=GvLsaZ+U`bLis!P2uZQ%~A*NYQ{n5L%@l)k=?p2?YZwfL( zV~zyB-Tb>&mh)H92Y*Duy3r3OeD+RX4laIdI_8$fC;Gx7$i#SE|JmAaewmrt8ZExP z-|I4B`pp>BVZ@4ptcU<2Sy#74xzA^ndJXBFL8J;^1jz@k+TN`ZHS3)qxOW^!A89nFK4XEtlz&V{>dQx2n2lhD8|8Fv|=rQ7}@YAoBm)HC$(y?kDm zwb!=fZf)K!5uPu8^0V1-C!Damc4&~BpKp`Q@#~%m zY4Pgks?eA3^dabb67PKW>#Ih1jib8mkvKn}EjiUf6^?%Gh?t+%__xNpW$W?3J5GdA zihAHc)T%H!zb&r+DtWjZM5oE(s>+_+ZG8QKk(}hK3W;dj)2~)^`@FsizJ;F!yvPPs zHbsWs-A!4)s2F(I)A0$%S?EyXETP*>&d=4_j_Y#@qi^I{uaQFx6Ulr5#O-78q5q28 z$B+r3#KDws|Lpkg0B2SP9UUFS**}_K0gz%sv>|9??12M~+uwPQZKSh{KOW~+sxf@8 zf9EwfEvjv{-BaMvc%)7KVBbB_Z8QG>n~GAaOEOC;J8_^vQ|C{bPqBFCPbYc1O}zQv z8ziZcTlE zeZO@*c53~!IP56<(Wzu%#9r(EL|W_*2TO<*pTMdV3v-pNOm}sU zEglpgx5WRWuNTkvXiwj1*S2sXTQ1PN|7&(&pvkZlj&CCUv+QTJ`GFxxsI&H>tA;eNITI#Hpe)jVTC^6M z2d=k&K7AK_-zeksXwT8tt8AR`N6)35nB-*Q+E`VF?*c*>E^&;F*I%(c4ZsVOAINzp zkD$K#r0L0P8=*>h=`kc-vsN_uqC0mxRE;kO-}TA+D{7+_Tyelg|LMG z(%SOlSG>ao9!jgRbSwTdc$1j&^?n_qUKRxorfAKb_OUF+2KAIJZra+6Ga70ecswfdrTbv4%%vdpf%EOCe8iMn)<)x z-fQpFe9Oe-2GcoK3{t`nnAs#vQB_pLM5n?G{Gq?xA_@b#I-^b;E((>`WY-7pqey~@g!42S#B8|m&l#p*S*uLGKb;YLQ+qm>q-@AN4P37k>Jnd2wTT0Q%Y9wBOE%T$f?b341sUqaNl-n;ad8>#{{ zex0lR#M>&t>uN?3V-v@)=Yp%AhD|IFtvyU$A?oRznVWm@l>7@N?c!sM^A`RaxZ@v7 zVC)9^Oh~Ow1j$pba7Nd$N%p^;Fsu~FK`ImLFh>veMz8;}`0o6rU!@&@2pDJc@0>L} zneh%$8#`A*l3(sv-15;*Jhk>v+j-+>e)f6q=}IqrwdT3$2fsL$FF9CQY+0<;ajZ{l zbzdjZ(@PpZx7)7FMx;vYGSU;=u(PK-y0s=+H4#sf+~26>J@NKY(yx>K9M_WuXGY(W z@~i(tHrY8PF3TQqWgpFM_nJGGkBhe-P{m}St~`n&j3+;vV0>F5PeY})YwztskNx5# z7R##wZ9;r{JK7AARf*6p+bL2Qo)13Jy;S(&Lwb$_oY=>9c!mH%7HLWtl7h(?;Kjk1 zo)Qx^3cneZ5j9O}|KLXL=NvZ!%g2=6nm3+i9+PM;v7}1$W|*rBC6Ck)-Vlm^BTh)R zM%=GdBTmV&Y-Z)-)??eWejIPNGx6i@!>as-nlzb5I&yc^z$k$6=JGqdB& zzRJGz&C^x-=%5VOjUBz@qlloylUb1mf#Y&`*bLzi=7n+adeJ_u@eBRa4@El^3MtxZ~oTZTF(M_%V@eqX|D;1V#7T0{vXJhpxg{chd?V zTZ)>W6q${+OXvLOfCbYRQsyE6I8k8xFuGb*B^LNo94Xc?i(w$5OJNglTfOOH2%233 zSJ{x1Oi0AdjaD~#?dYIU&n88plkv;=&O!`(M6FV43qU%b>fh?i5yQy9hgnKls`9Dz)V3w9KUk6=K2 z1V%;~a`)nZ0ewD{6_R;fa1g|dZs(0&d6{f@7u3jHJLvevp{jM-Eid64@t$=&sz6k1 z=cFLD^XeP&zOzw_pati`I3O~WvJiahqVWu(9kTOd<(c;C&t&`!CN@^Y-UONZI5y$0 zOE>&$0BO=xVsguk zf9uah?tE&6kJw%GpBfHkYVmCM#jC%QjN>Js?^!$o(!wZ;?339J=z z_UCsS=mzBLPtHGJ7Ps*q)%O}7%BFoHhzNvY;5ESk!32^;dm~vig?sZ2sIhlz{!0=^ zA0w5N-nH~K8dC`x%X-~zHNRQ+wY6=WxAiHV2J?wv1Nav??rBwhE~Kj=L9SzsR#pP+RU8l_1@CY)NWgpwh1$TbrybH2E%@ zcW=~x14bAmmS0kD^l_hc1I$dXbyV5tJP$Um>0$~hO=~v3j$};tf5kM1PE&Kw>JS66o z+`BZ@ksIZVm1WBe1-TXUk_U&mJ*Ht{?kbRjO>H@UOh=z+57UXuB&ZYk2C#9p6meZkP8NKyOcU;{#L#n^obgky+j(3#Fm-}ai( z1m3-XN&T7j6(PGVuLrZ9;VX`H!ujjl-vEtt+%VQ}XR`EE_=9PSsCL&7)`{TFhbPke zJJxO{tA{5lZ&`Y-Bvn@I?mZ&-AAV7|(n>)SBuU$qbCD_THC^rEo(q-M#PJcyv6-4t ztn|_AL-F7;*~{e>M)rNdyE(6$Hzq5MlPiUg6mY*qiu>cJ+8MzZb=fpj8QW|7{&FzP z=1+Nh_x|7Nybn%}Lv$fF+zZ7dlTp?=pvy(K@P8qHPzs4w)Ap^^dUj*TdZT= zqsB<`I2mpbm3U)jT}5b;;-~FBtVB`G^T#Ve;y+#Ltgics&Aqgd{FqaF=kUuf->n*A z>Bh)C@|wIGSIIR^1WuD{2W$B0uk2-a7$=~VH76JPu>Q+lheS+)zb6RUt9NSVH1OP_x z&ny7eqL2ByT$ycgv*ba4&?23;)#C7qqtT#6SJxVWDedkZ!Z|idpuC7b`#2LnnDc4d zdjq@heJO;B=du;2DuPn-xpc;mzR9?4-X_Fz{~dy<8E=n$r_7QA=tEXH4hll<_?Q-=mhAV~Cl4&*hGzV+ zKbgm$O|3_&vLqt7AaRt+F5XCua$MoLVDyM|J{XdJQkj75&s$c3CaJUJ)aI|+ z@Sfe&bDlpRpDAPbZ}ajW$3W2?OjcC!*Bc&LwG7ny1z3mSe=ERuJ~&;GC*Yfad9iRG z=NNeCsock$@{-LH>}Pe56z(1|k>TgK&$~7!`gbhFkDp1X&K2aHv(7yeiCNHPS}+Y;1!GY^h`Z2}4#NDK2nBD1u? zFu^k4qdpcqW^nni%*&keR76SpV4h5rY4r}V-~Jv#5h8M-WiZw?>lkI6#l&Y!DJy3$`tZozp0I;#Jv z4faGFud><_RHGX`Vl zvVK=LU9SJethe>$s@dZ65c#E;J%-TLlclUQoYh6}qC@X_Q9m9VAmr8PSW%5sGJSp85SSE1%P$~$` zk#{C_Gc}~Ob%JK-$&#(1C5c^k%i2Q$& z|HbBJtx-I8kcqq}-0Li2hqnixp9Ios8-LHxq_l2gNG9tx5RsM)L6%b{8ybsc1^o%!2(U}byxY$swu z_t~x4>Ai9PhZ75PImzB*Yjkt(!mHc3i7fGr1!j%I!fUtbvPOX}u`twTq|tiJ*>&OJ zO|6bHo0rdFFFbbbY(} z0p&ItKRLi=kSk{>9rI+=)H>YkhZC=5+4%aV^YvP$4%Ya9V*7?}c^Gb#!)4qcNw5s3~CDfaz%!={(ZM+!HKp=&4Lu`-Ev` z_f!*Ulxb^y+K!u)3bg2cTJdE^0*hfx-oU#v!c1BQ`mWGV`)Gd{eEdsPaN$-^NONNy z-_RM}>F7PJ@65j!_Ewsp7i-IV(eDOi&Mg+DRL*wgLk)o*?$%M&FbP?EJSJW7qm4@i z8X&thPtwYjGaTAK*r?Zh^?VTSXWU6@!PcI63{AiqDW=pf@2JM6sUlkNjXC(S?Shz2 zaY^aCKJAg$XP@fHdj>*1@;J+X@-3gA*IP5D%u5iQh2Naay?;?%NFjKWP(Ig@)K8Qybu~C&?3&l3>UN4iPrt1U}?4 zB5h@zlRW6Azh{hKRYlQnJE;E(wk`4AD)|?#SVX`Bzn?;aDJ3K(p}w&adv_Khx{F>pNEZ)Kc4}5B^wf))2)%9y1{`kb5UCbBvzqy#XpJ67nG zVm=UKICy0Kx3{B%1&3ay}XsJ z@JpP~IL*W7Y~kgk#bnFb@5?&{G5aUiFa?*MZ_Zla<1+W(iTP6hXBG}_sNYu5T&>YN zf+~k_T>VDEnsWaid_0`Dr(np!#g>q#H`BIp~&p$%K! z{f@-9NdfaNUhOPcc{G zn?VkOud3@)y3p25zU^!@0C%2q9FlpWIP7EZtpirR|9NOT$7qbDXhfJh@Pjf9jmNW-E_x?4e{yFp;nB_Ps$=YqY@Ipf}O_Za@!F28Sn zbI$jjZ#>U4HIHfmCfE56mzFGWDb-Kvn#)2b7t~Mt`z%)R+_ob>vWtFr9P%eRW8tpJ zVboaJ7`;=*)xH$H@QjU!6R&W+N2qf-;cg`b6S>9dT9w>BM!7fvw#mR2e^gq+7wp?W zP3bkdMogh(FA=hq^`zgBrN`^q6IaJn&!cw7&&Ese{D(}Ru;3l7-=pu~I;_NEX1t_0 zVrk(*j0lR!j(o4{t40iPOWzH8SG5BPJDpZ*xwhaz;^Y|-aAlnOhgxKVm3I=NC6zD! zO&UQHE?-IWH-h-ZTXYA&U^3s#Z{7Mme*-avW|sk0HE1Uw8KV(ANKk?J*iw=Tl4x~b znUxV;7XxQK&_9`imKbNf76wav_}4a9>`j@kHX|?DprvDHclRO*=tP2Akup8ln#5*)5(a^uU|g{gh3(%Oa|>O<|bYqFt%?n zH}Ge9^`s7=0b)jxK@riCKmNCKKFYxAq>Qb8&T{BZBP5nKV8DTCB$YXYIN%N>2Tj80 z=x9Izm!$v^wB5gmo1?TTOF$CI!&WEpEk?o>jA@sb3CfyX!~TT@Xz?d_uII}mm#v^W z(Y2W%1tH3Eo0m8u=L!km9`?z0nwpxrcpBy^?Tva!?0ag=Zfjt0l-Oo^T)6GOkgHl*0CK#eNl9PYW6x2cJ zmEry`LL?@xUfd!eC_?@Ns3>r+-vU%7RW?tbN>Sj+05WeW?SCP(GLxX7;N&+9V<^}S zG)Ro7Qi^NS!?*~D_NXS#S$v$B(MM`VLJ!@i{~{_ZT|WOGM2Vq_u5LMDkgDKI z3I#h`ZxVwLICvzpF3V@kdXJr}GQDn1I3vei8QQ~^C$ISi7)G~)Oi(Y{gCr>i89nr-NKufek&V~65 zw^*E#>u2HzNc_v4$9c=vii&su*)ob;fitiI2OpaBn)C+Pm%;tYcz3+j@`_0-9hp6l z*q~^k1GGcqbRkf;Skk_Egt;pDZf+{8)OVALy@&eJ($bEOypGC{QRKcwQrZrX*;PjG zO?KsNFshDo7Q(P`gP|IYytCQ$6cFNb);fxA0ZWH(X%3C=y4`}uUVZ!zF9ONE`;tm} z;W*b+!pY*Pbn z1Ar6VUAe)DLQEf2-S^cK_ZM>nyX^B(Nlq&L?W1;E4gksw!Eq*~Lg@jJl4@#drybFikUNMlko|<60;Toe_}Whp0A?!+S7N9GAa_84DnW%)jH6kdR4p`KlJxfxBhtm6b6A zIu+&Rr51NOvB9TC0R^f+7N-VLPEM70C@6RV9x_GfFMfZcLt=>~^Mvm)^q z4ntx(GgCZC0ccSbV0Tlg4JCmy0+Kl#Yg_8VBhGa}#O%UH1h z02xdNBnDR}Wp;qM_1k}Y_8mYXBUqhJ!&%aqbwLL*t%jJ0NJRz}{_>Y?2jzv#gRx#H z7N{~N*8)D~2~#-RCaC`q!0zkeoE3WAB6v3@E=SdF$0^eAFZ8y~+toY}3V@pKbLKad z6KZXwrGSP4^+O~(qVNM?M`XwgA*^FwX)Xh0zL z3~X{*)mv1R(tDGN1aN#xNp!U%BIf{l++iJia|3>Z_y)LAB;woSt6!R|@8`N3@EwG} zAMwf0-@m-Y3EBxTWpGx_s4oC9rDO#JJdh1J+F&+xkN5XHUcn>=*DCVXPyon2$~{p= zT!aQQ%Eg~CsqbeTeP?GUz|A2kmnxu+i`*d8#NgjIlD*?f(3}Po69{d_vf*Gg%GnQe zpO?jt&hr>XIRPty=3Ib}ZgeZhyx>P_vun`xib2RC( zgPj-{W*8!&L4_EwqL%HmYkn$<4Tw;1xfrW~5qwol)grLTEJQ{t;5~2dH4K?9t4#p( zCa?{H6d(n)k46XLfFH>1arCbOz>^X4sv95`WYG^i_}Yfd;}c9`bM#F7)WRz}73C$8 zHGFk8(W00tA{Y_t&x7y+QWMF^$v}&t%qs%D&@k*uR$LKDIbhfgIVRGP#P`l&1k^n7 z3t)^tz)2X$X0Ui5e zf_rBK4bVQp#Wi(W0*V%twQrX@BS&cp?Q_D>KI7jH?2 zUIKAQtQw#JnI>TCBV%r$qD$;W z0!mNGc|JztG*Wp_RaMn5p%E~ITU+iC68h{I;^%~E99}f0crHJUc(JT0FZxd=El?Cu z(!~ZGyGJPApFiPZ72Z?`?ttSa5cgG~dgc(O{j2}X8)6uvg)@B|;|nAj?-3fqRD+M_pQjOvRf6+guYaC1BLzbQMOaw2fOZp@bNF+BmKENq z^%bH;zyXYm^dAQy;Nm%S{^Mmp5He-$h^E-9L+23&ae^!ZkA`H|WM4HPM8(|*O+lV~ zkN$bjnnTqJ{?4j)LSOBfXupmkAXRu>2LHo@aI5T54 z=>faj<<^yK8j3^&ZIwOr5ECO;MMp>2bP)0L*BX#bslZU~z*Ky9j(vb+Z{;6)70d)u z>wwY;E)enfXmKOr0Zcav*{DQ{uc_Exoa`Z0t#BnTSs^~kmYnO%3?9U&65S(llQy)^ ze1avg0pQa?q5}mpxDJ>h{%-oi85N{X%D}>H;7m;ew6EfllUZ>HzzJ9eGM>QTiwk#8 zEdB&};P0uQfr(GtTZ%iVBS2+(ek6Fc#5)Qj_seN)Y)pstS!ab|^R$*_Ua_ZUQH>I0 z(On)89exW7MkB|0l%7s34Br{ z2!XW&Z;_HFkN-nI!|TD6^DM>L3`G<8WQ>K8&afITNaiM0Xtw1*= zB$Z*3#k06S0EP@4X#@s168Z#I4|iX~)95848V|Ze9jiu-`XfC&HR|TeI&E0; z+_3+)`61}r#qt0LKCJ!flIp~3mg-!>b{+fmSC~W}v5rhwSMv`;Adhu@D9peBh$9eV zIDm@&CI-cCq_#?;0tgaX{3@GRg@S{gC$}vb_1UT@3s!Ycp z=nzOkc*4O~%?X3*;7dhZC}hzmDntUqfb6PzZQ~vTW>oq3{C3GNf~337zch|j^~Dst z04q1Dy8p0&zr&=Fqq*i}LLo<-5&Y2ULZ8)(D%;m%Ykb4uqQ*ML6CI%i;)g&Q5Y=!a zYb5xc0JCddlLS_IIRLx{yO#gF$SY?6K`rZ3AotrV^gH9ZW6S($@H|)0RUkDZo;Lr~ z^ws>U40M7q?NFr&iwX$Y6}X*&X~AUUrNOTtl1 z<5~Xo4x^ww_ISfWPq3}5EJN#3BN@{LqnRiTZ}$VV#m4f{Au~T9f?OUnuP86 z7nw_OO(Tv#&(rai70$D^kJQhB!olSjh9O6DbIqvW^l|_U{H*@@)vVlQ3uW+k>L@}` zX}EB~_(LXxU@6b4u1kPvZN$bBE?V2S+`&%Iq1$te8%r(crm33=4}Cvhlb zI4|!?L>O1OnOSb4UmU3G202~4x|1)V^KgInTKPE|KkB5jneX>+aoD9u`U(_*3MNbn zoX3NxASg?Fd*IqMQTWY<8KRs%eCE*dyJ+jNe_<-kvu?56V&$KEiIX^A(whI4q&-D$ zFx&2t>Ib(U|>w}2z5gNO0d3&|FYs9TdhjsoWl*JiJfW)FAx3XWG zbqxbO{hj@gSA~vsuaQGwdN9zg!3RpZmlPee*FVqK@o{~$S$pWNAM&M}5hk3b`N5(^EZc2P@1@e~9C z=UrZ2{-d825*!R74R{xD0H#pj05Fk(%oH66WRf_c1msmDKw*``b1uwvo!~w>5PK+0 z9+i9mA;5p};>AhH;7vUU5(D_nKC1u~jtVdlfQ`!$o|S#u9)u{PAr+VkPKOkCrz9d; ze|#c>zyYw=Te| z6=;bg_n8nrdOr5!PbEcl?M+H4x{os`PeeM?Ub9Fi#_5eoe5|fNqzcBcZ`+UdwomU$^dY_io3Q&m$ar11W%f2rL zK4a`%R1~v99WU;g`jO)$$hVBiIo~>uNT)T7kMMGb{Z)m7kz*hg)?mMecW$p)LXOI% zkgpr@2WWEw9mVs704md10x1mxxK6OiKx(KAR7q(7AR8C(cV`>o0OhCIGt=0i2Mgun z-uB~|L^JXyq6OAU)m@zaL-nb0{QUF@$P)Mxch=X z$z&$%jD7a051hJL;5)*|(C|<6@=`~5G(J#Lf6)+6O(1(OWlnzn6ATiv^2=#j5`u}U znh8{DBdESfNJyNt0WBaG7new?Z%`<7{a*bF>PI+*EioNXs(x_gzVoYYxee%8#ah!y zyabDz%_u0CaBu`P*Eqil4ru~j{+|OI54a8p_&;yPy04npT5wxEK(7tNU=5cJU*ep831MAlUx!)dGF- zESqmoZ~y(7j(=Yz7xyHRs=m3oc|97L4|w6U0J*8k9!!*vevfOZ7k~txz`X+a7jP#X z+q3la^sE{$pc8&|b+z2m-`|gcM}g3VgAqUWSXj9EfL!0`jdN>Yww|Zn|2})?m*8K?iTU~Y_ICdZejad- z)#-jEHW>eC;5}(Bpw@peo3F+&oefg~0)k&?-@SXsLZgr@5a~1or;f-2Yqt*+5P`(% zBGA*r*=1I|PerBFkOg+cC+pN4n|dhyz#)5Z*b{SS8+)e1D8{rI0&hH2E|nBP`%f2x zRTUMFyk=%*fbKHYW1#LR4wR|T_7M`)(L|f6$!J(XKwQE!4)5mM{2q$B(oJs5$;shU ziHQKq2kPq0Af*I~ryu16FpX7#0yXpL3aGgw1p(>SQF9=qV<q* zK9B;kW~AA#xU@6bm!F?4Ma#aZ6EEF0_ zsR`DDfCwsM?i!!zpf~spb%fAmb4>FDdb~(v)vwOUD^(TI%@q|fFLy;ZQ^-d+ije0; zZ7=yGs7g%xgOM>4k&yIkR0jnGRm$DT0191fPl2*+6!)D<^RdkXGfe^tR4z6){pLm> z6Ml4bM3}3g7OkJM+^N0|zl;38*-e3vt zy7_yaTZw^5fcXO`k%k)@8Fk@W7HL<3D-f!HY_-J%wY4y&F<(7E?SnFsp4a6ekjB8k zz!=U1YV}eLrqp-1L}6#E`n~ODF+kv#dZYB zl!ix1qPgTk+WQ>V1hWAL5*9IPcA##?@W0 z*dMX1p4hDpHuPTc$BSmh+vpoQ9cFsj^+l7DH?X?!dhEx%!moD9)+60V=WVqKc*!d} z%8K9AMxp;Rc}ko2EfKFafh_yl-vv_Wma8`t8-UxpO$l(7Vys3o`c__Te2qRW2y(Y+<&*T&TIRdm{4+b{|p+ zJ9HmiW2ZED_$y~NQ(pc$#i;)!m$*XV8J6ND57e9Z_j=z2PpqJB*M;*JCufv|q9@lc zS{}n7)ZY~}QPHx}pMcm(v+FmNT+1Pfw-Z>GQ>(l7wp8}l;?uKTF%IXn_D2@0argzV zO*YpsA(aMhN~{0eUo8pk4HY+YW++}K8^pKP=wm-$P&muQfE-|-J$7~Fw!fyY>VJEu z{z%x~0s6Wc^tB7A>>b4Hw@J70(7E_iG!$6>++6d7K$7TX)l^_mVad@4+RP zTW^~f^=t(lC4a2Vt+#Glz2vE?O&J8Ce-v7r*y^<_v@IsBbe;0XQy*voA+E+U zHmiSr7vyu~&^$mtXc1R$x-2UazKn)K&{x(rdb6pR95{pP-C_l+nm0w+H)HW3=xDrl zqocce>9Z?vlZ;E7L7U3e-pw*IUXNtDan;>fzQyTJv_TYMZ2dL~q|}j;60U!;tNac1 z8M>@BVnp&-J?ipO4d{y%ost;dEtH7-Qay^2<)Xu~cbRcxvwfhX;kLivm3jOx?M=oedC18v z3}>ZL>)!r2tufWi)C&}z8MaDQUfRha{7%vAT9e%nLiv2-w`mgTMhe+370IgEyK}m= zQuu^@p%B*UH$Fte%BO`E%HLM4uvul!WD3jYDEJy~$z9xR>LKEptrNuygp`{f`O2^x z-%H*5?g79$0hCQ<`1h#Q#->UP%+U;Ne{X6Kv1w1)9L$O6Ru$YkEC8dle{ZLb$@GFN z80xWRH?}z}>^f9p0YS7MttA+RjLv^xn@ZipcGa#q76irkhme z>kl5;8;D$jOErUkleJ$ojhv(md_zC*6;>bTKI}$=j4aeV>(*LZ=zbGVhyqc9&q5vV z5Cmq9B(3u+R=i#Mu{s%wo4;U}`}Bj){@M~(hRh9^w^5;-FnJT3Z`; zWB}#D6>aQDey#hqNyR1_Vn9m)a`T1sk~HI^<`*qu&~H+cqVcC4c{I*AX7IEbmBStM zLm@PXn$bJ`)!(mRPNI+2kHmY^kJl&YAo0}V3Pw8@at$Fq^3Pk?vgTR)zTl(twOU)Q z^%Af(H@|VG=kyn>$Q5+xdiAWgQx0p{<}LZ4a1PdFci@)({W3{^`E?@p>XJZe{^ZrR z@1dtU-@S8w=HpgkU`JrBw>tOoUuVugfPNdDoQpoSc(e_;nmc;_-sBMK-j93IZbRQW zz}>Cy3ddEl^}-q@!4}5>K$ZnF1j6dhl}h|;G?H6kB$kh`RCb5 z`k`iR_a=`K?USt(0RB*WrKqAH@8n$OrmWRH^}=#x*;R}%!4Iu|(O40$T4Mrxpj!HV z7RHjXjf2g%dFD58D*dSI_7iz5&x<_VLp7716bW)}r4W^|?T}u4n_-&(FcSA;F8gig zX%BV#OZD%TTZ%15kZ&=9&A%``h9Kw%b=!kYP&n~SeZths^#+Z252N)7a!5Sdx)`w| z8G-3B{H$oma!T}3Kzs{uAZ7=%Zb@>wDKCf8NP@ne$kbjvyE`=$^mtoPrz>Zo0Za3z z-^qLSWY!v6g&<>NvbHym4^MR-vNcfj}}5N|KRQKpC4G@LV82eayap=H!2t&$X-7z(0-Ka z>In5FE307N-&Lw?coJBhS0ib^Iy7F6{wCE}doOT!eV(`~`gB&GxbkuT?}7dO(VSz4 zs*a%J{_Yh^+Ee!<>3S(Vth>xG*AHfuQsp~i=m&#_90-LVn=2XQ3=R*4Z}5vlIN0$N z4R_`dvyNjM)0`$6UT2PI5R)HA4|~PWaVMA#-@)h48kl`}56@5__QuEX)fR5otk-F$Y$ZVrnd{?h0dDjJ3Fhd0pc!B%Y~-jZ3*@G#cu$&r`9Fb~c6zS?vka%W=aJ z7YXpZgm`Pg(x0(`^m^-~ul)?&*dcN9J~{Ea&{^GNNE*?fma660oZ)@{_R4Kh71-Ig zN-J|(Obn(F#7GIlkAW+kW<>7J!=`Uw5tX;;LE0X&HjqonE85cv;%zv|9b5+#iYZ_6!_KB{O?3FrVo@M>+9GV{c?ZMqZD^oD7(at^ zduNh*2`Fhv+T~^#Plr6mJK~!x^T=6Illya*fn?(#IIR9RGTaA9Q0yV#NQkvE=H?*q zHy2GIPV*-5`tOEHFd)jL^Z4%`{uwD>PoX)*DJ@<*Iu{GSI#-;(_MH9k9fMcRU)u0}A(GrJAy zzBydmH0U?@x(#}4HTpqH&-*=e2&}vb!+xLLAf7zU)>{`b9E;lfww2kFimR3Z>flym8=xh~$3s-=VQ2M|`j z9&z#pU*FFFYvO1$%FX%e)vGFt+GH)J2R~f;BaDAfo_!E_&qt{>*+2gCfz5Ud@dZNF zfvNRx4Yw`Q{ah5?&M%A3dVhi|ISeBA&-p@co7>N=>6$B(YR6o!QHTLn@_dnz*Vr%b zPc`I_Z}AlRIYtZvnLu zZO$SKVcPtiD88CBysuqrLO*1>`7(Ks&$*mS)`th)Zj^)`gx`m-Mu>~?f7nG=0sCBt z+XEgI5?vIVbb%btPL>hG4#F#O9!3e);1D5>adfy;dddd(H0$5TeaY3tn2<^aal;Iaq+{k;^-d2udH_<_d!kY z4j^c`uAD?&u>XAban%~Z$h z)Kl`PNDK&p9<#`-h(e1l)cdhfEu2DTbx2$YQmT3Md9-+`n-6gup6wL@G3avVf7zf| ztQY5*<>de|Sa|*GT^=4@2Dt46`t3D9=D1ArCwexZK;%}FpuKIvXS>rAN`vA&kka@4 zmSY*a-Q0-lxo6jZAdr)a*CF6J>v7OSNf-nh!%;GV7I|Y$JhlOxMV{n%$mS&klB}73 zJ$HGDdDejjA?^>;Q;&Sl6C(n11*d8N2ILz!RDE?G=%;5NYM@;%p+Np&@fCf8GDJi= z2r+m9K8R!6!{0L#+?r;)n<&-UCw78a_>~O0{fYDs>p=*E#(gL z0d=IJPVg9_%^|QPc~pow!IqqVmWPiQ&CMF3&2;+5zhZvpL0p%^s~#L0IHDi4iyq)Vmjp*p-}5`A0MSxb zmO9Gt(beUk^V41e$m!MxK>;sw&)?NW{vH}Xgh<=pjbwc1BY3F%$vdlb^(w`ZL8V>c zx=Ifak~2!;Yn+299O3!d)mVN)(_*Wj-h94typ8ScXpa#e;t7^&Jm^P2>;cBN9WzH9 z=ACbA-sI`Z?>ByT?a&(cPm_5^rM8eswz~}4APMlP_`BV^-#2kgxnB!^0GZw5y{TzU z7BllV@_877m630Uz*7?$YoU3QqO!R?e34{gdKrNablXvq2Szl_>+$J`KBrkWtj= z-u)4Ueo%ycfG=(TrFnQXI2#-bYV1+XfnQgo!?u87{yPAXg-x4!y_QJ4Cb?^PFNP%P zVAG6rgvh-|P5D%r%>otWj~eicy;yu*BYT4+ch}RwhLQPi!*DqqGhbGNo8{BwFdVjL zk#=G9Aa@y2#Tey7{_URUx#q;y@nzCMcAD>st!+^GBWG@!mVyW#{_9M~1UcvyGWfM@<>F3Gl+E10shQrx z!>IKXwH_f$w~}m$GzqJOVU%!Q=$ztcxJCml6LKAgbigpDivAns^egs~tt16$WMC@ZWW-O4qyLBjJXKyHOr_*YdCzz;bO#FPE z%Et_hEwKz0Y^z7*akTgO5pNFB<$$1E<+Jx2h@%M`bk7uv&mVD2;UxDh;Mae06hTy= z`kzG+Q3tj_AvwLLv(v{rX}KfpEoPbez}k5JnW5o2FRO)Ko~xb(KnK5}Qu5qmCG-gE z9lJWiZRzI)Qi}Ja%FuZh=Xke;1v;|;cI(z(!3n%z5c!0(9J8q^s<}-457FSK-t^Zg zu_LkVxa*>s>r^l}?4HA*e5vfgsZ;5f37f3_hK;!SUl7G({|L>8xEGrIg@4~c?P~M97^^j}7rs6Df_HpPQVG&x4$xIEpD_`nj zN38f~#c%Z_T1KabnVyf`LZ#tNR!v&25u(KYsh`IDMIf;0h`EUXU-G4|PKrGyUd>Lg zg|6IHsd%#=|4xncZQ(i-TzK4G?4`nEWBS5&KVn@O|D`MyegG({jCXysIsPo(psMg% zBbnRB@A>TUo1M<`_2IA6E=(T>YVdRfGP-6Jl*V_3fP1Rv@Dq=7gug>2f)V}f>1+NJ zXVgUC{Ic^@Qtj%zJU;De;Y&H#`|+)x4Mgnf1g4_!vF^FH%l&p9`1&g@WRo}K5c{vA z^m&om2(M^ARmvXBUfW{Y$B`*Y6Bdzo3bK#`3#uu`Zj7Mc$Pf&D)fUE$>}^+h~$3Vxc^l0;&ijj zj)dXD_$BjIcSlW@%axibAzwe~_HG4_ zJyaU@j7|KV(nP^XF#1KR%K|wApN!i=?zNSQuxc7Ga$74Iw1#~Px7ZJ38+8m1P-aOU zpd|$U=bh04U0F~d@I-Pi^N}jxd-Uz1Wd^;k93^Hn>VIVM{wY)&8Jn4f&dNByz?SAW zHP;A=b9#AF6A&o5{}kQpA$?V{%%d1(lFUDhfB3y@aCR!FKWZyL{ennL0vKG6x0$&2g)~ScQ4WJ!p;^SCNyY?^5NFljmBiFWe|I-2~E#n(o<8S^WHzu zAMH&1d7>4Rr)y4NjxanoPgRC7Bpn$Np6`RWg+Y))Cz*W>0yu1~8);@_-BSne<)$!T z&&ZKW zWbv~q9F-u6{%yK7?H8CiTZvPU3t}kP%4>IY^(^#Yn*jGH!$y`{nuKAstc+#cj@F{? zQtJ}upZNi79iFc=Kn|@XmZrL4QYaN{aE-(lBsihW0@?p8@nFHU$BaSMbGU=?ba@Xvs9cysF6-D1!@AE`C|{vNNB1{>c8*EkW@ua z#&L#LecfEzj3H*teYJ+G@O`7*odfRQcuXz@qG7Qwb>uLzL9ddX`=EG!y=7pdmYc&O zG@q(e)9I8iVR%ls#fHx;_q@8q>27LZA39N6^>q0{?`}hRvwg*6?uG5k{z1q@yfXg0 zIXR|9e(iSq4g>Hj7m7Z3373tsMNp6T)=A$sVL$8~%R}xs$yBW^8q0|}IF|{R|H-F; z%fQYGopmxqo%brO47;V9hep~bh5Cutv{Al#q*Ug zcm=x#e@}=}L6JQFJK>k7>-S4NRAZ0@B@6Q4LHY}<2XL?v(qD!4lQeWuUey-;v(r23 zEgc}qXNyiH@k$-PJ&(D#c(->h>#&siL-&)CEs6sNYfV#BBd=#g(95p8monpIQXo%) zheCNFM~II+A z#=F=aA67PWUiw6a^2T6Y{g~@t1p!x8RpK~YozH>f`Q`m2A+9+a0#3wn=$ATt5E-#M z<%Q_HpEWpM$xJGHH|8OnsczTAKxqal!9H5g55A}j{P;)V`_<+e5n-VZH`ZPYdkA?& zuu2Ts-?0X^DJ;*#I2l(J@0t;-ND>NJypy^+4^Zlj1D?1Qk#$?G#8-kH84x@AE;o)3 z5zcBUQ_omnd@!p|B2u=jyr=AVmnwMo&MYH)t2?LIy5pBcWFx*8D&H z_$piiD%#u5BoCLIhMB~^FFy4oUf?`(LG>^f+x`A;&JLk{r4Apr=GMGoapab>r+ z-c=ZMdCgf*cFr=4&htY4AvdB)X*;d6`683kYZW2AOhR}qNSi-!cWnQM3iPC^&#R(W zS#${g`b;GW508r%z*`}!;Ks|k_emoY+(8+ z*IOM+ncqu}j^#6A>H-Fw64TyS$NR0?p6b%79}qL&>t1tXt7(b449@jSvwjMaF3c01 z)|R#`KY3r$v*nj&VU;}jtNzSork?*<=HtNhjaKWwxTaa4(t28!dROh7VG-5z;wfTC z>kq1x$6A+rFX-$oGQcH?5?De%8E{6s8YgtWkQ5#^a2R=-J8q$Uedf<x*_%1(Du z5H!R5@Xg5(DM6l1)I&Ofw_^h6JQ5?-Fa9o*4^lP7$S3|#k7W2sdIZ*(;MA3jSRnVS zUSNY1u0a;=I@n|HKMo08hszqLYBc0MB+j@iZOQ%sw9_Y3O))Fop4~Mz|1Xs7({>qilC2|WGcE``&Y;uOE)TVhe;rI*U3O1&J+pTb@$y`t~YqpL7)QdV&M5SDXT zqCXkOBCaG;*7?th&AW1?ksY$uyTM8&>$1a`(hD5=pwRk>mXKCbXr0JK1I9~p<*0l7 z=A%dVZ_A=~TE|iqavzv({}3kA&?DuJAA+?&Yan~5)0@vyi3hb5FP-au56?V)8gP^S z`!)T(`lG#-gC0w~(n}t2*v!+lJ;C3&Fm`15o+NyVURe}+sJDl9iPvu>srpx!_tU4c zqmf3DDtg%dhl0m-EEk6E^oRQv=$)DY%AMZL_F7kBIL7c6arh+vkBgI`XlYgfIqmwI z-jc9KJIe+v#6jqLh8XGu-n}2ZzN4CJ?9;Jhf8Jnjd`c-8tnJxvw;0;3<)>W#vWDeF zr+gP^2{6S=Kf);TP#fb>ufq55WR-I!Sx6`#B<=TBEM9vQiJxG>E{Yb5TIZGN2J5GH z$TBvo-H0kme4}t`IU~i>EEuQbW#M#bvR<@gSToIUb*I zp}q-kX^}WJ44d{I;XsKnTT2aV>>uj_tffi`2lm1~+OP+@L)jf4U%dMIt}F(amM=0G zSwam3!3gSqiDy#P&b3Sn78F7qu*JZ;H7l-=t#4xQy`788R?MDq^0&Ugcd&>`F$2zSyAE?Dx+V@uCZ=YX1tyu z8}(dXTxJwBWuvk5hI@hYN>tY{EM$#x<{k9TG#hBmqa>cpwVbl(TVBo|_XfAJJbT{r z15!S6E<`?vz8mz(;28e@noCpma$L|4w?Gxps~FTI&7f-GGTxPYpqzs?4jFzszo zadX-%e=Nl%jV`K`Y+c)Hr<)xDgbgx_l(^vht$;=tdLdhCV#t=7wqeGTHgm-bhEQ${ z-qs{{5sFdbB>8P;+wxc)!Rv>^zt8#xy~HCu>dubs7f#4~mk!@Q=i1P)%hC_!w1Ay~ zo;Vk-{`*<C^5 zkPuXPhlS2NZXH8XCs=5r`h=8TnfWMd%qYaaO$UYoP}0c#SJcIo`H2HD@<6u9dO*Yq zj*Tu1X3sh;QNfPj^Xp&LLEXWdL(&e4VhbQzQIXtDAPK7{*5e3$RCOt}Z5K#-3VQC{ z!{9KWKYY&ERX|Df+uEv+JqAdm5<;do_b3)XI6W*Nn1?bQ{Op>b?LB&>Cd3qn6OnsF zg2(3<$$@x2spU)}Ty-dENKO+}6!CLqQLL|c-c)KZYZVu2_H$jEHuAF-oQfbwBTS_f z3JUZM<_7oOj{`1?e}ZcC@cFQ-ronYpFZ!c{>~cwfd84 zN)5jQzR(^342@FR%>Yg8{f`~~f{B_T}P^=k|VqN_G1fB9Aa z>1?8Ii$a(8QG|O~D<7hrc&wdq##^lFE6g_dlvDaum`dokohME{(7oqxH@R3N)K;mv zT=yf5;8a?Z#d1!06`5=;FHHlm@yi(xDWeK3 zv_-sY{-ZlA5tUip0^XCrDKi}mcD#=0OB)K*55l&;Lc2T&M<0$(#Zo3oi63s(S2?P? zzC6KdTD0w@G(_ZWGZ@a@!>B?(=r@~^P*S3)tf>gXKX&C4zVEmJ&@-H@YU<7~*M&fO zV;PVSb4mD5NzmdzPWeq2oz9ec!w zob1XC_8Z zkrD1ak{qr(0#FSlkX66)2Bn3h?@iBi9m)E8K6m4 z7_U555m)1i`4yvE#B)34fHo_mHv-s4$=}omWK-T^)dFd(9GkyQg|qCNcEwEFz4Z0M z(GTu))$knzo<0}^U$>}W4(l=P+IRs>(f}R;1oDi>Mse1a^wpcp|1q$!4?0}(28IYq zzdrM0J*5a|u*Op|caEGBMi~}5Tb+`X-=6XQA8$5@dSJo4wkIibKu#1I3Tny1%R*JV zl6~XX2ylY`}NHnRfN>e zuc_mtEPDOU@GMX5KAaIw#Pb*6V|#TGy5;9^8f zjtP9en30D5@8@e?+O%kAUQmw<@z7vz^YO%82er#qnEKs_!GT9*>UskuOw_TN|JRzl z=}XfFK>NU=60h0}2pM$B);lHN`k#(Ci}8khl1X{$W8uguPwKx|v^%}U?FQ>5G-X&Zpk*F;Vd2GymErd3$9y%mA9~v{Uf$#4^ z)pHXj|2r&kjaTrK?XFNRfZqrx*xK)q?^z0Eqixls$sXG~lZd+yL_I^I*Yqr z!6FMtz z5S=IJb@8~BSwh|&BXc3p!Vf02amGltJM*UNXXw$h05>+CypLrUtn$i`x^gtH-vpm} zu-~(lTI)!Ny>+=~<`yLnw&oibFVIboowwv^2Mg`wU-B?1=eipRTBJZzUMo3%PZE{t z{x~bhmh`(H71#8y3Hoq)M`Y>bu6barj6QO?P5)NM9WclICfKjP32x9)vLHxNkJi4w zhJOLp?#=zfzw6-MA?io}HGBAQmalqV%niF3rd)YV14NB{s^NSE zRa<3}n5Li7aZSrB5mi-VD~=iATAtT*^V)~!BYrrhsGF7oZ`(S;Jzj-9e?+;#$1czO zv9Y+{lQI=H!@=bP?eo;7lrbmqYpk$&Yz^Pdb6TYMZS~NaDf-DU0!3ZmAvj!v(BbB8 z0D)d#7iC|n4P^gd5n9MEz_EC#5UzIbGZt^%?<{Y$HdTS|@3TR7GzF^gg*c`;+UrR! z-c-sH^vWD7^($&Pt>6E{)>p<=-L2b#2q>vYr_xACcXvydba!{Egmi;+gLH$m64Ko* zz36Th+{wGoK6jsc?gu~WZ#nwualB!i-PGoMqWY9s#zmKPPNseNrC6kQFJQYYZzJ~vsHJSfVi3$Pm7a}Rg z$M2ybLsDy?MZ13aU$FpjWvoslV1fk*JZ`(-pr+T$=&)>5h&Dmk!GX$ASy=0uPkS|M zNz;v1@^{!)zstC!--<>Qi4yIXN~?cr;#6uHo9m0ozQfe5n^yUH9#LUH3013&Twp%K zcRp9c5xZ|~dXv0j1J3NJ@so18HX6g(V3NSDCu_h4k~NB;#((J^QrZWy6k|46VUpP@ zaNzJGYK+~EF5^6_Ia*lpsV4Nff#)uu3Yn$YXIBE-uZasBrIAkq2Bo|F+xp)}iIPp$ zmi3c7|G55}2HHwEhAVk{8akG9WC^j-7_ES9wem!pTMD7*Fms*AqoYmiLV+ZI#b136 z&=*6C7ytJJHNbNZ|MS?)^8gQNNb?A7skE)+k;m2?kh7nTcR(@M`h!p4d=%9lwrhOS zt8(AqEcr?hQ{?GeXW(0Ud$}t%jg;@tQZ~MawfGH+9ZRW&gOp2aUxo3*=SsX47Gf%= z%7#@$%J$}>=NBZd+kWgkDj!JX?n0aeMv4ww&0h%_Aw7zci+k3{bRRu_ze~t3moNTV z1rF@U#@BE!O>-{h(c4=8g512MCyq&hVUt^$y0ejyDh{T3y?cEsG}|IZw%_0Pqvm|H z>KXM=9qJ-m;3yTN>qogi4fXZ&Y7n}8A@_BRz^79)yVa{nNsXR*6xytid7!4UBU zfwk$Hi>G*+`WljNT-9MdueOQ)3@AvW3w;2&a6+~)Jvc51IYqUaK*nGM1K?DJ=H7u@1`3SuE!rJMA*Oh z!MWSp60AACE%VTE-w{~H9jy&#V+Yu=v02G&nXqrIbqe_hTEdS}uTdaG6V-hiLe3rB zWg!%H?9S!6@r`8bdycgk~ z5kWl%HM6;3TlOIH+xBtrr2V@k47Mk=Y=v{k&FapF(v-_LJHBl(7x;ldF#~9vu_B@T zd4RNi8#m|eV-jF9zlE7PnLc*(2J^Ct`vd--`43Xf?m!Q~%n;A;m6wXym`P~YTFKw? zU{UNBDnazMDG|cy!ZOAiR;@<HT}tXFN~=4{k3eGTpj>T%|Eh$*TPvq=NqS*cPz-E(g|7PFq34q%qK7J2Gm{d z<61jBs=4T%@l+?fa~lG-XzLW2{Bbzog4yt&kItH3)aveMnTD3@2Ejw|N58$EQVw^( zq9;xc^|im}6|jS9b1@+7(gMb+Q?_){|Ec6NLt9TX05+@%$(qm%Z)}7eU+1QId1Rn=@U^zFJC#>y9lqmC(Lf+>-glU6ZSwbiPfg5hl66BDtY$eAGRB1 zG?N^~7}+6zalkVvY|YC^uF2s6Gu%W2!A#9LCf-rGoO!+X#i7Ne(?B^VtUV3CuzT{% z|Ndn!ew}Kv-0PZ$G8B1R$HQ$G66|wZ!Q25M@w9f)A}|U2*4Bl;`PSLpmq-$immp_I z{on%2ffSnU`A_ZMG7`)scmP_cdP|iWfcixRf2|0HyIoqL zxN}r&1&JA?I$hgKP_qkqZlXLX#SLB5P#Dm^@bXCHGT@U)*I4E@H*aePXJ;Y;iYH4? zs8d>nW@<}HaGm_2KXP{+s|1lBl5`B@0+2%WuOAdtSRh%JGxkD=fuZl(VYJkdR{@Z} zjV>qDVDnnY7DD=^KN^2M%oZ8FN>}i|Vf{cnGr=_@RvV3O4?n;*hu$0ug_k; z;>!Jt5SU^R1$feb@DVZSv8}9U5GF?o8Vw2I@T(4oP%dSC>C~y3mu<)%bam=d^7=pw zcZ5?Z@Q$LJeA2JmPfu7#!&<-}_`O{4mhbzuNuj#4^#b{ekyjFrZ8!JIEZ3|DRF1bd zg%#{33NO2a-D02ez|~7m)TaCpWM#Q~X8g zHYrnNv(E}QL;|dS_nJ`3H%4~UUwm=l)9si{U)ncB4f8>bY>^#l^lG=Yt=MV`{tpTADD%|6P)m3;Jz$M7ro?|zO z$yxnva4>Y*SqwK7WMK_wLj#ff6{;f%l}ttV#{^6q6U0=1DHV?d-LBf(xkhKgsW#)# zQanw2$^uX?&b^@jyu9$Dhab{(UipH5a={p`P5{~nk*g+o`l%Thp3-aN7?#5LLb!d7N+^J*k9h0@g0Mium` z#ElJ&&LgFtRN|0>^0Pk2zo|?1Z$h~pde<6%296l=b4%b*`Hp`u;mxi@CZJsW(jR1**2T|pNGuwY( zSCG4Dz@JYHW%N&ZXXROLsxmZ%#WSG1>QdO3oY>DX_NO8tEb519z#sW5g1-lYi6Z%u z`9kpu%qLw;gg{PfAP4KU_nE6KSj@k#h|QHMrp1S4!Id z=&W}RXhX#yFhZlzDz@GP*v6$1PBN;537JqH`5l|VN#Tw{!i*1{d@Wy5^^XD>EFzYu zK_|!PjHZ3iPu=1`BJ}>LK3kA$j4`f&j%~$fwAjlQHzPLyAKe2FRJLdX@D%b59FVF5 z15ka>zwQ%~0}>p$y0|RB4hrTY^OMZ$3oMOL0sn5Sm=K`E!t(JnvD4!){mI_je+Yn$ zm};}2|INq0s1$T57TY?*7?b(L2?JX1I`Uu4Qz5xs+HBfvE|8-9rP5F80YB$XWh`s( zo0(S7TFt2p&@2AyjevObnQ_%BOKW`joi*bDzY}4Aw(Ru>P1?V9S{5KldX2U3Yh5K6 zz}>1ca1e@7>(;{7JEQpl4eM++k1~=M3`<7f|8oYENHRd5_p9sLRh*Xh+P#?v_dr$O z1dPlu)K9M!3;yH%EF~rp>*Z(fufri`4dVjs}h^o5}{%INnCK)CtwZtzYp6mI+G71X>itO|KqGhR#385 z61!+@%bBwz62+`eT)@q->dBYc7H?6>)0t>Q4z9P;LbiL7B%~{t)LHEx8up;GD!3{R zjA(MB@SVB)GHGI}=P*C&E`msP}?&LbFufvaXb87o$B9c3r5-7ITa0KMeTtD~gGCC!MjvE9ndbk1}FJLTf!76UEJ> z8K)erjF>`R(bp&Pr2;5#sL!AO9PQxuv%IgM$ol>Aug3(x4dP&GB5~<=HDXJ&Ux7q7 zUExE1v6jsD6uzl#_&p4KsqZ?!_wdb__vY166~A28{1hC&akl#Rk7KB_qlFg+pT`s9ZvpP}+HWBxM7?o1=R`r1>5`TvWt^C^{Pd)~Dq(is z`rwZGG3CZGd(@KwCbVdJwB5+Tv!;HQR_P=>YAo{0~W>LjM>cb_(Mz}=Bn8CrCubP_zcDz5@f_`bM5P=@352&JFOJ6?t=cVQO>-*uv(}87$geQ;^ zBPM1xOUTIG!r3#;4@k0DW1rt}bA+EC0svoJ%(si29RnvGZ1z#G$*L73AgI2Uf808A zNU0A1LP^=3g;%PWmN1aT8&A$Ge5*LZZz+g9tr+XeTjOjMg>D0=VaR7dsBsRIoKv&R zOn;{sm;{ED1)dSC?XOTXrm&DV+WCquPzu5C{Qg0XQT6@dx)vIsd-4O}W7SysSdlFF z88iIV_Xo1SB??o3H;!NksO8zO5uow!2VXGs6-RL6+{^_9LHG3RlQv;k|K~OHO9UFw z*eo6;+C*#X(l?|nV5TU;Pj&T;z-O6%g>e4JJ(Zwr?TiyKQP^`+BVZ=6-D~ryM#nOm z)wrysVtEsx>+KDg*xZ_!s=C8ckpnE2d1!I$#5i~%m%2?)$pBEFkzgdd^PVc&nWyKl z2F1XFMT>E&8nk<{AzA`xpXZ~v@J7w*$BF3#xh27N%5IcrT3T2!a{KLReNTxjJx z8an~qUq}%p8!oVsi#q$y)%iC4gVKRM=?j;BofH~xql!E3@0Mk`3wZ=IBxikjD-6nS zWRshHUQ|GGTBOrDN-Y6e*75a0P_Mb9U@=VOt^QIO{FVOn5imM2*><-upQ~ady-ir;EP08Q9e$IHU|AJ>pMI8(LcPXW9;&xv1* zwzf7~`8JE0 zRJlqg>VyY2*Jp%xZr{4J!SMNQ^R0~_OVmMjUeofI;8*{wX=(iC+V?ENc66?1pLVod zmlu24*Mour8EF64c_YH%Ulf7a%eBVsA8}O$&f4LBsjK2N^y0~zbrioKFrDh@p$9`g zMuAIFpBwBs>P8|N@jmXB+}<;%Cf5d)Q6iEavTE7xV_C03X^nhZ=;5)UanDNxW^&G8 z-j>?_f_nq-MM@A&bldAC%>7H)tTG{Q_AAeX*5P^@E`Z#L2MOKT2#1GZP?-*K2U+^4 zzBoAYWL4KNSoUdokW2SrfgS^e?i;C?OJ#%5HUkgM+p^Dk^zDVkR$%kSkSm-zVk?uU z?IsNWyT&YSYbF%*mHP+J=`KKW;9R-2@<4PBm3juMm?e=HoQjjhl+If`=?_4G$&|+XNO!diAW|UvvC&ftd3;2?| z)zA3pVaFle>+6$U769M?fGqA4;9GV(ezXGLZX05gkv7G`nD(5(cwl70VULHNQ*TVB z&YSmBLP*@#vb1@@P4ejkP_EDqNB_Ao+Hki3=V&7?j@&V~V=2w!Zyo}xT=k#{Uz>83 zeS`W+^mYbpf&-l!hycb7iRzkLpqP>J!T5&$T}jWGM$y&o;>s~?ULdA4Ebw*`-AN0m zQb})s9D)cdIYE}L_sh9{3#s|`V6;f-H9BNgn^gss6YQl?CDr%z#qQmBLv>{^7}f#Z z4kiT2nCI*56}U^`gCr7Df&Jc}FP;qzovr~K?)8L(wS8P$)_#%iSJ7d2=625vqfg%p zou%0kxA{YuS*z)8J0^}`h`vv7Gg7>=8w$T_XT*eL5?%A>1r2yXF-FuzuEo`Z`)TNV z4J$d{Jjhyt&-_3{`i86|LsNX(F?E(_b>QWYVq6CQTN)mkzq4!Jm+Bv1j@4y+q8wGd z>%XO%s6Db_(BYz_|Ix&lg*>F`F(&!^>GLDuFAi7G0D*t*JeI^iZ@9z61LG~2EPcVy zv5c7W)HrKUS`O0_!VY;C$PufzWdYpK(wSOMx#CYep}L7v!j>h$oAIp>n^}RrF$4YX z3@bNvb)>AUtc%iiAb_yh8xDW0Fx@gmH}^*&QjqOZh{@a6?ZT0;LtC?J_W;fV#rDFO zkIUYndb5S50=)gJoBlF3>!xEBWWrNavi^Mn5wGtsJCru45I+INt~hN_{JNeYtH@h? zPG!Q!=>8I*RkdMmr~KerpkF;Ff*)rbEP(y(mzS+774sA;)3`Pr^aZWmu}|xT$FVnl}EzjhshknoI4z4e2iSZO|j- zqDi}!58}B4sG;GTk(=Ns1|>uFRzM&fUpaR51v!^fwcBPsdup)X|7U7s;;#Sh>okt! z{J5Aa$JzK53cyMEXEz9}Qa};m+vf5;ts091{rAq9^4HnS8k{zsR^f#k&8T-b=Ej14 zJh@mmq+V4t%r50_3ok+{tSklJ9IC`;RYX_TjMREt+K7Xmbf#2xqqNKAdYy`vB&7+~ z#~c2@;8|X(^NZhACemRb$A!RTUD3)&jobB(QKT+ev2JL$<&ZYvJQNy0w0r}_Wc8}V0e{?NbpQt1G?|4g8 z$iNx1myv1h4I3!o$l__zBbc%&xO zM$07`NoDo0k5?v49z4>y*YgVC`oqK{J>7NVr?Z2HQwl#^$y-TthFNM})0S&v<88T( zSW@b{^LB2R)}>Xm5+uv!@z@~+WON3(E*pD94I5|??eIsd${7&lU9*c|`5HZ6(md|+ zc$`Y9R5qWxxv*wMf$i5&pNAco0#JuBP~7x|;^(`>rY2sN$*ur*jHJsCu;X$~c%$Rd zv#%w!3+&-ajU=DdjlH|wD#b${o7`_gab_wtr_qkS5-1&Z@!%m$&1tx&!h!JsUd z*%eT{C91GrW*Q=90e4$Q>|khM$yBJ$o4hmVf%u7WYP`9upXcnt{_hNLs!`b498;GZ zkW`r<9Kb%ys{#06#V2{9;R1AoVqsbLpYzDW%v+T{ zmh5v!ArywY@|vR%&Py1Ik_r`w)F1r+lL6g8Df0Jd4pxLbCsl%bkq$h}BV zJ65C!!3?@iB@KN)X@<>TtJQoU`(9&#yqB<> zx$UG!{Re<$A*Gte6eIri^@*Ma_n;sARzm|(Ce)mbY++oXq1RX|JN_5nCX9ee6IFn~ zN-Dti!VhQZ$5C=Z*rRy95&^enhyRnGh^s0&^Sg;DA6`NfMfwem=%&q3`S@l<-AVFW znuTGYOa)u>zz)heksrl;k_K#&0xd!|d5Nn^B~QqYUx`|9T6D0VaYsi?gB{shKdGSG ztN$&SP}0k_z1$v={C||Mgn_)*DhaGPm-Rm zt7JL6v?E%m`;2YlkCLqcT9uctb%9D%EkbU+MY-c$V}_;DG4hRN!LKlbtQyAp4YE{V z#t~CGy1bB&ASHrbPB_7)miiU`f8FL-;$@S1m)w;f8K&J3(~S%krtws}8i3PW+PZv_ zq|MgH0Ue!pG0vp0+YXNV)izkW2|{ejE*moG@&8lZP?OB{nyZ?{E3eX?EZ5VzHKJ#I11nzySjxrtvJba51Ni11`TvVxZA=0 zNMp4+fW7r+3N+dc#A9bnC7c-|e&!_bEJ~EO_)Q*HUbcHYbIr}lh~Az>^!GqiDWx2? zHJ9UH3^osa$MnCnO{{l{crj%WE+Z|+Dg4EM07bE!%kaKD0CtI1p$<~gmGarzHW4Qd zTJFJ@UX!2^4N7}#ZvgIS-F+HGY#-B;mVSnUXL4>VQ7^*WoRoH76a3Q(#gXO#NP`~H zK}cJ>Dp-eo^YhY%s!|rC36XrRud{gxX3mmX$TdHRIvUe+q*1cEpy>W4%dTX9f8lIE3`6RD+qG@2q3Rn;&iSQusl6 zQ-}+6nzH+&pun?+U66$wSeGdph$o6E>?|DDnHhn)Sp^L|=k)t6LiV!uLl)@%Hd8gd z5%mB_#gf(cpIhp1&L2g>_G-)s@7y_Gq%eZ70@TgD=N#mKi~!#UxHyR=gBe47rP_mc z1V8_26*t+*rU~JE48ArPA(}t$D{QNimh0%m8<&Fv>$T3-j3G3ry}c2k0LQxyWV@%_ zL&QLi0y&(jaqIpgh4v#j7Rs<45#04<|2yS?KN_lif@Hz8e+CFQ&3CT`mb%L=!bzH_ zv#M@QJx&rX()%cNP|R7M#}qoGxtV~4(JulcW9(=MKpxtc1J(k1#WUQh1Lyzf4e;2v z2|rV4r$1kmM^hQ*f85~ana@$wp*#x)_Yej~<3ER*z@5_(pd3PXPF8`Fa^VEM);#o-B;P~2L>>SX1(@*)2pF}z?$<`=Ykl4>H{X8z)eu8YZmh2vSuUL z>amU^>_xLA(e8vg+0egx;=OR# zXz;~K;bpajl3MD9AcpdnGBB3|a!I;WNleMoOz@GFF{-s0mI&U^@JAUUKVVaFwLWF) zEvQ9N5w)wRC>%Wd%B_aH1?0ydYYt>?>U0jAfDLO~b#0acAhh88e3=C^9#_kjm1$R& z;)?(Gz}dPA@=K|`-jYc1EB+{ee^UWVzJd7w#HM||02LgXvt<28IeY3UZQ9bV;g427 zP?QS`-F!X;-@QB>RQl$d^tYA96W64RS_Ngm2<(q-^_|T}=}Md04cUBp`B@wF4) z6}{0*;OD}_%4l~nTML9TA1R->lln5QZ+I1e99j6l|7J7fFci2nY)}6&q(?gt_ZU0G z;{wzWyPbJIn3Xx?`a3G7QrUWvHvE|Qolbfu#mG{;xw5&6sfWEq$W)NIyYI{z{&3Ne zEbRkJ8q0L6iM7)TU|boUkyi$LaG2wH%x?A32$&Bg)(W9Qrl!umfoY9gSLiKWMdZ4j zk3YXKz;M|4b{}emK|FPtladc3ow54etvE~z2q+* zeo)Na1lH%ab~I<_TWd(;xI|)4ba!Vi_5I|RPpe|9UTwAx<%4?4S#kQ2>+U(mXYTsQ z!c|L!Cy**jFL z#VpjrR{2)i^YR32MXnLCg?n2{08LzMKAjW6O#Jd|*D9g|8-TG0U+ur?Rel~KF0aAM zT=&zXmqu;kD8}L2+bon4v1Gm=oJS8A2Wrby z8wV=drk6U_dNoA69ZSv^DU!6eGI9tDQcZfNQsut$Ob8JFe|XkP{k{p}Fgp5^pjeY9 zb^?SkPYf&?r5(RcPl;AwS0||P`ybfvKd>MpJe}E$FG!cQ>yeN zoY7aY)yewj|6go*y*;*JJIFnxM96*p>~)*K!7t&fa0oF^Hr zbH%^JIc~B8{MD69i8S=$@#5>r#WzJl6No=38~&xw`3mZVgYQVRERc;qYHDjRcBLCb zfXid7su!UBK>7zX$!PQ%A9FYj2z13eKuyULGPVS9Ah?@~WA%0p64;Co*A}sz4E_OA|uZ5Sw_V(8%LJK}5TR=Nq zjp;9blk3M=cA*g=%|-d~eae?OnZT82RZp}~m*5hyz!_MH z&X|;V>x`)nG7=Qme6Rq$33sMQ^TISUxCA=wHW`ozr{a%U>NS&mzz1nyQU=h>FG+6} zf8c|9`3h#d!P&jWm~()4leRu`8-z2E=jtP_3Euje(z;S^I4z-?cLlP+KW$e_2l}0( z(H4zl+RAA2&Vv~XVHe=HhbU#^>r*}4wk9^A zph2iTw*qC<1Q*D_kaM#}1MaM0@rM;69E&Jo=ehP8m)%CUP-2yzIM znreGpfU%WPSqtJvY~VB)_4$wWZOd#n1DAwcj!#ii4=)Uj_O{!Eol(Jazyrqo^o$3_ z;CO;ff$X=SkSYFFC`dQFapwdY&Lg0SaYzBdzuuvO5C=8^sBl?t5_M6`w@#XUXnXSV z*^bF%4qgMJ1F%N_0C4afBD@KpD_K%uuD62AEEKWPuW{VUqJoVP@T5tW)kZsZT^qz| zt!`sUrih>*LcfLNxRRT=XQiHZ_%J^uQqYP}2#L)Kqv`|2?(fDCYoJfnzLlPczipf5 zCF7V~flp5YjoUaE?!i9t>E4%mV9;>pT(>kkYJJ&doAJ+fs zKAcs;74&SH0JJB-7RC7)t;W#sERZZEYhF+@8=;^`O#WnlrGk!+vjKJY^^ar7tLfI{ zlZ71D+de`Rb0?84pHHSOmWS=F=|jik5b&85D}`Xo~Vh}xDxSyK3ge2uiqHI4ugnD|w*nF&o^#PoeGZc*6gTQ?$Pg&TA-3JZeTSw(N zdgQb{_DMFd0kAZ>=yUwAKmV--IAVJ0KgVH!&DSGviA87ruJ528wo$Q3bX+Q;O|S>f zK#39kE%Jw~Aylp_2Wr$Ry`j9!n20SX2zo`C6%jnNWre+4je z8S&?_(u3bg{ zp52pcT?8r&=13&};8IB~4{xtNaK0x^9;GE-q1(uRuEdD)76<&AS9Mnoz`bU@Gpcm6 zz{{3WSPM?T`(sToYhv63=%kv>Hhx5m=l%n^{hLh>gk(a`2?$`QQ*D-tQKX~407b^; zNmg~OD=Ln6P357SjL+&4AyAixg!?jJllTUk;s+CPXtJ_p{nLMrJvCWK2*$ve7YBNY z0E4tOE;OG`Xsi#^iXO6FOfOexMB$s`8t!0Ofae>l)qfvMTpVmbBA)tT@mm5i>1rJdQ=BTTjMErc|9S$yEu)C-2N z<5doo{-!U0IM4zPQZZb0rJeC@sM>7Bnv7jbJ)OU2I^R_%YNu}e0@LqsX*|~YmXOI# z69F!b*Kb&VBV`2?MT4+ZOjBdI+c$X7yASzMrT)59xOXHVj}j zAM^oPky}Tb-&JM^>W-CLu*%yr=ZY1YUrahHx{1@Qceai;Y&2+>dDRkL4)=@syt?SG z6jYxq8&%A51nS*n0ahT%tV$y+x1mnCb96Kiw^y;BiVl>T5Nk2VF8R<}cA)8Q7D zvxnh9o{5Q-IdZKIEYlf=bawov$#SDaWSr#HCXOA-fUwy~9EFrb`??8o`nG^a=z4&W zo1W8|%N^=|vpvLszSa1OP5qO1L)F0JNiXWac~mi@V6g4JM{xKHo%(kJjP;XisC?;_ zGIp)LkLy>sYE?<`$?6-84>+j-{4?BNJ?R(BR=d4i4_2BbAmfK@zMBM51ki4x%?t!{ zSIq(y0S0HW8lSUD?zyR>={jYD5oiEkuqsPKPO|4MHlq9|m#^i_yF8sczS90`xbUz+ z{zlw!FT_ZVxKgr+%lT;f=0^p3y0+oRGad!J)O0%5-oX}i*B~;jWx|fN&BIXx#G!}# z+M^0>)1nM$XTa!&G`;Gpa=7Kj`i%^qsWGxn#pc1xV6qNG4%zoLFiOl7%VV`?ZY7L@ zRC+9G=eMeQG@@#Y$#DW{J_8KduLEo3$L22gmnqVCtNk#B4-u2T(_Qays8{^tuB(Nd zdK@wG1j+<6FHn7wC8t$lH8|*aO_LTRGxRPk@A1=ZWNfBi^9fT4872D5$E!p=TP@C# zV+(BPiwEf4+V|Oh@6#rJ_KD)*3U1_{N~Tr08VT9f@vr0 zY6t&CcU-~d4JC21w*B04qic_yU7cZ}JdunG#qHIh!w^$$=z|R#JY@8d`^AqG{h1H+?aDWCcBQ&Q`0yTln|W?6&|b$} z{b2h0)|X$KoEaLUE_6#5G!fAlS9Ihkhlyc2`6tRmAKk_Rn`^-%BttZU?T^L-^m+vS zuv{G0K~a9ar(rg2w(T)ibajJDe2!QCWNrjnmS|K5HLlAU4G(`QI$x7lV6*ssg%t~9 zY=nngJya{7y2zEc;5=7X_J6qRGwRe$9n9_#PVanYcV5YFRsPf6wnnqcZzRW!QWyB& zEt0uwBvp#1mMw7*es#p5qw7o4*5PC2tB|vpztM*ETsGkTq+$HX!#b64%ZPFL$G%#D z^2N;axpa?)stBBB0}|cxqmFFFZ5@XAN6(r+*gBLXk1pGzgHAZ^2STm&gl*^FraZTG zpF1B;GBxsXF=jW)5!P8B8q%#)m~sBLr7EacH6y>?|?F1B+u zVH|77X;`2i#nraod#2#Z02%tWkIZx9GOp;Y?W2e&e7_e1Q3M#he>D85mSq+S!^*1Q zb5&WE6Vu^#Ln&D~DM?t!SZbD~afi7L7n7|47VrWz~o1-L>(~PM44As^O8_Y|nx0*36H`QzLUEbV{zn;eE0Q zXSO_#QP?6QU&m|Xs~d`%zdtH)iZaOIA@(ryes@8a*l38s`z`Y>J+_W_QmH!5*9bfj z#NRoD)w)xb;&37r@H<1HUb-hWF^xlizY z-RBgwC*rV1bUj4iQ*ONHw$uTAldSwo`$;}+*g3@U;T@Ty9rQ(T5=ic?^NwK&nP(a6 zYSIrEK9r@r&{!KMKLUA00yG;s`{1~>GAiuGIBu@)!-l1-{j1}ziJIhuuWb1ZCfXky zCn^I{D;5}!_O3Sc=v;~v*TqYqoQu1{&70`~q-+IvIFfXu{Jvz+R(VpYa}m8Z?>qa? zGc7%c`E_|kMv}`n>DU^@m3pXyMf^w1rj5-C3%m<`NK36aj0=aLu3V(AAfiBX7uvaN zEee!iZ*x=KbnT8~`@n8L@%2?)&z37W3D40$v(MeduI~Bt1CcoUvH+|5-y8#2M;>Vw zyENx5*iJ9{`H#Ja^Z65UJyFBZ&iMi6{9A__Tg&~H6|X;ub1yr*k-QEKE=rQMV=GVM zVm(I=bS*J*Y?32MB)Sf#An zZW-L#pvjf|){Z>FK1~YSD>pkLQxdX(U9O_*R&5B&ZC#ShC4y<)JjU#H?^%=Aa=EPM ziZqbCFGjSrl%-?oCRfmCxqO9Wkm_gGk+m3gv9#yP>u^*TE5~YBw@g3!4ga)z`=4S~ z`>r-6qtlHjmate_&IyFHcIiR|B$^@nH@TbIVcS$_zOe!m(J=$;yPp7jiie*@xQ!xwwk#Q`;l-_WlQ6!XW{g6Zc6F-qpL+VTqpd zqVG4#5K~+qhHEdMImZ^X1ytTXbFS2=n}vvcG{ zsw9_k>n?x~zwKzG_dOj}mNu$a`LBvs}_q6nAD=d1I?T?7eR6ukj^cI;sCR2Psm9$Mc$LI)! zhZtLr$ft8ZJp5gQD+|bnZei1sFKpL;IpR8H*AMo{-QV(Ai6*CWW!5b6DH!haR%~O# zp%onQY@|pmwJ|BY`C66X@$1<1-h-L&07>SG&PB9$aDgc4Xy&>nP5CX~qPANo{>q3uiW zj~YOQtYqsiHC>hXc&?Ot8KJiB@RKdb^{_@Wxkp~kPXJTX|FjDjI0w6$4~L(-3A`?% zl=BhZ%na_<&GXBsGZ|N)VY}C7&(@xA)!ued-@)RR_GmBG@b_Kc4T1B#qGl5`WCx{m zHlLnwlkEJix*dt{cR{)Sfl2Zy^rx&2@~G4er?XgLdX-r6%_oq0=NN33N2fAZ_&p}- zZWH}_Iq@0|7b2u1zbp1UJ5N@CrRhm5T!~BEq!&@?4-z@M!O+ z7gSg--9Ytkq|vKEmSehGAQD$|Z#=f4Vq1RI1nJOmBni1a_`H{3$E9Rt`cMIm{_pS1 z{PtN@a$Q(Y=x>?CWQ9|$1A7~$_1iT!XfQb(HA&4KJ~#(;@BxfbqP2ZB50MJYiTTa; zjFsa(Z|q_>gev1MHF=KI!TFya^;6%!Kw@Outsb-FLTmUFP-62!Siq)VcjqXYw2U`9 zxdr-hekkGGo1GS#{!z5J&r5-|%tgS{abN6qI+50x=QS<5M&4Rj(c$BPnpk1c7DYf{ zMazK9_|5HH!>OLzVlmAx$sM0$BluRBr67xP{ElZ6yaaWd%mv>~hXU z!|~ij<01b1f+8c>+YKLr%-Tb6$CT_mWgVf6>{9HCV?07}D^FGGlt)$L`+j`zVYSv7 z|1IUlq0jsJEkk%nW2UHHHG2}6JrbdxlC-?DbB@+%mbZ?j@RRJ?MPNjlU3mgvo$CB zbs(?N%6sBGeApnk zfrl*K%C6cC6kb-#Ci{M3zj!aW-8M<33r};-fp~7~O|S~)qW5f?L?GlJeYWLjR|1H+ zuVv`QIh!QM2-4!@YLDu__K#R*z65VvCE+PH*?K^}hn6*Jz;f683ddORJU!i9pgMx= zuQc~0Bck~2rC3Th^X>imgGicKx@HiXXTZ4F>a<+ph&-wqd8|}mdewDrH_NSZ_1%y8 z(-<6A)UKaH&jy>CHSa4oFb^H;1Y`1-kg{!tjXWK0MmqzvyPWg{X>^8=rFhUg7Eu?C zmdW3dy>Hm`mb>I#LDvXJHC~Ys90Nq$jQxqLWkP!iMVOn#1QMmnwTd)b3Uc$>4X1T zf;sg4raL01;WHJo-|uWJglWk`s=F=h1Usq^loKzpQ2g@6((Iy z$)ABGMXA6Vwn>-1$=j8n+1s`T3H5{Z=S*Lxiv{`l(t8%oz4CEdix4av#zvBsWQjA^N*d))6Mj%+R0KyQ=>D)(_g7i0Gec88Ypz zWf}P_b3sqc4I1)|{J=)7ESPj2{a+D1Sbhfjy0JV-VGO#=4GS_Y`5&Z|{2%^oZR$R# zWVYCgbWeZEfhqHvDE$ORGT+uFoWlts9`?n2ckoRZx%me`OZ)yl&T?ajibl|FZ@#D> zoK;tqdX+~?vP_TMZG9kh=c-TWoxaDYfYI3% zYnlUdz*&FfgkHfvA)tyLEk_VCa z=w77Q6EWTL6x@T`sT02}(D^+YRvjjrFD3mLv7{)JFNbB@Nm;g`wW*|KgJB%`?M|VR zH@x;LajBPPB;q=G0z(yE0mH(|J?w*WPZRi8p1Z7z)6m`!fRYf@nP%?$CE**dA6U69 znhZLMJ+{o`EVbR$C1g;u8{4zj#hb*^7~y1H;jd`8gNIbjx86Geu!CK@*{&y&2nkio zG^WCC*_}rn88hptE#Js&Yu+ESmqJ_0ud;9X6B?E@=YO@0BJh!wUd!KiI|gJKQP|r$ znlYU62-8dY=1<;W1PcFD4{4z7RMT3^7uZI(OHrv7);^ePl}XSndL;EYlR_E|yV}>< z98WBoGg+ZdQH3f6`ZYnQcR-f3}7ix)f>eO5&^%()n+tJqwa3xk`gfM-d|jut`KMD&6y;J>g<3F z6sNnI*jdurSM(usseaF`UYE)4;Pzs-t-A@@;flO>eRB|3!Bc-C=12e0-}hnMCa(ME z(I9<^W?&_paUr88m3wy-nN%2d*0!%w6%EKkBfhk^9d#Fjtu`}=zRgooccsv}i@WXV zppMTpcHZGW4MZvqGZmB<^n!X16A^KphCX(<-k0cahM{A(dMn;H-A8?c zZU4yHv0?$Vh9+K5X%=mcgVFFp7KQY^Aajd>d#@gqEs zw(@(vTWjuN&Z@%$E>FX5&9-tI3{ewtr#iGcmEGd}S861Q&h?0@roa5@G~1gBUeArI z25pMQo*#qE8~IZIKFzC@HixvmI>ZahOS70ka@;DdqtOb#&Cr9h0*k(XHG_$TYgu@6 zgdeIL6g1otNEfo#dSdc6kw(#pb>x&86#dZ(h;ZF*=9QnouOr`s0mQ+lBGK3`cR87W z$hwW)OAgL%JCYiDx_L8H$M8~O_4vp&RUqh>twZv`D?LQsNK;tUfaC+}cdEi{Er1s01W0DxR!>EQw+m9LWxLCrTaiYo_POZp zO7fHTms`-+{QTzGJ<`{sC{fY03n0dQs7WB?&@#oUJxaVKOqfUe15goJ*ViS-9pNE6 z>M9fQGB{b8yi|v$b?w$%-aGqd?L+EU*)mb{)Lgi&dYx{z;GEh$yOY)@7e6iu?hFk$ zc(D>2V(-n>NiiB&9eHf14{>>z`J8Lr4*Yj?B?kC4j9?&};E)}?D{XDCAXG1bG!#u0 zw!uTN7tHekh{;f+YwHUajIP();tZbH(laX{ihdZT8_!$x`nXj!!OZwLWDvde-Ph#y zuJT)azliD*nWpHVL6#W^Dy53jO|*nlc!&*$7UkW-6L@4w39=SDkLFp1YU?(KHaSr_ z;z;=iz*Y7qnY?$+-&6H(JNY@*XP0{Q?H-_BanQ4eR73-0&{v_d6YkP2DC*e3f{WStmhP9z7@*ohe<+l+07jCI!M@+|+s^W&LcW?u8U=U%S+v%Ht< zzQ($ii*#Z6J+o&H8)cz|l;*9th3czg>L2&b1DtwH2VM6CX}vXpy3OuQb*Hp-T+z^a zT!q}z+@a@OKI6aCZOj`TYW`1e&QWZVYZp08g+rf_Od`fgwCJz&w3swZCHf=>Yd5&7 zM)2}u<+a!EdR?Nf4sVz03?j~|Z<|D%7o>g!BPKDbPYQuXJZjlzsh}XQUx4X|uNLl< zO)9oTxyLs~?0Nj{Wp8X1t2lzk)!O(M^q-w~ zhY?T%LPew@DR__J$0foc*&4sO_G}gTa{?E9%9MseBg68&a-{+&n&NK>I8mdMt;> z(XYpprn4Hb1IAmIRdc!RmMAA$uXtUa&?H-X|3XgNFM_Yz40BA#;)iL99JSx#r{f@Bzo5rk3(r5W7@d;)n)`wRW-Y4`XkmC@oV26%Q#jfg-x~*QkGn!s{#s# zqmCoYcDBDd%NaoGZ}r0SglHF2an5Rut}ZJRc{DdDdxEk%C36OcaI5!FoR;iz!+XVm~q08s0i|oUGbKCCP z!uwvL$F$#^GL8??y6he3N<8`qSbidb5A~@8bPfXf{fmX^l{+m5+rT75#rL?M%cE!t z?I~_Cg|G}tf<`H*%JA^zvbF>E2&hlpY;mPK%T{z+HnRooX3boAcOWW7_#(~v+gvU0 z3y{7U3Wd;SG!rU&$#$qhyf4J3zc?kL97xheO5$<-ndq6KBJ#1SKU_6FQo^QcSw|N7 zm5r_drB%})*mP>tve&JdRi?{3#lo-lFq>uH`u>l3VX0tHA@yDkGF~d}W-hv;sT5Nl z0O|UkoI0&R*X?|Dv-uzpH9W?T?!fE%sZ-CY;(GPKX!h8_55fz}Vp>v!2B4TF10 zUlnuFl=p-P-yRvm+0*K>QclDl3X7V#MtL8YANo#GvhQ(~sZ~vX&cMH23;KdCvvewn zfNI)}iUq4Mo%m85wx+o8V9UW>+b;{Kg+pXN4IX{>?cL`I|MHppe8U=LF1MYs9os_@i-`^I_YG>|h@JHD6i%uVG zlQtb_(H71Hkcl)v9*?|z8ctO7Jwv_SmY8y@Lrc6c7jJpFnvdi@@gEbSFrS1*Ce0s}Eh=VI>nJHFB=K0%8|_ zIfZBDVi1@BFP%J;8uzT2+okU?8CfX<1oS*U3h0K{5q7@_Gs7TISDl^`Y9L_=ZW1p!dwQ$&g zP>N#|5}l_apkD8ZT^UUG+U!Iv?X^1-5`DUe`sU+tFf-TYrvHwL_q?by;h2l*n(YiE z<&)fCf#%eCLUl6r&0VDbHOFbk@am9dwlBM7;qTeaT&aC0k9(yaRx=`p58j){M**o_ zpDA5(TzC?D4PTYpb{t_GK(>?h)7eJ{;^BB_mzNE3(|&nhESYG<$F*7q@(0GRhTq?b}B zT0(Q1(Y8HV1`o&j4r#V@uwr@|Ert12aKlIt&TXB?OfnvIoZwrqKM$`b_HKV#rAhyp zd!@B4&wrZk8{@>fh$wz?F_OQ!la5aC=#b1TQWo#)6*O_LyOcu&ZIU(VwSJy}&n=(F zd>+;wQ5ahKIhbTt95)1aC?>*C1c);`7Ueo>+Z<|TX#H>+{Emytf z_D)UutQwkKn?<3;H!0%R0_YN!FPhfqCurm1eb_8mF!ouHySBmgqm#5hW7<~mskC7J zyvOTQ+sCLC+r>B0=f(f`uG&%HlU?LoIBvfbgf+NiHwevtX^yB+P-^_EoM#57)JZ#Q zZAE1iTwSStAiV?}l4U67c#ceV8hE4`Iv*i@sx^f^cNhd?k})1=#7 z&;|`)>@kbQR@JDdS`BLNnjG06dJaE4 zDf70jY2)+x{41y>=h^2??6a*x|Ie|Wi!NF&jqqArDVNn-+{HMACd!{T!%)fp#*)_I_1(!4)ft&2|>le5L+9MXRNnhJLWPuhy zc8){>%kG5^nl~PBd?Q zU+-N|O7YmFZupFA33N9LV%fb>RLSnhf(vTvO~l41(ORL3Nx-M=A}#D2eTDVCz%E#* zS7{DmT?iPewZJdU_23mrpQ4us%cd|KM4e!@#G-VWK@88YzA)d)3dpQmNMxqAoI4Mz(cono@+Ga-w>A&>xzlr zR;SzbKkhuU2D*Stc|WSiBaiq2>%CN9A~+$?tx&2%jPEFxzLF%kJh0Q3hjJ4f!g&uD zKKu6X8c!d8ZC;yCW-4k=R}|i`x#-97iYWYToB>=P;{2*)n^oI8VF+c!lRksL{Ajl@ z+L_CfT*abMOB;kiXz5FHy<0g>_!7ZR4mqvW`5qQZ@EeWm9pkRy^oESDI!;sUzEDnw zowVtX0Gu%{3cmqP_uQ%AdoNMtu7tq2K8|>9THmLGgNKQ}syZuO6sFb$ijknqGeN?2 zel(`)N%WvT<)0L;VY2z0cN&!|L=AsWED(VXKmIF&A6B>;-~#M$>AY6^H)?Wv-{nb& zd7IcbtsvITcu(`G+XFbf8pq-99b zc_-TbEFzX?=OnoyZ6&G-Ag$49Wu{;u?+sSKG*)8J?aZNrXvVZ59szlPFImP4v2fF? zUac;@B?7+gLUr=n%^0cNyZ07L!m) ztV{X0pCLm%%%I?VX10{vNyLCecg4mcbihqF8I|X)<#V0-z6v7W5`g6To*u~i&8Gf` zmCK}v(vdADB`UGbiA?gx z-Z!2B2Yq8S;08M}>cO$<`8Mw?r}vCYO|C2mJs zeK0~F1}?6cRHDQ>3*^lR!#c31c(A7$y8$=vOl-3oml>J7`T9(_H-r43X@mtYRMJRaa2)>Tn(Z1bb4S_ZD1vRw=o?uTSC62b>bKmX!Wi550r zfZ>hGOqJ&H6o-6Wa*u()?ZH@LV9)DYtw;r$UkNHxzXYvZSwM;>>!p|T8szXX3)ia}VnneGs6 zDsBwvT~dDK<&c)$*4K#jhvSkUCh(4;WTEmCb67X<046G!JHcKI;mLu?V@iH1p{)MZ z{W09~CU7|@Z!XoW$Zxg@I+|}W(hp<^o|HAF?s@$aE29&@s`t_Nf&6_hMQNkQj#)C1 z7ep2qaK85rOsXjPod^XBvu@c87eaCa#AVBG06olvRYNq%euMsAwyvM}k612G>ipV% zNWIbu-$>B2H+i2ipmQio%~ep&NQsu1p6BX)P2M8%GJcV6xO694ijF=NG#WZEX{99b zz?D#kNT^{mD5xW{K2Zvnnx|eKhrG4N{s?ZLPcL^E z+qHw}N&T8jj?q#kQ97Ky{sp9Z@;}uvueWXHIypH}O*6 zlg&r%Zl}v!kZ}I~ve;nX{JU5bF`P^Nqe#Y!D|0B@Rbo{+)x4aB@nC?;fhd4n9&8N^ zXH*#vkkWtgi9v5BNstqIW4uT#IhEw;rmgSf0~*2J9%QlL>qfF?{t?{JyMv_0W(XGp z%#yW&n0UGU`C^bGE%{MTR>vh33@!PQK#TaP}A|OUdNZ3FIn`^j~;D; zTsCeIyylgeYHukFt=_#1 z)AqjJZZy)8b9)x*jpyekYuH9pvVH_JK#iH~R?@&>MA{toeT~r(XS#QtwiaJmCxo=F z-(W7Gy1d!zHrfaNE2ZH;8_#c&_2z9>b*>eYp{b{F~#~u2O|7IBmt&@qzd1W=JbeUqOAwGq~~cD&#VC zv&#r8f)B288-}WlP5<=n&ck|ZB5qXcpqD<83sp-=V`k1Lq~%l5EK2Z=K;#dx;OU7@ z?OQo>U6DT7z-VsdCj^+`TC&koTyiAzrs(M}<>73jBKIwu1^AF6I_w# z)AON}15q@8630kHAq!5;c?~~~Sa)W;p&sl0VYg``+WV^yicMGLY%fS&`yK;726@1w zhZjuc(+?!sh<-IIO6PM{$#s|q9|In;JAL#kM%*`HSVvL+xmC(g zG@yZX(kh-PBPy}Pf~$H>x?6Y;7#-Q#w{WQ9g?5B4%SJjvU& z`67oLgT=_>Ks{L3x^H^ZbCC#g=^06R4awdJ1XWgBL^uoT{V_x$ zF=%TYdL`L*hv_a^P0NllV>A%7bxKPo!frma-Zwu@eDIGfuD8b}d0iW+N@l+v>Xf}G zd1<59aU5+T3XJ;+^1vkH Date: Thu, 12 Dec 2024 21:46:43 +0530 Subject: [PATCH 6/8] API updated --- docs/api_reference/anomaly_detection.rst | 55 ++---------------------- 1 file changed, 4 insertions(+), 51 deletions(-) diff --git a/docs/api_reference/anomaly_detection.rst b/docs/api_reference/anomaly_detection.rst index 92084f4c72..7db535a9be 100644 --- a/docs/api_reference/anomaly_detection.rst +++ b/docs/api_reference/anomaly_detection.rst @@ -3,58 +3,11 @@ Anomaly Detection ================= -Time Series Anomaly Detection aims at discovering regions of a time series that in -some way are not representative of the underlying generative process. -The :mod:`aeon.anomaly_detection` module contains algorithms and tools -for time series anomaly detection. The detectors have different capabilities that can -be grouped into the following categories, where ``m`` is the number of time points and -``d`` is the number of channels for a time series: +The :mod:`aeon.anomaly_detection` module contains algorithms and composition tools for time series classification. -Input data format (one of the following): - Univariate series (default): - Example: :class:`~aeon.anomaly_detection.MERLIN`. - - - np.ndarray, shape ``(m,)``, ``(m, 1)`` or ``(1, m)`` depending on axis. - - pd.DataFrame, shape ``(m, 1)`` or ``(1, m)`` depending on axis. - - pd.Series, shape ``(m,)``. - Multivariate series: - Example: :class:`~aeon.anomaly_detection.KMeansAD`. - - - np.ndarray array, shape ``(m, d)`` or ``(d, m)`` depending on axis. - - pd.DataFrame ``(m, d)`` or ``(d, m)`` depending on axis. - -Output data format (one of the following): - Anomaly scores (default): - np.ndarray, shape ``(m,)`` of type float. For each point of the input time - series, the anomaly score is a float value indicating the degree of - anomalousness. The higher the score, the more anomalous the point. The - detectors return raw anomaly scores that are not normalized. - Example: :class:`~aeon.anomaly_detection.PyODAdapter`. - Binary classification: - np.ndarray, shape ``(m,)`` of type bool or int. For each point of the input - time series, the output is a boolean or integer value indicating whether the - point is anomalous (``True``/``1``) or not (``False``/``0``). - Example: :class:`~aeon.anomaly_detection.STRAY`. - -Detector learning types: - Unsupervised (default): - Unsupervised detectors do not require any training data and can directly be - used on the target time series. You would usually call the ``fit_predict`` - method on these detectors. - Example: :class:`~aeon.anomaly_detection.DWT_MLEAD`. - Semi-supervised: - Semi-supervised detectors require a training step on a time series without - anomalies (normal behaving time series). The target value ``y`` would - consist of only zeros. You would usually first call the ``fit`` method on the - training time series and then the ``predict`` method on your target time series. - Example: :class:`~aeon.anomaly_detection.KMeansAD`. - Supervised: - Supervised detectors require a training step on a time series with known - anomalies (anomalies should be present and must be annotated). The detector - implements the ``fit`` method, and the target value ``y`` consists of zeros - and ones; ones indicating points of an anomaly. You would usually first call - the ``fit`` method on the training data and then the ``predict`` method on your - target time series. +All detectors in `aeon` can be listed using the `aeon.utils.discovery.all_estimators` utility, +using ``estimator_types="anomaly-detector"``, optionally filtered by tags. +Valid tags can be listed by calling the function `aeon.utils.discovery.all_tags_for_estimator`. Each detector in this module specifies its supported input data format, output data format, and learning type as an overview table in its documentation. Some detectors From 407d8bcc8772883e42cd6d12fc31216b2d241690 Mon Sep 17 00:00:00 2001 From: Divya Tiwari Date: Thu, 12 Dec 2024 22:23:12 +0530 Subject: [PATCH 7/8] Performance metrics --- examples/anomaly_detection/anomaly_detection.ipynb | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index 49e072249f..09d2e09804 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -202,7 +202,7 @@ "id": "bcfcbfc5-8d6e-4a5d-ba3c-b952c32dc8f3", "metadata": {}, "source": [ - "Another example is an adapter for PyOD anomaly detection models to be used in the aeon framework." + "Another example is an adapter for PyOD anomaly detection models to be used in the aeon framework. Aeon also provides many threshold-agnostic performance measures for Anomaly Detectors." ] }, { @@ -214,8 +214,7 @@ { "data": { "text/plain": [ - "array([ 3.89696881, 3.02367572, 2.09615194, ..., 0.42186597,\n", - " 0.33378197, -0.02893244])" + "0.9829922398772353" ] }, "execution_count": 4, @@ -227,9 +226,11 @@ "from pyod.models.ocsvm import OCSVM\n", "\n", "from aeon.anomaly_detection import PyODAdapter\n", + "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", "detector = PyODAdapter(OCSVM(), window_size=3)\n", - "detector.fit_predict(X, axis=0)" + "y_scores = detector.fit_predict(X, axis=0)\n", + "range_roc_auc_score(y, y_scores)" ] }, { From 0ed83afa3994b812548aaf396951b5b754b468c9 Mon Sep 17 00:00:00 2001 From: Divya Tiwari Date: Fri, 13 Dec 2024 19:16:51 +0530 Subject: [PATCH 8/8] use correct performance metrics --- .../anomaly_detection/anomaly_detection.ipynb | 70 ++++++++----------- 1 file changed, 28 insertions(+), 42 deletions(-) diff --git a/examples/anomaly_detection/anomaly_detection.ipynb b/examples/anomaly_detection/anomaly_detection.ipynb index 09d2e09804..9f393437e9 100644 --- a/examples/anomaly_detection/anomaly_detection.ipynb +++ b/examples/anomaly_detection/anomaly_detection.ipynb @@ -19,7 +19,7 @@ "source": [ "## Data Storage and Problem types\n", "\n", - "The anomaly detectors in the :py:mod:`aeon.anomaly_detection` module are designed with diverse capabilities. They are categorized based on their input data format, output format, and learning type.\n", + "The anomaly detectors in the `aeon.anomaly_detection` module are designed with diverse capabilities. They are categorized based on their input data format, output format, and learning type.\n", "\n", "### Input data format\n", "The anomaly detectors in aeon accept time series input in either `np.ndarray` or `pd.DataFrame` formats. The shape of the input can vary depending on the number of time points (`m`) and the number of channels (`d`).\n", @@ -29,14 +29,14 @@ "* Pandas DataFrame: shape `(m, 1)` or `(1, m)` depending on the axis.\n", "* Pandas Series: shape `(m,)`.\n", "\n", - "Example: :py:class:`MERLIN`.\n", + "Example: `MERLIN`.\n", "\n", "**Multivariate series:**\n", "\n", "* Numpy Array: shape `(m, d)` or `(d, m)` depending on axis.\n", "* Pandas DataFrame: shape `(m, d)` or `(d, m)` depending on axis.\n", "\n", - "Example: :py:class:`KMeansAD`.\n", + "Example: `KMeansAD`.\n", "\n", "### Output format\n", "The anomaly detectors would return one of the following as output:\n", @@ -45,17 +45,17 @@ "\n", "np.ndarray, shape (m,) of type float. For each point of the input time series, the anomaly score is a float value indicating the degree of anomalousness. The higher the score, the more anomalous the point. The detectors return raw anomaly scores that are not normalized. \n", "\n", - "Example: :py:class:`PyODAdapter`.\n", + "Example: `PyODAdapter`.\n", "\n", "**Binary classification:**\n", "\n", "np.ndarray, shape (m,) of type bool or int. For each point of the input time series, the output is a boolean or integer value indicating whether the point is anomalous (`True`/`1`) or not (`False`/`0`). \n", "\n", - "Example: :py:class:`STRAY`.\n", + "Example: `STRAY`.\n", "\n", "Each detector in the module specifies its supported input data format, output format, and learning type as an overview table in its documentation. Some detectors support multiple learning types as well.\n", "\n", - "There are a couple of functions in the :py:mod:`aeon.datasets` module to load anomaly detection datasets. Here, we'll load the KDD-TSAD 135 UCR_Anomaly_Internal_Bleeding16 univariate dataset." + "There are a couple of functions in the `aeon.datasets` module to load anomaly detection datasets. Here, we'll load the KDD-TSAD 135 UCR_Anomaly_Internal_Bleeding16 univariate dataset." ] }, { @@ -115,22 +115,22 @@ "source": [ "## Anomaly Detection in aeon\n", "\n", - "All the anomaly detectors inherit from the :py:class:`BaseAnomalyDetector` class and can be categorized into one of the three categories: \n", + "All the anomaly detectors inherit from the `BaseAnomalyDetector` class and can be categorized into one of the three categories: \n", "\n", "**Unsupervised (default):**\n", "Unsupervised detectors do not require training data and can be used directly on the target time series. You would usually call the `fit_predict` method on these detectors. \n", "\n", - "Example: :py:class:`DWT_MLEAD`.\n", + "Example: `DWT_MLEAD`.\n", "\n", "**Semi-supervised:**\n", "Semi-supervised detectors require a training step on a time series without anomalies (normal behaving time series). The target value `y` would consist of only zeros. You would usually first call the `fit` method on the training time series and then the `predict` method on your target time series. \n", "\n", - "Example: :py:class:`KMeansAD`.\n", + "Example: `KMeansAD`.\n", "\n", "**Supervised:**\n", "Supervised detectors require a training step on a time series with known anomalies (anomalies should be present and must be annotated). The detector implements the `fit` method, and the target value y consists of zeros and ones, ones indicating points of an anomaly. You would usually first call the `fit` method on the training data and then the `predict` method on your target time series.\n", "\n", - "We currently don't have any supervised detectors. Still, the problem can be treated as an imbalanced binary classification problem and time series classifiers can be used from the :py:mod:`aeon.classification` module. \n", + "We currently don't have any supervised detectors. Still, the problem can be treated as an imbalanced binary classification problem, and time series classifiers can be used from the `aeon.classification` module. \n", "\n", "Following is the list of all the anomaly detectors available in aeon." ] @@ -170,31 +170,28 @@ "detectors" ] }, + { + "cell_type": "markdown", + "id": "d2ce2a07-915e-4d20-94cb-0fc8aac96a8e", + "metadata": {}, + "source": [ + "For example, we have STOMP, which computes the matrix profile and records the distance of each subsequence (of a specific size) to its nearest non-self neighbour. The matrix profile can directly be interpreted as an anomaly score because a considerable distance to the closest neighbour might indicate an anomalous subsequence. We have various performance metrics such as `range_roc_auc_score` to assess the detectors." + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "efd5f400-41e8-40bb-8a8e-6317386c7532", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9984002133048927" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "from sklearn.metrics import accuracy_score\n", - "\n", - "from aeon.anomaly_detection import MERLIN\n", + "from aeon.anomaly_detection import STOMP\n", + "from aeon.benchmarking.metrics.anomaly_detection import range_roc_auc_score\n", "\n", - "detector = MERLIN(min_length=4, max_length=5)\n", + "detector = STOMP(window_size=200)\n", + "scores = detector.fit_predict(X)\n", "y_pred = detector.fit_predict(X)\n", - "accuracy_score(y, y_pred)" + "range_roc_auc_score(y, y_pred)" ] }, { @@ -202,26 +199,15 @@ "id": "bcfcbfc5-8d6e-4a5d-ba3c-b952c32dc8f3", "metadata": {}, "source": [ - "Another example is an adapter for PyOD anomaly detection models to be used in the aeon framework. Aeon also provides many threshold-agnostic performance measures for Anomaly Detectors." + "Another example is the `PyODAdapter`, which allows us to use all outlier detection methods of [PyOD](https://pyod.readthedocs.io/en/latest/) for time series anomaly detection." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "743fbbaa-a7d0-4f56-993a-07453f6a9442", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.9829922398772353" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from pyod.models.ocsvm import OCSVM\n", "\n",