From 994b81809cb229d6246f25ed0e1a38e3b5148165 Mon Sep 17 00:00:00 2001 From: amrit110 Date: Thu, 26 Oct 2023 14:54:09 -0400 Subject: [PATCH] deploy: 735c3a53f32839eac9c2ac40d5709826a30ea379 --- .../tutorials_nihcxr_monitor_api_10_1.png | Bin 59653 -> 73603 bytes .../tutorials_nihcxr_monitor_api_12_0.png | Bin 57307 -> 53252 bytes .../tutorials_nihcxr_monitor_api_6_0.png | Bin 46181 -> 34635 bytes .../tutorials_nihcxr_monitor_api_8_0.png | Bin 57427 -> 53743 bytes api/searchindex.js | 2 +- .../kaggle/heart_failure_prediction.html | 64 +- .../kaggle/heart_failure_prediction.ipynb | 520 +-- api/tutorials/kaggle/model_card.html | 120 +- api/tutorials/nihcxr/cxr_classification.html | 56 +- api/tutorials/nihcxr/cxr_classification.ipynb | 190 +- api/tutorials/nihcxr/model_card.html | 20 +- api/tutorials/nihcxr/monitor_api.html | 18 +- api/tutorials/nihcxr/monitor_api.ipynb | 124 +- api/tutorials/synthea/los_prediction.html | 120 +- api/tutorials/synthea/los_prediction.ipynb | 3280 +++++++---------- api/tutorials/synthea/model_card.html | 138 +- 16 files changed, 1951 insertions(+), 2701 deletions(-) diff --git a/api/_images/tutorials_nihcxr_monitor_api_10_1.png b/api/_images/tutorials_nihcxr_monitor_api_10_1.png index 4bb3988a01208eb056e0b0743532be8f21c3e086..0d2dbcfd5d408816024eb5a1bc517bf72d9f4470 100644 GIT binary patch literal 73603 zcmbTe1yEJ(+c&yVQA!k%E(H|@1e9(A1eBKU5RmSYk}d@S1%XY7Aky6>-Jpa>H%NEa zcP;$C?|J9Uyyu%a%rK7+?7i1ocU;%6ZUSVaL@(ozatqVpL$EEyRBTYFD>}$G#&@9MzN?&#I*vwK>8qD%k8$7_PdnA1JRsTvc|GfXdueUhz z1ZQ9CR~ckP{{5Qn`y--%KMYAYr||DR{GR{)LahJig?~Eme_kyaF%PB@lG4_`K^)Yu z#7%qlbvaOnbb4-%kc7mq@6Cd}9MN|U%cDK3$*HN1;G1W!=uV*GvG$(rh>5qZ#_1); z!o$13aQe{8(^EogLUx_4V~= zE83(USr3M@8=>ERVMR_vp^hc0AfWW%nzBL%O2 zowKm8(ACu?eV}`RQ7J2f_nUrOgfdeEp?MeoCLT)5{g^{jQ`2;~BzLeh-u=W0K21zg zGSe!~YN}BuSGi;{t6b`jLzlp=cCIohm-#UEp<=e?`woMtK*lU?ov|9POS#HSrh^6Z zNnJedCr9B`f;2Sof=*mlYN8)?YeOA`1Z`)X^(Ybm}5ABzfIZgk(EiUHb zvtQC~{dliEUMT+a=g;}A*J)_F{Ak>oaak&4IEKu`#j#J1=7fxXB{qoDusN)#uavI} z&UVJOtxq)t1O@S>&*9jSXkqmT|y}Z1zQ!G7O>McXFI0$=i=wS#h6bvhtgG?94z()?h)NafL`M}ozV&>QjX&nug7~m|1EGsdEFmAq9&^@ z^US0!83s*RrubDH-_I}G^KH&`8Pi^5}j*QK>C7j;o z@6Ru-{RP&yY25a1zKCGrnfOy`GxyM-ofP#fm@=Se{?&YUG9DFg!Lu&Raa&$&EUfQE zWe*R+oWxu917R3d@Qnp#G+&#Cpke(g4^%+J)OuF>?bA7o7~(b&${#rRlD{M_(L22Q;5h^n8r;q2QuR=W=tzn(irbQD#+X@ ziAJxw(8n+LrV{%2_!w?~u0B0p%}~zugpalUB|>7gJL16RcC?o-*yT>aV|`0sU;krd zB%5x%KjDs&y!;yMuZ^S4u6)=BX+u!9VUtaHT@mD-n=`-rUkj<`D|!=tL|2H z%I<#hZN%i znf&?XsRWukbS}<0*xUO8%AhN*!xIztGrQ3>mg=L2sCd^MvT%B7gZ?ZHj#k6`0a!eS zeetLLky>uzQd0QEBlbhs_mlfI9H?yOIx~yn7EYPz&i{lLCFJ#OzgjGOCL*F!_{N+s z`D`7nE31^Fy&b-*IB*iIGezzxFyFSxvy-7o`)Vfx8}IK>F$+t@+PTBs)s%_~^xmH1 z9f{ADwGZ#VOp(%G=uPb_8gZJ7MYi9t{gBz#>R3GAO37E_`1p7lWb436SI}^cm6{U| zs_((Z!Qq{$LYBJepPX>}smhhI#m{Vp_-aL_sk}{>xrQxktwe(bK1g#7vQhj1q) zDamnbgN^uWYP*sP^~S~q7CwICylvdh<=ap<3cpr-_w4H_&e`3hE2z8^hw(Bz=WwB{ zOw5(x-o2YVJW=)a!Y(extTJ2I)h`~&<3ut0=RCf$I%vT|)MZ(=DN~@VK#Rfjf#;QqV-tK1I?8WN@p}fgw+k*eeY>|3|xHn1 zhnAm8RXFGA9~RF<6Uqn+FQEU;0@g&VSHRzEo-^00rI-}`|B^p(G?a|KMcb{MdWJ

WnDi^)#=}${LnftH%iZ%;;=sF;AKwG;DM_!HAAXNzo5Ef3V z`uFx8{gvjK^aqT0pEEYl*B&M{T_{?6xtQuNMwg;*005$1-iRW9Z>N*v|6wTv#`%BV#47`x(GJE>=HQv9}gT)FuDu06r;oBog8 zR~`$`eK2nFcEGt~rpMW&WRNrc<+A$mzKF7!u{aTd(YxLTCAgeK{^D0J`RnEW3)HPY z=bYnZ&`)Nj#l234&zHx)M_2BfI+;ixUm_w1ee>mP93>_gY^gU5+x0!x&%8u5N5=3wlq%rO3AnJw{qs_(E!GDn}=q`EW_N zV1JeCp;Wp1baN>E^zmBV6=`nwxz1RC0??OFy!1W}na}vJ(%X)<=UvW&Qxp?6jZK9VpB}Znm}(Z16W)=cC(wAZAeiBg zrm1^t$EY)!{^1h_fvn{s&A2D-m*6`B<*-riGBQrZI*fi%NLNfsPR@P(i#9JWFO7=J z^}rT(q>P!Gw)Tuq-v1p%uv<@yjY0)B9-^hAlP(_{DnIbHT3=tUINli=hTf(y1pVTv zu`$ER$qBvdsMF@`dNW-JtHmfk(ogbR)qB(nCC^YKDn>Z2XtVRht4trqNXVs7*}|DmAzJ&+;FR572+Hx%RG_ zjM_gx@KQ4` z^bO_G~O;tOVzr|PUwdu*GN5JqaUGkqW&Qvml$w=#+a7{IE=aOdQU|%Ydp8I zdeD<1JvZVorr(#QfJe%jplH*NX@{VB`qIk{Dh*>Jcy zmCv_l+>V3x!!hPd^GAz|TXmSb$6}WkDF`;Qrky&>aXMNVbaXlspZkBI6odZ_r9=t2 zyH(zIC!(V2@F$>ioQrip-DO~y{2C&td$hMsFW|N^TsrJH^`4s&-@Ib|89+KU7M`w1 zRfL@+Ns*a*DL~Cztn)(IQV#`c?mE;@xSI<-r981$pD|ZYHjm&h@fdUCCk;}_KM3rN zd7u<`$}?;(CY@Tfd`0g@m(yf!yo=UU&)~BCY*&1jH#Q+$itFz{K0RQ+3115H8~5&| z4(5gk9^8HTO(M0(ti}@)kDUGc!bt;(Ry#E2J=g^Mf4_!cUAdBN?_z1mx;#`Yq?ZM) zDeUdrw`qrw8unzT2mRGjr8MKYP1IBHonB-HPsYDD?!|wyH|4U?LSJA$61_4~9>3<| zAVaf~{4Kys*4IC8oWQ%|*7yy>59LdZ!~Z>ZXGz-#ldG+szLl|uxkJ&{-Fm4x zb@Vyq3}RpYp7Sx0pW4kzO=GI7cg_Wgc>(6`w*sNc0l?U#A=z6CLteIMMc*y?_fGE!~S za|gCy7GbAN1Cf#ppxQkBw$EG^qxaOLh4#kJs}`F}7MqWt{*G4iVPj(>$j`9-4fB8B zycB?{Nk2}@wU{YoQ{p$H{T^9UYI>V_vAd;S*UtSF$fYxhALwk1bojN=t$Z#f-X-iR zL8|`hX^KIUWZ9))YQgQ3=2Nnvy}`q|xOw-zy**Z!Jxc^95lZvdEEkV;78vyaWl`^Y z0Nn}xJzFpr#j10z^3C?-dJ_$TyWWv=q#2to4y^6&3N72j8lAE%+4FIYxv=$olhj)t zEOgl!Fr*hShm~hKRz+2Q+;82rY|jRp-n@LdNVQFaJa9Q~U{+dS0U2 z%*;5M`&c9;Mvd?nhj*YZ$qVYiw>q!q$wJ>uOsVC^bm0Gf`H@1A@sBA7pT+&Z|99ji zZAKyF#{VLcc_uAAiK47xQe54f!1h5nF!icWhFCuc@Y%h=(?nR5=M;M_T{YLQh zYdvi5bh5W@n?JgxsJ0wTSUAXp7c~;zm8B{Ht=#^EE=o$z*x{KB0nZh8^aYz!6Jo8p zz3pnMa{10lwKgcEdn2oEO?&H8;?JMcva){IUhHQx?4-;pTSzl*nw^`A&xuO~MDzIZ zV@h6I{{TYzjbWRv&<`I_Kb8jaRRDH}-;tc0iPX9{p5G;?w=vx^r?K@ZA|mcXgLyF_ z0fAL*E)}2sEjBhnA))HUM^UHCwEO(fhjM4Z~;oxO9uA*S>yG^w_G%K0h?Z_qM^HhV|W> z7}=M?oj1M^@o)1Vm84ly^|sq$g?O*;K8nY%%fmN*^a$m8xHIEJqN#m)at!E}5I7~2 z0IAP^3<1nZ#PSvn7V)iCg!gN?#}1j7zXxh((?sPUzr4A*sR!*1VI^tl*+5Z%SkA>d z1El~7Kym0dbjg>*Ri}I^JuI!Qt&XS1yH2nRq{~iW+aN^EaJIdb>?1A?4v+}ZLOs|q z9q^XUdrD?GqzhMUo;0-94DAWHbTjxPv>Tat7WWUyx2=wIk_j+)(?Sp!uCLqwY0WsPm%xayFzJLUGb3} zFU7g5x8PehDWdi8Saf<}x;XXVpfBOBzu)>cM78Yy_nI6_W|U(2rv&0pZ8u8VEae9Il0rLn?ADyAEP@9OVdokJOn&MUl_iFB+C*6>cp1jnA-uJNkp_^;@N}QpA zESV{0EBqdrFudnXd1FolFT#%C)1 z;h)2Oqu*NB2?#=x&VW*9z=Utyu3bWd$OAmrERs+SY4wooDjx^DJM%lOA~XH%!_Tbj z16lwXPMiL_PJWns!BvOSn59@Lm%2(a*82fJi{jYYTtl3BucApGsc7=F(OaC&2paQj z2;}t21^Jt+pE2t$Rq#^1Iq?~|FaCbP>rYa53?>S-#kt+&7bo37rQ*rfqw(`bzBE4 z9_NuANzB-v^N)s1CQG&DNL)MxoCWHj<=rTe!D?{*oA80aXx~)OOH_IBd*Y{w7~k(&%WBH&SkuBHApAFc(vCIcVg1?|W7rca;gAhn38axeMo^R$s^?rxN_wV1w zL8j8K^Sx}l`1}3X(JWWew{LM95AglGK;YVdewF9EYp&_CE;9ON@%}f-7{%q1goFg+ z{VPU^qhG$bQs(awv;kzlB3@^@`0@^YUg{QG>D0_|}Sxfc@Giu=!r)u=e=qX z-!(Ka$a7fLgb{lI6dr}BirJMrVfd^4NH#(jY>NeUy?p z>iYWg82eePYTam4*&VjGzH(g#EgRUN`=xK@axpZ}B}{?>npo72KzdGD#e}RX+PSof z?~@;k1Lc(Ztel1T+UNS2Z=Z@0_?BD8O!!vR`M9_(%h{hU%DnG%W)?iz)8n=M`{;0I znT*W<50tTVMO(MEnoBT~6{=PTV@$+tDgtA$7D?e{Jo?8Xj*i>_+7Kk1oRjk*Fc4?d zWm7USG4U^~?Dt*y7Gr|M%<9d6!_&-I?%qZ8K;Zq*3>k_n#@sZW76Z>P*@d^0OLA4E zt{f_S=;@WVl(|YOtkvu98s69o_3|r>iIzcSBNs8coe^E-^To#a#7kP(ZQHynTdFjB z|HJEN6$2rF*_dLEdc;E>s9*GL({3P)!CEDi0Q!m&teWO~Zrv)yeNv>M6l>S`AAKw# z&pGR#Jfp??p;L0sybZZLxqKfDHj?m^i}-_i5fQvf!L^!nU|ZJTQahe-?`R9^Dzwa#kdxD?&pVuDeglHc*p&`LAZii0UEDsnfR zmh3@T?HDe#kv=Tx*K~d_8N>O&?Z`1OFfeVo916mp0;89OLm(?F6uJXbt2kUKpZhe# zinmfaM~ysAl?%_}@60I>%w}5|vv1wLjrq1SmXGl2)ko0$0h_B7Hn~G*1|2-q{bV!V zWxG#_e|OkM;qYL0wd+r=-aKGfgbDOW3*CPBFbpu_X2rvP%tAz2W4Chh!&R|dWWWg~ znjl>1c$+F-P{F>FQC`Avq2r?Pubn~SrQWxVDRO}#;W?3y}MC2D~;ITmA8ysnaS zPmp#cMi`OL72-3cB>Pb|(ET0D`ER#>P11J73s1Rh#SCMfzNVSO-k9)Y25({__UUUY*)5*fU0VhQ21igAolf@WN`Xf;C2w zL2r_aR`b>R$i8+1d7nptF>S&ZnrTM3CV6$iqfyZBE?iPt6?6gi^Z@Y_$`+Za z9mg@M4i*@hOQ)|vrEW}k?1gptvVK>b0Ep4!-icWt$a^Pi0-1n{jgpAtkJ4)jjxCC@ z0099itwLsmTCw?UK-3_^TqP!c2B;JJ^5vNbHFF)nn|lWb8^6^oLeXfK)e_)<*a+@ zw(!4+)6Z4;Yj4PB@9OyBMB4Ut+EBpAAbI|{BF3xLR=tsYzQ3C?^h=`*aw5iR`5vL_ z+w)iVRu$Bku8!vJG{rs9-yPMd^Nj^HO5cMQ@PCo%z!NC-LB#hbf&5NG=Q03n4|d-* zLc-Feyf(*ik4tF+_5gwvG~T-Zs&aLpMcphtNQp2XuRSv`FH@u#$B4Oz?i8Ys5o~Kb&LR{Y+h-IiD`T zpWW!+(tT^Tx@5RN6~j9Gft4wuD#W2dXmGdvis?CKmxtJ^+L!kIaQD|ssQ|(d7zJpt=lzClyrp$Lh3_O(qVYFnI3Zj|$Qt9XwTTH3cMQMyssm~L@hx+v`*y#KBp3s`U#tbmG3NV=x%L!)gxW!;5TPZN zeaIJ=V?|;StsfPB8-7qly33;cNPsGcFFhs&ek_iBpmR z1?Ar`b*MahaU3Hm&QUGxHDQ22?R_*QvUQ**dN$N#2aVU@X)ZudRfhTv@2g!gp{sDc zszc?afR6k6cXR>;qALr};f5r*&)v3v)n34@c`!a_nMluNY#xpdQc8%$jbD0JE{m;f zMx6hZLwPyN?6GMz77Wk}YW0vI{m)e5Nj2gu{H;|>9Vr#bB-EKbb6S;eI5=b8GlEN% zbl3*O5Ojul(3(J81iIIgsmAq9BB~Z_p7dY}*cyPByoUf_b}@8;)+r8+Y<+thX+!B* zWr3joDe%p{>e_^|AGbaXMF2>+0MO4#(04(!IV<-&Li^L<^yuQJe)xu;s;j;SWy74D zkAPy0Ii(cpUUD^_mE}MvRCuzPn~9hxWv)&)m(|hW%E@XVP4j4M(`@zfm!^^Bj^Nwf z+DRnTGo7bfUxqc_wCYhRT8vg~h$bGz^o#oKxCs|CbkqXb+kGe;RE~G%~Ue#+WboK!^A%D7No7CKHo9JHZ-K3<%9906P6^5@#i65-{ z=YzVV(fwveyw3JM<p=zhVH zYdVl;e8^n(msZpLc-wfWwzf9y*D2p66g zL>@c<(X%=4n4-eM!;x4B{92(26S_gMg%+%1*Y0z6Ky3K8dtVl=1%g><+*M-{lD}7T z87zRM<0aP0lituD25tsT#@9?WInPD!%UNc8{WO zT`+6Gl7zFZG?HX9Yw*I`$c|#;p&Z3(p~cPiGtv=g98UYcQjQtcfKz6E_X!YoAg0jC z+jfeBkDvAC#O?TifSkM)bf3oefbqJgfF`y9VUjd5x@To&g%}L5o_X~2^sKlk`|hYN z{K=&QGz=jC1(PqwB4T1#Ai3*}Rk_X`M;ph8+O2oAx7W{D6HrlUm^s_pvcqdrT+lu9 zw{F}(v9Pcp5f4qbeSN6@_%hoi`8;#eE#X$Yu?oeYyr8?AS%HX_X7gp0VOA) zTNTw_fjw_#2^Tun3!|9Ic17(g+>WIHk9nZ6DgGvsv4;5>jVx&o&gdRH4|G zeLHYufjiws{(sLppvD_1ET+L$ol1#!NqU~kL1bIr|UaAjpoJR zD#WA>v0yGht7rzD*RtH5s=VB=i&|GtPg)hs-dl1ID9C1L6$lV5Oo&VW@#7J2I^oBU zF#v1pPSyt$822q?Tx2-qvandqkKcr;Q`*~2ygd-RWj<^b~UVc ztsAW>r?pboe4DOa{Tg(EHf|bx&|3hQ5ea*IeBAgDNy;En7x3nW^H=zPfCSKRJ5s$C zF;3WDUj+dGgnU0tTw-~X_uj2H-<}SYKtBrb_eU{Mlmt`pHNrDHcdK8*ryK{LZW1;h zf=a%MhHSL6b&W4Bwt+T={{sj$4J5q&_Gi-ojz-EB6!&mH>NkH2viBiAf&^A3P7YV+ zK(NqT9j!!uD+H`Zp^1O%=Tmj3nNPQF-9l1=Fnqop27LN$nNhHx-uwIiFvt&$h)^_h zR#hD|N)awn%~X&Ch0wteyl4OxL0_KwDGD&(zn?ispOh6m~hd_ zuYX|^w1de1pPY!kfD`nhIDl;!AHu?{2UVdXNG9B}$XYI}2U{VwAqp*C!w& zYx&`EUM1h)a%gC1$|){~M+0|HMZshi!|@0pd|Unn@Cclo8_ZC+W$YD*YF$lJfNQ>^yFOMS=FoJF*rmfAavdDbVP& z{K5hMF!pLfxI=%~K{&{eUD=wR<+a*B!bko(K79ervo|0t>>5(-BqTDT>*q`XS!Qzf zte)a*;Ae+($TaZ(vnMvb6U)B^bIH2xe(}$82MfRq=Mb3`2NyTpU>h7HCV2ex=zAtm z9=BFTVgOCOR8?(-p4NdtA&$0)2Hgp1_X9!%ahVMj-MVvUqn=RuDhWwbpOTi@?>6uW zc6-_81t>azh1q1#cH=84_Q$}Bm&M@&M)^{a6`iL=|hr3%%}5;FMUtX_7}UqR>E8rC#K;d0whZQW0~^N zk2k?gl2e|3d;~pwoZVMbTmCXZ7S;qGDwti`H^KC%7uY_wMME)x(%d#44^Mx6SOy0TIa&HRaJm8Jh^VQe9r4JLUsE8^H;CwV6lP5m* z(Q9*@IQq)FT0Y@64DmY1h+$!4-xLs_LJ0{8xeB(3|95~F#3k+g*u!D$Gm0!RNi}&R zN%Dri4-wwy!S@k$dR<31*2P9nf5uJwG_~-_H|K#>vS;Gj5qwLI5wf)8`JD&h?qSO!~%-#EiuRc&hGrVHRiUj?Ibw*i}n#GomR_gW7h zI!JTry?fFXpVZrX&-Q>E3n$gc;3D!O)!_ET;|Tsdj5__FE|%jxU(2HkbX)&tY!-2n>mWG4xjceYflub}%WyMK${Q5L+>b{F=qZt!+lJx;IOPD_n zF^@u3!7$qhA(D+&k{Ydfk{I@6o-Y+wutw8Bl#mbtCOBha7J=Q*l?C+D|L2<~vn=yX zH>8i8d*y<&J5e{1>`78#tB-pGF}GxhWGi38`DOlLKd>P479SsGx=`8HQuk zAu_W}5VpH{)9ifip>gfex2*ky*Q%#QJfu1Bq4FO~V0HXgunnEEk%&`O?tK4J^@|$g z;-7P0I&I1N$;*p7mR?WZJP;Zs0!EJ$LXvMEQFZ|PGw|%GHf18TI=PCoXaBU~nT{$R z2>$-msi&r6_m@%8ZH8*80R$tAKbr#tIC6mXp% z&uT7nLFG40;`9c%md7>b;N+p$Wh@gCF>%Fv7`auT{SvHmmsK#*(pm?bj&kA^dnkSI zLPU}w69shZ{I**H9mp~mcO^h-^>03vLuS93rd-&`#`Z;;@l7N(=#DK6iu6x#D zR|9!I6YU0@D>;bty)vMCRaH4*LZ+l~;&!Ssl_G7K_h+gXA8;R0O;a&VH<8$X zrVEz?a8mIE$0ecl-k@19arg(X<=I0*={DJ zMy8C@EWUtbimYYD=aZM=Yav??_`=wqm}MK)d=W{#P>h$rUp6FgBJ?T$d?jPTReO%v9)|E`?cx zmZysYn=LvyPePmrd-fv~xsTWtyn3^LI0`-AY;7K#c-1ysiKwNi4#?moiKpMq8b5UI z533mGCNB38B;@AKE3TTfO*0~K~4hkEl`jFpob(OQb^>4*blwn&hG7t23g)A8K;?0B2R3cu zSY91{K>GHm&IbU<+1(YNB@o_gF8DH4RoYyh!6fo7z<&ix+JA-2+@2|vJa;hjk4r{I zOeK|4R7~6K6`4p|0;7U|?;89yGtX3E#(})c0mtBPzJLPyxtCta#GeH`x!(qPpby8u zhE3``TL#Y-}YX>oZ9e3KJ;4<$M+pi%4c<*JT zlXSnvEXKc8q;<>cn2v12RQie9C5UKDqwT8GscxE`yVsVjJ@AzRa0f)Hkjf+WRgd%{ zQ8F|7aC6?t!uev?inxr8d%j)(nk2oh^}~QbuJQy_*PRnJ;0;OZXZrtpUma4@H+oz_ z^jY+V4%Embu7zwo;E580hz;eF{-MEhIrg)hphBOGSoy!}0Q?pbEcUw0vO?=rW7W-R zK=^Sy#KAvu;FqwmLf7FSVQ)80261q92P@LAGrJ$M!PAVFYFtcDvCshv0^_gC3&z1y zW(>dTs@bIVT&Drj6IHE1w&zew?y+S2bk-0izl;dyVqUY%4*6T5mp?V6CR@`+jb5?s zzG83G`$QGcbYKjubwLjD+o0(>f5_^oz&{V#r%gIfSup@&0{ZOtQOeKa!N} z&G(IHKdQa`;FkI|6Z=~Pp__>oR>-9)o>oE(knosh@p;OFOnLP4aKhh${@@>6(4Hyi z_(=3I1}z5BpuaKd01Q3?h_t>k9qxQ1{-GgHt}E?pch_@@G0>!Fzy00Rra#hiz1PX4 zKNB8<%%-*G*(iHK9rS0hP_0qo%Fc);y;EVsN}L|}(x{Bm&HiH70>%#ymJq8~D$$6| z&>4F+09Vm1XE~`2d?MXk&k0|D=9|h)3{oQCEOxIrV52fV=LKxk;vh>5O@S=2$@M&Y zo_*`HbQHG#rv6$xbpd5oi?;BI+L$Qc6v@m}*=N{?RouD=T(L<+&|dx5WQ*3>7{1>! z5lT{kota(ADBy`;DNkBeoTEfSOiq3{*boa4NyRNMj!?fA>MMc-|0Z+0x%BhLe=B^P z`Gkzs&8{7JD+X)wToU1*2V5Pf=8+f+@TEfYQ;=X3zoJYV>PXKeKmtequT3Q+l8s^b zLkxEdr?7HfevoVbR=xZHR#&r%EKXdtgropb$tGWDw-mG3> z_90)Gzu5b;jvJD@pIMpl#u|4qe(dpYW_pYcm-QfMP$0vzj|bd><#3l*qFYuiZ7y0mRA~ zR6qyz-p)o{QuO;2IEjOQ|M|^{Fl7XHpdTUKtT*OASrrr_fQ&Rfb4<}@QVeQo5stoC zeb?f|W{MXlnAocC^fDw0bC79G`!!<>EtJG3wZVnM025@A>y(U%-02_JI$>%T!X5lc zFglJbl}MSK9M79a>L530e74hK+xX;vL{@7BZ$84PA{fPUDdP*Eo6J#$w$V!;)KEJv zwEJVlkj_wAFlO|R+5dQ7>u7jWq@JRTWd-S}vew1Uzfy2Q0Cqz6tNo_EZU;FM6lZm5 zB>MdR9r|ko`=5~cO=W|8{ocVW(RIU**FKJSjT^q{)_wgBx>|I_PW6i$v!0g2`sNXW zB^Y&?Syi-?WdH6k_i&IE56g_A8IV-KA_a2u6LVQrY|gryxnYGpEfLkL9I#li#=l^K z6;t)RE#f{L5%>aH3ItVFJ~mZ>#{i*l|DQi)K=UJm1B=*AArxHrK@D=toYvFLWo~3( zp+*nsk26@=OuQT+-B4#qEb~DS-N_4W8zE-L5;mANEOW91MR|gCENZ0$H*lJeQ%`WZ zD%Wc28CcehocU~l`Q9|JE&Re%+L`@(06 zXTs#k6&3uRUS3_wFP-FZja5|0C^${E5j}h;Ly-|AbiM6`J`P?`qX+<-diyA>J}-LJ zat&fW$Vo;>x^Tfk(iHxt43&I18I@Jm(9{$^gnNy^=t)skWeAb9>ACZhmqZCi)EZL7 zo)Qf6zRk2)oys?ffb7ao7=dnYCwY(PsHlLp8CqW6vca9de_w;RV`3YOoNO#9DKQpV z0W;PKbW9aEw3=_&WdH{g)$IrLOVqQ%F0i)d-vE`FB5QOuVQ>npb zqGx0+NXCkxTjo!N=`<`;4%HH*3UXtp5;z4m+u4xQl1wUj7ii+O+(}1} zGbrvWkO6_v6>>}f^f}9x+mUEXYKTQwR8}tD2DAOC0qI4A-vE?E{akO1*oyu`u=NEO-dR&;FRY&l92w9vudBh*Y`*N~x}{RJHji_{dFc zUHo-$ZWOY8-9{m1eQC&{-ld`9)e5#Ce|CLmApvy-w0&jfWmoEzj7j+<4;&*Ru@GlI z)Y_Dmz07AtQ%IE>1%g_;y|Iy~lBTJYUCW@y>qSE2|Ui#z=o5(9Rew9qj-2xm!f z`oPd74)o)&PoG3gO-(bWi3yC-AVNf2q;)}H#Z07}MKi$^EHC)WWH7&!wCwvhE=X7M>zI756#I!@j02t;kf3IofM z`o3DxEe?)PVB|s=5_R|P-Epwd4M0#td|5EutRRH?>zgFSvv;IAttQtAjG7sqtCwh8 z^f79H+R{B&BV}9umCD{C2AaS$zsjt<*ka6WE0c?F!R@X*kj z-qe@i?Ic6C4Wzyt3Zj zu1<-V;4oGNjZr}9lk+^St132~Vhyl?bj5sAAQGDtAarpG9SmEI@GzHx~}aNs?0yh3aD zl=zga5*>z?gQ&9@;o097kh*p?=9AeKsUZ|eGYS~&-P^C&XF4E?zZ^AHn| z)s(?_boAl1=e2VZQnw{V-b~1DJkOUZ6>8=~-#wk({w#)ibDT=&S0#NDqN!~(rvY#T z18E!Va%Y6=`36^$%EtH`2)vN7=U=J`z_`G<$p)mIVBSZxbW_Onu0#Brhtz(3FRvd zieZ}}n|-mM;ohUt@~wjV8FCJZ&b|=Lp+#M)mVCeigQjTw7`AdzwZmHu_fZ@Kd~e17 zzI=L@Cj4c9f66>tF5v`~C$8e zKlm9y1Fo zpJ(rfjjZivD|3Vy6+W9 zs%VYpPHvcx$4~5!=S|}#dP&tK^kXx96 zku&{P9@_dyxBa-TP{WW_Khe37!xCuRg~#0 zcF3Aoo0R9QDhPsvLG9orolQ9_hFR-UjrvQ)Gp+Uxy2ZRw&KVuLDfJ5eZJ8}j{Ew@f z$|fl$Bz26j)7|4tC50Edq?753&?j8y#cUL)`(?fhMZ$;6M5{G&Ib_|`v%!b#+wmQT zn_2F+&e&16_kF1olFBLHP(??qx9PmY4IEUfJj=W(%nq2G>F=k9GjI9YHW>6_60<>S zl5}g1+QoT}eCKFPXHXrQX*?DJX33&BDioD^ruaPoe}z8F0?FQ|IP;F7G<7hF?>NOa zxx->AR~g`_)9Bd4@GHdvqLJS*-mR`*4|QAjK6r+4b!P0SAViXdA5NHXp5z^vQ&X=w zTzksbFZzndXYb~(f&)o5d>5y8+g+>UtyeC^K&YzayHWp$|3%yT(ug2Io_nMxfiu9E!xmd2eK0K3$s(t+A9wtT;Bp*-!*$+bJoPv=JNjxP2fmwv zp#|8Bnum{PGW5;cFmDuST;*gb^z!fZDZI_fNP&OK&!U2RnC*UIQLP>rM>s+Dq60ap zL+=MsI^q$wcc9|x!YP~d|14p>pe%zIgBxiNz6t0u^QKH*zwv|7o8He&I7v(2Vcc~f zrZ;Q>!;|nl>+nUjlPVG=mGk6Vce+upO2^kFO86@CZd}f-t*luyXXmGoEnS&+56W|r zvU;pWn>a5a`WwLZD3TE@wefG?! z)r$koa zW^1XDvl%YRz9RlC2HE4!ZtC2stxn$Fp2{SpHaL-L`I-8}#OeSy2J_9~53$qMQ%?B9 z8)vnQ+>bbUZ8_E?^{%~6uId_&?bYFjKbDlcy_1P3zYQxqw_Z`KJ=S$hI9~rl-*x@;7yXJ{)@uj8rj4Dx0~@i9!m~_eV521&lj|$G73*SPRpe+|uA|$R%%A(1 z>5uDqE_XDk z(>t0-$H?G5vOv|kPWfYteb1jWrS6R|dMZ@kHY;f*f{}`OiQ`V+E#e>_+!TiCo~lE={gLDMtfOTX$(<9QTlLWF_oYziI@PnlY%yt?H+|qW?=Ezuj+_kx)b2 z;JJ&pG>MxMq6F4LjI?{U#lMDYpc_9Yi4Tw3T;#efSv!_~()d`7iTH_Y;=b)%o9T0n zT6g=?%+WZtG?UFCM*xns8ag>kzup`FBIfb2K0iBG^ZmNAA|B^>@yXhoTf?MSZju?g zCga7g8!JKOa+g z;%@AH*znM4oR3N9h)m-v%dHgd%28Fz86_BEIPl-;;EJ0g^a2I_N5=@80)%}gpsx@0~onih$zX$~}SW$3Z~ z_T?@7E_UC#j3M&OS2=Z=|MRI*W!~7x*yy*vyD1T7D5GmNBMJs_jE3Ar6w&X?xe@0VnYw^+Cv zbCeFR^$A>$i3H24Te$Qx7qbJ8ADXYUXsXh2=tvxEUfVvycuDaRrhX|C-_||U`APSu z{k+Nx&!oEe{shHqj&%g@h9eqm*;wBwV7G1NQc45;x`Xc4!;U3OldI}0mjsPDGZK+S zjB%GG4H5gTTof)U`uTvVFKrvbnt|{7Rs7h7!hF`;#q%hSGRu?;17LKLRz*|r<`RK3 zL9xAin4Hpss$f)L8}6Fj>-Xaa#KW|xueI7ei_Wik63K1`9Jt?*d>TM8JEI`wE&qQQ zd&{sa*Qjj}L_it?L{dSzrCTILxjiQ|h*GVQvaWICl|W z1?*UyE+yRiE!#Y2PtL?}S1by~-oNV|u}i#ehiwdXUh`U5rZZ4he)#ZH#Sdy|m7%77 zectbkUH<1>W~uxSQNDt2-HmpF{ej=a-N>%COkN?rt3QCy>9@T8<@&ixX3)K}b-~iO ze%J#3z%QCdcU64PL6|>8QOUPvjYg}jQ2_504?bsW&pbNYNVfNTWv+EM$wpFU%1r85 zFneu=y5F`c0$O7Z*dr126x&IOvHL+5%!c=k!GH%a2XARyx%urlmSZE}yqh?PFq(@v?L<)auP@{y=mJHpt ze2CE^rmg&%n+9lqhe_jK%1VA0?Jdtd)g_kFudai(#VokXLjiL>Qk#ym{#MYg_nTWk zYRV_pi2eMI@&NR1eyTeg1oAHg;hUK?m_s0hQ>lt$>|l71kEcy z0`iie?#g1V-086s3FkH)%;z@S+zi?TG#O_6@Px&TP8#p((>|mBKKLJzG$HyHk6=6s zPlh^SMlE3l^_|%OpROz5pfl^RJh;2}_|Y4iQ=>*Rm>;GejH{1VfAlH)K(us`X3gLb z>D4lygq4)4X2PT8_lZ+LIS=C~xbb5J@3)f3RSlfQYd6-RCqK(4isF1=bT4^hMGw*e zdS3M)hX4)l+J8^vA`I$(E_N<*HmOzSCF4yK->Lch+;Yt}6$<{lcwxod5e02|X~VDO zk1c4`t0bVLfcw}FWyRzp&?%9zTIuM_q#65sK3$l z^^R%ERz^}A5AT8O8vHP(=w(@amWL2a(0M^y((Vt1WmQ2($GSpE3b*5g zD{oAdw}|UVG6)-Bjo$ksXYvXPfKDt1&GU9i9a8+zh$S!DlKJCLyo$J@>J^Mcy1_mM zZu%Myvswu3x>;PkpfhwKD!TQVi8|h`#Y-48$qj6GAIYBne28^YBbJp@jGCk4SU^*G zD07fvfBE4;58k8yn7eEHCa;{=h{5~zlA6MCCSU};+gX)@N&GEUd9TBrTNT`>wRE}D z_F=5mq!{`^;=rN!tIDbU&BZuV z2wGiG_gSGt=*&sSU-+ASND1&0Ehjh2P&N zx%m^gv#k|aH7#eAg9P*LooT;#oVL1*Ou=Na-b7w=-RZ$C41$cCR5?utBz4m31gYAp zgh5QXd6j5$U3I_SHQyG@U%fV8OJtx*mILRk3SrdQ>q_3O1)M&~da)-A@*5G?*%IRG zp71-Nk^WY_%d7dLXJpUE4zqPT51C214z4)C*jlpR{xE3!^6}MD!DH?ewbbOS6K#pJ zviS%yRwZSnc3Oe!PPvy6-66x75}z;@zPjT!nw2QVC~&^Wn9M6o2SP7>3U|E8pZ2GXAJ#A7{da z-lrM8%u<=-C@Jr8GFv*%**D9RI;mu%=lq$&6V-tUnvJM@doP7EaU#l)~q%z+lM7o5qPjAYrgoL%1Kie@+-IJ z-GvLf$<@@#Q{C*qTgp}T-y4I>$JKP5>Vi=$aI&?zTYtDu+Wk!DimZ(UTdMx-wKvjl zl6`0T5bX=1m&oP*eDLyWGO;pg2lq1K)tk@;v!b`)o5*&}K%i;dU{n@HH0X=M_a?UP z>?M=tnM$3;p@XJ>NS^#^{|SBC$L^o6Z=|blap~&y>eW^iw;V3rVnY(a3c~f7Lw{bu zKoy#DgF{JampNm{+&;VZ6oyV(dK20rM!#kWqL$h1_IBu)92N~vmcyIpB8Vm@HZ^g= zd(1K#b$S>7hO)B}jW05sb5;RMELix%nM zUH)eu5=8|UzVo@s1d#$TxwsHMgf`p^Ul|K8COamVn={!!c?Z)@s9qi{=PL`(oMDnE z6Ylv7_nD^8YV95+Il-Bl;eKfufMxERELMlJF4Gwr>-zaX2Fb6^4VjtcD;HlFr?^z- zLM*A~)xrKB_TQb>=^E$W1(MUl@t~_?Q2c-xOAV{`)(RpN)MS$7X+VKWu~C-^JO9f) z{UCDHuuPLLsg45PAyMPyTUq&0$M5P7Y@b^4co>Q18!{$Ocjx2bEt0JDWTHl!n6hLQ zJxjF_8me?sH1j|?-Rk=g_q%^8bJmm$KnmxmO6KT-*4#9&l6mes*g!_pvGvJ1H?jI;-&PoGo>IsJO=b1uGAXo}o2 zNjN4Fj;ug)7sim*TJ+apT2&g^wj{edFHS$O{H<)brM>y4@{=sBWyw&-L_g6maX*-e z@J0T$3_(bVDj2Z=ceGvz+V(M1a>_D>qHL+qd8t*cEYHq_N3L5f6rfYk#$i8yVMR-j zW%qPP5&8WYfuq0U-SO$ZP2R;P$B_-)q~oeuPIbOyt>_KX?z@9+DQ$?0ggJk!+lYWhu8+T z>mc3Xk`cu~Mncf8=tYd;5+u9TH~s2X>Vva@1W}G^f$77O%0MfoY(2@AH5Ur>v}`)N zf%RhE>RX!#HjexEP{iK&70>KdjO7D^T6XyJL(iVkVD5c+H}Rv77fPVI{3|NU>lG(GzZ49*Qcg(l_9RmKqcEiZfx9WK9w*SgD0MAUy$DIT2$LW3kJzdSu zcw%otD5qJ~b@DczXO#pgu)GlmRwrzW9li?oaM9t^Qp}hLltA|+M#gE(^4kqu$nyoY zKgj=M^_ayP5M7>b;zkMm36nUv3RvL2BeRP@le3|$onW*u9PkXy9_9V`aQ0P8mj7HZzn7^8anisiwh{!wI( z#)U6zCci#32Ztb_Ne-*%s@Lc3X!h=|cXIo$w(SqkRisF-iCbBZ3N$vU4})rA_Jq=h z=!+?4?U~CLvKDW>**nW0<4tn+&=?aku}_dRt}g{90_k50`nMpR1_aRV@f=P`v4J-6 zCOx`4)->Vm0;$G3?pB#6uBmD4nOrICnUr!G5?O&Q?pl2dEm;V!tC=}ZmL)C2gTXXX zW%of$-1}=|2`H6svVWVJ8#aB@N$AYkApaW|ax(t`FU3wX9C4dKH9lKV5P4hlny75| zp4Z|Plm%t=Z2AUw0Xiz4j+{a}gNkf;hU9QyjBNz4!e#fnVM9PQM8jp3Z&`o8?B< zu-7V{i)$wwc3!gbZ>;+@pJ&*|RvYU@6Id!=ek20lkn6z;Nz;9?>xSA*Iid-#z^aajk{+=8-8F{L29&IKN;S6QN&%rm1$F*6?#3vi2>dk zls3e9ES7!_nGdfhc4XS(wX%34ogD-%Mc(JXJ$`T5+IKf?vDDM-{wSG;U0dyfr|&}* z)g()c6iEV{-%aI}@2gh7)R3@B{=mLtWI ziy-tTk}w)8{w+Y?N%w?pHngAnyk_)UF0-d`@q(hn@>RZWxi94AvVXy{iAzW?oZW_WsP3q&#&Y6;0s35^-d{ z*NQIoZRLsie`0c^*8+(MG~JiJO@>P{MqxIGRRv_SZig^aOtI-v(&uFQp zf|`s*-dzMoj=XI#Dk`Bn6H_NU!(+sAH-K0AV~dM9V1wdI)wF(xH;Gjh=g8L#I-4de zJ}VUlb7m9@tZC9amf~u?{Tsh}W%fCsFB?oF15g=Y8l@;bQBD8He!^pcF}imV2j#n- zfdVJ}g%# z!o{L9OBblH>cc6CB5&<1dt+79lU(0JKJkD4J-?~r2Ei7Vn>=>F11c?LwhNtD@`jxF zDt-yo%llAgEc6^jj3<})QlsBUcxVJfg_*wf`+Z5qW0!dQxz@rH*!X*JhVR^NKDTK| zjl2m#NS3Gn-io--rGvm#v14K|vRDF28^nI>Moa%&#fo!Rl$RW15>M-}iLQG2r-s=r zO!(={KvasOJK};%HX3ZTZ7R9K=LB$tv{>hOI{%*d)0AUqs+aq^EYn@O@h$%Cf14;G zF2zlKgJ8^Fv)6t%ihl9dVyA6tPyFX8Y;#&`#ifIZ93?Fq}qm26st6|KU1-0i4 zFvD&JvnCL{6Rq9JsIO&`Vi%YEuKK?^*IR5Y~7Aj_hB&&q*5Alg&<3pZVg- zLqD!Do|ypdZ@uo&c~+>&6qLZ@0TYwPm5-@UGrBjW30kma{YgsmOh~!S!r4AtUU&ucw@W&95f1IjTsS$u zYv?-p!cBm)8ba=cu%kRweDClmW9TJ_F2SyN73?p0m-r6;$7uN8gS4`L;>3V9Yu-dB znmgqbtNn!jgq_i-)UpTW9j`{S9V>jW?KLM zs6f8R2*VCDGqW;VwEv{Db1F$CrKR6NacstO*R%q=ykR|~T$Lar(ZWIk2B+DGQ;E|O zul&d{?&6}h`B$E$x-|nW!90O%sr#+7UlW(63yPzpSCgC4IB^c&9`=g)B0$26D)jD< zp-fUHe}N$bGxhh@lij)#kq+v-pAO)kuePRQ4tICUf{tfkU|{68l$2CNT%1;_t>F2; z)aI5JHG;G{!EN(iz7L~JKy%8m01}z;g|c|FSOw!WQ(|dY;*H=P!hiNsZ_Z^m1Df=F zJ%>ZNr0R?YJn<%?YcM?Ulj+c?wNuqe`SPCcD2|d}_)$5zI9|2+)6)Y2rR+^G{mcUl zY>@Pu^cMN%;ceXN`bUaiah_oYQY%Tiz|aYk||b4yXpf zY^S$PO8mp_5D$N`C%p#t{HECdFL@_1J3D*E=gt2+BT0GWSdZL+d-V|973kAhYJz>k$RR{H9Y}(Y++k@g*fDgYx8M8*t@f|BJ_%XfNV|HX_D4%cwX4ds#7dIi9 zCG&tD-KtV&#m_7ymm3TC1dV3p-+r^l1J?u~Rqu!euZ~zN1vJt;4@JgH_S?E&eV(}K zt7PD3QIo5sI6FBFfMo@^`KXrlY}uE94Kesexp%hp#B%t9COFO)>){}1*Uc`#A_vSE zv+R3;;x?ME<1%UcRk|tKz4Y5S7A4W~J}PL7h;y1!h4<6K0NGvSp5}d3EKSc_ACuNG zJ$FLDOhvWI)L8JyQ`dmBXR0kx?|4OVTOO)^=_h$8Yt{-bNN%nc=)R`w+&cFGJ?ZuT z5Zc5ep^q$i+VCMP^8ORl^*;r}&q6QCxUCn(5k7OXPOD`0+tVE&)|WFdcv3#=%!mYD zMepJLtF?hOK|F1eqZhax4}k9kkl+yHrIZw;Omoq*q2%jkaPB_^Z-0dE68s}-7M2j8 zMNS4q(*a@uoKOn#)3DOyV)`Ct^pvv{y4*jdj7N_PIOE&gE8=*@L}VnDmUq8JJxRnk z#AWA+;dC58WizCZukcsP$0?iGy+2LhPruL+vP{FF+~&X=gSOp1Qi6&8O2{gWOTQjdg^}0l0-o z1{14o34)Mz37ipb&xc8o{{Xu;@Mrg)p4LXgC2eo?#p?Tz0Iu0dUfBymPcJIH@22@~B2o-JiXO!Y>G(b_gu^2G?1tzeWR?!9x zGBBbW&s{fk`VIP`Vh1%ADxy*POsyp;6(gcuF?N_Rtcl<`Y_Fn}-)=+|*5a1zRq)nE zR}=rM{cfjssSBLYd3A#Tu17KcglPMKQ(*J8b3Z34tTJ=i1U^2yY`{wE8`FoWr_1&6 z&wla!pEsxrHZ7M+NON;Fy_>w`3y!GM$;f!daJnjSJ3f7o6zWl1%OL;j8d2tufR6WvYQWgRm;FzsVYH(DpG-sb#*M*qE~D{PrJ~t$Db^SQ%Fo}wrzV*Hod^3tbdmcDj8AU?4AP!cyzMiL`^T$}*eaFo^ z(?9*cLHmn*K)#UnF0z{yR+pa%s%m^}>%wvk7S=$FGt3fcdNU^;IDd?Aw9xXmA0p0J zWRF8HGZ^$esQlAJA&tG|Rq}KQ&Tg3YS@ z<~NdlgXBhGK263wzDi67KA2^q;6p3_c*ly_NmVRXz=mFixq-FQ|Jx|?_-B%9)Xd11 zU0H6@3n#fD@>w>PJ1mFDIkSYyeF&lrF>2GtLZg-6*^8x1JNMT_et`}p>!IHEnLxSw zfcGzS5oBn(fMY8dH?#iuNyUAFZJQ(=F3+?0q-StI-5sF!F+!n2ZlBT9w~0H5P(lth zYW(~N+Q}G#>j$@3CF>vSffnpP(Pw5MV&?i6$Hg(1m_QCd&|#NGFNnT zUCSpr6b6Pemib$!Qm~EUbow&8burJR1FVgLEyM8D?Ybo43m6BC95U-aFY#1r#tVUR zB6yR|S7f!mH#yq&8kOsIrmg$345#_=HRY&Ff~GZ9S%(!mAlr+70CY=K)>1=MI#MYOA}>aq%($kYf(`3Ic#i%IlkO=YSs*P z1u5MfO>@ETj=xT6x}9=7hYhW1b@k3u?i2#l_wwYI_C&A3Zvclt`>~Un2z>Y-AZs2` zuw)nLefdc8J&mzS4hN&HZS+jGabz7!PN=*HCLhw~ z)#aTwb>oZP< zWo#F{q1Q3}{N9#1^zji4g!#UephizgK;=L477Y{)Y3h~JEV_C(Y(j7S?veF-HGj(= z&m|smrvWNaJ#5}BBE8W{dU{?nZPRrnHYl?_-gT>@SxS7d-I3Q;ukcIzFFh9*pzP0j zo`(z_x*g+=^b;&g9f|7!N?^_svE`A#afo=r+c`qEFMB6i1Go)=o^~qW#idffX~x%L z`i>w2=^Os^BY!!yp0)2Qkp~^`1L-;#mc69-hwNNQhKkMUJYy%m0W4HkqOly8F6P>h zU+~Hl331V#@PHTwV?A=j6_q6Eui1qW%74O#ZHQ5{M^mp(b#db{ zK<#;F^pRPP^US%nHg#C^EOAk%5R&y+wG(4-fCg@ zjc8DU<47jLzPZw0S>vt;jXb|Mpn}3R`{*ec&HXQv#U;C9gWUXKXq#i zwZ6v_KQENQbiNc9O1LzDO;1k;clb`XtdZcu0S#dd9YY^g>MotBufKKFJJUyx?miZ- zAhXYT2!s^tvFOho^zv}yZTo@yPtp~55jwD9(1BH{_tjMsN&c$8gypC^yX$&e7F)@h zK@MR>pT*;Hy@L;UUIuUKckem!HlD$F4_*<(Sbv^-x&r5+!(m*9_tjjA@%mXV;eAT(Z5b#Giuv2c_=(W24s2=^?_wih!&r*7OLQPWiS^)6 zFB*_?vl{8_qk7-ZU*7+{#J4yN0;|wvlK;GFk<8t9kYcB7`_8lp(5jQs$9~o0wM2?^ z7VP@-{G4BTygOF#$@J&p=)*An_aI5F`5j-zQI6aX41VB$lOY)65iel^nN)W$IC&BK z+wlQ4fv?Y5t4Xj*mDmS0`jR^Ej^9DvQP}5%!{~Ow;Bezy{gO^{wto;2H@PkwQb;w z^5Q)xT_BS+trj>P!q3*?jvYbHXkYD%CrxO}ga(X7}Z%QtuT zpC}Rx(ff{fMZVMdV@Ek)i_EjCF?hsn-5w%sECc7yNZT(f8|+embaQ*AMpSH3!p%;% zs~cRh6Tsak;_Bk$0D6@E+}9klE;~ZdJ3ivI(*L-^83|Z4A5Y7~ufN+pw1yvWLCMp& zow|&bww*>u`W6^6+EWXa+Gig6IEgy*9|*TYB)MiWRt zv5|M~Ua)bbK_C6DIvZN1MwqT~>RoXDOy<7wYFGE=+|3mQ1O(NCbqyE|eiWO*!~ z5doHTNgJjTX`VUY(YYfcMJL1hW7m-#*Igj7?Kd1!cH2NMuwio$ps;W!EMEXjfO7Q* z0z}+kNAcKPR4oovu_h_2%9SykC;MCfwj89+StP~TTZ|m@mhu(CzlwS76f4u$NR-$B z?9Xb?N;nV+1{3dHP`m8{c(`E2oBdnQalsetnv2vMzeqPJ|1avi!CDi^#4`t0B!u|U zY3+^XdeoGat@R?#p`{zD!;xE*{elo|W9?4GuRj8*yJYfSDFt-QO z&DDsP;>0OH=BlkUnRkst=Hw!Dzi3pHH z*QK8-Q>{+t-o1an2~bt}@mer!*D5o!)BPcyW&pDcfEqQv6;VHw@<>D;`ZI9tfB_u= zg1b2-BGl1r>9;p)kTL}-vjv_0X@Ju4N23Ol##E}h4QE`C<{c#s>$hF z^?R3B52Eqs_~vu^H{H4BP=5LaVA7ZJQ42 z2LuG{;Ftv>IH!L<{3}4FXvUEm;Ft*_Y=rsv|HJE(TJE{zf|B2FQi;HMd+d)+HBn)A zv(Sa@MdDC@8z_uQlAmqcH2G75)lwQkmQ1bi7fkjFQFdQQ4Zg3t+w9#eJ%@2<94Dpx78?6(tHykBW06Y_j zd$*>8j{j?&hE+tR;&)}_zLom$#mS|Ib5>tI?mg}N=#)^ifngJvgPU0o3Rhp^lO#jIo*VK$~}m<)QI zg25ijhIMomL~{uW?QA@U^$v$LmHG-pgIA5jBOb1o=M1C69dMxe-f1 z3D5}u7-((+T};W}dD?dm$d-d~LU+f)UKPD2X^UfUvT)>kD9nt6a;LC9*prPPsnKi~ zi$6^VA5`wSKU6nP!f;%18p(Xo87DJhSWoY(1-z~pjLFfSm+yiFwH_3=N1 zKhvee03a~vqCGH9rViG$w`Q2zbPv7ai=PN90zQ9$V<9?vb%O-6l&wpjNJK(aD{T4U+g?~z@Pucu1%TL*|y#0 zjiO6CS%-=Q@CZz(ViMM;0dTJxT=>G&ZZDRALyqS?vCuz5(rcv4oio$Nk%t!rx$Pw!|+^V$vVcg5G2D0AEbgOBp){a*7~{auxu zds@)RC5L|Ql}9M-1r7tkRrVN_E(fOe22ZJ0xof|qiJSfGg&!m@FFfWCo3^b+MaSgA zQy7JN8Mc5fkqWpc_JA8GJ2bi)?*2Y|TQXUsWe&hxb!kGg4p^318R*F@98UGJ6Z@fEr^>9L4qvpn~TGD zoJP`ciuW+CP6?<-)Y7BJhep;${OP}TU@jVMohJ#4IJLOBa4lYv0A{7^qf6nfMi|mm(PaSgAY~A603}@9C1%zL7?x zA1H19tS)?ED-e*-{Q4?Kv)a*GXBq!)!~^3ZEo=ff>O5C)Ci{1Q`>Q;P)8G3JRu6j0Uj&0umts^)3pibn} z`B^=)rhP2`TGF?@t@MbSyVc)ow0|epYb~kgJIb=vt1|GRN;o^ zbd&R5C&sovjjgB}qaGRdtz1>dBq``mZEe0he1MKV5C)!ywgvwBAnqUz&rdZCL zqd5^e)?&L^DxOebTgf3MtfC9!PS^aL;H(vJ5mw_Xdm8e^=#b=!>|qf8$IOwOQzK(z zTd-j+aXK*i#82jbFx_Nn4yhKe6qtQXsAfHNPif=W#x#izHSxk*n|b-4E~MK>=dXT| zh-Meudm%}qcPsPC^cfMQ_(jUt+DqJZdG!gj+1IvsvuJwTWS8X5iB}vgG&sTqem6O) zS8_P@m2M1tayDLjY>B#Ug~l!%Tb*+gJUq#t5B8-=nG5P(kd7`I`y%EaU;zz*D!WHV z@4;IfG37>#?5!4F2>8+Ym@xmEVpy~LAmt}C_a_b|#>a9w*}it3H|Bo8RD8iT%2Q8(y6fyKBQN08SYoiN7-0J}!(ZYU_eWP`_VelR zHg1D_QyVMs7rdc_ta~_ESWDnF04F&4Z8WrEvk?Z!duz9>yK05;)Xb)rO#btzQ%nct z(xM=pxt)w9-!K2L`44rSU+}ljm6-_1sdMlVym@V}Pb_wi<`v1KCzF+xF10DyMLtPN zO##oSr+z)x@>Qe$B{GtIam`;riNtc`{g^gSY*^KJ;lkD}mAfwKdx_zrGZ{T8|EJ

B^E{83Zp%e^wK;bXVIy5~!Py8ZAKI-Q|+D zVbJoGD`7Y8yUDa*GS*w>R*O$rk_%73d_olAjCx;S7W=tqk8Urf(uihFzAJr47RQ@>HYBUx z<T zuT-0GrVh8~=iL$3?@ttr@ncMEK4~@v+hI`Dd)3T+BOdgw@W6#7b!U%O5>M|~-Tb{} z3n6CtwxQ;tFKFwomruCH=e-$D(=eGl9$I-~;Q-wV7g-jXQh~ ze`odIHy^7^4hs(3To~`cM13ZI*LPEtAm6-gjF0KFE0qa850h7e!eR3R<_<3tp-QzX z)52lp2}PN{OasIZuvU&s->Z}B`-VUMrNV>r9*V${>8|kFQuIiZ?eez|N+%rE3;gax zn<)9SwN>w4p(2T{T{}9KTVRntA^kCOl9B|utN~Nh880e|CmWsBWoNL7p9TjSj1TLE zCwx^es`?~^*PGm(-Z*Dkm^s2#^HWO323`VnqI}zk5JkFWw+Yg*nngx~@<~P;!JGLx zYO6OzGVR#NzvwAg;WVqm-dL>K-c319H^w-FM@ zQTqIzfWjf zjtmcy?)c*BhJ^gKJ3Kd*0vo5g;MnQ(Ki7hC17do2$y!BiKV+P@&u zZy{B~D=qD*fZv*CF}hj>W_(L)xD>aQzV%(Pq<>!kjq#+X5&W!EXy#*O#@!>Sw1w;y zWbjvg3+-_;tqj8v=96COh>`A%oSk26=M>#zb}z*I3vgNX{268OM{#v~38!oPHrLSE zTh473f=b?rWLuH+^ki+SKc(m4iAIEM;fY*+0}pD$qKdvrrM|Ox4EBe75_d zc%OGRUyOZF{_86@t`kufHl{w^I(a!)L3JPkpZwkTGL3%;{{HLt!Do>j($^KL*2JY& zw__61YwZ!Gd3IH;{qtjx5T|;~nSG-xaaYf$TFO|_P?;N(1@g&KX&BGn2M=Te2y_1X z_b>jVN2Z}em`~z5lr0Um^K#l_BSL4zr0S}w2Dc_vU&>456q*qpYiy_GXMOb^aS6cs z_qZbqe=+E<*1TRzHoo!~0!kdKz#@;AWg>gM)4G4vjQRMRxtGZPwtS;?xmCE7Q}e)r z&K7p3(mW0s#fMCqh@fF{$o7DAE@sFufGGQ5n5Vn6(E6NWt*p}+&6Ik1zxvANxY6O7 zLNb&8vSFSY)l7VYE~{(lTsPGA)!akgf}y;@CDl6vZ{>(x6)_&JHVvZfBK4kl-voM) zG*O)~U)-13I5^Y4N)5p+lcNs)^2F4fGY%(8q_SgR;m$z1R&_HEH+K~50gxx(2kt~2 z&0p^C9ozR&G8H##@aFjKOB&4-CJcUzbUY;{+SPa9*Z$_wOSZm+l@YG?+w*e0M*M=7 zf-macv>4(u&@%ssBKQW)1#b&;V-a->t^N+hF7M$p^$EpB^bPWbP291CA19Q|Pn!(( zewUpWOGXJ+d&);s-yLK}s*|RG&%61N>5)(-VtGKk1v~g4F$WL4m-b>vh^$QiqQ`oZ z_@@yKO?WzS_>1*mpyv2Y?bj{YSRu5)o^FwgI$14gKhi0C`=ZJW0_!{YKa_P6EG_;fNf8p6va^tvVD+$Tr z(PkK*)xTM=Qkn0FP?>K(=>3_nQ}MZ#&8$1}BYx4++9&>7;g?9`XEVa)a@^=rPsKM% zUKMRa7E_@?6B^=JvgUu%O#j_jEvzp4djNR@lH-}nJr1_%1|ROMzo z8uoR`igPBEmy2X%arlj>TkK6G%Z1ndu99Bp+7FSIuIj5|4Rhe+TG>3K7+JA#o4%&e zIC9NAx@AuZ+a9-jg2ZvD_we75p`C4(5Zspm>hGiNZrQJ^Qo)Bu{e~gty8IWDyUqQ| zS6};G*6(Z({{2_z6K`6~y}w^vcX0j3KlG3{7;p2xufKF;YCagVBKJ9U#w>qDBgJg6 zM@RY!Z3Yre!Sf`tBdpc5xq6R^68g*z)4ak&`RQ+Wkbb*I8DibsJ zIfbPoJ$CwSQB|z5U21fPWs1*w&ohRU4D(Y8S0E=YLa51J#x!E-3)#Jpo}Wd#NoC`? z&-k9oH=6q`2aZTRv}=9kibpQk4#8h?julMo4}O-BYHGJ%PH7l?EWJC*_3cUDA!TU< zymPze(+n#M|M&nr=?uNi3VW3x@jC_-zJ!ihq$^`N7eAaU_>oxHJd?@NC80Q_ZxCu{ zc++>WLEiZG!{UO$O;bvFS1(sa+Rz%&xh!jKJX`Yq%!s+;2lSeok%<8V4C9}sg=X}w zr-a`L#WZhgJ1`znV6&*FrnM;Chg278KE7DU?#iz@)`CQU)04UzIW@INp{4sjXQl); z9v@ifd?_1{e7A$|S2rvbu43T8M=)hlIvfGZ6YUjqstSwoi}NEyH5J$Mv*Dqdl|ikH z{89Zx?XLW7Z36w|zlUr!@}amWIk>$>aewFy>DiXLaG=du< z%^RFx?zm~;xMxkrsMIDCIC9mt@;g}$NfeiQNY0ne8!;Ii-6@DhTuSf z`8+PUph+K5OIAe#XDx5#SD_K6m}0be2`YOl-X^_GImP0-)TVnDh>LH>a)i(l>NsZx zd&9Ylji|d!elrJK*&$b}BHIzCxik*_*wv&TjkNG*U<0o1htHz|sh<;S>k#3Iv6c)MUu|8coX5Kxw4f&Get5r?-7MNO4kl}s$ z*N^N0_FafjN<=o@cJ+aL?bY#{DpY#;`r!1vmX6rBpVu5s?;W)SW4q>}yDj{$LczZ` zkR~Tq@|94MGgU`}4jEZEexroy)O;T1>2Ea7dJXjGv!-R5J0HCX%AvG2EaD1NDT-YD zgJPly)Gddp-yirBLkv`0*srpw)8s-jeoO}`1eC*r2r(+jnp$(~c?l0S6#|(6sU>@< zD0-FQ1li_mb9Hq(UiWFkB5){bF{Zf518d{*=I)@_W8j2L(Bm{>oztT`eC|23ju$!8oKuglCvUQ$%q zZK$(U@4f)Hz5caN)$s4!ox7JpMMC5=1hz-BR5A$LRv%286YG)wG9}JQn(I8QWB&2( z11ged2Mu&hI$W)b=SK!3)l=YnoGIc0tAL@F6-BL452!e^J&{ zLqZ!T;_8lq*{-Dh;LGX*o&D_N($DxGrFVX`J=nl{KD+;xGoI<_V{|sdLWIK;*0F)G zakW5W#U1zxkOqf_D(Ahh+S}Xf&C2#CjFb1EizxkLlJ=jg z@H`fSm!T|qq@efk#)UwBb!+5=jRyd{2TXrMkS*b`!btTmfG^_(FJB2-vT6f*q&}LU z=S!N3Ns7h9l%&a+VMcmOx~JVdQT(sCf}@+{-s1(G9lInP18U_=~K079LsezuB5l8}P7Ja3lF$cr<*OHParn!?crAgCff| z#f+9cUDdT_y1XLAKh!gw7;9s8Ty(#}^%Ft2=+95q876_FX7_@qwqvPY!hK5UF z7c67^8Tq*6b?B5o4Z8W*Y)EgT3h*;n--|+HteYu{LkYwjaQgS}prH3t6*qx*^oL-3 zj8%I_nkPBtDA%(Mf^2^c2gO`o{?di|AoBIXh)s-}NjQxnp8J7+pd9{unqg*b5;9aK z<65N&#)Nu3M>R?BOctF&aC6i?oxQw;j-JT3Z z<+KoAiTZI{gW=nEu+=;Mt-sIqFnf8|$FJ$=#>dTD+oG{E{{wok|lw@!hK z9aI}hoH*q1w*-D?Um%OJottxE5V*GZ#1U>5N9d&cp;DtmXA{oC1d9)9hZfZ5nl8I0%* zQ(}=FSO0xKO2QS_ww}-F_m)`IYJKr(%crpWq8eJom`Cpm&7UI?t)lC)9Nx3DO*TgU zMfui<}gvFz+#Tuc&6%llvP zvAtyqioacy(f+EsTED)K8>@s1oD7Hw3H=)MQwj@rE0!SY%YH_L_`w5le_XQm&QAU{ zLl`1L$FR82(EPx0ak(z3RpjQG-Bg**$28qK<+9YQEKih+#|&u1>kVUg*xM&QTbSmA zBq$WyP?Uvn)5~`@V^w`99kn(cqZdD?wt6{6ubia;_`(tQX`RezHF2(o68gi~;qciz z8BT5Eu4pyr<%cewgMVB*NiZkg6vdu;$U9ao$eNvOD0d`I=dp$kb;3&EiySNE?5?Ehh_ko%p~`o7umi{WMCX}qqaF%_>YUc3Jll4o{f(0BM6V2!o%*c^*)+MRzpCR3(7M{4)hUf=N?MFqxC+W?k8N1P zX@*Z*mWj*K?XQ4GopDvYu*_D$(aseyWC-Z+{dqD;^$AIfOKR`U;+_!Pj_pHoXUXb* z^qLIZWHzc`0wdVbwanITjhD)A@r-cp?)%~DJf^PEd73dV&XI|PIHTe|>!8bG+ zaZXK6PEx6T28EFs>UKu`{G~m&@$!!pE)7eHcWKz&7YT}< zsJcx4s7LE=G35Ce9tU#s8=ON##w^o3QD+l+%Cm~iQY7T79bt2oGuVb!&PWgY$mPUh zW8=J!9MPRBXZrp{eZ?Aa*=Z-@avF9)dO%SBdTAucR6u+ApDM;W=#FHpt=U1WLuQ_ybJEb~rZyD|s06V(;|bYG;C$c8rYto~NfoSCw+W*1TTSisYeQ(1c(kV!D2q^_g=`K-eX{3=nba$5uQVK|?#G%1M zcO%^;-Q8W!;{N^LFYgD3V>pcE-fPb_=T)n*(QYG_Nzatj!Mt%)EwQ?Z(vT1bf*vedG+MVR_EV+S2q}a<3vwXi2ExZ9o_X|<;o8b4|mS_q0U{VKd#Dj;&zdnNz;r)Lnv zj0}i{u=g+K8O&@%$mgd&OctoT7gelu<%_NTba^J|Y{~b>i3X0ObLRT~AdeyXpQ@uH z(+8!bj5)fzP^}`^^}%fdZ0TT0^qD9D7FE{YPhE`sTv8eib{5Z0Y_x-a&&#~eMFW*f&IQ+PDnj4m{&!4ps`0pGJ@Lsy#Ae@G7 zXIyPt)9Oih8djTh{>8M70=}vs)D?ZZ3y`;)+84CH9_LEFv-UgJVDqn;Gdec+a9kp70*y1bFBxAq9zpnadoFC#i5BKH zC6~P5Yb7dpGH+Ym+?X!ELg4SXB%duwcgbfP&h@e6H$r=xQM-Skl}9V9$g2BAG&XKe zSMCRWkl%G!lz%c@qPRR)dHG7ctCd!*?pH;pq}YvC2n#w40^fOh#$G@e#}-gCI)1M@ zo1wv%g^LA2=AEBs<+Xo^%@M$KwAKBJLvrhcf@tppAcE=yFifV|dPMw#y}lp5;C~_i z75pmNlGHhO%NVETc;0>&$zrV-Ul(!so)>C!3+%Pe%n{CKm4}eXTmTR|y$dzg=t%0l zIveRQ=OHf%(Il1LdF+P^rrqh^`#0Au-AC*`ncKgH6+Q$&-^HY-2KA#9 z#7n5rXFDl%K`j@5Gp0Y_RbNH8XsKGPJnUt@s7qwk$VngFVehy$!m{ywz{zwT`^D^L zdNi)djX8PY@dURa_woCoFf$2EW&Jks3Gi+wCzbp zaV2$@=kk=T^im+y>cvW=2@0-kF@6?nZ7l!GQ(9q+%}`W=wq-W7{uYWAKx?ZiJ!6kvvwV_zFgTx=IX$omHJze>K*$=$<|H6P#V3#227n(Pj=EW( zEAvt>wDgHI1c0c_cQiUg2g%REioT}MTV5L6(PoF9u2XrZxhB!U%_T|ee1*hryWitW zOYsX(OsZ%jHF;q==C!tdbz&iXzh>Wt5MyA}? z|JY@PQ3FHadL^7-b?3sSXT>L>KGH*V?z)X0cy{E(N*%8&yaiy_q^?>OG}XJs*0*&v(yVROd7m4EID}WWDck zx9GuMG~YOW&BUSWwMqZx9|AzdjYSV*%RbiKxOdfEx+6zD9}Cue;PMjE_0_UcDGELq zNKn)jN6IHyAvrPvp!n*)*F>7^n2mRGaP_BOz699+P<7a5b8p#-qHlDeLx(b3=7rXb z8V>7pPiIBw`ar@3$O8h#?aByPit^bk((?zt^&7P*=Z2I>);-PqVQ6WH%Dpb5r{h8U zS*3(w2R@c zegX{8f|gcFurlLWDobD?gLBi5T&4i75mceo-?{Q*+-enYb5h%q#`xaax!&qO1r(4G zQf=@Vx;&IpHNB*Le7T{KyVpo#+1(-IhaI7%8gT6P!E966E_vl(k+)$zl&VXK$|Fl@ z7cj;z<@hhz0@uPave8BaC9Q;>h6)&~!#Q(95++#y%}trcZd)jy_!CO(!pnZIQ{Kz_ zhUbh)Am}`rhg0H@O04=g&wAG<=Y{M*<|m7sqG&O$w$^_qN7ITrm|uYbw3mgAooh+R ztKk*`nII)S6ytRuCRWxSY6jKp&F-`wXzq_(tb*A@l%X&GafPR$p}$gqCl1c_ zU_sw>W5ELVW-;IR_$_@N`+?kC{jwcvc^B$~;+!!iOg7Y#)6)n2Ftu1$pONdCJZI>UQ z4(-Twa(v8M%a-6k6o@`ne#sm>naW?w*gObIe9Ny2nt^*lg)p9<95?HPzX}r3VRbo4Z*-bcK_y=PR@K>cNET;&vpPes}z(&PY}Z7%L1C7kV&A zW@!O9207`db!6>cX1n;Irm9!u`isMy`%e#+2FMp%WaZ+pYxWp}z z=0jpnSqG-wZ38tTp8mg}FoU{G&rg^MftI$+653e+486fmR{qe2FWEL`bu8g2>7%m6 z$o7iJ=8`_py5qmC_$d7d7#VX|ORN__eK#7?c!zcG-0*3nPV^mQClL`0k>QOo$pXJD zOXTtPG~|T-c`^R86MU_UaAMjV!BT2jSL=PqfJKg+HR!+khe*Fuv;{ZKj<%Cp@v+%m zbV?8>K3AF392japLFWqLU_FCY<>9;v;uG%OUaE2EhUeXo&ckxIsHvg`Oa@-HGk)v1 z@yRYP3sU42=^6XjRO?jMp8nidrcyPks_<9(DZ&en+AGWGeiU;9xY)jr^J~^;^$ifR zI6j%UOP;{jN6ycJk)b8q$8VvehlV{C&`Rg_snMANsAc)b%8GBDGH-yz@F`ub&%>J~ zH&9?qckiQ~eIQmD&+~vi_6{>?M$7Baw@C+)BnSMEbo8keO#RJ173KZ*uui_SS*no4F_qL3X?8f9QZRjfrKil; z`D}jfZT$6&74&9uGEGc8Re*>Bh#GMJo)s$2b>MHYvVIm@j zr#pD@emRpB`)3d`+qluKGi7v+_eh~I^dy*Q)C_Tn`ZS%+fI*}l(uXaJ0l z@`=?fmVQ0%BV;%)O~QfFsSwa^+||ht5%qLDf==97zw$3roY0?$xf3Qt5|W0ffoHSB zXHE?3E#;WmK*x85zm52yG$~ZHB0{m?X`xjS-Y5CiZ4V2e70Y?OCqAFXTw@3ch zSdDv+6HPqsq^CKTqi3+^Sb82)jifmb2s;`DBdZ7=NT)qP92*jYBFF8F3b0NcFb6%8 z#uWu80m9=4;8|9Tn935m3mStkVvx>3>(wmD;4t$&VB7m2k0ToBU2Jw9m{vxM?VaAU zC|6eCYySw*|M@J9oiPD@f{U9OT9$WV$tHxcrK>8~(umX|xjvtq0Jaq9UFO1SYSEpB zG>4@=2Q&R98F_`!&_y+&Gd|w>Z>!Y2cstfv^Zl7?;1*=WvJG2=DFk(Vv(IRp`!q`V)YSjM*KYBo@W-kDmDk znkcL)ungDJm*5!&k&kl4{%ll7jOB5*%8pFr-eZOQoq-oWL)ru)huvd&Rj%h* z7CaJOM(mrqX6+KWqr*zW-I+HNOXN1j$GEgZ1YyTaq*X|;W0*kZMrf_~#THi>yM zE`2vlK+Tz>=8LnGY?b&uAd%vJeuE!N0Qb5x!o}&m#258HU4?Nc#kQ&!E5ZODL-$!^ z>2*i`pQN*MU<3~47zQJ`o`2kjd+QAccKG72?lrfIe(n_-t@yeQ@5Dt|J1*rgO#M+m zD`aC*Dta;~qF-dxOOZL#g;qt64l^eP>Nw7wS`*cc2}7*2N8mCsEt#@+mYg9u*XH~8 zs5DG<=DusvO6!0Te1O@Pk7Yv=QqErace1y7jW^So4GY>}iJ42I_`7v4_Mq5k;;c81*`^)X<;5e< zs11$B64It-wOKfZxkyB$Sp>HP5pY)XN{ESD2a4FQDZYFcJ=sCet+;J=n%8t@1n2Dq zwol|~)cm8OkGC?LGL;$J#wRTA=~MzYVtniEm;krG1{rtWp07nRmaxAgn@Wde{ zks*au_J!xQIK~9HZ&Yi1R3mag^TV`jq$UaH#-D8A(kL5$)nT?a-#c z2aR$6Pd+QbT5(UpA*h8svN*u-9)f1S|8R#S!%NNkr7u5EbMs>NJDqxIS$kagw|$ z8U$)yw1R#aBPFx{KBaQ#p)+^AF1j<>rvFhR3{5$4cNLFi$ROWDc9?Ts%bN;_S^L~m zY(P2cuRI$SE_8FjM9E6U^by|aE4Vg22w@Wm!Y&y1R#YSr!tYcJ8N-34gxfLyZ@e$= zF&%$7tM|b~0bAVSKF9^bNsb!X9aggatwTxA<JV-Ue@!4B=jZotuKIiAI zH++Wpevf-n-r6zM1-=Ebx4{$!IpS3%@ z#WLsl4#ZXvwjZeAu%mTR!qnbd+W-e}TY2zyqaN-ZDro(OYC=yFT|fWd;!F+-h3W%^ zjyUtPTS%6JUv;9JRu!si9@T9Z)mqa|;hfR2lNokv0Z5eF=w4EV zKFiE2+h!OkVk6uiHM*guO8--5u742dOUs`}+HxAIU<*$|Yq$;!y+w6&{l6uCp1iox zJnlxpnnwv~8cI5(Y`J_Zu(WIZfurvdF^)B8#K>7LH}JtL@7e=B7s#!4U(I&gdN$=K zo|-?t-K1fzUn;0<@sd3mM{OZzZ{we-!xZCFDoB z0!D6+PAyE0Ea0sx7tQDWLp4b=pJ#l(IEk3Q``>u1+ZLyIUh1$-gaD|xH;2QNm%-!^ zigEoRF3kqHJvF%IA}nd#UX-9v=Lw`vqKox-pxa-aDp~fTy9G!upEP&1>4PR_$rOjH z#c{2)UVJW+GtWDR;I8={5m0og0?l1weT`T3{l|UMQ%azq`sYPpJy=$CjVu6$JkCKAiSs^7r0)P!%K}UdK5gAj5OziFav8 zGDz#Dy#qv7@nB)TPY#u;cos@(GaZ4>9_~>&T<4F!*k=-E+yO9|{2b&FmW=GqAUPdq zLRHBPY=!@xdQB$S_&4KZt;g4A$Tu~ZyVuZ`DA~70V4@XJTP zQqj-#Wwg)NrzE;rQNei2B}Eo~X1&y1nftCM$`v_n-p$mCC7IZBZ#J92qvQx-NbZ{d z)(@0_LPz1*1n3Hw>64;@<>#V;ns*~S_ygZs+4^J~%#M!10kQcbKW6kN4jv^sA`X-K zE1ihfN7g7>%RBWhUMX!OT|7^Q_G(o<7Ut0v^?Au$%QVxo(V&*;wfL#t?+J;%d+gP{BI^&#MQ~AWcB%2AyhRSwogukc!%O&_3Yd6(#gfrdhcRz zml|$ao0|XIFv9V5&a(^4X|?i*Zy_%bI9C{eray?h)Z*|nc5+%tj0%MPFX}u-C^-@s zp(BVbWSS@6_`%&zYPKflS2Yfto!;kWUyQw3HWfV;AV;mz>X~0gm-SHz+(Gnzw@`;> zpZI^xVvQzQ5sA>*7$L8c{=Y9d7CVYZ__*m( z+8dM~+VG^0J@NLF%~cS{$XAoSAW)a(Ewwbp($3x{xOr+QiwUY4+}7O=3r zVOrw^9g(u62l$?|aP~vlL_-L}#=Qd(Ez>e?xpIlWe@8(P8cX?kREC57k&o>^CPj5) zNXf?ZyubI=w2nWJ$V9VU(A&7tdE}dB{H+>HL&V(33wRoeO^NO*yjoqYBW5D`%0~}z zRn-c)*_{0u@vrs8ydXR?=tqc3dFN$lW^cC7mtAde5#CpC0YAM^^JkQ@f*LRs9{U#2)HgIT%+K$`p4MI8}uf+f#-}rDz4jKdr zuT0Ho!IYUCCZOC+J`tE3sYEQig!tSkc&EnTtT48vFB#mPSQXE!L~^u;?dh+VUS++O zZv0ne{h8245?^&`jH$dJ!F`T%8-x7tJ3HrQ86{oBHy0<(;+9^_R@$+CD-==RLtHq%*ZH?UsY(0!dulTFn1OowA0R zy65>?p}HDKk5ff<7}Zx5a<$vVFf7jrEM&|ZFcbS>Dq>av*S~&Z%z278wXl0}T_z~p z?Tr)hc$bn}MRD;YuG*JVsU?eOEze>N)V5E;TCSyEmP!0V^l-1!cRXrvJigo;7zfF9 zoSR=$+p*&u*38amaB8-&FhZ*Y@OWlVX_zq zTK8$r(k%YcLOt=sr06bDd2YCvb8HNE7(LlT-0X)7huu)SEen;5zm_B=>F1tLJ=cn) z3}bI@Xkq_bb0ff;lw$!_JMxfP9CU&80;c#YOVYuck`j-o*hbzI4J#-eFYjewf@r?B z2csI|C7rCBj+(tStLq@?I}=F`AY+LJD)PtPoA|c{djXE#tI!cimh@1vr5S|oLz@dh zk|6StEhkX449t4S^mOkPpVUiM`*wGcoVpZ_9jst}H$9`u%YMJdjg7;~$8lPSN@JP7Jtu zFZlHQ+Ev(IJu_>D2Csz~>^p%u+$wZ_kJQ^>^CfpdV$A*&$zHQj#o<&y*vrDX!G#+k z#>bY;fl-_OPZNM^+y0`QRhtKX8t^QpA~C7|P+h|k-0OB&)kEThl1g&KBAf}*c#V@L zS~6?w&KA@pFrr)$lm*1BZb>_|N-KYE`D#BQ3wO^IMW;3KjEiDwbXk)_hp7cR9!I3{ z-(@KY*FP)P>pr7^e3*dLf)8Q>a-A5V^6&o8na@LEj$>ok@zy(KA9wPqjbD+$Oo0+} zUoTAY7q4X605x?_kZ&QT*=%u6_IpB=PmLrpVUd)I3Mv&aNom+xGpiKfk`sfIzZG$5 z^}B5ff56>adt|?I?aI|8gd5auNZu@>C%fk5S|k@zKi}(K_7+vLET$9wLk!x6T>V8h zOH{9WL@T|LeSMb{DCx=$>PHS2?SRtLuitksIZ**`0MK)pPlITn689wFk8xw$IYvuL zmWC0~H$c|+&8B*HhG(fg&AGx34g%Lr%=KFPfCb|-l`Efx$UX^-YTmwwyNhlzS7y=K zx7x>X(7n%H@mG7=V&c1EN0`6=sL%#glgmGTYaXMj;+&Jrt!yj3;1(!idQWds8kJ8N zY$}`9`)QJ3wSDtz{kaC=mr;@e|-nUsV$ zCBo{D?0PQsX7jakt*5iF&P{A8?SpI4{|rj(d)(_%2j)mm}Q)){-H5 zmt4DYkT%fV{B^+HSS&L*5Zhx8Jpyv#?OQuwai5fSxbWWfmj3~|m{rph*>tT-ap8Fx z=F2M|xbauvPStj##nKS(^?`wu;JU@(3z|{_?Qd~Mw=v`-30|)>oSF{Waamb|Y(;{D z9mOm1AF3PP82*#moF>L{`Pb!P72Zf0?)95Z?Zy&K0ke6;yrZqoxtRW6SYJ9%AoD-_r;4vmTdY10bxhg3?$SGm+z>h! zx?QsL6=L({P&(9z!0{kv3m57c)B`oau-%C zZg(ZVj9x6DZCW}xJ(u8{FA;p`eJE;313A5VX{EoE7CxVQsK>S8>bLIdw>adXs!XOr zJO3<*9a<%wI{^7Cp%%=+N3$xOrzXcvfirMB$HTqAo3CSnps8j&qj7%F?VE`rqu@S* zwrIadOtMS$OaJ=|=MKSi@3R#rh`<-V(^fNhYb6lT;=%>?F-yhISOFRuctazA^CWOa zs5Pcm=(Zu?RerCEwQ+5-W8U|^{CF0Kpq#7K6LdyJhKHYWmW)3C^ixizj4$mnErngu zSLp(&$hvT6ZjUUx=%>jTDG&MADeRU{rta~2%S6M@emSPRoEJ@9k+OfVPhX7udTWoQ z$as0Zgh;M%_=~nH@y+|pl*K7@O2_h@WGHn9WhDRj;QUBe^H)@>#G8YYT;H&!>kEs{ zgBB|lOvt^AqXq%`(msPreS(K93%Wn#rR~f11woP$Y3AGOlv?hw0@NIrpraqE!9E9- zkhvpvvPUcqsOfKrS4VHo2dq83mplMg&4H2vrFYct$yihB^4hbTcULUk+3f+|8Grph zxF52zwj9mOgzi=qN9akgVD=X~RFZo8!^F+EfrqW1m9&xWQ1V-4<~*mp^?Sb>$J?$S z1;;-F#1l7ZWai{n(wy}A)LjzGeaXs;hm9a}Szn53{^_o^39e?*uq#=^av7yBw5(em z#PD^dW6N@WrSgPdU8!BU*%ICytcaaysBO~xf4VaiNbKim zx8Gd93f1<_@?a4hf0EuU$zm3S7dmAH=zF}a`0o4G@Bg^m_?ZQ_7j1c4ew4rxR8Tb* zFCtIquXZ(u3_yZP8CN$an!Q6gDx5Ug0(ZMiVS;ubL5mEy96hmz@7liXLNLVaP<^rV zI4Snk0Ta9>+I+3{&P?OXL+vLJj7JR*7QZEZUCH3UR9OymE)arKae6L;sqAAM{I;@^CU_4qw$C{Z>@Z*2>?PUeEHxPB&VIa$02 zOzXCDdY}3ILlUW=!^G3^ajx-Czg%Z!j^pAD*tbbf)EU6KhCKhij-Q1-3Jla}50i!8 zMWTkbZdNQ69^uW0k6bdQ17Y&M+Yf(xf_!Tj0zkdRf7ymZo)Am$jeNaMmnIt{A_5v$ zzD{OE03AUE`xk6lw{4eK{<(IClSdA>l}u~v&irj$I8JcgVH#@Ve;}7oO;dXGDMbA@ z9&9IA7IMAWIADG`qZH9%PJMD;{GZCALh%aYX1e0B-L}*a(=8GJFG}j{z%}K3OJwen zYr)Dp_|gyOAOE7f(XEPNlU5C0t3}N+6AjI$xngS=T83{r>_$l)!!rJnANlE!ohR{O zPMnEsEM#Bv!O2SqQrCW=*)#`FYWM1%u8 zlnzBYj=Tyw;CvGGiF`WtnUlG^CLj@zp|9{YfNODVVp9~(NH+XV1a>A!+x2qB zh1E)PFrLI09wpUuJ!0ZPmB3m7$SDMkSReW8tK7m3gHfx0{5!9K3W9@c$?INtHIN7E zcFbXYJ-VqpXzVMvPYwO&p59*2sW_nm4RGZ@t_A(#5l{a>MsPQ|KsOWFzM7R5A1`6p z=yAk-ou@UPZlFe3%^qph1l7~@K#F&+i>&rjMGy!z6BwT-qY1)mWo7!ibTeo$;8gl) ztjwmtV7CVLJ!wN9w{P3oBb9&WUE|cP5bX~(2H=>mpdl?HcYX6{cQrNO18EX~0eDfU z9R&ok3R0#@N#dqT+Qn{;{B^!lia@Zm9QyY00b(5EU@Xo$B+80BXl@ z%7Ks8g#%adhI#jP1dK+5H-U#6G3F{#cWX0#E|rZ9rFuZ`b2ek*lxu zmr=z$89w#c|Ma}IYmThiBLWq%Cd?olJw_QqUVtZoW{CS8oBhP>@`xFJhD={^fH`X0sm2hX}?$m&~z@*ZGFB+j-VtCN|GAsGw4-6ee5r!O@$Q$)hV z6e04G9@3eNDJh+L6jlWmw1ig>-{Sf3k9@Kd*(YRrdjiJT`tg?yu~B$LuS=8l*pj6 zjlAt<|h(0icJ& z$9g#aY4a-5>3V*;W0>Zv`U{|s{Sa_Hd48MNn|U*t0gjuSP>$6ttS9)%xf^kIEFkz` z1lVA2?y0J?cjk1)*7Qk)?+-6j)iD6+_8T)Jdvo(Bi0*PS5Jr{!_>APZuqR4FMvS^C zo^%9nU#_3A8@!b2_#=)+=J}WR$s$-ykUm9fNZg(^JYH9Hf8_#|C;eiKxBZ$q1(Q&+$w&}b-ZM+MxDMSxqtR}`JWmpy^{MXkJkE_gj= zTV(fDN2E>y!jX}eGZ`$i;MXRwdwaCiC(kGwh2HY=D(~9t>s8f?9}HJLlz{bC<8K$yCt*Tn@&Tv>9uNc1;*o(xZ6+~ zGu-;K7n+HS=QlJBDzmrjU|H>k&hTyL{0u#BIfcM#slzk1w4GU>!QTqGZRsZl+D8~x ziJjNC3!B7(#r-RMJgFIjr^`1L8!C+j-@ZRoh3QoRm_w+w3;@Drbk%{PC|UkL(K$|= zn#enwRr;LJ?eW#Vn+UNV=s=TTLtNxYD~y$e-{lP0!xPg*N{amFW;3W2>GR;@QSq43 z!%8Ye7>az0^8TO(x@5BBHeO5hC(hq}fyJkfwy<2}i7cW7O18=3?seQrisV%9BeaM_ z1EQ3`7|T^tD94W-y1hsp6{T1IQ;*|e;QKq?3#b+ceNNTPPanIX!uAOl7bD9!{D zK(xAP?7vLVc>X7@&H^qR=F~a4dG37qr4np#Vl{0fQEzOZP3x3)E|erD5k=<= z4XFBz`7A{Sq(V$Ao77iai(Gd^taZ*S3UZ~!6cMKTx8yPM6?r#ZxG(~mA=$=zR@=7W zt+Fz{bXGLD21LE+lk-RyxcMd)ldV1qBvSx0VoAgK3~8E5a`L#9Ny|@etA`h%K+*ab z-tu#wttX$)8f4j&6jgr*-jdeFZ`KIFOjK#(fC1BhUSM4wXxN?gL)FJkRfIQFz0quH zo-|&aUc#Cm_gy|0PjjSxYmu)C6*>91+*ZG9EA3!g>99gd@)I|*`j=q488{Q2lsC~e zd6Y=5pv!TTEj_p#oxBappV40#(v+HYg!L`&lr)+C{rRf?X20$+Z)V1_gu~_^AGjyt zgOpTz8l}F5#ygOw_LyqmI_Dg(JMc=OFn^#Gn9c3&EI2%HA;J&y{$IikPE!?lJ85*p zeB^m#&mW|I6h-eIvxLhvUJhCtfBI2EC08poSMJjMhst11X1sPiNpWfRA%A+TY(wHe z?$8dFD+7+ofo3EBt2bi~^T*20ubEIfFqaCTphrVnZ4vt_yv6fwnH|?J8oAhfazzq;P&Ku#FabjeEhJj9T}V!;9HZavK*zr4=tsN*Iu^dNqUUf z%z76o;viQt`3Br@5yz%KpR-|QWt9zsZC7i1s_9R3Kelhx#aN1>xG2IifdldM9~_tz zv|X-Zp(+5Zm0`Oj~t%c}b+Z03=tIj38C& z2;U+(Tp=hF*qe)=}2t5a~O&#w24fpLG zD~Sh@CJXQ!5)FouvxN+b+By9M`+w%|J_Ne{rxFE+(yxpF;1F)hCe_=fU&0-nf?U=% z2%9T2V^z81j_1xFhRiMO-c!sK%hMNXU8h zV)Y`+^WA_3Ej)r;k6CVRe%&1eu$f&BXWo<=BkK4F8=tCmg{1yYeVB98U+wo!YXdA& z_~tW;ycux^C0?xSN_ogB3ZzYcvp@0&vC^N$aDa~wt$kbN`7ZuPIQN56TWc#j3D$EW zB1+eKaC=E!Ufg#X_-XR|{Jfi>I~+cts;wDm8x##PK{ka?PirbFPBZXXK!M4EmHnK( zX_ApJFw4CAK><87}q`s-fvLzBum0}xF|5uJ7~me>ulb1q<*zlG6-sP>s3Pk z;!1AniR?Gk;ri-!m*E(>l2J$J&Nd)xQ2vxQ2t=h|V;r79XFXm6oeGHSVdU}s75sZ3 zAjd+Py{DhQ|5|{JjqUn$tL^scB$A?H+Nv*2G|e9gRiK}LPWK4JdL;kxTcDaj@S9ip z{|caOacvk#69f4id-x*F&cH`ViUsxEE**w__lxJX`h@B1%)Vn*yLpmsjHI z^atz#g?C!$w+oh~HnB{Wy;1MI{$R)IT9yD};h^yA~z1fMw~FaLa9Y}k#gT5^L$2DYKZTF!Mjis-_%sRv^a7rQ~O zdg`8P3*N}(E&;nq&m_<`)O3*_JaO;cx%*O3((1F+dl0gn1Nvz8YS%PD40f+$uFB`) z#aZi;$}2skhS1a~oNeL!q8{nbziZ@6*k(#{5E=}2(=rM6 z8T;M4%PY&g0<|uD6@y!25(lEoOFkYEM&(J>{*9zmkO77a8ZH1`%m|@?0H<}_i_()M zih(-^w8o|58+-c4gO*j?S$aP;-E>f>yVY|tQ;RF}I)rm|EA=VEl^|G>5fix#8du0; z0VLz$lBea)&OK4zwzf`0v5afm)i>E=^TVs-3iuNV8e&+L_lpkDhBnt?_mo9J5>0z?t2d<-Qsn zD%B;Dy2gbr5ZIBf#bp#K#eN$+4?@63D_I|SZEk<_>Rb6xV#Dc}g#T#V$Sa7ex$N?y zQKn)X_@EQ(Aw~}m8Hc=eG`w&bFGeuFD#4TZLs+0DjJjYC@jA?rQIKq1EVoo+8+By+ zxq8ahj-0)cabdKdI#^?JE%tX?djhAN9++x@9)vuF5{H$=^kVR4c6TOo@6STV=@Wz` zHOUI`HPrxOHDXq^{&8FHBlUk zsv0%s@z`XpH!csej@$1~7_Y%YYQ!mlUL8wLrDa82p*H8C!*VTxJkL0+t(O|@Uk|1z zXn7-!x-<@slj2rQ!`o_~4&Q}1@ibH=#SYrg_-N8~E}qYNw?TUF7fkIY=^N$YL`)YG zO0`)r?ZY2Q0#`+=_`I>>1VA>G!L+|VX}rgGPt|Q}AXa-$4Eq+XD2lKyecAO~?Ms`e zSVKY#=QS85sTRCoHtFZFI8>NGEAJH}+2&w^%+25T_d19M{RfaYeL&kCGo7qVEV|{h zK#b*ww?}?4-mOCdZ!L4p7#9YqQ3-%qkR{CzzOZoOl7~YTb;M$*K)Mj>I|iK(pzphF z&}V~VhE z`)JAA2TGqpGFhsH?qb`EXHlzNhZ`OQ|7Z{t^|iKG5*0_eQZ=1OEsxhZ1=cVI_Uvfw z5w}Q$K}1C2PQ|n;?d7qu+x2~xx2j~D;VHk}gc};Rqg6N<;C3-ifaXCQX`t?O(>@7X zd1O|XYIGv!zZv;>IwyZj8|v;drszgGW7<3QccZd&ZACacjcTwozhQPYF>@_3N#%p- zhyv(UK)PQ4w+pCVW zumqeT_-{?A*FOPHEw!2ok{CC=B%Qj!loa$GN-2ZP$E>-hAnlRYmHJIy|Kj$WJOHDg z9B9h#XG%Cx3V=a40Vvvy4rAy8Gq1ShC38n17PLUvDm|F@Y_Cq)R@>a%@9K@g62(-K z^QoH>d$@o$;bqh^rNo!>!dm=F>ySny8rL-F4%|&7+coH3vRM}8<7_L)^6SZ;l8HY= zeDt`TeYdHwjJL{1pnf?X6dH*o^*;|uadLX1W#TW-UKtGac%#FS(TB!jr`p%(pp7KV zyqefxTMXT^b#TA%jC?(7FBQ1&VtZh7dFH8Ew`?9pmV#4z7ErbUYfvlCl|dWU8=EYM zWoYt3`u^Q+@bkMW@%GNZ;jXeHxTJbgYy)8u;^7I_i;w`1~YFmT1C)}FbZthp2dFC$}TG>)D1s0^O z#1gaGplLCqOZpcD-S$x&7xR~FDpj2*m*;Ot_;EtQe`0|7=fVIE40uaBimFMhN^e&?6;x*-0ogJ2 zZ`d~hJ6A{z;>JwQf*-Z%U&V`N1AwENV>6nvuiEt0^c$QQ7d@e)winuGiJ52JkF^O- z-xw#&OgWDtY-m4mT`Yn*Y_*Q6$MoWND}SQ*GzzTu{`+jgcr)N zR7OnmTEbKfsKjJP4aSu(k7?mu @E)Oe{Jae`!DEQgz#d4EgO0C}8r0Mk7@v%0$& zlBJw$9lVLfFcsrNSem2H$LrUaRP7jB zC^6Ar!`bilN)h|~3#@{$g=Tx*Ot~XAAV7_~7bXLz+b@VToH7T-?Q>If8lATFv%@P? zs34MGWXUH8r%lX^8B_8E@S1@*%xG| zidoq&hgi^n*W$_uj^#GM|x7Y3S}ejsl(fPIZ-JE!?08`Wk+P;BOX{{*lh0p1ZJUx8zu*QBCl`!~BMnbjPc zdYHie8gk$}*qzQ+ipd}hFVmWkxRq_8m0m;gqrX6$s~C2&$*Ia$n4K0t^2X!hH}U7G zL`%eC0&15tY#Y?s!5lkrZzlMP!071vaf|3g6-OcZfu{?11$qtEnTIRlBzIS!f%w+i)-*QT2uoaL+ z$bq-Oc}QjqvU5yCab3W=xu7iS#*B)}jynf(BapLdKZl6$;x6X!0tY(2 z6Z=>wA3-fP(p9@Zz|mH&(Ax3SD*r!bDbdvSNZX2+}^I~Q%iIV z&Q{78wH6fhgTkCrI>%3?G?Gffl10EhOsSS;JD0>8NmM`!eJzROq}_!b3CmH(JAi?c z4vd6gFpbcj@%*KLo1q(swnsK~-hv))_#eRleD5y{`*%`Z0;mjZtNc%J70e$CUD4SucG4M!14yI8%6_wSx zgS9cwzZ%HT3jsXi(&uK!DU4zadd4c`Ev$Jm!}CaC6f_UzKx?Bh%6P}7lfd=A z;j5sYSAM2}K-n0Vn!B70{;-?ZQioUB))lR=f=WOg@cjL3rmj9EbK82LrqP0oa$Rm& zUM~0=POxznfc#l+ApcK8f2c(faVLf`kBgB2(NMPY&(%K=ck|7M>e7>@>dYH&u-7xW zT`61|B^u)PE*;o{$^U-Z6KM4T>pBN><{0Hyhu7834@(U^K(;u*!o5CRMst9?CYN8UTt z1~XbD^~Mnc2_K@fP3Bx9e6jhAUv;MY z%czDv2wK@bnapi%L4xf`mfUamo3iCs0@tDO=%>qN$?lna5R{OQSX4&vJ2Ibd8Tcnh zU45H@X6E>KieD;mSAVktm{$%u2fK8}rK&(=&#fzOoxbEPcRp~C4*W`gG{T(A@SRuX z1`ZAQm)C3D_7sme154^>{k2NkuI%sOe6-*tTs|Y}7Z%9Ez@vbfY7dg(E=5O#DWWa$ z)wworP&LP$Z8yeOZNr6Z@ab|tkTcsSqsT%)Ig|iZ@rU4itGsurMeNRQLTWDwVuj=QHb>-`Wry3D1GstyRBv z8g-si5qnlR3xWQtpu~WfXlO7lmTe77tr~6Fw4Xf|-+4!+ILi&o%Xjq-QR5ob5SFex zMmD~(A*bV4&%~?)`PMwsQsDTJQwVQ`ns*NR=U1F#vFkf4@~tHW=2pdU6FSzZx5L-nd_^5;ut{efANkCyZq zHC`IEY1EDVK})qitYI^JryZ)qQzC6)b_+!ti}uUq`%iaG6APJJ^+dE|Qre0n8##61 z=wa1?C7&pw9P1!(XnyN;JQDt_>o_BM*_^9FWQ1K-Rzp2sl$LoS(Vn7i2!9IH` zUbTryHq&o{i_(#DdR+|sQ()`;i0SBCkn4zf%vfHy`t?C|yl#5$bbP`^3EPD^{o)^5 zvBjTm)d^QbE{2bNA31S?YS-B`!LDHPAyFtLZQa`Rlqc@%7{kVN`qhU4?T3T$8N**3 z1QpG}9nX8*`*kiqM4{tiDJS&T<%ST>;E5hHZ1;Vq8PX~8`j#MbwSVEtG?~$$nIo2C z(R%vntMeDE6vI?YZki0$ak?=SKyqxwk*vU??=g?xlCCZCPfKTvZ#Xx7#=9jY)yLt~ zgya~_qRR_330Y|SR2htJxU@0e{kWG5n>ur$CRR>4b1!V-HH}@n-0@GJg5<#GZqEqT zL|-4?&blU9pho%O!L1j+Mp^A~mdHWl1)Psd{~0dCNIw`|8=Lw0ox0C3SRkWu7@+Lt zeR5#N#qjJmOx0JT!4kXmgd?Iu?lVC^fa&D&C~Ma2-K8wF&0uJ@T^SiAC4jd5OGC#Y{LZUs^+OxblE&y(U5UiGOIN7&bS==@9-M?zfqaxe> z(yxqJvDcgyEoa}E%@i)VEbV~fdmep&WV);B9j>~0;e?;X3Kt!;i z>f{jmH;l_|SL}G-$*cu57$?}d+=`xut`7N<^(cWT=yUUzsPoTu7JL$s zU$RBWl2?LZc!H87%X%T<+sx1a?07@QyN2r6&poQk8bHGghHh!(x0aNUizm(K3%WW->Z${6{TC_~%E}rNIOk*F@8b(Oij#Y_ zRz={E)ceX7!G_aQ?WsdLj?-#d#8s4U!-sl|G@`93@`x9kNS%*f!5s`E8eo1eK8Y{F zkk6DW3_L|47|J}MRcVay!wj?qbOyVaC|(g~ zN=C+8D>t9RtfW04=|~8cNyiNfh{hOq$-K2M%<}=t1d>B%V!Q<3@GM2}c4}5#mSCNnZu*Eew8{=s(?%udV60bA-;-W;-`8P#og^VAAV5VK zO~^2{Hr)hdL(mb5G~z)UG3{4!o0i4P&o*a!F!eKm1{Js)^b5kTO4-~%WR4bC+|g>A z?(BYVYS;VBbwBB}dnv()#y&gWI}0}>Q$F5fkNTAk0WqtW+1d2(XFgUk($b8FB~;70 zBdOI#=N#@u8a*q_9Ab@?+I{mfJG|(bv;udsMuURVY2w~-oLBGe?0#i!@g1pg#^wG( z6iIYb(v9lLKMnLGoqy+GOuii+d6rx0d6%5~o9u9WDY@M^Tp`LG#_14MK$XX*Ishkvw^; zi&npJ{056lYNoDXqnGvdDn>Yq)Y~u%@*IMW&Imfs$ z#NY+C6ihf_`imX;^37*qITtTxMe?1hcw z$f;!Qh41~otl#-gFY|~+OBXC+G;X+pW#)7%m1Q@d^VN%|kplS+^@0Jbkyv*}b()i0 z2Cetfy$v!gDWSZxr8~Jnd^hGO_$&^UC z1r}*5pV&1BKls=(#o*Gj6heQDG1`d~Re`9Lq=KmN@8}@tH=FfQ?*pK%+}irw(k&cQ z81pJ(FLrrXP1APAu^f-wg=L5GUcaQz%19x!LZZqnRrYHGOx2Go$-dw4w{n4&K1jl# zpxEqfy~8hxSj%xuKDF}}8GdPMEuPtXCpTSJ2@Q^K|E*nM9Fc&Ap&-^8fbPb5tJXr! zu?Y20_qCsfY7em7qc5xT_2m$Idfccv#5rj547Q?uIJ`OUTkzVaY6F_a-(~-(F`-Ib z3o+(~WEi{KAlA>0vlz0LpEqa=^gnhPm=$f!VqVG|(w6?2V0GJ-=X>C%GFb(Bacr{B zy0u`x`R?YnCnqJouVdFwg}u&gXqQ|Qp|WL}TlY1YY|6StNOT#~Y5B67)b-Vg3rn%7 zyYH-IFr7C0TMO*oTzlEQqD;_h9omFWNlU-4nsg{)8mL( zccvFVl0N4c&0sbE%Ft)rYTVrRPgzTr@g?8U@2^<2BmnqES`#Bew)U|f7i;fLf6}_D za$}#bQg4-FQp@!s=M zLZObvvlfcjd^=4{uIzU*>2xJ#30I|f(TlzfLhTAk?$W-SP|w2}A(G3@Cf`203rwSBT_kaR#1x}BhPCPB%hqfY^7`LW5^U{h*}7j$OOd2L)4vY#9@ zBeZcbZ_*j?XwXH(02+lApsh~eckyG#QM0|g>i-Z+fscnvrQ6*&DvF(xTUhwMR7B1h zzCpp0_d?=0>n(XbrLM)hxA2wLXXdSK*_<#-e`~pa5>zrAEZEy!1A=OX-58$Zi%fxZ zI9Y`FeY7P6?r|6Fu-8QWJ))5I(&68`j6|pcH3;4GXC;@AFG#DFf4d6pcHh+%w8V#0 z6+=xw&6#=0XeUUG`no)w57lgF-n(eKRHg$_J_}nE2T8G&Z-?g=# zY%SnIh+vB94il-y$A6mVm{!Gm`vl(=9ijbLz69f9k`>hu%9yv=1;ctBHWAxQuO_VQ zy0XH_@UG|Kvf8hr70HUnWFB%%$A&B0FCE9pdFVeUDx%SN-y5u07yvzJzbQEJgt{I< zVo%eK8sS#!JPlYzx2F#be6bT9`8u%g6i9*)Q67>6nPO$Gj)Zuh%zIh-zg@Lq%AcG3 zY4qBJ@B9#Q=D)4ek%do_8*+&@N~AZTNVua%RV9P57Wm_Kmh*9Mh(3MDD@>~ul4b&4 z8MbG6L--xVn{n@Y^E-7a^88Iy&+xDLJ15hK>!;LjF8#wGL9+K}rYBmxWP=ZOn$2K} zFXi117%kxnbZPGZv*!!41tx3V0@>8mR>P%M69$3)G8JCt(Mns%el``eZ<3i`oJbMg zvh?!dQ3QscvYGi4hc$1AzB<_*kdg1C*N<8sazqd9)z^}X)K)qU+^IPc(4^Jarxy(Y z6A_ih&a%b`B&(5QTckif{E(L(A_c$gg1c46A?iHPRWQf>y&W@_8j`l}?>s`!mhK3~ zxljQ769nVvDB%6H-h=F82t)b43YL234Sf|F%;*+<`G5=|r-ShJk zG^kiq|MUA|r*o+;)05GC2{fwIX6z#9{&+H-P?lfd91Wvz06yJ1?batTNu|w zWRI`I@rtnCRq&_PiP`VPKDn6^^4b;%mZ03%xGE}?X?(%~1NZ!io zLl2m+7#*xt{up35`obtH+P8GScTGQR=(9HFt%(tbhTK$Rqcl86t3ArZ{aVXmu=kTn zS+zk{x4}O?p)DuSemTofio-Vxw-)L*VcZ5n<&L1VZ;kB~VKxs34f&ll7JpkkzNw65 z%C5>KZ=dkR)3IK~uj~{!8{Vq`%j)+^Dhqnf6h0t8LD;RZQ$>t+<$INc`jgOh537`y zNw4B1cY?jdU(+OE$xWmNz78P|bg14^Q(y#AOp&Kv@`gHZtuSQ0xtmL!K676!y0gf4 z+rA=_aI)8>^YZ)s}QPmTm~v@eu$E=R~MRfl zA1|C#Z3_Sk4$dY%EzE3sN-)`bbk;#*P`G@Udmu_tX}oZU-}U9$#!H~&fm%PtsN@EP zcIMuT3R>aqKCzZY#xsZic8b1V;NWibdT2UqIS`oKdW!yhj)&Z>ENhi9CcGav8ZtDg zskh|h({O1Iqxv>v8F?;E+d)vNEqem}(RNP*>QAgs`#)u7QMNQWg409aYIQJ;+J3R z^L)hCz5XbG*DC{UamVL4vXcb1>DhBvr^YT#Cow^DZKL?1^AWLugdxzf0Iev^M_YH} zy?y6H7iw_{htmRN6i_aSVKwbWoDUCVK}g_W3d~bde6k}Vz(0KZvjPYBQ?aL)XNaf2 zR8OB;PQ~wo?*T7AaRFFFF~2;JCYdDaOiZM{ncUEdmBsi~2&KUH=)ICLY2$EY4cf4J z)fZq1?R{Mz)I8F+x)DG_E&ijjFri`Qcu?P@r0hk?y6nGDn8BtZ;3vWW+&@bT(NaW( z8*5`?JwG;l_Dp_1S0&^Bc6r?pPSYT&`erfF&8U=fY7y%$RiCF9 zVWRb;}9FDl%KcH2A zdu>27K}HH3Zd5_}3l1nV>^RcdxWRf+DC`2H zC-&t_>&r?T?OYTLCEk<%x$kIe-{lL0{WGgOD+oSHGRM4_-6%eO9|-a{H|0KfURQCZU)v?rFCwYOpMQd~A*-qU3MB0DAcMjJj z7p5{fwc>cduDm|`wm@8iZSBm+q_-ezs=|3b)jca;Xf;h@DwHjE#zEp9pD#%8QO-q5 zOj_>P-G^|kC$6Xiu$Td??P8eqHc({5|Jg#=r}&-=O81jGk3~GKcd06qPwAA*I>5Dl z2Yr0ex%owfdhGDCs9&@t<s$WF91PKW-$h0oV)@BL9l(i?-q|21eWiupK2N#Nh_1_Xz}e+8x5>uM1Q?-hWVR z1sM&w31LF~RYhepg07sZ{7gF`!%6iq8(h&-H$<`8PT-t3o_?pNJ02E3IV1YxSozKteZ?{BS|* zHwTJeYcBVRHLdIR-Fq?F&P`{>;+)>E*uE-rX9fvlcjNKK;7#g z55>0*6*u107XM)rJ^rOM-1qKN)$M=S;LK^DL|h#>zpiwrT?J+l4?Db-S!9?@2mb~y)um>7d5@$D0wP-t|!er_i7Zgay)73JcsM4&5L_ev83uSoOdqDs?&nbkK8@H zbT_+;fjxucFCA};tNn*Glg}3U7?i+-HAQUmheFlimbu|72=u;Dsrry&-3O;(T`QHI zRAfp3t&HKMPQ)Hnjt)MjvRIJATBe~g2Ns`LkE-gWdiQjT%e%{J61@IUKnJm&o zEL`E+l#2~TnNkp_OX3r^G=TwpRk>H9o8RgBv$_+ltvJCiA(TJ@CQ&VZV-zo+1Wf7e zTA(0iguVaK*6JnHgP;K)>jSKXF0b*?ejORPc0)0L?`;+zJ*=zY9MQ@oGw`mQ^KhkIgrRQUX)>8(`c+9j`(uL>E z91mz(e)|0RZMQK!l;A37tHl1*8SeGs1do5GtZI?0Cnm%V97} z{M@UgT1STtH~#HoWq|posJF*0kj?`5G(VmAWl!*!_heCo)8!gripJp26QaEh0bbi- z(j1itA>uRp%dUU&BxIUyPW_;q&Q#TFbV5W#!^d|bRKpfCb@wobp9fg+&>7rvWGg<6 zoH$FAD;FIm=WzM=*z;%`c<0xjMcq|Q+gz{S2#JhrGs~}Ssj!|MslP-|4O7F!)MU+a3o&YL_Y+6Kqua5&8>?-RgLrYb8MY_Q1PXfu$)+9cS-D!id&84@I`RIFGymO2Q4X$-CNW5ABh0 zG2(xQ$WLZZuM-w-+f?1Ei;RriSs4t1u^z;z=#HnZuKX}$m2ZF2<2;_)V<|OUK~uB6 z#H2UyIwhMPih_ayDf{+}dYqe)F$F`+P35H^GH`m`f=*E@T5>X?5b5!!UCqtJ!TqDu zb1*XtT?D7HkHh1pX7wk{!onKe$}?Wb(6u%ld>~|IdhwRfJikFf(xQJlSAY8L@o8mx zkzwz_Zc+19%d8tUMr_3O*PMH9HLQQT6@C!aVK|Vd-Tu2fIp})t%C0}PK-7GDc>Jad z6g|6-&0c_sHv8$VtXdVp@84r1qhXoL$AnExmc(a`|E-6PwsJdWEZZInSs9Wq`ZAIx z0R%3cFtGxgR>M1;;TwZ+{bQ^BWH$Hv07>53cd^s4J71=W>?m+8JaWv@ z9-Yo5>%TuCP_1++Ci@&;wgzqROe92+_H7dq580AMh#JbJXuhuI$e!to%81r}oz8eaQ^GcGgk`fXH8o5uNQtKu?ynjE0itlyIrVB1EuFS!KmzNjaek%+q z#3a*tWMgyRWp8!50gL8k<6E42x^>r^aE{`np;nq9&an|+=#v#GFzU;)RCzUyxB={l znLLb7@p;L^dyn?5m4~wc-t{yK(o0ttL>eBe)l1w;tM7RGmY$r0=v{!q@e|I=D$G6v zlU_Y9O2c=mG!xJDI63%sX~SaPz0K60m|%OEQuPJuy(6HM#ihL*2=xq91{nOGRbf~L z%`)v>ydC}fG+!pi$%N8OXIy6Hx3N@r9lqaCC6BV#hz3jbY7t$$!^T7`wSaSQRMd|~ zpGy>+rhW0$=TJY*X7J-P-F|jf7S!5XhN_9K{7Re+$#b~x(q$yIYK8O+VozfOy#yrz zyS3G1C^YyC)lly(e#Nr{|*V!zt_+N*F?XN;6`qW08 zi_t!Wjh`&mx9=qi%G-VTKKG~}|8Ld9KL0xVc)q0mhQBv3e?dSqB!PNH3CC1T4Vn7U z*w{E52os>MkdW9nZhY|b&ADg!vgN|u(Eca!EWK9Ei>D#V~6Acj_? zkZV1m$H|>H8|l3-!&A#a`yge8Mc6#sECaadYwFq#QuAj`k{$#eV}+PM+Nbh-A>YnS zM=FfX0CVyxGFGUNZd|Q!NttF9VYe4Jdk-}#q%D83LuR^fZXmID#W=^bk?&F5V2mlV ze|R{V+tJQ*7~monIQ)tWCc1RN5GL6J0dy>U{`D;sj8PLT#c zRL+x{nweFuSFNBbj+V2LDM5k!XMud{dILG?HJ}_Y)_!)9L+ z)39!)#+6i`mk5s)Vcvr7z6e7p?(%n=i6&%Tm@XMKJ(0}DdxcMY58|7cDWed9CLw`8 zrT!jv^ia-Nne}0ELwDor9V)2QnY>GE568)&;P0K_V;3UzdTJ}Z&w+H3uF;Rb z9Plzo_rmx*;39k|R~ib3fwtw&3p52}BB(I73tRN_aa;uYrGD`wY;;4g{EbCTXR)Af zA{(L#a^ZBkn!7V9Tpqzb4#5m$UDpB}f?fE}+pg$N4}b%Mv=LaA*R{OKibZ|HvI{}1 z!Pp0gs*>LZkGF9RCN5fw4_2d~-}PmnNG%Cdbryp^zQ>2OTXp`vD zM(MYm#YfgIDnh0V=S?h>kzmlsh9bl4pzMe+X&9V1s8jVK7!dGNF5Vpbt;_Cw%)T6O z)<@bmOFSd37>g-8xffc7G|q}I(bkg3ITFF#jF^dTBS;n_Ckf)I`9?P&#a~=+gGKp= z>;hpAt!KoA=O_qW^W3lDV)!Sp#On+7FfhG&NVQS~QXzj2VR>P#BMA()eulpe1xYb% zlWM3ef`LLy9lCCJ?j5z(_jB=Su-VWPwC}|33bDqOGIH(^Rbs zk3>b-kC_~xmAa5iL;Y}!^tEeP(;4O=eqfO~46oG^R@SGmm+qe%ToujlMI!ZoWk3ZG zPTS-0OyfHXg_z-+%YK?TQe53jJ@hExz9zDj4%PR5g%0j>`XKZ>{P_GGs#fOD8fZeU z&5X2D{(bkiJVf3To=_wLp=nB7(odRUqDUVvnL$cj#U}&VS2lLK{(e)n2Jh(%8ZQ!$A2q^D-kh0)R`ImF zf;UvUi#)tFD!uN!b-3w({h=UrNV6`CF#gM)2ET|rJ2|f*ZJ)Z$jQC z2Y=+Ko}8p{E3lVFdxP;BJ%4eQJ*a5%aNR+WgOB)lA~N>FufDnc{+lHn4$GJ*)K~kq z$@08Ee*4Qx>K{}VPo;_PPbe>yFZNZ~MqD?dDmv13ITwob#LeF{Xb|FbnWYafKG*c= z5BN-9`f8}ZN%;Z_g^Jt#SSI`3;*#=qFvdf-plqjVp0IVk^w~*LrW|`W*>Kg_if?4p zM*iJ=4|{huW(1B}*Y{r*U)&_8fGR?%=XWS}Z2$;U2t7UKR`pV{-@4Y|WnMGHcFX z$B6&wMfj}>JYABUeq!A*T77Gkk@UbIXoQfe`Keu`b`MIf;JX>IuJ;7V@X|xC#aBIAW_xQ+0v2Le)zf{r|c-JbRD=PVMJz? zCC|D38q*il=?<0e-A?jEZ}k&Sihf%z`R;Q^H@rdQ*|ZHTZUlaz(s4dno_Q zjfA9@SYFj63NxDE{+aCfa`z{Kq%@+#vsdNP_pXz}#h&Ha%cokVy>7aW_>87w6U4e^ zniCS{9h!+JA#Q#+febNda8<>fPPh~1!NsMCGR3Rc2ou-49sW6tC}rVCq5%g-P(Qdb zo^T%18A@+&>{@K`Dp)R?g#o^bG9|8NR)VeNUF26ZiRTsM8a7WF zvuWHt%ZxiNwzAkq6f>Ro9Lri0zm*ZN+RMERD?vfoF(Y>IJ#MA_z~?|J{eWrjd&d3(K# zXA6C&(suCce@@k-zbCve%Z+B~jqJ!u=-9&$`e@Ea)`@F4e}CQd>ID+AQfAB4PF|V2 z3?8McWo&WQxNzqwzbOz6Id~)3=fq!C0i3@Xk}DNi;UiC`cN`ga7W*o%7_7UCoj;7s zuL<#JTb|9PWo{)#WEG_)AM@BJ`u^fWfx;7fxBYnYYp%Ub%T#<%*c-O8^h=$p9>7JY zL7vWp#(;cTs#4R46leEc63zOssD4Wk9#D2DD`l&TBq8+r{`4kYTWb=5wUV(>DR*f3 zBl&j!9t=|+Z`|CG{ZDb~FjP>WOf+O}LsNC_rt`lKpd9nKwlkK;?Mc2xOhT^#0}1i< z{0r7}eRtS?^cop2Yv;J`;vLK73U*eP2vm{9L9ILDFLPRqzmeS?tHR=BHj9vn5xiN= z>~Do^-B%{-i@VnbZ+>D!!T z?NZGHmd*MvkqaC-S+;6ht_vwAh<(}XP14R^8p;$2NvR0ld#i|it`o$OQTl7T$D5JY zHmA`^(xE0jdV=0txcx6bW_)@+Y#Qe_!7b z6O9r1|F2Aha>!<)bl!Msw1a`jn$@q?`;V+=-b6mips0&uL4~Ku>wR9pA4y5#Xk}c} zFZaok*XvWxL6!@Z738UL3H0|(&%Y;Y!RiBY%KslX|9jmgE`}2Ffv4(?|F`Fx3cFyx zw>s<>5D->h4}(sYk^(5XbqdaP&yQ5c?RusE?C+~abD5VoZoMc!Y$1oZfihg6^5~H# z%x&I*vCi6U%E(Wkv~KFkl5Of=BE9!3MLhI@QRj90r5@2@!w!Y>v(vEX=>uddW@qnCNlmTP;k%AcOhI9C|Nec( zRUw$IYYQG?Whp#MGX#7_$o2hummWZKQcpD>*{y0KAs z&~EDM1-cLa<3E|#6rVqz#@tR&)t-T9in{r5k?7sKck{StXlPPXQtt5aRX6UxG%~8* z4No~cJ+4057_U}6JUp~;^YZrYPn8UpX#M&9yPTC(ftg=mVBUJx-!s6{n9Z^!;JlM@ z9p4v*WR8zCH8qu*4d&rahQ`P1Lbm_Jb?{`{JZ$%{5PW3go)q!CK~YgrxWmBF?n>o1 zGGyIZTwagZBS9M!B}nG_GbeV3C{8A7oN zFJ2@Wc0{H2^e6<7vkQmVO}g!8!pA?pe-EY)pb^q(Yio0!_93%_y)ps!eWb>z`0Sun zNFwAO>Brkr5ebv0>kfV&KiUcy-oAfd0otJV`b_@{ocp&GwB;_3Gh3>MHcu4OWt83B z>qbh=(xKsaP&yFim9o;rpT{1o4dg~IbVR2^ueE|K|JK`?nVAyf?wf-ga<`TWTd5UI zOfunkrV=yAWWb`z0Kl$7SCC6EVC*n1eC+G&*igHSFzp`t=9 zGJ-YJ z3Ldv$NI-i;qMoaAW{5VU_Nc!gzK><%#7X=2&n2fZdK{G1xVWU`wjGYA>Xh0eW#rS+ z)9slnlNxy$8P6IT8W`{0+ZnO^=XDr)yfxY(mX(^G-fmdzRCN`3$qdrY|EN5p|I)bs zi{+KOEOb2F)tB%X{W8VgGbvtQWcsK8T{ucnA4{70`0Kb`+ki*7p*!hE%O+ z!6K1Q%3SywHdyUo2}0ylh4*!qhg42^m8pUdZJE>GG9A^Y0xE8Ktg;x^t}jEL4$*Rz zm6e=T{-C_fekD9cmIl0&B+-wItK2SoubNw0eoRj4YFF8HL)gl!1b<3c$K%w#)O=Ls zb+tD9D=sdsQS?$gfW=#XdW6I6$WHwpsdNNe;?q_G-G+X!-0!y5u6m9zGBHV-n3!nP zyVrhU)=YzVUZ!rnJ0ml5ZoxT7hW+|jrOg_sQ@O{FANy=TkWX1y*vqghPTPd)U)}vV zE-o)uvm7Yk9z#HD5N7+bS~h!1f{3Rs^`@JF;3M)0WDCiB>lGrCuG`W+rw>VyxK>9j zpDK(oPHR=ldNTmQFj-^imo46hIqcL2G; zk6rajo7Bpcyy`sRW|Y4t5rL&dj_ZCL=R~ z#V`yB4edDCnq^vTtwXbHp-^JAFSN`!c#ZpYH>IFJF68Q!D+(5IK|w*JtU5XH9!qt{ z>!H0%eQ^RVeK7qR*+Q7^JY>MUy*gYxQe^NE^x@c-u#k|X)z#H@O`?QrpvWPjnh$#J z)~#D%(Ord}5QQ9nJ!)&Bv>h!SZJ*c)(^dp_wwCBN*54yJb=6wvu2sp+1Z)0JIyg}4N>^PfH2w7YepfV$z9%Cw?rK#-O7=T$;i0V z$mFSE^fQ9}XtlVj#AaRsjG}gd<>SY15GDTD`{L4ETWA!I^^4LkRHUT0v->sH4Qc;1 zb^}9SQ#ZG_6}J~U`U~}e_x9x-IOnXjI|`|Ai1UOr%U*TEiSJJo!8$xT`Y|}D3Xkc0 zI3GU0vclxsBtLx&+ZX^?w*dA|SS;{*cUvff{pQqTmROXW8M*!g!=L2LS`~6kDmiih zeprUiA>Xt-TAn{MJG;BpN@I5TXI`T~?+vxvPIr4=!~bk#7dP(m|5&nH{u#+Z(mAVE zA-KB+lPBDE!)$D9w7|l}+HKVCGcquUsbrUqxxmBZR(vZbof*B08mxlD))CDu>v4WO zNpkh-Too@pc=0g0+QKbXCjBBFc|t@4IkQ^9qq@2})8V32L_Y|+9c6$eCW#}tbN6od z`gpZ={{H%Sc1+B5pSN!_OnTEo0L}d`?D*iu`5Pv4U~#&}rso+2#{|R-`R#0#+#aE` ztsomWSWR8fG4t-pH00zNzkdC~^{DQuL(5-S8pWQSQED)+AqM%6VAt2mr^5F&0d!NT z>4^TwoIbO0DYtwgtKhsRRk9;fG5trW<#<*9bsnpP#{C?v%A!Vtl(e*}U6=VVRVgq$ z0rBzi;!;u%RtEF)*OxCSbJ@&2gFEY=oSaAjB$1x1bL)URF|C0#ZnNw}Q0T*=)!u6% zx8+1<&sy-ElqRc<+ z+qW1Y&x^e3?yyn(T~=0Eun>E8;@~&*=cpH(+WnlUbuohkBPZ`|l$I=bKc4Li8yoo) z9EQI^=jK`8+B3d{_YlGXJ^N~d^xuv%d{T;KErypWf?FUV9B|a70o~vWjj3lY?{DB&e5@A8TKE} zYi3T4R49Y2%Cl$hlai8lpgtgs(qk}B8@c_->1ltJy_tc5L1oAJ(MmoiC}J?jvS3oK z3A$DUQVT3cO@d7ipx}5)d|OIRO)cVRe?xcLnI~2TG@P`&ynNB{E7QTe*!vtD+S|;e zAIn}1C&7=x2K-)1kI%D;2@*VdWe8RF&?SaF(*0}reT9#&c9_Wa$*x20hYKLsdd*b5ZDV1A{ml$=$*~B-DRoaqJ zQRP+(&r*WN0IbV+ z;+k^qUZ>w0*bm+bC}ypAH-k|@Knh?#kvAHvD5Ul{u?`Cht87^ruP$#1q{_x2zMTQO z6qkTtkkYay@n7r?5^XwBQ~Bkdy28@Y?)tbUWryFY*Y=#gZtox5fW@)d38oc|jnKk>7AQ)67UndtR;pCT=mJrXe9}t`QP{CsYft}!|LDbq7 z4`I^>=z{o@z;J_WChmRla!;XtE4?^_OpIK-pj%&^n~V8GjXE4-#yfX@{Yn%WZ*hdu zin+zbba1*%J7XS_4D7=*E%}h?{Mzmk%4e-VQ9uBYtjCd}+y1yiNJ>o&f2nD|L=gX< z_kaHU*@4ymLP-a*0BWZ9`zN|m+eMjNjZz7R38!>__K*}%iK%I6rm!Xm8iRd!U^VrK zgp8~@VEZRW*Ka@ za&Rb)mYMg0KGIrR1Q3u222s^^;b%8gUL=1YW>_YxKk!+(|{0PEI2wCW@(}XOrh=W{T6yY9g` zrR28g8!FV_&DK4YKx{9ZyfhH{EI^Zpu*k&mt3Q_plu`&{_P~7P!TkI@q8EBRx^^a< zS8{>Js(NQWT-UV03sa-mFu=^k0#<*vC7>6IK|Vt~09>@)<^J~4H~_ZJkR{dDe<~)X zS`}~sMZyN*gm-;?O17<34&bpbjg)3nJhVw+Z;9}^L>5JJws=cGV4R+wzM|G);b-LS z+qYZHbHKFCTJdO^1I}Orjbc~J!o1ZT5nmzR%k^x1vOy2G^W zHRMSE-{cL#8E!ExjRHTmY6K+dg{!Npl#Wgud&}uIzuQqsCa41Sa*J3^0RsL_PYSMA z5%A_9ijhQgPnApr{O_JeQYvnhdCYuZkori<3=s{x(vCQR(Y|ceu2K(r28L*;n5bBY zpX7c=K(lVz-rmj;BK7+9>r~F>Z{H#!IgGw&w#p|ztpt}U{``TUpx{DZrs9I7$8mJw z=vrDvMn)rh=)Xu=`!;H-5dxQ}j50)($2v@O!--*a@dw(;w$??Db9K-UNPtKyKUXCi zG@kvz=JWD>z?GF}Ybz_+z{yv+JXGgat&*pe>AbsKnHFtPB_1-y@@8$Qa2C^yiIK4< zQN$NPx8-|)K6HC{8R+RVEGO$sd(#5e>y9mIw^}GI>L-~&W|1gOMFkHg7FN4Od3H+5 zljHpj2g-C1_&g6^Uth_P(K19^fs>*P&aSE1NJ(bY1Ctr&*w3Ho(|a?=#>Q;y*C%Q< z8D!)7ptfhGY;D4AUwiQNaeuyh>f!c3hI_TJwFGcBqops+(N@_cgqtA3G{#c7Hc?wG zAt5o=8Fy|)88gZ4UUQi~Y*d%U5Ro3Ri{k^;9_MG^FppF_0inCoH zf>d?>LW;6l`Z1XZ)?X5~iWP}#DW*wvgMr@Q>S}FmwZ~a;TI`Hv;(iJ-lQA95dtY!z zV`PlNRu}=AK~2E}{T6?l?Q7f?t49ucdU|24Ir!QASqAN2OvbD1HLC0~b)4o&P8YcA zyTPmK%Teb5?XwIb>{SBZ(~qC3){7R82};Ar~wLz+28)9TOWyCSexAt_x#=VQ!$ z5+M*hq6gRH5u()*unjlQgw2qUlNncsg%W{ushepm-4-9~M0bN(I7Q zCJ@6zu+bM5?JNLp%OD5?VEL5l-A^TTiBO)Dh)-_%4Lpv&8Vy7vxAu9{yB}R$4wo`H z5AlBPt_)V6X{)HDfh%xFP*5kN4{*2}I1_*~lmW7jr%B>G1z#BPfvR8UY90f~>4%az z$*jQ_g&8jU>ql#r9@%gp+5lKzB4e=&;09*V%h!42;JM)CY^xI$?N=x3b$#)vY-)8t zD}(6u0qU`F1K@ub76=r6_^5@@iL?O{&Wei4sy~@$jp8)>g2ba#syny2-HxWuie9g2 zfJ1Q?qG}_v=fQwL!SmD9gu9l2k+oSFuW_=4^$QR6(a?ES@b!wrm#bT}2Wq;rko!AceHKt%DArRFEYW!vSsM*WPVxuk< zEv*>92%NSHQdC0jT6AK8pXtG^TMFQw_*L+{X=g}3=q^3|lWK>J0pLO0O&2;92QT4A zdnCV8QBQBL{cHet76MLJa?3x5h9*OeUi}aKS71ev;2vxe5~x6oD!4V)n9Cq)P*vly z+?N>&j^hKTKQA#cFtkQ4X1)br-L~qAtI8`vy_ty2G>SjI)+Cc zf`;Ia$~}Lc2xWIn)#G`f(UJHuL=~05O|V<|N#V9PY?QZ~5$H?ft_I#x4;)rbaG>TF z78nHtv_o%8+f-xJ-2T?mVhX0>sQ&y|2;w}DmRuUFKP!cxXBL>r-{JKqsc7ivrTVS- zhzE%valzwNj?)$0bA^1JS|ql^tW%>Vlio|Rqpz=D3Ji*Cqvf)2N5yB)o&^kEMh)IS zdjSXYCs?K*+6ojjH2H%%F4D+?Lu`c0VoYtC%7S$ejx8sIU2f6S8-qzdnhTZHatIah z80ek0$^fH?C@dsa3ARMn#+aPjBIdSqREik2(-Q;_8XBe{RX-3DQ+C3GwLqdkQ(xa4 zwaL0CAyG-znoTe8&~At$uCx8gfwlebUyqxBp9HEyVOx?Vvx@HnG8hC6oC)qSVuejd z%W`#G)=DLgg#b@DFK4EI22^lx;5ZH&d<2^V$ecs`TtJb>;Kx-Yj#TTs8`5vu4@VS$=A4E-C3%yH}3$*2Bn?Z z5Dv)#rz48jKIk|Ic>ARvMub!adEQA$Ap1?ldRE(Hkz5fNz+DN!lu?v8_^ba!{> zT^szr``P8P=bo=V9_c5fsfNjzc4wCjEy_VRIW z#h1@l-g^4I-jJ96`ryi|kC9Fq1ID3J7eBOK6BKW-rVlNeigIzU`Pta?SJu8{@SWeu zmo$Nvy*(zw>6d;oG27{%_NUG| z?b^unv~QE1jlJN1z1^V2^gxlVgp`yHDqO7V8?oAhix)5Yy?twAPxCH+Rv&@ePEPF_#%nwXeaJI8%>bt9we@M8G+_|lTb=>AOn!_#}X`$L(n zQeS~6toZ8o_JgJ6=(mdhuBydGQ0ZRs($^ouEF6?Nnog5sIyyQlYikS~9Pfgt`9Jk+ zxA6}rsbp&4oH;{s>5@c+`{CP-g8s$U2rjR)1XLr7i*oRzo*cvAd+|Jm?YGQ&ZXFyR zp2Nr2gIC0edz3#ck3Ze+CxI<*dL~=KdGwnv8MMbVeY&YO(~*FExW7GJ>rYy@rI}+u zaL<=;vShhjx5kG+LQ>MYhJfE{p(ndk%yzn6f3*4yyGCh5yvI?b^XA;I3J(ts_-nUe zPnNdXaCvNI#Zgoko66eZ_ArmdK;ez!@_(^q&v98t({c1n{X5#0L5E?vXq35XsUsIG z8T+%JNj*o)<@+2&A}biQ!uCl|zblt+cB#P@DQ9XBP$XtuAZORw+n612o=?xU9FB7s za`N@|#!>#EidhL4()8`=cgOkk0>6NO@wxArQR6X5W9Ny>NRHOPepN$6Q#7dBMaf8-4;*EgNfV`lZmjkB5Kw2S zvg6YTWHp-N64BM_ac94r@6r*s?ft1==Pu64naSO~$j(8g!^6YVTk6DfxVPD2$`jqT zG2b0bK#lr1OE)_^tEaC|LPLWOQHm=qD{EJ1c>6YnwY9a=_CS_4_N!N~>=yg@G8JIM zZ$-Bq9IT`3+~uXD&ZecM5r&^^@<;TX9YqAjxO^pPYEZ+gb5br(DMcJ_RUGZHSymjP zN*tD_*3sgr3h`nwVs3p2!#|$o2)G{<*6>-6NnltM&oWB|QdIeoFg7+eVrVtIqm_*p zi&fEZdIN86^Y;=NSsgCDYvr2@m)H7W?;Bl~%kINM2DmA>x{8pp$WQDqS7dgKL|0UJ zge*HiaM=u}78m^?5l)qltv1QKNJqESK}Sc|-X1I5Vi(*%?-2u^Q`!-=&v~V8kY_uu zwpOCLx|&AA!Pb^hK%lVJ<4&Zg3ra!!&@R&C;>j8QqMTZzoBDaBa{j^hUt;_?d&+A~ zqD@Ur2P-v1a4$YXWiHsnw6t>DOM?L$o9pXre1YXVV*$CQ-R~lK4A&NN(A+i?(iTON zhMoeGpFGYX7&_dQlPbtw-*nI-q)#XaKSyyO^nGG8PmeU@)V zsUWf{Q6$!>#~mhYR)Q5y4n)ak@&%T<@D#je(KBk z5Ye}PV3NrS-@KgQW1AMz^Zq?CJNbnN4;~OM!hgaKF28nKpW-rXrzzi^qV6lOjPH)y zFHcIkM#XD1VugN3D?GKyT5A2o-X3Mx9>cn7D=I1qXM}y-ffQN$v_+1-(Qk=i4i%)z zg;PDr`$^&3>2&uC2U>>#Ylx#2%$9J@R5*HshuBzHMsQ3b%WGCfYa&tR&xbw^ZO(VESK-oU z8MH+)xQ&mGQ;N9?U|^rczrw`yCeO6{H@q*m8~N>r56_pEmlK+oId6`A&n&;OE*evn zETtnVA~N$kS!O6j_Vy67oT$qd`>GoxvB^loIPJgRo)XSK$Bg1NzAGW&T_dWbq_n#` z9lyTRn`5Z(Av6?Dr>#ajoEP%wHYs&=b$vHcWC2L5F}-k!n6F-ag%&@K!NfibmqvvI z&`mf6(tgepw(z{<@dsZ-LuNE0D<>TYSZ8dTm5^p_R z8>N61B&w^Ddy9Q}0T6WDb~BGE@uu$i;9nIJD-W!|Ix8+-!7#j>UNCf{`}D3bQ)DBn z8$%dp`|{WZOB@WVUY|)yPM#!445r|E4GD`A8HW3YS&j~oq3v1S+aXrR$6$pH(vzbPHRmpMbmLE z5rVcUH5`?2cf6)uKmP0o(h7g#cu~BvU2z;cpIH$nyi~|&nQkr_NGBR~j+7Z=rI{`B zQa_8BSlq&bnePvsf_?}*yad2=x&gQfltTR=_v)L383+pu!BPl$Pk_Yl*l5WGh zAJ6k{Sn8(K_!8-&rirdpZ1tJy85r0!&^ibV7*4fDifsN-!VuZ&(S?QLvKSD6{Vm#? z`~ED?B&?>m*J0Q#94?@vz*4i-fCskM0IJ;6<2RDX!8JJ-nr5ctjOpxnFQTreHmxu$ zhB>g?#nCG{1~ZSLFV{HK0mtxJ8PuyEY6b6xJ&t2%W{j39j@_k#>Duzm`@cffeltgL zOA8}1GBQGzd2f4Jb7B+jaL8$zb_yMjCg2|!S?T+H$IJ2fa63TsTPn)Bi1@-S?P~8* z54Y<-R$N!$iZs^TnM3`btAF$Z3DGZXhYOtNj;?Vp{uLw%J%kjN5VbX zNbWa~8c1kq2`F+TD)#2sQsiTsTR1E5DK(ZNQjaTU(nD+c%@0>E4egKU-yc4oK~0 zF(LhDNFl3>bL$;Cl3Hisu4{GFFr5JxmYF_%CU*VLn!-W>4cAr4t;PQP%F5)AA3uiZ z$!{d#<1uK}st=?jG~XU_;mj=CqJqy)Fhs`%QuA{x^U>4O!?}`*7js)nAxlk4xBbIf#gbrouCW)Eu(vcF3MMP~i^H<#%g24AX#xEHZ}PfAG3#m?R;M z!0k|GIgWWhneVjjZJtWXc-gmE&~uS@+5dje9p;>DQlj!tPbXaRWeU)0$Umrb-n%Ap zd@0wQD3Rs1-47+1OUQd+GG^JQJD&X{RR5AisAba0iE!bdW=GH=OLTh`Ev_|6GyR9@ z1XS5jUeYmTi*74?=-d|dqHOb|_WM1-l4HsE{*KPapYdujYi;daStVU(Yw3utmQ!rs zqUe|2@{XP-BfcDMxt?a|-P>+$rS>bf71<66iI@jh^Ygi*gT5EC)*0|f@Ot&A<+6z^id0nUN1V{8;Gjjv( zJKNts>+<)kAtAXmNyEe!c|`yxgL~ zTEZ*#j1mDStNq0DuGx!`QB2W{v;mi5m)rXs^b zcUtiL%q=$BnqS9yREFHEkLq_T31VBN5D_?UAbQrag3z?W$7?_62nh#87kz_jpXH#2 z8}@4x50C^z#cvUqn#uwnvaviI50!L!)8F5}N{^eY2vQ<;Ww@rhzppQ@TE3Ze;r7Aq zI`k}8xwx`hHv_5ozV)E{N*pZHGKnbOi>q-L3=xU#d~Q7K&o>Wf($j$|EvXR=sEj<| z4P=AzMpJ;ujJIxO4ZOVk$-{@00%9APK4$O2?@#NO8~W3L~h)8n=BJ<)BR%izWxy)KmhoPnHps} z;tow8n2?eI{fYN|dbeD4zVN(^<`iPKc5M_jCW^Z`TU3-F<*=sb>Tb{IL5>-xhGz|K z;QYSj+CDB0Wx`gRf5&I-O?O=6-ikzUw`)FNv6y(0=*4@S@5@JvKTf>J#KN*(LnMwM z2yim_Me|MwNnSXBWjRkQ2&Uhh=bZLMqTcF|>O@30N;MS8ans25h$(WhP zfDUcE?y}|t{F{U%r3YH$r`|c_XOQ<ziVmpJigKCx1Qh=FKo_GTwTA^xUxfnTq4{{6y{I zcdu{zT|L<-=gXWQ(}!iyl-QTG4&2g5#^XIIUr&$J6>zv|axQa+PtDH>Wvw)?^<)V7b_!Le|;@ zyHPyY_KTig`+?%b+l#0^g?Mj%O>QakL|t3D=(~E189s-hJ8q0+7Z4;8fSjwaJz= z&5H7S$DW=T&-2Zknu?V()crFvEr-^rElWd!#rD3|c4Njlj-5lkee-`M-d~5dWgP)U z!Qw{|DRR-j0Yo#zhet&8 z*y4v9q?&Gc%IVb&4S->HUR4}SauQ|^ z+xMFfz<2nMS!hcp^SU)!Tr?aBOh>yhUwe543>c17z8ZkG&=mj;a(dt%FYh}oR~(B$ z+n)>@h+((T69O>l6Q^c-1eY$yGfSuWZ?fmJ6-!iMJBzHzE?qQP2rRX9IC|b+i=#WS zRjDz4!;?DFc&DeLVP<~v%!O$RYSv+KCJ&RGHUl%o zlS;nZTrBl>%;4ZfvjN*)RIj>SuZmz-`6m^GBgxj0EE-zxQnSpq7!-0pK6Ln!m{@dt zv`;DO%nR9=&6ToY`1fo4CH z=cZuktFAr-J*YQ(DP1kET&gYN8D6kAa(k04Y|SL>w#N&M3=8z7DQ^2)JG;Bt&?`^1 z$F@L3&2?-8EhyRUvbnliH9Q;-o6iNz6I|_m$aWaV2h-xaz}Z9yy}ZF$alnlc@3uwR znIfk<-5wkM)Imy0D&+aVzyRL4bF9A$fJliEdU>9nK3->;jGkTr+IyfpP2ZMwrK|A+ z_*`!f%6p!Uv|kCqQ1_sn;c$2nCV5pkDQkpy=JCvN$Fpa*<|W5x;y=fDr2p(6trXMk z{MyN)y{@o(C@PX*EqioU@mDtSAkJKvTSzI~uXOrl+6xM3Url%3c7M^ASaZJBGpiko z%{QJkPtMQFsH@X(mhUh?JTS7c1$_N_9fC?tkLL>|kHIZyy?pcG41{-UxCC+O)^|Wx ziBN~zQNh7PP%RSyo;!8ERZ>*!fj(BhyUgV84WtF{=z^qpvBr9oAjL-Cz+i3J<2V}9 zKxt1m&`GUOrJW#}F$6r0L}GD}>4c!pZ1&(m4$cjc|t? z!0M;Kh%QX&bK%F|?iC+d6pXJaPuO^S-<75>{Hfrsrp&z1kL_aH-ABih@iE70%{x2c z$wPLkGnNk{UVj8TL&*`MoF zQMFW<{3`Y+8bLjp<4`*qR7Q(OTT4G)juZ8Ry1V1dPm7gKIZw97eq`3n3JOMpbk8pv z<@9Uc5m{({W*RL_D>y5lUaFP5+!r@ItjBGCk4_X*$brA_hqA7ON@D&As%inAw~#8d zDFQ`9j#;)hRiIglw{Kq+7h6x6Uo^U<{x-XPt2ON)FTWpEle%1!cNjEyXrr%3QS}T- zwn;v~@TRermVP$J9dFhzpf0((+^6k5r0z>t`NmHAf&GAl$EOt-Vk2znRj~+1h7!i*7zN(iCLrj2ix-FA+rv&9snnEk&NF1BXZY z;#T^~a={hu8`C}UScd&)&zk-@f#f&Bc7MN+NNVSbPI#-U4Jj6rvnCid{#cLWl z%s(1I;v7u(`sHv{iDi{|oM-kutqt0hjl`8L9o2OYD=T_E{jh@~B+61_hawfz;|sKF z&I0d+jZ4VBwb|M#8|QK4gs@UjM)C0QK0_WtSWRzl?^1VQZmSVcd={VLQHJx7<2V6u z&0ZB1;?QwVOiVDi*)I>ZK`whJEsb|{bfgZ2-*tP?A$iE5JL84A%VHh?m+{F-NkFFn zOIf~G4#W9YQ%-URw%6vb<_q47Yz+D;=*vPcZOsCV&+`*NV^LjICiPX+{ZGVeSB^98 z$A1fL0LUb;iL3@6hi_fz%~{_gqPXP= zU{`2hI434rFC{gVN2?M8Zo3ki`?rg>>uvm*0i!;}A-e2ib-rxDG2QnnLBy zv|^A}iREP)Q8S$WT3xGAEIoq>h5PyaLBF085$!bG^=sS>ZrHUQnIy2-(*>y{2_;;@ zA7;sHHZd^Di?MYyjmMH7m%Q9Eh-WgTsUPohbl`*l2PkmB{^&t3BC=Xb>Xcal6fZ)- z0LJ*0rgY>JiBPx>%R@bj_P+a0&d#4G7+OqzCq2Lbia4dGTOn4cE>6_NFE}_Asuo6T zB#*C(rdyc!(N1D5IyfkZ357x}J3)~G!UZAltY!?kBRTekpWq%YK9cz66EAM(&y%69 z{GDr2VAsrD?IE{%>$!Z^ruwh!l`PBEQ}Lf4=W_K?huWr?+jSlcet;dVWztVJ4Bv@r z(y4Ci#Hx%#6XsYmJlcsrDl(9<*D!LxjY}DbZgo#-v21x<+?NKn``p- zErb-e>y8z(TvRRW)|wPOP)%+K!tuqJUlDB9^PNBjBNP*C z7t#fSFe3z1jNR<-$>02q*$&GZKp59877Qtp&vs)U71mf-;&{oQYL*KrD z2>7=_r%BK^|Ixl0J+)GZXjx^{I(=&B1F0F&+~LA5_t;_aTX;HAlQHJeIsQ-dK3aPq zNvPOC$m8*tX^^vT=kX?8%pPOpa_Zr8Gy0Oh?*>f_2Se4;hJ`%evteQGWG#2Ai$p(5 zI*y1f_*7Dlz~Fg5ZD0oGr51@#7cb`b@9de5FJD{iFPKJn=HoXD)KTX4UUj~;a}4SP(6 zsX&n>=jP_7;J2X3F=%UcN!M@rgg{l`n?gT)s4jI{FI;P8>n(Q^4x-{K{!z>p4SK17 z=55dMrA7=}4X*0>C0eVQ+ebzDsR~q#MQR>g8R`~GR?}7#BCU>bu>_ZYS8{&dEBA$@ z`0?3czLbFI=6xG~w}H85Ux)M8j@8OdyE#x61C79}8MwZSrvAtGN?YBW0g{~Q=FK2P zUjdCuxYT|zuX_!l|8vja;!Yo`TOFE7_Ac7j2h%I4s!|{*erRY2(aQ#GT5cYIWW{UL z;R!ovGt+Su12noiB&@fOK*O+I8W42fU*tysK7to@gGJBYM#qWH0_CkS%$8aD0g%-C z-hAeID-T-TelhF;9Aov+I;`xrpzohQ>h%b;5n0uUBMKKV0aa-pwHFv_Jm2xA!oO z^B>h46tiG292XbI{yf(Y+NLOe%cZO)5{ksM5ej)*z~jNd{y%-_#YXIKY;paRr=bn_q-Iu=bEh{Ud9ik^AJ98TVbk_59pa$Q;0j7}Q^5C>_ za7a~3m0uVvDR@|+Bnib`y6qw{-e4v-0^6+|#e2@1Wm)YdtzoCf?qbUzToHBfX%-eeW>;6>mAJ^zQaeInYm* zOVeL`A3f5S!*b--TEx0)~SlXRbLF&Lv-bi7>DEY)@D28NhNZq(Qcp z<|tjmiGa&N9VH&Y)?`n_Ypv7G?UZy2^nwuZ{>1--l;O!I5LPLX=Dr9+_h^so*;Bt zFZ6Iho{7C^DkCd<4dheHjrHwej|Mb481ODB4UH^B*tzrPb%3ORPsFygv>b9>mxJ2$ zXzW=52BP~eEiHjIN2s8nfHLnxvKw$5c<@7v)+oLJI#K72AJ2Gzv3EM!offp4A>JOa zRp|Aw{_YP)@{h)ys`{rL{cM49N%qUUQkKEfF{UnC_%UMo>d_v}%gdvf9`OX`!W3w9 zGF5`=^vO!R^-=M)nNKZC81d(0{yJQ2vXzIF0nG|`Ni24XtM;eta1i8z9Oq3Fa(2~9 z5Nbe>qC!OnLH@NG58~%`TGR90&Ol^`V(SdZ%|C6Y{QUfgZu8S++X7C+ zuV24pBW_iV3de}Ld;(73Lqvo$?8b{?yHQAgNk}i8_%Z(m6alkj0oR z-HZMHT!FT(eX;(}xOWVXIcYFuVt(h5_JSY&%I3qJDVaiR;=o9)Tzy085>{XSTUZ$o z(X`h8o52R>0a!c6#vbk;D#nQxL5fwpFg z_R@l;G^?T3BwGi@>Sx|aPM9%}Mblf|W8^hKY#fT1$S+QkT{KZIipuw-ws_@MqJ@?h zu<1}WjJCp83>}sDVr{M-e;nsrzB1)oAEoA*G(JL)_a?vkGJ=HfBflP2RfGM!fG6X% z{I%)~+AI%;BWA@14MF3s?`T?=3ye5uL~)ejyyNPb36o;@?3D-uJa6>GK1KjIGh)q1 zd#FuP|98IlAU_bFv9-8}QoMiheVllCCWYhMnD-S7lNa8@!Htj~P44H%WG;Y3AG~ z-e*lMvOY3@)3vIGRGyjga^*bd zRJ;du=bu6wbV1PDreQ9m!KppN3YMWiuQP(`_H^wfuwsYJDM(TYpe!?g*ow0Xc%FU78x~vT)Ai)Rt z&S#Xj8*QiIqNr(vvF0nh9L7hBSVo3QwCZda53pin_)^#n^@TRS`};|^IWR0_563_Xn}7NdabPiz{_+;q?A%`hu%I}zwDk8V4C3fzvOx?7UnQ1aFSm4?zze3U zTR?TNYnI1AmJ5l9Xav0Gh=4o|kJY-%&k8JPp|pR2LhiKqUnt+!@cQJ+iJ52YrwK?O z>O*KKMk4zQId(H0uR!$soojrw>I(NXx%Y!f=9AS_E2aCv>iq%RBNrM{Ql+H$=;(fZ z58t`Zv$b$1LYm@u0k^P>JIQaE;j{1KO9i7fy<9k$jsyGtT zuT)=MEhx30-}c_7t7upTBD%G1ftSM|RS>6)-IV!qPG{rKPju=nHk#3cpSnMpw|wg; z6FXSB_dL%;5?V#X(*SM{>#y6$h5`I?z{@%l*<7Sj3pE3nJB7&o)a2xByIDoVtnlQ? z#~M>`r?FhS=DjpnayhL;06b8FU~bSm*ja^E)V7XHWd&L??Y!r-dyBm}zKxC2I=Z@u zv&EN)p2N26F9Pc%n$bG1Mu>Guzoy&-9do6xBx`~+VvDG7-O@_m(&%@q0CS_sn;P7a zean&9Ztbq|TT}Am_yYsSD|EtU8%^!@OJE2IX|ZqceLS$LeJPDHb-hpQa9w|C=hoY9 zl&JFP{<|ZE`^euISxeUX%6pGB-YfxYhX!v4rtS?ZaBP@%XSBJv0gIPw*86)@6zmby zVB*->+XIzCMW{62X0o}o+a?nl5{~6ffbt!M*5fi=n~24$>=^LK7=&egVr3I1AowxR zQ6}K;1}h^~A}f{Hr2qw!his3J4qXpc8&uPk0UOF7Tri@zgPIcE%>vds0R7p(yJ@&A z@*&zS$UXuNO9M=%85U9U>`*-#(DcQ%lqwkm7$wsR0)@k>hAsybN!O_7t$_04&^)V# zs!qSBH8VIeWgx3RRM8L-730zlHK0+yFVFNcHNV9Kd=z$|aqd5O|L50l1dQkA@{)6? zHv&ppUg<;FbJNVSsSa|Qm_P%iUbY#>V{Ky_=f=6*QQYonxWh_Ac}O z1ATL1>?v>Wzh^42I8#|O7Tev$xs^Ftj+n%HdwU6~1)+x#5fy!wYea&HiOFj@6!W6Q zUa&Otj@MaFU4gcznHr)|e5T4tWu;hHSZiVxpd^9$akbYlo(m4N&VEB(-72&x??XcF zfb~+Xz@p9NWK3BPty$S_?U_fJwxiwPnhj-T=r5L*)-%LxSoSYm0 z)(#QiJVfrV1nJ$ttm6c-3MPe+-E~cmLq0I|nZB7XQ@1anEg1wGVAH%`fyLlMz=_aM z6&4l!0=A=+jlWjo<=>~!^<0j=>2Y%v@`f?=BiWebfkBx7bHgtWyzrL^s^x%$%c_); z1r9EV4OJk3z!!?JI^J*Ikj=(}%M~~-gzFDwQQ+8$t2kH-sChjJl!+HoA%vGpI}?U` zvUMY2--c|#ivc()s;KDB?JVn8Bo>zR=bH?*x(Ry|UHw>E*0U9VT;MbInWHd) zuVU=#CJUeOrLqXl`}p}#<$+rlgtI2-Oo2_u$HaUm^Ykncoh(ETP;d0``BMR$<)1Bw z%UfdvLp92r!4+Ezw}praG*Wz30v}koxPB$HwY3GkCmyHQO_htT2MFy5%lS6l6w(4x zTc|AxUja?_j!v{0?4B!ITT>~~7FZ({$A_FCGjGiN!UiF!92PjvWl50EX(B|iVjWU6 z`io?M<4l5Ygn%`J%htjuc%8;U`)i$FZ)6O%YPnf}cn|O1#R4O4txonw0jvdCFnsvE6Gu9~{H#JzoFCe5Fx-)y%hK}h0YA`B^L`W<0ns3RA)Fu7etobtf%_K9 zO*P~YRXt*JXj>$uZh}>y32d8~;1+dED>-y_a#}_7Ypp7*_Bc@)M@NBfbz1@m2i+mh zIFM>HOV`fBPSsl!bRtoR|1=6Tk|R($xuAQ3PgDS@+6flY5mNN}9*wB;M?e-fdmF~E z1c*IrcQ!Tt?F6fe)=y0n@hOuJk&!@s6484cAg1SLQ+h$0Yl!P}TKt%G^AL(X;%x)Z zX4oEONJt3Snd(OIzlyo-Wx4I!Lpudv8DU67b|*rU-_;xF=?UP^r>3M_BqNiAVg$r) z_)#?!e!k!B)P{X=GJJHy>Ub;{gNUPr_2)XbN2Xuaqc`u_w&%48cA`>Pz4twQp@dZtXLHuiewrlY5dioDx)|xl795 z!&X+Te79CsR!Tj}3e`a5BYUR-CH z&%BQpEQcSqMI5dHa8Q3=A`HU8<;vQaSqR?hrA4rE1XC(~2({_zTN>0?FUlwb75u7_$rIY>smPK@7AaHpW*c^5d+ zqpd2?KoR+(uvJyXG@D|2VDAr)lzCa+ozL*OPpu+}=SN5HfZ(fI=KOLfN)@zQ2=_>E z+362^fF%SH9GBHdrOZ~Fs9FEPS@ffRQj z<`F-$mf4?NpwCO=gP}A7?Y6qP`BK2B#~=s6c~b9a{G+4CRfY;I>6gp4>G%ijNWs5j z2!b!d(3oZv`9RiI>Ewq_H+*_rR~_1?eww|Zh@7siQWCSy&Z{C=Ka{2Ae3HiN^Q>PS ziI$cI-HI*iLRtRZltGlM1kK#Mn-9#2JE3b^jv0_~S$EfQSaCmq2nK`_;T zZHEWg+i&|{An1e(OYYr%Z;dIk|H1kPy6IX^7+mQN*Z4yr3$q(}rX|*s#g^wc<}Sp= z)&IKPKuCSHsC=H*_zHuf>e90`@J%|pxWJr<^-K68hGcusP0nyZozxUI^rsGZbKDoD z&|`q!9^$n-x^=|5`)afQ=}HAU!Bm0b_-po5mxb^0kzrsD>fJ9mvW1sQ1`(2`skxbf zoBOwR<%6UvK(#@>2He1FF+ib|Fv-Wa=0}V4)@>~p`ttcR)a_l4BJ-!wl$}A;yu9`z zVj)Hwf+2VTnz!6hk%8ShQRD(gss7e6?-7}t6ilAb9wP320lS&^IV~iY4N=hn1X+{G zAw3U~){rEP_t`U$f8x)BD=0jkDiPkAY`7xM66_8GFZ1Q1D215={GMP(w|(M&`XuQZ ztSi&Iu5cCw)>`F@Z;;9$U+NjP495&CUqIuE|_vx$;bqefU43{e9lbS zXy`n&$x0P)09birRiPN?b47mE{%HL|?K4=Hwidk0ghv~%GWHngQ+0j}rs3T?*q4L4 zA~j&T@NGc}JyWnwq`Q&-IG4m8#0wJ)}C5i=hMuC%6( zj2vrx;)%?>!<`pBWi>m!I$WrrX5sF9f9rhn+gdN(!5yEbk1#kANoK7Ex;L^j>3NOt zVvDI4DJdZ{7d_E!GgD5nrg;P+_o;H?5np-5&-hb?|C8@*S9nWx_`tshs*wzloH02` z8e=YV{s$vD7}Ao)RA!?Q(+X^+8;JMFSey-&+$SuhwV*cf9=B!9awn+G_e3o|EUy@} zFq6%jARCXns%^E6-Enwh@hdxnBDOK|9)1GhbyyHrDtB0$vP^{+2G5+kAE-tBs{`*# zszUX01=VyP$_(GbqdlT)VG)~6oa^0zzdxUpW_a0I;rvP5u1=QG-PF(KE)=3w6q96d zFiu)XiEj$GKXX0rJN$Nl`cC#6T~lN`GL8*oYYAPv3}42And9_JT{#QwBGP6X`r`v^ z^7dD|mQQw7pb}4?&SOnOtHEP@h;`*B$$(YBuQ=;pc-apc68gjHznbfSPWaK4Gdg`I z3;4ji(}A@@W(Cgi^XGV^3^9NH)cS42*aHnqtlqy7H~pz4Lb|x*Z9$hlf%DQ3vrI@3 zk)%CqH5k9qv};RT@pW$w;=|;04vw$IODQNv|Eh<>*g!))7ct$r?hP-u5rU$%w)hNCX_^;><4N`dj=6$FbyRdNNC(b6!O*78dbh(89! zl1SNlE>YT&s!zynT5UIai1O0BzBdxhsnvqc+PrM^>-=+#(b6bMi6q67DKhD{NyI|R zdc+rSCZmFqn|nlpq0os!34{x-u0JMEl)(@1JL=?4%32L}JRGVc&egZqV^Ej-u6lUM zi3=8>;3kPr@AXiYSr;c?`;zN^7^H}HyPauS0o&%@zrd0So_^Zk=p=zs{400tpvQfh zT{T{bT7Nw%4Q%aS!nL18xJ$&U;Z)-mI$9K-@R(P<6wk5@zmvH#;jleJUP6N74niMD z%%vFI)IOqK>E;@l)i>MQm~mTar+#n!>*SNpot}iHpQl zNP*n@NKB6-OhX0Xk{cu;LNZA5g1fIX^Vz5OK`jQ&)`HkeMvOy8H$p$Ma9&bk4nOE* ze)1zYDBLVOIYcWP8jM^HB_jkbb{)i{Jm6lKPd{Sv@8^*0U!ALo@9pWx+ulSHQKKOk z9wJ;S2sPfC80@N81;=CqgW%SPa3ylx;cRTSLG~x_)5+MTv}93HmFE4L1O5aj)5dlm zqG2D_ZHj4dcuOZMos1L~sfPrO6uq$;)?3;18gCeBYFcR)5%6e7cIVHH)2j%p(tGWW zJvv#CFwG~`@9;e^D89=rH7H&};)Z{dMR)wyu&XD?6+=-`5oQD&PKG_l%qq-L_MA`w zMJ&51>0GEv%VO1F?Cin~g}C3+NUD*3I@57R<2lyxvV2zIoq9b+}nz;f7x5nJ38NW7RohR9N2?1d*yT$pX7Ztn&~60W(b^K=6F>h zju&4#KTwSP`$?RK%#BSGaml@kx%mL=bzp2DtY7O%k&{Rgdd9x8!!D48Yi2L40OxtM zN84zS@NP})iv&MEnbY;L_F--dY6yK`rNvn)MBi#a;mki^7mXCUBs@75atKH3lnyI! zw+B((GdNpXMnPAGrig{i$8Zwa5;5u8x)J+SfyHQn`&%oR5khohm#f}fKn_T4^2sst zE5yp7v?#~ccz!mv*lUnbGu;#Sesv1L$xHMj7GOlTSMm=swRbvU0B)@czzRr-U7CJL ze9RpqXfCqcM_Mn2vw1HDmROqX`hP94AKTj!Dh+q)jEY{0UDshe?>8RI=wWGH*IlE2BuY3Xab{{#9@Ah=GCciPPFgHNL+vHHqje=;1<+ zq?#@Bl`8IW*WNTVMYK3Xn9T`)#-K>*w12yw(CQ_I)(d34u zrQ^usLPwH!v_3!BVYFapxdHM?J+CfQ7%4_16EX0`hKFbGDjhyjJ!CpqV+6u=NDV78 z5?RG0#nS`xhy2x@TY8srjV)uC_GevIItH`M15N^tw=cHvgOP&{vYnLn!mh^z>}vSa zih=nW-Bn+4uUzR!OZNA>lq%xXJSp`*P2D|kR&?5{45_M@Yw8lW5W&@TkvfX!uv1{6#&%^qsJo4 z&kHPKYvvL11K}v>-8M*oLuG-8sRHkY0eU8Mii7KWGVK{4_%;Nl0{!EAS?rbcwHSqkW_W<6P>KbOi3(R9FT`3D8*!x%BJ znF7msZPENNg*LfT1nd08db~c_-QvH`xVG5XdL<-kA4PY3AyCa3P4uWzi9ZQn8|z47 zm$^TzFls$II!d?*|AC%Hd8Zr(XpsR$aKNsDnZkOc5)<)80R4dhigs;HjaOx5rH$JX zwAi4dHkN$(^5uql;>m*go8cTgo?G&ucwXSurBYcw_&y{d?ru{P!))d7Dpj1NoNpOJs~@W#u|l(qBt_(_rh3`^R}Bshj@mjb_SHdW#I|XRR66xfzPL~+2ZV6W4xRx| zxl&-x&p?6%_y@n}oD0{Dnm@7z(g=P4cH(!Livy_Sx1T?!0(z7m112y&YuEU+#)(qj zLV5K;KRJYkF=Aba7-08*gPM;4Y#IqNfD67O5VEx35U8?~gTw#)ZJ2e2kuGg`cNlVm z*)24Tr6UHFNC##lEvKyx431jrR+;cg>0yc;@gHo6imY7EH5*52ht2=LnZ*qzy?Aj3 z!hL8RnS-?)5`oc+Hei}D1R#*X7C5;v+lfej;O<`sJ&lBlDr&p@vZgCZPH!tX=bIWE zKf^Yc0zLJ2J^5sv`~h04-8SL=aY!W}>p4qwXE-|WeRPQr$g&UA=VE%IWT%nBE}b6w z0Y5;~6U@YM`?%FTxC!?;b#1Scggitvc6eaIDQ4>G`$;mS3UD9GZsz>9oKMG!;xIFV zqB0DRapYu&!hZV|er+Gr9_22aMd=o=T1Jmlm5ih z$SO59dpiu>YX3A?(gO8Cnig?&!I%DJT2q|f$J<^7{4`=10PoZ|YfcN;+)1ZLLc&T9 z1&j{=!mv-fwN^pR-tx%KW2Az-^pv5^21_6$XX!_`L{^D<3hCspQEA%`TNrh8L|D)e zPXHWBB0q%#e(G^cDwK>lN0{~NoZ?=%M`w?>HK)6n81g=x7OE%jI;?ct7V>YtwMi3W;d%VwCi{fwp1Qu+OvMbjvm6js4{ErnC((RFiH0z^kf(q^i zbV+^9dk#Zs2+Hi80hF2Kb6V{~zG-t)b_NU%jE;fD_+p$3)|4oWNeGk(k)$P@7W5>O zI+R!Vu+RKiimI(tdaW^O-Eo%aHe|&J5QQARo}&Ay=^Sy6vWclsJ^`0Ug2G4qaYq~P zAPJ7o@88YDlc$fq;n!}w4$K?0*Q3^m+m*5Ac`@`8sl>ae3NiQ%FJD0xNAZW}6-Rqanm)_GX6AUlj93qfdBek8ZHe{oRA5qeH1>TV}_ zz0txu^Q?`hfQ!qIr2sGdxpN9g6?r63uY1UeyD-}0z24Z4q}{*5Rn;5jFU@!QoQ#mD zL~|EM<8zRb1_reDEpHj)yB=`p0d9ePJndW<6mtqoYKH5pOZh(=%QUvXCi+BGLb2R8 zibTWPQ@kME_U9{C>oXi(`q$3o@2zlB#Pw)eLhXl;Jt70II1*A(Eqbqy3M#Ahp^w&0 zO#Q1#MJ;|Wt>Mc6fz5=a zgAqCdrF;v+*r;egB_R)We)jV-|fP56*)`7h2Qvj zO(#pEB$8B5hDJ}WsV^;#lh+(mnlTQ zyX#sCo_^BEfh7Y;`;>eE`0ROM_Py1g9k9Ot*e9=-_1sHnPnSu0*J+2KB$ALZ$9>53 zhu92<#>fXtj)qikDDm9Rg3I7-0 zFO|$}0a+fHvpYIhy>eUG!OmLXD|DN!Q(wsToAD~DkBS}3y_#~{2{nSvoku%GO>S`!2BF;aaf~dx^viys1i}V`1xm%Ry$Yp3@WR8-@B4So}rQwEV&!cKS z0iTCZSy@?Gci@pbB{azDrC9(Z-WGS9CJ=}>$=xiclN!Wz$81lRtE~$=ne>1ikVxW= zkhL1baGazq{1*Hxf6FxD)^Eo@Jt5M`9nxk@zfKoOU`Lm&6h;5x9W}b&-g7_YwwI6m zO_^FUm=;0aqqa9=2JgbB%FkEprxf}XS&4JtYSi6lrCd z3*E15%$(8CkWxEnxn+IL?5GLZrYrxp>2H%Bx0FE*Rb|`@D-JUGYj^vcAZ`^IjfAq1 z1n0-Ivs)#znl*KNCI8GdW(v%1125*bN*!zaHw+vP{%u41*b-*-Cj@Jyd}jxQ)_IXNcqDSiBS2_EAU`{LwTPJis8Jzn!`yas7! zI(A%Z;2!bCDQJkl-G+Ybw4stve;iH+!!w?0K{FFsqAd9IL7`qQdd*0(uhl3cWsbo zRe2Mih3pXM6eD2$rRNA{Z)@x8x#2k^P(0o)=GZSjheiqwk9nyD>w_FPU-<{D zu#Ju1U6=sq*s?m_u$}>6U3qW&^f@H&fuh;1yEUeXN&)CN|?v=FM+q|CAv-St`T7t+iDTri|g40-Tv`@DQm?O>vZ2&m-e_u#aQi zwzA5)w-1j{l7W#C6?N+~Fw({-Dq8kuAEq_nD9)YWcE2Y@Bg7G6xCUi-=6yB0wvc)b zeu8rVK>|l;op_Tp8j~K5wa}+f+X=C5MXN`IAUbz~s9_^z^gI#Aas&J9YkPys6B#sv4( ze4#*k@i;`yhe7AJC#qL^1V5k zhW~ht9g9Q5drw=d;En-vQi`XCC)B&Ni~s3spf?uGWnp3pD>PPe0&DB5e@}|)1fP@Q zetlgXW){qAT)g)KoOR${2!C<%IHN{=QyT2`VNYG(D{R#A%y+6E_Z(3Vi1hSKswRE1 zisbvhH94-DAoRdfOm^UTKRe(MH~yW}#5A1qa$XsOinjyH=_CbSWhXbHFr3hU&re#h1_uK6EtrHsyl~JjV(RjIg~wJ+9J+!PI^W`dG4|GR zS+&g?D2Rk0Er_Iuq#)7_3P^(zBHi5~Eg}-qA&qoMONStmf*_zE9g@-_C`jX(1-{?@ z_WsV^=kUk-`@Qn8p0(~Z_uMnrTyxD-2=qQfMu8T_+>R9Q{s5zQ^Vcsr4h}-70;51@ z&Dj50))Sh2cn8^B3!I6)9m%73sfNEW_+8GWc~HJy$2DfSeLJf1#-%r`H(lECnh_=f zP#b~hf~cUd>eSt`_p}G~3m~=_!j?m!6m~BgjUEC)B;ZJqqbM+_mZw)D1;%jl_aMiH z^cDLYZD9hJ2od_4u&w2QlARM(TsEC6atrO>bY!0I-7WiVZTy9ihK9_MMu)E%tKr(s zECezUw^977CwJ3&iDq;4vGHZ&rx5=7N<-_5^BPf?GTo=s03B+n1FP8XTWodC=j*(h zH^h*z_(a8+&p4V~^6d1=xml)R@BvGMz}{c0h(y%mGDD=VSPg_7(VTxz@&fr*ie@hM zR{zfCPjJ$-sYW&j(xm7n$t3-aPX~DbZ+U}C+KBd09}}1S&M^VM=Z8@gx%L1=eCpKv zRl-;ITXPs^jyz=Ko`zjCZe0I!e0hV+%wL=EayC+!gOH-WZ{#xYjWZ1PPAKMO(irnB zzA!q>)+qt|=A-9`?@bO>Zwa3+*Ly+!=_uEwS;B zsUXnOVPW0F?C})#iB-=@{(D%;tO&aL2FTCG3pLMSG}-&gX3u`vr4^bO8BD9__0+!;yhkh=G$b^!8?JesPl7&Yd( zbuZCiRqFZcc_SJcdnniGp}P`J1D=$#tSm?^eCA;DGk1`smGC{q088STbgr2mW_T?; zXMj4y7i{eI#vxl>*P&;z=?IHhx+UR8vz3dhi%Q!+E4Bl{W{=)^We!MG<5?k+z~6vV zTgC5RzqcGex7dj1Lxq;6*U|SratiauTf_6((enS?<*I3U^LL5aT>8(iV;m_zEzz|4 zxwQ@Jr|72E@yATr)e1EPjbHsw$Q|uM?Nj=D3n9MU^cdm6GoOv05Y*RsQYnfw{WUQ$7RPX=l+ z*Z*eQDze4udq;E@V;!SM7n;RT&OK{xYkN~I8~6DvIUp=}!%ABB{icNrI=H5ed(LV8 zvf1HNVYy0w_bB0r39m$q8Pq$IzCaM$mo}^{?2-a#5y7b`Fh6+2%IZnMdwkxv#bNsv zp#E0aRd(h!81ycIekF3|fMJgGR>dENd( z8Wne8V`FoP=GHgry-liYMJs4MJ+n6tyEkZyO&5B#C0uI=jq<_*sG0y7EI92$0571z zb(iKDP|7L$b8) zx-`rf*Jo0@<}=iNKdsFJL4P2tw3#>uunuu>$1m5^u;}qk8Awrb|5XkFe2)6NP@BB` z%%7gDhD#m6a?TJRxP&XTM-c9|z8wiq4MPkkyZ-J=>hOr$R__22C8*qJ)dY+#&a7O({N0-M1|aY#B%hT<|Hn4J zn{(dDw!pGSp)~}=^g6?Bh)8e5I63{31zj6@rOOC0sS@S$u#59HV_bP*oxZhx@8{OB zzY+Znu&=p|DzEU`-jXtH(m{n9R1xt_y7niaP6m27G|-3i-C0!>nOy`Go$?}8f|yz~x%asM7V zfLL<*PYsYT@5~RhZE!sMej);{%wavK@F7Dt;%y8s7+6H~3=AEa-VoM8*sN!F7S3IJo&AKV zClLoaY*D;!l0;DYLxXgX7cJB17|HMz66#_1@J*z*i6m zI_WM(T3wKD-`l7766Wna`zvzh`OxMZS4Y3F3LA?skq~s%f%GX*!RSRqQh|4b0Zm%S zpF_<+BZ=n}7YeG2-;^;fftLM@KP4TWW1k&1@!<%l{%V>|{1PC42tEWruE2J@s%2!P zw!*4N85EC@bW4WHq*g+XGes1L%vJ#$)q8z}GqbZj-Bz(b7YeW=+ZTUsZe{@gKf#OQ zbYY{DJ57LuY<%AJ3x>vFHKEOjH|DOG4Y-fE!dAL+h;UeTc7QFamYdVKvIDBsj{Mi-(dy?1V6apF8fkRiq3$# zYba1asnrW~nI>{?(bd;@}ruV zsor-uO5A!oM6%gT2aPpvT;5#G3($Cq$ir#dy$OJnOMTH+H|_87bSXL&m!{E}vNI+Q zos_T6JKyn*^?aTVKtCj+j5XIGYtPBaQQJF42$hh)01=$<^5srwuajSI0CFQjjD^w- zykK-3Qnlr@%j|kcFTh}_K8?P>c9ICX5@2*HMThqx7yWQKZ4(uuC{h!|ufw=dfmxUN8 zUt?u0mib$-dh@GKC`s7EDd2|y5IjM06G!x5nGWdNiMI^T)lNSV0s*$t&%0-bf#c^4 zTH!;Y#|_vgI}FzunqRU*t#anuxAj%4SnPX@b^|%VZO)Zr3ed}fs6|2s1sQ4S~14AJ2KRF=Mcbek*|5vgCAl&IBh0vhkxBmww zy7oZnXYWNn8sUPxFJMnx6Du?Zps<*KagteFV$~) zEBa5FDsU9~alomZCP(V|QfFt9@#ABIpN~HD_|stm1nqu&mp|Gfg^hx6WgS~rE4MVb zBp(bB8a6KA%d}E;ljLuV$gD$t);+l!a9NMZjf1BPc$}0^L;w~hF*!T5j>tQa=#O1A zpoVI8MidJh1OJ}!pgLvjjdoCQEHzy`;2+x<1;1aQu3DdaSvU6cFC;w|673CjUXpk};6ok6m48)Thl*u*RypIK4&Mkg=hNnCGu8V;D z$%@yY5eZ)T;qH&6nhCpkq(9b#Gulh#-iDfyH99FB{uNGtDC;7Eoggy1GFnatqD$Qq zi7U`8li0iOrA&YQ`sV`GythkWbH>mUvb`|{>Sx2TD`_Z*1Pye!1K%#r4yk%hKRLxzaGk6Im>Mb3phcyB+IAOx3S|_hvEz*dYbY}!w+PEp#z5- z6faNnY!I7-jt-m2x@b5Re}croPY}3D@F30~{|i-07RL5s=zC+(yoEmbaLdl)KQtF9 z(7uZHz22w*jhP2~DH=sXA?fMmQ_g^&fGiz~q_lJ;l)6K{o*nR#A;!EEUxM`YFm&gd z?f70@xkrhv%`4bYZ>2iRQ;q;Gh&ly44=ptL0BSThJKF;4TX$@1Y)prlPE)~aZ_fD{ zj*HDC>})Q6bXdGZIf+wJn!iO?VX8b!0Fp+J1_lOD5>)K8|{OQ(eeLBq7-2>{X#09^o44}eaT+K{hjvgVR3^2hg=9dRY4 z4AGO>PZ-%Eeqc7g;0jDS9JTyIg%yk3b>NJHUTWbpJXukKm*VxxE5z zgD`Y-BS;0&nE)nX^v~{}KNmwcNr5mzUp8tB7~YfK{EpU}ssR$Lx1qHKtkf6~7D~ZK zL2w9fmHHfqA`$`W-e<2?CB#OE^24*Cl}#yZ;V0M|!gvpJW3Lqo25zZlcrcl|1*13lrr|LV<-Sd11PrA}jpLG2bC-tsrOD5|FmRXSv)3>ouDPe90WtD0DziH15Hf?2t^VQFUAS`cX#mq5CZyPI>pp zybIUZK7YU^ucwG)o4neAtI}&$%)mwJ)o=M;J?07=EsUQEkCoyIATELOIkcn%g1+!( z2ZcS-R5G)o>G$qbWhW?zcx*k&Aiz5*!guF3uSslK{|iilm3U%xXKL*_2~}txyubdY z)T{H}x1JYwA5GPkeAsAj>d?FMd-+29aE7WY%j5Pwh+exuB~aGDz<@qMjcWzrJ0n&W z(Tf*6e6-T^1};si!%7?e#@K%-5dEArj++I1!&LVb!H0Qcn!Cr5VTTJOGFbQNbnPUazLNJQ~s;83$1UJ;M7$4|A~btRn(0tndxLfhQY?;ZVI0ePrD z8YDH?ge%5-sI*v$)sy8eoE2ZL@x)Ex<=IBuaq)>SpB;EZ)LM8YsO%E4y2w=x!)iHQ z*B}u{F-qSZuz+S{oO0xuSB(=;?fI~J!Yae;d2rFdf5;(p-mbY^z_9uqx-hhN=mtov zg9OVJ*mclyiE$NLH4!_7&KF&4@zVpiy`Yb@vSr{MtISQ>;^bScRxQy(lV#*a9DRl) zBll+sz_fM;>Rqfw_ZNtv1hBu=N#n7So3ID%e~2s!NW~ZT2%cptAu`Nne!0h*4zFU% z$h9m?s)H%2esYhKsO#znxH0~Qt|kb*vVlb6VGDB&5CuP!$J_nVajJ&6nNz2nM#;iH z0d3!?K3LJt`xh;uMAn;m(y_E;(CNy!L zfN}yCbZB-Lo@%N~T9rN0?pGc<){N4S)4=5vQbLPx^&6({*e}8^Qh}2Iy0}0NE^hkC zVK}J4RRHu0tvw2Ky5r>JWa{k?+PbHe+WMI+Ts)J@ zScOR)#?MIPut`w^UZF}aDWKUcV~AoGiSyX5t5HI4S-s1e6y&WezfQHruBr5Lt~-<^ zMh_W}(LaCi2iCz+NyBylW9O23?h7@m(`!R%nx2cXm{hOUlbb?Lu(4xgfghKj#hl!M z&TXD2;6Z`hU!26{c03gx3D{V)4U^@Vm5Az-`@M}?9%OXIPs{=yIO3hbCDB^fKiZxD z;*CCBwDIv-Tdw~DnwWX+87Tp}qJFy>YmCa)!n!kJF<~@0lMrL^278s=&V5pogiY#F z@h?xErPYm{q|`~&a~b}kT#kn74dtTMGu$)Y0){+A{=4qq!iq^Lb!}Jh%I#*%QA1k` z2Mzw-aaGNg=plGj`cnTj?`TNnK4`zIQYN!4q z-to7U)CKbqca-RW;o=&H=dU(q>{0pP*(LsZh{C6c7t2~IrvC6Ol-~b=TUeQv1G$5p z2CkY03F_Yu-3%3hb^K~z=|if+L|a_1%w5du#`hWpr|w2^NbFxCP0q!2SAIjb`j!D- zR+$^L#ad4>;8pTD^GQ8KBT>K0@(+BC0xpS!3)kI;G=-hMSn85s{i_0ss?Dbc0S}_( z{-WeAFAz_2*KhTm0{=sC4IVT?(oV}}#i>kFC5@f?8YBfyXV0p#mdb2s!t)*)BZvv? z98)kTUlOZJTvETAB6kLcT7_4>w<)V)$9W-tp~w*4*}Z@V&|z_wwDKs)aZs>+|MZM2 z*MI0UydvYNj0f-qoDZ${uOX7rh}UR0O|2xsDoENFO9%fW-d{G}j;r-XBJpKwp^WWc zq(;fDFW#rds)cYkX86Ul>hwC;**Mksg8u`1cqlFTvwJC2OfIpwKb4Ly3twkYIp5FI zZ})k;jekJO@IlVTUp9qYf}I~4+;Z$c+q4h22zRns+VFv-(^=Lm6QEnx;%n2nQ+QZ; z6^;k;`|jjrFUu($B=|Ntd}W2sg~~9W-TK0do8vPEuKkwHofR#5tzJhs0Ye=E4XAu7 z8YIyl|K7U?Y^3}|AuYj}YYe1`2TMADRhuutG8OybsXD=@YqpPN^--uU3FcE!>{%vQ` zXV`6ap9iRnrof(Z7SC49@eXXFB$ZjqX3WZ~$R2{beD>w3cD+O~#hzw!7F@6T=^1!H zJO;NiiyMz;Mj?cLx%12U==hZA2W}P?zN8zU2dU11-Q>id;j;x8yh4<NW*5zc%iny?4!a1J#w9_n+nLNQT*2{5wdB4$QiMy2ttsD7c==Uo-zafWWG7;SiXUAHHd(;-L{6OkQxGtJ>nf)nd^E z&0$#Y#Pt1lKebev8$2S?@w#|G<9B$k`K`$wEAEqz+~he-LJ;bl#P&qZ@jKM_Y}I$h2AcK&Vdm$=K~9QG}9ZTYe5E|_s5&` zbbXS?5^#vVp)C3`TdsQF-C;Xt$ReHMoVRyaO(nRfAH5z5b)oz|sk!b2`FZH~^u$CB zn?Dd&QHY6&*}=U${|)jmQP3L=f>%AD!h zMuU`r+!hc{&XZ9Hm4L`Q%;dOqmKB=;EMI9u`3 zv2vDfaz!m-e0m`zi-r>(HT*HPLfcP@{qW#6jr|@Env!PtkNz%II`Dulz8_PGwN(-5 zD0-Z|p&6^7?Vw-9bzHGz`LS+u-e$f9M*rMh4uLMGwH4;9> z5w#;MOPS2utFLaeT)XC^iNp-;xh7-sX<6E3B7DVZ)#6&{XayJOqDTccazE!=I`lCW zoGSpk|AicVGI3p1m$9>ROK5rph!~DJ(JW_mU2q~_EW$r{Y^z^IEM6J8W{v_UHs%1Q z!c=3*ZLNX6dUQS@aY;m>i?eme6hw|`h!rB=ui}MVbnwm<*G@WJm5`*@UNxWl*%OFm zob#4R-A?jLFB~~b-F23h@$1>A%Ms*6LNn}B;j!790pf<-ZaGymgHzAeq*ZMkwm4z?FGZ44vfHiNb-GV5?hz-F z$iNNd1R}@%oAV7*iBG(`ZfhCTZ)m+r7uZs`3@R(+xZ&nUqUV_DR-)2sBU+aP57FY`|kolzb@yWJ)1#U6Pk?_43LQ$L_#o7-3+XehXZ!?=&#n& z*dA?%mcU|IveXAh+h^V4ERZ!oqaeD%>71opUy#M@)FYh8SO)D` zox5L{q^`29)Fr$)R}gu+bf=w(CEto&&%A`eP!}=lP13@Ob*WJWX8vnhC_mZja*UMgA_)l&;1EO3NTIRyX+juvFC3Ea~P|NVwG0k7hX zZpy??M)>K;+lIf8uljyS(~y>6Hl8pwJj2KOYAD13iYHZRWH0 zlZ9MH#N4{=`U0iym>K&I>Yf>jBuf%}$)i;wRNjZgd_k`4o;NMa=zY5Fq6B>W2Zwn5 z&&J->OVienVwc0{jVgoX)d$tpVC(Rs#!y*_6V*M^^<^}mF2`4um>|u0@@)F2jA9mn zHjXB=BMaW={;(+Ob6w_(AF}=6@K+8h$vrqA>sP z{oFY*TCx{Aq;X^7%%j8aRGgXHhn+}ccK!pZ zFhCB(Yo31d|JMiqHi;rnmP%|XEempRb89DQI~R&Z^2u-13;zWKu2l~PS7lEgPTPTI zoZR}n-S34R?I#5schvv6C98tLurIk78c#t+uYj)lwbcK0TUG_3W0rk4GY9@s%b^3& zLVG~atb}THos%W?FFggOffe;8?=Bl|HCugjb-EjO@9#Bp0)^u169m>a7Wa2{%YO9m z;$8m}@p8!K;$p?-N@8> zaFhA*DpRG(Y3DyIfefxuwQ1$ahb5g4f}b9Q+tY6yp|YCs+1Yx~uu0TA-5q`4Uhjc6 zGr#d^t@cP|RiNzeLe+OS+5EFmJRe^9mnJowP(PkLwpf^^yvJwH%HX!KvCVB~wtMZk zZI!1`nRZ9zpBJPvy5D1>l*>p!^*K3ar4#EX9-UwYZiz(0?8>5IOtY&-52<;ArtQBz zK3Lk>H2;Q1>i#t2i|1Z9J4`qYc{SaCCc9nOwGBgN#0C!`a^T2Mdw_sO8M>czY_mbg9Cep>hi;{>``hW~V1S>CExH z^>xSejnOWWdW?Te>#~C=S5kV!li)Fv*}zwATfZ*8%GBpdMt6JUYI*Y?`dCh~=|sie z$>%&1O|IoDE?kTQc=Lu^M)!*jEvbm4C(3SYmAMNvC?p_C0`EQkLmJHL4-(RWD#|SA zyhBvH2Qtf4dNe#%ml$HYWHji!b>Orq6H?6%<05y%wqw?P7Yu0?SJW9W*NupK z&JX7Q)R@$SyJ<3|Pq z`+&LmT%rj|1P)N_f4c@?q?^0<|1yX5Fzs|%RorrPgnL^Ss6Z5*=o3xWJGbkK=SpYB z8GKqgr_x6=eoJE3bQldoyu<v6OiU2@+fCJ%Tl8N;0VW6xVpB-rkA_kt>53SP z;e#rf#v3P7J?%(pk!2`bhF)gJ2#X$FSTKRHhJs`pi4UkH>EEd^)}rvgxU;u(Gf9wVWR{jn;qIdSPKzM*?VX zAXa8(W>(?x%M5V#gb_1dq^*>Og4c%Ww+Q^#VHqE~Y5!b19x2w80F`W5(S?)4A4@=D z$J@oac(Dhk3M_xCPo(s8R88azXbb zz@pTimRwCBFB1KqwCsP+3ln!A!w|(usE~164)_Ce2E^&_A(FurXA=ov1Q&451t!}- znrvpq;Q8StZkX4Y_T*qCW>1q2BwQ{%!HmV6dLP0a>MZbj?cURjmJx>coM9bDwqPm<=-H3lcxl)AUbp z8*>jx3G(as4%^GhO6j7Cz!~_t5uK9K1O3Fm4jw}_5^97XS$A{k2IR0ZBwcc!77q)w zH8?DAr|(UO{|>M*Pq==~r6(|3zie~qxyH{mg^1J%oTqv3Un*0X6OC9rJ!eDWSP-r8 z6y)!2!EjipUCDtmC8CE0yVm@y!c6h4ETeTuX;=ikn2(f_F65qCSAnyPQ9%)cUQrV;fQ=c%CpS> zmNjNiRGNGAR63>EF5`1lESpvNg*d}1vPli0E1@6gb4_2Bv8`aj$$!y;7n{aSKkG<) zHIef2+30m8(~mWd@aiLq73V&TU+>#cdfz+I8ZQ63ecf)|uYdJ5L*i5~cRfSc(`C=% zqcy?*CPKgMQ;8`(d_8(|URLQOO`*Hx*YzJ_3?_xy zln#Dho3jCt+I_P>UUf-R!Pfjwzg&(`{nVD;Qo=Yuo5g1L)m6oAx@9p2H!->Lcn%%U$2n!5;vV4uZ&cWV0_O{^vHjUQuHNogq0f`b`dYZxugIBvy1!a` zp?Q5#C66FPhGg8qgg%r{B(PMPARJ9=)^MwRqBkL$%G`)|<LZy6a4!y+szszEJn>o}{Tnvlxz;o-t+w32GATG!F>^bi+zNy6() z@_|>qE?lSWGW{{JdwlHZK!kc1y#jjH=cygiKQT@pK5lw*efQ)^hc*_i50raKY6y(SK($ZWE9{fMP6>P1vVHPy= zENm9E4yzGM_>4woimhYb&Sz7QRz#^*52w|YZ)Gb)3KHPGw-LF}l6Yj{h5*d(~ z64(~O=)XK>HF3iNmnV9SOzy#2UJm0p2R(#wQ z_KrL}YZUp@lL~br&tTq3PaZ=Sk5!p___}u~b{zIVhnGzEt@J@rY}$x)Q`6F4OQvG9 zA4Q|qIz?jlQs+5yqiue=I{rA|9UGO!qV*@xz4n>RWa;C#SoR&|#u{q+pJRiq(>oJ)eT%#Y3ygtzBwX-c=%m<^JXlskkChu{s1)P+YR zqoBwpnGGx^h^FgDjb=av2={JiX^{&-an)i7p*WHquY|yTuaQmxuOrF<&=LR4(x9<8IVY z%WSOo+lk!Ba5W}YY|s+4Wk8Se)?&#M<9ux`$mGvo_}#^Nf2p%}mcxAOJMBZpZi-{k zz&wTaoJv>2AMHzBGQ|Y&g-VIT$5dO)t@4l6UKi5kUQH^my(mApl$Hh_Na4Vp@2k;e zPwP)pL#idp(XobxKWU^q>&r%a|fyz$;Y}M|o%<8}kL{!@nNdvvRe1C$>>v(W4;p zRccAHB)kuN&qvo!=wo}teUU4)f9HKYxH{}N7+fPU3kB|+zhw7Rr zY(|NG^>YrQs(!T;CW+Ipy4Pm=w(`(YLF-2LmwR~}GiWny6(eMEp4e@w3>nHF_jKJD zP8ZvMPwJT>?L|Bhf@w|nUj5n`?FoWXH9zMJTv+Qtj++CW4IF|8i}jy#F5<{uiEnpq z)Rpgd@!KjX$+J@>Zdtq)_tCmQM-S)rERP>kG8a~y++Ehf3>CVD3~WbAR(78lpW+7G zmo2h2-nRt9sYCnva?zXOxCI)c&Lgkd|8_9*psiKb+d*k~?gKW($7@Nl7LT?H$}Ej1 zmsz#m-rcG$=|Z!y*#6nF`O8LAT}oF3>!}8-RBydhcf6Wv!@KDEUd7RhoQ#U%g&`M<62QG9fD1%C<22TxG02^rE&82EVj&tSdHN_VcQo zx34#{_~{JeiWaIrG}SRTc}A-cow?1Zfq6UK+N<&o>9KlhGAjDA8i z5%yf-UgH9<`OGcP`Z~9{wHGVx$O7<(Wic@kqF3J(eOA9aB`3Pqdm!03oYSHj6;}J< zZigiPh9O>+)L87Yd;c;|qN+lo>Rfh->IU`Q{TGjZvlj*bczk4egY9tKa%`k;*nHQi z?`;?V^U;2C?y3i_1`5OS_ir3rzpIRO7mcin;aoIcu_vZg9C`Mz!mU-tPaKq?dKl3M zMEiSUze1x6ywSAqJW6#Uu;)&j4HvKv}@#pNP z{@FH4LnG5uQDn$^RdaWSw~7JR(s;^5X@_$gv*P65wvuh8nJaH`_FGdS^My|xIr7&& z@)D$~P7mo4>>bQ!`dDsDnp-sC4jUzndOfL&p&m)Piz-D!{+pYw@-0ZgHNgcs2cXm2tmW#Dw%%j63B`QTPwEnUBUY+R1fj>$UEFRC-1z_9h}z zt!n-HglN>A*G3^$ zUen~*XZ@B@{n)6t%LF9H4u{lkmga)z@AaCv?VGiR5ySI7v?Y^qklgIkv*VlucbO~S zB(0+?ljjyRw^y8n%bf7wym@0cIZgD9IUX7i%rE~|ctnz<#ELxX?U1a`WsUS{wbg^2 ziTW4Y7B~s4@QfAtvU#j!n1e7&t`LjV1qxo@B#<^KTf1`W#!J-7IXX?-p(ov6i(0?3 zUwCSu8kAZZTmCIc#Kzr-k-RZE77lEBYVbpKfdcof7XpL$pIo|2OZL4vzj_A0){w8h zKfgM$N?p7kLA{@N@yW^_2F7eBIS3Sfea`C&_W4Iac2BrNP zM~@yIX=zR~;j~UZTTdE#+`4z|i}7&9^Ar-Pt1jwn=3-nImackT-nqZcK;iw--RXPD zfH?|fGI0A2%M(>P^OPyL8b*7;gSE(-WJoYLt@sl@_?>b<@nFmii`m1=E z_2-fnrM_oT#GT6vx0at#Ub>sQX#P-plJ&@{_v{v6zN=k}XdjS<=ae z(RK_4I3Qjdx?bd!^`)!N(@-<~(JJ@b=f1@E4yk0<>a54S9w?O~AKa#sB8JKI)2VPe z@2_2#St%l2KfuTLc|_;x_i|dv8>2+)dGAbzabHyUQv2nz!$9(~mirD%H|aw+7B2qn ztyKTe6tZ>CVoQeo40g}+)lzqxxHy;Hq%crU0V)ZZq>rnrC^VqIOWnwFUmb`(NMs!9K@m=)A??jbW!MXeTfA&`o(; zoRi2a2tY@a3}u)6R?kIJBJ+N}akrWe0rHc8)XMnI%YiykBY zN+;wi;~*)+eVXfox6CN?`8Vm0bKf zUFbSO8z+(;g*=ywzV^FN5sSPCYsZGTlksU5)1bZWE2DzQpY)6%>K{)`tE<+n z^Y#L1$zpaSkCexYj$uJKd#j!YF>^eN##0cyK{ZRNv&da#MWx=D>cB0j_w!bmmi$9E z)ooOw)0^oun4V*apDy=}7&pjsf_%X~nmFdmQ~IHxe$y!Yg{*tdxE1q)QOS&?p|jc( zGW#!BtcLkss6;7v<);6Z6N@Wa)$smd*263X8$TzZ-A%N{T~YTj#+=OXL-yHVo+hJ= zqC{s5UDT&-%d>7@^bGRNDFwrOxCt2}Pa~psTG(Hv_CnusMJL;+w(zIkp=8G2qN={b zjlq*f*ySFYnsaY^=1N*vAFGd206$T+mf|?sWlLin6ebRIAMYqUeH0&E$WHD`D?ZgN zjjLR%c0#dvL(0kEPUh$#Dx1VvIHFq>kj!j9c1JZMo(r?5qqs6UIyO>@t?5sD7o)XN zB(Wwj_4$gOr0~&z5LZncqs&(ck+28 zH7#-DK3e5ZjY@vWD{(LgIoX!(n4>glTi{S>dXwZJ?LTP5s1i2maj%kHiZ7= zv%xO76*xbK;(l<7&a?h}@%&>LvEyV{vIbbi9GT=Z<*!YKxN$eEgZHw7o#NC3yz;4* z41}y2$*t=zG(LE4ZsK(Lw|dZ5>GT}NKkEuVKlPo@$nLv0mSqJ-)H=sQXoF2`gI&3J z!lqXGTDn9vT4m8Ry+c{NLLv1;#Hlq_dF zapnT|>Kqar{%2fPB|ZRR<{DN_)kD*-<6HNrwViR$QnKsJ*1b`dxji=$1@S}m;y(m$ z$e!Is-|LZ&V?TSSVUhhMT%UYqw1ixc$S%^rNxLkE+!5dHk)~JxL8nr#%IuYmX6G4m zUea*o9c7X4qCQ6+8kvULTlJ=KES-5}Lnf~$-gK&WkUY>GRs@Ni4Llw=uk4RRi=jhI`+RcSv zo?1>;Q(<%bBiXD;nhk?8FK)r`cQ{^*2ZE;wvSev46SiW5b^%jKCL}C7^`5ao;{DZr zUCFEHSH0h2Hr|y!@ZalTuXtHN(3q~3ea#~yT(*h*{MAv)EE`82I7d`!%YzpiZ6UFN z;}Vs?05lI1xT`c8UdV@@paAm<*9Y@dp!mkNmay>3n$A8~wPg#kg0$dVkH@ zjgB6jmqd>2xpu3fpTD}M-R|pYNlctXD7g$jB1U8XKx-XxIi~aJn6YGTe7`zvyZLzG z(fOC+!Tn=X_7BQd3Z{ZR!}S#9qGQ5dxZhtRUtI1ls9F_d@$f#J&td$=b-l?k&u(*DhPm zPq~MtESJ>3{$56qrCs?dsq9q}(Wh#4T#V<`)8n32e7uA<7nQ3kA`0VN9R8@kI<8K9 zVs4V5QWSJP%i`iHHOY1flTS=St!UVn*lv4^`3!fUtayBIYU*lGhU8 zbZ(YdXTbUcNCGMU)}`xQv$b@n_fh@WyWgi7(>hmN){eNVGJ{3?j#t`_DBvMI>Xs>X zH3j)tZC!Hm4lcKyeaj1@tS9nZDB5ZJu_5?YGVH~#He$1(JSqp!3vC|;z6b6yA&h%3>x{f`P#B!W_pT+S^adxk-lSAWREz)wbf1p57B|a(a0&`A6T~|`t za4z4F-o$S*AN^eljQWr_o;{7hKeuyEkf?B&o%S8}X^@(wtzv|YE2QOsTZ2Y$L2~Ti zNbj-rD(dHnNAA*8ld}8)3y-z0r56#1r?6UulgE5~+C}n-%KNu-Og?{ zK0f;3$}NRk@Lma{r~B0(fh7Gy7m~Y~bdz$n(&%f2q|Q<&k#m+#FMi%HG8qn87Bad1 zn?E{QgH}?XbR&o$l!?q{i|SczQ(Yv!y>@zjty!`xo6{VdQ?vj*x_qH5Vt}3{2eyzH z7Pi*b_>Ls1uuXim%ESy}ldTG_)fev4+g_ z=V35jNNauJdxaj+NCoYaE>Rv@C9=~caBQuPt?qOqy1Ao#tV|{0lPKK;naIT&R-CWb z7{QN#MQuemrGDNqgyl-D!6*Lby}Ry`1z*-?`1m9 z<6C07iq*K!7d(wmXzHR6N^3Wjn;(fum&zWV;WgiEoE7^~;Y^$*6m{>#mc!WZiw|3PuWYr0AXUnV+>O#&S2)rm&EkU%4G;D% z9`MB9F;Xn?9eEd?Ptc{DjMhocBnvPHSnx|(MTdrUG4mhL%VJZ~`tOmq_Fv7kAdr87 z`)P&YRakOIx9J~gJ=vSbPBF_P_71{|N;ho&Oe+CGV>&P93YifAAumK*Bo5s1(+Kef z1@}c*4@hCGjFQQ9b*z3>^q1mlk2NH~r5%dp(|#s9C{{tw6BER%?8IUzd-d`b|MrV> z(r|~q2#VW2u6-p{G_?{#C!XnHEZOl3oX`1U6Xhb4)P_qpOx0M6GVP3sPwEZwj8}@a zZnC8M_GJlK9$%i5i~{TRCy;j8{Q9ls_M;td?a`CgcYrn()8>A@@@Ybl8GVF6NXrk> zO7WoiJIa@iW_ihv9u$}AwO-jzFygA))N+b&_q%W2&$<2lnCzhK2~&vp)S?+AEXtKP z-~B3oZ=L0FPYBM05FVeYkl|w4h5Tw$O{L$=*p98T2%zA7yH2F_grpiaFW@@HKY!qG zc!V)2qf7hTkR2m^o1FsTt%;Io)H!J>Z8+s9imayE{g+~ zSdmaQ>pl(|`Lhs%YZ4U#ZM%aiT>8Qc$xGsFge;=PcY;~gJsOBm5TPr8*v3?RqN8J`3H|z{RZrjgbCj1S`J22vR0113Wq_cM0e_ts_4mq8 zeKobUZSj5R38`)^WbmE7idmC6VNTsY3H#~(K^1~tf7bgR8Ci34Cpy;Bto_?Q;B`#O z0d77uMAm^<0N1K2r&j6Y=NP>XalLp7(=XoRfIo)|_aGvj@1A=T{F8O!>P0Qp%fDG~ zu1C(4;u2Y1P<=PpS&v!kQaG|agk&eMt=!ZDi}Oer9J%8*onf`>|)L0hFV`~Z| zVIPG06+P-+GI#L`96bvnzGES>6aETK^Jp>7ef-&M}noW4C~L<7|q2Yi>$df)OI zyFU$kc#UouU16xDeYdvd&bLBOUA(e*QxvWL;i>UY0n$5YZ~5wRK_rAgDZe+#p5fAp zmk`>FnI((bD&{nIr7W}vj7CUJAB(YGpp~>IR5gNQfE#mdbouZp*H8BMhTAh7=D$ay z<>KT5++uNl3o!{%pVmx{4aG(3SaV8*}dXO z+L&I9_#4Gyf;qq_0|2y_2h1HzSI|mK-b~-=P)0?H0gYfI2(!WrftJLBVshVlV6xjD zQ%H!=UwlMD7#sk(S~$G($9ALyXeJLh%0HOzUgD8DOKf%O0!JOT zYqNN|N^NNUm$8d}H}!g@i*^RC;@7$SY*H>7%+YebGD8kAn2_Z*Db;abPsVi86_mX- zKLB4}l~ka`N!`t-S11!`?-sLd*S{>8s~r7b0mhYZz26oDozEX%BZ4Qp4jUIv&u{XN zO%#-90kY7}r9ny;Sq8FP??-A#1-eq^=$YHX6%Xp;HFAhqXXd4A zvUw~&q{qE)7s9SD8_B9&j<1zKWLcb?ojJ(Ek%8yHS6arIXxePW0U`ofcK?SULOK{E z_$2ElCEl-(`X?_4>7<~}FLUqS(ttdBZbe1K7Y_LAc_B~gpLK^D4fj6mU!XmRsOiS5 z>6>V45Yz4Y6YllW?X^Z%y!r34og@)}N5It?ek{K=50j7nbTFqlORHa`Mp^Md6dZ zdE{|1sokG~UVa6rp@7y2o^{jzQ`}oeW!ZjffFKGgC`dOdjii*c(jg@wf`p)ScY`7l z5)w)YD2+7I4ZeVgph$Ojcf;&Qf9K3P>&&eAXV%QObS*yx-sibv$F;A$FBm<^UbS$0 zt~0S6BqKXOhqE0t=wM0^6_D>UQ&W){hnvIWXCGd@x#gOu^a(;u%^4Mj7#iyZ@|xm2yPE4x)^<+j&H3W;LsxBY?I!))t}V4;!$ zjlH3fg4mst-W>(%m#P+x799^#|3p~Pyp|qa6}{`?k$=nisl*BzDuoL+^Ky9ry2|aV zL<*iOarMa}+xr*>d*p4gc**V``8q?Jhj-nar1pDP`J6$|mJ()9Ou|%o%$vk-Bi_Gn zMs!09ymxcH4f(8ljHlMtCL)sxRQW)f?y1Gb>iO>)<=0@C?^MdY{YIgu3>q(Je^No7r=3pXZLlb`(cYbVx zt|#}Nelu%pY4FRo_XOXM0|Vy-qyrg&bwqPAfi>bf9co%CQzzMI@Xlc4>!^f|wszgj z%4@t@e|)v9;gRS8xe5UEnLhP>k)>J{w+P9sCgU~1gM_{fBkHeDa^&vPioT!b*QY|8 zU=ic)MgQ%SHnLyl3GC}>z-+If8OX2wDL1=|OQf#{k3EAT#KN)?- z{k1c1r~j4%%yn^E*i~BaxtS78ciyW30yfI)4wL>QDNI+xMfS8Zt0ZI`WI&-#uV5aA zkQ%OyRFdKZD4$ltdkA?cDkxw-dGdrYe;bUWyeeV5bNM5^CED{5Rvc#$Dqe3v^9vW3 zBuo{_g=wQU!8*pqzVe9GUAu~XOOn6riy?aYvEFl?QFCwJMg1&w7Kq@ve{1wW-kxl< z>8OV*v=;MCj?G!MHX{isQY`ra9G3|x#PUE(ZhyukrGZ4cCSY}+%a`e5+&TL5MC~d^ zt$E0=i6$4QRsNc%<=y@6(h6g6!n02s(VWNYyjb;W4ix5}z^v0SS-K_|RYk^Q`uo`~ zrr%^>V;&@b!3VTZ%1`|VC=aomqP-rzJ@fGc=6y(Z$}F+TqSR#NV<0=d+(jj2q^h}^ ztPJUW0e!%iGG($4cf*RvxZ^&aS{1MO{X?DXLRKm@rO%$D(fZs)1Lr^7BRM!_?n%QB z5P9lrqNQ>kjKY)rYbDVxP=!zYu4>UXu zn_=fi0hyEd$Vhzn`yMli41a7Auz!)R-kiRpl@5&S_2;7OG0O_a#W0Vd$9(q&G)al? zutS|o>o7A^J?{%Gc?kWFUO^%#4u`b4^GNf4P>*_e0{{u?iTdP|mn7vUp_9x6hjLx( z=$y-zBZ7-!3p_{cYl#O85OyIjvNWo{n{siW$oxK{zSAnB88U`qV z*~V_z?brVWLJgJcb;k#N<#o*M`U;i9Bih7IHk~in%dPf{6>e z?m#xbHAbuRJ(Avxw*-BCc;pmWV7y!BI{AX{JJA^P!E;nB&Flv z2v=aQe4PNK+t^GTg*vx>7C!l~%4p+*Euk;6Y4N#!4c^-~d)4 zD4Zr4Hs+si6olKDn0LCw#`&0~jC?A3XVxB58sYU6DV$E3!|OCBnb2FvdrD|Q(ZHoZ zJ4imSNo3^4IGuS-LbR$<31;PF7g3$Oa4(zRK$0xrzE#QBe8$_xE*)nNkBnrG81zrV zg+BQD60lb&bDQqm9b$p1fg%s_v$Ia0P1KM+(4KzmPE$9T$v3}HT3*TzvSz^#{G&?n z3F`98)FaX!rS)pH(giI#E6*Vjd9JiaJzv8+ld;W2i9$@BN)6S&I3_g}Vlu@t1inWoxU- zxCdc*h~;>CFJK~B!;UP}g)36uZ~Za<5!0u#ZHFPR{_Kd>sK|9%XL~;J{ez)<{A5v3 z;=T8q{Du2FD55>j*{}arywyKAdT^!whKGyOJzJoR%3cBACAD|JAP64!9!q* z^K3Jo(<2n;&^!NcuujcF{uas^7S9KBA|y&6+Xi8DM#AfTMloCTcVF zQT9fd{UjC9ujeT!!VA|P{4`@2p{3-pv}x2QdA`3)wBlWR!8eVS+vLRJRobRN8?pwD z769m(;+V7Q5fsUWRD~)6v>}Styhj1eeu<2@J?9=pFUKDhVragr>FAW z|HLkvB@i-}G2MMg1XVqxjL+uWHXlhKng0cV<)Al)P(w+rfu#G7dHjAE_rY-`PSs5; zG!L;mZYF8dz>E;+Rd|$fsZ<)l5K=NNuJNTj$}U`3>2X%)91sB-c-+Hbv=DOa#j>+3 zjp8fG3xe)%!+_cLXZ`0xw3A(Q$SGBpr`Fq56k@H;hh?Cwr>MLt{Wq6*=q~5ERAlnJ z*pkleqy~4jFU_WJvTe{(bx9`5r`|SkC%^O<=YF0VPzXLNM+$9# zuSuw>oTfSPI94! zprNxhA--^5A?2Bcu*IvCM_Qh%=5REiJh4e_DL{P1JUk)r&~M>!v^*m}0g?SQVnf%YB?S-ChsB3ntXeJVQf8lU9@W zaSI#|(~(71R|JSt`V^Ul?0rF-+~X`N6#o`REPAlpnm80&D-9(}{O<4$YA6hqSs?Z4 z6dlu%!KOMXjcB-t)t}Mn_nsuOr{I}her8VwH7GKl;makMebn{@uTCz{TdQ{U0j&w(bk~ z?O6pcgtDcl`zyLy=Gm{J3o6w`5z6C3G|DfzyZlTf?+Sh$5h4%|teb-B^HeC6@}pS6 z_cTOf=)Ou;H{nx4wa=(8+wnvg5c*gFi6s^`ag~P&?Gu7ohGddbo{UmHTr1$)t?<_2 z&TrhC9-Os#h#;~`v8GJ&y)wJN&;x$Tip9FRer=WL4U}lYM#NVguqX6TtgQrDM;y!?lCFb9AI>B zvm3VDGP4JgTz20`{&Fl{ErQtqKM`OI#pE(o+h$+MQ3PBl&qoK0sRSSQFTrO7s41r4 zSv(v>Q%ZBG=rLJ%2kdh{?)@f>+^!b&Mv4X~etdwzo{Y=7nR&(+I=l;QO1?(-=Z4g! zf#WAd^G~KNbTZQ>zL&});PQgs^|?wM83{?F8^8?$%GQMjqfV5yql+D`|AN6Wne z!h8+n4c?&Psimx#ji4Z;)w(unk(Y?)B{x9xc?tvI>p&a)-on&NhWckt^R@Sg0EoP@ zKHdfQ_ftmgs}BaJs>@cGB+MUvMh(z*jZ)Tlc>f-bVav4hP?)Oh{L-wmfYMo^GqS(h z`I3+4sID422?Cm5xp~^C*4}ls{-We$WmigH%VRvKl2uojh+j^WQy2@u*)o2Pw)rx+ zzg|ZkfSfQ+&+JEj7O@!pg-H(Ea<2yTvf#6G#z-aZ5eu7BBn~wY%;$c%Ulx9))PJx( z_XA*HUqru5=VmhAFvSta8If2;z2bTl{4otlbf%hDxMcrs%QWo9+x?$L?<{cIG6|FHG>HZ*501LL~bp3{T`i3mxv-d9+cUO$PEbQ;zJYM8-`W}O*&+;3l$ zTP)hvRB8Fd0}qT<%a>oF9=KsmpMfe|uoPMA z7>OxeTmSSg^EygQlmm9kS=G&!=NRUBSAn&I6sW>orrDf7moHUSwL{sZ>N5$SSOYi3 zy0P@RLTuN$A=+Kb)5GPT`y1a^*lxL(gve2MdbpX-I~B3mURxBfc!nE!vkX_KcwdUp z5N9(4P|;5s%gm$4_#Bp%$#R49c4}=#3q6~>5kC6I6g%X!Uop>D-uagM%d)=GSbv9_ zWqiU)`EB1$!ZUsYD#$`Q1WsMHI5J5@XckpLHucU|%P6tU^E|`Bdr_`^#5NO-#l zZ?A5uXK}JqTSEzQ|&6o2*j$fSv|qxqL+T@a@RSPqEuc2 zLCMXQ{ouz3B*;@s6>xpl%yx6!?O5QIt`pjMgSu>^lNDru3LjmN&RMJO)_Wf-4&3-3 z3SL0c2sxd#9m+X@d>j%hv@}8nh}fWH3Z-ib^4gFxpo87xqj_Bat0f-S>`I@5Q5!89 zoJFH|I)7&1sPJDglw#B_y&k}IDS#_o?a*L9_S=x2$Q;K{_7gU$NzERXYmKtP6CxH3 z;bzzP7%q&(;-|CqyuL~#So$*fLt@ZYxyu=aKWXYCCs zF;k!E_<7$X==^)w@hm40)wrtv`o2I8l0e5o+x>H{CZQ|UjZ(Gs!K&HaS?gI-JvHl5 z%cOmoHk44;B)($kaVw$GU&-p=k8ly6-!esQm~>vVu`QMJN7~WGTKAt`NElrbpnX4G zK4c?_dm&f}y+Ax9OcooM+_f#=&-!%l3r~2t?4N(eGkVw(f;#&6rz)tRd!v0I{0ew2^hNvv{rN!YMiEu;|n9ouY32co=w*~f0h)}u`1v- z+aN^A=jSGZ^{hV^8J3~0;Kk*~(lpf+r8)RtOY0AY4(#LY4i z`J(4rD1jWWOXFAV!C zpYpFgfU7MSnpB@(fh+>9%vk(o*MtkDuuit|HL;F;)9!4y&n~~evy3pvCxX#|bOUTa zG^jROWE|eVO34@(Rjbwg@NZoe^%$z}VNXoHJ&HYLAbzY;Z2W?qm6X7NJ8Y!<+(1#@0JUzbi zZu;)O`y@cRo2s_lWf7uY>P5U!S3mDYNXTHEC=q#!%y)nPB)q>&tP`;X)*~3CJPeNH z82jwi_Vt+3{QSwYT<%wZjc}+i&@^2ELcAMHtA-nrdc4fDvOQ_#yj&4 z#gh)G)r`Zn&f9c!b zFiLZF*{|hi_}ewOVtr2PfAB3EtdwFXy%4^Gsnx89aQUKMC%G*d#I9(B8A{;#q~!X_ z;d7v60Zi5zqz6+>j^rYO9k(hq^8{w9MHS?Lbq38r&sT!m<#g?dcXi?i9*FdvVd4w| zb-(gMnJleDzT7vyk=MNKl16ct+Vv=mqu$5w-9@`y#M$0Hj%MIqzpmzDaCHZYVC@M) zXN{=b?Ft|UK_iEwIjzsp#G?>x?8k)Xo9}#G#30NTp8JXF?0^S$uix!Wx6IK|k*I!vDwPMe_q;EJewK{8$_wQBN627(Kj6FQ5Hy>2^!%sbol+_yG)co-nyWq(zGSC9 zvWebeXxJ`d-*kk|+tOB^oH*`?fIk5D=A(5a5u(lgIJA!O#ztSs>=Ae$ z3Blc34(-T~SGwn)@2&E%x(g}3B!`ZGRNkKNW^N=A^Q2+YY&5Ho$|j#=5KLhM;Q(^A zFPgnvJCWX~rmIlxj^8hVsO|G#mi{{3N3JeEi!6-1$+v0~rak08kLYev7iMS`FU(VV zYgHr1hIm|&X^D~a3SRJ$88)v&+xPL`__^yP7RB=EEylLb`krJj2V#{?ng#+X^@1&M zfbd}kZS{HkNun|r^RaL~OC6S9U!b1RRQ{j0u>ViN`2_j)_iIY~)dv$Q@Uc4#@z*6G zY?8Xx$LFt_J(A}M#<5OpXY$Zo*#w@7t=$?l)4JT13BV3&{`D}x)Vb+EW@~;#v)YS} z_EV2Qj%@Q<+Sadj8299OH#5y(GpT~`-rt^`NUiRfqUVt@!YhT&0BXXMB&hJ*=UfWM zs%a$lR=36KXslAQa4j{bx^8rpI5E-P=f%m#)LVTTY?u=(FU908Hm$E5aoDW82g?n9 zttdWFCgm{(BkIC_NX?MwFJQE;e6gn2>NsmT3z%Pi!ymoH^8+A-qu0puC?tDIT1pJp zQZ<$@RI#+3Y=NZ-s?mHV2s;%oMFDvmbYOT5Zp`nGM&r&$m5pj3bGGaac6)*qAjs z{ib-Js4`d?Mkxu^Fa13L*#NH=-yFNUr274j*-|tJRe;(6M5E`5a*gq3iyF+f>MRiXv{{ch$CKUE8c=1Bi<3mMER8234+Oe{uO&{(vcDTz0fI?woG!jGshtvuzo z3rG?Jq+(FN7=^2E3z9#7)f|bKp)>P>(+VO4PMsZNXkLO7{{>r>S$Lf`}f&$B!Mq zKJNPBWV$QJIv~sE)>bl0)5kFWnK;awy`!W^pb7ao_Tkc=O>@cAj09%>e zM@cD-+tDa^xdmg4YS-eW>x4V!gqO#By_?B?_e110LXW84V5D}`yRTD%;!%C36Jfy7`Ca~x~MTkw-L-BWb2eiqeCUn2o;n}c z_*{766a&hfzhC;6i;VmzH9D709S?Y5BwxY{cp>BNH?yrj__-KU=<91O*U(mM2A!gP z_mz-%sPv_N?LHfr(faru_1hC{7Gu&cmNTCZVn*7%{`3@lnFa)0heGGg2S2&F%<_O+ zLK-ESXprW&c<{aF3Gw*2QXn0FBYZ9s1f=f<#hnbPqhvu1H2hhN_S&nF3|{U zgN=RrE^^-IN?Y%Q2hP%&Z6){9R4q8O^I{z}SETu&))G+K=0dzRol+ghTqJ3p5@w6d zAL4OOv?=G5b)s~^>iN)?U7Q*OiWqnhBWId%{}z}?tlwrH8$H!$F!4zk^e;F1P1=dim9=*6GW7y->K)C-}ZQX`XW-wfab+ zfZ~dTGbD>xq*LQdL$WL=A8qWw>M*bq!ZK3~dM97*nR;e*_nS zvK7ysN^iM zjU<9U6sD`Mlz(vHoD7R_m8Qd(pqd2$tWoHP+?+Z`pQA&8Jkm||E0bZ1nttL`b&?A0 zb?Rpf+L{iiUNV!PBx2|VbV=0D+<>kKQ(XRTYU>Qz5owKKYBb0d6hk(s$w4e{N)u=3 z;PU!4Y^7Ur$v@YXy_mV0s&k%UK=$6|ghR{WHV{ zkg@Kg$Ij4==6(sxXgNd_Me~WL(HxtqmBnGEH{-L^%rv06Y=SHzM7w~&MVcJrSIo!H zDIVQsJJ2Bkwu1RX?zg4wME{@cHB`Q`tdz4!&`)QXm(4!g|?AdyYn zrBUmv39Xr#1WefuKR+0+w!65v-_5!^`LOfda$9k0_dBBWM6juxM%>2ERWIqGWSW4S zr8q4*=`7w5E`hTLwdpm9vo79|S^R>#;LU)r5A3JL2otG14Hpd;|9hXIEef*hubppN zBki=jzznh7zs;)(rc@}SGqq7a6k_{Zn7%;JzyJ7#Ss&#|1Rwl@Y^UcqCu{$)YHy#w zgl@^az~7g6Gydva{;qLIx+1IEvl`HGHbqr%EttV8(gLjOWu%^CfH#Hr`T$b z;V#4ou>=r+8`%%rC=x5dB<7b>p**afdSt%x~8|6Knm%3%O|JQ67FIj?!8}5$BB! z>pb1&%+Q4IF2r}g>v~3lchetF&GG4k0_I_f9W6;{#U)?X;rqcYkU5GVRfGqxwCqNt zE~u8Y{JPME>wU^~ZCD5Y*Q%RljZ`N__v_kN5u^qq$&aV3_`D^HH_B38AXjw!N$kOS zV~->l9Us9}C*XIImKX!9hNh=L{7snt`4A>Pgn$9B0Dm&YE&okzy-2uGSGzpUI<_sW z=DkM zRC5l%<+z^FVE2i)O-DH}X$8*I_3lZU3Ao-*J-=_;@&t8#DHSM-u`%Jk*$jc=D- zrz>8;2ttWNMx;GVz9}+5*adZ9uB{w~km6TYQy_8k}W0 z-F|j%Imls##Rp|*%LmG*FpcEy;o21~OkP##6b z;!F5n1xBSz#ih4nVOMz}7Dl^kh4D9IR)7~6hK#v&<@cid@L73`hR;xRmfEsAm-G)7Qmo7r(4O??sl0Fl`M1 zffwXdk^ChTP`h~r;OEcrL*;yOFMiC>AmP@_WT7O$laYQkAe!>8uE$~3uS#*ida1LR zyvZs;a&*l9y{n7>dZBTwzPsL?c4Gh#5Wye9ys|8b8G!8=eX*$aX$5UlaJA^O`J7pt17~6aYZ|v6#rNV? z&*l=h*yU%wQr>eK%{agmpHq1YUgiOdbvK-yhaI;?%b5!k@&v zRx#>_KZK4daEe()a5{rE9cMWJ%+6GEd=vW@Om=D|KOwv^h4$PvXUN1UR+WeXiGZea&ZcK3+W_8JK1{LaNlH`s@`!(pT5N@}j=x(jKSQuC7MC+WXZ z%A41pht9BvPzEV-KNm0S$xplKbk{$-8{Z3Xcf($xqo3>h5VAy>QTS@+rRKT`K~SR7 zTNMDTw2j!W_z9j+*jfP8Y&9LK?k&Yae0XnJn2KEE@crgQtZEKVaYZe%-n6W;Ekr>O z^RTu$FwIgSQ@8+u7q@aN*+h!fzzkBV>9ICwVLPRY2abh<@<=fTfy~K;#-pZ#N zYBayXKwl>e{-dP0lPaJRppiUc)%ONLuJCik%xfWvZjOv|J%ciVE98wo$xpa5X1z&# zz1wIHwij)lb@?1VqJXN*=vz35Dp2_NFm|*VQ`Y`5QXJS!1NN+1j}-r+&Q)Z!eStCw zFIywKvQK1@ZJ4VcACvZyq1TkS`#CEa&fED+1e1KoS?3L`Apnmrz#EueR>vrOzJ7l2 zX+Yn;$6c156OTUWNJYIK7T!B|=Dt=MUxFQBQW9gctC6n9g&Kpj zm!YU(PswB3UJ4_8vzRc+OumhSL7K@@<1S+J7AT}?W}98%Lwomv0uUO(Tc-q&u}>kT z%gQ%{P=JS_L(E)Phl8#@m011hYUr69Ck+>_Hc>N=b}%|pi{IlnK(nT-P2BVApCDM2 zvSB1$yh(bEvq9sve0qkyz3@aR^Npob-Yk{Rnr5aOkGn}X&{eATr9_!L-a|^_oQhq_ zA}a4OI=snSbskqr234L=?QdIK{DbOWLsClA$P&HXQ)ry1j*BQp^qHL$6$;O=F|-1e z>-CD~eF#lZe#=~BncO|xNw0dCBQas5l{-38Tmdh%xhvs1A50`;{i$Jr;zhp{9KIaL zxuKkO{BoomYgP#Rx&`6<^fSQbYo?MYSy-4egS@{+m_Kr)~#hV$DW>UES1j}^7U=6-uHanzaW*`SDd@z#Mj4~de2JG=&mTXtHn?|JZ$p% zm?~R=tKU>eH#opF#!$(;NX07voRz=3Sr+pnc1B>zG*|vw{t3YMNqZ#x77HB5C z3gFmjrMyQ*U5wAV7@b(wT?{b3NH7OCowENla}Gc*Z(He!+kxfZ^(??F|{5U zV{ui(P&)seEdp0iP=QRbpn9kn{iS_TJqdv@ z%djxU<_xG_bPOwN0){B=s5WZ<@;hy~u(-7~yL4)V-E_-lmkf>9vttrH*OwrZXv9NK z#$|YMd;ZtDdI8f_M3@w2JjOm`$WTu11PKSBQZrN2iK(|7EyiDge15PQ?(u1@P6c)0aNXWF7)V&}sAqmBfi1aS|< zDW-wqY5-ytmg9Aa(b3W0hB)-=KE7qI3IuIJ`@KPv?Uk>;?ppM*B7D}3$<0Ltgx=}4 z(TD|ld1n%Oderr&t2S{04h%YFmW`%77i>41Nz^nnnnA1eO2D_L&Vw(W!pvIXn== z>QL$TJ3EdqfXppp=>ueMmhq#$a>4hZ6#Pn+OJ8ybrBtJ}STkToD$D+uhfPXdcD5WB zDb5dvUm}@I!6_hGB846eik*x@HK%*SP=a~1w?6KJ>c4jJg1m!+!<4F?UOrxO#T{I? z?-JBo?*fEpZvFe?0eSe`+#E+JS#_`DlnN7SsMI1()XP2PVV)*8s4!~g-m|ZYg~61L z=D3_7swjCZ;<2Zqt2>$N`0(rJNZI zl9V2vsK3b5eA~VFu;j2t$ENl@P-vMqYtqyGb8}fW^Pe*R9rA_z%&9x378XV@gSWM( z40zfiL5&6t2Aq_dqA(xVl23AJWLG}NnrSt9v>Vt~Mk^d_0 z+3^c2b8~aSUYXlB$jQmWj;j7WI82wT1Mc+Fu6yv?w@*q;Ra01r-y&mq<>lqW4tVM5 z=@W-ASf5sKc8LP#x2LB^$x2&WTdUMuBSle8?uw$KVySG{ks7yDcVz?)7^)O zg72Y8DBQ+tS?6a0e>g~yZh$o1h0PIr{t;UYpM!5Sq?`u#Z#OHsy9<6b?zk|e=KJg3 zG){iS)KzMq6T7BKWHTp^$InF<7Z-KysbFXRJ_}|3@TH>hqTnOLq-%sc= zLP)s(z9^*?lE;64eM>U@)`P#}1CirNE%A3W#@}xddGr6<2Mpz}-M9J7bQO(_4Jwla z|5>4hC)Zn4v8KOp+U~X3O_F`d|GbEMMqi)iit{l^nJRND{!%b>Y&K`* z=7N6pmG|6xVN4o~`3YffzIM4=AzSl6P1~ zcaL{p&=z8~{oOg#{8NVxUft14x2moky5Y5ok1>iUD4%<`IZlawN>9H8lZnARmDfz) z{?Ep=%1av_qxm&9P`Qk}Y=k-c`Tu!26#u`Wl7GL>9p`Tc{oiHzf7({NS6;`%;|u}< zaoFq?pK_{iOU6VdU$V=cslEN56NI|re7f7ez07=Y!G7!O&9L`1k5MGRg$R$T zeu@N7iV1w7Agz?Y_6AQ^$(tBe6uBRKa2q_Noqdb~G=6s^3dL|4wY-atmXw#j3Rlh$ z<^JW*pGWzXbL3zY+x~eZM3DxB zCuz&{%Ph$zZ4U~!U;y{T_957*)zwu(Lc$I3&y=M!|K%oKM(nUa#V7UCC!~oF`g68- zl^zBbFo7;q^?F@GsYQRWaR<4Q$gX;)(7G_6&6p=@4;BfLj3!VPZu}lWTVI%1^H{Px zJ&-!5q(of+JmLG25}r#cdf*qo4GEuYe3MsF3Nz#+jV>~3WdU(z!Q77H_4)$Od7*KE zD8)Zz>x0No z>3`~=nAtC{to#_-Rfe+@Px6Tv)$Hwyx59l+rVH&4pWx-?<^AwQ$B2fm92OAx*2u3v zJEAR%+7Djnd`?$}bzCf@`*-(Zz|vq$I11wCsO9wjOcqOqxFHs4h0$VdSsSeu*zHk^ z11aMAgJzDpK8rWvxBBvR+o5$%sJ8Dv4RrlNH=wECl;;OD$(Y*MWTvIf6uQF`z%yR^ z>&d+e!X(G;qzH98J7J;vUFPCU?BO?c=^3=0-9Z$SV|1O#(2#fqk%B%y>aRcOE%0_K zu3`pa-&0oN={FSC22-dY92(J+Kr6VFPe9cSRD$NP#17EG1sfo9r1u7lVD`oG(o&#w zs%!0-(yU%V479@0Cn+b3CV~{w@3Ao@V`Byo#cH~Nfpe=aAHoZ0R}#O8*I-_{1rw<` z9T$Gnmc38*ud@J=>s+1kFo-E=)I8w8t;qxZa%n<0AAB*dDuYWUBn-U}yL`(~OgI36 z>H0!lXJ@{1Pza3^bYg+w?$gQVCyXrbmtOCK1k#w$WWlYYvIl;cMbHTqpOD}NArvCZ zm7bn1bM5)8$>()u4vu-4GHx>2Xq;OOXxOn&!H?RTyoHRXx_#&>V? z&=Z0v?^Gk~O$}&Q+S=Y`W@k4Bzlz+7a)|S?R*{E!0wS-xyu5@&M4x-jyUa_YKYZAl z@WV~%H4l};7*x<(E#NS>0n1tFx@owzwN+Q> z1?`EMiO%GRV$^JmzHwKIlrYjk>$%;bUyb|Uon_HXZ<`HSwM%5ZlY`m8cwff!lgW*O z-B>K?Rx*M>Hdnu1bdwbNwn+3nxt9A2WKz2*_jtGTZj8Qv$hek1!yW^vNcCL2g)2n0 zZXkLEJy`NT?-mWO#y-qYvN9?nHA!*VLJ2i4@Vi#A6)FDt94#ZxeReDmdrqtClUjLBq07Tn3@?G5n(s5 zHw!5Z0lJK*Fg%uw<@s9mI*WJxdEpkc3^UKp&O*Z-^je(<7I1m&tthP`v*784gmk)= zcYh2N8NGjh=dpKpH^Ee_uJ_?AVs%pbmx;n#Q$IQnnf(BB=Fx-LuIooBghIi$*NM8k zvLU!uGXBjPmMpRE7z$ds^=G>UAMJlI2MOsS77`H%1obZ2k=Y~Y1#5-IuIoB?{wt6W zd3mu&WFV4W0qJA06uzuqbfV`lLZ+u@$^S_FqGbf#RqinRkPJ~dvfxfTB%%+DfV?< zLeM3-w6x}U2b-960+x^8rtXkILqh}XSWQl~B&3MA!W#{CB6HAf2hNQmY?%wv=>n*C zA%i3!RjdU0bW=;qm8E=2{?9547Cx(Bk>fy{7*27;)BNdyoFp4i3V_gt>six9@Lh0Q z14<7W`t^|H@b4lA5*UN!fNOsja+1`t%I!`e+yw381`OPH2?+_V-nJ~u$!9x^HF_M^ z1N^Gjs@_5?DqH!;tL{ZQ@PL^h%T_oIE+`bjUIWNoK~);8R^k%B)es8KeNeA?^YX5J z8-0w*sK=UU@Vr#J14Xe)F@JHGt8L;2^)oZu8xe5Fl?>~EX3l>ARCr6zH@guq} zqyF;gQ;27sveWb2l#&i1ecATBY1W<#5-`XB(6wsac{7u}1+B{|sz*DaCG$zSwQgp? z<7{{`4o& zlXY;|zH#ij;W^)#r~tVwLsSs-;KK(uOL`d@(IS?GIvgvi4F#Sv?BMj}!T$b`ML{~m z{_o}AAB@qg4#j-rN;K{Gu(}oxYGcqa){PhVqgUo-O zr6Fl)bYN@UKq(^J-5Vaz2%3M9&d@CNFG^prL{By(7?hJhuIBcgJ8zfstJyb2F1Uh9 zHqvV;oqD67zx@))(|7lNL8x=KS6)vaR_U@nmb&)<4oSq{p&V1MyG(gb7L|RbJ_k^z zq@+wyP+35#rLL)2?6~;o4E(7*oJm_Si%}q30UfFb@U=fQ9_u;{ERWc?vz?sGi_L@1 zOGaASYw#QaM@Q~x@$vEZzyy(j%#1}}?z6^MmkvBd*}-`#wH^R4;HOt$GFbcq8G#_P zGCqE`#}AfD^lU4pexr>s84|3Aog({BLACPAeGLsFP?DUU7dsb#vyYhl0IJk4UxE<~ zqMf7uBchJ|ybZro?A()33jI@Q>HER2Cfm@LT5LZn5AxUzPfQY}EG#TCr;#RnWMae% z@UV!)1=5WF)Tvhk>F9^PWK1d5SZ))g%z{BU2j`IA!4mUpfrvJ6N^x0ewjwSJ);*_Z`~JNv+F|v-;O2uK9lX!U6%GSC0h< zyLfjBF0&$;0|+X*t`xN%z?-$voNWYrt241KPe8meU0rr4(b|)(cJSheii)a+h7>Fj9u1f1bO`rBr%T;8#$?YXkcRmcK;s=-kp#;> z2~GRRB|_qAM9LL}Lg+fb!ft_B>utNAxw*NidPZPdWj-Tzl(7N4)qA+;%BqA?WMbYm z_J^}EFcFWbpk^!BejzzoXstqmnTcs(rxbiiJ)GC>MJ=!>^3cj0eT|$)Hcr^he#(sw zUlp?atr3S`%<#S7ZdB1`VUv|g0QQkLHqNmBC9)6j3sXJBJ@4V|9Nky9w(Kvdl-Z2u z!{0-it^uc~Cnm|p-Mwn7RqU)tZ7`P0C>rJ~2yWJV`gFf?I!q|cJp>|b7T;nx(i~nQ zfb^L1e4(SKXSS)?lup4>v94Y%Lu~^F!{oaK$BtWd4|yKxRIbgFFrhUS9`evfs;Uu8 z9t|8D%QZ&yI10S|QypRvQE__bCg^;rjj^j@q3_!>@x7H+`U`c3A4k{sHaeK0W;~ zFJW^QnUw|B-33-}1ERzLIR4(NMmQ9F2)rPs15qnXN1;oy;30p?P6ALE z&Gv<^RDx3wS;9bC>os$DH%WzEIqzpKmNhpw>kpKF_(05Z=T6)`JHyN^&~EZhcVK5? znn0o_RNxKdD9*0?oO@*MqC}I!;o!Uiaz+$^YSpxBy+KrfKe$8BBgGolh6GVu&&$vX!ll5w_fmB z4m2PqNoigLj0b!S9JooyN*Ng$1H4aBC$ z1z5Q{E;tF0u25yK4qt(W1e z`n`maKHu_(KU)ZkP~##YM8S(uc~XRtE0dSUl&?GIq}?a7r~?1OcwPLL{RBNfccP_10eJzx&)z;xF;_(@&X8& zMhciZ;=BP=GsxV6Nd&4oI_+?~OxR&k0|0mp!#4Gy9mYcJ1;>#6a~d?DAt3{(mMMeR zkJ0xQKtqyMA8;C#3n_0Q)%AYaz4*L8Ul#!wA)}^&1j@)eQOCN{AB?9TNNKBSX?<5y z7HI|vFgHkM!wyLyu{VU=2EiYqqX|K;O$xU5_5w9{aU?TDCQbmLh4@>@M+%%Dgf*G+ z+y(h9#A*Ki^QQ^s{kB71P&Y{6dCOcSm@@$jDO!lG(36 z_kt4&zDgfHJA_6o2}#+al20zaZEk|k(5kS#g)4T#iA1Z(-bdhU9^L{32}Fen-Zta& zW5B2;K)H!PkqfZ?DM!I@k9wpE6g3_XXHzL2g2?5<#RMeXgVM)rgbej(z*dl%P=P}G z-nuU1Ui`@KOP4NrLY`0q!;gY=?Qz4Bq*GNvF{%+}MIo`U?LA6;fx-&-Srv;J2?tP& z=!Abkyg8`Q31VR4+?z83-7VyOAUPkR!GE<1d&YnPk^%!39A+|6XZfi z#Kd5sWGDfe(3B3NYy)XC!2RG{tW)fBgVr}J%Qn2Hs)|a3vd@tk;%poM8}^zu@)?8h zwi$+IEQ0tJgq48>rEmaVw1!bVOcIV$O_jC-uN9S)blvyc$jiZT+=yFv01zRY6{e=#MAH0u5b vPyC-4?mvkDBtQTF`2X&U{@+59r_a&rVp$ltGp>oF!GCg6N|O2F2Cx1PgEo@c diff --git a/api/_images/tutorials_nihcxr_monitor_api_12_0.png b/api/_images/tutorials_nihcxr_monitor_api_12_0.png index cef2866daac16d20ed88b8578aa088e7b8e64508..fa94032fd111e11b1227a9c85d866475d46032de 100644 GIT binary patch literal 53252 zcmagG1z42Z+CDxeiXvMD3kh+M21NmB41^h^OWFVt5D=u>K)1Lhh7u{IOOTcv6p;ZG zlx{>onxQ-X&pYpU&e?nazU%Mxeb?C+zAjcg>xuimpY=>tS)Pve7%c{ap~ER$R>xp= z;4qkN^1F7zPxgH7x)1*&W-oWmUc=_Lz0(alQ;gCL`+Ik7?C+Z2WOFpNyKin|eOBPS zz-d0VJNEYX?u!cwTK(q(0UJ9rLE)^tG#F&}Jq2ADjQ$4l|67t}lFTufdRyG(3!2W6 zjNFMMxal5N|nVDp@o=K$Yjx-6!2 zf5S)9PdSbi?^<|x;-SL+0K@C#CO0oH?^T6|yY4P4yAbQz?>m~2zm(`mRSi}|6^4!# zWxynmKWr*4S834y#CYTWil_eP%(0W)=zn^bd{#jA1OBjOo*`^S|C40-|M%(gRT`7C zRkm{#^fJkw>nquOwf6hae;c4>(Au1+6w#Efd)7trSKW{a_U#i^&!r3edby{gwxA!G z~u{ZPf*H_$s?0TvEF#a<88;7x$kt$X%&BjEfvyLUVu{$W= z%eE*csM{IptWFkZe(ZZUa{TT=TAzW%)%k9xUhWWlHF+^Yx#NUD*LKR+-swX{8GN-h zZD$#0+v_)tgqEdl9>r0YIUsxm?3nV$<;Cw=%NpIH{w7R7>k;aw-jenWIL?&Z+$Q@g zSFZm*{@!?H!%a7{^pwYD>Bxbm{lCW@IMo`Gf4BWD{g|J`fK{hO7<wP*iH6DuZG{;EKy zPk(lM?$y$s)c!@_P{iUEyPaZg*GIplmtK+uYy4Ju*m%wOQZja)+-p46n%QcZS*q>O z`Gk&Xu0c_7gx+iEQnQtQ{#`X&+pHd!A?8x}@r<+NXfJVdAM1Uc?t2w6z#WqI0Be-;$(NR}$~536-*~DOh2z0xw5H8fu_}IzAh*m;`f(_RCfoFuX>CivGBPkI5R5$L zwO$-K6Fa&9ztX7c`2D}?WH#Lm9h$X@))zV%_wJ-jxa)p>NY$*D;PK605jN+nI?k0Y z_PFU4SQ|DUjpx-)Z;K1GA8ANPO;6V(_mmbCO9d(<->UK}UUv?E{M%#;tFzI(@+fney^U+gsh3WJ3vA_NaQaBs*BJ4f-+PDYd>>|%I>((vT3OI9 zwCcpKPUxi7n|0=cl`|zYj-+-TuloMBpeOaYK#_uvj}H$Y-&*FX&&3n-BaQO(GHdV3 z))(~iEL*{N9a2{|R_4d%n{|vw>LkcbXR3~$#1UOjB*zF_G%2L%WM-@s#l~K&4dqW; zS^0K}UhDLcrkTOooRx%miI2a%^;(1HYW3Se+c~os_nBvWwGC^_v(0hx^ul+($%3=d zaB(U8?9WPUT;1rS_xgrxRXrSB@g5PJes6(2xpc9m#AUWA*IeW6wXC0_tmMZBmA`GH zmwb(EBTWC5h}EkSBb(+$xEGlX4eGGpc)$MP;W&S77`exFj33P7&X4j3ZX5I;Pycom zBnK zN?&WnU3p>O|3Y-scylET27i6h@BBrus_uTTo#Ewno3k%=zN_Wo;nBQyEqO-lKE#WN zx-H(vvfg-kHSR|I<-5)U&mOmA8fUIA_8PxYCE3>rFiE?=7qRY2reSseW{-dStn%i{ zWHC7xjG6b3U3V8teSK;3th@CF2M4tdrwM(S?R<)QUQ9BbVkqDD8&8>Ui=QO_CyefV zpOK~_vmP9HZrfBeHTHUM2{kpZctV$WmaXnTK6vQ2kH-9V=cFwyH6b&l(@)Yvc$Jbc zm;eX#6SmqNEUpT4;*OdZVx%p;WyNi!mogFGjTwzJ)rEkPG4@LNgV)k>?{ib#L=8_y zG|Zcvu^hz9DdJ7U>RK|H<-a$m*KzF#a4Ip%#8&59Rd6h+$ZSe1FZZgQ`Z>CQtSF$e zq}N?TIAG%oK55V}|7yywuXI{lWV>gbC4DcmuS{35a^U*zosX+G_(GF0R+DeFzSQpz ze>3AOwCJvcS_A23nN!blW~YI_{7B1Nx+aXbF4z?z9_|4Cz{!*wGQ{}{*aImmPC+Tu zr760UZ5s6z-siw&m{xZzJpHa&oFl7PyfoFFaV$hLg|<_ zR!^?p-VNMy+`tvTBF}CnQCeCNSfm54#6%mQFrkmN04uDuma&gQgrI4!lB9K zTP~8Re7r%}9Wn0e(WbQ)v43E^BR`Ri zhD{!gM6#!B4dvwhLm8iQH>U*s3^Nwc)qWvqzw+lv2ATf7x*4_Y;AqyiQpXM`4^pYw z98I%Z_iUGwvs>&)yTaNVeGY;??`AQ%rr_R<7t$hd{J!Y%3q781RM9eX{w!Fc3cGE& zCI;5XNc^!CY_^7>+5vum!H`6#8-4=W@U^A527dh-FpwEn%R!g%Gq(nwT%7Tk_Sp*A z9dG>>jI78Fil6h|`@?P3%7?{SmS7SVTGQ>dsplq8V6IX5WG|e(*_QzI6!-xg&=z#= zFGCf$^z@468yl51en#H6Sm(ZV@(PKYFW70>ZpY@@ke-(`1|xJ7{rJeCqIqo>;)b5` zqn+8$edAS=TVfjdxdNmtg0bB0wfqFD)|LBko>W&;&Q@R0%OmNQ4|{(1WkZxiYy15w z!BWk-velX#>!^>S-I`|gdoZ$z=+rS920GsZPnuTt;~ht0mlDESCT5>rOpQEmJC@|s z%RiPBGrF)0p5hO5W3DUw$f_Ff>ZdDw;C&Nzo#|Zi?2}JoLtGx;E@X??Xghc0ihFs& z+gL6{9-nac%eHijvnP{s8cnJE}?ueT|?E<%3 zJcGxn4|G^2*mvC9kHO^mqc(04o@X7!u92ufxOvz(U9&#=$#X{mZFTk}PY;(cevPSU zH_Gs1)Zs(rPAL(Fl463bJGHakT;j-VwQo=^q&vn1{{%-{AI)ER^T~7AMnmd0K6lR~ zX8gG2U!BL-o{(U-*jaDiAn?r4B_ZpJCUV}p@y2+9gh-&92M8rW<#tS zuSEOO`ec^2Aa!`&6Y5$Y4AlxU!qvbLq}<;}R?v$Yk`R@!`tq=D*0KsAyXsY|VR2x;3Ix#XlM z1HsS$(z92}9rr^MYO;lJ>OaEG29`&8D=vW5NK&olLQ6*;t&Q6=*Sp`?5|kys-&31U z@Ds;BJE$Bmc;5GJ@yml4Od+)(uuwH=z`E4bhDcAzRzBGmB3U?HCE(U^t}ryA+%mIz zwN8RE6}7@?FH0nnlQ0`PMru>fI0sz8G4qFXriYKj9}C)u*;I=)mcgmpH()D-VJ_+E zM{z@SQE5T(fLq@V$|ZT3egwzrF{c(18fsA! z%&nJuCs5pe__a}~tNBDno`hsscDA}zNA7nh+nRID8>6HbR>{Q7;>jY#r>vgNR`HbI zbg)l1XJCC<q*XWO1?r?e?pj&F7O$6)S1z2w1t=8f?`WgY6jEuO#0<`-SoLqMyFJ z=k9!CV}^pXXtqv*@!4hjda1?nsih3V;?cwi>jtQPlCGa#O7ky+$dhaIn=Fkk4f z%kSev1tY&v|ze|{a){i$&DOP(@$P=rCJ+J~* zYIBZReQKv9i@)o_cuGO9XR)5Y)ZMnzQH~wvol*;RpZaaJG&S!oA}|G8y_g?6q%7*( z9R6ZgAb=|!Zsu_!mzw$?If0|?b&d;GI3lnW^LhnFH!QG@Y8c6Vb}OEP(0ZVz1!lD!KN><@~ZQWu6|5 zxI6(7`()&*I5qS5SQEREtI-@febU_OTGmY;Utiz3R^o=%8`V2uWKrv`^C4kjIv(lQ zvSJVMBC|FTx16`|fvv`6DGF+(%G2juaNsx=0-gwK;A9`}xNa>uOv=<3%RN`VExRQ{`1p2AdGu`fzBa~$gaicDXoiv2 zC!L6mkz)6AO=}N9I<7!CckhR+uuV_N=(^m&XOE>f*Je}ebutW6DxV)t19KPu;k`{0 zkiEQ3|1_m5acUEQ3T!$^Agf&q_%a?a> zMqzoQT0$)|wV-Zj#C>iiH_9WGN35BOmFJuq_ycb~br_`Ot$tcbN!ReaSN6}V6ugeK zYa|y*;~UI5{3Vp9p9=^X2JiQjj%Xs@dsD*ddFTC3jCXex>h_Ddoa?1#`1P=&$GTU9 zR$b?>a+*H2f4a{%#vzoiRmqF}JnPAGq32Sz zu?Cqs8;d^{2;U+X7Ayp2{e%6B-j={M<%^C$MWI z;SDw*bCO#kic?N*=jOv5w33UFFq$LoS=Z+mN}Q(H)mN4SPby|SmJ_!6OWqWx!FU@| zab<}dmmKz1gOfPlnEQSNvuN}4T=xrKF!938#BxjCPYlK%g{yzP70qtTl`r3}3w9dB zi>*$kwf(uD&}$>k9oMd7%%5~&n+tK%=!3-M>8Nd(asg^mY20U+efa}KN^Co1hvuzy z_tXw?LEh(@3wd9smAYv{9~%baNlp646?hWv;GRK7^YigR2FPn0<#bO<-w9~=`l>p| zv-G2A&mCm$y`Rv1m-wS()f%ZF^)sVyR#vK?vk>QY+x{q zm*}IRxqY$YLBqYqDUK5+R_(DzsOqFAoC@f30D;a0EJ2WM$P%2sv+o%C8iZ}HthC#A zzWkorWOAgCTt?N&O?{i{#STkkkxPPTk`RFB*3;vubqE=5*LXHtFKQU95xI?C)4fxs zkrd2L7(0d>KDC0IF&fS&9#|bXC^kRFt8Kt1$DSBu=l47FlhOK-l7s}Icho}IXn31Ti~C}`21G0q+q86WgpaL;oC%FcX0{}Q&SWNRjK}V)a5fKbi1^yQ zN*el5;4HN2d>Ieda&rYN_HJrV33{CY+mfZ0OITsb&*!A&Z zC0-HW53&34*S_3^E$l>1m!zmI=M#IY3Qzbs6q7k%o^({8hCYioBcL(MRwKI^BNxyd;KMc73>e_v=9Xqn0KTlBjWUp?7UzmKWIaHSoHNh$6 zbFe9-`_!?Y3>A|RBmk#Ao|+cC(M{fac<@ZEx~XnBmV0V#cE)|95N@2TG-|W~tOh!P zYH<{RK!S$?5QJycSf)&NBL)%L&s8*W6TIoma>|8c6Wku{ihrPE6gak6DE*G?1m95JWvDP%{4ELShBcPh?WK$uxq`MMuDfs-*ame_7 zUaqFp6*K!O<&byeVYpdl6M9HI^_Xua)SHR#CSF&0$tLgLc}(3!Q_jGm2t#$4F4He7wxiZ2DMiGpyL_h+g(!Gao6fCW$ zMv7|@7~H2W=%(pJOu*+msGoC4I;2XDwQzT*hK%=BAm!5mM~#`k&IZFJ)XjRy9>hr* zeGB#Tip3UvTB*5!-z94w`}qFf*7fD=jQi{hWbzqQ>tX`?R8%bRbX^(2&yj7;?`jSd zU%1BMC!ws(lYroNU@T&Olv+quc~`e<8o-(-%`!_FczFmHPc-6^PeDlm#Y%|I!1|AE zP#}04p#G3;%q3Jn%X#50PW|q%YJ!A=kip|u0n*?%Ru@9B0O6cAfD<)EPc%DXj7Quq z9t!a43`0iZxKN{9@`Z&@&%kdi%Ph&3g&7GU=dVWnTJJ}o{D9Z2QsIZ!0LT^k_#_6Y zK8n4Lhd@e9Megz_V?mb8O9m51|yXWw;8aTx#kJY=-J@!tNcxafNi3G&Pw@Eg&D z%lkOhYw=zNhyXJ51p4!`SvM-M^j?wG`cMa(H)3%cVMYX}>6oB^n*%q?+j}s0QevODwet2UPz>+Xr-PKKQ2p&~yW?_ryh(yO zp2Z;;(!*B*7kAo4h-g71z~cJGGQ-rlE95ZVchU5T?c@$tcU2_gWZH*#b6VP~FqukU z-vI^&Qn7pC!`WZq;8;vfpjZEBm0r%3z7!1(T-Jq0!Qy?}b1snXSYLQ>Gwuq6^ZUOP z@~uQ*=2RZUB$D*$*^H(pm&3OUzI4nyE=zHH90eTPP3>NZgqU@U!#=bl-n@1y0ugt3 z|Mk=Oz3KbjpTxOJ1Cg#xl)aoZUohIk0Yf%3qO?l6KRxE zKS1liP;}bd1Dcu~^B_PzDI0^L6|$dqA$3Ks2WOIP|J%csNVR?b$##RGvTKDGZF@cLB_-H#{f?*K}k7bBD{Q;S~B081R^|lQB z+<_s=ye{KezQ*=}BkU%;zk$4;&u82h!V#Y+f?E1@gQ*_3!PTitg@L4&u=dtW8}Z$` zeDlS<+_p8`*~jI-Pxm9)e>Mii2;c4;b;yb~9uyLPyZKoCRzY*aR%@*~WT^cw3@3%7;ui+gW&+(VEY~h-#l6h4Jw#$aj#ev6pv3rK!h)?vG z7G%$xhEeE|HTB~=Tl zLa0=_Y}6ESDR!%130vz}h@l>jQ;Y`~V?7pV-4s7&)5@C=Anrnu6L0~w#ilYyACI2B zLST?tG~iH?>Av^J48b%d8*z-zzmB?;ZFC^lf~p}%b_hIrc3{1aCSkE#Cu{#nEU_16 zqd7Vp7c%_)5<;52w?!_-QFhBK4TblIQnk{wX?-q^w!Dq#aU+)+ODzDwu0ix%Z%WnT z)eTJ$yZ>vg^$dVIs;wD@T7VS@TX$XaSeg5-W9*^zTm~uH17d&ziT_dJLWsWx?AW7y z5Q^Q+R3a$M+k~)7uLtFvx0zHbtN=ZonwzVsk$ClctlaaaHWj&#m#xNLhCswNN{~z903*TQa4sTO&-anwoc6BxqJf4@hEVLNy&W2`9gq^Dwmb$3&qrB1i!>I4B>W!n@h zw}*~Z+5lmsM?CcNEE0bHyvca@FnR7htJj*^3ntN)F+hqDiAGq_-)L%bg9nDK(Q3H2 z*M2L8?df*%`%B-S9UN>)OH!hjNpDh<4_Drb#YGR+YAqG_R^v};P5*d=U*A|v48pJp z0_K(snj;=TK^ z{1%cJ#JZPfkb(?gpXm>eIBJCqcigRF5yo3%*~y~4TvM$?vI}X zGo+IeQr2DXZDEm_D}dz5F*72%wf0H?plJE7v9%5bA>5+c$wpkcmo1YpLF(pXKm+<; zsI%XI;CCJW**_li420IP=2ya&`NbYzUkMXk)e^gHL6R}HJQUNKmRXhx@)nz|=?4KJ4T%Rm#@7f@mBn5!V%gHqJ#>Mzkt#V^kerGa=)uhcb9JJX22Gk@qe)tR-qASk*i=W@Le^ag%yj0pN`T{Lp) ztx>5SvxH+4B7?E+F2WI9=Z4<^(mqnQIvIprU+vxO=?i}Ya<~#Bw+Ue!VV>9Km>0nB zqXa9R^K1N!jEta~(OixcJw)L@2?Fknm+^(#Jf(Gzlk@}fI z+TMGQu-uN#T6ib2_4{3IN-X|7NlF0NK;a67S}pj>*88bl(Xy!3#rbz{i1iqRvmQd_>wRdx(RQ+Iu~%ofY!z!hRy(d=Ai}i0>5n8OCZyg2wh@n}#T%nC9J~rpgBO5>9<7R6o8357)@M1C>!-58Dy# zuD+pT9D?8tJOm2f;H}3LSgoEP*8%)9ZZ4{|XpG#7iP_)mr$mg~U&ok&!tVa7sC_eoL~Muw-B9iBnt zi2_E~A6wwwLFC?Chck5`K^WIQg`<^Tcqv&M#!b%G8B}*7sUKvY$!-=R7od5)mgikr?ule)SK~vxeI|Y705zc~6r7Rl?-xq}c z7QS2BzebBFWtehLt&Gi-ts`)1xixyzW;B=ly@D5wHwih5J%|A0Iu!T;%?(dkQZH|A z5aNeHT|vQ`$IeX&1I}j(2xpC~=3di3yw3ues>4td1j;Z*#RvmJRci6MntV+dksvBBD#w9)Qe6~MEF-nG{%u`d{`xiZLTj4USUU$e`Ov#tqZ3^mo1*f4P?i7 z5Rp-~9Q6|AWrEYfXxngv?fSQ|Iw&Yi`2WJHC7!>V1$d~ zki5H=&JZ`t+(FNUSd}2U*@d@j56N9YpdHX1%zQZu>j}UnIapQ-mRSW3P7h+f2C`;RbS>li_S15&=e zo!sGIXP5wrm+DP$`k<8#YQpC5yvWj#>< zI6Kno!`2qBv;3d z0-;TG`U|G73&5kG0Y%8*-&`c7Df<(wfz|mnf`J)m--a-2$3lU00zOFcYI6ad4T3`F zck126kQl0RmKbS)C=OD$nLpFXL{SK{g=mWoNfSz6;Z{VhJPN*qRq|RkU!xXgRAHt` zBV@EoS6eb;_|r3F@=2(*9Ig2uF7kJ2@LB-)+$5DS>@pllbiEAa7f?g3?BB>pvX{;K zUEaG>thMF^}_+Ux22;>d|*cd)P{H&vo|5S00a=)n4r z4EPpVWi%QJ{k6Eh$XK#ygqDX&OmNy)@vQFe4h%&C&#(LnTbR)Y#gi23xK80tG4k_= z3>M)5T{*CA=SzXD0b9mF?6Py~48Jb>=KXre;k_eKiJM8lH)bc&;_{FkEv?Dn!-|1_ zj$koI%guWXvT6l_uyTniVaKZ=%Nn2t!%NO(tIB;$Hb6&Z6yZmD*X!=>_r7|7mp=qJ zdr->hH0#?z`LPo{rQQDzHgapgg{~)b1<^MIxPhW&@QU|uo4@|s46v91K!;2JT zzyL3HAns@{1C!)6B-W-Iz5T{tfOr6^^7qJXE8xo>?*>~3{;a*Y0CwRh?}0!m&yxS4 z-bi$meeu50mo4PdlA%>(s)z_2D4TBb4*8eBl}Qc``WkqZg3l%GMnAvsFFtY$$yqvv zC+TV+$OYV0A)+DfWfeWrMC<|I;85%JTqhfjI2R>pmuRE z%e801q?VA0HgKo2wE-Y{cKISSExrM9o32AdRlgdbXY+svnSHs1 zU`@q5K#j~0H;Mqs&iZ*1^q}Ac0}>i0I(ferWP+2u@v!e0hGYcdunheI;5kg|a| z?3B{0JM%0?A8b@u&$9TZ#m3$&ribCKJ6(e;8W;_*9G}BH?P+0PIqh}P=hZUmIX9oO z)+`nfkxP<}AYrG%&)j-KyJt_DG)N0?K<@`|MW_DFvU>9kjT4DK!wlGQzQG|lzgxhb zWUKxk&{v1+7u){NDJ3N(qqi!38QtdAU{sPySwW9Fbhtqd0Tg&BKO}4pf4o1$<5|h5 zUh0~dhGFAHq2p{VIXNFVPc3OPKUz=&h+$MuicHO@V3L6hT=7^TK~o*xaU%7rzcfz) z5SNVJ1|bT`?<{o?J=6m9$t9_X8g9o(AY!%OIy1n$P}Iu%E~E3n{$=Rqu|5rm%gYB0 zvJxwuBVX6HAr5sM?Xjz%}z01UVa~lnh!%Q_~2?LKV_m<(AU6a#m@IFab5q*sA z?~enkgMh@nFd|}w$hv1-Av+?`03L=&CK0O5tX&AemVjk6577JJ)=#&C)#Wm1IS@H| ztPgm07i@FfQ-VAzKfT@d)9B}~4n60Fk_*di`}rt-Zp`fbF4>+eJ}u>lQ@EMU zb@GhoTwot00(a3EL$?P~19Cxc4M4Hrsnt(I#U;yvXSIF&Gbu)()uvYOO1?ZUT&_5DjIe@1B+zd!~G|D!>sy z1TuKs@g=gL91mS@h|ly`1ON)-7H?O>0Bk>k!?@d&+<<-j3r(dQwM-M(muIu~IU3?CWDOj>oZ?$=s^H}Apty^HBs{9LWS$5mDEK4t6-Y|Ye7Tjxxs??w$0S<24y|ou|)U5jT}O*pofB6-}tsMu?%i=@)=cRool6#PW^m*CEJPl6&dd)+Pex^CAOi*L%dGKK z!alLf$Zen}Uff2y@UO;*(L=;O1ezy)+-y*!m0V4yZu=>vtDIY8-#&}+Dr>OnAZe(* znaXQLZnZditH(*dzWN&6e}Ux&ES4fl-G#9T|IMi`&zeHX3f?7Ig5q7u_x*=z&K;pr z&6w9M94Zn@DJT6X`IJjGGI>=p!l3Bl1xKMg2P<@ z>z@q{s?^RE+y-=7|L{3H;95#ik>~h>1)Q!iZ&!zw9W3s9wotV#RAjOfC?FwXrEc=& z2u^*i>wbvjx;+vYj*g>!hysSG<(KG@{6>#b1bCMmhAOa!)l=2A*Q3BFol*s2j(+F{ z-+uB67?)@d7smm_njI;Y&X(ySv!d|9WfqkBKW~cBu-pNbPe_p>Vj}aC5sjQDg1b-~ zS4hSw!LcXhYwV75fL4AQ5v77J;iN)hpvuT~$w3a^g>v{LeHeH^Kl`$EsVitj6~8CL zGD!*`*j9(c-30`QODkhY8uWq!Nyr+_xNnxScW%74F?LB%muE3Y=JK*v(5;36D1rwx zCT|Tb!7*AKqFm`2|1ln<{I0<#Aibwk*Q>LE)snrU80nIzKG9hV5a=lt5xCRBs07EQ z>fanEL|91{N?@dwoDY`l^`wlCx{MiLEvUZp_XK=GdsamaAlRQoVr~@+FK>vt38k@1 z^%lkL2IwHF+4|9Dyr@kEBECixl9=q8db6OF!vevJ74&qi&~id4kY#(OJf|K!gzh&S z_}sx40U2?W8jz$1s#iq)0?D;Q>Oj%$N0`^I&}q*CFT(3yK?h0Kj}Is> zhslod_$q8yabp=^-Usq5h+Ax?6m{NRzlxBn4}fDTOg6Jbol<%S%y*qqVqg*nBtKeA z1D^nvTON&`sPaF1RxiQ52sk^QmWi+_CB0smKbW16Do~078+k{XhJ^77Atm3&QyFR% zNIHXrU4Z(0Y>Jgq(~+FZ4Mj!8cn!2q)xQ$P7u-1k#1^6h-i{ID{Ff+wVHd?<>?S@#?ScQroc=xJYJG^0_LOAW;5N#-?0oS>LAGbFF4js-SxgAUq2Rp-a{Ix8l07aL<4l!S@{%0 zI_rSnoFkO5dfkPK;mC}x^U4peO`U&Gb0CV1FSb{Ub+Xa*t7wnMWTVN5-9rj12SP5y zK2TQ9dktYYARKHI^bp{24Im+@Jp&*F1v+%L-ydIamwRv{@<2WM3hRYXEAF@stO?oPbmF!HJe6dfcQGmqgh0Y>2Zs7KKD9i-aV3)(?VkjJ;YIZf}<>x#UDq?yv90Ksc9 z1)9HHXTORT6niX9^@&e^I;f?mmkOGTktPilE%B%fZ7yWgmmnav2u1|N*4+%O=ScSZ zfn$rO7rXrIeqA7hY2{#wu?#C}mH1eCPE(1? zeSU-Ys{WEQ3VF96GYW`-4*%NCdaoKfACtq3)%<)QDZsQ{T6nx0$r+)wWpjB5e1Lbn zg&F8cxDT@PR~k%&3cKB2 zeD1eJp5BpXb_kY?-gF!GSZJ3Ax!2n|4Ol!I{j!ml;RoWe+1h#aiABA=02 z7!T6tF+Pr!1;m*qUbe}z#H-=#wb}NgDM{8&T}iOb z#)7p-wcvPatxBe5HPfpaDO=^_8U%Z2STOqM}aF)W`QHYXD99|!C_yStCx2CnBDQ=6C=<#??xi2SNEa>4ulmDeYNS82q zCp|qYNhVPqyY+d1J_fQZUc}QJzvqoS>0;=?*O!Sxz|PBnk#ocmZhXX z3Nw&90L|6K)>qN1>A4FhzLDlm)Nt7aON<~mMvFtcpJ zGgc!o-B!~u!$kN{0py5kG14A$BS|sZ%o=cgq45wvF_wnI#_n%Gjz;vCFi}Ab788iVcfYc*l^nqD);827Ogi(?_^cYTNu#~tj&sa$5!ec*+!=Bwm z4jLeYbbtja5{_4?JwL>M&VA|L3ucK0q95z&>)s$vjD?4o%0~0y^{6z^7HXM2&$sC{ z+O~7=w|HDos^187g}N?IzJ;f(9RH>(oMJWmwpBL2;~SAUvK`~XC+_?XXyG@YXHNZD z%{eGyc^h&)Rr%5RiL`Q=jW?g3aaur(gx(BQc~B>UP|XtBW7A&0F2DBF#%Z5*b7}}* zZATm@yv&lKX;DB9Q^3ykJ!Q4%%IEFD!9!}l@lx`XG`#AS51U<3{8lH^sI$~~R85{a z!7Ro@JIu&61u(S4RwFkhxO90NOI0!(7K)NAI+84zD@6}^$#99Aj%@N~axHy22xOO5 zXsA%YR@vyRBqfa>(7@+B$mjwM79#?N;KoTOi-ifWKX(4|4N~w*&)-#vg2Xkur;EA( zK=1Q6UVkp?T{e;5X%7-S3wVSssD3D@s{cDYh-BYoTeWUc``6L4@QPKg#o9TzrEPC& z@N!oWAp*P)lC8puX!*s%&|VO&sgxv!@Y5F}j9jaM8j#G^KRH4KcuKD(*{uL%X2`Q) zQs7lOyf)X7hrup^^iCt^_SYt8o@VYb0}*3q)EhnUF^Bl|nzV$#jO%FYUvVHcemnGR zK(+J08$zY`7HB6ynl9H^-Pw8-1-YBk!r;d)Hw&()AB4A-u$irC(06>-X%+Sp9(8JD z?nwggDk0UAZ}kHFhKkgDu(HI&Ia~vrQ3k-J(5kFU6eobX5uE+#20TZCS=vDc#Xl-a z2l{e#0r41#=b=yDbGnkY+oOQJF);-i%HqX6<#@f{LSf7qs-$~DSa{U`!K{BTLy0akPtk#%a=2#t{vljV`2C^C2e29 zqwq56AYkZtb#rrggD4^iI^~Ux-KTw?>^&OGub+SC+v^L2_y^l)tetKo#m2JpUwbDj z3j9IY+Ki%7l(-hF`_uzOZ;4X}F)RYrzF?8k%Pd`Z1yFJ#I8YUO$tmxZg$c_6GjJg! z`TrFWJWrsA2rzr^FSg4sO!YzfMklIJ4N_Rl^6M)`)@;B>&lgD1-$m69PBjS0LsE~XB|69EgN=&@+G$rMLESeV_T&A~86_Z4%bIi^8< zC+Ic3p25>b4PPT_b8b|$gmrI|AKJSU+On%GKo;EW!UN+CV91;?Tyse>0FcJAi_x&&g>pLJ zJ4h2cm5XSlryon!h8~4PAPo`$k-KfGAoopKaRp0;*NZG>iNVTAd5~11SyZ;6_*-cI zOt;s2*6**g_HmCA2;Q<~XC5C6hLBCE%s2rH3y6cU5V1Qv(3iYY z-8q*!-Ct!nH~hT_ylxY`QfJln4j}_m1Fys4O<2dCvN9Ad=s_+yQycO#HX2&r116hr z&l;7328%#%d0m`Fb{}%;bz(!w^8PG(B@;Hb$3*TXx96Dg!ULV~0tSzOKnrO8b2m3u zErD4S{=Ns9HUqPaQOmEdcxJmlZ|WFwK#MgNBO97R6(>rNNsBeSNEUR&7#^Dh^?vEv zOl@bkVwBHwm}EF=%^AG00l1iUa8fRJvCaQxom1IF)Uk?I2A3xvyfpM{Kj2)dzV zr}qeI0?fk@E+ogu8Ch;zv?cjW^D&&4Qj&5M4txItJ)(TFL3hK&RgV8p98{LNRWWVq$(88pyL{q%(oxX9o=n)#14!9+8`m z2jI0(=!-d1VBYxFrjwo9eSKb?#}t^S3`o9R;bqMfx#x$DoxS}Rh{d13vVbH-cHfkM zZY-#0!Q6m9$h9;>%xw?ySP>MShGhnTc|o2{+7C0Aa2Sn;E{+j+L_~dcVFI#ZdGnqU zm(=9sNWe zQHhau{S|JOYd468RsqGQEUw8goP<(@h!UjEvPx1&@gj5$@J*!iu(_=C=OY^EyM=@* z;L*RD#!L#2G2y(-fnryKpoY{{pnI=&Foe@(qTIE;KfU^P?ATZkg$Lk}{=n67umVg# z8lolv*5bFCq;nfvT62xU3hd#XsUIKUvCDAlQh4bH>F2QQPk|W)aP#mmFe9x!tfytG z8PJ8r$2AJF!pW?#Y2m!nKT(D-g-3){XCcJQ^A|We; z;~0b8YOIA<-{863qKuAwt29M{!pmU>cF*7u8Y7Ljh)t{Z$USF>6jdRB>Z7)AeRvDT z0ZZ*#LwfLi{a7=V>Lro;a0nbh5xH#D0-ogsmj$H%`s!k90=&)%%`_Q_@FW~2U<5jM z6ctSiGY&|(@z`!GBR1)h2rSIB3&422|0Q6$rQoR{wVdA`r{ z{e9ly&$mDN+{512zOM6H=Q`Iq*0GND6hWJq-rca)Y24;D57ST5j*RVIopmX@>w_GY2=pQ{!%!rtSV= z4>EzT2ce}$z}HHnkq5_FCI+XgN554JDUPQxZK7Z(N-I*D!V_2%@HLNG7vdR=5g2iw zc`##11uV3Hq)4J^HGzg5%U{mIQQ-6O+-!K=_}Ob4YT{#_jdDAQ6&jcQFGL}9@81XC z_Q$8HLi_M@qwPoGPFJLS3)E+twvW%johBJ`aQOOhgQADrf(Zf|f*cyZTShV3_GXGX z-yYyUwcIkKN|D{UU%&SeslD%K#)aX4o`_x{EnluZAz*^4MoS$<7jK0#o3|KMS@Lds ze|mZ{HEi=6;9768LE#4j@-N;@!fy6~>QQPk2*^BZC@BA6Gr9>vL#r7F1Wx|YJzPcu z4-sN9Ki5sgI`=mX1&4`3*8~pR^6ct$bR5!-Irc4?x7V$}lXLQb%{p{?DnIhvY~<3q z{D-k?yr(}5r{J~Ib-!kKg+LVM9SfX;Tk)XJ^lUz5MWgG^%NChnNFR;tG&%yI=w)Td4j6CetS5{+$vt znL~ax?G0ZaQI~}PENJW}?cP4PKl`kAfpgz2kYLc6rYZH}4_xu_?7iC)%S1COe~epU zC$t?EM^r_dxZUtVRTT9hoa%wcyNGW-2!9&m@qsf6mNE_0Xfa z+)ib)2zGT;h}ebTKBW9$rSJ#PF*tnh1OM4Z=gmqGCWu0Xbjd*GxRhwymP5$N z(2#^G1z#U6BF7<-OZEb_)Gl=;uIwU40PHt8f5WLU=zRoOBGy(?yv!zMBi(mTB*bi# zZe!QtZemj5HBUm#yD=9M(xHjkLJ7;YWOkBPCXy?mjg06h6Li2Xm1t|bJQ@8`$kOIj zIwJ^BkV7ofHHH+WV)jlE)va$TC>%>@BWP3Ni3OXfrvJy6uj5hjZcUQ)3VapEox5^H zrh5(-tqU{~@Wx^Gg3H|8@l9V1RszTKfvJM=pcy2QrXExI~l4 z&qd66{PWQ1A0d(R7XwkFL%o}yMjN>SrNOsqf3-W7=yYayfsV=Z7#I57oDoBSmD6}~ z`R(aIXvnci0V%O;9#cQWNxL>^-x7ZzhegQ&a#9ZQyGhj$g@Pg-b$oJYs(R-j1*!pS#VC$JDZ2rO4Y5FGUnIWC`gOCG4UWWsd zGXdaKq3h@GKbp<^Z#tF`#oF6_uab^pD;RVjPm9=m=-iI6?&XWQ<+9)0$KxiSKD!}0 zVERXM&-~d$D&^SoGV*$*;{T&FO+p1PfUuij$jk)j$S;a0aLdZ

j8@t#5)u*{-c1r@E%bKwJA@T0C~I zpC@uVgY$hvt_=|tk%c#jacUhm_9!+J$ehq*LX@4fnDKaxwV%|OXA`-e{s!8U>Za~- z#A@xXbS1~CE*XQ++_w|;jiFF7Ixx5U?jBD1>6~w|xM=A}hnLq62rhrhP1Fc8yncHS zoN@4*OxNrM_Uyg=Pk(T(tbuVXSmk`^U7lPbJE?q zz2~q!pM}Mz=G51ZiN5~>l%u_Q37+02)NgB}+^%-7D~6Uh!ftRh4q=#XWP zflR((*5>5>D;-k(V$59$Ni9o3BkbQaGv%n4<)BY7a{lR0^QJ$gjtEOyvjO4FT)Du6 zisYHTL~;TrOZNG3b5Js+zf}%aQJpmb6;O@hoxbS^$}w?zGbpuE6cJh?1;Q_dLzAV` zHgXHRXQU!Ls`rFV?i#P|4;dW_JOpN z5TVj?7Ig2R9N;ZSRR#lWAY)JoY>zS0ZbIb8SR)X(mlff>A{3BEC`}j`f|9NsMincD zF8`Mv2BPf%$d8J&rG+c>H;XD94aKH*p`<@}{sqhqv>*7Uv1tpc+Dg`hI!_c1CZTY! z%^nABY&vz&c3**T$PkdZnZ6*ZMFlDelv>q!HfE01)kk_s$P%?xXYR$D2x0)n28o|> zMR)rtqT9r?%cJ<8;vjT`kvxB*nzoeIUfvVIUyrE?>zury2CTmJ%;LGU&Sw5oBl<&b zr9mNk$WcUY-MhmN7#}0@->6CmREQ!5E}!*Vt_rzU^xhp?aPPTP9jsfT)zFzV^u15Z z3jRo%R1o*@w&mP`Idndfw@F_RJ{`{(vLu8Y z$6|pF#VHVSls3*7i%z?Ess6{@R(?7l9|oH|>F7ddv2C-~x8b^=>>rcGJ%l{pK+%uA zO}qb{rzBqT8igC0an*mer!_1id%~NG=;MraoZ9OkBZg2i)f~O^U(7Cw60pzoPgUU+lBRQGZ+}}ONkaSh! zt<_YSGNAlcS49ofK>ktAGarPLTQrhw%h;%Kw%I&v^m870L4s$lf{|CL3o#ho&K4U= zcTv52n=KWg76tD3t}XCuH0&P%4ew7>jkR+3O=(dmmN661!loYWar+$)N@V`O`8fVN zn1enm$#c%9j7yA&auG(T)~nkl;!QJ{0MDSz#{EL@zUY8Mde*gS)9iamGtYT#w9B8P zU|4IW8H?y)%%TO94g~V`?2aEIjGXgJu>F5`5oXc>DbcSGNNMf%8QnaXiqwDgsNI_> zJ!h&x%)G~>XrIH@)O|XE`WeyWkQp)~@yPYnRfTpTs7%?(Ehw95wy0c&+}+po)Ln$% zL^tnL8ZiLmR=chIk(02r9lxZnb4ccovM<#UfzGIQ+M)WO92bBX;5Nwtl2A7I*(LY0 z^E?NOkMrw8m-@(Hj|>aa8y|)KGSXDOb+rNVvK$m!-u%KIrKJk|FJvd$!JCAB{ag6z zYi&3(je*<$H*&61=yrbz(SVE^m<)7Z1AjprcE{;oA2CVcuG|L?-$pj7ox?fi^fp;*u;HiUr)MOY@$^E^MY%o@#sD7ptM0wiR7*nHp8zOl+ z09Ce^mgc^73G3tK>H2>mIf2Rm3c>BD3_#V@sPP*nO8Bfad%kl4cLGwP6j0EKQb_cf zUwu)z|0W=--O4LzT?z8F`e5N82zXlpAV?Vw@R5k5&)%!JvHoQuAcuieFvpwknj|^P z-9;#B?{*54WwH^+qtb%Psr>dc(BFMiVQeIHJ^o|+^)fieu;JF}wWm9-gPK4Lt4c{t=9uG8aRi64wN6k675@Hzp z*B}eyk8)`;77-TKsqo{Lavj(gg~u@vujoMD_AiFLO9(4=gbw5=A=C#s%7h?xK(8d* zJ-AXoGz5>eXl3>I!vz!b3je}kOJq;Nd`{-BLuamHYR>*Vu=R4ogK6LCL zL)$jLY}5al$B&=l^H*RLb_w)hd{4UE4#-=;07>a1SN;QZOq!;~2hAT7y_T{s!-N3W zY%Y#a;keICD-zp+u{&5%T#&jRN%qS(3xl5s`S1p8p;W@#)@U_P@KWXa}Wb?wW`K zvtcGwHl#57BNorTr-#NBHq>rVqkD-yfJRIt*=Sa@kLm-Ed4)cJ95|oUE`PaCLeF9p z&Aq0EV;UhdvmMM1l597YoE}fLKt<^e=|_=ncaJB83ItzXFQ%skr$61W@JktvLA=r# z_HUCXU{`CT(2*DILvV?M zgW6#|1BV`W59EaXi{kcGlsY^lF8mLD5UK%;7W)PrTmDeYV!`_B8{5KR`B9;!EhEGtEyJLvsitUuJC^<+IVDTZ4| zDB@=X8Z+Tko5p{V{wAhJzQ`-2N*wC?Dp{QHLjJ;YoxtZ0=ZpBJU*}*q=9s(Ya<+Ex zrr3ik?}^R4O%`cmgvw5S%0yK37PDT;n1Q7I#w>N z>e?cYs8`6k1P$Bo7*$hU{*02*=$7N&4^-&@ShTODt=U#N{Mv%a?=?MzaXZHt2nG($ zk3Z=$HV)3R2gcukwxu=qiL^Qis^1@8rM_y5@`_UPhoKuoPE~L-%MZKausXPGy7M8+ zh7C2jwn3)iMF(BEc+z#q94z+b;4xNj@hV}xw5#Odd8=S?M^{8A2YLAfRHmTbM~{YX zUraq3ySU=12bdpPe5!|A-jEr5s{d{^rV8NEXx_{+uL^D!qdS8A=?8`s#uCT_s8A2U zst_btRd6&+IVEKX9m|$k;oPv9;JFTYjHeiLl8>W4^z?Y`vpG+MsV9)EEpbkH3sHam zDrx8BYYqA{*XMs8e@S`qn(C9C-nd{m_kznB!DOe}=<>u~xOS~lF{42J>>7v7vPcSq zj@xhRA+jV4s5y$6p}39kTdv_sfI^eMQ;r8`VSK znhH!D=RQFzzr@7IyH3{oQ$rfgv*u5G&tl23vm$G+eb`v6I$Xh++5PjUg|XMKs%|AE z&p5I)GRs+aEqr{jih1)+QPbU!*1U|CE7*PuSK2pgokhE*0GHvTk1wn%X1!+-z*a5e z?DXv4D2u-Fch1$)?)R=SdTRZ!LDXk;%vn--Z-39eY_(hgjat7sPZm=v!)rY@>E=;! zJ&3<&hskXjy5u< zNo>_#6_n`oy8ADsmKodBXPgMxY=47a*y))Ri*vs<0};9Q{xL}jOMb!Xk^T$@c+4;a z34gMdv6mm9rQ{eyWJL7?yg=>#yp6K_#A_@Z-GKa<-5x`9Vd-XA8t2UA=cXNPt{!gC zWAb+tkBS6GJ2;T*e;OMsF?pvRV)?9^dFq#QRu|1`HMN|5608EO>o#fd#1=uF;KAo@ zS;R0i%lbnck*YCIeb^%`--?&jLfNHLL}W(krV6Hj#9Ieq34zSR)YI58)6r-L>9gH~2Zr^T-4cdOZ3-30VUUU^jJ{gsf zd5gM%jdHHI>dDC|Vb>@@tSDxOhk68)W>v4CZa&EJK-TUroRa+XlCyI-c8r=*d(eHUUDKX?63Ptuaau4|$J)~PzAs6)Jr!ZWlVpcWl%*8u@jh6QO{3Och z;MTo$JeZx@f-=jrG!4FxmZ&0}Ywzx1Sd7FuY7fpecKs4;^Jp%eS-N-KJV={%|AlmD z?byX7Vefuam$=@=i*&$~?x7s4%ucMUWm~Cv+{MK`rM+vbs!g!hv?s1rhxDwG(T0_M z*AW)4hY|Nx$YbD(_wvr46DRU9L+@f^YUDLmPAR(^#6dmTGQE9N$TaSped(lNvt&In zgdfz(`)$~u+D@%J)CN5c6h_>{`{UW7QxeE0K#zuZXIr~fcBys&hrsE< zCe*!3ZNjDLkaw~eV{n>fc<#EWMyFe=!YnUa<%rmTz0B-ybF*r@_Yk|J?=Q-S&@>D~ z6}%=My&G&bdg#ZkN#3mXy@N?w)$-Gx#zRBh+>%cRWRy~Ve|iuCcGfncuOHl>m>kzI zY~MzlYxQBBI`s1LS4>ZBXm#r#HhkJW4^iLO4SxA2dLp))Tx=9E-+l3#T_fcHo%VqL z6@2PPW9F*)IP>)=Z|0P+>ryxO7G);;$0kjWX0xNj*?2GSTBB%&Z&zemW!B%cffeY_ z*yOq1`xTb0h<8K`c&|#yMp2I`v;N($A0ekO|A>DONX?-ek$(tnr{3w*!SQzs@?%bE zF}u;t{tgXONX-Y=gd z8*a?}n807(kb;Iw25+o0Wsm{gC;J3iAbh=B`P>AS{K}P?dl8u0d7d3Xz_DTL{AjOs zvWV1!zK^zQ-R1$FF|)h0i*25G(Q{(&w4^#Utx(6GPIRWtFK4tx$o{E4L@dnvLD=S# z*W6ypGZ|i$}-T)&3aR_ z1om!_O4)no_x!eMPWf#^^()PTtp?3tVghT(vtBk|u%ud^w4RP$Todf}ObR{@N30wU zTkT{WK4Z4E!8Y&z!qNy(7JS*H{nENnq_ynnW(V+y{FfdRPeW6wu-2;vhEyBV z?X67JX4q4(81(}VG9b~`?NtmplvhInoazwDBWT-uVqnYTzTnAA>$<@UAqz@{3740& zw5UIrK10`a@LyYvF?4t{2;0Qttu@DxR580xf87ZIGuQ)z9OK>(I#FtB`rOKqYOMEf z>t9}{=$!nBPR~5{nx|Pq908Qj7GnBJ;Wp!#;E5<~#`NbercD1JTfC>o*d?|Qeh+BE zAydNrvy%IdwO=$pBp`78)k8wh^Rs4eKNUgNzf6Cs@QdyPbq-^WWSdY-F zx6?l_4Jb~;I_WZr$1%&G*1xvrTcz~L?1aiw0yR^UFsWMV%&!*f58XJVFmaE+%4@WZ zCCAZ`gk0Xt^?5dXeLgh?Ny(c;FIcx#p6#F|6KzWzJ}rQ~?!dF1?|1q*o?9Y{QQ zbw%dS$sxZra3y8>)brNfx3m#`2jP1aErFVGG@wu}a5f17WptR`6N6q>&uZcD53Z%| z=!3i9-cXkcHXpl2rq^?xDhFwDjw@YZSD5ykx2a?w47Xiz#fjPP|D={j&%P$C=EqPC zVw`~WEX{XpW}f$%^zmB=!|EUn`1wu-tp5ge76x;Bco4r+zM1{wOO>;0x7SiP^WFy% ztY;nC_~{ExZ?YfvC7wQ*^GoUUfsvRqx-zoH6AJ5i;;Zz31ymE0a>QI^!(dJG&^gGM{8Syv*zB6~o>+RZ3f4 zzBbe|WB8v|afnt+AAw?W-SdTWvApatx&}EEobtbC3>g75HJsTe8z>1E_#f$@*l1yt zD}|c^M2mk~0&X3}b_I1yvUm-Na6KSJ=F+R>^Qj@wu~X^qci$Hhz|%1~s=<5 z;(ye}OtB0BM771NS%gv38!tW9;VQ(k`(8fXB9RZ7U~FSK{oQQ^ALf&nF^Az;Y!D~s zV%e6rH(FSaJoQyw9p?PmvoPpZ8MFYucI%w|jrk{RfE8tzjf)*9*lJzG@6z{Dz}i!W ztBGir{>iudBM6K;9Q@wCHMO#_sqlnh1kIg2#KygPtT)6+1e2d<4h23gwlR|L=TH9- zH)fH&x>86(U4>q7YSaN_SA)Czr9O;60$D^<^iJ8hrIi)8+XQel-om!@onEHFKix^A zMR0KNM^ylmdl`GNZFG(+4qqDx4`&Gz-O$nbkXb>cB;FEyPHiqM(I@=GcEl8UvENoQ zHWX2RDR~H59T7WD7nphUa2Sh)E-$E0)``zX-u$rw4Cgv0b$E=Wfaj`*pDh0$`Lwsh zQ#%7HMuF0A&9549JWgF2*2yqcpT6y;ro8LG<^0nCFjFA`b9NL%tKV#?5J#(oyxSLj z5MwR#TCMV0b8KG;E{61j%cA_~@R}Z727^(H&``+wVjLoMt8e2W&8Z;_=oia9RCLG7 z>3lLz-bJ_FXJz#=)@u2a**KylQ{i;9lJvj?ehL2ltvsK{hY0x$Eg3>zci`{KoZ@s) zF|YfT{%A7!0XYydGy8=g{rOzYm(!DGI9@7c>OyFx4giJl@h+wa^`+nPIC z^V1FKy}2Bsd$i_6%DQIFiA=tBjrGTuHWPw^wdNj!xoyQSkw%X|ej*qRDm{T11?cA& zEHT6){J=nNil<0Ou^4N<8{~^3{*=SzzNwHOu5EUb;lesatd@2#Et1CqXkA7;;ek=H z?8Yuq85%1|GC>PI7Wd&<$f-6L#_6wTi!3TWl_kYQa4(eMQ!n46OaBCJ4~VnJ30uFf zuWx@oBN5F*%MV1_LIveb$>~$h6C~Fln$_gT!JA7bW3UPDQ5H1OrcGoCx;;8)8M_S{ zCOaG$`%-L_*+NB^+8@TA`h^_Is`~b$IkP_Ms4G|JwR@{B9=gGOE4#X`&QM+5cVzF) z+m?n6^R#yl^tHDQ{GoJq0D2AGWU2#q#~GI&by(W9w4)fLdr)jHM*8+K(ZM+vo}x`YK}->UQoxGK!nTWNpeIp zyTNLV3*AUX=u2ZfN89f>T3B_o%Oy&MK|hVy=xpR@4Me;4bv!DzsoWqfr=G*(tAew;D`K?2WIQl8E(<&@H643#Tk>9;W8nN}6J^|yAfh`Emwo>t|?JbZfH3UO65#>L39Y4gu~-oiXvEnT`j;L!a_ z5<9)gp_a${ahl;6TrWQ+aceYb-rU1BH*yDc=HM__UquxD9(-6mufRo;B+0ZXvB8%b zA-*6_N5cIoXUNNAn2cON`>lWF*xkUEViX|Wz}1bPn|+6 zkwn2m!K<2T_v9x*?|5uJtM&tr6DLS$7OM^28IA`->c)=_SqI%}pkOnBU5GOeJ+`c> z(K8z#sV<&DCup5A4EW1KIo02K!ZN+S`M?F}?Iv-h`doBY*4%eaVa+qa>76)}CFApt zuCEqGFodx3(OfF5j9g8g^q^c#`hh^{M&?9b+wFtBbrdvIc4Y`uG1er#k;Xecp3|pc zW=R)_ncb+9hql4^h;T#f*@@A&e8wJE;g7WUuadH>?8u1WuNr>7OIOH}!Q0vWZ4F-F z=8)463zEveG0H;TpR zu1J4f)Nf2NNSQ3ood8e<#WCd9I>15LIA8wA`vCul}t<8J$YO%Eh@t z-9Bm?pWE3JA62*sKgMTE&z>2xn zm4;w@9`1t2PRHaB7z#1_PmMFnFUj+|q5gVD`RMpQqTI@#m$ z+0J1tE+45=a%H*IJ~4)vsZeW}c?9srQ50HdbNbueMoNmAhK*$x0A$^U&?;2UmNS*XB z^U%}fEdLXuP}(wtIXHalcjlU(2^=<`mVj5_A&=|ybRR-Gn;}a&K`A+kKJrorOzZbI^e&mPP{C&TI5l+xIukS-@`pMt^E*Qb5#kJ8tKFAb3?A>H2h} zPCb|(HXu``^HUSfJMvFIj0+zwoeufE`l$xg>eI6L`75O{P`PS5#`Z-WWAN~-pj_<9 zzw_ni#f3xZ#ZBExxcp`lq^hF95hDF1r-!t{4+C|jkOUz{wCcK$WBrCccMPtYdR4#M zOwt8f?FT2J-m?pkz8Gg&R#i(#FL9bnz1jr(4zG2xKI;FTEYs9|i;kx2e0_Vu>f0DR zD!~$FyuDmpz%r6~NSd>3AE=}xk*e6RJFue37H z*ZrSRh0?<1MPHTYZ7NU;J*eGICCh5m;iK{dq3F<#pRvf+uv5*P&Qdvpw(b}ih$Rb3 zX0Bt&7TLuVuHmcpC|M_yOD>_)*rp0j#_~ROme2(BKSBd#yt!1JS!D6_M&x{I%c_)p ztJ=VTaLjL%B1H5iLFH%yk-t$zOs*QYi9s5BoH{roqvtH>E`|<-?w9M*B7>jqXtK@4 z&UwjWnwO3ZAIk56u5s4K-G5!Sp^PBg9Syq2TDSS68if%s`uDo4FT^n@fj%Qi$sW&1 zm#i)xKk8)Avqpo%Op;7I-o?e#F-tHXQ{yQp|DX=z@mF;XP?`4})E<#~7`sdYxZEX< z2j6vjMh0xjIZF#xE}1oleUrIaZpWFR{`}rrzwM>ZjH#CxK9_AVTMk!K=B}B+Z4PD1+&44VjSfSfZu0OtAr1l_t zh3dL0SsOpr!ULohpr2|wR~JGzorPJnV-`)(!jsbpl0huHUt!Dk3CN`&q;graiWNrjA-APK<{tukL@o zGqgwnA)}N$+xtJL1zitQ3WRwI&=H8RryBvm#RCYP06spk{&QCm4AwUacR`)?}JB*JC(Cg9lux2j;0;RaG^#WZfqE(4L!uW1b)8D~D6|>37(? z#FT)~36-`ZwzgDmsq}UTrR_(JsZ|B1@)Y(e9CGCf)*;5pV`vs#1ezmrHpcmsU6)4r zjXdhJ&xUn4Ncfu-r-f_Uiv<@b8&jz9;Rk4b9*OZXlo3$DyXE(M)K5ON z%XkSFx!bI+SXRpG=rk=})9o>^1@!N%AOW3B6 zaVWX-kHM1%M%WR6h!*PvjG7%Yh5*YrL|U#A+@NesysVPKqPf8Vfq#zm8W;)xotD%5 z>TynyFc9ORx=N?9sj6w@Gl;q&4vVZW zTw5aLn!lB8wuFAwLJX=|#-{3+D{jn-8poA~_=@+dpz*v?VU$bzrzXj{)^c21o%nz4 z-NfAv|7Z?@UO+W^^n=k8Qoi}{(|wN)wSY$~ydK}yo;%<=Q+38@mrVN(2L?~@Zsgav z?yW;DIU0WDSFMn~`1vgI7g1bXcmOg*vO@qD;UaweTFP&!Svs&8lp30(mCfV9%6@Cmcv$P9; z@>wqQv3zYW_q{tv!meqnEh;TEZ{EZAg|LeR%un5oBwh2D|74N2vvd^+<|SJ;c!jKN zrnre9uFeqK&)-@dvZZ-tNP_m{FP7U$sv0kx&d+DQ;%r-+Y;Nv>o)X0B%C@%b_##b6 z@*J3TN=aIbGv9oQ;&=CT77ikhPo%uIfB_HBcwWbDtr$G`NKcK`!nubpUa)J@zh_lb zY5xMfp~<#-Rb8?l9sPgErm~*0++|pd) z`K-6F8aJnh;3}1SUI5H*%M-U&gXej;Vk5iyULz!DET}rfo673FCsver7I3L7$U=UO zr6H5xh!%Yk$>NJ+Cwnt?m9ey=U;2(g*lQEF{Nda+W*O&tqu0p#lxVJ*C+{4flftyT z&pvb+*P;2bqrT}7vx9-D#mfZuHcf$UlV+G@5qCk=aN(Ls0g*T?-r2j3rMGRc&~2%)`TjH2XE+KM3&iCxhz->R=e!saS)>|{LzX=UPTfTz zLRbyyt9MZMnjNMWn?+j-To_a19b~I%+gbkulmew}Og}(%dme!Qw6?yTs-MDW_y;Qm zQshDgN1><22prQNc#2PvD2`}p`ITVyB_@4o0nuu1FFqyZ{`KI2Q=fBweYokk=k+d{ zlJUWoyuBj=8n>D`a%`-h+DqGS^NHA6RhoXJ!cW4kIdmZLgQ^79Qu{q8$rO(S6k&=b zAZrn)SDMVnhg`*i53YE;nKf%y$Ds^r=ES)M+1@Vw#|n>w=jG>!eE1GcD)* zF@Q)ie?0cNHP7he$pWw-jXRt7m3Zcpj!b+v1wAws&w`_(?gj_5e~#IvSyf?7Uqw^D zy1lLNCU=}OzzILA8cPgP#Fn-KE@15U^M&0$F_z|xb$Y%RF?TOgYxyp0f7Q%}N3ev365xISL?hGyqMA`Sha9Mp$PR=cP zKxS0{b8Y(GKhRNXzT&D%4~y|qK51#A{QUeE^;ub2dp$QGm)zx$J|JrN*NXl7A83zo zhyr)G%DF66PxayV?`m64zEEM2vgOTh6P!n+QQzwyzU9fMD)O#my!*_EF{vu4@7XHP zp`Hu`^h_iVDYgu$x-)HxyoDq5fRk;;d0i6Mk&jTj|gu+-~q4kJimA7!y?>) z&@9HOA55U~q+}b7`r+Wi1<(90EF);B&3-epXosS2yE!k$5iJoPKafB5YX=$z2JB&o zg)+ReX}(tvRpwf2c7GpJxoT;i`B#%-lJu%pTAAse@) zesWprFn(7_c@%jY)+x|Z*5 zqX^N5R_KisF%hYE|NJv14$PtP$`;WEeHbOkb`Q!8TKl)GsYPe%04o{>95w8&b#cLJzk>O^` zp_+3C@|ccA-!78=$n8*MWb)3l9sSZqZT)!v9S~FZHOt6W_F-XUe%g%66c}XjS$}a~ zT~U?maG=)pv9(^Z?W;-`mFazF+mj76GF$ejqoPiW6`F_9A08`Bc`DwQ1E`BXKRxE( z5~%$M#glAoVbq1fp8BuLNJ|q-OiDUh7b*LqJbasDyIB99lAh=TS16wP)xTQaHEp;5 z+PfDQud#zJf?RLp34S_6Zf?T0O{+6szItU*(ljwP+z2s(!!6=qWjTF~2yNLE>d z<6-XoRZsscm3p6GX=5WQAfPf1g`P~*F1Tny%T5)zNy|T~8XmWr{;)=N#iroXFL((( zAoSp7Yc(M$se;+3?^&q&tlk#<)*)y%aUYv^Jvx?G_2b>8>Z8qC3wDe>8IaLVFxQwt znf>z(pNAX5KhLHu%_7Az~|VuFWM!WUvCZM@H#xqZl_aGciyR z?(bT*9QSi|1r@6mx=M)X|GuA>kK8&=Gm6J<4=7HH^_n;egYbv@nN8i#+#1JH6{DCu zal|H$rRDb0Zyk`zUJbs5xYGUMJc(5OcNrzP9h#P}PEIO;H0#mIBk7Vl8*}$uMKawh z0Qhb80fVb0Of0bLE6exNiDg-BPBQG!iTh~(q6o7<8`6YZ>;(mgN7Od_iKK{tA@E1( z&?(BtoCfZNf+S33_aCsA4c&CTjy)&Cj6194-hK|d2DQJ#ha@eQq}%Fjm*{7y)iFjo zb$wObh5Hsg;X4lGpZ-Pa-zf)q5q+11zZ# ztA6s%IaNIpGw;fs2{Pf^`t2J~o*7b@df?erfs{m$q(ki2ixgT)$F)WjwXi(qn=~98 zY6Aex2ZrB5ugl=MMeK6p_EYn&%i z83FZF%ygCU*{K?FAn!u1YUREx5LP*OvS0{`V98b*074VWa3R`KQD9r=Ufr$&`$_>X zJ(5!rwUr?9sJ0-Npz zHKkqgv~Rurgm2p#!#m2?*QlyPi9f{cg^Gl(o`Ad}(EylVvFV{USET)WPSU4e@0-dpMKmqz>AG{MWt+SP0pFG zw$`81me^p~)^|^@hn#w)$-sPuqOPaH<5$J^Ij;A^%Th94#-?GC+Z}eRzgZ(c|Gov2 z$?J2ZtpJ)Cf#(`N*L7&^BzXys))@4@`lH+|&5QSAA&okJ_2YCtOtARG@Iz{?d5FPF zU5|Ck#j6gv;|g}Es4Jk&DLCMux+z%?p{i@ErMgSU9X!aWDq%hL2Vdl>tdZLJ&O%Hc zysgl!x1Jr8!|FP}wl562%csB8U!45Go23%Ol+m0^uAt@ZRk<|Z%tM9BF~6x05Cu!Y zN|qv_wN$Zm>9g8v!3nCFt%>_{hW5rezWhs_9++k*`I1EON-5UCq8p~4#|=TBq8>v` z@{BTGYXryb!4C3fet5p#woR_DH-<0qvZ0}$sVy~7XiO}A1(+1Bb?j;0Z?rA%ZMFok5trip8Tbzb26d3 zdm{YS_0=jX%ozE#YGmWn0{FpFT`nk;VkDkFHR(u@1fBScEh$bHi52Kbvf8%hPLzi> zrH6QyyxwBwct-e5>{&RYd?NA*#&b7XHdu0xtRIIE8!AU^#XoPg1X5ov>yx-v={tOB zZ;W}?ZaF6&*K0K#vMxKM3_yFed#$am79h6bgap~4Kq0^bOrWcmd{`|zJ#IdRww(v` z)m%Ww-+w3**oA`cp&wFyjSj#Ai+SrPj?NMKO(P?re*N8Xqf#$_cDSj z1BG_^T0C0ocR`ZuaIGf~&j-Hxuw-Stu`%`jte+vfdo23gC|OCEm@v)C+wuKhvLEWh zPCT6561vGCdDjA*)0fG<-Z^o`wygkbW|FsC>yD!Ck5Bg#F+2lw=^5iK3tukRFGd*F z{h#wf0JmiJ*7Xe8TCu`()dCwocuh)q-TruSaiBq;zkZ5t!ctAm>%q4gQxPNxAE(}b zd5yI7wa6@+nyEIoOs|Ri=Zwscx$?8G$0_hln4P)JG4#xotgbJp<_SXEb@LfxiAUmh z2OhmvU}~K}kSf{@G1J0a#`s9c__}HBN8MF#ro&2|uH;AnN>%dw<5J==bcCrir(KCu z9~`alqpMY#A4xg%xYBF;+TV+(&f9VC5aU^dKfxb({>$b zyQ!$RRZ?_mu5S z!S0Lw{&kGEdD(UaJ;-w5c0V`|{5PtBJ_KobH)w zF6~B1es^Q4bj=4ms(J^3 zc;@ui^F>`1{m;=}?aCiHAFuwy!b^Nfwd>1 z$gBAQuL5(y5Zq3<96M*P>6=vH|VGA zoC8f2H66k!)C(lVA*Z)2@q=vwZO z2d=@N+dWxM2MNN6nT>Y8A3y-5QWpTiaU$pLtK^q|UGoupUSvZ-gk@d)@lL6lEf-;PA~G23hWCOqFV96L`~ zE+uX>8OjhYz+p~apRlmkZL)8D!rnT}96K~^-c<&vm5K+w75o)9)${oa0g|PFibzy0 zdD=NIW}iMa)i->!d3Uypmv+mia+;G-l^+aE;tLpKsWz*CQ`P6uoJ)TZCCE{5K^DXG04L|uq z;M&@a^%$o->L(9%%mICwS-}ZP3a|g;;5@w2c9HZSukCyxk?Uc6Spz8HUrsmX``og;u+1UNuGz1aT~i|n z3Iog_c<%{Kin9aDm%X~siZH)030qGUv~=fY#wv6q^Y_jdbZ(_J`8e>hxw z;r;oUXzQ9eC}@$Kq?HXvr4@hf?ErvqjoMtw9EcV3d>v$v)kw-A;MH|U%B_BX=9-@gCK`T12p9xWPLvWf>#be7)E;DpO=3)s|-Hh)+d zFYopgzBVC3YB)pmO?HZYNs(pF?_-)4>~I#sgdh8otUw}*rUwNNcL!Ok!dd+3=vh*k z^VgDk_O=2k*TQmMgN(H%I3=I>o}hj?_Q-4dYDo9eaaJ~i^c5DkKHhL+mR|XGb=}}& zXO%1)$7*Wnwe4t5^SjaGd=69cN)G!hd+OKt z(8z3yyzbk0ewCJGEc7+fA7XY)_h;yCUL}9qb8Ld+;~MEu(4-@^e!EL=cS{_0LzV*5 zJ>m?{iTi0#3jJzq6CNdkUd3C^i~j4p#A+f{nJ#D(yg+$7+T}k`0xGi#tCDp~U$)!7 z>1<2ZGe7obA-Yb%s*Bkcs#8)bl9GwLE*-9LSAZF=*%|;+`Lbnuu25kzO?wq-z1!j# z%|);&%PY#5i|LwxMn+$zWBpoW2*VmLPLChXlaJDkk_$YXZMHo{hfi2oIP;I?)FLV^ z>4>w7J*}mgGOm-D;uX63L)FQnTlsLL|>1FPq z$VhYFZFcO&?q~u0eaLyYOhDsuv^G4`+Nx^%>4FJ9)d+6kbZzGQ?=y#_luC2@byJ(~ zYG#f&Fc+`>%P?;Aq~_nR?Y(>P(Dk)_R?s6Oc2Ex^*7eHKD`cZ&aKEx>$bsaWBFCyp|I=v<(gQ8 zWzL6{2(kj1-_=|Tf!12yMw%Sh?|VDp-fA$v@u%!cW@`uzDkfO7{W;B~qj7hRopqA$ zqTmFf*YC?;rWvbd$a&ppTy1RH_f`12O%#W*=M`P>o$#kKxaYu$>*pZqi>6HE=zY&g ztzLK&p8v?H(G{ivhUr|gE+q~JFeIRoRqy7rV5+7>x*28;8g#3Z4nczAzr7dNl2Wa3 zd-B~8dAAh`=d*40rZ}4SF*OoCHBtp0{9c`RC+2ECzaK9a7l2BAb;*%HvC>Iv*EEUi zOm@{fx;-D~g|fw%&FpG#$vRZz_^a}rQ<&ht=YMyWwRt8XYk)pV-}n%5@ivCW)DM-r zhJ7Dy^5s#P9ynHujFMyd>W6bs`>w1!xVw*2H&y*5Q8@>s6Vm$G@VDyPx)T!!Er>it zB}!jVA^Zi&QWVlI#z7(X4rum6?h2ov;f_ImZh~%c-yd|&wykCt=HnacShQ&~#o|MH z{`TJFuRBxD(HVFgdyq_+Nok@|0)0?)O98c6^Ty_#XdJjpNWz$fc%-*3YM$TpP>pzF zmd)O_Gvl9sp?(3?G$Vq(-!aPY^Yb9`2^LRTpGC^*fpo8@tb7i_guJC4=B9-PnRsL= zhuYt9iud6a;I3Yj$}NyYvFhc^J?i?o#;ozN949f{_y0=`O8D_+ePB|ANB$V?75tx3 zUUsAK!CwL7Dw@YE;;lQrTPfKIBTrw70 zA-nguJr9@Rp#!7gt3~PQe1>_7;rBh>-Fu|6waGT8dZ>a|Perx~oVs5XuMf(k1yot= zx)^s*#`NR9!bTC3g{#+WW9{L#NPBiTaMIu6S=xQo9g2$|Yj?6<3-OyY-IS8?=UJuL z=M}DUQ7^oDdwRHsJm)( zedgr1H~W`8z4-VLq~4V)_TM#Z`8={rvS~AGPfrg=)xN9cyq6yTIXSVfK99NC(D!s* zRP8=9KKJXLQt!JfLND}g&-io9{rYA6>-VQas~;YHQsp-}d_vZ#>$;rt!+DvQc&pu3 zAYs=KF!j?cG^F{m1@~xq3d&d*?qGefP40f}~>2ia+*_PX4rr>rsDvVeaB%uQ&f1 zdk}NxYsB}19(&dz%J9zZs8bH#hkKnhEgskdqb z=)VR>KL08`P{;y_^WzqcKP?2mMRLo3ukz#e@#Ahu4*Ybon3*tE=+-}ZtQDV0ICEM` zU7g9l5#>h5wOx;DCttm65ED>ge*Y1@{Uj<{$`$Q8dFlH@MK{p*=XjM)dBFT=!*eTxR5j+danF;^pWi4~ zOeNYG95_&n>3cPU&F90zef&O~=4Ym780#^!rI$W`5aYGUc|avZr0@3r9De$OUF+N2 zo+QdlrJWud`V(7az(Bh{Y8{&rS7Z>O8;Xdz)Jahny?6r@ES206&q<+@EW29A|N9@tWI1UZ%d?L`5ar{o8xx z3N|j8&A+m&AD){Rq3Crc&SD(%iuZeQg=vIsY9xO5<7=YD(!Q>u9;z)Cb&)@_Owbv0 zeOg8kEiH-Ajmmg_U%JgX2$JGlbYKG0X+*B&`F*rl6yryQ=}>tsHbb>uxxx$+KI}S5 ziusA;ilMIUg8!+`gda@A{FH*{_g9)aoh7RD z^uR3DX!rM5u!JaSHB+Mp(N&GY;znnCO!;U4ghLOMb`N6lV57fmv5C1Gz5Uc~Ma8Dq zAUvdCZpMpx=!ftBxTX@MiBEp=UoM}4yrXByHEAOWO&EWk*;lm~*gJWB(b`x_x`6OTIdZpT5)iW&Lb znzUrc&(7p=@E}xCv0p7=srEg0;c`LZMo~)+IWzggs4>Ib9OICpUi#n2e+^9?QocmP zm+E%wosx7iuqSG-f=;ItUE@2SXr;I-}Se*CV=O)^g z$n28cj(D(P=P&uaPsQGnu`A?A(oOiDrklvJNP5=gqhfCb?`bGr)OlqAYbXBH}%A~bT)vNnP z(j0z&rdk4Cf&kSfnAW`ccbM>juB+Zh4K zGe7clw9CsEuyF+hcVvZ^AHES4%MQxW9SD9ObDaS&XtDaOnO0Z98nJrQoW#F&vTx0e!ug_@jPA3%=4Y^ z_j7sQKCk!dHAN0Zzx|(PTg$K43Qd8VSu9qj#m&&7qaQLWD=Xs|V4Z_Pkr}!bW8>n| zL0^X=t~|r#YY6a?#C+!wH72w8aSl5C42PC2$285sdk$cVTqh9b8WWH|805kVGU zN*~f0vlnOxwCf#G&;|G$ov<)8s(Io~_nen9+eiF^fLQ^!K~)9hKX|kC(Wx!rYd-r$ ze)b3Lw5e#|ht?Or`}A!bPFj3o_*mnwgGIv`^x(7-+OVFqZ zL#H5NnBRFeI4}W*wVsEd0R_)?;gQZ+iw1Fm20#*!AZ6Z)ayrtR22MJ_;6XD!15k|$ z;Q&w`sp!ufHFxfVf|VIUYYe~O#`-UdKfYMTf9WQ3f#4hfw9#h)c7bq;V`bb=$DO1* zzz19pZoH)hjkj^FzM3tH;SGU*%bBs%vHg?3LA1mIGB}&g7z81pG7%l5R(@qtzab$g zTj*OCLTOx6)0FERSP1@CVn4<%HMeTi+GBBd*U4{7|D|hs(vo3;Xz6bG!xw0!Z9&7Rr zp;l{`t`q-T(xWKvlU$XhgvQ@KBu%#N?wL0D2EM|HZ6p_zfb}K994%kh^J5Nwto$MO zMP(&>WW@5OQS46>YgbHbGes};fvqd%|FUBj+AQBU7F3}@v?9(;h=oD7y6X~Y#cUq=*csl` z)Fe6tw(P{3``*Io{6U2rk5Kx~;9~CDH)0*tP}r4-J3&P95Ijp-dDFz}7%q3g;DW12 zjMD)_v`6|m)4&tSL6hgP%0mi~TXq(B)`9LYbn!^JTITqMp81*>d)m>~xqCL<+h4OG zk~tiA_Q?&LkbU9NMlyR)|_#Jv9T&?wZ{dYHlT+%z@lCvLN`9if>0YYK8)z74wy4=iK7cR`a|h&>h$>1ru?zr zNRy_;UpSYT2!SOkfX-ktrY&B6_RVc}{+`l#^JW0IxVBeI1BPstw0i$tWs|411A)mH zWnKYVM+sjC+v}!Z69cRn`q)3E^Pj(Zb-Aj0S-R2Rw_*z)XM>297zPshT~G_Y$k_FG z+HI%eQMQ8vI(Wna==^1om>-(I0Z6Uiz8Y)}ckFKyKj)7>{gk3;nYvBwP7ZVLi zza_~$`@H>0B>APZB|MH|VkXzL1@G zkN)xq{Xb)Eo$OpA!(N9AnKp>g{IIgKP@`Cjco70rn&cE}K`83?d*p6|*vHdeq$09h zUDUP_J2|<)Hp^!FnRV+Bn1_vo2_ocmO?s!d!EHdK^CHa{jc~Su9s0a4DdT=jJ1e;L z(6UJphV}Yts(su%StfUw9q0sz?B5zSV@w8)K1|F+hM^`ihZIntBjIiS)KJ!Te!|Y+ z52diT2wnT*a$%xtUtQOA@tpnjxwm)S<{22buX`vjPRj(`I$rPi9VFfGMWG#xGX~2- zyM?4tWvp3j1ry8#N59-%+`2ns*?Gl4HoW6;Jq5xoleCF15mBV9e01#nA4zZr{3cB% zbNsyI1EhA$fm`K0S6Lkq+ugq^=FrrQ0;ugi(EdzSFM%UJ9ZN8-PAdM*9FxCHN(0C~@-h7If&Fq4i&dHFu$w&dXx89WWdb zqtBAkC9`iDK2#8=x#L|dAk_kO<#r1!fBD0TQ$TGW_^IzG`XwuluVZoaCqMfHm%fX0 zRxrzd+BXoGX_M`cfh^H7s-3+wvs)NxFp*9QHQ3E>4CgU@1@%4EN#~%8a4D{_>(zC+ zQ*0hjSw%(VnTM&du_YKm0cCx-pGC-NNIYB@yl6rAGNVftl1m@K80Dptd>q^Wx9|;# z{VplKzts!Jt!_IMu1NhmlQ>isLfFv>qNArEcxi-gd?!s@d=qRQYPQ7j8yg*+gx$Mz z%^Eu-G*%cJ0a9C$@RoCl(>L=JtdJ9&)OYsXS8C^W9i}7$o&?yX7IW;s4#^m}Ry1)q z)%pemsu5Q-zyDSQzYscsE``8aEHeaTGULzM+Mb*vRg)ooxm6z3c-cB>!wf=*KIShc za|2KJK&sHh3;^h%M+Tq!&v7^$t27gN>vXgAlyJkU`a=6=nVK4leyvV2^n*ZK|2{ds zHP$7+G2|@t6s>F>yo3LIIA}s;M#A+Y3HPhrzWP~ZJNVKPrM~?t&#g*9UdL;3k+bT` zyVF&+U|KnO#|QU)Xt=wDmIZLN`#h^}oPmJ=I#kgeQ6%N%piIHEWT{!EN;TZFYO4xu zAGweC#qmhF3Q?N?;hP+}`))&WP?QLQ90?FjA@MncC3P?ifZ+ugD}s6eqY_D_KTRvGlU_LT8bQ|JHn$I|PF^N{=>J0rP@ zFl4vPg#papLhx2>-m@n;Ix1=xHbhe#QS6bN02oWPknn!wPEJ+S*+Q;jUV@ znwqY@W|C$O*;S#w^PJLO5n3=jn{-l;J6)*gTtSbfv~uOjeU$?Vw!wCYs=c4gSRkXp ztw^`Kn>x^6I*;#R5M4h#Mq}XQLq&110K6)D1Ub&FUJsf#!!ocDM93y0RP$b`SiVQ* z!q5Mi(Ok6r|ITXHU2DAWor0wU`*rXr9dA6An#z&AZy?dRv*VYyc)P(o6uLpt&-O5M z2^?hf#e)Wd+w96lmeGqvc)1k)xOBL*YZm2er**o$bObzr#a!^(wA6RLMBKOHJ^T=^ z0j%9sWfnRqYHF*O&krM2Hy5W%L(eVWxV?>^`&iCaYqUr zG6AA{1)geRDZvKj@V-&-U)-fNrX-O(H~y}pv-1FA@FEZl)#2VL|3X>=)@zDWa9cLJ zA}}kPN{%^0J}0}V7s!bn=13KZpd9{IN?zCP)gnjK*VLUKJ;c)UYuL-}!9HX5G9o_* zeLv^xPml%bg-5;&eOW}?zQXX;?(jx4y3nk4ok*AS^0biHsw`QeuB~lti=JG{6gHGh zVho?VF|f*Xse<#04Fy!NH+)Vb$suBXA>G|Qd-nK_fnP=w85gc|Gc{Gg@8#>#y+XW} zgCx{pfunDfoOFf-YcF+xcOre7U`EvDGiL6maBPcDZXHAr#s=lJ%50Er6svxq@6By^_paC5JJ>G#B;Tj zis4=mQ5Zu9P5=W!Sv;DuG`hT3dp6082UQ~#RkyL@eAS>}nrP?#=!TObgmN1Dt201-C6*%v4q7--l4@9M)#6TK4FtJ~_G7o@~!nG)o|h{L+xa z!0^TKKO^jz%dOaZzu8lWGIMxCzPE653QpiIC<~$09o{klHt~fVHqM=1fBV&RhGb^& zhBBl=yF0qN=EoKP3Z9!Xcm^-QIApsR?t-O;oB?jh=g#yo2s710xl*Qeuih!F;c|5J z?yW1}@I925ELk`y37T9$f}i2A2C^JBghZ9P0z)Phx30iQx!$tBr`{C^x6Yn0_&7FR za|k3v3}soWsG6_wIdX&zv1zHIsRhjM8OEUKWMc(opkJn@im4C@*QU%Of4r|UE(v2J z8pa~X0Pv8*!$+R!QTW0Gi4_y#2t}w7y5wVB_d*^aM)_qNLn&Yk;ixi;>v*)meD*fG z+ctQVf%Jw<{-u~0-a*I}q>rsx^xbx7S!%lrs;{l_K~d_=ba89(iDS)2DwZ<}!exne z42cg+MIR#JaL>C99t*)?&Pyp#-9oJ>ui)3>#+cA~&xGM#b?7Y*>A~uv5B(C5qK{OV zM+yi0eQP!?kRJ8J(?v}&HcsPPo6W5M`))%Pr`fX}9@0rPtd#QsByOMJXN))jLn8CG zddM^R4aNADpamcDY2nH}sFBo_KvWCSLbulNk~d)9LPw~nJMOs`?F1x%+kCX~(5$b| zL57^7oimbUq7xQ~bYO^bt3S**WjgV`iu+BJGPgo{Jn$3(nwmzG@0PJh3yq2fA_>7J zR72`Mzfz7Q!la5rS zM?~g}GI$U2XG`qcy}3JpfHJ%eH#H?<`%=69xjVD4WKt0}31LZA3uL2RB6P1ws%DKQ zY>gIIpbFsBHeA|z2?_Re@y=|AHL&y9j}O5aaCo4z+I(1!vFAKAqi#)(l}y&64)b$J zDR`%@_Q9;bMvf)5oJ+UvG3B9jvR$O3R4q*|l)e*{%#LS_xM^^gjf$zlLqcQtH=VSi z%Q-3}mlQ;LmZ3(_M%g`<|wZD2_-lYQNv-Sq@dz3%=Jay zD=ps?2o+WZSF?V3K~pm9EEUM=p8oce>0h}dxW;-tmA=lcE~SHX zi-yqO5i#c-ZhbV}?UHBHeRB>qprzy!f!W%FTF9heQmL<$bOT854f}rJ3LA(@vtIA* zQx4h7|ETM)GFsXpFj-KBU!qpR)02lrcn$7ai3Q8Gw=%zUww`o;RY=akfR3}4ArO^t zdy5p}K)0?mw$fYkf*I4lD28+7@L+o72Pc60Mhs=k)o@gnA3ueMeOxp3%ii*#pH>B2h3=IxLA^$h>Fmd{e7=@<}QODKWbq_SzEZ*`HkJM z=0+S6(THnA1cfBIU89z^rpQJ5NqF^P$ax`EQCqi2EcT9k?t>rn-YxqLa5sxjr|<#2CY zTn`lRX_Ny(oc>TQ$!M%QFh$<*{Wqf$aSoQ?RJjr6C3)GaR@lN!6sU5$3*TLk6JQ&_ zxEj(@=cpJ<%?d_`gthkm>UDJJSaeLRARp9-{x;#jeq_d^iI|6i>`fkz3ST%qy9lLk z4Ok>b*THQGdUiHh)|l;teN_p!*DYSmBAZGhfhIF28ceKtib8-l)y%ZbNl@@_`(m!5 zz5_<#cy6GnGKeBYBv6B{#7sQOc2v%-2;Bkc9PHX2urP+oPEuVP0mK_B97E~Q2*od3 zoFL)=P?!23kqN{!zuFmy5LB2e6TG~BCKkqx9Uj<6uscRT|7%nut~%Xbs!dy#e*W9B z!#`eJ=OB##ufwC2|K_vQdN%d*J2!{O-g3gIcwTqb#}Y#WPww@?CfA;?`&gvh27P{>4v4fov^?e zf#ZDa*PWdmZiopA+WzYU0`^YV1W#q~`fZdiXBSXv5C4a_7&VRX;Dc zMUM05y9J)qjFO+Pd3ktVvnzysiMv=f&Q{=Vq7@gnd{wBYUR0FksK~tc5zkK&;x{U@ zZ9DeBB+x&mVay4es6XP^yH3Pyr2hQg?)d-rj|CTLnp~2#6o*?fb&u~0vCvC^H%9;X zKDpA7_p$fQOA&j42j!j9=k{w@w=T{OH)mTYHSVW=M&^P+k!$)~kB^U8%Qc1c%O6bD z2s49;Q97#!s2_MAx$Qx@*y8Wlr}ox6l247436dGM-mR&v6*gRdAM>M)`CkXVCQv_* zGZopVv4Q&YfBaY=&qMufw!dwMV!Q_Y3+v;oqLag?ME^jR{z3VIq?+4R)liQo7S(dq*B=@}^d%v1Fm)ptx>={rddXp4xQ$`1LiMlL&FQ)#k}O+u=|HyX0Gmd36!u!^4?n z-Gk3}lR6JeR_|9dR zC!lb;PW+T@`^m4bL^a3D)=Hh6oSNd5_R1X&sxjM2&z!`>tMi+vYyADOU}=)i%CHF! z4^PVE_+IKfWES7pbmS?^k33R|5F1?R4``VgY~;j)P4XM$HEHV_#UHdQxSVeib@8Y+ zyt8tYl+oR-bOhTDtM1~$jKW_Bj66qk32_9%u2Yr`=WqP_qF&~`GE=eFSVL?&C$4mHCPrGAuNw>vF4zCDdU(hXeR zZ=k6nPiv2wOE0qzhufRyTOS)O^O@b85Q7%3lKIAygo`gUVp|@aKfDM#)%@nNth+>H zgw$SIzorD`gT#=KeO)a=efyKME?uk*<<}(FMewrSIKw>DS(wx9HQyyEChzWE*f&{l z;N*37yncyC?rEFWH;tmx)6>n#nu#?xN|(lpQgkzoNe_AOu+5?6z2BB#dBa;CU5KUi zJ16c%T|joawmdDJycV_}Pxd>p-i&@lj^riZrm1;(!fd=gC#>GK^UdYEop$*N%HavH z6g(m#x@hv<9>eK965e1=R?R6yjpQbk0Fzsj6$egSIZqll@upZrIDX!5)sz(U@}%YK zJ9jpw>*rd9v5dCoHkQnH7d4u(h&g{t&+pc!ENSt?i;oq^(y%xO-uV#So^PK?j`Xz4 zEbVa)*(o~&mJ)LDXmcDsID*x_A?^`+NqKU?>f$h*x#^nTnKF}kPcoY0T!(#Ggw(iG zVN>+kyGF)teV-oH)YoS&UvJAMvCdMrCpwfER;!)aZR~SqZRVJsG((6E4<|Wl^=%7T z&7EQ#?>$w0j1VV!ch0Zrhq=vXBdbj^}EX2-e=4D#)o!I+IHt$DT`GgGyK z*6)2c5Ce!rqGeN(y2p6QJo;sAuS1gCcz2n5ouo@8i`(_F&cb(Vt4p)h$D)4M`>f_{ zWnfK1mb`SU&iSVhi`v1X$}i*Gr+*F3wwPotHs-nYe?Dm6Ik>erM(yE=b*H2~1+KAX z)ODCbjzY(p6W{;&Y2%|~kx4GYKUS=KQcTK9>!jCiNv_N^iY<-gaN_GF=U*K-Wfdn} zFOf4<0H^0>VrKX$>(G~{tP|>`*9_%Z-3N@_F2t%UzdVUo3g!EbKNi7(x9Z4C@Ah7@ z{rd8hQeu?%qH&b4k@=^<_9XQqq7f>#odu4Gl_GYXNnj7FV=XF?5_-W$l^aqsOH*P4 zq~1+Uu6cKTetNJuO)n>+&V3}SQipZARwPG}V^=$@m_X+6tOsecM>v-S*wp zkw4zml4fv>tS+xDbwrzBlc-s(DoxaX+nQ=U{!~?pa$eGNE{$28Q@dMm2Dy-^)tSVa zO0YM%2#=xUp`Nm`)rkkv*B567r#wnL76%husyV}|dKb#}Ci|?+wj2<#dnL8-bCYdb zcKpF`1Sdr zN>W&@;!z9n2t~;WU+0Mrry3V>&;o=RxwfeUz|rwUXNs1V))_2SSE{>@d^R86C)?IQ zuw;fH<~ov;-|e9kd`z`_kQo+DD^(}`@$-UGH2-lFB!3+#rUB^VVjcv})q&a_z7c`yG83 zd9;;Ldmqi|Utf7E+so<2FOs8tN@=%mKLxX^=yx=7ClQBQGJ7&tAFRi?5mSbP2?Orr zYW-Zrx!W{~OXDW7EvZRK-&#z3(#?hRD3^GM4R0+Qo!czJv`cmC1u3_rW`BQ|Rh3?O ztCOxDFH0|aezYS$b9ouOajp|d5rvn;NRJeb(W$SgtEuX-7sN~4)VLTMhMH4(eS8*r zAJh)dT7lW`UlCgEA)Ju4>M8Zgb)UZ44c3nr6qoXmG1us}!<$Np;BEeyJ3?gQuHdUQKT;HPelXT=U7|!)ZA)5E4_tLckTa z6gc(|IE(R0v+dY=%46m_ihPg>iIGAywKOzdyLG!mL~)03@~w33?r}b|`&#&SQ#HJ9 z6phujHF|TxOqW~F=-jPPi@_fT_RZkD-_@GIr=wv}t?Y%U4%XLd8acT@S^DpAP*Y32 zmQv#4&cmsRM2E;@c4%d+CDP@Gp4GSL+d3JB$=)lAqcfrp%43d2-O|uC@ybPu%yMhu z!%H!jlsZxJE2GHq#o^2rw;r!VcwH26VU5Klk7&0~9=-JRz^R4^x9(*7vK1W&#Ep)X zEQu)GN3HFT+f=(sQl?W&KNk}?H9egRL)RR{QJqfoT*Z|Wwq5VEX_y_}t7Iyuxak_X z5Yb*8i@f>KS@G%rF(xw?NpD`aQSPW)Q;(jB4RJN@*9-WQ=P77HI+ar|Wu~CKX}?Q= z)_Rx?zZ4n|In|3g->>2SMlY{s zLI2>$kha6j#EWayLz{(*3I5?ZK9%1UQ>_mY}%?Y zSpbjVWX8kU<;joz!qZFq!6_8NZguRRMJ3@JdcD=sZD_Vm`IP7%H^1u$0AsD z<@@d#7=|z3wUwFRTbc<9gu}8Acw8nvl!+a6Y{2t)&8WB0KPY~@m)GIzsa^8~6cqyH z(5h59m3oc^#m1elK5fg$B4i#`Sy3--V$ql|y701+Eo^PB!#eqIG^sCUu;%QKMSr9P z+5h0fYs9@#b8Sp0^SqVj<3sH7Nad)Ow(u_&zV_qh5%lr6P4~ehv!DBX+^m=Nd>5RU z>t)`aGpD}uW~fy}t#aULmuEAlB&IJaA4QXq(MPogyOO9h<$U*+?HqU}IXMmQRaPI{ z3jTa9A%|$b4xKt}-Rjw))Kf%ckT}cR=!I>}J7}NmKZ5sBuF##&YKkrt$XNAG;4jrp%L3aE`!OFnOU6 z(uE($8Q`K*khLV0cM6vA&5Ms$xfZ!e9Bdvw)u4XD?$v9rr5eTf5KjupX=OUnHK4OvD zW~oUNZIi}_=+}OJghtELRfAfCuSEUbhB#7RpZm~VLRuK&%~3{!ym^4l>TylYK-5V* zfw5#nh%-R+v%|OgVq-sYy>8#bBLjc=g|w6uF-tF-zN1CYxIV^)sS+`}&wNmXr#bD# z;qnJcp%!9;Yn$(421&!rUis@Qt?MX{b?u%;ZS>GY5jKz+_}GD{)5-+K{iiRw4*$4% z*ZXeQo0yN_NpgFgvaI*^*dz3gw^nkC10;{){TZ36?OMM%d>>b0x%KAb z_B~Z|OQh8aS(35qi;#;ispFVjM&rvRoIGuM~|N1CR})+p-9(NI;I zpa#j%yU@HNAqTyV=`mzoO*=Cfqyotmdo9G7k-3P9CIru2>U$!rwfOUHHVeJ`UcSJ! zU*XEfu&JqFE3n8TlAC95*NnYWA`JLUoTZGFb~x-rLoBml z_Jy18SreG2VXK<+VB+Q>Ahc$Q1x~4gS^EzvoxL_sHSwe(a!xoL=2@` z%X*xXxou!iBr0-y*KqsK4q1pL>cr{iX(bh0N;Xx?VB%#Q>CpXSlvTShSG2FC!@f4h z{E^KASrmUEuxq5_Sv-<3qofRYk4uv1S$H^usTO9w0b77aw!Fu$KH=bY{VwNk;&8h+ zx;<>9RlOHN>8Mln^i*w4;Sf)C9@Y4h`mrr#kLYx^Pm^ntyqpf*ycu%m;@=d(PTYD2u6|riEK)o#E5-lmm*ARrg-W4|{ z>=!`iQ7sHx-$TU#Qaoh09&YWj(!(&o-LlWIettY%08#!JsH+aNpW!8Vk!K3(^{ihVi!iOXyxwFcJH`CKhJOVn|a@!%wI#xb^=T$Vw zhvu~(OSw*xJo@R}jlHz<8k2uN+)3!M7vnZ-Res5ZtdZ_u4P`VRv&Jy0j|}{P_FO?e zIb>u1XFTCbR}ruGSmFJKwMqtC>G11Xs~vV7leLg|O7w0n zKl_6JdvdTM`Ag8KbIsCx8Lz`^Gr@L!ZkMsS375*V2HhCdk3Vo)3$i??i53^_ih6NX zi^jy-JU+^=IwB->jd-*KLOY~ifW=%$8|@$!E!n|B|5l5qmuhuT@7h^3u!|VLCWxQv zxe-vzAYqC*F`1*-34Xeeph7fTa^*D0*Kz)>)+p+tG85pVMQPSmB5cg>JX~Mwe*ZBV zz?>G_$$Ms_`V>|V1p*_u)ULP5y|3SKJ57J}-*JGWHjg-+C|$iWRhtluJ%OV>ffCQ} zNxLdpQz7w8xG^LEHreII`y)3MLPnwNgPPf~BC1WsiDFOqPB*HOyyo=q;*n2NmT#5I zxx6ZmY0%4IlpZ|^-vk>{U3kThYAcpcU`KF`HU_s*XvoT`*GY?h@1Pbsag)(|h@117 z=mj0iw{qaamS6axtE=5i(ybVfKTCws(03y`)f0!_o_Q5gM`|%bOWZ5 zcI_Vt%IgmuLNmJvH4>J4@^Y#5y}I#b&iqi3u_TJ8RzEJ*%?=Y=(!o@E` zR=m|c()|K>HFNU7NP$R7t%VpjZ`;?G+;6W3Y0M7SQof4N8sEy&Qok&=9iEXnfHviQ zh(9gGzRU|tLy6Tr?&I{Y>baW(-u-kWMkai62-S`)QNoJbl$LHlDk8H|Q%`z{ zZw@6bDEgrY0nvtqwcAm27^>5TCN};-^$2`Nm}#UeF;Y zYpQi&NAfhezUBA#*i%=(?DJZltQ7n8+zfFGw(V~RMFIo`1$8@sk+E#x0RCY{B!B{# z|17W)7LFVOWzqNIf z`)YVO@wdb62%aA{mdl}6zF7V6kPE=bJbZjj-(qB40ARBI_2mWa=8Z1_nX_m0f%yz) z6pd*B;V>}k;WPalaMM)F#sngpVClkZdRS6~p^LNZ{cE$Un1G;QW_y`5PG-)*dpWDP zURtmtn~|F@EUgxBl~ML+DhhmTvsXCyS8nip(9L}Gbrn#ye!E=pm9S{4zORG!FX6y z^Tf(<0RIKcmb3LUvK)HP+4htg$$C#-W!toM$F~hM%+Wxe4L7PvHzU}a$g4X9&~^{0 z@b&kk*Wa&`#${vFa!+o=2}Latbq4^EZnK+zb}e?SPkXn5tV*KdwR)X5x`Ks0Ub-Ko zycqSf&nL?=oH9ZzZggneN}&EhfVX!VIegdxObU!4J3y55|k zEjICflh3r_?qe!V0FGI`-HQvny=xoA$NRL^NZ(`PndytMqOCN>nQ&~l$3Jc->1UmD z-?D|aWHCj9-{7SHDi+kXH>}3501`!L9BhIebo00cz8}C-g_EBTYHMg{P_kS{+j&ILOJ3`iDAdW&;@PJam!#k%rhM$O$-pn3 zVuu()xerQzSy%)vqr{r}kyGq!t$!M@ZptdKqaKZ$H`%xrCCS_SAE;9W*gKBU|Yh z_1`KaA5|V$?xi6yF)DP{Ai6JU8tjOQ2?Oxy0iZrckOlZG$JW%;oP;=mZvf84x&Nt% z4l5fFptmSa{3fM_z+3@Q(Ktn_^_jic06dR8o0ryI8ivr?+-W0v1kz6SS6a7aTP%V> zzPAAD|~WBT(|Fmke%}i2=hH{-<@V)S15Pzw9U6H2sE5Zp<1OInt}Kja9Hj+ zTI{{z!Q$5GNGywzaL+!f8kr_scSbg|Y}JF*pIN$?0xua02V-UEC2N2@`bC0DL`rtH zP_9iIf}t8r))re{E?t1>JUm+3IL~UJo=>x=hxz@BKrH^khRas_Dd-b49wZ-fX2H~NuV83bQqUlW zmT*hBJx^<5K-x`1;6HWRWEm#Y%-~z155VJyd;moF!hG(No0fnfyr`P|Dl#QC_nd=U zL+jH+eR)n5apm}&tJSV&2E10?x7fX_#{7cs0jxx)!^*Vt=O$Gm7>64xo$0OAiVZ}F zU0(Sd)$`wEr@CCisOe|(Q%K=Ybku=M;}RF?*GrXibdTu{#7#ww$Fn{^ ziH~f{q1%f&0u>a4v_v{WeZK62g1&f~vF!KZ^0P_&L8+NK6>O{Q_?p61kHWnIC$V}f zx*b6hYF|4AOE~72#%t$1P(|N=BI2IDmScylzp>LJl&<_V0R0_xsi=?j60x6v zHBi@hU3_Pw=%3=rlnsL$l$RMKQ1PyqcRi-+&1Hhc3za*TAEG}B7Dt<>>Nwf7$C(0e zlV^7d8^@FlTLQW?x`PAZQYh3*8M>zG+FmmB3qjB-2b7G1tP7nVSP-|W!dRD$a1=I+Z?4)oV_bx_7eXy!dnyw%WV+OehW2CrlUI%|Y zZzdNRB4Ee*%own@*^=SbLnO1U1+H7nz;l&zFO^=ogeqRE3mJ8&TKXb9wKi%`O%F$~ zGHs6dr%dbh^9RVMm)+d|W&kTsSwB6V;PZJ0Wvs9gtLlBbP-J3Q!0P%MQ3trm=6Mk?aqSduFS!d(0-2YI{$8efDPZs4jZ4g-u7~t$ z_^Svt1TN4mH__nU8)u(8L>UJlf{{hyQu2{!#|`g2xvIzG`Ex@>otTo2LIdN`obqTgHqIaHmc_c@X+36v3M?P<+fr#F4OE~o*VU)AZodfXA zT<)+UvwPf?Fk>q*8kZ*#VTlfoS%jr=!OG81!Lvqr3t6u1CoPv7L zh{x=gtuZRu2Ss#lPF{gRBxJg1oN?s4EDx_R_KdkCHcH^y4+8)_{G(i7UXk>CS$CEt z)T&vrSMG5L?N-v_i13!BIpUQQSY|AvgU}XJhJw{uL3d+!IVjhvCVDm4O~tmk7*2hY z)n5D}ob4rr0zFg*iq&yg&dP3$jQ@=|lxt>{ii9ghMLgcCl&hYBr*<8C72QzD8VqGU zU<4auFW`Y2LD_VbB63S3_sM>H2Nnl*x83$dMK@Gy+w?fP3v+4`RKslbrX*)Wmis@y zP(kaQjqxaD#|{wJ@;Y3XvrVKq#a)D$@7aLAlqco)2tW$KgRZbdug&JskPLo`bRnwi&&F0J-Z%Y z>+h7lk9Apa2G_7i8D`}7oHjx}!h8$b4)44D57-8vB%rbbIalvL_KkwN9)42+kxz#& z!E|la$$RbvRzyYDp$+*dvH&%Q&dHQdCn~vql$8xKn_jcaS0vYI_qCc~2APux7&6ti zhE8I`e4qep33MgZR8Xn*-hRD^>c|Z?>o~oo=-+jHbR7aIe(r^G8$u#F7+H5VHj2I% zXroYQnfCIw6&JpeUQH^t>oT|PvBP#SDHwU<1s}uAlB4t1nbod^$nvL_02eH)wQJYv zt$@<&n+!UxZ?35(7k6S#PmPRLZNlV_schMz=saeVN7E{s3=4=VdzEK~QQj#(eK%-G ztPiA#4fKNCb`4o8P(cWl6bKvSFb&`gICHL-GHu~m;#5XSaH8EFuT?`G=NcM^9nZux zrTUy5?>{r1Xx-%SKcc5^h0|F0_l#0IW>^ z5*HwP9Hvp9ik#Kk-dYkkdin!ueU?VCy4&|1VpjrZ(`-8isHGaz_3gRBhMTpN(@doQ z$@d(MkpHey$xDBaNcNlLI)As}t9dj@c-DZAs?zEI zLHMjc@ew_tIHqetTuRS3FX}?ak#Z{`*!Lw7c*5<1Ty1&j%m(>9t$;l!hhN01nEiLr z0(%kgSQU_VO_j4wtk#A>O=)Ct7Qs!BiHf$QYZ{-uou*cW?R(%(Yym2T?IR6Mypy$# zCm~wN`K8K6tEXMXh36jc;nvQKd}ZPl|FdGR5)%^!OZvX8M3WOBk!U-;i#WK&9HI%8 zEZ5HbmE@JYi9u_@Z!bUuRkPzhRKACb2Pl9H66c%N2YB2wx`QEB?aq}Y>io6NyG+kd z{f=ewUbJju4d?`|-K(G@f2=Yv51Ufp;9F2aj!lHY*(&}ZaWh7uYyDB}eX?SWg0e>Eang-HRDmqLx-&{bWg-G? zdW?IIMFE>zo8z49Bv59UZA%UK=_s>m;)W!fKqU*Lm(RN1-3meJ-4Q@Mc)TXgB2v6w zGV#5#2sIL0(SeCYt89D1Cdm6jYk44;X}G!8nO=?jX*;c!Yl{(hNm+(!5%+AYVu^Oe z7Hmh0=XmvWgHkrjGZy>f#9&AclY+0v-cMR4UyI#(R3(J`U7l)?r;vI>O{6t%quFN# z!j-*-(FojH)x&O%S=8^pitXa&`MwG#ubCj5DAn&D&s9Ug zvbo8fY4=gC_M&`LupE)Enf-haOK*J-VBYK2WWmP%N+rT;uSG7$8g7UUe3$ijiq^a< zKog4c!uYTn%D4|Ts9J4C$Ex4`PXcNz37|YDjZevj+|MM9!TtbV@8!wtnp&ojW~F}> zEL!5;4V!W1dB7Az^_-OuyZJ^#p7}DE(Ae--_1Dut{<`Xi0n+E8fkLTL>4ItuV zhj$fZAri7VDd-hwc1#0UH7t{{j4fIpZHiFn8swkaT3m28{Ea-42bpmMz*2Y+m42M7 z#h-~5sy)W1RE8)8p}B!UuS~qT$AeWB35Q`tGj!{1{`OS_ut)pmhmDZ;caE23cE-rY zn>SFSFeftnCj(`(I(o?=eTfkXm+B)ymcD#0+f3dM^69)<&iYzGUUEt*$9lOkf-+;7 z&yFRtp%OqUcl4^6FF0`jeLQ0GPZ5qfDT8?7t5tb??&SI~pOH%#zWyH6JzH?*p<0Sm zoH@gY38a_hXK~)DAW+bjf~buVKJ(qySZ3HNf=#MVuRsm(T8pI>lup>?19w!kljX!Y zk`LuER3ASKNH4@&Fqijc9v#IONB(4Uv>}+ z^KgqWFnfxVAPK3nb{deqt-E`=|K7oyITA1mHU!TXc z>Rqe$uV5VMF{x9PrdGZqK~MH+kn5u|k>v2p^An38SabPuqB2P-OrRa~efF#-#>N?- z9kptR!&?DvD^TzGX{Y?}N;aRg<(tFl`L2u0fU%`lJl;Fpb}g#;pnY-7>~PC)-($gy zALDemH`i`~8hWx(RWZ*h{`a>SFohA`e1mvZiJ0?CbJ;aDjKvFAL`zqGU}4~m!Tu9* zDldfO{QUfo+L2y**#QX$Z_CK!T&rLG3gGbS%`TS~BM>V_6&sHW)DMH;7^YbhaN+3z zZSM?Fy9G#0Jp^s{YtVohAel4hVpEW~4#dI6xjq36B@`7H)1E-HrI4F#$%&*T!5 z_n0UrgP6jzC~pnpe7kJ#CW`(KFz#TdiO)(dNYb$-g1S;a=c+P^j6|n5B}yAz z7$j%Za;=(&+%gY7y}(54=eh{U%u}Df^B4!i9NX%<0aiq7lenO23{+>GK0W}qwF0xd zo^rRwzh%?Nw`qHAE(EyX%aNIzAoqO>=$Mz3Jph^~uhlVwCi1-S)i2+`2wd7N6S;aXrpt>7vb~&c9$G@$S09HxA02yVoT&?PNhG76dkT#W0~E5fMUkODJ(~b! z1$=&!@4)2;+)#^SwX3^4u&qk#e2eKZOX)Zd309Zi-_C-Sr{CfuDq0RU#ET8a1*eRP z#mC2kSe^)Ity@$G&ZD_4 zDIk9r`{}#sEdZ!b_X}T8Nz`0fnh)bZvdo2v-UE^zZ;>FhxLCr-rCA%jj&t9mV+R%z zak#dM9Mju~KOK3+jKY}ig=ONFXxctH1#!tx9GC0cBj*0EMCZH+~1Mx zwKUfZEP+ccfByCI`*dP+t!BCA)r1<6Xt49A%+J<5okpRp$feVy||X`bR{ zq~(^DHc8dZ{N#ldzibsj!HqQ;mORX`luZP9d)6CXCszj_qu>x<2 z?0I=pl?MHT8enoK8&!gtp7OM2M!n+Id7gpB2t|c@932vXT{*$op05k?hDK3b!|lr0 z2~AaQ$1f+c6_uDq+BBbymtq1EUp}N|Q`lZdswEk{ks)ZQ171vqNELW`;|9MdALHsx z3?XL2Iv5<_7>ni7=KCQC`?^DU_xuDQu2q!G)m>5qmOc16Lsu20t1nLl-&pY=xr_^> z#!&(PY@|PUe`dZR-l=Rh6tr&Z%3?wLc%jx!I0bTnzC+mjPt3dq(P zdCd&*TuIv?tH2$BGGQP}OdC^zCFj|G-MXPlA#1}Xtp+FhY~At_vtSkkdncV53o{8_ zif}XkcX5Ynk}E8vYCqen{)=T3LYkgvSB_o8`U_8~qCqGL)WX{64sap9UioDerJSK= zxZ8-<50iGV-$iACmEU}F;=u9{Z~W90QboK{fs}FROp*IEie_j*IUDS;>TtE$Gxde!M8GRDdL@gM?v9H5}x85T}-W zH|^NFq3Xh;MitG$a)w$&6ZLM{Vpsef19pUvVj=nh08gG=7%2xNpynbr*9-oV$-`KpvK0_37WC7*v$=nw zKClIQVDsmIFyciX@4iR=vx8=0G&m?H)MK?KE2R|)8$~+V=eG+|UENVc`85620u0gg zd>PCZ@!w8P@ZK`<{*(O(c7lxGX>CEi(Ae`U_6iNy7B05sEu*@wj~tZ=z{&yojlL#t z_=w6;8Lfrn8vjbB;t?TH>D5*&+iE}}yXf{plUwRz4ALQ^vpfKSxl6z){Mmxk$aBy8 zBE+gwlGNU~*^8Dg)=(cp2uQRCVxvG<<3BeT3)l)6i*HZwEYgMVdcY*EJ*W7A7-9+Zy}(9v&QMPiCA zb`3Ks@Hz_#aaC18*~2X&AY#)F2J)QkkxH{Pn z^n0lnQDX8u9C)?6d`cD!649+~*J{BgnUBjr_{3Bt>6OTV|vUSo% zi^eA&m(*`4K$Kc7*u^4V!y~8*dI-_d)ppFjthPzd!b2~aXse~&vpKkh010@&auv!^ zt4BSV6FK!fj#Y=8YB)*~z9O0W8p;{@_(Ol2*iZj)>o&Cd)Av z$@3r;!T=yOY(qRNvg{((OPW13cBrJmHAxjYn)&VJ*GRc6D_XXs0@P*lTPhD}g^b@c zbK#gu2-71j$0l`ux_zcF&J%pDRj%1Zx~)iQfq2VHU$COy3PpO;08!nvK+sA2TQ;~6 zS5A1qngVZAGxy)ZPqj_RQat}lHb{?)^Bb6~Or6Q9G%qrog0|jQ(ku9NWGd+@ibj%s z9xE>Hp#sEFXC8>a1M}f38-3sI9PiY#00C`B_CSP)>Z)Tu_#NQkU!tvX6(nl>B&)Ok zC@F%3*80p}xCYfUH+y0aAiLE*Jzi5P;b`HZiw4=_8-qx4WH1O{EXV^~sL=sBzhMAI zejW`GRWIa`iC`nTmdgVx6}M$6?662yvj8Q;c-D11Da=KIovG8mRjI#Smfk zt3?B6@clTFFx%`M#n25Ej8s0_>?&u$Whmaz|!lJQecSO(v^9lcIO+d2S9 zeieIjehkvJXgh^myYz{Ve%Uu}kQQe&w?Mhm=SY1-7JH#qR- zJ@jN?r#u(x2g=kP%kS2x13r_-0;8}zu@~|IdZF?ek)wi^Hss8bO^zf^jfPW2LOmV? z!5xDk^W#3crZd;qNh0c121JQ^i6elI!9s^LWB7Rf$7jA{a^Z zMjXwIg@NiSnT`>1zz&CCo+F8&$+zwWxJUU9r&Cpj>w?Lj4ED z#;`EHQJDA>1j(~Q)s)$me~3TXQ^B*yoL1x*SOb&y`C!d{QMd$y z`Vc32=(L5!lqxDzpqqnG$!rEMG~o$N53d_Qatu<9B8Qlb08*}p?QDcLn(yy{8v|@Q zleBWKuJ<8u;NIJPf?E?=(c4nBpL& z4VMM@N8Nl4I(XC;bg8fb*_TWNvM-ki+uF71@L)vfF28=fgJuIR#jhoGVws}I?065_ zwx-^>g@KM5Qz^Nime3|gkn$6jR_1|Z2GR4-D!|2K85%TSXEi?Czgc@_|#(r&%m4vn)-vGNS*%N4aTbMh+w#`VVN?GT8j}l z#OAY{^-T2FJ_M|UpSE56+Fx5++ni@>fUX;Wp81upX5BaY>5LpJn6P^jYCuE88ZZpk zf@tdLwW(Ax?b$PgfxHE9KS=7z(k9=ReO`UrM0L|Oc%*$QbJQbHVdZxnq!s* zh2oTMm~0v)rNMCpx!P!zEb7f5Du7cugV@#@?gt>mfeaB~$f1JMH_ouBu9D=Gef1x3kF{N-Y(k8jdB*NclO_~4`BlHGCm-L|c7V)QeD6ys6reuPsEx7bwG z5neX7zU!T2)Ij7PhV@kmRTO~qQ&hBi0+h4U*K0z+6=3K5bL8C(1-?{|u99tucAK3&i2(5Q@YVndsa$wvnLc{9FUi}Ff%O8FIT#M(dWK@1DaZ=}IB9j&Y(PDxFIR7}Vz4g>)xN4xltjx}v6 z+#Zw^|DY7j^8h!OWDO8={v92!6s98;su=&)c~Ar03bAy{xFgSnMARMf+iUzydX1tA zO3thKCOM#+#Upw09OWHOt#M<1HxDmSV>v{Vv1s86L`&S=!?E(I|IJ4Fej)A@9AyLS z6+pY2quKdx~(MgB_#S~#Hh&1=`>tBZ(Q3$TZXPe>b1!KxETpMBu(%_n} zU$u)|M>$4simt&`1@V1-SGL^!P_vZ&i%hN^pSK2;G@z!5&b@wnQvmi~oM}{bnQhh` zhX1#GmuIv68MP5N5;+{{8K^iL0pu+OK3{+@UmTh=(y;N#v&=ICO=z{zr zjI}A4GuP$`_uEP&sz!kV{+-evp)Mfo+NDjaUxe$mKskI0$OU`@wEoYB*B_Vce?0-u=}&Ghq%t&5cBUaIpXax~l$Kj-$ivucP%F0PO}S={* zuCW&|gy8AHTFS~iLI~pHF@My+ilX_Mp0}1xHeD&^wV}{ZhKjzx4H`@1naH)e!V$7R$eW8A_Z$ZVkYR5Ilesdp;(GviB9J@PO}IBox@s z=TOO#eRwD5GYO5X+na!8#tB`_mZk9{*AY=4p7dHQ5*OKYpt4U!A1Vgl9i-*u8}5YPgnoT)hkai`(&s9#4d*W}Zd0xKHVFe&-b={9xxtNz?tQ9*g$Xzwg42b7(4~sKSos#m zfby8*k>sc_z?98tHX>0;VHa|LE`AotR~k6vpgj)ivHUk`8Ts9q<``3=um_)yo$7Jz zcxEy;oM@h?0;LG3&C6r5UkEda5A&g7Y}3{%%3vavEI$EA!FNZW=fFvy&A4+&z$!X_j)rPSoj@D4+RSg%)G2v zka^+OM)Pa^FA&enU8-1|7XcutN+?+;9gV}YF9fdGU>s>t^{>FP*d`uWXoVZVDicAo z$B7RDT{Ffk;@Y^}TQBo?{o0R71&U)kFPFzr1$#0`hvIv3?>;J1IM?RyGH!?Lz5`fU_+R%jESxfDK!InQgarDqDTG-YW)|x$VNS*H+X^>aKU{R)CEy~t9P31{dcsv7dLslSEvd_QKq+AR@{fiz`WG_bmk5J5 zjPg1P{apxAA_;*-%EbS!3gTgt)F*@pyQWE~N?IUDAsR_hWW-XE0^vpoc<&9HLPlCa zD1gJ2?E$7rZYx752MJzaUV0UsPe|&}ejJ$k`jV6U&IPOW01|_q^KDRBfOa05A+AF$ zO~8`?AZ`&K#~7d_Bqj>H`aea705v$A(K>A%3Ob9Uya%-30DUaI^aaa(bCFS=?2i(X zd#0vF#m=bgq|LC;Csxh+wqPBjDX?>&lpqWwsz>TAKikR&Xys7^=s9h1WnuufdI-mnFo{ zT~wl$eOZx`#52QufDhobfD%UfOf+g_19O*ti0D7)uQwIQ& zvD_G^6}~_rr`oi`g#_bSl><>{z9ZD~eJQH+kjI$@>pmTFW{6Ale0i)0Pj7<3bIEp{|zm`$o z3qRrCeJ>kv(Q~8$E~M7Wv*oS)k&fNaFntxP;EE9Zlv}+nJ(M1os1PjvX2SrzpU`RX z-&clUr4x47g0KF-YNa%4UT-tbL@>2+CL~d~C)wd5y4OK!KhzvNothcak{ZVN=y@H) z`UNI&68yZ)#TDHIR|4k4u~ zj@_0cKuUjIueZ`}jSzx$RFnuFEc$dvXpK)L3y=;LVcafF>exWi7;|(apbD5HqXMu^WG(LN2Omn1o+8h`l&_wZM5r9gfA{lP1 zQbM<&5vt9J=Qgl67Ib^*M*1wv{xgZj2?YQRDtd!)ya}%(=ZD?w(5#Dn`5yT4ccvfE z&5DXpC*LBOv4IwG44P-*CNw5S#w5ANd(bU*AfM1BUBXrE`$Mg7*~{hqVGG*8D%1ba zsREPvjLrKvY8=SENW9-vFST@0p2a1k*w~(A1{XgCTj+U!o?~Wtx_%f`F@g2u#*wz1 zROaot7avP*W*OFoR?ksTyD_?313;WSQ*>{QTDCT?=xKYMH`cm(26@;?)QeM;9Rf{8 zE?kN@KGRwTogX9;DOo;%l@JFPq7A)mRB06Hxur=|+931mPs7e=qx>UHl@kDC%5<5E z*d$Fn7UcOKSDom+R+P1pVC@;~PW*md3ti8pB1JJ5WnOa|g5FGPxQ$f1kP&CTW85sD z8ZH1(1bq(@YbRyR&u#*le4PGUwLgth%a}Bl+Bj9UQG0Q)6mDqL&$=Q<8t(zz0xqqK zxHVM`?YUTuVS?s*^)J7HvS9(d2B4eq&(3^AVc11==;vJ~<2N#SJ%aNO*m(XIXKx-) z<=Tb~uU)%IvuGY9vJe$2Ln5Uzq-7o=B`cAHLT2qYMU!D6rOfk`Ia3-a7EuWasgTGJ zie!3^Yc1_(?`L>_-}m<4{_R%xeP6>l9OrQ!=Ws>^-@7J6x3BLB%}*foC8C3d9K18& z(5{y^w;ngu4}y;4*q!S*Ce?0sKr-U55IQc*jB<`;LYeP+a_HhS{ntoScRHkk_HZ{w zg%D__cm#HO*t**wi-4Dxj|8^%fva;77Mci$A{(VSRzhbj1}V{(k|l?dZ8uS(#ZQep zjhrBHJh^unG2~u!<&=b&C4PG}i{)t7H8~@c{9c*YaIKPjL7b?5DG0yQ zmIx^OD+5^GYLz}6h}+`7>!+R~5uQi#5-7iFgL&<5i?F@- zqY3d_;i(Nt!9!{KnX$^tNZK3tU@hg9A9}mm(bfW01|cz*`&_e0rpGg?$5M8IwniKH zEg<>|85Tbv-0ytIoxNV{ye&G1MV;)a@q~04uiu-vv>6^*i1WADC-Vsc?M5m33<(}A z(G~{!uRf?g)vsbWpms}GEAYpiVrVU%)m`iRHc>mtbuG-7=)fjJYW+Yv3a^AfmD@;` zpy4^237i%#pL%1L25QotL7UXO!Y`@@|Awq5S9ebr@@L&WE0E?ys*jv0IQ?w%=y%Nw zO#wj3L^o|v@LP8#v*7)-ya!_%j?D#0ZMC{2mm?oyy5*fE2W7~QRd<|lMz>cJsXI<8 zLtEr@Te~1>P9_CGbLe$A1ZSVVO`~<_1Xkys$ILeI{KIur zIsGPy#eFXfb*)V(7yu(P@&0{1Vck@qb5QUqmVOG|L;zQ{Hd2%=%>E|}2V{SQv#U*x zXURUlAC6uit3@&#T^i4@bDyBYXBsJ@l9r9>NG(e;+)sUPnmY_}D1Z}aG3{qQn{ZaH z$PjWrqaOrott}ep_m`}o1uPd_4{bhSxW5}243zw|oWQj9q@M-4P_qnGf`ufHdL-{v zfX1BGcGvuGgVlI{9@l?#@Cp@Wx#RBqgzn>}uxg@WxLe@PIe)&+N3+(w-Uitrm`fFZ za7<$wGR<_QmQqyDPISmYrwx)eq}dnJVKbFN&t7M)Jugpk;xl)Db+N4=#umz^%SqWN*yFmxjMLzM$qf)*0=vFkEiJummb z>@fZ`)0qmx~dv$C|#JQ#_EsQD!M^h)o& zZDCuE;m!@~f__f!T)2Vc{}9yijm2W#MFRw!j3?OCRHgjSkyjW20a3)7*Bd$9L7V)p zJC2`1Af;MI4PYp78Lc0h6RSM#b;n_ zuhE&$-*Y};k|C(cIKU@CXL1oBoB;O=B;QjYIGQT$^jo+N(53PnbRU48=i)Vb-mVaZ zAE>SB(*^(j?|kL$maNd>)m3_9j#0C~IEdWe;ldy&d|JBu-V>q|0HUbwFkMXH?>PES z;4?%1;CBQ}UYNzOY}AQZX3S`g_9nN7wkP@8sz8U{JZS9Yw)Rcv9`Q3uyGyQr#u5A* zC@Vm@)a0CTlAnW$IgTLIqm7ru&Gay6{W0M@ver$H=+c<%CE911wM^d;W_+A4xsgYC z!3{!O8v+A~F2_YEcDK)t*b?2dtk#Bj=Bvg4+_r1}m)50z6|aVbSvv$V+M)i{|2w=u z#u04lP*39*#DTAk9GYXunS`Qh22>wnXjCmDSO4gd!H!ekKEGTke&Xt`i66(;oUPp2 z^(y8HWW2uJeij@YoCI-FXm$JF+I~8}x})%CS5h`Qd}R@YI6>EeFn*1o?_K+|=(huV ze$ivtJ$RBx5?|-+jZ>TAANRFPrapvVQ0?Rp-i_y?*2weNU8CLN&+p0}CJMMHv23cy zXoD<0n78v+i0uXZZy&fp#;z+G#{5VP`cU{{Gz5v-0?}o)y8pqeW&ADd-zi9JL(uy5 zkvo~7+2eTD5x^SiyDI(<#s*Z@*uOdE1gmX_7`0bj=FIjPrfY|j(uCgRrXfiBAaIxV zvFR*JLT(Rg=~1B-n^6=@y#Lce8;X}C4=t1K95|t=?X)kEZ;fM{-z_n^51txF;Bx5c zYVTZI-;{UOt~akE5!IXg?{$ZXc7I>Z$iK=rlg3$3B8Y}Qq;gZz&x#(-QSCqP5_fz7 zn}u%?mNu7XI$yxR4T1%I?@lPuHw)=Oy>nlwXx`p-(it=cMa9193t5HvSS`l`77YS{fqtYE433EK;KK3DbKk0tEm;{UGKliJbBEi-5x#RPF5NsI^W@nJ`?$J+)*JF ztEycnB$5m5mSx%S>KvzstoD)Ahj0+lpFMv21u6K7^6qF!E(_nCVR={z&3zDUv?Rv$ zb;imS8ZFL!ve#BiX*|EZqaH03d`i&&coTOPJA44l@>Nh7@9f93dk3yXC$@%S&9$Me zk7GXEl~3wW%F4<@a}>Rxx=OSh0RuWe>6P!>WPNcW@B=_((QKlAzjC|f zO;k|7b#(>seeY@1g-mdrNN!UkWU4>EIew$opI=3&WfNICTgi#O@@&iyskfQzd*=o9 z@;Y+NpRO?b3}v8}sI$tiWN|H^5`6?H$I5Gohyt8E96EpRTKz1yg*o~;3M3y04(_mG z2{J}!hpr~XSx2M%vOJ4G2I{qVS00R|{Kbgtjn63HQ zePTZe_TZGdLTS`!Sk(CF=#tM5bYje+-TF(HNw@j#NlcnSvAFa123E01$U4iOAUySt zrA(P&L#g7CeUG5=0NfPEy{{tjjRMsDzM+yh%i-54U`**F*5Ez z7IvT=sRMP&AqxXDLUUhPSEj7aaRm zlOek{ujTw6DYRg-IZ9fY7V9+l6F72>W_Y~y`&(~o?bt>~81w=@H7UgyNN_cN@O5~} zI|CWi#|{L8T@2jm-*}oGA?00_R;(km`4C|E@=ggtxDYc0TBg0-E|IT%huQV9fQ>AX zu38A_$qxyN1R_g>1~asBy5ge}0%O{L)z|gmZv;(}H`3Z_pBxExTgK`3BQ~Ch*jX5mwpP!cE}TRn9?t&pQ87nYIViQK*jC52e^Kq?~I8e1vPJ%!viQzCZ2lygWAf;E|2}&`jnP5h* zLv8$D@DnXg>JHIY>km-#2NM!XKj6Ze6JWbLN@!sMWcf8Phz7SeiI5T?N3=%9{xybS za4oo$6t$R=sEAr33yr!Ht=IRC00Z*dzdCW>-Eqi!!*$;c|6sw7h65Y1u}K$w@WX`2 zGhrYA%&hlb1rqRCJs3Gb`h4at6HSiQ0tGg8xFxuw&pjBZCpd}>(macd@CrC6L;%$8 zlvIwT`rjZO=LBRKr*oTQ!LNWvCPQR&Bvxu;zyc9|SJ_+y#k$j)eea6CKSU`&cQd71 z_Ln@+A9J~izbcT}n5Xg0Ke+WA_I;wB3M9&Uc!Z338UB7{+^ravFak!G*?DfFYmkga z8SZT+C;1c)@MR6iTUM~;`N?J z?mno2el#iXAUY=U26H_XENWt}!N*+fJw-wSw%V54*}Z|$nC3v-G1d>&WHW8i8I;kD zYjxRGU57LLYEq>H_$fP)fDQ`)KlUK-?_Vbf;3sdI! zC`Mq`ixZUgzo8U9-fII`P~-CGBqjcIgzuP~+^St*P_bKq;q5OGt><%fn^ucs8y79z`1^?k!#kuyAgf_Und&b0|abS3|(&a)Kb;v*0q!<2|3tK#vY z#QGEAFv??NE-6Qf-hM9d`K@8&77GUGk`d5u5-vRH2O0+}DD3u!!f<;SO3G|?$v%jT z!myZc_&Zw%nO|TuX)gu;;BDt|oIV-jl8o0mrwqQ)|=Gq+XS{HC|e%K%fDKbTYejC2(Nq1!Kl!9OTF2r*qC}2jb5v zX!q%&0tUVE9WY29Hg--2#zoM|c(DE6&kvW@S%Ce4K|t9y=26QvF1&4b`XU0&x9;=$ zP2CMdTZ3Hqqt%MMqzDGxWNKho+6#YvWvD+BoaZVtfBpAh%f?g}dkZ7b++Ig&DLZ0z z5PXr3eP3$ErM3P|R;+@Ev%qSdcb?0b-$yz~y-@+9VzjO-B^dUWjYeKtmlEchJ-N&g zZtv)GrQP6xVNOfhfn)18;1Ol2|3fcJzD|65^fdWwpMCA?TKH>!LryK`bj6a(hv~<(F9`(3??03 zR3r@S4s@PV=@L+O)=}~v3lLC&biWtX?sEjI0Fk&K)pByXbE*ZPStji8^{8No1Th^t zfU!W|%Q^k8rein+?O#L((6N}p%)9~R@=E8K)7h_PD)B7e?%$y*O6}%fJO^;6 zkoZmJ78A?G^93cpvXc|uOXx(($u-Ml`b;#rV)wW9ua(tuL!pLm^6H9#=Ce0Gz%yse z1!?7KyJ5NliiA_*%t>abmQljJ6fe!!1oM>0yEbpEZoRPz**%Xl`RDaOayM=KXWiEi+H9i0Ff)uq6)!Ce9&;ll07^Ex z&$puce3|hk6j-c|MHrrWz&h#DP+XAzq!h!2>N~{y>kjr0R*3`Kr_d_?)+&xR2ua#h z8w4#5b;vTwzj5c$lmbc}5;uhZ6)~@n;6?74rBA1G40Z88OVlPOEIt#vZW@Fk4~ChDYacxTjibZ4L2TQDHFY+ zRrNj41phc8x!b+5pzkZS%kxUBS7U+^2UdIl#RZAwdAa2)zoH-AG9<(&I-0uWq;IVa)=N3?WOF{#sbh`ek|phL^0mE@ zLq7_yO{Y+(;#7^_N`7yw0`=jGu|J=?!L7=b+cv3@o%y*JM8*aHc{QzmXvO58w|0{v zug)`kzH0b^8H<(_YQ~n$p2_{-tHO+*-_oT4o5&|{Klsc`uMXQ>^T8J~ZKY6Y4T&Ys zx$MZ5Kv9+*4HB^}?)Tyo+$?fXSuIDBELi8#t{1zdz-O=7(&jiqcN9>|l7=DPT7Aq8 z?%;52!hO;>^9VviU2khY%Hwm=*FzocYKyz>n2|&I=#N$(oz&!`&@-PLF7^p^O00aF zopOCPMPBuTj}|57*4cJt&}0HT&oSjJZ?^U8t%056Iq|#KLfzh!TxI`{0;)96eb*ST zQ*H}MSZ%8RW5&`r)P{0s(bK3!yo()4UoaY1HSZhAoEpT#OL!*n)Rv@RzVaU}MH<&m zYV||W>P=B2^CbQ!7kOk5R6yH*{5%yhXU?V6eRkG6@HrmGPb}v+;?VpdV8h~u+%vg6 z2pQ77v0t5x;JEtzXnpF;gwrX)$9CtrNaH7TZ~SI5H#k9DXcF=Bg{v{ z+qP#}`;egM#}TfTe!4|;_-wV3{{Vt`{p`e2Z}R;3Xg)d)ge`<_VxMmuhWcfmt8>E- z3ZBZr`dPnP2rrbZ;`PO6q!rb5N#aNJ+sDcvki~TB*`_G0ynxfIow@2o#Omhit#ybml=q^iU7IG5tvw&U5HFT-!}V zyS-tkM>Ffh!I4V~0_Q#HI)GpP5P#x}JdOcYhmTG?5Al9hHT$aQ70GHdDG!yfdk@8) zPK*y!-4#(9Sh#{W)6MnNbiC>N%a4IHoPEC1vs!BZKKmR_NHaLR;uGXdmqrpSFhfy@ zQgI$leSQH^))!}T%v*lc4!(%O^(fis@B?n)w!TJPe?t4`KXUfk)fTr*$rTg|{|&4s zzh#U2=Hams9PzIo#FDhqiFu)1_o;|!g0C1t3HaOjHgq2I=UVWOnuW1W3nzI^PMZLM zipzVs&>jiOT*`IC2p-3YYRQ9zyJ*q&r^&a!;?^x2Z#wJG+6WI$tmpgav#2*4URhR= z)zEfGC4iGN`8v#|N9!V~zMUgtVqCz@_EDpMpv2$5VZXAYqt5>6l<+s|(bFXJR5jlb zqXz7dJO3lc71pxsy{V+cBud>At?R^2ZzoB*67c+-mRx^!uR9GgZ_LoP}TY2 zud- zhjt}&qhq+!z9Pggq774<#s{lY$eFQ;Ixxdp<2>+$9Ah_iEc^&ExT(`dJ@#l~ynXcj zbVZB$AY!46-13^LWd-0~usV__hO#Tg(U-Jb;>sv$V&Z~I`nV2F2qnxj z%W+4PEn1G`tdU8+j(1hZyV4?&q?V6=yn+*Vd9j$VP8RFLZ90}&?p|-iG`LTD$-zxp z+{#bCEgN|>OGjWj<$XLc8R|9x8_O^6Ow6bP&M9?qs>3_K*34>Cr=1+p zG0-im^~1{0I7nMu-^GPo2}Xx=Tt^w_Fn*`{_S9BTJz#(P=gwT%+@Y)yjso@JoGeK= zJBo7VfhS%AbW`f<{Oqf=1N{IGeyB%97w#km*cTUAKK(5ImC@^Kd~^~?AY$68MQ&oT z*+zarl;s1R8&8vaVeD?Cv_`sY>R$Nn#k$;Fu`3KCuay1Arq2>`8fsW70Lv|3d9Q)c zGw33Q@#I>#jHdWEX8C~zm?I67fdk_c+<~@G8EbjJd^_&tJKwOp%jMl4fWYLPxc1mA zOk{)1VQ-8Wl5xynUkcjwQZ2uqZ2^Z8G0AS={7y1eb{DkNi`Pt|7KHhGRsH}D06-Dd zl4*nUpHRPHPT7fg zw_R-4uH;G}($rwJ#6d+HY}j3z=ni4rx|ImQlV#Fwn8rwOO~8PO01FK9pPIQ=%~@dm zk8O2EM)&nR3K^!$y)fib{iqQ{eT24HL4JupKSe&-2a}7vTGFi1Q+~wZ4l80@^rgwF zeb;wJ#yZEnv5g{T`@H-QXZufGRO+R9H4+Dt_N=}JvQZK`HwaA#c-~1ZRJaJIniQCV~e9n{OKfim5 zX5!h?TKNUHZTo(?b-1T7ZWttXj02Ume22Z#%Cn^tMR`)-e*EF<;>?-Tej9OlFl}M@ z&aaq;iA!Z!vk&d7$t;f{w$XNCWFW;TiT0(A6$CjYecXS|e_(*v3#x!gRex4B^1O}CnZJIp^k_9VJM`>rmJfK*o( zQFu40n}sQA<>logH&A_F{e0uJtCR$INQvUM{UQ;FQAhL1Ja9YC3g@ruv}E z`_|GH(=Pl`!036lE`_|zEWC{Dio4FeQaG1@8RH&)vuS;rfRdo}4RpN+Y}XGlD;>u^r;CVot< zC`A75cbRwwx|S+ef@rV`y3ho_uBYLQ3hN}(0OZUyibWUGjHfEEP%oLgW)h`q(i(-S z2Go_DQhXnomaychLk(X z%vYz+Vr}*W(RYj}w!pjp>~o(?3O?eblneEwpTm7= z5O53Xo;iK%!$Am8O@0fA3>03Mzp{3zcpMkGD%@7Nwg3_v0OG^aLphQa0)2W;91>&e zf~4uIQwWF~Z0z=u=)>VyCgMC`j)eZiEajf2Jk1=rKuHw3(~qFRx7oR`h%muO!RF-4 zNJZ(s!aC(+uHMfM)`hK_L35=B- znYB4<{g;@Q-u`{~nMf9jERh(awpq4^HOZN3ERavf9)LVqgb zHOWkZ$rV`i1YyzYC8N+3`VO(G1xD8r+e}#>Pw|}V@R_FtIv!zmB*jwx+^G9bP&4}E zYu3}?;og4^iT$`y)%Vyfj+|3*?`w4r-G6Dlh?S zEh5$YOxL*l>J7PbXS6P{Glhp+uSi(%u|hC|a!kdut`PBAw@sN@k%@>uoIxU~tiqjS zJ^4le*`T7$x?+@P)x?S&J|lRhNs{CyX+^QoP)8r_-%mmY<*oQUMNCE&9<7!WHb}WO z4K~5+7|S`lFL`6hgkUngXe=YLyxbOgZvJnx{6MiFA+fR`e(7F;B+hZI<6i%^bz*y8 z)~IE&f;XDWV~A_y68C?H|FBLF=g*-)2PT)`%52uVMjV*&HMek~+$Q`lUWiRTe0V|U z8KO*zkI^@VX4~OBjfqoff&Z};3|@pA*GbIMRJ{v>YZk5(%{17?-hBR&9-ZErL&Bq& z#hBkrM)^hSq?op3lp;~|(D;Lw`{80o$T=_O5PY4LFs;b>F#D+4Jo;^V+u?jIKZ9)3 zmdyQ12?@4EU;iLjOAH0+x&l&7RfrMz|04sYsQDa-+Q$mQ5(5#0%|U_o{wgcqF*mZJ zo5)GC2H-*_q((sPKfggXG$3VNS z+c!VexWN~-@m9I4P|+ix8aG0AnEItXnETg_$j=U4=XTOgjN&l^H-d|6LB(islKMqV z;A+ZAiLt?AQj$Dn>=~P7jXoft5)oFJ0})CG}{JwCgI%wT+nu9hNNzOF@SzSu1NgHc>!cQ(z9(WimJ*>i(>C@LfNLb)#*L zv%fFKyD=qH>MUdY>k}~}Q351uBdsCAxlg%_)H5C6l6uF6y>z3T{zbn zgHVD0>YAkctIjakyP!N98L*PS!za}h4GkP&jh)M=*g25c=jc3i+6^Pjuf#18cIp1b)4EjiatrhGw5`|h(qff*SgSr z_OYI}kt;!7ssn;lsl)F`eiRn8k)MwRu2C*4y_B|VJDjb20V5M`#(q3erDalmSIlj) znbPL0E^(}b0f@I1|HW`>CGZ-6@bxx6;jL%L)W-1|Lik;e#J z=H;e73sng!%R6>aHTUquhbR2O6|7yC4_Smd?1LB;?x{_9oe*F+cc~sW!Fn21oO014G# zG3D-Wwv=1t5?qo(`*f3Ai29``H*kl2vg#AxjzC?;>+~o%5mP`d54T1hn8A!n-h?7H z#-uIZBLC8Bz6eRTDU4&=Zx8eq9Ma!h6n#>|e>Dc18jLrAa3SXUmx^d(1mCeYwFbOAFtNi0$JWhUud-YB|vJok`4aAAVJxp!H z@8q~}LY`WTcd@7F13h%kvQHU+tEbljfdssO`upsefsmy0StV;QFiK9@kGXhQsCBaO z=K5DLFgznQucCjPXS*<8Q=RzmWN5Bob9FT}e3ZvaQaJ7* zPHk~zI(70Z2KcuDr{&MK;P3F^W_{g4MIqV($rk5B&_JF_dqsm;wv58X8+uE~*eU<< z{FP-vfiZtV_#tHbLH2cMUyI+>fntTBAFPOj8&!9<7yBXeGT*NKZ?~b6rjT|-Y{A+r zcofDdtjmgyyK=@ulM}ixeYo4#ZCA~(d4t545_6Yrlw^;4sRpa0f*$`mF|ga%9}T#!!F@uXcm@$Y;-jg-*m1E+ zAm$n4L$`ThD&@}KW2JkP)t3-^F)PbxpZ!%#f4``Ff8<2zwY4H6;|L@?Y*AOBMqLea z59;WQ15kbJi78@V7Jy4xy*X2123^D;x-k7XeWe%5kde`9cT7h+1>jNS^MI2v)NL^8 zq^v^o2L=Omx3sXbY>FBKCJ9|MLr^chb(4yiL6DdvozyHuAdIE0-gY>Sxp+v?e-Q-14}x)k9S zW+2D6Zb5>E9QWb`L~ipn99Jm3lHA$lP%6S4E72OOVBB^=(JGd4p(y|HEGiU}sv9tM z@z-TIjA8S^I4&MyK=7$5k?L$5_lR8}-i;k8(LVlx*@n625+b*qVm@%r^_RBu{}L0N ze0>Imz5r*%>;5~p!`DR)n?5aFgGjxTRr8>D1n|HriHVDG<^M2Q@hF@V+!h;X*4DqY5ER0FPfdmO&nPx?YsED#Wxb z?u81KYloVmuB>|G%kAwLKbv9fr1Y|6uT+keHf*kKvY|Q7*kwlv5&0EI zSMQ|uRfZrq4&Uvw{p3*_UUJBEIAUU%!FN-?hL|xw4GQ){B+c>jdUljlvI&=V-q)QN zfH7;KkVf%?U*!Udyx^h~-9rzwlej*=+~bAI`STpVp>xXBi?hgO--^Y&Y?EVD&=w*C zh!>Emeo_$olrdJWy?jtd94LqN4)wfp3*d<;?*C?hZ zbvM?t&zN$=`xg-dB1VZPudM`1kb;BRhFhO>{5N@3ge)dE$166sqly5d`NKM-yqxWv zX$FcAt_&fb!6p6bQQDjx&C&NU>2KO^WGzYQ%R~?EL`+rl5eQ6QyOLOdG2&QpVI8eH znLcV?>}^xr1Xy`;6B@HC;8AE@2wJu>(-Dg&>6D+7Y+LtNvQFVyiePn>+>r(hL?u@N zjXxNkb6q=$0O>I+6@?aE`RjzaW+OZ`xJ{A``N}zVy_<+VGP^5yyE?AqNKsqJE+e)* zis8i1$owo({BvTht?gLN)eG2$^YZSO7^X%H5(db?JwrHS4n_!~P^0|RQk z8JOlKu~i*2K#9G@5GGP6*tExwqhxj;LlEJbCpf17Hj`S(MWD`7xFb{XubYsryV((w zg;?h3p%N0s5(*&I3N-zS&EGSTB_SwS&bBK|?~_GN0~m@YKirG6WV28<8XkpidV<8b zYC^a|S>gxTcE#><_zP`aefF-8wDwawj3+gIU4>LFR;b!+fg3An4g;@IHS*z-?FrMe zbJFePl%1d2g;0+FvJ1vJ*o9Co?1K4H?=n^#j^>lE^x&zdFrGfDoBW=SM(Mk5{;KUk11|; z-O_nj$RNs#fIw3TA4O6%{;b)^s~T>p)_DysyZFHgsD^%rgar-@sh!9RnvVTUVvRN@ z#VB$9QdY=9u{OnJU_g&xTD{?h4J);qvdon~Hy;LmKrHSXn3fn{b|~%>YUA}+C{FSD zglz~hV~oGp!?H-&23VvstZkq!CC@uP+UMR5W}ZJ|hsmFniQ4Q_M%lkv((SLO zr|kM%I3fdBfQ_hedunKMcfBfoyOW%1>qz*8gQ4-==;tJNVRJD#WwyyjPLorHVvD@s z2g4jP^p>7)NUAM)&Fp>mFj&G<_LBf)MiW{%Mu%HS1_PkR zzuAhF>Qkt0uuB-TD}^WnM;=O&N~rWq)anwu#QXmXub(x4CEt@R96QPM)T5Obrq6nv zh8bY|{0R!49ArE|MrP&}*GAfPx>*G}qR?}UY24c_PqMNX3RNFAZv^dlSx$Bh4m}zg z&#v1R4fr4d<@|NK3QKa9 zwBP7}5^!U&vEQw23!7KfPFY_Hzo_j8PZHY2E6!bU!Gm+(4V|XvJ5<;q1-&JX)`HS4+h4K-x|0%#z?V;=K9G;L&xNa63LMU~x3!(@|5!RwL-?4axwZ-gj0Hx1 zPG8^e7BR?*f6+1a>Gm1cq*V+=#pH$rK6A|a%>U>koOP;RlrVD3n*KuX3jF>3uiIs+ zKV1PBSfM6X%=T=cGn6xLtYM@36v;foy*TI(WlZ>}$B^>vo-vmz#i$X6KzR;Y<`@hv z`iT<;Z_`RkxDy^7rJ*@U1M^V_;u-2ejOkCWTfr`z# ze4ORR`!TtJBl!tLI5lFF_q)9N2iQM26zOu$b@)!x<_A=zfg8)$XKL`X9i@kz0;un~ zPT9P1VWNitd_rT!?2{@x7(3wO$D1Aft+Qr7>1iR1=hLsCNTDdxVwo$+KHbpt#d0ger7CJDYSaQmxmXo?>D^B*rS8dxEE%9)xCa0 zF6dM_XUO)(h=n(OZm`pB3S+^MG~2tbI)A(0s4KAVH?0a~g)WTb0A`q?m(J^MAhEKR zOf$)#S11IN^LL@&AMUr>nEgyBVR-M+MkEX z8I$P|q;oSgboKnTJN5h}jy|N286qRCC<2^-WR)s3zh2=@k^qo@CIgYtox!iv(9rPn zncPGK$}V~`_T4Y=8Rw>yw{$Hm68Sn&q0qrN=fI$#SoBrxCGG$FZmdl~cV9e)>k3RN z%7?sn>64Y(HLex=EK1(sR-3Ipt5{D_jyVvpk!1oL&;Gj!VC>nd89mh7d|2G9>NP?C z$=!xE_#66<`eFGxceFMD#WSu$Q&erd`UWzC1E#=_G|(u)7?Vc02z2LkkxoMt+G@#` z7!~MP<;u9rlJOl?(qA?aWdscR_7pAprt$oa%y664PA24HY%n_&G?=cKul&J!ELsXh zc?Zrny=eIEfK{Qof1Cb2c_qYIInKE#YBP4Qtrk-ZcA%{d^8z2-8I4isHM^@mw#I>K z5b=jg--D#jc^wAq&o}WJ93SR(Z@orPtXrYU-HwA?nBEJQt9-M%LsMDV2ghHer{3g~ zj~36T5to6go9mT0=B+;9%|V>uGwSP@D=ta8!nQv!b7E_-3=S6 zH4iEIkC~f`eR<6$tWZj}r0GgQL@m_3##H)_ZHit5noLY5yM%#VQ&B{?5b$smQ3vGa zcMTh2ut_G1K0sm2A^vCSW}UyCQN(8OKMZv^>^Yh4qbbCNvWKU1^3aE!sJBo`n!~v zSliF{(+O1pqI6SK1;B3QR=N-$WIBDA0eYOROTVZ8;oP7NvdysT%-Q&1^xh~RBt!mO zg#*f-(hfQ7xRBu*A!j@GJLm1W zuy{k;;X=Ka7PY36odW!JpLyz&(hTm*lb;va{F_gRUx?s;=F`*`-h;P=z`;P==Z~t= z2d`CJJFG&v<{=x9pNC}CCj`^WFiFZmv8z_|=+~p{z>vZh5-2Qus^6r&+4>@1xj58C zG&Jem?@puAm5$N@bstHmF{%c+RhC3ZwuL?m7o_-BV;80`q@J2^`Hx*NLHU4owGisS zI1cyKH_S*YsAA{S9twY^7HW{iBsXEr&iE1tj5+=|RxW`cEu0H=93O925Dr!aje-&! z5^BlD3Lg6H)zn~x*k~uiML=&csj7S!)W^38g4e>eq>r6X=bYv){An@uX+9sn3w7jm zFvRITs&Sf_qZJ6^NVl;&2|;7Pg`~|xZdRUc8>4z@&xMFR{Au;<#L6S;*m4exBt*NR zAgV&$a^umc8j(Do+#fo&dM)YKQ+K@cZLQc$fBLKfLAKPdTxa#HOj|l#wR zj^qlDSD?d4E_N#hS@wYcG}lp~SIHqP02+{X2{xgvoar>-NHzgOm)fB9s#j2ltFQ@? zaww%56z~V9f+Mw)2zPag>j~=0MWlm*XTMu0qD4OE;;g_oMFl%i+huW~^!~&H)t>eh zSCE<~3$?jxzT-8uK@)U2X27=Lh$v5`B3}mG!>SE{hqkcV) z|LGR4|D+xpfuS~UmpYF{>w)A zV?|fbtCWrw4qLmYNJ7VN ziPp_F!vdYy8o@~j4m>-~&OAI670`r6k`}2RiG~GhwM>S42f)5Iy#?b!Bc+EZZtpS~ zXub))Nx2QyBRi?49L+lkms3;2UDzc=a-EHbbROH)mwz2WsXP285Q_cftfbmygZTR0 zxVPmW*ku4pxWxZuBYYNPVI*wN5=R4OQL&p3a>l8n7UOe_y2UoyrLQRNWVS^{UsD3Q zn%bayPp$+iISvOZXc+*0G` zX3SiW#dc!A!g`puck1h-Y5D36)__9fK+oQ0Beu2<#flamBWX2+pp`VJ7SR@1$b z%%|0-G7Ynp{WK2+_6)ZKo`1e=06;M7Mtl{(a)zCUv?oY|D3}huC{mx#W(dIH@;JRU zXQJl!l_^X|2!J{Y|F|612Tk)U0b2}>c{wG02si5_2;WDbX3kCvt8jlq!&7m8b;Mn@5Kl{U-)nm zD&d4F{4sUHB#D1`oxr@c@xnZbkn&G0q`;jkKHJ8I={Dim zzVoXGTyZMBf`K}IcZ(a$XLx1Q0mZy_w+}NKCI{DntuF~y$f`@Ex<%ocx!j8k{AnrZ z6Eet7?8fkKFwfha1`px}(N{iS%#1@7m8e6^m^qL3t?BGkF7`0pTjESA!G&*a5BxAJ zeC&sHX|w%pR;WY`z

0lChu0?%Mxk5QFS1o~g`D7txO_T)Y8(8XACLu8IwHr%SG& z4bh?_o3;*h`vFAZV+BigU3%+tNb_q@bO$yC!sJ7MOZa6K!ZY1ld(oJyu_** zeWiz4`w+AC8YffY3F@eyRN=u$9d%bDJDQ;A2@{iKn7Qwfs1fRkKDQ;+=hPc-rqi(x z)LI<0ShAft(`Rud9M-+?=pwrgM3L`Yv@EV0Y)%<-e{vt7)`s>JOZI(u9eF@81OfVg z(0>tS37RY)XgMF{f->I8+Qf{>A5s1wHiG#Kay$bbg_;ReGyo^Q>0ZPi^+sl_wg~X5 zW01Cx?Wk`->noM>!n>d94!@U4BxD$CIQ$CJ|9oZ6v&=3X*8gj+~&72yWJLsuqi zC_dmSJ>dq~bGq_dO5zLU4reX?E+R$NdR zH8l@}^6Iho-??aB-~7NA??OVezs{J6rb}T)N40J*GK)0}(3P%j+nAGay77da*h5aN zrOcQcVO46$IuVT*Calb-1NHWSa{7 z$xgkO;??X<0nk&gpcRT#1JDrnDq`-fe{F%<0&KyYyBM%8BChGj;F>XWeg->6qR<1L zL=>9WFuqrc6X!hEEl&8H&whS~_fke-%;fIr4mHP5RiM+=yfUqeKgQ0(D9oG}+v=%* z+XCccgFjkXTR`2)m2lbdU4kb4_lq3H)))TbG{QBnclz1Ma?L)%tb4F!_nhf759sT| zH7g^MGr2W>WpkU`xE(?=Oun(rm`XON`#-h@jz6Buyd$YjTr--9tr~T5PB)^i$tB~X zRU-P|qHzjy{S#mJZzl<-;AEgsM&Fx=HVg_vw= sZgxXYbYH-9=$#oOncx|-!Uj* ztmg+Bz>UoA*ahuedjm=&@%$=5ZPjS%&u_gh^ZMSgJ*Cf{B>^NOtXntqw@>>LZ(RIf z-V^HAo7+ztcw4T!e(h|n5O`lCzmYo^Kh-9POJXbBlo3Nx5wtmy+x6-dm&0g}1{GYN zaJ`cMXIMXwul>`fF0lA(T!)+Ei1H=`LxZq3v8kShwUXwq_<9;L1tzzisJzM9th3tl zz}|O+l=mnpP3OL=Gn=%AA{!DzPz*>N%zq~kqY~j%U-ZC~mU}EQyLrOWCDh>m3D)=A z^$Z}O9W)&~p)jQdvOfQsd|o?Dga%K)+opX8v_wzQzoLF>^riWR@~G&vT7VfT|CkwuXg8*Wza?8X)e@dqVT%;O0;b+kX0% zkZx-7Tomjvl9(pBde=wkX;a|6kN%OnP<{tRsop3K_~(>Cp}2GBe_Bb028oGk&=i4E&C-)O zXYxBY5)B_5tXG#8Z;-I-(rR`a?b%;RrfLgGNl8trV-!hLv+8Pou!}P$$1?xfv3ss`} zGQSPmPStIao%qqp@%uE-RM7N$u{X7O@*-( zci~+}8a5Y(H^IV7ueL=<)P}af#+>I}rVqDzd-G;1) z|F(%6oZpG?7-;iX3ab@x!v2G*AO7RK*>ghN^uwr0kdvmOZi37V0I>#X>o4p2pePgr z%3)pO{|AykFnfof)xUuJX|0n~_2S71XIIE#B^u`0{)4RFQTP6QL(ZAt(Yy`>aUY)0 z2hI@lj9%b5xt0keX2iIneMjzcq6@xhjl%(!ljN03USOR?78Vu1M9ichI(*+iwHFc) z*ne>r6xHX0#s{^OI;h}6rH_cL#vWWnS|p6}&%J!?ITsp9VHUk$kS_>~mvtU`O$MA` zK)EodcUy~GHi|j6d4>kXZbcZA9bSQHS>E2>N8eDwI`Rw zTdfQ4il(9o%JqvZ4-qWXi}A&dpH?`c)Hzfwr#m!vbJ@j2|4JX?&&%EU$6RsrY5!8os$rR*DxgPu^qUq9F4HO*tn_MzO zF|-0P0_*DUKR>+n=(Z;k2Wz?((L(^f+uCA~TT9ir#*JWeq5iHUG8%RU#JYzDt5rZE zPGE1})_bm#MVzBUf#?fDnq>RzhA!qo@q-?@^52VrvKs0Sq$bDDNXh*?0KCTCU?o4A ziSdVi;M-MkM#$aN_t1StrjI%-=gb9+j0~VITu5(TiL4HCAgEyY{)L{6S?O$4kV3WZ z?`OL8E4HiMo`A1Fn&j*YrqVZgr7iPDo?TTZ zlpuq=jPAxjzYuLFoCW9n($9pI zmecO4M@MRjxs52kYsdr=ync^qZ1hxeLkZPdaLE?4sq2a-_{Bxlst(>}xOB$Yx{RgQ zmebV84@o&>DuZ6W7HM0|AEI~Y{y?WISCZ@?vkwpLeeJbqq zc=dQoTXE+`Ryfhl%)ktJ5>6OqA&Vx(8+E6fv4up556r#4n8+acuJqjeK#!C~5DQ%` z+{q8_OrMd1(uaGsFKJ#_mDkpy`n(-p^Z(I_i&GZE&`-JL0%>eH)60D2G7hQ#+65i_ z>>N5!ci2So3BJgTdmMaZ3V{SYOlmdr&elEpn3{U^O2A;vp};1Tl-Idi;`4Un^SGPl zE-jk~o21?f-9+NtU*reMq74eNPSmrd9Ex4_!Ki36-ee~=9g^iISTGHZ9FLvQhr5f+ zUqt8=Ie(P_ySzpzx%D~3Oh{bT=uy=j`d(h2;=Wz{^H%cF%|HyHBdFWd;X0tyfFtAg zHhW=neaSC20mVdJfklz(9Urd9YHT+s)GI&T$Q){R&=J_P0z^ZV70$!V!L|TE&o3gx zzSK?ObCk>GUhAPMEezr`cZ#7t2_6tyhhA9mbrCykUp#2UI!HWb(^pi17kx z`1jk8uflOD<+|5$)D9}%J=Xxu0pV3B$OkjGIk2x4~A=nIAfbbYUO=NDFA=vAU^l^Kf||OjX2=R zzxqpEdA4PU>g|Id!T!ZWhVsUy*HM?mbdzalL~3vwJ?M?ddi=E}E3)2-(t$wO6chQL z%CdhIwJ6kukx*M9cs%sw7JE}C9*iLF#Vj@%I9sPX6 z$#rm^92e`c&qLL3fF4}V-UY$^em()h%eB~8O_4K+V5d@c`Ml%EF^Sk)%_Pbfe^XN5{W~r!8eRz6}PK;97B1gCo z);4HdUOHFc+K0h%nX|DtX4c`69ihj6ZNs||Sf$}dvk2)*X<%Y7Oc`}OkUsQ7=f%ud z6jED>9B_(_+!hwfdKP6szHb zY5DylAp!LC(iVp>(ma-t325~SE^SFe@q|Gt#Ax|OplYEX@dryg1MfPahPwdjY#s*1 z!Z!fJ<2SYsfegVJJ25>J4$xR`?tk^9RzzJ2cX!?MDbq<&M3Q;tfxiBk;*^sxNsuj3 zMk9We!#i|eZGs8pY}d=&hk_UVZO_2MHA1DOQ)PTKk9%Tha|pV%&~*mZKL3IhynW3M zD#R*T`{Pa92u!hMH;SiSLLhXdJZHHEiuG!+97*#$TJD1Q95I26EX~#NQ4uw-0JtLl zr)Vlc0g4w600)~du7+JoL+$(72U>4s2=kWs{@8lq4uLQ#$@X`RlysR&D#3b+P5aGJ z;Lxsb^=OSWf7kR;p1p$tqOX&JS@*%psKISq+gtaD;Bd_$l~{8-3C0$9huJ`Vm=Dgz zO*JY$O=YFg6sFh7i!+(1v^)ZHmRP0XBSdzFWtI3Z+s`enk~~i%bzh~4-50q|zj!zW z)lf<|t>4|Sxj4BA^CZl3E8qyolEMYulU_j@YJBdH@~O;un%~dw+{?vEer*N+lPzuQ zWOVe;6veQ%qC-v@QYKO@uJv#aXsDs;pOfqs0=Ir0OteW&5?Q&)JI)GTGakDn5f^{EOq7q*f5> z*^h`&gSsX7{p;&7_Uva%ZTv4rGPnfZp=#}zf|`seMNUPwZUf$=9lJH)P_mjA`e}Sj zmcXgZtI@h=B!iyZ>?xeUgI@sxnk?T?7uZv890*|77Sdq^3Vt#t{D5M*b+Q3}M_27s zbKqZC4MlI`+wcow^{LwTj_+qV=_1`Z7iXzRN#Tb!kq1h>uk7UlRhCci{P$Xp+%soR zuVwAK$15Ie19HJ8=f1$HZNPFixczv9^RfWu@G98M?iC_p6T{|bz4-dF%t={y`*PNg<;_2x6LP5AM1LN#0jz_;fI|%8w8LD` zHO7UeoM)vIEt4pC&s7!YWoiCU7qfrYJ^OoCOeME47jQ#@Q-8#4obhipuO>we_H+W+ zZ{-AZACmJ&!430VVSj%Ubr$8G;_-&_^b2`%k*5k6G( z?;=1w_U${z?m-I)r5@H}7wzHXWMDWEvuOB8h<$+}C^BS)doHcRAu8UY8t}za#s9B} zJT*F+FR7JF=J>D`A_sro|1K#e#@KTH9&+cht5-CKG!F@$tJT(dmJ#}Q zF3o*7y%K1Id{EgO13_-`M{+m*L;KU)Y>pikeDL(%7iX5Emsms6(oO=#lJ$2;>+Q;I zZ7fr@02Qk+Hv{5J-xRleG)38o{dbg?#A)U&luJ&=fZNXROf-a%+i<*fUs6CI`f1pI zNnVgEf5?CiQ9WMtKn;CKBaSBzrqBDv&I|-N?7u}*R;-p*A*vg1A}*ph*bgD+9}#op!2l1Sw~>g;9q}v`C_YDHfSli0RhR1WKqefsCxT?-LuwvGi%LS^BzAG0gJD`@7{Cw+2@?S58#%B z%Q(epmA#6$MTFBeK@Yz#c>T|Z6{L$5w^BbLZevM4|JR?t3Jm!5(?fqfv0t~NK)Li)GIq9fzfd{) z^9Cgx>_ zQv46pIdHy26WN30<4#Wv3sF|12un%nLmaeuyjD4z7zx~QD*Zzag)WGB5Uzxl1Qmrv zS6B4@8Gs7g?mCb5`yD%mK{4o`IPu;ZDkwJ^2CLZen^*HWn+wVq(agk}orBk0QJE_? zzb}2=T|RdA>H{=L+@cNh+9~wE-l(<}k+Bg73)&v`Y8|6>#N+MAl%CWcc;E7@<#*1g zw!K;QPDsW^y8#v;J8$JAzkA%zGs>1#<@sIiefdk5kZvC53>!Jsn3k6M^X;zq=Qih% znQ!`oHf*hwyX0D)6S6C`KPV$Zlz1ytR8_%KpnhGXxc%D7xY^C$UM6j!>gb6z{$Eld z-gwVh&+-}@4bbqcM#}>kG15^?mc*<0_?VassG&H3s~l=b4Qk9AHbBHR+ zp0g&bX7ptmI66AswQ*?9%0yFbFBKHiSzZOthjlsVs)kfZM>u;D3nj!e*$K4h*tOH@ z-hEN0!RUNaVZ40tVh}zOHEeGdbG9|dM)j^utWGM{V?2A32#LIdIursKoj&ynT^AbH+RURK0QV1(4S^(~2LD%PacH#DG``GG(PubfMgj$H3X^}wkfPNkXdAe+F|Tkb)~-;kCEjfv%S z*_m_@t`^XOzdm=9kOx?|mT< z@^q|1@Vo12&&J)=meIsG5D#Ek188si=!&sb=(|K(cF?%{F%zCY-|((+?FFMe@bjKJD=f33+MYSsmw51FAR&+_OR zy!L-oUVeO@x-e;cXnPowI-_hrIOge3yWFB6yulioQD%vr(ITzjRTpG=9vGz6uZss@ z}f(4DliVq|~gVc4sc^Fpb!0c(Sk!?ZBnW8;I@>d>%Fyk?_d! zVDeKxK_LdBOio=&GLG%C)GAAHVa~uZ`Sab+*2Oo&-Fn1>e^1At8`oSDi!L86nQZ?t z{zyoU#8jx0(kW+rz9&bb$u^gFvef9ump=yRZnsIUcNTRGkxi23sl<~JUc*rA52Eoe zT~%N>x$yFw&@q?mc$`FARQbAzeTpq!Ep&|hqUqSDWB_mrxUJs>PBT!ej&=)8*@ zGBmfTx2MxFRidG(*?_pQape_h;!lSyhgJ9JgZtOjb$$$EE3Fv)Sipcn5iG^>FMPJZ z12KUDO(px`{n&jM`nvA?LL&m>PnlztMexpZhat!Cb*UQIT3s7cp4F6h3w=ZS;Gsr9 z-g|PS#RHrduX{#6gnA7oN0Q%5dcsleQS_dugT365&VaACiKvS%~Et&Yr*V1n?0*YgIQOB#4SgklWkQf*Youl9^D2tH0 z6ktshgjDlKUgVnx@%DOA=KahujWd)0jZaHmu6TI8e8 z3H|fotW{^H6Mnf&>b-J2Yw;E`U%B6N6RDk_hvShO${!+PVmh048VI>nS<*u{2i(1~ ziks_+W4Gr<#^~cym0ZNO0t#kf&BJdXs-DT<^ZEF&{}`r*#7e%X;Qq&=0_Tm>@LJXB ze}M`A;4!{3`~I4E;{!tx;>D7tuL^$~aS66v4M)(fUU#Z;bbPWA;{CvMJc2#n8&@?z z;1E4;?;^*N(ZhbsQO2=S-zSi>$NW`NOiT<@l1L|Y!a3zBYi?NM$dP6e?I9AzUyubq zbYi@5MF}#@nr|?vM$A<(GWLep*c~@U$b@dxEegm+!D7X~@{UpGM;0egs z@s-`;uJzbf5Z=F_2s0w==q6a%EEM+qtR0;MnDIgR<@GJDTD6zQ(nNWVtLCn`GT59Q z4^%5c90jg%i#NeWMcQ}f3v)J1?I7Us-VV2$a2@vp1I7OSd%97mYW1LN#CICCUz zk+QXe0~(^DBfc-k{Zm1B^TL9O|E>lg-FD@3IfgBJHueDk z?GcQ^L{?RN0pL}G#A;WV90UgQ%brj0!oA@))F}@cdqiiy#gtOjhYufA`gv=oe@=KD zGcY<62=ln-C4e|%^isG+R0E>Yb|?O_qgsB`~&U3=rE0`8}pR zkTr4va(rHu;nc{+9pJyV)u}|+@PPrE6U>vaWOP#OxhlOc-rLwbdOIr0wA<4J`H{8p z@5w1CtPy##lCaOXOvL2mOmWyMzhgUPSkf(E(s8S@cjt}Mh;efmdOBj)f$&4F4iMi6 zmEAW$%6@QPX5@)NG{}Z4_%K<81T_)4Gm1L!T0~{9{704`awFxG`QW6Kl%dSR&U#F3 zZ45pS7R##klv8+t2NqA~tcSnM`c_m{inu9KGw2U{btZhu!^W7iUb+PlsS68sjmT+< z0}(KG8Zlr+@YBT|nMCyCos>tO&XY@SHyJ|>EQ?!BO^fPmmPOEMkGYuj9V zjHPL)slB3V#meWm#Q6+0rg1bq1;RLSeD4YF`FRu#{#Dn)i(8zhEKOw#R<;F%38-?J z?ENW+&fQl!|EWO`wHeabK}?&7wL{|E4bkp{n-HqqhmFh;GRPz?!lYYc<@sJC zi+>BZOjHf6Vc)o}uOlZ&^#&5%xO#;3Rna=;3%flcT}n6c1X8(z4u;3?zOX~u~LTQs+{J6_1i z*f5j+UY3u}nsXrVi8wr=s(;+d0@uCLcWjn_SWQEN);w(3oMlcX8<}3XH#Kw@Uox19 z1NdSu4sGAYwt|zd*Ta`mJ<##<=mjsZaAu2vN@o zC{Q{~4!wbRp0ETR_}4`~mQ1nYeBU-$x8ohr3B+)tLSKJw+Xz5v0c9}4*g_Xz(J=pv zp?W=fB>XQO*ME%G65gr!YwtV7$^*uV==|B(mz6;4ZK>w>%n$~QblZ-?&7>^k3RDq? zx}RUh(SH+Gb=%dCq1=4$54($OtAw+{+4J+GttDQ$JB>2be!IF}*No9ajBY(fTJ5`^ zU4)rig>`Miqf?r@OdlmZ%j>O-?9UdEl+*>D_Jybh-_ z^9H>N%pb1K7O_zs&}Wz1Qxb9s=7?O|QD}#VFk9)5J)xncMLN0+)AR(1cM1lc!R?u+ zl$ReQ|NJT_STaIHY&wns7eyOOpU5hA`pV`ouMLdzhMk9|m2`an0VJpHAl?vm=7AFgPM} z?2T#q*G7hny0fqLc(xb0rz4q71<8U?EJo$^C{|=gatWx0G;V;23rTGc1gu8wQe?8# zA$t(9TlvJEG6`u!XYDuzQ_|Cd#K5h~Z#oT!EpeS@J-!!`{<|3%ooHF-dqg02->1h1 z2u`vIRW#~8SEcGIwx6u0VuOiVy->&}lNW$mZUvxk)akgz-vzT8`)I-__6GGv_qEUH0Bbm6v<$*n_^p`(_ZZcY3gh*cP@gZt*>v% z7H)3%KXi36VU~HVst`H4f39m&;ehUXMwyLII_?oAY4QD(#X^;N*nt_WVXMn*;#C9!Em z+&Y4^$xF(fx4K)2?NtGkV+OnMw~w7|q_%B)em!&%*PN`0l44vxxLtL;emNsUHiRLt zaGfBy<+XrnJ9ELS_R*jr@{(1LKq3O}jldq{H+fVxOTZSrTAvAii1lc-2jD_OrV$dJe5>b|R2Zji zZHJIEka7P_{QTmDZFt~k25FjNi}J2Vg(&LL64ojg3n$oKK5So~%WzEMSjB~wSbuqF zSciv)Q{Zg~nS_CqJC5~4PTDR6J%@dC4`Lk#C)#`1ej*5_Sm%FWvyL-}^Cj^@!d4(z zBFH$>+q&;;ES0tQ`@phjVD=itX)|C##GONgZX!@sW!x7?n7AF{o_z2qCjS*7I1PS( z(MT57SyFNHn957tV)*o-&f&uWap-{~v*%Ej)&&X*zj5P+G8;qa;b;d2=K}WeLW`mS zTZjUjrc)gwr%w#ok~JbG&?jsMobo_FHKYKumOvXrKxu}+JH39 zu)PyOn~*RMZ}{8?Mwt%~(+5F4h(Gkj6-UIyWwkjLYm;b2+MNk|j4zXHsn?@8-=$ZB zu(m~mDR&ydjuN{|a#R9U?GvO>2<;*Mu#)FD z>w>QZIISppJKmc6Yv}VKx%%bu3IecBfxUyQhaXN9`H{SaJe*ZFT|~RHV(SL{DWRPU z^Trg4D_ThctJ_dI5`LXwoZV?XPKlxs$%Q+hTWjLu?%Z*(@E%M?Sr$idpM7eq?WdL; z0V)f}>Iu^DidT4kiG!nQ7JkdO!F^14DnMnV)YPgIYpe>1K{TyucD%p;NIdST@aq!L zP}NovCRR~uloMHS09%c|SX<@w@wKN*(dHPqi+lBo>f%pDmEk8XoL_7T+|`UmPPFN4 z4j@@O*KIU5%IeeJ;?yTGk(|@x{a4DYb5%q6a3%1Lx%{jtJ?0rxP&`fInhggLK1!5(0vhNJ79kymZ~or z*%G5%#LvojOS~!ooCYlq`PlMdVn_?TM_4>0{M@(Ei>yctdTB}U1n)o6Rp=B?NrOao zpr6pKYFmBe(`$=|@`ZqKhFbH7tt%0mNOFO|U5`i{9uZ+UFotx*0Ly%pRxw}>MV+jf z`?`sQ6=r%ty#t}VFsaP)rgAai^00)sO&pP<5j5w|994aa*4No_mZy2v{*LMaGluI5tK>1le&JW!`QRh zx?t`{Ndp?CB@H&DqD*R02gs{q<*>5#(Wz^E{xjCWTci$?(;h)j+OF;=Jc&bzy3lCl zydq}Y;-CizW0++&8#KPkN1HX2UAX-QrLZlQ?G5WUdFmVzM2b3i<#1FTE;E~nCIjQ8 z^&+_$z6QHV@oX#q^s!9S+!UUWGU|kumR2|$(XhOmlTFQ4U+_AFJ6u*2WgBh_;a$_B?WCX=t}26YwD~ z1mZXe+tR+X#EUR{fJ4bMGc$=bE?`*((JI(vdO9|lutInQh(Q{x;|t=TatRFVTcVjKfXFZ_Z6SMiwxefI4-W1+u&4 z)0BlWR9dN8Z?BetwjjBfO14G^&TPtHII^^}7NEYzvX#~ZmJuD26{|NVkRk_Gcq0I< z5iJLR;RN3_xtd=oy_To8tPR{%ZC%(#QhRl_<;l1p*kum}K>e74IQ!&TiLz4okXjp) z;b+8VZZoA~)o@OLnTb1J+B1siz_(}D=G8-}yhc#+&$UfCMWQ-0^fotDPhVe+7z%B~(V0A3M1B8@h4XA{9Mj%VvLB;kSfCIBPSIsA15~yV zU6}fJSL}4#d_m7^()eI7xV)hd6CpUM_s#eavXKfu;%w)~I5Lm~S<)sc!T7->|f z1gM>a^aFb0EeHYOF$Az3Vc@9?G4x+Ac{(WL;wb8bVMo&12*oKR`szqo!`%kTuCjdj z>u6I{kOsbSsPkztMOabMJXCS& zHp)|rNji8mV(38p3rD)>mN*)ku)_RX_Q2*sA2iOzx?xFlJ6aT^l#sLnOlx z%&%WxP0#`eT4R(D$VKPaFwV?;c}GSRF7|uE+4*Gd+X;x769BMs#bsI&T3?LeYib0h zj~wT}ZP>9W?N1ys38S##XRSJ7u!@3TY-9658tQWn5I`r95@%r}cvtfN2_FykK)yf5 zb>;PV92lKcwjZI9=a69#u!16N8iD|+9syN~1YTytk#yO{7ZRZp>lT5lMlgT?(dZ{f z5c{Ywz8j;^F7Q_&<)626oDcm+yN2=qdus=av*@CnRByb;V&j$iA)SL+Dpr^N8<_4t AeEX%GV>6+xss6a*0!2|?)&5s(Iv?p6_y1}PN~X_4+ykXE{3iGY#| zkW%8i=VI@D_Bs1I|3Aj}j={i}xz?Od+;QF4eLeHpJ!Ls^5;_tT3Pmn|=cXzOg_n*( z;pPz$!e3m%uQFu$`N4TFoo$Iig;dJllu>s}S)O!v z({~;{THon<*!ys)hx&5a@)zC9ua|?*noTR<=#gmNGUFo9y>UuC7$nQnx5EbON zYLSqa0{o8lFwm?fy;Jc}u>GT1=VeY#7V2l2H4NB~Y6YBsU04{wQ!x?!=t;}C*a`1l z{c@Xgb8~aQcx93A{wdp;@{BB~nDPi5ru>7^QCG(gQc-twbbOT;1(!1qv&zfkH#RmR zY|1VKN_iEI4whF|jvNnU^0$1bz?j1w~a?m}nAu^Ax_!^;@?H1_uX!@!rCHU0ND@KjXH$p)L%3iz}p8<-;Yn z2wH)FfB@VE43{lTq_{Zh43{SA!{@VlnN=+Cu^f0PebgP+s{spN-^lhGMBMc8kwS54 z<{N){dU7>@3x?y<5+X}qJWtyT_Yu}&@qXC7^v(1otmi;FtyE2;fo+_;*ELwh9BILS zEM_WN?Vl6!WgGElK|=BeSbnJ_%$EVni)DEmd#_vc*V^NFU~ z)lV$j1WC_w8~0_(mzI^SZ*=krdH;DrLQ1;V<64i`*v_7=WNpp0GvP#O+WNv|Z)1*I zzw%`qmzGO)k#%8J$T=bNC$G3TIT!0rup>)oB;d9EF*w{o=Zu$^tc4j%N`sH~8o^f) zJx-k4;dGyuM@z2!d>!~cKmXw_291i1k9Xl#Gc@d3Z)5I#dn*j}zOZn<$oGItMRFZK zl;JTEYTT}d=kH3z^wk{=b@hQ_Cd3T?h`ZKI-jqSzZ7(!fJmqk(=p4u%bDQ=QIEtJv|mRkDA?Z(uj z&X1(r>6fspnmb#6bg-s&|9%sb=W^3svB}b@0$Y^pw6|D)w#u4tLUOXAlao`zY93f| zbTqYCx|v&lS^J(&+9UPXX0>i- zDx#X6j&9^Z7?0cUZ*rqBKz~ePZ*8sQgJRm1m6g)cQim>qHcXz;!E>-~6qCo#3+7#k zGHPn4;G<&5dSs{jT&-EF_iXbHXS()^QhNWxtf=+=;ZDQC>p+S{f4Rc4f;DHvt+P5? z1qa6j?K&6mC?y-al7y-jbMv%IAD#4EEagPF#GJ9w9Pdf9oRE# zrc_Z#Ec2h`Gwk~QIm{g$Op_??$Rfw={Um$AVyf1i+psnq&NLN%xIrQ%fR`JMY^|3TXiTo{m`#^7#XXj?YXkKlCrY61VfK! z#HTJj>AID1|B_N-qf_s-il*_w;o%Y(tc!5h-f+WVS(BNbuI~GSf|Vzur=#vvE?H~w z{#^()uoa6vcd_w{cBQ`75Y6Z(OqJcr$+zDw-@SX6UjNeBFdUmFNd_*X!66|b-CVa} zLHb@t-`A7M%gw#RO8tW2%E$iwFHslW54MJ3AsAgJqH0#F!zNv)Q(wKJv#UfCQb=O1 zA8r--7HuX%>ByNt5XAm*Y)TyW=BNPPTRPEJmg zcAbX^Y?=_b1_KxYW2d^77FfEBVA2nW*WX`R7n+1Jx)?8{x|+{*#HDyF=A=0FTv@LF zyL$S}nf32se2-y7PT)nZ)kW3S)e(PnGhV+o&iL!42veYw6y)@BX~Q|e&t!7a!u>isq;$fZDaC$!Bakh zOQaI~v0qtYeI}6d@y|D*5D(3F=0$3FnY}PgUOP3@Y+PJn;J&--vq@t1O!f^2qPDiS zu?Y!6rmbZ4>*4lKs*AXaYNO%jWB)`#!@ika%}V){rysJ+=U)=dF!S7t^D_|`;i}jX zHQsvnf+W2qg#6;!uwt*h4du|=VXSJhYwx?S z7>uO9`jQUGsO3jh!$u>-(?rBcD%R?bqaRxL0c9<2E~O}Q_8`}*~3 zvFFc$9JM4N3+1+rVSOiZ-~Cx;*ala`^DR=H&q8$b-TO|{kXRUs1q#V*Cxi4PhbxM{ zzpA3w4DH;aIafB$)A*x3CMQ4tM+a9?=G(Uc*LEgiV1M#6h`;q^DL%U`-O-h&Rb<}! z;+(p=`aMm}R;jh95c_7-MYRWid!Y9GkmGpl3qxLbvG05h_G^O zOjkic;eAeyPPUNse+!)lKiG3|dWW+R+_IyM=Vdy_&bKr92K~nSS81_QS^10c6-`*% zXu=wk;xq%^Qx7NXDv<&rLar*v3 z$LwHf^lV3?8={zSg^t@8g!Ha*UL+bPTA?HJYdOnt77@ zpQFP*-=hjPoi))K&%NpPV!^QLz&dQ&OUEXF~=InH7+A2C%{-0sqjn7%LQ#l zK9EaAta&X+K;(%oFE1w_`g{0yEhZ+W1?xFWV!vs9*29dRY@UOKR`p%iB~{8J3bfEU zT`z*KY{cb8!D_d+Oe%xolf-kK#O2~L86iqM)EHS4qWV>*HJB+Mqy51c2X*Gm8GXmk zXBeEi1h@^VOZy-HKR^zNbhn>Klmfd;oqzuPdA0u9&uL&c;;$1duH=XdLA?d5Ok$ot5IW=p>o)nbQN-5RR35~ zBBs3tak<+vJGE6!<=W@XUg=&4Ov5_n-Mz-PunqLyOkkV?4cERAIceTdM2$aeGBa~9 z_NmTWycW_sRsMsqa>?9nC7qd|J1!1$kY_SHiB2hMDL3_)D;&1T!lxCnI$dJXI}f@2 z?Z=PN=$24dZY^Eiv0`MZZOp!sVP|=8UsqSZYJhwox&G-C!{aX(P&N?pmMf-xmL`nu z-@o7H;i1NTcIk;U;GX>a{H>{-{k_dm+lt9bNMz!(FNDMwQk)X6I9)oy$6?LSmD0W&W;2YaX^M*+SXL~+}a=f zn2&bbZ0fd#y3(b6>(EXs>HdhX)~63QALWIhMy{Qhk8O%L_*u#^iMFel9Gn;^>!fTT zEoz{fxOPwk|oRzCH~QPeQS2o8Xh5U&(oQ=9BF=c4s49@iW~dl_i-L z(4ow$rCfHt_W?VGi%osWN-*ruu}Hlo$+R}|wnj>Z3Iq8$ZrI2-H6?`sC3Wyir33)l z<6#3=XI&TAOa`|Z|K-wtWqSQ-qr(jvI(qsfeiK|ILN@Gv=6m$$(a$6V&u03tpKRR9 zn+aLucVkMUoTeAW1hQAomHh3w>asZ;rPTbKQli&;i~GGnue|r+PVZ8WcK`y8+$1Tk z%gW;E*%T8*O8vR~?a@NR6{RG>U~TT(o}LmsJUoaa04O`*m!HqQTL0G$U|tjKN$Zs7 zTK9!ehUQ<{={>78Yg3lj0B>E<(s#U-T{V4lxWn+|t4y-Kr^+iS@2k>#?J90RqU7Ab z&D*;Tc2W;!nU4}B)@oyhhBTo{`qJ4s8$inYv+@wk-fSpeXaA?1Gz(LtnwD1E(c#+B zMh5f2ql}$vzK34Q(g#lfgM#7E1IkA?onZd4T++K*I`JyKY0S`bMWlSxJb6{x0TP>8 zY%8mmDVt>6Sosr=lU`Z0&rRtYCafak|7WMz9|O+agn?J| zKi-`0MlROwZ*)rj`I%9>H=hjf(Flh3BSyu1^rIQT#|9H9Ll8q=1^_wXy)&6QH)`-0#KZ(qOmLp)*0V7^PJ_aQt? zaY)iyu)$8c?$D+&IHp%$a^`MRPlrg z2EW3~Wx6i%iEY)WQQ%I3-R>VWvqaBY+m1`ayGRah?uW}G#f-Da?m*#yr0&AH?XieG zs8OA{D`q1kRAEMFd+z5J(OyP zVAYbZQ*yO6l*!}r^xvtSAy1`;u4NF?AvX0$ZYoh+`9TGXRfLojAK(Elx`mmUnHk$) z?+H*B#UJcUrvt)8-P70a;>b>E4j>>!LID!4vr}Dk<*px2uK5gqTNjmyO2miBlgOC2_Z4iX-`=NlXAUb!TEf@vhs8U$8!3@;sirT?`p@= z1NvO;vo|x)yyeU^;mvI>Wy>2LH5aI#QH9^Ov>MwL$h3AjnizRpv;2`13L1?0pwNR) zM7LkieiP{F=y;Gz$%B5Rm_=D9T=X`gv9K>_VYrmKX3=uqVh;UW-NC5m0{on(xtH#)?swaob^?UI>9zKcW?fMn7*uvBl{+ z>8w?h5`tjyq`9g~mH8xexFalt3Gq!A_Ub(P!d}eTRn)BnjDApC+>fv}G_gLMbg#ht zG3@K>y8)B~B)9#)(?|3Bo4wf(xlAKhylSnSokig9NL|F9?iG!c%8V`}{;950Nl-jL z-bIHxt<~*pK!%Eeyq$1@+3V#}QA65nq;!41$KvAR)m8g$;7ZDenWowI;O0F$^*h(V z7FA&OCoF6b9}bRteH_|GT@Ue}4aBa2~kNxQRqovB@W=ATkobO0*K%OF#TV z^-ejpV^GWJu3*{VRqAIBJy#Y!cx+k0uaxkqBP8vaB^V5i6YqRFrGnJI`>U=EONn}$ zLfU^H_umz>U2KVmH6Xl0=%6OzWpRepulGkVqpd45KBKMJC2D-@p1%U(gF{2As;W2c z-X;J1`EzQawVmBOKmap{oEQ1{>^Il)%g&rVAm1Kk{^%s?>rEHyv5%DFnN0oEjT&6& z$Z+)TryJr8_zEX5IFa<7>elp)Dl&u}?fwUp%C2V+DC^O*h_A0o5T6q2NIFD3{rkf=d;iqKMS zJX`?FLaB;f8=_*G9VL9x9x|H6U%1ngHd1s<16rOUz?sk>tr!TJb*$pAA!{AcdJ=fd zy_FJ=;U?CQFe#_TAN<4xuMSBn6z#~CLq=Kso|ia5qiM673g_L~rWe$gKV+=1A5&hc zG~Cvqq+I9~(J$eR!-_MdEMLlt?qx56w7{$qNd zV#4_?*o05qOCv~#YqD5=RMS2oR25m^RrhXq?IQIvo8OPO2DLV8rVk<@tDoi39r#s# zw03kL4KeXH_#p~g&94A38wKoI3AXdd&W>=VKmAbHXs<(SNUsgBi_l{yr<~l}G2;TL z$H~qK2AQ;keCzA`*t3-(7iqX=TDe-iRQ>li!a^Fj_!_uT^vxQ{)q{9TR4WB0@qs02n5ZSM_3@TbNz zl+Opc)gsEa(7Bc@GyV?zL0bUrczH)^oh|IwntXjWnNUZ24LfeTnd~|T>0^df4M<+n zhbryqO_o$-8YqZ084%p^3ag)QZ@av^o7@TryG9w+b<9XY3GUQP%+7DpjE|s39ETwt zi*Ex1pCAMe{1QdTF@(}vNb_91ZJjFZD}_KUxN-4@H!E2owN(vI#!6n2DF5^(%}bINqt+#KkVZnUPl_Tvn zK^d0wckm?-3OC>0XX1BnERw)FkKU{adLKcbkgQ3B1hROt%mix5T)*Ryuq965^R)}4v z+}Ty64}^-MBJgH2DDq+~rlc(X& zV9_oxhTQ-^Mjew2%Ezoyx1dN%?^lsdwfEUfPMtqm@I4IO+gxxZLHaJL_IC&|vvieQ zgC~ABN>ea(B# z^Crtl^WL<&Z{umeBYt`y0MvSXL=|G2K)k2$dH+%bO6_rIU zfr5Us#ep12u>O4AvRu=>>gub&US2h@vi8hY`v8%FFf<-jU$v1j<9_3UzQm`Q(PXl8 zZ{xgPiiKSx#^!a(0_VNK3pQIxXv-XSBHW&9JNgvjGpPFCS$xi2G^bA^<-5~TLB$4C z9d?*);9a~9mW&quEEQ$HdGp2;TPtlX4yHJ^p8GL32g8$w;t}xskEa(Sv=^!`Mu-RO z?|Wg%{3?WL1k&c;qYLN9KOA3Xwxm$XW5#_jc2Xgqw*(JKz|Z{IIjJETr@ss$h&+$NmbVVSs1jdNT3)5L`+A(^=`e?1QCbB-n#M|^`i3Nb=d){an?7TEYnF{9 zxZ3wf#fF1sy&sz>pL@|RZ*LG7g(b=Q{07c8PH12pa^>bTaT9e&T9jlg(fv~dfwk;0 zf-j2XAQ-D?CIofn&DcY_ufK;cr11LdrgvsI+UerQl9rf#VNucO^fd}}2U&P{c%xhq z(oS^VHj&WRCO)zZoFMY5!&m0|m|T3r_!3_# z^^{BLB!}3fhNW#tFYoiu4}5+h3oWR`;^#hy*{pW(dgN32*@-kpxpiF*+axrC#3ZKG zeCjTGw^ZD#4FCD0d2G}%V)~Psi?6SKPL#HTuwEb6;%w-oZCuhnF2z;op#v0l`Ek~X zy0`;PNiUdVx*i=Pw}aNrZU;-YffM|kge4s_AG|Oxo+kBTH!jC$x51;*1+XaNM>M^x zv8%+`HvRFp&hZe<4AE7Vh%zw0JWr^4jvrSrhFfWOX21FZg9^7_QWpP2so?9~ zS)^YbE4n2Qb=ct$*~uBwTl^XLBrA_3W{zi_hOghhH&rh^yyliLjxc z$6*rQIe6(x+VH9~DB2q=>K)I!&fE-_H#f+)ECfB<$>yNxI5MdjcuY&H7P)1U^J?RC zQd31Xel~Nkx$y9upw#!HmT zmyl6eV-<-p3XkK5U1K4dDW|K<5M@|P2U{>sUj99l)n$xbkl3k+p35?tsCs6L&A@HUx zHE&K(g6??|umz{*7p5c17xDh_HMfRqP>^LZZm>9*aHO0LwV@}?iNr2*CeCU)5BiFv z7Q4x#)8=taQOGjqQeu}m6UWBnqJA;8F7AjZDKB5(!Dhcf9N|T*sSBeBaQ5>le6-1N zK4{sEd~>l;uIB1DHanhP;x?+#i2O*8IU=ylkQ?E;sO(PQ#9vq8<-Pt}A<~Eo9J>odx%Ms0N zW|%76r{^T&E(mTs`B*XWm=x;2OMHBh>$7c}&7^!!ez_o2Fw{RO4$Z`Mdase7B$H+t z;FJ*B5>Ys8~OI&whKaY9-82@wx`dUi^u_`&Df_7xA?sm;4z zeU>@FI3O2?C~SW1G&nolXA>s;ph%%FO-8|S$5o@RT9DW4aOQ-xF|;?7(xp<74l#hd zaDdeaE+(J46m`d8KFMMO8s*6XrmeqC%HUS29;|qY6?FAAbkvP9dkzQpM%ctA8U@iBHrm*Z0eNp8wgfwE#WL68F*exP1w%bi}pok%+2? zg2dM~HP0d(S^A%W?5mTP#KaON^j(7>x2qm)s0wxE2UVkPe=^oQ#}Gw%5_~`X%ln3? zA3uJ4=#~2W!2#Wj+@rlYnDokwAKs9T4=|$NX!%sU0ZZw%x-=dMgNbHBsq^bY&*oX2XSrh54byfswYVL1epQ@kpJV=T%4=)kTu$MV_-W8wXqcl%wdIT=V+);_S0-QByf~tUK z$a(+X7jrCe542C|8JYHG5e&Z_Q2)LHNEHs>q26|+eyuP#3jub|zSB+MWi7*v{@z6)0~ z$u?vpQW|jMOJxo5f%T4=eYvupy10OKE!7aWECd{A$kS4jON=pTpza4|6QZzw9*c8U zwD}Cz8#X`Nl9>&`5oyQ>^4Hq1p(^V>Rb!pAgQ*C9V_Fq|Hd%SEysMdN?{U7i>|<)& z(P|0YLc86->EaAN>7zJ|`q8P2*j8W&YgbM{cJc*WC5`|-8rl2YVD@##C}ykp%!-2v zTkSW(D~;}1Cc9n5#V-l@v11tsow9+#*5nrM^9%=h4zEJ02ZSo*4%ir%6Diq`pP(gU ziqdOsR?A_~eYu*OS#N?3M}HcRhMIOXWpPK?*dYvB+g0a9|B1jRuEksJD7)Mc&&B3- zi_K;yH@mphp^uqT$GSBSc_->ZePUlbk~a1>^b*^>2XcDkH;x}ilds%(D-NT;^~vuX z8-E$m#@k9=y+AC zO>_{8n6eG6B}y?O$^L}E#XgVYk=*@lr3mbqhWzEQ=6u0KZNwrhp(NN*z}tivj2T{w^9Dfn<3?(J_>flJP`|<%bvzh0b2wK=%pK zqAM(OI=yNTvX1^+B9auIKvLkig_(PB`FU22V22Eo&}KbHQ;bWNP5|cec)?g>2{Z(I&?WG- zB5Py+jNYfdT%1ubV2X7ix&cl0nePg_7GMv2@r?s4Un1Yt2wECrXJGmDk3D^g5%F4u ztPDOcXYi@-FU{oia@w#uKh!eY>q&NL6KVv>W0PeS7z~XogU*pSK2;wlx^gnLT?5m24P>hP6 zjs6phojcx8gYL{*1?{AukB=bRKft+3o~D|P1>ly~J>H(2&ku@6mA40UcFbId)wDE4 zwxG{%b5nQMcaREIv(T6#I3fV0ntWjL@eOaY_#IWy`P^rYpzIA*>@?1t_Q)#Tw7m?$ z3g?Xgu~Y6$Q8#+`H_NVq!09%aUFJ*d_YMpJn(N~fRmg}s7;-3akkpJTbdX_pb<5hm ztT{ODUXRh6mM#Z~vn$aG=R$ zCOQ2M?0Jy}%psandjeNrW`a6V6Myg96Rk@*T-bH)a8n$g*;jBYVkE)BG1^dWdQ$m= z5bX5MtuS}m9@EJzy@GgZy%oPK=2EFPlX={;Yth2u8BUbn{Luoo%P2Q?jFLx1FSt3SsA z2Ag;v7YPo3u3?u;u=%4)Pn+q_xR+hC^ptu#sV|Ld~*q zV+G2zOsT=xAp86v_T=XEUyi#9#K)#Eg~gew={ZV4;lWTdjy7s3pkKWGP*afUkM-kB zsr7UuFmO`eJMACVIWuwt&|;PhHkM`@?sy?pS_ta)=jK@n%I?soO=@()*ri<5pd4;F z$`8Q*U@8hxwr2dBCf$?cD`(nKKKgjD&a-$(WVqa`Y;O?VhfP|UOvBXRsA$S?^ls3N z%GdY`2iqLvy7dQ$wm9RzOd;igcn$4F24o7=t5`Q4FpV#7+Ka;&N}Rqq6QC+CfOR>- z<2~^B^!+SU9$*UGBncG$#IQTD*vQsr{#O=Hzl>VfUtpBK=5*^^b`7U|(kCQ-W!jPc zCs!0L7>V7t$-ZN*NLD;e9VMh_k~q!JRuIZe4hB2+&{-a0<$tT#xh(OpGaqARE^i5r z!=z(-LJG!@qZn=3Y*VneG}zYV(l5o$jlM^R6lp(kpVCRPdE;VjsQRDG&d60#Uar92 zv2U8HG}vkXe&%!k1mptN4|>WFOco{(Tb4eKm4VbaU3nqt!@;w%n~)_&n(d>aMWdMM zVUW5-?^wN@*d)K5j!+3}(NcUZ9uJ1TrGho0f(%X^8jTS0kdOX+35`jgOy#agoB?lCvMr|selY}vT*025n!O#9%@94(^9 zjw^P1GXF`)xH+NN2uoOX-cQeZrf1B->pD{xZ{P-0>}>xC_T&!=aJ)R(_Ui>0n2m^N zVy`DnQ?qUHOKSoOBh10J2ag{4(?ZFfVnBus3zp}xh>84$;y9G7isKigacSb4pE*M3 z?PaDRcE{-+8e*x#tHiPyKrv>JEW=n_v1>^fB`w+*$MxpS2rsrqLy|;hQzK#w!bJEu z6KW@Q-Jy2p~`zm0vDPPTQ*1bEfeR5|JB_?t2?+gb4ZG~g2aVr z#I`K2-@ylLIiUshdOK3DlQyi#cTo8thZ(kYEA;Ynez%AhO@p#%{J`u?vUo@I zCTLTP=ev^hod-3zbxWT@O%n)WnvV}YKi}d-8r}7K-S!)x>RCth1JGwjB>RV$Q0d=} zb717&vr!(qVjYDk`CCDiF{2)515{AL)kj$E=W#P=yX&|jhfY#ADzlVbIpe0*K z_mu*n*=pa_?I}-tsiVE#&D1q7i_|4uUEN2PmcM9+q3vyGsC?Y^zP5QDEYU-Ts?k=& zLC#iuN*bw7M3GXlZ$?v3Pf=dJo!vb5#n{H^*jR@mU)`r5;!{`WK>~g1lF|$`cm94F zKOxfy)fLhu72ll+9Iokt8i<2~<5wj#5_hx@U>0>*B195ng~VxXXPV1o$YEPhTagQeXw*#{@{z>?iXk?Jb?Oc05skX=o{ueDeZ{@ zrs5zi9M%y<%EyU`dOkQ{kwZUa9Z1TFZdiHAjF9x1;txH&vNL@yWi&5*Fll-E`2n|4 zeQp2###|?~cLxaVX~J)xlW^fid9GHmgM_OgNy2s218YjY>;H{KiwzHE-BJSquI+zH zg^;xcB*4jmIi zXi=wN^}z!?D>(Xq+%$mQ;q+5fFmY@3HjT-=o8Y*xw`^^2jXfOcLFK9d7lF~o(mXSr zZ1)oHpOBaD1G!_)zQil|z$=YXB&lf9|3DdW1-IXNIlG=3O-O}ZTDrO^I>O!#{Mei5 zXu(~51ZzeU(qb>YlAJufYBhekaYzTqL5dzAtD^^wVUQ)}Kd3)>kZK}9Mkmg#d zBS~Q3)Ra9XbOkJ-%tN{YpOG>z8Xx;K(Q#=#D0&N(o4;O$?01nA1Rb%N6xcy_V-{}j z4kUyJ zCPYlz04V$m11w%$z)jJ?7kd8Ya{OusD@O@7kySr(N19IdwatLFk7#CovhpZm78%bK&8wG+flNb2M)N<~q7X5VD ztpyDu((6Uoy)Qk7Kh^#SrSFk}KyxwbJCZnLZK>m1||}k@#ZDZA$Kg27nL@aLSChK)*L5m{1JBg&&`UK1^7TErkT_o6ti&h4iV>p z2q#jf#2isMZ#@i$uO{1?T=cRe$X94C*Kt_6m#9tzyanslGrKhx3D0et=yp~)21k5o zbTWX@4w~44)YJe^4uB(OXFzbj zk$$uv`sa^_%cvR6_jNaw!69}lYir1SZ{EEN`TqU;lZ1<~LiapT(?;Z!z;h52@aLVr zJ%Y?vNlR$Z-1ql9kd$!J!e-S9Sxa?U>Us61enf|Lx+x*-*c~h#7GW3WUs;QK8~|%M zI;jD*nr;05V8zyVmE{$Z?LvEvj{Xd%w!%4@T3C&TW@hU-D&E*mLf`jpqyo}p1!^QA zwt0aBnwGJdrEv3ou!!OweK^bm3JOs;@%7#O3{t5u_6j%6X?&$^j7KzRp=S|CqW*oo zXE_?_!-^ott*>#|f;?2J5Z2?> zt5<1;1%Mbm*zhWI=vH$`+tRxON6kK%xc~}f^;Uu{jr94Qp*(w@XcyB$i2$osP{`Dc z27}#rN6)kBS5Y5M0M?Ia8CHD#3H&J5v6@k8p*wllk^$1Pl$SU{&Gw{RNj6A?=d}Lc zD7olTsY!}-kTBV)3_+`Bz)W!rKsGQj;Gdj)c=K?W8=uMRozXw z;$)tXaL`2Q^P&ASP*%2G_VhF3TyGd@lgUdIP1cZd9F7g$B}reBs95-ObH6_WELjE$ z^P%Q~oRB|%=bKZ&%U}`%_BN`!`<+IIeR|GtqXI2G)No;d99{GJ0x9|}HSJ=PZ1g~p z&34cFcIX2C(1LlU60_=D^?4h zg$VDoiysicfBQ>DN8jOm#RDL|5lQzo?YE#4wlTEz&pBg);SOuA2x)wXxR&+g)W#kH zWPz*-@sk5`+ zQ(~otBWgJO@ZESGJ;KKFal1}>``T|pa1bj~<|5I|lx}4yCs~^3kH)PQ0FuJ@o_KaH zz(`v`|I`JF;5In5QFZudslB}&%`Ux13>x@f?Q{@RQPKZ)VvGhCBmYCzmPVq+aA1WZ5Eu!!ZYgA08Cc2ep{; z3zGo%grBI2Z^Rq&R(q@?mUS!O&WCq*Z7H?Otj?I3nK3fj{|)@tF{w9|mEx0gccO)j zd2FDlYA#4wI)zuSiSU@pM|+$Xoy96J8d6}l43d?++S_V`LroQXEFB;D| z!YY%iPuhw91})h)wOnvK-aR?R1q0jRY*9bo^6k6V0IdwKU_cDh4kval^YJZ!5OHoU z!DzL;8Y$RRw}_jZ)DmJ?x1_-pj&^*H+CrIqzEsq2@gR!d5;$&DvQ${J0VpzoO)>tr z@iT~p4NHqO5hkMNpg_xOqob%hI{Z7~dvq`XYP4F=1eaD+?0mBFOeGHmE5AlAuYHd` zt-;CEbKth$NddPficj6Uz&NfP`$3MQLX+v08%pSvYt7Njv z^Vev1u3K;Oqj~aNRO5OpH~{*QS`3pu1Rrn|Dthynh26 zW9!cD@|%(}un;m}xb}@jY6s6ogtj%CUR8;ZeYWs3meuxVWDvwT;SY6_Bx0H^LOJF) z2u_M$7*u*MDN&FGZvc4KO_2_#HF2a5*CQdox~U96Miczfanr4^$7ooV`sqoR{A(pd zE_y0L5KToh$I+Wf7HMeYl+~}kEEE~N=nFVYn2gBMV7GR({b85ZMA*^wS3$uPo|9n> z4CN0NadGC4rFn zn)%w_IJ|DNoGh%Gr-KG%!)hTMU)(BYv&FPnxqakdShzJ)c?06W_?Xh2=mA+sP0)U{ z0XX3Wv!A{P%|(H=vo~Q;LkEc~>j;^0L-EnY;-NH+vEs{7r1xEm;#jv{Lk17yttRq= zQ}Sss%2%Hu$2D)VG(pYVxixGwt+oYGkZFjz@xYnCfsju1#Po6;X*NyM8_`oRmS{WX z%_^_G$B2a>(GbpkgUnLEzHaMm)SV#YxPprBpMK;NF!BS(yK}OaRt1X8WAfryQ!br zndBzjv%`02M1M`33yA;c6iOQ94WGsX7?a>LApf)GyUQfAWCx|6N@$TrM zK;e^Gb}yN10W@Ds!|rbW-9gZbN16aD@E_#P zIq5ly&HHqf4HRQU$;95GgG!I4C7-(MXR~tPAr;Fa`xvgSfj=cKPKs1^J<$I$UsySA`qqWn4hl7&uf3{NY=3Sd|WQ5q{l7yaS&+10$j z`12_e{CpFiU3`;?!Pu}Y-MQD-{6d$|<#gI{R+mamMN{sR$(^}g2xA-QMTV}Bao*1& z*~Cz!w*PET_=~1rw;Uc^I6V$_EPNfQM-*bj^jf~|c{zzk$jy8bSBhwfAGPGU3#N#) zK5`;8%3C6>p}|B&btov6dma|Q2iY(3dr0Z{=ig#l(${~*G}&y}E2LpG&`FIW*+*r! zlCKct^sgB9?e`G7ys8n>(i{j&vkzg;52=9F3(0;(oT^U5hP^!ta(j=a@dDrlC1b2E zsZ;r%g3ol=UV^L{ge>~j>$qna{nUoSP*y6uOR(}Q=7?RM$C|kk>(hEnw0d z6wb>hud1Rzhmf!$CjY7zkajb}H0bh=&_*Y$;8%=ee+jtyq8H|ck% z@MWsQw8sJXjDl*s3J&Ap+_@BksQ95a67t+$R;l%7iF%!%{{m>jBsjs~^L^vrB?If` z!+i2|yYZz>lzHg(cMd5u{<9<|3MXx>v5VC|s1$d<%?l1NZ6HS)EP7HRsvZe<01t?a zwz@Q;we>cLPG|wKcof-NTE2liXsWNLSL1$5F`6*^zoOl$?0<`PmVxk|0Zk1JcuNcI zmB$aW_o6P!ZGd*y)shi8D5+;x#ReRs_S%-yLeJbdycfZIm=j)3poY!ph$egwa=ktUaSL}^NvDerk0qCl|He@R z5R#vab8NT)JJ4PV@=!5526&wcv)Gi|MRIa-5E?Q1At%KKHT4AGB?hSaow_wgsMIz# z(~nZ%-7@fk2%PJZOzcdN*(;8BqW>hk!k@TL+K?aUa{h*!c(Hi#ae zEF-{NXPnR>Eo*mJ?Z1L{Obw+OfnbuE7LDZu(-zdC3A4}pwIw%iI@eD#_1LCbU3y}3 z;k5aHKL z2h(e{gu!P%-xueAd&D+ufs}>bmmF+9=($T}7M)MB)umW+B~Hk169Ok#Rv&Q5tLh@* z_CQyb`s_fnxAWKNt4>?~?r@s&FA{7sUm!%{mf{MIq&{WEU}G)}i?Ia#;(>TB>M;N$ zR&4V+#|U6<`MG(G771JQ#CzHlLwxqrH;R!I*Kr3TqH4psxi#(8^#6uk5j2;nf{>VI zwH+?Ij^z9AaB!QA4CT%QEi6rpLXuhX{vY!e79&bE9bcxjK^oRGC8 z8}_yKzaX9xGPm#cP=^u<`wl8bdrU@)?wW{5j-HO#WP!X4G9+6hiS)fjR&49lzd{%l z0W@GZ97itFyhUuO?=^PAD&tXLXnP`GkuhsQ;>N-;%X7?!i0R1a>43=1(V_<0Ts*nG2zu=l-CM}{{#ijAU&tr9HmKqM1?i zL@n~Q(qN~I2M!)5Q|=LcMmAL58|ky+jU*SC{Pe27!q9fn;Q?&=F;-&5vi)>^|0oR! ziHM|^BRPp6HekIE+cFjQ14xqX?4ZIU3|1W88`wXKN%Hz4h&)>uU|t^z-g*-i;$Ur| z0f|dWVxsR#F$$z+1uD_3NLV_U*zCpx4q`OnsTf$B7?L-i5NqI&g`k#`?oo5bEC4q_gviT8vxItKG+5tU;z`eG>TMyw3TCAR4R zS5sdP!En>6lOPjNj%@jd?+sYPqX*-zC~R1MKlv zP~+s#9e$;aa3WGk63ilMJ)ZOfnDio!vwWfv-p;TLy@}A9-uaeuj}98RrRR!c@V(QD*6v{6Sb)fBiiOU);VUaa))&MQ89+?J-EsfT_Za@o_v8_= zpXEL4|1I;$FP?;N2de(BnJ?476>jj=z5X1W&7D$!E9|39^TbyQXB*8V0WL=aGrl-eSS zgp$%F2#AD;gc1VMAxKC|BPA_LsDOaTCZtGu5T{5=N!*H_kQCW<2Qbc zasD`NSZnR|zVDpRtmnbI2A}mdAABYETDWWtc;r7l-`sPesNmrjZlCzUCZJcT>(W!P z&QjMb&_Hmg2{+{hI5Rbc{ps-9NJAb#6?hLWqOh8#y!~wt)tHVgodA1r&T8j8tC@sm z8=m-Ef9R*W?|};?%X3$9Ly@FMn8@&Hjgrs?~DMC7TV zUnWz}oN#zg2zDDWoDtUv|H&DV_JapUHVT4MHVj#IGHAq*h2UqmWc{@%#g=>Ut!3d` zPYX~MgL{BciUwmBt$Tz$>(W<1FY5oP;r05;T14FvPP334=?2mKFq+d9A#`k zY$-1zfWy_G-_ry{PC24~AW!I`fE}LM_4hOZ9czO@YBI9_eZ2Ly0jvB2dNLhC&q!WV z*oLbm{^$!Ce%sS8&JawJd?9M-x;%LBT700%MlIaz`0$8JNj9EF8idNx#3m=}B91(< z0PrVIQvfE9Ux&2jT{9#`2hE5kWj_r6VitT!`uO1Z@=n3STgCpBClCV@a>Y?w5wfXj zc;I2CWBk?){Y4|6(M^eQVcmfQNMe>xdYr;bLjcdW}$=#c5fbI2$w#2MVzyi=O>%fnL(ubxTOm+q05`+auflRQzwY{g$B8WCguamf&cQfy zrwVvTVV=VlIA|`Kw`Yi9G-yiTX!qXaF2znV^QYp@Ls`>f#YIb%-m!!dTR{-BBq{Ds z&by&7sB%DR1mu=DPp+{-q&f)`*CuPTJrH>!%ve*ef=l2({-0tV~44;IPi0Lz23@Y4_)EH3?c+5#*>2xS5EA$`9HJuC=85{w$$4j)j4 zQZR5B(T_m{e_E%wUGEIe#vK+wPfbWt#3%rgB6m=m)P>=QQP@feA^i8QoeHS6h&qb@ zD@Jz=&sLsZ(6vPJHHZGbjwA6bMeUQ2O<%;?c?wJwnIJJalmo+>5MHQx!1iE-PTi`F z)9`)IBaR(4#_frA%Y(jWIF3m%7xlPco?rF?r6KT@CjN-y(i= z*?o1Y#CWj@3g+ymevtU=Qy+Dmpca9}X?MMJ@BiL`qx2Z4l?uc5Js02OBb-UVYuKG< z_KYn$5UZ;JT`5eZ zHyVbEcDDMOLK0mjg~9!XE)es=(f(3(OALqE(q5u`U5K=&VVINMO)7rw5XGh6x1 zo(_N80qZq*`a7}&ykM$vB!0`eI<`DIcg5grpQSW%CfdL#tr{Yc1d=`acQgZu)sY+1 z>HkDCPDSvymqPSZ(>(5J3HPfLdzv0=xa^yLEnW@R@W7&^9)qslt)SoSsU*0Wv1P^| z2Oj9NzQ6LhI!HCBh{F2w!AIpkd>b52tD-I=cmcf%({AUakNj6K(}v7OQvV&yz@C@g z!T^?06Cdx=Jm;hSJC!Mn{FwXLKSww>-ND*L(pe5o+PS;zpldEIh`Ycmcr+I@UX8G+o4HBx|`Y?1}%xD zCs&$B1W!LYfLS_5>4m)T%`&^F#L~pndPu}EkA`?9BsP8s|CMxw1%;%ni`HL-r1)=p z4{crONWKJIIJ`Mf(6lT5mUM-_qxdT$lr0seE;Gnj@fP@k=_8&NN5f~_*x}io!9mLV zsoX^zBpR7`$;AIh!KnYhi_p}=TRE;75ACqE_KvKNsJNk+WGZlAi{$;4&Fp_qxvn)s zpO>wQm=P@`-XhWP${ZAb4>tA6!%fG_-1F(;_f-q(aH|vD0E3t_BaYDwUbh;?O>vEhjqDV|Cza;|? zhx4~kqI6_a==eb(5?LUBEEz$bJ9%Ut>ck9yNUWYy@SoUnV>)>N@St59{+uIN2U1Nr zMgC*?NDV3dWUa~&2V7gNc+)`}d(eWY_yH#Xq0gZ;d48}QAIDP<7Sl|E+s#}h>pxdK zAie!~h%b}J7*Zv06 zTz^(P%tb1WttTRMp{wVs?JR0^I!Od>)-ll3-6t72cLUl^G!NGbkY^@V7AN6~1pqu# z7E9I#DzFJk4(lCY8(~C*#`bUZ4tsFu;cQ-X2V1Ot5C6{&bs93a=!5W@}iY8W@GTea08ehW{~dye)in?JO( zbs;bdZ4CLKEx+F@9*>cVhty%k1FmlBuWVfL7m^uHMJgVY2Ne%kPUu;W$@?pk(0lt^ z4h@oTcQ@2Onkm$Z7D7uLl0)kiKMsN8qr)5;oGtpg^h+@7@>dScQt%yH|7iRvB^#(= z1IB1s%nVul&sgRlPu4a)rvXFkNq`^E2i6e6QJ6SLK1AXjt4nXYYm9B3P1w>Ck9*g5 z@@Ik?Zg~$9gUHj^9!s9S@h4J%UE*$UU`IxGSn+@rk+|gcMj8Eg=MSj447GeI(YvS`+D_DK)-qR+FD8_$6tAv|<3#e9PsZrTSa1_H5LhiNo$b5IxL zZ~7~ghY%=QifTFw8Rb-ayd*KO{CmBT;*>nQ+Xxq`3yh3PDx=>ERyDcKa*(C zrUHw>X+|{sNT}o;rFqmy|NLbLs_Tdjt%?~GKYKHN%VWowyr;eb6PhOv!g1ItRAPma z(7jeMotsH2A%F6EXh7UZl^v`y+6tS+xAXTD_KF|4k3-J~ZOO1$DsjG>e72tS1!Vju zgxKI)|B*qHR}-KXyYhPi&Eh(|YqG-xHlRwFSMY(x(f^V`gRbw>KS2#<~69 z9bc-L{s!F(Fbr1yK*@1kM^FHqLRHkJBLsyLr4I|^u z$n^`puv!NRG(aMs7B3@{`$^gxX7oq`?eAfPyaG?1iFoYq2{c-WzEX}day=W|Le|8L z7=B^(&!Z<>j|4b*qzZ?teXfp5j7Oc>%d@~&hd1DZeop`5?S%gv)I-JCc;kx%5$(u! zmcfW@dIG^oBuC z#J{rJU@@@jOz`A!!9R!Nw-i#y4et~ClpF+<^3!s&0na4&TH7Ap>RZhu9$qb~$~>TS z1Fym3qwX%X*xx>Ze6w>m;MqUra?UICb^P`Zx`>k#XqRTg4!M@-cm**(TPNUdL05*` z@)!3Rj=!+@`0jJ=;NCGT?+gp{Tai1Nn$8KAm+z%ZGCzYuuZjxz z5inqrMLu#d$#tUa1L9iM_Tg2iB1M<{=NlW~8 z37-DXT^4P3ydmqGbI93z3$jeRqO{05>KFwd}oh&PHFBZe3R7XQ@D#e z-);AJHFUQPONM7{>2P5@EAmpOL?d1VBUOL$+V6!^n6<{zU)3qxdH6A^{U(|qkd*0# zvw8^2yr6IX98ZcYw8Zp8It^ohe-a`oq(;RAx;wmWIS z#Jcx{7>U*FnkGS!qZUFwWQrV!6_HLn+B&!rEfHZL&&>f~IxDM(#Rn+g( z@0(1Y?b;%42UOd8Kl}J^V^AWgajbxR35;%l||uT!96ya_LFg+aK9@NxNIsUMMv# z%-7n@h~#rD>FDRq55-cHX?pI@%{W~+)0(hjx>Ts4%6&e3?mBhHMUR#Ah3~q_n(Lg* zPXu@aE~ZlebgI-)N7{7JePOvvBFzEsXolSGn*rS&W~% z8(D-q%YUu2n`yTmZDp&!n>i8nt@HYYv$fstif1&N@T+xX>+qWyL@|uFozUc+1-oW- zfvKI_K(Wy7`AvZ|l)SM~K88#}(Ro{0Y5Qcv+uYZ{GJSQoe04_bI4)_;)0E(#AHGc* zRsC1z+W0>`mb88^SJ>Vqh3ZQvqu2T{vYYL)CT*^pqjh7dSD6bnk=%H;oSQD&BP(Y5 zj1{hymWpOuo=WOH3S9Z3jeb)*SxRh`@(l0kxLcTagM579y(|G0ZEU0}+aAJML)RsE z6}TU0Hn^l@X-K_FGzf@^Dz>6V5$AB2tP)YsXNAfL<#=|lsUG$BWjB4RkiR^KmPAYC ze$b%j3cA{Hl)0HB`gAVErm$a5Q>@b)&DHV9>wK8q&zu8zM+2@f5}0NZXSfI_C|kdy zk1*XR&bvBc$4u;Sa<1~o5m!qbiJAP4w|jN840ErGU;DS?u+b!?vaQ~4?Zk~)NELf4 z7w(rZYuiIXA@k;zTJ8$l(eGb! zMsEm9N_uSUU@{owqd$U#+bt`zZ595kx%qux>{?* zrFbalMQStwY35tTvhh3l#phEN?YAA?zH!_$#BDT^3$^gzz%rUVbMAC!=SAJh3js!# zr0izHa9M67-xxS1Qa%D*$ajvLC6nB>F96|CtPIm%>3AD-f9OxY;o zyEf;<%65FHIHK1)e{b(tgfrHrp5SZ4N{;pYBqN7rIj)i66bd;O74_b>`<}#wJ?P#P zv{<2NW1?E}b+K(Z#*7 zYtrG<861<7P7+VhcJn{g58&Q_oiMM=ZE#l*pB!H5`W^l63q>tKCv@?F91^ z_kCsAWGtzrp%BRB`s6r)pS7(^0Wv-$PaU%#g^pbJ3 zdT=E=4fo|{LvP8S=Eout;l5x4G#BFK0vC9&WnSS*HsBx8(jv@%7nQjxYi8DXhEb|% zJX1vz7pq>^`B}F`4(WiCGP4{V4W`(fB1m|Fh<$#8dwZ%$|GKsC!-sS9Guc^9_{;_L z9=wu2zb%%!jOo?uy2f{h^+nkClZ{wC*ETKNoMdp9dabK2N&4ujZ-0X0=$YmbWnM2J zvc;9y9vma|_hWF3SdSy$US~Io3xA=%Oh(^gwOWBs*y|JCS*p=oT1xm?mftCaeUR&# zyX>qKx0_7K;&e>?;KnJfx?r}%wN{rb>ph&HADxRu7P|K$7WcJEu~LdG6teki^NWeD z7Rbt5mh!z{j2Ky7U6mQ~2-Z~PXKC4(KF5JrecGA`I-(omblNYENzTa+*vd( zc8lQslM!?P$?>x~I)R+-ai)&Z%klV5{f?V0x~WUM9}BCb-F~^rFz4<{GPt*F4tG$k z#;8X6-S=jauJa?8XJ+s~)pR$lu|(2WakHOxj_EC)?&`I0Yi{A(SqPJp{q&izt9&*Y zD=Svw$#bF)X(N{;C5((IaIu7Mm#}LYkE{nz?5|Va3^o@7b)rd@)&0cqM%H3MDbk?D z5pnXj8=xc=dqsuHLyytx>K%G?l;!?}azwS2e953V=S1CE4XJNSGQK(G?JiCtV#ynn zJ)}XoI}?5jzoH0>`B^RJsB`lw-=tR>!JFRHNQMb zTxKMHIo)w@(4~a6;*v1?st76wsH;On?TP#uNUJcnIJFyTovMbGw)4g&{Z3PXl8K%J?*^9Yde)wj zkv=L*Q>t+85PrL9-F@!zk=(iaEL8T>u_#`T?k_9v_I6wCEI8U6L^VR!%voL4b554U(7lwePT2WE4q2y3{OV=5Vz#m@c zUs(gzIyyQJfnfb9w}Di{ZC)DVT3Oh|y&XO=3bXE~m* zd;oiVw?)8tbQ34Tn!4&Mr+ivz3|-mei2}Eg;LOIWlLPuJ5w=~gysIOD1o85b z)xRR$pUNSP*y=5GJaToePsW@pgwI~4v$vMv(Jb93rEGx+0;lpo8AkE_6dLVkOr@RY zJKtC&mhz36p1i9y!brdv-%Ib@Oy|38>kz%1=a8*zBzgfqKUYdZH*xdWm*ic|MwZnRROPSMbm&k2&j$eD>Bg_(HiH~@=8aBKN6Rj63n*a3d z&iNCK4o*Yb7BEd5(8PKo)7|Jsq`XkjM`oG37O_hH<<^*+^}RD;MpuF`T1BFaJ+az7 z&qQl)6q{}CCGPC5?h0&dto3bPw&U7YGfyLJ3L|gUUg=ijd)Jv$eRVuV@0P`B9kdiF zE{KIFZ4rb#*mB=mn&IaTwA7Cezf;<($8u+b;@x%kwjaISliy$8USD!o>8Y_zbikvn znjd4hOy*>##id+*kE$A37?wkg{4 z6ckHokc_`3Sy4a-^0uctuHMm0w8&XV*(Q3dz{t*Cfys=xYKS9gEml7^KCtYbH%xT$AYFaahrAJ4cYb`k~-C2 zC|Ug9t0pT-8$d9d^kQe`!t><|?6(@aHp%%OTov8kS#)Dg5s&$J^vt=sW9Ryu`Pt5S z&lTyk5N~V1bW&VwQ^w+D81ew60z{~Xw;KAEK~$`8urGv$&pax&#rf2ids)IZKQ^Xz zNAyg++mqk;przM)$iMEol(9oMk`7$`gIHI?W?e|N!?WXo`7Nt*WYnT|IK|PalIC8< zJ|UKZ1w$U&_kj%@%b@P1RZ)*WsFBUkS_-yzcYk6D7iqib1b8{0dB00dUOU<2 z5V&)?Av#G2@hS9%Cj!H8I5e+EAl>Xpg#-~~`K>pPQKaKDA`OA`69OCmLqwHj5e6=t zqLd4fM#j)i4b5Ky-6s2s&Il={yw43h6CVsRhCN4F%MlMOtG6Fc#K6FGE^q@cgIRIl zrO+;WaISSqt|17~!`Oj_`N_xZm|@zX4Lo=tQDEb@jq^77*o4rN?~NaXbi|f8jI>7m zu`N}xtvb+`2618?^m-e&KRoy!c^sq_2h7X=??4T?^Zz1Y_>WjGE@mFP4ORpN({|r) zBjZFc%VLq={_IeQu(P8B3)=0IekdTHWbVJo%1TL1HS+0_^HzSGj)Sycf;grf|LPDC;qqfp|yp^Vl|L-0y5;tc{TVVyDu<{4zCV~Zt{jZw?^XrKYYP4KE;Mcw6$$$czlF;l^ykP7_Sbn$Gu^ z&VnQyyu*vY$a-CH1a}aq&`wu|!x+qCaB{JTs^nF4Ef54(3{L#BlX|LEC{@`Gn0t}a zcjNZ$YoJ|+PiKn)<+=seUt(_J$1VWPH$6-(z%)dLT-VHu3{ZIXgm1<{_hf67vRGqR z)fyitrByGbi|+;%74e4&O|yZ|>@45mX%Ugu(diRf$*EkU0oww83_j={+b?#SR8@`H zXQ&yf`-aNTAP61(^g=XIWpz!Ogwx-Q#)?Vl7|@0qs`SSh8YA&%v| z)2lChiHGMRXeFS}oO${7?J=5AyoiVhL8tltIfsxb{tWf=sjpwZDkwO)y}lr4YRU#< zmxOd;Zrzq1r;o1}fv(6~X!sr|cd9lJ`+CAW50@_(hVL&rFY33xl*Y#bd}bU!4)d2m zDp?)1(14ALiyJsPdN)c02nkLCvGlL%Z+DrQo8W#4C`OosQR@7;>hiMHv>vF(ueiHc zM(e7oGDRz-T~$;pH`^rM{fGw_=>@F z+-);|ae0{>RMcQ-M-wJU8ZD8hYjmD?7vDcSOWtyUthI6!h7?gK6BCmt2MrC4PhY=& ze1wC)2<7ZLeePi>iV_eLe}E+nf&t%tjYJT(I;)_lNOt}Dnr5jxm1)pIUfw@Xl!y2sPh%%U;s=Rtdki6bB&U~KrkPexXjk3w24baUV1XJa&f z48x=(j~+cLaalc2M@RQ0CgunO1H-$A4@LT)U%GS&B-Q*QBO`S_w>*^Mpzvv-oa&8F ztFM5ig#pvL0St_NGcWwq(qhAnnt)dWG=gYWrDwkV{kOY&dk2>t6VA4iFBSDs zPRW|Y!bmI&^7M$*04#^ULfTz@1sSIp7)7+~$v*-+##bS&%wP~$Ly!SaXyhlr+%ENo z7I1+~>rPk7+S8Su!BNLjY!Z!T#XOM2C3)7i1M__A%2A3tbczA%T zqM6ybuZh9|NusW`y}dM@ot^NI4Il@qZ)HVXscYjX4ErUB%5CsN)#eY{1aZ&Pw=lQg zgKKnF#337?@Ffil4weSGN5|RjM{u1X?tNfgC#9ssv$3(U>@7glzdn8WQV;F#*5$-U z{je)tR;9q!N8jV7dIHTILu%%Zcs^L3J4*pQ4kD-LMGXuLHb8s{`~j!C%ldpM%ox0? zsGzee?Kh%@C&_f~vm&bJQIz?mM@dT?tSq*PtLL$KhK`;-O;|{~*qT^aSU9But_V8- zP8!_Wx3G}U&CTt}^XI|ezNtKz?QWgz*@5v+;MA0FY6xeL0}7Ue#Ke*Lc@h*d)ls0$ zf!x>23zeFlo`S(#cj-);8hY~dX<$YMTS>PLirKF(Z z;$pdS$GI=mE{=*0PCYMTGQn`^I{9eR|iyP?QFUWBnG=7XOQGae~dR~4m z5}y9o&+qs!^0K(TZZrRb_cJh+FTyHmzjb56g)t3$6O&2G;-SF6j0t_@fmi{%9*Mu^ z-`=jC@{6l#1N&0^+j#Ac3OM@uHa6j(KPvzMJ)e`kb~LD+yuDLSsZ(V6=z6+zvGu^S za{0x@MF|&|EqV?fG#ZzXkkF~%8uKexLgc$#YU=EyG&3_RabCJ>FEA0d3Bq(Xu6-bv z`ckjj1D4FR3o}4(*)gyHmUZgeOPsB>x!|?AzB=gVK`Y<*m=qYeM~i&z2(6Kk5!0b6 zQ3(EMzQ9zFWt5J}_wV1~Uzs@c$|qJ}qu{i>3wuKDg> zsQe~~Z%p+Sb6HwhwPx!Gj89I|X2Tfp0vQFx5qx}nwit!_`ubrY0&3fO@`MyNC=(MC z7AGfXU`PmW-*GU#ppV5RTFZmq%7{na|iM?9rgHxK02UQ7Ru6>MyPr}H}O)Bkw?0ru4-ci&B>;_UA zn#ZR_UG(?&c0V;XzVCTwa?x$WHsw-hwhq3Z)FTkqc&nLLYq?Fyc{}jo$`1nog@O;J zNSRq!km>(1&@{wFO+66W*xI@aDiGg(J^&GuXY$lSi6kttAwcVIX!~nQb+)&VNLh6C zq%SGT3p}rMRtG4*p+Iy-G>BzqxFjVh4R|J}@zU8q{W;0hlgA8R9O~My_7F zRu6BD$yWk^dn-TiNp1Z4uAKL9&~BWd$g0mDmgp{YnAzUHZ)Q7-IWiBA2?N5!VY^Vx@Sd77MN}*VofjEn zx%9$1vTltv3k>o0n81$gF1BG{y`B&VGi%f!3?e;LEHnxw>y^!=-gr^+?Kn89_MpE(-ZxVt9-P}YXqoP!`w1U7?9HZivm^RVZ zX9Nus_Lv^(P94Kgt7>eFX=1gWU;$26)r`#hG6W3PeICp+)eSwRktE-F1)zvl6pbihN0G{g3bJGAI9PmoSw65fvv?P51>ozhm;Rh$OuCb92B1pme zqrR$_;n2rbvBxN&xM|qI2!X)wtJkk(73`gys1dY8Dn218yqYosIj5-8L_zxl{H)^4hgyVtX57csUqy zGS#v8Dl)IYz$qadykinZMriUAC!l4PEd_}g+N{#Lb|3}GYTOTjxNH-kV9RIk^-Crk zL9*@crzcDhweV8UW=6 zkaXk*KMT~{FO(oJv_L>cMm-V(PQ5G>Z~`0!YN(RR{={G~g7!Z`R8cNx_Z!9lk)(xjcDq^xadNLQVUX*41@ zdbAdjP7rO8NW4^mT%Y23E-|swt*x!dz66@1)~1MOqd;a4yQ`p}!f8QQQ%mcmMHhzk zp}U)#zM-KHIIpK6eXA|pAPO#()iDY}R8lT3LXaA%WG!ClC5px*bAhav?SlspF__zJ zcHLk*2`5|D3^qwUKL*BeOZ)9XN497O$HfQyO3y4*PrKX}qATI|nuHhM<1%v@% zAPKw?+r-AnDGQU`fB|jych^%&N~qxeao)yUTwHD-{x&sGE&vqv2(x>4PY<891dYCh z1yQy}Fl5i+Ea5FJEl~mCP;aq~O=MSVD;Y2k)q#P8J^w97PyBY-1486A+1Yt(WPN~b z@Ga{W7x=+ZxOWZE+N65$et_}mCrL?RkdtTT2Nz#GWZGYSZtQjERu9vJ<4-P~n3(n<1fqP9G!N@wGFJJB%=a+-P3dpmLK~(7{oAMqo)0Zm3 zHOI8qubKJyC?K>0`kxH|IH|=L-E!^Bt{DK(SmNU1Xf*m@SCbHoj*TICj%$fR6doQP z2vh(BN1VldY)quRx7m{9BP{y#d>)8h+9x^>` zj_eLw2Ztkz>+e!GH#cGJ8NO9}f~jl80SQv?=g(67x@1brSIEHJidz-l=@R|nrKsPHCqJhz{!TD=|)d(w=|3mOnJn%gsfymL@Arh>{0f*kv%gXWJG3WiI9;kg_}@jWoF)zWRqm` zJ1;$--}n3d|9;2u|NoC8hv#wM*LA(G_jQif`8r?c6|JRln~a2!1Pcp`Oj+rsHWn7{ zODrs$av}ow%PUurhww}4f#TfvDdYwa1vsalA#9{7KJ79gZ)yj$Q}!;`it^Sd0n5Z zl__5z-TALwo9p+h))T`yFUHV%Ptpw4$LL=g8+XvVNo{zuwC-D|QO7rz4i?>4BP9*P z@XyH6(SE8-pUSw_#S>eE|Ncyx?};R398MKyhVri?lTj0tMovZ}p+fqM%#Lr47U$XM z^U6t_%Gysl6q~S+|4M2N;X{7MP!Z^!exfFvr-J<5Dim7}`AvO}^d<7UAl>463jF5m zz{4#;e)DqEYjGimh@k&H1@Y!*LCmsC^PWw4;}6D|*x0YW3)zi}g-_{4tlm3xHE;Dx zoA}H|yxr_%3i$KYwl_mGO0-|{`C$6jYTK=!&l7%4`>#JvN@|lmSZUWYFzpbZaH{SX ztF5o!>Yy`EeEeAcBK3HB8V_>KBMr{8`Gtj%=rF=_boD=czoe7Aq>$Y`FKW|^88LRX z{{G={Z?<%r$i2>uTdC=Kjg$nlTQJvm%hXeVCpFoMlV zkAc@SK?h|zrh0mM_ib&fA1rEL78M;`ls(<%>z|BJ3KtpSlHUy;uREJOIomVbzOKQ+K)LoUO%Ci4cKMu>dU$I z6fPV8boCvwnxP{r?z}(Y$R52t`ZFQ3(TeNHBH{5vt)!&ns*7rx;A%bzVgtn|F&VTo zlanTGv%*|lT-p6;Po8LLX;pqs2wo~K`t?0o^Vi|lK*jbmlaPV}&ZMLy@qMXN+gTR0 z!Evy%;%HiLmOeu#_`R~795*y|GU5!KI~?e;F?WlDs!%hx|6ani&guc#9$I6k&x$mv zu(r>mBO|v|RaI+xwB*tf6R%4$Pyg(S&Iln9zSl|0t(nuy>c4{ihw4JQw6~zAr>9!> zRhkgxWbW4=xt|LA|8Y8h_wM49_sr_i;Ih-OyZQF35$ zpWUuG>g&_;^77vCNr8)g>5RR}K}|=eV`#{*s?CX2Rb9<{@#5}CMB)f{VO<7xx`Zq1 zsEG$zM@Pp>Rgd7L&s^LiW~s9wM>{jQ`8T56M@_x4Hx`R>%e`(r7T@wMF)v@ey8kIpzM!zsMfYN&_${#f=H{F) zU$l{{U%GVIneN%AD?+Dr@A0X}zAk|0489No#`ZWl`P`~ICswMExrV(xud(a9ZHe)~ zQ8AtE($n)7G+PXd6@l5@DZjb@rDVM;u918E)SI`p>$dq)A9UNbg zNAX+gJh#r4!1CFtNjxXr-QDS>y)K;`@3csF25c{HZf-i&eYNRL7YQpYEQ}Yiwzkf4 z@8(7R#>vHXM@y?UUF4q6;)}Gaoun*RYXCu64RWb)F4fcrkB~p!>xzHz;zdOLv|nto zPEk^7YNvIYDM2gMZDx574^eP$31gQ=FjTHfCJl-6J!#&4<*GvOo}AN^c|F+p=+UD! zf4A|Arvb5;&FC&g%f+Y)&Ggh-{Dq4C)#k)E4xQ)n^72B!#}fi~rqZgaF8})V3+qFF z{~EpM{STDn*M4i!3K-#Cx3=aIdb@!$AbW7>`K>q{i>*Eh+xP!a&G?kVHR8MOtWHqNf0=AN-CeO8N*ey(oJ z>P%8{^275Nk7pJ>7caa}XSZf4YA#gEXoEW#z2j|a$`TO~A+z^Ad8KZ`Ol=@PnpqA z`8Rm^_?8+5_=1nOa=*e>H(^qiOZ9gG8@u1w@Y$ZGjO~}WUi=AO@!%29f3?lE zuAlOnD{op``fak1<)P8#QTAr_brRr0clt#ZUPDlOZu#=Ev0c~wtAlUNYVDwfbNB1ir4@v<)dYCb#--!K)70W?w}#%yeuo5zb&_arz^d? zTLn_CJ%sdj3c2rJRE4aU%j#{KR_pHV{hEKoBoRNCAeWQvH6H!w5vf`5k!N}Hz5`h5 zCyfC;9i72VlTrvUZ|c+@UC#Ss3nYeY(yzlBt&dEW{ zHAB>v`~*zb&feY<@?@6Jx9F_k9(_CAY`ldPSx0bjvO&G zZ6T}cs}cInC0RSQ;?sx1S(yx3Le62uTqtj`hQTs8v|ze5P|LpHGHi$AecKpr?J_uX}P?qVLsIuqMyy;mlxSZbi1mzI~Y$AV9eq}S52 zvU=f>_N{!aj=srpMM5I^>C>jpQMa$wX^(SrHC0u67?+O#A1JG--QV4qEj`-LG;7DD zkg0qatT59bGT!B!hDSk1u-dxXDxinb=Dp;di{u4Gj*X>Q;lRr#-n?1jT#{=)6xBN8 zSyb=bp;A;-L<0Nnt`E7?_e9r6Pa!41-vWMp0QZTSH>bN*M4(E9E^WCZk6*XEA^ z1^Nit0$L#hpaC!r{F?8PzSjRD&1`%#pwwlsNYi}5FN&O{8!*U0*<2(k!zmg9$j}}Hc2}}G1|7BvT;l2(=Tahx?9}0o30|e(^16Mp1p;c z3MO3j#_H+8WX4)XWn*@AEVl%j=8Fybyl|+gsZrCu3$G!!XzT0WGBY#lYFJrWiFX71 zIs54bh4trWidee(`oF+4H&&_#E;|fe#1fwg@VNDeelu>N=fj7eK3QwjM6+U(8X7QdxLnLVt zSt|`DyoP2N3gVy+bz?wj$~rMpq^xdKYo{9!m(?9Y4YSP>N4D(MG)v;MzKW48wNW!T z+MlVnT%%e3)4)_*SC@LQ)|6n`9tj|+y3>s%fM@}5N27w+c?ZL+DQNdu%%9@&ldFAEt z5QuXEekp(Zc6A_EJze-AN>a_yksmSzv-=13-YlteA>a#ZZN0Tz#+EF-|?$wUrm8qh?6_sOnAG$R0^0a z@Lq34@R6enB+SgGPwnmP*`=g1Ao-WgEe{r|&w-u%=wx!`1XPll$;cYCYv}?7U#;v8 z^5o!h>XOVh;N(t?^Qo8aR)+BE6h2_AUs}hMP6zIoA{8H5@T}u3se{~;vvf5(W>+Ya zZfxZK$eZ7Z*tav_h8TK`3Be_|*!FH8=1%ProdWeyO6lJ^iUaWA_ro zM!aEfy?Z|u78Ob;JDHch63P=xbptLpC`z6zmGmA&1(DYee@(O zamMFZFPPB$9T}@kgR0rdlBDfO0!t>jG*4T`{DvWamKk}MO{@LaM9LU1PYCYg=XZ|W zraH5CJaz*a%I(`7U1kNnii} z5z=^O689o_CSc|V+}%8E)|wD=a9$LY*A#433SBEpFfBpc$6)8+!O={Q|thk4mgWmO*Ein-gn@yzBQ+#BQ$qhHd*MJ!phhBr=dZTk#QtQ zeYr$`ur_WY=wQ_sB9lZ(4pd1ZKo7*vfrp5Hy`L;d=8$IQ$IGw($^vlceQWDYShFyM zEVF>k4%eEi6-j_)Ao=x!x%ENnP_~=@rVoaH4_3YaN#Hx6#~+!F<%H~KN=&j#q-T#X+&n8&)25ifNtO>x)P?yjrbE{hc z3_R*oX*I6}P!HwLdt#of){WVA=_zit^j3%`>QKjY?Vj4W=Yo|Afv%^dq`=+li_*ke zY!5wB2}Mi$%6|c@8QO7b<}z_-IsA6^gJEN>s)iXVVAFG|u7k97P*YCb(vlr26)3Bd zf{&KXkr)Eib<1D_6nffs?|wj%1UMk%WtQEo+!M8c02v78$k(MjepCYSA`-BR5^TWW zAfXV_(CDI@;MzsvV+z*F>4Z^X+lDx{aJ9^n!milw@$g7{TH=?=H-rs?gV-> ziIkxkQYdHgf+);$TFE!`$4)&JfKq3kq!anC+DhsTF=*A^no$jlBC(jjvwPGkl-AzS zlW!?D`WfL@zI0yBb0{M@i^&PKILM$xtF&}cw|5DAE?0vOwaxEcAFhv~KpWy>I%Ph@ zocW`6t&TqDWt^8ae44a>7Pn;N>&c7O_K3=R99I-E-)MxZnU|dR5Oh0)Px{hGNXjxj zm!iv-K%gbKIBK(FL*Ao?KN4xkijs-k3)CcK+g|Xzw}x~yAE&2(fJ|_8YUj;z`2`7yv~S<6umEI#acc<=$3u$k zpv_qGPh(?5t?|-pAK-z#9lyHvZpRF$uZu!L=J(3T$jC^+hJZ55%xwP3D*52&r{U&E z{))HmH%@%QvGq(Z0p{-y6Rl!r^(m(6q(NJq-lUbbY9RkWZv8)96#l6Twt~-U^J^z^ zN?*QwIVTPn1$ad1Y-zw9duWQF)&k|^^egt-w}k_Cro1!69BCzH0z{!OV@Z&JA^@1B z-QBqaq$Wc=yy54wSS&!Vj1)2(Sg^X8m-ir0AMSR^aZ5;e$JNUI`fy>3#Z;dzDb}3wqNA=x91M5 za)*Ypepb9#Y)6*732evAG;Dg_JzF%?)DI@1_$G=_$nJkpRD>%GN!$TQ4_K0&v$OTD z?;j6;-j=gE+}+G(75ZD300eIX7w?3`0$i>wR=T7c2dr8KR++uF1si?}Se?gfYDfIK zSiFVZzTE`y_5otr>v0hV28Qe-R>#7D9*cDL2mDgQWKh$4 zTtC@_0t+>C=e8UCY$C`T5>1)g+cz2kHD|PDQfUc2eqr*B(si&aB=7}V4CdQdKYlLP zfY`9!Xj?B=w(0mZzT39f_0O}zRh69Fy&n!;H^({P-&-HFm3v(ehqsS776r>LX&g=8 z487dyyBkdfYZV7biU4BgBa~gOVHC1T8XEl)od_LNP*gNHKCYspa}EfL-lr1lJ>!7# zAT9rZly4a;PcY_ zU*z#0Zb;%=%8qM)Udcp+g0L*^8gGdDxBO=;8hjDPeEEH;=HQvEY1Qp%Xb<&ogKghe z5{E_c%Q}Iu_1Rx6QvTmC>|ZizYpJy2GOPj^h;FFsK5uS^kzf5sU1I9V-qr$cC2Ce7 z^^#%RP_YiS{i@{V`*U9*Deuh-1%H$$WOwYh zuf{2>s@@x`w7f37jFb`{V7(7a%o*wEx}NarG(gRQY$if-5;CK(5E>H;cozRI4i#cz zVyr_ro8{Lk>zRh>TA+au9{rQN`LWdZA3wZgTHZv8zFfM)>xQxxG?7GrwZ7;h{>ZeY z=cXA*Vn|G6Z0R%(&~CUl$vx9|0SqO-f-6*u`TJUWpAF&niorRrC`kC(05cvVWgfEc zIvP?D2(bj04RgX{ufYa(b54yU#JOCHCZx0)IXF6v? z_@$wtVfN@1;pD0CebMi~))-Y&bA=olFRlTrP&GpR6#*ttF*gYnh?1qHWzmEXq?+x( zulq63-E-?-GX8&J2~EEe+8{k_GAgAr=oeC`>kc)gu;du^xVShL{=sqql=3xu=b5zS z%;GG0J+)o=t1?hpjk7pGIFFB;C7l3jTyf%0^ba$7 zW|QVWjUBJj5bAW8<*$X;IJ&?dNEscCnSI>udZse&Z2#hZ|fTb#kTMv$0aos-_h1%90`EQ!EyCy_hNd;t<*YzrB=|YpMXC=bU^N`0(Sh$NEmNPn;rNJKg(czZthfgKVK1K_3 z5RZo}^UeOg{L&%zFJ2o-!PX1BWy1`JOQVNeusrBBAdYrp^}k^6%Nyam5>4U=3l;9k z#}r|0TGIrBDwzKNu#7SG1iIVG(%kr^higwsS6kom?Hcmxo~BREj!SUaSHC6GpVF;7 z&cu2^kWIc4p;*7xbaZ8pE9e<@fwFl0J*m_5ws2Dby0>&<} zmW*Nh_xol_BR_2^4#^u~4U!}DTxd+(YE3RADUIsQeT`$DkZvhR`&GIWA0 zM7-=iMN)Qqf#tHNNKpAG8IGN1U)(sr8RKAexPY>eGO5Keayo^zeowIVaKu$ZX6V{9 zzl6>aI?gs&?M@<3kh8dpUh755*&8T%a+4HXd-mNt{7yQwsOR79U1B4B`DK*(3F00j z#Gfc1dZe{m_vC;4d#Evk){9~KEUX>c*4^Kj-Pczo>xg6>qg_nF@3hTruBVKgXUAJ0 z50`38WFTa#Z~NS_Q8fN{f49{MbQg^usYC$4k9wciw-ZW(l9#$tbNdt!7rRC`hWJ58 zO7y``EocofNV#)Xy!$iGc-p6Dc6(0+{kSJu7vE1ddPwYxyclMnrtqF`|4vGT9b6Jc z+_BN}VMRLA_W+E?pz+TjYC{U%Rmx&+Za$D|?D8?iz)V?QUOr8x zu!ehGVfxp*U2b4V%Z+H#aXk(lTZ z+os-#D@BSvi6OGqbv866$H(tNN6oOtD&iN8P6X}`bzZYMsY>&XZwb*Q}|y0#en zdYqsut<_ZTlgp|9{Vx0V+_cr84?4FWlXwFMU3-&1fUm3ejP^np|B9OQNK?Z^*z`cx42^HJ#U zkzw;#qifWNqMjkJ2a=cHlY zVR48w+2&+}DtQHBe`CpK>+31c&P$P(e~5@BRB$Ic9wfa@gWlTy_q?pQCDe*?8c{O~ z?Vo&JMz=D|qgF}mUY{DzQ6jcpOjX~x*@p^}R|F)RTWGoI+UZUqQgN4F;;`tB$^x!8o^U71Q+*+P9eLrDz05?T3ktW0jz@8B)3xi={u{w$33S1Da~W$6 zUDB}PsM2e31dYhaR{#Cf4?gXj=V_h77Z$-)Xk}ZrP!I_?y>2XVj&b*JZ=)`K^ zrKtL5q0G;$gO@OR{XPB@qJbjO zG+p!8SZ+Z$HCt5=80ov@e6n<7;=!IgcLBKuWS^u{D(3v(da}@+LB5nsMY#0~)9a*! zW#$s@rdZiP7wC>K=dK2|@yqewBb(~u%kN)L+0WA=ith>E+tZ?qoL@yL5Fgf&jGuBe zOT(ELAZ41EzW(FJeqIimz}rfOrfp!)mJ>YaPzCxF$n(&ObLYLZuIS7P2D1Ofzfbrz z{aQJ3xyY0_yX48XDS-h??lr8ocXnqK;30Rt=s<7#qNcHw@XTCEjDpM^ADv$p)ozL|k?*I(b)RflZbve$qwXhOa&PT1wojlVwYrGD(LX3TlFtp2S)!;c4}1!H~* z^^gXd2_2KDt&O?b!thZaNp>BtP2M3paUhj7W%{m(3)_|ch4tZqqlFpQ72IO>^ep1r zMK?M}ZPeP?$1eDKCVXic`^LX5`rnGFb)-wt@o$$xPu4X8(9Cv3efe*jzyOA`jA;u{9~jQlPI zOOQ*W4pnu>7)YT#$xfUfK99ky{{v@1Beq zMbsBbhVd1*7ZRg_v6&mI=Dp>eE2(}brT!cNZf=O~=s9crC6YFKdPxoz7gjvN7k>s& zB%rV{Po@mr^1x&=D^cJx$+MY~YEGOIFI*1eVS(xcd{Hr8V^>%O`yZN6!j=i@ zQ<&|iVT`v{9Cd2;sAT{yqXz^i^J~FYZjLdaemhOX(@_ z*$z}S#=O^t;MLdKA1yDOol49wYiBTe08&!VXw&4`S#+g=`=>ANrd(P21bWV=8Kig_ zI{hg1%D~=l+)Cv5`43!JS5k1;>l4172Lm$Y7Db5C!RmjB(spAc6Bfu86wm~j5g6!R zzkye$9`aQRr9yjO%JIz|P_w?#y@;1A8n$ZptNm{+6aQ~5vu*X5NXC#5YLwi#L@c%5 zkf*;abRAHO$>QZn*oAk1blozZ97mAj}}_m^?4-e)le zjq(e3fvYQBmPRh6Zp7UCF$|3NCdOPH%@bcCaSMUp1)e)Q`F;VQ0|dU$pBY^=CsF}22r@LZd{8}Ohy%9p$v{P1P;*qAAzqmrvb zEiy6w=`N6@3RqvOpS^T3{4*~lHgHlrsn-qv7B%~YX$EWS$GX?TL)c#6R~Hf%F=#YF zC8M379357=Wif)KM1ubI!y;woKSWSoGfPk{j)p+k7x9Zk?Gd<^BiHGZC|8&EWx-Tg zy?qD{1=+-7DU56j)7rC0?A%hI$e~t@Wdw(1D<+F?$z|-#WX)WAWv>ZnBqm1S+$>sQ z*U|wM;7X<@@bHw#)~0Zb!DCC!y-Si?ub{!)%Z!!w9dF%m$OHE588v}`g$PsIVi%`%xWl*m0dm%+x+iB$- zXvBe11e>B6ekOG@jh`+K)bh}VWQ+(Mt+BpY{;eYX-R;LMj#0p!w|aSl-UFdLJu*W#^9 zX!M0TdRW@ps^3!p!Zxv>f+?FonvsF=-Fw$lsH?x-K=ofwPbSQ8L*o#fi+`@j|63he-x5 zkFC6ogYkn9al_YeRZy5qL#mqn_&=A}lO;&1XjVs+bR3{+KOX zXBk{QftsLny%x(rw^gV+e*}57`!AOMV1DI#bmv#Gh{r~$dLYcu2g5-#h&{yyjFgcR z2uBjecK-}h=rx@aN5p=Px>d>0uNHHc!+Ju3vS`6XvuH0hyqrc38I3uE{`eI&@o%AK zLb_vsq#xD?QFN&8tW$v`8mbKhV>>Cha$rREzyHCH2Z$PS)dYP=6M zjlnDWyCeDCEhY-=42sHuO&H>N(S;`SMR|#HVpWJkf5G+2X}qc_mvthiz5e#y)!k3N z72h!@JNHD{J2(e<_D1=N2S_3?>1qu;Yy?r3ivFm+Zr6X=dnK_|M_7p)Sk%zn3~YJMk|?9?mdEAwV?#e|#bkEcjKvP+7b`|-UkhgaVA z$!Wj}n*Tt$Ybr|`9aZAB>&q~yg?%ayq83vu1GP}Y8=Opz%0Ld2lrucmr(RLa18H(w z+L#-`g(wFoC%ws-&2hNOkZTnjbDe$2<1qV@!>5e+>_cS+)IGtKr#No?nSZ1qs2pj# zGavHN2{O>vP`;ajnnBi_4RNL=w(bdwTTmm5;8Gy-0K(B_Dr>m%hbA6xa>q+A`PF9rc}WRa%=6w5P*R#MEz&;FKL&f<9RsziD|HfBsi&sbx zuqcd%5TB$hXQAxnPyvMfGT~&>O|M)<@qt;w<0*z`mGWFiCi)2C-$NDVrF;5(3{x2 z=;p%PHa?AynzP}M=8im{25GkVo)F57`+>qw_y3Yu+ADU^t$iuNwRnK?RwN~Du}pGs zCpZvm(o)EALWO)Nh}v`+7SqrhHRx0@>8oM4b9ZtvIwG4 zyA(-bbp9G%k#)OXW99Se!f~ws7bflmM317DZFq(1PdgFSBho-cexRu{C1L};I)rzb z1kLs#^daYw$q?vu&qwp*{47r6Do$XakCgs38{UhF`U#dv60wfN$fet|jt}j*AjZ_- zG;!g_p3&$JsYzF1SW-ubyema*jcn*`5qC#*@*L3a)5B+sW9==-&`J%MW50l59rIq~}+661WVllI@N2p`%|9?G0)YxN)#E+?9Hi9u>1&p0JK)(SE~ z`#l4lO4Hu&qz&wJ$3h+b?4H>`2u{tz$-jZnj|Y*`BijHfI2O5+y@9=nnd7?71JDd6 z!;DY-#FrPU{*_kB@9td08>|X-)FF(>uMxOwbFy~IQf#|eJELwfn7o0wR;h<`$VXUc z+uphh$)uUvrul`thX-*~`;#Y69$Yoo)>e?0M?0+Rz*x}8Cba4i<+lvT?G9no2G61> z+sg_xeI{T8Gj_93KN9~%)?^XHXZ zBOv9Gin7cZ z8ySHRon5eetA{kIy{6v(VS2&ZV=B6pG(UZ>O4JwrIN;8n#+`3n_%g#d@_+ zxT6fod+9AC^>$*4T`Pd+7KW}f+zAEfV$ndC7nhdagWe~WTIQ8#b@8v^Fy4lXf{wK) z=x3p`d^KpdoehzIZtO2tcz|jOJ24{zY5hvV1cQ>YvL%r63owy32UBIU@Rfw|kud&1&#DdjI-6FC!k)v-jd3YeNg8!fKl3X%v56M~g0$opIRyN33apWc5o zq+I72Oe(l&XBXw0vT-nukrZM!iFa3Guc4Z;Fy*@a zXFlQxYza66r5a@j(S&+yWU{`w4R^b>ISRY2O?Que*0W+D91cIl`KCo^m<}t>~FQLNsZHe=c^mo%RC} z+WsrbQ;<{>G@oD60M$`jLmHPPEdTIdlEKq7K13HVezs=o_zj8C!>MEo4MDa35Z%EL zt4A|QNz@lQ3xVoB|-)-y@s=6&>n8((#{nCVtX1{AU)LkA6_V=;+)bDS_eLwQKX?Beb zWXc#3XaZ;8j?Uwa5(N$+9C0o}CR;-r;M(G2Nqkh(OAGGwmw90dx4EPpI=ffK1rMkviLg$jaO{*(DvZCesNG z-OzF&oNpX6=?=7V$%Lhy4CH-Vn;Yftl9nQ1w}(^@%}HcTh@`m*&X}20 z;n<|!2=qtV0*dV(+vwWDfgwWIrQjq235wA5B78O%m3PPpp5Nmv;ogC$IZ+U)wG6fI zTJ7eY)<%9R>e41PGIyU__)0sSrAYhU>WiEI$xvt89oDyG{eNoSi1l5caBiDch_~%N zvrrGE+z8WckfI1mr+tMZwC#l)^x_sLp+HL$m;4!&HGY;JYe1Et@{%#*_-qLcN`;rY z9-Su)9dEiHtv1BaZ&|vE9Mb`~t3C|#L=9R+;&`JM-VL7eIi~Yvg%)xvga(4~-WDpA z3hSC**gwPKM37_VfsqiACMxl@yl5ZB%Y|~S1?pqysn~5yp-Q?wj;e83&((ewthD*o z%J+5#Jf0LVeY#E6^vY>(N}6FjL@Gu29cjsF{b3*YZj|&~tYwf`4epG3BoHhC1e^!t zW`XH5(@8^7_uu0?o7>(eWU6hMgh>~R*T{AFYi?owlvr#wB^aF-CD#~;VfQp63 zQx3U~2%l_f@IhQ~$$DX^8V4ZtvEpvgt* zOC@fa*znL(R?%upeScfrii(^#2_tR096rwxuMj)u@*(1#4!Fe$6xanpR?9biUSc)p zl2BpdmoVc)RU=3j=SUT#;Q9nmlh;G|}9cMAy)k`d18sQ9x%v?<< zYVI=dib@+~03sS=#2c*eFF6s#*Z%`-6BKKde-_Gz&8_+mMIUh?8FiZ2j>-utf$C`~fYq*0L8iS4?If;97_5^5H4 zqf@8&sc-n1{Ap){;XYN~f?6P(lCZ|EV6O>^3ribfXUA*ledy>8V4AJ26&zO86ui7(tiFMFH={96T#_{# zp|n!Tqx&;emfx>=D}Rw-P6t{Ck>&@T=`|rgaSHb!yk5oTtfI}MPSsF*cCDEBPX;SJ zA&(6@-~PDw$w=DCeTZ~=iAX0u{1IlqSZ)X@-J=l$pKUp9o|rLc?5IaFZCM0F&gza+ z9#=o+EV~mx1g$8KEiimpXEKGQbEllnvr;acdOdW3)vmefhUl+er-sXPbx4xXUI*LZ z3@i=Qfy_*wLtj&6`W7e1ko6J)hw@QhmjIMFE@h$O76Y8=$!EH(6=K%QU&MWrV^{ui z^&_HkNq+@tFc+!%ie`BLm zSxNs18!!%YtY9sF_>X^~%2NA(`X8T2SDw)04cccUQpLmiW`I`bEUTyDHxBYv52c01 zg=7)410;!|uA3a_C=?W?eWxt%??{<3etZQNwhNxT;N|vVq^HUkk#f0rfLX3)sZ|NVf(* z?UG3)vjaz^HI7Nn8Qc3e5Slj`AsIM}RquDAD+nQ7Q&PMggUB^oR*1Ok@(;Fc)crfC zj|%jXmsx*uu8J#oqnXFWvSV|5 zx9x%ELfc2`@G>Hg$mKCBAu#$`*l3&!6=L|VI5T!g>kV&vRoEZ@@GYsz1a$cRn@b=X z7|!B}a0Ov@+_jHUXv@2wS)CpPJ95=Dc2^W!^sT0Xkw^Sm^rn0r(t&9KcJsTqNe%ihVm6O5vSBv z*wFBR){IFYgoUArT18CGQr?T5ceEg>8>mL~`5lHjyoJCUDc{*{|7{`e8S4YxfQ)(6 za7>36MB%tQ^ibh$JXJz3VLVtzFJ`j0Vhsb>16+psu8X=KHSxwA>#;hym7nRLv zqFa+ap>jO_N<`fHp0q)P?vHAOq^7^yHsnn$0@$}Fc(*hP!B75H4Hs=ade zGl6Yddqhvz@@PvkR!esTbYOU7A?J;2LFCp}Q$aW#OASHri>@-%1ChgGaY&UhbQo8dfK{|enJBBQ;2wx6<*+QB+gh&vSN zyWFldl*xL6PxwONEn0W!++l@j67?2AN4x2suoOrOa+iQSI;=p@))KFf7cd+K+8zII_olT5Z? z-^~w|{idK9*}WxC5E1Ys@OrPT&CN;ju8HA7Cwpka)Up?3vzK%zqf+I1He$0>v?B37< zViNDh*FHkM`rpJu0xjAVU(P->=CgDs4){W1=!Vi4H+hUZ!415Y@-?8nm6x`w+EB~q z({pH6qIVCwhhb5K? z$qjoZAV#-J^8jw~a)Vct|LYYLX!wS@;0m%hA!KoZ-KQF$Vc z7zo%uJf{M>9Xo6<_0VN^O9$?PJ#xLJ`ojew)|y-QSNszyzz^y1FBM%332CEiJTDn~HcEKh)~#`km98q4&QB11 zVbTxTQ7)C3%gPt zN=~8p?2k@2z|4vW7~HQ?iHxB;Mwpizckj}zb{C9|jFFG(wQ8zDv-3mT8U3E-i-dJb zh}-#HfV)xVk5h&jrBG+#)ADt9*xk-O#e9)z8Y$mry~WU>_XrDG)qT+pUzK48#;-Hc zXqm%YE;8-`tWO_|3t%`qgGBxqsB|8FX%*Uz|cTFS$Vxi;+i?|0#sZkhc{ z0V}_oA|M>P&FUKtgkU1Po(LKGgK-EfczqA@dIMAaJWl0n0_=FAgl*f6iBzE{wXLAS z)O7}9eYV_g1G2w*gBwhh-j{p(!tMb-+$^JPY7rLv#3X2MeiCLI5C7D+aIw$!#FD~m zc#abkV`)8=-9QgAcw7t)?S&|&4f!$8{?gk8Im5IM_w*n=0*k-Mo}NQyu+XLf_r0XR z#H=*~^W{Jtwzy^uz*}Uhs@(56z>At}zW)_pT^JkOOz;%Wrzq#XH*W1Tk=Qeg03cK_#d*Q{XJw6E?x0um(`;gwLz0Yo;WM6@9vW5T$~ zP-;{%`nXhdTH0*G;FAs(0V0P*43H`Z)c{-i7hL}k0sKc88Tm_?xa9!a=_*XI^T8-4 zGK4#GwC0@^_~$F~E*Mk8=P(~+`Clrps()2pdTd+&p-)Tit|t<~tczy#c~L~!w{5-2 z5pSfm#Bit_NSvh%lMBTqD!LV#?ZM=D<1vbnk3w-?e zaiw7{UIqqaXXv+PPL9)Hkh}{P@EG1(g1mGpiAz=djlQz>N!@MtPrsUOpRK_%&})6< z{t~2u?|RLbElloEYRo;{Toe|fXijsGOc|;cZ!nyLkzUi~QFu2^+jdYdj7TQ}iHU-V z{mZZdP(iRQBh6nu9Msfd6RsCK^=i>6G#hFA z7@sX4NvSjBYGky8$ptxeXo2RYwKtAMbE&Dc$hhjaxw*Z5@8)WG|NQv>zukm0@D>?< zEEqw#0^>20KmsqD4)E3Osw9ncv@p$P%|m0?GBl7Qp$Cf5*NYJZ4Ng)|>Inrr9ZF2uQc0fYRM5B`Mt<(%p6E^8M|z_a6J4bI%>;j`P=H4Ahqw7HiEl=QE%9 zKA*XB=P@ua00GT_fcUy^Wo3ong(p`@B1|?Qi;h0p+uOU{QuZ^3kSu_O zghs^m9x4if{=1T@#Bbkd0QX679*h-_U=hff(1M&vU4o-E;p=M9y|^V`wdc6y%-6*9 zI4`Kf@fAiti6vhGu8)3wle4)wO_H87GANX^c`nv8Hl~ACrsIBO#zOL5;Ily~@7r%VA`Le;ZMB%>RRQdFRd(l*`p#>Xp9PPML0N zmo4E!oIGcS3Jcf(P5`72tFBKTVJL;X3tE!4`naUfl1niB6&5f1`}YV?G${6+LLXTm zEmU>-lN06)n)3h&s{sN{IDy&owi2df5a^x-4WvQ0D?|`0hy(tCC>?yaS^KRw(3>2) z@#Vn#>LTnBSGhG;8i8b1DhklRM9h%0qHSWLnJa|}*f7wC+8$EM@-BFob zu<)#4CLel=82o3cG#wqC)}9Xv3d)3lFzD0-ZQfw<-6)v=%#k3})6G2*_vw?QY8<1k zOdQYPIiyuKPv~%oa+FtR3*r{!ol;icMg|o{)T9oJBB-JW4BS*#jX<37FxVGBQ}J7Y z=*Y|)WcW%Lf<2H*f@GT!zxJ<}+{s%;QR=C?!O09iNGXt}W07K+qok9Mjsw?7baPFw|)wA^2# zGtlAJVFG9B#|r~gO-)e~s>fKMQ~%)G%Laxs^Q>wRTr|&@L90#T?}YASKLRZwnCHkh+7sMAJ?F)W;r?Zxb#aHa3e^Fu@CIW1&GEE(js zT!rM17%41W)CEb}C4^)5f8%_2tLRod#-;H3C65Mp2y!rc{KNH7iczmoNXr}5(8JiV zXlns%{1NQ)96|L00OGamY+b1;DLr2a8gh@m*5f?Sxb$lS*<$Ok9|b)e*|+cnzTO_2wHb}O@{ zbW`_o(ACwQd`$-`-;hG|(_@QI|3M-8Bv{KTp_~Vp(8ehO5{C>Jk%7QFHS)m&JlUo> z#F9-n@PaEqDJ1`Rl@Sf`rn`~^(1iSp;{Xmvol$N zoEm+q8@Bqy3B?OQu%jm0T z$p^aV=1)Les#$$&Q2t2RI^av>a(e*VMB<|FkI(4Eh5i=XL901OAdSB$AL0>IkSlB& zWT7%UGt<-4149n32>g^s{sX*=>CmdQ`5sSIkR`x|PRihKhZ9(2Irs)6i7Bk>98}se z&%GtsY_1i24!ibfp@gKj$bch5!NK$r;TS;SoB$L<8F*|qu(?9eEdYzD+pC_P0&Z19 z`AbO56bN6N2VgZH0vq%)EOhVO*w9ZzV`gJKOS#fWs*C3aBc^ zG7N0c%oQJ0kSLar3LHHcBfe3E&Jx4Waq0(cx_hQS2Aqx^CdIQ?32i3(X-{l|ViTNB2vrzMZR54=i zUu6*Os=OT|Z+7qD+ah;$)(1-g5Vbkbz(!eK9{?56rq)&_ePjHBczNC@0>Ek5`> z^A6@9Ls1eY8>r1t4E?RX@+VJK7pi3L3{Y**32K=HipoA`!yk|*C;P%C2+kTbkbp|L z8H5!BJ-dxSK%uV<1kyT@SiS#I?@D5=w_c-pmCb+Y|8F%2;*-ET#dy2<#4$`T@0&$# zmoahi@F2e1ApqS(LX11mB^&k@|bFxZXXbHsyYA;;xHH^{EN~+;H-ZUxwxz zp9FAEp8(dg*h33+Per@wPaijEa#X@jYPMVble?^x^Md!)MDNB$TITwM3N+oY2sTJ1@O4&_rD=D z6enos1;EROQWBsS#B-AYq5wZ_j3M6k0rf+j=lz2r0fSR#@5?o6Z4v%ueL$B_KCTGe z$6W5MIH@=T&n;>Q?jtN1v<><{a~Bw}K_bD(9)SyIx9pud%WfW|CFRGLm`|M=y`FKEcH~`ZoiedfnYezHXa@muT`wzUqW4YyZmjU zq45sTwWTXjIzEK8#pa-LXhB;2RWoivHVGcE&Ts*stNLP)=n2zAMJ>P9`o&k!W-!q( zUWs&*nyHP!IAb*G0m{O@>@X-9+zGj7<-cKFK^Hg#JELJ`En(aSimmzGL$q2ENVq$r zlJ{4*TSj5s)HL%Lq}*eGKj)i_cyb*01}7c5Ks8v9fFffI`uhz549?`&CwhPs`T%^z zB+wjJT&N7P*FntK|4Gj+TRTn4x&4{KWPqQDXxK!Cbw5x~pWX3-OdGOqSejc91*xIx2#yA1@?Dt3-S z0(c;6(ebx1DLX?)+&G{hCx-%{ruYEOaSYZH|hR_OBR`uW736^*`<8qtrt>hii z*Sne9jxOIOv$yh$msm%)f`CvNR3PD2mwqyVTu7Y$xJ2B<_t~11X~65EV`koa%L0%t z0B~`9I;!mOrvoU;=?LOrT3G!%Mxo|TuseC%_p!npa3lGVx)-74-OzMIy<-#ZaAc6x zw+iyhRZ8Iq0ay$G_Z?B%%rN^zRp1UXqJ<>20!SkgheqXH^}+(ZVnx&FB81W$aigDNKI)y{3U4Jr|1rl&UoS{ukl zLJTd4Uf)Rrr9+?6=2{2I`DtaFM(fSLgUJ5}U^(?bum`2KGr++31|T0lGAS9L zg#7*P6=JR9yHX2l)yIQmFd}j>ZtC-^CmOXL{7lv;(e83!my)KD$R`^hl~>h_loQ

6p0fYi|!c(ZH9iT<5`bbXK$dmBaTIz{X4b&cRSi#g$Ky}6iC7g6>b`6TC6=0Qmrh|+iRZ&vx-`0v$ z&{~0q=cvR#RxRC7?yC745%6QR`*LWonP6Ps*_VbW3?d#lk;~3#fz)GM<-$Mp_`H7= zw(#2gyg=Hg;tUj5+C#I?pN}q21}$Ze876AQ;lA*F3Gy&JGJ1e;6N=UAXwMI7{Az}M0++B>&PDcXnfGNn)CDa72L$*ypZ=}?TPFbYe}5=9)J@eo z8cN`7RGvVH6nX}944ZA3BWQVXxbk4AsjR-3b+s`EEmiJjzt{=s%8oi;RQz4^Bj=fU ziGNmtm@M&H*m7V%8$Q1T_GjHRs;xBWhYI9D;fNY%zb6d%1_xtS)NCy%Gha%79jwq0 zyg@^?4q6R@G|?pg)KXOyK+ziLR;x@@!MC`BzP4H@J4{rVK8piNM>Uj>yZ`nFr1QeC z5I$a%-Ay3^!uc+cx{j;Y^CSU+OA8C6o5 z72(I-22$CGclY^;zytzTpb5uvrZ)iz$PR<_`=ugvrK?k)gFL*i8bW-;vLEwDJR}SA zjs*Tvebq_fB&q_aGeuUlW`+hCsQUuw+X^=vzAxvh(j9zGT#V5*l#_j;GW%EqCo>-D zZ#DHTRe|PFcQr6&ZlE(v@t^i|!{4B3&yUCU(xh5yX~CNB8=d8zBB&gdFQxvi9La7c z{2jV(l33W5zOaFnobS&-?+Wj0(H+m~TNXRynUKxbQ0d6{i9(*4_{&;Bh9s)9zZmxI zS3su>hj17)271r-i9kXxLbv6v8SJKda6o;N2C7FW;#+f|luBK@4;Hakr!>TUAA6wc zZdk@MjD=kPrwx=n!{c5&Rn;`uF0zA;&4gs=%%{Ke=UqfF2U8v9(oF|2Uar?Jeiz7tR^d4@fWPYe^q1fYu)fFDOR1y!@#*{tx%YoT z`o>Cp@Lp8X4Je>Z?jN}G|0jSx^aP;w;(v$1|9|2VPvT+E{uk4hHY8qvSB8TU0yymU zPc@mL=bIq_wDSKMG2nlqQ@)ET10)^-0HhZo6!LI%Z$X2uIN})rUX7| z2jy2G&LsejLf*Ef;C(2uz2WXu85A7Y0=HDpZQ2YXT?3*~$!M)vUm`o83;13%EXIEP zIE=$81uhy_NovfH&GX+U1(;P`Kx$!uC?o<-bA>!8!AIgg=LB6IqGT=VNP`jqNET<< zLs!b($5Vr?`X8j{?kA2k^f;}Iw6PYi#Orm=c%lGI$w+K zVL(b<-Z@7&_j%W+IVzSJ`yk)+?=1ce1_*;D3q;^9|3l#|#3Kh(28h27xXd_YWJ5p# z+NEug(Fw@inm5U^va+V;=5F=Sh2rWp6%fY$J(o4*R18QAfGJcCl5HjD!XTUKo_7Q@ zBy^(CeF=bf(GWisq9v{JcpMr+ywv;Sq?Wgfk$P&26PfQdlVsVDjjNXG$Z9y)6Rkg_X4_VEy0&YdZ^62LrHR+`f< z|0lZFm*5g)2?Pdac#}iZO%y=B%2DN zOR6d>9rx=t%fa%8So%wCK`CGv14zHre>2VR>-?`l%LE6YK(o~Up@h(&0OJCxASNKt z(%-vGdgGgdj6=f)%0~;{CyE$?yKK9R;DuY@;j)glmm1$hr@yp%*^a8u%3?rxqB0U_HMdB{<>QY7#aYr zn|87Xpc`WVab5Wu?9odqD$MggAycwbFmUr9_#8U-hq`1<m1XXYloW)ZCIC`LjaWKAM0o5(INS8nfV~fjIo$n5$w4nBpmr6=X7ma^U7aBo~ zEFgUS|3ei5R*dfjKs!QnL0R~U%FfR2*<>CD0BHXF`SVYs3Us%sw7{$QAE0DVDgF&5 z<9jh}LADQ0c!5~1SDRTwP;3nVc7veh2v)1wmJn))fq~kP@;0x*G=pgHi_c3SB5GWv z8+#3=HIp-RD#>S)D=+mm+cdmA4iJ$PXzoM10sOG);;)lU=9Qz4%=N1j0I#%+asyif z>0hJF{`J2hFe#@t_P&TPtwT7;l`q?LKqbeNVRu9UX0>on_cg@M|I|tQ!H?y}W;5!?V zbs^9Dj5$^&xC?LKwU3VcSzZBt)wH*w$~g9cSLVi+{E={hKvp#u4ce=$t{Je z(DL(sBqE}Hl9V3b7$>V_+}PgV07afly+-9un^|@`+!R9=E_+u5@AlnxU*%Y_Z|s-P znn|3FJBA4h{feXXB(V~|ZTd7H;IXDQ-VbOWEm{lT?L2LCoeiVP?N%$bzzCAwc*Vz& z6fkRw9l=>Dr|JNycVu_I)>Vg?u_hvLe$MjYe?C)7nG8FB-7_$?|A!gIQtvkq_CLwp zppMr_7WXug>n`D+wTK1(Hhw9z{F5!{VsJg&X{h3M{#Ue(3aq%(#|jA^iEfNuSOn1`AFyVyAxF33qN>T*l%B-wo9;QL{FZw3C814enwb0rP?2UOF z)Ra^6Ub+smz)f${N`SZdWu?RVp6@YZy!A`5qRL1{eF1AYtwr4OZ6i zURIRfy*Rxe|51WNI+@E2z_`qdy(y@O`aGF-Oztkc zDiZa{mHbyO^-2{mD6kzI=|%~;bXt=Ne~$vWQ}NObp19V)=ER1v{BFB3?Xr-)J*s1! zH13|-1fPG`m>HmpzLsAO0-ia_}mfv`(btqRUYTb1TqQE>k$hA8a`N z`SNAy2U$)SxkW>v(rSCS5zpOzcJ%Pf#9Y_Yl25O$4^Xg2R;(PtRT>Hd9&XQ{Yc!JC zzKZ{pA-de7hXTDb%COx7Q`^@?ak<6oXxjpgP-=)+iV|yEo>yLWxhU@fgIS0j_;uWmzBe=Qc6cHWGG`Bib7@!ind_&v0|CWhalNUy1a*U~>Q zidGUg76dJYGmc0HpR8XG<8J*CJeoCcz3o~j2s7}JNCD>0V^Mh@RQ0ThwqLU2+e6#h_eywuiv=sI zBFu2)t}4#M*g}*^Z8vMNW#Oo-va0Uoo$k*7c1Cs^xhTr+wO_S$%rL92b9^}tq3gIk znB60KqwS-k206`@Wurc7&havg3KU*_Xl{J=@-X1N%F3X|Cnv~wtMU=C7HC=(I2di6 zeDyw3C0svvt$HNJy;t<6S&_)?&y(}as^?&V0DJQ4&5TUfH>+2LT5+D3y5LNz2(dk8 zXlW>$m~mb;VC?cebbrapd+XF`eGvaCLG&!o^;x$riBR(S#N(|Hdg$TLOVLzM;$&sc zTF#Z9{(h)dtA$iHX0^rE{_Mqqt&>@b3&M$o8f zFD>tQZD^rIZDn#P0%%?HxtCGvg&r-b+sZZt*M-s0UssKpGP=QQ0pTC&><*&Kewc5A zM-@E46W~keA5fJYl-<=z&o8N2>5BMy3SWKO_RaPxNHJikBjoy+hA4^mmFAcz9m(h7 z`~XZc-CqFmi$LxZj|=8{+3L}vQ7coosZRB@^ER2-}~ zj4X^Ox!9qW=%4YJJrv(|`EWgD>leJKk;DkJeD|^QL@IRFhRuCTH1g7b>G+ zp(r*nTb)~;MicQR$P&?PfBJx?6UZp?J;zNOj!d{dxXo)FI|4OUfG#Bh(>h;ULwfnW z-pH}q=Q|Fne{{w=ksGOr(M{s8_yb-s)PAhrfI-mNnQdnG{gGum;ncL*YFF`?HdOE& zMUgnYy|sw5cTOlHw|}8YyqKI{FxDsx!)Pi;0>-k?(!95#^HXDUdf~vRRguG&>rsJ) z`aQTrVckcf8yi-P7e?qafodmlU#_sJ=r{h6@yYAku;J7@zxrIVPdxK_yg$}Y_+#}q zw;A0T?R$GLfzhj}DKJ!B;#-%+uz=Gc&PMGp)na$Gf3wJC=Ssf_(v;A}?bvEBXReC(HjiFAE zjuG^m+h6M6rOg>aX_IB?VX}Z zXtLOY7xj7fT>jye`0pQPpIX$U#`CHwYozjTOA#LnPicAYApOh>2Ah|3+j|mKL>ej3 zf~^_@y8Gg;bBEs0ne~c9XxdM!O7A^AJ4}cg=LpMnEv!vVP7yUWZwCt=@7qU>WVwxD zE=2nbF>3>qqXHxGoR6ruRzY7kXetJ32d2&EHekjQcuH5AwgCx-$zj7!*;mO+KKNnb zfgWbMgCkHqP-?q(#??*Wz z40M&Q1QW)X3U*G<01)g9mJtZQp;SedBQ4QQ!#8jZ2RGgHjhEE1ME- z%WH&MomPqfQ`NVvbsU2oabJ}VOif&c9ex5C!d@%-derUNCr!dySDFy7b#@O^mMgC{ z0xjw@&q1&egR{a4ZpQGrRgSNZqqT>ZCUsJ6bYQ18KWFsJI>ErmXR#Xnjyk|XL7H{< zb}bpMY{y01+B;b#X**QrzSn&H#6j_t6l?z3W~>Ml)#yszx*mNlKqa z7`o9CkM^tYuj86~p}qy9{C{2H({8}u| zEzZv|N!SA4NtH|cF*OOd^5=t}mpdt$`nvfe|`-IyU1I zo_JMNk>vZ;)f$a&#TsgLs|HM4gL;Rey@r&;=H!pCnd-*63!laoCDY0j9vm@U3V4-q{v7x`Ij(R#E`VvXflNPz^VTO^#`e|I zMHZE(Z?Lc)hoL>{Jlo)64)L{itG+uuD6p7Ql;}-Mlr0sO?A)+UoG+s&FPg`BiY)ws z-=0HRfx=aW5~Ji<11*M^I0=4aS4aej;=n!rb|2C7u{VX_!koO1_UQm+~B__pk3sSnG+@=tcJ)A>>m!H?pAxU6*(DXbq21gvTCbH6$e-Wsw#Cc{>~Eu*rTp z;tNv=ABbD>y>(JBVC@m>!W=y3WA#wISo?~D3Y`f28dqfde9QKYa_4h3wbCwar>_E6 zw`cQ}V@SQESueZ(;0_!yQc6d4LF1bAoz=ngM8r*U1Eq-hR66R4>C%lfQGOtM%dRWXg@o2J6b_-+tkc8S*3FF?%6}#bx{5ulg9fYwxHp3*{!v#Laa9pkm@~7eM(?%;uGd_ywNWo9q7C9XVH{}${* znkJ?5PiWGf!Yn7Uc_k*Psr`wu#zM#C^@n{o%E9k+ZrHjTF{@=)?aha?XYM|?dnuDR zw-z8gx241>_R5vdQv{VJQyvPVNogJ#NvO8q9P?}?2pCs_!HMIrs%+xI8ON-v<%w5 zJyBFwWV+pw``~!Kp}g@M%uL%Fj>GiCt;ZbmE`>P$=ryjkelZlpeCqHpHWnWbjD=P^tGiozbkHkO_BPN4TS#d(dg%UfykcUy*M6H*| z2C*>IX!!NKf{*s|0avlzOg^1kF}IdCUHcb(FfKyg6Sc3q=7m13!ER$PQPO~gOc|C# z{Up~diO?f7RU9{ck(jMvyZN-J6JCx;aIV-b3B1PY{^X95KQZI>)K>mUxxS=&y%@F_ z)U?>KnFGdUOLA^2a&B=Pbg}G{nBPp*sih7QRF7V9or>Gc~h*}P&*qm-#F#EF0*0JE-@!C6SfpMkQw%8~7ZpF_ueajo67lkT=ezlSr& zfGaat)um@w;tV|Fl6E_X)3QCztG!Mp!Dvi8s60stY6s6E8GB{*8WnrPB_#z6D|ENG zse=sE`sqtPl!~o3({Oa>8WX5o$)bZx?drU4FP5VAe|K`Q;y&2=EeC_Ln+vj={haNH zU}2B#70}u!Sesro)D#+Zr+Ga_nziUc$e()sECrS8VogCcj3)NuURPR@=r2*$%ybV< ziQ@e~k?%>cBF6&-2RduhLLWV`Mk%`pa*kMF{o-uGouF&|e#r4vl^?0bkNIE6@5q*w z+~Zy>q=#{rWl8fQNJYbf<+hew?qGwCNBrYCb54~Y+}Q3Zjx|~k!l2LjTF?ANqUcA* z;a2OKWPPWKx!5}K7j;LqY8kb0&!%3uxpv}VT@g*E^BjLOkL4XtpQM>0`0OeDEEpD! zh=GV+UkzDwIj{+)R+Yr`-~eOJMpdBMTiIRxjbyZcC)^q9bSDh zQ_2zcPh%%*)fhX-TK96&Db@J4!Bw{x$$LW@FpbYM;JC3e>SqwMBJtguNX z<3;#%VI+-)`l#^tr|@lEPrMQn%e<@z!ShUCSTQeeR2S5HW+isgpD4Gc{2?dd$Y(@Z z>Qh!h{UO7A{S>Vx<&6C;afs*RL{S?J{JeWe&G`-Qszfag<9uNJs@S97vq(=e=;S_%lO-jEET0 zEa@DKFGe^fUQ~Y7Zw?4%dJ1MXq1!u=kx*(^WhtrX=hk9)vGz|f2;n0xEPCd*@(y@C zmz7)}mF_Py+j{I)DAg3r=}7<|0>)HXhL%E*^o{Q$dN`k-7WH7etUqC^!g~S{B2g$E^U5t2qAJ?#FYLk)CSJh#rCrh4k5%-l35*8L;1li>STCi89kK z_l417SL1+b<+uJNQf%BE*c7`a31(Wb?o1cFk!Y7C=buV`yQy>yq3I)YdCO=)0g3&4 z{8y3VpW&q_DV-%rgpuwB2fr>(*THa&bE%yg5PE5Tisu-X29AuKk$+6qT-RpF0c`6i zV`h4|x%<-YkrkC`UBk%}$!Oy3m05)C6~;$V>I1Qf!*HBy(Jih71wlAg=QX8!3G;ilHb{Uxi=O`f$Z%i+q-W@o~m)(w>>43v~*y&I(~z2O$O%pC-NeQZQO zQm3nGgI5yPU)z4qxjqrl*_IgF!&q8Wivc^oqTfThBO8g8k=?)(R!ZrYu-|)}^2@%b z#Es+GG3SJA424F}z$hiZc)u{(U$;rbm`d4Jz&LI-h&7X%P1mhB^WGG@TyLsrLoesf z{B*l|6&nkSp9q+7M1$e9fTyR3**WF0$1ygPb(pC;I#c)7SYYd@ZZ@PjlHZLNJq>O{=FC$1+fq~7ZR!ARBpRD*cJoM{|c!2$L!w!4%M=b84?X_jATYGA5)GL+HZyzQ%)XxQ#dQ7Gq}MK z@86TyF{6tSF{D}+TjkuYG9*4+NdIQkBCl(e9?a#s&PtQNe!sR~kl!;Y!<GO| z#=^Q1n4Wi3BaiFdD^+|Q&u($}cnkKz6K4JAn*aV{$VlWm{N#{~mo>`2* zumTSh+8s#Dc^6M(>){wFDY0!Do3IJa-`qi;F!{_VDwVatapMWOF*4r@>O+7_DD3Bg zNhVT;9*L)lNE^q=o>6#IU>cM6>l>#U;6Z{&$L#jXR77*<5{sUAHBcKMv?2RZX4_hZ=iku^px(~fz3;V#qls~;sLCD#}yRLOFi z3uA{2W;(0=4ro;7o}ATpTGJS$o`;oDou9o`W0s2k04A5WR67OS!-8kv8HFn16=mQ3 z!LHO89>R>`F8Xk7NjU80x>V+cNX=8HBPWmY`O9mnk9tebDLNGO8i5-MUaQ0IkrVL9 z*v><9J~SE8jb}~1s;vDKS7n~GBr5wnA>{um! zJL^-~%H=&`xPDq<0~mf_RKE=?&lkfiXNzqQODd)E3Y6QIvIk>@^3^cc3)(9Qf!7Q+ zSYT1r4oF9RkfnqFHy+~=VZ3NxmU^(e+?x2F!1hDxWH zz#E|SG8O>-KQP@SDJw5?^Ztn+7fs@g2)H!3Yg{{-Pffxr*vto?fOr8|00RL(_Oq{1 zd(~Al%v4V=KR+6)e*ueKzIt>P-M#EeCsF(!c)!%Ccg(i$ia*AUZ&dQ2<0-x>>9(k? zz1-W1CBa+%nDZc?J6jN0(&O9Kr80IofVttPFkSBh))FP5qb`3u35b}Uz02fb3jby> zV8{HK|A^|ThwS!d+m?%@S&t%d=4k?1B=|M&J4`IrFi{DvIv$~Q7%onL2r@~f-kuC> z{jx+$U$;jES{o@^{>)$=ZP0vtK@qi!Y?T`$7v2Ab1=|KU`xiR}@K)R`cJy-W=ACR% z?zHSQny=dFfxicwD7E^o+tvAB85}5ryqMh;7D!Z_m@D|8U|==zgLyXT{P!K*1nVKn=EPp0JHq zNnhmky}x!d>eVK#U@~6RL?9j>6gZ@%c0THb^NAJ79Hc@z53apq&M88USUFm!{Rp;J z?{BDovj9^1PQYTW)f$y)$&ke=Tt9XxJ_V=wbas#9HtEVuhcbY~K@KSTgdWrU;@LIn zED`D2D35(-jKY7@0G|iqDxwLhV|7)3hBty8Y`{;6*pAR`7`?q~+*GC*!~I;x(v$Zv zBlUY_B;U?XY3KeZ`v{&maEb(@&<%Cf{#hvFbik-hx3M}l3>fm*Xssd*j+b9QvNIsC zJ+RsoTT!S?$fzlz0*469%ySl2i-Tk5*7O{57X=2B60*<4cgG28;8&-Bgq$_XT`szE zJCVhLtFF%?t?l)!Ti{s^M$Pn{FX4j05sYa@a>S?&FWiV8C;uzsW~8u5QhH@zGbAyx zI*r>>)>>26$j!22UWu?|zqP|_SY-U_ErAEcGe=0%6_p;1B^!N0(vwR-9LE+UHu=ux}M{_S9XUTem!oBn5O~$ zDOJDXdG0=-*dNoKvD~#>wu6xY=6TO#zja023zmdkrvjK@2_w15*X6*)U1)@VERmfX ztElWZ2t{(Gv_E6HS;qUZg@mI%wS}W-t(Ve^Ca~h!qP(hM6dh3FJS?0hT>3d31Y!cL zLd>!&KM{-3(Jc4cBF0WJ6Ll9V(LEm>{C9a~aY9gFMaX^kca{Cb|1ltUe)$%Jr*L*B zJ$Ya@Pd(w#)q)d?SYZD8N8iOVvBP51OMdWYG1Vt$g3|(i#Mkx(;ko!Cddr3c1d+@zAHr_Hf3nPP-4!mWY&1$2v zKP;)>*=3Y1%CvwH=XaVgOh(8_WTPh2P&qZA$0O5J15wrfYg+EvI~c7!e2qIRPM)v? z;QT>OEO64(Dc&S(m}{NDenLifpyQaaJSHl6LD!sH9AtR_9lwwjY<@9v%;r6l8E z^haN#Zgj=&#&+2@(Lq81E= zYji{OZf+pzdhXMPqlb_H$6?Sjz=V}+XgeSFc^#~z3%xX1$A znqtZuG=5~Yrl3SykX3Vov7}{0aJbBIxznMdjbTOTxX#Vyi*i~TN;wcTgHXnYX+2)8 zvygd5;o9(sz^GKKE$Lvn5~*uh$xX6;)jUeRV^R>-zlasL{4^RpBa#1j`_Xk~0-Pua z_1DQXmj@!jP=xWal7>y@9Ki$QK{;Xu2GJ`qq3q_41GqdD4)W+Z-yGM@fOSlFTjWcZ zbz{Ouc+xs50m*`*iHcMk2$8CEKCg-bmofW>qRG}I(i{9;i z)n`g;2ypDNOnX|1^Tk(BG>fx8yW%*WC~>|n;8FpsC@Qg6th~Jl!q_(+j@P@Xn{v?7 zYQ|kaI~{ooyiT>&nb-+(+@-D`Ir+nS z=TaJsYi9u>ck4Ta`e7jbSPXo(19u2F>{~o5qXgYl6J5lM+yQ}D>{evmORoQ9dfJ9=5CF3NzWYi5r7v&Wg#vg z(Y%-hl2r8-*U~XYq9)JBZra`;1;D^>lP@_uT%KQKYdW|zgp_nFl7{?VUEby&9(lw( z@C+#rX1Vn0j~H(DIa@&owk&a`O(&t8*60?W7J5Gdr|@Ux>O!8pz`C zBB$5`-|W?4#=@?)Mpx$7_1E^_%@^~kxIXcg-Br!!qMa`vc4B2Me`_ubC_hsN!_tvP zy!e8-uq~TVbmFG%AZ*Utv0-3R_-aZ)V=PZ3{cUO;(^6R0fb}d5?)HrDHGXm%lc*U2 z7guXD3LS3+%|#{5X&+bz=;O4mcgtD8%~k>?6e`Ef>{pOkvKtTr@ve51+#5x^%% zMV;11w7r~xVBTl$W9Wyv`BaDd&UJWH1G zQ`9)h{$+JoU(M>5?Ze!eoPfT$;q>tE z1Vd_?Lv{MbxxFqc3r6uBp-1D=SpX|Cw$R2~HYYbum=`2GX@q~=#^S82Qe#LZVXZO8 zv>Cr+g|bhg|0Z|-JF0|e9kyk=mb>de)n@AZS+pNBE_4W(QzBcWcO=^&v2S=kWuANp zF2A;oxU0iO@_e=MQA|z!WAIZlq)2`i7sW03?I2`R+OgP-(y-tGGKpq>Wya-F!d7k` zai6BT{;j}R6Y~33{DHd|cubF`CE4Jgl7}>beP)PRPUqD>rS6d0Pb+5099jau_+f0` zl4Sm&iahwZY>vN3Dyi+=Kt54^`G_D?Y@L?dajC%~03`CEl&qv=t{3hBdqbJIX%+Q( z0aKXYeNzzv=dp{Qb2bLf4;bEgqeQsFi65nZ6qDbgq(8iHNom3Gz2iJ_&qG~VwW@xX zZ!Pzn{wra}a+hQMyY(sx_*=Vq!zOK~wGXnCUJBUSCUG)<*5MlNdUV(u(u`WauWtzD z&##1XB>2X0Mo>xm({EMP29K1Us=l7v;8_$fb1Dr7?%(fCY=J)&t*n ze{L44I!H#5&66h6_1-I6dd=@}293kvJb2&?lqEOc>6^_Xo6oPT2-tV-iX0B_ApGRF zLu(|@3V6B(H>@(a~UIB;0Zz?P7)EmY{^ zjvCgnOEez@qPM@(&VIR+AHPC)7=IVmRtrv0BrNB-Q7gI2^k!Cy2?nO^w5|R1vCjt* z6U0auugL650yo_~%K(WKi&v3o0)z;N4Cmyqp`YX{~GWd}6uHlleR?p|s_CQEvt@}rO@+0QH~H8VL6iDj7sPr`tTEIoI+O=6M6!g`9*e5_VS?n;mb(&b zc~`ER$zBZRKeJ0RI&{Olba+BeTOFBr(p~tR!bT4ynp%bIW!zu;nz=Rl!~{qYwMN=B8j_zuP$A^6k?jLY6>uF*yxQ;AWv)sM@-I z-P?ll(BQ*=IzpR_?{cbdVljIVwO8U5E-b3v}=q&pN z=C%`k*E26UPCdai{i8V0Hw;uU@A|fU;=XWzMa@M!jeWrm#V)2%vYjdU{%10De_v>< zNsv);QF4)(SvE#$s^M62wd;a6f}6Lw5-f*fex2a`FB)Br-F;uV zstc(iTelGg!D$Hoz=Xzthm6dW2(9_&Y4wTCNYY5FDmPLY3^G@lyq6d;w6g=l@;860 zcEBeeOG=wB0LkA=_sI$e8|ItpiSn(jp#gtaM@rfUH!drQ`1-Pq7fB$?2R_1I;F+0c zC&iBQ*q1vBGI_cC$$t~6-?6o^v0x+6kDiBZX=-6U)T<__whdl;&FR7<`mSKw*>i~p zDZ1mshbAHN+PB?lJ6)YAJ8!NfYFF}ZblEe^8Ou4O@HeA@-xK{?_e7Sd zDJ;YPEZeZ`uY}vyv-Q2cPP^*?(`I+B=8gmwVQ&^~%;V9kTOE91XKUIMDh#LS9wQia zSi2C#vznAiA|+MWTM?_(`EtKjZ_PIP7;jOn8Y*F~-x$8pu@`OMiM#bVRa^wrc;eYP z_{(iO9fUvI`ryIf{|Fh&36EuJ1IeJ5-)i|MtWpV~5#txiQJMAVV~y^p)EVGs(S6}Y zhHD?hSQ2m_)W77symL7AJgi!|VLrV$SKa&3Wn8}@Z)J-**K=YT@P<`Od*X)Ii#h4-BseuZ#1*l z^`D-JnD!%2f4lwrg(l@23J_yNhy`;>Ijfj1_|7PX@jTQ5A(W{ zOQ+MlnaSXsc`lBZv^bl>l^=_o<K!!`lrRs#dg92$~^7g*E0?#mu*GxM{h!k-;EBjU;M5S`PrQ&wm!zW z^ueWH<5dHY_m)ep^?_=9tWfqI5y~gtVb!NpT4bNg>QhpO?qEI@l^Az?$uh3eg<@h* zxdZi&ICh4wqvg8)CAyHnus0DK`cWonV;{;*)|wn{u89vK1rsUdhiFbTOWO~m4)2z} zxbA*s#cFo3nspONL-2BIWA#O?y8^zXLo-@Qqod``#iY2|yV;Wkp8=m|9xOK){tj=9 z5wnmWe4n<#+8|9DQT!`y)uIgWvMzrnL}O$RiV|xq{AA`?IHxB;HlI%SP3IP7bR^1q zufSw96*AvTqkGUEqN1wW4gl&N;Mj3+Zh&{*&cjL8!0+0RechDP(ZbQmLy8)jvp7X)cZ^WG?`f_1`MLC840}=1Ra8gE4c?!`dCOm5r_4T>B+~wL5~x zdLXqz3U5Tk2!*}*V8B5TyeFGOn_GEV-y9a3Z?`pDR)AF6 z9GuBot5?igq}FeC#Qble*zISY57k*6FWc2CC|cgQPpX2jX4nUB(Fy&`+{R2!M^|&S zC~crD{#lhR;`U7aAP;wS!e|Lhal?S^Z?ur5ni?@X^!_?@&-IiYrh7~#4{FZ`4V$+r z(?nWb2M;Nq9>puMPgZrdH9NbIn0tn%(DgL?k!-Z-uuHAg4!<6Ss>ndtQMO#BZ1vrK zYs78Q(}}^VBffd&mrrSW8Zvb4g+d}J^lUAu^lU3i#H2N69s3c z_EV*>C6ivCqE8R&_OGcYR+_Zhm)%MT@fnv3ZHXK*y$;29qpMl#RGx?%m1@t%5m(Y^ z^1Vj#eHYrX@w9e)<{UG?s4^?7P=0-;wOm9j5Q}HWrc5%%3i;W~It^W2AM}pGicahd z)n-P3Hc7DI&u&I6ec-4dQ7Qg?e|O`Y_~-g1KG`8=-?G8Tb)K}Oacf)4DicA$7T24% z{9N#*V@Rc3T%ti_@;-2hcOo6rO_ukAUpE#{6U|o~(xL!I;(Et7U|Bxc5cRxfvDK-x zEa}Z$&8QgPkEc;j2R<-Wv^?-68lp0r&=_C#=Sn2bSTmu+e(=Dg@?aBcz7yNHhfz~Y zeBbj5lpfU6cC;?i_P7pq)*jp#sW9GKjKFo2zCC&Hbai$8K*YV>M|}5zU}S8}bjp@z z70?ADbN~S_iFomLgI914a0-SXOy8Hpd8h7YT|KeS(L2~a?9J8?`V0>HBvQI*csYI9ss} ztah$vryl1>?4H|2a&~K09Zyy!%wF?b*#;ZZW>pPp*_kKR$<(=Op}{>DZZ1mmRKrR9 z_VUC)|0$tHWQt0zdTR_dZo6Z5jXtsBfL1ETU4{82_Ln~NP`0`KYTHyrW-Xneo1Dbi zYtnI~(#lH1T0OgP^+0B=M;QsTSH0{)3)MEoIi&I~IH4WW=h~<=@c1;^Z z#HCz3hEvbX&sF>#3FPh0p7LBD^Zu4Otkic=jMCbMd$QTex8zX$_51M?2J8LN_ZO9S z8CLdPD&m?Bf#>T!+te{SyD46#uMh5b6bD8r^!kBE)t$U80;NToy~!yBR{1L@47O zJC4%Z%Vw^3GYPg5eW2ep!9r)rd?- zOt@J!C2z0nIP!u1QZ&%}6u|^SCnlZB_pXUuIoJi=Xu}f@4wsnCHt1oaKk?L zr7r50pz;HZO1X9W&TLw%p)q9eLyI^iT`4 zU&?<~oje0}B?I!Ma~s&%*(2<1U0q!jmYGw94C(M=ermI0YHv_N3%G2tx&4THrG#!7 z(Lr{V^nHIbSb%0Nc?eX-9Z6Gw zNi<0LO{@pj%^NX9#O*OFL20gLUFG-f*$zXbb{(Tx?g`+uNPeZj$mbSi7-#n{H0!=~%-n?d%B8EQoL( zZhAjHmQNZynLw#h_U*K$q7!tz-0J&iX9$f^VV&vjv2Q6?`Qx;-Fr?6w)&{LdFWGa};~6v{$~w(M z119IFN`Wz=cc+;6Cg z9n>}OFa1!F8ur0SBgs#V7t9xwqgkNP3LEnA<=@#h|;V*MC+q*mR{^q=8k$ z6E6Sf+9|SjFMZF2N7onl<_wR%40abcU*B|UKBel-$|g``C2mRn3!l0R3bzS&>f@c= zR~2`aUUxj)sz6y(c(Ns0?!q>U^xVSaolPU|`?QS)vxPaIu}t$N7M>7pI#D0577ryt zpZ2M)s`?C-cAn9is^zf*^K?Q0X9_7QDjG{{wLo&@rd^(;2Z1BAQ8F?F^MK>vv*X&R&r^|_BUN!q~xF0NW-#7$ZwXe z&zK-;vNu$Izt0#Oy#T3e2RA6{$F-sDDR!ZOov=zrnlh z%^B$|YVCu4nzpuyE|C2Gc5|_du^0z0l5uEFdG6ca*T;t_7H(?b@6gY<7Zc;iY+Pm< z6!v59rKL5X43Gwy@sDG%N-cS#1q>yMLHw4RHumbFhL)Z|zu^M*c>^fGb_fchkcN#+ z#__Rqy|7gi6X4GFUGO9B z!w&tc41v78IL$$w%p(G{$TMx>p+R2NrPF-X;79NXF(<2;l-`YrP>Sz#3k8FwEGl60 z7GZgr04WgxJ4ZXEhi6173IbmcfX@?H(+<_5au38`Z)_+95WVJLSk?$C7x7*TSDCD` z8q0n9^xE;=8o=0on5?Ti_S&&+^CeY45ab*`9{Sf$1b|1tJ%St?g@YJG^*1Hx9FUXi zneg@o7Ku;E{f{8Vkg0+nu$68p=7kT)>9_OsJ^jmRJa~Q8bWEQJs0bT!9^St+$+pdO z>D7E7Y3}p)w9m|FJy5n@iTbg>mHI7u1KE&aEMa+R5fi^7>C_#r)a?iU_=!PFPfAP- z(U*h><>^L7h;J>!*xg%NH{GKgmAaIE3uEN>+27GEdT?7`Bh7;2gAY7N=o9kJLS=XzsIhm zdWT4fgjcWbrKF^Y>^18y~sMW9zT7$5)OIV z6pDb0iwpMTbxtAgE1(e`esVnM6Apf+HcsC#p&x<&FhB3(Io6e$#v|NxmM`!PNDr5A zHOV4<{ggYVPD_9OdEB#l)>c-SE&pWtcA|5nV)84wmFWx{^$Hr>-z<>-w&;< zN&!}70@-xDMm+uVw2w&f5yX6#!cId*V7V12wd#I;s)WyokHsaZbPwDISDfm9l8w^2 z&DfcKYM)+T2vOjO(sFZ6~vqnrT3tQ#@XdDV2 z>EDH3&R~dnBkp+PjG`c2YIZWm)s#x7eR8_xcla>tix)2h!#Zhrx29f6n;uAMaoQDO zve&sT?x6Cd%FpZgi+!f;Bj1{7B#5ypQ3bv7YEKica_N$WE5-$1%9Kxv#av{?zTcP>=VRZ;-7?jv)(EEUB|t5N zy^<^dAIE8Ga&mn6Bi9-{W|7q_UKlNm!N~l`gGPAk;TP9kLD3RSXoE1wlg13WkoV_R zyN*2^m!+LtNl|@Y$s@okUoz?@)E@%nzpFzMwDw9!ytB^C@LQZ8P5bQ~@OBRsT!fKq z{&}Y5<>kR$y~T*~24m98_wPINajc;HbaZa!&p;_y2NCh3C}Bi#VaH=9AU2EJ&%vYd zR)zIoqNztZ+AMc&-!5xSHN(`lp_h?Zq67c}_~3uiEG~!k^jHHP-PL*ODa10doYcyx zvujRt0mEfS%l@MpVPZrZ^kw8zBu7gYoPZTBDBP>1=jJwLe4sLtm_Pb*;MT{;2;AcpVEO$~SzG{AeZEv=B@JJYEZ zuj(t@XjT}x1m!96>#uhc5`OtoAFU+n$CSqPU1VkH^s)CRzBP%N_*qS-*J7#1#1D zS~fPKECBcrCQr=8w`)@@ib*X3c5O`sucg4L%4P$#&~o}2s3ww*1zX3E76uIb=Xv~@ zy*mgwp2(xwFY=|;Anb4)a&eI<58TiT-_56RVuV`DH|GcLf#O;%O7Unpg)Jslgb)Fc zH#=Y8szc`Hg1??`I5*hz7ZpuS(!xah5&#R{Qw@NUI!Kl>U0c&F8^POH$?W0&%(H;^ z$(>HO2({uPane5e!l&005U}Gz$>~{V-mu~zzL%v@ng-23Rj&Eh9_m*U%HW+4Ff|h$%y0J zI6`NkxJV>9p|2?F8gqA-UJpKC*uAL8NZO#{)=d%IdJS7>;C=tc<1YfpHyY32XOF<#Zz7T zE=ltp*t~i3Ei7QGG*|h@%(FqO_&_8wItQh&6=#{2hb1F@D$C`0S|vaj{5fOLCv4KM zojUFDYD7sY(6959eDij5b-hK(3d7pU(Cz0bU|&WIKuqihZvT@VKUAU&%d?D(aow5o ze1V(R-QY^6ITXzgB}dp?ae@7SR$RJq=3ACq#7ZG+xb1wrEJf0&Zw_Xo8=~uNvD=li zhOuM^QcfouQYemmn%@>kmkfxf?X{6o;V&CE`i-;o-qDqmK^g;nMWq)>f4IqoPjg|W zLt067rgXMK>K6In^ot5v!zeT4RD=xrreqLldK2#I|5?;E-Wt^%o;`W;X0~*BL9KjXiXZJW!3T=u@1S?UaF550 z@{4%(e^Z2rC({2E$V^k!V(|i={5T%v_7@gRYwGIGfyNRsfb;y}!^s*rS-g%uVE239 z3{(|_#r7BP12=3pF7lQU3KyH?Q}LEQbdvWY%sB*JIpM{II}0QCoJiEz7r)=8W~bm3 zDO47}HS^0@ujVy(-vq3+_lMa|0PzWn8R1R)*s*s5Q}mgIwj2kelZq%%ZZYGcqSpT8 zkp?FNJ4eR^F!i%iyvDk|4Yi0*QlW!U5MzPc)8Zh3h?v!*a>RR_$xcx!3ae-HUawXo}S3Kh*rLwKc zIS5zhd9Nyh#~R!#|CkwozaGG3QX%5rgOXkeVsE&1S z0n+k%{(O$#n5Lo)hPo;DvxzCRz>8uC}U4gF$X?eyfrVvJYUr4D4j5sTV}hA?Iv zkZ1k-!>`xdo<&V4VObIb(}WOW3tR|+Nn4mwOk)$J7*&xc!Vbz|fELHGT3O$naW z0o*E5saGjlwcPy_!K$$tHezb4#h$~WpTtABFBxtF`d!|=8wl|p_Yp`rKMQuexAuZk zhugAEd}R4SPQTK45{99mk~$rr(JJW|GdxF3yL@NNyL=e?M)P{PM~kNPPp6t5{r&f* zCp)03Gz`~6TnTI;AT(ktg2F1;G%hYqAN;bDw|x!_lrbUrgiARC)LT22+tmELv{1Sn?ZIfp zfglx+aDRQ&uR&ntL|c^yqBwoFSxNAwd7)<}Hb&LdM%7uCH+Pk%I;r}BP|ws zj@Wy3aSNP?ii;zT2Xo!aiBl{ z8HVBXIfS~Au*-O-C_u^_UqGlR5qLDoEW0Hn`k-M3Ume5qP)eBlL<3&N`rR5yu)v`r zuRiZ@PSksbasy@748fq0c!{*j6W{1{^7{ffJ#b+z!$lzt_ZKf;_Pk6^V?BclrLr{J zOEMDarJ+}zap993Tt*a^2K<*eqw8Dy7drqguZvZV#GQv+sN)S;WFp{KV2wLL8jkkNIv)YSu9|-KMtC z)<`5TpmSe>)R;Tr65`lZ6oq1E5;uw_aEE9D^4G0qPtxs}7=nFLR}ctWJx?}Kf6e`$ zw{As249{l8q3Nxg6dw#qC1O{~V(A=cf4Pz(4&}`kL{nR^?5Jqv2-@ZyBuqPA@(dLPgAC4sqC)@HW8>cHW#Z;2o;!PYp7UEhC1XTqRa}|jjF^XL_t~`wNls~1T--z7}Xe#m|`+)^3-t>9>~|b)ju8^z{Cg7hYF+r^#1<0 fA^6{K>oV2OsJ7-ua-A<`!I#caeT}rkXRiGZ!l%*6 diff --git a/api/_images/tutorials_nihcxr_monitor_api_8_0.png b/api/_images/tutorials_nihcxr_monitor_api_8_0.png index 7749fa1a0df1bd77cde21608ce251677a79f3bb7..2e3c0c223beef8165dc5869a36f10ab5e217669a 100644 GIT binary patch literal 53743 zcmc$`WmHw)7dA?FOLrq6B`uxOARr(ON;gP%BZzc^fF49ZLg`LHB&9>*kV<#QyAJsK zzjuuL<$k+o42BNQX6?P^n)%FU?l&50Pq8t`G2r0fuoV?#HR0e8Kf}Qxl%S!2-&}pj zdkp>&el4f>TFc4uwWpb@1)QqcYiE0>*Y-B%v>q0&ZZ=MiyqrRu+>dCjU%z&Cd&0%# z@V^T={Y$W*IS8gEvK_s<&&FovUi^@bAb{}Jp8*4 zJhJxvfA;Y)KGWZQmL3pl++W$n@-Qzt>h7biAxU(9VQyX!+x>;&?L!*~8;Ef|oxzBx zXqcFt+*l=j_ZJoEHW_o(*VlJ-cbivIHRfo;S7YE%3P~CoW{+FkuQ+Dsd%9C#(U;oe zX9&LS=;(ku9C-aChgg^4Zh6htm6a7d5)$3_rSIfmf9|>I{v_wWha7+Xfi5YNQSp99 zejy0O_p6>E{cl6)|6Tk4eM3_4&;Ng0aI-OyK<_%`yzp)#3Sw(X@W=J*?-}Umg4^08 zFHW}mW~%MtytkIV#|W=yyE)ez)){i5;85!QJu~Z!Fe8-@pIvMZwY#(!<18nc@I(CQ z6v;vp%+uL_X$3^#{pMfmV?>77ss2z*GHpdeAalZtgKJP0#<*f z{C+*E>bL)$srfkxSE0w&3_LGO+k;A4 zhJoK{?``LE`QbfwE51FsQ-K6qo+jf3Ti4CmTCQf_Q%h(^IGmR#_0rN3shAI+)+ZHV z=(%GpMOxqYj&RKS?V4G*N@RX^_L$Ro-*SEaRdDpw)M_3c0&0bo`jelZJ=h6pZfsjF zx=J5FaY5f(S=ek~yT8xJV%tv#MZ{x?i39;>P*j*iT81nud7Kv|b;^y63hW382{rWf zm0rDi6@JaGTL!Ti{q%ZkD#3TRIqm4^=%w+;y1Ml2>}(9d+@*a=vF!JMc30a)52iY2pi+qN3vShVGT#q&Km#rFgEWxYYd;x993QU-a6S<0J;tOagl2 z>13Q6=bkoqzl;-;_ww>O=iuZlyWmo~SFGYLU^BKOP2Puk-(I1E$6@5=uJF5!6`a>= znJYCAZ1CJwo2_wpQSZt`V)Xi1!KO;ila&4Cp0tIb-ri>l_cmB$AwC{LPDK?v=g1-~ zT;FlCvhvE@-29#IP6#q)x}XzvZomcWT%)JN&R2(-D!M06G96}XsE+r8g5a{%tirR@ zzfSkhey!`e2n|% zckVhUdf5L!e5l3$QbAq4(ED(WU(+*(f*1Koj-?}o{C5fX$0KHaF}r?GGq73!UCUyqK_%fu;XK# zxRRu#WKK&BxdrL)y8WMD6cd4{%@@I)G=on_sIzcdZ;&vlBcP}4@$98#I(J9E66WVf zrERj}wwd=nY8D%lCbC>YaMwhb(6PSKT+sCXAf9M)O-iMZo7Z-x7demTPsc2nU<~z# zKURIuc;^&bl;NxMA7fRH9}WImd)sQK)1Dfuqsw&novVttAj)&wsYUj7BJ3hXJVJzC z)Vvp8#-IPQ{SQGiq(@>DqSRCM*aruuvn0a}VqG;=4Ug|{?Yglvt2bpo{7DQ_*uG?C z_VeLX_1s4Bz1sdPLayfF!TK{rX83H8GxSQ-t=pt=ZsX_uw}cuLInE^g*A!c`4mHI) zGdTOD?a+#U3)`%WeXr%gzz8|Xp?y5lbUQ=>J^r4@5cx}yokr?v7bhW|Yi1Yc8=u2e^b7T&@S7^I(i zGD^#5R`cJf6Q49Y#$s?)G?b8b++oM$nW?JLctH@`_)F5^V(hSpj)?*81D&%YiGK$& zVqMp4+4uLC&mr}EEp26h`{{GZ6s^%m#2)tH-!#uIO`rp51MXUPIswtS)-$YMJyI~H zopzwsHF=o#vxq_%zf7n~XXBfo8Pw02W?k?;K~a05PbA^0dn{xEl9Iwf?!}Wv=(~iq zleF8=X|4Rv!~YbN{EM~k0QEX1p+>=)p$GQcK(s5$3KuaIijr#0yM=5OHK+G!1cNL+ z{*Y;~td5}}YZtw+%E9DamvKhhnukw%+R=Xf-&XScw9s&^C@=RrV8(RA{`0YYl}@ZEYjZe;qqfX?Jdp zCzr4!CMC&uc?nHVPYdO!mGl)!Jmxi>K$>bLp}gUS(g__t`lmoewKb`2QLla+OUJ>b zKR_P1?k{*1@JN6~Hd=hFbTUm}tt7K(EQGDPhY0=feW+TYlnr`+Zc*x8dY523qlSaRfK&G5w-D{qA}8C-`eoydDNrAblxB zTngO77aWx%GD`3y=4@@pS}t@l_e+@q%-M9Yr1+S*W(wOmRlp}1IR}VkrJTMhiYXs! zerIIbylkHc@z0*cJD0;FN1<8tdp(9juQd>H3-gGzzbd8eI8XDhc_{Mu z9h2E#4$NfUc{0DCNlbOij;F|`sNHpo(RglShBAdgY!f_K*0b>6Di>GfxL=BXF?soK z?bM5>I87;5jtf*claUh(7BAIO5sLN7wRC}7Ni^37riBu`aQpzdVd4b z-y29z8PE%X#;gOjTg3Lnf}Y?=_ML4Ocd3)Y1L<1XzCIuyAwn#%6InC^sPJ?=mgR z>_4Yx`@4pKGz&w{_|FdRs3G2X6ba&UaQWD+pVuE3G>nLxS`h@_Tp$_miBo zL10PAxAzFeXEk+5>8)ty2GYiyO~=u~qvdy76-(I+zSJ~zBIEno8Or&G8r-x6B<+4C zulV|XaX2ZlCg<%v&NAwfeRrDgdN;!hD;GQcc!Of! z1+mNY$5m=RV=y**x>3#U6f`;nSBpBLnGZqABKd(o-y0e}y>`N3=Il!*OTu#sw}D)Kb7 zty}^;%#K3Uzie5YJ*b{+?IeZHSzQQ;Eto28p1SB33sGJ@VvU223-wc6D9xn4Q3`Bf zKJx{lj!>ILWKOIQ7ks=gNAA)-F%Yw;w-Mf$`7$IAStEKrb*}f7-n%*Se3NK1Y4T)v ze}sY6R~_5Hh9y8C?ZhX*z?DGvA4G{mCFTeUY%aU^xRAlEhvC19H2#E-UYTWLW?I|M zuxUxAM0|$^z2PXA;3zI|ORC(hOzT%YN z^(KTc8~hUw-gjftT#PK`Hk(k^1ZR)<@#o_v7@tS>+&AIf-iJG<37d_;V#Zx8#+J;O zG*q%Q;z1(k?zP}#Wf(6!hd^ASoUjjbBj-f=rvqx8!Y8)-_lxfwA`4I$VX{3rI-#I_ zd~U|6Yy`Pt_1wXN{ix^&-U~_R3h&!e-71~Cl2^O0^2J_&IBDBww7MsR_85hQDjc6j z&iI_rH(NqaQ`1fS_ym9 zYfi5tbBlETZV^S2kC4ejQJr|$oHJ4v9wzOFP&c=?C7SLcv9hLjFTJz$-566_a<8Dn z5L0QSu!pMSLd^yAIDu$rVMah4{)N`1cY*7C?vr_)1d&qkJUsU}GSs7_#5NtE6L9*~ z5yC@~UPz2IVW{#QgAV_k3GwW6jMD<6)tWEO=>#c& zhUW$VmjsJbUllZgrwV?ATXhl|X^6KDPCP;tT(0m;b#Y&Axa9#MN&9b9v(?*w!Ei(G z(=1k@qNBB=pn;l*E$4tfhz$o%>GJn}XgROFSX)s9Nqy}C_oND}>PSH0T0nfMX{Jji zJ7cQEc!X8eP}~iC)rvLc&0brlb1%`_HB))p!mTZe4(w@cDwLYId9VXGf;AACpLPg? zFgWf7@E6LM0Tj>JaEaURO}DML9m~9VbyG79caB`Au9;g5dxB6Z2tJA4EU&{R2c*Qp z33iSlzmnQ_P&X&rlH&A?5@6Om#$8TA-4`~5(T-aufxwB0(pk~)ty>QMIfQMnk@V9* zc$9+BI{j4Z=popbm_Z_ss$;-F#=bLH?mAU~m<@xwEmfley-Yh{hhS}2(|D5ue z-EA5o*LwPFXO6QQ=^1ObzBJh7tFud)SsY7~II1_!r>gf37D9c=jr;!R<60*)e#BxK z=-6Yh==iTsVt$uQE2|y8%6yG5H?JUeWC1UlXy#}_j`gvFP654F)8|sI*ly#a^Le#% z=XQmiNOJPOeQE!~mE#u1{+-_UL*;D{cOy_;gklAE;VZML=3)B5rdBH!zZ)Je(TRz> zBB`gk0I9n`8r@kQPV`R*ngqzLK8A9QwGg4|w{5R0K7S{3udqCwZwZPxHhP1*&_ z97=96f=t%z3oK;qgiJg_tei&RRLcyod3&;!?{d~ID@byCGiy9FSSMglgOO1PXv{lE z5SymfU@IwgHX?j*d>!(qw;@%8|B^Y{S>T(Xc@*Yb+;hk|h~V-Y1#lM3IrErnqL_nd z3Qyp0e`7X!+?$KSN1Z>xJVh1FgoXm?TOr0kHJF}$1j~;S%(Pv}4Sx|{dDU6PQ?dW_ zetDZ@rDmW?0;!TwW)Y)@A;9~g}FPu?fg{%9X^`E>oWm)d%>Zy43&>=qYX3!qjcS^h#b7>XL@U$| z2%7T@kRmL~aFpsI0f)H~x;IZyS>oiwhDBrHfeb@) z5|ms;x!I6UQlzv*RNSP};7UpowF=Z*>03T1dy@Jz);C}I>+NwTr|RA354dbcZFSmH zI`_%_{L$t_>Gv(F?w? z@XmJsy*7oZ%F+8w^U?BgM~KQ<9o$wMDTyJzxrcO8L?;X4hTD+XQ)y|0;Nak+?b)i>~zm2*silsu9!lwH$8pb_)p!Ar*3_ee>o`_4Q*}HPx&W z?m&FL*x)S{FK4W)Z|(nXw;xBT=W4w&ItO+3e>g>?MnRd2`B^!Q)8guEzdu_z>3DO} z?c#4Cuk|pkrl#gQpR1oK?Dc;?Wt}a>aOpMs@VmIU7`pZ{Z7sC6UZul8vaQOYgAPot z3d;dU9Dw?D(zD+jh`I~H|V zK=MYx*tvP^Q_8ud;D32i;JG{h$gmC%F0Mz$(2(-u(>TkY$*e4*3)>#cuJw9#&UCeN z9u#2PQ&W1(D(P)qZyvz^7##fcqSCVIVx#1@*K#aNlcky(4ipM~Wo5Pe`ic5BaT%X?o8aLcc+dI{$JPmK@|5lj4YinIz7b8?IVW*a@7uyt5nF|ZH^ zMKSej9`6FUWoU5pu23Dxvu6i&s>VwA&JFhf^0NS-`tI$82)3_Z^LaGpfNllGWl~ZS z&^qCrdQ9R+rt?kSen5X-@6I>--9iu5)-vJY;e~PrF$D2*3Gm=bXWCkC&`tbN;#6xJ z=Ep56oT)jE3FROQE4h%mgOBA zw*|~!zO3IUF8Sj1hM&_vrkhSc-(jZHtz5>Q(o3|$pM9n1F7A&>)Kk}SA)lm5JrDuW z*%2Nl;DL1?S6r$m$zx;Mmi_5c02CJUIWj#zSY1t`+@7vP&DuUSyaouZmZoNh`=pUB z^a-!k;H!x*FW^M?+7MxnK568ckdpE}2p&Y!mRRT$?_kG zk0Jq5;-|W7)dP-^HB_dz)KdrS{h4r{agp#OdUt+LA%)O;@kaZslrS3>f&*ZFQfIH; zUBf?tt1L9Lo+EN-$3S`Kyp;ojCg7uy0h!<<#1n)DW&Nt#52+Kl&ZV_{);|vn}t1C29r>aWl0mo~Oj97>f^Ejs} za9P-(|5V9rbC<*KvzD*4QPi)UL^+d^!%7Z$b>59J-Uzc{J9{f}vac8EJzjIhBLRc- z+v|@)vXBT!p>bku@+a^8S~6*QtucXRq%j_Fs9eN3L}@aL$Qz3Oa?yQXyC zUoai{?(t>j;Jg31=)KzOPdzw9G`tC;v9)n$4jcB-c!clJVY`d0G@zsylJS6+pDZNA zbc6EG$URyQNjNXV>xM{^Ih*12Mdy3T>Qh)o_izvA=?&fan$6)(lQS0QdR}w`*A0mhfaxndf&7Lb&mt2WXWHbo$rA z6tTsYY}X27S`lV={+}-$RSnr8FTSV5e^igjo89wRh4Q=E4d$m#`W+-7fFNO1j? ziVeEZRp}kjMP5f$!2M(#tFZ9A^K}|HiyVCoa25s{{=q=_e64T$yC^jSelmL2k{RQA zQi=aQ@PV2PF@7nipE;PY@SVC^Q-ecbxb8DB=)}36UfNL*nF%%2JzPkq}V- z?%R2PJ&y9iAC8fqKjD#Lp`NslR?-O(cB>&r&Km%U7YS!LlBSqPjOvM~#AyqqECVn_ z-;5L`pQk;n8%cMH#$O-FYT%DCwQ*(5dSWHZg1x(#oHPLZ|#O6$AvVD{jk6Xa$aoN_ozF{;d2&v z7NMvN&?As(5}1Iz-hJoC9=WGuB}N?8j(}mNuKZOv4-!B2BiwSJQbhX3Zl^>V={i_# zL$?aA)(;RZy;RYdM8zS}eRi*&vIt1Ho0US8J=3o~3%ew;6X5Jbu5sL#DI4|XTyNFG z2bdPC+LIOj(7awcl;uOI#^6I47o?na5c1Wf=CQh(FZJ4f(CX@}I>R&6eDVA6=n;LiPx20oYfn-50~}zt{wqEA=E9;P4M1Z_;kTw9!nv2xMc>@ zsE(p+z+JGxdpGMawOmluwk2SgXrOf1@(Zcq>Yry|5-uPtNYhAuFSRy7+3@=}3Y>3V z?0DRj%G!xxcBwSF6OpD_=_JbZ!AuQ9*weNhsy^;Z3y^ro)|UOb z==sBhoa}550|Rzr`Qy5|3Tt@3bBV*7)yWPAPrJLwB3yiWu$X}5b~$9`Y$AlT@iLTB z>ZOL9T7sF#huJ;vltye43*u$Yea%eW=*B4-?RDoXnd=|wY#$g4lRJ)p-p453(1q>^ zqI1g3{_b(bRwec0T~-|5_^6O#5@oE&kCyrd5L^J#C8VlUT|Rt&pa<=0(0 zsM!wiu1gJ}W_qNZ=Rzr3i-@^lavL~YCRAG`{2b#%^cgfl2+Mnv6^&UH zC0@lhi>3W;K3}1FL@!a4*R;xCW5U(}c%R!%TvmSa<`72z8Jf|?@+^=HChgF1L2ZU@BKvuGTrE=3Qt>oIrmNVMq#CL7=x(9zF5*Q)T%N7;@r4F9S7)8Lm z+PFsFW(^0oFWbthv@B!4kF;F@xF4Tpqmr4|<5%8!B@+FyAwsbTiOV30OJRIAukM5% zJJ&*+{4MXCMJeic1dZeu{VWVEA{*yGXjQGu@8_2r(D? zJlp2%N9tsaZq-WF>_A3X%r~X8mBH~KGULt%IMp@Ow%)ND1n+ovNS9SAD;IYI!t^;B!JGPvPdMF;~+>>~=K0xjo^YG*jHSw4flYHMF4@8@yx=c>GdZ@HRQx^7h%g9W zq~xc1YD~Ve);8#%p$9kTGcm3V!vHW>d`(q~$<#gqBwG`zZ43G-3W%0dSRMLE4<_jA zI&S`q(p$UPVDdKIq{>X>qh}r9RyRd_N7hQ$S245YA#1g$JkAkIDWe)=4B8`xe{EZz zRMR*gCSeq}KE$w>?6ZeCBEbRMpW+)5=1=YAOnA0!D+;7|pocz!_g4#|z~KJM%fa1D z1%y0mu7Y!mms|<=&{6tOM-qxD{U`Q-^z%Ihj{OxBd1PnR%O-!{8nlF%xML=g-!GGzb&o` zK59BgIg@um@>+&bB%;wx73FZ1XjAH1%CmS)St%ua#f8FUTZ(8i(lg{~Mg6qyxFH9A zD{@C7+nR>%l2rg>LIkc&aGJ7TI^ueE!yoaM}@FJ0}r$V@6NwGABiu7CHR!J2jNo z7z(>XgLr>v252e`Ymh#Gml9A`PV`aR%$uMEU};iv4l=HKh?q$G5d1QASirjWrlxUR z+SQ8mh@eGU4CrlZ*mO}~$yU3i%W;4%rs0Et$ebccWEj=i3oGi+YXHE`M!}vz~ zae(N04lII&UzStU@sTn&(yF*o{$$m>^9=Ok8ro=w)FCDU>AbAJc?dY08{~64KuoJ` zh2t{*i6QExB(`<4McXJYqDO6f`{xR|ISN=V@JVyIRIk=qQ2Y0Mx>Py`&+rBW9?{ds zjsR4d4OuD3Ez~p>lYWd$(KZOd78{`JMa;Dn#DkILy6LC4uf=}*=;o+guX7&82j<1U zEuDVl(8u6uB7UwYcT`s><9&AZ_j~Eq#X|eX_cXsB#nY9auzYYQg%kEYa3N_#kJ0iX z!cT~>QAU5O5aGvlgb_6TY*+;xkiv7`lzjvD7Mdp$vwI|UIPDtWChqoSQdsYN}Ll8vnl_UH_td+WpvRL^2 z&DO;V99%n3aEiW>0giR?B4CdkPrTQxLes3*YkVpARQkw>C?|NZ+3U+mTM_H48jQ}*uwU7=xn_KJeZrJiE{c$G6I$0 z3I=Vu0NvX_3ULN zMf_5>=HPp8pL3TIt6PyI2=~e8-AGR6J}?h8KnAFNimZ zzcuwuWpwyH)k_Nrq4_+s|q|f9L*AOA!%@7PJe0Fxg;}o08VmT zb0gfPsG%n#xF2vpQc9Wpb|1*ZHkWK zd(HK#v-PkIZU;NkPNw~nGn=^VON|mqkW<=bOkxU8$ETXl$Gwf81L3(#K(BKrR=x0b%jj=X{rAVUbfHZL~coKApDXLI1H)*2!DQ zb&3md3pps-Se;fo9z-%{w05FN)aVobaK;*A|N1|4RM~$E-$)tt~{kv zC@o^?18UjiP6{pUWD^7Sr;%*@wy#+a3<-qGAgE+umEo3!9L>cBQ)$>@)n_c0|7 zqyg<7l=%UVXj(EsC=yzhajto6MJZrl9QySV79P5E{gZEfg_^|)cV4q)M>_!rhrfW2 zhvp$M)%44d-F?P7eIH4-?n~L1_KM4CG#eeBBiw*`31`{9%?ZPAHS6)Ut?|ReYsv2{ zkR1Ix;E{@Tl&Nai}+^VwBP<~vZ`Q* zWRY`njx)ydA)^u1h@2t6@Ci!D{d-twWLhugRTQUmCfd~{Cp{xO%N4oo`$UO=?K_yC8v zVy9?e6?z;uV>lD?{?xq-q#l!-t0k-wH}e)k^Vu#!UkYIAs`h)n6%*#|_(>QvAV%)yfT0eV^FbJR$ z0JM8q;D((wec&@gwf)D(j^faV$F_oYW5pQW1ME+56!D7Xv6@eOs02om$C^50fz1 ztD4?>JT;gxw)EH^oO$1umNc0bscR+uS$KIbzuNd?qdeYkzTux~O!VgU+O=eArzui^ z2o@W{3BzV%bN=m4??ndS6c%&!U+Q9qXYGy^FJVt5aFhcKBgAlxrDTPM_w1(E#D&EB zosaP;{`OxV0}6m95i*i|+aKRv<;4~;2>%SUk-UjAZo1UdfR(Sh^JBuW!Gon3+~LQ( z^druItx<t>Pt(Hg_}fj`(`Ov3rMrcaNn(2?1C%CUNnKh5k6ytgX~jO8bGA1-{?4aB^x#7oeCjyKm`1iE!L02H8I{mcOm2 zGo3M^^87sOjPI?8To(hp0vD}QAUwyQqf*c)O;9p6nV+C11Krnl;B(u(WlKtU#gVKG z(F7tpN}|rnG`UpNLb1Q&*|)tS$)*9=Ej^1m7(>`dQ!W=sd;g*eZtxFkFKYe^Sj&jU z?649a52`L1CSk@AYQqbWJAb*ODJa}{*sIddWarC?za4;EH{E!+??dZk0uR!-sn+79 zC*_EixoWe0%X^qrN&5x@s1p$ATR|%-90z<@`ujDx2eWbn#*jm0ZdfD$MM@zoAHDhO ziSv3BT@jU*izGURRzPjef3*MIK4h+q5^W{88#rPq@XQmaP zNf!_t0P-MY)7eYk^W$Tx)pc@@(D*f}RgQ8aCkGj?&EY4$M)V_f-pv)X55zg)M7SrA zb;GPPQHolRY}8~o3gb1bN2xs+JIc1XeKAzBk*S^-7-fvffG7gCTHtMwyq0AYDU*r% ztI4DaAa}$|oG04W-k_l%2a3;kcoH4~-*Ok`=ViP)b~hWT9lf#b$i6ss(!2g7UT;ej zgf?hYuxBfYeFBp#$7yDgvp9P*nDGfX<*jJA637Dnf6_8vdQN?9+(M$xR1_-K z5}J#sMFi(PpQ3JQj*~Ha_(MWNQo+qlK5^vg@rdC16-X(ajXE8j&kfdxs~Rzbjs~U) zA%xJkxX-+Z64+j3VsgF8nyrm!FI97X2<}^GVOcEM*B^CO*CFk7?&LgAi-r+$HnBKt zyJF6vo)P(5>>T3M%k1X+UU=4dWY?~_Aw~=a4+u&3??wj(uqNct(7?U>Bj6|7XBSzr zypKCNR2`L`y53m@+IN#-9bsr_Bd7XHomKsBqxh($q}CF7GyYmCZ|qRTC!jQ6+Tp#^ z>s|w~M3of!Gy^~YVX6Va9-v;|YiLH2JzKUX(gYitIv}5(S5!Ep1)#~)LHQhG`H&Fb z&hhDs$WDD=tq>l)=V-W8cip>m=Ws0>A(+xYo#g-r{^t70l_y!Y2%fwu!W~PTO$ZdZ z3cm{F;95N?2B$J*CU7cOs~>(Oz2%M+CKf1vd%XaOZxRo^UeInTGZRFM#a=4*9O{u6 zvv(MnNr*AyB{Y^JkIIcUGbgVLu0((aqd9N13foA+ZfS;&&6}3PgLSsSTlw7mfC2Aw z%Flv;3X!e484;K2^555g_nwr411#`vwLZY1+<5F)H|zW+5&%HoJlD)9H^eF|=l>;d zQ1txP^4&~4r9OC79{UX&T1^_p)V6fOhbJ9>?^=Erd|cRXy8)2iofkbOQ_>n?wJvSL zT4jsKOC9N)2KuOD;@>oOY5k@2&C4m77mI@{ zlmO0AprtAH!_m?KXD75&8qER!?SMK*Ila!T35qA9OeV#Q$l}+B*2_-7BRtprU(7ll zTQgbwYNHX4`%Bey4Z(W819Y=Dena;%Gt&J(&sI!t*iK>lsQ=Ln_`SY7nb646+EE`d zM_VO__bjKQ{?D}d>LuoAG+NZ2t`|`e;$KJJ>>c*d)BtWl3^h#9mrY3ecppSIzFzx6 zp>F`Gi7ubppSo%nMvxcp)Y`}gE^p*Yv!=Nw{DC8bV zIITixeoQB+TgNb1QaBPRd4){7Pmz&mG8`8GwJz53iZxV*i2pnWbml324AreYXU3z^ z&qPI$_ZW5+8m)BNbR z&sND*DbD9TuXtGB!V}b6Zi%fMkARz}^e1qQWX8)S6ApD%d2eAL>5{LnO`tm^436Sb z2;GLbupYF1);{}6+Tj2=JBFk%*E{}i_vAqx2>I z$I8L9zg_QKQrskB1UA5G>;E`4vkwG38qolJW=(Rx=V*XW5fetoKguK3MS$^{A4UfX ze#jnbMIAbBl6hL*Oy)@W2|(4hQ&7iFfCvy;q*k2AQUP#}QKIOxbgfyCWi$O>!D_mBH z#1OSnte6CW`Us=W_w(28VnkAOw3Rut#YU|~kR){f{bsj#VKA`$me0-J)+4om=?$L; zh(kv?elyQHS%W4Ko4Y-_HMA1>G@RBu+@|l?vW*WYFI>{(0YOlfoGTKMUrvI$=Rd#p&j&=*KY}Md}a%KV7uZk^VtM z66E%4K(AH*JECAiyudd6sD(aO(h|jf79ru++BG1Bu3F1N2ECh&M+B{Ow>In~L6^i= zm-+CxipC}EN!?bi)dTVpIl6*?Sv{;%h-5y=1xm-q1iaE0<`DQ1uoyi{ClPw{rOXI? zW#8_%T2NVVw)6uY7dcfYchnAn%UDk~I^EQjJ*Z$7uQyv(MkWWl$Bj*>m9MKl5_Ur} zp;cKDDxR#_(Vx8~Wmgn^Cd&AOn#`nnkd-kD;tY1h;X%hQ_c-(MmJE2AL9#qvN zsY-lq8kf)3*nRz$}a)0 zVdOa&=dQ~WHs4CwM>N@fJ=!(|FL4Vu+5_ua(=4H#y!@ejtyl*UhB|cYVeWtYqSs8- zRrD#d|eR`3*yrc{3{u%DM}QB)E)E7z>~wjVhhi7Ae^n{ z%~szQL;bjeF7G-+MZqR3pFwbyE&fS_d`K+aQ>LM5?{Rs(#Q>f&fkD;-YdxgU_vK;j z*V`=Z8{bVqLaQ`Aqf|cg^0hVzw}EYu9Osv$fKes)4lt-?S6(4SwtYb!_5=f<72ARj zIlqAhk+Afp)K@HhKVnd#jtE%pLx;Olg4i(aWp1htP%m)&@{pTF?f_0V ztPPP9?b&ENpE+z?7$Puj@7zvnjqfzSQ2D@E|MLVmZGv&u5TL+mph(BijGj7yikB!S z%9}gji+VlGeoymCEgIs?rpbQ+IE2DPQc`%iaJN2G;sLnSS&qQaUBfC;=?u>Yy^os8 z8A>_d(gAiw6SX^a(oAL1l;LI-;U6kEEh9Wfz}!o?kb%iVTlfmvkkw8Gzix0tmE zx|DPe*@Ad?Hp2qbY2Z*>lwY1&p>mWdk$=bmJV7=>uly1TmAEiwSm#x<6{fUfVP%D# zhfb2x1`jy}CKWk;_2<*KbWO7nK8?!RU2|Ni{ACA{?`@qlMr;_%S?QkT{Ff8PoKI1> z23$>gO)Uh3JO|W@%l6=A=ONXKfYLh!=~$A1aXgy#=CV!nPLG)ROd#3CDloDTqaa^x zr_8fqo;Fy@!2$-Y6EahWQChAET)&ykzt-FT3b;i-=s>`!k2{9U=4`{A{rxW6;%$vc zT`OIzt=@v#4e2O*9QCbzOyN&Hkf;NIa_Fngu4G!vQ!cKO_R0f8^lgAY0gf|cDn%Uk zH8NsNOLHXA%ZVjU7XzcrFF|K;sP zA({5v1>B=0_6BUH9`Cr^V5n7vl^lVI+WD`ILGe5_0D|*>q8i#A=+6QRNk=Q*SnG18k~pXSGv}&m^`*9HAE^e$34PlkX+jE4Uhz@fl0#!$FZFJI@#8*N^ zy`J>_rY5&V(FToMh1ffHqP&j|n~9t!IB+$+5W1fYUBT z_OV$hQ^4QO{2zZi7b0hy9Z(7oSbc52egibB22gQnXqvxMCBo0e!-Ed3I^pDbFIWSR zPUo$0pOe#WO~a$F!vKP3!v5V>FU8G+Ak8oP0-|G`2w@v$RHWBrS5l_qK*qosASa(C zKnUf-hD61IzLcKuUsMlH2{_4emz=*Y>Zma#i48RcNb-y_EhIk3ZP^d4H>_ZJTpY6^ z0sg)_4l17$ylPg49kHkguCbRA=supIWp#EVtLG}NXBi;IpC}W5uV*3#EHoQW*0)ML zoi;3}qJ%|2sa!EC;y;E%`#qMgbI=2)J+6!l?=tzX1Y;5W^2(#^ktw^ExDU zN{Nn+j)a0TQtMQA0LnYKi ziFS;;^KU&cJolf42pb~>%4C3Z2xIMCUmRx#T>Ql%=Zi{CPX0)30_de#0uHc=b{-y{ zo+ii1G9zKHJ=62;ET@*D(llCn*9|O0{qE3QniXn&cK6OxAQk#ZzFn#Tpi;ElBe~gg z=S_igX|J|_R5v^lmUHA1O~zRu0`n3NjJP$R+K@p&dGy~XDkSAGNAmpbf@;+88X8Vw zOe-Xm4rck!KlhyO%w@5>_X?qKYDx$SdWnm*cX-Ia%>3z!n2O5CfJ;giVks-%*|eVL zZ}pHfq<(ZUMjggh!FjVqI+%OoPEGe!3Upx(p|K`O2W}DL7K;4d&3j(Ve)@18riMs{ z)E}O=%6?hog|7yK=QIIl!Qf3Ask@ca_JGnHKVG7nJ5v4N!2|HJfdbVmf!}_I16$LT z>vcUOBqV@oss&}H*IbvDh9zeqdtoEfH0)Azs+qxPcTvNKb#my0|f9;`W5EX5@A_nj0*j^{W7iPi?6h=y;^Ux=<24^MWjN{n}5}9Xm=m&&4-4rPEU!9dU6YE<5Oc0ES}> zpRxF%-JjiE^>X88y|3nS=i=a%DS*r?wE3mdcFX(V;9#o7Ut}Olu>7kXi0m0|s2xW= zwA^SlwxrRj9@x(E?Nsn?j(%tP@2^ zNolGzP=Z3h{`2sYCoW5}EmsH1u(vJLE(G+A>zaTUO{{;)ax6ZaWTDq&XgCi0H+;p% zqE3LH`|En}TckE?!DHAs*QFJo^!#~MOLvmrsg_-#ArqJs@ z8k$;K89Xmhz}qyA{`?9B8kVM>U1c>yZC|s%dkjqA58w}XaFx zw}H1#)B*ZE?9CI3Nz7QJ+(El7m+zQWGsphgJ-{z|1_;$x>rkgdXl9n_rH=L7AZ0ll zeX7XzNez|&(3lRHX@S_a*_3HlQ#Q2>h z?f5Lo|C`Dtk7J{6Szi=?_>jBSL4fyjbd>s3EnE1IeCcr-QfSb9$h?UPr?f%qOqZtK&Y?33KyIh;teZLgJeV-`d z|8Vw}QCaoT_9zX~2uMn*bV;`g3P>pmN|%5%(%q#92nI-s(%s!5poG$$0@B^}U)vM+ zoO?gqJMM?qFT%iVaLJ@l>6uwuB4g-YrTCMjkwMWLzNJjj67@DXSC zsjRlHT7bq%&~{c~HjY1x6R%<|*NBrS0<$Po-gsci3h^MIA2I9MijT3hR%TM;26(h{ zwSRQ)yUM4_1#RbxP2a(rg3sv+s(O~Q{{vEv1|jX$C{k$tNz7lKoinCzae9!}AIT#5 zHa0@|2C;{RK$03ya^!gum!gX%d;p+hkbd5Jg*xI_#9hhMx~9X#R1cCQ*;K~j@!3z} zP&q4LBT}6*q@%qPbM*@gFjNx3Sbm=`U-#`lA!PZ0nQhQn&H7yOZR*=&PKZ)l zonWxc;Yp;?K{<%pwHcZ4stG_{Z5{RVttWgF@6p=yL^z1)UI(%w;^9 zsA+FX4v9aMJ||x64UE33`12~s`))(#Vh0gm%X=O-64m;&Kqe=LOx~-=>Bkq+yo`XN zX;2L%gT6adKxp`kY9rpNXRpRau#2_;uqw~MZX06Y5Y4S!wLko`d*P%XT_)3z++GYa zhqQb76o7Dome&b7lF$TP|b!WYmLRlDYPG&w3VjUroP1Gsbcb2`@Gh_C|Hi zQ7nrDfqn#U$8lQ!&4^6k?b#HrIk2L8FyXn(D(q ze>8{h)6rMAhJCq7az@Xs@pVQ*fQjPzlYxUTW@{)#$mv$qGo0c z;sng?e{iWVY3#b!K~48AF7D0vdOC=kMlQBSQ?fs2zw+7#8DG0u$=&y+#8+{F+58oA zk)`D{BS%oZ75gYe6vt8YX!dh=Wtb$iDGtAOAhF@#0qWI`zf703&k-SzOH5UK&m=4@ zuacnL(+;%TFg7#Tl~b*pfS9k4z9% z7Y;GdzI{%H-7tjl03{A3=)E*`QV=vU=2Manxcx={OVvYapIUBC^gktQja2l_A8nK6LRRONJ&i5r7K4#OL-K>o$(oqi zmJm`Jny`ij={&76qqUKu)QpU0A{$V3w)5iSw=gg;_+I1EvTGxMR%kU^{IJ*Qs8V|B z6Q?FxBPjCt@B?ylb1fQ%K=yWhzszQ&5VeJOq|A~6rN$X(IQT+VBX$F)KAz$8-@|j9 zmBWJ&tGK7iv81Nb=#rytMP2XLxZdl-Td8Lex)>+-xPGeqo$x}kG`qM%E`$&{p{(}R%lNY zJUu;wj9u17nm(H)DHs_s+1Bk}181*!XfF&v%9Y?WqzRT}Per_2LrG{QT;HvaSL{Nm z^D9k}!lrin!AA9J{zIo)u)2ajENouS&dJaIJxonW*`df&Ckq;{aJ^>{$e>s@yJwm{ zT>_VafN%41+Q@a;cJU+&#`VC1gwu0&G-2!M+0HK1P}aW^oz2+-y5p{2gW$CxcW?Yb zarMVxyr87)$(;#a>3-UH?PakL8xYgBt1q~-jE{JKr!qqv_)<(9MvF~a+uQp~%zAO# zBd7&&L6RnQzV`&F&F1Fkscyf(N%lIRIz8Mp8!I&@k02r>ymjkV@FySoq;>L!@UHL^VQLRRW?Th#M{_uqTPcGyRfwq#~ zcUa#i(YF3L{lOcc?$W=gXpx*Z{JXw*9yuKnw40ZwAp0Xofaq4^0t%7m=~i#9er4NI z&Dr9AqrI^!{u|IPtGm3ZwL7TKp6r>lE&o8L`es}rpM97k`+MZ~t=Sg0g^$mEY&L-V z*u<;$>*Nug9UU|8RizC7bVPj|au0v~dUi41)^xcqjpb~#_WWS{Rh5q1`ii!0NDT`8 zM!JUtNaf}eU8W2ttDOW7&g?x02{SiqcL~9wjER*s6!daYb8|*0(7=2acd-d4XJulN z>fnWuh2g-BEh66tT?WZPBzhE%67MjNsM$4?@1JY|M|0m z#me^OX5pxnfl zr-c{ir_ZuCR|^`XvMdEO2I=gjJPxe%TxT&s)>poR+KB?sQwUB)e$*UD=G%37aoqQU z-wJg62%Iy5*(81;lMz)5?_co+0spV)N8U}h$~i~x8@lsRNVwf{_L-)dRbLFk?l%Bu z45}?(JZ}8*>g;NYmyOw<)Ww~>NZ#f*Z_PE_Z=q8EyG{K(+$TR?z`WTcM)!M8LH!$; zx)uNAW)>4W$!s5zU9B5lZ98hY*5-NW6wX@rsBpDC4 zV|O_txv$i`pFOd}ak=l|>=``R{2d1&)4>ah73g>7Cmee7N~I+v&OVrST_uTf|4wes zeIFf62i{hpA8Ztt-XP&~YABIhk{BXwu_-+D!Pew+&l|k?ao63@84Cm?ga);ESi;SA zxQj0CcgwRUPcT7>P0R6&`VN1Mn~r+^v&l`;5F+{>_zpi1Pdqq%9FkM{y5bNL;TXwD z`oOQ??TDn#iKSOHlQ(YN>Z^8I#XU7PGI|LRNKkyT{<_lPM7g#0gSV7TAK0E`(^k&U z&9M(2w&(ZQJHpumxWsC_Jg@Hp-kV?{>#+p2$KcS>3-)g1=x0mUMJkDsd{+LLO%X?F z{hnQM#o}HIU>=C9+>MW_;hA2EMsdA`C)FV={dS51^;wXwaw3mPhfU*+n5y&kiq)<2 z*WA06V*&ugKze26;xhU5;5*;&KgHm83zbg{ZD0saN!C|-Z^QkjKI8qao)NO5T2<%i)fAR+0 zDiNzg?)oDmtC&5co{$ojg|7p#2Pmpwj zFD7^OUnX2!T#>^GhbuTZ26L^i-_l7|-?Ahli8^8Gy7HdhZ{S5or%=t=HS)W|8^A&8 z`->@ZzP-7^a6W8xo7CZGI#UozKWrGiCJt@e&`HFb*j$R@2M77d%abkxiipqrX7=#Tzl(~xtE7bgOxKAV(6}47Zx=UjgA~|w zd#>&1^pupG{5}F;R6DW(Eaa4CdHHI{z_*>w<1O2h^bgaQ^LDZ|lPa2;RG65UX9wf9 zA3l9b>iTH{-$df_Y?q9jJaNFy-X4FG&D%=@w7TWi;~hVLegnCEU~urzQ)?+^P%Lk4 z*+Ga@Jqm*d9`eGh$tR`>9N#+B?b_uLr@H4G0W=LvPBOQEJb}fvAy<<0*lDMh1J}SF zoMbreAxaqPS!p~F-6LxBp*-#3qp?IL4Y8?SXLbHTw#)Z30K&v@>;CP}Pz}J(fHy*U zmc-5#_cC%ifQ3G|?yUDA~a}3V7$(l?*uF%1}XIysph3_MK$9u-vk!t;nipCsx@sW+FzF%B8UT`R@dDwMAg^`=Ro1J_lvLVDW^ zj1Mmf(SCWTQB+^1d4)^0_aIck|JKvUg-hM912@3FN19`8hwbz}xpt=uG5R8|TMS@1 zN^&^up0!Uw{&fX+h;WG-spFjD#V^OPfX97W7OLGTPgC_Iayc>}Zv?Lq3Qj zzoPm!v~D~dM9;8UR+FY$c?zt|#%nyN_SAgP64W;)VOsh=w-PB)Zl7WUGy3Yb@+nJ)Yx=1&pu5 zlP0GhOdRuBX;y~~gll?=>~U~=ty}rWC%N9Hub@azYZ^1c5in4@p#w8XCYF>jR42&v zf|{TT6A*G1f17~wSosUXv<|TgDHB{7_0!p0nJgSK9rYkFTfos!mu&CWoA*l`;@H{eD!vAIk^;N z8LAziqd0VfX}uZ{D??-&nAV4Ljp-VAX{Nfd(J9GMpN04<&z2s1{Lr{k&?SAJ+V?$f z=6ri(!HzQA+aEA;RHp&2oeZ;Ht->66XIi@A(voD`c|Uzh z7NcW-1w3z!k45F`W8|hH&OF5J8~T@1!SbW_^5V2z@^Wh(`XA}E!+lwm?6A!!o-%DfJtoKc^#Dz<;zqy0dArrVXjbmv`3_sv z>~s4RC9aD|Q%a;ix0I@u!Ej+iK;6G=2eqvYDxyW}W#3@;A*ns`F|nzJ}0Xm&7wn1P=<1BDxw0RYId z4<6ihSnR@G@;Vqbb6g+$2x*Paw2LwrpQ;%;kRO&}XL@vOA|jQ|=PjFt;D-`+jcX3t zD`N<#oZ97Yqh+{u5HE7oWT}N-Y{(HlY6Yh+5})2VhkC{$hY$o};M5i=jjb(wHxRGf zR&ZE#kRqAs(+-h?-EsWVqjy?LkKOsGwC!;$$-69Sq$5pZ+jXuPIjlIja@{V^*Pn}5rX3;=$9;^H8B48Jl)n?TMl!V+e|*Rva&k9AiOSS*RL%6vBEP6n(_#32N-%FQ#P=e zSp)-{7wcnXoSMafn+=!sYOHVHykUY~4o?0x&gYGzX30>bSt8~(kGAV1KEWM@njuo^ zgtb><7hm6V8`JpvTQ=$R%*@Q@-N7IW)o90&bmjOF=rr7ZVC{A{%)Q}I+3y46CQ3m| zo2i5K;5nnCUU6b_g7e;gQMMbHsUQ+{_Mri3&GGQQ{_eUP*Q?7a^d(FhC7clAp#CQg ziUgkC2d)eY25nx2P< z^2cf_dSyTXuZugk&WfCz)SH`|zc4jLidIrRJOj8#XY=%znS}*_v#4=cLJm%|^MG04 z)^2oUl_;KV(8FZc#V4})dc z_?5qOIQ9pZ7zHtH8fdI6(n*SbY^cS07}98&wupua^_|t}T6OU-oper(obT7TJ-pa# z$-XXZLbf*Uu+|=bmcp_4^)km>S+2(O{Vg5mQ4_=^32qm6&D!m+V54K^TY+`UddI!) zqn0LA?#mCkvgIt-CF0;K6vCc@d8z5n%HzfOpDcW007IE{C5l%x*B2J@4i)I@8&1<} z$23_*5fT!bLOxy_uh`7YtA!lUZdwwRoU96CY@~9AS4Z4NSx$-vd2Qz6baM&b zs65_VBka(&1mWn_92Pr!^VbbqgDi&#jfhwT<0+Irb7mUi_zB$tr5g1ZCr-_*yQ z&Mh5JMg9g;iYvJ^xeb4J*mm$L#2%B^i|GmRxrl3iYm}pCee_+)PbK5?cu2s{S!wZP zQz^ZlSVVM63JO=!($W@4cWK$!+24fm)W_ZryY~3`^Hb?)yeP38I4bbx$6z`?`;rXKX_-LHz}M48-IqTRq41Pn6ut z{&eT`vTI;rzBc#MVCUNmJ&LwBg3dc-HllxfVkpN}iIliKA_J@xE_(Pe_3lC!hTedi zzuz8zd}}@`ZZMlb;_QaRR^ez1Mwc|bE2XKVrln#RZKq8|2|Q{*3Z9yC{x!s-;7*0D z2*?l~roTlln|ffC-_u5+uLP-+Jt)QBA1yjM8krySB_|1{;x;+>ig!2UEoS zcOTp`Y69=H~kAoXLJe#T0`~}0> zS&MMfB|@~CA+G`4V*-X+D6kv=84A<6&iFkvwp0DH<>B^+i=ThDatZOBf;h8Xxv8j| z=}iB2xj!T3k{DVvW*zV2g#@sRlf8ex2rwUNjz>_6>J-$2Zzr!uc2#RLI0;nR zgjs|*CwjB_J!-+vSh`4Q2Q5mJPW>O+;;Yb@@3H{5@2hH9Vd~U_7Tzztqrbih=Ux)R z5KnHhr*XwmEb`P9FapgAo9oa){|TnCVPIwsfXta+y}}L~1vJfT=V5<`X00pV#l;1D zoZmUmcs_CKx({$S!*E_M%_~y!;CYZjP>rDLvIdYy$JL>5gru^ywKZRV@U646GX*TpJ|55mtUT>~8V;a)1mrZ0 z=LLHkHJhdoyB2i4a#rBhrNSdF@;u%Feg(ieS`{|k0Czkuefjufa!xwcR2*fMr~HEl zSlltIfy(>)%Kz;pm%K&s82DUwcJ6`~ai1CkE8=pQSOY|T`GEO@=2FL_QWP|NKwdfS zMKlP!0>`0R8y?^_rkSf!@YO{~xv3ay08S1`!yFLPvY~#mZT0rk(R_>a?B7{KOw7zQ zIwC;EpdZK;?edvg`>ydEg-9ADrvawfQCMxOLGAqAb{5#6)n8!2$v}CW*wQ)mzMaTt zvTH|NqvUiqwVL&rEOn>__JH_iZAr7197GGJA5?@6$w3=tcQn~@burThwNrIu*}<3+ z;lof;p$-T8=e=1c&f^YE8KwHi9Imt_(4HREjZb?ckFK`v8y6S%12(L@4hu8xOI271 zhydrG6{R~2N*?$&4cOdu)L=9z-F1zPiDk2!_BkTCcWCZ5oN>9D_~*?3jPk5c^9KH# zwpG7^3~PgyFm20>=OpHjAo)%ETzV1GNp#2%Nwz}s-AiSba2HTM0E#yhdT)c%j+(Wz zxX!EVt>pq>M~AAYA26@v&B)Y#FzxyLor#S;J+6ybQv)?~CGf5&q`Yn=+FB4a5~=&O;dBsd*^i3(D?0z}JXiyFsS)y+z!%u|YrN zm>(RjQ>TuriA~eu#{&Xp>+NvFf>gtTyT4p^Z6xrelcL1yO*aiy3{F0ijmR7O*+rXG zTKd6hb*Ko8523uc59?Aa=8EB-v#>A_bJwE@U80%o1*M`BlOmMp{pJ%UR|a7A`QC}8 zL^YJtpV~>?ryY%I>_9p2{wxb309>^&9GkDBmiJSOpIF>Qs&Is#1-a80qA9rKU+gAd z7J|oMT(+vL?4Lh-Qg}%|=wgn+XA&c2=o2Q=gy^iGs5rmrb14pQ5VD9w$MN@lEsF=T zvbp5cSk~gP;|iyS19^pR-(U&CQRe@mj<^wo^tY^C2r-F=%sY8#o_F+m+MUPzVX-vg zCpp$FLtlYiA_&3)j1_J$q`MAIfP$K^C8ZjT%Gc#^)-oui*-JSZPjfv z`=h6zlcY(IN!f`foJLq4wE^pn9!7@4bg6}xQpoB#v`mK^CQ#GE7O6Qv?s7EpD8-#Q zmzS5xBg6qyGxvskDaUrV1as|r|;eSoD+ z&^%CzIkLiPC2|Ytp}g_|`8$K|8ca-c{cFyYl2rM}C2p53J}vzYUvizLyIvoCRVxtH z-c-FwYz6l7X9kuQSKi>xytxUQLbDqiOv1@i=hwxhhF-+0M}gnqVZLT(Ine=H;re-9 ztxIw?E`>vM>WscgcI`sSxiT{|6nd8TM@L7v?HeOS0sOrZ?^|1Cr`%V$kgtcw zQ``8TG_;P6j!e6=P!eY3jE+~>F2O7-1sWv8Lv#Xc6+f=H!hj6^&>(0$3N`N*(q%&% zk2ICYaPQ*cumQ^Tz@2tHTwJ78B9hobfpos}s-OsHUG+zEc=-(%?sOh&d?$ySsx~$@ z$FHE?|Msjv&uX&zBV_F;;5Tl=^FW#o++MfaP>ks;OS-S-RiMZ#D=&Nv<7T>jJ6cuh zI1rHg-5(qU#56PrrHzzDCD%?59+DfBN9=1kTx9|nddMV-66?%OL{`|(=Zv6U_lhPJ zuagjl=H9YSYJ^+uqF6@G8-_+-1@6k$IeMRegu$U?a3?amtjPNC=hH0rE91k06hWJn zs6KyVA9K?Yf-|(P!yDU^lAip#ERf3dB9AMF(ZR^WZ8MH%`SOvAnBMeC;II=$vL~6y z|JBdW|AQ$0b9Rc1aQ^4))Zf4B*2(|>rhaZ|>DdLXObDR~Op`sgny_oZmlov=V3u@v za7v$V2Z1f@!N#Pbj?M&M0cJUvCfD?^f`6;39{*>mR# zvs?rxQAS8xYpdOEw=gpMBRy#Szm0M?$I4byd*BRz1uNUL3z)RGa#c*+gY_(c4$nd< zoY&_h=CslZJHCGC_XiWu93dbWui-s_1x9Awd@R%eTQD&(sXg7Oks78c2XJ;UTfNj~ zG7g?I{PhQb|HVqU-4+!s8_&A~x$DEe6ds-151k0pX`5J2p0#NtL6)s-iWUkG;m>T? zRdSQ9NfE0Mm5dS?oeDB?N@4ic>EGC1UrXMu^jd8x8T?uuOzNf8`YLi8uZVO|uy)9z za^aKq5uCdb$nrHVo2KvqkZC%3go2V%>*{`AmBZ3Ey>AXnJ<#Gu$Hlo%EEWI1&4tyI zi%&x6y=S_Gr~d#vgwZv8h}57!?vzV2QX^KOZ`>p@Wpo;+oxh1 zNOhk!zu1$Acc;Po+!apgG30I~aLh$+=xdfF2H0QrP;emK!ezS-Zrbuh6+bdJSIKy9 z+i?ELEXgg{Wy&oCLP<0*Kn7kU!l4q(@nVTRr0q&TSR@I7Gv)N{DR3K5kysOY_lF6t4`&g`6zu;-%diQgjDgF=uu37b#`6@;PnU zVz)}$d~YEp3n%HhJA9ca!31Bc9}xT8EwRu&e1T6dl>kqV$QL?|{JweU^&p)i#AA46 zbeFdwWP@R~iipy$b?5i7vGQPu%&`{v`gIC){25!gD!DNFMIzYI@$tvrd!Ci)!+IX! z@CNQ|$A=suoD6l`%y?m2Zu$}r5moEa;vm@Amb5kTt?r<(u<&8YwUEHTge}Q8Fn~T; zEm(s8%r+*f_SQz<1A};L%(5V{Fk(y%=5>kUF1K!`%{`q7Hr(o9jx2=6=Lt9L#7R{F zx5NoE!#}cH4vqQicCD1mQ72aFj9nt7aqTyoc*SV_EnX0Wj16zR5rOF-&%qt>&Q<=S zUS<6!v3hRi5#RsXajZnml6Ad~b`M|I$x-yB75e`?9cRhi|6jB50)$%ws~*Af{GOW# z<`Hb(>*(%YfH~cN%#`5)hXERuXy+EsrCpAEhn?X8SY}u4xZDhkmDla_S&nB}oG}i! z&(6=y$IH2nkB^;pJloaQ3!Xp-`u_L#w`0JA*}F78CMJgWFXtl64cQf01L9H(SYY%a z3M=f|ou>j5x0{7c-;rbm|EKEy!jG2?Enz>`;m%KoxL2dny{5;BIiO%#0B+i!KA$Az z=Rb*o!>9wI*-HCSH@g>`kI=u04Nr~qGmox2!&%U&_Yy#G1)s|cWYP`{?|v|=2C~mH zNEfnDfXDKisRYeK@CE9?SHMmI#YZZFoxOfl2Pn8SVs)=|{rUuk;D-9opHnh2He6a+ zO;kp~5!g9}5fFe(CC=+wh}|?8WIxU`e9cq0cQr=WnJOdM+db$Uk#-<(8$wC|Tpi`Z z$B*DH9@sw5C@Gl;-SKT8wzxy?OI3LPW7Qq19mGN&7Htv&Li!DmR@g(h4JPP20LJe? za!GPb1kS+6sc$W-MHfrbmxfSvA=MHvY+z5qPY9PCa`awL4beC`U=A2TBTVQE6A_1Q z!Qo%OHy5whVoXOfHg+$I!2AShuy zCtHIL)Z-ei6E+o3G&COEy^Gp#en11@7|X6bs=SKIV#DP*53oy2)7q_sXb@Z*Znv7- z9UyP%n@`n>0>=WT`N*M!k!KaqIG~DVm9Bsp3&J=Mby(yzYQ^d6>w5`*7a5fyWw*lc z6*$lKal*EGhWdaBL*ax6kBt%3I9Et-6#@)}hKBZXx?b|~s9m~pe@HuL-&3${OT*z0 zi0*EKdSM10HM{MQ3S>H7d_cjKpyc^GRVNORr5UU)71VV}{L$ITC?TPf*8(nYZ7lS$ zB_~NPe6kPxIDWw9lWZ$}Dty&o0bxhG#tHCV@R{57)b+M!obEb$z2RdX!aNe0nLq2) z_pwX}2n|Ir9{|Wi*}&k-;6ms??EJaaGr=e7;$P@;zJ${VYvANV>{3kJkI#oKQgFFP znpA63hBh@nzbL#NZDyZq>6P5>xvbs_XsY)Cm~ zC=T|B5U49-3{bDHkx)|p3!dG@2rRI9bbNQ!xzLRn7zbqJO}UuF<9#4mj9?!||F`Sy z^>sQ&!CE_9;@`L4PMl+&?feiDYN&dDQKFL3PjCg4eoTU?NnP_mzcoUQCx{aWm@|UK zWo+MOD)d33m>PYL=&f>4UK+7Y02}vT8t{K815b<4=C@aC>7EPDvj|CCYKP}murUoi z0a`tYXwtutd@IYHyHZ_oIF|KbY93{urWYHHMe~Mn#1~t~?rtG0N#X_;=uD@cV^y1u z+)jvDIp|EoT_tg|nD!G9W@g9hz#4;%=`X9xkF;>X;X85~bh9L|YD?sLJvZ9FRa-Q! zB9dLa_&sq`iF)&5sD;ay&O2>KPOc9*3YeF3239x-l$FnvIDnHL`t-g5aaSx6IyXiE zh29BGZT-l^wW5DZ-kRhv7qP4PNVYQGeXnq|jr0(^J@cE6QfQ>=3?+HuJh2zMp=C7~ z!p(D?*Ye%$tgI-FvvyBUsrMc4b3(U`2i|aaah1yZHeJ_ig(3wxkkAYno$gF?Ds&3ZkcT~eG(rv zU<=haAgi!z0^oGk%d6>P7LK1k2R{U-m{9qfKc_=tlF=ivRaQ~o(+7RQlHdqG`cUCu zXp>&$Wl8z=t*`F`YL&Ty@w{OiV&$+H7a@_f?_RFMmTC3iW}#U~B6`#)>h)tfx=YeOFXAP$9Fh(k z!&0fVlg+qUp5VXT3y^DOJX(|M6STanF_x6mV424`FzlMf{#|WWnGt`Z@9Uu8ljU6@ zh`SE2yoF;AB^)K_^XpL$&NG`8`WWije6iKK?Zi4LE-2IOwBl!XPl?AwLz}ypK1~&W zVz!Kyn7l!fg^sYdqa4UdiBviLLp2uHlYRW>%nQI)7$4O=!W9`{BCO0#vIaOIK zP;C79;llmZDcMfv*hZ?slG#IAY;D=}b%!}3;S3#!DPL#g#c{R@KDFV;V|$(O&A81@ z757DD%6pntH{ic-ruP{z{FZLNr>jbF3jTj8lS8PwNdPq)kl4Sj(G|PLvu|St2 ziXZfH@gX{POR|X)Fl5oOQ@x#9$^5(q%FPE+{Wm(~rkt1-HKrLe)+f51{xx(xzu)o1HhVzX5A`sk zaf1n`yjf!Io=Z+fHM3*Pe{5%!f9Stla zGo^5=)?`wHRa8*lF&-Z^stf!2pwNyH^>027($9)AY&2#uFrhm_dD3E#3rmJdM6Cjy z@mm)Qqig4H&|jWd?x+d!sJGJmdp-iC;K(p_4=2*Ml+5)d(^=)u{=BO^#*~E&*QI*+ zF7L*Z;SOW?4$jR63qC25(c-_zOA+z+lAn%m^C{3)UYw;>6;@1b`xhq7@r$;%{uqT? zVvCOQo}!KvPa!{Q=fSnwk0yGTUN~oBIA94&kga~mn}0HFccy(B{XVu`Zn)r8;-zYBkxRi%8@2x>SOD!kj~CW7&>#Sa zc{4DinM_5pd&81hMCzUIOZ^G%fXm6Eq;))(Zx^&t-w~4ora6Teh@?HYW4?`6U>|I3 zL!2O4ucy4kP z_+=j6hy*p?PRy}wqHPfPvF^9HI>VFUHa%AJA3ve?LfEbzU z0|5{iyx~xsAygJv5}PwAEi4=Z%ne?j3(v3LzO61GSET!$CaxnkRIB&=rN-f)R#tb9 z0)>)N@C}OAk*=ivU0S%@K%ujjx`skJGmF4LOe}4!Of}>sr=;G z%fBnPt*1x#WSUf{b`X>pXjwx`;%W4Gc) z(Z`dZBxEL6iBI?|MWVCab00r;DtzV$Ma@V4(DA)&+|7b3nNu%dnK-aZKIjtCORX*6 zdL1gSXj~zqBvHzpRkXD;Z7}nPV62_PFN^&JQxQ9^S&&VLcOR3lbGFk)r5R(<#|y9h;1Cb56nan4b!cf|wA7dc-H zM7%MEJk8S+-?JE;&~B+y=sYRpMVIQ}+wE$7babELIehZk4x1EO-T1EH22aIT^HyM0ArO|*!Ye<1JVUUxN|0_6xXg51GUU~ZRD<@A){O*oe7lv zYm+qTL^bL^kOwztYmn9s1yE-`a6PEK zI9WxZ6|qCwvuC31fPp`+vNuWW(WKBah0#y#_b8dIzXA@KwopkX5GD2;I_9MM7ue4> zJ+(uR!ETf>B%V>%MN>=0uV@+*6(8L#2te7ZtYbc>UkkoZ8h)QW%QAhZ+IJ-!DS}#a z5|jc)`)5WB?L=n%qp|vD~^A!E`2UUfD9B&2WK|UwOh<6=X8JX z6-Xy6&1$@Mjk#4pm<@4p;PG>eE`1yd$B8+^S;2gUts2RxAzrht@BB<63<&y4X754zo!nQHmQ+bnst>zVS=kVdbx6Uyi%xk<88YRP$&OLptz(`-Bp-o$0> z!YyGos7%h#zBzSc_Yq<8RBiA?s%HYD)LSoWoG4uxD7?&V=qk|(>3K6e5`I_&9bvdG zE_}C9CuawzBBs8+)>fH&a=Rx@=P=k)fWX2?M$O9w+i3i*xw7iaKF!9pp=m@?bE+2u zCe?jU_kQXKYx!-*l2kT!ZXus!GqaDjr=~O#0wikD*+dxzHU-O4JZa0P5c9iVcVk@_ zYstYgoQ^DWjS%+js15jithyxS;<0{Pk@mF{;YnJ=Ei$-ch~gY z1K%CoO*rFw|0}|8aj92^n{R=x3rC%$qwlx+1X9eJd?oevpQvf-?`Q!n5@x4tH(N2PlA0i$M!gTk1z ze`}uk-e*VDYhHc71^z64z;95_UD}5&Q+o@`3@D&O)hapDr)1 zyI&zAzxVt`YwKr50TGOZdb0y@_Z^wV(g^x`zDWMJI|aJcE$i=B4nOP!S@tLqIev02 zMxtL;#=GwBE4~*AfuR};<5TXK_2V{JzjM3b`Dif;*6WPQR&6BN8k5H>fOL?ey1-crf|13kN!%!uA1;?9&BI} z_3%k&#c1XI5ac+h_AR>LWybgau%1SDb~MX4bY_kn{F9;D2jd$BQ8UY(b2)+^ybS!` zYM1z#u2%2yZYoM(y|)z%`6aalWuyqxRK-s&i8thfLRtAji;TIc z$n{0M{(E3dc#+_utbKOxdTwm-=hkj>Py7C1NGLec*YLcv(Nvue_5xk?=z@Ic1jDGu2Js`P#$@-tNhSS(9cT z`pLA<=Z~+N)@I+bQU%k{Y1*IDhLLoNKE9JDhaAg3&P4ej(f%$ILRuAv^#MF=i2rmk zYWNj5oA@Y7OIqehe%)v0P9u3i-)r3VM|rP)RRZnn8C=;vtqLo1&PZM{UktxuZ#Ane zR4fVG1>i_Y+jP;^gM-(L^2EL$ZaC!q9&rt|($#w_U+e#0kbo%%3(dt2@7YnnaqpDw zli9u^s9M@zRCgQJd4w_iz={l#k(P#xs};AGT*BJpggQgBe1o_8N&}A1m^D_9cR*vs zpI4l2*DrRR_g9@Yo@%{imAE=L*1Yw2ag$Bjr{s{I;g>^O=pV=*kUK&X^zMAAl4vUN z@l&>(3CieZVpww7cl=4Mn?$prDAZyeOHPOTM1;H-XPsDfC`yOW`&B}>&$(ZUgS-6{ z&p5QF>gvzlcJ$JF3pTBFcKr-$rx)^&Vd>@d5Nev(a1NMk=M*8MdrT(_Cw)J@u8uNF zfbw6m{nXQro!;#~tmoT|Ryf|BH0XOrwIcU78P%2n`x<8;3ep#oWItx6BO_ZMCPk{YNpTuieW$B-jzen+x+N;ebWGLu{|!;tbTg@21{WIv&1Cwwa8~^W6O)2K$*4VGDAB+MLeQW1$t#gcI)i#kLO*Zv5eTb&(jhiHe zjgd87ZENu(tFo&p6`NzNXR#wQDs4f4{jdkkHnLItZjOL(l?J_#$T!RCj5{;QVppk)`Ir_oB zMwLn3NrLENN8+sX=bGf`;kT+hD$Qt29+TK%7L1nerkQVc?0VTh3&!VZ7GEr&$8Rf6 z%3WNOMhn(?d5u}CVT{vG<9)G_Y{s6y*mYuJtB8ckRc~_Us|Ei;0Mnplyn!C!8vo^$ zi|O`XV%eIxA5Sz1CEWUIy{#F}7%6FSGv?S#+Cr~#5Va{w5wi5nWW{{pO+JvEz=#}s zFf!}(C%@*+TO)DCB9+2XM%dMHb7Bi8^sRrT>&rYN**?d=>JVs{$`t?Nh?h6U^W1%G z_+FpG$(pX~A%8*SL5PP;qw00JKFZ?Tv!{s}Pg}XJq_wpP(jU$r64T<|Y5k=_+3v|k z`1%{~&$bv&_R*^Uu5KX*=2YYeeJw2Wm+Bhx%H^GB(Jo0hkaG2Sof?h}3V=q!9W4~*oLbmrgr!K0 zFONyj%{Tl&0Kc+OS}7h~1%Nm89OgDVkE^NGGZOT!5@=$XtXTs$SZFl3;PS%DB4mGt zy(cD#VrAw~4>kpd$*qS`dsflvZ(90`d99XFJ~nhFxja30k9J9DwW#2o>y&b&qOU?%aSZHW85V3N8wY~8Wc5qd{`rP1Gviq;~ zitE&61WXxEhLzI%kQGw)i^00MjI&#Rn74LSx7uIxE}55TWm0t7x1_!RNnv@@Ti&34 zQy!L@+V+xkBiVja8OOJpi6@PE4mO#Qs%}9R^z;rrPV$tpA21*xyd))JWYmM8x>(4! z66EK3p0U1mzQr4#eBoCarbXJv{i|P@RMdFoZXWvnKiqZZm$&QQ-41!-u*xp?t79%Z zjyWhD4lS)@BacxSHDa$5Qk;5JtK0YBf0LlSb}eo#n@~4qp6niJCB9R(-8QT$V~*ln4fTXuL3f5l_3PAsJaMtduNP zlHh7&-0muzvbs~ZQic0iN{W63yG_$*{4Iu7zH;$5oJ=U+fe&`K5Z{7K`*1;UgW!?HSBhaIllIt-tJZp|%0h?RuVTp^n${HRUgT<~a!6bqYrUtWjYCv$2$y{w*^pSc z>o2mWe$|q0^zQQ|h6fD-IC5XypR}F+Lo7S0@NfdFdK(+2gQd`C8>K=mhyMywU(4d$pX0at^N#AFYEtI;QJ;}4Cg#HURk7yOWu(kn zU0bq+H_276MI!iC&tl;$PVqduV^rxrM)H{{!Mq;5zkcN{!!VbA;%Tv8#QFgC@W1El2jRE5#4aOdp)*f(prjc;c&_yUm-h?mM3yYj++^ zq()o(pZ4A~9Lw+j_a=oxsLZoeWQ;Nu3W;RMOft{&l*~jZLsFrPDKds|88THelR0yS z%tPjR+Mjjx{r>*{ec$^y_I`3dxQ}CB$MO3;_~tsV^IYdzpK-0%yM!W?JpAxjSV?nv zVwARzO^vsVKCTuR5$)&f@1pvM`j3W^Upe>DGthbBMW~iXlTLob8i~34oS}C%crNsomauOAf8+f8-3q=L}=zhGntx}!O5ZQDVDgoRRc3x>M!iR zCA??$?wQ%fXP!9G*mHPC^rY>8)aI{AZ_mvyVRDlz*CWL7Zr+^Scpuzi{AK<)X~g3v zgoL47xnXn+?_r?tTz|!+StaD(YIiSqR1ku`Gf9RctARJ!NaZ`Uz#{P^Yj4DUyL%fU+Ke)0#29l6G})>XGMq`kS!*& z$~}0?DhD=feWaaWbYQ_OVqoo7vgWX}=uoBPmN%;T2IDbC`1MYfgvdBvg4V+YmE_@( zm4#LbTg^|(S8|bvJodc_0C9|TiMP@ytH9^UMD~4Z3&LYa4Ji0 zN1hs>i=h_PRM@xF%hZyb8X-aaP$?jk_iC0Z180z@O0z?M1A$qf(5Qosnp$P8xn-+4 z)Ui#%`lMo`k8~~yZtkxhab{5Fi*e-O5%j!X&DtzKLpGR42+@bmcJanJTTOh4N?$_1 z@v$uc!7NDX z)-AlEUEL_kvze~ccC0BM)VO83Qp-g23)i-wF{V6FKDgrN(Kq2xgK0aT1Jfx|G=z!# zW>N!H-a_fn-qKH>mCh41%hF6F+Y}KU|5>me7Zvs>2hU(La4`EHAxVdZE|0<=SS>ytNZs zk>qm#(FBRvX4T0&yB~_gLf?rkYfMA&4K`IJqPuuWmZ2g>zG#B^#-Pfaf2Dk%RO^5v zUN(Thth=8`%is$ttC19aW2&aT6#r8#jGgtA&+66s*5GpbtvO>e%-B->is^%Fje=ld z;S{#VGSR{98tw3Vj1Rw(9N!AOy6rSe`Rt?oXlGmx|3>#$VW}=o-f$%`$j`9tGW>ie zk@79#l91%iV@qjuEv8C}cWu6`scF3-@S)sN6%Hd!pY)ny<)wPY^zjL zzIer1*omz6@Y{TgOIgdWLDHBmuRAcr9fl>T6)993yi7rJb7>Fti$IH zF=brf6CRdCyBC*F(~bd7fK@@NReWix>g(XlbCbTta;3&F*W`ze2tZMGrcdr4V=HkcJa){zLp`iUEpZRvF+}pff=F*aaHm1jnLHR zFG=u%UaLDm_5;=t2L_oVvi$eaET(=%S0hH~s50AWSt|+2#qwbT3)z=rAr=bx)e&1S zGjMuui%aqDdyKXYJ1zyfm+;aBtcTZhC#4h_N#FQthO@pJd^PQIt<)*Y4#}&>Mv0xD zJ2J<$F7|(Uvh6+Xb~7HDel78TZQSQ;Li9XAQZ){F^?N(sxXpF`3td-k$oCQp*%g0H z^mF+9{kvu&5iO(P$!govxkl2g>)*+ztG9%L^2RD#=npPFjUu%>wR%@#(|e3^`X{~nHvQMq1~ zaX)WbirEqe`+cXM#CR%04(V)u(HlXGT>fWklv}=WnI7S4t3BB+xN7al08z=#u77@p z?E6psT)JyhzqW1(_ie8zd95}oX=PLH3=P+GD!ep9Dhhxd<7^@cRw7BRG2?5=IH$Fv z%qaPOVe;G$`~(IvV<9Ok%bR*JxC9?HbSbSd)n4~0IW@APh!RLvsX;-LGBnUfwQP)T zW;`y&_uYd1n)$ zc`_TkB+;;=h1$&SV^Z;3b&@*^yB}^-3@ zssmN&vh_r0_XnX5U8LLsssz}kK`c?R;g7>?YH`w6USfazb)49ff3A#{w4bYZx8U8( zOlyZ{F1GLcnYGTKW%j|~^5V@xIi*QA3v0bnS5~QDQbQBJgX5IT_p(%GI+{&dPtN_8T%H4Z3pogT-#>$-YS zn;tsGZ*WV4<)#+19Fq@m7bwzG=fe3=_E$-k{0m{M?SI>hsY+REVhmn9G|OK_)$kQ zYp_Qr1>W&{XUi$x6hEB^Y`;S_gjiTZ*Im)Vl1w4TG=q|e%#eHL7$&BbbiqO>cp|9F zQ7`>T{meMq#HY1rj0t1&*FW~gYw`gWoe~}BLLkS2y-*4ZO%RP>7E$u8(Bz_XQUM<+ zE{;d9JvrYCyBdhoeSXS#QnPmWQI$ic?@ zBI+#oNS_f57C-k=0Gq0-M63)sl$3b!Sk#ck>d8OVr8_5&`8$oq{aghJ3m@HQQIjrz zJ=9bCS`&YDh;A32=fz72vs3*Xgk`xcFx$^#OY7srrNqv1(W-Dq?EY+2U4jqK59;VK z6uW5I1;0quk+dCACZvcw-)vRfvHCE9Lzb6qPL}6_tLgK-1-Z}rMR=51K3^MVurEvv zL<3vyUwQm@1*$cDI*4uzeEQC|?FWJAKynm=*A+HR=~xl-bUd59m4lD;+Ar^P6rI*x zQPa;Br(NO1Aa5T@$vtqtQhfTWYS6@FR|mx`NSJahkaOS9JXd6rLHU3Z+MAQaPS5!S zgWk`n3g^vJHz`s&^@ZXsFKZr+jGgIbfoh{(vf+kDqR#7SgUCL4ozp~<g$itp7O!%~yV+NB6hktyA{2az zY+(vIRA)Ox)7Ozr`F@o;Nc(v4=M$yDp~-Kft1K#`SIBg9IEroM>@8jm#S3M+Cj;$f z>U#tYwg9Sh=2VxU-<&wz)9w%Y<=lZk0n;9Np0o8nhVQPWy~U(uwAIs#_cK%39@q9K z7m$9BY9{JFlw@U1B=^xH)U1+#5LN$H#J#JIMwivG#OgP`* z#P49!T&R=LlFuC1U{~pCx8vzK^$y*I9w%RI(p>a2w*4tC*eTRe*$P`XaBjVg057J8 z-OEB7W_Ud$YlqAuh1#kq`9^f3GuTHn!*@T#$qex%T8IE}`+`e@$S}db;aa@Z?86zZ z-F;Dr=>ilrYdk+&jvYhmay;I&H0#H4ZjyKIm}a5}I*p#!-^&M0*7m)9iDz_sGSDgm z&KOT}2#fnA@b1gr@F36nV1JeuJ`aq7{D zYNylpmGAX!@>x%aB-`4w{UQ+Ext`uK&kaj9quvO^H~;C^*3F%K=j8~m-B~>_>m}2? zRGHuGiBLY>@r@ck-T|4xyxw^h?#u}-U-@pbewg@uF!A>a)k2|y#1dK~S;cXGDZ(ZH zVA;NJchqo2=?wqM_CargppuXh?HlkAvI0@6)gH+Nq0DK~Im%4U4{i*oQAg8h=IdG5 zG^lXLQbF#%{&CL!>ImH*lkT;Ttzq)I+d+x@j!Pb)iuD^R zD_mCF31_e_USRM%r7`GU+$4jSKW$;0{OLmSLmIBVR>+gI3VjC;GBz<7RLW!Vpdz_0 zBsrS?_7lvC!=S}gw%(SDrK1{4-6~Kq88hu)i}>z9m$Lu-25iHjOWJzIBUE&u#1`!L z`EWGPzRRbSDP}ysm#kuUgImhUn|nemIaU9+{7<6ri`tntk%>#;_@u=x?MiCeR8Y3{ z2jjiB_LT*e;45sv7JHcwvdBo8S5raNHR{2?87~Hf>3o*7z221}k9pNh6wV7*sW}N( zDTVH^=SeT0S7G}isAr2lpq zh9@gNQj5#SSj#ze``Ad&sBlyGTM%4zVSht43kDDD&_gp0UBc4KY zbetG=?r}g%1>ROXw2Ck$vQVaQBBheD6}i0Bm8vt}_VZ-I0+}TNb}5i&q?CNAq1v<= zpYA`C_wD&DkB*7j4e$u@kH90Ks6XRo^Z;=JLFOB}Sk0z7&y%-3Zg8Ht5K{1_=Bd#< znL#@ma@Gf?5BsV2JD6c>LAZAkz3I@Py+!xgSM`4k6YW5h1V+np85V;|GV|S<+ocz* z;I$vqNl;-C`oQ+M2y-KkWc~!KZ8-R9Rm?w@Q3*3Sb}rR0nXe4$BWPt~&6LhjO_*Z@ zA(=sKzj@MabEIBlR^r<%;zTYK29Q4P`;tKhv#*1li;@VBX;u*;dz#&XhHK9Q02igK ziz663S$~-T0=3XBc?=zv(<%FHqC_kQ&|+PX#%m&Opm9b`m~$xO6=4 zG;C`eU%0RZ`N@%Y-ZT|%Wa0-QLJA2!{;VD2<0UO^+M-m2V@}+C7?;2ugB&8-`-WO} zHgbgF1yX+M@)#&^T&8eMBMI%6v{PZ!4tCla7t$B_gGt^B61g^XS%e^S>>m7&vttXw z0G&%-8iXkvm{;`}oXDa^s4c}hIz zZ^A1-qhs~~1tph>i*XxrGMftNxsXG!&4@Ozo*<*O4L(|?{g!<&GgSxm`(C{C6wnqm zWZ1Smwk0D>b689EO%yf=+rRr8N&HZ#6*NAd&E8@xMFI=aLW`1E#wg$%bmrq}Oh+T%7Y zIu|bO%yY`vF_C3BTD1IocJtO!-$&~PdCs>ZCgec_U)}}Q#hrnPlKst-|`|@ z&y-WuergZbDV!qCNI7!X;m7Bz$rm#8_azrFur6Q?6)Q<=-LPUD?#Ft$#y&l7kMj3Gl928KsytIOinwN2daA*F^ zB9rxZ;=c-R>)1}Vl?RWVAGc|p0><~rW%K*?vwrrEU#gSZP1N@^a8duA4h_BdbQd3sTV*0#Lej_#OG{f( z2&7ZQ91inU?$+s@;t3u7o=6NyEZPQgB-D@^v(`j--OTJf3kwVEG9#m>pP!kOJ#9e! zd_MEqg~CFiHH%AKMJ5(`7|FoF7mV%yeIZECBIl1&a$-o>f8p_ij>Bz|49FZH37%r^ zysr|UUdVShkU3Vr>6V0|`~>7Ct`^qdyPfa(O$I^(?#bnJ*z1|H&}cB{TqfJ+{^D*Z zKH1e2Oj=y`7_7y3nM%5o$jEr!?6l^Hw0>`r5q$#Il?wMG!932#=mKtc90qVgR&~!z zm2T}lBMEcMBxR_rfvzWatma8d-9ig;@C#czJNynK*8u>cHg^Drd>E;89dIivE0HWj zs(g&b=R=??)fjzJ`y9q_wTOku|MI08e0m;#jQf)7`R1#*4=+@2z2_Sz+`CB5cqcHB zX36p7JQp1E9Mzbf^^5dzP$j!Yb|O^gYucqHL*jDUW4=!xdrFP=Gfz)ERxSfpA$-!d zkM`0WPAZS_ScRAMj$~!r+a67lfQCDFY|r}JO4zn`2aYz@gi;B>jUocp>r2K!vmymR z*MgHikUoTdxt&G$U*O1%0I6&epl^l%=>ip#3Y)4=#&s*OQ8a4ERWu=d#qGN%HGWEI zR_=4p66z3Zk)OjV`FG?kAF*C7ERZt4D&7B8Q_2a=i&(a@&RWw@g5~wnObYq3DzIzsUa_4i+RQhQt-<+T zCfo?9YTb+lKKmZ915n3u0ER2`aXTEa0*0+MfP4^V0ec7(9uqUg#m@c~KJ%_-W|P|D ztd{J7`1BckRE0*&3R$Ll>BpS)EVLiJd*PnU;N8sFj?Ew7j##z1DDkiLk_>un#eH0MXSb^9AU1-vW9kP5K8fV8dU5{jfNr&z2#a z*{lPA+NVAn^&cX@R$=sBYxi)IcH$YtXZs8uS*m7I^c`kgSu%S99G9omz(BGTcez;SbRxqOj^3`(ERePV2n;AIePs{rF}U3`E}2hM#G0JJC#m(*#k$9)>bz4t zp8UymdLGa5+^VE4HhckoaJDPwDAm!`?k;W<#>Ub2bQ%H&;mJp9cEF>+R^ns-wWIV{ zkFGO`0i~P{5WQZWSIFFW{WXA~z^LW~@WE7yY`0e?hyj>TyXZV#PmT_O!5KSm!KxZg z{t>rJ^z1o0DeQgftB#b+-%vJOD0}zTRhOklWGK6V4{2Wi$1o2^oFoOUTN{zO;-7se zeh*XM)-tD1v}M<$_`CJ=@$u8sJ-JMH_{0WZ{f~{0k2?(57UMbqAsfg6P37*ZI3=*v z8gWGsw+@(tBJ$B(h5!;k;13)VbMsX=c&wCW6Ue0-0h-GkZHrCwgYd%}F& z$=ba$c}p%%)y^H6&GlL5gctN0l(m4}wgzfbaJDmwLJ~lWWE$4)FA!NBPC^SJoDiHo z`~c^+y|zY!O|9Sd+Xz-}PtqYKRYzwheAy6;CW3`2_)zCI`7IoKKI<<_r#qcpHJbD0 z&xvXWPA@Sv;{W7(@|+X@Q@RJ^YI*y}yZw@#3UaOrTW+W_C85B;WXWj&cS{ZR~>rqHq+<~0m z`rlL#T=*M~M()YgGQsC19N|AJs;R-~+C0n5@|RWr+khUi#eHy-=Z;dKs;@)po27>O zmFKhkBPCh04ub(VCr|NU_Lr)&qoy7geY)YvcmDWtgPzeNnC8Z7Cb?Q&)tQCPyROgG|eJx4(98mNlaL1a+dM9*>E9UI^5lbpADJ}k|&SaZ~~uelV&avkeBKPj1}eWUn8 zxh3wq+tKmp3-FB|sLUH5h*OtUg?BA>Y)@-uHOMtW)Dp`E->5aa6!YRyCJSb5qcCsh z9x1BFLPeI>>^hrfxfrqe>wsoF5igf&tB}<>VF{&1_P2sJ&hEFQn&>3(4;EuSuACA; zuYpV<^2=aH-`1{`Mi zBx=Mjvcg8&KaxT?hEoK_tCoG<0WdGPceSm?Y$&Ivh>Y=?b3qUZ`^V;+7^t@JJu_fB zQ4q2<(wN)3IYX`_lqH80e$dAf=c_HAa3OzW7+zm?d{NZmaR1cId3P~ve-s7+pWnda zq3Av^yGe6@nA3=>f53vv!R2FO!KXIii06VK$JD!XM-E6lrp~E>L5W4lJ#oL6TVc8+8 z{O=YO$eGR-Ko;*p#Ns}6Tu^LT?6AV;%7qORkI&kSNMsj%#&Dq~?)tu;?980f*9PnX zAo}4KvomJyudo$ya3aA+rMDfsJ*%Hi(@H{C*<`2R3J0+u{$c#PEeS4i;|Cdm=AJXP zE&k0h9+%aLafNxG#kXm36XX!;Vn2*#n*{vM#$9sd+&@P)39YkV7+4$J~7ieHob=HZEn9&GA=NkOUe;&K0 ze=T$*q&Rq#?ho^-!ATfiiWv!Wh+s*ul{omB?@fV8OyATO!G2-=R^suAD7_%7S@6Zj_wRO<8QIb)bvdkQ+!CP5#*pJ%h8@5$uqLMq#N_gTu3I zIaYQ-zd|N{!YXEJ&E&Xs=c>^(Qqsey zK!Q!QdxoNdCxjw$?&GOv99+ElhbxLAAWb3`(#j?q2}V0UD|<=u|` zJKvU1{QH*1DE|G*cg}xrGKLpZz=N03%Tn8qcXn_7&3n6so&>HRD<)OJ!hSAYy~TKt$OV=%B-bA?tHJnx4&&r*STj@|iWi&fni$99Dvy zF4Y^3pTeOogYc<%z*s}M#54w|XU^UhSli0#M5}k@yU>*@6fsRV)zs8d7XUm9nmJ|t z#sIX1gO!&qeVRY$u)VV2oJk{K#s~Vg`2&AbG9-KYcXtTX!d%YA>*?uf zolV#daDEC-xFG{pJEe}>J?el^uLxXH)Ux1;G9^SnTeyT+>4g$sRwkO7Hh_32*52M8 zwb9VrYyy0rD~WJY4}EBpdPtTbIk|ZH{GGS@RX#`>6VNx$U%sriEgr)Ef*4jdHe67o zz#D;rhA}8kU0ZDyKv-0S`38*4(n=FAp1~)r*Zig@d;zZr946;JegSyTX!<02y#8k# zPr!LtR9ldhlQRUlSfoq?DJ0@%Ym9@^i5DSXADW|K}wC2GZb*N**{~=V>4l^F_dh8L!4+Q^U*&bfC7T2 z*?O~rnTH<;J$Pk@rXO?^x~;Zm6(x46U=Ou%|M>p&wAj#5aEJd~RbKVb-SUb3>6kZf zr*sl9u6?Sx!^Z2g-MWA_ z19T3oi>;+<(ySr-|CcGRD3VGMuoSU~gNCixQPR za;LhjZ_)-F`Fmq286ZkQ0r)~hb>h;mj0d>|nFvNe>?&e*bAb~5?%lhK1HN0M1aJ&E zQ|vsb;~-FvMf*G20fa?DUH}n*0pvK{okay+fJH=Pd!F%iJxvHOAmL|`^@OGEFq0bN zwBD_`y1zNBSyuInC?7!2dLA?9Kukb;&d1~9lOF!>oFKf{SORGNFet@R`z?PZU{dQt zC&Cf2ju?dO!K8fYEparC@nzFGdpL%E`BMm)JAO%;-UD2d~cF*52+h-Y;-0#UpV zL=+Gf->x_g*inw?xdFGt=e@HAke~l@C+a(T|CA}sH?K?oc+@mu1nefL5b?P!ja=M> z8-TYm4hAuItswK69_Luge4?G7n*CsjBOs=8YIhHJda71}CH5W-*p-p^L1h>TG^|AU z?ggOY>!(-_){YSXJOM<>5Nsl<1NVD_liGqymQKG5-Do3^ObNu4Iixsv!i=FS9=-H5j70`4se|kVd4ADSA zMhxj*yTLU1q{T(@s}k<(-Pfw%LK{H)ivy@0VXT2ooP;n;z+b18%KSyd-3sjZQdoRi zm0luHvwtA2m`kxNLpwHbw0mPl4O?mj@9hXgD4;E>z>GMfcQd5{X@(#kqrUe#1=t?U zpSV}AzJo*f8l|ZxXJ^L)KXWMJy;xr_Ya@6s%82D}=*!Lt3fu^sWejzdv7PwTYBNx_l?`P zPn4FH(u+7rOI?soX#jzHVEv&h0~d@;nBDS*VQLw*U->0ys3~WeO&xRR`sZwr#nBM+ucRYVTRK%shfW z(=Q!=A>SyT>;Yg(;Bg*7EV8~dRkTu3`9G>OnE1U?x||CQpX)u4kE@cI8J{ktqjaFYIu(Wg9kl}bXX z%@DTJfYu7?iTxQG=xh-=iwKWUURMZ67zSo?$#3+=EB;IY#_N|9iiv(Sek}eKQe$KY zJ|L7| ztJmtjTtg**&>v!Hu=5M;P+>K1>FJ3(@O z(98i?qi2r4YY`_+P3;U)dCADgFx>=IfSNn8m!AU4gp7u!0kx#O>A_6^3-uHgRq$Wk zC6EHjp7#Nq3s@5Ln@~9eOr4r%A~Jl8Vjd$?Q@5n0kAhtWt~r4p1X#{}rZMC+G$`@` zKV<->H|klj78lZaK-m{a_jH1qE69Vig0xX7pm&k9AKWsigXA{0j-bh1UAq&(TM9)u2DFZ`;beKd$L!wZwfEh<*C#?sFRW}#2c;ftF;v7Fk|6XxkxmRO>iEvmN&w@5 zF|3{K&f1>SKWqfGyHbcg2#JV{A+bTkX_QK;SGER_iq|7CDCpJETyx{o)N?-v>NGeY z595sd`)D8)NO4g@0s?KWQ$RbEN_8%W>{gimK`v7Vz}mVejH{QO9VfYq=9kb159568VNN2I}PSGDg7JbT2<-v($a(8O^7 zCw>WZX(0E3`A#;U!uv$g0580138y-Vx*sUQ6talA2D1MMUH-i@D5EqsU4QQS@_6dFg!lqf@0#qgF{FcGPMcaA;H=S5te>fk6 zbz22(1mhebgvhrAY2-JC7@cq!PP(AG+f#3nlI+1a&_T5(t1EGM|Ifo(ppsg^n4pZK z7IJotv^qm-b>GRLyZg?EkbBU<(y1JEEo&+Q#kzWsH0Sd44x_&G4Po;8_EU4iK?u0i zW&kEL$rr6EAMbEmD{!RCFo+uNVOV7mTYcYs47!Q(@@u~#&?qLSr?&wFw1(#o^0PA3 z;5YTdhoPQem4?owh?px~TRv)EzKjp?zv!GZD2$L~VfI>8z;uB88he>-K_8@Zh~C`c z2;nGCnO*U~Oi!-9_ElgFHUJLvrh$P$-vqFXandgzErQYorU+zOUIOVCxY2F52O!+^ zkclV-0(=-mN?=|cAWlSJceU6m6cI8VED<1rC)idfu0TK58Di<%Q#0^d`qsfQ_}y+* zhnoSoEj&cDtu=%^pFVvuEhZpk1dKAcIx+SBQXqypIXjom=Qmvxdx&7AeXFkUqy-=k zfHp*|&2)lP%l(9;;1EUcKM!H0gT4nRNht$B`$3Z~%A=K2oB#$_H3{!+I&D3Cr;+=>T=n$Gi+ZYLifp#9I z!&@T@V?$`2figT0(OS-M8Gs!wWl;C&DZLNBizdOfsl&rdXIRTqd* zNQDQ;u3MlC=zg$0wcVL?@Ex`;SVCb4LK$Xm?r>x;K$z)dT4w`1OV%0E%&qcQ_@%Q& z2@THbcU@ftaX_QMGF6#q#6=XBTF8zb#>)hxm{3d^dG;2+BMHoeNO6`(N$3k*^>DjdYm27{*r#Wbj7Wx9yG zG&n;~e+PK#5b=I2^afi1q66z;{npSTg7_L+k**++Pj=G3) zQn-jx!4w+mYn(2KgHH?C?pDw=}VdJPdH7RL)9H@EGH znAXyS)SF2cEA_yK$U~(Ctl^Z`K?p?sV23)2Ko1O-q=cILAl&JgvFSA;V61 z@?=iTVwpudE1LMAQ|ks|St9N$Y@q&g6$d45kRpwu@-7S(ghB;Kmx)XJ8?5GskSirr zVZ9}|(wW{*XFYeA{{BeN61gadqd-Fd zywO)kQ;viF8QA#d*ejtB0(O8R9H<&^LD{*Hv97cP@ZefdvWBpQ7j}gh!6HSvU?BOR zL3>1x2|8I?6&_d6W*K-xpz63%Cvgx73PqPJJJ@^C80!RkLa z_ZKjYkP?A_`-%xrguP`JvgJVMY6}952Jq-K@S32gG!815>5hj;W*O;fqO=!QOYJj; zCU`5iL2C-PAk6^+8bmvXblkY059Bu*M2ZCj1*2g=ki+*_pT#QwEc$F-1q~|qA5-Gp zaE~G|O~I?DfWCvDNHNkW*n_2t{Hw0pXdrrlP$e0~sZ z!ZUza32YLSA8x}M%4}u=FW;{nrg{M%3V^Z&{N_LC#i9J`!Jq?ss5{91?P~u|9W*TL x1Jwopk4k}%Tl}xvEPo*vV0ivpckbIEo>gx5Z^eu>jNJ%#c3W&6Hi-44bbfKxEp-G5~$iu-QWWmARD|(0sK8Y@sHwFK2 z+l#8&D_9xXJL%aP!b$7dTbojMXAzKn zUfn~cdIh(xI`H5@>_~au52iev2U?iE&l&#|Xf;=q6g+US*vu&^P#twB{-zo>>$V#f z7B(9he|TdP@#@K|hZ08A=N}6;HJ6iIR(ts=;*&1L3024IVqcwkCGEGow~*Ylu*faQF0V zluAjtHAteEq~s$E!7JummBI|MP%>0vj;}w(LPy6Nii*Ncwx?LlM(Kb2{Ap$$86Dm3 zi?1C}S;={OId?l==fc`Ac+01!r-yc~d^fda5;fFTW=&F&^qcF;p-L+qZP#@wxFi{x z;^N}BDSYwkLz$`j)iyRZZ{uJ3(kf&q@(O&su*YN4?v)k1R$QwB-}J{Ir+)tYVNkpL z+F=0f#U|#b7Zw{UwmF(#tS&nzyD<{LlKjrZgf>?>U%%rkp;Bj_GM%tLx?xuYm4m?U zTB>JXkzQ-nf*BOc|LXP$2OKUC?5m8YkUNT(V_R)NeP50TETBpN}+`@>6h!zA4zNRm@bXAu- zwZ&80UkO<=I@WrVxFf~LKORLbA)%8z;o_?5EB-h7>gwvYjt(pw9QzJO%gApWIGIBd zsRAFJH?lL~{*2_}6LVU=?@Qv&l$JB@jT4fTlpOI`;bi6FQbo>z-9nr+*`hmbiar8| zkT4Kp^pt@ie{#R9tZazUOOZHVy`0C;*)o<kHaDt1}@151ia~ zbUodHwD_XrhHxj#;4y0cYWjo#ri8*&7iKY4p~PG9@zQa!+)QQ7?#ta1ukCUv8fa;0 z!QA}74hqAJHzL|%c4o?A2eH+AV4`(57n{&`flK6_$H+gee8GyaHz>ulzg5~Fk&qzS zpTh65-;TdN*C+@VtL;h)45jX{j~8xhrZ)EM>`bAL#d%K`k3n6r{I(rmhuNS5)p2WF z$laX}4Aoe({q#LpY9Ak;u*gWej)ewvfA5l#67^J-HZ2!7H-pj%O;SJT^w==VA=%T6 zLJnfamqtW>LZ+rKCMG6=$&q|_u05wG7nJVcoP^ZRF+`GkDy zN9gU+-c>E{du12sZvMK-XB5f1XBi6eQr2p=IK-B}P zth)tU#Ebe?gXs@X7o;cj>AdjgClt6~QeM2E_7+m7X`60?NNVX8{bFXYCZ?SjBwS5r z`%5M>HSCVH$&TBTHvRmk)?4GnrrV75NAmP|D0o2+$jQm|_4TVZa&qGYJ#T0Wl_qwU zEq11>;po-NfCt)Y8@iLZEQb@#Z362kR;35~fX{5UsZNhKTA-z|bkntK(;Y?Y5F!Ed z{v3tR8`OAsEdi~}`_K8Td#{|@^bVbczXS&xf_viN;wq8SF*2@tV+ec$RyQ)aGh3es zjH}3?6J6kHPuOv1x+{s>PPNLK0s#sA87ph4`Xw?pwe|8ZOfdZRPIAw;^72@mGxpfn zG%|ctA(e9;iBH8s6sHQRR})-67B)0A)Sa&;f+_yg?x%yT*s-vo(R5SG>ncG6nH^?n zr?}44KpzUUGV#3!_?k@xTD4%>0zcmb-_yOhIxn-BiUCGuYC$8N;4@>nwprL%@IrX7 z!a~FKctaEf4ClROB$wk6mE6h8%S&uhQnXiz92Nx3x^T(7PDWQ}`_s-#!KkD>WjPif zK73G`@*B)npi!?d?*bG4)p&DUi1?8K7I?XxHiRtT{NP3VCN0j-6zZRB82l#WU8J5n*H8Q6#Si2DlyU)oSl~&D$5Q+*8ieqL0pBBnWzQ@oTxylW^_~Mpi}^d5nHtgvM5332gDUpUdMq- zt2xuopFe*}h>K@k5_2Ju^4RNwB=a^^AQ>!2sd9f{a4_s?D4K1#Y zmOD69EE-UM~yu7^g$%L`l zh&F7bL@BOvGfj^@OQYTfMIN-oRQNHl*pwfn3mc4Y=*QcaYDM?t>`#%Gp z0!OMs06-@v-)VQ&EB>L?R7EgI5_YbQr!!9EpFVwBJ0gKOsgt9*SxBxh z7Zcu30p)s=MO!;NWMpIt8k)7J`vp~1ar|y4kIBhH3wPD115T%b3!J;Q$l99jHC^b9qeM=(pTgw@UUoQ zi(cZmda$k4(>0|7*VNaKZf$RYCwwy+TjVoD1jC%ZJ#IB2 zV@(RHZS?9dxco$#y5nTwwI=>Abs%+*$3a3_N9UhA=yT=tPqUFAhRD+h%IFApbeNCb zS4H{wlMskphi>5m#RrA7v3BMD{xe3Zqf z$Lm*fQTlh?-;|vhD=~FDYAL!tlLf!RT1e3$^Dp9U6ShdK#1{)+idsq;cYE}L6(mXU zS`2?Q_(k=+1rH9(gIH>CcPW%64KLu&BDb9^9qnJ(y#J~vWmZJZgZn$O&`~v0pch_4 z0s4BKNkAbiUIh4eff=SSVSK`Mef!oNtBdxzkb_`)deO4j;T$4|99FEz70h_o&2ZMt;GhNIpwuSgHj;Vf#12iyF;;KPHurd z$PSrx%7(R(mDP4@_liFJrUbVIm|^YM!_Lmm1>}YG>1tMxfJDT^SndDFt@fve>Jcc} z$i}~1iCSv_5XQ&PPkE}4)<{R^(7SoTn)$=P6A*4{OxL=)x+pj}Vvxw)AA8liohrL| zh=|-*(X@GBHkz01+7~$2gI$SzJ14_OMndv-uHoZ3fJ%L5SKZqHHt`qk=0cZ&t>KsD z_oLL=XhzOXY(c(`k%eJms;r$BX5EQ?W*1bxTY_o+(P_@Fr2@z90ExbI+R^;-C1n!uE)j4I=2mzrf$6`#=PP)Fne{0Je5sj!#@Ma&ws5h>8Vc*uC-N-W%vAJARegpCOyyZRHP( z?VV3B|0JL;?S6LLCSFcws+zwCMITq^hlK2=IB8Jd$$)@*uDYgnrnW~WLCk+lN-Aw{&jwH)3CNEWJuwUhAg;eU-d$N&^uPdl$_1c- z^G%)GZm3MCF!M*sh%EBw?X^xIG&4{B|yJ-2; z%>|D6%m;zYlFqV&IR;|A69r?zT^9l{5v#^)$B`QitNQjTo28ucuHN3|+`@)Mz_;=w zW#~kKH<%7TUxg&9uq&R}1F9yhs;XLlGO2_PKU?c$6&%W&ECZbeKr%U0nbG$U5cUsD zso+knNM2n7u>}F;nlN%|a^I3vSrB>R+&L`TcYxbcUPAR+cE@V%KW(3JVZiBPexj zRlo=2bGq72t?a`)r9`mqx&(+?HTK#2B;YRv9bM4Tkv#x$E|X&=>iPCtakZ-90_B z>xK~E?##?Zlf^+y4RN>EyPisyctO4@Iv=9}a2cp~bIOoS_2?Mu>+4$u?n1-N9OiL- zk<5}YrtOxWGe#WZi4pYWi#1PjTYI|=NPD9j{4j8FId#4cFRwzSC`?O05QvvNegzJR0(re>z(%tn&p;IsiJ&L!oob|UO0Gx7BFJlUFP1*jk}D2O*5 z0t0mUI5;>14IkaWj*BTO4ig&i?AvhN@K4vfaVliWtN_mTjE}EQc~=dZ_@^~&lxcfX zC+@$>B%b`ex|mhU*UCyOWFdZgCA&Q-oq8YXH1%d5j|PJ4zLM$?y3W=SO5U&KH1nXT zhpExMdkLgt5Tz^3CtAwE>U1vwP(wja{}s@~quj_vE?lY}KDUEiXddz{B|3lx>Qy%rSr>*Fyf7Mgb5%y+-suFQP) z2W;|F5bA)LndUx7rOk*`^1#5x!g2;z&yNc|Az-ro<3C}fEk&>WF!6cc3##X%sXLX_ zy5(wpH_tHAxUV;3u)gA*;AJI(EdYwIe4qw??B*>)$!mitUHh_ z$MuAlS5R=dy*>pYbD1`9tZVk;sTM#gRe(YF^R7+;Yn-+plCHbGxg-&AKa1)-JUpaj zVk$fxW1y#h4^aEn0*2>&Ne|-^gmVr`zkB0Z*P<$b4g(<6_m1T<#*5=kvEA8vmxC^v z%@uc8LgvY)3oX8$GxApso#MqBpPXyQWG4ZcWPWFTpgQr~!SJ)nxmB#?>JnP7QE9PZ z4<3irtb4cSfFu-20$l|v9g%xpg#-Rpq}zOtvGK|l@Jks9zegx264KHu^UdD$S~XE1 zDTIQYOsa%D6$Q&lAZdeqv4aVpibXUkuA z>#2eAQD!=_5w#>1LZUZSVNrK=&;w=hp0`U*RhA%315BdBK3rMxqA`|+h{K!!1&^L< z+s+SzTn6YT0PH`5+Fibm6wEdKOQ$7*EWhKV;}3m&O{p!w?nKRw`+ptw1Y zWIku`9g+He3b&^m_}C`d^>zXeOg7wZ)t=zJW!Lw-qNG0!n8wNSYXyB#3d!R9Svwt; z{_wF$^~um69>As+eH{?50Xc|RNdC;v!Of4K;Qa!+EtHE!CmtVtZbNS3>%4v8&V1NH ze@Fj=uG8Ws9)wb<)2}9T-lAW-NA2|)Ytv=WzWe<5r*D)StD;+ew%-@I9K0SySFKNw zog8(a&4xWPAhBmW3bN|CgZRYdH6?|O5MxM|%|GmEgCOJIyy!os$3?OFBj%a*)aVEW zJI_Bc#!`3F-|g3UTySh-@G>&8^Cf5F54wMY((GkMb^Pe^&@1iTw#f+7U^`~@n?tFt zJN!QVwOwCt-g{=^bQ`ng>lZ6Lc3HC@WPc@Ld^$RMB!o#c6JO7?!rWFHLaHpwKjWiG zZ(NcAKGiQ)ty&72zgfR{erZV+_8k5On^YB|GG&~RZ25^EsPuz}3? zm4rJ2&fa(1NNZ�(uhNL-JY!=%T7NHRy1Yhy;?Y8^*0`}XJ(Lio0 za@y5S@whP2_Plo3sXv{~=|n+6iRX1gTEM2gpD@DmY14(?8o=$aD{dDq(PgLqmiw-#=VfSa1h= zller6FI4OTa)*MPT+_$X8I^4pi^;Nu;!d($$G1?2C7PR5sVv^g4yI2o$8-ze~5cV((JCbF^-&MP^UbpFpIz28+nlO8gf6aLC)6h>Vz@m`SQ-~D_8&+K{Q zK3QQQ1&B|11yoD4tULr#%F@~z(kl>A)`3(FrPzQmERU$@i(5nFa>N%t?6xk<*fd6mCeZHdn2O*urM}j$xg}u&4QWGLhd1S zMFD^;+2GFy%DQwShs|#Nsj`#|ov)u?ib7I3CV>0^2?HbQQoF@~+CkefYU_Ng*ys{H zXTT1&P$zMM4rC|+AYu6HrO&r1C&e7zKM77I;a+kW9WH%kXRlx(LG){#UlS~(`XTi& zeC9R$*;tqrKrs2EC!_g|`F2Tvf%4XrF?CB2K9dnpazR?j%7K9R!i?$-Q0uUB2>2To z9WCnW$^(QlzHKwLd}SWTEoaG>E{70KAiU?0P5~{Bw)LyoBCwfEndH5b69Ka+Nl?I9 z1nTI#O5+8tZbKtTa>`Wk;EB{!RK9}O>$2qusS^MJGyu$#W2Z!x8=Nrf1}ZoJ(PlYr z0R){jit-3hCk*JDS+@y}%y}OEX7~0cly|4f8vy71T%GGL8kW_*Sy@@??G(^d5^UT_ zgCwryf|?i_y;DgwkNw`sLEI9^96_S??AZNZgC{aAmT+~S{xB2wZXfs|iUL4$GuRGq zJp!mG`RiB0CgX_MZjv5B=Ch9gGQKXSXw*gjo*xVzR}A!o(u%1fJp>>magm@)N=Xp{ z`4S3_bWeydh&l2$KHgr8HLi|oSgq9VHW>U0qgXpi={t5)9CEt_>SzEcAvkUU(3Z>L zL-Yhd<&906RQHIeD4d4N9o`rQjSe7?L+eIofE=vP)?aeBKZ8yREt4zPXQki#-nXG( zn>Otq=)RJYa>N4+xnfBtEJ2XBMnJBp-(7D}!H!9)*iP?1$&MMAsoFcZKq+YfMS|+h zf?7Cdpf$?6@KI2}1C<0ks_JCYOxDF6@JJtD-`_wBW^D**UC`|eAw2>#b#njDpAVqW z)3-#Lqy;wg?CP+8|KK1;-M8vx22`3H9v;p)PmJ!I13+4xDhsMOCX`MU8;HSGSk6cm zYS;H&OoQE&lN+GT#Y4J(Kk3*F2NSayjdQvQ`c{zT7X@E(1}^Rll(~K)m_E!F674zh zTEdaG*#hB@Kmh_??u!?SgFseY-rjYXg!HVPh4sY+sXv}z!0C~=CB~x@al}a9HhgEy zzk5z3;Leqvo*oq)%>o1h1+vh9fO!yQ)_}WZS1dBU0D)CkRNtRR098nOYp@*F+j(5SRDZ2tT}X*U~)+8~+i zgJoH==aDt>6j-tC^PREG1hgNJB(`oC?H1p*CnS{(T9yOi)w81VMv(96HiR4yGB-up zlLjtR7=z`14#JRjOirQM0g!xHfNNqKy^Mq3-}}mxWI^V>;qO4DFC${>7PFj@bX%si?;wm$+vIcay|yNzYZni zS8Z_TuAH_-LBVHyMoqn%G+rHMq(@> z(M3D=v)eUK$9?|+V9YBSiIzXW?LfHqyg4KX3*BRiq6-r!HVF+8y~#F&KIjij5OcCh z%FT%MiZYySUo~4V9*Lre*qc;UXVRdc3)pcdwzY{U7ie~Yl;0c7IV)~tME(2sZ%sQ0 zJ0E!N$g1$YI#7*(zJd4$yywqMEhmW5xi$fNUAc$|&`K^q4Jz9@0OyR5RZf^k#l|YA zs{R5p*>gHN-q=c%SL*6SQ1X60?~Nhj(ra373-UQs7Eq?j0$cJ!DOY*ioa6QD*W!|r z&^gGXI`a(+41s!A@-oxiHAd+w;*Zzh9L!j z(GA}Pz#UlA|Ej-fpQ2Wyi;ud{-~fb3tp5NI zKOz7bj>#%V6CyI+2o6Ela*gJrFka%^9Kx%EHj&qp4{Eq5PuCogH*{_e@TuR%H36Lq zx~V`K1>1560{O^z15g?GF1Jdde0-$aJ&7I|I>1xFzN|peNZD%N)y)mT=m-5^l;X`> zm7A|n#pYKv?OUb}-liZ9{PMJ=^5O#Y&ZkvMq{E;91gaJ7pzfX%0xoiO&kRb?$xkmoKrvY}M7(%a=?{ zOdNK))XGh90ak><*({z_Yc6PVbXiZ21G}`YVABYB_k@WO z=ca<&AA#~pC=kyJ(7S$CjZjK}Qu%DXTdDOzOOAm|X5<}HXyQq>1DP5sE0>#%DO-HJ zIMN%=dI!24l#$b+WF=rY=-`K~2dMJEk$MdIao~$3uMpb4tlSWKATA-H2PO?*u0H?> zEPymUdi02Z(~<-Z8fjTrSU|MFX2w^ZtaGs&8H4ugY)_WoM?{PW3wtFe_jsnpA@_s` z9v&WeYLcxc6yk}b6^u62Bwq>po{3%ze0+V6qgLY>$@PAvlpG*35&(l?@E<(}MY*G` ziAW%*D>5XN0YZ{Dx^(*+^wwmk6lyD7LRDhu+ZzeP2;}x)(gxT(*o4)1ZPji^W#DS& z5?(p~zDXtM62pN++@DZWPTx;I;JG*N%agI<%pd{SWb&@QRGO>Fn zrQ*NzJL=orwIu(fnRAijN1cM*_(4kSvi<&l+or;Keoe=C$EM=1h@!(?X`BHU?a$=? z+as~|>)pokWmL4gE#x%Ks+W?D$p46W-*3N4$B245^2EyMUukl6?&byP*%ESp zBbhC_?J0iI$%q9UG!o)<=>a)l{oh4-DL=iv9)nGrC@zz563MmOAb=dDzx*4N`ef-m zv%iPHmyxO0V4Cj2ThKfIlOg0$(p?PcKbj)I1&&n!Nb(zc=l^{XFVS~A=;uSzCD{Xu zK-KyvnGw#BzZJ0T2f4AnjJNo*oy3!v(;Q7B3v3eq&8BIcxk&YinHw1#X0o>y$-AL+ z0TbBHgtLxc-B{k(4TUc6Ff~DaUG~}?P4sWI?w1Kp8eXyK3EtqCabz{S|A5aE760M1 zf5HcMaJsDJx4Ho#)lS`-5R=e$eH$>-gnu(7+PW2puAd`m*7qgD#@-Lwa{yOU{#}g( z6uoqL{}|5xOqzJnzueJA1mgYQ#k`LW#OMA&dqGFgOrdO*E)y&?XLvVcY_6VU672~1 zSEm}-52X~<;yZpEhu=viGP=hlFZcSp((-xU0JXT9C&@^Z|L=~|0J_p{S~}n=hqCo) zwg5d-?cciI!!e90S{<=i-s(bnQuovEgoERsded}}$yt0+sG)YfhbtS$E+JRb^i^5;igN&ABaxOe~ z7^LL>+Q-=mone2^2t3R!1F9qoyOL=kbU!y4NcYf+Yr#d?zgx+sgYJFfx%I|5_OXf> z<$t4mzN-u0XNv&WC{CWoF~aA7^e>e|Brh)L1&AubaQiJ=tOatAcNH&{3sY|c znHm0xiK5#NH?b-%N^a220oz&=ycmJ%PY17&fUvjjD&hS%eL~cP#SihwHF_s~jFSxDG;LbHCr1{3nf#k3HPa8N=fXFW_>JN^ zd9y^~8wE##ceQ2dgwkFKKom}%ShZj&DR(6Q+gs``Ya;qOH*VtD*+4x4pEjf#o@d-B zX#22!xo~Qesm9ad)6J(OXZz0?B3k#*dJ-1@ zjm3Ckh?o{a`}7%OnB~%>L2=G%HqF1-A>F-@zP@#F^+fes=XM}i^ja`Os(fjd-++x88=}Sx&#t`v7-+xQ@5}jW1sj(Hjz;{Q$655O^!G3Wgsdq@il0m}P3So7Y zKi}Vz7|KxA`ql7|aEtwo4ryJ#rXz%dmYF|KB3AL>WF!22+~R%+eSZ^8uXgz^_SSaG zhqMAVq`S^kK6MEh+Bs>ht~Ji9SF0zQEww(-op}21hrg8@ihLIhsLp&ZAJ1^!%avxA z<&XY5&V5y}F{2WPS#3Diw_D=Yd8;X|FaP~6JZS}}>Luu?0i-0NE$jg7Nc2Vi`<-~= z?3DECsAe5Py?9ETS8Snait9g-bAP@{x121E)j1? zU+s;)x9F!@Kiw`?En}8f_-Dy{Awzm$Y&raXtxZfVog)k4{J$UTz<#$+5F(Ex<1e%` zdk>B>Lh-QdKdqR|U}P)b-SL{z_GEPl+q82dKPrRvzn?vfpRyf4+hIiY7qT%z7P7d~ zB8?bkow0-!mN6@hxzId3K9KsgejD);=h2?)8=;Uuu>Svnxlrg&YXOu(2~gihe)Q;v zVNVQ{t(Z?$f`x$LObhburysTOICO~lsi_x9zcqi84xggRf>x)1TH;Uc5jT>n zGW1gn14<78VKXy_lEKT9?Ra)GJive0E6zbTY#f^jmQ*a`3Sf5M^YXsZ6;y$4ozH&m zcl~nm7++p~QQN;Vf~%P(5!-}k0PD?$bB6%9-htA~`cx$oRL+0{;0UO$)UqkP0NV7Q zO;=TMWxS1ViK12L8)xL_PX+?Q0l?wbKq*dQ&j4r-kfC3o_6ZteM|baHdf(iSOO@iy zLvNoljOFf1M6Ad8)tM<772MGp$KPUNVoFR$UI6^7?g01|5ESKN=+)${3L_&U5neev zI|qlxMn#1sCTdu~IxxdKRJ0XkP73&nUV#uIDU-+rdYk`eAFrsXscC(k-_ZJd(6gus z`k3rN*H{M!t8w46XV211gMx$eW5-|=BiwFjWv$&8<9NPB%yr;qjHLiwj3`cnDl1qI z$nNITRpFowB&7lZP&$}X_9Qw9*FXR!47G@-Up*C7HOEuO2!OdNiHWXd`H|B5_s>9G z2lVWkb`A{=%GAOQR#ySh3f1lcuT(Ff}C|avu8tfP%3I2*h8%zNd@6c?~%58E8s*_~=niLO&4} z*5}mJRFv5}V4T#q!-Q_*EP<$88yzxM*-o~+y%ClcUZoXJ=mi>l1C3KZ#taWDLS+LW z=ZOOggRcS&IH07w1WMNMx}ZMEeiDFA#uo>w6!5yms;2JAP$?XgLQ>et{ssaQ5-RTW ztIM&uckX#Upaq6yUbaisf?cOSXf4Y^W+etWr}i%*7~}$BC?wzR6l=UAm2Qqu#F`jw z*WwXh33|>Q{?+*##G3M4a+b@^{{ab0y@{d3Ca>|HAL3O!qazCeD1W0qW+n4{vt?Oz z8WlhFv&EI%giJ{IRQ}?fH$g%d*ot(&pU!9n{2^N0ux=Cm2W_PcEqj`+6LgTu_XRC* z(M>Mc+EifST+?W-<*EC@lxUud37wzbJhmyon2WFfM)WqS?nm8f2W^OOTg|f~_M-!{ zt1^FKKvmmDqec3>IOYw*$8`@4D6*n~BzxS0-8Jnu=~P5(#;GGhn7hV3J<`v>t*ic} zgTbsMbno`iyjE+1GP1`Kotsl2!PsE`OCs+qEZMP4=592A}`9`@DYz(AU5ix8BT07n;LX|g`(pB)KUaCz^Z6p-#v3Y5(g5& z(Luza7Ttix%|)#63+zdO01D4-F>CiHc)2?QT{c9pwX-zPqnJ*8$Ityh9Q5qsB(s;hWu;`bn?Q7XZQ%bp$b&TfTt|tk@!v6nGWx%t`x78^g2>5x^zpCTsa-} zn>)MV`{_L%Wf>o#2qeA}EG+e;yR!K+X%Jvo*cXbcW7jonY!-lCZqTT*QhdzSlT@~aE3=lUc&I3I z2j!}gTD?;HO5h?Mum(yae?l`XDn)gSeeH*R5|udR`KY(T@muN4TlHF{D`EqLpa|vg zHI z%2-;6nvu~uYrBU??epp@{uTS>Fi+NLat6AohLv4ygMQ;QvWBFdA+mYCxaw)NPysd` zexgSDD2MpTrvltSGa9SC5qITq*HQrYzOYIm1GZDSPg*p|_Eo6w?VXvlF^MwsBOIVT ztZW%(CfLE?K+#;VDi}>%VD>*gIbaDL^NYo;xlGVVzekhD$kAW@vv%Hdw%thLVbqhqsauEB1|_vTGHKg8XfKZgAAjVsO=W-7JuRTaYw z9^vYs^$_jVhu4p_8;BIi3#>w0F>)cFV^e{p z4&U)E6P)!GYb(F%xNuwo-20Uegu@awE7kMEK5$k_Qpt?VJ)#TJb8B}WMOX>j$*LZf zUK!Aht_feI-<6{foNgp))wAx(LkVasDbE$g!uSD`=7^^Kuvobv)~~!CQ2V!(f7(fH zI#>N3T3D^rZPRq1uc~4iQ!}CTs8i;-!=VdWy5)<_(wyO7HU~ewqN6~xD17b!@kif} zo5blazQ@|#;VzZp4bb-Jd{H8<9(1Jwi27%`YDCBfYECo7qwujexL_e%y0 z^eG$k1c1fzeT7-9$>SBox;P8Qfcw^;Fy5m0%1H0ImS@OwCQ9ikiDoBi89#TxHemHf z%pDGPt{J>~86!|k=H-)XV8U4z9rP4SHVrnV4XpxfqxVe_UqrSl;jL93im*k}VKC7> zgPP1xbPe~imoR%&4R^6XuH;Mrjq2eb$0PZm{m_H-OABPxdizY#J}uUBR%oFp*ie@6 zt+fC3$|_&)l<>mptK&zAstfv_cDeR7@|;uMz1u&(IMT${tXuly)qKI`jHqVt2N4AE z$$5oH%Op}Y7v;X<#Rq^*!d(yT!YAz1bzk}5%D3poHM-u)WC}>mD57J?lq5^>BIH+B&+bZzkF`>-fNV25u91TIK&|3O9a;S03?Tb}8?Nz8XnpwsA zjl4fo{ay&&Xxq4QwXx`yzAq`G1~TJXmX=_fg45X=Q4ukF$aD4t=?$q#&iJT?o)$TlCboJDAKy|rl%0Kd zcyHPBeT|&wx2rA+JyAc?ngq<%hr4R9s+eZBs!__CX1?!Dy+WgPi0+o>Y|I@O)XGjw zVvVXMfLxrBZ8erK^1(L_mWCw5A}?wqu`6Rnb~m=FHQbr z@p8|}a|X_iz-%=st8ToRDKws4b_yeu;-;GbU+kIm9ka?Vw?)wY^%>(3a@`=n%z%ou zW$cks98{M=iZZo&=b?U25@H@}TrpNB*zIpePK-V$srJ;I%TNsvs@w{MX>_Kd(FDh=m!x+RQu; z(R5}hLqSJ;i-|yGfA8F=G-onQbAH4r^oK z77fQH>+-RBq3l4qVRe#JF&OQg)O;ROBT*0s-O(L7Ad%qZ`u_32l$1#q`qTdvgiS@e zG!pqiJ&kxZ%lB`YzoLvTa@y|nDQ*}!AaCV;ABNn5r!u7H3v{FuqGLO`nAN#k+!r|q zSjFTA3|5IpkF+y0Ae1CMHutR+Uf6)!lb3&TyzDMHb6>SL`m@qTh7EU561|5rsT%LW z^FI2OADwQ1`mA_%%~)JPUs#Hzm*cz?c?l34PJZR-V_B zdwq;0j>~#LjOfX7u5)MCQzZ(4I!3rrL9f?Z$^1`uyT(+eY4S(gdPDs z4t>DmrXs7Zkvj}gBlK&I=p0*m?)W$y=k;KgGyO$|1j0}%IrQ14VzeMqwW9BpH<7=d z?|5>kuib|2GckVvXO~-6+51*qmeHZKV0ik4QcO*@rVkj7O!;mikT@vNf?{D39-2Zh zt%)-X@+Eu+NNs@S`O9zVX!5Ti@-NjXl=!rgDyx$m_IOm8t{DyQ# zbSmM(&Zbl+(JWSgbyvgmj=`^7XwFaZwR>1jYbW*8<6Pb`4&sccWG7nUN|*r$GbmQC z`m>*6e$i6BDJx#UFwO@%uFQ}-G_9nq+a#isn z5){b?QAu9-N7;|RGdg(`jN!-9JYOh-iTQ0+o|Cs5mSK_&SFc9zA;iSSX{2WWTU%%5 zs^P$9B4^!1HzEH6og9n}47TOd`cj z*uqoGzHlgY*ddGIYxq<}r~cba-ni~Mno)ddP&Nq^VXzGej4XzXOPfBqG`m|7pdQ#c zyr)dbW=yvfI0O#wD;A`7{<)f2-g*kIg$EJj5|a>^jk5_EBJw3aI0QlKQ-ie*L7Q7G z2V;1~>f3Zn4&`3s<0yGfDPxN?`o<)n=tgwd%Rv-ukYRB``fWxl$zW|p8;Pm2!INps zdlb^v6!@49?rsbe zQ<8e7HEJ(g`|mmVHK7UVs)z*y^f;@1_lG7Fr?)FCQFr;RzA`%>XnX~5Y!p16|;I~KCH^G7p%$X|_-p+P@*Qo1t&D6pT^WmbB z2%lIKz9#NN6{Rgs;?=BujT>6;4|PiYD%b6vd`ar5kSVppupw_wJl{8v+BJpP*6P;n3@RW-6RF_r0VKr9~5xC!+u) zLlmWns@d;iSgFYtEI#%_cfQSGucxeASALIS^#5I5h00DYsS1F|X%Qm2 z$00e9vPTY~&+aMS*CfqaRul;dI`o@+(EJp0FZ9D11>&qFvXgJq$_?{YzO)bdugsyV znY=RBef1RIu664PimD4{zBoOP(L5#=d~IwKddt{vec%ulXk8)mIMY)u)RqPv2H;>D z0jSjF7k|{#qX1nb#^8`5I36+9!wn>%>37-i0I8I!&BHMs_^v(G^GeNqijC={qPAh= z{_yRL)7NpPQ-p#23K0)-Bg3%5>I+U1Cc8s(ysy&UK^RS0PnlVPCiJd*hnI#+w9z zP|auAWo1#I_7BvQuQ>+FxzXQ+{Y&0=MZDn!oetpKdFR-69|V#=WCn!oVSh-vwG~eq zchlTGC*PLiPxJW{^^~?D91XQWTcj@I-t`hGajFd5zC*D!YHIH`jK%=EdX_4e%HD;3 zW67zlFr5qpDMCU*&}a1H$B!J+&@W%~fuI%a*awbfAN=_lI=X%gSo+4$=X=g+dW%QTiqJsO%v5HI%kvm?# zfW(eS%_xo<^J`@jhf9K@ZcPwqtPYEakS|XKk=oom59KKoD|F~J(Q)rNwm(TB6R`Gt zZohA1qDr~gjDF*eBUbGFtAOHJa7d?oC$Cn6@}FH}%jlP+Mi!%X3?R26sZW8rd5GQr zRoTtdg3G4pTVGo84h=1U17^y8khJ1()g|+kRiJZ8PKZ z3SNB5H#Cynys`b|n83OG!m1m-Y@bJft;($Zu>tvU2?q*Tv3qH0Y0O5w1Q$o^!i^r+ z!0kA|Y00m|oakQA*0i`d0wDb&#|1sHn60bc*aQti#g5zR;4BREVCe|VGnykP<5J*V z5Pj72ObImC3okM#Z9OV(%$Pv$qEWv|6=`m)*^V;^Q|k+ESE>!zqd19&YEFtrB^lhk z-1*TfmQ8QfvFbxlOB@eBIcn>UtQkZufu06%FiJvJcI~@A86`Men!@i^qTBpAM>OPQ zcP@0q^(uB_Z=v-FbW)h!f=0_F@HW_+Z-#dNDX#$#3mOc(P7ie*Ik;GuT6WNUJ#wsG zP_EG$7;rQKK`kgY8VEa5?0SdAw1C6sk@#j z@Z89IAvKft+>ZgoMB4oC7E)9DHF-1J1ET4c(|%&3&?>5l%wW&LEb6$bX^g>H}wgs_Mq-tLo|zMF46jsH1DLJ=NPQ@yfn{)0au1{Dxr>=$GBi=xL^{ckSg`S?qgetIUFnuJ=daTI5XZ!Gp-li) zpHhQ*Q-3N9&K^VHlMi5Tu``7VCn{~+<9D<^{ zw&@qy!;m|Db`TB~VHV=uXy^@Zf8=NKePC*rUKR&48ys6d+eyL2$-p1F-03SLBw5)A zONhF7?hvUNUY0WqiHDU0vyIzjocRvV>e73zo>1ZF*J%;O1hDDx3UI&L=r$O4&vSm@ ztcNj-BqPiVB0g@r(PB+5r3r)&Fp^9lC1jHj<;inW!!XwI^Ze;NtD5TJI16rkAR13hZi{>vtUmPlCh5Qa=oiT*y;B&rsSuSP=Xe(ZOOXPoGFL}dRb2|&*Gf& zu(yrf69Rs@*hTT2uY^0mmDSeaZhH=Ov|~zW0c3{-B$Oz1qXP801%NZ5aX<%mn?5Na zN#3US9$)N(*bA3FYaQ5vIz;^?*w%Sw2)gnQeKreo1K2)CB*}wfVKI_hBv7-p1roMq zqeR?VM`o=Z+C8MxEghpjEupS4GrQ@JMZF@QV`Z~{dVvkbDUVbOfGZ*iJ?9$24B0)- z2MM`6Ujo<==d8&QZ?bs-=r);nIt?m?2)V$v*3S3DS0_>vZ8GRbAuzR{TMS0Efo>{J z_ZH1t@8+9>gh(2Tp(O+yuXsE|(=zfsAgHXskOO~RD0vgMfu>!|^ZA<2RGlOoBNJ1A zL-z>#Ne7A#6|LpAf=&k+Q0sNRbXmauA+ZZlVj{OIpS2f*9Lh1og`OUmZ2j7RsT|@A zINS@r-=5XzQ?-A;S(U8MKZ;cpA$s@s*vrCi{X7noYh!!RYOwg4Bgp0F(al!{aNeI} z*iI*IjXvzY5t;+}g`13Md2cJOpP<^(8U+;h`t z`Q&yY9Y;Wk}^zrw5x??D3RZ=$@R=WhZ7ozK@_Y9~-%zX#YV>B$|Xrdqw(g$XkwI&U#;Fpc9Q(X4jv z!}=30bMn5jXl_vQ zco5bwYM4s>a8HZk>YM=?;DBRx8%O3OM5{d?nR=C_Pv4% zU2JAD2sbGxcDB9O&v>WF5xd9~hc!hKGC;$ayo%8xv5|}y#wNkmVt@!y$|IV<0P#|# zKWuA>%G8-EXRU|fk}{%g{CO^hygJgVEf<`i5n{i?Xe_;!?c)Z>Y~e=h*4%*mlN#zG zbUbJs*WDXCP6Z(~kF?s{c}kNYTl7@!vjGAq_lPgSs5zuB2fB?DG7l?SqU6q`;haqx zvIDB%TTP12!W{@!5PyH9{9|vxtK$D`@B@@piI*O{BjbyFfc~TpWe3e0d#&9b7i#o^OB_lJMsJUX^y31VBc@!4E1IC#0j6qcEo#y)2h_ zb>HRFnHZJf_<#j90sK=jCqC&y9mH$-mMG9G==P8E^9m=6*Esel1p zeYH89me$U1<6h4bd6Lauil zxa^+M zX{w{pDP8gACy?{Q zTG)%Q|2+8s^jcuX4yKv|z=9Qn-%8H5?w#20^4t>6Y#;Md=0!Y3c3;rB%AQQMv>HX*hHFzWd$3ea_fpoIf7McpPxYT64{L z#rONU#sr871RAY+%z(%3VOdl7I_BGph?2j_d`{4N^yibKjgCZa)paA*pY!i!;vPX2 z1(?;#44p3%#rz0yUJeEe#v}(|nHm0TH+hw7B3oMY7Nc(7g+rBu`V$WaLr~I$L}BkH zlrgLieLG$0#UrI9B*dzVrsi-A$+GM;Jp;cSG8vTW3O{&vRV20# zsoRK$CH+U>pR>-+02e2haQ|m()N%5uP*R8OWA!Z zG4PA9ZsK$cuuXwhVly?gc7HaeJnT8e8RVrK#nS49&)%78MTUZR0MB-cXd_L~#ahMN zL!vcs(U<exe)n z{F$=>7s{d`b$XP-GFXx(GpV6j z#aPJzVLXlf`8{rYqcBlsRWev>OTb zMrfdlmcM9#rZs71A~YBLmL8<+7p1!);Mak=$3)%mUH+(I*L8yRb(uBvBy#`T%<}aO zQFwXiR)a8DRWL)>?o1~K1qf_lGxz??LcV{FG0&K|zR+eGDaS`o&3G@D-R%_Tkcs*5 z0U7f;z?&hopRf4d07u6eAt>VUQA6FC-sK%jmxUjcmU7)u2#m$B3Agpb5v%2_|Iqj; z^w&)ZJ-fa56-y4vAAl4AB<&Fi34{nPE-sLlXO~?PJYRkTQ03{uj-RZ%FyP4V(dqHO zC;03u7Y>O;D~)_8Z#Ez%YdGS%bdV!@OlaaP;Li6*8b4Ry>9UR#G0{w?zA&9Dd+{$$X zARJ&oUQhZ(Pfu@dWtF3@0ybxva?arE3HmEP=_gnCc$*{A^Y`Z@p0&VX*Aq5|>=`V=8KkUOle~jzpl# zB$G^FNm%!#{ZQ~a3 zA4DBeAA$oyRR3e-1z?^*R1aVpoo{}_03=k5K;;COWH;@hKEOEz6f1qRv#Ef3Sm$^7 zhqv(_6kxXhiwWcQd*vf%*G=uhuglBJCHGk)V30wRkdOc*!89N~^8We+2K#!z5xQEO zF;oiLLAXzS)xeRWpw+lT+XhiBfas&rC^nC2?CI*7Zf~#4E(01aBp-z zrxj5MsswO{dL2GbA?^}De*i#T{g=O8H-E4pI#<(*4=X^*tYz3$LjRcosF=0`_#@qF zQ$)y>Ghb_gtyQG521+46PrgY={PvQSK?z_B;VyN~rGix)X$US6qI7QE>d49T`x688 zu*+W(4?mZnKIKWw1N?Pp8D)cFYCea$-+s;4@rw&-Jh2zWLI3v>oYFNwx@ZDFE3gPX zcee)yQXp8b`|bwU0JSOk-3;9g%+HWIA!_lngPQghHwLgvDmL8K*75*9 zOlWE<v%BfKpj+8~8o< zncv;z7Gwbhp4>X%eFEgIaq#?U{1W<4TgKKoc!Z{ih1P0REqWy{fB=5V@nW}67 z`P{t`%XOzv$N&~l^RWmKmomGnY0Q@ExenI=u^g~i+WU-e@^0G;7vR&QXdLv)=x z28@k#eg_J0&eYq@#sHL4aNx}^+M5l|z*5&N7;H?o5&13+k(Fg8rcE55301m8>GrJx z?~5$$cj{3eadnz{L?mg$U}qKMUE02X_e;MCeuli;fPw=N<^YpqXTFXZup>(JF>HWb zZ`wy19bldSzN$Ot?vxoa;1UuMU7k*=?|?@`I}->cc>q>W?gND^u;Br3%P$~KT#pes zw2p%o=8Ju{@X{UULBviH*UFtix}zU)#soH&z|I!exJefL;|?Lr^Tp1y_y~=i~2EkUhkgvw(hM+(DRW*}UCVCc@D~5E+5Z7a&ota~$_T zXpP;IG@sR&MSC=X1Hz1DfZ72x9Ck{6`|ejY8Z9%L8%M^Gea|I;JXIm|%994P4{>up zk}grAk%Hzq3jx6_52Oq=hy|HWG>w^C>k}x#;KO#OX?6z=A;7r%2E@e2=jT?yns)<& z7|6;5#F*2sqrm2dKa7)>j;XHws2JIA?NR}p>@ZW0RyP`Qc@4@yAC>@sZNF0rLK6kO^(aDO^(#Z$H%9?S97egz0um1#{u?J z4XCE~c;&!rF=Jz|efy8}v8=K~Mg~E!l_Rmr(Lkw~a*;ry{VC$}jB~n)G_bkc?WhNS z%FRUx`by*be)VQkz|ik_05oB^z}@x1){v1I&zTIGb%B2{bnVKd2C<7L?d%su*iW_p z63AyywDZ(@ZW5%B?Su0)<0IH3fPt~>N|>HL80`EIRR-|0rjNO0eIs#Oli7kxiza=?AW)9ydYr%tks4(VK2c*Qk6E{$;%f^Myf_*k# ziKDOReQBmgWhYoD;4iiA;$Z-lLL5|?zewc%EDXeSHrcy=Z(SB0no6^PLW9!aIZl^4 z=n>+x-aMgl#D;cv&BQ|W%HsCHXI@*T2?49d3`nwu1>>ntc6N4f0Ne;j!~sq63qYFf z@^XO7$L@-pd15t#?}$`Gdo7eR3ELJ9TA(02jdVU!nER;9_AwFjgAe30nta}^kIxBEy7gs zOr?!N+8t~OFJ8X<0`_6OMtgGK^Fu}!me1m9puE&jU-oo_T=(GF+d?G6V5Cz<6ZU8O>^vg>i4;KW)WB&o52)8=cMtrZ% zn>cArDN2Ya04-}F22i&FR;&HnzZeMg$H5>El!1%-XuN*)YH)Pa8~{Zi-m;kh%Fx0{k$AQaJw=R(Ragh zeIoXgYvGkH(;9*3bhz;R;JkJ2;~CH56STXT9?lyA1Q&& z6)vZu)$A77zoDu%-NGu^z6LROHv)Xcv_Av&$Rroe1s$I8AR6=c9?C=P@H?nYrD?ap zhznU#f-cM|Y=D9Z#6B?Q$sjHQ0s$S{?!@$?93E@I14_|Ax@xPce{E$xU3VT zp7s&Si1%3@vj21(7kMV;wv7+aNm%TlFBagf4KP?%4B$@(`E!4>ez6*_m zW*>6!d&`|XWQkZ3q@mPl50NJO=rrvrqNAqt7BW;C#}~07PY6mARG;$H*;38{;CTdq zqa6gfm^?s^zY(OmImx+SPq)f0t-6qr$!H&t{qkCrs0GoQJvV#gyoW4z_nd zcM44GQB!i@vp`HB5SW5!=PNjdyaD_LLTCc0A2?4UAV>g+TobdhMnNjO$7=~{hd@@> zA?n3VnI-wDNoch`HYXaHh`+>IZVG7St20Pk_0 z{?FDozvKWfZEug!P%8S-TLphsZus%*Ba6Hii8(ybf#uiN)dbY?4O))%Uj(bqjn{Lx zK7MRAbzx>Bd7w=2rVwbM&V<{BK%Uy^e?0;C#aE&gQ3{DJ^-$ZYbjlFJ)D3@$0_Rz4 z3;4cHy&{qD#~yKf!p7h!MgQ@K5{P20rQfD!Sqjp9hcpWf|DL2O;6=dk%B2Km+?(Ep zhpkb}kWOks;VuzR-CT=_L*(@^eL(tsz}D8CGCan zkXP5wEp;(}$wG_%=XkUhqXX@SCeTNLY5p_OQFN=z9!a7-B*QlgPvRom#Cr)< zVL^a#*ARQ^uIF_*4;*VzoJQmAFA(_qL*%L`b}1Va(@6VP7|7R&}JrRVWoW*}kEDME z8Yl!p>QG&Hd=gUGQpX6<45$tFJ_o?DRSg)A~Juk*7|;L2GmStc7vf$ zbaG@PE}D$FA!)z~3eTw*!Lra69XcYR874NE`B$g$3ly_bflmUx{BG`2P?ven5|BMs z0nv6SN}Js;LINAyTFkFN49Xalrn&Vr93fo7KZ zGkATl%>Z}Ow|EP3x7Hf!+ah;hDTZmrx(PADTyQ;-d#7+f4PBh1nE}NSi>HeOTZ4i>}3_&3`n3p#173CulxY+K?`Jq`%jmGTck!o>GN=L zbsc>ymO^{}0uUz>n+h?4csUEf2mj8d##Grv zXZLatiN!|%_!-f`@&l(1l+0OLb-CEWzPeb+S#z>L(t5k!6Zo>C%ezo#M-S@kh~+^I zx}CdNP$R9=Wk0=4`bzG8r+hg2$u`^1I3aT* zg6Ak<%T#>O4nM|d=GOpsKdKB=kKuC&9NwQv*?q$yO{R_H?z1x)wc?hKd#^!A1Vo9c zsI(iI*B}Y#)UbB)1lk|iSc)0hL#t%XIDHgJkPNV83_qGEC`E60BAkL7-5F8-x-v$l z|FyZ0>BMthp#~`6*II+Fp`p!clRQMBn!6pKNAQz6%VV3Eg8q($EhsgcyUURhujA_3 zH}UT+tX_MeM(gH_%JhuXxFX%N|0PF+=VzXR^h+zlS}x%VKiB^`ud&@ zGp>HT@~bm^f;Xkpfs1m_Y`Oortn83paz3~rvcXNt_4(9R+?UB*{?m@^>%Ho3fFJSd zdpF%b$o9I(i%pUN9OcHi^0?AXH25@_h{4qsq4O&yXZ;12&J;&Jxb4|?hn{tP{$_FI zK-UA~NUagd9Q%EM1Q7}U%c~W|M-pxJg5#d0IPpK!v28;~gndE8U1{zbyfGQ>8ei2z zIzw#_$=!v`MtpP|$!>1Od`b}t8IK0}PW(7KAN;7GJok@kmdqo%^TX*Bm|Ze$mQcYA zPieJahW&H_s6$eLdxfhjdwo3np6{vGYX?<$)&%N0i&;ut2ie1~xD!XS{msrax5eP2 zC(0Esw-+Xt5Qay_WRpDX!>gTLB_!}X90b1IZZL1w82jhR)4C2;D-b!CPyx%(IL`^P zvmw`eOtiocR7Aj61$NNX)3f=1J7^pb8y2MhaB45ETQuut+`1Fxn?AtZvp#CTJJzh|P&V z_chu0v5g#KiqC=+Ks6ZlVU+zE$2j290?fxbIy);2yuV{J>AZP=AaS+LAnbYg9&iXD z@El;viWZJR{Hv?oGKlz2M;8Q6%UbaJ5kA=2#j+3k8$)ra^Zb!iT4^~b(YR|ie_ea@ z6`#Q4uTA$GxK7lMy1vn!Rond{DMjrI@`lnKUZD@?A67hlDP_y z(Lf*DxK zfng8q%)XMiK0f+fxQX_iXMxSab@~2T*rD4y-zU7LFPZ!_O%L(@JSCNWH%W&;K+vT1 zQa7eSsz1R}fpN2Up?mSvbCC{+{#>`gl`Ya$4KRD1RMb-JiG&t$Q>MBy!{tN{lp z9OAPH^8+dyAEO33RSATid%q3Mu`JI6U;vOi z5YBLuWg5-m!BiCie5C1WjFX++V*0Z-G?8OBZQ9lXU~fS$q2S9F^i$tA$gD3yyTMcV z3eIbBOLGW5|-zT~P1H}P$aPrG@O-(6BN5=+LmO1q@pJ*?Uzo?45$%C z4k_hBW89I*_qVdm_jsGHl#|aTm-dXK_}&=&clkkY^r}efUwgVa-@VGg>O3`4_wT7( z`;7xw1sU8;R|nq)2fNpt@xfq}1Fsh^9s*{|Q*q{}PtV>T0IjT2K!7-)*FsoGh?r&Q zd(dDA3X%{x>uEM<{G@|HH!mCj&-d);Fry^bl^2ZHf4tDuY#dW2wk@aPU&E#4+0PkuLDXkv`%qoL@$#552O!cX~PCBg2S)1_^nam z;FA+N)=5A6J_=WbqZOm0p-A{1`m)c;J`K#Rqup0#(>8> z=Ya=!=V4OVO;<@o1o1=%H#cv2*WQPMb7Gm(=`2XU=zQ_VV)|Ym8 z5ccRrLlaP(9xSFpbC&Gt{>@pMG-{P2p2i9gA-v-wTDc;l@j;6)d2o!mDWy~zIX3aM z?hfU3RdD3*@CH^R>ISbMWD72S&^_x27LkJYI72nI!}3RMNgL`;D7n8L97ItYx>|)r z@o_qOUZw#26=-2>1L!G48Uq~;0Gs1-ToOcZY}p_IEao&JS0;q>C09I91la^lIxA3H zAN?qj3c-u7_NsI~x&oXWRPY78z_1aHw)YT{A7I;{l3&YDcC55$c19CZM+Bky>U}HS zF04ieaFTC|PGet5Tc_!DG6|6W-49w{!KeB;aWvr125HzOD~W)H{prI1Y6N~Letu(@ zDX}E|#`4LtnmaezRm3v!W?2*gmH!*PDC{4-h&hR!oX;Ac*6#>0@M<#03m%LPfzT$^ zB`Q7#V>ur*%<@=`Q$bA+V0>4FfP|n_*#b61ird>Eo3OB6;x2B1zM$nrvz>3>^AhzI zHmxcAZc*88Cdylc4+yFk#~N|by;_!IYx%f9k?Qg&mCYG}x_xa$gDf|G4F;ph+e^bu z+DIiLq*c&R34-`X0&D**S#3@0L!8$;>QXnGS9e~k%5@ru`(4j)wCno4A~n7F?>;j7 zQ<;)d)~BYHOnxs-4^7sw&%>V?I6;*^SVL+JH)RlTYqa}mBdZjYmH`hHN&__KDg7sG zP!tWsvLmpIeUoY0*cfh7gWYM;goLr_kiwxs=WhR<>duF3-S?}l({1XzD6cR;T=u)k{C$LZy!q*?&%|aziteeWUV$F4 z))&6@{W;&>K5Et>cAa)DG;jGN)4j|LfVR-~zBFw9U7^IMz?BYsAn?K6XjGH=o;Bzz zPhNiz@B?Fp0`Y6tGnsxZK>AByo&mjm_^fyw=x~Fras8o^MxhNRX1JM7W~Nzp<$+~y zt3^J@syvve#I0%YFv8;SFK$-9=L@*0@Qf@+8N4Aoa=~p+v^zXM4F>X0*}`|SKvmM| z6zgpIoC4S(#TOfD!e&<8SSTf)SC6T0Bd*0 z53qLixp^R$#L?{}Tk#=}DU>!>Ci7%IZTtQ!B6EjFJp)fdqItXa*Y$v`Gv9Ri00o)( zjPaPTPs4^j4)03-TL)-sGSDa9I+!%eY>`;~#B?Xu9^3PdlaB5nn znWQ5>)kx6${J2!?7NJdN`29FKr<202BS-yI9bW$z@1^C(>VK38Kx*cP+zXyooQUgC z+veyfuk80@^Y#Hw0tiD*jD!?b+P<>8)2o&}!5y2k$$P6W@7gRF+6A$*1D6@|rXgn~ zSK?S4m#bY4hSO+hp+sM+cC|n6x_NqE@N88?U^|)_0WmA`dGH~rWZX;7O1%Hk!~B=$ zTDy>rfFO2*a#MN=m0)iFC1I7_9@H(%{_|&7n@w%qE&GmJppb{RA(w}DEX%ArhP`@! z>)k+#hotxX@mJQZR`EZg7uG$Itt{N|A(e|y-AlClpy!IecQ8>6Ds!U57AYE_6^U&9 zgB_~Cd5QN0gKJpSrcO&aTshYHVmG!{57bUT*z zjZ{Tm;bMw6fAbUlgz9%~QkU9rFS}{@QYin_rLJ@5i)J-aeyFM6f2(eyrjqNH-VRvi z!JP>Z;*F$1k0=VA^$9=GkLi)If>Z0yWem1lAd4PP&XF(_?O#H`J&5%8jgiRq!MMBa zoFAbeL|gVrJ29$@c_tb6mE&?*gFh*4$R+|AiH%F>VdVo9Culc4wg&d@4B$Xg%>M}l zZJ#oVVOzs%2nGYoR~FH01L%5?wwl_5R@RX_TxEyFljF8j+$EV#D-6LJFl6Nxs~cI^086%GUUA`-Ci1Nk9i-% zO*Ge&`afyX!Ni#1z-*{Bt}VMY?&m2zy(i|Dk^$nQ0a!6q<#%3Q<^!*k{quaQEP1J# zL7Bm#&)0i1z=_tCyFjfq04Qd>HU@AvP0pwRq1i?P7NKlu95i;QCngB{u&Em%WZtA> z;_9R8!F2Om>%yF)pShd*iKR#;X}vYbz@FxOaQVB&M}o!L zVqzOl#3P9rl$HNwRS50NcKYdrQ$E1SP9b9!>|Ja(hQ<3v%)8cClJ4f1+f~7tsHPYK zFxq?^RoA*x=w#|I#{}I)Oc>m-kN%^(Q^Tk`15XMQT$I`O%RmoOLZzL({AlUDjxO{(WQnM1Y!qB|YyLz92D`90S(CD7K* zrsw(+Ytr#OwftjCeT(2Q*hTnas>CA)qT!owHh-pGdT+X?$8tXM;ICRt@RgR5KS|3< z-pri|+01>nI5hH9$KT+H-0z-PP)*l<$Tqy6gE*S~jN8tx|JH9=H9j7@#fbUM&g$kAVj$-==S-+Z=j_cve@NC}z8ybHT1aQiCx7lo)c z6`BT9XmmpB>22Z*pmnsQ{|u4sem={u%OAU-SUXUZi_GVgpJ;}rW6XFCt+5j2P&xAT z|8^J5D1gcu6SIRV6X=~`?2&;v1@yHBB_RGTm5|3xkEX0Jwyh-6G)8+87*X2ig zeR`%e$o7kENWk}t&z~A9p5=u3$^$x4eX+U{p|QAXl}rX7gGFJrcW6uVk6jDP5T8%g z&dmY!LVf~|rrM8JQAUvXQ2=IV!m#0kfB@H4Wu*}&YaqAsJe9C%B~_DM+ohKWiQgId zX%|_9p|>%ofnzuJ{E^wf@Q%Mfypu~|ih>-x^~@Jf&-e}oJl|@8gFx#1^%tUAUw*tZ zp0KDkQ$5SPe#zR;oaU;S=pza0He~Cqx|xVz$*QI=iL$nzZ&0tXEKVn zKEdOvtK?OX-d(BZSjo3Vt5KFZ^rrjtb#?jAlbcq_W?r)1GfW29g!u+Fo9WodBEmL* z5i)y1A0!;07QK!07IXWQYsyQ1ur!thBc3E_cY8jmd-{>YMh#{F$W!t{7Z;Kv)^$x! z&#HwY&syYrY(v!)H>D6j;N^*Dm$uzi_OQsZyT|3Irkk4HSu5_c0VotzA%*cTO6P|n zQTrrHsn6f&@}){I>3Iv?+)$UBJn$??JKN;t?LK@x4u*8{5>0R{)3>DRO{hbPXa(09 z?xCgb8~C^|T3eH}47Om3W95q`3uM2G6mNkV`5bWUUP)S${Vz|%f8z%%OJ9PMp%og} z5h_t&c2*k)pb4uQw4lY!yKDqk8}sP2G3x0V6S=x+gZ5=Q*z#B^E7^=cx6c(VpQTeMU2S9g!)CkhhBn8(<- z(=0KLz=1Rq^71&@vZ}uSrrM<8;nv?VT|DZNzs`Cp2JI#nZMObzfsm-JQ*dym#kXbq zElpUzO%K@{g1#VsU(+$36Jd)*rn;^Agjmj{6W~w`sme)Dx+;;YDzhdwAt;sbpe1wW zmcKGoQ4%h&vgEd2RFZSLOYi@!ZapV;;2XuPRx@>vWk?hsDHM^08MWjFNovGlv|FD-U@epJ*gF9T0 zRaid>);$c0`wR7sGm>jH(=M%Ccb|i3>etE&$dl6JJoYTcInPd9jj~);KmmVHZ|qy7 zUb+Tu{Ygj?SMy-gtA&U$H1de@?$c50_ZNaYa3Q$RiV;gn=5U4IYdNo zjBg;Z)c40qAfB&P472OQz@kZN{DkDNlG&uNf(TMyWkfHMYft_uAFt4ZST0{h5%*B+ ztMrZ{{%N=a%jpmdR;+E=eWG_%1$qr#oB$B>*Ghk#g9{%#-+o=y2)Kv5c2UGm0*O;O zBpH;T=+SYNUT81uuKkf4WNI(sUiJ@@Ho_mjc~zs2p*;27xYWwR!qVw+_gd&tKh8-; zu0y-fGzPp$AVZ)vF@at~B2+cG5=FK^P7n9uHCj0v_qh{Bm;{RSc1wWm1$tVcJxySb zp}oztrfvgHqZ9ufg&eD-G9 zs%l?qC(&&fHC!)oXItVUQBoQmhy`^nhI*xUW8MX8@!#|E%kop?a<%>Jo|RoFxz^=B z1ZTe4|8}QLou4Tz5a?bp*z018G^WYcsh`d^(KF55EO6C7Y>$e2!R0$Sv2MiQs&BaXl;Dt`U5&LyXI!}qHZ|v zaKLQRBn`4%yqSo>2l@IvPVyO2Qd?OsuY(_B;+v?uovn$nMDohTXWJN{E$S)W69=j9jal6OqyplceVku#OK^JbrsaL0?>V)Jh4+2njl0~qq6?3r z6p}f2FHTqoRqiK216%5b3Qf0=24%WHN#TJo@0%F5 zpO{t7d$>wV`ltX=7G+%VPk=bhIYZ0WmT&F}b>iplMmcy>UaC@IAMD0%k`iFN@{Ekc zxcj`AL=o?U`U`lcGzj9yUyrp{OzoF32oH?G4Njb!lfsSEByBKnHe_|{Cj3%pE94u8 zninN#BVvLI9)5^Rc=<5*9fmu7nl>)>-q_W;Zdx|V&YE9jR7}J+PGb7ETDZJ=-!Nm` zkLA-w8kXR$Tk@H5E?RrBYpQV^QLHOmjao*6c&%<4i@wcz?u#Pvf>UK2PuE9;dL;Yv zHk&bjDXn>qe9BaKWJZuY>n{xtbDV0=&qtBZ(_63D58^j-vk}IgCCBWWc8>}yZ=$0+ zYaywq2F)pSD$w9axijAUV&si1+?L$C<>oFTS%}~7HsbA_R3nJ@sI>Pdb}xKT5W62D zVl2(MK}K?e9&m=eos8bJx>)Of?EAd}9f#T?fvObErx>Cx|4Dzz-;HdcK^-C^vXqFD z>f24e!{9)Ln>hA6rESdT_?hk>M9>B?5J)e;W)+-L0c7} zzet`{@L->9#C(O*&~h^|Ceu`0t7AGY$$s<0m;MZ)Vs7rZ31Y`)v89R>4<)e@W=XM< z34#uNWc8O)_}e>S`RW~OsAfFlj3H(Kz<-t_Q$VA3&h`aYP^J+t0 zFzb66-swo>4N@XeFA-^Hqf%rcf_4E>DMki;M;T=gnjs~66SLGS`A&Fx8cJf9ISE(8 z%MoSO-#dTVHM9{eB2{*;8EET&cQ{0#Y(4lsZA!57;@!)nngd#2L^2m;TG1V9GwTn2 zpZ$H|w@qf#Jab*eXPy0zh~oazn*R}Y`!==pDa!*(Lss}LyDAg8<{SK1_RjKPk~v=G zxQE#i(JWz`O`3U%bJ(vbynUade;asvvc-a5o<~oMep&(UP;->`S7~nA*2sf{?-Cv>$ z=9|JzC+mb?y?-Lg0h>_sKEKqxy&*yPQ~bBc?pZ2VlR{@)hAr3#Mj1P;;If}2JszNjYd@7c^V<=1P6kG4PS+`fGytm?E={ll{3 zGhs@A^=_r(vh{9tfTned>K=R?osMG#C-u*?R69>Vw8C}GC1)EZe}uxA`m1gMm%e4H z%XT(w(0OAt#Y)Xgu$x)K`TJJUc^q}~oW5_gK6yzTJ~M6mR^{q5%LL-VN#A>(xwXj0 z@FJOJdYV@+7_QeD8T$rIc%m)R>ARIB!c_P20y`DEOf=02RYb2J;n1MPijyC{jE>5R z3t2p=7I7%RQGy981}nE|Z1IvR#-bD^Z&aLHxe4T`cU-40ti4sVj44$J(+Xml=wY)9 ziX6@rMp5(tdta(gcWJyxam<({&&}oouDzbzAv8m6%xjNK1b#BU^k78I3HY?cvno-o zJ0?bUdsR{bp_B}N_jTi`j~us8uNXcNA4C!>t4OxmP@^ETUmH4ZPkuuwl#a+;%});3 zTcAZPfl-H9^o%qdohV@-b>zc4ACZ-PE1LYa#`VlfiQ@Foj!-@6VblIB(<3Sx zt-kVmw{I}NJJAVd6`AX$zDS&uEFjVZ_jeqH-K&$r_%!1TNkB9W1v$y8_%`~iI}Cda zNy7w>xUkwOR`TL{qWWp1p(k?B%vmOLC0x5UqVgV*W>z&6q84K8% zmG;>*D`1Nt#7fP`X?V(h4er<|H)hP$mUIt>7Mk2WHUU_}2a2i~ohE3sPt(pu#GG|t z1v|-0+ynjfj|?feXHbgdHw$KFdqVCd!4ZC~&5PrufrvwCfi!cdclC(@=CE(@qW7=~ z?^T_2kAweP#kaVgiNd%ewzr+vbGOx=VAZRzol;}mqNfM(HN|qUy`u{4oOxGIwo_i)~%kc@0P=TmvUsXm>`Q1|d^_e8~_8q09K?xc2d4?*4|xEcH{ zwtAcFqfc%-Ig(8^rTs;+iN4Rzodt?tTbD$xieXbq1k;LI_^7dC`~2m`jJv|)=_rtv zPnYp(^?LlL)IP;RZ_QDr;KZi`%o@@(GE$WaVJaO-Zz`anVF{(cPB?IO;xQz|#`XV# znZ_vvic)_Ds=cZA7k;#QwJqa%-UwHvJ7JWR^o`lOa7n}hJ?2PW*I@cq(J+#>ZVpRg_ zycSL}1=6)rBqvUKA-oIE?wWii-Ain<1B!lzYIq6KEmwcRnHB^6Qp}iLKg&T!TOErf zVxe&G)XL;eAS~&i?PYeyq}%zpB0VZWDav}kpKXbV5kAUQ|3@}X6)NZt_qbb!;EM_MUpv}515ijp~#9#rp4PPMe^6R4S* z(o)bT_e>6Nx{yRj6?(_C2Oqfdag;*gH9ba(N6CUwn(yg~LzW#kbD2DYnN5OfHLY#= z^n!a(io-|7kSt3U;?0#WrN^rI!DB#-zI7e}b3Do27lAg zki~1lS$6&Ndulbk4mdY|F3fK=n9p?_;|?C6zRk#CC3Gb4-nj-G z9tNOQ2lLm!WT`$dHc8^_0vaFT!E?b1R8h-AYEV>?$EbyPVW15RUMEUZD++Oyp)k=|8HMMVKp-X2fR_{ z!bem_e!Y-k=hVd~9W}RfD9KQ_<>3`>SgUZ!A~G#o*!>MPNaiA~>@cbo(x&!Le&`;2 z{YW+t2U59K5oak{6ZUc=o6YemV@Ds=*q2%kKI#tqyh)x{=X{=Wk3#0zMrwPPqw+tH z^F~0UodE9~2_GvQ4Ep{F872Wi$=3A-IQF8yZGrKZE7mLgK*0|UZvv{Gj3#P9=OFNP zkNz$?xz1apa{FJoK@c)22jL%|YGFBF<YCm80k5EeqhSTs=6eE@L&s8RhytJHTPN(HmWK`Jn+b=H&rb8E|rurebqmZlR2|qBs6euR@ z0#X^bQ=Rz`q2*}W58K7{p+v}1mN_e(hd*l)D269`gb<4M;A+%^kDq^HZf>sq@XSYI zi-AB$d?e()EZFLEH`0|{`ehNrtKKE9c+TDfe%E%#c4Wt9>d&mIoN|v*_GWwYUU^-w zTGoihfWcS2gu2e^ z+a|iiMbDOf#~`@vJ$S6}ioW#yZ_&Zzir=LbkqQ~!2<9q()yD=mW`4X-neCVbn4*s3paH;2L{f?G!&@W9#<_Ie7r|3G<(IX78vH%?+Leg1`8VcE3%_mhp@kZ3qtY_IcfcjE4CNTd3xQa zCE2csewP_X4SEICwx5sdE@~R4Qu%pK7yZfkD@>X*PcJVr>B#Jd%G>O@+b^ih&-pg0 zx>53)R>S|eycrIM1q1Lu<`^b8EY(wc?_2QQ|6fqB)^VC1%p zhsVfGv?2LdsxGDTl(G7mdwoyLj+_TI5=7#E>0L&vg3tYPheSH3 z%G}JeF_ga2B63u8qIcs+kzVDJ0*BV&ult^vk0Lq_cd)g-u-DH~euAB97>lHPiy<_K z6B^A0wubIeS|i}p1X4GVO$RWB_-k)CwgIE4K5#Vyhchx9Xj-hm%!Ud_Uf>s~?7H)D z1ato;{|L&&{H@Zzc!PQo%*MQwi9+-$Nm2uaYe?lwwjD`O0!;S`v7cHkTjROo@Z61U zrI5!;4n6(QJ~rC4FU&=Er&Wpn)`AoPwP_Pw29SQ3udpc>CRYg46gfZC7*>^O!fRI{>ojbX}2jEPx}TeDauWW zfHc<_Xwi~VQXt5nrlH{|u;`>q1O~Xm3OanxUrDgAE`ph&5OfhTq+hO_cghyv(j|Ow@m4qK&!hW##>6qFoGQtGZXgM^CwE~+(eK4JeRg;!PI9R8FPk%)_kA- z#fcL=cPXPTHBWDOv>Oto$%5?V7u!#wY7t8bk-SV&;`T4u!*%Y>H#2CBrnzQb^LU?+ z2vp5xW#bo?HNjrV7*bCu6bP66@!mh}JJp<{MQD29s2)}|@XSpl4eQZ_qi)I*ftvmN z6m#XfqLf?y%$?`d;uKhN&sGsBm>yGWYE|TR{sS$UTWyK5WdKC zC!gnRb7$miE|5@_$A{H?d(3N`|%Yb7PRV zu;m6m6rDqjU?Hy`Oedg5{>7YuntMUVc8S1W&fTE1=eM~P?2k|V;E7Gql}I@2ky zM1tnL6_hdxpT`6o8nn5U_3=zqv_TxVgw^sNBU&DzpJ)ES1d*Zh6=?gfGHRLrl-)dk zocaWrh{$z!kq$;30xELjL7zZaiDbCl}V=SZpdU2(_HfmNoemL@2UsZH#ndFA4;NZ~t$`X;Pl;mMT#l6_PwE1NFE zfhTG?PDeaWVHZ;mUoLo<09|I(na6I-^ri=X>QjZTqv@!vSht#OQ4fj-R|@ zCN~Aj;z6?5;1T=sCFKA&^2y1R#41iCEg-f1?7Fyp8$}ffBF)4jefH7HAOv6m28GTT zIn;Oc>e+&I2%=|pS`_(5&b5U0K6oD;*{wjc8rJYUBF*#fOZBMGZ% zJU2^tfXVjENka4!la&f+@QZzY12m)Izm;9QZN7-+6NW3mzrAX8S7uiZ+w$S`>+W&A67;Qi6 zWp=>AE8~BLRZ{9W-?T5Sg@p#Mt?G3+m((O3Qw8PVd<5-v-gX1@*d*x=R3mDTw-}OP z-(Pn~$_S?yn5VhfjI>sfpy6_hRd^%AHEQ%`%EMAk!dCpp{5nJ8<7Q2UQr2B-*rG2+ zb*VT+P@B#eD-Ft@^mbTnt?_V;n2s=2OR)>M$V;yVTXNV~#^^1vd}oKntGa8b2mL>- zeFacg?VC1Oh#&|EDk(^pBGRdVfTVyl(jZ;ZDJUTdA}C!-cSyH@fbgTc8|m))?&G`v z-JPACFXr1bzRtV|{LXpK^W1se*R_=3AWMnmvHZ^2@4hhZ>$;0A@(CRdc!$h&33r~T zy};Z)$6T)(bMee-3M=DLXs6v(^fi4?s$5+CK809lvEPU^KI?&@4&!fiim)!SBQja* zN3VY@PL*WtMf$&;fZ*~Pe8z>|_EgqOg}-%mtJG@f`*&he%-Y`g$9%YWth0!b=aDU0 ztfW2Q;<=yob*lcNZ(6vNx+;;fGlPAd(~=ZPzpEVRjBbLiTH}dqtgzB z`%Aoi?&WfgL3mXY=gQ=`!r;HRHB_~{h(Z)8Z+OWm*KGyM6ePP>pyW~azI~hgom&s8 zn%M#Pln^Y9pK6V3^TnK%;G{@(s48shP0s8mu6$}C7cjq>WcaGN)L*oc;A46(1+{J1 zi|ZT0ca+}Ut;6XFowUW}<^Y~4S;ABT-(WVZh_(y}%jB-f)T(P4dJmFmpz=M0n4K;@ z{qU~K_+8PWEioA*=eET1@|2W{$cxL$VmuU9J$oi9TQYmI)Zo1uGgh&sw|wUc)+L=y*SrmuP;9zXy4R8% z^}LSj57gB9?D_>SU&wPlow_96s(VgeI7W6I61XXy1wRhbv=!em6a9@Hyi%-(^&>EL z$S#bY@!G`$%R?R0Gm~RkA1apAf99^46^bUY7Yrfs`0l-yuZ&F6tG|cU3wxF7gN>qI z{-C@k{Vh51yZW4$X^(zN_k##QHsId(*OLM;;u}E0P(R zOjNNx7nBxz8GVc-taR@UdS%W?f9w$j-ZcqoYwotraXfvLi@ShQSkzPv^qzj8K8qQl zD?T+Cd_|Cla~qpg%LFSEYI9a53>OQ{T%j_zNjqa%^Z0=|HMn@n%G!*k0R`=03C;c6 zS0-Zo#uCeDsk(XSCA%@@KPLs5n1xFED&2hhiUcAHl_npl33SmUfvcCKKlTy&g|e(p zYzwt~)qL>Ee^HH$^S8mZ;>GK0Kd`Q7_-eUkPR(bkpWWw-F7xlSx`ExyRW%{mVsUmq zIbLZi*Gvcv>z5GcTXgMZXJ3l9{>|yB)RI*3)3LlxKS+t0B^+x%7?j?!L*S>UN~Sh=%qE7Lh?*wCs3zVY~2*m?#zafkmY!L?;=<9y1&;^|T@Ua5i%` z!T*P&mcmp46G~5w7QLgOaeg6ZH6_)uN!c!o4(FvC`O%JFYxRZvxQ*hfs`Ty1tX7-N zPeQ$Im^oPwZe3Y%pl7m37vr7j(%<{+5vA+6Lqppr1EoA}{hb#wqP(}NmL*fCup(df z>@T5?X!5P)@L%_3f`uZEMzuXAdoY+aaG3DQv-Qj(TDMPuzyQ=acGf<9y2@89{4A=4 znVE$d3uoosi&kmx?=s76^5bWR)-z=D5lIH6YfstTO60Wt1ovyFCRluyZcq>Dy5MUrp{EM&izO8^plEsf+hhhHaHJwya=v4@ReM-E+RkUKqo2a7A z9{$KPZBanroyhn7YLzy>32hPcfmiM4jS}BN`KH9s@$haYJi~wjLlk!}f+&u+ihMMRUTZlK2Lb_5N2H|gNP}y_T0Um*#YC^gzHlz*_yJa z;POCibZ&InH-h^QF@rQ&*~564nl2?>#FCDTJLu7VX^FzpNE1+%la7j}EZM0ePd=oh z>UL%j{(_Ure-$^ZF6wC>x^|%72*KT{UhN$syhX^SZ!75#p~d5Vb${hTQeycwh4xh% z*U%da&WQ8-?2g;51H!qL8^x#oNU`2K=MHU;jlPd0e$mN2MZRL4B%XG%>vGj-cg84} z+O$wvgG+{EfqZDUZSBZgoHI&TsbZty)W~ykL>frYJoYcDiZ%TvPxyg|McJ z3))a^txCVX+d25^LR~UN#D_X4tS}cdq|=tk+ka`XbWOQXv&5)c(cMO;RfX@yVZErL z^YfjNXD)x~hHZ~x)K^Ov$@&Mm{nylx9EwS&%i=Yod#u>NUV1=PjVQ zxRz>S`f$$r=?4xG>tMQ@4~t%6g#-9@u(}ajPLPWBiZ}YUYZgX9WwC z?JN7sL9Z}GESG(lWVeBD1U6X3t%AuiQ>QMoek{!morl=gekEE|RI%@SF!kHPm%Z$J z|B(GPCo8&Xl^^>oEBkRzH#ku5kd!74+TuCQuZHtgXJdQ9dt@p@ zHt@6FP$z>?qa;-$Mbf(0>J#SsiX_od!qzssitd?}9P0gm)TV=+cz2k+m8~^W6W7)Y z)8G70#r}C;(a*y-+-k4|%^xg>yH4haO7tY(!V77}!~U5fr!%BCSb)B_@@OO|4~j*? zw>htMS45U&A$8~O0(B1@;tO0dKD%a9!Q1Nb^>eyl^PyLKQf?JbK!61iy}j$K=ha6pr`+GnFOfV! zy9Q%@LEq3`3@0b=Zj3!l%fCBhIUoHJA1kPtxC*8`n(*V%ZfWWfJ=hix_jq-o_v5m9 z2Zew-y}{R)&Ksxk5w$yi66;ol^B9_Ia@F3_-Sed^nTMF2W>f|rFMu8_lU6?Sf!+1C zZ0{SLbl29TyxGbRn5nuw{?=O-xuUo3H_h0Bp>|jboiI;zDGByv6v}C!CuQvuCcxu~ zx}apj6~zqIj*}7N{*Hh*$DwznByq(=d4oEn4SGHNek-l#7_R0PU-)#MJ)Sg2=T{~M z>mrVx@dGRQF_d}wIU;juC;a_1@b5mp6$d{@R58Y!*txl1B=Xg8b8{yChWWJ+PU^_B zfjQp_pqju-cdu@hN-aplt5!4jRt!dkeJW#;tb;*vzt{UBMwSdec2dX>dG1cESot}e zEuditu{M$$>NjUQi_&>K(^7#gBQ21I-*qArx4+_qTkELsD*wtmY4#_dl zg=W2+hHqo~WTWb#>ZneC&Fq^Bd`htnWk3Wq&UxoA_*M{VtJ=mpwY!>9?-G8bHgNqp zMhX#rO702%bBANyrwl7NURb3pcsYT7w^3gKVC#@!Yj(Z;HijBOcm3E+_|{ao#b47d z5o%Y8pa!h#@=%i$ix?BO&1%bzJ+Hj-jHW!HU zSfYXsmoESym>11OJ4ABr-PqqZvnPh05o2?n!%^>YDx5-KL|ykb!a>ayh**=p^mSO& zIp)O;=5%3~(crpKbADqw z+QCE7yu+{^Ufn@D3ir`0Nk-$YY6tqf`sf1U7C1?X1tGJ7n9%8z2ou-N2fiZTae=uX zM+xN6sujdiKzfzZL6<8KG6o5Z=i1{EA}{XM9o<0FI--R(3B1VgqrBN4dCUjyMT^BMkc57RV@UVgz?c=&DkaOxOe6Js)hFv9tZi*paT^1E~7BFRb1+5W1;`!g= zh^2&AG=A;(Y8O06I4*pAOav7Huee)|sRPLZ|GWq}T@k+dT&#^ck$07XbMAr>Y7z;8 zH~ROoOx{`%MNd8&zrtQoTS7nxw7e2cJ1thlQ)4sru`7DbEY94TLDpQrf?#E2*K{vuD<5Yih~+UfVBlt_7`!+%v66amBZbU6+zfT3GNa>gZO0*YuL7iiF-e#% zE!Hdgzqflh`;~tb^Jo&x>5E4VC9#rs!X!*R@aIS}qg>^J=RR7wi9&1#m8AMSzT=BG zuBj_o?&8N21~RP$CijkwUt<5Q;OiWo<$} ztx{6*1wr!?Q;l2!gDxb3WIxivsRoCf)sGjen>6KZ+q-;(qlJnrp0HqJjqQ+Px==5l zZ)b{*%9fEq*uu{xCf1?trBXDR!W9~uAt_@;m0saF&if9=t@LZZOoejZ(nnQ#5dV&T zMQ=6Tx%7Mf)IGOdchK=@`hDakXtOR!_U&7wDO!z515;EdXFMD<>UqIQPl??QLZoaqDvT z=TpoI{>(qJpCrzV@|Owxm0}58!i!$83j&R^lTF31yH2pAbc90#56%gWB)TbI-^|LR z>XTqh>(_DABuUs3C%q#>E1c60^#WS`X_iNKl#iKPspM}ZrT5D{DfW9au>Vs-l zxcd17-I7lUH97VYzq|D_tqBWYRv3x8hO&kSM^&nUDYq= zZ9*y|&XD-S-(NIk*vI~+V8tD;#{>Hcdhpae^i&sn7vfxlfTS_?=B-r4P$+$3~fFW0W`Z^N|t_I$Kef!%^E@H$3B z9P`L2@)pMQIL}OsGe<^9Ir+!*B%1dgtOR)&e0a2g`{+{L71@#;g+PCa1H@tc6RqGeXs0jqs=Rc5hcr`w^cHD=LvIS99?&Q@Pc$3Iu?RXx27lMeml2&E7*yv$ zn)5(e3($W&b1RNqxo|ur}_51g;wXy~B>Ye2K`L=VvAMpQdUMQI~{<5(& zb(Hc?iDZ|-a9q>o`D+#l0Q%WRyVs@M@Q9C3i9*BV$=^A&HQKIJ(Y6t&`*ow}csXrM zTf9BV5>=l95QK)`@m5rv0#OYy+&v>vV-MZ9^NnM?Re-l38ybpw>lKZIq@33**QKQ2 zr(}Uum*s3N$m$SWX@h6O{{HmiG!S@QzI*dp$d@mTT8?9lz^B_ge*hhDqWF#`;N+1t~^O)ItW)zG*8#;Hg zOfQjG+mpJ{;o-|ElHNAIHa)id-8MQrBep0BSE?>n$MlD#vprX|_K@A^i0r|Tbpf5x zjX4|0ENH|4Z#ooH|7Im*8}u4uvcx7>4335NL-xNgw|lsyr6j=@lc0b`m4Q)|o*sY* zVZmmZw*abd4}=5~9`MI7C-mc9dYvi!daI*mTZkAe(j2%U-TIpyp`uitwX*k}C;t_e8W z&jqUSd$x%QR~srudcTeA4Zkd|9OjL%r+XUmNq?ylC%!-UEve!R$1JX{-S8t(DQIH! z>u^1+@q-gi)m4$QuFvH~1gUnbL6|%97k7*UzTLD0KdCA? z>YCc4!;=Fup98Z~>b(#5mMdQq)Dm%Zs`iNj5T#p=k>NDy^Jrjsml6Shzl|ZO%iFbb zmHC`9&znn*Z#(VZ@t&sG><6-uC}(lIl*kew&7$59!bPGO-gnR>m*c9L=-?R8^f6Sc z-1v#rBwz_-5!o(=RWI+WsgV_qt2KLTzo@sual~J!n0#Y!n{-Oi+}wd=&FR*flh^If z+xWNL6e()MTGN><5^OcD_wku-tZm{&6JL+GPtsT6cHoZwn@d@-I=>uMmV403lY}Gt zbM1$nwIsv1fFQnYTb{1mQ)~x6xWSO1wrgrx$EOb6SLg`lY(5VbuD(-%{VxDrTp1ae z#wlxg1%+-?SI|ZS9kZrNnWs;GVUl@LbC1Cu2By%p)F4-fim}o$Jo3cY3-**fW=Aif zpv+B_Lic#U-$mKYgERWN+`w&qE&XQxOnW_+)&g$F_6Hfo1M+2KQm8my(`a^_0Y&X` z|I13|oOs7aLTfq`t}Gq*UBf;eNXyx+FN3e<>aZ_jmJ-MXCs{jRvu4!Tfpn$olNO%h z&6zAv07dw!glN@FxhCt=54Y=}yBzt@@S4tNY8IdhHt9(r+vbEmv;Nf2chd2KRn8+o zWp|s)Qgiz#Wb!qz@F5Jtu(3&Jq+?sp(AwzVt$J)zLnIAB*&vQ!6}Wo5o<^CrS6pLH zQ;UZtlxFA#ZzrX5dev|WD|9$TWt`G_3G@m#TRpGJ{JNx7q~jA8>N;sX6!6}zVE$cp z*v+re&bi`Cl}3^AHj(+ixgjUS=?&+|+e-h4wo_Tk$8Z|@bL1|hQ?%cpg7x2Zpo9Oi z`;l0dz9AQIdEmNdj)oXST3}&EC^iklwSEEdOdHTz9`5FPUXPgE$;iz42Bws2+{s7h z!63g7LgrVh8Lz;X?4#bL^Ke($?`{{)K5bnxxpl@iu8P~u25~$)ZU-5Zoay*NF0Oo0 zp>Ku-hthgRa()J;U#sZO*)ZCdkxH1X_->?4hHtk(Q${T6y#K$H@IW$ zxrvOW8>((X>X~e4$Jwbwa5rCt6d+r5MC}IHP|c8UaA&IC8#ZHLVVUa^r${a>jYt&n zd+2#~GA}9t-m1~kYH^-K$b%p82Dm~e4YmpfxvHf_nXv3!(`t9=sF7Y5ftVjA%@)qYtjtul zzGMdnH~7W}aUJMOv+u{{bo~uiQwePB&}I1Y z>Eoqm#Aqbogg#ABPoxblHL7R^*Kl?&IXQj~4D{r~@f!(Z5`0f1^iq5*(f8|zdQ_y} zj5VDY!zUmNy$lJ!%j0fWr~QLJ&XTya{A}2MQi|-5aC~u4!+0SZ{QmB!_7yYYx+`uh z{}99L#ld6jLqWgpz(k(Hwl}?|J8VCp%8VDDV5tAuv z9)BU1LF*vDQcJeMSTg?DLjUG-h~t|#Oim@6H{JH?AYUJ*7hd&xLd36qy@tyMQ{-=b zZsj0Zi|^ySEsvJ^xgug<(H`|5$ox!Mg+r6ms4^-nok7C%s!M4#HL)_pN2)c?+afgq zLa74UB)h3?eL$OOsy~BV1RBh!$UrzT7qle>+W&l=s?2esF}{I^Nq0GIRkhQ*@1s1? zTO|x^JLo{-hSy`zsxgNuXl<@+@_S& zsayY#tD$VP`y1c8;{rucY=d~IXJ!fOM*N+?+|oE55&vPI-Cpvng~`tkfd*Ud9xV!R z<&pp`ha{9B{?15@3AfB^MQw6^LWlYxe@qun`Hqx%;px(0=+z^i-W85JewmJa@~*Wzxb(#s(84-tk;gy4sC$6_jBuKm!>nBVCO{eHoC;ack;rwFh~IK6$>I8{6uLEyXi@rh6ess##6Atxme{)!%D-7Y zl3i{mXCdVf(I9!R;r1*`bAhaVRYsBd(vU9W6d5W9zw3@qZmzwh|Gce!>t*p1qH2*{ zCCltOhhFP#+T3`+DG)P8z1lir&`=Q@D$kfdbyhKATM0TVosPXk?9F`I0-2e9^g=c5 ztdsvL@kdk+6C2gmVb=K!4r7UnO(WkOTeEm&$4}N>ssC-?+xdQ`cThjmOiBanX`{*AT)wNT>k|D%!h?pWf<*`V2s3!OcfBs zuj)drU=hIYshpj+ld6B<$(7tj(Gi4j0tP()Xov+3aLQa-e)fR!2g;9E*W^bcdkHF= zUB5L2ybVPe-Q(nl8ti@L@?m}0$TLAuSkVFvx+Nzht`uKvO5c==>|Tm~;wQCu5aoxX zVxB%SjN`?O% zdCL0H&OY?yI{gljBHcY)MU>(qS`3>k{L$gy+BooZ`R;Br$K-M7m%Ea@f-0IfwXi-fs(%_ab%k?zv3i_f zeT|s0q!0Y?ubpyo+2NbfL?3fY(O>CIV9=PKwXXHk6+bq2c@)57gMBKiT^=lRQ5ut+ z=@s5w*GP(l>-^Yi{i4GU=YKxdN`1BSa{h*a=?HI{78xyf)!n^=62e97Z+fJVR8tuW zD@3~|3SR~AREqjvd_+#0Wjp5zQw|**oE%u~=_|IZ&98DMT&&d?m^{iuB5rbl*~Tvv zXju4xO(wfUCa07&t{_JBVv0Q)y_uIQ_Ky;b=e&O#=r2m;9&MV+>i;f)jaI`-t+S1X zY+kt|z1cXQZ?!a36aF+9yZmuCG1{)yxeLbIg|S3Qv8b^4MI;HN2s*!KV7u9-<$ddO z?f{o!dw=CAn2trdFDvJh@=|EgGnBkT$z_&Qx(e0lQ5@UFdY4)BDxUV7504_{F3}(n zIJ)zB>0uw5(`T)RCEGZwzMnH0>QEsC$i}b48ICUCqRb?RmAZw)nuho%>_>$5i|+7} z&e`C{uTru#)o)ZBabeWdrQCH$)ta*vl?|2e)Z5fI%l9T4Yf6Us#I}y=5P$!-A1?qW zD}heoi2pK?lxB%)M8vo$c*09d-jDi@HV3XqEZq@re>a9>Oq!EgU5Qt7U$`;Fl#%mA zC|?=ho1?p&sUOMChns#0ty2lMisD7Em9dgo=o*7myOWZ2(4qz%9D2Ly;*!{)mJNaeIlE;7XrU#<08 zcIkFEUoswjA}ab*20g>S*<@bUw#VW}g-klBmtFjA**?zuUq;L}*7nF=EEmN=Z5OhW zFB(;7<|m#NgWIytw=FDiNNmRRC7Ri)a_ZNY^q4t0f4`_f->3c1|G;;EBNoHTY%WGGGd`koi& z23-`+m2#Qy7WB|U;I<)YC3e-kDdoZHqK7vj3z$* z60QkW^w9TUWc($m^dU5Y)e=nsgN9=ri}MQ9XTIB3AnEm^FplXDHfT4}u%I-OQubQ5 ztGj@6p?b}yr6H*-f5?30JnwBvl(601<@dq7_k!hSUq5RO(Ek}T1@(Y| z0SltDXsdNu+eq?(`l=j}njX1;sH8CjIj+~i-F@w|P3a>QjenZNRNZxhWSsbRGm>o| ztqMD?sr7#0)OnYX{!1>6K@`5*`{QG`#@$EChbCF3-+p8Sz!m~`U?A59`Dzp4y1}Ac zzoLec@qCq0Bl(&=hn-IFPV@V>p^kDaXGeFHYcJfLx^4I!Jc2FEvf(X?nmcIN5$Tpk z#uLnAV@T^Imy29ivfPFx@{OvpfBR%!%A=VADf|`ezBqWl%rGH8Hb0tgVwvN+x+GcLXMaA_N0Or`!U(!m`x>_B`Y(3wFOe3|8vJc2*gpLGIk)D6f`5+P&+jX4J#*qj zJL4640SVzhF6VyiYDBlw1(Q9a5j-Zyqs;>H=qj=RT;t>~k5!4+&#qH2PHp&AKhErdF zl(lfTPrxhPIqFk-g9K#PU%c!ggu6SZ7PYv3B?YMp$F zbtr!aIC+4|F~Ylc{S-K5<~~VqgsK$r-%sXzNIvx&L6c z7-GgCYb4sLMuWj5s_r#7NYm^x_{lL?>g$D1h|bE;zaUanm4O9H`F2Iix1 z6#AhsgffO1e__a_qh_lRp)@FA_?W-8n7|O;@;^El$ZnIEzqr-?a)D7yPKoPo>B5t}Y z`V6Y`JLO3jANxKkS!O2My0VOq6&*ZV+9d3Q@ID9-kTQK9jKwZP6?vtovQ=3U8v*Gn zLx7mVgub@K>Km77mxc_@c7|f($9PO0#UT)Kg>rJMZ39^!?^Wb^sQk(Z#tc5Z4rME_ z$Q}^ZzX5TBj^Zc4lJM{wl)f3Bc3FDAjCq;4($x*pS0p~O+~2Ab$;=tX5NDJ6fg zz`-GeDd*C5$0KZ28<5*b4y+}45km1lj`p&p(bUjA_D8l`2*KoQKI?L_hNMzSinwmm z_!>cfN5|`5Mog_~o^n`OZ5`Q^u3jP$fgJw(o-i5or!B^X;PqZ{FTC~KMi$H;l(qKi z>oOT)36Lfa>ot??jYw4Qmq(K={ze-t?s*N&h zacRS9KI;j0I5jmigHz2Ofwi8zL8|KJpY1s^GC$hIzzP+=+k3Iq263S{kZuZhT20CR zajP>M`o#z}bUHK<%a(iT#v{@lu-QBidy2>Nk@Pvv=VR(mqjZU=YS`QBSd+QCp@Zx} z9s{$aNQ?_{z3&z+@o%QI(U&5x3rM`a5gIak>RklD0Ho>E^AWtcn?w2322CNOT-73b zmA>9Y&_`*!_BeRcWL68HK(qlc$(w=(+MU$WD>x*OJ&a;LZ?F%h+5fhNI?np^Ze3pB z3&aY;FGXx$N_lBGrlEVm_Ap$|px038TdJJ#lo!qotRNt~C3fh`2yIUTUu=rFW8x8x zaxJ6Ev6J7Hzx!WIeXxHp+t2?^ZpQ^#2y^KTl2_Pz zj>GqgHDx#7KSaBtIS^{8KSEmoecnAeh!y-L`1A{hpp8V!$r43AQ&Bubk%rr;L<0T~ zo2)%{G`1X{pt7IL^zPv8hjXus`#QI$$={w(=GEsMVyK-@JHRfF(ybSNh(mF9XW$-Z z7G?la<3Gw{oqgH;U26{w45~2%T$-A6`x!sY7Bc^F2;vkp0i)U4<(kdohWWefv)aMp4wN#cNKt;fI+8-2nnd~V`3iZ0MwzKZty0OG{a*m zT6hUyy{A{x(w(m&>LWX^SW};wme@( zs*r(s4p(sVnU5daJY>s3KN?1wDJ^5E-Ea$a~~lMP$uemu$94}vGk zcGlNY8rpuEFmW$$Uf4=WtNh?EAr62N_>k%wem$~!vZUbQEcVnW0|SIebRXiZrzIL` zYh|kEQhNG>`y$S48y(!ezs9{6s|paAO)yrHYoWrX9^H%X@>Ep#M1Nn*><{?RhJAmU zYH{Wv0u9`bBIdxOt*2QD@1P2-m&-lc!b;2(9c0HUaVJdlcS3{y8o;FX?z@#u!&^;K zEF3a#NO?LV8BCe($Zz5k8hJ45KSOTyPNzZOqLLn*or{0B8x`L7rXiE0c{<(Wcq@8N zJ}BFNYf>|rGKql2!~1(9)Ovu(dpgRV@WPu~ejU&{#)-i10{8hG`ozD&&)Lv}Sl=4^ zgrP^dg1|@DUvm@UuU9w(t=*ezMsRd)zw*z8)(~8xm;Rb`S5$@5a`tx-LC#~OIti*nqEgB zIrISZ>WolPV!FDNmoHzIj^mFM@xvk8KKR#gYP3~#Bt`CNpgRei|1kxM zPESwIz1Rc@B;<-`{pNe{ul|{wRgnPn&yX3-@f7HsDFzE|ZEwfe0W&qs{c}P`N{wz8 zRFjZvwn0n@*E9(kO3i$!2JK5_<*&X6|LmQZxA#QcgrxqrlZ(M8%nS^PARn3?bcgr# zAGVG~hYFjDF^l4b`FW2eClkECm&tPTkCqPn=OS)etheqx{h$NA8`KEA2Snp>wA7}l z5>;w5qfUmKr&H%WKjXT$BKh>`(@g!!m-m5*pKpx^P91WyZGmaz{}VkGFO-q~(az$@ zA8{e7`}c$61YEb8?*(_l+lJkRpW_GWu*h|O4_e>Q{r%eAtw8R+^Bi6ZJS6@0LWh;C z?GMNGmn(Z;gC;g-nvkLCO{8sKweB8VS;-GgR(n`-*rT8s09$L+VZ=h|#S5FgA!ByW zGq-QwzjqwW5d2-55dFYX-n5WE5@fv{8AG7=gRSn z|GZWBKm5j+DNA8j9W^4fm$_VRz+{VDCZ0hJ3gUX1GcaRv!PL|g4Q9Kq*+F|4xrTG2 zqKE`>FpCr9CCgMH#dIHXqHiwN#oy%-bT=gkdTnL`WH|34#vdu^%~z> zbBR!|?5KYtNL-LS#!o-L-&z2}w+Mn_aJ4DkB!j${K&ND+rL7IykzMe`lPBJPD?YQ< z+&gg$-Y>Ci^>+A8w5blycTEIR#pxv>mwnCaIPPp*8hsZZ?_a;Sd;DCRczUGWxe7>= z2-0(Mf(DrYKZsm(PXy9dV@6hu`m@wZ$JVU5{%!kWJ;(ib3%vV(VW72ZRjRiLKZk~v zEoEf>9J?8k*KPJ#HY|>5%jB5;`Bol3Berh+PU<=l+bYkTqc#A zSrsP)wFLx@<^4YWKC=afO6lDXCe znC*v2w}Ed!orj0#>&R;H(_nH77LDR86x({$e%vnn{4VwnRngfUp4WdqT~-YwVu1|$ zHnuug{r(p!T85V|yqRB(=CUg8?&-;9xshoRlw@6C^s!u zDYqxh&KfKu%n{M6E-Vrw6qYE~al8e((EUrE_RKCh64Z9S73j9+;hMIX z@L!9~J|bPc055s&7m3v+o$*R%`2X`MQ1lSM71-&FG);l}#jR`aA_U!ejbrVJK!Ll7 zKLrk7C2$19*tPSxoi1w|gV&RedK7T$rPEZ>dP!E+2b~8JE-o&as~}@(K3-Yo><%O3 zSy~lovUDW`z8@l z0hj;+-j^MO0WcXil6X%g72yD=ltK30_wwy_KD8PT0Sfn>u2k~y*jN<6v*@7tE}Z+a z>Hy#+ekdcp)m2x}l#@Mnmsse})fR$dECK*lrmlrivRq0nkV5b50$ zF{=u{e!YV^AqyWW3RVHfeYh_j9Ps=upm1z<`&1A>7ifv5PreWxjs*+@#2Qu`bArjbxZUPYw|#9EHut?_Yi5c<`3(W-$&4FcBC{xeDy@7Fr_O)g## znZ^gy5dtX#IR^L9nO*fiZpaIUR{Cv0H*VQf0+3+;CKh-A0a8HEBK2)-@@)s?Wh!_L zzo~*XfLU?{_$VMS;duQ-bohE}K?Eom=T_|z0}6x{6h}^aXMp;lbkZB^_5&TX^MfWK zRT%n5R2TO_a96UVZqAksl#q#7H3utMZeGi`o_bOh|4jAW6G~zdlHfAA+;{DZpo6G0 zR&JlAR+!>^hsTzB;@}sTYN63}9KY`G1%eAT3l$C@%iGI#R-w zyFOeL$YIvsnJje+RQu3Dm*a|Js;uE`bI1pBfhbT$p58u;S~w4ku>=`-M4XnsLw(9``t2<@~#u8k?F*BEPS1I>GYu%#XUm0sQuk?_-`DXHdv5*PTCI{U87y zHQ-a1(Dz+Dn3JIrO2qdjBU%IRS39-MoF7k|tMBgaS`^h#@?X{n6a;|a|M5`i&ptgpdfz;fkTfBml|Sr;2*lXyR)!ZOKXqTJ91o1vlqEhWZ-P+Eeps9x=)D8j=rk-&5oR|5I3JQVm|^@jI&VI1F%y+M@NrTdKfq@%6V)NfFqH7_H25yiGuFN z)s5PVxX2EF$j6>{R>l>Urkr@<8qavcFkbgFr|eq2YiV zjoO!khyd&l!iNLG;(+4ZVxthJzxnq!v$GSFjG+FFT4LKH8ubg|QaRadf*^o@Z&1I9 zzhmNTkLwC9xwN!2HUk3#Sa8hv4vboGs-*yXla!I6VPgxyB%qa7RUKT~1J{piK~3!NO0+Xv@?}m zGS0g;6Ad>BE_w=%ZKF|P(bDcYaBpxg#Mt=<1W+E$#$dZGGs#D`VqEh2r-p8M= z7`s6NjilQ5-4~_IM~bg|efvgr1%%~+eMTIP0GN5tXuF$)gf?UL12$lm@=bbZ5gVCP zx%G^>^+G~Qif_7N>K0(X@?S41Y;A3of-)K8xhD2;6lWWLqt^A;!SegUV{QYGEf$1v zV<8|cu$_aS)&t3-Y)eF&39(LFVBn$;w-*XQS#7O!CTQkN$fzR@MR9&04tzsR@Mxug z4m+IZsu^-ap9CPWE-XML$gY_W|3E^BH@&^RGiR$om-fZP#IjWLr%OGL0bOPdd?*hg z>xT@z6$w+-9mXf8q!hRv+5%>owR$^t-5OjpyT^$W1eAz?Q7J^c>6w{) zM$u=R!JeNWq?`#Bl7%&aU+=ZJSamQRc(|le^P4W-?I*aTpHSlHJ=xbRv!!vIaOZ-e z1vfyWb-TC2jKGp9%nv#&4Y-6*^5 za`#3aKY8$Oc+E*;0dp%T;)Y*V1r)G};IH+4jmXHzegZjuU8oP9ZuklPfH3=;@_rRW zE?9ZR=0ke$b(x3Kn}N(FQ65d=~Lm89^JD5>kEjyf#&W01(4v$hk{hlcc zEb``CazDeO(|C>k{PR5oMwo>>e4>1s^N3xCfHAl$4k00k;dEl%_l8i%!Telc?~djz z;B^t{u&5QxmRcao=CK*5{lobKcnXb73kr{eM8lGi?4u)>W!QcdUp{@(ajqKIkFx_d zpnd_!tVWNad@7ep4o3R$Cj{}BXY1!@!7|aDu{%6)PTr1#pB^I#1MGuhSV1Pk{ zG$gHW72?x@lM0AaAnZV`s#Z9#K-xeK;VMe%{OlB6{rVd+2%S=kQOmow8^o|j@<~XYuEn9T>MTCfMAZJ<`8A2j+$U$V3>kcqk&=t6ol~c@zb)i4Z+%Fx3e2G zf101@>FGgNKg|X&Ve;x#?eZ?CRcm{Dig++NY8g(-JqXVXTfcIyj#j(#fy}F>)6~1{ zGY2Gbj6S!VIBa)x0_HcQw1MbgQ+S?iVxE%)?bUc5!^5&!j+QEmSiyX{cx|fL+_RaWw%L z+O}DAqA*l)?08{;kr@Ic4AYo6QqFXDsHk$jx{QZpemuapq@F!uUaW74_3jDXXwLAk zdQR!|HFyaDoX23#`%fyGW_YITJn(S*d=R-baE#j%=V#%F05rVKV`h!w58yc-0{ata z%{v1h7+CwdRR?x>8qOAE z>_jBYgb>V&Z@n@Uv6=D9)v|k5=V~3{p{FFh{r$Gbt0i{(V~!<5kV<@n;2dzeU^wH? z&-QEhkN$i|+$I<#ZvZ5BoWp}B5h96yGXonar=5XNHE_RuRvzi;?xtsDwIoc6`%N4H z8L`Egz>TYUrhSZ8uy3Q)f{OJ}o-QH-z5|(b-wm+{%aQ6?mP&%4#s( zD-L|1TxVvtQv2p26z^8XDoRijtABXDn3$L#apd{_`56UJ8qN*WobBGSTnI^$6Bp73 zry>mw6YPuj!HKwCndT**I^_{xT#9zs|0Dcsg6>C?wq_)JPF`r;DQ^I(8S zh|P9Z0_u?nB_A6bTl_k%EooO*7qUFazHq8~xmJB_nNv1T{P5w!VNwzbiVg^{J0O*l zg6Ilr2`S~}QH_DOpd?%i833XedWTK#60rMy%g+x1X!rL+8#g@1Q5y_op^;z)JS3E3 z-a)7e#p@5sBDHewMn^RT4<;{G?-k`vIaENnHCHil$_kFd5WacTww1++pPbWjv>6cb zPEc!f242L$GCMjLLSlvOkL>5SPZH;ag@DQu>_NWkz9r5q;L-f@eeqYGQcO;Ypb}kGzAORWV(0D+TRufg&LcX|*eZ@!f zb(a_@R7Suy4Gjz1Df97#cO4BFcNOak}ORZ}*|wfi)7lB8yw@uzFNB?EE)GgPy@GWQ%j(0$KH%KT-0Z zpKLb?ocRfzPzLjFT?Jw{qgpR?O}EWPq&AUg+!5!s$N*&_S`lCDsZqNQn1k4Ws)y&4 z!``1RAFc_v(7D!dB%4O6FqYMaM&up`Q}^B0&Uaz5sSwu5Wq(aZU7c8GzL-`0hkv$4 ziETU2!UIr?Me4$E6mf(V6!4KGnH%op4kXMFk-3a&p8#rp^Lc3vb%rp4xv&qll+L+yc4q9t$8HBDns? d+B|R1af!GK{&dmz*`mQEDJCnL^F+t{e*vn;*`oje diff --git a/api/searchindex.js b/api/searchindex.js index c524c7621..c4ab423bf 100644 --- a/api/searchindex.js +++ b/api/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api", "contributing", "index", "intro", "reference/api/_autosummary/cyclops.data.features.medical_image", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage", "reference/api/_autosummary/cyclops.data.slicer", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec", "reference/api/_autosummary/cyclops.data.slicer.compound_filter", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null", "reference/api/_autosummary/cyclops.data.slicer.filter_range", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains", "reference/api/_autosummary/cyclops.data.slicer.filter_value", "reference/api/_autosummary/cyclops.data.slicer.is_datetime", "reference/api/_autosummary/cyclops.data.slicer.overall", "reference/api/_autosummary/cyclops.evaluate.evaluator", "reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate", "reference/api/_autosummary/cyclops.evaluate.fairness.config", "reference/api/_autosummary/cyclops.evaluate.fairness.config.FairnessConfig", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.Accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.BinaryAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MulticlassAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MultilabelAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.AUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.BinaryAUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MulticlassAUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MultilabelAUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryF1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryFbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.F1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.FbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassF1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelF1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.factory", "reference/api/_autosummary/cyclops.evaluate.metrics.factory.create_metric", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.auroc", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.binary_roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.sensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.specificity", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.stat_scores", "reference/api/_autosummary/cyclops.evaluate.metrics.metric", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.Metric", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.MetricCollection", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.OperatorMetric", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryPrecision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryRecall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassPrecision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassRecall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelPrecision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelRecall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Precision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Recall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.BinaryROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MulticlassROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MultilabelROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.ROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.BinarySensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.Sensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.BinarySpecificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MulticlassSpecificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MultilabelSpecificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.Specificity", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.BinaryStatScores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MulticlassStatScores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MultilabelStatScores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.StatScores", "reference/api/_autosummary/cyclops.monitor.clinical_applicator", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift", "reference/api/_autosummary/cyclops.report.report", "reference/api/_autosummary/cyclops.report.report.ModelCardReport", "reference/api/_autosummary/cyclops.tasks.cxr_classification", "reference/api/_autosummary/cyclops.tasks.cxr_classification.CXRClassificationTask", "reference/api/_autosummary/cyclops.tasks.mortality_prediction", "reference/api/_autosummary/cyclops.tasks.mortality_prediction.MortalityPredictionTask", "reference/api/cyclops.data", "reference/api/cyclops.evaluate", "reference/api/cyclops.monitor", "reference/api/cyclops.report", "reference/api/cyclops.tasks", "tutorials", "tutorials/kaggle/heart_failure_prediction", "tutorials/nihcxr/cxr_classification", "tutorials/nihcxr/monitor_api", "tutorials/synthea/los_prediction", "tutorials_monitor", "tutorials_use_cases"], "filenames": ["api.rst", "contributing.rst", "index.rst", "intro.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.rst", "reference/api/_autosummary/cyclops.data.slicer.rst", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec.rst", "reference/api/_autosummary/cyclops.data.slicer.compound_filter.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_range.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_value.rst", "reference/api/_autosummary/cyclops.data.slicer.is_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.overall.rst", "reference/api/_autosummary/cyclops.evaluate.evaluator.rst", "reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.config.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.config.FairnessConfig.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.Accuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.BinaryAccuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.AUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.BinaryAUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MulticlassAUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MultilabelAUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryF1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.F1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.FbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassF1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelF1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.factory.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.factory.create_metric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.accuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.auroc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.binary_roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.sensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.specificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.stat_scores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.Metric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.MetricCollection.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.OperatorMetric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryPrecision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryRecall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassRecall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelRecall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.BinaryROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MulticlassROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MultilabelROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.ROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.BinarySensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.Sensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.BinarySpecificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MulticlassSpecificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MultilabelSpecificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.Specificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.BinaryStatScores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.StatScores.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.rst", "reference/api/_autosummary/cyclops.report.report.rst", "reference/api/_autosummary/cyclops.report.report.ModelCardReport.rst", "reference/api/_autosummary/cyclops.tasks.cxr_classification.rst", "reference/api/_autosummary/cyclops.tasks.cxr_classification.CXRClassificationTask.rst", "reference/api/_autosummary/cyclops.tasks.mortality_prediction.rst", "reference/api/_autosummary/cyclops.tasks.mortality_prediction.MortalityPredictionTask.rst", "reference/api/cyclops.data.rst", "reference/api/cyclops.evaluate.rst", "reference/api/cyclops.monitor.rst", "reference/api/cyclops.report.rst", "reference/api/cyclops.tasks.rst", "tutorials.rst", "tutorials/kaggle/heart_failure_prediction.ipynb", "tutorials/nihcxr/cxr_classification.ipynb", "tutorials/nihcxr/monitor_api.ipynb", "tutorials/synthea/los_prediction.ipynb", "tutorials_monitor.rst", "tutorials_use_cases.rst"], "titles": ["API Reference", "Contributing to cyclops", "Welcome to cyclops\u2019s documentation!", "\ud83d\udc23 Getting Started", "cyclops.data.features.medical_image", "cyclops.data.features.medical_image.MedicalImage", "cyclops.data.slicer", "cyclops.data.slicer.SliceSpec", "cyclops.data.slicer.compound_filter", "cyclops.data.slicer.filter_datetime", "cyclops.data.slicer.filter_non_null", "cyclops.data.slicer.filter_range", "cyclops.data.slicer.filter_string_contains", "cyclops.data.slicer.filter_value", "cyclops.data.slicer.is_datetime", "cyclops.data.slicer.overall", "cyclops.evaluate.evaluator", "cyclops.evaluate.evaluator.evaluate", "cyclops.evaluate.fairness.config", "cyclops.evaluate.fairness.config.FairnessConfig", "cyclops.evaluate.fairness.evaluator", "cyclops.evaluate.fairness.evaluator.evaluate_fairness", "cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values", "cyclops.evaluate.metrics.accuracy", "cyclops.evaluate.metrics.accuracy.Accuracy", "cyclops.evaluate.metrics.accuracy.BinaryAccuracy", "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy", "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy", "cyclops.evaluate.metrics.auroc", "cyclops.evaluate.metrics.auroc.AUROC", "cyclops.evaluate.metrics.auroc.BinaryAUROC", "cyclops.evaluate.metrics.auroc.MulticlassAUROC", "cyclops.evaluate.metrics.auroc.MultilabelAUROC", "cyclops.evaluate.metrics.f_beta", "cyclops.evaluate.metrics.f_beta.BinaryF1Score", "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore", "cyclops.evaluate.metrics.f_beta.F1Score", "cyclops.evaluate.metrics.f_beta.FbetaScore", "cyclops.evaluate.metrics.f_beta.MulticlassF1Score", "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore", "cyclops.evaluate.metrics.f_beta.MultilabelF1Score", "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore", "cyclops.evaluate.metrics.factory", "cyclops.evaluate.metrics.factory.create_metric", "cyclops.evaluate.metrics.functional.accuracy", "cyclops.evaluate.metrics.functional.auroc", "cyclops.evaluate.metrics.functional.f_beta", "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score", "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score", "cyclops.evaluate.metrics.functional.f_beta.f1_score", "cyclops.evaluate.metrics.functional.f_beta.fbeta_score", "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score", "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score", "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score", "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score", "cyclops.evaluate.metrics.functional.precision_recall", "cyclops.evaluate.metrics.functional.precision_recall.binary_precision", "cyclops.evaluate.metrics.functional.precision_recall.binary_recall", "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision", "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall", "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision", "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall", "cyclops.evaluate.metrics.functional.precision_recall.precision", "cyclops.evaluate.metrics.functional.precision_recall.recall", "cyclops.evaluate.metrics.functional.precision_recall_curve", "cyclops.evaluate.metrics.functional.roc", "cyclops.evaluate.metrics.functional.roc.binary_roc_curve", "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve", "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve", "cyclops.evaluate.metrics.functional.roc.roc_curve", "cyclops.evaluate.metrics.functional.sensitivity", "cyclops.evaluate.metrics.functional.specificity", "cyclops.evaluate.metrics.functional.stat_scores", "cyclops.evaluate.metrics.metric", "cyclops.evaluate.metrics.metric.Metric", "cyclops.evaluate.metrics.metric.MetricCollection", "cyclops.evaluate.metrics.metric.OperatorMetric", "cyclops.evaluate.metrics.precision_recall", "cyclops.evaluate.metrics.precision_recall.BinaryPrecision", "cyclops.evaluate.metrics.precision_recall.BinaryRecall", "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision", "cyclops.evaluate.metrics.precision_recall.MulticlassRecall", "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision", "cyclops.evaluate.metrics.precision_recall.MultilabelRecall", "cyclops.evaluate.metrics.precision_recall.Precision", "cyclops.evaluate.metrics.precision_recall.Recall", "cyclops.evaluate.metrics.precision_recall_curve", "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve", "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve", "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve", "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve", "cyclops.evaluate.metrics.roc", "cyclops.evaluate.metrics.roc.BinaryROCCurve", "cyclops.evaluate.metrics.roc.MulticlassROCCurve", "cyclops.evaluate.metrics.roc.MultilabelROCCurve", "cyclops.evaluate.metrics.roc.ROCCurve", "cyclops.evaluate.metrics.sensitivity", "cyclops.evaluate.metrics.sensitivity.BinarySensitivity", "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity", "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity", "cyclops.evaluate.metrics.sensitivity.Sensitivity", "cyclops.evaluate.metrics.specificity", "cyclops.evaluate.metrics.specificity.BinarySpecificity", "cyclops.evaluate.metrics.specificity.MulticlassSpecificity", "cyclops.evaluate.metrics.specificity.MultilabelSpecificity", "cyclops.evaluate.metrics.specificity.Specificity", "cyclops.evaluate.metrics.stat_scores", "cyclops.evaluate.metrics.stat_scores.BinaryStatScores", "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores", "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores", "cyclops.evaluate.metrics.stat_scores.StatScores", "cyclops.monitor.clinical_applicator", "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "cyclops.monitor.synthetic_applicator", "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "cyclops.monitor.synthetic_applicator.binary_noise_shift", "cyclops.monitor.synthetic_applicator.feature_association_shift", "cyclops.monitor.synthetic_applicator.feature_swap_shift", "cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "cyclops.monitor.synthetic_applicator.knockout_shift", "cyclops.report.report", "cyclops.report.report.ModelCardReport", "cyclops.tasks.cxr_classification", "cyclops.tasks.cxr_classification.CXRClassificationTask", "cyclops.tasks.mortality_prediction", "cyclops.tasks.mortality_prediction.MortalityPredictionTask", "cyclops.data", "cyclops.evaluate", "cyclops.monitor", "cyclops.report", "cyclops.tasks", "Tutorials", "Heart Failure Prediction", "Chest X-Ray Disease Classification", "NIHCXR Clinical Drift Experiments Tutorial", "Prolonged Length of Stay Prediction", "monitor API", "Example use cases"], "terms": {"cyclop": [0, 132, 133, 134, 135], "data": [0, 2, 3, 24, 26, 27, 49, 50, 52, 54, 69, 72, 89, 95, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 131, 133, 134, 136], "slicer": [0, 132, 133, 134, 135], "compound_filt": 0, "filter_datetim": 0, "filter_non_nul": 0, "filter_rang": 0, "filter_string_contain": 0, "filter_valu": [0, 133], "is_datetim": 0, "overal": [0, 7, 21, 121, 132, 133, 135], "slicespec": [0, 17, 112, 123, 132, 133, 134, 135], "spec_list": [0, 7, 132, 133, 134, 135], "include_overal": [0, 7], "valid": [0, 7, 9, 17, 121, 123, 125, 132], "column_nam": [0, 7, 9, 10, 11, 12, 13, 133], "_registri": [0, 7], "add_slice_spec": [0, 7], "get_slic": [0, 7], "slice": [0, 3, 7, 8, 17, 21, 121, 123, 125, 132, 133, 135], "featur": [0, 7, 9, 10, 11, 12, 13, 15, 17, 112, 116, 117, 121, 123, 125, 131, 133, 137], "medical_imag": 0, "medicalimag": 0, "__call__": [0, 5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "cast_storag": [0, 5], "decode_exampl": [0, 5], "embed_storag": [0, 5], "encode_exampl": [0, 5], "flatten": [0, 5, 132, 135], "task": [0, 2, 3, 24, 25, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 44, 47, 48, 49, 50, 51, 53, 54, 58, 60, 61, 62, 63, 66, 67, 68, 69, 78, 80, 81, 82, 83, 84, 85, 90, 92, 93, 94, 95, 98, 99, 100, 102, 103, 104, 105, 110, 131, 133, 137], "cxr_classif": 0, "cxrclassificationtask": 0, "__init__": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 123, 125], "add_model": [0, 123, 125], "data_typ": [0, 123, 125], "evalu": [0, 2, 3, 121, 123, 125, 131, 133, 137], "get_model": [0, 123, 125], "list_model": [0, 123, 125, 132, 135], "models_count": [0, 123, 125], "predict": [0, 3, 17, 19, 21, 24, 26, 27, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 67, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 94, 98, 100, 102, 103, 104, 105, 107, 108, 109, 110, 123, 124, 125, 131], "task_typ": [0, 123, 125, 132, 133, 135], "mortality_predict": [0, 132, 135], "mortalitypredictiontask": [0, 132, 135], "list_models_param": [0, 125, 132, 135], "load_model": [0, 125], "save_model": [0, 125], "train": [0, 3, 17, 121, 123, 125, 131, 133, 136, 137], "metric": [0, 17, 19, 21, 121, 123, 125, 131, 132, 135, 137], "__add__": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "__mul__": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "add_stat": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "clone": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "comput": [0, 17, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 123, 125, 131, 137], "reset_st": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "update_st": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "metriccollect": [0, 17, 21, 123, 125, 132, 135], "add_metr": [0, 75], "clear": [0, 75], "get": [0, 2, 75, 123, 125, 131, 132, 135, 137], "item": [0, 75, 132, 133, 134, 135], "kei": [0, 7, 17, 21, 75, 121, 132, 133, 134, 135], "pop": [0, 75, 132, 135], "popitem": [0, 75], "setdefault": [0, 75], "updat": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 134, 135], "valu": [0, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 131, 133, 134, 135, 137], "operatormetr": 0, "factori": [0, 7, 133], "create_metr": [0, 132, 133, 135], "accuraci": [0, 132, 135], "binaryaccuraci": [0, 132, 135], "multiclassaccuraci": 0, "multilabelaccuraci": 0, "auroc": [0, 131, 132, 135, 137], "binaryauroc": [0, 29, 132, 135], "multiclassauroc": [0, 29], "multilabelauroc": [0, 29, 133], "precision_recal": 0, "binaryprecis": [0, 132, 135], "binaryrecal": [0, 97, 132, 135], "multiclassprecis": 0, "multiclassrecal": [0, 98], "multilabelprecis": 0, "multilabelrecal": [0, 99], "precis": [0, 24, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 64, 66, 77, 78, 80, 82, 85, 86, 87, 88, 89, 90, 92, 100, 105, 132, 135], "recal": [0, 24, 38, 51, 55, 57, 59, 61, 64, 66, 77, 79, 81, 83, 86, 87, 88, 89, 90, 92, 97, 98, 99, 105, 132, 135], "precision_recall_curv": [0, 132, 135], "binaryprecisionrecallcurv": [0, 30, 92, 132, 135], "multiclassprecisionrecallcurv": [0, 31, 93], "multilabelprecisionrecallcurv": [0, 32, 94], "precisionrecallcurv": 0, "roc": [0, 28, 29, 30, 31, 32, 45, 132, 135], "binaryroccurv": [0, 132, 135], "multiclassroccurv": 0, "multilabelroccurv": 0, "roccurv": 0, "sensit": [0, 121, 131, 132, 133, 135, 136], "binarysensit": 0, "multiclasssensit": 0, "multilabelsensit": 0, "specif": [0, 7, 17, 115, 118, 123, 125, 132, 133, 135], "binaryspecif": 0, "multiclassspecif": 0, "multilabelspecif": 0, "f_beta": 0, "binaryf1scor": [0, 132, 135], "binaryfbetascor": [0, 34], "f1score": 0, "fbetascor": [0, 36], "multiclassf1scor": 0, "multiclassfbetascor": [0, 38], "multilabelf1scor": 0, "multilabelfbetascor": [0, 40], "stat_scor": 0, "binarystatscor": [0, 25, 35, 78, 79, 102], "multiclassstatscor": [0, 26, 39, 80, 81, 103], "multilabelstatscor": [0, 27, 41, 82, 83, 104], "statscor": 0, "function": [0, 3, 5, 6, 7, 8, 16, 17, 20, 21, 25, 35, 41, 42, 76, 93, 102, 104, 107, 109, 110, 113, 121, 132, 133, 135], "binary_precis": 0, "binary_recal": 0, "multiclass_precis": 0, "multiclass_recal": 0, "multilabel_precis": 0, "multilabel_recal": 0, "binary_roc_curv": 0, "multiclass_roc_curv": 0, "multilabel_roc_curv": 0, "roc_curv": [0, 132, 135], "binary_f1_scor": 0, "binary_fbeta_scor": 0, "f1_score": [0, 132, 135], "fbeta_scor": 0, "multiclass_f1_scor": 0, "multiclass_fbeta_scor": 0, "multilabel_f1_scor": 0, "multilabel_fbeta_scor": 0, "fair": [0, 17, 121, 123, 125, 132, 133, 135], "evaluate_fair": [0, 133], "warn_too_many_unique_valu": 0, "config": [0, 125], "fairnessconfig": [0, 17, 123, 125, 132, 135], "monitor": [0, 2, 3, 131, 132, 133, 134, 135], "clinical_appl": 0, "clinicalshiftappl": [0, 134], "ag": [0, 112, 131, 134, 137], "apply_shift": [0, 112, 114, 134], "custom": [0, 112, 121, 134], "hospital_typ": [0, 112], "month": [0, 7, 9, 112, 132, 133, 135], "sex": [0, 112, 131, 134, 135, 137], "time": [0, 7, 75, 112, 121, 131, 136, 137], "synthetic_appl": 0, "binary_noise_shift": 0, "feature_association_shift": 0, "feature_swap_shift": 0, "gaussian_noise_shift": 0, "knockout_shift": 0, "syntheticshiftappl": [0, 113], "report": [0, 2, 3, 110, 131, 133, 137], "modelcardreport": [0, 132, 133, 135], "export": [0, 121, 132, 133, 135], "from_json_fil": [0, 121], "log_cit": [0, 121, 133], "log_dataset": [0, 121, 132], "log_descriptor": [0, 121, 132, 133, 135], "log_fairness_assess": [0, 121, 132, 133, 135], "log_from_dict": [0, 121, 132, 133, 135], "log_imag": [0, 121], "log_licens": [0, 121, 132, 135], "log_model_paramet": [0, 121, 132, 135], "log_own": [0, 121, 132, 133, 135], "log_performance_metr": [0, 121, 132, 135], "log_plotly_figur": [0, 121, 132, 133, 135], "log_quantitative_analysi": [0, 121, 132, 133, 135], "log_refer": [0, 121, 132, 135], "log_regul": [0, 121], "log_risk": [0, 121, 132, 133, 135], "log_use_cas": [0, 121, 132, 133, 135], "log_us": [0, 121, 132, 133, 135], "log_vers": [0, 121, 132, 135], "thank": 1, "your": [1, 132], "interest": [1, 132, 135], "To": [1, 3, 5, 132, 135], "submit": 1, "pr": 1, "pleas": [1, 132, 133, 134, 135], "fill": 1, "out": [1, 121, 132, 135], "templat": [1, 121], "along": [1, 112, 132, 133, 135], "If": [1, 5, 7, 9, 10, 11, 12, 13, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 135], "fix": 1, "an": [1, 3, 5, 7, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 51, 60, 61, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 132, 133, 135], "issu": [1, 21], "don": 1, "t": [1, 5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121], "forget": 1, "link": [1, 121, 132, 133, 135], "onc": [1, 75, 132, 133, 135], "python": [1, 3, 135], "virtual": [1, 3], "environ": [1, 3, 132, 135], "i": [1, 3, 5, 7, 9, 10, 11, 12, 13, 14, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 133, 135, 137], "setup": [1, 135], "you": [1, 3, 5, 75, 132, 133, 135, 136], "can": [1, 3, 5, 7, 21, 25, 38, 51, 69, 75, 84, 85, 95, 100, 110, 121, 125, 132, 133, 135, 136], "run": [1, 3, 132, 135], "us": [1, 2, 5, 7, 8, 17, 21, 24, 29, 30, 31, 32, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 60, 61, 62, 63, 66, 67, 68, 69, 75, 76, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 98, 99, 100, 102, 105, 107, 110, 112, 114, 121, 123, 125, 131, 132, 133, 135, 136], "all": [1, 7, 8, 9, 10, 11, 12, 13, 15, 63, 73, 75, 108, 109, 110, 125, 132, 134, 135], "file": [1, 5, 121, 132, 133, 135], "For": [1, 21, 76, 121, 132, 135], "style": 1, "we": [1, 3, 121, 132, 133, 135], "recommend": [1, 76], "googl": 1, "guid": 1, "appli": [1, 8, 25, 29, 59, 62, 63, 66, 67, 68, 75, 76, 93, 104, 109, 110, 112, 123, 125, 132, 135], "black": 1, "format": [1, 5, 7, 89, 121, 132, 135], "docstr": 1, "numpi": [1, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 115, 116, 117, 118, 119, 123, 132, 133, 134, 135], "also": [1, 3, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 135, 137], "flake8": 1, "pylint": 1, "further": 1, "static": 1, "analysi": [1, 121, 132, 133, 135], "The": [1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 60, 61, 63, 66, 68, 69, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 133, 135, 136, 137], "show": [1, 132, 133, 135], "error": [1, 131, 137], "which": [1, 9, 10, 11, 12, 13, 21, 90, 121, 132, 133, 135, 137], "need": [1, 17, 21, 132, 135], "befor": [1, 17, 21, 22, 125, 132, 135], "last": 1, "least": 1, "type": [1, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 131, 137], "hint": 1, "our": [1, 132, 135], "check": [1, 14, 89], "mypi": 1, "current": [1, 121, 132, 133, 135], "ar": [1, 5, 7, 11, 12, 17, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 97, 98, 99, 100, 103, 104, 105, 108, 109, 110, 112, 116, 121, 132, 133, 135], "strict": 1, "enforc": 1, "more": [1, 7, 16, 17, 123, 125, 132, 137], "api": [1, 2, 3, 131, 132, 137], "becom": 1, "stabl": [1, 132, 133, 134, 135], "start": [2, 17, 132, 135], "instal": [2, 132], "pip": [2, 132], "develop": [2, 132, 133, 135], "poetri": 2, "contribut": 2, "notebook": [2, 132, 133, 135], "citat": [2, 121, 132, 133, 135], "pre": [2, 132, 135], "commit": 2, "hook": 2, "code": [2, 132, 135], "guidelin": [2, 3], "tutori": [2, 132, 133, 135, 136, 137], "exampl": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 131, 132, 133, 135, 136], "case": [2, 3, 75, 115, 121, 131, 132, 135, 136], "refer": [2, 3, 121, 132, 133, 135], "toolkit": 3, "facilit": 3, "research": 3, "deploy": 3, "ml": [3, 132, 135], "model": [3, 16, 17, 21, 121, 123, 125, 131, 136, 137], "healthcar": 3, "It": [3, 38, 51, 75, 84, 85, 100, 105, 136], "provid": [3, 7, 9, 12, 17, 21, 69, 110, 121, 132, 135], "few": 3, "high": [3, 132, 135], "level": [3, 21, 132, 135], "name": [3, 7, 8, 9, 10, 11, 12, 13, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 123, 125, 132, 133, 134, 135], "creat": [3, 6, 7, 21, 42, 43, 75, 84, 85, 100, 115, 118, 119, 121, 123, 125, 131, 133, 137], "dataset": [3, 6, 7, 16, 17, 19, 21, 26, 38, 39, 51, 52, 58, 61, 68, 69, 80, 81, 83, 88, 89, 90, 94, 95, 98, 99, 103, 104, 112, 114, 121, 123, 125, 126, 131, 136, 137], "infer": [3, 17], "popular": [3, 132], "effici": 3, "load": [3, 17, 121, 125, 131, 135, 136, 137], "differ": [3, 24, 29, 36, 37, 46, 55, 62, 63, 64, 69, 70, 72, 84, 85, 90, 95, 100, 105, 131, 132, 133, 135, 136], "modal": 3, "common": [3, 132], "implement": [3, 137], "scikit": [3, 132], "learn": [3, 132, 133], "pytorch": 3, "canon": 3, "mortal": [3, 124, 125], "chest": [3, 122, 123, 131], "x": [3, 114, 115, 116, 117, 118, 119, 122, 123, 125, 131, 132, 134, 135], "rai": [3, 122, 123, 131], "classif": [3, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 44, 47, 48, 49, 50, 51, 53, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 78, 79, 80, 81, 82, 83, 84, 85, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 122, 123, 125, 131, 132, 135], "clinic": [3, 111, 112, 131, 136], "detect": [3, 133, 136], "shift": [3, 111, 112, 114, 116, 117, 131, 136], "relev": [3, 121, 132, 135, 136], "card": [3, 121, 131, 132, 135, 137], "librari": [3, 131, 136, 137], "end": [3, 132, 133, 135], "mimic": 3, "iii": 3, "iv": 3, "eicu": 3, "crd": 3, "python3": [3, 132, 133, 134, 135], "m": [3, 132, 133, 134, 135], "pycyclop": [3, 132, 133, 134, 135], "base": [3, 5, 7, 17, 19, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 131, 132, 137], "packag": [3, 126, 127, 128, 129, 130, 132, 133, 134, 135], "support": [3, 7, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 72, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 107, 108, 109, 136], "process": [3, 112, 132, 133, 135], "transform": [3, 17, 66, 67, 68, 93, 123, 125, 132, 133, 134, 135], "downstream": [3, 132, 135], "addit": [3, 75, 121, 123, 125, 132, 135], "from": [3, 5, 7, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 119, 121, 123, 125, 132, 133, 134, 135], "other": [3, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132], "thei": [3, 69], "extra": [3, 121], "multipl": [3, 8, 17, 21, 75, 121], "could": [3, 132, 135], "combin": [3, 8, 132], "both": 3, "set": [3, 7, 17, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 132, 133, 135], "up": [3, 132, 133, 135], "henc": 3, "make": [3, 132, 135], "sure": [3, 132], "sourc": [3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 131, 136], "env": 3, "info": [3, 132, 135], "path": [3, 5, 112, 121, 125, 132, 133, 135], "bin": [3, 21], "activ": [3, 135], "In": [3, 75, 132, 135], "order": [3, 5, 17, 107, 108, 109], "depend": 3, "test": [3, 17, 121, 123, 125, 131, 132, 135, 136, 137], "built": 3, "sphinx": 3, "local": 3, "doc": 3, "cd": 3, "html": [3, 121, 132, 133, 134, 135], "sphinxopt": 3, "d": [3, 75, 112, 133], "nbsphinx_allow_error": 3, "true": [3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 24, 26, 27, 31, 32, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 112, 114, 116, 121, 123, 125, 132, 133, 134, 135], "welcom": 3, "see": [3, 7, 121, 132, 133, 134, 135], "jupyt": [3, 132, 133, 134, 135], "insid": 3, "ipython": 3, "kernel": 3, "after": [3, 17, 132, 135], "ipykernel": 3, "user": [3, 121, 132, 135], "name_of_kernel": 3, "now": 3, "navig": 3, "": [3, 7, 10, 14, 17, 21, 75, 121, 123, 125, 132, 133, 134, 135], "tab": [3, 132], "cite": 3, "when": [3, 5, 17, 21, 24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 75, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105, 123, 125, 132, 135], "project": 3, "paper": 3, "articl": 3, "krishnan2022": 3, "12": [3, 7, 132, 133, 135], "02": [3, 69], "22283021": 3, "author": [3, 132, 133], "krishnan": 3, "amrit": 3, "subasri": 3, "vallijah": 3, "mckeen": 3, "kaden": 3, "kore": 3, "ali": 3, "ogidi": 3, "franklin": 3, "alinoori": 3, "mahshid": 3, "lalani": 3, "nadim": 3, "dhalla": 3, "azra": 3, "verma": 3, "amol": 3, "razak": 3, "fahad": 3, "pandya": 3, "deval": 3, "dolatabadi": 3, "elham": 3, "titl": [3, 132, 133, 135], "cyclic": 3, "toward": 3, "operation": 3, "health": [3, 132, 135], "eloc": 3, "id": [3, 5, 112, 132, 133, 135], "2022": [3, 7, 133], "year": [3, 7, 9, 132, 133, 135], "doi": 3, "10": [3, 132, 133, 134, 135], "1101": 3, "publish": [3, 132], "cold": 3, "spring": 3, "harbor": 3, "laboratori": [3, 135], "press": 3, "url": [3, 133], "http": [3, 121, 132, 133, 134, 135], "www": [3, 132], "medrxiv": 3, "org": [3, 121, 132, 133, 135], "content": [3, 121], "earli": 3, "08": [3, 132], "journal": 3, "medic": [4, 5, 126, 133, 135, 137], "imag": [4, 5, 17, 21, 118, 121, 123, 126, 131, 133, 134], "class": [4, 5, 6, 7, 17, 18, 19, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 69, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 119, 120, 121, 122, 123, 124, 125, 132, 133, 135], "decod": [5, 133], "none": [5, 7, 9, 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 123, 125, 132, 133, 134, 135], "reader": 5, "itkread": 5, "suffix": 5, "jpg": 5, "read": [5, 17], "paramet": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 132, 133, 135], "bool": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 75, 76, 108, 109, 110, 112, 116, 117, 121, 123, 125, 133], "option": [5, 7, 9, 10, 11, 12, 13, 17, 21, 24, 27, 36, 37, 38, 39, 40, 41, 43, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 69, 75, 80, 81, 82, 83, 84, 85, 90, 95, 98, 99, 100, 103, 104, 105, 108, 112, 114, 121, 123, 125], "default": [5, 7, 9, 10, 11, 12, 13, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 123, 125, 132, 135], "whether": [5, 7, 21, 75, 108, 109, 110, 112, 121, 135, 137], "fals": [5, 7, 9, 10, 11, 12, 13, 14, 19, 21, 29, 30, 40, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 105, 107, 108, 109, 110, 117, 118, 123, 125, 132, 133, 135], "return": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 135], "dictionari": [5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 75, 121, 123, 125, 132, 135], "image_path": 5, "byte": 5, "image_byt": 5, "union": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125], "str": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 135], "imageread": 5, "monai": [5, 133, 134], "method": [5, 7, 19, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 133, 135], "attribut": [5, 7, 19, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 123, 125, 132, 135], "call": [5, 121], "self": 5, "storag": 5, "cast": [5, 132, 135], "arrow": 5, "arrai": [5, 24, 26, 27, 29, 30, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 60, 61, 63, 66, 67, 68, 69, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 116, 117, 119, 123, 133], "convert": [5, 25, 35, 41, 48, 61, 69, 95, 102, 104, 107, 132, 135], "pyarrow": 5, "rtype": 5, "structarrai": 5, "pa": 5, "string": [5, 7, 9, 12, 17, 21, 75, 121, 133], "must": [5, 9, 17, 21, 121], "contain": [5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 27, 103, 104, 121, 132, 133, 135, 137], "binari": [5, 24, 25, 29, 30, 34, 35, 36, 37, 47, 48, 49, 50, 56, 57, 60, 61, 62, 63, 66, 69, 72, 78, 79, 84, 85, 87, 90, 92, 95, 97, 100, 102, 104, 105, 107, 110, 115, 125, 132, 133, 135, 137], "struct": 5, "doesn": 5, "matter": 5, "list": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 66, 67, 68, 69, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 115, 116, 117, 118, 119, 121, 123, 125, 132, 135], "arg": [5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "stringarrai": 5, "listarrai": 5, "token_per_repo_id": 5, "serial": 5, "version": [5, 121, 132, 133, 135], "dict": [5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 75, 121, 123, 125], "access": 5, "privat": 5, "repositori": [5, 132], "hub": 5, "pass": [5, 17, 43, 75, 112, 121, 125, 132, 135], "repo_id": 5, "token": [5, 132], "deseri": 5, "np": [5, 11, 14, 21, 123, 125, 132, 133, 134, 135], "ndarrai": [5, 14, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 115, 116, 117, 118, 119, 123, 125], "metadata": [5, 132, 133, 135], "emb": 5, "encod": 5, "input": [5, 24, 46, 55, 60, 61, 64, 69, 70, 72, 87, 89, 95, 115, 118, 123, 125], "state": [5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "itself": 5, "otherwis": [5, 14, 24, 27, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 67, 68, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 108, 109, 110], "tupl": [5, 7, 66, 67, 68, 69, 75, 87, 88, 89, 92, 93, 94, 112, 123, 125], "classlabel": [5, 132, 135], "translat": 5, "translationvariablelanguag": 5, "sequenc": [5, 17, 75, 123, 125, 133], "array2d": 5, "array3d": 5, "array4d": 5, "array5d": 5, "audio": 5, "subset": 6, "hug": [6, 123, 125, 131, 137], "face": [6, 123, 125, 131, 137], "object": [7, 19, 21, 112, 114, 121, 123, 125, 132, 135], "ani": [7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 133, 135], "A": [7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 25, 75, 76, 104, 109, 121, 132, 133, 135], "each": [7, 8, 17, 21, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 67, 68, 69, 75, 76, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 108, 109, 110, 132, 135], "map": [7, 8, 22, 43, 75, 123, 125, 132, 133, 135], "column": [7, 8, 9, 10, 11, 12, 13, 17, 21, 112, 123, 125, 132, 133, 135], "one": [7, 16, 17, 21, 24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 67, 68, 69, 76, 80, 81, 82, 83, 84, 85, 95, 98, 99, 100, 105, 123, 125], "follow": [7, 17, 24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 60, 61, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 105, 121, 132, 133, 135], "exact": [7, 13], "select": [7, 112, 116, 132, 133, 135], "thi": [7, 17, 21, 24, 25, 26, 27, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105, 121, 125, 132, 133, 135, 137], "singl": [7, 75, 121, 125, 132, 135], "row": [7, 132, 133], "where": [7, 8, 9, 10, 11, 12, 13, 60, 61, 63, 75, 121, 125, 132, 135, 137], "e": [7, 9, 10, 17, 21, 75, 116, 117, 118, 121, 132, 135], "g": [7, 9, 17, 21, 116, 117, 118, 121, 132, 135], "2021": [7, 132], "01": [7, 29, 31, 32, 132, 133, 135], "00": [7, 132, 133, 134, 135], "min_valu": [7, 11, 132, 133, 134, 135], "minimum": [7, 11], "specifi": [7, 17, 75, 112, 121, 123, 125, 132, 133, 135], "min_inclus": [7, 11, 132, 135], "indic": [7, 21, 27, 60, 61, 115, 118, 132, 135], "includ": [7, 11, 21, 72, 112, 114, 132, 133, 135, 136], "rang": [7, 11, 29, 30, 66, 67, 68, 93, 132, 133, 135], "work": [7, 27, 103, 104, 121, 132, 133, 135], "numer": [7, 11, 132, 135], "datetim": [7, 9, 11, 14, 121, 132, 133, 135], "inf": [7, 11, 132, 133, 135], "max_valu": [7, 11, 132, 133, 134, 135], "boolean": [7, 8, 9, 10, 11, 12, 13, 15], "greater": [7, 22, 135], "than": [7, 11, 22, 48, 52, 54, 123, 125, 132, 135, 137], "equal": [7, 11, 21], "maximum": [7, 11, 22, 29, 30], "max_inclus": [7, 11, 132, 135], "less": [7, 11, 48, 52, 54, 135], "match": [7, 9, 12, 13, 17], "between": [7, 21, 38, 51, 69, 95], "1": [7, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 116, 117, 118, 119, 121, 131, 132, 133, 135, 136, 137], "dai": [7, 9, 135, 137], "31": [7, 132, 134, 135], "hour": [7, 9], "0": [7, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 114, 115, 116, 117, 118, 119, 121, 132, 133, 134, 135], "23": [7, 132, 135], "negat": [7, 9, 10, 11, 12, 13, 133], "flag": 7, "doe": [7, 9, 11, 12, 13, 17, 21, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 121], "keep_nul": [7, 9, 11, 12, 13], "keep": [7, 17, 21, 135], "null": [7, 9, 10, 11, 12, 13, 135], "conjunct": [7, 133], "its": [7, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 60, 61, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 135], "own": [7, 132, 135], "callabl": [7, 8, 17, 21, 76, 121], "import": [7, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 131, 136, 137], "slice_spec": [7, 17, 123, 125, 132, 133, 135], "feature_1": 7, "feature_2": 7, "feature_3": 7, "value_1": 7, "value_2": 7, "2020": [7, 9, 133], "5": [7, 24, 25, 27, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 114, 115, 118, 119, 131, 132, 133, 135, 136], "60": 7, "6": [7, 24, 26, 35, 36, 38, 39, 49, 56, 59, 62, 63, 78, 79, 80, 81, 83, 84, 85, 87, 88, 90, 92, 93, 95, 97, 98, 99, 100, 104, 107, 108, 110, 132, 133, 134, 135], "7": [7, 29, 30, 31, 36, 39, 40, 69, 80, 81, 82, 84, 85, 87, 88, 89, 93, 98, 100, 105, 108, 109, 110, 132, 133, 135, 137], "8": [7, 24, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 40, 41, 47, 49, 50, 53, 54, 56, 59, 60, 62, 66, 68, 69, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 99, 100, 105, 107, 109, 110, 132, 133, 135], "2000": 7, "2010": 7, "slice_nam": [7, 121, 132, 133, 135], "slice_func": 7, "print": [7, 132, 133, 135], "do": [7, 17], "someth": 7, "here": [7, 132, 135], "filter": [7, 9, 10, 11, 12, 13, 17, 21, 132, 133, 134, 135], "add": [7, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 133, 135], "detail": [7, 132, 133, 135], "registri": [7, 132, 135], "gener": [7, 69, 95, 112, 121, 131, 133, 136, 137], "slice_funct": 8, "result": [8, 17, 38, 51, 123, 125, 132, 133, 134, 135], "bitwis": 8, "AND": 8, "signatur": 8, "should": [8, 21, 69, 76, 95, 117, 121, 123, 125, 132, 133, 135], "kwarg": [8, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 114, 123, 125], "given": [9, 11, 12, 13, 14, 24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 75, 80, 81, 82, 84, 85, 98, 100, 105, 108, 109, 110, 121, 123, 125], "int": [9, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 116, 117, 118, 119, 121, 123, 125, 132, 135], "compon": 9, "have": [9, 12, 13, 17, 114, 132, 137], "nan": [9, 10, 131, 137], "nat": 9, "rais": [9, 11, 12, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125], "typeerror": [9, 11, 12, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121], "float": [11, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 109, 110, 115, 116, 117, 118, 119, 121], "valueerror": [11, 17, 21, 48, 50, 52, 54, 58, 59, 60, 61, 62, 63, 69, 121, 123, 125], "either": [11, 30, 31, 32, 75, 87, 88, 89, 92, 93, 94, 110, 121, 132, 135], "substr": 12, "ha": [13, 75, 121, 132, 133, 135], "find": [13, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 133], "perform": [13, 26, 27, 31, 32, 121, 131, 136, 137], "datetime64": 14, "target_column": [17, 19, 21, 132, 133, 135], "feature_column": [17, 133, 134], "prediction_column_prefix": [17, 123, 125, 132, 133, 135], "remove_column": [17, 19, 21, 123, 125, 133], "split": [17, 112, 121, 123, 125, 132, 133, 135], "batch_siz": [17, 19, 21, 112, 123, 125, 132, 135], "1000": [17, 19, 21, 112, 125, 132], "fairness_config": [17, 123, 125, 132, 135], "override_fairness_metr": [17, 123, 125, 132, 135], "load_dataset_kwarg": 17, "datasetdict": [17, 123, 125], "load_dataset": 17, "argument": [17, 21, 43, 75, 123, 125, 132, 135], "target": [17, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 116, 117, 123, 125, 131, 132, 135, 136, 137], "prefix": [17, 75], "ad": [17, 114, 121, 123, 125, 132, 135], "model_nam": [17, 123, 125, 132, 133, 134, 135], "remov": [17, 21, 75, 119, 123, 125, 132, 133, 135], "mai": [17, 21, 132, 133, 135], "expens": [17, 21], "memori": [17, 21], "wrappedmodel": [17, 123, 125], "entir": [17, 132, 135], "being": 17, "note": [17, 121, 132, 133, 135], "chosen": 17, "avail": [17, 121, 132, 135, 137], "first": [17, 21, 25, 76, 104, 132, 135], "eval": 17, "val": 17, "dev": 17, "batch": [17, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 123, 125, 133], "size": [17, 21, 112, 123, 125, 132, 133, 135], "neg": [17, 35, 48, 49, 50, 51, 52, 53, 54, 59, 61, 62, 63, 72, 81, 83, 85, 98, 99, 100, 105, 107, 108, 109, 135], "integ": [17, 21, 121], "configur": [17, 18, 19, 123, 125, 132, 135], "overridden": [17, 123, 125], "prediction_column": [17, 19, 21, 133], "keyword": [17, 21, 43, 75, 125], "onli": [17, 21, 24, 27, 29, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 62, 63, 75, 80, 81, 82, 84, 85, 98, 100, 103, 104, 105, 108, 109, 110], "found": [17, 75, 121, 132, 133, 134, 135], "group": [19, 21, 22, 75, 121, 132, 133, 135], "group_valu": [19, 21], "group_bin": [19, 21, 132, 133, 135], "group_base_valu": [19, 21, 132, 133, 135], "threshold": [19, 21, 24, 25, 27, 29, 30, 31, 32, 34, 35, 36, 37, 40, 41, 47, 48, 49, 50, 53, 54, 56, 57, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 99, 100, 102, 104, 105, 107, 109, 110, 121, 131, 132, 135, 137], "compute_optimal_threshold": [19, 21], "metric_nam": [19, 21, 43, 121, 132, 133, 135], "metric_kwarg": [19, 21], "take": [21, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 112, 132], "allow": [21, 22, 132, 135, 136], "intersect": 21, "treat": 21, "multilabel": [21, 24, 27, 29, 32, 36, 37, 40, 41, 49, 50, 53, 54, 60, 61, 62, 63, 68, 69, 72, 82, 83, 84, 85, 89, 90, 94, 95, 99, 100, 104, 105, 109, 110, 131, 137], "same": [21, 75, 116], "uniqu": [21, 22, 29, 30, 31, 32, 66, 67, 68, 69, 87, 88, 89, 92, 93, 94, 95, 133, 137], "limit": [21, 132, 133, 135], "number": [21, 22, 24, 26, 27, 29, 30, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 98, 99, 100, 103, 104, 105, 108, 110, 112, 116, 117, 121, 123, 125, 135, 136], "continu": [21, 132, 133, 135], "veri": 21, "slow": 21, "larg": 21, "denomin": 21, "pariti": [21, 131, 137], "across": [21, 116, 136], "linspac": 21, "monoton": [21, 69, 95], "control": [21, 115], "usag": [21, 132, 135], "rel": 21, "small": 21, "32": [21, 132, 133, 135], "avoid": 21, "optim": [21, 132], "oper": [21, 65, 76], "necessari": 21, "nest": 21, "second": [21, 76], "third": 21, "omit": 21, "requir": [21, 24, 29, 36, 37, 49, 50, 69, 84, 85, 90, 95, 100, 105, 110, 121, 123, 125, 132, 135], "huggingfac": [21, 112, 123, 125], "runtimeerror": 21, "empti": [21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "encount": [21, 135], "unique_valu": 22, "max_unique_valu": 22, "50": [22, 132, 133, 134, 135], "warn": [22, 24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 105], "score": [24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 66, 70, 72, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "liter": [24, 25, 26, 27, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 69, 78, 79, 80, 81, 82, 83, 84, 85, 90, 95, 97, 98, 99, 100, 103, 104, 105, 110, 121], "multiclass": [24, 26, 29, 31, 36, 37, 38, 39, 49, 50, 51, 52, 58, 59, 62, 63, 67, 69, 72, 80, 81, 84, 85, 88, 90, 93, 95, 98, 100, 103, 105, 108, 110], "One": [24, 29, 31, 32, 35, 48, 59, 62, 63, 69, 95, 133, 135], "pos_label": [24, 25, 30, 34, 35, 36, 37, 47, 48, 49, 50, 56, 57, 62, 63, 66, 69, 78, 79, 84, 85, 87, 90, 92, 95, 97, 100, 102, 105, 107, 110], "label": [24, 25, 27, 29, 32, 34, 35, 36, 37, 40, 41, 47, 48, 49, 50, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 68, 69, 78, 79, 81, 82, 83, 84, 85, 87, 89, 90, 92, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 109, 110, 116, 117, 119, 123, 125, 131, 132, 133, 137], "consid": [24, 26, 27, 36, 37, 49, 50, 62, 63, 84, 85, 90, 95, 100, 103, 104, 105], "posit": [24, 25, 29, 30, 34, 35, 36, 37, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 90, 92, 95, 97, 98, 99, 100, 102, 105, 107, 108, 109, 110, 133], "num_class": [24, 26, 29, 31, 36, 37, 38, 39, 49, 50, 51, 52, 58, 59, 61, 62, 63, 67, 69, 80, 81, 84, 85, 88, 90, 93, 95, 98, 100, 103, 105, 108, 110, 132, 135], "decid": [24, 36, 37, 40, 41, 49, 50, 53, 54, 56, 57, 60, 61, 78, 79, 82, 83, 84, 85, 97, 99, 100, 105], "top_k": [24, 26, 27, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 108, 109, 110], "probabl": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 80, 81, 82, 84, 85, 93, 98, 100, 102, 103, 104, 105, 107, 108, 109, 110, 125, 132, 135], "logit": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 72, 80, 81, 82, 84, 85, 98, 100, 102, 103, 104, 105, 107, 108, 109, 110], "top": [24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 80, 81, 82, 84, 85, 98, 100, 105, 108, 109, 110], "k": [24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 75, 80, 81, 82, 84, 85, 98, 100, 105, 108, 109, 110, 134], "num_label": [24, 27, 29, 32, 36, 37, 40, 41, 49, 50, 53, 54, 60, 61, 62, 63, 68, 69, 82, 83, 84, 85, 89, 90, 94, 95, 99, 100, 104, 105, 109, 110, 133], "averag": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132], "micro": [24, 26, 27, 29, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "macro": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "weight": [24, 26, 27, 29, 31, 32, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 133, 134, 135], "calcul": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "global": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "unweight": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "mean": [24, 26, 27, 29, 31, 32, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132, 133, 134, 135], "imbal": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 119], "account": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132, 133], "instanc": [24, 26, 27, 31, 32, 36, 37, 38, 39, 40, 41, 43, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132, 135], "alter": [24, 26, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 105], "zero_divis": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 105], "zero": [24, 25, 26, 27, 34, 36, 37, 38, 39, 40, 41, 47, 49, 50, 51, 52, 53, 54, 56, 57, 58, 60, 61, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105], "divis": [24, 25, 26, 27, 34, 36, 37, 38, 39, 40, 41, 47, 49, 50, 51, 52, 53, 54, 56, 57, 58, 60, 61, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105], "act": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105], "pred": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 135], "75": [24, 25, 29, 30, 66, 67, 68, 90, 92, 95, 103, 104, 105, 132, 133], "05": [24, 26, 27, 29, 31, 32, 36, 38, 39, 40, 49, 53, 62, 67, 68, 69, 80, 81, 84, 85, 88, 90, 93, 94, 95, 98, 100, 103, 104, 105, 108, 110, 135], "95": [24, 26, 27, 36, 38, 49, 62, 69, 88, 90, 93, 94, 95, 134], "p": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 115, 133], "zip": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "2": [24, 26, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 56, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 75, 78, 79, 80, 81, 82, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 116, 117, 121, 131, 132, 133, 135, 136], "3": [24, 26, 27, 29, 31, 34, 35, 36, 37, 38, 39, 40, 47, 49, 50, 51, 52, 53, 56, 58, 59, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 116, 117, 131, 132, 133, 135, 136], "66666667": [24, 26, 36, 38, 49, 51, 61, 63, 81, 85, 87, 88, 90, 93, 94, 95, 98, 100, 104], "initi": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 135], "two": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "scalar": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "togeth": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "multipli": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "variabl": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 137], "attributeerror": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "alreadi": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 135], "exist": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 133, 135], "copi": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 135], "abstract": [24, 29, 36, 37, 73, 74, 84, 85, 90, 95, 100, 105, 110], "final": [24, 29, 36, 37, 74, 84, 85, 90, 95, 100, 105, 110, 133, 135], "reset": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "_update_count": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "_comput": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "sigmoid": [25, 35, 41, 66, 68, 69, 102, 104, 107, 109, 110], "them": [25, 104, 132, 133, 135, 136], "875": 25, "problem": [26, 88, 108, 109, 110, 137], "highest": [26, 27, 62, 63, 103, 104], "determin": [26, 27, 29, 30, 31, 32, 66, 67, 68, 87, 88, 89, 90, 92, 93, 94], "dtype": [26, 27, 31, 32, 38, 39, 40, 41, 66, 67, 68, 69, 80, 81, 82, 83, 87, 88, 89, 92, 93, 94, 98, 99, 103, 104, 115, 116, 117, 118, 119, 132, 133], "float64": [26, 27, 31, 32, 38, 39, 40, 41, 66, 67, 68, 69, 80, 81, 82, 83, 87, 88, 89, 92, 93, 94, 98, 99, 103, 104, 115, 116, 117, 118, 119, 133], "binar": [27, 29, 30, 31, 32, 34, 47, 67, 68, 93, 94, 109, 110], "output": [27, 69, 121, 132, 135], "classifi": [27, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 102, 132, 135], "correct": [27, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 93, 102, 103, 104], "per": [27, 75, 133, 135], "area": [28, 29, 30, 31, 32, 45], "under": [28, 29, 30, 31, 32, 45, 132, 135], "curv": [28, 29, 30, 31, 32, 45, 64, 65, 66, 67, 68, 69, 86, 87, 88, 89, 90, 92, 93, 94, 95, 132, 135], "max_fpr": [29, 30], "rate": [29, 30, 66, 67, 68, 69, 131, 135, 137], "partial": [29, 30, 133], "auc": 29, "automat": [29, 30, 31, 32, 66, 67, 68, 87, 88, 89, 90, 92, 93, 94], "applic": [29, 111, 112, 114], "4": [29, 30, 34, 35, 36, 37, 40, 47, 50, 59, 63, 69, 82, 83, 84, 85, 87, 88, 90, 92, 93, 94, 95, 99, 100, 105, 107, 108, 109, 110, 131, 132, 133, 135, 136], "35": [29, 30, 69, 87, 92, 95, 103, 104, 105, 132, 133, 134, 135], "9": [29, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 49, 50, 53, 54, 56, 60, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 89, 90, 93, 94, 95, 97, 98, 99, 100, 103, 104, 105, 107, 109, 110, 132, 133, 135], "6111111111111112": [29, 30], "89": [29, 31, 32, 69, 135], "06": [29, 31, 69, 135], "94": [29, 31], "22222222": [29, 31], "625": [29, 32, 35, 103], "aucroc": 30, "confus": [30, 31, 32, 87, 88, 89, 92, 93, 94], "matrix": [30, 31, 32, 87, 88, 89, 92, 93, 94, 115, 116, 117, 118, 119], "f": [33, 35, 37, 38, 39, 41, 46, 48, 50, 51, 52, 54, 75, 132, 133, 134, 135], "beta": [33, 35, 37, 39, 41, 46, 48, 50, 52, 54], "f1": [34, 36, 38, 40, 46, 47, 49, 51, 53], "form": [34, 47, 132, 135], "6666666666666666": [34, 36, 47, 56, 78, 84], "harmon": [35, 37, 39, 41, 48, 50, 52, 54], "8333333333333334": [35, 37, 50, 59, 62], "85714286": [36, 38], "9090909090909091": 37, "83333333": [37, 41, 50, 54], "55555556": [37, 50, 103], "90909091": [37, 39, 41], "85": [39, 80, 81, 84, 85, 98, 100, 132, 135], "total": [40, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 108, 135], "count": [40, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 132, 133, 135], "predicit": 41, "constructor": 43, "arraylik": [47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 76, 93, 102], "ground": [47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 93, 102], "truth": [47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 93, 102], "npt": [48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63], "7142857142857143": 48, "estim": [49, 50, 66, 67, 68, 69, 93, 102, 125], "shape": [49, 50, 51, 52, 53, 54, 60, 61, 108, 109, 133, 134], "expect": [60, 61, 132, 135], "like": [60, 61, 75, 132], "n": [60, 61, 132, 133], "l": [60, 61], "sampl": [60, 61, 63, 119], "presenc": [60, 61, 133], "absenc": [60, 61], "rest": 61, "ratio": [62, 63, 105, 135], "correctli": 62, "observ": [62, 132, 133, 135, 137], "precision_scor": 62, "tp": [63, 107, 108, 109], "fn": [63, 107, 108, 109], "intuit": 63, "abil": [63, 132, 135], "recall_scor": 63, "3333333333333333": 63, "receiv": 65, "characterist": 65, "decis": [66, 67, 68, 69, 93, 121, 132, 135], "fpr": [66, 67, 68, 69, 132, 133, 135], "tpr": [66, 67, 68, 69], "25": [66, 67, 68, 88, 90, 92, 93, 95, 105, 116, 117, 132, 134, 135], "softmax": [67, 69, 93], "1d": [67, 68, 69, 95], "33333333": [67, 85, 88, 90, 93, 94, 95, 100], "non": 69, "evenli": [69, 95], "space": [69, 95], "increas": [69, 95], "assertionerror": [69, 121], "03": 69, "stat": [72, 106, 107, 108, 109, 110], "abc": 74, "other_metr": 75, "postfix": 75, "userdict": 75, "collect": [75, 132, 133, 135], "want": 75, "behav": 75, "themselv": 75, "intern": 75, "similar": 75, "reduc": 75, "els": [75, 132, 133, 134, 135], "keep_bas": 75, "iter": 75, "underli": 75, "moduledict": 75, "hashabl": 75, "v": [75, 133], "correspond": [75, 125], "keyerror": [75, 121], "some": [75, 132, 135], "pair": 75, "present": 75, "lack": 75, "metric_a": 76, "metric_b": 76, "metric1": 76, "metric2": 76, "unari": 76, "appropri": [84, 85, 100, 132, 135], "375": [88, 90], "suniqu": 90, "45": [90, 105, 132, 135], "42857143": 90, "15": [103, 104, 105, 132, 133, 135], "57142857": 103, "sum": [105, 108, 109, 110, 133, 135], "_abstractscor": [107, 108, 109], "fp": [107, 108, 109], "tn": [107, 108, 109], "classwis": [108, 110], "over": [108, 109, 110, 131, 137], "labelwis": [109, 110], "prior": [110, 132, 133, 135], "modul": [111, 120, 121, 132, 135], "shift_typ": [112, 114], "shift_id": [112, 134], "induc": [112, 114], "synthet": [112, 114, 131, 135, 136, 137], "categor": [112, 132, 135], "origin": 112, "util": [112, 132, 133, 135], "load_nih": 112, "mnt": [112, 133, 134], "nihcxr": [112, 131, 133, 136], "hospital_type_1": 112, "hospital_type_2": 112, "hospital_type_3": 112, "hospital_type_4": 112, "hospital_type_5": 112, "ds_sourc": [112, 134], "ds_target": [112, 134], "num_proc": [112, 134], "build": 112, "hospit": [112, 132, 135, 137], "drift_detect": 114, "experiment": 114, "sklearn": [114, 132, 135], "load_diabet": 114, "y": [114, 116, 117, 119, 125, 132, 133, 135], "return_x_i": 114, "x_tr": 114, "x_te": 114, "y_tr": 114, "y_te": 114, "train_test_split": [114, 132, 135], "test_siz": 114, "random_st": [114, 132, 135], "42": [114, 132, 135], "gn_shift": 114, "x_shift": 114, "x_train": [114, 125], "noise_amt": [114, 118], "delta": [114, 115, 118, 119], "ko_shift": 114, "cp_shift": 114, "mfa_shift": 114, "bn_shift": 114, "tolerance_shift": 114, "ds_shift": 114, "nois": [114, 115, 118, 132, 133, 135], "prob": 115, "covari": [115, 116, 117, 118, 119], "proport": 115, "fraction": [115, 118, 119, 135], "affect": [115, 118, 121, 132, 135], "n_shuffl": [116, 117], "keep_rows_const": 116, "repermute_each_column": 116, "multiwai": 116, "associ": [116, 132, 133, 135], "swap": [116, 117], "individu": [116, 132, 135], "within": 116, "cl": [116, 117], "etc": [116, 117, 132, 133, 135], "floatnumpi": 116, "shuffl": [116, 117, 132], "permut": 116, "placehold": 116, "shift_class": [117, 119], "rank": 117, "changepoint": 117, "axi": [117, 133, 134, 135], "x_ref": 117, "y_ref": 117, "normal": [118, 132], "clip": 118, "gaussian": 118, "standard": [118, 132, 135], "deviat": 118, "divid": 118, "255": [118, 133, 134], "placehol": 119, "output_dir": [121, 132, 133, 135], "serv": 121, "interfac": 121, "popul": [121, 131, 132, 135, 137], "modelcard": 121, "directori": [121, 132, 135], "save": [121, 125, 132, 135], "output_filenam": 121, "template_path": 121, "interact": [121, 135], "save_json": 121, "date": [121, 132, 133, 135], "jinja2": 121, "json": [121, 132, 135], "classmethod": 121, "cyclops_report": [121, 132, 135], "section_nam": [121, 132, 133, 135], "model_detail": [121, 132, 135], "section": [121, 132, 133, 135], "repres": [121, 133], "bibtex": 121, "entri": 121, "plain": 121, "text": [121, 133], "descript": [121, 132, 133, 135], "license_id": [121, 132], "sensitive_featur": [121, 132], "sensitive_feature_justif": [121, 132], "log": [121, 131, 132, 135, 137], "inform": [121, 132], "about": [121, 132, 133, 135], "resourc": [121, 132, 135], "context": 121, "homepag": 121, "spdx": [121, 132], "identifi": [121, 131, 133, 137], "licens": [121, 132, 133, 135], "apach": [121, 132, 135], "unknown": 121, "unlicens": 121, "proprietari": 121, "justif": [121, 132], "field": [121, 131, 132, 135, 137], "descriptor": 121, "new": [121, 132, 135], "pydant": 121, "basemodel": 121, "subclass": 121, "As": 121, "long": 121, "conflict": 121, "defin": [121, 132, 133, 135], "model_card": [121, 132, 133, 135], "cylop": 121, "tradeoff": [121, 133], "trade": 121, "off": 121, "interpret": 121, "consider": [121, 132, 133, 135], "affected_group": [121, 132, 133, 135], "benefit": [121, 132, 133, 135], "harm": [121, 132, 133, 135], "mitigation_strategi": [121, 132, 133, 135], "assess": 121, "mitig": [121, 132, 133, 135], "strategi": [121, 132, 133, 135], "relat": 121, "img_path": 121, "caption": [121, 132, 133, 135], "full": 121, "whole": [121, 132, 135], "left": [121, 135], "blank": 121, "instead": 121, "param": [121, 132, 135], "contact": [121, 132, 133, 135], "role": 121, "owner": [121, 132, 133, 135], "quantit": [121, 132, 133, 135], "slash": 121, "fig": [121, 132, 133, 135], "plotli": [121, 132, 133, 135], "figur": [121, 132, 133, 135], "plot": [121, 132, 133, 135], "analysis_typ": 121, "metric_slic": [121, 132, 133, 135], "decision_threshold": 121, "pass_fail_threshold": [121, 132, 133, 135], "pass_fail_threshold_fn": [121, 132, 133, 135], "explain": [121, 132, 133, 135], "fail": 121, "regul": 121, "regulatori": [121, 132, 135], "compli": 121, "risk": [121, 132, 133, 135, 137], "kind": [121, 132, 133, 135], "primari": [121, 132, 133, 135], "scope": [121, 132, 135], "usecas": 121, "version_str": [121, 132, 135], "semant": 121, "v1": 121, "dt_date": 121, "dt_datetim": 121, "unix": 121, "timestamp": [121, 131, 133, 136], "yyyi": 121, "mm": 121, "dd": 121, "hh": 121, "ss": 121, "ffffff": 121, "z": 121, "summar": 121, "chang": [121, 132, 133, 135], "made": [121, 132, 135], "task_featur": [123, 125, 132, 135], "task_target": [123, 125, 132, 135], "atelectasi": [123, 133], "consolid": [123, 133], "infiltr": [123, 133], "pneumothorax": [123, 133], "edema": [123, 133], "emphysema": [123, 133], "fibrosi": [123, 133], "effus": [123, 133], "pneumonia": [123, 133], "pleural_thicken": [123, 133], "cardiomegali": [123, 133], "nodul": [123, 133], "mass": [123, 133, 135], "hernia": [123, 133], "lung": 123, "lesion": 123, "fractur": 123, "opac": 123, "enlarg": 123, "cardiomediastinum": 123, "basetask": [123, 125], "multi": [123, 133], "ptmodel": [123, 125, 133], "skmodel": [123, 125], "properti": [123, 125], "splits_map": [123, 125], "64": [123, 135], "compos": [123, 132, 133, 134, 135], "unnecessari": [123, 125], "pathologi": [123, 131, 132, 137], "represent": [123, 132, 135], "tabular": [125, 131], "fit": [125, 132, 135], "columntransform": [125, 132, 135], "slicingconfig": 125, "default_max_batch_s": 125, "filepath": 125, "pretrain": [125, 133], "proba": [125, 132, 135], "pd": 125, "datafram": [125, 132, 135], "notfittederror": 125, "destin": 125, "parent": [125, 132, 133, 135], "dirctori": 125, "best_model_param": [125, 132, 135], "y_train": 125, "seri": 125, "nonei": 125, "drift": [131, 136], "experi": [131, 136], "dimension": [131, 136], "reduct": [131, 136], "techniqu": [131, 136], "roll": [131, 136], "window": [131, 136], "biweekli": [131, 136], "kaggl": [131, 132], "heart": 131, "failur": 131, "constant": [131, 137], "distribut": [131, 133, 137], "outcom": [131, 137], "preprocessor": [131, 137], "creation": [131, 137], "synthea": [131, 135], "prolong": 131, "length": [131, 133], "stai": 131, "queri": [131, 137], "inspect": [131, 132, 137], "preprocess": [131, 132, 137], "drop": [131, 132, 137], "nan_threshold": [131, 132, 137], "gender": [131, 132, 133, 134, 137], "nih": [131, 133, 134], "diseas": [131, 132, 137], "balanc": [131, 132, 137], "w": [131, 137], "showcas": [132, 135, 137], "formul": [132, 135], "patient": [132, 133, 134, 135, 137], "o": [132, 133, 135], "shutil": [132, 133, 135], "pathlib": [132, 133, 135], "express": [132, 133, 135], "px": [132, 133, 135], "dateutil": [132, 133, 135], "relativedelta": [132, 133, 135], "kaggle_api_extend": 132, "kaggleapi": 132, "imput": [132, 135], "simpleimput": [132, 135], "pipelin": [132, 135], "minmaxscal": [132, 135], "onehotencod": [132, 135], "noqa": [132, 133, 135], "e402": [132, 133, 135], "catalog": [132, 135], "create_model": [132, 135], "tabularfeatur": [132, 135], "classificationplott": [132, 133, 135], "flatten_results_dict": [132, 135], "get_metrics_trend": [132, 133, 135], "join": [132, 133, 135], "load_datafram": 132, "home": [132, 133, 134, 135], "amritk": [132, 133, 134, 135], "cach": [132, 133, 134, 135], "pypoetri": [132, 133, 134, 135], "virtualenv": [132, 133, 134, 135], "wizuawxh": [132, 133, 134, 135], "py3": [132, 133, 134, 135], "lib": [132, 133, 134, 135], "site": [132, 133, 134, 135], "tqdm": [132, 133, 134, 135], "auto": [132, 133, 134, 135], "py": [132, 133, 134, 135], "21": [132, 133, 134, 135], "tqdmwarn": [132, 133, 134, 135], "iprogress": [132, 133, 134, 135], "ipywidget": [132, 133, 134, 135], "readthedoc": [132, 133, 134, 135], "io": [132, 133, 134, 135], "en": [132, 133, 134, 135], "user_instal": [132, 133, 134, 135], "autonotebook": [132, 133, 134, 135], "notebook_tqdm": [132, 133, 134, 135], "offer": [132, 133, 135], "document": [132, 133, 135], "through": [132, 133, 135], "technic": [132, 133, 135], "architectur": [132, 133, 135], "involv": [132, 133, 135], "intend": [132, 133, 135], "subpopul": [132, 133, 135], "explaina": [132, 133, 135], "go": [132, 133, 135], "tool": [132, 133, 135], "progress": [132, 133, 135], "subject": [132, 133, 135], "data_dir": [132, 133], "random_se": [132, 135], "train_siz": [132, 135], "sign": [132, 135], "com": [132, 133], "Then": 132, "profil": [132, 135], "usernam": 132, "trigger": 132, "download": 132, "credenti": 132, "place": 132, "locat": 132, "machin": [132, 133], "authent": 132, "dataset_download_fil": 132, "fedesoriano": 132, "unzip": 132, "df": 132, "csv": [132, 135], "file_format": 132, "reset_index": [132, 135], "index": [132, 133, 135], "2023": [132, 135], "26": [132, 135], "14": [132, 133, 135, 137], "16": [132, 134, 135], "171": 132, "chestpaintyp": 132, "restingbp": 132, "cholesterol": 132, "fastingb": 132, "restingecg": 132, "40": [132, 135], "ata": 132, "140": 132, "289": 132, "49": [132, 135], "nap": 132, "160": 132, "180": 132, "37": [132, 135], "130": 132, "283": 132, "st": 132, "48": [132, 135], "asi": 132, "138": 132, "214": 132, "54": [132, 135], "150": 132, "195": 132, "913": 132, "ta": 132, "110": 132, "264": 132, "914": 132, "68": 132, "144": 132, "193": 132, "915": 132, "57": [132, 133], "131": 132, "916": 132, "236": 132, "lvh": 132, "917": 132, "38": [132, 135], "175": 132, "maxhr": 132, "exerciseangina": 132, "oldpeak": 132, "st_slope": 132, "heartdiseas": 132, "172": 132, "156": 132, "flat": 132, "98": 132, "108": 132, "122": 132, "132": 132, "141": 132, "115": 132, "174": 132, "173": 132, "918": 132, "13": [132, 133, 135], "pie": [132, 133, 135], "update_layout": [132, 133, 135], "histogram": [132, 133, 135], "xaxis_titl": [132, 133, 135], "yaxis_titl": [132, 133, 135], "bargap": [132, 133, 135], "astyp": [132, 135], "11": [132, 133, 135, 137], "update_trac": [132, 133, 135], "textinfo": [132, 135], "percent": [132, 135], "title_text": [132, 135], "hovertempl": [132, 135], "br": [132, 135], "class_count": [132, 135], "value_count": [132, 135], "class_ratio": [132, 135], "8070866141732284": 132, "39": [132, 133, 134, 135], "20": [132, 133, 135], "wa": [132, 133, 135], "li": 132, "et": 132, "al": 132, "most": 132, "features_list": [132, 135], "sort": [132, 135], "help": [132, 133, 135], "essenti": [132, 135], "step": [132, 135], "understand": [132, 135], "u": [132, 135], "tab_featur": [132, 135], "ordin": 132, "might": [132, 135], "17": [132, 135], "numeric_transform": [132, 135], "scaler": [132, 135], "binary_transform": [132, 135], "most_frequ": [132, 135], "18": [132, 134, 135], "numeric_featur": [132, 135], "features_by_typ": [132, 135], "numeric_indic": [132, 135], "get_loc": [132, 135], "19": [132, 133, 135], "binary_featur": [132, 135], "ordinal_featur": 132, "binary_indic": [132, 135], "ordinal_indic": 132, "num": [132, 135], "onehot": [132, 135], "handle_unknown": [132, 135], "ignor": [132, 135], "remaind": [132, 135], "passthrough": [132, 135], "let": [132, 135], "done": [132, 135], "independ": 132, "everi": 132, "uci": 132, "archiv": 132, "ic": 132, "edu": 132, "databas": [132, 135], "cleandoc": 132, "misc": 132, "cc0": 132, "demograph": [132, 133], "often": 132, "strong": 132, "correl": 132, "older": [132, 135], "higher": 132, "panda": [132, 135], "power": [132, 135], "easi": [132, 135], "compat": [132, 135], "22": [132, 135], "from_panda": [132, 135], "cleanup_cache_fil": [132, 135], "num_row": 132, "cast_column": [132, 135], "stratify_by_column": [132, 135], "seed": [132, 135], "100": [132, 133, 134, 135], "lt": [132, 133, 134, 135], "175872": 132, "straightforward": [132, 135], "maintain": [132, 135], "instanti": [132, 135], "line": [132, 133, 135], "sgd": [132, 135], "logisit": [132, 135], "regress": [132, 135], "sgdclassif": [132, 135], "24": [132, 134, 135], "sgd_classifi": 132, "123": [132, 135], "verbos": [132, 135], "class_weight": 132, "mortalitypredict": [132, 135], "encapsul": [132, 135], "cohes": [132, 135], "structur": [132, 135], "smooth": [132, 135], "manag": [132, 135], "mortality_task": 132, "best": [132, 135], "hyperparamet": [132, 135], "search": [132, 135], "grid": [132, 135], "27": [132, 135], "alpha": 132, "0001": 132, "001": 132, "learning_r": [132, 135], "invscal": 132, "adapt": 132, "eta0": 132, "roc_auc": 132, "294": 132, "wrapper": [132, 133, 135], "sk_model": [132, 135], "295": 132, "296": 132, "sgdclassifi": 132, "x27": [132, 135], "early_stop": 132, "loss": 132, "log_loss": 132, "rerun": [132, 135], "cell": [132, 135], "trust": [132, 135], "On": [132, 133, 135], "github": [132, 133, 135], "unabl": [132, 135], "render": [132, 135], "try": [132, 135], "page": [132, 135], "nbviewer": [132, 135], "sgdclassifiersgdclassifi": 132, "28": [132, 135], "model_param": [132, 135], "epsilon": 132, "fit_intercept": 132, "l1_ratio": 132, "max_it": 132, "n_iter_no_chang": 132, "n_job": [132, 135], "penalti": 132, "l2": 132, "power_t": 132, "tol": 132, "validation_fract": 132, "warm_start": 132, "29": [132, 135], "30": [132, 133, 135, 137], "y_pred": [132, 135], "only_predict": [132, 135], "len": [132, 133, 135], "184": 132, "7588": 132, "variou": [132, 135], "perspect": [132, 135], "metric_collect": [132, 135], "how": [132, 135], "certain": [132, 135], "70": 132, "33": [132, 135], "fnr": [132, 133, 135], "ber": [132, 135], "fairness_metric_collect": [132, 135], "34": [132, 135], "dataset_with_pr": [132, 135], "7234": 132, "8686": 132, "76358": 132, "gt": [132, 133, 135], "18891": 132, "87": 132, "21256": 132, "11325": 132, "97": [132, 133, 135], "12048": 132, "46": [132, 135], "9979": 132, "20261": 132, "right": [132, 135], "36": [132, 135], "results_flat": [132, 133, 135], "remove_metr": [132, 135], "796875": 132, "8260869565217391": 132, "6785714285714286": 132, "7450980392156863": 132, "8819444444444444": 132, "8623853211009175": 132, "8676470588235294": 132, "9076923076923077": 132, "8872180451127819": 132, "927972027972028": 132, "842391304347826": 132, "8686868686868687": 132, "8431372549019608": 132, "8557213930348259": 132, "9152319464371114": 132, "plw2901": [132, 133, 135], "lambda": [132, 133, 134, 135], "plotter": [132, 133, 134, 135], "class_nam": [132, 135], "set_templ": [132, 133, 135], "plotly_whit": [132, 133, 135], "extract": [132, 135], "slice_result": [132, 133, 135], "dict_kei": [132, 135], "roc_plot": [132, 135], "roc_curve_comparison": [132, 135], "femal": [132, 133, 134, 135], "41": [132, 135], "overall_perform": [132, 135], "metric_valu": [132, 135], "overall_performance_plot": [132, 135], "metrics_valu": [132, 135], "43": [132, 135], "slice_metr": [132, 135], "44": [132, 135], "slice_metrics_plot": [132, 135], "metrics_comparison_bar": [132, 135], "comparison": [132, 135], "reform": [132, 135], "fairness_result": [132, 135], "deepcopi": [132, 135], "fairness_metr": [132, 135], "group_siz": [132, 135], "fairness_plot": [132, 135], "metrics_comparison_scatt": [132, 135], "leverag": [132, 133, 135], "histor": [132, 133, 135], "gather": [132, 133, 135], "merg": [132, 133, 135], "recent": [132, 133, 135], "wish": [132, 133, 135], "metrics_trend": [132, 133, 135], "integr": [132, 133, 135], "purpos": [132, 133, 135], "three": [132, 133, 135], "dummi": [132, 133, 135], "demonstr": [132, 133, 135, 137], "trend": [132, 133, 135], "47": [132, 133, 135], "dummy_report_num": [132, 133, 135], "dummy_report_dir": [132, 133, 135], "getcwd": [132, 133, 135], "dummy_report": [132, 133, 135], "simul": [132, 133, 135], "random": [132, 133, 135], "uniform": [132, 133, 135], "dummy_result": [132, 133, 135], "max": [132, 133, 135], "renam": [132, 133, 135], "folder": [132, 133, 135], "dummy_report_path": [132, 133, 135], "date_dir": [132, 133, 135], "dummy_d": [132, 133, 135], "todai": [132, 133, 135], "new_dir": [132, 133, 135], "rmtree": [132, 133, 135], "previou": [132, 133, 135], "report_directori": [132, 133, 135], "flat_result": [132, 133, 135], "trends_plot": [132, 133, 135], "audienc": [132, 135], "organ": [132, 135], "store": [132, 135], "regulatory_requir": [132, 135], "releas": [132, 135], "team": [132, 135], "vectorinstitut": [132, 135], "linear_model": 132, "e501": [132, 135], "next": [132, 135], "use_cas": [132, 135], "These": [132, 135], "fairness_assess": [132, 135], "well": [132, 133, 135], "taken": [132, 135], "ethical_consider": [132, 135], "51": [132, 134, 135], "clinician": [132, 135], "engin": [132, 135], "condit": 132, "improv": [132, 135], "bias": [132, 133, 135], "lead": [132, 135], "wors": [132, 135], "retrain": [132, 135], "below": [132, 135], "By": [132, 135], "52": [132, 135], "report_path": [132, 133, 135], "view": [132, 133, 135, 137], "torchxrayvis": [133, 134], "functool": 133, "graph_object": [133, 135], "lambdad": [133, 134], "resiz": [133, 134], "densenet": [133, 134], "loader": [133, 134], "load_nihcxr": [133, 134], "apply_transform": 133, "get_devic": 133, "devic": 133, "clinical_dataset": [133, 134], "nih_d": [133, 134], "4000": 133, "spatial_s": [133, 134], "224": [133, 134], "allow_missing_kei": [133, 134], "func": [133, 134], "1024": [133, 134], "newaxi": [133, 134], "densenet121": [133, 134], "res224": [133, 134], "No": 133, "adjust": 133, "285885": 133, "93": 133, "2511": 133, "3567": 133, "int64": 133, "originalimag": 133, "width": [133, 135], "height": [133, 135], "originalimagepixelspac": 133, "unnam": 133, "float32": 133, "__index_level_0__": 133, "arang": 133, "nih_eval_results_gend": 133, "append": [133, 135], "scatter": 133, "mode": 133, "marker": 133, "perf_metric_gend": 133, "title_x": 133, "title_font_s": 133, "768": 133, "selector": 133, "55548": 133, "57209": 133, "57798": 133, "65": [133, 134], "nih_eval_results_ag": 133, "perf_metric_ag": 133, "56494": 133, "57448": 133, "07": [133, 134], "57569": 133, "91": 133, "57849": 133, "male": [133, 134], "showlegend": 133, "bar": [133, 135], "balanced_error_r": 133, "nih_fairness_result_ag": 133, "balancederrorr": 133, "fairness_ag": 133, "56813": 133, "09": [133, 134], "56801": 133, "58156": 133, "fairness_age_par": 133, "slice_": 133, "itr": 133, "enumer": 133, "dummy_reports_cxr": 133, "112": [133, 137], "120": [133, 137], "frontal": [133, 137], "805": [133, 137], "fourteen": 133, "mine": 133, "radiolog": 133, "pleural": 133, "thicken": 133, "80": [133, 135], "remain": 133, "arxiv": 133, "ab": 133, "2111": 133, "00595": 133, "inproceed": 133, "cohen2022xrv": 133, "cohen": 133, "joseph": 133, "paul": 133, "viviano": 133, "bertin": 133, "morrison": 133, "torabian": 133, "parsa": 133, "guarrera": 133, "matteo": 133, "lungren": 133, "matthew": 133, "chaudhari": 133, "akshai": 133, "brook": 133, "rupert": 133, "hashir": 133, "mohammad": 133, "bertrand": 133, "hadrien": 133, "booktitl": 133, "deep": 133, "mlmed": 133, "arxivid": 133, "cohen2020limit": 133, "cross": 133, "domain": 133, "autom": [133, 135], "2002": 133, "02497": 133, "medicin": 133, "lab": 133, "radiologist": 133, "scientist": 133, "inabl": 133, "addition": 133, "poor": 133, "qualiti": 133, "artifact": 133, "geograph": 133, "region": 133, "ethic": 133, "ensur": 133, "divers": 133, "regularli": 133, "human": 133, "expertis": 133, "address": 133, "rare": 133, "qualit": 133, "detector": 134, "reductor": 134, "tstester": 134, "plot_drift_experi": 134, "plot_drift_timeseri": 134, "shifter": 134, "source_d": 134, "target_d": 134, "25596": 134, "62456": 134, "dr_method": 134, "bbse": 134, "soft": 134, "txrv": 134, "ae": 134, "sensitivity_test": 134, "tester": 134, "tester_method": 134, "source_sample_s": 134, "target_sample_s": 134, "num_run": 134, "detect_shift": 134, "chexpert": 134, "chex": 134, "padchest": 134, "pc": 134, "source_slic": 134, "target_slic": 134, "51837": 134, "51321": 134, "50814": 134, "46828": 134, "48835": 134, "49544": 134, "48892": 134, "49769": 134, "rolling_window_drift": 134, "timestamp_column": 134, "window_s": 134, "4w": 134, "longer": 135, "v3": 135, "instruct": 135, "etl": 135, "postgr": 135, "cycqueri": 135, "op": 135, "qo": 135, "datasetqueri": 135, "num_dai": 135, "querier": 135, "dbm": 135, "postgresql": 135, "port": 135, "5432": 135, "host": 135, "localhost": 135, "synthea_demo": 135, "password": 135, "pwd": 135, "def": 135, "get_encount": 135, "nativ": 135, "sequenti": 135, "patient_id": 135, "birthdat": 135, "race": 135, "ethnic": 135, "patient_encount": 135, "isout": 135, "encounter_id": 135, "extracttimestampcompon": 135, "start_year": 135, "birthdate_year": 135, "addcolumn": 135, "new_col_label": 135, "stop": 135, "lo": 135, "conditiongreaterthan": 135, "conditionlessthan": 135, "get_observ": 135, "cohort": 135, "conditionin": 135, "categori": 135, "vital": 135, "conditionequ": 135, "groupby_op": 135, "groupbyaggreg": 135, "n_ob": 135, "observations_count": 135, "observations_stat": 135, "pivot_t": 135, "aggfunc": 135, "add_prefix": 135, "obs_": 135, "get_med": 135, "n_med": 135, "get_procedur": 135, "procedur": [135, 137], "n_procedur": 135, "run_queri": 135, "cohort_queri": 135, "to_merg": 135, "extend": 135, "to_merge_df": 135, "067": 135, "orm": 135, "readi": 135, "040": 135, "successfulli": 135, "041": 135, "finish": 135, "execut": 135, "345333": 135, "858": 135, "859": 135, "816837": 135, "153": 135, "154": 135, "117480": 135, "323": 135, "324": 135, "162305": 135, "405": 135, "406": 135, "081821": 135, "list_column": 135, "payer": 135, "encounterclass": 135, "base_encounter_cost": 135, "total_claim_cost": 135, "payer_coverag": 135, "reasoncod": 135, "reasondescript": 135, "null_count": 135, "isnul": 135, "600": 135, "respect": 135, "larger": 135, "thresh_nan": 135, "dropna": 135, "thresh": 135, "length_of_stai": 135, "length_of_stay_count": 135, "length_of_stay_kei": 135, "5573997233748271": 135, "obs_alanin": 135, "aminotransferas": 135, "enzymat": 135, "volum": 135, "serum": 135, "plasma": 135, "obs_albumin": 135, "obs_alkalin": 135, "phosphatas": 135, "obs_aspart": 135, "obs_bilirubin": 135, "obs_bodi": 135, "obs_calcium": 135, "obs_carbon": 135, "dioxid": 135, "mole": 135, "obs_chlorid": 135, "obs_creatinin": 135, "obs_diastol": 135, "blood": 135, "pressur": 135, "obs_erythrocyt": 135, "obs_ferritin": 135, "obs_glomerular": 135, "filtrat": 135, "73": 135, "sq": 135, "obs_glucos": 135, "obs_hematocrit": 135, "obs_hemoglobin": 135, "obs_leukocyt": 135, "obs_mch": 135, "entit": 135, "obs_mchc": 135, "obs_mcv": 135, "obs_oxygen": 135, "satur": 135, "arteri": 135, "obs_platelet": 135, "obs_potassium": 135, "obs_protein": 135, "obs_sodium": 135, "obs_systol": 135, "obs_troponin": 135, "cardiac": 135, "obs_urea": 135, "nitrogen": 135, "1126": 135, "170221": 135, "sllearn": 135, "xgb_classifi": 135, "los_task": 135, "n_estim": 135, "250": 135, "500": 135, "max_depth": 135, "reg_lambda": 135, "colsample_bytre": 135, "gamma": 135, "58": 135, "072": 135, "073": 135, "074": 135, "xgbclassifi": 135, "base_scor": 135, "booster": 135, "callback": 135, "colsample_bylevel": 135, "colsample_bynod": 135, "early_stopping_round": 135, "enable_categor": 135, "eval_metr": 135, "logloss": 135, "feature_typ": 135, "gpu_id": 135, "grow_polici": 135, "importance_typ": 135, "interaction_constraint": 135, "max_bin": 135, "max_cat_threshold": 135, "max_cat_to_onehot": 135, "max_delta_step": 135, "max_leav": 135, "min_child_weight": 135, "miss": 135, "monotone_constraint": 135, "num_parallel_tre": 135, "predictor": 135, "xgbclassifierxgbclassifi": 135, "logist": 135, "use_label_encod": 135, "reg_alpha": 135, "sampling_method": 135, "scale_pos_weight": 135, "subsampl": 135, "tree_method": 135, "validate_paramet": 135, "226": 135, "4130": 135, "3977": 135, "3689": 135, "78": 135, "54786": 135, "8076": 135, "76": 135, "8701": 135, "8181": 135, "8197": 135, "8672": 135, "amp": 135, "8287": 135, "62": 135, "5032": 135, "8470": 135, "8341": 135, "66": 135, "8582": 135, "7892": 135, "96": 135, "859504132231405": 135, "8648648648648649": 135, "9014084507042254": 135, "8827586206896552": 135, "9529577464788732": 135, "8823529411764706": 135, "9583333333333334": 135, "8214285714285714": 135, "8846153846153846": 135, "967391304347826": 135, "8870967741935484": 135, "9036144578313253": 135, "9259259259259259": 135, "9146341463414634": 135, "9615274188917599": 135, "9193548387096774": 135, "890625": 135, "9047619047619048": 135, "9467516447368421": 135, "8849557522123894": 135, "9103448275862069": 135, "9568752660706684": 135, "xgboost": 135, "python_api": 135, "53": 135, "statist": 136, "goal": 137}, "objects": {"cyclops": [[126, 0, 0, "-", "data"], [127, 0, 0, "-", "evaluate"], [128, 0, 0, "-", "monitor"], [129, 0, 0, "-", "report"], [130, 0, 0, "-", "tasks"]], "cyclops.data": [[126, 0, 0, "-", "features"], [6, 0, 0, "-", "slicer"]], "cyclops.data.features": [[4, 0, 0, "-", "medical_image"]], "cyclops.data.features.medical_image": [[5, 1, 1, "", "MedicalImage"]], "cyclops.data.features.medical_image.MedicalImage": [[5, 2, 1, "", "__call__"], [5, 2, 1, "", "cast_storage"], [5, 2, 1, "", "decode_example"], [5, 2, 1, "", "embed_storage"], [5, 2, 1, "", "encode_example"], [5, 2, 1, "", "flatten"]], "cyclops.data.slicer": [[7, 1, 1, "", "SliceSpec"], [8, 4, 1, "", "compound_filter"], [9, 4, 1, "", "filter_datetime"], [10, 4, 1, "", "filter_non_null"], [11, 4, 1, "", "filter_range"], [12, 4, 1, "", "filter_string_contains"], [13, 4, 1, "", "filter_value"], [14, 4, 1, "", "is_datetime"], [15, 4, 1, "", "overall"]], "cyclops.data.slicer.SliceSpec": [[7, 3, 1, "", "_registry"], [7, 2, 1, "", "add_slice_spec"], [7, 3, 1, "", "column_names"], [7, 2, 1, "", "get_slices"], [7, 3, 1, "", "include_overall"], [7, 2, 1, "", "slices"], [7, 3, 1, "", "spec_list"], [7, 3, 1, "", "validate"]], "cyclops.evaluate": [[16, 0, 0, "-", "evaluator"], [127, 0, 0, "-", "fairness"], [127, 0, 0, "-", "metrics"]], "cyclops.evaluate.evaluator": [[17, 4, 1, "", "evaluate"]], "cyclops.evaluate.fairness": [[18, 0, 0, "-", "config"], [20, 0, 0, "-", "evaluator"]], "cyclops.evaluate.fairness.config": [[19, 1, 1, "", "FairnessConfig"]], "cyclops.evaluate.fairness.evaluator": [[21, 4, 1, "", "evaluate_fairness"], [22, 4, 1, "", "warn_too_many_unique_values"]], "cyclops.evaluate.metrics": [[23, 0, 0, "-", "accuracy"], [28, 0, 0, "-", "auroc"], [33, 0, 0, "-", "f_beta"], [42, 0, 0, "-", "factory"], [127, 0, 0, "-", "functional"], [73, 0, 0, "-", "metric"], [77, 0, 0, "-", "precision_recall"], [86, 0, 0, "-", "precision_recall_curve"], [91, 0, 0, "-", "roc"], [96, 0, 0, "-", "sensitivity"], [101, 0, 0, "-", "specificity"], [106, 0, 0, "-", "stat_scores"]], "cyclops.evaluate.metrics.accuracy": [[24, 1, 1, "", "Accuracy"], [25, 1, 1, "", "BinaryAccuracy"], [26, 1, 1, "", "MulticlassAccuracy"], [27, 1, 1, "", "MultilabelAccuracy"]], "cyclops.evaluate.metrics.accuracy.Accuracy": [[24, 2, 1, "", "__add__"], [24, 2, 1, "", "__call__"], [24, 2, 1, "", "__init__"], [24, 2, 1, "", "__mul__"], [24, 2, 1, "", "add_state"], [24, 2, 1, "", "clone"], [24, 2, 1, "", "compute"], [24, 2, 1, "", "reset_state"], [24, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.accuracy.BinaryAccuracy": [[25, 2, 1, "", "__add__"], [25, 2, 1, "", "__call__"], [25, 2, 1, "", "__init__"], [25, 2, 1, "", "__mul__"], [25, 2, 1, "", "add_state"], [25, 2, 1, "", "clone"], [25, 2, 1, "", "compute"], [25, 2, 1, "", "reset_state"], [25, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy": [[26, 2, 1, "", "__add__"], [26, 2, 1, "", "__call__"], [26, 2, 1, "", "__init__"], [26, 2, 1, "", "__mul__"], [26, 2, 1, "", "add_state"], [26, 2, 1, "", "clone"], [26, 2, 1, "", "compute"], [26, 2, 1, "", "reset_state"], [26, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy": [[27, 2, 1, "", "__add__"], [27, 2, 1, "", "__call__"], [27, 2, 1, "", "__init__"], [27, 2, 1, "", "__mul__"], [27, 2, 1, "", "add_state"], [27, 2, 1, "", "clone"], [27, 2, 1, "", "compute"], [27, 2, 1, "", "reset_state"], [27, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc": [[29, 1, 1, "", "AUROC"], [30, 1, 1, "", "BinaryAUROC"], [31, 1, 1, "", "MulticlassAUROC"], [32, 1, 1, "", "MultilabelAUROC"]], "cyclops.evaluate.metrics.auroc.AUROC": [[29, 2, 1, "", "__add__"], [29, 2, 1, "", "__call__"], [29, 2, 1, "", "__init__"], [29, 2, 1, "", "__mul__"], [29, 2, 1, "", "add_state"], [29, 2, 1, "", "clone"], [29, 2, 1, "", "compute"], [29, 2, 1, "", "reset_state"], [29, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc.BinaryAUROC": [[30, 2, 1, "", "__add__"], [30, 2, 1, "", "__call__"], [30, 2, 1, "", "__init__"], [30, 2, 1, "", "__mul__"], [30, 2, 1, "", "add_state"], [30, 2, 1, "", "clone"], [30, 2, 1, "", "compute"], [30, 2, 1, "", "reset_state"], [30, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc.MulticlassAUROC": [[31, 2, 1, "", "__add__"], [31, 2, 1, "", "__call__"], [31, 2, 1, "", "__init__"], [31, 2, 1, "", "__mul__"], [31, 2, 1, "", "add_state"], [31, 2, 1, "", "clone"], [31, 2, 1, "", "compute"], [31, 2, 1, "", "reset_state"], [31, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc.MultilabelAUROC": [[32, 2, 1, "", "__add__"], [32, 2, 1, "", "__call__"], [32, 2, 1, "", "__init__"], [32, 2, 1, "", "__mul__"], [32, 2, 1, "", "add_state"], [32, 2, 1, "", "clone"], [32, 2, 1, "", "compute"], [32, 2, 1, "", "reset_state"], [32, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta": [[34, 1, 1, "", "BinaryF1Score"], [35, 1, 1, "", "BinaryFbetaScore"], [36, 1, 1, "", "F1Score"], [37, 1, 1, "", "FbetaScore"], [38, 1, 1, "", "MulticlassF1Score"], [39, 1, 1, "", "MulticlassFbetaScore"], [40, 1, 1, "", "MultilabelF1Score"], [41, 1, 1, "", "MultilabelFbetaScore"]], "cyclops.evaluate.metrics.f_beta.BinaryF1Score": [[34, 2, 1, "", "__add__"], [34, 2, 1, "", "__call__"], [34, 2, 1, "", "__init__"], [34, 2, 1, "", "__mul__"], [34, 2, 1, "", "add_state"], [34, 2, 1, "", "clone"], [34, 2, 1, "", "compute"], [34, 2, 1, "", "reset_state"], [34, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore": [[35, 2, 1, "", "__add__"], [35, 2, 1, "", "__call__"], [35, 2, 1, "", "__init__"], [35, 2, 1, "", "__mul__"], [35, 2, 1, "", "add_state"], [35, 2, 1, "", "clone"], [35, 2, 1, "", "compute"], [35, 2, 1, "", "reset_state"], [35, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.F1Score": [[36, 2, 1, "", "__add__"], [36, 2, 1, "", "__call__"], [36, 2, 1, "", "__init__"], [36, 2, 1, "", "__mul__"], [36, 2, 1, "", "add_state"], [36, 2, 1, "", "clone"], [36, 2, 1, "", "compute"], [36, 2, 1, "", "reset_state"], [36, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.FbetaScore": [[37, 2, 1, "", "__add__"], [37, 2, 1, "", "__call__"], [37, 2, 1, "", "__init__"], [37, 2, 1, "", "__mul__"], [37, 2, 1, "", "add_state"], [37, 2, 1, "", "clone"], [37, 2, 1, "", "compute"], [37, 2, 1, "", "reset_state"], [37, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MulticlassF1Score": [[38, 2, 1, "", "__add__"], [38, 2, 1, "", "__call__"], [38, 2, 1, "", "__init__"], [38, 2, 1, "", "__mul__"], [38, 2, 1, "", "add_state"], [38, 2, 1, "", "clone"], [38, 2, 1, "", "compute"], [38, 2, 1, "", "reset_state"], [38, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore": [[39, 2, 1, "", "__add__"], [39, 2, 1, "", "__call__"], [39, 2, 1, "", "__init__"], [39, 2, 1, "", "__mul__"], [39, 2, 1, "", "add_state"], [39, 2, 1, "", "clone"], [39, 2, 1, "", "compute"], [39, 2, 1, "", "reset_state"], [39, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MultilabelF1Score": [[40, 2, 1, "", "__add__"], [40, 2, 1, "", "__call__"], [40, 2, 1, "", "__init__"], [40, 2, 1, "", "__mul__"], [40, 2, 1, "", "add_state"], [40, 2, 1, "", "clone"], [40, 2, 1, "", "compute"], [40, 2, 1, "", "reset_state"], [40, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore": [[41, 2, 1, "", "__add__"], [41, 2, 1, "", "__call__"], [41, 2, 1, "", "__init__"], [41, 2, 1, "", "__mul__"], [41, 2, 1, "", "add_state"], [41, 2, 1, "", "clone"], [41, 2, 1, "", "compute"], [41, 2, 1, "", "reset_state"], [41, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.factory": [[43, 4, 1, "", "create_metric"]], "cyclops.evaluate.metrics.functional": [[44, 0, 0, "-", "accuracy"], [45, 0, 0, "-", "auroc"], [46, 0, 0, "-", "f_beta"], [55, 0, 0, "-", "precision_recall"], [64, 0, 0, "-", "precision_recall_curve"], [65, 0, 0, "-", "roc"], [70, 0, 0, "-", "sensitivity"], [71, 0, 0, "-", "specificity"], [72, 0, 0, "-", "stat_scores"]], "cyclops.evaluate.metrics.functional.f_beta": [[47, 4, 1, "", "binary_f1_score"], [48, 4, 1, "", "binary_fbeta_score"], [49, 4, 1, "", "f1_score"], [50, 4, 1, "", "fbeta_score"], [51, 4, 1, "", "multiclass_f1_score"], [52, 4, 1, "", "multiclass_fbeta_score"], [53, 4, 1, "", "multilabel_f1_score"], [54, 4, 1, "", "multilabel_fbeta_score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[56, 4, 1, "", "binary_precision"], [57, 4, 1, "", "binary_recall"], [58, 4, 1, "", "multiclass_precision"], [59, 4, 1, "", "multiclass_recall"], [60, 4, 1, "", "multilabel_precision"], [61, 4, 1, "", "multilabel_recall"], [62, 4, 1, "", "precision"], [63, 4, 1, "", "recall"]], "cyclops.evaluate.metrics.functional.roc": [[66, 4, 1, "", "binary_roc_curve"], [67, 4, 1, "", "multiclass_roc_curve"], [68, 4, 1, "", "multilabel_roc_curve"], [69, 4, 1, "", "roc_curve"]], "cyclops.evaluate.metrics.metric": [[74, 1, 1, "", "Metric"], [75, 1, 1, "", "MetricCollection"], [76, 1, 1, "", "OperatorMetric"]], "cyclops.evaluate.metrics.metric.Metric": [[74, 2, 1, "", "__add__"], [74, 2, 1, "", "__call__"], [74, 2, 1, "", "__init__"], [74, 2, 1, "", "__mul__"], [74, 2, 1, "", "add_state"], [74, 2, 1, "", "clone"], [74, 2, 1, "", "compute"], [74, 2, 1, "", "reset_state"], [74, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.metric.MetricCollection": [[75, 2, 1, "", "__call__"], [75, 2, 1, "", "__init__"], [75, 2, 1, "", "add_metrics"], [75, 2, 1, "", "clear"], [75, 2, 1, "", "clone"], [75, 2, 1, "", "compute"], [75, 2, 1, "", "get"], [75, 2, 1, "", "items"], [75, 2, 1, "", "keys"], [75, 2, 1, "", "pop"], [75, 2, 1, "", "popitem"], [75, 2, 1, "", "reset_state"], [75, 2, 1, "", "setdefault"], [75, 2, 1, "", "update"], [75, 2, 1, "", "update_state"], [75, 2, 1, "", "values"]], "cyclops.evaluate.metrics.metric.OperatorMetric": [[76, 2, 1, "", "__add__"], [76, 2, 1, "", "__call__"], [76, 2, 1, "", "__init__"], [76, 2, 1, "", "__mul__"], [76, 2, 1, "", "add_state"], [76, 2, 1, "", "clone"], [76, 2, 1, "", "compute"], [76, 2, 1, "", "reset_state"], [76, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall": [[78, 1, 1, "", "BinaryPrecision"], [79, 1, 1, "", "BinaryRecall"], [80, 1, 1, "", "MulticlassPrecision"], [81, 1, 1, "", "MulticlassRecall"], [82, 1, 1, "", "MultilabelPrecision"], [83, 1, 1, "", "MultilabelRecall"], [84, 1, 1, "", "Precision"], [85, 1, 1, "", "Recall"]], "cyclops.evaluate.metrics.precision_recall.BinaryPrecision": [[78, 2, 1, "", "__add__"], [78, 2, 1, "", "__call__"], [78, 2, 1, "", "__init__"], [78, 2, 1, "", "__mul__"], [78, 2, 1, "", "add_state"], [78, 2, 1, "", "clone"], [78, 2, 1, "", "compute"], [78, 2, 1, "", "reset_state"], [78, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.BinaryRecall": [[79, 2, 1, "", "__add__"], [79, 2, 1, "", "__call__"], [79, 2, 1, "", "__init__"], [79, 2, 1, "", "__mul__"], [79, 2, 1, "", "add_state"], [79, 2, 1, "", "clone"], [79, 2, 1, "", "compute"], [79, 2, 1, "", "reset_state"], [79, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision": [[80, 2, 1, "", "__add__"], [80, 2, 1, "", "__call__"], [80, 2, 1, "", "__init__"], [80, 2, 1, "", "__mul__"], [80, 2, 1, "", "add_state"], [80, 2, 1, "", "clone"], [80, 2, 1, "", "compute"], [80, 2, 1, "", "reset_state"], [80, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MulticlassRecall": [[81, 2, 1, "", "__add__"], [81, 2, 1, "", "__call__"], [81, 2, 1, "", "__init__"], [81, 2, 1, "", "__mul__"], [81, 2, 1, "", "add_state"], [81, 2, 1, "", "clone"], [81, 2, 1, "", "compute"], [81, 2, 1, "", "reset_state"], [81, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision": [[82, 2, 1, "", "__add__"], [82, 2, 1, "", "__call__"], [82, 2, 1, "", "__init__"], [82, 2, 1, "", "__mul__"], [82, 2, 1, "", "add_state"], [82, 2, 1, "", "clone"], [82, 2, 1, "", "compute"], [82, 2, 1, "", "reset_state"], [82, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MultilabelRecall": [[83, 2, 1, "", "__add__"], [83, 2, 1, "", "__call__"], [83, 2, 1, "", "__init__"], [83, 2, 1, "", "__mul__"], [83, 2, 1, "", "add_state"], [83, 2, 1, "", "clone"], [83, 2, 1, "", "compute"], [83, 2, 1, "", "reset_state"], [83, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.Precision": [[84, 2, 1, "", "__add__"], [84, 2, 1, "", "__call__"], [84, 2, 1, "", "__init__"], [84, 2, 1, "", "__mul__"], [84, 2, 1, "", "add_state"], [84, 2, 1, "", "clone"], [84, 2, 1, "", "compute"], [84, 2, 1, "", "reset_state"], [84, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.Recall": [[85, 2, 1, "", "__add__"], [85, 2, 1, "", "__call__"], [85, 2, 1, "", "__init__"], [85, 2, 1, "", "__mul__"], [85, 2, 1, "", "add_state"], [85, 2, 1, "", "clone"], [85, 2, 1, "", "compute"], [85, 2, 1, "", "reset_state"], [85, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve": [[87, 1, 1, "", "BinaryPrecisionRecallCurve"], [88, 1, 1, "", "MulticlassPrecisionRecallCurve"], [89, 1, 1, "", "MultilabelPrecisionRecallCurve"], [90, 1, 1, "", "PrecisionRecallCurve"]], "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve": [[87, 2, 1, "", "__add__"], [87, 2, 1, "", "__call__"], [87, 2, 1, "", "__init__"], [87, 2, 1, "", "__mul__"], [87, 2, 1, "", "add_state"], [87, 2, 1, "", "clone"], [87, 2, 1, "", "compute"], [87, 2, 1, "", "reset_state"], [87, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve": [[88, 2, 1, "", "__add__"], [88, 2, 1, "", "__call__"], [88, 2, 1, "", "__init__"], [88, 2, 1, "", "__mul__"], [88, 2, 1, "", "add_state"], [88, 2, 1, "", "clone"], [88, 2, 1, "", "compute"], [88, 2, 1, "", "reset_state"], [88, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve": [[89, 2, 1, "", "__add__"], [89, 2, 1, "", "__call__"], [89, 2, 1, "", "__init__"], [89, 2, 1, "", "__mul__"], [89, 2, 1, "", "add_state"], [89, 2, 1, "", "clone"], [89, 2, 1, "", "compute"], [89, 2, 1, "", "reset_state"], [89, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve": [[90, 2, 1, "", "__add__"], [90, 2, 1, "", "__call__"], [90, 2, 1, "", "__init__"], [90, 2, 1, "", "__mul__"], [90, 2, 1, "", "add_state"], [90, 2, 1, "", "clone"], [90, 2, 1, "", "compute"], [90, 2, 1, "", "reset_state"], [90, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc": [[92, 1, 1, "", "BinaryROCCurve"], [93, 1, 1, "", "MulticlassROCCurve"], [94, 1, 1, "", "MultilabelROCCurve"], [95, 1, 1, "", "ROCCurve"]], "cyclops.evaluate.metrics.roc.BinaryROCCurve": [[92, 2, 1, "", "__add__"], [92, 2, 1, "", "__call__"], [92, 2, 1, "", "__init__"], [92, 2, 1, "", "__mul__"], [92, 2, 1, "", "add_state"], [92, 2, 1, "", "clone"], [92, 2, 1, "", "compute"], [92, 2, 1, "", "reset_state"], [92, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc.MulticlassROCCurve": [[93, 2, 1, "", "__add__"], [93, 2, 1, "", "__call__"], [93, 2, 1, "", "__init__"], [93, 2, 1, "", "__mul__"], [93, 2, 1, "", "add_state"], [93, 2, 1, "", "clone"], [93, 2, 1, "", "compute"], [93, 2, 1, "", "reset_state"], [93, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc.MultilabelROCCurve": [[94, 2, 1, "", "__add__"], [94, 2, 1, "", "__call__"], [94, 2, 1, "", "__init__"], [94, 2, 1, "", "__mul__"], [94, 2, 1, "", "add_state"], [94, 2, 1, "", "clone"], [94, 2, 1, "", "compute"], [94, 2, 1, "", "reset_state"], [94, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc.ROCCurve": [[95, 2, 1, "", "__add__"], [95, 2, 1, "", "__call__"], [95, 2, 1, "", "__init__"], [95, 2, 1, "", "__mul__"], [95, 2, 1, "", "add_state"], [95, 2, 1, "", "clone"], [95, 2, 1, "", "compute"], [95, 2, 1, "", "reset_state"], [95, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity": [[97, 1, 1, "", "BinarySensitivity"], [98, 1, 1, "", "MulticlassSensitivity"], [99, 1, 1, "", "MultilabelSensitivity"], [100, 1, 1, "", "Sensitivity"]], "cyclops.evaluate.metrics.sensitivity.BinarySensitivity": [[97, 2, 1, "", "__add__"], [97, 2, 1, "", "__call__"], [97, 2, 1, "", "__init__"], [97, 2, 1, "", "__mul__"], [97, 2, 1, "", "add_state"], [97, 2, 1, "", "clone"], [97, 2, 1, "", "compute"], [97, 2, 1, "", "reset_state"], [97, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity": [[98, 2, 1, "", "__add__"], [98, 2, 1, "", "__call__"], [98, 2, 1, "", "__init__"], [98, 2, 1, "", "__mul__"], [98, 2, 1, "", "add_state"], [98, 2, 1, "", "clone"], [98, 2, 1, "", "compute"], [98, 2, 1, "", "reset_state"], [98, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity": [[99, 2, 1, "", "__add__"], [99, 2, 1, "", "__call__"], [99, 2, 1, "", "__init__"], [99, 2, 1, "", "__mul__"], [99, 2, 1, "", "add_state"], [99, 2, 1, "", "clone"], [99, 2, 1, "", "compute"], [99, 2, 1, "", "reset_state"], [99, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity.Sensitivity": [[100, 2, 1, "", "__add__"], [100, 2, 1, "", "__call__"], [100, 2, 1, "", "__init__"], [100, 2, 1, "", "__mul__"], [100, 2, 1, "", "add_state"], [100, 2, 1, "", "clone"], [100, 2, 1, "", "compute"], [100, 2, 1, "", "reset_state"], [100, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity": [[102, 1, 1, "", "BinarySpecificity"], [103, 1, 1, "", "MulticlassSpecificity"], [104, 1, 1, "", "MultilabelSpecificity"], [105, 1, 1, "", "Specificity"]], "cyclops.evaluate.metrics.specificity.BinarySpecificity": [[102, 2, 1, "", "__add__"], [102, 2, 1, "", "__call__"], [102, 2, 1, "", "__init__"], [102, 2, 1, "", "__mul__"], [102, 2, 1, "", "add_state"], [102, 2, 1, "", "clone"], [102, 2, 1, "", "compute"], [102, 2, 1, "", "reset_state"], [102, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity.MulticlassSpecificity": [[103, 2, 1, "", "__add__"], [103, 2, 1, "", "__call__"], [103, 2, 1, "", "__init__"], [103, 2, 1, "", "__mul__"], [103, 2, 1, "", "add_state"], [103, 2, 1, "", "clone"], [103, 2, 1, "", "compute"], [103, 2, 1, "", "reset_state"], [103, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity.MultilabelSpecificity": [[104, 2, 1, "", "__add__"], [104, 2, 1, "", "__call__"], [104, 2, 1, "", "__init__"], [104, 2, 1, "", "__mul__"], [104, 2, 1, "", "add_state"], [104, 2, 1, "", "clone"], [104, 2, 1, "", "compute"], [104, 2, 1, "", "reset_state"], [104, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity.Specificity": [[105, 2, 1, "", "__add__"], [105, 2, 1, "", "__call__"], [105, 2, 1, "", "__init__"], [105, 2, 1, "", "__mul__"], [105, 2, 1, "", "add_state"], [105, 2, 1, "", "clone"], [105, 2, 1, "", "compute"], [105, 2, 1, "", "reset_state"], [105, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores": [[107, 1, 1, "", "BinaryStatScores"], [108, 1, 1, "", "MulticlassStatScores"], [109, 1, 1, "", "MultilabelStatScores"], [110, 1, 1, "", "StatScores"]], "cyclops.evaluate.metrics.stat_scores.BinaryStatScores": [[107, 2, 1, "", "__add__"], [107, 2, 1, "", "__call__"], [107, 2, 1, "", "__init__"], [107, 2, 1, "", "__mul__"], [107, 2, 1, "", "add_state"], [107, 2, 1, "", "clone"], [107, 2, 1, "", "compute"], [107, 2, 1, "", "reset_state"], [107, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores": [[108, 2, 1, "", "__add__"], [108, 2, 1, "", "__call__"], [108, 2, 1, "", "__init__"], [108, 2, 1, "", "__mul__"], [108, 2, 1, "", "add_state"], [108, 2, 1, "", "clone"], [108, 2, 1, "", "compute"], [108, 2, 1, "", "reset_state"], [108, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores": [[109, 2, 1, "", "__add__"], [109, 2, 1, "", "__call__"], [109, 2, 1, "", "__init__"], [109, 2, 1, "", "__mul__"], [109, 2, 1, "", "add_state"], [109, 2, 1, "", "clone"], [109, 2, 1, "", "compute"], [109, 2, 1, "", "reset_state"], [109, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores.StatScores": [[110, 2, 1, "", "__add__"], [110, 2, 1, "", "__call__"], [110, 2, 1, "", "__init__"], [110, 2, 1, "", "__mul__"], [110, 2, 1, "", "add_state"], [110, 2, 1, "", "clone"], [110, 2, 1, "", "compute"], [110, 2, 1, "", "reset_state"], [110, 2, 1, "", "update_state"]], "cyclops.monitor": [[111, 0, 0, "-", "clinical_applicator"], [113, 0, 0, "-", "synthetic_applicator"]], "cyclops.monitor.clinical_applicator": [[112, 1, 1, "", "ClinicalShiftApplicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[112, 2, 1, "", "age"], [112, 2, 1, "", "apply_shift"], [112, 2, 1, "", "custom"], [112, 2, 1, "", "hospital_type"], [112, 2, 1, "", "month"], [112, 2, 1, "", "sex"], [112, 2, 1, "", "time"]], "cyclops.monitor.synthetic_applicator": [[114, 1, 1, "", "SyntheticShiftApplicator"], [115, 4, 1, "", "binary_noise_shift"], [116, 4, 1, "", "feature_association_shift"], [117, 4, 1, "", "feature_swap_shift"], [118, 4, 1, "", "gaussian_noise_shift"], [119, 4, 1, "", "knockout_shift"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[114, 2, 1, "", "apply_shift"]], "cyclops.report": [[120, 0, 0, "-", "report"]], "cyclops.report.report": [[121, 1, 1, "", "ModelCardReport"]], "cyclops.report.report.ModelCardReport": [[121, 2, 1, "", "export"], [121, 2, 1, "", "from_json_file"], [121, 2, 1, "", "log_citation"], [121, 2, 1, "", "log_dataset"], [121, 2, 1, "", "log_descriptor"], [121, 2, 1, "", "log_fairness_assessment"], [121, 2, 1, "", "log_from_dict"], [121, 2, 1, "", "log_image"], [121, 2, 1, "", "log_license"], [121, 2, 1, "", "log_model_parameters"], [121, 2, 1, "", "log_owner"], [121, 2, 1, "", "log_performance_metrics"], [121, 2, 1, "", "log_plotly_figure"], [121, 2, 1, "", "log_quantitative_analysis"], [121, 2, 1, "", "log_reference"], [121, 2, 1, "", "log_regulation"], [121, 2, 1, "", "log_risk"], [121, 2, 1, "", "log_use_case"], [121, 2, 1, "", "log_user"], [121, 2, 1, "", "log_version"]], "cyclops.tasks": [[122, 0, 0, "-", "cxr_classification"], [124, 0, 0, "-", "mortality_prediction"]], "cyclops.tasks.cxr_classification": [[123, 1, 1, "", "CXRClassificationTask"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[123, 2, 1, "", "__init__"], [123, 2, 1, "", "add_model"], [123, 5, 1, "", "data_type"], [123, 2, 1, "", "evaluate"], [123, 2, 1, "", "get_model"], [123, 2, 1, "", "list_models"], [123, 5, 1, "", "models_count"], [123, 2, 1, "", "predict"], [123, 5, 1, "", "task_type"]], "cyclops.tasks.mortality_prediction": [[125, 1, 1, "", "MortalityPredictionTask"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[125, 2, 1, "", "__init__"], [125, 2, 1, "", "add_model"], [125, 5, 1, "", "data_type"], [125, 2, 1, "", "evaluate"], [125, 2, 1, "", "get_model"], [125, 2, 1, "", "list_models"], [125, 2, 1, "", "list_models_params"], [125, 2, 1, "", "load_model"], [125, 5, 1, "", "models_count"], [125, 2, 1, "", "predict"], [125, 2, 1, "", "save_model"], [125, 5, 1, "", "task_type"], [125, 2, 1, "", "train"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:method", "3": "py:attribute", "4": "py:function", "5": "py:property"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "method", "Python method"], "3": ["py", "attribute", "Python attribute"], "4": ["py", "function", "Python function"], "5": ["py", "property", "Python property"]}, "titleterms": {"api": [0, 136], "refer": 0, "contribut": [1, 3], "cyclop": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130], "pre": 1, "commit": 1, "hook": 1, "code": 1, "guidelin": 1, "welcom": 2, "": 2, "document": [2, 3], "content": 2, "get": [3, 133], "start": 3, "instal": 3, "us": [3, 134, 137], "pip": 3, "develop": 3, "poetri": 3, "notebook": 3, "citat": 3, "data": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 126, 132, 135, 137], "featur": [4, 5, 126, 132, 135], "medical_imag": [4, 5], "medicalimag": 5, "slicer": [6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "slicespec": 7, "compound_filt": 8, "filter_datetim": 9, "filter_non_nul": 10, "filter_rang": 11, "filter_string_contain": 12, "filter_valu": 13, "is_datetim": 14, "overal": 15, "evalu": [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 127, 132, 135], "fair": [18, 19, 20, 21, 22, 127], "config": [18, 19], "fairnessconfig": 19, "evaluate_fair": 21, "warn_too_many_unique_valu": 22, "metric": [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 127, 133], "accuraci": [23, 24, 25, 26, 27, 44], "binaryaccuraci": 25, "multiclassaccuraci": 26, "multilabelaccuraci": 27, "auroc": [28, 29, 30, 31, 32, 45, 133], "binaryauroc": 30, "multiclassauroc": 31, "multilabelauroc": 32, "f_beta": [33, 34, 35, 36, 37, 38, 39, 40, 41, 46, 47, 48, 49, 50, 51, 52, 53, 54], "binaryf1scor": 34, "binaryfbetascor": 35, "f1score": 36, "fbetascor": 37, "multiclassf1scor": 38, "multiclassfbetascor": 39, "multilabelf1scor": 40, "multilabelfbetascor": 41, "factori": [42, 43], "create_metr": 43, "function": [44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 127], "binary_f1_scor": 47, "binary_fbeta_scor": 48, "f1_score": 49, "fbeta_scor": 50, "multiclass_f1_scor": 51, "multiclass_fbeta_scor": 52, "multilabel_f1_scor": 53, "multilabel_fbeta_scor": 54, "precision_recal": [55, 56, 57, 58, 59, 60, 61, 62, 63, 77, 78, 79, 80, 81, 82, 83, 84, 85], "binary_precis": 56, "binary_recal": 57, "multiclass_precis": 58, "multiclass_recal": 59, "multilabel_precis": 60, "multilabel_recal": 61, "precis": [62, 84], "recal": [63, 85], "precision_recall_curv": [64, 86, 87, 88, 89, 90], "roc": [65, 66, 67, 68, 69, 91, 92, 93, 94, 95], "binary_roc_curv": 66, "multiclass_roc_curv": 67, "multilabel_roc_curv": 68, "roc_curv": 69, "sensit": [70, 96, 97, 98, 99, 100, 134], "specif": [71, 101, 102, 103, 104, 105], "stat_scor": [72, 106, 107, 108, 109, 110], "metriccollect": 75, "operatormetr": 76, "binaryprecis": 78, "binaryrecal": 79, "multiclassprecis": 80, "multiclassrecal": 81, "multilabelprecis": 82, "multilabelrecal": 83, "binaryprecisionrecallcurv": 87, "multiclassprecisionrecallcurv": 88, "multilabelprecisionrecallcurv": 89, "precisionrecallcurv": 90, "binaryroccurv": 92, "multiclassroccurv": 93, "multilabelroccurv": 94, "roccurv": 95, "binarysensit": 97, "multiclasssensit": 98, "multilabelsensit": 99, "binaryspecif": 102, "multiclassspecif": 103, "multilabelspecif": 104, "binarystatscor": 107, "multiclassstatscor": 108, "multilabelstatscor": 109, "statscor": 110, "monitor": [111, 112, 113, 114, 115, 116, 117, 118, 119, 128, 136], "clinical_appl": [111, 112], "clinicalshiftappl": 112, "synthetic_appl": [113, 114, 115, 116, 117, 118, 119], "syntheticshiftappl": 114, "binary_noise_shift": 115, "feature_association_shift": 116, "feature_swap_shift": 117, "gaussian_noise_shift": 118, "knockout_shift": 119, "report": [120, 121, 129, 132, 135], "modelcardreport": 121, "task": [122, 123, 124, 125, 130, 132, 135], "cxr_classif": [122, 123], "cxrclassificationtask": 123, "mortality_predict": [124, 125], "mortalitypredictiontask": 125, "tutori": [131, 134], "heart": [132, 137], "failur": [132, 137], "predict": [132, 133, 135, 137], "import": [132, 133, 134, 135], "librari": [132, 133, 134, 135], "constant": [132, 135], "load": [132, 133, 134], "sex": [132, 133], "valu": 132, "ag": [132, 133, 135], "distribut": [132, 135], "outcom": [132, 135], "identifi": [132, 135], "type": [132, 135], "creat": [132, 135], "preprocessor": [132, 135], "hug": [132, 135], "face": [132, 135], "dataset": [132, 133, 134, 135], "model": [132, 133, 134, 135], "creation": [132, 135], "train": [132, 134, 135], "perform": [132, 133, 135], "over": [132, 133, 135], "time": [132, 133, 135], "gener": [132, 134, 135], "chest": [133, 137], "x": [133, 137], "rai": [133, 137], "diseas": 133, "classif": [133, 137], "multilabel": 133, "pathologi": 133, "balanc": 133, "error": 133, "rate": 133, "pariti": 133, "log": 133, "test": [133, 134], "w": 133, "threshold": 133, "popul": 133, "card": 133, "field": 133, "nihcxr": 134, "clinic": 134, "drift": 134, "experi": 134, "exampl": [134, 137], "1": 134, "sourc": 134, "target": 134, "2": 134, "3": 134, "dimension": 134, "reduct": 134, "techniqu": 134, "differ": 134, "4": 134, "shift": 134, "5": 134, "roll": 134, "window": 134, "synthet": 134, "timestamp": 134, "biweekli": 134, "prolong": [135, 137], "length": [135, 137], "stai": [135, 137], "queri": 135, "comput": 135, "label": 135, "inspect": 135, "preprocess": 135, "drop": 135, "nan": 135, "base": 135, "nan_threshold": 135, "gender": 135, "case": 137, "tabular": 137, "kaggl": 137, "synthea": 137, "imag": 137, "nih": 137}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1, "sphinx.ext.intersphinx": 1, "nbsphinx": 4, "sphinx": 60}, "alltitles": {"API Reference": [[0, "api-reference"]], "Contributing to cyclops": [[1, "contributing-to-cyclops"]], "Pre-commit hooks": [[1, "pre-commit-hooks"]], "Coding guidelines": [[1, "coding-guidelines"]], "Welcome to cyclops\u2019s documentation!": [[2, "welcome-to-cyclops-s-documentation"]], "Contents:": [[2, null]], "\ud83d\udc23 Getting Started": [[3, "getting-started"]], "Installing cyclops using pip": [[3, "installing-cyclops-using-pip"]], "\ud83e\uddd1\ud83c\udfff\u200d\ud83d\udcbb Developing": [[3, "developing"]], "Using poetry": [[3, "using-poetry"]], "Contributing": [[3, "contributing"]], "\ud83d\udcda Documentation": [[3, "documentation"]], "\ud83d\udcd3 Notebooks": [[3, "notebooks"]], "\ud83c\udf93 Citation": [[3, "citation"]], "cyclops.data.features.medical_image": [[4, "module-cyclops.data.features.medical_image"]], "cyclops.data.features.medical_image.MedicalImage": [[5, "cyclops-data-features-medical-image-medicalimage"]], "cyclops.data.slicer": [[6, "module-cyclops.data.slicer"]], "cyclops.data.slicer.SliceSpec": [[7, "cyclops-data-slicer-slicespec"]], "cyclops.data.slicer.compound_filter": [[8, "cyclops-data-slicer-compound-filter"]], "cyclops.data.slicer.filter_datetime": [[9, "cyclops-data-slicer-filter-datetime"]], "cyclops.data.slicer.filter_non_null": [[10, "cyclops-data-slicer-filter-non-null"]], "cyclops.data.slicer.filter_range": [[11, "cyclops-data-slicer-filter-range"]], "cyclops.data.slicer.filter_string_contains": [[12, "cyclops-data-slicer-filter-string-contains"]], "cyclops.data.slicer.filter_value": [[13, "cyclops-data-slicer-filter-value"]], "cyclops.data.slicer.is_datetime": [[14, "cyclops-data-slicer-is-datetime"]], "cyclops.data.slicer.overall": [[15, "cyclops-data-slicer-overall"]], "cyclops.evaluate.evaluator": [[16, "module-cyclops.evaluate.evaluator"]], "cyclops.evaluate.evaluator.evaluate": [[17, "cyclops-evaluate-evaluator-evaluate"]], "cyclops.evaluate.fairness.config": [[18, "module-cyclops.evaluate.fairness.config"]], "cyclops.evaluate.fairness.config.FairnessConfig": [[19, "cyclops-evaluate-fairness-config-fairnessconfig"]], "cyclops.evaluate.fairness.evaluator": [[20, "module-cyclops.evaluate.fairness.evaluator"]], "cyclops.evaluate.fairness.evaluator.evaluate_fairness": [[21, "cyclops-evaluate-fairness-evaluator-evaluate-fairness"]], "cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values": [[22, "cyclops-evaluate-fairness-evaluator-warn-too-many-unique-values"]], "cyclops.evaluate.metrics.accuracy": [[23, "module-cyclops.evaluate.metrics.accuracy"]], "cyclops.evaluate.metrics.accuracy.Accuracy": [[24, "cyclops-evaluate-metrics-accuracy-accuracy"]], "cyclops.evaluate.metrics.accuracy.BinaryAccuracy": [[25, "cyclops-evaluate-metrics-accuracy-binaryaccuracy"]], "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy": [[26, "cyclops-evaluate-metrics-accuracy-multiclassaccuracy"]], "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy": [[27, "cyclops-evaluate-metrics-accuracy-multilabelaccuracy"]], "cyclops.evaluate.metrics.auroc": [[28, "module-cyclops.evaluate.metrics.auroc"]], "cyclops.evaluate.metrics.auroc.AUROC": [[29, "cyclops-evaluate-metrics-auroc-auroc"]], "cyclops.evaluate.metrics.auroc.BinaryAUROC": [[30, "cyclops-evaluate-metrics-auroc-binaryauroc"]], "cyclops.evaluate.metrics.auroc.MulticlassAUROC": [[31, "cyclops-evaluate-metrics-auroc-multiclassauroc"]], "cyclops.evaluate.metrics.auroc.MultilabelAUROC": [[32, "cyclops-evaluate-metrics-auroc-multilabelauroc"]], "cyclops.evaluate.metrics.f_beta": [[33, "module-cyclops.evaluate.metrics.f_beta"]], "cyclops.evaluate.metrics.f_beta.BinaryF1Score": [[34, "cyclops-evaluate-metrics-f-beta-binaryf1score"]], "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore": [[35, "cyclops-evaluate-metrics-f-beta-binaryfbetascore"]], "cyclops.evaluate.metrics.f_beta.F1Score": [[36, "cyclops-evaluate-metrics-f-beta-f1score"]], "cyclops.evaluate.metrics.f_beta.FbetaScore": [[37, "cyclops-evaluate-metrics-f-beta-fbetascore"]], "cyclops.evaluate.metrics.f_beta.MulticlassF1Score": [[38, "cyclops-evaluate-metrics-f-beta-multiclassf1score"]], "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore": [[39, "cyclops-evaluate-metrics-f-beta-multiclassfbetascore"]], "cyclops.evaluate.metrics.f_beta.MultilabelF1Score": [[40, "cyclops-evaluate-metrics-f-beta-multilabelf1score"]], "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore": [[41, "cyclops-evaluate-metrics-f-beta-multilabelfbetascore"]], "cyclops.evaluate.metrics.factory": [[42, "module-cyclops.evaluate.metrics.factory"]], "cyclops.evaluate.metrics.factory.create_metric": [[43, "cyclops-evaluate-metrics-factory-create-metric"]], "cyclops.evaluate.metrics.functional.accuracy": [[44, "module-cyclops.evaluate.metrics.functional.accuracy"]], "cyclops.evaluate.metrics.functional.auroc": [[45, "module-cyclops.evaluate.metrics.functional.auroc"]], "cyclops.evaluate.metrics.functional.f_beta": [[46, "module-cyclops.evaluate.metrics.functional.f_beta"]], "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score": [[47, "cyclops-evaluate-metrics-functional-f-beta-binary-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score": [[48, "cyclops-evaluate-metrics-functional-f-beta-binary-fbeta-score"]], "cyclops.evaluate.metrics.functional.f_beta.f1_score": [[49, "cyclops-evaluate-metrics-functional-f-beta-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.fbeta_score": [[50, "cyclops-evaluate-metrics-functional-f-beta-fbeta-score"]], "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score": [[51, "cyclops-evaluate-metrics-functional-f-beta-multiclass-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score": [[52, "cyclops-evaluate-metrics-functional-f-beta-multiclass-fbeta-score"]], "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score": [[53, "cyclops-evaluate-metrics-functional-f-beta-multilabel-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score": [[54, "cyclops-evaluate-metrics-functional-f-beta-multilabel-fbeta-score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[55, "module-cyclops.evaluate.metrics.functional.precision_recall"]], "cyclops.evaluate.metrics.functional.precision_recall.binary_precision": [[56, "cyclops-evaluate-metrics-functional-precision-recall-binary-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.binary_recall": [[57, "cyclops-evaluate-metrics-functional-precision-recall-binary-recall"]], "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision": [[58, "cyclops-evaluate-metrics-functional-precision-recall-multiclass-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall": [[59, "cyclops-evaluate-metrics-functional-precision-recall-multiclass-recall"]], "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision": [[60, "cyclops-evaluate-metrics-functional-precision-recall-multilabel-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall": [[61, "cyclops-evaluate-metrics-functional-precision-recall-multilabel-recall"]], "cyclops.evaluate.metrics.functional.precision_recall.precision": [[62, "cyclops-evaluate-metrics-functional-precision-recall-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.recall": [[63, "cyclops-evaluate-metrics-functional-precision-recall-recall"]], "cyclops.evaluate.metrics.functional.precision_recall_curve": [[64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"]], "cyclops.evaluate.metrics.functional.roc": [[65, "module-cyclops.evaluate.metrics.functional.roc"]], "cyclops.evaluate.metrics.functional.roc.binary_roc_curve": [[66, "cyclops-evaluate-metrics-functional-roc-binary-roc-curve"]], "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve": [[67, "cyclops-evaluate-metrics-functional-roc-multiclass-roc-curve"]], "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve": [[68, "cyclops-evaluate-metrics-functional-roc-multilabel-roc-curve"]], "cyclops.evaluate.metrics.functional.roc.roc_curve": [[69, "cyclops-evaluate-metrics-functional-roc-roc-curve"]], "cyclops.evaluate.metrics.functional.sensitivity": [[70, "module-cyclops.evaluate.metrics.functional.sensitivity"]], "cyclops.evaluate.metrics.functional.specificity": [[71, "module-cyclops.evaluate.metrics.functional.specificity"]], "cyclops.evaluate.metrics.functional.stat_scores": [[72, "module-cyclops.evaluate.metrics.functional.stat_scores"]], "cyclops.evaluate.metrics.metric": [[73, "module-cyclops.evaluate.metrics.metric"]], "cyclops.evaluate.metrics.metric.Metric": [[74, "cyclops-evaluate-metrics-metric-metric"]], "cyclops.evaluate.metrics.metric.MetricCollection": [[75, "cyclops-evaluate-metrics-metric-metriccollection"]], "cyclops.evaluate.metrics.metric.OperatorMetric": [[76, "cyclops-evaluate-metrics-metric-operatormetric"]], "cyclops.evaluate.metrics.precision_recall": [[77, "module-cyclops.evaluate.metrics.precision_recall"]], "cyclops.evaluate.metrics.precision_recall.BinaryPrecision": [[78, "cyclops-evaluate-metrics-precision-recall-binaryprecision"]], "cyclops.evaluate.metrics.precision_recall.BinaryRecall": [[79, "cyclops-evaluate-metrics-precision-recall-binaryrecall"]], "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision": [[80, "cyclops-evaluate-metrics-precision-recall-multiclassprecision"]], "cyclops.evaluate.metrics.precision_recall.MulticlassRecall": [[81, "cyclops-evaluate-metrics-precision-recall-multiclassrecall"]], "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision": [[82, "cyclops-evaluate-metrics-precision-recall-multilabelprecision"]], "cyclops.evaluate.metrics.precision_recall.MultilabelRecall": [[83, "cyclops-evaluate-metrics-precision-recall-multilabelrecall"]], "cyclops.evaluate.metrics.precision_recall.Precision": [[84, "cyclops-evaluate-metrics-precision-recall-precision"]], "cyclops.evaluate.metrics.precision_recall.Recall": [[85, "cyclops-evaluate-metrics-precision-recall-recall"]], "cyclops.evaluate.metrics.precision_recall_curve": [[86, "module-cyclops.evaluate.metrics.precision_recall_curve"]], "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve": [[87, "cyclops-evaluate-metrics-precision-recall-curve-binaryprecisionrecallcurve"]], "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve": [[88, "cyclops-evaluate-metrics-precision-recall-curve-multiclassprecisionrecallcurve"]], "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve": [[89, "cyclops-evaluate-metrics-precision-recall-curve-multilabelprecisionrecallcurve"]], "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve": [[90, "cyclops-evaluate-metrics-precision-recall-curve-precisionrecallcurve"]], "cyclops.evaluate.metrics.roc": [[91, "module-cyclops.evaluate.metrics.roc"]], "cyclops.evaluate.metrics.roc.BinaryROCCurve": [[92, "cyclops-evaluate-metrics-roc-binaryroccurve"]], "cyclops.evaluate.metrics.roc.MulticlassROCCurve": [[93, "cyclops-evaluate-metrics-roc-multiclassroccurve"]], "cyclops.evaluate.metrics.roc.MultilabelROCCurve": [[94, "cyclops-evaluate-metrics-roc-multilabelroccurve"]], "cyclops.evaluate.metrics.roc.ROCCurve": [[95, "cyclops-evaluate-metrics-roc-roccurve"]], "cyclops.evaluate.metrics.sensitivity": [[96, "module-cyclops.evaluate.metrics.sensitivity"]], "cyclops.evaluate.metrics.sensitivity.BinarySensitivity": [[97, "cyclops-evaluate-metrics-sensitivity-binarysensitivity"]], "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity": [[98, "cyclops-evaluate-metrics-sensitivity-multiclasssensitivity"]], "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity": [[99, "cyclops-evaluate-metrics-sensitivity-multilabelsensitivity"]], "cyclops.evaluate.metrics.sensitivity.Sensitivity": [[100, "cyclops-evaluate-metrics-sensitivity-sensitivity"]], "cyclops.evaluate.metrics.specificity": [[101, "module-cyclops.evaluate.metrics.specificity"]], "cyclops.evaluate.metrics.specificity.BinarySpecificity": [[102, "cyclops-evaluate-metrics-specificity-binaryspecificity"]], "cyclops.evaluate.metrics.specificity.MulticlassSpecificity": [[103, "cyclops-evaluate-metrics-specificity-multiclassspecificity"]], "cyclops.evaluate.metrics.specificity.MultilabelSpecificity": [[104, "cyclops-evaluate-metrics-specificity-multilabelspecificity"]], "cyclops.evaluate.metrics.specificity.Specificity": [[105, "cyclops-evaluate-metrics-specificity-specificity"]], "cyclops.evaluate.metrics.stat_scores": [[106, "module-cyclops.evaluate.metrics.stat_scores"]], "cyclops.evaluate.metrics.stat_scores.BinaryStatScores": [[107, "cyclops-evaluate-metrics-stat-scores-binarystatscores"]], "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores": [[108, "cyclops-evaluate-metrics-stat-scores-multiclassstatscores"]], "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores": [[109, "cyclops-evaluate-metrics-stat-scores-multilabelstatscores"]], "cyclops.evaluate.metrics.stat_scores.StatScores": [[110, "cyclops-evaluate-metrics-stat-scores-statscores"]], "cyclops.monitor.clinical_applicator": [[111, "module-cyclops.monitor.clinical_applicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[112, "cyclops-monitor-clinical-applicator-clinicalshiftapplicator"]], "cyclops.monitor.synthetic_applicator": [[113, "module-cyclops.monitor.synthetic_applicator"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[114, "cyclops-monitor-synthetic-applicator-syntheticshiftapplicator"]], "cyclops.monitor.synthetic_applicator.binary_noise_shift": [[115, "cyclops-monitor-synthetic-applicator-binary-noise-shift"]], "cyclops.monitor.synthetic_applicator.feature_association_shift": [[116, "cyclops-monitor-synthetic-applicator-feature-association-shift"]], "cyclops.monitor.synthetic_applicator.feature_swap_shift": [[117, "cyclops-monitor-synthetic-applicator-feature-swap-shift"]], "cyclops.monitor.synthetic_applicator.gaussian_noise_shift": [[118, "cyclops-monitor-synthetic-applicator-gaussian-noise-shift"]], "cyclops.monitor.synthetic_applicator.knockout_shift": [[119, "cyclops-monitor-synthetic-applicator-knockout-shift"]], "cyclops.report.report": [[120, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[121, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.cxr_classification": [[122, "module-cyclops.tasks.cxr_classification"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[123, "cyclops-tasks-cxr-classification-cxrclassificationtask"]], "cyclops.tasks.mortality_prediction": [[124, "module-cyclops.tasks.mortality_prediction"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[125, "cyclops-tasks-mortality-prediction-mortalitypredictiontask"]], "cyclops.data": [[126, "module-cyclops.data"]], "cyclops.data.features": [[126, "module-cyclops.data.features"]], "cyclops.evaluate": [[127, "module-cyclops.evaluate"]], "cyclops.evaluate.metrics": [[127, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[127, "module-cyclops.evaluate.metrics.functional"]], "cyclops.evaluate.fairness": [[127, "module-cyclops.evaluate.fairness"]], "cyclops.monitor": [[128, "module-cyclops.monitor"]], "cyclops.report": [[129, "module-cyclops.report"]], "cyclops.tasks": [[130, "module-cyclops.tasks"]], "Tutorials": [[131, "tutorials"]], "Heart Failure Prediction": [[132, "Heart-Failure-Prediction"]], "Import Libraries": [[132, "Import-Libraries"], [133, "Import-Libraries"], [135, "Import-Libraries"]], "Constants": [[132, "Constants"], [135, "Constants"]], "Data Loading": [[132, "Data-Loading"]], "Sex values": [[132, "Sex-values"]], "Age distribution": [[132, "Age-distribution"], [135, "Age-distribution"]], "Outcome distribution": [[132, "Outcome-distribution"], [135, "Outcome-distribution"]], "Identifying feature types": [[132, "Identifying-feature-types"], [135, "Identifying-feature-types"]], "Creating data preprocessors": [[132, "Creating-data-preprocessors"], [135, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[132, "Creating-Hugging-Face-Dataset"], [135, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[132, "Model-Creation"], [135, "Model-Creation"]], "Task Creation": [[132, "Task-Creation"], [135, "Task-Creation"]], "Training": [[132, "Training"], [135, "Training"]], "Prediction": [[132, "Prediction"], [135, "Prediction"]], "Evaluation": [[132, "Evaluation"], [135, "Evaluation"]], "Performance over time": [[132, "Performance-over-time"], [133, "Performance-over-time"], [135, "Performance-over-time"]], "Report Generation": [[132, "Report-Generation"], [135, "Report-Generation"]], "Chest X-Ray Disease Classification": [[133, "Chest-X-Ray-Disease-Classification"]], "Load Dataset": [[133, "Load-Dataset"]], "Load Model and get Predictions": [[133, "Load-Model-and-get-Predictions"]], "Multilabel AUROC by Pathology and Sex": [[133, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[133, "Multilabel-AUROC-by-Pathology-and-Age"]], "Balanced Error Rate by Pathology and Age": [[133, "Balanced-Error-Rate-by-Pathology-and-Age"]], "Balanced Error Rate Parity by Pathology and Age": [[133, "Balanced-Error-Rate-Parity-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[133, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[133, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[134, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[134, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[134, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[134, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[134, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[134, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[134, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[135, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[135, "Data-Querying"]], "Compute length of stay (labels)": [[135, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[135, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[135, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[135, "Length-of-stay-distribution"]], "Gender distribution": [[135, "Gender-distribution"]], "monitor API": [[136, "monitor-api"]], "Example use cases": [[137, "example-use-cases"]], "Tabular data": [[137, "tabular-data"]], "Kaggle Heart Failure Prediction": [[137, "kaggle-heart-failure-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[137, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[137, "image-data"]], "NIH Chest X-ray classification": [[137, "nih-chest-x-ray-classification"]]}, "indexentries": {"cyclops.data.features.medical_image": [[4, "module-cyclops.data.features.medical_image"]], "module": [[4, "module-cyclops.data.features.medical_image"], [6, "module-cyclops.data.slicer"], [16, "module-cyclops.evaluate.evaluator"], [18, "module-cyclops.evaluate.fairness.config"], [20, "module-cyclops.evaluate.fairness.evaluator"], [23, "module-cyclops.evaluate.metrics.accuracy"], [28, "module-cyclops.evaluate.metrics.auroc"], [33, "module-cyclops.evaluate.metrics.f_beta"], [42, "module-cyclops.evaluate.metrics.factory"], [44, "module-cyclops.evaluate.metrics.functional.accuracy"], [45, "module-cyclops.evaluate.metrics.functional.auroc"], [46, "module-cyclops.evaluate.metrics.functional.f_beta"], [55, "module-cyclops.evaluate.metrics.functional.precision_recall"], [64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"], [65, "module-cyclops.evaluate.metrics.functional.roc"], [70, "module-cyclops.evaluate.metrics.functional.sensitivity"], [71, "module-cyclops.evaluate.metrics.functional.specificity"], [72, "module-cyclops.evaluate.metrics.functional.stat_scores"], [73, "module-cyclops.evaluate.metrics.metric"], [77, "module-cyclops.evaluate.metrics.precision_recall"], [86, "module-cyclops.evaluate.metrics.precision_recall_curve"], [91, "module-cyclops.evaluate.metrics.roc"], [96, "module-cyclops.evaluate.metrics.sensitivity"], [101, "module-cyclops.evaluate.metrics.specificity"], [106, "module-cyclops.evaluate.metrics.stat_scores"], [111, "module-cyclops.monitor.clinical_applicator"], [113, "module-cyclops.monitor.synthetic_applicator"], [120, "module-cyclops.report.report"], [122, "module-cyclops.tasks.cxr_classification"], [124, "module-cyclops.tasks.mortality_prediction"], [126, "module-cyclops.data"], [126, "module-cyclops.data.features"], [127, "module-cyclops.evaluate"], [127, "module-cyclops.evaluate.fairness"], [127, "module-cyclops.evaluate.metrics"], [127, "module-cyclops.evaluate.metrics.functional"], [128, "module-cyclops.monitor"], [129, "module-cyclops.report"], [130, "module-cyclops.tasks"]], "medicalimage (class in cyclops.data.features.medical_image)": [[5, "cyclops.data.features.medical_image.MedicalImage"]], "__call__() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.__call__"]], "cast_storage() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.cast_storage"]], "decode_example() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.decode_example"]], "embed_storage() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.embed_storage"]], "encode_example() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.encode_example"]], "flatten() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.flatten"]], "cyclops.data.slicer": [[6, "module-cyclops.data.slicer"]], "slicespec (class in cyclops.data.slicer)": [[7, "cyclops.data.slicer.SliceSpec"]], "_registry (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec._registry"]], "add_slice_spec() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.add_slice_spec"]], "column_names (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.column_names"]], "get_slices() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.get_slices"]], "include_overall (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.include_overall"]], "slices() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.slices"]], "spec_list (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.spec_list"]], "validate (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.validate"]], "compound_filter() (in module cyclops.data.slicer)": [[8, "cyclops.data.slicer.compound_filter"]], "filter_datetime() (in module cyclops.data.slicer)": [[9, "cyclops.data.slicer.filter_datetime"]], "filter_non_null() (in module cyclops.data.slicer)": [[10, "cyclops.data.slicer.filter_non_null"]], "filter_range() (in module cyclops.data.slicer)": [[11, "cyclops.data.slicer.filter_range"]], "filter_string_contains() (in module cyclops.data.slicer)": [[12, "cyclops.data.slicer.filter_string_contains"]], "filter_value() (in module cyclops.data.slicer)": [[13, "cyclops.data.slicer.filter_value"]], "is_datetime() (in module cyclops.data.slicer)": [[14, "cyclops.data.slicer.is_datetime"]], "overall() (in module cyclops.data.slicer)": [[15, "cyclops.data.slicer.overall"]], "cyclops.evaluate.evaluator": [[16, "module-cyclops.evaluate.evaluator"]], "evaluate() (in module cyclops.evaluate.evaluator)": [[17, "cyclops.evaluate.evaluator.evaluate"]], "cyclops.evaluate.fairness.config": [[18, "module-cyclops.evaluate.fairness.config"]], "fairnessconfig (class in cyclops.evaluate.fairness.config)": [[19, "cyclops.evaluate.fairness.config.FairnessConfig"]], "cyclops.evaluate.fairness.evaluator": [[20, "module-cyclops.evaluate.fairness.evaluator"]], "evaluate_fairness() (in module cyclops.evaluate.fairness.evaluator)": [[21, "cyclops.evaluate.fairness.evaluator.evaluate_fairness"]], "warn_too_many_unique_values() (in module cyclops.evaluate.fairness.evaluator)": [[22, "cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values"]], "cyclops.evaluate.metrics.accuracy": [[23, "module-cyclops.evaluate.metrics.accuracy"]], "accuracy (class in cyclops.evaluate.metrics.accuracy)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy"]], "__add__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__add__"]], "__call__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__call__"]], "__init__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__init__"]], "__mul__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__mul__"]], "add_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.add_state"]], "clone() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.clone"]], "compute() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.compute"]], "reset_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.reset_state"]], "update_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.update_state"]], "binaryaccuracy (class in cyclops.evaluate.metrics.accuracy)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy"]], "__add__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__add__"]], "__call__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__call__"]], "__init__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__init__"]], "__mul__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__mul__"]], "add_state() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.add_state"]], "clone() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.clone"]], "compute() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.compute"]], "reset_state() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.reset_state"]], "update_state() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.update_state"]], "multiclassaccuracy (class in cyclops.evaluate.metrics.accuracy)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy"]], "__add__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__add__"]], "__call__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__call__"]], "__init__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__init__"]], "__mul__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__mul__"]], "add_state() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.add_state"]], "clone() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.clone"]], "compute() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.compute"]], "reset_state() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.reset_state"]], "update_state() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.update_state"]], "multilabelaccuracy (class in cyclops.evaluate.metrics.accuracy)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy"]], "__add__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__add__"]], "__call__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__call__"]], "__init__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__init__"]], "__mul__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__mul__"]], "add_state() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.add_state"]], "clone() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.clone"]], "compute() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.compute"]], "reset_state() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.reset_state"]], "update_state() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.update_state"]], "cyclops.evaluate.metrics.auroc": [[28, "module-cyclops.evaluate.metrics.auroc"]], "auroc (class in cyclops.evaluate.metrics.auroc)": [[29, "cyclops.evaluate.metrics.auroc.AUROC"]], "__add__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__add__"]], "__call__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__call__"]], "__init__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__init__"]], "__mul__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__mul__"]], "add_state() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.add_state"]], "clone() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.clone"]], "compute() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.compute"]], "reset_state() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.reset_state"]], "update_state() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.update_state"]], "binaryauroc (class in cyclops.evaluate.metrics.auroc)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC"]], "__add__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__add__"]], "__call__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__call__"]], "__init__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__init__"]], "__mul__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__mul__"]], "add_state() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.add_state"]], "clone() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.clone"]], "compute() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.compute"]], "reset_state() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.reset_state"]], "update_state() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.update_state"]], "multiclassauroc (class in cyclops.evaluate.metrics.auroc)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC"]], "__add__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__add__"]], "__call__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__call__"]], "__init__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__init__"]], "__mul__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__mul__"]], "add_state() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.add_state"]], "clone() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.clone"]], "compute() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.compute"]], "reset_state() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.reset_state"]], "update_state() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.update_state"]], "multilabelauroc (class in cyclops.evaluate.metrics.auroc)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC"]], "__add__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__add__"]], "__call__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__call__"]], "__init__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__init__"]], "__mul__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__mul__"]], "add_state() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.add_state"]], "clone() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.clone"]], "compute() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.compute"]], "reset_state() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.reset_state"]], "update_state() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.update_state"]], "cyclops.evaluate.metrics.f_beta": [[33, "module-cyclops.evaluate.metrics.f_beta"]], "binaryf1score (class in cyclops.evaluate.metrics.f_beta)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score"]], "__add__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__add__"]], "__call__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__call__"]], "__init__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__init__"]], "__mul__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__mul__"]], "add_state() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.add_state"]], "clone() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.clone"]], "compute() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.compute"]], "reset_state() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.reset_state"]], "update_state() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.update_state"]], "binaryfbetascore (class in cyclops.evaluate.metrics.f_beta)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore"]], "__add__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__add__"]], "__call__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__call__"]], "__init__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__init__"]], "__mul__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__mul__"]], "add_state() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.add_state"]], "clone() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.clone"]], "compute() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.compute"]], "reset_state() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.reset_state"]], "update_state() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.update_state"]], "f1score (class in cyclops.evaluate.metrics.f_beta)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score"]], "__add__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__add__"]], "__call__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__call__"]], "__init__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__init__"]], "__mul__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__mul__"]], "add_state() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.add_state"]], "clone() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.clone"]], "compute() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.compute"]], "reset_state() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.reset_state"]], "update_state() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.update_state"]], "fbetascore (class in cyclops.evaluate.metrics.f_beta)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore"]], "__add__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__add__"]], "__call__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__call__"]], "__init__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__init__"]], "__mul__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__mul__"]], "add_state() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.add_state"]], "clone() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.clone"]], "compute() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.compute"]], "reset_state() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.reset_state"]], "update_state() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.update_state"]], "multiclassf1score (class in cyclops.evaluate.metrics.f_beta)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score"]], "__add__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__add__"]], "__call__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__call__"]], "__init__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__init__"]], "__mul__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__mul__"]], "add_state() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.add_state"]], "clone() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.clone"]], "compute() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.compute"]], "reset_state() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.reset_state"]], "update_state() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.update_state"]], "multiclassfbetascore (class in cyclops.evaluate.metrics.f_beta)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore"]], "__add__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__add__"]], "__call__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__call__"]], "__init__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__init__"]], "__mul__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__mul__"]], "add_state() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.add_state"]], "clone() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.clone"]], "compute() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.compute"]], "reset_state() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.reset_state"]], "update_state() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.update_state"]], "multilabelf1score (class in cyclops.evaluate.metrics.f_beta)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score"]], "__add__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__add__"]], "__call__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__call__"]], "__init__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__init__"]], "__mul__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__mul__"]], "add_state() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.add_state"]], "clone() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.clone"]], "compute() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.compute"]], "reset_state() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.reset_state"]], "update_state() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.update_state"]], "multilabelfbetascore (class in cyclops.evaluate.metrics.f_beta)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore"]], "__add__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__add__"]], "__call__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__call__"]], "__init__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__init__"]], "__mul__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__mul__"]], "add_state() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.add_state"]], "clone() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.clone"]], "compute() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.compute"]], "reset_state() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.reset_state"]], "update_state() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.update_state"]], "cyclops.evaluate.metrics.factory": [[42, "module-cyclops.evaluate.metrics.factory"]], "create_metric() (in module cyclops.evaluate.metrics.factory)": [[43, "cyclops.evaluate.metrics.factory.create_metric"]], "cyclops.evaluate.metrics.functional.accuracy": [[44, "module-cyclops.evaluate.metrics.functional.accuracy"]], "cyclops.evaluate.metrics.functional.auroc": [[45, "module-cyclops.evaluate.metrics.functional.auroc"]], "cyclops.evaluate.metrics.functional.f_beta": [[46, "module-cyclops.evaluate.metrics.functional.f_beta"]], "binary_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[47, "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score"]], "binary_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score"]], "f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.f1_score"]], "fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[50, "cyclops.evaluate.metrics.functional.f_beta.fbeta_score"]], "multiclass_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[51, "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score"]], "multiclass_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[52, "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score"]], "multilabel_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[53, "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score"]], "multilabel_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[54, "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[55, "module-cyclops.evaluate.metrics.functional.precision_recall"]], "binary_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[56, "cyclops.evaluate.metrics.functional.precision_recall.binary_precision"]], "binary_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[57, "cyclops.evaluate.metrics.functional.precision_recall.binary_recall"]], "multiclass_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[58, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision"]], "multiclass_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[59, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall"]], "multilabel_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[60, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision"]], "multilabel_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[61, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall"]], "precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[62, "cyclops.evaluate.metrics.functional.precision_recall.precision"]], "recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[63, "cyclops.evaluate.metrics.functional.precision_recall.recall"]], "cyclops.evaluate.metrics.functional.precision_recall_curve": [[64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"]], "cyclops.evaluate.metrics.functional.roc": [[65, "module-cyclops.evaluate.metrics.functional.roc"]], "binary_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[66, "cyclops.evaluate.metrics.functional.roc.binary_roc_curve"]], "multiclass_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[67, "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve"]], "multilabel_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[68, "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve"]], "roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[69, "cyclops.evaluate.metrics.functional.roc.roc_curve"]], "cyclops.evaluate.metrics.functional.sensitivity": [[70, "module-cyclops.evaluate.metrics.functional.sensitivity"]], "cyclops.evaluate.metrics.functional.specificity": [[71, "module-cyclops.evaluate.metrics.functional.specificity"]], "cyclops.evaluate.metrics.functional.stat_scores": [[72, "module-cyclops.evaluate.metrics.functional.stat_scores"]], "cyclops.evaluate.metrics.metric": [[73, "module-cyclops.evaluate.metrics.metric"]], "metric (class in cyclops.evaluate.metrics.metric)": [[74, "cyclops.evaluate.metrics.metric.Metric"]], "__add__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__add__"]], "__call__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__call__"]], "__init__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__init__"]], "__mul__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__mul__"]], "add_state() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.add_state"]], "clone() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.clone"]], "compute() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.compute"]], "reset_state() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.reset_state"]], "update_state() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.update_state"]], "metriccollection (class in cyclops.evaluate.metrics.metric)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection"]], "__call__() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.__call__"]], "__init__() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.__init__"]], "add_metrics() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.add_metrics"]], "clear() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.clear"]], "clone() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.clone"]], "compute() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.compute"]], "get() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.get"]], "items() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.items"]], "keys() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.keys"]], "pop() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.pop"]], "popitem() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.popitem"]], "reset_state() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.reset_state"]], "setdefault() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.setdefault"]], "update() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.update"]], "update_state() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.update_state"]], "values() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.values"]], "operatormetric (class in cyclops.evaluate.metrics.metric)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric"]], "__add__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__add__"]], "__call__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__call__"]], "__init__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__init__"]], "__mul__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__mul__"]], "add_state() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.add_state"]], "clone() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.clone"]], "compute() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.compute"]], "reset_state() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.reset_state"]], "update_state() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.update_state"]], "cyclops.evaluate.metrics.precision_recall": [[77, "module-cyclops.evaluate.metrics.precision_recall"]], "binaryprecision (class in cyclops.evaluate.metrics.precision_recall)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision"]], "__add__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__add__"]], "__call__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__call__"]], "__init__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__init__"]], "__mul__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__mul__"]], "add_state() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.add_state"]], "clone() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.clone"]], "compute() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.compute"]], "reset_state() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.reset_state"]], "update_state() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.update_state"]], "binaryrecall (class in cyclops.evaluate.metrics.precision_recall)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall"]], "__add__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__add__"]], "__call__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__call__"]], "__init__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__init__"]], "__mul__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__mul__"]], "add_state() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.add_state"]], "clone() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.clone"]], "compute() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.compute"]], "reset_state() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.reset_state"]], "update_state() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.update_state"]], "multiclassprecision (class in cyclops.evaluate.metrics.precision_recall)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision"]], "__add__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__add__"]], "__call__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__call__"]], "__init__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__init__"]], "__mul__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__mul__"]], "add_state() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.add_state"]], "clone() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.clone"]], "compute() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.compute"]], "reset_state() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.reset_state"]], "update_state() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.update_state"]], "multiclassrecall (class in cyclops.evaluate.metrics.precision_recall)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall"]], "__add__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__add__"]], "__call__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__call__"]], "__init__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__init__"]], "__mul__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__mul__"]], "add_state() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.add_state"]], "clone() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.clone"]], "compute() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.compute"]], "reset_state() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.reset_state"]], "update_state() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.update_state"]], "multilabelprecision (class in cyclops.evaluate.metrics.precision_recall)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision"]], "__add__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__add__"]], "__call__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__call__"]], "__init__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__init__"]], "__mul__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__mul__"]], "add_state() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.add_state"]], "clone() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.clone"]], "compute() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.compute"]], "reset_state() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.reset_state"]], "update_state() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.update_state"]], "multilabelrecall (class in cyclops.evaluate.metrics.precision_recall)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall"]], "__add__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__add__"]], "__call__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__call__"]], "__init__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__init__"]], "__mul__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__mul__"]], "add_state() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.add_state"]], "clone() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.clone"]], "compute() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.compute"]], "reset_state() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.reset_state"]], "update_state() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.update_state"]], "precision (class in cyclops.evaluate.metrics.precision_recall)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision"]], "__add__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__add__"]], "__call__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__call__"]], "__init__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__init__"]], "__mul__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__mul__"]], "add_state() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.add_state"]], "clone() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.clone"]], "compute() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.compute"]], "reset_state() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.reset_state"]], "update_state() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.update_state"]], "recall (class in cyclops.evaluate.metrics.precision_recall)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall"]], "__add__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__add__"]], "__call__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__call__"]], "__init__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__init__"]], "__mul__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__mul__"]], "add_state() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.add_state"]], "clone() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.clone"]], "compute() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.compute"]], "reset_state() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.reset_state"]], "update_state() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.update_state"]], "cyclops.evaluate.metrics.precision_recall_curve": [[86, "module-cyclops.evaluate.metrics.precision_recall_curve"]], "binaryprecisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve"]], "__add__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__add__"]], "__call__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__call__"]], "__init__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__init__"]], "__mul__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__mul__"]], "add_state() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.add_state"]], "clone() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.clone"]], "compute() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.compute"]], "reset_state() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.reset_state"]], "update_state() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.update_state"]], "multiclassprecisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve"]], "__add__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__add__"]], "__call__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__call__"]], "__init__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__init__"]], "__mul__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__mul__"]], "add_state() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.add_state"]], "clone() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.clone"]], "compute() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.compute"]], "reset_state() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.reset_state"]], "update_state() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.update_state"]], "multilabelprecisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve"]], "__add__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__add__"]], "__call__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__call__"]], "__init__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__init__"]], "__mul__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__mul__"]], "add_state() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.add_state"]], "clone() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.clone"]], "compute() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.compute"]], "reset_state() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.reset_state"]], "update_state() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.update_state"]], "precisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve"]], "__add__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__add__"]], "__call__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__call__"]], "__init__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__init__"]], "__mul__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__mul__"]], "add_state() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.add_state"]], "clone() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.clone"]], "compute() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.compute"]], "reset_state() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.reset_state"]], "update_state() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.update_state"]], "cyclops.evaluate.metrics.roc": [[91, "module-cyclops.evaluate.metrics.roc"]], "binaryroccurve (class in cyclops.evaluate.metrics.roc)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve"]], "__add__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__add__"]], "__call__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__call__"]], "__init__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__init__"]], "__mul__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__mul__"]], "add_state() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.add_state"]], "clone() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.clone"]], "compute() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.compute"]], "reset_state() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.reset_state"]], "update_state() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.update_state"]], "multiclassroccurve (class in cyclops.evaluate.metrics.roc)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve"]], "__add__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__add__"]], "__call__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__call__"]], "__init__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__init__"]], "__mul__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__mul__"]], "add_state() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.add_state"]], "clone() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.clone"]], "compute() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.compute"]], "reset_state() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.reset_state"]], "update_state() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.update_state"]], "multilabelroccurve (class in cyclops.evaluate.metrics.roc)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve"]], "__add__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__add__"]], "__call__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__call__"]], "__init__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__init__"]], "__mul__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__mul__"]], "add_state() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.add_state"]], "clone() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.clone"]], "compute() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.compute"]], "reset_state() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.reset_state"]], "update_state() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.update_state"]], "roccurve (class in cyclops.evaluate.metrics.roc)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve"]], "__add__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__add__"]], "__call__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__call__"]], "__init__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__init__"]], "__mul__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__mul__"]], "add_state() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.add_state"]], "clone() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.clone"]], "compute() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.compute"]], "reset_state() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.reset_state"]], "update_state() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.update_state"]], "cyclops.evaluate.metrics.sensitivity": [[96, "module-cyclops.evaluate.metrics.sensitivity"]], "binarysensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity"]], "__add__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__add__"]], "__call__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__call__"]], "__init__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__init__"]], "__mul__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__mul__"]], "add_state() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.add_state"]], "clone() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.clone"]], "compute() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.compute"]], "reset_state() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.reset_state"]], "update_state() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.update_state"]], "multiclasssensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity"]], "__add__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__add__"]], "__call__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__call__"]], "__init__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__init__"]], "__mul__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__mul__"]], "add_state() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.add_state"]], "clone() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.clone"]], "compute() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.compute"]], "reset_state() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.reset_state"]], "update_state() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.update_state"]], "multilabelsensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity"]], "__add__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__add__"]], "__call__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__call__"]], "__init__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__init__"]], "__mul__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__mul__"]], "add_state() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.add_state"]], "clone() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.clone"]], "compute() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.compute"]], "reset_state() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.reset_state"]], "update_state() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.update_state"]], "sensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity"]], "__add__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__add__"]], "__call__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__call__"]], "__init__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__init__"]], "__mul__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__mul__"]], "add_state() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.add_state"]], "clone() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.clone"]], "compute() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.compute"]], "reset_state() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.reset_state"]], "update_state() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.update_state"]], "cyclops.evaluate.metrics.specificity": [[101, "module-cyclops.evaluate.metrics.specificity"]], "binaryspecificity (class in cyclops.evaluate.metrics.specificity)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity"]], "__add__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__add__"]], "__call__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__call__"]], "__init__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__init__"]], "__mul__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__mul__"]], "add_state() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.add_state"]], "clone() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.clone"]], "compute() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.compute"]], "reset_state() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.reset_state"]], "update_state() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.update_state"]], "multiclassspecificity (class in cyclops.evaluate.metrics.specificity)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity"]], "__add__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__add__"]], "__call__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__call__"]], "__init__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__init__"]], "__mul__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__mul__"]], "add_state() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.add_state"]], "clone() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.clone"]], "compute() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.compute"]], "reset_state() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.reset_state"]], "update_state() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.update_state"]], "multilabelspecificity (class in cyclops.evaluate.metrics.specificity)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity"]], "__add__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__add__"]], "__call__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__call__"]], "__init__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__init__"]], "__mul__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__mul__"]], "add_state() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.add_state"]], "clone() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.clone"]], "compute() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.compute"]], "reset_state() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.reset_state"]], "update_state() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.update_state"]], "specificity (class in cyclops.evaluate.metrics.specificity)": [[105, "cyclops.evaluate.metrics.specificity.Specificity"]], "__add__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__add__"]], "__call__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__call__"]], "__init__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__init__"]], "__mul__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__mul__"]], "add_state() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.add_state"]], "clone() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.clone"]], "compute() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.compute"]], "reset_state() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.reset_state"]], "update_state() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.update_state"]], "cyclops.evaluate.metrics.stat_scores": [[106, "module-cyclops.evaluate.metrics.stat_scores"]], "binarystatscores (class in cyclops.evaluate.metrics.stat_scores)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores"]], "__add__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__add__"]], "__call__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__call__"]], "__init__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__init__"]], "__mul__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__mul__"]], "add_state() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.add_state"]], "clone() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.clone"]], "compute() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.compute"]], "reset_state() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.reset_state"]], "update_state() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.update_state"]], "multiclassstatscores (class in cyclops.evaluate.metrics.stat_scores)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores"]], "__add__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__add__"]], "__call__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__call__"]], "__init__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__init__"]], "__mul__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__mul__"]], "add_state() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.add_state"]], "clone() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.clone"]], "compute() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.compute"]], "reset_state() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.reset_state"]], "update_state() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.update_state"]], "multilabelstatscores (class in cyclops.evaluate.metrics.stat_scores)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores"]], "__add__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__add__"]], "__call__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__call__"]], "__init__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__init__"]], "__mul__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__mul__"]], "add_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.add_state"]], "clone() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.clone"]], "compute() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.compute"]], "reset_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.reset_state"]], "update_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.update_state"]], "statscores (class in cyclops.evaluate.metrics.stat_scores)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores"]], "__add__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__add__"]], "__call__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__call__"]], "__init__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__init__"]], "__mul__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__mul__"]], "add_state() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.add_state"]], "clone() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.clone"]], "compute() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.compute"]], "reset_state() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.reset_state"]], "update_state() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.update_state"]], "cyclops.monitor.clinical_applicator": [[111, "module-cyclops.monitor.clinical_applicator"]], "clinicalshiftapplicator (class in cyclops.monitor.clinical_applicator)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator"]], "age() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.age"]], "apply_shift() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.apply_shift"]], "custom() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.custom"]], "hospital_type() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.hospital_type"]], "month() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.month"]], "sex() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.sex"]], "time() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.time"]], "cyclops.monitor.synthetic_applicator": [[113, "module-cyclops.monitor.synthetic_applicator"]], "syntheticshiftapplicator (class in cyclops.monitor.synthetic_applicator)": [[114, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator"]], "apply_shift() (syntheticshiftapplicator method)": [[114, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.apply_shift"]], "binary_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[115, "cyclops.monitor.synthetic_applicator.binary_noise_shift"]], "feature_association_shift() (in module cyclops.monitor.synthetic_applicator)": [[116, "cyclops.monitor.synthetic_applicator.feature_association_shift"]], "feature_swap_shift() (in module cyclops.monitor.synthetic_applicator)": [[117, "cyclops.monitor.synthetic_applicator.feature_swap_shift"]], "gaussian_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[118, "cyclops.monitor.synthetic_applicator.gaussian_noise_shift"]], "knockout_shift() (in module cyclops.monitor.synthetic_applicator)": [[119, "cyclops.monitor.synthetic_applicator.knockout_shift"]], "cyclops.report.report": [[120, "module-cyclops.report.report"]], "modelcardreport (class in cyclops.report.report)": [[121, "cyclops.report.report.ModelCardReport"]], "export() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.export"]], "from_json_file() (modelcardreport class method)": [[121, "cyclops.report.report.ModelCardReport.from_json_file"]], "log_citation() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_citation"]], "log_dataset() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_dataset"]], "log_descriptor() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_descriptor"]], "log_fairness_assessment() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_fairness_assessment"]], "log_from_dict() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_from_dict"]], "log_image() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_image"]], "log_license() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_license"]], "log_model_parameters() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_model_parameters"]], "log_owner() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_owner"]], "log_performance_metrics() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_performance_metrics"]], "log_plotly_figure() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_plotly_figure"]], "log_quantitative_analysis() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_quantitative_analysis"]], "log_reference() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_reference"]], "log_regulation() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_regulation"]], "log_risk() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_risk"]], "log_use_case() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_use_case"]], "log_user() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_user"]], "log_version() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_version"]], "cyclops.tasks.cxr_classification": [[122, "module-cyclops.tasks.cxr_classification"]], "cxrclassificationtask (class in cyclops.tasks.cxr_classification)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask"]], "__init__() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.__init__"]], "add_model() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.add_model"]], "data_type (cxrclassificationtask property)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.data_type"]], "evaluate() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.evaluate"]], "get_model() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.get_model"]], "list_models() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.list_models"]], "models_count (cxrclassificationtask property)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.models_count"]], "predict() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.predict"]], "task_type (cxrclassificationtask property)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.task_type"]], "cyclops.tasks.mortality_prediction": [[124, "module-cyclops.tasks.mortality_prediction"]], "mortalitypredictiontask (class in cyclops.tasks.mortality_prediction)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask"]], "__init__() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.__init__"]], "add_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.add_model"]], "data_type (mortalitypredictiontask property)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.data_type"]], "evaluate() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.evaluate"]], "get_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.get_model"]], "list_models() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.list_models"]], "list_models_params() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.list_models_params"]], "load_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.load_model"]], "models_count (mortalitypredictiontask property)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.models_count"]], "predict() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.predict"]], "save_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.save_model"]], "task_type (mortalitypredictiontask property)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.task_type"]], "train() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.train"]], "cyclops.data": [[126, "module-cyclops.data"]], "cyclops.data.features": [[126, "module-cyclops.data.features"]], "cyclops.evaluate": [[127, "module-cyclops.evaluate"]], "cyclops.evaluate.fairness": [[127, "module-cyclops.evaluate.fairness"]], "cyclops.evaluate.metrics": [[127, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[127, "module-cyclops.evaluate.metrics.functional"]], "cyclops.monitor": [[128, "module-cyclops.monitor"]], "cyclops.report": [[129, "module-cyclops.report"]], "cyclops.tasks": [[130, "module-cyclops.tasks"]]}}) \ No newline at end of file +Search.setIndex({"docnames": ["api", "contributing", "index", "intro", "reference/api/_autosummary/cyclops.data.features.medical_image", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage", "reference/api/_autosummary/cyclops.data.slicer", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec", "reference/api/_autosummary/cyclops.data.slicer.compound_filter", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null", "reference/api/_autosummary/cyclops.data.slicer.filter_range", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains", "reference/api/_autosummary/cyclops.data.slicer.filter_value", "reference/api/_autosummary/cyclops.data.slicer.is_datetime", "reference/api/_autosummary/cyclops.data.slicer.overall", "reference/api/_autosummary/cyclops.evaluate.evaluator", "reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate", "reference/api/_autosummary/cyclops.evaluate.fairness.config", "reference/api/_autosummary/cyclops.evaluate.fairness.config.FairnessConfig", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.Accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.BinaryAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MulticlassAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MultilabelAccuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.AUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.BinaryAUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MulticlassAUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MultilabelAUROC", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryF1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryFbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.F1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.FbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassF1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelF1Score", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore", "reference/api/_autosummary/cyclops.evaluate.metrics.factory", "reference/api/_autosummary/cyclops.evaluate.metrics.factory.create_metric", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.accuracy", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.auroc", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.precision", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.recall", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.binary_roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.roc_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.sensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.specificity", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.stat_scores", "reference/api/_autosummary/cyclops.evaluate.metrics.metric", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.Metric", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.MetricCollection", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.OperatorMetric", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryPrecision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryRecall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassPrecision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassRecall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelPrecision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelRecall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Precision", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Recall", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.BinaryROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MulticlassROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MultilabelROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.ROCCurve", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.BinarySensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.Sensitivity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.BinarySpecificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MulticlassSpecificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MultilabelSpecificity", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.Specificity", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.BinaryStatScores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MulticlassStatScores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MultilabelStatScores", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.StatScores", "reference/api/_autosummary/cyclops.monitor.clinical_applicator", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift", "reference/api/_autosummary/cyclops.report.report", "reference/api/_autosummary/cyclops.report.report.ModelCardReport", "reference/api/_autosummary/cyclops.tasks.cxr_classification", "reference/api/_autosummary/cyclops.tasks.cxr_classification.CXRClassificationTask", "reference/api/_autosummary/cyclops.tasks.mortality_prediction", "reference/api/_autosummary/cyclops.tasks.mortality_prediction.MortalityPredictionTask", "reference/api/cyclops.data", "reference/api/cyclops.evaluate", "reference/api/cyclops.monitor", "reference/api/cyclops.report", "reference/api/cyclops.tasks", "tutorials", "tutorials/kaggle/heart_failure_prediction", "tutorials/nihcxr/cxr_classification", "tutorials/nihcxr/monitor_api", "tutorials/synthea/los_prediction", "tutorials_monitor", "tutorials_use_cases"], "filenames": ["api.rst", "contributing.rst", "index.rst", "intro.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.rst", "reference/api/_autosummary/cyclops.data.features.medical_image.MedicalImage.rst", "reference/api/_autosummary/cyclops.data.slicer.rst", "reference/api/_autosummary/cyclops.data.slicer.SliceSpec.rst", "reference/api/_autosummary/cyclops.data.slicer.compound_filter.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_non_null.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_range.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_string_contains.rst", "reference/api/_autosummary/cyclops.data.slicer.filter_value.rst", "reference/api/_autosummary/cyclops.data.slicer.is_datetime.rst", "reference/api/_autosummary/cyclops.data.slicer.overall.rst", "reference/api/_autosummary/cyclops.evaluate.evaluator.rst", "reference/api/_autosummary/cyclops.evaluate.evaluator.evaluate.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.config.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.config.FairnessConfig.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.evaluate_fairness.rst", "reference/api/_autosummary/cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.Accuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.BinaryAccuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.AUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.BinaryAUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MulticlassAUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.auroc.MultilabelAUROC.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryF1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.F1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.FbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassF1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelF1Score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.factory.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.factory.create_metric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.accuracy.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.auroc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.binary_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall.recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.precision_recall_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.binary_roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.roc.roc_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.sensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.specificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.functional.stat_scores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.Metric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.MetricCollection.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.metric.OperatorMetric.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryPrecision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.BinaryRecall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MulticlassRecall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.MultilabelRecall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Precision.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall.Recall.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.BinaryROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MulticlassROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.MultilabelROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.roc.ROCCurve.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.BinarySensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.sensitivity.Sensitivity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.BinarySpecificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MulticlassSpecificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.MultilabelSpecificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.specificity.Specificity.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.BinaryStatScores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.rst", "reference/api/_autosummary/cyclops.evaluate.metrics.stat_scores.StatScores.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.rst", "reference/api/_autosummary/cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.binary_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_association_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.feature_swap_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.gaussian_noise_shift.rst", "reference/api/_autosummary/cyclops.monitor.synthetic_applicator.knockout_shift.rst", "reference/api/_autosummary/cyclops.report.report.rst", "reference/api/_autosummary/cyclops.report.report.ModelCardReport.rst", "reference/api/_autosummary/cyclops.tasks.cxr_classification.rst", "reference/api/_autosummary/cyclops.tasks.cxr_classification.CXRClassificationTask.rst", "reference/api/_autosummary/cyclops.tasks.mortality_prediction.rst", "reference/api/_autosummary/cyclops.tasks.mortality_prediction.MortalityPredictionTask.rst", "reference/api/cyclops.data.rst", "reference/api/cyclops.evaluate.rst", "reference/api/cyclops.monitor.rst", "reference/api/cyclops.report.rst", "reference/api/cyclops.tasks.rst", "tutorials.rst", "tutorials/kaggle/heart_failure_prediction.ipynb", "tutorials/nihcxr/cxr_classification.ipynb", "tutorials/nihcxr/monitor_api.ipynb", "tutorials/synthea/los_prediction.ipynb", "tutorials_monitor.rst", "tutorials_use_cases.rst"], "titles": ["API Reference", "Contributing to cyclops", "Welcome to cyclops\u2019s documentation!", "\ud83d\udc23 Getting Started", "cyclops.data.features.medical_image", "cyclops.data.features.medical_image.MedicalImage", "cyclops.data.slicer", "cyclops.data.slicer.SliceSpec", "cyclops.data.slicer.compound_filter", "cyclops.data.slicer.filter_datetime", "cyclops.data.slicer.filter_non_null", "cyclops.data.slicer.filter_range", "cyclops.data.slicer.filter_string_contains", "cyclops.data.slicer.filter_value", "cyclops.data.slicer.is_datetime", "cyclops.data.slicer.overall", "cyclops.evaluate.evaluator", "cyclops.evaluate.evaluator.evaluate", "cyclops.evaluate.fairness.config", "cyclops.evaluate.fairness.config.FairnessConfig", "cyclops.evaluate.fairness.evaluator", "cyclops.evaluate.fairness.evaluator.evaluate_fairness", "cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values", "cyclops.evaluate.metrics.accuracy", "cyclops.evaluate.metrics.accuracy.Accuracy", "cyclops.evaluate.metrics.accuracy.BinaryAccuracy", "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy", "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy", "cyclops.evaluate.metrics.auroc", "cyclops.evaluate.metrics.auroc.AUROC", "cyclops.evaluate.metrics.auroc.BinaryAUROC", "cyclops.evaluate.metrics.auroc.MulticlassAUROC", "cyclops.evaluate.metrics.auroc.MultilabelAUROC", "cyclops.evaluate.metrics.f_beta", "cyclops.evaluate.metrics.f_beta.BinaryF1Score", "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore", "cyclops.evaluate.metrics.f_beta.F1Score", "cyclops.evaluate.metrics.f_beta.FbetaScore", "cyclops.evaluate.metrics.f_beta.MulticlassF1Score", "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore", "cyclops.evaluate.metrics.f_beta.MultilabelF1Score", "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore", "cyclops.evaluate.metrics.factory", "cyclops.evaluate.metrics.factory.create_metric", "cyclops.evaluate.metrics.functional.accuracy", "cyclops.evaluate.metrics.functional.auroc", "cyclops.evaluate.metrics.functional.f_beta", "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score", "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score", "cyclops.evaluate.metrics.functional.f_beta.f1_score", "cyclops.evaluate.metrics.functional.f_beta.fbeta_score", "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score", "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score", "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score", "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score", "cyclops.evaluate.metrics.functional.precision_recall", "cyclops.evaluate.metrics.functional.precision_recall.binary_precision", "cyclops.evaluate.metrics.functional.precision_recall.binary_recall", "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision", "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall", "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision", "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall", "cyclops.evaluate.metrics.functional.precision_recall.precision", "cyclops.evaluate.metrics.functional.precision_recall.recall", "cyclops.evaluate.metrics.functional.precision_recall_curve", "cyclops.evaluate.metrics.functional.roc", "cyclops.evaluate.metrics.functional.roc.binary_roc_curve", "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve", "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve", "cyclops.evaluate.metrics.functional.roc.roc_curve", "cyclops.evaluate.metrics.functional.sensitivity", "cyclops.evaluate.metrics.functional.specificity", "cyclops.evaluate.metrics.functional.stat_scores", "cyclops.evaluate.metrics.metric", "cyclops.evaluate.metrics.metric.Metric", "cyclops.evaluate.metrics.metric.MetricCollection", "cyclops.evaluate.metrics.metric.OperatorMetric", "cyclops.evaluate.metrics.precision_recall", "cyclops.evaluate.metrics.precision_recall.BinaryPrecision", "cyclops.evaluate.metrics.precision_recall.BinaryRecall", "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision", "cyclops.evaluate.metrics.precision_recall.MulticlassRecall", "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision", "cyclops.evaluate.metrics.precision_recall.MultilabelRecall", "cyclops.evaluate.metrics.precision_recall.Precision", "cyclops.evaluate.metrics.precision_recall.Recall", "cyclops.evaluate.metrics.precision_recall_curve", "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve", "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve", "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve", "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve", "cyclops.evaluate.metrics.roc", "cyclops.evaluate.metrics.roc.BinaryROCCurve", "cyclops.evaluate.metrics.roc.MulticlassROCCurve", "cyclops.evaluate.metrics.roc.MultilabelROCCurve", "cyclops.evaluate.metrics.roc.ROCCurve", "cyclops.evaluate.metrics.sensitivity", "cyclops.evaluate.metrics.sensitivity.BinarySensitivity", "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity", "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity", "cyclops.evaluate.metrics.sensitivity.Sensitivity", "cyclops.evaluate.metrics.specificity", "cyclops.evaluate.metrics.specificity.BinarySpecificity", "cyclops.evaluate.metrics.specificity.MulticlassSpecificity", "cyclops.evaluate.metrics.specificity.MultilabelSpecificity", "cyclops.evaluate.metrics.specificity.Specificity", "cyclops.evaluate.metrics.stat_scores", "cyclops.evaluate.metrics.stat_scores.BinaryStatScores", "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores", "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores", "cyclops.evaluate.metrics.stat_scores.StatScores", "cyclops.monitor.clinical_applicator", "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator", "cyclops.monitor.synthetic_applicator", "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator", "cyclops.monitor.synthetic_applicator.binary_noise_shift", "cyclops.monitor.synthetic_applicator.feature_association_shift", "cyclops.monitor.synthetic_applicator.feature_swap_shift", "cyclops.monitor.synthetic_applicator.gaussian_noise_shift", "cyclops.monitor.synthetic_applicator.knockout_shift", "cyclops.report.report", "cyclops.report.report.ModelCardReport", "cyclops.tasks.cxr_classification", "cyclops.tasks.cxr_classification.CXRClassificationTask", "cyclops.tasks.mortality_prediction", "cyclops.tasks.mortality_prediction.MortalityPredictionTask", "cyclops.data", "cyclops.evaluate", "cyclops.monitor", "cyclops.report", "cyclops.tasks", "Tutorials", "Heart Failure Prediction", "Chest X-Ray Disease Classification", "NIHCXR Clinical Drift Experiments Tutorial", "Prolonged Length of Stay Prediction", "monitor API", "Example use cases"], "terms": {"cyclop": [0, 132, 133, 134, 135], "data": [0, 2, 3, 24, 26, 27, 49, 50, 52, 54, 69, 72, 89, 95, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 131, 133, 134, 136], "slicer": [0, 132, 133, 134, 135], "compound_filt": 0, "filter_datetim": 0, "filter_non_nul": 0, "filter_rang": 0, "filter_string_contain": 0, "filter_valu": [0, 133], "is_datetim": 0, "overal": [0, 7, 21, 121, 132, 133, 135], "slicespec": [0, 17, 112, 123, 132, 133, 134, 135], "spec_list": [0, 7, 132, 133, 134, 135], "include_overal": [0, 7], "valid": [0, 7, 9, 17, 121, 123, 125, 132], "column_nam": [0, 7, 9, 10, 11, 12, 13, 133], "_registri": [0, 7], "add_slice_spec": [0, 7], "get_slic": [0, 7], "slice": [0, 3, 7, 8, 17, 21, 121, 123, 125, 132, 133, 135], "featur": [0, 7, 9, 10, 11, 12, 13, 15, 17, 112, 116, 117, 121, 123, 125, 131, 133, 137], "medical_imag": 0, "medicalimag": 0, "__call__": [0, 5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "cast_storag": [0, 5], "decode_exampl": [0, 5], "embed_storag": [0, 5], "encode_exampl": [0, 5], "flatten": [0, 5, 132, 135], "task": [0, 2, 3, 24, 25, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 44, 47, 48, 49, 50, 51, 53, 54, 58, 60, 61, 62, 63, 66, 67, 68, 69, 78, 80, 81, 82, 83, 84, 85, 90, 92, 93, 94, 95, 98, 99, 100, 102, 103, 104, 105, 110, 131, 133, 137], "cxr_classif": 0, "cxrclassificationtask": 0, "__init__": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 123, 125], "add_model": [0, 123, 125], "data_typ": [0, 123, 125], "evalu": [0, 2, 3, 121, 123, 125, 131, 133, 137], "get_model": [0, 123, 125], "list_model": [0, 123, 125, 132, 135], "models_count": [0, 123, 125], "predict": [0, 3, 17, 19, 21, 24, 26, 27, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 67, 80, 81, 82, 84, 85, 87, 88, 89, 92, 93, 94, 98, 100, 102, 103, 104, 105, 107, 108, 109, 110, 123, 124, 125, 131], "task_typ": [0, 123, 125, 132, 133, 135], "mortality_predict": [0, 132, 135], "mortalitypredictiontask": [0, 132, 135], "list_models_param": [0, 125, 132, 135], "load_model": [0, 125], "save_model": [0, 125], "train": [0, 3, 17, 121, 123, 125, 131, 133, 136, 137], "metric": [0, 17, 19, 21, 121, 123, 125, 131, 132, 135, 137], "__add__": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "__mul__": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "add_stat": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "clone": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "comput": [0, 17, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 123, 125, 131, 137], "reset_st": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "update_st": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "metriccollect": [0, 17, 21, 123, 125, 132, 135], "add_metr": [0, 75], "clear": [0, 75], "get": [0, 2, 75, 123, 125, 131, 132, 135, 137], "item": [0, 75, 132, 133, 134, 135], "kei": [0, 7, 17, 21, 75, 121, 132, 133, 134, 135], "pop": [0, 75, 132, 135], "popitem": [0, 75], "setdefault": [0, 75], "updat": [0, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 134, 135], "valu": [0, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 131, 133, 134, 135, 137], "operatormetr": 0, "factori": [0, 7, 133], "create_metr": [0, 132, 133, 135], "accuraci": [0, 132, 135], "binaryaccuraci": [0, 132, 135], "multiclassaccuraci": 0, "multilabelaccuraci": 0, "auroc": [0, 131, 132, 135, 137], "binaryauroc": [0, 29, 132, 135], "multiclassauroc": [0, 29], "multilabelauroc": [0, 29, 133], "precision_recal": 0, "binaryprecis": [0, 132, 135], "binaryrecal": [0, 97, 132, 135], "multiclassprecis": 0, "multiclassrecal": [0, 98], "multilabelprecis": 0, "multilabelrecal": [0, 99], "precis": [0, 24, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 55, 56, 58, 60, 64, 66, 77, 78, 80, 82, 85, 86, 87, 88, 89, 90, 92, 100, 105, 132, 135], "recal": [0, 24, 38, 51, 55, 57, 59, 61, 64, 66, 77, 79, 81, 83, 86, 87, 88, 89, 90, 92, 97, 98, 99, 105, 132, 135], "precision_recall_curv": [0, 132, 135], "binaryprecisionrecallcurv": [0, 30, 92, 132, 135], "multiclassprecisionrecallcurv": [0, 31, 93], "multilabelprecisionrecallcurv": [0, 32, 94], "precisionrecallcurv": 0, "roc": [0, 28, 29, 30, 31, 32, 45, 132, 135], "binaryroccurv": [0, 132, 135], "multiclassroccurv": 0, "multilabelroccurv": 0, "roccurv": 0, "sensit": [0, 121, 131, 132, 133, 135, 136], "binarysensit": 0, "multiclasssensit": 0, "multilabelsensit": 0, "specif": [0, 7, 17, 115, 118, 123, 125, 132, 133, 135], "binaryspecif": 0, "multiclassspecif": 0, "multilabelspecif": 0, "f_beta": 0, "binaryf1scor": [0, 132, 135], "binaryfbetascor": [0, 34], "f1score": 0, "fbetascor": [0, 36], "multiclassf1scor": 0, "multiclassfbetascor": [0, 38], "multilabelf1scor": 0, "multilabelfbetascor": [0, 40], "stat_scor": 0, "binarystatscor": [0, 25, 35, 78, 79, 102], "multiclassstatscor": [0, 26, 39, 80, 81, 103], "multilabelstatscor": [0, 27, 41, 82, 83, 104], "statscor": 0, "function": [0, 3, 5, 6, 7, 8, 16, 17, 20, 21, 25, 35, 41, 42, 76, 93, 102, 104, 107, 109, 110, 113, 121, 132, 133, 135], "binary_precis": 0, "binary_recal": 0, "multiclass_precis": 0, "multiclass_recal": 0, "multilabel_precis": 0, "multilabel_recal": 0, "binary_roc_curv": 0, "multiclass_roc_curv": 0, "multilabel_roc_curv": 0, "roc_curv": [0, 132, 135], "binary_f1_scor": 0, "binary_fbeta_scor": 0, "f1_score": [0, 132, 135], "fbeta_scor": 0, "multiclass_f1_scor": 0, "multiclass_fbeta_scor": 0, "multilabel_f1_scor": 0, "multilabel_fbeta_scor": 0, "fair": [0, 17, 121, 123, 125, 132, 133, 135], "evaluate_fair": [0, 133], "warn_too_many_unique_valu": 0, "config": [0, 125], "fairnessconfig": [0, 17, 123, 125, 132, 135], "monitor": [0, 2, 3, 131, 132, 133, 134, 135], "clinical_appl": 0, "clinicalshiftappl": [0, 134], "ag": [0, 112, 131, 134, 137], "apply_shift": [0, 112, 114, 134], "custom": [0, 112, 121, 134], "hospital_typ": [0, 112], "month": [0, 7, 9, 112, 132, 133, 135], "sex": [0, 112, 131, 134, 135, 137], "time": [0, 7, 75, 112, 121, 131, 136, 137], "synthetic_appl": 0, "binary_noise_shift": 0, "feature_association_shift": 0, "feature_swap_shift": 0, "gaussian_noise_shift": 0, "knockout_shift": 0, "syntheticshiftappl": [0, 113], "report": [0, 2, 3, 110, 131, 133, 137], "modelcardreport": [0, 132, 133, 135], "export": [0, 121, 132, 133, 135], "from_json_fil": [0, 121], "log_cit": [0, 121, 133], "log_dataset": [0, 121, 132], "log_descriptor": [0, 121, 132, 133, 135], "log_fairness_assess": [0, 121, 132, 133, 135], "log_from_dict": [0, 121, 132, 133, 135], "log_imag": [0, 121], "log_licens": [0, 121, 132, 135], "log_model_paramet": [0, 121, 132, 135], "log_own": [0, 121, 132, 133, 135], "log_performance_metr": [0, 121, 132, 135], "log_plotly_figur": [0, 121, 132, 133, 135], "log_quantitative_analysi": [0, 121, 132, 133, 135], "log_refer": [0, 121, 132, 135], "log_regul": [0, 121], "log_risk": [0, 121, 132, 133, 135], "log_use_cas": [0, 121, 132, 133, 135], "log_us": [0, 121, 132, 133, 135], "log_vers": [0, 121, 132, 135], "thank": 1, "your": [1, 132], "interest": [1, 132, 135], "To": [1, 3, 5, 132, 135], "submit": 1, "pr": 1, "pleas": [1, 132, 133, 134, 135], "fill": 1, "out": [1, 121, 132, 135], "templat": [1, 121], "along": [1, 112, 132, 133, 135], "If": [1, 5, 7, 9, 10, 11, 12, 13, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 135], "fix": 1, "an": [1, 3, 5, 7, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 51, 60, 61, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 132, 133, 135], "issu": [1, 21], "don": 1, "t": [1, 5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121], "forget": 1, "link": [1, 121, 132, 133, 135], "onc": [1, 75, 132, 133, 135], "python": [1, 3, 135], "virtual": [1, 3], "environ": [1, 3, 132, 135], "i": [1, 3, 5, 7, 9, 10, 11, 12, 13, 14, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 133, 135, 137], "setup": [1, 135], "you": [1, 3, 5, 75, 132, 133, 135, 136], "can": [1, 3, 5, 7, 21, 25, 38, 51, 69, 75, 84, 85, 95, 100, 110, 121, 125, 132, 133, 135, 136], "run": [1, 3, 132, 135], "us": [1, 2, 5, 7, 8, 17, 21, 24, 29, 30, 31, 32, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 60, 61, 62, 63, 66, 67, 68, 69, 75, 76, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 98, 99, 100, 102, 105, 107, 110, 112, 114, 121, 123, 125, 131, 132, 133, 135, 136], "all": [1, 7, 8, 9, 10, 11, 12, 13, 15, 63, 73, 75, 108, 109, 110, 125, 132, 134, 135], "file": [1, 5, 121, 132, 133, 135], "For": [1, 21, 76, 121, 132, 135], "style": 1, "we": [1, 3, 121, 132, 133, 135], "recommend": [1, 76], "googl": 1, "guid": 1, "appli": [1, 8, 25, 29, 59, 62, 63, 66, 67, 68, 75, 76, 93, 104, 109, 110, 112, 123, 125, 132, 135], "black": 1, "format": [1, 5, 7, 89, 121, 132, 135], "docstr": 1, "numpi": [1, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 115, 116, 117, 118, 119, 123, 132, 133, 134, 135], "also": [1, 3, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 135, 137], "flake8": 1, "pylint": 1, "further": 1, "static": 1, "analysi": [1, 121, 132, 133, 135], "The": [1, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 60, 61, 63, 66, 68, 69, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 133, 135, 136, 137], "show": [1, 132, 133, 135], "error": [1, 131, 137], "which": [1, 9, 10, 11, 12, 13, 21, 90, 121, 132, 133, 135, 137], "need": [1, 17, 21, 132, 135], "befor": [1, 17, 21, 22, 125, 132, 135], "last": 1, "least": 1, "type": [1, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 131, 137], "hint": 1, "our": [1, 132, 135], "check": [1, 14, 89], "mypi": 1, "current": [1, 121, 132, 133, 135], "ar": [1, 5, 7, 11, 12, 17, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 97, 98, 99, 100, 103, 104, 105, 108, 109, 110, 112, 116, 121, 132, 133, 135], "strict": 1, "enforc": 1, "more": [1, 7, 16, 17, 123, 125, 132, 137], "api": [1, 2, 3, 131, 132, 137], "becom": 1, "stabl": [1, 132, 133, 134, 135], "start": [2, 17, 132, 135], "instal": [2, 132], "pip": [2, 132], "develop": [2, 132, 133, 135], "poetri": 2, "contribut": 2, "notebook": [2, 132, 133, 135], "citat": [2, 121, 132, 133, 135], "pre": [2, 132, 135], "commit": 2, "hook": 2, "code": [2, 132, 135], "guidelin": [2, 3], "tutori": [2, 132, 133, 135, 136, 137], "exampl": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 131, 132, 133, 135, 136], "case": [2, 3, 75, 115, 121, 131, 132, 135, 136], "refer": [2, 3, 121, 132, 133, 135], "toolkit": 3, "facilit": 3, "research": 3, "deploy": 3, "ml": [3, 132, 135], "model": [3, 16, 17, 21, 121, 123, 125, 131, 136, 137], "healthcar": 3, "It": [3, 38, 51, 75, 84, 85, 100, 105, 136], "provid": [3, 7, 9, 12, 17, 21, 69, 110, 121, 132, 135], "few": 3, "high": [3, 132, 135], "level": [3, 21, 132, 135], "name": [3, 7, 8, 9, 10, 11, 12, 13, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 123, 125, 132, 133, 134, 135], "creat": [3, 6, 7, 21, 42, 43, 75, 84, 85, 100, 115, 118, 119, 121, 123, 125, 131, 133, 137], "dataset": [3, 6, 7, 16, 17, 19, 21, 26, 38, 39, 51, 52, 58, 61, 68, 69, 80, 81, 83, 88, 89, 90, 94, 95, 98, 99, 103, 104, 112, 114, 121, 123, 125, 126, 131, 136, 137], "infer": [3, 17], "popular": [3, 132], "effici": 3, "load": [3, 17, 121, 125, 131, 135, 136, 137], "differ": [3, 24, 29, 36, 37, 46, 55, 62, 63, 64, 69, 70, 72, 84, 85, 90, 95, 100, 105, 131, 132, 133, 135, 136], "modal": 3, "common": [3, 132], "implement": [3, 137], "scikit": [3, 132], "learn": [3, 132, 133], "pytorch": 3, "canon": 3, "mortal": [3, 124, 125], "chest": [3, 122, 123, 131], "x": [3, 114, 115, 116, 117, 118, 119, 122, 123, 125, 131, 132, 134, 135], "rai": [3, 122, 123, 131], "classif": [3, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 44, 47, 48, 49, 50, 51, 53, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 78, 79, 80, 81, 82, 83, 84, 85, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 122, 123, 125, 131, 132, 135], "clinic": [3, 111, 112, 131, 136], "detect": [3, 133, 136], "shift": [3, 111, 112, 114, 116, 117, 131, 136], "relev": [3, 121, 132, 135, 136], "card": [3, 121, 131, 132, 135, 137], "librari": [3, 131, 136, 137], "end": [3, 132, 133, 135], "mimic": 3, "iii": 3, "iv": 3, "eicu": 3, "crd": 3, "python3": [3, 132, 133, 134, 135], "m": [3, 132, 133, 134, 135], "pycyclop": [3, 132, 133, 134, 135], "base": [3, 5, 7, 17, 19, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 131, 132, 137], "packag": [3, 126, 127, 128, 129, 130, 132, 133, 134, 135], "support": [3, 7, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 72, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 107, 108, 109, 136], "process": [3, 112, 132, 133, 135], "transform": [3, 17, 66, 67, 68, 93, 123, 125, 132, 133, 134, 135], "downstream": [3, 132, 135], "addit": [3, 75, 121, 123, 125, 132, 135], "from": [3, 5, 7, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 119, 121, 123, 125, 132, 133, 134, 135], "other": [3, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132], "thei": [3, 69], "extra": [3, 121], "multipl": [3, 8, 17, 21, 75, 121], "could": [3, 132, 135], "combin": [3, 8, 132], "both": 3, "set": [3, 7, 17, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 74, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 132, 133, 135], "up": [3, 132, 133, 135], "henc": 3, "make": [3, 132, 135], "sure": [3, 132], "sourc": [3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 131, 136], "env": 3, "info": [3, 132, 135], "path": [3, 5, 112, 121, 125, 132, 133, 135], "bin": [3, 21], "activ": [3, 135], "In": [3, 75, 132, 135], "order": [3, 5, 17, 107, 108, 109], "depend": 3, "test": [3, 17, 121, 123, 125, 131, 132, 135, 136, 137], "built": 3, "sphinx": 3, "local": 3, "doc": 3, "cd": 3, "html": [3, 121, 132, 133, 134, 135], "sphinxopt": 3, "d": [3, 75, 112, 133], "nbsphinx_allow_error": 3, "true": [3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 24, 26, 27, 31, 32, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 112, 114, 116, 121, 123, 125, 132, 133, 134, 135], "welcom": 3, "see": [3, 7, 121, 132, 133, 134, 135], "jupyt": [3, 132, 133, 134, 135], "insid": 3, "ipython": 3, "kernel": 3, "after": [3, 17, 132, 135], "ipykernel": 3, "user": [3, 121, 132, 135], "name_of_kernel": 3, "now": 3, "navig": 3, "": [3, 7, 10, 14, 17, 21, 75, 121, 123, 125, 132, 133, 134, 135], "tab": [3, 132], "cite": 3, "when": [3, 5, 17, 21, 24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 75, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105, 123, 125, 132, 135], "project": 3, "paper": 3, "articl": 3, "krishnan2022": 3, "12": [3, 7, 132, 133, 135], "02": [3, 69], "22283021": 3, "author": [3, 132, 133], "krishnan": 3, "amrit": 3, "subasri": 3, "vallijah": 3, "mckeen": 3, "kaden": 3, "kore": 3, "ali": 3, "ogidi": 3, "franklin": 3, "alinoori": 3, "mahshid": 3, "lalani": 3, "nadim": 3, "dhalla": 3, "azra": 3, "verma": 3, "amol": 3, "razak": 3, "fahad": 3, "pandya": 3, "deval": 3, "dolatabadi": 3, "elham": 3, "titl": [3, 132, 133, 135], "cyclic": 3, "toward": 3, "operation": 3, "health": [3, 132, 135], "eloc": 3, "id": [3, 5, 112, 132, 133, 135], "2022": [3, 7, 133], "year": [3, 7, 9, 132, 133, 135], "doi": 3, "10": [3, 132, 133, 134, 135], "1101": 3, "publish": [3, 132], "cold": 3, "spring": 3, "harbor": 3, "laboratori": [3, 135], "press": 3, "url": [3, 133], "http": [3, 121, 132, 133, 134, 135], "www": [3, 132], "medrxiv": 3, "org": [3, 121, 132, 133, 135], "content": [3, 121], "earli": 3, "08": [3, 132, 135], "journal": 3, "medic": [4, 5, 126, 133, 135, 137], "imag": [4, 5, 17, 21, 118, 121, 123, 126, 131, 133, 134], "class": [4, 5, 6, 7, 17, 18, 19, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 69, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 116, 117, 119, 120, 121, 122, 123, 124, 125, 132, 133, 135], "decod": [5, 133], "none": [5, 7, 9, 17, 19, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 123, 125, 132, 133, 134, 135], "reader": 5, "itkread": 5, "suffix": 5, "jpg": 5, "read": [5, 17], "paramet": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 132, 133, 135], "bool": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 75, 76, 108, 109, 110, 112, 116, 117, 121, 123, 125, 133], "option": [5, 7, 9, 10, 11, 12, 13, 17, 21, 24, 27, 36, 37, 38, 39, 40, 41, 43, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 69, 75, 80, 81, 82, 83, 84, 85, 90, 95, 98, 99, 100, 103, 104, 105, 108, 112, 114, 121, 123, 125], "default": [5, 7, 9, 10, 11, 12, 13, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 121, 123, 125, 132, 135], "whether": [5, 7, 21, 75, 108, 109, 110, 112, 121, 135, 137], "fals": [5, 7, 9, 10, 11, 12, 13, 14, 19, 21, 29, 30, 40, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 105, 107, 108, 109, 110, 117, 118, 123, 125, 132, 133, 135], "return": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 115, 116, 117, 118, 119, 121, 123, 125, 135], "dictionari": [5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 75, 121, 123, 125, 132, 135], "image_path": 5, "byte": 5, "image_byt": 5, "union": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125], "str": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 135], "imageread": 5, "monai": [5, 133, 134], "method": [5, 7, 19, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 123, 125, 132, 133, 135], "attribut": [5, 7, 19, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 123, 125, 132, 135], "call": [5, 121], "self": 5, "storag": 5, "cast": [5, 132, 135], "arrow": 5, "arrai": [5, 24, 26, 27, 29, 30, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 60, 61, 63, 66, 67, 68, 69, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 116, 117, 119, 123, 133], "convert": [5, 25, 35, 41, 48, 61, 69, 95, 102, 104, 107, 132, 135], "pyarrow": 5, "rtype": 5, "structarrai": 5, "pa": 5, "string": [5, 7, 9, 12, 17, 21, 75, 121, 133], "must": [5, 9, 17, 21, 121], "contain": [5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 27, 103, 104, 121, 132, 133, 135, 137], "binari": [5, 24, 25, 29, 30, 34, 35, 36, 37, 47, 48, 49, 50, 56, 57, 60, 61, 62, 63, 66, 69, 72, 78, 79, 84, 85, 87, 90, 92, 95, 97, 100, 102, 104, 105, 107, 110, 115, 125, 132, 133, 135, 137], "struct": 5, "doesn": 5, "matter": 5, "list": [5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 66, 67, 68, 69, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 115, 116, 117, 118, 119, 121, 123, 125, 132, 135], "arg": [5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "stringarrai": 5, "listarrai": 5, "token_per_repo_id": 5, "serial": 5, "version": [5, 121, 132, 133, 135], "dict": [5, 7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 75, 121, 123, 125], "access": 5, "privat": 5, "repositori": [5, 132], "hub": 5, "pass": [5, 17, 43, 75, 112, 121, 125, 132, 135], "repo_id": 5, "token": [5, 132], "deseri": 5, "np": [5, 11, 14, 21, 123, 125, 132, 133, 134, 135], "ndarrai": [5, 14, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 115, 116, 117, 118, 119, 123, 125], "metadata": [5, 132, 133, 135], "emb": 5, "encod": 5, "input": [5, 24, 46, 55, 60, 61, 64, 69, 70, 72, 87, 89, 95, 115, 118, 123, 125], "state": [5, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "itself": 5, "otherwis": [5, 14, 24, 27, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 67, 68, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 108, 109, 110], "tupl": [5, 7, 66, 67, 68, 69, 75, 87, 88, 89, 92, 93, 94, 112, 123, 125], "classlabel": [5, 132, 135], "translat": 5, "translationvariablelanguag": 5, "sequenc": [5, 17, 75, 123, 125, 133], "array2d": 5, "array3d": 5, "array4d": 5, "array5d": 5, "audio": 5, "subset": 6, "hug": [6, 123, 125, 131, 137], "face": [6, 123, 125, 131, 137], "object": [7, 19, 21, 112, 114, 121, 123, 125, 132, 135], "ani": [7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 66, 67, 68, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 133, 135], "A": [7, 8, 9, 10, 11, 12, 13, 15, 17, 21, 22, 25, 75, 76, 104, 109, 121, 132, 133, 135], "each": [7, 8, 17, 21, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 67, 68, 69, 75, 76, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 108, 109, 110, 132, 135], "map": [7, 8, 22, 43, 75, 123, 125, 132, 133, 135], "column": [7, 8, 9, 10, 11, 12, 13, 17, 21, 112, 123, 125, 132, 133, 135], "one": [7, 16, 17, 21, 24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 67, 68, 69, 76, 80, 81, 82, 83, 84, 85, 95, 98, 99, 100, 105, 123, 125], "follow": [7, 17, 24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 60, 61, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 105, 121, 132, 133, 135], "exact": [7, 13], "select": [7, 112, 116, 132, 133, 135], "thi": [7, 17, 21, 24, 25, 26, 27, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105, 121, 125, 132, 133, 135, 137], "singl": [7, 75, 121, 125, 132, 135], "row": [7, 132, 133], "where": [7, 8, 9, 10, 11, 12, 13, 60, 61, 63, 75, 121, 125, 132, 135, 137], "e": [7, 9, 10, 17, 21, 75, 116, 117, 118, 121, 132, 135], "g": [7, 9, 17, 21, 116, 117, 118, 121, 132, 135], "2021": [7, 132], "01": [7, 29, 31, 32, 132, 133, 135], "00": [7, 132, 133, 134, 135], "min_valu": [7, 11, 132, 133, 134, 135], "minimum": [7, 11], "specifi": [7, 17, 75, 112, 121, 123, 125, 132, 133, 135], "min_inclus": [7, 11, 132, 135], "indic": [7, 21, 27, 60, 61, 115, 118, 132, 135], "includ": [7, 11, 21, 72, 112, 114, 132, 133, 135, 136], "rang": [7, 11, 29, 30, 66, 67, 68, 93, 132, 133, 135], "work": [7, 27, 103, 104, 121, 132, 133, 135], "numer": [7, 11, 132, 135], "datetim": [7, 9, 11, 14, 121, 132, 133, 135], "inf": [7, 11, 132, 133, 135], "max_valu": [7, 11, 132, 133, 134, 135], "boolean": [7, 8, 9, 10, 11, 12, 13, 15], "greater": [7, 22, 135], "than": [7, 11, 22, 48, 52, 54, 123, 125, 132, 135, 137], "equal": [7, 11, 21], "maximum": [7, 11, 22, 29, 30], "max_inclus": [7, 11, 132, 135], "less": [7, 11, 48, 52, 54, 135], "match": [7, 9, 12, 13, 17], "between": [7, 21, 38, 51, 69, 95], "1": [7, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 116, 117, 118, 119, 121, 131, 132, 133, 135, 136, 137], "dai": [7, 9, 135, 137], "31": [7, 132, 135], "hour": [7, 9], "0": [7, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 114, 115, 116, 117, 118, 119, 121, 132, 133, 134, 135], "23": [7, 132, 135], "negat": [7, 9, 10, 11, 12, 13, 133], "flag": 7, "doe": [7, 9, 11, 12, 13, 17, 21, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 75, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 121], "keep_nul": [7, 9, 11, 12, 13], "keep": [7, 17, 21, 135], "null": [7, 9, 10, 11, 12, 13, 135], "conjunct": [7, 133], "its": [7, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 60, 61, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 135], "own": [7, 132, 135], "callabl": [7, 8, 17, 21, 76, 121], "import": [7, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 114, 121, 131, 136, 137], "slice_spec": [7, 17, 123, 125, 132, 133, 135], "feature_1": 7, "feature_2": 7, "feature_3": 7, "value_1": 7, "value_2": 7, "2020": [7, 9, 133], "5": [7, 24, 25, 27, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 114, 115, 118, 119, 131, 132, 133, 135, 136], "60": 7, "6": [7, 24, 26, 35, 36, 38, 39, 49, 56, 59, 62, 63, 78, 79, 80, 81, 83, 84, 85, 87, 88, 90, 92, 93, 95, 97, 98, 99, 100, 104, 107, 108, 110, 132, 133, 134, 135], "7": [7, 29, 30, 31, 36, 39, 40, 69, 80, 81, 82, 84, 85, 87, 88, 89, 93, 98, 100, 105, 108, 109, 110, 132, 133, 135, 137], "8": [7, 24, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 40, 41, 47, 49, 50, 53, 54, 56, 59, 60, 62, 66, 68, 69, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 99, 100, 105, 107, 109, 110, 132, 133, 135], "2000": 7, "2010": 7, "slice_nam": [7, 121, 132, 133, 135], "slice_func": 7, "print": [7, 132, 133, 135], "do": [7, 17], "someth": 7, "here": [7, 132, 135], "filter": [7, 9, 10, 11, 12, 13, 17, 21, 132, 133, 134, 135], "add": [7, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 133, 135], "detail": [7, 132, 133, 135], "registri": [7, 132, 135], "gener": [7, 69, 95, 112, 121, 131, 133, 136, 137], "slice_funct": 8, "result": [8, 17, 38, 51, 123, 125, 132, 133, 134, 135], "bitwis": 8, "AND": 8, "signatur": 8, "should": [8, 21, 69, 76, 95, 117, 121, 123, 125, 132, 133, 135], "kwarg": [8, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 43, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 114, 123, 125], "given": [9, 11, 12, 13, 14, 24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 75, 80, 81, 82, 84, 85, 98, 100, 105, 108, 109, 110, 121, 123, 125], "int": [9, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 116, 117, 118, 119, 121, 123, 125, 132, 135], "compon": 9, "have": [9, 12, 13, 17, 114, 132, 137], "nan": [9, 10, 131, 137], "nat": 9, "rais": [9, 11, 12, 17, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 69, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125], "typeerror": [9, 11, 12, 21, 22, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121], "float": [11, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 109, 110, 115, 116, 117, 118, 119, 121], "valueerror": [11, 17, 21, 48, 50, 52, 54, 58, 59, 60, 61, 62, 63, 69, 121, 123, 125], "either": [11, 30, 31, 32, 75, 87, 88, 89, 92, 93, 94, 110, 121, 132, 135], "substr": 12, "ha": [13, 75, 121, 132, 133, 135], "find": [13, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 133], "perform": [13, 26, 27, 31, 32, 121, 131, 136, 137], "datetime64": 14, "target_column": [17, 19, 21, 132, 133, 135], "feature_column": [17, 133, 134], "prediction_column_prefix": [17, 123, 125, 132, 133, 135], "remove_column": [17, 19, 21, 123, 125, 133], "split": [17, 112, 121, 123, 125, 132, 133, 135], "batch_siz": [17, 19, 21, 112, 123, 125, 132, 135], "1000": [17, 19, 21, 112, 125, 132], "fairness_config": [17, 123, 125, 132, 135], "override_fairness_metr": [17, 123, 125, 132, 135], "load_dataset_kwarg": 17, "datasetdict": [17, 123, 125], "load_dataset": 17, "argument": [17, 21, 43, 75, 123, 125, 132, 135], "target": [17, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 116, 117, 123, 125, 131, 132, 135, 136, 137], "prefix": [17, 75], "ad": [17, 114, 121, 123, 125, 132, 135], "model_nam": [17, 123, 125, 132, 133, 134, 135], "remov": [17, 21, 75, 119, 123, 125, 132, 133, 135], "mai": [17, 21, 132, 133, 135], "expens": [17, 21], "memori": [17, 21], "wrappedmodel": [17, 123, 125], "entir": [17, 132, 135], "being": 17, "note": [17, 121, 132, 133, 135], "chosen": 17, "avail": [17, 121, 132, 135, 137], "first": [17, 21, 25, 76, 104, 132, 135], "eval": 17, "val": 17, "dev": 17, "batch": [17, 21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 112, 123, 125, 133], "size": [17, 21, 112, 123, 125, 132, 133, 135], "neg": [17, 35, 48, 49, 50, 51, 52, 53, 54, 59, 61, 62, 63, 72, 81, 83, 85, 98, 99, 100, 105, 107, 108, 109, 135], "integ": [17, 21, 121], "configur": [17, 18, 19, 123, 125, 132, 135], "overridden": [17, 123, 125], "prediction_column": [17, 19, 21, 133], "keyword": [17, 21, 43, 75, 125], "onli": [17, 21, 24, 27, 29, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 62, 63, 75, 80, 81, 82, 84, 85, 98, 100, 103, 104, 105, 108, 109, 110], "found": [17, 75, 121, 132, 133, 134, 135], "group": [19, 21, 22, 75, 121, 132, 133, 135], "group_valu": [19, 21], "group_bin": [19, 21, 132, 133, 135], "group_base_valu": [19, 21, 132, 133, 135], "threshold": [19, 21, 24, 25, 27, 29, 30, 31, 32, 34, 35, 36, 37, 40, 41, 47, 48, 49, 50, 53, 54, 56, 57, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 99, 100, 102, 104, 105, 107, 109, 110, 121, 131, 132, 135, 137], "compute_optimal_threshold": [19, 21], "metric_nam": [19, 21, 43, 121, 132, 133, 135], "metric_kwarg": [19, 21], "take": [21, 24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 112, 132], "allow": [21, 22, 132, 135, 136], "intersect": 21, "treat": 21, "multilabel": [21, 24, 27, 29, 32, 36, 37, 40, 41, 49, 50, 53, 54, 60, 61, 62, 63, 68, 69, 72, 82, 83, 84, 85, 89, 90, 94, 95, 99, 100, 104, 105, 109, 110, 131, 137], "same": [21, 75, 116], "uniqu": [21, 22, 29, 30, 31, 32, 66, 67, 68, 69, 87, 88, 89, 92, 93, 94, 95, 133, 137], "limit": [21, 132, 133, 135], "number": [21, 22, 24, 26, 27, 29, 30, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 98, 99, 100, 103, 104, 105, 108, 110, 112, 116, 117, 121, 123, 125, 135, 136], "continu": [21, 132, 133, 135], "veri": 21, "slow": 21, "larg": 21, "denomin": 21, "pariti": [21, 131, 137], "across": [21, 116, 136], "linspac": 21, "monoton": [21, 69, 95], "control": [21, 115], "usag": [21, 132, 135], "rel": 21, "small": 21, "32": [21, 132, 134, 135], "avoid": 21, "optim": [21, 132], "oper": [21, 65, 76], "necessari": 21, "nest": 21, "second": [21, 76], "third": 21, "omit": 21, "requir": [21, 24, 29, 36, 37, 49, 50, 69, 84, 85, 90, 95, 100, 105, 110, 121, 123, 125, 132, 135], "huggingfac": [21, 112, 123, 125], "runtimeerror": 21, "empti": [21, 24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "encount": [21, 135], "unique_valu": 22, "max_unique_valu": 22, "50": [22, 132, 133, 134, 135], "warn": [22, 24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 105], "score": [24, 25, 26, 27, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 66, 70, 72, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 97, 98, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110], "liter": [24, 25, 26, 27, 29, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 69, 78, 79, 80, 81, 82, 83, 84, 85, 90, 95, 97, 98, 99, 100, 103, 104, 105, 110, 121], "multiclass": [24, 26, 29, 31, 36, 37, 38, 39, 49, 50, 51, 52, 58, 59, 62, 63, 67, 69, 72, 80, 81, 84, 85, 88, 90, 93, 95, 98, 100, 103, 105, 108, 110], "One": [24, 29, 31, 32, 35, 48, 59, 62, 63, 69, 95, 133, 135], "pos_label": [24, 25, 30, 34, 35, 36, 37, 47, 48, 49, 50, 56, 57, 62, 63, 66, 69, 78, 79, 84, 85, 87, 90, 92, 95, 97, 100, 102, 105, 107, 110], "label": [24, 25, 27, 29, 32, 34, 35, 36, 37, 40, 41, 47, 48, 49, 50, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 68, 69, 78, 79, 81, 82, 83, 84, 85, 87, 89, 90, 92, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 109, 110, 116, 117, 119, 123, 125, 131, 132, 133, 137], "consid": [24, 26, 27, 36, 37, 49, 50, 62, 63, 84, 85, 90, 95, 100, 103, 104, 105], "posit": [24, 25, 29, 30, 34, 35, 36, 37, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 75, 78, 79, 80, 81, 82, 83, 84, 85, 87, 90, 92, 95, 97, 98, 99, 100, 102, 105, 107, 108, 109, 110, 133], "num_class": [24, 26, 29, 31, 36, 37, 38, 39, 49, 50, 51, 52, 58, 59, 61, 62, 63, 67, 69, 80, 81, 84, 85, 88, 90, 93, 95, 98, 100, 103, 105, 108, 110, 132, 135], "decid": [24, 36, 37, 40, 41, 49, 50, 53, 54, 56, 57, 60, 61, 78, 79, 82, 83, 84, 85, 97, 99, 100, 105], "top_k": [24, 26, 27, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 108, 109, 110], "probabl": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 72, 80, 81, 82, 84, 85, 93, 98, 100, 102, 103, 104, 105, 107, 108, 109, 110, 125, 132, 135], "logit": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 72, 80, 81, 82, 84, 85, 98, 100, 102, 103, 104, 105, 107, 108, 109, 110], "top": [24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 80, 81, 82, 84, 85, 98, 100, 105, 108, 109, 110], "k": [24, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 75, 80, 81, 82, 84, 85, 98, 100, 105, 108, 109, 110, 134], "num_label": [24, 27, 29, 32, 36, 37, 40, 41, 49, 50, 53, 54, 60, 61, 62, 63, 68, 69, 82, 83, 84, 85, 89, 90, 94, 95, 99, 100, 104, 105, 109, 110, 133], "averag": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132], "micro": [24, 26, 27, 29, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "macro": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "weight": [24, 26, 27, 29, 31, 32, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 133, 134, 135], "calcul": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "global": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "unweight": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105], "mean": [24, 26, 27, 29, 31, 32, 35, 36, 37, 38, 39, 40, 41, 48, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132, 133, 134, 135], "imbal": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 119], "account": [24, 26, 27, 29, 31, 32, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132, 133], "instanc": [24, 26, 27, 31, 32, 36, 37, 38, 39, 40, 41, 43, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 103, 104, 105, 132, 135], "alter": [24, 26, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 105], "zero_divis": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 102, 103, 104, 105], "zero": [24, 25, 26, 27, 34, 36, 37, 38, 39, 40, 41, 47, 49, 50, 51, 52, 53, 54, 56, 57, 58, 60, 61, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105], "divis": [24, 25, 26, 27, 34, 36, 37, 38, 39, 40, 41, 47, 49, 50, 51, 52, 53, 54, 56, 57, 58, 60, 61, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105], "act": [24, 25, 26, 27, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 78, 79, 80, 81, 82, 83, 84, 85, 97, 98, 99, 100, 103, 104, 105], "pred": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 135], "75": [24, 25, 29, 30, 66, 67, 68, 90, 92, 95, 103, 104, 105, 132], "05": [24, 26, 27, 29, 31, 32, 36, 38, 39, 40, 49, 53, 62, 67, 68, 69, 80, 81, 84, 85, 88, 90, 93, 94, 95, 98, 100, 103, 104, 105, 108, 110, 135], "95": [24, 26, 27, 36, 38, 49, 62, 69, 88, 90, 93, 94, 95], "p": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 115, 133], "zip": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "2": [24, 26, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 49, 50, 51, 52, 53, 54, 56, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 75, 78, 79, 80, 81, 82, 83, 84, 85, 88, 89, 90, 92, 93, 95, 97, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 116, 117, 121, 131, 132, 133, 135, 136], "3": [24, 26, 27, 29, 31, 34, 35, 36, 37, 38, 39, 40, 47, 49, 50, 51, 52, 53, 56, 58, 59, 61, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 103, 104, 105, 107, 108, 109, 110, 116, 117, 131, 132, 133, 135, 136], "66666667": [24, 26, 36, 38, 49, 51, 61, 63, 81, 85, 87, 88, 90, 93, 94, 95, 98, 100, 104], "initi": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 135], "two": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "scalar": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "togeth": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "multipli": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "variabl": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 137], "attributeerror": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "alreadi": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 135], "exist": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 121, 123, 125, 132, 133, 135], "copi": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110, 132, 133, 135], "abstract": [24, 29, 36, 37, 73, 74, 84, 85, 90, 95, 100, 105, 110], "final": [24, 29, 36, 37, 74, 84, 85, 90, 95, 100, 105, 110, 133, 135], "reset": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "_update_count": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "_comput": [24, 25, 26, 27, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 74, 78, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 92, 93, 94, 95, 97, 98, 99, 100, 102, 103, 104, 105, 107, 108, 109, 110], "sigmoid": [25, 35, 41, 66, 68, 69, 102, 104, 107, 109, 110], "them": [25, 104, 132, 133, 135, 136], "875": 25, "problem": [26, 88, 108, 109, 110, 137], "highest": [26, 27, 62, 63, 103, 104], "determin": [26, 27, 29, 30, 31, 32, 66, 67, 68, 87, 88, 89, 90, 92, 93, 94], "dtype": [26, 27, 31, 32, 38, 39, 40, 41, 66, 67, 68, 69, 80, 81, 82, 83, 87, 88, 89, 92, 93, 94, 98, 99, 103, 104, 115, 116, 117, 118, 119, 132, 133], "float64": [26, 27, 31, 32, 38, 39, 40, 41, 66, 67, 68, 69, 80, 81, 82, 83, 87, 88, 89, 92, 93, 94, 98, 99, 103, 104, 115, 116, 117, 118, 119, 133], "binar": [27, 29, 30, 31, 32, 34, 47, 67, 68, 93, 94, 109, 110], "output": [27, 69, 121, 132, 135], "classifi": [27, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 102, 132, 135], "correct": [27, 47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 93, 102, 103, 104], "per": [27, 75, 133, 135], "area": [28, 29, 30, 31, 32, 45], "under": [28, 29, 30, 31, 32, 45, 132, 135], "curv": [28, 29, 30, 31, 32, 45, 64, 65, 66, 67, 68, 69, 86, 87, 88, 89, 90, 92, 93, 94, 95, 132, 135], "max_fpr": [29, 30], "rate": [29, 30, 66, 67, 68, 69, 131, 135, 137], "partial": [29, 30, 133], "auc": 29, "automat": [29, 30, 31, 32, 66, 67, 68, 87, 88, 89, 90, 92, 93, 94], "applic": [29, 111, 112, 114], "4": [29, 30, 34, 35, 36, 37, 40, 47, 50, 59, 63, 69, 82, 83, 84, 85, 87, 88, 90, 92, 93, 94, 95, 99, 100, 105, 107, 108, 109, 110, 131, 132, 133, 135, 136], "35": [29, 30, 69, 87, 92, 95, 103, 104, 105, 132, 133, 134, 135], "9": [29, 30, 31, 32, 34, 36, 37, 38, 39, 40, 41, 49, 50, 53, 54, 56, 60, 62, 63, 66, 67, 68, 69, 78, 79, 80, 81, 82, 83, 84, 85, 89, 90, 93, 94, 95, 97, 98, 99, 100, 103, 104, 105, 107, 109, 110, 132, 133, 135], "6111111111111112": [29, 30], "89": [29, 31, 32, 69], "06": [29, 31, 69], "94": [29, 31], "22222222": [29, 31], "625": [29, 32, 35, 103], "aucroc": 30, "confus": [30, 31, 32, 87, 88, 89, 92, 93, 94], "matrix": [30, 31, 32, 87, 88, 89, 92, 93, 94, 115, 116, 117, 118, 119], "f": [33, 35, 37, 38, 39, 41, 46, 48, 50, 51, 52, 54, 75, 132, 133, 134, 135], "beta": [33, 35, 37, 39, 41, 46, 48, 50, 52, 54], "f1": [34, 36, 38, 40, 46, 47, 49, 51, 53], "form": [34, 47, 132, 135], "6666666666666666": [34, 36, 47, 56, 78, 84], "harmon": [35, 37, 39, 41, 48, 50, 52, 54], "8333333333333334": [35, 37, 50, 59, 62], "85714286": [36, 38], "9090909090909091": 37, "83333333": [37, 41, 50, 54], "55555556": [37, 50, 103], "90909091": [37, 39, 41], "85": [39, 80, 81, 84, 85, 98, 100, 132, 135], "total": [40, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 108, 135], "count": [40, 49, 50, 51, 52, 53, 54, 58, 59, 60, 61, 62, 63, 80, 81, 82, 83, 84, 85, 98, 99, 100, 132, 133, 135], "predicit": 41, "constructor": 43, "arraylik": [47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 76, 93, 102], "ground": [47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 93, 102], "truth": [47, 48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63, 66, 67, 68, 69, 93, 102], "npt": [48, 49, 50, 51, 52, 53, 54, 56, 57, 58, 59, 60, 61, 62, 63], "7142857142857143": 48, "estim": [49, 50, 66, 67, 68, 69, 93, 102, 125], "shape": [49, 50, 51, 52, 53, 54, 60, 61, 108, 109, 133, 134], "expect": [60, 61, 132, 135], "like": [60, 61, 75, 132], "n": [60, 61, 132, 133], "l": [60, 61], "sampl": [60, 61, 63, 119], "presenc": [60, 61, 133], "absenc": [60, 61], "rest": 61, "ratio": [62, 63, 105, 135], "correctli": 62, "observ": [62, 132, 133, 135, 137], "precision_scor": 62, "tp": [63, 107, 108, 109], "fn": [63, 107, 108, 109], "intuit": 63, "abil": [63, 132, 135], "recall_scor": 63, "3333333333333333": 63, "receiv": 65, "characterist": 65, "decis": [66, 67, 68, 69, 93, 121, 132, 135], "fpr": [66, 67, 68, 69, 132, 133, 135], "tpr": [66, 67, 68, 69], "25": [66, 67, 68, 88, 90, 92, 93, 95, 105, 116, 117, 132, 134, 135], "softmax": [67, 69, 93], "1d": [67, 68, 69, 95], "33333333": [67, 85, 88, 90, 93, 94, 95, 100], "non": 69, "evenli": [69, 95], "space": [69, 95], "increas": [69, 95], "assertionerror": [69, 121], "03": 69, "stat": [72, 106, 107, 108, 109, 110], "abc": 74, "other_metr": 75, "postfix": 75, "userdict": 75, "collect": [75, 132, 133, 135], "want": 75, "behav": 75, "themselv": 75, "intern": 75, "similar": 75, "reduc": 75, "els": [75, 132, 133, 134, 135], "keep_bas": 75, "iter": 75, "underli": 75, "moduledict": 75, "hashabl": 75, "v": [75, 133], "correspond": [75, 125], "keyerror": [75, 121], "some": [75, 132, 135], "pair": 75, "present": 75, "lack": 75, "metric_a": 76, "metric_b": 76, "metric1": 76, "metric2": 76, "unari": 76, "appropri": [84, 85, 100, 132, 135], "375": [88, 90], "suniqu": 90, "45": [90, 105, 132, 135], "42857143": 90, "15": [103, 104, 105, 132, 133, 135], "57142857": 103, "sum": [105, 108, 109, 110, 133, 135], "_abstractscor": [107, 108, 109], "fp": [107, 108, 109], "tn": [107, 108, 109], "classwis": [108, 110], "over": [108, 109, 110, 131, 137], "labelwis": [109, 110], "prior": [110, 132, 133, 135], "modul": [111, 120, 121, 132, 135], "shift_typ": [112, 114], "shift_id": [112, 134], "induc": [112, 114], "synthet": [112, 114, 131, 135, 136, 137], "categor": [112, 132, 135], "origin": 112, "util": [112, 132, 133, 135], "load_nih": 112, "mnt": [112, 133, 134], "nihcxr": [112, 131, 133, 136], "hospital_type_1": 112, "hospital_type_2": 112, "hospital_type_3": 112, "hospital_type_4": 112, "hospital_type_5": 112, "ds_sourc": [112, 134], "ds_target": [112, 134], "num_proc": [112, 134], "build": 112, "hospit": [112, 132, 135, 137], "drift_detect": 114, "experiment": 114, "sklearn": [114, 132, 135], "load_diabet": 114, "y": [114, 116, 117, 119, 125, 132, 133, 135], "return_x_i": 114, "x_tr": 114, "x_te": 114, "y_tr": 114, "y_te": 114, "train_test_split": [114, 132, 135], "test_siz": 114, "random_st": [114, 132, 135], "42": [114, 132, 135], "gn_shift": 114, "x_shift": 114, "x_train": [114, 125], "noise_amt": [114, 118], "delta": [114, 115, 118, 119], "ko_shift": 114, "cp_shift": 114, "mfa_shift": 114, "bn_shift": 114, "tolerance_shift": 114, "ds_shift": 114, "nois": [114, 115, 118, 132, 133, 135], "prob": 115, "covari": [115, 116, 117, 118, 119], "proport": 115, "fraction": [115, 118, 119, 135], "affect": [115, 118, 121, 132, 135], "n_shuffl": [116, 117], "keep_rows_const": 116, "repermute_each_column": 116, "multiwai": 116, "associ": [116, 132, 133, 135], "swap": [116, 117], "individu": [116, 132, 135], "within": 116, "cl": [116, 117], "etc": [116, 117, 132, 133, 135], "floatnumpi": 116, "shuffl": [116, 117, 132], "permut": 116, "placehold": 116, "shift_class": [117, 119], "rank": 117, "changepoint": 117, "axi": [117, 133, 134, 135], "x_ref": 117, "y_ref": 117, "normal": [118, 132], "clip": 118, "gaussian": 118, "standard": [118, 132, 135], "deviat": 118, "divid": 118, "255": [118, 133, 134], "placehol": 119, "output_dir": [121, 132, 133, 135], "serv": 121, "interfac": 121, "popul": [121, 131, 132, 135, 137], "modelcard": 121, "directori": [121, 132, 135], "save": [121, 125, 132, 135], "output_filenam": 121, "template_path": 121, "interact": [121, 135], "save_json": 121, "date": [121, 132, 133, 135], "jinja2": 121, "json": [121, 132, 135], "classmethod": 121, "cyclops_report": [121, 132, 135], "section_nam": [121, 132, 133, 135], "model_detail": [121, 132, 135], "section": [121, 132, 133, 135], "repres": [121, 133], "bibtex": 121, "entri": 121, "plain": 121, "text": [121, 133], "descript": [121, 132, 133, 135], "license_id": [121, 132], "sensitive_featur": [121, 132], "sensitive_feature_justif": [121, 132], "log": [121, 131, 132, 135, 137], "inform": [121, 132], "about": [121, 132, 133, 135], "resourc": [121, 132, 135], "context": 121, "homepag": 121, "spdx": [121, 132], "identifi": [121, 131, 133, 137], "licens": [121, 132, 133, 135], "apach": [121, 132, 135], "unknown": 121, "unlicens": 121, "proprietari": 121, "justif": [121, 132], "field": [121, 131, 132, 135, 137], "descriptor": 121, "new": [121, 132, 135], "pydant": 121, "basemodel": 121, "subclass": 121, "As": 121, "long": 121, "conflict": 121, "defin": [121, 132, 133, 135], "model_card": [121, 132, 133, 135], "cylop": 121, "tradeoff": [121, 133], "trade": 121, "off": 121, "interpret": 121, "consider": [121, 132, 133, 135], "affected_group": [121, 132, 133, 135], "benefit": [121, 132, 133, 135], "harm": [121, 132, 133, 135], "mitigation_strategi": [121, 132, 133, 135], "assess": 121, "mitig": [121, 132, 133, 135], "strategi": [121, 132, 133, 135], "relat": 121, "img_path": 121, "caption": [121, 132, 133, 135], "full": 121, "whole": [121, 132, 135], "left": [121, 135], "blank": 121, "instead": 121, "param": [121, 132, 135], "contact": [121, 132, 133, 135], "role": 121, "owner": [121, 132, 133, 135], "quantit": [121, 132, 133, 135], "slash": 121, "fig": [121, 132, 133, 135], "plotli": [121, 132, 133, 135], "figur": [121, 132, 133, 135], "plot": [121, 132, 133, 135], "analysis_typ": 121, "metric_slic": [121, 132, 133, 135], "decision_threshold": 121, "pass_fail_threshold": [121, 132, 133, 135], "pass_fail_threshold_fn": [121, 132, 133, 135], "explain": [121, 132, 133, 135], "fail": 121, "regul": 121, "regulatori": [121, 132, 135], "compli": 121, "risk": [121, 132, 133, 135, 137], "kind": [121, 132, 133, 135], "primari": [121, 132, 133, 135], "scope": [121, 132, 135], "usecas": 121, "version_str": [121, 132, 135], "semant": 121, "v1": 121, "dt_date": 121, "dt_datetim": 121, "unix": 121, "timestamp": [121, 131, 133, 136], "yyyi": 121, "mm": 121, "dd": 121, "hh": 121, "ss": 121, "ffffff": 121, "z": 121, "summar": 121, "chang": [121, 132, 133, 135], "made": [121, 132, 135], "task_featur": [123, 125, 132, 135], "task_target": [123, 125, 132, 135], "atelectasi": [123, 133], "consolid": [123, 133], "infiltr": [123, 133], "pneumothorax": [123, 133], "edema": [123, 133], "emphysema": [123, 133], "fibrosi": [123, 133], "effus": [123, 133], "pneumonia": [123, 133], "pleural_thicken": [123, 133], "cardiomegali": [123, 133], "nodul": [123, 133], "mass": [123, 133, 135], "hernia": [123, 133], "lung": 123, "lesion": 123, "fractur": 123, "opac": 123, "enlarg": 123, "cardiomediastinum": 123, "basetask": [123, 125], "multi": [123, 133], "ptmodel": [123, 125, 133], "skmodel": [123, 125], "properti": [123, 125], "splits_map": [123, 125], "64": [123, 135], "compos": [123, 132, 133, 134, 135], "unnecessari": [123, 125], "pathologi": [123, 131, 132, 137], "represent": [123, 132, 135], "tabular": [125, 131], "fit": [125, 132, 135], "columntransform": [125, 132, 135], "slicingconfig": 125, "default_max_batch_s": 125, "filepath": 125, "pretrain": [125, 133], "proba": [125, 132, 135], "pd": 125, "datafram": [125, 132, 135], "notfittederror": 125, "destin": 125, "parent": [125, 132, 133, 135], "dirctori": 125, "best_model_param": [125, 132, 135], "y_train": 125, "seri": 125, "nonei": 125, "drift": [131, 136], "experi": [131, 136], "dimension": [131, 136], "reduct": [131, 136], "techniqu": [131, 136], "roll": [131, 136], "window": [131, 136], "biweekli": [131, 136], "kaggl": [131, 132], "heart": 131, "failur": 131, "constant": [131, 137], "distribut": [131, 133, 137], "outcom": [131, 137], "preprocessor": [131, 137], "creation": [131, 137], "synthea": [131, 135], "prolong": 131, "length": [131, 133], "stai": 131, "queri": [131, 137], "inspect": [131, 132, 137], "preprocess": [131, 132, 137], "drop": [131, 132, 137], "nan_threshold": [131, 132, 137], "gender": [131, 132, 133, 134, 137], "nih": [131, 133, 134], "diseas": [131, 132, 137], "balanc": [131, 132, 137], "w": [131, 137], "showcas": [132, 135, 137], "formul": [132, 135], "patient": [132, 133, 134, 135, 137], "o": [132, 133, 135], "shutil": [132, 133, 135], "pathlib": [132, 133, 135], "express": [132, 133, 135], "px": [132, 133, 135], "dateutil": [132, 133, 135], "relativedelta": [132, 133, 135], "kaggle_api_extend": 132, "kaggleapi": 132, "imput": [132, 135], "simpleimput": [132, 135], "pipelin": [132, 135], "minmaxscal": [132, 135], "onehotencod": [132, 135], "noqa": [132, 133, 135], "e402": [132, 133, 135], "catalog": [132, 135], "create_model": [132, 135], "tabularfeatur": [132, 135], "classificationplott": [132, 133, 135], "flatten_results_dict": [132, 135], "get_metrics_trend": [132, 133, 135], "join": [132, 133, 135], "load_datafram": 132, "home": [132, 133, 134, 135], "amritk": [132, 133, 134, 135], "cach": [132, 133, 134, 135], "pypoetri": [132, 133, 134, 135], "virtualenv": [132, 133, 134, 135], "wizuawxh": [132, 133, 134, 135], "py3": [132, 133, 134, 135], "lib": [132, 133, 134, 135], "site": [132, 133, 134, 135], "tqdm": [132, 133, 134, 135], "auto": [132, 133, 134, 135], "py": [132, 133, 134, 135], "21": [132, 133, 134, 135], "tqdmwarn": [132, 133, 134, 135], "iprogress": [132, 133, 134, 135], "ipywidget": [132, 133, 134, 135], "readthedoc": [132, 133, 134, 135], "io": [132, 133, 134, 135], "en": [132, 133, 134, 135], "user_instal": [132, 133, 134, 135], "autonotebook": [132, 133, 134, 135], "notebook_tqdm": [132, 133, 134, 135], "offer": [132, 133, 135], "document": [132, 133, 135], "through": [132, 133, 135], "technic": [132, 133, 135], "architectur": [132, 133, 135], "involv": [132, 133, 135], "intend": [132, 133, 135], "subpopul": [132, 133, 135], "explaina": [132, 133, 135], "go": [132, 133, 135], "tool": [132, 133, 135], "progress": [132, 133, 135], "subject": [132, 133, 135], "data_dir": [132, 133], "random_se": [132, 135], "train_siz": [132, 135], "sign": [132, 135], "com": [132, 133], "Then": 132, "profil": [132, 135], "usernam": 132, "trigger": 132, "download": 132, "credenti": 132, "place": 132, "locat": 132, "machin": [132, 133], "authent": 132, "dataset_download_fil": 132, "fedesoriano": 132, "unzip": 132, "df": 132, "csv": [132, 135], "file_format": 132, "reset_index": [132, 135], "index": [132, 133, 135], "2023": [132, 135], "26": [132, 135], "14": [132, 133, 135, 137], "49": [132, 135], "263": 132, "chestpaintyp": 132, "restingbp": 132, "cholesterol": 132, "fastingb": 132, "restingecg": 132, "40": [132, 135], "ata": 132, "140": 132, "289": 132, "nap": 132, "160": 132, "180": 132, "37": [132, 135], "130": 132, "283": 132, "st": 132, "48": [132, 133, 135], "asi": 132, "138": 132, "214": 132, "54": 132, "150": 132, "195": 132, "913": 132, "ta": 132, "110": 132, "264": 132, "914": 132, "68": [132, 133], "144": 132, "193": 132, "915": 132, "57": 132, "131": 132, "916": 132, "236": 132, "lvh": 132, "917": 132, "38": [132, 135], "175": 132, "maxhr": 132, "exerciseangina": 132, "oldpeak": 132, "st_slope": 132, "heartdiseas": 132, "172": 132, "156": 132, "flat": 132, "98": 132, "108": 132, "122": 132, "132": 132, "141": 132, "115": 132, "174": 132, "173": 132, "918": 132, "13": [132, 133, 135], "pie": [132, 133, 135], "update_layout": [132, 133, 135], "histogram": [132, 133, 135], "xaxis_titl": [132, 133, 135], "yaxis_titl": [132, 133, 135], "bargap": [132, 133, 135], "astyp": [132, 135], "11": [132, 133, 134, 135, 137], "update_trac": [132, 133, 135], "textinfo": [132, 135], "percent": [132, 135], "title_text": [132, 135], "hovertempl": [132, 135], "br": [132, 135], "class_count": [132, 135], "value_count": [132, 135], "class_ratio": [132, 135], "8070866141732284": 132, "39": [132, 133, 135], "20": [132, 133, 135], "wa": [132, 133, 135], "li": 132, "et": 132, "al": 132, "most": 132, "features_list": [132, 135], "sort": [132, 135], "help": [132, 133, 135], "essenti": [132, 135], "step": [132, 135], "understand": [132, 135], "u": [132, 135], "16": [132, 135], "tab_featur": [132, 135], "ordin": 132, "might": [132, 135], "17": [132, 135], "numeric_transform": [132, 135], "scaler": [132, 135], "binary_transform": [132, 135], "most_frequ": [132, 135], "18": [132, 134, 135], "numeric_featur": [132, 135], "features_by_typ": [132, 135], "numeric_indic": [132, 135], "get_loc": [132, 135], "19": [132, 133, 135], "binary_featur": [132, 135], "ordinal_featur": 132, "binary_indic": [132, 135], "ordinal_indic": 132, "num": [132, 135], "onehot": [132, 135], "handle_unknown": [132, 135], "ignor": [132, 135], "remaind": [132, 135], "passthrough": [132, 135], "let": [132, 135], "done": [132, 135], "independ": 132, "everi": 132, "uci": 132, "archiv": 132, "ic": 132, "edu": 132, "databas": [132, 135], "cleandoc": 132, "misc": 132, "cc0": 132, "demograph": [132, 133], "often": 132, "strong": 132, "correl": 132, "older": [132, 135], "higher": 132, "panda": [132, 135], "power": [132, 135], "easi": [132, 135], "compat": [132, 135], "22": [132, 134, 135], "from_panda": [132, 135], "cleanup_cache_fil": [132, 135], "num_row": 132, "cast_column": [132, 135], "stratify_by_column": [132, 135], "seed": [132, 135], "100": [132, 133, 134, 135], "lt": [132, 133, 134, 135], "201326": 132, "59": [132, 133], "straightforward": [132, 135], "maintain": [132, 135], "instanti": [132, 135], "line": [132, 133, 135], "sgd": [132, 135], "logisit": [132, 135], "regress": [132, 135], "sgdclassif": [132, 135], "24": [132, 135], "sgd_classifi": 132, "123": [132, 135], "verbos": [132, 135], "class_weight": 132, "mortalitypredict": [132, 135], "encapsul": [132, 135], "cohes": [132, 135], "structur": [132, 135], "smooth": [132, 135], "manag": [132, 135], "mortality_task": 132, "best": [132, 135], "hyperparamet": [132, 135], "search": [132, 135], "grid": [132, 135], "27": [132, 135], "alpha": 132, "0001": 132, "001": 132, "learning_r": [132, 135], "invscal": 132, "adapt": 132, "eta0": 132, "roc_auc": 132, "203": 132, "wrapper": [132, 133, 135], "sk_model": [132, 135], "204": 132, "205": 132, "sgdclassifi": 132, "x27": [132, 135], "early_stop": 132, "loss": 132, "log_loss": 132, "rerun": [132, 135], "cell": [132, 135], "trust": [132, 135], "On": [132, 133, 135], "github": [132, 133, 135], "unabl": [132, 135], "render": [132, 135], "try": [132, 135], "page": [132, 135], "nbviewer": [132, 135], "sgdclassifiersgdclassifi": 132, "28": [132, 135], "model_param": [132, 135], "epsilon": 132, "fit_intercept": 132, "l1_ratio": 132, "max_it": 132, "n_iter_no_chang": 132, "n_job": [132, 135], "penalti": 132, "l2": 132, "power_t": 132, "tol": 132, "validation_fract": 132, "warm_start": 132, "29": [132, 135], "30": [132, 133, 135, 137], "y_pred": [132, 135], "only_predict": [132, 135], "len": [132, 133, 135], "184": 132, "8645": 132, "variou": [132, 135], "perspect": [132, 135], "metric_collect": [132, 135], "how": [132, 135], "certain": [132, 135], "70": 132, "33": [132, 135], "fnr": [132, 133, 135], "ber": [132, 135], "fairness_metric_collect": [132, 135], "34": [132, 135], "dataset_with_pr": [132, 135], "7559": 132, "7273": 132, "41": [132, 133, 135], "51238": 132, "gt": [132, 133, 135], "20488": 132, "81": 132, "22011": 132, "69": [132, 135], "23109": 132, "80": [132, 133, 135], "21006": 132, "21266": 132, "21336": 132, "right": [132, 135], "36": [132, 135], "results_flat": [132, 133, 135], "remove_metr": [132, 135], "796875": 132, "8260869565217391": 132, "6785714285714286": 132, "7450980392156863": 132, "8819444444444444": 132, "8623853211009175": 132, "8676470588235294": 132, "9076923076923077": 132, "8872180451127819": 132, "927972027972028": 132, "842391304347826": 132, "8686868686868687": 132, "8431372549019608": 132, "8557213930348259": 132, "9152319464371114": 132, "plw2901": [132, 133, 135], "lambda": [132, 133, 134, 135], "plotter": [132, 133, 134, 135], "class_nam": [132, 135], "set_templ": [132, 133, 135], "plotly_whit": [132, 133, 135], "extract": [132, 135], "slice_result": [132, 133, 135], "dict_kei": [132, 135], "roc_plot": [132, 135], "roc_curve_comparison": [132, 135], "femal": [132, 133, 134, 135], "overall_perform": [132, 135], "metric_valu": [132, 135], "overall_performance_plot": [132, 135], "metrics_valu": [132, 135], "43": [132, 135], "slice_metr": [132, 135], "44": [132, 135], "slice_metrics_plot": [132, 135], "metrics_comparison_bar": [132, 135], "comparison": [132, 135], "reform": [132, 135], "fairness_result": [132, 135], "deepcopi": [132, 135], "fairness_metr": [132, 135], "group_siz": [132, 135], "46": [132, 135], "fairness_plot": [132, 135], "metrics_comparison_scatt": [132, 135], "leverag": [132, 133, 135], "histor": [132, 133, 135], "gather": [132, 133, 135], "merg": [132, 133, 135], "recent": [132, 133, 135], "wish": [132, 133, 135], "metrics_trend": [132, 133, 135], "integr": [132, 133, 135], "purpos": [132, 133, 135], "three": [132, 133, 135], "dummi": [132, 133, 135], "demonstr": [132, 133, 135, 137], "trend": [132, 133, 135], "47": [132, 135], "dummy_report_num": [132, 133, 135], "dummy_report_dir": [132, 133, 135], "getcwd": [132, 133, 135], "dummy_report": [132, 133, 135], "simul": [132, 133, 135], "random": [132, 133, 135], "uniform": [132, 133, 135], "dummy_result": [132, 133, 135], "max": [132, 133, 135], "renam": [132, 133, 135], "folder": [132, 133, 135], "dummy_report_path": [132, 133, 135], "date_dir": [132, 133, 135], "dummy_d": [132, 133, 135], "todai": [132, 133, 135], "new_dir": [132, 133, 135], "rmtree": [132, 133, 135], "previou": [132, 133, 135], "report_directori": [132, 133, 135], "flat_result": [132, 133, 135], "trends_plot": [132, 133, 135], "audienc": [132, 135], "organ": [132, 135], "store": [132, 135], "regulatory_requir": [132, 135], "releas": [132, 135], "team": [132, 135], "vectorinstitut": [132, 135], "linear_model": 132, "e501": [132, 135], "next": [132, 135], "use_cas": [132, 135], "These": [132, 135], "fairness_assess": [132, 135], "well": [132, 133, 135], "taken": [132, 135], "ethical_consider": [132, 135], "51": [132, 135], "clinician": [132, 135], "engin": [132, 135], "condit": 132, "improv": [132, 135], "bias": [132, 133, 135], "lead": [132, 135], "wors": [132, 135], "retrain": [132, 135], "below": [132, 135], "By": [132, 135], "52": [132, 134, 135], "report_path": [132, 133, 135], "view": [132, 133, 135, 137], "torchxrayvis": [133, 134], "functool": 133, "graph_object": [133, 135], "lambdad": [133, 134], "resiz": [133, 134], "densenet": [133, 134], "loader": [133, 134], "load_nihcxr": [133, 134], "apply_transform": 133, "get_devic": 133, "devic": 133, "clinical_dataset": [133, 134], "nih_d": [133, 134], "4000": 133, "spatial_s": [133, 134], "224": [133, 134], "allow_missing_kei": [133, 134], "func": [133, 134], "1024": [133, 134], "newaxi": [133, 134], "densenet121": [133, 134], "res224": [133, 134], "No": 133, "adjust": 133, "318522": 133, "2511": 133, "3647": 133, "78": 133, "int64": 133, "originalimag": 133, "width": [133, 135], "height": [133, 135], "originalimagepixelspac": 133, "unnam": 133, "float32": 133, "__index_level_0__": 133, "arang": 133, "nih_eval_results_gend": 133, "append": [133, 135], "scatter": 133, "mode": 133, "marker": 133, "perf_metric_gend": 133, "title_x": 133, "title_font_s": 133, "768": 133, "selector": 133, "57120": 133, "61": 133, "58220": 133, "59787": 133, "67": 133, "65": [133, 134], "nih_eval_results_ag": 133, "perf_metric_ag": 133, "57013": 133, "59988": 133, "93": 133, "59422": 133, "59819": 133, "male": [133, 134], "showlegend": 133, "bar": [133, 135], "balanced_error_r": 133, "nih_fairness_result_ag": 133, "balancederrorr": 133, "fairness_ag": 133, "60061": 133, "59439": 133, "56864": 133, "63": [133, 135], "fairness_age_par": 133, "slice_": 133, "itr": 133, "enumer": 133, "dummy_reports_cxr": 133, "112": [133, 137], "120": [133, 137], "frontal": [133, 137], "805": [133, 137], "fourteen": 133, "mine": 133, "radiolog": 133, "pleural": 133, "thicken": 133, "remain": 133, "arxiv": 133, "ab": 133, "2111": 133, "00595": 133, "inproceed": 133, "cohen2022xrv": 133, "cohen": 133, "joseph": 133, "paul": 133, "viviano": 133, "bertin": 133, "morrison": 133, "torabian": 133, "parsa": 133, "guarrera": 133, "matteo": 133, "lungren": 133, "matthew": 133, "chaudhari": 133, "akshai": 133, "brook": 133, "rupert": 133, "hashir": 133, "mohammad": 133, "bertrand": 133, "hadrien": 133, "booktitl": 133, "deep": 133, "mlmed": 133, "arxivid": 133, "cohen2020limit": 133, "cross": 133, "domain": 133, "autom": [133, 135], "2002": 133, "02497": 133, "medicin": 133, "lab": 133, "radiologist": 133, "scientist": 133, "inabl": 133, "addition": 133, "poor": 133, "qualiti": 133, "artifact": 133, "geograph": 133, "region": 133, "ethic": 133, "ensur": 133, "divers": 133, "regularli": 133, "human": 133, "expertis": 133, "address": 133, "rare": 133, "qualit": 133, "detector": 134, "reductor": 134, "tstester": 134, "plot_drift_experi": 134, "plot_drift_timeseri": 134, "shifter": 134, "source_d": 134, "target_d": 134, "25596": 134, "65746": 134, "87": 134, "dr_method": 134, "bbse": 134, "soft": 134, "txrv": 134, "ae": 134, "sensitivity_test": 134, "tester": 134, "tester_method": 134, "source_sample_s": 134, "target_sample_s": 134, "num_run": 134, "detect_shift": 134, "chexpert": 134, "chex": 134, "padchest": 134, "pc": 134, "source_slic": 134, "target_slic": 134, "51805": 134, "53031": 134, "09": 134, "44752": 134, "46932": 134, "46296": 134, "45996": 134, "46760": 134, "92": [134, 135], "50103": 134, "rolling_window_drift": 134, "timestamp_column": 134, "window_s": 134, "4w": 134, "longer": 135, "v3": 135, "instruct": 135, "etl": 135, "postgr": 135, "cycqueri": 135, "op": 135, "qo": 135, "datasetqueri": 135, "num_dai": 135, "querier": 135, "dbm": 135, "postgresql": 135, "port": 135, "5432": 135, "host": 135, "localhost": 135, "synthea_demo": 135, "password": 135, "pwd": 135, "def": 135, "get_encount": 135, "nativ": 135, "sequenti": 135, "patient_id": 135, "birthdat": 135, "race": 135, "ethnic": 135, "patient_encount": 135, "isout": 135, "encounter_id": 135, "extracttimestampcompon": 135, "start_year": 135, "birthdate_year": 135, "addcolumn": 135, "new_col_label": 135, "stop": 135, "lo": 135, "conditiongreaterthan": 135, "conditionlessthan": 135, "get_observ": 135, "cohort": 135, "conditionin": 135, "categori": 135, "vital": 135, "conditionequ": 135, "groupby_op": 135, "groupbyaggreg": 135, "n_ob": 135, "observations_count": 135, "observations_stat": 135, "pivot_t": 135, "aggfunc": 135, "add_prefix": 135, "obs_": 135, "get_med": 135, "n_med": 135, "get_procedur": 135, "procedur": [135, 137], "n_procedur": 135, "run_queri": 135, "cohort_queri": 135, "to_merg": 135, "extend": 135, "to_merge_df": 135, "681": 135, "orm": 135, "readi": 135, "409": 135, "successfulli": 135, "410": 135, "finish": 135, "execut": 135, "397262": 135, "189": 135, "191": 135, "779844": 135, "423": 135, "424": 135, "117588": 135, "580": 135, "582": 135, "153356": 135, "673": 135, "090819": 135, "list_column": 135, "payer": 135, "encounterclass": 135, "base_encounter_cost": 135, "total_claim_cost": 135, "payer_coverag": 135, "reasoncod": 135, "reasondescript": 135, "null_count": 135, "isnul": 135, "600": 135, "respect": 135, "larger": 135, "thresh_nan": 135, "dropna": 135, "thresh": 135, "length_of_stai": 135, "length_of_stay_count": 135, "length_of_stay_kei": 135, "5573997233748271": 135, "obs_alanin": 135, "aminotransferas": 135, "enzymat": 135, "volum": 135, "serum": 135, "plasma": 135, "obs_albumin": 135, "obs_alkalin": 135, "phosphatas": 135, "obs_aspart": 135, "obs_bilirubin": 135, "obs_bodi": 135, "obs_calcium": 135, "obs_carbon": 135, "dioxid": 135, "mole": 135, "obs_chlorid": 135, "obs_creatinin": 135, "obs_diastol": 135, "blood": 135, "pressur": 135, "obs_erythrocyt": 135, "obs_ferritin": 135, "obs_glomerular": 135, "filtrat": 135, "73": 135, "sq": 135, "obs_glucos": 135, "obs_hematocrit": 135, "obs_hemoglobin": 135, "obs_leukocyt": 135, "obs_mch": 135, "entit": 135, "obs_mchc": 135, "obs_mcv": 135, "obs_oxygen": 135, "satur": 135, "arteri": 135, "obs_platelet": 135, "obs_potassium": 135, "obs_protein": 135, "obs_sodium": 135, "obs_systol": 135, "obs_troponin": 135, "cardiac": 135, "obs_urea": 135, "nitrogen": 135, "1126": 135, "181185": 135, "sllearn": 135, "xgb_classifi": 135, "los_task": 135, "n_estim": 135, "250": 135, "500": 135, "max_depth": 135, "reg_lambda": 135, "colsample_bytre": 135, "gamma": 135, "853": 135, "854": 135, "855": 135, "xgbclassifi": 135, "base_scor": 135, "booster": 135, "callback": 135, "colsample_bylevel": 135, "colsample_bynod": 135, "early_stopping_round": 135, "enable_categor": 135, "eval_metr": 135, "logloss": 135, "feature_typ": 135, "gpu_id": 135, "grow_polici": 135, "importance_typ": 135, "interaction_constraint": 135, "max_bin": 135, "max_cat_threshold": 135, "max_cat_to_onehot": 135, "max_delta_step": 135, "max_leav": 135, "min_child_weight": 135, "miss": 135, "monotone_constraint": 135, "num_parallel_tre": 135, "predictor": 135, "xgbclassifierxgbclassifi": 135, "logist": 135, "use_label_encod": 135, "reg_alpha": 135, "sampling_method": 135, "scale_pos_weight": 135, "subsampl": 135, "tree_method": 135, "validate_paramet": 135, "226": 135, "4370": 135, "4128": 135, "58": 135, "3874": 135, "66730": 135, "8195": 135, "9100": 135, "8875": 135, "8978": 135, "9258": 135, "amp": 135, "8864": 135, "8828": 135, "55": 135, "8737": 135, "8760": 135, "66": 135, "8940": 135, "8667": 135, "90": 135, "8771929824561403": 135, "9491525423728814": 135, "835820895522388": 135, "8888888888888888": 135, "9564941251190854": 135, "8627450980392157": 135, "8461538461538461": 135, "88": 135, "9353846153846154": 135, "8925619834710744": 135, "9857142857142858": 135, "8518518518518519": 135, "9139072847682119": 135, "9626543209876542": 135, "8761904761904762": 135, "8695652173913043": 135, "9375": 135, "9022556390977443": 135, "9504573170731707": 135, "8849557522123894": 135, "9280575539568345": 135, "8896551724137931": 135, "9084507042253521": 135, "9545338441890167": 135, "xgboost": 135, "python_api": 135, "53": 135, "statist": 136, "goal": 137}, "objects": {"cyclops": [[126, 0, 0, "-", "data"], [127, 0, 0, "-", "evaluate"], [128, 0, 0, "-", "monitor"], [129, 0, 0, "-", "report"], [130, 0, 0, "-", "tasks"]], "cyclops.data": [[126, 0, 0, "-", "features"], [6, 0, 0, "-", "slicer"]], "cyclops.data.features": [[4, 0, 0, "-", "medical_image"]], "cyclops.data.features.medical_image": [[5, 1, 1, "", "MedicalImage"]], "cyclops.data.features.medical_image.MedicalImage": [[5, 2, 1, "", "__call__"], [5, 2, 1, "", "cast_storage"], [5, 2, 1, "", "decode_example"], [5, 2, 1, "", "embed_storage"], [5, 2, 1, "", "encode_example"], [5, 2, 1, "", "flatten"]], "cyclops.data.slicer": [[7, 1, 1, "", "SliceSpec"], [8, 4, 1, "", "compound_filter"], [9, 4, 1, "", "filter_datetime"], [10, 4, 1, "", "filter_non_null"], [11, 4, 1, "", "filter_range"], [12, 4, 1, "", "filter_string_contains"], [13, 4, 1, "", "filter_value"], [14, 4, 1, "", "is_datetime"], [15, 4, 1, "", "overall"]], "cyclops.data.slicer.SliceSpec": [[7, 3, 1, "", "_registry"], [7, 2, 1, "", "add_slice_spec"], [7, 3, 1, "", "column_names"], [7, 2, 1, "", "get_slices"], [7, 3, 1, "", "include_overall"], [7, 2, 1, "", "slices"], [7, 3, 1, "", "spec_list"], [7, 3, 1, "", "validate"]], "cyclops.evaluate": [[16, 0, 0, "-", "evaluator"], [127, 0, 0, "-", "fairness"], [127, 0, 0, "-", "metrics"]], "cyclops.evaluate.evaluator": [[17, 4, 1, "", "evaluate"]], "cyclops.evaluate.fairness": [[18, 0, 0, "-", "config"], [20, 0, 0, "-", "evaluator"]], "cyclops.evaluate.fairness.config": [[19, 1, 1, "", "FairnessConfig"]], "cyclops.evaluate.fairness.evaluator": [[21, 4, 1, "", "evaluate_fairness"], [22, 4, 1, "", "warn_too_many_unique_values"]], "cyclops.evaluate.metrics": [[23, 0, 0, "-", "accuracy"], [28, 0, 0, "-", "auroc"], [33, 0, 0, "-", "f_beta"], [42, 0, 0, "-", "factory"], [127, 0, 0, "-", "functional"], [73, 0, 0, "-", "metric"], [77, 0, 0, "-", "precision_recall"], [86, 0, 0, "-", "precision_recall_curve"], [91, 0, 0, "-", "roc"], [96, 0, 0, "-", "sensitivity"], [101, 0, 0, "-", "specificity"], [106, 0, 0, "-", "stat_scores"]], "cyclops.evaluate.metrics.accuracy": [[24, 1, 1, "", "Accuracy"], [25, 1, 1, "", "BinaryAccuracy"], [26, 1, 1, "", "MulticlassAccuracy"], [27, 1, 1, "", "MultilabelAccuracy"]], "cyclops.evaluate.metrics.accuracy.Accuracy": [[24, 2, 1, "", "__add__"], [24, 2, 1, "", "__call__"], [24, 2, 1, "", "__init__"], [24, 2, 1, "", "__mul__"], [24, 2, 1, "", "add_state"], [24, 2, 1, "", "clone"], [24, 2, 1, "", "compute"], [24, 2, 1, "", "reset_state"], [24, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.accuracy.BinaryAccuracy": [[25, 2, 1, "", "__add__"], [25, 2, 1, "", "__call__"], [25, 2, 1, "", "__init__"], [25, 2, 1, "", "__mul__"], [25, 2, 1, "", "add_state"], [25, 2, 1, "", "clone"], [25, 2, 1, "", "compute"], [25, 2, 1, "", "reset_state"], [25, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy": [[26, 2, 1, "", "__add__"], [26, 2, 1, "", "__call__"], [26, 2, 1, "", "__init__"], [26, 2, 1, "", "__mul__"], [26, 2, 1, "", "add_state"], [26, 2, 1, "", "clone"], [26, 2, 1, "", "compute"], [26, 2, 1, "", "reset_state"], [26, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy": [[27, 2, 1, "", "__add__"], [27, 2, 1, "", "__call__"], [27, 2, 1, "", "__init__"], [27, 2, 1, "", "__mul__"], [27, 2, 1, "", "add_state"], [27, 2, 1, "", "clone"], [27, 2, 1, "", "compute"], [27, 2, 1, "", "reset_state"], [27, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc": [[29, 1, 1, "", "AUROC"], [30, 1, 1, "", "BinaryAUROC"], [31, 1, 1, "", "MulticlassAUROC"], [32, 1, 1, "", "MultilabelAUROC"]], "cyclops.evaluate.metrics.auroc.AUROC": [[29, 2, 1, "", "__add__"], [29, 2, 1, "", "__call__"], [29, 2, 1, "", "__init__"], [29, 2, 1, "", "__mul__"], [29, 2, 1, "", "add_state"], [29, 2, 1, "", "clone"], [29, 2, 1, "", "compute"], [29, 2, 1, "", "reset_state"], [29, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc.BinaryAUROC": [[30, 2, 1, "", "__add__"], [30, 2, 1, "", "__call__"], [30, 2, 1, "", "__init__"], [30, 2, 1, "", "__mul__"], [30, 2, 1, "", "add_state"], [30, 2, 1, "", "clone"], [30, 2, 1, "", "compute"], [30, 2, 1, "", "reset_state"], [30, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc.MulticlassAUROC": [[31, 2, 1, "", "__add__"], [31, 2, 1, "", "__call__"], [31, 2, 1, "", "__init__"], [31, 2, 1, "", "__mul__"], [31, 2, 1, "", "add_state"], [31, 2, 1, "", "clone"], [31, 2, 1, "", "compute"], [31, 2, 1, "", "reset_state"], [31, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.auroc.MultilabelAUROC": [[32, 2, 1, "", "__add__"], [32, 2, 1, "", "__call__"], [32, 2, 1, "", "__init__"], [32, 2, 1, "", "__mul__"], [32, 2, 1, "", "add_state"], [32, 2, 1, "", "clone"], [32, 2, 1, "", "compute"], [32, 2, 1, "", "reset_state"], [32, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta": [[34, 1, 1, "", "BinaryF1Score"], [35, 1, 1, "", "BinaryFbetaScore"], [36, 1, 1, "", "F1Score"], [37, 1, 1, "", "FbetaScore"], [38, 1, 1, "", "MulticlassF1Score"], [39, 1, 1, "", "MulticlassFbetaScore"], [40, 1, 1, "", "MultilabelF1Score"], [41, 1, 1, "", "MultilabelFbetaScore"]], "cyclops.evaluate.metrics.f_beta.BinaryF1Score": [[34, 2, 1, "", "__add__"], [34, 2, 1, "", "__call__"], [34, 2, 1, "", "__init__"], [34, 2, 1, "", "__mul__"], [34, 2, 1, "", "add_state"], [34, 2, 1, "", "clone"], [34, 2, 1, "", "compute"], [34, 2, 1, "", "reset_state"], [34, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore": [[35, 2, 1, "", "__add__"], [35, 2, 1, "", "__call__"], [35, 2, 1, "", "__init__"], [35, 2, 1, "", "__mul__"], [35, 2, 1, "", "add_state"], [35, 2, 1, "", "clone"], [35, 2, 1, "", "compute"], [35, 2, 1, "", "reset_state"], [35, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.F1Score": [[36, 2, 1, "", "__add__"], [36, 2, 1, "", "__call__"], [36, 2, 1, "", "__init__"], [36, 2, 1, "", "__mul__"], [36, 2, 1, "", "add_state"], [36, 2, 1, "", "clone"], [36, 2, 1, "", "compute"], [36, 2, 1, "", "reset_state"], [36, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.FbetaScore": [[37, 2, 1, "", "__add__"], [37, 2, 1, "", "__call__"], [37, 2, 1, "", "__init__"], [37, 2, 1, "", "__mul__"], [37, 2, 1, "", "add_state"], [37, 2, 1, "", "clone"], [37, 2, 1, "", "compute"], [37, 2, 1, "", "reset_state"], [37, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MulticlassF1Score": [[38, 2, 1, "", "__add__"], [38, 2, 1, "", "__call__"], [38, 2, 1, "", "__init__"], [38, 2, 1, "", "__mul__"], [38, 2, 1, "", "add_state"], [38, 2, 1, "", "clone"], [38, 2, 1, "", "compute"], [38, 2, 1, "", "reset_state"], [38, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore": [[39, 2, 1, "", "__add__"], [39, 2, 1, "", "__call__"], [39, 2, 1, "", "__init__"], [39, 2, 1, "", "__mul__"], [39, 2, 1, "", "add_state"], [39, 2, 1, "", "clone"], [39, 2, 1, "", "compute"], [39, 2, 1, "", "reset_state"], [39, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MultilabelF1Score": [[40, 2, 1, "", "__add__"], [40, 2, 1, "", "__call__"], [40, 2, 1, "", "__init__"], [40, 2, 1, "", "__mul__"], [40, 2, 1, "", "add_state"], [40, 2, 1, "", "clone"], [40, 2, 1, "", "compute"], [40, 2, 1, "", "reset_state"], [40, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore": [[41, 2, 1, "", "__add__"], [41, 2, 1, "", "__call__"], [41, 2, 1, "", "__init__"], [41, 2, 1, "", "__mul__"], [41, 2, 1, "", "add_state"], [41, 2, 1, "", "clone"], [41, 2, 1, "", "compute"], [41, 2, 1, "", "reset_state"], [41, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.factory": [[43, 4, 1, "", "create_metric"]], "cyclops.evaluate.metrics.functional": [[44, 0, 0, "-", "accuracy"], [45, 0, 0, "-", "auroc"], [46, 0, 0, "-", "f_beta"], [55, 0, 0, "-", "precision_recall"], [64, 0, 0, "-", "precision_recall_curve"], [65, 0, 0, "-", "roc"], [70, 0, 0, "-", "sensitivity"], [71, 0, 0, "-", "specificity"], [72, 0, 0, "-", "stat_scores"]], "cyclops.evaluate.metrics.functional.f_beta": [[47, 4, 1, "", "binary_f1_score"], [48, 4, 1, "", "binary_fbeta_score"], [49, 4, 1, "", "f1_score"], [50, 4, 1, "", "fbeta_score"], [51, 4, 1, "", "multiclass_f1_score"], [52, 4, 1, "", "multiclass_fbeta_score"], [53, 4, 1, "", "multilabel_f1_score"], [54, 4, 1, "", "multilabel_fbeta_score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[56, 4, 1, "", "binary_precision"], [57, 4, 1, "", "binary_recall"], [58, 4, 1, "", "multiclass_precision"], [59, 4, 1, "", "multiclass_recall"], [60, 4, 1, "", "multilabel_precision"], [61, 4, 1, "", "multilabel_recall"], [62, 4, 1, "", "precision"], [63, 4, 1, "", "recall"]], "cyclops.evaluate.metrics.functional.roc": [[66, 4, 1, "", "binary_roc_curve"], [67, 4, 1, "", "multiclass_roc_curve"], [68, 4, 1, "", "multilabel_roc_curve"], [69, 4, 1, "", "roc_curve"]], "cyclops.evaluate.metrics.metric": [[74, 1, 1, "", "Metric"], [75, 1, 1, "", "MetricCollection"], [76, 1, 1, "", "OperatorMetric"]], "cyclops.evaluate.metrics.metric.Metric": [[74, 2, 1, "", "__add__"], [74, 2, 1, "", "__call__"], [74, 2, 1, "", "__init__"], [74, 2, 1, "", "__mul__"], [74, 2, 1, "", "add_state"], [74, 2, 1, "", "clone"], [74, 2, 1, "", "compute"], [74, 2, 1, "", "reset_state"], [74, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.metric.MetricCollection": [[75, 2, 1, "", "__call__"], [75, 2, 1, "", "__init__"], [75, 2, 1, "", "add_metrics"], [75, 2, 1, "", "clear"], [75, 2, 1, "", "clone"], [75, 2, 1, "", "compute"], [75, 2, 1, "", "get"], [75, 2, 1, "", "items"], [75, 2, 1, "", "keys"], [75, 2, 1, "", "pop"], [75, 2, 1, "", "popitem"], [75, 2, 1, "", "reset_state"], [75, 2, 1, "", "setdefault"], [75, 2, 1, "", "update"], [75, 2, 1, "", "update_state"], [75, 2, 1, "", "values"]], "cyclops.evaluate.metrics.metric.OperatorMetric": [[76, 2, 1, "", "__add__"], [76, 2, 1, "", "__call__"], [76, 2, 1, "", "__init__"], [76, 2, 1, "", "__mul__"], [76, 2, 1, "", "add_state"], [76, 2, 1, "", "clone"], [76, 2, 1, "", "compute"], [76, 2, 1, "", "reset_state"], [76, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall": [[78, 1, 1, "", "BinaryPrecision"], [79, 1, 1, "", "BinaryRecall"], [80, 1, 1, "", "MulticlassPrecision"], [81, 1, 1, "", "MulticlassRecall"], [82, 1, 1, "", "MultilabelPrecision"], [83, 1, 1, "", "MultilabelRecall"], [84, 1, 1, "", "Precision"], [85, 1, 1, "", "Recall"]], "cyclops.evaluate.metrics.precision_recall.BinaryPrecision": [[78, 2, 1, "", "__add__"], [78, 2, 1, "", "__call__"], [78, 2, 1, "", "__init__"], [78, 2, 1, "", "__mul__"], [78, 2, 1, "", "add_state"], [78, 2, 1, "", "clone"], [78, 2, 1, "", "compute"], [78, 2, 1, "", "reset_state"], [78, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.BinaryRecall": [[79, 2, 1, "", "__add__"], [79, 2, 1, "", "__call__"], [79, 2, 1, "", "__init__"], [79, 2, 1, "", "__mul__"], [79, 2, 1, "", "add_state"], [79, 2, 1, "", "clone"], [79, 2, 1, "", "compute"], [79, 2, 1, "", "reset_state"], [79, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision": [[80, 2, 1, "", "__add__"], [80, 2, 1, "", "__call__"], [80, 2, 1, "", "__init__"], [80, 2, 1, "", "__mul__"], [80, 2, 1, "", "add_state"], [80, 2, 1, "", "clone"], [80, 2, 1, "", "compute"], [80, 2, 1, "", "reset_state"], [80, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MulticlassRecall": [[81, 2, 1, "", "__add__"], [81, 2, 1, "", "__call__"], [81, 2, 1, "", "__init__"], [81, 2, 1, "", "__mul__"], [81, 2, 1, "", "add_state"], [81, 2, 1, "", "clone"], [81, 2, 1, "", "compute"], [81, 2, 1, "", "reset_state"], [81, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision": [[82, 2, 1, "", "__add__"], [82, 2, 1, "", "__call__"], [82, 2, 1, "", "__init__"], [82, 2, 1, "", "__mul__"], [82, 2, 1, "", "add_state"], [82, 2, 1, "", "clone"], [82, 2, 1, "", "compute"], [82, 2, 1, "", "reset_state"], [82, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.MultilabelRecall": [[83, 2, 1, "", "__add__"], [83, 2, 1, "", "__call__"], [83, 2, 1, "", "__init__"], [83, 2, 1, "", "__mul__"], [83, 2, 1, "", "add_state"], [83, 2, 1, "", "clone"], [83, 2, 1, "", "compute"], [83, 2, 1, "", "reset_state"], [83, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.Precision": [[84, 2, 1, "", "__add__"], [84, 2, 1, "", "__call__"], [84, 2, 1, "", "__init__"], [84, 2, 1, "", "__mul__"], [84, 2, 1, "", "add_state"], [84, 2, 1, "", "clone"], [84, 2, 1, "", "compute"], [84, 2, 1, "", "reset_state"], [84, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall.Recall": [[85, 2, 1, "", "__add__"], [85, 2, 1, "", "__call__"], [85, 2, 1, "", "__init__"], [85, 2, 1, "", "__mul__"], [85, 2, 1, "", "add_state"], [85, 2, 1, "", "clone"], [85, 2, 1, "", "compute"], [85, 2, 1, "", "reset_state"], [85, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve": [[87, 1, 1, "", "BinaryPrecisionRecallCurve"], [88, 1, 1, "", "MulticlassPrecisionRecallCurve"], [89, 1, 1, "", "MultilabelPrecisionRecallCurve"], [90, 1, 1, "", "PrecisionRecallCurve"]], "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve": [[87, 2, 1, "", "__add__"], [87, 2, 1, "", "__call__"], [87, 2, 1, "", "__init__"], [87, 2, 1, "", "__mul__"], [87, 2, 1, "", "add_state"], [87, 2, 1, "", "clone"], [87, 2, 1, "", "compute"], [87, 2, 1, "", "reset_state"], [87, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve": [[88, 2, 1, "", "__add__"], [88, 2, 1, "", "__call__"], [88, 2, 1, "", "__init__"], [88, 2, 1, "", "__mul__"], [88, 2, 1, "", "add_state"], [88, 2, 1, "", "clone"], [88, 2, 1, "", "compute"], [88, 2, 1, "", "reset_state"], [88, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve": [[89, 2, 1, "", "__add__"], [89, 2, 1, "", "__call__"], [89, 2, 1, "", "__init__"], [89, 2, 1, "", "__mul__"], [89, 2, 1, "", "add_state"], [89, 2, 1, "", "clone"], [89, 2, 1, "", "compute"], [89, 2, 1, "", "reset_state"], [89, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve": [[90, 2, 1, "", "__add__"], [90, 2, 1, "", "__call__"], [90, 2, 1, "", "__init__"], [90, 2, 1, "", "__mul__"], [90, 2, 1, "", "add_state"], [90, 2, 1, "", "clone"], [90, 2, 1, "", "compute"], [90, 2, 1, "", "reset_state"], [90, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc": [[92, 1, 1, "", "BinaryROCCurve"], [93, 1, 1, "", "MulticlassROCCurve"], [94, 1, 1, "", "MultilabelROCCurve"], [95, 1, 1, "", "ROCCurve"]], "cyclops.evaluate.metrics.roc.BinaryROCCurve": [[92, 2, 1, "", "__add__"], [92, 2, 1, "", "__call__"], [92, 2, 1, "", "__init__"], [92, 2, 1, "", "__mul__"], [92, 2, 1, "", "add_state"], [92, 2, 1, "", "clone"], [92, 2, 1, "", "compute"], [92, 2, 1, "", "reset_state"], [92, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc.MulticlassROCCurve": [[93, 2, 1, "", "__add__"], [93, 2, 1, "", "__call__"], [93, 2, 1, "", "__init__"], [93, 2, 1, "", "__mul__"], [93, 2, 1, "", "add_state"], [93, 2, 1, "", "clone"], [93, 2, 1, "", "compute"], [93, 2, 1, "", "reset_state"], [93, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc.MultilabelROCCurve": [[94, 2, 1, "", "__add__"], [94, 2, 1, "", "__call__"], [94, 2, 1, "", "__init__"], [94, 2, 1, "", "__mul__"], [94, 2, 1, "", "add_state"], [94, 2, 1, "", "clone"], [94, 2, 1, "", "compute"], [94, 2, 1, "", "reset_state"], [94, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.roc.ROCCurve": [[95, 2, 1, "", "__add__"], [95, 2, 1, "", "__call__"], [95, 2, 1, "", "__init__"], [95, 2, 1, "", "__mul__"], [95, 2, 1, "", "add_state"], [95, 2, 1, "", "clone"], [95, 2, 1, "", "compute"], [95, 2, 1, "", "reset_state"], [95, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity": [[97, 1, 1, "", "BinarySensitivity"], [98, 1, 1, "", "MulticlassSensitivity"], [99, 1, 1, "", "MultilabelSensitivity"], [100, 1, 1, "", "Sensitivity"]], "cyclops.evaluate.metrics.sensitivity.BinarySensitivity": [[97, 2, 1, "", "__add__"], [97, 2, 1, "", "__call__"], [97, 2, 1, "", "__init__"], [97, 2, 1, "", "__mul__"], [97, 2, 1, "", "add_state"], [97, 2, 1, "", "clone"], [97, 2, 1, "", "compute"], [97, 2, 1, "", "reset_state"], [97, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity": [[98, 2, 1, "", "__add__"], [98, 2, 1, "", "__call__"], [98, 2, 1, "", "__init__"], [98, 2, 1, "", "__mul__"], [98, 2, 1, "", "add_state"], [98, 2, 1, "", "clone"], [98, 2, 1, "", "compute"], [98, 2, 1, "", "reset_state"], [98, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity": [[99, 2, 1, "", "__add__"], [99, 2, 1, "", "__call__"], [99, 2, 1, "", "__init__"], [99, 2, 1, "", "__mul__"], [99, 2, 1, "", "add_state"], [99, 2, 1, "", "clone"], [99, 2, 1, "", "compute"], [99, 2, 1, "", "reset_state"], [99, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.sensitivity.Sensitivity": [[100, 2, 1, "", "__add__"], [100, 2, 1, "", "__call__"], [100, 2, 1, "", "__init__"], [100, 2, 1, "", "__mul__"], [100, 2, 1, "", "add_state"], [100, 2, 1, "", "clone"], [100, 2, 1, "", "compute"], [100, 2, 1, "", "reset_state"], [100, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity": [[102, 1, 1, "", "BinarySpecificity"], [103, 1, 1, "", "MulticlassSpecificity"], [104, 1, 1, "", "MultilabelSpecificity"], [105, 1, 1, "", "Specificity"]], "cyclops.evaluate.metrics.specificity.BinarySpecificity": [[102, 2, 1, "", "__add__"], [102, 2, 1, "", "__call__"], [102, 2, 1, "", "__init__"], [102, 2, 1, "", "__mul__"], [102, 2, 1, "", "add_state"], [102, 2, 1, "", "clone"], [102, 2, 1, "", "compute"], [102, 2, 1, "", "reset_state"], [102, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity.MulticlassSpecificity": [[103, 2, 1, "", "__add__"], [103, 2, 1, "", "__call__"], [103, 2, 1, "", "__init__"], [103, 2, 1, "", "__mul__"], [103, 2, 1, "", "add_state"], [103, 2, 1, "", "clone"], [103, 2, 1, "", "compute"], [103, 2, 1, "", "reset_state"], [103, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity.MultilabelSpecificity": [[104, 2, 1, "", "__add__"], [104, 2, 1, "", "__call__"], [104, 2, 1, "", "__init__"], [104, 2, 1, "", "__mul__"], [104, 2, 1, "", "add_state"], [104, 2, 1, "", "clone"], [104, 2, 1, "", "compute"], [104, 2, 1, "", "reset_state"], [104, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.specificity.Specificity": [[105, 2, 1, "", "__add__"], [105, 2, 1, "", "__call__"], [105, 2, 1, "", "__init__"], [105, 2, 1, "", "__mul__"], [105, 2, 1, "", "add_state"], [105, 2, 1, "", "clone"], [105, 2, 1, "", "compute"], [105, 2, 1, "", "reset_state"], [105, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores": [[107, 1, 1, "", "BinaryStatScores"], [108, 1, 1, "", "MulticlassStatScores"], [109, 1, 1, "", "MultilabelStatScores"], [110, 1, 1, "", "StatScores"]], "cyclops.evaluate.metrics.stat_scores.BinaryStatScores": [[107, 2, 1, "", "__add__"], [107, 2, 1, "", "__call__"], [107, 2, 1, "", "__init__"], [107, 2, 1, "", "__mul__"], [107, 2, 1, "", "add_state"], [107, 2, 1, "", "clone"], [107, 2, 1, "", "compute"], [107, 2, 1, "", "reset_state"], [107, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores": [[108, 2, 1, "", "__add__"], [108, 2, 1, "", "__call__"], [108, 2, 1, "", "__init__"], [108, 2, 1, "", "__mul__"], [108, 2, 1, "", "add_state"], [108, 2, 1, "", "clone"], [108, 2, 1, "", "compute"], [108, 2, 1, "", "reset_state"], [108, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores": [[109, 2, 1, "", "__add__"], [109, 2, 1, "", "__call__"], [109, 2, 1, "", "__init__"], [109, 2, 1, "", "__mul__"], [109, 2, 1, "", "add_state"], [109, 2, 1, "", "clone"], [109, 2, 1, "", "compute"], [109, 2, 1, "", "reset_state"], [109, 2, 1, "", "update_state"]], "cyclops.evaluate.metrics.stat_scores.StatScores": [[110, 2, 1, "", "__add__"], [110, 2, 1, "", "__call__"], [110, 2, 1, "", "__init__"], [110, 2, 1, "", "__mul__"], [110, 2, 1, "", "add_state"], [110, 2, 1, "", "clone"], [110, 2, 1, "", "compute"], [110, 2, 1, "", "reset_state"], [110, 2, 1, "", "update_state"]], "cyclops.monitor": [[111, 0, 0, "-", "clinical_applicator"], [113, 0, 0, "-", "synthetic_applicator"]], "cyclops.monitor.clinical_applicator": [[112, 1, 1, "", "ClinicalShiftApplicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[112, 2, 1, "", "age"], [112, 2, 1, "", "apply_shift"], [112, 2, 1, "", "custom"], [112, 2, 1, "", "hospital_type"], [112, 2, 1, "", "month"], [112, 2, 1, "", "sex"], [112, 2, 1, "", "time"]], "cyclops.monitor.synthetic_applicator": [[114, 1, 1, "", "SyntheticShiftApplicator"], [115, 4, 1, "", "binary_noise_shift"], [116, 4, 1, "", "feature_association_shift"], [117, 4, 1, "", "feature_swap_shift"], [118, 4, 1, "", "gaussian_noise_shift"], [119, 4, 1, "", "knockout_shift"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[114, 2, 1, "", "apply_shift"]], "cyclops.report": [[120, 0, 0, "-", "report"]], "cyclops.report.report": [[121, 1, 1, "", "ModelCardReport"]], "cyclops.report.report.ModelCardReport": [[121, 2, 1, "", "export"], [121, 2, 1, "", "from_json_file"], [121, 2, 1, "", "log_citation"], [121, 2, 1, "", "log_dataset"], [121, 2, 1, "", "log_descriptor"], [121, 2, 1, "", "log_fairness_assessment"], [121, 2, 1, "", "log_from_dict"], [121, 2, 1, "", "log_image"], [121, 2, 1, "", "log_license"], [121, 2, 1, "", "log_model_parameters"], [121, 2, 1, "", "log_owner"], [121, 2, 1, "", "log_performance_metrics"], [121, 2, 1, "", "log_plotly_figure"], [121, 2, 1, "", "log_quantitative_analysis"], [121, 2, 1, "", "log_reference"], [121, 2, 1, "", "log_regulation"], [121, 2, 1, "", "log_risk"], [121, 2, 1, "", "log_use_case"], [121, 2, 1, "", "log_user"], [121, 2, 1, "", "log_version"]], "cyclops.tasks": [[122, 0, 0, "-", "cxr_classification"], [124, 0, 0, "-", "mortality_prediction"]], "cyclops.tasks.cxr_classification": [[123, 1, 1, "", "CXRClassificationTask"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[123, 2, 1, "", "__init__"], [123, 2, 1, "", "add_model"], [123, 5, 1, "", "data_type"], [123, 2, 1, "", "evaluate"], [123, 2, 1, "", "get_model"], [123, 2, 1, "", "list_models"], [123, 5, 1, "", "models_count"], [123, 2, 1, "", "predict"], [123, 5, 1, "", "task_type"]], "cyclops.tasks.mortality_prediction": [[125, 1, 1, "", "MortalityPredictionTask"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[125, 2, 1, "", "__init__"], [125, 2, 1, "", "add_model"], [125, 5, 1, "", "data_type"], [125, 2, 1, "", "evaluate"], [125, 2, 1, "", "get_model"], [125, 2, 1, "", "list_models"], [125, 2, 1, "", "list_models_params"], [125, 2, 1, "", "load_model"], [125, 5, 1, "", "models_count"], [125, 2, 1, "", "predict"], [125, 2, 1, "", "save_model"], [125, 5, 1, "", "task_type"], [125, 2, 1, "", "train"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:method", "3": "py:attribute", "4": "py:function", "5": "py:property"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "method", "Python method"], "3": ["py", "attribute", "Python attribute"], "4": ["py", "function", "Python function"], "5": ["py", "property", "Python property"]}, "titleterms": {"api": [0, 136], "refer": 0, "contribut": [1, 3], "cyclop": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130], "pre": 1, "commit": 1, "hook": 1, "code": 1, "guidelin": 1, "welcom": 2, "": 2, "document": [2, 3], "content": 2, "get": [3, 133], "start": 3, "instal": 3, "us": [3, 134, 137], "pip": 3, "develop": 3, "poetri": 3, "notebook": 3, "citat": 3, "data": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 126, 132, 135, 137], "featur": [4, 5, 126, 132, 135], "medical_imag": [4, 5], "medicalimag": 5, "slicer": [6, 7, 8, 9, 10, 11, 12, 13, 14, 15], "slicespec": 7, "compound_filt": 8, "filter_datetim": 9, "filter_non_nul": 10, "filter_rang": 11, "filter_string_contain": 12, "filter_valu": 13, "is_datetim": 14, "overal": 15, "evalu": [16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 127, 132, 135], "fair": [18, 19, 20, 21, 22, 127], "config": [18, 19], "fairnessconfig": 19, "evaluate_fair": 21, "warn_too_many_unique_valu": 22, "metric": [23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 127, 133], "accuraci": [23, 24, 25, 26, 27, 44], "binaryaccuraci": 25, "multiclassaccuraci": 26, "multilabelaccuraci": 27, "auroc": [28, 29, 30, 31, 32, 45, 133], "binaryauroc": 30, "multiclassauroc": 31, "multilabelauroc": 32, "f_beta": [33, 34, 35, 36, 37, 38, 39, 40, 41, 46, 47, 48, 49, 50, 51, 52, 53, 54], "binaryf1scor": 34, "binaryfbetascor": 35, "f1score": 36, "fbetascor": 37, "multiclassf1scor": 38, "multiclassfbetascor": 39, "multilabelf1scor": 40, "multilabelfbetascor": 41, "factori": [42, 43], "create_metr": 43, "function": [44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 127], "binary_f1_scor": 47, "binary_fbeta_scor": 48, "f1_score": 49, "fbeta_scor": 50, "multiclass_f1_scor": 51, "multiclass_fbeta_scor": 52, "multilabel_f1_scor": 53, "multilabel_fbeta_scor": 54, "precision_recal": [55, 56, 57, 58, 59, 60, 61, 62, 63, 77, 78, 79, 80, 81, 82, 83, 84, 85], "binary_precis": 56, "binary_recal": 57, "multiclass_precis": 58, "multiclass_recal": 59, "multilabel_precis": 60, "multilabel_recal": 61, "precis": [62, 84], "recal": [63, 85], "precision_recall_curv": [64, 86, 87, 88, 89, 90], "roc": [65, 66, 67, 68, 69, 91, 92, 93, 94, 95], "binary_roc_curv": 66, "multiclass_roc_curv": 67, "multilabel_roc_curv": 68, "roc_curv": 69, "sensit": [70, 96, 97, 98, 99, 100, 134], "specif": [71, 101, 102, 103, 104, 105], "stat_scor": [72, 106, 107, 108, 109, 110], "metriccollect": 75, "operatormetr": 76, "binaryprecis": 78, "binaryrecal": 79, "multiclassprecis": 80, "multiclassrecal": 81, "multilabelprecis": 82, "multilabelrecal": 83, "binaryprecisionrecallcurv": 87, "multiclassprecisionrecallcurv": 88, "multilabelprecisionrecallcurv": 89, "precisionrecallcurv": 90, "binaryroccurv": 92, "multiclassroccurv": 93, "multilabelroccurv": 94, "roccurv": 95, "binarysensit": 97, "multiclasssensit": 98, "multilabelsensit": 99, "binaryspecif": 102, "multiclassspecif": 103, "multilabelspecif": 104, "binarystatscor": 107, "multiclassstatscor": 108, "multilabelstatscor": 109, "statscor": 110, "monitor": [111, 112, 113, 114, 115, 116, 117, 118, 119, 128, 136], "clinical_appl": [111, 112], "clinicalshiftappl": 112, "synthetic_appl": [113, 114, 115, 116, 117, 118, 119], "syntheticshiftappl": 114, "binary_noise_shift": 115, "feature_association_shift": 116, "feature_swap_shift": 117, "gaussian_noise_shift": 118, "knockout_shift": 119, "report": [120, 121, 129, 132, 135], "modelcardreport": 121, "task": [122, 123, 124, 125, 130, 132, 135], "cxr_classif": [122, 123], "cxrclassificationtask": 123, "mortality_predict": [124, 125], "mortalitypredictiontask": 125, "tutori": [131, 134], "heart": [132, 137], "failur": [132, 137], "predict": [132, 133, 135, 137], "import": [132, 133, 134, 135], "librari": [132, 133, 134, 135], "constant": [132, 135], "load": [132, 133, 134], "sex": [132, 133], "valu": 132, "ag": [132, 133, 135], "distribut": [132, 135], "outcom": [132, 135], "identifi": [132, 135], "type": [132, 135], "creat": [132, 135], "preprocessor": [132, 135], "hug": [132, 135], "face": [132, 135], "dataset": [132, 133, 134, 135], "model": [132, 133, 134, 135], "creation": [132, 135], "train": [132, 134, 135], "perform": [132, 133, 135], "over": [132, 133, 135], "time": [132, 133, 135], "gener": [132, 134, 135], "chest": [133, 137], "x": [133, 137], "rai": [133, 137], "diseas": 133, "classif": [133, 137], "multilabel": 133, "pathologi": 133, "balanc": 133, "error": 133, "rate": 133, "pariti": 133, "log": 133, "test": [133, 134], "w": 133, "threshold": 133, "popul": 133, "card": 133, "field": 133, "nihcxr": 134, "clinic": 134, "drift": 134, "experi": 134, "exampl": [134, 137], "1": 134, "sourc": 134, "target": 134, "2": 134, "3": 134, "dimension": 134, "reduct": 134, "techniqu": 134, "differ": 134, "4": 134, "shift": 134, "5": 134, "roll": 134, "window": 134, "synthet": 134, "timestamp": 134, "biweekli": 134, "prolong": [135, 137], "length": [135, 137], "stai": [135, 137], "queri": 135, "comput": 135, "label": 135, "inspect": 135, "preprocess": 135, "drop": 135, "nan": 135, "base": 135, "nan_threshold": 135, "gender": 135, "case": 137, "tabular": 137, "kaggl": 137, "synthea": 137, "imag": 137, "nih": 137}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1, "sphinx.ext.intersphinx": 1, "nbsphinx": 4, "sphinx": 60}, "alltitles": {"API Reference": [[0, "api-reference"]], "Contributing to cyclops": [[1, "contributing-to-cyclops"]], "Pre-commit hooks": [[1, "pre-commit-hooks"]], "Coding guidelines": [[1, "coding-guidelines"]], "Welcome to cyclops\u2019s documentation!": [[2, "welcome-to-cyclops-s-documentation"]], "Contents:": [[2, null]], "\ud83d\udc23 Getting Started": [[3, "getting-started"]], "Installing cyclops using pip": [[3, "installing-cyclops-using-pip"]], "\ud83e\uddd1\ud83c\udfff\u200d\ud83d\udcbb Developing": [[3, "developing"]], "Using poetry": [[3, "using-poetry"]], "Contributing": [[3, "contributing"]], "\ud83d\udcda Documentation": [[3, "documentation"]], "\ud83d\udcd3 Notebooks": [[3, "notebooks"]], "\ud83c\udf93 Citation": [[3, "citation"]], "cyclops.data.features.medical_image": [[4, "module-cyclops.data.features.medical_image"]], "cyclops.data.features.medical_image.MedicalImage": [[5, "cyclops-data-features-medical-image-medicalimage"]], "cyclops.data.slicer": [[6, "module-cyclops.data.slicer"]], "cyclops.data.slicer.SliceSpec": [[7, "cyclops-data-slicer-slicespec"]], "cyclops.data.slicer.compound_filter": [[8, "cyclops-data-slicer-compound-filter"]], "cyclops.data.slicer.filter_datetime": [[9, "cyclops-data-slicer-filter-datetime"]], "cyclops.data.slicer.filter_non_null": [[10, "cyclops-data-slicer-filter-non-null"]], "cyclops.data.slicer.filter_range": [[11, "cyclops-data-slicer-filter-range"]], "cyclops.data.slicer.filter_string_contains": [[12, "cyclops-data-slicer-filter-string-contains"]], "cyclops.data.slicer.filter_value": [[13, "cyclops-data-slicer-filter-value"]], "cyclops.data.slicer.is_datetime": [[14, "cyclops-data-slicer-is-datetime"]], "cyclops.data.slicer.overall": [[15, "cyclops-data-slicer-overall"]], "cyclops.evaluate.evaluator": [[16, "module-cyclops.evaluate.evaluator"]], "cyclops.evaluate.evaluator.evaluate": [[17, "cyclops-evaluate-evaluator-evaluate"]], "cyclops.evaluate.fairness.config": [[18, "module-cyclops.evaluate.fairness.config"]], "cyclops.evaluate.fairness.config.FairnessConfig": [[19, "cyclops-evaluate-fairness-config-fairnessconfig"]], "cyclops.evaluate.fairness.evaluator": [[20, "module-cyclops.evaluate.fairness.evaluator"]], "cyclops.evaluate.fairness.evaluator.evaluate_fairness": [[21, "cyclops-evaluate-fairness-evaluator-evaluate-fairness"]], "cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values": [[22, "cyclops-evaluate-fairness-evaluator-warn-too-many-unique-values"]], "cyclops.evaluate.metrics.accuracy": [[23, "module-cyclops.evaluate.metrics.accuracy"]], "cyclops.evaluate.metrics.accuracy.Accuracy": [[24, "cyclops-evaluate-metrics-accuracy-accuracy"]], "cyclops.evaluate.metrics.accuracy.BinaryAccuracy": [[25, "cyclops-evaluate-metrics-accuracy-binaryaccuracy"]], "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy": [[26, "cyclops-evaluate-metrics-accuracy-multiclassaccuracy"]], "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy": [[27, "cyclops-evaluate-metrics-accuracy-multilabelaccuracy"]], "cyclops.evaluate.metrics.auroc": [[28, "module-cyclops.evaluate.metrics.auroc"]], "cyclops.evaluate.metrics.auroc.AUROC": [[29, "cyclops-evaluate-metrics-auroc-auroc"]], "cyclops.evaluate.metrics.auroc.BinaryAUROC": [[30, "cyclops-evaluate-metrics-auroc-binaryauroc"]], "cyclops.evaluate.metrics.auroc.MulticlassAUROC": [[31, "cyclops-evaluate-metrics-auroc-multiclassauroc"]], "cyclops.evaluate.metrics.auroc.MultilabelAUROC": [[32, "cyclops-evaluate-metrics-auroc-multilabelauroc"]], "cyclops.evaluate.metrics.f_beta": [[33, "module-cyclops.evaluate.metrics.f_beta"]], "cyclops.evaluate.metrics.f_beta.BinaryF1Score": [[34, "cyclops-evaluate-metrics-f-beta-binaryf1score"]], "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore": [[35, "cyclops-evaluate-metrics-f-beta-binaryfbetascore"]], "cyclops.evaluate.metrics.f_beta.F1Score": [[36, "cyclops-evaluate-metrics-f-beta-f1score"]], "cyclops.evaluate.metrics.f_beta.FbetaScore": [[37, "cyclops-evaluate-metrics-f-beta-fbetascore"]], "cyclops.evaluate.metrics.f_beta.MulticlassF1Score": [[38, "cyclops-evaluate-metrics-f-beta-multiclassf1score"]], "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore": [[39, "cyclops-evaluate-metrics-f-beta-multiclassfbetascore"]], "cyclops.evaluate.metrics.f_beta.MultilabelF1Score": [[40, "cyclops-evaluate-metrics-f-beta-multilabelf1score"]], "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore": [[41, "cyclops-evaluate-metrics-f-beta-multilabelfbetascore"]], "cyclops.evaluate.metrics.factory": [[42, "module-cyclops.evaluate.metrics.factory"]], "cyclops.evaluate.metrics.factory.create_metric": [[43, "cyclops-evaluate-metrics-factory-create-metric"]], "cyclops.evaluate.metrics.functional.accuracy": [[44, "module-cyclops.evaluate.metrics.functional.accuracy"]], "cyclops.evaluate.metrics.functional.auroc": [[45, "module-cyclops.evaluate.metrics.functional.auroc"]], "cyclops.evaluate.metrics.functional.f_beta": [[46, "module-cyclops.evaluate.metrics.functional.f_beta"]], "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score": [[47, "cyclops-evaluate-metrics-functional-f-beta-binary-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score": [[48, "cyclops-evaluate-metrics-functional-f-beta-binary-fbeta-score"]], "cyclops.evaluate.metrics.functional.f_beta.f1_score": [[49, "cyclops-evaluate-metrics-functional-f-beta-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.fbeta_score": [[50, "cyclops-evaluate-metrics-functional-f-beta-fbeta-score"]], "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score": [[51, "cyclops-evaluate-metrics-functional-f-beta-multiclass-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score": [[52, "cyclops-evaluate-metrics-functional-f-beta-multiclass-fbeta-score"]], "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score": [[53, "cyclops-evaluate-metrics-functional-f-beta-multilabel-f1-score"]], "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score": [[54, "cyclops-evaluate-metrics-functional-f-beta-multilabel-fbeta-score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[55, "module-cyclops.evaluate.metrics.functional.precision_recall"]], "cyclops.evaluate.metrics.functional.precision_recall.binary_precision": [[56, "cyclops-evaluate-metrics-functional-precision-recall-binary-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.binary_recall": [[57, "cyclops-evaluate-metrics-functional-precision-recall-binary-recall"]], "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision": [[58, "cyclops-evaluate-metrics-functional-precision-recall-multiclass-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall": [[59, "cyclops-evaluate-metrics-functional-precision-recall-multiclass-recall"]], "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision": [[60, "cyclops-evaluate-metrics-functional-precision-recall-multilabel-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall": [[61, "cyclops-evaluate-metrics-functional-precision-recall-multilabel-recall"]], "cyclops.evaluate.metrics.functional.precision_recall.precision": [[62, "cyclops-evaluate-metrics-functional-precision-recall-precision"]], "cyclops.evaluate.metrics.functional.precision_recall.recall": [[63, "cyclops-evaluate-metrics-functional-precision-recall-recall"]], "cyclops.evaluate.metrics.functional.precision_recall_curve": [[64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"]], "cyclops.evaluate.metrics.functional.roc": [[65, "module-cyclops.evaluate.metrics.functional.roc"]], "cyclops.evaluate.metrics.functional.roc.binary_roc_curve": [[66, "cyclops-evaluate-metrics-functional-roc-binary-roc-curve"]], "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve": [[67, "cyclops-evaluate-metrics-functional-roc-multiclass-roc-curve"]], "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve": [[68, "cyclops-evaluate-metrics-functional-roc-multilabel-roc-curve"]], "cyclops.evaluate.metrics.functional.roc.roc_curve": [[69, "cyclops-evaluate-metrics-functional-roc-roc-curve"]], "cyclops.evaluate.metrics.functional.sensitivity": [[70, "module-cyclops.evaluate.metrics.functional.sensitivity"]], "cyclops.evaluate.metrics.functional.specificity": [[71, "module-cyclops.evaluate.metrics.functional.specificity"]], "cyclops.evaluate.metrics.functional.stat_scores": [[72, "module-cyclops.evaluate.metrics.functional.stat_scores"]], "cyclops.evaluate.metrics.metric": [[73, "module-cyclops.evaluate.metrics.metric"]], "cyclops.evaluate.metrics.metric.Metric": [[74, "cyclops-evaluate-metrics-metric-metric"]], "cyclops.evaluate.metrics.metric.MetricCollection": [[75, "cyclops-evaluate-metrics-metric-metriccollection"]], "cyclops.evaluate.metrics.metric.OperatorMetric": [[76, "cyclops-evaluate-metrics-metric-operatormetric"]], "cyclops.evaluate.metrics.precision_recall": [[77, "module-cyclops.evaluate.metrics.precision_recall"]], "cyclops.evaluate.metrics.precision_recall.BinaryPrecision": [[78, "cyclops-evaluate-metrics-precision-recall-binaryprecision"]], "cyclops.evaluate.metrics.precision_recall.BinaryRecall": [[79, "cyclops-evaluate-metrics-precision-recall-binaryrecall"]], "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision": [[80, "cyclops-evaluate-metrics-precision-recall-multiclassprecision"]], "cyclops.evaluate.metrics.precision_recall.MulticlassRecall": [[81, "cyclops-evaluate-metrics-precision-recall-multiclassrecall"]], "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision": [[82, "cyclops-evaluate-metrics-precision-recall-multilabelprecision"]], "cyclops.evaluate.metrics.precision_recall.MultilabelRecall": [[83, "cyclops-evaluate-metrics-precision-recall-multilabelrecall"]], "cyclops.evaluate.metrics.precision_recall.Precision": [[84, "cyclops-evaluate-metrics-precision-recall-precision"]], "cyclops.evaluate.metrics.precision_recall.Recall": [[85, "cyclops-evaluate-metrics-precision-recall-recall"]], "cyclops.evaluate.metrics.precision_recall_curve": [[86, "module-cyclops.evaluate.metrics.precision_recall_curve"]], "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve": [[87, "cyclops-evaluate-metrics-precision-recall-curve-binaryprecisionrecallcurve"]], "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve": [[88, "cyclops-evaluate-metrics-precision-recall-curve-multiclassprecisionrecallcurve"]], "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve": [[89, "cyclops-evaluate-metrics-precision-recall-curve-multilabelprecisionrecallcurve"]], "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve": [[90, "cyclops-evaluate-metrics-precision-recall-curve-precisionrecallcurve"]], "cyclops.evaluate.metrics.roc": [[91, "module-cyclops.evaluate.metrics.roc"]], "cyclops.evaluate.metrics.roc.BinaryROCCurve": [[92, "cyclops-evaluate-metrics-roc-binaryroccurve"]], "cyclops.evaluate.metrics.roc.MulticlassROCCurve": [[93, "cyclops-evaluate-metrics-roc-multiclassroccurve"]], "cyclops.evaluate.metrics.roc.MultilabelROCCurve": [[94, "cyclops-evaluate-metrics-roc-multilabelroccurve"]], "cyclops.evaluate.metrics.roc.ROCCurve": [[95, "cyclops-evaluate-metrics-roc-roccurve"]], "cyclops.evaluate.metrics.sensitivity": [[96, "module-cyclops.evaluate.metrics.sensitivity"]], "cyclops.evaluate.metrics.sensitivity.BinarySensitivity": [[97, "cyclops-evaluate-metrics-sensitivity-binarysensitivity"]], "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity": [[98, "cyclops-evaluate-metrics-sensitivity-multiclasssensitivity"]], "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity": [[99, "cyclops-evaluate-metrics-sensitivity-multilabelsensitivity"]], "cyclops.evaluate.metrics.sensitivity.Sensitivity": [[100, "cyclops-evaluate-metrics-sensitivity-sensitivity"]], "cyclops.evaluate.metrics.specificity": [[101, "module-cyclops.evaluate.metrics.specificity"]], "cyclops.evaluate.metrics.specificity.BinarySpecificity": [[102, "cyclops-evaluate-metrics-specificity-binaryspecificity"]], "cyclops.evaluate.metrics.specificity.MulticlassSpecificity": [[103, "cyclops-evaluate-metrics-specificity-multiclassspecificity"]], "cyclops.evaluate.metrics.specificity.MultilabelSpecificity": [[104, "cyclops-evaluate-metrics-specificity-multilabelspecificity"]], "cyclops.evaluate.metrics.specificity.Specificity": [[105, "cyclops-evaluate-metrics-specificity-specificity"]], "cyclops.evaluate.metrics.stat_scores": [[106, "module-cyclops.evaluate.metrics.stat_scores"]], "cyclops.evaluate.metrics.stat_scores.BinaryStatScores": [[107, "cyclops-evaluate-metrics-stat-scores-binarystatscores"]], "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores": [[108, "cyclops-evaluate-metrics-stat-scores-multiclassstatscores"]], "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores": [[109, "cyclops-evaluate-metrics-stat-scores-multilabelstatscores"]], "cyclops.evaluate.metrics.stat_scores.StatScores": [[110, "cyclops-evaluate-metrics-stat-scores-statscores"]], "cyclops.monitor.clinical_applicator": [[111, "module-cyclops.monitor.clinical_applicator"]], "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator": [[112, "cyclops-monitor-clinical-applicator-clinicalshiftapplicator"]], "cyclops.monitor.synthetic_applicator": [[113, "module-cyclops.monitor.synthetic_applicator"]], "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator": [[114, "cyclops-monitor-synthetic-applicator-syntheticshiftapplicator"]], "cyclops.monitor.synthetic_applicator.binary_noise_shift": [[115, "cyclops-monitor-synthetic-applicator-binary-noise-shift"]], "cyclops.monitor.synthetic_applicator.feature_association_shift": [[116, "cyclops-monitor-synthetic-applicator-feature-association-shift"]], "cyclops.monitor.synthetic_applicator.feature_swap_shift": [[117, "cyclops-monitor-synthetic-applicator-feature-swap-shift"]], "cyclops.monitor.synthetic_applicator.gaussian_noise_shift": [[118, "cyclops-monitor-synthetic-applicator-gaussian-noise-shift"]], "cyclops.monitor.synthetic_applicator.knockout_shift": [[119, "cyclops-monitor-synthetic-applicator-knockout-shift"]], "cyclops.report.report": [[120, "module-cyclops.report.report"]], "cyclops.report.report.ModelCardReport": [[121, "cyclops-report-report-modelcardreport"]], "cyclops.tasks.cxr_classification": [[122, "module-cyclops.tasks.cxr_classification"]], "cyclops.tasks.cxr_classification.CXRClassificationTask": [[123, "cyclops-tasks-cxr-classification-cxrclassificationtask"]], "cyclops.tasks.mortality_prediction": [[124, "module-cyclops.tasks.mortality_prediction"]], "cyclops.tasks.mortality_prediction.MortalityPredictionTask": [[125, "cyclops-tasks-mortality-prediction-mortalitypredictiontask"]], "cyclops.data": [[126, "module-cyclops.data"]], "cyclops.data.features": [[126, "module-cyclops.data.features"]], "cyclops.evaluate": [[127, "module-cyclops.evaluate"]], "cyclops.evaluate.metrics": [[127, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[127, "module-cyclops.evaluate.metrics.functional"]], "cyclops.evaluate.fairness": [[127, "module-cyclops.evaluate.fairness"]], "cyclops.monitor": [[128, "module-cyclops.monitor"]], "cyclops.report": [[129, "module-cyclops.report"]], "cyclops.tasks": [[130, "module-cyclops.tasks"]], "Tutorials": [[131, "tutorials"]], "Heart Failure Prediction": [[132, "Heart-Failure-Prediction"]], "Import Libraries": [[132, "Import-Libraries"], [133, "Import-Libraries"], [135, "Import-Libraries"]], "Constants": [[132, "Constants"], [135, "Constants"]], "Data Loading": [[132, "Data-Loading"]], "Sex values": [[132, "Sex-values"]], "Age distribution": [[132, "Age-distribution"], [135, "Age-distribution"]], "Outcome distribution": [[132, "Outcome-distribution"], [135, "Outcome-distribution"]], "Identifying feature types": [[132, "Identifying-feature-types"], [135, "Identifying-feature-types"]], "Creating data preprocessors": [[132, "Creating-data-preprocessors"], [135, "Creating-data-preprocessors"]], "Creating Hugging Face Dataset": [[132, "Creating-Hugging-Face-Dataset"], [135, "Creating-Hugging-Face-Dataset"]], "Model Creation": [[132, "Model-Creation"], [135, "Model-Creation"]], "Task Creation": [[132, "Task-Creation"], [135, "Task-Creation"]], "Training": [[132, "Training"], [135, "Training"]], "Prediction": [[132, "Prediction"], [135, "Prediction"]], "Evaluation": [[132, "Evaluation"], [135, "Evaluation"]], "Performance over time": [[132, "Performance-over-time"], [133, "Performance-over-time"], [135, "Performance-over-time"]], "Report Generation": [[132, "Report-Generation"], [135, "Report-Generation"]], "Chest X-Ray Disease Classification": [[133, "Chest-X-Ray-Disease-Classification"]], "Load Dataset": [[133, "Load-Dataset"]], "Load Model and get Predictions": [[133, "Load-Model-and-get-Predictions"]], "Multilabel AUROC by Pathology and Sex": [[133, "Multilabel-AUROC-by-Pathology-and-Sex"]], "Multilabel AUROC by Pathology and Age": [[133, "Multilabel-AUROC-by-Pathology-and-Age"]], "Balanced Error Rate by Pathology and Age": [[133, "Balanced-Error-Rate-by-Pathology-and-Age"]], "Balanced Error Rate Parity by Pathology and Age": [[133, "Balanced-Error-Rate-Parity-by-Pathology-and-Age"]], "Log Performance Metrics as Tests w/ Thresholds": [[133, "Log-Performance-Metrics-as-Tests-w/-Thresholds"]], "Populate Model Card Fields": [[133, "Populate-Model-Card-Fields"]], "NIHCXR Clinical Drift Experiments Tutorial": [[134, "NIHCXR-Clinical-Drift-Experiments-Tutorial"]], "Import Libraries and Load NIHCXR Dataset": [[134, "Import-Libraries-and-Load-NIHCXR-Dataset"]], "Example 1. Generate Source/Target Dataset for Experiments (1-2)": [[134, "Example-1.-Generate-Source/Target-Dataset-for-Experiments-(1-2)"]], "Example 2. Sensitivity test experiment with 3 dimensionality reduction techniques": [[134, "Example-2.-Sensitivity-test-experiment-with-3-dimensionality-reduction-techniques"]], "Example 3. Sensitivity test experiment with models trained on different datasets": [[134, "Example-3.-Sensitivity-test-experiment-with-models-trained-on-different-datasets"]], "Example 4. Sensitivity test experiment with different clinical shifts": [[134, "Example-4.-Sensitivity-test-experiment-with-different-clinical-shifts"]], "Example 5. Rolling window experiment with synthetic timestamps using biweekly window": [[134, "Example-5.-Rolling-window-experiment-with-synthetic-timestamps-using-biweekly-window"]], "Prolonged Length of Stay Prediction": [[135, "Prolonged-Length-of-Stay-Prediction"]], "Data Querying": [[135, "Data-Querying"]], "Compute length of stay (labels)": [[135, "Compute-length-of-stay-(labels)"]], "Data Inspection and Preprocessing": [[135, "Data-Inspection-and-Preprocessing"]], "Drop NaNs based on the NAN_THRESHOLD": [[135, "Drop-NaNs-based-on-the-NAN_THRESHOLD"]], "Length of stay distribution": [[135, "Length-of-stay-distribution"]], "Gender distribution": [[135, "Gender-distribution"]], "monitor API": [[136, "monitor-api"]], "Example use cases": [[137, "example-use-cases"]], "Tabular data": [[137, "tabular-data"]], "Kaggle Heart Failure Prediction": [[137, "kaggle-heart-failure-prediction"]], "Synthea Prolonged Length of Stay Prediction": [[137, "synthea-prolonged-length-of-stay-prediction"]], "Image data": [[137, "image-data"]], "NIH Chest X-ray classification": [[137, "nih-chest-x-ray-classification"]]}, "indexentries": {"cyclops.data.features.medical_image": [[4, "module-cyclops.data.features.medical_image"]], "module": [[4, "module-cyclops.data.features.medical_image"], [6, "module-cyclops.data.slicer"], [16, "module-cyclops.evaluate.evaluator"], [18, "module-cyclops.evaluate.fairness.config"], [20, "module-cyclops.evaluate.fairness.evaluator"], [23, "module-cyclops.evaluate.metrics.accuracy"], [28, "module-cyclops.evaluate.metrics.auroc"], [33, "module-cyclops.evaluate.metrics.f_beta"], [42, "module-cyclops.evaluate.metrics.factory"], [44, "module-cyclops.evaluate.metrics.functional.accuracy"], [45, "module-cyclops.evaluate.metrics.functional.auroc"], [46, "module-cyclops.evaluate.metrics.functional.f_beta"], [55, "module-cyclops.evaluate.metrics.functional.precision_recall"], [64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"], [65, "module-cyclops.evaluate.metrics.functional.roc"], [70, "module-cyclops.evaluate.metrics.functional.sensitivity"], [71, "module-cyclops.evaluate.metrics.functional.specificity"], [72, "module-cyclops.evaluate.metrics.functional.stat_scores"], [73, "module-cyclops.evaluate.metrics.metric"], [77, "module-cyclops.evaluate.metrics.precision_recall"], [86, "module-cyclops.evaluate.metrics.precision_recall_curve"], [91, "module-cyclops.evaluate.metrics.roc"], [96, "module-cyclops.evaluate.metrics.sensitivity"], [101, "module-cyclops.evaluate.metrics.specificity"], [106, "module-cyclops.evaluate.metrics.stat_scores"], [111, "module-cyclops.monitor.clinical_applicator"], [113, "module-cyclops.monitor.synthetic_applicator"], [120, "module-cyclops.report.report"], [122, "module-cyclops.tasks.cxr_classification"], [124, "module-cyclops.tasks.mortality_prediction"], [126, "module-cyclops.data"], [126, "module-cyclops.data.features"], [127, "module-cyclops.evaluate"], [127, "module-cyclops.evaluate.fairness"], [127, "module-cyclops.evaluate.metrics"], [127, "module-cyclops.evaluate.metrics.functional"], [128, "module-cyclops.monitor"], [129, "module-cyclops.report"], [130, "module-cyclops.tasks"]], "medicalimage (class in cyclops.data.features.medical_image)": [[5, "cyclops.data.features.medical_image.MedicalImage"]], "__call__() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.__call__"]], "cast_storage() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.cast_storage"]], "decode_example() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.decode_example"]], "embed_storage() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.embed_storage"]], "encode_example() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.encode_example"]], "flatten() (medicalimage method)": [[5, "cyclops.data.features.medical_image.MedicalImage.flatten"]], "cyclops.data.slicer": [[6, "module-cyclops.data.slicer"]], "slicespec (class in cyclops.data.slicer)": [[7, "cyclops.data.slicer.SliceSpec"]], "_registry (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec._registry"]], "add_slice_spec() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.add_slice_spec"]], "column_names (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.column_names"]], "get_slices() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.get_slices"]], "include_overall (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.include_overall"]], "slices() (slicespec method)": [[7, "cyclops.data.slicer.SliceSpec.slices"]], "spec_list (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.spec_list"]], "validate (slicespec attribute)": [[7, "cyclops.data.slicer.SliceSpec.validate"]], "compound_filter() (in module cyclops.data.slicer)": [[8, "cyclops.data.slicer.compound_filter"]], "filter_datetime() (in module cyclops.data.slicer)": [[9, "cyclops.data.slicer.filter_datetime"]], "filter_non_null() (in module cyclops.data.slicer)": [[10, "cyclops.data.slicer.filter_non_null"]], "filter_range() (in module cyclops.data.slicer)": [[11, "cyclops.data.slicer.filter_range"]], "filter_string_contains() (in module cyclops.data.slicer)": [[12, "cyclops.data.slicer.filter_string_contains"]], "filter_value() (in module cyclops.data.slicer)": [[13, "cyclops.data.slicer.filter_value"]], "is_datetime() (in module cyclops.data.slicer)": [[14, "cyclops.data.slicer.is_datetime"]], "overall() (in module cyclops.data.slicer)": [[15, "cyclops.data.slicer.overall"]], "cyclops.evaluate.evaluator": [[16, "module-cyclops.evaluate.evaluator"]], "evaluate() (in module cyclops.evaluate.evaluator)": [[17, "cyclops.evaluate.evaluator.evaluate"]], "cyclops.evaluate.fairness.config": [[18, "module-cyclops.evaluate.fairness.config"]], "fairnessconfig (class in cyclops.evaluate.fairness.config)": [[19, "cyclops.evaluate.fairness.config.FairnessConfig"]], "cyclops.evaluate.fairness.evaluator": [[20, "module-cyclops.evaluate.fairness.evaluator"]], "evaluate_fairness() (in module cyclops.evaluate.fairness.evaluator)": [[21, "cyclops.evaluate.fairness.evaluator.evaluate_fairness"]], "warn_too_many_unique_values() (in module cyclops.evaluate.fairness.evaluator)": [[22, "cyclops.evaluate.fairness.evaluator.warn_too_many_unique_values"]], "cyclops.evaluate.metrics.accuracy": [[23, "module-cyclops.evaluate.metrics.accuracy"]], "accuracy (class in cyclops.evaluate.metrics.accuracy)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy"]], "__add__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__add__"]], "__call__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__call__"]], "__init__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__init__"]], "__mul__() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.__mul__"]], "add_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.add_state"]], "clone() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.clone"]], "compute() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.compute"]], "reset_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.reset_state"]], "update_state() (accuracy method)": [[24, "cyclops.evaluate.metrics.accuracy.Accuracy.update_state"]], "binaryaccuracy (class in cyclops.evaluate.metrics.accuracy)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy"]], "__add__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__add__"]], "__call__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__call__"]], "__init__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__init__"]], "__mul__() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.__mul__"]], "add_state() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.add_state"]], "clone() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.clone"]], "compute() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.compute"]], "reset_state() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.reset_state"]], "update_state() (binaryaccuracy method)": [[25, "cyclops.evaluate.metrics.accuracy.BinaryAccuracy.update_state"]], "multiclassaccuracy (class in cyclops.evaluate.metrics.accuracy)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy"]], "__add__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__add__"]], "__call__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__call__"]], "__init__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__init__"]], "__mul__() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.__mul__"]], "add_state() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.add_state"]], "clone() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.clone"]], "compute() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.compute"]], "reset_state() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.reset_state"]], "update_state() (multiclassaccuracy method)": [[26, "cyclops.evaluate.metrics.accuracy.MulticlassAccuracy.update_state"]], "multilabelaccuracy (class in cyclops.evaluate.metrics.accuracy)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy"]], "__add__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__add__"]], "__call__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__call__"]], "__init__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__init__"]], "__mul__() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.__mul__"]], "add_state() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.add_state"]], "clone() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.clone"]], "compute() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.compute"]], "reset_state() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.reset_state"]], "update_state() (multilabelaccuracy method)": [[27, "cyclops.evaluate.metrics.accuracy.MultilabelAccuracy.update_state"]], "cyclops.evaluate.metrics.auroc": [[28, "module-cyclops.evaluate.metrics.auroc"]], "auroc (class in cyclops.evaluate.metrics.auroc)": [[29, "cyclops.evaluate.metrics.auroc.AUROC"]], "__add__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__add__"]], "__call__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__call__"]], "__init__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__init__"]], "__mul__() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.__mul__"]], "add_state() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.add_state"]], "clone() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.clone"]], "compute() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.compute"]], "reset_state() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.reset_state"]], "update_state() (auroc method)": [[29, "cyclops.evaluate.metrics.auroc.AUROC.update_state"]], "binaryauroc (class in cyclops.evaluate.metrics.auroc)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC"]], "__add__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__add__"]], "__call__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__call__"]], "__init__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__init__"]], "__mul__() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.__mul__"]], "add_state() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.add_state"]], "clone() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.clone"]], "compute() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.compute"]], "reset_state() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.reset_state"]], "update_state() (binaryauroc method)": [[30, "cyclops.evaluate.metrics.auroc.BinaryAUROC.update_state"]], "multiclassauroc (class in cyclops.evaluate.metrics.auroc)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC"]], "__add__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__add__"]], "__call__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__call__"]], "__init__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__init__"]], "__mul__() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.__mul__"]], "add_state() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.add_state"]], "clone() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.clone"]], "compute() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.compute"]], "reset_state() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.reset_state"]], "update_state() (multiclassauroc method)": [[31, "cyclops.evaluate.metrics.auroc.MulticlassAUROC.update_state"]], "multilabelauroc (class in cyclops.evaluate.metrics.auroc)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC"]], "__add__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__add__"]], "__call__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__call__"]], "__init__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__init__"]], "__mul__() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.__mul__"]], "add_state() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.add_state"]], "clone() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.clone"]], "compute() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.compute"]], "reset_state() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.reset_state"]], "update_state() (multilabelauroc method)": [[32, "cyclops.evaluate.metrics.auroc.MultilabelAUROC.update_state"]], "cyclops.evaluate.metrics.f_beta": [[33, "module-cyclops.evaluate.metrics.f_beta"]], "binaryf1score (class in cyclops.evaluate.metrics.f_beta)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score"]], "__add__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__add__"]], "__call__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__call__"]], "__init__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__init__"]], "__mul__() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.__mul__"]], "add_state() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.add_state"]], "clone() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.clone"]], "compute() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.compute"]], "reset_state() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.reset_state"]], "update_state() (binaryf1score method)": [[34, "cyclops.evaluate.metrics.f_beta.BinaryF1Score.update_state"]], "binaryfbetascore (class in cyclops.evaluate.metrics.f_beta)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore"]], "__add__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__add__"]], "__call__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__call__"]], "__init__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__init__"]], "__mul__() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.__mul__"]], "add_state() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.add_state"]], "clone() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.clone"]], "compute() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.compute"]], "reset_state() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.reset_state"]], "update_state() (binaryfbetascore method)": [[35, "cyclops.evaluate.metrics.f_beta.BinaryFbetaScore.update_state"]], "f1score (class in cyclops.evaluate.metrics.f_beta)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score"]], "__add__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__add__"]], "__call__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__call__"]], "__init__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__init__"]], "__mul__() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.__mul__"]], "add_state() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.add_state"]], "clone() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.clone"]], "compute() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.compute"]], "reset_state() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.reset_state"]], "update_state() (f1score method)": [[36, "cyclops.evaluate.metrics.f_beta.F1Score.update_state"]], "fbetascore (class in cyclops.evaluate.metrics.f_beta)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore"]], "__add__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__add__"]], "__call__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__call__"]], "__init__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__init__"]], "__mul__() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.__mul__"]], "add_state() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.add_state"]], "clone() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.clone"]], "compute() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.compute"]], "reset_state() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.reset_state"]], "update_state() (fbetascore method)": [[37, "cyclops.evaluate.metrics.f_beta.FbetaScore.update_state"]], "multiclassf1score (class in cyclops.evaluate.metrics.f_beta)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score"]], "__add__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__add__"]], "__call__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__call__"]], "__init__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__init__"]], "__mul__() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.__mul__"]], "add_state() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.add_state"]], "clone() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.clone"]], "compute() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.compute"]], "reset_state() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.reset_state"]], "update_state() (multiclassf1score method)": [[38, "cyclops.evaluate.metrics.f_beta.MulticlassF1Score.update_state"]], "multiclassfbetascore (class in cyclops.evaluate.metrics.f_beta)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore"]], "__add__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__add__"]], "__call__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__call__"]], "__init__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__init__"]], "__mul__() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.__mul__"]], "add_state() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.add_state"]], "clone() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.clone"]], "compute() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.compute"]], "reset_state() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.reset_state"]], "update_state() (multiclassfbetascore method)": [[39, "cyclops.evaluate.metrics.f_beta.MulticlassFbetaScore.update_state"]], "multilabelf1score (class in cyclops.evaluate.metrics.f_beta)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score"]], "__add__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__add__"]], "__call__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__call__"]], "__init__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__init__"]], "__mul__() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.__mul__"]], "add_state() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.add_state"]], "clone() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.clone"]], "compute() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.compute"]], "reset_state() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.reset_state"]], "update_state() (multilabelf1score method)": [[40, "cyclops.evaluate.metrics.f_beta.MultilabelF1Score.update_state"]], "multilabelfbetascore (class in cyclops.evaluate.metrics.f_beta)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore"]], "__add__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__add__"]], "__call__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__call__"]], "__init__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__init__"]], "__mul__() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.__mul__"]], "add_state() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.add_state"]], "clone() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.clone"]], "compute() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.compute"]], "reset_state() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.reset_state"]], "update_state() (multilabelfbetascore method)": [[41, "cyclops.evaluate.metrics.f_beta.MultilabelFbetaScore.update_state"]], "cyclops.evaluate.metrics.factory": [[42, "module-cyclops.evaluate.metrics.factory"]], "create_metric() (in module cyclops.evaluate.metrics.factory)": [[43, "cyclops.evaluate.metrics.factory.create_metric"]], "cyclops.evaluate.metrics.functional.accuracy": [[44, "module-cyclops.evaluate.metrics.functional.accuracy"]], "cyclops.evaluate.metrics.functional.auroc": [[45, "module-cyclops.evaluate.metrics.functional.auroc"]], "cyclops.evaluate.metrics.functional.f_beta": [[46, "module-cyclops.evaluate.metrics.functional.f_beta"]], "binary_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[47, "cyclops.evaluate.metrics.functional.f_beta.binary_f1_score"]], "binary_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[48, "cyclops.evaluate.metrics.functional.f_beta.binary_fbeta_score"]], "f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[49, "cyclops.evaluate.metrics.functional.f_beta.f1_score"]], "fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[50, "cyclops.evaluate.metrics.functional.f_beta.fbeta_score"]], "multiclass_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[51, "cyclops.evaluate.metrics.functional.f_beta.multiclass_f1_score"]], "multiclass_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[52, "cyclops.evaluate.metrics.functional.f_beta.multiclass_fbeta_score"]], "multilabel_f1_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[53, "cyclops.evaluate.metrics.functional.f_beta.multilabel_f1_score"]], "multilabel_fbeta_score() (in module cyclops.evaluate.metrics.functional.f_beta)": [[54, "cyclops.evaluate.metrics.functional.f_beta.multilabel_fbeta_score"]], "cyclops.evaluate.metrics.functional.precision_recall": [[55, "module-cyclops.evaluate.metrics.functional.precision_recall"]], "binary_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[56, "cyclops.evaluate.metrics.functional.precision_recall.binary_precision"]], "binary_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[57, "cyclops.evaluate.metrics.functional.precision_recall.binary_recall"]], "multiclass_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[58, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_precision"]], "multiclass_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[59, "cyclops.evaluate.metrics.functional.precision_recall.multiclass_recall"]], "multilabel_precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[60, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_precision"]], "multilabel_recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[61, "cyclops.evaluate.metrics.functional.precision_recall.multilabel_recall"]], "precision() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[62, "cyclops.evaluate.metrics.functional.precision_recall.precision"]], "recall() (in module cyclops.evaluate.metrics.functional.precision_recall)": [[63, "cyclops.evaluate.metrics.functional.precision_recall.recall"]], "cyclops.evaluate.metrics.functional.precision_recall_curve": [[64, "module-cyclops.evaluate.metrics.functional.precision_recall_curve"]], "cyclops.evaluate.metrics.functional.roc": [[65, "module-cyclops.evaluate.metrics.functional.roc"]], "binary_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[66, "cyclops.evaluate.metrics.functional.roc.binary_roc_curve"]], "multiclass_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[67, "cyclops.evaluate.metrics.functional.roc.multiclass_roc_curve"]], "multilabel_roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[68, "cyclops.evaluate.metrics.functional.roc.multilabel_roc_curve"]], "roc_curve() (in module cyclops.evaluate.metrics.functional.roc)": [[69, "cyclops.evaluate.metrics.functional.roc.roc_curve"]], "cyclops.evaluate.metrics.functional.sensitivity": [[70, "module-cyclops.evaluate.metrics.functional.sensitivity"]], "cyclops.evaluate.metrics.functional.specificity": [[71, "module-cyclops.evaluate.metrics.functional.specificity"]], "cyclops.evaluate.metrics.functional.stat_scores": [[72, "module-cyclops.evaluate.metrics.functional.stat_scores"]], "cyclops.evaluate.metrics.metric": [[73, "module-cyclops.evaluate.metrics.metric"]], "metric (class in cyclops.evaluate.metrics.metric)": [[74, "cyclops.evaluate.metrics.metric.Metric"]], "__add__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__add__"]], "__call__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__call__"]], "__init__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__init__"]], "__mul__() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.__mul__"]], "add_state() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.add_state"]], "clone() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.clone"]], "compute() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.compute"]], "reset_state() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.reset_state"]], "update_state() (metric method)": [[74, "cyclops.evaluate.metrics.metric.Metric.update_state"]], "metriccollection (class in cyclops.evaluate.metrics.metric)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection"]], "__call__() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.__call__"]], "__init__() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.__init__"]], "add_metrics() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.add_metrics"]], "clear() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.clear"]], "clone() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.clone"]], "compute() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.compute"]], "get() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.get"]], "items() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.items"]], "keys() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.keys"]], "pop() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.pop"]], "popitem() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.popitem"]], "reset_state() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.reset_state"]], "setdefault() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.setdefault"]], "update() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.update"]], "update_state() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.update_state"]], "values() (metriccollection method)": [[75, "cyclops.evaluate.metrics.metric.MetricCollection.values"]], "operatormetric (class in cyclops.evaluate.metrics.metric)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric"]], "__add__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__add__"]], "__call__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__call__"]], "__init__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__init__"]], "__mul__() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.__mul__"]], "add_state() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.add_state"]], "clone() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.clone"]], "compute() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.compute"]], "reset_state() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.reset_state"]], "update_state() (operatormetric method)": [[76, "cyclops.evaluate.metrics.metric.OperatorMetric.update_state"]], "cyclops.evaluate.metrics.precision_recall": [[77, "module-cyclops.evaluate.metrics.precision_recall"]], "binaryprecision (class in cyclops.evaluate.metrics.precision_recall)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision"]], "__add__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__add__"]], "__call__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__call__"]], "__init__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__init__"]], "__mul__() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.__mul__"]], "add_state() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.add_state"]], "clone() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.clone"]], "compute() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.compute"]], "reset_state() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.reset_state"]], "update_state() (binaryprecision method)": [[78, "cyclops.evaluate.metrics.precision_recall.BinaryPrecision.update_state"]], "binaryrecall (class in cyclops.evaluate.metrics.precision_recall)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall"]], "__add__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__add__"]], "__call__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__call__"]], "__init__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__init__"]], "__mul__() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.__mul__"]], "add_state() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.add_state"]], "clone() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.clone"]], "compute() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.compute"]], "reset_state() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.reset_state"]], "update_state() (binaryrecall method)": [[79, "cyclops.evaluate.metrics.precision_recall.BinaryRecall.update_state"]], "multiclassprecision (class in cyclops.evaluate.metrics.precision_recall)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision"]], "__add__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__add__"]], "__call__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__call__"]], "__init__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__init__"]], "__mul__() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.__mul__"]], "add_state() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.add_state"]], "clone() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.clone"]], "compute() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.compute"]], "reset_state() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.reset_state"]], "update_state() (multiclassprecision method)": [[80, "cyclops.evaluate.metrics.precision_recall.MulticlassPrecision.update_state"]], "multiclassrecall (class in cyclops.evaluate.metrics.precision_recall)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall"]], "__add__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__add__"]], "__call__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__call__"]], "__init__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__init__"]], "__mul__() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.__mul__"]], "add_state() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.add_state"]], "clone() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.clone"]], "compute() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.compute"]], "reset_state() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.reset_state"]], "update_state() (multiclassrecall method)": [[81, "cyclops.evaluate.metrics.precision_recall.MulticlassRecall.update_state"]], "multilabelprecision (class in cyclops.evaluate.metrics.precision_recall)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision"]], "__add__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__add__"]], "__call__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__call__"]], "__init__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__init__"]], "__mul__() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.__mul__"]], "add_state() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.add_state"]], "clone() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.clone"]], "compute() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.compute"]], "reset_state() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.reset_state"]], "update_state() (multilabelprecision method)": [[82, "cyclops.evaluate.metrics.precision_recall.MultilabelPrecision.update_state"]], "multilabelrecall (class in cyclops.evaluate.metrics.precision_recall)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall"]], "__add__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__add__"]], "__call__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__call__"]], "__init__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__init__"]], "__mul__() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.__mul__"]], "add_state() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.add_state"]], "clone() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.clone"]], "compute() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.compute"]], "reset_state() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.reset_state"]], "update_state() (multilabelrecall method)": [[83, "cyclops.evaluate.metrics.precision_recall.MultilabelRecall.update_state"]], "precision (class in cyclops.evaluate.metrics.precision_recall)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision"]], "__add__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__add__"]], "__call__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__call__"]], "__init__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__init__"]], "__mul__() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.__mul__"]], "add_state() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.add_state"]], "clone() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.clone"]], "compute() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.compute"]], "reset_state() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.reset_state"]], "update_state() (precision method)": [[84, "cyclops.evaluate.metrics.precision_recall.Precision.update_state"]], "recall (class in cyclops.evaluate.metrics.precision_recall)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall"]], "__add__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__add__"]], "__call__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__call__"]], "__init__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__init__"]], "__mul__() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.__mul__"]], "add_state() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.add_state"]], "clone() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.clone"]], "compute() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.compute"]], "reset_state() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.reset_state"]], "update_state() (recall method)": [[85, "cyclops.evaluate.metrics.precision_recall.Recall.update_state"]], "cyclops.evaluate.metrics.precision_recall_curve": [[86, "module-cyclops.evaluate.metrics.precision_recall_curve"]], "binaryprecisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve"]], "__add__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__add__"]], "__call__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__call__"]], "__init__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__init__"]], "__mul__() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.__mul__"]], "add_state() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.add_state"]], "clone() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.clone"]], "compute() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.compute"]], "reset_state() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.reset_state"]], "update_state() (binaryprecisionrecallcurve method)": [[87, "cyclops.evaluate.metrics.precision_recall_curve.BinaryPrecisionRecallCurve.update_state"]], "multiclassprecisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve"]], "__add__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__add__"]], "__call__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__call__"]], "__init__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__init__"]], "__mul__() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.__mul__"]], "add_state() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.add_state"]], "clone() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.clone"]], "compute() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.compute"]], "reset_state() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.reset_state"]], "update_state() (multiclassprecisionrecallcurve method)": [[88, "cyclops.evaluate.metrics.precision_recall_curve.MulticlassPrecisionRecallCurve.update_state"]], "multilabelprecisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve"]], "__add__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__add__"]], "__call__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__call__"]], "__init__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__init__"]], "__mul__() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.__mul__"]], "add_state() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.add_state"]], "clone() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.clone"]], "compute() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.compute"]], "reset_state() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.reset_state"]], "update_state() (multilabelprecisionrecallcurve method)": [[89, "cyclops.evaluate.metrics.precision_recall_curve.MultilabelPrecisionRecallCurve.update_state"]], "precisionrecallcurve (class in cyclops.evaluate.metrics.precision_recall_curve)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve"]], "__add__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__add__"]], "__call__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__call__"]], "__init__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__init__"]], "__mul__() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.__mul__"]], "add_state() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.add_state"]], "clone() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.clone"]], "compute() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.compute"]], "reset_state() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.reset_state"]], "update_state() (precisionrecallcurve method)": [[90, "cyclops.evaluate.metrics.precision_recall_curve.PrecisionRecallCurve.update_state"]], "cyclops.evaluate.metrics.roc": [[91, "module-cyclops.evaluate.metrics.roc"]], "binaryroccurve (class in cyclops.evaluate.metrics.roc)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve"]], "__add__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__add__"]], "__call__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__call__"]], "__init__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__init__"]], "__mul__() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.__mul__"]], "add_state() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.add_state"]], "clone() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.clone"]], "compute() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.compute"]], "reset_state() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.reset_state"]], "update_state() (binaryroccurve method)": [[92, "cyclops.evaluate.metrics.roc.BinaryROCCurve.update_state"]], "multiclassroccurve (class in cyclops.evaluate.metrics.roc)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve"]], "__add__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__add__"]], "__call__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__call__"]], "__init__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__init__"]], "__mul__() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.__mul__"]], "add_state() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.add_state"]], "clone() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.clone"]], "compute() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.compute"]], "reset_state() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.reset_state"]], "update_state() (multiclassroccurve method)": [[93, "cyclops.evaluate.metrics.roc.MulticlassROCCurve.update_state"]], "multilabelroccurve (class in cyclops.evaluate.metrics.roc)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve"]], "__add__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__add__"]], "__call__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__call__"]], "__init__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__init__"]], "__mul__() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.__mul__"]], "add_state() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.add_state"]], "clone() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.clone"]], "compute() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.compute"]], "reset_state() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.reset_state"]], "update_state() (multilabelroccurve method)": [[94, "cyclops.evaluate.metrics.roc.MultilabelROCCurve.update_state"]], "roccurve (class in cyclops.evaluate.metrics.roc)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve"]], "__add__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__add__"]], "__call__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__call__"]], "__init__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__init__"]], "__mul__() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.__mul__"]], "add_state() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.add_state"]], "clone() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.clone"]], "compute() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.compute"]], "reset_state() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.reset_state"]], "update_state() (roccurve method)": [[95, "cyclops.evaluate.metrics.roc.ROCCurve.update_state"]], "cyclops.evaluate.metrics.sensitivity": [[96, "module-cyclops.evaluate.metrics.sensitivity"]], "binarysensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity"]], "__add__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__add__"]], "__call__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__call__"]], "__init__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__init__"]], "__mul__() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.__mul__"]], "add_state() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.add_state"]], "clone() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.clone"]], "compute() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.compute"]], "reset_state() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.reset_state"]], "update_state() (binarysensitivity method)": [[97, "cyclops.evaluate.metrics.sensitivity.BinarySensitivity.update_state"]], "multiclasssensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity"]], "__add__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__add__"]], "__call__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__call__"]], "__init__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__init__"]], "__mul__() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.__mul__"]], "add_state() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.add_state"]], "clone() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.clone"]], "compute() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.compute"]], "reset_state() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.reset_state"]], "update_state() (multiclasssensitivity method)": [[98, "cyclops.evaluate.metrics.sensitivity.MulticlassSensitivity.update_state"]], "multilabelsensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity"]], "__add__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__add__"]], "__call__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__call__"]], "__init__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__init__"]], "__mul__() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.__mul__"]], "add_state() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.add_state"]], "clone() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.clone"]], "compute() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.compute"]], "reset_state() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.reset_state"]], "update_state() (multilabelsensitivity method)": [[99, "cyclops.evaluate.metrics.sensitivity.MultilabelSensitivity.update_state"]], "sensitivity (class in cyclops.evaluate.metrics.sensitivity)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity"]], "__add__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__add__"]], "__call__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__call__"]], "__init__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__init__"]], "__mul__() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.__mul__"]], "add_state() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.add_state"]], "clone() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.clone"]], "compute() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.compute"]], "reset_state() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.reset_state"]], "update_state() (sensitivity method)": [[100, "cyclops.evaluate.metrics.sensitivity.Sensitivity.update_state"]], "cyclops.evaluate.metrics.specificity": [[101, "module-cyclops.evaluate.metrics.specificity"]], "binaryspecificity (class in cyclops.evaluate.metrics.specificity)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity"]], "__add__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__add__"]], "__call__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__call__"]], "__init__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__init__"]], "__mul__() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.__mul__"]], "add_state() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.add_state"]], "clone() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.clone"]], "compute() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.compute"]], "reset_state() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.reset_state"]], "update_state() (binaryspecificity method)": [[102, "cyclops.evaluate.metrics.specificity.BinarySpecificity.update_state"]], "multiclassspecificity (class in cyclops.evaluate.metrics.specificity)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity"]], "__add__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__add__"]], "__call__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__call__"]], "__init__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__init__"]], "__mul__() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.__mul__"]], "add_state() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.add_state"]], "clone() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.clone"]], "compute() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.compute"]], "reset_state() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.reset_state"]], "update_state() (multiclassspecificity method)": [[103, "cyclops.evaluate.metrics.specificity.MulticlassSpecificity.update_state"]], "multilabelspecificity (class in cyclops.evaluate.metrics.specificity)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity"]], "__add__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__add__"]], "__call__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__call__"]], "__init__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__init__"]], "__mul__() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.__mul__"]], "add_state() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.add_state"]], "clone() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.clone"]], "compute() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.compute"]], "reset_state() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.reset_state"]], "update_state() (multilabelspecificity method)": [[104, "cyclops.evaluate.metrics.specificity.MultilabelSpecificity.update_state"]], "specificity (class in cyclops.evaluate.metrics.specificity)": [[105, "cyclops.evaluate.metrics.specificity.Specificity"]], "__add__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__add__"]], "__call__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__call__"]], "__init__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__init__"]], "__mul__() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.__mul__"]], "add_state() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.add_state"]], "clone() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.clone"]], "compute() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.compute"]], "reset_state() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.reset_state"]], "update_state() (specificity method)": [[105, "cyclops.evaluate.metrics.specificity.Specificity.update_state"]], "cyclops.evaluate.metrics.stat_scores": [[106, "module-cyclops.evaluate.metrics.stat_scores"]], "binarystatscores (class in cyclops.evaluate.metrics.stat_scores)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores"]], "__add__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__add__"]], "__call__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__call__"]], "__init__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__init__"]], "__mul__() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.__mul__"]], "add_state() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.add_state"]], "clone() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.clone"]], "compute() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.compute"]], "reset_state() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.reset_state"]], "update_state() (binarystatscores method)": [[107, "cyclops.evaluate.metrics.stat_scores.BinaryStatScores.update_state"]], "multiclassstatscores (class in cyclops.evaluate.metrics.stat_scores)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores"]], "__add__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__add__"]], "__call__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__call__"]], "__init__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__init__"]], "__mul__() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.__mul__"]], "add_state() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.add_state"]], "clone() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.clone"]], "compute() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.compute"]], "reset_state() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.reset_state"]], "update_state() (multiclassstatscores method)": [[108, "cyclops.evaluate.metrics.stat_scores.MulticlassStatScores.update_state"]], "multilabelstatscores (class in cyclops.evaluate.metrics.stat_scores)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores"]], "__add__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__add__"]], "__call__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__call__"]], "__init__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__init__"]], "__mul__() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.__mul__"]], "add_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.add_state"]], "clone() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.clone"]], "compute() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.compute"]], "reset_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.reset_state"]], "update_state() (multilabelstatscores method)": [[109, "cyclops.evaluate.metrics.stat_scores.MultilabelStatScores.update_state"]], "statscores (class in cyclops.evaluate.metrics.stat_scores)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores"]], "__add__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__add__"]], "__call__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__call__"]], "__init__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__init__"]], "__mul__() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.__mul__"]], "add_state() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.add_state"]], "clone() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.clone"]], "compute() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.compute"]], "reset_state() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.reset_state"]], "update_state() (statscores method)": [[110, "cyclops.evaluate.metrics.stat_scores.StatScores.update_state"]], "cyclops.monitor.clinical_applicator": [[111, "module-cyclops.monitor.clinical_applicator"]], "clinicalshiftapplicator (class in cyclops.monitor.clinical_applicator)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator"]], "age() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.age"]], "apply_shift() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.apply_shift"]], "custom() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.custom"]], "hospital_type() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.hospital_type"]], "month() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.month"]], "sex() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.sex"]], "time() (clinicalshiftapplicator method)": [[112, "cyclops.monitor.clinical_applicator.ClinicalShiftApplicator.time"]], "cyclops.monitor.synthetic_applicator": [[113, "module-cyclops.monitor.synthetic_applicator"]], "syntheticshiftapplicator (class in cyclops.monitor.synthetic_applicator)": [[114, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator"]], "apply_shift() (syntheticshiftapplicator method)": [[114, "cyclops.monitor.synthetic_applicator.SyntheticShiftApplicator.apply_shift"]], "binary_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[115, "cyclops.monitor.synthetic_applicator.binary_noise_shift"]], "feature_association_shift() (in module cyclops.monitor.synthetic_applicator)": [[116, "cyclops.monitor.synthetic_applicator.feature_association_shift"]], "feature_swap_shift() (in module cyclops.monitor.synthetic_applicator)": [[117, "cyclops.monitor.synthetic_applicator.feature_swap_shift"]], "gaussian_noise_shift() (in module cyclops.monitor.synthetic_applicator)": [[118, "cyclops.monitor.synthetic_applicator.gaussian_noise_shift"]], "knockout_shift() (in module cyclops.monitor.synthetic_applicator)": [[119, "cyclops.monitor.synthetic_applicator.knockout_shift"]], "cyclops.report.report": [[120, "module-cyclops.report.report"]], "modelcardreport (class in cyclops.report.report)": [[121, "cyclops.report.report.ModelCardReport"]], "export() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.export"]], "from_json_file() (modelcardreport class method)": [[121, "cyclops.report.report.ModelCardReport.from_json_file"]], "log_citation() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_citation"]], "log_dataset() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_dataset"]], "log_descriptor() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_descriptor"]], "log_fairness_assessment() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_fairness_assessment"]], "log_from_dict() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_from_dict"]], "log_image() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_image"]], "log_license() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_license"]], "log_model_parameters() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_model_parameters"]], "log_owner() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_owner"]], "log_performance_metrics() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_performance_metrics"]], "log_plotly_figure() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_plotly_figure"]], "log_quantitative_analysis() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_quantitative_analysis"]], "log_reference() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_reference"]], "log_regulation() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_regulation"]], "log_risk() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_risk"]], "log_use_case() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_use_case"]], "log_user() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_user"]], "log_version() (modelcardreport method)": [[121, "cyclops.report.report.ModelCardReport.log_version"]], "cyclops.tasks.cxr_classification": [[122, "module-cyclops.tasks.cxr_classification"]], "cxrclassificationtask (class in cyclops.tasks.cxr_classification)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask"]], "__init__() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.__init__"]], "add_model() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.add_model"]], "data_type (cxrclassificationtask property)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.data_type"]], "evaluate() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.evaluate"]], "get_model() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.get_model"]], "list_models() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.list_models"]], "models_count (cxrclassificationtask property)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.models_count"]], "predict() (cxrclassificationtask method)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.predict"]], "task_type (cxrclassificationtask property)": [[123, "cyclops.tasks.cxr_classification.CXRClassificationTask.task_type"]], "cyclops.tasks.mortality_prediction": [[124, "module-cyclops.tasks.mortality_prediction"]], "mortalitypredictiontask (class in cyclops.tasks.mortality_prediction)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask"]], "__init__() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.__init__"]], "add_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.add_model"]], "data_type (mortalitypredictiontask property)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.data_type"]], "evaluate() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.evaluate"]], "get_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.get_model"]], "list_models() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.list_models"]], "list_models_params() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.list_models_params"]], "load_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.load_model"]], "models_count (mortalitypredictiontask property)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.models_count"]], "predict() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.predict"]], "save_model() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.save_model"]], "task_type (mortalitypredictiontask property)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.task_type"]], "train() (mortalitypredictiontask method)": [[125, "cyclops.tasks.mortality_prediction.MortalityPredictionTask.train"]], "cyclops.data": [[126, "module-cyclops.data"]], "cyclops.data.features": [[126, "module-cyclops.data.features"]], "cyclops.evaluate": [[127, "module-cyclops.evaluate"]], "cyclops.evaluate.fairness": [[127, "module-cyclops.evaluate.fairness"]], "cyclops.evaluate.metrics": [[127, "module-cyclops.evaluate.metrics"]], "cyclops.evaluate.metrics.functional": [[127, "module-cyclops.evaluate.metrics.functional"]], "cyclops.monitor": [[128, "module-cyclops.monitor"]], "cyclops.report": [[129, "module-cyclops.report"]], "cyclops.tasks": [[130, "module-cyclops.tasks"]]}}) \ No newline at end of file diff --git a/api/tutorials/kaggle/heart_failure_prediction.html b/api/tutorials/kaggle/heart_failure_prediction.html index 9b92e0f6d..5cbb99240 100644 --- a/api/tutorials/kaggle/heart_failure_prediction.html +++ b/api/tutorials/kaggle/heart_failure_prediction.html @@ -551,7 +551,7 @@

Data Loading 
-2023-10-26 14:21:16,171 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
+2023-10-26 14:49:50,263 INFO cyclops.utils.file - Loading DataFrame from ./data/heart.csv
@@ -633,9 +633,9 @@

Sex values

-
+
@@ -575,7 +575,7 @@

Performance Over Time

-
+
@@ -818,8 +818,8 @@

Model Parameters

-

Penalty

- l2 +

N_iter_no_change

+ 5
@@ -827,8 +827,8 @@

Penalty

-

Epsilon

- 0.1 +

Random_state

+ 123
@@ -836,8 +836,8 @@

Epsilon

-

Early_stopping

- True +

L1_ratio

+ 0.15
@@ -845,8 +845,8 @@

Early_stopping

-

Max_iter

- 1000 +

Shuffle

+ True
@@ -854,8 +854,8 @@

Max_iter

-

Alpha

- 0.001 +

Class_weight

+ balanced
@@ -863,8 +863,8 @@

Alpha

-

N_iter_no_change

- 5 +

Tol

+ 0.001
@@ -872,8 +872,8 @@

N_iter_no_change

-

Fit_intercept

- True +

Epsilon

+ 0.1
@@ -881,8 +881,8 @@

Fit_intercept

-

Eta0

- 0.01 +

Warm_start

+ False
@@ -890,8 +890,8 @@

Eta0

-

Class_weight

- balanced +

Learning_rate

+ adaptive
@@ -899,8 +899,8 @@

Class_weight

-

Warm_start

- False +

Early_stopping

+ True
@@ -908,8 +908,8 @@

Warm_start

-

Verbose

- 0 +

Penalty

+ l2
@@ -917,8 +917,8 @@

Verbose

-

Power_t

- 0.5 +

Average

+ False
@@ -926,26 +926,22 @@

Power_t

-

Average

- False +

Fit_intercept

+ True
-
-

Learning_rate

- adaptive -
-

Validation_fraction

- 0.1 +

Eta0

+ 0.01
@@ -953,22 +949,26 @@

Validation_fraction

-

Shuffle

- True +

Max_iter

+ 1000
+
+

Validation_fraction

+ 0.1 +
-

Tol

- 0.001 +

Power_t

+ 0.5
@@ -976,8 +976,8 @@

Tol

-

L1_ratio

- 0.15 +

Verbose

+ 0
@@ -985,8 +985,8 @@

L1_ratio

-

Random_state

- 123 +

Alpha

+ 0.001
@@ -1300,7 +1300,7 @@

Sensitive Data

-

Reference

+

License

@@ -664,14 +664,14 @@

Example 4. Sensitivity test experiment with different clinical shifts 
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 51837.31 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 51321.09 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50814.95 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46828.39 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 48835.16 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 49544.24 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 48892.07 examples/s]
-Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 49769.51 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 51805.52 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 53031.09 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 44752.22 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46932.32 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46296.32 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45996.11 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46760.92 examples/s]
+Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50103.00 examples/s]
diff --git a/api/tutorials/nihcxr/monitor_api.ipynb b/api/tutorials/nihcxr/monitor_api.ipynb index f3ddb8bcc..1eacee91e 100644 --- a/api/tutorials/nihcxr/monitor_api.ipynb +++ b/api/tutorials/nihcxr/monitor_api.ipynb @@ -22,10 +22,10 @@ "id": "8aa3302d", "metadata": { "execution": { - "iopub.execute_input": "2023-10-26T18:22:46.038823Z", - "iopub.status.busy": "2023-10-26T18:22:46.037745Z", - "iopub.status.idle": "2023-10-26T18:22:53.267571Z", - "shell.execute_reply": "2023-10-26T18:22:53.266910Z" + "iopub.execute_input": "2023-10-26T18:51:15.147155Z", + "iopub.status.busy": "2023-10-26T18:51:15.146663Z", + "iopub.status.idle": "2023-10-26T18:51:22.238126Z", + "shell.execute_reply": "2023-10-26T18:51:22.236885Z" } }, "outputs": [ @@ -69,10 +69,10 @@ "id": "e11920db", "metadata": { "execution": { - "iopub.execute_input": "2023-10-26T18:22:53.271317Z", - "iopub.status.busy": "2023-10-26T18:22:53.270989Z", - "iopub.status.idle": "2023-10-26T18:22:53.828742Z", - "shell.execute_reply": "2023-10-26T18:22:53.827571Z" + "iopub.execute_input": "2023-10-26T18:51:22.243673Z", + "iopub.status.busy": "2023-10-26T18:51:22.243399Z", + "iopub.status.idle": "2023-10-26T18:51:22.788391Z", + "shell.execute_reply": "2023-10-26T18:51:22.787548Z" } }, "outputs": [ @@ -89,7 +89,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 36183.24 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 36322.87 examples/s]" ] }, { @@ -97,7 +97,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 69845.80 examples/s]" + "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 80514.17 examples/s]" ] }, { @@ -105,7 +105,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 62456.31 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 65746.87 examples/s]" ] }, { @@ -159,16 +159,16 @@ "id": "54a3523a", "metadata": { "execution": { - "iopub.execute_input": "2023-10-26T18:22:53.834672Z", - "iopub.status.busy": "2023-10-26T18:22:53.834269Z", - "iopub.status.idle": "2023-10-26T18:23:05.725209Z", - "shell.execute_reply": "2023-10-26T18:23:05.724576Z" + "iopub.execute_input": "2023-10-26T18:51:22.793515Z", + "iopub.status.busy": "2023-10-26T18:51:22.793263Z", + "iopub.status.idle": "2023-10-26T18:51:34.357111Z", + "shell.execute_reply": "2023-10-26T18:51:34.356461Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA58AAAKrCAYAAAB2jTlzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAACGuElEQVR4nOzdeXhcddn/8ffMZM9k684SKLXsyFoooOyFFmXfSgFbClZFZSsFWkVA5LGI/CwKCAiloCAiD8ojqLgUAVkEoRZBZBGoBUrapG32feb8/jjT0NCFNE0yk+T9uq652u93zpy5ZzIzmU/OOfeJBEEQIEmSJElSL4qmuwBJkiRJ0sBn+JQkSZIk9TrDpyRJkiSp1xk+JUmSJEm9zvApSZIkSep1hk9JkiRJUq8zfEqSJEmSel1WugtIt2QyybJlyygqKiISiaS7HEmSJEnqV4IgoK6uji233JJodMPbNwd9+Fy2bBnl5eXpLkOSJEmS+rX33nuPrbfeeoPXD/rwWVRUBIRPVHFxcZqrkSRJkqT+pba2lvLy8o5stSGDPnyu2dW2uLjY8ClJkiRJ3fRJhzHacEiSJEmS1OsMn5IkSZKkXmf4lCRJkiT1ukF/zGdXJRIJ2tra0l2GBrns7GxisVi6y5AkSZI2meHzEwRBQEVFBdXV1ekuRQKgtLSUUaNGeV5aSZIk9SuGz0+wJniOGDGCgoICv/ArbYIgoLGxkRUrVgCwxRZbpLkiSZIkqesMnxuRSCQ6gufQoUPTXY5Efn4+ACtWrGDEiBHugitJkqR+w4ZDG7HmGM+CgoI0VyJ9ZM3r0WOQJUmS1J8YPrvAXW2VSXw9SpIkqT8yfEqSJEmSep3hU5IkSZLU6wyffSQRwIvN8FhD+G8i6N37O/vss4lEIh2XoUOHMmnSJP75z392LLP29VlZWWyzzTbMnDmTlpaWjmUqKys577zz2GabbcjNzWXUqFFMnDiRZ555pmOZ0aNHd1rXmst1113Xuw9SkiRJUr9ht9s+sLARvr8aViQ+mhsRg0vL4Ihe7GU0adIkFixYAISnjLniiis45phjWLp0accyCxYsYNKkSbS1tfHyyy8zffp0CgsL+c53vgPAySefTGtrK/fccw9jxoxh+fLlLFy4kJUrV3a6r2uuuYYZM2Z0misqKuq9BydJkiSpXzF89rKFjXBZFXx8Q2dlIpy/fljvBdA1WyoBRo0axezZsznooIOorKxk+PDhAJSWlnYsU15ezvHHH8+iRYsAqK6u5q9//StPPPEEhxxyCADbbrst++233zr3VVRU1LEeSZIkSfo4d7vdREEATcmuXeoTcP3qdYMnhHMB4RbR+kTX1hdsxq669fX13HvvvYwdO3aD5yx98803efzxxxk/fjwA8XiceDzOww8/3GlXXEmSJEnaVJEg2JxI0ztuueUWvv/971NRUcEee+zBTTfdtN6tbQB3330306dP7zSXm5tLc3Nzl+6rtraWkpISampqKC4u7nRdc3Mz7777Lttttx15eXlAGAI/8343HlQPeGZryO/inwvOPvts7r333o66Gxoa2GKLLXj00UfZe++9gfCYz7y8PGKxGO3t7bS0tHDMMcfwq1/9iuzsbAAeeughZsyYQVNTE3vvvTeHHHIIp59+OrvvvnvHfY0ePZoPP/yw4zZr/P73v+eggw7qgUeuta3vdSlJkiSly8Yy1doybsvnAw88wMyZM7nqqqtYtGgRe+yxBxMnTmTFihUbvE1xcTEffvhhx+W///1vH1acuQ477DAWL17M4sWLeeGFF5g4cSJHH310p+dn3rx5LF68mJdffplHH32UN998ky984Qsd15988sksW7aM3/zmN0yaNIknnniCvffem7vvvrvTfV166aUd97XmMm7cuL56qJIkSZIyXMYd8/mDH/yAGTNmdGzNvO222/jtb3/LXXfdxezZs9d7m0gk0mfHG+ZFwi2QXbGoGc6v+uTlbhoGe3dhA1ZepGv3u0ZhYSFjx47tGN95552UlJRwxx13cO211wLhsaBrltlxxx2pq6tjypQpXHvttR3zeXl5HHnkkRx55JF861vf4otf/CJXXXUVZ599dse6hw0b1um+JEmSJGltGbXls7W1lZdeeokJEyZ0zEWjUSZMmMBzzz23wdvV19ez7bbbdjTM+de//rXBZVtaWqitre102RSRSLjra1cu++eHXW03lBkjwMhYuFxX1hfZxPC5bu0RotEoTU1NG1wmFosBbHSZXXbZhYaGhs0rRpIkSdKgklFbPquqqkgkEowcObLT/MiRI3n99dfXe5sdd9yRu+66i913352amhpuuOEGDjzwQP71r3+x9dbrbqKcO3cu3/72t3ul/o+LRcLTqVxWFQbNtQ+uXZMjZ5WFy/WGlpYWKioqAFi9ejU333wz9fX1HHvssR3LVFdXU1FRQTKZ5K233uKaa65hhx12YOedd2blypWceuqpnHPOOey+++4UFRXx4osvcv3113P88cd3uq+6urqO+1qjoKBgo/t8S5IkSRo8Mip8dscBBxzAAQcc0DE+8MAD2Xnnnbn99ts7zlW5tjlz5jBz5syOcW1tLeXl5b1W3xEF4elU1neez1m9fJ7Pxx57jC222AIIT4Wy00478eCDD3LooYd2LLNm9+Y1uy4ffPDBfPe73yUrK4t4PM748eOZN28eb7/9Nm1tbZSXlzNjxgy+8Y1vdLqvK6+8kiuvvLLT3Je//GVuu+223nuAkiRJ0iCQCOAfLVCVgGEx2Cu39zZg9aaMCp/Dhg0jFouxfPnyTvPLly/v8jGd2dnZ7LXXXvznP/9Z7/W5ubnk5uZudq2b4ogCODS/b18wd9999zpNgT7ukxod5+bmMnfuXObOnbvR5ZYsWbKJ1UmSJEnqioWN69+QdWkvb8jqDRl1zGdOTg777LMPCxcu7JhLJpMsXLiw09bNjUkkErzyyisdW/wyRSwC4/JgUmH4b3/8S4UkSZKkvrOwMTyEb+3gCVCZCOcXNqanru7KqPAJMHPmTO644w7uuece/v3vf3PeeefR0NDQsXvo1KlTmTNnTsfy11xzDX/84x955513WLRoEWeddRb//e9/+eIXv5iuhyBJkiRJmyURhFs817ev4pq5G1aHy/UXGbXbLcDkyZOprKzkyiuvpKKigj333JPHHnusownR0qVLiUY/ysyrV69mxowZVFRUUFZWxj777MOzzz7LLrvskq6HIEmSJEmb5R8t627xXFsALE+Ey43rwmkbM0Ek+KQD/wa42tpaSkpKqKmpWacza3NzM++++y7bbbcdeXn95CeqAc/XpSRJ0sD3WAN8Y+UnL/fdoeGhfem0sUy1tozb7VaSJEmSBrthsZ5dLhNk3G63kiRJkjTYLW/f+PURwq63e/XtiTw2i+FTkiRJkjJEEMD8WvhxzYaXWXPijFll/essGoZPSZIkScoAbQH8zyr4TUM4/lwBjMuF22rXPc/nrH54nk/DpyRJkiSlWV0SZlXC31vCLZvTiuGLxVAQhWPjYVfbqkR4jOdeuf1ri+cahk9JkiRJSqNl7XBBJbzTBnkR+HopnBqH7FTAjEX6z+lUNsZut32ptRWeeyr8t5edffbZRCKRjsvQoUOZNGkS//znPzstt/YyWVlZbLPNNsycOZOWlpaOZSorKznvvPPYZpttyM3NZdSoUUycOJFnnnmmY5nRo0d3Wteay3XXXdfjj23JkiXrva+1L3fffTe33norpaWlvPfee51uf/7557PDDjvQ2NgIwKGHHtpxu7y8PHbYYQfmzp1LEAS0trYybNiwDT6O73znO4wcOZK2trYN1vv++++Tk5PDbrvttt7rN/QYfvGLX3TzGZIkSVJ/8VoLTKsIg2dZFL41BE5fK3gOJG757AtBAC88A3fdAisrYdgImP5V2O8zEOm9V9WkSZNYsGABABUVFVxxxRUcc8wxLF26tNNyCxYsYNKkSbS1tfHyyy8zffp0CgsL+c53vgPAySefTGtrK/fccw9jxoxh+fLlLFy4kJUrO5946JprrmHGjBmd5oqKirpc7+jRo7n77rs59NBDN7pceXk5H374Ycf4hhtu4LHHHuPPf/5zx1xJSQl5eXn8+te/5txzz+WPf/wjAAsXLuTWW2/lqaeeoqDgo53kZ8yYwTXXXENLSwuPP/44X/rSlygtLeW8887jrLPOYsGCBcyePbtTHUEQcPfddzN16lSys7M3WO/dd9/NaaedxlNPPcXzzz/P+PHj11lmzc9gbaWlpRt9HiRJktS/PdEIc1ZCSwDbZMFlZXBgfrqr6j2Gz9625B2462Z47Z8fBc2VlfD9q2HX3eGcr8O2Y3rlrtdspQQYNWoUs2fP5qCDDqKyspLhw4d3LFdaWtqxXHl5OccffzyLFi0CoLq6mr/+9a888cQTHHLIIQBsu+227LfffuvcX1FRUcd6elMsFut0P/F4nKysrPXe9/z589ltt9247bbbOOOMMzjnnHOYOXMmBx54YKflCgoKOm4/ffp0br75Zv70pz9x3nnnce655/LDH/6Qp59+ms9+9rMdt3nyySd55513OPfcczdYaxAELFiwgB//+MdsvfXWzJ8/f73hc+2fgSRJkga+n9fC/6uGANg9B+aUwY796LQp3eFut93V3NS1yw+/C6+/Gt4mCDr/++9X4cbvdm29m6m+vp57772XsWPHMnTo0A0u9+abb/L44493BKR4PE48Hufhhx/utCtuf1FeXs6NN97IpZdeyllnnUU8Hu/Yors+QRDw17/+lddff52cnBwAPv3pT7Pvvvty1113dVp2wYIFHHjggey0004bXN9f/vIXGhsbmTBhAmeddRa/+MUvaGho6JkHJ0mSpH4nEcD1q+CG6jB4Hp4P3xs28IMnuOWz+846dvPXkUzCig87z331LKhdz0l9/vfP6859gkcffZR4PA5AQ0MDW2yxBY8++ijRaOe/OUyZMoVYLEZ7ezstLS0cc8wxzJkzB4CsrCzuvvtuZsyYwW233cbee+/NIYccwumnn87uu+/eaT2XX345V1xxRae53//+9xx00EGbXHtPmj59Oj/5yU945JFHeP7558nNXfed/eMf/5g777yT1tZW2trayMvL44ILLui4/txzz2XWrFn86Ec/Ih6PU1dXx//+7//yox/9aKP3PX/+fE4//XRisRi77bYbY8aM4cEHH+Tss8/utNyan8HaXnvtNbbZZpvuP3BJkiRllMYkfGMlPJXatnR6EXylGIpjG7/dQOGWzwHssMMOY/HixSxevJgXXniBiRMncvTRR/Pf//6303Lz5s1j8eLFvPzyyzz66KO8+eabfOELX+i4/uSTT2bZsmX85je/YdKkSTzxxBPsvffe3H333Z3Wc+mll3bc35rLuHHjNljfV77ylY4tq/F4nKVLl3L00Ud3musJL7/8MosWLaKgoIC//vWv613mzDPPZPHixTzzzDMcffTRfPOb3+y0a+6UKVNIJBL88pe/BOCBBx4gGo0yefJkAHbdddeOmo8++mgg3GX5V7/6FWeddVbHes466yzmz5+/zv2v+Rmsfdlyyy175PFLkiQp/SoT8MXlYfDMBi4ohQtLB0/wBLd8dt+9j3Rtudlfg/eXbvj64R87zu/H93a/po8pLCxk7NixHeM777yTkpIS7rjjDq699tqO+VGjRnUst+OOO1JXV8eUKVO49tprO+bz8vI48sgjOfLII/nWt77FF7/4Ra666qpOW/CGDRvW6f4+yTXXXMOsWbM6xoceeijf+9731ntMZHe1trYydepUzjzzTA455BC+8pWvcMwxx7Djjjt2Wq6kpKSj9l/+8peMHTuW/fffnwkTJgBQXFzMKaecwoIFCzjnnHNYsGABp512WkdA/t3vftfR8TY/PzxK/Oc//znNzc2dHk8QBCSTSd5880122GGHjvm1fwaSJEkaWN5qhQsroSIBxVG4uBSOKeyf5+rcHIbP7srrYhuqWBZEo+Euth8XjcLHdrXs8nq7IRKJEI1GaWra+DGka3b/3Nhyu+yyCw8//PBm1TNixAhGjBjRMc7KymKrrbbq0RB2zTXXsGrVKubNm0dJSQkPPfQQ06dP5+mnn15n9+M14vE4F154IbNmzeIf//gHkVSjqHPPPZdDDz2URx99lGeffZbvf//7HbfZdttt11nP/PnzueSSS9bZxfarX/0qd911V6+chkaSJEmZ5bkmuKwKGgLYIgaXlsEh+b160ouM5W63ve2C2bBT6vyOa15ha/7debfw+l7S0tJCRUUFFRUV/Pvf/+b888+nvr6eY4/tfLxqdXU1FRUVLFu2jCeffJJrrrmGHXbYgZ133pmVK1dy+OGHc++99/LPf/6Td999lwcffJDrr7+e448/vtN66urqOu5vzaW2trbXHt8n+fvf/873vvc95s+fT0lJCQC33347b7zxBvPmzdvobb/85S/z5ptv8tBDD3XMHXzwwYwdO5apU6ey0047rdMxd22LFy9m0aJFfPGLX2S33XbrdJkyZQr33HMP7e3tHcuv+RmsfbExkSRJUv/263q4oDIMnjtlw9xhcGjB4AyeYPjsfduOgW//P7js2zA0dXqTocPD8dX/r9dOswLw2GOPscUWW7DFFlswfvx4/v73v/Pggw+ucx7N6dOns8UWW7D11lszZcoUdt11V37/+9+TlZVFPB5n/PjxzJs3j4MPPpjddtuNb33rW8yYMYObb76503quvPLKjvtbc7nssst67fFtTEtLC9OmTWP69OkcddRRHfNbbLEFN910E1dccQVvvPHGBm8/ZMgQpk6dytVXX00ytdU6EolwzjnnsHr1as4555yN3v/8+fPZZZdd1tsJ98QTT2TFihX87ne/65hb8zNY+3LTTTdt6sOWJElSBkgGcFM1fGcVJIDP5IUdbXcfBB1tNyYSBGvO+zE41dbWUlJSQk1NDcXFxZ2ua25u5t1332W77bYjLy9v8++stRUW/Q322R+yczZ/fRqUevx1KUmSpB7TEsBVK+GPjeH4xEI4vwRKB/ABjxvLVGsbwE9BBsrJgf0PTncVkiRJknrB6gRcUgWLWyAGnFsC04og3/1NAcOnJEmSJG22pW1wfiW81w4FkfA0KifGIWuQHt+5PoZPSZIkSdoM/2iGmVVQk4ThMZhVBhMGaUfbjTF8SpIkSVI3PdYQHuPZBozJhtmlMK73zp7Yrxk+JUmSJGkTBQHcVQu31ITjcbkwuwzG2Fd0gwyfkiRJkrQJ2gKYuwoeTp2W/egCuLgUhpmuNsqnR5IkSZK6qC4Jl1XB880QAaYWwYwSKLCj7ScyfEqSJElSFyxrhwsr4e02yI3A10vgtCLItrFQlxg+JUmSJOkTvNYKF62AqiSURsOOtpMKIGrw7DLDpyRJkiRtxJONMGclNAdQngWXlcFn7Gi7ydwzuY+tfL2m1+8jEols9HL11Vfzu9/9jpycHBYtWtTptv/v//0/hg0bRkVFBQBnn312x+2ys7PZbrvtuOyyy2hubgbg05/+NF/5ylfWW8fPfvYzcnNzqaqq2mCtTU1NDBkyhGHDhtHS0rLO9aNHj17vY7juuuu6+/RIkiRJXXZ/XXgOz+YAPp0D3xtq8Owut3z2kbbGdp675p+89fB7bH9iOQdeuTtZ+b3z9H/44Ycd/3/ggQe48soreeONNzrm4vE48XicqVOnMnXqVF566SVyc3N57bXXuOKKK7j77rsZNWpUx/KTJk1iwYIFtLW18dJLLzFt2jQikQjf+973OPfcc7n66quZN28e+fmd34ULFizguOOOY9iwYRus9aGHHmLXXXclCAIefvhhJk+evM4y11xzDTNmzOg0V1RUtMnPiyRJktRViQB+sBrurw/Hh+XDpWUwygTVbW757AOr/1PLwyc9wX9+8x4A//m/93j4pCdZ/Z/aXrm/UaNGdVxKSkqIRCKd5uLxOADz5s2jvr6eq666ivb2dqZNm8axxx67TgDMzc1l1KhRlJeXc8IJJzBhwgT+9Kc/AXDWWWfR1NTEQw891Ok27777Lk888QTnnnvuRmudP38+Z511FmeddRbz589f7zJFRUWd6h81ahSFhYXdfXokSZKkjWpKwqyqj4Ln6XG4aojBc3P59HVTW2P7Bq+LxCJk5cYAeOvXS3n6qpcJEgFBMrw+SELt0gYePulJDvzW7ux46rafuN7sgp7/URUVFXHXXXcxceJE3n33Xd577z0ee+yxjd7m1Vdf5dlnn2XbbcOahw0bxvHHH89dd93FWWed1bHc3XffzdZbb81RRx21wXW9/fbbPPfcc/zqV78iCAIuvvhi/vvf/3asW5IkSeprVYmwo+2/WyEb+EopnFEUdrfV5jF8dtNP9/7tBq/b+pCRTLx9f6pereapOf9Y7zJBIiBIBDz9rcUM3bmEYbuVAvDLI/5E8+rWdZY/9/Xje6Tujzv88MM55ZRT+MUvfsEDDzzA0KFD11nm0UcfJR6P097eTktLC9FolJtvvvmj2s49l6OPPpp3332X7bbbjiAIuOeee5g2bRrR6IY3rt91110cffTRlJWVATBx4kQWLFjA1Vdf3Wm5yy+/nCuuuKLT3O9//3sOOuigzXjkkiRJUmf/aYULKqEiAUURuLgMji2EmMGzR7jbbS8auutHoXJDsgpjDN21pG8KWo8PPviAxx57jIKCAv7617+ud5nDDjuMxYsX8/zzzzNt2jSmT5/OySef3HH9kUceydZbb82CBQsAWLhwIUuXLmX69OkAHH300R3Hme66664AJBIJ7rnnnk5bS8866yzuvvtukslkp/u/9NJLWbx4cafLuHHjevR5kCRJ0uD2fDNMXx4Gz1ExuHooHG/w7FFu+eymqYs+v8HrIqlXaCQSYZ8Ld+YPM57b4LKHfn8ckchHr+jTFh7Zc0V2wYwZM9hnn3345je/yZFHHskpp5zCIYcc0mmZwsJCxo4dC4RbK/fYYw/mz5/fcTxnNBrl7LPP5p577uHqq69mwYIFHHbYYYwZMwaAO++8k6amJgCys7MB+MMf/sAHH3ywzvGliUSChQsXcuSRHz0Pw4YN67h/SZIkqac9XA//swoSwI7ZMGcI7J6b7qoGHsNnN3X1GMytPjucYbuVsvK16o5jPgEiURi6aynbHDayW+vtCXfeeSdPP/00r7zyCttuuy3nnXce55xzDv/85z832NAnGo3yjW98g5kzZ3LGGWd0dLidPn061157Lb/61a/49a9/zZ133tlxm6222mqd9cyfP5/TTz+db37zm53m/+d//of58+d3Cp+SJElSb0gG8OMauCvVB/TAPLi8DMqz01vXQOVut71szdbPoPOepARJ2OfCnTtt9exL//3vf5k5cyY33HBDR4Of733ve0QiEWbPnr3R25566qnEYjFuueWWjrntttuOww8/nC996Uvk5uZy0kknbfD2lZWVPPLII0ybNo3ddtut02Xq1Kk8/PDDrFq1qmP5uro6KioqOl1qa3unU7AkSZIGh5YAvrnyo+B5QiFcO8Tg2ZsMn31gq88O5/hfHcLxD611+dUhbPWZ4WmpJwgCzj33XA444AC+9KUvdcwXFBRw9913c+utt/Lkk09u8PZZWVl8/etf5/rrr6ehoaFj/txzz2X16tWcccYZ5OXlbfD2P/3pTyksLOSII45Y57ojjjiC/Px87r333o65K6+8ki222KLT5bLLLtvUhy1JkiQBUJ2A81bAHxohBswoDs/hWep+ob0qEgRBkO4i0qm2tpaSkhJqamooLi7udF1zc3NHB9eNhSmpL/m6lCRJ6r6lbWFH26XtUBCBC0rhxDhk21io2zaWqdZmtpckSZI0KCxugYsroSYJw6Jw6RCYkA9pOhJu0DF8SpIkSRrw/tAAV62EVmBMNlxeCvvmp7uqwcXwKUmSJGnACgJYUAs314TjfXJhThmMyUlvXYOR4VOSJEnSgNQWwNxV8HCqR+akAphZCsNMQWnh094Fg7wnkzKMr0dJkqRPVpeEy6vgb80QAb5QBDNKoNDzfaSN4XMjsrPDk/w0NjaSn+8O4coMjY2NwEevT0mSJHX2YTtcWAn/aYPcCHy9BE4rsqNtuhk+NyIWi1FaWsqKFSuA8DyYEVthKU2CIKCxsZEVK1ZQWlpKLBZLd0mSJEkZ57VWuGgFVCWhNAqXlMHRBRD1a3zaGT4/wahRowA6AqiUbqWlpR2vS0mSJH3kyUaYsxKaAyjPgkvL4LPuwJgxDJ+fIBKJsMUWWzBixAja2trSXY4GuezsbLd4SpIkrccv6uCG1ZAEPp0TdrTdKTfdVWlths8uisVifumXJEmSMkwigHnV8PO6cHxofrjFcwuTTsbxRyJJkiSpX2pKwjdXwhNN4fi0OHy1BIrdZpSRDJ+SJEmS+p2qBFxUGTYYyga+XAJnFofdbZWZDJ+SJEmS+pW3W+GCSvgwAfEIzCyDYwshZvDMaIZPSZIkSf3G881waSXUBzAqBrPK4LB88IyImc/wKUmSJKlf+L96uHYVJIAdsuEbQ2B3O9r2G4ZPSZIkSRktCODWGrizNhwfkAezy6A8O711adMYPiVJkiRlrNYAvr0Sft8Yjo8vhAtKoMwk0+/4I5MkSZKUkaoTcEkV/KMFYsA5xXB2MeRH012ZusPwKUmSJCnjLG0LO9oubYf8CFxYCifGIdvGQv2W4VOSJElSRnm5BS6uhOokDI3CpWVwZIEdbfs7w6ckSZKkjPHHBrhyJbQC22WFjYX2zU93VeoJhk9JkiRJaRcEcE8d/Kg6HO+dC98ogzE5aS1LPcjwKUmSJCmt2gK4bhX8uiEcTyyAmaUw3LQyoPjjlCRJkpQ29Um4rAr+1gwR4AtFMKMECu1oO+AYPiVJkiSlRUV72NH2P22QG4GvlsDkIsixsdCAZPiUJEmS1Of+3QoXVkJVAkqiMKsMji6AqMFzwDJ8SpIkSepTTzXBnCpoCmDrrPBUKgfZ0XbAM3xKkiRJ6jMP1MH3V0MS2C0H5pTBzrnprkp9wfApSZIkqdclArixGu6rC8eH5MNlZbCFiWTQ8EctSZIkqVc1JeGKlfCXpnB8WjxsLlQcS29d6luGT0mSJEm9ZmUCLqqEf7WG4ePLJXBWcdjdVoOL4VOSJElSr3inDS5YAcsSEI/AxWVwbCFkGTwHJcOnJEmSpB73QjPMqoT6AEbGwo62h+VDxOA5aBk+JUmSJPWoR+rhmlWQAHbIhtlDYE872g56hk9JkiRJPSII4LYauKM2HO+fB7PLYJvs9NalzGD4lCRJkrTZWgP49kr4fWM4Pq4QLiiBISYOpfhSkCRJkrRZahJwSRUsaoEocE4xTC+G/Gi6K1MmMXxKkiRJ6rb32uDCSljSDvkRuKAUTopDto2F9DGGT0mSJEnd8nILXFwJ1UkYGoVZZXBUgR1ttX6GT0mSJEmb7E+N8K0qaAVGZ8HlZTA+P91VKZMZPiVJkiR1WRDAPXXwo+pwvFcuzCmDsTlpLUv9gOFTkiRJUpe0B/C91fBQfTg+qgAuKYXhpgp1gS8TSZIkSZ+oPgmXV8FzzRABziyCL5dAoR1t1UWGT0mSJEkbtbwdLqiEt9ogB/haKUwughwbC2kTGD4lSZIkbdDrreGpVCoTUBKFS8rgcwUQNXhqExk+JUmSJK3X003hrrZNAWydFZ5K5WA72qqbDJ+SJEmS1vHLOrh+NSSBXXPCjra75Ka7KvVnhk9JkiRJHZIB/LAaflYXjg/Oh8vKYEuTgzaTLyFJkiRJADQl4Vsr4fGmcHxqHL5WAsWx9NalgcHwKUmSJImVCbi4El5tDUPCl0rgrCLI81Qq6iGGT0mSJGmQe6cNLlgByxIQj8BFZXBcIWTZ0VY9yPApSZIkDWJ/b4ZZlVAXwIgYXFoGh+dDxOCpHmb4lCRJkgapR+vhmlXQDmyfDbPLYK+8dFelgcrwKUmSJA0yQQC318BPasPx+LzwVCrbZKe3Lg1shk9JkiRpEGkN4Dsr4beN4fjYQriwBIaYDNTLfIlJkiRJg0RtEi6phJdaIApML4ZziiHfjrbqA4ZPSZIkaRB4vz3saLukHfIicGEpnBSHbBsLqY8YPiVJkqQB7p8t4Tk8VydhaBRmlcGRBRA1eKoPGT4lSZKkAWxhI1yxEloC2DYLLi+D/fPTXZUGI8OnJEmSNAAFAfysDn5YDQGwV27Y0XZsTror02Bl+JQkSZIGmPYArl8N/1sfjo8sgFmlMNxv/0ojX36SJEnSANKQhNlV8EwzRIAzi2BGMRTF0l2ZBjvDpyRJkjRALG+HCyvhzTbIAb5aCqcXQY6NhZQBDJ+SJEnSAPBGK1xQCZUJKI7CJWXwuQKIGTyVITLydLK33HILo0ePJi8vj/Hjx/PCCy906Xa/+MUviEQinHDCCb1boCRJkpRBnmmCc5eHwXPLGHx7CBxbaPBUZsm48PnAAw8wc+ZMrrrqKhYtWsQee+zBxIkTWbFixUZvt2TJEmbNmsVBBx3UR5VKkiRJ6fe/dXBRJTQGsEsOXD8MDilId1XSujIufP7gBz9gxowZTJ8+nV122YXbbruNgoIC7rrrrg3eJpFIcOaZZ/Ltb3+bMWPG9GG1kiRJUnokA5i3Gr67GhLAQflw/VDYJTfdlUnrl1Hhs7W1lZdeeokJEyZ0zEWjUSZMmMBzzz23wdtdc801jBgxgnPPPfcT76OlpYXa2tpOF0mSJKk/aU7C5VXheTwBTonDd4bAltnprUvamIxqOFRVVUUikWDkyJGd5keOHMnrr7++3ts8/fTTzJ8/n8WLF3fpPubOncu3v/3tzS1VkiRJSotViXA321dbwy/zM0rgC0WQl1GblaR19euXaF1dHV/4whe44447GDZsWJduM2fOHGpqajou7733Xi9XKUmSJPWMd9tgWkUYPAsjMHsITC82eKp/yKgtn8OGDSMWi7F8+fJO88uXL2fUqFHrLP/222+zZMkSjj322I65ZDIJQFZWFm+88Qaf+tSnOt0mNzeX3Fx3hJckSVL/8lIzzKyEugBGxGBWGRyRDxE72qqfyKi/keTk5LDPPvuwcOHCjrlkMsnChQs54IAD1ll+p5124pVXXmHx4sUdl+OOO47DDjuMxYsXU15e3pflS5IkSb3i0QY4b0UYPLfPhrlDYUKBwVP9S0Zt+QSYOXMm06ZNY9y4cey3337ceOONNDQ0MH36dACmTp3KVlttxdy5c8nLy2O33XbrdPvS0lKAdeYlSZKk/iYI4Ce1cHtNON4vD2aXwWgbC6kfyrjwOXnyZCorK7nyyiupqKhgzz335LHHHutoQrR06VKi0YzaYCtJkiT1uLYAvrMq3OoJcEwhXFgCQzPuG7zUNZEgCIJ0F5FOtbW1lJSUUFNTQ3FxcbrLkSRJkqhNwqxKeLElPE7u7GI4txjy3QajDNTVTOXfTSRJkqQM8kE7nL8ClrRDXgQuKIWT45Dt8Z3q5wyfkiRJUoZ4pQUuroRVSRgSDTvaHlUAUYOnBgDDpyRJkpQBFjbCFSuhJYBts+DyMtg/P91VST3H8ClJkiSlURDAvXVwYzUEwJ65MKcMts9Jd2VSzzJ8SpIkSWnSHsD3V8OD9eF4QgHMKoURfkvXAOTLWpIkSUqDhiTMqYKnmyECTCmCLxdDUSzdlUm9w/ApSZIk9bEV7XBhJbzRBjnAeaVh+MyxsZAGMMOnJEmS1IfebIULKmFFAoqjcEkpfK4QYgZPDXCGT0mSJKmPPNMEl1dBYwBbxsJTqRxakO6qpL5h+JQkSZL6wP/WwfdWQwLYJSfsaLtrbrqrkvqO4VOSJEnqRckAbqqGe+rC8WfzwnN4bpWd1rKkPmf4lCRJknpJcxKuXAl/bgrHJ8fh6yVQYkdbDUKGT0mSJKkXrE7ARZXwSivEgBklMLUI8qLprkxKD8OnJEmS1MOWtIUdbd9vh8IIXFQGxxdClh1tNYgZPiVJkqQe9FIzXFIFtUkYHoNLy+CIfIgYPDXIGT4lSZKkHvLbBvj2SmgHxmbD7FLYOz/dVUmZwfApSZIkbaYggDtr4daacLxvLswZAqPtaCt1MHxKkiRJm6EtgGtXwSMN4fjzhXBRCQz1m7bUiW8JSZIkqZvqknBJJbzYAlFgWjGcWwwFdrSV1mH4lCRJkrphWTucvwLebYe8CJxfCqfEIdvGQtJ6GT4lSZKkTfRqS3gOz1VJKIvCrDKYWABRg6e0QYZPSZIkaRM83gjfXAktAWyTBZeVwYF2tJU+keFTkiRJ6oIggPvqYF41BMAeOTC7DHbMTXdlUv9g+JQkSZI+QXsAN6yGX9aH4yPy4dIyGOG3aanLfLtIkiRJG9GYhNlV8HQzRIApRfDlYiiKpbsyqX8xfEqSJEkbUNkOF1bC622QDZxXCmcUQY6NhaRNZviUJEmS1uOtVrigEpYnoDgKM0vh84UQM3hK3WL4lCRJkj7m2Sa4vAoaAtgyFp5K5dCCdFcl9W+GT0mSJGktD9XDdasgAeycA3PKYDc72kqbzfApSZIkAckAbq6Bu2vD8Wfz4PIy2Co7vXVJA4XhU5IkSYNecxKuWgV/agzHJ8Xh/BIosaOt1GMMn5IkSRrUVifg4kr4ZyvEgC+WwNQiyI+muzJpYDF8SpIkadD6bxucXwnvt0NBBC4qhRPikGVHW6nHGT4lSZI0KC1qhkuqoCYJw1MdbSfkQ8TgKfUKw6ckSZIGnd83wNUroQ34VDbMLoV98tNdlTSwGT4lSZI0aAQBzK+FH9eE431zw462Y3LSW5c0GBg+JUmSNCi0BfA/q+A3DeH4cwVwcSkM9Rux1Cd8q0mSJGnAq0vCrEr4ewtEgGnF8MViKLCjrdRnDJ+SJEka0Ja1wwWV8E4b5Ebg/FI4NQ7ZNhaS+pThU5IkSQPWv1rgokpYmYSyaNjRdmIBRA2eUp8zfEqSJGlAeqIR5qyElgC2yYLLyuBAO9pKaWP4lCRJ0oDz81r4f9UQALvnwJwy2DE33VVJg5vhU5IkSQNGIoD/txp+UR+OD8+HS8tgpN96pbTzbShJkqQBoTEJc6rgr83h+PQi+EoxFMfSW5ekkOFTkiRJ/V5lAi5cAa+3QTZwXilMKQq720rKDIZPSZIk9WtvtcKFlVCRgOIoXFwKxxRCzOApZRTDpyRJkvqt55rgsipoCGCLWHh85yH5EDF4ShnH8ClJkqR+6df18N1VkAB2yoY5Q+DTdrSVMpbhU5IkSf1KMoBbamBBbTj+TB5cXgZbZ6e3LkkbZ/iUJElSv9ESwFUr4Y+N4fjEQji/BEr9VitlPN+mkiRJ6hdWJ+CSKljcAjHg3BKYVgT50XRXJqkrDJ+SJEnKeEvb4PxKeK8dCiJwYSmcGIcsGwtJ/YbhU5IkSRntH80wswpqkjA8BrPKYIIdbaV+x/ApSZKkjPVYQ3iMZxswJhtml8K4/HRXJak7DJ+SJEnKOEEAd9WGXW0BxuXC7DIYk5PeuiR1n+FTkiRJGaUtCM/f+X8N4fjoAri4FIb5zVXq13wLS5IkKWPUJeGyKni+GSLA1CKYUQIFdrSV+j3DpyRJkjLCsna4sBLeboPcCHy9BE4rgmwbC0kDguFTkiRJafdaC1xUCVVJKI2GHW0nFUDU4CkNGIZPSZIkpdWTjTBnJTQHUJ4Fl5XBZ+xoKw04hk9JkiSlzf11cMNqCIBP58CcMtgpN91VSeoNhk9JkiT1uUQAP1gN99eH48Py4dIyGOW3U2nA8u0tSZKkPtWUhG+shCebwvHpcfhKCRTH0luXpN5l+JQkSVKfqUyEjYX+3QrZwFdK4YyisLutpIHN8ClJkqQ+8Z9WuKASKhJQFIGLy+DYQogZPKVBwfApSZKkXvd8M8yqhIYARsXC4zsPzYeIwVMaNAyfkiRJ6lUP18P/rIIEsGM2zBkCu9vRVhp0DJ+SJEnqFckAflwDd9WG4wPz4PIyKM9Ob12S0sPwKUmSpB7XEsDVK+EPjeH4hEK4oARK/fYpDVq+/SVJktSjVifgkipY3AIx4JxiOLsY8qPprkxSOhk+JUmS1GOWtsH5lfBeOxRE4IJSODEO2TYWkgY9w6ckSZJ6xOIWuLgSapIwLAqXDoEJdrSVlGL4lCRJ0mb7QwNctRJagTHZMLsUxuWnuypJmcTwKUmSpG4LAlhQCzfXhON9cmFOGYzJSW9dkjKP4VOSJEnd0hbA3FXwcEM4nlQAM0thmN8wJa2HHw2SJEnaZHVJuLwK/tYMEeALRTCjBArtaCtpAwyfkiRJ2iQftsOFlfCfNsiNwNdKYHKRHW0lbZzhU5IkSV32WitctAKqklAahUvK4OgCiBo8JX0Cw6ckSZK65MlGmLMSmgPYOgsuK4PP2tFWUhcZPiVJkvSJflEHN6yGJLBbTtjRdufcdFclqT8xfEqSJGmDEgHMq4af14XjQ/Ph0jLYwm+RkjaRHxuSJElar6YkfHMlPNEUjk+Lw1dLoDiW3rok9U+GT0mSJK2jKgEXVYYNhrKBL5fAmcVhd1tJ6g7DpyRJkjp5uxUuqIQPExCPwMwyOLYQYgZPSZvB8ClJkqQOzzfDpZVQH8CoGMwqg8PyIWLwlLSZDJ+SJEkC4P/q4dpVkAB2yIZvDIHd7WgrqYcYPiVJkga5IIBba+DO2nB8QB7MLoPy7PTWJWlgMXxKkiQNYq0BfHsl/L4xHB9fCBeUQJnfEiX1MD9WJEmSBqnqBFxSBf9ogRhwTjGcXQz50XRXJmkgMnxKkiQNQkvbwo62S9shPwIXlsKJcci2sZCkXmL4lCRJGmReboGLK6E6CUOjYUfbowrsaCupdxk+JUmSBpE/NsCVK6EV2C4LLi+D/fLTXZWkwcDwKUmSNAgEAdxTBz+qDsd758I3ymBMTlrLkjSIGD4lSZIGuLYArlsFv24IxxMLYGYpDPeboKQ+5EeOJEnSAFafhMuq4G/NEAHOKoIvlUChHW0l9THDpyRJ0gBV0R52tP1PG+RG4KslMLkIcmwsJCkNDJ+SJEkD0L9b4cJKqEpASaqj7dEFEDV4SkoTw6ckSdIA81QTzKmCpgC2zgqD58F2tJWUZoZPSZKkAeSBOvj+akgCu+XAnDLYOTfdVUmS4VOSJGlASARwYzXcVxeOD8mHy8pgC7/tScoQfhxJkiT1c01JuGIl/KUpHJ8WD5sLFcfSW5ckrc3wKUmS1I+tTMBFlfCv1vCL3ZdL4KzisLutJGUSw6ckSVI/9U4bnL8CPkxAPAIXl8GxhZBl8JSUgTLy9MK33HILo0ePJi8vj/Hjx/PCCy9scNlf/epXjBs3jtLSUgoLC9lzzz352c9+1ofVSpIk9b0XmuHsijB4jozB1UPhBIOnpAyWceHzgQceYObMmVx11VUsWrSIPfbYg4kTJ7JixYr1Lj9kyBC++c1v8txzz/HPf/6T6dOnM336dP7whz/0ceWSJEl945F6+NoKqA9gh2yYOwwOL4CIwVNSBosEQRCku4i1jR8/nn333Zebb74ZgGQySXl5Oeeffz6zZ8/u0jr23ntvPv/5z/Od73xnnetaWlpoaWnpGNfW1lJeXk5NTQ3FxcU98yAkSZJ6QRDAbTVwR2043j8PZpfBNtnprUvS4FZbW0tJScknZqqM2vLZ2trKSy+9xIQJEzrmotEoEyZM4LnnnvvE2wdBwMKFC3njjTc4+OCD17vM3LlzKSkp6biUl5f3WP2SJEm9pTUIO9quCZ7HFcK1QwyekvqPjAqfVVVVJBIJRo4c2Wl+5MiRVFRUbPB2NTU1xONxcnJy+PznP89NN93EkUceud5l58yZQ01NTcflvffe69HHIEmS1NNqEvDVFfD7xvDL2xeL4fIyGGLrSEn9yID4yCoqKmLx4sXU19ezcOFCZs6cyZgxYzj00EPXWTY3N5fc3Ny+L1KSJKkb3muDCyrhv+2QH4ELSuGkOGR7fKekfiajwuewYcOIxWIsX7680/zy5csZNWrUBm8XjUYZO3YsAHvuuSf//ve/mTt37nrDpyRJUn/xcgtcXAnVSRgahVllcJSNhST1Uxm1221OTg777LMPCxcu7JhLJpMsXLiQAw44oMvrSSaTnZoKSZIk9Td/aoQvLw+D5+gsuHYoTCw0eErqvzJqyyfAzJkzmTZtGuPGjWO//fbjxhtvpKGhgenTpwMwdepUttpqK+bOnQuEDYTGjRvHpz71KVpaWvjd737Hz372M2699dZ0PgxJkqRuCQK4pw5+VB2O98qFOWUwNietZUnSZsu48Dl58mQqKyu58sorqaioYM899+Sxxx7raEK0dOlSotGPNtg2NDTw1a9+lffff5/8/Hx22mkn7r33XiZPnpyuhyBJktQt7QFctxp+VR+OjyqAS0pheMZ9Y5OkTZdx5/nsa109J40kSVJvqk/C5VXwXDNEgDOL4MslUJhRB0lJ0rq6mqn8O5okSVKaVbTDhZXwVhvkAF8rhclFkOPxnZIGEMOnJElSGr3eGgbPygSUROGSMvhcAUQNnpIGGMOnJElSmjzdFO5q2xTA1lnhqVQOzk93VZLUOwyfkiRJafDLOrh+NSSBXXPCjra75Ka7KknqPYZPSZKkPpQM4MZquLcuHB+cD5eVwZZ+K5M0wPkxJ0mS1EeakvCtlfB4Uzg+NQ5fK4HiWHrrkqS+YPiUJEnqAysTcHElvNoafgH7UgmcVQR5nkpF0iBh+JQkSepl77TBBStgWQLiEbioDI4rhCw72koaRAyfkiRJvejvzTCrEuoCGBGDS8vg8HyIGDwlDTKGT0mSpF7ySD18ZxW0A9tnw+wy2Csv3VVJUnoYPiVJknpYEMBtNXBHbTgenxeeSmWb7PTWJUnpZPiUJEnqQa0BfGcl/LYxHB9bCBeWwBC/dUka5PwYlCRJ6iE1CZhVBS+1QBSYXgznFEO+HW0lyfApSZLUE95vDzvaLmmHvAhcWAonxSHbxkKSBBg+JUmSNts/W8JzeK5OwtAozCqDIwsgavCUpA6GT0mSpM3w50b41kpoCWDbLLi8DPbPT3dVkpR5DJ+SJEndEATw0zr4YXU43is37Gg7NietZUlSxjJ8SpIkbaL2AK5fDf9bH46PLIBZpTDcb1aStEF+REqSJG2ChiTMroJnmiECnFkEM4qhKJbuyiQpsxk+JUmSumh5O1xYCW+2QQ7w1VI4vQhybCwkSZ/I8ClJktQFb7TCBZVQmYDiKFxSBp8rgJjBU5K6xPApSZL0CZ5uCne1bQxgyxhcWgaHFKS7KknqXwyfkiRJG/FgHXxvNSSBXXLgG2WwS266q5Kk/sfwKUmStB7JIDyNys/qwvFB+XB5KWyZnc6qJKn/MnxKkiR9THMSvrUSFjaF41Pi8PUSKLajrSR1m+FTkiRpLasScFElvNoaflGaUQJfKIK8aLork6T+zfApSZKU8m4bXLACPkhAYQQuLoPjCiHLjraStNkMn5IkScCLzXBJJdQFMCIGs8rgiHyIGDwlqUcYPiVJ0qD3aANcsxLagbHZMKcM9spLd1WSNLBs1tELf/3rXznrrLM44IAD+OCDDwD42c9+xtNPP90jxUmSJPWmIIDba+DKVPDcLw+uH2bwlKTe0O3w+dBDDzFx4kTy8/P5xz/+QUtLCwA1NTV897vf7bECJUmSekNbAFetCsMnwDGF8N0hMNpTqUhSr+h2+Lz22mu57bbbuOOOO8jO/uhT+jOf+QyLFi3qkeIkSZJ6Q20SvrYi3N02CpxTHO5qO8QDkiSp13T7I/aNN97g4IMPXme+pKSE6urqzalJkiSp13zQDuevgCXtkBeB80vD83hm21hIknpVt7d8jho1iv/85z/rzD/99NOMGTNms4qSJEnqDa+0wLSKMHgOicKVQ2CywVOS+kS3w+eMGTO48MILef7554lEIixbtoz77ruPWbNmcd555/VkjZIkSZttYSN8aQWsSsK2WXDtUJhUCFGDpyT1iW7vdjt79mySySRHHHEEjY2NHHzwweTm5jJr1izOP//8nqxRkiSp24IA7q2DG6shAPbIDY/v3CEn3ZVJ0uASCYIg2JwVtLa28p///If6+np22WUX4vF4T9XWJ2praykpKaGmpobi4uJ0lyNJknpQewDfXw0P1ofjCQUwqxRG2FhIknpMVzPVZn/05uTksMsuu2zuaiRJknpUQxLmVMHTzRABphTBl4uhKJbuyiRpcOp2+Lzmmms2ev2VV17Z3VVLkiRtlhXtcGElvNEGOcB5pWH4zPH4TklKm26Hz1//+tedxm1tbbz77rtkZWXxqU99yvApSZLS4s1WuKASViSgOAqXlMLnCiFm8JSktOp2+PzHP/6xzlxtbS1nn302J5544mYVJUmS1B3PNMHlVdAYwJYxmFUGhxakuypJEvRAw6GPe+WVVzj22GNZsmRJT66219hwSJKkgeF/6+B7qyEB7JITdrTdNTfdVUnSwNdnDYc+rqamhpqamp5erSRJ0nolA7ipGu6pC8efzYPZZbBldlrLkiR9TLfD549+9KNO4yAI+PDDD/nZz37G0UcfvdmFSZIkfZLmJFy5Ev7cFI5PjsPXS6DEjraSlHG6HT7nzZvXaRyNRhk+fDjTpk1jzpw5m12YJEnSxqxOwEWV8EorxIAZJTC1CPKi6a5MkrQ+3Q6f7777bk/WIUmS1GVL2sKOtu+3Q2EELiqD4wshy462kpSxevyYT0mSpN70UjNcUgW1SRgeg0vL4Ih8iBg8JSmjbVL4nDlzZpeX/cEPfrDJxUiSJG3Mbxvg2yuhHRibDbNLYe/8dFclSeqKTQqf6zu35/pE/NOjJEnqQUEAd9bCramG+vvmwpwhMNqOtpLUb2xS+PzLX/7SW3VIkiStV1sA166CRxrC8ecL4aISGOrBQ5LUr2z2x/Zrr73G0qVLaW1t7ZiLRCIce+yxm7tqSZI0yNUl4ZJKeLEFosC0Yji3GArsaCtJ/U63w+c777zDiSeeyCuvvEIkEiEIAuCjXW4TiUTPVChJkgalZe1w/gp4tx3yInB+KZwSh2yP7pGkfqnbfze88MIL2W677VixYgUFBQX861//4qmnnmLcuHE88cQTPViiJEkabF5tgakVYfAsi8K3hsBkg6ck9Wvd3vL53HPP8fjjjzNs2DCi0SjRaJTPfvazzJ07lwsuuKDLzYkkSZLW9ngjfHMltASwTRZcVgYH2tFWkvq9bm/5TCQSFBUVATBs2DCWLVsGwLbbbssbb7zRM9VJkqRBIwjg3lq4tCoMnnvkwPXDDJ6SNFB0e8vnbrvtxssvv8x2223H+PHjuf7668nJyeEnP/kJY8aM6ckaJUnSAJII4B8tUJWAYTHYKxcC4IbV8Mv6cJkj8uHSMhhhR1tJGjC6/ZF+xRVX0NAQ9jy/5pprOOaYYzjooIMYOnQoDzzwQI8VKEmSBo6FjfD91bBirb6Ew2MwNAqvt0EEmFIEXy6GoljaypQk9YJIsKZNbQ9YtWoVZWVlHR1v+4Pa2lpKSkqoqamhuLg43eVIkjRgLWyEy6rCrZzrEwO+VgpnFEFO//kqIUmDXlczVY/uzDJkyJCeXJ0kSRogEkG4xXNjf/EujMAZcYOnJA1U3W449MUvftFTqkiSpC75R0vnXW3XpzaAf7b2TT2SpL7X7fBZWVnJpEmTKC8v59JLL+Xll1/uybokSdIAUvUJwXNTl5Mk9T/dDp//93//x4cffsi3vvUt/v73v7P33nuz66678t3vfpclS5b0YImSJKm/6+oXjmE2GZKkAavHGg69//773H///dx111289dZbtLe398Rqe50NhyRJ6j0NSVhQG56/c2N71EaAETF4dEuIecynJPUrfdpwqK2tjRdffJHnn3+eJUuWMHLkyJ5YrSRJ6qfaA3i4Hm6rgVXJcG7LGCxbz261a7LmrDKDpyQNZN3e7RbgL3/5CzNmzGDkyJGcffbZFBcX8+ijj/L+++/3VH2SJKkfCQJ4pglOr4Dvrg6D58gYXFgKPx8F3x8WbuFc24gYXD8MjihIS8mSpD7S7S2fW221FatWrWLSpEn85Cc/4dhjjyU3N7cna5MkSf3Im60wrxqebw7H8QicGIfJcdgyO5w7ogAOzQ+731YlwmM898p1i6ckDQbdDp9XX301p556KqWlpT1YjiRJ6m8q2+HHNfCbhvA8nlnAUQVwRhHsnAORjwXLWATG5aWjUklSOnU7fM6YMaPj/8888wzjxo1zy6ckSYNIUxJ+Wgf31EJzqn3h+Dw4PQ6fyYcst2ZKktbSIw2Hjj76aBYvXsyYMWN6YnWSJCmDJQJ4pCHc2rnmvJxjs+HMIphYAHmb1VFCkjRQ9Uj47KGztUiSpAz3fDP8YDW81RaOh8fCYzpPjkOJ5+iUJG1Ej4RPSZI0sL3dCjdWwzOpZkIFETgh1UyoPDutpUmS+onNDp8ffPABP/7xjxk+fHhP1CNJkjLIykR4rs5f10MSiBF2rD2zCHZbTzMhSZI2pNvh85lnnuGss85i6dKlAAwbNoyzzz6bb37zmxQXF/dYgZIkqe81J+G+OlhQC42po2vG5cLkIjg4H7INnZKkTdTtlgBf/vKX2Xnnnfn73//OG2+8wfe//33+/Oc/s/fee/PBBx/0ZI2SJKmPJAN4tAFO/BBuqQmD53ZZcMUQ+OHwcKunwVOS1B2RoJvdgvLz83n55ZfZYYcdOuaCIOC0004D4MEHH+yZCntZbW0tJSUl1NTUuMVWkjSovZhqJvR6qpnQ0CicVgQnF8IQu0RIkjagq5mq279Kdt55Z1asWNEpfEYiEa655hr222+/7q5WkiT1sSVt8MNqeLIpHOdF4LhCmFIE29pMSJLUQ7odPs8++2zOP/98fvOb31BeXt4x7xZESZL6h9UJuL0GHqqHBOGxOIcXwJQ47JELUXevlST1oG6Hz4suugiA7bffnpNOOok999yTRCLBvffey/XXX99T9UmSpB7WEsD9dTC/BhpSB9/slQunF8GhNhOSJPWSbh/zuXz5chYvXszLL7/M4sWLWbx4MW+99RaRSISdd96ZT3/60+y+++7svvvuTJo0qafr7jEe8ylJGiyCAP7YCD+qhg8T4dy2WXBGEXy+EAq63YZQkjSYdTVTdTt8rk9zczOvvPJKp1D66quvUl1d3VN30eMMn5KkwWBxS9hM6NXWcFwWhVOL4NRCGGozIUnSZuj1hkPrk5eXx7777su+++7bk6uVJEnd9F5buKVzYaqZUG4EjikMj+sck5PW0iRJg4x/65QkaQCqScAdtfDLOmgHIoTHc06Jw955NhOSJPU9w6ckSQNIWwAP1MGdtVCbDOc+nRMe13l4gc2EJEnpY/iUJGkACAJ4vCk8X+f77eHc1qlmQscWQqHNhCRJaWb4lCSpn3ulBeZVh02FAEqicEocTovDcH/TS5IyhL+SJEnqp5a1w03V8IfGcJwDfK4w3No51mZCkqQMY/iUJKmfqUvC/Bq4vw7aCJsJfTbVTGjfPIh5XKckKQMZPiVJ6ifaAnioHm6vgZpUM6FdUs2EJhRAjqFTkpTBDJ+SJGW4IICnUs2ElqSaCW0ZgylFcFwhFMXSWp4kSV1i+JQkKYO91go3roYXU82EiqNwUqqZ0Ch/i0uS+hF/bUmSlIEq2uGWavhtqplQNjCpMDyuc8cciLiLrSSpnzF8SpKUQRqSsKAW7quDliCcOzAv3MV2f5sJSZL6McOnJEkZoD2Ah+vhthpYlWomtGM2nFEMRxVArqFTktTPGT4lSUqjIIBnm2FeNbzTFs6NjMHpRXBiIRTbTEiSNEAYPiVJSpM3W8PQ+XxzOI5H4MQ4TI7DltlpLU2SpB5n+JQkqY9VtsOPa+A3DRAQ/jI+qiA8X+fONhOSJA1Qhk9JkvpIUxJ+Wgf31EJzqpnQ+Dw4PQ6fyYcsQ6ckaQAzfEqS1MsSATzSEG7trEqEc2Oz4cwimFgAedH01idJUl8wfEqS1Iueb4YfrIa3Us2EhqeaCZ1UCCU2E5IkDSKGT0mSesHbrXBjNTyTaiZUEIETUs2Eym0mJEkahAyfkiT1oJWJ8Fydv66HJBADJqSaCe1mMyFJ0iBm+JQkqQc0JeG+Ori7FhpTzYTG5Ya72B6UD9mGTknSIJeRLQ5uueUWRo8eTV5eHuPHj+eFF17Y4LJ33HEHBx10EGVlZZSVlTFhwoSNLi9JUk9KBvBoA5z0YdhQqDGA7bLgiiHww+FweIHBU5IkyMDw+cADDzBz5kyuuuoqFi1axB577MHEiRNZsWLFepd/4oknmDJlCn/5y1947rnnKC8v56ijjuKDDz7o48olSYPNi81wVgVcuRKWJ2BoFL5aAneMgJPikJ9xv2UlSUqfSBAEQbqLWNv48ePZd999ufnmmwFIJpOUl5dz/vnnM3v27E+8fSKRoKysjJtvvpmpU6d+4vK1tbWUlJRQU1NDcXHxZtcvSRr4lrTBD6vhyaZwnBeB4wphShFsazMhSdIg09VMlVHHfLa2tvLSSy8xZ86cjrloNMqECRN47rnnurSOxsZG2traGDJkyHqvb2lpoaWlpWNcW1u7eUVLkgaN1Qm4vQYeqocE4e5DhxfAlDjskQtRd6+VJGmDMip8VlVVkUgkGDlyZKf5kSNH8vrrr3dpHZdffjlbbrklEyZMWO/1c+fO5dvf/vZm1ypJGjxaAri/DubXQENqf6G9Us2EDrWZkCRJXZJR4XNzXXfddfziF7/giSeeIC8vb73LzJkzh5kzZ3aMa2trKS8v76sSJUn9SBDAHxvhR9XwYSKc2zYrPG3K5wuhwGM6JUnqsowKn8OGDSMWi7F8+fJO88uXL2fUqFEbve0NN9zAddddx5///Gd23333DS6Xm5tLbm5uj9QrSRq4FrfAD1bDq63huCwKpxbBqYUwNKN+e0qS1D9k1N9sc3Jy2GeffVi4cGHHXDKZZOHChRxwwAEbvN3111/Pd77zHR577DHGjRvXF6VKkgao99rg0ko4Z3kYPHMjcHI87GD7lRKDpyRJ3ZVxv0JnzpzJtGnTGDduHPvttx833ngjDQ0NTJ8+HYCpU6ey1VZbMXfuXAC+973vceWVV/Lzn/+c0aNHU1FRAUA8Hicej6ftcUiS+peaBNxRC7+sg3YgQng855Q47J1nMyFJkjZXxoXPyZMnU1lZyZVXXklFRQV77rknjz32WEcToqVLlxKNfrTB9tZbb6W1tZVTTjml03quuuoqrr766r4sXZLUD7UF8EAd3FkLtclw7tM54XGdhxfYTEiSpJ6Scef57Gue51OSBqcggMebwvN1vt8ezm2daiZ0bCEUZtSBKZIkZa5+eZ5PSZL6wistMK86bCoEUBKFU+JwWhyG+5tRkqRe4a9YSdKgsawdbqqGPzSG4xzgc4Xh1s6xOemsTJKkgc/wKUka8OqSML8G7q+DNsJmQp9NNRPaNw9iHtcpSVKvM3xKkgastgAeqofba6Am1Uxol1QzoQkFkGPolCSpzxg+JUkDThDAU6lmQktSzYS2jMGUIjiuEIpiaS1PkqRByfApSRpQXmuFG1fDi6lmQsVROCnVTGiUv/UkSUobfw1LkgaEina4pRp+m2omlA1MKgyP69wxByLuYitJUloZPiVJ/VpDEhbUwn110JI6c/WBeeEutvvbTEiSpIxh+JQk9UvtATxcD7fVwKpUM6Eds+GMYjiqAHINnZIkZRTDpySpXwkCeLYZ5lXDO23h3KgYnF4EJxRCsc2EJEnKSIZPSVK/8WZrGDqfbw7H8QicGIfJcdgyO62lSZKkT2D4lCRlvMp2+HEN/KYBAsJfXkcVhOfr3NlmQpIk9QuGT0lSxmpKwk/r4J5aaE41ExqfB6fH4TP5kGXolCSp3zB8SpIyTiKARxrCrZ1ViXBu++xwS+fEAsiLprc+SZK06QyfkqSM8nwz/GA1vJVqJjQ81UzopEIosZmQJEn9luFTkpQR3m6FG6vhmVQzoYIInJBqJlRuMyFJkvo9w6ckKa1WJsJzdf66HpJADJiQaia0m82EJEkaMAyfkqS0aErCfXVwdy00ppoJjcsNd7E9KB+yDZ2SJA0ohk9JUp9KBvC7RrilGpanmgltlwVnFsPRBZBvMyFJkgYkw6ckqc+8mGom9HqqmdDQKExONRMa4m8kSZIGNH/VS5J63ZI2+GE1PNkUjvMicFwhTCmCbW0mJEnSoGD4lCT1mtUJuL0GHqqHBBAFDi+AKXHYIxeiHtcpSdKgYfiUJPW4lgDur4P5NdCQaia0V6qZ0KE2E5IkaVAyfEqSekwQwB8b4UfV8GGqmdC2WeFpUz5fCAU2E5IkadAyfEqSesTilrCZ0Kut4bgsCqcWwamFMNTfNpIkDXp+HZAkbZb32sItnQtTzYRyI3BMYXhc55ictJYmSZIyiOFTktQtNQm4oxZ+WQftQITweM4pcdg7z2ZCkiSpM8OnJGmTtAXwQB3cWQu1yXDu0znhcZ2HF9hMSJIkrZ/hU5LUJUEAjzeF5+t8vz2c2zrVTOjYQii0mZAkSdoIw6ck6RO90gLzqsOmQgAlUTglDqfFYbi/SSRJUhf4lUGStEHL2uGmavhDYzjOITxlypQiGGszIUmStAkMn5KkddQlYX4N3F8HbYTNhD6bD2fEYVwexDyuU5IkbSLDpySpQ1sAD9XD7TVQk2omtEsOnFkERxRAjqFTkiR1k+FTkkQQwFOpZkJLUs2EtoyFu9ceVwhFsbSWJ0mSBgDDpyQNcq+1wo2r4cVUM6HiKJwUh8lxGOlvCUmS1EP8WiFJg1RFO9xSDb9NNRPKBiYVhsd17pADEXexlSRJPcjwKUmDTEMSFtTCfXXQEoRzB+aF5+scbzMhSZLUSwyfkjRItAfwcD3cVgOrUs2EdsyGM4rhqALINXRKkqReZPiUpAEuCODZZphXDe+0hXOjYnB6EZxQCMU2E5IkSX3A8ClJA9ibrWHofL45HMcjcGKqmdCW2WktTZIkDTKGT0kagCrb4cc18JsGCAg/7I8qCI/r3NlmQpIkKQ0Mn5I0gDQl4ad1cE8tNKeaCY3Pg9Pj8Jl8yDJ0SpKkNDF8StIAkAjgkYZwa2dVIpzbPjvc0jmxAPKi6a1PkiTJ8ClJ/dzzzfCD1fBWqpnQ8FQzoZMKocRmQpIkKUMYPiWpn3q7FW6shmdSzYQKInBCqplQuc2EJElShjF8SlI/szIRnqvz1/WQBGLAhFQzod1sJiRJkjKU4VOS+ommJNxXB3fXQmOqmdC43HAX24PyIdvQKUmSMpjhU5IyXDKA3zXCLdWwPNVMaLssOLMYji6AfJsJSZKkfsDwKUkZ7MVUM6HXU82EhkZhcqqZ0BA/wSVJUj/iVxdJykBL2uCH1fBkUzjOi8BxhTClCLa1mZAkSeqHDJ+SlEFWJ+D2GnioHhJAFDi8AKbEYY9ciHpcpyRJ6qcMn5KUAVoCuL8O5tdAQ6qZ0F6pZkKH2kxIkiQNAIZPSUqjIIA/NsKPquHDVDOhbbPC06Z8vhAKbCYkSZIGCMOnJKXJ4pawmdCrreG4LAqnFsGphTDUT2dJkjTA+PVGkvrYe23hls6FqWZCuRE4thBOj8OYnLSWJkmS1GsMn5LUR2oScEct/LIO2oEI4fGcU+Kwd57NhCRJ0sBm+JSkXtYWwAN1cGct1CbDud1zwtOmHF5gMyFJkjQ4GD4lqZcEATzeFJ6v8/32cK481UzomEIotJmQJEkaRAyfktQLXmmBedVhUyGAkiicEofT4jDcT15JkjQI+RVIknrQsna4qRr+0BiOcwhPmTKlCMbaTEiSJA1ihk9J6gF1SZhfA/fXQRthM6HP5sMZcRiXBzGP65QkSYOc4VOSNkNbAA/Vw+01UJNqJrRLDpxZBEcUQI6hU5IkCTB8SlK3BAE8lWomtCTVTGjLWLh77XGFUBRLa3mSJEkZx/ApSZvotVa4cTW8mGomVByFk+IwOQ4j/VSVJElaL78mSVIXVbTDLdXw21QzoWxgUmF4XOcOORBxF1tJkqQNMnxK0idoSMKCWrivDlqCcO7AvPB8neNtJiRJktQlhk9J2oD2AB6uh9tqYFWqmdCO2XBGMRxVALmGTkmSpC4zfErSxwQBPNsM86rhnbZwblQMTi+CEwqh2GZCkiRJm8zwKUlrebM1DJ3PN4fjeAROTDUT2jI7raVJkiT1a4ZPSQIq2+HHNfCbBggIPxyPKgiP69zZZkKSJEmbzfApaVBrSsJP6+CeWmhONRManwenx+Ez+ZBl6JQkSeoRhk9Jg1IigEcawq2dVYlwbvvscEvnxALIi6a3PkmSpIHG8Clp0Hm+GX6wGt5KNRManmomdFIhlNhMSJIkqVcYPiUNGm+3wo3V8EyqmVBBBE5INRMqt5mQJElSrzJ8ShrwVibg1prwnJ1JIAZMSDUT2s1mQpIkSX3C8ClpwGpKwn11cHctNKaaCe2bC5OL4KB8yDZ0SpIk9RnDp6QBJxnA7xrhlmpYnmomNCbVTOjoAsi3mZAkSVKfM3xKGlBeTDUTej3VTGhoNNzSeVIhDPETT5IkKW38KiZpQFjSBj+shiebwnF+BI4rhClFsI3NhCRJktLO8CmpX1udgNtr4KF6SABR4PACmBKHPXIh6nGdkiRJGcHwKalfagng/jqYXwMNqWZCe+WG5+s81GZCkiRJGcfwKalfCQL4YyP8qBo+TDUT2jYrbCb0+UIosJmQJElSRjJ8Suo3FreEzYRebQ3HZVE4tQhOLYShfppJkiRlNL+uScp477WFWzoXppoJ5Ubg2EI4PQ5jctJamiRJkrrI8CkpY9Uk4I5a+GUdtAMRwuM5p8Rh7zybCUmSJPUnhk9JGactgAfq4M5aqE2Gc7vnhKdNObzAZkKSJEn9keFTUsYIAni8KTxf5/vt4Vx5qpnQMYVQaDMhSZKkfsvwKSkjvNIC86rDpkIAJVE4JQ6nxWG4n1SSJEn9nl/pJKXVsna4qRr+0BiOcwhPmTKlCMbaTEiSJGnAMHxKSou6JMyvgfvroI2wmdBn8+GMOIzLg5jHdUqSJA0ohk9JfaotgIfq4fYaqEk1E9olB84sgiMKIMfQKUmSNCAZPiX1iSCAp1LNhJakmgltGQt3rz2uEIpiaS1PkiRJvczwKanXvdYKN66GF1PNhIqjcFIcJsdhpJ9CkiRJg4Jf+yT1mop2uKUafptqJpQNTCoMj+vcIQci7mIrSZI0aBg+JfW4hiQsqIX76qAlCOcOzAvP1zneZkKSJEmDkuFTUo9pD+DheritBlalmgntlA1nFMORBZBr6JQkSRq0DJ+SNlsQwLPNMK8a3mkL50bF4PQiOKEQim0mJEmSNOgZPiVtljdbw9D5fHM4jkc+aia0RXZaS5MkSVIGMXxK6pbKdvhxDfymAQLCD5OjCsLjOne2mZAkSZI+xvApaZM0JeGndXBPLTSnmgntnwenx+HAfMgydEqSJGk9DJ+SuiQRwCMN4dbOqkQ4t302nFkUbvHMi6a3PkmSJGU2w6ekT/S3pvC4zrdSzYSGp5oJnVQIJTYTkiRJUhdk3LaKW265hdGjR5OXl8f48eN54YUXNrjsv/71L04++WRGjx5NJBLhxhtv7LtCpUHg7VY4fwV8tTIMngWR8JjOO0fA9GKDpyRJkrouo8LnAw88wMyZM7nqqqtYtGgRe+yxBxMnTmTFihXrXb6xsZExY8Zw3XXXMWrUqD6uVhq4Vibg2lUwuQKeaYYYMLEAbh0Bl5RCuV1sJUmStIkiQRAE6S5ijfHjx7Pvvvty8803A5BMJikvL+f8889n9uzZG73t6NGjueiii7jooos26T5ra2spKSmhpqaG4uLi7pYuDQhNSbivDu6uhcbUJ8O+uTC5CA7Kh2ybCUmSJOljupqpMuaYz9bWVl566SXmzJnTMReNRpkwYQLPPfdcj91PS0sLLS0tHePa2toeW7fUXyUD+F0j3FINy1PNhMZkh7vYHl0A+Rm1j4QkSZL6o4wJn1VVVSQSCUaOHNlpfuTIkbz++us9dj9z587l29/+do+tT+rvXmyGH6yG11PNhIZGwy2dJxXCkIz5hJAkSVJ/N+i2Z8yZM4eampqOy3vvvZfukqS0WNIGF1fCl1aEwTM/ApPjMH8kfLHE4ClJkqSelTFfL4cNG0YsFmP58uWd5pcvX96jzYRyc3PJzc3tsfVJ/c3qBNxeAw/VQ4LwL1CHF8CUOOyRC1GP65QkSVIvyJgtnzk5Oeyzzz4sXLiwYy6ZTLJw4UIOOOCANFYmDQwtQdhI6Lhl8MtU8NwrF+YOg/8ZCnvlGTwlSZLUezJmyyfAzJkzmTZtGuPGjWO//fbjxhtvpKGhgenTpwMwdepUttpqK+bOnQuETYpee+21jv9/8MEHLF68mHg8ztixY9P2OKRMEgTwx0b4UTV8mGomtG1W2Ezo84VQkDF/gpIkSdJAllHhc/LkyVRWVnLllVdSUVHBnnvuyWOPPdbRhGjp0qVEox99U162bBl77bVXx/iGG27ghhtu4JBDDuGJJ57o6/KljLO4JWwm9GprOC6LwqlFcGohDM2od78kSZIGuow6z2c6eJ5PDUTvtYVbOhc2hePcCBxbCKfHYUxOWkuTJEnSANPvzvMpafPVJOCOWvhlHbQDEeDQ/LCZ0N4e0ylJkqQ0MnxKA0BbAA/UwZ21UJsM53bPgSlFYSfbbEOnJEmS0szwKfVjQQCPN8EPq+H99nCuPNVM6JhCKLSZkCRJkjKE4VPqp15pgXnVYVMhgJIonBKH0+Iw3He2JEmSMoxfUaV+Zlk73FQNf2gMxzmEp0yZUgRjbSYkSZKkDGX4lPqJuiTMr4H766CNsJnQQalmQuPyIOZxnZIkScpghk8pw7UF8FA93F4DNalmQrvmhMd1HlEAOYZOSZIk9QOGTylDBQE8lWomtCTVTGjLWLh77XGFUBRLa3mSJEnSJjF8ShnotVa4cTW8mGomVByFk1PNhEb6rpUkSVI/5NdYKYNUtMMt1fDbVDOhbGBSIZwRhx1yIOIutpIkSeqnDJ9SBmhIwoJauK8OWoJw7jN54S62420mJEmSpAHA8CmlUXsAD9fDbTWwKtVMaKdsOKMYjiyAXEOnJEmSBgjDp5QGQQDPNsO8aninLZwbFYPTi+CEQii2mZAkSZIGGMOn1MfebA1D5/PN4TgegZPiMDkOW2SntTRJkiSp1xg+pT5S2Q4/roHfNEBA+OY7qiA8X+fONhOSJEnSAGf4lHpZUxJ+Wgf31EJzqpnQ/nlwehwOzIcsQ6ckSZIGAcOn1EsSATzSEG7trEqEc9tnw5lF4RbPvGh665MkSZL6kuFT6gV/awqP63wr1UxoeKqZ0EmFUGIzIUmSJA1Chk+pB73dCjdWwzOpZkIFETgh1Uyo3GZCkiRJGsQMn1IPWJmAW2vCc3YmgRgwIdVMaDebCUmSJEmGT2lzNCXhvjq4uxYaU82E9s2FyUVwUD5kGzolSZIkwPApdUsygN81wi3VsDzVTGhMdril8+gCyLeZkCRJktSJ4VPaRC82ww9Ww+upZkJDo+GWzpMKYYjvKEmSJGm9/KosddGSNvhhNTzZFI7zI3BcIUwpgm1sJiRJkiRtlOFT+gSrE3B7DTxUDwkgChxeAFPisEcuRD2uU5IkSfpEhk9pA1oCuL8O5tdAQ6qZ0F654fk6D7WZkCRJkrRJDJ/SxwQB/LERflQNH6aaCW2bFTYT+nwhFNhMSJIkSdpkhk9pLYtbwmZCr7aG47IonFYEpxTCUN8tkiRJUrf5dVoC3msLt3QuTDUTyo3AsYVwehzG5KS1NEmSJGlAMHxqUKtJwB218Ms6aAcihMdzTonD3nk2E5IkSZJ6iuFTg1JbAA/UwZ21UJsM53bPCU+bcniBzYQkSZKknmb41KASBPB4U3i+zvfbw7nyVDOhYwqh0GZCkiRJUq8wfGrQeKUF5lWHTYUASqJwajy8DPedIEmSJPUqv3JrwFvWDjdVwx8aw3EO4SlTphTBWJsJSZIkSX3C8KkBqy4J82vg/jpoI2wmdFCqmdC4PIh5XKckSZLUZwyfGnDaAnioHm6vgZpUM6Fdc8LjOo8ogBxDpyRJktTnDJ8aMIIAnko1E1qSaia0ZSzcvfa4QiiKpbU8SZIkaVAzfGpAeK0VblwNL6aaCRVH4eQ4nBaHkb7KJUmSpLTza7n6tYp2uKUafptqJpQNTCqEM+KwQw5E3MVWkiRJygiGT/VLDUlYUAv31UFLEM59Ji/cxXa8zYQkSZKkjGP4VL/SHsDD9XBbDaxKNRPaKRvOKIYjCyDX0ClJkiRlJMOn+oUggGebYV41vNMWzo2KwelFcEIhFNtMSJIkScpohk9lvDdbw9D5fHM4jkfgpDhMjsMW2WktTZIkSVIXGT6VsSrb4cc18JsGCAhfrEcVhOfr3NlmQpIkSVK/YvhUxmlKwj218NM6aE41E9o/D06Pw4H5kGXolCRJkvodw6cyRiKARxrCrZ1ViXBu+2w4syjc4pkXTW99kiRJkrrP8KmM8Lem8LjOt1LNhIanmgmdVAglNhOSJEmS+j3Dp9Lq7Va4sRqeSTUTKojACalmQuU2E5IkSZIGDMOn0mJlAm6tCc/ZmQRihOfpnFIEu9lMSJIkSRpwDJ/qU01JuK8O7q6FxlQzoX1zYXIRHJQP2YZOSZIkaUAyfKpPJAP4XSPcUg3LU82ExmSHp005ugDybSYkSZIkDWiGT/W6F5vhB6vh9VQzoWFROC3VTGiIr0BJkiRpUPCrv3rNkjb4YTU82RSO8yNwXGF4XOc2NhOSJEmSBhXDp3rc6gTcXgMP1UMCiAJHFMDpcdgjF6Ie1ylJkiQNOoZP9ZiWAO6vg/k10JBqJrRXbril8xCbCUmSJEmDmuFTmy0I4I+N8KNq+DDVTGjbLDizCD5XCAU2E5IkSZIGPcOnNsvilrCZ0Kut4bgs1UzolEIY6qtLkiRJUorxQN3yXlu4pXNhqplQbgSOLQyP6xyTk9bSJEmSJGUgw6c2SU0C7qiFX9ZBOxABDs2HKXHYO89mQpIkSZLWz/CpLmkL4IE6uLMWapPh3O45YTOhwwtsJiRJkiRp4wyf2qgggMebwvN1vt8ezpVnwRlFcEwhFNpMSJIkSVIXGD61Qa+0wLzqsKkQQEkUTo2Hl+G+ciRJkiRtAiOE1rGsHW6qhj80huMc4POF4S62Y20mJEmSJKkbDJ/qUJeE+TVwfx20ETYTOijVTGhcHsQ8rlOSJElSNxk+RVsAD9XD7TVQk2omtGtOeFznEQWQY+iUJEmStJkMn4NYEMBTqWZCS1LNhLaMhbvXHlcIRbG0lidJkiRpADF8DlKvtcKNq+HFVDOh4iicHIfT4jDSV4UkSZKkHmbMGGQq2uGWavhtqplQNjCpEM6Iww45EHEXW0mSJEm9wPA5SDQkYUEt3FcHLUE495m8cBfb8TYTkiRJktTLDJ8DXHsAD9fDbTWwKtVMaKdsOKMYjiyAXEOnJEmSpD5g+BygggCebYZ51fBOWzg3KtVM6PhCKLaZkCRJkqQ+ZPgcgN5sDUPn883hOB6Bk+IwOQ5bZKe1NEmSJEmDlOFzAKlshx/XwG8aICD84U4shClx2NlmQpIkSZLSyPA5ADQl4Z5a+GkdNKeaCe2fB6fH4cB8yDJ0SpIkSUozw2c/lgjgkYZwa2dVIpzbPhvOLIKjCiAvmt76JEmSJGkNw2c/9bem8LjOt1LNhEbE4PQiOLEQSmwmJEmSJCnDGD4zVCKAf7SEWzSHxWCv3PBcnG+3wo3V8EyqmVBBBE5INRMqt5mQJEmSpAxl+MxACxvh+6thReKjuWFR+FQO/L0ZkkCM8DydU4pgN5sJSZIkScpwhs8Ms7ARLqsKu9WurSoJVamtnfvmwuQiOCgfsg2dkiRJkvoBw2cGSQThFs+PB8+1FUfg/w2DuMd1SpIkSepH7IeaQf7R0nlX2/WpDeD1tr6pR5IkSZJ6iuEzg1R9QvDc1OUkSZIkKVMYPjPIsC7uStvV5SRJkiQpUxg+M8heueH5OjfUQygCjEyddkWSJEmS+hPDZwaJReDSsvD/Hw+ga8azysLlJEmSJKk/MXxmmCMK4PphMPxju9aOiIXzRxSkpy5JkiRJ2hyeaiUDHVEAh+aH3W+rEuExnnvlusVTkiRJUv9l+MxQsQiMy0t3FZIkSZLUM9ztVpIkSZLU6wyfkiRJkqReZ/iUJEmSJPU6w6ckSZIkqdcZPiVJkiRJvc7wKUmSJEnqdYZPSZIkSVKvM3xKkiRJknqd4VOSJEmS1OsMn5IkSZKkXmf4lCRJkiT1OsOnJEmSJKnXGT4z3MrXa9JdgiRJPa+1FZ57KvxXktQl/T0bGD4zVFtjO09d9iIPn/AET13+Iu1N7ekuSZKkzRcE8PzTcP40Vl77Y7jg7HAcBOmuTJIy1kDJBpEgGNyf9rW1tZSUlFBTU0NxcXG6ywFg9Vu1/HnGk9RVJAiIECGgeFSMI+44hLLtM6NGSZI22ZJ34K6baXv1NZ5bdihvVe/M9qWvceCWT5L16V3gnK/DtmPSXaUkZZT+kA26mqkycsvnLbfcwujRo8nLy2P8+PG88MILG13+wQcfZKeddiIvL49Pf/rT/O53v+ujSnveW3e8xP8d/2fqKtoJiAAQEKG2op3/O/7PvHXnS2muUJKkbrrpOlYv/oCH/3M6/6neEYD/VO/Ew/85ndX/+AB+dF2aC5SkzDLQskHGbfl84IEHmDp1Krfddhvjx4/nxhtv5MEHH+SNN95gxIgR6yz/7LPPcvDBBzN37lyOOeYYfv7zn/O9732PRYsWsdtuu33i/a1J6S+//DJFRUW98ZC6rOatBp79+r+AAFIvrs7C+ZGfKSOnJJt4eT6jTxrZce3rdywl0Zz8aPG1VlEwKo/tThnVMX7zp+/TXp9Y9y4ikDc0hzGnbdGxC9Tbv1hGa01q035krZUGSXJKsvjUlK07ppb87zKaV659/M5HjyWrIMrYySM71rv09ytpWtEK0SjEYqnlk9DWTiwnwtizt+tYywe/W0bDB80fPa4gXHckEta0/WnDiKTW++GztdR/0EoQjRLJyUmVEUBLePtPTfsUkaywpuWPf0j9u/XrPg8EEMCYo4uJ5oTLVr7cSO3SNsiKQW7BR7XU10IA25y6HVnF4f2tevZDal6vWVPo2k8vAOWfySe7MBytfquV6iVtBFlZUFjSsVCkZhUESbb43GhyRhYCULuoguqXV66z3jU1j9ojh7zSKBBQ+347q99OQDRKMHRYx3qjq1ZCop1hh5WTt20ZAA2vLWf1CxUdq41EOq9/2PZRCoaEK2isSrLy7SRBJEKwxVYdL4lI1QoizS2UHrAV+TuFr8uWd6pY9eSSTjWu/cou3QYKh0UgCGipDah6O5xPbrMdkWj4t7FI5XIiDXUU7b0FBXtsA0DrstWs/sMbH+2mt6aG1AMoGp6kKPVx0dYEle+kPqxHj4Wc7HDZFRVEqldSsPMoCvffHoDE6npWP/yPtXb/Cz56rgMoLA0oGpGEICDRBlVLwtdt4lM7QUF++PyuqCCy/APyxoyg8LBPhzdtbmX1fX9d62n4aP0RIK8wSfGIRPiyC6Dqv1nherffiaCklAgQqawguuRdsrcqo/Bz+3Wsqnb+YwTJZEeNa34tAWTnJSke1tYxXr0sh2QyQnLsjgQjU58HKyvJev1VYkOLKDj5sI71Nvzs9wSNLZ2egzUvi1h2kuIhbR2Po3ZlFslEhPbtdyYoD9+3kdpqsv7+DJF4AflfOKZjvS33/55kdc1HL+HgoxVHI1BU2tJxfw212STaoyQ+tT3JnVKf5/V15DzxR8iJkfulKR3rbfvVYySXrej4bFjz3K55PuLFH623uTGb9vYoyW3H0L7P/mG9ba3k/OaB8PF97Rwiqc+k5O/+RPKtd8JlPvazA8jPb+l4D7Q2R0kkYiS32pb2Q4/qqC3n53dAkCRy7tlEisPfM8HCx+Hlf67zVl7z/sjJaiUaDa9sb4/SnogSjNiCtuNO61gw596fEGloIDn1C0RGhT/PyDNPw9PPdNQZoXO9WbH2cL1BQCIRJZmIEpSW0jrtvI47z/n5nURWLCcxeQp8KnxvRBb9ndhjj35U6Jr1B+FrLxpNEk19NicDCJIQ5ObRcvG3wuUikH3/Aj5YWMniioMIgijBWn//jpAkEkmy56in2Xq76o7HGI1ANPXrIRlESCRjBJEILV+fA1nZBEDWY/9H7I1XSR42AQ48MCxv6VKiP7s3LDMCRKIEQccHBVlZCbKzk6n1RmlpySUgQsvkqVBYBAHEXnia2GuvEN13TyJHHBqut2oViTvvCyvu+DD76MtgTm47eXntHeutq8sHIrQdfjRB6dDwuXrjX8Ree5nYjtuRc9wR4W2bWmi6+ecffUKmXlRBEIFIhNycduIlzeFPNICVlUUEQPve4wmGhR92kfeXEvv3K2SXj6Bg8hEdz231vAcJ2oOP1rlWzbm5CcpGNneMK94vTH1G7EIwfES48OqVxN74F1nDiyid9tF6q27+A4nm1vC2a15mkfD/WbkBo7ZrSq02wrK3C0i0RklsuTXB8JHh8vX1RN9+i1hRNiPP++izZ8Wdf6VtVUvHc7v2WySWDdt8uqXjLj94PZeW+gjBkOEkhw4PJ1tbiC59l2h2lPKZB3fcdvnPXqTx/ca1vkN89HxEIrD9QR99z/ng1Rj1lVGShXGCIUPDhRJJIh9+AMDYy/Yjkh2+MCseeJXat+rX2mt8zc8u/O+OR0bIygnXu+yfAauWQJCbC8Wl4TJBQGRlFQQBO1y4K1ll4feL5f/3FlWLaj5a15rnI/X/nY/JJrc4QhCJ8OE/Eqz4V4IglkVQGH5fIIBIXR0kk2z/5U+Rv21JuN4/LuXDJ1ZC0PFEhK+71Hp3PD6f+BbZEImw/OUW3nu6mSAShdw8giDoeI5JJtn+7HJKdgtf1yueWs5/H67oXO+a918A259YzJCd8iASoerVZt56uCa8Mjsr9TwAiQQkk4w9cwuGHxC+rlcuWskbd77f8fux4+eXuo+xJw9l5AHFEIHVbzTx6m2p7zKp7xBrnmOCgE+dOoqtJoXfT2rerGPx3HfoiEGdf92z3fHD2faYERCBuqVNvHjVO6z5btgpOQWw7XEjGHPqlgA0ftjM87P+/dFyay0cBLD1pOHsMC383ty8soVnvvqvTve95v4JArY8fChbThjGC7NeX+vJ/Lhw/sCbd6Vk+8L1XN936urq2GOPPT5xy2fGhc/x48ez7777cvPNNwOQTCYpLy/n/PPPZ/bs2essP3nyZBoaGnj00Y9+Ke6///7sueee3Hbbbess39LSQktLS8e4traW8vLyXngkkiRJkjR49KvdbltbW3nppZeYMGFCx1w0GmXChAk899xz673Nc88912l5gIkTJ25w+blz51JSUtJxMXhKkiRJUu/LSncBa6uqqiKRSDBy5MhO8yNHjuT1119f720qKirWu3xFRcV6l58zZw4zZ87sGK/Z8pkJu90C5P3PN3ji+QOobhm+zi5JpbmVHLrnE7R84YupyUjn3WAjkXDXooICyMkN51pbiTQ1EcSiEP/orxCR+tq1dgVI7dtEai+MNXNE1trCH4HsHMhNrTeRCHe7iEQgP/+jGlqaIbnWKtaqa525NeNoFGJZ4SAIIJnaHXjNHIT7cHWU8rF1rU9kPVeuM7e+Zbqyrq6sewPr+vhkl+rsSk2buZwGnc3a6WVjN/2k1W7k+k8uaf0LfOLtNlrvxm+80as/4X57Y8eijj3EPrbuyJpdKhPJdXffWmsQfhyHyyYTqX1kU5JJOonGIkRia5ZNhrturlXE2uuOZEWJrlm2PSDZllrZmj3CU7dp+f6d/P31/Tf4+Pbb+TlyL/9SaqUQy44SzVrz2FI1s9avv0jko19Xax5bJEIQBEQikU36CIx0+n368es2MIhsYJmPXbnRXyNd+BWzTn3S5trY59OmXPfxfVDZwHCddQYbvu7jH1wbur/13q7TJ1P4TzIRfo+MRFO74wbhB157e+r/a61grcNYOv6z9v3k5nXs0ktzM7S3QlYO5KYO80okiDTUp3b1XV+da6/vo/sLikogKyu8rqmRSGMDQW4+xOPk3fJ9nnjxoA1ng/HP0fzN75Jua3a7/SQZFT77Qm5uLrlrAtRaRo8enRndbkuKOGjrl/nDu8etc9VBW7/MFiOGwVGfS0NhkiRtnqD86yyb8iwrV8fX+RI1dEg9+/zwfCKjP5XGCiUpwwwp23g2KCmC7bZbzw37Vm1tbZeWy6jwOWzYMGKxGMuXL+80v3z5ckaNGrXe24waNWqTls94F8xmqztv5vjo/eFfaILUn6qDJEP32RLO/Xq6K5QkqVsioz/FPtfH+cOMv3WaD4iyz/VHERk9cgO3lKRBaoBlg4xsOLTffvtx0003AWHDoW222Yavf/3rG2w41NjYyCOPPNIxd+CBB7L77ruvt+HQx2XieT4JAvj7s3DXLVC1AoaNgHO+Bvse6G6UkqR+LQgCVv67Blrb4PVXYOdPQ3Y2Q3cucddSSVqffpANupqpMi58PvDAA0ybNo3bb7+d/fbbjxtvvJFf/vKXvP7664wcOZKpU6ey1VZbMXfuXCA81cohhxzCddddx+c//3l+8Ytf8N3vfneTT7WSUeFzjdZWWPQ32Gf/8HhLSZIkSYNTBmeDrmaqjNrtFsItmZWVlVx55ZVUVFSw55578thjj3U0FVq6dCnR6EfHiRx44IH8/Oc/54orruAb3/gG22+/PQ8//HCXgmfGy8mB/Q/+5OUkSZIkDWwDIBtk3JbPvpbRWz4lSZIkKcN1NVNl1Hk+JUmSJEkDk+FTkiRJktTrDJ+SJEmSpF5n+JQkSZIk9TrDpyRJkiSp1xk+JUmSJEm9zvApSZIkSep1hk9JkiRJUq8zfEqSJEmSep3hU5IkSZLU6wyfkiRJkqReZ/iUJEmSJPU6w6ckSZIkqdcZPiVJkiRJvc7wKUmSJEnqdYZPSZIkSVKvM3xKkiRJknqd4VOSJEmS1OsMn5IkSZKkXpeV7gLSLQgCAGpra9NciSRJkiT1P2uy1JpstSGDPnzW1dUBUF5enuZKJEmSJKn/qquro6SkZIPXR4JPiqcDXDKZZNmyZRQVFRGJRNJdTie1tbWUl5fz3nvvUVxcnO5yJKlf2Hffffn73/+e7jKktPO9oK7ytdJ/ZOrPKggC6urq2HLLLYlGN3xk56Df8hmNRtl6663TXcZGFRcXGz4lqYtisZifmRK+F9R1vlb6j0z+WW1si+caNhySJA0oX/va19JdgpQRfC+oq3yt9B/9/Wc16He7zWS1tbWUlJRQU1OTsX/hkCRJkqSucMtnBsvNzeWqq64iNzc33aVIkiRJ0mZxy6ckSZIkqde55VOSJEmS1OsMn5IkSZKkXmf4lCRJkiT1OsOnJEkbcOKJJ1JWVsYpp5yS7lKktPP9oK7wdaKNMXxKkrQBF154IT/96U/TXYaUEXw/qCt8nWhjDJ/9mH9ZkqTedeihh1JUVJTuMqSM4PtBXeHrRBtj+OzH/MuSpP5o7ty57LvvvhQVFTFixAhOOOEE3njjjR69j6eeeopjjz2WLbfckkgkwsMPP7ze5W655RZGjx5NXl4e48eP54UXXujROqRPcuutt7L77rtTXFxMcXExBxxwAL///e979D58Pwws1113HZFIhIsuuqhH1+vrRH3B8NmP+ZclSf3Rk08+yde+9jX+9re/8ac//Ym2tjaOOuooGhoa1rv8M888Q1tb2zrzr732GsuXL1/vbRoaGtjj/7d351FRlf8fwN/D0ACyqAiN4sigQQaIg0skokKAIKTVKfVkRqOWWoKIa9HXxLACygV3zcylRSiTSkWSUDEQEUG0VChMUnPcOiaCWTHz/P7oeH9OM6iIE5jv1zlzztznPvfZ7nOH87kbGg2WLl3aYDsyMzMxZcoUJCUloaysDBqNBpGRkTh37tztdYzoNqhUKqSmpqK0tBT79+9HaGgonnjiCRw+fNhsfh4P97aSkhKsXLkS3bt3v2E+zhNqsQQ1i/z8fDF48GDRoUMHAUBkZWWZ5FmyZIlQq9XCxsZGBAQEiOLiYpM8O3fuFE8//fS/0GIiIss4d+6cACDy8/NN1un1eqHRaMTQoUNFfX29lF5RUSGUSqVIS0u7afkN/cYGBASI2NhYo7rc3NxESkqKUT7+ztK/rW3btuL99983SefxcG+7fPmy8PLyErm5uSI4OFhMmjTJbD7OE2rJeOWzmdzs7BLPLBHRveLSpUsAAGdnZ5N1VlZWyM7OxoEDB/D888/DYDDg2LFjCA0NxZNPPokZM2bcVp1//vknSktLER4eblRXeHg4ioqKbq8jRE2k1+uRkZGBuro6BAYGmqzn8XBvi42NxWOPPWa0n8zhPKGWzLq5G3CvioqKQlRUVIPr58+fj7Fjx2L06NEAgBUrVmDr1q344IMP8Oqrr/5bzSQisiiDwYCEhAQEBQWhW7duZvO4ublhx44d6N+/P5599lkUFRUhPDwcy5cvv+16L1y4AL1eD6VSaZSuVCpRUVEhLYeHh+PgwYOoq6uDSqXCZ599ZjYoIGqK7777DoGBgbh69SocHByQlZUFHx8fs3l5PNybMjIyUFZWhpKSklvKz3lCLRWDzxbo2pmlxMREKY1nlojovyg2Nhbff/89CgoKbpjP3d0dH374IYKDg9GlSxesXr0aMpnM4u375ptvLF4HUdeuXVFeXo5Lly5h48aN0Gq1yM/PbzAA5fFwbzl58iQmTZqE3Nxc2Nra3vJ2nCfUEvG22xboRmeWzpw5Iy2Hh4dj2LBhyM7OhkqlYmBKRHeVuLg4bNmyBTt37oRKpbph3rNnz2LcuHEYMmQIrly5gsmTJzepbhcXF8jlcpMXb5w9exbt27dvUtlEjaVQKODp6YlevXohJSUFGo0GCxcubDA/j4d7S2lpKc6dO4eePXvC2toa1tbWyM/Px6JFi2BtbQ29Xm92O84TaokYfN7FvvnmG5w/fx5XrlzBqVOneEsDEd0VhBCIi4tDVlYWduzYgc6dO98w/4ULFxAWFgZvb29s2rQJeXl5yMzMxLRp0267DQqFAr169UJeXp6UZjAYkJeXx99SanYGgwF//PGH2XU8Hu49YWFh+O6771BeXi59evfujZEjR6K8vBxyudxkG84Taql4220LxDNLRPRfFhsbi08++QRffvklHB0dpTs6WrduDTs7O6O8BoMBUVFRUKvVyMzMhLW1NXx8fJCbm4vQ0FB07NjR7Nn82tpaVFVVScvHjx9HeXk5nJ2d4e7uDgCYMmUKtFotevfujYCAAKSnp6Ourk561p7o35CYmIioqCi4u7vj8uXL+OSTT7Br1y58/fXXJnl5PNybHB0dTZ6Jt7e3R7t27cw+K895Qi1ac79ul8y/zjogIEDExcVJy3q9XnTs2NHkVdZERHcbAGY/a9asMZt/+/bt4vfffzdJLysrEydPnjS7zc6dO83WodVqjfItXrxYuLu7C4VCIQICAsTevXub2j2iRhkzZoxQq9VCoVAIV1dXERYWJrZv395gfh4PJIS44b9aEYLzhFoumRBC/JvBLv3t+rNLPXr0wPz58/Hoo49KZ5cyMzOh1WqxcuVK6czSp59+ioqKCpNnQYmIiIiIiFo6Bp/NZNeuXXj00UdN0rVaLdauXQsAWLJkCd59912cOXMG/v7+WLRoER555JF/uaVERERERERNx+CTiIiIiIiILI5vuyUiIiIiIiKLY/BJREREREREFsfgk4iIiIiIiCyOwScRERERERFZHINPIiIiIiIisjgGn0RERERERGRxDD6JiIiIiIjI4hh8EhERERERkcUx+CQiIiIiIiKLY/BJREQtTnV1NWQyGcrLy5u7KZKKigr06dMHtra28Pf3b+7mNMqoUaPw5JNPWqx8IQTGjRsHZ2fnFrffiIio5WDwSUREJkaNGgWZTIbU1FSj9C+++AIymayZWtW8kpKSYG9vj8rKSuTl5TV3c1qUnJwcrF27Flu2bIFOp0O3bt2au0nw8PBAenr6HS0zJCQECQkJd7RMIqJ7CYNPIiIyy9bWFmlpabh48WJzN+WO+fPPP29722PHjqFfv35Qq9Vo167dHWzV3e/YsWPo0KED+vbti/bt28Pa2tokT1PGnoiI/hsYfBIRkVnh4eFo3749UlJSGswze/Zsk1tQ09PT4eHhIS1fu+Xz7bffhlKpRJs2bZCcnIz6+npMnz4dzs7OUKlUWLNmjUn5FRUV6Nu3L2xtbdGtWzfk5+cbrf/+++8RFRUFBwcHKJVKxMTE4MKFC9L6kJAQxMXFISEhAS4uLoiMjDTbD4PBgOTkZKhUKtjY2MDf3x85OTnSeplMhtLSUiQnJ0Mmk2H27Nlmy9m4cSP8/PxgZ2eHdu3aITw8HHV1dQCAkpISDBw4EC4uLmjdujWCg4NRVlZmtL1MJsPKlSsxePBgtGrVCt7e3igqKkJVVRVCQkJgb2+Pvn374tixYyb7YOXKlejUqRNatWqF4cOH49KlS2bbeK2/KSkp6Ny5M+zs7KDRaLBx40Zp/cWLFzFy5Ei4urrCzs4OXl5eZvcP8Pf+nThxIk6cOAGZTCbt+4bGPj8/HwEBAbCxsUGHDh3w6quvor6+XiovJCQEEydOREJCAtq2bQulUolVq1ahrq4Oo0ePhqOjIzw9PbFt27YG+xcSEoKff/4ZkydPhkwmM7paX1BQgP79+8POzg6dOnVCfHy8tI8AYNmyZfDy8oKtrS2USiWGDh0q9TM/Px8LFy6Uyqyurm6wDUREZIrBJxERmSWXy/H2229j8eLFOHXqVJPK2rFjB06fPo3du3dj/vz5SEpKwuDBg9G2bVsUFxfjpZdewvjx403qmT59OqZOnYoDBw4gMDAQQ4YMwa+//goA+O233xAaGooePXpg//79yMnJwdmzZzF8+HCjMtatWweFQoHCwkKsWLHCbPsWLlyIefPmYe7cuTh06BAiIyPx+OOP48cffwQA6HQ6+Pr6YurUqdDpdJg2bZpJGTqdDiNGjMCYMWNw9OhR7Nq1C0899RSEEACAy5cvQ6vVoqCgAHv37oWXlxeio6Nx+fJlo3LmzJmD559/HuXl5XjooYfw7LPPYvz48UhMTMT+/fshhEBcXJzRNlVVVfj000+xefNm5OTk4MCBA5gwYUKD+yMlJQXr16/HihUrcPjwYUyePBnPPfecFNy//vrrOHLkCLZt24ajR49i+fLlcHFxaXDsrgXuOp0OJSUlDY79L7/8gujoaDz88MM4ePAgli9fjtWrV+PNN9802WcuLi7Yt28fJk6ciJdffhnDhg1D3759UVZWhoiICMTExODKlStm27Rp0yaoVCokJydDp9NBp9MB+PsK7aBBg/D000/j0KFDyMzMREFBgTSe+/fvR3x8PJKTk1FZWYmcnBwMGDBA6mdgYCDGjh0rldmpU6cGx5iIiMwQRERE/6DVasUTTzwhhBCiT58+YsyYMUIIIbKyssT1fzqSkpKERqMx2nbBggVCrVYblaVWq4Ver5fSunbtKvr37y8t19fXC3t7e7FhwwYhhBDHjx8XAERqaqqU56+//hIqlUqkpaUJIYSYM2eOiIiIMKr75MmTAoCorKwUQggRHBwsevTocdP+urm5ibfeesso7eGHHxYTJkyQljUajUhKSmqwjNLSUgFAVFdX37Q+IYTQ6/XC0dFRbN68WUoDIGbOnCktFxUVCQBi9erVUtqGDRuEra2ttJyUlCTkcrk4deqUlLZt2zZhZWUldDqdEMJ4f169elW0atVK7Nmzx6g9L7zwghgxYoQQQoghQ4aI0aNH31I/hDDd50KYH/vXXntNdO3aVRgMBilt6dKlwsHBQZofwcHBol+/ftL6a3MjJiZGStPpdAKAKCoqarBNarVaLFiwwKSP48aNM0r79ttvhZWVlfj999/F559/LpycnERNTY3ZMoODg8WkSZMarJOIiG6MVz6JiOiG0tLSsG7dOhw9evS2y/D19YWV1f//yVEqlfDz85OW5XI52rVrh3PnzhltFxgYKH23trZG7969pXYcPHgQO3fuhIODg/R56KGHAMDottRevXrdsG01NTU4ffo0goKCjNKDgoIa1WeNRoOwsDD4+flh2LBhWLVqldHzsmfPnsXYsWPh5eWF1q1bw8nJCbW1tThx4oRROd27d5e+K5VKADAaK6VSiatXr6KmpkZKc3d3R8eOHaXlwMBAGAwGVFZWmrSzqqoKV65cwcCBA43Gbv369dK4vfzyy8jIyIC/vz9mzJiBPXv23PI4XO+fY3/06FEEBgYa3QYbFBSE2tpao6ve14/BtbnxzzEAYDJfbubgwYNYu3atUb8jIyNhMBhw/PhxDBw4EGq1Gl26dEFMTAw+/vjjBq+uEhFR45m+EYCIiOg6AwYMQGRkJBITEzFq1CijdVZWVtJtpdf89ddfJmXcd999RssymcxsmsFguOV21dbWYsiQIUhLSzNZ16FDB+m7vb39LZfZFHK5HLm5udizZw+2b9+OxYsX43//+x+Ki4vRuXNnaLVa/Prrr1i4cCHUajVsbGwQGBho8iKe68flWpBmLq0xY3W92tpaAMDWrVuNAlYAsLGxAQBERUXh559/RnZ2NnJzcxEWFobY2FjMnTu3UXXd7tjfbL7c7hjU1tZi/PjxiI+PN1nn7u4OhUKBsrIy7Nq1C9u3b8esWbMwe/ZslJSUoE2bNo3vCBERGeGVTyIiuqnU1FRs3rwZRUVFRumurq44c+aMUQB6J//H4969e6Xv9fX1KC0thbe3NwCgZ8+eOHz4MDw8PODp6Wn0aUzQ4+TkBDc3NxQWFhqlFxYWwsfHp1HtlclkCAoKwhtvvIEDBw5AoVAgKytLKi8+Ph7R0dHw9fWFjY2N0cuRmuLEiRM4ffq0tLx3715YWVmha9euJnl9fHxgY2ODEydOmIzb9c8wurq6QqvV4qOPPkJ6ejree++9Jrfz2guUrp8vhYWFcHR0hEqlanL511MoFNDr9UZpPXv2xJEjR0z67enpCYVCAeDvK+zh4eF45513cOjQIVRXV2PHjh0NlklERLeOVz6JiOim/Pz8MHLkSCxatMgoPSQkBOfPn8c777yDoUOHIicnB9u2bYOTk9MdqXfp0qXw8vKCt7c3FixYgIsXL2LMmDEAgNjYWKxatQojRozAjBkz4OzsjKqqKmRkZOD999+HXC6/5XqmT5+OpKQkPPDAA/D398eaNWtQXl6Ojz/++JbLKC4uRl5eHiIiInD//fejuLgY58+fl4JlLy8vfPjhh+jduzdqamowffp02NnZNW5AGmBrawutVou5c+eipqYG8fHxGD58ONq3b2+S19HREdOmTcPkyZNhMBjQr18/XLp0CYWFhXBycoJWq8WsWbPQq1cv+Pr64o8//sCWLVukfjTFhAkTkJ6ejokTJyIuLg6VlZVISkrClClTjG7LvhM8PDywe/duPPPMM7CxsYGLiwteeeUV9OnTB3FxcXjxxRdhb2+PI0eOIDc3F0uWLMGWLVvw008/YcCAAWjbti2ys7NhMBikIN7DwwPFxcWorq6Gg4MDnJ2d73i7iYj+y/iLSUREtyQ5OdnkNkdvb28sW7YMS5cuhUajwb59+8y+CfZ2paamIjU1FRqNBgUFBfjqq6+kt65eu1qp1+sREREBPz8/JCQkoE2bNo0OCOLj4zFlyhRMnToVfn5+yMnJwVdffQUvL69bLsPJyQm7d+9GdHQ0HnzwQcycORPz5s1DVFQUAGD16tW4ePEievbsiZiYGMTHx+P+++9vVDsb4unpiaeeegrR0dGIiIhA9+7dsWzZsgbzz5kzB6+//jpSUlLg7e2NQYMGYevWrejcuTOAv6/wJSYmonv37hgwYADkcjkyMjKa3M6OHTsiOzsb+/btg0ajwUsvvYQXXngBM2fObHLZ/5ScnIzq6mo88MADcHV1BfD3s6T5+fn44Ycf0L9/f/To0QOzZs2Cm5sbAKBNmzbYtGkTQkND4e3tjRUrVmDDhg3w9fUFAEybNg1yuRw+Pj5wdXU1eV6XiIhuTCb++bAOERER3TVmz56NL7744o7e7kxERGQJvPJJREREREREFsfgk4iIiIiIiCyOt90SERERERGRxfHKJxEREREREVkcg08iIiIiIiKyOAafREREREREZHEMPomIiIiIiMjiGHwSERERERGRxTH4JCIiIiIiIotj8ElEREREREQWx+CTiIiIiIiILO7/AB9zg140vYK5AAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -227,16 +227,16 @@ "id": "40b5a90f", "metadata": { "execution": { - "iopub.execute_input": "2023-10-26T18:23:05.728333Z", - "iopub.status.busy": "2023-10-26T18:23:05.728059Z", - "iopub.status.idle": "2023-10-26T18:23:13.762230Z", - "shell.execute_reply": "2023-10-26T18:23:13.761390Z" + "iopub.execute_input": "2023-10-26T18:51:34.362819Z", + "iopub.status.busy": "2023-10-26T18:51:34.362620Z", + "iopub.status.idle": "2023-10-26T18:51:42.363272Z", + "shell.execute_reply": "2023-10-26T18:51:42.362455Z" } }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -285,10 +285,10 @@ "id": "9ba03fac", "metadata": { "execution": { - "iopub.execute_input": "2023-10-26T18:23:13.770749Z", - "iopub.status.busy": "2023-10-26T18:23:13.770364Z", - "iopub.status.idle": "2023-10-26T18:23:30.667590Z", - "shell.execute_reply": "2023-10-26T18:23:30.666304Z" + "iopub.execute_input": "2023-10-26T18:51:42.371280Z", + "iopub.status.busy": "2023-10-26T18:51:42.371064Z", + "iopub.status.idle": "2023-10-26T18:51:58.533161Z", + "shell.execute_reply": "2023-10-26T18:51:58.532321Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 31465.41 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30527.08 examples/s]" ] }, { @@ -313,7 +313,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 64903.58 examples/s]" + "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 62839.41 examples/s]" ] }, { @@ -321,7 +321,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 51837.31 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 51805.52 examples/s]" ] }, { @@ -344,7 +344,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29662.51 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 33327.25 examples/s]" ] }, { @@ -352,7 +352,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 56120.75 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 60675.57 examples/s]" ] }, { @@ -360,7 +360,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 74640.76 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 78776.03 examples/s]" ] }, { @@ -368,7 +368,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 51321.09 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 53031.09 examples/s]" ] }, { @@ -391,7 +391,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30161.42 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 28972.79 examples/s]" ] }, { @@ -399,7 +399,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 64834.20 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 53570.54 examples/s]" ] }, { @@ -407,7 +407,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 71233.00 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 57311.78 examples/s]" ] }, { @@ -415,7 +415,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50814.95 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 44752.22 examples/s]" ] }, { @@ -438,7 +438,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 26076.71 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29685.00 examples/s]" ] }, { @@ -446,7 +446,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 51267.38 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54094.31 examples/s]" ] }, { @@ -454,7 +454,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 61867.28 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 60562.35 examples/s]" ] }, { @@ -462,7 +462,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46828.39 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46932.32 examples/s]" ] }, { @@ -485,7 +485,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30097.85 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30145.36 examples/s]" ] }, { @@ -493,7 +493,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 52346.67 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 54276.24 examples/s]" ] }, { @@ -501,7 +501,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 70188.60 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 62321.53 examples/s]" ] }, { @@ -509,7 +509,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 48835.16 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46296.32 examples/s]" ] }, { @@ -532,7 +532,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 33005.55 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29617.68 examples/s]" ] }, { @@ -540,7 +540,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 55189.70 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 51339.49 examples/s]" ] }, { @@ -548,7 +548,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 64221.69 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 60679.01 examples/s]" ] }, { @@ -556,7 +556,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 49544.24 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 45996.11 examples/s]" ] }, { @@ -579,7 +579,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30268.31 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29312.35 examples/s]" ] }, { @@ -587,7 +587,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 52838.56 examples/s]" + "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 52136.76 examples/s]" ] }, { @@ -595,7 +595,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 68926.89 examples/s]" + "Filter (num_proc=6): 83%|████████▎ | 21330/25596 [00:00<00:00, 62915.35 examples/s]" ] }, { @@ -603,7 +603,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 48892.07 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 46760.92 examples/s]" ] }, { @@ -626,7 +626,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 29598.87 examples/s]" + "Filter (num_proc=6): 17%|█▋ | 4266/25596 [00:00<00:00, 30974.47 examples/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 50%|█████ | 12798/25596 [00:00<00:00, 53045.51 examples/s]" + "Filter (num_proc=6): 67%|██████▋ | 17064/25596 [00:00<00:00, 64107.22 examples/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 70782.13 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 71256.75 examples/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 49769.51 examples/s]" + "Filter (num_proc=6): 100%|██████████| 25596/25596 [00:00<00:00, 50103.00 examples/s]" ] }, { @@ -662,7 +662,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -726,17 +726,17 @@ "id": "77e4b383", "metadata": { "execution": { - "iopub.execute_input": "2023-10-26T18:23:30.672220Z", - "iopub.status.busy": "2023-10-26T18:23:30.671795Z", - "iopub.status.idle": "2023-10-26T18:23:32.990924Z", - "shell.execute_reply": "2023-10-26T18:23:32.989894Z" + "iopub.execute_input": "2023-10-26T18:51:58.539220Z", + "iopub.status.busy": "2023-10-26T18:51:58.539014Z", + "iopub.status.idle": "2023-10-26T18:52:00.779875Z", + "shell.execute_reply": "2023-10-26T18:52:00.779197Z" }, "tags": [] }, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAABUkAAANHCAYAAAALxtxzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAADPcUlEQVR4nOzdd5RU9cH/8c+dXmAXEQFBkKKiPihICdhRUSQ2EBSNimJDQNgCSG+LCioSLEQNsRusj12D8YclGrsI9oIhgkgRkF1g79R7f3+A+0iCCGz5Tnm/ztlzdmfvzLxXPcflw525luu6rgAAAAAAAAAgT3lMBwAAAAAAAACASYykAAAAAAAAAPIaIykAAAAAAACAvMZICgAAAAAAACCvMZICAAAAAAAAyGuMpAAAAAAAAADyGiMpAAAAAAAAgLzGSAoAAAAAAAAgr/lMB2Qzx3H0ww8/qH79+rIsy3QOAAAAAAAAkFVc19WmTZvUrFkzeTzmzudkJK2GH374QS1atDCdAQAAAAAAAGS1FStWaL/99jP2/Iyk1VC/fn1J0oqZM1UQChmuAYCaseGss0wnAAAAAADyxKZNm3T44YdX7WymMJJWw88vsS8IhVQQDhuuAYCakSooMJ0AAAAAAMgzpt/Kkgs3AQAAAAAAAMhrjKQAAAAAAAAA8hojKQAAAAAAAIC8xnuSAgAAAAAAIGe4rivHceQ4jlzXNZ2T1yzLksfjkcfjMf6eo7+FkRQAAAAAAAA5IZVKaePGjUokEhk/yuUL13UVCATUoEED+XyZO0VmbhkAAAAAAACwi1zX1Y8//ii/369mzZopEAiYToKkRCKhdevW6ccff1TTpk0zdrxmJAUAAAAAAEDWS6VScl1X++67r8LhsOkcbBMKheT3+/Xdd98plUrJ7/ebTtohLtwEAAAAAACArOe6rizLytgzFfPZz/9eMvk9YhlJAQAAAAAAAOQ1RlIAAAAAAAAAeY2RFAAAAAAAAEBeYyQFAAAAAAAAkNcYSQEAAAAAAIA8dNJJJ6m0tNR0RkZgJAUAAAAAAACQ1xhJAQAAAAAAgF/6/ntZr70mff+96RLUEUZSAAAAAAAA5CbXlbZs2a0P64475G3bVt6TT5a3bVtZd9yx248h192tzNWrV8vv9+vWW29Vly5dVK9ePXXo0EFvvvnmDo+fN2+eWrZsKcdxtrv97LPP1uWXX1719UsvvaTjjz9ejRo1UpMmTXTWWWfp22+//dWOAw44QLfccst2t3Xu3FllZWVVXzuOoxtuuEEHHnig6tevr06dOul///d/d+vnzUSMpAAAAAAAAMhNlZXyNWiwWx/eESNkbRsfLceRd8SI3X4MVVbuVuaSJUskSffdd59mz56tDz74QC1atNDFF1/8X0OoJPXv31/r16/Xa6+9VnXbhg0b9NJLL+kPf/hD1W1btmxRcXGx3nnnHb300kvyeDzq37//Dh9zV91www166KGHNHfuXC1ZskRFRUW6+OKL9Y9//GOPHzMT+EwHAAAAAAAAAPns448/lt/v15NPPqlWrVpJkqZNm6bu3btr5cqVatGixXbH77XXXjr11FP18MMP68QTT5Qk/e///q8aNWqkHj16VB139tlnb3e/efPmad9999Xnn3+u9u3b73ZnPB7XzJkztWDBAh155JGSpDZt2uif//yn5s2bp+OOO263HzNTMJICAAAAAAAgN0UiSm3cuOvHr1wp72GHVZ1JKkmu16v0xx9LzZvv1vPujiVLlqhv375VA6kkFRQUSJJeffVVjRgxour2559/Xsccc4zOP/98XXXVVbr99tsVDAb18MMP69xzz5XH838vHP/mm280bdo0vffee1q3bl3VGaQrVqzYo5F06dKlqqysVO/evbe7PZFIqGPHjrv9eJmEkRQAAAAAAAC5ybKkaHTXjz/oIDl33CHP0KGy0mm5Xq+cP/1JOuig2mvU1pH0oosu2u62d955R40aNdJZZ52lo446qur25tvG2tNPP12u6+rFF19Uly5d9Oabb2rWrFnbPUbfvn3VsmVL3Xnnndp3333lOI46duyoRCKxww6PxyP3P95PNZVKVX2+efNmSdKzzz6rZs2abXdcMBjczZ86szCSAgAAAAAAANu4l16q9CmnyPr2W7lt20r77Verz2fbtr755hul0+mq2xzH0a233qqLLrpIhYWFKiws/K/7hUIh9enTRw8//LC+/fZbtWvXTp06dar6/vr16/XVV1/pzjvv1DHHHCNJv3ohqJ81atRIq1evrvq6oqJCy5Ytq/r60EMPVTAY1PLly7P6pfU7wkgKAAAAAAAA/NJ++8mt5XH0Z5988oksy9L8+fPVo0cPNWjQQNOmTVN5ebnGjx+/0/v+4Q9/0FlnnaXPP/98uws2SVvft3TvvffWvHnz1LRpU61YseI3H++EE07QAw88oNNPP12FhYWaNm2avF5v1ffr16+v0tJSjRo1So7j6Oijj1Z5ebneeustFRQUaODAgXv+D8IwRlIAAAAAAADAkCVLlqhdu3YaNWqUBgwYoPLycp1yyilauHChGjRosNP7nnDCCWrYsKG++uornXfeedt9z+Px6K9//atKSkrUsWNHHXTQQZozZ45OOumkX328MWPG6N///rfOOussFRYWaurUqdudSSptvaBUo0aNdOONN+pf//qXGjRooCOOOEJjx47d438GmcBy//ONBrDLKioqVFhYqPI5c1QQDpvOAYAasaF/f9MJAAAAALDbEomE1q9fr/3331+hUMh0zi4bPny4Nm7cqAcffNB0Sq2JxWL67rvvtPfeeysQCGz3vYqKCrVu3Vrl5eVVF6sywfPbhwAAAAAAAACoDUuWLNFhhx1mOiPvMZICAAAAAAAABriuq08//ZSRNAPwnqQAAAAAAACAAZZlacOGDaYzIM4kBQAAAAAAAJDnGEkBAAAAAAAA5DVGUgAAAADIAdbKlfK98YaslStNpwAAkHUYSQEAAAAgywUeekgNOnZUQZ8+atCxowIPPWQ6CQDqnGVZcl1XruuaTsF/+Pnfi2VZplN+FSMpAAAAAGQxa+VKRUtKZDnO1q8dR9HSUs4oBZB3vF6vJMm2bcMl+E8//zv5+d9RJuLq9gAAAACQxbzffFM1kP7MSqflXbZMqebNDVUBQN3zeDyKRCL68ccfJUnhcDijz1zMB67ryrZt/fjjj4pEIvJ4Mvd8TUZSAAAAAMhivnfe+a/bXI9H6datDdQAgFmFhYWSpLVr10oSI6lhP7/1QSQSqfp3k6kYSQEAAAAgS3m+/17huXMlSa5lydr2h9FUx45yOYsUQB6yLEsNGjRQQUGB0uk0709qmGVZ8nq9GX0G6c8YSQEAAAAgG7muImPGyKqsVKp9e22eO1f+N95QdOJE+ZYskefrr+UcdJDpSgAwwuPxZMUwh8zBfy0AAAAAkIX8zz+vwIIFcn0+2SUlctq3V3zIECWOP15WOq3I+PGmEwEAyBqMpAAAAACQZayKCkXHjpUkxc87T8nTT6/6nl1WJteyFHj1VXnff99UIgAAWYWRFAAAAACyTPi66+RZvVrp5s0VGzFC8v3fO6ml27dXom9fSVJkwgRTiQAAZBVGUgAAAADIIt4PPlDw7rslSXZxsZy2bf/rGHviRLk+n/wffijf3/5W14kAAGQdRlIAAAAAyBbJpKKlpbJcV4mTT1bivPN2eJiz//6KX3KJJClSViY5Th1GAgCQfRhJAQAAACBLhO68U77PPpNTv77soiIpEvnVY+1Ro+RGIvJ9/bUC8+fXYSUAANmHkRQAAAAAsoBn+XKFb7hBkhS76iqlu3ff6fHuPvvIHj5ckhS+8UYpmaz1RgAAshUjKQAAAABkOtdV5JprZNm2Uh06KH7llZJl/ebdYkOHymnYUN6VKxW8/fY6CAUAIDsxkgIAAABAhgs884wCL78s1++XXVIit2HDXbtjvXqyx4yRJIXnzpU2b67FSgAAshcjKQAAAABkMKu8XJHx4yVJ8T/8QcnevXfr/vGBA5Xebz95fvqp6uX6AABge4ykAAAAAJDBwmVl8qxZo3SLFlvfY9Tn270HCARkT54sSQrdd5+sH3+shUoAALIbIykAAAAAZCjf++8rdN99krT1ZfatW+/R4yT69lXq0ENlVVYqPGVKDRYCAJAbGEkBAAAAIBMlk4qUlEiSEqeeqsS55+75Y3k8qiwrkyQFn3xSnmXLaqIQAICcwUgKAAAAABkoNHeufF98IaewUHZxsRQOV+vxUj16KHn00bKSSUUmTKiZSAAAcgQjKQAAAABkGM+yZQrfdJMkKTZkiNJdulT/QS1LldOmSZL8f/+7vB99VP3HBAAgRzCSAgAAAEAmcV1FR4+WFYsp1amT4pdfLllWjTx0+ogjFD/jDFmuy9mkAAD8AiMpAAAAAGSQwJNPyv/qq3L9ftlFRXL32qtGH9+eNEmu1yv/u+/Kt3BhjT42AADZipEUAAAAADKE9dNPVWd4xi+8UMnevWv8OZy2bRW/4AJJUmTKFMl1a/w5AADINoykAAAAAJAhwmVl8vz4o9ItW8oePlzyemvleewxY+SGQvJ98YUCjz9eK88BAEA2YSQFAAAAgAzge/tthR54QJJkl5bK3X//Wnsut2lTxYYMkSSFZ8yQUqlaey4AALIBIykAAAAAmJZIKFpauvXT005T4pxzav0pYyNGyCkslHf5cgX//Odafz4AADIZIykAAAAAGBa69VZ5v/5aToMGsouKpFCo1p/TLSiQPWqUJCl8yy2Sbdf6cwIAkKkYSQEAAADAIM+33yo8e7YkKTZsmNKdO9fZc8cvvVTOvvvKs26dwjfdVGfPCwBApmEkBQAAAABTXFfRkSNlxeNKdumi+KWX1u3zh0KqnDhx66d33y3rp5/q9vkBAMgQjKQAAAAAYEjgscfkf+MNuYGAYkVFchs0qPOGxDnnKH3QQbI2b1Z42rQ6f34AADIBIykAAAAAGGCtX6/IpEmSpPjAgUr26mUmxOtV5bZxNPjoo7JWrDDTAQCAQYykAAAAAGBAZOpUedavV7p1a9nDhkler7GW5MknK9m1q6xEQtEJE4x1AABgCiMpAAAAANQx35tvKjh/viTJLimR27Kl2SDLkj19uiTJ/7e/yfPpp2Z7AACoY4ykAAAAAFCX4nFFR47c+ukZZyjRr5/hoK1SXbsq0auXLMdRdPx40zkAANQpRlIAAAAAqEOhW26Rd+lSOQ0bKlZUJIVCppOqVE6ZItfjkf+f/5TvjTdM5wAAUGcYSQEAAACgjni+/lrhP/5RkhQbNkzpI44wXLQ9p107xc87T5K2XlTKdQ0XAQBQNxhJAQAAAKAuuK6io0bJSiSU7NZN8UGDTBftkD12rNxAQL5PPpH/mWdM5wAAUCcYSQEAAACgDgTmz5f/n/+UGwrJLiqSW1hoOmmH3ObNFbvySklS5NprpXTacBEAALWPkRQAAAAAapm1bp0iU6ZIkmIXX6xUz56Gi3YuVlIip359eZctU/Cee0znAABQ6xhJAQAAAKCWRSZNkuenn5Ru21axoUMlr9d00k65DRooVlIiSQrPni3FYoaLAACoXYykAAAAAFCLfK+/ruBjj8m1LNmlpXL328900i6JXXmlnMaN5Vm7VqE5c0znAABQqxhJAQAAAKC2xGKKjholSUqcdZYSffsaDtoN4bDs8eMlSaG77pJVXm44CACA2sNICgAAAAC1JDx7trz/+pecvfeWXVQkBYOmk3ZL/PzzlW7TRp6KCoWvvdZ0DgAAtYaRFAAAAABqgefLLxW69VZJkj18uJzDDzdctAd8PlVuu+BUcP58WT/8YDgIAIDawUgKAAAAADXNcRQdOVJWMqnkkUcqPnCg6aI9ljztNKWOOEJWLKbI5MmmcwAAqBWMpAAAAABQw4J//av877wjNxSSXVwsFRaaTtpzlqXKsjJJUuCZZ+T56ivDQQAA1DxGUgAAAACoQdbatQpve4l6bNAgpU480XBR9aWOOkqJE06Q5TiKbLuYEwAAuYSRFAAAAABqUGTSJHnKy5U+8EDFhg6VPLnxxy572jS5lqXAa6/J+847pnMAAKhRufF/awAAAADIAP5XXlHwiSfkejyqLC2V26yZ6aQak/6f/1GiXz9JUnTSJMM1AADUrKwZSefOnatWrVopFAqpW7dueu+993712M8++0z9+vVTq1atZFmW5syZs9PHnjlzpizLUnFxcc1GAwAAAMgflZWKjBolSUr06aNknz5me2qBPWGCXL9fvkWL5HvhBdM5AADUmKwYSR999FGVlpZqypQpWrRokTp06KBevXpp7dq1Ozy+srJSbdq00cyZM9W0adOdPvb777+vu+66S4cffnhtpAMAAADIE+FZs+T97js5++wju6hICgRMJ9U4p2VLxQcNkiRFy8okxzFcBABAzciKkXT27Nm64oorNGjQIB166KG68847FYlEdM899+zw+K5du+qmm27Seeedp2Aw+KuPu3nzZl1wwQWaN2+e9tprr9rKBwAAAJDjvJ9/rtDcuZIke/hwOe3bGy6qPfaoUXIjEXmXLlXgoYdM5wAAUCMyfiRNJBL68MMP1bNnz6rbPB6Pevbsqbfffrtajz1s2DCddtpp2z32zsTjcVVUVGz3AQAAACDPOY4ipaWyUikljzlG8YEDTRfVKnfvvWWPGCFJCt94o5RIGC4CAKD6Mn4kXbdundLptJo0abLd7U2aNNHq1av3+HEfeeQRLVq0SDNmzNjl+8yYMUOFhYVVHy1atNjj5wcAAACQG4L33y//++/LjUS2vsy+fn3TSbUuNnSonL33lnfVKoVuv910DgAA1ZbxI2ltWLFihYqKivTXv/5VoVBol+83btw4lZeXV32sWLGiFisBAAAAZDpr9WqFy8okSbFLL1WqRw+zQXUlGpU9dqwkbX2bgc2bDQcBAFA9GT+SNmrUSF6vV2vWrNnu9jVr1vzmRZl+zYcffqi1a9eqU6dO8vl88vl8ev3113XrrbfK5/MpnU7v8H7BYFAFBQXbfQAAAADIX5EJE+SpqFCqXTvFhgyRPBn/R6waE7/oIqVbtpRn40aFr7/edA4AANWS8f8HDwQC6ty5sxYuXFh1m+M4WrhwoY488sg9esyTTjpJn3zyiRYvXlz10aVLF11wwQVavHixvF5vTeUDAAAAyFH+l19W8Omn5Xo8sktL5e7hSRxZy++XPWWKJCn0wAOy1q41HAQAwJ7zmQ7YFaWlpbr44ovVpUsX/e53v9OcOXO0ZcsWDRo0SJI0cOBANW/evOr9RROJhD7//POqz1euXKnFixerXr16OuCAA1S/fn21/4+rTUajUe29997/dTsAAAAA/JctWxQZPVqSlOjXT8kzzzQcZEbizDOVat9evk8/VWTyZG25807TSQAA7JGMP5NUkgYMGKBZs2Zp8uTJ6tixoxYvXqwFCxZUXcxp+fLlWrVqVdXxP/zwg4444ggdccQRWrVqlWbNmqUjjjhCl19+uakfAQAAAEAOCd90k7wrVshp3Fh2cbEUCJhOMsPjUeW0aZKkwFNPyfPtt4aDAADYM5bruq7piGxVUVGhwsJClc+Zo4Jw2HQOANSIDf37m04AACCjeT/5RAUnnSQrndaWa69VfMgQ00nG1e/TR/433lCiZ09tfvRR0zkAgCxSUVGh1q1bq7y83Oj1f7LiTFIAAAAAyAjptKKlpbLSaSWPO07xiy4yXZQRfj6b1L9wobwffmi4BgCA3cdICgAAAAC7KHjPPfItWiQ3Gt36Mvt69UwnZYR0hw6Kn3WWLNdVZOJE0zkAAOw2RlIAAAAA2AXWDz8ocu21kqTYZZcpdeyxhosyiz1xolyvV/733pPv5ZdN5wAAsFsYSQEAAABgF0THj5e1ebNShxyi2JAhkoc/Tv2S06aN4gMHSpIiU6dKXP4CAJBF+L86AAAAAPwG/4IFCjz3nFyvV3ZJidzGjU0nZST7mmvkhsPyffmlAo88YjoHAIBdxkgKAAAAADuzebMi11wjSYqfc46SZ55pOChzuY0bKzZ0qCQpPHOmlEoZLgIAYNcwkgIAAADATkRmzpR35Uo5++6r2IgRkt9vOimj2VdfLadBA3m//17BO+4wnQMAwC5hJAUAAACAX+FdskTBu+6SJNkjRshp185wURYoKJA9erQkKXzbbVJlpeEgAAB+GyMpAAAAAOxIKqVoSYksx1HihBMUv+AC00VZIz5okNLNmsmzfr3CN95oOgcAgN/ESAoAAAAAOxC8+275liyRW6+eYsXFUjRqOil7BIOyJ02SJIXuuUfWhg2GgwAA2DlGUgAAAAD4D56VKxW57jpJkn3FFUodfbThouyT6N9fqXbtZG3ZovDUqaZzAADYKUZSAAAAAPgPkTFjZG3ZolT79ooPHixZlumk7OPxyC4rkyQFH3tMnu++MxwEAMCvYyQFAAAAgF/wv/CCAn/7m1yvV3ZJidx99jGdlLWSJ52kZLduspJJRSZMMJ0DAMCvYiQFAAAAgJ9VVCg6ZowkKT5ggJKnnWY4KMtZliq3nU3qf+kleT/+2HAQAAA7xkgKAAAAANtEZsyQZ9UqpZs1U6yoSPL7TSdlvXSXLkr07i3LcTibFACQsRhJAQAAAECSd9EiBefNkyTZxcVyDjjAcFHuqJwyRa7HI/9bb8n32mumcwAA+C+MpAAAAACQSilaWirLdZXo2VOJ8883XZRTnAMPVHzbP9PIlCmS6xouAgBge4ykAAAAAPJe6K675PvkEzn16ytWXCxFIqaTco49dqzcQEC+Tz9V4KmnTOcAALAdRlIAAAAAec2zYoXCM2dKkmKDByvVvbvhotzkNmum2FVXSZLC110npdOGiwAA+D+MpAAAAADyl+sqcs01siorlTr8cMUHD5Ysy3RVzooVF8spKJD33/9W8C9/MZ0DAEAVRlIAAAAAecv/3HMK/P3vcn0+2cXFchs2NJ2U09zCQsVKSyVJ4TlzpFjMbBAAANswkgIAAADIS1ZFhaJjx0qS4uefr+Rppxkuyg+xK66Q06SJPGvXKjx7tukcAAAkMZICAAAAyFPh6dPlWbNG6f32U2zECMnnM52UH0IhVU6YIEkK/vnPsjZuNNsDAIAYSQEAAADkIe/77yt4772SJLu4WE6bNoaL8kvivPOUbttWnk2bFJ4+3XQOAACMpAAAAADyTDKpaGmpLNdV4pRTlBgwwHRR/vF6VTl1qiQp+PDDslauNNsDAMh7jKQAAAAA8krojjvk+/xzOQUFsouLpUjEdFJeSvburVTnzrLicUUmTjSdAwDIc4ykAAAAAPKG57vvFL7xRklS7KqrlP7d7wwX5THLUmVZmSQp8Pzz8nz+ueEgAEA+YyQFAAAAkB9cV5FRo2TZtlIdOyp+5ZWSZZmuymup7t2V6NlTluMouu1iTgAAmMBICgAAACAvBJ56SoFXXpHr98suLpa7116mkyDJnjpVrmXJ/49/yPvWW6ZzAAB5ipEUAAAAQM6zNm5UZNuZivELLlCyd2/DRfhZ+pBDlDjnHElSdOJEyXUNFwEA8hEjKQAAAICcFy4rk2ftWqVbtpR99dWSz2c6Cb9gT5ggNxCQb8kS+Z9/3nQOACAPMZICAAAAyGm+d99V6P77JUl2SYnc1q0NF+E/Ofvtp9ill0qSItOnS45juAgAkG8YSQEAAADkrkRCkdLSrZ/27l31sm5kntjIkXKjUXm//VaBBx4wnQMAyDOMpAAAAAByVuj22+X78ks5DRrILiqSwmHTSfgVbsOGsouLJUmRWbOkRMJsEAAgrzCSAgAAAMhJnn/9S+Gbb5YkxYYMUbpLF8NF+C2xwYPlNGokz6pVCt1yi+kcAEAeYSQFAAAAkHtcV9FRo2TFYkp27qz45ZdLlmW6Cr8lGpU9bpwkKXTnnVJFheEgAEC+YCQFAAAAkHMCTzwh/+uvyw0EFCsultuggekk7KL4BRcovf/+8mzcqMj115vOAQDkCUZSAAAAADnF+uknRSZOlCTFL7xQyV69DBdht/j9qpw6VZIUfPBBWatXm+0BAOQFRlIAAAAAOSU8dao869Yp3aqV7OHDJa/XdBJ2U/KMM5Q6/HBZsZgikyebzgEA5AFGUgAAAAA5w/fWWwo99JAkyS4pkduypeEi7BHLUmVZmSQp8Mwz8nzzjeEgAECuYyQFAAAAkBvicUVLS7d+evrpSvTvbzgI1ZE69lgljjtOViqlyPjxpnMAADmOkRQAAABATgjdequ833wjZ6+9FCsqkkIh00moJnvaNElS4JVX5P3gA8M1AIBcxkgKAAAAIOt5li5VePZsSVJs6FClO3UyXISakD78cMX79pUkRSZMMFwDAMhljKQAAAAAspvrKjpypKxEQsmuXRW/7DLTRahB9sSJcn0++T/4QL6XXjKdAwDIUYykAAAAALJa4JFH5H/zTbnBoOyiIrmFhaaTUIOcVq0Uv/hiSVJk2jTJdQ0XAQByESMpAAAAgKxlrV+vyOTJkqTYwIFKnXKK4SLUBnv0aLnhsHxffaXA/PmmcwAAOYiRFAAAAEDWikyeLM+GDUq3aaPYsGGS12s6CbXA3Wcf2cOHS5LCN9wgJZOGiwAAuYaRFAAAAEBW8r3xhoKPPCLXsmSXlMht0cJ0EmpRbOhQOXvtJe/KlQr+6U+mcwAAOYaRFAAAAED2icUUHTlSkpQ44wwl+vUzHIRaV7++7GuukSSFb79d2rLFcBAAIJcwkgIAAADIOuE5c+T99ls5e+8tu7hYCgZNJ6EOxC+5ROnmzeXZsGHry+4BAKghjKQAAAAAsornq68UmjNHkmQPGyanQwezQag7gYDsbRfqCt13n6x16wwHAQByBSMpAAAAgOzhOIqOHCkrmVSyWzfFL7nEdBHqWOLss5U65BBZW7YoPHWq6RwAQI5gJAUAAACQNQIPPyz/22/LDYW2vsy+sNB0EuqaxyO7rEySFHziCXmWLTMcBADIBYykAAAAALKC9eOPimx7qXXs4ouV6tnTcBFMSZ5wgpJHHSUrmVRk4kTTOQCAHMBICgAAACArRCZNkmfjRqUPOECxYcMkD3+cyVuWpcpp0yRJ/pdeknfxYrM9AICsx28VAAAAADKe79VXFXz8cbkej+ySErnNm5tOgmHpTp2UOO00Wa6ryIQJpnMAAFmOkRQAAABAZrNtRUePliQlzjpLib59DQchU1ROnizX45H/nXfke+UV0zkAgCzGSAoAAAAgo4VvvlneZcvkNGoku6hICgZNJyFDOAccoPiFF0qSIlOmSK5ruAgAkK0YSQEAAABkLO+XXyp0222SJHv4cDmHHWa4CJnGHjNGbigk3+efK/DEE6ZzAABZipEUAAAAQGZyHEVKSmSlUkoedZTiAweaLkIGcps2VeyqqyRJ4euvl1Ipw0UAgGzESAoAAAAgIwUffFD+996TGw7LLi6WCgpMJyFDxYqK5BQUyLt8uYLz5pnOAQBkIUZSAAAAABnHWrNG4alTJUmxQYOUOuEEs0HIaG5BgWKjRkmSwnPmSLZtNggAkHUYSQEAAABknMjEifJUVCh10EGKDR0qefijC3Yudtllcpo2lWfdOoVvvtl0DgAgy/CbBgAAAICM4l+4UMEnn5Tr8cguKZG7776mk5ANQiFVTpwoSQr+5S+yfvrJcBAAIJswkgIAAADIHJWVioweLUlK9O2rZJ8+ZnuQVRLnnqv0gQfKs2mTwmVlpnMAAFmEkRQAAABAxgjfdJO8330np3HjrRdrCgRMJyGbeL2qnDZNkhR85BFZ339vOAgAkC0YSQEAAABkBO9nnyk0d64kyR4+XM6hhxouQjZKnnKKkl26yEokFJ0wwXQOACBLMJICAAAAMM9xFC0pkZVOK3nssYpfdJHpImQry5I9fbokyf/ii/J89pnhIABANmAkBQAAAGBc8N575fvwQ7mRyNaX2devbzoJWSz1u98pccopshxH0fHjTecAALIAIykAAAAAo6xVqxTeduZf7LLLlDruOMNFyAWVU6bI9Xjkf/NNed9803QOACDDMZICAAAAMCo6frw8mzYpdcghig0ZInn4Ywqqzzn4YCUGDJAkRSdNklzXcBEAIJPx2wcAAAAAY/wvvaTAs8/K9Xhkl5TIbdLEdBJyiD1unNxAQL6PP5b/2WdN5wAAMhgjKQAAAAAzNm9W5JprJEmJ/v2VPPNMw0HINU7z5opdfrkkKTJ9upROGy4CAGQqRlIAAAAARoRvvFHe77+X06TJ1os1+f2mk5CDYqWlcuvVk3fZMgXvvdd0DgAgQzGSAgAAAKhz3o8/VujOOyVJdlGRnHbtDBchV7l77SW7pESSFJ49W4rHDRcBADIRIykAAACAupVOK1pSIiudVqJHD8UvuMB0EXJc7Mor5eyzjzxr1ij0xz+azgEAZCBGUgAAAAB1Knj33fItXiw3GlWsqEiqV890EnJdJCJ7/HhJUujPf5YqKgwHAQAyDSMpAAAAgDpjrVypyLXXSpJiV1yh1LHHGi5Cvoj/4Q9Kt24tT3l51X+DAAD8jJEUAAAAQJ2Jjhsna8sWpQ49VLGrrpIsy3QS8oXPp8opUyRJwb/+VdaqVYaDAACZhJEUAAAAQJ3wv/iiAi+8INfrlV1aKneffUwnIc8kTz9dqY4dZcViikyebDoHAJBBGEkBAAAA1L5NmxQZM0aSFD/3XCVPP91wEPKSZamyrEySFHjmGXm+/tpwEAAgUzCSAgAAAKh1kRkz5P3hB6X33VexESMkv990EvJU6uijlejRQ1Y6rci4caZzAAAZImtG0rlz56pVq1YKhULq1q2b3nvvvV899rPPPlO/fv3UqlUrWZalOXPm/NcxM2bMUNeuXVW/fn01btxYffr00VdffVWLPwEAAACQn7wffaTgvHmSpFhxsZyDDjJchHxnT5sm17IUeO01ed9913QOACADZMVI+uijj6q0tFRTpkzRokWL1KFDB/Xq1Utr167d4fGVlZVq06aNZs6cqaZNm+7wmNdff13Dhg3TO++8o5dfflnJZFKnnHKKtmzZUps/CgAAAJBfUilFR46U5ThKnHSS4uefb7oIULp9eyXOPluSFJk40XANACATWK7ruqYjfku3bt3UtWtX3X777ZIkx3HUokULDR8+XGPHjt3pfVu1aqXi4mIVFxfv9Lgff/xRjRs31uuvv67jjjtul7oqKipUWFio8jlzVBAO79J9ACDTbejf33QCACCHBO+4Q9GJE+XWq6dNDz+s1FFHmU4CJEme5ctV+LvfyUomVfHgg0r9/vemkwAgL1VUVKh169YqLy9XQUGBsY6MP5M0kUjoww8/VM+ePatu83g86tmzp95+++0ae57y8nJJUsOGDX/1mHg8roqKiu0+AAAAAOyY5/vvFbn+ekmSfeWVSh15pOEi4P84LVsqfsklkqRoWZnkOGaDAABGZfxIum7dOqXTaTVp0mS725s0aaLVq1fXyHM4jqPi4mIdffTRat++/a8eN2PGDBUWFlZ9tGjRokaeHwAAAMg5rqvImDGyKiuVat9e8cGDJcsyXQVsxx41Sm4kIu833yjw17+azgEAGJTxI2ldGDZsmD799FM98sgjOz1u3LhxKi8vr/pYsWJFHRUCAAAA2cX//PMKLFgg1+eTXVIit1Ej00nAf3EbNZI9YoQkKXzjjVIiYbgIAGBKxo+kjRo1ktfr1Zo1a7a7fc2aNb96UabdcfXVV+v555/Xq6++qv3222+nxwaDQRUUFGz3AQAAAGB7VkWFotuuHRA/7zwlTz/dcBHw62JDhsjZe295f/hBoblzTecAAAzJ+JE0EAioc+fOWrhwYdVtjuNo4cKFOrIa72nkuq6uvvpqPfXUU3rllVfUunXrmsgFAAAA8l74uuvkWb1a6ebNFRsxQvL5TCcBv65ePdljxkjS1pF082bDQQAAEzJ+JJWk0tJSzZs3T/fff7+++OILDRkyRFu2bNGgQYMkSQMHDtS4ceOqjk8kElq8eLEWL16sRCKhlStXavHixVq6dGnVMcOGDdNDDz2k+fPnq379+lq9erVWr14t27br/OcDAAAAcoX3gw8UvPtuSZJdXCynbVvDRcBviw8cqHSLFvL89JPCM2eazgEAGGC5ruuajtgVt99+u2666SatXr1aHTt21K233qpu3bpJknr06KFWrVrpvvvukyT9+9//3uGZoccff7xee+01SZL1K28af++99+qSbVc4/C0VFRUqLCxU+Zw5KgiHd/tnAoBMtKF/f9MJAIBslUyq4KST5PvsMyVOPlmb77lHikRMVwG7JPDkk6p3xRVyIxFt/PBDuY0bm04CgLxQUVGh1q1bq7y83OhbW2bNSJqJGEkB5CJGUgDAngrddpsiU6fKqV9fmx5+WOlqvD0WUOccRwU9esj32WeKn3uuttxxh+kiAMgLmTKSZsXL7QEAAABkNs/y5QrfcIMkKXbVVUp37264CNhNHo8qp02TJAWeekqef/3LcBAAoC4xkgIAAACoHtdV5JprZNm2Uh06KH7lldKvvL0VkMlSJ5yg5DHHyEomFZkwwXQOAKAOMZICAAAAqJbAM88o8PLLcv1+2SUlchs2NJ0E7LGfzyb1v/yyvB99ZLgGAFBXGEkBAAAA7DGrvFyR8eMlSfE//EHJ3r0NFwHVk+7YUfEzz5TlupxNCgB5hJEUAAAAwB4Ll5XJs2aN0i1ayB4+XPL5TCcB1WZPmiTX65X/3Xfl+3//z3QOAKAOMJICAAAA2CO+999X6L77JGnry+xbtzYbBNQQp00bxS+8UJIUmTpVcl2zQQCAWsdICgAAAGD3JZOKlJRIkhKnnqrEuecaDgJqlj1mjNxQSL4vvlDgscdM5wAAahkjKQAAAIDdFpo7V74vvpBTWCi7uFgKh00nATXKbdJEsaFDJUnhGTOkVMpwEQCgNjGSAgAAANgtnmXLFL7pJklSbMgQpbt0MVwE1A57+HA5hYXyrlih4F13mc4BANQiRlIAAAAAu851FR09WlYsplSnTopffrlkWaargNpRUCB79GhJUviWW6TKSsNBAIDawkgKAAAAYJcFnnxS/ldflev3yy4qkrvXXqaTgFoVv/RSpZs1k2f9eoVnzTKdAwCoJYykAAAAAHaJ9dNPikyYIEmKX3ihkr17Gy4C6kAwKHviRElS6C9/kbVhg+EgAEBtYCQFAAAAsEvCZWXy/Pij0i1byh4+XPJ6TScBdSLRv79SBx0ka8sWhadONZ0DAKgFjKQAAAAAfpPv7bcVeuABSZJdWip3//0NFwF1yOuVXVYmSQo+9pg8y5cbDgIA1DRGUgAAAAA7l0goWlq69dPTTlPinHMMBwF1L9mzp5K/+52sZLLqbScAALmDkRQAAADAToVuvVXer7+W06CB7KIiKRQynQTUPctS5bazSf0LFsjzySeGgwAANYmRFAAAAMCv8nz7rcKzZ0uSYsOGKd25s+EiwJx0165KnHqqLMdRdPx40zkAgBrESAoAAABgx1xX0ZEjZcXjSnbpovill5ouAoyrnDxZrscj/1tvyfePf5jOAQDUEEZSAAAAADsUeOwx+d94Q24gILuoSG6DBqaTAOOcdu0UP+88SVJk0iTJdQ0XAQBqAiMpAAAAgP9irV+/dQCSFB84UKlevQwXAZnDHjdObiAg36efKvDUU6ZzAAA1gJEUAAAAwH+JTJ0qz/r1SrduLXvYMMnrNZ0EZAy3WTPFBg+WJIWvv15Kpw0XAQCqi5EUAAAAwHZ8b76p4Pz5kiS7pERuy5aGi4DMEysullO/vrzLlil4992mcwAA1cRICgAAAOD/xOOKjhy59dMzzlCiXz/DQUBmchs0UKy0VJIU/uMfpVjMcBEAoDoYSQEAAABUCd1yi7xLl8pp2FCxoiIpFDKdBGSs2BVXyGnSRJ61a7cOpQCArMVICgAAAECS5Pn666qhJzZsmNJHHGG4CMhw4bAqx4+XJAXvuktWebnhIADAnmIkBQAAACC5rqKjRslKJJTs1k3xQYNMFwFZIXHeeUq3bSvPpk0KT59uOgcAsIcYSQEAAAAoMH++/P/8p9xQSHZRkdzCQtNJQHbw+VQ5ZYokKTh/vqwffjAcBADYE4ykAAAAQJ6z1q1TZNvIE7v4YqV69jRcBGSX5O9/r9QRR8iKxxWZNMl0DgBgDzCSAgAAAHkuMmmSPD/9pHTbtooNHSp5vaaTgOxiWarc9lL7wLPPyvPll4aDAAC7i5EUAAAAyGO+119X8LHH5FqW7NJSufvtZzoJyEqpI49U4sQTZTmOItsu5gQAyB6MpAAAAEC+sm1FR42SJCXOOkuJvn0NBwHZzZ42Ta5lKfD66/K+/bbpHADAbmAkBQAAAPJU+I9/lPdf/5Kz996yi4qkYNB0EpDV0oceqkT//pKk6KRJkusaLgIA7CpGUgAAACAPeb78UqFbb5Uk2cOHyzn8cMNFQG6wJ0yQ6/fL99FH8r3wgukcAMAuYiQFAAAA8o3jKDpypKxkUskjj1R84EDTRUDOcFq0UPzSSyVJ0enTJccxXAQA2BWMpAAAAECeCf71r/K/847cUEh2cbFUWGg6Ccgp9siRcqNReZcuVeDBB03nAAB2ASMpAAAAkEestWsVnjJFkhQbNEipE080XATkHnfvvWWPGCFJCt90k5RIGC4CAPwWRlIAAAAgj0QmTZKnvFzpAw9UbOhQycMfCYDaEBsyRE6jRvKuWlX1/r8AgMzFb0QAAABAnvC/8oqCTzwh1+NRZWmp3GbNTCcBuSsalT12rCQpdMcdUkWF4SAAwM4wkgIAAAD5oLJSkVGjJEmJvn2V7NPHbA+QB+IXXqj0/vvLs3GjwjNnms4BAOwEIykAAACQB8KzZsn73Xdy9tln63slBgKmk4Dc5/erctt7AIceeEDWmjWGgwAAv4aRFAAAAMhx3s8/V2juXEmSPWKEnPbtDRcB+SN55plKHXaYLNtWZPJk0zkAgF/BSAoAAADkMsdRpLRUViql5DHHKH7RRaaLgPxiWaosK5MkBZ5+Wp6lSw0HAQB2hJEUAAAAyGHB+++X//335UYisouKpPr1TScBeSd13HFKHnusrFRKkfHjTecAAHaAkRQAAADIUdbq1QpvO4MtdumlSvXoYTYIyGNVZ5MuXCjvBx8YrgEA/CdGUgAAACBHRSZMkKeiQql27RQbMkTy8Os/YEr68MMV79NHkhSZONFsDADgv/BbEgAAAJCD/C+/rODTT8v1eGSXlspt2tR0EpD37IkT5Xq98r//vnx//7vpHADALzCSAgAAALlmyxZFRo+WJCX69VPyzDMNBwGQJKd1a8UvvliSFJk6VXJds0EAgCqMpAAAAECOCd90k7wrVshp3Fh2cbEUCJhOArCNPXq03HBYvq++UuDhh03nAAC2YSQFAAAAcoj3k08U+tOfJEl2UZGcgw82XATgl9zGjRUbNkySFL7hBimZNFwEAJAYSQEAAIDckU4rWloqK51W4vjjFb/wQtNFAHbAHjZMzl57yfv99wrecYfpHACAGEkBAACAnBG85x75Fi2SG40qVlws1atnOgnAjhQUyN72vsHh22+XKisNBwEAGEkBAACAHGD98IMi114rSYpddplSxx5ruAjAzsQvuUTp5s3lWb9+68vuAQBGMZICAAAAOSA6fryszZuVOuQQxYYMkSzLdBKAnQkGZU+aJEkK3XuvrPXrDQcBQH5jJAUAAACynH/BAgWee06u1yu7pERu48amkwDsgkS/fkodfLCsLVsUnjLFdA4A5DVGUgAAACCbbd6syDXXSJLi55yj5JlnGg4CsMs8HtllZZKk4BNPyPPvf5vtAYA8xkgKAAAAZLHIzJnyrlwpZ999FRsxQvL7TScB2A3JE09Usnt3WcmkIhMmmM4BgLzFSAoAAABkKe+SJQredZckyR4xQk67doaLAOw2y1LltrNJ/S+9JO+SJYaDACA/MZICAAAA2SiVUrSkRJbjKHHCCYpfcIHpIgB7KN25sxK//70s1+VsUgAwhJEUAAAAyELBu++Wb8kSufXqKVZcLEWjppMAVEPl5MlyPR75335bvldfNZ0DAHmHkRQAAADIMp6VKxW57jpJkn3FFUodfbThIgDV5Rx4oOJ/+IMkKTJliuS6hosAIL8wkgIAAABZJjJmjKwtW5Rq317xwYMlyzKdBKAG2GPHyg0G5fvsMwWefNJ0DgDkFUZSAAAAIIv4X3hBgb/9Ta7XK7ukRO4++5hOAlBD3H33VeyqqyRJ4euuk9Jpw0UAkD8YSQEAAIBsUVGh6JgxkqT4gAFKnnaa4SAANS1WVCSnoEDe775TcN480zkAkDcYSQEAAIAsEZkxQ55Vq5Ru1kyxoiLJ7zedBKCGuYWFio0cKUkKz5kj2bbZIADIE4ykAAAAQBbwLlpUdVaZXVws54ADDBcBqC2xyy+X07SpPD/+qPDs2aZzACAvMJICAAAAmS6VUrS0VJbrKtGzpxLnn2+6CEBtCoVUOWGCJCk4b56sjRvN9gBAHmAkBQAAADJc6K675PvkEzn16ytWXCxFIqaTANSyxIABSh94oDybNilcVmY6BwByHiMpAAAAkME8K1YoPHOmJCk2eLBS3bsbLgJQJ7xeVU6ZIkkKPvKIrO+/NxwEALmNkRQAAADIVK6ryDXXyKqsVOrwwxUfPFiyLNNVAOpI8tRTlercWVY8rujEiaZzACCnMZICAAAAGcr/3HMK/P3vcn0+2SUlchs2NJ0EoC5ZliqnT5ck+V94QZ7PPzccBAC5i5EUAAAAyEBWRYWiY8dKkuLnn6/k739vuAiACalu3ZQ4+WRZjqPo+PGmcwAgZzGSAgAAABkoPH26PGvWKL3ffoqNGCH5fKaTABhiT50q1+OR/4035P3nP03nAEBOYiQFAAAAMoz3/fcVvPdeSZJdXCynTRvDRQBMSh98sBLnnCNJik6aJLmu4SIAyD2MpAAAAEAmSSYVLS2V5bpKnHKKEgMGmC4CkAHsCRPkBgLyLVki/3PPmc4BgJzDSAoAAABkkNAdd8j3+edyCgpkFxdLkYjpJAAZwGneXLHLLpMkRaZPlxzHcBEA5BZGUgAAACBDeL77TuEbb5Qkxa66Sunf/c5wEYBMEistlVuvnrz/+lfVW3IAAGoGIykAAACQCVxXkVGjZNm2Uh07Kn7llZJlma4CkEHchg23nmEuKTx7thSPmw0CgBzCSAoAAABkgMBTTynwyity/X7ZxcVy99rLdBKADBQbPFhOo0byrF6t0C23mM4BgJzBSAoAAAAYZm3cqMiECZKk+AUXKNm7t+EiABkrEpE9frwkKXTnnVJFheEgAMgNjKQAAACAYeGyMnnWrlW6ZUvZV18t+XymkwBksPgFFyjdqpU85eWKXH+96RwAyAmMpAAAAIBBvnffVej++yVJdkmJ3NatDRcByHg+nyqnTJEkBR98UNbq1YaDACD7MZICAAAApiQSipSWbv20d28lzjnHcBCAbJE84wylOnSQFYspMmmS6RwAyHpZM5LOnTtXrVq1UigUUrdu3fTee+/96rGfffaZ+vXrp1atWsmyLM2ZM6fajwkAAADUtNDtt8v35ZdyGjSQXVQkhcOmkwBkC8tSZVmZJCnwzDPyfPON4SAAyG5ZMZI++uijKi0t1ZQpU7Ro0SJ16NBBvXr10tq1a3d4fGVlpdq0aaOZM2eqadOmNfKYAAAAQE3y/OtfCt98syQpNmSI0l26GC4CkG1SxxyjxPHHy0qnFRk3znQOAGS1rBhJZ8+erSuuuEKDBg3SoYceqjvvvFORSET33HPPDo/v2rWrbrrpJp133nkKBoM18pgAAABAjXFdRUeNkhWLKdm5s+KXXy5ZlukqAFnILiuTa1kKvPqqvO+/bzoHALJWxo+kiURCH374oXr27Fl1m8fjUc+ePfX2229nzGMCAAAAuyrwxBPyv/663EBAseJiuQ0amE4CkKXS7dsr0bevJCkyYYLhGgDIXhk/kq5bt07pdFpNmjTZ7vYmTZpo9R5ewW9PHzMej6uiomK7DwAAAGB3WD/9pMjEiZKk+IUXKtmrl+EiANnOnjhRrs8n/4cfyve3v5nOAYCslPEjaSaZMWOGCgsLqz5atGhhOgkAAABZJjx1qjzr1indqpXs4cMlr9d0EoAs5+y/v+KXXCJJipSVSY5jNggAslDGj6SNGjWS1+vVmjVrtrt9zZo1v3pRptp6zHHjxqm8vLzqY8WKFXv0/AAAAMhPvrfeUuihhyRJdkmJ3JYtDRcByBX2qFFyIxH5vv5agfnzTecAQNbJ+JE0EAioc+fOWrhwYdVtjuNo4cKFOvLII+v0MYPBoAoKCrb7AAAAAHZJPK5oaenWT08/XYn+/Q0HAcgl7j77bD07XVL4xhulZNJwEQBkl4wfSSWptLRU8+bN0/33368vvvhCQ4YM0ZYtWzRo0CBJ0sCBAzVu3Liq4xOJhBYvXqzFixcrkUho5cqVWrx4sZYuXbrLjwkAAADUpNCtt8r7zTdy9tpLsaIiKRQynQQgx8SGDpXTsKG8K1cqePvtpnMAIKv4TAfsigEDBujHH3/U5MmTtXr1anXs2FELFiyouvDS8uXL5fH83977ww8/6Igjjqj6etasWZo1a5aOP/54vfbaa7v0mAAAAEBN8SxdqvDs2ZKk2LBhSnfqZLgIQE6qV0/2NdcoOnaswnPnKn7FFVK9eqarACArWK7ruqYjslVFRYUKCwtVPmeOCsJh0zkAUCM28PJPAKhZrqv6ffrI/+abSnbtqs2PPiq3sNB0FYBclUiosGtXeb//XvbQobKnTzddBAA7VVFRodatW6u8vNzoW1tmxcvtAQAAgGwVeOQR+d98U24wKLu4mIEUQO0KBGRPnixJCt13n6wffzQcBADZgZEUAAAAqCXW+vWKbBsrYgMHKnXyyYaLAOSDRN++Sh16qKzKSoWnTDGdAwBZgZEUAAAAqCWRyZPl2bBB6TZtFBs2TPJ6TScByAcejyrLyiRJwSeflGfZMsNBAJD5GEkBAACAWuB74w0FH3lErmXJLimR26KF6SQAeSTVo4eSRx8tK5lUZMIE0zkAkPEYSQEAAICaFospOnKkJClxxhlK9OtnOAhA3rEsVU6bJkny//3v8n70keEgAMhsjKQAAABADQvPmSPvt9/K2Xtv2cXFUjBoOglAHkofcYTip58uy3U5mxQAfgMjKQAAAFCDPF99pdCcOZIke9gwOR06mA0CkNfsyZPlejzyv/uufAsXms4BgIzFSAoAAADUFMdRdORIWcmkkt27K37JJaaLAOQ5p21bxS+8UJIUmTJFcl3DRQCQmRhJAQAAgBoSmD9f/rfflhsKyS4qkgoLTScBgOwxY+SGQvJ98YUCjz9uOgcAMhIjKQAAAFADrB9/3HqWlqTYxRcr1bOn4SIA2Mpt2lSxIUMkSeEZM6RUynARAGQeRlIAAACgBkQmTZJn40alDzhAsWHDJA+/agPIHLERI+QUFsq7fLmCf/6z6RwAyDj85gYAAABUk+/VVxV8/HG5Ho/skhK5zZubTgKA7bgFBbJHjZIkhW+5RbJtw0UAkFkYSQEAAIDqsG1FR4+WJCXOOkuJvn0NBwHAjsUvvVTOvvvKs26dwjfdZDoHADIKIykAAABQDeGbb5Z32TI5jRptvVhTMGg6CQB2LBRS5cSJWz+9+25ZP/1kOAgAMgcjKQAAALCHvF9+qdBtt0mS7OHD5Rx2mOEiANi5xDnnKH3QQbI2b1Z42jTTOQCQMRhJAQAAgD3hOIqUlMhKpZQ86ijFBw40XQQAv83rVeW2cTT46KOyVqwwHAQAmYGRFAAAANgDwQcflP+99+SGw7KLi6WCAtNJALBLkiefrGTXrrISCUUnTDCdAwAZgZEUAAAA2E3WmjUKT50qSYoNGqTUCSeYDQKA3WFZsqdPlyT5//Y3eT791HAQAJjHSAoAAADspsjEifJUVCh10EGKDR0qefi1GkB2SXXtqkSvXrIcR9Hx403nAIBx/DYHAAAA7Ab/woUKPvmkXI9HdkmJ3H33NZ0EAHukcsoUuR6P/P/8p3xvvGE6BwCMYiQFAAAAdlVlpSKjR0uSEn37Ktmnj9keAKgGp107xc87T5IUmTRJcl3DRQBgDiMpAAAAsIvCN90k73ffyWnceOvFmgIB00kAUC322LFyAwH5PvlE/meeMZ0DAMYwkgIAAAC7wPvZZwrNnStJsocPl3PooYaLAKD63ObNFbvySklS5NprpXTacBEAmMFICgAAAPwWx1G0pERWOq3ksccqftFFposAoMbESkrk1K8v77JlCt5zj+kcADCCkRQAAAD4DcF775Xvww/lRiJbX2Zfv77pJACoMW6DBoqVlEiSwrNnS7GY4SIAqHuMpAAAAMBOWKtWKTx9uiQpdtllSh13nOEiAKh5sSuvlNO4sTxr1yo0Z47pHACoc4ykAAAAwE5Ex4+XZ9MmpQ45RLEhQyQPv0IDyEHhsOzx4yVJobvuklVebjgIAOoWv+EBAAAAv8L/0ksKPPusXI9HdkmJ3CZNTCcBQK2Jn3++0m3ayFNRofC115rOAYA6xUgKAAAA7MjmzYpcc40kKdG/v5Jnnmk4CABqmc+nyilTJEnB+fNl/fCD4SAAqDuMpAAAAMAOhG+8Ud7vv5fTtOnWizX5/aaTAKDWJU87TakjjpAViykyebLpHACoM4ykAAAAwH/wfvyxQnfeKUmyR4yQ066d4SIAqCOWpcqyMklS4Jln5PnqK8NBAFA3GEkBAACAX0qnFS0pkZVOK9Gjh+IXXGC6CADqVOqoo5Q44QRZjqPItos5AUCuYyQFAAAAfiF4993yLV4sNxpVrKhIqlfPdBIA1Dl72jS5lqXAa6/J+847pnMAoNYxkgIAAADbWCtXKrLtis6xK65Q6thjDRcBgBnp//kfJfr1kyRFJ00yXAMAtY+RFAAAANgmOm6crC1blDr0UMWuukqyLNNJAGCMPWGCXL9fvkWL5HvhBdM5AFCrGEkBAAAASf4XX1TghRfker2yS0vl7rOP6SQAMMpp2VLxQYMkSdGyMslxDBcBQO1hJAUAAAA2bVJkzBhJUvzcc5U8/XTDQQCQGexRo+RGIvIuXarAQw+ZzgGAWsNICgAAgLwXmTFD3h9+UHrffbderMnvN50EABnB3Xtv2SNGSJLCN94oJRKGiwCgdjCSAgAAIK95P/pIwXnzJEmx4mI5Bx5ouAgAMkts6FA5e+8t76pVCt1+u+kcAKgVjKQAAADIX6mUoiNHynIcJU46SfHzzzddBACZJxqVPXasJCk0d660ebPhIACoeYykAAAAyFvBefPkW7JEbr16ihUXS9Go6SQAyEjxiy5SumVLeTZuVPj6603nAECNYyQFAABAXvJ8/70i2/6gb195pVJHHmm4CAAymN8ve8oUSVLogQdkrV1rOAgAahYjKQAAAPKP6yoyZoysykql2rdXfPBgybJMVwFARkuceaZS7dvLsm1FJk82nQMANYqRFAAAAHnH//zzCixYINfnk11SIrdRI9NJAJD5PB5VTpsmSQo89ZQ8335rOAgAag4jKQAAAPKKVVGh6LYLkMTPO0/J0083XAQA2SPVo4eSxx4rK5VSZPx40zkAUGMYSQEAAJBXwtddJ8/q1Uo3b67YiBGSz2c6CQCyys9nk/oXLpT3ww8N1wBAzWAkBQAAQN7wfvCBgnffLUmyi4vltG1ruAgAsk+6QwfFzzpLlusqMnGi6RwAqBGMpAAAAMgPyaSipaWyXFeJk09W4rzzTBcBQNayJ06U6/XK/9578r38sukcAKg2RlIAAADkhdCdd8r32WdyCgpkFxVJkYjpJADIWk6bNopfdJEkKTJ1quS6ZoMAoJoYSQEAAJDzPMuXK3zDDZKk2ODBSnfvbrgIALKffc01csNh+b78UoFHHjGdAwDVwkgKAACA3Oa6ilxzjSzbVqpDB8WvvFKyLNNVAJD13CZNFBs6VJIUnjlTSqUMFwHAnmMkBQAAQE4LPPOMAi+/LNfvl11SIrdhQ9NJAJAz7KuvltOggbzff6/gHXeYzgGAPcZICgAAgJxllZcrMn68JCn+hz8o2bu34SIAyDEFBbJHj5YkhW+7TaqsNBwEAHuGkRQAAAA5K1xWJs+aNUq3aCF7+HDJ5zOdBAA5Jz5okNLNmsmzfr3CN95oOgcA9ggjKQAAAHKS7/33FbrvPkna+jL71q3NBgFArgoGZU+aJEkK3XOPrA0bDAcBwO5jJAUAAEDuSSYVKSmRJCVOPVWJc881HAQAuS3Rv79S7drJ2rJF4alTTecAwG5jJAUAAEDOCc2dK98XX8gpLJRdXCyFw6aTACC3eTyyy8okScHHHpPnu+8MBwHA7mEkBQAAQE7xLFum8E03SZJiQ4Yo3aWL4SIAyA/Jk05Ssls3WcmkIhMmmM4BgN3CSAoAAIDc4bqKjh4tKxZTqlMnxS+/XLIs01UAkB8sS5Xbzib1v/SSvB9/bDgIAHYdIykAAAByRuB//1f+V1+V6/fLLiqSu9deppMAIK+ku3RRondvWY7D2aQAsgojKQAAAHKC9dNPikycKEmKX3SRkr17Gy4CgPxUOWWKXI9H/rfeku+110znAMAuYSQFAABATgiXlcnz449Kt2wp++qrJa/XdBIA5CXnwAMVP/98SVJkyhTJdQ0XAcBvYyQFAABA1vO9/bZCDzwgSbJLS+Xuv7/hIgDIb/bYsXIDAfk+/VSBp54ynQMAv4mRFAAAANktkVC0tHTrp6edpsQ55xgOAgC4zZopdtVVkqTwdddJ6bThIgDYOUZSAAAAZLXQrbfK+/XXcho0kF1UJIVCppMAAJJixcVyCgrk/fe/FfzLX0znAMBOMZICAAAga3m+/Vbh2bMlSbFhw5Tu3NlwEQDgZ25hoWLbzvQPz5kjxWJmgwBgJxhJAQAAkJ1cV9GRI2XF40p26aL4pZeaLgIA/IfYFVfIadJEnrVrq/5SCwAyESMpAAAAslLgscfkf+MNuYGA7KIiuQ0amE4CAPynUEiVEyZIkoJ//rOsjRvN9gDAr2AkBQAAQNax1q9XZNIkSVJ84EClevUyXAQA+DWJAQOUbttWnk2bFJ4+3XQOAOwQIykAAACyTmTqVHnWr1e6dWvZw4ZJXq/pJADAr/H5VDl1qiQp+PDDslauNNsDADvASAoAAICs4nvzTQXnz5ck2SUlclu2NFwEAPgtyd69lercWVY8XvVKAADIJIykAAAAyB7xuKIjR2799IwzlOjXz3AQAGCXWJYqy8okSYHnnpPn888NBwHA9hhJAQAAkDVCt9wi79Klcho2VKy4WAqFTCcBAHZRqnt3JXr2lOU4im67mBMAZIpaG0lff/113XLLLXr66aflOE5tPQ0AAADyhOfrrxX+4x8lSbFhw5Tu2NFsEABgt9lTp8q1LPn/8Q9533rLdA4AVKnWSHrfffepU6dOevPNN7e7ffjw4TrxxBNVWlqqfv366dRTT1U6na5WKAAAAPKY6yo6apSsRELJbt0UHzTIdBEAYA+kDzlEiXPOkSRFJ06UXNdwEQBsVa2R9IknntC3336rrl27Vt32wQcfaO7cuQqFQjrrrLPUvHlzLVy4UI888ki1YwEAAJCfAvPny//Pf8oNhWQXFcktLDSdBADYQ/aECXIDAfmWLJH/+edN5wCApGqOpJ9++qkOO+wwBYPBqtseeeQRWZalBx98UE8++aTee+89hUIh3XPPPdWOBQAAQP6x1q1TZMoUSVLs4ouV6tnTcBEAoDqc/fZT7NJLJUmR6dMl3qIPQAao1ki6fv167bffftvd9o9//EMFBQXq06ePJKlp06Y69thjtXTp0uo8FQAAAPJUZNIkeX76Sem2bRUbOlTyek0nAQCqKTZypNxoVN5vv1XggQdM5wBA9UbSZDK53XuNxuNxLVmyREcddZQ8nv976H322Udr166tzlMBAAAgD/lef13Bxx6Ta1myS0vl/sdf0AMAspPbsKHs4mJJUmTWLCmRMBsEIO9VayRt1qyZPvvss6qvX3/9dSWTSR111FHbHVdRUaFC3jcKAAAAu8O2FR01SpKUOOssJfr2NRwEAKhJscGD5TRqJM+qVQrdcovpHAB5rlojaY8ePfTVV19p5syZWrJkiaZMmSLLsnTqqadud9ynn376Xy/LBwAAAHYm/Mc/yvuvf8nZe2/ZRUXSL94HHwCQA6JR2ePGSZJCd94pVVQYDgKQz6o1ko4fP1716tXThAkT1KlTJ7377rvq2bOnOnfuXHXM119/rWXLlql79+7VjgUAAEB+8Hz5pUK33ipJsocPl3P44YaLAAC1IX7BBUrvv788Gzcqcv31pnMA5LFqjaQHHHCA3nrrLV188cXq3bu3pk6dqqeffnq7YxYuXKgOHTrotNNOq85TAQAAIF84jqIjR8pKJpU88kjFBw40XQQAqC1+vyqnTpUkBR98UNbq1WZ7AOStao2kkvQ///M/uueee/T8889r8uTJCofD231/yJAh+uijj9S7d+9qPc/cuXPVqlUrhUIhdevWTe+9995Oj3/88cd18MEHKxQK6bDDDtOLL7643fc3b96sq6++Wvvtt5/C4bAOPfRQ3XnnndVqBAAAQPUF//pX+d95R24otPWiHry3PQDktOQZZyh1+OGyYjFFJk82nQMgT1V7JK0Ljz76qEpLSzVlyhQtWrRIHTp0UK9evbR27dodHv/WW2/p/PPP12WXXaaPPvpIffr0UZ8+ffTpp59WHVNaWqoFCxbooYce0hdffKHi4mJdffXVevbZZ+vqxwIAAMB/sNauVXjKFElSbNAgpU480XARAKDWWZYqy8okSYFnnpHnm28MBwHIR5bruu6uHrx8+fJqPVnLli336H7dunVT165ddfvtt0uSHMdRixYtNHz4cI0dO/a/jh8wYIC2bNmi559/vuq27t27q2PHjlVni7Zv314DBgzQpEmTqo7p3LmzevfurWuvvXaXuioqKlRYWKjyOXNU8B9n0AJAttrQv7/pBAB5LDp4sIJPPKH0gQeq4skn5TZrZjoJAFBH6vXtq8A//qHEiSdq8+OPm84BUEcqKirUunVrlZeXq6CgwFiHb3cObtWqlSzL2qMnsixLqVRqt++XSCT04Ycfaty2K95JksfjUc+ePfX222/v8D5vv/22SktLt7utV69e271f6lFHHaVnn31Wl156qZo1a6bXXntNX3/9tf74xz/+aks8Hlc8Hq/6uoIr7wEAANQY/yuvKPjEE3I9HlWWljKQAkCesadNU+CEExR45RV5P/hA6S5dTCcByCO7NZK2bNlyj0fSPbVu3Tql02k1adJku9ubNGmiL7/8cof3Wb169Q6PX/2LN4C+7bbbdOWVV2q//faTz+eTx+PRvHnzdNxxx/1qy4wZMzRt2rRq/DQAAADYocpKRUaNkiQl+vZVsk8fsz0AgDqXPvxwxfv2VfCppxSZMEGbXnrJdBKAPLJbI+m///3vWsqoe7fddpveeecdPfvss9p///31j3/8Q8OGDVOzZs3Us2fPHd5n3Lhx252hWlFRoRYtWtRVMgAAQM4Kz5ol73ffydlnH9kjRkiBgOkkAIAB9sSJCjz3nPwffCDfSy8p1auX6SQAeWK3RlITGjVqJK/XqzVr1mx3+5o1a9S0adMd3qdp06Y7Pd62bY0fP15PPfWUTjvtNEnS4YcfrsWLF2vWrFm/OpIGg0EFg8Hq/kgAAAD4Be/nnys0d64kyR4xQk779oaLAACmOK1aKX7xxQrdfbci06ap4pRTpDp+RSuA/JTxV7cPBALq3LmzFi5cWHWb4zhauHChjjzyyB3e58gjj9zueEl6+eWXq45PJpNKJpPyeLb/8b1erxzHqeGfAAAAAL/KcRQpLZWVSil5zDGKX3SR6SIAgGH26NFyw2H5vvpKgfnzTecAyBMZP5JKUmlpqebNm6f7779fX3zxhYYMGaItW7Zo0KBBkqSBAwdud2GnoqIiLViwQDfffLO+/PJLTZ06VR988IGuvvpqSVJBQYGOP/54jR49Wq+99pqWLVum++67Tw888ID69u1r5GcEAADIR8H775f//fflRiKyi4ul+vVNJwEADHP32Uf28OGSpPANN0jJpOEiAPmgRl5uv2rVKj3zzDP66quvVFFRIdd1/+sYy7J0991379HjDxgwQD/++KMmT56s1atXq2PHjlqwYEHVxZmWL1++3VmhRx11lObPn6+JEydq/PjxOvDAA/X000+r/S9euvXII49o3LhxuuCCC7Rhwwbtv//+uu6663TVVVftUSMAAAB2j7V6tcJlZZKk2KWXKnX88YaLAACZIjZ0qELz5sm7cqWCf/qT4kVFppMA5DjL3dGiuRtuu+02jR49Wslf/M3Ozw9pbXvfENd1ZVmW0ul0dZ4q41RUVKiwsFDlc+aoIBw2nQMANWJD//6mEwDkiehllyn49NNKtWunTU8+KfdX3m8eAJCfgn/+s6Ljxslp2FAbFy+WolHTSQBqQUVFhVq3bq3y8nIVFBQY66jWy+0XLlyooqIihUIhjR07tuo9P++66y6NHDlSrVq1kiQVFxfrnnvuqXYsAAAAcoP/5ZcVfPppuR6P7NJSBlIAwH+JX3KJ0s2by7Nhw9aX3QNALarWSHrLLbfIsiy99NJLuu6663TggQdKkq644grddNNN+vzzz3XxxRfrnnvu0bHHHlsjwQAAAMhyW7YoMnq0JCnRr5+SZ55pOAgAkJECAdmTJ0uSQvfdJ2vdOsNBAHJZtUbS9957T506dVK3bt12+P1gMKg77rhDoVBIZdvebwoAAAD5LXzTTfKuWCGnceOtF2sKBEwnAQAyVOLss5U65BBZW7YoPHWq6RwAOaxaI+lPP/2ktm3bVn3t9/slSbZtV90WDAZ17LHHauHChdV5KgAAAOQA7yefKPSnP0mS7KIiOQcfbLgIAJDRPB7Z2066Cj7xhDzLlhkOApCrqjWSNmzYUFu2bKn6eq+99pK09Wrzv5ROp7V+/frqPBUAAACyXTqtaGmprHRaieOPV/zCC00XAQCyQPKEE5Q88khZyaQiEyeazgGQo6o1krZs2VIrVqyo+rp9+/ZyXVfPP/981W2bN2/WG2+8of322686TwUAAIAsF7znHvkWLZIbjSpWXCzVq2c6CQCQDSxLldvOJvW/9JK8ixeb7QGQk6o1kh5//PH67LPPtGbNGknSaaedpmg0qvHjx2v06NG67bbb1KNHD23YsEG9evWqkWAAAABkH+uHHxS59lpJUuzyy5Xiop4AgN2Q7tRJidNOk+W6ikyYYDoHQA6q1kh6zjnnqEePHlq87W9xGjZsqNmzZyuVSmn27NkqLi7WokWLtP/++2vatGk10QsAAIAsFB0/XtbmzUodcohiV10lWZbpJABAlqmcPFmuxyP/O+/I98orpnMA5Bhfde7ctWtXvfzyy9vddsUVV6hz5856/PHHtWHDBh1yyCEaNGiQCgsLqxUKAACA7ORfsECB556T6/XKLimR27ix6SQAQBZyDjhA8QsvVOiBBxSZMkUVJ5zAX7oBqDHVGkl/TadOndSpU6faeGgAAABkk82bFbnmGklS/JxzlDzzTMNBAIBsZo8Zo+Bjj8n3+ecKPPGEEuecYzoJQI6o1svtn3vuOTmOU1MtAAAAyDGRmTPlXblSzr77KjZihOT3m04CAGQxt2nTrW/bIil8/fVSKmW4CECuqNZIetZZZ6lFixYaM2aMvvjii5pqAgAAQA7wLlmi4F13SZLsESPktGtnuAgAkAtiRUVyCgrkXb5cwXnzTOcAyBHVGkk7deqkVatW6aabblL79u111FFHad68eaqoqKipPgAAAGSjVErRkhJZjqPECScofsEFposAADnCLShQbNQoSVJ4zhzJts0GAcgJ1RpJP/jgA3388ccqLi5Wo0aN9M477+iqq67Svvvuq4EDB+oVrjYHAACQl4J33y3fkiVy69VTrLhYikZNJwEAckjsssvkNG0qz7p1Ct98s+kcADmgWiOpJLVv316zZ8/WypUr9eSTT+r0009XMpnUQw89pJNPPlmtW7dWWVmZvvvuu5roBQAAQIbzrFypyHXXSZLsK65Q6uijDRcBAHJOKKTKiRMlScG//EXWTz8ZDgKQ7ao9kv7M5/OpT58+euaZZ7Ry5UrNmjVLhx56qL777jtNmzZNBxxwQE09FQAAADJYZMwYWVu2KNW+veKDB0uWZToJAJCDEueeq/SBB8qzaZPCZWWmcwBkuRobSX9pn332UWlpqd577z0VFRXJdV05jlMbTwUAAIAM4n/hBQX+9je5Pp/skhK5++xjOgkAkKu8XlVOnSpJCj7yiKzvvzfbAyCr1cpI+s4772jw4MFq1qyZbr31VklSw4YNa+OpAAAAkCkqKhQdM0aSFB8wQMnTTjMcBADIdclevZTs0kVWIqHohAmmcwBksRobSVetWqUbbrhBhxxyiI4++mjNmzdPmzZt0imnnKJHHnlEK1eurKmnAgAAQAaKzJghz6pVSjdrptiIEZLfbzoJAJDrLEv29OmSJP+LL8rz2WeGgwBkK1917pxIJPT000/rvvvu08svvyzHceS6rtq2batLLrlEl1xyiZo3b15TrQAAAMhQ3kWLFJw3T5JkFxfL4f3oAQB1JPW73ylxyikK/P3vio4fr03PPGM6CUAWqtZIuu+++2rjxo1yXVeRSET9+/fXpZdequOOO66m+gAAAJDpUilFS0tlua4SPXsqcf75posAAHmmcsoU+f/f/5P/zTflffNNpY85xnQSgCxTrZfb//TTT+revbvmzZun1atX67777mMgBQAAyDOhu+6S75NP5NSvL7u4WIpETCcBAPKMc/DBSgwYIEmKTpokua7hIgDZplpnkn7xxRdq165dTbUAAAAgy3hWrFB45kxJUmzwYKW7dzdcBADIV/a4cQr87//K9/HH8j/7rJJnnWU6CUAWqdaZpAykAAAAecx1FbnmGlmVlUodfrjigwdLlmW6CgCQp5zmzRW7/HJJUmT6dCmdNlwEIJvU2NXtfzZo0CD5fNU6QRUAAABZwP/ccwr8/e9yfT7ZJSVyGzY0nQQAyHOx0lK59erJu2yZgvfeazoHQBap8ZFUklze+wMAACCnWRUVio4dK0mKn3++kr//veEiAAAkd6+9ZJeUSJLCs2dL8bjhIgDZolZGUgAAAOS28PTp8qxZo/R++yk2YoTEK4kAABkiduWVcvbZR541axSaM8d0DoAswUgKAACA3eJ9//2qlzDaxcVy2rQxXAQAwC9EIrLHj5ckhe66S6qoMBwEIBswkgIAAGDXJZOKlpbKcl0levVSYsAA00UAAPyX+B/+oHTr1vKUlyty7bWmcwBkgRofSffee2+1bNmyph8WAAAAGSB0xx3yff65nIIC2UVFUiRiOgkAgP/m86lyyhRJUvCvf5W1apXhIACZrsZH0lmzZmnZsmU1/bAAAAAwzPPddwrfeKMkKXbVVUr/7neGiwAA+HXJ009XqmNHWbGYIpMnm84BkOFqbCSNxWJav359TT0cAAAAMonrKjJqlCzbVqpjR8WvvFKyLNNVAAD8OstSZVmZJCnwzDPyfP214SAAmazaI+mzzz6rTp06qV69emrcuLECgYC6dOmimTNnatOmTTXRCAAAAMMCTz2lwCuvyPX7ZRcXy91rL9NJAAD8ptTRRyvRo4esdFqRceNM5wDIYNUaSZ955hmdffbZWrx4sRzHkdfrVSqV0qJFizRhwgQdcMABevrpp2soFQAAACZYGzcqMmGCJCl+wQVK9u5tuAgAgF1nT5sm17IUeO01ed9913QOgAxVrZH02muvleM46t+/v7755hvF43FVVlZq4cKFuvDCC7Vhwwb1799ff/rTn2qqFwAAAHUsXFYmz9q1SrdsKfvqqyWfz3QSAAC7LN2+vRJnny1JikycaLgGQKaq1kj66aefqk2bNnr44YfVtm1bWZalUCikE044Qffff7/eeecdNWnSREVFRfroo49qqhkAAAB1xPfuuwrdf78kyS4pkdu6teEiAAB2nz1hglyfT/5Fi+R78UXTOQAyULVG0kAgoM6dO8vr9e7w+507d9Yzzzwjx3F047YroQIAACBLJBKKlJRs/bR3byXOOcdwEAAAe8bZf3/FBw2SJEXLyiTHMVwEINNUayQ98MADtXr16p0e06VLF3Xv3l2vvvpqdZ4KAAAAdSx0++3yffWVnAYNZBcVSeGw6SQAAPaYPWqU3EhE3m++UeCvfzWdAyDDVGsk7devn9566y19+umnOz2uZcuWqqioqM5TAQAAoA55/vUvhW++WZIUGzJE6S5dDBcBAFA9bqNGskeMkCSFb7xRSiQMFwHIJNUaSc855xx17NhR/fr104oVK371uM8//1ytef8qAACA7OC6io4aJSsWU7JzZ8Uvv1yyLNNVAABUW2zIEDl77y3vDz8oNHeu6RwAGaRaI2m7du309ddf65tvvtFhhx2m6dOna9myZVXfTyaTmjBhgj755BMNHTq02rEAAACofYEnnpD/9dflBgKKFRfLbdDAdBIAADWjXj3ZY8ZI0taRdPNmw0EAMkW1RtImTZpo06ZNkqSKigpNnTpVBxxwgPbZZx+1bdtWBQUFmjlzpsaOHathw4bVSDAAAABqj/XTT4pMnChJil94oZK9ehkuAgCgZsUHDlS6RQt5fvpJ4ZkzTecAyBCW67pudR7ghx9+0AcffLDdx7p167Z/EstSmzZt1LVr16qPTp06KRKJVCvetIqKChUWFqp8zhwVcCEDADliQ//+phMAGBQpKlLooYeUbtVKFU89JbdlS9NJAADUuMCTT6reFVfIjUS08cMP5TZubDoJyFsVFRVq3bq1ysvLVVBQYKyj2iPpjnz33XdVg+n777+vRYsWaePGjVufcNv7WXm9XiWy/E2SGUkB5CJGUiB/+d56SwVnnCFJ2nzLLUpceKHhIgAAaonjqKBHD/k++0zxc8/VljvuMF0E5K1MGUl9tfGg+++/v/bff3/169ev6ralS5dWjabvv/++Fi9eXBtPDQAAgD0RjytaWrr109NPV4K/MAEA5DKPR5XTpqmgf38FnnpK9ujRctq0MV0FwKBaGUl35IADDtABBxyg8847T5JUCyewAgAAYA+Fbr1V3m++kbPXXooVFUmhkOkkAABqVeqEE5Q85hj533xTkQkTtPnhh00nATCoWhduqo6fX3YPAAAAszxLlyo8e7YkKTZsmNKdOhkuAgCgblROmyZJ8r/8srwffWS4BoBJxkZSAAAAZADXVXTkSFmJhJJduyp+6aWmiwAAqDPpjh0VP+MMWa6ryIQJpnMAGMRICgAAkMcCjzwi/5tvyg0GZRcXyy0sNJ0EAECdsidNkuv1yv/uu/L9v/9nOgeAIYykAAAAecpav16RyZMlSbGBA5U6+WTDRQAA1D2nbVvFL7xQkhSZOlXiGipAXmIkBQAAyFORyZPl2bBB6TZtFBs2TPJ6TScBAGCEPWaM3FBIvi++UOCxx0znADCAkRQAACAP+d54Q8FHHpFrWbJLSuS2aGE6CQAAY9wmTRQbOlSSFJ4xQ0qlDBcBqGuMpAAAAPkmFlN05EhJUuKMM5To189wEAAA5tnDh8spLJR3xQoF77rLdA6AOsZICgAAkGfCc+bI++23cvbeW3ZxsRQMmk4CAMC8ggLZo0dLksK33CJVVhoOAlCXGEkBAADyiOerrxSaM0eSZA8bJqdDB7NBAABkkPillyrdrJk869crPGuW6RwAdYiRFAAAIF84jqIjR8pKJpXs3l3xSy4xXQQAQGYJBmVPnChJCv3lL7I2bDAcBKCuMJICAADkicD8+fK//bbcUEh2UZFUWGg6CQCAjJPo31+pgw6StWWLwlOnms4BUEcYSQEAAPKA9eOPikyZIkmKXXKJUj17Gi4CACBDeb2yy8okScHHHpNn+XLDQQDqAiMpAABAHohMmiTPxo1KH3CAYkOHSh5+DQQA4Ncke/ZU8ne/k5VMKjJhgukcAHWA344BAABynO/VVxV8/HG5Ho/s0lK5zZubTgIAILNZliq3nU3qX7BAnk8+MRwEoLYxkgIAAOQy21Z09GhJUuKss5To08dsDwAAWSLdtasSp54qy3EUHT/edA6AWsZICgAAkMPCN98s77Jlcho12nqxpmDQdBIAAFmjcvJkuR6P/G+9Jd8//mE6B0AtYiQFAADIUd4vv1TottskSfbw4XIOO8xwEQAA2cVp107x886TtPX9veW6hosA1BZGUgAAgFzkOIqUlMhKpZQ86ijFL77YdBEAAFnJHjdObiAg36efKvDUU6ZzANQSRlIAAIAcFHzwQfnfe09uOCy7uFiqX990EgAAWclt1kyxwYMlSeHrr5fSacNFAGoDIykAAECOsdasUXjqVElSbNAgpU44wWwQAABZLlZcLKd+fXmXLVPw7rtN5wCoBYykAAAAOSYycaI8FRVKHXSQYkOHSh5+5QMAoDrcBg0UKymRJIX/+EcpFjNcBKCm8RszAABADvEvXKjgk0/K9Xhkl5TI3Xdf00kAAOSE2JVXymncWJ61a7cOpQByCiMpAABArqisVGT0aElS4uyzlezTx2wPAAC5JBxW5YQJkqTgXXfJKi83HASgJjGSAgAA5IjwTTfJ+913cho3ll1UJAUCppMAAMgpifPOU7ptW3k2bVJ4+nTTOQBqECMpAABADvB+9plCc+dKkuzhw+UceqjhIgAAcpDPp8opUyRJwfnzZf3wg+EgADWFkRQAACDbOY6iJSWy0mkljz1W8YsuMl0EAEDOSv7+90odcYSseFyRSZNM5wCoIYykAAAAWS54773yffih3EhEdnGxVL++6SQAAHKXZaly20vtA88+K8+XXxoOAlATGEkBAACymLVqVdV7osUuu0yp444zXAQAQO5LHXmkEieeKMtxFBk/3nQOgBrASAoAAJDFouPHy7Npk1KHHKLYkCGSh1/vAACoC/a0aXItS4HXX5f37bdN5wCoJn6LBgAAyFL+l15S4Nln5Xo8sktK5DZpYjoJAIC8kT70UCX695ckRSdNklzXcBGA6mAkBQAAyEabNytyzTWSpET//kqeeabhIAAA8o89YYJcv1++jz6S74UXTOcAqAZGUgAAgCwUvvFGeb//Xk7Tplsv1uT3m04CACDvOC1aKH7ppZKk6PTpkuMYLgKwpxhJAQAAsoz3448VuvNOSZI9YoScdu0MFwEAkL/skSPlRqPyLl2qwIMPms4BsIcYSQEAALJJOq1oSYmsdFqJHj0Uv+AC00UAAOQ1d++9ZY8YIUkK33STlEgYLgKwJxhJAQAAskjw7rvlW7xYbjSqWFGRVK+e6SQAAPJebMgQOY0aybtqlUK33mo6B8AeYCQFAADIEtbKlYpce60kKXbFFUode6zhIgAAIEmKRmWPHStJCt1xh1RRYTgIwO7KmpF07ty5atWqlUKhkLp166b33ntvp8c//vjjOvjggxUKhXTYYYfpxRdf/K9jvvjiC5155pkqLCxUNBpV165dtXz58tr6EQAAAKolOm6crC1blDr0UMWuukqyLNNJAABgm/iFFyq9//7ybNyo8MyZpnMA7KasGEkfffRRlZaWasqUKVq0aJE6dOigXr16ae3atTs8/q233tL555+vyy67TB999JH69OmjPn366NNPP6065ttvv9Uxxxyjgw8+WK+99po+/vhjTZo0SaFQqK5+LAAAgF3mf/FFBV54Qa7XK7u0VO4++5hOAgAAv+T3q3LKFElS6IEHZK1ZYzgIwO6wXNd1TUf8lm7duqlr1666/fbbJUmO46hFixYaPny4xm47nf2XBgwYoC1btuj555+vuq179+7q2LGj7tx2JdjzzjtPfr9fD1bjynMVFRUqLCxU+Zw5KgiH9/hxACCTbOjf33QCgP+0aZMKjzpK3h9+UOz881X5xz9Kfr/pKgAA8J9cVwUnnCDfJ58o3r+/ttx1l+kiIONVVFSodevWKi8vV0FBgbGOjD+TNJFI6MMPP1TPnj2rbvN4POrZs6fefvvtHd7n7bff3u54SerVq1fV8Y7j6IUXXtBBBx2kXr16qXHjxurWrZuefvrpnbbE43FVVFRs9wEAAFDbIjNmyPvDD0rvu+/WizUxkAIAkJksS5XTpkmSAk8/Lc/SpYaDAOyqjB9J161bp3Q6rSZNmmx3e5MmTbR69eod3mf16tU7PX7t2rXavHmzZs6cqVNPPVV///vf1bdvX5199tl6/fXXf7VlxowZKiwsrPpo0aJFNX86AACAnfN+9JGC8+ZJkmLFxXIOPNBwEQAA2JnU8ccreeyxslIpRcaPN50DYBdl/EhaGxzHkSSdddZZKikpUceOHTV27FidfvrpVS/H35Fx48apvLy86mPFihV1lQwAAPJRKqVoaaksx1HipJMUP/9800UAAGAXVJaVSZICCxfK+8EHhmsA7IqMH0kbNWokr9erNf/xhsdr1qxR06ZNd3ifpk2b7vT4Ro0ayefz6dBDD93umEMOOWSnV7cPBoMqKCjY7gMAAKC2BOfNk+/jj+XUr69YSYkUjZpOAgAAuyB9+OGK9+kjSYpMnGg2BsAuyfiRNBAIqHPnzlq4cGHVbY7jaOHChTryyCN3eJ8jjzxyu+Ml6eWXX646PhAIqGvXrvrqq6+2O+brr7/W/vvvX8M/AQAAwO7zfP+9ItdfL0mKXXGFUt27Gy4CAAC7w544Ua7XK//778v397+bzgHwGzJ+JJWk0tJSzZs3T/fff7+++OILDRkyRFu2bNGgQYMkSQMHDtS4ceOqji8qKtKCBQt0880368svv9TUqVP1wQcf6Oqrr646ZvTo0Xr00Uc1b948LV26VLfffruee+45DR06tM5/PgAAgO24riJjxsiqrFTqsMMUHzxYsizTVQAAYDc4rVsrfvHFkqTI1KmS65oNArBTWTGSDhgwQLNmzdLkyZPVsWNHLV68WAsWLKi6ONPy5cu1atWqquOPOuoozZ8/X3/+85/VoUMHPfHEE3r66afVvn37qmP69u2rO++8UzfeeKMOO+ww/eUvf9H//u//6phjjqnznw8AAOCX/M8/r8CCBXJ9PtnFxXIbNTKdBAAA9oA9erTccFi+r75S4OGHTecA2AnLdfmrjD1VUVGhwsJClc+Zo4Jw2HQOANSIDf37m04A8ppVUaHCI4+UZ/VqxS68UJU33yz5fKazAADAHgrPmKHwrFlK77efyj/4QPL7TScBGaWiokKtW7dWeXm50ev/ZMWZpAAAAPkifN118qxerXTz5ooVFTGQAgCQ5exhw+TstZe833+v4B13mM4B8CsYSQEAADKE94MPFLz7bkmSXVwsp00bw0UAAKDaCgpkjx4tSQrffrtUWWk4CMCOMJICAABkgmRS0dJSWa6rxMknK3HeeaaLAABADYlfconSzZvLs369wjfcYDoHwA4wkgIAAGSA0J13yvfZZ3IKCmQXFUmRiOkkAABQU4JB2ZMmSZJC994ra/16w0EA/hMjKQAAgGGe5curziqJDR6sdPfuhosAAEBNS/Trp9TBB8vaskXhKVNM5wD4D4ykAAAAJrmuItdcI8u2lerQQfErr5Qsy3QVAACoaR6P7LIySVLwiSfk+fe/zfYA2A4jKQAAgEGBZ55R4OWX5fr9sktK5DZsaDoJAADUkuSJJyrZvbusZFKRCRNM5wD4BUZSAAAAQ6zyckXGj5ckxf/wByV79zZcBAAAapVlqXLb2aT+l16Sd8kSw0EAfsZICgAAYEi4rEyeNWuUbtFC9vDhks9nOgkAANSydOfOSvz+97Jcl7NJgQzCSAoAAGCA7/33FbrvPkna+jL71q3NBgEAgDpTOXmyXI9H/rfflu/VV03nABAjKQAAQN1LJhUpKZEkJU49VYlzzzUcBPz/9u47PKoyYePwM72k0TuE0FGR0MEGKoqujRUUWRUEG4i0UJQuoGAXsCyra1tZ1y6uDZcPkSYioqCoBFCqEIqUAJnJZGbO98fIhJGEGnKSzO++rlySOe9MnhPPTCZP3nNeAEBxCjdsqNy//U2S5J0wQTIMkxMBoCQFAAAoZu5nn5X9558VTkmRb8gQyeMxOxIAAChmvvvvl+Fyyf7jj3K+957ZcYC4R0kKAABQjKwbNsjz2GOSJH///gq1bm1yIgAAYAajenX5+/WTJHkeekgKhUxOBMQ3SlIAAIDiYhhKGDFCFr9fwZYtlXvHHZLFYnYqAABgEv/gwQonJ8u2aZNcL7xgdhwgrlGSAgAAFBPnu+/KMX++DIdDvsGDZZQvb3YkAABgIiMlRf5hwyRJnmnTJJ/P3EBAHKMkBQAAKAaWvXvlHTtWkpR7663Ku/JKkxMBAICSwH/HHQpXqybrrl3yPPmk2XGAuEVJCgAAUAw8kybJumuXQqmp8t17r2SzmR0JAACUBG63csaMkSS5XnhBln37zM0DxClKUgAAgDPMvnSp3P/6lyTJN3SojNRUkxMBAICSJNCjh0ING8p64IA8kyaZHQeIS5SkAAAAZ1IgoISMjMg/r7pKgRtuMDkQAAAocWw25UyYIElyvfGGLFu3mhwIiD+UpAAAAGeQe8YM2dauVbhcOfkGD5bcbrMjAQCAEijviisUbNVKltxcJfxxHXMAxYeSFAAA4Ayx/vJLdAEG/4ABCrVqZXIiAABQYlksypk8WZLk+PhjWX/6yeRAQHyhJAUAADgTDEMJw4bJkpurvNatldu3r9mJAABACRds106Byy6TJRxWwujRZscB4golKQAAwBngfOstORYtkuF0yjd4sIxy5cyOBAAASgHfAw/IsFrlWLRItiVLzI4DxA1KUgAAgCJm+f13eceNkyTl9uqlYJcuJicCAAClRahJEwW6d5ckJYwbJxmGyYmA+EBJCgAAUMS8Dzwg6++/K5SWJt+AAZLNZnYkAABQivjGjJHhdMq+apUcH35odhwgLlCSAgAAFCH74sVyvf66DItFvqFDZdSpY3YkAABQyoRr1ZL/9tslSd7Jk6Vw2OREQNlHSQoAAFBUcnOVMGyYJClwzTUKdOtmciAAAFBa+TMyZCQmyvbrr3K9/LLZcYAyj5IUAACgiLinT5dt/XqFK1SQf/Bgye02OxIAACiljAoV5BsyRJLkefJJKTfX3EBAGUdJCgAAUASsa9fK89RTkiTfvfcqlJ5ubiAAAFDq+e++W+FKlWTNypJ7+nSz4wBlGiUpAADA6TIMJQwfLksgoLx27RS47TazEwEAgLLA65Vv9GhJknvmTCk72+RAQNlFSQoAAHCanK+/LseSJTLcbvkGD5aRkmJ2JAAAUEbk3nyzQnXryrp/v7xTppgdByizKEkBAABOg2X3bnknTJAk+Xv3VvCyy0xOBAAAyhS7XTl/vNdwvfaaLFlZJgcCyiZKUgAAgNPgHTdO1r17FapfX/577pGsvL0CAABFK++aaxRs3lwWv1/ecePMjgOUSbyLBwAAOEX2BQvkeustGRaLfBkZMmrVMjsSAAAoiywW5UyaJElyfvCBrOvWmRwIKHsoSQEAAE6Fz6eE4cMlSYHrrlPgr381ORAAACjLghdcoEDHjrKEQvKOGmV2HKDMoSQFAAA4BZ6nnpLt118VrlhRvsGDJZfL7EgAAKCM802cKElyzp8v2/LlJqcByhZKUgAAgJNkXbNG7hkzJEm+gQMVPvdckxMBAIB4EGrWTLnXXy9J8o4ZY3IaoGyhJAUAADgZ4bAShg2TJS9Peeedp9xevcxOBAAA4ohv7FgZdrscK1bI/umnZscBygxKUgAAgJPg+ve/5fjqKxlud+Q0+5QUsyMBAIA4Ek5NVe5tt0mSvJMmSeGwuYGAMoKSFAAA4ARZdu6UZ8IESZK/Tx8FL7nE5EQAACAe+YYPl+H1yr52rZyvv252HKBMoCQFAAA4Qd5x42Tdv1+hhg3lv+ceycpbKQAAUPyMypXlGzhQkuR59FEpL8/kREDpxzt7AACAE+D4/HO53nlHhtWqnIwMGTVqmB0JAADEMf899yhcoYJsv/0m1zPPmB0HKPUoSQEAAI4nJ0fe4cMlSYG//lV5XbuamwcAACAxUb6RIyVJnmeflQ4eNDkQULpRkgIAAByH5/HHZdu0SeHKleUbNEhyOs2OBAAAoNzevRWqVUvWvXvleeQRs+MApRolKQAAwDHYfvpJ7meflST5Bg1S+JxzTE4EAADwB6dTvvHjJUnuV16RZdcukwMBpRclKQAAQGHCYXkzMmQJBpV3wQXKvfVWsxMBAADECPz1rwqedZYsOTnyPPCA2XGAUouSFAAAoBCuV1+VY/lyGV6vfEOGSElJZkcCAACIZbUqZ9IkSZLr3Xdl3bDB5EBA6URJCgAAUABLVpY8f/zC4e/bV8GOHU1OBAAAULBgp07KO/98WfLy5B0zxuw4QKlESQoAAFAA75gxsmZnK9i4sfz9+0tW3jYBAIASymJRzsSJkiTH//4n23ffmRwIKH14tw8AAPAnjrlz5Zo9W4bVKl9Ghoxq1cyOBAAAcEyhFi2Ue/XVshgGs0mBU0BJCgAAcKRDh+QdMUKSFOjWTXnXXmtyIAAAgBPjGz9ehtUqx7Jlss+bZ3YcoFShJAUAADiC57HHZNuyReEqVSKLNTmdZkcCAAA4IeH69ZV7yy2SJO+ECZJhmJwIKD0oSQEAAP5g++EHuZ97TpLkGzxY4SZNTE4EAABwcnz33SfD7Zb955/lfPtts+MApQYlKQAAgCSFQkrIyJAlFFKgY8foLAwAAIDSxKhWLbLopCTP1KlSMGhyIqB0oCQFAACQ5HrpJdm//VZGQoL8Q4ZIiYlmRwIAADgl/kGDFE5JkW3zZrmef97sOECpQEkKAADinmXbNnkffFCS5L/jDgUvvNDkRAAAAKfOSE6Wb/hwSZJn+nTJ5zM5EVDyUZICAIC4lzB6tCwHDyrYtKn8/fpJFovZkQAAAE5Lbt++ClevLuvu3fI89pjZcYASj5IUAADENcecOXJ++KEMm02+jAwZVaqYHQkAAOD0ud3KGTs28s8XX5Rl716TAwElGyUpAACIXwcPyjtypCQp94YblHfNNSYHAgAAKDqBG25QqFEjWQ4elGfiRLPjACUaJSkAAIhb3ocflu233xSuXl3+QYMkh8PsSAAAAEXHZlPOH+Wo6803ZdmyxeRAQMlFSQoAAOKSbdUquf7xD0mSb9AghRs3NjkRAABA0cu77DLltWkjSyCghDFjzI4DlFiUpAAAIP4Eg0oYOlSWcFiBiy9W7s03m50IAADgzLBY5Js8WZLk+PRTWVevNjkQUDJRkgIAgLjjevFF2VetkpGYKP+QIVJCgtmRAAAAzphgmzYKdOkiSzishNGjzY4DlEiUpAAAIK5Yf/tN3ocekiT57rxTwfPPNzkRAADAmZczYYIMq1WOJUtkX7TI7DhAiUNJCgAA4or3vvtkOXRIwXPOUe7dd0sWi9mRAAAAzrhw48bKvekmSZJ33DjJMExOBJQslKQAACBuOD76SM5PP5Vht8s3dKiMypXNjgQAAFBsfPffL8PplP2HH+T44AOz4wAlCiUpAACID9nZSrj/fklSbo8eyrvqKpMDAQAAFC+jZk3577xTkuR98EEpFDI5EVByUJICAIC44J06Vdbt2xWqWVP+QYMkh8PsSAAAAMXOP3SowklJsm3YINdLL5kdBygxKEkBAECZZ/v2W7leeEGS5Bs8WOEGDUxOBAAAYA6jfHn5hw6VJHmefFLy+01OBJQMlKQAAKBsCwaVkJEhi2Eo0LmzAj17mp0IAADAVP677lK4ShVZd+6Ue9o0s+MAJQIlKQAAKNPc//iH7D/8oHBSknxDhkher9mRAAAAzOXxyDd6tKTIeyXL/v0mBwLMR0kKAADKLOuWLfI8/LAkyX/33Qq1b29yIgAAgJIht2dPherVkzU7W54HHzQ7DmA6SlIAAFA2GYa8I0fKkpOj4LnnKvfuuyWLxexUAAAAJYPdrpwJEyRJrtdfl2XbNpMDAeaiJAUAAGWS48MP5fzf/2TY7fINHSqjQgWzIwEAAJQoeVddpWCLFrL4/fKOG2d2HMBUlKQAAKDMsWRnK+H++yVFTiXL+8tfTE4EAABQAlksypk4UZLk/O9/Zc3MNDkQYB5KUgAAUOZ4Jk+WdccOhWrVkn/QIMluNzsSAABAiRQ8/3wFLr5YlnBY3j8WcwLiESUpAAAoU2zLl8v18suSJN+QIQrXq2dyIgAAgJLNN3GiDItFzi++kO2rr8yOA5iCkhQAAJQdeXlKyMiQxTAU6NJFgR49zE4EAABQ4oXOPluBbt0kSQlcmxRxipIUAACUGe6//132n35SODlZvsGDJa/X7EgAAAClgm/MGBkOh+zffiv7xx+bHQcodpSkAACgTLBu2iTPo49Kkvz9+yvUtq3JiQAAAEqPcJ06yu3TR5KUMGmSFA6bnAgoXpSkAACg9DMMeYcPl8XnUzA9Xbl33ilZLGanAgAAKFV8w4fL8HplW79ezlmzzI4DFCtKUgAAUOo5339fzs8/l+FwyDdkiIzy5c2OBAAAUOoYFSvKN2iQJEXO0AkETE4EFJ9SVZI+++yzqlu3rtxut9q1a6evv/76mOPffvttNWnSRG63W82aNdMnn3xS6Nh+/frJYrFo2rRpRZwaAACcSZZ9++QdM0aSlHvzzcq78kqTEwEAAJRe/nvuUbhiRdm2b5f7mWfMjgMUm1JTkr755pvKyMjQhAkT9O2336p58+bq0qWLdu7cWeD4L7/8Uj179tTtt9+u7777Tl27dlXXrl21evXqo8a+//77+uqrr1SjRo0zvRsAAKCIeSZNknXnToXq1JFv4EDJbjc7EgAAQOmVkCDf/fdLktzPPisdPGhyIKB4lJqS9Mknn9Sdd96pPn366KyzztLMmTPl9Xr10ksvFTh++vTpuuKKKzRixAg1bdpUkydPVsuWLfXMn/4K8ttvv2ngwIH697//LYfDURy7AgAAioh92TK5X31VkuQbOlRG3brmBgIAACgDcm+9VaE6dWTdt0+eKVPMjgMUi1JRkgYCAa1YsUKdO3eO3ma1WtW5c2ctXbq0wPssXbo0ZrwkdenSJWZ8OBzWrbfeqhEjRujss88+bo7c3FxlZ2fHfAAAAJMEAvIOHRr555VXKnDDDSYHAgAAKCMcDvnGj5ckuf/1L1kKOYsXKEtKRUm6e/duhUIhVa1aNeb2qlWrKisrq8D7ZGVlHXf8I488IrvdrkF/XJT4eKZOnaqUlJToR+3atU9yTwAAQFFxP/OM7JmZCpcrJ9/gwZLHY3YkAACAMiNw3XUKnnOOLD6fvH8UpkBZVipK0jNhxYoVmj59ul555RVZLJYTus+oUaO0f//+6MeWLVvOcEoAAFAQ66+/yvPEE5Ikf//+CrVubXIiAACAMsZqVc7EiZIk5/vvy/rLLyYHAs6sUlGSVqpUSTabTTt27Ii5fceOHapWrVqB96lWrdoxxy9atEg7d+5UnTp1ZLfbZbfbtWnTJg0bNkx1C7memcvlUnJycswHAAAoZoahhOHDZfH7ldeqlXLvuEM6wT94AgAA4MQFO3VS3oUXyhIMyjt6tNlxgDOqVJSkTqdTrVq10rx586K3hcNhzZs3Tx06dCjwPh06dIgZL0lz586Njr/11lv1/fffa+XKldGPGjVqaMSIEfrss8/O3M4AAIDT4nznHTkWLJDhdMo/ZIiMcuXMjgQAAFBmHZ5N6pg3T7YVK0xOA5w5drMDnKiMjAz17t1brVu3Vtu2bTVt2jQdOnRIffr0kST16tVLNWvW1NSpUyVJgwcPVseOHfXEE0/oqquu0htvvKFvvvlGzz//vCSpYsWKqlixYszXcDgcqlatmho3bly8OwcAAE6IZe9eeceOlRRZdTWvSxeTEwEAAJRtoebNlXvddXJ98IG8Y8fqwKefmh0JOCNKxUxSSerRo4cef/xxjR8/Xunp6Vq5cqXmzJkTXZxp8+bN2r59e3T8eeedp9dff13PP/+8mjdvrnfeeUezZ8/WOeecY9YuAACA0+R54AFZd+9WqG5d+e69V7LZzI4EAABQ5vnGjpVhs8nx9deyz51rdhzgjLAYhmGYHaK0ys7OVkpKivZPm6ZkVtQFUEbs6d7d7AhAgexffqnka66RJB2cPl2BW24xOREAAED88A4bJvcrryjYpImyFy/mmvAoMtnZ2UpLS9P+/ftNXf+n1MwkBQAAcSw3VwkZGZF/Xn21ApT5AAAAxco3cqQMj0f2NWvkfOMNs+MARY6SFAAAlHjuGTNkW7dO4fLl5R88WHK7zY4EAAAQV4yqVeW/5x5JkueRR6Rg0OREQNGiJAUAACWadf16eZ58UpLkHzBAoZYtTU4EAAAQn3z33qtwuXKybdki18yZZscBihQlKQAAKLkMQwnDhskSCCivTRvl9u1rdiIAAID4lZws34gRkiTPjBlSTo7JgYCiQ0kKAABKLOcbb8ixeLEMl0u+IUNkpKSYHQkAACCu5fbpo1CNGrL+/rs8jz5qdhygyFCSAgCAEsny++/yjh8vSfL36qXgZZeZnAgAAAByueQbN06S5H7pJVn27DE5EFA0KEkBAECJ5B0/XtY9exSqV0/+AQMkm83sSAAAAJAU6N5dwcaNZTl0SJ4HHjA7DlAkKEkBAECJY1+0SK433pBhscg3dKiM2rXNjgQAAIDDrFb5Jk2SJLneekvWTZtMDgScPkpSAABQsvj9Shg2TJIUuOYaBbp1MzkQAAAA/izv0kuV166dLHl58o4ZY3Yc4LRRkgIAgBLFM22abL/8onDFivINGSK5XGZHAgAAwJ9ZLMr5Yzap47PPZPv+e5MDAaeHkhQAAJQY1sxMuadNkyT5BgxQuHlzcwMBAACgUKHWrRW48kpZwmFmk6LUoyQFAAAlQzishGHDZMnLU1779sq97TazEwEAAOA4csaPl2G1yvHll7J/8YXZcYBTRkkKAABKBOfrr8uxdKkMt1u+wYOllBSzIwEAAOA4wo0aKbdnT0mSd8IEyTBMTgScGkpSAABgOsuuXZE31ZL8t92mYOfOJicCAADAifLdf78Mp1P21avlfP99s+MAp4SSFAAAmM47bpys+/Yp1KCB/PfcI1l5iwIAAFBaGDVqyN+vnyTJ89BDUihkciLg5PEbCAAAMJV9/ny53n5bhtUqX0aGjJo1zY4EAACAk+QfMkTh5GTZNm6U65//NDsOcNIoSQEAgHl8PiWMGCFJClx3nQJdu5qbBwAAAKfESEmRPyNDkuSZNk3y+80NBJwkSlIAAGAazxNPyLZhg8KVKsk3ZIjkcpkdCQAAAKfIf+edCletKuvOnfI8+aTZcYCTQkkKAABMYVuzRu6nn5Yk+QYOVPicc0xOBAAAgNPiditnzBhJkuv552XZt8/cPMBJoCQFAADFLxyWd+hQWYJB5Z13nnJ79zY7EQAAAIpAoEcPherXl/XAAXkmTzY7DnDCKEkBAECxc732mhxffy3D45Fv6FApKcnsSAAAACgKdrtyHnhAkuT6z39k+e03c/MAJ4iSFAAAFCvLjh3y/PHG2d+nj4KdOpmaBwAAAEUr78orFWzVSpbcXHnHjTM7DnBCKEkBAECx8o4dK2t2toKNGsl/zz2SlbcjAAAAZYrFopxJkyRJzg8/lPWnn0wOBBwfv5UAAIBi45g3T6733pNhtco3dKiM6tXNjgQAAIAzINi+vQKXXipLOKyEPxZzAkoySlIAAFA8cnLkHT5ckhS4/nrlde1qbh4AAACcUb6JE2VYLHIsXCjbl1+aHQc4JkpSAABQLDyPPSbb5s0KV6ki3+DBktNpdiQAAACcQaGmTRW44QZJUsLYsZJhmJwIKBwlKQAAOONsP/4o97PPSpJ8gwYpfNZZJicCAABAcfCNGSPD6ZR91So5PvrI7DhAoShJAQDAmRUOK2HoUFlCIeVdeKFyb7nF7EQAAAAoJuFateTv21eS5J08WQqHTU4EFIySFAAAnFGul1+WfcUKGV6vfEOGSElJZkcCAABAMfIPGyYjIUG2X36R81//MjsOUCBKUgAAcMZYtm+XZ/JkSZL/9tsVvOgikxMBAACguBkVKkT+WC7J+/jjUiBgbiCgAJSkAADgjEkYPVrWAwcUbNpU/v79JStvPQAAAOKR/+67Fa5USdbt2+WePt3sOMBR+E0FAACcEY7PPpPzv/+VYbXKN3SojKpVzY4EAAAAsyQkyDdqlCTJPXOmlJ1tciAgFiUpAAAoegcPyjtypCQp0L278q691uRAAAAAMFvuzTcrlJoq67598k6ZYnYcIAYlKQAAKHKeRx+VbetWhatVi1x/yuEwOxIAAADM5nAo54EHJEmuWbNkycoyNw9wBEpSAABQpGzffx85hUqSb9AghRs3NjkRAAAASoq8a65R8NxzZfH55B0/3uw4QBQlKQAAKDqhkBKGDpUlFFKgUyfl3nyz2YkAAABQklgsypk0SZLk/OADWdetMzkQEEFJCgAAiozrxRdlX7lSRkKC/EOGSImJZkcCAABACRO88EIFLrpIlmBQ3tGjzY4DSKIkBQAARcTy22/yPvigJMl/550KXnCByYkAAABQUvkmTpQkOT//XLZvvjE5DUBJCgAAikjCqFGyHDqk4Flnyd+vn2SxmB0JAAAAJVTo3HOV+9e/SpK8Y8aYnAagJAUAAEXA8ckncn78sQybTb6MDBmVK5sdCQAAACWcb+xYGXa7HN98I/tnn5kdB3GOkhQAAJyeAwfkve8+SVLujTcq7+qrTQ4EAACA0iBct65ye/eWJHknTpQMw+REiGeUpAAA4LR4p06Vbds2hapXl3/wYMnhMDsSAAAASgnfiBEyPB7ZMzPlfP11s+MgjlGSAgCAU2b77ju5XnhBkuQfMkThhg1NTgQAAIDSxKhcWf5775UkeR55RMrLMzkR4hUlKQAAODXBoBIyMmQJhxW49FLl9uxpdiIAAACUQr4BAxQuX162336T67nnzI6DOEVJCgAATonrhRdk//57hZOS5B86VEpIMDsSAAAASqOkJPlGjpQkeZ55Rjp0yORAiEeUpAAA4KRZt26Vd8oUSZL/zjsVbN/e5EQAAAAozXJvu02hmjVl3bMncto9UMwoSQEAwMkxDHnvu0+WnBwFmzVT7t13SxaL2akAAABQmjmd8o0fL0lyv/KKLLt3mxwI8YaSFAAAnBTHRx/JOWeODLtdvqFDZVSqZHYkAAAAlAGB669XsGlTWQ4dkueBB8yOgzhDSQoAAE6YJTtbCfffL0nKvekm5V11lcmJAAAAUGZYrfJNmiRJcr3zjqwbNpgcCPGEkhQAAJwwz0MPyZqVpVDNmvIPHizZ7WZHAgAAQBmSd/HFyuvQQZa8PHnHjjU7DuIIJSkAADghtm++kevFFyVJviFDFK5Xz+REAAAAKHMsFuX8MZvU8dlnsq1caW4exA1KUgAAcHx5eUrIyJDFMBS47DIFbrrJ7EQAAAAoo0ItWypw1VWyGIa8Y8aYHQdxgpIUAAAcl3vmTNl//FHh5GT5Bg+WvF6zIwEAAKAMyxk/XobVKsdXX8n++edmx0EcoCQFAADHZN28WZ5HHpEk+e++W6H27U1OBAAAgLIu3KCBcm+5RZLknTBBMgyTE6GsoyQFAACFMwx5R46UxedTsHlz5d51l2SxmJ0KAAAAccA3cqQMl0v2n36S8513zI6DMo6SFAAAFMr5wQdyzp0rw+GQb+hQGRUqmB0JAAAAccKoXl3+/v0lSZ4pU6Rg0OREKMsoSQEAQIEs+/fLO3q0JCn3b39T3pVXmpwIAAAA8cY/eLDCycmybd4s1wsvmB0HZRglKQAAKJBn0iRZd+xQqHZt+QYOlOx2syMBAAAgzhjJyfIPHy5J8kybJvl85gZCmUVJCgAAjmJfvlzuV16RpMhp9mlp5gYCAABA3PLffrvC1arJunu3PE88YXYclFGUpAAAIFZenrxDh0qSAldcocCNN5ocCAAAAHHN7VbO2LGSJNc//ynL3r0mB0JZREkKAABiuJ99Vvaff1Y4JUW+IUMkj8fsSAAAAIhzgRtvVKhhQ1kPHJBn0iSz46AMoiQFAABR1g0b5HnsMUmSv39/hVq3NjkRAAAAIMlmU84DD0iSXG+8IcvWrebmQZlDSQoAACIMQwkjRsji9yvYsqVy77hDsljMTgUAAABIkvK6dFFe69ayBAJKGDPG7DgoYyhJAQCAJMn57rtyzJ8vw+GQb/BgGeXLmx0JAAAAyGexyDd5siTJ8cknsv74o8mBUJZQkgIAAFn27pX3j4vh5956q/KuvNLkRAAAAMDRgm3bKnD55bKEw0oYPdrsOChDKEkBAIA8kybJumuXQqmp8t17r2SzmR0JAAAAKFDOhAkyrFY5Fi+WbfFis+OgjKAkBQAgztmXLpX7X/+SJPmGDpWRmmpyIgAAAKBw4SZNFOjRQ5KUMG6cZBgmJ0JZQEkKAEA8CwSUkJER+edVVylwww0mBwIAAACOzzdqlAynU/bvv5fjv/81Ow7KAEpSAADimHvGDNnWrlW4XDn5Bg+W3G6zIwEAAADHFa5ZU/477pAkeSdPlkIhkxOhtKMkBQAgTll/+UWeJ5+UJPkHDFCoVSuTEwEAAAAnzp+RISMxUbYNG+R6+WWz46CUoyQFACAeGYYShg2TJTdXea1bK7dvX7MTAQAAACfFKF9evqFDJSnyx//cXJMToTSjJAUAIM5YfvtNnsmT5Vi0SIbTKd/gwTLKlTM7FgAAAHDS/HfdpXDlyrLu2CH3tGlmx0EpRkkKAEAccc6apXLp6fJMny5JCrZrp2CXLianAgAAAE6R1yvf6NGSJPc//iFlZ5scCKUVJSkAAPHAMGRbskQJQ4bIEg5Hb7YvWSJLVpaJwQAAAIDTk/u3vymUlibr/v3yPvig2XFQSlGSAgBQRlm3bpXz9deV0L+/yjVrppRrr5XFMGLGWMJh2TZsMCkhAAAAUATsduVMmCBJcv3737Js325yIJRGdrMDAACAomHZtUuOxYtlX7RIjoULjyo/DbtdCgZlOfI2m02htLTiDQoAAAAUsbyrr1YwPV32lSvlHT9eh154wexIKGUoSQEAKKUs2dmyL1kix6JFsi9cKPvPP8dsN6xWhRo3VrBFCwVbtFBex45yLF6shBEjZAmFZNhsOvTkkzJq1jRpDwAAAIAiYrEoZ9IkJV97rZwffCDfiBEKN2pkdiqUIpSkAACUFjk5si9bJseiRXIsWiTbypUx1xeVpFD9+gqmp0dL0XCDBpLTGd0eqF9feZ07y7Zhg0JpaRSkAAAAKDOC55+vQKdOcn7xhbyjRungu++aHQmlCCUpAAAlVSAg+7ffRk6fX7RI9uXLZQkEYoaEatZUsGXLyGzRCy9UqGlTyeM55sMaNWsqSDkKAACAMsg3caIcfxSltmXLFGrXzuxIKCUoSQEAKClCIdlWr5Zj4cJIMbp0qSw5OTFDwpUr558+f955CjVvLiUlmRQYAAAAKFlC55yjwPXXy/Xuu/KOHasDc+eaHQmlBCUpAABmMQxZ166VY+HCyEzRJUtk3bcvZkg4JUXBFi0USk9XXvv2CrVpI6NcOVPiAgAAAKWBb8wYOT/4QI5vv5X9448VvOoqsyOhFKAkBQCgGFk3b46uPu9YtEjWHTtithter4Lnnhs5hb5NGwU7dJBRqZJksRTyiAAAAACOFE5NVW6fPnK/8IISJk/W/iuvlKxWs2OhhCtVR8izzz6runXryu12q127dvr666+POf7tt99WkyZN5Ha71axZM33yySfRbXl5ebrvvvvUrFkzJSQkqEaNGurVq5e2bdt2pncDABBHLDt2yPnuu/IOHqyUli1VrkULJQ4aJNc778i6Y4cMp1PBli3l79tXB599VvuWLdOBDz+Ub/Jk5V17rYzKlSlIAQAAgJPkGz5chtcr27p1cv7732bHQSlQamaSvvnmm8rIyNDMmTPVrl07TZs2TV26dFFmZqaqVKly1Pgvv/xSPXv21NSpU3X11Vfr9ddfV9euXfXtt9/qnHPOUU5Ojr799luNGzdOzZs31969ezV48GBde+21+uabb0zYQwBAWWDZt0/2JUui1xW1Z2bGbDdsNoWaNMm/rmjHjjJq15bspeZHMgAAAFDiGZUqyTdokLwPPyzPo48qcNNNksNhdiyUYBbDMAyzQ5yIdu3aqU2bNnrmmWckSeFwWLVr19bAgQN1//33HzW+R48eOnTokD766KPobe3bt1d6erpmzpxZ4NdYvny52rZtq02bNqlOnTrHzZSdna2UlBTtnzZNycdZSRgASos93bubHaF0OXRIjmXLZF+4UI6FC2X7/ntZjvjRalgsCtevH1OKhuvVk5xOE0MDAAAAceDgQZVr2VLW339Xztix8g8danYiFCA7O1tpaWnav3+/kpOTTctRKqatBAIBrVixQqNGjYreZrVa1blzZy1durTA+yxdulQZGRkxt3Xp0kWzZ88u9Ovs379fFotF5QpZECM3N1e5ubnRz7Ozs098JwAAZUNuruwrVkQWWlq4UPYVK2TJy4sZEqpTR8H09EgxetFFCjVpIrndJgUGAAAA4lRionwjRyrhvvvkfvZZ+e+8U0pMNDsVSqhSUZLu3r1boVBIVatWjbm9atWqWrNmTYH3ycrKKnB8VlZWgeP9fr/uu+8+9ezZs9DWeurUqZo4ceIp7AEAoNQKhWRbtUqORYsixehXX8ni88UMCVepElloKT1deeefr9C55/LmCwAAACgBcnv1kvuZZ2TbskWehx+W78EHzY6EEqpUlKRnWl5enm688UYZhqG///3vhY4bNWpUzOzU7Oxs1a5duzgiAgCKi2HItmZN5PT5RYtkX7JE1j+dORAuVy56+nywXTuF2rSRkZJiUmAAAAAAhXI65Rs/Xol33in3q6/KP2iQjALWtgFKRUlaqVIl2Ww27dixI+b2HTt2qFq1agXep1q1aic0/nBBumnTJn3++efHvPaBy+WSy+U6xb0AAJRIhiHrxo2RQvSP2aLWXbtihyQkKNi8eaQUbdtWwQ4dZFSowKrzAAAAQCkQ6NpVwWnTZP/xR3knTNChY0yQQ/wqFSWp0+lUq1atNG/ePHXt2lVSZOGmefPm6d577y3wPh06dNC8efM0ZMiQ6G1z585Vhw4dop8fLkjXrVun+fPnq2LFimdyNwAAJYRl+/b80+cXLZJty5aY7YbLpWCzZgqmpyvUqpXyLroo8tdmq9WkxAAAAABOmdWqnIkTldy9u5zvvy/fiBGRxVSBI5SKklSSMjIy1Lt3b7Vu3Vpt27bVtGnTdOjQIfXp00eS1KtXL9WsWVNTp06VJA0ePFgdO3bUE088oauuukpvvPGGvvnmGz3//POSIgVp9+7d9e233+qjjz5SKBSKXq+0QoUKcrLqMACUGZa9e6OzRB2LFsm2bl3MdsNmU6hp08h1Rf9Ygd6oVUuy2UxKDAAAAKAoBS++WHkXXCDH4sXyjhmjg//5j9mRUMKUmpK0R48e2rVrl8aPH6+srCylp6drzpw50cWZNm/eLOsRM3zOO+88vf766xo7dqxGjx6thg0bavbs2TrnnHMkSb/99pv++9//SpLS09Njvtb8+fPVqVOnYtkvAMAZcPCgHF99Fb2uqO2HH2QxjOhmw2JRqGFDhf64rmhex44Kp6VJDoeJoQEAAACcSTkTJyrl0kvlmDtXtu++U6hFC7MjoQSxGMYRvzXipGRnZyslJUX7p01TssdjdhwAKBJ7unc3O8LJ8/tl/+YbORYulH3RItm//VaWYDBmSCg1NboCfbBjR4UaNpTcbpMCAwAAADBDwm23yfXhh8pr104HPvnE7DhQpF9LS0vT/v37j7lW0JlWamaSAgAQFQzKtnJl5PT5hQtl//prWfz+mCHh6tUjhWjLlso7/3yFzjlHSkgwKTAAAACAksA3bpycn3wix7Jlsv/f/ynYubPZkVBCUJICAEq+cFi2n3+OXlfUvmSJrAcOxA6pUCGy+nx6uoIdOijYqpVk4l8hAQAAAJQ84fr1lXvLLXK/+qq8Dzyg7EsvlSwWs2OhBKAkBQCUPIYh66+/RlefdyxeLOvu3bFDEhMjhWiLFgq2a6dgu3YyKlQwKTAAAACA0sJ3331yvfmm7D//LOdbbynQo4fZkVACUJICAEoEy2+/RVefty9aJNtvv8VsN9xuBZs1i5SirVsreMEFMqpU4a++AAAAAE6KUbWq/PfcI8+TT8ozdaoC3bpJdiqyeMcRAAAwhWX3btmXLJHj8Ar0v/wSs91wOBQ666zobNG8jh1l1Kwp2WwmJQYAAABQVvgGDpTrxRdl27JFrn/8Q7kDBpgdCSajJAUAFI/sbDmWLo3OFLWvXh2z2bBaFWrUSMEWLRRKT1dep04Kp6ZKDodJgQEAAACUWcnJ8o0YoYSxY+WZPl25ffpIXq/ZqWAiSlIAwJnh88n+9dfRU+ht330nSygUMySUlhY5fb5FCwU7dlSoQQPJ5TIpMAAAAIB4ktu3r9zPPSfbtm3yPP64fOPHmx0JJqIkBQAUjbw82b/7TvY/Tp+3L18uS25uzJBQjRqRmaItWijvggsUOvts/loLAAAAwBwul3xjxyrxnnvk/uc/5b/3XhaDjWOUpACAUxMOy/bjj3IsXBhZgf7LL2U5dCh2SMWKCrZsqWB6uvI6dFCoZUspKcmkwAAAAAAQK9C9u4LTpsm+dq08DzygnBkzzI4Ek1CSAgBOjGHIun595PT5hQtlX7xY1r17Y4aEk5IUSk+PFKPt2inYtq2M8uVNCgwAAAAAx2GzyTdpkpJuukmut96Sf/hwhevUMTsVTEBJCgAolHXr1sjp83+cQm/NyorZbng8Cp57buSaoq1bK3jBBTIqVZIsFpMSAwAAAMDJyevcWXlt28rx9dfyjhmjg6+9ZnYkmICSFACQLztbzvffj15X1LZhQ8xmw+FQ6Oyzo4st5XXsKKNGDclqNSkwAAAAAJwmi0U5kyYp5Yor5JgzR9YfflC4WTOzU6GYUZICQDzLyZHWrZPWrIl8bNumxCM2G1arQo0bR0rRli2Vd9FFMlJTJTs/PgAAAACUHaE2bRS44go558xRwujROvDhh2ZHQjHjt1wAiCeBgLR+vZSZGSlFN22SDCNmSKh+fQX/uK5oXseOCtevLzmdJgUGAAAAgOKRM368HP/7nxxffin7woUKXnSR2ZFQjChJAaAsCwaljRsjhWhmpvTrr5HbjlShgpSaKtWtKzVpov0DBkgejxlpAQAAAMA04caNlXvTTXK//rq848Yp+4svWG8hjlCSAkBZEg5LW7bkl6Lr10u5ubFjkpIihWjdulLDhpGC1O3O305BCgAAACBO+UaNkuudd2RfvVrO999X4PrrzY6EYkJJCgClmWFI27fnnz6/dm3kOqNH8ngihWhqqtSggVSvnpSQYEpcAAAAACjJjBo15L/7bnmeflqeKVMUuO46yWYzOxaKASUpAJQ2u3fnzxRds0bKzo7d7nJJdepEStF69SKzRZOSOE0EAAAAAE6Af8gQuV55RbYNG+R68UXl3nWX2ZFQDChJAaCk278/vxDNzIyUpEey26VatfJPoW/SREpJkaxWM9ICAAAAQKlmlCsn/9Ch8k6aJM9TTym3V6/YS5ShTKIkBYCS5tAhad066eefI6Xo9u2x261WqUaNmMWWVLEip4AAAAAAQBHx33WX3DNnyrpzpzxPPSXfqFFmR8IZRkkKAGbLzY0ssLRmTeRjy5bItUaPVLVq/kzRpk2lypUjM0gBAAAAAEXP41HOmDFKHDxYrn/8Q/577pGRkmJ2KpxB/IYNAMUtL0/asCH/9PkNG6RQKHZMxYr5iy01aRKZOepwmBIXAAAAAOJR4KabFJoxQ7ZffpHnwQeV89hjZkfCGURJCgBnWjgsbd6cP1N0/fpIUXqk5OT8maKNGkUWXnK5zEgLAAAAAJAku105EyYoqVcvuf79b/mGDpVRo4bZqXCGUJICQFEzDGnbtvzFltaulXy+2DFeb34p2qCBlJYWuQ0AAAAAUGLk/eUvCrZoIft338k7bpwOvfii2ZFwhlCSAsDpMozIivOHT59fs0Y6cCB2jMsVOXU+NVWqX19q2FBKSJAsFnMyAwAAAACOz2JRzqRJSr7mGjn/+1/51qxRuEkTs1PhDKAkBYBTsW9fbCm6Z0/sdrtdql07f7ZokyaRU+qtVhPCAgAAAABOVfC88xS45BI5P/9c3tGjdfC998yOhDOAkhQATsTBg5HT5g8Xo1lZsdutVqlmzchM0cMr0FeoQCkKAAAAAGWAb+JEOebPl3PBAtmWLlWoQwezI6GIUZICQEH8fmnduvyZolu3Rk6rP1L16rGlaOXKks1mSlwAAAAAwJkTOussBbp3l+vtt5Uwbpyy587l8mllDCUpAEiR1eZ//TV/puiGDZFV6Y9UqVKkEE1NjZSi1apJDocpcQEAAAAAxcs3Zoycs2fL/t13sn/8sYJXX212JBQhSlIA8SkUkjZvln7+OVKK/vJLpCg9Urly+aVoo0aRa4y6XGakBQAAAACYLFy7tnL79pX7H/9QwuTJ2v+Xv3CJtTKEkhRAfAiHpW3bIjNF16yJnErv98eOSUjIX2ipYcPIfz0eE8ICAAAAAEoi37Bhcs2aJdv69XK+9poCvXubHQlFhJIUQNlkGNLOnfnXFM3MjCy+dCS3W6pTJ1KG1q8vNWggJSaaEhcAAAAAUPIZFSvKN2iQvFOnyvPYYwr07Ck5nWbHQhGgJAVQduzdmz9TNDMz8vmRHI7IKfN160ppaVLjxlJyMhfbBgAAAACcMH///nK/8IJs27fLPWOG/MOHmx0JRYCSFEDpdeCAtHZtfjG6c2fsdptNqlkzdrGl8uW5ZgwAAAAA4NQlJMh3//1KGD5c7r//Xf677opMwEGpRkkKoPTw+SLXEj08U3Tr1tjtFotUvXqkEK1bN1KKVqoUKUsBAAAAACgiubfcIvfTT8u2aZM8Dz8s35QpZkfCaaIkBVByBQKRVecPX1d006bIAkxHqlw5dqZo1aqR0+oBAAAAADhTHA7lTJigpL595f7Xv+QfPFhG1apmp8JpoCQFUHKEQtLGjfkzRX/5RQoGY8eUL58/U7RxY6lWLS6SDQAAAAAodnnXXqtgs2ay//CDvOPH69A//mF2JJwGSlIA5gmHI6fMH54pum6dlJsbOyYxMVKI1q0rNWwY+a/bbUJYAAAAAACOYLEoZ+JEJV9/vZyzZ8s3YoTCDRqYnQqniJIUQPExDGnHjvyZopmZ0qFDsWM8nshM0dRUqUEDqV69SFEKAAAAAEAJE+zYUXkXXijHokXyjh6tg2+9ZXYknCJKUgBn1p490s8/55ei+/bFbnc6pTp1IqVoWlrkFPqkpMgiTAAAAAAAlHA5kyYp5eKL5Zw3T7ZvvlGodWuzI+EUUJICKFrZ2fmnz2dmSrt2xW632SLXET18Cn2TJlK5cpLVakJYAAAAAABOT+jcc5Xbtatcs2fLO3asDsyZY3YknAJKUgCnJycnci3RNWsiH9u2xW63WKQaNfIXW2raVKpYMVKWAgAAAABQBvjGjpXzww/lWL5c9v/9T8HLLzc7Ek4SJSmAkxMISOvX588W3bQpcq3RI1WpEjtTtGpVyc7LDQAAAACgbAqnpSm3Vy+5X35Z3gceUPZll3EZuVKG1gLAsQWD0saN+TNFf/1VCoVix1SokD9TtEmTyMxRp9OMtAAAAAAAmMI3cqRcb7whe2amnP/5jwJ/+5vZkXASKEkBxAqHpS1b8q8pun69lJsbOyYpKX+maKNGkYWX3G4z0gIAAAAAUCIYVarIP2CAPI8/Ls8jjyhwww2Sw2F2LJwgSlIg3hmGtH17/unza9dGrjN6JI8nUoimpkoNG0ZWoU9IMCUuAAAAAAAllW/AALlefFG2rVvl+vvflTtokNmRcIIoSYF4tHt3/kzRNWsiK9IfyeWKzA5NTZXq1YsUo0lJXE8FAAAAAIBjSU6Wb8QIJYweLc8zzyj3jjskr9fsVDgBlKRAPNi/P78QzcyMlKRHstulWrViF1tKSZGsVjPSAgAAAABQauXedpvczz4r22+/yfPII/JNnGh2JJwASlKgLDp0KHLa/OFSdPv22O1Wa2RxpcOLLTVtKlWsSCkKAAAAAMDpcrnkGzdOif36yf3yy/IPGiSjYkWzU+E4KEmBsiA3V1q3Ln+26JYtkWuNHqlatdhStHLlyAxSAAAAAABQpALduik4bZrsa9bIM2GCcp55xuxIOA4aEqA0ysuTNmzInym6YYMUCsWOqVgxf7GlJk0iM0dZVQ8AAAAAgDPPapVv0iQl3XijXO+8I//w4QrXrWt2KhwDJSlQGoTD0ubNkVJ0zRpp/fpIUXqklJT8UrRRo8jCSy6XKXEBAAAAAIh3eZdcorz27eX46it5x4zRwX//2+xIOAZKUqAkMgxp27b80+fXrpV8vtgxCQn5p883aCClpbFiHgAAAAAAJYXFopxJk5Ry+eVyfPaZbKtWKdS8udmpUAhKUqAkMIzIivOHZ4pmZkoHDsSOcbkipWhqqlS/vtSwYaQotVjMyQwAAAAAAI4p1KqVAn/5i5yffCLvmDE68NFHZkdCIShJAbPs25dfiK5ZI+3ZE7vdbpdq147MFE1Lkxo3lpKTWYEeAAAAAIBSJGf8eDnmzJFj6VLZ589X8OKLzY6EAlCSAsXl4MHIafOHi9GsrNjtVqtUs2bsCvQVKlCKAgAAAABQioUbNlTu3/4m96xZ8k6YoOxOnTgrtASiJAXOFL9fWrcuf6bo1q2R0+qPVL16bClaubJks5kSFwAAAAAAnBm++++X6+23Zf/xRznfe0+Bbt3MjoQ/oSQFikpenvTrr/kzRTdsiKxKf6RKlSKFaN26UpMmUrVqksNhRloAAAAAAFBMjOrV5e/XT57p0+V56CEFunZlklQJQ0kKnKpQSNq0Kb8U/eWXSFF6pHLlIoVoaqrUqJFUp47kdJqRFgAAAAAAmMg/eLBcL78s26ZNcr3wgnL79TM7Eo5ASQqcqHBY2rYtfwX6desip9QfKTEx//T5hg0j//V4zEgLAAAAAABKECMlRf5hw+SdMEGeadOU27s3nUEJQkkKFMYwpJ0782eKZmZGFl86ktsdKUVTU6X69aUGDSJFKQAAAAAAwJ/477hD7r//XdasLHmefFK+MWPMjoQ/UJICR9q7N3+maGZm5PMjORyRU+ZTU6W0NKlxYyk5mVXpAAAAAADA8bndyhk9WomDBsn1wgvyDxggo1w5s1NBlKSIdwcOSGvX5hejO3fGbrfZpJo1868r2rSpVL68ZLWaEhcAAAAAAJRugZtuUujpp2Vbt06eSZOU8+STZkeCKEkRb3y+yLVED88U3bo1drvFIlWvHluKVqrEinMAAAAAAKBo2GzKmTBBSbfcItcbb8iXkSGjVi2zU8U9SlKUbYFAZNX5zMxIMbppU2QBpiNVrhwpRevWlZo0kapWjZxWDwAAAAAAcAbkXXGFgq1ayb5ihRLGjtXBV14xO1LcoyRF2RIKSRs35p8+/+uvUjAYO6Z8+fwV6Bs3lmrVkpxOM9ICAAAAAIB4ZLEoZ/JkJf/lL3J8/LGsP/2k8FlnmZ0qrlGSonQLhyOnzB+eKbpunZSbGzsmMTF/pmjDhpH/ut0mhAUAAAAAAIgItmunwGWXyTl3rhJGj9aB2bPNjhTXKElRuhiGtGNH/jVFMzOlQ4dix3g8+TNFGzSQ6tWTEhJMiQsAAAAAAFAY3wMPyDFvnhyLFsm2ZIlC559vdqS4RUmKkm/PHunnn/NL0X37Yrc7nVKdOpFitF49qVEjKSkpsggTAAAAAABACRVq0kSB7t3leustJYwbp+x58+gzTEJJipInOzv/9PnMTGnXrtjtNlvkOqJHLrZUrpxktZoQFgAAAAAA4NT5xoyRc/Zs2VetkuPDD5V37bVmR4pLlKQwX06OtHZtfjG6bVvsdotFqlEj/xT6pk2lihUjZSkAAAAAAEApFq5VS/7bb5fn73+X98EHtf/qq5kIZgJKUhS/QEBavz5/puimTZFrjR6patXYUrRKFcnO4QoAAAAAAMoef0aG3K+9Jtsvv8j1yivK7dvX7Ehxh9YJZ14wKG3cmH9d0V9/lUKh2DEVKkQK0dTUyOnzNWpErjUKAAAAAABQxhkVKsg3ZIi8Dz4ozxNPKPfmmyWXy+xYcYWSFEUvHJa2bInMFF2zJjJrNBCIHZOcnF+KNmoUWXjJ7TYlLgAAAAAAgNn8d98t98yZsmZlyT19uvwjR5odKa5QkuL0GYa0fXv+NUXXro1cZ/RIXm/+6fMNGkRWofd6TYkLAAAAAABQ4ni98o0erYSMDLlnzpS/X7/IJDMUC0pSnJrdu/OvKbpmTWRF+iO5XJHZoampkUK0YUMpKSmyCBMAAAAAAACOknvzzXLPmCHbxo3yTpminIcfNjtS3KAkReH27pV27owsmmS15heimZmRkvRIdrtUq1ZkpmjdupHriqaksBobAAAAAADAibLblTNhgpL69JHrtdfkGzJERrVqZqeKC5SkKNjixdKsWUevOn+Y1RpZXOnwKfRnnRVZfIlSFAAAAAAA4JTlXXONgs2by75qlbzjxunQCy+YHSkuUJLiaHv3FlyQVq4cOXW+bl2padPIDFObzZSIAAAAAAAAZZLFopxJk5R83XVyfvCBfCNHKtywodmpyjym/eFoO3cWPIO0Z0+pb1/pkkuk6tUpSAEAAAAAAM6A4AUXKNCxoyyhkLxDh8q+aJEsv/1mdqwyjZIUR6tS5egFlg6fXg8AAAAAAIAzzjdxoiTJuXSpkrt2Vbn0dDlnzTI5VdlFSYqjlS8v3XJL/vVFrVbp5psjtwMAAAAAAOCMC1eooCPP87WEw0rIyGBG6RlSqkrSZ599VnXr1pXb7Va7du309ddfH3P822+/rSZNmsjtdqtZs2b65JNPYrYbhqHx48erevXq8ng86ty5s9atW3cmd6H0uOACacoUKSMj8t8LLjA7EQAAAAAAQNyw/fqr/nSeryyhkGwbNpiSp6wrNSXpm2++qYyMDE2YMEHffvutmjdvri5dumjnzp0Fjv/yyy/Vs2dP3X777fruu+/UtWtXde3aVatXr46OefTRRzVjxgzNnDlTy5YtU0JCgrp06SK/319cu1WylS8vNW7MDFIAAAAAAIBiFqpXT4Y1trozbDaF0tJMSlS2WQyjoBV6Sp527dqpTZs2euaZZyRJ4XBYtWvX1sCBA3X//fcfNb5Hjx46dOiQPvroo+ht7du3V3p6umbOnCnDMFSjRg0NGzZMw4cPlyTt379fVatW1SuvvKKbbrrpuJmys7OVkpKi/dOmKdnjKaI9BQBz7ene3ewIAAAAAABJzlmzIqfYh0IybDYdevJJBW65xexYRSo7O1tpaWnav3+/kpOTTcthN+0rn4RAIKAVK1Zo1KhR0dusVqs6d+6spUuXFnifpUuXKiMjI+a2Ll26aPbs2ZKkDRs2KCsrS507d45uT0lJUbt27bR06dITKkkPyw2FlBsKHXW71WKR44jGv6Axh1ksFjlPcWwgHFZhXfeZGitJriNWtz+ZsXnhsMJFNNZptcryxyJTRTnWYbXK+sfYYDiskMljQ4ahYDhc6Fi71SpbCRobNgzlHWOszWKR/Y9jzayxRz4/DcNQ4AyMlY79XC6prxGBQKDQx3Y6ndF/5+XlHfN5XxLGOhyO6PM+GAwqfIz/dycz1m63y/rH960kjA2FQgod45iw2Wyy/fHaWhLGhsNhBYPBQsdarVbZ7fYSM9YwDOXl5RXJWIvFIofDEf38WM+3kjBWin3OncxYXiN4jeA14uTHloTnPa8R+XiNiOA14vTG8hrBa8TJjv3zczlw4406dMEFsm3cqFDdujJq1JD++F6WldeIYx33xalUlKS7d+9WKBRS1apVY26vWrWq1qxZU+B9srKyChyflZUV3X74tsLG/Flubq5yc3Ojn2dnZ0uSRv30U8wT6bCzk5M1oF696Of3/fhjoeVKw4QEDW3YMPr5uJ9+0sFCDqw6Ho/ub9w4+vmkn3/WnkIOqOput8Y1aRL9/JG1a7W9kMsJVHA49ODZZ0c/f3LdOm32+Qocm2iz6dFmzaKfP/vLL1p36FCBY51Wq6ade2708+c3btSPf3zvCvJcenr0369s2qTv9u8vdOxTzZpFS9X/bNmir/buLXTsI+eco6Q/fvi8u22bFu7eXejYyU2bqqLLJUn67/bt+r9duwodO7ZxY9X4YybxnB079MmOHYWOHdmokep6vZKk+bt36/1t2wodO6R+fTVKSpIkLd69W28e48LM/dPS1CwlRZL09Z49em3LlkLH3lG3rlqWKydJWrV/v/65cWOhY2+tXVsdKlaUJP2Una2/H+O6Jz1q1lTHypUlSesPHtS0X34pdOxfa9TQZVWqSJI2+3x6dO3aQsf+pWpVXV29uiQpy+/Xg5mZhY7tXLmyrq9ZU5K0NxDQuJ9/LnTsRZUq6aZatSRJB0Mh3XfEpTj+rH358uqVmiopUiIO/eGHQse2SEnRnUec+nCssSX2NWLSpALHlitXLjrzXpL++c9/6rdCjkuv16vRo0dHP3/11Ve1sZBjzeFwaMKECdHP//Of/2jtMY6JBx98MPrvd955Rz/++GOhY8ePHx99ff7ggw/03XffFTp21KhRSkhIkCR9+umnWrZsWaFjhw0bpvJ/XIrk//7v/7R48eJCxw4cODD6s2bBggWaP39+oWP79eunWn8cl0uXLtVnn31W6Ni+ffuq3h/Hz/Lly2POnPizW2+9VY3/OCZWrVql9957r9CxN910k8455xxJ0s8//6w33nij0LHXX3+9WrZsKUlav369XnvttULHXn311Wrfvr0kaePGjXrppZcKHdulSxddeOGFkqRt27Zp5syZhY69+OKLdemll0qSdu3apaeffrrQsRdccIGuuOIKSZGzR5544olCx7Zr107XXHONJCknJ0dTp04tdGyLFi3UrVs3SZE3eJMKeQ5J0tlnn62ePXtGPz/W2EaNGqlXr17Rz6dOnVroG8i6devqjjvuiH7++OOPKycnp8CxNWvWVP/+/aOfz5gxQ/v27StwbJUqVTRo0KDo5zNnziz0Uke8RuTjNSKC14gIXiMieI3Ix2tEBK8REbxGRPAakS8eXyOOV6AXl1JzTdKSYOrUqUpJSYl+1K5d2+xIAAAAAAAAAE5TqbgmaSAQkNfr1TvvvKOuXbtGb+/du7f27dunDz744Kj71KlTRxkZGRoyZEj0tgkTJmj27NlatWqVfv31V9WvX1/fffed0o+YudixY0elp6dr+vTpRz1mQTNJa9eurZ1PPFHgNUlL6qm0nG7P6facbs/p9scau+eI19k/KwmntZTEU2DMHlsSTn3jNDlOk/vzWF4jeI3gNeLkx5aE5z2vEfl4jYjgNeL0xvIawWvEyY6Nx9eIAwcOqFGjRqZfk7RUlKRSZLp627Zto9Pfw+Gw6tSpo3vvvbfQhZtycnL04YcfRm8777zzdO6558Ys3DR8+HANGzZMUqT0rFKlCgs3AYhrLNwEAAAAACguLNx0kjIyMtS7d2+1bt1abdu21bRp03To0CH16dNHktSrVy/VrFkzeq2PwYMHq2PHjnriiSd01VVX6Y033tA333yj559/XlLkrx9DhgzRgw8+qIYNGyotLU3jxo1TjRo1YmarAgAAAAAAACjbSk1J2qNHD+3atUvjx49XVlaW0tPTNWfOnOgFajdv3hydCixFZo2+/vrrGjt2rEaPHq2GDRtq9uzZ0QvEStLIkSN16NAh3XXXXdq3b58uuOACzZkzR263u9j3DwAAAAAAAIA5Ss3p9iURp9sDKIs43R4AAAAAUFxKyun2rG4PAAAAAAAAIK5RkgIAAAAAAACIa5SkAAAAAAAAAOIaJSkAAAAAAACAuEZJCgAAAAAAACCuUZICAAAAAAAAiGuUpAAAAAAAAADiGiUpAAAAAAAAgLhGSQoAAAAAAAAgrlGSAgAAAAAAAIhrlKQAAAAAAAAA4holKQAAAAAAAIC4RkkKAAAAAAAAIK5RkgIAAAAAAACIa5SkAAAAAAAAAOIaJSkAAAAAAACAuEZJCgAAAAAAACCuUZICAAAAAAAAiGuUpAAAAAAAAADiGiUpAAAAAAAAgLhGSQoAAAAAAAAgrlGSAgAAAAAAAIhrlKQAAAAAAAAA4prd7AClmWEYkqRsv9/kJABQdLKzs82OAAAAAACIEwcOHJCU37OZxWKYnaAU27p1q2rXrm12DAAAAAAAAKBU27Jli2rVqmXa16ckPQ3hcFjbtm1TUlKSLBaL2XGAMic7O1u1a9fWli1blJycbHYc4IzieAfODJ5biCcc70DJwfMR8eR0j3fDMHTgwAHVqFFDVqt5VwbldPvTYLVaTW24gXiRnJzMGwvEDY534MzguYV4wvEOlBw8HxFPTud4T0lJKeI0J4+FmwAAAAAAAADENUpSAAAAAAAAAHGNkhRAieVyuTRhwgS5XC6zowBnHMc7cGbw3EI84XgHSg6ej4gnZeV4Z+EmAAAAAAAAAHGNmaQAAAAAAAAA4holKQAAAAAAAIC4RkkKAAAAAAAAIK5RkgIAAAAAAACIa5SkADR16lS1adNGSUlJqlKlirp27arMzMyYMX6/XwMGDFDFihWVmJiobt26aceOHTFjBg0apFatWsnlcik9Pb3Ar/XWW28pPT1dXq9Xqampeuyxx46b78cff1S3bt1Ut25dWSwWTZs27agxCxcu1DXXXKMaNWrIYrFo9uzZJ7TvRbVfKD3i+Xh//vnn1alTJyUnJ8tisWjfvn1HjTn8dY/8ePjhh0/o8RHfysJz60T2oSB79uzRzTffrOTkZJUrV0633367Dh48GLPft912m5o1aya73a6uXbse9zFRssXz8f7QQw/pvPPOk9frVbly5Qoc8+efIxaLRW+88cZxHxs4FUXxfFy1apV69uyp2rVry+PxqGnTppo+ffpRX+uLL75Qy5Yt5XK51KBBA73yyivHzWcYhsaPH6/q1avL4/Goc+fOWrduXcyYE3leFeT777/XhRdeKLfbrdq1a+vRRx+N2X4irwUoXeL1eD+R91JffPFFgT9/srKyTuhrSJSkACQtWLBAAwYM0FdffaW5c+cqLy9Pl19+uQ4dOhQdM3ToUH344Yd6++23tWDBAm3btk3XX3/9UY/Vt29f9ejRo8Cv8+mnn+rmm29Wv379tHr1aj333HN66qmn9MwzzxwzX05OjurVq6eHH35Y1apVK3DMoUOH1Lx5cz377LMnsedFs18oXeL5eM/JydEVV1yh0aNHH3PcpEmTtH379ujHwIEDT+rrID6VhefWiexDQW6++Wb9+OOPmjt3rj766CMtXLhQd911V3R7KBSSx+PRoEGD1Llz52M+FkqHeD7eA4GAbrjhBvXv3/+Y415++eWYnyX8cQBnSlE8H1esWKEqVapo1qxZ+vHHHzVmzBiNGjUq5rm2YcMGXXXVVbr44ou1cuVKDRkyRHfccYc+++yzY+Z79NFHNWPGDM2cOVPLli1TQkKCunTpIr/fHx1zos+rI2VnZ+vyyy9XamqqVqxYoccee0wPPPCAnn/++eiYE3ktQOkSr8f7ybyXyszMjPn5U6VKlRP+OjIA4E927txpSDIWLFhgGIZh7Nu3z3A4HMbbb78dHfPzzz8bkoylS5cedf8JEyYYzZs3P+r2nj17Gt27d4+5bcaMGUatWrWMcDh8QtlSU1ONp5566phjJBnvv//+cR+rqPYLpVu8HO9Hmj9/viHJ2Lt37yl9TeBElPbnVkH7UJCffvrJkGQsX748etunn35qWCwW47fffjtqfO/evY3rrrvuhHKi9IiX4/1IL7/8spGSklLgtlP52QQUldN9Ph52zz33GBdffHH085EjRxpnn312zJgePXoYXbp0KfQxwuGwUa1aNeOxxx6L3rZv3z7D5XIZ//nPf44af6zn1Z8999xzRvny5Y3c3Nzobffdd5/RuHHjAsfzHq9sipfj/UiFvZc61u84J4qZpACOsn//fklShQoVJEX+0pSXlxfzF5smTZqoTp06Wrp06Qk/bm5urtxud8xtHo9HW7du1aZNm4og+ckpqv1C6RYvx/vJePjhh1WxYkW1aNFCjz32mILBoNmRUAqVhefWn/ehIEuXLlW5cuXUunXr6G2dO3eW1WrVsmXLijQPSq54Od5PxoABA1SpUiW1bdtWL730kgzDKJLHBY6nqJ6P+/fvj3k+LF269KgZbF26dDnmY2zYsEFZWVkx90tJSVG7du1O+/eNpUuX6qKLLpLT6YzJk5mZqb17957WY6P0iJfj/WSkp6erevXquuyyy7RkyZKTui8lKYAY4XBYQ4YM0fnnn69zzjlHkpSVlSWn03nU9UKqVq16Utf36NKli9577z3NmzdP4XBYa9eu1RNPPCFJ2r59e5Htw4kqqv1C6RVPx/uJGjRokN544w3Nnz9fd999t6ZMmaKRI0eaHQulTFl4bhW0DwXJyso66jQuu92uChUq8LMkTsTT8X6iJk2apLfeektz585Vt27ddM899+jpp58ugqTAsRXV8/HLL7/Um2++GXPplKysLFWtWvWox8jOzpbP5yvwcQ4/fkH3O92fEYXlOfLromyLp+P9RFSvXl0zZ87Uu+++q3fffVe1a9dWp06d9O23357wY1CSAogxYMAArV69+oxcXP/OO+/Uvffeq6uvvlpOp1Pt27fXTTfdJEmyWq3avHmzEhMTox9Tpkwpsq89ZcqUmMfevHlzkT02Si+O96NlZGSoU6dOOvfcc9WvXz898cQTevrpp5Wbm1tk+VD2lYXnVkH70K9fv5jHBiSO94KMGzdO559/vlq0aKH77rtPI0eOPKEFp4DTVRTPx9WrV+u6667ThAkTdPnll5/w/f7973/HPGcWLVp0yhn+7Oyzz44+7pVXXllkj4vSjeM9VuPGjXX33XerVatWOu+88/TSSy/pvPPO01NPPXXCj2E/lcAAyqZ77703uuBErVq1ordXq1ZNgUBA+/bti/mL1I4dO07qAuAWi0WPPPKIpkyZoqysLFWuXFnz5s2TJNWrV0/ly5fXypUro+OL6nQvKfJG/8Ybb4x+XqNGjSLbL5RO8Xa8n6p27dopGAxq48aNaty4cVHEQxlXFp5bhe3DpEmTNHz48Jix1apV086dO2NuCwaD2rNnDz9L4kC8He+nql27dpo8ebJyc3PlcrmK5DGBPyuK5+NPP/2kSy+9VHfddZfGjh0bs61atWoxK4Qffozk5GR5PB5de+21ateuXXRbzZo1ozO+d+zYoerVq8fcLz09/YT37ZNPPlFeXp6kyCU3jpXn8DaUbfF2vJ+qtm3bavHixSc8npIUgAzD0MCBA/X+++/riy++UFpaWsz2Vq1ayeFwaN68eerWrZukyIpxmzdvVocOHU7669lsNtWsWVOS9J///EcdOnRQ5cqVJUkNGjQ4zb0pWIUKFY76xaGo9wulQ7we76dq5cqVslqtJ7cqJOJSWXhuHW8fqlSpctRzoUOHDtq3b59WrFihVq1aSZI+//xzhcPhmF8eULbE6/F+qlauXKny5ctTkOKMKKrn448//qhLLrlEvXv31kMPPXTU1+nQoYM++eSTmNvmzp0bfYykpCQlJSXFbE9LS1O1atU0b968aEmUnZ2tZcuWndTK3qmpqQXmGTNmjPLy8uRwOKJ5GjdurPLly5/wY6N0idfj/VStXLkyprA9HkpSABowYIBef/11ffDBB0pKSopeLyQlJUUej0cpKSm6/fbblZGRoQoVKig5OVkDBw5Uhw4d1L59++jjrF+/XgcPHlRWVpZ8Pl90ZsNZZ50lp9Op3bt365133lGnTp3k9/v18ssv6+2339aCBQuOmS8QCOinn36K/vu3337TypUrlZiYGP3F4ODBg1q/fn30Phs2bNDKlStVoUIF1alTp8DHLar9QukSr8e7FLlOUFZWVvS+P/zwg5KSklSnTh1VqFBBS5cu1bJly3TxxRcrKSlJS5cu1dChQ3XLLbfwZhvHVRaeW8fbh4I0bdpUV1xxhe68807NnDlTeXl5uvfee3XTTTfFzOL+6aefFAgEtGfPHh04cCC6XyczswIlR7we75K0efNm7dmzR5s3b1YoFIpmbtCggRITE/Xhhx9qx44dat++vdxut+bOnaspU6YU2axU4M+K4vm4evVqXXLJJerSpYsyMjKij2Gz2aJ/kOjXr5+eeeYZjRw5Un379tXnn3+ut956Sx9//HGh2SwWi4YMGaIHH3xQDRs2VFpamsaNG6caNWqoa9eu0XHHe14V5G9/+5smTpyo22+/Xffdd59Wr16t6dOnx5xafCKvBShd4vV4l47/XmratGlKS0vT2WefLb/fr3/+85/6/PPP9b///e/Ev8GFL3wPIF5IKvDj5Zdfjo7x+XzGPffcY5QvX97wer3GX//6V2P79u0xj9OxY8cCH2fDhg2GYRjGrl27jPbt2xsJCQmG1+s1Lr30UuOrr746br4NGzYU+LgdO3aMjpk/f36BY3r37n3Mxy6K/ULpEs/H+4QJE4657ytWrDDatWtnpKSkGG6322jatKkxZcoUw+/3n8i3FnGuLDy3TmQfCvL7778bPXv2NBITE43k5GSjT58+xoEDB2LGpKamFvjYKJ3i+Xjv3bt3gfebP3++YRiG8emnnxrp6elGYmKikZCQYDRv3tyYOXOmEQqFTuRbC5y0ong+FvYeKTU1NeZrzZ8/30hPTzecTqdRr1694z5fDMMwwuGwMW7cOKNq1aqGy+UyLr30UiMzMzNmzPGeV4VZtWqVccEFFxgul8uoWbOm8fDDD8dsP5HXApQu8Xy8H++91COPPGLUr1/fcLvdRoUKFYxOnToZn3/++XEzH8liGIYhAAAAAAAAAIhTrG4PAAAAAAAAIK5RkgIAAAAAAACIa5SkAAAAAAAAAOIaJSkAAAAAAACAuEZJCgAAAAAAACCuUZICAAAAAAAAiGuUpAAAAAAAAADiGiUpAABAnNu4caMsFovq1q1rdhQAAADAFJSkAAAAcaBu3bqyWCzauHGj2VGKjcVikcViMTsGAAAASgG72QEAAABgrpo1a+rnn3+Ww+EwOwoAAABgCkpSAACAOOdwONSkSROzYwAAAACm4XR7AACAMuyVV16RxWLRpk2bJElpaWnR09AtFou++OKLY16T9MhT1mfNmqW2bdsqMTFRlStXVs+ePbV582ZJkmEYeuaZZ5Senq6EhARVqlRJt912m3bu3FlotrVr1+ruu+9W/fr15Xa7lZKSoosuukizZs0qcPz+/fs1duxYNWvWTAkJCXK5XKpRo4bOP/98jR8/Xnl5eZKkBx54IOY0+yP398hLDuTl5WnWrFm6+eab1aRJEyUnJ8vj8ahx48YaNGiQtm3bVmCOTp06Rb93X331la666ipVrFhRSUlJ6tixoxYtWhQdO2fOHF166aUqX768EhMTddlll+nbb7896jGP/H8QDAb16KOP6uyzz5bH41GlSpV04403as2aNQXmWbdunfr27au0tDS5XC4lJiYqNTVVV111lV5++eVCv/8AAADIZzEMwzA7BAAAAM6MxYsX65///KfeeecdHTp0SN26dVNiYmJ0+/333y+32620tDSlpqYedc3Sw2Xj/fffr8cff1wXXXSRKlSooK+//lqbN29W7dq1tWrVKvXr10///e9/1alTJ3k8Hi1ZskQ7d+7Uueeeq+XLl8vpdMY87ttvv61evXrJ7/erSZMmatq0qfbv369ly5bp0KFD6tOnj1566aXo+JycHLVr106rV69W5cqV1b59eyUkJCgrK0tr1qxRVlaW9u7dq3Llymn27NmaPXu2Xn31VUlS7969Y772448/rkqVKmnr1q2qXbu2UlJS1LRpU9WuXVuHDh3SypUrtW3bNlWuXFlffvmlGjRoEHP/Tp06acGCBRo+fLimTZumZs2aqVGjRsrMzNTKlSvlcrn0+eef67vvvtOgQYPUvn171apVSytXrtTatWuVmJio7777LuZxN27cGP1/0KpVK3344Yfq2LGjKlWqpK+//lq//vqrEhMT9b///U8dOnSI3m/16tU6//zzlZ2drcaNG+vss8+WzWbT1q1b9cMPP6h+/fpauXLlyR84AAAA8cYAAABAmZeammpIMjZs2HDUtg0bNhiSjNTU1KO2STIkGRUrVjRWrlwZvT0nJ8e44IILDElGs2bNjPr16xsbN26Mbt+1a5fRoEEDQ5Ixa9asmMf8/vvvDZfLZbjdbuPdd9+N2bZx40ajWbNmhiTj1Vdfjd7+6quvGpKMK6+80ggEAjH3CYVCxhdffGHk5uYWmL0w2dnZxgcffHDU/QKBgDFq1ChDkvGXv/zlqPt17NjRkGRYLBbjtddei9mWkZFhSDIaN25sJCYmGv/3f/8X3RYMBo1u3boZkow77rgj5n6H/x9IMipVqmSsWrUq5n4DBw6M/j/y+/3RbX369DEkGQ8++OBROXNycowFCxYUuv8AAADIx+n2AAAAOK5JkyapefPm0c89Ho8yMjIkST/88INmzJih1NTU6PZKlSqpf//+kqR58+bFPNZDDz2k3NxcPfjgg7r++utjtqWmpurFF1+UJM2YMSN6+44dOyRJl1122VELTFmtVnXs2PGo2arHk5SUpGuvvfao+zkcDk2ZMkU1atTQnDlzdODAgQLv3717d91yyy0xt40ZM0aSlJmZqf79++vSSy+NbrPZbBo9erSko78nRxo7dqzOPffcmPs99thjqlmzpjZt2qR33303uu3w9+Uvf/nLUY/j8Xh00UUXFfp1AAAAkI+SFAAAAMdVUAnXsGFDSZLdbtfll19e6PYjr+0ZDof16aefSpJ69OhR4Ndq3bp19JR0v98vSWrTpo0k6dFHH9W//vUv7dmz5zT2JtaqVav05JNPauDAgerbt69uu+023XbbbQoGgwqHw1q/fn2B9yvoe1KhQgVVrFix0O0FfU/+7M+XB5Akl8sV/X598cUX0dvbtm0rSerfv78+++yz6PcLAAAAJ4fV7QEAAHBcderUOeq2w9c2rV69uuz2o99WJiUlSVJMcff7778rOztbklS7du3jft3ff/9dNWvWVKdOnXTffffpscceU+/evWWxWNSwYUOdf/75uu6663TNNdfIaj25v/8fOnRIt956q95///1jjjuc988K+p5Ike/L77//XuD2w9+T3NzcAu9brlw5lStXrsBtaWlpkqStW7dGbxsxYoQWL16s//u//9MVV1whh8Oh5s2b66KLLtJNN90ULZcBAABwbJSkAAAAOK5jFZAnU06Gw+HovwuaMflnLpcr+u+HH35Y/fr104cffqjFixdryZIlevnll/Xyyy+rTZs2mj9/vhISEk44y6hRo/T++++rSZMmevjhh9WmTRtVqlQpevr9eeedp6VLl8ooZJ3T4+33yZa2J+rIPF6vV3PnztXy5cs1Z84cffnll/ryyy/1zTff6Mknn9Q999yjZ5999ozkAAAAKEsoSQEAAFBsKlWqJI/HI5/PF11l/mTUrVtXAwcO1MCBAyVJy5cv1y233KLly5fr0Ucf1cSJE0/4sd566y1J0ptvvhlzDdDD1q1bd1LZisK+ffu0b9++AmeTbty4UZJUq1ato7a1adMmOms0GAxq9uzZ6tWrl5577jl1795dF1988ZmMDQAAUOpxTVIAAIA4cHh2ZDAYNDWHzWbTZZddJim/pDwdbdq00T333CNJWrlyZcy2wws8FbbPh69reuSCU4d99tln2r1792nnOxWvvfbaUbcFAgG9+eabkqROnTod8/52u13du3dXly5dJB39fQEAAMDRKEkBAADiwOHZhz/++KPJSaQJEybI6XRqxIgRevXVV2NOwT9s9erVeu+996Kfv//++1q4cOFRY/Py8jRnzhxJR5edx9vnpk2bSpKefvrpmNszMzPVr1+/k9yrojN58mStXr06+nk4HNZ9992nrVu3qnbt2urWrVt023PPPafMzMyjHiMrK0vffPONpIJLYAAAAMTidHsAAIA40K1bN82fP1+33HKLLr/8cpUvX15SZOGfI6/7WRxatmypWbNmRVeRHzt2rM466yxVrlxZe/bs0Q8//KCtW7eqR48euv766yVJCxYs0PTp01WpUiW1aNFCVapU0YEDB/TVV19p586dqlmzpkaOHHnUPj/++OPq3LmzLrnkkuiiSY888ogqVqyoCRMmqHv37ho3bpzeeustnX322dq5c6cWLVqkCy+8UDVq1NCXX35ZrN+bOnXqqFWrVmrZsqU6deqkihUravny5frll1+UkJCg119/XW63Ozr++eef14ABA5SWlqZzzjlHycnJ2rVrlxYtWiSfz6dLLrlE1157bbHuAwAAQGlESQoAABAH+vfvrwMHDmjWrFn65JNPoivO33LLLapbt26x57nhhhvUpk0bzZgxQ3PnztWSJUsUCoVUtWpVNWjQQPfee6+6d+8eHX/bbbfJ4/Fo8eLF+umnn7RgwQKlpKSoTp06GjJkiO666y5VrFgx5mtMnjxZVqtV7733nmbPnq1AICBJGjt2rCpWrKjrr79eCxYs0MSJE7Vq1Sr98ssvqlevnh544AENHz5cl19+ebF+TyTJYrHorbfe0qOPPqrXXntNCxcuVEJCgrp166ZJkybprLPOihn/0EMP6eOPP9ZXX32lr776Svv371eVKlXUrl079enTRz179pTdzlt+AACA47EYhS3XCQAAAKBYbNy4UWlpaUpNTY0u0AQAAIDiwzVJAQAAAAAAAMQ1SlIAAAAAAAAAcY2SFAAAAAAAAEBc45qkAAAAAAAAAOIaM0kBAAAAAAAAxDVKUgAAAAAAAABxjZIUAAAAAAAAQFyjJAUAAAAAAAAQ1yhJAQAAAAAAAMQ1SlIAAAAAAAAAcY2SFAAAAAAAAEBcoyQFAAAAAAAAENcoSQEAAAAAAADEtf8H8g9ZQSpnUloAAAAASUVORK5CYII=", "text/plain": [ "
" ] diff --git a/api/tutorials/synthea/los_prediction.html b/api/tutorials/synthea/los_prediction.html index bf05645a1..424082abc 100644 --- a/api/tutorials/synthea/los_prediction.html +++ b/api/tutorials/synthea/los_prediction.html @@ -677,17 +677,17 @@

Compute length of stay (labels) 
-2023-10-26 14:23:42,067 INFO cycquery.orm    - Database setup, ready to run queries!
-2023-10-26 14:23:45,040 INFO cycquery.orm    - Query returned successfully!
-2023-10-26 14:23:45,041 INFO cycquery.utils.profile - Finished executing function run_query in 2.345333 s
-2023-10-26 14:23:46,858 INFO cycquery.orm    - Query returned successfully!
-2023-10-26 14:23:46,859 INFO cycquery.utils.profile - Finished executing function run_query in 1.816837 s
-2023-10-26 14:23:48,153 INFO cycquery.orm    - Query returned successfully!
-2023-10-26 14:23:48,154 INFO cycquery.utils.profile - Finished executing function run_query in 0.117480 s
-2023-10-26 14:23:48,323 INFO cycquery.orm    - Query returned successfully!
-2023-10-26 14:23:48,324 INFO cycquery.utils.profile - Finished executing function run_query in 0.162305 s
-2023-10-26 14:23:48,405 INFO cycquery.orm    - Query returned successfully!
-2023-10-26 14:23:48,406 INFO cycquery.utils.profile - Finished executing function run_query in 0.081821 s
+2023-10-26 14:52:08,681 INFO cycquery.orm    - Database setup, ready to run queries!
+2023-10-26 14:52:12,409 INFO cycquery.orm    - Query returned successfully!
+2023-10-26 14:52:12,410 INFO cycquery.utils.profile - Finished executing function run_query in 2.397262 s
+2023-10-26 14:52:14,189 INFO cycquery.orm    - Query returned successfully!
+2023-10-26 14:52:14,191 INFO cycquery.utils.profile - Finished executing function run_query in 1.779844 s
+2023-10-26 14:52:15,423 INFO cycquery.orm    - Query returned successfully!
+2023-10-26 14:52:15,424 INFO cycquery.utils.profile - Finished executing function run_query in 0.117588 s
+2023-10-26 14:52:15,580 INFO cycquery.orm    - Query returned successfully!
+2023-10-26 14:52:15,582 INFO cycquery.utils.profile - Finished executing function run_query in 0.153356 s
+2023-10-26 14:52:15,673 INFO cycquery.orm    - Query returned successfully!
+2023-10-26 14:52:15,673 INFO cycquery.utils.profile - Finished executing function run_query in 0.090819 s

@@ -774,9 +774,9 @@

Drop NaNs based on the
-
+
@@ -695,7 +695,7 @@

Performance Over Time

-
+
@@ -938,27 +938,19 @@

Model Parameters

-

Reg_lambda

- 0 +

Random_state

+ 123
-
-

Gamma

- 0 -
-
-

Seed

- 123 -
@@ -975,8 +967,8 @@

Seed

-

N_estimators

- 500 +

Min_child_weight

+ 3
@@ -989,27 +981,19 @@

N_estimators

-

Max_depth

- 5 +

Learning_rate

+ 0.1
-
-

Objective

- binary:logistic -
-
-

Colsample_bytree

- 0.8 -
@@ -1020,10 +1004,6 @@

Colsample_bytree

-
-

Eval_metric

- logloss -
@@ -1034,6 +1014,10 @@

Eval_metric

+
+

Seed

+ 123 +
@@ -1049,6 +1033,10 @@

Eval_metric

+
+

Max_depth

+ 5 +
@@ -1074,6 +1062,10 @@

Eval_metric

+
+

Enable_categorical

+ False +
@@ -1084,10 +1076,6 @@

Eval_metric

-
-

Random_state

- 123 -
@@ -1113,6 +1101,10 @@

Random_state

+
+

Gamma

+ 10 +
@@ -1129,14 +1121,18 @@

Random_state

-

Learning_rate

- 0.1 +

N_estimators

+ 500
+
+

Colsample_bytree

+ 0.7 +
@@ -1147,14 +1143,18 @@

Learning_rate

+
+

Missing

+ nan +
-

Missing

- nan +

Eval_metric

+ logloss
@@ -1166,15 +1166,15 @@

Missing

-
-

Min_child_weight

- 3 -
+
+

Objective

+ binary:logistic +
@@ -1186,8 +1186,8 @@

Min_child_weight

-

Enable_categorical

- False +

Reg_lambda

+ 1
@@ -1220,7 +1220,7 @@

Graphics

-
+
@@ -1228,7 +1228,7 @@

Graphics

-
+
@@ -1236,7 +1236,7 @@

Graphics

-
+
@@ -1244,7 +1244,7 @@

Graphics

-
+
@@ -1252,7 +1252,7 @@

Graphics

-
+
@@ -1496,7 +1496,7 @@

Quantitative Analysis

BinaryAccuracy age:[20 - 50) - 0.86 + 0.88 0.6 Passed @@ -1506,7 +1506,7 @@

Quantitative Analysis

BinaryPrecision age:[20 - 50) - 0.86 + 0.95 0.6 Passed @@ -1516,7 +1516,7 @@

Quantitative Analysis

BinaryRecall age:[20 - 50) - 0.9 + 0.84 0.6 Passed @@ -1526,7 +1526,7 @@

Quantitative Analysis

BinaryF1Score age:[20 - 50) - 0.88 + 0.89 0.6 Passed @@ -1536,7 +1536,7 @@

Quantitative Analysis

BinaryAUROC age:[20 - 50) - 0.95 + 0.96 0.8 Passed @@ -1546,7 +1546,7 @@

Quantitative Analysis

BinaryAccuracy age:[50 - 80) - 0.88 + 0.86 0.6 Passed @@ -1556,7 +1556,7 @@

Quantitative Analysis

BinaryPrecision age:[50 - 80) - 0.96 + 0.85 0.6 Passed @@ -1566,7 +1566,7 @@

Quantitative Analysis

BinaryRecall age:[50 - 80) - 0.82 + 0.88 0.6 Passed @@ -1576,7 +1576,7 @@

Quantitative Analysis

BinaryF1Score age:[50 - 80) - 0.88 + 0.86 0.6 Passed @@ -1586,7 +1586,7 @@

Quantitative Analysis

BinaryAUROC age:[50 - 80) - 0.97 + 0.94 0.8 Passed @@ -1606,7 +1606,7 @@

Quantitative Analysis

BinaryPrecision gender:M - 0.9 + 0.99 0.6 Passed @@ -1616,7 +1616,7 @@

Quantitative Analysis

BinaryRecall gender:M - 0.93 + 0.85 0.6 Passed @@ -1656,7 +1656,7 @@

Quantitative Analysis

BinaryPrecision gender:F - 0.92 + 0.87 0.6 Passed @@ -1666,7 +1666,7 @@

Quantitative Analysis

BinaryRecall gender:F - 0.89 + 0.94 0.6 Passed @@ -1706,7 +1706,7 @@

Quantitative Analysis

BinaryPrecision overall - 0.91 + 0.93 0.6 Passed @@ -1716,7 +1716,7 @@

Quantitative Analysis

BinaryRecall overall - 0.91 + 0.89 0.6 Passed @@ -1736,7 +1736,7 @@

Quantitative Analysis

BinaryAUROC overall - 0.96 + 0.95 0.8 Passed @@ -1766,7 +1766,7 @@

Graphics

-
+
@@ -1774,7 +1774,7 @@

Graphics

-
+
@@ -1782,7 +1782,7 @@

Graphics

-
+
@@ -1790,7 +1790,7 @@

Graphics

-
+
@@ -1841,7 +1841,7 @@

Graphics

-
+