From mboxrd@z Thu Jan 1 00:00:00 1970 Content-Type: multipart/mixed; boundary="===============4223201462222227330==" MIME-Version: 1.0 From: kernel test robot To: kbuild-all@lists.01.org Subject: Re: [PATCH net-next] inet_ecn: Use csum16_add() helper for IP_ECN_set_* helpers Date: Thu, 10 Dec 2020 23:43:23 +0800 Message-ID: <202012102301.chq73ELs-lkp@intel.com> In-Reply-To: <20201210105455.122471-1-toke@redhat.com> List-Id: --===============4223201462222227330== Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: quoted-printable Hi "Toke, I love your patch! Perhaps something to improve: [auto build test WARNING on net-next/master] url: https://github.com/0day-ci/linux/commits/Toke-H-iland-J-rgensen/ine= t_ecn-Use-csum16_add-helper-for-IP_ECN_set_-helpers/20201210-190846 base: https://git.kernel.org/pub/scm/linux/kernel/git/davem/net-next.git = a7105e3472bf6bb3099d1293ea7d70e7783aa582 config: i386-randconfig-s001-20201209 (attached as .config) compiler: gcc-9 (Debian 9.3.0-15) 9.3.0 reproduce: # apt-get install sparse # sparse version: v0.6.3-179-ga00755aa-dirty # https://github.com/0day-ci/linux/commit/fbed6705f871b8eb4e522c874= 927ac8e57c8889b git remote add linux-review https://github.com/0day-ci/linux git fetch --no-tags linux-review Toke-H-iland-J-rgensen/inet_ecn-Us= e-csum16_add-helper-for-IP_ECN_set_-helpers/20201210-190846 git checkout fbed6705f871b8eb4e522c874927ac8e57c8889b # save the attached .config to linux build tree make W=3D1 C=3D1 CF=3D'-fdiagnostic-prefix -D__CHECK_ENDIAN__' ARCH= =3Di386 = If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot "sparse warnings: (new ones prefixed by >>)" net/sched/sch_choke.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add -- net/sched/sch_sfb.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add -- net/sched/sch_codel.c: note: in included file (through include/net/codel= .h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add -- net/sched/sch_red.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add -- net/sched/sch_fq.c: note: in included file (through include/net/tcp.h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add -- net/sched/sch_fq_codel.c: note: in included file (through include/net/co= del.h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add -- net/sched/sch_netem.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add -- net/sched/sch_sfq.c: note: in included file (through include/net/red.h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades= to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades= to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument= 2 (different base types) @@ expected restricted __be16 [usertype] adde= nd @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [us= ertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] = [usertype] check_add vim +97 include/net/inet_ecn.h 67 = 68 #define IP6_ECN_flow_init(label) do { \ 69 (label) &=3D ~htonl(INET_ECN_MASK << 20); \ 70 } while (0) 71 = 72 #define IP6_ECN_flow_xmit(sk, label) do { \ 73 if (INET_ECN_is_capable(inet6_sk(sk)->tclass)) \ 74 (label) |=3D htonl(INET_ECN_ECT_0 << 20); \ 75 } while (0) 76 = 77 static inline int IP_ECN_set_ce(struct iphdr *iph) 78 { 79 u32 ecn =3D (iph->tos + 1) & INET_ECN_MASK; 80 u16 check_add; 81 = 82 /* 83 * After the last operation we have (in binary): 84 * INET_ECN_NOT_ECT =3D> 01 85 * INET_ECN_ECT_1 =3D> 10 86 * INET_ECN_ECT_0 =3D> 11 87 * INET_ECN_CE =3D> 00 88 */ 89 if (!(ecn & 2)) 90 return !ecn; 91 = 92 /* 93 * The following gives us: 94 * INET_ECN_ECT_1 =3D> check +=3D htons(0xFFFD) 95 * INET_ECN_ECT_0 =3D> check +=3D htons(0xFFFE) 96 */ > 97 check_add =3D htons(0xFFFB) + htons(ecn); 98 = > 99 iph->check =3D csum16_add(iph->check, check_add); 100 iph->tos |=3D INET_ECN_CE; 101 return 1; 102 } 103 = --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all(a)lists.01.org --===============4223201462222227330== Content-Type: application/gzip MIME-Version: 1.0 Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="config.gz" H4sICAgk0l8AAy5jb25maWcAlDxJc9w2s/f8iinnkhyST5sVp17pAIIgiQxBMAA5iy4sRR47qthS 3kj6Ev/71w1wAUBw7JdDLHY3GlujNzTm++++X5HXl6fPdy8P93efPn1ZfTw8Ho53L4f3qw8Pnw7/ s0rlqpLNiqW8+RmIy4fH13//83D57nr19ufzs5/PfjreX6/Wh+Pj4dOKPj1+ePj4Cs0fnh6/+/47 KquM5x2l3YYpzWXVNWzX3Lz5eH//06+rH9LDHw93j6tff74ENudvf7R/vXGacd3llN58GUD5xOrm 17PLs7MBUaYj/OLy7Zn5b+RTkiof0WcO+4LojmjR5bKRUycOglclr9iE4ur3bivVeoIkLS/ThgvW NSQpWaelaiZsUyhGUmCTSfgfkGhsCivz/So36/xp9Xx4ef17Wite8aZj1aYjCmbFBW9uLi+AfBib FDWHbhqmm9XD8+rx6QU5jMsgKSmHmb55EwN3pHUna8bfaVI2Dn1BNqxbM1WxsstveT2Ru5gEMBdx VHkrSByzu11qIZcQV3HErW7SCeOPdlwvd6jueoUEOOBT+N3t6dbyNPrqFBonEtnLlGWkLRsjEc7e DOBC6qYigt28+eHx6fHw40ig93rDa+fQ9AD8lzblBK+l5rtO/N6ylsWhU5NxzFvS0KIz2OicqJJa d4IJqfYdaRpCi8jcWs1KnkydkhbUS7DNREFHBoGjIGUZkE9Qc57gaK6eX/94/vL8cvg8naecVUxx ak5urWTizNRF6UJu4xiWZYw2HAeUZZ2wJzigq1mV8sqohzgTwXNFGjyUzhxVCijd6W2nmAYO8aa0 cM8fQlIpCK98mOYiRtQVnClcyP3CuEijYLdhGUFDNFLFqXB4amPG3wmZBvowk4qytFd1sAqO4NVE adavyighLueUJW2eaV+SDo/vV08fgg2dlLmkay1b6NPKYiqdHo3MuCTm8HyJNd6QkqekYV1JdNPR PS0jomEU+2YmfwPa8GMbVjX6JLJLlCQphY5OkwnYMZL+1kbphNRdW+OQg4NiTyytWzNcpY2ZCczU SRpzfpqHz4fjc+wIFbcg4IrLlFN3HyuJGJ6WcUVg0FFMwfMCZaofSnTzZ6MZJ6IYE3UD7I1dHpkO 8I0s26ohah/tuqeKaKShPZXQfFgTWK//NHfPf61eYDirOxja88vdy/Pq7v7+6fXx5eHx47RKDadr s8CEGh72JIw9o7QbaZrQkVEkOkUtRRnoUCB0xCDEdJtLx82AzdQNMVI49mh2OGUl2ZsGkd4Mxc7v x8C4XJhFrXl0u75hoRwTAYvEtSyNQnHZmTVXtF3puRA2sD8d4NzRwGfHdiCbsclpS+w2D0C4aIZH f8QiqBmoTVkM3ihCAwQyhj0pS/TZhKv5EVMxUJma5TQpuTnt41L68x8V7dr+4aje9Si5krrgAtQw HKsJVEp08zKwcTxrbi7OXDjuhSA7B39+MR0JXjVr8A0zFvA4v/QUUFvp3vmlBUzLaLThCOn7Pw/v Xz8djqsPh7uX1+Ph2YD7yUawnirfkqrpElTzwLetBKm7pky6rGx14aj1XMm2dmZck5zZk8wckwZO Cc2Dz24N/4Sc7DwmaEa46qIYmoFuJ1W65WnjDEg1Afkk+xZe81TH3SeLV+mCO9rjMxDnW6YiYt8T FG3OYKWckdbgc/kKAgUHR9LjTvWXsg2nCw6fpQAeoZaZTZqpbHnExhVwfAeJ6rRHkYZ4lgfcXvAt QBfG2BWMrmsJoos2Bnwax1xa+cQAyDB2eYKNh71MGdgB8IRYzCFXqEsdjVyiet0YF0M5MmG+iQBu 1tNwfHeVzgIUAC0HJ4BcDEwA5wclbhsnljLfV963HzklUqLZ87ULxMCyBnvFbxk6eGbvpBKkop7V Dck0/BHTxUHkYbUGT8+vvSgFaECfU1YbT9Po1NDVobpew2jAduBwnEnU2fRhbcL0HfQkIHziIPGO atBwWtC17yZXL5CMHhGZXFaAAihnEZR1bhyo0abhd1cJ7gbjzhbM5zoOKSHgVmdtfDhtw3aO7sJP OOXO6tTS9WU1zytSZo5AmJG7AOOdugBdWK05DojweAAMjkSrAldnapRuuGbDysbOMvSSEKW4u1Vr pN0LPYd0no8+Qs1i4dnFIM5bxTo7sa0oICbSdidujBImh6aRAYsKvHOrZ6bTqdnv0UlDO5amUf1i JRx67cKQwgBhQN1GmFDN0+L0/Oxq5kX1Cbn6cPzwdPx893h/WLH/Hh7BJSNgeSk6ZeBfT+5VtFuj kuOd9/b7G7sZGG6E7WOwzU5fmNgiYOvdoEWXJPFOYtkm0UXVpVxCkAT2SoE70OdNYtoJidCioivW KTjNUviDmLAYtYO36Nl0XbRZBt6P8TrGUDquBhsmjDnDdCTPOB2SAq5RzngZjw6MRjSmTbub4GcS B+Ldu+vu0knPmTC9S/dgPCGazALtCtSuBdONaqnRwimjEPE750+2Td02nbEGzc2bw6cPlxc/YT7Y zTauwZB2uq1rLxsKXiJdW1d5hhOiDY6ZQG9PVWAfuQ2Nb96dwpPdzfl1nGCQra/w8cg8dmPKQpMu dTObA8JT3JYrhF29yeqylM6bgNrhicIEROr7FaOOQd8c9dYuhiPgynSYnDY2N0IBUgQnrKtzkKgw tQbenvXHbMCqmDMlE5kMKKOtgJXCFEnRVusFOiP6UTI7Hp4wVdkEElhHzZMyHLJuNWbRltAmEDBL R8q5c9tzMCKlB7UFQxr0lXckOi3qGawkt/su10ssW5M6dNAZWHhGVLmnmBNzQ4g6t7FQCZqu1Ddj NNXfKmiCW4YHAfeFUZt0Mzq7Pj7dH56fn46rly9/2yjai5l6RrcSOATxwXDAwplljDStYtZj9lGi Ntk5RzBlmWbcxFWTGWMNeAsgZZHOkIkVUXDcVOlzT3huBzOyQijbNbDHKDe9UxNV2kgJOg4T57WO h0lIQsTEJxKgjB6IzjqRcC8X08OspCxMbdz7PpcNQWDZ+ka+lx2ueHyUNtyQgoO6hIgAM3U4r5hp KPZwlMAdAh86b5mb/4NdIhuuGs9I9LD5BOYkuuaVSXsuzLPYoCoqE5BIMErUSwKvwWAHw7EZ1rrF vB8IdNn43mS9KaID/XrqayQd0gcjE3H17lrvorNEVBzx9gSi0XQRJ8Qu5oheG/s4UYKughhCcB5n NKJP4+PyP2Djl0ZivTCx9S8L8HdxOFWtlvGYXrAMfBPmZ+cm7JZXeCtBFwbSoy/TBd4lWeCbM3A1 8t35CWxXLggC3Su+W1zvDSf0sosH2Qa5sHbo1i+0Ah9PLBypWT5x0GiqwilY220zadcuSXm+jLMK EYMSKuu9zxqd8xrMic196Fb4aBB3H0BFvaNFfn0VguUmsBG84qIVRs1n4DeWe39QRv1A1C60ozQ4 AZ2IhqfzYn6k34jdzCR56SimUQNqVrJowhrHAXrZLoaX2zJgIwOe0ztgwFbMgcU+d9OyIxc4faRV cwR4rpUWDJz3WBetoFH4bUHkzr2uK2pm9aHTRepmASrjU2mMRMCrSlgOrS/iSLw1vL4KcX2og4UD PsaBWNukhWdXLFDQBbk2ZQQdqWeiLSNAxRSECjYtlCi5ZpXNNOGdZyBlQSCCAMw3lywndD9Djbvv 2WFEwC4vjJxUlGPcGevK3FzqAryfGE9e/RaXRXOyCgZRUDnZTevHOdHw56fHh5eno3dT5MTaw7Gu wvzBnEaROpammBNSvAly01EOhfGZ5NaI3hg/LozX23mzFXB+3TCx//KW7Pwawqdlr07WJf6PLXh+ jQTNl8Q8W/5uHYoXShO44TbxP+llTkGPgJpd2DJPVfUuLPd2vpJ4QQleX8xNs5gr71KsB15fxdya jdB1CQ7gpddkgmL+NLoWA8lF3L+b0F/lcB53t0B9yCyDUPDm7F965pdM9VMKV4pg5NJw3XAahkoZ eOLQAjQSiQR3JtRYRht9PzjaWGHgKHdeovCVg++M9/Ytu/FGWjfBqTZ2EGIQqTExp9o6zLKYEAXk B51OMXQ8kVoGCxJkqyHwSmzr6F7RKOXJIXxjsMcbHr+oQVY1CT1rMOUaQkjUCMS/ajLoMTnlzUQL Ui+eOfBCl5FWYzR6Z1Yd5WFhpCHhbDUDArxjiec/s7iLVtx252dnS6iLt4uoS7+Vx+7Msbq3Nwhw i692LGboqCK66NLWDaXrYq852j4Qf4Xn5dw/LoqZpF8v2lP0bHYL70YwB72wsiaPYhjoSIek5HkF HV74xxMkt2yNj+FcHozy7KDP3PHYxIiLjV/X2TzYJtXxLaQiNYkn6DBmkWD/ebbvyrTxblMGY3Mi yeGJutUUw9nsBz2a2Kd/DscVmKy7j4fPh8cXw4fQmq+e/sbaUyez3SeWnCxkn2nq71A9/6dH6TWv TV4/pjhFp0vGXOnoIV2Q8QA4HgODi8footuSNVuKzmsRcFtKVwCKlo593P5ujXxnwjjj+Awu4XSn AyFKPlO2ftYLF9TBzb4Gt8AIvgaFKNdtHTAToJ+bvqAPm9RuLtRA+qy4HbHxXbSTHp5ui5DWrEAe 1aeWV01V1wQmxiD8LTMwxTad3DCleMrcvKPfJaOxKjWXgoQzSkgDlmofQtumceMNA9xA3zKAZaSa jaIh8WjaroqMGiuDM6GbYiARWgf9TAFX6DIGaJ7O1nNEBnBfM/nDnBiSPFcgOvFbEjtf61kH3Gmr IeruUg26JeOle7U+5r375ULd0da5Imk49BAXkbDlpa4pypKMBQR2hBKCR1COS+vCZR8p+Wx1Es8g 2rbsxN73SwJhaSFPkCmWtlilifdXW6LQgpf7mN0cDyqpmXPcfXh/a+13gYgTMlo32YlZmL/DQtBR vXEsMwCJCerFXE9KhCG1zvwh1p4/P5T3rbLj4X9fD4/3X1bP93efvDhtODp+TG8OUy43WNyMqYpm AR1WfY1IPGvuyEbEUNmNrZ16i7gNjjZCFaphp769CSaUTCnNtzeRVcpgYLFQO0oPuL582L9+jxKb DETb8Jhj4S3vUkGKR/Nt6/H/WIdvnf83zXtxvqNwfgiFc/X++PBfe3Ef8b5ro8YXnf2aUuwc+16+ VOltRkjkssFVreS2Wzu5AB/xyyIi8CJMtnRn/C7wRHw4uGIsBdfAZuQUr+TX8KHl96k4LZZQWgRj qq/sNcRsUP3SdJW5Tr/wkaWsctXOQiMEFyDhi4vOJklVMzl4/vPueHg/92v9GdjXFH5UOCLNJTIW ZpJ6Htm6NbwRbTjKIn//6eDrRt8tGCBGrEuSpr5r7aEFq9rFgzZSNWwh/nCJhiuoqC2zqOG6yg1D xhk5t4LmDM3r2YfA5ashh1mq5PV5AKx+AG9hdXi5//lH97yiC5FLTE7EXHmDFMJ+ev6/waRcsYUq S0tAqphZR5xt6oQtAHM6cqC0Si7OYH1/b7nybuKw/iFpY+PuKyMwDxw0iF1TU4xnHVNtvgs1mvCx vSzr+FUdxMXxi6CKNW/fnsWvkHIWXXNMilZJoBv2OktcgVnYV7vnD493xy8r9vn1011wSvtY2lwM TLxm9L6bBQ4dFptIQeoh4M0ejp//AUWwSucGgKUxW5RxJYynBxG0ZTTlAQTn0RdngttqR+9iAraH VJ0gtMCgv5IV5nAgSLF3xhNptu1olocMXOiQOXDHkkuZl2wc7Uz9NYePx7vVh2H61v65JeQLBAN6 tnDeUq83To4Tb59bEKzb4KUWBhWb3dvzCw+kC3LeVTyEXby9DqFNTVpTaOG9+7w73v/58HK4x9TH T+8Pf8N4UavM9PwQOdirmlGSbRERGj4nzjRzkraEzFHNAwTd9vkZW9valog8/NYKsCwk8W9bTAqY dmu215i5zRaeovZkmP8ZyYKRTumJtjKJLizOphgjzrOi5qFqw6su0VsSPkjlUjGs4oqUOq3Dyh0L xcKWGELWcXjPBpy/WemcwWdtZevlmFIYMZtbo+C934b51b7TK0fDsZByHSBRo2JEyfNWtpEnZhr2 x1hC+/guWDVTBSZVg9m4vhR9TgABTJ9jW0Bas9GJ2aLbkdsHzrZesNsWvGH9gxaXF1Zv6bH20Dwx si0CusuLhDeYhe5mj0G1wFxV/4Y53B0IDeGMYuIOC6t6GeptkUen3WjO3zh8b73YsNh2CUzUvisI cIKjbzWhtRlOQITxBVZTtaoCFQpb4tU0h5W/ETnBwB09UvNSwtaNmRYxJpH+hyJe1S+Rn9+e9tM7 8iewbkF1TyZE2+UEEzd9igUTrFE0PoCKkfRyZ8+JfXfUlyoEg+mh9kJ6AZfKdqGMEN9C25epw0v5 yFQ1o2i7T6D6CkvHjQmbLBE6rHAzSpCcADkrEHS1r4NZzOCYyfIGTHa/4aYALZSKyKPEULglCo9b PeFprQpv9lCBY0kmXinG1htxyAPNowoVJxzq4Y6QUayIdiRGpi3mlVH7gwFBkYvoKIMZLlpiw/Tq hEMLtAN9E1WefquxYrj3gH0VAaEiXofAMoMLkzp9SPxpBZ731wqXMwQJbMToLaIaxI2J6WQIdUHV 9r8doLZOdfAJVNjcrm20eQw1rWYNu3B5Mdx49bp4lE7UUG6Jf9Tddt5QgAND1b6e1SdPvsPoNlG5 +emPu2cIhf+yjw7+Pj59eOgzdpM7CWT9Apzq2pANXlFwQ3WqJ2+U+GMk6JrxKlqO/xUHb2ClYMXx jY57OM2TFY3PLW7O/dOAUjPU3ocHxd2IntoW/Zdy4fagp2qrUxSDzT3FQSs6/rxINBafRh8ZZT+n aP2uQ+I973Hg6IkvcEWH/GKhjtKnertQzOhRXb77Fl4QKZyeCEhgcfPm+c+78zczHqgCFFsoe+5p sKx9C36H1vijE+NryI4Lc28X6bytQImCytmLRJZ6rmLN8+jx/m7sLynjN0s16Z9kjkFOdT59tZX9 2RxTemyEi4YPAqYrRhvrQvDnDMo8WTONQZ7k1rtNUVsNamMBadTPAm4MJMxvl6RTXfREsowJG6tt vOkMPmocjJvxsrEkdY27RtIUt7kzOxfT08Orsi5hGf6Dzpv/4xsOrblm77YKmLtznq64jY5k/x7u X1/u/vh0ML/ttDJVXy9OrJnwKhMNmtSJB3xQ7xFtT6Sp4q7i7sEgld71FrZFZzOaU1sakBmtOHx+ On5ZiSnjNr/bP1U4NFQkCVK1xI9gx3Iki4tlT2xjn1tnyndtO9fNG9lZuxVGGPj7Irl7Md6Pd/wN BJcVFmzVjRFkU7F5FTRK8PD75Tc9yHoPNPxRBde1oLPXZlgFphiexXhVfuSHa2xA2gVva2yVv0Tn yPf5nWhnSjnoWHHdcA9kXC/7Uyipurk6+/U6rjuWH2b4mEhXC67rVIgYwcOMt2Qf065RamGft3pn ATx9W9cV2yD3XRl8jA/inbobcqIUAbEwBKJvfhlAt7WUzoG4TVrHy729zIKS11ttH3hGmI/5Jnzy NKRbJl4mB2FmjZmMtR9GCDguHLMijrCYZE1WuZoK38dsZuEOqDNTSI0/bhLP6UJAnIAXWQiiYqbK pCfwshvc/NoUI2cx9Vw3zEYgxPMFl3XQwKFyL4D1OrFvnIYkhVFk1eHln6fjX3h9N2kw5wjSNYsl 0MCMOh44foHO9UqCDCzlJF6v0JQLb6cyJYw1WcqfY2ovluK3U51y+7V9SY8/axRlBQRwBvCGFgwj 1oLHwlUgqitXlMx3lxa0DjpDsKlxW+oMCRRRcTzOi9cLpZAWmSt8iyna2AMgS9E1bVUF2dA9Kl65 5iy+2rbhpomXRiA2k/G7sB43dRvvALelI8UyDpzDZSSv0TAs7PY0XReIAheAGloPYJ99m9bLAmoo FNl+hQKxsC8Qncr4jzph7/BnPkpbTEUPNLT9P86eZLlxHNlfcfThxcyhoq3V0qEOFAlJKHMzAS2u C8NVVk05xmU7bNW8nr9/mQBIAmBC7HiH6rYyEwux5o6VfUM3d06D//zH99/fnr7/4daeJTPg28nV u5+7y3Q/N2sdJWXa40UR6bQZ6EldJwHZC79+fmlq5xfndk5MrtuHjJe01KOw3pq1UYLL3lcDrJ5X 1NgrdJ4AY6gYKHlfsl5pvdIudBVPmjI1WTYDO0ERqtEP4wXbzOv0MNSeIoM7hbY+6mku08sVwRwo XSnFVpUyLr1NpGDe7tIws8q6ajFvG2oX8c5zEHCPlZjvFCTD9X2/SLm9V2oluE6z0kvtBTRaT0l+ y6q8gIQTKonj4Lks4sCZXQWSIMFMB6y+ko4bSceSOsOEtMZ4VfHE1k/q3zXfZNDDvCj88TD4LHCf GHS8JsNElA4bzy4ReWOMILLCfRrl9eJ6PKKdcxIW5ySbkKbWcQY/bCuljFLHfo/pikBETBkiaOZg PKPaiErLPl5uC48VmKfFoYwosYMzxvCjZlNny7fQOk/NHyppD6zNXJICmVUEU0PZXBfs0rYJa6Cb 3FuK2br7ffp9Ag7sT5NxzHH0M9R1vLrrVVFv5cqfQgVeC4pRbtBlxQuqmDrHKM+qhqCyYz4aoPZF 6AHvqBYku6PGr0Wv1v2q4pXoA+E86ANlFPoy4J7oa6whSESARW8I4P+2xN2Wq6o+MLsz/fAH5XZF I+Jtccuoft+tL01H7OoUGvD6rsX0KoyjW0q+64oSa2y7pmoq+aWKTDxEb9aIqey8/62jQDM+69Bx o9HqKy9SNENxkUiQg9xg4W5ZF0pP0mfMzCd8/uPtx9OP1/rHw8f5D+OU9vzw8fH04+m7l20cS8Sp NwoAQK26l8HUIGTM84QF/YkUjWIrpoHpQIL1gap6N6F0wW2lYl9SpRBOc2Zta3DiXqg47uW3aweh pJlSu2JSUGsIMszG6KjilZChwBRM2x+dJOYWMg7wpxZJvrqXoX1gSHbGv6qPwXDty2VVRnpioCIy 6rfdxbBine0Ur6gFnqMzgCgw97pt3ZFZhNqtPQVr/gwg04iEJ65PuYUhg5gsfOamJbbrdDMyWhjU 8njsUlGyfC8OXMa0vLLXtzaluUI+mee3PQkyK8mkcDgFuZ3+cyuq3umpOgLsTnBxpRPM5Y1yWojq rpJhFUkeC0o6quwspNVapeq174+jG6xs8lsqxr1yU+j1KTRb73EHFaZuFfe1m4VldefcTJjo7gsP LWfc8ealAVdhdXU+fZw9+6rq6q3cMDqFh2JwqwIk5iLnnn9Cq1TrVe8hbEVZx1FnVZSoi10HAD58 //fpfFU9PD69on32/Pr99dnRq0UeK9sNOsmortz9g5noWBKQhWD1UAeSgifCqycTazxiQjWFFbqA 7PvNAbBJoNFsT+2C+vz7dH59Pf+8ejz95+n7iQoUgLLbmO+iKtgZQO/hH92ZrNo7utFgk9YcrGGB ViFhbl3fxpT0dOAVS7UTYDdp6w3y+I5fr/62BvFyOj1+XJ1fr76doIdoU3pEe9KVkQ5GHYPQQPBm UrZYlQFZpVHrInrXt9zeUfp3b34NmOfljh5UQ7Apyd2N22VZujt6WSpbSp/JXpbBANA44g4Dib8v EmOFcO71yuwEnUcxZuUWYwzoTb+mJ7gUERzhoaubrx12nNKkNLcoZpVDq0k3UBtMdsNSm8tDY0+x d+1MTG5lUaTNDROS1Jk5IJvNlOj13LlaO8RcWFew+dW1iA5C+3SF53lGm9QUCbrZ92tqXJCB2yxk r1pl6A99A1RoSejeD/MahHCAyuqnzXVtO42zMZZBEnpeARGRPKLCCC9u2cCoBJx9IjJkiyRCO6Am JVsLBNBZZHWZ9YrWpaROJIVaHTxqONqpoxIxKnJC+PTB+O0YAzSV7a0Je3cf6VFRp3K3ciGYvrYH jKQ7y8onAk/TXopnRPJi73cSFm5oiuoSJChKw6va8f3E9aDtBOp2e6kl+lRDy0MRoU/oZYq/Me+a jFVj/I/FRnZrP7QlVLgP1bxNFJfxMJHYureiZmqg4PfXl/P76zOmxO+ucHMqfTz96+WAsQxIGL/C H+L329vr+9mOh7hEpv0qXr9BvU/PiD4Fq7lApe/dh8cTJgxS6K7T+GRHr65h2jbIih6BdnTYy+Pb 69PL2Y+nYnmifK9JftMp2Fb18b9P5+8/6fG2t93BCBOSOZmPL1dh9y6OSKtIFZU8sTVVBlBLwW/G oz5cGVBQe1/s5OeJlcmkITBHB4gE8liHPMHa2rIICmy8aJwWGzipuqZ2WatP8XBois/7YOWbVsea 69AvfTy8PT0CXyT0MBIcqzUksxvKLNu2WYr6eCTHcjZfUF+IJUCKobQzDUl1VCQTe9oDfe4ie56+ G77hqujb+nfaNXbL0pK8P2FwZFaunYujgYFItfMXuCEBPjZPInRCpo68SjfaxoOpF98++3Flz6+w Sd+7tb8+KLdRx5usASlXjwTfA7HYl6OsorYRKzFDV0qFH+hvtz+QJGjjy8gP7opcdJfE2DPf26Yf GGa+vJU+IpUuZd96rln2FOV3SeM8qDV9mHMnqfg+MOMKzfYVE/1iKPuZssAgoC8+bS9Dskh5DBpi Fa1ENNcmn8a0z8BiBF5EQ/R+l2K25RVPueS2c3LFNo7zj/5d83Hcg4mUZ45DmIFnmXPwmQrsx9Pw eFLhAmqZrd1cibDOWB6z9oUI1/W6vwPbwFwtqFonfLblnseaBljOV1b8Z1PckosKkFkCgRib3I5j y6TjawU/1bSJPhPw8H5+wn5fvT28f3gHIhaLqhsMXyFPd8Q36ZkUjdMBNC6pHIwXUDrcS3kaKr/d T6NgBSpqT4UB2DarPhm6YKEHlnN99r5SfeYO/gTWA58E0k8IyPeHlw8dJXuVPvyXGI6iKAPO0oDE DnB0X8S0lkrd1xvvKsr+rIrsz/Xzwwfc5T+f3vqMgBrWNXc/8gtLWOxtIITDJmpfGnQ6AzUo7Wmh csyFpg+3wirKb2v1plA9civ3sOOL2KmLxfb5iICNCRgGw2vNuP8FWSL6axkxcA1RuvYGjVkuvGUS ZR6g8ADRSrDcfR4rPF2au314e7MSZSgVkKJ6+I55x7w5LVBBccRxQ/O/8L8K07B5ufUsrFjF9cbm OhRQxbJjSqZ1GtlaavU5WXIzP/a+ksfbPpCJ1bgHjG8X19M+rYhX47ppz/mCnMnz6TnwAel0er05 9j6aVACqfqoMCfuqzl0nTVUqjfDVI/KqHZoT/VDY6fnHJ2SlH55eTo9XUOcFVaZqMYtns0CuaByT 1OuOM6+9pQf/fBim0pOFxLR/qBu0HZENFu5HYR5gGI0XRkx7+vj3p+LlU4wfGNIkYYuwRDZWSNhK WZtzuMmzz6NpHyo/T7sRHR4srcgHvrB3XuYs97LXOHh0yPMJdPRAHEPz/4IGKRmPwrZ6feyGIk7L JKmu/kf/fwxCVHb1S7vYkmeuInPn6U69pdydr6aJ4Yrdr9ytQotcZeL31GIFlRbTT2+no0rdV01C gLp03/AxUOB/eURb0ruCyvY4RKOUY4FHmRqy6LhY3CxpO3NDA8uaMno7LrvKX1fxqxlw4iAatJ7P pWWY6YhNCiC9QvcZo9QPDlwf7E8f3/sMXJTMxrNjDdK/HUveAV22FJjz7N5/j5avMgwvp4dhC9IA medd8nXWe/VJAW+OgQz2PBbLyVhM3ewkjfyUx2kh0K6DSaN47IoDsZjNJrM6W28CuX63wD+nlIkh KhOxXFyPI1tXzkU6Xl5fT3zI2E7KwnJR4EOxgJnNCMRqO7q5IeCqxeW1HT2axfPJzOIyEjGaL6zf eyOZ9mNbSnR02YaefaJPeFshVBvzWefgho8rHWuRrFnAarEvo5yTmWHH7l7Wv2FRQTeiqh6P1Cjp s5LBCZU552QzzwpTRzIQlmjwOrMmtU40PouO88XNzJK/NXw5iY/zHhS4wXqx3JZMOLe9wTI2ur6e kje39x3tl69uRte91a+hQbNTh4XtJkBMlnaQhDz99fBxxV8+zu+/f6kXxEziqzNKANj61TNeLo9w DDy94Z/2qErkPskv+H/Uay0ws6RTLiZ4klD7C71iVV7s0nGUR04pszMotqDaNTt0cHkM+C23FNsk pphRs332mW32YfHWkrExLA/6GmPWCFe/rTAVJm6mGb9tBBJFVEdW1fjSp3P7OmezY5biSZv4RqDL hOFSuo3RjDQga51trWNyiAKWcmcnqOeD0U/0ajRZTq/+sX56Px3g3z/7za1BykUzs6XcMpC62Lrj 0yJo99sOXQhHwr3YEWv40ZMG894b1U3A40W/b+Q7tXkbcFWoZ+hpTzy8+kgM9n6z85TU3Rlxp1Ii XYgWksxn+7tP24feeuFlELU/hjCongqowFawAXYJ7Uu2CbifQ/9E4BKA70Kuuwi9bS5XZlJooz8q HOh7Wu7oTwN4vVdzWhVC1IF290xuqf2v/AVUKNovq5NpFnrAZ8v91dx5ZVS+n7nnVdBfpY3Yc35/ +vb7DEerUY5HVgIER4hrLFV/s0h7omHyGCfeDgdsD1c9nGqT2M2Lz9IJPYRwZQdcPeV9uS3I4Fmr nSiJysYU1LKbCqSSw+NxMFDBhrm7lsnRZBSKOWsKpVFccWjEEfBFyuNChHzk2qKSFV5CaeYxOB1K 32aSzFZvV5pFX+2YYAflKIfg52I0GtXeyrUYPCg7od9YwsR8xw2pyLYbhBMqlzyie1PFNBzXUuHw 2JFMA92QKc3QI4LeqIgJjfDQVO+qonJ8SjSkzleLBfnWgVV4VRVR4u2E1ZTmNFdxhgdqwBksP9KD EYeWjuSbIqf3HFZGbzmd+x3Z6FDBkNdi98Gxl8Z7lYc8fk0ZLOClDYargPLfdQrt+S4j11K8Zalw /bUMqJb0wmnR9Hi1aHriOvSe0kvYPeNVtfMEycXyr4FFFANH6HyNf1wQRVQCAWfVxscaX1KnmRGa l7IqTFjPU1/uUk5xv3Yp9PJyzGjpmLYgil2eBHxErPpYtkuZIzKt2Hiw7+wrPn9HLhWd7ZJEbXfR wZYXLBRfjGe2ntlG+W9hsRF5RjDzGopDdx2QNza0uA3wfSCi9Rgq4h/fHWYabJ0+X75kA5MFMvie uU+3Z/ssCSjBxO2Gbl/c3odCNpqGoJUoL1zFeXqc1gGnaMDNFK8eworDRfQ6FOnR9IfHlbsIbsVi MaXPb0TNRlAtrWa8FV+haEgW9BotzDpvS8Ow3EwnAxecKilYRq/17L5yxVP4PboOzNWaRWk+0Fwe SdNYd5poEM3ai8VkMR44ITFSrfJSqYhxYKXtj5uBlQt/VkVeZPTBkLt958ASYXaRHBhJjJas/Tu8 X8Nisrx2T9Px7fAM53uecOdIVzm6EloGsQoWt06PUYUXOgXw1YqBq0XnxzC+Sq6zaqSSEpMV3zP0 1VjzAW6+ZLnA3IHkwN+lxcYNGLtLo8nxSLMyd2mQNYI6jyyvQ+g7MmOB3ZEdam4yh6u7i1FzFwpQ r7LBRVElzqdV8+vpwKpHF1XJnPs1CojWi9FkGQgIR5Qs6K1SLUbz5VAnYBVEgpywCkOMKhIlogyu fCe8SODd5MslREnG7ugqixTkPfjncJIi4AwPcPRpiofkS8FT9/kfES/H1xPKZOCUcnYG/FwGHlMD 1Gg5MNEic3N4sZLHocfZkHY5GgXYe0ROh05TUcSoMDnSgr2Q6sJwPk9msPD/xtS5bzFso7K8z1jA wIbLg9GKrBgDr/LAfcF3A524z4sS5ByHLT3E9THdeLu3X1ay7U46h6mGDJRyS6BHMnAYmPtBBMJ0 ZUrGRFl17t2bAH7W1dZ7F97B7jFBKJeUCcOq9sC/5m4qAQ2pD7PQgmsJ6If/rMq1qYcw/kRHHj46 DU2awliHaNZJEnDx5mXAQ1xFJa6Q6abVINv7UHhNph1y99779saRWVBeEq2Pcg9rtZgGkiGVJQ0X XgHV0vb14/zp4+nxdIWhQ0a9rahOp0cTgYWYJuYvenx4O5/e+xr5Q2o7DeOvTuOW6VuHwsmtex1t Lz3gJbezEN/jVprZMZs2ytKvENhGcCZQ3ovVPqoS3GHgtwUalOjpqbjIZpRR3q60E4YoJAPGLjim NmdPoKvICNkUruUQKKTgNMKOb7ThMkD/9T6xGQAbpVSBLHc1EYeBzCSNTtuxynXYNT7RGBBvO6rt QXD69thnR1Rx0gfJ7guXYlcHMhrBzpkGNfXaAOG1ap02bUCcZRPgIiFMZi9vv89By5yKbbTNmPCz Fwepoes1Zl5MQ+7YmggjlUOGE02h82nehh611URZJCt+9IlaJ9JnfJ3n6QWOmh8P2mHEL19gRtyL /fhS3F8mYPshvHcUWcMd8gzTJW/Z/aqIKkeT3sDgQKRvJYugnM0Wi79DRHHbHYm8XdFduJOj68CT vA7NzSDNeDQfoElMcoBqvqADulvK9Bb6e5nED8SlKdQiDaSzaQllHM2nI9p9yiZaTEcDU6HX8sC3 ZYvJmD5EHJrJAE0WHW8ms+UAUUzv4I6grEZjWrfd0uTsIAMGyJYGs0ag6mugOSO9DUxckSZrLrbm DY2BGmVxiA4RbRHvqHb54Irid2IesG50qyAb17LYxVsvs2af8igHG8zkrXoSk7rTuqPNcv3An3Up xgSojlI7Y0UHX90nFBg1IvD/sqSQIOlEpfs0PIEEodCNuGhJ4nsVykO2y9ds5byP0+FUytnmYZaO a27xLEWeIJCWxOogQxYtoKKxWlPTSCbT6IjW+C6Jb63t0PtM/X2ximaUvOIXPEQ1gU7vhp28QLSK s9nyJpTLHini+6ik89RpPA6q7xHlkezF8XiMLlUSPIzNt7ZL5nJDHV0ofUF7n2O+TdogpElUdslA NltNgCMr4ooFdP1mB3IR0svxaU/XrwWph/dHFY7G/yyukANz0pw7Kc0Ix2OPQv2s+eJ6OvaB8F/X i1GDY7kYxzejax8OrJjmAVxozJ3zRENBgNXQTmRU8Co6kIOhscblAEpeIAJs5gWHu5VUcU30SN/l bp92IugYvYky1rdFG2GamqDW14vioDXP+fPh/eE7Srw912Up7x2VSigH9HJRl/LeOhbNq+shoH55 5fN41kYrpCp0GOP9MGqy9cc7vT89PPcd7/URo1N9x05yeo1YjGfXJBBEbzjAVThYE+NE02nvcWeK G9RoPptdR/U+AlDoErfp1yg9U4k9bKJYu3IFOm3nXXd6aXtT2gh2jKpQ/zOWA/dE+RLYVHlV71QI 3pTCVvgSU8ZaErIhlWY8IS0HztcdvJdhXeTg+FZyvFhQFjabKHXelneGg7fLLX99+YQwqEStO6Uo IhykTXH8+JSTid8MhfsqhQW05tuv9UvAz9+gkVPgdEJEQyHiOD8G9GMNxWjOxU3ASmSIYH5XrEqi gJ+foTJH4xcZbYJZm1zSITK0xQ1WVQUsGBpdleGDGtBrAcNYDrWhqHi+TtlxiBR31NfRhBb9mjEv fe/VNk7KOeO8xZLFstK5mYilkmPUFcbVBxxjW3FDSpqnz+tNYLXlxdciZH7eoeI5UKOKd66Fl8nI 7zhGonuP9HY1wPVQVnBmkq/6VEoV6Rj0y2YzUfSll0LK+KESJTr+CCQX4FryJA28BpCtjK5b60XX keuotT2Yx96Iwsj6cs/5TRT5fUAjnx3oNEMmgtMsi+Zb48XNZP6XB83hWvWXD/Q+C9iRAHXr4ZqK 9k7wIRAaJq378JI06MJQbtST3v13XWUM/0oydomlsXmUyTZ7pPe9ZdMkcOlxMRZLq6YD1uYO03SV 9FsGDhGGhuv0DX3dGDD6fQ2kHcKlHqgHCD5qyzbOwzQIVSIyhgg62slxbJ7Wo5YcIvF1TbZ3q8p2 x+buyn4/n5/enk9/wQhgF1VsJ3F3mWJhwaYhSGU8nVwHku8bmjKOlrMpZfh1Kf7qdRszKvSBWXqM y9SJsLj4XXZ5k5TDzUqFCE+YR1CU4uvmsg+E7jYDio21DDWmVugG09i1rqBmgP98/TjTOXqc8YpS PppNqNTlLXY+8XsEwOPEXycYoh14C86g0Z35Er7OSsqBDLEgkY3cXnBhZ83VkEz6nSo5P9LSOmJz 5ZcTalO78cCS3Pm1Ci5msyV9sRr8fEIrZw16OadYQ0TubX9sAyirNn2nSqQVmEsRu3djdzD89+N8 +nX1DTNxmEjyf/yC9fH836vTr2+nR7Q3/mmoPgG3iSHm//RrjzG7RyAdI+ITJvgmV/FULoPpIUXq ZZ7z8BgPj4nIhptx5AzEsc34urcEWMb2oSl2L6UGUusE2vopajdgH0luWQanQaDGQill3Tph+7Zf 5R0CPNMhERasNb6bl+fg8ngBTgxQf+qd/WAswT35U7XVxk07fZZRIWq4X3vLozj/1EeYqdxaI45s oa92z9m7E+NDp5K3QOWOEvAUiloVCmiCLS+VU7GqIPH3PlsHTQZdRDsSPGUHSEJXvH3zWuUmAVkg wFOJMiNjCG3TL/z4P8qeZTlyHMdfccxhozsmelukXtShDkpJmVZbylRJyky5LhmeKne1Y112he2a 6d6vX4LUgw9Q7r04nABEgiRIgCQIaGpaHn91auC3OeadAD8+wLtNJQwnLwA0tmKd6WGV+c8VN4B9 3wCFJUMAG+vCdDsUmlUiW/2NsLXQwhUqceryHtEo6Kh1PRON83vm8qvIMPv2/GLrzb7hbXj+/D+2 FQWZYUjI2GUy/eTEFJFyr0b3E7g03TtyxUBo3df7+ys+0/jc/SKC9/AJLWp7/W9XPbDjVMZJx92c aieuzHtGG99fI9CzoRn4U42fbhhkB9NNcfKdsXpy5qPcw75RYazcS1tRIeD/LYAprNaCULYEMCnH IjEpkBjzVdUErrOG+p3HVr7sBhJ6A/bxJr3lO8rSEW9iJOKbi7a9PZWFozdHsup2PyARGM0a28Pg uhOcK0z3fOtdGUk0bLKC78y5KsK3whNVXuxPRftelUVdl323Obb4gjGR7Yq63Jfvcsb3ru/S/JZ2 DeSCfYesKs7l+3x1x31bdsX73d+XO7tSGQSLryivd69X3x+ePr+9PGLOZC6SWcj5IiXPGnWAyOvR gGeWjDEfEqpSXMZoIMZHZfvRfFwgp4rDdBNFdbedmtxRwDJtXzeDLidiQM2wdgIobsu9ZR8oQ8p8 u/v+nduaghXEypDNqnNHqA6Bzs9pg99VCTQcMbux83ritjEFXamuvbI9GxZ1sbYaSHix/0Ro7OzY 8jAYJZ0GFoYGbLb5jI64bMebUD2rJtaNUovx5faXEQu3LUZHq6VvY8KYWWXZs9hqYpdh3sITyifE LOVc7uFBvVXQuSNRFjBca6xxPm9kBPT+z+9c3dotGp13zF6UUD1IoiKiHgalZpNGqBkBR16TwWGC j59aLwQx5nM7orcsRESrb8qMMuKh/YX0hpxo23y9lzY554XU55NVnwyV4+Lyt3T/6dL3ldExcjNk AJu0qtPOqqDNwj5kvlOYKsrmcxatI9Azf72ruij0WGTwIcAsMgdTgBNijnz/sR5YZEttzfBX5jM2 NEviwCQJtIlrD8scT3ldqOWxicXVpmeOixIpr1ybHpzTtrGWNxHSHNylidmHIvC3QNHAQLV55lMy qO1E2mMu77sdVxipI/Kn4J1bl2rS7zOZtAj55T8P4wazvnt903rrTKa0NOAUpr8xXHB5RwOGnQao JOSsuinPCP2wYIF3u1LtAoRJlfnu8e7f9zrf4x6W24u1wbXEdPjR94yHRnmhxpqCYGiZEiUijJrB gjFS4ruKj5zFO5zuVBrm4YdoWjmOkzSdBjvt1SlcLfD9S6bGOtCRDEeEapQuFREzz4Ugrp5ihRlE CiUiMaoJdLlSbFeR66EtOvT6Y84E0VSaM4UKd6ddUImuz7V6l9DkqcRrS9Zod6V5Btmw+KzB7+m4 mmUJDWUB2IiKtfgCEqutEBI8VatAwRtBh4qQzxaDcCqyg3N6bi14ESZMI9uX7Ew9oky2CQ5DHHk4 nGmPSDXMWlWCgNpFdmrSyYlzDSgfuRrA6fPNRxoPw4CxNKKcbmMm3XWOZSec+U8Tz/dsVjmchFhX GfB0aKhnDStAuT23PRZ8Y5ketcy0Y0FcjEjsBWivjzj8Kl4joo63eVMzuN3GZcXH7JmJRMiz52OS VjUsRvcNE4GubpYSxciiJfZ+FGLypHBDgjCOsVI5n0mMlSpR2DnJRMGFISDhgH0sUOijSZWChmjN gIpRg1ShCGXNCIIliOgBImEIoqs3foD0jLQsE0RahezBfSRNAoKgRy8HTAbbPvQc7uZTvW2fBKEj DdvUljxJEvQ9k7Eki5+Xk5oBT4LG43F5XCB9je7e+JYLc20bQ1Ruyv64O7bHpSgLpYn7jM3jgGC8 agQMKTaviadm9dARoQsRuRCJA+E76iBxjDeoTmjgeNo70/S8UX+HBpu2GkVEMe44IvZw7gDlcv0Z aTof3ZQu+CyO0J4fyss23YO7Cre0K6z+GwbRiVarvyHeuzTbtCbhtdMcmBmqcwjm0O5u0b4Ax+6u dvlmTY3dOB9qzyRN4XAbHAn6oSEYBxn/k5btBYLer1aRdxFdZwICwNI1acmLquKrWY3xUYY3vK9w H8uxw2PCbfKtPebiuIhudxgm9OOww+qrM+LHzDffXZkFdNm1fkg/YXZVSJjT8XCmod57NNwyw93p FQqXc54kuC6vI+KvTZeSb12NhXfp99BD4v3CvSRMAXSoehavMvRb5rBhJgI+aVpC3xEnyHeRolFW Zgqh4JBlViJiJ0L3PtKQCdIZ4NhDQmS1AQQVFrfNPaAotp3XKAL3x44ndDoN7rQyCzm3qiIvwuwU jYQgikcgIkTrASJB+pbDfRL7SP9BXGN0tRYIH688igJErwhEiOoVgUrWJVPyiFp9y8LQ+KhSr6uh LSAlJTKL+iwKA+STYr+lZFNnrtlXtzFfIlCrpKojzHxf0LGPCGQdY9OhjrG5UMcMr5it9Q+8R3R8 tq7TOQG2p1jQCTqsHL46i+rEwU4SUh8/u9BoUGc8nQKdo9J5dH2OAk1A10Vy32fyfK3srNzKJmnW 8xmJG+cqTRyvzXhOwXf9yNzaN1kdqyHplmZsWZgoU6Ix37HNlLXLSVk1W2kUrfAnKGK0zzd8X91s 11QCxP3PttsG5a7cd82xvZRN12DxVWey1g8pRa0ljmJetLZXKNumCwMPWT/KrooYNzqwmUj5bj1y aAKaxNgWV6HwGa6DxuV/jV25ymPscgz15HKOFcxxjjwt+lrL3lE+fhAEuMpgEcO0T8P7A1njmqHg egxLddB0gcdVMYoJ/ShG1M8xyxMPM4sAQT20T4a8KQhdt3s+VREeyXEiUO6ZTMx1T5BmczCmqzjY /xMFZxi19Bm1EXldcI2OSGzBzefAQxQQR1DiQERwPInUXndZENcrmAQZPInb+Am6A+76vovRA6fl +5obEvj+NCOU5Yyszbo072JGsTMB3k6GDUm5T6mHyBrAsUWXw32KFdRnMWJq9Nd1httFfd0Qb02F CgJkyAQcNRE4Bk94ohKgvNdNSJCqILhU1hzHHYeNjFiUIoieUIKu06eeUX99eTozP4597ApBpWAE mYuASAi6LRQourYTFxSoySIw6zYUJ6n4koqmCdRpoj2yH+aoiMbXyB5aYorrLcqYdc2MkuixQ1Zd wufZAg8/rCMUm6y/8QjBFk5hOaXaOc8IWskPP1F0fdqXnR7cYMIVddHuij28vgX2DtstHF+kt5e6 ++DZlQn7fqWqc1uKZ+iXvi0bpLoxUfZldzhxtormci67AmuVSriFgxuRUBPtPewTkY21a9Js/RN3 6QjhKr9AsEn3O/HnnYIW5lwlydu9MUGlw1peRvBYpT2aTuQJ0ueBd/k37K20TM0iqsqqVF2RuE1y aW7goq5uMNGTX3aH7JL33USATwpO6gfegHChlgYkWDnzDetqWUaDsmuN5/mZO9YZ06fq7SjS4HPa Z9f5AV1Fuw3vw64rN9oj7W6j/YBXnmrkNfFVVkKoNPzrCasDpwy5WSneCuNf6kTaQrdgHa6Im6xO 1WKXexOOsEZYPMH6/cfTZ5Hi1IrlN35ab3PjOYqACFcnHQbH4bqig1gx0n8MDcQpPkp7ymIPqUJE CvG0JJcAVbyv1GLETScGG0/RlhvyLUQiyosWC9UvGBa3roP5DUBD6o4HMpHgqnFCR5iJMyN9vQHm da5gPSP+YHbKCESb2tCIYqG2uJF9aSCNulIpwHgZmmkPhcip+fGYtjfIq5+qyXR/TwB0egSaZc0R XZxd9zBVHa9j5xrhdb/Qvn+HzoiuiJA1dXbZoGkdBI2IpqQ3XPjsZfUhV9sLCNNrD2CMNTXTt10L 2C0WAh95mIOclNX5ztmQ4SGOowQ7fZvRLDAkSl6nxwiQhggwwSgTZgD7yI9MKeUw6+PpmFEHt0V/ 1CGTW4BykjNCYIFHoLo4ikJnxzoVKC6NDZj0qDQ7tyuylbj4QFAGcTRYNBoFJPiVkkWNNtsbaAGt Q49YnABwJVwRkNzcMi4i2MqSbobQs9PlpRufjGAX97ddph4FA6yHDMy+H3Jd32Vpbi01VeMnAX7u J9EsdkQGHEuvaiygsRAJyx8W3AqIFzrSKQmfA8e9sUTGrvlme8Iu0MSa3MA1bxeaeGD+TnOgnaHS f1YvTcItLaOT8DXG1+SkP1eB59vjqRJAjPW1AT9XhMY+ooar2g/NWbP4ExvwyQFYgU3e+lpD07b8 dNinq9qUb9YC9BhqRPrm/B6d2KzZZnoYLzCUVjoeq7AsT/xA89JdNZ2mbyG3XZXK968mSHqOqN2y oLblUPB+O1Q9frG4UELUi6MIPbPvjkYMhoUKNixivzLToX2+fMA10o6h76s1mlHZWag06xmLQhSV h37CcDZHS/Ad3qSRucrabAsin09G5Tu1zFbmO3TSVFzlhpNQfaYbOPwASBGHdM9N7RA7oV6IdBW4 wMuuSnwvxKuHs34aE8wQXohgVY8JVrbAULxo4Zz3Xi8D0TvtgnuDkCVo9RwVxRGGwmwmHcvX5NV6 xcl+gNYrUBEq+ZZ5ZaAoOikESs1RbKASd4GqLaahhGnobD63DOk77R93FLo+0PExc9XAkQy9D1Vo GsZCvH+57UhQgQMM9V2YEO+LyTrF+BRW6iqbs+FhY7I0CUJUDBQDFqm12R4/OVJlKUQnxjwX3wKJ Xn8bNAnKXpt2zQae1jalGlTykvZ9ub9Fv5itZBvVB0y3WlWc00tTJapP6OnAQtJVu9BMIrZg4YKM RI48jhpZRF1X4TpZ6FFsW2USxQ4dM1mQf6sm1MvDICI+ujjYtqWB015oabjJhLRwpl2lY0LHEEir CW+vvZEaMdm4x1oqA8j+0JfbUnVXFykDBA7eSxihOUQh17HvuNIUXxUZvnc6wjndseoKBnROkjYt 9911mh/OJpnGIMKchuBmHQTKw83dkXCTtycR6agrqiLTDirGR8BfHu4mc/Ptr+/qM6ixm9Ja5ASf mdGw6T6tDnw7cHIR5OWu7CFIoJOiTeElmwPZ5a0LNT0YduHFqxK1D+cHu1aTla74/Pxyj0XMOJV5 ITKXOIeL/wDX20qVtPy0WbbKWv1aPVr9c3io5++wF7AHZK4HisdKtkoYkxp/fXi7e7zqT3bJwKeW jBgAXK2P6YDb7gOJlr4AZH67T+HcrS73hxZ3fRFkBUTr4hMGLiQulUgHjR6bA/GxKpRdzJxY2WJb lVz7+mCUjqycBh9l7hRUiwS5s5UDWyaZdoLO5XOtGK0zhES6ck5PedWvfpqTrf88ZZjR2gclQWbm vDf2Erp8q++0Jeju6fPD4+Pdy1/IkbyczH2fqqeu4zw67oVISx5+vL49f3v433sYj7cfT2jfiy/G 46qV1UmS9XlKRCjVv0HIKKrcLKp4QBsx1qXuPQxswvTH9hq6SMPY4Xhq0+HObypd3VNj2+gg0o5C TZzvxFHds8rAEvR5pkoEKS+MAyUFO2TUo5iniE4Uau5EOi5w4uqh4h+G3Ro2RhTjiM+CgNsjjnND lTAdKIkc5+iW2KB+MSrZNvM84pAtgaMrOMc4jlVTZ1sZa7uIdyS+ymlFHdPE896X366kJMT2MCpR 2SfEd8yylsmQb/jQ+R5ptzj2Y01ywjsjcHSUwG94Y7V3/NiSpK5Vr/dXfMm82r5wvcg/mcNxiRO3 17e7py93L1+ufnq9e7t/fHx4u//56neFVFl0u37jcatUV5IcCL5t6vhI8Ikb4n86VYHAo54dIzYi xPvTrAqgxFDSfDLoD0cFlLG884k+B7BWfxYRs/55xZXMy/3rGwQY1tuva/N2wPxKhG4bl9aM5rnB dgmTzOB6z1gQUwzoT6qGg37pnOOi8ZUNNMD9ZGYs9a0+6n2C2/iA/VTxUfWxI40Fa4pCeE0C6lmD xtdJZsvHJvLQHfv8kS1pQihwScPV5zgwfGuP7T6nYfM8PcTG9JXrlQXgT0VHBvSqUHw9rhG5ucde kHLI8DV6YQBTj7KMdJx1lhREGDBGgNZIcTm1Z1LfcT3nGic+xzyTC4hKlBKsQznDMbEmJIh5f/WT cwKqHDbcPDG5BthgNY/GSO9wILXEB4TWx07WxglvzOYqCmTYBKttgcHFfugju3d6PzSmPcwlP/R1 YF5uoGvrDQ7OzGZwRAwIVzskukE+S1xZLJWW4XeMQJBuE4+4pkGRWTIKs9WPLHHMKdeZ5r4RoAEx t5NtX1HmexjQHlxYet3Mf8oJ18awRzvkqFxmo4JYWXphIcBTjC/9R1F5sVdkuezFFitp33FO9nyn /MdV+u3+5eHz3dOvN3wDffd01S/z5tdMKDO+L1rhl0sl9Tz8HgHwhzYkrhuUCU+c82WT1X5or9HV Lu99f6XWkQA3RRWCCH8yKSnM7HPmLPcMhZIeWUgpBrvwPkQXCmKvX2WXry9geimJI9PaOBuZWymK hZV63WQfiIp1A+G//p/c9Bl4YLkGU9gjgT8HzJtOIpSyr56fHv8ajc5fm6rS1+umqiw1IJQfb6jn oQ+9DRpx3C4T3BTZdLIzxa0X+WiFlYTYaX4y3P7mqKDab65paAkpQDG/rhHZ6E+DZqir++AuUIuB NAPtgiTYtY7Cvt/QD9WuY7sqRIC2Ck/7DbeHHSGLxvUoisI/Xe0YaOiF1nwQOy+6pjxANzguKwB9 fWiPnY9dlYqPu+zQ08Ks9bqoir0d8jJ7/vbt+Un4tIocoVc/FfvQo5T8/E68+UnReIlr5LuGIrst a1MlCu2fnx9fIbYul9D7x+fvV0/3/3EZM/mxrm8vW+Ro1D6nEoXvXu6+//Hw+dWOBpzuNMXOf0JA fLTjBQ7NOS4wtWLtjIAoMMsWrn+OEmSAer0Qmc9eBUBMYgNm5CQHULHdllmButdL78Ndr2yzT7v0 krYbCyCOcXfNUT/CBWR3LnuIiHvAHH9zNS46/3Gpy6bkBm+pQ3PeR8fBzjkhcCKUSV1j0K6otnAc quNu6m7MzmDDt5sFtYjvXCBnpO4gpV1zqA6720tbbNHzXP7BdgMxU2c3eb0qiYRUw8Lb/gM3VGx0 VaQipnQn4sDpBUAykEuRlzkc0tYQFN/qsUyNLw+wvjcKgQwqaE9wShS+K+pLd82ZQbEno/iOD/uc ywo8pu6fPj9/gQuDl6s/7h+/8/8g34C+UvDvZHoRbkmj++KRoCsrEgV6hSJ3w9CII9ZEDXFqIccL QSWkpos3aRq2tZKMbnkeoID1JrRpjk8pQPLpbqSWWKC8XfhSvlBkJXo6shCA31TTG4Mz4nZp20tZ 3s4mTpo1Vz+lP748PPMVvnl55s15fX75mf94+v3h64+XO7gPMQcJIq+kjkDgf6/A0dp5/f5499dV 8fT14en+/SrReCIL8tJpERlXS1e/3h+OpyJV/HlHwJQjMOsH+wJwopGXSSEKnp4HffCXtugEdY3n /9Gp+MqKBfVUeBex8SpIcWnIfaK+bJ0gF5EA5dK0h03x4R//sNBZ2vTHtrgUbXtokc8hNxBknp0J 9BkMJKMYWlbEl5dvvz5wgqv8/l8/vvKB+WotAPD5WZTsnAyCxhWmUCfgXaw6mc3I7swNg3023gZe DhvI+tGtEcq0UXm6Qxs8Rts+ukRUlrWoJLuE6nDmEnfiSlfkLRPhyV36RanytKnS/c2lOPFVB2Ff Ek0pEptanSHIYOiDxKfv7w98v7v78QDJYg7f3x64XTbNT0uqRDdBPYdjD1pN12uzXMgnc8Jj4Ng1 xT7/wK1fi/K64KvVpkh7mdjslFZAZtNxSSzqpp/r5RsCiwbsk7b4eITL0c2xuz2nZf+BYfx1XLur TbAIRIqHCvKt5cdWKneC9Ohaz2lqc2cq9xO3REz5ONXn3RY9qAS9XKdanKERFhnHohLqR/jmFxZR 01Sqd+mOmiV/HCodsDlk18a8adJ9US1bWrkMN3dP94+vutgIQpdrF7aWj4Vo9bdlvisQBhaMxsey gdm8PHz5em+wJL1NyoH/M8RsMOyIGZs3GHt22foAFP0+PZW4fy7gs7LlG7bLxwJ9UyAkYXMYhJ+I YREKTWWOd587haYllJnkfLwd1Nr2QrChbkAERXpKzVEoBulCBN5bfOZhQnL5P86eZbmRHMf7foVi DhPdEds7Uup96AOVmZJYypeTKVmuS4bbpXYpyra8thzbNV+/AJkPkgmmZ+ZQDwFI8A0CJAikOWZs keuovNlzw2aRDeOrKhlePY7rt/vn0+CPjz//BDUtsJMEgwrvxwHGt2r5AEw6aN3pIL3xtRYtdWqi C4BBoL/hgd+Ykw8PJgmvJazCGv0noiiHPaWD8NPsDgpjHQSPoQtXETc/EWACkLwQQfJCBM0LhiLk m6QEmctZYnQBNqnYVhhygiIJ/NOlaPFQXhGFLXurFan+PBs7NVzDsg+DUt+hpRHm71dWm8CaNLJl ACxOg7CyRUy+BY9k2wsuX+p3p833OmlVx0sFh0IuQoNhFnv2bxiTdVpiyqI0SdTQ6D3l34FEs49u WjTLzenEwKLBBM4GkIO1afOFbhhRltFaHpObfZZM9Jtc7NcNs9ilsP3K1GU0SzEKrIebyNY6f2hA 5sOUFtx5NdKiGrWVLj/nB7vGCHK+wKnx7mRWNcUnBfO5GUYY53a4GE7n9E0HTkYZft7RDGkQGl2j QPbb1xZB1o+g620pK+5GpAONwlklM0yF7ugPxG2OxAef9KMYm6t43BGk9sbRgDqzqQIz3w8jqyaw P7n6AHYqumpJmIKM5GYZu7vcFEVj2EKtwhCkauEqU1L0zNFDmgZpSjlHIbJYzDyz2wpQZEJLOLB8 Z9Uri6nDbZRFLI/tDbGCwR7LYrQdjB41kP4edGI60Cbw2YQghF1I+QzUscBWoI4ei4mhuSI/In6y HEX5ZozmFYew9JI0NluIdzmeJboqmPTc3FgTscZ1l2TXy1Bv4nxkHF6T6oncgVb3Dz+ezo/fr4O/ DyI/qN2DO0fNgCv9iAlRJXZua4mYbkbJZgk6vmrxuyLw9FvwFmO/l2wx3bgCLa56HkQOf0slA/ES vddS3PhpXN5GYUBVQLAtyxmFadz+qUKDbLEg7yotGjOQcovsCfOucVCv/2gO0Kez8fKzznG9DGxJ zFdyGv/D1BvOo4zCrYLZSH/SpRWY+0c/MYyrTyZmzQNUD4zqo82sbRBrZ/VgGBlZpPE3RprFVMqw PIk2ahRSrzF5VRg/2heeZ7jjda5p2kJFuk+6jgZbUOs762zLjTBT8LNNtVDkYbIptuTgAWHO6Ox+ +y1pPyDrKtpLrY6K19MDXu7iB8SdGX7BJkXoO6sAO1C+p2/7JdZedCZ2DzYDvX3JbgijHaeNAESr FIc9aA6/evDpfuPI1obomPksino+l+6abvSdPJp04mHsNqnM/+ckCfEqaO1GR6Hv2BAl+usudNd+ E8Yrngdu/Dp3s95EYC6njiigSHAA1TYK6EsExEPN5AGLm+DO3S23LCpSOnC7Kju8FWnCaa1HVv8u 7wSNMgi4zxzKhMQWbtwXtsrdc6K45cnWYdWqbkkEmIqutJdIEvnuUGoSH7rHNAqT9EC/5pLodMN7 V7pUU2MYd3f7YxibvKf6Mbtbg3LgLgOscLkw3By4n6ciXdMGiaRI8Zy2Z+7H+6jg/fMvcUTXQVya FyGdvRSxGVjPIJdghbgHIgsLholX3QQguXAHdOIjhg+jYJK712CWc9CLnGjBeF8zBIvFPqEtOonH ZAR27D2TogiZW4QANowE7EShuwVQgSzqkTJ57B6kDZ7cMtEjoEXM8uJLetdbRMF7FgxIIRH2rLdi C4vZ3QXFNgeTRmVxcxLtcY8vM0H71khxyHmc9oikI09idxu+hnna2wNf7wLY4XsWpArgWG73dFpU uc1HmVVApUBR2kdzUW8qSw1DvEK31BszEbz+WY3QgbU2hM9T0y1YYI7TQsQT71IRDFIWjWF6eSDB Psp4N2W7RgD/TVyB6BAP6vG23DJRbv3AKt3xBdiStVKHRNhUTaNr4Nn3n+/nB+jz6P4n7SeVpJlk ePRDxxUBYlUyWVcTC7Y9pHZlm9HoqYdVCAs2IS3pi7us79lxCgOqnH5Imjim3yLHGCp0186BGtKc HWrphMX1/PCDfpVZfbRPBFuHmARvH3cd2XQu28v7FT0Dave1oIdrwddxGVMnpQ3JF7lLJuV4cSTa kk/1aMtJiNe9gXbQjL+UDW+YUQ207GzhXRK5x8Impt/bS/QqR/MvwVv77S06UyWb1j8HtReiQ+WH LKPuoiRKnhoMrYIk0KOA4y5QJaIwC1QBM1xlJmExMa7lJPQ2Z1mHkcrQS7+/kQTOAGWqehgZjI4P 0ODJ+GUVdjo0/USrUQoPmFeYU2dZba2n3S8r+CeVRqqZIyux6ikVbwoteYcMacgcD2YlvhtDyMQ3 YRZczVwFnpEpQQKrgItiYgWCV/NCRW9xMSx8huEtLI5F5E+Xo858ITLfNfOU9NKV2LQwrsMVJy1A oLWWpPP0H0/nlx+/jH6VwjffrAaVpfCBaXKpjXjwS6vD/KqdCspOQ80vtmsQHe38yTUcRsE9ROiK 4caCjjtfrHpGWIW+Q1+gmIgDjK0s3s6Pj8btmvoQJNHGuLLTwdLBIu80psamIMG2KXXbYJDFReBg 37iVOPC6IxVdBT+jfbYMIuaDCssL2hgyKB3KiEFTh5mWl0iyf8+vV3yJ8j64qk5up1Ryuv55frqi D6P0eBv8gmNxvX97PF1/7Uj3ptdzBjZwmHzasypWh7NzwAzj1BZvEIEMNzx5LQ54Bpc4S2D7wCEA 8U4GowjzyNXxHP5O+Iol1CldCAp3CSILQ04IP99rl80SReikCCc45YVfGpfVCMC0DrPFaFFhGh6I k3s3wSjAmL94mq97RTSw7sWqhjt0bgaVF0zMuk4TAAQtfGPcESGsidQH2kICZqOJxRC3euEMI8Uw 0JA2QUzduSnhzgGpu+5i6GwAaYDoaAIwbqMJOcIIJ0cwkZKbOCuDTCGbisjrhC0WVMabmJrRLYXW pFssxI7uU0G7ZErtr40tsTdrKNZlVaum1/2n8+nlqvU6E3eJXxZWa+GH5bTeDA4G9Qk0lqv9uhtm RTJdcyNA962EGuZM9Tm1SBSqjNNDWPnN9JHVLo2OkCSKCISuwxK1mtH0zf4YcJFFTPPZ2QaTiZFn eyeGKomG8buUi3T4F+gKFkImGfjda2vnr9lm5C1mE8q24zEOkc85Xrtp05Pl0qspq3zcGjB6M1XI 34cWOE/liExNsFLCQYMQwrgCV1jpYVTj/vY3rdZbluNN4CoqU8cxtU5CuepoeGlTWGW3PytCzTjX 76LgR+lzI68FgjIMCLUJE55TaaKRIsBXBIrC5Mb0ZwsIAB3FT8W4UwRe4KobFdoWBRrYZWj1RTLI 96TERVy8nnmahDqsAcZBzdlL43dkYiy6JJWUen0l3BXnWyJjK+ZTi+U5Gc1HQ3Pjzk1BUCmljLZD kGmSBn/h3bYGkbkBeFpEKxuYK1eqtiAJtctR9vX54e3yfvnzOtj+fD29/XYYPH6cwMwmzpS20J+5 I8TQJ1zq6m3y8G6lu2uBZbPheoYWkPVhwO3fzeZpQ5WmJUUb/xqWu9Xv3nCy6CEDW0KnHFqkMRe+ FtbJRK7SJOgATflfAWvRYsOFOJRBknXgXDBnqZkfzXXXMA2sT3wdPCPBZgqzFrFwRL3QKSgvNh2/ IEqMx1QFWZxF0MU8BesM203USZFkvjeeIYW76IZwNq5YmXhYXkYAYR3sdWcT80moGM3iEVFNwMA+ 1VtB+TH96YL0NtS+o2oO8NmEqmThLYbdOYJgYupIcHdkJHhK1RYRVAAgDe8dqQ/jeOwx+miyIllH 0xF1LFOPMG4zPB155YKaKCjreZ6WffOT41zk3nDnd1rsz44YjjDtIOLMn3kTqsTgZuRRCkiFT4Ck KJlnJNUwcd3SJCImqlEjRrOu3AFcxFaYF4OY+LD4WECu9jhgny13fIPTt9zjvbmN1X2Gh5k31IlP LfymHrUWUDtw75oV0cKbdicsAKcksCS6ZKf+NSw9QiRRotlQ+a3u7x0Xx4cFPdR5ui+sjTsvIqhw Z9PmMEDv13t8A2TfXrCHh9PT6e3yfGrCBNZP/UyMon65f7o8ygfaVSyDh8sLsOt820enc6rRf5x/ +3Z+O6ng9QbP2mYIivlYdyKqAI1Hn1nyZ3yVnnL/ev8AZC8Yx9PRpKa0ubFE4fd8MtML/pxZ9VAF a9OEghA/X67fT+9no/ecNJIoOV3/7/L2Q7b05z9Pb/894M+vp2+yYJ+s+nRZBX6u+P+LHKr5cYX5 Al+e3h5/DuRcwFnEfb2AcL7Ql1sF6AyNk5UsKT+9X57wYPXT2fUZZXNxSUz7uo7Ku9NK/KiUy7Lj g1TN2G9vl/M3c5orUMsC42rewh80bbjDCav2gu0edtUEolxnG4Y2omYvJVzcCQGaol7lSkGW9mTu 8FuqaTr3uxbe7ZHeUJCRV1tsmuEBK1W/jn+Nhc/ZbdvUGnjgq9xMVtE0Vz7+CvAJPVWa49S1RltO wDV4z/JufOPN/fuP05V6Im5h9DkQRgEydBl+O9BAXXE/biIywcYtup20/SB/Vq8/5avS3xequuGL DGGE1xGVLYWL5P10Gtye0XEFEZ1nMTI7XxOD1j6SlKkmb3VfUPhRruLUOBpgEQ8T+czq1uVAsme3 IXei1QEishZ4cHFb7rOAOTwwWtpiu0+CMF+lEZlD6xhXNW8+zUJ246zDkbM0dleR+WG+DehDGcSV uPIjl3+ionCxRo/OchM7nD6ZwPXFMpeLnsT3ly4pHKWHYQgKSA//wA9WjDzDDqMIBOmKp/r5cQu0 u1+i+gpCfL4qyAALCrfv8BNxuli44ugggdVuCwX/EX7Os8J67V6jmeNGoiFwuQeymEdpma93PHK8 Jtl/4YXY93VITSKToDqkcwaCMPV3YYGJWei1l/W8adlm/VMH8Y6JU/gjzNpF9y++AgGFVO9THoQs Y0Ffg+vUtdugc6pcF7rlyQ652Gn4DOkg73pE5tWv4g2kdOU9WBdiFg38DULaKw/OO3lFF4dJlNIe 44ogZbsit3wCLJKDNePb3tjna0yTNK4yuKZZHm5cXrY1cZan43K1L1wur7HgfSOAaNeAZ766rZCe I2RubOXiWPE3tKsKc+NIgib3mSIVW76i/QsrXLkq+tZUTbV1Tp+KwC3loR4+WPQuqzdjvSIs2vRh M5Yw6UjduwbS5K4XfyeKMJ7P3Iko0YuyYHkfE3T5kzY4zEGgTQpubbf1dIiO+rske5U4ellhc9G3 wqSzqK9e2PaQYY5MR8KOigB04wJq4nerJ/y983Reo6iaR5SAhaMo0XTQWn3PeGaEV/O3oIGHDTPa lyyKWJIeiYdeyqGj3KZFFulH1xVcPwLYskNY+pHmUQc/ZFipNN3tsy4hhtEAw0HPayIdOyom+uSu oETq8i4NKJTLycI+Aayxgk/HE+rxk0UzHVHVQtRkQmL8wA/nQ/toqMHKmHqlGb+oS9ZN37i9FRkH Ue4b3tfqoODp8vBjIC4fb1Q6YmAncr/kC+P5HUDDQ2FD5c8SCzEoV1HQULYnClSpzbSADWWVGoeo mU+5K9QX94q4roa88GLmFb8CEjkvKvP8+XI9vb5dHro9kIfoLA3bjrECWygMmW0KNaZ8h6sq7fX5 /ZEoKIuFeeCFAHmRSXlqSKR0B9igb1XbfhuDABur3b/VlTUqpYtrMEJQhep0mkj9wS/i5/v19DxI Xwb+9/Prr4N3dAv78/yg+aOqA4bnp8sjgMXFN/w168MGAq2+A4anb87Pulj1YPXtcv/t4fLs+o7E qwOoY/aP9dvp9P5wD6bmzeWN37iYfEaqvJ3+Jz66GHRwEnnzcf8EVXPWncQ3GkqKaZ5/rzwtjuen 88tfHUaNQSh9UQ7+npy91MeNF/6/NPTa6pWm9zoPqWv18Ij7ZF3n8K/rw+Wl8vTRZpFBLDPPf7GO Y2rUMfMceW8rirVgINqpG6eKoHq3an/XqM7jyZK6Y6nItGyIHcR4bCZnbTEye3RfrSvHT3e5du68 GlwkU+N4t4LnxWI5H7MOXMTTqX6rVoFrD35t8wYRmGteLlxHwg+8Yl7rwRZbWOmvSLBxR2DCbS8v DYte4W1GVg2/k5FOgMoEV/54YUDWUP13LchvOqSyVFFm0hFRkXg6ibjtvGqvwCTHtmrSgqsXBXGR Ue+AwTEaT6aODMYSq2eSqAD2KeEqZqMFbbkAauI4fljFPkysrvFdoQPmLcxAXmzsih0OynIwpF+c SxyZvkJ2ZKHKL8fsyK0xa3BoDVj43VEES+un3Su7o/9lN6Ij2Mf+2DN9COKYzSdT10gg1shiCoDF RI/2D4DldDrqJA+v4DRPwJjJ1GXiICrHK2BmxhWhKHagBXsmYMXMEKP/wT1ZM9Pmw+Uon+pzb+4t R8bv2XBm/y65MvMZhpjVHboAvVwe9d8cZD0vrczoKhsxQqn1IPcGM629j3kIhiMTGLAlTu1NZnHf HumYB1HhexM9dZYELKYWwPTbx41iPCNnFxgfs5GZnN7PxhNH2sU4TMqvI9UwglvC9vOFmdxC7RWq fbSNLTt2uBhRDCVSwMrQ2tdmqzc6slIyjnU//rt3qDKG9iCsw9Obn2vISsl8fQINxJiH29ifVOHc G12zoVKS9PvpWb4rE6eX94slXosIuinbVid31JSSFOHXtCIxxWo4c4hV3xcLciZxdmNLAOEHRLb5 GokvwHMMQic2mSmRRCbGlNw8fF0sjfTqnR5QoSfO3yqAvGxUUdT1UaAJdAkci+bIU8lWZTiIrP6u y7SLtES6yZDGVT1oJia4DO7VtKHF1XQ4M655p2Nz9wLIZEKpfYCYLj18fyFCg8F0Oc4tDrPlzLFB BGIy0T3E4pk31h+jgUiY6pl6QB5M5p65AAPmT6fzkT60va1v/Ce+fTw/1wHl2z7BTlWB6MPDJkys 3pahhRTejVFqjHEU0SFRShhpg3TqVgXEO/3vx+nl4WfjPfBPfH0UBKLKMqGda2zwRv7+enn7R3DG rBR/fDQhmo2TCAedJMy+37+ffouADGzN6HJ5HfwC5WDmjLoe71o9dN7/7pdtKKbeFhrz+vHn2+X9 4fJ6gq6r5VcjfzYjXedQvztxoo5MeJhyhtRbsv14qFsPFcBmUi3AzV2eKlWLEm3FZlw/j7OmZ7cR SgSd7p+u3zXBXEPfroP8/noaxJeX89VoM1uHk8lwYiyc8dBINVRBjOBXJE8NqVdDVeLj+fztfP2p 9bp2N+aNR5QSFmwLc1ffBqh9UEeRgPGGekLpbSE8PVeQ+m2KwW2xN9OHCD4fOhLnIMobksuu0zYl KGCFXPHJ3/Pp/v3j7fR8gs33A/rKmHHcmnG8nXHNfEvFwsjAVUNMul18nBmN4cmh5H488WaKlDwz P+AMnckZatijOoKcupGIZ4E40nLI3XT1ZlDGoKJmAt63sMhxbR18CUrhsolYsD+OOiNUIzFTJLW3 AwLWmJ5jIwvEcmw+SpWwpSM3OxPzsUcH9tqOLKcihLgUnBi4LOimIc7xuhlQ9BNqQMxm+nH6JvNY NtRPKhQEWj8c6hb/jZjBOmH6I7BGhxCRtxzqDtsmxtMwEjLSd9wvgo083YLKs3w4tRZgxU89Iyct h3yqeytHBxjYiS8McTWxssEqiGa/JikbjfV0QmlWwKBrfDOoqzc0YYKPRuOxKS9GowklucA4HI9H lkdZuT9w4YiiV/hiPBlNKOsBMXOP6qUC+nhKWkQSs9AuGxAwN7kAaDIlM/fuxXS08Awn4IOfRBM6 yq1CjY2DukMYR7MhrU5LlJ568BDNRvpzr68wGtD5hmJmigz1/OT+8eV0VbZ2dzNnu8VyrpvTu+Fy ae4m1YFMzDaJ6xiCbcajkXEM4Y+n3kSDVOJQMqEPVWr+Nrrxboj96WIydiJMMV8j83hs7NIm3Pa3 JPvqv5ocrq9Pp78sXcyAVxvaw9P5pdPfmtQn8JKgfh8++G2gssU+XV5OpuK8zeVzcPqYT+YFyPcY +588BURnRfQ4pNHiTqyFhmoqTFer2qFeQMMBG+Ab/Hn8eIL/v17ez9J1uDPVpMiclFkqzBn7OQtD M329XGGfPLenlq0d5BnpbfF9h3mMBcYOnXcTzR5DYCMAFr0hB7II1TtyJ3fUjaw39KGu20RxtmxS tjrYqU+U1fB2ekddgVQLVtlwNowph9NVnHkLQ4PC3+aSCaItSBw992gmLOG8zYaUGOV+NrKU4Swa 6ec4/9/akzW3kfP4vr/ClafdqsyMD8Wxt2oeqG5K6lFf6UOy89LlOJpENfFRPvab+X79AiDZzQOU s1X7kHIEgGweIAiCIKB+u98D2JlL1H44d5J502+vEMDOPgbCg2Ic8lC3fPdhdmzJkFV9enzu6G2f awFaxDk7zcHgT2raPXpMM6s9ROppfPh7f4fKMDL+V8oOfctOKikIHyI5xPMsRY+VrJPDJmLEm5/E lKI6i8RVaxboqu+zuhGgzeKY23/bq8szewuA304sYyxnLTDcDc+OTx3u2uQfzvJjJlL0OPwHB+3/ 1yVeSeTd3SMe4CMLjiTasQB5K4uIY1h+dXl8fsJHzVHIyOx0Rc3n/CKEtQI6ENy2+kW/tVZiJDjT jVHH66xLM/gxZGnnAlT4qk46ywQRyEB1FWEiJOiqirvCobLSzkhPxBhxw33SuSnkoN6y0qjDT51H hItLhcSJuDxJrmaciEd0B4ro7MKtfyHW0vnAAyZpZ+vPkB5OJo5qOhaM3S1jIQwt4xjKt0VQB74O xyxrYWBekAAXGNEGN3hHXQmKjPKvFslaD92kx1WiSWEbw5d6/OlKvcCA0lXSscHFQZ7KDi8ou6bK c1uDUJh5kxQtzCX8SkTuY7sMlYxkupWvV9dH7euXZ7ryn/qrH7L7TxUo4N2yQDC3wyXFsK5KgWSn uqgZ7dX1UF+J4fSiLIZVmzmM7CCxLC8rgSqpE1GHseQsCnWVj22UXmC3SYY5XR5biJ6qiRuyS7sD ijriBZvmEmj+kGyKgiJxYqrAT9+nz8Lk9WhEr3dPfz483ZFcvVMWK+eduunFAbJxzm2vARi1mTeX M5N5b9g2Xijb4PGQUXDKtKkiIRfDh0Wp4Cxg5Ualj5wEFQKU9hu0YbU9enm6uaXtO3yt33b86yE1 c360bGMFC6u0jKb1MuLIK9lbmsz2UMNfg3nLY4HzrPAkAYKUK1fSNXEn6yYJHU01Oql6JHB4rPI9 V4026wpGZWzHXGBqEdhxdxKRrOSwrZpUBy9y1AOB6g6oOsAytWha9nUU4qoW01UllvxRmZcWTnUG NszRt3Coas64jGFKyPfQiaBQACPiK6trHz9NZDvIMmmuaz++84TfgIzr7BRMBuRHYpgQ8z7Lu6yE mV6WAjMS2ll5Wj+nU+oDMgUwu5EpKEa6qfkapmcBd+sia4HBSt7m+KmvOu6NPua0W7SzwfZ7UTAH tIAWDe7sJF50Z8MDKp6HXRjzxubi2is/QTGUc4bJogb4w1TJUYp8KyhBU55X20i1WZlK/umRRVTI TmBOqtA/9ub2+87RLxYtcT+7hDS1EtHPu9evD0eYTS9YQOQ56g4Egdb+Na+L3hSRa2DC4p7d2aF9 EFhj6pmiKjPnsaHyXF1ledrYN3uqBAbgxWiyKtSiX6juSWsAeWTZ6mVT2lNNQtrSdIva7SsBpvXP K89AcSU6OyPsql/KLp/bVWsQddNa97JYpEPSSCfrg/pjWHraI8N5GuvBECgoOtSrBJdxGwyCRLXx fnskVQY20/Ifi0V76iwOA9Ey5TiA097rO6xNWIzngsLHFiMK2/ZFIZoAbI3u2OQRc2hmRqJWJr0r GRXKJFlFp4SK5GrQz89O6AEFyz9XYVvIisYOr8b380i+B90WSrVWViW3aGySuskq3Rm2CgyU8+Z3 FmJT9Q10hNuIG1HYE65+u/Eem6rwBK6C4MtjdFe85sjRN9SG1m3nOZAoCIb+yXFXNvPDiRFFCT0Y qfyKATk7iFwlNtpvxMXslG2AT/e57dKfaOmBL9mdMGGPDnV5FlCzldr9e7vaoMp3P/49exdUC7/a is01pAn02wAXuKB0u0wrG8E99yllBwrb2pNnBukxHv7enHq/HSOtgkSEBCFnv9955LOBN481GDKu jAhSLIl6h05qnZYcPxgi3IlAp09Lry9p1uIr06FPay4eJpBwkTWXDTkjglyt7LCsoEb6P7G3zgfV exxrS+zLxg6Ron4PS+Bda5Q0NJ4cL5H1it9TkmzhVIW/aQtv2ZhCiBWoNeETOZTkZoAd5Q6ptlKs h3qLweX52OhE1deY/SaOp80m1pAgHugEjdwTj3j0IqoxPwzPPIrwjfZVqYjt4iK+wV/W/ESU9lUz /JjW/v754eLiw+UvJ5YEQAJogCQ9bXbGOfg7JB9to7qLsa8HHcyF7cHjYU6jmHhtsRZcnDtmYg/H L32PiONVj+TswDc4g7dHEu3W+XkUcxnBXJ7FylxGh/zy7DTagcvZ5Zsd+DhzK87aCplquIh87+Q0 2hRAnbgoihvqN898IT6BhiI2ewZ/xjd9Fvsi54pg489jBT++2dTYQI+dDdhsxPCXBA5JrOHrKrsY Gr9mgnJxLxGJMXtB07MTjRhwIjFnAgcvO9k3lf8dwjWV6Pg8yCPJdZPlOVfxUkge3ki5DsEZNFDY cSJHRNlnHdc66ujh1nV9s87syK+I6LuFmxU85zSgvsyQ9x37pgLBEaEpRJ59Vgm9TWxg9ozvmMSU R/Pu9vUJr7uCoMa4L9mfw99wfP7UQ+UDY0Ywepxs2gw0tLLDEhjClNtotOFKptxnhnSFSZJV4jJ+ AzMHOIxq25Ltv2sy1ooYHvUMxDn1m/q0pslgatFZM0dvuFeiSWUJnegpZG59TWpJIryQJQEZZ6wD rQ6NYS2cwuwnV6gFZQmVxHOayrz9Blo19d1vz1/297+9Pu+e7h6+7n75vvvxuHt6x4whsAxmfTw8 0C0wMJ8AayTpqqK65mNyjDSirgU0lVOnRppr4YT1HlsgFnjFk6UMjhTZaluiL6RjheYIBimanJsE MtISldbFYVYSdQa3K42QoTl72UTzD/CFCIuJhjPhR9DXBcdqbSOrBk1WWrt9E1q01wVm6gUWiaqZ FnWfZpGID2xwebmxXmfDjwH1ZFAs+96eI0KkqdKinXBEKpzvtJTtkO84j+/Qv/3rw7/u3/9zc3fz /sfDzdfH/f3755s/d9CK/df3+/uX3TeUXu9fHu4e/nl4/+Xxz3dKrq13T/e7H5R0fkeuEZN8+48p IdHR/n6P3rH7f99o5/pxULIOFxfMmZ59G4FvdXGhuzkirBFVNAvYWiwSViJH2mHQ8W6Mr058AT5Z aUDA4j6qDMBP/zy+PBzdPjztjh6ejpQwsIIlEDH0aqmiD3Dg0xAuRcoCQ9J2nWT1yhZdHiIssnKC pFvAkLRxYk+PMJbQMpF4DY+2RMQav67rkBqAYQ1oTQlJQWMQS6ZeDQ8LuNcqLvVoKKDUAAHVcnFy elH0eYAo+5wHhp+v6W8Apj8MJ/TdSro5EDTGV1A8lsiKMeVC/frlx/72l792/xzdEgt/wzzE/wSc 23ghqBU05TJ3aZxMkqDFMklXTDUyaVI+PrRucBEOFQi1jTz98OHk0nRFvL58R1e725uX3dcjeU/9 QRfEf+1fvh+J5+eH2z2h0puXm6CDSVKEU8rAkhWoZuL0uK7ya9exelyfy6w9sf3DTS/kp2zDDMlK gEDbmF7M6dERahTPYRvn4ZAmi3kIc233I5S1DZlmhNXkzZapplqwgZ0NAzNNvGJWC2yXOr+Zty5W 8YHFlIpdX3AM1LZuekF183/z/D02kk7+EyP7OOAV16ONojS+obvnl/ALTXJ2ykwXgZV7AY/koRg/ nhMuV1esGJ/nYi1P58xQKcwBToDPdSfHabYIlwP7qeh8FSlaeX0YQ5fBEsCIchknypoihcUUby7i 7XdFE/j0wzkHPjsNqduVOOGAXBUA/nByyrQUEOzzFCPDzsKqOtBg5lW4t3bL5uQy5IRtrb6sNI79 43c3IJORP+FqA5gK/hKCy2xkxpBVqi2G/OIto5pbBAb3yg4I70TgedaYvMPybcdZRCz0OVMslQc4 eMHvoq3IW8FMvpHo3JTKpo6FS3RJhraVp8OHCzbCvuGAGfMJOA6/NcaaxK9dccHD3SN6NJunq/4w 0b1evE3qZtWFXcxCzss/zzjYKhRweDtnWLS5uf/6cHdUvt592T2Zh7TOOcAwYtlmQ1JzambazCmM Qc9jtMwOOk646K2ERZTwVw8TRfDdPzJM5ibRf7K+DrCoQQ6ckm8QvN49YqOK/EjRuG5KDBqWzYYN BOeR6vNFtCpZkr5bzfEGMhKUeBRl4pB6gX3GTHX+eenH/svTDZzPnh5eX/b3zEadZ3NWohG8ScId BhF6jzNOqGxhTcPilESwivu9nYgOLC2kGZXUg21xdNkQDcKO7abZeUE5zz7L308OkRz6vLWDxzs6 abyHuzzumn5Vqy1T0DWgUC6qqYkWsu7nuaZp+7lLdvXh+HJIJNr3sgS9GJRj4URQr5P2At04NojF OjiKjyYxWQSLZyos7Ji/siXaHWupnAjJywbbkDHZQxN8KPwnnU6eKYvq8/7bvfLnv/2+u/1rf/9t 4v6iSnvMOp2Rwfb3d7dQ+Pk3LAFkA5zWfn3c3b0z1OrKfugwB7wy+TaOr2OIb53saxovr7pG2CMZ MzVWZSqaa/97nMlRVQxrDROQtl20aRMFSQr8n2qhcZ77icEzVc6zElsH8112CyNv8qigybNSimYg ly3ba0V4DqPzDLQ1TL9isZ7xcgdFrkzq62HRVIVnQ7BJcllGsKXshr7L7Othg1pkZYoJFmBs5u7N SFI1aca95YSuF3Io+2LuZMpSBn/bp3/00k8yjINpn8cMygOPueQXqNZRIN06z+wuEQX6W8DqhY29 rDrhOXrBCQOO27CLOiAvAxTQqGMIK26gXV0/uBW4Ryc8M5m7Gq9ixIA0kfNr/lxhEcyYoqLZxtaG ophHrLyAZW+hE9zH7KbbCZ+zeXiSTCzjgn8AJCMzt3UBg6dVYY0K0xLbBWuqEqGpDOHoqoebeu64 Un5WW5YH5f3GEMrVzDuSBR5kFjXbPttRzANz9FefEez/xjwRAYwee7iRlzUmE+f8JbDGCzbe8oTs VrBsmXoxBQq31jV6nvwRNNLLuTr2eFh+zmoW4RwHHPiMhWtl3xMZ9iWdEZ7JyvlBbm0dxXKzXc06 2IJaiRKEgw3rombh84IFL1oLTo8hNiIf8MxtDZVoGnGtBJmtebRVkoHc2siBCCYUyj6QibLwQZRB 1ZGVCPfz4OIbhQlQStg+W4WAzWFp34AiLHGTACOolg0IdkIFKka6+/Pm9ccLPoF82X97fXh9PrpT 9x83T7ubI4wV9N+Whl0IlQ6ymF8Dq0yOxSMCvoX+Aug/fWyJMYNu0WhDZXlxZ9NNVb1NW2RsfleH xE4LgxiRgypWYGDMC3e88BwT+MwZ5WOZK2a16lrJZO1c+xkEOrc705t+snfSvJq7v+zdx0x37jqt J/lnzHBhT3HWfEJ9nHOcLGo3LRvTyCpLB8yUAMqGw7LAxmZxbtK2CpfsUnboTl0tUpvX7TKDvb06 iI6UDft1iH4hkKy3wg7XTqBU1nZ+J/QrKJfuTj0+u/ZUNvfi0WjMBH182t+//KXeFN/tnr+F7hak Dq6psfZ4azA6ALJvkRLlfos5oCjj0Hi79TFK8anPZPf7bJw2faoIaphNraC8yLoplNGZXSTpdSkw kXvcBdShCOICWvp+Ma/wPCWbBgpw9iFVA/zbYJqhVtoTEx3s0SK1/7H75WV/p5XzZyK9VfCncGrU t7R1IIDhi54+kY6hwsKavSiSjMaibEFR5RUziyjdimbBb+DLFNY0ZcyJvOpQ1pKiR3snihHODQX2 OznAN8rfL04urbTduA5q2HfwFWPB199IkdIXgIolWAEBhhymxBasCFEdhRMcuTIVWVuIzt5rfQy1 dKjK/NqfGeVdsehLVYDEL4gISzyprtZVph8ZMsWV+zAGfa57m8F+moWI4ciuuL81kiHdfXn9Rsnv svvnl6fXOzdDfSGWGb2HstN0W8DRoUBN5+/Hf59wVOqZNV+DfoLdoi9XmVCic7fzbcjLo8t1zBN5 JMNbaKIs8P1mdJLHCl3/CtoLlDIF/Gy3A39zFhpz4OvnrSjh9FNmHe7CIneuDAjLemD81PS4bVce PD7P4Isrc5zXbh1jZZacR1kLOiAGP3VvHFQtiKc9n/euw9LVtvSjTtpoYGlMZcMaPKZvwHJd+D1o qlR0wtPLxwFWNNursM1b7hH0eHbv0M3d2dMIYp5mR1tZzfHhees3UoMZ9cXFLxw92sVRgCOGyw0e XQDfatfQJD2JtNhHUKUEpWx6xsxSaalsNt0Tv0ltLji2p3WiuRGU/RwkVdgdgzmwYJWPU9/GFN8W dopUU8kyjW4cHpdsiqFeUnI0v9+bImwnUOONte9T6tM087Ay+Ayc5JfMXE5N+InmZk3Xi2BFT2Cv bpVAgBy9Doyt3jnw0MXvmIpslS1XUOHhSaYZwEe/C/VaOJzEEJkk1MW1QMEYGtgVFlkdNdyymkRn mo4P01w3tUmeeQ1YZbRd6VMeEB1VD4/P748wgOzro9odVzf335zXyLXAhFv41JJ/GO/g8Wl+L6dD oELSoaDvJjC6WfYoWzpYXfYJv60WXYh0NFw67duE9A2mYXFiv5Xo+ut91QtLw1BYlojxQxYZfehn aHRjHJGCXxhWmPCtEy23kLefQKkC1SqtnFs8ul1QlbO76OFJV67moCl9fUX1iNkWlZzxohIooKt4 E4xu82z+5Op2WRQ5ZS1lrWzmyuaOnkzTfv+fz4/7e/Rugi7cvb7s/t7Bf3Yvt7/++ut/WeZ48uPF KikpMfMgr25gKZqADXFfYOxDVNqhqabv5JUMtj+TDSvQPnjy7VZhYB+pttqF3ZM/zbaVbFo0ncgW G+tZIcibWtZhXRoRrYzSP4IemstYaRxUumfW2zvXMGoSLBQ0LihtxXo8OPWYfQkxstHCqYFl6v8L g4z2PXp3CeLQbEssfCjtzL0kv82LWNMNPNnAsA992UqZwjpQBnJml1eqxIEdRlMMmE1UtGEYHLV4 /1IK8Nebl5sj1Hxv8foqOAbT1VeotSI4fqBbhiUo+EcGWhgnX1E/KgdSRpOKwk0aXdkRN5EW+59K 4ISuPOzD6DugxHHiKMZcqPNh2KmQaSyCQ4VB6367AlQa6Ig87m+nJ241xCz8KRyw8lPLGWFMUDqn y4Eu/0kffhtSXjiTE7RO50MkISZNsC5LPgC0TK5VqlFzAkOvjYnTLcmpCUqKGor5mL3j4HiKP4xd NqJe8TTG7rTwFhmDHLZZt0IjZfsTZDqUC1rnfHJNVpD6D/XhZahHgkFPaJKRkgwRQSXoxeNbShNd m6rakzANmp4Hr5uqKYm7bZBd04/KQUkPiN65W8YJhjOrDtMXjLFVlT7Ut1vbEl3DUayANdx84vsa fM+cHf0PacKQd/yJRaWI7L5B1SEzjQuA5SROqEW46W1G+nkeGtsC2gT6bjgNVcexaPtgnEEvXTA9 VApVtOBqm4suGPSiyCqvp7r9moH9XQ6WeQlHnlUVMqdBjGcjl1HmsMcBf+k+Bw+vDFzf2mNcESoQ ueDpgX4udSKPA/M4ZvpwexdZuu11CbMWZgdZoXeIDm0cSXFM1aolFkbVc8loiQxzkKKrQjT8Cd5e diyl912B91I1uQI5bK8nsxOwbdWMNsR87k3ikWniJJZUoDuF2I5oDTgKBs88hdp2lsqhWiXZydnl jC7V8GBuzZjAnC1udDoC2TMSeWdq06kLkLfp6Eb2ENkhjU2TrLbA21KsiQ8O1oWZAg8R6FyseRZz FNZ06lckYISm2SwwNRLmNC5SdCc6aJAFMox6mGlDrxzdbv++OGd1LldDDmR8qEGHNPSy09xU9a1l MLq6OB/0XRHtDnZCZrtUpK50vowUoJihV+nccbbQZ858vsj7lnuERDv5tEiYYyQ2GF0ZUlxuh44y mOuI1s/xFZsa1MK7t1Qjoo/f6Y00fiAqr6vqhpDcJfgL9FocuhekOkjZOYCnGT80EmrI6Hah7nmZ QznJ8RQavXTvyy2G3WuCC6JRgXb517717XbPL3hSRMtH8vA/u6ebbzvb4rXuPYvhqPqqwxBeeFaN 3hwyOwZPXfBElvosO1yYb1CpuyP7A9M+ILI8Ym1GlLo18AwBXnXjC3e/XtBnOsndEvgVWDdQEQrr CAE7mWNZ1ybNFnSCamOEttXaBvZzUgqBgXAz0277k11mnUbCrSrzF+7wrZeF2yUpshLvBPhA3UQR LT+fTkbA4we24Tl6CB3Ak89OlVeYUz0uNGx3owN7tLrDiOzMyppzPmMdGam3K3mFdz0HhkP5V6jA BRG1SdO1Sc1LB+X5DBRdxbEYoUdvWxs4z7rCDYhMYHy/Hv/QVXx7J7yxw8cpGvSIDG4mvIGLvQ0h bJbGQvgim64P8DB0uYrcRhB+U8QvH9Xg4DHeD2LhfaNeHECiJ/WqoiuvDUtGDsXQzje0X6ptkTXF VkTcSBTjUGTRA/2J732a8SjCRjR8AhE510UHpIMsEjhgceZR8y00gGbhOoKS/mWTMwy4iFE+tx6H L2rrXRJU4vswHdy3giAEyqXpfwFazpZ7j+MBAA== --===============4223201462222227330==-- From mboxrd@z Thu Jan 1 00:00:00 1970 Return-Path: X-Spam-Checker-Version: SpamAssassin 3.4.0 (2014-02-07) on aws-us-west-2-korg-lkml-1.web.codeaurora.org X-Spam-Level: X-Spam-Status: No, score=-10.2 required=3.0 tests=BAYES_00, HEADER_FROM_DIFFERENT_DOMAINS,MAILING_LIST_MULTI,MENTIONS_GIT_HOSTING, SPF_HELO_NONE,SPF_PASS,URIBL_BLOCKED,USER_AGENT_SANE_1 autolearn=ham autolearn_force=no version=3.4.0 Received: from mail.kernel.org (mail.kernel.org [198.145.29.99]) by smtp.lore.kernel.org (Postfix) with ESMTP id D8822C0018C for ; Thu, 10 Dec 2020 15:45:06 +0000 (UTC) Received: from vger.kernel.org (vger.kernel.org [23.128.96.18]) by mail.kernel.org (Postfix) with ESMTP id 7091A23BAB for ; Thu, 10 Dec 2020 15:45:06 +0000 (UTC) Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S2391924AbgLJPo4 (ORCPT ); Thu, 10 Dec 2020 10:44:56 -0500 Received: from mga06.intel.com ([134.134.136.31]:22858 "EHLO mga06.intel.com" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S2391247AbgLJPoh (ORCPT ); Thu, 10 Dec 2020 10:44:37 -0500 IronPort-SDR: 33N7Wd44KxuSlNKgPfPeO68rVBaqOFkoHxORJ2Eup1xASbbD0L8PAmFLxou5z/++B1aFNaxqk5 izvWzDhQIM/A== X-IronPort-AV: E=McAfee;i="6000,8403,9830"; a="235868435" X-IronPort-AV: E=Sophos;i="5.78,408,1599548400"; d="gz'50?scan'50,208,50";a="235868435" Received: from orsmga004.jf.intel.com ([10.7.209.38]) by orsmga104.jf.intel.com with ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 10 Dec 2020 07:43:51 -0800 IronPort-SDR: vYH9PQM39E+MEX669uQ1bGM0SCdXmuBZv9zFMCJP4IUXYlqt4hnNm0ikVPmAlvN8llW9gaV9f4 WxCbjc5b7KWA== X-ExtLoop1: 1 X-IronPort-AV: E=Sophos;i="5.78,408,1599548400"; d="gz'50?scan'50,208,50";a="484491364" Received: from lkp-server01.sh.intel.com (HELO ecc0cebe68d1) ([10.239.97.150]) by orsmga004.jf.intel.com with ESMTP; 10 Dec 2020 07:43:48 -0800 Received: from kbuild by ecc0cebe68d1 with local (Exim 4.92) (envelope-from ) id 1knO6e-0000L7-0A; Thu, 10 Dec 2020 15:43:48 +0000 Date: Thu, 10 Dec 2020 23:43:23 +0800 From: kernel test robot To: Toke =?iso-8859-1?Q?H=F8iland-J=F8rgensen?= , "David S. Miller" , Jakub Kicinski Cc: kbuild-all@lists.01.org, netdev@vger.kernel.org, Toke =?iso-8859-1?Q?H=F8iland-J=F8rgensen?= , Jonathan Morton , Pete Heist Subject: Re: [PATCH net-next] inet_ecn: Use csum16_add() helper for IP_ECN_set_* helpers Message-ID: <202012102301.chq73ELs-lkp@intel.com> References: <20201210105455.122471-1-toke@redhat.com> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="tKW2IUtsqtDRztdT" Content-Disposition: inline In-Reply-To: <20201210105455.122471-1-toke@redhat.com> User-Agent: Mutt/1.10.1 (2018-07-13) Precedence: bulk List-ID: X-Mailing-List: netdev@vger.kernel.org --tKW2IUtsqtDRztdT Content-Type: text/plain; charset=us-ascii Content-Disposition: inline Hi "Toke, I love your patch! Perhaps something to improve: [auto build test WARNING on net-next/master] url: https://github.com/0day-ci/linux/commits/Toke-H-iland-J-rgensen/inet_ecn-Use-csum16_add-helper-for-IP_ECN_set_-helpers/20201210-190846 base: https://git.kernel.org/pub/scm/linux/kernel/git/davem/net-next.git a7105e3472bf6bb3099d1293ea7d70e7783aa582 config: i386-randconfig-s001-20201209 (attached as .config) compiler: gcc-9 (Debian 9.3.0-15) 9.3.0 reproduce: # apt-get install sparse # sparse version: v0.6.3-179-ga00755aa-dirty # https://github.com/0day-ci/linux/commit/fbed6705f871b8eb4e522c874927ac8e57c8889b git remote add linux-review https://github.com/0day-ci/linux git fetch --no-tags linux-review Toke-H-iland-J-rgensen/inet_ecn-Use-csum16_add-helper-for-IP_ECN_set_-helpers/20201210-190846 git checkout fbed6705f871b8eb4e522c874927ac8e57c8889b # save the attached .config to linux build tree make W=1 C=1 CF='-fdiagnostic-prefix -D__CHECK_ENDIAN__' ARCH=i386 If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot "sparse warnings: (new ones prefixed by >>)" net/sched/sch_choke.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add -- net/sched/sch_sfb.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add -- net/sched/sch_codel.c: note: in included file (through include/net/codel.h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add -- net/sched/sch_red.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add -- net/sched/sch_fq.c: note: in included file (through include/net/tcp.h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add -- net/sched/sch_fq_codel.c: note: in included file (through include/net/codel.h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add -- net/sched/sch_netem.c: note: in included file: >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add -- net/sched/sch_sfq.c: note: in included file (through include/net/red.h): >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add >> include/net/inet_ecn.h:97:21: sparse: sparse: restricted __be16 degrades to integer include/net/inet_ecn.h:97:37: sparse: sparse: restricted __be16 degrades to integer >> include/net/inet_ecn.h:99:45: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __be16 [usertype] addend @@ got unsigned short [assigned] [usertype] check_add @@ include/net/inet_ecn.h:99:45: sparse: expected restricted __be16 [usertype] addend include/net/inet_ecn.h:99:45: sparse: got unsigned short [assigned] [usertype] check_add vim +97 include/net/inet_ecn.h 67 68 #define IP6_ECN_flow_init(label) do { \ 69 (label) &= ~htonl(INET_ECN_MASK << 20); \ 70 } while (0) 71 72 #define IP6_ECN_flow_xmit(sk, label) do { \ 73 if (INET_ECN_is_capable(inet6_sk(sk)->tclass)) \ 74 (label) |= htonl(INET_ECN_ECT_0 << 20); \ 75 } while (0) 76 77 static inline int IP_ECN_set_ce(struct iphdr *iph) 78 { 79 u32 ecn = (iph->tos + 1) & INET_ECN_MASK; 80 u16 check_add; 81 82 /* 83 * After the last operation we have (in binary): 84 * INET_ECN_NOT_ECT => 01 85 * INET_ECN_ECT_1 => 10 86 * INET_ECN_ECT_0 => 11 87 * INET_ECN_CE => 00 88 */ 89 if (!(ecn & 2)) 90 return !ecn; 91 92 /* 93 * The following gives us: 94 * INET_ECN_ECT_1 => check += htons(0xFFFD) 95 * INET_ECN_ECT_0 => check += htons(0xFFFE) 96 */ > 97 check_add = htons(0xFFFB) + htons(ecn); 98 > 99 iph->check = csum16_add(iph->check, check_add); 100 iph->tos |= INET_ECN_CE; 101 return 1; 102 } 103 --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all@lists.01.org --tKW2IUtsqtDRztdT Content-Type: application/gzip Content-Disposition: attachment; filename=".config.gz" Content-Transfer-Encoding: base64 H4sICAgk0l8AAy5jb25maWcAlDxJc9w2s/f8iinnkhyST5sVp17pAIIgiQxBMAA5iy4sRR47 qthS3kj6Ev/71w1wAUBw7JdDLHY3GlujNzTm++++X5HXl6fPdy8P93efPn1ZfTw8Ho53L4f3 qw8Pnw7/s0rlqpLNiqW8+RmIy4fH13//83D57nr19ufzs5/PfjreX6/Wh+Pj4dOKPj1+ePj4 Cs0fnh6/+/47KquM5x2l3YYpzWXVNWzX3Lz5eH//06+rH9LDHw93j6tff74ENudvf7R/vXGa cd3llN58GUD5xOrm17PLs7MBUaYj/OLy7Zn5b+RTkiof0WcO+4LojmjR5bKRUycOglclr9iE 4ur3bivVeoIkLS/ThgvWNSQpWaelaiZsUyhGUmCTSfgfkGhsCivz/So36/xp9Xx4ef17Wite 8aZj1aYjCmbFBW9uLi+AfBibFDWHbhqmm9XD8+rx6QU5jMsgKSmHmb55EwN3pHUna8bfaVI2 Dn1BNqxbM1WxsstveT2Ru5gEMBdxVHkrSByzu11qIZcQV3HErW7SCeOPdlwvd6jueoUEOOBT +N3t6dbyNPrqFBonEtnLlGWkLRsjEc7eDOBC6qYigt28+eHx6fHw40ig93rDa+fQ9AD8lzbl BK+l5rtO/N6ylsWhU5NxzFvS0KIz2OicqJJad4IJqfYdaRpCi8jcWs1KnkydkhbUS7DNREFH BoGjIGUZkE9Qc57gaK6eX/94/vL8cvg8naecVUxxak5urWTizNRF6UJu4xiWZYw2HAeUZZ2w Jzigq1mV8sqohzgTwXNFGjyUzhxVCijd6W2nmAYO8aa0cM8fQlIpCK98mOYiRtQVnClcyP3C uEijYLdhGUFDNFLFqXB4amPG3wmZBvowk4qytFd1sAqO4NVEadavyighLueUJW2eaV+SDo/v V08fgg2dlLmkay1b6NPKYiqdHo3MuCTm8HyJNd6QkqekYV1JdNPRPS0jomEU+2YmfwPa8GMb VjX6JLJLlCQphY5OkwnYMZL+1kbphNRdW+OQg4NiTyytWzNcpY2ZCczUSRpzfpqHz4fjc+wI Fbcg4IrLlFN3HyuJGJ6WcUVg0FFMwfMCZaofSnTzZ6MZJ6IYE3UD7I1dHpkO8I0s26ohah/t uqeKaKShPZXQfFgTWK//NHfPf61eYDirOxja88vdy/Pq7v7+6fXx5eHx47RKDadrs8CEGh72 JIw9o7QbaZrQkVEkOkUtRRnoUCB0xCDEdJtLx82AzdQNMVI49mh2OGUl2ZsGkd4Mxc7vx8C4 XJhFrXl0u75hoRwTAYvEtSyNQnHZmTVXtF3puRA2sD8d4NzRwGfHdiCbsclpS+w2D0C4aIZH f8QiqBmoTVkM3ihCAwQyhj0pS/TZhKv5EVMxUJma5TQpuTnt41L68x8V7dr+4aje9Si5krrg AtQwHKsJVEp08zKwcTxrbi7OXDjuhSA7B39+MR0JXjVr8A0zFvA4v/QUUFvp3vmlBUzLaLTh COn7Pw/vXz8djqsPh7uX1+Ph2YD7yUawnirfkqrpElTzwLetBKm7pky6rGx14aj1XMm2dmZc k5zZk8wckwZOCc2Dz24N/4Sc7DwmaEa46qIYmoFuJ1W65WnjDEg1Afkk+xZe81TH3SeLV+mC O9rjMxDnW6YiYt8TFG3OYKWckdbgc/kKAgUHR9LjTvWXsg2nCw6fpQAeoZaZTZqpbHnExhVw fAeJ6rRHkYZ4lgfcXvAtQBfG2BWMrmsJoos2Bnwax1xa+cQAyDB2eYKNh71MGdgB8IRYzCFX qEsdjVyiet0YF0M5MmG+iQBu1tNwfHeVzgIUAC0HJ4BcDEwA5wclbhsnljLfV963HzklUqLZ 87ULxMCyBnvFbxk6eGbvpBKkop7VDck0/BHTxUHkYbUGT8+vvSgFaECfU1YbT9Po1NDVobpe w2jAduBwnEnU2fRhbcL0HfQkIHziIPGOatBwWtC17yZXL5CMHhGZXFaAAihnEZR1bhyo0abh d1cJ7gbjzhbM5zoOKSHgVmdtfDhtw3aO7sJPOOXO6tTS9WU1zytSZo5AmJG7AOOdugBdWK05 DojweAAMjkSrAldnapRuuGbDysbOMvSSEKW4u1VrpN0LPYd0no8+Qs1i4dnFIM5bxTo7sa0o ICbSdidujBImh6aRAYsKvHOrZ6bTqdnv0UlDO5amUf1iJRx67cKQwgBhQN1GmFDN0+L0/Oxq 5kX1Cbn6cPzwdPx893h/WLH/Hh7BJSNgeSk6ZeBfT+5VtFujkuOd9/b7G7sZGG6E7WOwzU5f mNgiYOvdoEWXJPFOYtkm0UXVpVxCkAT2SoE70OdNYtoJidCioivWKTjNUviDmLAYtYO36Nl0 XbRZBt6P8TrGUDquBhsmjDnDdCTPOB2SAq5RzngZjw6MRjSmTbub4GcSB+Ldu+vu0knPmTC9 S/dgPCGazALtCtSuBdONaqnRwimjEPE750+2Td02nbEGzc2bw6cPlxc/YT7YzTauwZB2uq1r LxsKXiJdW1d5hhOiDY6ZQG9PVWAfuQ2Nb96dwpPdzfl1nGCQra/w8cg8dmPKQpMudTObA8JT 3JYrhF29yeqylM6bgNrhicIEROr7FaOOQd8c9dYuhiPgynSYnDY2N0IBUgQnrKtzkKgwtQbe nvXHbMCqmDMlE5kMKKOtgJXCFEnRVusFOiP6UTI7Hp4wVdkEElhHzZMyHLJuNWbRltAmEDBL R8q5c9tzMCKlB7UFQxr0lXckOi3qGawkt/su10ssW5M6dNAZWHhGVLmnmBNzQ4g6t7FQCZqu 1DdjNNXfKmiCW4YHAfeFUZt0Mzq7Pj7dH56fn46rly9/2yjai5l6RrcSOATxwXDAwplljDSt YtZj9lGiNtk5RzBlmWbcxFWTGWMNeAsgZZHOkIkVUXDcVOlzT3huBzOyQijbNbDHKDe9UxNV 2kgJOg4T57WOh0lIQsTEJxKgjB6IzjqRcC8X08OspCxMbdz7PpcNQWDZ+ka+lx2ueHyUNtyQ goO6hIgAM3U4r5hpKPZwlMAdAh86b5mb/4NdIhuuGs9I9LD5BOYkuuaVSXsuzLPYoCoqE5BI MErUSwKvwWAHw7EZ1rrFvB8IdNn43mS9KaID/XrqayQd0gcjE3H17lrvorNEVBzx9gSi0XQR J8Qu5oheG/s4UYKughhCcB5nNKJP4+PyP2Djl0ZivTCx9S8L8HdxOFWtlvGYXrAMfBPmZ+cm 7JZXeCtBFwbSoy/TBd4lWeCbM3A18t35CWxXLggC3Su+W1zvDSf0sosH2Qa5sHbo1i+0Ah9P LBypWT5x0GiqwilY220zadcuSXm+jLMKEYMSKuu9zxqd8xrMic196Fb4aBB3H0BFvaNFfn0V guUmsBG84qIVRs1n4DeWe39QRv1A1C60ozQ4AZ2IhqfzYn6k34jdzCR56SimUQNqVrJowhrH AXrZLoaX2zJgIwOe0ztgwFbMgcU+d9OyIxc4faRVcwR4rpUWDJz3WBetoFH4bUHkzr2uK2pm 9aHTRepmASrjU2mMRMCrSlgOrS/iSLw1vL4KcX2og4UDPsaBWNukhWdXLFDQBbk2ZQQdqWei LSNAxRSECjYtlCi5ZpXNNOGdZyBlQSCCAMw3lywndD9Djbvv2WFEwC4vjJxUlGPcGevK3Fzq AryfGE9e/RaXRXOyCgZRUDnZTevHOdHw56fHh5eno3dT5MTaw7GuwvzBnEaROpammBNSvAly 01EOhfGZ5NaI3hg/LozX23mzFXB+3TCx//KW7Pwawqdlr07WJf6PLXh+jQTNl8Q8W/5uHYoX ShO44TbxP+llTkGPgJpd2DJPVfUuLPd2vpJ4QQleX8xNs5gr71KsB15fxdyajdB1CQ7gpddk gmL+NLoWA8lF3L+b0F/lcB53t0B9yCyDUPDm7F965pdM9VMKV4pg5NJw3XAahkoZeOLQAjQS iQR3JtRYRht9PzjaWGHgKHdeovCVg++M9/Ytu/FGWjfBqTZ2EGIQqTExp9o6zLKYEAXkB51O MXQ8kVoGCxJkqyHwSmzr6F7RKOXJIXxjsMcbHr+oQVY1CT1rMOUaQkjUCMS/ajLoMTnlzUQL Ui+eOfBCl5FWYzR6Z1Yd5WFhpCHhbDUDArxjiec/s7iLVtx252dnS6iLt4uoS7+Vx+7Msbq3 Nwhwi692LGboqCK66NLWDaXrYq852j4Qf4Xn5dw/LoqZpF8v2lP0bHYL70YwB72wsiaPYhjo SIek5HkFHV74xxMkt2yNj+FcHozy7KDP3PHYxIiLjV/X2TzYJtXxLaQiNYkn6DBmkWD/ebbv yrTxblMGY3MiyeGJutUUw9nsBz2a2Kd/DscVmKy7j4fPh8cXw4fQmq+e/sbaUyez3SeWnCxk n2nq71A9/6dH6TWvTV4/pjhFp0vGXOnoIV2Q8QA4HgODi8footuSNVuKzmsRcFtKVwCKlo59 3P5ujXxnwjjj+Awu4XSnAyFKPlO2ftYLF9TBzb4Gt8AIvgaFKNdtHTAToJ+bvqAPm9RuLtRA +qy4HbHxXbSTHp5ui5DWrEAe1aeWV01V1wQmxiD8LTMwxTad3DCleMrcvKPfJaOxKjWXgoQz SkgDlmofQtumceMNA9xA3zKAZaSajaIh8WjaroqMGiuDM6GbYiARWgf9TAFX6DIGaJ7O1nNE BnBfM/nDnBiSPFcgOvFbEjtf61kH3GmrIeruUg26JeOle7U+5r375ULd0da5Imk49BAXkbDl pa4pypKMBQR2hBKCR1COS+vCZR8p+Wx1Es8g2rbsxN73SwJhaSFPkCmWtlilifdXW6LQgpf7 mN0cDyqpmXPcfXh/a+13gYgTMlo32YlZmL/DQtBRvXEsMwCJCerFXE9KhCG1zvwh1p4/P5T3 rbLj4X9fD4/3X1bP93efvDhtODp+TG8OUy43WNyMqYpmAR1WfY1IPGvuyEbEUNmNrZ16i7gN jjZCFaphp769CSaUTCnNtzeRVcpgYLFQO0oPuL582L9+jxKbDETb8Jhj4S3vUkGKR/Nt6/H/ WIdvnf83zXtxvqNwfgiFc/X++PBfe3Ef8b5ro8YXnf2aUuwc+16+VOltRkjkssFVreS2Wzu5 AB/xyyIi8CJMtnRn/C7wRHw4uGIsBdfAZuQUr+TX8KHl96k4LZZQWgRjqq/sNcRsUP3SdJW5 Tr/wkaWsctXOQiMEFyDhi4vOJklVMzl4/vPueHg/92v9GdjXFH5UOCLNJTIWZpJ6Htm6NbwR bTjKIn//6eDrRt8tGCBGrEuSpr5r7aEFq9rFgzZSNWwh/nCJhiuoqC2zqOG6yg1Dxhk5t4Lm DM3r2YfA5ashh1mq5PV5AKx+AG9hdXi5//lH97yiC5FLTE7EXHmDFMJ+ev6/waRcsYUqS0tA qphZR5xt6oQtAHM6cqC0Si7OYH1/b7nybuKw/iFpY+PuKyMwDxw0iF1TU4xnHVNtvgs1mvCx vSzr+FUdxMXxi6CKNW/fnsWvkHIWXXNMilZJoBv2OktcgVnYV7vnD493xy8r9vn1011wSvtY 2lwMTLxm9L6bBQ4dFptIQeoh4M0ejp//AUWwSucGgKUxW5RxJYynBxG0ZTTlAQTn0Rdngttq R+9iAraHVJ0gtMCgv5IV5nAgSLF3xhNptu1olocMXOiQOXDHkkuZl2wc7Uz9NYePx7vVh2H6 1v65JeQLBAN6tnDeUq83To4Tb59bEKzb4KUWBhWb3dvzCw+kC3LeVTyEXby9DqFNTVpTaOG9 +7w73v/58HK4x9THT+8Pf8N4UavM9PwQOdirmlGSbRERGj4nzjRzkraEzFHNAwTd9vkZW9va log8/NYKsCwk8W9bTAqYdmu215i5zRaeovZkmP8ZyYKRTumJtjKJLizOphgjzrOi5qFqw6su 0VsSPkjlUjGs4oqUOq3Dyh0LxcKWGELWcXjPBpy/WemcwWdtZevlmFIYMZtbo+C934b51b7T K0fDsZByHSBRo2JEyfNWtpEnZhr2x1hC+/guWDVTBSZVg9m4vhR9TgABTJ9jW0Bas9GJ2aLb kdsHzrZesNsWvGH9gxaXF1Zv6bH20Dwxsi0CusuLhDeYhe5mj0G1wFxV/4Y53B0IDeGMYuIO C6t6GeptkUen3WjO3zh8b73YsNh2CUzUvisIcIKjbzWhtRlOQITxBVZTtaoCFQpb4tU0h5W/ ETnBwB09UvNSwtaNmRYxJpH+hyJe1S+Rn9+e9tM78iewbkF1TyZE2+UEEzd9igUTrFE0PoCK kfRyZ8+JfXfUlyoEg+mh9kJ6AZfKdqGMEN9C25epw0v5yFQ1o2i7T6D6CkvHjQmbLBE6rHAz SpCcADkrEHS1r4NZzOCYyfIGTHa/4aYALZSKyKPEULglCo9bPeFprQpv9lCBY0kmXinG1htx yAPNowoVJxzq4Y6QUayIdiRGpi3mlVH7gwFBkYvoKIMZLlpiw/TqhEMLtAN9E1WefquxYrj3 gH0VAaEiXofAMoMLkzp9SPxpBZ731wqXMwQJbMToLaIaxI2J6WQIdUHV9r8doLZOdfAJVNjc rm20eQw1rWYNu3B5Mdx49bp4lE7UUG6Jf9Tddt5QgAND1b6e1SdPvsPoNlG5+emPu2cIhf+y jw7+Pj59eOgzdpM7CWT9Apzq2pANXlFwQ3WqJ2+U+GMk6JrxKlqO/xUHb2ClYMXxjY57OM2T FY3PLW7O/dOAUjPU3ocHxd2IntoW/Zdy4fagp2qrUxSDzT3FQSs6/rxINBafRh8ZZT+naP2u Q+I973Hg6IkvcEWH/GKhjtKnertQzOhRXb77Fl4QKZyeCEhgcfPm+c+78zczHqgCFFsoe+5p sKx9C36H1vijE+NryI4Lc28X6bytQImCytmLRJZ6rmLN8+jx/m7sLynjN0s16Z9kjkFOdT59 tZX92RxTemyEi4YPAqYrRhvrQvDnDMo8WTONQZ7k1rtNUVsNamMBadTPAm4MJMxvl6RTXfRE sowJG6ttvOkMPmocjJvxsrEkdY27RtIUt7kzOxfT08Orsi5hGf6Dzpv/4xsOrblm77YKmLtz nq64jY5k/x7uX1/u/vh0ML/ttDJVXy9OrJnwKhMNmtSJB3xQ7xFtT6Sp4q7i7sEgld71FrZF ZzOaU1sakBmtOHx+On5ZiSnjNr/bP1U4NFQkCVK1xI9gx3Iki4tlT2xjn1tnyndtO9fNG9lZ uxVGGPj7Irl7Md6Pd/wNBJcVFmzVjRFkU7F5FTRK8PD75Tc9yHoPNPxRBde1oLPXZlgFphie xXhVfuSHa2xA2gVva2yVv0TnyPf5nWhnSjnoWHHdcA9kXC/7Uyipurk6+/U6rjuWH2b4mEhX C67rVIgYwcOMt2Qf065RamGft3pnATx9W9cV2yD3XRl8jA/inbobcqIUAbEwBKJvfhlAt7WU zoG4TVrHy729zIKS11ttH3hGmI/5JnzyNKRbJl4mB2FmjZmMtR9GCDguHLMijrCYZE1WuZoK 38dsZuEOqDNTSI0/bhLP6UJAnIAXWQiiYqbKpCfwshvc/NoUI2cx9Vw3zEYgxPMFl3XQwKFy L4D1OrFvnIYkhVFk1eHln6fjX3h9N2kw5wjSNYsl0MCMOh44foHO9UqCDCzlJF6v0JQLb6cy JYw1WcqfY2ovluK3U51y+7V9SY8/axRlBQRwBvCGFgwj1oLHwlUgqitXlMx3lxa0DjpDsKlx W+oMCRRRcTzOi9cLpZAWmSt8iyna2AMgS9E1bVUF2dA9Kl655iy+2rbhpomXRiA2k/G7sB43 dRvvALelI8UyDpzDZSSv0TAs7PY0XReIAheAGloPYJ99m9bLAmooFNl+hQKxsC8Qncr4jzph 7/BnPkpbTEUPNLT9P86eZLlxHNlfcfThxcyhoq3V0qEOFAlJKHMzAS2uC8NVVk05xmU7bNW8 nr9/mQBIAmBC7HiH6rYyEwux5o6VfUM3d06D//zH99/fnr7/4daeJTPg28nVu5+7y3Q/N2sd JWXa40UR6bQZ6EldJwHZC79+fmlq5xfndk5MrtuHjJe01KOw3pq1UYLL3lcDrJ5X1NgrdJ4A Y6gYKHlfsl5pvdIudBVPmjI1WTYDO0ERqtEP4wXbzOv0MNSeIoM7hbY+6mku08sVwRwoXSnF VpUyLr1NpGDe7tIws8q6ajFvG2oX8c5zEHCPlZjvFCTD9X2/SLm9V2oluE6z0kvtBTRaT0l+ y6q8gIQTKonj4Lks4sCZXQWSIMFMB6y+ko4bSceSOsOEtMZ4VfHE1k/q3zXfZNDDvCj88TD4 LHCfGHS8JsNElA4bzy4ReWOMILLCfRrl9eJ6PKKdcxIW5ySbkKbWcQY/bCuljFLHfo/pikBE TBkiaOZgPKPaiErLPl5uC48VmKfFoYwosYMzxvCjZlNny7fQOk/NHyppD6zNXJICmVUEU0PZ XBfs0rYJa6Cb3FuK2br7ffp9Ag7sT5NxzHH0M9R1vLrrVVFv5cqfQgVeC4pRbtBlxQuqmDrH KM+qhqCyYz4aoPZF6AHvqBYku6PGr0Wv1v2q4pXoA+E86ANlFPoy4J7oa6whSESARW8I4P+2 xN2Wq6o+MLsz/fAH5XZFI+Jtccuoft+tL01H7OoUGvD6rsX0KoyjW0q+64oSa2y7pmoq+aWK TDxEb9aIqey8/62jQDM+69Bxo9HqKy9SNENxkUiQg9xg4W5ZF0pP0mfMzCd8/uPtx9OP1/rH w8f5D+OU9vzw8fH04+m7l20cS8SpNwoAQK26l8HUIGTM84QF/YkUjWIrpoHpQIL1gap6N6F0 wW2lYl9SpRBOc2Zta3DiXqg47uW3awehpJlSu2JSUGsIMszG6KjilZChwBRM2x+dJOYWMg7w pxZJvrqXoX1gSHbGv6qPwXDty2VVRnpioCIy6rfdxbBine0Ur6gFnqMzgCgw97pt3ZFZhNqt PQVr/gwg04iEJ65PuYUhg5gsfOamJbbrdDMyWhjU8njsUlGyfC8OXMa0vLLXtzaluUI+mee3 PQkyK8mkcDgFuZ3+cyuq3umpOgLsTnBxpRPM5Y1yWojqrpJhFUkeC0o6quwspNVapeq174+j G6xs8lsqxr1yU+j1KTRb73EHFaZuFfe1m4VldefcTJjo7gsPLWfc8ealAVdhdXU+fZw9+6rq 6q3cMDqFh2JwqwIk5iLnnn9Cq1TrVe8hbEVZx1FnVZSoi10HAD58//fpfFU9PD69on32/Pr9 9dnRq0UeK9sNOsmortz9g5noWBKQhWD1UAeSgifCqycTazxiQjWFFbqA7PvNAbBJoNFsT+2C +vz7dH59Pf+8ejz95+n7iQoUgLLbmO+iKtgZQO/hH92ZrNo7utFgk9YcrGGBViFhbl3fxpT0 dOAVS7UTYDdp6w3y+I5fr/62BvFyOj1+XJ1fr76doIdoU3pEe9KVkQ5GHYPQQPBmUrZYlQFZ pVHrInrXt9zeUfp3b34NmOfljh5UQ7Apyd2N22VZujt6WSpbSp/JXpbBANA44g4Dib8vEmOF cO71yuwEnUcxZuUWYwzoTb+mJ7gUERzhoaubrx12nNKkNLcoZpVDq0k3UBtMdsNSm8tDY0+x d+1MTG5lUaTNDROS1Jk5IJvNlOj13LlaO8RcWFew+dW1iA5C+3SF53lGm9QUCbrZ92tqXJCB 2yxkr1pl6A99A1RoSejeD/MahHCAyuqnzXVtO42zMZZBEnpeARGRPKLCCC9u2cCoBJx9IjJk iyRCO6AmJVsLBNBZZHWZ9YrWpaROJIVaHTxqONqpoxIxKnJC+PTB+O0YAzSV7a0Je3cf6VFR p3K3ciGYvrYHjKQ7y8onAk/TXopnRPJi73cSFm5oiuoSJChKw6va8f3E9aDtBOp2e6kl+lRD y0MRoU/oZYq/Me+ajFVj/I/FRnZrP7QlVLgP1bxNFJfxMJHYureiZmqg4PfXl/P76zOmxO+u cHMqfTz96+WAsQxIGL/CH+L329vr+9mOh7hEpv0qXr9BvU/PiD4Fq7lApe/dh8cTJgxS6K7T +GRHr65h2jbIih6BdnTYy+Pb69PL2Y+nYnmifK9JftMp2Fb18b9P5+8/6fG2t93BCBOSOZmP L1dh9y6OSKtIFZU8sTVVBlBLwW/Goz5cGVBQe1/s5OeJlcmkITBHB4gE8liHPMHa2rIICmy8 aJwWGzipuqZ2WatP8XBois/7YOWbVsea69AvfTy8PT0CXyT0MBIcqzUksxvKLNu2WYr6eCTH cjZfUF+IJUCKobQzDUl1VCQTe9oDfe4ie56+G77hqujb+nfaNXbL0pK8P2FwZFaunYujgYFI tfMXuCEBPjZPInRCpo68SjfaxoOpF98++3Flz6+wSd+7tb8+KLdRx5usASlXjwTfA7HYl6Os orYRKzFDV0qFH+hvtz+QJGjjy8gP7opcdJfE2DPf26YfGGa+vJU+IpUuZd96rln2FOV3SeM8 qDV9mHMnqfg+MOMKzfYVE/1iKPuZssAgoC8+bS9Dskh5DBpiFa1ENNcmn8a0z8BiBF5EQ/R+ l2K25RVPueS2c3LFNo7zj/5d83Hcg4mUZ45DmIFnmXPwmQrsx9PweFLhAmqZrd1cibDOWB6z 9oUI1/W6vwPbwFwtqFonfLblnseaBljOV1b8Z1PckosKkFkCgRib3I5jy6TjawU/1bSJPhPw 8H5+wn5fvT28f3gHIhaLqhsMXyFPd8Q36ZkUjdMBNC6pHIwXUDrcS3kaKr/dT6NgBSpqT4UB 2DarPhm6YKEHlnN99r5SfeYO/gTWA58E0k8IyPeHlw8dJXuVPvyXGI6iKAPO0oDEDnB0X8S0 lkrd1xvvKsr+rIrsz/Xzwwfc5T+f3vqMgBrWNXc/8gtLWOxtIITDJmpfGnQ6AzUo7WmhcsyF pg+3wirKb2v1plA9civ3sOOL2KmLxfb5iICNCRgGw2vNuP8FWSL6axkxcA1RuvYGjVkuvGUS ZR6g8ADRSrDcfR4rPF2au314e7MSZSgVkKJ6+I55x7w5LVBBccRxQ/O/8L8K07B5ufUsrFjF 9cbmOhRQxbJjSqZ1GtlaavU5WXIzP/a+ksfbPpCJ1bgHjG8X19M+rYhX47ppz/mCnMnz6Tnw Ael0er059j6aVACqfqoMCfuqzl0nTVUqjfDVI/KqHZoT/VDY6fnHJ2SlH55eTo9XUOcFVaZq MYtns0CuaByT1OuOM6+9pQf/fBim0pOFxLR/qBu0HZENFu5HYR5gGI0XRkx7+vj3p+LlU4wf GNIkYYuwRDZWSNhKWZtzuMmzz6NpHyo/T7sRHR4srcgHvrB3XuYs97LXOHh0yPMJdPRAHEPz /4IGKRmPwrZ6feyGIk7LJKmu/kf/fwxCVHb1S7vYkmeuInPn6U69pdydr6aJ4Yrdr9ytQotc ZeL31GIFlRbTT2+no0rdV01CgLp03/AxUOB/eURb0ruCyvY4RKOUY4FHmRqy6LhY3CxpO3ND A8uaMno7LrvKX1fxqxlw4iAatJ7PpWWY6YhNCiC9QvcZo9QPDlwf7E8f3/sMXJTMxrNjDdK/ HUveAV22FJjz7N5/j5avMgwvp4dhC9IAmedd8nXWe/VJAW+OgQz2PBbLyVhM3ewkjfyUx2kh 0K6DSaN47IoDsZjNJrM6W28CuX63wD+nlIkhKhOxXFyPI1tXzkU6Xl5fT3zI2E7KwnJR4EOx gJnNCMRqO7q5IeCqxeW1HT2axfPJzOIyEjGaL6zfeyOZ9mNbSnR02YaefaJPeFshVBvzWefg ho8rHWuRrFnAarEvo5yTmWHH7l7Wv2FRQTeiqh6P1Cjps5LBCZU552QzzwpTRzIQlmjwOrMm tU40PouO88XNzJK/NXw5iY/zHhS4wXqx3JZMOLe9wTI2ur6ekje39x3tl69uRte91a+hQbNT h4XtJkBMlnaQhDz99fBxxV8+zu+/f6kXxEziqzNKANj61TNeLo9wDDy94Z/2qErkPskv+H/U ay0ws6RTLiZ4klD7C71iVV7s0nGUR04pszMotqDaNTt0cHkM+C23FNskpphRs332mW32YfHW krExLA/6GmPWCFe/rTAVJm6mGb9tBBJFVEdW1fjSp3P7OmezY5biSZv4RqDLhOFSuo3RjDQg a51trWNyiAKWcmcnqOeD0U/0ajRZTq/+sX56Px3g3z/7za1BykUzs6XcMpC62Lrj0yJo99sO XQhHwr3YEWv40ZMG894b1U3A40W/b+Q7tXkbcFWoZ+hpTzy8+kgM9n6z85TU3Rlxp1IiXYgW ksxn+7tP24feeuFlELU/hjCongqowFawAXYJ7Uu2CbifQ/9E4BKA70Kuuwi9bS5XZlJooz8q HOh7Wu7oTwN4vVdzWhVC1IF290xuqf2v/AVUKNovq5NpFnrAZ8v91dx5ZVS+n7nnVdBfpY3Y c35/+vb7DEerUY5HVgIER4hrLFV/s0h7omHyGCfeDgdsD1c9nGqT2M2Lz9IJPYRwZQdcPeV9 uS3I4FmrnSiJysYU1LKbCqSSw+NxMFDBhrm7lsnRZBSKOWsKpVFccWjEEfBFyuNChHzk2qKS FV5CaeYxOB1K32aSzFZvV5pFX+2YYAflKIfg52I0GtXeyrUYPCg7od9YwsR8xw2pyLYbhBMq lzyie1PFNBzXUuHw2JFMA92QKc3QI4LeqIgJjfDQVO+qonJ8SjSkzleLBfnWgVV4VRVR4u2E 1ZTmNFdxhgdqwBksP9KDEYeWjuSbIqf3HFZGbzmd+x3Z6FDBkNdi98Gxl8Z7lYc8fk0ZLOCl DYargPLfdQrt+S4j11K8Zalw/bUMqJb0wmnR9Hi1aHriOvSe0kvYPeNVtfMEycXyr4FFFANH 6HyNf1wQRVQCAWfVxscaX1KnmRGal7IqTFjPU1/uUk5xv3Yp9PJyzGjpmLYgil2eBHxErPpY tkuZIzKt2Hiw7+wrPn9HLhWd7ZJEbXfRwZYXLBRfjGe2ntlG+W9hsRF5RjDzGopDdx2QNza0 uA3wfSCi9Rgq4h/fHWYabJ0+X75kA5MFMvieuU+3Z/ssCSjBxO2Gbl/c3odCNpqGoJUoL1zF eXqc1gGnaMDNFK8eworDRfQ6FOnR9IfHlbsIbsViMaXPb0TNRlAtrWa8FV+haEgW9BotzDpv S8Ow3EwnAxecKilYRq/17L5yxVP4PboOzNWaRWk+0FweSdNYd5poEM3ai8VkMR44ITFSrfJS qYhxYKXtj5uBlQt/VkVeZPTBkLt958ASYXaRHBhJjJas/Tu8X8Nisrx2T9Px7fAM53uecOdI Vzm6EloGsQoWt06PUYUXOgXw1YqBq0XnxzC+Sq6zaqSSEpMV3zP01VjzAW6+ZLnA3IHkwN+l xcYNGLtLo8nxSLMyd2mQNYI6jyyvQ+g7MmOB3ZEdam4yh6u7i1FzFwpQr7LBRVElzqdV8+vp wKpHF1XJnPs1CojWi9FkGQgIR5Qs6K1SLUbz5VAnYBVEgpywCkOMKhIlogyufCe8SODd5Msl REnG7ugqixTkPfjncJIi4AwPcPRpiofkS8FT9/kfES/H1xPKZOCUcnYG/FwGHlMD1Gg5MNEi c3N4sZLHocfZkHY5GgXYe0ROh05TUcSoMDnSgr2Q6sJwPk9msPD/xtS5bzFso7K8z1jAwIbL g9GKrBgDr/LAfcF3A524z4sS5ByHLT3E9THdeLu3X1ay7U46h6mGDJRyS6BHMnAYmPtBBMJ0 ZUrGRFl17t2bAH7W1dZ7F97B7jFBKJeUCcOq9sC/5m4qAQ2pD7PQgmsJ6If/rMq1qYcw/kRH Hj46DU2awliHaNZJEnDx5mXAQ1xFJa6Q6abVINv7UHhNph1y99779saRWVBeEq2Pcg9rtZgG kiGVJQ0XXgHV0vb14/zp4+nxdIWhQ0a9rahOp0cTgYWYJuYvenx4O5/e+xr5Q2o7DeOvTuOW 6VuHwsmtex1tLz3gJbezEN/jVprZMZs2ytKvENhGcCZQ3ovVPqoS3GHgtwUalOjpqbjIZpRR 3q60E4YoJAPGLjimNmdPoKvICNkUruUQKKTgNMKOb7ThMkD/9T6xGQAbpVSBLHc1EYeBzCSN TtuxynXYNT7RGBBvO6rtQXD69thnR1Rx0gfJ7guXYlcHMhrBzpkGNfXaAOG1ap02bUCcZRPg IiFMZi9vv89By5yKbbTNmPCzFwepoes1Zl5MQ+7YmggjlUOGE02h82nehh611URZJCt+9Ila J9JnfJ3n6QWOmh8P2mHEL19gRtyL/fhS3F8mYPshvHcUWcMd8gzTJW/Z/aqIKkeT3sDgQKRv JYugnM0Wi79DRHHbHYm8XdFduJOj68CTvA7NzSDNeDQfoElMcoBqvqADulvK9Bb6e5nED8Sl KdQiDaSzaQllHM2nI9p9yiZaTEcDU6HX8sC3ZYvJmD5EHJrJAE0WHW8ms+UAUUzv4I6grEZj Wrfd0uTsIAMGyJYGs0ag6mugOSO9DUxckSZrLrbmDY2BGmVxiA4RbRHvqHb54Irid2IesG50 qyAb17LYxVsvs2af8igHG8zkrXoSk7rTuqPNcv3An3UpxgSojlI7Y0UHX90nFBg1IvD/sqSQ IOlEpfs0PIEEodCNuGhJ4nsVykO2y9ds5byP0+FUytnmYZaOa27xLEWeIJCWxOogQxYtoKKx WlPTSCbT6IjW+C6Jb63t0PtM/X2ximaUvOIXPEQ1gU7vhp28QLSKs9nyJpTLHini+6ik89Rp PA6q7xHlkezF8XiMLlUSPIzNt7ZL5nJDHV0ofUF7n2O+TdogpElUdslANltNgCMr4ooFdP1m B3IR0svxaU/XrwWph/dHFY7G/yyukANz0pw7Kc0Ix2OPQv2s+eJ6OvaB8F/Xi1GDY7kYxzej ax8OrJjmAVxozJ3zRENBgNXQTmRU8Co6kIOhscblAEpeIAJs5gWHu5VUcU30SN/lbp92IugY vYky1rdFG2GamqDW14vioDXP+fPh/eE7Srw912Up7x2VSigH9HJRl/LeOhbNq+shoH555fN4 1kYrpCp0GOP9MGqy9cc7vT89PPcd7/URo1N9x05yeo1YjGfXJBBEbzjAVThYE+NE02nvcWeK G9RoPptdR/U+AlDoErfp1yg9U4k9bKJYu3IFOm3nXXd6aXtT2gh2jKpQ/zOWA/dE+RLYVHlV 71QI3pTCVvgSU8ZaErIhlWY8IS0HztcdvJdhXeTg+FZyvFhQFjabKHXelneGg7fLLX99+YQw qEStO6UoIhykTXH8+JSTid8MhfsqhQW05tuv9UvAz9+gkVPgdEJEQyHiOD8G9GMNxWjOxU3A SmSIYH5XrEqigJ+foTJH4xcZbYJZm1zSITK0xQ1WVQUsGBpdleGDGtBrAcNYDrWhqHi+Ttlx iBR31NfRhBb9mjEvfe/VNk7KOeO8xZLFstK5mYilkmPUFcbVBxxjW3FDSpqnz+tNYLXlxdci ZH7eoeI5UKOKd66Fl8nI7zhGonuP9HY1wPVQVnBmkq/6VEoV6Rj0y2YzUfSll0LK+KESJTr+ CCQX4FryJA28BpCtjK5b60XXkeuotT2Yx96Iwsj6cs/5TRT5fUAjnx3oNEMmgtMsi+Zb48XN ZP6XB83hWvWXD/Q+C9iRAHXr4ZqK9k7wIRAaJq378JI06MJQbtST3v13XWUM/0oydomlsXmU yTZ7pPe9ZdMkcOlxMRZLq6YD1uYO03SV9FsGDhGGhuv0DX3dGDD6fQ2kHcKlHqgHCD5qyzbO wzQIVSIyhgg62slxbJ7Wo5YcIvF1TbZ3q8p2x+buyn4/n5/enk9/wQhgF1VsJ3F3mWJhwaYh SGU8nVwHku8bmjKOlrMpZfh1Kf7qdRszKvSBWXqMy9SJsLj4XXZ5k5TDzUqFCE+YR1CU4uvm sg+E7jYDio21DDWmVugG09i1rqBmgP98/TjTOXqc8YpSPppNqNTlLXY+8XsEwOPEXycYoh14 C86g0Z35Er7OSsqBDLEgkY3cXnBhZ83VkEz6nSo5P9LSOmJz5ZcTalO78cCS3Pm1Ci5msyV9 sRr8fEIrZw16OadYQ0TubX9sAyirNn2nSqQVmEsRu3djdzD89+N8+nX1DTNxmEjyf/yC9fH8 36vTr2+nR7Q3/mmoPgG3iSHm//RrjzG7RyAdI+ITJvgmV/FULoPpIUXqZZ7z8BgPj4nIhptx 5AzEsc34urcEWMb2oSl2L6UGUusE2vopajdgH0luWQanQaDGQill3Tph+7Zf5R0CPNMhERas Nb6bl+fg8ngBTgxQf+qd/WAswT35U7XVxk07fZZRIWq4X3vLozj/1EeYqdxaI45soa92z9m7 E+NDp5K3QOWOEvAUiloVCmiCLS+VU7GqIPH3PlsHTQZdRDsSPGUHSEJXvH3zWuUmAVkgwFOJ MiNjCG3TL/z4P8qeZTlyHMdfccxhozsmelukXtShDkpJmVZbylRJyky5LhmeKne1Y112he2a 6d6vX4LUgw9Q7r04nABEgiRIgCQIaGpaHn91auC3OeadAD8+wLtNJQwnLwA0tmKd6WGV+c8V N4B93wCFJUMAG+vCdDsUmlUiW/2NsLXQwhUqceryHtEo6Kh1PRON83vm8qvIMPv2/GLrzb7h bXj+/D+2FQWZYUjI2GUy/eTEFJFyr0b3E7g03TtyxUBo3df7+ys+0/jc/SKC9/AJLWp7/W9X PbDjVMZJx92caieuzHtGG99fI9CzoRn4U42fbhhkB9NNcfKdsXpy5qPcw75RYazcS1tRIeD/ LYAprNaCULYEMCnHIjEpkBjzVdUErrOG+p3HVr7sBhJ6A/bxJr3lO8rSEW9iJOKbi7a9PZWF ozdHsup2PyARGM0a28PguhOcK0z3fOtdGUk0bLKC78y5KsK3whNVXuxPRftelUVdl323Obb4 gjGR7Yq63Jfvcsb3ru/S/JZ2DeSCfYesKs7l+3x1x31bdsX73d+XO7tSGQSLryivd69X3x+e Pr+9PGLOZC6SWcj5IiXPGnWAyOvRgGeWjDEfEqpSXMZoIMZHZfvRfFwgp4rDdBNFdbedmtxR wDJtXzeDLidiQM2wdgIobsu9ZR8oQ8p8u/v+nduaghXEypDNqnNHqA6Bzs9pg99VCTQcMbux 83ritjEFXamuvbI9GxZ1sbYaSHix/0Ro7OzY8jAYJZ0GFoYGbLb5jI64bMebUD2rJtaNUovx 5faXEQu3LUZHq6VvY8KYWWXZs9hqYpdh3sITyifELOVc7uFBvVXQuSNRFjBca6xxPm9kBPT+ z+9c3dotGp13zF6UUD1IoiKiHgalZpNGqBkBR16TwWGCj59aLwQx5nM7orcsRESrb8qMMuKh /YX0hpxo23y9lzY554XU55NVnwyV4+Lyt3T/6dL3ldExcjNkAJu0qtPOqqDNwj5kvlOYKsrm cxatI9Azf72ruij0WGTwIcAsMgdTgBNijnz/sR5YZEttzfBX5jM2NEviwCQJtIlrD8scT3ld qOWxicXVpmeOixIpr1ybHpzTtrGWNxHSHNylidmHIvC3QNHAQLV55lMyqO1E2mMu77sdVxip I/Kn4J1bl2rS7zOZtAj55T8P4wazvnt903rrTKa0NOAUpr8xXHB5RwOGnQaoJOSsuinPCP2w YIF3u1LtAoRJlfnu8e7f9zrf4x6W24u1wbXEdPjR94yHRnmhxpqCYGiZEiUijJrBgjFS4ruK j5zFO5zuVBrm4YdoWjmOkzSdBjvt1SlcLfD9S6bGOtCRDEeEapQuFREzz4Ugrp5ihRlECiUi MaoJdLlSbFeR66EtOvT6Y84E0VSaM4UKd6ddUImuz7V6l9DkqcRrS9Zod6V5Btmw+KzB7+m4 mmUJDWUB2IiKtfgCEqutEBI8VatAwRtBh4qQzxaDcCqyg3N6bi14ESZMI9uX7Ew9oky2CQ5D HHk4nGmPSDXMWlWCgNpFdmrSyYlzDSgfuRrA6fPNRxoPw4CxNKKcbmMm3XWOZSec+U8Tz/ds VjmchFhXGfB0aKhnDStAuT23PRZ8Y5ketcy0Y0FcjEjsBWivjzj8Kl4joo63eVMzuN3GZcXH 7JmJRMiz52OSVjUsRvcNE4GubpYSxciiJfZ+FGLypHBDgjCOsVI5n0mMlSpR2DnJRMGFISDh gH0sUOijSZWChmjNgIpRg1ShCGXNCIIliOgBImEIoqs3foD0jLQsE0RahezBfSRNAoKgRy8H TAbbPvQc7uZTvW2fBKEjDdvUljxJEvQ9k7Eki5+Xk5oBT4LG43F5XCB9je7e+JYLc20bQ1Ru yv64O7bHpSgLpYn7jM3jgGC8agQMKTaviadm9dARoQsRuRCJA+E76iBxjDeoTmjgeNo70/S8 UX+HBpu2GkVEMe44IvZw7gDlcv0ZaTof3ZQu+CyO0J4fyss23YO7Cre0K6z+GwbRiVarvyHe uzTbtCbhtdMcmBmqcwjm0O5u0b4Ax+6udvlmTY3dOB9qzyRN4XAbHAn6oSEYBxn/k5btBYLe r1aRdxFdZwICwNI1acmLquKrWY3xUYY3vK9wH8uxw2PCbfKtPebiuIhudxgm9OOww+qrM+LH zDffXZkFdNm1fkg/YXZVSJjT8XCmod57NNwyw93pFQqXc54kuC6vI+KvTZeSb12NhXfp99BD 4v3CvSRMAXSoehavMvRb5rBhJgI+aVpC3xEnyHeRolFWZgqh4JBlViJiJ0L3PtKQCdIZ4NhD QmS1AQQVFrfNPaAotp3XKAL3x44ndDoN7rQyCzm3qiIvwuwUjYQgikcgIkTrASJB+pbDfRL7 SP9BXGN0tRYIH688igJErwhEiOoVgUrWJVPyiFp9y8LQ+KhSr6uhLSAlJTKL+iwKA+STYr+l ZFNnrtlXtzFfIlCrpKojzHxf0LGPCGQdY9OhjrG5UMcMr5it9Q+8R3R8tq7TOQG2p1jQCTqs HL46i+rEwU4SUh8/u9BoUGc8nQKdo9J5dH2OAk1A10Vy32fyfK3srNzKJmnW8xmJG+cqTRyv zXhOwXf9yNzaN1kdqyHplmZsWZgoU6Ix37HNlLXLSVk1W2kUrfAnKGK0zzd8X91s11QCxP3P ttsG5a7cd82xvZRN12DxVWey1g8pRa0ljmJetLZXKNumCwMPWT/KrooYNzqwmUj5bj1yaAKa xNgWV6HwGa6DxuV/jV25ymPscgz15HKOFcxxjjwt+lrL3lE+fhAEuMpgEcO0T8P7A1njmqHg egxLddB0gcdVMYoJ/ShG1M8xyxMPM4sAQT20T4a8KQhdt3s+VREeyXEiUO6ZTMx1T5BmczCm qzjY/xMFZxi19Bm1EXldcI2OSGzBzefAQxQQR1DiQERwPInUXndZENcrmAQZPInb+Am6A+76 vovRA6fl+5obEvj+NCOU5Yyszbo072JGsTMB3k6GDUm5T6mHyBrAsUWXw32KFdRnMWJq9Nd1 httFfd0Qb02FCgJkyAQcNRE4Bk94ohKgvNdNSJCqILhU1hzHHYeNjFiUIoieUIKu06eeUX99 eTozP4597ApBpWAEmYuASAi6LRQourYTFxSoySIw6zYUJ6n4koqmCdRpoj2yH+aoiMbXyB5a YorrLcqYdc2MkuixQ1ZdwufZAg8/rCMUm6y/8QjBFk5hOaXaOc8IWskPP1F0fdqXnR7cYMIV ddHuij28vgX2DtstHF+kt5e6++DZlQn7fqWqc1uKZ+iXvi0bpLoxUfZldzhxtormci67AmuV SriFgxuRUBPtPewTkY21a9Js/RN36QjhKr9AsEn3O/HnnYIW5lwlydu9MUGlw1peRvBYpT2a TuQJ0ueBd/k37K20TM0iqsqqVF2RuE1yaW7goq5uMNGTX3aH7JL33USATwpO6gfegHChlgYk WDnzDetqWUaDsmuN5/mZO9YZ06fq7SjS4HPaZ9f5AV1Fuw3vw64rN9oj7W6j/YBXnmrkNfFV VkKoNPzrCasDpwy5WSneCuNf6kTaQrdgHa6Im6xO1WKXexOOsEZYPMH6/cfTZ5Hi1IrlN35a b3PjOYqACFcnHQbH4bqig1gx0n8MDcQpPkp7ymIPqUJECvG0JJcAVbyv1GLETScGG0/Rlhvy LUQiyosWC9UvGBa3roP5DUBD6o4HMpHgqnFCR5iJMyN9vQHmda5gPSP+YHbKCESb2tCIYqG2 uJF9aSCNulIpwHgZmmkPhcip+fGYtjfIq5+qyXR/TwB0egSaZc0RXZxd9zBVHa9j5xrhdb/Q vn+HzoiuiJA1dXbZoGkdBI2IpqQ3XPjsZfUhV9sLCNNrD2CMNTXTt10L2C0WAh95mIOclNX5 ztmQ4SGOowQ7fZvRLDAkSl6nxwiQhggwwSgTZgD7yI9MKeUw6+PpmFEHt0V/1CGTW4BykjNC YIFHoLo4ikJnxzoVKC6NDZj0qDQ7tyuylbj4QFAGcTRYNBoFJPiVkkWNNtsbaAGtQ49YnABw JVwRkNzcMi4i2MqSbobQs9PlpRufjGAX97ddph4FA6yHDMy+H3Jd32Vpbi01VeMnAX7uJ9Es dkQGHEuvaiygsRAJyx8W3AqIFzrSKQmfA8e9sUTGrvlme8Iu0MSa3MA1bxeaeGD+TnOgnaHS f1YvTcItLaOT8DXG1+SkP1eB59vjqRJAjPW1AT9XhMY+ooar2g/NWbP4ExvwyQFYgU3e+lpD 07b8dNinq9qUb9YC9BhqRPrm/B6d2KzZZnoYLzCUVjoeq7AsT/xA89JdNZ2mbyG3XZXK968m SHqOqN2yoLblUPB+O1Q9frG4UELUi6MIPbPvjkYMhoUKNixivzLToX2+fMA10o6h76s1mlHZ Wag06xmLQhSVh37CcDZHS/Ad3qSRucrabAsin09G5Tu1zFbmO3TSVFzlhpNQfaYbOPwASBGH dM9N7RA7oV6IdBW4wMuuSnwvxKuHs34aE8wQXohgVY8JVrbAULxo4Zz3Xi8D0TvtgnuDkCVo 9RwVxRGGwmwmHcvX5NV6xcl+gNYrUBEq+ZZ5ZaAoOikESs1RbKASd4GqLaahhGnobD63DOk7 7R93FLo+0PExc9XAkQy9D1VoGsZCvH+57UhQgQMM9V2YEO+LyTrF+BRW6iqbs+FhY7I0CUJU DBQDFqm12R4/OVJlKUQnxjwX3wKJXn8bNAnKXpt2zQae1jalGlTykvZ9ub9Fv5itZBvVB0y3 WlWc00tTJapP6OnAQtJVu9BMIrZg4YKMRI48jhpZRF1X4TpZ6FFsW2USxQ4dM1mQf6sm1MvD ICI+ujjYtqWB015oabjJhLRwpl2lY0LHEEirCW+vvZEaMdm4x1oqA8j+0JfbUnVXFykDBA7e SxihOUQh17HvuNIUXxUZvnc6wjndseoKBnROkjYt9911mh/OJpnGIMKchuBmHQTKw83dkXCT tycR6agrqiLTDirGR8BfHu4mc/Ptr+/qM6ixm9Ja5ASfmdGw6T6tDnw7cHIR5OWu7CFIoJOi TeElmwPZ5a0LNT0YduHFqxK1D+cHu1aTla74/Pxyj0XMOJV5ITKXOIeL/wDX20qVtPy0WbbK Wv1aPVr9c3io5++wF7AHZK4HisdKtkoYkxp/fXi7e7zqT3bJwKeWjBgAXK2P6YDb7gOJlr4A ZH67T+HcrS73hxZ3fRFkBUTr4hMGLiQulUgHjR6bA/GxKpRdzJxY2WJblVz7+mCUjqycBh9l 7hRUiwS5s5UDWyaZdoLO5XOtGK0zhES6ck5PedWvfpqTrf88ZZjR2gclQWbmvDf2Erp8q++0 Jeju6fPD4+Pdy1/IkbyczH2fqqeu4zw67oVISx5+vL49f3v433sYj7cfT2jfiy/G46qV1UmS 9XlKRCjVv0HIKKrcLKp4QBsx1qXuPQxswvTH9hq6SMPY4Xhq0+HObypd3VNj2+gg0o5CTZzv xFHds8rAEvR5pkoEKS+MAyUFO2TUo5iniE4Uau5EOi5w4uqh4h+G3Ro2RhTjiM+CgNsjjnND lTAdKIkc5+iW2KB+MSrZNvM84pAtgaMrOMc4jlVTZ1sZa7uIdyS+ymlFHdPE896X366kJMT2 MCpR2SfEd8yylsmQb/jQ+R5ptzj2Y01ywjsjcHSUwG94Y7V3/NiSpK5Vr/dXfMm82r5wvcg/ mcNxiRO317e7py93L1+ufnq9e7t/fHx4u//56neFVFl0u37jcatUV5IcCL5t6vhI8Ikb4n86 VYHAo54dIzYixPvTrAqgxFDSfDLoD0cFlLG884k+B7BWfxYRs/55xZXMy/3rGwQY1tuva/N2 wPxKhG4bl9aM5rnBdgmTzOB6z1gQUwzoT6qGg37pnOOi8ZUNNMD9ZGYs9a0+6n2C2/iA/VTx UfWxI40Fa4pCeE0C6lmDxtdJZsvHJvLQHfv8kS1pQihwScPV5zgwfGuP7T6nYfM8PcTG9JXr lQXgT0VHBvSqUHw9rhG5ucdekHLI8DV6YQBTj7KMdJx1lhREGDBGgNZIcTm1Z1LfcT3nGic+ xzyTC4hKlBKsQznDMbEmJIh5f/WTcwKqHDbcPDG5BthgNY/GSO9wILXEB4TWx07WxglvzOYq CmTYBKttgcHFfugju3d6PzSmPcwlP/R1YF5uoGvrDQ7OzGZwRAwIVzskukE+S1xZLJWW4XeM QJBuE4+4pkGRWTIKs9WPLHHMKdeZ5r4RoAExt5NtX1HmexjQHlxYet3Mf8oJ18awRzvkqFxm o4JYWXphIcBTjC/9R1F5sVdkuezFFitp33FO9nyn/MdV+u3+5eHz3dOvN3wDffd01S/z5tdM KDO+L1rhl0sl9Tz8HgHwhzYkrhuUCU+c82WT1X5or9HVLu99f6XWkQA3RRWCCH8yKSnM7HPm LPcMhZIeWUgpBrvwPkQXCmKvX2WXry9geimJI9PaOBuZWymKhZV63WQfiIp1A+G//p/c9Bl4 YLkGU9gjgT8HzJtOIpSyr56fHv8ajc5fm6rS1+umqiw1IJQfb6jnoQ+9DRpx3C4T3BTZdLIz xa0X+WiFlYTYaX4y3P7mqKDab65paAkpQDG/rhHZ6E+DZqir++AuUIuBNAPtgiTYtY7Cvt/Q D9WuY7sqRIC2Ck/7DbeHHSGLxvUoisI/Xe0YaOiF1nwQOy+6pjxANzguKwB9fWiPnY9dlYqP u+zQ08Ks9bqoir0d8jJ7/vbt+Un4tIocoVc/FfvQo5T8/E68+UnReIlr5LuGIrsta1MlCu2f nx9fIbYul9D7x+fvV0/3/3EZM/mxrm8vW+Ro1D6nEoXvXu6+//Hw+dWOBpzuNMXOf0JAfLTj BQ7NOS4wtWLtjIAoMMsWrn+OEmSAer0Qmc9eBUBMYgNm5CQHULHdllmButdL78Ndr2yzT7v0 krYbCyCOcXfNUT/CBWR3LnuIiHvAHH9zNS46/3Gpy6bkBm+pQ3PeR8fBzjkhcCKUSV1j0K6o tnAcquNu6m7MzmDDt5sFtYjvXCBnpO4gpV1zqA6720tbbNHzXP7BdgMxU2c3eb0qiYRUw8Lb /gM3VGx0VaQipnQn4sDpBUAykEuRlzkc0tYQFN/qsUyNLw+wvjcKgQwqaE9wShS+K+pLd82Z QbEno/iOD/ucywo8pu6fPj9/gQuDl6s/7h+/8/8g34C+UvDvZHoRbkmj++KRoCsrEgV6hSJ3 w9CII9ZEDXFqIccLQSWkpos3aRq2tZKMbnkeoID1JrRpjk8pQPLpbqSWWKC8XfhSvlBkJXo6 shCA31TTG4Mz4nZp20tZ3s4mTpo1Vz+lP748PPMVvnl55s15fX75mf94+v3h64+XO7gPMQcJ Iq+kjkDgf6/A0dp5/f5499dV8fT14en+/SrReCIL8tJpERlXS1e/3h+OpyJV/HlHwJQjMOsH +wJwopGXSSEKnp4HffCXtugEdY3n/9Gp+MqKBfVUeBex8SpIcWnIfaK+bJ0gF5EA5dK0h03x 4R//sNBZ2vTHtrgUbXtokc8hNxBknp0J9BkMJKMYWlbEl5dvvz5wgqv8/l8/vvKB+WotAPD5 WZTsnAyCxhWmUCfgXaw6mc3I7swNg3023gZeDhvI+tGtEcq0UXm6Qxs8Rts+ukRUlrWoJLuE 6nDmEnfiSlfkLRPhyV36RanytKnS/c2lOPFVB2FfEk0pEptanSHIYOiDxKfv7w98v7v78QDJ Yg7f3x64XTbNT0uqRDdBPYdjD1pN12uzXMgnc8Jj4Ng1xT7/wK1fi/K64KvVpkh7mdjslFZA ZtNxSSzqpp/r5RsCiwbsk7b4eITL0c2xuz2nZf+BYfx1XLurTbAIRIqHCvKt5cdWKneC9Oha z2lqc2cq9xO3REz5ONXn3RY9qAS9XKdanKERFhnHohLqR/jmFxZR01Sqd+mOmiV/HCodsDlk 18a8adJ9US1bWrkMN3dP94+vutgIQpdrF7aWj4Vo9bdlvisQBhaMxseygdm8PHz5em+wJL1N yoH/M8RsMOyIGZs3GHt22foAFP0+PZW4fy7gs7LlG7bLxwJ9UyAkYXMYhJ+IYREKTWWOd587 haYllJnkfLwd1Nr2QrChbkAERXpKzVEoBulCBN5bfOZhQnL5P86eZbmRHMf7foViDhPdEds7 Uup96AOVmZJYypeTKVmuS4bbpXYpyra8thzbNV+/AJkPkgmmZ+ZQDwFI8A0CJAikOWZskeuo vNlzw2aRDeOrKhlePY7rt/vn0+CPjz//BDUtsJMEgwrvxwHGt2r5AEw6aN3pIL3xtRYtdWqi C4BBoL/hgd+Ykw8PJgmvJazCGv0noiiHPaWD8NPsDgpjHQSPoQtXETc/EWACkLwQQfJCBM0L hiLkm6QEmctZYnQBNqnYVhhygiIJ/NOlaPFQXhGFLXurFan+PBs7NVzDsg+DUt+hpRHm71dW m8CaNLJlACxOg7CyRUy+BY9k2wsuX+p3p833OmlVx0sFh0IuQoNhFnv2bxiTdVpiyqI0SdTQ 6D3l34FEs49uWjTLzenEwKLBBM4GkIO1afOFbhhRltFaHpObfZZM9Jtc7NcNs9ilsP3K1GU0 SzEKrIebyNY6f2hA5sOUFtx5NdKiGrWVLj/nB7vGCHK+wKnx7mRWNcUnBfO5GUYY53a4GE7n 9E0HTkYZft7RDGkQGl2jQPbb1xZB1o+g620pK+5GpAONwlklM0yF7ugPxG2OxAef9KMYm6t4 3BGk9sbRgDqzqQIz3w8jqyawP7n6AHYqumpJmIKM5GYZu7vcFEVj2EKtwhCkauEqU1L0zNFD mgZpSjlHIbJYzDyz2wpQZEJLOLB8Z9Uri6nDbZRFLI/tDbGCwR7LYrQdjB41kP4edGI60Cbw 2YQghF1I+QzUscBWoI4ei4mhuSI/In6yHEX5ZozmFYew9JI0NluIdzmeJboqmPTc3FgTscZ1 l2TXy1Bv4nxkHF6T6oncgVb3Dz+ezo/fr4O/DyI/qN2DO0fNgCv9iAlRJXZua4mYbkbJZgk6 vmrxuyLw9FvwFmO/l2wx3bgCLa56HkQOf0slA/ESvddS3PhpXN5GYUBVQLAtyxmFadz+qUKD bLEg7yotGjOQcovsCfOucVCv/2gO0Kez8fKzznG9DGxJzFdyGv/D1BvOo4zCrYLZSH/SpRWY +0c/MYyrTyZmzQNUD4zqo82sbRBrZ/VgGBlZpPE3RprFVMqwPIk2ahRSrzF5VRg/2heeZ7jj da5p2kJFuk+6jgZbUOs762zLjTBT8LNNtVDkYbIptuTgAWHO6Ox++y1pPyDrKtpLrY6K19MD Xu7iB8SdGX7BJkXoO6sAO1C+p2/7JdZedCZ2DzYDvX3JbgijHaeNAESrFIc9aA6/evDpfuPI 1obomPksino+l+6abvSdPJp04mHsNqnM/+ckCfEqaO1GR6Hv2BAl+usudNd+E8Yrngdu/Dp3 s95EYC6njiigSHAA1TYK6EsExEPN5AGLm+DO3S23LCpSOnC7Kju8FWnCaa1HVv8u7wSNMgi4 zxzKhMQWbtwXtsrdc6K45cnWYdWqbkkEmIqutJdIEvnuUGoSH7rHNAqT9EC/5pLodMN7V7pU U2MYd3f7YxibvKf6Mbtbg3LgLgOscLkw3By4n6ciXdMGiaRI8Zy2Z+7H+6jg/fMvcUTXQVya FyGdvRSxGVjPIJdghbgHIgsLholX3QQguXAHdOIjhg+jYJK712CWc9CLnGjBeF8zBIvFPqEt OonHZAR27D2TogiZW4QANowE7EShuwVQgSzqkTJ57B6kDZ7cMtEjoEXM8uJLetdbRMF7FgxI IRH2rLdiC4vZ3QXFNgeTRmVxcxLtcY8vM0H71khxyHmc9oikI09idxu+hnna2wNf7wLY4XsW pArgWG73dFpUuc1HmVVApUBR2kdzUW8qSw1DvEK31BszEbz+WY3QgbU2hM9T0y1YYI7TQsQT 71IRDFIWjWF6eSDBPsp4N2W7RgD/TVyB6BAP6vG23DJRbv3AKt3xBdiStVKHRNhUTaNr4Nn3 n+/nB+jz6P4n7SeVpJlkePRDxxUBYlUyWVcTC7Y9pHZlm9HoqYdVCAs2IS3pi7us79lxCgOq nH5Imjim3yLHGCp0186BGtKcHWrphMX1/PCDfpVZfbRPBFuHmARvH3cd2XQu28v7FT0Dave1 oIdrwddxGVMnpQ3JF7lLJuV4cSTakk/1aMtJiNe9gXbQjL+UDW+YUQ207GzhXRK5x8Impt/b S/QqR/MvwVv77S06UyWb1j8HtReiQ+WHLKPuoiRKnhoMrYIk0KOA4y5QJaIwC1QBM1xlJmEx Ma7lJPQ2Z1mHkcrQS7+/kQTOAGWqehgZjI4P0ODJ+GUVdjo0/USrUQoPmFeYU2dZba2n3S8r +CeVRqqZIyux6ikVbwoteYcMacgcD2YlvhtDyMQ3YRZczVwFnpEpQQKrgItiYgWCV/NCRW9x MSx8huEtLI5F5E+Xo858ITLfNfOU9NKV2LQwrsMVJy1AoLWWpPP0H0/nlx+/jH6VwjffrAaV pfCBaXKpjXjwS6vD/KqdCspOQ80vtmsQHe38yTUcRsE9ROiK4caCjjtfrHpGWIW+Q1+gmIgD jK0s3s6Pj8btmvoQJNHGuLLTwdLBIu80psamIMG2KXXbYJDFReBg37iVOPC6IxVdBT+jfbYM IuaDCssL2hgyKB3KiEFTh5mWl0iyf8+vV3yJ8j64qk5up1Ryuv55frqiD6P0eBv8gmNxvX97 PF1/7Uj3ptdzBjZwmHzasypWh7NzwAzj1BZvEIEMNzx5LQ54Bpc4S2D7wCEA8U4GowjzyNXx HP5O+Iol1CldCAp3CSILQ04IP99rl80SReikCCc45YVfGpfVCMC0DrPFaFFhGh6Ik3s3wSjA mL94mq97RTSw7sWqhjt0bgaVF0zMuk4TAAQtfGPcESGsidQH2kICZqOJxRC3euEMI8Uw0JA2 QUzduSnhzgGpu+5i6GwAaYDoaAIwbqMJOcIIJ0cwkZKbOCuDTCGbisjrhC0WVMabmJrRLYXW pFssxI7uU0G7ZErtr40tsTdrKNZlVaum1/2n8+nlqvU6E3eJXxZWa+GH5bTeDA4G9Qk0lqv9 uhtmRTJdcyNA962EGuZM9Tm1SBSqjNNDWPnN9JHVLo2OkCSKCISuwxK1mtH0zf4YcJFFTPPZ 2QaTiZFneyeGKomG8buUi3T4F+gKFkImGfjda2vnr9lm5C1mE8q24zEOkc85Xrtp05Pl0qsp q3zcGjB6M1XI34cWOE/liExNsFLCQYMQwrgCV1jpYVTj/vY3rdZbluNN4CoqU8cxtU5Cuepo eGlTWGW3PytCzTjX76LgR+lzI68FgjIMCLUJE55TaaKRIsBXBIrC5Mb0ZwsIAB3FT8W4UwRe 4KobFdoWBRrYZWj1RTLI96TERVy8nnmahDqsAcZBzdlL43dkYiy6JJWUen0l3BXnWyJjK+ZT i+U5Gc1HQ3Pjzk1BUCmljLZDkGmSBn/h3bYGkbkBeFpEKxuYK1eqtiAJtctR9vX54e3yfvnz Otj+fD29/XYYPH6cwMwmzpS20J+5I8TQJ1zq6m3y8G6lu2uBZbPheoYWkPVhwO3fzeZpQ5Wm JUUb/xqWu9Xv3nCy6CEDW0KnHFqkMRe+FtbJRK7SJOgATflfAWvRYsOFOJRBknXgXDBnqZkf zXXXMA2sT3wdPCPBZgqzFrFwRL3QKSgvNh2/IEqMx1QFWZxF0MU8BesM203USZFkvjeeIYW7 6IZwNq5YmXhYXkYAYR3sdWcT80moGM3iEVFNwMA+1VtB+TH96YL0NtS+o2oO8NmEqmThLYbd OYJgYupIcHdkJHhK1RYRVAAgDe8dqQ/jeOwx+miyIllH0xF1LFOPMG4zPB155YKaKCjreZ6W ffOT41zk3nDnd1rsz44YjjDtIOLMn3kTqsTgZuRRCkiFT4CkKJlnJNUwcd3SJCImqlEjRrOu 3AFcxFaYF4OY+LD4WECu9jhgny13fIPTt9zjvbmN1X2Gh5k31IlPLfymHrUWUDtw75oV0cKb dicsAKcksCS6ZKf+NSw9QiRRotlQ+a3u7x0Xx4cFPdR5ui+sjTsvIqhwZ9PmMEDv13t8A2Tf XrCHh9PT6e3yfGrCBNZP/UyMon65f7o8ygfaVSyDh8sLsOt820enc6rRf5x/+3Z+O6ng9QbP 2mYIivlYdyKqAI1Hn1nyZ3yVnnL/ev8AZC8Yx9PRpKa0ubFE4fd8MtML/pxZ9VAFa9OEghA/ X67fT+9no/ecNJIoOV3/7/L2Q7b05z9Pb/894M+vp2+yYJ+s+nRZBX6u+P+LHKr5cYX5Al+e 3h5/DuRcwFnEfb2AcL7Ql1sF6AyNk5UsKT+9X57wYPXT2fUZZXNxSUz7uo7Ku9NK/KiUy7Lj g1TN2G9vl/M3c5orUMsC42rewh80bbjDCav2gu0edtUEolxnG4Y2omYvJVzcCQGaol7lSkGW 9mTu8FuqaTr3uxbe7ZHeUJCRV1tsmuEBK1W/jn+Nhc/ZbdvUGnjgq9xMVtE0Vz7+CvAJPVWa 49S1RltOwDV4z/JufOPN/fuP05V6Im5h9DkQRgEydBl+O9BAXXE/biIywcYtup20/SB/Vq8/ 5avS3xequuGLDGGE1xGVLYWL5P10Gtye0XEFEZ1nMTI7XxOD1j6SlKkmb3VfUPhRruLUOBpg EQ8T+czq1uVAsme3IXei1QEishZ4cHFb7rOAOTwwWtpiu0+CMF+lEZlD6xhXNW8+zUJ246zD kbM0dleR+WG+DehDGcSVuPIjl3+ionCxRo/OchM7nD6ZwPXFMpeLnsT3ly4pHKWHYQgKSA// wA9WjDzDDqMIBOmKp/r5cQu0u1+i+gpCfL4qyAALCrfv8BNxuli44ugggdVuCwX/EX7Os8J6 7V6jmeNGoiFwuQeymEdpma93PHK8Jtl/4YXY93VITSKToDqkcwaCMPV3YYGJWei1l/W8adlm /VMH8Y6JU/gjzNpF9y++AgGFVO9THoQsY0Ffg+vUtdugc6pcF7rlyQ652Gn4DOkg73pE5tWv 4g2kdOU9WBdiFg38DULaKw/OO3lFF4dJlNIe44ogZbsit3wCLJKDNePb3tjna0yTNK4yuKZZ Hm5cXrY1cZan43K1L1wur7HgfSOAaNeAZ766rZCeI2RubOXiWPE3tKsKc+NIgib3mSIVW76i /QsrXLkq+tZUTbV1Tp+KwC3loR4+WPQuqzdjvSIs2vRhM5Yw6UjduwbS5K4XfyeKMJ7P3Iko 0YuyYHkfE3T5kzY4zEGgTQpubbf1dIiO+rske5U4ellhc9G3wqSzqK9e2PaQYY5MR8KOigB0 4wJq4nerJ/y983Reo6iaR5SAhaMo0XTQWn3PeGaEV/O3oIGHDTPalyyKWJIeiYdeyqGj3KZF FulH1xVcPwLYskNY+pHmUQc/ZFipNN3tsy4hhtEAw0HPayIdOyom+uSuoETq8i4NKJTLycI+ Aayxgk/HE+rxk0UzHVHVQtRkQmL8wA/nQ/toqMHKmHqlGb+oS9ZN37i9FRkHUe4b3tfqoODp 8vBjIC4fb1Q6YmAncr/kC+P5HUDDQ2FD5c8SCzEoV1HQULYnClSpzbSADWWVGoeomU+5K9QX 94q4roa88GLmFb8CEjkvKvP8+XI9vb5dHro9kIfoLA3bjrECWygMmW0KNaZ8h6sq7fX5/ZEo KIuFeeCFAHmRSXlqSKR0B9igb1XbfhuDABur3b/VlTUqpYtrMEJQhep0mkj9wS/i5/v19DxI Xwb+9/Prr4N3dAv78/yg+aOqA4bnp8sjgMXFN/w168MGAq2+A4anb87Pulj1YPXtcv/t4fLs +o7EqwOoY/aP9dvp9P5wD6bmzeWN37iYfEaqvJ3+Jz66GHRwEnnzcf8EVXPWncQ3GkqKaZ5/ rzwtjuen88tfHUaNQSh9UQ7+npy91MeNF/6/NPTa6pWm9zoPqWv18Ij7ZF3n8K/rw+Wl8vTR ZpFBLDPPf7GOY2rUMfMceW8rirVgINqpG6eKoHq3an/XqM7jyZK6Y6nItGyIHcR4bCZnbTEy e3RfrSvHT3e5du68GlwkU+N4t4LnxWI5H7MOXMTTqX6rVoFrD35t8wYRmGteLlxHwg+8Yl7r wRZbWOmvSLBxR2DCbS8vDYte4W1GVg2/k5FOgMoEV/54YUDWUP13LchvOqSyVFFm0hFRkXg6 ibjtvGqvwCTHtmrSgqsXBXGRUe+AwTEaT6aODMYSq2eSqAD2KeEqZqMFbbkAauI4fljFPkys rvFdoQPmLcxAXmzsih0OynIwpF+cSxyZvkJ2ZKHKL8fsyK0xa3BoDVj43VEES+un3Su7o/9l N6Ij2Mf+2DN9COKYzSdT10gg1shiCoDFRI/2D4DldDrqJA+v4DRPwJjJ1GXiICrHK2BmxhWh KHagBXsmYMXMEKP/wT1ZM9Pmw+Uon+pzb+4tR8bv2XBm/y65MvMZhpjVHboAvVwe9d8cZD0v rczoKhsxQqn1IPcGM629j3kIhiMTGLAlTu1NZnHfHumYB1HhexM9dZYELKYWwPTbx41iPCNn Fxgfs5GZnN7PxhNH2sU4TMqvI9UwglvC9vOFmdxC7RWqfbSNLTt2uBhRDCVSwMrQ2tdmqzc6 slIyjnU//rt3qDKG9iCsw9Obn2vISsl8fQINxJiH29ifVOHcG12zoVKS9PvpWb4rE6eX94sl XosIuinbVid31JSSFOHXtCIxxWo4c4hV3xcLciZxdmNLAOEHRLb5GokvwHMMQic2mSmRRCbG lNw8fF0sjfTqnR5QoSfO3yqAvGxUUdT1UaAJdAkci+bIU8lWZTiIrP6uy7SLtES6yZDGVT1o Jia4DO7VtKHF1XQ4M655p2Nz9wLIZEKpfYCYLj18fyFCg8F0Oc4tDrPlzLFBBGIy0T3E4pk3 1h+jgUiY6pl6QB5M5p65AAPmT6fzkT60va1v/Ce+fTw/1wHl2z7BTlWB6MPDJkys3pahhRTe jVFqjHEU0SFRShhpg3TqVgXEO/3vx+nl4WfjPfBPfH0UBKLKMqGda2zwRv7+enn7R3DGrBR/ fDQhmo2TCAedJMy+37+ffouADGzN6HJ5HfwC5WDmjLoe71o9dN7/7pdtKKbeFhrz+vHn2+X9 4fJ6gq6r5VcjfzYjXedQvztxoo5MeJhyhtRbsv14qFsPFcBmUi3AzV2eKlWLEm3FZlw/j7Om Z7cRSgSd7p+u3zXBXEPfroP8/noaxJeX89VoM1uHk8lwYiyc8dBINVRBjOBXJE8NqVdDVeLj +fztfP2p9bp2N+aNR5QSFmwLc1ffBqh9UEeRgPGGekLpbSE8PVeQ+m2KwW2xN9OHCD4fOhLn IMobksuu0zYlKGCFXPHJ3/Pp/v3j7fR8gs33A/rKmHHcmnG8nXHNfEvFwsjAVUNMul18nBmN 4cmh5H488WaKlDwzP+AMnckZatijOoKcupGIZ4E40nLI3XT1ZlDGoKJmAt63sMhxbR18CUrh solYsD+OOiNUIzFTJLW3AwLWmJ5jIwvEcmw+SpWwpSM3OxPzsUcH9tqOLKcihLgUnBi4LOim Ic7xuhlQ9BNqQMxm+nH6JvNYNtRPKhQEWj8c6hb/jZjBOmH6I7BGhxCRtxzqDtsmxtMwEjLS d9wvgo083YLKs3w4tRZgxU89Iycth3yqeytHBxjYiS8McTWxssEqiGa/JikbjfV0QmlWwKBr fDOoqzc0YYKPRuOxKS9GowklucA4HI9HlkdZuT9w4YiiV/hiPBlNKOsBMXOP6qUC+nhKWkQS s9AuGxAwN7kAaDIlM/fuxXS08Awn4IOfRBM6yq1CjY2DukMYR7MhrU5LlJ568BDNRvpzr68w GtD5hmJmigz1/OT+8eV0VbZ2dzNnu8VyrpvTu+Fyae4m1YFMzDaJ6xiCbcajkXEM4Y+n3kSD VOJQMqEPVWr+Nrrxboj96WIydiJMMV8j83hs7NIm3Pa3JPvqv5ocrq9Pp78sXcyAVxvaw9P5 pdPfmtQn8JKgfh8++G2gssU+XV5OpuK8zeVzcPqYT+YFyPcY+588BURnRfQ4pNHiTqyFhmoq TFer2qFeQMMBG+Ab/Hn8eIL/v17ez9J1uDPVpMiclFkqzBn7OQtDM329XGGfPLenlq0d5Bnp bfF9h3mMBcYOnXcTzR5DYCMAFr0hB7II1TtyJ3fUjaw39KGu20RxtmxStjrYqU+U1fB2ekdd gVQLVtlwNowph9NVnHkLQ4PC3+aSCaItSBw992gmLOG8zYaUGOV+NrKU4Swa6ec4/9/akzW3 kfP4vr/ClafdqsyMD8Wxt2oeqG5K6lFf6UOy89LlOJpENfFRPvab+X79AiDZzQOUs1X7kHIE gGweIAiCIKB+u98D2JlL1H44d5J502+vEMDOPgbCg2Ic8lC3fPdhdmzJkFV9enzu6G2fawFa xDk7zcHgT2raPXpMM6s9ROppfPh7f4fKMDL+V8oOfctOKikIHyI5xPMsRY+VrJPDJmLEm5/E lKI6i8RVaxboqu+zuhGgzeKY23/bq8szewuA304sYyxnLTDcDc+OTx3u2uQfzvJjJlL0OPwH B+3/1yVeSeTd3SMe4CMLjiTasQB5K4uIY1h+dXl8fsJHzVHIyOx0Rc3n/CKEtQI6ENy2+kW/ tVZiJDjTjVHH66xLM/gxZGnnAlT4qk46ywQRyEB1FWEiJOiqirvCobLSzkhPxBhxw33SuSnk oN6y0qjDT51HhItLhcSJuDxJrmaciEd0B4ro7MKtfyHW0vnAAyZpZ+vPkB5OJo5qOhaM3S1j IQwt4xjKt0VQB74OxyxrYWBekAAXGNEGN3hHXQmKjPKvFslaD92kx1WiSWEbw5d6/OlKvcCA 0lXSscHFQZ7KDi8ou6bKc1uDUJh5kxQtzCX8SkTuY7sMlYxkupWvV9dH7euXZ7ryn/qrH7L7 TxUo4N2yQDC3wyXFsK5KgWSnuqgZ7dX1UF+J4fSiLIZVmzmM7CCxLC8rgSqpE1GHseQsCnWV j22UXmC3SYY5XR5biJ6qiRuyS7sDijriBZvmEmj+kGyKgiJxYqrAT9+nz8Lk9WhEr3dPfz48 3ZFcvVMWK+eduunFAbJxzm2vARi1mTeXM5N5b9g2Xijb4PGQUXDKtKkiIRfDh0Wp4Cxg5Ual j5wEFQKU9hu0YbU9enm6uaXtO3yt33b86yE1c360bGMFC6u0jKb1MuLIK9lbmsz2UMNfg3nL Y4HzrPAkAYKUK1fSNXEn6yYJHU01Oql6JHB4rPI9V4026wpGZWzHXGBqEdhxdxKRrOSwrZpU By9y1AOB6g6oOsAytWha9nUU4qoW01UllvxRmZcWTnUGNszRt3Coas64jGFKyPfQiaBQACPi K6trHz9NZDvIMmmuaz++84TfgIzr7BRMBuRHYpgQ8z7Lu6yEmV6WAjMS2ll5Wj+nU+oDMgUw u5EpKEa6qfkapmcBd+sia4HBSt7m+KmvOu6NPua0W7SzwfZ7UTAHtIAWDe7sJF50Z8MDKp6H XRjzxubi2is/QTGUc4bJogb4w1TJUYp8KyhBU55X20i1WZlK/umRRVTITmBOqtA/9ub2+87R LxYtcT+7hDS1EtHPu9evD0eYTS9YQOQ56g4Egdb+Na+L3hSRa2DC4p7d2aF9EFhj6pmiKjPn saHyXF1ledrYN3uqBAbgxWiyKtSiX6juSWsAeWTZ6mVT2lNNQtrSdIva7SsBpvXPK89AcSU6 OyPsql/KLp/bVWsQddNa97JYpEPSSCfrg/pjWHraI8N5GuvBECgoOtSrBJdxGwyCRLXxfnsk VQY20/Ifi0V76iwOA9Ey5TiA097rO6xNWIzngsLHFiMK2/ZFIZoAbI3u2OQRc2hmRqJWJr0r GRXKJFlFp4SK5GrQz89O6AEFyz9XYVvIisYOr8b380i+B90WSrVWViW3aGySuskq3Rm2CgyU 8+Z3FmJT9Q10hNuIG1HYE65+u/Eem6rwBK6C4MtjdFe85sjRN9SG1m3nOZAoCIb+yXFXNvPD iRFFCT0YqfyKATk7iFwlNtpvxMXslG2AT/e57dKfaOmBL9mdMGGPDnV5FlCzldr9e7vaoMp3 P/49exdUC7/ais01pAn02wAXuKB0u0wrG8E99yllBwrb2pNnBukxHv7enHq/HSOtgkSEBCFn v9955LOBN481GDKujAhSLIl6h05qnZYcPxgi3IlAp09Lry9p1uIr06FPay4eJpBwkTWXDTkj glyt7LCsoEb6P7G3zgfVexxrS+zLxg6Ron4PS+Bda5Q0NJ4cL5H1it9TkmzhVIW/aQtv2ZhC iBWoNeETOZTkZoAd5Q6ptlKsh3qLweX52OhE1deY/SaOp80m1pAgHugEjdwTj3j0IqoxPwzP PIrwjfZVqYjt4iK+wV/W/ESU9lUz/JjW/v754eLiw+UvJ5YEQAJogCQ9bXbGOfg7JB9to7qL sa8HHcyF7cHjYU6jmHhtsRZcnDtmYg/HL32PiONVj+TswDc4g7dHEu3W+XkUcxnBXJ7FylxG h/zy7DTagcvZ5Zsd+DhzK87aCplquIh87+Q02hRAnbgoihvqN898IT6BhiI2ewZ/xjd9Fvsi 54pg489jBT++2dTYQI+dDdhsxPCXBA5JrOHrKrsYGr9mgnJxLxGJMXtB07MTjRhwIjFnAgcv O9k3lf8dwjWV6Pg8yCPJdZPlOVfxUkge3ki5DsEZNFDYcSJHRNlnHdc66ujh1nV9s87syK+I 6LuFmxU85zSgvsyQ9x37pgLBEaEpRJ59Vgm9TWxg9ozvmMSUR/Pu9vUJr7uCoMa4L9mfw99w fP7UQ+UDY0Ywepxs2gw0tLLDEhjClNtotOFKptxnhnSFSZJV4jJ+AzMHOIxq25Ltv2sy1ooY HvUMxDn1m/q0pslgatFZM0dvuFeiSWUJnegpZG59TWpJIryQJQEZZ6wDrQ6NYS2cwuwnV6gF ZQmVxHOayrz9Blo19d1vz1/297+9Pu+e7h6+7n75vvvxuHt6x4whsAxmfTw80C0wMJ8AayTp qqK65mNyjDSirgU0lVOnRppr4YT1HlsgFnjFk6UMjhTZaluiL6RjheYIBimanJsEMtISldbF YVYSdQa3K42QoTl72UTzD/CFCIuJhjPhR9DXBcdqbSOrBk1WWrt9E1q01wVm6gUWiaqZFnWf ZpGID2xwebmxXmfDjwH1ZFAs+96eI0KkqdKinXBEKpzvtJTtkO84j+/Qv/3rw7/u3/9zc3fz /sfDzdfH/f3755s/d9CK/df3+/uX3TeUXu9fHu4e/nl4/+Xxz3dKrq13T/e7H5R0fkeuEZN8 +48pIdHR/n6P3rH7f99o5/pxULIOFxfMmZ59G4FvdXGhuzkirBFVNAvYWiwSViJH2mHQ8W6M r058AT5ZaUDA4j6qDMBP/zy+PBzdPjztjh6ejpQwsIIlEDH0aqmiD3Dg0xAuRcoCQ9J2nWT1 yhZdHiIssnKCpFvAkLRxYk+PMJbQMpF4DY+2RMQav67rkBqAYQ1oTQlJQWMQS6ZeDQ8LuNcq LvVoKKDUAAHVcnFyelH0eYAo+5wHhp+v6W8Apj8MJ/TdSro5EDTGV1A8lsiKMeVC/frlx/72 l792/xzdEgt/wzzE/wSc23ghqBU05TJ3aZxMkqDFMklXTDUyaVI+PrRucBEOFQi1jTz98OHk 0nRFvL58R1e725uX3dcjeU/9QRfEf+1fvh+J5+eH2z2h0puXm6CDSVKEU8rAkhWoZuL0uK7y a9exelyfy6w9sf3DTS/kp2zDDMlKgEDbmF7M6dERahTPYRvn4ZAmi3kIc233I5S1DZlmhNXk zZapplqwgZ0NAzNNvGJWC2yXOr+Zty5W8YHFlIpdX3AM1LZuekF183/z/D02kk7+EyP7OOAV 16ONojS+obvnl/ALTXJ2ykwXgZV7AY/koRg/nhMuV1esGJ/nYi1P58xQKcwBToDPdSfHabYI lwP7qeh8FSlaeX0YQ5fBEsCIchknypoihcUUby7i7XdFE/j0wzkHPjsNqduVOOGAXBUA/nBy yrQUEOzzFCPDzsKqOtBg5lW4t3bL5uQy5IRtrb6sNI7943c3IJORP+FqA5gK/hKCy2xkxpBV qi2G/OIto5pbBAb3yg4I70TgedaYvMPybcdZRCz0OVMslQc4eMHvoq3IW8FMvpHo3JTKpo6F S3RJhraVp8OHCzbCvuGAGfMJOA6/NcaaxK9dccHD3SN6NJunq/4w0b1evE3qZtWFXcxCzss/ zzjYKhRweDtnWLS5uf/6cHdUvt592T2Zh7TOOcAwYtlmQ1JzambazCmMQc9jtMwOOk646K2E RZTwVw8TRfDdPzJM5ibRf7K+DrCoQQ6ckm8QvN49YqOK/EjRuG5KDBqWzYYNBOeR6vNFtCpZ kr5bzfEGMhKUeBRl4pB6gX3GTHX+eenH/svTDZzPnh5eX/b3zEadZ3NWohG8ScIdBhF6jzNO qGxhTcPilESwivu9nYgOLC2kGZXUg21xdNkQDcKO7abZeUE5zz7L308OkRz6vLWDxzs6abyH uzzumn5Vqy1T0DWgUC6qqYkWsu7nuaZp+7lLdvXh+HJIJNr3sgS9GJRj4URQr5P2At04NojF OjiKjyYxWQSLZyos7Ji/siXaHWupnAjJywbbkDHZQxN8KPwnnU6eKYvq8/7bvfLnv/2+u/1r f/9t4v6iSnvMOp2Rwfb3d7dQ+Pk3LAFkA5zWfn3c3b0z1OrKfugwB7wy+TaOr2OIb53saxov r7pG2CMZMzVWZSqaa/97nMlRVQxrDROQtl20aRMFSQr8n2qhcZ77icEzVc6zElsH8112CyNv 8qigybNSimYgly3ba0V4DqPzDLQ1TL9isZ7xcgdFrkzq62HRVIVnQ7BJcllGsKXshr7L7Oth g1pkZYoJFmBs5u7NSFI1aca95YSuF3Io+2LuZMpSBn/bp3/00k8yjINpn8cMygOPueQXqNZR IN06z+wuEQX6W8DqhY29rDrhOXrBCQOO27CLOiAvAxTQqGMIK26gXV0/uBW4Ryc8M5m7Gq9i xIA0kfNr/lxhEcyYoqLZxtaGophHrLyAZW+hE9zH7KbbCZ+zeXiSTCzjgn8AJCMzt3UBg6dV YY0K0xLbBWuqEqGpDOHoqoebeu64Un5WW5YH5f3GEMrVzDuSBR5kFjXbPttRzANz9FefEez/ xjwRAYwee7iRlzUmE+f8JbDGCzbe8oTsVrBsmXoxBQq31jV6nvwRNNLLuTr2eFh+zmoW4RwH HPiMhWtl3xMZ9iWdEZ7JyvlBbm0dxXKzXc062IJaiRKEgw3rombh84IFL1oLTo8hNiIf8Mxt DZVoGnGtBJmtebRVkoHc2siBCCYUyj6QibLwQZRB1ZGVCPfz4OIbhQlQStg+W4WAzWFp34Ai LHGTACOolg0IdkIFKka6+/Pm9ccLPoF82X97fXh9PrpT9x83T7ubI4wV9N+Whl0IlQ6ymF8D q0yOxSMCvoX+Aug/fWyJMYNu0WhDZXlxZ9NNVb1NW2RsfleHxE4LgxiRgypWYGDMC3e88BwT +MwZ5WOZK2a16lrJZO1c+xkEOrc705t+snfSvJq7v+zdx0x37jqtJ/lnzHBhT3HWfEJ9nHOc LGo3LRvTyCpLB8yUAMqGw7LAxmZxbtK2CpfsUnboTl0tUpvX7TKDvb06iI6UDft1iH4hkKy3 wg7XTqBU1nZ+J/QrKJfuTj0+u/ZUNvfi0WjMBH182t+//KXeFN/tnr+F7hakDq6psfZ4azA6 ALJvkRLlfos5oCjj0Hi79TFK8anPZPf7bJw2faoIaphNraC8yLoplNGZXSTpdSkwkXvcBdSh COICWvp+Ma/wPCWbBgpw9iFVA/zbYJqhVtoTEx3s0SK1/7H75WV/p5XzZyK9VfCncGrUt7R1 IIDhi54+kY6hwsKavSiSjMaibEFR5RUziyjdimbBb+DLFNY0ZcyJvOpQ1pKiR3snihHODQX2 OznAN8rfL04urbTduA5q2HfwFWPB199IkdIXgIolWAEBhhymxBasCFEdhRMcuTIVWVuIzt5r fQy1dKjK/NqfGeVdsehLVYDEL4gISzyprtZVph8ZMsWV+zAGfa57m8F+moWI4ciuuL81kiHd fXn9Rsnvsvvnl6fXOzdDfSGWGb2HstN0W8DRoUBN5+/Hf59wVOqZNV+DfoLdoi9XmVCic7fz bcjLo8t1zBN5JMNbaKIs8P1mdJLHCl3/CtoLlDIF/Gy3A39zFhpz4OvnrSjh9FNmHe7CIneu DAjLemD81PS4bVcePD7P4Isrc5zXbh1jZZacR1kLOiAGP3VvHFQtiKc9n/euw9LVtvSjTtpo YGlMZcMaPKZvwHJd+D1oqlR0wtPLxwFWNNursM1b7hH0eHbv0M3d2dMIYp5mR1tZzfHhees3 UoMZ9cXFLxw92sVRgCOGyw0eXQDfatfQJD2JtNhHUKUEpWx6xsxSaalsNt0Tv0ltLji2p3Wi uRGU/RwkVdgdgzmwYJWPU9/GFN8WdopUU8kyjW4cHpdsiqFeUnI0v9+bImwnUOONte9T6tM0 87Ay+Ayc5JfMXE5N+InmZk3Xi2BFT2CvbpVAgBy9Doyt3jnw0MXvmIpslS1XUOHhSaYZwEe/ C/VaOJzEEJkk1MW1QMEYGtgVFlkdNdyymkRnmo4P01w3tUmeeQ1YZbRd6VMeEB1VD4/P748w gOzro9odVzf335zXyLXAhFv41JJ/GO/g8Wl+L6dDoELSoaDvJjC6WfYoWzpYXfYJv60WXYh0 NFw67duE9A2mYXFiv5Xo+ut91QtLw1BYlojxQxYZfehnaHRjHJGCXxhWmPCtEy23kLefQKkC 1SqtnFs8ul1QlbO76OFJV67moCl9fUX1iNkWlZzxohIooKt4E4xu82z+5Op2WRQ5ZS1lrWzm yuaOnkzTfv+fz4/7e/Rugi7cvb7s/t7Bf3Yvt7/++ut/WeZ48uPFKikpMfMgr25gKZqADXFf YOxDVNqhqabv5JUMtj+TDSvQPnjy7VZhYB+pttqF3ZM/zbaVbFo0ncgWG+tZIcibWtZhXRoR rYzSP4IemstYaRxUumfW2zvXMGoSLBQ0LihtxXo8OPWYfQkxstHCqYFl6v8Lg4z2PXp3CeLQ bEssfCjtzL0kv82LWNMNPNnAsA992UqZwjpQBnJml1eqxIEdRlMMmE1UtGEYHLV4/1IK8Neb l5sj1Hxv8foqOAbT1VeotSI4fqBbhiUo+EcGWhgnX1E/KgdSRpOKwk0aXdkRN5EW+59K4ISu POzD6DugxHHiKMZcqPNh2KmQaSyCQ4VB6367AlQa6Ig87m+nJ241xCz8KRyw8lPLGWFMUDqn y4Eu/0kffhtSXjiTE7RO50MkISZNsC5LPgC0TK5VqlFzAkOvjYnTLcmpCUqKGor5mL3j4HiK P4xdNqJe8TTG7rTwFhmDHLZZt0IjZfsTZDqUC1rnfHJNVpD6D/XhZahHgkFPaJKRkgwRQSXo xeNbShNdm6rakzANmp4Hr5uqKYm7bZBd04/KQUkPiN65W8YJhjOrDtMXjLFVlT7Ut1vbEl3D UayANdx84vsafM+cHf0PacKQd/yJRaWI7L5B1SEzjQuA5SROqEW46W1G+nkeGtsC2gT6bjgN VcexaPtgnEEvXTA9VApVtOBqm4suGPSiyCqvp7r9moH9XQ6WeQlHnlUVMqdBjGcjl1HmsMcB f+k+Bw+vDFzf2mNcESoQueDpgX4udSKPA/M4ZvpwexdZuu11CbMWZgdZoXeIDm0cSXFM1aol FkbVc8loiQxzkKKrQjT8Cd5ediyl912B91I1uQI5bK8nsxOwbdWMNsR87k3ikWniJJZUoDuF 2I5oDTgKBs88hdp2lsqhWiXZydnljC7V8GBuzZjAnC1udDoC2TMSeWdq06kLkLfp6Eb2ENkh jU2TrLbA21KsiQ8O1oWZAg8R6FyseRZzFNZ06lckYISm2SwwNRLmNC5SdCc6aJAFMox6mGlD rxzdbv++OGd1LldDDmR8qEGHNPSy09xU9a1lMLq6OB/0XRHtDnZCZrtUpK50vowUoJihV+nc cbbQZ858vsj7lnuERDv5tEiYYyQ2GF0ZUlxuh44ymOuI1s/xFZsa1MK7t1Qjoo/f6Y00fiAq r6vqhpDcJfgL9FocuhekOkjZOYCnGT80EmrI6Hah7nmZQznJ8RQavXTvyy2G3WuCC6JRgXb5 17717XbPL3hSRMtH8vA/u6ebbzvb4rXuPYvhqPqqwxBeeFaN3hwyOwZPXfBElvosO1yYb1Cp uyP7A9M+ILI8Ym1GlLo18AwBXnXjC3e/XtBnOsndEvgVWDdQEQrrCAE7mWNZ1ybNFnSCamOE ttXaBvZzUgqBgXAz0277k11mnUbCrSrzF+7wrZeF2yUpshLvBPhA3UQRLT+fTkbA4we24Tl6 CB3Ak89OlVeYUz0uNGx3owN7tLrDiOzMyppzPmMdGam3K3mFdz0HhkP5V6jABRG1SdO1Sc1L B+X5DBRdxbEYoUdvWxs4z7rCDYhMYHy/Hv/QVXx7J7yxw8cpGvSIDG4mvIGLvQ0hbJbGQvgi m64P8DB0uYrcRhB+U8QvH9Xg4DHeD2LhfaNeHECiJ/WqoiuvDUtGDsXQzje0X6ptkTXFVkTc SBTjUGTRA/2J732a8SjCRjR8AhE510UHpIMsEjhgceZR8y00gGbhOoKS/mWTMwy4iFE+tx6H L2rrXRJU4vswHdy3giAEyqXpfwFazpZ7j+MBAA== --tKW2IUtsqtDRztdT--