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 Received: from mail.kernel.org (mail.kernel.org [198.145.29.99]) by smtp.lore.kernel.org (Postfix) with ESMTP id 8F2CAC433EF for ; Sun, 14 Nov 2021 22:01:27 +0000 (UTC) Received: from vger.kernel.org (vger.kernel.org [23.128.96.18]) by mail.kernel.org (Postfix) with ESMTP id 6947F60E75 for ; Sun, 14 Nov 2021 22:01:27 +0000 (UTC) Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S236222AbhKNWEJ (ORCPT ); Sun, 14 Nov 2021 17:04:09 -0500 Received: from mga11.intel.com ([192.55.52.93]:55392 "EHLO mga11.intel.com" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S236208AbhKNWDy (ORCPT ); Sun, 14 Nov 2021 17:03:54 -0500 X-IronPort-AV: E=McAfee;i="6200,9189,10168"; a="230806836" X-IronPort-AV: E=Sophos;i="5.87,235,1631602800"; d="gz'50?scan'50,208,50";a="230806836" Received: from orsmga001.jf.intel.com ([10.7.209.18]) by fmsmga102.fm.intel.com with ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 14 Nov 2021 14:00:55 -0800 X-ExtLoop1: 1 X-IronPort-AV: E=Sophos;i="5.87,235,1631602800"; d="gz'50?scan'50,208,50";a="535308112" Received: from lkp-server02.sh.intel.com (HELO c20d8bc80006) ([10.239.97.151]) by orsmga001.jf.intel.com with ESMTP; 14 Nov 2021 14:00:53 -0800 Received: from kbuild by c20d8bc80006 with local (Exim 4.92) (envelope-from ) id 1mmNYS-000Lou-DS; Sun, 14 Nov 2021 22:00:52 +0000 Date: Mon, 15 Nov 2021 06:00:13 +0800 From: kernel test robot To: Alexei Starovoitov Cc: llvm@lists.linux.dev, kbuild-all@lists.01.org, linux-kernel@vger.kernel.org Subject: [ast-bpf:relo_core 9/19] tools/lib/bpf/relo_core.c:1198:5: warning: stack frame size (1288) exceeds limit (1024) in 'bpf_core_apply_relo_insn' Message-ID: <202111150609.9hiur3MO-lkp@intel.com> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="ibTvN161/egqYuK8" Content-Disposition: inline User-Agent: Mutt/1.10.1 (2018-07-13) Precedence: bulk List-ID: X-Mailing-List: linux-kernel@vger.kernel.org --ibTvN161/egqYuK8 Content-Type: text/plain; charset=us-ascii Content-Disposition: inline Hi Alexei, First bad commit (maybe != root cause): tree: https://git.kernel.org/pub/scm/linux/kernel/git/ast/bpf.git relo_core head: f17ef55e5e0bac0d11d3186cd1c468b5a1e047d7 commit: 03c354f8c71c2478c421d5552d284e0befb03861 [9/19] bpf: Prepare relo_core.c for kernel duty. config: hexagon-randconfig-r045-20211115 (attached as .config) compiler: clang version 14.0.0 (https://github.com/llvm/llvm-project c3dddeeafb529e769cde87bd29ef6271ac6bfa5c) reproduce (this is a W=1 build): wget https://raw.githubusercontent.com/intel/lkp-tests/master/sbin/make.cross -O ~/bin/make.cross chmod +x ~/bin/make.cross # https://git.kernel.org/pub/scm/linux/kernel/git/ast/bpf.git/commit/?id=03c354f8c71c2478c421d5552d284e0befb03861 git remote add ast-bpf https://git.kernel.org/pub/scm/linux/kernel/git/ast/bpf.git git fetch --no-tags ast-bpf relo_core git checkout 03c354f8c71c2478c421d5552d284e0befb03861 # save the attached .config to linux build tree COMPILER_INSTALL_PATH=$HOME/0day COMPILER=clang make.cross W=1 ARCH=hexagon If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot All warnings (new ones prefixed by >>): >> tools/lib/bpf/relo_core.c:1198:5: warning: stack frame size (1288) exceeds limit (1024) in 'bpf_core_apply_relo_insn' [-Wframe-larger-than] int bpf_core_apply_relo_insn(const char *prog_name, struct bpf_insn *insn, ^ 1 warning generated. vim +/bpf_core_apply_relo_insn +1198 tools/lib/bpf/relo_core.c b0588390dbcedc Alexei Starovoitov 2021-07-20 1147 b0588390dbcedc Alexei Starovoitov 2021-07-20 1148 /* b0588390dbcedc Alexei Starovoitov 2021-07-20 1149 * CO-RE relocate single instruction. b0588390dbcedc Alexei Starovoitov 2021-07-20 1150 * b0588390dbcedc Alexei Starovoitov 2021-07-20 1151 * The outline and important points of the algorithm: b0588390dbcedc Alexei Starovoitov 2021-07-20 1152 * 1. For given local type, find corresponding candidate target types. b0588390dbcedc Alexei Starovoitov 2021-07-20 1153 * Candidate type is a type with the same "essential" name, ignoring b0588390dbcedc Alexei Starovoitov 2021-07-20 1154 * everything after last triple underscore (___). E.g., `sample`, b0588390dbcedc Alexei Starovoitov 2021-07-20 1155 * `sample___flavor_one`, `sample___flavor_another_one`, are all candidates b0588390dbcedc Alexei Starovoitov 2021-07-20 1156 * for each other. Names with triple underscore are referred to as b0588390dbcedc Alexei Starovoitov 2021-07-20 1157 * "flavors" and are useful, among other things, to allow to b0588390dbcedc Alexei Starovoitov 2021-07-20 1158 * specify/support incompatible variations of the same kernel struct, which b0588390dbcedc Alexei Starovoitov 2021-07-20 1159 * might differ between different kernel versions and/or build b0588390dbcedc Alexei Starovoitov 2021-07-20 1160 * configurations. b0588390dbcedc Alexei Starovoitov 2021-07-20 1161 * b0588390dbcedc Alexei Starovoitov 2021-07-20 1162 * N.B. Struct "flavors" could be generated by bpftool's BTF-to-C b0588390dbcedc Alexei Starovoitov 2021-07-20 1163 * converter, when deduplicated BTF of a kernel still contains more than b0588390dbcedc Alexei Starovoitov 2021-07-20 1164 * one different types with the same name. In that case, ___2, ___3, etc b0588390dbcedc Alexei Starovoitov 2021-07-20 1165 * are appended starting from second name conflict. But start flavors are b0588390dbcedc Alexei Starovoitov 2021-07-20 1166 * also useful to be defined "locally", in BPF program, to extract same b0588390dbcedc Alexei Starovoitov 2021-07-20 1167 * data from incompatible changes between different kernel b0588390dbcedc Alexei Starovoitov 2021-07-20 1168 * versions/configurations. For instance, to handle field renames between b0588390dbcedc Alexei Starovoitov 2021-07-20 1169 * kernel versions, one can use two flavors of the struct name with the b0588390dbcedc Alexei Starovoitov 2021-07-20 1170 * same common name and use conditional relocations to extract that field, b0588390dbcedc Alexei Starovoitov 2021-07-20 1171 * depending on target kernel version. b0588390dbcedc Alexei Starovoitov 2021-07-20 1172 * 2. For each candidate type, try to match local specification to this b0588390dbcedc Alexei Starovoitov 2021-07-20 1173 * candidate target type. Matching involves finding corresponding b0588390dbcedc Alexei Starovoitov 2021-07-20 1174 * high-level spec accessors, meaning that all named fields should match, b0588390dbcedc Alexei Starovoitov 2021-07-20 1175 * as well as all array accesses should be within the actual bounds. Also, b0588390dbcedc Alexei Starovoitov 2021-07-20 1176 * types should be compatible (see bpf_core_fields_are_compat for details). b0588390dbcedc Alexei Starovoitov 2021-07-20 1177 * 3. It is supported and expected that there might be multiple flavors b0588390dbcedc Alexei Starovoitov 2021-07-20 1178 * matching the spec. As long as all the specs resolve to the same set of b0588390dbcedc Alexei Starovoitov 2021-07-20 1179 * offsets across all candidates, there is no error. If there is any b0588390dbcedc Alexei Starovoitov 2021-07-20 1180 * ambiguity, CO-RE relocation will fail. This is necessary to accomodate b0588390dbcedc Alexei Starovoitov 2021-07-20 1181 * imprefection of BTF deduplication, which can cause slight duplication of b0588390dbcedc Alexei Starovoitov 2021-07-20 1182 * the same BTF type, if some directly or indirectly referenced (by b0588390dbcedc Alexei Starovoitov 2021-07-20 1183 * pointer) type gets resolved to different actual types in different b0588390dbcedc Alexei Starovoitov 2021-07-20 1184 * object files. If such situation occurs, deduplicated BTF will end up b0588390dbcedc Alexei Starovoitov 2021-07-20 1185 * with two (or more) structurally identical types, which differ only in b0588390dbcedc Alexei Starovoitov 2021-07-20 1186 * types they refer to through pointer. This should be OK in most cases and b0588390dbcedc Alexei Starovoitov 2021-07-20 1187 * is not an error. b0588390dbcedc Alexei Starovoitov 2021-07-20 1188 * 4. Candidate types search is performed by linearly scanning through all b0588390dbcedc Alexei Starovoitov 2021-07-20 1189 * types in target BTF. It is anticipated that this is overall more b0588390dbcedc Alexei Starovoitov 2021-07-20 1190 * efficient memory-wise and not significantly worse (if not better) b0588390dbcedc Alexei Starovoitov 2021-07-20 1191 * CPU-wise compared to prebuilding a map from all local type names to b0588390dbcedc Alexei Starovoitov 2021-07-20 1192 * a list of candidate type names. It's also sped up by caching resolved b0588390dbcedc Alexei Starovoitov 2021-07-20 1193 * list of matching candidates per each local "root" type ID, that has at b0588390dbcedc Alexei Starovoitov 2021-07-20 1194 * least one bpf_core_relo associated with it. This list is shared b0588390dbcedc Alexei Starovoitov 2021-07-20 1195 * between multiple relocations for the same type ID and is updated as some b0588390dbcedc Alexei Starovoitov 2021-07-20 1196 * of the candidates are pruned due to structural incompatibility. b0588390dbcedc Alexei Starovoitov 2021-07-20 1197 */ b0588390dbcedc Alexei Starovoitov 2021-07-20 @1198 int bpf_core_apply_relo_insn(const char *prog_name, struct bpf_insn *insn, :::::: The code at line 1198 was first introduced by commit :::::: b0588390dbcedcd74fab6ffb8afe8d52380fd8b6 libbpf: Split CO-RE logic into relo_core.c. :::::: TO: Alexei Starovoitov :::::: CC: Andrii Nakryiko --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all@lists.01.org --ibTvN161/egqYuK8 Content-Type: application/gzip Content-Disposition: attachment; filename=".config.gz" Content-Transfer-Encoding: base64 H4sICMCAkWEAAy5jb25maWcAnDzbcuO2ku/5ClZStZVUnZmRZFu2d8sPIAiKOCIJmgB18QtL I9Mz2tiSS5Kzmb/fBngDSFCT3VQlM+pu3Bp9bzC//fKbgz7Oh7fNebfdvL7+cL4V++K4ORfP zsvutfgvx2NOzIRDPCo+A3G423/8/eV78ffm22Hv3Hwe33weOfPiuC9eHXzYv+y+fcDo3WH/ y2+/YBb7dJZjnC9IyimLc0FW4uHX7etm/835qziegM4ZX38ewRy/f9ud//PLF/jv2+54PBy/ vL7+9Za/Hw//XWzPzvbq+fm5KDYvX28m98Xt9H77XNzdfn2GHy/Tye14s51+fdncbP/4tV51 1i77MNK2QnmOQxTPHn40QPmzoR1fj+CfGoe4HBCGi6ilB5idOPT6KwJMTeC140ONzpwAthfA 7IhH+YwJpm3RROQsE0kmWrxgLOQ5z5KEpSJPSZhax9I4pDHpoWKWJynzaUhyP86RENpomj7m S5bOAQI3+pszU+Lx6pyK88d7e8c0piIn8SJHKRyQRlQ8XE2aZViUyMkF4XLPvzkVfEnSlKXO 7uTsD2c5Y8MhhlFYs+jX5krdjALrOAqFBvSIj7JQqB1YwAHjIkYRefj19/1hX4B8NMvzJUos i/M1X9AEGxtFAgf5Y0Yyog9o8DhlnOcRiVi6lvxDOLDSZZyE1NVRiqfAY+f08fX043Qu3lqe zkhMUorVFcD9uNrF6SgesKV5Xx6LEI1NGKeRfiJ9Ao+42czn5o6L/bNzeOlsrbs8houakwWJ Be/vTUPmbsqQh5F5/YJGJJ9nUnKkZPTYInZvYCJsnBEUz3MWEzi6pgQgxsGTFLaIxfo6AExg R8yj2HLd5SjqhUQfo6AW6oDOAtAvrjafcjWkYlVvu43YJn6tPfBX23kALMUOhD5sjyOBWZyk dNEIM/N9fUVztnpckhISJQKOEJPcJQFaUJZpGq3j633hJPsiNqc/nTMcwtnA9Kfz5nxyNtvt 4WN/3u2/dZgPA3KEMctiQXVb6nJPyiomoA+AN+67i8sXVxYOJ5zqg+Bnc3yPcuSGxLNK6j84 gqawsH3KWYgEWJie4KU4c7hF6oBxOeDa08KPnKxAuDQp5AaFGtMBIT7namilKRZUD5R5xAYX KcKWPXEBktRqgoaJCQELSmbYDSkXJs5HMfgVzW63wDwkyH8YT1sOljguBjVFrcawK3k9uG1Q JeTlkatLtcn9xobNy79oVm0ewOBSCdW18e334vnjtTg6L8Xm/HEsTgpcTWvBNv5plrIs0UxY gmYkV8JNNMUB+451UQ/n1UjNoarf+TKlgrgIz3sYjgOihQI+omluxWAfrCaKvSX1RKArBPh3 bYCF8dVKCfW4Pq4Cp16Ehgf5IK5P+pnh8jgRxkRSheXsFc7uEMvpPLKgeMBllhQwhzQFl0ik lRvccEQ57jFZOTTDmkMAwBOQOW6bKSB4njAay8CJC5YajkBxOUeZYGpue7wAd+URsKsYCXWH 7egOLl9MrEeFiA2tLXNLGQMmquAm1aRD/UYRzM3BtmMiA592Mi+fPdHEvpCXu4Cb2BTWy8On CGk2wctXT4boSQo2NG/4dD2EeuLCs+JcxkRe/t12MzhnCTha+gSBKUulF4c/IhRj44q6ZBz+ YgsqvZylSYBiCOdSzSx2DXgEroaCZKfGRc6IiMB21W7aepryti9R+LA8RBpWXMI4XVVxhZ0g BRmd26TElHYXcWBENrSFDJIwK4YkzBxTH4vOYhT6mvipTeoAFebpAB6AtdRSJsr0HVKWZ3CY mXUbyFtQOEDFRpvGwtQuSlOqG6q5pF1HvA/JjaiqgSouSb0TdKFF1vLiVbShn2aOI83Iw+rE 83RjDTEWUdKZN8Fwe2t4PLruhRhVxpwUx5fD8W2z3xYO+avYQ7yCwF1hGbFAMFnGZtU87fTW +OcfzlhveRGVk9VuTmOcTNeQgJh9bmhAiFy71IeZa5OakLnd8XBzKXjWKp6zDQoy34dMUTlg uCnIA8Ega2K15oJEuYcEkik09SlWIZwe3cpE1ohJVbShDLwRr5uZrGK1Km5YqxkOMNUJyqpH GxEGZIVm+uoVIE+CNZfROvhHzdmDxYajyY3okivTJPAQdfauyRlKw3Wl9Zp5irRwqsm1eBb1 ocGSQLqix6aQmc7L4Ku3Wj2oNCaKHdFm+323L4BDr8XWrOnUBwVZ0Y9Yg1EK+XKdwLd1gMhT 9Yc2S+e6XsWpCiketChTyYG05/n13C59LcV4OrfJYUswhTkMzWwwk5vpwPSQGo5HoyHU5GZk WREQV6NRJwOFWey0D1dt/acMNoJUpli6qPYvwqjFbI6APgMGotpPz8U7jALFdw7vkvTUXhpI Y+7zjmLIEoAfohnvi4Qya+oqFWXA2LwvL3CFKnvORSCDec3my4FXE5eqvDXX5g0Fq7PPWqiZ l4WQVYP1zUnoK7us6fRMyLwPkpAFCfmDUVcCQ1WuIX1SZ3FV11LJrqltuuXjnTFLBJheqFCy G7PFp6+bU/Hs/FmaiPfj4WX3WmbHbWkFyPI5SWMSWg31xWm6xuknV9uEagLCFvDrRDuOcnA8 kt5v3OFzl/EybAJJCBkyotcKmcUSYfPDzKt0nFuGQdZbl32HwqGaktojgQotrzmVRQMpM/+I sBv+DpCZ0W0XOxi0VoTSPS1lAsLB27QpRk4jqUXWuIV5qo4Hlgdyul+/nL7u9l/eDs9w/1+L X7u6oEoHIeicnmC6Un7NHDR9LD1lRwPa9DRPl2D4hYmSWYXLZ70igYYLqduHS+c1g/R23Uc9 wfV4fTDYBSaE6ZL7ODjp0sQvXdED5NGj9YRUlnJIjNcDWMzM0qOBBA9rT2rKXYJDzLvVUY2A g8VmCbIFzhJdltxz2Fu6TsxQxYrOfbjMqnSgrEqyOZ53KgQRP94LzZonCPyEGgIRs8yJdNuL WRq3FIOIHGeQTiGdNV0KQjiz5wtdSoptQt+lQp7ugrrYhC0hGSP40o5SyPnpwJYgg2oIrRSM +z+hQBGdoZ/RCJRSO02txQjb+B9xj3EbQhZHPcrnECWT0DBLkOuvwDe7l1aTpUxgS766mxqT t50HmAR8GWnXsGbIkX20RChFsC09owNLhiL96XXwLP4JxRxBxn+R1cSnNo7KPs70zr63KhId Wrpywl3V0xU3eswXFAazjm3OAxZ6JDXrQWWXh7U1SE2NYSLKysDPg/jJbNBpyPnaNUsSNcL1 H+2tG2O9RuF4PNay3bgyQDyhsXL0undpq4HqAOTvYvtx3nx9LVRr2FF55tlIUl0a+5GQIZzN HJZIjlOamNa4RMhanr0QyFLiZVFiPebQrsoMpng7HH9A/LzffCverEExxL7CqFVUbbymPaBJ VBJCoJkIdc8qVbnuDHKlJ+6ImgxS8YDoqhQkJTJgMPwjmKC0szj8IeT9SVevVSa4tvO6TxJF SFZGpa310ofr0f20mSQk4CkQyI02cYRM1UB9Xe9jBzyixKsymK2cBzgwQYg/3Nagp4Sx8OGt +elmnvbrygddgt/N7E8qnGW2Np7ipMpDZDKjVUq9upwgc5h5yWW9qCJbQTI8su14BvFB1bRW 4uRtzhsHbbfF6eREh/3ufDh2gn8PRQPGZGhsk+MNiqpWxyb9LqlX/LXbFo533P3VrRhhjFKv N0ClFrttNcJhjUK0lrvMjwISAn8sbIHITUSJ7sZrCASvkFMZZR+BYg+Fnci93mFaruTTNFLe SXX4a277u+Pb/2yOhfN62DwXR01llypV0YsoDUjdNRjFzNcrvHD3zSJGrbwdJwt/w0du6er8 QM/PuzttjK1KEWSgW5swnWUysPRSujDNegUni9TarSjRUiirsWA+IqZXMBUO8XWMa4ry8UCj V03RCCS/7NBoN5mSGViP7u+cTnAPxpOI9oBRpLvEenT62B+NsdvuyZN2I4DbUVfnd1gCSB9C 5FKHiVW9BqS67Lp/nJxnpSaGmENgUaUzssiYh5E9xhfjHCX26pDCragVJ2cOc7pKrlernNjH P4IwAY7a2jFRQIEVvGVRBdC6W/UzAO14puK0RkHB4cAOV21VWdw8Hw+vqk+umRwqi8QvG7An yfFwPmwPr12GcRxRKVACcip7feP/tYqmkbUtAJcaIXsXy8XR9S2wNV6kyH5rM8Zm0hdUk/Xf lhTfjhsIGCpGPStG6e3iAYKextcsbpQr5tqdyV+QIaYUhR1gJOZ2BKep32LaA0lc5q4qlE1g hFm8EZ4yBbx39jasfd8cT2YwCoNQeqviYq4vLxHA9enVqozoreUNoNEDa97dDgClkaURmGiB Bmo+LZ1I7UmeJJG2IuHhxb2AMVH9xXovFpRHIUgXKnJToe+nsbmMMQXEx1VLwtp379PLeiiL w7WurH3mqzvJ4K8QFshgvWzYiONmf3otew/h5kfvliAHAdvdY7E6xsDmyvwkZbV/jQ/nwjl/ 35yd3d45Hd4KZ7s5wfKZS52vr4ftn3Ke92PxUhyPxfNnhxeFI+cBfDnXZ904+MImlDGAdTmS v/N0aaGkFWntJXwvNwCc+x7WfkYK3Tk+Y8mQNDSpHriYCHHR5jVgQ76kLPriv25O353t9917 pe4dlmOfmlL0b+IR3HmZJ+HgYJsHe8b2YAZZrFHtahYP7VR6RxfF81y9/cjH5uQd7OQi9trE yvXp2AKbWGDSf8nnuj0MijzetzUSA8Ge7XFJjc4E7d1Yx4DrGBZ1iZHLIVy0ep0Ll1j1st7f Id6ugaqfp6g2KiTv3DTEeHB2yU3Ii2d9Qxas+ZBvUtYJ30xG2BsmgEBe0QwSCH5zM9B3UhuA 1LTn+ppm0eWTlm+liteXT1twz5vdvniWal2FEHbR5wlBKegc7XKCh8MXmASA6zoR+HfIZzc2 ahKJftri7U5/fmL7T1geZTjpkZN4DM+urLz5+bFLywg5i8kACVFBvKkOYE0kpnvICly2qNfl c7DBI9fEVVj+UzqOIp7FtlKcTsVEYt1qPllJ2zSTV9NTxaU6Z4/1BGNg4DfVgfx4fz8czxbm AJG5YA3N+TIPEOQFRoHDTgAihrvb0snc7sPqughk2WGNU5dZtvcTz0ud/yj/nDgJjpy3Muu2 Cr0i0+JvAD2Cl2JNPtUs8fOJ9UnAf5qzSoe6DFUDlcvyoVGyaTwucauH9ZORySCJlU8xI+tz 9ppiFmbEtnCnoyTBwRqSYJl76C1sN8Jg+ac39kdezFbwA5drNpQrAORMd3e391NdbWrUeHJ3 PTxVHsvgyhCRqpXUE9p4ERGHN+LaRiAAz327mimcQOms+6yxFiV9ztKt7E5bLbesgxMSc5Zy uC9+FS5GE70z491Mbla5lzDR3oUGVJl2mxZnUbQ202eK+f3VhF+Pxjr7ZA0zhBjJVhqDzDlk PIOUCqLqOt03c1DMKKTXA11aRSEFLE1s06PE4/d3owkKtSCb8nByPxpd6XssYRPbM4iaYQJI wPFpJdcK4Qbj21sLXC1+P1ppjxYiPL260QIaj4+ndxOjJjXkgVby5RCkWJ5PbCeVBeocAvuV oRiUU/jPnKzzjNseoOBJpQSlLSVgPaK+HS3hcI8TLWargCGZIb21WYEjtJre3d7om6kw91d4 NbW/8SsJIDjM7+6DhPCBp4AlGSHj0ejabnHNc1RJ9d+bE2T3p/Px4009PTt9hxz52TnLZEbS Oa/SRD+D0uze5V/NjPv/PNqmb1WpqtWMEEJ9JEPTxC7eBAfs0mXLazX6WggPvGlOFgmKux68 jsp0Q1GGYJjTOvroiYNEypcAWkKEqKe+zdLKC4qq6uAbwA6Jp0r87arVcs75x3vh/A4M/fNf znnzXvzLwd4nuNY/jJ5O1avn1sfmQVoiNWvWwGa69msPCy7Ng4PO3hvr1Tt2LGvLZpVBYUI2 mw09LlUEHKO4rJD2vIZij6gF7tS5EJ5Q6xVw+X1dBe8shaRUuvCHzeopijTRxtZxamcbvRMu 1VuooTm9oMcUL8hTD9msWo0OEoiwegcABIkuDUNhhnpb74i17qesCaLl2UhkZpjlG3yPCIJt D0cBL+tOSI/XPKUzmtOoIOMezbhPdH0zNWDqVbh6t6NDVb9tbYQjqrs28BhEnsuLVJdDUMuj G89Ily403dUkPmV6Ab1qDZRVHvmqY0ZS9dzNCLw7dOpFqCqrd6lcKgNdynXTIt8HyCddXMiu jXyFa+Ay0MiUJsQzoOpliwHhMUrUp3vmYUVAVd1kQeWjMWpNceR8qn351oVA6vBoQFXWVRPr 6xDX2lgBRGqeB8u2VWdwRAe+WQWclJsO+RNJbc5FTtQI1JsNmj+GAwguBhDBIIYy1JGUEK1N SNYZXPYAzS6MbK1CnGM/j8x1hTlpCaqz4JQxESAeyOd+hqRVZL756kfKyZKKgS9pAQtmvrzj gdtsX6p1rkSWAC1Dyri/TPKbUwgME5UpkjaLhMrPpantbiUyMZ1wyFjiKkVTawCifBFCCHHG V/fXzu/+7lgs4d8/tFBAb4mQJU3tja+Lk5TL7N8/zoORBo2NL8nVT4g4Pd6F+b5syYRE72+U GK4K9nOjb1hiIiTfAFWYptD9KvtBu7oJdOrsRfaQOQFzaDx2MTDAX5StLLzvkHGcEhLnq4fx aHJ9mWb9cDu9M0n+zdZyF2/dXZBFJ9/sYLUX9CXre9UqYwBolMtQari7GgaZoT1T1QiSm5u7 O8t2OiT37a21GDF37es+ivHoxl5/NGhubdmcRjEZTzXf2iBkk2guuy/TuxsLOpwP7Ysk91cr 2803FLNEb0Ab4Fx+4KR7pwYrMJpej6eWcYC5ux7fWcaUsq2LR7v/6O5qYvu42aC4urLOurq9 urm3Hj6yPrBs0Uk6noytG6LRRZ7FZCn0t0YNgiUQfoNB5Nb9zFjo+RQsev+zpC4pF2yJlmht 3R1XSiCj8ssCx7MY5OInNEE516XNiGiSC5bhACCWK1hVWtG3F0ZEKgFgiKy9e4XrtndLKEqS kKjVtUhSYVwc3dzfXhvFZIXAa5RYw2ZWfi8B2YxRNTLh5tuNDo5HZZ3PwC74arVCqDuh0qwO DJIolAiKuW0HLbJMobvWlTffuVSYGpZD9Aru3XLkluJK0+MWqlqElvk8emkyzNxU+96kgc/8 iX1/EFfYaq0GPtdf+7QYiP1DEunVvwanPgxD5v80oUFy6kEQEHvW50kNlYg8bFmVqg88rPOW qIHaVZdqcjWxzL6UH2Oy1Dq97PWH4YBmt4eTX06w9OIWFI1rfMzZ4uTzSf09WMuQJfXgh4Xb TwGJg8x27Z57b5WiGYoItr7jbJfLUlc2V/yVTTr5zWg8tiwoIw35OKw/ZJUgz7oXiYCQ7DJb FZGM5S6TLVE4B8EDZz6+dLZklWLLFv+XsmtpbhsH0n8lx93D7PBN6rAHiqQkxgSJEJBF+8Ly JN6Z1OZViadq5t8vGiApAGxQ2kMcG99HvB8NoLvx4VLXWPiB1XmyGvbSkkv3XCT/hu43iuYt TAMkHawprx7QcmisU95ecnTjqJEe9uIPTcS/IrQ65kzXuZowNZOLeio6EtnSr5zLlRSpFesa OGYZJVniGaKCjudlmqU7vGgGDVvUDIa8BCCD6WVHJ5yFDFQPRd3fTG1/DnzPx2SYFSvQREsd LJ6ygpPcjzxXyRXj6PuYFGkSOWd0voB1xCUpYhm6K67Ius3FGNZJsk4p850XYhdVNikO8Mop YW3sOzwDp5xQdqpd+asqXjuQY97ks46YgzIUoed5OHg4v685O+NZPnZdWQ84dhKrU0VdtXV6 EoHiZ5QM+H2DTq6bWnSqu3jOGUGngb+SmyyWsKc0waY/owbO7XPl6szVAz8EfpDeiANEL2cU DXqgoDHkFDReMs/zXZWtKLfHgdhp+H5mXiMaeCGWK4cOisEjzPdvDQUxMR1yNpKaRngHIuwY JGHmzIz843ZjkyE5NyNH5X+D2FaDlGbx1B5SH3fUorPE3kiqu99q8JKPBx4PXuJKrs8Z3Vd9 /0Tr8XC5XeH1ET2B1Dny915a/6PVLX+/1I4VS60R+JeXkmege6vkfbwHis2tj204dRKst6Bn 1TExjF19GUhqTrsjNpq3YvbaiirEr31tWs0xlaZVvqSsh1cg4Gqy2MhNSQroqf7tISYz1a+G gItZVnDp+bCRNVA1EgKNtQ1d0TreOed0ILwHPcrbk6usrZtTm2QFjoUNwOcn3net4S9t1SRC HCui2Nhu2iQ18t1x5Oxprhe0NPL3mgc3hSPRtnIl7hxDkBWB5w0bcohiRFtguhV3OtaukvZk NG9OjcWwbqocP2cxaeyOZYZxX+0a8Tg4OdgObTDauY2wHbzBGbIkdlUWZUnspQ7h5bniSRCE +KfP89YZq8XuRCYZ2PF1/YHFgytZ8OJVa6or00mT4dJMhc1biLFr4cxqdTAu8Rl2Hk2LjYYf GdsQPdzRlhOFF8E1/XUMcudR5NQ1RynaXuwGYs/eQFXh4Ila5MYR5FQXJM+i9RfyMHcvBM6q R6FSbNNLy1r2ij7W+x47TpuLUkuTKl4FdtxwsCgWmQlex/4w8Pc7Z8TStJ3k2IdPYsKuW0xv X+EF8b2dXTd9dTw30nLpJE/Z7Nz2YoEa6aXHazYfaCDak1YP69bklybxIm9dURbvLP9zFzhv iJD5rlmwMkiLQxankZ0z2UZ9x/P+CZQ/ZENaX5Z5GmTeVO7VRZXadi1DZYUl4dKNDUyJLSNS V+XQhNFg86dgWxJSYE1EyYvzRu2JqSFIdu6OKPAkSHI71YLk5v7NCLb3rFOp+0c5eqcKcyYp eUm8VOyqVIqQYhFZTKkWJ7v81ozUg4chsWvXu6kVkVjJQNx0Ddqe1NHqXEAG4tOZhBjR5AMZ cvDCdci0epvhQTmpm60SPPjY/nGCgjU9xE49JihC6PhQVGAcr1SZTi8/P0kTtfr37p2tzWQW TP4JP6Wm4lczmOa9cR+iQpt6T1lgh/b5xf580r9DyCIIVBRWH/TFxL5qIkmga2gx5pThZhVT GUBQcNzKKIa6CGTa5H62agOOeS3faFPI2LI4zpDwRrXXdCWP1fxyXY9dxqvL/r9efr58fHv9 udYn5rq7nUctY+I/1jXSLrZlyl0B05kz4Rp2uqzDBO8aDN4YSkMZCHyH7LKR8idDYlRaqTIY bZCmBA1K0Nqy3UdNFig/P798WWveT2etWRB7dheYgnV/qm5LKv0DP4ljLx8fcxFkawxqtANc wOAnSjptqqgbaZa6K1UdIPJsZW92+xls+/EszRUjDO3B3TaptijVwKu2tLzQ6qnnrWgzh4Mq nSitsSfVc0crgC4eMG7WWM+wqduI7KK8TaGf7wsSZGGMq3yYsbiiqLsC264ZmeRBlg2uCDqX vZdRuTyJ0/QmTQwoeqor/BBeJ9LhVr1J6/WNekuD1F8Nvfb7t9+AIELkGJS6wIje0RRXTvZi /m48x0HFzIItwBaBVMzlBEgRioay1Pfxk9+Jgxg/2RRZJVsEXBliAmFowcHyanjOwHWI+uvc nYS4gu1T5xLazqWnmmH4udQEP/LMZQs4MW51zkbMevXmMGVF0Q6od+sZ95OaCUEMyf6C2SLX qmVqsq/6Mnc4QJ47khIY3vP8CPXsztNEBNKqtTQMDrrlhLeaMHXSPj+XPWzOfD8OPG/dRgMT a5mVnxVJyBXjLZKQb7bgAxPtRW9FIl18bMYDy8yzH8bbsZAQP+eeI3ms9uebJeoumwNftDiq uWhJAFbjkIL3zazNZUfZKuX/0vJvs9AWzSQhOSEdqD03jSlUTfFKA4UzJiBIJy6QI/EZ9Cb8 yIo/ub10S8Bw/Es1UWyJg1JLu3BBlL+xDfGjpqQelYdxfcsMoWAwrBS29Q2WRMBkRWlw4ds5 ICk3MUpB5WC5YNF5UunV/JSxGrMNlJj4sbcyKp/aKbujnX/YScLzJ7rNG6PwmsUDKB8BZ08c Vn1UrINidrpJnCLc820a5PqeGhEytRDLy45cy7IEKRfqdQeex7QiXfF9HoXYdlJjKKkIi1ze eY59exQzGYpLrzQYAiuJKzyUJvkIODnCQ5BWfFfWDxgk5x4MkHICCnA0omp4ajuGpi4aHgsf hPhl6vpXj8oF3HUkF+Ifxa6BhrppnsAPT9Hkuiq0DIeLB60557buz2K+BscgyocUOhuut39K g1gsqWudbV2jD9T4pOIi2CabwcpBshUm5Hql1qwFkvMwqyyTv7+8ff7x5fUfkRNIXNrbI8Ih fJb3e7WfFpE2TdUe0YlBxb+ayq/hBNflnvCGF1FoXp7OEC3yXRxhw8Rk/IN+XLcwoW983FfH VTWNpBkKOrmlm237tqrMTFg5GJO7YkfCs0bm0vr5lz+///z89tfXX0YHENLLsdvX3MwhBNLi gAUaVlpWxEtiy9EFOJVytPqpHuJTacgN166qHD/9AS6pJv8T//H1+6+3L/++e/36x+unT6+f 3v0+sX4T2xBwTPGfq24l52F87gWY73w3OAy148waxklBNhRCZsZD16LHsgD3BWF8b/fjAiYE 6ODOeMv8UfQ3bG8g0Qoe2ZBur8zrQAtkTf7oRmGRhwdS7NyV9bEuugZVGgB8Uuw1vpHDen5Z 7710kuQsGzyKJrYYq5nNoDCHmzRQUCb4hk5hYgagro2FZHQ0dKgVAfz+OUoz7KQVwIeKUNPF pBzmYhu/ESPhaRK4eyB5TMTiuvH54Dg3F9gkqjhy261U8WWoa/MnwYtrghMTwrXDWBVAW3f2 rTMJA1O+BAp3S/d17W5IFhZB5DhkkPhpJGLGw0VfwGsCnqOtsjCHWCshIYIccIcWVxw/z5H4 uU2EaBpc3AVmT+2HsxAP3SNDHleMe2q7t9Uom+dFOmHE9YGBAq74c147tt7AuBB3TSnHCG64 cedtaOhuYzT0RW58O3kaFnLQN7E3FIzfxYIoVpOXTy8/pHC0MqKSE1wHBg1nWywqaJD4sRk2 OyUxVsi+23f8cH5+HjuxXTE/4HnHxP5oNUh43a5cPphVXoMblQ7zwdq9/aVkhal02nppL4aT vOHo84sPamNlR1dxY6jw896sgXllMXsvBE7OINxdXJLA1SA85elc4MDm2D7/uiIgomx+Ou/K tVIiElboOBKh6KEcNf1ZMbl9FgtVmKSO4zZgEEZG8b+UflHWybHUUYr4YuT03Ufpa28l5Ato 9OMsUw+pTjcdUqtXjZFv0uc1PT3Bk7JgUdlWHF7qBe9kcnvJeE7Afdm7t+8iQXD19yqG0Sfp fVCMLZnsr//SXXCsc7NkRgnL15EhAtSmQSOI37S7scnL7BVYKkK1KCJ/X6tKYeBqbhOX1/nY Xd9MIGIKCJmXmVsmG10jbPBjzzjsnBGx/gUxPp/plHSb4jrynfGGis0lCD6rHtO/fnv99fLr 3Y/P3z6+/fyCTRtzJL1oAsvzxDqvh2KsSOV4l1Bn9VmeprsdfqC4JuLLKhIhPtRWRIeFxDrC O+PbOcxbESIu661zmN0ZYXgn7850d8m9bZLcW+Tk3qTv7TbZvSk7Zt41Mb+TGN3HC/M7O2x0 bw6jO1slurMOozu7TXRvQYp7C1Ld2Rui/F7i/jaRndLAu11koCW3Syxpt2cQQUsdXjlXtNvN BrTwrrylMb7LsGnZ7T4labjzL4sW3jGEZEnvaoU0uKekA+6X07WoTY94fPr8wl//d2vJq+D5 XsIf0NidEazkATid1c6F5/CCRWkTxmsxQQI7TbMJjk2Md7OmAOk/T/piUT4cYz+wGXX/oTjV dC0o2edK2nfqrQ0ztbGwfGcsgeMjdlQq4etT5Pq7KV9ffvx4/fROZgCpdPmlKJNjaVbpIpf6 Ol5ecmpV11V0RM6zJMF5miTRusMusiVE9lnCUkMFWoVX7bNlumURaJG5DnUUYcC3HhOIC2FK F81xoKL0YK3tsYWKjQH0GWfvqLt1WQeo3tGxb1VNCp6tHNZWqoZLHgZRODhGm7PvLMfEMvT1 nx9i22Js5lXyyqXJupHANYZDEeJKCDYaSd4GhM6+KGHdy+QUCmrK2m2ZDOW0LoLM91Z9k7No Z2dS27RaJVej7VBu18i+FPnyyeXRysRiYGpmQZ1qukrZ0HAXhVZMDc3SEBkXckLcqFCp4u3G WRNk9umFVV3SMsNcsFb4zrcbhX8gQ5bYgUpv3QpVat3Gfc26vpVvIjEoNtvheqKjn0Ugn8no Hj//fPtbbLS3p8/jsa+OOUcP51UbiEF+plZ7LVeuSy7Q1K6JXRwSF9yrw7v3FXZyo1B4F7Yx jE70cKePOlrmiqhlfZrY87IY9znnVa8p4U/q9+AnSy/uFKxi0m9o4ZkbGYqkPUV+Nb2/asye wAVXLycZLzGMX+eP8oJnuyjGroBmSnEJPN/wvDojJQtSx2bHoODNYVBwAXOmMNSL3Vw8gV7L PDsDhMCvdlH2H0C/fsAqYoIcmvRLXsEU3ltHvJ6c5swJxEdfTtY+VXZCq0+V2crGp4pwzc1s 5zJ1xFUZwQo69aKt3EyUYN2JNM8OFlIzCt9gRRDRZTvHzmbmwHSMWpLPBNPL1TVq2dBroOFh EvvrbMLdup8EzRqBQkdxmqJIttulSOo8CRO01URHivwYazaDsfPWqQEQxKkr1tRUcFszYpGu 4+M422HNrjN2GZ6lOBmQRmdkH0YpNisc8/OxgtoOdqi6wsKb9Nawntrz2AsxJeY5+Z6LWSvG Cgs2NCE+4xzOVTNlT1nabNVIudvtdBPL04WYqjriT7E0GReqKnA6/7dkVaWN/PImFivMEGBy 8V2mkW+MJAPBXN9dCcT3Aq3fm0CMRwoQJo+YjJ0j1tB3xeqn2IjWGLsg8rBYeTr4DiByA2ix BZAEDiD18JwDhI2yhXHiPv4pXH12hJ45TPpxWw0uNdKJz0LUn+AVL+DmHcn+AC8gtPKNzF5/ UPL6JWiUIOF8oGh7wctu9BFXPlaMQvzI636EB4DWEc8o1Z2pzGDJkgBpNnBUj5Vusme0fJvN KDjfHLaa55D6mRcf1vECkAWHI4bEYRozBOCMV2eeC+ELy8uxif2MYVpzGiPwGEE/ThMPN3dY cKTnnupT4odIbdZ7klcEDafVgITDuYI5ny0Qz9J16PsiQvIjBI3eD7Dmlf6pjxUCyGUBnYwU lDokMIO1w5KUAJJLue7HSF8DIPBdeYkCx6mnwYm2eqNkJOh0oSBseZwZIJQEKfYtIImXbKUs KT4ybUsgyXBgh7S8CA/9FOt08CSDGttYDhOX2pDBCfHTNIPjuDMwOI77LYOz21qRVDGxfkUK GnoBOnHywnoixsYpC8IsQboe6VMxN4RInyQJGpqGaC8im4uVgJEWFaEZHpljL6cRcEleI7hM MBbCViM0ZIcPFuK4e9MIuMXbAsdBGGFVIYAImxokgE4NtMjSMNlauoERBUjNt7wY+anqSc14 12ORtwUXo3OrLMBI0xiJvOBiM47MfwDsPFSodKvgLYzngY8Pff5QtQEWQ1cUI81uzNnyVHGn VTMlxmuvC28KRiXKIMEvegzO5nDYV3AzU2EJ7Gk+9izxtpr1wOgYPmFfi2V2LA4H9DnGRbCh bBd4+R79vmX0LPbRlG1GUfdhHOAzkYASL9haTgQj8xJkDNQ9ZbH1wtKCsSbJ/HB7zAaxlySO 9ThFVpoJuPr00C2TFkqY+Ugnh1UqDj1sRlWLIlJAteThBRRY4KWocwKTgskPas3I8GyGURS5 1sYsQZ2YLwwaZBm2RFNRb/icVJMoDLbipCRJk4j3yKAbKiEqIAvfhzhi730vy5E5hXFalgUu 2ohVL/KiAPdKsFDiMEkR+eRclDvPQ+MFKHDahirOUNLKvyG3PTeiuNvR0As8AuYy4FUc3ZGd lKU32Qy5HLApe248ZrQEi/UC3QyJrej2cisYN4QwwQj/2crTiUuDknVwgW3eSCVERWThq0jh R5i4I4DA91DZRkAJnDxv5Y6wIkoJkpMZwfYDCtuHmLTLihMcd63fmdJxbGmXQJig7cQ5S+Pt chAhwmInFYUfZGXmI5NBXrI0CzBA1FvmWCXaPPC2JW6gbAoEghAG2M6dFykqY/ATKdAD8IVA qI/JLTIc6TMyHJVgBRJtdhggoHknNPbRXvjI/QB1dTMTLlmYpiFyrgBA5iPnMADsnEBQYtmQ 0JZYKAno2qAQmM4cFmAasRGrGUcEMwUlLV5MMSBOyJGLQioUmq8pp3ApFBtOfFUAPNnEa2Y6 vpqxilT9sWrBLctkMKve+hkJ+2/Npn2mw/M54OF6hGebMDFrJpaVMgU6dvDUW0XHS22+hoAR D3AAJt2IoMML+wRc8ShH75ufuGNHiHp+EXift0f5AyvO/yNPFTkrBzybLEIcNmIP4cxB4VnT Y5tU5D1GmGDCH7ReNb1P8/b6BRTDf341fPFIMC9o/a5ueRh5A8JZbp63eVfHR1hSMp79z+8v nz5+/4omMmV+MrzbLD/Y7rXsJoWhlbRk1JkbxxuXG5nm9ci6YjO12/Epb0kvX3/9/e3PrWZw UabXcOuyzkVqf/582cyxtEYSmZZ5xrvyYrC0WdOSFnojVxMcWvjNXGmKEpraAJKkzP+Hv1++ iEbD+9CUnJOjrRAU3odxDqHFO8G/dsjKtmUB2u6SP3Vn/OpjYSm3DdJIfKxamJQxuXihw8M8 0shDRKzP7AtBKgbeSLKXTghG2ldTTKtavby8ffzr0/c/39Gfr2+fv75+//vt3fG7qLNv3/Wr uiXKa1QwdSK1ZBLE4tfo67OL1naoSp+LTsGlxXUlxWj6oqPo/1oldj3axboD171UXLuODjgq daJONzpab1pikVC8QGgLTk4sMY7OCNBskqo9BD685r3xPehDeskOjWBSyNnM4fSQ8kYKz3Ut nT+u/X3MPiGx2pm3jNup52L0lvkYgtOPbSLf+T2BHfRtHsvJ7kZ0gpLHZbRV7CIvhXhWoYU7 8EvJPf9GXiYr2Rs95LKNq2fUtjlgV7zNoO0QeV52q7NKw/dtkpB+xGS0zenbmCf+jdTYuR1u xDO7g9mOR2x/QtB96jk+UK7rHEsDR2zzYMqHRK9tYzTmQ5omwY3mrMkQgON9F5ieG2rjc62B F2Cst5FuAL9XrljVKr+ZK2nt7fpeenEZj8N+vx2J4t2gCCGBVw83+tns5GCb1tDCz261/mQ4 5yzcjPfPuYsy+RLa7mQgbGwyHmsmfrs1MggrQj+sbiRWxNDN0E6iFIYB1PumkJUjOVAcRZxd ZWwRUi/MNnrukQqRztmFKGR5lWd9JRvzwHfiZ9KgVTLr+v72x8uv10/XFb94+flJW+gFgxZr gY/BSxMdY/Xe9NvF0AfURBXkOl0LNv8aTx3jIHgb6mMAlH39CNp11g7dJLFDkzPM8boexZHk xViQ1pWEw9BEUSrtWVPp8OZ//v72EUyJZ2e2KwUucihXUjGEKR+9R4o/aAUMUATyjQdulME1 GAYE2LmZ/CjnQZZ6aJKwgIsGdfmxA4p8fdFDz/kkvKjg/2skaqmbXsOsBxEP5cq66BpmuxCX NQemRT52d7egYWyXUwY7LpwXHNV6vKL/x9iTLTeS4/gretqejdiJyUN5aDb6IS9J2c6rkpmS 3C8Kla3qcrRteX1stPfrlyDz4AHK/VBRMoDkCYIACQKONngkR4Onsjlh3r/iO/AR6DnySA06 rxKffMKYOso1WXVwuAZs/kTxHmbQosLvQAAJL2JuYnfl4ncgjISFGeMPs41EZWK7Zo9pRtE4 vrNSGwfRJYvWlOyXUzge1TXwNbPtIK4FTJPgm9mxzJ8Q3kYZbxZY3sTnU0QcAcazS1jyfHKg h1BK/vacySdfYrlPg/Jj7jQjMDiyzASG544zAXpMPKHDpasOEffRxt+DTXjHxLOah/QMDBUg d5lWa6fQ1ZXKRwvOUL3ySkXAVN0hM30FiqJK3yRrj64z3MOFEZTqqzixxG4ZurbMINyJWa2n TbzOMzjSsB0jS5hYN1RE8mXgH8bM7fKX5jtGhi49+RJ8Apo2Q0ZwcxtShpYEZRQfPMvSWqlu fBAvqE0wD0lGcAvqnjo6VP2LStf1DpDWx7xpDu+6lI/hJQF6sz6UXJS9zJL8SZdwVt4Q37a8 gwyhI2SpkEDZB8enXer4crhxF4JG8ddo6jjAd6FvYjfhpZj+2cp2jNGJByIq4Qw+8t2+WFqu PrkzmqVLkSO3Qan7wnYCF0EUpeuJ6b9ZE8YnbfLm2+a/g459rfH7MlyibjoDEu6VPnUYthcD xrO+qm1liO7BeLjbL0NDEG+OL12HzjA7e/yCitGYd1tIHmXihiFhkaKtDVmMkI7fbKMUEhQa UrawrxN4gASrHBWi47nVpH+K0SFNyvJsps+OP4KZOWb4Mb2xmynW+YGam7u66MDH+FMngBeD PYtBXpG+FB8JzTRw1cVuumaqJ52KbvIbugwNqEFTQDoxKAeY59RMBI/vQvHiX0Clnss2UKTs qKL/YWfEAgm3D9CSNXtDwHGeuV60YgXMGOTlrjCr2gs5GYdGNpJIHFEKKxgbq3IdVZ7rya+E FGyIRkucidRokTMmJwXVoDGtSKLxncCOsMZRqejLYl/A0d0tQPPryCQOWjA8pzvgjQYcan0I JF3ieuEKLZmi/MDHix713quFM/e90FyCWUNWyQx6skQW+kssQ5lC46NcBahw5eLzc01jVqi8 62w9a8+mAlZ4oCV1PAxmgUpmCMekkIXWl62mRI6PDlwTht7KMMMUh6o0Agk1B8S08QoGlZWT YaFhBv0ObUwT5xHmBiJQJNFKSsgnoHZUcsjujwryC8HCaFaGzYMdNrdNiZ1xKVRDXD8cCSk+ dzyYoEYgui/KedUh1iPeLjByUP8qmcRFpfFgDaEY3/bRYaYY8MI3NOabY6NpyUWacuegnEG/ 9gPTfkScsoksXD+WqYj9JZVXhoH/lazQH63qJLMhpuOKjWcrbrMClumXcV2TzqCFqrS7NlvH PR5gVaVt9l+VCTZdjIdgEQtjqvRxVxoyEQikdCAs35AhTqQKnSWumytUQXV15MFb2fZdA7OM NuNXRfiOQUhxE1FMqqriAlTdEkxOHGe7qIIw2Y4ITjWgFIFSRHEei+kEE8XeowBIqiBmTssN KWDaZExdihs9DL/LkwwN/TAcktA+cI+eDMkcwO7SGGkrm0ATHKJ6aPlNxhwFQpFyicNXgkUr gql1UkipT0ZsnLY7FgyfZEWWdHP4qfuH02gqvX++iLnwhpZGJZybj9VqPaH2QFFT03yH90ei hZvDDtITocQSaRulLMmgoVaStn+jvjGs1d8gZUFOrs0IMlJji3d5mkEq3J06KfQPeCVdsAkZ ItXcny/L4uH546/F5QXsVGHAeTm7ZSGsnBkmB1YV4DC5GZ3cJlfRUbqbrpOEm05AcSu2zCu2 G1ebDF8HrAJ243UsKD3LxIHMGSfbV3RBCU0HYERuK37jMkXP0UdAYMW7y/P76+Xx8fwqjI8y CQiNyMyyb+XgVrT48fD4fn493y9Ob7Tlj+e7d/j9vvhlzRCLJ/HjX0RPPT6XoM6YmZaza5RG DV19osrD4F0WeYEnylHO3fkyEI1ZVoUC4+HWZdj8te3qX8uu5PMSYCik6WMNalllG4o5bQCU krjVmrGNWimzsQDGr4GgpBuq7WEnfIBrI0j7XNVKc6iZbquVs4EVn10NlUdREFj+VidfU1vF 0VvLjw9NK2Agysl4f4suAVhWVG1xxq1BgyMLm8FL2tuGoF+UUVHUCYZK4Sp6Iy4smfuFBXF6 vnt4fDy9fiIXx1yEd12UbCdP5ZaFT+O0i9PH++Wf03r5/rn4JaIQDtBLltYNFzR5q55xcpfn j/uHCxWndxcI4/Vfi5fXy9357e1CVypE2H56+EtqKC+r20V9Kp+JDIg0CpYuZjdO+FUov4gb EFnkL20PO2UUCMQgAxxcksZdWho4Ia5rhTrUc+XnvDO8cB0sFMNQebFzHSvKE8eN9c/7NKIm CL7GOAVVqIIAv6OeCVzsmGLYXhonIGVz0OsmdXV7jLs1tarx2IR/b355nO2UTITqjNOV7I8R CscIpiL5vKmKRSiNpdsgROYwdpPjXb2XgPAt/AR+pgivzkHchbZ5hCnW8/WKKdjw4Jjjb4hl o1GrBu4sQp+23A9UTgTBaNsa23LwQQWzw7hAjGAow0GL1TWLbtd49hKzSAS8p7WBggPL0jSf bu+E1lKHrlaW3i6A+hhU7/KuObg8eIjAQsCZJ4lxdWZiY2WIOT8s64PjhWowbFH7Qdn3/Gxc AYHt4DMpvv4VWDnQesvBKLW7NDC+aziumyk8w/nDSLFywxXmqjXgb8IQ4bktCZ3hLEEas2l8 hDF7eKJi5X/P8JpjAfmttMHrm9RfWq54CC4ihjUv1aOXOe9X/+IkdxdKQ4UZ3DKh1YLUCjxn S8Tir5fA356k7eL945luu2Ox85sTBcX394e3uzPdgJ/Pl4+3xc/z44vwqTqsgauvmNJzpLAj w5btqCoHZEEt8yZPLUdSOcz184Vzejq/nujUP1PBrydbH/ig6fIKbLVCrXSbe5hw3ObhEosa NjS+PDj6FgxQWxMjDLrCoB5aQoCWgAxgeXDRcl0PUQPqneVEtnl7qneOv9TqAKin1QHQEKUN 0ZqdAA3IOKI9fxlgn6kxb7TPAkTXYnDs/mdGr9BGBg76gHlCKzdOE9w3ZCSYCdAgaHO52KiH 6IZd71Zf1bYyXRtNBAF6oDyibTfUuRLAMbK7Ed93NGYtu1VpiUaUAJYPGGeEfVXGU4rG5Msx UXQWemw/420br3xnoY+fBbyr6QsAtm2tj6S1XKtJXIQtq7quLJshr/XDK+vCaPfBpdTKCewj RKTXamjTKClRb1oRr7W5/c1bVrZeHPFu/MhsNDA0sqdT+DJLNmbFjBJ4cbRWm5F1YXYjaeC4 WGcSv6Aw3cwcFQIv1C2p6CZwdc0k3a8CXV4D1NfWAIWGVnDcJaXYSKklrG3rx9PbT+MulDa2 7yHDBg5EaMClCe0vfbFiuRq+rze5ujvPG7uKk23zrq+YbwjfTz/e3i9PD/93XnQ7rg1otjyj hwR9TSG76AlYag3boYNKcIUslDY3DSn5oWkVBLYRuwrFOH8Skp3omL5kSMOXZedYB0ODAOcb esJwrhHniOGFFJztGhr6rbMt21DfIXEsMXSGjPMsy/jd0ogrDwX9UIwjqWMD7c5gwCbLJQnl MCgSPqJKEhrvT59y29CvdUL3AMNYMZxzBWds2VCnwdlcIMxg5L6kWidU7ftqUZRhyEJ1WZ2x VX20wnc8eYU6tmfg5Lxb2a6Bk1sqQo1V04l2LbvFssBL3FnaqU1HdmkYdYaPaR+XktTHhI98 +KifNDKxtXk9vfx8uMPTHJeHY970O9fs0JvKz+O5OUZhsySfbSwBzGX+K92rFt8/fvyg8jUV PhjKXuM3w2XZHNOcKIlCRwGPlcnDK5zu/nx8+OPn++I/FkWSjrdFSLcpll+mDNeMyIxBeP4i 32w7iVB4yzThb7rU8VwMo3q/zpjhNYkU/VrEeaaY+CMRf5NYGLJHz3TcJ+Nq/9R73xlDd+Uw FAW3ggpQlB4sXvhM9U2UBst3V3JQ8RE3+j9c7cfg3IgUrXryCZXuPMcKCsyhciaKU9+2ArQ7 bXJIqgpDDV6y4hL+gj/HMrYpc+vh/HqhSt7jeXH/8PbyeBoXtn7dvduw605Si4/v0r4sb78A 0/+LvqzIr6GF49t6T351PEEQfdGkkU4TO2P5pO4r6cUGqfTEsds81Xu5VSKu5+mcJaJrs2rT 4bl8KGEb7fHbMKjI8M0Yj0NrHHk53z2cHlkjNW0bPoyWXZZs1bZGSdIzdyuE2zi+7YVVM4GO ayHdKIM2ino5AXPcH4fhSY8ZUAzVt1lUaEObFTc55h3DkV3dQMOk5sb5Js4qDZxswdlMheX0 LxVYs2jiKrDfRAqsjJKoKG7VJidsHzU0OaGd7HJYlbHliWcLDHnbtBkhMpByzaauWgip9CRu iCOUdtRQV1YSbRSyIqpUSEbFuAqrFcDvN5kyTJusjPM2VYDrVilqU9RtXvdKp7Z10WXSlTGH mDuzy3dRkebqYG86P3SxpDSApG1mrC7XfXOrsW2fFPUmRx/75fBovKB8Jheyy7M9qXnkE7lB t60WmEpA5xCTQi4q7xTAb1EsvlwFULfPq606czdZRXIqb2oFXiRKJiAGzFIVUNU7ZZphFAah gUDhj6YROzxh0GkDbNuXcZE1UepIAgRQm9XS0oD7bZYVhIOlcS0jOkElZSRMT+IERdeqQ1FG t8xzRYa2GV8+6tyVedLWEFnGVEVdUTmfaSu+7IsuvyZUqy6XW1B1bb6RQXXLV4QAaqIKoiPR FSRtOQLYvF6arKKjVXVqW5usi4rbCjsCYmgI65AorDIAZ8VC3glGNGUxok7biEuubAoNlUkw dXmCOyANNLcsFp8h5BujaXNqqBo61ma0CnXptXWSRJ0Mo7JfmwgSlaSvNupYkqwEWkONRNpZ 2G25ztYscEWRV8ZCuixSJCoF0SVCdYJMEaq0iU0hx69mnSyxfOxMVoHDdURyQdhMIKytZdR2 v9W3UImhRLq11Wr9VHaSzGAjMPyWCjHskSZHtj3pygg8h8WCRbiyCGTJDkrXsSH4PSajcNa/ Zy32+pTLfm1z3Od5Wasy+5DT5ab2HMpVB0smuE2pcnWFpXk4t+O2x+5QmdZUNNqEQ6J1R427 Ox41IlrjlI4P1XfB8QrReRuDyjqQU0tV01jHKuILhTavl/fL3eVR112Zb1iczjKGuX6B5Ffz AF4pTCWb9P8xKabcVylrpIQaEVIhQsPqbZIfi7zrqJmSVVTxq+SGz+6rAnCI7vspDxt4V8K+ YPCJ64smP/LHFNJn9GdlekANeGof0q07IsdtIg+q3Kaoquh+kWTHKtuPjs+Ti7B0yQtjqjmu Mt/AIc5bk7UkJ0qf17TYvMo7JonzTOtGeltFEM6C+aNi8oWNdgfB3uq0T7qC1yCVAeg0JyzK anagoqGKCnXxqENO2JhDxi2IQYO7drLxAcfpnsrrKuVhXn91ZAatxiN6xnOXt3ewUkcnVS2y HZs5PzhYFpsXyc/xADyFQ9N4kzD/dhXR0H9DpCTksym575Pcf14THUnzEDESLdmzRrDLYvwx 70RizK4LFEPEUSM+G4bESFAfese2to1KJJBAmkDbPwwjK30NKNd3rny8prxFK9CnpR4nS+XF AQ5b/JWOz2Q81JyJ8UeyoklcR84gKeEhkBy+2UlkQ6C464QEP8mY8DxKi5EmqQiLzwK0X3XL xKC97V6bFVKEtq0JthlMJ7aWUW0Y+b63CrApgw8g/pKhMkCzNwIlKJLKt2OUMvp7S9Ctb4hx mTye3pD7USZiklJuLNVoq0605AC4T0sZ0JVTQM2KKib/XrAh6Gpq42SL+/ML3cHeFpfnBUlI vvj+8b6IixuQ8EeSLp5On6OX/Onx7bL4fl48n8/35/v/po0/SyVtz48vix+X18XT5fW8eHj+ cRFPs0VKrPf50+mPh+c/9LtXJjzTRHM0Z8aYpIkznkoror9/mTAQ10vZainY1Snd4yZKNxlG bCrkKBpxMzQvtcVYdr15CZaMldIWO21g++A+cdUCAcb2/yvfjM3WP+Q9NTaI0aQQraCtC33y msfTO531p8Xm8WOM0LogmJbICrrJqJ1WV5k8VBTlyEMKkLHJ/IbodP/H+f1f6cfp8Z+vcLb7 dLk/L17P//Px8HrmSgcnGdWwxTtj1/Pz6fvj+V7lRVa+KZLDRNC11KSlrEZIBgbbmijzvgWH tyxS5n2AUuMiwemPJdEk2YRDDnS1vSiQ3Q2mVcT6jcqOnpBAfGnK1iOtRwxqP8OEA3l5BXPs V00cyLiLgUk0c5oop/t+bKwpam9cuilfL4MfAqsDOvZk66JZXgWS/Zaa6dss6tChgBdpcCye FZkuVcZKGrr3H/CR5Ie2xzI0dDErm8zEhAPJukvpbpvXaAU7uoe1aKvyJvpmGBXDcYvYLCoU rqi7ChUXflg569B20GcXMo3n4sO3iVqq8aOovNnj8L5HhwNETxNVkAv9Gh4t86YgOY6o45xy eoLzTpl0x94RXdBEJBw54Z/VJAgcC/8KcLZ3bKJWffeoUIWo36ZIdOgHjsaKqKJdGWHn1AJN UziSy7CAqrvcl7wRBdy3JOrx6f5G9xmwftHPSJM04cEztJdE6y9kDcmzto32eUuXMiFo/eS2 jOvCUENn2l+npR5n7W90x0CL3u8NvFU3cGyJo8oqr1QtRPgsqU1i7wBHQ1TT+ELy5WQb890Y HVLS22gsK3HGOgdtXt+kQbi2pMycYvtaQ53anjxtbvIpA7rLZWXuK82hIMeXmxClfafz345k G7VNRbapO7gsMYxBoVoWo7RPboPEV/XKW5bLQAbm6Xh0JRqTIO/ZVZx8BAO3p+BzAqcLUykM eizXkHWZdDw9uNYPk4FEFZwqyXZ53MqJ7ljT6n3UtrkKBhNHlTrZlmQdN37W+aHr0dBsXHmB +4H1Xi3gln6CHc2zwn9nY3JQJnbbg1YTO559iNX+bkmewA/Xs8ya9ki0VN5vieOWVzdHOtrM sZBokpIOdk3otoH7t7QJj3DS5BU1ulGWbn5+vj3cnR4XxemTqs0oTzdbKdrJqECPOKTlVd0w 7CHJ8p3Y5iFyIf0K8IY+w1Ehj8oinqdH21195SN2QCJHb+S8AoHLlVbKBrxitUzI335fBoGl fysc3BpGT+oMt+KUZnGoLmqMRMe16cRxoILxgkv3vXzqN2AHw/dY9eUx7tdreP/tCLUpWjfO KufXh5ef51fa3fncULVqhtMf0xkVrAjVjh4Prvo0kRGbdoDJ5xjD+Yhx2ISDDuwpOePaQyT5 BQOs3OktAJirHKdBIrqVok/FaYI1lW6cjhOYH5gPk8PTYxiphtjxO/yOhVmKzBdpPDUU+ROd Mlm+xFTbaWoiXfKzaWGnPRJoZBOVEIfWcXZQYSU4CQ7MqOLWRIX0u0QF8YseCYSeQa2Pndp6 /lOtZYSituiE5Adf6q0Gx0E/DRMz0Uj9NhW0PhZUDbhyXDkTGqWBQANDhfeGj6y5GcOIfl1F x4ZlkhDD8cfL6xneDl4gnP7d5fnHwx8fr6c5TIZQlOFec14bGk/0VQK6qhk+Hg0ot3My32Gr sAO1xaStblAe3+C8twF2gRx7akVHxY1OwqXxptG/ACivBbuAF2gw9t0c91mcRMo6gPtmYVcS xMXX8yfsx7cNGveU1QAukWSfdyxgg+CwjAYrzkpIXygco44QJcvA+eny+kneH+7+xGJEDJ/0 FVhhdA+AeKVChCHStPUxLmqpHjJBtBrM12Nzd8Y6wS0QXMNw5QRuKwfHkwHC7vSYvzQG45Fs pABRM4458rC8JqiUYJRxCxpuBUbAdg8aYrXJdNdRcIrWhpF9L3gmywVHbZ5hadAYknlxW1qz GRjfAWc8tk2PWH/paIWym6gDvmPywapjOiXUlo9xb32RqI2+mWkgwKGHHh4xtOoyzRsNkbcx fX7CeoL6MAA96VnQXLdngPKqPzWU76ofDNGdwTepVxluStcg9+CKbz3DTzHPTJ2MUye0HH1k OtdDg+8zbEUcpXldEkFsNxVaJN7KPuj8OYQKvcah3l9aZ+tOSZmtLA92o/T98eH5z3/Y/8nE ZLuJF8Obgo/n/2ftSZYbx5G9z1fo2B3x+hUXrUeKi8Q2QdIEJat8YbhtdZWibcthyzFV8/UP CYAkACao7ol3KZcyk1gTiQSQyxM8NgxtVia/9JZDvxoLbA2HOmJME8kObFiNrkIE5kE3RZh2 aaJh62wXp938Oi39YW+FOyBElqjP74/fR4RDQNmKnAUm/7J16rhDRqrq5QxNWiKW8Yb47tRp BTxUWr+fvn0b1ioNIEz2be0ieGDw4ThJLDuq0m2BPy9phKTGdmeNZBsHVb0WF/V4IZ0J4vX6 wnJ3rb6AaTX7tP5qjHeLRuVPi2yNXXQm4UN9ervAm9TH5CLGu2fl/HgRQcjkzj/5Babl8vDO FAOTj7vhr4KcpnFuHxQRn+9aZ0uZeRHHgQdDbq2Bx1y6PuR1jV8EwOsKZGViJzELBWR+s0WW iyBvT2uPNIANo9spuD1+28goFE+w/rMmzjdpHmvVNF0sd7bV53GmNAL0kgoe+zaMTruHOaTw Kf6ABmXC5ccSd0fkUb0C1z2MoCG1BY69G69bpiKMCB68NCUbeIs38S2W6eIZXGgGcy3RuoQX ZRPYCr7xrXWSMGliK5Iw9XwMWVuR++ZgUeMg/4/ts3xdJnIEUbwIOXsVS3a4+sST0Fm/FnqX ffb4bZvnNEG5thYiaFzHPhdMnNs/P8D5zj5X9U2zpWPY8NaG5c6CrFEIY3HUFhirIRuivMD2 iB7GWDziqdW0EHsSqthbJ5xxekB7bxWpyeDoFn7HbFvRM6xLOC6qIPG3dY6U67ERovsBrpsc vhh0CVQQ1eSv5tzZgKsCXQdaDguxEjNjCjqRFz6fIOm1esrisTmb2i4SGBxuxrHyIEDzwA6U l5ekqq8fveNQ5fAsPjZkNoM0pNjHTV7UaYLde0uiVuDrUBpnCbSUDjBMqyiHUJ6zmevtli+A oo6JanRsdLrbB3aH9tWmKwneabTXo200BZmP2P5KDCZxCcxQmKZNptsssp8etlXKR2PY62Pl toL/7F6UHQNcFXy+Zn3xAiHOtpA0lBpWXXofmcbdFLq7gIrBrdsVCls2xbYT/Z0NauO8T1LF jQl+MQ5K2ShrGbY4vLUyRFvEKYhhuN5jmRYwGvqzqtUTo/gN2cN2A+AaInrqD7sSk+YlmsK6 LY1gVTBgExJwIombgZ6055kuZTP66kT+S7AHRPvK0VzASJt2eCgNwq8DKUBOj+/nj/Ofl8n2 59vx/bf95Nvn8eOCWdZfI+2r31Tx17XFa4KtnjjCnunZQmaqW3edlTJW+bhIk0M9GnDw+Hh8 Pr6fX44XI2W9gRHUrw/P529ga/Z0+na6sGMo09xZcYNvx+jUklr0H6ffnk7vR5GoSCuzlSpR vfBd5WlbArr8lXrN18qV0djeHh4Z2SsEsLZ0qattsZjOVel3/WOxM/Da2R+Bpj9fL9+PHydt tKw0wor1ePn3+f0v3rOf/zm+/88kfXk7PvGKQ330u8bOVmbmPFnV3yxMssaFsQr78vj+7eeE swEwUBqqwxIvljNN/5WgYR6vjq1spYrYp8eP8zPchFzlsWuUnRMLwvz6zXdM0EdEuYhEJBBN YIioe9xPzyIeZXi7ZuD9bNCIHNTsYIarRjKmGdN4xmKuVkV4AwaV18rhGcrtejJ8PpBpwevT +/n0pC9uAWrHaUObpNwE66LQn57zlOkMtLSY+xMuVeE5MGeneWwXSdI4i/hLsxpIflNkUZLy S2sD0pRpqT24hNuKCchun7Nkc4uzLMiLw/h2WGRlyI5QriV4sFBj2JkddwzZ3tEyzeH6fzC+ 4fP58a8JPX++a3mMe7uKkHhLf9bYc9Z1ubDtJNK/YYyiS2E+QnPHz1p2gqSuSeWw05adJD2U cKgbycAHC2I+QlDcZSPYKhobB5H+2Y4Xy8CO39dLyMFnJ8jLkCxG+xdQsvLmY2UwJqKQbUNk 9YSUnbgrUZiVFIIUjw3mgY41ljFlFY9NVs4HhKeZK6+3uEwphE23XI1JojYHIz44FdkvCD9q pCG+lIKagJae4leeAktxZNsC4b7clHf4Uk8omCCTMRY85OxMW5VjgwvH/xFGhJuPEfRWIJkm i3elIyD1Dn87aU/eTMri3eyKqC38FcshYMOJbxrttB/wDW679GGtkApPmdahXTyut8SXeONE y2CHZczShPUoY7KNholvC8OEjJfc0UXNfW/4/spI51PDLa9VBzE5rpQRpNm6wLQMfjbTc5UI UO8rKzxCQGM6PU44clI+fDvya3XF+aRft/x7OOFtau79ydcVRdt9rVi9TfyyIdG8VVuEuP/n F7J1lYbYwW1ImgX3X+2FQb7zmu3huw3uegd6kfgEXwKtWmQnYYJg5qQjBDL1+kgJ/sppwvDu GsloS0FYjHwPwmKAltryy/lyhFQCmO4AWUPquGTjgE498rEo9O3l49vwSa4qCVWidPCfbA8x IfLsr+Yn0MoT4ZpYk36hPz8ux5dJ8ToJv5/efp18wMvmn4wXI+OI+sJOdAxMzyHWS6FCh0G+ D2waHhW6GftfQHe2LNmcasMEShGmeYJvYoKIWIha/Rhpr+gIN66z9UOa3oGiyeRZhournobm RWHZRQVR6QVXCxrtxrC1qtxcufB1Ywl80OFpUg24dv1+fnh6PL/YRgK+Y1rt3LeYXXA85iLd RnXDyhdn6UP5JXk/Hj8eH5iMuz2/p7eDRrQn5Suk4m3zf8lhrBdsg1oStI2DL8VFDVOPf/yw lSiV51uyGVWu8zJGq0QK56XH3Gdwkp0uR9Gk9efpGd5nuwWJtCVL65gvAyWnF1rr3y/9X12G nvr4l3W1w7MKiXAzGkCyw2Jg2ey5mM2TKggT3BAaCEoIu3BXBfjaAgoalkzpsKIJGWDVmKBm 33jnbj8fnhnDWlcEf/rZxHnaUFx6CQK6xjU1kS0wC/FhQfOb6lhKIqCwE9yFOaWIuJE9R/un LxWpsI7v5JsKD27DJY3Q66349r1QJiqHIHblgGlNen+UXqVOtbt5fnYbykc+p4fT8+l1uMjl UGHYLl7N39o2+2aUBJZDUsW3SKvjQx3ypxchBH5cHs+vrVs+YoEoyJsgChtw+rIWyM5PwWqq plyQcN2ITALbPNXqtU2P8v0Zft/SkywWyylm5dVTyIzOOlymIh6A61ym4jGr6tL7gn809lYp 6ap6uVr4waD3lMxmjmZdKBGtE4q9SEbB+As8DFSXZkiSVmnKs9wSm6hMcCGxrt0m89gqxXUC eG0lKb7A4FAMb8x5XDehnSRN7JKXEouVRrCEZ/6osrWrPcpWZWhpnbhUSEjoNbFFBLanftQs OFU5M4VHKe62gsGacN3PrQLWnql1uDSjwb4Cm9kiB/Nho7KbJE04lQ6WpkVMc8NaKP6ruu0r 3wxIea0UwhN1JIqXDhDRO3swZolHC+9bGe9jHnTP9tTUaovRIfMX3vDNoGVcEkxRz8w1Cdly Ff7q/QCr0O59qOM2D31cjgLf1Ry62MRVkYPfTQgclsSMY1xNgCSHjC5Xcy9IzP5JgpsDjVb9 CPKfem7Tm0P4+43rqAkpSeh7evIOQoLFdDazDiLg52jiBoZZaqmsGWA1m7mGVYmEmgC1UTwk /0wp6BDOvZmSzILWN0tfDW4PgHUw0/Jb/Rcvjx0jLZyVWyktYBBv5arPhou5Mzd/M8EVhDHY BARMkc3096zFaoWfQ8QhJyDBLPJgY0SGNjiUnnMApG4RV3rLpfmJdqOfwrHdUmgUrIC/N2Wg +pBtD1qs7TQPvIOoWbOJAS1yUHCPJ4dFZMVKhztLs7I69KYLVzGqBICako0DVtpmDwqAP8fd ViGHzBzNeEPC0p96ZrR+8Isg9dyfO2a3VTTTNsBGxdZHEufNvTsyO6T05t7KMgZ5sFss9X0e 3gQs1EILMSeSqxp7ULSG1jG9GpLiRfYE+0ANyNLDGVgLKsAtuDZfq8La4079pmx92GjuN15m LYGG3mLIN8oNV8zaYMUKO8KERsSeE0Elwkem5l13lq7GGRxKmWzF83Cx3XFdMJHeaDMknCib Q7us/6nVQ/J+fr2wk/GTfrxnW2cV0zDI8PP78GN5Pfb2zPR/PeI5CafeTGtbT/Vf2D6ATvzP bR/C78cX7rFMeWIktcg6Y3xfbmVIT002clR8X0gcuuvHkNPtp/5b3zLDkC5dVxN9wa3JPe0y JHThOEq+CBpGviN2PwOm1SFAnTNcz4sQirpKQevflJYkWhqNJVEaLamocgQrakcJ9vdLc+Nq 58+cGBHg//QkAdxmImTn9fOrejTFCVTNj1A5a1TqXZ35EQ2Z+t/zgWadoeHE/TAt25qGzRgi NaW01prw04KTkyttdAT/XiDhKV9j+DKYOfOpOs8M4lvs1hlqOsWVR4aardAo7QwzX85V3WU2 X81lN3rlk06nHubyReae72tbD9tAZy7uvM520OnCw0/XUpbaTHIZYjZbuKp0GR3BjgeePl9e 2uwUJg9oOOEnBNHOjq+PPzuDqP+Ar1MU0S9llrWPA+Ldjb9iPVzO71+i08fl/fTHJxh8qXWM 0onQbt8fPo6/ZYzs+DTJzue3yS+snl8nf3bt+FDaoZb9T7/s8+aM9lDjzW8/388fj+e34+RD WUSdANy4qGafHALquY6jLoQepi8QUu58Z+YMACb/yYXENQY/OKT4lV1ab3zPzDFlcMuwT0IO HR+eL98VYdFC3y+T6uFynJDz6+mi7ydJPJ06U4P3fce1ZbkSSA9tHlqTglQbJ5r2+XJ6Ol1+ KlPTtot4vqsowNG21k+Z2yhkbUTDtEehBynTepPpmnqea/42Z2db78xg1+2qTtkmh2k5gPC0 /X3QIbGE2dq5gOvhy/Hh4/NdZAT+ZAOkdHhNUsaJ6tYMv3VGSw4FXS7UyBwtxDys35DDHO9M mu+bNCRTb+4MdkiFhLHwnLOwdsGjIpAtIqNkHlHFtVWHmxanI8MiXA15fqEha0S/Q5hbVzub 7g5uOxUtDBKYYUubIdgCUy6dgjKiKy3gCYes1OkI6ML3dLVovXUXljysgEIvS0K2P7hLhRcB 4HvabwbQbOCJP5+jWWs3pReUjn5mEjDWPcfBb/zSWzpnzB9kuPTpNACaeSvHXWJcr5F4WrxC DnM9bLH8TgPXc5WuVmXlzDxtUbdF2/3N62qmpp3N9myOpyE15NfUzNKno5Rcx3kRuExWq40o ytrHs+6VrAee42uJb2nqur4WaRUgU1Rc1De+r+ZyZEtjt0+pp9y6dCB9gdUh9aeuJqY5yBK2 ph3Hmk3HbI4NJMcslRsoDlhpswGghaUGhpvOLOl6d3TmLj38eXsf5pllcgTKVxO4xiSbO2pk OAFZaAt9n81ddLHds5lk86XpW7pMEQ+nD99ejxdxZ4ZIm5vlaqFei904q5W+FclbUxJscotE ZSgmr1QNgYT+zJs6AxHKC+HKAY5i5ZvodrLZ0XW2nPpWhHEEk8iKMJZUeVKD6wncvwYk2Abs D22DSLTvs9gQisH9fL6c3p6PP8wXeTjzmN6NbWnqN3IDfXw+vQ6mSNlJEDwnaF3VJ7+BKfvr E1OxX4/97EIztpU058Nu5HngmGpX1h365V+GQifMMrUycMnbUVtpFcoavNOzoijxdgm3sx7V DQXeYbmfvjIFjQcQeHj99vnM/v92/jhxDw9EN+Y7xbQpzdDs3Uq6XpqmiL+dL2yDP6GvGDPX tR4HbbG5IsqWvfX+cza1BDSB85uxsWm4GZpisi4zUHuxo5vRL7TPbCp0h8mMlCt3kErWUrL4 Wpy13o8foCthExasS2fuEPwhf01Kz+Yqnm2ZUMWiKkQl9TVNunQ03SQNS9c8KnRnoMx11QcN /ttUuhmUCUb8ME3obG5JGQ8oHwshIkWlyH9nClARclPfV2dT9fpqW3rOXBF492XAtDPlYkEC um6051hzVnr99RW8ZxCRNUTK+T3/OL3ACQIW1tPpQ9wVDrYlrmiBKtS/GaRRUHHromavvkit XU+/2yhTi61HlYCvln62a3eFKnGUHOr0sNL4gv2eOdqbGnyAKY+wyfuOlrs9m/mZMzgcXBmI /1+XJ7FVHF/e4I7Dsrq4OHQCCF9KsByrJDusnDnX0TSIqt3XhOnryhsa/73Q8K6r/mZi3jF0 MgYxdatW9CMdUKb9bpj7OK1ueap4LK8wxNtOcWUmgnAV7FvtMUTaGrANMwRcmeIOux1ddTvu +V7dB66dirH/MiyziNdn0VCnS1BgKtzirX3Pq8OdlaZtynZJ7fWwj8Fjq9ymEOAkjSwpEoQB LBCbNivqEyIQQGI2iwYBBHltiwrRmlyzOsKCrNPcUgx4DW+gNWUIrl+WxzomMG3jQsJt2cTm h60uZvJUJ7JKSJKw3mmqq4zQXoS1GhaPCWowlyla+0hVfAlcUG8XK8ukcfyBug4+TIKAm8lO 8Z1HUsRVZmViTjCSbEijkI9TI4RbGuFeOwINj8sjaEh8mNpYmBOUobu02AQLCj6l4HJ4GBsS zsTX8CLKTxNUYyMDb7sj6HGPKEEjIoEUllCcCk1pe3nlJFVAyzXk9LUEGJZU/JV2R9cQFNlq HC5o+avMCHqQ7sIkKEJwCx2jMP1RNWydwvEg1EMDCNT91xznE+mHKbkx9Q17FxvdnJ2vB7sK xGKmn398cHPLXmWRIT/NQNUwoN10m9EZlTuFdZNtiDVGNJ+WIBdRriCetUX2AZ1wZrTVJClW 4xTSyh5ocOvfzrkUYo+PNluM4mhtYkKukiyukYAsgq1hvDk0ZbtMzucCP0IBWbt/4uE0+aQe gsZb5oQHUFdZUUOOVgJUYz0ipPSvE0D9ljaGZRjwIOi9TgZgLjZE7HeTVRWUZefk9dYLuGUt LanRgKQKeNT4sdYLA5M49wdRsnWy1sok4r8sEb80StYwXAIBldQjSFqmwv/dvk6kbejoJMLb MZipuOzIAIWO8F5POr1Omm6nzmJ0/LghuruaNqWHO4gBkbCiHSsmIkvI92cj4c50UlexLnQm k8HJHruK5S1lhbuebv4oVhlEYrqJY7IO2MQYsXlHSMf6I8M7MWk6WqC01YEtgxg+He0ZTRP0 XX8gEYbILtlfMdQlFrGLhFpKBvbT9Ddtd6+gSyKqxlVojyp5VBWmP5Ul5kIUKO9T+Z7ESq48 /lNcrWl2aBzMle8UV1p6iiIsatwBRtBITayJwbtwrLCWcLw48FO3VwnG43Gys3i+iBJy4IY8 KqwVcYF3m5jN1YcMzONoFGjZyjphY29CRzLeS9hLrw2suFmFsBX4qHZHtGsDsk/mTE6NDGrr yXitIJrvIWbwpsSueKtgH7Nhk3yg2UAJiz976dyr9VrllW0c5IhC0pF8XwXDO4Ht3eTy/vDI b6aGtwKGU3x3mAaJUm+H4RzrbbOpsahcHZrtIOhnpcV5viNA0nK0r/DDLvTfW1VrduzGbr7S QouYC7/hEGs/xtMsJba4U/w5gf0/j1E/77DY6YHq++eHMNdixKgPCQyFN0R7mrBRgfPKbYze aGkJLHnYGa74qIlDRTAaEWemv7/WfaGEUdLp+TgRO4V2rbYP4OKyjpuEghU5RR9EEu5IrkYv jA+11yR0AGgOQV1r9wUtArJnHJogxGKjtzQ0DncVBPJVi/XNenytOKMuXy0Hr2pqFjgdK3Bq K1Ansiep4eibHeTM5oENkUb9vo60K2L4bc16CYky1iE7rsXqhU3KZg6yTVD9rkaCGbElLIny pZg7lOp3ToCiDgNUe95MqGe0Z11X9oLyNBNfoNjEs395X+SxHZtAqHbMUMmY9G66IHiFziEC IvIgNIUahRJCLjYAFkHr1ErjPKy+8pxxtmaxQzLOpAkVwTP7eqIOoMgyDuLhz/EaAmsIzttd USsWODx1hgA2d0GVG50RCBs/CmxdxVro1duE1M0ee8oTGOVWnhcQ1socQJL2hE61ZDcCpoFg D9bWcqilaJORFdUvCjbkGVPP1Y96GFsMUVqxraFhf8YJguwu+MpaU2RZoWVJU4jTPIrxOz+F iMSs70U5jMkYPjx+V+Na55DnRYmt0rGKIQokwAyvyoHArcpo9DAl+nZnKc+rF02JfmNK0pdo H/FtBNlFUlqs4IbEsgR3kZmMp68HL1u84hb0SxLUX+ID/Mv2T732js1rbT4JZd8ZgmcviLCl FtRd2JawiOISct9M/YWK74W37l9rLTSvDU7lAGNGOKy60wG+0XABy+4PzYE/n+KSU36I77G9 WjA2nOLm8OP4+XSe/IlPMnfsQvvLMewgkUVVrCRDvImrXJ2Z9mTXaj6k1DvLAbZOaDSDnao7 HJAkasIqZhqNwvv8T787tsfnYW+7clIqwh+L4MGqtKggmq4xvUHUFq4DtOkNEuOrmO8PxoR3 QBmw1/ZYvLVveAxVZjvLrrw2284Bg+D7a3vxw622UxK6Hd+AyPIdVaGQmDu2BcbCJ9haJN0R Eugu4933Nk4QBBCmEZYNuKuI/K10WMr9/1V2LMtt5Lj7fIXLp92qZMaSbdk++EB1U1JH/XI/ JNuXLsVWbFXiR0nyzmS/fgGy2c0HqGQPM44ANAmSIAiQBBFH9GaNRMf3VIi7xImrS3Z7i3oc pW41QQLqpUnBVvGzK0hyTOYprWCyiDK6p11PnWjCFlld0LwDf5YQKAgI7gIzjYay5/qWdQRQ IgHFLqTAZRXaYIZd5q5j3TfKgbDhrm/QM11XM55WUcDMDL0B+Nfm7JIQacj5nohuaeg8J+VN zcqZ3nUKIk1BtRj3TqCBltYD7S0qwpBj38Mop1MyK7tNKLJdkVXqBBguT6dS6cgd163DeGdI RxHf0++8agSUJPZ139MVlwdHoTkTz+aM47mYFNpKowh4MuZhyEMCNSnYNAGxkWMmCzjtbMdb x5lKohTWJY9SzBKfVpzl1ly7SW/PXNDIWkFakGUzFG09NgTz6eDbCndtrjbdB7QILLn20o0z ct9IkoE6dSrKMXMgKbB35cLUNrb2kdNYLAV6ibXqAMqPLzJnhBTslx/ZSqaDU/6gwhGbEwp1 H+UkHwAHwSezsKR6Bhr4oYzQ6+PN7u3y8vzq8+BYRyvbtAHbVLMrdczFqRGgbuIuqBvxBsml +XyMhaPPlyyi36jjwmx1jxmdeJp1ORp4vxl6vzn1Ys78rRz9ugGjkZeZK0+VV6cjb5VX59Tl TevzoafKq7Mrw1cw2LmgFTISgbeGEtbQl3CNYgbDXzMINAOz6SLZhcm0qtOiVOAhTX1KU5/R 4HO7mxVi5GmCwl/QtV/R1QxOPU3zsKXH0SF8nkWXTWHzKqD0MSmiMVcO6F5G5bJT+IBj3ktb JCQmrXjtefS9IyoyMKAO13BXRHEcBWaDEDNlnIYXnM9dcAS8sjSkeI3SOqI2541eADapb6u6 mEdkFhKkqKvJpbbqpBGKsLHgSBAY6UXC4uhe2JNdShrSqTb21WUI+PrhY4sXZ52kOpgTXq8O fzcFv6l52VohlG/NizICJxQMFaAvwCrUFo4xUWq77wirOGKIEgHchDPwh3jBlEuklup2jWtC 8D7FpSXxJK5L4EImVDEpr5ZZMScwOau0l/Axf4+4zMIL9F5mPM71iAsSLYs4/mv3dfP618du vX15e1x/fl7/eF9vjzVbUtUYZyz03YrtiDDEhjJhFB7z5pa8igzJ1aoAtyJbphhxeagUFH3b fJJvltP7wWqDqh8epqcALJPr4x+r10eMFP+E/3t8+/v108/Vywp+rR7fN6+fdqtvayhw8/hp 87pfP6F8fvr6/u1Yiux8vX1d/zh6Xm0f1+JOfC+6f/Q5f482rxuMF938d9UGqSuPKWhmTLhh WbNgBTQlqrRchIeoMLe12QsAxMtoc8dTpmhYHKuKyDNOg5CsC+9LxjAWnkSQDvEE9JmXtnsu k+wuhfb3dvdQhK1CVItus0Ia3vpek8jVZb75IWEJT4L8zoZCGTYov7EhBYvCEcz/IFvo/jTo lUxdBAm2P9/3b0cPb9v10dv2SE48TSgEMXTu1Hgi3AAPXThnIQl0Sct5EOUzXU1YCPeTGdPT b2hAl7RIpxSMJOxsd4dxLyfMx/w8z11qALoloG/vksLayKZEuS3cOGY0UU0YleLRdeeU0vcB v60w64TnULMlnk4Gw8ukjh2O0jqmgRSPufjrr0X8CbWjjbaTxM5QQBRIZsPLP77+2Dx8/r7+ efQgpPtpu3p//ukIdVEyh/PQlSweUDXzIKQMlB5bMqcdPCjCkhGFlQkVLqQ6pS4WfHh+Prjq XjL82D9jSNvDar9+POKvopUYMfj3Zv98xHa7t4eNQIWr/cppdhAYF4zUAAfUWqc+mYFZw4Yn eRbf2YHe3eyeRiXIiL+Qkt9EjhqCPpkx0MoL1baxeCgFjYCdy/mYGolgQt2dVciqcIYhqEpi aMYOa3GxdOiyiUuXI1824a2eyVApAH6HLzu702WmdazVrZjwraoTl+Gy7DtthimtPX1mJOJU yjLRLQ/FsexeE7iQlCocc73buzUUwemQHBhEHNJAt7czOtlgix/HbM6HY4d9CXcHESqsBidh NHEwU3LN8PZ6Ep4RsHNXHUcgvTzGv8SUKJKQfhZHTYgZGzhFAnB4PqLA5wNi9ZyxU4fTMjl1 CfFkf5y5q+Eyl+VKY2Dz/mw8OtVN7pIYX4A2vtwsaqCy5cTy5BwpYZgCK6Ls9Y5CpupKzFgM DUtt+GjokdNFIXelZyL+ukLV6j63S3mRy+dl7e4/I9gED8ruCdnlby/vGPdqmuKKy0mMR6B2 DXh6Y8Muz4YEnSvGAJu5CqE94pHxoOCDvL0cpR8vX9db9WQVxR5mQm+CnLKxwmI8tfJt6hhS /0iMnKd27wlcQB4RahROkV8idCo4XirXLWjNZMQH+W1b+Mfm63YFlv/27WO/eSV0ahyN2ynh wlvdpOJ0CEnQqPzNQSIpeV1JVG2ShEZ1ZoPGyyEyEk1NFIQr1QlGFJ64DA6RHGpAp4L9rdPM DorIozAFipyJsyXR7eAvJQnHbRKxsVLd5aYfppB5PY5bmrIem2S35ydXTcCLKprgISZvb2L2 BPk8KC/xYHiBWCyDorhQSX49WLR58WP99ZxpihlXuDyexAtpk/4YVYo2PhL1TViMu6Nv4Ojt Nk+vMsj54Xn98B0c2D+0tNF4XaKpirpsN6CKSJ/kLr68Pj62sNKt0LrD+d6hkKd3ZydXI2NX JktDVtzZ7NBHVFguTK5gHkdl5eW8pxA6AP/lNqDgi0x2oiSwC9Hwqgf6+1C/0d2quHGUYvPE 7b2JGq/Yq4OkT6/7+grSjMFJAtWq79RhImJWNOK6i6awMIjY6JhxBAYCplPWhkmFGoLtkAb5 XTMpRNCWLpE6ScxTCxtkRajPe2hiwsE5TMZQUS++cv+SaS+od0GOQWRfWVYoCwymHzg2oO4N 0GBkUnTWoaYToKiqbminNDAeFcOf3TayAwe1wMd3xjteBoY+yGlJWLEE+T9AMSY30wE3OjM4 MX9d6IIw7gz1nuBS0163po0MIhNmCdlisDW6Ozn9oCFU3lUw4XjtAJfZ2NAB93J1sQwcsGyI khFKlQy2DEkNFg4N10vpOQHbp/GAKfrbewTbv5vby5E+9i1UhHF58gG1JBEbUc+ptlhWJESx AK1mMI/832EUekB8KbIJek6zW5Jx8IX40N5qabF9D4kz8r5bNIS4FmLNX7Fdi7k9NYmTKcHi TBr6BBQL1USWlZhADLTHAnMMFkwz8XCPOsqMuDQJwvtKjaE6EG6kz0hFjVOZIZCnU/2IA2HA RMwKzMgzE8alxlARzER5YvcVaSdZ0SaxMMuYxtmYxSAemRGpgCiWR96rD4jHQElC05fTWHaq 1tdQiV46/ibPweyxqTIQE0OzxPdNxYzC8IEMMMyoQJAkj4ybZFkUwjBOYREt9COnqWq+AuAe f8jzrLJgcpWGxQRzhJx0KNCJiRkgCTJvna32V3vGX9iUDnRy1lrzNEUZRwL6vt287r/L13Re 1rsn93gQ/oC0YjjBNIYVNe62lS+8FDd1xKvrs677WvPPKaGjAHt0nKEdyosiZfrdRSE4Dfy3 wJfjS3ku2rbTy3vniG5+rD/vNy+tkbITpA8SvnVbOimgahFpcD04GZ5pNhuYyjmmGkZGydtU HF+/wbv1ML76/jFYe8KgSqIyYRVMJiy9ydLYuEYpGwlTKwCLt07lJywGK7ix8qO2HywSsIPq W3Pe66UsOZuLVDRBXutd9tud8oeeI7UVnXD99eNJ5H2PXnf77Qe+zKp1X8KmaDndlYVmy2nA 7nyKp7ihf33yz4Ci6t5/9+Jw57UGhcE1G7dtfEl0aynUyBL/T86kjgxPGgRlggF35NpgFIhH gbraFVobxng+DQ3Ngr/J+64lcw8iBbQZAwdh6UGKpcEhoT/89RflLJpUNjCMFs7BpMTUacHR ix17HiuRVKCdMB7FeydYMZtR6lYiOdjVPVvCSZQ9poUU/paAmqOHt/957IqJnYZMP2Huyu2F XVwxAocP0w7ol3tlYYhVa5dVT4dqp4GaFdQdC6wjW6aGyyz86Cwqs9Twd/rCYWWa2HA5HMTk aBGeyyQkKR41e+eGIhLBpaWHjaa9fOGpoAhqoVB/gxd5e1gFyP6SK7PH+00eSSWX5hqXKk2B BzM0ngSKp6EMWXR5X9Bh1a1oidRy4qz+AFWrrVGnk/toUgPMGc4NdwdKYrFjQSxAMwFVVMHs a1gYtua+fRGgl2qrH2aR0ODyjASJjrK3992nI3yR/+NdLhiz1euTGYbD8N0XDGjISP4NPMb3 1rACmEgUmqyuejBeKahzYKuC4dMt6zKbVC6yDxPJsgpzTyU6oaiDCj3xEttcyqqaGb5tUrFy rou3XNk6VNeWwfCE4qsn/DVbFq3N1fIGjAowLUL9MESoS9kWMwT70GjKe2JgFTx+oCmga73+ DgiBNsUHWz7nPJfqSW7+4BFor5z/tXvfvOKxKHDx8rFf/7OGf6z3D3/++ee/ew0rolpFkRht 5IZm5EW20INcDXDBlrKAFDrCihsVcLy75dUX6FHVFb/ljgYroVntFSlzitPky6XENCXYC+bd sramZWnEcUmo4NDyfWRsSO4AcCelvB6c22BxDF222JGNlQqtKjBLmyS5OkQi/BVJd+ZUFIHC Bv8RrH5eq9KGtnZrqQ/ov3YkhbuoliNKkYjOgQlf1YW8FKKPbd/hfq+wDCbm9/pD4P+HpHZz TXQR6MxJzKbEGqswFCvYx+J7oxHoI+DVsDotwXWH9UfuXR3ovLlc4n5NAdZBzJn5SIim6L9L Q+pxtV8doQX1gFu8mo3fDlVk9nq7gCH4AAcluQEgUCIiPDIsBrFugyHKKob7rvjaRdTmbjOU mYdju/KggI5Mq8jKcCAPCIOaNPGkJglqR7mAgWLuoFvi2EKRTqQfc8QUMfo3VEQLkIAlZxag 4dBUEM5mt9QMBzpeSZUG4jdERLXZeEt93bTuZKEcyRadigfIoQJtTZbTIDC1I55WOLlVRRZT QW9obvgDMxechmWEnrJdfg52ZwJCAG6gQIERnOp3UpzyWoC2cnT9PxEl0HduGb7D6ArJ8/qf 1ZPI6NwLir6vUq13e1QZuJwGb/9Zb1dP2ovyIla73wOUoduik/ULynREt4TyW8GYIy4WmRAI j+ZUswx3WTK86fpF7k7olUk7pkP5DVEwMINs0Y56bjg6BVjjeDBSybVXnJuTG1WHus5Sh+Ce YehhE2ZBjeFx9E1AqTnHkWwhHdpv7YP9DwJhjPU3uwEA --ibTvN161/egqYuK8--