From mboxrd@z Thu Jan 1 00:00:00 1970 Content-Type: multipart/mixed; boundary="===============5840212387792661964==" MIME-Version: 1.0 From: kernel test robot To: kbuild-all@lists.01.org Subject: Re: [RFC PATCH 3/5] tracing/hwlat: Implement the per-cpu mode Date: Fri, 09 Apr 2021 05:39:22 +0800 Message-ID: <202104090545.du72PBvJ-lkp@intel.com> In-Reply-To: <1c99ca2d7403474508aa7b025869c0673238400a.1617889883.git.bristot@redhat.com> List-Id: --===============5840212387792661964== Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: quoted-printable Hi Daniel, [FYI, it's a private test report for your RFC patch.] [auto build test WARNING on tip/perf/core] [also build test WARNING on linux/master linus/master v5.12-rc6] [cannot apply to trace/for-next next-20210408] [If your patch is applied to the wrong git tree, kindly drop us a note. And when submitting patch, we suggest to use '--base' as documented in https://git-scm.com/docs/git-format-patch] url: https://github.com/0day-ci/linux/commits/Daniel-Bristot-de-Oliveira= /hwlat-improvements-and-osnoise-tracer/20210408-221655 base: https://git.kernel.org/pub/scm/linux/kernel/git/tip/tip.git cface03= 26a6c2ae5c8f47bd466f07624b3e348a7 config: openrisc-randconfig-r013-20210408 (attached as .config) compiler: or1k-linux-gcc (GCC) 9.3.0 reproduce (this is a W=3D1 build): wget https://raw.githubusercontent.com/intel/lkp-tests/master/sbin/= make.cross -O ~/bin/make.cross chmod +x ~/bin/make.cross # https://github.com/0day-ci/linux/commit/4e2f5d30c69f77756e8cf223a= cf55c2aa2657393 git remote add linux-review https://github.com/0day-ci/linux git fetch --no-tags linux-review Daniel-Bristot-de-Oliveira/hwlat-i= mprovements-and-osnoise-tracer/20210408-221655 git checkout 4e2f5d30c69f77756e8cf223acf55c2aa2657393 # save the attached .config to linux build tree COMPILER_INSTALL_PATH=3D$HOME/0day COMPILER=3Dgcc-9.3.0 make.cross = ARCH=3Dopenrisc = If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot All warnings (new ones prefixed by >>): >> kernel/trace/trace_hwlat.c:122:28: warning: no previous prototype for 'g= et_cpu_data' [-Wmissing-prototypes] 122 | struct hwlat_kthread_data *get_cpu_data(void) | ^~~~~~~~~~~~ In file included from include/linux/err.h:5, from include/linux/kthread.h:5, from kernel/trace/trace_hwlat.c:40: kernel/trace/trace_hwlat.c: In function 'start_per_cpu_kthreads': kernel/trace/trace_hwlat.c:496:25: error: passing argument 1 of 'alloc_c= pumask_var' from incompatible pointer type [-Werror=3Dincompatible-pointer-= types] 496 | if (!alloc_cpumask_var(&this_cpumask, GFP_KERNEL)) | ^~~~~~~~~~~~~ | | | struct cpumask ** include/linux/compiler.h:58:52: note: in definition of macro '__trace_if= _var' 58 | #define __trace_if_var(cond) (__builtin_constant_p(cond) ? (cond= ) : __trace_if_value(cond)) | ^~~~ kernel/trace/trace_hwlat.c:496:2: note: in expansion of macro 'if' 496 | if (!alloc_cpumask_var(&this_cpumask, GFP_KERNEL)) | ^~ In file included from include/linux/smp.h:13, from include/linux/lockdep.h:14, from include/linux/rcupdate.h:29, from include/linux/rculist.h:11, from include/linux/pid.h:5, from include/linux/sched.h:14, from include/linux/kthread.h:6, from kernel/trace/trace_hwlat.c:40: include/linux/cpumask.h:767:53: note: expected 'struct cpumask (*)[1]' b= ut argument is of type 'struct cpumask **' 767 | static inline bool alloc_cpumask_var(cpumask_var_t *mask, gfp_t = flags) | ~~~~~~~~~~~~~~~^~~~ In file included from include/linux/err.h:5, from include/linux/kthread.h:5, from kernel/trace/trace_hwlat.c:40: kernel/trace/trace_hwlat.c:496:25: error: passing argument 1 of 'alloc_c= pumask_var' from incompatible pointer type [-Werror=3Dincompatible-pointer-= types] 496 | if (!alloc_cpumask_var(&this_cpumask, GFP_KERNEL)) | ^~~~~~~~~~~~~ | | | struct cpumask ** include/linux/compiler.h:58:61: note: in definition of macro '__trace_if= _var' 58 | #define __trace_if_var(cond) (__builtin_constant_p(cond) ? (cond= ) : __trace_if_value(cond)) | ^~~~ kernel/trace/trace_hwlat.c:496:2: note: in expansion of macro 'if' 496 | if (!alloc_cpumask_var(&this_cpumask, GFP_KERNEL)) | ^~ In file included from include/linux/smp.h:13, from include/linux/lockdep.h:14, from include/linux/rcupdate.h:29, from include/linux/rculist.h:11, from include/linux/pid.h:5, from include/linux/sched.h:14, from include/linux/kthread.h:6, from kernel/trace/trace_hwlat.c:40: include/linux/cpumask.h:767:53: note: expected 'struct cpumask (*)[1]' b= ut argument is of type 'struct cpumask **' 767 | static inline bool alloc_cpumask_var(cpumask_var_t *mask, gfp_t = flags) | ~~~~~~~~~~~~~~~^~~~ In file included from include/linux/err.h:5, from include/linux/kthread.h:5, from kernel/trace/trace_hwlat.c:40: kernel/trace/trace_hwlat.c:496:25: error: passing argument 1 of 'alloc_c= pumask_var' from incompatible pointer type [-Werror=3Dincompatible-pointer-= types] 496 | if (!alloc_cpumask_var(&this_cpumask, GFP_KERNEL)) | ^~~~~~~~~~~~~ | | | struct cpumask ** include/linux/compiler.h:69:3: note: in definition of macro '__trace_if_= value' 69 | (cond) ? \ | ^~~~ include/linux/compiler.h:56:28: note: in expansion of macro '__trace_if_= var' 56 | #define if(cond, ...) if ( __trace_if_var( !!(cond , ## __VA_ARG= S__) ) ) | ^~~~~~~~~~~~~~ kernel/trace/trace_hwlat.c:496:2: note: in expansion of macro 'if' 496 | if (!alloc_cpumask_var(&this_cpumask, GFP_KERNEL)) | ^~ In file included from include/linux/smp.h:13, from include/linux/lockdep.h:14, from include/linux/rcupdate.h:29, from include/linux/rculist.h:11, from include/linux/pid.h:5, from include/linux/sched.h:14, from include/linux/kthread.h:6, from kernel/trace/trace_hwlat.c:40: include/linux/cpumask.h:767:53: note: expected 'struct cpumask (*)[1]' b= ut argument is of type 'struct cpumask **' 767 | static inline bool alloc_cpumask_var(cpumask_var_t *mask, gfp_t = flags) | ~~~~~~~~~~~~~~~^~~~ kernel/trace/trace_hwlat.c: In function 'hwlat_mode_write': kernel/trace/trace_hwlat.c:800:6: warning: variable 'ret' set but not us= ed [-Wunused-but-set-variable] 800 | int ret; | ^~~ cc1: some warnings being treated as errors vim +/get_cpu_data +122 kernel/trace/trace_hwlat.c 121 = > 122 struct hwlat_kthread_data *get_cpu_data(void) 123 { 124 if (hwlat_data.thread_mode =3D=3D MODE_PER_CPU) 125 return this_cpu_ptr(&hwlat_per_cpu_data); 126 else 127 return &hwlat_single_cpu_data; 128 } 129 = --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all(a)lists.01.org --===============5840212387792661964== Content-Type: application/gzip MIME-Version: 1.0 Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="config.gz" H4sICE9xb2AAAy5jb25maWcAlDzLctu4svv5ClVmc84iGVtOMpN7KwuQBEWM+IABULa8YTmKknGN baVkec7J399u8AWQTdp3kYrV3Wg0gEa/APDXX35dsOfT4eH2dLe7vb//ufi+f9wfb0/7r4tvd/f7 /11ExSIvzIJHwrwD4vTu8fm/vx1+7B+Pd0+7xYd358t3Z2+Pu4vFen983N8vwsPjt7vvz8Di7vD4 y6+/hEUei1UVhtWGKy2KvDL82nx+czie//32Hrm9/b7bLf61CsN/Lz69u3h39sZpI3QFiM8/W9Cq 5/P509nF2VlHm7J81aE6cBohiyCOehYAasmWF+97DqmDOHNESJiumM6qVWGKnouDEHkqct6jhLqs rgq1BgiM/9fFys7o/eJpf3r+0c+IyIWpeL6pmILORSbM54slkLcdFJkUKYfZ0mZx97R4PJyQQydt EbK0FffNm76di6hYaQqicVAKGKxmqcGmDTDiMStTY+UiwEmhTc4y/vnNvx4Pj/t/O13qrd4IGbod dThZaHFdZZclLzlJcMVMmFQjfDsLqtC6ynhWqG3FjGFhAtPaNS41T0VA8mUlaKyLsWsBa7N4ev7y 9PPptH/o12LFc65EaJdOqiJwVtNF6aS4ojFhIqSvAVGRMZFTsCoRXDEVJtsem7A8gsVuCIC2R2nJ lOYNrBue23nEg3IVa38a9o9fF4dvgwFTomewuqIRQI1HF4JCrfmG50bPIqtAFSwKmTat3pu7h/3x iZpuI8J1VeQc5tP0TPOiSm5Q77Mid4cKQAm9FZEICQ2pWwkQfsDJYyFWSaW4hp4z2BrkTI3EbblJ xXkmDXC1u7xX7Qa+KdIyN0xt6Q1QUxGSt+3DApq3kxbK8jdz+/T34gTiLG5BtKfT7elpcbvbHZ4f T3eP3wfTCA0qFloeIl/1cyC18ITVotvKkdAsSHlETsMrBHCMFHQudJEyA9ZmtNlUWC40sfow7gpw rnjws+LXsMzUROma2G0+ADG91pZHo5gEagQqI07BjWLhAIGMtWFp2iung8k5B1vKV2GQCqv83VT6 4+8Mwbr+wx2+WCecRQPNtHOod3/tvz7f74+Lb/vb0/Nx/2TBTQ8EtrOcK1WUUru9gBkNV6SS1sSV DhNfK3y0FJHHrwGrKGPTjWJYrhtrWFyt0dzoOUkivhEh7TAaCmACam/mmYBdpPSpwH3T0DDDPNkS Hq5lIXKD9sIUihbCzpR1r5YNTbPVsQYhYJ+HzJATq3jKHC8QpGscuXXByglZ7G+WATddlCrknsNX UbW6EbQIgAsAt5xCpjf+0rm46xtKYGxTeDsXIe+nmNxoQ407KAq0e8NtAEFVIcFEixtexYVCsw// ZSwPydhgQK3hD9ejbnhViuj8ozO/Mu5/1ObG2x/oCUE1Fb2cK24yMBG4rhBipTNrPkcR156W8gc2 VKo9lO9nQBvXJDNav3kaw+Qqz1kFDGKIuPSFakUqISTvJ8b+hM3uNueymBqxWOUsjSMSaQczgbNh wwROJ2CrCEmZcEJwUVSlqn1eH/VFGwEDbRaANjHAOmBKCX+dG+Qam20zJ9ZpIRX8T0DtxOI+NmLj z7iMKUXoI1eVFeCCIgXtPPOIKOuoY2rrrMPMiTRhKDyKeOTpS3h+5m1I6zGa9Ezuj98Ox4fbx91+ wf/ZP4JnZ+BLQvTtEP64zuWVLfqON1m9qHVEM4qznMSGGQgXaZXWKaMDep2WAWXM0yJwR4/tYYnV irfxzoSGlXEMEbdkQAhLBFkTGHsySitikXqhlQ0RrI/Qrrv387yWuJA8V0I7IQkGTQGuXR4J5gQT WebEHG2EnVxxiF39KFkUslCmypgcyITxfJyyFVigUiINEbHrMnNyC0io1nXTUQsM7cF5OQirHPJ4 2O2fng7HxennjzpAdCKTdtDqfF2dL8/O3JWB5AC8ZnWlhOEmAbe5Soj5bufLJqkQ0laRCdDf1dHx /e3T00KIhXh8Oh2fd1hjqHV22Nqaf5FrU8XxOakBFGn6alLwFXPC94SR2LhKQo+gW1xlg6PPy26B 7GZ3U6HzszNSRkAtP0yiLvxWHrszx2nefEbAaDigdFqCh1VVpB1P4Q9XJywqrqqVVI71DLPIVkfa FYz2X56/f4csYnH40a5eQ/pnmcmqlEVelXntJCNw0rDTMLlwZ6HrloNAHQU6wzp8I/MaouMWNafS XgXn9rj76+603yHq7df9D2gP9nA8Eohuq9iZBDs9mPHX2zQpivV4Z8JS2zS2gr0ByUBPYBtisQni bMu6zO1OmiIJU87UFNHFMhCmKuK4Mp7lqVbMJFzhSiuWr5xAKjVFm/q25EVUppBMg6OysQZ6Pyfx XBnMLqsUfEGKqjy0/LUIGBpQQR0YR5CBx7EIBfqS2J1ItEmuj9FdjS0sNm+/3D7tvy7+rt3Wj+Ph 2919nS/3VRMgq9Zc5TwllWSWzdDOv6ANXciMxgIiKu6MwwYNOsOI4nwwq8NpxtAWzHNauCrRoMqc BNctCGRTFhz3Ael0VzK0UU4fLDUEgk4cGzSuLKRLmljQhgJd7FWVCa3BlVZYSNTSeqwMnYt2HZnI QHbQraha+5GYC62uEnAjNuPua5WtYtoSQQq7rPSsZ4DqQ0WVOj///ND3Uhd0weKJ3M5iuB5sIlR8 W7GMLBFSOGJOY4aN1RXddATvk2irzfy/+93z6fbL/d4W6Rc2Pjs5BigQeZwZ3JxOqJjGw6QAf1cR mt22NITbmShF+Gx1qIQ0MGP9zNYIWF6qRIfdYC+uH5wagh1ftn84HH8ustvH2+/7B9LEQphjIJB3 pggAYDkgogawHxtpmYLBkcZaD+tf3w+MUjisX/XBNMaHiqOSguJS8bh2hGinMYP+YTZgN0WR+vz+ 7NNHp0Mwz7VtpsPjiYz8RhYFFXLcBKWzyW+sUSnCXp1bCMZ6jsm3nsYODl3S2i8dcoVmGBu4Nd9S 2sMIdxWnF6qLaLgbu3I8XlihpXBWZx2AEzc8b02TVYF8f/rP4fi3Hyg4qxKuOVUqhM177W3la9DW zDUhAIGwe+Vqr0kpZb+OldMQf4ERWxUDECZr7payQF0GkM2nItxO8AXdWClm+IAZ2ER/stZ86wra gNrWU8w5mh4TugcS3jIIWZc6mmJ9X4WUmD9jtQVMUAE+i8qHgMji8MQNjHk0YCBzuhJll15OlKlq 5AqNEc/Ka6rKbykqU+bgugddZlYeKjXc5rC9i7VwvWrNaWNEryYIKiOHuwOPi9JdAZzIilF5i8XU C+hTAwzjLXTGM83aBfPbConmaapZI++DB7TK/dOnCyUFxiEPt4JFKHZlEeRadZ3AWmmjCvrUA7uE P1edQhFj6GjCMnBL9q0RbfGf3+yev9zt3rjtsuiDHpTQ5eYjFX7I0UaoYYMZATie2UJ8DQZYrX0l kEY26h5v3T7bRjLZ2ngWLGYmaTcBpDHks+4JWwfqJskJEJWIwDb3rR6aBPhw3KNtBJ952h+nDtt7 ziNr26PgLwhz1hQqZplIt40QMwRMyRnOeCxl5vA22pkjSIvVHLrQsbOJseaZ59abeVBgMjq3acDA KOIbzxA0XSCr+lyO7KAa6IiLGmuQi73BY88J3PCU1UOifoGd89RviLf6RymfS5iyAPMyrxeDZS7I 86LQ3SwuZqU4jdChmWgC9gXiLj4pMcsgyadOjjyqGNg/THBILpYXL7UXKpxYw/7AmsaDNgSi0BUE JTSBzjM5gZJyRmzNcjr286nEi0tp6snx5r7d1oPOO0SrSJMC9JQJTyUZA4y34iotISIwniLkzJ9Z +F0NBUbYcIUQNhoZAiGLEIqPe4HdqMGWKEhbKHMBYQAo5PXW42drFJoAtUbB6QNmpMxWPPfn1FQT p4+IijHbnfb6tnVdJHdFsGdieX0XyAOjIRx0bqkmWON8+Azs1PmgLnrw2BbBn4rHk+OavsdTYwtD b2iU4E9cusFosSA0FCFhOpnsAgPciR7wWJePuIGTmKA3I60wtK5EkPmMXAUQe3B/+a+iBkNvYase dZnMhuYPJI5yTNed4tp44Nqm0E+L3eHhy93j/uvi4YD3AZ6oWODa1G6L5Go1r0F7nE+3x+/70xRD w9QKAiRbdsWTBZp3S9WGUPNUvSBuoEXSNb52KqkYt5nYsQRppMPJZGVEnFCZOUk4swINCWbm9tz0 pQlIyaiapLTmY4ZgRirfihNtczwWly/QxEMLRhLNWBeKvrBe8JVzgAkz1+YlIVof80qujueZnQDo +wWCodmhaPDwYJ6kDT5fGCXkPxlZrZ0gLqSBXE/IoXF4uD3t/pqxNni1E0tgZiunBK+J8GbGlMw1 RTi6aDNDm5bazGyghgqSAgi4X8kSrE2wNV7IQFPZ+PplqsbJzwtYW8KXd0RP/6ot0ZDLclZMmwzM SwgO7v+xLGhSZ3vkYT6P1y8JhLHDZGREkNsId7bPZH4t6wrNC1IJac/UXruO6dK8ckZTnq9MMivg MJocU2QsfAE/6RwaAiwm2jsCc1R5bOsB8zM1iNZmSa/yF5KTjrSuxb7QtUy2eiJgI4jXprFpcyxH 8fAcceOgXk3OWUpGwhRpyPP5xbFZ/Pxg6vD6dT3i0cFLk4M0QhtBvxsgG6gXams9be3gZoeMMdYs QXmxtENob27Mld68grTm9CoCajO+4Svk/7yiohdjWVQxW+d87+UL9bqM4XVEUcP9EkyT6CKGTk8a Ar+852ctw94wFxoBscY2ZIKwRqYeWCeHYwaY3GOVBQ/MxbhK0FRZ+ukFuJBdhuROPGCacIQs3jsE nhNyEUp2tVgCa0w67rJuMNlhG7SNks4a7YXeXgsq7PQIuqB8INDLUW47pnyVTjFvwisxmvsGX88g 3TNM02Snil0NOYL2DCs1HaJbjUFPgGrkJ+94zO23+ooSDx/3p1dsSiDMbd5SrRQLyhRvELqnoy8x Gu+4puLuVRmao4CMGzbaXGN6r77pI9szhbjiwXBSGxwgsCxamnEzRJlqPOceGjSWujbQk/xxtqwu SN4sw6MeEqMkCRc0eBRZO7hhND2m6B37GKd9H+lgNinL5/nCIBSX6ZZkHHk7fSBvNTUYahNTdHqw KhQNKNO8/G0G32La3eceKvunB/VBYdiftNWbCwCLMBTR09SuahhVSLSs33AMDnw79AW5wSe76AVo rjYnt7u/BzfEWvajtyM++wEDR/bmPKS/aAy/uyPN+jQaq0AhHmHSN5OnGuiEnROLNEk/fK9mCWck mCLDfgfLWvfpHaCqyDEn8ANDMB/QLmUDgpQhdDUbf4OVA654ckSdFyNBqLbSeA9QLHjyuJoZKkjG DMvlgb+pZ5QuenMxajFxncHiuKECDe0elq282Gh42tpsPrGC8EfnRSG9OzoNFg1PY5QpdDbuoApj 51qLbQ9G+fySglWrjfLmyUFlgCIGWPtE79p37SUnL5SkqXP2Dz+WvdYww1KvloWPo5iUKUcEOffX S0qnUyaDnq1MCq/oLjjnOKgPTlDaw6o8bf6wL4YE1oyYfwelp51MkmAjNV141050c0XXmqDL5/3z HszJb82zvoFpauirMLicvEWD+MTQDyg6fEze0mvRUglvf7Vwm+VfzjRU/jOUFqzjeXF0PMfU8MvB jRwLDYblwmZmJmqaFgsZ3mjyIfCy4x3BVziaETTS48shCIf/eUaQKzWmzS7pHvU6aBDjcSXFmrrg 1OIv48txP2ERDeqQFhxfTmFCtuZjNvHlmDJJyOmXYrLEVeNT8rlavz563H3j2HyDUvu6mN4GvSuM hre8RwzGRAMSCHPioorri3IDXCPi5zc/vt19O1Tfbp9O7jOVu293u/G9HEieBqMEQJPeDjYPIkwo 8ohP3YhDCmtXB1YF4fHVuBusaPQvM2qAfWQ5ho713HamN5ISE+EfJ9feipMWVzOjqKv7xMTImBga 8BpUbS3cJuJ4f93DcAv2WfOuBBWuva9uOMiQvkrTE9jzAJJvXTkawwc5XI+wn0ShOA3uWbcjZVMH VPWWBZ111DUMnB+5xnfNBX4hxEsrIE5ieF2Zqn/ie5uNvhI4jZ30m9rVeS9VW9goHBviUwhnAu+i 1kYoIwqXK40YX+VqbqD40Wgmh9sMIdVKDwxvrh3NSPTwyKiqRz1ZFq7SC6zFYNnQuzNyqYyjn/ir 0lnkMrcwU1JZo0VliRjKkodaEORKOiNVMSpH6N5ita/d1XV9dQMvxvuB5LXbvHkUj701YcAY0d/7 dWQD/kGpt1hpd7oOhr7bVhDrBN2/57047Z/8j1vY7FEVsoLVFoPCyqjRAOHeGu8Wl2WKRXZM9TtG SN32p4W6/Xp3wFc+p8PucO+cZDKIJJ1IFH5VEcMHWCnz3/mCoKrISOOnCu15RNsxu34HMepjM4Sv +3/udvvF1+PdP/Xz21aB18KtX3+U9W7pN6y85Phgi07s2Ra2SaXxpmZEOQ+HIImcC/MNXDI1gnHp FT+2bDDkZgVmR9c3D+l6iWMEbSWKR97leix6xGgAyLZVkHO/QFODqiwkCrEjqvrS4jxhIiI65UMc /dwZiytU7Gbh/vc0sOCjY/QFNH2ToHpzpHkad68yenDMmSntffpBGcOqYHD/vD8dDqe/JtUPy2u5 caNEnKYw834r4+OTUASm1AEJtB/L0KUGXxJ5I+gIAvehhovIzJpGoAD+uAFVMvIjMk27MFueXVyP 2El2fnY9kjsmBrOBfx4sU5uhFMwkF2Sd0eBYUEDXmk2uhlPFiMHCqolPbAFyHVJbQhvFmd3Ng0cq V0LxlH6up+K1cE14/btVVR8oclmaEXQlh9nNp1HM+Ek2fn0yZvxEluA66yGoS30hl3je7SxZC8Fv LRmzHVSgOiw+yfMiI++8jEqWpWYQUPjnh5WI3WezV8PnJC3E/wxJpMGl4mswpy6kCpAtHUYw1pFn 2hlAzERabNxQGHyCKYq0jYq6qmutWNFwm8swZGrw8YgsFGxkMWT4dnd7/Lr4crz7+t1e+OkfRt/t GsaLYvhKr6zf6jbXLH6SYNBPk3gfnNuYTLpmroVAKFB/KasvaRq8Qp4WOfk9FVV3EwuVXTEwhvaz d+2UxHfHh//cHveL+8Pt1/3ReVl4ZR/buvJ2IPtiLgJG3ndkjGJdJ85A+laldbTtJHTSkwSwqGka sOEr9lET6sltZ1KGg2tFumK5sUU070lma0fsE10XO5E940P7+rMlZOZs0XyjuOfcaji6qqYt7LYM lJfsw5Ixvc3Dlth+lY/orvuuhSybj0a5doqvvIeg9e9KLMMRTMvMMesN8Op8BMoyNyxuGbrf62th F855tX25n4B6WN2JXd1CVMzzkDfX+4YfFBnvrtqFPz817sINGhOBYbgbAzWgmfMMl1NnGQswVCFG 3d0QVrkbj+IvDB8F8069LRhcXIOi1ss2FCruW7uYMrgm2GbkR6Qi4yxjEbt/4ztP04RFPRCfCOPT cA/ImUq3NGpdBH96gGibs0x4vdrrjd4LK4B5+lDg9QYY0wYW33uxXCPQ4XgwNOjeV8HAf/uXqxoA JCV//PH7p4/uTLWo8+Uf1N2OFp0XkOCFXQ62yfhCP//4cTie3A8AefD6YTZ+83WkdprnulCQ1Qt9 kW7Olu43J6IPyw/XVSTdDz06QH8vgs3Jtnb23EP9UH+6WOr3Z9ThF2ycFKJKMO44w/7uZzLSn/44 W7LUvT2g0+Wns7OLIWR55vbZjsgA7sMH6tMpLUWQnP/+u/MFlRZuO/905kSaSRZ+vPg/yq6lO3JU Sf+VWs4seq7QW4teKEllptpSSiWUTrk2edxVvrd9pl6n7L63698PAUjiEcg9i3LZ8QUQIAgCCIIk NHQiI2keYrkPpX73GOIP8XGxP1TGJmF/35fnGjNPTjWr+Q+4KwwmrH7lP4QO4MzvVdXDIvZl6QRz +wo6N2hD7WxEEZvqWNIHh9yWU5pn2tpZ0YuITqkuiqLX+/GWF6e+YthKVTFVFQmCWFeOlsQy4ujT X48vKrLOFxGp6uUPPgt+evf64/HrC/C9+/z8lRvYvBs/f4df9WCaN+UdPscD/f9nhg0Is4eXsLos wW7pNcuwoidj3x/iKPDFDZvg66FK2xiIMlAk7A6pBYTzGQGE6Bh6A2IJDBtcTaeuw9rX73++eoua FwXr+AWCWEBgjj8CPBxAMzZSjVoJZfjbOz6lepO35TjU052c6oWIl5enH58hjs8zxCj75+PHJyN8 gErWQTgRdGtPMvzWPVh3eyS9urdSOTi26JbN5ljiVlo+ZHcdt8n9lQWptY1O+JMvRwy9shB5f+s9 QfAWlt0DVtiKN92x5v/3PVIouK6V/chV9CZ4Yy2YJaiE9MEfOWbmEf5MIuAankfVcLuWD6I3asqH JYSMwVfRWmndhZ7uatxnZmU7QKBtu1S3TFVzA1AWjkWVB9tQ+Ko7JbKjbVJksVt5+lD22IU7iUKz CPXzxU43I/Bvo5oLm6iFt5x7Nk1TWdpSmxsBquZLlzDUog1ak9YyHMFdGNtYkQzCqU2zM+TfN7H5 RCtaGhvwOlj3Y4WvtzSuU3nmyyfsWENjugPPurXSGtLz2ZKZg0ChsjfwtRlf0mFGm6ocdAxGuY2q HX5oRFghQUBTI9iFjud53+apsEhWCTS83GfcmsSqZzBpqxoDGEgQEtXbMHxsq+bWTsZxlsFw6W59 PdEa33zVWXeXkAQEu/ntcIUFLg7Ea+/OFTcuz3lEcp9Q9CGnY1uSGDMBXcYjIYGveenDOLJeLK3f zAs44zmE6waHt7X3ZREkoa9WsIzhveXNdj6Vbc9O9ZsCV9VY44LyPt+UEy6jxFZdiIpQTTQK0NiF Otfh8ls9souvvseu29eYcWlUtt5zkxKvBre/Q6Lb8jrIUvaQpQSv5PFy/uD5iNXdeAhJmPmkrnCf UpOlw/MWuuR2zYOA+LKXLD79r3Nyg56QHF1+GWyUJRBMEm2HtmWExB6sag4l40ZvH/uEbcUfbwhQ t1N6afiajfp6U32uJs+es1HaXUbwkNmGvq3OYj/yrW+0h6gHyRSk+KcSvw8ixivaOuL3a3321cnV mdjH3o95Nk3mpGswtFwNeqcGmL9gv7FjNRpoyZG3HkMS+XLjH0iM+rc/BOcMgwAPLu7yZW9INrS3 0TM3srqpyj3+AVjN/O3GRhLqzh4m1h5G5sGmPE0842HsWZoEmUfdfKjGNAwjDyhCP/oafuhOrZoW 35o86/csmTyK+wOEo68n10Czw/+t5+BtHTszn1j7nB5/fBIb1fU/unewkDS2lQwXBPEn/DQ3wySZ LxLvdnub2tQ7uTIyqMY1E0lSy3JgtjNmYSv9z8wEA8W4yx4rEAxhxb00ycUZAQo4lm2larhuqyra 7cySJN9IdGtifQsDa93lGgy2ipfr0T8efzx+hOsqzk7fOGr7Pfdaq/D/WNeIPewzk8+B6D4848yg bYddXRrnW8kQVHJvuKpA8L4iv/Xjg2FGy30oQcZcf/ewmQKHzyocqtwtefrx/PjZPfdWtrjYFKa6 m5EC8jAJzI+uiNo7D+JVAll/bUNx5SRpkgTl7b7kpPOIyaxzH+Ac/g4VBM4OcOA83OCUGQJdIugA b8W01RaLCMi41yOS6WhbnsHVZxi9dSxZD8+i3NuH8SizOBmBbd83mmJfjeKWoPE6kl4v5mmO/RVO hT0Q/jmHMczzyUnTHW49797wKsWvKhbC+dvXXyAJl1r0KbEz+KLtpps5QIs0+DSqOEy7XyO6A0aB rD5Yjw8YwJzOXySj9Dz1br6C7C+WkrRmYFcIke12XGA/oiZWW2yljn8by6PHm8NkVB4VXgxMWNld 7c6uM+3Ky14E5SQk4eaETyrB+2aLqj37nknh7BYo9ZhXKw0GrhSUWOCBNbemRyu6Qt4vxf+qJnH2 Wx9ryjXh8DdYvLlB90VrNQPQ3ks91g1nU+daaVs6DovziN30Zy6JOOpHN0b5MksE4V03ry9NA3MV ZuTI553EZRTNvJFPIMi4afMEdU/XxzlMceCM3NjW0+iiGnCHsDMfl+GkrZdcBIQeqPe9EXhK+cM6 36bu2/UVty8GFTSW89CRROD8SMYdxDaegEWeAghvsOFgxJ8RMKttAtc5Fkk88bfXrw/KwsGLuzsY Vwk4sHOKxA64rhCmft+1hkEhSfLVmbqTh61L1iu+K+MIW82uHLKRsbwp/766TbIiU92fuH4wfR7u rXdsdOjOh42U/+txjOuV5sHajF1DFDiW2yzm3DLDhY3i9aXFxUWeTYQUOcnRFz2wCywOJurzwbge BICM44afiADM53fPOQtH28s0i9H++fn1+fvnp794DUAk+sfzd1Qurit30qwWDv/VWb/nrDKVusSS VNKtGMMORzPSOAqwcLIzR0/LIomNzRUT+muzgL4+g57YKGCojmaNRGTkOaFb2baZaN/sdet/szVN mZTrkee50OUEYDZ5ILfy87++/Xh+/ePLi/VlmmO3q0e74YHcU8xrb0VL/XjSKmMpd1nTgN/K2jfW bixe23v3O3i1yJnm3X99+fby+vnnu6cvvz99+vT06d0/FNcv3HT7yFvlv80qUHCYMU8v5BeAt66E J5kdSN+ChY+4p6oa22JJGqMM67aiq8/Ps/4mPHM82XcgGjMF5y3rKYvVLTepTW5pufy6PDXA1clX Pm1z6B+8E/Bmffz0+F3oGHvxJKpXd3BodAmtXB0nFiAO3a4bD5cPH24dzBqGaGPZMT4ztfYQG+vz g3ssrjHc17wrO2eoojLd6x9yLKiaaJ1E93vxdjOzIDaiD1IJSF0SsEnKnQFDwFsJvJbs+kq/Oc+x wcpghqZf6fO5q1a1pTYzc6Q/TgV+sJyirrVoLmxXnbxaPtxKMtg1J4a+FtCJ1vgSsMeusyhHvHXa Ri+99L3Wx/kfy+Vy6a3as3cfPz9Lpw17+gBu2oi3Ze7mOIsuJLYNUMR1x1oxpTQWIdRj2t9+OKqq H3su4reP/4sIOPY3kuS5fF13zq76Kh7L6E8P8OIx+DacfdHHX7/xpnp6xzs7H6ufnsFrkA9gUdrL //jKAT+cPOyjyHAnc1gofu3Drc5SipqxVu9m5bGpgNvyTumaQJoELj9MdIfLmVrbS5AT/w0vwgDk mHBEmkUpWZSF2qbeQp/6MChcekv7MGJBbh7E2aiLwBs4jfmW8IxMJAlw42RhGdsDdpg14x2tmm7E Mm9rOnRw8nlj0FEd9Tjw3vry+PLu+/PXj68/PmOq0cfi1B2sy9JtYcrirIkSD5D7gCLwAdpmK9TJ 2OdRBHF9F5zM1c2zhCzPq3WHebq1ktTDe/Wkt+XAbDeclk4+/mnmxfWpfi9wId3uiUV1Xi4WVL6Q zqJgNZDleytfHr9/52aMkMWZhUW6LJ4m6ausbyP3y1a3rwqUf7czrSwp9lcjfoK0Rkb4LyABXg99 m8yAB3uJL8in5oq/BCpQcEGi95ijo2ykXZ6ybLJKYmVbJvuQ95dud7ExZ8NMkTtsZM0fl/9p5bPY SkYLt/vbQTjx2O/kYN9tMVwF9emv71y1u9+z3PcJ18OOxIpub57aTGfMeU5+j+tNLhvcXmd/WUEN 7WYWy51ociRT9G3JBFOGHe8r+JAnmd2+Y1/TMCeB3sBIA8oBc9j/jYYNA6tS5VB/6M6lRd3tM5KH uSXObs+rQNrrvdOppReGr3JNHxVx5CSSmtOXaKDJmOSR09ojjZK8wCcO1WgsTQqCPxApOd63U45f 4he4PCLexIsiRq0D5CtIN01uxm9+ndWW1z82kkxkd//84/VPbuls6MXyeOQr61LebLAanls49kve qkA04zlf/ZLIlcDG3KytyS//eVYriPbx5dWQhnPOF8dZGBeG97mJoQ7iOgu5tnhqz1y1MrBjrbcr Iq9eD/b58d+mHy3PSS1eThUaln9hYK0eH2UhQ/2CxAfkXgCin+5FAIGfKAeJfElTq6lWCD0T1zly r6RR4AOIt7joreLiKPcltkxEhCPLPSJlOcGBvApiX3l5RTJ0YJg9Y7HQYGNXXHrRDis04mxn6yaq htrd1ssEv46+A0aduRlpWCS4S4/O146p9aIMysY1iAz2+Dc4HQERLmVaoE0lsXWvXLtmBtu44NSv H9BIbhSD2zwtDskC4aHn5gGnunH4+n0pObCpStmA5Z5CZBGuarS4h9yMyIswkYn1HOUEc4Nhbeth k8NXrLg1OGeraKr41ft1vRt8goikg7AAgpS4Seg1DEji0mEUpdrw0um5j24oAgPB1PvMwHZ6cA4l sCSu+yzluVTkjZx278NsEkexjhQK8voC2nynPXZQv9RJup6uN15g7ay+mV460LnherhUze1YXo5o jASVJ3ggZkEcuN9CIaEHCQnywf1doWY95OYCos/ql8ZmoOnzTLhwWnRzVblmIz4Vks0YpQnRv+qK 0JikIXZUMLNIt4hOSEniNElR+bMsLZAKiJoVSA34145JgjSSAIoAB8LEk1UWJSiQyDKcegPEmxzt kDpPkWOrB50jnSZkDLW7KEZElb53BTKKRR+VM0mMKIthTIIIad9hLOIkwb7shTISBPiktMi/L4oi wS4FDOdkTEm+jCtFPl1bfZEq/uRWtHHzQRLVvvLJjGEpPVoeX7mJizlHqbuN+yzWnXkNumG0rEgL 1wNQdwmdI8EyBSD1AYUHMC0vHSIZ5iiqcRTcTMRyHbOJeIDYD3jk4FCK3vzUOTJfrhnWUKeRoJdY wdrCB9LKQbPU/Dwuz1TfDiU8qHoeB/Ql4jU38P1CBBynnrhkyn+U9XCj/dBh4s94zy4bhe5Zil/h hTu2mz1PTljmzZYZO/AFf5AccCAPD0cMSaIsYS5wZBST7zDypdgF3kvAPQFV4iYhOWuRXJskDFCA 2yglSg4RqjxfPGMCnupTSqKtO9D1ri0rRARO76sJocOGn6moFmjMM5f6G40RobnqG0gYImMEnm8v jxVWG6nAPZGXDZ7MNogwrgLtcxLaGt5iWk9Q3QBQSNDIzDpHiLSIAGJENwggxVpKAKgcYE2E2WZD AUsapFuyChZS+ApI0/zNEootfc0ZIpJFSNXgHjof+R4gQiYOAWA9TQCJr4wC6bFSLLx3tLSPgje0 7UhTdNpf8J6FUZ5itavOh5DsWuobYu2QcZURIX2hTVFqhlOxbtZmSFtwKmoUNG2+PS1xBmyTRINR GfIML63Y0mEcDj3J8A0BjSEJI/yOisETb01CkiPBJOhpnkXpluzAEYdorc8jlZtzNfPtVyysdOSj cavBgSPDvjsH+MoXGTgAFAFiKp572lqewXNdDnlSGAqp91w+XpJcWzV3WQDbjbp74kLmhhJSCU7G tAUnR3+hZIqqzX1bcX20rTerlpI42GpqzhESbJByIIV9CUSiltE4azeQAvlCEttFBdp9GD0l6RuT gOCJ8IODhWccWZZsDQDWtlzFYuYuJWG+z33rCpblIXYjZuHg7ZVj37U+l8aZvk4390k0JAo3TcmR ZkhfH08txSaPse1JgCodgWx1D8GQo1nGWN8AOtYInJ4QpJfdjyQkCP81j7IsQqxeAHKyxyoDUEHw 012DJ/wbPNu6WLBs23acpcnyxHPtRudJz3g9+Xg4ISsCiVRmZPQFdM7OFIPQzdb1Z0maQ/jhHsKK Z3lDDavOzFS11XCsznBFSu0i88V/Uz7cWvZrYDPrcbRm2nWo5atf8AQoc/E5IPqxu+cSVf3tWrMK q5LOeIA1nbj5s1lDPYmIksd63CN8TmDm7Qr7ppDAsCvPR/HjjYJWiYwdnv4yc21+lou8KOfKqILM reeT2n66P+PFzf6nTbGuFC3kc3ctHzozXM8CyrsEwln8Vp3h+2N3LxZ2CBI+vy3/a4DkJ7xjnL2m K7wg++nbv971P55en788ffvz9d3x27+ffnz9ZhyXzrlAIE9ZCHwApE4mAx+BRiQ5Hxu8r7JVO4td 3JvQztEQNr3jSvafVo19QTlZdxj1OxOrNtEBrSxEcLWp4XYJANIIzV6e4isAyXNdWWjZ2mczG+nV lSgs8Ye6HuCsEUu9MAkO1m8VodyXsHpfkWso8yaqi8DCLppwYXm7X7aEYGPf1pSgaVuIdBISuJDv DIUL2/3y++PL06e1i9DHH5/0cKps11O3bgzCy3SM1TvjspYeuxhYVCRN82hiR9sSSQxki+nUMRGD Vq+OANihKRkWnEGgc6nwkhRtz05qTSr89EkwoWGsxQWHf/759SN4uPojqR/2jr8+0Eo65kWc4O+z CgYWZQQz92bQcBVthX6WzkQ/Dc5yDPMssHSwQERMnENTTVT3CV+hU0P1XUkARCioQD/SENTFB8nM RZy9mRmo8zjDYxXotsfmSnNCSEGDgvMlukm1oLqX50LMMWIRYETTPwCaF3RXhLq+zqh+8Ag5KTVo 3TJdENxUnGF0f34BI7tJOJUk+IaGgJuzL79jOVbgyS33iC1B+VIxQk5ndY4+TMPCTneqU27yi6ZB 0vHVK5+VWE014x9ovJS+2dt51e9ZGuKuXwDfVS1P5JFOnLbqYWFWYoIQrQBVshdOJE4yfBWqGLIs Df1tLxnQOJ4rnKf2gFjOTN3M8hhbnik4L4LMHkjC5wEhFhhnkTttMKb4HtAMOvnMk7VJ1hzqDDrM aXaZPT0kvJ/7KmqfeAqa654oyHd5gC3SBSbnYDMfVlHrTrmg1nGWLpfNjRJYm6ABigR295DzDmTo k3I3JUHgXKsx8+SrY2ylIbDZZcdIMda3so2iZIKINNxY9yR2PT8lNc9yXyvxnJv2Yifpy6Yt0aVf z1ISmCfr8miboLG01ogzZn0EPccuQq5wEaDJQttlzKoNr23kVymKI0n9KlqVgp8fLAx5ik0YC1wQ XPqChBsal7NwlRYZDhvjtYmDaKNLcYY0iF0GLd9rQ8IsQq2Vpo0S71BU3r/mkJb+vE4+HT2dy2OJ +U8J80B5Pv9EiFbMxnmmDmO7kGubEI9nwwx7PJElDGrR10Qtph45NUajxClQxrZyaJhhAEgSbHx8 6edsqU8RWQncwydnEM0YN0S8OnBJrnuXSx0k1hI2sT1oVt28Llq6jX7/12ceL4lnX0bds1CR7Bdb V+BQTxDdpWvGUr95vTLABfqLjCrBLm2F5g6bJ2LvZOVCcuJWwZEPYg9kmhYWlAYZhoHhn+t7zRq0 T6LC2GnWMGnHY744K4+1KtAQyxRfkcWiR0tVvQJfEJtcE6bpdJ51jYBk4b2sYLLoR4QGQkKCdqGy CAn6iQRCcGEO5TmJkgRbX1hMeY5mbl8yWpGaNUUUbOfMedIwIyVWIa6I0wj9kDCtZ8SLhHhVhQfh 9pcTM2HiSS51/3Z6zpNmKSYZ2NaJOUsYYJ7G23kLnjTAWmq2hPFiTXvYghJ0DAkoQzvg6sboqQef NN6qx7wGwHMQa4G3s8jNEx0bDTFbSmOiPeEmFV77PolJijZZn+dJ4SmWYyluZ+lM77PCs37SuPgy A90PsVhQzSpd61HpOZLkPkSsaxBh4OZYjK7oNJ5DPgUB3i794fIB3hR4o879PVcw6MLL4sl95QCI eh5oPNcWa7L34ok++10aHYS4nPcyLofDMJSs31XD8NDXViBmiKCAplCLNwyyF2kaNMZ5gKp+e4Go I+196GkwFrZ9ia7lTB5melZqYNLmWYovPjQutSjcLqc5ckM2QLXbbJkh1WM86yAtPVAexhOaIUDZ Ge/ufMWRED6CNsXVVn0oFkYpOlnKdVyIfio3GKmNFWjzCIxEnkkPu42IMclVFvqZ7+0AOgjPxpVF g8m6t4gPtqbc1TttS32wtygGiILRr3839WCsLgaIyQEPTg/ow3sUeb+K8uFai4eyRmPLox5up3pK Tnt8kcXhuu2w6NEKMWOg1jDxVMZbhzXcP4JoeJFBky8KftBrCTkeuwHe9rZzqI+XUr9Ey0kjvNtY GxdBeCXn54B9lZFX09EoxzM6Tlb7gFfQ6AnzxCvivE9loJ4g9FzUaddNt/09+j4Hr16naVbqbGL9 H2VPsty4juR9vkLRh4nuw4vmImqZiXeguIh44lYEqaUuDD+XqsrRLssju2K6/n4yAZICwKT85lBl OzMBYkkAiUQuCMmLmsVM93zKopD5AluRt84Bjc5YRlw58ZVk6To0K4jUA03KoxVSTpJUPst54ofF YZJMtqJrwegtZnt9eP3+9EjEWwnVoEPwhwwME3Km9gLhYdn6zfFOtDBBJNwWsmxUWMD7bKgThXcZ H+Ui7OHxhsjQd6sZGpdxzMJQFmmxPcHqiKlVjAXiDQZ4IJ73b0jM8YXp9oLf4XgZo9PIF2FduPSb NRqEodtamIxwSDpIzlc3pMZ0KshtBOJFgk6BQ8eHYATnl8fLl/N1drnOvp+fX+E3jJP1ps1qF9tt aamhzXs4Z6m9UIyhejjGE63h6rdeHe8gvZFz/1SDZErlKlNiFd9yEStgfVz224hyVxYoYAa9O02o mRAgqAr8Cp+UkzCjohMNJOk+NCorfZkTVKblfHp7fX74NSsfXs7PxuAKwtbHFsGBAZykZhtVCHjD 28+WBayZeaXX5jXcm9cLk2ck8aaI4NzA65CzXJMp9TTSem9b9qHJ2jxdUN+GNdwGGdE/2W+iBGd6 1tQbJkpZ6Le70PVq23WpOuOIHVne7qBNcIY5G99yJshOaL0Tn6yl5cxD5ix81wrNGZTEDCOW7vDH erWypxZKR5vnRYoh+Kzl+nPg0xX+ETK4bsOXs8jy6MQVN+IdbLg+JpkDUnrCdizfhoyXaK21C631 MpyIhq8MfeSH2Ke03kG1iWvPF4e7rVAKQIuT0F6Jx8QRXV7sfaQTHKaGfSFJFoul41M0mZ/XDOMV +rHlLQ+RZ1NURcqy6NimQYi/5g3MfEHSVYyj+2PSFjXqAtc+PZAFD/Ef8E7teKtl67mkBeCtAPzv 8wKjo+73R9uKLXeeq6rGG+XERYtqbeWfQgbrqcoWS3tt07UNJCtHv7wqREW+KdpqA3wWkp45yorz M97AcuCL0F6EZAduJJGb+M4HJAv3D+tokStUo8om2m4Q4SbyFzsQrVa+1cKfcG2LYsumZ1ql9/2/ ODhFDBVONThiu6Kdu4d9bFPmPgolSC5lm34CLqtsfrRIzu6IuOUu98vwoCdoIcjmbm2nkUW7i6g7 aw1cAQuL18sleX2eonXJCS/yU+sHx7kz93clRVGHIHSnwIEHnrjkblBXTXrqDqRle/h03JIbwp5x kJJAqgaWXzvrNT2tsAGUEUzVsSwtzwucpSHudke+caKqX9tULNxGVAsGjHYosz514GwzJL1WiopA iSFn5vQFCYxtDbWiZES+2AnBrNvUgyHRrtauFKrAXSCt1wvb2Cd0XHMMDDQcvS3eMg14hlkSElai MX5YHtGkZBu1m5Vn7d02PpjdQFGsrHN3Tiq/5NhVfohJ7lYLPZaHgSRzZyENiIjwj60Mx02JYmuL 1Ir3WMed60MmRYt+LrWu1wnLMUZNsHBhdGyQCgx8wRO28eUzqvSP1Fpj4CldAUG2vPuRld56Hbv0 DCycLHE5t0fjhElL84UHbEg+0vdly9B2uBbLAjFwwmFgtSP8cly4qtugiV2uVIMzDRuW05XCUHrj m4Ef7peeydQKAm8seimx1LIkLFfefKG+cE4vVX2Yojr394xO2SkaXQXltplEbzPbadwJ/bhYcyID yd09FwSVKK/FhbD91LBqx/v9Jr4+/DjP/vz59StcakIz4wpcS4MsRIfW25gATGgRTipI+b3Pd493 RK1UAP9ilqYVbDkjRFCUJyjljxBwYdhGm5TpRTjcTsm6EEHWhQi1rmEIsVVFFbFt3kZ5yMi0Z/0X C9UBIcZY8THIXlHYqkatAEd1UiqyaqlQDHvT3Xb1avB6hc2qZcKZ8cR87+MBj+xLoXSzj7ii5wII WsPLSMx6P7kdCnO+iR5mPGjio9YyvH2qf7MNMOSxnnuWZdTdvbmTXIp9j/DULzLK+AS/La9mRnNB ZHYtOvYTybcyN/3D47+en759f5/95wwk+MlUwSjdB6nPeaf5VHc3xPUhJCnT4n569Qp+jfG7OnQ8 zdjqhisP9+se0iiOMKMHzBtKKIsPqRoB4YY0bVFumFEgQg21Wi2siVKr1ZIsNQ5zoxSTtgwUSrxv Wz49YAJJPQgrJLBLe5oRzA1359HlRqQHXlcq3sPwLNOSGoZNuLBVm0ulp1VwDPKcQnV2MxRKzt7A 6h8wdF9+z8KooLcYVBT1+0pweXm7PIvE4eL0kjsKoThtsuw0TvShgeFn2mQ5/31l0fiqOPDfHU9Z 1ZWfRZsmhm2TymJzyyVxv5XKSi22BVnDSCV8K8OLJh+HZ09YOB6FhGnmyPDnLTpWXcGVoKbTPgNh 5VOaj4aosXOwGrWIv54fMWcNtmy08WNBf47KB2WeERZUavzqAdSq4dsEtNSUYQLUwKmZ6mSbKN2x XIcFCWoc9LIg3cNfJ4NQRk8xOxwUDW0DiMjMD/w0PalbgCgjXgAmynRpvM3vwBxsixyVNBPlIlS9 G+MSpRE6RGidiz7votN42rINI1MCCWysvjwISApiWKEm7kEoSIZ+GjL9e/A1mYpbI92dIrMNBz+t Sd8xWXV0EFokox2nyngaQChD7yW9Fawefe8Pf0OGh0VcfWB54udmkV2UY8TxmnwRRII0MMNIIVA9 wSQgL/aF3maU9ccroIfiH6X6ItrDxYyrOwirmmyTRqUfOoAkmok02/Xc0pgFgYckitIxD4GcyQKR VtyEpygGmcCT8GHSoeIBdDuixSDq6IlnLo8Mr/1VdJrajVrMI8lGiWw1ErjZT+KKaipJOWLhloGe ocDgtBe1oIlqPz3llPAp0Jj2KxjtjR0YxMH75SiRW0UDPxkLD+S7XGiiAm4OJipHuLwuTX21wocM nUNhp5O5ErW6Oi3fRD0iWFUqM3/pxerIp8TDDgdMB0dGZHQJPlSmzag7FflMJLYC1BX7nGl66wFI rwXxocyv6j+KU/e1/lxVoKMlUbN9oY8XbFw8ikYzjvqSLZ1+SqIxi5QMITzROkykd2hL7urfOzDW WTIowCPLM6Ndn6OqMIexh00PyedTCOdsMdr+pBN+m5DZWsShm5ZcFfeoU3+IV60LKdojOqIm1x4b yzt9dZsLQMvr5f3yeCFykmLVMrmt9jWxuZGC1wf1mmRa8leR+4buokgUhRtRTPbyhiYSruu5ddT6 h1y1akuUbhZJwDCxYA3CrNQO3Lga8TeTCAUI4kOmMwJCYQfCEAbUToDoJi1Zq1nTyary3PCZRTDc K+B483mbBKGGMT/q5zkIu0HU5tGht/AZcUL29PZ4fn5+eDlffr6Jibm8otX+m8livVc53i4YaeeA VDF8CpM0o1uXvkeJOk65jw6TGcuLysAVNTqWF2ET1CnUP0aGjItQEJggtsr9FJeVToX7vBhmEeSS b8azg2nGeAMbbx7KSBS/O/+hMfYQQV2w6OXtHW8j79fL8zOqGqj1ESyWR8vqJkMbsSPyD8Anhirq 0PqkC2iFkQ+ge21dm5UKfF3jnHIQxellPxDGnArXoH6dyJYhBvyIybqTcsRkIlKtvTh2Ldc+GcM0 Qak7fS76Pv+ioGqqMK3ixnYds1aNgKcr277z3WrlLxb4FkRME34YXb0niiJac2jvgSLmcxfNemCZ LrJC8Pzw9kbvp75qtyBspGT+TR14CA2qOsNXFRkkFQ6y/5qJbtcFyInR7Mv5Ffayt9nlZcYDzmZ/ /nyfbdKdyArKw9mPh1998qeH57fL7M/z7OV8/nL+8t8zzNak1pScn19nXy/X2Y/L9Tx7evl66Uti 79iPh29PL980ixd1+YTBijQ8QMu7cuRcJqF7YpUYJOj/P1nrvlEd1SXM8B4SyxQ1+YQRW4+jw/uI XokJD0n7OLGpHQLXXAkIE7v6ZK2CwuzYmGLrh9toarMVFCE6O1VSTyPzez0/vMMM/phtn3/2MT5m 3FRudOUdY4MGiGhVX9n24cu38/s/w58Pz79dUSnz4/LlPLue/+fn0/UszwtJ0p+gmAIM+OsscoZ9 GR0iWD+cIDJhKa0wHuiGnk133xlHhx8we3Q3Jm//Awkm393BacQ5nM9wszKPq+EDos0gWQQGUyUM pKvIN87tDtoiZ5L0/RBTqIxnE9Wx7Gjy2YAj1Eg0YR1tyUt8v7Vr8eUVIH0QLBd210vta0MZDNxh ziBJKfn83mz3lCN+Rx4UnEdutw3nS8fokjTBNYS5IRd1p/PUzxeJ/WiUh9zn+KAx0Y8+5TarAhRn zBkdMnLvXDhmP/qSVNDd/1KQuHq6WAV3SODamkQTGXQVQkzNDUdXEKWRaYRLfLEEKeA4MYaduq7N aM9ChTLKymhKYu4TkNchJoIuqPlF846iImeZlf6nieaRpt5qo4BP9SSMBBIuvCQ+XtmO60zMBSA9 MsaIyoA+3OfNa0jfpwP5TdY0E13dRSde+jnm07j/1Y6Q/Owu1Q1RVFSxQTuL4ANuyYK6bRzXIasX T4dkv7KCLydWtsTZ3pAwk6wZaFbzifLHZrJc7u8zf3TB65Bl6rjWtCjRURU1W6w8yitQIfoU+Kou X8XADojXTBLJy6BcHT0a58fRJAIGKwyjkMazqKr8A6tg+euadpXolG2K6SO9o5pQM2obxCaq/jB8 Moit6zDBkUXZKbjJoc9ylk8KVEoNQZFPsPURtTpt9uGWeWA82RT5B+cA542tunmp81zTa6Ipw+Uq tpYuXexYTTScDmWFx6euBFCTb6o3xoyRcYk6nLMwP+uHTd1Mb2h7Hhkyehpti1p/DhBg847cHyHB aRksXEOOOokwhSOJJBwprdSLKx4jUWoylHgl6+zl1AoFvM1iJvJ7yowZ09zAOPzYb+loX6J/U7dW EE/zINqzTaWHSRAdKg5+BTJpZfZ0wglFzFKCeZ3EvTVmx7pRHYSkiIU69Pigf+kEdMZGFH0Wo3Y0 +BM1FvDT8eyjeV/mLMBfXM8a3ZV63HxhUfZtYoRYvmthEjBNCrTelI/9guMT3Y8bQ5fff709PT48 z9KHX1S+cCxXJtqk5kUpwMcgYpQLEOJkZkBNUVf7yb5ApFrXAJQS8ObUa8/uyLZuZ1asqEgneqG1 SMjN+jR0sjRxCe4w3c3I5By1XEv6GKl1YKZ61LXrGrQO26kT2rzJWvnizxW6sbB9m7jz9en1+/kK nb4p3vR5i5GPdJNpAe6UT004LZ9vKxOtIHsdkTks5dF3lrQ7H6Kz/Z06EekauxfPS8M5r4dCPUIF pmMwOvXaWGkboJR6D60tcLI5hn3yeG6GzLH6lVnYboz0MCo3klOjL9MNyCVlweXjsTo5Y7VXDEdf mxoa3J41TGiEm/qovCT9oUOLjblbxW0+/nhEtKfZcHMtxW2VwwlgAhMWmiBSjSd/jUciUw8nvEEp qtHgDZhxbwfUqNMDZtR3FUMOw0DQjwbdmyii3+40ojIpDAmMpouBO4BH/gph/JeojEcymug2jbc8 sVLB9Xo9P15+vF4wTOrj5eXr07ef14f+qUSpDJ8LjeOyTkYAeiQRcW8Qtzird5d3zEcbY5e2fnpH J1fSdhgKU+cUonNxt8wnqzRMnSQw3GwpexWx+/qH22Gm59v9cPiHs/hURpouSgDaOiipMZPIJHQ5 dx01dGpXSgQKWB1NOEc/cntxy4yObax/vZ5/C2Tcq9fn87/P13+GZ+WvGf/fp/fH72MbM1lnhs59 zBUHm9dFLlBG4P9bu9ks//n9fH15eD/PMtTeEvK9bAa6Qac1PmlMHgL3a9QmFG0A+YHVwkRn+FKW Tfl4ZxhNnrr04fOlbsIhHvqE+S0Fa6VFzQ8CIwxhgiItKgO9qVD0zfFWkRxQpMy34i4sBgnjjxOD Jgr2tq6U4RDifb+2HTWrn4TmruV4a98Ec3cx9zQLWAnHvBu0WkG2PsgWLhnj7Yb2tNB1Ai4i+9G+ BTc8JU30WC1l0QBcq5naB6hlm1AzMI8AyuTtzmgIOvhUZmlBo9vvyi9jwMu58Q0E6kE5O7Bnkfbx PdY7Hvv3ffMrnqcmubgBXQK4GA1aufJ0b7wevCJ9oG4Dops7q/C7A4U0GEpMH5YuMiEaP6k3HIEb QhwbHyPN2AWKCO8nmTF0tIQ9squ1661NVhjFtJbv+4GPYWGMCuo08Nb20ezSOPtoD+7i344Z3vv3 VI/UwLZ6ObT0X5BZ1wSacdeOU9dem83rEDLri7HViFfYP5+fXv71d/sfYu+ttptZlwrhJ+aep6yF Zn+/mVH9Q/F3ECOP1+jMaMI4nKzsa3qsSOW7wGL8xBG7ykCx3QKZKjkOFCRbsc1cWyhjpf/L88Pb 99kDHDv15QoHm74Ba1xWrzwR9msYvfr69O3bmLAzCzEPjN5aBPMZVKNR6LEgsPKkoG7vGlkS+VW9 ifx6sqLBUHF60+1Jg5J2C9OIfJDs9qymHMA0us6KiK6kt+zRZ00M6NPrO77jvs3e5ajeeC8/v399 QimgE8Zmf8fBf3+4gqxmMt4wyJWfc3RGm5iEwM+0QPAasvQ1Y2YNB/eJMNqbW8JQEG328zG/9mPY hNM75dD0+qRKAxtcl9TyMjc7qbnaavpIfEbDrAcYAoK22WXwf842fk5dWKo6wJv27UsI6IWhoQoE JkFdwPqeqAMwdZEEej0dsPdq+tv1/dH6m17rSI2s4ESm936gADB76v0jlfWIhHCziGVeFv37Ao5z aXSlh7cNi0SAm4kGhNW+VxEORoXYjtHu0RNTod81HCna9RT+ZuN9jrird0JiouLzmoIfV1o01h7e x28dtSLkpiMcQSCyf5FFl3Mz5QdFtiC1Rj1BcspW3oLoJSYrWWtR6m6ILhb96GudA9udz/WR6EeD RJzbCkpE0bxTbcW9AAaZGijGU9uxJmLdajTOvXHqSBbj8TgC3BuDRc5BLfydirCoIRcYdxKjZ4fQ UCv6BjGM4NyuJ9Ji9iSbT65D3dCGlXcLbD7G9BH1zFkZAvcTCCN/a4/icC9ZW/SLSk8Tg0RBRigZ 6oeFSDUV4N7KJuEyTfjoU1EGt7l7K7TaAwHBzwhXH75v8NXKIieSe7RyaMCHsFesRuc4L9n0Nig8 4HP0RxgcBZEexa/x9klsHnCJu7cogPcc21lSvYRRWQfkgpQ4mUj27hwubHsQGwfLtw+bbDsr2rZG IfHoiLMKgUdwLe6lK6+N/Yzpfmw6wYf7sR5YmSJZOh9Xs5yv7m20SLFaEfuSKOqMGTbkztyaE/SG u7IKp3ckXu/sZe2ToZGHDWlVG2GRFYx79wQBAo/cOjKeLZz5PX7dfJqboZR7biu9gI4P2xEg01pU yXvhQDuSz6f8U1aOVu7l5Te8CdyVYbrcY+NpiWv4zdKCoPfDYCRuGbq4dIUmYvCK5eeXN7iI3v3+ tkjDmKlObCHmbzJCit5g5tOkgtn3KBkQJ/PHcSkA2Eb5VotLgbAh8H/i53mUqlHgAKtml/TTGpNL ZXwLGIXs0PpHhtSKQXHM0axLJWMixg4D2EITurokc3Ie27AMM/qAEl76CZZvs21GXSpvFFrjQpHI THs57KCKG1bclrLcMIDB89P55V3bCH1+yoO2PrYhafYOUCFD/xoPOYbrDJXp2TSx4jDSNwJrj5mW Eu4goKrkJku3WbGPuqAidFOQaGTl28H7qJv0+09HBNfy0iDoo7joHRj4oznerD46GFp5aIYoSTif L1fWSCHYwRV+yXDAA8baVDWhTWp7sXO1hEWAd6jbWmfG1kVw/HEDy3Bx0sbNMsBVIWZA8b+XCKnb bjO4gvoTZitdX+H2CsuGdrlSSagDWsEb/q1GJxrNvYkVwL64o0Q5q3QTTUCFGDFUoqhXJCxcNeqb AG4JrUzzZ0B1dYiEoIKvISreh6WilMC/0MJXXfo9rKVNy/bCCoQVdaqYxUhgxXItE6aEmg3pPLUe r5e3y9f3WfLr9Xz9bT/79vP89q75y/X5Vz4g7duwraLTRnVK7wBtxDVdL699WPx0bsx+86fZRCyO Nkgn3IcPIGjmaaE/+8id6vny+K8Zv/y8UpkVhbpOzxgsIMD1G2XXge9yjI2dqUwm3KjRHr4t2f9R 9izLjeO67u9XuGY1p2rmjOV3FrOgJTnWWLIUSXacbFTuxJ12TWzn2k6d6fP1FyD1AEnI3XfTaQMQ nyAIknjko8GUvraxtdYfiiCcxuQOt3JXKqL5qpnYandB0oPxrWG+FMDwrIhXinK52B135/1LRyI7 yfZtJ+/giAtHE+fiB6R6PVKIzuroT+nucLruPs6nF2Znl3G/YTTp9VANK9zqhqZsB1OUquLjcHlj Sk9g6yU6Cf6UEsOELTMTIrfG+9JpugWDABNbrnniaqu3jfA6BghBU1f7EBW7nV+z75fr7tCJjx33 2/7jX50L3sF/hTlonANVYODD++kNwNlJP4pUAYIZtIpgdD5tX19Oh7YPWbxyC9skf8zOu93lZQss 8HA6Bw9thfyIVN39/jvatBVg4STSl04/nXB/3Sns9HP/jpfF9SDZl/hBTg1Y5E9M9YomGXkahyG1 qCmxq2nqwzwFz/6fg6ZJP1+5bOvD5/YdhrF1nFl8rc1AA/P6sLzZv++P/7QVxGFrd++f4iiyg2NM 8PUs9bn9z9/kbuO/6v9zfTkdWzM9K2KZ1htNrekWVKJmmbgbTLgLlJJA90wugZHY9PtDEtCvgRsP cQ3CTJ9TYpJ8OXTYPCslQZpjTh7BfJpFw2GXO+OV+MqW02oNIGBm0Q5Dy6ULki/VjEVnyb0ovBn6 qARcUICAlg0/SiPIpsgGVrhTjrTQzxwavDz9cFg0XrBSmiF+MQtmkkoHl+8bvle1UMOq/84y9hu9 M1WtGbrK1SQ9SpI9NtHWdHBFTvMoaI3z1/7SjvwvXl5277vz6bC7aswtvE2oRZUsAWY6YQkeW+kW a/w0Eg67AgAxoCGZ1W89s/E0coF7lc8UD9XpPdHTM/l4gs95BAyQel0aFl0C7gwAPfUvNpl3Z/zU a19s3L8wvrmeG9Tt99gr1CgS4wFd5SXAyO0MwBF1JwTAZDDUrlYAdDcc8iGGFY7NPLlxYby1u1gA jXps2rYsX0z6DrmcQsBU6IH+DVZS7HXcwiaNDq2v+7f9dfuOT50gRk1mG3fvnFTjtnGPhtiG3yM6 X+p3EcwwASGGq4VdLtT5cnx3x98WCS+QlxSixcq5zLHNZ4BF5GSCSCL4lms/jBMflmauwgE3J9nN mAZLDZait9mUXzdn1tztDcYcn0rMhIyLBNwRyw1MAdenaf0AoEcdjtykP9CfxVTO6ZYeLsVqrFmX SK1wLZTJonZOr3NcFYHRpQaz5mtpCABPc46pXFX6AGee3GWj2CvNYWhiXFlCl4//L5GZ03U0Pl/P Rk63dfqbJMstA1ReUm2qLlfsf4vV6WKYnU/HK+har2QFoKhO/cwVpTerXib5olSMP95BvTGu5ueR O+gZ19m1qlx/oL74tjtIrwh1NUmXYh4KtC8u4+6QFScR/nNsYaaRP5p0zd/mRuG62YSVxYF4MHNC w3Fj3O1yUitzvTJLL6VX0LYtSGFv5AzCDgUpxvbN7hNWWGdJ1ic9XD9jvl4yT9Z4qrvf/Wt19wvz 23FBFT4dqV7LE1CeiLJyuLNyPNUpKkuq7+xCbaSmKeRGgTyuHOEypLpiZ+DsreJHTYYTuTrssuG1 MS+rvi8DZDDg3pkBMbzroZFP5lNpD9B+apQwuhu1JPr1khjDv1LNIBsMetpVczTq9fstWY7FZuiw KYwBMenpsnUw1h80QeRAzcMhK8+V5FEtI3Gwb4yvssAA5nj9PByqOJ663Chjp0vvE1P9ozilAXKX oxZlrWo3kZ/MJpRBlnf/+7k7vnzvZN+P12+7y/6/aEDnedkfSRhW53h1IyQvWrbX0/kPb3+5nvdf PvHGmHLuTTr1Ovlte9n9HgLZ7rUTnk4fnV+hnn91vtbtuJB20LL/v182cZJv9lBbIG/fz6fLy+lj B2NbiVWiCN87I940YLYRWc/pdlvkV5Ss+t3WpNblwr1/SuOiD2pNZq1picIHahOd3/erJCUGK9o9 UQJtt32/fiObRgU9XzupsmQ/7q/6fjLzB/jcSfWTftfRfc9KGJ8Ugi2eIGmLVHs+D/vX/fU7Nwsi 6vUdTsP15jlVmuaeC23UYoUAqNdlkx7O86xHpYL6bW6A83zV42RCFoyVHk5+97RZsTqkhAIskyua rh5228vneXfYgY7wCQNERn8aBc5I25rxt9my2SbOJuNu+w66iDYjduterovAjQa9ET3JUaixyQAG uHkkuVk73lMEsy2FWTTysk0b/NY3RdDXpO2NYVOmiTJS9MXSi7y/vCLr62lLhbfaANtyPCHCvvZo DL9hsZHrCJF42Z3hgylhd6y9usjG/R7l0OncGQ+7+m99k3Uj+GLCzRti+kTDh999akHloq/BUP89 GpLK75OeSLr0kKAg0MNul1yP1KpGFvbuujQFrY7paVnbJcxhzdv+yoTTc3TLgiTtDtmVFeap6ROw hkkZuNweCDIIBJUllxDGhUxfxsLp68fnOMlhPvlzeALN7nVb0VngOGy2GUQMtFrg5N3vO/xGAly/ WgcZO3K5m/UHDhHEEjDu2VOSw/AP6ZFSAiYGYEw/BcBgSC3TVtnQmfTIU+/aXYbl8GqQvn4y86Nw 1GVVcIWiUe/XIRwUtfl6himAYXbYfURf2Oo5cPt23F3VhQWz5BeTuzEZMPlba65YdO/u2ENNeUcV iXtyXCZAUwQDDGQLf0/k9oe9gdbPUsLJguSmznFOOZtwKhxOBn17mkuE2ZIKnUbAZW1Kx5OIxFzA n2zY17YqdkTVWDe+dtqOLE85Kz7cqPZNuem9vO+P1owR6c7gJUHl09D5vXO5bo+voGgfd7oiPU/V C2jLVar05ExXSV4RtGhjOTonYFLctoKkzTZXSN0NvrHl/nQEhUhaFm6Pb5/v8P+P02WPOjI3ID9D rimxH6cr7Ih75kJ42NMNf70Mlh97tQjHpIHuf4YHJZD/vH4LOBAenMxKQlQLOQ3VaCbbBRg6qgyF UXLndHmFV/9EnTXOuwsqCIxgmCbdUTfSjA6mUdJrsfz1wjlILjZhZwIaBZFo80S3WA3cxOk6bDhI OKw5Dr09lr+Ny+Mk7Dv61VeUDUeswEJEf8wIGRkmhu1XPhyw0z9Pet0RacZzIkDbGFmAWvJURzRz wBt97IgRM1nuNpHl1J3+2R9QY0a+f93jGnphT2VS1RiyBomY0jSV76XFWuflqcNb6yaGIUo688bj AfuIlqUzeijKNncaI8DvId0rkZwoT7h1mmaS63DYD7sbW4evR/fmmJTmDJfTO/rNtd3XE2uFm5RK 4u4OH3iS19dQM4zh5q47crgLI4WiGmoeJd2uZscqIdwlTQ7itUtUEfm7p2WE4VpWT+Ijce/DNOSG hw2CLFM6BIo8wrSJLhenHPFoBTnLiVUFAqVvrbbmESp9S3WrY7X7pQ8ycbLtAw8YNMeh9pnFjPp6 oXloKgplEVYprFFCSeLUWRQK0uynZo11hQmG/dQMn+ogerGb0/AjIED8nDVGUJhp6kZZPi3vvzXH FIlXxqL3XDYYRYB59CoXTLX+50+d7PPLRVoFNINUhp4soxbZwDK3uRHUaOpGxSJeChmWCcl4BR8+ L22IizxOU+OtlaGS9Rz4ElSwuB8UkIlwrZngIRK5LIg2k+gB29tSQhRsMJcv6S1BJhtR9CbLSAaV ItxKUTgUOsoFnpXBn3RwJBIZq6SIvGik3RIgNnb9MMZL59SjVs6Iku9DKrSV/g1BBK45flVKWGxf S99l6Imeo2kBOr+QIjGQGvSrRW2ZsvBU2BHixfH1fNproXzF0ktjM+VA/eyjyGs9QRD7nso/kP40 hVQJxOe1zBPUkktlzSp8NEIjcPVBSjwP54+d63n7IvdWU9pkOWkA/ECbwTzGS3vKMQ0Cc//l+hfW bTUCs3iVuj6X2MomahyFDwx2hmGJqZGElCG5FkajghVGEioTnbV8Bqx267Mk14J61nDL97O557OH vCoVjWXoZZI0j0zSIjDjYFkoIxqWtLqp06w23Kg+m6W+/1ylYW1/+kzQ4dmNV0lIrV1k0al/H9An 4njGwyvrH+0esrIIEjPeb7smWAZxVo4o7EPFsm8ka2++YPNTZwE1QcVfuJNZHt5ZGEQAb5nk1FXZ f4mpbLzSw1fBdo+RRj2P5kRtbGdBVwBhnpRBBBvBYkU0rw4ruiGaeoHZv4MOIwWXpl2tBeqvoLti tCiRZuxpFXFxhuleXbJf+xs0pdUDblWwYoqWxkVsOgNUBQahXyBFm6UzBrhauulTYuYGohRr2JBb vLo928uBrDCJk5EV+KJFq4/EwyrOiYG6/ImW6NIWVs42Wppomy1GdioJH0W6DNgcRQpv+OooYA5r jcBmUV6sidqqAD3jKzcnE4WZOGbZoKDGZQqmgWYwIAXdHdyVHriwNN1ng1vFMBmY6pp+38Awy1aA OWQLT89Sx5GI8FHIJLBhGD+yE0S+Cpaez3mNE5LIh/GIkzqogLt9+aYl3wV10p1rS6sE3ZDBZSFK jbzsPl9Pna+wxJgVJq24W6KmKQvveRB6oAgyvVj46ZKOqLF9qz9qGokyzrSnESqZchfC4AN+RCcr RV8VgyV8uQR5UOnWolLpVg1KRaQdgjA4Jdl11O86CsYCzcWnTzkodZivu2uThSiaMAKaTDtItThF Ej7HNZpT5CqqAS3EQs7dW3VMBj22DpPuOcu9n2hMXdP3H3S3GiZNmtgd4uKdMvSkjzcipBptrJvw y+vu6/v2uvvFIjTylpZw6XVgtzsVvEs1SNDHOF1QzuTeUkKquoakdfvLaTIZ3v3ukPgZSODGnp9g cNRBn7sC0EjG/bFeeoOhmdo1zGSo3a0YON5qxCDi/YoNoh82fkJfbw2MQyfCwHGnH4Ok39Z33SXT wP1Mt0a8X7hBxDtna0R3fc5GSCcZtg3QHb0/0jGDu7a+jwc6JshiZMBi0jrWTo+1rzdpHL0t0pnR HOaqMu42kuJ7fBv7beVxV2wUP9TbVoFHPHjcVg37TEq71dpA/RKQJ+HeNJFgEQeTItWbKmErHYaO v3DkFUuzEdIj2MeYhi01KALQAVdpbLKBxKWxyI289zbRUxqEYcDbV1RE98IPbzYDU0suuA4ELoZe 594YaorlKshbhiTgRwUOJgvDK5FQrPLZhLg3h1p8JvjZGuRotQxclfJLBxTLOI3g2PIsE4fWfsnE RyIuHh+oQqQdfpRl5O7l84yX3JZDNSbgaIrCX6CZPqwwkLtSE+mupvLzwZwjIfqXcttWeZDxPVU2 GUD4XXhzOCX5KlMw/z7rrvCYgx65mbzJzNPA1ZSCiqRFyZQZVl153MF4oyprOffKVGplTY2CBgHL oj9/QcO719N/jr993x62v72ftq8f++Nvl+3XHZSzf/0NQ1C94bj+9uXj6y9qqBe783H33vm2Pb/u 5BNMM+TqwXd3OJ2/d/bHPZrb7P+7Lc396tNagDHO8RZ5GdM4BBKBflVwTHBJYDWbAm8rdILm5Ziv vEK3t722ojUZqVGFYc7j+sxx/v5xPXVeMNPb6dz5tnv/oOaZihi6ci9kYBgO3LPhvvBYoE2aLVyZ B6wVYX8y11zJCdAmTfUzQAVjCYlWazS8tSUVhhgaKcQiSWxqANoloEJrk4JIE/fMoJRw+wMzGKNO X6fNbA+TYHzgb/JU2OQ68f3M6U2iVWi1ZrkKeaDmUF7CE/n3VqPkH25rqAZxlc99Pa5EiWnJg1Fi 6wAi6rD8+eV9//L737vvnRe5KN7O249v3621kGbCmm/PZkjfdRkYS5h6mWAmL4s4Jbgak1W69nvD oYyNpa7oP6/f0ATgBU5Drx3/KDuBVhL/2V+/dcTlcnrZS5S3vW6tXrluZPXqnoaIr+jmsNeIXjeJ wydpP2Yv8/sAQytZpWX+Q7Bmej8XIA7X1SxMpQk1Jhi82G2c2kPqzqZWTW6eMrzg3mJl350yn4Qp 92ZXImOm5gSbaAI3eWbLAv/pMRWJRbuctw8sJh/OVxHTTowkqaUrUQ8gGD+1ZSQjYQ/lnANuuEFf K8rKZmV3udo1pG6/xy1JiWgf1c1mrsXtLsHTUCz83tRqiYJnNgukbu50vWBmfXHPbh9k1C2R6HHH jxppT1QUAE/L50mXWdVp5DmsgWy1TObCsYoEYG844sBDh5OpgOAMW2rB0reLwtvcaWxvl4/JUJqr Km1BJv+w+Un4GdMKgLal+qoolqtpcHNDEqnLn61qBogf2yOPlMwgIh+OLtxzdE2BWrThHEhwQ2Yi Ec6d7qstwbe5cib/MmUt5uJZ3NjhKqnLTbXfkv+5xqcJ/5Zfc8PAmvTct/e4/DHW43vp8CbpueKU 0+EDzaE0lbkeGnmfyIxD+Bzf6suEjdlWfzuw2iyvF60W431oxdHp9vh6OnSWn4cvu3PlusM1GqMW F26SLu/t3T+d3stwPVZNElPKVbMzCidus64kgv3sBp8BhVXvXwGGO/bRYCV5srCoLBacPl8hClYG 19hWnb2m4EaJImHxrG1luKaQ54fW7/2l1GXjKV7w0iRDtSgTeR3gpjzfvO+/nLdwnjqfPq/7I7Mj hsG0lGI2vNxkKkubWzQsTq3em58rEh5Va3p1CRaba2Qs2mvpW7XxgVqLsVScWyS3OtCqtjS9I0oj 18KWPW7+aK8qf10aq2nWohYWVfB2LNbXHTC6PFDY0cEIEhNlblyfiz5NqFxXex6l1UdhfB+4xf0m ZESgQcG99JUfiOwpwtSoQIj3NZi+xdIBXfQV+irPBBeZTOCyfzsqu8GXb7uXv/fHN/okqJ44kJkx RH1W3x7xL40/UXbV/WmwFOmTenOeVWszbF2UIZzLRFrIlz9Ns0CjPf6hehqABoMx3sigV6ZxoNws 3eSpmKXS9IpKLkoS+ssW7NLPi1UehNoTZ+oZ9ltpEMlsd1NoBdPC2lDPDTAYl0jsegwwaLHARyDL NZAz0hnTLZSqy/IjlJmvCr2Afs/4yVxVlvAwcP3p08SosMG06WaSRKSPIm+JLigpYM7asKwfNsC1 Pd4lD2OYd646czQE5BhaniyodaZYenFEus9U+YyiKVgqheW7Bm3UmKoJz7E0KzKfUflXXuN5V6Pm StFecYktOoIJPQ1G+owI9kK1fkeFNttsKG8u9bQtIMy8AjbcWFORKRSvk50R/wXWSHG5D4diWOHu nIMViyhh4dOIBc8yAhdZFruByIO1D4eHlIbvhPnHFUaNDxGEQZaaZwAME5vQ51zshozTKhKpWhgR Z6FnoZAPznOpcZGWYNuwAhkKFWlncVpFP9LKQK2Gi27aIIqMk3lVs6Yw9KBlpiTzRHYfqmkkyyFZ wemTyhfvgVzT3YfxVP/FiIUlcHzOCEhgFjjujuhJInwucqHdqgTpA+oJ3MYZJYGWzgJ+zDxSbxx4 0gIPxPQTtZeE7cDzk1izzcQbeVzqBUhoDD/VpS8OuJux6524ZBi7kv5YUO2ZEvpx3h+vfyuHhMPu 8ma/2rjKDqGADT2E7Smsb5nHrRQPq8DP/xzUQ6OCtNol1BQq6TbMgp+mSzjr0neE1hbWp7T9++73 6/5QbtoXSfqi4GfSn4YvJT+CSOTypcxSqF+ak0nDGX3kE1icaOnL2lCkoPNLvV5k2hXX3EfDf7Sn gplleadcIL5McIiGRJHIqWAxMbJ5RbwMiWgo893G0vS2zJUI6ydA/0R67STX9KNY5mVPk1ja1hF5 ocH5Ch59sZAR0mBF0rn66dn4HxoitORMb/fl8+0Nn4KC4+V6/jyU8Z4rLhKoUIJ+R/0nCLB+hlKn qz+7/zgclZmZ1cbhPe9KRiL/5Rej85k1HJmUUY/4ryb6Kiy+W0iCCC1TeYNKvaSlkWy9pltNM/OF 2wigenMg9Yaj+Zsfmt1BG7ZKty3f7+rCmpmQBhWweWHMHj3plioF8VJyc1ZQ+G38uKScJWHAb1m8 1EzemtJgbc1MeDz9C5ZFZldfIlpkJEuKz5it67Iikp6pFgdUWDSyam9L6q6kHPhhJbCocJez7Jh1 qvICoZKjjiFI5AayQplLZIg7R21Govwl6GFz32UavOYy4ZXMIeNHyqdfZs6VMMDNnJONc3R6IK1D G9IZcLtdkIbmNGhXaiQLAYuBOc8rLE4GbpPLGKiCPHgGoex5pTZqvlA3HG4M41y5aqlHAiTqxKeP y28dDGHz+aEk23x7fNNtUjHBFz6NxzE7EhoebcRXvhb0PHAlo8UrEgsdzblXSRPbrpnUeJbbyLot 0zjOMTJfRAllHdzxs5XYbKWqqpijm08usgXlUiWCa1TdF4cqMU1VDeGPm2XQmq16fIBtDTZHjz4B 4Povh0/zOb45m8qMBbav10/cs6gEbIwTGLTOPtjzhe8nSqSp6wJ8GWyk86+Xj/3/VXatuW3DMPgq O0GA7QaOYydCYjn1I0l/BUFmDMOwdmizYccfH5ItypSH/ksk6mmK/EiJ0gvuFkIvfv5+DH8H+DE8 7qvVKnx7Dk/7U5VbQn7jNdueZxp8esCd+I+T8T1iqsDCRIh8SkVrIJYxTXeterBNipmo87ecz7SG Tn4+cw5Y2vX5mIWvR7uWzq04J82p1LEI+GMaoOS5tHAZCyKecT30oSi0R5unanByyTfsH2OI5grW FoaJXKU5MQ1ysjQmyJiXopiO1j/AFCNbY2gVWiNgv28VFehz1PgtkK8cmhUUI0AJk37tLW7HgKZg 18LCxO5ZGSXn1OWD6j4UWVtIQfqDkcrX2+P2CSHKHZ1u4eso/FlMq6kaTE6j6O28BMWcGN2nRWoV zPusy9AjhpdLGHmQabHHcVN5A7NnO4CR8xBEAAEqnOKVmgebLzqvIYig+ymV9KjE5CGCPABPQTnN cgEiVN5kn4wi+8tnWQ0xjcoQmFs8tQsxHXLokZx4clZF4+0Jb6rT/R3QaKDx+D/eb3H1TCzWRC4F FBnL8a3VdAkf0csIi9qiS8a99j1rOajKIfX2HLoijoAhK2AesCOoKBlQreyfaM97HrQhqpK+jEaM qhE/VlB16KkANV66yvXDi4QI5wQesp0PWZea/9YCztuFXosoYwSEcpL4I61BuMAM8+MuUcdFXpG2 hjxBZmF1Z3gElEsWKujyxMDenkxpdGmynm23U65vHAl4ZMxaxsaSUZIRa0w+L21FBswW+sZmzYG5 iv4zHJ0ecMeEsLJBPhyTAiBoLiQVkiSgGUMbif02xaHL9GkflwTQZM/+3ZBpVjO88XIuKV9/DS9v 39/vQlyGXqxueH+gtkTQlr/+Gd5u34YQhe97a7SBek2Ajqa6cR/KhCdb6xLk5RJ1cICb3uv9HxUj YqWlMjMHtm492pm+mixDezoYZ6f5q7CWsuf3J5IVeB9D2qACQymvT46vjuGDKWCJkpBhBBkdEzjs N+GFGoy3cd+tZdE1MSzmVMbSO1ZKNyg/LrT2WIXgVZJ713j0J1aKofM+5jtybeHaGQtq3MumeFzW O4qX/Qs0nF1x2fSVjk55vOzN5XPjCbni6Npc/fyUvYf8rr7Mpps3K9PVrk1XpT9G35uNuO4FEy+0 I5EqMpr1cbEG3dkdOi0WZitTAx0oD6RNzGT7mO1gNLjvIRNPFS8umUonMShwNariWMYpuJG8q8nZ chJxrMZusEldjMuRlaapAGEvjL0n+ZhcFhxm4GIcxIIqqhy0tNhsat2hfnLUxB8CChib2LTkvuKK QKmksyOUT1oyi7J5du6ftyH+ATLhFt9AgwEA --===============5840212387792661964==--