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=-10.3 required=3.0 tests=BAYES_00, HEADER_FROM_DIFFERENT_DOMAINS,MAILING_LIST_MULTI,MENTIONS_GIT_HOSTING, SPF_HELO_NONE,SPF_PASS,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 BEEE1C48BDF for ; Tue, 15 Jun 2021 21:14:05 +0000 (UTC) Received: from kanga.kvack.org (kanga.kvack.org [205.233.56.17]) by mail.kernel.org (Postfix) with ESMTP id 23C9260FEB for ; Tue, 15 Jun 2021 21:14:05 +0000 (UTC) DMARC-Filter: OpenDMARC Filter v1.3.2 mail.kernel.org 23C9260FEB Authentication-Results: mail.kernel.org; dmarc=fail (p=none dis=none) header.from=intel.com Authentication-Results: mail.kernel.org; spf=pass smtp.mailfrom=owner-linux-mm@kvack.org Received: by kanga.kvack.org (Postfix) id 6C94F6B006C; Tue, 15 Jun 2021 17:14:04 -0400 (EDT) Received: by kanga.kvack.org (Postfix, from userid 40) id 652976B006E; Tue, 15 Jun 2021 17:14:04 -0400 (EDT) X-Delivered-To: int-list-linux-mm@kvack.org Received: by kanga.kvack.org (Postfix, from userid 63042) id 47DB96B0070; Tue, 15 Jun 2021 17:14:04 -0400 (EDT) X-Delivered-To: linux-mm@kvack.org Received: from forelay.hostedemail.com (smtprelay0216.hostedemail.com [216.40.44.216]) by kanga.kvack.org (Postfix) with ESMTP id F37E86B006C for ; Tue, 15 Jun 2021 17:14:03 -0400 (EDT) Received: from smtpin34.hostedemail.com (10.5.19.251.rfc1918.com [10.5.19.251]) by forelay05.hostedemail.com (Postfix) with ESMTP id 8D7B1181AC9CB for ; Tue, 15 Jun 2021 21:14:03 +0000 (UTC) X-FDA: 78257210766.34.7010F6F Received: from mga17.intel.com (mga17.intel.com [192.55.52.151]) by imf14.hostedemail.com (Postfix) with ESMTP id 5CD0CC00CBF2 for ; Tue, 15 Jun 2021 21:13:52 +0000 (UTC) IronPort-SDR: EO7Rkc36iH7cwYubcPY9RNVPbTCs78JmGqexPU5vL5u0+UeeWMjPSSMurWP6HL+3AaY4v7nT60 Mlkb1ce14jVg== X-IronPort-AV: E=McAfee;i="6200,9189,10016"; a="186445325" X-IronPort-AV: E=Sophos;i="5.83,276,1616482800"; d="gz'50?scan'50,208,50";a="186445325" Received: from orsmga003.jf.intel.com ([10.7.209.27]) by fmsmga107.fm.intel.com with ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 15 Jun 2021 14:13:56 -0700 IronPort-SDR: T2tixIUwcUFcAxdEkEnb4ydTP/dTSrnGSdjS9ApH5XQAsO3WFjo67lV2i6aHR2FoihXN5GK+OS Qwc/9Guvj+8g== X-ExtLoop1: 1 X-IronPort-AV: E=Sophos;i="5.83,276,1616482800"; d="gz'50?scan'50,208,50";a="404344940" Received: from lkp-server01.sh.intel.com (HELO 4aae0cb4f5b5) ([10.239.97.150]) by orsmga003.jf.intel.com with ESMTP; 15 Jun 2021 14:13:54 -0700 Received: from kbuild by 4aae0cb4f5b5 with local (Exim 4.92) (envelope-from ) id 1ltGNe-0000eW-1o; Tue, 15 Jun 2021 21:13:54 +0000 Date: Wed, 16 Jun 2021 05:13:11 +0800 From: kernel test robot To: Pavel Begunkov Cc: kbuild-all@lists.01.org, Linux Memory Management List , Jens Axboe Subject: [linux-next:master 9208/10007] fs/io_uring.c:7082:42: sparse: sparse: incompatible types in comparison expression (different type sizes): Message-ID: <202106160504.hghSUEJt-lkp@intel.com> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="bg08WKrSYDhXBjb5" Content-Disposition: inline User-Agent: Mutt/1.10.1 (2018-07-13) Authentication-Results: imf14.hostedemail.com; dkim=none; dmarc=fail reason="No valid SPF, No valid DKIM" header.from=intel.com (policy=none); spf=none (imf14.hostedemail.com: domain of lkp@intel.com has no SPF policy when checking 192.55.52.151) smtp.mailfrom=lkp@intel.com X-Stat-Signature: h1cxkoibimd1yg9c68ac3teq9ti77cmx X-Rspamd-Queue-Id: 5CD0CC00CBF2 X-Rspamd-Server: rspam06 X-HE-Tag: 1623791632-162087 X-Bogosity: Ham, tests=bogofilter, spamicity=0.000000, version=1.2.4 Sender: owner-linux-mm@kvack.org Precedence: bulk X-Loop: owner-majordomo@kvack.org List-ID: --bg08WKrSYDhXBjb5 Content-Type: text/plain; charset=us-ascii Content-Disposition: inline tree: https://git.kernel.org/pub/scm/linux/kernel/git/next/linux-next.git master head: 19ae1f2bd9c091059f80646604ccef8a1e614f57 commit: 9123c8ffce1610323ec9c0874fa0262353f41fc3 [9208/10007] io_uring: add helpers for 2 level table alloc config: mips-randconfig-s031-20210615 (attached as .config) compiler: mips-linux-gcc (GCC) 9.3.0 reproduce: wget https://raw.githubusercontent.com/intel/lkp-tests/master/sbin/make.cross -O ~/bin/make.cross chmod +x ~/bin/make.cross # apt-get install sparse # sparse version: v0.6.3-341-g8af24329-dirty # https://git.kernel.org/pub/scm/linux/kernel/git/next/linux-next.git/commit/?id=9123c8ffce1610323ec9c0874fa0262353f41fc3 git remote add linux-next https://git.kernel.org/pub/scm/linux/kernel/git/next/linux-next.git git fetch --no-tags linux-next master git checkout 9123c8ffce1610323ec9c0874fa0262353f41fc3 # save the attached .config to linux build tree COMPILER_INSTALL_PATH=$HOME/0day COMPILER=gcc-9.3.0 make.cross C=1 CF='-fdiagnostic-prefix -D__CHECK_ENDIAN__' W=1 ARCH=mips If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot sparse warnings: (new ones prefixed by >>) command-line: note: in included file: builtin:1:9: sparse: sparse: preprocessor token __ATOMIC_ACQUIRE redefined builtin:0:0: sparse: this was the original definition builtin:1:9: sparse: sparse: preprocessor token __ATOMIC_SEQ_CST redefined builtin:0:0: sparse: this was the original definition builtin:1:9: sparse: sparse: preprocessor token __ATOMIC_ACQ_REL redefined builtin:0:0: sparse: this was the original definition builtin:1:9: sparse: sparse: preprocessor token __ATOMIC_RELEASE redefined builtin:0:0: sparse: this was the original definition fs/io_uring.c:2921:24: sparse: sparse: incorrect type in return expression (different address spaces) @@ expected void [noderef] __user * @@ got struct io_buffer *[assigned] kbuf @@ fs/io_uring.c:2921:24: sparse: expected void [noderef] __user * fs/io_uring.c:2921:24: sparse: got struct io_buffer *[assigned] kbuf fs/io_uring.c:4228:14: sparse: sparse: incorrect type in assignment (different address spaces) @@ expected struct file *file @@ got struct file [noderef] __rcu * @@ fs/io_uring.c:4228:14: sparse: expected struct file *file fs/io_uring.c:4228:14: sparse: got struct file [noderef] __rcu * fs/io_uring.c:4833:72: sparse: sparse: incorrect type in argument 4 (different base types) @@ expected int mask @@ got restricted __poll_t [usertype] mask @@ fs/io_uring.c:4833:72: sparse: expected int mask fs/io_uring.c:4833:72: sparse: got restricted __poll_t [usertype] mask fs/io_uring.c:4837:21: sparse: sparse: incorrect type in assignment (different base types) @@ expected unsigned int [usertype] result @@ got restricted __poll_t [usertype] mask @@ fs/io_uring.c:4837:21: sparse: expected unsigned int [usertype] result fs/io_uring.c:4837:21: sparse: got restricted __poll_t [usertype] mask fs/io_uring.c:4862:29: sparse: sparse: incorrect type in assignment (different base types) @@ expected unsigned int [usertype] result @@ got restricted __poll_t @@ fs/io_uring.c:4862:29: sparse: expected unsigned int [usertype] result fs/io_uring.c:4862:29: sparse: got restricted __poll_t fs/io_uring.c:4946:49: sparse: sparse: incorrect type in argument 2 (different base types) @@ expected restricted __poll_t [usertype] mask @@ got unsigned int [usertype] result @@ fs/io_uring.c:4946:49: sparse: expected restricted __poll_t [usertype] mask fs/io_uring.c:4946:49: sparse: got unsigned int [usertype] result fs/io_uring.c:5098:41: sparse: sparse: incorrect type in argument 4 (different base types) @@ expected int mask @@ got restricted __poll_t [usertype] @@ fs/io_uring.c:5098:41: sparse: expected int mask fs/io_uring.c:5098:41: sparse: got restricted __poll_t [usertype] fs/io_uring.c:5186:22: sparse: sparse: invalid assignment: |= fs/io_uring.c:5186:22: sparse: left side has type restricted __poll_t fs/io_uring.c:5186:22: sparse: right side has type int fs/io_uring.c:5188:22: sparse: sparse: invalid assignment: |= fs/io_uring.c:5188:22: sparse: left side has type restricted __poll_t fs/io_uring.c:5188:22: sparse: right side has type int fs/io_uring.c:5193:22: sparse: sparse: invalid assignment: &= fs/io_uring.c:5193:22: sparse: left side has type restricted __poll_t fs/io_uring.c:5193:22: sparse: right side has type int fs/io_uring.c:5195:14: sparse: sparse: invalid assignment: |= fs/io_uring.c:5195:14: sparse: left side has type restricted __poll_t fs/io_uring.c:5195:14: sparse: right side has type int fs/io_uring.c:5207:67: sparse: sparse: incorrect type in argument 4 (different base types) @@ expected int mask @@ got restricted __poll_t [assigned] [usertype] mask @@ fs/io_uring.c:5207:67: sparse: expected int mask fs/io_uring.c:5207:67: sparse: got restricted __poll_t [assigned] [usertype] mask fs/io_uring.c:5208:52: sparse: sparse: incorrect type in argument 5 (different base types) @@ expected int events @@ got restricted __poll_t [usertype] events @@ fs/io_uring.c:5208:52: sparse: expected int events fs/io_uring.c:5208:52: sparse: got restricted __poll_t [usertype] events fs/io_uring.c:5334:24: sparse: sparse: invalid assignment: |= fs/io_uring.c:5334:24: sparse: left side has type unsigned int fs/io_uring.c:5334:24: sparse: right side has type restricted __poll_t fs/io_uring.c:5335:65: sparse: sparse: restricted __poll_t degrades to integer fs/io_uring.c:5335:29: sparse: sparse: restricted __poll_t degrades to integer fs/io_uring.c:5335:38: sparse: sparse: incorrect type in return expression (different base types) @@ expected restricted __poll_t @@ got unsigned int @@ fs/io_uring.c:5335:38: sparse: expected restricted __poll_t fs/io_uring.c:5335:38: sparse: got unsigned int fs/io_uring.c:5472:35: sparse: sparse: invalid assignment: &= fs/io_uring.c:5472:35: sparse: left side has type restricted __poll_t fs/io_uring.c:5472:35: sparse: right side has type int fs/io_uring.c:5473:54: sparse: sparse: restricted __poll_t degrades to integer fs/io_uring.c:5473:35: sparse: sparse: invalid assignment: |= fs/io_uring.c:5473:35: sparse: left side has type restricted __poll_t fs/io_uring.c:5473:35: sparse: right side has type unsigned int >> fs/io_uring.c:7082:42: sparse: sparse: incompatible types in comparison expression (different type sizes): >> fs/io_uring.c:7082:42: sparse: unsigned int * >> fs/io_uring.c:7082:42: sparse: unsigned long * fs/io_uring.c:7287:9: sparse: sparse: context imbalance in 'io_sq_thread_unpark' - wrong count at exit fs/io_uring.c:7298:9: sparse: sparse: context imbalance in 'io_sq_thread_park' - wrong count at exit vim +7082 fs/io_uring.c 7070 7071 static void **io_alloc_page_table(size_t size) 7072 { 7073 unsigned i, nr_tables = DIV_ROUND_UP(size, PAGE_SIZE); 7074 size_t init_size = size; 7075 void **table; 7076 7077 table = kcalloc(nr_tables, sizeof(*table), GFP_KERNEL); 7078 if (!table) 7079 return NULL; 7080 7081 for (i = 0; i < nr_tables; i++) { > 7082 unsigned int this_size = min(size, PAGE_SIZE); 7083 7084 table[i] = kzalloc(this_size, GFP_KERNEL); 7085 if (!table[i]) { 7086 io_free_page_table(table, init_size); 7087 return NULL; 7088 } 7089 size -= this_size; 7090 } 7091 return table; 7092 } 7093 --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all@lists.01.org --bg08WKrSYDhXBjb5 Content-Type: application/gzip Content-Disposition: attachment; filename=".config.gz" Content-Transfer-Encoding: base64 H4sICIAMyWAAAy5jb25maWcAjDxZj9s40u/zK4wM8GEWmExs95EEH/qBoiibY1FUSMp9vAie jpNpTF/oY3fz77eKukiKcudhDlcVi1exbvWvv/w6I68vD3e7l5vr3e3tj9n3/f3+afey/zr7 dnO7//9ZKmeFNDOWcvMHEOc396///XB38/g8O/ljcfTH/P3T9elss3+639/O6MP9t5vvrzD8 5uH+l19/obLI+KqmtN4ypbksasMuzNk7HP7+Fjm9/359PfttRem/Zp//AG7vnDFc14A4+9GB VgOfs8/zo/m8p81JsepRPZhoy6KoBhYA6siWR8cDhzxF0iRLB1IAxUkdxNxZ7Rp4Ey3qlTRy 4OIgeJHzgjkoWWijKmqk0gOUqy/1uVSbAZJUPE8NF6w2JMlZraUygIXj/XW2spd1O3vev7w+ DgfOC25qVmxromDBXHBzdrQE8n5mUXLgZJg2s5vn2f3DC3LodygpybstvnsXA9ekcndpl1hr khuHPmUZqXJjFxMBr6U2BRHs7N1v9w/3+3/1BPqclANrfam3vEQp6Jd/Tgxd118qVrHI6qmS WteCCakua2IMoeuBW6VZzpPu9OCsZ8+vfz3/eH7Z3w2nt2IFU5zaqyiVTJw7c1F6Lc/jGJZl jBq+ZTXJsloQvYnT0TUv/ZtPpSC88GGaixhRveZMEUXXl3HmvOQDYk2KFC68HQlon2MmFWVp bdaKkZQXK/e0XZ4pS6pVpt1T/3W2v/86e/gWHGa4Iiu/W7hMkKF8vGAKwrVhW1YYHUEKqeuq TIlh3c2Zm7v903Ps8gynm1oWDG7HDKwKWa+vUO6FLNzNAbCEOWTKaUSUmlEcTi7g5LHgq3Wt mLZbVPGzGS23fzhl5l4SSAwFUP0n7184/IxtE6mG0+wX0w6O7AUxVVEqvu1foMwy72UoIVOQ ESBhynnbMLBULJcktTO1W/LX1REDIROlgVOymq5fVwffyrwqDFGX/jH5VJHVd+OphOHd0dCy +mB2z//MXuB4ZztY1/PL7uV5tru+fni9f7m5/x6IBQyoCbU8GiF3noAV0BjS3oqma3gfZLvy 306iU9QQlIHGgbFmGlNvj7zz0DwqKD+xpV7PwWa4ljkx3Mq0PRJFq5mOPAo4vhpww/rgR80u QPadNWuPwo4JQKDJtB3aPs0IagSqUhaDG0VoZE3agEQPD9XBFAxuQLMVTXKujY/LSCEra+NG wDpnJDsLENqM37GdQ9IEDzYigcGqa9SUtUjcJ+Ef/sCZb5r/iXDlmzXwYSqi9RqRs4qxu119 /ff+6+vt/mn2bb97eX3aP1twO30E28vKSsmqdCYpyYo1j8l96mA06Sr42ZnjQcXkm5ZfzPha RLP2gVFGuKp9zOCLZOB3gXU656lZRzgqU0d5tjOVPNUjoEoFGQEzkOcru9th7hK0ntHT+0jZ llNPkbUIGImvenokvKwsMg615uQYwTUdLduaXJcT+ky6BDGMrnvN6KaUvDBoksCzdCxXq8PA cbOcPRcLLiFloGQp2Nh0GlNvl87DYzm5DCUDTsw6fCqNavhESlTi4WsYjonWsgRVzK8YOiX2 GKUSpKBRVy+g1vA/geIGtzTFl0qtaSOG1Az94aLTmoOz/5NkUpXgTIEPqgrvnKjJQaFSVhob 5qCeGPChphVgfjlaXPf49IoZ9BU7ux49oOZGDlFkja8XN7BS84uoo9LbWRCdTcx98IWQ5Rmc lYrPkhAN91FNra+CGDCKYaX0x3R75quC5G5sZnfgAqzr6AL0GrSXu2LCZUz/yrpSgcNL0i2H DbRnHHtlwDohSnFXd26Q9lLoMaT2PN4eak8J3xAGCu78KBfWQcvSyNwbKjx1rDT7EiGDFbI0 dd+yfQ74nurQz7ZAmLXeClicdHRQSRfz4876tDF+uX/69vB0t7u/3s/Yv/f34J0QMEAU/RNw cgenw5+rX7BVaKM5o97QT87YTbgVzXSdYfOmxZiXmDpRm/i7ykkygaiSmEzmMvEeL4wHuVBg Vlv3OjZoXWUZhGHW+tptE1DRnhoxTDT6B3xwnnHaKSDnhcqM5yCyMR8FtY7V/tr1S/wkQS/E 3DoE9mrF7vrvm/s9UNzur9vcTT8jEvZeyYapgsUftqUjOVgZEXfvifoYh5v18mQK8/FzXMe4 q4pTUHH88SKuaQB3ejSBs4ypTIh/hQOe0DXcMQVPFe9mmuZPcnU1jYUbZAW6eTIWeeYEopAv 7r3bQbmUxUrL4mg5zbijWbLsbaLT42maEtxs+K+vN/1jgrdryCEO9NBKt+p4MXUJiC9AkFmR yolFKgLPIP6a7XDw0HPDNuBBTpjSFa95uYwvsEXGBbZFfjqAPJofQk7MyZNLA265WvMiblk7 CqLExCMceMjDPN4kAL9fiUMEOTcmZ7pSB7mAPpY6LiMtScJXk0wKXk8swl6xuTj6fEiCzMXx JJ5vlDQcxCM5mbgPSra8ErWkhoFLp0Mj1fnjuagvcgWuLZlwehuKckzR7VLS2lyWrL449x1q RBBBrnD2mP+L+NiYDLTwhGZqFEudNimaCE/AMKIvT+YfF0vXiIxNRBiwrs8ZX60dL7fP38FD TRTED6A2m5DBi0ik4AaiMwhramu7vOQTxguKOHlWyrYAOXYyqlQr6kMaFY6RcyTliCnTWldl KZXBtCImeB1nCEJHPBoq10yxwnjG2abyGVH5Zesou9giWATmZxL0xIqUEz9WGKaPEkDAjY6p DfKCTeULOE04NXAleGbOTvpEmGe6nRXgqKNlrRZB3NsjljG5arcaMjiA9mTFX894Of4hIcxl bwh4UabmmoAK354togd3tExAahpvxGf3Bska3iBoNtZUEXpnyfVwX3487odTtGyCzAkGmvXx xvMAB8TidBP3JQeS0+NNzKu0eXhQXRc1vHkmVQpvYbFwd4fnDtF4xoxb1kBM99rSSpS1yZNA crKyOxl/GMgs4KoxsJGzMSNhai3KETB81VqYCZl/C2+D42gpops/K0kWy6N4bLagUDFp7QfZ lsWAiiUVyibXqDFFqAVRxo6RCsZSJVu3OZTfpU0ib/kkigUovEhUGsFBEs3T9nHPxwiQRH32 KSrtWIZoQt7omQKLxWmgBDOIPWEIKCCsKzpP5DweJ3rXDj/qIInkXLFLWSibaBtysHY/dgYN rxHrTtQN2VyqZiz+R5DyzClhrq/q5XHk9gB+/Mkrz1zVi3ncGUPUhBOH7E8mRy1PTicZzg9M No8u2Ts5olBp2cx0P/Ds+NPAZ8MuWNwRoYrotX3+0w67BF2dlafH3YSR5WBQLp3CENZ2DS/A MQg1AbwPUpZgvuAKG6w/GSaJXILpZYGF/0lKKlKsooO/IMXPUQJRzS4wkPhJruhzTGoHj7w7 moa9awFdS9LHdOCPpCyihjHM2jSVhRGuXDVF/xweY45vqCkKvj7PHh7Ruj7Pfisp/31WUkE5 +X3GwGz+PrP/MvRfTjKG8rayB04EWxHquGFCVIFmEPDaalU0Cgf2XAxKJ4YnF2eLkzhBl3Z5 g49H1rAbKo0/u1knDZK2ScPetJcP/9k/ze5297vv+7v9/UvH0TkhR3eWos+7DxCSbjEPnY5T 8ilgrTuRylhaBrA037gDzr+Aq3vOFHYKcMoxZzWdNUIFWApXuiY303uDDYXoKQDR4/jX273v JPoV7g5Sr+QW4oU0dR1yDymY7a3x/MoeCUGTu5kmzwSvp1/DLH26+XeTMRyijDiB61g2y3ch o81ajtnN091/dk/+NJ2fxZWwTiCYPxDByKGvpFzBRjpCJ6hpEJj8tsGJ8W1ni8bSmSy0BNTd MG3HFGOdpMoyEICOz/QSBlY9MfD0abZlenbXdkbsvz/tZt+6zX+1m3ePeIKgQ4+Orc+ucmUq iOSugppIE5fAEyZFjQmZeptqeRb0J+2eIHp8gYDg9Wn//uv+ESaLPsJNGI79id5sThLmtTnY 3BsmI9F0gpmZaGSyOgbtWaeoE7+3aKOYCSe0aXIOB42qCZAmQI1W2ECnOBWCBxC7KKvt11Ju AiRGn/Db8FUlq1hRGE7DvrDGPw7UNvqUEEIYnl3WWlbKDR97ApxCQ3xVFdbDD3k0UZPMsjrc OfawCZm2HWDhRhVbgfcCqtxaM+x5sK0PZbh9rFfEihI4PgbHakjLE12b2GF6MuLu5ZyAYuUl hcBLYamhbWmLsNCMogk6gAJVkJugYNdgYl6o7RnBRaMEMepn+UO4K9YOBn4qGc3z50Z2HTbu hCg44IpY4dqMe1hALGCU51paWX+z+QUuvT2KklEsSjiPX6ZVzrR9ZOjuKT/UbtmzCxSpoulm wz1GxNKOBvmRYlzEHfscAYGdIPok/FGfxuLRNUMZWabyvGgG5OQS+0kGq5dLdPdg5WAM0lhd qXk2eI5T4mCnbDsvVb12edhMlVO6itUcG5FqBLnNaUFYFBP4qSK1e9RoSdql96qayu37v3bP +6+zfxrv9fHp4dvNrddGhUSjlErP2GKbAhXzq54RzFCeOjCxt3vs/y3zasVd5eMDB1Pbg2t6 Se1Wc5SSy5ihHWghIsZbgH+ULC8nGKKYNp270arlT9q8bnZlaoElcdd02NqwxsrokP5qnhr6 tbVtiTCjVxgC2nRS37nnI6sCEbEQJ6bjJ5V/tyxF+9Zg9+KHVUeW0O4l2tvhkAQ9jg5Gr8ni 8FigWC6Pp4dPhfI+1dGniTqZR3WyiCVTHRp4I+uzd89/72BJ7wI8SqhCkxm2Tob41RWfCPAD wouJ6mNAdqXNRGTcEGKd+hx7kzRYhqH7qObCZiziO7a+EJhdA/v98PzXzf2Hu4ev8Kz/2vf7 NooLEEKwHGm98TsnXGh9vuaG2Za/d8GZ6aZHMQc3ys1EJW3LXP9zU2uqOdimLxVzOwe7fqVE r6LAplM8gGOBYqVAkRxA1WYxd6+wI8CkbuzF2Za6NqdgNbwKR58n8WJ0wxkbKsKubHf3WGQp SbxgiATNlwmg9ai6LMOidhM+755eblBvzcyPx70bNRNwN23LUxceu0snYGeKgSbmovOLAT8c qdSZBx44Cr4icY5O1wBR/OCsgtA4e6FTqd9gn6fiDQqs8h6mAI9DuTuPs6kOn92GgBmI74Nl b60Av6s4/fQGkSOUMaouHREIhyvW4osNHLn0n4vNZzTfYMihcdWL0WEkl02tAFvzch4tVzpU m8vEzVR04CT74nob/nyDGGPTjWvuioWrj9oXoksw/2g4Ry4sOsD2i5TUEiGFo9CmMeFgdR4f OoL3ThfYi1qC7c1JWaKGJmlq9XpQPhzaX+0hs//ur19fdn/d7u1nXjPbYPXiXUDCi0wY9Ban QuuBwkbkntlqcZoqHu3ob/G257VPZ6Bb2kZ5/YVNrbRJKO3vHp5+OLmfcUqhrXI4JwEAOLXU plZsXcEPJmx7+Mq1KHazG8ZK2/nn374uc3D8S2N9eFvnOB52A/41HTVwYRFQMTSe8S4u0HAq yLHgE4IIpU4qz8ndaBEZ30U0NpIRvLAScXY8/3w6hDOMNNG/Kx8QPYa93lTEOzeukDAy9VUp Ze5mvK6SKu5cXB1lMo/ZwivdNiG6XFqYlbIoO5sTsSfbBb/xAixTtmgGbOLmEq596qu4/sGV 2NaCkTDxwphpUXQ6QZgZmdZ0/++bazdL6cpiSbknO5TH90Vp0FoyZN9urlveM9k/jcEONVHn muVltNwBXosRZeb2R7QQ8MGaT3Hc7EWRkjzeVgK+pp2pz7zaTwY7bdQnHW8fdl9turKTyvOh hhuC7H2Dfq+cYhW7gPsfsraDxziMsgmlZsMxpg4a+2jypElXDKnjnrLzoKM2MdxRN1Gbc9i6 qq4TMOtox3FTUPT8+g+2hnpEA2dbFf1IoEGjmLdjQR0J6TchWyyxXS0tjXXpY9Fz1+WDH1dV RgZfTCq28lRs87vmSzqC6ZwL1HB3IdxNIvYwwUeE54sRnRCu69FN7n7DZfOhTR26ycl7JwnI DPziRq+w6FVPvLK+UPfVPnDnZSeKCm2SesV1ggVfN1q+MMy4qk9zTFxgCQlOJmYs1ry1Cj7A KVI5ZbRuKf3dFdo5bmFSd2r4acVAj/TK4PA97p6eQ7/NYLbwo/UZoysGvOtX6nBOmY3HegRw Vbb0EqEaeaTdAu0Kq2esEz2g79e0kJun3f3zrf1afZbvfnj6F2cCVxVekX9EbXw5BtVKDtDM 5MOPYvQL/DlXzDjCImelstTnpHWWeqZRizqb6G+1ZynLqTuwtt7bRe/lw1MQ4AQN3qIi4oOS 4kN2u3v+e3b9981jWzcKzotmPLzNP1nK6JTuQALQH+1X1ncBKwwp7Qc+fsKpRRYy9FY6TALW pe1gnciUtIT5zxKumBTMqFjyEElQpySkgDAdP2KrF/5iA+zyIPZ4vFG+iMACLtJETwIzEjnY xKlniGcsUm3ffQAHa07G0Mrw3IeCaAQAGQBIgk3AVi66DyWnxan9HOHx8eb+ewdEl7+h2l2D 9gplrknt4kFi/KPDcyjXlzpe40Vs22gfCG0JIQIsMqpb3lpc86Xm/vbb++uH+5fdzf3+6wx4 tro3/m4w+ISwRK/DhfSI+lxxw5qvQuJfV/jkIBCTVIKuy+XRJkh9uqpBm+VJHq5F58GReIc8 kgP4J4TBb4hiDMmbL/XckKTFMmUz34hdLD/581sluxRm7OemN8//vJf37ylew5Q/bc9H0tXR sKTEVmQK8HTE2eJ4DDUQyw2f1755pXYtBTjB/qQIsc7byNAVDHETR4o9UYjuCvtq958PYM12 t7f7WzvL7FvzfGBJTw+3o/RJN3MKk+S8TqeUgCVCl6SdbIRqvqMpOvkM8W0DeggWF5yGG7aI VTn1RUlHga8PSxyHqSg44fHPQnsSAtJEiugqrGas85UYCZO4eb6OXCD+C/8SR4xZyvVGFvjX PKYeVMm9u2SUglh9t/30r4+PD08vkRmZ+7dvXCjYK4gOwbN1K7UTBOAgHOCS0LWXaoksq8NZ ubaLz8s0VbP/a/67xGas2V0T+0Z1myXz5eYL+DuyN/r9FG8zHp2pVCMt1YBtTu0Yc9/27+sc uhestJ+XeIltK0+Un0OCpeyt/cMLE5/ZhuMwdTSxgirh/tkAoD7PbTFbryWEyIGWtAQJS9o/ 67Oc+9MiFhNY4oBHgzSrvGIJn15UWEVB8PoSgmIvQEuNI1xu06jEPzbCjfHqLQDE3BsWbTxg 80VFFLWRyZ/ujQAovSyI4LGP5gDZJj89Hl60J7FHAUL8Lfq4blawQcjcD4MB2uRWJ/5gCVFh F21Y9Bnpl2Ir2Ez3r37IDQG8zia+Mdrav/6E30VEfRKPZ6/FxpEnOGJaYgco10f5dr509D1J T5YnF3VaSi/+dMAYs8fyYg4FRuqDcFRCXNrTd/hxqj8fLfXxPFa3BX2eS10p/GxM4V9ecPPd Zao/f5ovm57OgZ/Ol5/n86MItwa1nDu52nb7BjAnJ3MvjmpRyXrx8eM8wq0jsOv4PL9wF7EW 9PToJP4RWaoXp59idWEUd9giqOLyqP2TCE6ch65Tv+4L/OD3otZpxrw/hEWXofQ11oWBahVj y9LAa2L8gvgAPondSYNte4bvRsMEuTj99DH++W5L8vl/lH3bc9s40u+/otqH8+1W7ezwTuph HyiSkjgmRZqgJDovKo+jTFxjW/lsZzZ7/vqDBkASl4acUzWZRP1r4n5pAH3xswETNSd4GIII SZoeiE7JctsWZLB/XRSu4wTyNqJVX2hF/rh/W5Qvb++v35+ZBfnb1/tXKsa9ww0A8C2eYNv7 TKfN4zf4p6oy+f/9NTbjxPQwxhzDLJMLdM5SOIy10rmryLbSpRZ4IlFky/bQprsSN6lX1gXl qrnMpaWX/+CXPU/n+zew+6My9eWB1Z9dmPz6+PkMf/71+vbODkFfz0/ffn18+XJZXF4WNAG+ a0urD6XB22yrXGlPj+cUJKnFmhjAje3lXHxNE1ZaAb4Bpz2nssnUGxL+7kgLCMc2ShiHyq+/ f//jy+MPvcijr6NnPdNRTh2lOtAyGM8ExuRjKgh1I+mndWmZg4O6TtpQgUtar+Ab7stmzkCk zGwLFn+nQ+7Pfy7e77+d/7nI8l/owJeU/seCEtlvzrbjNEmLeKJtEFq2VYs3r9LKWAaEnZpS XH+NMVTNZqPZdDE6AeVhdtts9BOrcz/OM+WIwz+lchZrRFueBNwvslb+r0GvyhX9CwH4Y7CW E6WD00JQwsWf1BlX15rlmQ+RWm201jkyMw91fwOEXXcy1y+2Wu7XZJvlWk9xojxONfSU5Tsi 42pdgCM/ZmCfhZ3JdFZFj4aPXXYJrSfLFQLsTZhv7W2nTa9pnZQf72EZYT0lSQ/co8aqAT3V rlPVbgE01NDmIgHcql3OR+F88F785/H9K0VffiHr9eKFro9/nReP4J3ky/3DWRmzkFq6zcpr LcrwrDikiiQGxNumK2/RYkK6BLWKrKXZP64ItXrTn4PmPhXD0a/ZQuVIpxROcU2KyRSEkZYR s+4ExTD8eJKf2OM1Lm6v2Jv1lW0gr0dVf7PKuTT+c8O+h325Lhu5tCOXuN6v0126KTqmrYu/ 3EMiJRxsS9LslNxa0I0kTLeUKZrI2B7cwJZtkStUpoqlUMgubZlXS7WI/bZkF+iHErQvbS/f kKLeeDLIrhdtzUvxYkWUwlCpRCtHpr/8ymBdwqTDk4ZxoqT9qZBfUuDrcczg1NNtpZVlhgh+ OFN4tj/DVDa4aMIGiXY+lKA9UTuRP7irT4xw9L0pLCnAjW9/p6TBSeIu+NTRvQEUS3Ud25lx XWCiJYyoY9lnaqPSHZqPBbW7Z63HiSrM9MXF5kjMKK/2QAY00PsuG5XWMmFnIlVN08Jju0hY 7lF6vDXlt5dv39+t0la5a/dSw7Of9AyTE50G/nmLuiqIgXBd+5tafiXjSJ2C8p5ApofFJ/DW Oa35b1pZQF2CFHQdUkRUBaENku6xk47GRrKuKHan4d9gT3yd5+7fcZSoLL81d7wUCrU4oES+ e0vtbbtc5x/QQcx8nMzL6kg5pXkbhp5y5FaxJEFqrrEs5xLOSH+zyhH6be86oWMBYhzw3Agv Yc4M2/KyixLsjDzxVTd4YeDO20I+gdO7Avuoz9IocCMcSQI3QRA+NBGgqhPf8y2AjwH0YB/7 4RJtjzrDBNEZbjvXc5E0d8Wxl3fGCWhaeqqgKwlBMJLWZL/bYEjfHFN4fkCg/Q7vCtLX6jlx QspbEnnY/JtLSad8gH7b196pb/bZFl/HZ75jFTg+NvoGMY7NtLO0dTU/WTrLKqvxfupB1Ra9 LJUWDNXaAC7OW4JbMgBG6KaSyo/AjJrdpW2qE+m+uGOaPs96BiNiufbQmEitqrgw9ECGYUiN PNWpJop8R0WnvsyIKIy+IBLwDi23wkg7UZGvQm26Zw5fGmUzNVdueiZ61qw6XI6YWDZrD3PA OeNUVkRyBDKdeBhCJeKqqGWz4Qlj7pbSDINImRfHcge6dybY13mGJceOUVbg5PkeAh7Bl6b6 kjNhNRW5KzoOrrcZ03VuOsyljsqzAlsipK6giotX9Fjm9AeCfNoWu+0+RUudktBxsRvuiQO2 4T3aXS0Z2jRXr9IR8CQ7b5/wNSnTaKUPcWajLp2G+W8xk2nzZ02tLGviK1jPuCBhXz8Ul8Wc liRtnTjDqdnRdUgvSprHbjDgVGx2CkS7ORVYn3nRmJG1iKD9XcEiyuqjp7+qU1cWE4Tk4w8O PQ/2ymY1Cn9DHEdL/7RlSwoizqVDsvRCs1Q6X+b6ceKf2mPHc7I3ck23e/W5ggNMflgVBa67 K/HkBTg27vS6MOwAvtnMtG+G/relNdWu2OwrcF46N4M6dloShZ6bzLVDxhffC3+iAUbOsagK uB9lfS35NluHTuTTBq4xf+4TUxLGyOBvj/VHLQssaIm6m8QJoVrI+GdN3jUQAgEuUkWvaJnn aewljmhaXA9yZFw6offBBDhSCc+FWWLOraHyscnIyPhs5JCyOHGISk9etDTagglVkUHO6tR3 HGPeCbIuM4jKdgc23ZFmMfmicOTT8+BwbINJDwKTq69eXV0GhhIPI2oSjArShsLUOhlUr7Tk 144vvQ4IClulG43Ty8Xjkc7vugbF0ym+YpsoaBYXqxzEJRYOhte+DJXTEjtHbu9fPzPF+PLX ZjFe6YuPtKqyn3BHT8VRnQo+IZWnNCCK1zJNetWYKFpb3DPzRLrsxDPUU29XuFzMYX6Yk0u6 1+qzSWtm9iMnPdJOO0IPuGixJ5ZKa2pxL4616GTtht2S8Pvor/ev9w/v51dJS2DW/rdcwAqJ gdnH7i0LE50+ItwQunS29UqEVeDC5zqVBcbtUTiBUJ7XRyL3+142dYHdms9sqzTwpYkwA8IU 0czulGV9pz5MzdhQttvCIrbTquClAfU9PZQOOHZkdNAp98JJrafP6J+2xsrVt0pTMM4Sb3mB 6WuOgcOJKuss3u1kJiY5fchVUsquQHdvmW23PzSaGACwkYeEHWjl4WZ7kG8dx0r2vv+p9QI9 PRmzHC4NNmU3G+hh6U45bo4UruA0e54yJtDc9bzruj3pJW9yk8UrLZV5YSnbw0B7sQM86Mmp 5Mlnh0wDn6bK9R0l1vthzLD+/vT++O3p/IOWFTJnarJvpgYS6/NuxVcymig9NdJTETaJefr8 rP1fNQFOr/GLTIFXfRb4jvIyNEJtli7DwMWXFoXnx5UM2nJH53OFlY1KrtbE80L6+ErydTVk bcVva0a9imttLH8vTMvU+HnzJYfSh2m1acD5rEGkLSCPpmkHABufuWPn0cYiEi1+BwsgobX+ 9+fL2/vTfxfn59/Pnz+fPy9+FVy/XF5+Ab2Ifyhj85TBBMD6Oy/A0x4z6bvyogmcRV0cPL3P dW1gCSrrjVrz3z4FceLoKdwUdVvhRqYAN+xa0ZIFbcf59VvtjbLui0zPiytimQpXP+gy8HL/ BI39K+1H2s73n++/sbVhuilnnM37Vz5ABJvUJWp7r0kpDy9rL6vlIz0arIJBVXootDpWzHcS U5dSW5ojoL0JWpx6K/BHUT38i8EAA9UcLew9VZcdpFoac8ZXeoEpLFCaMFDC3rWOEq5IcYfs +pd12ZaMY5tJG4JifQgXL9rLMZAmaymZVkw+DmBHru/fYDzMSgPmIwpTZWEirJqSEGszNZoT AAPXf6HLtBJZFGh03VilO62cQmder884g5VKQ0MqcbQEjVlUys0Kd1F6ODsJ2w3taV0Vg00s AR6rTQCAdB2gf6+xgxTPu4Izglp2ICrbOhCF73tCMr384IC/3GFHaEC7JrtRQ3UClWRuUpLI 8fTESLmmYp+1OmAYYclogLh3apn5iqPm/Olud1u3p82tUcO0nkwM2JiTNiVTIQ3KMgsJwN++ Xt4vD5cnMVi1oUn/KFIGa9Lp0baQH7oB6qsi8gblrMlSgaUFrb9s0LuV1eC2TANqFof4OZIO OdX0ZSY/PYKSpmRKD7ppVEia029bRYuY/uTT2ljaKTKmZzZhy8IMMPetN+xoMpdZgsSLxJSc CLB8eTX26rZvaWaXhz91oHhh/i/a7R0d6Sws5K7oIUowWLexUxHp0xrM3xbvF6aqSfcZugd9 ZkawdGNiqb79S1ZqNTObyj5JTzOBjxSJgf5rJow24AYggj0hCQIBCXMzInXWej5x8BPxyAQe qWyRzkaWwQ0dPALIxNLX6+scIlbPVR526XeVo8mKqsE1TaZag4d5WGpORF8SuQ0YHTpv92+L b48vD++vT5LoMNtWWljMzCqa0y7doFpfU4Hg4CLd3o30jARx5YZmjzLAtwEJAqj2Y1Btxe+W IDCjFqaIw+1eQndyJ9+sR8FU+6TsbsWyLR3XYTxatxt28jH0LFU404w6ZMwIi8qo7CXdmY9j 3Kbp+f7bNyp3s7J8Nl0Usy/jYBjYhmvLUIgLzwpx3uXV1PJj2uIO0xm87uEvx8WsH+TaIfqk HO7Ut1dG3FbHXCNVzabMDsoezJtplUQkxs6NHK7n5ULprrROw9yj46lZ7Y1Ezc1YQfsWGX/M v6Z8Xc6IxyxfKrfljCr2Z5WYghpjtlXOh/ZOn45pjHr+8Y2u78rmy9PkSjJaSQVVtXESyK7V SJvjiZ9azcHpYFRvMIaQoEN+9oHEzuc+vqTODKiVjYDhUWYw+rJvy8xLXMd6dtAakM+2df4T DevpDZB25admp8+sVR67CV329JIxuodpMAmYVtetjwctOf58Y6RmnmiV6dMmsa+POCCGUYj0 V67dXMpol4V9mPjGV0xHxvbR+LSnlYCRl6rfQxnAr+Q5x209JJiBEEeFyoyeLn/WsqcKeGgd YxRdLhWzIWSgTILx1QFEF2k3CszlLB18d+na1zM28Vx9r8h8P0n00diWpCGdvvR0qUtbxsyY OZFBJwlSF1bHw+Pr+3cqJmqbkTIhNpuu2LB4ldpSQU9H+1ZuSTS18Rvmo4dl6v7yn0dxm2Gc N46uOFOfcuIFifSoIiPuUVF7miHLrdLMQDbK9QpSFLmI5On+L/WBhKYk7ki2BXrhNTEQxcp0 IkO1nFArvQRha4nC4fr2j7G5pHB41o81IRb7WFZfUwHXWiQfM9BUORK8laj0jmcHV4HoF3Hi 4kBSOIENcWNkPIh+nyRWFrIDzIflAC0zcVRIUc4zEiz8PuEnAInPMnp1FhbhO+16W3YVLc3S YpUq89V95Hv+h2yTyscHRZtLhYBCsrqCcVIjazeJSBt1k6smZ5xfQpGSgWV0raWg5A0Rd6o7 vUScagSBkbHtsVZqkqccV577mCaQRhb6GHBtIjuZFGSEOXR0KvPONtLmu9EtaM53TKJxIkz9 bJX2dPW7O6VZnyyDULrcG5Hs6DnyuW6kw7yKpIkv0xPV2bGMXCsEY/DMJIlsajLWCYhSw3JL HIVz/Hx168XDMJhVEICq16KD21wxV9fhvD/taU/T9teNEsz6M+HuWv3TJaieGeWkw8aNncCO eBbEc6Vajw03qkEpl+gCYwPUwaf+yAOSpRoGV2PQH4XmxFknXfmy6v0odLGCwQOhG3n44/PI lBciTgZUPohQb0ZSTZnWnjmwWBssExOgXR644WABlg4OeGGMA7EfokAIeRi9BgDtGqxdAVom +Ou9zBNZAt9OM6pe+QHWseOY2qT7TcF3kcA1Z8ymqfJ1SbbmYOz60PF9s1JdT9ecEOvvfUZc x8Emy1SjfLlchtL23e3CPgIFQ7YKSpoTyqrMfp4OpeKVnBPFK5bmtIe76OAGnMitzOQFIo99 F9d8kliCn2GxRJGeWGrX8bBlVOWQlmwVkOxHVGBpAXwXB9w4RoElFTgxoI8HF3WxAZCP3jLJ HIH94wBVrVY4Ik95d5Ch2BKZW+HBb3Innm2v30LoHMT/KB+SxdH1jh3Alc+OhfXpmgppY9IW cvymid4PrYvVP6P/S0sIqdlZnts0xpZgqrMjF1Nv6gslKugIkchDRgU4Q/FchJ3rsaayUcGI rWOXHkrWOJB4642Z3DoO/Tgk5iejzjWeUU+PhnvwY06wkbepQjdBHW5LHJ5DajPlDRWdUpTs mYXnd7HpDuvAbbmNXFtoecFT9gm2qo/wb1ngmUWhq2jnepivHGYYvikQYHw+wBqL7xrYUVLl iLFaCsiivqVzEdWfiAIvr60ynAPpASZ9hOgMAshzP6hX4Hno8sOgjxol8CJ04eMQrhg189yC G2aYklf5QGByry6iwOHFWDkAiZzo+gLJmFzMfEDhiBJkilJgacvZp0IuroErs6iXhRIWaQsu zuN/UO4oCjxL+aIIvXFUOJbINsrLvUSmX521voMtmX0WhQFWz74lnp98MFLqYrf23FWdcWHo Wpm7mK5qPrbDZ8OADvM6wo8UM8MHeyNl+DCFq9OoxmQVSk3QmVUnV5eJWr0hl+jXJwFliD9g WH7UDstro53CSL9Qauj56NBgUHBt3nMORJhssyT2I2SAAhDgS8Wuz/j9aEm0ayOTNevpaoBd EsoccRxi1aJQnKCnB5ljybyE6UCb1bF8WTBXa52ES2UPaGvcBfv0ybFm+zbSEmTV2zSkRg4q Ul4b0hT3EMGckv0fKDnDxKy6oKsjuucWVDQKLFcBEo/nfswTwRXStarUJAviGquNQJbo9snR lb+8Jt6QbAsnX9CTrhtUhmIc6I2GwuFHZvuRvidxiBa8jiL0CJa5XpInLrrwpDmJ8VdDhSNG JZGUNnTywXZW7lLPubabAQM2+ind9zw03z6L0SD2I7ytsxDdf/u6dR2bYY7Ecn18MZZrTUYZ AsfFmhuQDxqMsoTutVXo0LueiwyAY+LHsb/BgcRFDmkALK2A7JlTAZAFn9FDrMYcgUXJotAu MVZxEvbowYeDkcWxkMRFZ9V2/RNMxRZzUzXxsHtwbASZL7oTC9tmUqyKY1x26UpdUEaF2vnF agR2zZEFgL2SnDBx4p7Zih0YVOdIFuDXgunnQThZx4CZqtH4Hnq8f3/4+vnyx6J9Pb8/Pp8v 398Xm8tf59eXi/I0On7cdoVIGeKtI5mrDLQZpTsEG9OuadqPk2rBvyTadBLjGIdqTPZaa1o+ G/NR28fmhIY0637ub3kky4CUF3b5CaoaQ71fywkJTNxSmCOKaxQgGc9i9oiio1c8/WA8Ekfo IGUSrmGlMk2pfirLDt43ryQrNMWwuh4R4njnilV1tNG+WlE4APnD1ZqO9rdm7lwt5nTMZfOn etPmGaNJZRGK3kfc5z2dkannqukQsjq1DSGl5gGc0rFBkkHcxZldIqu/hL/IJtPIIqRSXbZE Q1ruZl9+TwMyYWRbUXbjR0geG4h1mdU7I8kRt+lCciZUKZrplX/5/vLAAudYA1usc81mASjS 0+M8LIBO/NjFd+cRRo/+MFomHS41p7T3ktgxlnmGMacQYJZABUVskEw82yqTLwsBoC0TLh1Z bmJUSclLzWtoPWewebpZ54aS7UxT3yoluqL5zxpaV8idiD5GTEK9RRjZch6dcYs2AfQBrI4+ pu00oaGnlkSsp9oFnoTYTOsnFvwAPsIRXtoJxgQ9AbqqDAvUTdoXoHJPThti7cnM9Qd9YAii akgkA2Ynt14kv88AbVtGVHpljSm9doFn1pSUmXJDAVSaJq5DCGlxT1dq+nx1VQvIPbkYLcHJ 9rZneGTRuueDeHCDMMZvRwRDHEeefTRyBvSybYaTSJ87+iPwRE0CX+sb9jQeI0TPmDr8CRk7 Uc5ooqXUR8ptykhb6jmOAoScZ/GJ2cBaAjzAXNVRCdv1g+pEHohUIsIeegCa9AdmTZfRsQq8 oijBKQXdor/EMtKehhlt0kKViTeJo7WZkD705ZUUmc0WkcFlEEeDGX8HoDpELyoYdnOX0DGq rVjcedxoMdXXjw+vl/PT+eH99fLy+PC24Iqz5egEEhFRgUEYe89CBiMa9nGjzubPZ6MUddS0 Uqrcg42Y74fDqSdU+LMvr1XrLwPbAgk6IYnWOzTlqt7r+bVpVafYpRloBbtOqJzzmKYwbn/A IVUnnOXK6KryMMJwZV8T6sy22QvV4grXRm0nlWsztcRod6AnEbY9TjBXnjapHk5VXQYoiLHT UISu47JPjFFcNwW0EUn3uarYQ4HICZxrk+1YuV7so5Otqv3Qx+92WK6ZHyZLa/swDXF13Z7f H1WpTyjuY0SzyUbAaDEm7niBSjzWoet4et8CFR20HIStQU/G3BAoLXAcg+a7A0bTVxAJwT0O jQwhkkXomO0i1OPV9bfZ1tzSYTDm4YhRqQ27nFM/9/SFnSPi4K32sjgO6sR6rYkvhnUOl+BH fVyTiDUhDwpWu86J7rjoYnz15DMdk0dNWUlbafKXpqmUzsC6HIoc4kr1/Ll9KtnMAu5r9mnF 4izs6wJ/UpnZwf8Q83qIfmCwU7FtQ5coPGshCl5NAI52ibwiqpCqcCpheegvEzxbcYa7nq3e yzM0ndSeEUzY8mCf6aNUg9TxL4PiCHi1vMhIl4bHaBKEIa56Ja9gnkUjSWPC5B1pDKa70A9D tFEYlqi6vjNqkfkkP4Hs/IJVjCOHULYrmNGSVEvfCS1Q5MUuOqjodhP5lm5CDV8xPioDxfiV hMaEXUzILEnsoeNTt+VSkTDE21pIHh/mmSSW+vPN9qOKUa4oxiWrmQuOciFqvKXwJFGwxKrJ IPkgpEJwArN8BecwtHYMRBWvNZ4lOsGloyOeODtCftwqsf4kbWXzPmg9cUugSmoqHsvHJxWi DYU2Yda6VDDGsTZU/I7LSJKoLsFVLMLP/DLTbbz0rq/ncA52XbQ+gKBLAbdhsX0T4j2tncFV ZBnjkw9sVwP04kHmMXXuJXSdDM5Hi3W73n+CyGbX8znQ9RivAoMSO7TEoWONte5t1tSjgw6k rAzek9XpYHMKOPN2KWlXRdfdgYeQ2cEwhEzCHbBIn+r3BBKk3hZIwHRnYEJU6kQ/6YPEQTcq cXmBIvXBQ7ev6R4Bw6oNPU84aE8Ycq8E0RSdKMX7goKJF2DnKI0n3mFp04Ni6NKphI/98dB+ NXVg8iwTi5/G8Yk6HfFtmHutWHDo/rBYoRdYRILxAP7B6MVsmTEmftjG2ncyZjaFe91j3Qxd MXVWmT5Yl/RDpoJwc2h8clfpqlytlKazXrpl4j5OOnUVeZkyOpjONbJtIGPexr4nTRDObrAq ZAgo06vWbyO+yrsDc69HiqrIzOiY9fnz4/14dIMgemrYbF7WtGavLDwz/NKCMfL4AKf+8BO8 ebkpIfY5zqywdmkOZvhTE+iVzLufyG900/FhbsyaUM5s8llhtNT44aHMC3ArfjB6uWHGCtXs 7vLw+Pl8CarHl+8/FpdvcG6W7kN5Ooegki65Zpp6LyHRoZcL2svyrQ2H0/wwHbGn9uAQP2DX 5Y5tQ7sN6hWQJV8XtQdmo4q7K4awh1YIJHTK6L+Ijh53TV7ITYhVXhqFswMrs2n09qRz8nYP PcVrLcfmpHVgXfT1/p05fjozd1GfzUy68/9+P7+9L1J+SVQMbdGVdbGjw1L2IWQtnDyBVMeG 4gp68eXx6f0MoVHv32izwp01/Pt98T9rBiye5Y//Z64tH/FpnrZ0WkuP4mImlEEsn/C5h0aV NnO68mPFNAk48KwnIdN4En2RhrHq0EGknaZx7ETYM/z45ZqeHNRdigH8XhQbbjAyV/u1py2Z Mx2ZG4xOx2cjaw/MSF7zYVNu0PTqtKoafFr1rfxJUPGVgSsKEHNCHcoaewgdQc3qTiLDsn/9 Q+YqmblJjgIkWw97sx9RcKrPvQup65jsboeT7l8eHp+e7tHI9Xx57/s0246zLf3++fFC18OH C3jO+Ofi2+vl4fz2Bo7cwCXb8+MPJQlenv4wXqSr5DyNA1+xI5iAJd2lrfWjozMK3DBDvgTE 8mbKOWrS+gF6puB4RnxflqNHaugHodmVQK98D/eLLYpUHXzPScvM8zE9Gs60z1PXD5CmoEJJ jOr5z7C/ND87tF5M6haXlzgLaXZ3p1W/PhlsYsj8XFdzn2w5mRhlSULklKaRFttsdtUmfznv lVdSo7sbWMNZm4TjPrIpxkEi3TPN5Eh2iaGQYZKanQ5gEuAXG5xj1Scufrc04aip9oRGkdmn N8RxUZ1tMbCrJKKFjmKzwLBouxbBXua4NlzYfVmMPoeOk7wNIayNkTsDUKl8wmPHMYSf/ugl srnCSF0uHaNzGRVpMqCjz1LjLBl8Tz6zipZMh6XHbr+kAQlD/l6ZEfoqx9pQPruJJWLwwiRw DIkIHfbnlyuTKNa6H+NAY/NJEyM2asvJITYD/MDHuP2lbzY1AKFFeW3kWPrJEnf9IjhukgR1 FiV6c0sSz0Facmo1qSUfn+l69dcZQrXzGOdmk+7bPKInUffa6s15Eh9dvGw5zTvlr5zl4UJ5 6NoJD2aWwsAiGYfeFvcKfT0xroqRd4v37y9UvpxzGNUnNIhv/49vD2e687+cL9/fWOB66VO9 3WPfMYZCHXrx0jFHgk1jTdSzZz6mc/1ieJRT7KWaPJFdK+uGuFHkyZKP8YUk/ACWci/lb6bw r6DacXG/m0932fe398vz4/89L/oDb2dDiGL84FC9rVStAAmlUo+beBblfo0x8VCzXoMrHvQ7 BTmv2LWiyyRRNhIFZocDi56qwYdqlUhcNSkdx7XlVfeeg8Z91JkUTTId8/F6UsyLIivm+pb2 gfCpqmsGGR0yz8E1ABSmUIn9pGKBciOqFGuo6IeyXb+Jxsh1icCzICCJg+o0yWzp4LmqJ0Vz 8FicdsiM68zBQwAaTB5eW4b51yeMxauizFhAe35YDLpNO9aGS5KORDSVa5dNolT7dOngOnXK UuC5YWyrWdkvXVSPWGbq6GZo3A1O48B33G5tGb61m7u0ZQNLqzN8RSsbyFsttsqx5a+/XJ7e wOf25/Nf56fLt8XL+T+LL6+Xl3f6JbKsmidOxrN5vf/2FTT7kKgseWcGv04pTQ7bNO6UEpnR 16/3z+fF79+/fIF4A9MHIuX16pTVENpYunigtF3Tl+s7mST9u+xqFuuDNlmufJXRP+uyqroi 6w0ga9o7+lVqACXE91xVpfoJuSN4WgCgaQGAp7VuuqLc7E7Fjnayog9JwVXTbwWCDDhgoH+h X9Js+qq4+i2rhXJHs4bQV+ui64r8JF8JQEZpdlOVm61aeHBYJyLGqMn0ZcWqCnFLx61Y6eyv Y7AQ42aDfj1HW1YrRejYh7dmvD7lqj5thj4I5bWb0oVWkjyfoehF3zW7psaeCCArLg4ofUho 7k4szzt0/LLKru4f/nx6/OPr++L/LKosNwPBT2WhKL+sFWHHkPJMja8wyo0zc9z0uRdim8jM Aq+o6LemSziESSg3XM2BPcocuckhkoT5XmSwIPHYFTBJImzX0HhiBy/A+AD+QVUR7S6Ei6ny OJgTY41H0jCRkDYJwwGvJ1eSud6XEMtLNs6ZIfNpd8aEJ3QszwNt9rjC1PNnplUeuU6Mt23a ZUO2w9YcKRMxMsQ0+mCyjN9v81p6T6kaNXwP/AZvQBD/ik5sJH+J47BJ3cjydVbtqeyJxzI0 NsI5BdLsd7mxE27L3IzLQYmSjUyZz64l+67YbXrFvo7iXXpEqrPnyciMowt8oxjk2/nh8f6J FcdYceHDNOgL4RFepmYdGiiNYa12XmLEPd35MDNnVsuiuil3+ifZFtQtLJ9k25L+ulNbK2v2 GzlYypbdx2dpVemMTHLSaHct3ViIXgraxptm15UED9cBLEVN90vMMpyBVZHJUaoY7RMP0ar1 UL0qO8zyiaHrrja+qJqubCyaK8BwKA9plWNq1YDSMoyRpZWvbu6wrQaQY1r1TavW5VAWR9Ls FOMuKNtdl/ZlY3RqCZa71gKXvS3r31It2DMQ+2O526JSDK/fDuKs9GYhqow5X7R8B2vQs0rY NYdGozWbEpsYIx1+tLih08SCjhlAu329qoo2zT0eLF35dLMMHO1TBT9ui6LSB6RSRipulllN B46tsWvaz53ZbHV6Z1jzKgxUPGSzxZYsBIkBk3ZthjYQTtWcEBDstmQj1JLeri/VlJquL25U Et0GwcUCnSlSp0pEJRo9+6Do0+puN+ilael6A1sQXpQWLLc7mAZEy79K70g/ToUx/5lo5t/R 48CgJkLSkldMKRFJa7LfYaboDC3q0mgN5neRbmY6uS9SY3WhRDqO6KZR2BcYmn9bob6F2HBQ 9e7YsgA6cykpMaGIJVinXf9bcwepzmWUqdqMYMtAecB2dQbRY0yhz+d+S5cFbUnew156aomv l/hYlnXT25esodzVuB06oJ+KrtFbSGW4y+leap0y3IPIabtfGd3DkWxPetBxZL+suaRVi19R Y9v/HJ1TEVGmBFkwUJht2PI1g6dN0+TlIN/q6onqH03mV2OAaIQX1DWbbVaqB1l5SADHFc2h Wtqp2mNHilu6gdfKs7Qg86MensZpVTWZNI0mEtfTIf9OpEsiioFCgqnRVWe/kpz+VzaL7eXt /WqQRUhFs8EBEsl50EclN0aky0y/xl19Mx7c3gpyOar1yo88KT0XSl9V+2JdFrjBNmcphrtd Q5Bvt6UfL5Ps4Fl0igXbjcX2ruZxVVGNAwD3tBHKqGsqR61NdqtEyQTSltzKl2416UulbwVl an4pGhZ5f3z4E9PCEJ/sdyRdFxAoYF/LmoWk7RpjDJGJYuTw4fDYFUeQXKTbFvgltL0Q2snw 1yFhbOul+x0aM4HxrTo4r+2owAwxv7MtqKXlY9NQDrNJ2Gf04Ox66vsTp+98xwuX+KMe5yB+ FITYvsFh8N0mvRjwQmZ15MvmUDM1TDTerHMcN3DdwChbUbngSBK/jWYczETU0RJkRE/LG477 shvbibiU7WwmquMORnHMqBcyykNY6TkIqhZujkEIiZlQB3ohKVEN+STIIf7UM6Lh7NNOTzAM PRdLMLRXD9AIK0WiWcQbeKzrsmg4fm00t16o94+gYg0IUCSbRzHqaOdKBb+9PiWFiateL2so BoFmrhcQRw5OyPNX7/EY7VoUFD4tci9RbYR50/R+uLT2x+y/Rhuk3LbH9lmfpaAyrxW7r7Jw 6aq+WXlq14yXpskW/rDjkzsKOwvcj0ao21IGl8R315XvLs0ZKSBPnQfaSrj4cnld/P70+PLn 391/LKiMsug2K4bTb75DSCtMGFv8fRZY/6GtpSsQ5GutDfU4Nbz21UC7XxuOYGSrfwzhl+/6 wugA7iNBzGN7E2JmFioH2dS+q27Z/Ang6f7tK1PX6S+vD1+1HUQbyXCTj10zCzQJmanV1P79 6+Mff2AJ9XQP2xQddnxJs6wAL1kllTLvxm2N9s/9n9+/gX7x2+XpvHj7dj4/fFV0NnCOMdWu z05KjFAggP/SKHETE9H2biBts76hXYwSx9v/v72+Pzh/kxko2FOZWf1KEO1fGdroQGRhiYzu o8jicfTqIe358EW569eTv0Cdrijiy9TTvqSyPZVDVBhMJETE5umYApkb0sbIzM2/B70azNRi tQo/FcRiEjkxFc0nzBPpzDAkija3oOdEvAmh9FNW7Pp9d4eVCzhiPP6GxBLhtruCYXtXJ2Hk m9nrHlBGOji4WzrKDiRBYM96JbfZvRAGaCbyM8TMVa8ky43jkBJ1JMz8q/UvSeV6DlJPDshW OxoSmchA6UjlmKdlz8cqxyDcT5fC4stKLgpiBRI0wzpwe1S9dmQQLi6w1lzd+t7N1eEmjL+u JD/5gUHSH83KrmYhbCQ/5MGd848chIrcSyfFWmhNNx5LAIopfTqTbZZ0M0uYfFATmoqH+xgb WYqannauTajuQBmQ0Qt0Hxm7HVjG+ljjk5yuNomxYMPhX1030SFlsxeXWTCnyspi59kWOXQT lxkCtEoMudZ6wCCHuVLWOBeZ4N0yluOZzj0ZhAlGh+UnsC6iHrpUeK6HLMd11oILZHUL9DJ6 Hs6Fa5+pu0A4+nC7ywk9GyIF4HQ9yJRavBgbV7T7lhnagxwzwzAgEyLS9NiFCdj9O5WJn69X KKvVuyOpjz2L9yyJJcTdhkgMIdIpsLsm4H26Lqs7S+aRJYaCwmLxVDGzxN7HycQBqh4ucyQJ sj2xT9Gh4AWyiv5E1x24jCtIf+PGfYoN9yDpkwin+5g0QOnhEpmXpI481Y5m3psCPEDCNLja MMPmLgxOxyTrnqlkeojwm04ER+TT3e5WDiA10kc3hWyMX15+ydr9RyutcGZ8pZbrnv7LcZEC Go47R4DfjSIfcBcR5lRnMZ2fZ+UEcqZnmNePyj4G00MKn4PLVzhbSIL/TDOPFxJ2wN33Ug5T GRCsBovdRlEGBNrkc2qb7nZFpRbi1CivSCnYZKd0IG4gC7QqrEETRcDJjyzUGQXxa4U1qU4F nmBZb051nsG3cjm4H6+SUiP8ACC8ZvPBd8pbPHWmZbWFZE71ppaeW2dAafijUQeBkPUJshiH BeSWPT2eX96l9k/J3S479YOoy9xi4pxmdNOpS8vpqpiSV/u1aUTMEl2XmlPrI6OjLcMTOtXN oRCKoNfYjPGlM5CiWkMFsMsBwbIt0lYdVROVHa6LWlZl0io6td5+yEsCD8JzShA6ssokLdVt HsDYm2ODqPSZAMMqJVlZgvKg8mLYu9ENGsuaMnrSU2ub0rki7vNPdUGIEkKNozwkscD+9rc5 F1Hw06qi8wt7HZQZlDc7CbCrGOxRT12HdSnpZsAv2v8lbSnFpyej13R9wVKgK4VkLCxRVfNX +A03iWrCnLwC42SLICRYyl2Leu4fE65VzTuJPGo7i7sa1PyfOWw3CsfduGcEe+rjIKhkEPGK eqqKTZpN913Mfevb5cv7Yvvfb+fXXw6LP5gRvvwmPFkAXWcd89x0xd1KftqnQ7rIlfdLTjFn qA7zYAtsrpafitPN6t+eEyRX2Kh0LnM6RpZ1SbJxHKAdKfhKkmJsKlPihaFeTUY8kRSp7g3/ uyoxc2PBw9YjI01GPRVDqpspKfiuPHXNvrfocpA+pSuzJfRBSdezyRsBMgInziuywBbiOWTV jbTIVTdwzUcnjRJMnDHyYajyb4/0ELRjz6OjBdfT5eHPBbl8f31AQ94ydSMIk3tqyz4KNBvG 0UACS0RKIy2rVYO9b7EVRnXkwUmzPxJuIXF+Ob8+PiwYuGjv/zi/g4+LBTEn0Uesaj5sgqwV TfgR4GO+TQnpt7TXN1iPNOvTuEjyK9zz8+X9DObiyGGsABWYtmsUu+qZesq0hVUSaDbFrsxO h3ZPRXXKinYCkjcv07fntz+Q4rRUUJMkW/h52hGdMi3gcz5KetLwBw3dY6l6BeIHb1riv5P/ vr2fnxfNyyL7+vjtH3Cr//D4hXZUrroSSZ+fLn9QMrmoIvNo5ILA3CTg9XL/+eHybPsQxXm8 56H9df16Pr893NNxcnt5LW9tiXzEyngf/1UPtgQMjIEF89iyqB7fzxxdfX98gsesqZGwZ5yy LwaILiJ5iUEHxs+nzpK//X7/RNvJ2pAoPkmctDj9dOMyPD49vvywJYSh0xvQTw2ZSaJiS+q6 K24niZj/xCIoCYgHRWLKSVSMyos63SnCnszWFh2s2yl+vlQ4QSWW0OVXktklePLJa82JLjd0 TzQPbaI+uTkO5sqfikOxw4SjYugzpjrAR9uP94fLizWOEmem5650GahHNYFYY8YIHMKZ+iF2 3TIzaJEYZCAJTKDtd+BIASlM1yfL2MfOb4KB1KHi6FKQQb9NmGkYAB3C9P++pxip1o360MRP sqddYXkOL1E5mysVzD+mNz1Jba6+crACVIpMA7/xXARXn63U/OA4ve5rPUcWE8TBNYcAZxcy VpSpaqAXbKyKImyCROqPUjAyQRAq4/xFsrtlJuumcQdFQBqZk0tpfUrZf7kIndNJ2mj0B5fA slaR7MeNtc/2wIOunkZRpkTpPL45rWQtlLQDnfUU9ERI0ave0Kb93kAkWQCwVZfVpF/Br0w1 9tAY+T3HBjNe4Qw8EJaed1/O+g38Dnl7R6Wj39/Yaju3tDB2OVF4TkIiMmcGp5zDU9FWWX26 AZ/6dAZ5wIYNCvrxGHuMff+MISStDtLJESAYvGU9JPUtJK9+VpcDeG+fyqSAIiTYoRs8x6HD tVQWYImjoyMBSo4PdcrXDunJS3b1aUtKy4SQufS0JJ4mK6qGniuLLhemmGLEqf0hJQx7izXw S7YyNo32/ApvA/cvdJl/vrw8vl9esWPnNTZpzKmBPLis9vL59fL4Wd6N6C7aNWWOTqWRfdoZ ZRV9phMxdzf7aa6OgtzWdBDnKa6Xy3k6TMVie1y8v94/PL78Ya4sdJmSlOV7uqp1TQ93EqRU 9JpnCNRFsO0WOPJ9Xd+p6VEBuRPe+JuqQLFtkXb9qkh7PUOBr/suRWUQvhb0W2nNE5TTBqUS lFqTPUJte+VuYaIj29Soc262s3Sj226w/XpNlFzoT6aOC+db8KuIf0GXe9KLOHb61wLa7rHb AIkhZQYd+tdECw2ngqtiXa7RMEeg9EtlymF2ksKiRnx7Ov+gIrep47wfTmm+iZeeHAKOE4kb qKobQLeFVwLDzFo9qWEZSyJz07bKvXCJHtFJVdbKZRMQ+B04c1mrDJeO/nvHzfalG5Q9IPiq 1ZAeHUCacMrV2x5BGYytirLzm7Qq87Snc4NQ6blT9PEoiR7PU0laoEKeR8maIAmk05D2PabX SXH//1X2LN1t4zr/lZ6uvkU7jV0nTRddyBJlq9YrlBQ72eikqSf1mSbtyePc6f31FwBFiQ/Q 7TeLSQ2AT5EgAAKgKmID8AnIDL5TbKXt1chGxJ3MWu74A5KFW+HiWIWLP6nQuwki6KYrs7YP 2eA/LxPrwhB/B22G0IdiGUfx2jo7pchgygGXsg0QYpr+z84oDbAenw11YjWIEGO+MC7AqHen 25nueJByyBZ8GbgGApKLrmp5qXdndjVIEUjBi6iqJGtzE8uOz/yFRNtI8ubunR48M6urtJlb 07ps1RcwP4yG/WYQIxl8VxBncROv3GXmE8sOFKsIltaVv7Yc6tAgFBbUXWHnOJ7aEGl/CcJm 4A6qzHI1DdxSnXvrgUC4dI6WUEzAKxeaRYfGWMJ2eTW3KT9NqjQZG7PyMzDPLHADopsB1ktB 1hkb8MbvMLHDnWCuGQ0ZAq5UppKprSwX+FLKJmNjI1O87IjlVU0hmb9YMGhmK7vOhr4ny8LS xs07k4wAQ/AgEFlRuDoiv4iGDb7IaMkpMpo6bhUQNzBUDgnbQQFpo6psK2PdChFa3wrbSmGo vhdpAdxo5gIM/xIqFbfWCYDZzNNmEVo/Ch1Y1jBVzkaIu0AQ/nBVxtZTwYfLoyunqgmKQcsZ 5uvp4c/R8hNllG8jypGT59U2UG1WJoJ3hDeICgFTVtXWslIqyc3tNzu/e9rQAcZfYihqRZ68 lVXxLrlMSOqYhI5pLTbVx7OzE366uiTVU6Ur5ytUtr2qeZdG7Tuxw/+XrdPkuJJbi+cXDZRz PshlGuRv0fS0OmZIrvHWefH+w8QN3PoVRJfJKrxcbkT76fXL89/no4t72XoHD4FCu4KQcmvd 6R8bvtJjn/YvX39gcnHuS9DdCTtodauyzvJECoNLbYQszZF6SmZb1Gx96s80YK0/+90bPxJe hiIvVa4M9u6hLPShnRsluh0boCZPw1KHSBDzdQVcDRzcDXiWvvaOTICoWPHA8R7s+9KR+oRX 9ec0eHZ3y8wZlIZgolU0wSfqBJzqHwny64qBXquYkMkDYkQ0bcIOTVGAmFmII0HQYz1aaHDh nDwwDaZr16IEMTZyD3y92mRUmNOgfqvjWkV8TDxdoYqWiyBuLrqoWdtLQsPUOe/xRJZKsW1j 22hsgrlZ6h7ThDieRg4F6cb81TlHiYc1Ov4dLeBpbT7JteMP4FPk15z/tYGu2GHtro+VwtXF FltgDPTlMt+Qz8XxnoliKZJEHPuufSqjFb7ioL4jVfrp/age7zwGXWQliIf8OV84e29de3v3 otwtvJ1vYs/CWDk0wDHrprXeQFC/xwNog5fRGFbXfJqdzBcnPlmORgAtG1vmXUUCX3FEB9vH pWBW4iHX8YR2+3q+mJtItwO4IP6gB0dqcEepZ+fPhqOpmYrtgXHVuiXMsf6+G14XXn//7+K1 V2msTKPH2kWnhHA7wAeZ0cFW4/bPVXPp7IwutDiFrLx9oGFHbutGkjCXGkmuMy4pXGm63MKP aQIPTz/wNb63s9cmWst2Pch2hm3dxHwIYz5YiW8t3HkgO7NDxF12OCSngdbPTz+EW2fjyh0S Kwzewf2+X2fvj7TOHQ8OSXBY9oMGDo6Lw7JIPr4/sxfAhDk9CTT50YxusjGLj4Hazj8s7DKg 4OD66s8DVc3mwfYBNXMnkxxqA4PVTc34HszdujSC88Q18Qt7rBp8yoPPePAHvlMfQ52a/a5X s0C3Zk6/NlV23ksG1tmwIorxXLXT92hELDAHSaBHiqBsRScrtrCsQDiNeGvUSHQlszw/2sYq Erl9hzZipBAcb9b4DPrv+OWMqLLL+BPKmhQ+P68maTu5yewELojq2pTLC9qV9LqQJc4rUF+i e1CeXat0ZNrvnbuoq/rthan+Wrcayl1uf/vyeHj+5fvzb8SVdQTh7/FhMMayoY9BIZsM9E8Q EaGEBDmdO+Na2QFNohqZNEplz9NwQ4m+6pM15hJWKQpNfXpQe/oE9E3yOmhlZmkOnqlfQ1Ku mlK020puGEwdUTLPAZyCmoamPnVXapDjhUFMVkPMqKwSKv8Grap+/e7py+Hh3cvT/vH+x9f9 W/WYgiG3jD3JqyipM26pjSRXkR0wMiIwt1Ej2owV8qcGQOurtmWfNwVbi0nQi0jmvNmcjL1E h2YQkeOsxbh+S37tBOjZe4LjRQiboMob5ezHRx+MZPBNti6bQw1p3WBacJHhCIQT9fr7zcNX dE5+g//7+uM/D29+3dzfwK+brz8PD2+ebv7eQ4WHr28w8cId7ro3X37+/VptxM3+8WH/nZJp 7x/wLnvakEZqqVeHh8Pz4eb74b83iDVclfDqDVYXzAZOrzmqVRz3dd6tshKT0nZxm4to49m1 f0O+vJKCz095hL6Pcu4Kg/pKNnv4AOPE2uElmiYFpm2QsMbUwNRodHhmRwdQlwnqnu4qqWwg pm2MAp2GxHcWrBBFXF+5UKjDBdUXLgQDrM6Ae8XVpWmEAWZY6Zv9+PHXz2d8luZx/+rH4/DW immbVOR9mtWsPVFho3xlecBb4LkPF1HCAn3SZhNn9dpkdg7CL7JW+cx8oE8qyxUHYwkN3dPp eLAnUajzm7r2qTd17deAaqdPClIBSKl+vQPcEjdtVJ9kTbTMRfBC3SEXu1ZGQ8SJ29oqnc3P rdQsA6Lsch7oj6Smv+7qVH+YNUIWx9iDU4ihCxyDQZUF/uXL98Pt23/2v17d0pq/w0zdvyZe p790E3k1Jf56ErHfCxEna2bmAdxw3kIjWiZN5M1AU8z9WenkpZifns4+fhpfdHz+tn94Ptze 4DOl4oGGhm9L/efw/O1V9PT04/ZAqOTm+cYbaxxbuRz1Z405h1xdZA0yWjQ/qav8CjM3eH2M xCrDmHx/QOIiu2SnZx0BX7biRlQ4BIXkoNTy5Pd86U9/bL7zoWGtv0liK3hKd8Ivm8utR1el S2YINXQnPGW7tmHKgPS5lRFnMNH7ZR2eYwzSa7vCHwa63uu1scZUWoHpKyJ//tYK6HZ0d3Rw l1hIhwUf7vZPz35jMn4/Zz4Xgj3obkfs2x3YMo82Yr5kVqvCHOFk0E47O0my1Odf7ElhzLr3 nQcUeWIf4Z3JwufMyakPy2Dpk8cvN++ySGaszUjvp3U086oE4Pz0jAOfzpjDdR2994EFA0Mn gGW1Yrq5rU/tR5yU8HD4+c1yEhy5g3+MAEwF3LgfttqmGfOBNGIKg3b7hK+fg0J/hOnGEaqb Oh2lX75puVAAA33mLVF0gXZhaeB0G5gowyNlDeoFcxj4Kwo0Spoel3aAT6NTH2R6d9D7KINN 3meA15VX+/nCX0b59cKjI3M882ncC0sV3wcqzo/7V+XL/Zf9owp/dBQRvVTKJuvjGsU2b/rl cqXjnhnMmuN4CsMxAcJwhwciPODnDF/nEBgQUV/5fBVfXM/K1JW5vx++POJDWo8/Xp4PDwyX zrMlu2EQPrA9Hc3gz/9Ew+LUEhyLc00oEh41CiLHa5jkFa4PSWBsI5uVdBE4P0ZybPzB83Ma 3RFBBokC7HS99deFuBzilTLmrJuwSm50d8WExxZPFkc4F5JmxaoVcWDlAt6IePWRaKvZxYJT oQ2qOLbcvsx+Fvi6RNyvdjnDOh2KoBcNKKgFvrAFZGiHa6/M+CoDWXfLfKBpumWQrK0Li2b8 mLvTk499LNCshn4KwnOwrjdxc45+cpeIxTpcCl33AL83S37Q+S/Yej+o9+fxkRfLS31VCnxg QjklkIMo40OhWMX+8RnjRUGGf6LMsU+Hu4eb5xfQ1m+/7W//OTzcGY74VdLleHtOts1Pr2+h 8NM7LAFkPWg+f/3c3493bcqfx7SZysxUh318YyX5GPBKQTRmmPcGEPCPJJJXbnv8rSZWDNwL X4Nq2mDXJgpisfgvrodSXFZqeomE95z7g3nWrS+zEgdCrpWp5ul5kJkrIwwZZyYnggHWL0Gb haNJcjcI6HsdyZ58rOyr9Cjk1LrMQFDDfBTGLtHxbmlWJvj8IUzX0nxiMK5kYvJvfHhEgLZe LDENypQfimYwMvT6sppi6eKszyryVLdiFWw8i9Jgg5HEwH3gRGUZVDyz2DHsfE+2hzrbrrfs AUrTMJt4P2evOFwS4D1iecVnDLdIuOvVgSCS28jOpawQyywwxDNLnIoXNpuNuZSHcFj5alZs 5Gpz9SqJz8IVxixMpI4PiAFVrk02HF2TULqxJchrdb47UNN/ZeoKQo2aDTjnxxJyYEFqtn+m y8pUDYG5VnfXCDanXEH6XSDN4ICm0Maa05QHgiw6W5iLYABHkrO2TMh2DTuRKdfAgXOktWX8 2fj4CmYn4tIbkOzlmBB+ooejP+mbKq/sLJEGFC/VzgMoaMpALWNj1UVNU8UZcJJLAWOTZrIr jDkGXmAGUCqQz1UQbiUZK7F5ynIW1XQZZgoJOqAZ8VuZKd+jpZcmDnqdR+TAsyZRnqmBbOpI m1ZyemKTawfWVM3UhKiyKjWiL6xhITaqMzdgyAL3jZsqj8Z87BhpVrn6xgbvzaul/YthA+P6 aKsii89MBTS/7tvIqAGD00GONs6Gos6sNObwI02Myit6JWwFx7G01gCsC93uZdJUfm9WokWf 1ipNzMWTVjCbU8IdE3r+r3liEAjDDRp8j8+80MXg5MoYAd16JaKuWgemhA04NEHam49OfA1w c+tr4o1xuZrm1fC29mQFd5Ck+DXrPMne+zMwIGUQmR9DxkWdmDcyJq5zkVFTznCnVwnJvfaN ohY+Cfrz8fDw/A/lpv16v3+68y/+Y+UYh0/T5SCi5ON1yocgxUWXifbTYlxTg6Tt1bAwRb5i WaHCIKQso4IPkwh2drSUHL7v3z4f7gfh74lIbxX80RjatA9pb6KOzwUuSOgIRd+ozGRTZ2GJ 1DDJGCRecPZEKaKELmsi+8Z8LTBDBMaWwIpkL0MHZqFCsdCHv4ja2FAWXQx1D+P9LE1F1aLu vtOujIeYKHyN+v2cC0m+LIb3VW2Rzqxni/e46J7g+UVrUfxP599KqzUsy2T/5eXuDq9js4en 58eX+yFH5eQ5jG8eomZg58qwO9ownW+IiW7dy2efDC/YiLLAqOEjjQwVDhfrJguks2SzSqxz H39zirQ+d7plEw0hjZjdLsqtCCzCsvP9RzNo9105RfiThLEingo73KOP9U4sgTztQH/E9zlt O66qDvF0cnHesli22pZ2+hGC1lWGD5EG0thNVfch3wNFUi0xkpF90xC/0TATILCgZ4LfeY0J b046TDo7sWYTr1GSIpQoExV26Vd+ycmMkwSiaIZXzf3CCnFk6CpVEHlLHJtDtYlxs/9mlmg8 GO6WOnFyDJpTicjU1W8iXOC+vVFh0b8KD9yymrZAkoxe6LZTx7QYnW+yxqQ7OgEXEr2qfvx8 evMq/3H7z8tPxYjWNw93T+Yqxvw7wBirykwDa4ExxL8Tn2Y2kgSZrv1kZKDEV1HRM6SroUct rD/2nSaF6tcdjLeNGsOvTPu0jKixkdncaAYzt4L+EBUGIfWJsymEaIdBjSLQ9gJOEThLksqQ XskApkZkikDH51b5EALv//pCb9wZrGPyrmHQ7sLCsW+EqB1eoAw2eLk8Mbv/e/p5eMALZ+jQ /cvz/t89/GP/fPvXX3+Z7y1V+vW/FQl3rsRZS0w7PAQdW4yJEPi0KFVRwqyEGBQRoBIT5Byo D3Wt2FkJtdX6nVJj2huaJ99uFaZvYOPZnohDS9tGhfxZUOqho1KokK7aA6CVo/k0O3XBdOnf DNgzF6sYIGWKGUg+HiMhiVzRLbyGMhl3oNmBNCk6XdvcH5DVeQVWmg9MjvBxwzcmzWzMEW2P vofN23ZSaO+U8QtP0x52sm3i1C5v6g//j6U77kOaKOBuaR6tvC/qw2l6qZDVc5QS4cvjc5ag c8NRpexIwaW6UaeozVL/UZLG15vnm1coYtyindVKb0gznJna6HDkcEBHJSYYhcxnfGJeOtzL PonaCI2fstMpABzuFOim3XgsxeAQ2uhBwnpjpR3FAuKO4Qtx55mm9Xez15BWDKAAplYTjk0H 4c6qMTDoOxkqhQc5KRPjgTGfWbW6SwGB4iIcJk1dJK/pfkVrD8SFrErMabYnymFNF4O+IOWQ 8s5Cq4wPIDuiHcaYmLKqVVcNaxYJIqP6chwLXa3XPE1yBQol2tH1VFgVqP1VUKYg8riUiUOC GetofpESZNTS5BcqL/JQUNVinKFUd2wzdrIuLLs0NTtK2TOJ3rovgT8tzlWzzVDXc4dXSyEK 2ATygu+cV98AMM6/cVkE03s1lJfa0qwUaBgedZ3brhYVXbpYL6BbaLIphuuYuJEDh224NN03 NFyKNoTC5FkeVCUcjvPMcuIYkOpX6tc15qSH/9VXfbrUtpb7A4hFDCuxmTn3Gch7fpgtTqIe 8sgvLZPXALVT+Q+UKJ7LjM1hpoUO75xTmSp0gmpW+XRGaFqY2v3TMx5vKBbGmAP35m5vxJFg VihzHag0UUxbFt5mfAo2pEyfjlkLS1s2mCBInzNob6rk77LfhDPkuCrcBl22XRUHFBsA651g CF82Nf4C5ZEMR3i/EUnUhxuHAG1HsqOg68i03CoksIJIikhFYp/8uziB/0bOA8wJrwhbJcR6 jw3km6TlJAKlCuB1bWMJzQQvspKezDA/ACGaKpCgirBJdhl4IWXY02aOJ04W0FIOyWLuKbtE tzdvXViXHcELRMolBxpSP9bBWTEoPYCzKrXBnbHJm279brdoPtZil3QF51WqJlMZpVWAUON8 AkA2cW0ZAJX7AiBaNrEeocd7cBPoWsMJ2HVZMg2TQDt9C2Q3ecQYQHiJEn+LK9yfgdCrIYTN Es61Rq3MTeF0DgaBKr0NvCyU5uO2i5EvvZvDR298vHqH2qZ7Gm/EaSYLkK45/goFgcXkic/z pBiSak7Mjfe+ANGjzQNUeteSpwXDJC03BG/V6fPhNz1AtSuMVROYiJw9vIfFSSFudmSf4hyi iCNYht5qJi+JrPXJM2ca1QfAbYpmQTaVDfJlTEQFpd1TbgCxx9vRo8yLJFI3Kv8DXbdfAdFo AQA= --bg08WKrSYDhXBjb5--