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 DEE2AC433EF for ; Mon, 6 Sep 2021 23:50:59 +0000 (UTC) Received: from vger.kernel.org (vger.kernel.org [23.128.96.18]) by mail.kernel.org (Postfix) with ESMTP id B714560EBA for ; Mon, 6 Sep 2021 23:50:59 +0000 (UTC) Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S234026AbhIFXwE (ORCPT ); Mon, 6 Sep 2021 19:52:04 -0400 Received: from mga12.intel.com ([192.55.52.136]:54267 "EHLO mga12.intel.com" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S229866AbhIFXvz (ORCPT ); Mon, 6 Sep 2021 19:51:55 -0400 X-IronPort-AV: E=McAfee;i="6200,9189,10099"; a="199588336" X-IronPort-AV: E=Sophos;i="5.85,273,1624345200"; d="gz'50?scan'50,208,50";a="199588336" Received: from fmsmga004.fm.intel.com ([10.253.24.48]) by fmsmga106.fm.intel.com with ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 06 Sep 2021 16:50:49 -0700 X-ExtLoop1: 1 X-IronPort-AV: E=Sophos;i="5.85,273,1624345200"; d="gz'50?scan'50,208,50";a="523518249" Received: from lkp-server01.sh.intel.com (HELO 53c23d017ad5) ([10.239.97.150]) by fmsmga004.fm.intel.com with ESMTP; 06 Sep 2021 16:50:47 -0700 Received: from kbuild by 53c23d017ad5 with local (Exim 4.92) (envelope-from ) id 1mNONy-0001Dt-SL; Mon, 06 Sep 2021 23:50:46 +0000 Date: Tue, 7 Sep 2021 07:50:30 +0800 From: kernel test robot To: David Hildenbrand Cc: kbuild-all@lists.01.org, linux-kernel@vger.kernel.org Subject: kernel/fork.c:1205:24: sparse: sparse: incorrect type in initializer (different address spaces) Message-ID: <202109070724.Fh8LRlM1-lkp@intel.com> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="envbJBWh7q8WU6mo" Content-Disposition: inline User-Agent: Mutt/1.10.1 (2018-07-13) Precedence: bulk List-ID: X-Mailing-List: linux-kernel@vger.kernel.org --envbJBWh7q8WU6mo Content-Type: text/plain; charset=us-ascii Content-Disposition: inline tree: https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git master head: 4b93c544e90e2b28326182d31ee008eb80e02074 commit: 35d7bdc86031a2c1ae05ac27dfa93b2acdcbaecc kernel/fork: factor out replacing the current MM exe_file date: 3 days ago config: i386-randconfig-s001-20210906 (attached as .config) compiler: gcc-9 (Debian 9.3.0-22) 9.3.0 reproduce: # apt-get install sparse # sparse version: v0.6.4-dirty # https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?id=35d7bdc86031a2c1ae05ac27dfa93b2acdcbaecc git remote add linus https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git git fetch --no-tags linus master git checkout 35d7bdc86031a2c1ae05ac27dfa93b2acdcbaecc # save the attached .config to linux build tree make W=1 C=1 CF='-fdiagnostic-prefix -D__CHECK_ENDIAN__' ARCH=i386 If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot sparse warnings: (new ones prefixed by >>) kernel/fork.c:1005:19: sparse: sparse: incorrect type in assignment (different address spaces) @@ expected struct task_struct [noderef] __rcu *owner @@ got struct task_struct *p @@ kernel/fork.c:1005:19: sparse: expected struct task_struct [noderef] __rcu *owner kernel/fork.c:1005:19: sparse: got struct task_struct *p >> kernel/fork.c:1205:24: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected struct file [noderef] __rcu *__ret @@ got struct file *new_exe_file @@ kernel/fork.c:1205:24: sparse: expected struct file [noderef] __rcu *__ret kernel/fork.c:1205:24: sparse: got struct file *new_exe_file kernel/fork.c:1205:22: sparse: sparse: incorrect type in assignment (different address spaces) @@ expected struct file *[assigned] old_exe_file @@ got struct file [noderef] __rcu *[assigned] __ret @@ kernel/fork.c:1205:22: sparse: expected struct file *[assigned] old_exe_file kernel/fork.c:1205:22: sparse: got struct file [noderef] __rcu *[assigned] __ret kernel/fork.c:1557:38: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct refcount_struct [usertype] *r @@ got struct refcount_struct [noderef] __rcu * @@ kernel/fork.c:1557:38: sparse: expected struct refcount_struct [usertype] *r kernel/fork.c:1557:38: sparse: got struct refcount_struct [noderef] __rcu * kernel/fork.c:1566:31: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1566:31: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:1566:31: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:1567:36: sparse: sparse: incorrect type in argument 2 (different address spaces) @@ expected void const *q @@ got struct k_sigaction [noderef] __rcu * @@ kernel/fork.c:1567:36: sparse: expected void const *q kernel/fork.c:1567:36: sparse: got struct k_sigaction [noderef] __rcu * kernel/fork.c:1568:33: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1568:33: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:1568:33: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:1980:31: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1980:31: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:1980:31: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:1984:33: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1984:33: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:1984:33: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2287:32: sparse: sparse: incorrect type in assignment (different address spaces) @@ expected struct task_struct [noderef] __rcu *real_parent @@ got struct task_struct * @@ kernel/fork.c:2287:32: sparse: expected struct task_struct [noderef] __rcu *real_parent kernel/fork.c:2287:32: sparse: got struct task_struct * kernel/fork.c:2296:27: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2296:27: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:2296:27: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2345:54: sparse: sparse: incorrect type in argument 2 (different address spaces) @@ expected struct list_head *head @@ got struct list_head [noderef] __rcu * @@ kernel/fork.c:2345:54: sparse: expected struct list_head *head kernel/fork.c:2345:54: sparse: got struct list_head [noderef] __rcu * kernel/fork.c:2366:29: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2366:29: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:2366:29: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2384:29: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2384:29: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:2384:29: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2411:28: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct sighand_struct *sighand @@ got struct sighand_struct [noderef] __rcu *sighand @@ kernel/fork.c:2411:28: sparse: expected struct sighand_struct *sighand kernel/fork.c:2411:28: sparse: got struct sighand_struct [noderef] __rcu *sighand kernel/fork.c:2439:31: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2439:31: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:2439:31: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2441:33: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct spinlock [usertype] *lock @@ got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2441:33: sparse: expected struct spinlock [usertype] *lock kernel/fork.c:2441:33: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2850:24: sparse: sparse: incorrect type in assignment (different address spaces) @@ expected struct task_struct *[assigned] parent @@ got struct task_struct [noderef] __rcu *real_parent @@ kernel/fork.c:2850:24: sparse: expected struct task_struct *[assigned] parent kernel/fork.c:2850:24: sparse: got struct task_struct [noderef] __rcu *real_parent kernel/fork.c:2931:43: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected struct refcount_struct const [usertype] *r @@ got struct refcount_struct [noderef] __rcu * @@ kernel/fork.c:2931:43: sparse: expected struct refcount_struct const [usertype] *r kernel/fork.c:2931:43: sparse: got struct refcount_struct [noderef] __rcu * kernel/fork.c:2024:22: sparse: sparse: dereference of noderef expression kernel/fork.c: note: in included file (through include/uapi/asm-generic/bpf_perf_event.h, arch/x86/include/generated/uapi/asm/bpf_perf_event.h, ...): include/linux/ptrace.h:218:45: sparse: sparse: incorrect type in argument 2 (different address spaces) @@ expected struct task_struct *new_parent @@ got struct task_struct [noderef] __rcu *parent @@ include/linux/ptrace.h:218:45: sparse: expected struct task_struct *new_parent include/linux/ptrace.h:218:45: sparse: got struct task_struct [noderef] __rcu *parent include/linux/ptrace.h:218:62: sparse: sparse: incorrect type in argument 3 (different address spaces) @@ expected struct cred const *ptracer_cred @@ got struct cred const [noderef] __rcu *ptracer_cred @@ include/linux/ptrace.h:218:62: sparse: expected struct cred const *ptracer_cred include/linux/ptrace.h:218:62: sparse: got struct cred const [noderef] __rcu *ptracer_cred kernel/fork.c:2343:59: sparse: sparse: dereference of noderef expression kernel/fork.c:2344:59: sparse: sparse: dereference of noderef expression kernel/fork.c:997:23: sparse: sparse: incompatible types in comparison expression (different address spaces): kernel/fork.c:997:23: sparse: struct task_struct [noderef] __rcu * kernel/fork.c:997:23: sparse: struct task_struct * vim +1205 kernel/fork.c 1170 1171 /** 1172 * replace_mm_exe_file - replace a reference to the mm's executable file 1173 * 1174 * This changes mm's executable file (shown as symlink /proc/[pid]/exe), 1175 * dealing with concurrent invocation and without grabbing the mmap lock in 1176 * write mode. 1177 * 1178 * Main user is sys_prctl(PR_SET_MM_MAP/EXE_FILE). 1179 */ 1180 int replace_mm_exe_file(struct mm_struct *mm, struct file *new_exe_file) 1181 { 1182 struct vm_area_struct *vma; 1183 struct file *old_exe_file; 1184 int ret = 0; 1185 1186 /* Forbid mm->exe_file change if old file still mapped. */ 1187 old_exe_file = get_mm_exe_file(mm); 1188 if (old_exe_file) { 1189 mmap_read_lock(mm); 1190 for (vma = mm->mmap; vma && !ret; vma = vma->vm_next) { 1191 if (!vma->vm_file) 1192 continue; 1193 if (path_equal(&vma->vm_file->f_path, 1194 &old_exe_file->f_path)) 1195 ret = -EBUSY; 1196 } 1197 mmap_read_unlock(mm); 1198 fput(old_exe_file); 1199 if (ret) 1200 return ret; 1201 } 1202 1203 /* set the new file, lockless */ 1204 get_file(new_exe_file); > 1205 old_exe_file = xchg(&mm->exe_file, new_exe_file); 1206 if (old_exe_file) 1207 fput(old_exe_file); 1208 return 0; 1209 } 1210 --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all@lists.01.org --envbJBWh7q8WU6mo Content-Type: application/gzip Content-Disposition: attachment; filename=".config.gz" Content-Transfer-Encoding: base64 H4sICIyeNmEAAy5jb25maWcAjFxLl9w2rt7nV9RxNskiTr/HOff0giVRVUxJokJS9egNT7td 9vRJuzvTj0n87y9A6kGyoPLMYuICID5B4AMI9o8//Dhjb69PX29f7+9uHx6+zb7sH/fPt6/7 T7PP9w/7/5vlclZLM+O5MO9BuLx/fPvn1/vzD1ezy/enF+9PZqv98+P+YZY9PX6+//IGn94/ Pf7w4w+ZrAuxsFlm11xpIWtr+NZcv/tyd/fLb7Of8v3H+9vH2W/vz9+f/HJ29rP/17vgM6Ht Isuuv/WkxdjU9W8n5ycng2zJ6sXAGshMuybqdmwCSL3Y2fnlyVlPL3MUnRf5KAokWjRgnASj zVhtS1GvxhYCotWGGZFFvCUMhunKLqSRJEPU8Ck/YNXSNkoWouS2qC0zRo0iQv1hN1IFg5i3 osyNqLg1bA6faKnMyDVLxRnMvS4k/B+IaPwUNu/H2cKpwcPsZf/69te4nXMlV7y2sJu6aoKO a2Esr9eWKVgiUQlzfX4GrfRDl1WDAzZcm9n9y+zx6RUbHtZUZqzsF/XdO4psWRsuk5uW1aw0 gfySrbldcVXz0i5uRDC8kDMHzhnNKm8qRnO2N1NfyCnGBc240Qa1bFiaYLzhyqR8N+pjAjj2 Y/ztzfGv5XH2BbFt8Yw6Ys4L1pbGaUSwNz15KbWpWcWv3/30+PS4/3kQ0Du9Fk1wRDoC/jcz ZbhkjdRia6s/Wt5yctQbZrKlPeD3yqik1rbilVQ7PD8sW4att5qXYk58x1owgcmmMgUdOQYO k5VlYGtiqjtUcD5nL28fX769vO6/jodqwWuuROaOL5zteXDoQ5Zeyk3Yv8qBqq3eWMU1r/PY DuSyYqKOaVpUlJBdCq5wKju6Y2xercGCwTmsZM7jNgqpMp53tkTUi2ALG6Y0R6FwgcOWcz5v F4WOd3H/+Gn29DlZrNHYy2ylZQt9+n3OZdCj249QxKnhN+rjNStFzgy3JdPGZrusJJbdWc71 wd72bNceX/Pa6KNMW8Eis/z3VhtCrpLatg2OJdEur+hZ07pxKO0MdGLgj8o4pTP3X/fPL5Te gUtagSnnoFjBuMDBLG/QZFeyDvcNiA0MWOYiI06H/0rk4So6WjAnsViiLnUjdW13e30wxsHQ N0WyKBxI9ne3q2568JOaG0qN+zZMovuYmABy2rpRYj0YK1kEnYNdUKj8NgcRHjhd/LBRvJQs J4m2rfJwrvGAh31UnFeNgTVzPn80dh19Lcu2NkztSIvXSRHT6r/PJHzerxmoy6/m9uXP2Sus ++wWxvXyevv6Mru9u3t6e3y9f/ySKAnqF8tcG/6EDz3jKXanZGRTi6tztGwZB8sLgoG2pRy7 Pg/wCegyQicdk2CDSrZLGnKMbUcbhueoQk6Mblw/LUgb9D8slFtQlbUzTR2wemeBF44Iflq+ hZNE7Zb2wuHnCQlXxLXRmQ+CdUBqQWsJulEs48PwuhnHMxns/Mr/I7D8q0HBZBaSl+AF/Oke YBziNTiNS1GY67OTUTNFbQAas4InMqfn0Ylva92B12wJjsbZ1V6T9d2/95/eHvbPs8/729e3 5/2LI3eTIbiRp9iw2tg5ehFot60r1lhTzm1RtnoZeI2Fkm0TzKhhC+4PVGgHAFFki+SnXcF/ 0pb8PEZqwYSyMWdEzgXEJazONyI3S0JhlLFkm11Pjch11Jwnq3wCLHb8AozGDVcUePICy3bB YaUO+sv5WmSc6BEOEh7N6RbhQBTEd2gsJ7+phM6IIQCkCDCIRPPVsZhhYR8IRAGjgO2h+ljy bNVI0FH0WUaqaFpeFTEocU2TiwnuB3Yv52CCM3DuOSmk0JRRFrNEM7d2AEaF0A5/swoa9jgm wNgqT8IeIPTRzthffhAqjBwX5sSidFzgWBdTLIwIqClJiX4otiMQ1coGHIi44YgknR5IVbE6 UaNETMM/qEgyt1I1Swi7N0wF4HcIIiK7IvLTq1QGbHPGGwd1nX1MIVmmmxWMsmQGhzlyvUmP AglsnhhjBdBCIJqI9AnOU4W+qsMsRxSKkOgNCUw8AmAeHA5wKzK86W9bVyIMsCMvz8sCdk7R 4VayJtTOMwgDijaE0EVr+Db5CeYqWNBGhvJaLGpWhhkaN62Q4LB2SNBLb32HkTIhidEBPGhV FLiwfC1gxN1KB0sH7c2ZUiK0+ysU2VX6kGKjoGGgutXAc28ASMYA0+HOcArOTWHaZ+wZhlVn bjeCtrMwGwNBVxRxOZPoqMTsoV2e56Hv8IoOg7FpaNNkpycXvevt8n7N/vnz0/PX28e7/Yz/ d/8I6IiB980QHwGmH8FQ3GIyOMeEFbDrCpZJZiQa+x977DtcV7673lUHE9FlO089BSapGMAB FzmNZ65kVDIAG4jFJC3G5rB1CvBCF1SEYwAeetpSQACq4OjKaoqLwT7gvUi326IATOSwiFsy Bj4qjhwwS0jjcWfanHOLwrA46dcLbz9c2fMgZeYCfpvvwH1C+FokZhKkQ2+ljWozZ05znkH4 FBwc2ZqmNdaZe3P9bv/w+fzsF8wshxnAFfhPq9umiRKXAAizlcevB7yqapPzUyGwUzV4Q+Fj 8esPx/hse316RQv0OvKddiKxqLkh+aGZzcNsY8+IVNK3CqFO53tskWeHn4CNEnOFqQwXFxHG A8M3NDhbggc6AmfDNgvQlzS9pbnx0MwHgIoHA645AKCe5YwINKUwlbJswzR4JOe0lRTz4xFz rmqfaALfpcU89GZORLe64bDEE2yH6N3CsPIQpXYtOIXBnAvm2gKrUICz5EyVuwwTXSFwbxY+ AinBoIA/GOKTLievWc29TuIi8syfRGclm+enu/3Ly9Pz7PXbXz6EjCKVXqGrhjimePIKzkyr uAerwebLMi9EGKYobsBlijh9gC343QdwoyjQgBJ8a2BNcZ9GXBM10fdG+n8UAEuCeeJG60kR Vo3td4ECKSukLmw1FxNjVXl2fna6jQwOmCcrlIj8ikfoshJgYQAwYzYNB0kFNcsd6Cd4fMCV i5aHObqGKbYWMa7rad5/TIxyucZjWc5BJey6V4jeWYNjSvrxecumxfwZaFRpOvAzdrqm134Y zPdzMINoH0APjVQXH670lmwfWTTj8gjD6GySV1UTPV1NNQjnHQBzJcR32Mf51VEuHctUq4kh rf41Qf9A0zPVakmre8WLAo6CrGnuRtTZUjQZGUl0zPMoZVCBoZ9obMHBAy+2p0e4tpzYnmyn xHZykdeCZeeWvqFyzIkFQxg78RXgGSqWcOYozX31JkjVOIWMwdnvcklXoUh5Os0DJ7qoK4SL YVA42jaE55lsdjEPIWsDRt+nC3RbxWyjk0ECTt9my8XVRUwGLCGqtnKmugBYVe6uLwcMxcC6 oRewUQiLn62r7YF/CPEfZlUxWOYlp3Mv0B24Qz/DAAl3ZLflEfTrOazKD4nL3ULWRCuwpqxV hwzAb7WuuGFkF22VkfSbJZPb8GJr2XBv/iL0m1eUA6kdDNGItgGIzPkCGjqlmXihdsDqQXzK AELk63CJGvKexO12gpeRgBnQki9Ytkt9L6szgWpZxf7So4sgFPr69Hj/+vQcJe+DmKtX4zqO HA8lFGvKY/wMM/LxHUUg47y83MR+dggwJsYbT9gvAyj3hENAmdOrOXmL486rbEr8Px7mZ4yE Mz8P8Kv4sIo3QXFMUAEG9Lne0RCJDE4S2JWp3dQqOePox6M8o8SrLwCU5HQ63gXlszve1UUQ FKwr3ZSAas6jDE1PPaMvOHr2KY0MQN9lUQDcvz75Jzvx/0vGEM+xYQTMZL7ORRuRURlVB3IK QIDQGhwvRoB7d2E8zXZ2rL+6x8u4QFFFiXpT9jAP73dbfh3NojHJuXNGG8I0qTGfolqX94tF UCcQOVV9t6Og/zwIPI1S8S8MDIQRN3yS3k10MCwnE2K4Mpg0chZntELRBkDISe69WzufX5hQ YQ2Ba+LXKpFQ/OE3eusWvrsbJWKAUYIGIYQkptFJWV7QcGN5Y09PTqZYZ5cn1FG6secnJ+GY fSu07HVQX+YjiaXCm9Dw+xXfchrnZorppc1bMqhrljstIDjAw6LwwJ125y28E8B0DirGse8d YoHvz5LPu+zDOtf0qmZVjmEiqjYVEMKWiGJny9xEF+i9BT8Sz8Zpi2WDRwZTIj6axsMzHGvv vZ7+3j/PwBvcftl/3T++utZY1ojZ019YRRhHyD7wpxYkClebajIwA1ZWBgd284d3VdZBcOdk D7J0ccoABxfwDn71vsvtnballKu2SRqrwBKZrnwIP2nCnI6jwLIbsIF+bM7b6iDNNZ5rlHVz XZBhrW+ryZQfTtKJ4msr11wpkfMwmxI3zzOqRieUYOno58yA/d2l1NaY0LY6YsHqwwlBcDrV l4PHisO2aZ00NaLdAZ/Q7LhEJWYeDGb8jC0WYH0xxzo1OLMEwMHKZLddNadjOzvfNgvF8nQA KY/YYtqyuzFmAtPPE7bfLZsEBA4nXk2L6DmduHHM5cTdpW+81RCowQE3S3lETPG8xTIxzGRv 0JfJuqTuPcdzwRoenK6Y3l1WxV0gY3oAeWOKo0sE/04r0Qa7IfD6EfZfxE4tdL7VEIP0JTaz 4nn/n7f949232cvd7YMH5qMV7nR5qu6E+HpoWHx62Acl1tBSp9VR6y6ZtJBrW7I8p2/zQymI fdvJJgyf8CWhUJ+4IrfVs/okV+hShhkFOUAHEFCQDiO+6zh8adjbS0+Y/QTHZLZ/vXv/c7gJ eHYWEgHgxM0msqvK/zwikgtFR9iezerAHCIJe4wpvoWY1nccuTegZ/X87ATW/I9WKMpW4uXC vA066G4bMCYN2wIydWeVIaII7lrc76U6jLEBgNDJopqby8uTU8pSQnhYRzdnDn/udDEnd3pi C/323j/ePn+b8a9vD7c9XIgh0PlZqGeH8rFxATOGNzPSg2HXRXH//PXv2+f9LH++/290m8nz 8OY5z+NiwUKoylk5j4ACxsZmRXeTH65CSO8BGrF8CykXJR+aD1voWBjdu0KLA/joi0H3X55v Z5/7aX1y0wpLpiYEevbBgkRLuFoHYSLmmltQkRsWR1XoENfby9OziKSX7NTWIqWdXV6lVAjH Wj1gyP7K8vb57t/3r/s7RKK/fNr/BeNFwzACyQiaxzmQ/iINAKCK0i8rf0NEbMTvgO3Brs7D CNQ/8oCwYKcxVC1MlOLvuIinCe7BXZSvdR1waVs7tI81RxkikgRlYBoS30QYUdu53rD07YOA +SISJ+74VmTPK7xNohiyoeldM4j10xthxy/a2l8DA+JEFFb/zrNYL5xYVIQylti7FpeAphMm GjVEN2LRypaowdawS849+ep0ApsVAKox3OmqqQ4FNO8zDhNMb7ltdbDofuT+iY2/BrebpTDu Rj9pC68t9XCl7opW/RekXC39xXrCPD+bC1e0bA+eKegKw7ruLU26dYBr4EjWub/B7BSs8xWR nC8rIXcV3/1Mfrjc2Dmsgq+mS3iV2IJSj2zthpMIYe4d7zdbVcPkYb+iApy0XoVQIkSdeOvl 6gP9BW1fcXjQCNF/X52iuiXCyJ7abMoqUFyi+qeqWgvhBcQQXTSAtR8kG4t/KZFOKf0h8mW1 3W1DOpjOknQ6idm/RKL7zr/AmuDlsp24fsdXO/7lR/+mi1gMzTN00kdYXWVCaI87zmTY4L7G HSpBnZKmD67nx1YjzlTicsgBlEb6J4dTSYJBAA5/eFGB9K68/2DUG4GynXq5++pUB4lC+/Qo SVTVNq3k8uQqJfd2tXapRdg2rJYgdMGrFfCwNCvNZbj9dkzoAP21Sj8Hs9PndnkGBzfIFgCr xSwJOi8sMFQHx0bLwuC8wcDITbc6hBV2H7v8prghhx8V+KQ+dgtGk3QP8VdDqU+HrmM7l5US M2owPsBledCHxHeKYtFlr84PGCzxggNyRVuO+03NZ5isXXmN6ZL3g+iEwER6y3kyA/7S9G/6 1CYosDjCSj/3W0p+TrHGGeFbmfOzPvMZOyk03GElXwp+uvJIgG+Z2jUHNUwj7kqtevc0pnO7 lGpP1RPHB74rYYSz4wrxUjF38QIO8moonVxkcv3Lx9uX/afZn76m8a/np8/3aYoAxbpln7JK OHcn1r8JTtK1x3qKFgNfVzdluxA1WRH4HXg96B3sM1byhtbLlbtqLOsMbjf80Q+tcKcfvrgQ 303RVQFeqq2PSfQ451gLWmXD0+KJcuteUlC55I6Jx1Uh6knfbaX8yQe+qeDEQ91ULK2xTwVR Ezf4QkKDoxhfOlhROZ2lZ+QwPN4jLa/f/fry8f7x169Pn0BhPu7fjR3Aca5gA8Bq52BddtVE W860GzhiYxZ8aGJeTiR5dX0axES1fwIPJwhcI275gfUfE/M+eIewmIiM3Evg3DXjHoBOi6gN JeCf3dcuXV6ypsEVZXnu9sGtKmUQ+yJtO+cF/qd/P0jKumsWu1HQ+JhC5P/s795ebz8+7N2f Ypi5m/PXIJadi7qoDLq/IBVRFnFw2wnpTInQNnbk5AmNxDRt1YQWYGoUbojV/uvT87dZNWbi DgJu+tJ2THx098EVq1tGmbnxTtiLBI6l5xCkg7+44MMVfOq7CG9kumEJLcs0TYG7093OdlJL adBEht2haW+M81CuDuSC+rwTw8oIk9beOX/vMMBEfd8CAzRUcrqqrxILxVIIgQGxTXwRXhs6 rbXGXl3Mw2fRvoBQdjnDMfuhqfvi/nGsQ0f+XXOuri9OfrsKTcQhrKSqccLS4FX4Tg7we+0q tQJaWC0NPw7K93tSoWNin2sLSFjJrK//NQ74pqHvQm901S/jKNvRnIpRObo+m4SVxH3mJWzA JSTcxmJaY0Xv61i77QrWvB2M4O0gcYPQEzMhEQTsqWHPsNaujAqf1FIjx6KsKC2FlAVHFUcz vnFVFMQIkO3wfXg8V6ggfQQ5mJNpizFqRZg1W819dXKfuHBmp96//v30/CeAmEN7A6dpxZOC XaTYXDBqocHLBIgUf4GtrBIKfhs2acqJOudCVc7ST6XFMTtIf5k37q0iJ3dG1PGUROOfkWVM 0/d+IMDyNb6iA6cmW7ruGYSaOvwrLu63zZdZk3SGZFcJM9UZCiimaD7OWzQT+MczFwprYKp2 S5oJlLCmrevYd4CPBWMnV4LTu+E/XBv6VhC5hWyP8cZu6Q5wWyyjK7MdD0DXNFM0E8kMxx2m GxI7PYzksuZAPR2jzZsDnY8lFNt8RwK5sC8QwklabbF3+Odi0DZiOoNM1s5Dl9x7kp5//e7u 7eP93bu49Sq/pBE47OxVrKbrq07XMbSjL3udkH9JimVqNp+IInD2V8e29uro3l4RmxuPoRIN Xd3ouInOhiwtzMGsgWavFLX2jl3ngPIslkabXcMPvvaadmSoaGmasvsTUhMnwQm61Z/ma764 suXme/05sWXF6Noqv81Nebwh2AOXaaUQU2OyJjlEjpacLk/rtGxsFv+0CiYiK6aiV07oJBvM +0HEVUS3SP1HgMBcRgfcb9VM/SUKEPZ5T5I7b44wwVjlWTZponU2Yb5VTu8pbDpVwQx4PZwd /IRZC8qOIatkcVyMNAhB6T8/gMy5Orv6QD/HKM8M1Y02IaBXwY+5EnmY0PS/rVhUsBy1lE2U RO24axhyl3um2EQHNiuqZMNtTt6tu7Y/nJydRu9zR6pdrBU1xUCiWocDyHkW4SX/uzODQQqt zKIf4d2rYWFNHP7NAYhCSx6TRZPnCSgAAqbcGK1U27NLYh4QPQfP8ZqlTHDNFYDMZuL1iuCc 4xJcUn+LDCfd/wkMBxH/eNu/7QEg/tr9DZCoLL6Ttv/P2bV0N47j6r+SM6u5i55r2XHsLGpB SZTNsl4RZVvJRidTcU/nTCapk7in++dfgNSDpEC7z11UdwyAD/EJgsDHKHxw+w3J25qK5R24 iXlk7qnOrOjJZSVox5leQC2uVFx2L1CZUb89USYhVZpMLuVU84d0mlUdJlNiFMopEVYmqtCa Xf1I0O7obbYXiCUupxfqDv/nZAPHlV/XVu374NbObbNdiBJEG2yLHaeKfLjYypHtlt6Tkwcf J2I7TslPadst0VWlICsJ5QHnQj1tS8bYx0THj7cGQzG97pY8kK3fs9UnX5To2+WCpQH2vKRQ tpup7thV8dvffv76+utH++vz1/lvnZ/c2/PX1+uvrz8cQFJMEaXOVwIB7dQimpLrSOSxgqqw 2hhZapX1rUYokBypZPvF/EKaSh7KaS2QejclJ6kJCdhTNdwN8YVlQmdhbhU9PUNYJOvSRB1o FJmidbdzJuynwYzIg44hkIePNSfz3S/mbit2HIwd807+TgZBZ6/JRCwX/vUJG4ORzn3DXIPx aU2OiNo/4hx9G2SRHuxL7RC2DoZmtANZhaLk+UEeBVSUUgw6m4FhzOsojjY7kFNQe0LrHhXN f6IYJQboP0OXRhBbVz1OpdsvSGs3klpuFQsnjKVTYaLcDGLfymqymqlvB73E20PpAkarxHOf T+qhqv3bRB656G8ds4ONUro9vYkYElrzd/bqqkGj6mNrY9mEDwMiaGfJujmfvs7O7Z8qdldv OK0OKSWzKuCAXOTC8VsYrG2T7B2GaUEbLeVZxWK1I+oojecf/z6db6rnl9cPvD08f/z4eDMM bgxUPUvDh99wrkZ7ZMoOni2oMrFOqmL052PNP0BzfO/q/XL67+uP3gvRCgfJdsKDNXCH9j6i 1LB84OhAY/QDe4Sx3qJfTxI3JH0bWyv/I8vIdr5Y62Gw2NEOCA5dsSM1qoATmjZIJGyO9u/v wf3i3iYJWdSD6yoQbmJdkYnzKgofdHVMSkPUUKYRo3zekQezzc4hYmmE3hJoXrFhIpG7OzBs 0jISPKGX21Ivxp7ionZSZQ2xTaFWGNyIsqUofrRazZwMkYQuHpO8FOMCxpXqgETg/00AJyRn VNdnVyqXXfy0krMd0ZJmx31nGFjmJuSZdAu1+Mk6uJvRAAF2H3pF+qr5BdLmwnd39ab6oGdR vWALot+Qx8CiBy46bGgMEhp8mJg9w+pgKcIhIkbx2GOugb2JWgAV3fQSAkImEwWQb9JYIUuX Nt5xmcX0HsV0YT1GQH9lpt3n334/nT8+zr9NV9kx5TYSYS31bmCWB/Q9IyFMNfMA/+zPqw7p hNB2ORvUekfRsKxvpiu/r+7GNpTA7luVtBERmLuImsWJCNuqc03pSEdR8VS7XI+DKNmgKcKa J3rV7Rnvp9PL18354+afJ6g0Xp+/4NX5TcYiJTC2ck9BBRpv0xDxqdF3fYO/TJXshKlB6N/O IOqIIi/31hDt6JvSew6+dw4L9yOMpjFvBG1Sj3i5bR3I9FFLSSgTYikZ6IOOui8S6yBLmXd7 TRrho+zb4Q1CcPDUPdcp9M9MWh+SMJGiJweRMegHdVGkvcbbzxTfNqr9B52FitNRNR2ClzEp 3B8dGLuN4QErPN79gxJJNSNwmSwzKxtFoY7sA0/FlUpHNfOIoYfNXxIeYTu9gm1Z00u2inWS 1IaAHBXl5LbKhaBIFQ9Z76kjGLLQAQOn84hEaqUUBX2CQB4cAvw8JgUZnIxFdh7hdmugDykM cRVK7+lcJePpSsVDL29/e6PEX+oYLcirOf6HFOtcX9x9Wx8QgPbj4/38+fGGUMvjNtJNnq/X f70fMWYIBaMP+EP+/vPnx+fZjDu6JKZdjT7+Cfm+viH75M3mgpRen59fTgg+othjpRF6fpLX ddkhHpFugaF1+PvLz4/X97MbesjzWAU9kCqIlXDI6uuP1/OP3+j2tqfAsTs51y5IgZG/Pzdj fW/S1ll/jIIiVnmAjVkpYnvHGQO2Xn90q+lN4fpv7LWn75anpXlWs8idi6LxusihzkpbKepp cD7e5yTWes3ymKVTvH1V0BDBp56amXzFEAf39gGj5HOsfnJUzqtm1QeScvqJEeh8ZIJ6V7Ex oG/8pjGVCjwZ2mPcxygB2N006hzZJ2OS3o+UHBnuxw2aEFMICQfbT7BXqZTDqcn12H2V6q3e UCBtvp1mXnGnN5GOGm6Xtq04BjbQF5coxpQHZiesHFuJ4gYITYS33NeF5/UVZB/2KaJKhiIV tTD1w4pvrMAI/buFXVFMiMdgQsoyU91VblwYRqGGSeJiWsFI4bCS63g4su8802uIh9b6sjHf sq2wg4U7wlQD7Bkqplm3DFkFsxhjGSlAm/ME2GxyM6IRf6HxxfImU8QM3wugGFJUCc3Zh83I GD/F4zxdUDuxi9qhw5tcNI6ORO3kpo+VcrBSgzmD6QfrweDUVk5NayDcYYxoY+Eh49SmZ9H1 Zvn69WPa1TAps0f3OR4RZhgfSC/vW5jxBc2rRZJNMMH7RS2P0kLiiRMfDsJDtlnitmxBA/ca gMrtnj5LyMq1vvXfdWwbxPZXyoxXQ+s3W98rZA2CJcNgiRNuukTPbXRh/RuaEirDqnYeLGd9 93AOy0dmaBJ9ayh6e7+ImjvzEOvIG1MlXAWzSdt28dt/Pn/diPev8+fv/1HQ11+/wUr9cnP+ fH7/wnxu3l7f4TwMA+D1J/5p6gU1asbknP1/5KvNtW/n0+fzTVJumBE0/vHHO24dN//5wNc3 bv6OSBavnycoYB5Z+Asa5yHz4HYMXPh3RaBuaImD3sQPGWlv4tHWMmygNzxsZFHht3ApkQqh q3wSWxaynLXMc1Y4lCz3LJzWrNXPm+DVRGfimIwrZKJDvTmmqATG/r+XjqO5fpaMc34TLO5v b/4OW//pCP/+x1KIB+NIxdEaQusWHbPNC/lIft7FYowWZhH0a4EAWmrzpk6+cEDvrHfOxaXz TEdY5LHPEKhWQ5KDn7HZ+xRb/qBQDS74+Nbcs1DBpx184Kyi9LIOjY+D27BHCQph3O9jWgnb eDzBoH7SA2kG3wV/ycJFZOlXlj1dQaC3B9Uz6tU9T+oDr2lPyzzNfMC4oIxAvrR1r4ocVs+o s35QWaorkr2jAbm1x62u83DzzHXk8tzPwykj68o3WlDkiXlsJsiEdQQx5Lx8Eder1XxJ4+ui AMtCBkpI7N4eGiLbohJPnj5QZfg9+TDsaT6b0V2u8vazYKAV1MRXN3iWxxsOLjixwTe0i6iw LkoOsFNzGqGmfiy3BQnmZOTHYlbW3Ar+6EgKuy8RpPJjZrDh9nrE62AR+Hzc+0QpiyoBhVjP UspURIX0rIVj0poXDqQan+w2w4GthvNMW8trH5GxJ8cdYGRZOKfwcx0EQeubzSVOy4VnOGZx 22zIQ5pZIKy9eS0sgyt7cKOniHRVRH8ADqfCWQxS34RJ6XsxZPhGchr4Gv/KKAgrOKg74zm8 pf1iwyjDjYBeDMO8ob8n8g2MWmyKfOHNjJ5QGrPQPQGZCX0eNOMHRw76XZhTlnQjDSZwHoqC LcznvDkkOggTp9tkbXkq7buujtTWnjvRnk2318CmO25kH6iDp1kzUDuternTmkiiYgbtS9Sm xafIaHWI3jKNDGN7KdRBKrTnt5mqu0kZC0rntHVK7vPYtVNP8+PZPrXd8UI+v1p3/oRA+WSn a+AskrXds6MJQmiwxHq+bBqa1aGpj30VkCiz3L2fVwTPSWdDn4mBfvBEujS+JO4yO3JuvaVf GWsKIxtv3c3P+Z5d6Uo4QB+4/c5FdshijzOx3G3o2sndo8+hsi8ISmF5YY2aLG1uW49vFfCW /vfOgCuPF9kJ5dXjNJc9RHZyvb71PPANrGUA2dKOtDv5BEl9HhVuH3WzYEgNzbK6XVzZi3Tv 8oyeCdljZaEK4u9g5umrhLM0v1JczuqusHGt0ST6jCDXi/WcmmBmnhzDOWzdSM49I+3QeMKV zeyqIi8yetnI7boLUGw4Rq+DOqgemHD36mkO68X9zF5r5xOfGqLcg4ht3Ui/zc3Jp0ONhMXO qjFa4XxrBMK8XlkMunhbnm9E7lj+QJeEEUhm/MjxAiERVzS5kucSwW3Ihn9Ii42wNqmHlC2a hlZZHlKvCgR5NjxvfewHMs7RrMgerUiZpb09oE8h94W1VdnVQVHF1qdVd7PbK6Mejpag5Fu7 7zpY3HsOs8iqC8+Dn+vg7v5aYdDbTJIdU6FrcUWyJMtg47cuGiTuUO4pgkjJ+QOdZZHC6Qz+ 2U+2JnTLAx1vzaJrZwgpUtuLT0b389mCQjm1UtmvUgl570GuB1Zwf6VDZSatMcBLEfmQ8FH2 Pgg86joyb6+tmrKIYM20PNBMbq02Buvz6gwG+F/our39tDwry8eMM8+zpzA8PG9nROhz7bGw 5GJ/pRKPeVHCucVSTo9R26QbZ5ZO09Z8u6+tRVNTrqSyUyCgOWgSGBcqPWEydUq6wxp5HuwV H3621dZBj7W4B8TYEjUFvm1kexRPuR2bpyntcekbcIPAglR3jcz1VYuZeXf5gstjKjwxxJ0M a4R/Ge1k0hT6wyeTxLHHci/K0o8VIEP3tYhxw9w++hzgUJ0lXrvr/Dkk5eE++GdMuEaJpefN aec8pjLcfnydf/l6fTnd7GXYW9yV1On00vklIqePFmAvzz/Pp8/pjcMxNR2w8ddoKsv0RkPx asuSBT8vQdrX26VP1bEzzcxgOpNlmE4Ibn+SJljOM1guq4IdwFq1Cll7ol/LSsiMDFQ1Mx3P PxSTgy7nbVNTmSfYFetO3RRvUAoophQ0wwy+M+m1R/7pMTZ1AZOlbHg8t00TRza9l8IborfT 19cNMM17qOPRvQ7ppoyVwFj1sgYNj/RisP8uarlv/RAiMHmloLcfXBYod9Lx4Cxj4rrt/efv Z++tXu++a/50HH01LUkQl8r1TNY8jYOFCDzEENQiGasr0ewMsPP91+nzDZH0X/Ed5V+fLd+B LlGBsG8q7MMpseegby+J4uKISTgBg4bdfAtm89vLMo/fVndrW+R78WgFn2gqP5BEw+ddN73P n1cn2PHHsGCVEcPRU2DR2YWWxXvgpDvg0OfzXsT1v6YlVKiYJ1p6ENR9d1km58fac28zyGBs Idox6LE/iHUq+pXKd0/EdiDMV3KsiyM7MvrCbZTa51dbtamviqDXED7hQc/Qcch5By2MNtk9 /tTRe0oLh3o4c1pWr4G1oA6JIzsWRH5REVaMoG+SOVX8pjJ3AIvcZiRnj++kZUVNVlntcnSs 6yAjRcyPGBJdEdnXmX1GHXNWlgiyBwaZI75v77mKHIQytlHGvMtSCpWxqGiVzJYKGflgySiE kUS2H974uUcRfy8oTXoQedryfLunuhTXMMeHcuA1pQeXaJAom4o6ogz8RAp2F07XaYXhQ3Vw xy720Vavu2OVDSIM29V6dU/zose6luXECWMqcuu3nprCCMxbVvSiacptWVbKrc8xxZTEFwJZ 7xB4VbrTDYjGMqU2+/zJ+73c4+hmyhwZWoaO6xn5YoopmakfdOPDka8xPUmtdLtVMPdVEfYA n4u0Kab+rtALki5D/X0UnmEDZ12WLRbLpnsSl26I7H7lsdWZYnBUUf7LhfSdFCf1EvU8oPU/ S1RGamhQYVLdPiHs2msqzIng1q/z8EUzm7x/pllqxw85d1y7DWbMER7Do12OYgcB+8YFoaNA DLe8DWsPLHL3JXXK5HUhobyva8/LzYMaBetr3kleEFRxRqDRXJJ55OpAc0EiyoIZZaDUXPTn SvHxNjQ1Wbiwmr+fBM11dTtmXfd4s0YR1fpk31ZFzdS7h6oXXREWN+nitnHJUcYWztWgZsDW y0p8TSSFv0KP50+Vien6qu0Az58vykFT/G9xg6cOC6bXAsEhfJ4dCfWzFevZ7dwlwn9tp1lN jur1PFoF1ndpDhz3S0nd4Wl2KkJgT5PR0fOa13m3kOmAiLBL/rRV1CW0yFo9N+l7p002LOOu X3hPa3O5XK6JMgeB9JZMx7N9MNvR7geDUJKt3ajt7kxMdfrgkUmdRPVJ+7fnz+cfaASaeJHX tYVud6DaEUFT79dtWT+aOP76bUwfUYP7f5svh2fZU4VbiHEZGLXSn+Hk6fP1+W0araw3dQ27 G5lLbcdYz5czkghrbFmhXwSPFTSk9fiAKacd+a3B1LOCu+VyxtoDA5Lv9GPKJ6hqUygVplCk HS49lTbxkK1amnGlJoM3rPLVP0MtgfScMaXyqsVIbAPm2uRW+CpJxgcRsiAF5huT92fW1x1h 0vsqGx+vtm9Vz9draks2hVLrGVGrOcQw3PKP91+QBpmocadsp4RzdJccP961YNsSNjy7QTT6 2831uydAo2On6BRHI3F1EjKK8sZjMtYS0HUhr2LmcdHtpMIou1t4dLROpFt5v9ds40IEeESv ieF99NWsKs/tnmZXJa2rdOxEQiOW18pQUiJPUt5cE8X59BQslhf7pHQ9zHsnfnuFc4ZKFtWV juYnBkoOQ0jFNXqc1wcrTV3Txhc4z3jGWl48FT4XjD1euJBXSl290JxnRZkZdPU9kNrdOYGE hvS8pvW+DrYquuCVLspMgP6SxympwG2P3UM5Y7UGkn7tUhTW8wIj17kqGBmOS+zICNkteVc8 Smy49TzEyHCu+EZGI8ot9+j9COIpIk/wlizyR89VV3akQZv0M6D9uOuIZbReLe7+dKg57Nru +ISOzjiFuJEfKmY0Mci542BbkgYL6NdNtOVohukeJu3HTQT/7EfBjW4tPeFsmEh4gHAj9U1e nrqL8nJhwYCzuccUiwJRRe27yDnUCK1QFY3x9iCMu8h9RKIRafo4idHuw9gnqpxxVugapdrL WgG86/Dg6ZXFPKL2PCRTRZrihvTCs0SX1GWfHT8r1UwWUizuVtYBYkvjRpQ2YEQpp1eO+gq2 lDc/3l51xNv0AzFhlKoXOHdqlNG3faOUUlnpCvUi3cwYiv+XemLp/GGBEGhuXULlPn78e3pL hFjTwXK9bvuxoCMQFdDMTXcdjVc5uQ97+vwB1TvdnH873Ty/vLxilDBsOaq0r39YTWCVhHsx 9Xm2kH4H1pfFIaP1N0escN2U+nvxSasYWYgcdxPqlAWtrlVKm6BwRRFOAHb2TNTflsHclGi7 +FsnkageXE9KHdTrveRRmamnSDyVayPnXm0gtgdq31DsLgy77//ucY3/PP/8eXq5UXUh0CH0 d2VxSb5eooxOR43TbCfB88OVipB4ZUogC9d3ckVp5JrN86dgvrJbupXC9tnVpq5mvaT1qv7D 2iTakkPnQvvoCQej6ZeOi8dlpwXNYoLZbYuuHbdr7lQaOYh8AWdCmgNpHEayCuC0MvlS3TLU pqk4ol5PGizaLoJgmtNR5BgK6cvpKIO7SNXKeGL9QlPowZbEmnr68ycsNdQg2/GsTGkdl0g9 YJxcybULM/IPgbBee44oukXTVhR0hJI28bijx2aKvncvCnEtNacDP7RtMI4Wc9c/zwBnoZrn 8Pp5/h0W6gsjk202Fd8w/cip9d3qAVyzk8ncBpeNoF9Wgl/+eP08KVyH7Pnr7PTJMejRV/Fa uqAbfhSKZXCkhvQoYSuTI11uhFl5olZmbeXb839NsxXko/BIWwz5yqz8NV1mNsL4wIjl/HZG rzi2DGXdsySChb+Au2uJ5wui0sBYz5Y043Yx8zECbz0W9DWJLXPtQ5ezhi55tfZUabUOPF/H Z7dEr3e9a+z8Cq2s4pI8KwxYZmVqGS9N+lRDpIS2x8yOIihjpiUoHR6BaEobXztkNQzmR/XR d5Yqa3LWlAelJRBMswwf5qumabyMdhs/TJkZa4LV3N40oi2rNmpGN+v72YKoSy/hnvPGlBju QOvLvUxaL+6WlG7TV623VpiHYguaWv2E87Fz5kai1sdQS5so/fnzGZY6SjWSPJdFJVsmFyuP V3Uv0QhQHXP12F5VeByXO9ndGkMjL4sEs6syCcuC5dY72Iaa4esdMjPuuMavKrn5mkRPj3ma wvJnrX49Tyx3GCZ+oTxUXmbLhEqs9Jp5QnsOjSUsSX/hno+mUWybab1tBain4ni+p+jVajmf LaYMbcSY0YzbOZFVXkd6GxHSfUu8l4jqu7s1NXFMidVqSWReRtmqaahc5bYOfCbFTkLI5fL+ ikwmo9tVRk08WyRcUO0IOubyDhYTFybe4lOtphiLuykDvdKjct918qS+wL5b31GhwINEHcyD gExbr+ek2a0XOK4XUNktOXw1j2+TyQqCp8KL6wfwpxN1KlbvZkFADX41ukwwq45gQJM6DPXg Kfr+yCmPZxwW3Bzv2bBORZLgnGew9stvM8MY1YmrBdRfqfZYCeVGhE8B24aWXqJ/nmNTHKBi /0fZky03jiP5Pl+hqI3YqYqd3rIOS/JG9AMvSWjzMkFKcr0w1LbKpRjbqpDkma79+kUCIIkj QXsfustCJhI3mMgzysH+ABMqYvgLjxRtKvleyjzuHTfi6iFtkUTgbRexFgHB99Il/987DXU9 0iSaiyK6U5bUagJ8mj0zioE0WL3sn0FKc3rBdJ4iABJf1CD29OMjYDQL6rCkTVP4Tmao48nV FmlHpQYoGJ0uCn4fLbNjoNRBiGk4ZQA5lLO4CSbfqq2xSWm5R8jpEWZKlommxFC9tcVptvHu M9XqugUJ5QEXjcpsviGCBXa0XLwGRJQD1SK4BD9dOwUXL0L4yYaOfHxtdpeHH4/Hp0F+2l8O L/vj22WwPLIRvx7VndBS6ijAjkQ6qyOA59HvL+8hQa6090nlENlKe1kgiOoBBrJ9s+Ko1rSj z4/Lqhvc0tVNoShBFIDSFtIjEE9dTW9QMpvQY2RCXB0o/ZGaehgjLWJYINs23CCFjLsBBSwC 8QKIlBxBV9Tu8cScEE3G2UcvJoyxHF/3IsyGV0MTQYIjn53S8XwiW5alnC2cW92hOfiPsZON GdlRRmlByjwYoTMdVUWGjaS5M/wZo6x1gviJp2eU2XgLdsxcIyXT8dVVRH03QgSsjxPKhtUD nM+Go0Uv3Alc5X17SMjZ9KHTYDhqp6ObfqHFwyeQ87vDsVknXZvL1emPhAjL2e3plT1b3Voz 9spYL1Y4G02sXjPm0NqdTY0EjDaF2NMaLIONZ/6sZ17Lu2Q7nzrBwJni7TZ8o9kmK5/PZtYq d9AbCVWPdLD6Zs1DHeWMxx6jByEl7FHunnV2O86uhnNHH8A0wRsNZYON0PW3P3fn/WN3mwa7 06MZUzoPsD2oXIQl7rdH2YHKM0qJrxkzUV/7AfYQqiqe1woIeNnhtRuoQSUkWU+dBqyXNmGC A8INcZSq3Way0HDWvkNzJFzwIQO93TlfS3DPkcQwILg8it3CNSFSC6BonAYO78ZhVW36Dm7c QYK9AjQ0Q/ojYKimNXl7vhy+v70+gKrRmXIkWYSW/wAvY89ahzUNgBPQWuN2EKsy4MH5A1yw CbUFs3lXecUtqLTdSrw4D2ri0A4AjDpgXSNgcsZfhx/Bc8YgbdHyJKh9R4I7FcuN8YeXfmML nYXoXgUM8XFRriuY73IqZAMtrSrwh5MrO2qt1hii8VCgNLm+Gurt8CJDIcDL72mgyh6gTHMx 8HQ/JAHvNXDjKPxr4AS7NY8FVyDkyOgbdVff7m9G0VqpdwPrDNebcNwWYEG2EetbFpeemuu4 QwBDqYrbGKa00ixlOhx4uPJ3ay+WvhBdOWyHoZYEToeNMEGYjnI9dxG+vnETvpn1Es43CUaV R2Kx4rOrQHAxWQt7NaRljhJ7PvExuWgRGO9MVqAFiI+JGo3Qzxe8BCL5RpqxOiuT3h/Y05FD 12ZsbVJAdCGcvywYi8nWlzicSQrEKFmFptU6c3mUkqJRMTvps/42qSddJHrcsgDqIMwd5esA gnJWuSMEu8CRcOXaUItlzm/t2pBwPyzW3A6dPZYj3T1S2l08HnbNqb78+qnF1xfd8xKwdHX0 QHiR1uXahRCSJSnBSNeJwVM2KkBzDGHx7vQ0thtuKlyfhZBRDCysiWjaWJMwyvSUgXJqhAYl Vq+ccO03h0iqvR/3x0l8eH37q81X+R865fUkVjwzujK+fDypOTHB7GlsXqsCIK7UhKQ8lEK6 1M8Yp7qIIVNXzNB44k90NrBeK/tFybrSjcnc1u3kwJz0zDlCTKTfOTwdLrvnQbm2Jw5mOdGu JijRoutyFG8r494W9PfhVAWBh2ZCAjFVVK8WRkkFrpYBCDbZ2Yfwz/ozBrCqOMKigTRZgeze qwfOyDQkfg6+HyAoPXvM7M6M2vP+4QJ/XwZ/X3DA4EWt/Hd7znn2HOdZEQetnQ/1vhZHkExm VxiL04F1MwBRziaZ8L+cx1PoWxQPFd7zh+PLC3AUIt0Hfjb8ajEyvkldOXJueHkSJVlO0RoJ 5HpR9YsJKJ68lI0iLNdYeREYB6i7bJAg7xoi68eI/dcTDB62kUmumZ4kCb7Ck28Ap2cn4tqo skHoIY/QU6xVCbO+v5Qtt3t9ODw/706/kKeLuMrL0uMuukK6/gZJcx/3D0cw4fkHpM992J/P YOsJ1pkvh7+MoD8fq8BrFCFtEVUSWnl3e6q4SilQ3WmNqrSMmkq9/asLu4Uo2IcXNpB/7SGz xODhx+EnUs1G6Wbwq0Bhe/3nic0GsNAWlX5E4TkWFoPL2ys7J1ZtE9RmUdmz1X7dH9/Ogx/7 559Y1R4smXLsBVJknPevbBkVdzolzxiGwDFiVobFhtLKRUKq5935B0ZdB4h5YM9P12gsmBjD 2/lyfDn87x5uYj5P+ggQsFrvvOfnb3FiHylWpd2C/FV0vrA9uDs9Dj6fdxc2kYfL/svg+zuo D9zQ+b8G7Dizhb6AvwxSiTX6G+2nCyjl4PP7dALZKAL22INz8Dk9ni4/Bt4LpF/avX69PZ72 u9dB2RH+GvBOsysSoUFo+IGOcCx9RP/5warNp1TBGhxfn3/J9fqax3GDCjkgJD/RbBweI4xP Z4MUiA8PaWIFDT5H6fXVaDT84kpOp28We0/ol69903Iiy9Pu54/DA2qt7y0xbdJ6yW7lQpE1 ygLO1yzzivM03SeHAUXevKhwxMIM9czDYguwMuTwqcXimJ7YYR/8+fb9O5ucUKkgaS9wHg+t xuv5u4d/Ph+eflzYRoiD0BlWisEEpypfjer3GGC9OZXh0RZzZ3CVBNrT9/rT5ikz17FT11Wp aqMEP+uMUtNvUisH7R3rGlHFxxqVNDS9hKAoD/QK4FQrwtbaIBrddVOnlP/hqYGBmhLpsK89 d6joMCj7NcFyCk+NLePw8fwOsqMAtXrPJwAScxp5Yxuw5bOiwKXK2ZgAB0PPO8nOC6R7pL+P R3pTzQs6i0N4Z+GCcehSkQU1qggHKOPbfPbClj5/Rr/0Z1pb1FQyBx+Ucb32YhJaZhX2Qv0h nyhIG2themxtkpoutSyOcoNUoMktzL7wnWOml8Eq2ksMVRvTgC4PrIGQgE8Bj7qlw7zgZlaD /Cgwe+TmpcXGIWYFLxzO5zfOdfViOnZFoRXgiSt0poCT68k1btDP4ZSsenYVW2Li8m1uwVy8 5nCGBKRqPh/29JCBR/3gcQ9443A+Bti3cjwezZ1wv5zPHO7WsM29q+EVLrDm4IS4dAj8Mtre Lx0R8HltOhnN3avCwFOXJ3gqNTbuOREKHa9yRvvnh3i7cPc+9IrY61kUdif2gWPvvre6II+7 b7Tk3WBB3g1nT3lcXyU+B24Y5KwbOyxqU9D4hGTpnlIB7plzgRD+8S4F98o3JNwYfSFNFHgP gZQOnRbaLbynATq8GbsPHYCnbjASbEX9robUfRkB0H0LMeZiOBu6LwsO79lUXN0337rnpUFw d+E2K5bDUU8f4ix2b854O51MJ454koKHgIxnGa6LlZyQM7QCA6fJ6Np93+XBduWwuGXQguQl CR1qe4AnkSM5lYTeuFvmUEemNfGdnrp3MyV0djV0f15plpJgTfyeeS0LNrLUPW9r4s1HPbe1 hL/zleSaxIy6b4/1djRyT8J9ssDyx6/C37jwRjOk5mfFExsWfWi0tf5mVMkhwHHMGE1KvkW/ X2nckia1FAWCTdJjU0hI49Ta9yoAAqHFL8li7iVCRi5GS8WieUgWKJkEODn3jaLgjP96F6uI 0syhRxO8UiJshh09luFfYEz1ZkVoGdvsbhhRskx52lVj6EK4cAyklBNECovTfn9+2D3vB0Fe nQ3ZQocq5dpIlf9RhLlynBCmxaOFxfU2MOpI16jVr8KEoPGDVEJqsGwN4FpMAEZG+wgKe4Et SIzTjuTQENA2WNuPDwkr8oRipoMNDkm2fNDVVpWD9y6W8WlhW2JFpqPh1Ts7niRLu/uskFMg KTaABoob56pYuVeww8/2sGbOrWLwtanVOJU21Fk5Z1seMthkIlRuCg4JHrrRpPU9hZCqecxe Z5iVe4MMEYr9MljTECNFswVKRBynMjk8nI5czXU6voJEhRWx7xgcYiHYP9sZw/8ftez+SJtm Y5VdaJzFh0RECXfq+0gVvgg907UtId80dKDRs/BXLhISTr3im5ewdQF71ezqauSADIdzN6Re bRDg7eT6eoKVX4/nU2yB/XA0n6I2Mi1GWRv5/xpIaAS2NsARnQ3HE6wig4wmqGdU8/Urk6lq j9XtbFDo3Y6vxuhgEm97Mx9fz1B/rQaHJvOb4bTeBGEXQbsHR5o/2Ejse3w9vBp5NoR1kD2r 7XJKEnaS2Lcwj8mCmKIdBUPchCbMdbFTmozGupO4iQHJ1z2Ey6hCbzgeIxtmk8yvR+jaMchs 3sNsymWYXffxlIGwrurbAq1hllU19ar52OWLKHHiHGTFG+qBhbMjjLOOu/44arH9MGqJojba CO3m0CZA7AOQeNZVSWJz6TqwOUGGDaGEKHymYHtJiCkxoBjldxX0hiSYqWWrgNQxKRkrxp69 IVEztAAcMeaBYrBcYY8GXJAACFWck9qKKaUgsD9Tl40zwHkI2ZXHuIIgNFp31BCCRz4ZgMQj LRo2HlCe//h1PjA2ZBDvfhkqJkkszXJOcBtEZO0cgIgm4wqb1dOSQcYLl47cu+V97kinDhWL jC2Z0DVhRh+JzlfAV9SMOtihctMs3ehB2D1A7h/GT7cKudCeLajuin0AMBqudIlwW+i2UG4x TFtnm0RcLhKc+gL+HWPusYCz8XV2ic8CWSSskrNPgT9zSHkBuuaGcuwvR4sV6w6ZsmVTfMU5 1buVGnMVilb0zupbRlfE93rnLHHEOuzmZMsecI6kpVHCQ/8jnU+jjZFCBn4JZR5WJqzbUAgE TiZBFutu7xzB59Hh04hhrTYQ5CFd6nFehUkDK7MYNV5fWAobraZROZnr3vC8fFOguW04zPRK 4IU5GnWGg1S7a71SXI6vbzDGjEOzcsSjdBvj4m+lP58Pr//8PPzC75Fi6Q9kUpc3COYzoD/3 D6CAh0tdTsbgM/tRlyuSLpMv6vEUcxNvWS9dHQHrbavv4AU097E3LAczdiROKsuLn8PoMhkP J+3QhAkJWAqVx9PDD2MJ9VaLcn6t8wXt7JSnw9OTvezwFVoapnQqwKnE1JAytu9WWWkMpIGu InZz+pFXOhtpVdzo2dJQ2eP3fSQvKMkaz7Kn4ekm9RqocXnly8Nn8fDzAiYk58FFTGW3odL9 RZg9gvHF98PT4DPM+GV3etpfvuATzv71UkqE2tAxUm61/N4QcumOi8FE/ixnAzl/E2L6WX0y 4Q3p3h+OULZeEETgN8UelQ4MHpqT3cloZLSIvexrdiuAOpwGRaVYkHCQZYhdlEGthRiEgiQY TqZz9lQ04lkDzLIclrAQ3Jka036rrNVTt7QU2Bp3vmIYirVJV00m6dWa6fxF2A2eRrHeiSYg YjfLIoZzQpfQiD0YIQghDDjV3jLg/2/U6GDx1oRJCHe9WAG1Olkm2s7tQNiUboBgYPmWyXK0 F00d3KlxRSsjBvuizkWBsr+4L5fRpXZFAhF4VDNhovdpAC8WxzAgT4bqvNitYV14XdByVuxX C9scmFNfEMOzccPLcT5VUkLnh4PqJFtHjONmrBd+yCSaO+WjRKBRvIDB4S8OicQu8hzn140h t6+uahsSmseeEkIX4k/FgWIetAonk9n8yvoYynJtSRNYpIAQsJpCVohdhlEsmR/GklGqOWUJ KI8m0cA+feqIy47VflxnC3zSVRTs3lTgBhNX6VdoBdFFUTEbQHLweFlGKSnuNApsHaKkA2jU PNdbR6QECTKKq/0qGVZRqlucOJCU29XdotLTIEJhspjqulJ1GAvNBmu9cKWCZ3dhv4m6iEKe RCmWJ2od5np2Xu6ZayJLY+OH0/F8/H4ZrH793J9+Ww+e3vbs7YZIb99DbVpfFtG9pthi+ztS 086J36bhU1sq2A9+Nsm3qL71fx9dTeY9aIm3VTGVSCgSGfIn9U6nxCPU+wga7Bq3LZNEmo+u r63BQWFNtbWRkFvxr5HPV8dJCXu8V6UIS6OD+A2Gl0IAXZAZOaCSqO5rREtvSdCwQxC2oPN6 sD0BeQiqjSNgvxdExSrEbxiA1RtSRJBaFMdIQvA8xmHhmn1QRLYrHIFWtJ+8PjKxvDxGnaMz JM7qYnFLHAhNhlc7812LsszZgCBFPaRzxbu9yjk/iTfRDKdmL5DbyMFr+glj+lAtkIwGtgo9 1eEGnQYj74BgMDiTSvNRLaITGewHu23izBFxOxC8AwQNrBzyZJH7s75zyE0aqYZf9q1Bg7Wy vt7qbg2S3BEh/p6WUTKbuj3nyyxnR6WoYy8vM1xZLhlRRwcEtKCO2CxCm5ewVyQrSQ0fUAMN nPKdbvASpUpJyfriyknKWwsqp6xIwUDstpvVTcT7pdsubfaPnOQaCxisCnYVtcTwKUp44s1s 29cmrYoFeJG3lLTLTALZKQGbZXypGwJFNq57rpEGr4t2jPHo4JYfxIotcVMCViK5V+h3MbjX CWzBkvMg84pMB6z5i/33/Wn/Ct77+/PhSffZJIFj/0CLNLcsTBsdxMca0j5Vsq+mWKu7RIUq dR3gsorVhuYkhTj7FiMiekOPbycsQAdrkxZBTdgHVBHVsdJoXSKlfhy2pcZ4jRZa6alHYl8P +54HmES2eXgayITNTYX5bwqnuf3L8bIHrzpMgiVyFYKVOrpOSGVB9OfL+cmeKm5yoUgD4Cfn zzU5AC9NUQ9/DuLP2qUe89GEQIFNVLCm+Ei0HiunCrwqNgTJHQiGAZ/pr/Nl/zLIXgfBj8PP L4MziDG/Hx4UrYLwhnl5Pj6xYjAfQTyAMLBwZjkdd48PxxdXRRTOEdJt/rUzT7k7nsidi8h7 qELS9t/J1kXAgv1Nyb0RHy57AfXfDs8gmmsnCSH18Uq81t3b7hkcfl09Q+Hq6oKGxFra7eH5 8PqXiyYGbc1IPrQpuu9REwuzFVOIn1g8wyZqJg9RKFStWRpGiae69ahIeVRwI5MUi73JESBK JmX3Pw5uI6A4anuUknVk9tyKNtgN0nTJiLbAOzQEor8uD8dXZ9BCgWxpMmSxZGUg0uYNpmqW aOw5Nh6rjx9Z3ig/FZUVu/gKRULC7WvYG7mOEt3egTjeyWmJ59peM67C0LA2E6XGZoEkqjxG pnbtb5IeqRGHbnAGCmC9EZQ6BISVEZ6WxR13gMXe3xasZcdz8Cgy4saIJPQQGm3k8oERudAI e4KUjvgnPLC9Eg3C6m++uh/Qtz/P/DB2+6jNy7LS4t5zC4BlAsXY0qzu68BLhYIAVOi6dskP kvo2Sz2gMuohkW+9ejRPE8jRpOgHNBCQ0NacAaXUlHUvSsxXZpMeQxttS5nHwfX0KLgQvlX4 eGHyVJEnrDkFgSaiZz8dsl+AxHkbgjTfnxjX9rIDZu3l+Hq4MBYO2TZ9aMpKe06Ti4m16t7r 4+l4eOzWm12PRUaUK1IW1D5ht2cBTw5NSKBBUc88g0Ajafj05wEUT//48W/5x79eH8Vfn1zk ofFWyosuazMcRezrYSK/dG3k6uAFzii7EgopxGioJpsTgEIQE0Y5m8HltHs4vD6hOT5L7M0j YxWv7Kd3uXrnBccQHAYSLXxZKjLctpQ9ypHSvCRIaacvakyJ7EGqF7+yeWQSZVg6S3XCvxHJ smiwgjWmkOdYfkHCpVZZ1mHfyehbJOF975i84LGwqhzP5MhbYe9Aokrvm4+Y1Wn4sC0SvL0W wVtgAt0WnJKMNo95L6hTM2F3i4jfIGXUshLsT4zvUosV5iLL1UQIIj+W8gs+PnZu0Jgk+EeY WzFLiYb6FK5SzesVElfdVV5Yz/XtVRRVzr5KugZZRlOMsN2QaJE34ZeV9owX0hQ3gjPYJWGd cGActPgSqPxj4AWrqN5kRSgVwV270jE4qhcUDMapOlQoyijZskqKnC3awmtSZ0+astoX6epy bIJB11YDXJMVAxcLIot7E95tHwo544v73HRfVjHYdxm3MFhQoY9TWFmzgIgCy3Rk4fWo8u6q rMTUkhAabUEn9UKZZFGmFS1YY1pBUOme21KZssC/gBkbLyQtWNhOJcHu4Yeq2k4jWBjE9FEC Sq9EV4vyXaMvhNhIVhULA9xhsmXh4fKYBsvN0DYYmQ/MSh0Tx0dSjlVwHuf92+MRIl7trRMg ne0VxgsKbvUwCrwMommWsVGYe6DAzFKixaPkoGBF4rCIUrMG+9Jzo0+YK1XtdBsVqdqRhtNv 7sIk108WL+iOIS7w5ThbryxRwV+1jMrYV1uRRXxc2sMHossWEbsO1AsQ/ml2b8fA2bOt3Fug 2oLjLITV+F5p0nE68Bos1eaC/Whd/z8dzsf5/Prmt+EnFQw2zHy5JuOZXrGFzMZatFYdNsPt uDWk+TVmh2mgjBytz9VHqAFx9XiuZuQxIEMnZOQc5XyK2fEZKJOe6phxv4Ey7amOm+JrSDdj 3MFUR3p/IW7G7mm4mXygI/P/q+xIttu4kff5Cr05zcHJE2XJcQ4+oLtBNsLe1Iso6tKPlhiH zxalJ1Ev8Xz9VGFpYinQnoMXoqqxFmpDofAbfccYkURXIwmO9PVop5rZxY/7CjjeasroCrfI tDnzh2UA9NmVjRFbfAO/pFu8irUYXymDQaWmteG/R8b4PtbkjApmcBCC3i5r8XGkGOQEHNxe YLxTW5f2JQJTnHKM/KXKQeEb2pqAtDXrBVnXuhVFQdW2YJwuB0th6Q8PAWCNFsxXF32cahCU +e+MmOxoP7RL5wE6BAz93LkUkxW0zB8qgXRO6d31uLq2RYujxCqH+/b+7WV3+B4GcS352hGu a4xgvh4wN2ugvjS87UCRkA9Yc5j0akELpkTXRJkq+Dg4z7xmtXYalMOvMcvxRZyWmUSVRuzz dECFFWOIOunS6lth2x0GISxx9YOpIi1OaRcfchf1TBXsj/B5pbA2fAU6doiYszbjFYx2kJFM zXqUd8zdZN0B0gkQKNpFkTD3HfcQC0fRNYyK9JqDaosafFcPrZvPSz4FlspK8D5/zosm+hCZ HntXxvIyTyh9XdbryH0rg8MasKfK+geNrVkk7vPYHTZHh6d/FcpHQ8Mpq1fVWHSRpAUTJr7j VJOXI9BeXWiyc4zYheqKWFSgzrbkNp6wMELZtYIjQ+Q3lAvJBFEdtwizI6q78tO/v232D3hu +g7/enj6e//u++ZxA782D8+7/bvXzZ9bqHD38G63P2y/IOd49/n5z38rZrLcvuy3387+2rw8 bPfo9zkyFRV8ph7G3u13h93m2+6/G4T+y3pmTPRIVWDmVnXF3YkSGKGutoMVsk7OlkJFp48b 3O4/0O33w4Djw5hOpHyuOan0yK1q43NJX74/HzBP6Mv27OlFJ7y0DrklMoxp4WSNdoovwnLO MrIwRO2WqWhy2+/gAcJPcieY0yoMUVsnLm0qIxHDtGKm49GesFjnl00TYi9th5WpIa1LAlW/ GRsrd5RaDUIeR5Ca++GYiU69V+gF5ymsaigKsjDsofyHWOShz0EgEv3zXd3eaotyCtlu3j5/ 293/8nX7/exeUucXzJD4PSDKtmNB+1lIGTxNibIsJ/oIxR3l1ZnAbeZFSurel7TabeZqaG/4 xdWVm9hGHVu8Hf7a7g+7+w2m5uZ7OWBM1vv3DlOovr4+3e8kKNscNsEMpGlJ9GaRUozVfJKD YsQuzpu6WM/e2w9mT9t0IbqZfWHcDJJfixtiJnMG7OzGLF4iI1oenx5sJ5RpOwlXIp0nYVkf Un5KkCt3T6l0adHSIX4aXPuJRV1wA508Bb8l3WVmh/M13sYLt1Aen268p9wP1DrilZ2bgGJy vIEWmd+ShROcU4W3ain8Fm+8CyAmXe729RA21qbvL6hKFCDMV0Rgxb6GVSjK4cTXt7ekEEgK tuQXFFEoSCSEemq5n517+TaCvYXtxvsVXeYyuyR6VWaUK8UABWwtLlPgEJ+2ZTb7QPkUzG7N 2SzcwrDzrz5QxVczQjTn7D3J7MhX0DWwB5UmqUOpu2pUE0rp2D3/5cbSGd7TEQ1CqResE8Ir 4QcEG2A1JCJkHaxNLwnyqVfuI+seILgWY0iHYUSoCMVRytAUjX3U9VfUDoByKpTFSDhyjuby 31OUu8zZHaPu6XiCgeD7PJTzoGw03n1JFzJ2Hb8YryJvSE2kRDlyJoWBErVg5uJKnKpVo/iN m3xaJh9+QH2g/xWu81sLlLs6KPt4Ge6X4i4kKSjLqe171/XhhfAWDJunx7Pq7fHz9uVssd1v XzzzYyLqToxpQ2m3WZvIhw0HGkJKAwVRDNXvqISl5NGChRFU+YfA66wcg3Rs49/SVUfKoDAA WsefoJbRQKnBEqet6MMlHw/NkvjgJjT15PFYJ11dcIJIsMN4i9Y3rL7tPr9g6vaXp7fDbk8I 7UIkmu8R5RSXQoAWZyao6RQOCVOb/eTnCoUGTfrs6RomNBKcRQZtZCjo95i2cHYK5VTzUVl8 HN0JNRiRJnHpE09Oa5isW5clR1eedP5hzpOQAW1fDhgTCkq9SuKHEe2bwxuY3/d/be+/gi1v X1LFgzFcSbyS302eS8st6GNIOsT/qeuU5qT0J1o1VSaiYu1a5R6fG2ouomRciIoz6nEojACk L2wlArQEvMRjbSMTngcKRJWiU7CVj8/ZNrCNUvAqAq147ycpMqC5wJS1ooVpSoQT5dFmTlxe K0oOdi++kmhVo9y4dvKtKaYwFRhtb2v+BuQVT6mA5qgZyBQiTeEk35IYGB4AxASMvqp733uc 4rt6KTBYp2jmEWo6ntRmoWf9MNIWudLM7Z9TsFpQXoiUJ+uPXttHCH18plFYuwKBewIjIQ8r APbB4Ysul0ydA17YyMoiiTXzkWjBty+kU9PiNlaMYpXVpTVBRGWgK6AO2LRO6A2WYiCXX36H nAcEiauK3CmO6ZWCZkLUjKVUzaCLkNigodDlZC23d6OXR1+V+Lm+fLAMSW2o6ysaQXhpEXQx I29XHYF9DvvU797YNczODapLk/SPoMzLIjCNeFzciYYEJAC4ICG3dyEDIE5HWNfVqYBtfcNh BC1zTkc6ZBm89IvkqxIOK8FyJ+1CBYr62KlMCMAfnXBJCZO5IlgjT0UsKpIcRya0yLJ27McP lw537Fai7gvHqEbkNOLWlxU1IppHy3Qi4VUK2mhrP2+xKEbvQbXs2ma3RZ24vwi+VBUYCWPx g+IOD7+OBaK9Rt3AqrdshJOtBAN1MYQSBI2zNLBcZlVvsq4O13rBe0zRU88ze03tb0abtTqA XsqczlsWedSwYvbFPVmU8cZO8aNOJKTsB1kG4uTi/Cj8A9l9pMNqhseX+LCqEfTTwYJRRWTp 88tuf/gqEx89PG5fv4RHseqpEjkKR5BiYcoKPzsy9rdv8dpiMgi8GUfq4CD2ahkVuJApXycP /W9RjOtB8P7T5bSyKrVEWMOldeCLOSh0TzNesEjuDv0czInkHTZGcO110tfKpAbJOPK2BXTn ig1+Bn/sd1z0Ckanf7Jpd9+2vxx2j1qdU0/k3aty4i1E1Za2VoIyoP1sSN0L1BbUcDtOH0la mB2oNuRdgyNKtmIt5vStC+lHts5gqAolNq1R+FiUY61hORILMk7ZtTHpnSs9iyzB/EaioUMi W1ivEequPs3OLy5tYoZPYDNh2D0ZxdaCiSmtR8Cx28s5PtaOwdOwHwrK3alGZV4OLUVXsj61 2LoPkd0b66pYhzM4r1vYbvOhUp+wQiwq5PXRoTa1cIOfb0pQ9odbLYbI6lecLTHUIkwNZiyR nyVW9cYZ+kB294YrZdvPb1++4HGn2L8eXt4eddIgs9vZQsgYTjtVi1U4HbUqY/7T+T8zCktd QqJr0BeUzENFaGS5s9D5O2reSZm2wr+JWevkkZxEKDHY/ARxTzXhyXMsPENKjSVQst0W/qaC lY0xMiQdq0Adr0SPSUuYLRwlzK5MIfd01sE0tSpM8A5t51UVKUWajYC6XMz7sAeZuBnveEsm XZUIQwUbD9hK4iZ5Mr2o6XBaBeZg/Z0Ay81Tes9MuJ12J/MYrYvBSRKF3B4/RfAugWFMMSdI C8NwA/eDDiuY6rUEOMpJftvzqhN1FVaHcKmckYwVvq1Xlc0sZBmwEHyyw43oP9YHnDGSUEui tHXGehY7OZ6oVyGvbsM2VtSVqclo77OhdBiZKlHfRiLvVb0qMD2S9L0YEoMWCbNCjFjgvdzE emHBDCiAn4bjMpATXVQa4YDaD6WGpDnaChKH4/tSObff7tMPypVhyzelPCTE4LoTjQNWS5+y TvBmAbbsgrxm566rzsFHdEUBTjSj7vLKaJuobF2iLYIWmsXylOatrkx0FoaWce474V4tDo7X n1ws8pJT5qy16nJR8ILHHFi9304EqLnukiHnCX2iCorxgbATQXoceRMYfI5xb4mQOXfuj0+/ j0JFlpgQu0jIpEGS6Z7UTakbmOrZ+bmHAfzWbKtPF1dX/ve9tK8l85QivPtkGTgBUws2Q44X lYODbcQ/q5+eX9+dFU/3X9+elSqSb/ZfbMsGk3pitFbtvALuFONFqsHyVSugtAWH/vjyDsbd Dchgehin7RHo6nkfBaKBgq95lDZa46YajeP4XcPISq8pmTHAJoIAg2rIQot2xsfxO6PqH3PM q9SzzmFBSlWbQNNsYja1sKEJLd4XF8XvyuoalGdQobN6EchrNQRSYJ8mIhW/DGruw5tMOW6J 3aM4kLw45i5RUNdUk2VSgNhGItWMvxFwDpecN55fXrn4MXDnqHD85/V5t8dgHhjY49th+88W /rM93P/66692Gl288CfrxuRsYSbYpsWcmMS1PgVo2UpVUcE004cFEoyD9Xkh+sOGnt/yQOU2 CX388gj6aqUgIJjrFYY+By2tOl4Gn8mOee4qGYbLm6AAHdndp9mVXyyN3k5DP/hQJZ+1m0Si /H4KRbp/FN5l0JBo06Fg7Qh2y2Bqu/DJQ2NHhaXJAVxwTog3vcrqhJZKYWqvKDAVDCVW7tfH aRNOS3H061kbZe58Ru7H/4eKTatq+kBESJXkuHhu+ViVwqeB8JujZ8vuujTLgVbAJsFwCtjR 6uDghPKyVNIwIrW+KvvgYXPYnKFhcI9neYGHR54Del1udKEvIiNOLQmUF2EFnVtSKbqj1NTT Wt6DFm4o88ke+02lLcfn+8DIDu+2AgmTlotiJakV8kBTF6AAEbGCKve+OB7wAAysFOs7cp5k FbjqlPsHYPzazlZtck854wmMl2utS7aBN8VsJAbWWrrua4vhyIAFy70acGR8EUSCnOsaN5Zn 6DR0AYZ/TuMYD+jc0H8cOK5En6OT3Vc+KbRMtKgloBvZR9dopbyoD/XhUa6Hgnd6cetJTOnT CirBOBXf05/q2lTVHlNo5RPg3jBVV1JX8uBxPWaEnduzJfMhSXwvcSrox7e9fgklmGOrKu0I 6la2KdK0nJew+9preqxBe8YS9hvSiCHt+AuLypU8twiqDolpom6SkiiRE6GmHxPSz9PQ1Bfg IXgTOTykiPcP5hl07TkxQqWzhR8eHb8r2LrxmuuuqkXHw3XB5x+OX9pNlqWoY/xHT4PeB758 A25RscZ9McIDGIeuR29aMQFJBsSqJtBT8hwYj/ktDVgHO8DUqe94SLsERLfhz9UAtSZc7TQb u5kHZYZc/HK6htMcx4ViGId9+8CMwvEgdOsK6NNvBnMo2A+COHOu+IfK5eTB5KanDldt7mGD j5aOrpoV8nwWl4QkXj1ENXL8Z2i7aGoOTXo9A+nanBCfVudiyATqlL5EMqGMFz1zQ5COHFGe 7MXqtJYAmeLoa57OYpzKxAaaiMi4fIls9v73S3nYHXE/KZeIe5tTeUmOifpp96HCsigh4oi0 8dT55o/xZDhCtLdGKSU6na9gJ3K2lPR3qp3lXMwjdycVgs4OWojoK84KT/2K+Gk1jrHoT7sZ Ze4roU9X3HNPdTFX4wQK6T8fP1AKqWc3BGI0tCtCHM7aYm2OpYfOjnD5+GHUZ8RSAA8N/VWk rixZRD5QD8Vl9iUd7TUoknkx2PFQUkOaZA6VXgZ7iZExmO3sRGwUPkAgd+b5rfe6xBEQOWie MIb4qf2E44seT9dWMQDoJopcEWlYPKBF1mBUSG8C5Lld5JUklfoabepo3UO1Ugnj/BPZyYZw SdAO4ei3rwc0gdEblWJa0c2Xre13Wg40YzLGHoYo1K2WMt6RUFPSaFR18t0jGt1RC6W7b2ot frbYgbyvbwxfc6PxQOhKdVR5k2JvUUwhlrCn3eChY4F/05aezeA6roqa+R9dBM1uTGIBAA== --envbJBWh7q8WU6mo-- From mboxrd@z Thu Jan 1 00:00:00 1970 Content-Type: multipart/mixed; boundary="===============1004750109120966829==" MIME-Version: 1.0 From: kernel test robot To: kbuild-all@lists.01.org Subject: kernel/fork.c:1205:24: sparse: sparse: incorrect type in initializer (different address spaces) Date: Tue, 07 Sep 2021 07:50:30 +0800 Message-ID: <202109070724.Fh8LRlM1-lkp@intel.com> List-Id: --===============1004750109120966829== Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: quoted-printable tree: https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git = master head: 4b93c544e90e2b28326182d31ee008eb80e02074 commit: 35d7bdc86031a2c1ae05ac27dfa93b2acdcbaecc kernel/fork: factor out re= placing the current MM exe_file date: 3 days ago config: i386-randconfig-s001-20210906 (attached as .config) compiler: gcc-9 (Debian 9.3.0-22) 9.3.0 reproduce: # apt-get install sparse # sparse version: v0.6.4-dirty # https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.gi= t/commit/?id=3D35d7bdc86031a2c1ae05ac27dfa93b2acdcbaecc git remote add linus https://git.kernel.org/pub/scm/linux/kernel/gi= t/torvalds/linux.git git fetch --no-tags linus master git checkout 35d7bdc86031a2c1ae05ac27dfa93b2acdcbaecc # save the attached .config to linux build tree make W=3D1 C=3D1 CF=3D'-fdiagnostic-prefix -D__CHECK_ENDIAN__' ARCH= =3Di386 = If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot sparse warnings: (new ones prefixed by >>) kernel/fork.c:1005:19: sparse: sparse: incorrect type in assignment (dif= ferent address spaces) @@ expected struct task_struct [noderef] __rcu *= owner @@ got struct task_struct *p @@ kernel/fork.c:1005:19: sparse: expected struct task_struct [noderef]= __rcu *owner kernel/fork.c:1005:19: sparse: got struct task_struct *p >> kernel/fork.c:1205:24: sparse: sparse: incorrect type in initializer (di= fferent address spaces) @@ expected struct file [noderef] __rcu *__ret = @@ got struct file *new_exe_file @@ kernel/fork.c:1205:24: sparse: expected struct file [noderef] __rcu = *__ret kernel/fork.c:1205:24: sparse: got struct file *new_exe_file kernel/fork.c:1205:22: sparse: sparse: incorrect type in assignment (dif= ferent address spaces) @@ expected struct file *[assigned] old_exe_file= @@ got struct file [noderef] __rcu *[assigned] __ret @@ kernel/fork.c:1205:22: sparse: expected struct file *[assigned] old_= exe_file kernel/fork.c:1205:22: sparse: got struct file [noderef] __rcu *[ass= igned] __ret kernel/fork.c:1557:38: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct refcount_struct [usertype] *r= @@ got struct refcount_struct [noderef] __rcu * @@ kernel/fork.c:1557:38: sparse: expected struct refcount_struct [user= type] *r kernel/fork.c:1557:38: sparse: got struct refcount_struct [noderef] = __rcu * kernel/fork.c:1566:31: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1566:31: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:1566:31: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:1567:36: sparse: sparse: incorrect type in argument 2 (dif= ferent address spaces) @@ expected void const *q @@ got struct k_si= gaction [noderef] __rcu * @@ kernel/fork.c:1567:36: sparse: expected void const *q kernel/fork.c:1567:36: sparse: got struct k_sigaction [noderef] __rc= u * kernel/fork.c:1568:33: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1568:33: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:1568:33: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:1980:31: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1980:31: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:1980:31: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:1984:33: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:1984:33: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:1984:33: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2287:32: sparse: sparse: incorrect type in assignment (dif= ferent address spaces) @@ expected struct task_struct [noderef] __rcu *= real_parent @@ got struct task_struct * @@ kernel/fork.c:2287:32: sparse: expected struct task_struct [noderef]= __rcu *real_parent kernel/fork.c:2287:32: sparse: got struct task_struct * kernel/fork.c:2296:27: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2296:27: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:2296:27: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2345:54: sparse: sparse: incorrect type in argument 2 (dif= ferent address spaces) @@ expected struct list_head *head @@ got st= ruct list_head [noderef] __rcu * @@ kernel/fork.c:2345:54: sparse: expected struct list_head *head kernel/fork.c:2345:54: sparse: got struct list_head [noderef] __rcu * kernel/fork.c:2366:29: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2366:29: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:2366:29: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2384:29: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2384:29: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:2384:29: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2411:28: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct sighand_struct *sighand @@ = got struct sighand_struct [noderef] __rcu *sighand @@ kernel/fork.c:2411:28: sparse: expected struct sighand_struct *sigha= nd kernel/fork.c:2411:28: sparse: got struct sighand_struct [noderef] _= _rcu *sighand kernel/fork.c:2439:31: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2439:31: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:2439:31: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2441:33: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct spinlock [usertype] *lock @@ = got struct spinlock [noderef] __rcu * @@ kernel/fork.c:2441:33: sparse: expected struct spinlock [usertype] *= lock kernel/fork.c:2441:33: sparse: got struct spinlock [noderef] __rcu * kernel/fork.c:2850:24: sparse: sparse: incorrect type in assignment (dif= ferent address spaces) @@ expected struct task_struct *[assigned] paren= t @@ got struct task_struct [noderef] __rcu *real_parent @@ kernel/fork.c:2850:24: sparse: expected struct task_struct *[assigne= d] parent kernel/fork.c:2850:24: sparse: got struct task_struct [noderef] __rc= u *real_parent kernel/fork.c:2931:43: sparse: sparse: incorrect type in argument 1 (dif= ferent address spaces) @@ expected struct refcount_struct const [userty= pe] *r @@ got struct refcount_struct [noderef] __rcu * @@ kernel/fork.c:2931:43: sparse: expected struct refcount_struct const= [usertype] *r kernel/fork.c:2931:43: sparse: got struct refcount_struct [noderef] = __rcu * kernel/fork.c:2024:22: sparse: sparse: dereference of noderef expression kernel/fork.c: note: in included file (through include/uapi/asm-generic/= bpf_perf_event.h, arch/x86/include/generated/uapi/asm/bpf_perf_event.h, ...= ): include/linux/ptrace.h:218:45: sparse: sparse: incorrect type in argumen= t 2 (different address spaces) @@ expected struct task_struct *new_pare= nt @@ got struct task_struct [noderef] __rcu *parent @@ include/linux/ptrace.h:218:45: sparse: expected struct task_struct *= new_parent include/linux/ptrace.h:218:45: sparse: got struct task_struct [noder= ef] __rcu *parent include/linux/ptrace.h:218:62: sparse: sparse: incorrect type in argumen= t 3 (different address spaces) @@ expected struct cred const *ptracer_c= red @@ got struct cred const [noderef] __rcu *ptracer_cred @@ include/linux/ptrace.h:218:62: sparse: expected struct cred const *p= tracer_cred include/linux/ptrace.h:218:62: sparse: got struct cred const [nodere= f] __rcu *ptracer_cred kernel/fork.c:2343:59: sparse: sparse: dereference of noderef expression kernel/fork.c:2344:59: sparse: sparse: dereference of noderef expression kernel/fork.c:997:23: sparse: sparse: incompatible types in comparison e= xpression (different address spaces): kernel/fork.c:997:23: sparse: struct task_struct [noderef] __rcu * kernel/fork.c:997:23: sparse: struct task_struct * vim +1205 kernel/fork.c 1170 = 1171 /** 1172 * replace_mm_exe_file - replace a reference to the mm's executable = file 1173 * 1174 * This changes mm's executable file (shown as symlink /proc/[pid]/e= xe), 1175 * dealing with concurrent invocation and without grabbing the mmap = lock in 1176 * write mode. 1177 * 1178 * Main user is sys_prctl(PR_SET_MM_MAP/EXE_FILE). 1179 */ 1180 int replace_mm_exe_file(struct mm_struct *mm, struct file *new_exe_f= ile) 1181 { 1182 struct vm_area_struct *vma; 1183 struct file *old_exe_file; 1184 int ret =3D 0; 1185 = 1186 /* Forbid mm->exe_file change if old file still mapped. */ 1187 old_exe_file =3D get_mm_exe_file(mm); 1188 if (old_exe_file) { 1189 mmap_read_lock(mm); 1190 for (vma =3D mm->mmap; vma && !ret; vma =3D vma->vm_next) { 1191 if (!vma->vm_file) 1192 continue; 1193 if (path_equal(&vma->vm_file->f_path, 1194 &old_exe_file->f_path)) 1195 ret =3D -EBUSY; 1196 } 1197 mmap_read_unlock(mm); 1198 fput(old_exe_file); 1199 if (ret) 1200 return ret; 1201 } 1202 = 1203 /* set the new file, lockless */ 1204 get_file(new_exe_file); > 1205 old_exe_file =3D xchg(&mm->exe_file, new_exe_file); 1206 if (old_exe_file) 1207 fput(old_exe_file); 1208 return 0; 1209 } 1210 = --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all(a)lists.01.org --===============1004750109120966829== Content-Type: application/gzip MIME-Version: 1.0 Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="config.gz" H4sICIyeNmEAAy5jb25maWcAjFxLl9w2rt7nV9RxNskiTr/HOff0giVRVUxJokJS9egNT7td9vRJ uzvTj0n87y9A6kGyoPLMYuICID5B4AMI9o8//Dhjb69PX29f7+9uHx6+zb7sH/fPt6/7T7PP9w/7 /5vlclZLM+O5MO9BuLx/fPvn1/vzD1ezy/enF+9PZqv98+P+YZY9PX6+//IGn94/Pf7w4w+ZrAux sFlm11xpIWtr+NZcv/tyd/fLb7Of8v3H+9vH2W/vz9+f/HJ29rP/17vgM6HtIsuuv/WkxdjU9W8n 5ycng2zJ6sXAGshMuybqdmwCSL3Y2fnlyVlPL3MUnRf5KAokWjRgnASjzVhtS1GvxhYCotWGGZFF vCUMhunKLqSRJEPU8Ck/YNXSNkoWouS2qC0zRo0iQv1hN1IFg5i3osyNqLg1bA6faKnMyDVLxRnM vS4k/B+IaPwUNu/H2cKpwcPsZf/69te4nXMlV7y2sJu6aoKOa2Esr9eWKVgiUQlzfX4GrfRDl1WD AzZcm9n9y+zx6RUbHtZUZqzsF/XdO4psWRsuk5uW1aw0gfySrbldcVXz0i5uRDC8kDMHzhnNKm8q RnO2N1NfyCnGBc240Qa1bFiaYLzhyqR8N+pjAjj2Y/ztzfGv5XH2BbFt8Yw6Ys4L1pbGaUSwNz15 KbWpWcWv3/30+PS4/3kQ0Du9Fk1wRDoC/jczZbhkjdRia6s/Wt5yctQbZrKlPeD3yqik1rbilVQ7 PD8sW4att5qXYk58x1owgcmmMgUdOQYOk5VlYGtiqjtUcD5nL28fX769vO6/jodqwWuuROaOL5zt eXDoQ5Zeyk3Yv8qBqq3eWMU1r/PYDuSyYqKOaVpUlJBdCq5wKju6Y2xercGCwTmsZM7jNgqpMp53 tkTUi2ALG6Y0R6FwgcOWcz5vF4WOd3H/+Gn29DlZrNHYy2ylZQt9+n3OZdCj249QxKnhN+rjNStF zgy3JdPGZrusJJbdWc71wd72bNceX/Pa6KNMW8Eis/z3VhtCrpLatg2OJdEur+hZ07pxKO0MdGLg j8o4pTP3X/fPL5TegUtagSnnoFjBuMDBLG/QZFeyDvcNiA0MWOYiI06H/0rk4So6WjAnsViiLnUj dW13e30wxsHQN0WyKBxI9ne3q2568JOaG0qN+zZMovuYmABy2rpRYj0YK1kEnYNdUKj8NgcRHjhd /LBRvJQsJ4m2rfJwrvGAh31UnFeNgTVzPn80dh19Lcu2NkztSIvXSRHT6r/PJHzerxmoy6/m9uXP 2Sus++wWxvXyevv6Mru9u3t6e3y9f/ySKAnqF8tcG/6EDz3jKXanZGRTi6tztGwZB8sLgoG2pRy7 Pg/wCegyQicdk2CDSrZLGnKMbUcbhueoQk6Mblw/LUgb9D8slFtQlbUzTR2wemeBF44Iflq+hZNE 7Zb2wuHnCQlXxLXRmQ+CdUBqQWsJulEs48PwuhnHMxns/Mr/I7D8q0HBZBaSl+AF/OkeYBziNTiN S1GY67OTUTNFbQAas4InMqfn0Ylva92B12wJjsbZ1V6T9d2/95/eHvbPs8/729e35/2LI3eTIbiR p9iw2tg5ehFot60r1lhTzm1RtnoZeI2Fkm0TzKhhC+4PVGgHAFFki+SnXcF/0pb8PEZqwYSyMWdE zgXEJazONyI3S0JhlLFkm11Pjch11Jwnq3wCLHb8AozGDVcUePICy3bBYaUO+sv5WmSc6BEOEh7N 6RbhQBTEd2gsJ7+phM6IIQCkCDCIRPPVsZhhYR8IRAGjgO2h+ljybNVI0FH0WUaqaFpeFTEocU2T iwnuB3Yv52CCM3DuOSmk0JRRFrNEM7d2AEaF0A5/swoa9jgmwNgqT8IeIPTRzthffhAqjBwX5sSi dFzgWBdTLIwIqClJiX4otiMQ1coGHIi44YgknR5IVbE6UaNETMM/qEgyt1I1Swi7N0wF4HcIIiK7 IvLTq1QGbHPGGwd1nX1MIVmmmxWMsmQGhzlyvUmPAglsnhhjBdBCIJqI9AnOU4W+qsMsRxSKkOgN CUw8AmAeHA5wKzK86W9bVyIMsCMvz8sCdk7R4VayJtTOMwgDijaE0EVr+Db5CeYqWNBGhvJaLGpW hhkaN62Q4LB2SNBLb32HkTIhidEBPGhVFLiwfC1gxN1KB0sH7c2ZUiK0+ysU2VX6kGKjoGGgutXA c28ASMYA0+HOcArOTWHaZ+wZhlVnbjeCtrMwGwNBVxRxOZPoqMTsoV2e56Hv8IoOg7FpaNNkpycX vevt8n7N/vnz0/PX28e7/Yz/d/8I6IiB980QHwGmH8FQ3GIyOMeEFbDrCpZJZiQa+x977DtcV767 3lUHE9FlO089BSapGMABFzmNZ65kVDIAG4jFJC3G5rB1CvBCF1SEYwAeetpSQACq4OjKaoqLwT7g vUi326IATOSwiFsyBj4qjhwwS0jjcWfanHOLwrA46dcLbz9c2fMgZeYCfpvvwH1C+FokZhKkQ2+l jWozZ05znkH4FBwc2ZqmNdaZe3P9bv/w+fzsF8wshxnAFfhPq9umiRKXAAizlcevB7yqapPzUyGw UzV4Q+Fj8esPx/hse316RQv0OvKddiKxqLkh+aGZzcNsY8+IVNK3CqFO53tskWeHn4CNEnOFqQwX FxHGA8M3NDhbggc6AmfDNgvQlzS9pbnx0MwHgIoHA645AKCe5YwINKUwlbJswzR4JOe0lRTz4xFz rmqfaALfpcU89GZORLe64bDEE2yH6N3CsPIQpXYtOIXBnAvm2gKrUICz5EyVuwwTXSFwbxY+AinB oIA/GOKTLievWc29TuIi8syfRGclm+enu/3Ly9Pz7PXbXz6EjCKVXqGrhjimePIKzkyruAerwebL Mi9EGKYobsBlijh9gC343QdwoyjQgBJ8a2BNcZ9GXBM10fdG+n8UAEuCeeJG60kRVo3td4ECKSuk Lmw1FxNjVXl2fna6jQwOmCcrlIj8ikfoshJgYQAwYzYNB0kFNcsd6Cd4fMCVi5aHObqGKbYWMa7r ad5/TIxyucZjWc5BJey6V4jeWYNjSvrxecumxfwZaFRpOvAzdrqm134YzPdzMINoH0APjVQXH670 lmwfWTTj8gjD6GySV1UTPV1NNQjnHQBzJcR32Mf51VEuHctUq4khrf41Qf9A0zPVakmre8WLAo6C rGnuRtTZUjQZGUl0zPMoZVCBoZ9obMHBAy+2p0e4tpzYnmynxHZykdeCZeeWvqFyzIkFQxg78RXg GSqWcOYozX31JkjVOIWMwdnvcklXoUh5Os0DJ7qoK4SLYVA42jaE55lsdjEPIWsDRt+nC3RbxWyj k0ECTt9my8XVRUwGLCGqtnKmugBYVe6uLwcMxcC6oRewUQiLn62r7YF/CPEfZlUxWOYlp3Mv0B24 Qz/DAAl3ZLflEfTrOazKD4nL3ULWRCuwpqxVhwzAb7WuuGFkF22VkfSbJZPb8GJr2XBv/iL0m1eU A6kdDNGItgGIzPkCGjqlmXihdsDqQXzKAELk63CJGvKexO12gpeRgBnQki9Ytkt9L6szgWpZxf7S o4sgFPr69Hj/+vQcJe+DmKtX4zqOHA8lFGvKY/wMM/LxHUUg47y83MR+dggwJsYbT9gvAyj3hENA mdOrOXmL486rbEr8Px7mZ4yEMz8P8Kv4sIo3QXFMUAEG9Lne0RCJDE4S2JWp3dQqOePox6M8o8Sr LwCU5HQ63gXlszve1UUQFKwr3ZSAas6jDE1PPaMvOHr2KY0MQN9lUQDcvz75Jzvx/0vGEM+xYQTM ZL7ORRuRURlVB3IKQIDQGhwvRoB7d2E8zXZ2rL+6x8u4QFFFiXpT9jAP73dbfh3NojHJuXNGG8I0 qTGfolqX94tFUCcQOVV9t6Og/zwIPI1S8S8MDIQRN3yS3k10MCwnE2K4Mpg0chZntELRBkDISe69 WzufX5hQYQ2Ba+LXKpFQ/OE3eusWvrsbJWKAUYIGIYQkptFJWV7QcGN5Y09PTqZYZ5cn1FG6secn J+GYfSu07HVQX+YjiaXCm9Dw+xXfchrnZorppc1bMqhrljstIDjAw6LwwJ125y28E8B0DirGse8d YoHvz5LPu+zDOtf0qmZVjmEiqjYVEMKWiGJny9xEF+i9BT8Sz8Zpi2WDRwZTIj6axsMzHGvvvZ7+ 3j/PwBvcftl/3T++utZY1ojZ019YRRhHyD7wpxYkClebajIwA1ZWBgd284d3VdZBcOdkD7J0ccoA BxfwDn71vsvtnballKu2SRqrwBKZrnwIP2nCnI6jwLIbsIF+bM7b6iDNNZ5rlHVzXZBhrW+ryZQf TtKJ4msr11wpkfMwmxI3zzOqRieUYOno58yA/d2l1NaY0LY6YsHqwwlBcDrVl4PHisO2aZ00NaLd AZ/Q7LhEJWYeDGb8jC0WYH0xxzo1OLMEwMHKZLddNadjOzvfNgvF8nQAKY/YYtqyuzFmAtPPE7bf LZsEBA4nXk2L6DmduHHM5cTdpW+81RCowQE3S3lETPG8xTIxzGRv0JfJuqTuPcdzwRoenK6Y3l1W xV0gY3oAeWOKo0sE/04r0Qa7IfD6EfZfxE4tdL7VEIP0JTaz4nn/n7f949232cvd7YMH5qMV7nR5 qu6E+HpoWHx62Acl1tBSp9VR6y6ZtJBrW7I8p2/zQymIfdvJJgyf8CWhUJ+4IrfVs/okV+hShhkF OUAHEFCQDiO+6zh8adjbS0+Y/QTHZLZ/vXv/c7gJeHYWEgHgxM0msqvK/zwikgtFR9iezerAHCIJ e4wpvoWY1nccuTegZ/X87ATW/I9WKMpW4uXCvA066G4bMCYN2wIydWeVIaII7lrc76U6jLEBgNDJ opqby8uTU8pSQnhYRzdnDn/udDEnd3piC/323j/ePn+b8a9vD7c9XIgh0PlZqGeH8rFxATOGNzPS g2HXRXH//PXv2+f9LH++/290m8nz8OY5z+NiwUKoylk5j4ACxsZmRXeTH65CSO8BGrF8CykXJR+a D1voWBjdu0KLA/joi0H3X55vZ5/7aX1y0wpLpiYEevbBgkRLuFoHYSLmmltQkRsWR1XoENfby9Oz iKSX7NTWIqWdXV6lVAjHWj1gyP7K8vb57t/3r/s7RKK/fNr/BeNFwzACyQiaxzmQ/iINAKCK0i8r f0NEbMTvgO3Brs7DCNQ/8oCwYKcxVC1MlOLvuIinCe7BXZSvdR1waVs7tI81RxkikgRlYBoS30QY Udu53rD07YOA+SISJ+74VmTPK7xNohiyoeldM4j10xthxy/a2l8DA+JEFFb/zrNYL5xYVIQylti7 FpeAphMmGjVEN2LRypaowdawS849+ep0ApsVAKox3OmqqQ4FNO8zDhNMb7ltdbDofuT+iY2/Breb pTDuRj9pC68t9XCl7opW/RekXC39xXrCPD+bC1e0bA+eKegKw7ruLU26dYBr4EjWub/B7BSs8xWR nC8rIXcV3/1Mfrjc2Dmsgq+mS3iV2IJSj2zthpMIYe4d7zdbVcPkYb+iApy0XoVQIkSdeOvl6gP9 BW1fcXjQCNF/X52iuiXCyJ7abMoqUFyi+qeqWgvhBcQQXTSAtR8kG4t/KZFOKf0h8mW13W1DOpjO knQ6idm/RKL7zr/AmuDlsp24fsdXO/7lR/+mi1gMzTN00kdYXWVCaI87zmTY4L7GHSpBnZKmD67n x1YjzlTicsgBlEb6J4dTSYJBAA5/eFGB9K68/2DUG4GynXq5++pUB4lC+/QoSVTVNq3k8uQqJfd2 tXapRdg2rJYgdMGrFfCwNCvNZbj9dkzoAP21Sj8Hs9PndnkGBzfIFgCrxSwJOi8sMFQHx0bLwuC8 wcDITbc6hBV2H7v8prghhx8V+KQ+dgtGk3QP8VdDqU+HrmM7l5USM2owPsBledCHxHeKYtFlr84P GCzxggNyRVuO+03NZ5isXXmN6ZL3g+iEwER6y3kyA/7S9G/61CYosDjCSj/3W0p+TrHGGeFbmfOz PvMZOyk03GElXwp+uvJIgG+Z2jUHNUwj7kqtevc0pnO7lGpP1RPHB74rYYSz4wrxUjF38QIO8moo nVxkcv3Lx9uX/afZn76m8a/np8/3aYoAxbpln7JKOHcn1r8JTtK1x3qKFgNfVzdluxA1WRH4HXg9 6B3sM1byhtbLlbtqLOsMbjf80Q+tcKcfvrgQ303RVQFeqq2PSfQ451gLWmXD0+KJcuteUlC55I6J x1Uh6knfbaX8yQe+qeDEQ91ULK2xTwVREzf4QkKDoxhfOlhROZ2lZ+QwPN4jLa/f/fry8f7x169P n0BhPu7fjR3Aca5gA8Bq52BddtVEW860GzhiYxZ8aGJeTiR5dX0axES1fwIPJwhcI275gfUfE/M+ eIewmIiM3Evg3DXjHoBOi6gNJeCf3dcuXV6ypsEVZXnu9sGtKmUQ+yJtO+cF/qd/P0jKumsWu1HQ +JhC5P/s795ebz8+7N2fYpi5m/PXIJadi7qoDLq/IBVRFnFw2wnpTInQNnbk5AmNxDRt1YQWYGoU bojV/uvT87dZNWbiDgJu+tJ2THx098EVq1tGmbnxTtiLBI6l5xCkg7+44MMVfOq7CG9kumEJLcs0 TYG7093OdlJLadBEht2haW+M81CuDuSC+rwTw8oIk9beOX/vMMBEfd8CAzRUcrqqrxILxVIIgQGx TXwRXhs6rbXGXl3Mw2fRvoBQdjnDMfuhqfvi/nGsQ0f+XXOuri9OfrsKTcQhrKSqccLS4FX4Tg7w e+0qtQJaWC0NPw7K93tSoWNin2sLSFjJrK//NQ74pqHvQm901S/jKNvRnIpRObo+m4SVxH3mJWzA JSTcxmJaY0Xv61i77QrWvB2M4O0gcYPQEzMhEQTsqWHPsNaujAqf1FIjx6KsKC2FlAVHFUczvnFV FMQIkO3wfXg8V6ggfQQ5mJNpizFqRZg1W819dXKfuHBmp96//v30/CeAmEN7A6dpxZOCXaTYXDBq ocHLBIgUf4GtrBIKfhs2acqJOudCVc7ST6XFMTtIf5k37q0iJ3dG1PGUROOfkWVM0/d+IMDyNb6i A6cmW7ruGYSaOvwrLu63zZdZk3SGZFcJM9UZCiimaD7OWzQT+MczFwprYKp2S5oJlLCmrevYd4CP BWMnV4LTu+E/XBv6VhC5hWyP8cZu6Q5wWyyjK7MdD0DXNFM0E8kMxx2mGxI7PYzksuZAPR2jzZsD nY8lFNt8RwK5sC8QwklabbF3+Odi0DZiOoNM1s5Dl9x7kp5//e7u7eP93bu49Sq/pBE47OxVrKbr q07XMbSjL3udkH9JimVqNp+IInD2V8e29uro3l4RmxuPoRINXd3ouInOhiwtzMGsgWavFLX2jl3n gPIslkabXcMPvvaadmSoaGmasvsTUhMnwQm61Z/ma764suXme/05sWXF6Noqv81Nebwh2AOXaaUQ U2OyJjlEjpacLk/rtGxsFv+0CiYiK6aiV07oJBvM+0HEVUS3SP1HgMBcRgfcb9VM/SUKEPZ5T5I7 b44wwVjlWTZponU2Yb5VTu8pbDpVwQx4PZwd/IRZC8qOIatkcVyMNAhB6T8/gMy5Orv6QD/HKM8M 1Y02IaBXwY+5EnmY0PS/rVhUsBy1lE2URO24axhyl3um2EQHNiuqZMNtTt6tu7Y/nJydRu9zR6pd rBU1xUCiWocDyHkW4SX/uzODQQqtzKIf4d2rYWFNHP7NAYhCSx6TRZPnCSgAAqbcGK1U27NLYh4Q PQfP8ZqlTHDNFYDMZuL1iuCc4xJcUn+LDCfd/wkMBxH/eNu/7QEg/tr9DZCoLL6Ttv/P2bV0N47j 6r+SM6u5i55r2XHsLGpBSZTNsl4RZVvJRidTcU/nTCapk7in++dfgNSDpEC7z11UdwyAD/EJgsDH KHxw+w3J25qK5R24iXlk7qnOrOjJZSVox5leQC2uVFx2L1CZUb89USYhVZpMLuVU84d0mlUdJlNi FMopEVYmqtCaXf1I0O7obbYXiCUupxfqDv/nZAPHlV/XVu374NbObbNdiBJEG2yLHaeKfLjYypHt lt6TkwcfJ2I7TslPadst0VWlICsJ5QHnQj1tS8bYx0THj7cGQzG97pY8kK3fs9UnX5To2+WCpQH2 vKRQtpup7thV8dvffv76+utH++vz1/lvnZ/c2/PX1+uvrz8cQFJMEaXOVwIB7dQimpLrSOSxgqqw 2hhZapX1rUYokBypZPvF/EKaSh7KaS2QejclJ6kJCdhTNdwN8YVlQmdhbhU9PUNYJOvSRB1oFJmi dbdzJuynwYzIg44hkIePNSfz3S/mbit2HIwd807+TgZBZ6/JRCwX/vUJG4ORzn3DXIPxaU2OiNo/ 4hx9G2SRHuxL7RC2DoZmtANZhaLk+UEeBVSUUgw6m4FhzOsojjY7kFNQe0LrHhXNf6IYJQboP0OX RhBbVz1OpdsvSGs3klpuFQsnjKVTYaLcDGLfymqymqlvB73E20PpAkarxHOfT+qhqv3bRB656G8d s4ONUro9vYkYElrzd/bqqkGj6mNrY9mEDwMiaGfJujmfvs7O7Z8qdldvOK0OKSWzKuCAXOTC8VsY rG2T7B2GaUEbLeVZxWK1I+oojecf/z6db6rnl9cPvD08f/z4eDMMbgxUPUvDh99wrkZ7ZMoOni2o MrFOqmL052PNP0BzfO/q/XL67+uP3gvRCgfJdsKDNXCH9j6i1LB84OhAY/QDe4Sx3qJfTxI3JH0b Wyv/I8vIdr5Y62Gw2NEOCA5dsSM1qoATmjZIJGyO9u/vwf3i3iYJWdSD6yoQbmJdkYnzKgofdHVM SkPUUKYRo3zekQezzc4hYmmE3hJoXrFhIpG7OzBs0jISPKGX21Ivxp7ionZSZQ2xTaFWGNyIsqUo frRazZwMkYQuHpO8FOMCxpXqgETg/00AJyRnVNdnVyqXXfy0krMd0ZJmx31nGFjmJuSZdAu1+Mk6 uJvRAAF2H3pF+qr5BdLmwnd39ab6oGdRvWALot+Qx8CiBy46bGgMEhp8mJg9w+pgKcIhIkbx2GOu gb2JWgAV3fQSAkImEwWQb9JYIUuXNt5xmcX0HsV0YT1GQH9lpt3n334/nT8+zr9NV9kx5TYSYS31 bmCWB/Q9IyFMNfMA/+zPqw7phNB2ORvUekfRsKxvpiu/r+7GNpTA7luVtBERmLuImsWJCNuqc03p SEdR8VS7XI+DKNmgKcKaJ3rV7Rnvp9PL18354+afJ6g0Xp+/4NX5TcYiJTC2ck9BBRpv0xDxqdF3 fYO/TJXshKlB6N/OIOqIIi/31hDt6JvSew6+dw4L9yOMpjFvBG1Sj3i5bR3I9FFLSSgTYikZ6IOO ui8S6yBLmXd7TRrho+zb4Q1CcPDUPdcp9M9MWh+SMJGiJweRMegHdVGkvcbbzxTfNqr9B52FitNR NR2ClzEp3B8dGLuN4QErPN79gxJJNSNwmSwzKxtFoY7sA0/FlUpHNfOIoYfNXxIeYTu9gm1Z00u2 inWS1IaAHBXl5LbKhaBIFQ9Z76kjGLLQAQOn84hEaqUUBX2CQB4cAvw8JgUZnIxFdh7hdmugDykM cRVK7+lcJePpSsVDL29/e6PEX+oYLcirOf6HFOtcX9x9Wx8QgPbj4/38+fGGUMvjNtJNnq/Xf70f MWYIBaMP+EP+/vPnx+fZjDu6JKZdjT7+Cfm+viH75M3mgpRen59fTgg+othjpRF6fpLXddkhHpFu gaF1+PvLz4/X97MbesjzWAU9kCqIlXDI6uuP1/OP3+j2tqfAsTs51y5IgZG/PzdjfW/S1ll/jIIi VnmAjVkpYnvHGQO2Xn90q+lN4fpv7LWn75anpXlWs8idi6LxusihzkpbKeppcD7e5yTWes3ymKVT vH1V0BDBp56amXzFEAf39gGj5HOsfnJUzqtm1QeScvqJEeh8ZIJ6V7ExoG/8pjGVCjwZ2mPcxygB 2N006hzZJ2OS3o+UHBnuxw2aEFMICQfbT7BXqZTDqcn12H2V6q3eUCBtvp1mXnGnN5GOGm6Xtq04 BjbQF5coxpQHZiesHFuJ4gYITYS33NeF5/UVZB/2KaJKhiIVtTD1w4pvrMAI/buFXVFMiMdgQsoy U91VblwYRqGGSeJiWsFI4bCS63g4su8802uIh9b6sjHfsq2wg4U7wlQD7Bkqplm3DFkFsxhjGSlA m/ME2GxyM6IRf6HxxfImU8QM3wugGFJUCc3Zh83IGD/F4zxdUDuxi9qhw5tcNI6ORO3kpo+VcrBS gzmD6QfrweDUVk5NayDcYYxoY+Eh49SmZ9H1Zvn69WPa1TAps0f3OR4RZhgfSC/vW5jxBc2rRZJN MMH7RS2P0kLiiRMfDsJDtlnitmxBA/cagMrtnj5LyMq1vvXfdWwbxPZXyoxXQ+s3W98rZA2CJcNg iRNuukTPbXRh/RuaEirDqnYeLGd993AOy0dmaBJ9ayh6e7+ImjvzEOvIG1MlXAWzSdt28dt/Pn/d iPev8+fv/1HQ11+/wUr9cnP+fH7/wnxu3l7f4TwMA+D1J/5p6gU1asbknP1/5KvNtW/n0+fzTVJu mBE0/vHHO24dN//5wNc3bv6OSBavnycoYB5Z+Asa5yHz4HYMXPh3RaBuaImD3sQPGWlv4tHWMmyg NzxsZFHht3ApkQqhq3wSWxaynLXMc1Y4lCz3LJzWrNXPm+DVRGfimIwrZKJDvTmmqATG/r+XjqO5 fpaMc34TLO5vb/4OW//pCP/+x1KIB+NIxdEaQusWHbPNC/lIft7FYowWZhH0a4EAWmrzpk6+cEDv rHfOxaXzTEdY5LHPEKhWQ5KDn7HZ+xRb/qBQDS74+Nbcs1DBpx184Kyi9LIOjY+D27BHCQph3O9j WgnbeDzBoH7SA2kG3wV/ycJFZOlXlj1dQaC3B9Uz6tU9T+oDr2lPyzzNfMC4oIxAvrR1r4ocVs+o s35QWaorkr2jAbm1x62u83DzzHXk8tzPwykj68o3WlDkiXlsJsiEdQQx5Lx8Eder1XxJ4+uiAMtC BkpI7N4eGiLbohJPnj5QZfg9+TDsaT6b0V2u8vazYKAV1MRXN3iWxxsOLjixwTe0i6iwLkoOsFNz GqGmfiy3BQnmZOTHYlbW3Ar+6EgKuy8RpPJjZrDh9nrE62AR+Hzc+0QpiyoBhVjPUspURIX0rIVj 0poXDqQan+w2w4GthvNMW8trH5GxJ8cdYGRZOKfwcx0EQeubzSVOy4VnOGZx22zIQ5pZIKy9eS0s gyt7cKOniHRVRH8ADqfCWQxS34RJ6XsxZPhGchr4Gv/KKAgrOKg74zm8pf1iwyjDjYBeDMO8ob8n 8g2MWmyKfOHNjJ5QGrPQPQGZCX0eNOMHRw76XZhTlnQjDSZwHoqCLcznvDkkOggTp9tkbXkq7buu jtTWnjvRnk2318CmO25kH6iDp1kzUDuternTmkiiYgbtS9SmxafIaHWI3jKNDGN7KdRBKrTnt5mq u0kZC0rntHVK7vPYtVNP8+PZPrXd8UI+v1p3/oRA+WSna+AskrXds6MJQmiwxHq+bBqa1aGpj30V kCiz3L2fVwTPSWdDn4mBfvBEujS+JO4yO3JuvaVfGWsKIxtv3c3P+Z5d6Uo4QB+4/c5FdshijzOx 3G3o2sndo8+hsi8ISmF5YY2aLG1uW49vFfCW/vfOgCuPF9kJ5dXjNJc9RHZyvb71PPANrGUA2dKO tDv5BEl9HhVuH3WzYEgNzbK6XVzZi3Tv8oyeCdljZaEK4u9g5umrhLM0v1JczuqusHGt0ST6jCDX i/WcmmBmnhzDOWzdSM49I+3QeMKVzeyqIi8yetnI7boLUGw4Rq+DOqgemHD36mkO68X9zF5r5xOf GqLcg4ht3Ui/zc3Jp0ONhMXOqjFa4XxrBMK8XlkMunhbnm9E7lj+QJeEEUhm/MjxAiERVzS5kucS wW3Ihn9Ii42wNqmHlC2ahlZZHlKvCgR5NjxvfewHMs7RrMgerUiZpb09oE8h94W1VdnVQVHF1qdV d7PbK6Mejpag5Fu77zpY3HsOs8iqC8+Dn+vg7v5aYdDbTJIdU6FrcUWyJMtg47cuGiTuUO4pgkjJ +QOdZZHC6Qz+2U+2JnTLAx1vzaJrZwgpUtuLT0b389mCQjm1UtmvUgl570GuB1Zwf6VDZSatMcBL EfmQ8FH2Pgg86joyb6+tmrKIYM20PNBMbq02Buvz6gwG+F/our39tDwry8eMM8+zpzA8PG9nROhz 7bGw5GJ/pRKPeVHCucVSTo9R26QbZ5ZO09Z8u6+tRVNTrqSyUyCgOWgSGBcqPWEydUq6wxp5HuwV H3621dZBj7W4B8TYEjUFvm1kexRPuR2bpyntcekbcIPAglR3jcz1VYuZeXf5gstjKjwxxJ0Ma4R/ Ge1k0hT6wyeTxLHHci/K0o8VIEP3tYhxw9w++hzgUJ0lXrvr/Dkk5eE++GdMuEaJpefNaec8pjLc fnydf/l6fTnd7GXYW9yV1On00vklIqePFmAvzz/Pp8/pjcMxNR2w8ddoKsv0RkPxasuSBT8vQdrX 26VP1bEzzcxgOpNlmE4Ibn+SJljOM1guq4IdwFq1Cll7ol/LSsiMDFQ1Mx3PPxSTgy7nbVNTmSfY FetO3RRvUAoophQ0wwy+M+m1R/7pMTZ1AZOlbHg8t00TRza9l8IborfT19cNMM17qOPRvQ7ppoyV wFj1sgYNj/RisP8uarlv/RAiMHmloLcfXBYod9Lx4Cxj4rrt/efvZ++tXu++a/50HH01LUkQl8r1 TNY8jYOFCDzEENQiGasr0ewMsPP91+nzDZH0X/Ed5V+fLd+BLlGBsG8q7MMpseegby+J4uKISTgB g4bdfAtm89vLMo/fVndrW+R78WgFn2gqP5BEw+ddN73Pn1cn2PHHsGCVEcPRU2DR2YWWxXvgpDvg 0OfzXsT1v6YlVKiYJ1p6ENR9d1km58fac28zyGBsIdox6LE/iHUq+pXKd0/EdiDMV3KsiyM7MvrC bZTa51dbtamviqDXED7hQc/Qcch5By2MNtk9/tTRe0oLh3o4c1pWr4G1oA6JIzsWRH5REVaMoG+S OVX8pjJ3AIvcZiRnj++kZUVNVlntcnSs6yAjRcyPGBJdEdnXmX1GHXNWlgiyBwaZI75v77mKHIQy tlHGvMtSCpWxqGiVzJYKGflgySiEkUS2H974uUcRfy8oTXoQedryfLunuhTXMMeHcuA1pQeXaJAo m4o6ogz8RAp2F07XaYXhQ3Vwxy720Vavu2OVDSIM29V6dU/zose6luXECWMqcuu3nprCCMxbVvSi acptWVbKrc8xxZTEFwJZ7xB4VbrTDYjGMqU2+/zJ+73c4+hmyhwZWoaO6xn5YoopmakfdOPDka8x PUmtdLtVMPdVEfYAn4u0Kab+rtALki5D/X0UnmEDZ12WLRbLpnsSl26I7H7lsdWZYnBUUf7LhfSd FCf1EvU8oPU/S1RGamhQYVLdPiHs2msqzIng1q/z8EUzm7x/pllqxw85d1y7DWbMER7Do12OYgcB +8YFoaNADLe8DWsPLHL3JXXK5HUhobyva8/LzYMaBetr3kleEFRxRqDRXJJ55OpAc0EiyoIZZaDU XPTnSvHxNjQ1Wbiwmr+fBM11dTtmXfd4s0YR1fpk31ZFzdS7h6oXXREWN+nitnHJUcYWztWgZsDW y0p8TSSFv0KP50+Vien6qu0Az58vykFT/G9xg6cOC6bXAsEhfJ4dCfWzFevZ7dwlwn9tp1lNjur1 PFoF1ndpDhz3S0nd4Wl2KkJgT5PR0fOa13m3kOmAiLBL/rRV1CW0yFo9N+l7p002LOOuX3hPa3O5 XK6JMgeB9JZMx7N9MNvR7geDUJKt3ajt7kxMdfrgkUmdRPVJ+7fnz+cfaASaeJHXtYVud6DaEUFT 79dtWT+aOP76bUwfUYP7f5svh2fZU4VbiHEZGLXSn+Hk6fP1+W0araw3dQ27G5lLbcdYz5czkghr bFmhXwSPFTSk9fiAKacd+a3B1LOCu+VyxtoDA5Lv9GPKJ6hqUygVplCkHS49lTbxkK1amnGlJoM3 rPLVP0MtgfScMaXyqsVIbAPm2uRW+CpJxgcRsiAF5huT92fW1x1h0vsqGx+vtm9Vz9draks2hVLr GVGrOcQw3PKP91+QBpmocadsp4RzdJccP961YNsSNjy7QTT62831uydAo2On6BRHI3F1EjKK8sZj MtYS0HUhr2LmcdHtpMIou1t4dLROpFt5v9ds40IEeESvieF99NWsKs/tnmZXJa2rdOxEQiOW18pQ UiJPUt5cE8X59BQslhf7pHQ9zHsnfnuFc4ZKFtWVjuYnBkoOQ0jFNXqc1wcrTV3Txhc4z3jGWl48 FT4XjD1euJBXSl290JxnRZkZdPU9kNrdOYGEhvS8pvW+DrYquuCVLspMgP6SxympwG2P3UM5Y7UG kn7tUhTW8wIj17kqGBmOS+zICNkteVc8Smy49TzEyHCu+EZGI8ot9+j9COIpIk/wlizyR89VV3ak QZv0M6D9uOuIZbReLe7+dKg57Nru+ISOzjiFuJEfKmY0Mci542BbkgYL6NdNtOVohukeJu3HTQT/ 7EfBjW4tPeFsmEh4gHAj9U1enrqL8nJhwYCzuccUiwJRRe27yDnUCK1QFY3x9iCMu8h9RKIRafo4 idHuw9gnqpxxVugapdrLWgG86/Dg6ZXFPKL2PCRTRZrihvTCs0SX1GWfHT8r1UwWUizuVtYBYkvj RpQ2YEQpp1eO+gq2lDc/3l51xNv0AzFhlKoXOHdqlNG3faOUUlnpCvUi3cwYiv+XemLp/GGBEGhu XULlPn78e3pLhFjTwXK9bvuxoCMQFdDMTXcdjVc5uQ97+vwB1TvdnH873Ty/vLxilDBsOaq0r39Y TWCVhHsx9Xm2kH4H1pfFIaP1N0escN2U+nvxSasYWYgcdxPqlAWtrlVKm6BwRRFOAHb2TNTflsHc lGi7+FsnkageXE9KHdTrveRRmamnSDyVayPnXm0gtgdq31DsLgy77//ucY3/PP/8eXq5UXUh0CH0 d2VxSb5eooxOR43TbCfB88OVipB4ZUogC9d3ckVp5JrN86dgvrJbupXC9tnVpq5mvaT1qv7D2iTa kkPnQvvoCQej6ZeOi8dlpwXNYoLZbYuuHbdr7lQaOYh8AWdCmgNpHEayCuC0MvlS3TLUpqk4ol5P GizaLoJgmtNR5BgK6cvpKIO7SNXKeGL9QlPowZbEmnr68ycsNdQg2/GsTGkdl0g9YJxcybULM/IP gbBee44oukXTVhR0hJI28bijx2aKvncvCnEtNacDP7RtMI4Wc9c/zwBnoZrn8Pp5/h0W6gsjk202 Fd8w/cip9d3qAVyzk8ncBpeNoF9Wgl/+eP08KVyH7Pnr7PTJMejRV/FauqAbfhSKZXCkhvQoYSuT I11uhFl5olZmbeXb839NsxXko/BIWwz5yqz8NV1mNsL4wIjl/HZGrzi2DGXdsySChb+Au2uJ5wui 0sBYz5Y043Yx8zECbz0W9DWJLXPtQ5ezhi55tfZUabUOPF/HZ7dEr3e9a+z8Cq2s4pI8KwxYZmVq GS9N+lRDpIS2x8yOIihjpiUoHR6BaEobXztkNQzmR/XRd5Yqa3LWlAelJRBMswwf5qumabyMdhs/ TJkZa4LV3N40oi2rNmpGN+v72YKoSy/hnvPGlBjuQOvLvUxaL+6WlG7TV623VpiHYguaWv2E87Fz 5kai1sdQS5so/fnzGZY6SjWSPJdFJVsmFyuPV3Uv0QhQHXP12F5VeByXO9ndGkMjL4sEs6syCcuC 5dY72Iaa4esdMjPuuMavKrn5mkRPj3mawvJnrX49Tyx3GCZ+oTxUXmbLhEqs9Jp5QnsOjSUsSX/h no+mUWybab1tBain4ni+p+jVajmfLaYMbcSY0YzbOZFVXkd6GxHSfUu8l4jqu7s1NXFMidVqSWRe Rtmqaahc5bYOfCbFTkLI5fL+ikwmo9tVRk08WyRcUO0IOubyDhYTFybe4lOtphiLuykDvdKjct91 8qS+wL5b31GhwINEHcyDgExbr+ek2a0XOK4XUNktOXw1j2+TyQqCp8KL6wfwpxN1KlbvZkFADX41 ukwwq45gQJM6DPXgKfr+yCmPZxwW3Bzv2bBORZLgnGew9stvM8MY1YmrBdRfqfZYCeVGhE8B24aW XqJ/nmNTHKBi/0fZky03jiP5Pl+hqI3YqYqd3rIOS/JG9AMvSWjzMkFKcr0w1LbKpRjbqpDkma79 +kUCIIkjQXsfustCJhI3mMgzysH+ABMqYvgLjxRtKvleyjzuHTfi6iFtkUTgbRexFgHB99Il/987 DXU90iSaiyK6U5bUagJ8mj0zioE0WL3sn0FKc3rBdJ4iABJf1CD29OMjYDQL6rCkTVP4Tmao48nV FmlHpQYoGJ0uCn4fLbNjoNRBiGk4ZQA5lLO4CSbfqq2xSWm5R8jpEWZKlommxFC9tcVptvHuM9Xq ugUJ5QEXjcpsviGCBXa0XLwGRJQD1SK4BD9dOwUXL0L4yYaOfHxtdpeHH4/Hp0F+2l8OL/vj22Ww PLIRvx7VndBS6ijAjkQ6qyOA59HvL+8hQa6090nlENlKe1kgiOoBBrJ9s+Ko1rSjz4/Lqhvc0tVN oShBFIDSFtIjEE9dTW9QMpvQY2RCXB0o/ZGaehgjLWJYINs23CCFjLsBBSwC8QKIlBxBV9Tu8cSc EE3G2UcvJoyxHF/3IsyGV0MTQYIjn53S8XwiW5alnC2cW92hOfiPsZONGdlRRmlByjwYoTMdVUWG jaS5M/wZo6x1gviJp2eU2XgLdsxcIyXT8dVVRH03QgSsjxPKhtUDnM+Go0Uv3Alc5X17SMjZ9KHT YDhqp6ObfqHFwyeQ87vDsVknXZvL1emPhAjL2e3plT1b3Voz9spYL1Y4G02sXjPm0NqdTY0EjDaF 2NMaLIONZ/6sZ17Lu2Q7nzrBwJni7TZ8o9kmK5/PZtYqd9AbCVWPdLD6Zs1DHeWMxx6jByEl7FHu nnV2O86uhnNHH8A0wRsNZYON0PW3P3fn/WN3mwa706MZUzoPsD2oXIQl7rdH2YHKM0qJrxkzUV/7 AfYQqiqe1woIeNnhtRuoQSUkWU+dBqyXNmGCA8INcZSq3Way0HDWvkNzJFzwIQO93TlfS3DPkcQw ILg8it3CNSFSC6BonAYO78ZhVW36Dm7cQYK9AjQ0Q/ojYKimNXl7vhy+v70+gKrRmXIkWYSW/wAv Y89ahzUNgBPQWuN2EKsy4MH5A1ywCbUFs3lXecUtqLTdSrw4D2ri0A4AjDpgXSNgcsZfhx/Bc8Yg bdHyJKh9R4I7FcuN8YeXfmMLnYXoXgUM8XFRriuY73IqZAMtrSrwh5MrO2qt1hii8VCgNLm+Gurt 8CJDIcDL72mgyh6gTHMx8HQ/JAHvNXDjKPxr4AS7NY8FVyDkyOgbdVff7m9G0VqpdwPrDNebcNwW YEG2EetbFpeemuu4QwBDqYrbGKa00ixlOhx4uPJ3ay+WvhBdOWyHoZYEToeNMEGYjnI9dxG+vnET vpn1Es43CUaVR2Kx4rOrQHAxWQt7NaRljhJ7PvExuWgRGO9MVqAFiI+JGo3Qzxe8BCL5RpqxOiuT 3h/Y05FD12ZsbVJAdCGcvywYi8nWlzicSQrEKFmFptU6c3mUkqJRMTvps/42qSddJHrcsgDqIMwd 5esAgnJWuSMEu8CRcOXaUItlzm/t2pBwPyzW3A6dPZYj3T1S2l08HnbNqb78+qnF1xfd8xKwdHX0 QHiR1uXahRCSJSnBSNeJwVM2KkBzDGHx7vQ0thtuKlyfhZBRDCysiWjaWJMwyvSUgXJqhAYlVq+c cO03h0iqvR/3x0l8eH37q81X+R865fUkVjwzujK+fDypOTHB7GlsXqsCIK7UhKQ8lEK61M8Yp7qI IVNXzNB44k90NrBeK/tFybrSjcnc1u3kwJz0zDlCTKTfOTwdLrvnQbm2Jw5mOdGuJijRoutyFG8r 494W9PfhVAWBh2ZCAjFVVK8WRkkFrpYBCDbZ2Yfwz/ozBrCqOMKigTRZgezeqwfOyDQkfg6+HyAo PXvM7M6M2vP+4QJ/XwZ/X3DA4EWt/Hd7znn2HOdZEQetnQ/1vhZHkExmVxiL04F1MwBRziaZ8L+c x1PoWxQPFd7zh+PLC3AUIt0Hfjb8ajEyvkldOXJueHkSJVlO0RoJ5HpR9YsJKJ68lI0iLNdYeREY B6i7bJAg7xoi68eI/dcTDB62kUmumZ4kCb7Ck28Ap2cn4tqoskHoIY/QU6xVCbO+v5Qtt3t9ODw/ 706/kKeLuMrL0uMuukK6/gZJcx/3D0cw4fkHpM992J/PYOsJ1pkvh7+MoD8fq8BrFCFtEVUSWnl3 e6q4SilQ3WmNqrSMmkq9/asLu4Uo2IcXNpB/7SGzxODhx+EnUs1G6Wbwq0Bhe/3nic0GsNAWlX5E 4TkWFoPL2ys7J1ZtE9RmUdmz1X7dH9/Ogx/7559Y1R4smXLsBVJknPevbBkVdzolzxiGwDFiVobF htLKRUKq5935B0ZdB4h5YM9P12gsmBjD2/lyfDn87x5uYj5P+ggQsFrvvOfnb3FiHylWpd2C/FV0 vrA9uDs9Dj6fdxc2kYfL/svg+zuoD9zQ+b8G7Dizhb6AvwxSiTX6G+2nCyjl4PP7dALZKAL22INz 8Dk9ni4/Bt4LpF/avX69PZ72u9dB2RH+GvBOsysSoUFo+IGOcCx9RP/5warNp1TBGhxfn3/J9fqa x3GDCjkgJD/RbBweI4xPZ4MUiA8PaWIFDT5H6fXVaDT84kpOp28We0/ol69903Iiy9Pu54/DA2qt 7y0xbdJ6yW7lQpE1ygLO1yzzivM03SeHAUXevKhwxMIM9czDYguwMuTwqcXimJ7YYR/8+fb9O5uc UKkgaS9wHg+txuv5u4d/Ph+eflzYRoiD0BlWisEEpypfjer3GGC9OZXh0RZzZ3CVBNrT9/rT5ikz 17FT11WpaqMEP+uMUtNvUisH7R3rGlHFxxqVNDS9hKAoD/QK4FQrwtbaIBrddVOnlP/hqYGBmhLp sK89d6joMCj7NcFyCk+NLePw8fwOsqMAtXrPJwAScxp5Yxuw5bOiwKXK2ZgAB0PPO8nOC6R7pL+P R3pTzQs6i0N4Z+GCcehSkQU1qggHKOPbfPbClj5/Rr/0Z1pb1FQyBx+Ucb32YhJaZhX2Qv0hnyhI G2themxtkpoutSyOcoNUoMktzL7wnWOml8Eq2ksMVRvTgC4PrIGQgE8Bj7qlw7zgZlaD/Cgwe+Tm pcXGIWYFLxzO5zfOdfViOnZFoRXgiSt0poCT68k1btDP4ZSsenYVW2Li8m1uwVy85nCGBKRqPh/2 9JCBR/3gcQ9443A+Bti3cjwezZ1wv5zPHO7WsM29q+EVLrDm4IS4dAj8MtreLx0R8HltOhnN3avC wFOXJ3gqNTbuOREKHa9yRvvnh3i7cPc+9IrY61kUdif2gWPvvre6II+7b7Tk3WBB3g1nT3lcXyU+ B24Y5KwbOyxqU9D4hGTpnlIB7plzgRD+8S4F98o3JNwYfSFNFHgPgZQOnRbaLbynATq8GbsPHYCn bjASbEX9robUfRkB0H0LMeZiOBu6LwsO79lUXN0337rnpUFwd+E2K5bDUU8f4ix2b854O51MJ454 koKHgIxnGa6LlZyQM7QCA6fJ6Np93+XBduWwuGXQguQlCR1qe4AnkSM5lYTeuFvmUEemNfGdnrp3 MyV0djV0f15plpJgTfyeeS0LNrLUPW9r4s1HPbe1hL/zleSaxIy6b4/1djRyT8J9ssDyx6/C37jw RjOk5mfFExsWfWi0tf5mVMkhwHHMGE1KvkW/X2nckia1FAWCTdJjU0hI49Ta9yoAAqHFL8li7iVC Ri5GS8WieUgWKJkEODn3jaLgjP96F6uI0syhRxO8UiJshh09luFfYEz1ZkVoGdvsbhhRskx52lVj 6EK4cAyklBNECovTfn9+2D3vB0FenQ3ZQocq5dpIlf9RhLlynBCmxaOFxfU2MOpI16jVr8KEoPGD VEJqsGwN4FpMAEZG+wgKe4EtSIzTjuTQENA2WNuPDwkr8oRipoMNDkm2fNDVVpWD9y6W8WlhW2JF pqPh1Ts7niRLu/uskFMgKTaABoob56pYuVeww8/2sGbOrWLwtanVOJU21Fk5Z1seMthkIlRuCg4J HrrRpPU9hZCqecxeZ5iVe4MMEYr9MljTECNFswVKRBynMjk8nI5czXU6voJEhRWx7xgcYiHYP9sZ w/8ftez+SJtmY5VdaJzFh0RECXfq+0gVvgg907UtId80dKDRs/BXLhISTr3im5ewdQF71ezqauSA DIdzN6RebRDg7eT6eoKVX4/nU2yB/XA0n6I2Mi1GWRv5/xpIaAS2NsARnQ3HE6wig4wmqGdU8/Ur k6lqj9XtbFDo3Y6vxuhgEm97Mx9fz1B/rQaHJvOb4bTeBGEXQbsHR5o/2Ejse3w9vBp5NoR1kD2r 7XJKEnaS2Lcwj8mCmKIdBUPchCbMdbFTmozGupO4iQHJ1z2Ey6hCbzgeIxtmk8yvR+jaMchs3sNs ymWYXffxlIGwrurbAq1hllU19ar52OWLKHHiHGTFG+qBhbMjjLOOu/44arH9MGqJojbaCO3m0CZA 7AOQeNZVSWJz6TqwOUGGDaGEKHymYHtJiCkxoBjldxX0hiSYqWWrgNQxKRkrxp69IVEztAAcMeaB YrBcYY8GXJAACFWck9qKKaUgsD9Tl40zwHkI2ZXHuIIgNFp31BCCRz4ZgMQjLRo2HlCe//h1PjA2 ZBDvfhkqJkkszXJOcBtEZO0cgIgm4wqb1dOSQcYLl47cu+V97kinDhWLjC2Z0DVhRh+JzlfAV9SM OtihctMs3ehB2D1A7h/GT7cKudCeLajuin0AMBqudIlwW+i2UG4xTFtnm0RcLhKc+gL+HWPusYCz 8XV2ic8CWSSskrNPgT9zSHkBuuaGcuwvR4sV6w6ZsmVTfMU51buVGnMVilb0zupbRlfE93rnLHHE OuzmZMsecI6kpVHCQ/8jnU+jjZFCBn4JZR5WJqzbUAgETiZBFutu7xzB59Hh04hhrTYQ5CFd6nFe hUkDK7MYNV5fWAobraZROZnr3vC8fFOguW04zPRK4IU5GnWGg1S7a71SXI6vbzDGjEOzcsSjdBvj 4m+lP58Pr//8PPzC75Fi6Q9kUpc3COYzoD/3D6CAh0tdTsbgM/tRlyuSLpMv6vEUcxNvWS9dHQHr bavv4AU097E3LAczdiROKsuLn8PoMhkPJ+3QhAkJWAqVx9PDD2MJ9VaLcn6t8wXt7JSnw9OTvezw FVoapnQqwKnE1JAytu9WWWkMpIGuInZz+pFXOhtpVdzo2dJQ2eP3fSQvKMkaz7Kn4ekm9RqocXnl y8Nn8fDzAiYk58FFTGW3odL9RZg9gvHF98PT4DPM+GV3etpfvuATzv71UkqE2tAxUm61/N4QcumO i8FE/ixnAzl/E2L6WX0y4Q3p3h+OULZeEETgN8UelQ4MHpqT3cloZLSIvexrdiuAOpwGRaVYkHCQ ZYhdlEGthRiEgiQYTqZz9lQ04lkDzLIclrAQ3Jka036rrNVTt7QU2Bp3vmIYirVJV00m6dWa6fxF 2A2eRrHeiSYgYjfLIoZzQpfQiD0YIQghDDjV3jLg/2/U6GDx1oRJCHe9WAG1Olkm2s7tQNiUboBg YPmWyXK0F00d3KlxRSsjBvuizkWBsr+4L5fRpXZFAhF4VDNhovdpAC8WxzAgT4bqvNitYV14XdBy VuxXC9scmFNfEMOzccPLcT5VUkLnh4PqJFtHjONmrBd+yCSaO+WjRKBRvIDB4S8OicQu8hzn140h t6+uahsSmseeEkIX4k/FgWIetAonk9n8yvoYynJtSRNYpIAQsJpCVohdhlEsmR/GklGqOWUJKI8m 0cA+feqIy47VflxnC3zSVRTs3lTgBhNX6VdoBdFFUTEbQHLweFlGKSnuNApsHaKkA2jUPNdbR6QE CTKKq/0qGVZRqlucOJCU29XdotLTIEJhspjqulJ1GAvNBmu9cKWCZ3dhv4m6iEKeRCmWJ2od5np2 Xu6ZayJLY+OH0/F8/H4ZrH793J9+Ww+e3vbs7YZIb99DbVpfFtG9pthi+ztS086J36bhU1sq2A9+ Nsm3qL71fx9dTeY9aIm3VTGVSCgSGfIn9U6nxCPU+wga7Bq3LZNEmo+ur63BQWFNtbWRkFvxr5HP V8dJCXu8V6UIS6OD+A2Gl0IAXZAZOaCSqO5rREtvSdCwQxC2oPN6sD0BeQiqjSNgvxdExSrEbxiA 1RtSRJBaFMdIQvA8xmHhmn1QRLYrHIFWtJ+8PjKxvDxGnaMzJM7qYnFLHAhNhlc7812LsszZgCBF PaRzxbu9yjk/iTfRDKdmL5DbyMFr+glj+lAtkIwGtgo91eEGnQYj74BgMDiTSvNRLaITGewHu23i zBFxOxC8AwQNrBzyZJH7s75zyE0aqYZf9q1Bg7Wyvt7qbg2S3BEh/p6WUTKbuj3nyyxnR6WoYy8v M1xZLhlRRwcEtKCO2CxCm5ewVyQrSQ0fUAMNnPKdbvASpUpJyfriyknKWwsqp6xIwUDstpvVTcT7 pdsubfaPnOQaCxisCnYVtcTwKUp44s1s29cmrYoFeJG3lLTLTALZKQGbZXypGwJFNq57rpEGr4t2 jPHo4JYfxIotcVMCViK5V+h3MbjXCWzBkvMg84pMB6z5i/33/Wn/Ct77+/PhSffZJIFj/0CLNLcs TBsdxMca0j5Vsq+mWKu7RIUqdR3gsorVhuYkhTj7FiMiekOPbycsQAdrkxZBTdgHVBHVsdJoXSKl fhy2pcZ4jRZa6alHYl8P+54HmES2eXgayITNTYX5bwqnuf3L8bIHrzpMgiVyFYKVOrpOSGVB9OfL +cmeKm5yoUgD4CfnzzU5AC9NUQ9/DuLP2qUe89GEQIFNVLCm+Ei0HiunCrwqNgTJHQiGAZ/pr/Nl /zLIXgfBj8PPL4MziDG/Hx4UrYLwhnl5Pj6xYjAfQTyAMLBwZjkdd48PxxdXRRTOEdJt/rUzT7k7 nsidi8h7qELS9t/J1kXAgv1Nyb0RHy57AfXfDs8gmmsnCSH18Uq81t3b7hkcfl09Q+Hq6oKGxFra 7eH58PqXiyYGbc1IPrQpuu9REwuzFVOIn1g8wyZqJg9RKFStWRpGiae69ahIeVRwI5MUi73JESBK JmX3Pw5uI6A4anuUknVk9tyKNtgN0nTJiLbAOzQEor8uD8dXZ9BCgWxpMmSxZGUg0uYNpmqWaOw5 Nh6rjx9Z3ig/FZUVu/gKRULC7WvYG7mOEt3egTjeyWmJ59peM67C0LA2E6XGZoEkqjxGpnbtb5Ie qRGHbnAGCmC9EZQ6BISVEZ6WxR13gMXe3xasZcdz8Cgy4saIJPQQGm3k8oERudAIe4KUjvgnPLC9 Eg3C6m++uh/Qtz/P/DB2+6jNy7LS4t5zC4BlAsXY0qzu68BLhYIAVOi6dskPkvo2Sz2gMuohkW+9 ejRPE8jRpOgHNBCQ0NacAaXUlHUvSsxXZpMeQxttS5nHwfX0KLgQvlX4eGHyVJEnrDkFgSaiZz8d sl+AxHkbgjTfnxjX9rIDZu3l+Hq4MBYO2TZ9aMpKe06Ti4m16t7r4+l4eOzWm12PRUaUK1IW1D5h t2cBTw5NSKBBUc88g0Ajafj05wEUT//48W/5x79eH8Vfn1zkofFWyosuazMcRezrYSK/dG3k6uAF zii7EgopxGioJpsTgEIQE0Y5m8HltHs4vD6hOT5L7M0jYxWv7Kd3uXrnBccQHAYSLXxZKjLctpQ9 ypHSvCRIaacvakyJ7EGqF7+yeWQSZVg6S3XCvxHJsmiwgjWmkOdYfkHCpVZZ1mHfyehbJOF975i8 4LGwqhzP5MhbYe9Aokrvm4+Y1Wn4sC0SvL0WwVtgAt0WnJKMNo95L6hTM2F3i4jfIGXUshLsT4zv UosV5iLL1UQIIj+W8gs+PnZu0Jgk+EeYWzFLiYb6FK5SzesVElfdVV5Yz/XtVRRVzr5KugZZRlOM sN2QaJE34ZeV9owX0hQ3gjPYJWGdcGActPgSqPxj4AWrqN5kRSgVwV270jE4qhcUDMapOlQoyijZ skqKnC3awmtSZ0+astoX6epybIJB11YDXJMVAxcLIot7E95tHwo544v73HRfVjHYdxm3MFhQoY9T WFmzgIgCy3Rk4fWo8u6qrMTUkhAabUEn9UKZZFGmFS1YY1pBUOme21KZssC/gBkbLyQtWNhOJcHu 4Yeq2k4jWBjE9FECSq9EV4vyXaMvhNhIVhULA9xhsmXh4fKYBsvN0DYYmQ/MSh0Tx0dSjlVwHuf9 2+MRIl7trRMgne0VxgsKbvUwCrwMommWsVGYe6DAzFKixaPkoGBF4rCIUrMG+9Jzo0+YK1XtdBsV qdqRhtNv7sIk108WL+iOIS7w5ThbryxRwV+1jMrYV1uRRXxc2sMHossWEbsO1AsQ/ml2b8fA2bOt 3Fug2oLjLITV+F5p0nE68Bos1eaC/Whd/z8dzsf5/Prmt+EnFQw2zHy5JuOZXrGFzMZatFYdNsPt uDWk+TVmh2mgjBytz9VHqAFx9XiuZuQxIEMnZOQc5XyK2fEZKJOe6phxv4Ey7amOm+JrSDdj3MFU R3p/IW7G7mm4mXygI/P/q+xIttu4kff5Cr05zcHJE2XJcQ4+oLtBNsLe1Iso6tKPlhiHzxalJ1Ev 8Xz9VGFpYinQnoMXoqqxFmpDofAbfccYkURXIwmO9PVop5rZxY/7CjjeasroCrfItDnzh2UA9NmV jRFbfAO/pFu8irUYXymDQaWmteG/R8b4PtbkjApmcBCC3i5r8XGkGOQEHNxeYLxTW5f2JQJTnHKM /KXKQeEb2pqAtDXrBVnXuhVFQdW2YJwuB0th6Q8PAWCNFsxXF32cahCU+e+MmOxoP7RL5wE6BAz9 3LkUkxW0zB8qgXRO6d31uLq2RYujxCqH+/b+7WV3+B4GcS352hGua4xgvh4wN2ugvjS87UCRkA9Y c5j0akELpkTXRJkq+Dg4z7xmtXYalMOvMcvxRZyWmUSVRuzzdECFFWOIOunS6lth2x0GISxx9YOp Ii1OaRcfchf1TBXsj/B5pbA2fAU6doiYszbjFYx2kJFMzXqUd8zdZN0B0gkQKNpFkTD3HfcQC0fR NYyK9JqDaosafFcPrZvPSz4FlspK8D5/zosm+hCZHntXxvIyTyh9XdbryH0rg8MasKfK+geNrVkk 7vPYHTZHh6d/FcpHQ8Mpq1fVWHSRpAUTJr7jVJOXI9BeXWiyc4zYheqKWFSgzrbkNp6wMELZtYIj Q+Q3lAvJBFEdtwizI6q78tO/v232D3hu+g7/enj6e//u++ZxA782D8+7/bvXzZ9bqHD38G63P2y/ IOd49/n5z38rZrLcvuy3387+2rw8bPfo9zkyFRV8ph7G3u13h93m2+6/G4T+y3pmTPRIVWDmVnXF 3YkSGKGutoMVsk7OlkJFp48b3O4/0O33w4Djw5hOpHyuOan0yK1q43NJX74/HzBP6Mv27OlFJ7y0 DrklMoxp4WSNdoovwnLOMrIwRO2WqWhy2+/gAcJPcieY0yoMUVsnLm0qIxHDtGKm49GesFjnl00T Yi9th5WpIa1LAlW/GRsrd5RaDUIeR5Ca++GYiU69V+gF5ymsaigKsjDsofyHWOShz0EgEv3zXd3e aotyCtlu3j5/293/8nX7/exeUucXzJD4PSDKtmNB+1lIGTxNibIsJ/oIxR3l1ZnAbeZFSurel7Ta beZqaG/4xdWVm9hGHVu8Hf7a7g+7+w2m5uZ7OWBM1vv3DlOovr4+3e8kKNscNsEMpGlJ9GaRUozV fJKDYsQuzpu6WM/e2w9mT9t0IbqZfWHcDJJfixtiJnMG7OzGLF4iI1oenx5sJ5RpOwlXIp0nYVkf Un5KkCt3T6l0adHSIX4aXPuJRV1wA508Bb8l3WVmh/M13sYLt1Aen268p9wP1DrilZ2bgGJyvIEW md+ShROcU4W3ain8Fm+8CyAmXe729RA21qbvL6hKFCDMV0Rgxb6GVSjK4cTXt7ekEEgKtuQXFFEo SCSEemq5n517+TaCvYXtxvsVXeYyuyR6VWaUK8UABWwtLlPgEJ+2ZTb7QPkUzG7N2SzcwrDzrz5Q xVczQjTn7D3J7MhX0DWwB5UmqUOpu2pUE0rp2D3/5cbSGd7TEQ1CqResE8Ir4QcEG2A1JCJkHaxN LwnyqVfuI+seILgWY0iHYUSoCMVRytAUjX3U9VfUDoByKpTFSDhyjuby31OUu8zZHaPu6XiCgeD7 PJTzoGw03n1JFzJ2Hb8YryJvSE2kRDlyJoWBErVg5uJKnKpVo/iNm3xaJh9+QH2g/xWu81sLlLs6 KPt4Ge6X4i4kKSjLqe171/XhhfAWDJunx7Pq7fHz9uVssd1vXzzzYyLqToxpQ2m3WZvIhw0HGkJK AwVRDNXvqISl5NGChRFU+YfA66wcg3Rs49/SVUfKoDAAWsefoJbRQKnBEqet6MMlHw/NkvjgJjT1 5PFYJ11dcIJIsMN4i9Y3rL7tPr9g6vaXp7fDbk8I7UIkmu8R5RSXQoAWZyao6RQOCVOb/eTnCoUG Tfrs6RomNBKcRQZtZCjo95i2cHYK5VTzUVl8HN0JNRiRJnHpE09Oa5isW5clR1eedP5hzpOQAW1f DhgTCkq9SuKHEe2bwxuY3/d/be+/gi1vX1LFgzFcSbyS302eS8st6GNIOsT/qeuU5qT0J1o1VSai Yu1a5R6fG2ouomRciIoz6nEojACkL2wlArQEvMRjbSMTngcKRJWiU7CVj8/ZNrCNUvAqAq147ycp MqC5wJS1ooVpSoQT5dFmTlxeK0oOdi++kmhVo9y4dvKtKaYwFRhtb2v+BuQVT6mA5qgZyBQiTeEk 35IYGB4AxASMvqp733uc4rt6KTBYp2jmEWo6ntRmoWf9MNIWudLM7Z9TsFpQXoiUJ+uPXttHCH18 plFYuwKBewIjIQ8rAPbB4Ysul0ydA17YyMoiiTXzkWjBty+kU9PiNlaMYpXVpTVBRGWgK6AO2LRO 6A2WYiCXX36HnAcEiauK3CmO6ZWCZkLUjKVUzaCLkNigodDlZC23d6OXR1+V+Lm+fLAMSW2o6ysa QXhpEXQxI29XHYF9DvvU797YNczODapLk/SPoMzLIjCNeFzciYYEJAC4ICG3dyEDIE5HWNfVqYBt fcNhBC1zTkc6ZBm89IvkqxIOK8FyJ+1CBYr62KlMCMAfnXBJCZO5IlgjT0UsKpIcRya0yLJ27McP lw537Fai7gvHqEbkNOLWlxU1IppHy3Qi4VUK2mhrP2+xKEbvQbXs2ma3RZ24vwi+VBUYCWPxg+IO D7+OBaK9Rt3AqrdshJOtBAN1MYQSBI2zNLBcZlVvsq4O13rBe0zRU88ze03tb0abtTqAXsqczlsW edSwYvbFPVmU8cZO8aNOJKTsB1kG4uTi/Cj8A9l9pMNqhseX+LCqEfTTwYJRRWTp88tuf/gqEx89 PG5fv4RHseqpEjkKR5BiYcoKPzsy9rdv8dpiMgi8GUfq4CD2ahkVuJApXycP/W9RjOtB8P7T5bSy KrVEWMOldeCLOSh0TzNesEjuDv0czInkHTZGcO110tfKpAbJOPK2BXTnig1+Bn/sd1z0Ckanf7Jp d9+2vxx2j1qdU0/k3aty4i1E1Za2VoIyoP1sSN0L1BbUcDtOH0lamB2oNuRdgyNKtmIt5vStC+lH ts5gqAolNq1R+FiUY61hORILMk7ZtTHpnSs9iyzB/EaioUMiW1ivEequPs3OLy5tYoZPYDNh2D0Z xdaCiSmtR8Cx28s5PtaOwdOwHwrK3alGZV4OLUVXsj612LoPkd0b66pYhzM4r1vYbvOhUp+wQiwq 5PXRoTa1cIOfb0pQ9odbLYbI6lecLTHUIkwNZiyRnyVW9cYZ+kB294YrZdvPb1++4HGn2L8eXt4e ddIgs9vZQsgYTjtVi1U4HbUqY/7T+T8zCktdQqJr0BeUzENFaGS5s9D5O2reSZm2wr+JWevkkZxE KDHY/ARxTzXhyXMsPENKjSVQst0W/qaClY0xMiQdq0Adr0SPSUuYLRwlzK5MIfd01sE0tSpM8A5t 51UVKUWajYC6XMz7sAeZuBnveEsmXZUIQwUbD9hK4iZ5Mr2o6XBaBeZg/Z0Ay81Tes9MuJ12J/MY rYvBSRKF3B4/RfAugWFMMSdIC8NwA/eDDiuY6rUEOMpJftvzqhN1FVaHcKmckYwVvq1Xlc0sZBmw EHyyw43oP9YHnDGSUEuitHXGehY7OZ6oVyGvbsM2VtSVqclo77OhdBiZKlHfRiLvVb0qMD2S9L0Y EoMWCbNCjFjgvdzEemHBDCiAn4bjMpATXVQa4YDaD6WGpDnaChKH4/tSObff7tMPypVhyzelPCTE 4LoTjQNWS5+yTvBmAbbsgrxm566rzsFHdEUBTjSj7vLKaJuobF2iLYIWmsXylOatrkx0FoaWce47 4V4tDo7Xn1ws8pJT5qy16nJR8ILHHFi9304EqLnukiHnCX2iCorxgbATQXoceRMYfI5xb4mQOXfu j0+/j0JFlpgQu0jIpEGS6Z7UTakbmOrZ+bmHAfzWbKtPF1dX/ve9tK8l85QivPtkGTgBUws2Q44X lYODbcQ/q5+eX9+dFU/3X9+elSqSb/ZfbMsGk3pitFbtvALuFONFqsHyVSugtAWH/vjyDsbdDchg ehin7RHo6nkfBaKBgq95lDZa46YajeP4XcPISq8pmTHAJoIAg2rIQot2xsfxO6PqH3PMq9SzzmFB SlWbQNNsYja1sKEJLd4XF8XvyuoalGdQobN6EchrNQRSYJ8mIhW/DGruw5tMOW6J3aM4kLw45i5R UNdUk2VSgNhGItWMvxFwDpecN55fXrn4MXDnqHD85/V5t8dgHhjY49th+88W/rM93P/66692Gl28 8CfrxuRsYSbYpsWcmMS1PgVo2UpVUcE004cFEoyD9Xkh+sOGnt/yQOU2CX388gj6aqUgIJjrFYY+ By2tOl4Gn8mOee4qGYbLm6AAHdndp9mVXyyN3k5DP/hQJZ+1m0Si/H4KRbp/FN5l0JBo06Fg7Qh2 y2Bqu/DJQ2NHhaXJAVxwTog3vcrqhJZKYWqvKDAVDCVW7tfHaRNOS3H061kbZe58Ru7H/4eKTatq +kBESJXkuHhu+ViVwqeB8JujZ8vuujTLgVbAJsFwCtjR6uDghPKyVNIwIrW+KvvgYXPYnKFhcI9n eYGHR54Del1udKEvIiNOLQmUF2EFnVtSKbqj1NTTWt6DFm4o88ke+02lLcfn+8DIDu+2AgmTloti JakV8kBTF6AAEbGCKve+OB7wAAysFOs7cp5kFbjqlPsHYPzazlZtck854wmMl2utS7aBN8VsJAbW Wrrua4vhyIAFy70acGR8EUSCnOsaN5Zn6DR0AYZ/TuMYD+jc0H8cOK5En6OT3Vc+KbRMtKgloBvZ R9dopbyoD/XhUa6Hgnd6cetJTOnTCirBOBXf05/q2lTVHlNo5RPg3jBVV1JX8uBxPWaEnduzJfMh SXwvcSrox7e9fgklmGOrKu0I6la2KdK0nJew+9preqxBe8YS9hvSiCHt+AuLypU8twiqDolpom6S kiiRE6GmHxPSz9PQ1BfgIXgTOTykiPcP5hl07TkxQqWzhR8eHb8r2LrxmuuuqkXHw3XB5x+OX9pN lqWoY/xHT4PeB758A25RscZ9McIDGIeuR29aMQFJBsSqJtBT8hwYj/ktDVgHO8DUqe94SLsERLfh z9UAtSZc7TQbu5kHZYZc/HK6htMcx4ViGId9+8CMwvEgdOsK6NNvBnMo2A+COHOu+IfK5eTB5Kan Dldt7mGDj5aOrpoV8nwWl4QkXj1ENXL8Z2i7aGoOTXo9A+nanBCfVudiyATqlL5EMqGMFz1zQ5CO HFGe7MXqtJYAmeLoa57OYpzKxAaaiMi4fIls9v73S3nYHXE/KZeIe5tTeUmOifpp96HCsigh4oi0 8dT55o/xZDhCtLdGKSU6na9gJ3K2lPR3qp3lXMwjdycVgs4OWojoK84KT/2K+Gk1jrHoT7sZZe4r oU9X3HNPdTFX4wQK6T8fP1AKqWc3BGI0tCtCHM7aYm2OpYfOjnD5+GHUZ8RSAA8N/VWkrixZRD5Q D8Vl9iUd7TUoknkx2PFQUkOaZA6VXgZ7iZExmO3sRGwUPkAgd+b5rfe6xBEQOWieMIb4qf2E44se T9dWMQDoJopcEWlYPKBF1mBUSG8C5Lld5JUklfoabepo3UO1Ugnj/BPZyYZwSdAO4ei3rwc0gdEb lWJa0c2Xre13Wg40YzLGHoYo1K2WMt6RUFPSaFR18t0jGt1RC6W7b2otfrbYgbyvbwxfc6PxQOhK dVR5k2JvUUwhlrCn3eChY4F/05aezeA6roqa+R9dBM1uTGIBAA== --===============1004750109120966829==--