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=-10.5 required=3.0 tests=BAYES_00, HEADER_FROM_DIFFERENT_DOMAINS,MAILING_LIST_MULTI,MENTIONS_GIT_HOSTING, SPF_HELO_NONE,SPF_PASS,URIBL_BLOCKED,USER_AGENT_SANE_1 autolearn=ham autolearn_force=no version=3.4.0 Received: from mail.kernel.org (mail.kernel.org [198.145.29.99]) by smtp.lore.kernel.org (Postfix) with ESMTP id D7F57C433E0 for ; Fri, 7 Aug 2020 11:14:22 +0000 (UTC) Received: from vger.kernel.org (vger.kernel.org [23.128.96.18]) by mail.kernel.org (Postfix) with ESMTP id ABA5222C9F for ; Fri, 7 Aug 2020 11:14:22 +0000 (UTC) Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S1728387AbgHGLOV (ORCPT ); Fri, 7 Aug 2020 07:14:21 -0400 Received: from mga02.intel.com ([134.134.136.20]:49101 "EHLO mga02.intel.com" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S1726842AbgHGLGI (ORCPT ); Fri, 7 Aug 2020 07:06:08 -0400 IronPort-SDR: GGA1kcb90c8kt5/OKDsx5692J1ayQi4BMiCq5K6hMa9ne3SkXu7Jn/xxfYzuX2T+l2EFsxPUMT u0wbpegCTs7Q== X-IronPort-AV: E=McAfee;i="6000,8403,9705"; a="140924184" X-IronPort-AV: E=Sophos;i="5.75,445,1589266800"; d="gz'50?scan'50,208,50";a="140924184" X-Amp-Result: UNKNOWN X-Amp-Original-Verdict: FILE UNKNOWN X-Amp-File-Uploaded: False Received: from fmsmga007.fm.intel.com ([10.253.24.52]) by orsmga101.jf.intel.com with ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 07 Aug 2020 04:05:30 -0700 IronPort-SDR: vDv73aTwDJfs3/y6n2N2CXwwEQw/Z+v+dWejKqzXkMtrBcx/kEAbRDBPGMT8JaoSLzOT4kM00+ GDJm0f9e9f9g== X-ExtLoop1: 1 X-IronPort-AV: E=Sophos;i="5.75,445,1589266800"; d="gz'50?scan'50,208,50";a="275348067" Received: from lkp-server02.sh.intel.com (HELO 090e49ab5480) ([10.239.97.151]) by fmsmga007.fm.intel.com with ESMTP; 07 Aug 2020 04:05:28 -0700 Received: from kbuild by 090e49ab5480 with local (Exim 4.92) (envelope-from ) id 1k40Bj-0000Cb-Lx; Fri, 07 Aug 2020 11:05:27 +0000 Date: Fri, 7 Aug 2020 19:04:53 +0800 From: kernel test robot To: Luc Van Oostenryck Cc: kbuild-all@lists.01.org, linux-kernel@vger.kernel.org, Masahiro Yamada Subject: drivers/video/fbdev/sstfb.c:337:23: sparse: expected void Message-ID: <202008071937.5jgrWQOc%lkp@intel.com> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="GvXjxJ+pjyke8COw" Content-Disposition: inline User-Agent: Mutt/1.10.1 (2018-07-13) Sender: linux-kernel-owner@vger.kernel.org Precedence: bulk List-ID: X-Mailing-List: linux-kernel@vger.kernel.org --GvXjxJ+pjyke8COw 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: 86cfccb66937dd6cbf26ed619958b9e587e6a115 commit: 80591e61a0f7e88deaada69844e4a31280c4a38f kbuild: tell sparse about the $ARCH date: 9 months ago config: s390-randconfig-s031-20200807 (attached as .config) compiler: s390-linux-gcc (GCC) 9.3.0 reproduce: wget https://raw.githubusercontent.com/intel/lkp-tests/master/sbin/make.cross -O ~/bin/make.cross chmod +x ~/bin/make.cross # apt-get install sparse # sparse version: v0.6.2-118-ge1578773-dirty git checkout 80591e61a0f7e88deaada69844e4a31280c4a38f # save the attached .config to linux build tree COMPILER_INSTALL_PATH=$HOME/0day COMPILER=gcc-9.3.0 make.cross C=1 CF='-fdiagnostic-prefix -D__CHECK_ENDIAN__' ARCH=s390 If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot sparse warnings: (new ones prefixed by >>) drivers/video/fbdev/sstfb.c:337:23: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected void *p @@ got char [noderef] *screen_base @@ >> drivers/video/fbdev/sstfb.c:337:23: sparse: expected void *p drivers/video/fbdev/sstfb.c:337:23: sparse: got char [noderef] *screen_base drivers/video/fbdev/sstfb.c: note: in included file (through arch/s390/include/asm/io.h, include/linux/fb.h): include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val include/asm-generic/io.h:225:22: sparse: got restricted __le32 [usertype] include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:179:15: sparse: sparse: cast to restricted __le32 include/asm-generic/io.h:225:22: sparse: sparse: incorrect type in argument 1 (different base types) @@ expected unsigned int [usertype] val @@ got restricted __le32 [usertype] @@ include/asm-generic/io.h:225:22: sparse: expected unsigned int [usertype] val -- drivers/video/fbdev/kyro/fbdev.c:725:23: sparse: sparse: incorrect type in argument 1 (different address spaces) @@ expected void *p @@ got char [noderef] *screen_base @@ >> drivers/video/fbdev/kyro/fbdev.c:725:23: sparse: expected void *p drivers/video/fbdev/kyro/fbdev.c:725:23: sparse: got char [noderef] *screen_base vim +337 drivers/video/fbdev/sstfb.c ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 330 ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 331 /* ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 332 * clear lfb screen ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 333 */ ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 334 static void sstfb_clear_screen(struct fb_info *info) ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 335 { ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 336 /* clear screen */ ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 @337 fb_memset(info->screen_base, 0, info->fix.smem_len); ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 338 } ^1da177e4c3f41 drivers/video/sstfb.c Linus Torvalds 2005-04-16 339 :::::: The code at line 337 was first introduced by commit :::::: 1da177e4c3f41524e886b7f1b8a0c1fc7321cac2 Linux-2.6.12-rc2 :::::: TO: Linus Torvalds :::::: CC: Linus Torvalds --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all@lists.01.org --GvXjxJ+pjyke8COw Content-Type: application/gzip Content-Disposition: attachment; filename=".config.gz" Content-Transfer-Encoding: base64 H4sICJovLV8AAy5jb25maWcAjDxdc+O2ru/9FZ7tSztnts3HbtrcO3mgKMrmsSQqImXHeeGk We/Wc/I1jtOe3l9/AVIfJE0pO3PONgJAEgRBAARB//jDjzPydnh+vDvs7u8eHv6Zfds+bfd3 h+2X2dfdw/Z/Z6mYlULNWMrVL0Cc757e/vvr6/nlyezzL59+Ofm4v/9tttzun7YPM/r89HX3 7Q1a756ffvjxB/jfjwB8fIGO9v8zw0YfH7D9x2/397Of5pT+PLv85fyXEyCkosz4XFOqudSA ufqnA8GHXrFaclFeXZ6cn5z0tDkp5z3qxOliQaQmstBzocTQkYPgZc5LdoRak7rUBdkkTDcl L7niJOe3LHUIRSlV3VAlajlAeX2t16JeDpCk4XmqeME0u1EkyZmWolYDXi1qRlLgIxPwj1ZE YmMjsLlZgIfZ6/bw9jJIBtnRrFxpUs91zguurs7PUL4dY0XFYRjFpJrtXmdPzwfsYSBYwHis PsK32FxQknei/PAhBtakcaVpZqglyZVDvyArppesLlmu57e8GshdTAKYszgqvy1IHHNzO9ZC jCE+xRFNicKqmZRmZXsJOXxHJehyP0WAc4iI2J3HcRMx3eOnKbQ7ocjAKctIkyu9EFKVpGBX H356en7a/vxh6EiuSRVpKTdyxStnL7YA/C9V+QCvhOQ3urhuWMPi0KMmtBZS6oIVot5oohSh iwHZSJbzZPgmDZifYDVJTRcWgV2TPA/IB6jZVrBHZ69vf7z+83rYPg7bas5KVnNqtjBduBqL kFQUhJc+TPLCMU5t80JyRDqSqkgtWQvr5eyOlrKkmWfSX9jt05fZ89eA2XAwY1dWR7Pu0BR2 7ZKtWKlkN3m1e9zuX2PzV5wutSiZXAhHwKXQi1u0KYUoXf4BWMEYIuU0oi62FU9zFvTkrByf LzRoqpmDMaH9nI94HIYF3WZFpaCzkkXG7dArkTelIvXGZblFTjSjAlp1kqJV86u6e/3P7ADs zO6AtdfD3eF1dnd///z2dNg9fRtkt+I1tK4aTajpg5fzYaYRpC6J4itHOIlMgQtBYecimRrH 6NW5Oyn0F1IRJaNmoZI8qlXfMTfHoQDrXIocWBal250RU02bmYxoE4hUA87lFT7BCYLaxNZA WmK3uQ/C1jDTPB+00cGUjIEPYnOa5FwqV5t8Bvv9u7R/ODt62WuDoC7Y+kvHyecCnV6m5YJn 6ursxIWjsApy4+BPzwY146VagqfMWNDH6bkVprz/c/vlDWKk2dft3eFtv3014HYmEWxv59AE yqaqILaQumwKohMCURH1NJHOa9FU0l0TMLt0HtWdJF+2DaJoi9KSLnxXExJUPI3rZouvU99H hvgMNOGW1XGSCtzDiOq3zVO24pRNUUAnuK8m58DqbHoQMOAxMwg+Fsw/7F1X5A2ukIyQoz8t fVIJgVqUFsRqabuhmPK+YVnoshKgcGhkIUhlbr9m1UwYN77A4FUyCVMD40iJisYTNcvJxjFV oDEgbxOg1k6obL5JAb1J0dSUOWFinQbRIQCCoBAgfiwIADcENHgRfDsBH0T0ogInA+G7zkRt FlPUBewNTyQhmYQ/4oGQF7zYbzBrlFVoH8FyEeoa9iobPqzxG74LCMY4rrHT35ypAmy6PnLq dj0GsLtQyEKLifCcLUhpXfHgGExAZl1v1CGipXLiN2u5yoK7Yf/c7TEhEORkTZyBRrGboaX5 BA0OwjgLpkV1QxeO0WKV8MTA5yXJM0e9zCwyL4A3MU8WU1m5AIvnxIfc0RwudFN7BpOkKy5Z J1pne0EnCalr7i7dEkk2hTyGaG8he6gRGe6hNhYYZFllE8uJCmPieFcIEGJ68aUxSQYa6QGY Z2nqnmbNAuDO0H242C09AmFIvSqAHdczVvT05FMXLbUH/2q7//q8f7x7ut/O2F/bJ4gpCDgv ilEFxHRDfBAdyzIdGbF3gd85TB96FXYMG9l1YWa34+GoRJRO6mXc/uUkGUE0SUyzcpF42xLa g57Uc9adu6Lq2GQZHNYrAmRmtgRstbfpFSt0ShTB/AbPODVhmB/XioznoLeR/o01Mm7Ai7D9 zEKv1IUTbt1ChK1T1+5iaJOg9pQpJ074hYcMcBNd+OEwD0e5peHgGNcdURZrBgeBCMKal2Ng v2G0mZYXmPlRUKtNINlAcc2h0BB7pxMusB1Eb5Wr41xfN7xejo7SgPgT5qDl+eWJ82U8rShg wAx8YM+1M8TcpoVy0FQwMZ+9LZnDREFJDaf9gvfAo2C82j/fb19fn/ezwz8vNrB3Akm348LM 4vby5ERnjKimdqfgUVy+S6FPTy7foTl9r5PTy4t3KBg9PXuvk3OXYIhx+7lG9/Mw0Uk0znKK 4HSyOcxvCo2Ti2zffl6R6ZzGM0Ed9vNEdydaNaXncfA7ZqZ8glERttgRCbbYUIAe9vTkiJ1R mVlsKLKw8fkUMi67FhkT3cWnhKtj7xEz6YVjP8raHFKuLj71+ipUlTfGmh5H5ApMEJwMN5Fu F7cgP09IADn7HJc4oM5Hlsr2cxId4ep0yKlbhhY1poWc2IXdMOqnLQGgwf/EgmWjbjUEn6Jw uzCpylIklRey3cDUeTSrAxYf/SJaSSfgMd1itI5hlOvcpoygsZLF9vF5/0+YVrdm3STrILoD L+ePF6DbvRLgbaMul9ku9Xs0Nfy1CkdqqWSVg+eoilRXCv2sE0QLOEmZVAN6alGn4FMuh40O 8fViI5FT0Ft59enCiRfAK1vfHD/T4snxGN9izcVIuoGTLbhjQ+SK3pOsTbb+KrxkYz/MdcpF pH90uLBpsqakGOfIq9Oz3wdTL8FJ2/jdYVdSVNbYTqQw+8bxFYykBdK6LPsMGg7Tt8cXgL28 PO8PLsu0JnKh06YIDsxtT16zngdG0VJ0YXL1/Pd2Pyvunu6+bR8hbg3Ub8ET2B0mwsMjmuRJ cGwroiMf9TqcfddhjF8yxdOOndVuf3i7e9j9X3dRN4SUilE49JvkZYN3X5apecOkZwarsciW Ft4ykarKTRSLuzUeVkPcpBebCg65WexMai9uVo4l8XnzrNKqiI6xwpuP9sLBhrEQds0JjRlc w88w31bUgcS8e7q7/f2fu8P2Hk3Nxy/bF2gCizF7fkFSJwCzekRtTsazoz7MzFjY8N4l7SPO fl7/Bp3UcN5g0fNiGKGaflkGZwmOZ6MGztNwqMaEEMU0c2CJ8CSGV3yKlzrB6yGHk5qpaOfL OHSM3MsrDFc65uiwEGIZIOFUgtevis8b4W7v7pQAHtjcPbR3q8F80IqA8VQ823T5qGMCyVRr oQPkmpR4tGgNoDKZGnMNHE5AFroQaXtjGk64ZnOpwYFZC9rKHfZIKIb2VO9tYDwdY/sY3OQL bZ9opmJCHVRlGusmJjyWaKPtgQUzs2EcXjR6TtQCs5ai/etIunbBbRr8KNtj0C3UXjqP4FLR HPtnXB2TRrV3bt01dmSirV3GyMU7jY3B27oAI9vWOoraXEMFvU9eBQ36BRODDQd0mL16vwvU 7ZEtUmJQgvt40cwZnnaj0xWZ0in0uwlXTKRdaMMophecHJdIm5xJYwBYnhmFiEzFoLpgLFws UW26Cgfl5s9ojumFBIQJAUUqnXSvwFoCPpcNMFSm50cIQpW3pCYw1xEpGgZXcJp3wpTODfTQ saDVrjUYE9XFq/XayWBOoMLmVorR5h5quCVjmVn1sWQqhoFuViu01zi2DWBpvan668w5FauP f9y9br/M/mOTaC/756+7B+8eE4naWUV6NdjWHekgCR3iIowbEpN6V/qT/s3LR00w18ckEEqD C0IPTunVh2//+pdfbILVQZbGdQkesBUEnb08vH3bPb26wd1AqemGmuNdzm642kQDCYcarCSu Bfy/Bm1/jxp3inUY0UjOYy7M1r0TX/QnI9AsTKW7btakmiXmU69Og/3tpRasRtqzRS5I/Gav pWrKKYrO8031IGvalxT5KnNEyeOXlC26q3qZosEk4FoXHKLq0rma07wwibxY4FSC6QOjsykS 4SX/W8NoLqlziE8ax9Mm/jEN78QklRy29XUbSzoYvC1L5DwK9Mpehqs1xeY1V15hQ4fEg2Ds zsPc5BYpnq6tY6zD1uskFsLbfjGJmsmwBQpGVMRbNHvCudsfdqiQMwWnbzfjTyDmMocIkq7w 9s0NzCDwLQeKUYSmTUFK4h0sAgrGpLiJ6kFIyWlcX0I6koa1OSOElVizGiKD7yKuuaR8hFF+ MxBG1kXILCqrArxjFKFIzT1EPxRPivhQTj6RTjJTyFTIeOdYupJyuRw7mBS8hJnKJom2xooT EJK++f3iHRYb6AbCCDY5WJ4WMdkg2Oi4453nPEYJx9zaXRiP16Z8h8clAb/3Dg3L+KSosebu 4vcYb87udpjrsgPBjnS3d3GNGRffzgAMI1BzM2pzOGIoPXE2NNBxYdOEKQR4fh2tg1xuEt/i dIgku446QX+8wX5g/sDRalmeBkGKLeaFUBYLaeuNb4nHKHSymCB6p4/v68AvCBwlkeQoDeiS ob+dZMYSTLPT0kwzNBANtSMRWhvWT8nZUHwHepTngWKUY49kXISGbEqEDsE0O++JMCCaFOEa fDmblqEl+R78KNsOySjXPs24HC3dlCBdindYek+UIdWRLLE4/50d0t/UEiUwZ1MXTk7UBMq2 MfhksS7dM3+9lnCOGkEalkZwwxnQFnHAPEhVGQpjUNl/t/dvh7s/HrbmacXM1DIcHNOa8DIr FB6tg04HBJ5flSMJAPnZw5ZU0ppX6ggMcbB3j4NtRzPbY/y6tylD9vk44dlfm4TJB3sHgqE7 K93CgeEW5gavR1gMtYJ/8BwfXtQcURwPamJ0XYqU6Qk8XqBE8BmRSs/dkN+szJKxqm/rKJid Yl8/6522vGuimMO3tz/m5sfeI34K+k3wVONGAi3AXk8GqZIYDGLGmoRkmFrVYbUPSoOkaa3V 8WVoIppypMpyKYvIzLqXCGYFIQ40PV99Orm86CimM1sxLHC8JhvvpBIlK2xVV7xaxiM3xSOU QLDiZq4YHF4CmFcmA3FeEFH2oEz6QBiKyKvfPJVwcnURFm8rIbzEy23SxM58t+eZyNOrR4dQ 2nKq+GqwusajrslM2GokrNr0ora0q1PCvPwyXm9ka1xWJjvq6A6rMZ1p6tSd3ExT6YSVdFGQ 2kvPGQcqyhxTh5UpxoxeCPVWtlLMpi9JfnV8GRkxTF0PJevTY+X28Pfz/j+7p2/H5gsvTIHy 0f+GJSLzAYiHEM8v3eD9ozstA8NG8dqDPH7IvMnqwuT1o1gs+12ymKLwhq7c5ScZQmKEVgrD Ole2LpWSkfdjQNCd4HUtwIHWsV4rXZXuGxrzrdMFrYLBEJwIsIFjgyFBTeo4HgXAq5F3WhY5 x5MhK5qbCJuWAgs/bMrTKapFGyqWnMWXxTZcKT6KzUQzhRuGjQ+Ay6LJYhzH5IjELGto7EdW e5iuCzTq7IMUrTqw332TVuOabChqsn6HArGwLmByRDxriqPDn/Ne22IWu6OhTeLeXXT+pcNf fbh/+2N3/8HvvUg/B4nFXutWF76ari5aXcdoJf4YwBDZGnIJ20enI8lRnP3F1NJeTK7tRWRx fR4KXsULpww20FkXJQOL38L0RR2TvUGXKQSSJpRSm4odtbaaNsEqWpoKr2oxiBvZCYbQSH8c L9n8Qufr98YzZOBw4jk6kO5R+shF4stevCFEhzVJA8GSuZICj1dUgad0ie0tYxSbVBNIsB0p paMWU9IRa1qPvMWBNRjJWqp4IUV+pqLPRFU1eMSidj6SmqcQwAXfms8LYLcUorL3pMP0LX6V k7K9i40HHIbg95Oz02vX2Q1QPV/VMU4digIoXMtBS+Ps+84sZNzV5Tl1yeEzXpdIFMnjanNz Fis5zEmVOEHHQgR8XeRiXZFYopAzxnBun51nMANMl3n7h3mPAmpaKvea3aGUwkQ+XvEptbgR Z2oye11Qdf22fdtCSPVrm86zd42eJwR6TZPrsQ1i8AsVL8Tv8Zkc3c+GoKqjtWYd2hghJwPR wSEiduXdgWUWK/8fsN47jA6s2PWoWbEESdyrDEIaD0QQD8ZkgitFUAaDNnXw+cgcUxnauIAA /use5/t2dX08SHEdH1wukziCLsSSHYOvs+sILbie/BicXY9hKFmy2AqFyehQyRbT61Px2Imy x3p1oL20wiLSLmyxrHvPeSyim1aUlY5Ijkylw4NnyYTJY0yczFserz68fN19fdZf714PH9qH 0Q93r6+7r7v7oGgQW1D3nrQF4OUzp/5CIFhRXqbsJlQ/RBlbG6/R7kiy9Yi4Edmcn3nJLQua eMfZEkw5dsuYXMW8iYu+OJ5pBpY6Ns3j57Wh5NzXg25vrD4epcAnIUFZhjkoFOFbkSNGCI2p Qr8zQF0cHaXeM6e0lPjCVOCPg8QDGYghiLn0jaJFxcqVXHMVLeFdWQ/kJXU62NH5osOby6uh 6eMIontH7goy5+UyOAkVVS5DkSJMz2X8pzoMEjV4IvDTpYxNdyHrI2tsRJOyuPSQIj+HpZd4 NJmiKqmMxf115WzYOjO/MeAeDW/85+Ltg2fsMHSqMRqaEyl57PBgIip8Ji832n/gmVznvs6j vre/c+Mna2aH7eshElNUSzVn8StWc/CsBRz9RMmVCDS2TRwddR8g3CSRM20SH5JkMNF6LL7O 9JLGQ+w1L0i8PqDOljxaYYWzu6x86V1Ww22uJ4bL8XfrlPDM1UP8niTGDkH5hq1kgI1MvF5Y tYB1jAZPmXcZAZ+wCedckZGYCfAljakzYhaUe5sIQHKR5t4CtGp0t59lu+0Dvhx9fHx7al3a 7Cdo8/Psy/av3f3WKxLDvqry8/k5zm2UNUvBz+iIdn3XqH3cLwlWovmLyjMH0J16jyG+/0ix 9N1PXMNOhWXxXjZnhOdiNdxVpYafWbrf/eXd/tuiY7d2IPxofzPHsx8AZngbB/s+snrYqJA8 aDCqeYir7ePO7r1M+8tUXnOpmvhaIRJfWqvoe17EevlqBDBKCh/CxSocECzjSH8VRELeQ/Xu NVjla7OtpALY/fPTYf/8gL/58aVfAauRd1+2+PwZqLYO2avzcMWROSx8ykq31N2FmqLKERSr 3Jz6u6O6s80U/GsfrTlQ+wQj+AmXHtHW8Ic6o2/whfPNkYzS7evu29P6bm9Ygg0Ff0jn5U73 NGeKrK+Ticu7Xwv29OXleffkSxafkZgnvYGetFBtYVm4CUCsWeQX2hxO+tH68V//3h3u/4yr hKvu6zZmUMzmJZxOx7sYeqCkTp20Ay0oJ649tRBTHaopj21j7MG+u2p5/3h/t/8y+2O/+/LN rQjcYOLB7doAtIinTywSdEPEY1mLjyY3W5SQC544N3VVevHb2eUwV/772cnlmTt3nAkW+ePN la+UNal48IJteAu0u2/t5UyEd0mNrdxesLxyKwY8MJgJtXB+JAU8qyoqX4c6GAQyTRl9hqpI mZJc+O98q9oOlPG6MCVz5sf7jmaR7faPf+NueXiGDb8f2M/WZuFd1nuQuRxM8VeNnKqEG1WT fjRnTkMr82YllEcUDb4pzxPivYLs6boSYNdahdPoWplnIlgx21U9OHf5pko4jgugzlpgjWxa 81U0P9ii2Sp4lm7haATattq+BI1naJGMyE1JO2LzkioyXP/jb/iABNyhoXNcvoNeNTl8kARs q+JuuXjN5l7hg/3GkOYIJt1HSz2s+H/OnmS5bWTJX+FpojviOUyAi6hDH8ACQMLCJhRIgrog 1LbeWNGy7LDUMe2/n8wqgKgsZBFv5uCFmVkLas3KNRm2UQc8eSNQlpnhV/pGTGMgdPWS+6DS 6yqmdoSIjNUlpTzF2KPUsR/VKt/+/WYwef3rrWjqiLj8mWSXI64A/kopuS/ftMulpL9aWLqo k6bADEOA9YjLt2j6pIo7HDexSHLYNqNqs5rwFPBTLRc55igulqA/Hn++6ZuDFAuqG2VD6jCk BwrT0pQ7+5Gm0ObJpsU8QGEOUc17DRUmlRrUc2dF/8GjjZMqlN+icgxnNYNjejQrQKsCc27H I6KG5PCG/rTf0QJVR5apfz6+vr1oPj19/EXtYKGlbXoHG1za06A+w9E5bWRUFeYqiGv2WQdg +qKp0emI1e5apFUc2pUOKm4Zh5w1iMywCJ2hoihHH3cxNobNqQUQowVXBdnHqsg+xi+Pb8B2 fH3+MeZZ1KqKyZMNQZ+iMBKuMw4J4CDTZ6BdEipDaZNSxRZs8DSkwqNmG+R38M4O633r0e+1 sP5V7JJisf3EY2C+PYIKmtdRCteko5vqYzJ4uoXcZ8IlzwWY7dGHOqHHBLAtmbX1ioxSBNvO Fm8IoOieRG0A+PjjB4pEOiBaB2qqx88Yh8Gaae1XhUOIKr/xosKoBQGvvVR4wVtcaBxypo7x UOPcHtFns6JfXKZBrcdlMB2a+CQdrPHp5d8fkJV+fH59+jKDqrprgl/jZSZWK2/0vQqKgY3i hDNQMWgsozLEoEV8nAZyb1d7QXQmtCp2FG9mQckLVq+rFr3Yl/7izl+taR+krP2VdVzItB9Q MrUAdHYB/lxDq9PSxzEePQGf3/76ULx+EDg/LmmF+shC7Ax/0q1SHgFr32Z/eMsxtP5jOSyI 6bk2W8rRtVtZ45KFBiclYlhgN0N6uqyzt6Pon8bWOdCj3TPXU/gNHpi70RmgkJEQ+HTcB8CU UVW8gwTuCV6mqc+Zkyrj6BEwjW03FGoO0zIMq9l/6X99eDFms2/aem/YSqR+XYBj+KarGnXE PhA6oHJ8XiqDjy7I+3B1AgXycfeHIIT/O77ysE1oxQBoT6lyiJZ7NM80DV57gm207aTdQ2TZ Hoc6O8KY94hdeoi2oztUVXeFBdmf4VWFL/VLL8Pa4PCL2Pxm4OoOeVI7wsYDFg6QuiYxAQB4 V2w/EUAXOoHA0PiXBJ4AGHkGFHFnqzn87nROBIZySxKRFFgUGnauA7RBs9nc3K6JXKNDef6G M27o0Tnyk0b3O+dLYrvS+WPmhzTFH7xarCNC6ZaUeP4l5cJveIl/T3zIIv6I7AlSYNSuEoTV lrdKu3R6Ai/vJvDN5iredciLEHgMVN2I8Mi3gNFjcIbbqB5HvpMfMRHFny/fP//lvIX7LjQl HoHDmhdSAsoABNLwTcVf7SgIi4JG4s4mjE0Bk4JQbaIuRyJOYJQi9dwxDZi0qnJq8UxNZiWb scw0P2aRISTtHxUAte6sy5IAFHl/IKm23AtqTo2pCOJgC7eVcbZoqBjVVAtWBa5QQbWj9kcG eLTYGZJYWO1f4Fh43BWNrW3Tul51Y46c5n2f3z6PZQhBuPJXTRuWBem7Aba1QwwFClaGJXnI sjM9E8t9kNdmYLU6iTNrBhXopmkM0QtMye3Cl8s5YUWjHAZEHqoIhQvHRERsBKayTVLyXg3K UN5u5n7gMmKWqX87ny+YyjTKJxHt4OUh4aZta8CtHKHteprt3ru54cLY9QSqb7fzZvj0fSbW i5Xxlgult94Yv/ECS1DnIcpFJ7kn3bPOrn4iTHl/TXz8tdailWEcmYFV0GS2qqXhL1AeyyA3 jWSE311c2mEsggMoG6t2NBxORt94gw7A1VBfB9TRrkbgLGjWm5sx+e1CNGsG2jTL9ahBeAu3 m9t9GZkf1uGiyJurYMGDTxn9pMt3b2+8uRUHS8Nsn5oB2AZSHjL92u9HrH765/Ftlry+vf/8 +5sKEPz29fEnsO7vKMnBJmcvwMrPvsAGfv6B/zUzYcDD2lSd/D8qGy/YNJGL1mIWeaKR5rjf bWgGG+CjvxzHXUhe359eZsBZAe/78+lF5W0a1otFgiJN/UbqcVIkMQM+wglLoMMNVZStpcW1 Gtl/f3u3qhuQAjVCTBec9N9/XMI6ynf4OtOv5zdRyOx349V36fulur7XSsmognwb83tt7AwR bZSf7jlpVCT2hbW7g1RgqHlhPgD6XU8V5QMYzSUMV8NtkAdtkBCxhHnbdGMmk57fGZ0OKu5I VphRwoMkxBQ7ZtBkpKK/aNhnBelMoywoZsrQsUGGznS90LE3f4Md8de/Zu+PP57+NRPhB9jx v4/5MUnE12JfaSjvfXQpxKlaLmUNx6wLzAz3rLovlGIvp9ZlCpMWux1vb67QUqBBGypjyKfX /WnwZs0BPheZUQcuhAUn6m8OIzFrmQOeJlv4hy1gzyZC1UYgsWo1qiovLQzSD+vrRqN1UsGr +ZNNLag9y09xq/fCCtU0zIsOgL4tMBhZVdmWW31jSFZS35gu99xgqvA/z+9fAfv6Qcbx7PXx HU6I2TPGjf/342fjFlB1BXtzrypQVmwxxlVaZmi4lsBtOrc6gIWUVBGDXXFcHuJFdDSDWSDo vqgSw8JZ1ZUAV+Ct/cbqRKA0013vaOsySX3uAatwcXxZsfDxn+1R+fz32/v3bzOVkmU8ImUI 65WeDVjpvSR6MN2JxmBIVNjPTBfUbQOE74AiM68ZNaGJI1COaio88belnizO41Fh8uNo5JAh 4R2D+4FlBptjlDXqeLIG5ZCOZ+vICqw7VI2J3C5mFP/5oJVqiaSsFZJCZaaFh4JUdUF02hpa w8jz8oQOX27WN5zYWqFFFq6XjdWSOGs1PYVGcVCNmt+X9WK9dtWO2Bu7dgQ2fs5BF2z9zaIN 2SR9iiKpN743LqjAzs/+BIxERYyb1EoMKjgiU6tncK8KBprkn4KFb0Pl5mbprax6izTE/THq I2q/rnwZ7GV/7t+My+EmL1JOcKvQVRAm8iyp4AzhIb8LFZIPz6xR8NyMKnSas5cEbLf1Zj4C Suv7e6seSldXSZxG9uo4WpZMCDsl+bbIx2qFMik+fH99+WVvudE+U6t8biumyNR3EzSezLk9 mTBpFuhigEWA3eUy+hpUvbsvHj1TDxj/vZe89/YR/358efnz8fNfs4+zl6f/fvz8ayw+w8Kc eR7Cx1GUB0E067erBS3CyktVC3hGuiTWiEQhnOkxhLCy42BJLWg2xNuRoeAHjYi6PjjkFsi1 jAn643lb9qKpS0/iAw01q39T85gOFosxmcm5dTAM4i53kRkBvcOIOrUb7lnW/rZAJ76Zt7hd zn6Ln38+neDP7+MXQpxU0SmpiHSvh7XFnl3SFzyMgs8WzNlBG9CFPJPX17WuXoR3ysA8EaaV Us6sINzLPOeuRGhmf6N7FTz8SmSFmM1tiQLCKLCc3xCCb62o3VZFEGLwBBdBVRzysAIukrqe mBRWygSKxRAuxwiX8KF01YBGcToBoKlMDAS6vRLdHoBqh7o9KZGaRR0bFwYPCIcJ287hlAx9 kJFjpPGJVqTUF7CDjTVJgKOuJMoZpFAZIzE6a5pS+7H6wH8DwNujWlwqUDyb2uIY1fuRZxHR UeWpdi4aWNlKWDtD3yVBEhuCJEbhqZwpakckWoXEB6ZMA8fAK5I96/6jUJ1/yCDmw7Dd9Ftg MI4RLMqqXYiCaPY7c+CFWN3wDnsDwYbPn1Ofy33BBjw0mg3CoKxp6pEOhPKwCg+XiQp2ET0p otpbsEycWSgNBCrkaf4lCa++QnKMPylaRzTcUyAieMxdk+zV7OPDrDQLHsxLhqCIFAV+bjzP s3VlxqxA2QV/Q3YzlmciZd3LzVbhDM3rJOC7VAkejuurIFKXoE5dTvOp50Twyx0xrmGemu9D VVRU7qAgbb7dbNikOUZhffDT3bFd8ptiKzI8KR0irrzhB0O41k+d7IqcT3iElTlezyq3HKoZ XAVdTqHDB6OXCPnenHtxGGVGfigEd0wOZPjqPVyVmEwgEW3Ju+6aJMdpku2OHwyTpnLQ6P61 peMeS5P7g+0TMEJafWQGYR+lknrrdaC25nfCBc0vgAuaX4kDerJnwBWTftnHGVME82zQeM27 CMP+Xu4Ytk8hz0EaFYf0KtBBjdLE5Uvdl+pk2ENDqe9I/girIbBC2o3ri7JDGpEn9DbyJ/se PXTp5YeBVJA2L9GJO4ebKtMR5adqig+fkloemOs4zo6fvM3Eabcrih3NL7Q7TnzxnvR7X3pT h+L+EJyihN3uycZfNQ2PQmNNMkJ8Q5F6ylp0c0eYoB3vBQhwx7mRNK4igHA0ghhXdUtXzwDh KuOIqhhn3pxfucmOvyI+ZRNT2wuozNfBMXOdZ/Ju50jjdHfmMhmaDUErQV6QfZOlzbJ1+GwD bqXeeS6sPF1FOyNF9P1JREVX253cbJb8FYyoFX8OaxS0yCtC7uQD1No4BEVWf4rREZELf/Np zdtEALLxl4Dl0TDaN8vFxGGgWpVRxu/V7FwREQv+9uaOJRBHQZpPNJcHddfYcIhrEP8al5vF xp84azAmTWVHZvcdC/jY7CY2BPy3KvIio9Fi4ok7JqfflLSNCjn6fzjVNzqBrH2kB43ruswj /85pGN+VLu23J9PzIzAo5K5WeUDCiLfxGgoWd+SbgZ6NMmqU6CJoRvkuyalT3B6eOLD22U85 R+icFycTb5IyyiUKUMxqYVVM8Sr3abGjks37NFg0DovM+9TJjEOdTZS3LvQ9665kduSA1gM0 fd69CG7gZnPaj/T4Q+Dg5u8FWuG4At9V2eTqqELq/7qeLye2I7rm1xHhtzbe4tYRtg5RdcHv 1WrjrbnUsaQxWEiBZA+vCuO9VCxKBhmwetTKC69w+93MlIyie75KzFsRwx9ybsjYoaWMBbq1 iin5h0wsyZ0Ut/584U2VorYNiXRl8AWUdzsxoTKTgjmYZCZuPegNfzWVifBcbUJ9t57neJ0i cjl15MtCoANaw8uqZK1uNTIEdYYZTaan95DTY6ksz1nkiEeCS8hhmC0wAE7uuNSSw0QnznlR ShrSOjyJtkl31k4el62j/aGmahEFmShFSyStKIG7wnCW0mHfUU/KiI70UoGfbbW3Mv0S7BHz /SZs+Guj2lPykFPbYA1pTyvXgrsQLKaeLU1S8XJORPglJ/uLw9DgncIoNt826qfl7yzvYvqa SkrelQTFBlr5YViTIZC4bmgIqr7yBFYH8QVRqKTeBqxmRKE74QutDvagAM4wseGdtISoH/dn PraPZq+RO769XWWmJhPe68av0jBFgh+YPajLnGsAYRxTkvYAgXauRoRlZUm0WgqGJusoAmB6 CfjCqlYbeJkdbJX/fV0TNZLkxQ4y3Q/WYco54bULK+VyT0hNY3lR0+DhsDYd8bvhoNx1wb4t iM4SaNQpTio0FzHtuta1ofmD3HaxszD2wNjqM3pV2SFOzxiz6rdxnKzfZ+/fgfpp9v61p2IU HSeHUsmISMp0YDg7MmSReSmY1h/KhFOLK1XfEPaohx7pBByztrT8MTob1R9/vzttMJO8PBiX k/rZplFoTLWGxTEG/0+JG5TGoAZLB9caBkQhdKaLO8tdlpBkASatQpJe4Y+u7S+YE/1i4PVm 9RZDishI+zSxcIxJdWicWAn7Hfjf5g9v7i+v05z/uFlvKMmn4sw0HR3ZEYiOVmQoY0ZcrqC6 5F103hYY6cZ89newNnAYsxgE5Wrl85cMJdrw3lAWEcfcDiT13Zbv533tzR1uEoTmZpLG9xyi hQtN2IVGrNab1XXK9O7O4ZZ0IdmVjlc5oVArP5qoqhbBeunx8c5Nos3Sm5gKvVcmvi3bLHz+ hCE0iwkaOABvFiteHzoQORIUDgRl5fkOYVRPk0en2pWTrqfBGJwoQZtoTtbFKTgFvB56oDrk k/NfwIHEqyOMKVvAvpiYjjrz27o4iL2VA4OhPKXL+WJijTf1ZM9FUMKLhRNuGSecYXKBP+G8 9BlQG6Rm8M0Bvj2HHBjlE/CvyScNSOBMgrJGfztTSjdGA3PAx94baHsLTbYilftFuTrx4tYL YQTvAlScX20KQ01EKU06fWlJzSvNSDBgY0wNfrX+LkqOVTO849JIVT2uF5jn1e0NZzut8eIc lIFdIX5nFxjJqq7HOP15LLLrM3OUTdMEBvuswXhO2rBhsknAJhup/U2sGxWuX0xe4dAtKBKV qsFhN6cJcHj1DX+FCh1fmK+tsmTZezEa8ikAuoZRIXkvfI3KjDD6ChLPjTgQPUQtmMKi9MPO Ncum97wRxLchCyLG7WDc8tKo1ap/L+wff35RccuSj8XMdihRvfxFfuLfyl3QfJYoBHCI1pFG 0SIhZ5OGwjuOgVbBiQhSFLAzXAFydxvSx4eX8TzRJSuhzkUbXHZtWw1pfkDyQqaDomFRuyCL xiYO3eOHG+fBJ4xh6/Vj5evjz8fP7xg80Xb8rWli5iMnU8U0ULebtqzPxuGv7WedQJ3p/Q9/ tTZHK0i72IR5qDnZfjmp+NY08IE4izQII/OFeX5APWtFD64m0MrXlM2YoPCYGqAmaxBexcgd ZsShuoe2O1ayXDwUGQlYkbAGanmLoXvJQ6zdsSnlVAy4LkvH0DcNlcR3TcVlsKYqVXlsMF6e ndS8I4D3B0lgCL/vNKALBvTz+fFl/Kjv5ikKqvQslGpK++B/f/2w8YF3f9PllOnf2IFPFwZm ceHN5/amuGA4bqQjQH1AqiPK2GV7lHKDK1gjR5syr9T/5ZBDvqOg/roGsK98hPwkM6ZPMokT 1gShxwuRN6V9zCmEt07kDcuYdSTdUfWpDnb4EfTgGfAKZ/fWwOGYq3yiQy5GhmgbHMIKBVWe B+/E+RVK1wAlcbNu1nPmWzun8lKOtD1WK6b53QBztog4mGH9cfYMV6U/KgCwYUksfAsby7RN S3aoFSrJ0UODHW4L7+yxQNG/iuWZ7BIBO7f6D0iM2kbbKcrbB2+xYi8La4db7aBQQ8e8HcNF XanzpjuRB9F5jblP4bhlBZKIIAlny/E4lCWJur4/9tFBiVwZoA7ZYWe3PKo3KbOk3cO9kpqJ JhQU3Uva0PLO1BiMYKBljtzzCEm0FFvdMFUcmPJshZb0QlAgOBJctZ0wr0VY7EaFSkyQUcSu NG3ZdtQRpon9abDDH8ayB6r0XsB0WAF5RmSX2PsjjNRq/g6O75JEqGB8XdBkFcvus5vXuNyv gnhjoWMo5ttYzlkdx4Be0rNFVP6Sf20nJbptoGSS3RfOnl7YEUwF2K/KnpEIGg3HoJnI1hia tnwn9hH65+AIcxe9gD+loZIwJqU046EgXSLtkCgaOgLQeCs9EN9lQZ2ZeYRMFJxRSR6ZOURM bH44FjX1AUD0sUbX4apoXGkPu/7Ui8VD6bvfPSNCl3EADLLNAhtqCXviOMF8nS98NtgKIpRL IiH1VhxTYaw1OMtqVr+nUMfa9+c2g2Zg3AzLPsMD8EgXNpQr2JRduAMxM1bUbzo9IBitYPa1 fxqMmbK+VLtYNoaHoQFf3Rp+i8csLXZVWJkQYchw8JdKBiyTh8hI8FzkGLDWPH8BpMIDVlaj x+xAVHHAIKRnS5YwRPC/sk/1HqoOGKC/PBgPChODEXUvMcC1vB0W6FjxYYoecDco+RlsGGqX 4wudlpq9MhC5h1LmHYfATGkfdACmv1/en3+8PP0DH4T9UFEguc4Ae7PVr0iVJCnKzXTSXaW9 NIV0T8P59LE9Pq3FcjFfj3oJD+zgdrX0XIh/xogq2o2BWdqIskvn3McCufbhZvkudnqXGcNA KHkTBQXprtCpvS0gdNec7cvDGWPDWFFmSjGDmgH+FeO/XMtWoCtPvNViZbcIwPWCATY2MAtv zICkHQxdX+goJpu5DSEBQRCCbrpLCsqVCZxvAZWhHCyKgzWgiVytblcj4Hoxp02jHc66oTDL N7kDwTUxVm/hdvv19v70bfYnhibvYtT+9g3G++XX7Onbn09fvjx9mX3sqD7AWxOD1/5OR17A 7OsFT/obRjLZ5So3gO1baaGvOBrblDRMBWJtDZCBuouykqYuR2gxUlCYMycMn2irJZlkNete iEj9luqXdvQPnIuvwOQD6qNexI9fHn+8uxZvmBQovD34gs7lEHaS9KQPIZkmu71DfgpUVbEt 6vjw8NAWDt4XiOqgkMBqZ7TdOsnPNIyQXkYY5bNTdqrvLN6/6lOj+0hjGdEPjDuOvJeJuba+ NeJ8Qh2FQifF0QwhsIsU5hwWHenLaec9kOBxNUHiuhvNi8wot2CjpJuO3eh/acUnUyFiVVR0 C2bIjIBfyx7fcHkN8WnGCmsVPUhJDAg/hNBGxxbSdrR8J3u7H9qL7aHGl096puDeO+sbbWfY yo428qZs8bVOeGhE2BcqwtLsZt6mKWs0A2j9+N/SjiFQjzip6n8Zu5bmyG0kfd9fodOGO3Zm TYAv8DAHFskq0SKr2AVWia1LhVYt24pQSx1Se8feX79IgA88EpR1kZRf4g0mEkAi86DmvCen bsjpMNhp4HQUrOtx1RQe0haECaEd4GfNksN7TAUDPOj3WUAZpDWv0S+z3NFod1/2n9vusvus GjpPke7t9cfrw+vzOFcMxVzWpqutwH96Z8/eDirem3XomyqhQ2APj/OOeMbM81r8QXFnxgkQ /7rhu5Sa0PGrh+cn5U3PCVklkonNLdjL38htoJ3nCMpTW7wWEwsiizXUXobmqv0GfjHuf7y+ ufpN34mKg59ZCxhtoEZDODCx2fvCgWvGUPdfv8oAFGLVkbm+/7ceuMotTGtGvYeTJaT50Crj ExoJ0n80+GwdHUzHZD60O2wtXWBKUh8/j29R5t0kiNCReblnAc2af+FbbImW4OI6RKdKQwjp pFOp88pV97f779+FBiNHx1mYZLo0cuwpJX0UlN8M4iLWdGp5qwJsm23Y9vArINhuV2+Hrm8Y 8NHtx8t1c1vaDd+whKeDU35b7e8ITX2l87zN45KKwT9sTlaWovsL8/WLJLuh04wOa8vLdrwN n7YW/kGYFVBJffzzu5jm7uCMlk9Wf+flvrNIu9uLUvXcKRFgVDpYVLmNCgerI0aq6S93RLYs Tu1c+q4uKCOB3glII9UM3ZZ/o/E0sOflsb477HNnuH/J93eXHg37IvFZGdaJTcfS0G4EEOMk RrotTeLAmRVd3gj1xFfsaH1jNaLveBJTwpxWSCAj2CWwwj+3A0vs3JRJjlXh25aFSHUFOcsi VGtDRuQ/pph5qyO16dngTqj6It9pk8RFKgXRyIKOZRFSMujTByl8XtBXKyUEG0nsAuQ9X0YG p1vUt4KbgCmGIgwZ8wqzruYHfrQGZjjmRIyMXQcVGEvbDiBtUSamfLPeRmNHMmeHJLMqYHq8 uTU8WN8SOBd01nLyz38/jZuVRX1akoyBk8H0z3yQumAlp1HmeU9gMDFs+uss5FZbLBbAXC8W Ot8Zmy+kJXoL+fP9/z6ajVP7Knho31pNUwjHLy9mHBoVxGhSCeH2lAYPwVx/m7kk3gI8hpY6 DwvijwoIiT5ldCD0ApdCvzs1QYaninVP4zqQsgDPKmWemrEqiPAkrCIpMifGsddUQ7gAu+Rn NMiuxI4V1x38aMRLzsOUUhyz1T4bgz973xtEnbnpC5rFHv8zGt/fzU/pPuvNHZnm60HtgLuS ceLAdZZu1iK5TWwxQwFzEB30ls1PXadvtHWqfWpgYNe3RozcSfHMy+KyyWH/brxDUQunygG/ L4IQkn4YTtp3MGuE/hIk2MPCsVA5dxNt3dbpzFi6DQRfpQwWfD5MLE21O1yqMyZSJha+MS9D xzYJMmbNJF9lH+1EU16bzzQdUIuSuc55FoTa1z134dDRYMDyVAjaSgW5A6TBjF22p6q57PLT rsKyF+oASfGHuRaL9n0bCKgxSAfWvINUqxNHpGcZGuth4lg0PicxaK/mrgdl8TynmFi87wqW OsohX6lj04dJTNwxhd6J4jTVDOhGpKx6GSFKsST6dYSWWGnhTmKFZCGaRqi8SHliXkYkHtwU EsiQQgCgcYr1O0BpiC2iGofYCiC58nYTRmim4z4B28ROk01OYbUKRMSdi8c+DkLD8e6U97HP onitvqeCkyDQDEstMSr/FSqoGYddEsczZutwUBnsKT/hyN34HG+kTEOCWfdqDBHRFnaDzjB6 SwJKTAswHcJ6weTQ9jAmkJnWTxqEPinXODKhsmF17dOBeIDQB0R+gGA1F0BCPSnSwJMijdG2 CiVnNXoML9LE0/m8qzyvkmaWfujW+rHkCUWaDvFo8DLr+OaSt6gRw8ixTYlQhrdYYoAY3aLh DGaWOExjjqVuCxKmLLQfxVlcuyYmjLdumwRAA966g7MTOkSOkpEhVkdz+d7lv66vExIinVlv 2rxCyhX0TvcMPdPhnO62PSBl1D1Lsa75pfB4CZoYxGJ+JBR1YrBEfNlX+a5y669kY+wBMqTF YIVA9LVLB8BMx2mYBCjS3xLwFB7RBI3apCBcy5snk1gixc9KfwBHEiRI0RIhmQdImNs8AMQK iiUIidrjYDVMEvpBDZMkxOuRJBFFPyKA4rV5IDn8lc0Q8dYWXagWCAvoiySOcAlfeEykxyFs dauLhZpis61NQ2RGtSk2a1pdcdKoDOuspkUPrDQYrSRDC2ZIlzZtFuAFZ/g7lhlGWyz2sWHk yS8WKtl6ljH6nXUFS0Pb/HuBIvSGYOLY94U69qmt0BwTXvTie0HaAkCaIoJCAGJrh34wAGXB muKz74o2HRCRK0/jM20Cd6NJkttmz9s4XXWhWMWFtL8U223HEWjPu5PY2nS848jycQxjShFh KgAWJIgeVx87HkcB8j3WvEmYWEWxyUPFZhtR06SQT5lHzgIElmKnJu99oXAW7pCRNVVxlLjo DBYYDdL4A3Eo5BND+h6QKIowySX2NglDJHY3VELGo9NebCoisW9d+0AFSxwmKarcnooyw02i dQ4aIJLurkkIRu9uW6mTOAC/7gmqdQpgdWkRePin2yuCXKBq4WhxtjoByrYiaYhvrSeeSih5 Ebp11zgokXcCWOLklgar7Wp5EaUtQftkxFZFr2LahNgCyfuep7En71Ysu6tyoyCUlQzff/GU UR+QogXmoi/Y6hDX+5wGiPIAdExGCnqo5JD7RRToY+UZvm6LGP+U2o74zFt0Fvz83WDBz2M0 lmh1YgADpsGc6zxhSe5207knFNscnntGQ/QbuWVhmoZrmx/gYKR0CwMgIyXW9xKi+MtajQNZ YyUdkZaKDgIFrDo8ZTZC0Pa4mwaTKzE9PWFcCU2v8ScyJlN1jVkhzjzTfe1Il3pHbtR/JIGj /r6G1+XYSj4xVW113FV7ePc6HtNfyqrJv1xargdam9jljg0/7h45Dj6n4AoGi3p40A4hgzq8 ZyfWstrmp6a/7A4QnbDqLrdWtLDVFNu8PgpJjhvRYQngVTP4+CkqrDN1zvG6p2kOhVcfmNL5 q4Kyou1E+MDE8GLaGerwelv+bhvK6rw9Vp+ndKuzCFQjIyDPBEmjofnrgyP8hGqzdqTrly0T qF+2jE/PsA+Db0RjOK83xotFrjkhABYO1q4GDhYGMiojmnpCbSI8vrJTLR+yweKprHqbBfnL F7t46SYTipn32Bt40uLmBWSLSdUeQiai3DNuXCzNAEd9zUp8qbOTdKoy+IIsWlyAGIy4ubpi Ga/wlvcov/7x8gA2fV5Xb+22dKzrgZYXPcuiOEfrIxl4mKLHJxNI9ahxrZzAkzmSmVHeU5YG K46zgQlevknD3uKAWQosPNdNUequRgQgvakEpv2tpJdZnJL2FrNYlRmqy7O/XJrjZmULjpTK 6ohGuYPGyws6pwZAjan3uZzG4vXcMrFguuUEJhQrOMGVqhEmHndeEm72mIose6Eg4KnY7LSR aFqq64DpHUYA13UitDHZdYalbw9vF3hd4FUHWGTVNfiZeNMJGPXTA4jxCAfqIA3iivZQ6sIE APdZCFAZ61o8MsuCxvY4SHKC+k1Qc2q86nPmGtzVoae5C6zrQgtVN35bqPq130xlkUtlWYDV hmXoHdCMZniiDFfaJd4nYo/lh6v9lpJNi38V1Z181+aJ8gUiz0Y1zLIJ05Bj1Z+8WXbFNhbf FLZ1lWlnyzyd6FwuSmoR9zHzZnTDAmZPvuM+7hPTrZyG8qpAhTyvozQZ1iUvb2N03ySxmy9M zE9q1wb2yLhlwWaIgw9EPRc7MUzHk5hjVwzUvha73TCMh0vPC5/HRGBsujCLfP2qLvXN8enh jchpWcfkOEtzVUN57HhCgtgTzgduoHEjbgWZlteyVElnuCfBhQF1kj3DlKRmvaEtylrXaaJt r6tl4sw0SWcJ3tiZIUMbrMHUqsVIdVeCGbEe3YyYkLjoPfFo4IFO+wnLT76YIoIDvMo7E1XL 5LYhNA2tJ/1yHrVhHFqC0zFilkTLFFnKnoHFziIhNiLX+3yXY8YiUicZLbr/QoiYpjJBa2pH waMUj94tG9/GJLCGEGjE0e6ktTR2QzCDzM6GRQGWTUiGVS1oZMHdr00M9po4mqo5s04ZeTuC +XDdCq0xJcwTi0FnElqbVx73oNgQc94IubfVZshR2sV2iwM6/bW1T62fE08H8noLZqL7Dgrh 2dZDJebjoelzT3CVhRdci5yUixt+wl1lLMywAZf775kdr6RQjXaWnHF4YJfCpORCMsjLOPQo GBrTXvzC9QSNSW5nPmLyG9QtTNNW5AM2tVtYbbqr1xsYRQWwxULw5Nt8H4cxuqNYmMw99kJX mjs+Igo7x+F63WreZKGpKxtgQlOCbbQWJiGDE/1ZioYIFSAlWMUlQvGKS5vA9fGwX8KYSBzj RVoLrwnp2oiGqMUETSSgJE2wVNpOAsVi5ks2mQsi3SIvzyLMZbTFo9vompDaTuBQTL1Qlnpr JPc4H3xf06bno5qn41Wzi40bV3P5N/FUv9c2IbFdwqGOiM72fNWwI0JPXBaWWTd1ke3prjLu DjXszFiQeMZYggw/C7C4PG9TNK5b/FX9wiGDA8H73tVmTlsgF1D7KQThtO3ywCPwAOQfdC2P W5YmKZr3sg1ysWYXj6EBXWxWA7A6iTyDBD9/M7iY5S3L4YG7aJKE6DwG7ZqG+Oepdhk09KUb ty44RkLPLF6xyHWY0DFWWDTg3Ya9/8PYVpxwa3qN51XzwjHqlEg1XT3WwKwnhCNLMW3UlwwL y6WlIEAMBd1haH30uCMGLxPFofRFqZA4eJ9DnyRU4FkG3n8od77LmfK3x69P91cPr4ZLqkUF lumKvAW/kmNyXFeWjEL/ag5iw3XGeA1OcJvYCyVzYTXUc8lzzOFV5Mel8vL4YXkwFt6CADxi x/0jfMCivNvYpTzjUT3PdVnB892zq6kbHEpHb+s9iL58v6s00S/ytqYSUIxY6n0Pdx3K24nJ Bk7xVFDzI/8XSXRojHWvSjXjQQFagZMoXhVw4yU2rhC0/oBvNYD91FQepwytnGTudYXsQogB swyN5M9f7p9ff/v569NvTz/un6/6s3zEubgqlEylD9WrxI2X+CPJOxAzXm9CIebbwuxIgHKm PyHXEsCvFi9tAi/yahVzK2KzFp58ghQ9k544Tm1/CUwzpwkqBugk79BBoRlFj66X4sUne8by PndpEGEbDJ1Bf2M/0Xcd6/gNluX+cM4vPfyJ3U1MXH0PDMhw9D0NgpMLHDohyAgyqtssCGKs IgoR4r499Njp0cTXFf1ZaA0VUplbSgKkkkW9r467L5cebcAZFA2kondJQFOsohC0YF/z3O00 d7x8fSr787I5lbuqt8tQWIm6vOItV9U+ns0qb2hBx4u+bvwYV1D71SLw5Fx1nvzkbx//5+H+ 2z/gk//p3pARnzAJAaTrUqifQsBPHreMNzZy4E4NrxisAWifAcsxr/f8Oi8Pt142JcpAAq+t V9Cgyb/HasgpWJHWGNWbf7VeP369atviZw7Hn1ojLZbt09vjLTzq/QmiXl2RMIs+XeULu1bD bX2syt4ayZFoB56aVsEW3jkdOlgo+CTFH16/fYNTLSn2p4hdS1FjPBDIuQUfUP65sTJrbO9Y LRgv5vvDpS1NWbUg5kqvrU/3Lw9Pz8/3b38tvu9+/PEifv9DcL68v8IfT/RB/Pf96R9Xv769 vvx4fPn6/sle0PhpIz4E6RmRV41YPe3uyvs+l/eTWuFw0ukORzGUVOzFlLes0QHp7E3FSWaW 0p/20qWnmvB/vP94/fb0f4/w7ahW2dWW/ODWrmuMR6A62pc5kf7lfbrSzMao/rTAAfXNhltA Srxoxsw3MwZc5XGKPjF2uVK8hFYsHIOnboAlnkZJLPRVTKA0ST6ol2AioafhEG2LeIoeChpQ 5sPiIPCmixSGV3loRNLYv5nQ2dLe0ytFFHEW+PslHyhBDWndOWFdXmn4tggC3HjFZqJ4NSUW rk1IX8pqrQu3BY09Zy1GJzB25InIx797GatyyoUuQnzF8ZqS2HPHrrHVfUZCz4WHxnZk9MMK icEPA3LceuZsS0oietZ8ueRwbETLcQc8mMzShdn74xXsubaTIJ4WPbmvff9x//L1/u3r1U/v 9z8en5+ffjx+WmS2ueDxfhOIPby5zAhiQgJHl+b9OciCPz0KlERJYOd0TggJ/kSySqxHawYO 3xb6rEqCjJU8VKbzWKsfpNO4/7oSC8Pb4/sPiDFgtt/c6R0HLFiA1PtGiVzQsrTaVcPHa+01 94xFKcWIc00F6Z/874xLMdCI2L0piTS0SuhDYhV614jRCxOMaI90fE0i6g6akKrMnROBrZZL Tnf2yDF3OTM7OSyHAQsdoqgoSxwlHNZOdIUD9FxxMmR2VuNXXxKn5gpSvexWQBQ0OOWf8oR4 t6AqpwQZL918YRlEu3vEfBqsXWLPxeJm8Yl57zQF3N7ldtGqF6UyMU+8Xih1vk9Cr0sn9Ay7 fkAbnIbQ1K6MIlJkloUWUXx31kfVJJHhwGdpR2QVvR96dzKKDyFGPoQwtsZ3OuHY4GTn/EEA KQCekR/hDknmeR2ltYvZqWDPjXp4ArAqPFI5TLBjaDUeQoumwdEeJUGNSGWRj31DWRhgRHtE Qf5ZEkJuVy/byq7gXUnEUgknagc0CtR5OgxRae1qskCfw8Uo3r2zFwQBo04vqf5GnxFpsDVT lHhLp/Lznovi92JT+ftV/u3x7enh/uXnm9e3x/uXq375sH4u5PojNmHeSor5S4PAmtSHY2w+ xJmIxO79TdGGsS1hm13Zh6Gd6UiNUWqS22QIWutMMPh6A+xyVI76icXUqp+iXZyNtMyJzDKp 5uW6UDKrkXlHT3xqLHC/DCkYaeCeHsiCzWX4Pz+ujT5dCrBlpk6BsNhHpoZpnNdqeV+9vjz/ Nap2P3dNYxYgCNh6BcegQepO7gU0ry7VQUhVTDEDplOiq19f35QuYhYrZHKYDV9+sabFfnNN 7RkEtMyhdZQgNGt6gOVDZE9JSTTtLRayTyDCVju05zBnu8Y5z5Rkj7mKzKnfCLXSc5E2Sogk iX3Kbz3QOIit6S53NxSZmCDkPeGMAb4+HE88xG9MlaAtDj31ncheV021n+OvFOogqp4ik1/9 VO3jgFLyaTWOxCR9A0e56+YTyf719fkd/C6LSfX4/Pr96uXx3ytq9qltvwgJv7bdcXY1MpPd 2/33358eEJ/Wpe4qWPxzaWs4LNrUGJUbVpdALzshqYYpHgre3cAmXRi16Po/w7xqttIfuFHy TcvHYCF22dsNhDGb31R5C4ewMBexbyzns0J/PTv7iFYD+97qqvMxb5e6acCuamXsHAyD9vgw SMevwYMfhp6t4nlxXc3BE8Aa8vHl4fUrnJW+Xf3++Pxd/AUBNt6NwR7D1wh1y9wdjAivG5Jg t9ETA/jUh3O0jA1Y+hm2X4xo/ot91VQKwrHVzuHndDrZLPWYlxXqYhHAvC0hHopVUUW9oJ7i Nbyob+wpNyJgttj1nteAC9sOogTKub11F9C86K5+yv/4+vQqBEz39ipa9v769gliLvz69Nsf b/dw3m0OHTjFzgt13z/1y9/KZVxA378/3/91Vb389vTy+FE5pWEprD7Qm+q4r5qLbVQ/VmU1 fz37/eF0rnLtem0kTJFli37QbnQtHnViH6Pk6fXpv8Kl5iZD2548Yz7xQVQCGQnFHvrzDnVL KyHxTWsiHnqwr+E2Z5cfnXxOJWZFIhPpoRCk0N3lO2rajQC5qI9icbt8rryNORa50GVuL3B7 ZcoMiTTnktvfxOfBV63Nobh22McQjWKWe1J1uZgqk3CapkZ3//L4bMkjyXjJIc/qyEXHNZXZ C4oBq7NC1JWD91tUTNuq/gJvgbdfhO5Ho7KmSR4G+NOwJVUN8WVvxK8sRD18IJx1xhgpsBbU +/2hgRhbQZrdFTnemF/K+tL0oo5tFcSere/MfFPvd2XNO3iHflMGWVoGkT1Zxj7KW37aQzzh zPLDiTA3gm8j9vyfg/U2A98uitPQnGIK3FdiMWiY2J5fN3pUco3jcM6hz/6fsmdZchzH8b5f 4djDRs9hdizJsuXdmAOth82yXiXKr74osrNc1RmTj9rMrNipv1+AlCySArNmD12dBkDwKRAE QaBsAzivLymSKudFeu7yOME/y8OZlxU9blXDBQa83HVViy/v1g7VbywgEvzPm3utH0arLgxa 6rJiLAD/MlFhLtHj8ezNs3mwKHVXvpGyYaLepE1zwWwmY9J5mvSS8AN8lsVy5ekhh0iSyHdU WMV72fdPu3m4KufSSkjSlZuqa9ANJDHjnU5Xilgm3jL5eP2NtGmwY/7HSw+IlsGn+ZmM5+Ig L8huaCQRYxPx2BOlfF91i+B0zDzSV2ekBN217vLPsBQaT5xNv9AJmZgHq+MqOZHv4AjqRdB6 eTonvwDBW5gSDrtdu1qZNzMOomjt1rF78qq8gJJwDpch27s2LEXa1hXoanM/amHxkA3sKRZB 0abM0T5JU2898o2BRtYc8gt+62G4XnWnz+ct0/UYa4cw9p+GJ3okwpHnDWNsMuM5bfP68OXb 1dpvlJMhjCcrz6tIf50s99akFPKcY/UVjl4bUAFYlzDag0MeNGCP6tJSelk6BqNAFWfHa4wP lNRndBvfpt0mCufHoMtOZmNQma7bMljo18aq+6j1drWIloZhAA8FHJcMj4xIngrB18qLymgz gv3ApfG3O15irP54GUDnPNg5TaZtJXZ8w9SbttVysvtYeNLCimQgKLN64VndBLAolyFMR7Q0 MTKhYnJchZ7nQATWrqSVwCOeiRxVJXPSFXjqtWMt2umK05mnbcmO/Ggz78FU1BDzLNHE9dal ZW0Lzz8E+lRj4jHE7M5REK6MV8sDCpUUn3yirVMEen7QAVFwEBfBZ8O7a8A1ac1qOjVyTwES LDQvpTTMKghdH406F0zWVpI5bFHYGM+nX3v1erUTd+TUayLZRnZkW1I3BeUjLVtphOg+H3iz F+bywoRVt6TdUlZlr3dP19kfP75+xdR6tutptuniIsEQrCOfDJ0aW55ddJA+JINtQ1o6iC4A g0QPhoGVZOiYleeN4WTUI+KqvgA7NkHwAoZhk3OziLgImhciSF6IoHllVZPybQmiNOF6IDtA bap2N8LHzgMG/qcQ5NwCBVTT5ilBZPWi0gMv4rClGWhzadLpj9yQGPYDI58ZtkI7Po5QTMTQ m3RM1njawe7Dd7Al18Yt4TMR4hvnQx4F6b7UhbY1qN8wQ1nVYXa6qiwnk34BpdU3rgR1aL96 9NoZbDYwlJSfh1wnojVrgBO2YAb3qsbtsknNURFeYgUOwdUv09taLehz3rqeAo8ULpftkYKe uYYf7ToRZNdoYQe3wkmxWyWOIVstDLUcl47MAeTqndP4hbPTXjw/stgp4K/aAVTTcl1Me6X2 2C3latLj6LEVgVWJCHCROb5MJX3NMVVA92T0eBbHaW5WzYWxDuF3F5iWlgHq0XEsceGkFYgv 7qh7f2kqo44gycwVjYBb23TGEuHs1LGqkqryDN7HFhS+wBQtoBzDrmTAWLM3ftdFYH7srCl4 mVIw2MgYKC1HM4aegYwPoiUDMuFQFiI+WP0/JHbHMSzu9twuwjlto8CequfldC1FimelqjD3 TbzD9C1p0sOky/V2ItgG7Ady5QOrE2IFXtjT3nRyNFb2q7RepSQVAyn0N3f3/3h8+Pbn++w/ ZnmcDK/9Jxc6aCyJcyZE/55r7DZi8kU2By3eb+dGpBmJKgTodtuMzF0lCdpjEM4/G37RCFf6 JPX5D1hDQ0Vgm1T+ojBhx+3WXwQ+07LMIfiW0NKAskIEy3W2NW8x+m6Ec2+fkXYGJFCascmu aosAVGJtc7qJLMdgjvghg6iR+GZAqgAgREtGkvpUUNVOg4RpRYpovfC6U55Smt5IN0RZI9gD KoqWc6pDErUiS8m39HNymCRqTWLqKAzPjvFxZ8/Ruou6MxlObaSZvrsdcdMYONrsWanDtKYd YehWZELokWiTLD0zCJY2jk18jktqdx5p+sgef9c883/xoQ88QPnEyKXakpTvAGlV0z7d5pWd jbyvfHJRPHAQ1aE0wpyJ0lh7Ukbt4OQxEUg7K6cNT8a8WG2TltuWisIGZA076QUPO/Jcg/zG T1B5a3y/3qN7CBaYXMsjPVuguVQbHoTFzcFYpDdgl1GRZiW6rs3IlhIoSI1cog5wCMrNejdp vuelzSTeoQHZwSbecfh1MfmAGi0Yb2zgYcs0DzWEFSxmeW6Xlo7VFky9sDGBMCvbqkRru2lJ GqDWYBmdSgvhHkt86iKfnppFft+nFyfDbVpseONcFVkz4QfcpEneUWR/mUznieVWUBgNeeTp SV4KmIO0vTRWmFeEckz4bM4Fb1O7hZ/YhhR1iGtPvNzp6WZUl0oBx8e2miyiPHalbZPYNDHb l6dldawmTKotx6/FwUVqwEV1EKnJrIBha6rSXnqXDHZT68uDo7VcPnbNBY+bSlQZdUiR+KoE KZJe7BEsDnnLP5rlsuVmC6qmTfc2G9h10DaXV84FVqctyy/l2WRWw8cJctvseQ9UNhsCTl47 6wS4EzhaMVCkiXCVjrlrFYB6VcoLhnhauOEFo4+eiAZpA6P2AVre0rjxmK4q5+UHHNqUUceJ HpfmAoR+KsyBhjrr/GAJLTikmFRbvJBjwrQn3IBuISUK1rSfqotZhQ7tZAJPox8tP1JReiWq qgUMg7300Oy+pcOjKHQDRy2Vj9dJdMD9s6uFIzoqSjbO7VfJBv7My8LV8N/TppKDcBvWAdLp GUwl6SWBbXT6eatQ7d3uQOUPk7tlXgtdMaK29fGhrqF6jEqD2HRuvaHmN/+pgcfmBcjq15f3 l3v09bS1B+S332iCUz5978WfkTP6A2Y22U3XGhy5SD0KbzBQl9JGZEI7IAyuWkurHZx3DTuo 2ZOJ2418aS6f5xqqGEBB3KCNgTKqyVgCec27jb5AFKuyVCq3AQZNedftmOh2sTm0dqWYv5xa rpJJWYKOGqddmZ6oWCbqmdPD2/318fHu+fry401OwORhsXpJrsLYoxbNRWs3w4x74WxR1W67 0w6kZM7JMOcDzSaX0l+0+DGYY49CXQ6zzCEpNn0AFr3b+E74ANK0TFRKgr/75tosB7VYLreX t3d0zRq8VpOpWVnO03J1ns9xOpydO+NS2pG7EqLTHm12R0IbzE0MPe3aycBKfNviFErvxo+Y ZyI3R2KocjQZWNyr88H35rv6g2ZjRllveZ62PIOJgsJThEwn5HtTREWOQEU0Uv8A88hTvJ7M tt8Q0ERKKI80sbDLNhH6Xa9XH/QbGZtB8AeoMGOgDGCZttlOMH1bZspyNYsf797eaCHK4sn0 gL5RtqS6ithTUtjtaIvpW/wSdrT/msnRaKsGw1V+uX5HR+jZy/NMxILP/vjxPtvkexQRnUhm T3c/h5efd49vL7M/rrPn6/XL9ct/A9OrwWl3ffwu3f6fMN7Rw/PXF7NPPZ25KnugHWpAR+Gh 0FJAjZKsZRmjdkmdKgPtxTpA6WguEp90J9OJ4G/WujiIJGnIJyw2URjSI/DpUNRiVzkrYDk7 JNTRRyeqylSq9nQVe9YUzMV/iBgBwxn/ajTTEkZjs/T1eF5yW2NCF6f86e7bw/O3afQgKRmS ONIdsSQMzzTTuea1K7ywlMZJad6V3ICdnUDDICnkx5o4IoKp4DIxZZzsUb7ZdoR0fbIP9arg 7su36/vfkh93j3+F/eQK38WX6+z1+j8/Hl6vandVJIMugi8d4Pu6PuPrri/2piP5uy7qbgRt A5sljKMQoIDA6VDYYgTda3iS0lf8g4xfLaePfLC9spXEXauceyFWZJh9uXDgLKKbdkaYDG1b mXYiDdvbrj5kO4bVmKIYb2K2cbJnzT6AHc05GD2ZskJ93Ih4Z/iEaBip5OxS1pJYjPyk7rnS qXI58K5hcz07+jB8uAUVQ1mjS4s63ZJNyNqEwyBWJPIIO2ozERoKx2v2+Vdjx+lDmN6wZJs6 4ssRVJ1untA7EXm+/ozRRIV6wFl9fckrOpIhr080/HAg4fv0ImpWdnXCPsKTzdjngu7Vvtqg y1o83RYUvojb7gD9/tUYy7u/jwe4qMRq5c/J9kmcF6JrnR3lz6KKFh/LgK44H6Zqeo8r2bGY HLoUqs79YB6QpaqWL6MwInGfY3agZ/7zgeV4dnP0RdRxHZ2pyz6diJmPkS0UDFeSOLKOG+Ir bRp24g2IAEEZx3XaS7GpckebW+rJjiEpNmnzCTYIR/kzCEvyiloXZic2OewO81DbD80ImqLk RmRFq3xc0R/IGe0hXeH6DE5c7Dag/Pxi8MTBs1WOYTW0vqNXhzpZRdl8RUbi1uW4nlEKN0vz ME2q+WnBl5N6Aei7dySWHNoDdZusmnIUqaVG5+m2atHQbQ9d/sHxddhT4ssqXrp0oPgivTPN 6nii7D1WZXKLgfO3a3nIu6L+tYa1ADic3zfHrSVU88QkA8WnjNMj3zR2MH/ZqurEmoZXruOT fFhpNTndibRVJ7mMn9uDUwHlAk3T2cls0AUKWJIn/V0OxdnSHPGwD//3Q+9s2Td2gsf4RxDa om/ALJbzxWRiebnvYBxlOC6naSXesUrs5f3AbcnWf/58e7i/e5zldz+pR7tYrt5pl2NlH1vw HKf8aLYQ7VjdcaNbggf1MuhfEWiGPkfNZse2DGMnEv1pL7XutCx/dm1cG8L9BnVYyRQ+w+F2 5FntOci4ydH0DT72o/35/frXWMUL+v54/ef19W/JVfs1E//78H7/59R+qXhjSNiaB7INYR8X Whul/y93u1ns8f36+nz3fp0VeBaZzK9qBL5azlu0XthD2jsDjliqdY5K9EXQgMLfiRNv5TXz bXyLgo4TXGD+UWPTGmDOkLhPL68/xfvD/T+o48qt9KGU2zSIukNBZq/BhI/dJq+s2oWCfVjv v2BLvLWj5VkBXD/ofPdJno3LLoj03DgDtgnXPgVWphOZTfHJiT0ITeygebi/K+sh0sgq3Xwo WDdcWY7eE4jbNCgTS9xDdieUNeU2nXpEoIfOZBHK8qyEjzBca740CiyC5SI0bBiqurhYBmQS mREdRnb7m/kco4UsLLj0SppbNUugT1EGk86js8+CFiE3/Nqn7w5vBHOP9BlDdB2zdWhGjdfh rsSXkqa/XrDqw2xf1CuXGzacdL0OQ5nVoVA2dJthGPp08LURT9+93fBL6n1lj41C80HYALa8 pSxstLRnVY6Y6X2lwz8cSaQxUqdI6C3MvcnQmaamx8aevxDzKJzO6YlSyCVqTF1kNmGT+NF8 ujryNgjXH4x57w7nJugzdbia08YMEx1M6m3zOFx7ZKw9xXaSglED68FXBrCd0+T2KZLBUyR2 3yb+Us8kIqFcBF6WB56ebkxHKLdYS1JJE/cfjw/P//jNU4GRm+1m1vsa/njG0A3EVezst/Hm +i+WrNugwlZYTZjm71Pdz88w7e45wtgLrkHAOLebi+lTo2ZI5u7rv2VSSLevD9++TaV0f/Um pnPe38m1nM5xZRDBua03fFNYOBTsrbEZUEWbODC7lDXtBk1uNJ5wuTfwcX1w9onFLT/ylva7 Miht6UFTDbepxNA/fH9HW/Db7F2N/7jEyuv71wdUtDDIzteHb7PfcJre716/Xd/t9XWbDDgi Ca683ummqFwUv5qvmhkuXQYOzvZJenRWUEvXRuoQaA4xphsc1wNaRzHLNIYRMB66cfi35BtW kmHfMM2wvOfWi4xQh+4IBNM3ZwDs0nJrvDlD2C2dGyg4ZZoLE1sZzi6obDUM1LwtVkG099Sx M8eCWoCETKDlU795xFvAHI/ZbKn5gstsQDuEdsW2aCmE9jbvJGuZZHzs4XTbZAmVlnsE9k3T OSAI6aZXn9jt+PHh+vxuaMFMXEo4VZ07x7AUrD+dT+YCY8ffXFQAvDlkU4cFyT3jVo71k4RT R2PFx1oyALkFYLI8GobXrmb1t6V7OA9WDc3deLFYRZoqwgschZjzzvSMa73lXs/R1Ftfb9FD bmAVgUGZZucWuKlk50MTrDRyOAsIwfTn4wq7QR+EAffv/z4gMY6VdMfLYWkbQ6Rj6BeGGoU8 MRBDb3WrL6GZD/QXhvCji3lm2D4AVGNw+G1a8sZxMQE0CYaQ+gUNI+NcIQY22LgShtIvK8an MtPLKoMGhCNpucPizUHo3kAAKrKlb9h2jpljO0EpRCUy0ND60PXRaYq0PEyAxgc+wsZHxiZq w/K8MtWUHiNTGThbi9UXZm96B6T715e3l6/vs93P79fXvx5n335c394N37UhRP8vSIeWbpv0 YlihekCXCiO8omgZyBSHR+YJdKeSPPLHjy9w2BcvP17vzWeoQ+A5Cn87ijOebypN+7ylpSh2 2twMO4ciHc0HqrTLyMdBnTt0TE8kr0BWPqDt9Rkjjc4kclbfgQYhw4uK6Zj/ilTbmWVNRHSv CUXvSsaEaHdNddhSkqHKFLnmVBqs5zfYOIcIjeOTwkzmqrk+vbxfv7++3E+tDioHDUjLWDdr ESUUp+9Pb98IJjXs7uNXLH9KOaYvNAWV+/JWmmoBQPRZkfXfrubVaFZ9Gw98gIJXSMOswop7 /iJzkiRm3hZRxbPfxM+39+vTrHqexX8+fP/L7A1PLF9hZkcrlQrD9vT48g3A4iWmFjeFVuWA 4fWLs9gUqx7xvb7cfbl/eXKVI/HKr+lc/y17vV7f7u9gOX5+eeWfXUx+RaoU7/8szi4GE5xE fv5x94g5WVylSPw4e7G61pYlzg9wuvynxainPIMOXJ67Y3zQlylV4uYX/C/N900AYbjJY9ak n296lfo5274A4fOLEcNTobptdex9MGD3T9IC9HFNS9SI6rRB6YY3NZo+pxPgRZVgx5Quf0uI bKhnenkQI7ANTlXPvhOJPZ5jf7v0aDwITs+tOoBLBuk/3+GgNXjuEbZcRd6xJO7whpUUeT1N Jth64chd0ZM47E49dpqhdkQEge5gNsItW4qOiBYBxUmaWWz4LW2q3eK6LUPPDmtpkjRttF45 os72JKIIQzKuWo8fLpYMW3fVaIoJ1w+OHFWUQ5bpsUJHWBdvKFJpsB7ybRv4vQwQgacDA9yf WtNkqMvAqj8zQZYxmzXUKvBDuZH42p6J+ufgvk1t+Qo/lHS0Ui30IeT4/f318fr68nQ1kyMx OLZ4S3+uuTQNoLUOOufBIpwAZJr4CRCUKAuoJ7PoASRVz+82EJuCeRF1IQ8I34zNviliWJfK w4o6XjJfP4olLDCCJhWsSeZLG7C2AGZWQDkLraqxC+BMTytA+7NI1g5M/Gnv0VH6izjw9cuJ omCrhf7J9wB7xAYw5rQiuAJ2aQZmB1C0CKlvETDrMPSsnJk91AaY6YlkWiQyJdE5XvqhEVRb xCygYzyKdh8ZKUkQsGG92X1QMsyF/W9j6ksZUbqPlw5CHSS5vfRhN9sWGKgkb5m+FleevzB+ +8ul+XvtWb+NECMAWayoVFWAWJqP9RWk4xnsdjKiWZ6T69egU5+OzmNFZsaSiKgz26pMEkbh NRXGTyICo6hKGzb+Xvsmfr1Ym7/X2rUJO9f+HJOoGusVoVGEUGo3KI9pXtXoNt6msXEDsuOw nWmfw+680j9oXjIMXaEq62Hq3siCtbG/0DOlSUAUWgDzIgL35blPhXFDjGfknFSQyAQEVpYz dl4v6eRbcR34eiYGBCzMfAEIWjtSd5Xd757d5ZIdZA53zdyEez3stwaZPLgcUc+Z3v3dcpJ3 nJ64keA4ZSrhANYGWSRSoyqqxL7paiXpXEWTHT00eqjDIXJALwSdqEPhPd8LoilXbx5h/pEP GHt+JOakyOzxS08s/eWENbB1xNFR6NWaTAqokFGwWFjDIqJlNO2AUFeMNKM2jxfhwrhT7RN/ wyokpxLQS0Rby+OYLb25ubCOHNSZTQX7pAnvzzLn4csfBPdHQloX4zKK/yw1cmfg3tuksHX0 aRZNnlqJ/nj7/RGOQZb0j4Ll0ij8f5Q9SXPbTK739ytUOc0hqWi39KpyoEhKYsTNbEqWfWEp thKrvtjyk+T5xvPrH9DNBd0NKjOXOALA3huNRmNpqJTE/7x/kbZKQiWu1JTZeQhbJl2W/pvs pM4if8wKL64rJhqzcm7LM7b+No3ETZcN3oIVBhkG2ROLlEoIIhV6ONvNw2RqGB9UyiqzZypu xeGpBHRgeMuED/RuyxPQKcH8oHJARCmXKFWESKvvSKFUihJp+Z3hB9vcbq0iNFk4N6rlcZrE aeDKCdCTrBw7KkFui/gw6o41MWE0oIYH+Hui/x72e/rvoSELAITzLQLEaNrH50zhawUg1Chh NB1wL7GI0U0IATLuD7MWSRGOvd5Ys6OAc3A80OT40XgyNn+bYv1oPB2bt4TRzWhk/J7ov8c9 o6U34yG7xxA15Q1QQP4YdHk7CNj7Ez50cZpgzCEauVwMh1QYjMb9AR0FOLtHPVM6GE3YYwdO 6uENzX+DgCmNXQscGmrvTvrSRkTn7IAYjW5ajjNA3mg3mhI2puKz4tmqeyR865XFrnyxYa8/ vb+8VCl9KUuwcGXwxv3/ve9fHz864uP18rw/H/6NthGeJ8oERUSlLnXNu8vx9NU7YEKjH+96 KgbHm476A9riq9/JktPn3Xn/JQSy/VMnPB7fOv+AejEJU9WuM2mXxte9OYiVvHpD4m56LH/6 b2tsIp1dHSmNHf36OB3Pj8e3fedcH0h10/DK3tXZDYJ6A0PYV0D+siCv/ToL22ZiSL3/ZtGi N7Z+6xu8hGmMYL51RB8To7kcTP+ewLUyonQ96NLGlACW5y/us0RdynkUxhG4gkYbGhOdLwb9 bpfbO/bMqAN1v/t9eSbiQwU9XTqZMpx9PVz0iZz7w6Ge61eBePaHisAun0uyRPVpe9mqCZK2 VrX1/eXwdLh8MCsu6g96enKsZc7eQpYoUtM7zDIXfXoQqt/6LJYwbf6X+Zp+JgKQkEb67742 PVbrFTsDvnFBs62X/e78ftq/7EFWfIfRsPbTsGvtp+GY2U/DFhVVYOTAU5CWmJYl0rjar6It m6k0iDe4/Mdy+euWnhqKrYpScDJRKKKxJ7ZtcHa/Vbiq/dX50D7YtAAcRwwQrRdbQRvdrjJU k2HhGB743SuEdgg6IZzVXc2M2Ek9MR2wO0aipsaULXs3LccBonjxPhr0ezT9KAKozAC/B1Rz 4qIZ8Ej/PaYqtkXad1JY3E63S3TStcgrwv60q6f21nGsxbRE9ag48l04PS1Ld5ZmcM/tcQW3 hnQM88w02t0AHxq6vHYU2NRw2G2JdFoiOZE4TpyeykZZUydpDhPL7ZYU+tXvDrpdjX30etTo Bn9TDbfIV4MBjcUPi3y9CUR/xID0LdGAtd2Vu2Iw7GkyuATdcJqEapxzmKWRri6SoAkv2SLu hi0QMMPRQPNcHvUmfWKHtHHjcKhFvlYQqmbb+FE47tKwmJtw3KNyxwNMQ79froCSC+g7VlmA 7H697i9Ka0v2crNPV5PpDWeqLhHavDur7pRXgJVvApGzIN6GBGjKLQ1CFzycxUDLQx1F7mDU H1LTLsUF5be8WFHVdw3NSB3VOlhG7ggf8NoQxgo0kFp3KmQWDYwswDqm5fQwiAyGz87r/9S5 1ZX/kqHIicqQk1URlLA8sh9/H16ZxVKfMgxeElQGzZ0vHZXW/ffxda/Xvsyk9TJ5TdN0EzIa S7ZO84qAt3LBlzc0NA6TJOUo6Wzfi7nQqiu7wTdWuwO8HS9wgh6YZ7xRX8sYL2BTDugS3o6G 5r11SA8pBSD6fbycdjXNNQB6lIMgYGQCel26U/I0NIXQlq6w3YRhoGJZGKXTXpeXwfVP1DXw tD+j0MHICrO0O+5GC7r5U+2BUP02GYSEaZvJS8VAfxZcpqzeLkrDXo/e/eVvvYISZtx6woH+ oRiNe9oBqyAt27VEGnIlQgfcC0bJjlTMU5NJKWdhTvxTGKOSfDRkh2KZ9rtjUsZD6oB8NLYA ek0V0GA41iQ3MuIrBqWx514MpoORdTxpxOXyOf7r8IL3B9iLnacD7snHPXdYSVFq1G1xhAo8 zDYY5H6xYZ9ZZ3pAi2zu3dwMu7rRYjZvuQGK7XTEP57CJ5pIuAlHg7DLhHOvB/Jqd0vLuvPx NzrH/PFptS+m2uWpL3rG5fkPZSn2vX95Qy0Pu4mBYwWRyhyUuMlaCw0Thdtpd9wbmhA60HmU GvlfJYTbFTnwbCo8yt9UdsLbdm8y0t4TuMbX4muuxRODn7AlOT0sYgKPmMUjQLnU5r6rg9Mg XqRJvDBLzpOEe1KWn/jZ3CJH/xEzUEuzjCIfIxpyUvYdcW6CH+qgozZECERPh3nOWUIiVjrb 6apPBOd3XAdKTBnDV0kK2a3Mo8sEjsxuMSaS7qlRzAN+M1jlkM2eYuglfgiAC/o52vfkWRKG 1O5HYTCXV+XwpXjM8r4j3n+cpd1e09jSwlx3wifAMhO1Qjc3UjcqVknsyCgDSMZOIH6OMZgx SKTHhXTWCZZaZGGKU/FMWuvAiQ6i7SS6xfa0kkXB1g+b/rS0J906RX8SRzIgAlllFIW91lEu LCcZuMDsQuSk6TKJ/SLyovG45faJhInrhwm+EmWez655oJGP2ipYQ7MfDUTg6qgqe6Pd5hxA cAvXWKW+SkgL0YwSusi+NRBnb/hh+IYDIEzrB7p0f/p5PL1Ifv+iFIOcKf41snqdS6vB8vn2 6XQ8PJFTIfayJCA2oyWgmAWxBxJ4kLptuLlo/apy/vr044Auc5+f/y7/88/XJ/W/T2TLWzXW /j4sH6j6QI/zWbzxAj6Lu7Mt/TOIXauzNaxYEcSxY+CshIHKnzULrUdXJkoofLRgj6rpW951 LqfdoxRg7OC/gmW2yr0sJ0HIK0i5Uog+sYQbKQpsAljSV2oq0pwv1/LPaxS4dseqUufpgpps Kc+JFGe0MN/SLaR0v2BaimUW0SKrvzCejE28u0kZZGnuYMjCNRrW6rBrPrraZJHjLrdJv+V1 VpKViT/NQcAQmA++hS2bleKGUdJSZrUv8xdBi1+XxHtzNuWQnigUflbBh4vYCI2qEanQ3e3e s4SGD4+NBEJF+6SQmY/2u2aTEpe9HWGUfBiLrRwNU09h2+Vj4BbHW9xM+5pWuQSL3rDLp3FB ghZjb0Shk1SLCsRyIkijIkk1OUYECcdQRBhEyiOqoQSQOpbcPOPmUmo7XDMvHqwXhNOSQITD 0Gqe15YVVLegV8/Ch98gCctDjAzpxsEbEtyO5gINIAVlngAKEjisDUP0fjHnDmPADIq5abU+ kAUnApPdulyfKxrhu+tMeR43mKFd4BD9EzA9o2wKX+BQq9QqkdalF90W+fP7zNNCPeDv9nx+ Am6VLnAQzYEi8wOBJyk/eN8lQqviD+P2vaUfCG/tBn6DSUIxNI1W27atYYu5wPnWNP2ugnEq 3zyzOlLBrvamJoJRA/keN8LC7FlNk61jEOFiQBeWq7BGa8Q7VkBHwDzkfMH+vADxIZjzsnsc hK09n/dVx180AA52QUWIkqzYOnme2WBmzVYoe39IjBovfcQlQjojOG7LASALlT6BQfwduA0f UhCH0NlqDAGFLGBjQU4zt7TtNrzGmhtYwVRoJ2Cl7FgGoV8gXmUkrTg1SI9onXrfgp/LvNfZ fapnvQEwTqk2cBXIXCANYrYO4GiK0UQ9djAqHZ1DUWfBbQRLBWJ5usRIryptkhz7k8bEd53k nJu+hKOHs3RplMfF3KEp4SSBm2uxKzE6/1wM+XWrkPrKlQyWdNitUktUZ4byLWbLS2AAQ+fe ZBk1FDPtBJigt/DYfDAcpRPeOTIpbhgmJFosIcX7xJbFxLgstnrMX4LewvTIMaD9I/jIh+FM Um2WlHi/e3zW8iULi+OXIMkE+OfXimIJDDlZZGymmYrGzudaIpIZbuCiJb+DpMENQya4gdml EhzbqsYOTA2AGgzvC1yIvnobT0oZlpARiGQKF31tlX1PwoCGKH0IykDa5e+1N68WUVUjX4t6 ukjE17mTf41zvgVzgw9HAr7QIBuTBH9XjtMuSNIphmkYDm44fJCg/7GA/nw6nI+TyWj6pfeJ I1zn8wkd7ji3Dt5GiOP7pHQG5/3707Hzk+srulcbJ4IErVrCu0skKsZyGu4CgdhlzN4RqGCf FOUug9DLfMJoV34W0/EzLs95lOptkoCrQoGiqI7K5olnvQAGOGPZD9za517hZr6WlbDO7bII Fk6cB6pnVMLGP43kUmlc7EEmd4pAqIgn0M/cj/gNDpz6LslWbXQVFQ1pAz+q1aItpqbMUNTr sRgO+ASvGtEN+9akk9yQNy4NM6E2fwZGk4kNHOd/ZpDc6L1uMOPWKnXTZAPHe40YRLzlhkHE mT4YJKP2vrPeYAbJtKWH08G4DdM6EVP6rqJjpEtYSzNZAw8kAU6Mq66YtJTa67c2BVA9HSVD 7ugTXZVvUFbgPg8emD2pEH/qxogvb8yDb/i2Tnnq3sBcBjXmT83qjfSaVkkwKTKzkxLKKfYQ icGi4Eh0YvMrGXfKBwmW07o0BCA/rrOE/ThL4Jro8Nqomug+C8Lwah0Lxw+p9r2GZ76egKRC BC7Gr+YijdUU8TrI9aGrxyGgkf8rDIjvq0AsdYQ8iKn9bMjnulvHgWup0SrzcapRUb42+8f3 Ez7iWgGyMC8BPSvvUb69XWPca0twLDN+wfQgYQa3HP50mZUlcUcnZufzPVUt6WZ5QyoxzIcA LrwlXMx8lT5UV2CVd1AM7iTkq1qeBS4ndtq31QqiiQhVeeU5yWBSh2rJ5yDH42VKJOuMXnuk UsOVdyyMmaxyD/8BrYr+9PX84/D69f28P2HmmC/P+99v2ptF3RIBKzle8/FkG6KoLSxFTZIn UXLPqSJrCidNHWhoxgxHhUJpZvknfC1HcFNYU7Ypi2rKMHG8VE8PbOJgXcHUsNESatJ7R49i 14yZM8fn2YAPlE+qcldechejsXPrS4hUGnFvQ6X8zY0xeR0yiDw2TB/U/+0TesE8Hf9+/fyx e9l9/n3cPb0dXj+fdz/3QHl4+nx4vex/ISv4fDm+HD+On3+8/fykmMRqf3rd/+48705Pe2n3 YjGLhQsSarheoAICdjII5r6zqh72VPjrzuH1gKbdh3/vSo8dMg5BjkveXRWxka2hpmFraItr yhPP7jNfC093hQyu8OF/0Y4NerIKvuWydxgDJoQF0RLW0yDFNxlCSUX8lsGs0O1TVbtGmhy/ qnwLW0squagiBXlvUk2ke/p4uxw7j5jI7XjqKM7TLAJFDP1caNHFNHDfhvuOxwJt0lm4coN0 SRmlibE/0vkOAdqkGdXONTCWkLAqo+mtLXHaWr9KU5t6JR9tjBLcJGJIQY5wFky5Jdz+YC3a qTGyLqbGUppqi2ox7/Un0ZpkkSwR8ToMLWoE2tWn8q8Fln88q2BnnS9BCtB0PgpjPsMrRcP7 j9+Hxy9/7T86j3K9/jrt3p4/rGWaCcdqgWevFN91GZi3ZJrju5knHKtBzvvlGU1NH3eX/VPH f5Wtgp3X+ftwee445/Px8SBR3u6yo2yxGhaX07JV0+FG1oC5S5DPnH43TcJ76fRgN9XxF4Ho sX4eJYXwb4MN0/OlA0xqUz1+zqRvJcohZ2uA3Rk3Z+6ce5qtkLm9LF1mEfrujCk6zO5YBlyi k2s1p9hacyC3uWCqAYHzLtONaKzhxSTB+Zo/9as+YEgya7Esd+fntvHECMAWI1NAs/AtdKe9 sxv1UWVCvT9f7Moyd9C3q5Nge5y2LJMF4rzX9YK5zUQkvVkMWa8GZ/KGDIyhC2B5Suswey6z yIP1zoKp8qYB90djDjzod616xdLpWUB0RAMEFsPQt4BHPZtbAnhgA6OB1TqQR3x/liwsRL7I elO74LtUVad4zuHtWTNdIN1wfHsLOj63NwBqpBuzKOL1LODucbS+zB3axwAHBHnlbh4wa6lC NNFojLXpRH4YBg6DwAtulbzbxtmrDqH2ZGI/PHaMDMtAEz2Xf69RrJbOg8PfPaqV4ITCYbNu GkeEva58X8uiXIOzFG73V46LyN6iuW8PL9ya5XxZtArejLxalMeXN7TX1zz862Gch07uWzWE DwnTgcmQs7yoPxkynwB0eYWJPggpqyg7993r0/GlE7+//NifqlADXKMx0n7hppyg6WWzhRF8 mWKWHPdXGI73Sgx3nCLCAn4PMP6+j2bI6b2FRWmx4ET6ClGw7LzGikbuNce4psli7kZvUpV3 BWt1mu+VtrCPOQESqwPLO1rawx93HjJ6GU34ykaQvF0ScS2VLP7PRQwKUgiDrQqxBh2YC/+Z UyEavz6MilwuRbOd8oD+QzOlLNDWUbTmc3LgsShAXxvShhBHrjvkDcQJMVyRs2RbuHGMmYb+ RK1e/ts7IfOZO3N/6/phSzdcF45VzhBB3EeYPzVwpQ4SU6GRB7wGma5nYUkj1rOSrLF1aAjz NKJUTJXbUXdauD6qFAMXbeBMA7h05YoJ5oTfIBYL4yhuqiD+LViZ3FDlAKzWSrCIfa9IfWXK Ik2OsAUB4dXouf9T3nLOMhnO+fDrVXmzPD7vH/86vP5q2KF6WqRK30wzjbHxgiQcKLH+Nker 2mY4rO8tCujIg/9t2J2ONX1dEntOdm82h9fuqZJnoUwVI3KeuDIA+A/GpGryLIixDTB3cT7/ Vscu+HHanT46p+P75fBKLwWY42JcpLd0LVWwYgb3ZTgwshWzhtAdRRvrGWwoH3MEkPGrfEdi Hy0AAvrcW6HmQezBPxkMwSzQjD8zT/O5yILIL+J1NFMZgap1jy/caLfjRunWXS6ktlTp5+ot 6MLeg5NJA/XGOoV9w3CLIF8X+leDvvGzNuPXD36Jgd3qz+75uzEhGOr8QmKc7A5WWQtTQgoY Kr7csSY/ufov8sQHcqV9N3NJDjvzMpY5sZdEtMeNBQsKqXAwhtreeVCiqwEFsQqFM+XoqUHR PtyGDxvqFwrlqFGUYgqXYI5++4Bg83exnYzplJRQ6fSScoJcSRA4dOxLoJNFTFkAzZewktkJ LmkE8NQrtc3c71ZlRiKbusfF4iEgSkCCmAGiz2LCB5oViCC2Dy30SQucDEq156UK29Fsa2bu Uvsho1XnMjRtRKZMWv5tMBk1HKZkAJwsc+6VWT09PEXiBnC6bPxCEjQotInR8h7FcGEphEr/ E/rxgj6+SZzMfeSkUkg0LWwQ53heVuTFeKhxsdoAR74SScJ1XL9FkoPmLkjykPhOIaUrG6jU K/ufu/ffF/RUvRx+vWPK5xelwd+d9rsOxu/6X3JLgI/xhMIHVXzsRpufXpfwkAovUMuBSdtY sZdSkZI+2goKWnLzaESsExKSOCEIBxHGDp+QlylEoI9e21vdIlSriSwRmfnDfAl103WRKZP+ quhbomdehMlM/0U5e7UOQt142Q0fitzRVIhBdov6J86oK0oDDDVEOfDco8aZgSc9YUROA84L dLdLQmNFyUeuOyekD8cI8vw0IUUKWIuGGwM+XMeL695nlrSgv95VMpiEvp0Or5e/lO/2y/7M vOlJSWQlc/XRhpRgFyNPc29vuP8Tae68CEGsCOuHkptWits1GlYO6wEvhVOrhCFZX/exEwXu FWew1g7WmoXD7/2Xy+GllMXOkvRRwU/2cMiajGtkA0Pb37Xra7dTghVpGPA3S0Lk3TnZnHfb J1SzfM4/THqw9N0sSNmrsB/LN51ojdottMBvOjEHTu0XUHf8bdKb9vUllwIzRrdC1gwwg+u4 LBZozEGhVhNLoMP0BQGcDA59JaoQRouSFBYdMp4gDoNYE1ZV4UI5AKBJY+Tkrm5XoGFkv4ok Du/tmVGs/Q7fb1OV3JEX4v/TpVKvX2cRSPvU7LZpGAHWL7tqUr51/9XjqOBiFjih2XU0WtWv qgqO1p3Wa0L5WOztf7z/+qVdwKTtG1yPMDqwnqhLFYd4yZ/ZGwR8m9zFukZHQtMkEEncdndq ikbHlSskWeI5uWO97xlUyo6cW5hL9Hstxyvyo9IYwfi8wrDGF3KVSYuEtZ4JT6E2kV3eJpIP K2gbdaXRQJXx8mONTxcgey+4fpVTLXObSGMBZuLUSkaRp3Vk5FG0coRDVdyuFHcktJL3GqwC ywH51rOMEZr1ZYlPKzfZWJVAWQBW3j5Fqr1fIf2V0RFLIztfKWRB/R2Mpvr+pvbmcvf6Swu5 ojICFEt0os8dwU363S0wC2AZXqI0YrVnIl84XfkxbFpgOgnvHaTh0X9x7WvpGFH1A4dssiZZ GgWsIs92dpDgNnWn+katWh8u5wZXVSOINa18P1VcVSkZ8A23nsPOP/6/smPZbRuG/co+YAiw Ydg9ceQmSPyoZNftKYchhx02DGgH7PPHh2TrQarooUBhUopEUuJLlF7//PyNed3Xz59+/X27 /7vDP/e3H7vdLn5CFquhqMsHskvWx+NWM2F4imue1ilQQ5yCKt92Aj01mec46+TZ75/by78r 6MvCEPDYhyU9l+d/aXGmK5rRCDPjlM5ymbFcbx6gTga8JbRT3NVorZF8FCaXHhKNiQZ+FxZ2 3fKwxTbNqnH4AS6Hn+UVCkuONqTMZiVgPCVStUA38JQwjQSyyLGHynq+8B6uUg/+/NEqgXbn qnYY34G7mo6iSrdz9m5mhtOAyQdeD+hoV+xItplFrUuLwjZRjkdjKiDRbTu6EkSMuLVkoAEK KgMyu9ZN5uuXGF6wET+aR6Emd7uKKZldThfYR9mysoJNlXKXxBqsEIyzK4kXGP1pmPDEHXvw 4bYc6by1Z9rNWDvYrU50cwSHFgyPGnZywJverJbxxLFWylO33Q+m0Dcv0yDtGD3dBQjsiHxV Utjt3LNZW4c+2P14knGCv9QGduvA23KeTujRuvx3GNxReT8R0h4zFCzKIlFDTDAG+6noBLOM L9lHnDh3Gy0MmgbGHW7ZmHkYTaoIyDvOH3WjJ80IP3EiUIRQ6vherIJgozWmAxcEbHBxHkV/ ITyWd+QRS+WYc0Hlr8bayBtfx0qTVR5VtY9uaFvfXjUdCslZQFzLMTH9PatdwTHXg+l5GhLb NAOtVqpbxNuIDqA4gDH+kWo6FJxOmr/ve1ice8zYcAMxGsb2UD6Jw/VCyb1QdJ7YoND/wQj0 9PA5hkfGg19F+fc6di4KlbW4bTRBKjwhJI4qi3Xrw3Nx2oMGGTUFEiQ4CcBhmW24j9QVXKbV ImegNhWzLdYPYL4z1mgpHA1WQ6dBQJ6vwRg0hoPzp98f0CMJgtUWFxGE4QAxMZ2FXRNd+LzG tr1fjpNs71BOlRKGTrsqj1BUKEuli++RkGtsgmlG5mDFfDhgKL4Cp8D6cB063Jc0rCSur6Nh FBqWuwpnK/n7N8WEjQl0Ms/HuZOPXzIFOVLKgWSx1MhjuWZMokKc9gbANMjnCgiBE7Q6nEO3 VTiYE1f5/BZhzLNSWUJQTpnocLwLoAV/R8ewmCCcMPRUoTKg6NDzUT6mwUJ+qayAp06LKvHU 8aQPXl8QrVsi2tjmXzABf8IwMmzjyeVNmJcGIleT4NRFe7Yd+CymkAEuLa8wiPYXXbKodisv ZGPp6oYKazvTNaBwJY0YekZ3MQ37hJb4XewaYPpmgWG0/kbRNtAQeG+1Zrm6Pb7kLN7IEYI9 84GiRLhBYfSWQ71rBwSV1Cm12rJIZQ4OOIyX250deeVLkn+jej6PEeeSNEjq0pamGZ+KmnCL iezIvb36oxmXODaUpVb+A+c5daB44QEA --GvXjxJ+pjyke8COw--