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=-7.2 required=3.0 tests=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 B1F53C433E0 for ; Thu, 18 Jun 2020 15:54:40 +0000 (UTC) Received: from vger.kernel.org (vger.kernel.org [23.128.96.18]) by mail.kernel.org (Postfix) with ESMTP id 7E9442070A for ; Thu, 18 Jun 2020 15:54:40 +0000 (UTC) Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S1731516AbgFRPyj (ORCPT ); Thu, 18 Jun 2020 11:54:39 -0400 Received: from mga02.intel.com ([134.134.136.20]:31563 "EHLO mga02.intel.com" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S1728090AbgFRPyi (ORCPT ); Thu, 18 Jun 2020 11:54:38 -0400 IronPort-SDR: Yb3PGfwUcc5Pt1Tz42uw4dhRYDiaiY+k4p9jgLw2AMg1DDplt3u1BfK7TuoYnDm3zQgbAOs7OI /DBBR685wmMQ== X-IronPort-AV: E=McAfee;i="6000,8403,9656"; a="131026030" X-IronPort-AV: E=Sophos;i="5.75,251,1589266800"; d="gz'50?scan'50,208,50";a="131026030" X-Amp-Result: UNKNOWN X-Amp-Original-Verdict: FILE UNKNOWN X-Amp-File-Uploaded: False Received: from fmsmga004.fm.intel.com ([10.253.24.48]) by orsmga101.jf.intel.com with ESMTP/TLS/ECDHE-RSA-AES256-GCM-SHA384; 18 Jun 2020 08:54:34 -0700 IronPort-SDR: +AIStREKsRO8oC8+2n8ywIY6SYiQlcXIsxPT282QbD4Q5Ux++ChkPNaO6HWQrAqc9cd1+Nw4jW Y8mxQmvnAwuA== X-ExtLoop1: 1 X-IronPort-AV: E=Sophos;i="5.75,251,1589266800"; d="gz'50?scan'50,208,50";a="299740813" Received: from lkp-server02.sh.intel.com (HELO 5ce11009e457) ([10.239.97.151]) by fmsmga004.fm.intel.com with ESMTP; 18 Jun 2020 08:54:32 -0700 Received: from kbuild by 5ce11009e457 with local (Exim 4.92) (envelope-from ) id 1jlws3-0000JB-ML; Thu, 18 Jun 2020 15:54:31 +0000 Date: Thu, 18 Jun 2020 23:53:33 +0800 From: kernel test robot To: Jack Xiao Cc: kbuild-all@lists.01.org, linux-kernel@vger.kernel.org, Alex Deucher Subject: drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:999:32: sparse: sparse: cast to restricted __le32 Message-ID: <202006182330.gdBW6ihA%lkp@intel.com> MIME-Version: 1.0 Content-Type: multipart/mixed; boundary="Kj7319i9nmIyA2yE" 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 --Kj7319i9nmIyA2yE 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: 1b5044021070efa3259f3e9548dc35d1eb6aa844 commit: 6698a3d05fda57f37add68c55a0696bfa7100413 drm/amdgpu: add mcbp unit test in debugfs (v3) date: 12 months ago config: riscv-randconfig-s032-20200618 (attached as .config) compiler: riscv64-linux-gcc (GCC) 9.3.0 reproduce: # apt-get install sparse # sparse version: v0.6.2-rc1-10-gc17b1b06-dirty git checkout 6698a3d05fda57f37add68c55a0696bfa7100413 # save the attached .config to linux build tree make W=1 C=1 ARCH=riscv CF='-fdiagnostic-prefix -D__CHECK_ENDIAN__' If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot sparse warnings: (new ones prefixed by >>) drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:163:29: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:163:29: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:163:29: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:163:29: sparse: expected unsigned int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:163:29: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:165:29: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:165:29: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:165:29: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int const [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:165:29: sparse: expected unsigned int const [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:165:29: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:240:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:240:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:240:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:240:21: sparse: expected unsigned int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:240:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:278:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:278:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:278:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int const [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:278:21: sparse: expected unsigned int const [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:278:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:319:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:319:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:319:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:319:21: sparse: expected unsigned int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:319:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:357:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:357:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:357:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int const [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:357:21: sparse: expected unsigned int const [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:357:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:398:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:398:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:398:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:398:21: sparse: expected unsigned int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:398:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:436:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:436:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:436:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int const [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:436:21: sparse: expected unsigned int const [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:436:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:525:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:525:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:525:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:525:21: sparse: expected unsigned int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:525:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:582:29: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:582:29: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:582:29: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected signed int [noderef] *__p @@ got signed int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:582:29: sparse: expected signed int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:582:29: sparse: got signed int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:650:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:650:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:650:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:650:21: sparse: expected unsigned int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:650:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:729:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:729:21: sparse: sparse: cast removes address space '' of expression drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:729:21: sparse: sparse: incorrect type in initializer (different address spaces) @@ expected unsigned int [noderef] *__p @@ got unsigned int [usertype] * @@ drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:729:21: sparse: expected unsigned int [noderef] *__p drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:729:21: sparse: got unsigned int [usertype] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:942:25: sparse: sparse: incompatible types in comparison expression (different address spaces): drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:942:25: sparse: struct dma_fence [noderef] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:942:25: sparse: struct dma_fence * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:943:17: sparse: sparse: incompatible types in comparison expression (different address spaces): drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:943:17: sparse: struct dma_fence [noderef] * drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:943:17: sparse: struct dma_fence * >> drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c:999:32: sparse: sparse: cast to restricted __le32 vim +999 drivers/gpu/drm/amd/amdgpu/amdgpu_debugfs.c 980 981 static int amdgpu_debugfs_ib_preempt(void *data, u64 val) 982 { 983 int r, resched, length; 984 struct amdgpu_ring *ring; 985 struct drm_sched_job *s_job; 986 struct amdgpu_job *job; 987 struct dma_fence **fences = NULL; 988 struct amdgpu_device *adev = (struct amdgpu_device *)data; 989 990 if (val >= AMDGPU_MAX_RINGS) 991 return -EINVAL; 992 993 ring = adev->rings[val]; 994 995 if (!ring || !ring->funcs->preempt_ib || !ring->sched.thread) 996 return -EINVAL; 997 998 /* the last preemption failed */ > 999 if (ring->trail_seq != le32_to_cpu(*ring->trail_fence_cpu_addr)) 1000 return -EBUSY; 1001 1002 length = ring->fence_drv.num_fences_mask + 1; 1003 fences = kcalloc(length, sizeof(void *), GFP_KERNEL); 1004 if (!fences) 1005 return -ENOMEM; 1006 1007 /* stop the scheduler */ 1008 kthread_park(ring->sched.thread); 1009 1010 resched = ttm_bo_lock_delayed_workqueue(&adev->mman.bdev); 1011 1012 /* preempt the IB */ 1013 r = amdgpu_ring_preempt_ib(ring); 1014 if (r) { 1015 DRM_WARN("failed to preempt ring %d\n", ring->idx); 1016 goto failure; 1017 } 1018 1019 amdgpu_fence_process(ring); 1020 1021 if (atomic_read(&ring->fence_drv.last_seq) != 1022 ring->fence_drv.sync_seq) { 1023 DRM_INFO("ring %d was preempted\n", ring->idx); 1024 1025 /* swap out the old fences */ 1026 amdgpu_ib_preempt_fences_swap(ring, fences); 1027 1028 amdgpu_fence_driver_force_completion(ring); 1029 1030 s_job = list_first_entry_or_null( 1031 &ring->sched.ring_mirror_list, 1032 struct drm_sched_job, node); 1033 if (s_job) { 1034 job = to_amdgpu_job(s_job); 1035 /* mark the job as preempted */ 1036 /* job->preemption_status |= 1037 AMDGPU_IB_PREEMPTED; */ 1038 } 1039 1040 /* resubmit unfinished jobs */ 1041 amdgpu_ib_preempt_job_recovery(&ring->sched); 1042 1043 /* wait for jobs finished */ 1044 amdgpu_fence_wait_empty(ring); 1045 1046 /* signal the old fences */ 1047 amdgpu_ib_preempt_signal_fences(fences, length); 1048 } 1049 1050 failure: 1051 /* restart the scheduler */ 1052 kthread_unpark(ring->sched.thread); 1053 1054 ttm_bo_unlock_delayed_workqueue(&adev->mman.bdev, resched); 1055 1056 if (fences) 1057 kfree(fences); 1058 1059 return 0; 1060 } 1061 --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all@lists.01.org --Kj7319i9nmIyA2yE Content-Type: application/gzip Content-Disposition: attachment; filename=".config.gz" Content-Transfer-Encoding: base64 H4sICNuD614AAy5jb25maWcAlDxdc9u2su/9FRr3pWfOtLVlR03PHT+AIEihIgmYAGU5LxzV UVJPLTtXknuaf393wS8ABNXcTKc2dxfAYrHYLwD+/rvvZ+Tt9Lrfnp4et8/PX2efdy+7w/a0 +zj79PS8+59ZLGaF0DMWc/0TEGdPL29//3x4Oj7+NXv30/ynyx8Pj/PZand42T3P6OvLp6fP b9D86fXlu++/g/++B+D+C/R0+M/MtFrc/PiMffz4+fFx9kNK6b9mv/50/dMl0FJRJDytKa25 qgFz+7UDwUe9ZqXiorj99fL68rKnzUiR9qhLq4slUTVReZ0KLYaOWsQ9KYs6Jw8Rq6uCF1xz kvEPLHYIY65IlLFvIRaF0mVFtSjVAOXlXX0vytUA0cuSkbjmRSLgf7UmCpFGSqkR+/PsuDu9 fRlkEZVixYpaFLXKpdU1cFGzYl2TMq0znnN9ez1HWXf85JID55opPXs6zl5eT9hx1zoTlGSd zC4uQuCaVLbYoopnca1Ipi36mCWkynS9FEoXJGe3Fz+8vL7s/nUxMKLuiQwwoB7UmktrfVsA /qQ6G+BSKL6p87uKVSwMHTWhpVCqzlkuyoeaaE3oEpA9R5ViGY8CPJEKNHzoZknWDKRLlw0C RyGZNYwHNWsICz47vv1+/Ho87fbDGqasYCWnRh/UUtxbam1h6JJLV3dikRNejKlzxRFvSU+S UjEXZncds6hKE2XEsHv5OHv95LEaapTD0nIQQxFnrBz3S0FbVmzNCq266eun/e5wDElAc7oC HWYwe1vEH2oJfYmYU3uFCoEYDsPaq+SiA+u35OmyLpmCwXJWOpMdMTb0JkvGcqmh1yI8XEew FllVaFI+BIZuaSwFbRtRAW068VBZ/ay3xz9nJ2BntgXWjqft6TjbPj6+vr2cnl4+ewKDBjWh pg9epLaIIhXDGIIy0HSgCO1xtC1KE23ZIwSBLmTkwTTyEJsAjIsJBqTirrRaSX/DHI0sSlrN 1FhPOrkBeuAEPmq2AU2xuFMOhYZmPginP+4HJJJlaB5zUbiYgjEwcCylUcaVdnEJKUSlbxc3 Y2CdMZLcXi0G0SAuEsI1vA62EDTCxQ1K0JVMbw5WzS+WgVj1MhPO9uGrJfgY2AEGE7T+aM8T MEU80bfzy0H4vNArMPIJ82iurv3dr+gSxGVsgGUS0VqqSkpRagUuSl/N31t2OS1FJZXNKhhp mgbFFGWrtkEQ3aAaLs4RSB6rc/gyzsk5fAKK9YGV50hituY0bDpaClhpf4+O+GRlcn4QMOCB pUSvC8Yf7IDj4kD6RXje6GNd1OAWS8BYJozHznfBtPMNoqcrKWCV0ehC4MNsFhr9wBhiehHB eSYKpgabnhLtLmS3W9BYWVFIhvZrbaKf0gq/zDfJoTclqpIyK0Yp4zr9YDtWAEQAmDuQ7ENO bPYBtAn5GEMqvKY3dksIG4UEDwTxYZ2I0qysKHNSuDoyQa3gFycociKb5htsIWXQCGI0mDS1 6COZ2Kw0NjMwqvHsuOBO1yg9P8hJGvfvR169h3Ushx0tWoaKZQkY3NLmk0CwklTOQJVmG+8T VNALxxowzeWGLu0RpLD7UjwtSJZY+mH4tQEmbrEBagm2yLJk3FpkcIJV2fi/Dh2vuWKduCxB QCcRKUtui3aFJA+5s0M7WA0/AwvUo42kcBNovnb2Fyx1N3xwb+HqmuA8CdtH4JPFcXDPGUmj 2tZudNemeHJ3+PR62G9fHncz9tfuBZw7Ac9F0b1DkGX5cqeLfmRjyhokMFmvc5iC66Z6V/iN I3YDrvNmuCbqchRUZVXUjOzsVciSiIYUaxW2TxkJ5QnYl90ziWDZy5R16ZBjBhGLTgRjirqE zSTyybEGwiUpY3C0odVRyypJILOTBEY0oiNge52NrFlex0QTzIp5woGAm2DHCmlFwjPQ6KDU 3US063dxE9nZUckVXVt6n1tx1gcIpWtwrNeWjTXsiiRRTN9e/v2p/ff+svnXMw/J2soYtS6M sGM8BENikmQkVWN8F5k4tscC9tuoNk7LUY8+p4G8PirBFzVRcoBAVfkYurxnkHtYvCRgIxkp swf4rh3DIlNtSgoZaCoYjutma8nn7QlVfHb6+mXX5ApWRFWur+c8oAotcnHDHaXOBTAPo8aZ uA+5nB5PCmuGAK2AL8Uoaott0shGLh8UzKKep5ETvQ0YCPTSKKzYeSj/1xWoSCs+T6lqrkhN O6NzfPvy5fWAxSiZV514HHJjIqXRv16FA636pZEOpSt528592m1Pb4fd0V4OSEuvLi+D8wTU /N0k6tpt5XR3aSfEt1f2djBB1LLEZNBm2ufQsBi9QuevX7DwZhlhmsew0U1A1Dd3KBv9e/3v 7jADC7v9vNuDgbX6GYxGHrQWk02dotb28PjH02n3iPz++HH3BRq7w9i+x5gasG3gxjGGpJjl huoyxiIshVh5SDA9WJDTPK1EFdjloJKmutBW47zWNLP6a6uFxi6B1dQMK3ymVuC1WvNSe/k6 judRwSZrkyTJKJpmf/8p4xMxYkJrZfGRoU2NgIt7cA3WnBpZYXzjhK8sMVyYICuok5i42d7S SQmapaNi/ePv2yNsoj8bfftyeP309OyUKZCoXrGyYFb8ZYAm6NX1Tf2LrbnnOu1NZFalvDBF RUpvLz7/+98XY+f0DwrVmwgNwS7EjMwSmTEYCsOOoVzcSn9kDmEWFJNhW0taVFUEwU2LHjkY SxG3ldVwYtbZ35L2BdhgaNjR2aWAAdYMH8Q0cf1oPBNSkKuzPDU08/nNWX4MzbvF9CDX72++ YZh3V/Pzw8CGXN5eHP/YwmAXo14wpoN8NJTidlvMVIsysBuVlRZGbikDM01FFYetdFcxux7U 5aCRSoNAiCCcQL1PWTVLS65D5cOOBgOn2O20td+1KfCWfsf3USi9a7rDkCdRPosNtB/J6U2B tRWSZCNTILeH0xNurJkGL+l6BQLeyeSikBNhkhuKWXMVCzWQWmlYwh3w4FW8Ee1J5He1pNyd GMDQAttZWwvGCk8XTHAxU49/7D6+PTuJClBx0fjaGPyB8ZdfA8jVQ2TndR04Su5s1t1B+lhN FVdDUzxFMsuqJFg6NBajMhq6nTzn4j7quGd/7x7fTtvfn3fmYG5mcqKTNY+IF0mu0Xs4yTf1 SjP4XcdVLvvzG/Q3bc0wpE5Nt4qW3C5xt+AcgrDb/XdW39i1LZApvs2k8t3+9fB1lodCjy5i g7DdiaERAD44ZhhaQxRq7eLGy0Jya/xbQ2Pj21MhrkRGXFVUMgNHKrVpCI5c3d54jSLMtuwm LaBJI6nbXQiW87QkPhmGKU0eZyUJEFXDforLWvt5FyRaMC0NsYNTYlCWeLpVzWHmMGZherq9 ufx10VGYUjdk4CZgWeWOcmQMdjEBxQ6mDk6RDD4nC5M9zjZBCCQQcqnbX/pcUQqRDfrzIaoc s/ThOhFZuILxQY0rB93OjLsEGUPElVO6SUqSQ7hmAjlL4qxEWXTnJf0gaSXriBV0mRO3SOBv ValZE9SRzNb9afUe1sJaXayxArelE/AikHkwtYpqttGsUG2yZjZTsTv99/XwJwRT410E2riC ofa26UZIHXMSWkAwURvHYG3AAjiqYmATrTXkttZQ8Bmok7fITVJa2otfsB9S4YHccqUBmSw+ wQro3oGrKoIcP+P0wWbXoJotOMWFWX+uIN1ylAAXYMVCjlvl1J4mfE4JhDvrzGVTg6VEudDO idalgLDV8feATXgEGs1Zo6ehUdp+JSY3WGdQXg+m25aG6GW4i4YIwtBIKOawJwvpf9fxkkpv FARHQuhQ5t+iS1JKX8Zc8lCLBpWi02J5tfF2i8RagpN+9PSDWgxdRCXE5SOx582U/WPBHuPN L7fF2It6YrI8V3m9vnLl1gDnTqnwAT2JWPFgT80M1pq7k6pia/6OMBNRBc1mixsEF+QbqcjS 1m0DYip8ksMb9tDpTfXWcun36G+XwWRQid4xPRdX9jS0iuxcuvOAHf724vHt96fHC7f3PH4H 6URw1dYLh1H4bjckHlCHD+oMUXOWgwakjkmIY5zzIiDZxVnRLgKydQfOuVxMzKTmGRmUpumu XwybzlEuA1Fcj+QAsHpRBueG6CKGeNAEXvpBOsZjPTGss1GbyZwzYchCFWEKN3XAiT2Y1Zri UbF0UWf3vt0YcODtqWNP8BIXlkYxCvA2mkFBwGZqMGCXc+nV1QfShGeeTe+BQS1vL88ddujY IXQ+7Q6jC3ajjkLhQ4uC3yDpWIVQCcl5BiFuyeOUnSForPZUz3hrwFaYAg/+isIEYSGRJOaa QWt19x4Y+oSQwQGP9aEHwuqED9gHClT9fyBpxpwggpyjylNWBGfSHzq589c4lYkGza09j7xR ykkORPTblAFC9F0lNJkYrmS/Mapd4en2CGyyQ6y1TPSXcOsUDAEmjvOmo/CO2HT3WpZiEwqs BtXatIu+b7bDxiSSx9nj6/73p5fdx9n+FTPtY2grbCAHwg3rNT1tD593J6eK4bTRpEzZ5MIF KIuk0fszJJ3t2J8fE9xSrsal2I5zSJsf/zgzV42XFSHZM6Y3zE9D1EbH+yFROWtmnOBnaqMB aj1mncv/fIP1SlpFQRt8Y2tVqyENfGwKRvSdmgcQMaRyYyiG1ONBm85dY5fYPdi2A40VkE6Z FkRjq7ABDLILogQUl73qO/DWjXjQVoPaXe4hc1KkGXN9uRmd3LtsdzWsM6vWLutfi//vwi48 1zAsbSh0cdZ4MbXGC9/gtGsU7rFfbp+Vs62mh+vWNhx6LaYWcdEsCW5EbNxU3kYE/TJba7YY rfTEVlwMqz7NG6y+bQLOr2nIXTbhQqhkaBBIySJ//i0OEJiMYXIVQunA/B10MeG1LKL3l/P6 epo7JCE5pgchBnDzh+BcTrAUjsAtgpG/t3B+eSlEI1caDfv5QZSe4m+dkVDo4k65ZDJ7mOgg 9kQenkWtg2IrWcxLxzLZTEPPQYTjVy2453Ej2YfWg3mLqalLGHOFv88o5fFxZKlsi4hkNZLN x3XNIN110HpOjjbw0p7gL7ePfzbnqaPuAwzY3XsdWPNWVFuai191HKUYOdLCvZtkUG2q3FQe TOaDiXH4KsdUA/8A8Z/o3fNzQ+aNfwaLgzln3XGogqHxdcne/sIbL5xgxcRubTATNTuinXon fNY0C5aoEAXbyzJlCMmlsI7wERKV88X7G3t/DVBYu0aLQze25+62xu+uvD5BXq+vbeYNiIeD FINjwWqgsnUpDdjE0fbkaQ46WAghnbJ7i0Uj1Fp2B23ObUw9SBEvQUZQgDPTE1j4q7uBhQFW p2u3vmih8vVEsBYz6qWInYAyS5fgY26vM8lWwyee4hIpM+aCuYxj6bpyANSsoMHXUZu5tQEy Ip0DZbkUYS4XmbiXpLCHaUEhVRnRFEsatneMMRTcu2AEi7kNnoJ2lvbubfe2A4v0c3sG6twW aalrGt05VQQDXOrIrW0aYGKfLnbQZmN6QFlyMSY1taS7Mby0D9o7oEqiEPDOS9sMWLO70PWM Hh0l40FppMZAyM7Gg2oSnk4a5DtWbXLpweEny8fguCxDM8rvcMwzc1KryHD1ddyWLsUqFJl0 +LsksAYULyKGekvuGtyZDilZsbEg2pN4X4mWyZmeJA901FXRRjzj5aTAAoxv/nZ+L7mbsDWd WwzPs8Mrd0odGFxFIuqEBF93dkQtd7cXx0//e9FWE5+3x+PTp6fHcf0QfJt36AgAvCzFvR2I YE15EZsL+460EWVi+vBFn44kCd1L7ZDV9dzWzhZ05oVMSzBxQtuzpdZyPA+ELsZgCC/v/S2C 8DMvknqBySll6zq249QObrJB5+WFOW4x4BCsuZGIT37HKJrLYJMietDemViLaUQ+hufgxvwl blGabSbKehYjpOChgwKLBG+UjKRPvDIlnkVB/m2KVhNPQluSlASfQnbonJdoP/fjhorkMpgq dwQjRhGIaUuIUXwqf5ZPxYMHZT16FTHnLXKHcK+fd1CMbcZQ7zGQ1XcuplbFzDRhIQk1hWn/ HNw9v02Y6X56I7YUrW8LNW4Ny9ktBhRn7DlaRusokFouPS4UvlUT+Mbdlk0EAQUxN9gC/QrJ irW657gT9wGge4BlI9abzA3HnFasYOuQxq2biplzf6KDTR+XNrffesIp+ZlTmol0B3aA5wAQ UqfKEqeBoIV3oncDhR0SOIgvlGW/lqr0PKqRBR7zOI2yazCICivWI1RBlfPaAb9rwXK8KFlj WQxSxLCbaB5SYh9+mBOioRlRatJ6lZs6qtRD7b47i+7sD5nUv/H+7Xd7NWd22h1Po5hYrnRz V7zP8UfkHsK+4jPwvyR5SeJgDEeJ8wIIPv3ir4WJ3Is+CEod2iaWIMUs3v319LibxYenv5or lU6jNQ2WnAxqQ22jhSCVjUDO8iOAkozWEdd4zG5rIOKSjJlO9zYwLUed/kaKDzWH365dUlqP SA0IYj6i8Tq7L5MWS0Nvcwye/vLLpdchgvCFy6gvg+hGCmsnkPGE48+JR31IkdfTQpeMrJBf lsTu1NVvpH2I4vTVgpHfiR47iikRsVz5AnLwHUcT3a/WBJ8l43Vfh1+ZbcZAJZLWLPXqWalo 9oSPBD9tH+1zMyR/j7k6EIRYNu2CHDEVI3buLmuq2p7GvI/gOY2Ige4DopgeuKKtL+kO7MbT c/tr7no3t+48f2B14W1gyyeGAimSgOkrpXPprYONDkICFIU5SMmECjuonnC6+lpuViSs/NB4 RfMA00qXjOTDDfoWjEdBpfvo456XLHMuT3QQvGxpQeHLe9JsQO1fYrBBSj6MiLht1JIUiytX ll/MDMD86R58qufoZ0uNy8oygVd48W8TgdaHyoA9Nb5hgImYh9F4t5ClcTRmwTw9at7yNCQY 7KkAXZ+EhpH+pd8OQ8uYjJ9t9uh7R8QOGF+yO40yHjVSG0FglAepoZWcxFEnQfKQesWdumGP nlZJiDgMSSikalGYrpm7FPhmsnnkfzmoGMBsjUOSZvOaR6K37ztUmay4HW8030BlPwhrgbyQ lZOctPBUTpZ4fvXSxl/l6E1FC+4eUvfWhieuNePJ9MVwRPp3ewywsYkdhMllndlXTDoIXsHT +mH8mLvDoxrbkX7wUNe+35XgCUDKNXGLUQAugp4JMUvjfRxitYwzOoqSit32MEueds/4YH2/ f3tpay+zH6DNv2Yfjf21r3RgTzx32UPtv3L9swEX766vfZ8RoODz8Pv6b+St40Q2WbKrDk26 2O2W+/Fl1A42URmJFexIfG8wdAtROKxm5mcjmNrUufLKb6BH7qU2c+/fvCoYTD3hmVjbxt+c NzAvRh8Fszax8+TI/2j/UpcKAsfvnDEEwyWNKucy21JoLC+atkgSkBaCiT2RFtC6VuecADA1 o2XogYRppWTu0yPsjKWzSIwHOE8ksdSlyHriz2o5ZOgXxsQj0uFvqbgSqGPpibeWejS7Ogol PCjtXHnrif5ypbwOJm0a4pSuIp+ei/BdQrPAZTgqNjgSTj0tBZnSGzoVbdtEainHhgobPr6+ nA6vz8+7w+xjvwuaaHr7cYd/fwOodhbZsXtqbz00QUlTErPxKrVQ87R3tDgdkslwsPpPDLgi TDT8f+qtPhIgD93OnFTQeoN/JmMzklW8Oz59frnf/h9nT7bcOJLjr/hpYzpiKkakJFt66IcU SUls8zKTkqh6Ybi7arYrpq6wq3Z6/n6BTB5AEilvbEW4bALIg8k8ACSOF9Mfazynx4GYbupv kY2ujvK4j98k+frh+zfg8tkId0kRmzAW7pQb4J2FidfDhg62vibpPwPpydja2P7rvz/9+ONP eWqwpvWl1+c0iXzc3K5t6l2k6pi/Vx6lsgRax3YP7Xv77o/nlw93v798+vDf9Ey9JkVDbuLN Y1eGLgRmQnl0gU3qQmDOmPgVM8pSH9MdDyOlqtTRx0xhET790R81d6XrrHWynvnHJKvoZs/A HTrwsGCc5yav9k6sHQvrcjROlo06GlXEKnOiHw4jX9sW92mdg6CR2DCgw3jvP718+TdO7s/f YHG+EI/Ni3GZp10HLr5WYz2k2yOtjUA2vvLYQZEAjvQs2zlm5eNEczs28taqMAwDdVMdpMcs Ky8enAMlo2skbJDnPBHiRhG89jgrWAJch301wLTmpefENGRKX4toIAZ5cyd9tjFaTXUaFABE QEgOzCXWPiOPOIPBmUt0LD3wEszo0E95XiENRoohOfQRvnyMUQX3/Asjcm/2fhPRQ/yknhVj I5/8fCV89BTmhIDHHaMEVrR3/Zz2FxDb+0BV0mAWVCGATx3MQ/Tz/MKAOYZOlBA6rfcy5rRr Z4i8idmD+ezsMgCBJCyAR9mPVOV+TkDQqn6w+MFCfvK6//788sp95BvUG8To1TeWEVDWyM94 Sxuf9ncB7xGrojsVfQArOWrAjB6jtZRFdmWH1qzP5lVO8Oddbn0DTLSw5uX56+tnK95kz/+Z vdwue4Sl4ryWfYk5qKvZ/dW+EU0VAExt1pusq0no37THEwk9dmuadmm9jyVGXucda8V89bJy XmMMsQDLz16sDFt4rfJ/1GX+j/3n51c4lf/89F063c2M20uiMGJ+S+IkMlsRbxV2oW4Au1Xh XZvxlivFYJRIhXvIThWP3SWNm2NHdh0BG97ErjgW208DARYKsKJJMtRMfXExKgfRNZ7D4UBV c+ipSZ3PBEPvAEoHoHYa9iSq9L3xuWw0hefv3/FqqAdiqAVL9fwH7ILOjEfXH3g1HCy01eOB WXHWYGwv0T7NYKn6HQFmpLozRkeqnemXqca+7eQX/0ZHzdvoj5//+Q45xWfj6QNV9fu5b45W ebRei3o4QOps6AN7RwD6JmATu98InoHNa1RmdXg0pkKPTWoTXQixQbiZbRyh3d6tEPHp9V/v yq/vInxvn+4BS8ZldCB3VTtjOlUAM5D/Gqzm0ObX1TTQb48h2zqKpAB20NlPLNBGE7x2lzpt Er5QBopB0SEWB/nTnWEDKmxxozj4P4WhSqIIZYyjys3d35c3CIxzvtNgrS6GdMaQZ1Uc13f/ ZX+HIEnkd19s6AbPVLMFJE7l7ap4TbpKccl43vy0c5YZALpLZkKX6WMJzLgzCQ3BLtn17gbh greGWDQb8y9spDhkp2SXuovF1IyzWFLjNYR9LJnlCXAhpyJtPKkIAIvxXNChl1ZgwyfKqMdy 9xsDxNdC5SnrgPGDY1c5AGMsaWnU0Ow5jykfW6LnD7BnZzw1afwZi0DdMoOhhtEGjZw4S1Wj nk947T7kEyUeokAVpyzDB5ERGIhQO6E1blJptQxb2WBmID5B928SZMAz3CSI65186zZ2+g28 fnwD325u4p29YdL6x3AeogFFFJ8994Io66O05Fi4u6Yzb434WyNQ63auKCrOeUI0QwMvCFB7 f/hFGEksIurzsdSt0B2GYK92NcYv+cKh7MrWgKwbmXwvQHttGYtPr3/M7yqAPdFlrWGr0cvs vAjJwaHidbhuu7ii+R4IsJc3JxnwlOdXXKCyLu4Ikrsvcu4BFa2RbHHapPvcjLSIhXHaLkO9 Wsjh70AkzUp9wotT2AXm1+g92RFk3Uw2KFJVrLebRajkKBs6C7eLxZLIBAYSLoj/Tj/EDWDW a2JNMiB2xwCtTGgUmh5jGt8u5K3hmEf3y7UUaS/Wwf2GRSY56V2v2ev2Wm1XGymWqUZeyaeM 9Oz9GDusA8Gund63OleqoCxEFJqr355nShJgFPK50tnCYa2HhNfvgVlyUBFzausRuWrvNw9r oWM9wXYZtSyeYQ8HoaLbbI9Volt/4SQJFosVZXqdzo9vuHsIFrPtwEK9t6gTtlNan/KqoYGg mo9/Pb/epV9ff7z8/GICZr/++fwC/N8PlIKx9bvPwA/efYBl/ek7/kkzj3RcLfz/qEzaIMyK n/YHdAFSKCxV2fBx068/Pn6+g4McuKeXj59NCqrpSzskqN2x3PKA01G6F8BnONoYdNj2ywqN +OaNH7+9/nDqmJARKpiFdr30376/fEORBgQc/QNeicYC+1tU6vwXwvSPHRY6S9yczN1FbZng yX33xugRxeDlid60mufRegukl7pEHWSEB+Z1slFIoiNxRjHLVmVROVhmTVzisKA9Nl0Tnl32 HxUI66pTKZ127MjpB1qng/Qy2wFMsM68JN4xtUpjzP/E4sBH9KbPlLHxISlkipMy7akIN3rC /TzGgelX3yETR/rub7AW/vX3ux/P3z/+/S6K38Gy/4VKECPXI+m9omNtkcxZeICW2pNYZqzT k61kqFXcTgYkT5Vl3toIlUrWjRqCrDwcmDGmgWq0QTPa6mFTMsPUDHvGq/PpUAwSPhZwLiI4 Nf9LGI1p3zzwLN3BLwFhVhRLsGZRddXXRRaa+x7OYFys1Q450xHeRGQBW5BRbJq8G06rUXvY LS0RO9cH3MrifN9jV7ThWNpFtDCgJfXaTMJUOzbVw0xaXroW/pmF5GvsWGk16yQU3LYemWQg gFH31anMBaBbqVLRrY6oNHqANskJYwGoKtcm2KW9CSE+MwMFSnlNnxcr17+uMQL7xML1RPas TQqMZiBZRjKyHPP6CZXUyaE3X7IpU/wjhCW2rcReDOjtqiVmYz1gnuPC7ovnG8Odn0+5uyma cFT6qmc1qTrKtaSsMNgEmgnZ3UoOzJfZiIvkIsdOGil6Pu0/MwQuWT6Tgd1ZitAQF7OxsTsw BRwtdQsfzmtF38qmekpnQ3Ha62Mky4T90gCeTEx+aBq71ju3/9eaNA57FbVSM4905fIn21XG OY8gIRh0f/K1y2AbzJfaIZadwc3GW80nhUmJKUtAA14FYgYE20dM/eN0+5qvl9EGJnTobskj Bq8Ney0PqvuMrWzgox0iAqqDxmRtMhVODENxv3LegNDkogGloXqCoxCGG+bVwnmfp0xZGXxg sqMcYSHbsQjQs46xGt/e/5TE7LiAZ9lh0p5T1V42e7EzI1pu1395Nx8ci+3Data9S/wQbL17 lmM1almq3GzqsxlY5ZvFQo4pgQfZ3oym27xV5PgKRcck02k5rBvnhR3VED3qHY6TaZak/cyJ 4I4naU5V6rDTpUWimA92HptTVloiPSoQyIMb9Kv1PWtzVBwxqFkVVwaKspNm8aV2g3nmxCAY iFc47dG98kS7H37UKebGnKShGU4nHH3dWDBKnFC70566yg/E/U1tDpLFAU5/fHByWDqUxqXW mCDIERyxKZg/cHBrGq4wNnaJOoVBQ+sSmBQMd8LEwGnF3U4BbszN5VZ0oSqTKJXW0xxTc516 TjHk8yD7kfrcmN0Uae5sblKAMC53Bn1m6YUegDDqFdrimMSDTjc8LBpg3if80hzrvqHMNB8H 1ensw564UITjaww35Ar2mXpMeA14idVcnTossNu7dmvkkxj1lw+LA2KG2GPlk8vZHyiBScgg vEUf7MvVDuFRkfruYhC5T7OErgqEVa5EgUD8kKHYK7wU2JklMdMU04aYZrkXdnwF9K7qkYSP OWknPZmFoEQo9qtH7yV7iKEo5eJ6mMD19ZiI22H00F6snUn7GA/lLlhuV3d/2396+XiBn1/m 6oh9WifoPESM0HtIVx6p9fgIhrEhXvgjmN1RTdBSMyuYm50aSluvC24NlqekL0XiujXtyiJm l61GTz89Jk8nkwrdjWrbebiLdC/5t2GBJlGOaztCDFfHgnazViaSujwVcV3uUjE8Kyc1Wfj8 VWHahnOCM//kc9KfiNE0cacyY5JOD2gVeeKtpZUJRZQtaZ54AyP6gercKKrwa5mHv3Udp80d xFDr0Amd8IufpIG/dCmKsM2JdMG2P+0Up6I7m+lhkquL5c9Jw+Ja9/dqcrCiIuMxf6Hqc01C 5uhTcUhytEGhVYL0KVeHYbSsUSSTTgwYZ6ynCNPK9EG8FFkQCEqK1K0SQDe8JAYKk+Nud6rF qw8kAtEMeI6a96AHGi8mGASnMxSbxs3DQ7BYu70z8HAtb+qGQDrqEbGHtR8uFk4YswFqGsW0 2hndkhhFgyqOpr4SEYvhLSe4oLgjm8gWMr68p58wgcvxrgN9x8hVhGCvYbzLGs/Za5CofNSZ z1XFkBzdhOcUOZc8BgufHy+ffv+JqnhtDd8VyVvG+jq4DPwfi4wbcHNEj02aISaeh2gCFiku 624Zee5QCY2KVeUY8AtEwFGTUyJpgmXQOhvNSJupyLCfMvvEKJuklPbM/rKo0Y7v2VAuV+9Z VqlCTePi6VTuC+0wEMDBVjTpLPzNgBbdqigBtl9quk6ykK/UTJJvEZywUgFRAqmsdfbMvrUT 8Og01qB57ordZkPTS5IS9kjlx+BuJYV420UY9JxybKhHJhe0TOnUpIeSBlawz93xwhJrGE00 E8JN1lqfrUxBPYPxqY+jhAlleRI+g7QxX6bXQpjpwO1PBkOCPki0MuWZPr2v0u36InVOabie 5ggsCgbLT6OuIkcdhZ898N2hlRH1gY2jbbOrGk+kx/Tp5DqkOCjWBfo2Vn0yvc6gT2lYNM4J 2gXyTVOPX/LLrR4qTcAJSbs2QNFHWOwviCIl3RPcMGYDHcy5tFCUu8pTOL3nG2tcuKma+iri JHK3ieYkBwqlpbjbapyF5AkOv5i7OQ4Qx9mCVJiAOE1zqO+S0PZ4WgkG4l0LPRp+CYV2iRhK 2SIN/1u7TXf68XpUl0e5t++jI3e1t5CuqHSvtMlRoZJ4+MeppiOr5Vj5vP5okZO6JKJr90ST bsJ124p9N6bOrOuydjtxg6oYgMcWyZM/GeBnT8aZ1lcEEJ5GEOOrbuXrGSB8ZTzx2PZ5sJCt 6dKDdHD+lifi4sxVfU5oyIP87LI3+lGMQA5Tj2dXgmevEpG2CM2pomSRDfOsXXXy3RViet0M Bc209yMhCm9iftOsXc9rWpvsx/T9EbavDsoBmappumEDTYqublmsAwPuvQQZZR/gTKyVB1Gb MGlV8lyngNKXmbndhNzPgioOo55GtTfcJ6Ep+z1jOLNU8bBazqJQ0gIadvM36r3WRMjCp2BB 4xrvE5UVMttVqAbrZ+1bkNSk3iw3ocyNwZ9J7WjBdCie0+eWdg6fBi9C9J1zs/zyNuqyKPM3 uBYqccIxiPU7u7HnG26WW2kPpFWfgcFgHLXJVhzLoadJwfKRdAqoS/ko7xMaJsUhLaiO6wgC AkycCXBN0Ntzn7oKCFuNvcWbqJ8ytWT3dE9Zz/NOd20GgutEupizyIEFJoUO3miWLaw6KCRf 85GeJHGXZeSKFAAJR9fMvgkhg70HAZWlzFKgdg/TMxHqSD3gofbFAXQnVTcUihaQLI1WnTvc CGmnFp3bKEGCMiDhJDbBchtV/Lkp2QVDD+rkiDcD1mhpmkuqWf6AAbsJwi2HdmUWY/w/YzRC 3m4T3G/F6VTDmmFGRxSH0RpJs/2zvMC0ylEr9hZvo5Pk6fZgYvLbeg8/lL2kpwQ8dHkUoz1d IUDnBl+A2eNMYdEuJyh6j7zRpZRpOHW0DRfLwDNbdOqNbzmS5FpWQLNhiNAl8kYc24GwMUfJ G29wYov7qKrqmidKOtSsbpOIMhhqku+7J3Eq6WtRVprG5IovUddmB1xoX+Yw1zyTVNUkx1Mj a7Qo1ZsUZ65MlEgu6fvCkzNqH8eeaCVpVYkGpCih9oZcTOPV2TgQDIK3W0Vqx4ZcnSMqbXZK vOQd6upYplEKNSG06I05QaFsVicHT8Ehr2FLl7yhGFUFvJvHFA0gE19IU0MDEz3CWxxZsWdI ysijVzPYXpsw9ag6XlngLX1BLfzEdMF50dTpAS/VLcJ6iKTpHTzOPTT7YiqPO1bPoGHroZNi TKctwkTl2GaxbHk18JWNaZ29KZiAm4d2TmmjrTrvN6jCOHWURipW7gVEry3w9C+GzzhUNC3H Cvm+kNeOwCbaBMEcvFlthAruHzhwn7ZJ7A5dGlUZzDW5c9Y/t72oK28zQ7u9JlgEQcSbyNqG U/bSmNvqAAa22dO0Ze957SM77wM3ztiM7DwvUJiM6irjxE8j4XS2WhbC/aL98ezpOR7IYyen FQGLzh0F4B+CRSvpfFB3DfMujTTv+RkNBnTidsiGHOoOsKDCGv8XqqwqpvOAx26nY2+KWMTH CfpSikFeqqpz8ychLK9oAlcDwQ3MibZWVSVLx4EAwoJWVVNxZJ/LhlQ7WImz/po4J86lzTDU GRUCdXakrAvgjGc7mgXSXBkGYWw5HZi5xsW/SJI59HgyweX6K0XyeRAVqUbiVxH1qC4JtbFC WJUclD5pDqybbBOsF27VFixpCBCLAu+GiiIIhB+mnh86r9rNJnho3RYm1LYLHjbSXeBAFsWR UYPO6wZMl1AehiKKKJcatUq3geJ2s12+S8VK4nx7LxoGDgS63j4sZsPaYzaimm4kgGX9sHaH d8BsRcwhuw8Xag4vcEulZqADAnfo3RycR/phsxToa8yobCOmiYOtTztthF40TL9F4g6JyoC/ X98v5XtiQ1GED6GsS0X0LskeubkFL13nsFWcZC8AJEgqXRbhZrPxLaUoDLaL+aJ8r061u5rM q7abcBksOscMYUA/qiwXb74Hgic4MS4XaoeBmKMupdrgNF0Hrew3ijRpdZTVGsaZMk1qvJ5z V+05u18IUyA6gjQkwNVTFNDIwZeMB3nH5+laN4fDT+gPI2qIBhAe5npMAK59AbsBd//ILKks pNPyjVOPZcuhh83avaSwzkRzV97/nOvlDeCNQuQmdGQIo5zHa0HInglZAwRNMhuUNeKIV0CQ uT7sTiwkwkjgGZoRz6KHT9VGaeJWd8NwFdHxjkgl9PWHezJx1hie8Y3xQ5oa5HeibyvRrZNJ wwYyhn8Tauwpqqx16+HBVuE0yhN2IFuIMcuVZaCewBol7y9dWRiHBYkTylqhgQYkFwuVJV0Q CYC5nVP0+LJOizIq+Tw3vV2vfOPes9Rie4wOUxq+va5r5fozMqxljN9srBbdDigFN9CjGI8i gZK8v8aijxKlMSJjUhRMifzUFOOq8O7H01F6ke3y0IQHtpk9KkyMQHv5lKv2Di0pP398fb3b vXx7/vD789cP8zAINmR3Gq4WC7KLUChfxwzDI32PdkBvtk4GUX6fvIV3JpYY1vpTc7bKmGr2 0ZElmVHHRCuHT126YjusgQG/Ll/2IVKdYo9vkEG3IXPAtya2gLj758dnY/H0+vP3Kcc7LxvX N0KXWArDv6elw6YMVrOedsZOrLJPX3/+dffn88sHE0/SCYpbYTK1//l4h1FH5d4dU61asW1e 1ikaKY/t81jxIQWZwqNVO17xvSQxnH3O3FamKxeUBaXJcmze6IsZJGEEWJHjPuIBwQaoERnd BgDOFoSFqnO+r9PmvQvXVZLEe9W6tafwd5FwnbPFXO7vt+Ilq8HCnP+NGczZ2ip6WdHDNLXO tF2PMxo6/pyzh67ambSf0/rqYfOr5z5OwPefP7wu7UMMf/roRPu3sP0eAxTxzBUWgwmKWBId C9YmGcYjiwNqMblq6rTtMWMkxc+480iJVPpC5QlkbRPQf5qpDIOx40+SZ5hDpqM6SWBT+DVY hKvbNNdfH+43bnu/lVcg8baTnMVeJmf/x/EFZrMlH5PrrrThinv4AAGOm+k0CLxar0OJleUk m41YKWK2EqZ53EndeAI5lMaNYYgHGREG90TcGBFxnz2svt+shXLZo+3B/JU9SScY3kxV7iU1 4ptI3a8CKb06JdmsAmnI7IwW683yzTKUzJoYxXIpFobz+WG53oo78EQUyUzjRFDVQSjpM0aK Irk09BpuRGA2ObS10GL3bt0WTkRNeVEXJSnZJppTIc8s3eRVIkyTEjaPlUDf5GHXlKfoyHyy JvQlWy2WC3ECtTi5b78KHJlBIDrMjyQ2rdnsCzQg0ObUOpDsN+REwEfYxkIB1KmM5sKZ4Ltr LIHRwAB+V5WE1NdCVagtvonsdM5vvEaS6FrxwHcTCkXExyFs/AybZMhcMyOgGW5sdjpxp74l KC2KRgukC2YCpGIH9mWEkojcA0/DNnyyt0mbixvbnBeF6bDePshxwyxFdFWV6OJrsDgmbhgz jpknXpHJzLvdIDzrtv1fxq6lO25bSf8VLWcWmRBk87XIgs1HNy2ySZPoh7XhUWzNtc/Ilo+j 3Ov8+0EBIIlHgfIijrq+wrsIFIBC1S3DL1oFh2OGlX20CI3mgskENZVsWU5Hhinn/TNlyk4Z E2O17SsUYLf9K1woiuJCzbv9kKHZHSof2+Cu+FD3SIZAnlSPLityrtlS06oPbBeMn2YYAWgX cKyL8grxSXEXOwsfbVF7lrUQbvekTtsG5IzpY/L5AabnLlzXbBhq3dn5grXZgds6bpfD3/h2 A2bXpPPsDcuzFYWgfG/22LUu2I9tpodjeTqesQ9yla0x9NSwYAsAiqHhvn/Bbn22KbD9bbBW BwrH7cosJn6Lg++8zNU3CSpU92DF9BWBDjTvUOCYndgO/4Bi93v2QzurWjF564R2qWQTkyeT krxrMct72VSYPoXSrTRqJcIpZ18OtFZt7lQ8K+IkTrcwfWLScW1+1aCBbRKIOcdijHAKPLXq IxINPoON2C2vBxzfn33ikQCvHwd9R+Pg5rg7lVOdn5JAVU41pg9JTtsDIR5eQv6B0rGf3+Wi PSFZ3u4JwWiaLGMcYkDQ0oos9dAbSo0JlhX9wb8KH7O2H481ehSs8pUlrV15MPluzMMVJxui KODctzzwHA8IVL7q/K6m4/mN6h+6rqhveE8f2WJS9jhWNzUTqxsuMGM0fogjgoOH8+nBKSfl Pa184sdv9wJ+RqqzdHjd+WwyXcF/i6seguVtcWVbLEISdz5sdxX+ymC17UgINsFpTGVTgXOo Wt28aAxCj3RIY93eonMz0fGtRtWn8lY7+q69j4mPQ2yzx4P44GhZ0Kmi4c2LcJz/PdSHo2MO 5H9fVYNoDQWfP0EQ3qB5juptTKDXgnJrLC3EjMbAdtnk5hplbnHRtX031tQRGUeTCRLECbap t9pbU981rbN28hmjc1WKMfie95ZMCa54O5N4qh0H5Crv0E6oC0ZtZqibMivwFo31aG5WNJgS XJfUmdqKOhZ54zRAg25JFO5cHw7txyj0YmznrrI9lDTyfcdwPQiFGl9fu6beD/V0qULHCjt0 x1Yu5I786/ejeAtmHm/W6Oc+tPXOcKTBSXqMJaCwnZ9BqVR/0DNlkUWV7hfSv67JryrAkuKb FP2gRdLwvbAAQ/ua5jhfCtS/d3emT0+9wvwn/KvH8RXkPq+1cxVBZaOGUIfsug6RIMnX4YJ5 tccTWY9+6wpUKVMPOXBhZmYC7/dozuLMcnRZzwAPkucha0u9C2bKdBrDMFHLWZAGH5cFL9sz 8e6xQ8SFpWrnZVReP2Fjt3oSRm4lxPXW58cfjx9fIWqfeQlKqWIgeVEjOQuHI3D4dBqbbPZT vXDODMod/9WmXahCnva1cEuz2gad6luaTD3VXVYKl5Kc7BQBtgE6CRe3RTbgh4yn7qHD33RN h1G7TxWPsIbuTNH7VAGPcBm/3stCuAeq+6RqCu5i+Uw7eN6OWRqXFxGSYknCKPdGmAcZROfH l8dn+95ONp2H2ci1Zx4CSPzQM6VekllZ/VDysF0bMZzUBBUcq9yjZdhDrYLapaIKlDf1ak5F Wq4E7PH8TgN/qDT+scPQgelXdVtusZQ3Wp6KsnB1TZudPvBwzZsCx1kzfrE5XaC0N5l58Dxn fAR9cGiZU5MVa+2YOQcYC5SrpaV+ktycndDd8MNKyQRx6aTLcUteTy/ffoNMGIULLnfqYnv7 1qV3Gtj3cpnGfW0NO/RuIyIWmdWYoVkE3W1eOBf5IQaHvtorREW+zfLfjZgVrATHuqovWCoB YHW2OPP8hFqkLziJ6hE0c/08wITdiK7RSFSux+9odtAfBer4OTM1AR2D3R//kKwPUWXaZ+di YDPRH4SETJfe4HTNM9Levh/xGunwxmjiLmEkOKi+3VbaKk+Bb2VYjc3U9ObsYEoY203yqKb1 oWaabqc9HpRMMCM+kCDUZWUJEKatD0b+cN++V41tj5c5+qiyXjMa8gFK/19zjynezdoaTjWL Rs2EU2FWEC40DTrEBBEhOjUlfMXAySZq/Mh5xIsxcbhfZepOgcO6+3NBYp8ZboED6DWj+bHo nOXxmOZdpbgrYcqL7XduIU6w7DCN0VjALTbRpWoWcMFVG26dZr09u85Dtd75ZDdBh1CXfrh4 6aI5+6/XVBmlcj2WPU/CNpXGWaKgajqRZISbLpdBrsqj2PUg6Ol86agJCsNfo8wLhZDxQ3fD rrfnLEcaBA+9v7MLmxHzKJR1nemnaMHYZNF8sO7y5gjmlvIszEz8HDH9UedVaDa/q2Y90+lk OBfJqEFjqoJu88OI8JxRWnS1fz+/fvn+/PST1QQK5/EHsRqwKW0v9jksy6YpTwftzkpm67p7 XGFRtpWuofku8DCrjpmjz7M03CkW7jrwE8u1r085HTAZmznggaaRsCh/LWnb3PK+EUYqc5iS rd7US5Ghq0Gtd5QhLrm/roKRPf/r5ceX189f/zJGpjl0+9oYeCD2eYURM7XKRsZLYcueEMLl GIF3+vyOVY7RP0N0nO1g8KLYmoQBFltpQaNAH1hOvAXaVAzktojDCJ+LBZwQgm1++VwC215j sNlKhT3MAKiv69vOrMCJHy5hJwQc5U4tmJSf9X4fa7afT0OzcEaOAvzEWsJphNrJMfBSZ3oZ jCDuWNaJ5J+/Xp++3v0Joa9laNX/+spG7Pmfu6evfz59+vT06e53yfUb07Qh5up/66KVw3Nl /sZZK6sox/pw4kHgdV3XAGfF3snA/Sy6k6sbPsDKtrz45pBsTDn3ZTt/oQq144ZSrlHPs7Xa WuHDfXAzh7Wluk9VoAol0drLlD/ZbP+NaVeM53fx+Tx+evz+qn02aj/UHZiEnM3Jv2hOvlmk DC05NXCo72jY0O07Wp0fHqaOqTN602jWjUyjMhpMa7Z9FYYgvAXd62cxr8nqK4KlV72SnqWV KQadTrS+pOe9XjwiG5wkY6qZYiBs593h/RYWmATfYHGt2uryrKQLHNETUK8FY68GWDmO+g9t bRcHq2OtTLGLuTknP3+BEGxr5x95aINMD2jf20GqwCXgx+eXj/+nzOxrAtpPJEwS8NmvuxQX cvzt8c/npzvxcP4ObIFPJb12A39MzXXXkWYtBJG+e31hyZ7umNgwUf/EA7Mz+ecF//U/qqNT uz5zi+QyrByRirDCMzAdhu6s2tsxuuatQeGHRbs6s2T6qSPkxP7CixCAou+BeCC6wdp7sl6G U1ELb/PeD0YPe+84s0Akk0bTshbkRkIPWxkWBtpWNyxll5dNhz53kgxszI+n7KAeqC1Vzgq2 gV7pMPFqzhMkgccUBk/+MuxwSJYoS11lrCZzknp4b7oAFH1tzu7riTfoojxiFnbWDeAcgXp2 SiECL399/P6dLXw8X0Rf4SnjnTu4C2cQZwnaAT1XjZ2uQDlcXLPe6C7j7JKTKgr/84hnsC5y aS1PAh7srp2OzbUwSO0+icb4ZqRuy9MD8WODd8zaLCx8cJu0PxspWN/n6t6LE8XiZxDB3Ucl b8tnTdk9GIv+wqlPP7+zOUZbYkSei1m8PgSS7jjylCyn3uy762QoCqJTwKzbYVOwMvi43Ym4 FILNSbDJUCUheuHJYdrXuZ8QT72uQfpGCHhV/EKf+aZYZUP90J2M73raF2kYk/Z6sXrYafKz oqGdqDnht2McfZedHiZKsd0Wx6UeZ2ba9EG6w273JZrEwc0aUyCHUbg1pgUeAGUZ8ThSb485 echDGiaBVUV+E+4eXLjtTiKrjhzwCR4Fe+VI0N3BiqfWFILY1c/0yNttCLqwynCVBmholsWI aarFvEUEVO4n623B3dNE9b4qP5x64j6KSWQ1B15/CtDHrDLEiBV54EtH5bMaYtdDvHoa93b9 llQIakpU5wgfcVUu568ETkfnxYr89p8vUltuH9kGTe0Sxtlm4KKOP2fplNl2RYrR36n+EnRE D+msYuSKv5teeewFWfYDUmW1KePz47+f9FYIdR6cirRaGwR9NC41FwBa4GGnCTpHguQpAHjl XEBMD61/Vg6iPfLRE2NHVBqHH+C5Jl7oqFBAXEDgbH8QTDl6x6BzJXhd4sRzAcRR+9LbuTol KUm8JRFy5Bd1kntrzy6qCs5JhhdFhWgojSYCf1LtvkblaGjup6Em8Sos02IqscIllR20AIGt 5/zqdZCAhpLHdwXvQs5yxnPfNx/sWgq6011xD37JgNHev2RFPu0zSiF68ypgfCKfREQbZawF ec5pHWc+sws6bpbGus+GJQh7UXANB4qHF2mHb7JiXOgi7AGkyqCKq0YnDrpywTbTm/LAtO1L YKcY99oVxVxpRkZbPAeRM3Aj0/17X4agtZosIeeDIJPvWGCq7NJero5hDWAI2yZuFmGzGAxg dRszzcDuNYn4DsQnyoI916gee0ijCtgMsURJ6gVobWceqb1sCBood2wTg3SG45RwLZ4PKlaz hgZRiJ0pL/3MjSy4p44b2UVqrEWldYbWqCNpgNWZ90mKW2jPPExIdiTENDONI0WKBsAPY6zN AMXoib3CESZYrmO7D3ZaprNcHLLzoRTT8Q53ljRzDjT0AkyxnwsZaLoLQ7vwcz4Sz1O+fiPW CP85XerCJMnTQ3EEIWxPHl/BJQNiKwVmliM8VtkRxTpcoyf6zfuMtPBIBb3AVzmUdumApujq EP70WOMJ3ig5ZdoNXm3KWoQG7NU4iDNxhFtUKhyxh7UZAKwzxjyOfIIBYESFDAm99cQmF2Pk I+UyJRhyR7paTLWwrm72ttgCb7LU4f2UtfizvpmnignTGauNrgOOxK8OdhuqOAzicLTbLC3i +VMqZLgqynT8M81c0StnvkMTksThYknh8b23eNja77IOWzi2pEccmOmuzmbsWB8jgi4XyzDs 20zfZChIX6LRi2cGOFzTJ5cFoklsD8m7fOfbvExzGojvox8ejwyMB7SYOZTDWjs5n2ixOVzj SJFPAG7jSYh8YQD4BPkmOeD7DmAXYn3MIVT30zmQesBKG3lRiLWbY2R7QuQ8EXb4rnKkyDAy eiSmBwwIUgew87Ee4JBDRdN4dCXA5ghInKIy1OZ9sL3m0DwKkWWsaaMAHbQ2xpU0hWFT5toY 6VVGTTBqggkn25Kg1BBrRRLjrUi3e50xbE07DA7Qb65lO80AO3PSOHaI/AgAaUOfJ3EQIR0B wE49sp+BE83FqUo90m6wE55yyoQfHV6A4s0BZBxsP4ZMZQCk+inBAvXcMfdmj/OD8BST1F73 brEkwMmg6/iY2sCm9Cmvqn5E5/shCP3N74RxJF6ENq8emyhh6+rmsPtsBxyh86OfxoljfgQI bJTOTcZGcltiaR4kZGvk5KSJNoFhvheHuFquTzTJG2UEu526X1SQJEqQr5z2447tIxGJYkgY RDEyp57zIjXCaamQj7r2nTkemoh4yPc0Him2tDEyNt8zcvAT5c4xbmkBZCuhbUniAPmIS6aq sd2uXQIDfKI+DFOA6Op7qOoKTot3cbsl4DNLiqziAtsHKVLRMT+GEbcbb1F1iOM+Og1zKMCO VBcOSscY00TGtmVLJ/b558RPisS1BWP7w82PhHHEiY+IacZ6N8H3BfUp8710a+5gDLebI2lg zDsWD83jrRWFHts8RD442vbEQ8aS09HJnyNbShFj2OHyBcgbzbjUWZRE29r+hRKfvJENBc/O G5W8JkEcBwe7QwBISGH3CAApQTaOHPBdKdA+5MiWgDGGhs2hFFm6BBSd8Lqzb+hYuZAShean 1AhdlRiuK2SNRWBfX0brUfc/NWNlWw6H8gQv6uTB91SUTfZhasc1qPPMbOyUZnJX2bTrUHOP JBBXpEfKLcoqOzd0OnQXiLPQT9d61J0cIIxVVg/i2RR+iI0kgWebwpvNLyeRNxsN25aZy7WR Sq+T3UizcQgMMWv4Pzi8Vh/rm1+rLTcAs6WjKC/VUL53iw0EH+VvO21IBixdKvS+G+olJ+yq VrnLmMtb7zLkywvlGkNSDLPUhXzqrtmHTnWkuUDiEcrEL2qEA/0C4eIGR/MR4fXx9ePnTy// uut/PL1++fr08vfr3eHl308/vr3oRkVL8n4owUKO1QCGwLKtWzK0HE2u80xX0a0nJ/KMSuma 1aCKn0u9lTgK1sSmXQKWdL0XKU8PXpRuM8l7po06yBsnpQ4SkI+vsJY91PUAl5Eb2XJ87JF8 pdEW3mXXzTxPIY1IguQpbyoQBLbzwe2GIEw0zmglRgouCclWTbKmbmOm2YB7jVVq6yjwvHLc c6rmFxLMaYCKZAUPxDKfyDSzfcZvfz7+9fRpFdDc9HIM7/bzrWEtqPDdPltUvJkj49nMcQS/ Vt041nvtIZ/quI6z5DX4jldZ155dcVznYLh4NeW6P9rnbYbUAsjKFSQwiUrkNVoRjcNVDMfH LteLmSsoelfPcayabMTeN6gJeXC4vD1ZqX+h5XNshvUt0f/+/e0jGPo6Y3+1VWH5rwJaltMk 3YUOx4bAMAaxQzecYR87s4GPxzZ940ky6iexZywWHOFewqqmvEEwiK9GSRw8Njnq2A84uDdJ T30sy6mYSR3P8Nb7nnUfrDAslrdaMkF1+5WEjh53cWM+9jTx5A3ccVTG+xVWjAC1RJ7R0Ne7 Vi5Q2qO2hR6aXcNXJNR99wwGVjYkNMZZPplQuy4nehhXhWi+8QPoWEdse8MbhfYF2/RPfTbW OXZpCSDL07BgbHpGRR8fATLmR71y3EQyb7tCNYQBwLaNBGqS9C0e4GhFrc7m5MjDz+mExN3I LozxG2nJEMcR6tB6hfVAVys9wQ4CVjgNjOECarILzFYIS4LNOiapj23RFjSN0UxTbG/MURoF aWy1qjxVPtm37s/zUvflwF98ODIGncCsSp9XIRN7TNJ4EmnXqAmJuFA3aLbJKiffJ+gZAMeE wqOPw1jm87tfLaOx3sXRzRkBHDja0CNGZkAyzL04/f5DwkTPN7lHzYlVtr+FnrdZJPdMJRcs 9uPLxx8vT89PH19/vHz78vGvO2GvW8/e7VENHFjsWXd+Qv/reWr1MuzLgKZ5WssKY74Uxs/m 6IEBDBpES2bYtLY8ZU2LBlsBG2LihZoNkzBZJg43e27vXbz42dzZqICgO1aZhcEn2Pn63KzZ 1tsmh+opoZJbglCT6IZQU+KhVU6Jv7n4MiY2BaNnVfP+AFOEZsyKlbKmBVttNO21IX4cbH0A TRuEuhN7XmYehEnqnvjp+/bmnJ4vt0S1xeGlLBfTulolnxpgRGzZ5eqH7/DKDc1tQ+K5tAMA 7ZHjVukuSeJggiTZOZdS84xtpdkqjnnuttJQXrCe12ds7puuiIlmDS+mNb5FNIniEZgkzTvg Za5WH6271PZ1cyrvoNTOWYhO69SVQ8SovXQNzQ6luumdGcCdxVl4MRnPreqnYuWBQy1+prVy /WNzMTXmAF8ykgFsMhLdbEABizBI8TcXCtOJ/Q+z5VdYxLYCK1/ZGCB5y6HdzFzdRNjDYOjb BhJiVZJKNI74+gdkYNjMpox4dgqDUFfpV9Sxq1wZ6rFJAy/EGgP3rX5MMgxj81sUOPp3mZU2 C4aFNSZYh3AE7V5ud+oqla9NbxTJ1im0qfMKhtVGTNp49wIYxbinhJUL9PkQndc1HsN0VcOS aJdiFedQ5LmgVFXlDChGpXFWzHFI7A+wwuSmTvdMo+NxgpfIoCT18VFt856wXsEftSlsbGfw xncCLD5eAWtbsWJ9dX4oCbosKUyXJPEixyfMweQXMkjRsX8Pbpv5Y3YERPYWCih2EW/0nNMG eGUZ/bbPPPRTBWgkOBS2SRw5unXeZ2yXK1dbPAeWgRdh7wdXHjBsIGzUseopWjeK+QH+VQkt GhekWTF3Y0nkzJME6JRnq9EGpinTBqa9TFSw5XmirR5wNwBI/W1/0TqGPm/QWDQ9K593sxrl 1NG6qjV1IzenlHyCQGmq4856wDcHA/iZyLuCKTNuHJyUOR6gQKBP/i7IcD3Fd6mHH4/fP8O2 0/LidDlk4MVpbYMkcEdgh/48/kEWH2CF6rSF/QBX6PVU6M7RgF70bKNym/1MYXdKwxzXsm2t xJw+lk0Fz4gcie/bUXpL0msE9Gq/QlrO1R681S0XkWg/Ah+4VJ1YfxZoPFqzpUwUHJWk1Oiv y5C1aLUPZTvx03xHk1zYxShgzI/8hnJ5Lv/07ePLp6cfdy8/7j4/PX9nf4FDH+XkHVIJn2Cx 50V6bsKdTUOinU0/3fqJMoUvTZQJxAJD6426q0K8xtnQ2n6DeR90TL41F1Uqq8o5ZEWpnpn8 P2XPttw4ruOvuM7D1szDbNtWfMluzYNMyTbbukWUfOkXVSZxZ1yTxH2cpM703y9A6kJSoDP7 0OkEAG8gBQIkCHQwaShkhcVBPw5UZCZjWhW0IvPZanjGN1Rt1QoDQcp13N0L+ywb/OJ/PJ7O A3bOLmfo+Nv58iv88fr99PRxuUdLSz9SquursCB1qPTPKpQ1Bqe3H8/3Pwfh69Pp9fh5k/Zb hrrFq9XobEjSchv6BldrUBNwmRV7SlhZxOomaUKCGyeL371+I41oobJvaP2TDx6jOueBwYTt KqTfKEgkfJGudVFwvHNbGe9OEVEGkbVSRK/ReOWvxo7AEohnPM9LUd2FznHlzM/xenod6BpY i4m2gfGoERF3e8rDAjELMIyEWUsdsBM/GAOe+YlMq24stuz+9fhsfceSELYXqAr2OeCWfj/a EciOWtxRGMHjjIzD25EsQ35AN5jlYTgbjm8CPp763jCg2uEYsneD/93O5yNGDKriSZJGGPpv OLv9xnyqlq8BB9MKGovD4cRyQO2oNjxZBVxk6A21CYa3s4BMDtEVSCMeh/sqYgH+mpR7nqRU 82nORShT76UFXu/ckp1MRYD/RsNRMZ7MZ9XE053NOjr46YsUA6Zut/vRcDn0bhLXkHJfZIsw zw+gBWjJEJwLuCl1CHgJSzKezq8t95o6ZRs5uK/r4WQGXbklzRu9QLJIq3wB8xF4Q3u516tI pb6sxDQYTYPr9XW0obf2xxTPNJKp93W4H3qfNRvOff+TZkO+Sasbb7ddjlZko6BcZVV0BzOa j8Ret3h6RGJ44xWjKHQQ8QKYxkEgF7PZcEQ1VuRldKiSwptMbmfV7m6/8vWN3frkDSmS82BF fuQtxpAa3Y3I4nJ6fDJzekvBKdMKQm/9ZD+jD8aksAwSQaqmZbyQOm7g03q4VABB/lSYO9WV yE+Ka9zJ1jxDf+Qg2+N5wiqsFvPJcOtVy52zHCpHWZF4N+QrJ8Uf1FiqTMyn+uspqeBxnC8+ Nx4pKgS/HY73feDYs7S3Ys0TDLDBph6ME9Mo26u1SMWaL3x1ATCbusSURTbrVQNSYZnRr0Rr vEimE5gkM9pNo0X6wXY2Ic9J5ERS21wNRJ1cVxbdi0svHBaJv+Vbk1s1UPNrNFdjzrJV6Zzr NRccfljXvfoi2ltSGADLRZ+TySHIqRjLcrVKlcqa5GC5t5Tg0XhuNbXy7e3dt1sW/pZ+bWjs P2FSSIuquit5vmlV3uXl/uU4+OPj+3dQ8wNbrwczjcWYMVqTDgCTVvVBB2m/1+aYNM6MUoF+ Ewp/S4/NbShak9jAMvi35FGUh6yPYGl2gDb8HoJjQsxFxM0i4iDouhBB1oUIuq5lmod8lYDo AWPe8L2SQyrWNYZcb0gC//UpOjy0V0RhV701ilR3rUamhkvY3cOg0i/ekRjkJ8b4e9EZrqnS HRSDm9SGq1k1an44/EIlaekvlz+b2KA9XzGcDakJ62sVgFlMHdAh9QGUlPFwOLQKtHBcPXRR H+QtsMocFI9FYUJKXGnWdLXJt+maxShQ3kbG2pehggmQeRXYgRtXux6Cno6cb32LCQhyOJk1 2H4jEkw3wWf6Oy9cAk04Kr1VBaxiTLObgDJIt95QYZbVuzK0GFxjaZfiDu8emzopeOmBesyu waS12KFd1524jIqDkr56MQXsanWNA+hcKEEdhSNcSm2L5wrodE7oKHzGyBD8SMGFNQrMGueR KnmDHE3MBRqmIPu4yeHNITdFjGfsXzVA9czqgERcGdQ2TYM0pV1EEV2ALuVgYwEaamh9/H6+ 6ckdR3EwuWN7d6thsH36oFtsTYXCQLJSFGTWBuSr6SMlIYKVFsvwzMHsKr52Xe2Lm4lrwur7 dvtrDdFASGNKD0D0AlhoSbIaJn2bV2aGXg17bS32DH0DC5asN6Q8NiQvVI7MVv0j9RC55Szu H/56Pj39+T74rwEY2nZKM830QDOcRb4Q9Sk80XT7JRuEHVs6fBeEtodqXXF6mPYev+1Th8vi +e3NqNpFIR1guKPsx2wkiGrH6KtjBJr5XH+BbqFmxnarIZUjxCc9kDf2dLgGbdSYfYSMytnR aD6CRA0uN/auH1vgwyzKqGEuguloOKMmEayCPUsSqlDtcKMvz08WYVMHKF349E5PeQfiKaVV LGka1XoVO7++nZ9Bk6qtIKVR0VdBrJ+9CMDwm3pjJFieRhGOhjIJyjg+9LMoGWBMtlnGifh9 PqTxebrDvDPt5wyiEPbVJSii/ZoJZJPcLMtBw86NyHkUdZ4Wvaugq5XXCnHhb8J0W98wNdlb rrNZEyWpHWu+rqF3S9eVEWmZGJ+2ivfOg/40rrnh+A1/dkHtijxMVgXlXw5kmDmz5W1JVFOL rV43xI/jA6aIwu701HUs6N/gEZ62OhHGWNnLl6wQeUkLKInNXNtCi+WUK4/ECj02hISUYJxF dg8WYbTh1JpQyCLNquXSrAgsrwUm/dYTOgGYrfF41K6frTn8RSUdktg0Fz7Pe4XSknZSQmTs Mz+KDmafmLwTtjqUjUd6AmkJO2Rgpgh7tmE9rNIED5cdjYZ412rxAfOC2xWFUWjln7LQlAiW mG+b8GD2dRXGC573V+aSPB1B1DqNMLOkzk0Jga47u7QqpnPPxWvok1q3xsA3h9DsacnwmJKZ VDs/gtVjT+2Whzt55u5ocXXIrZezCOX4RtFmhJUAW8N89Re5b9ZQ7Hiy9q1qN2GCOQYKu7mI qTigVt8tjcPAJOk2tTuITEFh4FzIwLM4LfUHzgoeoQ5qtx77h95rNoMAhLVcxq7mOGxouLNZ raWYBy482J3HTMFcTr6zwaSg7owVJucrs500t5cmR9f7BA8bo5R8ACIpmkzvVveysPCjQ+IW nhlmAGS0iijx8PXKuwBGO5lIGtxZqVN3xW4o3l+VecqYTxu4iAZxB3xwVFlfmpgfF3wtmrDD v3qSSIb9izCRrtUZUYS+WxoBNowwtyF5cCMpyiSL7I0kj3lPJuFdmC84pZ/KekBBKb6mh7qy bqvX4JaQ0r9dvk1NnoBgEWHYE4143L9yj7dY52BoqjDejqYwf+auyoRnDrkcL7+Fee8D3/l0 skGJ4zxOC+vT3nNYzSYI6zV53EAqM5SyJD4EoDI43Hkko2WEi2pd0qEVpR4QZXRyH0q1acO/ k+oXIGoVzPjwjHmpaSzfKCN4vF53l3+NalAmjqvr15Mr6bRtUm+9Vq0z6RrsdMfpMOJ7h+gI tGP7IAwsn3W19kW1ZoGBsYdPP2mWVSQJCDcWVkm4a1zeGlMmPr09HJ+f71+P5483yafzD/Q+ sd+ItxE40Crijofcku6Q+PgoNeZJ6nC8k/wpVtVuDVIqulYZ7IUCD41WMuqsWDi8W+QgQfcF bRQkeaAipPw+1tGKr0blO8nyhW8IhG4pYtI91iXdC2w1XJaezvbDoZyaFx2+x9mnocFixfzM nGOJ0PLNGN0M67oc40735Xg0XGf95jAM9Gi67yOWwHwo00ekTbd/UlDz0b+B6WegQ3Q58sb9 RkQ0H42ugKHfqVlPPvenU7wm73UNyc0gAA1U9D8QBMs46naA+HbO63gg7Pn+jci5JlcZi3vf XS5z3DqmZxfEZueKmDUBHxKQ3P8zkIMv0hxvBR+PP0CuvA3OrwPBBB/88fE+WEQbmRNYBIOX +59NYrL757fz4I/j4PV4fDw+/u8AM4DpNa2Pzz8G38+Xwcv5chycXr+f7Q+6oaQYwV/un06v T5rroFE0Dtjc4Wci0agDWvqHTsAzdwI5WV7OVkAmXpAiZsc8+ytBWFVGZC64Fo+hHRrBlz3f vwN/Xgar54/msehA9I8p28Lp0u0rVRONzdWJEKPN1f3j0/H9S/Bx//zbBU82Xs6Px8Hl+O+P 0+WopK8iabYUzO0Gk3yUyeAezaUoa7duklr4Fh88mwGbWlyR+2wDkyREiHrf0i2kuyZQ7PM0 IA2qRtbM9LNLDdj/bFsEhtrI1QlUu/bkuMmPTyXItvbRNim3fcqmYYljFors6vQ2Kcd5znwj CImOzDceiFxLsCmcOgJxdJCtPUesdo1Ibpfr0HftgE3+cL7i6l4nlBoG1Rk8tBju7W+oQarT iyqm345olGGchfRtoUa0LAJM902nl9PotiD3XVK0JuGZf+foNHlEpXc1WLnZ0SDBBiDndTkf jb2xY+4AOSEfnukLUN5DkXXzbEf2iZelY6ib8CDApMWcJNdbrQnJ6jeR4DQiXaBHFCvI3sas ADPFGzu6Ju+1rncqTsVsZkbitrDzG+qeRCfal7XuTFWR+NvY4dChUWXR2BtS14waTVrw6Xwy d7Rzx3zHqapOVPoRavXXGxIZy+b7Cclx4S9pYYOIKvPB5gloPA9zMDN5DoJACAe/xSFepHSK TY2KPIExJMYizL8amZ407B5EZhqTi22369lFNeuz+niO6k4aJzwJPxGDWAMzz7f0PqEJW8Vu y6PpIBfrRZp8sikIUY6GriV9V1BeNBpBmQWz+XI484Ykj9QW/9JtkKbJRiTXlFZDzMmAPjVu PDXZ7gdlUe77/N6KKwIeNAL63luZbqu0sM83JcJxXCbtqXrrYYcZm9IB0BWZdB506SKBOu40 bR7cheyzdDlyvOCo3cjddqjL+gJdCgzrLV/k9qt92ZN05+fAJdrlVZanH2Qpuw+zXkmDZcn3 RZn3tBsu8ALU4RiLBAco5NqZwm+SLfueKF+XC/x/PBntF46yawEGPvziTfQAyTrmZipDlOss 5MmmAh5jgiEYk4lkaz8VxtWEnJrClhp4AtlccenF93jFZQ+kDP1VBOqSa53s4Ydqov20sj9/ vp0e7p8H0f1PKmO4tPfWxil2kmaqNhZy6mEe4mRUx+3CdLAr/PU2RfQVldobjvR77StdNIe+ 8kGjoRZWccjMVOoSUBUsow80Fbpk5Lutuqx8aSofrbV8LH7+OP7GVMyLH8/Hv4+XL8FR+2sg /nN6f/izf/SmqsQU0hn3cIUNJ97YZsH/t3a7W/6zzBH/fhzEaH8RElR1Ax9dRoV9WkB1xVGj cYiB191ixwt5z2WfcslDLsdCQKu2WphH2uWOWjZxrL24yXa5CO9A2BNAZeno6wCoqoWdibzG CUzxWfp5YRewpZc6UYzZFxF8wUJXDtGMenqOfgZWBGvyaBNxqF3lvW7xZYzHJXQRtpiNhnaR LT71DWLSq1ziS1iK2v6MsFKsmQ0J1nwKM21R4pU4XpDCbJoIdgdDM0HNG4BMIoxOxgU1O3EY Y+xo49argfU5q+XoFu+nh7+IyJFN2TKRKiZsyGWsB24UWZ6qtWI0Kfrrp9fYP1kPTfNyFmPX 2+ya6Ks8a0oqb06Gw2rI8okebr8DU/OCh+R47tyNWJ5CSw80ClbJ+1ILs8hxa05Ql1nvcHtL Vt0zYrzh7bFdFvP9YjS+NR6HKXjiDceTW8reU3jhTW8mvt0JFk+9sRGhv4NPqPBlEi0d5oZW XRI47nVMOdddqQlT8tjtI/h2TE1Yix6asQYkXEXwcJVSKcC1WdahTag7s0Jn0nvVDYz8RgfF avFkBIsaO5kQ2RpanJnioAO7eQnYqT28KJtPhlRN6Ejo7rrkyoQ2XluCqSOfuiSoY2+h+1xJ Xeu2RHpEGwm0gydJoB73ylipwXg+7A268CZmVgD1DfRdLK31o2LQuHpbMB+jd1g9KCI2uR3p MUxVXb1wPRpYD6HZgO1ol+3XM/nb1aFNEYynt/boufBGy8gb3fY/kBo1Np/wWSJHXgr88Xx6 /euX0a9Sh8lXi0HtdPKBibWpK9rBL93N96+W0Fqgah9bfLMjLSo+RHuY6Z48wDACZI+Ly+np qS8l6wtB0WNAc1OIwd+p80CDCKx6sU4Le75rbFwEDsw6BE1oAXaFs3nyAQJFyMw4CQbOZwXf csdLBYPS4XJr0DS3t/JARPL39OMdLxXeBu+Kyd30J8f37ydUaAcPMh7B4Beci/f7y9Px3Z77 luNgBwtuuPabI/XjMLf3pwaZ+Ynu1mXgkrAIwq2zIHphJtYX0nKwNKL44pE4hgTHJ+qGBeeP RgfYsH18OtNcJBD85PAzAc0sCfQF3EHlMsZo0eSM2XSqN5+R+kFQ8/Zqh2C1rpl2CWpj2msi qg22Xy3IKLfR/sYcdIeYfM6NhHT30QhSlgcx3emtepOYbWsKqv5Fsses4tfbuAsDPcIh1Fbl +9BRI89STllVecEqfJz3UwdY6iCC1gx09wMNbJ4u/Ovy/jD8V9c8kgC6SNeU5YFY64YPQck2 luk05XcMgMGpeYVraNJIypNiqbJpkEutJQGV3tUBiTe+QR1alTyUAYD01SX7nW9p+xBdabDT hNndlPMXi8m3UNAHgB1RmH6j0kF1BPv5UA/GVcMDgS9dqP4qTMVAjpU55U2sE86MV94mxpH0 QSOazsb9nq0P8Xyih1pvEJjO4tY8YdZQjlCrDUUuJsyjmuMiGo2Hc4oTCkUG+rdIpv169wCf 9AchUw+OPao5iRpOyTh0Ook39SgeSNznpedk2/HNqJjTamNDsrjzxpTp3a7zXkC1BtFEaCM6 3URouzZzRHy/GiXA7rp1JPVtaJaxRyfmbeuHD8QKPdphJnMyoKJWdExMchiDpTrrr4l8C/B5 nz7H0IfEiheTuF+JCODznDcajMi4JUh0+YThDhJ0Wm1f7SD9/evjPxFAgQBL89rih/U0HjnH ecvGJFMlTmXp6onF1h/FOaRacozN0N4aZuLI26GTTK59JSiX5pNq6cc8Ojik29SRx8IguSaW gWA2nk8cY5jdfF7/bE4mxzRqGfeXVCDGN/qtRAtXFikNp/spg/le6YIoNqNZ4ZOiNb6ZF3Qw WI3Am5CyHjCTa7yNRTwdU2Nf3N0YhnS7JLMJG46otnCtXpMddkRuHT4hROG3Q3IXZ823e379 DYyfzz7DOmfUlV4sC/jNIcPqmLRXV5NItm7NSFYig8NeE4QzdUnTPh8Tx9c3MLEdQwswnUwv 6KQK2BP7i3Kp+d/WRcQhYRgQw8yptJNw+uqvrom6tLAa0aygck/cgjY18tS4XeNpxTjlSI+Y DNmyChOe39mFAlBca5SjsG9eTyEIzCWWkg/zZWv4Hrp+/fuiI8By3NtVZXkpKLMEcfFyOtak Az7crlRuJk2lV/Ha7L8xB0nZA6pUUT1YL7pMjVpgjkA9fEIN50mmZ9JrWoypbsQ4MSoETN/R ++Fyfjt/fx+sf/44Xn7bDp4+jm/vxsvsJlT9J6TaKiz8FU+oiysWbdA6gBFtSs3beY2PdQGH Kfoy34j/Kk9KEfd7+8r25eX8OmDP54e/1Ivz/5wvf3XfRVeCiE6L0LUIKK1NKwfK8y1sOB3P NZzgE29iiEYTOaLPhk2iGyrUk0kyM4SXhmMBC2dDOri4RUYn2tGJhAzLwjKK4f0wzgisE2CQ BYjEExp2yyaOIblD72tEKoeBncJScWsJ3yMp1xyLpSu/3oH+l5A3VKqQOH9cqExq8jDRzKEq IWAyL7T1m3PBtgr1u/6Gmaq7PUnxebRItU208aOv4rVxMojXVLlfxUBMndSoaiy/Cg4cLbV3 JsoD+fh6vJweBhI5yO6fjvIU0HB8bt5Of0KqnaHIlurorD3u5seX8/vxx+X8QKi1Ib5ewsMH nWdECVXTj5e3J6KSLBZ6gkf8U0ZKtmEylvpKOqIkfgEr6QoBAAytSOKVmCXXn9m3duvGR+bo gtfaIOeP18fd6XLUoncpRMoGv4ifb+/Hl0EK6/jP049fB294EP8d5qC7J1WRb1+ez08AFmdT x2ji2BJoVQ4qPD46i/WxKqTH5Xz/+HB+cZUj8eqBwz77srwcj28P97Bw7s4Xfueq5DNSdWb9 3/HeVUEPpw6b9tnN33/3yjQLF7D7fXUXr2gPrRqfZLTMISqXtd993D8DP5wMI/HdkmHKDVmW 2J+eT6/O/te5WresJPtHFW6f4f2j9dbKpbhJR9x0rP7TSMTbqLh14mKZXVk6mlRpEoQxnhXr wf40sizMUez5iSMNtEGLToXC35Ix9DS6NotN95Ub1fhCoAiwxtN75tUNvQq3xgVHuC/+j7In W25bV/L9foUrTzNVyY1E7bcqDxRJSYy4maBk2S8sxVZi1fE2kjzn5H79dANcGkDT505Vqhx1 NxYCDaAb6MWTT1yyguCvyz0cQN0phRW5TLH83Qj3YdJ0vOhUWBBaBgOaZaaF14+PZoV1Er8P KzWfKCtMViSjPhvevyLIC0wm4poDgzGVRj2HqbE2ieMtNtKcyMYhlXJDlJJl/BAOVnpzFoz2 F1YaI8SvF+FCUung6vkIRBCuLfXfhWDLWKSyVYHs3ZA4lETcWDGNKnBbo9q57+8PT4fT6/Ph onGn6++iwZDcw1UAPeKaBNIb4ApQJQOrgPPY7U9p+CFQoEe9KkQYC9XL+65Di/vuoE9D38Zu 7vf0sKQSxN1nSIyu1csRzVNRBl7VuNKjuAcgHMKiohq4u5AE3dZweElo4Nc74c+Mn2bOtPXO +77u9/r8+0TsDZxBR5a92J0MR6OuFLSA1XL5AGCqJ2OM0W6jb+ekU3C+TsDo+Qt33rDXYxNz 7ryxQxMkiWINepWjA+aunoPAYE3Fri97EEHQe+3h+Ot42T/hUzJsiCbzTpxZn7LpZNwbUy7F 32W4wFRkGMI3igIttAwQzGasNuH1Qd3p42ZLGRJz1sLOoyV5xKy1Ol2QbIMozerIRymRI1e7 SV83tyk8ZzjhRl5ipmQwJcBIX+ru+oMOO3dUTcdsrN7YywZDR7NnS8q7/nRafUZrmuxuJlM2 cZ8UdLd4FFUGSqRUk3GoDF02imdLsNXGrYUDmBxPwpeHXpz6lY1Pi5E5PrVKClm6p8WOlzAB C46MZpu109WD4dV5HWO+81KrHdRc0N61LMb9XjN+NWd/xMWUzxen15fLVfDyQJgbN5o8EJ4b BUydpEQl0b89geRlCfINVEkSj4dnafut7hjpUioiF86ZVRWagc7nPA7GHe9bniemLIuF7rWe dgfUn0lPj8KOLYU5xigWy4x9ZhKZoD4t27tpZbRU68bm56g71ONDfYcKY1gp9nRUeAI67rGo xkFU56BSsURWl7MrtZHGiUErNE+TGleNWRV+XbEMcM9ezTm/BY5646G+p40GHdMFqOGQezkA xGjmoOERjfIjoYNcA4xpBir8PRtXQ9SeylladMdyF8Ohw91pxWNnQDNYwfY16uv5nQEydToy y3vZcMLeYcE6h76MRhNyTqjFCmDKTB+Ot9IFgVke3p+f60BymiaIE6l0FRk9j9f3zAqqIMuH /3k/vNz/vhK/Xy6Ph/Px32gw5/viaxZFtcqu7oHkncr+8nr66h/Pl9Pxx3uTMEa7L+qgUw+F j/vz4UsEZKCsR6+vb1f/Be3899XPph9n0g9a9/+3ZBvt88Mv1Nj91+/T6/n+9e0AQ2fsUfN4 2dfCW8rf+oJa7FzhwNnNw3TaONsMetQEswKY4lq1SJe3eaqkPV7XL5YDK3mGwVv2x6kd67B/ ujySTbmGni5XuXIKeTleXnV10F0EwyGbsAT1u57hT1jBHLZ7bEsESTunuvb+fHw4Xn7bc+TG jhZe2F8V9F545aNgRS4sV4VwHEogf5szsCo2DnfMiBAOFSopwG9HEy6tnqqVDKvjghaqz4f9 +f10eD7AYfoOX66N8DwOK/5iml7Hu7EmyYXJFhloXDFQt1oRiXjsi52hsTXwZjutN43uvio7 Vxmn1J4I/7tfCk2DciPYYXuaPZyb+WLGB4uWqJm23lb9yUjTqRDSJRrEA6c/5XdrxLHWEYAY OMSYw0NLfvq6Ar/HI/JJy8xxM5hyt9cjcR6bo1ZEzqxHcx/qGGpPIiF9R3s2/y7cvtPvyB2a 5b0Rz5RVG2bc4KjITVv7LSzJocc9KMJ6hdWteQwpCNEt06yAqSOjkUF3nZ6EUTkr7PcHvI6A qCFvLgGa2mDQkUAemHWzDQV/3npiMOyTBCcSMHFMyQ9HqIAxH3WoLxI37cZNJhwDAWY4GpAh 2YhRf+oQ49Otl0RDIyWUgg24z9kGMcj7NEv0NhrjLQcpfgcTAePeZ/dWfXmql9T9r5fDRem7 zMJdT2cT8o4sf9MLmnVvNtOVyOpKJXaXSXeme3cJmwGbHj32BiNnaF2WYKAjrNE68oxZXMXe aDoc2MuvQpibeY3OY+Ava69sH5G5UVLj13pqGvpSvNlRXUkjrDb++6fjizX0ZLNl8JKgdiW4 +nJ1vuxfHkBCfDmYEuAqV0971eUbLyfIJ/kiyPNNVvwtZYFuAFGaZhwlnSw00SWXfs0X8f3W RK631wucKcf2bpAqDA67zkCbnvbIlKP8jRstlTgABEuRX8BZ1CmMdPSK7TF80YU6d8TZrF9n FemoThVRgvDpcMYTlVmD86w37sXEbHoeZ860Z/7Wb0lXma7ggjbQ73de1mURrEeyrmMxGtPT Wv3WW0DYYGJJDipYsKlUqkgIxuIrRkM2askqc3pjIhvfZS4c0WML0NRXqwTmGLYyyQsGAGPW mI2sZuP1r+MzimpoY/lwRG69P2gV0KN61GMvzUIfsyqGRVBu6QXXvG/EnMkX/mQyZO+2RL7o kfNL7KAtbXNEAj600TYaDaIek0WhGa0Pv7F67j6/PqGfVdflJ3nm/pBSbVuH5zfUBFk2j6Pd rDeWh3XLtBLGSmdFnPX0C3gJ4YzbCtiPaD4M+dvRImJyPSNXjwXnSbGNg8qRXX4c/KzShtnP bkhagHwznJKjG2ALdx1o5V/3pweueIjUIN2O6gsZpO5+nctutAAI6rTJr2UuXSZCaH6NierI qY4phzHqpbsrk/xbv5X6fDTcqE3h6mPKrLhVgNDApcyiULuSCTOM2DZnPS1hlwgKfGsqMJGA ljtbYjAnnPLFoylIYjvmfLa6vRLvP87yTbj91Mq2rgp7YQOrlNUaeu5hguHElXFEzIAZWKYy 7IRi7DrUSVbcGw8lUZGOyGUl4BYiKsN4N42vpXf3b4rLdm7pTJNYRisxu9Ygse9dDWeem1Ve 41rh2M2yVZoEZezH4zGrlyFZ6gVRiteGuR8Isw55ja5iqXQODqEJeXkRqersmOaXaEQFYEFL 4i8+dJ4gBfENHsaAfZ0gow0/dDNIBERZEwEzO5zQ5l3uos/q6oIzSPyIrOF2t00M/fJwej2S cIlu4ucpjSBcAcp5mMD6hPWlsYGOXXCrzqigtrX89OOILpSfH/+s/vO/Lw/qf5+6qsfG2dzs 5MlAfQ5RPMN5svVDNqyY7xKXp9pTjP5UsibZJlT+lDJAO6wmGs7q5upy2t/LY97c/QSNywM/ 0BSuQANWQW1wWwT6v2mxQxBlXbMSnEg3udf4YupVVrjWC5c4EBaYWJWEYqghVTANE7ostFQZ DVwUfDT6hgCW1ccEmR4xzSZgIp7UN172wJN9O1tyMSAWelZW+FmHei4TI3oNIVEhylVohGe9 dIXqCvCNJIIPSi5R8wBNK8xKU48VpDEDQBYFu9bYgYbyYayeMDqQ6y8nM4cbC8RWn0Qg0pqU Vy0tC6wsLtMs0yTGkLW7FFEYG0GdEKR2Z6/IuZRvUnWE/ycquSUxkt2YUYRbHUiXWtSrw/EJ 5C65L2uDs3VRhgb5GdTJzM0Fq3ECLkxjV/vGYFc4JbvXAWZQ0h2jAsApIDBrsBfZKBF4m1z5 WbeYoaqFNjlEMy3M0inb5xsfdrc1NNrSq+7KH/h97mtmSfi7O9mgAPXDc70VtfUNQoEHgzYq DRBIaSTEBo5mruhvrJk1k6rKnVsU/FXCd0nA9G6nOkE4ECHXm7TgXQN3dCg7Kdgg64hIE+la ILx8Q055gkGr/lCLg4fIGzfnA3Iisjv403IhOlgy9RSKfngNK1PH45SPBt8YBpZetMF9jq0F Q5t0N62CIsM2uY5Scs1AkTqvz4u8aw6TMGq+pmY6p55XCsAuaSxXkSnG0U6ACvHxVNdU9QLi uN+pGZppWEaUCJPvsJUZwTpxWtmUJl0rGW3U9S1GQVRMqVLPoxtGAXogrFWm20bTSny0Lrnt wC9k/vH8NtPz/ixEkx65feRWIHb3lhjJQqQO10yxLBeg8RMdkqRpuTwC0KaJaCUYQ74iw8Wi et6a5EhE1w6lsEUeEEnpehEX5VZ7R1Ag1hoIK/AKMiGYTGEhhtqcK5jB1gu5fbPrZBvkkXtr LtIGiomEQkzxXPohv+lxtG5048rEylGU8nEvSSmUrzkmJCQ7mFH5ZfSjCD4OYGTSTOMFddzu 7x+13NtCnRHPBkDtI/raUIhVKIp0mXdkzqmpuo8lhU/nuPxKzKJB3t0QhctAG/sW+sGeS4g6 OlgbKqgBUIPhfwHV4au/9aVcwogloUhnoBDzrLLxFzVb1ZXzFapL5FR8XbjF16QwGmtWY2Ew XSygDN/0tqEmpetgOR7Izhmm/R0OJhw+TNHLRATFt0/H8+t0Opp96RM9j5JuigUXby0prBUl QV2zLpH5jWb3wg+HUq/Ph/eH16uf3DBJaURvWoLWZmoGisSbJLpPSCAOEeZXCbX4XRLlrcLI zwOy366DPKG7iqGMFnFm/eRODIWoz716noN44ZdeHmg5PdWfdpzrOwV7bAjHhEK53GJsliBm T+2guEnzNaUiirY9rQBhN1+JGOhFtwP9ayVsaFYnbtgLGEVc9s3iJbHOymQH5Tnp3qbUvVRi 6uCOGjWoaLTEs9leKc2n4iBRCUBLTHgISkaYfPv0x+H0cnj65+vp1yerVBwuzVx8eZoWZWKP H541yugbjmh2TioiZDLQs4FIHwQjlg6AfO07fJgia+R9c3p8bn58e4J8NY5qvNjNVhJhope/ o6kH/2/pdJGMGSIYbDSmltk82g/Afpo/1QeR0YNPttNWIcIMcig2SZ555u9ySQMmVTAMlYbR MRM9CzmgQCjFEuU6n3MP+1V5Y0a9IFsZC68C8aJwTRPSTQd/qWNb0xIlGH20b2CApcgcdLsg SOJNhjlErTosNY8irUhdLZS/w23xeKWWyaQPHxA23eI6kPqudhi6lnbpfqiwKXopQnTiVVdh 9ERHWPRZ1qEmRXQ1R6I+W7XDl6Dr07sc0idXDTMZkAguOoaabGiYqW5CZeDYHV4nGX1QnHuL 00nGH7Q+5p5UDRLtFdXAcQ/LBsnwg+LcMjVIxh3DPR3POjCzwbizyRnrlWYUdzrHazbk06Hr PZtwJppIAiItcl057exf3xnxBlgmVde8ucILQ50P61b7Zqs1oosDa/yAr2/Igy1mrRF8WAJK 0cXLNd6Y8ebDOjrYH3bQj3T4Og2nZW52W0L5S3tEx64HUoeRNsbAe0FU0PeNFg76/CZP9W5L TJ6CZEMznDSY2zyMotAzJxFxSzeIQu6mvCEATX9t9yP0ML2FzyCSTVhwLckvDj/86GKTr0Ma 3RoRqMwQt6pI85SHnx+cEJsk9Lri6WtX2spj43D/fkJzCysmDp5yRGhVOSlhIhCRh8lSv3ur CnCGDpirNfBVfdRvVl0VVRj2UwBR+qsyhcal/Mrb2KmbNQx8I+SLfJGH+sX/B5dvNYoKJzKA CmidfpAEKpQoXk9IucQzU5BYZOx3oKQjk3YEOaY6WAVRxj4a1Kps+00uDdoq4m+f0Hfh4fXP l8+/98/7z0+v+4e348vn8/7nAeo5PnzGOJW/cEI//3j7+UnN8VrqBleP+9PDQVoVtXP9jzZ8 /NXx5YhW1Md/7yuPiardENRO/ARvXSZpElANr6Nkje5uuPEJMtmvbnSX5kq2p7KSDNOke00p GKilXnZrQnfUEU6BsmsTkruhPwa28dItsaJGzkzrxzLv9Pvt8np1j2keX09Xj4enN+naohED fyzdjBwlGtix4YHrs0CbdB6tvTBb0ftQE2MXWmkR8wnQJs3pJW4LYwkbkdDqemdP3K7er7PM pgagOQ8lZmdmSGFrdZdMvRXcLiDvlM3KK+rSDwVmHZQhXoRVdLnoO1MVg1VHJJuIB9rN42XG 9SbYBBZG/mHYYVOsYJu04FUUGnUB9f7j6Xj/5Y/D76t7yae/Tvu3x98We+bCterxV9ZoBJ7d XOCxhLkvq1T2IO+XRzQevd9fDg9XwYvsCizoqz+Pl8cr93x+vT9KlL+/7K2+eV5sDzgDAzUW /jm9LI1u+wPN7aFeP8sQwzdqd7I6in+noUTOqCMgVMUvKRxo42GHTwehgcY44bkiEcF1uGWG euXCfrutx3UufdUwh+nZHrW5ZlJTQxfcy1yNLOzV4jHcHlALowoWyUtRHZYu5tYcZNgvE7hj GoHT/SZ37dWerJr5tRYEZvkuNk1k5tX+/Ng1OrFrc/Iqdrkx20GXuwdtqwrV5tWH88VuLPcG jt2cBFvfB9Ci3/PDhc3z7KbdORqxP2RgDF0IXBVE+Nfe0mNfrRcbTB2bWjCsDq6SgdOzwGLl 9jkgVwWAR317ywTwwAbGDAwf5+apfZAVy7w/syfhJsPmqj3UO749aratzXZgcy3AtBymNTjZ zEOGOvfsOQK55mYRMjNdI6zrvppz3DgAncbeyD1XFN2FRDFimB7hnKdzfTYw376Qf5m61iv3 zuWyQdWz40bCdWx+qjd0ezqDwGc2cTi/M1BBPmgoHlqNFIE9YMVNKmfAolXwNmbDP6owiW9o xV87EpsjtYjcgr+Oq/fOOz4LToWeDllXv7qs/UkAW3Eb2Z0otFlQ5vL7l4fX56vk/fnH4VT7 PWsSfsPDIiy9jBMH/Xy+NEJwUkzHvqpwsKd99PGSyOOva1sKq93vISamCNAmO7u1sCoBBCOO 14haPjZ70+BFJbV2d6shxQEzZ6hBSjmf4eQO2xciqNeGTAbmhhtoNEMMYSh2pZckmBbpwxEH ajvOnk3jeZrBA+hLMWYfBk0WFXNM4Nf2jiCzzTyqaMRm3klWZLFG07SzG/VmpRdA/xahh3Z2 ysiOfna29sQUTTq2iMdabEO8hrhuqNNWD2ubAHcLgZeFTWsaViYJVekzic3iElX/LFDvfNsg Vz0OmdjjHroo/5Ty8VnmBzoff70o1477x8P9H6AdE68TjOOCRvfyCuTbp3sofP6KJYCsBFH/ n2+H5+YmXj2N0nuWXDPLsfHi2yfyiF7hg12Ru3Tc+asMUJQT381vzfa4yxVV8TySyXpE0dm1 lkLyvbS0kD2sbRX+g8GrvKp+nPan31en1/fL8YVKhUrNp+p/DSnnoGLBBpYTg0J0BtE6OofF FWCcYsLLtYcGiB6Jl92Wi1x6DFDeoSRRkHRgkwCtF0L6+OKluU+lNXX/5UZ2YQx3XFuaNkvY g7UL+6MG6o91ClsG9cqw2JR6qYFj/GwM6fVNTWJg2QfzW97xSyNhQ/YqAje/UbYFRkmYAb7Q WBOwvKFRlLsjj8K5LbF7xNV9t9MlccxzVDQxsMmsJH4a0zFpUHfQBm7gkWYoAXIA7Kx4myH9 EQl8WDbQZwJdeSVPzdWyu0Ow+bvc0YA0FUx6l2Q2bejS4ayAbh5zsGK1iecWQsD+adc7975b MH3A2g8ql3fU74sg5oBwWEx0F7sdiLQDPrTXEr3irRc+TYQMP6THRCFDpVFrlLm0DWx+SvvD LaZ118CuEKkXyri4MIC5S+QW4DdcxtSVRIHQFqDUljfCffq5CQjLCEEyKVjQJnPoM+JUnqty PISFREYkRqNHL3Jz9AVZSYFKx8og7ZpxqLgJ0yKa62Re3NwF+Yef+/enCzpaXo6/3jGD+rO6 J96fDvsrDG3zLyJ7QmER3gVlPL8FjvjWH1uYLMjxBQjNxPo9srhrvEDdVpbmdx5K19b197Rx yL3b6CQuiVKCGDcCoQAtdL5NyYMNItCFr8PmTCwjxXukLmkQjBKGW+Ulr+u6plYxkW6e5UV3 ZeHO6RYI085Z2ebXeK9AqoqzUMvABT8WPuEFdKfK8XqsoMFDF2lSEIuV1jAP4Jx4K+mnf02N GqZ/0dNJoPdbSvomHx/8IKOpBAXwsbYs8N0nWdKtmPhTG2KB/ixSS18S+nY6vlz+UN7Hz4fz L/thTFoSr2Ucck0mVWA0/OC1BuVqVUbpMgJRImpu0iedFNebMCi+DZspqiRUq4YheYhDw66q K37Ap5rwbxM3DlvLnmacOr+90YKPT4cvl+NzJXedJem9gp/skVKGKLoa08LQ5HjjBUbE4gYr sijk7bAIkX/j5gs+SwChmhcLlmTpz9HRIswKfucIEvkiEG/wdgUXJMfRcBAE0qL827Q/cyg3 ZrDhoy9grD2V5qAQymoByVnqJiAk+lhqnlKJkHg81OdAgJ7PrTNMa1ydAW/iDhWixwgvm6vq hDJiQ2PM2FVp01s5X8PIL0QXlFvjbLlxYf2qQchSee5RPwIKt+d5kaKf4U3grv+vsmvpbRuG wX8lxw0YgmX3HhzbmY00lmPZybaLsQ1BsMOKAm2A/vzxI/2QZCrbDgUKkVJkkuJDIiW+vxfP 1ah50P8qeNNCSVAoTsFFc3QU3Nw4HbQKgx8+vm00LHLWS9fllklL5mPYinTW0QAOp6LZ5cft ehXVMmsJLGAKtXCzo9ErdmRAILJNUHF4GHOu9HiWw1hTWhNWOfiQvsLOXBV7ATVA/pY3+iaW zFey5NUT+sduOyJ5JSwMWGyEBOPKwXNnYxZbsE5qna5whu+75lNsx1Cm7EbtE/r+pV8vzfzL D5vFaffM12A06pSaEx4TRf5nupR3WwSv8gz+Eo23wp2Bt2cR7OL709XL7bdm1+IwvKunG3Mj xACwL1CS3iZWvwH9fKRFS0s6M3p5bGw+rmRUtEhIOxhyDFUBdOCol+xojflAWE/kOE/NltRY FiabSmNY0MetcbmRTiI3OV5fDTV3wBVMZZ/n9T1FSUFDfqinNz5AnFkOVu9enn894aTu5cPq 9+318nahfy6vP9fr9XuXjTIW/Pmuzb9E3NVBVJSXVAIUZRAPToEMrLx9pG9byuJQvSV7nYPP pE+Iy8NI4uCHLt4bHWXqLNPR3a//oJbn8vFKcqfORoi0IllJnBAQmyUEv0OlvSimKJXo75Q3 W+Pu7AwUKm27pFuN5nuM07OyBMhFbCWZyDs4KblFFD+Q5VEeekk73awQgF94WPDHw/gLE4FC Nhi0JxKPK/TTxoUvWILG/KimLI+X8HiTDj+XtJG4AY3iAPicYkEk24nN6UiG/kDfPm8a0+hZ +nOMEs/kn2Wfgukq/doarQyEE8V2XSWeElPGicV86OcmqQsdZ3THdyNt48D+XLYFwrAwXW0A H7jGnbOZmixAQWER8xWY7JK5VUCNFOsat6wajRxSKfEdd9AFjWZQHkTI0BunRioiGeaIIIqr 0GdJm2AnFNfABdWvNkEljL6Muq1VEy653QnQHQ0VhIJ/AD9kJPw+3AEA --Kj7319i9nmIyA2yE--