From mboxrd@z Thu Jan 1 00:00:00 1970 Content-Type: multipart/mixed; boundary="===============1338800576824378956==" MIME-Version: 1.0 From: kbuild test robot To: kbuild-all@lists.01.org Subject: [linux-next:master 6635/14131] net/qrtr/ns.c:111:47: sparse: expected restricted __le32 [usertype] service Date: Mon, 01 Jun 2020 22:26:52 +0800 Message-ID: <202006012248.FkwL1jps%lkp@intel.com> List-Id: --===============1338800576824378956== Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: quoted-printable Hi Manivannan, First bad commit (maybe !=3D root cause): tree: https://git.kernel.org/pub/scm/linux/kernel/git/next/linux-next.git= master head: e7b08814b16b80a0bf76eeca16317f8c2ed23b8c commit: e42671084361302141a09284fde9bbc14fdd16bf [6635/14131] net: qrtr: Do= not depend on ARCH_QCOM config: ia64-randconfig-s032-20200601 (attached as .config) compiler: ia64-linux-gcc (GCC) 9.3.0 reproduce: # apt-get install sparse # sparse version: v0.6.1-243-gc100a7ab-dirty git checkout e42671084361302141a09284fde9bbc14fdd16bf # save the attached .config to linux build tree make W=3D1 C=3D1 ARCH=3Dia64 CF=3D'-fdiagnostic-prefix -D__CHECK_EN= DIAN__' If you fix the issue, kindly add following tag as appropriate Reported-by: kbuild test robot sparse warnings: (new ones prefixed by >>) net/qrtr/ns.c:111:47: sparse: sparse: incorrect type in argument 1 (diff= erent base types) @@ expected restricted __le32 [usertype] service @@ = got unsigned int service @@ >> net/qrtr/ns.c:111:47: sparse: expected restricted __le32 [usertype] = service >> net/qrtr/ns.c:111:47: sparse: got unsigned int service net/qrtr/ns.c:111:61: sparse: sparse: incorrect type in argument 2 (diff= erent base types) @@ expected restricted __le32 [usertype] instance @@ = got unsigned int instance @@ >> net/qrtr/ns.c:111:61: sparse: expected restricted __le32 [usertype] = instance >> net/qrtr/ns.c:111:61: sparse: got unsigned int instance net/qrtr/ns.c:112:47: sparse: sparse: incorrect type in argument 3 (diff= erent base types) @@ expected restricted __le32 [usertype] node @@ = got unsigned int node @@ net/qrtr/ns.c:112:47: sparse: expected restricted __le32 [usertype] = node net/qrtr/ns.c:112:47: sparse: got unsigned int node net/qrtr/ns.c:112:58: sparse: sparse: incorrect type in argument 4 (diff= erent base types) @@ expected restricted __le32 [usertype] port @@ = got unsigned int port @@ net/qrtr/ns.c:112:58: sparse: expected restricted __le32 [usertype] = port >> net/qrtr/ns.c:112:58: sparse: got unsigned int port net/qrtr/ns.c:138:47: sparse: sparse: incorrect type in argument 1 (diff= erent base types) @@ expected restricted __le32 [usertype] service @@ = got unsigned int service @@ net/qrtr/ns.c:138:47: sparse: expected restricted __le32 [usertype] = service net/qrtr/ns.c:138:47: sparse: got unsigned int service net/qrtr/ns.c:138:61: sparse: sparse: incorrect type in argument 2 (diff= erent base types) @@ expected restricted __le32 [usertype] instance @@ = got unsigned int instance @@ net/qrtr/ns.c:138:61: sparse: expected restricted __le32 [usertype] = instance net/qrtr/ns.c:138:61: sparse: got unsigned int instance net/qrtr/ns.c:139:47: sparse: sparse: incorrect type in argument 3 (diff= erent base types) @@ expected restricted __le32 [usertype] node @@ = got unsigned int node @@ net/qrtr/ns.c:139:47: sparse: expected restricted __le32 [usertype] = node net/qrtr/ns.c:139:47: sparse: got unsigned int node net/qrtr/ns.c:139:58: sparse: sparse: incorrect type in argument 4 (diff= erent base types) @@ expected restricted __le32 [usertype] port @@ = got unsigned int port @@ net/qrtr/ns.c:139:58: sparse: expected restricted __le32 [usertype] = port net/qrtr/ns.c:139:58: sparse: got unsigned int port net/qrtr/ns.c:250:37: sparse: sparse: incorrect type in argument 1 (diff= erent base types) @@ expected restricted __le32 [usertype] service @@ = got unsigned int service @@ net/qrtr/ns.c:250:37: sparse: expected restricted __le32 [usertype] = service net/qrtr/ns.c:250:37: sparse: got unsigned int service net/qrtr/ns.c:250:51: sparse: sparse: incorrect type in argument 2 (diff= erent base types) @@ expected restricted __le32 [usertype] instance @@ = got unsigned int instance @@ net/qrtr/ns.c:250:51: sparse: expected restricted __le32 [usertype] = instance net/qrtr/ns.c:250:51: sparse: got unsigned int instance net/qrtr/ns.c:251:37: sparse: sparse: incorrect type in argument 3 (diff= erent base types) @@ expected restricted __le32 [usertype] node @@ = got unsigned int node @@ net/qrtr/ns.c:251:37: sparse: expected restricted __le32 [usertype] = node net/qrtr/ns.c:251:37: sparse: got unsigned int node net/qrtr/ns.c:251:48: sparse: sparse: incorrect type in argument 4 (diff= erent base types) @@ expected restricted __le32 [usertype] port @@ = got unsigned int port @@ net/qrtr/ns.c:251:48: sparse: expected restricted __le32 [usertype] = port net/qrtr/ns.c:251:48: sparse: got unsigned int port vim +111 net/qrtr/ns.c 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 103 = 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 104 static int service_an= nounce_new(struct sockaddr_qrtr *dest, 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 105 struct qrtr_serve= r *srv) 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 106 { 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 107 struct qrtr_ctrl_pkt= pkt; 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 108 struct msghdr msg = =3D { }; 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 109 struct kvec iv; 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 110 = dfddb54043f0a3 Manivannan Sadhasivam 2020-04-21 @111 trace_qrtr_ns_servic= e_announce_new(srv->service, srv->instance, dfddb54043f0a3 Manivannan Sadhasivam 2020-04-21 @112 srv->node, sr= v->port); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 113 = 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 114 iv.iov_base =3D &pkt; 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 115 iv.iov_len =3D sizeo= f(pkt); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 116 = 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 117 memset(&pkt, 0, size= of(pkt)); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 118 pkt.cmd =3D cpu_to_l= e32(QRTR_TYPE_NEW_SERVER); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 119 pkt.server.service = =3D cpu_to_le32(srv->service); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 120 pkt.server.instance = =3D cpu_to_le32(srv->instance); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 121 pkt.server.node =3D = cpu_to_le32(srv->node); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 122 pkt.server.port =3D = cpu_to_le32(srv->port); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 123 = 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 124 msg.msg_name =3D (st= ruct sockaddr *)dest; 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 125 msg.msg_namelen =3D = sizeof(*dest); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 126 = 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 127 return kernel_sendms= g(qrtr_ns.sock, &msg, &iv, 1, sizeof(pkt)); 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 128 } 0c2204a4ad710d Manivannan Sadhasivam 2020-02-20 129 = :::::: The code at line 111 was first introduced by commit :::::: dfddb54043f0a377f642bd0e6a28aa40769e2e65 net: qrtr: Add tracepoint s= upport :::::: TO: Manivannan Sadhasivam :::::: CC: David S. Miller --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all(a)lists.01.org --===============1338800576824378956== Content-Type: application/gzip MIME-Version: 1.0 Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="config.gz" H4sICF8I1V4AAy5jb25maWcAlDxdc+O2ru/9FZ7tS/vQNl/rbs+ZPFAUZbPWV0jKcfKicbPO1tMk 3us4bfffX4CULFKCvHvunLldAyBIAiC+SOX7776fsLfD7nl92D6sn56+TD5tXjb79WHzcfK4fdr8 dxIXk7wwExFL8zMQp9uXt39/2a6nV5P3P//689lP+4eryWKzf9k8Tfju5XH76Q1Gb3cv333/Hfzv ewA+fwZG+/9McNBPTzj+p08PD5MfZpz/OPnt58ufz4CQF3kiZzXntdQ1YK6/tCD4US+F0rLIr387 uzw7axFpfIRfXF6d2f878klZPjuizzz2c6ZrprN6Vpiim8RDyDyVuRigbpnK64zdRaKucplLI1kq 70XcEUp1U98WagEQu/OZleTT5HVzePvcbRHH1iJf1kzBHmQmzfXlBQqqma7ISpmK2ghtJtvXycvu gByOmy44S9t9vXtHgWtW+VuLKgmS0iw1Hn0sElalpp4X2uQsE9fvfnjZvWx+fNctRN+ykliAvtNL WXr6aQD4X25SgB85lIWWqzq7qUQlCE5cFVrXmcgKdVczYxifd1wrLVIZ+dxYBSZIsJmzpQBZ8rmj wGWwNG2VAEqZvL798frl9bB57pQwE7lQkludpWLG+J1ncB6uVEUkaJSeF7dDTCnyWObWGOhhfC7L 0GbiImMyD2FaZhRRPZdC4V6J5WZa0rM2iG6eo0j9dcUiqmaJ9gX8/WTz8nGye+wJseVvZc7B8ha6 qBQXdcwMG05vZCbqZaeW1jiUEFlp6rywh60zmga+LNIqN0zdhSsKqQhzaMfzAoa3VsDL6hezfv1r ctg+byZr2NXrYX14nawfHnZvL4fty6fONIzkixoG1IxbHqBNf31LqUwPXefMyCVl4prPRVybuVAZ S3FpWlfKs6dIx2hiHODIz/gT9XH18pKYwTC90IYZ3TFFEKgzZXctTx+xImCyCDfbilLLQDNgR63f iKVmUSpi0ly+QdpHHwCClLpIQYBF3mpL8Wqih2fWgGZrwPlrgp+1WJVCUaagHbE/PAThaBBemqLX zYo8xOQCdKfFjEep1E41zQbDBR6P6cL9wzu4i6NRFtxftlzMBYvBXZMeHn12Av5FJub64syHo7gy tvLw5xed4cvcLMDRJ6LH4/wy8JVVDnsG5TXWaU9wK3r98Ofm4xuE68njZn14229eLbjZN4HtRUlY wvnFBy94zlRRldrfOzh8PiMPdZQumgEk2qHcqk8RlDLWp/AqztgpfAKGci/UKZJ5NRMmjWiSEmKX ObmCWCwlF6cogAme01MkUZmcngP8OUmA8V6XDBwLFZTngi/KAvRYg7cyhQqcs7MYzC/G1QSePtEw PXg7zsyIqhS6J2J6NAGQjk2SlJ9b4W+WAWMXa7xURsX17N6PqQCIAHAR+Im4Tu9H1A641f0YJr0v iGVaxFXgrYsCYw7+m5Iqr4sSAiEkjHVSKMgSFPwnYzkPxNsn0/APOgVzmVbw28XjKoe0dJZDWLUZ qxdqyqT74Vxm9zsDny7BbFWgbLDxDMNDE7tPaJugaPDJnOVx6oU8lxRiUqC8mOWcl5+0em40Yhrk UfnZQ1IZser9hIPv7bAsfHoNMmFp4lmUXYEPEEuRGx+g5+CqggxUUrYAwbNSQdxk8VLCihuh9J1f xJSSoXtpkAukvsuCAS2spsULWq271Oo4ClVpA3VCHz9YhYjj8Gy2muDnZ1dtOGgKu3Kzf9ztn9cv D5uJ+HvzArGcQUTgGM03+yBEfOMIL5/KnORdwkaHRKyJmKkjW1t1ppcy2gPrtIqoY5MWkaddGA3q UDPRJjUB73mVJBAiSwZ4EC6UV+AL6bNoRGZTXyw3ZSJ5m8t4yWqRSCgqe+64kVhYJh7VMHMxOgXh gAldOn2U+93D5vV1t58cvnx2qZUXp1uDZNOrbp/Tq0h6J/0e0u0aAuDlhXf6My8ngtyAL8DXQj6v q7IsfDfRhD0nFXRO9ZIpiescpv1gkjJSEABcKuoxwRwEIiRGaqFcKq2EVzjEmX+OE++HCz8FlMwQ pCGK1TZQWa/l7R18IGcuULWK886mdZJaaJDwkdBDYwlpiYJ0zbBcVhmh/owvZJ6Ku94KOgFdLYIi tof9sKAtuEd2Pv02uunVCN38vj4/OxtDXbw/I7YGiMuzM3/xjgtNe+01Zuyq0vPaCrfJQX8NkHom 62rZE9occruIgT8N7NGi+B1kqX5TBqIjGCimwmjQhYJc+vr8OIfOvHQgtzamr6/Ofpse11uYMq1s rtYzDZHbY9d0JRq6r9Eo+NdS9O1UZ97RAVNHs4005J8t9VGwbje8FBKQUPPCKSOEbOfWIhVQCzZz ZwUcrt7qoDSDn0bOgKZZao8igXJrFAmZodJiFB1w7xxnK+sq8w5yDqvTbQ1ytA4soCuohpFBxjxF zYtUYAVhVdrzF3Zu5GcdrVgZkWvpF2xwpFHG6E1wEZa2lnGPjRNbiiW1XdzgaGacgQo46Gek8dCc OPDmCZkMOCa1UAp28juo6vo5wAkth2bPsrTOk9sg7ouV4P4E1v8n2/3zP+v9ZhLvt3+3kbc1Iqky SPnsJjNG5+bgTSHAx1RfD7yuLy746XK7bv0WxBl2QflcQhjJi9xyTMDPRox76dusKGZwQtolBT0n h+JQWdic2Ya5wUaB7eQH8e9h8/K6/eNp021cYv7wuH7Y/AgV+OfPu/2hC3y4FghIgVYRhvOkBdbb mKEbVVCZFBJyVuoKg64lhp0HbPod2QDZNERAurIe1JdNpP9fNmWlYDaf9uvJY0v20Srdz7ZGCFr0 0FxazKlEwmUau3+gzIcEbv1p8wz5myVhvJST3Wds7wemV1Kh0XkTl0JgB8vPznu/kDKTs7lpDjtg 6zLmIX2bapXFLSgSe17oXPopiqW0te/MzwsCcN2vdBz7kquhNYY0gtN9Up+GcUoYiImYgWTlrreq qDIGPNlzADQyv2t2+m34psK5vvwQ0CWsPzIu/INqQeg0EyVu6lLrHqppQEItyq3ER9EyKPJCZA8u S0jwwkWF8TiUZ9M9HROq1Ud/ZjiEkO33zQIPPwPHNdT9nCyF3KBKmwIilTDzIh4MVCKuOMT6OVOx db5FnlJ9DUscRkc3ccaCugNEgz0AJWYQ3cZtzP17pFv/9aN7zF51KTioIriuWu8f/tweNg/oDH76 uPkMXJFJd+z9gFq42ibw8At7aULVcXZIg+5F5gUkPiTCOgdbj8yLwg8xTZ0BuZ41PrATqCLinlex 7W1tVAU5Ex4SE4SpAclYweB4u+EUkVupzjAha27hdI+FJckxhGKDlmflis89u236p3YGkIOBzAFO VT7MhIj7h69ToLT6qVARtwmf4Fi2evVgEVeYimFOJdLENut6o8UKktm+xJsy/fIiQqTMvKVjj9mv 83XbYZhBrvXTH+vXzcfJX6518Hm/e9w+uSuZLmsAMsiJVC5Suog+xaZfaX/Fwo9NPkizsa/km6Rt RekM2y1nPXEFbR4LavJ6zCbIo9xQVfkpitacTnHQih/vfkf6dC2lpHvCDRo1iJdUp2jQ/G8hXGvt rr6aZnItMxvpyaFVDrYE5+cui4qUJjFKZi3dAjteZHc4CF7YLNZcoxe+qYQOb8+aRnKkx+8aGnwq qW5R14k2YqakuQvnDVC1OT/zs8WWAAsZKrDYi44sxncGtS23VMj8NhrsBUB1dnNiKxAF64QWGkgJ 2yZQtbSnrlzvD1u09omB9C/M5Zgy0lhTipfYoKY2kOm40B1p0K4JwF1Q6s3ory67wYgdigBg6Mdk 0S5ZFt31U7BgoJSFq7hjcEcoVGrFHdXiLgpaRg04Sm6sDts772C+YxnPwktupvNz/5bCqlSXUMTi qQafF9zIN3gbSxz+FI4cewv2JsYG+8hmtKum/t08vB3WWHPgo56Jbc0evFgeQTmbGXT2nirThBfB ZbUj0lzJ0gzA4BC4fwZwbFxlJV0JjSzIrjbbPO/2XyZZl8AMcg+6C9KVwk2DI2N5RWaOQRfDUQXd 32MP5Js4eG0pmNi1HgbdDXubbO9ZylQcuw+DCZeueh80X9r2hXWBzRQ++0YU/l36kXcK4bg0dqDr hIWvjRg3vWTzaOwzNEh060GWAaWa6k3iZOFSI6/pNb+Dyi+Ooa7q96FtkmIKqF7C6w5NlZLtqwMr mUzmlqdr6HU7SQVz7VzqAsFPvOFHv2o4ghIdArEzrbvm5X1ZFF6/7T6q4q6Sub9MijT2z8C9zRQK qiC0KasVMOa2i94jk6aTZR92EINneP0rcg7VQ3Ml0pyr8aPTmaHfrhMGXMcMI37rKvLN4Z/d/i/I m8hCH5ZK9iarXHrXcfgL/ETWg8SSBbs0I6nAKlGZrdHp62tY9ELQ3blVXNa6wDVSYpNu810vr3QH krORxg4QtEGwVgXkgtQNEBCVuf+ky/6u4zkve5MhGHtRdG+uIVBM0XirrFKeQoImwYyzakUs01HU psrz0FnquxyOb7GQgtaGG7g0chSbFNUpXDctPQGqpWbzcRwkmONIWaIPG9F2t10f2NhhQMfLgXla RBU7xPgCFLv9CgViQS9QYxa02eLs8M/ZqZTrSMOryC/WWt/Y4q/fPbz9sX14F3LP4veafJkAmp2G ZrqcNraO79XoJyaWyL1U0HB86nikfMHdT0+pdnpSt1NCueEaMllOx7EypR98WGTPoH2UlmYgEoDV U0UpxqJzzAdsWDZ3pRiMdmZ4Yh9tXmD7jyPHxBJa1YzjtZhN6/T2a/NZMogefJxElelpRqCglEUj yKwEq6O9ED6RhgXwJnZ5vqI0JT7chsIyuQswdggkE7apAEExK4N8BCgSmZqwr3cEkoeqeZq+32DA gxT0sNkPnq8PGHWhcoBCYch80SUDAxS+yfPQCR7W3Mb+AGpf+bnHiF4i0SCAFZSVlFg9doRsfSx2 ZZMg5wrQVqVU8AyoElPSe4Gqg/cW3uFg+RFUW3X+Vf5a9vgbT8KEilsZz9JK1JxKUIBJzkzAFH4P NoIwt4UQ1l8QwjKmbyqhWCx60hwe4sGCV46m7b2ubC30OnnYPf+xfdl8nDzvsPB8paxwhTOrRX/o Yb3/tDmMjXCXylbCrXkQptoRhsbqEzgpEjroBuf4bmzk9A+JEzfXSY5QSkolaMUS5J5mTu7ym0QB jizTA01Bufrw5wkFGfweAaoUGw5o/o6IcgNDKnf54rUnTvquIKnUYjS5XeqBT5Tlf77BJSaYdihm Q8VV77y7/Nti6LgBBwSc0OruJEkMJU4fHzpDyJMHnrNZTgdUAu/ge3DYOaBkeTyDAbwJJT3o0RB7 d/pl51l8yXYjOlukawegzFg+S8WQA2SWZPfklI4aJf49PaVGWl10JhWoa5SkUdeUVlenhSmlsqkv z+mYbqZOVHgacIx7QD8gGGpvelJ90zEFTE9r4JSAyWMyHQ2LkZLxjM7pHArJRXQiNYxKt+2xcx5z Plpzaj5Sj6qRV/SQqJI33CZ8K2syyOUkFQUQlbI8kDfCsrKg83VERupi+oH2FumFIT9U80P7LPAW mf/Dybj/u5azDISTF0XZ6800+CVsobG2wcvOkDJT1PLcfSBWh5r18lYEESPsjB/OLs5vusV2sHq2 VF4XwkNkASIWPOgBud9Nyedd2KVBHgc/L0hVstQLXHjnwUqoY0KwLOO4DE8XAGqR85FnSquL98Rk KSujjmk5L3AfHtdpWtyWjL41l0IIlMZ7MqBgKtC8A7Xe8eZt87bZvnz6pbkBCL4Pa6hrHt2E5QsC 5yYigInmQygemDDtseBSka/MW7StFImJIUMKEggL1ElEAW/69oZgI26oPvcRHSXUank0kudaLOQx 1CDDvrLJGbmbWA+SNQuH/4qMIFeKmjy7+crkehEhxZAhnxcLMQTfJIQ6ePgwswUnNw1myJwtBEVP qWo+T06sv5SCGgRTA+bUOPfQdTCQ7qUepTx8O992pBL6urJFW0GcpGildZJIh9P0sBAYk6JOmDbD jlmzhet3nx+3j7v6cf16eNc0Bp7Wr6/bx+3DsBUAQU33pQQgfBggqbDY4g2XeWw/WRkMta53zC8h QXLbN2SEVpeURz4y1cuSGoVwMktr5wInSo0bfpfXF4D/fZHPbdCWsRibyNHftNiWr8WHDMWxFIJi yf9k3kNyshXsEeTRnREk38r/JMKDZxDNwvPaIIxYmf7W2mWwXNItUY9IlnRh1oqIjdW77jyDYXs2 zb24E+caP94r8A8CBM8SIOQwe69PzluUIl/qWwlro7KP5l7Fyz0aiGuce/McESlkT/gwmGJnL/cp riGCaoY1zZ7RpntW0h0s7DZoz6jmemCabvu9JltAkV5iSYfFOd2Ku1HGS6PwV60zL5pZiKmCD4Qs LJuP37DkXFPdalV6zUiV2O/E/bCzCr+8bb4Mta3WXgykaFwrlmp524QRv0LW+AjV/84uuulFPfQA zV+5CG8YJ4fN66H3ysuubGFmgk7ibGqsirIGg5C9z7GOpdmAfQ/h32x2uXgGBXr31qRcP/y1OUzU +uN2hw/JDruH3VNwEcroFBXOfSBwOG29EtLDRP4FKQJmt+Hv389/u/ytbTwBYBJv/t4++I//PeIl 95/6WsiKWI5OOaPu+hEHBh1y4CzldSQNXvSERRBiF0um8ZaIS5FQVmI51INlWRBkG8zgJ7DBk5EO yylzt3j+669nPYYIqqVmA14W0c5EmzuQSfvyPx/dQ9bsIRiUBbsY5e3IDPy/q9X71ShZKdjitCD1 7wy/veovQ2T6hLCSD+fTs/P+mE5vI8Pa1fQHHldJJTpOHKtm3HDljYIIhGcKPrZI7KsTz/x1CStu P5Xomf9cXp6fr0IWGS8v3ltg1zYdsjmyr3QUsg82/wGLW0tCbh0VAdiBfnSMYCpTsye+HTRUD8Es 4xE7sQSrngG7qjVeTwK9nYazuKeE7k8U0A/MCVd09P9BRR7hN7sipvtSgBz51MJiRrpP+AxSJ5h6 EVIA5OAVT4Rt/jQx7n2o1Wr09LY57HaHPycf3S4+9h1qZOzjTq9Ow63wrLe5OZeRoRXSYnXsl5IO WjFlKBgsVKEP/kKg5uFfG+gQEddUyutRMDO/XIyMHnkt7FFc3kpFlY0eiRMVtWoX4ii2ynx15htO twP9rc2mK+qxi0eSqWU6XAMs7OLscnxoVIJvWhHqTnrq7uGXc9KrRs06Qn4IqtFARi3dLProANkY 0vFgjhq2l7gkkLmpkr74B+SCUw/wEhnVqnnk3oDQLNLgNryF1K4uaqHwqw4fklpQ8ydxQpD0jJ8n M+zYBeErTy3IfoqHrz7pgNoMRB8m0gI/Msc/hQHBZOSPsrT0XEDp0X7AXxd5Rf4tgpYaH53Dju2H V/g+TMziaLh6+xGF+3DBkeDbtTAr75bb9CxOTts5OGIDKmbtJ3GneNwGGkpl1Aq6BwGGd6UB8nIU x6HiJpC995UZ4wNdtrBacXzTqY0SVDfAJ2ubTe+aPo3ePW8m/2z3m6fN62tr7JP95v/eADZZT/Dv GE4edi+H/e5psn76tNtvD396f1HqyDsTOvjg/4jox6E+ntCGz1S3DzzHrghCRjAkr05Npw3Dz4Tm 9st3/OS/+wrlVgKs04T92XC1f0Pi+vhhoEr+n7NraXLbdvL3/RQ6bSWHrEXqRR1ygEhIgsWXCVKi fGGNPfOPp2r8qBm7Nvn2iwZAEgAbUmoPk1jdTQAE8ehudP9wYqmxtanf8lVNa1ISWV429YR6KF3/ 6La0zb5tOYbv22Q38pawvf0Lk9CRLw7RVZFoeezwZI5870SliLHEDqwmntAmwc/RhRw4R1PDBQI/ Jmk8mrcPr7P989MLoJ18/frrm/Yizn4Tor/rhdmMGxAFlPlqubTLlKSOhfGEvFggJLcvRoYowvMe wA87WxkBesbiqpCZXTh52ipeh4H4P8GpWt4wxP9VD/VFlZxkZTrxabM9vvpjMWualfBaYVKM4+hQ FWLcKJAeo3Ax1mSw0RjYT1gKoABmK2h9rIsi7f1Rk1AKn7UuT/6shBeVJ2aR3B8avZHbRAT6B+wz WP93Db7bAZ9wNGMbWF1ZZ3YdGWcTAopbCTzYEk/cac8NLDLgijEFQfI9rAagi+GNEyOqsUY50AAA qUbxfoBrASMCgRVnt3VC7fA8XRJuYiIAScbHmSX0qcuOOa38SIKmtx8AzRvNDD0+3p7/+naB/HwQ lBE9E0wD+U2Si9UIIEjk1ikVbGmc2j9gDYSBSfGDWPm5KXdTkfVEvvUCKpPn+yfxxs8vwH5yX3BM WvBLKSP84fEJcKQke+zOt9nbtKz7skNCGv5thu9Gvz3++C6MZOtTiDGaSMwhe1D01CEv2x1iVIzS KXyE0ZKhtqH+t/99/vn5Cz58zClx0T7rmlqL7O0ixhJiUiV2a7OYYdEHIKiSdXQT//j88Po4+/T6 /PiX7S250rzGSqhIySxrWBNkLpUM7S2ErrGYjyX1AnplqNqubrtJlqlbWkbEAwcLw3ng2arFWH6T qRO8aeMgySafkmWyaxcrl6lCLX348fwIWYOq1x+nCC39szVnqw1mew51lrxr22ml8OA6MldA84kD zUN0FvdCVSuFFugo9DR/hAZ4/qy3slkxTQxqVG73kaYlmiQj+qnOSnti9LQug4xwNIKH5AmxgZ/K StU0oN1IeOt+VA6AJy/fxULwOk6V/aUHdflnQpJpWAngpY5MYSBVZMSvGdEmx6cgQUm/MFaowUYA cka5PuX6TyONy32NQaknudRNhuTKsUCVlY3zHKrxAaSnT1jdnm+mHYEV5dPHYDnTz2pEKyzICVIa kQxC+TDh1zzui1BA2+OIpwcrvVH9lpqkS+Mpy2Bh+urSTYgZTcsy0yLpCzXTbGH14EfxzeWA2Nsa HzD3NI+VTUfReeSZK8rx+OttagBkR9ap9o9+HENuWH8LodUCKIQR1JZzbv8CDyIjlp9JkjMABpYs dIFQj7JqjwiZIs2uRWrIavyou8CiVFysnzKGfHGN4TPuQoqEqWW5NYRlpp02naW1PY1oLo3zu/Ep G4FIJ+xbfkKdw583wuAVP3AfnRYCXYrzRHQFKxdhix/09MKNmBQ3BeDY/KZAUu3wLh8afYfPT3f4 bXSTXxHPkVdSFRkc4cbJGa8B4NvAkALrCRXQ5//3evxeD1Tc/grKNj9ndKpiA1V5KL/+l9uPgmWZ 1SCqkrWIp/1S5HjJ0IxqydyTXcViY+YqajypqPZE7SrmBCZwNLDN11SK+PPb5+nKQ5JVuGo7obsa HgCDaBv6JgPWVqO1YmPJrrCOYhP2KDatwjjcq9k+c7pbkjZta7ggRQdtFyFfzg2aWHnTAgD0YRGS x1NmI45iJU9xVzopE76N5iHxZaDyNNzO5xi+vmKF83Gt4DTnRcWFMpaGq5UFkNmzdsdgs8FwMnsB 2aDt3FDxjlm8XqxCI5aWB+sotGJwSV2Ll+5oXC78UNNczE2PdaLPwDSrBUhasZ4ne2p8ZYBx6ISC bWRzl+eS5KZqHId6vVbgElTs3ZlljPXfS3LElA+x6LaRu7K8KoqsLuXwP5aRdh1tVobGpujbRdyu EWrbLqdkltRdtD2WlBtfQvMoDebzpamVOS869MZuE8ydAa1oriNzJArdhwtlrNY4URrs7++Htxn7 9vbz9ddXiZj89kXogI+zn68P396gytnL87en2aOYy88/4J/mnRWdfUPB/6Ow6TBOGZ/4D8c5BZHv BHT0Mp0stABo+DLLWDz779nr04u8TujNXXfPRdlZipsgmFrQrUIM3YXmlw+Y7knjY2H55WFkkzQu fKEPw9C3nW8j2fGxHsmO5KQjWFmA3m/la1krsOUJZIlh3qgfSm95eXp4exKFPs2S75/lV5Ru0nfP j0/w9z+vbz8BOGX25enlx7vnb//5Pvv+bSYKULabCQid0K4VqqwESrHq6lRkD7eJYo8uGaYLAZMT 1LgH1iGxyzkknQLLGRfagVqix6S9fkPTE7NCbowWxD5EJc0XZSM7uWBolc9qjL7+JK4xnRcE4Jiy 2w+TFLr385fnH0KqH4vvPv366z/Pf7sdrvHpJjrmGO4y4YjHQL3F2i7Nkv1+9L4wsylv08lllmmO ZvUbRriYep0CTp40pNjvdwUx71/oOd63AjSAdRh4Gz8BeJK5IjReC20ZYaQsWLWLaWkkSzZL0zXS M+IsWS9bbNDWFdunFD3q1xLHsl6s19hwey8PPTBNbviajKG1sjoKNljYjyEQBgtkEAAd6ZKcR5tl sMLqKpM4nIt+7Ir01twYxHJ6wV6Wny8nT9BNL8FYRnypar1MGm/ndI1nCo7fJBNa1Y22nhmJwrjF xkYdR+t4Pg/Q+ZNIoB91OAwxszoOYjI9JGxcZgN6VoTB0lRXeCdwJwZ3vBUHqcgyePCTXxTko0/T trTjWGgqCvrO+GpABeRNT/QIsMEFhQ1BJt9e1TVWw3flhLZvbJRt9VudPh/on0EYjTVqXlocDs5p t1o5KaWzYLFdzn7bP78+XcTf79PvsmcVhTgS8017Wlc4wTVTiRwFJxrZBb+aWtLNNg3WmTx6ltaG oRMYq1lOhwCXcaAX8ho4tLXSWkI50MhDQ1CYEfqhkRcOGv4fCVxDSTalyJ2+21UFSQDcyE3oGEWq osmFzb5jqLVqi5I8Kbx1AYjYmYKTsyl9MuDx3JFU3nozGhwkhlxFJ4LiXHtSA1kJ0rjnoPVxYNc6 e5YtUtEmwes6eDJjRfs4GgMrXlf8ixepnSamaV1yzUnG7FRAO3xeBsAX8j4yiVOemrtzXu/6OIjR 18l0FqT1G45JZHQITFCbUxmccbFo8H4T9O4sh7e8qtETrnl2XDk9WTlyrGTTPLVuWZNJHpkJbs2b /AAY38erOWZJFeceFAPIDVYuZE9wl4womQqoE9hnYR89f/oFloU+9yAGNqx1htOfef7LR4ZpC/DV uQu6dhY2ubBDFnFhxUeehVmNair1tTwWkyyYvhiSkLKmuJlmih1ohX9BUyglMaBIojlAllxNzU9J Ymr5CNRvdVdLzQ5iYzAB35XtWHMnnbIvOyMfLVBRYWXd7EehFZrdKH5GQRB4XYwljKYFflZmlipW 3LxGD0NNqcq62c/kQJMLP6haL9ZURYXrCIaUWssL3O1qy8XEExhpiIFM7rmCzhI7O/feoFJHmnJv Tm0vBICZ5jdlsWc4x/K0COv2xDcAhFVo0+smZdYJWBjMl+2E0CXcAHJWD5k6FhC67ILZqppnHSgp Wu5YzyO1OwJwspgOxEXcNA7Xli2WWXRhOegUXbQ0sl+SbBvMjaxFUdEqXBvWkTrN71pWqZUG6Tk7 zilJQ/uOLaEeEGegTAuhWZOa17HtaOjk5iuK1y+u2eJ/lhLVUzHHrGZKbaJyq+746XoklxP6yvSj voMXG3zqepJ7I/7YkAtF4wNHGRaFK9N+MVk6YH50egbotUpUpvw4cnMP/NvBc0XUYXf2oPi1vkcE w1MJcHzFLX0tEwzfM54laJ8Fc/z8hx0wxet9hu8kGanO1FSwsnOm8vzGEX46oHcsnq6W8x1+T+PY kBpFdSQvLJ9AlrbLzpPTKHgraT34uPxyk72/3Bur4MLwQ/iZUgXMi38lyGl2Z/Rn18paBOF3MEe7 ek9Jmk8y43U5OandylAxCjgs+JmbJVUVeWFeTJDvS+uH67Q3nzZtPta1AO1CcqFVZRCQQi093Hzs zBJmeUFluGfi006MR4sT/uJwF8lddU9h9epQqLtaSElzDgbePbkPwr5nd+sGWxUSt+/JVcndoiA6 tKZYCrklJF6RcPSzVZBIPkn01kxOMjA5bpfOKZ3gcPSsIiXVXvzd3TI481mnlpAvcb0XyLilbtKS xb6b/UB2GwSYPSFZy3Du0cCEWQbWYuvHJ+gFa7kc3GlzY909UpbXjBLzblZp85lZi3C1hDXZGnRm 8WtelPxqfPbkEndtehAGuqEkDTQ7N9UoqKbHpjYq1L/xzrkBhKwlzuyuOn9hH30WrSGljmjxHTJJ 8HaIJRw9W5Fa0U7nEPeLsoqJBOeWsUUCUUV6WhRwKObM6lrFYPWOWPDwMiBRWg4OUeaN2CQxECG1 gGVOqdnZymSVtPootFFT2ZPUtjRjKcrjNWXmNa8X8CUYMyalCRwJyHvABGviExBtmQHdlxpKEpZ3 qsyekiXaYdETtOHoiLVRtNmudzZVdOumbVu7AEGMNghR+YfUGxp4ZcoydJ0mQn61DJZz9y2NOpZR FNjNiZmwDPuGj04UZZF5SkrEp+7rH2diGS2iMHQbBeQ6Fta5ryx4bBlhj0Xrza2H1lu7AXvW0sR9 ERaXacM9xUitvGsv5Gp3vLBtxY4bzIMgdhht7VagFU63hglfqEKeVih1ze2AQT278ZTi184XHfQ1 m5xLE5Sk7gvkrSjiPRHbRut9CVJH84Wf/aGvDWmn3s3tftS7t9sU2LhvvDLsYXY5vBb2UWtb8LQi YtKw2PfRz6ymHO4OtftbB8ccxGoQVvBf/5c68Wi7XWVG8lNZGjql+NHtOExQh5hQuC3EOukA8hTJ 0WBmZUntUmQwmrbhzXIKUqMpPoIzqVIG4uKeMsGVUbp1jX4Cy8nC06OlmwB3CG9GMe2lBKC61WYp 4FaB4wL417o/zTt+f/v5x9vz45MEL9BnNLLIp6fHp0cZCAGcHv6FPD78AGTOyQmTENJAMtInbPYF sGJSYwYmsE7C5q+P42cGWkkPhJsxLECs6jQKVnOMGLr1CaVwE6E57MAVf5ZZ0Tce9pJg0/oY2y7Y RGTKjZNY+lFRTkdNJcxk5DHCUC4QPx8Y2Y4hnCTbrs2wvp7Oq+1mPkfpEUoXC8Bm1SK9INVbxbE6 G3iHdB3OMediL5DDWh8h9cHWscOKzGK+iRaYA6eXqOB+AJWBhTwPXcWbHcdvVtFCH0lTNZPhKh9v o3ARzL3u7l7uRNIM9Wb3Ah/Esn252Fg3wDtyTMPvnxLb5ipone8JrzRF6AQOK4/4eREwOaNVRbrJ mD+na2wExMdtaGPiDPPgQxwEAeZHdc4aB9CCiwdwBB4YD1oyxxTFxTwfwpbJPE4ZUwrz+iNivW8d LUOqNXdrAqlK2Kh3BbUGc6dFGU2Y2NysXSmrN2Leo/ntguMEPUiSnZIuSbZVICh/z8PO0od7IiI5 gslZ5MYlOO34O0RQ6BQDvWpOcpxC5wvqPh2scKeB2Y8VcVOLMSGlVI09JfeayJoYirTBVLI6lcl9 ZgYNCG/DmE5K2IYce2XNM/H2gbQJF2RK2jmkIIooVhPFuwe4URiQG+2IG6uGC9uzCcF2BvREa9D0 RDdrUFcyGU36/TD6ro4L2jJe29LC8mvcNweasLQhWJviOQ5VfYmie2PCzNoWP7ptYG2HQEKusja4 NnIRUJztD0jwVncHsed2LVPE41QxRT5eE3J/iZamN83R48MBRuN44ZhionZpuUOMMclwT50eQFIR vTxnpJ1detCR3ev3h8dPcMPyJL1CQXCwcDmfG5WZVHu4WRwbuWMIP7hb+1AYMZxu4o3kgjzWJREr rF8AVDildJbGK6lqs7Fp+8ohgAlkU9pwZZ5LMrFxC8vCHE+iyS22r5TxYj6vi8KKCSOV12gRteGj 6Zy1cPCP+9Oa96zmTef3lCf0LEaNx7OG4UMwnnhygAwz/Jx15c4ES+8pw6qjA+p//PrpjWbsUVrG qoHgA69RzP0e7oy0EZwUBwKQVJCRU566pfKE3+2iRDJSV6w9qdxN2fLm7en1BUYoDq2nHyvg3l8P 6qoSeV9cccBVxaZnCx+mJ0IXfjW70AfKoR440asT+NxThO5XrlZRNH4oh7PFOPVph5X1QVgYpn1o MTY4IwzW1oY+sBINhlutIyxwYJBLT9CYadEazGdaMDDkYPBsQ4NgHZP1MsBQpU2RaBlE5hgdeGrM 3K4izaJFiM9cS2ZxR0asnpvFanurqcKaw5tZVkGImRSDRE4vtRm3NDAAWRnUW450P6+LC7mQK1on b/KTJ9FxkGEf+DrEHAhj52dhVxdNfBQUpHWtZ5RC0nBpxSsak9VQy+FnV3LLtTEQO5KW+JI6iuyu vtvPewk4dBT/Lz3xmIOc2BNICd6+fyvX8cwHlDNKx9fJpegTGXmViwMNMnJpClqJCWg+5ammYBKc ggnHLOeaUbP8sgzXsUaxfRGDlYDG9RlVYW1AUr4lXV31AdXfqBuOIbYbLAFQ8eMrKQ3FRBGhT+xk f5uueU5VA3fyUS2xMxcqNiFu2TaomX7zYZwgjRmZjmI8bFhwOaYnjEWKyCt9PLdzKQHoXB5X1BM9 oiek0MgwJT5jSwd5UZJ6OC6TxtEzQ8XKTJMIKPv5wilSUOQoKRx6mOhkQlc+CCaU0KUs5hOKBYKq aCtr01O+4ofXRwmfwd4VM9CUrJvDrVYicASOhPzZsWi+DF2i+K9MhP1qk4Vyq1ZDi5qyHVCtbGGg 4/jgiqfjZJ21VdfCQ/D9+5+tYvxBta1zLDGkUe8+QkqQjOpUX4fS5VzoPObbDJwUm+sDl2ZNMD9Z QJADb59F88Ae59rswb7omCaKqMZKxfzy8PrwGU4BJrZZXVsb7tl3p/U26sraxutUmcmSjM7JVMIZ AYoZ5DtMBid/en1+eJmeK6tFtqOkSq+xdX+7YkThao4ShaEgtqeY1DSRWXiFCRJnylm3VJuMYL1a zUl3JoKU19wdo73YHnyEmC/KFIp1/oOnjCTDQyOsdnrMN1MmoxIO+05j8krCG8KN9wi3anIAnRpE 0IpoW9M8wU+vzPe62FEHFsteCobq6zCK2t5Myr9/+wPIogI5PuTx1jRtTT0vtNhFMJ8OB0Vv3Tkv OPCOKavRSEclYW8UBtH4pDbzPc+Qmngc5y2upw0SwZrxDXropUX0wve+JofGPh1E+d4meuSExlkS jg10/QAI33oFtm/X7doTdqVLqnDvhGZXJbYAa+aep11aom8+sm5MNSnEckh7vfcmMJE+BosVuuw6 a5U72uK6SvuYHrfcXDROwndVnntwugP3hAcWHws8uhSQeGDhNs/3AYep47iP/Hju4aUm3Qjpopam a9Dla4l67J1PEAApMq8NX81I0566tek2hCgod1wyYVAJTSNPUvuSSkFN4I/Gdoo+MCRAY+Jm0ksO 4HJ0EhANVw9luTK+Sx3w7Akayi/lOHMaBPdwW2otEC9wdVFSeG67l42CK5+Kve/m+Gz3b1p0vPT5 hv9MSBIqUOgpGbWSp0b+5MRrIqGxic0UM2HLMPykTTRXVTX+PlkE8JO64wxcuZJOz/zPcLU23JP0 7MG2OpbU8uLB7y7D3W1iDB3iI41PqjeMcRqLv9JwOBv9VmaOHOOTXGNFVQA/U2IXV6Ya0nPA6JIn mtOHpLUmKDk1j0RMbt6ci9o+XAP25ITU4OEFxtXOLeQs3rmT19d6SpLvVS8WH8twibyx5rg204Tv 2E9mHFF6nXgYerDSiX46jij1waqG192uKOoBvFC5MsMYcQKbFipgXcjOLYRueLByuIEqHTyiE62V m6kjrZJgY1Myj6RyUNKAnDXYTg4cDX8IurBdP0kPxY7VU2IpQaSHlxz0fkDEG99YY2DMhGkq6F++ v/28iZOqCmfBarFyaxTEtQEzMRDbhSOZJZvVekKDNEO3OyAcNCvx7EI5GRwjx2TBdYRfbXEAmMAs KjlzZCZBaDdL5RuIfbmx34szYbJtV25zBXm9wDUZzd6ufd/3zIhdtyCIydYPUxiE+NfgccaswfzP 28+nr7NPv0YU/9++is/68s/s6eunp0eI8Hqnpf4QmjLgnvxuHSPA8AREE1BHPK1NKFzVIEFDbWXX Ydp4KYLnesh7Wqfua2D5ewnM6O3DE81KFJlDTlDHMSy/eUzQC7LUB8lqNPkcmCpssZ9B9G+xtnwT CpxgvVNz5UGHxSHotLIXWAE+tAbFjAeBqtgV9f7/GLuW5rhtZf1XtLq7U0WA71uVBYfkjBgRQ4rg jChvpnQdJXEd23LZTlXy7y8a4AOPBpVFHE1/DbDxbgCN7suHD7dOaQcaNhYdF8oIs6jN+Xk+IJNf 637+Kb6/SaS1uf7O2jv6rdrAXX9LqC2uViNL0uzgyx4H6sLV+9RqY4Fp6h0W34Svz9urXKHuWQ1C NwrKHGJwA6onlGz4OIXl2TJVANKcRlfRgWr6oFRHE2K4spcfc0DzZTp1rsogudonGYLciqmR/5+d LxuY80hA6hLqEbKxiZRlWIYitkMSDGCjDHsbp/TOXkTQOtUF8T2QwPupoPhWVICLCbKdqdjCZmLe DNA9HODNsdE7n6zxyXzxDLTJfiSlY2osW1/+8Hx+ZP3t9GhpHFsD/vX556dvn1//xoxfpRyXdYIA /sU96tzyVjuL/wx/E0Bbonoqf4JWica2Tujk2RlDhjAE0RIz48HNPRrwsjcDWoqfO7EFzmMPHE4t Ae3j50/KK57jb19kWbYNvOh7sDRrDZJnfLYkM2avQOs3/wDPwC8/3767qszYC4nePv4X86IowBuJ s0zk39m3CYuzdyf9KnNzhu2sVojmDB1A/w1/aSe8ysOXC6ipDctQbpgLPcjpQqyKPEiMM+gFgdh8 IQ8wM6aFhU8kDib3SwxU4cL9WMmjtCWaigctYRzLzQQZDxocuM6BSWNCdY6b6Rt4SdQMj/ZUoKrE bvDtYBsy488cNbGSoONOTVLlJXWwjlL2+uXt+z93X16+fRNqkPyao1QpuVnVj1Ze1VPRWzWwNbHj iU59/pAlXDcuV9T6/IHQ1KLyprMZ3XlrEe52tG8KF+eI/hKuKqKkvv797eXrb5biorJX5hm+ilZV GthlAip1ZZ3pHo+26gKlLPI4dJPO9P2kxyx26nfsm5JmRNl4aPqCVW7VI46VWx9GbQzNh+5sBERV vUGMxxhbtCSq9FSnjszRJolDGY9xFjqlH3uexDnxfmF8ZFOWWP3tUh5I5LTNE8tC02RxIed5hPci t1ZWZ4m7tXUYs8luDhkrBp4XkgRp46ZWIOrYVtVQVYbKj58RkgOTD1bhXfnksX6OVIbq1cQ/+bAy DLMMX41VURrecey1kRrKQyFaJtS7JCKsMvPiB2yIzqkQ1CxhZ/oNezJ21U8EDlKdNZX8B0K/yc3B prvoieY43WBf1GEa3sZScRrlgf59Hcm0HbaOkCdjd7ZBnk3oxsBPjV6pSEn0EvLPL4Y/V5GP2snA Wx5m1ZRCuM/H/MoBBQvif8GDWhrrHCS0akFLjLuBNHg8Zl06TxagLnH0XHRjAROwu5IGvf/lKMQd 4es8Qkl5R7o080iXZgQHsjqIfLWa1SRFJ0Czu2g6F5zI34orpoYobKi5efSskW9sTCzbO4RJ/Dsa 91UKhGCU7TNOtTerPTw8BlyrErkI3Gy3gjN5Yd6OZSGsiKQi0h6KUQy951uW9SxLAmPphpNNeBAO WkSQ4BPqkh6aLUF9l2oMmeG2x0Dezz3DDw8XFn5AA4TORRDoVlFLksMjhVfmXsA+37bh+wrTZmyu arxdRCOKVpiNnN3SOfqHwyLWNJIGEb5mWUz/IieK+r9YKkvrCxbS8B6+oFfKAol8sxwNFrBwtH2W ClUZ6WCelWHLGvzJDK447RgmsfbGTpOFRHGaeuXMsel74RANF5F4crOVQB64cgBA4xQH0jDG5BBQ nOV4g669lh3CKN1lkbplgHsNnlv8VFxOtairkuYRwfrzMMaBZ9ZfPjOMeRRjy430XaZfq4mft2tT GacWkjgfJVrOlJSZx8tPsa/Bjl/X0BBVGhJMrdQYIqLdVRn0DKMzElCjOkwIK6zJkfhyzb25hvg0 p/OQFG9wjSennolg4xlFdb3PE5HdcBySg2ClFEBC8VIKaD/Ih+SIkVx5mAZonrxME9TGfOWYmtux OC/+YbG8+7quEPo49UgBK55QVBQIP7IriZrKRSFLN9cmfhD7/QOW7zElQpvDIkPpHBk9ntxsj2kc pjHHsmUlCdMsBHF2sj61Mclsq6UVogFHz2IXDrHqF65Qgkxd6n1zn5AQrdnmwIp670OCoa8nNOmI vuBc4F/LCBFF6EMDoTRwkbY512K5wb6k5tC9mUFxpEiuCrBVChv23ZgbfOhsr3GI1Qvp1ABQggw8 CVB0OEvovQJHNEGbVEH7Mx6s1PiTdJ0jCRJEbomQ3AOYESF1KN/rLfJUIcVrQ2GoSwONJUnwZUVC Yf5edSSJR4EzeOJ3hciRTqgKkCOdnpV96FkOxzKJ91ZeVp+PlBxYaesC24xf6kr22j1YEqLdhu2u HwIOsczS2JPZ/nIqGDBdcIMzvG+LvdZ+Mo84u1NVy7C2EVR8cLIcV9o0hpiGe20nOSJsspAAWoa+ zNLQa+W58UR0r6jnsVQHNQ03ojeueDmKIYx2EIDSFD+k0XjEbnN/IAFPHuxVz7mXfsiQ1QMOq3Nj uPSe1zVLEn4/ErQ+BUD3J0nBEf69n3WJtOFi2uKqN6wW01iKCVMLlSFCd3EaByUB2jACSp4oasez ysR4GaUMk3ZGcmSxVtghxOY0Po48xdY7zliSoDUuJiRCsyojeyO/qHiaUXTnIIAUU4tF8TOKAM25 oAGyTgEd612CHlIso7FMI3SKvmfl7oowsh5cYrsZAh1tS4ngp3waS7Tb2MCAFoP1MUG/em2KJEsw LwUrx0goQdep6wgOf3aSPmVhmoaIBg1ARpDtAQA5qbCvSYjiVtwGz/4ULVn2pzLB0qZZjEb3NnmS M164hKb3Rx9S3x/R8snDRGenjhuvrSMA7CvV+eNmNAqTfGH49Z5JECVvbLj9MNViqlk9nOozvC2a TaghEFrxfGP8l8DN82lo5PtB8GbZ7+Vb1cpO7dRdwUVef3tqeI1JqTMei2ZQgZDRJsOSyIjXvHcC I1tJ/LkjjLvyAgNYFcl/3sloE868wL8eh/px4dxtnEsrXSYatwmIMfzSUcA5XMd5czCepfCD8UNI NOg27jJV2YCjMDz1gprEJYZ22cgHO1rKrbc7bPhQ3Ng8R5WHEqK5ObIBWTteBiZVCgg2h3KvuHFQ vAK8w3byEt/K4SRdZAdvvyVDQw3obIY/WoXoXjekLdXvf339CHEXvR5h2bFy4j8BDY55CK7vwLN6 ZbJAscVMpi5GmqUBmrN8Ux14wkxLhiqPU8KeMH8ZMvOpp3r02Y1mxvwFum0AsNF8vObrAVk9q2mO IaYk22+PbDx7B/ecLG84rhvLJoBDrBC7IFjRmNpCzwdf+ItpjcF+or4g2BnDAupHSSstRLIhsb/U VXvGDDBk+5QknEy/iBp5p0wLh9HiQhm/9QVvSs1YHmiCCexINvdDvaDpzg+AoEzdtU8oPxZ2WX8t zh/ESO4qdDICjtVqxUgnb3bQoBobGpsCuJdBqku7NywzPU2THNtArHAWhU5mWR5geWU59fdzdZOD bTI3NLO+NCawiTBpy/nFRq4/QLTborer/dr09SBfiXg+OtTjxS6G2CvGortiVTLb+NhO/iCj1VBG J8p7Gou22hxpRF6XS56GKLyJ0mRyzLl1DhYHxEkGRL9Bn2R5eM5Eh8AnFZUH6o2hOExx4E7nxQFe 8u4K+sxL/f0O0MZG7MHCMJ5uIy/hCN5qvrYP88jXNeF2MsucDFvmNmjRMo/PNbiNI0GML0Hqqg69 clFQ6kxBip5hToQ2OA/sgoLcojihfymUKbPkHYYcFVaDrYl5obpL4IqoRdD+lJiTPFdj41MbBaHb FXSGJIh2+8pTS2gaoiOiZWGMDk0pmGWXJ6eAKYutGXIxK/wHIWLr3QL51xW5QtPITvjEYoIa1y8g cXqCNAz0TZEStLq8oEX6G/qZFhJnDZrNTRr0VYzGwM24XwsSB/tJ89wtf1nlYYT32UHad/VIP1ns IPe01uXbEGhUbGg6w35mJe5Y1W88ysP+tWvHAo3rs3HCc9uLdCdw5hcm98xIfrBFkzu0le8dAcSC ffKNbIMLlnXU9enKVJRjlulXLxpUxWGe4dU0K+n7Wc+DoK06guY/40LjBLsolGVR17GW8pn0mizm JbaBUXTus1gInvxYnOMwRnXajcncZm30hrd5GMQeKKEpKfCvirks8Uz5GpNYA1N8qrWY9mtPGvNM mJCAxGifWZdY7JNjGcYZ5oLO5EnSBC/9opHu5gBMYtnDhAN9MYlyrEQSSgIfpFRXHIqpF8ozDyQV aBSbtxumumjiaeZNmuW4MH2WxXi5hbbs6+Je80eTJU/x5gbt+p1O2B8vHzwRADWma5YFSYCVS0KW u2cT9OyRN65HCOACb7B2ZXBUcA1SGjsqAqesLzxm6iYX95yXaFwxy9Jkv+/z9hTPgY6QHIQSFpMk 3B/0oMXRMPFnIbTRdzrFqukitbWqs/7syb+QMKaRP3ulsyLZuwfeGIvxIsNAlL4yI+WyATMo525s jo1uyDiU9mCG16zaatc2Q2mwK18og+6eFiL4rcCWtJE9U6OvhZZIsiBIkQXDr9fSk5R35+f9tLw4 P3eoQHC43aMIEwrOw6HyfHNi/f4nG2VhiFUBY1imsirB4YrHzSU4hpYW2ZZTEnkCevr+8u3PTx9/ YA8TixPmmOR6KoRKox1zzwTpEeXUX/gvZPWSU+lvz8QPoQPB+99DY1Kr/lZcJs39hI5JW0vGMCqv 26N8o2pgD4zPjimMm4A1lfga4+B6se/a7vQs+v4RrzlIcjyAx6D1csDLB/47bqKmK6E1D8zztnku a6kHMwbaODKHAJ7UxP78BKf9XWvC16FgWxGtdBgdQq7Lg3cEg+ryYZCO37Maz/VqSc3L+7r6RfP6 8Pr149tvr9/v3r7f/fn6+Zv4C/w6aCfrkEo5HkmDQNNlFjpvWpJE5lekCwwIYiXU1jyb3EQrGAf6 66w9gdSF4MA0P4JrOp1stvlQVPVOlyhYJYaDFz53l2tdYNEyZN2eaqt2r6KhTMqlas3SF3y0uzw7 FSfqiR8JeNkMw4XfHkUP90jyOLV2poeuvMcuJKWYyjEWeCcxhO2LswyJLKu2+vTj2+eXf+76l6+v n43athA9h8PQVKcayXVDjMybxR/53eH7p9/+MN2Sy+o6F+B3eBJ/THaMJkcgNzddjno8F9fmalfU TN65fpSjjBF6CU0zXXBpANj9lIVxih3SLhxN2+RU972vA2FkvI7SoQg9i1s4WBPQLHwcsdRD3Rc9 unwtHHxMjb2JRk/D2JpGlL9NayKrjpNJGYhuwTJ3bZNgeKuRnyuuhd1j6km5IpVRQfnIsf7UDfDQ X074t8dLMzxYXPBafPVeJnvV8fvLl9e7//vr99/BoYbtjPR4EDMsuHE3ToaPB7TDoVnJjxxePv73 86c//vx59z93bVl5owUI7Fa2BedL6M8vOtJGxyCgER2D0AIYF01+OgaGpZFExmsYB4/YZSPAqgdO Zm6y9+lGyUAcq45GzKRdTycahbSITLLmHkejFoyHSX48BYkjI+NxQB6OAW6lAixqLHnhbmShGEiY yQ68T2ulE2GjXv9xcefB/wbNt41fXERuzp7ausKSre+kHaSoxH43sR6iGSB6crXxuBdiWnp18oV9 V57O5BhiO1LS0lxjGqQtvlPe2A5VQgK8iTTRhnIqz2d09LwzRhaR7yvW6MqBowYvjLy7nLVmkT9v HefOPY+J3MBjb1ug4UT4WbfPOlezgz+D1OsR92bCrdbvXBdiU5d5nJl0oToqHz1uPvdPVd2bJF4/ OhMF0IfiiTVVYxJ/Fa1gfgwos59yQ3nnqjpAdTbqSZBZM9UDgHjtyHIJ1K0BqN6+vYiicTtPgGVF evKsns8F3Pmz5tzpe04pj9i5qAhZITVznbdNt64Vcw96ySG/PXTl7WhleoX7VfC6L0A/Jv2NWkVx zub1lKu3J7MFL+BoaUAa9sKY4VDV4N9pA0gMza+8j7oZu11jSwFNbkCsv0QBkV53TaDr29CM7qZT IUsTKco8vcEOuLTLNIf79Paoxq7loiIZekorwbFpzECzG1UGL/UMbLGPzRYfHxaVeuzMZ9jjok/C T6gbKoEcxsy8aV2JNwhW5DgU0rjKIiD6lkvSxPa8tFq1m57Fmoa0kaRb6XlEM2LLI6gJ7oALwHE6 Wl+siqEtjOdMgniSlscmrS2eXUaVOkJSR1hqi8iUUxVjrrIIdXnfSdNbo5DNuWpOmAXLBppL40av fn0n2WQKsKSyyPWZkzANMKLTJI4jfn2FqLjT8YGGu5uQApU1SVFPKUrcsW6zKbALoajW8vTQDSdC zYNN2Vhdi5pSAzQlURLV3G62yZlszozGVo/vy+nemjWHph+bqrYlGFiNHtjOWG5lLEm6ticn76bI TDP5jahmFfur8mK147i1imSYKPWJ9cyOajiroBnVf4q/fvv0ptk6y5a1urcgqMZx+0ChtAevLMAh 1B5J8HauYtYVDnXdm6uYicn6+IW4X+jBJvem/OXufEUuEmvkTKeIClYnAD6UNydWeGpCcVwbT7QD gwuUzXclVQcxPlEEsZ4Kex3WcDGbk2APDek+eoNh77THzLEEF/QUkDdhgD+sm9mWXZHmEmftju4n h9oVVUh3W30k2z0CWlksdUKOD/UvSeTMM7fzfTua6RS9kmYWQHRUJK+ydzFMvBVBrOBGMM2FfCmI Hgh7IZdFUzzaX1wBNQj8X78lx8auIhnFuQFf6yb9UFY0CAKXGQ4jEpfcdxVKvEfIo+iTc3gcpyAy 1IhvzQfxISyzWY0L1VU1Kmdr0k3HJ5PScNgamjSZYwenN2ad1IfugHDCt8XK2gTB5EHHgpcF84Cs Mw0mF9DjAF92sq40MxMEpeKaoQtmZHHrZ27vLLVWZsFAU/ZP1NLzum2Ab+qQJUtCaQ7Mb0/3DR9b c7JVfs3eyjs5hu9+f/t+d/z++vrj48vn17uyv/xYPF+Wb1++vH3VWN++gdXSDyTJ/xreMuaiQKSL gg/oo3+NhRf2ZmQG2CPHATFihY6AY5w3znywQH3V4JEPdK5ayPMuk9iNHj3xuBe2hk1S0At+Nr3b AMbooOCxIKEELNU4VraG+ZZsQCFS4GEsr7zC0vLuCLdpMkKG20lG9unj97fXz68ff35/+wpnLBwO 2u6g+71I4fWrx6Vk/z6VLatyTzmXE8fkbA1HwEy6jfHyycZG0PHYnwr8C9JBvFo/FsVLrpHISzN9 vC9bWhurisvtMjYt8iXASGovLxsyeZFkBzHtXR3UePiho2kQUA9CSIb1mgUTSuVOz1u5cLkeIhJE ON1wlbPRoxinxzGeT0JCnB5h5X2IwyxBS/sQx7HnEerC0pZx4nGWt/AcKpq9yzPeeOnbigJDycO4 tfXBDUDKqwCkghQQ+wC0KuCUoPV5htB5YuKxqzW50IIAkHiAFC1hRK0n1hqCu3HQGZAhpeiOmxQT 9euZC9M0IR12BvBRIcBQ+aZBgAiXNIxyXMo4bHE3IQvHRIOUIgtpVaQUGzsVaxCZYY/kmwVrnhKs 8wk6xYpT8ywkSOMDnaJzkULe6W0zk2X2vypnI0u8ZypyZTifu9vwEAYhIhorpjwLMqSlJRLGaeGB Ymz+k0iSeoCc+pAQGxoqM6Q7Mc6ynCRgzL7tpJ2K0blAvR7Re/CFW2i0JMmQRgUg1YNwWwA+FCSY I71zBnyjc4H3hydwZYkndwH4ZQIQXUsFGAZYZc/AjsASfldgMTIypC8tiFdkhfpkjgn92wt485Sg ZzyJkSJG2+4iMYxxQnAHtDoL7klh0V1PYxs7O2SJwOmPOgv1IHi5VnSoT4YB5MbAhK4sNml9q2wo MZW6GY6zmqrUx71COnsSG+eMhgGySAOQYHrbDODNvYCenijgKEYNeFeOsQixFQPoMdYSYyM2eIgK LPbjNMbWfwkkHiC1D8lnAF6nYQUCKEXtaQ0O+yJiBoSeiEzQo1gdI4LMZuOxyLMUA9prSIOiKSky RWsg3il1Bs+AW1lC4r2tMfmcmycNrsqJRHt6w8jDgtK0RmTlSrvxIJiefqkKEmIqgnxnhemnTyyL 7QPThU5DrGAS8R1yLgwZnmVKnCuYBfl/yp5lu3Fcx/18hZfdizvtR+w4M6cXtETZrOgVkbKd2uik E3d1zk0lOUnq3K6/H4DUg6RAp2dTFQMQHyAJEiAIzMnIPRbBgtgMNZzYxBF+Eawq8KTcITmvWiDJ 2bOwJiBXEWLWoZuKlmBNnWUMnJ7WLS4wo9GVnQ4AZRPQVV6tRpe4PeaTXlxdBoq8JI53CF+TesdX bae4WpXz86oSHs8ul6GLbE2hVoslOSs05vwmCySrUIi0liRn9Xp5drHn/bU4hZgTgtogLkgJUzIM vck8tnQ5Fx1ri1Os2XfR0YO0qQzoAdEbY7tLNBGP/e0AaFth4ecQyltVPN+qHcEbIKvYYbDy1qYY q5Dh1sRY0l5P95itF9swsiYhPbtQ3I7CoGFRVKuidtMOGkRFpnXUuBI4MfoAgYK6ctNYaV9eaUiN 1zIjxvD0WtA+0watirIJ5JbVBGK74blHYeGjHa+qW5cH0U7ALx9YVJKJym1zVNRbVvk9z1jE0pRK MIrYsipicc1vve63d2ZuneV8Zj801zDgkhL44nczhUXkIW/LikvplgLTZlvkFcZM6okHGLDGJeeZ RJhTLk9Z7kN45CTj1bDCZwX/Cj0NMGLLs42oRkthm1SUs4xGpUUlilr6teyKlE5Erz9Sq/XCGzdo VDfJbegtdztUR+jvHfnVHVgKsy445faCH2SRC8o2oBt0W40CRyFcYPq7wDdCcZ/8C9tU9G0yYtVB 5DsWXjjXPJcCJE1BhURCgjTyUi9oII99ZqQ8L/aU9VAjgX1jGdNBm/iLOwA9An7YD597uJ6rgxMu gKs626S8ZPGcXuNIs726mBKfHnacpzIsGjIGY5/BdPNmRQbDX42HL2O3ScpkQHSDdmfW3OgzEVWF LBIy/7PQHkawM/Db0Xd1qoSexIEPcyXcdueqEluX4UVlnB2ckkuW4/sDWGvUMwJNwXPgi+1dYKCK pbf50a21BFmaRjEJHHx0aTRMN0+WlSCIkPki8sRnWQk42LjEFQfSmLuEVRFFzGs5CHaCD5Jlss7p oAcaX+Qh0aZjrqciH5epOAtJN8DBfIQdnEt/sKEdZUrGWNVdysRIiFac50wKyglKF5ixSn0pbrFU 61hjQUf7Amw6hcs2EIKS85EAVzsQLKFOqh2msPbTltrQUcU1nnuaUi488Dz5yiuvSQeGu5Ivr4XI ChUSrUcBk9ktGsvVnLF61sHC8uLrbQyHJDdvuea1DhHZ7MjEtPrkk5bebMZkiPM2JnV3w0mc5vpE W+Qx0/h4xN46cc+eLU3MnbcaTrmbF4D2SUGJRBVYxvWGkhWIMQLU6sgn5fpkQ5rf/2pTgFN91cnF 267ZCYNt2t6TyC7Vammxi0STCqXgVM9zOIxZMZYQ3/p2u0DM1+oKde0tw2OMwkldkWs3nxQz4Nor zxSV592LCAvMKtwQmWx2thQ1CZQtMs9jWX+Z5yClI97k/EA9+TUhDR/f709PT3fPp5cf75rrrceF O5G6uJ4lr6Rwnw1qtOMtH+h2obboHKJ4KqTHRkRtUr0bSIVLheCo1CzFlCwA8B8n290GBQa0C9ik YhMv9fe5OyHzTkPScwyTxZ/LbqyHZnV5nE71CHx323XESQPwQFt4i3bHSkOrotA9bZTHC41VCket eyHrYx2v6x6ayJSA7sjHUZrpx3o+m+7KcQMx2dFsdaQ6nMBQoV+I12eHBoPtX8xnZ/hSDHxxviz6 FkfUvYRD4sROdT8mUqXrdXd+sGp0ayQaJdP1bNQZh6Jas9VqCap/uHBslxsTtYNKufFrRLDOXYZO raRkNg/9JtHT3fv7WLvXyyDyhls/HrF3XQQeYo9KZb0BIYdN838mmgOqqDCm08PpFYTo+wQ9tCIp Jn/8+Jhs0msULo2MJ9/vfnZ+XHdP7y+TP06T59Pp4fTwv9D4k1PS7vT0qr2Qvr+8nSaPz3++dF9i 78T3u2+Pz9+c59X2/IojOpQjIEXpeYQa2J5aiAO8QXkif18TyBy2bjhtzlyUjrnrjhp+UJPpZgyy iyTrdAUTvJPxEbGbeirEVeRPD4OAFpz7cMviLR8Jao2KMfZWVaTjqVU+3X3AqHyfbJ9+nCbp3c/T WzcumZ52GYMRezjZI6KLxKSdRU4aPXSNh2jh9wJheisMLitN4fdzTGF6eqbmocPd+1u3n0bmT+Q4 pEVfQlgemUayUhL9oy2xetx3AjOp0wp8J4K9tIL98sBW0ose1Qfm7QIGNn5pauGMLXMk9wySiSrC uOKB/ndU1fViNlsFyhib8cY00c5xNrEw+tCw47baZmHRSQDtljzt3IuJskvYs46BxrVWsyajrlYs Op6VfEsWn6hYYOprErkXsqhIjCjZDY2g6TnM82AXOyQoaoF+JuvZnHyT4tIsFyFGbRmomp+MoigP dJfqmoSjJbRkOSb7PIencakM9fW62AiY11FYdLSEWaRAlVyEV2pHh8aE813PCnnpXOt6uPVFAHes g6Oas30W6H2Zzhf2m3wLVSixWi/XAd7cRIw05dskIDBRtSFLl2VUro9LGscSHkQ0JQNVMw6IIF6B ni8qWMZS0kXcZpsiDXRKheRzv8g3vNKvkamijyDjCrq3h0OA/0Wprbh0c4osFzn/dPZhGRFpg7Ub hxaEJqMnyEHI3abIQ8JbypoOJGcPtpqTRddlfLlOppcLetqa84ylSrmqZMBSwDNBXoi2ODu1pD7F xrWqj379e+kL4pRvC+Wm39BgX7PpZH10exmtFj7O5M92VaG4s2DYShDK+/ZSxFW58dIrhr0b1E5y 7DVBkyWYtVEqk2M0PEkE6LGb/Zay4unejVQVVTFQ+PdiU2FwtsBnojiwqhL+pqQjY3n6I2Zy1opI Io6q9o7V7SOaxBP4t0DnjRj/qrl29OYZar/w/3w5O3rWjJ0UEf6xWPoSrsNcrOybeM0NkV/jK11e dV3xTlyskN5tlDt2KiNPW+VfP98f7++ezHGYPneVO+u6MC9KDTxGXOxdYaeT2++d1zqK7fYFIu0G 90CT+2Jz2xlgQkMKp8XF1DEYnmm60yJSTTDQM1F/fSKYICkPGX5cQumxxCCRKY2+4Z4T2FZtbPI6 azZ1kmAYwIGul/FFLq1DvubA6e3x9a/TG/BgMPK4Y9dZNGo7c6muu2phpK4fZEt5ZPPL0P6a7XWZ P33YwpNTMif02AbTel15i2gTR20zXc1GUvZgJB4Zj1gWL5eLFdFX2MHmczIcbo+1M9lrphXXtV8M 386n4a3QWIs0/0kPjfAwuqt/Azt4WUhzVWqPrzaFOKBuxvhQjhuA/zWlUyVNsfGFXNLkvsklabhf dQKyArZr5RNmGCeoneYjnBM7w8Ac74+2YMrAkzTKb5X5MxkprB287XFg1HuqEVd7jOYNjcrdF4gO DngV1pctokbWG3nmbNXTVjlsxf+gSDI6h0PSjhndJ2foQp1LmhRW9T9oTdIEwkt6VDWL6Ni4BF0b M+0fkofWu0WlZyTNDWJqWrhujobwKsps8b29e/h2+pi8vp3uX76/vryfHib3L89/Pn778XbX3VI4 PcFruvAOX8eBRB5aSpFuV1qmtSub2P3OjFVS5xFqCGdI7Jnz2Tar8KgY2v+3pJTS0XXaDd7d2WhZ EcUYOWAQom4z4FxzTV4oGyyIA0xM53PJOCoEvxrNlW0Tb7al31yEEdGPLORZmYUXyD0fnI3l8wnW n9NuS+4ktNAAmLCBSCcGXaOZfNwog9zFCykXcy+SoylWR2xej9MQYqPVz9fTvyKT1+H16fT36e23 +GT9msj/PH7c/0XF6DWlZ/WxKcUCD9jTpW/7sLjz/63IbyF7+ji9Pd99nCbZywORKc20BsP5pior 3CAqBpfvBYYjbvGfNTRQnzMV4IDYyINQTrqpLHJ+NBuMhESAunBg/UWBxCcXbpwqJG4VEGM4z6Lf ZPwbUn5+24gfdxcFFkjGO/vU1oNAl9OmTymdIGUD3hz2Bls9ICoRFTv8i5iXw4eY0JKqsExVkvlF GlSC/5OP/zRLRAI7dzz6lE4FA5hoc2m7ACNoj5Gp49Fo7WuYxx5pLXeRX1cN7RMrmAGhNrbhX9wj so2o7Wt33cib3ZjDO3lDigTNhkLuxIYFLhSQIlPXFOOPPHetTRnPMIMo5feIF/6u85S+P9fBKClY oz3XHAc5xG0q1O9ztJfsDqhA51sejyQSkI5Xtv5+HC9Sg5lcrC6WzIPqfERTCuhEd+rAoVzxPX5K PsHRaJPBwauqjNgV1uUzoYWH0l9qGjehiWkCZty6GLccwGR2lha7XOosF75TSY8N5Mse8NRlYo9d zUetXC/tkAAd8NJ+WjqwYXkcdaiFn2UP0qwWR6/ELkmSYsp1tdLYcRobHx/N5hdyGsgGqWn6LAVh EnwgTyaWMhOlTyvifqUihqkcwsWqNFpezQIJOfuZvaRSm5uKrcx83iLTt+Z/PD0+//uX2a9686u2 G42Hsn48Y7hywlds8svgmverfSAwXEDbGaUEmcakR+DkaIQwPdMZDugEcu1UJmWGenv89m0sNFon n/GM6Lx/RlE7aTJQ2OSuoLVEhxB0REqGOjQ7Dlv8xrmBdPCEI6uDj3R4c7p+BlrCXijq6tyhI4RM 34XWO0sLDc3gx9ePuz+eTu+TD8PlYXLkp48/H/Gc1B50J7/gYHzcvcE5+Fd6LLRZWWKM61D3WGZS wtI9LBntDe8QgX6N8WBp/pb6fUwewJpoWnYSRX0kEhuRenztKQT8m8M+nNNOPZWKzM5ItDrGpLba mW7gxgAbu3lYuP3IoGoi0GdsHAQc8zmYsE1ONUPuNNiOc566jTDHNpsTcGypGChmW6xk3BkT10wA 0s6bUKZHLG1gt444vUOyJtvaN1EDwmrHAT/208C00DGZc9gCIHdqbgFIZb/9TJrSkPUMjJ4eT88f jkGAydscTly6L+QwxxivT46znAB8UydjT0hdHlq6nShaBw2n9fe2pEDlgGqyYs9Nfhx6prZkXe6S wIxEEhBSpfRmXg/HtKGKe6pql7PA7a7FwPpIXGZ1fbPFUY0vz0XiAsq42qPfiahuXESMKUJahG0w QG2Gkx5UgIHtJiqkfR2EVcDJeOzZAggQJ0ePtKqdi2UAZcnKfTO4T8hTDK65LmLwUI1JXWEzvE1m Abs3lR1jH5fWxMZf6HhjVy+SaE+5l+/1LZAoVGqpHwZYwY7uw+LScl3et65qDgW20Gm4hoYurA1W 0qYMg9xLozB732Ang9/gwxfZuloPWR1ar+T7t5f3lz8/JjvQ7N/+tZ98+3EC1ZkI/fUZaVfntuK3 G/ftWAtquCSf6Cq2ReYOUrESMpu7OiCIPx47EtdAgkG5e7TZsfXSFl8xA9Tv8+nF+gxZxo425dQj zYSMrDnqt2dTBLa6Fu/LQRdbsqr1ffe/E5KdiaXdfQ6rdLR8Wtx6vly23usugsXwzwGjt8ZupBgb z7Do2ZR0axrTLW0rAYG2owAR6NXFOfTqeCT4MxDMpwEvozHlnHTZGNEtZvNz/VmganeuRZhS8Vw9 KY7Laj5dk9zX2Mvj4vMi1k5SJhd3NZvNiD50uDXx3R5xs8vZNIwj+dLhFme+uyC72mJXZ0dlb6a5 vSt2uKxMI8TAuLrHHYegjOaLFb0UOvxqcRYv5nOC0T1yMeYY/FI86lvuNyxmoGH7RsQOpxbTc/MU 34Rovk2Px1G9WxBWu5KUnLApHy/OLRQRlcFbhb7dNzpZvRvMtkV+qWguXmOi4bq9FvErjfQTmxgD tISr7YlGdbaYmAWLzuJAZnWPKqY21I51/GI6HQ9yxpEhI3AumtXSju1lw49HoqWIWU1pq4ZFcjk9 IxOAIGWbMiIXQq73CmoNGUxGzNFKxcv5lJhIcjWnUlb1+6XtKDDUAge4KIuJzuPtwac7Hexm4xWI Wxy970lGtPva/J8K6sEgIVLOiZNRtXpFU3zXg0IhFMHzHN8v1co5e1YqXc+u5rUDMcGZLb0aIXDy uy0VKAxRRmWMdInUtSj9IjvcgZej+rkLAXG/sZZjtb6cOU1cz9Zr7nis4G/YH0dvb7qzq1qt7CTP +vfq9yHoxt2/f7yibeX95ek0eX89ne7/ckLV0hTeqbPpwkGYVIPPD28vjw9OmsEW1BtEFG9Ax7/E DLB92/pg0/7tcHJQ6lbnsVGFQl9i/fxlCIU+4HX4E4Ne9B5XW9lgSNtNUbhudrkALVPCYZGUEAeR RpiGV1+afUJB3gpdy8upfSXUnd+9aOodGJtX2V69HaJLuTnGmPeyIwVBm/bIFvcUBe0nN+CLEo2E Z4l0xAWi3x0eveOI1lHuniMik/MwRk9Fkq4UFwvn7sCke717//fpg0pz6WGGghLB01g787mPinuC axBM9MHhJnUdaA4JvdVsizROBB1q4QBTJ9e3t8NtKBPppnAO52VE22s6cxmQU8ayIstq+HdvGbtE waTtw2BoWCl80PB82HD29Hx6e7yfaOSkvPt20gZb5xFSx+pPSC37ga5Jq54Br5OOon3Ny6RUO5Dk W4qXccaqRuHTH6svxl6Ipdj8tMCWMTpgeR1IZcRSLF/L2sCHmhPV6fvLx+n17eV+fNVYcXzdj2mz 7OffxBempNfv79+IQspM2nsZ/tQJhp3dS0O1yXOrvY2rkro0MWStdcjyN3GrtgyImAoOxfSo27KI Jr/In+8fp++T4nkS/fX4+ituGPePf8J8GJwHzCbx/enlG4AxyrvtfN9tGATafIc70EPwszHWJJd8 e7l7uH/5HvqOxJtnpcfytyH2/M3Lm7gJFfIZqbnq+O/sGCpghNPImx93T9C0YNtJfL9BY0gx0S3j 4+PT4/PfXkEtZRsVfh/V9sSkvujPBv9ovHt7FGao3icVv+la0/6cbF+A8PnFbkyLAtm570KXFXnM M2ZnF7SJSl7piPe5nbjSIcDdSrJ9AI3Xg3AUsFNxOF+D6IGzdG/Bb1s+cogZOtlmlusr40cVDRde /O8POFF176JHxRjihlXiq0nZ1a++FpNIdnWxpnallqC9fvO/gzPS7GJ5Sd8FDzSLxXIZLhsILi/X Fwu/c02p8uVsOR3BK7W+ulxY1uQWLrPlcjofkXdPD+z2ZyA2q8DlGGkEz5Vl94QfTSatTQ4BIlYu hfGzUnaQbgTDDr0tC1t3QKgqitSjgyk4qtJ7p6K/xOtJP7XMHrTeTU3vguVh/NRDVDc6u/c4uAje 38GhoLus6OKL+PT9FllihkvP1KytEY1CK0wgu3b/frKIvJDXw/7D8T0O/IBjbUplWMHDnfzxx7uW IEMHukQwziuVTZQ117AW9Ascjep5Cj/wFUMzX+eZfnDjHJ1sJH5Lcxio9EZp3uwQ08mjsIO8I0oB eDZ3UyMi3BwfuOe+1o+Ky4G+QBRVEbMSVmWRo5dmxrpCeWQBJi0dV7KKjcOo2Bpad5bM46oQMdnM XnsbLszFJt/HIqMOFDGzdLocJrbleK9/4uWe/a6mBZaZAF1LZyAy0SgPk4+3u3uMrDCa5FJZhcIP jFMIGvaGSftmbUBAjY1yEV3uUAsER5sq4tbDnDFucK0YnSddh+whgM+4G73eWtpJvtvTfFk1o6gQ OiNLtq16GukGr/bx0b4kkH3eF+rLjEW7YzEnsG3+eWtKtbXANse/8hZPTIS2vhI9PqKihk288oqu +NY8SrWBceK8mu1gTZLRXuc9AUuoe8we7bpJokUWWnTUbTK3eJan8Oh0hD7ILN5eXs2d7bgFy9nF lHqOj+g+a3R3A0hUYx0fitJOVOemx4RfKKm9sEsyFZmbUQsARlhFqnJ4qb3o4e+cR/T1Wd0+dxg6 OIpn0d3Cu0cXkyf+ES1EWpg5ng17loqYKd7g2xRWSdJsADhQs7TUs08k89ADE8AtzuAuPFyL+bKJ rRdp+Mt3YK64gAbi8w7bENcBox2PHKf+HqPTJIs8oQ4jVpnNkSlVkdU1+JQB5lOU0mjJo7oSygmu +EWjqMXX9aAnRchNXSjKIn/0anc+qmhjEqKKHFPtgWZcufHiHKIDq2jlGpGhm+dtIufOEGCeaRrS FPNoQ4DRV9NaFgZunnVmTF5jRg8SmTg30htVhXici7Rv0yCO5iPyAYeeoJSthh58fsRjotucDtZ6 +Bcl1TB0+mkQbzwChtUMuzza3m4dilBTea4N1yJgGgGKPa9oV8BEGh8hS9nyAcIAtOZltzFhY/ei FqVnr02rAegIou0bWrr5CQmH4xi+x2m/wBnpddwp0RMJBqhgu7MH+ibJVLP/v8qObLltJPcrqjzt ViUzlnzmIQ8USYmMeJmHZfuFpdgaWxVbdklyzWS/fgE0m+wDrc2+xBEA9t0AGg2guec3BEbhM1SA XytTi2nfZtWZtp4FzFh+swazHnOTjG9gJ96dsfwGKCZxjUvg9G3AJrbmKL1k6YGqNAOFPV+qzVCI YziIc2tYIbmFyaXuOIpIQxiOvNDmWAiM1cOz6lY4q3zQS7Rh70BidzsWpqDAlx3zecmmMJU0lv+j ROTT7zggmOGPk5RIQ0HkymVTD7NLVXBsq4YE72IAxGAEX8o8/TO4CUiyDoJV7qAq/3pxcaKtoe95 Equhn/dApC+oJrBjNmXlfIXC5JFXf868+s/wFv/Nar5JgNOak1bwnQa5MUnwt7SlYnr7AuNSz04v OXyco5ETjpXfPm32b1dX51+/jD+prGMgbeoZn/qfOuDg57UlNAnkklGELJeqvezoMIlT73798fg2 +osbPtIh9Oki0AKPA9ypFJFwttFYCwFxFDF5ZAzc3kD5UZwEZag43S3CMlMPZMb5rE4L6ycnrgTC 0G+iZg7seaoW0IGojcoZN0xnXcJ47aYb/0iuKKdwFt94pZwpeZa2x1URfOhuRvuP/ErZyQ9rfE5X pVKOqEYL8PfNxPh9qs6bgOAAcXUhUnPeEZCWj5eh5JuuyFz8EmWEcEkEOct2riPCmcaXoTOjL0Fc 0c1GExTK1Y9aB5cpErgYHIkLUAJy5U4JtQrzJ/ZWq7APGpJLrsnKwjd/t/NKf89VQN2JPvywiPi9 7cf6xsbfQoawzniIxWe3lyDuSO8OB59PvYxl6C3aYolJbyO+TUjVFPjKghtPu8bVEFuc9FCHj16P RzNHQWnJjhD+Rvs6sejwpQk8l8bruZXhr4WDC6tRAvBDsnSN5ytoKTRaEBraolVxl6e8zVsnuuSs 3hrJlfqEl4GZODHnTsyl3tUBc3Hi7MvVBad0GiSTI59zUXcGyZmrxRfOvlxcODHa65sa7usp5/Sk k6g3CsbHE3fBZ9yjQXq7Ls/MQQJtCVdYyxlztG/HE2erADU2y/UqP+bstWqdY7MvEsExKBV/qo+7 BJ/p7ZPgc576wjUU3Ft3Kv4rX83Y0aqxc8zHrs23yOOrttSrIVijV4HhOaC/qgnwJNgPk1p/GmXA wKmxYZMl9iRl7tUxW+xdGSeJanKWmLkX8nA4RC64dsQ+ZmjjpGxPkTVx7eixlv5dYuqmXMRVZNbm 1IyDhM0sk8W4tIfiO0Cb4X1rEt/T8zB9xI/qSNIur1XdWDMPCo+B9cPHbnP4Zccu0ZNDykLB33BM vW4wvZsliqR6K9K9w4wiPcaaKJJkOpQq9VV8xiEMWv15o87qYcHhVxtEbQ6VUI+15knTHMYKVXT1 VZcxa2NVjHgGZMaX2Omljlu4jqjwzNsHSQEaDqWgC0v0WonCpHCEo/ZlVanL86snqfM0v+Od4noa ryg8qPN/VJbkXlCwWVp7kjsv9diRwSSdcBY0L63sKkABzZdZm1R87hO0G80dhix5nBwmWA0KhBK/ fXp5e/j5+Pb39vOv1evq88vb6vF9s/28X/21hnI2j58328P6CVf55x/vf30SC3+x3m3XL6Pn1e5x vcWroWEDiLuI9evb7tdos90cNquXzX8ou4ty8odzHU6tv4BdmGkOTzF6JJHq6uuBtgYFXt7oBMMN BV+5RLvb3juGmNu612bJEVY6Qfi7X++Ht9ED5lR/242e1y/vara7zmvWS+aaf5oGntjw0AtYoE1a Lfy40FKoGQj7k0gknrCBNmmpBmMNMJawV3Gthjtb4rkavygKm3qh3irJEjB5kk0K0gS0GLvcDq7p lR3KjK1nP+zPmBQXahU/n40nV2mTWIisSXig3fSC/ioWuc5xG/8EFthr6giYvVWKmg2n+Pjxsnn4 8nP9a/RAq/Vpt3p//mUt0rLyrHICe6WEvm81I/SDiAGWQaVHX3R9acqbcHJ+Pta0W3Gp/3F4Xm8P m4fVYf04CrfUYNh+o783h+eRt9+/PWwIFawOK6sHvpp3T86Jnv5OUkYgfr3JSZEnd+PTE05r67fd PK7Gkyur4Cq8VnON9p2OPGBPN5I/TJGzUgL/vd3cqc+NzowNQuiQdcn1ho1R7ls0tVqZlEurP/ls asEKvom3x+oDHWNZevZWzSI52PYuxnjUuknt0USPNTmU0Wr/7BpJDHM3Gx9pse+y6aJHOvBGfC5s xJun9f5g11D6pxO7OAJb5d3eEo81wdPEW4QTe5QF3GYnUHg9PgnUGG+5qFke7hzfNDizKk2DcxsW w+oNE/xrc/g04HYBgi9OOOrJ+QUHPtUCBbutFHljW4DB/mSKAPD5mJGDkXfKrNQq5ewEEom3YdN8 zuyoel6Ov7JGtS4MphCNEDoAJUq1V6UX2nMKsLZmNIGsmcaVzd5L/8yinSb5En3xnQjLMCnXk5eG cNLzOA7iVTWf1kchYEO7OjkR2o2f8ZJsEXn3ni3JKi+pPGZtSE5tz3gYMqWEZSFcVu2V4IhwlAKT DfLrkMucHfAOPoy3WA5vr++79X6vabv9OM0SvBsw253c51bpV2f2Mk/u7fUAsMjer/cVqQvCA3+1 fXx7HWUfrz/WOxHcIJVxcxww60zrFyV7sSw7UU7nMp8Bg2H5rsAYKc9UnM+bjgcKq8jvMWaoCdE1 s7izsKjPUVCIzRQkyjJ3O8h6HductZ6iVD1rTSRp85ZwgqrJ08Y4SLxsfuxWcHDZvX0cNltG1GEs HsdYCM6xC0R0EsbO22HTsDixCY9+LkiY2SUkq/HZdBwbQbiUbKCjxvfh8DIUR3KskU4JOfRg0AlZ ol4qmd2MlkzXvOouxWdMYp9ML5hgcyhVQRbNNOloqmbqJKuLVKPph+r2/ORr64dlHc9iH53UhIea 2sxi4VdX6EByg3gsxenFJqvpChmchKGIS9j4VYUm4r4KDUtPFyxC1TQUzzOMgguFvw6621AjY4Vj rncHjLcAzX5Pydb2m6ft6vABZ+qH5/XDTzigqxmB8F5TNXyVWs4QG199+6RcsHf48LZGJ9NhzFwG mDwLvPLOrI8z9oiCh0cVXU1Tnl1ELkBvM1ILpQvDbwyHyOnmZBboEK9VPY1B2cFAamVlSSf1LERX gzjRMoWUgbqH8LFfegtgqiUU6d3cKS9qqmr9EmWA6W1NvMX10+LWj8TVahlqKq4PB7m41gwJ/vhC p+gU41cVFtdNq391apz0AdAbeR3aAJHAZgynd9wFikZwxpTulUvXYhIUMBN8uVrGDoOT+0rOAMy7 b51GfEU1748fiv98FuSpo/MdDagblIpaf84eoej5bMLvkRmC/Eo0T4d7wcQNHQeUG6ZkhHIlgzrD UoOSw8P59oH6M5C/amCFfhizewSr8ykg7e0Vp/Z2SIpyKLjPYo/NWtFhvVI56w6wOoINZiEwutu3 oFP/uwXT43SGbrbz+1jZgwpiCogJi9E0TbmZySTrae44NTDSKsRtzcHaRVqw8GnKgmeVAveqKvdj kBQ3IYxO6SlCBV/nBc6ihmUIEL3HpnEchGvJ8jI4OLSVSN6XhNm8jgwc5dzzCrp3UAUxNpAy/gVB 2dbtxRlsZQONlVHKOqSb5TINpeT/S5FQbBhvoMryzM8jUmTb1PAdp9oKd2qrap6IOVFYQdHAMVsd gOBasT1mie7a5Cf3GHCsVhqX16gocT4/aYE5MpSi41T7ndMb8HMQaKUyWY1fTfCwrctCjBHKlZbB ZHahFAMN3g4EYZGrYS8w6MYoiaIdjL2TqZao1K8wpI5B0PfdZnv4OYJD0+jxdb1/sm/2RF58youq 6VgCjF4ovDFZBOVg6oIEhHHSm8wvnRTXDbpCng2DJJQvq4SzoRWYhEE2hV5j5i9Ku/ej3X5I4vE3 IAnLEmj5VPDOseqPwpuX9ZfD5rXTXfZE+iDgO3tkhcNPdzCyYOjk2/ih9qC6gq2KJOblukIULL1y xtsC5sEUwwDigrVvhpkInG/w9rYLoehQsxLGhzyytZRyuDALYGMYLZZqN6MlnAmpNK/i7qujEAMg 0U0ZtoB6dSD6AVop3VencZVitjZldxgYahM9E2uWAczJDzvHLyVdrlRBf3fitMwK3XYK1j8+nug1 4Xi7P+w+XrssoXINe/OYPD7VHJUKsL/REwP+7eSfMUdlvidk49BE34QYTf3pk9F59UWsaaW9O48/ Mb5VYzECOsW0AY63RYgAXTSPoNGhPIHjUAq8nvNSIKKeQq2fjlWEZ3fhb02APgTCi9LeSGYX1Kvc vlz1oAHcCOR3mFWxnkBdFId4klB8PAN+nS8zllsSssjjKs+MEBAdg8MKA8PffRuk+FiN3UjhJ8/t eXoOvrulxtftrdApUcANfynfjSiFfdOl9RGqbhOi4sE1ZOHh2qCGfBtb19zDxJjFVhFGTZvzSfSj /O19/3mUvD38/HgX2ztabZ+0U2QGWwrYSZ4Xyo7RwBgY1ygmGYFEyZg39bc+ZSYe8xp8kbuGkVb1 xyqf1TZSE2ag/3qpSlg4slu7ibtWnqjDg5W1UZPh239sPvLl9ZAMU1FWYSeKolWGeXxEhaMQ8NHH D3qDxN5HYqEZMTsC2AnDQS4jlIkckY4LTDXmqsDpWYRhYRgyhFEBLywHDvKv/ftmi5eY0LHXj8P6 nzX8Z314+OOPP/5tim1UvRvQ5sPK3iIySRDrn0LbpP9SL3NZaV7sAgpnEFReqgQ6YeK6iDBhupU5 nJUTI4acwSrDxzVb3dtruRStGBzBFN/8/2dcNAW2Rg9zdUBIJgNTbJsMLzFgisX5/Ah3WAgW5djI PwXbf1wdViPk9w9oK7KUKrIz2QzajA4y541XDgWSotxiPj0fMdasDbzaQ3NS2RR1rPvoHG28WZUP qh8IRRDrdiqA0m+4PWVM8qB8+Q0lurAy8GoU6tdM95AE+TUpaD2/m4xVvJx5BRReq67wMk2R1n6z 58CGhLJWkoQ4Mh0inBKkMSZwcRi4oMkRcMmkEX51oUxuwVKjHSfz7+qcSxoYeXAul0+uib6qMTOI lYeMmTES4lMCtilFbINGjDZHhc8S0u/yig3MDzMthvYS2Kwuzrg1gHYYjPHIGlC2xxepftJFpIip xEvJ0qHbSaefm6jgB5XK6ZapMGsyw6UQiezsQ8okve3qybRe7w/IbVCU+JhiaPW0Vm/tFk3GmhPl xsSzW16CCPkuDgaaHy09/8CT8jGJIn5NlsUdcH1hAfEyP7/pplCNiClhptGQjHtKpFzUk6Ini6B2 vO1GYhdt5lXuiOUmkjTOKPu+m8L5/VTyamL/R5jDFP0GjuDJvpQneYrbwkVFgb43+Iz60cKAT+GW deKFIISVddSsTR2PwlsMpDkyMsKOIvxr+a0g6Sq/4M0K4v4HKGo2nx+hacfNrOONMOy4SwU8ZTp0 UzSNw4+WsLdkQ3TjMbZ3luRLN0WJdnVKz3dklF0hVISN2VzCYnEvtIcJCHaTWmcnrb94xYwxyIPR V4xUMTMheOUUoWVJvPgy7OkYTjUwsu0UGH2Ueg4n7e5FcpHE88jKoDDOIxNk2aP0dUV+3p3LurGk 0vzI1MIRy/dgYR1bs3Qj5jiCyUJMAmlaCNNeh9AdiHn+bHkZC/PifwHkazkY6rkBAA== --===============1338800576824378956==--