From mboxrd@z Thu Jan 1 00:00:00 1970 Content-Type: multipart/mixed; boundary="===============6199723159498557833==" MIME-Version: 1.0 From: kernel test robot Subject: Re: [PATCH 2/7] libata: cleanup ata_dev_configure() Date: Tue, 03 Aug 2021 02:16:39 +0800 Message-ID: <202108030231.7Ta42o7Q-lkp@intel.com> List-Id: To: kbuild@lists.01.org --===============6199723159498557833== Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: quoted-printable CC: clang-built-linux(a)googlegroups.com CC: kbuild-all(a)lists.01.org In-Reply-To: <20210802090232.1166195-3-damien.lemoal@wdc.com> References: <20210802090232.1166195-3-damien.lemoal@wdc.com> TO: Damien Le Moal TO: Jens Axboe TO: linux-block(a)vger.kernel.org TO: linux-ide(a)vger.kernel.org TO: "Martin K . Petersen" TO: linux-scsi(a)vger.kernel.org CC: Sathya Prakash CC: Sreekanth Reddy CC: Suganath Prabu Subramani Hi Damien, I love your patch! Perhaps something to improve: [auto build test WARNING on block/for-next] [also build test WARNING on mkp-scsi/for-next scsi/for-next v5.14-rc3 next-= 20210730] [If your patch is applied to the wrong git tree, kindly drop us a note. And when submitting patch, we suggest to use '--base' as documented in https://git-scm.com/docs/git-format-patch] url: https://github.com/0day-ci/linux/commits/Damien-Le-Moal/libata-clea= nups-and-improvements/20210802-170443 base: https://git.kernel.org/pub/scm/linux/kernel/git/axboe/linux-block.g= it for-next :::::: branch date: 9 hours ago :::::: commit date: 9 hours ago config: x86_64-randconfig-c001-20210802 (attached as .config) compiler: clang version 13.0.0 (https://github.com/llvm/llvm-project 4f71f5= 9bf3d9914188a11d0c41bedbb339d36ff5) reproduce (this is a W=3D1 build): wget https://raw.githubusercontent.com/intel/lkp-tests/master/sbin/= make.cross -O ~/bin/make.cross chmod +x ~/bin/make.cross # install x86_64 cross compiling tool for clang build # apt-get install binutils-x86-64-linux-gnu # https://github.com/0day-ci/linux/commit/5622af0f73c9776b5bdf272de= e77d57d62ea03fa git remote add linux-review https://github.com/0day-ci/linux git fetch --no-tags linux-review Damien-Le-Moal/libata-cleanups-and= -improvements/20210802-170443 git checkout 5622af0f73c9776b5bdf272dee77d57d62ea03fa # save the attached .config to linux build tree COMPILER_INSTALL_PATH=3D$HOME/0day COMPILER=3Dclang make.cross ARCH= =3Dx86_64 clang-analyzer = If you fix the issue, kindly add following tag as appropriate Reported-by: kernel test robot clang-analyzer warnings: (new ones prefixed by >>) note: (skipping 3 expansions in backtrace; use -fmacro-backtrace-limit= =3D0 to see all) include/linux/minmax.h:104:48: note: expanded from macro 'min_t' #define min_t(type, x, y) __careful_cmp((type)(x), (type)(y), <) ~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~ include/linux/minmax.h:38:14: note: expanded from macro '__careful_cmp' __cmp_once(x, y, __UNIQUE_ID(__x), __UNIQUE_ID(__y), op)) ~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ include/linux/minmax.h:31:25: note: expanded from macro '__cmp_once' typeof(x) unique_x =3D (x); \ ^ drivers/hwmon/max6621.c:321:10: note: '?' condition is false val =3D clamp_val(val, MAX6621_TEMP_INPUT_MIN, ^ include/linux/minmax.h:137:32: note: expanded from macro 'clamp_val' #define clamp_val(val, lo, hi) clamp_t(typeof(val), val, lo, hi) ^ include/linux/minmax.h:124:48: note: expanded from macro 'clamp_t' #define clamp_t(type, val, lo, hi) min_t(type, max_t(type, val, lo), hi) ^ include/linux/minmax.h:112:27: note: expanded from macro 'max_t' #define max_t(type, x, y) __careful_cmp((type)(x), (type)(y), >) ^ include/linux/minmax.h:38:3: note: expanded from macro '__careful_cmp' __cmp_once(x, y, __UNIQUE_ID(__x), __UNIQUE_ID(__y), op)) ^ include/linux/minmax.h:33:3: note: expanded from macro '__cmp_once' __cmp(unique_x, unique_y, op); }) ^ include/linux/minmax.h:28:26: note: expanded from macro '__cmp' #define __cmp(x, y, op) ((x) op (y) ? (x) : (y)) ^ drivers/hwmon/max6621.c:321:10: note: '__UNIQUE_ID___x523' is < '__UNIQU= E_ID___y524' val =3D clamp_val(val, MAX6621_TEMP_INPUT_MIN, ^ include/linux/minmax.h:137:32: note: expanded from macro 'clamp_val' #define clamp_val(val, lo, hi) clamp_t(typeof(val), val, lo, hi) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ include/linux/minmax.h:124:36: note: expanded from macro 'clamp_t' #define clamp_t(type, val, lo, hi) min_t(type, max_t(type, val, lo), hi) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ include/linux/minmax.h:104:27: note: expanded from macro 'min_t' #define min_t(type, x, y) __careful_cmp((type)(x), (type)(y), <) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ include/linux/minmax.h:38:3: note: expanded from macro '__careful_cmp' __cmp_once(x, y, __UNIQUE_ID(__x), __UNIQUE_ID(__y), op)) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ include/linux/minmax.h:33:3: note: expanded from macro '__cmp_once' __cmp(unique_x, unique_y, op); }) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~ include/linux/minmax.h:28:26: note: expanded from macro '__cmp' #define __cmp(x, y, op) ((x) op (y) ? (x) : (y)) ^~~ drivers/hwmon/max6621.c:321:10: note: '?' condition is true val =3D clamp_val(val, MAX6621_TEMP_INPUT_MIN, ^ include/linux/minmax.h:137:32: note: expanded from macro 'clamp_val' #define clamp_val(val, lo, hi) clamp_t(typeof(val), val, lo, hi) ^ include/linux/minmax.h:124:36: note: expanded from macro 'clamp_t' #define clamp_t(type, val, lo, hi) min_t(type, max_t(type, val, lo), hi) ^ include/linux/minmax.h:104:27: note: expanded from macro 'min_t' #define min_t(type, x, y) __careful_cmp((type)(x), (type)(y), <) ^ include/linux/minmax.h:38:3: note: expanded from macro '__careful_cmp' __cmp_once(x, y, __UNIQUE_ID(__x), __UNIQUE_ID(__y), op)) ^ include/linux/minmax.h:33:3: note: expanded from macro '__cmp_once' __cmp(unique_x, unique_y, op); }) ^ include/linux/minmax.h:28:26: note: expanded from macro '__cmp' #define __cmp(x, y, op) ((x) op (y) ? (x) : (y)) ^ drivers/hwmon/max6621.c:323:10: note: Calling 'max6621_temp_mc2reg' val =3D max6621_temp_mc2reg(val); ^~~~~~~~~~~~~~~~~~~~~~~~ drivers/hwmon/max6621.c:135:23: note: The result of the left shift is un= defined because the left operand is negative return (val / 1000L) << MAX6621_REG_TEMP_SHIFT; ~~~~~~~~~~~~~ ^ Suppressed 8 warnings (8 in non-user code). Use -header-filter=3D.* to display errors from all non-system headers. U= se -system-headers to display errors from system headers as well. 9 warnings generated. Suppressed 9 warnings (8 in non-user code, 1 with check filters). Use -header-filter=3D.* to display errors from all non-system headers. U= se -system-headers to display errors from system headers as well. 9 warnings generated. Suppressed 9 warnings (8 in non-user code, 1 with check filters). Use -header-filter=3D.* to display errors from all non-system headers. U= se -system-headers to display errors from system headers as well. 9 warnings generated. Suppressed 9 warnings (8 in non-user code, 1 with check filters). Use -header-filter=3D.* to display errors from all non-system headers. U= se -system-headers to display errors from system headers as well. 12 warnings generated. drivers/nvme/target/fcloop.c:1226:3: warning: Value stored to 'ret' is n= ever read [clang-analyzer-deadcode.DeadStores] ret =3D -EINVAL; ^ ~~~~~~~ drivers/nvme/target/fcloop.c:1226:3: note: Value stored to 'ret' is neve= r read ret =3D -EINVAL; ^ ~~~~~~~ Suppressed 11 warnings (11 in non-user code). Use -header-filter=3D.* to display errors from all non-system headers. U= se -system-headers to display errors from system headers as well. 15 warnings generated. >> drivers/ata/libata-core.c:2376:3: warning: Call to function 'strcpy' is = insecure as it does not provide bounding of the memory buffer. Replace unbo= unded copy functions with analogous functions that support length arguments= such as 'strlcpy'. CWE-119 [clang-analyzer-security.insecureAPI.strcpy] strcpy(info, "LBA48 "); ^~~~~~ drivers/ata/libata-core.c:2376:3: note: Call to function 'strcpy' is ins= ecure as it does not provide bounding of the memory buffer. Replace unbound= ed copy functions with analogous functions that support length arguments su= ch as 'strlcpy'. CWE-119 strcpy(info, "LBA48 "); ^~~~~~ drivers/ata/libata-core.c:2382:3: warning: Call to function 'strcpy' is = insecure as it does not provide bounding of the memory buffer. Replace unbo= unded copy functions with analogous functions that support length arguments= such as 'strlcpy'. CWE-119 [clang-analyzer-security.insecureAPI.strcpy] strcpy(info, "LBA "); ^~~~~~ drivers/ata/libata-core.c:2382:3: note: Call to function 'strcpy' is ins= ecure as it does not provide bounding of the memory buffer. Replace unbound= ed copy functions with analogous functions that support length arguments su= ch as 'strlcpy'. CWE-119 strcpy(info, "LBA "); ^~~~~~ drivers/ata/libata-core.c:5471:18: warning: Access to field 'pio_mask' r= esults in a dereference of a null pointer (loaded from variable 'pi') [clan= g-analyzer-core.NullDereference] ap->pio_mask =3D pi->pio_mask; ^~ drivers/ata/libata-core.c:5462:6: note: Assuming 'host' is non-null if (!host) ^~~~~ drivers/ata/libata-core.c:5462:2: note: Taking false branch if (!host) ^ drivers/ata/libata-core.c:5465:21: note: Null pointer value stored to 'p= i' for (i =3D 0, j =3D 0, pi =3D NULL; i < host->n_ports; i++) { ^~~~~~~~~ drivers/ata/libata-core.c:5465:32: note: Assuming 'i' is < field 'n_port= s' for (i =3D 0, j =3D 0, pi =3D NULL; i < host->n_ports; i++) { ^~~~~~~~~~~~~~~~~ drivers/ata/libata-core.c:5465:2: note: Loop condition is true. Enterin= g loop body for (i =3D 0, j =3D 0, pi =3D NULL; i < host->n_ports; i++) { ^ drivers/ata/libata-core.c:5468:7: note: Assuming the condition is false if (ppi[j]) ^~~~~~ drivers/ata/libata-core.c:5468:3: note: Taking false branch if (ppi[j]) ^ drivers/ata/libata-core.c:5471:18: note: Access to field 'pio_mask' resu= lts in a dereference of a null pointer (loaded from variable 'pi') ap->pio_mask =3D pi->pio_mask; ^~ drivers/ata/libata-core.c:5593:6: warning: Access to field 'host_stop' r= esults in a dereference of a null pointer (loaded from field 'ops') [clang-= analyzer-core.NullDereference] if (host->ops->host_stop) ^ drivers/ata/libata-core.c:5840:7: note: Calling 'ata_host_start' rc =3D ata_host_start(host); ^~~~~~~~~~~~~~~~~~~~ drivers/ata/libata-core.c:5576:6: note: Assuming the condition is false if (host->flags & ATA_HOST_STARTED) ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ drivers/ata/libata-core.c:5576:2: note: Taking false branch if (host->flags & ATA_HOST_STARTED) ^ drivers/ata/libata-core.c:5581:14: note: Assuming 'i' is < field 'n_port= s' for (i =3D 0; i < host->n_ports; i++) { ^~~~~~~~~~~~~~~~~ drivers/ata/libata-core.c:5581:2: note: Loop condition is true. Enterin= g loop body for (i =3D 0; i < host->n_ports; i++) { ^ drivers/ata/libata-core.c:5586:7: note: Assuming field 'ops' is null if (!host->ops && !ata_port_is_dummy(ap)) ^~~~~~~~~~ drivers/ata/libata-core.c:5586:7: note: Assuming pointer value is null if (!host->ops && !ata_port_is_dummy(ap)) ^~~~~~~~~~ drivers/ata/libata-core.c:5586:7: note: Left side of '&&' is true drivers/ata/libata-core.c:5586:21: note: Assuming the condition is false if (!host->ops && !ata_port_is_dummy(ap)) ^~~~~~~~~~~~~~~~~~~~~~ drivers/ata/libata-core.c:5586:3: note: Taking false branch if (!host->ops && !ata_port_is_dummy(ap)) ^ drivers/ata/libata-core.c:5589:7: note: Assuming field 'port_stop' is nu= ll if (ap->ops->port_stop) ^~~~~~~~~~~~~~~~~~ drivers/ata/libata-core.c:5589:3: note: Taking false branch if (ap->ops->port_stop) ^ drivers/ata/libata-core.c:5581:14: note: Assuming 'i' is >=3D field 'n_p= orts' for (i =3D 0; i < host->n_ports; i++) { ^~~~~~~~~~~~~~~~~ drivers/ata/libata-core.c:5581:2: note: Loop condition is false. Executi= on continues on line 5593 for (i =3D 0; i < host->n_ports; i++) { ^ drivers/ata/libata-core.c:5593:6: note: Access to field 'host_stop' resu= lts in a dereference of a null pointer (loaded from field 'ops') if (host->ops->host_stop) ^ ~~~ Suppressed 11 warnings (11 in non-user code). Use -header-filter=3D.* to display errors from all non-system headers. U= se -system-headers to display errors from system headers as well. 8 warnings generated. include/linux/hid.h:1007:9: warning: Access to field 'name' results in a= dereference of a null pointer (loaded from variable 'input') [clang-analyz= er-core.NullDereference] input->name, c, type); ^ drivers/hid/hid-lenovo.c:321:2: note: Control jumps to 'case 12544:' at= line 329 switch (hdev->product) { ^ drivers/hid/hid-lenovo.c:335:10: note: Calling 'lenovo_input_mapping_scr= ollpoint' return lenovo_input_mapping_scrollpoint(hdev, hi, field, ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ drivers/hid/hid-lenovo.c:224:6: note: Assuming field 'hid' is equal to H= ID_GD_Z if (usage->hid =3D=3D HID_GD_Z) { ^~~~~~~~~~~~~~~~~~~~~~ drivers/hid/hid-lenovo.c:224:2: note: Taking true branch vim +2376 drivers/ata/libata-core.c 818831c8b22f75 Christoph Hellwig 2017-06-04 2365 = 5622af0f73c977 Damien Le Moal 2021-08-02 2366 static int ata_dev_confi= g_lba(struct ata_device *dev, 5622af0f73c977 Damien Le Moal 2021-08-02 2367 char *info, siz= e_t infosz) 5622af0f73c977 Damien Le Moal 2021-08-02 2368 { 5622af0f73c977 Damien Le Moal 2021-08-02 2369 const u16 *id =3D dev->= id; 5622af0f73c977 Damien Le Moal 2021-08-02 2370 int info_ofst; 5622af0f73c977 Damien Le Moal 2021-08-02 2371 = 5622af0f73c977 Damien Le Moal 2021-08-02 2372 dev->flags |=3D ATA_DFL= AG_LBA; 5622af0f73c977 Damien Le Moal 2021-08-02 2373 = 5622af0f73c977 Damien Le Moal 2021-08-02 2374 if (ata_id_has_lba48(id= )) { 5622af0f73c977 Damien Le Moal 2021-08-02 2375 dev->flags |=3D ATA_DF= LAG_LBA48; 5622af0f73c977 Damien Le Moal 2021-08-02 @2376 strcpy(info, "LBA48 "); 5622af0f73c977 Damien Le Moal 2021-08-02 2377 = 5622af0f73c977 Damien Le Moal 2021-08-02 2378 if (dev->n_sectors >= =3D (1UL << 28) && 5622af0f73c977 Damien Le Moal 2021-08-02 2379 ata_id_has_flush_e= xt(id)) 5622af0f73c977 Damien Le Moal 2021-08-02 2380 dev->flags |=3D ATA_D= FLAG_FLUSH_EXT; 5622af0f73c977 Damien Le Moal 2021-08-02 2381 } else { 5622af0f73c977 Damien Le Moal 2021-08-02 2382 strcpy(info, "LBA "); 5622af0f73c977 Damien Le Moal 2021-08-02 2383 } 5622af0f73c977 Damien Le Moal 2021-08-02 2384 info_ofst =3D strlen(in= fo); 5622af0f73c977 Damien Le Moal 2021-08-02 2385 = 5622af0f73c977 Damien Le Moal 2021-08-02 2386 /* config NCQ */ 5622af0f73c977 Damien Le Moal 2021-08-02 2387 return ata_dev_config_n= cq(dev, info + info_ofst, 5622af0f73c977 Damien Le Moal 2021-08-02 2388 infosz - info_ofst= ); 5622af0f73c977 Damien Le Moal 2021-08-02 2389 } 5622af0f73c977 Damien Le Moal 2021-08-02 2390 = --- 0-DAY CI Kernel Test Service, Intel Corporation https://lists.01.org/hyperkitty/list/kbuild-all(a)lists.01.org --===============6199723159498557833== Content-Type: application/gzip MIME-Version: 1.0 Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="config.gz" H4sICFb0B2EAAy5jb25maWcAjDxdd9yoku/zK/pkXuY+TOK2HW9m9/gBSUjNtCQUQO22X3Q6djvX ex0727bnJv9+qwBJgFDPnYeMmyqggPqm0K+//Logb6/P33avD7e7x8efi6/7p/1h97q/W9w/PO7/ Z5HxRc3VgmZMvQfk8uHp7ceHH58uuovzxcf3y/P3J78fbs8W6/3haf+4SJ+f7h++vsEAD89Pv/z6 S8rrnBVdmnYbKiTjdafoVl2+u33cPX1d/LU/vADeYnn2/uT9yeK3rw+v//3hA/z77eFweD58eHz8 61v3/fD8v/vb18X5/X8t7z/+8eX+7O6PP5bny0+fdsvl3cnt+fLL/u7Ll7OzP+7OLu7vP/7jXT9r MU57eeKQwmSXlqQuLn8OjfhzwF2encB/PYxI7FDU7YgOTT3u6dnHk9O+vcym80EbdC/LbOxeOnj+ XEBcSuquZPXaIW5s7KQiiqUebAXUEFl1BVd8FtDxVjWtGuGK81J2sm0aLlQnaCmifVkN09IJqOZd I3jOStrldUeUcnvzWirRpooLObYy8bm74sJZVtKyMlOsop0iCQwkgRCHvpWgBLauzjn8AygSuwJH /booNIc+Ll72r2/fRx5jNVMdrTcdEbDFrGLq8uwU0AeyqgbpVVSqxcPL4un5FUcYzoSnpOwP5d27 WHNHWneHNf2dJKVy8FdkQ7s1FTUtu+KGNSO6C0kAchoHlTcViUO2N3M9+BzgPA64kQq5cdgah153 Z0K4pjqydT7lYa/tzbExgfjj4PNjYFxIhKCM5qQtleYI52z65hWXqiYVvXz329Pz0x70xTCuvCLx LZDXcsOaNApruGTbrvrc0pZGqLkiKl11GuoIieBSdhWtuLhG+SHpyt27VtKSJZHBSAt6ODhVImB8 DQAqgV1LR1H5rVp8QBIXL29fXn6+vO6/jeJT0JoKlmpBBdlOHGJdkFzxqziE5jlNFUOC8ryrjMAG eA2tM1ZrbRAfpGKFAA0HMhgFs/pPnMMFr4jIAAS67ArUmIQJfKWT8Yqw2m+TrIohdStGBe7m9Qxx RAk4adhL0Aqg3uJYSITY6EV0Fc+oP1PORUozq96Ya4NkQ4Sk81uT0aQtcqnZZP90t3i+D45yNGY8 XUvewkSG+TLuTKO5xUXRUvIz1nlDSpYRRbuSSNWl12kZYQqtwTcTzuvBejy6obWSR4FdIjjJUpjo OFoFx0SyP9soXsVl1zZIciAiRkTTptXkCqntSWCPjuIMkqmXu27R1oSWREuXevgGTk1MwMBurzte U5Agh3awpKsbNE6VZuphHmhsYFE8Y2lEDZheLNMHMvQxrXlbllE9pcFRyIoVK+Rbu3Ifx/LaZGGD HWzyYLcpNHV/ulylme6K1GpQwiOK3jb4GdszxBpZa6DXdo6uBWFt3Qi2GebieT6L2oDrA4wXXbJP lKPwBaVVo2BD65jC78EbXra1IuLaJd0Cj3RLOfRyu8h0BRoj5YJOuA249YPavfxr8Qqns9gB2S+v u9eXxe729vnt6fXh6WvAf8jeJNVTGOUzzLJh4Af6YBSsCKGojLQYeAO5DGAoJpte0Y87LjM0LikF 4we94yeIcod+roxtkmTebko2nHLGJPqR8aP8DzbKcRVhF5jkpdbhkz0XabuQEfGG4+sA5pIHPzu6 BTmOnbc0yG73oAn3QY9hFVwENGlqMxprV4KkAQAHhm0uy1H7OJCawglKWqRJyaRyjY6/ft8bTlh9 6pDJ1uaPaYvmAner2HoFBjFQPoMTjuODJlixXF2enrjteFYV2Trw5ekoUaxWEDORnAZjLM88fm0h YjExiBE1tDe9ZpK3/9zfvT3uD4v7/e717bB/Gc+8hbixavrgxG9MWrBZYLCMOH8c9y8yoKcmbUwG QVZbkS4hEJqmnoiNyjRB6w4Et3VFgIwy6fKylatJuAbbsDz9FIwwzBNC00LwtpHu6YCjmhaRk0nK tUUPu5utHFtzwkQXhaQ5GH9SZ1csUw7poI189NE5HnfdoES1iCWjYVmMpSxUZG60ZRtzEMUbKtwp gcskjSok2yejG5bSyVDQD9XcpB1UQu6Nb5rR4MxOUTGZTgbSTqHjQ/J0PYCI8uIxDHnAxwTdG5tj RdN1w4EV0A8A39ZZi9XmEPz2J+2GRXB6GQXbBR4xjQVjYF+J408jx8Bmaa9TuK46/iYVjGacTydu E1kQSkNDEEFDix84Q4OOl0dVnM3Gmhp0HiM960PlnnjO0T77Kg1kjDdgD9kNRe9eny0XFUit55yF aBL+iOm6rOOiWZEaJFw4Ohm9IOX410ZvsWx5EeKAxUlpo8MPrfVDVziVzRqoBOuGZI5QY6gcPeYP XoGNZSAFnlzIgiqM9HonbS52xmOdYvSqAVabuaGF8cSNL+q0am0e/u7qirk5GedgaJlrr8lzGPzV x91CAmFY6EX3tLaKbh2thj9BxziTNtwNgyQralLmDg/pZbkNOrBxG+QK1K1LNGFxxmW8a2ETYoqZ ZBsGq7CbLgMe0Pofz1J7TnnWXYWJqgkGxF1uwAsUJkQIRp22Nc50XclpS+eFhmNrAk4W7CCKDGjK CIY+CtQgmFdwWHMkLDBqaO1G2mD9ddrzQD92WrmqRFLPQdX6VLdGNhXGpVnm2i4jUUBMN4S4Dqst T84n3qPNkzf7w/3z4dvu6Xa/oH/tn8AVJeAepOiMQpg1uhgzgxs6NRA2o9tUOikRdX3/wxmdSKAy ExrXZRIP9lxatokhI25+edUQ8FDEeqY3iaW3cFBPv5Q8jkYSOGZR0N77d6QHYWjC0W/tBKgWXs1B MXcErrUne22egyPYEBg7kujRq0afsyFCMVK66gjz4Z6jptWvNqpe1sbPX/fIF+eJGzFv9Q2L99s1 libDjjo+oynPXDE0mf5O2yB1+W7/eH9x/vuPTxe/X5y7ae01WO3eB3TWp0i6NsHCBFZVbSBtFbqd oka/36RmLk8/HUMgW0zJRxF6fukHmhnHQ4PhlhdhEsgzAU7joEY6fSKeaRkSSKRkicCMV+Z7LYNu waPHgbYRGBw/DNs1BbBCmKMF79G4fSbEFtTxVXSo1YO0joGhBGbcVq17D+ThaQaNohl6WEJFbZKQ YE4lS1wDa/1/ienYObAOMPTGkLJbtWDoy8RBwcSyRgxZtpOufvUjjVYnlp2Nz8HOUyLK6xSTpdQR 1KYwEVkJqgjs13kQ0UhSU8OsuNk0NUKqNWxzeL7dv7w8HxavP7+bWH8auXlEIuE5JaoV1HjJrgpC 4PaUNNE8HAKrRiduHW7iZZYz6SX0BVXgGDA/X+TNYTgLHDgR96IQh24VnBhywTH/BTGRw8uubGRc eSMKqcZxbPwy42XIvKsSNrP84XDtZQYEemXrO10mgOAVcEwOPv4ggZERV9fA4OC6gINctNRN8sIm E8xReSbWth2xQwOKbFitc9sz61htUPzLBFiq2/QMNW5YNBO2BlMZkGnS602LuVng1FJZf3AkaLM6 TmiQWoslwXrUPlExDPInbP6Ko0egyYpORFJRHwFX60/x9kbGr78q9K9O4yAwnlVkAYO+dZ2+nm1F DfYMzgKYxmZrLlyUcjkPU26ErIWqarbpqggMKV4UbPwWMDmsaisthTmpWHl9eXHuImgOg+iqko6p ZeTsVGuOzovNEH9Tbed1ik2BYhRIS5rGsoNICIiWEWQn2LTNILzTxtV14abx+uYUfDnSiingZkX4 1r0bWzXU8J+DnFVexrUAvwdUAvgCMye+BdUau7HQZkuiQwaGK6EFOgtxIF7hfVxOgL2nN56LhTgt RtPIyvVodFOVThVSlWIMyWf0gb7571DvByzKI42CCo6BDAb2ieBrWptcAV5MBozmZwVsE2YnS1qQ 9HpWXVf6qg6YYc4MAdzjir4RrxrlipcRkLlOHQynEyF8e356eH0+eBcITihi7Utb+7HVFEOQpjwG TzHR70foDo42UfzKtxSDHz1DryeFNgAGt6otg3tlc5hNif9QNxHCPnk6tWIpiCvopPmjkTFTZo06 y8Lz/qh9mpkeGRNwJF2RoIMX8E7aEFMAJBVLPYWCGwX2F0QnFddNXLFjOjmWKdAemnZhzAgk4ioO 4Em4ZeBai/UeAF56lwGGBQWFBaxEpi97fwAvnFt6efLjbr+7O3H+8/evQUKm0uJuFGYzIUjgmH4X om3syXvDoICiJa164kZUM0BcwSkh4mYVl2nCzTlPCYKYkIi2YjFtObpw48YpU/nQren1xEE1uEpu 9eaHF55HECe7EiBg1neW72WxjcJozqLtq5tueXIyBzr9eBIhGgBnJyculWaUOO7lmcssa7qlMa9d t2O4FvI5RhYG2LSiwMjfu7s1IMlmMh6CyFWXtVHz16yuJUODAvILPuzJj6Xl7CE+0KkGX/YMN2HC GPNgvi7QwaDu5aZI+1kgji1qmOXUmyS7Bk8E3C7LUxDhcrckcJzOIMxDxokakukqlpMfu+EUQKzK tvCdu1HYHPCJ42XofFYcZmP+TSadQjcj/6GC9yxJiLLldRm3ryFmeLE/nnGVYRCHy4lHaSA0LIet zdSRhLfOCJRsQxu8v3NTQ8di1wmnwtZ3gZUwunbV4DlhysRE1Xhig143pv753/vDAkzn7uv+2/7p Vc9E0oYtnr9j5e6LqS6w3pJJFMSjlngIiv59YS3BrM3pA0ec11nC5Fd/PFo6JDhmfN2GKYaKFStl U9fYpcnSYBA4DgV2Q7sT2opS6aS4nOioYYYbi2iAasZqUtH1wup3zZss5s6bdTReTQw2Cbrp+IYK wTLqZmL8QUHr2OqvuaFJuNyEKLCo12Frq5Sv83XzBmaP+cFmRWTaIeO+eXRhOoQS9HPXSBlMP8Y9 g98XBzPvPsgHBu2+uvHJHAckRSGo1uhzZKsVOIFuQtcsppUQwnaZBIHWBmO8lhzlUHfX2aq2KQTJ QtJDWITZZiJ1XEPKMA0+y1TwtyKgk8Rk4H5njAaY699jMW7DGn8QmcQzSKZvWG0T2bqKqhU/giZo 1mJFJObir4hAn2RGS2t0+Cu2FaPkk4Y6+sNvt5eF/ogImJ8va1S8eKzffPg7j29Rg7aWN8B3QRmR 58sO8XZf0LXID/v/e9s/3f5cvNzuHr0QrJctP5bX0lbwjS7Ex1TzDDis8xmAKIzuvgyAvrYKezv3 2nHbGO2E6lbCIcykOCYd8GpPVyn8LT06iG8VixkYb9n+hXwUo6dyBn6cKMTgdUZhhlgBRHAEta0O np1sWJfLE/chTyzuDg9/eTeFgGb2yD9+26ZTvRkN0l7G228mQbhmzTTt+8/nkK3CP4oE3gPNwPia TJRg9VzKpTk3GU1wG/q1v/xzd9jfOV5JdNySJa4XFReiYS/Z3ePeF6mwkLZv0+dRgp8VdQU8rIrW 7ewQisaDKA+pTxZHtZsB9YnlcLF6RU7GXp8qIsbzJn/r/JmS3LeXvmHxG1ihxf719v0/nHQQGCaT rXACEGirKvNjbDUtmFddnni3Ioie1snpCWzB55bN3BUzScBxietYhGUVwZRczMBVWVcnIWNjPUoS 3ZmZJZvteHjaHX4u6Le3x13AjDoNPJNh2p45ZUo2kpk2TVAwkdhenJtADnjLq8CckqIpzB8O3/4N 4rLIQtVAMy8JBT9na6JzJipthk0EEcXJKsbiFh0gptwnchgahg/ZKpKuMIiCKAtTBcAX5trFJZHJ VLKOJXnM2OdXXZrbuiK3k9veh2pRMgvOi5IOa50Uaaj918Nucd/vp1G1GtIXw8cRevDkJDx3ZL3x qnvwJqeFc76ZlBv3XAwu5mb7celwDd6Arsiyq1nYdvrxImyFwLuVQ+jXVx/sDrf/fHjd32Jw+fvd /juQjrpgomlNWiOoodGJEL+t9yO95Ht/0YN630+lmJvgyGr/bCtQ6CSh3q2Zefeoc1+YbcxnXvFZ NB3E92iBM0jznKUMy1vaWksX1jamGCYEISXeomFVs2J1l+DDsGAgBsvHGDty278O77lNK14CxwC8 ibfbYTCKz2OlfHlbm5QgxI8YNMUeRwGaVyE3VnfpEVcQTAdAVKcYVLCi5W2kSELC+WijZd4wRVJo oLwUZkFsJecUAVzUSY7JA9oseDXZdEO5eRtqamC6qxVT1Bapu2NhAYMccl66Wtj0iOLV3FTVhPPJ CrMY9gVoeEDg2oNs1pmpRLBshLYoxJOuy+6fHb5Wne24uuoSWKupzA1gFdsC645gqckJkNBzxeKD VtSwRDgVr2IvLF+LsAqGZeiT6VpjU2jRVy9PBonM31eoCbtFmB6NHakn9UegkWLAqmo7iOwhfLeB NpZ3RcH4riGGYlnPiIp5LmAvjkNirL6wnIfJtwDD9jO3hzOwjLcz5TbW+LMm7cxjwP5BcgSXl5mD H9s1SVNEOAKyJUueojWQ2RBb98ajLIHvgqEn9TijVvbbXbXuQHBfebyc1UsYloqHD/hnEEBVuPfb 2I653diWXDHEtWyqy1RCXkalSLdKK8719NFTCEYXS48W4M08tAqty98+sqo4ylcbVqOa5ips7lV+ jXd5aP2wpCvCwLN4kamM3AAc60/DjKxmUg0EYtAzEdGpJM+1ulfXk3Vk/eUjTUFvOSwOoBYzwWih scgbdUJk++iWKbSd+sVv5CBwaoQBCr+qQ5TBHukZ+guQ2BK8IsfQ20AaoobS7zXWTUbGdYoe5wZx USJDWbBGx7ukkEzD9fa979SDgA1m5n3UUB46YtjwzLdeqJ0kK+zVwdkkvrFwEvgrQ4CUMFOIEttv ZLbwtGJtY4/x/mptVoqiSb2U1wxKn3Ga00naYVHgFqn+mwTiyqkNPQIKuxumjnaPgcbF4QtWiD/t DaLvpaDldou5Q/6x5fJ93cCUK3pveh4y+USIsfv2Mat1v2K6Ye6li6/KbaE7KCBdjR2XT4xExkja hDkp3/z+Zfeyv1v8yxTAfz883z/46VREsocTGVhD+8+cBC+QQ1g0nXCMBm+38EM1GDyxOloo/jeh Wj8UmJUK36O40q0fUEh8HXC5DNSnuxzLYfrjBN30PbSP1dbHMHpX+dgIUqTDN1VmHg31mCzmDlgg nrhAx9na9LDzAJ/9skmIOPOxkhAt/O5IiIiseoUP9iQa9+HhXccqzdTxFelgDzhdrS7ffXj58vD0 4dvzHTDMl73zgRJQCBUcAFi+DBTVdTUzljaP+klxeFua2JLR4SdERZhqEfSzX8g6Pu0ExWMvCBwQ vuFLZBFtNOnYoB2TkoVgKvoW0II6tTyZgm+490SjbwarypXyX1xMYbABVz78KlGThq76HF05w0fg oBy93IUDz1Mwe81MMswbJ+Uz9bYeVliP6GEZVRjNreljxBrrhpQhqUY/9yo+9qK92R1eH1CXLNTP 73vv7l8/czFxarbBG5TYDQdo+IKMqI6jJjMuYwBM+7nNY246IMVj20myFJdXfcY08qQNvWzGe2sA uzo8u3ZyW4DHuCkGzcBD8+1YBDh56+zgrK8TP5LqAUn+OWodfJp+GTbcvtvt91bWy/FXW9vTxGp2 rYgnLutYVqE4Jj5E5fC/tg+ms/F63WwAiDm4AjNAvf8zsCFdpj9ylI2l9iPKPCTsLK7iXSftg53G PDLWUJQoijW+fcy0lu5v7CYOV/8osEtojv/D/IT/pR4H11QEXQkY3F3z+EhcMxj9sb99e919edzr z+YtdGHqq8NqCavzSqGzMvF2YyDr1Lj8pCnG/MlwC4qRyfxXE+ywMhXM9exsc/CanOM1fNW4oji3 JL3eav/t+fBzUY2XSJPE8dGi0LGitCJ1S2KQGDLE14K6UcEI2tiSp7CAdYIRJuLw20aFJ9SG4uEL IAFbmAl6LJv2nvT+m3ZLlue0+AjDx2vq8BrjKDGwPXwzM66BxTyGSMWYu4MlRGONMioYC+7PYzRY NCzZVr5eshQk6Ba522kbjBzEIsGgTachBEX15ln9yEfDXMLU/3P2ZT1yIzmD7/srCt/DYgb4vJ2S UkrlAn6QQlKmXLpKoTzKL0KNXTNdGLfLcFXP9Pz7JSN0RIQYyv62gbadJBX3QTJ4YO+WJEzouHtD rEDLRnGC9J3pkim9c2oUdmfgPVcW6jhnYiRlpKqk/bjd7AOtXXaXKX3ACFeq46WpYWFWg7KfmEuL jkfhIAndTlRcokfqECGpS+ldTgh1XAz28Fgyn1xFCqwDOu1Qr+haBI4ymkJaKEaX0Yr5G2LHl0AF BE2O+Medtg0UZRPJZH1uDLPOEc5LY5WMkF5njKfnLnRpHB+I1K7AAknbVtcgi9AXRK3ijUUQLBWO sz+qcIWSLIGmnpooGuHNSijoEImstXjw0YyGRugSor5cjxc3l+HDoIY+K6IDdUM3ps26dPjvbVGe 4Djuh6e7WUGDTz5oGyaWGL6P09aQar+F6nDgiIdbzX5xjSVU6lDAD5ifQyufB8XVVz2///v15z/R Bmdx58GRda/rlSQEFl1ECbLAzyk6HfwF97X2Oixg5tfzZi4s/p1ZWwq2hvYESVElZXE1ShoRRYaO dpNXeu/yRsYFwbh9tH1GMwkOvXCRogxpgKiplOUpf/fJkTVGZQgWpuS2ypCgjVoaLyazsagCJPKA PFZanq5EMyVF352qynicfsQ7rL7PU3o25IfnjjYzRGxW0+50A26u1mIFg3QR7VcqcCm3jJhsmuXF R2Cn7qpAXJAGqGPNCNaLPyWNfQELija63KBALMwLvg/RyxZrh38e1sTUiYadYlUlOd7aI/7jf335 /W8vX/5LL71MfFoZBTMb6Mv0HAxrHdWgtJmNIJLxgNCZq08sCjXsfbA2tcHq3AbE5OptKPMmsGPz IrIjjQWtonjeLYYEYH3QUhMj0FUCEkmPvr/dY5MuvpbLcKUfeAw1xRBM2rJNBKGYGjuep4egLy63 6hNkxzKifaLlGmiK9YJggsSjNq1KbGDV2T7DqKT4VFtGLWWHj1um6Rp8A+U8zzTt1fg1MLrihQXu 4LKh/cyBdHogNkHTRhuvRfb68xnvRpAV359/2gLBz9/Pt6ratAGJI4Phzq0xIpeki/DLK7RFTZ81 S8qa0/u3wtBSVSU4MxsBOvUCZ2qtLFtbq3NTrhTVaPy5NujaxQgMnu3OPGtlS31Z839X5lLtguQV cMHTkbqxl01bXx9XSRLUfq7gcSitt7pEr33epigs2UlgEIAqb1bPDiSBNqzMxtqoDcP6r+B/PrD0 +awNrJVkGFgrfh4ZK8kwuLZbIrAP3TQsa70W3U5S9v35fW1oprua4eEGVYI8H6PF7WBQMtR1qyBF Nd7I/WWb7YRZjl/cTMzCgbYJvXo6W+z4qKMd6Aq3ozgy3ils8gFWo6IzaDVmOW7z5GA9D/uEU77g 5yKq+nDjOspryAzrD2e1QgVRagg5R+bvgSVSNMqF5m0EP6nkAlEXFZplMD7vRA3c9IggPri6vlJH 1ChPUc2xNuSXoKgvTUQGOU7TFDvnbzVWZIL2VTH8Q8RNzNFGO6KUB8oncksrUxaxZRU4VOLhgDaw ZjENr9D8j9eYq4J+QIKVFolXHBJdN2l15pe8Y0dqXQwSoTL5A8SQBCZwUddNrFtoiAcZqigdsfBS Gq9iU7gom4ISUcXxoMbAPfJ2Mb6io7CSrPuj8DCDAV6/NqqHtrNL1xXjFG/coNIDFzCIBUw16Gwb ZUDaTATH1tTlqHptr5LJQbPYRlMHXfVovcNzouAC6QAnCoXkERNtvLGq+MQfez2YZfxQ6GQZPnPL VCu6cuTu/fltiD6ujUtz3xkxxfVTqa1B0Kqr3PDTnI73RfEGQlXKKErTqGyjhB6ISNUIwwYBWVQH xKzUAYeLNtYA+eTsvT1dOL4iiDNbjgQcNcnzv16+qD4aWlFnRp5GAnVluicuAnlh/0B6e2nkLCoY Gomh3Emy/EiUFSlV1aE1qtKw9+cIrXwblqcZfXSJ+nt7cxnb7Tb6UAsQGphR4GUEWzHeWY5/q4FB hc9JT/RIALHJlhYN+Lke7esmje6J/qqT8ynCMA/mh2nJzUo1fBY6wcb5EyNtJRmbZicoriv9Htq9 HPgRQQ+9sKfUkwrIZRiL56xzzmxSzHJbTEeOdl3HGFA0TSgdIqDUVz/xM+EaoOSZSD6mFxjVvAEo XeScc2WGKX4lajkjuE9ZQt2hKomMPijdyr79/vz++vr+691XOQILv0748MjyuOOJsGdQ6wT4KWot diUCfT6Ss4yj0Z4LozgE9Zw+KBHd3Q9t0GDYAg02mV7MDmu2TiqMMMgi19bGJWcYtpdoVZbHfTsY sw2gS96mhaFYYNkBGS1tT8mTd0R8f37++nb3/nr3t2doND56f8UH77uBRXMUm5EBgo8X+NBwFOkH xOPLHM0lu8/Vi1P+NlbkAMwrLS/bAD006kDj3bhvzN/zQOtgIyY8i/JM/0U8sSEUPjeYHRV74poj JUubY2/kqFKkRirsTsMj4NlSvb15psdsIfRlI4uLoU7xuUuRgNoaGqLFtxbsHXJLpWqchi+paCIy Q9LuiCnwRu5y3I6L23liIdCYWzsQh19T0/E3CEUx8lyW21WQoNPrsqTRDRDEJNWdTKAqwhlBs3ky fwz5rIxw1Ll4OTc8aRVsxJtSK0ZAlJBfWlkCR7r3W8jw2ftPEd+IM4CEfWORm4UDsoX3zqWPsTkq a8FDMfZGd6JCUCMKrRzwqJlTAmhf5jUtOSAO1ogdF/GcYilElaZjpBgNNL+HfWML/DXRWKZS4NDt yT7eSPGnJkYSpq2Lf5Bko4GLwX9IS0SAfXn9/v7z9RsmSPm6ZJJxELIO/rRFEkMCzPA3PsYv6kie 317+8f2C7rlYnVCh8t9//Hj9+a66+K6RSROo179B616+IfrZWswKlezW09dnDGAo0HPXMdHWXJY6 wCxKUpgBwYOJjlpH4dPOdVKCZOS6btY8GWXSszLNWPr964/Xl+9mWzGQp/BMJKvXPpyKevv3y/uX X//EGuCXQYrvUjrs/XppykV2LazRBRrGopaWZtqoyQ1OaXavfvky3CB3tWkhEJ2ueZFHaA510k6i k3TTOKZFQz6Sw9XclY3u8D7CQGY+mcM8kACXUiVRYc1UJiqdnP5FFqfxLpy82L+9wlL5Ofchuwgf Ac0ccgQJi5UEUy0pl9i1a6OpEgyhNDVv/k44iVp7P9ONRvgqf2m2dOIFZca5s2rfOM6DMNGncQZU GW4hybT52TJDg6DTpnz5mWD85be91RAPrr+Hmiv5BdVyRAmRsF4dyhEuA1RLBnSqlzRyTXP0ZRHf 0ZLtE9HnU4EB4GNYsl2u8iBtetAsiuTvPlezfw0wXuSlZjI3whvV2XgAXpwFqCxVHnesSM2SKQyY 0MFQrLtMN8NGZCZOTOG3Rp4Vlk07hUGRUotqOH7Mzf07gFa4iZECjy/ictKikIw1KudUDXwxowOY HSo1egH+Almz1dJTCGCJ2dAoBM/bjMac4usCUerpg+GnWGHLZ8TZmv/H08833fS+Q/fOnfAC4GZp Y5hFgST6izR1Nn2rQGENiMDLKygZW0AYpAoz1Q+OXrlWhAgSIRzrLAr55RdoqLaMYbbwbxhHRAzU Cf4JrAL6Asi0LN3Pp+9vMrrLXfH0n8XQxcU9HDRGDw3fnkyNSV7JX4qI1qFDIvVgUWkftlmil8R5 pgZX5GVvFC1mp24sHmBdMnt+YBBuoWVfrJw2Kn9p6/KX7NvTG1zgv778WCpGxErJcn0MPqVJyowT DeGw4czow8P3+CYiTHHqarkQAV3VZlZogyCGe/MRrQ0Ns9cRXyj4lWIOaV2mRpgUxEmf3eq+Fwny esdShEHm3iiGyndGkIW3WhP8uXK8RXuwy7mtMwJJf2JruECG+uzW6nPpRITRsaQe0FwIZaKlexvh wENFS+gQok09B6LSANSl2Yco5qmFI15Z81LgePrxQ4n8JvRTgurpCwaNNTZGjbqW6/hWZJwUaOWu 3d8KcOFlpeLG8MWhHr5YJSnS6iOJwOUgkzy6xnkwENSU8KoSoFZM2tRrjeMx6w/X62Koy2QXXFsy Hjfic3a8EjOU8tg1PtJX4X242ZrFahScxS7aQnPabA9JqrR7f/5mRRfb7cYSXFuMh0W1L3olgr+d Wzi3KFZBfA6io1yrs5h6Y23JVKjP3/7+AYWpp5fvz1/voKil0lpvZ8l8n37JEONUQCusU77YTfC/ CcOA0l3dYVxtVMCqnhYDFrhXPtjGO3OQhem2dCUvIxUDL2///FB//8Cw3zZFIH6Z1OyghDSIRYCF Cpjz8qOzXUK7j9t5oG+PoXxHBalNrxQhRsgtccRVKWIW968EYwAZjMR0aXOLzaNKTDClBNXiUB0R 7hVv2MPyFIwu/dBGebc//fsX4H6eQCD/Jjp693d54s06CHMhifKTFAN2rS5+lS6x5LeZxtJQeJn4 8qrFHBnBg2Z+Wd5qTialVqG+Was4giWrPktPiCGp06EcR7J8eftCrBL8g+eLg03gYH5r6nlqHr2c 39cVO+YN0YIZKZkp1Qr0T9AKv8yPG2rOTGL0KflzzezjuBPrexyVlDHYbf+A/aUo0czvgYhoM0BR s3SMylL3MKMJgPldKSXWY7tTzZrMF3C7i8YXDYzS3f+Wf7t3DSvvfpMuKyT/K8j0JjwAA19PvO5U xe2C1UJOscFXA6C/FEoyGeOsFQRxGg/mIO7GxKGfZblkkBF1KE4pmV9sKtfwJwSwSBSmqRWSTpmL WkuvDLIi6kEsQQMBC5d112kxwQAovbhI1H0df9IAizgnABvXuwrT9BZ1pjsb1WiPieGB00RPhicR aNylwaTTsxlDTwmFL+OI6VkcbYC+0czxRqiU/Wnt6PRhn+UZHbdAoRHPP+Tb8kgUXcNwtw+oZsDd TTH+I7qqh/aPcNWdSPgSCR1cCdMxJEwYUwW+v355/aZ6cFWNnoFgCCmhNmqMMlGdigJ/2HqeW3IG jd+jPp9zZGzyxnOvNL/32eCRFqWgqd0qQdLG9gAVoh838PxKp4Qb8bYWsgS4ZLT5YsnZEgG+i8Qq xudYkmAwDrQN8tSCGz1ouT66ksc6l6nyZDN8gtBF2uhppPCT5TIU30gPlEjNYS/gx4tuzoiwLIrb XM3cIqHaDhQgwwdEQ0XtQbdmVcD4iMjhsLa4lSmE5vIhSDJmNHSC48e2FizcV8a7Th32iY1Z6lhB TuZ1y+FC4V5x3rgajxslvutf+6SpaS4vOZXlI562JDaPS4zsSe/bY1R1FuGuy7NSrA26VMb3nsu3 FtstYPuKmmM+QDzil8ZQA9mx6fOCTMbRJHwfbtxINXfIeeHuNxvPhLiKJd04kB1gfJ9AxEdHmt7N r0YDRtS539An07FkgefTmR8T7gQhjeK24yK59FcMfiLOReur5vio2Ftuc3xeq649T7JUDXDh6pee /A3LBBoTtb3riHGR7GPaoHC/YB0lHI4sV7PXHsDWfGQDvoyuQbhTTNMH+N5j12ABzZOuD/fHJuVX oq40dTabLbm5jMYrZ3G8czaLtTtEu/7j6e0u//72/vP330Ri8CHq/jsqobGcu2/ItX6FbfryA/+p imcd6sfItvx/lEvt/eFRad766LUhEuE1Fv+8IXEaLSZO2L60bOOJoLvSFGf5WHouLZIoyNGXB/rT lB1pRgnjsUDXWG237BQkLaZms1Ecoziqoj4i7V/OTVSpHOoAGN/X5k02wBeVjKoi9bSWeiG0dh+0 GIttI6KLyYwOyuN5nohkJdTjDn6gnFH4uRaLQEBmsy0Vivl++mxi70S7hgbJhFp/gbX2z/++e3/6 8fzfdyz5AHvlr+pinjge0qT32EpkR7EGZA7I6RPd2m6EMprpEX1hqDvCkBp2kqI+HGhDM4EWwfbF e7E2IN24+96MSeKYDAcnxRhVDHJGgGWIfgrDMWK9BV7kMfxFfmDOMUKFJY+WrFui2maqYVatGb37 X/pYXUTaTXUeZA9oFkvixLPeIs+AnJ/rIfYk2cokAtH2FlFcXd0/Q3OFmagtEkXq2gsYV6gH9yv8 J3afvaZjw2mfb4GFMvZXi6QyEsC82fGR1aJGoo+Rs9vSll2SIGJm+zV0znbXqxpmVQLwSVlYsA2G 6B8916RA0RutRUCi7kv+0dfyjo5EUo6UgYUoHk0jKyN+/5EopE2F6U3XYV7PhX2U2Z392nADwX67 RlCeV6ejPJ/KlWWXNB1cv/SNJetH/R9/XJvwlhn5cXV8Cu1zaXwJPJW4Kqr0YnMbmmhW0idPNOtD 0XTeLQL3BkHulStd5cBqds0DdTsL/CnjR5YsThoJtl76Gg2hgzbIhqid5qYHKZvWIsjj54Ru7Dlt nS/7/tjStuAjlh63gZVqzubpNeDhulClT/Gz1kRz65mIiD6r1hrNV7FJefWcvbNyWmXSznp9auBg XsNa7CQkskIziVV85JCpcCU71JhXal6Wi7nPP+dNnzYN+ZQ/U3C0l2Ndu/ied+nK+cMfS99jIZx6 FmlQDsLKrnkQKw81gCvXwkMR9dnKXCL+xhVbNKTXguxFXu6czaLrCfP2/h8rZx/2fb+jgw4Iikuy c/ZUsCFZvumnIae1XFyBJkG4sSgh5G7OzMFSsYr7jsbKHNOC5/Viv2ntPZqM+7FvEzU36QgVUeKW 4LRky0E+glx0MngSleUzJI/pLtYYStQrIDOp6r0BJB0h9VzqAD6nbVxjdgFMrGPVXIpw2RQXADhd iSyqb2avL6aYW//75f1XKOH7B55ld9+f3l/+9Xz38v39+effn75okrYoJKIduiYc4ZsnwCw9Rwbo oW7zB224sRA4U5gTuJYtLbsGLNyiIToNzwuXXvYCa0k+VtKLelAoWhVv2YlTYY3R8/7O8fbbu79k Lz+fL/D/X5fyaZa3KfqMaXfKAOtrWycnCh439ME2UVSWyCszQc1pY8HVDiijFjFgImtMPi7sfi1e 8YMHpqLKHcZUk2brKjFESl2nSmKwG4eTjbFPH0R6s5VgdJaTW4QVSy3aQug1RqCgVTiNFXW+2jBo /WDxTYqBRzklNH90sIQCgfZx0zVh7hf8i9dmfsZxuZ/oBgK8P4tJa2sOkrRFOWU8pYxg+ZCC742/ KS0pytoyTlwk4rQw3cfcuqyB37ehpO+hdY2mmKRpEX3xnFZJ3fYe0y210sKj2123Nn6ke2yONZlh T6knSqKmS3WFowQJ2zfctDcKOKT6lko7x3Ns8QTHjwoQa3OoREuPyYucwUzf+rRLzZTeqY2vHXSn Hb/ViTL6bMTgmFH6Q0yZhI7jWB/wGlwNHn1EYnrO64H0YFArhOOj6nLN1zJ6sORKVL9rGd0BXGa1 ceUXlhZ2Bc1HIYLef4ixDf6NVRC3dZQY6zze0tdozEo8sCy+39WV7g+zLYwuP9QVvaOE6om+6R+B 9xcpLWwfUg80eoeZkRI9rqjARMo3gwGVZkcSMcpFU/vonJ+0ce2OpwrdiYSmm+ZGVJLzbZLYYrCp 0rQWGtk+jNlHoov84WR6mxGdlBy6rmmWTHtnCScxoumZn9D0EpzRZ8pwV21Z3ra6twzj4f4PSnDV vuJM6415rBGfiNDg2lFxSIHDz6fLhe7JtU9ZZHlJNj5aVpro14UMmlrk5Ku68tXwfjFXVLi0kQOH 9WM63S7Lw/zDqfZaGKfuzbannwdLv3mQBaSvGo6h+eE2w8hWvXnULEuSOXXJE/d4ii5pTqLy0PVV da2Kwnc4rWW0qiM1Y6wIgOVt70ArqQBu2eH51faJeXPNmK219hvLt8yRtasz7YHpE21yonwVtedU T09VnsvEpji6t4SF4/ePVAA2tSKoJapq3dC+uG57m262uPp2qQ2w/LKKzij3JGO49CVyz8NwS199 iPIdKJZ+NL7nn+FTW1Qcc47MTQPDstt6N653ObtpSe+E8rHNtYGF387GMldZGhXVjeqqqBsqm48m CaKFEh56oXvjSIZ/og2fxm5y17LSztfDjZUL/2zrqi7pY6PS254Dr5j+z86k0Ntv9KPZXcRFIuo9 w12r3SFC0ZPQgpXyYX2vtRjo6xsbfogOn1aHvNKdho+RSJNODuxjis7LWX6DAW7SimNWQ+3Js755 h0p9q/rRQxF5tke/h8LKU0KZ17TqbegHMki32pAT2kPoGusHFu0wApU17NEDQwMZW1zmtry5ZtpE 63sbbLY3NkWbolil3eWRhZELHW9vCSuKqK6md1IbOgEVYE5rRJVqD+sqDiMrtiSKRyWwF7qOGS82 U54jvkzVnNYqoi5ATob/NR6d27TzGesznOcbi5nnhR7AjbO9u/EoB0LtK/3hPud7S9gQQDn7GxPN S66tjbTJmS0MCdLuHcciOCFye+uw5TVDH9krrRDhnbhPtO51JSz8PzF1p0o/aprmsUwtVt64PFKL fS9Gjqws10l+utGIx6puuJ4WJLmw/locjN27/LZLj6dOO2sl5MZX+hd5zxpgQDAOOrfEYO8KMlqh UuZZvyjgZ98ejXxNGvaMaVrzjrIUVIq95J+NSLUS0l9824KbCDySS1YKlxaSauGDzSQem4XNUWyg ia65/XgdaIoC5sNGkyWJxXgtbxp7hgweI1dPq5iOj7ZAZMgFDxYfKn4IN8Mp98UpfMwCq9RYWJKI NI3lEd34QNR0fH17//D28vX57sTjyU4NqZ6fvw7x5xAzBj2Nvj79wIjWiyeMS6G6i+GvWZtZypuJ wnVH/co6rsSuAKxvY530Qks1bq2KUrRbBHYU9gnUKAhaUC3PNRkAX/kiy/S0OS99ypFELXSWpyhk CryhdUxV4YBAt5FutKjhJi6CQqomkSpCfdBU4Z2F/vNjojIJKkqoWdNK155cbgTHHtX5Go85Y7Po Pi0s8vNMdbzwvFxsD3zu+vb89nYHbVCfQS8X8wFn2LLaB8qpW15RBU0fRqdPecdPvT0JA0bZyCl7 GvFuNYYVVJ5Ucp5YAhyfl33Mv//4/d1qOjuGhpzLRoAII0lNikBmGbqOFZrfmcTIjIP3mtu/xJRR 1+bXATPFI/n2BMM6PURrLrnDZzWmIbaEx5Ykn+pHOqSkRKdn6VllAKXRgzJANmds+cF9+hjXUav4 oI4QOP8YCW18X/WQ0DFhaMXsKUx3H1N1P3TOxqcqQcSORrhOQCGSIQB6G4Q+gS7u6RaYrsoaQsT2 JkWwiaxjUbB1AqJkwIRbhxonuZbIWosy9FxvrUKk8Dyy1OvO8/dksSWjN+9M0LSOSwkKE0WVXjr1 uWtCYFh81FBxsuZBeFor+VAXSZbzYy8CrVmK6epLdInop/WZ6lTdx6vTlT/wwL1SvYCdvSWr7pgH i5oWUmai0u27+sSORs44k+5q2QgsakAQotdEzGjRQjlh1o8XzJ5Fq80lichLYkl9IwmwZ5y1aUre dLIdWrJcCQvDpgyDzbWvK+gNiVWQRqVRsnO2lP5uQHfMtRYtWCwcVNFyExuXkaOeOsOZ6l03fXzq tFU+Hv7X3S7YeygGdvmim4AO9/udDcscbxd6fXNpp9LNOSzhqPApkWToThMZ6fQkXJxRcZo2thQt M1WSsjqhMwvOROc8biNiJkDG433cVbbMepIoFzH7upTWLU/3ENyx1UC5RnjtPu1X8CJcLhyka2U8 poKXXKFgpbOhlEYSiwY7InuOZWbbtDutTWvX8MB3nXCmsVbVXQpUotlm4CT+sn7dsCz0d9vld82l vL0+kEjUu7442rrDkJx42GthLSVJEu3ccDMM1IKvSqL9JvDsO/1aeFv6hJUUcGy7wd6SZ2+kCNzA 3gVWRp7xBqYh0IPO+nGepLAFMRgP/CuO2mUpvGbD2dFHbRtRd8AwEu1ZHFu2gUJ04CtooyJJMJ41 dh63xdgGvNGWp97irgFJzDHPz7bMt0YIHQHSo1YihJexAclUH9sRIqIj1AbcTQb3RZPecRYQ14R4 mwVEW/oDjFoLEuX7I+t8fPr5VQRFzX+p70x/L73dRNwIg0L87PNws3VNIPyp+9ZKMOtCl0mjaQ0O UohkE2bliISzvOHUA6REF3kM6OVnbXQhd47EDvZORsFmzdxF331r1TA6vaxbBzcxAZWstd7SE7cE vjhEZaqP3QjpKw7iBgEvtOUwgdPy5GzuaSuPiSgDlsQgGURnaq1MVqiUjCqlwV+ffj59QZXUwnm/ 67Sz8EyNLqY23sMF0j0qR4V047ECYfuequ6j60+xZwqRNxTj2GKA4HHx8+efL0/fljFzZDgTGdqF qQfHgAhdf0MCgclo2lTEAVWCRRJ0MvSItsRGlBP4/ibqzxGArI6dCn2G6i0qx5lKxKQlqaXRmuOs 2ko1Y4CKSK/6FaDiyrQCjo8ytVKpqla8zvGPWwrbwvTlZTqRkBWl1y6tEkvEVZUw4k0KE3I2nwOp objAEWLrWWI/RKaGd24Ykp4aClHRcMuyKPPFmQcoDGJLuGzJ8CSv3z/gpwARa1loh4mo8ENROASm 8l6n0G8+BaisIbPUT5YoGQOa51lusZkeKAq03aSjcIxlMFZdLWrzkcIJcr6zuTlKIpAkA2+dZLgN PnXRwfp+rJPeIhsePBp+kxIukTV0a/EiGNAZh5FsbtUhqPIK83fdImX4sCiio+eHnMHRaXHAktS4 8T87nr86SY3Nr3dYCLA2l62aoiJqx7WxRkvWtcVCmzUgK+knn9i8DyYNTNfRGpaqP1gWeVV/rm3W Mhj6qSOf8YZ2oce1Fp9MgYv+wNfD3T+/N44ewZaQUq1477C8PNk0sYPlP1vxOMiBXQbOr0oKWopu ynh4RZMvLlnElJPkeAEurErUl50JJFJMA/OjhTObscbTzoyQVuYLcBxtPYdCyHdYAmz6ss04BrNA Ku9mkmveHFNdZsXUo7BhLD4pF1t2Gxi9MiWfEc5avEygMxfFsbHor2DCDuyYsns5yEThHYP/m5Lu PyDoR2/8KLfksZU4lJh61vr0e6xKJJRVtpYNNHBi5VWqMmMqtjqda0MDgeiKUyItYsYnSI18rMPa YNZSfA1izh0mJcJMy2ah2ETeed7nxt1aZGxY3kwPXghXRvGIaQBE4s0lXK1koq0z8thcMuCKpDjM cnvinYhjIfNnLJ+goN3LlydVIkZXRjEVNXDAh1wTtwEqFLkYc1IT6HGBiDjS1HmCyCN8pT3+ALA8 XUcGvvz92/vLj2/Pf0DnsIkiKC/VTrhZYyl1QZFFkVaHdFHo4u6Y4fCnpYWILzq29TbBssCGRXt/ 69gQfxCIvMJjn2oFjKqlDUl649OyuLKmSMjVsTqEelEy74qQoiwt4UMOj2nNRN/+8frz5f3X396M 6SgOdZx3+gAgsGEZBZSn6yhr6gVPlU3yKebGmFfBYMdxB40D+K+vb+83MgfJanPHt/AyEz6g34sn /HUFXyY735ItXaLRYWoN35cWblCclgsZXkXaQgpJZGl5CAFkk+dX2s1DHMLCAtbeKGkyC9uJjjco FlDOfX9vH3bABx59oQzofWBRowIa7v81HBzfi4MPjzXbGuFM5/vmk/I/b+/Pv939DVO0DKHS//Ib rLtv/7l7/u1vz1/RbueXgeoDCHAYQ/2vZukMj3Q8kqxNTlKeHyoZGmMtrLRJS9rnIFFapmfXPEBW m1CLd09LcbBtyby4iGvvSUN4OYllp0blQ9hkjSaD7/0Bt9l3EAUA9Yvc1k+D2dNClyMaMkWz1drQ RTXvU8Lgon7/VZ6GQ+HKROoFDwerWXBmRpBQDi7ykDLWFZ1MUKCK6GzcXAI0BP2jMBgBEeMam42U 0YfscSonEjyBb5AsUqMpHSb66Fmk3IYMWtDo7glciCE5z71gRz0YHlUrqKMIzzWzHlLnzXMjjPwM /vaCMQiVlJ4iqEWkqOkbPY06/FxaxMkrp+FjeUueBD9jRY4eCveCMTfLHJBCcUn0UiFZxmqecQNL M7XnH5jD6un99efyguwaaO3rl38Sbe2a3vHDsB+ZVLkJRQLcu8GuEs2AqrS71O29MJbFPoHEXWJa EcyY+/b8fAe7CvbpV5HZCDavqO3t/9jq6e/PaqJRHZcnXeg2wgpkHrUFicVswCA8l7RezyCrLZFq lwM3tXniyEYtw5A0bUBgZtpTo3D3AJfM7ZIe2bjsBJ/pOmUsCf5FV6Eh5EZdNGlsSsS9naud/ROm JNOMDljxrukuyytZ43p8E+pCgoldYjisF13HOGGujr+h7oyJoCuzK/WlNF4gDelHkpqlhZpRd/p0 NNXsuSkdjCRx9Ni1UW6Jfj4QgQTeto/nPLUstYGseKyuRDJMc0aKBEO631vC347tAmG0s8iyU7Oi qqqrm0WxNIkwq6tF4TQuhbQ6p+2tKtPi/ohK6Ft1pmWZdzw+tZaku+MWEo67N0vLYX5v0XzCd4Lb 44oEWZ6a4pRJlV7y263np6rNeXp7yrv8sGyazJMCJ/rb09vdj5fvX95/fqOs1G0k5mKHY+5YRQft spu2UCJVXObK4Ntd4fkWxH5jQ5AnTfpwAjYvbvMTpQPD/ScfZ3SAyBmBEdWHpBK+MwVtrDMjRbpM DaUFXRpLydsH019THplW/lcUJsJ+2tGMNmwVuOGoNlonjBg3s4ZDZt/47enHDxAaRFsIaUR8udte ryJOr61C+VyhvQwLcJk09OqTXbDmopFmVZeoiRdl4gOh7Yusw782eqQ0dUTWpRlJ2a5Py7G4ULeW wAnPxfNi3OMw4LurCU2rz467M6A8KiM/cWHh1vHJxInHrgWwNkuGhcN0haU0QLuGvm9r+YUle297 XXwkpSP7aKC2IDNl/lH/Y19gki8EjubDgMV3f2MJahO7c8LQ7GfehYvhY0fPcUzCS15hXCkTyp2A bUNV/bPaokkGF9DnP34AY0ptlsGG27pVEjU3iFxyl16KesvduqGg7nKeBrg144A0C0HdICkcz+id WaO0f1vW2DU5c0NnQ049MUzyzMmS5fAtBs812xAnu43vhkuoEzo+BXXDRXvjBDrnlBf6kUpu38bb bykD8QEb7jxzaSHQD8w2mHfaNEXIKy5a1jK/80NaoSeHemklrc8ED/xNGBj1jRaSFDgMzI4I8N5x l/P8UF5DKjSnxErbSnUTEXM8pVe/tXVWdJNyErvQ8sQuhxjYsZpWQA5rOe8xjo2RNnRBlEoqSwRB OWkJ81yLe608iuokOueF+eKpZIenBun88vP9d5BgV87C6HBo0wOazpoLDGTJU6NOBVna+I1IdS0q dT78+2VQG5VPb+/GzFycIUmu8K2oqWU4kyTc3YaK0KZinEtJIXQ+aobzQ672hWik2nj+7elfqgEY lDMoqUA+0uuVcC6fhNVuSgR2YUNdlDpFSJQpESIDcxypj24ahePZPg0sCNfyRbjxLV94G2vfPHp/ 6TT0aaTTUFecSuFvrnTrdqG1dbuQcpTR+pxutpbRSJ0dsWKGlaEIBWjeLsOtU3K7wPJT0xTag6sK X6rlKCIj8VKTRBK/VKlECQNhv4NVr1gLo3bV/GAgmpw/ZgxqEg/4hAYX6CZwlp+wi7tRL8sRjoOu On2pcH2eNAw1TRqBuyySx3oMrqHJACYKk4FW2uEjo6T4wR3C7C9aN6DM93Ar3TGhOaaRDq5tZ7ex pAQwiCjzYY3EdYgps0+mcH5RDb9HBLIdquwwwvWzdC5GDCVRTOcFvkPB2dYJ3IJskbP1d0TVSdqJ tPaSJPADaqpHZx9inEYSmJet41+prwWKDJKhUrg+0TpE7FRtgoLw7dUB20VPvEqzD280yQ+uZAW8 jL3tbrV8ydvdaMTA6dEljevvEJ0OKc6su9+ubd7RXm256drO33jEYmy7/VaY/S8admLc2WwsoSDH 8Un2+z3pE99Wfhegh49+BhrHqvjZn/PEBA2vUFLzIi1aZXBrwih7yDYW593pcGpP2qOQiaSvxoks 2W0dS8B1lYS6PGeC0tm4DtkKgbIZQqo0ZDR9jUJxJtYQnrVmZ7dbL3Xvbuk8bkkHfbZEqtFoqKWp UQSutQLy0U6n8Ik+HzvdS2QAc29HghkIcQ6BuGKO2QrtHIFLLqhG3ocYrnR1EO6dzU2aLCod/yi3 xfqIAhuS8tJm+Dt2KLakcJgI0MadHPTu2qzNF4M/orztWWPECTLwDadi9YxUIsIDDspyzBNuyNMz Aq6vtaYlaVHA+VtSH+f+PUiitJvBMAM7B1jvbNkgoadyswNVbLbzvZ1v87qQNKM/KzCEa9VzdlTt Uyd4B4LTqYu6lC+Rh8J3Ql6SCHfDyZE4AGdIuXopeJcoUGjsjGhZA+6YHwPHW1tuOepYhyOemBp/ da2izQG9VAZl4aLET4zk2kY0bLLWcalslkVepdEhpcqcXjtWCpYXsU9+LlA7i0WnRrWnGiYQxLwI rs4nji5EuI6tLVvXtfkGKDTb9TtJ0ATrF4CkseQNGTcIMJcuzeioJMEmoCR4jcQh7j+BCEIasd+R cM/ZecQ0YMJN8qIQCI+uPAi2xMQJhE8edAK1X7uVZQuphVKyxrOwGR0LSJZswjfc9cKA6l1aZa4T l8zk0yaCdgfnjUeswjIgoTuPXJjlbm2GAU1ud4DTuaRnApKbV9BkI0OCsQCopQ2kDKOgSRYH4Ous JxD4rrfOewoakv/XKYjuNCzceQGxjhCxdYnNUXVMqt9yLrWVi+ZUrIPtRomDKsVuR55NgNqFFulC pdlv1pZy1bByp4aAnruVhf5eWeJNaZjHT5TlwkqNYJ7dgFY6azS79WM0Tou+yWwObwNNE/UtD2xx ISceouk92iFpujLjsmdZZksQNnJSDd+7m8iSfG0squLNqcVkYw2l65nIWs93qTMTEMHGggg3wZZC NNzfbqhPeBGEwGZRG9b1N0FAIPBK3YXkrpSoOZ7F+pXtaQ9W6u3ie1Rjh8uM6KC8qizfuBvbnQQY igWQt0RI7jPEbbdkFFqFJAxCcoDKxg3J11CFYE9v8CYvt5679m1TBrtg27XE7r2mcMUTY/Dgb/kn ZxNG5CHLuyZJWLDWV7j7tputS9zTgPG9YEdc7SeW7I0IGSrKXWVqr0mTOlR9nwvoIXUgX0ob/83j jsxzOOFBGCYWKICp3Qdg7w+ymmO3teSlmynYOqtHGOybR0+ZAu9FbOQU5KgtxWMAwnUsiAAV5GRn Ss62u3Lt0hxJKLZb4mKPYh9BkEPlILoqWSQeQeGuMXiCwiNOLd51nNzsIPcGAaUJSZjjhknokBs5 SvguXN2NEQxiSJ7SVeRu9lShiLE6TU8knuuur5WO7VbZ1WPJfGKjdGXjbKiNjHBilQg4IR0AnLxq EE6NB8B9hygfA/Sy5jRIsIteAjoIyTg8E0XnuA65iM9d6Fpe/kaSS+jtdh6ZzluhCB1SF4Qoa65S lcYlcyerFMTACDixYiUcDzvTQU2hKOBe69bYDkkTiDDkSxTsviOh6pGYVKBW/Xmm3YFujH9Cadfd bxyHug8EKx3prqUSNKajJwseaXgXdTm3BDYaidIybQ9phcFOsKV1ls0ZqTcmsSHhjeA6W8IubS4i 4fVdm6t26SM+SbPoVHT9ocb89mnTX3KeUj1VCTNUHvJjZPEzoT7BUDgYiZStf2IvnSBcbS8SxFF1 EH/cKGhunFoSHAgjFdnmJD1nbfqwSjNPLzKpuc0PeaAyLT0ngtGEab09wm6fIhkiqr4/f0OHjZ+/ PX0j/d/QJ18uP1ZEui58IAFmc2rsWbw6zisKcc09Pp+XzbRlfjOLx5hiScetrRSbGUi97eZ6o7FI Qg/IYIewWtai3+y4OrqSqmPo3lwXi1SZU8wkapDnUlTDA6K2geoSdeyY1MqxOEIWeTsnRFVfosea jKc30chID8IpfEhlnxBVYOxT4ewDpalZ7CcCu4H0XFMrPKBEtntZ0mKmL0/vX379+vqPu+bn8/vL b8+vv7/fHV5hvL6/GnZQY6FzYbhr7QXaogZjgiVibAcjRxVj7qkRRQyuNGldFKqBZZw1TOrNIiNf 06S1W6kCLZY3wZ5s4CWJoE8JNe1DjBrqq895LmLprVQ6xtpbdm2wHqfH67Ja5vCwvCwT1ane9Upj 5NFHVSeCVa5UOMYDXBYbsYcT5uCFoVOAyRlDocNcSfBUT1TkJfqOmyOtEeycjWOZijSGY8MLt3p1 4jUrTM3KeIO5HuCoseTxgrKyvGsYvSwnuvTU1mNf6BMt3kE1dIPzuIx4qx80Gexna1mBt9mkPLYT pChuWbHQ2RVkuHPczNZSwJojeGzWh4aDuLXs+oQWelXHs+Krs3Vygs2ylzM74S+mGkTU0XbeWh0S ebt4J/tJsTLCUtgsG2Uamn7krfXVCNBwt8vMYgC8H8DU0RSx4+dFr2CFpg0I1976NFT5fuPZ+13l bLfB04KuGS6pyHWGukdD5w9/e3p7/jpfBezp51ftMsFok2y1VVAg7U7PYXk3Ned5rEXd47H2A+Nh qV7b4iuWY3IK+usRa5SS5LX5zTzACoGloTI9A5YtAr3ZStHJ6OU3k5kOOuMFysqIrAERi2taRCf5 ++/fv6DP8DIRyzi9WbLgdRAWsS7cb3066IMg4N6ONGYZkapCD28HxfVBLyjq3HC3WUlUiEQiJjcG XjNCQy1ojgVTcxIgAobH32/UJxABHf0lFg26Nu5mYV+pEJjeDzNscNnVihNeew6l5puwnk9+FK5+ pL54zkDdOxCHHVkrj9ZA4WeI9l2rNelEYmvL5MW8+MQS4mVAO5bgVog+RF2K7vC8P5Ae/GLAmeNd zTkdgNQ0lI0buHRQcEQf82ALpxyOGElz7DAWBs8Z9ZaHSKjSCGeBxUpp5uEUtffrEUGKBoqwxJZB nDXuzCTticlm/4+xJ1tuHEfyVxTzsNETsRNDSiJFPcwDRFIS2ryKoA7XC8PtUrkc7bIdtjt2a79+ MwEeAJiQ+6HKdmYiAeLMBPLYN6jx0DH1xgZhGE15X/N36KxdmiCrcvLk2GopE4xSv7PiaxvnpSuF KtLcgHqbUVdpiFTR/ydbiQK7ZurUDFktXNvgt4NOfJlG+JWpqwhIf6IRvV6QfCPSN6tDR2tvRZSK 1nPX93aZBchCa+qaW2KbUL2Bm2UAShphSGSvW5ldWDTn1AKhGmGzruJtAFuFe6+45ogk8c0yItMU KqRp0ithyh3Nbkh9A0qCi41SqEw+Io3Jo1Pw5So8T84znSIPzJeYAejeICTJzW0Ek5W2CJAETV45 K514zSK04S3LF4vg3DYipq3ykEx5DtqF0UqffPrsOGf5dLhZBhoPJVxXIvQ901BdGYfTd8UStbIW 89RTcITa52VvVG5B+cQPUgMbnpAak4iAKhdEs7OVDyL1PRp6TjADKHWoAQ62QcezR3PKlt5iKliN aJk1woxajFxPmT9fLQhEli8CezV1vpsWUGpKdmtdDtqStRZAwZTGav61LNgVceyUR8vpWQDQhe/2 kulJgknyBptkvaYtjuS+UO5z5ZB7ptwGdRLTv9csbHrzajiQUM/5Yeta0erWxdqVulAu+kcMvu+G tCxT4JBAaqrJvL/y1KUWfH+LNeyHeohElxIyFO5NS8bGjNlTpJcDhdjyM0YmL7OG6REpRwKMTXtQ oZjFIU9J7vgiIR8krlKB5LAzPIoNlC2JWMjQo805RzLUtSLSnlOjSYLFOqKrUdrW9eL2aI+oqXak 4WwPdAulT2kdNWpW0/G0oh4ZGF9/TDYwc93UxcL4dLdsWREsAnLHsYiiiGRuOpuNcC6y9cIL6FrR +mq+8skEIgMRbKTh4kwzII2rKTo4klf0zm8RXZ8a0tWOHH37IDQxgaMDunPyep3q1CBZAypchRRK E9WJeqWNWUSbIBpUk4BWNFkULqnsThZN6Fj6iIxIP0CTxpLqLaRD1LOoVrTwbFGR8rvdMaZmYmEj 0udKI+q0bysDkIFfRa4aAAnKzGdfElc+yHKfklXB0v+ksVUUBeQMREzoWJx59WW1JsOhaTSgLPnk PiYx5G46qF0TDAYtWQY0ahudPQfm8DX1HbgjbHeOuhBF74UStaZRetiDESwFhi4CJY08iE17NEL4 jwQ1E9UGA79VXM8gCAdZw4tbssSg1k1RtvqmoUDQooca9UpHJGCdCPXLT4lC3+EaYhBZPiYESX6c k0Mg5nnFPHLOIUrQ01EEebQKHfuPyHaBnRF8SjRIoBQHUFa9kL5BNqiiuSN/m0W1olLgjTRoneqH C1K20LRLEjd3rD6lOs7JWTVVQW1cRB5jU3XUwvmLOd2jlB+0i4ic7ZRiamFBBvtkLI5oivYJjdLJ rrZTbgEZ2/CNngoutk+PuDUSKme81i6VNtVWQtq8TFKz0+I+ayV12SCxRx7rfoS8btPCuMwByJ6f g33iiMsNwl4DigMnU3rUXXYpowIi1wJAu/BdNif9W7KyrDDiCl2TCjbHa6MuFb7obNWFptMNGT4a FplMoGIV6LKqNDUrRM6bhk5gAnR69XFqjyNCirLhW27G65Xp3iW2JvX7AY0xR6xA1LKW/WrhcN+T pVLHAzKePdUhE2mEdE6SmvFC7FlSnpxkqold8yavcLu3u9cfj/fvVGIptqNsv447hikXxq7rACiD YQB48R8/1F6fASlOvMEoqSX1aJjowYHgjzbncKQmwggMjfCkatnh3OeNoDl1MQdyi6WCijTbYkwX E3eTiy77waRCWQqqzQVmCa7KrNzdwgbhsHnCIphio4X+TmBx1TmGZHeSAl9rxDTkLs0xd+fYMKvB LhyWE/sc/h+wQ9S8y/P9y7fL2+zlbfbj8vQKv2FseuNRHFmo3Bwrz6PE055A8MzXfWJ6eHGu2gZ0 3nV0tnvTQNsPFFrUOlczlVVgnU9TDcpOKWGaG5kkdFKzJTVLXIlgEM3yxJXGANFFeTimzI3na/It FVHHXWpNzCMMpt1Rx/y029LyhhzinAUO1zLZekEbUciVtWM72tNFdkvMarTb2ie6SDxgsmMyaeqX s8MCFXCbEmRiJ7bLSWV1tEZQdUmh5bAnj++vT3e/ZtXd8+XJGnlJCPsP8ISzlDXcND3QSMRBtF89 D9ZyHlRBWzSLIFi7ZrkqsylTOGJRPZ2v1onZKyNFc/Q9/3SAuZGFdN0Jhpqn7AJGkq5/J/A04wlr b5JF0Pj63fZIsU35mRcYucJveT7fMN2jwiC7RTvo7a238ubLhM9DtvDIj+KYq+0Gf6yjyI/pb+JF UWaYnMZbrb/G1H3SSPt7wtusgXrz1As8Xe8baW7gGGOibYRnelprFLzYJVxUaA5/k3jrVeLRV99a r6YswU/Jmhtgu1/4y/B0fRjGAtDQfeJHepiWka4ojwzp5CzyHQ3WiMJwNb/eRzkmSsSEPmzrBatT qjsOjVRlxvP03GZxgr8WBxj5kqTD4NPS7K9s8AJ4zUgqkeA/mDnNPIhWbbBoyEkI/zNRFjxuj8ez 7229xbKgR9GhF9OktwmHVVPn4cpfk1+rkURzR4UlCKdtvYHJlSxICsFycYCJL8LED5NPSNLF3nRM JInCxe/e2aP1akeB3L1nT6ijiHlwDollME+3Dh2fLsiYa3u3aMstcKb7IuU3ZbtcnI5bf+foCZDB qjb7AvOm9sXZo163J9TCW6yOq+RkviwTZMtF42fpZ0x5AyMP60U0q5VHTh6ThNw6YU7ftiw+L+dL dlNRFE0CSkoGk+sk9vT0aupDdtudJqv29OW8I5fakQteFuUZ5/J6vl7TnQDLuUphfM5V5QVBPF/N SUHJOhD12jY1T/T3Ju2o6jHGmYqeCm/f7+4vs83b47eHy0QgjJMC4/lQlpASvedVWaQtj4tw7lsD Ee9hBNACByVK+/Tqt3IAFZYPiZKlYT+EtZ81Eej8GxdyHfqTCWViD2daMZLSGijIvAlDnwxWJHnB udyioh6bLcgxJTV8O7p5JtUZ75V3abuJAu+4aLcnu0nFKRs0G0dNKB1XTbFYhpN5hiJrW4konE+O 9gG1tEqBjA7/eGRZCSkUX3tzt4yJeCuQhYHNK/QeISZas+cFRnyNwwX0mw9ihoUvxZ5vmHqUN2IX EdjrZVf2R1l4OsrIlJCMYiLJ4ADbVsvp2Q4IUYQBDCQZOMMiCa2vAK5V4s+FEQtUSu4Fw/D1Z/jl HC6WV7CrSH8NNbBJZbfWKBiSxli9esaS4yqwl7CGQIXVWt+4N+T7pIqCpfWhpCbRATtGk+1suheZ 35I2BTtyOoC4XJNnsaXChsl+qONqd7B7J+Z1DWrBlzR3a3RqvieOnAVSm9mU5yMHrdK1h8j09ZN5 lFxR82p/7p7BoMhd0azcOMGObEfZ2sjOPeMFUbvFi9ZU0HIgSJVp0UgHxhb9Zm4sKsy2MaQClgfJ 9u3u52X2x1/fv4Mmn9iq+3YDalGCkbz0ztluyEOPZCUr2dzd//n0+PDjY/ZfM5CMezuOSbYrlJpl 1tXudlW7tgWMls+ug+KNZsZ3+8ZRasTfNMk8MF4OR5yySiJ6fSRRT1REWRlB8GpReU19Mjz4RqRg IFQxCjOJ82+gosh8N7aQZJzHkWZqyGp0RrjwyCZJ1JrEwP4SkOymRnIjzs7qpPE7wqevMup2cyTa JKFvPn5rvVDH57ignntGms7kyNGClE7Z+slMHq5ccbPBN4Xunk9bht2mO3CcXPCOzRHloTCaoXLl 8WS6cvbcMB2HP8eg0k0NEmtDm38DYc0ojfuwNwLGAr8xZ45shni93GNKeGzOxCsE6dkS1VuTB4vr w9luqAS2262rgc5FJnGHOmWZWcsmzW54YVej8oA5KwFZEf6i8sVLbHmwbAsRmjP0E73CU97tu1je VnWqJ5hGIIzGrpSJqfSNu4dBJ9lNSHNhdZ2OzNLYyPqOsK836a3NZZfmG15T1vISu60tJrsMTprS DPaFcDj8WZbQ5v6Ih6rlpYOjopvb1OZ4YllTUhuBqi49yWsPq3W3tTwCbV4cPWIdrEDRsMl/Z5va NXjNiRd7NqnhJi0wi56ViE0jyGI7WDMC08QGFOWxtGAgJHYLyqiyh+MfFR0XdyAhJwpi60O+ydKK JXNriiFyt1567qKnfZpmgpiZOdvxOIdJ4uryHIa2NiJVS+DtFs7yyWfWqVoGLl6YLBD9xi1uqN3W 0/meH7KGT2aiQVI0lB6IGJDC5LOsQQ7yF8YJgHVBh3iRNGnDMM2gmwC2IDxVnPiMFfLOhAxR0lHc imYy+zXwtY22qvEu3cFaMG48RyuYvK6yO0OkOdK6GGG4ZIzGMinWpIy6hO9wMM3gAEqtHRPqr7KD Bax1xUZuCXjJyQTXcw71IDV3dZY5q5vfy1uTrw6dFGm4vV5h1xKpvbBR/95Ze+kBT+C2EgsTfOI8 L6eb0pkXOaXGIO5rWpddm4cyPezasH+9TeAMdjy5qfHE0DvtnsyNLE/hrMvM2wk1lHAw5oo3BJjR VgDT3EtxgMxF3KNLY6MZoe2uLBNurS09+bNWq82zMzXQwq9wsXe2U16YAQEWpasjWagH0jyZia1C COJFP4fO3ro5k8V7JPWFaINQ7mMOml/TZGmbFiCQaFsu4gnLCATDqYTBiGjnayQ4ZBWfpp/WCODX wuWLjHgZbWPPRLuPE6t2R4kq5v04IRF+qiZ8DvDqx6/3x3uYf9ndLyMx+VBFUVaS4TlOHfcViFVp AV2f2LD9sbQbO4zGlXZYlbBkl9KPws1tdc22pIQBVbYblOWL7k9XnWqRfsHErsZjYQdWFyg0j3bT JZ62QXDkFiUoNpGmr2BOsAOrSWdOKNcZdyjv8jz+t0j+jUVm+5f3j1k8ZAXXAsKMRjJ5PM0pZGBF snf5mWLVfJsDiRMfb1aO7AuIxRANIslz0sII8AeonIcwHp7ZUfGXfcxN0F580UdAtq2773T6yQJN 3tCxy3KQ/xtO2nUV6QnXsHaC4V/qpoSCtb3cNcVIcQlEEzNGsSTY1KgvF6DItPsTGv8Uu3Sqs6I6 PVESZfnpXYQEM9b487U3qY4VC28erOkrNEXhsA1RSLEIraAEBhojXC6spmziPFzojiojNIgmDZSX SdT1y4idW6wGr7gJp5AMvT9g13O72xDq+TZUpbW0q+2gk2sYiXTs2qoS9OVc2jUDMJh8WRV4Zmaf HhwMMT3dtXSXXOZ0TI+YKJJn1McE5IcHZ8sRZ0CFi0kB3RRdQnTfMmvmJ3NXcG2J7zzrxZI26VEf 2SyCtT3hJld/Ejo6P+jQJmZoQzxpXJPFwdp3BA5V/NyZpnp85/1tz9Xgf+2mTR24JRwvXcO1PSm4 WPjbbOGv7c7vEPPzkIF53DZm31/eZn88PT7/+Zv/T3m21rvNrLul+wvzQ1Iy5+y3Uej+p36cqAFE HYTSNyR2cHc2vjQ721EaejhMFBcrdBKcFMGoOdGGUrbUAErv5zHu7WTtT8ccwXMyzKviODFhV9+5 yxf+crrTZrt8so1vn+7ef8zuQMBpXt7uf1jbulm+bpYBGUqhw0aBfGAbxrl5e3x4mJ4PKIHu1AWq NcMVQgbAo82eDbISjqh9SUkmBlnCxY2zqryhbsgMkn0K4s8mZY2TyXDH/BmruDo4mbAYFE7eULeV Bh2x8Q1f2gX9lLNLjsLj68fdH0+X99mHGopxaRWXj++PTx9o6/ny/P3xYfYbjtjH3dvD5WO6roaR QZNvfI/69EtZbiVHN9AVg7XyGY8ibZL0eIUHXobTGq7ZswdXUBHz2xr61pfFcYrxldBCj6bg8H8B 4l5BzaUUFPEWThsMxSLi+qBZVUgUoaohnOBUNzG+9o3lEYBRv8PIjzrMwANxUtKjnh4xMlHv6jC+ Vg7QqViuzFdyNn1OBCBooDv1nKjBBk9rEB+LNBMmFi8ETIgeQJdlDQZOysUOMBrZSSYcA5ih8WxF Bj2WUxJgp9cDUlo22Pp+yRqrnHa7dm5pnl1ox6+3xZe8apPKaKJ8GdxjhW2+yxsKYXwPfsskYEgH p5+UuzJ0mLS9OLRGFWLbmg3sYvgo2DCq8dPj5fnD2PCZuC1Aj3H1AkAtu/5hHqCDRKJx3xy2s5dX dPbX44Ei9y034rGdJNS4NeiKkz0hUW1eHlPlRULtnB1RHzDALty7JziMphUR7P92OpDehsL8uGH6 Hs6dndX4cehCkcX61WGyXK4ibyINdHC9sTfC8z3aMoHnOFIx5611x9xvcHEy15ZexWppcFB1lt4D WBmcSuQY8roD16Ucp8AEK7UQ5EQhjFgLVWeiXTYD7h//sHoBxDRY78aln46ht3SNQmq1xNdan3Uw laCDdIiiJxPiqqQ+4osor78QvJEiQS8PRWEzZq5LHcyKntZxKWjL1UOXob17iXXSwFlIyZWyeH0Q wm5Pvg0dmdzlh2xpJ4LjllQR4XvbzW0lrw3stLq4z7cqRqHRCLTP2R1Sh2tEgXaisF/EmMeaOqKU s4JZDX5WWhgSVAd2XbN06GNSUXtYh92wLCvNt5UOw4uKDOrcNyanWpjjNFMGNlOHQmyJ9peMMsnL JttYQJum+24DBlPCBh2FcbHXAVWd48dJKL6tie7uuDOWmhz4+eP928v7y/eP2f7X6+XtX8fZw1+X 9w/qBn1/W6X1kdwlP+MyMtnV6a11M9svfvQ10iQG9bcdCGaAKjFY7vD8a9rebP4z95bRFTLQjHVK LfJ2R5xzEffTnN6eFB0X7O+Q4ZInyEyiaB4Ek49DYCvYBH6jfhryYYfqTcvsRkh4m56ZfVVMExa8 rctDQy5X0TA4+s340E0GjSHZ1o0I5uaJpt5pYPd5/7h7eHx+sJ8A2P395eny9vLz8tFrpv37iYlR 1M93Ty8Ps4+X2bfHh8ePuydUcoDdpOw1Op1Tj/7j8V/fHt8uKnSRwbM/+5NmtfA1u8wOMIRPMmv+ jK9aYnevd/dA9nx/ufJJQ32r1TIkF+LnfDordWwI/FBo8ev548fl/dHoOCeNyjl9+fifl7c/5Uf+ +r/L23/P+M/XyzdZcexodbC2YyZ0Vf1NZt0skcmuL8+Xt4dfMzkjcC7xWB+gdBUFS32AJGAyQE5W sqb68v7yhJdYn86xzyiHF0Zi8lvLS/kV96I1e/729vL4zZzPCqS933QlNyVzWDFseZ2eMBy78uom FvdOtNtqx1Cm04SrgotbISrTgupGrOjYfKA1ofIGnPhWOzS3PM0S2PJbpej3h0mOrwJ4FIjWSj+I VoAdrs86nZHu58hDiq7qmBwYfMl27pDCXcRXjFBC0sg8G53Jan+4U3JorjR+vd4+xXxbcTIcI7pt 5OnA3dyvJa4UbcYqy3JqSlNh3knHdt7TNBtHgmyUClqHKCtxNxtp20RfeWlPWFnG0PGm/xzqyDjU WwyyRn5xj1zA+DeNQyMYiaQBVltWdbpz5XnpiXeVI9R2h9+XTZU53iSHJtfl32oY24FEs3MkSNyj rWqc6dY3HQSTbcC6So2DHJTEjlrtm08v93/qF+noe11fvl/eLrgxfoPN+EHXt3kszDgJUI2oIvuB tD8p/h53Q1DoGkhGu3XQrZdkOGuNSPBgsfSpjpCowLfEGg3pU7fmJon+4mViVh6JiZM4XXmho1LE WqF3STIx9zxQ/CkLSL0VdpAeBHYBOh0NwNsx+Am65GdtuJatWyMz3s40+DEOSHgX9tLRPBWd0U60 pH1dtsvbeKcpOt1d2zHWYPuTqHjRWTBoa0G8/PVGRbNXyXCM/Fxdepxykxq9mx5BGYuUT8EI3WQJ ARUYYMbQ/6S5InqnwQ7fhMuNLkyQLRwKMp6Bsqx32nDG5Hv65buK6T26vzkFfvTGrOqSEj19oQTD dXBGLqkvP18+Lq9vL/fTfq5TtG2DXtWeDEcYLI7uKn8QiyasVBWvP98fCO5VLvSQn/invBKyYYWw IfLqddcZJzowCDCUF4lXKj25Q5rN1LZ+tO9HcWrSd6Caz34Tv94/Lj9n5fMs/vH4+s/ZOz5wfn+8 1+xjlGz3E7QDAIuX2HiN6+W8/2ftWZYT2ZXcz1c4ejUTcc40VAGGxV2IekA19XKpwNibCtqm28S1 wYPtuKfP149SqodSysJ9Y2ZjXJmpt5RKSfkg0Moq53zaPT6cXvoSkngls2/zr+F5v3972D3vr25O 5+imL5PPSNXL038n274MLJxEBkd4qrqKD+97hZ1/HJ7hqartJFvdJCoD/fETPqXj+0401Ofc75cg K3TzsXsWfdXbmSRenwqm132ZeHt4Phz/6suTwrYamL81gzohtImS197Fq08U6asmbuLpydB+ytY0 S/0gYSl6GNfJ8qAAFsVSj7Ru0ylBOuNCuNFeCzR062+XRsO1Y7QJzEZYEca69lbBJki1o0qwLb3u STT4610cv3oDlSliGSTuG/PQw3GNCjkTggutWVaT9Hpsr/H1+xOExyO9odRkdhyCDuG6+uVQB7cc dnaoHn+fNUFepuOh7lSyhhcleBNlRJY8Gfe53awpGoXL/lLXELCoFH9dI7CC2DkK6kUn0vfcCK5v 12GoX0h3sMqbk2D8IIbg5kumhgVVOMsfNOBXcKQFKgyun5XhrETUUP0bcjKNRSpL5bDkWhJH27Ph jeG2Po/SXQZ4MvOuls2ioa/bGvnC38bITroG1LcnOvDasQCYap6wke4OQ32bNJ6Yk/IJPqahmN5n ju6r1Geu4akgYYU/oM8mCkf595UYbJQebmM+nU0cFvb6il9tuU/lttp638BxkO4033MdF8//hF2P xuM+F/cCO5kgzVQ2Hem6egIwG4+HpsN+BTUBek22nhgEHHto600c0mU2L1dTV49NAIA5q30J/V/u Z9t5cz2YDQuqbIFydAc24nsymJjfVaQO7KxgQhiIEXo2QzI38yN5gKIDXngeOD4dAlbjkOkmiLMc 3nlKw5vGcnutn91UyG+cOi49Z3Q9NADTsQHQ9fVgQ3AnLgLUzjjamZS7IwfHXoJLQ3CJpBwK9zQQ YrrdD6dTXMeUra+num8rKTJvYHc0H65b375VpLLortxazIYuuyMQeN0Xch2zElWJ+3JzTjLfVuIs ZQ6D6ZAqRSL5EDmB2IST4QDnX585t00j/t2ngvB8Or4LURPHvgNeWwTcY+ZLC85eS1yfA16fhXSH DXETb+SMUd06qt9+MOjWwRCv1998JfCe9i/SDoLvj29IlmRlzMRetaxNjLQ1JxHBfWZh5kkwmQ7M b8zaPY9P0ZJiN5i5iSPb9QBFsPd8t41l0onlEkrzVYVrfZ42ULAALcDOji9yzKZ5zu3MDGzvvfrm flqzoKbrzT5VxuGHxxog3wg8ceI4HfUzA02g7/IJb0Otqz5tn9u4l0TaEKLXCIRTZ1ieNyXZ1bCR SAApjSrQON1sq3ldErNwp5YHPYPHgwl6zxm7+lwS36MR2hjG45kDSpu6QbaEuoiZCNBkNumZKX6e lUK80EUPPho5SMssmTiuS2n7C649HmK2Pp46OPqTl4+uSc8xgo2JcsdjfedQbMxnhoeXC93XToDH j5eXX/VZ05wACFe7Ftn/z8f++PCrffL7G3SyfZ9/zeO4ub1Q110LeDvbvZ/OX/3D2/v58P0DXjf1 Mi7SScL8afe2/zMWZPvHq/h0er36T1HOf139aOvxptVDz/vfTdl5PLnYQjQxf/46n94eTq97MT7d Cmp52MLw8N6Kjow7QprQV0EHs+Ls5Gt3YEckwqtncVdklQvva9bCkigwijTR5cJ1BgNqwtjNUnxo v3t+f9KYRQM9v18Vu/f9VXI6Ht6NXmBhMBoNqKt4OMMOhkaQJgWjHbCRJWlIvXKqah8vh8fD+y9t dJpaJY4KutAJ/8tySDv9W/og+pEW1L7nDPQYNMuSO3q0GvWN+d2yXOskPLpW4rb27aBRsVpRm7SK hQymES/73dvHef+yF3LDh+gVrZXzJBpO0K4K37g64Tbj02v9GNZAMN0q2U6QPLupIi8ZORM9qQ41 5zHgxFyefDqXY55MfL61JnINN9/pL3SEskOQnl2I9QkqyBWLqfdb5n/zK44CdjB/vR0OdAseFrto 8MW3WFDazQLLfT5z9f6RkJk+JIxfu8iD33w5vMa+WAEypfiIl4ikU/2BSAB0kzDx7ephCjywJBvj 74l+GlzkDssHurivIKJZg4F+vQERPIfQeVhIkps7j53ZYIis6DCux8mWRA7JLe8bZ0NHP2cWeTEY o2VUl2BGo4vLYqx7qow3YsxGHnprFkxHsCjSoKtGaQ6S0owNjThPWV6KUaZ8Geai2s4AkKg3ouGQ 9E0FCP1uRZymXVefYmIVrDcRd8YEyFxvpcfd0ZBWAZU4082l0ZOlGI5xT7heiZtewM16og8K3DUZ ekpgRmNXG6g1Hw+njqYvvfHSeITcGCsIjpO8CZJ4MiCNNRXqGq2tTSyOlxTxvRhUMXRDndNgTqIU JHc/j/t3dbVB7DKr6exal0zhW787Ww1mM3w/Vd9sJWyR9l39sIVgTNgo2R07uifKmmHKTGihoMnf RDeDL46Y4+nI7UXgvaFBFomL4gthOE5zxxK2ZOKHj1203ZFd+h9t8PLX5/1fxiWRPOCsaS8RKE29 cT48H47WkGn7CYHHhcHjaSXfPuwX0sbk7epPUOY6Pgq5+7jX1DBE8mWhHqC7+1iUO7wNFMU6LxuC nq2yBO0biAFCX+zyOx5yVEbdQLqG9V55FDKWNAbcHX9+PIv/X09vB6mUSOygchsYQfR5su9/Jzck Tr+e3sXmfdB1PLujmEPyDZ+L9esa3Hw8ok9d4khlbEwAGpOBgss8BkGUEo+NapJNED2LzWniJJ+Z oZN6c1ap1QHovH8DsYbgLvN8MBkk2qP4PMkdfIsC38YFebwUXFD3QJ9zF19tL/MBtTtFXj4coPWd 5PFQv0xT37g8AXMxER/jC0v5bSQSMPfa4lqGbzUditOX4xGeFMvcGUwobnqfMyEyabcDNcCUMq1h 6GTLIyhsEozERtYDevrr8ALyPCyMx8ObummzhleKQ1h2iXyICQHukzf4gnc+dMgZnyuV7EZoCkE1 WJfueBEOtP2Jb2dY3NiKCgwwueYzAfZfF4nEm3jsxgNLRP+kyf+/+rSKC+9fXuFOgVw4km0NmOCv QaL5H0/i7WwwGY5MCA68VSb5oOf9SKLouK4CNSRDdJWCTetjLL8dH/Froi1dzmlJq9hvksD049NM Cl2nC0I/yZ1CbyMALQtQDcfKJIirZez5np2bQpbe3Mrxlr4kBRyYcIYlpSUK2NpmcZGYWdYj2Zut ilDek2ucc6vVAOsxrezQlstaQBmR7QEk3VFMW4N4MCuDsDq2o0+BAb0xdPco+iOixT8fFLwaC7RG aDHzbrPOmbcydailLrjY4bzI6YlqUwf4ivLMK8mra8Fzg9LUskE4NWiLWzJ/RZJ4y7wCDfItrTOp qMCvt/TbYMla+fLuin98f5MaMV131qZ0lUDr1ZL+rxYJgOkJs7yrPJYqE3dwIdXjgmDuQeijlEGG jpkbyssPUi+oyqwo1Mu6UZRC+5/nwCMhEGoXCwjHYt1LHaBgMUXJdprcQBXNcpNoGzRu8fuLzres cqZpUi257okToaD9VqvEpM9t/1l6+SzPlxC8IPGTyaRn/gFh5gVxBg8DhU8r2QualjPAa+Q8M2vT oYPEVHhv9iY0h9q8QWPJY2hJRn4ciBy/BV5PtKcypxhYgjmh+OxhMICJ8/ZxJt+ff5zOL3KnfFHX ncjurqn/BbJ2sTJdJ5Jx7FO+Btg20mIUR9aa041OGl6V+kXW59zOMkiJo3m68aOEjMzEdB/7wYYE VKtED+mVbuzPdkvDQHhg5j7TI9PWIeoD0FFF24tKUog/Vhcsb6/ez7sHKdmZrJyXWu7iA7SMSzAx VcuoY7AtSlS1omxMgcJfJ8kdzo9n66KOTJ8h4/kOp/srsbGh4G+6qp1aIyVyztrAeu1qWwLTsaNN 0eeluiXgJWXK3aITvqarVn5SMOFarrlRtwewKRbsnPA+LPWp80LshfJtkiwTUlXJomjIvQ2l2y+p 2ggeZhFhEQT3TYQPInWtlpAXMobpOo/1A7/MWhm/dMAspOES6IexDanCJKCh0Dq90ghn15mms61z TCoWrony0yhrXG8JcaZK3QF+NWoJacYa4riW4lP60QN2kmZ+T70FUcJ4WfvboXNtKJbruVlAjWHS L25vCUKqodigRM0Dw1JOADNPP+QG7UO5+JfSJdbB7c6WVFmO9jVlyAexkrKCPjHwCFsnwDfIlH1d w+MoQSG8AaCUfCBMsLmcC08FIyJNUdZAYKWQ12NeSm/E4iBR3ayZ75OhPjszDXFGEWJUXq6xfoia aDcBbWaXZKbhRHOHg/WJ1Wv54Vmc2aRooetae8xbBtVtVvi1byF0Jc3gnC/O+CEHtTVO3v8JXJSh GMjBtnQqfc+rAdWWlSXqwAaRZxwi1HiUeN/Q8MBbF1F5Z6R3q554rAI3MnBdjZO5bLi2+waRaJ7A 6PVugYIUq1+3GGlPEqUhNfe0PNumE6i28WQBWsup16imxm3Cb33diSioLBFBvzNUmRx8fYNjULrr t7JWRHUXIXeM+maeghHU87KwmtfALk6ZlkiOm1xFC3PqtDTFOhUnl1SgK8v9DqK1hFIFZlwMFL36 uzKCEMKTGn6BOgEvins7IXSsPpAgGIKLKcwp14D16WagqCUmcaofL5QmnVeoAwne3+ucBb+Vd6Mk Mr7PKODIqoYCL+m7m4binpc9u1xXXEG6I74XR0GDA8Cw63I/3X8qdBIepAZWezPOcrLzInGOA7zh rwJsXUCf9A5R0KxMHL2Lu9zodh0spLoFnj9cTkaSoYRc+a/S6f1el1aRwjQeKJs8mJ1HA6t3GTAf SCI5H6h+uVlnpe7JDD7Bx4w0j5O7bqgODt0RuxDgmvCWFSndXQpveGpRwLIIUIY3YVJWG+o5RmEc IwOv1DW612UW8hGaSgqGZ9caYq5g83o6nkXt0MdgnWIUIYAiXpe11u3DEw7lGHK545HyQk2tyP0/ xfHzq7/xpchgSQxCNJtNJgPUjm9ZHOkOgO4FkY5f+2FT9aZEuhT1fpXxryErvwZb+CsEK7IeoWR/ 2gU5F+kQZGOSwHfjaAciOubgEGzkXlP4KAODTi5a9eXwdppOx7M/h1/06dyRrstwSsotZvkKQpTw 8f5jqmWeltb+2Yl2lzpHXda87T8eT1c/qE6T0opxwQ6glXmc1JFw46lPbgmEvoPwJxGyNFDWtcso 9gs95q9KAbEXwCM/7Fy6RL4KilTvJuPCpExyXGMJ+ETGUTRyB7yAj+DMNaH1UpbrheA3c3LPSwJw 0OEVASt1td0m3MAiWkAsZ9VJHV79dLt5c2tmj1d3PuDKMaHokjJItE7JCvB4Z7AT5tOAqrjVYKFB FMh9ggbVbvPQ093SSC++VQAQDTY3qyYBtgDVJycGoS3W2hJSd2ycR305eQVLDA4rIWpbFuduMsOa hnbIy8WBji/RjK0harduThbdGRWh/aigD5gtGVyqJHkFkZ5iOqOaQvrNpx9NKErYdD3svt4kN2TG Fn5veHRtEUIeu5SfEu3sZNv7S6lAhCMqMVrBRclcesW4pzsmSOaBOGxfGrYqLNgiCYS0oM6+Mi+3 3TS2xsxNIgghrUOyxFwBuQG4SbcjGzShQdaqKOoCKH4s/elojFV+t1vKChwFzO/EMeYfEHt3YJPF cJpvhHG0DSgSMV4tmn4NaehGJJ1FtfT04jB6OnL6kTAH+rEXmmC2sumd361mQ09krVeYytakR234 vB5WDb48/z16evhikRn37TW89kphVqIgY16JHWVjsNh1LzcuTMG1gZiidAs3+EgLJ+86WuyFi46W 5h4/TotDwW1WrPRdkkid6trB4qPrYUq4A4JGPqyEfEhn2JFc6/pBGKOrWCLMVLduNzBOLwbplxo4 WtsDE03oJ06DiDr0GCS9VdQNQg3MqBdzoVkTyiWBQTLryXjmTvowvb0/c/uaNhvN+qtJhkUAEnES gvlVTXtyHTq9VRGoIUZJ78pmJZoS+katwRvtasBuX36ftWhM5zehwdc02OrStjW0Rjci+ayGQ6OK qyyaVgUBW5u1AK/mYhtmtO+qhsILIGxRTyUUQVoG6yKjsveKjJXRZyXcFVEcXyxjwYIYv+e2mCIg YyQ2+EjU3/Cp0qLSdURtVKhvIhyftMGV62IVkS6xgQIOy+huKabDqq/TyLMexBqzOP0pQ9mC7h8+ zqDUZ/l2XwV36Kh5B5exN+AL2pbTIZByJLYQIRoKwkKI3z2B2Yq1oJLRZsntUt281QSo8MpfVpko RkbKRJsvIOUNWeQpJLVn19sj+P7mUiWpLCJPf1rv7m8NCN7p24zq3ZMSSYADlWwOeiZC2rAifrZZ 5Ix8M5f+A5es8INUdMRauh3P7ypwb+0x7J3AJNKLsXMIRRbg8JEs0ySGVvBcD0gYZoW8hlQaCFpH wYuGJ1NCXG0VVvsTtGz7P758fft+OH79eNufX06P+z+f9s+v+/MXoqt4YtTbJimzJLuj3fW0NCzP magFfbHRUsUZ8/OoxydkQwSWD5cpOAtB881UqbFLE4fp7DYFy7hPKAXLAeoePQvrnaYFioPaImXw QnopqQywgmT3qKeJwYZU+6mPUt1a0+2qReuEUL47PoKx8B/w5/H0r+Mfv3YvO/G1e3w9HP942/3Y iwwPj38cju/7n8CW/vj++uOL4lSr/fm4f7562p0f91Ihu+NYyrBk/3I6/7o6HA9gSHj4e1fbKdcV 8Dx5wwR3ydWGFaLdUdkEn9FumigqiNSKO1YAxbz2VoLxpHSnthRi0WnFUHkABRTRowgTQSQgtfy1 0EB9hYJnK7F74SBCnSEN3UcNur+LW+8A5nbRFL7NCnUxpLFtycyh59T1+PnX6/vp6uF03l+dzldq sWvjI4nhxYXpQWUQ2LHhAfNJoE3KV16UL3XWZCDsJEsUdlED2qSFftHXwUhC7ZxsVLy3Jqyv8qs8 t6lXeW7nAGdkm9SKBIHhdgL8YIWpIUiX3PgaX/WYahEOnWmyji1Euo5poF18Ln8tsPwhZsK6XAY4 GE2NMV1nGlMiStrYN/nH9+fDw5//3P+6epBT+Od59/r0y5q5BXLnr2C+PX0CXfunhflLoo4CzGkO 3BIUn1DwhLIoaTptXWwCZzwezuz+bFHgLbvpCvbx/gSmTQ+79/3jVXCU/QGGYP86vD9dsbe308NB ovzd+87qIM9LiEYuPGozaZIshaTJnEGexXemwW672BcRHzrUO5JBIf7haVRxHhDMIbiJNsS4LJng qpum/XPp6QJklTe7dXN7XL1wbsNKe/l4xGIJPDttLJ8jzB7IQlpPu10xc9IVlsJuiaKFRH1bMJuF pMsL49AhZQ/3l6gRss3WoYYUopmUa1ocaroH3Elaj7dLiILYMz4JswdoSQG31FBuFGVjJrh/e7dL KDzXISaBBCuFNBpJQ8XAxRSz3G7JbWkes1Xg2HNGwe1BruH18rbKL4cDPwr7MV3trAW9pMM6NdOx fwq18wIc9E9Ix9/1duOPrJol/tiGRWIBSzsJagsoEp927dLwhCUbEskALKY4DyiTpI7GGU8Ulc1r lmw8dPqRImVPGgrsUlVMLtUN9DXm2YJId5uLQvpTypGt5LhDGJlmRivp7vD6hBw6tczXnnoCVpUR tfAD3mZ8gZ+n63nEqeSFd2HSCOn0FkI3ECtBISxfeCZeTUuiYAiTGccRFZ3KoOjy6MGrPUowxt+n dPpJ4a6GbhTg7AUjoZdL5yXBLwB6KZlPzAIBc6vAD/q7NZS//Z26WrJ7QvxvhIZeRH+JPCAfQFts kSOXxBgu972+HmhoLnSSRtKfTUJVuwwuTL3yNiOnfQ3vJoiVa01gc+PLlJV7y2htTYO86wtbC+v0 8grG2vgI30wc+UxJ1Di+J2MSKOR0ZPNPpSppwZb2Ll4/rCsr593x8fRylX68fN+fG8dnVE0hym7l 5dTZ0C/mCyMenI5ZGtFREe7i/ipJKCETEBbwWwQ3EwEYgOZ3FhbOehV1HG8Q9Am5xWpHbrMlLU1B qhuaVOQ5v8UGqTx1ZnN43SVnBtxC0kouSg6EjQ100Y3LiufD9/Pu/OvqfPp4PxwJqTKO5vQWpzSa NoGk6JPBNFxj73qJ5pNSFHsjM1Coi2X0pDaK6D8xYvTloi7nQm0WAG/Fw0IqngyHl2i68i0moZFd 4lJdp3Tnz/4ZBNSt7GZmtaTtpxm/S5IAXiLkI0Z5l9vxJjzwi/ZDnrjfZAwfiNmj3Ag8PO0f/nk4 /tR1VdVDPkwYCOzE2+cYWnn1N/JumjmPUlbcKWXhsFklce/ygODBkypHAVYbWDUPUk8wOPL1BAJu sqKSSnpYR4X1aXL/b2VHths3jvwVY552gNkgzjjZzAL9oJbU3dzWZR3utl8a3ozXMGacBLG9yP79 1kFJRbKoeB4MuFkliqSKdbFYtTag0WINRnEoMd4eB2W3SvHso61Lz3UlUYq8ikCrHMNUjYyCGEEb U2VYdw1WeW2kVlC3mXPdujVlfqqGcs3FVaflwJMhmaF2uvKeGv9i0Ajymml3Yih0WjbHdLel04Q2 33gY6NLeoDJIwWxNYeRMpz6AGkFiVXU/nbFNVJye0hQkhdN0/sHFCC1GGG4/nNynXGsXzdyxerO7 bwhSmDRfX+uuHYFwoTyatAfQESL7GzHWRg9AAmjE9EwdXSEV5/Kw+UPjPxWBC77NDuSd1aU7eQvS Q7uwlQMS3XaMLkTBVTjxtDfMRr1WGaHmtmo9yzg1p1WNS0NsdXx6LBo1a/jHG2yW35NbUE9UP5cF 01X/RvNzWQSTSG3aNiZtqbwKWvsd7Nal92G+i4W3rdN/KR1HfM7zOpy2HKMVAtYAeKdCihunGP0M ON6ozW6E6MhXlNPlFmyhEyhUtWM+ylbsVnKBdSpI/Ji0bXLNHEcwm66rUwMMBhQKQphByKSAvck0 ANxEtc4dtoftTvmPikbF5euBl2/7nQdDAHRBeqAfeI6wJMvaUw+WiMPJEQJzLBKKHdzlrWP9dQev 5jOip87XgIYmb4H1jwD2H9795/blz2fMd/T8cP/y5eXp7JEP5m6/3d2eYa7ifwpNEx6mqsYYgwwD wMh4Wdl4BHfo6KIQVo17SSzR0f9iHUXOv12kRMuniyhJYbYVRgqvPoqAFQQ0JpqUaPxGk3oglnpb MIkKBksX66aTbQFohlPrkEt2KYVsUTvx2Ph7YsNq6I0bkJkWNxjdMTdgWXXQD8UrysYt4Aw/Nlkv yReof9x5V1lXh/txm/eYXrDeZHKLyGeo/p1TJ63DjCd14VE37pUGs2A4dtwEGuytsk0xdDvvZu+E RBEhZepB6PT6kMjal9SU5Y0sMcuH3GRhgWKENW+m6OoOdlvp5mfBwJxqq34QkZPNUzvd+IBRKabW r98ePj//wSnLHu+e7sM4J1Jp97Scjs6GjWnip0Wi2VDyjdN6MFhUUHUYcaAxqGPbAlTTYjrx/UcU 43LAi2AXEw3R9RGlhwkDC/mO48zyQtJJdl0lpZmTwWjNYUWF63Jdg0J1ytsW8PRyYPgg/IG2va47 ftx+lOhCT/6chz/v/v788GiNiydC/cTt38LPwu+yNnnQBnIoG9LcK2s2QTvQcXVRO6Fkh6TdnHrY MHTIp8Wu+9i6D8zH0ozEJtnhx8a9REM7rcmImplQBjwobU2jOik2LXwOuptJZe3dzdKATMVUOaUe cdfmSUbOkSQSXrTLMRcZ3ugC0i40tztPsOMLynizqkx6Kel9CI30VFfFdbiYm5qy1wxVau/XAv9G qRuddVMbmzvCYSc2v4IXWndVgvk4HFGKROfBIzjkyZ4KcYG0kFT8ajolqibX38OnkfVkd/9+uafK 3+bz0/O3F0xiLii6TLaG7gRS1rewcYrZYXfW6u33cw3Lr2sSwvA0e8C8YquffvIm3ymfpCPRejh5 Xz9Ew+gOwiwxoUd8hccOMTjKk3okOPZA7nIc+FvpbZZR6y6xmQZQ9UikrCWY7IyRgUOrhXlT0eEa i3xKM1cCWXv1UfQHf/xEtzObPhxlZq7ikV+MUq8xMwDpXAtYIAv0b8fgvIocptuRj9qasmLq2s/x vBgNTCixQFb+4GknI0kJQG1kGhonCZKHaz9KPyLj8hJTw4uReEnn0BrX48sdWwmlzpoxYmooQzWv BEP2A8bZRbRFRsmTtrget7E7M/QDAYMENkmcrVt9uHDhA8lyUDG7/erjWxU2Jc8RqtQ4J4Szd4Ev +rmT2oO4oJevMAl7DOh04M1+TtxDqGpeG8ZsczLcasxvCPK17Fa/Bu+0OKRODdW+wjjYujVbU/lD t5jA94cc3bLVNh9FjIMHBuZQUjl6eCfRBnCjvvYiAOxcthVSEIP1S+Ov4vAu+8NL2TLSg1vxQvJo /9kozKkzoYuiypcfeyxV5Yo27gXhZAfF4s5hAeVmojYgtK6uvDwZc3+YYmWB77c1yNkkRu8Tj2bk wzF8x0EzEidXZ58NMn0v//bqHdpG6k7epeP+mUMqgs0CIraEioqhs69Ao8zvur7lIkauCLhIbTqQ FhaZmL17LLJoqVj2DGxUYyfVoSuGNR9/eQzYUiuYhgUoQ+HyjZCFabIuNqChol26APaQWZwcuPaY Ckonnavy1Gx7yzG990TizYPHIj2bth+SYEdGmrlkLQVTK/uFNUZUMNWKM2QWc9X1DhYwadAhj1mA Sen0TN1xmUOsZWGahMJ0BmCgmucoYQnK0PBYTkKxvGyyDVUiJGO0yqt6VgSyzL9FTH0sD31DWunM stXf45UR7w6QhSVU1prEEAiD1bkQKYyBAnbkCe/evw/6Ji8oF3/AndJJZ5pF0kW8H0U/8+9gX+ww lbN/lEf4Z/WXr0+/nGFFs5evbFvsbj/fS38ELHGKcfx13cjr27KZ5eC8yRlIHqOhX00rgqdBQ6PU Ne3qTR8Cp1mgawGLrZYSkd6hHb9Fke0o386E0GbeW6n+uCTmCYP9ezglIIeyUXHCic2DEWg0mNfg +MvK/Z92A9A+KWSCV7CpNoGmxb+YlTbxogktPhYXxR/K4ZIVrswN3CM65imopLpMc3wDEMzc31/Q tlUUEubhnh+JG13XDLWNYma+LqL07bJbXLZ9ntsaB3yqjEHWs6b1t6evD58x8Bqm8PjyfPf9Dv65 e/705s2bn+eBUqow6nJL/kNmwtKhV1+pCcMY0CYH7qKCBQ3Oy+U7cI5RaYQHFUOfH/NAS+lghvh8 IG509MOBISC+6wNdlvMQ2kPnpLXhVhqhx/85W0sTNOAJaLc6f+83kzuss9APPpTFuXWAEspvSyjk 9mW8i+BFBjSfImlPl0M+jL2983mpxY4uOWvvsE55rkhX+8E5GsnqgZrkpoUDToJHCbSpVo9zV/PH WNIku3Tj9KCfn3QZv+uQmH4hufFf2QPjHHjFQfJsCkeIu+2nqjQ+2YTPzG5uuabk2MPrakOFkZLA BNhSXFAR9yyKI8LwDzarfr99vj1De+oTBp8ETmAMZPGH3NhGX/LqO5eBfEvY09xHRoyqNBjQaOuA 9YEWrnHvzi2O2B1c2sLiVL3h0nocM5gOqsHH3CcVYYAeFdpWNBKo7rTSHqNbhGH+zPk5ZeKIhCot eX8nKfbu3HmBm2wcm/JLmX9nLOriTDIwBC+thtuSEr3woTjxI9jBmLVIDQCGAe9AchZshFDOGipi IbgctFbpdV8LzkcBg+IEJ5ASFdXhApBzy/pKeKyXods2aXY6znj44mduV4Cng+l3mGc7MNkUNE6K RSdVr0FP2qBXCy7JwITXYvySh4KJ7Ig8ENM6rrxOMIb02muEvY8HLLZrD5jaV/lAHk3qCks6FFwP m41c1/wKw6UR30mzhqSAtNPBhNPwazRg45ewu9tLfTpBf6Ovwu/IIoZUtAkYJ6p5dF5rn1Fp36Mx XcjMslVHgGmBGr5Z7IPUtwWE3aFI+iWEuqtq0+VLKOQi+UE3ZWlqAuvLYfcok14snwR2fuoqsMeB H2h8HSQUEAHwWUohZ48kpPZH7TbuDdaFH1CDKDB/Guabx7J9/hceoKd1ziSpj7W7rmD/LSDsMF7T lvrTXs/rwUTPCYO9TUOU7IRPzKJg3j96+KW7pPCWpKBgDFwdFW+b1lfT8m1+/Bn7BIRUE9eM5Aj/ EvKUap02WJYXYESpD4mNb33sse5RKpsM+NcuNee//nZBoSnoEdK8XOSQcJOEsI8iGY6Z6ZoichnC YomvGqEbicfn/T/Go8Oo6GhHrUwZ9O4AOyBP9kRLS+/ZY2GDJYQWs6oBHzf5ckf8K5Kf0uKMpu+y B5qKvRh7+ujGBnC+F4sTKKPfP37QlDNPcQ74fKhYhzh8FGTDNLiQk4XgzRd7SkXyYWj0pyJ9Zett 5AGqkXXM5CVWa2sXawrz8QTxxIbF6OfQRRglRg9muC2XzB9T21319qjW4hVw99NMAD7dWu48km/D qo0UEYPeFPd2YJPEY87owVF38e2F0izPmReHzrwj+mxDnkS0UTVjb5Qd1cFUuMC1GlM6gf2IiEnv dulXRkP1d0/PaECizyf98t+7b7f3oo4t+TmFi5UGGxyzzN5Qvy0/Wp7jH5TyrFFh9O3tCWc0xjDc qG7nDPi6EHGz5C+xgT3IpcCB3YFMB3Fleah3FFjrmWxb0EpJZWOPEF3Wir0Yw+eBsbhrNDf4CVb0 rxJkYeFAtv8DII+Ix2gYAgA= --===============6199723159498557833==--