diff --git "a/app.ipynb" "b/app.ipynb"
--- "a/app.ipynb"
+++ "b/app.ipynb"
@@ -4,7 +4,12 @@
    "cell_type": "code",
    "execution_count": 1,
    "id": "7cd0d8fa",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:26:57.961833Z",
+     "start_time": "2023-06-10T09:26:57.950736Z"
+    }
+   },
    "outputs": [],
    "source": [
     "#|default_exp app"
@@ -15,14 +20,19 @@
    "id": "7195af18",
    "metadata": {},
    "source": [
-    "## Gradio Pets"
+    "## Gradio Dogs classifier"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "id": "44eb0ad3",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:00.455286Z",
+     "start_time": "2023-06-10T09:26:57.972014Z"
+    }
+   },
    "outputs": [],
    "source": [
     "#|export\n",
@@ -35,11 +45,16 @@
    "cell_type": "code",
    "execution_count": 3,
    "id": "3295ef11",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:00.471583Z",
+     "start_time": "2023-06-10T09:27:00.456280Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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kJgiRc67f7Z1LZVGToiiMdt5DAMAwDCmlx8eHGGNRFGdnZ3rSAPqu6x4fHsuqrKrq4uLik08+STHuNpucy7/7P/wfnVycf/TZp1LIcRgkk2VZDv1gndNGYwwhhCklznnXtozREGLXd7N6mQjQo7768vN2Gk7OzyFim81mvz/AGBhjhBAAHKWUMZbl2aEdjDFMSmPGqRtymSkzOq8327HK8rqu214JQbWGCSDrLGd1SskYTQgEIPhgUUJlVWm96/veGrvd7ZqyYgjXWWGsKiV3zglGvQfb7TbLhMxYVefj0Oups8YWRZGXNWECIRiGfhzHaZpiioyyuqnHcVpvbp88efLq5VcpTre3t0pr7z0hTEqZIui67vHxsS4LY6wNgAlJCJBShOA5F977rusIIZTStm2FzM7Pz/M832y31lrGWAjBORdjbNsWIcooSykFAFzwVVVRxiAEIQRrTDt0zWxW13VdlQTjt9fXh2GEmGWZ5FwUZVFRut6s9/t9lmWUUs45pdQaracRIVyWpRBF8GC5EAAF7z0AIMuyetYsqgbE8LC+76YxhPj53et5P3/vvfcymSGEUkqEkAQhAEAIuVgsyrwACaxWq2EYhnFQeowxZlkWgh1HZcZOa9N2u8PQIciaZv7+++8jgqdxmi/r1bxWnOz3/bBvyWxWj8PQdcPD460LXulJ8qLMq7qurdXW26fPXnrv9rtN1+0Z55gQF3xRVY+b/TgNyg4IhHlZWBO26z3PJpRCLjMEU1lUdV3lhTTGhBAIQZOaUooQgof1zThN64e7x/tbnpfDaARLEIBOqafnZwDEKpeH3T4SH70qCJ09ufzi7c1kh74/cE6Mne6uX1+enoKU3t5cQUqYkDNOUS7udK/UsI0+RvOkfN7UDSUEI4ww2mzWKUHKsLaTD+bq7mbSoyAoy2RTpWFoCQKzWdXvt8N+222msSgYYymkYRpZzgQKbbfrulbr0RjlnGvquTWmrqtZM/vKu18fxyljGWa4rJX1jzH5BBBCgnHpXMAE7tq9995a16BZkWeMk/YwclF47513CcCyqNq2f1ivJ2crawilDaV5nhOMu67z3scYeYbW+3WKqSoqSggh1CjtrRWMKzvKnC7mDYToYbtXagKYykz0Qxuia8oygTROY1EWkBCEkbJmhhGC0GrrXJo1ZV3NnDcQQW20FJnWFuOIGVR6PCDgrb26uur1JLIsF5kzFsSEmeCcj+OICfHeI4wE41ppRigCwBnjY8jy3I9TgHgxXzV1kWIcECF8LKpqt+swwJzx4XBYP9wrpShdyjzDcQZZflBvyeP9mlBCCBmHHgFYVtXQt0Ve7A9bSklV18ZYmUkAUgKp69pm1hBC2q7Ni/zjjz/8s5/99Ozs7OnlsxRQ3SxeX79e39+czOchxK7tGGMy41JKjHHf965zUkrnzPX11cPDQwwOUzJfLK31wVohRJHny8Vis3kQjEkhw2F7dnouuOzUlAAIPmplHu5vlR4WTRm8c3rC2o1t7+QE8sLHxJmgYHJKba3BkDx78RUhuNYKEgwSlkKOegAglWWBCRunqXf6ZfHydHlaZMJ7p9XAGUZljpAAAOR5ESMQZaWc//SnP9XGIoQIxWWWIYiddYTwaVKL+XKxOBGCHw6tsTrLpLEFghBEmGdV08xjjKvV6vXrLxOAXPBh6CnBRVEorZS2CCFCKKNpuVgKKR836wiBs25yk5ACabxaLSGEXdcN4/h4fdU0TVEVucy10rNm5lNoh2F1clLWmfNGaeVdxBgDiDAhwCYAklKTNTqGWFVVvZgDBIe+DzH5EAShZVFYY6y1wzAinDDBSSdCmQDIWrtv95OeLi8vZ1W9XC5he0AYl2WTUurbVkiZQBimvusPj4+PxhiCcdd2dVNHH4IPkcDz8yfvvHg+Gg1gwggzLpbL1TgejDZVudDaUUb220fvLONMK+2sgxgBiGbLJYGQqskyxjgl7W6TvNl3I2PUGN00szwvCCFd21pr+75njM1ms6qqfvSjHwWfKKVd1//O7/zOf/+/9z94//2vTebtO+++F4yyzj05Oz9ZnTw83LdtCwAQQqSUFovFdrvNMpnn+ZMnF7c3bynF1lptbCFFnudFnh/PMoTxann2Z598+sknX3zja1+7365H66Qozs/OEIpNKjlDRVEYnA77Lm8qD2IfgtYWRCwwL2pprR37IQGAMWZMaOOlyDDhbXsbY+CcE4ZAjCEEO6lSiPHQWa+tFAAGkXEfUgihmwzBdLPff/rl59M4gZROT8/eu3ifYHI4HITICCEYY20MIAQShBjJaV7VWYgOAbJanY2jUZMehjGl6Lw7HLrVapXnWZ7nRVGsVquHh/U4TVVVeeuttZTSF8+ep5Rub25+8fYtkVxmcjQKhwQhBCnFGEMInPOU0hHcJwAyKQnGxpgvvvw0z2sEcFlWVVURQkKwhBJjjPXeRt9Nw6KZUURwXiprjTE4JgwgIWSaJgAAwinG6Jzb73aM8qqs+qk/HA5ciGBdCCHG6GN0znHOd4cDHVtCSNd1bbu3Tk1aFVzmnEajp2lyMRSzWkiKCaQJaWt22621tmlmIaqhHzASrTU8ccpYU5Tbbtjvd4zCeZkRSgjBxDqT5Vme533XU0ITSCAGbw1jYr3eFkWtlArBbzcbAIC1VmD0/nvv/dlPfnoYDxACZw2j7I/+5I/Luv7xT/6UcbZcrPKsiAGUVUEo3mwetdbjOKaUhmEYhqEfO+f9xcXFyp8wBPeHgTN+xJ3WuW3bAgCnUd09bKMLV9e3Z6fnBFFGQZaJMs8SdG27LRghGLXW65TMOCBKMGOUSWsHKsiop3GYCJeH7d2seufs5OLN9UOIwVkNUkohYoRiCILR/qC27bqoeJ4xtWv3+yHPc8GlC1Ap1XXdqNyHH310/3B/enJa1zWGeFTKOpdnGUBoGAal1Ha3A4RKId99993z1WoY2kzks/npV77y9Z//7OfjNIQYMOaL+Yl3iRFWFbWUUmstpczyrO27h926yWtjNeMcuOSsLYq8zLK79SNeLNoAQEqLxQIT0pTlcjZL3uf1PM+ySbVqVJjy+4d7axSKePe4dc7fk7uLJxeYEOcMgMB7D2LMOCeEwggpoRghD4MgWArmrauq6nhSC0kBAHd3d+PU+uBPTk6aebPZPaquWyvVt9317U2EEDH+3qt3GCGHbnTetO2urJrvfPu73e5QZhmlNETQ9l3XtwDBjDFtlTFGSmm17bqDtXo+XxCS3d7cOQhmi7nWJmFaV5WxZBhHzggmPCVEGKdd31pndttD3cwQIRijjz78+emTZyGB9eODD75p6rqut9stxni33WUy+xt/42/8w3/yX05qHPrhq1/9ekzp9//l73nvF/Nn3gfGZAjROjdO4zRNEMK2bfu+z7Ls/Pz88NF+s9nc390RAjPBCOY4UoxxURTGWsb4688/2e32CQKKkQ9xmnRVlh4nzghl5GH9sH64/eZXv4op5zIvCjtt1pv9hhIlKEPJEZzadp8ipiwZPRa5JBhJyZVSzruyKNq27dqOZRmnBUIgwNSpUVBUVqXrupji/cPDw+YQYxzH8fr6oSjKd1+9a41Vk+rajjKOCDZKlUVJCdFaD8OQF6VkbLt+eP/Fqxhc1+KqqmOMd/e3PnghpbU2y4qymKZRWfcwd02e59vt9rDbD+MQUXTKdG2X5/l8Nq/Kkgv+rW9+8/Tu3lpblqX1HmN0enLinUUIUUq5lFQwBgQPMUQAIUSILpoVnMHtfnsYDp98+lFZVkVRIIQ45zDG47hNCYcAWus44zAGNQ3GuDwrU0oppaqqQgiMsdHoFNPV7VVVlUVZ2GEKzss8AyAZrRFM2+2mLgrOcJ5VL58/e/bq3bOTs8Nme/X6i7btinr+4tU7ehx9CCDFcRqv3t4cBz4IQV03UhYgmaaeddPQbg/WGgyh1eM4dVqPPvi6noMESF0uptHudz3nAgKglErBGTv98ff+9V/4rd/20TDONpsHrSYh5DSN+/2haZqXL1/OZ3PvHeP8D//wD8uq/spXvvLy5UtjzcP9DaWCYBaifnh4ACkeDgcA0sXFxTRNb99e6UnPq3q72cXgRZF5m6jgZVk2TdP1PWNstVpOfQsnJaW8fPoUQjgMw/x0FWPcbDbWurwoJus/+uzLly9fzGcrZbyHxCpNQMAgdru9GSbBi25/ELls23YYtPUOohhDQAg1TTMMA4AwpcQ4sy7cPe4uz1YuooSo8eCzL9/+6Ke/mMbx/Q8++JVf+XpK4Dvf+c6bN2+OdM+b169TTM+fP4cpHQ6HGCMEwFs19vuL8xMAgODcGP3ZZx8/PN5/+frzoiiXy6VzLsZwdna2Xq8Ph/2NGler1WKxmBfVZrdzKWx2+/1uP00TSGno+yyTEOI8z+fzeVWWq+XSGIsJBjB9/tln+/2eFrnMyyJvimrmrAkxeuuss5TSJ0+eVKYah/G45o4vv9O6a1vBBSI4JKCjhyGEAAklAIDNZoMxDiFYp6uqnM1m+65FGCGPxmFsZnWTFfcPD4e+Y0W2HbqmLDHG2miRibqeP71899WL96uqOl8+yTn70x/+qfdWTUbwPEyTD54S/vWv/apSWmtFKcUYe+dD8HVd2xjc2B+xSggxhDgpFTfr4BPnOYkIzBYL0nZZJigl1thRWc45IehP/+R7v/7d71hrpcwEz1IEGLNnL16+evXq5z//OcJYSFHk+Ww2o1xyzo0xs9nC+/vN5hECBFGqqvr+7oZS6pzXRu8P28f13aKpn5w9Gc9OX1+95kJUVSZ4FkI4UipDPwzDmGC6vbudpumHP/hBVZV5nseP4M31dZFnT589WS3mzkYphJqsD8HHSCCmHBMYy7xIwYIAD4fOpdR140cff2ZdzIpsNpsBCCEgIYTlcjEqK0QmKNq3h4f17d3NFcL4Yb0mEHkfzs+fnJ6evv/e+zGEGELXdfP5fBzHGON3v/vrGCGlFSZkmclxGGGMRvVRw37z8OGHPzs9OyGY/umPfpTnuRRMcI4ggQBRyiBMUgqleNu2bds56zPGq6o+9N1iNp/VTZbnnDGtNUQIQmitaZo6gTRNKqZorSmbClP6uNkQ1Z9hkmUNQsT76bDdMErny3nX99v9DiDImZCZpIRRxsqi7EPCVAOCEgTTOBljYgiMYppAluUEE4SQtXYYunEcirwECaAET5arcRpSSNW8SRDe/9mDGkY9TQ/uftE0s9miLiWl/Or6Zn3onl4+qbJs3/bWBeSC4EIwiTF13oXoMMJZXjprjTG73e7N22uZCSF4JrkQ7PHxEYJUFk2uKOfQOSelAACQz958/uzJpRBMTSPKs9VqManJ+cgp+/zzz2d19eL5c4IRowwAkADwPrx58xYhlAD49re/Q1Dquj6m3jt3cfGUM9k0zeGwXS6XCMHD4RBj4pz1ff/4eD+b19/85tenritysZjXygyQkOXy1JoAITgcDtZaBBHCWFkzTYoQ8vLlSwhS33V1Vf/ar35jVMOPf/iDb33r155evmzHQzcYwRlOgLhgvcGc3m82RVbmkHoAEWV5Xq+3+2a+ZExOk6GUVlUeQnDOPn36bLlort9+0l1txvGQIjp0w37fFUKerVanq1VZVZIRxoqY0n6/P5KRnHMXnIvw+v6uKIqmaTDGzlo9qcPDHY0uQCQEIQQ/vbiEEBFCiqKpqqapmmkaYnIYkzwvlksVQjgykVmW8SwLIRBCUkx3D3cI41yIsigxQm3bCim1tSmlaZrodkMoe/e99x+3G28disEZCyGkCE3jYKNTSkEAJS+ccylCBEmK0NrQNM2hPVw/3M+qmWQyy7IjYw8gZEJQBJxzmKCsYEo5bazkQqmpb9s8z4UQ2+0OpPTO05ef6E/m1QxnfHW6Wi6XOaEIs37S69267/cniyZFSGUhskxr5ayDCFrnXHRd21VVlQkRU0oQiDwzTg+H9vLsYl41ueCjGrlAVsnlPL9fbyghAEDy05/81Gl7tlhBCJxz2+1GcNF1/X6/f/Xq1eXFpcyyx8dHiLbvvfdeAh5BsN8+fviLD41WH/7iFwSEsqop4yGEP/3+93/jN37z7u4uz7PN9kFrTSmVInPOSSnfefVulsv9fgMrCCiWVXF2fl7mRVE0Shmf4vG4xBgboxFCT58+tdY9f/788vQCJuCcM85iQf7SX/yLzrvvf/97X375mRrByfK8kJITmhXL7X5b1YsY0/JksVidA5AYFx4QTNlisTjOalLKppk9PNz//Bd/dnoyS36URTHD/P527SJ47713z5arvu0SSFmWSSn3h269XnvvCSEIoWmajDXL1ery4uL12zfWWpwgRbBpGk0gpVSPw/r+tiwKzlgzW1hrpJRZlnvvskyOU08I11pz3jvntNbW2hAC/POfrMgqVe32ewCAEHwap2EY1pt1XlRSyhij1noxX8xmM+3sME79MECEGSO/9mu/Zoy+vr3Z+fjw+Ljfd3meV1VV1tl+tx/GQ5lljDGMAKEUQKC1iTFQSjEm3jmAAMbYWEsYKZoMJDR2h3EctNYppaPQhRFeLlZCFp99/qlyCoYoKSvznIk8ot7GAWNktN4e2mFUmBGESV1kjLFps9FWE0LGcXTeCymoEHPGOF1sd4/jMBhthRAhBqWMGcfgzdnZGaPS2kDOTk53m20pssV8hjGCEOa5HMbRWHvx9OnlswsIoBqG6+u3Pzrsiqo8OzkVFH/l/XdkUXz8ycdq6GNM50+e9G1XN7UxOsbgg/fBIYSF4CClI+jWximtEYJMyMfNehhHQQnFJFgLU0ohjOMYYoA0eG+dNQgCCMDV1dsQ4cnJOcCk6/scgW988xuFFJdN/eOT5osvrv/ksytv/Wq+XM1ns8VZWVa73bodFICwKIoIsTG2zsv7+3tjTJ7nb6/eXt9cBR+2h8Nh//jy+XnXDYMKH3zla5v1ehp7bczp6cmoxtVqRQg1xozjiDH23htjpBCc82kcOeeCi0zKKs/1NF2cn0n+Yrd+vLl+c9hvzk7PZL2QmcjzbJqM9xZjihDCiEEYOOfGKK0NYzyEAACw1gnBIYRG67OzcwCgUopSpvW+67oYU16AqqqKotjtdl3f+eAJoULIvu9nzYxRqrRKIS2aOSMsxbTres5YTAFB4L0zWnFKCKNCsOSD0ialkGIwWkMEIZDbbs85jzH22yHLq7JsmOB1U8d9IpQSSlOM3gdrXZ5lL1+8UHbkTKhRGaX3h9eTsQgTglD17DLGpKzZ7vdK6uADAGBSE2U0z7Kbu9vdYc8YOz05KYWkBD+5uBiG/uFh07YtwhChhGHa7naD0U25oIgSkrC27uFhjSBCCIYQCIXL5fxv/52/DRAY+r2fzFzy5uWzslm6GAGjPgQm+G8/ffLuy6f/5l9/783Ndb7bMoinYRinPssFxmgYXJ4zCHFT14yxtm1ns2axmHf9zliFAEDJIwgBALv9hiDsXWiq3KYwrG858mZonUkvXr0DKd5s7mJwy+WiyGmey/vbO2/NSVOsTs5kkZF6cXu7plQc2tb5sN7sLy7OCcG3t7enF8/K2SzGyCg9HHbDNFZN/b0/+UOlVAihyBuE8OPjvqmr5WIZjZ2VNYV4Un1eCKVM348YawAApRRCiBCKMRJKj5RhlmUZE97YEYQEoo4RQQzyEpDth598+rDd/cZv/uY09SmiTOTeGZ9A0ywgRJMaQvDOeu9jlmWUphgTZaLIRF1Xxpi2bVOMIKa+6xnjL168stZyKYqiEEJACL33EMK6mlcAhhAwAsm5x7a11mKMKaUvX7wSd9eHoW/bvSS4LgqGsYmRYEQhFIJixsdpRIh57wFIVo/GaKUmKWWc1LYbgw2MUVkU+65rh75ZzOumSd5hCO/u74apfXJxtpiftu34sNlu961zblYXajSvv0gmeIKgU+Z2v78nDwgiABLFsK7rxbwuJb+7u/v5j39EJTu/OD89Pc3LcmacNjr44Kzd9YeH7Q52B+DRvJwR7z1nXCl1dfVWCOG9n68Wv/Lk4otPfnF4XFNEEMF5npVV1amBcGnGMaWktcYYnpycfOMb33hzc+29v7w4O+q2lNIj03v8MzSbHf/93Xfe8cFdX3+53W0QBBkj3rvDvpNCUs7e3H8ZQ8iFPBL1s9ns5u19VVU0k9pMABgXxoxLOw1rNTrvi/Jl1py+vtsKyV+8fHpzfccF5oIILvI8OwoKj4+PHsIYI6cUY1yW5Q9/+MMvv3irtfbeZ9m2qurnz8VqdZJnmTHGWue9l0JopZRSd3d3lFIh5fFsPZI7R+4pxTT0gzUuBMdY1g9dX3X1vDKtARDMZjNrzDiOeV4BANu2zYuikKWUMjlLKXXOMIG1jVoP0XlrTZ4XjiII0cuXL+/u77UxQohpmoSQnHNCiXXu5uZGCIkR4SxDkFAhEMIpJRBCiPGIFL33SqmyKD744IMvrt4M0wQROkKInAmEEQFYUA4B8MFjjJ1zR9qfEAwhVJMyxozaHvrXGOOLiyfe+7IsrbXW2qasCpmDhDAkBAtKxcsXZ6vV6U+s/eSTT/TYO+e8e0gQ1LO6aRoEoOqGmBIheAIhyzKldZFl77//flmWt493h/1BSpktT07PTru+M8rEEPJCUIr2++2kVNXMSZ7nZVkOw9B3LSGkrqqzJxeMMQ69SBZ7kBDox96nVC84IzhZm1LigrdtlxJsmmaxWOy2u/eev5zP5yGE16+/BAAuF8ujgDGNU1lWnPO3b9+klNq22+/3MfhSckYphPj2/vZhs/7jH/7x2Wz5G9/6tsy597hpmrG3P/zRjy9fPGuaRUp4GtKsyGQGQoiTife77fXV467zCGspC5kJLmhZlrPZjFIOEszz/Pf+5b/cdi1BOM9zRjBGqB96CImURZEXZ+eni8Xi7OwMYzSOIwAAYxSCb9tDnmd1M0MQOueMtYyxGCPnfBiG7XabUsqyTAKJCQoR7PY7mPztJx9VCGYYEYxkJpFBIUaEMSU8eW2MPSvy4L3SWsrMOZMAo7TJs1z34zSNwzBYQ4ah//JLNSl1XDqcMymlc5ZgYqwNwe92W87yPCuaZhlh2u/3XHBKadfuORcppa7rpJTOOYzharmKm7XSmlGKMZachxDMNGIA6qbx3nvvQwh93yulYvQppZiidRYTXFUzBAAlBALAOQcAfPnF53VRPzm7SAlKWTjvtpuD4GWdV1VWEoCtMZRxRmkEKYZAKTk9ObFKG2ulFIAA7721VkOYZ/lsNqOC9uPoXdjtdovZnBLqsR+HwQUtOcdVs+6GN3c3hHMKQAQgFUU+DEMIwb99s76/+vo7L07q+ReffrKfnKhnkUjjAnceYbDf7znnBLPHx8cY4/nJ6vrm1hhzOOx3u82k1OnJaVlUbbtHMJ6szvK8sNY83N9dX19vNhtrRoLA8r33ndVvr7/8k5/+aFDT05Ozd997zyGYY5wx9vj4+JOPPvYBLM8vJ2UopY/rteCkzHjbdpTiAiKvR8YAilhNmjHadebm+n4c9WIxN0Yvl6u//tf/6na7VZNSWmVF7n34/PPPOBWci9PTk+XyZDabEUKmcYgpIAjn88Vqtbi5vtnut0jq0/nSKn1o22EYiqLouq7vewih8T6pSamOUpoQBGYoCDzcXP9sas9evNi0bddPy8UppNIHiGEoy2Lf9n3fVzWLCRLK62aO8QwEF51d1otpVJ9++ql1zofgvG/b1hgDAEwpCSkwQVdXbyc9zpomRL3bDSE4xrmQxNvxsHtIKZVleWh3IcSqrIUQ4zQ577MsK3gxho4Q4oOHCVKMVDBv77YzqxjEwTmEMKVUax2C98FgAihOIYW64GXRlGU9q+qrq6s8z08XK8ZYiDaEGJLfDR2h06V4ero6++a3vp3l5b7bffn6yyzPz89OijxHGJ2dnqeQ9oeDc7YsMozxp599FhjAGHPC+ggpZc56FcAetEVRFEVJCN5uHyKgxWLeanu/3RIpZQih69rLi4uUkjU2r7JpHH/wgx8uCmlTNNETAEIIR3/kNI1VVQEAECTb7e7y8gITBiCq63q32zJGBWcIot12f/9wc3q2cN7s9rrv+7vbK4gAwkkIjjG6vr09dIcf/eTH0zRJKZqmSSlNSp0vZyklxihCGMbYNI0xpus6a900b85WC0KolMI6XdWVjVFianvtkE8AQgApoSkmY4zWijNWFoUUgtIThJBzTjAWQqKUHvmdGNNuv4/eM0oSALvdrizL1clK5HLyllBKIBrG8fgAwzAcXS/J2q7vCkkLxkUCQmanVX4/KQfJ4e6x917IYrU8lVk+TRMtCmcdJjilxLkoikKbkfMMo8QJtEZ9/sVV1/VMCm3NZrv1zkEIjTEQQsaYVpZzjhF1Njw8bMqyygt+tPAqHVIKnPPjFlhVFcF0NltgjKdp2mw2QgjOOSHkCFs99mWRjRMe+pZSzCA2ytazxZMnTzBCw3ZnnJrPq4JyH+Fm3+32XSbzLMuKoqCUHn8bY6Zp2u/3j7vts2fPTperl6+eP39++ZWvvPu43vzkJz/54osv9KQoxSCBu7u7WTM/yvdd1wnOpZRTsOvDLiPceTeNU13XTVWj4xOmhCBKKW33+9OT5QcffLBY74j3HiEEALi/v18sFtM0HfaHps5B8F3fAwIx44OaYEhZlkEIEIIIoSzLIMB13UCIirx48uRiu90ccZ4QIoQwdu2zZ8/mi7rvD198+eXr169PV4sjGMrq4v7h8csvvljv9oyL50/PLs9O7zbXZVlCCHe73Xw+n8/nFxcXr99e13WtlEII1Q2tqoox5pw7PT19/fZLo40xppmX8yhe339MKC3zoiwrpcejx7uu6xhTiCEjMjpfZcWymd+t1857xtg0TdZaCKHWusjnnHOM8RFoMsY6PXZdB+MvzRnHNX1UawnG8/lcEMgwSs7IjLGMkSKjIitPToROz56/pESqcYo+JO9Xi1WR50JKCGFZVkJSrUelBh2sGodeTcoZzvh8sTgy2M65+Xy+P+zbtpvNGEaxLBsAUYxxPp8DCIJPwUfrNABJKZVlv1RJRq+mSR+f8yh8l2XBLN7v903d5FmRoocQLppaMjJ1w3azf9zsvPdnp2co49QDLPmTy5fdZByVgVLn3GazLYriOOZCCIUQlNL5fC6kXM7m0XnndFmWWS7mzayUWSmz9X49mQljbK1drzd5ntdVhTGAAGz3+33bzptGB5dSijG+efPmUJTLxaKua4IwIejs7AzipJPPE1iWNRnH0Xuf5/lR6QoxppjGrucYOAB9QMEFylNIUVvDBI8hTtOIEGQ0y7IcISwzJjMB99B7Z4yx1mJMqmomZSZ48bBZV2X+3rOziMj17d319dU0ub4fGMfvvHi2mK/ef/fdENx+eCzL/OzsLEUIWWbT5EM4Kt2CsWCNd67rujyTZho+//Tjx93D/f2OsLwbTGBSSKmsPjlZIsSGYSjy0rkwTerp2SJ6MwXsncMEu+gmNWBEtTJCyKHvOeMIoHGcMIFKOwhwjHG9Xl/f3hCMi7zgnB9NRghhAAAm8Hy1moZej52hRHK22+/M0O66QQJwwuni4h0hs/3+4L1L0T+u28n3q5PVcL3pDg3jRZ6VQoj7++v2sPfeM8oMMscDCkI4n8/VNOixwyAACENwCIvFYp5x1g699Z5gelwxw7CPwQ99e3Wzf/LkKQApxhhDOv5XzjnnnFIKAJBnZZaVQjCl49Nn75w7M42DylQ/6kkrhCCAkIuyIA2E4OpuIzOJMYowQsKoyNb7HUgRE2KUhik1TYMJnjXNspn3h+vtXhL6hHGZ5fzpswuI0Oeff3p9e7XZ7wgmECUfbUDCGTdOY4gBg+SMzusmZ/N5Wd8DvDlsHzYPdV3VzQxCJBl2LlhjHqdHmCA5arWz2SylRCldbzYIpqzOGcOUUUC5UZ5Qluc5AjD6YK3HGHftkGVwHKeqqjNKvVWc4l5P260VQjx9+hQAr9SQQGh3a05JM1/97OOPf/iTn243m0yUz549e/+DV1VVjv1orAYQhABvbx/Ozy8TTEqb/aE9Loiu67SevHOnZ6chBesMF3RZ5U/q/Hc3hzFZ4oi1fdf3xmofPAHk/PxJSiGlpI2GIEhOvcPL5YlSCiG4mEbvEqWSc04wxhgDmAghZTFzzu52uxiDMaYuSuschFBKiRDq+x4AJCTPMnkM8SRGx3GaxqGh4LDvRuUJF9FH5zxlEUJUVxUhcL2+m9re8XxWVO3jOpvHGFLbtiBBxqT3ihCSZRlC6KgjFEVR100oSymF96nvRwASZ5SUZTeNCCHBBedcSikl67u2qa0LKMTIOMcxjl3vQyAEaz3u9wEhcCTF2vZQ1VWWSYwJhLjvp7dv32R5efH06dnZZfAAhAQxijF+fPO6bdumaQiMzlkpCoLxpMy8LFOI3trVapVlchhV27Zte/DRRG9Xq1OYlVzwl6+eV2VWlNnV3d1+v/PGdkPvU1jN5l3XdX0HYtxtNof9vs4rgkg9b7IqX2/XKaWu731MHCVvNcNYZhlAgDjnMMbGmKObrigKpUaEEEJISukhLouGYApCXC6XIca3u4PMMgShs15KiTEqpMgYhmW226wPg8ozOY7D2fnJq1evMikO9+pu3X702Zuf/PznlNGqnl+eX7z76mVR5JxzyWSIISVAMBt69eb11YuXzzHGV1dXUsoj+3j65Pzh4WHfdYLTquAsF5iAeVWXkulOQZZVdc05J4wopc/PVtM0KDUBALq+Sxa+9/JZoJxgvpxXwzg8efIkRfjee18zxtzd3VZVVdc5xlSK+eP9/e3t7xdFWZall1IIMY5j13XOea01ADjLpPP+7durFDwBjlJGCYNBEcKEyIQoAaAYEx9CCN45jDGBCAleHIbx3Xfej5vNoe2dTZyJxeLEaKONhjB477U21hpK6W67LZsmywrtgtXGOXfYHxCEGMDgfYLQeaeUKopS8IwgmiIyPlnvCSFFlnnj+u02y4TS4/6w7fr9fD7PsgyAo2AS+r4XnIKjTpZlTbPKs9pan5I/MgBPz55whPt+ePLs1Dn385//2ags4axpmqdPL8d+6LrOWAMhnrre+43RbXQmeT1fPCmqhrNsvpi/CC9pJreb6ubq2k7D4XAAziOMq7La9nvK2dAP7aGjlK1WqzLLKIDDOLmUlHeCQIIAIBhbQikl8/m86zqlVPpzMyxG2LpkrY3QlnVNKaOEggS0s975GFOKwAYXU1yuFpiSzf7RA5sXbLmovPdPn15CjGOyCMOrm6sPP3v9wx//dL09VE3DOGua2W9+99fPT1aPDw9qUCIXy8UcJnBdNdvt559/8Rlj7OmzZ0VRGe0YJQim5XxujdFas+OYBvHt9hAQOjk7G6e3zieACEBwVc+Cd1dXbzhnECSIUJ5JUQjIy5IIZ9P+sLfWMkYwQd5bY/TJyUoKMV8t94edyPk3v/vrb+7f3F69TTEZq623Q9cN/UAp9c4lgKy13NOmrgSnZlBajRCG0Yec8YaiMueUQEpCUTZKmQQgRJhQZpwfJ3292WRZflo24zBqNaH5Yj5fbDabblhzxnww3lg9TjGlYZxGZQihzppMMJHxSU95XiyWy+1u2/eHqqoog85aggnnglMKYSIIpOjrKldqKIs8gfkwDBjjoiilFNOktFYAgBgjSFwI+eTJxTCNu25vgxdMRG8Ph93hABkh0Tqn1O39xnlHhVwUFcKYELJandRldXNzo7UGMPmonYntcOd064M9G8eLy+ez5oRSOV8uEsKSynHf9kM/9kMMoa7q2WKecdFPw7ya7/eHcRjGvheEzOdLANGkDWYURgdjAJC0be+DJ39u0YMxxpQSAMA6b4ytqkrbmLrBGLdarbjgfd+v12sAcVEWIQJCkHfGWfvFFx/C6J+cnr3z4hnBTEqZEP693/s3//yf/x4AQCnnXCiKHMP08tmzb3zjG0WVR5SqRcONLrNsHEbC+Dd+9VtcMGumm5s7KYu6bHrYSylCCHVZHXb76PxqPjtZnoxGI0LGKWIqmci6yW03a0owSJFgbLQTgsUQCAJ5ns2XK+MhAWFS43qzDj5ImaWUuq4/7itnZ2fK2NdvP333XSd4tmiafvP40G4JJXVdf/Deu955rfV2u725vfPeORsEZ8FHzkVOMY123U3TNMmCtvs1zaVYnO52W6VtkWchJoQwI0gBc3t7d3b+pK6bsijGfrh/uC3LarffjuOBcQYgBDFKIXJKlTVaKwySs1qBgDHuhyGkVBYl56xtD0rhx8d7kAiCmHPeNPWh3XPOUoxCZmWRW2c4F6enZ95755xSBgBorRvHkVJKCRVcZFnGpTwMB8rQbv+ohymG0Mxmkzb9MGBKlXFCyg+++oIQstvtjDGP68dlM2uaZrvbJRDrqppgOtyv990bbeKh63xwGLGyBIyKk+UyZ3xzd3voOsqYdlZbY6xBLkgmZFU2dWO0Pu6MsihmENJxDCAREKzWXMpjivOXRv/jVAsAAAAgCBNEwzDkeda27RGbXl5eHAfqthuUUkVRzCsRY9gd9jgBENO4bxmip6enP/vokx/++M+GcQQpxRTzvOSM5WX57otn3/q1b86a2c31FYr25GRV55eUSINihFAdxovzZ5iAY+J5GAcAQJbnKaXjK4QQwpjEEKZpJJTriA+73tqAU6AAXpwurXVdN0JIdrsdZ7Sua4zw/f198L4o8kkNhFAhRNcOMUSEUVVVq9XKO993WwzC7fWbZFOGw1mTPZm/nzUnACRMSIIAQPjBBx/8/Ocf/ukPfoAQThH07ZZAu2qKknJCicgEoyBBqJSepikAlhKKIXTtaOzEhbTWbdZr7wPG9HSxwBA+bjeP6zuMsBBF27YIIY5wnuceJC5oG+zD/VUCCCCMMYoRpJRAAscQJgBAa33oJkpZM5tZZwEAIYSU0na7PRIOVzfX8/n81atXu92u6zophHW261rG2HK5SBHs9/uua43RlcwZJg7jIx90jPQMw0AEjyn1fQ8AGMcxz/OU0m6/p5RWVR1jAjF4lywQSvnDw0OAgAtZFjOYYlU0lIq6LL7yla9gzl5fvc28H8dh6If1YU85rwgUmAjOj/zD0VvDOFfW4OSlYD6k49ojJngh5byq66rebDdt20KM8rIY+v7m7o4z5p27vbtvuz7L5Pvvf9D3w/39/WazGTr05Pw0L/J2v40JKxfMZv/F25/94qOPnY8Y0ZQSxbgoJRX5d775q9/+xq/GYJPvv3q+WDXL1bN35otlmZXF6bml3CqlhsPt9esPP/74ow8/rGrzeHNPEerHru0Os9lsmqYY46SNVo5FnAtirJ2mCRFEhYwpUowxRsY47/3R7+KDL6vq/MWL/W6XC9kPPUzAGIMJohhZZ1arZYhxv3lY37W727vtF68pQ4Thpqq10Qhjr5W2FgCwWCy+9rWvTdN0e3sbgkMoeaMfN2qLqRQsrxcF4zI5zBmBUBuz73TwhZp6Y0dCWIigqsoYY9+1s7IihNR1ud/vCUFlsRQ8u3+4BxQzLoI1wTuMSZ4Vd/f3CeKqKinlWVaUZYUQ6rvOWHN+dnZ7eztNerVazZuaEOy9OxxardTLl6+EEIhgQoh3viwqpcyXb6+kYAiCvu+/fP36ZHXCBWeGH+UkKXOKOaYEIkQpJYQQTI23xwACSGkcBqMUSgAjVNd1ikErSwgURb3AGGPbbW70nSU4nSxOMyExTHneEMzny9n79D1CyMP9XX84AJDOTk8Ho5SetE+MEMqYcXYYR230MI6HrgXBCc5AAkPfK6VIVVdqGGNMEMFf5rBSRAQvVquubWGCjOO27ZTSQojDoauq6nhwDL3P8ooLTBgfeo84Xa/3f/bRp9Y4xjglNIQwnzcffPUVzerzeZkxjHR8Oa+WeQ6c1p/+i+1P1gahvVwtf+03i6/8VrY6P1mdf/Nbf+Hh4fb3fv+f/3/+wT/u2zUr+C8+/MWsmtezppCCEMQIn3qdCTWfN1pPypu8qighyPkQAIB2sVwmEJxzWZ4754ZhQAgXeT2Oev247bru2YuLqi5Aim+v3p6enmAMD9ut6Q4Vz53MvEMAEjnPBeVWq6PO2ff9bDZ/enm53+8h9lnOIMlgTABC6J0eelOlJ8ssMEwwhCluNo/TOEEQQ7QYe4SwMQxBZNS42T5UVZ0SyLJMKc15BhFaeDtNk3YOIeyciwA182XVzI/H1zQpgnkMYLVcgXfD4bCPMXqjCYoPt1fB6bOzs2EY2vYgZUYZ3R/2xzSf867OGyHGBHCel86YadJa72JMR19BVhRCZhBAmHBEQJaFINw7V1VsHLtW6+RDWZZPn1zsdrsUI0DIWiuEYIxqbQalOc/my6dKDUYdbt5+VhbLIqtAchhjyjzlWd3Uz54+268fGaP91B/2IyQYMQIgsiEEa4dxvHt4SCkBkFIK2lofY51lq9Wq6zpCIwBcjE47a2OMZVnGGEmCGeMKk2O/CCHEWut9AMC+fv367OyMcw4TvLm5mS+qsyeX46Tf3j18/MlnztojL8MYns9Pnj9/+vWvfx2EeMbDUt2D/Xr49MG4gxAxy6ggkFNh1m/W/ef17Uflt/9OWL5CEJ2dXf5H/+F/8lu/+Zf/i7//9378gz+hK6qMtg+Ps/dfWj36aJ+/uFBqAglcPn2KOX17dYUQlBDHEI86isz4crlECD08PLRt+/z5c0Dg6uysWcwfH9acUwDAdru9ubn96U/Toim/853vDNvNF59+0WtLWaZjgloLIQAAh0NblsU0TVrbYRiqqiIMju0OBI9jQCjZyViNJsECzLOqIhlft31VZ0Ovo3cJHMlIiBDyzmYZ3eysD0aKahwHKTNjzDSNeZ4rpY4FJCF4zrn3HgAoBIcQSCmOqaNpmjiXT5/WQgjC2H6/I4Qcv7KqqgCASumua7U2R4PLfr9//flbbU3T1EWeP449xvjy8vKozx3TToxxa3QKQRs1badFuThKWSkl69zbt29PT0/Pz8+FEAgh78PhcAghZEd3KVbGO60DxDPB4ajXX77+eD5fBf8MJFLPEGGSEDqfz85OTzeH3WTk+nHtUyhnjfcxhJhn2VHLtNZqrbhgeVGkELwx0zgaa8nY9YCSyRkYAcE4yzJnzdj3wTqrjY+BENLU9W6/894ixI5YpCzLmIUYXFXm2rof/fRnP/nZL6xxZSYJIXmeXVyeV0XOKCFE/sZ3f0V/8kfm5/+K6D2I2sUUXS74XNQNRphDi1Fyn33vsL8vfus/oacvIS8BoC9evPO/+k//0x/9yZ98/1///pBMIHy7XoOkpGyEkFfXV1fXVy9fvrpYLQouP/3is+HQzefLvGoIIvPZrK4bQkhKgBJCKJ2M9s4bYxjn2+2aEAARJZRO03R1dbW/v6k4tSFMPjaYB4iPvTTOmSyTx21st9tfXl7meb7dP0YpPYglZ4KgkEtn/dXNIfr4nK9Wc8Q4rarK214FjxFhlGljEEKEgPv7awjJYXd49vxlXdcxpBADYwzAxLnY7/cIoaMrVwihlFpv1nXVYEwWizkhJMaojdXGQUSePX1RFuUw9sdSnRjDfDbfgQNC5PJyFbzb7na77c5qDzHKqiIlILjkXCqlEeqOa3SaJh+Cniatx6wsBcJ93x89exDhk5OTI949ui8AAJigLONK6c1mk1Ky1pgQOJNVc+YUtXEYusMnH3/IqCCUQUII5VISQsnF5eXd48MwTU+ePFnvtiklRmhEwXvHKD1ZrbQ22+3m0B1ibBmlDKGYgNKGmBjcoI9y7REmy4zKQmjli6KMKXjnqBQABkKRlEyIQimltRacIIiscX/4vT/68vVbirEscozxfDZbrJZf/dpXEEC/8Rf+0l/4znd++E//8/t/9g8bljAyeV17zAgCyCegTJAiEYRCNFqNX344bP43rJjLr/6l4hv/NSTmCOFv/9ZfuaizD3//v9p484maQIIF47v2IMscM/rFmy9DDMui6lenv9h3b+/uVs5dPHny9OISI5QXJeeybVtrHUKQIVYvasb4yWrZD/u7x/Xt7Z2UUlJqreqsRoQKRG2vxoCK3CfgCE2CC8a4MSbPM4wRpTjFRJlAKUGCAUIEhbbvR9UPLtts7iDnFOcni7kg5Th2AHjr3HazGYdDXa8SwG9e30mRWTM8e/bq7PQiRAMAzmXNGLPWOOdSigCAoihC8CerFca874au3ZdlGWLc93sAkAdwUc8FL5z3wzCmFDDGMQIp8pcv3pnNmuurL4OzBKNyNcvyHGOslCKE13UdgtNGHSdjhJDg3BrtvJ8GvVqcYmk5Z3meJxCGoT+ivrZt7+/vUwoQRYxxlmV9PxzLzCLHDBMT7OgwlXOihsN2e/94xcuM5YKNDGOMCatnzVmzeHN9xTmv61p7SxOy3k9epxBzLhdN462xzvV9v37coAQ4FxgzEmOMKcGUjl1TSinrWFWVQnAhMu/dfr+PMTqXUoqZxAkk5+zNzQ0XPMT44Ycftm3HuTjKUfP5vCzLb33zG9/5zl/4yq98Z0XjH/1f/09/8P/83xulGQgfnFeXX3+HCR70gJJPhEFAUwo2YJgvwOZa3X3qqDQ3Px0+/Bcn//X/Cb78VgJg9bXvSoJf//N/QKzr56cHPd52d/PZ4uTk5E++//1+HC7mq96Zy6eXh+0BQpgg3LUHjLFyru8GCGFVVV3XG2NCDMulXC4XXOAUAfBxnEarBoHB4E0KLgGJAcbOa624oN4b74b5nDPGjhaZoigoodGZosgxBChGbyEVrAIl56zrO9nueEVgcGVZxOiznCGIijxfb+6cS0bHqi7ms5KAuHu8L2UWIPEhYYytUUd3JoQAIRRDnM1mu92OUlRVVdvtQwjjNCrv8ryMMRFKY6Tcs64bMKIIEkRxCGm/3+/3+1EPWdmIvAreYYzHcTwGb4ydqqrCBDHG8jw/WmMxxjKT3ialpzwvUkq73Y5x8ueQI2RZVtd12x60ViGEw74fx5EQQhnFMp8385zllHfRSpPujR3u7++zai5FLlgmeCEQoYScPzkXn32kDvtpmhKGDKOu73R0FpngfJ7nznljjDEmxDgOo7W2aRqS5/k0Ts67GCOE8JhnUEpp5bKsyLOMEKKUIpDGlNRkUwJaWwAgZ4JxfrI6zbPy5GR1cnJyfn7+7Pnzd99991e+/quzZtVef/Gnf/9/+yf/+t/8ZKu3+/bb57Pz975eXbyTCWK6B+BtNJoxHlO0xph2DVMMCBtktSbFxz+5e/xfz//G/7T8xt9MERbv/9qrohL/4P/oD29+QZuBZ/tdm+fyyZMn/TRORitn7m4fnl08JYR042Cu3p6dnSmtx24qymI2mwEAHx8fj+URCOdDP+pxmlf11A8EIEygzEqEmZmiyDNMWYwRAggABAAcDgfG+DSNs9kMIQQhhBDFGCkjhBLgaVXXiUzWWO/1s+eUIkAR4EXhrB6HlnEBQCqKglFBq6yqc4wicv7J2cnJcv7FzY1SjlJ2LBBgjIXgvfdt15ZVyTh3zgkujz4ejLAUJM/zGMJ6/aim3nvjrKdYIkgwQpyz/X6PMKJSUAIppYJiIUTbtl13KIqcMUbpsdJwJIQeDRjGGD1pLnIIodIKI3yklpyzx3V8zCRZmzMqEkht20qZSSGMc8MxGpCXgLHpoFByej8Mw7BZb6Qs63JujCGUE4pns9m8mbVtixHyIIUQEUIwwhDCMAy73Q4iFFLMs5xg4rXp+n4cR1LIrJDZer3eDwNESGZZiFEptd5smzrkWdZUubE6REhFNijNiHj3+erZxZNvvXo2n9c8kxFykdf1YkazjLCMUBIiGA7bn/2j/92P/+D3f/T6cNe5TY+/cy4hDKKYsbz2wTE36uQjTV47AOI4KGs1xikLlEmZLVbAT4d/9n9QY7f6jf8OBLw4f6f4+nf8n/zfvl6R3lx8oQOi+NXTZ+v9WmslIZnGcbPbzudzCDBxWFJmtbXObjab4/BX12UI4egkMsbYEIKfILCIAcY553kAuDgRCGGM0WpW95PaH3RZ0JQcRAQAAiHMck4wpDKbhnZytiqynHIpMwRj13UE4v1oTipuvTe7u7HdAQiWZycmz6Ypd9744JyLkFJZzRMrxhAXi0V76DkltKqMtSmlEMIxpuecXy5XiSU1KS64EAITYowb9t0xAhpjFEIUBXFe+RgoFyBGLqiUwnnvnUUEEcKPXYXz2UxwsT/s1TQQAiEAatJVvRBCaK25yDBGCAOMIWMEAOoc6jp39DUfo3OUsUQghbjKC4AIAIAEhGA8dB1CiFMhCorFYlAHp3e678bDNExDXtQyRZ8Sknw1X9zd3TdlEzHCCCJMbm9vJzVhjAEABCEUE8YoISQot8xra4iLASHoUySUHq13dV33fV8URVEXh2GHUi4wKvNs0TDU0F/96q89WdTEj09evZidXVDJUmLOJ57JiFBMKfgYg/n5/++/+MX3/vT7Xx4+vwuPNhycedD6hz/9+Pz04t1f+SaAGCIIYhy7CQUQvScAEy4wShTgY84rQgHdsPnd/4yDVP/mf+AhWX3zL99c//R0uvqNbK8S3SdQUGG1g5DmmaiqepqmqqqKPKvrGiMkuGBcB++PyUlKaVVV6/X62IFqnUUAEEqMdt1kGlowIWSeQwDgcfOEKISQUkwpaW1WyxUhwBjDOOUYGXWwxu/3vaCIZjXLBFIThBAEB6PHOE7eU5FBAG5v7wGEQkgAgDYaQbhanZyenhtjhmF88fxpXc0O+17K/MnFbL/fj+Pw5MmToxvw2GaaF/k4jpxzQqi14ThiZ1mWZRlCGOE0TaNzDgBw9DEZY1KKEMKjWrbf7wEAVVEabfqupwxXZYMJNtqO4yCl5Jwf1cQj4T9N05H2xxgPw2DtL4UAF0JRlU1RjmTc7VuMsJQSUqK1dtYp77NMMlpW5cl63O33GyHrk/6krrS1llFBKLm8vLy6uTu8fdNNg9Y6hpBiJIQcE9VHuyOAEB3xsZmKoiCjUc65iEDd1CD9Ejg/ffrUe6/HQWJ0ihh1fhHByhmBfK3XjVwBtsgXFzSfQYphQoKjhCHCGMYIIeoP4+Hqc+MsB/DV08p1SN/GD3eWQcL+1R8iyRnGJTJ6UkPbAh9QBDFEIQiCyQcHMEIxYEw8yuZcd9//B/z8XfbiN+DspP7qX/Y//EenKf3VKv9nA2/HjhIRUjocuiO9BSEch3HWNEqppppJIY6BzM1mcxQ2CSGcc+ec8xbBZK21LozTZBxaLrGPYT6fgxQf1o/tMCHMEELGGMaY1sqY6er6NUbp+cXZ+ZPV9du7SQUVXRj6uWCyzDHGi6bkBDpvs6KChGql9WFrlMqynGHOvQEAHu2qxpjdbtsetk2zGHt9cloiyCiRGGtCsLVWa318r45ep2manPeMifl8jhAqioIQ0ratV3ZS4y+LPOu6qqq2badpopQOwwAhPHqNKSZ91wEAgk/DoDMpjfFHSvvY7qa17vs+xvhLQRH9kkw4inkQQm0NhNBo452jlBKMQ4wYwOgcRSihZK1HQNbVabe/Urobpna/PywX+pgzCwAuFou6rqZpPBwOSqm6quaLxfF9mKap67osyzDGw6SsMcMwzpcLYnqVOJFE5JKP06TVWGSFFEI5u8wWXy8bpM0nu7upVQCGeSHvPv+McvjBr/2VYlYHXkAEMfjlR4IJBAAgBDgkCNNZk82/+Z6HRHx65bRrFfzSicNPHyH649/+zvuolM4nG5JzHgJAYBKEYoygJNEHAkDwmlKWIoNmOPzu/3nx33oCF5fZy6/vf/7PsbWz1L5izePFE4bh1fVVXeTvvXq2P3Q2JG3t2A8wJgARY/yY/t5sNkdTfdPMtDYAIAwhhkBSFnxancystZvttizK5eKkntUuRHcYglWZzPJcllWGnNm3axwU8mnaH5q68i5CjHEMZtRD4CxBTNG63W+GKatmzAfKJJdlllWUMmtdWZa5yJ1z7aHjonTOESqdR8FHwWD043YbIGFM5ME7xvMcoGnsnHNKax8DSoBChBFuZrNfmndDEEK0nRrHUQjRd30KUU3q2PADYkIQEUqOQQCQABV80lobNYxd0zSE0JTANE3eeUrZ0YwOUBKCG2MopVJmMcYjQXs0K3JG19NuGkcfIiQsYcwQ7HZbLNj85BQBpLWyGHPeOGON9dPYT9Ngjda6J3lFpahz6fU0tntAmDJWW1fkmf3ztMLxcDgGaI/l4uT3fv/3aZWfNvN3XjyTmWRMrlbLt9c3bddNGPzo9uErl5eMi975ncNEm+a01qof1g+z80dyWkRAE4gAgJRSDBFgCCAY2jYa9erF01Gjfpy+ope9VW/WflTqdjL4wy8xTt/64GmG4mgS5+WkJsmAijAiBEMKLno7yTzzxjofCIr+6tP19//p+d/8H6esAfncm1ulJ4wyZ01TV0YvCaWZFCmBRFjbdiEE8suojWKMaa2Pns77+/umsUcRxVqHQMAYp5RiTHVdaW0SiJMa8yJ75933nz17/sknn2ptjB0jsLO8oowjQlFKh2F0CRDOorYEIRBdGA2khPJCZstu0MHTrM5SQtM0WmfzLBdCWGuHafI+yLzAmEiZcc6l4HWRweh2+4PSE8C8rGaCF3leIJh2u/Xjw2PbHqz3p8tVlRc2hLbdF2UhRQYhAgAwThACRy7MWltLKatq6AdrLcL4GBAAABzaNsVY1dXQ9ZxxzgSljAtwHNX74aC1poQijCglIYRh2Culj2vl+P3udnutFYRwmkbrHKJidnLCCV7OZls17HYbgokxRso8zwurDs75aRytMdZoL1mMgRK6Wi4vLy5u7m/b/RaBRAjBZX48Day1hBBjbYoxxogxSgCQ/+A//o//y9/7Zx999unpcv7Nb33z+vq6LKvz88jzbJrGf/mH39sfDmd1fTeZj6/dt09xTP23zn41TOb2Fz864xmbXR7jXcdDIUEIIOSMPTtd1UTfdWaM40mNv7pkSB+0QhuZrUf7s6ttUzAJvNJpsSAxIiZ4QMQjOI0d8HE5m3mIXPAJIJt4UHZ+/QvgNBIFml24zQ2jzGiXMAjBE0qllEJKH4EH6Eg7dF1HKXt8WBNKCcbPnz8/OTm5v7+/v78/6jScc5h8SL5u6DAZ74OQvO/b/WE9Tt3TZ89hUUD4mVJqUl3X7cWr90Vehf12MhpjMhw6iiCHkaBEAChApIyZCKuCzxYzQBgCBEAMCMIYS8Gc9+M0EsYIQUVRLhaLPM/HcTx2N0x9h2IEzg7DkCIoq1lK0Vo9TSNEsChKKnidF4WQFvi+75UaGOVNU3Vdb41BEPXHdACCwzAcsUEIIVrbD/16vZnNGs65sYYQcnp2DhKwxk5qSiBO03Ro997rXw4eedW2LcYYIQwhPOLC4zLNsizLJKWUMaq1TpjGGEMAhFIRRTf1RitjTFXNK+IJIco5pZRzLqaUUgox0gRWq9Wrd169ubkSh70xJgEPUzTGHkE2pdSHUGSZ5FIWeYCACEnfe/5sc3X1vR98f3myujx/qqexLsT1zVXd1PnJ8uObO6dN793ZoqpzlgLYb4Za7i+WC5BY8h6hBDE52m0ASCACIkV1coLUhrm0aMqcEhk9CXrs1HUfbkbw4zePxrlnPAEQexMyEnysTp+sDEwxEkYgFRkRAvvgvU8R9YdDDawf17gpLZnx5DQlBUCcoUTzI+kDMUM0jm3rXCiKKsX4/Pnzi/PLn/3szzjnmNDZbKa0aQ/dfD4bx04ZVRbVeHC79ZbLoqqL1Wr50Ufj7e0NF/JPf/D95XLJOBWCApAZY95evSmK4vLy+eHQFnmx2zzaqZMCUpCwDxlGiCPvdbe+O3nyjBCJIGGMhwQeN/19u8cY50XBOOOcI4j22130IYLUdfsUw3azXuSSi8xDYqyxm3sIMYSxbVtrLaEk4wIAcOg7RHFKwBhLMUIwARCUNQECzChEkCASU1RK5XkupRyGgVLivY8xCC6mcey1CTIIIQjBmZQIQYwwo1RK7pybpgkA6H1IKUKKjpH54yHDGHPOKqMpZxFAG6K3GmhjCUIxGe+itTAmM0233bAhYDXPAQwhxklNKSWQUAwhkiBENp8vq6oeJqWMu7l7vN+2s/mibupc5t56CDpjdAAxkxJiRJZN+Ze/+50w9j/95Msf/fjH1PTvvPvKW/zi6QXD+GsvX/1/X/9L76dvvffed776qmJgPputTi9lVcKyIXmWCPAhkl+CUAABACFmZRVmTxEvK3+nA8QAPX92LiS/ur3p7w8Hn1oN3zwe5ifVYMxar08rchIcr4qqkBgLITnAlIsiGgNRDMZSzr33Zn0nm1ceC6+D4NWyKn6hR6/iYtZgjH/3X/0BoWw2azbr7cnJaV3XgouT1YnWKgEAEbq6vj66D7/48vOqymMMzpqiyLXzeVkvl0shxKtXr87Ozrbb7TGhWpaFt1ZwrpRGOGWZlFIa4/q2fXF5eX/7BQaKI0gxgSm56DgX4zSqYRRYEkYRgiAGCBIhdLGYLxaLcRwF55Qx7yIAoMwLpfvbuzVEOEGcl1XS1nlCiUQIAhi0HgjBnPMYgvY+xigoefbs+WazSTF07UFbQxkjlHjvMyEpwiklpdQxPZLnmQuuLAsAkjUmhRh9PBz2JycneZ4fLyqBECIIKWEEMynyI8+VUnLBH4NNx0iWEAJCoLXy1jlrJ20IxsF7N/njHGm04YKXeTlpE4D3CSIIKaPGWu+D9zElAGEiBAsu9vvDdtdyKc4vn/kYGOcAQkJJLjIA0sPDtNlsOOeMMbIsip3dfftrX//q+x/EaPr24atffe+TT19ba7hg2toyL5YzeXkyX6xOQHAaYA2ARRhiMtx9zhYXOGussQgfu3Ng8I5QWj77qjVeoD/DCFlvAdb5hBeN5JudZKjOhYz2ybK52mzfbg4+yAj1rG6oTZyzcr5Mwd/fb6ZJyUwyghEtQUj+zc8spHXc7IUIXld4I9f+s97f3968fv3GpFjXzeFw0FprrZum8d7vdrvj258QOrZ7csK0UhinKsuT9Tzny/mciGwcht1uyxivqkpmmXOx67pj77P3XgiOMKCUeu+l5Ov7680uyoxxnKC1lGCvLeaMSK6VcmrIqgYmEEKMMUgpEY3W2uPDUEpJAnmRe+/HcbROE4IJxh7xflR26gLgIAMEYk5pXhRoUoRQSlkIUanpONSvTlY4gEkp410hpbfOAQRCdCEeb5Ox1jrnvPfaquOn0JPNsnw2z4+G0eOpPQyD955SCiE4MudHUTTGGEE6OvDVpDrfE0KKTKIINg8P13d3k3dPnz6TgqvBQggJxr2zSisE4aBNSDZjKc94CCF4f/xGsiwLIQpMEEZHtIAJkVJSziNIWuu2aznBIcayyrUpj8oRUdMAgylpeLoo89mz7/14/P0/+DfvvHhprEWM5E1VCpFhru2EGXv29B0EPIA0my0LPtvd389YTUVpnGWIgxgDANY5iBA6e54zAeG47TcgeTv2BDoIAUM4R94yjhPAyK04uQbeObsfwNubu5Lxi4sTksCg9N39GiPEGKMy5zmNAI9f/qT7+PtU5JwA3W+Smv7S8vl1byzEJ8vZ+tBaY9q2RxhrZ7txgAhmUhCKueCY0A0EnFIiWDOfO6tD9EVeICqCNofNdr3ZtId2sVg+eXIOAFqtVkbrzfrBGksoBQkAF4sixxgzRosqf3P9ZjUrFgIj7wlnjgIXEooQQYIhYDBBFBPwjIsMknG7G5QCEC6Xq6NR/Gj6HoYBwCRFfkw0KKWaourVNA6PRVkrgylmpEC7/X4YhjzPIQTB+/u7e845RZgJXpYlJbRv2xjCbr/zIUopq6o8Pz9PKT0+PtaiPo4HCGFKSUwRItgPA8YYQpBA8sE77xhjIYau6wCEx2R58MFaG2MKyRMCxqndbu45YTKXTVMd7m4P7d5aSTBkFDPBSu83m7VzLsU49IeSoaoojLEAAuuMc+aYGfY4lVle1/Xb+3ujtQtBpkgpDc4DCCGlhCDv3LyZuRgABGR1sux3CYUpYhID+O1f+45HeDKOcosxef/lO28/+Twr8+rk1CbYT3pWZUXRcJ4BQhbPX0GBIYqMsRRjgjCEMBkNESIkj0OnJgUZhF4xSCDnmNDTZuanfT9ZmTEusG8xpjT56CdA5+jibFFliHgLYiSSZZJH6iP0nCAIopo0TSHEAEGEarQO1avy7/61//B3f+8PlFZvr+52Xc+57Mbx6urKWSsF55xOahJS5Fn21Q8+uLm9Hb2WmaAAAaC1NiLnD5v7zWZdN/XZeUkJPbRjLjM16aLId7vJB0coYpz/2xIEq1WZZ7cpGG0GCwtKJm/H5FPwMeJAaaBYB8eCxYACBBFGjBBeV8MwYoyPTs0Y43r9KIQghFtrAYQERCFERIRxGQLAiOVFBWLAJGEM94feOQsASDEJwY1SKMsghMF6o3TX9957SllRCW30fn8w2kgpZ7PZsVaJUSazDCBojHl4fCSEFEWBMEYY+xCOpCnjjHKmlCKQZFLylI7XZmCcpqn3zvrgnA/KWZ/ScjHHBCEMIQI+BYzwYj5bLebDOO62jwh4RmWKGEKMEPLeAHgMFCGXIoophehD8DFEl8IQKSFO6eADIYRxDgDCGAOCAYKkKbPoZ70O6/1m8LEoCutTAsgl/OaLN8vFydnZGQrh5Pyimi89wNvB7g83pu+jm4rlaSMuUogRwKMk4GIMIehpzEBU/f7q0w9F+zoETfI8YzNAZAq3m+3eJ7cs5gXGvM7RwxoEZCJcrOZMQGcmO1hE2LPTOYLRBwOmA6U0RQgTQIhYpX0MBsuXf+0/Cs++WZ+/+g1Dfvef/s75yWI2q6/vHmdNs9lsQYgUk6Me8/DwGHxijEkhhk4VRVGIzJjDZrO9ub43zr3//nsQor4fgo8YoQQAhLAsSoTSZrtDECKMOecIIesc9p44u8hlDG70QdI8BB9TQBBb66CkCcGEobXW+oSMDxECCI8dlIfDwRhz7LY41kEeSRyEECIoIuhhggg2s8ZF6KxNMZhhBCAxztMvTZ/o6K8DABz5irZtY4yEkGMpM6PMJRdCOJ7UxzFca33oWsZ5WRbHWrK2bcuylFLO5/Nj4KcsSvvnUfpjZ9gRhECIM1lByCBERV6GEH1w1iqt9BGipAjKquGU7nZ7NU79qBAVzeJESMY5CyFY58Cf/wQfAITHawuOFZlHCogQqia12WyapuGMW61Ha2SekWHfAUiK1Rkpi6u3bzbdOJstNpvHlNLls+cU0dPTk+isMZ4xXkiZ5YXp9mrs2sdrNw4Z4PKyigQczcIIIc6Fs0ZPA/KewGT6HYVgVFrILJNiUYqykCEeKEwkgdHqStDgwXqw+xBJSdMwjW1rIfTDICmiFCSQUpYDgjHPPOYpeug01Ovp5heLr3zbAvCVX/l2KeQ//sf/D4yC4KQbhvlsbp1drx8ZZ5zxo00dQihltqL42AprjN7vDlXVUOvarq+KSgiREsAIz2czzkWMFiFU5pm1TiuFAKjqOqWYCR4iyxnjstRaH2lgmIB3lggiAIijwlmIlI9KY58SgADAEJOQEiEshJjNZq/fvDlZrcqqUpMpilIb3R621tuqrOZ1RTC11k1qjMH3/UFrBSDJ89wYExM8YkeMfmkrTikZa44tdyF4SmlKyRpzrL6RmTwagPKyoIRYa7MsE0Ic0WeMiWDIGFUTUNMUQqCEhhimaTxetFAUhVKKUVbXc2NtjMk6ba2DKdZVWRbZ/rD79LPP1us15zzF6L3X1jsfRqXKKhdMOusxJBBiCGCK8eiuSiAhBMdhLLKMCwEAEFIIwYdhAAmUZYERmjZrawyJgelgDfCEsuXyfByNsXa5WnjvmeDRg9lyrvp2VTcoeDt2OcWny7ke6UHtGPZO7UUKEBKMIcKYQAghCsF23bZ/vA1TV0vBaFmGmII23tXNTPI9hNF4s+/iYRxJ8BEzRvhydpEFojtzdXM7TVPG4WJWQkA5I3rsXISAGlbWbtIxpOjB4/f/qUng/O/8zyPJnr7/jb/2t9L//e//Z7P5LJFp309FWczms1xwzotxVDEChEhKoM6qeTnz3h/2/Mn58/mi7rreuuB9ZIxePr0wxmBMBJOM5eut9QYTDhmCgGBKsdcuIhQp1SDklGU+CIqSd8F55VUusAkaQ2pADBHKsqKU6mk0zo3aIyoYiFrrh8fHLC8mY6mxTEgIUVnOQEpKjc76x/X2yZPzWVNtt1tGqTHMhxBCOtaaIkJ9CEVdjV3rnIEQikxSITabNQSAYBS8+3NJ3Ruj+rE7IhPBZCDUecc4K6uKUoogQQAe9vsQneDMGo0x5lzu97tJdd46zuVuvR2mLssLRiUieDJTTIlLigHsu15rZcyQYNj2bWaF5AJAwBmnxBMcM8EJFpIXjGYIcZBgsBaCZPQUUnh28aRv+7quM873h0PXt/PFIg+ZmqbtFspMVmV5OBzIw9RiQqw1AAKZSc5yRDBEcegHZwNGeLlc3oy9hwlSAkDYbx67TewP25L7h23K6yUYXhSri2Mh8hFiy7zcr6/3n/1w+/GP5ticrJZFUWAqrDZ9N3rvIUIIoUnbyfgAsIcUMHte4GTD/tAaYzjnVFLMMp+CRKSpikG7UfupG7zSMAIck4vYvvkU7K7wyQcpgffe//rf/Pf/2//4d/7fq+XpyQoBCPNMEIKsDQgiLrjW+jjecs4vLy/ns7ngxNiRMrZczYwxIfpjf12KySPqvY8hxhCKIjfGjlodBUCjrR6U0WHAE/EuY5mNwYOEBXcxhgAWzRITCRCOIegQhJSUJesHANKxsYghNJ/NjvNyWTSc891+H2Jomto5r9W03W7k8YYBzjnn4zRpbadpghDVRaW0qrMCeHe0jFDKhn6YV3Xf984ba91xGD8e3OM0xpSePXt2pDlTSv0wdH1floUUuZASwGpSQ4qAEAIA6PseYRRj3GwfjdbWOoiANjZ4wLgoZ/W8afJMJqenwYBoOISns6YyztpIMeZCRAF9cHmeH2kBBKHzzgefYkwpIYz6rnfOlUXBCBNSzKoaIPT5j3745du3TT3LsgxgdEzICc6JJ9g4G52XWeZciDFmnIcYY0QEYwAigAAgFCCYLRfDbgNgZAQ2VQZjgAzTxapYLEBK8Rh6AtB7H0ASBJPxocBOqenh4aHtumVTppiMNvuugxDCGCJM2gfMeKeckHyVI931LoSiLBAAmBETYs7Zdn+YlBZFCVPS/QhixAgnQhzCaPu6++SH85MPPIgIwt/87X9HGfeP/uH/qy4La+0geV5mBPO+nyCGR3rlyOptNptc5ienJ7e3V957hPCRlHbexpiKWZFiHIYeAOBDGMap7zqAEICIYJxROgwKJGKNl4KGEHyILgbGmI8BIgoCFEIiyI433QguXQgQjhihox2dMoYxppQoBQ6HQ1EUmZQQ+KPqiDACKTnnj4WamZR1VWVZbJoZSElQXua51pozzjhLIBmlpmEIISIIQohSSkrpkWOijM0E0/rY9Sebpk4JQAQRRpQyhKDWCmPMGO+6QSsFEmCMp4S7rovBMUZ9sCAiFhPGlFKeiSIlaG3Y3D8SyvMCDo8bBkHCOGGAEsApQYQBZkJkx4oehDEAAEEEIIQAxhAeHu67rksIg2MCdr/PMvnixYvPvvji7u6uH8eyKp9fXuZ5gRAiIdLdbkdhiglRxlL02mghZJZlKcWb62sIYdEsPvro04tm8eL09MZ8eX3zJkztycny/Py5xDBYRSWNMcKYUkLajNP+Xq/XghBQCJDREMLk7dXNfQZT26nPOkWQPaPB2xAQChBOWv/qey94MuPUoarOCIJTLwvMeJ7JRiNsVRv0FG0c+olmOYJUUpqS9y72H/5++d5vw9PnCaSYwl/8rb/4vT/+40n1WSGVnuzOzmbzk9P5pIxzkXMqOM0kPxwO49AKwVMCnAlKyXEGyrLCWUspYwRrPSnlRm2cGxilwXoMnI0mSWaDJQgShBgVILngg/U24yL54HGw44YUxZg8Y0wIwWXm+oETIhiFEBJCKCHHanqMMICg73spJSGUUeacU2oklBzhYErQB5cJEiMKzgNADsNwzC1RTEMIx6tRjjV9IcVjCVyMiXJhjIEQYJIgio/r+3m9QBAc3cc+hHbYU4oSiARzY6PzmFHprVbTME1j3/aMoZQAJVlRNqvVmZQiJWCdGbv91X4bQLo8P7XKP0YEcVbnZYk8gFFrPVlflE0uq1wWR6dznuWMsgSBh0l4+MmHP/2jP/ojOZstl4vT01MHo5+Gqsi++t77w6j7abhfP/7skw/LvJ7NlgQhDBIY1WS9XyyWmJC+7475pOP1e1prSkni5Ccff3i6nL978fLVfNlt7kQ1y2WdYoYg++UciiEIkCTgD3uzuxkOaztOzurFYpFSmpxW03j7sOu23fksRwgq4zgpBu0EZE8rPg7DfkqiopRnwWijI0KhczuYgoc4+hgByIs8ImK98x4jnHwA+zefiN/7eyf/3t+N9VkAmAn5t/6b//7/5T//ezFaYzTGhPPReZdlxbHGUQpx3EStjUf9F2FyvG2DMUYhPTk5TSlBmGazGYTQB7fbbX0IUrJMcqX0od2pSQFvy6y0zgFvjmNv8N5Za6NnKPCZni9OOBfGGO88IXSxWBy7PVarFUjgyDcdr+z2zlNKvTPaGSGEMfg42lvrKMHjqAGMKcL9oRMsCzBAmBhg06SO2+RR7znezHmMdoR4DETDow2gLOoiH9X4y/GcUhq8d84iRLWZ1NRSJouqhjGN/fSwXocYGWPO6wTgyWp1+fT50XqntdVKj1O/3+8xo217iM5CQpyL0SiIEqWkLBtiXV6VRx2IM8Y5p5SkY/eeDybEfd9+/PlnyoflYv6rv/qrZ8tlsHaapof9rl4s/tq/+1cYhP/i9//Fp59+ue3eEM7Y6dmpHvvdfv/w8BBjNMYBCBeLhZSSUyql1FaXy/nj3f2HX3z6YnXaZGz59KlYPZ+fnIrTFw6zo6s3QRgBzqg4TEpvr0iyiRI1+a7rIIQupGGYBm1JhDXPgte9dRs/bbRnjJ4IGE3oLcmoOOw2rh0IRDIrEXZGacrl8fNlMnMAhmRjjIRiSohEcvf5vzK/o06/9dvsq/9uIMXXv/6N99599+NPfoEQEkIcvTz7/V5K+W+NsYQQCMHRoiuFIJSN43g0lR0vLkrRZ1nmvQfAY4z6vk/BQhjLMlPTEEKAMWKEGcU+OghwnmUIYSlkQRNBKVjFMaEQmxCdtUqblCLnkjEWvAcQOud+eUMhIAiio+9TCH7cDgFIlFJrTHBIZGSaJoIJY5gJElIyRrfd6Gz4tzad47F+TGmGEISQxwr6GOM4mCzDmSwRwMcFeiSSxnE8tBYhMA4awCHBYNSoxx5AQCmdzWY+GEY5IWIc+3EajTUQEMEzjLAUcjDqcGglw4gSjNI4KJQgSLpNAxa0rGshhJDHgn+BMTmyv78s8TOKZxL40LbtD37wg/PViiLcDwOlJFrzT/6rf2KcTd4lCqxzJFjX95O3gVDWth1CFBMak4vRG6ON1lJKq7QftA/gn/7ev/pbf/m35Mt3lJPRWd/uZ0xmZQ0QTpAgykiKSLLm8unukz+gyEDGl/MlJlhr5dJ+cn47aIdSMrrk8CG5nY4bHb9aiNWsud6tPS9gsJRgnZKLQUVPaTJBY4QREgQhSiBhPFE8jpYRSjNBGYMJ2U9/sN19lj1cF7/5303F8q/+tb/+Zz//yeP6Mc+LkOKxtOdYnCmlxITs9vssEzIT3mkAUJFnglPvA4AohIAgVMbd3d0yTrw1augzzhhhBKLkgyREY0wwhBACBDkjIPiE2NHVm2cymE5P26AOMJUyL31MxAdjDCGExv8/bf/VbFmaZAdi7v6pLY++KmTqLN1d1dW6G9XdUA1MDzkEODMcDudhjE/8KzTjC/nCMRJ8GJJmxAzAAWzQ0wOAEK1Q3SiVVZWVKjIiQ1999BafdD7siCzRaGEQ2zLSbtw4594j/Pjnvnz5WpyYgRkSe+uIKIEHYGYwSilVSCnH43HT7L33VV1LqZgTIhRaSHAphRQToZAkRKaGvRVjspS4751zTgg1/HgtFRIJIuYUg3U2Maeua+u63je9IMkgOSXr3GazBUxa43636/vOZJk2putbqSiw75r2+Vm/2eyyLDs8Om663er6wnYNSuksdE3oe5fneSa1tX2MwTk3rY7m47kSqKXQSpVllRe51JqQNInLq/OHj0+P50eeMaaUUurawBKVLDDxbtVcnC1dClGI3BhtCrndrvq+DzEaQ5PpqG2cdb1Swgfftu1uuz8+PkYEIbTOykjq9775r0ikn/ns5yullND7y0vFkFW1UATMgMTJo9BVNu1Qex/bYR296/p9d365OVvv25RsSh1mDcguYmKeVgXo7AePl7OZl7EfGT1bHPbB2sARhJAVA0tFwdvgYTSdUUyJOyEJECMkiCykdL7lb/wjMb1Z/sxvvf7aW//xb/2v/j9/9/+tlPTeex8GqGVwPLq6utput8EXRZEldoiemYZlXwBSSnUhWuvqut7uVq7vlJSEyMBSq4ur69Vm17tYZ4qRvPfIEWKKKaUUtc4Cg8ny1iXnmnI8BS2TiwM2SUIIyd77AUwABqVVAh5we0AYJGeHSeOwCBFjrOtRs9/2nY0x7pt95Bd1DpJApP1+v1gslsvVgI4JIQGyYYo9EEGGNdG2bYY1poEkv17vuq4t8mJQKOr7ZrNRKaXReDwwWpbLZQb5vun2uz0zayWMkTFY71ywLUcfYui7NoaYZUXf9cOBVmSFkGIxn8tBPUepuq611iEEjlFIhYkffPJg37ZFVRX1qCzLsiwxcrPbPXv27Pr6mqQ4ODhMwKo0r995JVdaxuSKykghGRwwee/zYpTnucmy68urlNKzZ8+qurI2RBDCFKtAv/v1bxcg7+y7V954GxJy3yZBFCNkwidWxHlVVzfubi6/a/0++jj4AV+drq8utue7rk+w8+HRqj1v0aIm4SDYx88vzlrIq3C13tB4XEoaz2ab7b5zYqTyBH2Ivdbo/XCiZUoVzrVEwALLokIGGx2k3f7qSQ2JWfzar/2Vh48++c4732TmlOJQbiLSYOirtWbmtmu974AFMBFhXY/73q7XK2NypdR+v3bOCSKdKW2MZ+dT8hxcTK31gkj1tlDMyJxSBE4JhYzSFMFtSedNu9ftRgIyKGuHhXfIi3LYQomRU0ppUC62tqorDqlt2yzLtJYDZXggiAhBWZb5du+9A35h9COE8D4KgBjj06fPBlLcQBAZRlbDGtpwpg/rIgNdw9oeQJLQmcmkFISyKMvEvigKIhyNxi8fKn9875OyGgTdLVJs2m3X7nKlMbpcCYtSMrNm70OKyWjDzLvdbjabHR4eVlVV5aosiwGRLYuyynIj1cXz048fPji4cdKfX15cnam1FFJiiNvVpuvayOB8ap51BweLV169efP4oNSZHFW11nq73a43u8SMSHmWxxCWV1eEdPfuK0+fPX3y+IlUSimVmYx1llX5h6v+8dV3Nm33yiuvjvIiJujFNhs5pUcEhLo0h7eKepFay8jQ9JvzC9vaPUsbSETvmc+bfh3RJhBIzrt3H15c7/svv3IAvuldRJQjUqPR5Hq5TRGk1ik4lMIDeyYt8lyS1sK6lkCZvAzeKgSXjdzychGTIFJS/sd/8z+5//GD3rZ1XUupLi8ukCgmn+Umy02mNSJwlhuTEYlhsa4oy9VqxQC7/Wa33wIAAheZatuWMAFhQh25Z0alpZCklCBKvetJyrwcCaVdcEIWIqLrOmTW0oDQDTbe+7oeDfWDIBKCADjGqKXp+951XghpdK6kDt4BMyFKQVKKrh0M+LJMZTILUuIQvhDdfrcbmMUppWGCKqUcMjSCAIDEabVaDSP18XhSFMXl5WXvXT0eSymYyDorJRpjjM7yPM9MBiA3672z6WBxGEIgZJTCWiuk1Erv29aYohqNyLqmaUKMw6JlhNS1QWg9mx9WeVFkRmsiQVLKPM+zPENBfbP9+L3vP3j0xGT50WKerHXO2aZtmxYR86JAEvP5Isvyk5OjqjLr9fqit3K93rRt2/ddXtQpJoAUY0JAQrLWLVfr6XS62+2ctZ45yzLmEBHzg8N2c/6tJw/vXV0eTMZ3jg5N8IvJeH5ym0ZHcnSgsjorj67b+73zHLyIcb/brFtAIsWSlO6cDyml6DKBnXMfnS57pNwoqWsAZhR9H5WAMjfR9aiyLCtDcoI4CWGqWqQkVIENSZBIMqEjkSskOvuee/BH5Rs/H1gd37j5X/wX/+X//e/8N/fu3auq6rXXX1utVk3TNM0esUohVFWd5wURDRVq23WpaQ+PjrTRy+VV4sQA+/1OaT2fzJvN+vnZeW9939sUk/fO2qhYmioX2sQYrA+Zzn30QpYKSQJ7a02MWsvB51MIkSk9mNJKpaQUROi9q6sqxMAcAbC3fdfujVEpRiGEECLLDAOkxCmmoshDsETEzIRIDCEEwCSFHGD/qqryPO+7PstN3/eDn2JVVc45RLq8vO57JzQJiTGFtrNGioODRYyRUGZZtt1uV6sNwItjajwZnZ8/3e0apYxWJstzQPIhbNseGGJM3oeYUkxJaTMfmUJSxW3odmY6KQptskxJpbWWQnTtbnl2+t13v/3R/fsD1UEAGWUkSS3VIFAcQ7Ddfj4djcpst7x68MmDFy/c4JQzDFpGo7Eg3O22VVVlWb7d7S8uLoo8Xy2XeZ7vdruqKieTSQqhZ9JS57OjJ5urf/IHv/ezb7yVOffG3RvVePL65366uvnG7uB2npvtemWIbt84erbad906JOliWm0bImJIRrARJARdNskUuN2tDifTlBIzeBd670kI70JWAgmhTCGFZylUniVnWXBWV4KV9Q6FFtI0HWq7PPud/2b2K7v6K78ZAL74xS+99dZbf/yv/9VsNhsOUCnVerMuyzKm6IPXrHNjhvVIQhRK7ff7EY3quh7wwrqqJHLf94AgpBhOQCloOEAHwFIIksoACedslqEQpFXe2711TZ5c17UxRSmFHDTxhYgxMgAAvLSbESHEAdsa9I+6rsvzAhFjSkrpPDO+7xJx1+1ba82nhohEUspBqm44Bvf7/QBRxRT6vvfeMSfrnNGahoYpBsVGJmQQQMjMMcXxeCxIXV1dPXnyxLpOCKGNDtEul+ch9kN1PmTKAdno+35AIYbnorVGBKlhCsjdVhoxGo2yXDDD4AwRgg+77fPTJ0+uns/m07KqY4jehRDibr8XHHOjYowPnz55+OTB+eXZfnt9lKuFkbPZVOZ5IaViQAKKnJ6fneVZ1ux2SishZFXV77zznZPj4/l87ryXUg47KsaYOo5sbx8/fDY/PDi4/Zn/23//P4h+98U33nj9ePyL1xc/85uzcnpYT4+ai+cc/J1bNzdd/O7ZBxcby4K2nS/zHAGloIxSUY667X6mcus8AighBZF1MSbWhH2iNonoYWy0UJTnhVLKRk7slRDAlDwok7NPIistH6T19cXXf7t642flaIYk//pf+5v37z/IsuLy8pI5Hh4eTkY1Ik/HkzzL8jz3MQJh4hScTbGzzmmt8txwSgQQYwwpcLAQPZHYb3dAoDKNxEYpRSKGqFXGAp2PHEIyGQMQstCms063+1GdKSTH1LaWuXPOkaDJZDKc8gzcdS0DKyEJhABkYJVpKREhFVlWVQURQqRd0y8325gSIMWUEMFkGWk1SOgjohA0bLivVisG11vrnQs+QUpVVSkl61HtnE0ptW0LkJRSSulgw6NPHpGElACRtSTvepd6FAYAfIgheK0zBOy6PsZARDFG5+wgmJgbTZqUym7fubtQBiXcuHlXCkEJhZAkFafoOrdvlg8efJwCF/mIUKhMK8O5VNNCX1yd7/eb6EJO+MbJMUpRKjGvRpnQj07PJAkZrbPWzSfTkOLl8jq1UJR113dSKkSoqurho0fDkoAQYjIZDe0wESVOrbUjZ1995fXPfPYzRb99/uBhXdC987M7p4/zfum6Rmk5OznORuO3+vDVN2z74dOtCz6x9YmBYmDQ4Ciz9mquxtoUKCQwpoRCSwHSpdT79Pzs+ubtEyBjjBIkuqYFaSRhig4hkhQkFUli67w0anKDN0/X3/+XB7/8n8YY33zzM7/wC7/6wQffK8tcaxnDC4aObZvorBa03Gwig7U2xfgCz+W02+6AARJ472LyiFESxhQRIM9zbYiSBY4hhMQAAhNiiilYB0oY4+vCCFEmG1y3S6YkTpnRzjnmSCQ5pWa/k1L2XVfV48HHQuVKIGZCQgqJoxYgiCGF6G3gtFxer/edC4kIO+sQQGuNzBx8WRYxhpQSEcQYAKDrmrZLdV0lKQBoIDfxC5xBEcmyLH3oCGk8GocQHj78JHBXVxNAtk33/Pnjss7rySEDGZNPJzkzIFDf9db1xpiiyBEHOMIASsb+1q27r995QwrQ2ozrCREoIAAhhZSQIIXHn3xy78En2WQuvNjvNz50zb4JRmO7F8kKcmWhvvrZnzaCTq8uPMA6xOW+v2KS19fXwKCU0kpBxOPj475zdVkwBGv7pmkWi8Vms3n+/Pnt27fzPEekoijbtkME59yNGyeu2z1//mxx67UTw2/fvbO6fj7RcqTBO/XgbIudkJH221XX7W/W9Pps9GjtOo5NtJxSsoHq2ZN1bwDnBfsY46CKgsTAzMkYE5zbbzuMURJpJYepBiAIIVIcCMGMiIJEFEJKAYGyqm4++sPpl36NqgUi/vqv/do3vvGH2pDSCgGH9dxdSH3XHSwO8izfDui9EM2+EUI474dNc6WUZIExNW0jIOV5fufOncvlilLMpXZ9F7xjAOEocqrzctBznNRl1/eZTrVSNnbe7rWpvXXWWin1aFRaa2P0UsqyqhBhQOkDJwbGhEJJ9un66nqxWBSjIsa4Xq9W6/X51TKxGASQ67quq6qLEQGHGRginp9vrbVVVZksjzERaqO1KsRyudzv99oYo3UIYTodj8fjfQPB26vrM058cDiL7FfLLZESWSHzysaYhVhVpVIKYOB8mNSnl/gXKKWKolDarFcbub2uT46Exsl0rrXSmSFmTCyFMkIj+9Pzy+997wegsqRUu7x89Mm97W59NB1X04WLaTKeLBaTdru/uL4inXUJNrZf3Lj9mVuvzppG7vabEOJ8PnPRh5gIECE1+/10NtJKbdZ7QhxmEoPQ6Hp1VWQqed/1fV6URqv1VXu5bvKyXiYndX504/U7R7dUcqNbr81/6q/+/t/5PwbvcumBedevUEQkIKbEIjLG6MrR9P1Hz1+pq1ykkGIfuJCKOe17B8mL3pbamOkUEscYk0TwXiojCBmYEWNKQqmiKDBB37TIgJSyN35JG8V2S/UBMx8dHf+Vv/JX/ud//NuTSS1IDEoEwgBDarq9NlkJGII/PzvnlAba5VByaa23201MQUoZeislMUNVlAJ5UiiIvQ+273spZaYFJkCAymgjhSKBKSohGFK3WynGvosA5L0fOCiAIJXKTLbZbgfYMjETkUtJIghlTB6btkMSAND1drPdOu9efe3NIi84paZtnbPT6aTruvVmE7xnYKVkCH673XgfUoKu7YL3Wsuu65xz2vt1CFmWpRS7viVEIeX18sr7oJSMKVjrvG/rsnr1lbsx+slkSkIur1fOu9F4wgkGHTyttdEKkXU+2qyu4vLJeHfOzzV95ktFXRsljBLBBiHRaCOlaLa7dz/44LyzAnj95Mnl2ZMqo1snr756fJzl1fPz1XK1OTvbROeeXVyvAifCvMyOA96YHxemkdY2TdPE2M8ObhQmi85z8Mqo/W4vpcqz7PLiQiu1WCwG170il/v1dbfbJ4FSwH6zDSy997JvMS9uvXK3Rv9g5/Szs9vB375z49bnP3f+5OPjDLNcM2bWrT27JmBIovdCaeiDbXb78eKYBSmt+hDROfb92XqnBaZ+PxvVSsq96/rQ3zhclKVodmuwfVmWWV4EZxOAd0EyeGv7tk3gq/nJ9Kv/0TAGHOaZv/Ebf/ni8vyDD76PAOv1uq7rhBww7ro9N11Z1Pu22242McYQwmQy6bpOKbXb74iYCIkUc+q72HW26TutpBC0GI+O6jyG4Lzj6KILvuud3feNmk1u1vUYAbuuQ+sVgZe6LsfL1dVqtZzOZkoZ5wJR7Hs79FsKZXABhGicG5jwwXkhbIoJUVSTKSqtFBmlGCCEcHH5fLm6GI/HRW6umv12u82yjFNsmz0zG6OloBTZez8YkyqtrbUxxvVmaV022GJZG5U2fW+7bp9S6rtWpf5wcQBCum5LJCmFFHi13GRZBpAG56fJdAooms2u2l9O7HXGGxm9Co5jFEpqZiGHgy5Z3z189vDRsyeb6w2kfZ3Ta196K69GT08vvv3ux8vN/mq9u9qse2sB0OS5zooqzzGmb73zzkcP7o/HY/n5z//Ux/fuPT89vVrtjw+PjucHMfpBTSDLMmPys7Ozx48f37l7d71eW2dH45snR8fX5xcyN87HH3xwb75YTGdTo7TMiz7J6eKo79qv33ugFJ0c3P7yr/7N9//R3/ft8916u+84ggzR+uB9kIFFluvVdg8STYZSKiUrIWT0PoUgU7B97Gxa92tJnAvhrev3+7feetsFjyEZo40ZEbD3frfbaaQX0lNMot1CgiCMfrlpYHT5m3/9b96///F+tx6WM7Uej8ez3a7Ls8J5P51Om6a5uLh46eYomGGzXueFGeZA3vvgAhEpKVNKztq2p6LMs7IuBIlkFcmL56ebZZeDdqAjSEjsInZ9l2ST5fOsyFZrZObVcjkaT6WU6/V6oHS0bTvoI8QYtTHtfn9+fqG1dK6fzWZCiLuLw4F950PI89yYTCu12W6ur6/qejIZT4dHKKVUSsUYrXXe+yzL6no8kAyd9y8UazPd2z6EAAADSVSIF0hOWVWScLltQgzW2byotM5JoA/BWaczM51NAcD65NtWnr+f756Lfu05BrfaXXwyu3kn5UV8oV3DKfonT+7/0e//7scfftB6+9qdOzePDgudRaBFyt5/vLzYXbdRFKODPISiLIWkdr+jyLbrm2Svuk5dr2SR15/97BeVys+vzq8uL9vd/uT4qKrr5fV10zRK6cXiYDqb9X2fZZl3vihrYfJiNNn3zXq395FDjONiXJYlp1QqWF9fg5J7VO8/eLioZjc/85W233z/H/634MP1eu+DzrRp4qA3w6Szy+U2L7SUXJZ1WYwQEvuolDqez5+dXa42bUesJCrnSmOSt1cXS5WbUVnEkNqmHaAbKZW31lorpUhgdqf39YM/zl77WRDZi1WYyEeHN3/ta3/lf/wf/ztjTN/3zrrZdH64OHbeEWGe5dPpxDlbFuVkMhnWG7Q2MURE7LoOAoQQEIEBQoysxK7pfAhlWc7GtQS363ettbIYsS6izBJKEtz5YPLc+T7BdteHGONsNvMhGGOYeT6fhxD2+73WepgP7ff7LMtCjG3T+KBccFlZzufzPC+1NiGE/W492KvWo9FqvQ4h7Xf7zOSTyWSAmYYTg5mFkMPsdEB5i7Ic1Bzads89DPiUMabveq2VFGTRxZhQ5USEKcksByQUyggEa5XS0/EICZ2PfrsW10/09hkKwYuTUmUZgLSNjJ4AQmKCEENsttcfvvv98+fPJqPi9aPb42oiQIIwIbCP4sbhoi7yajwVALbdVXU1ykaXF8un+yWV2TyrtdFFUcizs7Msy27euIEIq9Xq3kcfrTebzwkZE/hgUwrOuVfuvHZ+eS6lQiRvw37fJiTvOUWu66Io8rIsY4xSCKW0Sw453Ln12tnzxz94/NFP6fLOF3/22fvfOf32HwbClGIOwgC2kDg55NGe4TUty6LK6kmZyxRj2/sQ7C6FQotxLq7X+5WDUnJxfXlS5Of69Mbbr02nh4QYAiPwEOwgSGTae0dC9uf3rr4dbsxu4fxVYAYAIQgAfuPX/9qTp/fff/89qTSDX62uja5icEJBtw+2a6VEjYzBD8OLECIJHohOGPmFZhBhJOmZQkzRhqrCkOK+bRBR1lVFZjQeAyrn+rZvtqHRoFN0KQnruSiqsqy7rhsSZ9/3SqnJZOK9X6/XA0jU931ivnHrplJqNKo5caYzQeBdh0RK64ooxhhjIUW22+1Wq6W1tiiKuq5fVs9iPJ4OGXdoHpxzQ0ktpRRCKamVUs5ZLaXRWpIUJIhJKgWEIQVjjDFZUZRCCCFU23TWOsCYEIPv5eoxPPsYifPPfuHm618VbuOWj8aHd0ZFJRD6lHx0ttl++MH3Pr730dXqspqMnaer9S6EOJ2mqposDhYBIMRU5LlUiJhICIgpq8wdeQsQs6IgwSk5aYzebFZ37ty9q+8CwOLg4P79+4Pt7snJUfDO9j0DaK2lVFLK7XY7nEeD/OSn/8/zPMU4pAFEIBJRld94/6OD0tz67Bd+5T//P/yja7v/xh91IrjoEEApZSQRku1dPq+lGIapCoSIWq/3m227IculUZNR1u3d0byGsNvv94uDyTBoRsRByGoAWYwWw1lGscs8SS5BSvgT12/+9f8Fgv72t7+9WXetcnle1lUREgaftvvOhcCx711ApYzJsgyctylFrbURalzXm+326emZMplzThAUWV6WpXP9arUaj8fj8ViZfDqbWms3bbPfbyfjiYhkXSomUxvyXdsNtxzMBbMsu7q6yvN8qAiHFmQgNO33+6IoyqIc6IIDFSuE4J3zYXi+uixLRPTeDT7TWZbN53Ot9dXV1SAZWRTFAJEO5HZE3O12w49KzMbo7SZIic73GIAkmkzlRR5iSDExEzAhSiJRVZX3q96mcXT5xQfu7GMTvSpHcWt5+Wxy+1X9+ucPj14HIZqwiyEm5z7+8L1vffObT5+dCZProtr6wClJIZQxRVFqnU0WhwBobd+2e+d6RNr3e5kVPviu7ULCvNBN28qiKJxzT548Gb9ksjx79uzhw4f7fUMEnGLb9V1nB4GeyWS8WMydc8zsXjrODC+0MSaGQER5Xti+DTGCLsX0xj/7zjt/42B+cvKFL/9n//XDTbd/7ztMICRSAEophAAIZVkM4zvnnJZyYA9KBCmx9/7ufPbqzVEp092DN5ZXq8OjRVVWtu+FlFqpIX8qKfuuTSkWRZmsVZ/9qeztX6ZizvyTAXp0ePO/+t/973/xF/7Sd9/59nvvvbNeX5n8ZDY/LrK6mi2ePnu8vLjcN702ajabCiFiCoN2ujDY9XYg4foQhjUGa23bdoKSc+6jj+7dvXvn6PBIQJLAu67nJCSrKsvm01GfhDKVKStnLQAMRNVB+nlgHA/92aDumWXZ8fGxVmq9Xg+ADgAopRiAAQahvEG5syiKGCcXF+eDXqExZjabXVxcbDa7Yau4rMq+60KI9Wg0MLmMMVmWxZj2+3ViBxi1xhBCURjmsNtuGUAIkZlRlleEFIPzwZu8yDnIJ9+3D9/VQGwU+yZ88t6anZFUpPRsd1FOZ6qaeqDHjx4+/Pij2Ww2mh8KowCJPQ6Dg/1uz5G0zqTJSYgsy8pypHXGiZXKGYEQsyx3fb9aXzkbpZJyNp1eXFzsd7vFYvHWm28ywjvf+97Z1UUM/pVbt10KUfI4rwA4xjBkLyEEItu+zbM8pbBcX4/q2uhswJyJJEe8uVjsjX6y2334/gelOVgc3v5L/9v/Kvw/w8ff/U7kGJPAJNsAEsFgyrVEACG01NL2HRAFjxFQF+ZgvNh732xW9e2T/G7NigA1+BBl3Hch06r3vqon5fHt9uJR/coX1fh2/tbbMHkNokaEQWf+Ry9EePPNN998882P7n3+d37nf1rMb/3Kr/za8dERkbDOfuNf//E/+Ad/P8bUuz5GH33iRMYU621zdb3V2qAQHL0xRmDKDXW201LW9aLb2dXZ5Vhlj3hfyqpEchJ21iahqSWhle0aRjTGxJj2TTN6YRA/FiQ26/b84nRjN8MHflDZ3e/3RDQkAgYGFEaXWmdd1+W5Guhz3vu+7/f7nfd+Pp3WeYGEn33r7Yvz697uE3sgOJhPdtervmlNntdVLZTKckPeNUsLAWOEsiwKCZv1Tuu8FcFaRwhJepXL6ChAKzJV2aSfvu/O7yEzCREa3wlGivT8o+byPTGeH37uK7eKL8d8dH19/cEPfmCybFzVpBQAhBDBh17iLvmm7ZqUOqWozSOzknLoyMuynC8mKYXdbqc1GGGCzV2XyUH7uaqqQUhNKfX6q6/OZrMP3n//+vLq9PICiAZLFGNMXVfD5zvGmGICTlmelWUZUmBmKUUIoSxLZ+12u1OKANNkcfDeo4/uvLIaHYyLxeyLf+2vPbm8tI8eJ06C0UefKcy1LMtCkCAhGSDEwABFUbZtW9e1zky/vEq2d96ZrAoSA0aP3YgzXU15fqDz+eTOZ/TB7Wp9ocZHVB0GYgQSyH8yOj+9GMJbb35mOl2URVUU1QszTG1+/ud/cb1Z/sEf/F7btkKi7R0AVGUlADfrje2brMzyPEspKSnH4zEA7HY7hVgUutttbd9MoEKIAGS0llkGiCikzvK6KLveee9TDM77VGTe+xiDs17KvCjLrmsuLy8HR5EBHB2PRswMCINDUErMHAdAerdtd7utc857N53OAHHYCMiy7PbN2+N6+u4P3kkhcIzrtv/gg/efn1+SUm+8/vp8vnDWzKf1eDa+vL62wW93LKoMSLnAWZEP/plx03ZyDZmRWZU3q+zhN+PZx8SkCHvftwg1Zehj1/tpYcrRKJ8cUlY9fvz43e//IDfaGL3erBlQa22tXS6XSknnw/3TczL6+PbN1xcnWkjrXAjBGM2cri4vSTAndlYIhqFhlYPGQ1mWgy83Ec1ns+jDL3z15/7oW9+49+A++fjGa69TnjVNo7U2xrzQME9hWDPwy+t6NIox+hDqst5stohMIjnXQYqkBEyOv/X973/hbfQgstnJZ3/lNy43v+2W175vA4oq06OqUFJJqbRSDMEYE4wRSPP5vKqr5XJbIGR1aZMtxLjpOxPCuMilGam3vlh97muYjRhNEIrqEwZgiMiIgImA/rTwBOAkAOFgcTxoHwDAsJeqlPyNX//1R48eLFeX1nZK6aIoJpMJjutSy+fPT13flaN6s9lokUkhkKjr2mo6KvQ4jvOjw3ls1/XJPLK6Xm0gpMl4Pp1MAoihuVZKATAm77tGCPIpxpSitczp1q1bT58+GxqdocgeqBgxBFXmIQSjC2t9CNw0TUzxhRyayZg7IaUAzLIseH95dTkZzw4ODh4//uT08bNvfOdb665RSnGKj588KKUZjerJ8fSrP/PV2Wx2uV7FlLZNT6SJpLV2OCQ9Sdv3Y0jy7BN1+gPZXaGg5KMRCIAu4t61WVYcvv2V+aLKj07mdz97erV67wfv5sZMp5Plcnl+fimVzjLT2Z60HM2mQqqgi/FslggBoKrrsF4PLhdVVQlJ2+s1Ih4fHTPgsB8it9vN2dnpeDyuqnooopWS3ruiKIosm41n0fkQU6X1IF4nhWjaNni/222Pjg6Lokic9vt9VVdd12mpY4zG6MQYIqfEAlVVzR8/fcIfftdIrcz4xq07X/6Nv3z/3r2PPnivXa+OjckRqyIfT0ZKCYoIJvdZZbKUaRVd6JudEDydz4zJUMmCVK0lJqGPXy0//2tcngz5kHjQH08ILAABgX8sff7Jg34AZOBTXAYJhi2fohj9yq/86u/+3j/ve9XsN0UptQGSmdRquhgVxejo+MYP3vt+ZOcRjuaz7Xbbtn0uY6F5fX1eGtNcXW2jcAkVqoRqs7dN25JSUmljzKiqx/OD5Waz65rIaLJMkiRBwYfpdCqFyIti0ITRSnnv27bzPkwmktk3TSOFKIvSexlDkFI635d12TRN70JRVgkw06oL3a1bN5Jr3/nud7ddU4zqIsuD8zHGztlmu9yBGz94NMlrAMiUUQJDiMShMJK0lPM66dnc7bLH3zfrU/Jtx5KFVNwnRKG0QJ1Pb9z6+V+a3vmsVIp1fn19/fG9h01rg2ZSnY+w7juJuMAiL0d99DorZ+PJ/PDmaDxtWxddf3Vx8cnjx/umGVWjtu22+73zVitKIbDzXdddXV/J1Wo5m00H+hYzD2pB+2bvgxckfO+atpnN5wAQvAdjiEgQBQClZFEUKaWyrrz3Wuk8ywf3MesGcYtBfpI0icni5OOn7908XKjem3o6Pjx8hUR1ML/39JHYbXSeTcYjKUXT7WXgEEJR1pwccuraXbA9KUFKZUobLShlwcf69s38jS9zcYSDau6LAGQABKSXIfmjLdK/oRL9iXj90S9u3Lh9eHB8fX0uBSFgSlBXU+fTcrM2WWG7XiC+4DWSqIqyjbbIcsneS+pNhomFVJnUKPXB4VGMuNs3hTYoZdM0BDg2Rdv3kaGqamOMc24ARAfAcrB0Kopi2ERVSiFi17UxhgH3rcqy7aDrpJTSevTOl0UJBSKJzJgiz5fLy3a3mc5nv/zLvxwJmUCgCj5sNpucAyoa2D+AUNdlkedGiu12a53tYhhhNpMTevqD5uH7JXijhSPJGPt2hzyYcsTxrH7tqz9//JW/JMpRDPHs7Ox3f/cPL5fXoAQi3lG5McWNW3ddDEVRSiFUinleKqmvmy0Q1eVYZMZ7//Zn3m6bbr/bt21bFJUIOpOw223f//ijru9n06lkZiK8vl4akw8w73w+G8COoijGk8np2enFxblWcjSqB8h6kGquqnI0GmVZppVKKYUQuq6fjCeDN5mQEgBSSm3TEmFZljKfnC73t28e2egkCiloPBq/8epb3dlpWYhhS0GYzLnGe5/lOSfYbnd926UXZzAH21I0eTkuj29XX/5NeevzCRn/ZKP+7+Mqi9FnPvOFsvyqJGFMRoLyfIQIF5env/vP/+l3vvUNIXE8Hs9Go37fCI7zSVGSuXx+pmZTqeoYQl2PWIir1e7ps8eHh8dI8fLqvKwndV0779bNTuV5kek8K4IPg9bXwMotq6osq77vs8xopQcHwLIshzjW2iBi1/WIWNe1EML5kCIKYZQUWWaqus61tpvNyrs2qZ/6whdG9ej3vv6HSDIlIBJSopTCaMMhoBFFXggBzF5KaDqfF7OFoPjeN8Kj7+TsY1F5bWLyjEFIZI8xJiUQIPZlIcq5LKqwubx+/nDv+mXXxY4no9F614jOTquqlsaGEARprfe7rWvbJnRXlxfGlGVWXV1dBeRBTWQ0Gl2tVtvTZwoUIR8fHr7++ut1PZJCquvrVdO0feekIELa7fbG6N1uv1qvhRCTyWSxOJCSANg5SyilUDEmJG7bFgDyosgzUkp6H6ztQwgDdAcAWqld9AAiBH94fPPJ4wdny6uD+SEqkEYLH3JQMvGiyhEYgImQEaQQCqn1LrjeBVvkptAqWdtDkrPx7M0vz774y3jz81EqwRZA/Rmd0F/4GqL8hz8nz4uf/qmvwA9zMKeQEMVoNDmYH0zqyde//i8yJdZXV8F5I4V13gbfe3ab3WDLu4FdORpPxqO22To3EVKRZCJar9daa+tcUZXrzWa53MQQBgNCIiyK3Lnu4OCQE4QQnd0PNajtWiWEIgHIUsn9bksDnZR5MZ/v9vuu7UCK7W4XfeiUioj1aOq8azv7xmuvX5ydf/joYVWXudEx+Cw3AJCrjGOAFLNCJSKJZqLVuGvDB/fD848RY1I6Ivddm2cGmAJzIhYAKcbJ4ujVtz63OFxYZz98+Mn9jx70NvjAQPDo8dPdZr84PDg7P61zfef2ndKYznUXy/V33/ne8+16MZ3fvXmnLgpgZsK+aY3ORqOR6xpkFxIfzKeHB9Pc6EKR7HurtMkSn5+dZcaMxxPvAyBa66RUXXdd13WeZ3me9X0nBGVZ1TQtEk6no7Ishww6rJzv9/sQ/Hg8IpJd1xdF0bYtc0IUxijo+ebN28+eP7haXedZpfMsMHa9ywTPxrmRssrNftunGBRRck4Cl7mxfRu9F1p37V6JsZRq/PbPyDtfYkDgBKAG2uwQRAN0zz+SUxH/IrHLPxKInx70wBCZIw3tU2JMCUky6/Fk8b/8W/+ZMeLJw49CcFQLhHR9/tR7D4Us61JmShCR0swwHtUIqW22UlZFoQddY9v38/Fkdb28urwKhIMokiDOMq2UYggp+aqcOOtt6mEYg6UUvcvzPBE5Z5WWKSXvg9Y6z3Ol5LmzSCilJERgzsdjmed5njvfk1Sf+8zn236/321VLiPKQXSqd10IHBC6FEaaFs7L5bJ/ft+vNp6h0Eogsk8sGBIroEIoTzGBrabT6uSNlNTm4skPPnjvvXc/CKhiAo6p23XL6+v1atU769l/YtuHjx6P8mpve0E6RE2yePLs4vTJRV7posi3u+1+1zJjUeSTca0UTqczk5kiK3ftZpJIzmaz9XqjlBpNxufnF+fXVwfzxfHx8UDWH0xr9vu9VjSAHCHYoiiYeTab5nk+bMfudlsAfElRMwODZqioJpPJoHmeEhhdFHm92SzzrJRCDtwFqUyeZVowRKeJQcvko23bFGzfNLF3Pvg18LgwqirXvVs4r5GAf+g3NXzxqQPVv+eLPQybm8DAzAwJhdDyN3/rb7/7ve8kiDGm73//O7RdScPb7Ua6WI4LY7KhR3POEqdkW4G6zCpSOsW43++32+14NJpMJpvterfbHs3Gp5enIcaj+nC7a3fbrSATQkIE21ut5UBwAQDCoVnFAdtXSlnbd13f952KcTwZT8o6hIBKRKVICCbyIY7nh1/90le++c4399FFRq2Lw6PDIi+x77rTj8V2NWJW29X28cPYeKuZKHGUiShyRKQQPJEwWimZwezO7S985fWf+RtOld/4g3/5wf2HXqjr7d4Gd/v27aur66bbt137/Pnz8WSspN70DnS4fec1rQpmurFfXa2uZGas7a+urrQu67EJwRtjTF6UWaZUTqircpZlpihK6b3b7TZFUYwm48l8dn5+vr5eksAbJzcffvJot9sppZiTVhhjAkDnOpPJ2XSmtRqAT2udDx4R97sdAFhr8zzPslxr3XXtfrupqtF6u02R+m1b5CPreuedVgKAIabou+1+K6scIiKyFtD31nV733fe9slaIpiMq8ViltfV09XqxMfxj3Q//BetQf+Mm/GfchtGYEgJOEGMiAACAYYNNpTS/PTP/CJAiim99sZnm93ae391dfng43cvL86jw8yYvCxSYpkJBFQ6IyE3uz0zmDzvbM9bnkwns/mkrDKdyXpUrjcbxhhTREKlEEms143Wuu2sUdo6p5wH4uGckFIR+b53gLhvWpJamdy6FEtSplCZIaLNZmPyyXQyis4fHBwvXnnbUjpeHM9H0/F4LEluHt/71//t/yk8emS9W7aNE1EVyrNTPqXoIyEJETCGgEZLU+nxm2/d/rm/Xd34XPT267//T7/zrd9/5Y0vWSFX1rmwu7i4WK3XTdMOH2ej9Hwx9QwkaL4YFUWlpFlfUl7o2cnhjcXJ6uo6xnTj+MDa7vT0GQgZEk2nk8lknGmhtBZEkiFluZFKlGVR13WVZ+HWyfn5xSefPOp7u91uiGRZZm+8cYuTWK93SuNyeb1YLJp213bNdt22XccAANx3HRFvNpuTkxOt22GU13b7ve2uV7tgHad0dHQ0n5+sN1dKGyGkAQyhb3a7onfayCxXvut8s099t18vAaHMTF3ls9lU12MxqWevfWH6ytsvbJkQBzT7J8Lt01P+x4/7H5YBf2Zkf3ozQGbgCACQEgeLwoAYvk0AyDC4DAAhzmeL+ewAAF5/A37+F7622aw3m01Z5VlmhnsLUs65rm9X26V33nsPL1o/rspMCBG8v3X3rRDD48ePx+N8Pp8TYnJhOj86PjmRgjItiYRSKvjU972USms9iDKQlIO7zQtQgUgIIhIAgMAiWSDBpCOQGsC3FHmoXkOoj27d/fJfuv/xBxLWoyzPhdoHT14EZPYRUorIgpGI8jyry+nxyRfvvvWz69a+983ffvr935MIje2z0Ww2mqcgt5vN4ydPdrv9AN9uN2tgu216nRW51FVVIUD0vcoyYO+DRQFd2+x2qtuvu2YJQs0Ob85mI0hJsMGIbdNJY8zh4eHFxcUgvBtCWC6bsiy71o/Go9lsfnZ2XpbZcrmcz46Aodn3Wqvl9QYp7XZ7KfMYotKqqqrMGGu7xWIxcPOEEGVZkuC2t8pojgmZpZTz2bTruv2u16boQ6+8U9aK4MFDs/O97W3b+bbPchVTnM6qup6b8TxUBU9u3P25v8nZNCZGTijEX6zE/He5EAA4phcg/iAr8yd+50tVkuFrHo3q8XgMEIdwTwwcoSiKCYxOjo9e/tgXwgqQgJnxpYXt5z7z5SGmhyVPIejlEIGHz9bwqWOGlCK+vF48MHjhVQXAzC9Ut5M0hJBCD84nqZAkDKpUSCwAek/dupoRuEr3yntX+ui967re9q7zHojzzJRVluVaidRePnznn/+/Prj/4eb8NHVxPHkFWS6vNtsuZCbLj7LhyB3IxK+/fufwcLZc7wesbTabzqaz7cXl8/NT28fLy0tvHTOvt3tmGM0OfPTeN7v99WQ8RS1751yK0nsffJjNZt77j+99jITb3VZrU5bVYnH42muv/cvf/V2AtN3u1ut9Zsq+7/M8l9JIEc10CqASwOLgYFCAL4s8BI9I9ah+8uTJvY8+yhDqyRQau7y6uvvK3aqqY+Jbt+7cu3dv3zZdt6kgge16DWWeS0RhIwcnIB6cHPfOjkejop6EvOJ6pA9viXJCyZG3kbJPxTP+A1+cOCECQHoRBwPqigCQEBiQhoP/ZRANARQB0gCVAiPg4ICSEBgRAThyT0iIyEiAkJhfjBYQGYb94zTYoHKMiJQAmVnKF50Av5B4SohIP4R0GZERgF8Y3RMguqvzj7/+j599519Qkf3Mb/2Xo8/8SkKJ0QVrKSuTs2F3XY2UTEXlQ9e1qW9jkJ2hfSOwAZCiKEyeK6GZjOhiePDeA+/VePKqGrmNzIXKdsvrCGo8rVMKb7391he/+MX5fL5erZ1rjo8XxzckoKjNyNq+Xe4ZqK6nCUEL8toWRWFM5pzdbK/bfb8oDhbTm0IKoWUmhckzmWkThTTGYEHb9c4Yk+cVI33xS1+qivLu3VsXq+Xv/M+/c2OxUEpZGwBgs93euXtHMHhriWwAOj+/QISqKF65fauu633T9M7mZcGCrl1/fvZsu91urjZd3znnbt++k2X5bDZ5/OxB2F+NBEuMkiG2PQmhiPJM59OxqUv0eZIZFSpV2k5u1Tc+o+sSkQPIIUCGHu7Hoon/nPL0T34TkV427/zDXPgCfE0kACVgApQJMcboCYCBUUhGAAbs+353aqYHLMcADPyi6sAfAgL4MoQIXhjvokA5RNRAV8VP4QNmfHGXYTQaUQBAevFZ5DikWsBPn8iQOD99LsgAgJxiIAdPv/eH3/jv/s+bx4/qxLpS37PN4Re/Xc5fi6un7fL05ud/zaeU2qsyHzGStEFKGXPlnZNFlhVe5a1nUkKaTNSHo9nijSubte7MyEqOyuNyxOvtw9VGFfnBYpZledPsTaYX84PJeDIejc4vztfbLqVEiJ3aW+tTRG3kUJgVdW0mUxJiVFdSitNTbd1zFqS0Gde1C3boeuUgxOO9F0IeHh62bZsXBhBvnRzmWfbxB+9Vub5xdJB8nM/ndlCAiOHs7OzoxlHPqdvvJ/XUSPns2dPpZHx5ca61VloLLZVSVVneXBwqqcqyZCk//Oije/fuhRA/89m3Dg8Pnz/7ZHdxnSrtExRFzgy961mZ6XyulGIUpjQ767Zt7DDII3lrPs1MBojMAJwGmsuPLh79B7gQQBApTgxMEAJFCyYnbSJDjKADbL73zfP3/8XJ576Sv/0LZBTo+i+Gy/7kbf7C3d6ffzFT2tgPfufvPf1X//1+dakFoUyEanv/nnt6T+Z1Mc6yLH9+9QwQ2NnARBAYAhGglCGEPM+rosjrsvcJAYqxnkyPR/XB/YsnITjf7ztAnU3bLibmuiwH/oqUUms9zFyUVFLK3X472Pc0bRMDxMhC5oMJxLCRMhqNqiKPCIO0tPd+s1n3TSONLItSDNTLgUpYFGpwvBMiEYBt9x/94N3T06fB9q+9cvfZ01MAKIpiKC+KoihUPsrr6SvTpm0fP3kspVxvN0pJVAJSwESIqLXeda1SykOs6skXvvCF27dvv/vuD05PTyfTene9yhJ551CWgJxnWVmXW5RYFiCkSMojXYf+vevL/Eb15XxiWPgYZExCSAAYXpEY44tF5P9Ql4AXKnoIycfdsnlwJhDzyWL17IG/eLj58B13/fDp/Xfi7/9/b/6N/3r62V/9twu1f+O9/u2eF+Kgxhea3SYHllporVgiCSm1rjQ5xoa1bvYSAyHFFBJFQRjZc0qJUwpJGVnVuUlIiDKjIp/U1cyYCyJate2epAB4bnvvcbfbsXjBW08pbbdbZ11KCQFu3Ljx2muv7fe7e/fuIUpJWmmBiOPxeMiMu90uBi+lKIri6OjYOh9jdMwoURtNRFIiKaPBQFHk3rt+v4kBmOjrf/h75+fnr776ilQEKT18+BgABk3rYXC82W6klG3XaS3LIgsh9rYbXuWj4yPgOLxM0+mUIS6Xl0TS9c4Y87M/92XHuL44vfrkwQQSKNTGWGGMrurRSErY72yz9SpPH14tv/7ozBzf+Mrhrdloxo6jx6SYKAG8WIYejvh/t/TzKVCPiMNZyswJUCAIBs/ErAUFSgjRdU/+4O/vH7w/rsZRgu3b5L13YXv1WKeQ5dVQATIKZvwTuNWnvREDph/+cgAYvKKHsgJ/+Hj+gkOyl7QXBo4YwF+d3fuDf7A7+8CMsv11k5izfJwYhCQmDlJQYuk7wheVNCGnyCBUQgzcgZAA4Jlk4MzorCiEUhQicPfWa698cr75eHPltL5y7cmNG8zq8upqu96kEIcAlVK53EspAWlUTzJprnZXUhZGm7IopMRheDuUZ845IRUJKaQsCzWuUGudYpJKGqOd76SSMs/zlJIQSKSlIKnUoJAmhOydTynV9bgsyuvr68lkYq29cePGIBvUbLcpJaVEkeez2cy6Yrff931//+P7o1EppazrKtN5jF5rFaMvy0IIVIrqYpRzAmcLLfKqZGl0WYhRmapcBdzE7sOri29d7c+266LK3qjL0Xii8gylSIljjAAoJH1qgfVvHZh/7vsOAAjiRcQIQBaSQSP4Zne93elRRYi2t84FBBCEYX8NKQD9G1ZN/r09pj8dVksswXbr97/+/Bv/4vSDb7vdtUo+zw0iEZFUuRBgMoXACESJfbBCgBCSOQEnH6KQMibD7IQQCMSEMZGzvlBGKWV9P7919+6br3zjwWPB5GzUBoSkw8PDlFLf903TEBIVou/70Wg0Ho+ttafPTmMMVZmHGFzoiczAN+37vqqq6XQ6sGFiiACstI4x7veNVPJ6ed22W9m2bdu2u92OOVZVGYIfjBxTjCGlp6eXdV1rjZz48uICAIZh3UBVLIpiu92u13uj9SBfoYxeLA7quhqNyrZtB/HY3W5TVlnb7coql8pwBA7RCFVqeTSvGaJNKojJKqpd0vuevnXlv32+P9teV0UGJJhQZCoixB+fYf6Hx5iG3ySRERCZAjNRgtQ7iBxdbLZ7KSiE6JxXiGF3tX32cfnmL5FRfzJ5/vu6Pj0rfvj0EV8I1bquf3p/+f4fLR98S3nLKEgJxTBIkxZZniCG4ELvCFBJFThoIwFiSomTRwApCaWSQqaUAAUg9iH46FEKENooZfa7n/vSF59fdV9/dNH3HoM1BoSSQogXROEUkWgYH/a2r6u6yArsABQ4BzGkwXxs4Bu9eBYpUgSlJCBJY/q+z/MsxdR2bVlV8gcfvA8AXdsKIUOM5ahKkSGBMSYyNW1jnRvVtYO0Z6q8HZfVi0yuROI4X0z3WzWwUbLMlHUxmUz7vtttV3U94aygxCeHRxeX523bcAh1XY+qsVL49NGj1WbT12bXdFd6RC3a1dY/uYqJfAQSmfahJDJEVTFm1gwEAMboH43MPy9G/w1B/KOY5V/ojoNcEyCygNDa3aVdXW83a0SiIC1K711KzJqoF83pJ3F/geaVP+3hvQwvBP408ccf/SfEH6VnIbwU0v+TNcyPfGfo+yVAWD950Dx+oCO1wBHA+UiEAx7nbJNSZE4IFBOnFKSAaJ2LkYFRkNYisUcUBEwESgIAIyGw8DGmdgeC9Ho9KeqvffmN+9uu7dwWmuPqzqDVNaTDvu+JwNoWE+vCMMfe95tmG5kHKcZhtd+H0Pe9krIoCufDarWuR3VV14MnxNBUTCazyXQiGbGuKgYgoFwpUnK5XAUXRNMCQIxhMh7nmUEGIfS4rgkQALIs88E1+6au6/li7p1rmubO3Ttd1wTvijxTShKq5fW6Kk3buulkWpXldrtx1pqFiRxOnz9Z79p3Hp0j0atvHyNABBRCTsoyK4vL5Wlnw5hFWddSGSm0FBIAhvXZPC+G8/0vAIX+u2fZBMDAwDFxCPv1dXC9MTrESIRAIEEMRKQEKJGlfJE76c/5zT850PrxaP5Jsir8GUU2v7yDyeaf+dzy/je6i+eEQIQhpBB4WAcN3g4+mUKILMuQ2DlHEBEJCUEgEQpBSJgSA3OKHgCISGklpQBIydtmv8YreTAa/frbB3/40WpfzIVUKcS+74day1obUhyPJ9N6FFO01nZt2/c9oEhx2P8WRNj1PSEOwqUxpnoyKYpimH7leT70T2VZxZBkVZaTyWQ0Gq2vVyCELnJrnSgJEUPw1vYxxlFdLybTR08vS1NUdeW8z7JsMV+URUlExujgvda6bRqpaL9vtJ4Cy74PBwcn2+bq6bOnIYSf+vznbt68cX5+7nw/G0+vrs6T7ZfeF7cPLSaynYQ4mdSv3ridFcXH9z/obGhdNEWttDFGD9Ycg+WmUkrAf9DO/U9ciCwzx1Ae3lV5bYwBF0yme9cjJWQIIWKMjJh8oD//fP9hCfBnP4sX4fsXawEZUE5uLl7/ytVH7/p+z8wIkF78BAghDj1ljK5pHREKJC3kwD6RSgFgjJEHKXsiHLSME0dvFSopJbHH6F3XCE2/8vnX9Mj/4SdPjNFoqOu7GOPQPXe9M3rv9i5BEpoGmkviONiEFoNRUwhSiIH+MqTepmm6rhuNRnVdp5RWLz1n5KNHj16soTBKImSuy7Jru+F1G4aqUukYE6VQFkU9qkOMzX5/eRG01lVVOeuGJdrVajOejIRQzb6dTDIhhVTi6OgocRp01LUxr7/+uus6dvbOW28/fvTocDGfv3LsfAfJmZxeu337zVde3+y2k6oqCyOEMvl4PJ3P54uqqokEA8QYu77Pczko0nzayP/oO/0jX/OPvdN/7lv8E5EBCIwMBP0OV4/l5uzim/8k7E4RQRuBBHmWp5Ri4qRAmoy6PQXHDAARXi6UMgQAQBYACCmhwMQpARETEyBK5PiSnPVjwYoAnNLA208AiDh0+p8+wkFyH+DlaCAxJ/IRMXhne0aMwB7B9p0LliMTotQCEg9kDgRIRmVZLqRIKSbvgZm0JgQiElIRUUyMEIBAaK2zQpgsepecc675pS++LfL8nedXjXVllg/wFBIeHMzzrNis10gDVoHM0DQNIhLitB41+yb5EJj6vtOSnNJN2y7XKynkZDLZ7Xar1brvLTMjorx///58Nju8devBw0dVWSGis9Z7R0RlWVxfXwkh5vOF894oIqXrUdX3PXMEhuB98B4ABukBItpt9wDQtq2U+uTkJM9zo8TWrO/cuOmsff8H777+xhuzstYmf/sLX7r79ufqPH/w4ONPHnyoNQj0o6JGwKbtpFKF0ZPx+PDg+O6d1zKTSamFVIOdBhEB/7A4+4l50o/npD+NqfTnx+jLv3Ni7B99fP29v1farv34I+5apQUq0XWOI0opE0dGJhLt1Vlz+rg6/Ax/unUCgCwYICIBp7Q+2z95tzSoXvsqYgXJcpa/uFXiH05MX8YgpvhicIQvUAt8ad+RUiRM8GKU9OLTEOz64sm9pt05F0CSjdEmZu+si8SiLPLEIUVAJkRURoHEhJAACJgAlBAgkAiEIBJCKSWlVBKFEKRzlJqEjs4m55NtU3Px1q2b5214tt1u29511lunimxxMBtVI5NJ5/xgqMcMmTHT6bQoCiVkWeRd0zQ+FHleGmldv9+TIAEAw9rmYNlDSAAg79y547zfrDeEOKiaD2fooBY0iClvNmtr7WQyEUIMNW5Zlt45rVQIcZA62u12IYRBvGrQwUIipdTJ4cF8Mrk4v9htr6ej8vH9e8+VGI8mx0e3Vj547+/evX11ddr36yRQFVki3PdtItCZOTk5OTw6klJst1ujy5AxIGqtB2JmjHFA7F/Ch5/uIP37bKBpGKTvL8Plvabt+u3SW0e5GcYQ3kZrrfdBqKFKZu63AIlB4cvuZyghCSwjUpY9ffcd061fn9zFMobrS3l8CDoPrGjgT/1YsudPkygLxB9hbzGzRIYUgCGRSECAmBKff/LB2YffDM5HFt7FXd+nhJiiFigE9rZHF7UySml88UrGF37uxBIQUIIPg+qJUHLQiTGZliZTeUkkgw8xBk5MPvrtVX28OBnPTtfboq4gceDU9X3bdUVeGpPtdw0JGnJHVVVKqc1m03W91spxBIhao7V777lveq0VI24367oeKSmEgN52ACyLoqhHoyw3hS9iHGZQwRiDKLxvQvCDmJu1FoCloizL2rYtCj2bzi8vL9u2TSmmxFfXV7PJZFJPrlfLLM+lwsH3artarlbLjx98HKLbbDaIeOeV26vtcj4a19rsQiQhbt999eHDD7bb3SdPn/Bt2ve9DTHLirquTWascynt83xfViOTDZZzzCqlFAdeDieMn+7NfcrwGVqbH14v/vEnvvPj10/cnhkYEofr593z9zQYlIBGoCVgEEgRYowRCKWR3gfnbN801/e/l7/+VXH4GoBgjogyYuyvH7mH3/PLU4hWXnzUr69O/+n/A+sZb3b1z35t9LlfhRgg/XDd74fnOGJMnDgxBCkEDE8LkVNiSIMMIOgMxdCa8WxxODu6/ejJfevt3gYfLQcwUgROwTulRJ5pIkFCvFgdGyTHh9YeIAAIGIhUSAyQklBa6Iy0YakAJcaYYkgpCCWYmPens2yRSQ0UWwQAEEJ0TXvaP7d93zs7Gk+C90OZa20fgvchRM6lUiTF8HRjjFU1ijG2zvYxxt2uLHLgQCQODg7l9Wq575rPvP0ZRLy4uJBShhDX683BAWglBEHb7qVU2+1WSlQK26ZDoOBjYGIka3vb7Q8OD2/fumGEOJyNkdJquz4/f1xoeLbZGmUOTw4/fvTBct1Pp4t6VN/75Knv+/VV8+rdO9PFIkl94+TO6ekpVLzc7/DsyfV64z3orPQxPnn+pOns7VuvCikZUJACjoCElABTYibAxEhAA58fEYUQSJSYmROJ4f0GePma46fFAL/giPwYZ3QY8DBCkkDMGME122//8+7BN6TwnJIkpXSmTY6IwN6r5DiiIC2SFBS69uzb/yKvRouf+49o8UZkz5ulzKe6dVff/AN/+ZGDmBhIwe7x+0QqxpRkyO/+DBQ1Rk+I/KK4xIGwFBmAhtwaE7/orQgFQ0qJGZGlRIIEEQCJPS1u3vz1//zq8vn5e9/c7IJQUTEIBo5RKgFSAgtmCjEAgA8eQQhBKURMTESBY0JGRKmkRCBQgCIBKqlIqpRAKCmIe9uJYCBKub2YyXhYqgdLv1yu266dHCzyzPiuX11fdd7ZGOeTaZFlpCjFJKUQQiHzYMCAgL0NEVgYZVvX2k5pnZzvQsxqo03OLCUiPnn8ZLfdlUXVth0zJ05tuweMWilg3m63RIKImmb34YcfHSyOJ5PJZDIp6vrocPHogUyue+XuLaHk6dPnvXUpJaW1c+7s7Dz0/cnJ7Hp5PZueLA5NXdcAUJrqk48fNp7e+ej+68GdHN1SUo/qyS75qigBwDkbQ1DGsKBtuzdZlWJKw58YY4xKSUB8OYUfHh0NPdOgcCSEVFIOZ+LLfEg/sa70Z10ITJEgihC6Z/dWT7+TJ8skGFiQUMYEgQSQEIElMhEJRiuIpJSY3Nk7v7d69OHNr/3t6pWvPv36b9v1J9VoxPYMJEY39J1KakIQJER7fd7d+6P8S381ohqU+n4yqTO86JPSi4qTXhDuUt/3AKyxQCEAOIKImPR8PPmZr1197z27PxVlNs1NSJGk1loKQgYIwfvASqnEwJAymQFADEEIGWOQRFGEGGKQSSALQiSKKXKMgiQwIJEkASGFziLzuNy+VavTZSeVchu3Xq+PDw7qul6t18Jok2Vaae8DRB4EzJzzRIKZ/a7deecwHR/fRMQYk2Benz4rlFncuGGKcjY7cC7KLMsODg5OT0/XYuu9z7NsPBlNZxOAVJR5u++cc+v1pu/7qqqZebvdeh+Ojo6M0R9+8H5VZCfHt3brZdf3MSRCnk5GZcg32yVAOjk53mzOE7SQCAH7vo8xnj87PTo5aoIrTN7YvtntR9VYKQMgQkLFUNW10uvt9XKz345mExK03W2ul9cp0nTKWitEcP6FjlcETwGFlJzSkHtwqEaHBnrgF79gZKY/fy7KAIgMKXEggObsefPOH1Nz6nVSpPvtzjors4KUanY754PzIREKIJ1lAIxEnvN2uepW58Ftb/+86y6f2cff9lKX5Viq3OAAxLQhQlmMEqICPvuD/+E4cfWFX0skmNOnY4JB7hGGcS4Dp2GemQAxMaf0wkgJcJg+IIHQIbZMxY23b33xFz/63d/2AbZtXxmJzCHGFDkmjwRGG04spGBBntOAKDnvCTEIAsJsOPW1MmWhM4Mk0rDpkhInRkSIiSgmF6x0r5Rqf2S2+9La3nFcrpaHs8VoNOpjKEej6XTS7ZvNbjMkkc1mo5Sq61FVlJvzLdVFTDE6r5VORbbe07pvbuU6pbTdbACEvLy8NMYYY04OT9qmXS6XMSRAtbpe9Z2rqloIJZUOYV0U87KorPVa6966y4vT0ahgb6+vTiWR7e1iNnvl5qLtuqbl6/Om9TZM6vHibtt1bvX0aDx/8uRpXde7truVFQd63HetJLlp99uugehB4NbZiBgQZVEKpa8ur6fTue37zX5NUsYEIQalsSxzZBFC0FoxeABQShuji7zIi8JIIzByeNGj4MAexz+Vfv/DUQ0zcoqAgEwgIIbd0x9sH/5Lo5Aw4z6kyF2IGSInNkWZoHc+akHGKO8dIiYIJISQgkQZd93y3X+GtgVRoDIRhQ8WCHSmy6r0zsXIghBd74J9+gf/02s3XssWr6cUQNKLrhw5sQUAgQYCcWAhCIdQDSGm9NKsiJTWpHIGCkReEGl98sWfP78+W7/7zTaGnDDqtLci9L1QUOalEYqBBRCyBJC9s+zD4N6klTGFJC2NMTS0bYzARKSQKQaPSAipa1fGGJ20DT1bN7MmFzg/mAqtgo/btiEl67wo8+Ly8rRtGiI1yD4OSoZ93657l4Q0LK7PL/M8DyE0TTOq5vVJnUhvd7uU9lmWyTt37hRFcX19fX5+JQTtu3b7tJEv1Svbtq2qSgh5fHyslEKkG8cHMXgtuMx0kZWZpGa/ctYdHdar9freJ4+llErrwCozVVnPR7MDInF2ubz/4JPZdHpycqKUuX///iuvvDqbTo2i5fI6ywrn3JDkvEdCmedVVY5W67Wzse99nqWu82tcbdYXJpNCoiIzoLCz6dFoNCrLUsgXxzrAjx3lzCzkX4h7z5AAIyUAzwgE64v+/rscAyilpNrues9kqhEK7SP3fRcDB6a+9z4hxASZHNy6OCUiynLjQ7/frSB4IBKCUoIY2bmOsK+qXGuRUgJptICuXV784A/u/NLNJM0w3QFIAELJLKYYQtQEUiGnSITMMGDdw9hiGDNpaYiEUEIqhUTlfHb89ufOP7nPfWMjuU0vJAOIUhkWkgWSkoyQQkACIgEEfe+991hQSqlrWwkJBUG/U1pKITklIEFGCdYMTqbU9711NsW269pP4EafDkMInevzrDQmY05DuSWFHMatA4nJe4+E+32z3zeTyTTLMk5poI8M4M+A0nNKw7BehhCePn06nU51rs/OzgMkZ50UIkcEgO12u1qthBCj0UgIcX5+duto9vaXPhdCEJImdWHbfVUUWFZtaxcHhxdXl4eH49nioF7cijEYU1DHZaH/N7/1t/4vf+f/+u1vf/vs7GwynhLSw0cPBdxaTMrFZJQS8HjiVraxTa7yzJSpUtOpDRGdDWKstcqMzg/mJ6NRNRoVSglOOBSaUqm6GkmlEJGQBvbBp6kSEfHPnjkOAT0EKGKMQSXcPHj/6vv/mIJrHr1L7JhV33dMAoUSElAag6LzwbuYUICAxBRdCKEH5qIURmsSordtAhHZSwQiIZSkBFIOQ/M0OGYwQEASimtC//w9325xdgtS+/JjhgBSkPTB+tQTEQlyLnyqZo+InJKPITEIk+si4wgI4LxzyNWN27d/5he328vlRx+lXWs8S0o9g/MO2ENdkFCSZErMgNZGayMzMrD33nnsbRK9ZEG2zxiEIEmESJKSxCTJR60xMauUR7SrmIPOJ1nRB8eMzlkhxGaz3Ww2h4ezW7duMQsAGEQrDhaL84sLaz0RrVar8WhUlmVVVVmWXS+Xq9Uqy7KyKIhEjFFeX6+vrq6eP78oSz0qS6+LHexijF1vLy4vizxPKQ2CTYPxxeMnj7LcZFlW5Hmeq2dnz3/rr/0NTvD+vQ8TYuftZLYoy3EplLVWKSWlamN48PzZZz//xeVm+/z04vnz05OTwzwvskwdTufMTFKF9TZ4JjLGjEhKbXA8GQPSZDyXWgHGg9n41Tu3J5NpaTKtpItOCgGECRgJhUBMACmkxEkIHvbpeNgKGv6Tww4xvcTQGYFiQEaWiUFATIAhrLfX73398t2v75/8wPreKDI5yZSniI4hSkWILqWUApAAJFKUQrIhAIEi9N47i3o8lkZ1NsXOEgkQREq4EKSUwJyV2lmXIjsfhJCF0kor7xwLxckSJIAXuCZJSimlFEkKTipAFJiE1gyQmLUQEILSem+h9wH2OwA2Wpe6qE3xdPdJiHzyhZ+auHZZjx7/6z/c9V3svW5tqLXUUtiWjJHBIKGPadlZiMkImYQMzCEk6xS2HqlzJDjFUhsUxMwxAqKWCpTS1jlL3bS4Ndnps5AyWUihI6flatV1XdO24/E0MgFqTlEIWRZVpfW0qHa04hQvLi5CCFoLhAkzZJnSSjCzlllKkGIkQXK3WR0tZkVR+OiFVH0XrLUOnDSm2e85JUTM81wpFVM0OmPQm427vm5H4+LR0yeb7fKXfrE/WJy8+vZPn58/FUIpaULgUV3keb5ardbrdVmVnHgxm3/lp74cvH9++nS9WbZt881v7Z/M5pPpZDqb2Qjee5kZrXMppFZ55FQUVVmMre92+/XZ+SfM3XSymI2no7KKkAZ3lbwsBUhiiDFxAiEFvAC06cVKJL8QshuapE/7ehKIUjYPP9g8+va0ntiuf/L0o7Rd7x5/KPqdKishjTayHJUpIhMSoHV2mGF1vY+99y76EJ3zWikfrNIyxNB7mTMiCJRCAjJzDG6Q9x62GoINMYS6rIb5QnQWhJLlbPy5X9KjKQMiyRc5nYGkYKZIRCBDtICJUANj4iQLUlnGiQulRQgI4L0ngOA9AqYQbN+DFClwOT9avP1FyeGD739vu9+6JiW22AusSGhAIue9ByaCRJiYE0OM0fmQOCUOhFApwzCYjvMwupR5obSWWQmd6rr9VJMK4H1QUoaBnJrSZr0RQi0WC+8Dp4RI0+mUOJ1fXa22m8HsPs/zrrMXl9dlWXpvYwxKKqWUEDQskMjPvPFaVZWIdLVZb3cNkbh79+7F1cUwDN3v91orIiJEk+XMTAZEjoo0an3nxhuR/enjx5+9fZdiH+sRT+bnZ1fBp9PT0/F4PKCSgkTvemIotDH1+O2333h6+nR5fb28Xj54dgrPTr/4hS/UdZ04lWWZmRIBBQlrekBUUq02y6urJ8+evDca1fPp8e2TG3VZqawAgCIvJvVsPpuPRiNpygSIaaDnECIAEw0MeWRmxpcmwfBi7JQSC9g86d/73bOgEvDu8uF6sxVMkkg2m3pUCsmBkYF8TNFDs7cpBpnngnTk6AIDKdIUOHHitm0FJs8kWlsqPbzxSmsttKCXlE2EASvz3g97PIKSjyyLafb6z0P2QpKCgwdEkpoBEaMgwRwGNI3QDBInCYlAp8S5TjLYwS/BeZ9SQoTJZBIRVtsNMZjJ/MaXp9vz5+Nd169WKFCUZrW6alf7qtZCyMhpIH57jl3fsbcpV8akLNfOse2tyb21tsjKIfIYEYVOIIUQo2rR53K8FJRSoNT1Xdt3WZ4XRbFeb6y1q9UqeD+omHd9F0N4fn663qxRqCzLptPp3TuveBds77bbXWI3HlcppRjDsIAkq8nIe4/ARVEWReVdqMejelQ+ePBoR3sf2Ac4u7hGgTeODjKjci6OD6ZHi/nNk1spRgb2bfveh++SFJ5p74EJ+75PHIeZ5yA8xMzKyPnBpG92qW3vHN24XC731r7x6mv/6l//8Te/8+07N2+aohLCAImUAhETSaVlCLZtV/vdljk0nWs627r9YrqosqmQQil1eb1e7rbz+WwyWuRZmWeVlpJfjFxerIADIiQWSIkjs2c06K1ruu75D9rv/54KnlNIntFhcmw5KkWFoP3e1nVNSrRtv91sMYa2661PhQ+5zhFQEjEmAI4xoMCQwIYknM9ici5IKV3vXO+KXEtJuTLIyImIUBClCMEnKTVICSm41fPu2fdU9qtCZmQbbjduddGsL4qbr2ZHbyLpFOPL/Q98UToLwcxIzJGl0KwoxogALFhkBVOWEimhIkBM0ceQT6dv/+zPRud0XuroL999x1089hoYyFkrUkKOUoEPBJyEQyaXiEEULsSubXTZUhozSVIZIkidv9xRcZLlKJe34/iTrul23c72YykT8MnJybCI3XXdsLWx2++VlkprSFxUJi+ysijyPENwg9k2oxJapsTJpd1+s90tZdt1xpgQgrPeOZdSVEYBgBRKStW2nZQ6JShydfPk2EhSxjx/etpvNkezg+OTk+vra4di1bRCiN4nXdSMbDJtrb28vJQvHMcwxti2vRRQV2Wz69j6yXiy3+5GVXX79q2YklJKSAlMMQQhIXFIKRJp73uAMB5XREIIlReV1mOg0iWWCSBhJo310fpkvWfuAJQ0GSLgsG8OTAAENLRKRCKwTKS1d6tPvn39R79dp6UpcyRNqFGp5Xa/265Gk3FEKWXmEkEfml1zfXUdXG/ywidsuz5FjjGF6Img73tkSCEACpkZIOj63mRZCjGGGEO0tpUCU1EqZRgEIgISkwiJUwwyKQSO3W79/h8dLN5kMtsPf2/7/L6wzfLpE2VU/bmvTr7y19XomARBCkz8YumdaPCaQkQScmgpmFkIVVR1UY1weTXw2YJ3kELixESyKBAzUy3e+o0T1/XauXv/6p+47WNMUREjg9CKlAghoXdIGEK0Lmjtve2d64t6SlSHGIgkEnFKzofYNKmHYz1+bFlKOa+myGCtXSzmMfLgNwIvtVQVCiUVCtq3zWg0iimen58aqYtcTUdVlheN62Jim2yIvZRCDhpUWZaVZVkUxWq1+vjj+0iibVulVGbMbr9XUkxHi1dv3zw+mP/xd75bldmt23emB0cBZASpi0pp3fc9Jg+AMQQElFKGEFJKZVW1TRNC0Crrmt3i6CChSoQHSlTSHB8f72yX5bkS5AJIKZXSzG4A3fu+DzEqRW1rrQuj0VxJjUCIUpkKEBKikllVjhFkZLYpYHDaOa3VAH++xI8YEiNRSiwQhe93j947/97vK7vaRyeFUIq7dt/3wZR1d3Wddo0PsUwYmM5XF23bhBCsCyA5kbRd71N8sZgfMYL0wREPvF/SAgc3nBSTyUyWZYk9cJRSZiaTOosxNm3rUuChsRt8KZBwv6TrR9vT58v3/n/RtopUUejO2fX3/1k8e2/01i/Vb/1sGt/CH6EQICIB8UvW/eDeHkJQSpVFUZZF1++t7WFgezGnEDjFiHbXROt1Uc9TgZM7b1522/bqXEAiotIoHsyNEQduEEKSUuiuM10nTamUUqQoyxAghCCpFHJN/Tq1DxyPpTDe9ULneZ5b64zO8jwfEpBUUgpZIK2BZZVDTEop550CWIxqKSWh1CSi1FlR0FTs9usYvbxz81bTNM45Hx0JeuXVO6+/9dr19ebRw+eX16vOO+ls19rxePKrv/CLD5988u57H0xG2Vtv3p6XI2WqEFBK0Tf7TbczRu2aLZIUQvZdO/BlbNsSQGGy9XafmB49O/PsxpMxc6rH47a3UujgEzLleQbsA1vmsFsvQ2AhqbeNc12Ivm0bAE7sGWqlozba6ExKTUpkeaGl1jpXSgshY/KJCRITCBTEAMiARIxxd/pUtptuefr8w2+cffSOTDZ6xyCC9/12LUiqalTXo/V+n1JHJPqu81233KxJClNWrFVwaRgQDEPXLMu7rg8hKEEpJMAUgxu41THFru+IhJIgCACRCXyKSCi1Grw8tNEhJEBEIWzb7D76I7dZC2ZPuo0skk0ChZja5Xr5zu/p8ULOXsfISC+gsUFfBBD5RwgFg2FXUeR5UendLsbkgk0pACCRZGbGCJgwQt+skxJKya7xznMKHhiAY6moNJqVIE4awfvU7FtjmjS2yfW97RmYvBNSAYlkbbfv0XYydDmqVaDr63MfSSoSEo9PbmckOYSUolalkir0Xa2LLuciM8Ap+iS1APQyxrKQNjoI3vcdSlEUhkHKyWhMgNf9tZSkM10WxuSZkoqSsNZv92uE+JWvfPY//V//J9eXV+9+eE/I6vT0+u/+3X9IXfqlX/2aVKoP4fn11fOz56/cvjUfj1Blz88utJREVFVVt2+a3X4ymSwW8+Vq1bT+8nLpnRuNx0+ePSOSd+++gkhN2wMG5zsXhevb3W4tVZZAhOCGzmbYW9jvtin6lELwaTKepUQ+urIsZ/Ws2e2lkuPRGEC9nA0NE2QBQiWUAoKK3b3f/Qdue8Z+Uxcy2uhj33fdw6enD55d5Fl+Yz4fL+ZKSkDc7bbR9QioMs1EkZiRizxrfYQEeZaHENqmAWaO0cfwgmOI3DRNSslkGScWAoJzRpI3Weo6UkkIEVLEACQEMyilhxFL5/vN6QOFQkptMHjwznmhJMc+IMW+2z5+b37jTapuACh+IcaP8EKM58d42YNQqFYmz0prrRQ6ABNRYo4hAqQYY8c+06UPWixuZK+8tfv4w2a7bmzqiRckDEjvod87VIRSdKHr89Y1uxaJiBKnuAWQxuQlhRASodLH2L9pdz8AuTOF8NraXghMERnIuZC6djQaC4kp4ypRj0hCRh8BwAdou8QcJAnWGjAljtFHcMH5Tl5cXKQU66pufbff7VNMuZF1OZrW5XRUnl/wyeHsq1/+PNv1d98//eCTT7TBg8Ui+fD3fucfPr5+/rWv/WVdTCaz6Xg6ESkVQkSQZZZ3fTPQQ2OKHvh8eUUojMmyLMuMfvLkcdeFy8urLDNvvPHGeDx5/OTZZrt2rkfnCZPWpu8dBfIhCKGUzLxgAHbOIaL3odm3TbPXKt91WwHoD3qTZUJQSm6UpjHGPM+VEBABRWKIyH794TuXn3zX7p7Fdqc5ApA0WabF0UKAzu5ddqd7H3lLWQ7gAxCl0O63VVnFBKpQVV5y5GR9keUhBefcYGjUNE3wfhBdQsSyUAA8rOEmTkII4giRuq6rasXMIUQhJSKlmPq+F+KFTp2UMsSIADEGH8PLIQKkmEiAlLB//mD09APx1hwImIn/dAsTBBhs5qRURFqpqLTyPnCMiIgQBqIgRVZCxfrg5s9+7fCtLywvzpLvZwpWD96/2l/UNmlBzkiJoSxNjLHvWuYUYuAEQhmZFUZpiIKkhnqUheILyl8uP75WsspvBC+zTOeCd90qr+v55FDKRMJrmja7S+97TVJrqTQqosxkkf3aWQ2USKaYusbG5CaTWkopgZVUZHJdZKYe16vl8unTpwBqs13dPDn+uZ/+0vHxfLO+bpz1wLNZhgnG9Xi9355fN6en11ruEqfxeDKbTK3zKGA2mVytghCi63sSdHLjpG3aq8srY8zl5eXx8cnrr2WPHz/W2hwdHedZtltvlJST8fjsotmtr8sy52B9CpBSiDY4Dyh1ViaO/ebK9n1VVyHFyKx1JvYSI0OKUgqTZU23E1JLaUKIgnFgbl48vIdXD7bvf3N5+bAPYdhnEhi1pNKYIqtaq26MnlyBtQx98Aq8D6yIc2MQwGjNCaKNRpqEHGLQRgkhfAjK6IzTbhdiiFJQCK5p3WQyTilF50gQCSGE1HkWE4eYJAUciNVIJAkBGdh5LwRB4uADAAEyEXnvM2OQiAhdTB4hT4mtZ+T0giaYXs6/XjCb+aW4Gb6kZpZl2TvXNMn5XgglhY4xhtADIEdnfROTF1EBki/G4zsl2+7s/e+cPr+ofLMvrCbI9lRnigj2mxUgC20QhJRYZkFIEWyTqZHQFaemEwV1T4/05rJSUQoOcrdx/aY2o2I0GWdGJ+z29rnQ3ZKfnNtL3b6WiwOSiBT23QoFovA7e5YXi2l1WysVQpBSSWMMEqxWl5vrTVlXCR2iiJH2djeal59/9dVXD+bj+dRNFx88/P2TxcFsVK6WS+utT5gV1Wq3KksTE6yfb31Kycfjo6MsN7CCsiyHF9pbJ4jKsmjb5urqYjqdjsb1vtk1TTOdTBaT6bPtkxRdblSZm6vtFZAHjMFZ59rEgVhlpta5WV5fbNbrrutCmFeTUeRgQ0uJorfWdibTUur5/KgsJkpmiCCykqQEKWXwp9/9l2F92jUdCq21VMQiQSbQCJTcC+6mk2K163xMe2sPcuPcVhg1G9UoJBmNwkAUwQfb25hC5DCfz1Of9l2HAmWmnUXve620VGrfNIjIMTKDVspkRoaU58Z6F6JNzFobU4wiJ0JSUg7jAyUUaer6XhAqqRgwxEgMgpTROrgebbdfL41vWNbEyGBfyOwNmnhpOOhTjIGBB+NTY0xVV0i43WKMnSAiwhiVEIKE8MGm1BNHQMnepeDazWpj98WNO+OswrjbrK+ro5PJK5+Nzep698wmkaGBfaOg6eykZHApcoU0up2pG3T5dLPtVc+zLLsKvUhmVCoYBZk1Il3ZTqhCm3oL2fOYzgH8REoVciky5GS7nZIxwOUnq68bO3olfZmS0TrfNFE+u3j+2puLk/rw7Pzy+cMHIiOR5LicdLa7/8l92Kzf/Kt/eXN9Xk+PXrtz8/sf3Vut3MnJyWq9fvj0wxvHx7PZTBDmRV0UI99777vlajWjwfAvDoKjw3R12CQZTOvWm42UKsb44MGDdrt789XXW9c8e/KkLLNRUW63a6Up2RCtT+woy6RSm4un12fPUQzGpq5vtgMxOTNFb+n84rnJiiIrY8D59GA6Gee57HpQWQWByqrmwMG6aL0yEkJEAWDDptk+ur68eTwrq/rmYhwDb71njpp0EFhokWvpUSSGFCNyYvYh9QAUQ2ibRmidGeOcK7JcCeW6RERGGyFFjLHrnfOBlBCJQwghBG1KpkGIUaSUIEDkqKQe0O8m9HmeU5Q+euc7YwwKIaVhhJRSAPYxpYsH1eOPsleqKItPD3R+gVKkT1eaQgiIWBS5tS6hIFRSmM3mvO87IcQwx48pAsfECRhisBCiZ8eFOPrcT2d5XuVF2NmJ84e3bsi+u3znUc2B6pM7v/q34maL4Tr1XZmV7WrZb56XcEG3Pu8LH+dCps1habBxAjFR6PXmYvUwpouyaq5Wvpjtjw71YTYvF2Zk8kz2wfNytZVqt2su//89/VeTbVuanocNO71bPtdKn7m9O/uYqlN1uqq6G+2IJgkQEBEMUTcKKaQL8lI/gxcSGWQEFQpRBC2iQYANsNkE0AZd1V3mVB1/tt87febK5df0ZlhdZIHX83bGnGN83/s8by8KDoPRePlilZWBtxuvG2rYZDDs2oGcj9dM6Fa3jQwAGKTYnF2cJ1nxi8lqHZdB5HVb/pMPPux3WkfHp0rKj95/qiRI1msIwM7O/o1phwNxw96XZUkwooRIKSgmTd3YtuP7flmWGOM4jqMw2hj0HdskGC2XS4PgLFtByXmtHWoWANZlBYHSSiopTdNirC7SVeBa1Atv1gxS1jf5RSk4RMCxMedNCaBh2tfXl4HvCclDy2bJogTi+tsvrs9OULlSEGFkMMGZrFVeJst5lqxVtX76wdPNfggwnqV5khSOZXtWW7DKsihUqNbAsW1WNYZjKc0BIMSwIMJA63+jVUSGbWugqjxGELi+JwRvuCiqSmhtGAFCSAhZ1Q2xLAhRXTVSAUwwwRjUNUIIAsCV5FLcsBBSQSYl54oJbVmW1FoRA0Assnl+9A10+6C3izBCvyYCfs0la61v5vkYYymVYVp+oKhhU2JYlgMA4zeQo9ZNU0vFIOJKcIzUjeOBQc5UYyjSCAZEAzAxHFzHy+nRi2Q23tre62/fjTZG4eMfAAKwbCyN1+cXF5/9U50naHHpPf7kYO/9fcNSTX359uvnb35yevKtYQvVNFVd2LYNEV6v58uLDBi4tbG/zq+EnhoE1BIQw9k93HZpdH41x9zH2LVsryyFZzsksDA2S8ewN3o7HCylZHbkZ5mybG84HFyczo6my7Bi16t1LQElKEmLq8k4gvqD+w8vFzPWFJ4XBH53MZ/bltk0leU6gnPFG9OiRVHUghOMyqw0DWM8Hn/xxRf3796DQszmc63lDRAX+E7ou0rbSioLQuYFs1RxnSkkgQJIVWmWQ8t0HI8Q2jSNVApTX2pe1iUsEsWVbiFCpFSyanCWG/P5tZSqsB0C8eXLrz7/k/8+lOVo0DUIVk2lWQlZZRHidiO74+kiy+PVYHOHG7bjOSszAQpEXne5mksEw9CjEgJIEMFVltkUQYkAJZZpqromBrSoU0tt+0HRhIoXEAGllJIKE2hZlBpYacm4wEpLCCnGEGilYCWQRTCXkvGGAEghcj1Pa80YE1opCG7wAa0bJhuEMCa2SbHkzfTsqzIcDbyeNm2N0U3Y7tdWXa2B5AhAoCHChFLTdTHGtdaSYEjwBoJkNptx0WiglGqU4lJKrRnGBBsG1oapiJQcISSEgoIzpJFJhh987zt/9z/Y3tjttzo2xQgUQCFEiZbSCN2t3/g7fn/TDLoIGb8+G9vOwZNPNu99+PLFZ1eXP0dzaNo1ZIaDaFF1V2lVVlLEM40pkAnV0iJWvkpdIypArYTZxjsQiHfj1+k6J80WGc+vNrro5GKy0b1XC8Yb9eDekyxruJaLFVpYKeOsrGoA6fn1dGuj75hG0ZBfPX9bKTAY9mdX16fh24NDZJvYdcOyLJMkdW075U1ZFJTSZbkOWgGWeDqd2LZ9cHDgONb2zuYNT3jzP6rqyjC0bTt5nluG4zqBK/U6ZkArqZokWVe1oJRggm/OCRAALSSEikvRMAlg40eIYBMjsyzYSq8813M9nxBE/c7o3ntf//QvO83ModSxaI1YljSghBVt2l1/a2tPCpGnC5Mx36QWCQhEWZKbptnptDEBlm37lDJvZHV6i+O39eWlQWQBqrqpLAi04VE7JFW+nK+gEkCTgnGu66ZpmAZSSgNhWwMAgGEYCgClFASacyUkQkhrIIhSEBNCUJpntm0DBJX8dVwDAEAIwQgBLZHIGkEkJvbuI3d4C2EK4f8+s1cAKiXV/359glDfEFoYY8/zAABpmipFu92u1nq1WknJU14L2dyIHThjCsObwhaMDUoNSimGBEFEKbUMy6EmBQhIqQiGWgMhJRMKQrcThbijEdG8BlSBX5t6ENDQNPDTpz+4//C9V0d/8+rtXy/rFx7ULqmbPjShTajd7XQx9dfJQkszbtYvjp+btrO9scGBLpK1LNeWYSogiOEZL96+uLyo05XoDoLbh+/5XqB19d3vfOerbz/P4mq9ToqiiHOhtW5FLcskhuVCSp8fHV1Ornb6w83BFtrnTc2gUpxJrIFn2XVdrtbrnuM88nrXSVxR/OLlyziOb926bRhkuZr6ocOZWC3Xg8Gg1+9OZpfQoBpDpaBhOLYluavKfFazhkOOkHFj2UVIKakk51Uc13UlldIANY1oGtnrRZ7nMcY5K5J0VZS9TuAYKrMC7/7775My6fc7JkYWb46efTFev4ZcW8IREEVbu/EUKi0MBAihuBWIhkkp2+12nK4hwr5FrcP77Q//frTz1bd/+l8pURquo5TWTd3ee1DTwFe5ur6cHn2bQQC51lBBYoi6ycoa1ZwC4NumkhISIjgnhCgpqypnAgeB2zDWiBr5PrUtTQlQWkuuhMQ3u9kbbBUR7EWG37Zbw/bD3zDau0AqKeW/Aa2EVFwIAZRWQt0YnjD+tcgYAmhZthBCa9U0teM4St0wMSLJ+E0vMMbEwebNC02wZZqWZZkYUSmU1koJVWZZYxXA835td5KaaC0w0gpJhTDkCKib4QBEUEOKqQkUAKoytH7v4Lce7P3w1cmnf/3lPxLGkZKVahSCXhEvrFAIzIQk7Y0B02wdL+bJlW8QP7AC/5ayOYWMHC9enlzNIt2zHXdn526v08FCRCawKPjw4Z27e/uff/n65atjLisAUVpVhwfD6nzS1AAZwTpLaz4Fn/1kYzMKW3txVkvAGM+JFoYpDWItVutxzaaLuYZwOpsslgvGm6fvvwcg3esMzi8uiEF6g25R5J4duIbttVS8LIu6UVAZDmka5BhRzbiGWigBhMIIyYaxqoIYWK6pAchLrjWAXIim0bbTCgPXH0VhsDnYaHf6eVbEada99XgwGA43NhDGDrai+1+iP//jZj0nNJFIUYC8Vpcni8i2M4VMCgzXzOLMk7ZNDUoJs1qEc94ko8cfCscvpicYQa+/Hcdrz3Os1m7JdfPmp9Ors0DETNO6aUzTsE2jULKq5bpobLfq+RhyoDGt64pLhYjBmUyTynVdZGDkBpbnm4apNHAdv2Cg5BLzXBHSOnhg93dJtGl5gekGynC4YBAAhPENSAi14KxijGmAEDW00kJwYgDLNLXWUmgtNcU3hiWMEbItA8IWIQRorATM8kRwzurFTXrIcaEGVEqNEUAQCgW01pyzqknz3HS0a2IKlWJaYUBgI6WuOEASIM4lAMAyDWwhBRiWnJcZ57WEJGi1Ht/9wa3dj796+c/+7Kf/mbaNqN+eXZ0gVhqujUBtwihyuhTVQjV5mYU0Ooh2K3N+FZ8SDKwHw4/67sbO7lYrDLFWdVE4ri3qxrWtTtQLw7Zl0bcv3uwfbNu2sbO1OdoYTiaz68mM86oR9bvLi7/86c/+9u/0lCYM8LIqjVITQGzboRILCC3Pa7faX3z1VRxnjMk7B3thK6qWq0HYqhdTi9Cc8ciykZCGEKFFGtkwoBTTvt3hBkRV3TSK85JzBSkCGksJCCLw1xNFKYXGkEgpkyQ2DNojxijs7vWHg+GmALAoivVqxeqyLnOttMTU2th5+Pf/r6cvv51/+U/e5Otf5VlgoW1KI8siTEkpXc8tqzrO014rxHYQHn7HHx6atis1GO7ehvu3qqaRyDCHQCkhsSRK9Pfuv2ntVnEspAIQa0jsoH/7IJqen1dltq6FZWuKbz58umk44vLX2wRqUIgwIXVTl2VpUOIM9ncf/bAGpqxzxkW0fYAsG7FSQdwoiJXCGDHOtGggBEJIxRlnjVYKIHIjyOecV3UNgoASoiTHSJsESsMMwxBjXBQFF8JxPKUkJsiy7PV6XpZZXZdVxZjgUknXVhhC0zQouFFSYKlUwyqENLFsjAg1oBBNnlUAGZYfmZaNXRthSgmBUGMt6rK4vrpareedwci0DaIaU0ffvfsHyTL561f/vWBVv7fFYQwItkxjPpll5bSBMaaUS8QIKUENgRd6HXL7YPtJ/zchhqUosVYGQYASCKHvB3mRI6UO9waW/VGSrt+cvvV9J2zZvW5w53afGsh2zcV8EZfZF9++2BnufvTBJxZ1XJfUdVrVtUkMjEkYhJ7rFUWxu3Nwfb2YzWYnl+d1HZ8x+Xu/8wc5Z6fvTrr9diFrouHs6sxB1ebOTgvQ6SIv8rCUJsBrBBlGqGYFZ4pC4jqBYFxrLaSEgJoWZUxZlm2Yhla6kSIXbLJeIMNsRx3PcnCk5tOClTkEQGJytZi8vTielul73/lDefXitgkDzyZlhjWu6hIoZZiG4dhZvCqbKuzvhne/T4MBJFADyHnNJOAAQVEDiBE2kIKQKWEG/u6j/PKNYqIRSugmac6JocDoVjk+rcsVQIXv2FKDuqoIwRRBpTWvCiWl5dhiol3X0kpJwW1gene4NdxFGthSMg1UVWPBBMAKKsoVpUQpVZaJkEJrLRrGG04IpqYFMVRK3uDXeZZhjIkQAGiMEMaIAOo4DibEME0ppes6VmpZpmmYRlVkRVEWRV4UKWNMRVJKyYVpWaYBPSlVVVYIaM4rDJSmtC4qzjk1DMfteGELERMhCLXWspEQQYwM0yzLYjKZuGGbNxo1iKkJhOB3f/B/3Dx476tv/2eI1krUXGqD9iXIIGVQBUUptSxKubhYAdfsdHohaQ2ku3lVrgnOCcXUM9qCWEww1zB5WZVVSmIdet7DR4/G15eGaTS81NCUst6IXFATz3aFjC/Gy1999dXmYGNr87DgFWcXnrEHkKGBMhEqNTudzzvtcGO0kTEZdLqA6pPL68vVXMLYDSrXBYXCDFY1AkTjGmRhYPvUO75kTLgm0FCmCCEAIGMMIEQtAxFel5VWzHWIyOvV4qo/6lEjagSrRaMRVIjGWcGZbIeRRa0waDcsT/L1VydHmNDJ9fVv/zt/78n++9Of/ZNy/Ctq2VgpImRVllmVulZLSw0AriXouQFxPEYB4BIiLbRQUuMbbSiUGnCgoBAKQP3kh79n+8HZr/5VMnm3MdhNqY1aHeR0gk4vPXudJRNd5IbpC4DzoqIGMjExIMqbupFMa05AYJqmgYkqplef/2n3we+4w3tcSwE40hWHppC1lKIubnImhJVlWWRCMg2AvNFBUNuwHSG4VFIDYBJTCw14DQiyHFsIqZUiECCKLeIihGTghY4xXWGTGg1xCjtbEJkVitXNYnJJTdtxXd/ztBYEaBNq2WBAzCpNMl4LhUzPt+yAGpZgDCuNoOJNIxijTgCogRFqh62ZbUNMoYBMlI0sLc8BhDy+9cko3PzTv/l/Jvy0HXQ9GkV2G+CkajRTEFHi+TYr1TQfl8QgodtLy+mg/WDKLqomJfUtSDeBi8fpaVEnBnMKqU3PvndnEHq/T7HRcYLPX3wabHlX67Flept+hGAwnpbrvHx7/uY6mQtchoPYjTYpgDWfVay4uFwCSTudzp3bm9t7/miry1gBbJ/TOUbQcgmxWM8lSondYTuN11RDH4duZDTVgi9qHQSpEkop2/YwroUQCBHqGASReLmEQHu2ma2zNImJYZiOPZmMbUpNSHWkFvO5EGJ7NOoM+pNrfnEx+eF3flOwhq1LJ5Oqke0nvzmZvu0SQgxOsHBMe7maMk7qss6KWgHIkdkIBWUDNLpZLiopCaGm6ysNFEJSSi5ZXVRlWQa7d271t1uriUHdnsjnq2nJKmA6vd37ZBWyySUrqkTUDCha64TVUCnbsjBUVd1IDR1XUUKAhunxi3g+7x8+bR3cB+GmRoSzmvM6zWKgNABASlnk6U0llW07CGOtMAC44o0UQgEtlQRcVEVBESSWadY2VKSpmdbaMJBpEIgQRcgxYcu3MBCFZLUsTMNWGpV5kidrWWTrBLmuKyUDoq5yMpWKi8ZAoNvqjEa3HNd3TJcSpATjvK5Zk8QJY6zV4YHnA8kciw4GA8Mwed3UdQowohoCoFSTdqLBx7d/9y++nDjSzZaLqowhqpMi48BAgDt+K/LJ5exispqTyfniya3vi1JwMF9Wr4iPA0otDylXRaNWclnl1aqWAEvuOzoKWmEQ3FHbKV8Od2jZXJmt3uJcdUedILIMv9YkVTCv+GI8fdWisyI5Zhb84HtP3z07jZt0cxMNWvtNUzNJu1536FrVPLFN0IZgcXnaGFVp8axBTRFbnhN19/ZbJH0+Xq6463lCSs6Y1pJzXje1a9me52kh4nhJNSSEFkVhu6WCgFAwm09sYmKCCbVOLy4wQVEn9Nvd3//Df4CYrPJlUSS//PQvtQt39+917v7g+vRlz3EtkAduJrNqATiFRtlwCRGw2xVXtKmAohCjmx030LqqKmIYGGOIiWXbjXC5VkzlnoMtslM3aZabAFkSFYwtr0+P2GJOqsJCwDEtllexQlAqA5NacQxkXjVCQ5pkpmliw8KqAKvrTIuD9sCmbcU5E2XDysVilpbLsizruuaycd3IdXqhhqZBWaMMIafLSZEXrXabUHM6Pj47fW0Rx/F9RAnEWEhBKQ09P3A8ahiOYVgYc15jyByP5o3lOi1IsqpItVZVnQsl8jzOs+UiiCxCmJTEtndHB9hpaZNADXTVNEJAhOq6LosszdKiKDivQLfLq4yXdRCG2LaaqknTFGJsBZ4WDURYlvV2cAdX9ml6QilUiGGoudSrKmE8rVneNtuFqJMsI4E5VJKczr9NxYRiC3mr6/InzQXrdod7u48BZ2idIY1zFieoFLrWzdwwZNdCbSO8mtVpLty29/62t9Hr87wyqAUdIMEalMnJ7KzTNuK1eP3uldS8FVhQIEMyk5CyKXCj6rRhSqXr1bujSyiWwGtQN7TCzkbXSdDSrHwN42X6sqgGnrvh+m5VIQhBU7MsSwjgFm2HUacRIpktmlqKhims8zKxEOOYAd6Zjk9N29kabk8mY0R0rzsCGmpCrKgz2Ln9Vz/+YxgCA5L25p0vXrw2rRBCYphz00ZMYtaogkthowQRo+JSFBQZhJgAACUBQkRrxaTAnFNKlZIWNaUpoAZllQGKLCPQhDSKybgBARo8amnG0uksv7wolzPMQS2B4JJAbWKhgdRAI5oHjtU0zAhgsLHv9Lftrf3GDHmexPNJkk3TIpsupkWxklKZpm26HsKMojIWXGs1ny+ABufnp3Ga+mHU7bcm4zevnr2qmSSGHXSNO4+20jTP0sqlnomNMIx8x7apJZXmQg16Q5sGUJlSV5YVckcJoFkel2VW5EmRpq0w3N3dHwx2oqgHCE3XM1VVFnWIYQGANNAC6oY1+Wq2ur6Y+R7B0Lbc4d4d13Zr0TScs7KI6jYUgrFKK41NQ2J8eT3u+JbnWjVTcdJcLBadFo6TIhaNY1uCIWJHs+PJycbGHmRBPuO1SCfsknO4upov05Wuadv20iTV/ZnUiwUbW+ZWQ50GxJ6NDw47SVxb2FUVg1nSdTbOri5uPdzisJqs58QzkR/kF6s6Pv/g7qHket0sjupvXGtLrhGmjMspsnAl6sITSuKPR3ulS6f8Mlk0yvAX59dSldQCAcrrcq5kCwHXoMqgBoIoS2MEiOdFfhDyhsdJVue1adnrZNHUcVnEqzT17AAjfNHZ2N3ZDzwHaxwGzDBtTOnDh+//4uf/66tvf0UU/ui7v6OBePH21a2d7UG0372jnP5eXKOjX/7U9FGJXSEA06gQGcW14zhAI8YENagUggtW11BJCaQo0oRzXrNaSAEhMi1q26aZOY2sgdYKk2C0E/S2Jq+/nh6/uREeZ6zWSgGMNEYsLyIpbWL6Iept3fWGB0mdjF/84ibnVhbLvK6KplZMKQEER1muE1ydlMeC8aIqV6tVXVZZmkqoETWooTXMirIqSm35ectzKgW8jrcuFmt5DbVOcreeMQSR7wesYieXQTvasoxIIM2F0KQwKPGdkCDY1FVV1QAifz7vOQFx3LIuOCEoxGbkmAa9EQpJqSWlSZkfHb8pqmKwMbh/9+EmBI5hMNxAjDQHdVmVSVrXlW07VEPAhUxVWnI6CCA20zjTAjiWXeZVI4UQQglJGJwQVy3zF0CbVSnr8Tpou3bgMUkns+sm17C3L3CVrurQ6fp+LytzRQWEqm4q0wSGU11NLnzbs51eLivslLP16fd/9AEDX5+dnVgQhQPtYxpX7ywnrPiiIrO8Lj1zCyJGnKnSiNjBputNL5q/+OaLp9+7Yztk09pGGI6NRVbSaMMHtbo6nnImJLcJJo7jSiHW82a5WijNCbUMAwahzZkk1AqDTi4VU3Xe1LVotICNkl7oN6y4npRxHLf8yPf9bujdvfPo7fEXl1fHnaPR9fXl8dsvGZZg71Hn/u94vQFPq0PslNWyEHy5PrdsWwJViurGFEwpRRXCmNR1zRjjjImmqutaKSWAQBhrrZoGcc4QQQhDrW46iSDT2tkYgtUqvzpHEFLPgY7vt1qQYsOh0KDYtGHgnidrVD3XoAJSAADYzf0uS4q6rlLOuQZgWVc1xQgpLbmom4axJi8Ky3YgxZzVdVHTEAdbVhcQZJfIYmdXV612aAUaG1IqncVpnKdhx9w/aLejsJrV08mL2WlWpRarCCbaxhbBWAOu5c0Zs07T1fXi8nB02PEjN/IR1rZjeDaGAGVZnld1ELQePfxgc+vufL7EGHu23xSJcCPHNBzH8TxPK7WYjoHWnmWOp+ez+BW2VLqS1FSI6KZUnVYLclSnDLsGBAQoTgzb7vUHX3zx1enLbKd7EHqOqbiMQasdoC5ufAsh5fg11F68mGDP0AxZDjm9nBIECaSHB9vj9Kp2y4RKnpr7D28bNv6Xv/gjAxbvffg9i+z+4tk/DjobAFs///Rr07a6GwPlyNu7g7ycHo+/gdxxXZasWcarF2JqzmBEgk8++qFlolk+n66WSb4mWlEvNElTcEPqjgYAUcN2/SyLV6uJafoEYss2GcFIwcDzqZTLxVRDBQxCTauoqm9ffJsny9HG1v7eoQkU1TwKwyf33hca1lU5vji7Gp+Uonp58lwSeu/WdwaQ+ETnls1RZ766XqXTbtT3ow0NoBSCMQaAAgAy3qRZXNcMQWATorU2DTOvK6WUaZpAQyGkkhphoqGECGOIHGIEg+3Q8Se9iPGm1+8TtwMINm0TUiI4JwgjyDRQSteCs6YGRVnUVUOQ43mB6yNjiOusWK5W7VADqbJVXGrBmJBMtYKIWJhprQUhhoGcprftPL5/cHL85tXRbHd3W6nG8SwTWctpcn1UGKa9eWeDV3EMl44fDl3faZnH36S/+PlLCj3L5JaJDMPABFKT3rp1y+tal8vX2emCMsvA2DadTtTe27trU7MosmmW9npbA68fuHaZLQyEGOnOm8Smdru347guZw2vawh14NgEkq9Pf17ay8GgdfpVxhsZ+XS40V4uJ2WFsLaAQjUXgnOy2T/o9rdne8t8cSmQnC0SYtt5WWtLSVVFzq2yqPqDflVzrdX1tBhEg1Y/mmRX+Yo3tbo8vYzssJTVMq3WiyQu8+GohSljnByNXwbRmOH6+OhiM2g/3NuuNDhfT4kNJulVw4pl3CDIjJabxsVyzWzsLSfq3fy0b326vzcQwLC9sEdai2TKA7FYzEpAhiNQTrVWWuAGYsJrwOvENC3GGcIECW5SwxlEiMIkSUzTvjFMtaJW1O0IJd+8eRW4/qg/2JJDm6gnjz48Pzt+8fzTimXUMLI4efnsV/FicXjw0PY6aZlxLSnCVc3G80lQccOwMMFAg7IshFQEaS3ZjQq2kUBJyRlGCiIIZdEIjBTQStcmUVJqrTkASCNo2AQZ4U74VIhGaS6FhlABwBHnJoBKsEZqIYSSEEDDsVtbO73BxkYURRY2m7Isl5eXL17NiBaWOR7PSo1C29/sD2XDxtOJghgRaHmwM1QMFkWRImxv9e+8PUssF1mGI2SdpWI2ZRfjsjfoj8fAIOLgsJc13DSNtEhG+9aTj0bffrZOM16WyKBUY+CEuoHKANXt+/2+3/3Znx3VFdnodBbj2Tcvjna2Rk2RzRZro9N+cu/Jd558NNraMbVWVcnLpGFpUVUNE4pzCBWgxHO6tc5IWLbrHtWk7WkX+rDiQNWEmApBRG8GqkJBTTyju9Hp24QELdDvmPE6uLya1IxnpVKK7W3ztycXx2dAVPndx/f2Rl2kBUI4anlNnQkISoZM5C4mZVoXxIBlWZyeFLaPuaYtvwlDjhs6n+XxpLx/bxMaRKvGcqxSllo5lTI1KdZxWczYVjBq2HixqJTwjupLWRaxMMMwMCiUXHmek5VZo5Dr97/7+L1ffv1SDJ2jV2M+x0ijsixv0NubygfDgI3DOeMYARMDz6K9dms0PNzaGEKlqibTXMRlKerUhq4o1svVgjU3am2zrtTV9VGSTnvdfWqbkBKtMdCQc1VBBJUDDQNAAFUDlRQNB1xwxjTQFZJKSkIIYOxGbZKKBhKEMCYaKs6FEBJCDgEhBAGMtGFggrCWVFRVVddcKQghopRQMwzb7V53sDnY7Ld6rudSQojiWtbj8/jo7ZfTo7M79x/nhHS9tv84sDFerRcvj981usOpXahk9wHujtJVYq1WSANe1anrGUKo7nA4X1yXpcRG2/FF1GuF0UDFeT1xl3riuCBL6/u7d+78+w82t55/8/k5VAbnWkLY3rCgtc7q0hVt1kBeIZ5mC3M5CDvj8yVExt//g7/z3/5Pf6QxydJstVoHngshUEBDgoRWWVUCYtiWaWJomhZU6mcv/vEqfRHZAdGUd0G1ajC2i6IpQd3u9hHWaTztBC1KbNJyb3/11U/SdWoRbzDyqSUr7nGG0xUcDLtcKIM6x28mnm1l62pzUxILnF9dFKVAEC1W627U2en1ZldFkkz9lr0RjtI0i6e5YQHf7VtpOwSGDNnV9PLHP7+4f/eua3TrVRwQZBig5wRxBrre6M5WBN0ad9nXn1+1XDPsOVob81URGSJT9XQ9tUNKbWN3dPvN68vb2+//9m9+f5mdcz2dUjOdcAA1540QUgNAMAZKBq6Hoa6KQitZlnlRJqxJ04Q4hgnLTNS15HW34+pMmJpjQqnCUjaY3IgNcZIowc8M26S2ZVoBxgQj1EDd1BnQQAOoFJCCN4wDAG5yQ4QihCAXSmh8w3NwoSikGBuAEEAghdBA2JDaMAyDEKS1UrxhlW6QY7bMwHS90HHcTqfTDX3fcz030ApApDmvgSCiYlkynVydrbQ8+O7HO4ePCIK+aSsmzs6OFxkY7G+5rD1ZLg2KsLeWUkOkTBOUdRZnay3l+cnSRCGhPrVgwWZOgPdHwV4XNi3lRLK6Di/Ozywbff382fbmQbcX7j91jcBdr3ItZTsAW1vBaiUNBGeTOW+AZTiSadf2u2Hnqy+/+c0H773/6MlPXn6ducnl5XkUeIYSBoaWZTdKG9T0LMei2EAYSv2zz/+bz49+bFq8P9jSHEzLec5Q6IxM399Qhm1Y2AIEWwSQJBbk5DWmzm96pJWinz47flOnyqJ+zZntOO2ed3E+yZLyt377+wwkgU+lAjyTVaPTWSqkBNgVWuZV3op8r8xDa4MI48mdO2m6hgaVTfXm5dXiMgs7XiVVa6vVcNs1oyJv8rzWIG9S3HE3ETAcg14WY16rwXDfoGA5zmoL9O1BvE7n89z1vMt3k8jvtRz/CKH/7B/+1/+3v/NvPfrg41f+y9SFtdfj6QprIAGtWVUWqU1NiyKOhBU6TSNLUS7T+SBtkyissuzli0+vLk6gEp98/BuHW4etTqfb22TypoFKcMUQxDdSJwYgUAZrOMKSEmIhh2LDtEzT8EzDoZQQjCmFBqXUMCHUXJSu08bIRBAqrdI8hxCapgkRBvBGbSxuLARNXQsmbs6stu37nm+YpudQz3MJIVTrvKkkkIbSWVZorUvJ6zqDQnb6Wx/1+1HYcYjtmFayXqzyiRV5W8YtJ1nP1vNFPDZIs57VOZV5VrVaHV6Xq6JQ0K1lnhTl3m4bg2aGGono21cnJuubQb0Q7+JYIWJpShfFWq8vvv/0w0wsU11sbW+s0rVB4XK57reGG4756fNvk0SuAMrrggOJMJBV81fPv/jBh9+nr18XnC3i1Zs3ryKHjjaHrdaeE21iw0YQUMMSQp6f/uqi/AbYTk5tNssZz0qkhSkkLkeDQZGURNnHV8d2IB2c5swh78bPD7cfHg5+ALH31emPTVnWyi4Vl6J+eXwxn63vHB7u7G3mU7pIF07LvD6d2ZZTk3CRZn5glLG8ygopxdZgOOgMISereJk3Ja6Ig2Gr3WUK+U6oVnFoOhbVr99ddbou0JhgIy6yeZboUZTUqUPbqJCHnc4qW8exzCj77R9sXvzVxWy63kLDJC7S9Xo9zV3bxzL8o3/+r+dz3gu2U3++XlDDbKUF1IhVVZlm1O0almUROkjXMc9SqWoO9Ww99gMn9Fqbdz7ef/B9gjQGjTJdIwJ21PawIYSQUqobWyNE+Cb3gDAljmVZrXa7G7Z9zzMtyzQpvekJlBL/uneZ1HUZx9ONjX0EDQiB0iovC84FNSgmFCKMMaqqmhAspRJcEmj/ug4Fixu9N9LqpmkAIZhkawAAFTrJ0ptyPdMkFGFCCBDMNE3HsBtZL/IVtT0LO8iqUrZOp5NVcWUJKDnIoTAMs+N3WJwlcTopUL/jn5ycaMVIlkWQ1oS9O0/TjG8OYXsXJ2vmB37dFJ2ebyIwnp5IKQ0o7/bD07pGrl0ltYmo7/QI86lRHoadUbe13e+3W62nHz7lnn3ryXcOnh+/vjxDII3TdHd373DjoL99Nwp6gmmtWJnFCCLLoRBIxstVIVSRANjs9reqOGU4A8hC0GWNqktoOoHl4NEOIZP0bflqaUr5h5/8+9OTyS/f/sVGz3ek6ZskXq89YO33B7Ory/k8bYW2kQHPag88fzpZ+27QiUhdN1K5Zc1HW6294cZqXqwrxbT0sJsmCXbldbLiuv7+9w9MHU7m80pnV9NiFUvXISa2F5eTYLcd7W78+Mf/SuZw0F4h5RrW4NnJF0j9FQaG43hFoja7d9+cvjZcthNs7G8GP/n05y9e/YkfmpubA163ArfjqoCxzA/8fq/X73YGvQElfpZV8+VyPL+qWFEzYlqtg8PHYdQ3McqSJctjynXDcBR0kWH82nhHKDUMSo0w6Ni2bRqGY5uGYbquYxOshYAI3qQ0tdZKCa0EwggjoiWnhJqE/punyCKGgQg1DEINDRGEACrg+76UUislGJOS41/nWxVQQANQ5EVeZAios6t3rutl89VsMe+0251uF2h8owcryny9WlWoSMTKDlzAKWt0UuUvjp9dXTzr2uDroxNseRalNDTOTq5yWUBEdVZZA7Jo2MnbKwxxWVR1xdqBHTgC5FitHagwazTX3KfdlhfZlj/oRtouMGEORrNV0Y2628Ne5Jjfffr4ZPaTUtXTZMFlnaNm7+DB5s6dnW7vB/fvv3v9bKXZxnBr4/ajjcMnbmuohdaiLPM1gLDX6b+ZX79596ogUtSqZxsAqSK/zgWolQUk7rSMRFbtiO602m2nxZAk8+Rswa98M/of/vL/nWapqcMyldDQs+XapYFL63i27EZDmQphY8ybFrUM7Pr2tmewtuVQ31lnS7J5yODZu+k3r7693N4YVUUyX6dVARBaW57151+//c0K/cHH91+9PXJCYz0ulysU+mLYhnWtv/z6iw/s96qC2cSrhfQ9cDV5Cxi8Oil292xiS8PiSbLCvJUns6iF//qzL6qSipxLJRgbDzvAcfaljFNexUl8b3d/szscDkYmDdEGst+zuIZZUfqe1Qo8ixIEAeBMpFmTJQ3GeV3sbu8bTuA6LgZAMYEwcmzbskzP8yBEUgqEIEYEaqAAElwSjBCCCBMBtYZKCKmgFJLVlUQIAwV/rXuEQCqJtSKUYIgaVhNEJROyKZtKayQUEJWo66YEmiBocaknk8usWGZFPFufltXsV796tdne+8HTD0qC3TaBCFa8WSYLkQtFS2Xju+5wvL4az8+/fPnpy+MX60WsoWyA3umEq7PleMwkabDJDu/1AchAJSPbPTsu+kNPSgKgvHtrw8DV9Lo4Oq5KraJOihHnTTIPGt3WOxu7tQTrNAHYcRzFVA0QfPnujYmMrmOcz2NM0abnhF5n5/H7D+4/iZDVdd37Hz3eu/udO/sPehsDE5oszbMiq4tVkl5tdHZgI65n3+T1Guhg4PtKobSK3VAEoWdyqMqc9IetjdbFq3fT2Bh4G0AIUvM6W1c5bWzDJAQhS0GbmzZhMcjTUvBs/Tq+f8eRGAouYsWLpHnDpj0U3RncWuaJRr6Ui6q4jsvl/k5/MNyM09ox7ArmWVIZpiE1G/V637w4cr3eN6+Ohh2vJXTD9YuTfDwtPWo6Xvezz75RUq2TNUFemRWT61wbhXbIOomL2FjWRdsGpmm/PUm/eXba67qM1QumMGZh5LU74Q0HTDQ+ensyvZjc3xvsb/UdL8AA2pbTH275YVuVPm68HEgnirSEcVGu5stGCsMPt7e2TMullFiGRSApy7Kpa8F5WRS2ZRFCGGNSSIQQwripG+xgdPPlE5ILLqXkghV1DhDKy8Ik1q+7HCCUN1yRUqt0dT097YV9C5njxYnttMpKlXVW1UWcJ1JJy7RX2eXpxcs4mZW6YChZrabMMGYr/OptsM1qxLjte7bvOtSv69kiv9gk9549/8Xxu9dFsehjOYH2l+MrrbUAksk1hcar42vLJhsDFyjT9cLj41W3642v52nWjEaeY9tfPbsihnr/cKcp4Kevr5SGpgmPz1bDXffpwT2lwNvjcytywlanXC57bnd1GWvgvDm/KCR03dbd27f/3b/9b292u5bhwJJJx/ny7dtH731wf+8Bq9j67Lh0TI0kBXq00e9tPCLAaES9uL6q16LbdVzbYorCalTNq3ZkmgZIqpLxJgzdIAgV5+tyuc4zktUlpDStCiaBaUMvILXKocbDTm8yzipMFORH02uPmjhVyDfWQb7vjYrT1eySc4yX62VarlBL56XzcnVMnM7J5WwjMLptP5lr3oC0rGwXF4aVpLzMWe0pUcj5sqk4pxIyDOTZDBHZNMJCfp0T14dABYrpZZLxGtI6qDLUQO060HTa0wVfz9OmJJ2uDQkkVObF2kLt9XrBk6IqGUTk7fnq3elsd2+4tbnpK+/4m+Pj0z/zAmkQELr2QX9n7/Bhf/Mw6g6up9OyEYv5nKCVVGJ398CkrmVZpmlWVZGlWdM0juNQSpVUQkleVlmeU2oghDlnRZFleYIRZpJVsm4aaaQJkfkNS1TUpW07GKmiKLM8Hk+Orsdvj8/fffHub1rdzf3oIRNVkq4y1lRNpgGbx+ftnmsYGkKmeKJRaThQiCLJCyeZ0UZG3Y7VuFV6WbNJIpp3P/v02zefbnfbTaqX6fLpwzs7Z7PXs8T2rNPxfPfW4PBhmEyZlLKqE8uFl+c59RpkYsd1Wj2MkF4voKhwUTeVUELWQdgybTRfM0RR1wzqWhqOVwthysYicK+9eXU0PYnnV/N8VtVEMMeUZ6++dh59uLkZUNsyw2j74YNXZye6kJUstNK93ubu1uGo3/E9z3IjA8F4OcsUM1ujq3iNltd+21XaqivNNVcIYMOMk2lZrSzDtS2XcZRyTBSDbT9c5ms/ImHHRLhsMr7MxDKb53FFLe14ZmCYIxq8OLrY++6uTcNY5xc6vszXA7c3WUwTkVJpcqYMCjGuDdPlFeRtykpYMsSw1hm/1b97+m4satJwr6KiAYWDgVag5pyuxe7W8GQyswM8GxedkSWlKBstGaxzXcdLhB1EYMqY6VnVsqkTiCBENbC8oE4b7PHJ/LVmtmt5UYe0up1B29vqde7sHR7uHrQ6/U5/A9veq+NXNV/l0+siTiyna5nuLE7fjM/yYtm2w6ys1+XqyfuPR91Dot0osDG2K94skkVgtM5P33x9+jd3775vId93/GevP93dOQysXtEk8/VSCFk38brMqInreGRZjobyanyJIHYCDSBzrCHE9uvTN0Iuvn7z1t8effvi6xflKymBVobftbMsUQow0MxX6e/88Ht+C86mL0hp/PTzq91OIJ1GJrxScVRbkyxJ2WKhkq9eHL1+cdXu0Xfji8UFn6VFLZXjYCl4k0PHNMs0O9wN8mlV1mCVVNu3wukEtNq0P+IGkEXRuKFHPd0K+rO8bHjh+EBbtes6t2+1A8f/xes3g6j/9nJl+tTEBgE0SYvAtvCsbmpm23Y5K3/85U+fz5/xPnx0//YXzz998P4P/73/4P/yx//j/+frN19Dw7516+7hwcFWu+95HjUMhAnQkK+ndbyAhgLYzFYZ0Am3Wac/si24WGeI2EVTUsezPOIhN1kmtu0RrTVG0DSJkA1C1vX1QgCSxUIX2LP9rFg5rrNepa2W7Xr9rdbTRXYsQXV468H11dV4lhRlwzC3kOW3/aLIDERvH2wfvXpjp5aHTAUkA9CPIi905kdLy3OkkKs0ZZzdNGxDgIhNK8WDgCIiWa2EwAUrGtFwDYk2OKN1JYosD7uO45nLRaIhwFA2FXBDuyjYOolt0jrYu0UI0lACALOsTml6fvaySc5tgsPAP7h9Z6u97XefiI27V+lkb/99LeXJq5e//PyXJ8tzB8Jsla7W1a++/urOnf3Q7huYCCEB1etsncYqX2e/OvnV1sE/74ZeN+y6ln/yj5Y2au3sdf3Iuby8GI3Cy4vLV9cXo+5dAxhpuvQD5/D21uXi+apcPLr1wetvrktebe/42zut5yfH2FCyAZP5skiVOadaw6YW0YDu7vbfnZ1Fa5sya3lZZCt0LSpDvTs5/bZlOdsbO7UlnA3r7fJ8Op9JoJbLRDUcUcfwrZ99c401sjxTCh35zqjlcF55I296UiZz2RtVd+51size3DbWk4bVZpJnpo+xLQCkvSjo94C06nYQOaFvYKtIy9Vk7FlYgVRKvxVuXC+mF1eTEhrD7tb0aupuOYbrII98e/IX/9oOnn7wu45hEIBC3z09fffJxz96enjH0TpfnKjSjVodYpgQm9Pl8vn8uLcZdk3Hi0bDoZdRpaCpWdX3WxBbhUp43TR1AylGCNRNRTY2ugakpYEQglqSpqR+6BFfaoj3tm8dnx+ZhgkIfrOYH2xtP9o9fHPVLFbjx3c/jFflxXzKeON3aCeI7t69+/bkCCrhWdga2FA7poOr7FpwQEyoXA4jAwhFCbSInerSsmnUcmvGupvhOp49eHKQxnL+7CIt696mN55oUDecMQTNzWE4Lae0ZTRpk1fSdiyIQFbXFuMC4sVKvPdwe3N3Z76YSKkhwkWRnFytFnN0YZkGVD7Vi/Hx3sP3tslvVuWSSCXzGCH0v/zFP/+n/+JfDHaG8TLxbKOMq1aefvbyZyY0J+cFFwJZUGNQrGoE1IMfHRR1ggxTalyCYlpfvnv+xd9ynz4dPkybhdcw7nAz8r785s35xSRsO2HkDgb9Tmfr+dW5Oz2b5fPJchVu3PFrS6eFMzSigW/44vRoBgRuao4QKpMiSYrtW/cB4ldvk2cn153t9uR0ITFL4hXh1Hh1PbhnPOyObj3Zf//xJ//5f/qPiKlDPzIEBlRklaxzllciiKxbD7dFvawYeHi3F8D0/HL96svF+x9vBH5EicLSPD+qBMRhB7V7xmpZOLbTbpnAjgbuaHK9nMyufMtP0qowKoxyq03m19cvnl9cpTyM+t97/07FUFIWw+Hm/t72h3d/497ek91Bz4AYlLlQdYPFsoxXyyn0fEQULxQTvIcsMxgCwxu0dlocBN5GgYELKwHzhMm2094MBjljSyABkEWaV6Kq0jpVgrimXVWMOnbfjxDFUdQVshmM7K59WFXV1qgb2K2r9RS64Gx6+eXLr+I8B7j++tk3J+8uO91+nK4JlXt7+0LUCKo0rxopdnb2XOR8s3zFiXCQgSBe58vHTzavLzKLglKKR+3bvb6X8WSVzLgugiH2WtbuVufo+KrKUasXCLFUElZls9kPtg/sode9ms8sx/3Bo8PltH77zdK07VqAVrCzPWh1WpEWMrCjosowRsP9vXu73TvbtwzDj9PEaPLtobV//wNp+P/wx/8tFE2v9fyf//G/en51VGE4mcZQATcwoCMb0Dy8vVvnxSpNFmfsweaD23fbL49fQ0R1yvYGg7Ipwi37xfFLafEH39/KQYKI3Bx2l1V2VSSjUZcthNvucllVRfbF159+90d3P/zwwfgiUZTsHg5rxc/H8/3N3Tlf+j7lDPpPtuuSlkUzmSw8GlAbQqKrpkpx3r4VQaAOTb/OzVmsCOC9EbQ3vRqI4aDz6Y9/WRTZcDCgvqOXpayb7Z1ukzaXl2su2C+++Ob+vT3PtWaXqTa41/KUsq8XjetAVclWr6VAhJWmpNiI2kDxILL2RtslKG0rlOOpwEAguKoZNDEmNCmzi7NVVlHfDT56770Pn7z3f/j9v9f39/7m5//LPD3hRdqG7uc//nFqiN/+6EdVUyFC1001q4tWu+e5fhAEQvAiLyGIRzu7n9z9/uzqObdsNyBFkucVdyBZLmegC2pZ5zjd2RqZiK7jRiOJGSaLfC0lJNTc29q6TM4rloQ06jrB+w8ezJaLLEufffnm4MF+XtZZFsdydefe/aPzzy+nJ0CD1XzuRqHpgJrzNy++NS1jkSYqByNKcCCinsn4QFSg1+7Pk0laJZvDYaWnDONnn736oPVgvIi5EFKWH33y8PTdZPNpv9MNZmOOAMJQSkiURBBDiZrRVrvUZZKXtLt+uLdVN2A1XtjYu3vriWv1gWBaFbUWUkmpmWW1EaFZUz4+fPBB8N6Pf/FP/ot/8f/7B/wfpNLa2dwB3vYf/7M/LT3b9Toff4KbplaVFbSJH7QQxp2uxRyBwl5vxNaXV/3O0D28w7F59s1pJmrkknE2pY75cGtEKMEacFXULEuLbGs0sLDYuRd99ovY9exZvMS3WuPVem+zP3q8c3J+qjWbLKZW6Nst0BdhVZZhy6fIv56kuwcHkKiLq8mg0+769tns2kDgOx8+He5svPn2m3wBU8yQgrs77X6AXrw86rY71K73Dzvnq+Q//g//Q1XNfv7s53FWwitpziCQoBNFgW9BVM9n6dZ+sHnPu33rXpEnWbH45S9fPW6H33sw8IhxfHKZxSnCTdTubg86b45naVobii9XMxxhHNLuwEMAV0s2XhZrRQJLMDE5OvpicvTme09+689+/Kdvp6//8A/kk827//Cf/X8/T8b/ied0hr1+tz0Y9DobQ9MJACZCQ2g4rhNi1lgEbQzvH42/nKVvPD9QREgmm6a8TpcTWQy6kVY6Wa8Dz7cskSLOK0W6vfbicvbR3cebG+1x/q7b83kiDexdz95IZitClmJ939g6PbrguPry5XnPJlubw1SXTV0LjIM2KVVzOZ0wJW0DYxPUTTVbz3pRSAj50Q8/Wad5kr7eNjuU9E6ujjqjDlTyzidb2i3zi/XW9lYcJydfL65PZw92Sssy4/QSYKc7CJM4d6xw0O9k+fy2u2kY1DT9soEIzTZvtVBp9GnP8NbKWONmsynEnd3Rk4cPNFLj49eskNd67Z2/e16t/+iLf5zh9H/41/806t26u7373f2nERUffHw7DL7zavZzpcSz59PBXtuPFASkTvOoa6+XsT+E23sbzMgtU5qBObzfQZKVRQEYcqOAYI6h5kJwwPy2Z/ajeL2YrKaEdoQAhoF39zu/+1t/q64nncCaTM8PtlpvX58MWu2lyI7j8Sjq3rDSBStqlVzO67BnxrVXyfrt2VvQsIPh1ux6dniw8/DxvenlGLaSumkCU7ge7bLBV89e7m+Phrc9NVOuwbABozYkNl2cx8rQ7cD90fceLMs3KcvdDZrUjdfpJA3zA6+70cZ49/zsM21cX13N+q1OlVQoUMAEF9MxkCbmxWan9e76GljIp9SzzY3u4fNvj9ISfPCdw3bHnI+vF/ECSfJf/Y//k7BZe6eb8OL/9d/8J784f/6jH/woO7uwfbKz09WYYUwAIoViGJrUsIywDeYTxOp+f9/d3uqyxXqRG9Rww9Z8sWjt9QCAnNWWayxXy6rIqW2EkVUVJelRY+/ufZ4Vn80/K3G9uTmqu/pkdhULcGfz/bOTcX/UMQj/j/7vf/dPf/KztyeTql5MLq52djfiq6v2rp+zpMiaZMa3d/uMVaZrZw1dT/A3AAASxUlEQVSTQnfC7qA9oi4VJsKGW5WN3+L3Om2bOr4XfnHxOePN0/s73/vuB2Vasdp56QeCl7f3985Prw9H23tbo2fvvvbsjY3O9s8/OzYtwqW4s/9wb7Bxdv5CY/Dk3nuqjBW8ksBENtN6/Yc/+n88OHhY5+u1bzthV7tRFk+eTa+DnU02g6a59dHD9//zP/ov/+Wv/vzwYOvnx88Gbptpubux43uFQgoQMJssdYUCLypSdXo5v71LKZaAqRAjKFIoAK451fj8arrI0gcP9znjM01s6jiu1/aChqfUMjb3WgVb7uxsNOuZgeRsvZ5lC6/E+1s7F4tpnjYAQY/UjmdoLYMgTPOs3W3brtNt98fxBXKceFFkRxeNxn/yP/+r7z68pzh3IZWC0aBtdwb7Xn15ejFdrErebO+H37z4adcKeCx3N/dW/XW3R57s3lM6q1lq+UYG+DrLOqIThGFVr8tVOexthvbTF6++yUs26NKghXMdX04vnM2Hh/e/9+bZ53mTCSQUEY+29oRUnfDWcNPk+tTRfH66dtt9x/WOz6cSYSmBquVG0Ht9Ob/z+MnuaOvtfFyldX8jmlwszi/fhLd92/YaLfM0jpxenE6Wi+Ma0KvJSurGwMH56WU76iBkeZbrG+ZiNnOoN9rqnY6Pl/nK82i31yOirLTljfNlrQskQVMxN2pnJydF2tzaFu3IRoazmF+r5vKDB12k+KvLd9Hu4O3blxqyAjSFRtezdegbgDT9foDmZJ3mUGMHO+Or6evVLz5670ea+dIpcjsDCIa2KaV0cXTy7mRg47Znnbw+oqRTFqnf2lQN+Vu/99Hhxvbl4jhwOxCRhB09/XiY5vMnj27PLuKR9/Rvnv84h+nj22rz8ebxSakB9p3y9z/8nf/1r/67P/sCm4GzPj7dHO588P4ffHj3N6guymL9i2Sx3+kuLpbdTs9vWa/Pj5msU5n3Ox2Ri63OgPvV1cWp57Qdx3315hgC3WtFJgqrqnE74cnrk6Jcdv1drTAkCjnQM4y4yCm2Liaz3eFOwGHgtQnm66R6eG9rvLBUXsuqfH76srfrB22vY/Wef/ki6Lk7fni6mIzBZEuGvh+13bZoi6opsG9KknsW7AcdZ2jhillBK6uKUW/zfHEt0ixodaPAU5WkFGkJDIJeH1e9gbH9YbCcrM8vx8gy79zbj5w2TtDJ9DotqGc4T+7vjCdjC9miyNfXV9eTGSCv9rZu+15PCKCllKDGWNgGKPNxkvRNywOu8+CuBbkUJT+8tT/obT3oPfwx/Ytvz1/c3nv4eP/wfHb5TX4kPdXrtaPIujPoZLN+rKFBbOn6Hi5tBNpBlGfroi4irzOPx4w3tgInr3+ZzN45nS3UIECAZeC7u4ehHRyvxzWXjoEs31GFgMDoebtNwIr6hDVLwk0z52Xg477bU1rlWQI0NBA+uZo8f/dtK/LTKlFp/O27r7IiDf3uJAlNYE3LKybl0LYtg+SRa9jQccy0WEWDyFioTugDijlUcZ6tV1mn1cI4pp4hSFAogUS27W9HjwZclEdH472d+5YbmF17vhoHjq6a1cVSjOvzqBdRGJyeX+3cDlfL9E44WoH1Vy+/MbxQVXp8XUArD7sj12wPrOD86Oifvf6z3sbGRjS6mr9dH//04fHn/9aHfzeI/PPZZWj4y+m43aG3Nm7F7AJjNdro++0AQsIyI3L9b49/qUyIDHMVx73hAHDRqoUCCFLCS27BqGjErE4xxWHorJIEGyCKOp4XFXa+uzEKiZcViW+7QFJdU275soYt2+9vbzgBIxjVtVytUoF5txPsbG5oBHmaW216dX4Rp2tsIcbKMAqo0+VFZdlaMFYns267mxf5er2ybMfzg47jl1l+Mh6Lsh5tt//dv71PiVdWse/Yve5AShi5vchor/VimiwzwRzhRUbQ3ja/+PLVt/H5eplypgUk/c7GYLDbNJKDJM+KbsdEHKybtay/9M1eFqtWFI66UZKV19PTrJzCxOsF/u3Nrf2wQ6qqWq++d3Drl/wZK8uN/d3ZfHF+fh7t7dum6/gDDfN1+dby3ZYxJBIt1+NapMgELy++ii+OnbI4ir+tmqwuStkoikzVSM3kYrUCfb1cL1bT7PG+PwpbNU/CaHeRzQhx7apIbQwcmyR5xni1OEuBJPt7uw0o8xoIqfJavLzKbh9ue3Y/mMxxrEgNHj75DQzRenb06FFXISNPZVaLUkwfPLolKsAaEUVBO2u1u7YlyWdfrbLq6v1HP8SGkjJ1Dcc07JQJEzibo+2z6TEIr4mAWV0Oe5tCAtOMphdLIrIobBHq8RUcv4sxQ2Wzev/Ok1cXrywssnHx6N4DGzZb/jD35e89+SQIQqbMr37+Arre2+uL03/8n/793/t7HkLd1t7b4vyqercq1q7ZtREQFWlmsr/XK3lFY+5QB7donGdxssIE+4aBmU6LCkLdD60szss0lwbd7I3KqhSVZRmBZ7WB1hBgUHONS4rhapUuVllVKK/VAq51lZ/vdqLL9XVuSA9WnW6bW/wyXT3Zu13FcW5qoiEBAENiIMwZqxK2s3UPAvrlm59FnlVV9SDYibyoOXsrpRIc2BJjzj3bhpysVivLyas46Lfvaque53WcZ2WaFVV8MT7XTgOYFqCwTPbwYDgbXz5/e+HaXnvYZlK6jjHqb4maM+ho6LmO2bXt6eLaCA0hlDSFMsV4dSkFbDu+Z3WfXT1bTK5H/kHdsON6VfMqBG7HwKbbG2Dv+PgEENrv9OtiDRkqysVCnhimzZGCApRN7oSm1/Kuro8Yz6lNbSBRDW0zkASuk9QiTJd8PxhopVfCigW7bgqnZryYI+5SGhFX8rDVubyex9U42mwbyh2F1vRoZWvDtimo3e/efvScffrB7cOt7d50ct0y/dmitOHQq7S0DceJMBAF0+22xxbo7NW7pkM6QQsCgkDTaQWhNyRJth+OxsqziF2zpKhkCa8xtHzXx6Y6ufoWWZASd2u7E0/zbWfwbnKmKbWNACJpAcOsnc3IQYAmtTQdrGHVDcN1trI02W2PTo9f/+WbP/2tD39/kxwwvZ7l14+/86huKpPIqohfzr691d3rmIcevsxLsNEeIkGG1uDZ2fNJnq7X1ZODW+fjs6yqPQ/NF9eSIQwwk6VCyCAYWejkYjoZz4uCAUcMSm4g+uBgV0Gg6yZ0PN+20yThbRh5vWYKOFMbnTBvKmUThRXWoOd2CiyqotwctBarleJ2vMi7PV8jb3x97fl2r9W3DDKIdt+Mv83zq8jv73U2uZbMKZnU6yIN+506Scq8ViGqmXJcz6XkeBGnHBdZcnAYKGZqBAb9HqxEnGVrVrZ77aHbL/P5N18/2/Td7350q6gSzVqKCQqqOpsLd/29R0/fTb/1bd+H9qY/cFAk/MRFBlzWTVI1UgMt3ZI3VlU15a3BoRJ2CgsLwVqKpFls7+ymRS5YWQptRj1Y8RKDpDmK51MSKGqgWjXHsyPJ8rvu4WpaVEmxqitsOWmVhe2eawXT60skdIO5VE1EQsmYg8h37u1Z1Ky1TBqlmsyzHYRrbUrab/XjGati6BrhKAhu3+r5dmTmbZnUtm4+fvA+Sv3Fu8rReHNzsLu7r6T5+t3YgQFSrul3r2fzZTZxPBzY7TpFmrlZmS+TeDydJdP0zfVpAZZP7o8kr3LBG4Bdv5U3eVKu0iqtVAUgDHjEJ0U9W02S5VxWaZY+6Nwe0YGP7Ba1LA3DdhQXebJcptnk1m7vb//G746G3RcXf0naybQ4/clnfw5s8fLNxfXpqmM5v/XRxwEMLGlpZdh2cHnybLqYmdDtGJtS8jRbtzthKRRLZRazIi2qRWqj6NbwkVjq5SRxXaeqs/2Dnc2tDUWNoDcI+/0qNfKl4iWryixOp5bHQ0e3TDPm2VRmjRTQIIZjr+LC9n03MLmolea9fmujN7K0Q5mx528MsVNWZaZYNBwkjVbYt7Doen7H7e8O902M16sr00I2Nfa6G54kqNQeMAdexzGsIk6g1Ovxatjxen5ES//29r3r+blA0seOh2yJMaDkYGev50c6Y2zJF6fFT//6+OTV9Xa71bCqbGqmRFKUXOb5cuJQy2Rsi9rHJ6/b/U5Eh6ukVlJBIaFQGNE8r16/fpelrCq11irN01WSaIJD339/uBO5dgGa1qC73Y1mq9Pj5auL5Eh6TZwsNGcc5NNsnLL87PhdOpvWTXkyOfvp4u1Rsw4cL8CGZAASSmzYGvm5SINuu7O12UDl2w6R0ESBr3ygbVRAgCXaBP4nB9/95NbHalacfX1SV1W34/72J7/rt3t/8uf/2ySeXc2m64S+ervM64npCNtGlSw0r2az8dvz5/2t4Gx8nWf15oY/3Ohsb225rZAp5fruenF8Ojtn0D6eXdqBuRl1DWhNVisOeFKuz6/O18lyOptr7VaNhBgiKV3LHhCXcDlbjafF+Xl8mRR5tYgHTgerwFCWZRhxk33w4JMQeF+/+kyTqD0cLvKJVgbLNVGyXqcucI+/PPPy1tnb+NM3R5x7RJtUiW60kyX67eup43W///Tjy7PJWvLOYNgS/v/59/9P2+HW/GIOACAEaSAphXf2+h89Pri3NxgNzXbL7ASdUf+Q4CBNioJX0CWIAK1kVhZZUWgIseku07TWZW+j47rOcj1rslWaz8/57Eyu5qJalTlrWNcK3j987972exwGVqs9SS/zREbOyKORYpIASJg0GKACwVKamvR7PS2V4lLVvIjnkRO0raFNPC80V/l61B2WcaEIFki1wrBI0pPL87wRxI5ioGKJ7HBHY6Ip2j7Y7w83hSx4Ga8urr95++Knr7/e6LWWRxfgSlrcpgh7lrvR7viOX1V8vU59t1NXcj6fS8VqzjRGyDbeLM9TWQadcDja8k26TM7j5UJXyqO2b9iz8eXb0+ecp9udroRiwTPi0t1e36EUAFCm2U+++OW//OrLF2dnW53OZstPZhOeVx0r2Gz1NIHCMm2rtThbXVwsSKOlAMp17PH5kb6CUWsYQ8gKjR1P8KZltjG0l6vYBGQQ8XUieb0AsO72nG1ryERjuMHFfB4ZTq/db9kbvlumMdeYSwiWSUWtlvKNbscJvHavPdSKK51pWfMyHw26nDXX6UzUMs/ntvJ8P2rqdRiaTarnGZ6wVSrqktd227t/cPfo7YU02cHexrDVGU8vrteJGJavLmdt++6osw1YKjh5uv/wrXWksRSN7lrd33jyCZJofHmJlb2YrW3H72zu3B/d5Qz+9fOvH3YGYJ2kfHE6Ho/uf79KktV8/P6TTe+q8qgxZezo4mx3fyAUmy9i1oDDvd1ha0OkrJSlZbkYGMR1ltmEuurs+ro2tNSAmE1nY3A9XV5Nz3dGm8PBnfM3f2kLSzpuPIvbgVdiBQURlW5y1bciDOD+5i3PirQzWZYL02iZZtvTdM4Xk6Qwgc8kFxCsamV7HrV8qMHBTptYwgfW+dUlrShCRqvbmaaL7a1RIYsiSRxpDqMN3/OBacCqcZwudXApS8vUaZ4LAe8PdqZnl6fNqtvp//Dej+JF1vVHhgpfHL2Khm1KaFMVWOvA9E6qpDYYEjWERpwlRFSAGWG3R6nR9tsiW1NAdY4WWcI0REwkooSYD4dhs5A616wRk/UVq4tMs3Z7UBDeD9qci5N6dlXEgIDrrH55PhuYlkM6R+9OO4dyazjM4tU8ng7JxtOn313lM+KGw/P1JMVlZ7O9mM6JQz5++lsvvnolU/7zz/91NAg/fvRAQcN325OrOYVOH6aJqNZFmhXpRqfX6bor6Ry2HvdDcrp8bVFMIW7WqTXoPnj48GoyR8TUoG7k9Ue7v/On/+LP3S3bs/2u5xOoxtkaEyql9L0AA/v6Kvaj6Go8Drqbvh+MT+eGYQatoN32IAQHh7eu5mehE8SqeT0fm8Kdz1PX69aqoq6zzCYDr5uMZ4MgDAcOrBCxGhCR8bqJwtC0rSQrMCGu54+vTxoef/KDpypm66vJ/nA4u2og9pbw8mh6Nrp1vxiA6uoEAGDbdlEU5+OLJKkDr/vde++Px1MrCt+8O72zf9CwbLpcrCZz08SWbUpRWo4DAFNlFVBL2d7V+G0fdk2rrQpWrCagSjSEDjCiyDE0rlmyOdhKqrRcnSi5lWqBDCwlxxqsFkvaJRvDDd0YRV1VvPE83zWspqjqvNrwhtQB67TGjEimijIXq/rW3tbdrf0/+fGfXc/W1TXbu72hOKsYdzRJlswU8vT6S16TVr8/n8/BIm+7vmGZgese9u4lQZ4u5puDkZANNJCB4ZXkyyY3SyalNExKCJFMQgBKwTAyoARGI9bLqe2aQLCFeLtOE8aUIFJrxQoUChNhpCRfxGsOmOa1shBKJ3VdVQ01w24NgdUOQZw7jksNk0sluKybOk5T3/M4Y4zzq2S2ZmleL/7/XTBXme7SFNkAAAAASUVORK5CYII=\n",
       "text/plain": [
        "PILImage mode=RGB size=224x149"
       ]
@@ -59,10 +74,16 @@
    "cell_type": "code",
    "execution_count": 4,
    "id": "ae2bc6ac",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:00.520708Z",
+     "start_time": "2023-06-10T09:27:00.472584Z"
+    },
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
-    "#export\n",
+    "#|export\n",
     "learn = load_learner('model.pkl')"
    ]
   },
@@ -70,7 +91,12 @@
    "cell_type": "code",
    "execution_count": 5,
    "id": "6e0bf9da",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:01.075718Z",
+     "start_time": "2023-06-10T09:27:00.522607Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -84,6 +110,9 @@
        "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
        "        background-size: auto;\n",
        "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
        "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
        "        background: #F44336;\n",
        "    }\n",
@@ -110,14 +139,14 @@
      "data": {
       "text/plain": [
        "('basset_hound',\n",
-       " TensorBase(14),\n",
-       " TensorBase([1.8399e-06, 6.9743e-05, 1.2756e-05, 3.8912e-06, 5.5747e-07, 1.9816e-05,\n",
-       "         3.3903e-06, 1.2291e-05, 8.8069e-06, 3.3049e-07, 4.7154e-06, 1.0780e-06,\n",
-       "         5.2836e-04, 3.7090e-05, 9.8938e-01, 9.0365e-03, 8.2160e-05, 6.5614e-07,\n",
-       "         9.1344e-05, 2.7471e-06, 2.9094e-06, 8.0382e-07, 3.3430e-06, 3.9379e-05,\n",
-       "         5.8145e-06, 1.6686e-06, 1.6690e-07, 4.5227e-05, 7.4895e-07, 1.8120e-07,\n",
-       "         5.3700e-04, 1.2602e-05, 1.7675e-05, 1.2343e-05, 1.8821e-05, 5.7948e-07,\n",
-       "         5.5970e-07]))"
+       " tensor(14),\n",
+       " tensor([1.1052e-06, 6.3919e-05, 1.7038e-07, 6.9371e-07, 1.6348e-07, 1.2355e-05,\n",
+       "         6.1358e-07, 7.5065e-07, 3.3859e-07, 3.8036e-07, 9.1114e-07, 4.6855e-06,\n",
+       "         1.1798e-05, 2.7002e-05, 9.9844e-01, 1.1119e-03, 2.4641e-06, 3.0583e-08,\n",
+       "         1.8604e-05, 5.0454e-07, 2.7129e-06, 7.5549e-07, 3.4601e-06, 3.1499e-06,\n",
+       "         1.0551e-05, 1.9220e-06, 3.3227e-07, 1.1528e-06, 2.0841e-06, 3.1390e-06,\n",
+       "         2.6923e-04, 4.1458e-07, 4.6974e-06, 6.8495e-08, 8.1838e-07, 1.4024e-07,\n",
+       "         3.5885e-07]))"
       ]
      },
      "execution_count": 5,
@@ -133,10 +162,15 @@
    "cell_type": "code",
    "execution_count": 6,
    "id": "0419ed3a",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:01.079052Z",
+     "start_time": "2023-06-10T09:27:01.076809Z"
+    }
+   },
    "outputs": [],
    "source": [
-    "#export\n",
+    "#|export\n",
     "categories = learn.dls.vocab\n",
     "\n",
     "def classify_image(img):\n",
@@ -148,7 +182,12 @@
    "cell_type": "code",
    "execution_count": 7,
    "id": "762dec00",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:01.629211Z",
+     "start_time": "2023-06-10T09:27:01.080193Z"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -162,6 +201,9 @@
        "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
        "        background-size: auto;\n",
        "    }\n",
+       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+       "    }\n",
        "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
        "        background: #F44336;\n",
        "    }\n",
@@ -187,43 +229,43 @@
     {
      "data": {
       "text/plain": [
-       "{'Abyssinian': 1.8398773136141244e-06,\n",
-       " 'Bengal': 6.974329153308645e-05,\n",
-       " 'Birman': 1.275579688808648e-05,\n",
-       " 'Bombay': 3.8912407944735605e-06,\n",
-       " 'British_Shorthair': 5.574654551310232e-07,\n",
-       " 'Egyptian_Mau': 1.9816458006971516e-05,\n",
-       " 'Maine_Coon': 3.3902545055752853e-06,\n",
-       " 'Persian': 1.2291030543565284e-05,\n",
-       " 'Ragdoll': 8.806910955172498e-06,\n",
-       " 'Russian_Blue': 3.3048647196665115e-07,\n",
-       " 'Siamese': 4.715384420705959e-06,\n",
-       " 'Sphynx': 1.078016225619649e-06,\n",
-       " 'american_bulldog': 0.0005283569917082787,\n",
-       " 'american_pit_bull_terrier': 3.709043448907323e-05,\n",
-       " 'basset_hound': 0.9893821477890015,\n",
-       " 'beagle': 0.00903652049601078,\n",
-       " 'boxer': 8.215981506509706e-05,\n",
-       " 'chihuahua': 6.561360805790173e-07,\n",
-       " 'english_cocker_spaniel': 9.134367428487167e-05,\n",
-       " 'english_setter': 2.7471251087263227e-06,\n",
-       " 'german_shorthaired': 2.9093755529174814e-06,\n",
-       " 'great_pyrenees': 8.038204555305128e-07,\n",
-       " 'havanese': 3.3430071653128834e-06,\n",
-       " 'japanese_chin': 3.937914880225435e-05,\n",
-       " 'keeshond': 5.814476935483981e-06,\n",
-       " 'leonberger': 1.6686294657120015e-06,\n",
-       " 'miniature_pinscher': 1.669043996344044e-07,\n",
-       " 'newfoundland': 4.5227454393170774e-05,\n",
-       " 'pomeranian': 7.489487643397297e-07,\n",
-       " 'pug': 1.812025658409766e-07,\n",
-       " 'saint_bernard': 0.0005370019935071468,\n",
-       " 'samoyed': 1.2601573871506844e-05,\n",
-       " 'scottish_terrier': 1.767518369888421e-05,\n",
-       " 'shiba_inu': 1.2342902664386202e-05,\n",
-       " 'staffordshire_bull_terrier': 1.88211470231181e-05,\n",
-       " 'wheaten_terrier': 5.794777848677768e-07,\n",
-       " 'yorkshire_terrier': 5.596969572252419e-07}"
+       "{'Abyssinian': 1.1052484296669718e-06,\n",
+       " 'Bengal': 6.391949864337221e-05,\n",
+       " 'Birman': 1.703842826827895e-07,\n",
+       " 'Bombay': 6.937071930224192e-07,\n",
+       " 'British_Shorthair': 1.6347689779649954e-07,\n",
+       " 'Egyptian_Mau': 1.2354697901173495e-05,\n",
+       " 'Maine_Coon': 6.135849730526388e-07,\n",
+       " 'Persian': 7.506537826884596e-07,\n",
+       " 'Ragdoll': 3.385853801773919e-07,\n",
+       " 'Russian_Blue': 3.80361342422475e-07,\n",
+       " 'Siamese': 9.11137703951681e-07,\n",
+       " 'Sphynx': 4.685501608037157e-06,\n",
+       " 'american_bulldog': 1.1798315426858608e-05,\n",
+       " 'american_pit_bull_terrier': 2.7001642592949793e-05,\n",
+       " 'basset_hound': 0.9984366297721863,\n",
+       " 'beagle': 0.0011118714464828372,\n",
+       " 'boxer': 2.4641033178340876e-06,\n",
+       " 'chihuahua': 3.058348951867629e-08,\n",
+       " 'english_cocker_spaniel': 1.8603957869345322e-05,\n",
+       " 'english_setter': 5.045378657086985e-07,\n",
+       " 'german_shorthaired': 2.712880132094142e-06,\n",
+       " 'great_pyrenees': 7.554922376584727e-07,\n",
+       " 'havanese': 3.4600750495883403e-06,\n",
+       " 'japanese_chin': 3.1499175747740082e-06,\n",
+       " 'keeshond': 1.0550967999733984e-05,\n",
+       " 'leonberger': 1.9220292415411677e-06,\n",
+       " 'miniature_pinscher': 3.322655572901567e-07,\n",
+       " 'newfoundland': 1.15276839096623e-06,\n",
+       " 'pomeranian': 2.084072093566647e-06,\n",
+       " 'pug': 3.138956799375592e-06,\n",
+       " 'saint_bernard': 0.0002692256239242852,\n",
+       " 'samoyed': 4.1457849420112325e-07,\n",
+       " 'scottish_terrier': 4.697437816503225e-06,\n",
+       " 'shiba_inu': 6.849465705727198e-08,\n",
+       " 'staffordshire_bull_terrier': 8.183790214388864e-07,\n",
+       " 'wheaten_terrier': 1.4024345773577807e-07,\n",
+       " 'yorkshire_terrier': 3.5885142324332264e-07}"
       ]
      },
      "execution_count": 7,
@@ -239,10 +281,30 @@
    "cell_type": "code",
    "execution_count": 8,
    "id": "930cf172",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:01.633628Z",
+     "start_time": "2023-06-10T09:27:01.630064Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/prasanth.thangavel/.pyenv/versions/3.10.5/envs/fastai_related/lib/python3.10/site-packages/gradio/inputs.py:259: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+      "  warnings.warn(\n",
+      "/Users/prasanth.thangavel/.pyenv/versions/3.10.5/envs/fastai_related/lib/python3.10/site-packages/gradio/inputs.py:262: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+      "  super().__init__(\n",
+      "/Users/prasanth.thangavel/.pyenv/versions/3.10.5/envs/fastai_related/lib/python3.10/site-packages/gradio/outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+      "  warnings.warn(\n",
+      "/Users/prasanth.thangavel/.pyenv/versions/3.10.5/envs/fastai_related/lib/python3.10/site-packages/gradio/outputs.py:200: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
+      "  super().__init__(num_top_classes=num_top_classes, type=type, label=label)\n"
+     ]
+    }
+   ],
    "source": [
-    "#export\n",
+    "#|export\n",
     "image = gr.inputs.Image(shape=(192, 192))\n",
     "label = gr.outputs.Label()\n",
     "examples = ['basset.jpg']"
@@ -250,77 +312,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 25,
    "id": "4f463e23",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:33:35.129826Z",
+     "start_time": "2023-06-10T09:33:34.210481Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:3000/\n",
+      "Running on local URL:  http://127.0.0.1:7863\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
     },
     {
      "data": {
-      "text/plain": [
-       "(<fastapi.applications.FastAPI at 0x7f591050c970>,\n",
-       " 'http://127.0.0.1:3000/',\n",
-       " None)"
-      ]
+      "text/plain": []
      },
-     "execution_count": 9,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<style>\n",
-       "    /* Turns off some styling */\n",
-       "    progress {\n",
-       "        /* gets rid of default border in Firefox and Opera. */\n",
-       "        border: none;\n",
-       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
-       "        background-size: auto;\n",
-       "    }\n",
-       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
-       "        background: #F44336;\n",
-       "    }\n",
-       "</style>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "#export\n",
-    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "#|export\n",
+    "intf = gr.Interface(\n",
+    "    fn=classify_image, inputs=image, outputs=label, examples=examples,\n",
+    "    title=\"Dog Breed Classifier\",\n",
+    "    description=\"Classifier is fine-tuned on pre-trained resnet34 model\")\n",
     "intf.launch(inline=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "id": "82774c08",
    "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:03.544361Z",
+     "start_time": "2023-06-10T09:27:03.540346Z"
+    },
     "scrolled": true,
     "tags": []
    },
@@ -346,9 +382,11 @@
        "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (1): ConvNeXtBlock(\n",
@@ -358,9 +396,11 @@
        "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (2): ConvNeXtBlock(\n",
@@ -370,9 +410,11 @@
        "                (fc1): Linear(in_features=96, out_features=384, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=384, out_features=96, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "          )\n",
@@ -390,9 +432,11 @@
        "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (1): ConvNeXtBlock(\n",
@@ -402,9 +446,11 @@
        "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (2): ConvNeXtBlock(\n",
@@ -414,9 +460,11 @@
        "                (fc1): Linear(in_features=192, out_features=768, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=768, out_features=192, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "          )\n",
@@ -434,9 +482,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (1): ConvNeXtBlock(\n",
@@ -446,9 +496,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (2): ConvNeXtBlock(\n",
@@ -458,9 +510,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (3): ConvNeXtBlock(\n",
@@ -470,9 +524,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (4): ConvNeXtBlock(\n",
@@ -482,9 +538,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (5): ConvNeXtBlock(\n",
@@ -494,9 +552,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (6): ConvNeXtBlock(\n",
@@ -506,9 +566,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (7): ConvNeXtBlock(\n",
@@ -518,9 +580,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (8): ConvNeXtBlock(\n",
@@ -530,9 +594,11 @@
        "                (fc1): Linear(in_features=384, out_features=1536, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=1536, out_features=384, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "          )\n",
@@ -550,9 +616,11 @@
        "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (1): ConvNeXtBlock(\n",
@@ -562,9 +630,11 @@
        "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "            (2): ConvNeXtBlock(\n",
@@ -574,19 +644,22 @@
        "                (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
        "                (act): GELU()\n",
        "                (drop1): Dropout(p=0.0, inplace=False)\n",
+       "                (norm): Identity()\n",
        "                (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
        "                (drop2): Dropout(p=0.0, inplace=False)\n",
        "              )\n",
+       "              (shortcut): Identity()\n",
        "              (drop_path): Identity()\n",
        "            )\n",
        "          )\n",
        "        )\n",
        "      )\n",
        "      (norm_pre): Identity()\n",
-       "      (head): Sequential(\n",
+       "      (head): NormMlpClassifierHead(\n",
        "        (global_pool): SelectAdaptivePool2d (pool_type=avg, flatten=Identity())\n",
        "        (norm): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)\n",
        "        (flatten): Flatten(start_dim=1, end_dim=-1)\n",
+       "        (pre_logits): Identity()\n",
        "        (drop): Dropout(p=0.0, inplace=False)\n",
        "        (fc): Identity()\n",
        "      )\n",
@@ -597,7 +670,7 @@
        "      (ap): AdaptiveAvgPool2d(output_size=1)\n",
        "      (mp): AdaptiveMaxPool2d(output_size=1)\n",
        "    )\n",
-       "    (1): Flatten(full=False)\n",
+       "    (1): fastai.layers.Flatten(full=False)\n",
        "    (2): BatchNorm1d(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
        "    (3): Dropout(p=0.25, inplace=False)\n",
        "    (4): Linear(in_features=1536, out_features=512, bias=False)\n",
@@ -609,7 +682,7 @@
        ")"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -621,58 +694,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "id": "10d7900d",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:03.549262Z",
+     "start_time": "2023-06-10T09:27:03.545325Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[Parameter containing:\n",
-       " tensor([ 1.2545e+00,  1.9196e+00,  1.2201e+00,  1.0390e+00, -1.6480e-03,\n",
-       "          7.6568e-01,  8.8830e-01,  1.6302e+00,  7.0489e-01,  3.2909e+00,\n",
-       "          7.8756e-01, -1.2321e-03,  1.0008e+00, -1.1701e-03,  3.2963e+00,\n",
-       "          7.5332e-04,  1.9848e+00,  1.0214e+00,  4.4530e+00,  2.5485e-01,\n",
-       "          2.7261e+00,  9.2749e-01,  1.2365e+00,  4.6786e-03,  1.7861e+00,\n",
-       "          5.4500e-01,  4.6252e+00,  1.1814e-02, -8.0696e-04,  3.4503e+00,\n",
-       "          1.3520e+00,  4.1267e+00,  2.6889e+00,  4.1214e+00,  3.4020e+00,\n",
-       "          8.4680e-01,  7.3639e-01,  3.9801e+00,  1.2857e+00,  6.4153e-01,\n",
-       "          2.6896e+00,  1.1183e+00,  1.1701e+00,  5.5256e-01,  2.3371e+00,\n",
-       "          2.6110e-04,  9.7016e-01,  2.1527e-03,  1.1990e+00,  1.7883e+00,\n",
-       "          4.0231e-01,  4.4849e-01,  9.7238e-01,  3.9889e+00,  6.5864e-01,\n",
-       "          6.8973e-01,  9.8424e-01,  2.7063e+00,  1.2161e+00,  7.5966e-01,\n",
-       "          3.3019e+00,  1.6209e+00,  9.5479e-01,  2.1214e+00,  6.2982e-01,\n",
-       "          4.0345e+00,  8.9406e-01, -1.5776e-03,  4.0855e+00,  1.0646e+00,\n",
-       "          1.3953e+00,  1.6694e+00,  7.7575e-04,  7.6740e-01,  8.8671e-01,\n",
-       "          6.4291e-01,  1.3444e+00,  7.1629e-01,  5.4538e-01,  2.0897e+00,\n",
-       "          1.1951e+00,  3.0924e-01,  2.9660e-01,  1.4705e+00,  4.0818e+00,\n",
-       "         -1.9466e-03,  1.1466e+00,  3.8855e+00,  3.6003e+00,  4.8230e-01,\n",
-       "          2.1677e-01,  1.2715e-03,  6.5100e-01,  3.0062e+00,  3.0463e+00,\n",
-       "          6.9039e-03], requires_grad=True),\n",
+       " tensor([ 1.2548e+00,  1.9193e+00,  1.2196e+00,  1.0408e+00, -8.3188e-04,\n",
+       "          7.6621e-01,  8.8759e-01,  1.6324e+00,  7.0546e-01,  3.2889e+00,\n",
+       "          7.8554e-01, -6.1709e-04,  9.9950e-01, -2.2638e-03,  3.2981e+00,\n",
+       "         -5.7606e-04,  1.9837e+00,  1.0217e+00,  4.4522e+00,  2.5687e-01,\n",
+       "          2.7243e+00,  9.2496e-01,  1.2363e+00,  4.0675e-03,  1.7874e+00,\n",
+       "          5.4314e-01,  4.6267e+00,  1.1971e-02, -1.2741e-03,  3.4504e+00,\n",
+       "          1.3509e+00,  4.1251e+00,  2.6871e+00,  4.1206e+00,  3.3997e+00,\n",
+       "          8.4934e-01,  7.3714e-01,  3.9786e+00,  1.2864e+00,  6.4068e-01,\n",
+       "          2.6898e+00,  1.1186e+00,  1.1696e+00,  5.5259e-01,  2.3346e+00,\n",
+       "          1.6150e-03,  9.6813e-01,  4.2501e-04,  1.1970e+00,  1.7889e+00,\n",
+       "          4.0116e-01,  4.5047e-01,  9.7276e-01,  3.9896e+00,  6.5790e-01,\n",
+       "          6.8785e-01,  9.8607e-01,  2.7069e+00,  1.2161e+00,  7.6307e-01,\n",
+       "          3.3012e+00,  1.6203e+00,  9.5506e-01,  2.1210e+00,  6.2877e-01,\n",
+       "          4.0325e+00,  8.9295e-01, -3.2978e-03,  4.0861e+00,  1.0655e+00,\n",
+       "          1.3974e+00,  1.6681e+00, -3.4348e-04,  7.6648e-01,  8.8494e-01,\n",
+       "          6.4293e-01,  1.3438e+00,  7.1627e-01,  5.4806e-01,  2.0902e+00,\n",
+       "          1.1960e+00,  3.0883e-01,  2.9709e-01,  1.4708e+00,  4.0850e+00,\n",
+       "         -7.8778e-04,  1.1464e+00,  3.8854e+00,  3.5994e+00,  4.8365e-01,\n",
+       "          2.1725e-01,  4.5227e-04,  6.4782e-01,  3.0069e+00,  3.0469e+00,\n",
+       "          4.3883e-03], requires_grad=True),\n",
        " Parameter containing:\n",
-       " tensor([-9.7076e-02, -4.1602e-02,  4.1634e+00, -1.0902e-02,  2.5195e-03,\n",
-       "         -2.6698e-02, -3.1112e-02, -8.0897e-02, -1.3977e-01, -6.1426e-02,\n",
-       "          3.2092e-01, -3.3970e-01, -5.7320e-02, -5.0723e-03, -4.5248e-02,\n",
-       "         -2.6788e-02, -4.0929e-02, -3.8162e-02,  8.6140e-03, -2.3497e-02,\n",
-       "          9.3659e-03, -1.6219e-01, -4.0165e+00,  5.3178e-01, -5.3446e-01,\n",
-       "          2.8025e+00,  3.7767e-02, -8.2848e-03, -1.0448e-03, -1.1741e-01,\n",
-       "         -1.3899e-01,  1.9646e-02, -9.6837e-02, -1.3024e-01, -1.9224e-01,\n",
-       "         -6.6514e-02, -3.5839e-02, -1.2878e-01,  1.5046e-01,  7.7289e-04,\n",
-       "         -6.4686e-02,  5.7553e-02, -9.2985e-02, -1.1460e+00, -5.4285e-02,\n",
-       "         -5.4245e-03, -1.8200e-01,  2.2406e-02,  3.9471e-02, -5.9339e-02,\n",
-       "         -4.1768e-02, -5.6190e-02, -4.3428e-02, -1.2965e-02, -1.1264e-01,\n",
-       "          4.9255e-03, -3.7258e-02, -1.5767e-01, -9.7796e-02, -1.8840e-01,\n",
-       "         -1.1216e-01, -1.8182e-01, -3.2907e-02, -2.8298e-02,  1.4188e+00,\n",
-       "         -3.3801e-02, -4.1863e-02, -2.6832e-01, -4.7449e-02, -4.5676e-05,\n",
-       "          2.6773e-01,  1.8772e-01,  6.9789e-01, -3.0746e-01,  8.3766e-02,\n",
-       "         -1.0845e+00,  1.5364e-02, -4.4824e-02, -7.7240e-02, -6.7920e-02,\n",
-       "         -1.3150e-01, -1.6358e-02, -1.7351e-02, -3.9939e-02, -7.2383e-02,\n",
-       "          1.0359e-02, -5.8904e-02, -3.8567e-02, -7.9783e-02, -7.3791e-02,\n",
-       "         -1.0318e-02, -3.7690e-01, -9.9740e-03, -2.7374e-02, -6.3630e-02,\n",
-       "          1.5712e-03], requires_grad=True)]"
+       " tensor([-9.6988e-02, -4.0686e-02,  4.1637e+00, -1.1528e-02,  4.3803e-03,\n",
+       "         -2.5941e-02, -3.0891e-02, -8.0677e-02, -1.4107e-01, -6.1215e-02,\n",
+       "          3.2222e-01, -3.3768e-01, -5.7922e-02, -4.1583e-03, -4.7281e-02,\n",
+       "         -2.6027e-02, -4.1479e-02, -3.8102e-02,  9.5216e-03, -2.3337e-02,\n",
+       "          9.8850e-03, -1.6398e-01, -4.0166e+00,  5.3007e-01, -5.3390e-01,\n",
+       "          2.8043e+00,  3.6653e-02, -8.9412e-03, -2.2900e-03, -1.1790e-01,\n",
+       "         -1.4030e-01,  1.9541e-02, -9.6028e-02, -1.3067e-01, -1.9375e-01,\n",
+       "         -6.7558e-02, -3.6966e-02, -1.2916e-01,  1.5244e-01,  1.2842e-03,\n",
+       "         -6.4242e-02,  5.7126e-02, -9.2993e-02, -1.1459e+00, -5.3462e-02,\n",
+       "         -4.4450e-03, -1.8404e-01,  2.3861e-02,  3.9773e-02, -6.0168e-02,\n",
+       "         -4.0611e-02, -5.7472e-02, -4.3456e-02, -1.2489e-02, -1.1251e-01,\n",
+       "          4.6585e-03, -3.8195e-02, -1.5746e-01, -9.8000e-02, -1.8399e-01,\n",
+       "         -1.1046e-01, -1.8166e-01, -3.2026e-02, -2.6843e-02,  1.4199e+00,\n",
+       "         -3.4307e-02, -4.2792e-02, -2.7001e-01, -4.7914e-02, -7.3771e-04,\n",
+       "          2.6696e-01,  1.8685e-01,  6.9680e-01, -3.0849e-01,  8.2255e-02,\n",
+       "         -1.0847e+00,  1.6044e-02, -4.3925e-02, -7.9718e-02, -6.6874e-02,\n",
+       "         -1.3106e-01, -1.7186e-02, -1.8771e-02, -3.9757e-02, -7.1355e-02,\n",
+       "          1.2518e-02, -5.8448e-02, -3.7163e-02, -7.9703e-02, -7.2437e-02,\n",
+       "         -9.9623e-03, -3.7771e-01, -1.1318e-02, -2.7118e-02, -6.3289e-02,\n",
+       "         -8.9539e-04], requires_grad=True)]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -684,80 +762,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 12,
    "id": "008537b0",
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:27:03.555385Z",
+     "start_time": "2023-06-10T09:27:03.550194Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
        "[Parameter containing:\n",
-       " tensor([[ 2.2773e-02, -1.6051e-03,  4.0450e-02,  ...,  1.7370e-03,\n",
-       "          -4.5070e-02,  8.0949e-03],\n",
-       "         [-1.4383e-01,  1.6965e-02,  2.5983e-02,  ...,  1.2606e-02,\n",
-       "          -1.0443e-01,  5.6370e-02],\n",
-       "         [-6.5471e-02, -3.2719e-02,  5.6796e-03,  ..., -4.1571e-02,\n",
-       "           6.5921e-02, -4.0347e-02],\n",
+       " tensor([[ 0.0229, -0.0015,  0.0404,  ...,  0.0017, -0.0453,  0.0080],\n",
+       "         [-0.1439,  0.0169,  0.0260,  ...,  0.0125, -0.1045,  0.0563],\n",
+       "         [-0.0653, -0.0328,  0.0059,  ..., -0.0417,  0.0659, -0.0403],\n",
        "         ...,\n",
-       "         [-8.8080e-03,  6.9815e-02,  7.1424e-05,  ...,  4.0177e-03,\n",
-       "           4.1478e-02, -1.9052e-02],\n",
-       "         [ 2.0792e-03,  3.2267e-02,  2.9801e-02,  ..., -2.9897e-02,\n",
-       "          -3.0278e-02,  5.5432e-02],\n",
-       "         [ 1.2097e-01, -3.5444e-02, -4.6078e-03,  ..., -6.3829e-03,\n",
-       "           2.3691e-02, -1.1242e-02]], requires_grad=True),\n",
+       "         [-0.0090,  0.0698,  0.0001,  ...,  0.0042,  0.0416, -0.0191],\n",
+       "         [ 0.0020,  0.0321,  0.0298,  ..., -0.0299, -0.0304,  0.0555],\n",
+       "         [ 0.1210, -0.0355, -0.0043,  ..., -0.0062,  0.0238, -0.0114]],\n",
+       "        requires_grad=True),\n",
        " Parameter containing:\n",
-       " tensor([-0.4047, -0.7418, -0.4235, -0.1650, -0.3028, -0.1898, -0.5534, -0.6271,\n",
-       "         -0.3008, -0.4254, -0.5997, -0.4107, -0.2172, -1.7935, -0.3170, -0.1163,\n",
-       "         -0.4482, -0.2846, -0.4342, -0.4945, -0.4065, -1.1402, -0.6754, -1.7237,\n",
-       "         -0.2955, -0.2654, -0.2187, -0.3914, -0.4150, -0.4772,  0.2365, -0.7542,\n",
-       "         -0.5852, -0.1820, -1.5272, -0.3626, -2.4689, -2.3461, -0.6109, -0.4115,\n",
-       "         -0.6964, -0.5764, -0.5878, -0.0318, -2.0354, -0.2859, -0.3953, -0.8402,\n",
-       "         -2.2398, -1.0876, -0.2295, -0.9004, -0.7584, -0.8833, -0.3755, -0.4549,\n",
-       "         -0.3835, -0.4047, -2.0231, -1.0263, -0.4106, -1.1564, -0.2224, -0.4250,\n",
-       "         -0.2494, -0.4222, -0.0975, -1.4017, -0.6887, -0.4370, -0.2932, -0.4641,\n",
-       "         -0.4958, -1.2534, -1.0720, -1.2966, -0.6276, -1.4161, -2.3080, -2.4538,\n",
-       "         -0.4259, -0.9987, -0.4638, -0.3147, -0.2416, -0.8744, -0.2829, -1.4208,\n",
-       "         -0.3257, -0.3202, -0.0602, -0.1896, -0.2497, -0.6129, -0.2976, -2.1465,\n",
-       "         -0.4128, -0.3675, -1.9815, -0.3815, -0.3785, -0.2292, -0.3700, -0.3256,\n",
-       "         -0.5584, -2.4192, -0.4590, -1.7748, -0.3996, -0.4092, -0.3518, -0.5332,\n",
-       "         -1.6534, -1.8191,  0.6263, -0.4058,  0.5872, -2.2074, -0.2438, -2.4540,\n",
-       "         -0.2283, -0.6865,  0.6988,  0.6477, -0.6445, -0.3454, -0.3275, -0.5701,\n",
-       "         -0.5173, -0.2774, -0.4090, -0.3018, -0.4874, -0.4954, -0.4073, -0.4356,\n",
-       "         -0.5103, -0.4128, -2.0919, -0.2825, -0.5830, -1.5834,  0.6139, -0.8506,\n",
-       "         -0.4669, -2.1358, -0.3417, -0.3766, -0.3345, -0.3961, -0.3886, -0.5668,\n",
-       "         -0.2224, -1.3059, -0.4601, -0.3928, -0.4665, -0.4214, -0.4755, -0.2865,\n",
-       "         -1.5804, -0.1787, -0.4368, -0.3173,  1.5732, -0.4046, -0.4839, -0.2576,\n",
-       "         -0.5611, -0.4265, -0.2578, -0.3176, -0.4620, -1.9553, -1.9146, -0.3961,\n",
-       "          0.3988, -2.3520, -0.9689, -0.2831, -1.9000, -0.4180,  0.0160, -1.1111,\n",
-       "         -0.4924, -0.3177, -1.8912, -0.3101, -0.8137, -2.3345, -0.3843, -0.3847,\n",
-       "         -0.1974, -0.4444, -1.6233, -2.5485, -0.3178, -1.2715, -1.1479,  0.6149,\n",
-       "         -0.3749, -0.3952, -2.0747, -0.4657, -0.3782, -0.4958, -0.3281, -1.9219,\n",
-       "         -2.0018, -0.5307, -0.2555, -1.1161, -0.3516, -2.2185, -1.1394,  0.5366,\n",
-       "         -0.3218, -2.0387, -0.4656,  0.1850, -0.5830, -0.3129,  0.6182, -0.2124,\n",
-       "         -2.3538, -0.9700, -0.9784, -0.3668, -0.4503, -1.9564, -0.2662, -1.1754,\n",
-       "         -0.4200, -0.9024, -0.3604, -0.5172, -1.1882, -0.4191, -0.4770, -1.5558,\n",
-       "         -0.4011, -0.6518, -0.4817, -0.2422,  0.6909, -0.5080, -0.4303, -0.6068,\n",
-       "         -0.4001, -0.3329, -0.3596, -1.6108, -0.2371, -0.2467, -0.4545,  0.1808,\n",
-       "         -0.3225, -0.3918, -0.3514, -0.3756, -1.2178, -0.4000, -0.3578, -0.2883,\n",
-       "         -1.7485, -0.2364, -0.1599, -0.2640, -0.9769, -1.3066, -0.4148, -0.2663,\n",
-       "         -0.3933, -0.4628, -0.2174,  0.2141, -0.5733, -0.2766, -0.3658, -0.5171,\n",
-       "         -0.3484, -0.3365, -0.6445,  0.6866, -0.3738, -0.2902, -2.0863, -0.4882,\n",
-       "         -0.2597, -1.0497, -1.6616, -0.3399, -0.5111, -0.5661, -0.3029, -0.5048,\n",
-       "         -0.2877, -0.2841, -0.1981, -0.6910, -0.2872, -2.1120, -0.8928, -0.2299,\n",
-       "         -1.5010, -0.4734, -2.2293, -0.4020, -0.2925, -0.4198,  0.6646, -0.3047,\n",
-       "         -0.1687, -0.3750, -0.6434, -2.3348, -0.3102, -1.2732, -0.8192, -1.0592,\n",
-       "         -0.0931, -1.6385,  0.3426, -0.8484, -0.4910, -0.5002, -1.0631, -0.3532,\n",
-       "         -1.1562, -0.3843, -0.3172, -0.6432, -0.9083, -0.6567, -0.6489,  0.6336,\n",
-       "         -0.2663, -1.3203, -1.1623, -1.2032, -2.0576, -0.3001, -1.3597, -0.4614,\n",
-       "         -0.5024, -0.4949, -0.3158, -0.3273, -0.2668, -0.4280, -0.3296, -0.3011,\n",
-       "         -1.6635,  0.6434, -0.9455,  0.6097, -0.4234,  0.3918, -0.4943, -0.4285,\n",
-       "         -0.2588, -0.4951, -2.1992, -0.2601, -0.3935, -0.4564, -0.5817, -0.3487,\n",
-       "         -0.7372, -0.3589, -0.4894, -2.0108,  0.4556, -0.8057, -1.7749, -0.3511,\n",
-       "         -0.5359, -0.2100, -0.3956, -0.4780, -1.1457, -0.3976, -2.2114, -0.2840],\n",
+       " tensor([-0.4048, -0.7419, -0.4235, -0.1651, -0.3026, -0.1900, -0.5534, -0.6271,\n",
+       "         -0.3007, -0.4253, -0.5995, -0.4106, -0.2174, -1.7934, -0.3171, -0.1165,\n",
+       "         -0.4483, -0.2848, -0.4343, -0.4946, -0.4065, -1.1402, -0.6755, -1.7238,\n",
+       "         -0.2954, -0.2655, -0.2188, -0.3912, -0.4149, -0.4770,  0.2364, -0.7542,\n",
+       "         -0.5851, -0.1820, -1.5275, -0.3625, -2.4690, -2.3462, -0.6109, -0.4114,\n",
+       "         -0.6963, -0.5763, -0.5876, -0.0319, -2.0354, -0.2857, -0.3954, -0.8403,\n",
+       "         -2.2397, -1.0875, -0.2296, -0.9002, -0.7585, -0.8833, -0.3754, -0.4549,\n",
+       "         -0.3833, -0.4048, -2.0232, -1.0263, -0.4105, -1.1564, -0.2225, -0.4250,\n",
+       "         -0.2496, -0.4224, -0.0976, -1.4018, -0.6886, -0.4369, -0.2932, -0.4642,\n",
+       "         -0.4958, -1.2535, -1.0720, -1.2967, -0.6278, -1.4160, -2.3080, -2.4540,\n",
+       "         -0.4259, -0.9988, -0.4636, -0.3148, -0.2416, -0.8743, -0.2829, -1.4208,\n",
+       "         -0.3258, -0.3202, -0.0602, -0.1894, -0.2497, -0.6128, -0.2975, -2.1467,\n",
+       "         -0.4129, -0.3676, -1.9814, -0.3813, -0.3784, -0.2293, -0.3699, -0.3254,\n",
+       "         -0.5585, -2.4192, -0.4591, -1.7747, -0.3995, -0.4092, -0.3517, -0.5333,\n",
+       "         -1.6534, -1.8191,  0.6263, -0.4061,  0.5872, -2.2076, -0.2438, -2.4540,\n",
+       "         -0.2283, -0.6866,  0.6989,  0.6476, -0.6445, -0.3453, -0.3278, -0.5701,\n",
+       "         -0.5173, -0.2775, -0.4089, -0.3019, -0.4871, -0.4952, -0.4073, -0.4355,\n",
+       "         -0.5103, -0.4129, -2.0918, -0.2825, -0.5831, -1.5834,  0.6138, -0.8505,\n",
+       "         -0.4669, -2.1359, -0.3416, -0.3768, -0.3344, -0.3959, -0.3885, -0.5667,\n",
+       "         -0.2225, -1.3061, -0.4600, -0.3926, -0.4666, -0.4214, -0.4754, -0.2866,\n",
+       "         -1.5803, -0.1787, -0.4367, -0.3172,  1.5733, -0.4046, -0.4838, -0.2577,\n",
+       "         -0.5612, -0.4264, -0.2578, -0.3175, -0.4619, -1.9553, -1.9146, -0.3959,\n",
+       "          0.3987, -2.3519, -0.9689, -0.2831, -1.9002, -0.4181,  0.0159, -1.1108,\n",
+       "         -0.4920, -0.3177, -1.8909, -0.3100, -0.8138, -2.3347, -0.3845, -0.3848,\n",
+       "         -0.1974, -0.4444, -1.6234, -2.5484, -0.3177, -1.2714, -1.1481,  0.6149,\n",
+       "         -0.3750, -0.3950, -2.0748, -0.4655, -0.3781, -0.4957, -0.3281, -1.9218,\n",
+       "         -2.0020, -0.5307, -0.2555, -1.1159, -0.3517, -2.2185, -1.1395,  0.5364,\n",
+       "         -0.3218, -2.0389, -0.4656,  0.1849, -0.5829, -0.3129,  0.6183, -0.2124,\n",
+       "         -2.3539, -0.9700, -0.9784, -0.3667, -0.4503, -1.9564, -0.2662, -1.1754,\n",
+       "         -0.4199, -0.9023, -0.3606, -0.5171, -1.1881, -0.4190, -0.4772, -1.5560,\n",
+       "         -0.4010, -0.6518, -0.4817, -0.2423,  0.6909, -0.5080, -0.4302, -0.6067,\n",
+       "         -0.4000, -0.3329, -0.3594, -1.6110, -0.2370, -0.2468, -0.4545,  0.1805,\n",
+       "         -0.3227, -0.3918, -0.3514, -0.3756, -1.2178, -0.4000, -0.3579, -0.2881,\n",
+       "         -1.7484, -0.2363, -0.1599, -0.2641, -0.9768, -1.3065, -0.4147, -0.2663,\n",
+       "         -0.3931, -0.4627, -0.2174,  0.2142, -0.5734, -0.2767, -0.3659, -0.5172,\n",
+       "         -0.3486, -0.3362, -0.6444,  0.6866, -0.3737, -0.2904, -2.0864, -0.4883,\n",
+       "         -0.2598, -1.0495, -1.6617, -0.3397, -0.5112, -0.5660, -0.3026, -0.5047,\n",
+       "         -0.2878, -0.2840, -0.1983, -0.6910, -0.2873, -2.1123, -0.8926, -0.2302,\n",
+       "         -1.5013, -0.4735, -2.2294, -0.4021, -0.2926, -0.4201,  0.6647, -0.3047,\n",
+       "         -0.1688, -0.3748, -0.6432, -2.3347, -0.3101, -1.2732, -0.8192, -1.0592,\n",
+       "         -0.0935, -1.6388,  0.3426, -0.8484, -0.4910, -0.5002, -1.0630, -0.3532,\n",
+       "         -1.1560, -0.3843, -0.3173, -0.6433, -0.9083, -0.6567, -0.6489,  0.6335,\n",
+       "         -0.2663, -1.3205, -1.1623, -1.2032, -2.0579, -0.3002, -1.3595, -0.4614,\n",
+       "         -0.5023, -0.4949, -0.3156, -0.3272, -0.2668, -0.4279, -0.3296, -0.3011,\n",
+       "         -1.6636,  0.6435, -0.9454,  0.6099, -0.4234,  0.3917, -0.4945, -0.4284,\n",
+       "         -0.2587, -0.4952, -2.1992, -0.2601, -0.3934, -0.4566, -0.5818, -0.3488,\n",
+       "         -0.7372, -0.3589, -0.4896, -2.0106,  0.4556, -0.8056, -1.7750, -0.3512,\n",
+       "         -0.5357, -0.2100, -0.3955, -0.4781, -1.1455, -0.3974, -2.2115, -0.2838],\n",
        "        requires_grad=True)]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -767,10 +845,160 @@
     "list(l.parameters())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f5b680dd",
+   "metadata": {},
+   "source": [
+    "# Export to .py file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "3dcb61fd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:33:40.646224Z",
+     "start_time": "2023-06-10T09:33:40.619958Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Export successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nbdev\n",
+    "nbdev.export.nb_export('app.ipynb', './')\n",
+    "print('Export successful')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d4b0f6ca",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:33:41.112545Z",
+     "start_time": "2023-06-10T09:33:40.945206Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 231664\r\n",
+      "drwxr-xr-x  17 prasanth.thangavel  staff        544 Jun 10 17:33 \u001b[34m.\u001b[m\u001b[m\r\n",
+      "drwxr-xr-x   7 prasanth.thangavel  staff        224 Jun  6 08:28 \u001b[34m..\u001b[m\u001b[m\r\n",
+      "-rw-r--r--@  1 prasanth.thangavel  staff       6148 Jun 10 17:29 .DS_Store\r\n",
+      "drwxr-xr-x  15 prasanth.thangavel  staff        480 Jun 10 17:18 \u001b[34m.git\u001b[m\u001b[m\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff       1257 Jun  6 08:28 .gitattributes\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff         30 Jun  6 08:28 .gitignore\r\n",
+      "drwxr-xr-x   4 prasanth.thangavel  staff        128 Jun 10 17:22 \u001b[34m.ipynb_checkpoints\u001b[m\u001b[m\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff        273 Jun  6 09:38 README.md\r\n",
+      "drwxr-xr-x   3 prasanth.thangavel  staff         96 Jun 10 17:29 \u001b[34m__pycache__\u001b[m\u001b[m\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff     141084 Jun 10 17:33 app.ipynb\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff        813 Jun 10 17:33 app.py\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff      74775 Jun  6 08:28 basset.jpg\r\n",
+      "-rw-r--r--@  1 prasanth.thangavel  staff     483187 Jun 10 17:22 fastai-prd-apps-pets-training.ipynb\r\n",
+      "drwxr-xr-x   2 prasanth.thangavel  staff         64 Jun 10 17:22 \u001b[34mflagged\u001b[m\u001b[m\r\n",
+      "-rw-r--r--@  1 prasanth.thangavel  staff  114793185 Jun 10 17:13 model.pkl\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff         55 Jun  6 09:53 requirements.txt\r\n",
+      "-rw-r--r--   1 prasanth.thangavel  staff       3783 Jun 10 17:31 requirements_all.txt\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls -a -l"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b83b2703",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:33:41.527411Z",
+     "start_time": "2023-06-10T09:33:41.397490Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.\r\n",
+      "\r\n",
+      "# %% auto 0\r\n",
+      "__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']\r\n",
+      "\r\n",
+      "# %% app.ipynb 2\r\n",
+      "from fastai.vision.all import *\r\n",
+      "import gradio as gr\r\n",
+      "import timm\r\n",
+      "\r\n",
+      "# %% app.ipynb 4\r\n",
+      "learn = load_learner('model.pkl')\r\n",
+      "\r\n",
+      "# %% app.ipynb 6\r\n",
+      "categories = learn.dls.vocab\r\n",
+      "\r\n",
+      "def classify_image(img):\r\n",
+      "    pred,idx,probs = learn.predict(img)\r\n",
+      "    return dict(zip(categories, map(float,probs)))\r\n",
+      "\r\n",
+      "# %% app.ipynb 8\r\n",
+      "image = gr.inputs.Image(shape=(192, 192))\r\n",
+      "label = gr.outputs.Label()\r\n",
+      "examples = ['basset.jpg']\r\n",
+      "\r\n",
+      "# %% app.ipynb 9\r\n",
+      "intf = gr.Interface(\r\n",
+      "    fn=classify_image, inputs=image, outputs=label, examples=examples,\r\n",
+      "    title=\"Dog Breed Classifier\",\r\n",
+      "    description=\"Classifier is fine-tuned on pre-trained resnet34 model\")\r\n",
+      "intf.launch(inline=False)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cat app.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dafd4644",
+   "metadata": {},
+   "source": [
+    "# Pip requirements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ec1e403c",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-06-10T09:31:38.907095Z",
+     "start_time": "2023-06-10T09:31:38.091291Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!pip freeze > requirements_all.txt"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ff9d5c73",
+   "id": "4e0d6255",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -778,9 +1006,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "fastai_related",
    "language": "python",
-   "name": "python3"
+   "name": "fastai_related"
   },
   "language_info": {
    "codemirror_mode": {
@@ -792,7 +1020,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.11"
+   "version": "3.10.5"
   },
   "toc": {
    "base_numbering": 1,
@@ -803,9 +1031,14 @@
    "title_cell": "Table of Contents",
    "title_sidebar": "Contents",
    "toc_cell": false,
-   "toc_position": {},
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
    "toc_section_display": true,
-   "toc_window_display": false
+   "toc_window_display": true
   }
  },
  "nbformat": 4,