{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "\n",
    "def is_cat(x): return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p3YP7kHvJCQybpVLhBMXM2DEhzie1o5iVHATTfHR6hE7fuvl1MhcKJgekcerH1DIW+fGLTzNcFXvjmSuvTIrlZ58Z3Tu6cHJyf2cvyNDG3IkkBIs9z0v3zLVxoAdxeOisYh7ldA4AaoZmpmpmAgoABKCgAGhgBkZAxAhICIgARohADGiMiIgKoipgSohmpACIaj8M+0f/A4hGBIigYGqgplkVkByzmiSzjNCrBTACJIZRwQ455wwIJWFgAPIx5xhzFVxJ6hgNMakZmBpOPDuTykHhEYiHAQsH7CgLMsMo+MAYJYFBwbg1LklSisk5y0YHi1x3sjUdpF+71Lc9ENF0PALEYegANGXLOcWUm7ZHsT7Jg9NzXzjL4Lwvr1178myxHIZISG23euKJ6//03/zL8+6+kJIhEUpWLgvJfennn3jmR7f9nz82Xz+2V756J54szBUOEWKfiADZiQozilqXLPdmAASIhpINRKK4k7MWcfMkiY1MJKOaAZFlwmDQG1zff37ZrO7ev/34/gsfef6lk/N709H0L17/cnAm4rKgRwXVoU8Hi+Vv/+l39ycT49hEeenZz82ryzf2m8u7V1+7+ce3bn/r8a3pnUVrAt471dSrFKX0PQEkQMXgg4AhmIa27wU0DTH4MqpT3N4bH9Tu1rqvdx87uHuW7t/jwLpVNIcah74LYYyxM+iQPXvggndrHbk7J8VPTMvrR92i6c4ASU1JzRmIaQbIYAoAAGIKiACEQB5JSA2AAQ3QHuVRQEAgAH0UhoqgYKY/TKAAm+8BAAMFIGAjMkkIQAaKkE3NwEwJEdQQEBFAzDMxqpEyESAmshhzFK2YsxmziSkoVR48eyEOkFRwVLiq8CnbvHJ1yZ6dERFgXXhEOF0uAejC7g5bXq9WnnR3Olln18WhKv29s84b7cyn5axMiprz4elKGUtfBHK+mAbitu88Y8zaxiyGyOS++vK/aIb17uh5VTeapD/5xm+su8XR8v3j1Smx25QmIEImQAxcjMoQhz63qaS8N3bLNUqUjFiMir5PZsqOzMwxm5maIRkCEhGorbrIkjkQGYqAJlWFog6BLKsRQgkYVQpfXJxd/4XPf/Zf/dY/qsPO89d+zK5wFx98+7U/rtzF8/bI+VxYZZbL0iWAm/e7bj9xdWGrvlS7q1vjK9ORPnH5M213enT/2z/+kY9+6QevHy7aqvRqzIRI/XkcGFFVJrPSe+k6NDNiYAQVbPqhGu+G4smuOwt8Fgr8wbsHk8L8+ul+fc712dbs+SFjlNhnEFXIGTEln987GW0Xgu5ssYyny4dJERBMwQuYqKqJmhgqAAIYGMKjIAQDRGJCRjJDFVUzA5NNa6AqsukOHoWqASB+WOkRzAx/WOYB1MAAgEjNNgnbEBDQozEBE3lkIhSVyvmcJZqCQOFcIGQiRmNEUwCQwrlgkA1MkRw55wiMTSu2nFIXc1UEyHndNg6xKj27EIc+x1hXtWfZmfBlpDcPEVLqLQyIyFqSOq4iQVmXRoRqRSDnACQOKQIFYvAutOtuVKL78ze+uFzI7vjWxf3Hvv7q733rna/1QyYEHwJ6Sz1gBvBgYD6Epju/9eCNp6/+0iBvLxdv5OQQD820j+CyEpGIalZToABZDcyQUMQMDBmJQQYtKpdVVRWRkAk8oUlOcQx+4vhuTB+58fFnrnwK8uiXf+q/fPOdlwuuynJ69/C7H3/yl3/s47/2g5tfeeXW754vmkFTNXKjsrgwLgTyujn59Au/mgcY1zMTIJbbD17m8Njulb+G79xzRYuMmlAlxZgVrPIUM6UkPhAiIbEYAjpPVkwmu9OnmXBS/WQ73KpnNy/zlcOby+P2CIKv3NbVnWedG7/74Ntdd2yABDqbZ83uzsOtN9o7e9t3FbDtzhVIQEnNCZpCNstqogZECmCIBAYGSGibWENCRDXLoGYKgPrhh9kmUHUTnkj4w+YW/tOQBUMDQky6qfUWkBHVUFWU0YiBQBlQDMxMAZKaGZmZC1h516Q8qBSemCw4B8Ztymrg2LKYDVkdDlHEtM+WCQHVI/jgNcmqiaEwU+tj3JrVY3CHp6uP39h7MfD9syZKHiycN0MRNGlU7epAYjiejBeLs2bIgTgl63Mc165Lqc1WKrshGztapYM/+sa/MjyKXWZm55kZk6RNAkUlMohZMuU33vsuZL20d62abbfnr6hYIgSAHAVAAGHT4IMDi0YEAIZmKkLs0IgR1FDVyMgHX5R+6AYRfe76F8YwPW8PL5SLjz/9S7WbkcjV7aeXe03TrtWLZP+jH/+1/Z1d5Ze+9eZvtrELFTGjI3iwWC8XeWsX33r/2597/q/kPu1N37buuynZ/+Sv/F8BU0p5iKkZsppNRhZ7EwHNZmZDPzAUYsKWGaHteme+CN298z+7sf8ZcZOjeP8j18dP7Fx4o7PjRbL6Ru0urFOzVdaTeud0/R4ocaUiKsv6scvFunrmnYdv+qJQzagEoISAhmKSNGdVMwQ1QzCzDEgIbLiJO1VNmxFX9MMnKWD6KCfih1kTAQwNAAAJwT6MTofAYGKgagxsKg4MwNSMzQzQIRUEAJBE0bD2DhFVAclGnslMTUber6N5coGdSWpjKkNgpmEYHCEANoMkhSjEIJNAmgUCV1XJJa6bdj6qm65jLBCoiTmZ3TxqL2wVjDAttB67k5XPwEPbKLhAkPshDhY8O4PZZDyulX0xJBmG89JJCB79hMicqcU212PLg6sKF01cHYC0XacQTYmx8rFtAc05moTRJIxH4/LOgzuDGXvqVxkAmDejqSIClyQZEFFFQAAIyTGQEWFVF6ZigKaQYrJeru09/Us//ffy6sTg1cPTH+zNf2Iy/uTQN223rqumqh4iXIt67erla3dOv/+bf/KPF+s7lSu7bEwMrKuFoIPdbffY9nPX9z6/P3OXpv960R3v7f6qH/3PDk7Okrbf+MHvf/3V3w8BRhU2nRoQklZMFfuerGS6MK7b2DdZQAOJFmRVOZnscAprULi6VcpxuTp1Fy9tDVJ3EU76I3C9ySn3zpw0A2/rhSs7TxZjeO32m4u4VKPNuFIQFQopxlU/rGKWzdhNj4aiQM4Ri6mYqpmagSioAqIhGtimuG96LbUNrmRqAIBmhogAmwdsW5NipywNUVVNNaokzWDgHLMKGiazImBBrhkGRqwcZNVsyIST4AggZTHQmK3wXFdF0/Wq4L3Lmjxi4Tgma7IlBEpuFCR4E1HPPBuHoqzOl2tTavq2LorxqELLo8J3MV6/MPXs7z14WNflqlcqRkCFY5G4RixOzhZE6BzUhR+yc0w5pxgTALL3DoGIyEDJkTFWpdsq3ck6tee9G9Oo9j/y7DVi/fpbt7MxMqCndepS6g9X1ichRiagwDJkVTUDA3AeDZAcOmZAp5sJVg0Bk4k17aiu+pjikCwimJ63D7//+p9dnr29NX7t4t6lArBtD8pi19Y9+P/Q2k2CS1vj//roBJeL7hc+/TffvPXVVz/4hmRPBTRNZFb2fHLWbY+lHU5VHr97uO2L45OHr158Kl/Zv/Lg6P1muDWqEBC7wZDQVLJCa6oqiliOXC8yZMXMcYghYBgD4xr6q9bOzeC19+9//tmtpy7tvHn77oPj80vzHdZ0slyBxBp01JfTuWmzun/6fljzY/svvnXwnT4OmzmHEU01SR5EshngpiMEEEMCQBNTBVCwR9VcdDOcK4J+OKXjh6V88/8PgP9JM4pom/yKtmlEkRQ22PRmHNNNS5lVtRcKIIApCQEpoBgy02JIJZIjE1VGVsWzddslQ6RCMhEWwZkImhECJBuXwgRDMiaL2Y7Po0A7rkLbRhd8WYWmbfem1Xw6Xh0c33m4AIQkIbYUs5r0Rq7AjsCWzcKHgJKbNoq6wKkZZLFuOYTr+9s5JQeKde27NgIoGEdJy04yAKFp1MJZyeq8VB7Jl6HCVavepYKxXadNFVIloiwAmwYJAIBQowJgYiUiUQU1y0pE9bRgpLYZsipkNANGv3/hxeuXntX2+10vkM4zfVCOny+r3Unsm3ZthRDdgdVfjKq/tju9vmjui+R+0NnIPXb5BdJQjWY/eO/rvvS37r19++G7z1x+5nMv/PrI9cgVykwt91FjlPEI2w5aUU/sHYPqGP3WeHLWtVnx4HQ5nY0lxapyky0JdhXEt43zZdqaXCMbjldS0nLQGjmctKHPKJHYVciTh92d7etjhMXyeEV5fLC6vxYZB4zCSEhICppVMpiC0aY2IwEYgCooASqoiKioPSr+GySe4EOI89G7pgDwCMvfzPnwCLInREYEQjCwDXRq4BCzgaAWSGYaGME4SbYMjwB/AzFNAiYKBWWFpGgmhJAVCY0Zg3Oq0vbROWAkJgtsZuYciTEDCoAoFkUBgApZVbu2V4XzplOQ8bjuB8uStiZFF23VyPWLI8i26sUQQ3DIBAgBfJ8iGjDztC6TwWK1ciAulC5liSkb4GaqaQRSVm+IBOuU/uLWXUe2yGk+cTuTkHLf99D0xkCiIEnVCYB92OkDMRqCiqGBZRDIj94EVLU05HbIYADMJmqmo2r6mec+tzo/7Vd7bbpXuXo6r579yJOnJ4s2pfPFE5NRS7QDk4/tzPdn81mfrrlAB4tjSd3jOy9ZchcuXlqfHt+49uyfv/bFVb++e/by73/j+ONP/+JLT7x03h1fvfDYs49/5OD8x//4m2+r+cKRd05U2PDFx678T//SZ7791nv/4buvOUeWMgLOtuzsmKdlvH7pyba/4/xO39+aVJPR9BMn/c2rlz7Wy3eOz942NbV+5Lb35k9QgLOHZxdnL53bzTg0demDaOlLz8mAMasATMpCAfvYC27m7k0iNAQ1BVFVeTQMIQDgh+OPfQgl2WZhBfAoXX44G5nhJpsiIKCqqekmPh0RIsacTTESieigQogeIJohYFRDAAEAMTAaBEQVzJiIgACydwRmROqJGtOo5IAqb8TZVDRDzogFaU4hIDOu26SmorjstPJoWBye92UFIFAGrktHmMJO/eR2/cHhSWzaRFCE0nIGs75Pvqz6GEdjX1LmpB4pJ3EpJ+kViRyj82hIKQs7zoN5RUFZiUo2IrKU7p/Yaj3sFsV0xO+f91ZCnfnyqLrfd21KoECE6NlUEdDQQB/BICpAREYWU8bNHk2F0BSp6de377318ad+/qEfndytblz/XAh85/bNZfOwqC4/uft3LJ4MCs8/86Mpr7u4dhW9+PSn7x++c/fee82ynVXbxwf3Htt62tbpqb1PLvvbt07eXdn9B9/6lyjwweHbzz798aqqvvf6H4oysh8HiikhoGMacvzKN7+ZKWyPw1ETg9MLl1h6m02U3KHZzt5op4+pDJfF3inrAasvnC7eUx0AIGkLhmmIdWVXrvz40KQnH//Y15p/crZaOtAru2NVGJXOjE8WAxXuwnT88gdH2cA7EjMzo0cwvIKaZjNVQ0P4cAxCtEe5Fgw3QWeP4vVR4nz0epMXEAkJN1inKRIQkxJYiSYAohnRAhKgIaE39ACOQQTAAA0CUUEUwcDME5JpQAQzh5Ri9o5HjrIJABaeBVUVEdGR5ZQ8OhXrcpcVmR2ilA4qjzEOoizJUs6DxHEL29NyFAjzulu20xEvu5yziIGa+UAO80oy9dGZiElSSUmdJJOIhEBsmjfQHCqiK5TVBAjYLCoHGxR7iaMi3JhVgXPM+CBhVWNFWjvXUSJgVUVCzI/qjn3YPG1yAKARogECGiEAEwmIyqJbPDh56/zs6KlrH1ms7r5z55sXp9cvXn7m4pWtrXrH8dX7B/fZFcB9n/Obb3/dmjeK/u0xz07OD/dGl2Im8rxqz568+OK7D/uUb6EzYPuT1/6dmbxx9y/qsjJcqRUhuGEYujgEDqr5vYcP7p3VV3YiYR4XONsykHC6aFNOF/Ye35v98vLsS2/d/sbj28+O59tDG7d3J/PJj+xt3/jeG7/VL9ZEPBnriJsdXPutPE7f+skXLojuNE0snTFqAkbgO+5sPKrGTl65Tdd3xmfd0MbMCIqbqgJgJqa2qc32P6JHj7AnMARG24Tk5j3TH6ZYwE3UMdIGU0WEbEZoCKSagFBFGQCRvBNmNoUkkgHRwDliUwDzZEiakmUFcpZAEQkACM1vhhQXglLw3PU5CjABw6ahwKJyOakJlcEhYfBlFRBBnCARsEPMYVQWfdYHDcejbuL67e1xn80HbCUgcU6ZLDZZkWHTqbhMg8KoDo6RR2OYTGTVQgcIYrHT2cxPKzpfqXeUUnboiHTImhPuzwKXdPtBl533lDu17z9sqcDRpEbjZtmwx5xRRZmZHaeYABkINl2VJUVEYATEqvTtcuACX7/97cOTmxcme+ORv3P87vcPXhtPLz739Ge+8b3f+9Ev/NV1d96n469/59//6I/81H/4j//DVmX18P1f/Njdm4eXv/b6C2a39uf32jjx4YmmWa9Xw/70eiNnZ81Z0nMwRNMurQtXhOD62PUJiV1CCQ4Jeb8uf+a6PejhS2/l+/dz3zZlhTuzYt0t7iz+fVodW6ZFPqrxc6PiycXpw2jL49XtRXuI4C9sF1dmocDzkmFSeBx0XgIRXtqea0pkBuyIcFIBGUBOP/vipZNO//S1D6JR5dghPALfN3O6gW32TGakQAZqII+CVTbYPgIUHJwPapIlMXEXBwAQM7/JqwAExmAAJmaIwEhqyIgpJwTK2RQtGWRVMRgxOkI1U7QMBKBIoEAIpGqImIzUDAHqANnUgJ2nUHgwA5NJXQ9pQADP5p1fD1awYwYVLYJzHpzjEBy0mUzLIrSD1KNZjP6wHUzTqBo51XboDLUI5aSuQtuJDjHlvteMMJkWOJoWN67ZqJah59Mjfxpl6G1/G67tu6ahg7PURBTLYAYKhFwqMFhUG0yM2BFNnMMCLITz05VmowpkMBCsJ0XqcxZFQiAdjYpmHbVXQHCVC4UvK1ucRN38ZRwF5Mvzi+v1+WpovHMvXv8U2OAnO5joU8//zG/84T968YlP3jt6u9eTjz52Zbp+Y7Z14TD9POU/ferKO0Mq2vjSUfNjvi7B6Q9uffWVm38BTIhGipWH61d3To+WXZbVAJ4dYC4cjwPu1Htbeh7GfKvtjo7rUe0m9aICvnMsLuDj08/X5XC/eeuJiz/1xIVPncWjOw/eROWHh69uzdLuiC/Vo91JWXosC3JFgUrM5L0TkU3pJodmpjENw9BHWUV75f3z947bQSwJDENvREMahiGxc965UVnVRRm8SzE6X863dra2dgBxZ3v7xo3H9y7sXr92bTyaGejQ98vl6kt/8uXvvvzds8Vp168d5ADGjAgGaBmAzMBA1MAsm6ohgXomMMtqnswzOkBgAMAoAmpMKEqIWhB4ctEsmzLArCYwFWUD8+zMNKU8n4yAcb1ce8d1UTZRY1ZHUvgwpDiuaVLVsU9UFKx9FXjoDUORUozAYKA5qyTnfekZidiFfkjMAAbn666Psj32ON2uLmxbXee2hYOHpIQOgAjJJedo2UDUBIY4UMUO2KqpObSzc0tRAXE8K5+/PkoaX7/VDJ24QMBgii7Q3Pvn5hMO/uu3Hgxoe9vVah2HTiQBAM53gnfaDdB3mXCz8zNEkz6zkXj14OuyPl+fb0/2/tKnf+WPvvNbJ+vT2pc3bhSXZnt+zQSP7e99LvZfK/139meNwONr+DsZsGnbd+//8ev3v90lmVa+TxkBmK107tknisOTePuhVgFQyFfZZZz569eu7h43XNC2Kw/LfL47ubno+Ps3aRJe+sSLP/n63d97bO9T58sPPJdArGlZwgd7M6sHIAIw3ewq5/Pt+WyUU/LOAWhZFMTOzESzZTGzmLNm84zraIcrOWvltLcw2o5iUXLh/Wxcvfjss7vbW1tb07ocjSaz0XxejcehGpfVrCgLJCNiM6JNw4kYZVgulv26P1+s7ty788or3/761752987tpm2MMpJ5AgUUM36EmRqYESKAMWMWdWhMVDoHoFmA0DIAgFVMnj0xJdEck/cGZjGpAhK54LAf0rx2oeA+CoCJwAbjrQoqRyOiEKzx3h+ft9vzaR66kded2agHpxmaPmbVdRsndVkG13St94GQ1l1TFuN+aMVElcYF4c7lKZuw5UHEcWE5JSAzRaRAflLw5b1Zats7B6utcZnSUG2rOPvgQJsFAKkfuYoBEZdNAkJDI4cE7rHd+qXdSZWGM7AvvX2s6KqC6gAnxzKf0HOX3GqQg9bOFkLkCA2ypixIaGKM5AIOpqowcn5cz2Mazpo1O37icn1p1p8eTz7zyb957dKnlufns/n47vvfXpx8icO1w/UFhX48npy19//8B1+iwryBJBQi9hwAblyBOtid+7BKZJgnhfN5vLM3urL1wmz2iePln26NPjqi3aH5ndK9duvh9oNDmm7ttV4ppwJHhUIZUsGnF0pnfQcimb3zwTksPFfFtKrCdBKYSHIWkboMjlBNwIwRZMhqZCiO0IeCXAHFqJhd8juPdV1jq4dkPRN4V/iyGs33RjuXXT31oa7G83K8FcraF96xAyB7hDibATADaDYAJJYsp8fnH9x6/933br7yyvdefeW7J0cPkg5t1xBj5RAAuwSIRqpAlE1rjx7RDJxDACIwxg1USN47M2A0NR1i0s3G34B9EDXJaVJ6M4tJNpmaCQGYGSeTUeqTakxqwTlmTjlf3d+SFPusl7dn09IfLtvFukXElDMR5ZyJsKpCitZ2zc5s2ikeHp+6FCVUwYlZxqTi0D2zO2fGB0fLZcw7l/xOPawsX90fFalPxLHloyb1PWAw7wp2umySCTKjkiEhk8+dlErBxTbFd48GRUdoXavb03Dt6uTXP0kffWzRi/3hK+63/yI7Js+omoApigKjAKCBY0Sk0k9+5LlffvPuy6v1609c2b5QSDg93/Uzk+5b3/2tt99/9ed/+m+LXrpy/X/71u1vX76yt1qtv/H6F48X9xmRDHKkHKUcYeFARe8dYHDaR1d5ZMLzlQTfFW1/ps1k/G3TYZD+9fe+uF09vT0ul+1J9sPDxa357tOesG/vUki11ZbjedNZAgRWgLFnQjKklAbNQ448qoq6LkpfSE7RhB2BiRGSRzI1M8eBwED6IGDru/3ybk4p9n2KAyJzMXJFsTw5nZyfb1+8ilsXe8QQSi2KLECEjkkBCUlVTbMpmpGqaooqMpmWz3/k6Rc++uyv/Re/evTw6IP3b7dt8/oPXvmDL/3B+++/SWzIaGqEtIk4NCC0QU2FSo8iBqpqWHhkZs35wtY8azo4XQ59dATBk0MR0YJhSBHRIYIL6LiMfXKEYoCaU1YgDg68oyTWJ113cYgJkICLUUm4SAYA7JerzoGFwGUIqjYMvXeuGdL5ahgGdtliVic5d1mM6Pru1l/5wiccrN+9d/TFVz545YPV/YLWfZ4VhU8ZiI1ZDH3FpohIKqko2YSCs94MiZ1DVHjnwTJr6Hs5GtQR5zhUZcWEICF3euteRsand21euPMYJeIgGRGRkIAkSeDJZz/6c+/feX1/fu3S7Pn1ali1b41om4ZR0uOUD7/5Z//vh63deOxzua/eO/j6Lzzzt07i4R/9x3/6q7/wv/z16/+r3/j3/+i0OUAlUCtLLjxLFGIDsGag4FBybBSJKPh4vqJLM0rd3bPVByj44PjmnfjBS09+LqMN9n4RphO6OMAqyvsIYXnchaBXxtwmMaNZEbIJRZCoDMkse6Zzh3XhZ+OKHAXHnBRMUI0cOucJ2TQboJlFBEgDIZPCqCxycJKV2PXrlQJz08LBPTAcmXTs/GjEriRkJHRE9mjeRwNAZEIkJiOX06CaiEE1jifVSx97Iafh4x979hd+5qf/8I+/9Ftf/J0P7t4uPJCzElHECACAEaFgmgRfVEXTDU07IOH2dHRtZ7q7Nbt19+Dh2RqAq8CiWdUKRmDfDxFQSseSdIODI2LhwKOMSoo5O8BhiMjOg62WKzVSsFsHD6UrSgZP1sXIAH3KqpZiymrB+dQPAkkFAqsbBQLLSsCBJds7x8dfefn7s0DrIT+5LQcPXRjjUSPtYigYy2BdTBvaDDImyMRmyYLHumJNCsAoECOKyJ3TuF5pmHlUVYWUkqvK4wfxT14vnrvip57eeKDLzlJWQkIEU/PeG5kkXbSL+/ff+/yLv3Tv4Lbguh63n3zyl49P4nuH3525bQ/rsy4uepiNZu/d+5Mf3Pn2z7n/4sb1p4py9K9/9//+X/+1//3VqzeO3r5fE1ulYrBc9mUdskrOUAZKEgexLFZ4cxSGbG/c//r90/L5a6NnLr49CZ/8wa2jm3deCU6Roq+mTXuu9n4TdbGW0uH+OHRDBvD789EkYCBDYmaHaGiB0FQ0i52vG89IiMH70jGmSAQJo3eBmMghEmIaiJg20E/OwM6HoISjYt4O0cDMl93QlaIpiuTMROx4Q8chImaGbCpioABGG26OeVIBVXQMiEPXtM35wc23MQ+f/9iNF578X3ztz7/5zZdfvXv/HlM2g2ToPU+ZLs/rcRkywMV5fev+8fZ8/tGnrlyej5g5DvOD47Od6ajt+j5ajlKVNkgmJDMdkvYKpWntyQUEMx9CME1LRfJF4FAU0VESjTGTY0J/uOwf3x9f9P5wmdbrblx4YE4CWYWyjMZ12zRcuKqqcOdiKEIgjfuj+awev3bvPoCUNjUZ707QU58KfvvuMkpXePAMa+FAaEPOpFiQmjpwVck++JgSAwNyzpkp5QjDoIAkZjlmz+7ZFy+d329Dkcwc6PzuwztDzHXtDSyrqCgSGsKNiy/uTC6dLA62J1eevvpczHfq4rvbxbMHi61v/eAVp6Gz04P2rosujHCdYuHKz770k5Wbz7e2/90f/pOqHg82pDiMg4spZeWdrb1Ve5KTOgQljYJINGJXFSoAQwY2MdT9evvjTz7j6On3Dt5764NvgYMq+PncXZ6HnfHqW29IG4uLu7iPYVaFWeGyZjQoQlkEXwQMwSEigjGgmSpkVt0wOwwUVUgNCZiZmAmQicgxMqkZERsicJXVjcZ1UY2MimIy3732ZFHPt3YvltOd0c5ePR5554jIdCMPQRERzSLJRIkYEdU055SGnr1Xka5Z3X3vrcP33yFM7fJkWK8d6xDz6+/d+48vv9FGKcvgYbiyVYvoi9cvZMsA7u17x09eu/zsla2CMJmdnZ0t18NxK+/eO1y13apPDqgMBCCenLLrkzJAXWLB1KVEBIXjrMEsERkAgaGgxZjLIsSsJnJ15kzzeS9ZkJmzKBMZsgMpnJRkajIaVW61zsXcM0LlHGvcr/jWcjjt11tVdzygZhpp5ZATg3O04f8DIRSgQixKwNmk7SEgFAGkk9TFKFqU1DQZACDLRutQzrwpbU0cFPHgQX9ydkdycsyh5JgzCIKBqjlkQB2PZi9e/1xKsYkHVfW1vXKxaO/tlNd++lM/dfthd/fklSrWwNLrAIzZ4u37H8zKpeTh+euf//69PzMSVPI8euzSk96caXcvRcT+2qR452zdq3iyDUMwm3ninCwpfXB22r919/nHLjz/5I/szC5+760/Auorz7tzOj1UM5yO8xyq3dJZHpYG43G5NanKIhAzmCEigRGYI0QTTQaIYKI5qqgaDimpmXOOGX0oHCClhMJA4ABM0XmOMbuEWPBoPN2+fHX70tVyvD+azMNoFIqKkAkdIQnII1oIEwIxsGy0JRvknh2HUiUjUihH+1efsAwn995DYO+Lg/ffOzo+uri785d/7FPfefP91XrhMIDq84/tBICL29Mhol2hKzvjwAZqcchd31MIRw/OhhSdc6MASMQbbRTh1rTOYn3bjUonihgTu9oVblSUHrQsij6lmDHnwU+oLqpOAFSTtFltNC0LF1JsoooD6WNcdrkqK0tN4cAhOemtHzKU+ObxiWZVsEFQLWUydZDA1kNu++QqTGpgqoqiyghm5rwjDClnAOjXuRF1hlvBEeFymUHITMghIJraeFr3Xb9endaAqxZGY6IcmiblJICAAqSoAoLy3vuv3T14987eO89c+yhX97yVbOejSgt/tyR7evzSxYtPv9gvbx9/8zvv/DkRWNbF8viJi8/7XPz4x/7yWX9wcPJ+qIpFu5guT7Xrh3T2/JOfv/vg/cFMrPUsDjGKBEACymBl8LmPZQhHi3vlw2qQ9OS1F44WN947ePX+sRFwyNOqXu8VxS77jDSezmc1E1rqmma5EgVEct55h0zgHDqwkghNVJJE6YcBEAFgSIpRA1PXJSUMoXDOISgimiElGs93OPhqNp1u7c63L9WjndnFywZcjkbM5D7UODDzoxdEagiGzG5DzENEIgohiJCqOMR6tnXh8aeA6cG7MTW32IW9KzeaxbJd3nn+go/bWwBkpjXLuKhQYVxyWU5Lh7EfiLlrWxeK2w/OhiHu726dnp1PqzAkbYYMhkyosTNEBV00PRowUdd1ba/720aeN7wVQxc8itrRYskmRVEoWKZchVSVoWR/tmwdWs3qx4WZZnRo3nNwChCH7AijELlSJUmOoKCKMcPZuc1mgiWbCdojxZcRZoCiYDLNeXCOY8yajDxn0XPNREpIVKJGsA0XNMB8O3RdJ2ynD21DOR2S1XWZTZ3jCBHwQ/0Nwqjee/7pL8xnfnv6VL949tbpH9U+oRW+LosRXLy0V4YnT/s7ZoDiPv7sT165+OTJw/v788td05Q2+pUv/K8X3eGXvv6vYxK1eGU+vjJ66+IzT1TjHzt/9TcenL2D6pgAkdTAKbU5CxCKhYAnq4dNv/rg8I22PSEfECkNAijbhbtcVL4K09Kzpm7dZzEAiikhAjs2UACPnojYM1hM/TC0XZ+SAiIjiIiaCuSlYFEEQogqZkKgzOzKKWM9He8i4XS+M57vTfcuYVE7X7qiYiLnmTa7RaJNwt7soXCTO5E2UYuIG+I9kUd0MSZicsUeiORhyFu711+gm2987+Tofp/y8YOFD+7y/lZVFQzU9T1gaaLOOxmiggK7qLLuYrMersynUbUvy2FIfRerohDNqpqMIIMnjpIJYFQUXmHIUUVbo7PFWT2qx6Ny6JOaWBp8XZuBZy68r5gC80qz825aVyNv7ZDOmriMvvawOGucLx0TZtEkVJGhmQ9ogXzFy2XMURG8IxsE0dQIvANTBQJViwMaA6QICUCAvYIzZDRBZiAFLryiSdRRWaSY1st2yLbFxaB6uogIKMmuXp4qDSqcklFFGnXi67/5q39PYtTcEG6xmzx+/flRWfSxOTpt+pi+9da/Pz9/4Ctij4GKT3/08yPYKdN46JZKZx957mefufGZ3/2Tf3hxfu2nPv43mhSXh/8D813CBxCfGld7cHzTFcgMYy4KL0eNRmVSGHkqHd9PS9F21Wtw7MwKBE2qLLvVZFw4Ikh92/QxZexV9JFMhAPSqPSjup5WwbOpDOvVcLZo1l1GoqrgDeXIABEIUVNKznvHXBUj51xZVfVktrWzU0y3OVT15MLs8mNFNfXVJIsUjhEQAIkZHpXyD2l4Kgiw6T7NjIhE5MNIBVXx3ptBjGl+Yb+cjPJ6/fD2O8Rhd74TA17YKtdddqpOhB0AkaYhqoEViICEKQ6SUtMOV/bntefD88bETlZdXQcEHJL1fXRDKpjNLKmUTG3fO6ahG+63AyIx0uFZOxmtgXzKQx2sG6QqKzUa1s0i5yzHReE98EFshpQ0CzMDFwoaQnAOKGcxRkQbckJQUhCQmDCpupINsSxlaEUMQYEdmqEIgioiQlQwQADyBAkokIIicJYcQiWQ1UTU/MifnHeCmgbsOA2KQMRAo8p7gU5tVBcAHC1ljjkOv/37/2Q6dc/d+PEHsX/2sc+VNd97eOeJa09AWH//B3/+xJXHb8LBy29/V4GG1Pz//sM/+dnP/BoqjcPNi3sdFZ997+1v1W702MWXxvXW0Nyf7f7ieA6H9//N7sWZM3BeyHOKvsfU9NIkYCIEGJKsJVWEPmsLyoR7wRubWLxQVmOHKQ6Uablqu6gGSIGdx5Jd5cNoVIwqPy7JkTCoSoqaFZicmshynUU3sndDJAX0jtksgYQ69JKdoWpGVGa6cOmyr2bTnX1y1Xh7e0giOXvvNwwmZgJVIjJVAEAiU9nM78y8idEfNgAAhGgi2TlnZqGoUzcoudn2Tr84brHPA1yqxxIHxuSc+Y3UUdUgipCqiYBq3h37svQqMA68XQfkWdP3iJySCkDOIJrNIBt4JlQl3Ih5SQyISZF8VRIXJc93a59SWwXX9IMahFBULhCKBwNXSpsyDIXnalzlISqQUwRSICAkSFFU1Cuzh74VVEJnizZOSiYCAFBFS2AKRMAbtgtTUlAnCmZx8zwADX3pgcSimCiBzzENOXGpQ49Q8OV58ZGrFw4Wywdn3e2zRT3ySQYRE1AEk15P7INlMz64/7s//9N/c7AFZH3n5jem9VZZltevPvndt//odHleFdVqGc1s78L+bOsKrM8LentePDhaLavil7/wib/22BNPvP6D7z48OPrRz/385Ss3tvc/1ff28z/xdPvl5tb9V8uAfdShN0RDgqyaALznuQ+dyRaFUUHJ1BlcGJXb5FIXzbkhx2xUlc55VlFmGlXFqC7m47IuWDWnPjbDQAAVEQZks7VCH2EQBMebsDHg3GV2eTYCXCyfffb52WzOZObIlbWh874sqqkagfOlL/KQMARENFVDpA9L/CY12IeODxthHTMTkaoSMqCJZkRCVADQnCUOsWsGycV0G5gNtDm5j9qTY5Xc9hGRvAsAJpoULGYrnauKIiuc9YMCzMdFVfle6qPFUJWZMiTFPiYAbocYo1WBxpUrq5ECimoVaOZ8jEM7tM6HMRaAcnrSL3rrh1h6GpWhT+IxN/1ZWRV1Pen64eGdh6hQO3DG6AiRDNRbypoMnBWBmw5DCTsTd94MGdAEgAERtBdV0EBJFNVsM0caIyAWiKDaA7CQd2kjsxFDJylDWViTMJSeEUCNoJlXcN5zycXIw0GfsibpARWrigh92za+6L78jX/z+MWP1SN/cHSQuXvjzsvMcnn/xju3v7Na9Rum5Hq1vH/88jY+tZYxERV4XIf1YnUa3LOf/sRPVuXOuN7Zmu7Xbruq6Bsvf+kTz/zI0cFrzkEUyCaGpCIGUHhPTEs0L1Y5Y8uKsFuGbeYhxi5a1Cgm+9PRpHDtMJRlub01LQN6NsbctUOKA6gRYhJJwzDELIae6cKWJyZGTKrrmJtelEtX1ts7Wzeu7F9+7MalG0+Syer4SIhdWQKgMQRfacpFWVNBqkpEG68HQHq0T8f/UZm0gZw2mXKTTUUUUAAzGJqZIaaY4tBLlvFktx7NU3P64P23JGXAuu86suwJAS3JAKKbVFw6xz5Eyes2DilntbN134oNWcQgG7YDpRgjQFT17NHxIDosU9JYeGYErF014ofL2HbRUTw6a+oADDRkSQY4SDHEIRo62y197LTploQCqoK4szXd4MWKiKDZe1cFLrxygK6TJNb04IBS3ohgUQcDAST6kO6JuzthMpGjI+sHQ0PZoPhqOQs6NENANDQgASBJWAYMHs5zPDtITYzmXUEgWbOkTaPvnTmypgeoKCocnh4dH/4xOahC/Xt/9o/vHdxBUjZumgaMEK0eu3V3b7X45GM3rqX+F+8f/+b2mLuUOPD3v/eVp5546dLepbquu7YhiLfevz3y8/355VEoRdptV6WaH8akwglE2AqGGdG0dFVpVaCJcyS66kQM2iFnse3JiJ1vYprPpqO6zCkuuliEgKaFp422Q82YiZgU0cxK7x1jzOl8kHWf+2TKHiTtTHB7ez7d3Vu16+r0+NLVxyfzi8dHB2hmjtZnZzuXx6rZLDvnN8JjYAIAEyFANd20oUT0wwb0w6U2mZlIQkQAFhMkkphi6jkUu1ceV4kPPnjn5u07zXJJYEkGAEgZhs2PmDjG4LxzLKqpbaOhICE7TLmuym7VmWHfJyRjT2YuOI5ZAmLpsc/QyKNtxbjgk2Vsz2MrOitYDNcJGwGTXHgOzCZ5o0rvE3akD7q8N/Yzhj6Don1wvHYi2UjFEL1XlbYbPFFsLA9GBZ4vExoUtQejHM0ZAKGSIWyEiTYeQRlsNKFWlAxyJwhIjkABDZABFS2ZBl71ygUBpZRc7XziGIoipnSSOyIApbqgXjIygpLzCgBTV0ZNa81mCBzfeus1dOAKRkMxcw6K0uUss9HVH/vUr8zHu7dvR2x+6fA8X3380xd2LjSr9x48OLi2//SsaIeuX7drx+X1GzfWy72/+kv/x+/84PcO7nz34sRd47JLeRAwxtpBSeQYa08OrBuGJqkaS0xmsDsttyuXZRjVdR/t5OwoMFV1UYRcoErMSt47P8R+3XaWJCvEaMs2RbWUNSsloC7nIeb5bFqHotTkyYVqfHp4NBpv7V9/cnbxqmb1VY1mqsLsVJXIkFBFGBhww/TceA0BIjjncs6q6r3fMN0BNgloI1M2AIsxxiEWRe1d8EXRrs6btq9nu91qEdu1KlqWHKPGKGaMEDzRxmaLQIiyIqIFz464TDIpaBDosnUxnTSyFkdEzZAJwZHgEMdTl1LiYjwNsl62LDT3viaR2HHwQ0qhmE4cna8bQx8clBSG2HFZ1xBF4biL7D1l8kV0o0BOQRSjirSZDHK2IQNXlJOgIRJuCsoj0SuR0UbCBQhweCLjGhatEaMOysAUgBxmFVU0MiRgQucMicGBRtCsrYugGkbMzmcDk3ypGAeEB7peiS2zIlqBMPKERkkJmPteNuAwGhFDNUI167oEgkdy/yvf+ddboyvj0SXyo09/7kdKN+67yLh/8bGtS1e2zx++2bdDGkBke1jJrN79lV/8e7sXL/zD/+e3dMk7dZ6WuEXkiUHFABjBYmoU1QjFzIyJt0ZlHbCP0Tl+eLLsoo7roJLr0mPCTFzVtcqwXq+bJg85pWRNVDEsHDMTMGWwNupqyOjG9Xi2tb0d6nEW29u/zs6fnZ/Or2g93yUA9oWRF8mhKBUs5+SC30hnkFg2ShowA0VkMNgU90eCRQLVDGCP9kyS4zDEmIqyZMcpDmYqAFduPBNIOa2WtTs/Pu2HVZ8SQJGlKwA9oWRjD0gs8kgbMQwJTE2yQ/aBRwXgyF2a4INWupy26tKQY46TvXnOKua4ntXQx1lsoAqjSSXrZnVcFJNoNtu5VOaFDOsHDY8ns7btrrqhqCbbqzWhU4WqKk2l4t4lzYSIjnQlpuAcBc+uoCamzVRoZqjKQL7wQ05qioLkCAOww2apdfa7tXvY9OzZF85IclZUIAPziESQBAlRTVv0QO0QzZA8ScwKZGYBrYRYh8JnV2C6OAog8iDqnbZzAIUnAZUsxEiegIwL1ARDJ5CBPanTr337Dz75ws/dKItb733/7GSxM7v03FMfn0ymcXl+e/j9qvo80G5qe81NNX+zb6v7B8WrP3j17/5X//07b37l3be/ZIYEjtgn7U01ioqBiOQsYoSEs7oIZClJFGibwRFujwsznE0mowBGMJrUue+adYvEZWlBIWUNTg1xNdhZK03SRUxiYOxGjrenoxtPPTvfmc2m03HtZ/tX4/6lvuvK8QRcKEdjM84xaZEYwNBUN4IjMQU0VVMiQqSNK9lmct/0XaoGYMy8yabDMKScfCjKouq6RjUPfV9V1bA4yymPtnZzTof9QZuhSSYpMZKg6RDH4FxVEJBjSpKHGGPKQ0oqgkRkEIjAISNcrnyrfNIMD9ayHKJKIpMoGJrjHgGAt6aj5dH9+110jFs1zJ1/8OCDNHTTkuIgWCVpujXltltKn3JgM9c1Smxaj13MGoIHJdHEBfrgEkjqJfeCjpTARNW7mMUTOKTCMQr0WYTNyGaT8Os/++yshj/43gffeW0hpRmoDjoK01E1OukOscjOE6itVuIQIyEycAD1xIjdMjomLnkVY1bI0ebEbImYCrUeAAl97S0npx5R2CEg5UElKXsvkFEsNeKIL1249OobX2XJn/jYr1+98sydO3dVaqTh7PD93P757u7fCP7qfPydSfjNIl1L65/7uc/+1dVqcTMuZ15GPAGAYYiWM6CpacoQFRSIEScFe5Q+WhTIplURRsERWl04hNgI78+mqKlbrwV4iDKkJEm7IQ3R1tnWCZtkUc0TesdtBsqxyGuMq/nssbIeIaTV2YNqvDWufI5dCGVOUk3GOWcV2XSW+ggqeuTOpJqJPGzsBEUAYDNFbZT1vPFSNFNVZkaiIpRDjCmloe99cN2qiaLXnvlotz41kauPtcvF8u69tBi6LibIaWvsfQGYMiYVkayas0YBMTIgSznl1AEye2ZjiMEV41Cs+zYLny37Iatj2C6xiZQsO8wAFhOeNrIalju1e7CGIebdMTvUMCBa/uBcntwBZrt3mh21N3bCTuEPmxNHBmIqvTASl8ikNmhKG+ceNYKNRWtZsCiWaDsBkkmZcWkYB4ERnK9PDIGJXnzys+8fvNkNq88+/WNPXP5ocs3vfu1fubj1o8+FJ3YXX3ndv308GMI0sDlMxAxYBD8MKQqsvB61uY2wPypq5pQMAdCACbJIXVcxN0aMwLnPsUuIEAIBO2Aa09Q7/Mo3f2c2zlfG/Adf/QdVde1jz/7apatXMg0XL/23h/ffOT9ZhPCgKF5u14dny7ujermWy2eLYjJ76q2b3zWqnC+Q2qyrIfUeITDWpfPebaRqfcrrXiZ1dWEUGMXQGFkkA9J8OjaV9XJp6PohmpFEXPTSDRTVkhmhOaLSWeHwYS99tAv7k60KVofvn25N9648g6XPXSMZyum8CJWaqqnm7J2XLITExGiI9ijmNkYNIsL8KF9uBG4//OoPpZ4igkieycxUsko2M8nZgPb2L/frs8PFSdt1OWm7WqoKcEEMiNglowG6FNFERaOKI/ahYIIkGZHqsmJCyWCghpTESs7P7dddFjVdDVxXxWrdSOE1WyLbKUScm2rSZN7BuNCiDLuTcL6Od0+7J/dH+1PX9v32bNQRr5uVAb/xsC0DuN2RO++EK8+GJlSE3EeVrKZIDkDMAEhtWvmTNvsCRzUaadfRiGsxOO+H//Cte3VF7br6W7/wl9+6feMr3/udl5791NTvH6zee+ryi7/2l/43zdkXlwe//9JVf3cdFUzJcjbvMA4iSVFMBFYJU1bP1FqMDccovYL3AAApiQuSo5nkrNnU8NFCD4hk/8JHPv+EPjg4fW8BoThR0qPlvf/sE//lCx954V/85j9wUP76z/3vrj7xie2L53E9nJ8+vVx9b2fmc249paKajGYzJt82p4CmlhnNE7BDQsw5xyGaQUZok4zKYn9escqguGj7gmlaV945zZIsZ2AjnEwqGTJJ6gViBgYDoEYwge2N+HTIZ4M9sTO+tl155305PXlwmCLuXrlCPuTcuqIiH4m7DbOyHI2ZOUuGjM45kYy4ETts3K6UCFU3+3dApEfWYrBJsSqSc05EJCoIKpLUBMCI0ZUhrs9uv/Pq2fED57BL2ZAg9u1iqWhF4dAXUTEgBmZmpoyAYDmS95tflFUVzHMARFURyQwy58zCay4UnTi+fGk3x9xGYed60z5aVZajsTqMJaTSO1d6R365XNzv8pUxjqGIivOATssPzgex4tKscuxMESWmnM0jROeTIgXOSQwQDIJzs6o8WTYC0CEtTMvCqSGrEqBXGxwTlqPR6ObD73/uoz99dPrA28j5EPzkJz/5N7fGs1de1Ut7f+/07penxfvnXTMYkGMTjTGrqnOcs4IBMZYl9clyLwYWPCHpIICIRVYZFMEQ2TGbg5xzllxiyLZ670Gx5fn6hfq1O03vlqzF7375n//Gl/7J6en53/8b/2A8nr7x7jc12XZ9rQy/dHwui/Urk+KF3YuXdXl+794tdq7pOwIsPBce2BTVhiwGGAWjIQI49he2xqSpy/Zg2WjW0Wycs/QpucAkmZwLjnOMTR+PVnExZESfgW6vBAye3SuaId8+H0Lwk0ChHucwihy2JrO+a+7evr1/+XIooevWXITsmRxjjjklZgdgkhMRqKJzDPZIFw+IkrM8SqiSs/1w4amqOaecE4DlnAAw502wIhO0bRfb1ftvvbo6O63K6v2b7wx9B65wZcm+WbYxaSSwsq6YUA2TJMsiqoRQEsxHFTru+rRYx1W/qsrgHYJZFkORMbkAMiLXWfHB7Qc9uP35fHl+LqD7W/v1pBJFpxkCl+XIWbxQupP5vO3aJdvOmHKvO9NylCmfnpUcrZw4y8SWu6joiAkHlQ02FJgBQRCvXrhw3pxnNVAYQLqK2l5bAYQcO90IxKZbVz/z7C+98d73z/ZOv/Dxn4GeFatZdXVntnX/8N3Llz6SOH3z5k3B2EewLEWFidEUTI0C5mgGGDwlAwNAUiQ2UhGQaIiqJagSoHgGAYFNHQNLLq9Xt0/8/lFbLVd3htitWkET1YdAMBlvFbU+OHvt1de++pNf+OuT8kLfnnLzwuOP/+cxUjna2cKqfzWuGwq+LDEHNlBlRBNQpT5rrxa8GwU3q8sCdMh253Td9PHidCQmZ60VYUM64zL4FIem6Vdt6hW99+tO7y5iBv7MtVHK6f1lGhQnoDJ07dn55Qu7nghUdvYvRNGz4+P59h4R9W3w5TiLOkBReYQ0iWgGYt6kS2Z65CaGZGZIlJMQMyKqCiLGKCkPKiJ5I2wGESXEIca+WfXr5eH9D1KKHPwHN981EV/VXXtEpqHwcdUOGRAwuDjfqid1iapkoKZiGTYCK8RRWUTBe8vueL2cj8txwSEEAAC0CsSBakojJg9YkDonJ+s14ox5ChaZyfsKpG0Wh9X+hQCgTpEou5FwbCVWIZRpUVdFWXo3BSdoQjSYsKeYDAFExAMD2LiqFuv1oukQydQ0WzRbqxWBSc0HUoOcVBJc2Hpy65OXHh48uHb5MaC2qBpPQVO2xJP57He/8m/rauts+VAGZcShSb5CZjQFYPCVTzF7TzmaolJAMkXCnDb7VSCwvZ0qiy5WHT0CuEDNGJyardrD9VodwRRDPfKNpEXsUsa2W/7Lf/d/m06qvfn+2fqDC3sXv/PWH//g9Vf+/ov/vbZn5XQ03ps89tTH7tz5xjgAGCFkAoxZh5wHgSFR4Wlcewb0nsT0aJ1PmrxVFYZ22vSOy92tURKpigpVRHI7xAxYMJ+s42mTPfOPXh8TyMtnaZG0IJt6urIzmZaU2mZrew4G6/OzyXzmK9+eHcSusJwd8mxvPyfvCp9zZGYDSzl73Bh6AOIjdIVo84JyToFKM9lU/JxilhRj3KBRG9emlGIc+qFr18uFmR4dHp0+OCjLQMR9u3bel/VIFw0RJ5UmaRXhfJVihMCZQZiAwAi8EK/XfcxaFsW1/e3js2XKuWNImIioS9YP0THGnFMG58Lh8emq7VVtuPMB3r03KrgOengqjFHZHfO8Obrv0mlMVo5mi2boYr8/Isty6wFe2j13xOQZUJAATC32UgYCgCgyqqqiKI/PzhxjzqqGlceRxyFj4bBvJWYDRHIUyoDeprhn23y+vH9p6y8Ib2W6bvCTLlxbrhe/+pf+7vZk54tf+Zffe+9rnTUaM5CFgIDMHmOnDjdLNgUEZUSz3BsAcEA19g5ffHy0bt03X2kVCNA25B5iKWsnovUIR8AX/TyRLM56x8EkuQJOu/Oz/vR8sTh6+P944cmPfvetr/ZpKtDdPnz78crtji998hOfvvP2b589eF2MkTDlHAXMnIgy43xaImRmTipxgIfnDRGhybIVI74wIgXtE1+YOxY5P2wB/bRyy6ZznoO3p3bLrVLePM7H0Rjx8qR8bKvemVa7F+ZFVeY4GLrxeNq37dCvLSXX+Xa1Xi6OYlpefPxFEzGnOSfn3Ma6aQNzith/4juCZpZTdl7MRFVTSpKTqkiOmwje/Mhmhk8p9n3z3tvvdG0z39shtdXiNOfEzkdy3hcMHRCMiqqPeqZ91/dmimYetS59VYALMC7CQtP5cl2Xfns+bru+ibHrIxEPKUexETtEdqga+77XOOSaLUQTxa6zYuK3am7XApIP7r9dxWGnwi4ni+cjtD6mRig43HJcQ3SdpqL2TpNkyoKQwTwgIDuu6+p8tfCVx6RkBoxcEACCaIo0RHSBTY3Ybt5/8/e++s8/+8Iv7+9esrNztW/fP2vadO+J3bPS/vZ076npbMLe/sZf/ntP/eDpf/7Ff4hgpsaMHFDUTKEoSeRDLpqoGFpSILRMADJkPFrGs8WKHGkGNEAi53g2xqy5j7BZXi+pTVGziaGGCvu1AXNRu1BLm8++9f0/HTQnbd57+1uXb+z94//Pf/d3fv2//eMv/aujk4cOnGNTESKsnes6yWrzifdog5EzVJGzJi+j1oV1gp547ImZVsvmyt6WI7x/cOZ8Ma+r83U3GPVDquvqylZ13g2dmqBOCxpXfm9vtr03I09IEGNf+7BYnoTCOw7LVRub47KqlicPU9e4UF958iUwyCoMG7xzMxXZD+2ZVBUQEQjNNGdAEEkpDWAW+95Q1AyBc9YslFNmopjS8cOjUVVY7trlYnm26vtVKAIhGIeqqrKen65jueVmowJNsmkdvMboNwx6VHLAqNORR+fbbiBMpXegmlTEdFI4BWMk9DYyzEoh2JpQII7qAhVVMzv0yBoQALaGVV36bOg8ookPMLtQH513WQRNV6uVO4/at1EcG6IouPCIqVWW4XRxnk2ZgNAeefcTrD9shpjIwNQUFEIxc37rfDns7U7q6eNtnIyrFlAsPyhrqssKVNfr9puvfeVPv/lvcxJyqEnN0HseOjNLPvjcKzEREgPEmIER1DQpeRoSvHt3lRJS4QCTCZhZUagqrTqzRAi2xLxan5feJwRAkgxEVo3AF3yyira5WMRY+PxHX//X/tX48N7b/+if/h+GYcFDt18FMuXgQLQdUidWFq4qOGYh9mbSJzhtYzZVIwH0gIC0aroLs3pv5hbLNVCYT4qUMjF6x9NJcWm7XnbDWaN3Vl3sdXvsS5Kh64ZYVmVtACr55PSwqkers0S+qOoaidt2DaIP379ZTubT+c784mOGZU4ZEXPO3vOHVmG4QT0R0VANchZEhCH1OWUQUUlIGyw0q4IYIVrTte1qVY/qYWh9qMvtcjbbVkjDEFdn53lIbFrX4d6qv7fovOO9cYhZ132aloXlIYsGZTZEtzGNUAA0NXTk2MWcafMVZkdskLKic8ROisJ3KaiklGLhMGcRZGTfDXFUkAKqCpgNWQm0mBTzmofsNu2K6yL2CsxKRAysJjHmIhQxxQy2UdsAgzKAwcayp/C87iQNOQQHgWTIn/jIz/xnP/E3NGM39JifaOxvn55/dRhunclzIcw4hKII1XjyhU/8dFnDb33pnzX9amMlaFk0mifKWRFQshgDbpyGzRBA1VDNoRkiBBIQH1wgbyb1xJo1g4kjM2JCVYRhc8sLiYJyhVVRdetoGXZHiIDLPkDK33nvu9UIZlw9PD5Cp9tMwREbtCn2SYcEznEdMGVRch5UBZqY11mZHYlms8FpqVoHt7czi1HaLgWG2KVFP5ytUxIg545XbS8UkywyBMbS4agMVVHklLt1I0WVkcWgW6wJlKw5P30YXJFECY1zPrj17mS+xWUx3rqYzXtfbFImfugrZgimmx19FkkGoKA5Jcub4o6qIAIqj2ijbbM6Oz2M/cpsqCuGhKvFslsvum4dh9g2Q9esU9cGHcD0tIN82mSwS7OCHa/6NC68onRRFAYKkNmDcYqdSMYyMHNVFqYKjBszXjQiNCAK6ALD2HugsgsxphhVRUyHKArLAQtKCCBgqojA503PACmJ944Y3BATOAQDMqsq13WGxCqSTYnJspBtrFaBGYB1Ez+pExPLoiRggN945Q9fe/u7P/HRX3z84ovzC1uWbjz55GfWq7M4ALr5bGtrNB4Pufmzl7/x7e9/KVpHQICqYEDoHDm2bkioHLwTUxNF3NylQOfIBXYekiiDOXtknh8ImzPLmEEgibInRkICEZNBkio5RDaHyXmcsdupd6Z+unXtPlDxzlFxuI4pUeV8SfHCyAFIFAEDQgoBAkA2RcGCwIMOKqtoaECmmVCSlQWb2KTiQHq27KvgvOVll9pkQtxHWbYDBw+aMmDKMAs4rYtxXRV1UZQheNJhnbP2yQYBZVd67x0OfWeGRFAGPPzgZlWUhQ/0lK9muyrMzpkKGCKhbYiMZmgkElUzImQVEJHYiakBb/ycU8qOXd+t12en7dnx4vDBenEy9G27WuecAEAFGX1weS3SpoxEYwcIlKK883DdDHZjy1fepRwdo/OBiEX0aN1rhiEKmric2XEWLZiROYmoKiMSgEjemJA554AxBB6EJSpbJIcggMyeaKOtrEdlTHFICqald4Epa3bgAAxA0AiySj0eBR/brscMrJAVNrA4JFNABgN0bZMNDJFM1QTNrI1nllenZ99cL7775n9878nLP/Nf/er/idzdi/tXv/eD3zt+7fZkdOHN9//id7/8/xqXcH06vnOyFCJT84xAOgyiQmbqCwfRJAHyholrIiKdcmAOrmsFRdNCqSArqe1z8ESEAKBRRcQXPCb39BOXH54u750tDWHR9QhcYB6Nn9yvh8d2b8WcGTkN24vUWOpGY++MhiwGZIIggmjR0ATLAJ7UDBaDrpMaYunA1MrCezMCHJW+WTeasCyDiTJxXTixGCHPy3C0al1RrLJul+7K9mhW4big7XEoHLFJn3K3bvsky7ZP5sazrTIwZDFTIB3IKsfLo3snt2ej8ZbzpRuzZAMA3vismm5qfVYTzZKSSFaRlJJoRjWTrCIxJkTq+65rm8XD2w/ff7s5P2+bLltGQFIlduDdql3HIanCMKQuJgOoPDL5Iebj1SCiN7b8OLicM2QBR97RfOzevrMYskwK9kkC8ZBt6OMGG1LNOYmYiRqAMrssCWBzvAl96SzDxp9xyFkVvGNmFombO1C20Rk5qzA4+1CsqoRi4LMQGDGnPhkpMQHDiF302mdhT0OnIhi8F8zoeHNtomC3O61u3X/NV/H+aSzde8vV7dPF3VDax1769D/8F/+dqatDCWjrXsRaQFNEQiQBVcvJmFEJU/rwzNXmBpuZGZoBM+YhaQJVA4b9eTHjUO/Q6wfLmBXUjMxVRKDPXt37z3/s+VsHZ//2T15eQSIgAusTR3URZ3eOXR3UkEB6AvQOnTNBU6CYIgEPBiqIQHVAz9REbTpZb4YnRkcqSIERVWajkoH7Po2rUHgaRAfJD8/6ZS+IKJpbg0IhA44C9c3aylE9GnlETkOOPYiUgVNWMktxuHfv/nw+256PY8qpHaoQvPODpJyH5vSg2t4JRaXIxEToRDaHkGxzbEkkZ4maFVTTENFxTnHDa0xxQMP18vz44Pa9d18/O3rQLNZqKARFUWgWlWxIfRe7vss5MT1yK20Bdiqqaq85Y7aHC5AJ705rM0mi5KD2tD0bvXn7WIUDsncOzVS0b/sY0QcicqIqBp4dAIEaoIIamWWFlMwzsKehS565LEHVJEuMBgDO0TpLEYgRHBiSB1X1GqKqD0poWQQJzSEyq0rbZ0lUFcQA3UrJ4WTse6E+Z4fkaseER6mLSaaEynTz4JVvfeerV6899i//3f/5o8/8eL/qXv/ge2UVNvyghICETrBkjlHbPkMGNWGPZgRkXDpJ8sj41sB5ErMQKpEc+2E+LZ7dGmMXAfXyrLh53CKikRmhEd89O/+zV9/VQYuClgOSgCgUwd+4fDU12ufPjuuyOzi7tre4c/i+N8Is6EOUZEhg6j1vrhcNWc8XQ1QjJjMcBVcyJAHnCNFGo6IM1A994ZxjLit/vmrXfU+MdV0ksb4ZogAb9TFJjqOxN8RmtaKhmZbsPImRgoXKz3jqusENeblY93HYno+Cd+x9XdeBHDD1XTc0K1+OjUMoi0cWMxtXPTVCBDGVLDmJqGQhU0kpp9x1XR7a5vx4cXJ0cnB7fXx4enp2fr5E08l4WoaiHI+RGImmBuvV+vz0eBgSYmKEts/jArYDe18UjogtZm2afjYtCUnNUs5VIGJeD7l2WAZNj1xOEcT6PjqSIgSBzXUdSSomJGreOVbzRG3XOKTpdAQ5x6RRlG0DHVqfBJRyL+bMgZoJoqKAkCdiQjNC5AqyAhIwcBatAl69zAQ5j6iJslpHAwNU8Bj7NJkFNqeqWTAbAfRnw61r4crNw1dee+9lT4HAETnzhqYoQAHVtBmiRNqaXrpy4UbTLfrYLFZHF7av3zl4J5Q+x6xZwcAVhuTUoO8HH0jYPjhrL5e0XseUaDxiI/QMnrjv5HA9fPn12zhYREUCyeYKJCd//sofPLX/HDpXtPOyHvt+PKK7qD2hzymLmmdGMFXph83hNVXEjZ3NuPQVwSAigCVy5aFgzTG6IpSVn00KAThr4rQsg9f7Z8OizU0yT+F8yOtmmARkJjSQnCkUSUwRDElUDdAHDpJdSo6sXQ3jMmxNS2IEy4ERUm+pGVanvhpTMfbeZ0wIiKJqslHjg6mmnOIgOSNyyikP7eL4ocQ0tIvF0Z14frK4d//w+PzwNC6HITgHMGRbjs2YvKqmlIGwrismR8SriDr0q17mbEWo2pjnkyKgAnKMVhXEgEOWgqgu3HJtbYZRzgyYVNWkIArsyTTGaAhopAAClAWzSJYMIFXwPlSLdV+UNCp4sZIhybz2GUAUfRUqYmc5Z3HkaHPi1FRBzbNvupiTcoloAKKiBsR7F6CqYoxW1eVTN35+PB197eu/4ZyPbazqYlbWx4uVoA2RLNt8Et5478tv3P6zwXTQ3Mbh0oV6e8vuPsQsbEnUaDaunq7qgyF/9CM//8yNH227Ltv6W698+anrHzHD+0dv5AhIgATkzQzXq54dhArXy7TKCS9PovDBqldRFzzWlPvcd5ITqANFIABGcBUjYexxezIa2vMW8qScTSeXxVfpHoKYsaIaIq9j3nBXEMABKhIBOk+VIyboAbJiweRJw+ZQtlFResfMDm8fHCOiEt47HE57FQN0VDh/92xRMteOHKCJZZEmJh9qB5bS0LcpqiaFJqYsRgRm6eGD8+C3d7Zm4IKaMihoit06Dy1zSGlgcwAIltkRAIkOIkNKTYoDEJvk1A/L4wfH9z+wbt2dn2DsTk+O1+suQvAVT6sKEJKqdX3X9b4IznsDSymRJMhCGgumuq6y5JNBLm9xm+3hYpgGLJwmTUhQVcXQy7obPJmqiELMUjpHzElEsuZsdWBkBFAxHZJkAwRwTEwg5pLKsh2AqeujZnTBJZW2i53AkKGutA4oYIDslAGiusKhJ1Pphzi0KoaaZeqLS5Nx37ZLLR3mLE1Z7HzuC3/3qcd//K27X/qJz/z18Xb9xT/4Z09vTylLPa7u9roaclmQZjs+O0mg0SGwOqXZHC5fgC5i22K1VRLT9XI0RgvSy+k9uSDTYq5S/+Sn/trx6f1nHnvWU3ll/+lX3/ja7u7uyfr2ctVo1nri+1VmZCO9f9pGAe9dlJSGnAYrGBGRvQFsmE7giDVrNi1pdHF2ZTra3kZEq1GBXUAIUfpB0ziEdT+YcwBkIJtrBAGpIGJCRWiHnNVqT57MkUnOiUJdBgQjxPPVernOReHOVimrFE5jBg7hvI9gEBgcWorSu5QLwMLlFGObuqynq74dRJJ2AFGRzByTgd4/OBt75wvqUxjalfdFkYa+WdflJMVhc2MeHRIAaMqqKbUxtikNxEUehuXxw7P7d5YHt+P5IZMhevJFMQ4OOhhWqRs2h+xMyQCIuir4URHKqnCOoMRMOIo9LloBAFe0WS7OwlsPO5NcOgAjzz05Ct4vz7qYMjoQUVUXRZi4INfmAZG7mNTEe1eW5J0bknay8RsDRCiK4mIxarpm3Uif1OUIBsZYbmzpRBnJAMCyQzMIbKraixI11pYlxEG2uf749uUixLt9+/iNzw3SNf3xZz76P9+ePeGrgpR/9sf/+jde/dMf+9Sv2OH3Mp3PRkHx0gf55MrWxarc1sxVXdx7eDtys9TDB4fD2SkkYxVrO7m072eldl17aVqgyWo4UU3eEsL5E5demYbFRx/769X4yU+/8DN9bH7/z//Z+dHr1YxzEhAKlUPUlISIuEBIaFlMYUBgBDAQUfYUChqGbIqA2OX2rDuuw/ZstovWG74ONtrdfvLo9GbTnfSkhKQGAuaQCibHsDk1GwV6MQMceSoIHIKKouOsxoHIKBTu8PAczIrghmzTKizbiJ6SaZcEaVPdgBjNtO0zAayXTR9l0eVl1C4DgBltJlQsEMcOz9p0uOzCtMjsjh88QKLx7sVueRbqKQCYd2ZG5hEgxcEg5zSYKCioiqmdP7x/dvDB+ujAuqaoC0FNYm3Td4t1bON61a3aoVdgclWgUaAsKeU+aMiOInI2JbT9eX0WLZucJLq8FSZFt+oseM5Aoth0fVlVwXuFSAYblrQQDkOsvffEKSVGQue6rE2jjqhgnlceAJOooKQkQ47EVFdl1/bJtE9WFQQAjKKIZrS5yOeIgBwN61j44sUXPtUu2vn80nt3/+KpuYe2NYzjyfSjL/zUqukPH96zQdLqm0ctwuBOT86fvv7C8chevv0nO/NyudAnr3xiNJxd2nlsVO5PRltR289/dOf+0etv3/zWO/dunndNPQrOgaEMQ+5G5Apc9MO3Xv86vfWdx3Y/8tkXfm46flDmL18Y63T2BITn6+JqGPHff+r/8tWv/+YXv/pvN7eT+5TKkvTRzUAkh6AEbGqQkhJhKJg99q2IIKIhIRn07YOumlJXTKuY4Ztnrcx3niv940cnrktt1j6LGGDJGAgAIIomMzNwxAHNmapiVA2OkVAAkbAqy5yiKEzGNaOOCheH6JmyGmQZxLJBAujECjFM2qd80iQAaQY97m2RLSkg2ebeyTRwZhRVR/DgeHXxwnZLUbOVyzacHI63MMelr8qcMzIRguYY+wYxSxZJaUOs1pQspWax0JwFWRM07XLdpvuHq1Wfzvu87pKhqwLVAWcjPwpF4Z1ZypL8xhNGDSSvF30vWJWUctmIe/bqzhu3T8ykSxZ6HYEfYF16H5xLTlSSqpFCEhmQC8aMOAhUZLUnARpEFkM66XpELL2vCw9IQ4yDKqiyY9ucpUdWRAHNCtYLkzl2DpByks987Gcu7T5XVZP9j16fjy6/+s7e0fm/PV1C/xCff/bXUq4c8f7Wflr+9iq+agIT//NDO7t7/PbJ+X2a7N4/PkL29fTSc+Wzbbeez3ZCMR3z/u7WfHd24SNPffr/+wf/8J1bb4lmI8xRj466qhIHcNpol/NWPf+Fn/9bs3DxwT374ODp8eSDlF9B/7lM4fzugwcnb3zz9a/IoMgIiGZqQkC8oc36wMZOYiYAZfSBTKxbb24Obq4D2pXppCzT7aMffGR0gXms2U8njbM3Lkx+8cL2J1b93duH7xwe36yDBWZEzGKiwEjOYUDYLLQUDBGIgMCMNtafKQ65j3k8pspzXg/ZMJptrg6bWfDkCYJHdkDORPFBI4soXbakCAR1vRWCN0CEtFgtl04rxilRAD04Oh9VPK/DtE+pbW06qCQzQVNGltwPfZOGxnvSJP9/tv4r2LI0u+/ElvnM3vu469Jnljdd1bbaoLsJgCCaoBkRFIKMmeEMpXmiFJqQQgq9KBSKeZdCT1LITOhJo5BIxogcDmaGJAACJAGi0WigDbq7qrrLm8xKf+1xe+/PrLX0cLKaoKR8yDh54j7ce3Odtb9vrf///zMAZGfGqd+UNDA7bidS8/nF5t7j5aPleLLMqUJScY6ZQAyKQSrmg4JVUAuMVs15IgX2GAKWXiEr5PV9Jy/dPHz26l7f9yI5pVrU5lMHJggqKgC80/sjcl/EEzNzEa2iznH05D2p8XJIF31e9oPjIXjfhVjTkETX2UBBDM43hQiITA2Hao0zEnHPLbpHy/7ibPO1z7xIHuq2uom7dvTUxfYVfzhembz0+a/8+32+OH503m8+vLL/+tl2s7+wvPn2sH3hypWXr1+5+faHDx4tbz9/8y/u7d00UbDq8F0t7az9kiM6Xj7+2YffffnpVxfTox+++x0mbgK3gVTcw7Ns7HyrtaS3PvjO5fmLz13/8p17o1LM2rYxatVb15857+9vx4xohOgjM0PTOhylKhkoMaaxSJXQOjAstWp6ArDcqXe9I9fUYtI29LPbf3r56meePvrVGb9fIfr2xenB5Vvx1Vdf+dbHH3337bd/t9pYqgoQM0bE1rGi5VrNoCoEB9EzERmgR2y7bnVx3gR/NJ/Wsd+kMkp1nnMWQsdYGGzqXccwb+KkoTTmdYKLasWqY5jPj/7ef/q/eutnP75758O27e7dv7deHqd+3Tto1S42A1ict+2o0g992CynpdSSnYsKhig5j7DTx3ty7IkdGAHgOPY5Z61lGMZHF/3ds/F0nYYC2ZCBkoEJOLGqqiaMlaNzqLnYbveGAFUBAByaGCrCyTotTs/nbet82O94E+ThasC+NtEvWo9AYyqisoMJGlgWmHdh049JFQoiSggOCfe6wIxnvaQq/VDWQ502fihlI7tEVRIzUHWIqqSqRiwK7te/+tK333rvzsn5Rx+/+/JznydPD0/fXm3ufPnWfzrcKMOwBMjb5aoKY5gv14cNl/VKt/3zL7z8mcne3t27P95enI+Es8nRYnq0HcYsP9uM/+xsDTfgzbOzLz5e4rNPvxpc89b7/08zRqLG4XxOj9a5shEUM+pp9Xvf/wdfeOEvTBb4e3/2T371y/+Dp6/dOtk+fHRy++Hx7a9/8Vt/81t/55/86//CeW08p7Esz7OpcmRQ7PuMCL5zWrUOAka7PBhAUNXA5AI+3JRoMGbNdrq+/23Jr331xb9++fKNJiy2+fzNj77TEQ/9I8CideeehEAQGcA0VRFTAjSzadN4R855rTKZdMwOkff32lm0T45HEWyDC4K1KBJ5pOipIewcNo4Cs7FryLoQxjIymWN+840f/ejHf3L++P7B0fX9K09NF4fj8v7q9L4hjmOy2WSdy2JM5yfH070DUyRmI0O0WoUpkCNyTN6xGjsGg9A0Viqobtd9LnXd59VQi1IWqQgJoVYjQDbL1XIdS67bhuat7zyjaB6UvENiJopsgwAyLbflfBucjhUQjLomzIsOKacqy75kw6zCQt45NVCDqialTNqwzQWRsuB2W6MzdNDEeOTs0UVfjEz0vM9AXJJkVERCJrZPHQNIqagauJ9+fPfqwdf+2q/9XQNNF5XYHi6/e2l669L87XU/PZw8P6bx6vVnptvl2WP29PfmU1tt18+99AsUJ/cfv1GxWjdND/XN93+6P31hf3410HSocjQbSv5D7ePU/8KjTx4bpS++/M3YTn767g8PDuPpsiRBMiqiR86Zs43Vdz/6wQd3fjzq9pOzn37uM1/+o5/8sy9/6Zs/ev/3v/OT11+6GT9zpXn70cWqkPQVDIN3CkaMiEiMeawmRoZAGDyLaC04n7Ky5iRMtMrCzg4WvLrguw/u1vJ7X29+/eAAgncvPffV3/rt/2NZ3Wd2SMBoZOqQRK0oEJFHErFpdNPWpyKBsfVMjDmNSLw/bS8uluepxOjLoH0uu5hjJmwddsF1nrrIngAIZ40babpO1VA3m7N/+S/+K0WMDjbLR5v1xbRtbsyi+Fg1jcXOV4MLfjvk+WIRZgeCjOgIGNGZcz4SgLFnIqcqjgOoOudjCLWWYvb4YrPuM5pVlQSWAKvtIA5WzQAQizVjOSooRWsXvGciCKZapVQdi1Szyh6YHpwPDU2CQyUYknSB+6S5aFEVsFqtOKdiaLjLYxqy7TumEMREgValupyMbFtoEv3+tPMpn/UyJgWyXkAMAIXQmACL7nhktGPh/fj29rUvzX/nO//FxcVxo/s3bk2uTpsrk99u6JMRbgH8bcKXTDNpunrtqcns8nwvfPze7cPLT5+uH2z7h//iD/6hhQsXqa8P7jx4I49rMFb+G1LfdbB3tP+tvYMXs+RHJx+9/vbvf/TJW5M5a4TNhZEDAHjpcjvr6snGlo8glexi4UA/eONfHc2uuHb49vf++dM3n23hzz784HEZaOH9o22GasSWUgYj9syOZBQAJEJ2dLAfr19rT8+2e+auHnbfffssAl+axW1K6ujiNC1ml//6r/yHv/k7f/+bgb//43/x4Z3Xv/XN/+jWrVc+eHuJ1htQrjDzhACjmHfkiQA0EO5Ng2itBjXXg72mjL0Stk1glXcf974JQyqbIRuxQ6tCE6+Nh4DmGDxYSxQCFbEcwma+WG6WZuIDAhMhekSDkTQzTIGoCvUKXlEVGf18fghSSZXRE0UX2zYEU6lSiPkJGxcdGjgXVI2dT8VO+7LclmoEpgSoBtlwb++Sd83Qb8bU55ovah0HVRWw1DbOOTdCNcIkKoZiWqq2nqzKakhdw4Q+7njKxEnqDpokIqWaOvAO+0GQydTaUtvoCNmY54w5U18hqSyXo6EdTWNLfFbG0WBIAjtOFhjhzgiHhMAEjOQSpj9+879LBVKuOABMP8PlxSvTczpI2/yeh59O45cYp4tL+wn749NPwvTW6x/81qaeGCy/8/3/dqvr1kEZGGjzs7t/9LP733vx8te+9Y2/s1yfzQ8vnx0/PprPu+nkleefvXXj6OE/fi/B7XG0ZuJLKeSA2qognsHE2BEhlrUmuPju678dZ/727Y8mbfzM1UVK7nzcJA0T8ZMpDeByKVmzKYioVSMPPnIapFbbbldsemu/7WBz0LlPztPpAALWn2VEOl+eXyw3v/QLfzWP/VjWr778K9euPPNP/8X/OYDE6B2joRHBWITJNc4zGiFGT2RS1QiBHJjUXQRJdPHu41MBdKXUatFzUauAqerBxDWepIhUU2F2EAMbYj+O1w4P+tynQQwMVRQ5k5DjLrbeOTMtxGraGSKTC6Hm1I+pFTug2M0WvmlM6pgKM8fYIKKMqg6cd9ODK1X9cjUsN6NUM4NsUIAyWRLbqUkmsyMfJ814NgzrftuXWk+yMuNExbMGBufQEIsIO6pSjYkQZZdkWCsiMQGYFYWdOhIRS9VqHIkcW66mYOtUfSDH7JgQYaM4Ozg4kKrcbLPWWg7nNaHduxhsx8ID2KkLAHZHNCMEBHXeyTZrHlENbl659eu/+r/W8e09/W5Q6rw+PFmn+m7bdo/P052HH350593Lly+/8c7vv/H2Hy8m3Qf3P2xmDoDAXJaiuC4jnm3u/Zsf/sPPP/tre1P3j//5//2ND6+OQ3n12S88Ov+o2vq5m994dHbxcPl+IHJMHz/KESyNWMHYkaSdeBEfLj/xyalq6ofVZvr5l//9y8NKx36eHmu95w+/9LjSH/7wt8wQDXdh5LUKGF6schWYtvTe494rX/QCBFkt9QqA+7NLv/r1v3n77ieffemzKY+XD5/67Iu/aMo3rr2yfPw2os1aDo7XQzHT4MhjjZ6l1MY3Y8oGjlAcoWk1cN7jcrN8fD4s5h0atVFWfQXEzVAD0SSGhkGdq6WqQTVwjE6hw9KnzaX9g3vjQ9FKhAzgyDuE6NxpX/pS9kIEFVAYc1GE2C2uP/P5y8+8fHB0iaLXKrUkkdI0nfehlExI7BxzmO4dHdx49kc/+MF6s11t0qCQDQSpmKgBIfWbi2EsoWlaXxmBEBApKZwlJcSsuhkB2ZQ8MzswJjQgAyu5QuNcIFFF5p2NU9SIzDHlKtuEDuteG9dDFrFUtE/SIiJKwxhmzaaMzWy/7aZHxI5Dqelof/8z2Kz6vB2Gk7Pz9aa/GPqc6y7ZR3eP+EGtHwEMSClO3Fvv/d7zN97isC1JTs7pw0dyGe5/8MZ3X3/nzT4LRYAPwDlu3HJ5DgzOEW6HnQSIAdAF+vjsrdvHr19ZPLX+2e37q/c/ungDCq03y5vXbj371NO/8pX/6U8//MOH33vbOd9viww2MqJn16qJmSo7VgBCJGIfahnrwdGXrl77yhevt2/90X++Plm6BmuRbU6GABXMjAM5T7mYi9C2wUi3yVapDkOqik3r0lDBbDo9/PJn/7KT8OpzXyCBaZhNmv1Js3e6PP8bv/Y/e/NH//X77/xeFV9VUrUQnGhhYhFEYjMVBUAJjPMmspXdiOB0lZQCMXoOZRzNUERrBR8coDK7xnv17BFDDMGzYunGMWwuWj+9ceXoZL3pt9uktYrNW7feDhdDdk0z9YFt2Ov8pcOj2eKwO7zWzPaZnRHiDj9r6HzjfAPkAM15Zg7IXNlff+4ljlPDTaHUiwxClRAA2BuzY7MqfU5bEl9VDcB5zwBNwNZDqTqqDlm3kmLwuMNy1srehcCAHH1grMvtoEC5FkFytptBY1Hri4oOs7YZcy01p6LsDNFMK4jOmql4NgBNPQTYOc6ns9l8ZuwI4RkwOt+sTs8vluvNg5Oz0/OVAjgvZMWILET36OyTH+dHrz61P2QZ+smHJ5d+/80/+YbNv/H1v/fg/P9y+957iACCmo0itwuuoxDBOJqjqoVc5wmr9wA+/PDD31n1K+C05xdXjl59/qnXtv27Nw6+tt2eDds6nxycX6w8dRAGVWBvRJirkmcRia0PXbPtx2nsfuUX/4PPPvetiZ/ce/yby/rBhbZ1WW9cvb48+wkZ7NaesUVk8IQgUEykWB1ECwBjbCnnAogcXT9e/PTt333l2a9c8S96XhzOeDJ93FC7P38q8uTGzS/70Nx5/w9WeQtCaOIZSzVyyESpCBIx0v6kcQQIhCjrjW5GY4fBYcm1CKCBIwIUBGiCR0IkCNFRVauqCKzWMMwDSN4YhSvTLnddFlMVk9yXujefO2YX3JWum1J99sb1z732javPvoSuCTGy410aqI8dkXchuODZVVMlZHKOiUbjSzeeOj45AyAAEkBDC45NYXfLBkQAG8cMiKAWAwfnJlRab4CgBopuWaqUQp4aBGPMVTajTltmEocUfdz2CYCqKIMFJg9Yq4oPfRGzcdLG6JqaSs7VM5lzfRJM65DGs7ECWjM7GEW3m/4i3RZAVWu8n0/a1XYDiAHdc9cPGk+3Hy0dex9aLX2dTtBF3D+gDz8a0khn2+7Dh9X5+qN3/sgctBNi2h0MQA02gwxO0QM6ZoLdz55NsQoaIND90/tGNvHda8//+v78+vn5u/MIEz8Oq9Xh4pkvPfPXnn/hy3cf/OR3vvMPu+jitBnHLGZagdVPJ23Eusrl6rXPfe75r2+Hj+5+/OhB/8PMQ5qkZ67y1784X+z/7ZzHZf6oVBpHwaI+YFYjY4AaIhqDkikBR0eMChYU5u3w7p23OVy+uT9t3Z9cm//R4/X1/fbvYvhLN5999d697zU8bgDa1tWUkBCQCcy0FoMQeN7E6HhIIyKM2fpiBXHiGSFULbM2ipRtruxcrgUtNtGDKSk654zAwIiBHXceEGGV8qYWAorszQGhKVOtY00WDm7GhmZcXnrh5Zc//1o8vCKl+h2dFsh7QvJELsR258nRXQIcw7bW67eef+0b31w+urceM2LilLeqoxkoSlUzBVMkAAQ0M0J0jAitQybzYOAZCILDJFoNzNQ7RCUxWfVp4nHeREeAYIhmaH21DNB6JOUdwnsoWiRNmxi6VqWOuRKRZ79NyQbtkM77cv/iwVCVmS6y1qrF1KOLZxcKKmqt98Fj2zSTxrvTbdJie/t+NgcAKAYfPsBPHtfT/qKKTDqfbfjjt37LEWBDUIEiQVVUU+bWWdqIGAABN1C1ymBdi2qaR7155aVfePWvtaFbb08O2jvTvXcVOLhfPmj/4uVLX//48etvfvBdoqDVb8+LczRvpm1b555i5Vx1XFz5xmt/+WJ18fjRucHjp46+WPrb158fF6Fo/t5nn//P9vYP/9G//N8/6u9pBgBl59mDFg2MBQQCADAYGhoAMlCt9Xg1HO1NX7j2zCQcrNLAy/R49eE1/U2QWw+Olwf7T08Cyp3X++2amIgIyQkYg3kPk+ijw6Qy5opIY6nAHkzBdL3tQ9OISp+F2ItWI1drBePppPOIUgoRljQgADJ5Z2q6CNCajaWqFVEzpAqQjdh02KwHi1efvfGZ176yd/0WxYlUIcc7TBtTQGJidqEhYjM1EzA1sMkE4bJdfebFZz/z6uOLVT+WTLyqUBHVRLWImhmgAiISUwgxetfW1HlofXAkFQxH3dFEqlo28MZMFlwIHoeqPI5NE5BAqgSH1XjImkQnzhEpIynQqFb63AlOGl8BxiLoUInWRZntYV+XAkUVUyUKITiSmosAUWAORElVqzrKs847EpxOeLaHYxEpeL7qh76vaBAsEGcFGao3MgbnGAOZlOcvzRade/PhEsxqAXOoZtobgAVmIJBcTa1riHm8fuXLDx7+yMm9xg0CENx3AF4p5anr+59bvLb34Uc/ffrK5+4+/uiZW5+Vqu/f/r2Je9cEv/3OZhLi2fL+md3P6fzlp38lb7SOnzy+/6OHtL119ahzw3S6+Gvf/E/+y9/+v5Z6wZGR0FDIEMUrGiIgoCRFAegweB6Lng16sn7zbLv8a9/425fmrz04kZzfWVG7HyevvfIKff7ry+Wx/Kv//P2P/ogM1ayIIBF7aJtgWjdjrWZkmGtNAhElOlJ0jTNCu7sayDAQZK1VnfOOkFC1bT03QcU2GkrKqgbME8etwjDksBOGopFjAyoCvTZI7vJ0/qXXXtu7fouaiePABEhI3jkXmSIgsXfk3C6rVgUInIEaCzt36dazn/nqL96+/cnjkxXFYLknU0ZsJxMRTTkLgHPOe+8Zo4x7zhpPgIAAkxiAdJKgL6kalifsB3Wo3riLcaeGbqN3VAgpOo7E2yqDCBhOgqKZIxa1VIGSAGhx2CAAUVbzVj0xVGOiClJqBYHo2LMTVSUw050AP5UcQ3DthPf2dOhxvbFuirU4JUMxTZqgWAUQYASpysE1waVkCBIceMK8S/EXm7ZhzNmzM9SUDZC4hduP35GSrh996frVL3zw7veMLjznCjPXeAJoG4yzo7ffKg3Hl258fT658tzzz8cWf/j66a2jl7/yMsfofvzuPw1Rnrv8xdn8ltv35fLfPTn5ktS+4Euhmfzsg29/fPcHewfTsa4pYE2FPBsCkHrkkgTREI0ja5HtulBA56iIPD795L/8l/+n3/jWf/zZW/+jPn+8mD5VdfLw4b3l6v4bb/2r5YMfTJvGRMaqSBQZG0eb7SACBJBTbdqgpgZAQLWIEJnD5TZHt4s4VgAac11v0/40dsERGoCZVo8oRNm0cS4yrLfZe89mYwHvXBe9A12rqxKv7c1+6Ru/8MW/+GuTo+sYWzADQs+e2DkXiAIwIRMSmhoo4G7VpU+InbGZ3Hz+xZc/+9q779+t41pBPEBkMoUQmuCDATIambDkGVkkBEPnyBFOG0dU9yNsBxzUVABBHREiAoNWmXZu249iFAMLgKF23nmGQZUERcQ7JrMd6aOoOsJtkhisAoxVOTjRNFTBneeHEBGLKAIE70FFwVAUiI3RMbp2gn2Ci5MEin5Bwo5Skoq4m9ABsUN05J0DsmFIJesHp+XBCrYFvEcCYoZJ6yXXUiooAKMLRIzO2b3zj//sZ7/7pc/8lctXf0Px5eXFJ2ovXb787K3rzyuPfV5O99vf//5//dd/+X94tnp8uDw8XFz64mf/zmsv/422hbc//DN058ebP0Y/Ezs9uvTUW2+evPT8r0ffLjfnQ04vPPW5H7/7u8dn99qpV8FcwCGaRwGrvXjPVYSIlEySgqEm2LvEA5sMsNiHe48evnRj9eP3fvQ3f+mVGzderVbG4Zn5fPqnf3Dn8eP3F11EqorceJIyZsHoaOzTppgFaxySaRVlxOhJRHOpTdtCTkWtiADieijH55tIuJg1TQwYIk5tLFqrkMGwHYIn4l2uJ8fgL186vFhv0xYOZ4tf/PqXf+lv/PrB86+6pjVTAHhSmsxIDoh2mABU1J2w5UnMiMGOOefDdH7w5V/6lbv3Hj3613/QgBFo41itqGk2UEM2CygtWctGBKUKo8272ARGxvlYJ73UqoagZoGxVnNTL1VFjTxKL62npE/kV4REAIqAQIwYGEy1KiRF8jRkFTVFzEpWoBdkop3vXFWQyBFF4lSrmLFDINxleBOgS0NdLdUEr13xKaHUSogpV9c5ctgailkWVYDgqZpp0UK4IURCAQNnWeR0vYmOUSgGQgfOESH0Ywl8+MKzX2na+d6Vm1WPusmZc4f/+vv/aH92+Qsvfn25uv/91/8wyfCvf/j//uXP/83+9L+ZNm/vXftL0S9PTseuOWDjWvQHP/r2x+98uHew38Urr7z81e14ztz/5Kc/uHJ9j1hDQBS0qtHB1758/dHDi7c/WM0mjohXF4UdgQH7HRHG+aamQtMZG+qbH3x/c/HxcnuvP33vb/3N/+yVV39tGLY/++m37z36yAyKyqQJ66wIVgXb6APDcj0UABHd0fFMLbZBwYZUPDOhiUMRqKpjxWLubJti40NwqBaDaxpugpMq681IzNEDV61mTRcOj44iwP0SLu/tff1rr33rb/3tS8+94JpIAIaMSMyeySMREO+WLWBgKKAGT0JHCAFVdnNkx6Fr9/a+9NUvv/fhh/1b75gmh7LLI2kAkhYC80YeqAkYHSlaF/yk68gBovjoZq0OfQazaigG80kY++JbQw2myIRxN8AnMpGGWQWISNRUzTE1xP0u8ZEBAEWAWAUgpbLNFRgRSc2YuZRqaLxDZoLWqgSgKCG62XziVhdKzNyBc3a+kjEDkZqCpnp50TrDh8OoZijASEYqRo5R0TyjY8gCwIwAJUMzdTGqAg2bCmjjIL/yF/57i0X37se/z5F++u4ff+3lv/Xss9d8A7/zR/+vP/ref3fp8EAws6c7x+997+1/9eVnr105+Imvb7H9aez+5+in7N1i+sVH+f276ZMPT97/2su/fP/RT1ardU6r9+/96KMHdnb2sRnr7uGG8Ojh+XYtYAZs6/NiFcQpKmq20PJ0YTU5VFG19VKCSw/O7/jqhy789j//f7z/9p/65ujjOz8YJQXvt8W8U0YopRTgzqNWyUaeMZARgnPkiLzjMWdHHL3vi5mYGaExMahp18Zc5MHpxeF8MocoKo3jNGatamDsyTvnsJ3MZ4eHh8fHq6tX9r/05de+8q2/cvmZ52IzMUBmBuCdcNDAdkY5AzHZvQegn8YoI4gAERExsfkQuvn+0fUbzz3zzIP7D5fLU4AKAATSOheBAJHJ8ElsHRBRDK6NFELwRbdjQQAEJAAkGCtM0bpJGId+r6FSsYAZYetwLNYXJKiEJASmkNWKVCLyDh2wmkWExrtlpbNxHKoUMFYi2GlAwXsnatuamTA4MiNVc09UFuRcYHDQRV5utCT1kdWQHPiAAShr9cGhM8sGBSUpmBnapAsq5aBxFNzDTWKvyahIzRegIrHjnBQKfXj/Oz9+5zdRQ9J6sbz4hc/9e2O5d/r4EVTY4HJzvHzm+sKRPLoo7x3/eFt/+tLlePPwku7D1RluNp989sVvPn3jy79J/7cfvvOvtKe3br/54OxuSmNJeZuGo9ZNgqr4Snxqg1Z77/ambaIPNBY1QopkZFoAAfIopTNCN5lxTXr1kp9MrI5h5un6PhF03/3B77z80l+6dOWlfnuMMgyqLhVHHhCYzZmtszjv5g0HVHbcOHYODKHflCsHrZlVBTIUAwCMKE1w3tHebOIJAKmoYZXAyGxqMHMNAU+66eLw4PBwb3ux3Xb2jV/6S1/8C7+4/9TLrolEzgzUgAkRUU1KScRsqrtkK0Qi58zgCebCaEckIyLnvEom549uPvXFr37l9kcfjsNWNKvk3XCpCwygSAimACimkWjahCY6YuqQ99t40urpWFSe5CJkqU1gJD8W8d71vTqmKhXMPEEWc2xqRgQlS0GMaI5JVM0AHW9EPzgbVlmQITCqiAETo6qqGjPuHLwMpKjI5ggdMQE5Lda2hAbri93OXlSBPfroNwS1WqmCjtDhsAvpctBEj2AM4A1MREcThdggmm0HVUXPhgauwccXH6hwqlsRRfSvv/9v3nynvvfBG0iICDFgZCWrKlrUltvyxoP+/QfTb7zyC7PD8ftv/KNHq0c/eucPlutlTRUqrPuL89U5AIACETXTaTdXdpqKuyiIbAZAjFcn3fHQW0QyFLVmQmMR5zA0xmSMxmA1w/kyXn+q+caLl4dHfhi3129+7mDvZsXDjz/+LmgRobGgabp8MJExrTfjRmg+iVfmsZaCSF0MQ+pzHvdmoWH3aDWOotMuguSu4eBD5ynG2DVx0Tgw8d5F7xw7P58sDq4cHhx2k+n8cH86aR999B6a+7Vf+AvXv/SN+bVnfIyIULWC6q5JIhKoSi21jGCGKjs1Nju/S2kSYSQ2M1ERFQBk52KIpZ0899nPfP3e146Pj9ebNbBJ3jUqB8RqKmqOeRLd/iw2YXe0VQRoHc2DTb3vrVoB9kCAKVdiN5R6sMdDJjBeDSMhMELdQTnVQCw65zwFghFIVJAsobu3GtaqzOgZO0+ilkV3iVFmgGaNYxUTqyE4kOoQgnM1V7fDqm43xdA4IjJMomdkLdpvEwKqGGhlZm6dVjXVNFbZmnd4lhRJQ0fVgAzVFL15piIKRtyhISKjL9Y5Pzh8+6PX8yYbAQCwx7blhydjGTSDAWFONGQ9rQ/f+uRPz4d3737yITf73/7Jv1GELgRgETRmJU9VwQUQAB14GtymALBpgcY7s3q0WBDo/T7lJGiwt3dw+eAzzz3zwg/e+scmtFz3AmRZg+HJMfTNjIsUOZ7v36p17MvFZHJlWI4OkwCNpfSpErosRVXnwQWEtml2xVDFwAxJl2M6G9LebJKrVDEDPJjPri7ag/29rvGdt4kPTbc4uHrz0s1nLt18dn5wpZtNEMXSdntx7FzwsZ1dvtkdXMbQGIJUqVJQDYl29iMtRWp+kuFtpoBAJFoAAJABmb1DJDNQVTNVU0N0Pjbd4qVXP/vlDz/88U9+XIoqWZXKYZc9hgE4OpxPmq71TROJKGdRRXah63xc5yxW1BRYqwhAklEY+6HuT8L5evSOgvcAijtbqsg0MpFNm6gKw1DEbMh4dxySoQNQUDM00UCEBma7uCGsojtFKAGSwm5CAQom5lzEXNSQ4oIYEA0RCYByqYBccyVCAKy5smMkQLBaAQUE4Exq1yGxpY2MjIhGYbfXAO+hDEqmQKQChCZjrcWACUTRITssGTYXFYmA0DM454exAuvrt//wZ/cLUui2uVuEfswlVQULnU81K5v3DigPbYp2cL4epkdPP1v7EA7eu/vjVOGD5TKKk6xoCApfePpXvvLKX22n3UF3dOfxT3/vu//cESyapnN1TPXP7ty+HKk4vXX0L7Vcd/Wrz9547afL2zt8GxBvhuTZzbpWrBJIzdJMWlEZxuSQarUtmJnGxlURQuyrnA61S/lGs3ft1q2bt57q2jibTA6Oru9ffWZ6cBRmU2S2ksq4VTDfLi69eAl9dM2UmgkAllq0FikZVJGZVU1FapKckZCYjAg/zQZSMBMBsVLY+YBABqCmpaSciyr40OwdXXnlc6+cPr57fnYqGpabLYJFj4E9IQTvpl3DjCkVx84HrlW0SPRuFtwwiqBsS228jwgFcKxwssoHM5xPu25iVXG56ZsmbkdpPUbPCtBEfrBMRmYGp6MOBRis7BRLYE/a3i57AmHHhChWiYmZFLDx3jE4z86jM0MAdp5UNPeFiMWqigTP4HZmbZBiiCRFwYwDE5mMgGhMiAgpGwcGBDE1NABj7wQUCaqhVSDPvYEKAAI5BEdAYEKbVQUAIAXArmMTAwUmMAZEVgOF9cElSg89gDpzkuRgHtdb+Utf/o1tufjx27//5S/8ct/nZ669KOViU9J793/o2MZCQ81kRExf+cIvPnv1S2gsGV559hffufPjg/2rr738yz/6ye8NY4qOHqTTizp5+pkrm3rXpdsih+vt6HwDpjkbI1Q11doG17V+O46ui+fbDQC3TczbkUOTc2Efau6RatOErAjMQ58eHp9N54/3rz119dpzh5eu7u0dxtnCOadZFCqaIjVhf9Iy73xWyAHZ65P9omjJqgKVVGqtJY9DGgZiarqGffCuQQBDMFOVaqImKLUSOwAUraVkqcUAkcF306Mbt15+5aV33ni9qnTTZtxsg6foyHkGtFqL9x0zSJU0VkME5OBp0fl+LGPRorgt4ryPjoacC9ids3Ea87wNq1GGCkgoZvuN77MxwKNN3mRpGEM3uXk4ORqygXrvaimISMw7pLiZqJohqknXtIBYanVM3rvpdLI3n82mUzfpOCfYbrNV6xyDwaTzZxsbk3pB7wkRS18QERmIWdTQaZixCKBCRTAyzCBq4AEM0EjFQMF35DzWolVkV6ZGRgqqhky7jAQk2GU0O0fjVgAR0JrgyXIxELAite3Mafi1z924PJvd/ujuz+5vPr77zt/6q//jiAfkp5957lVT8c3h62/8VoXa+uCZxEGfpUjJtYbFbLAhUHv//OPjs/P/xX/wv3OBvv+DfzkUWLj9JGeXr10mvb7pVwfTddN2ewevBqbj4z8bdQzICFpEl/0I4Eh03YuJHswjGu104JPWn6z7UtEHEpO+mFapyqmKgvXjsFyv5gdHgJj7VTHrFvuOPbJ3LjjvDM1MTRAAFa1KKWOPplZLKbnWiogl536zWl6c1JLarmume9PJookNEFaRXf68ITof2AVArLXUKiZVzQyJQ5gcXb1085ntZnN28oAcyrRNpXdopqCAUnXMGYjZR0QsInkYczUx4hDYjM0CUyoiVpQYDAghG6Ui27EC02ZIwRESFyup5rFi9L5o3b90FLtJyQUR2XsiLKWoVDUjQkIDZAMyzTHGYRiqSds2YM4xmZhUc+tzKaWaIgKEYE9f2rtxafKTDx7evSiqlrOAKSPuJKXmDD1gpTDDnAEKiCgzKIKLZAhaVUYFj1jNKWvWGD1VGcZqUoEAmBXM0c4yDMAACiAGCrhLMSM1MO9ZKwHUGGm11JTk5GT77P7szio9GCvnDx48+Pirn/nWo/N7iylK8hfjyf2T9/7CZ3/9q1/4ix/f+9nv/Mk/IK8Q4Hx8p09/EjGMcbj3yem3vvAbZQk/vPPHtcDUd8M4IOt6+Wh1+vipy8+7vVuqB48fv3Gw2C/lmYfrnzaBd+7sUdR62W/iKLXxTFTHXNm5SMYEPvjVmGPjLetGVMVS0aGCOj/fW+zNpo2jlNf3fvrGpVs3p3t7wBTazvuABrY7MzIAUS3JpFoex2GDqlJzP/br9Xp1sTo+Oblz93bu+xjDZL44WBwsFovg/ZhSFem6LrbtdG9/Op35GO2JV90MCJAAmZvJ/s2nVcrNWzcGrdvVetye11oI2IUGiLJqLtUUixZfKzVj7ccARdN53fbz1gdGRiyitUotGjx6dLnWLMKqAtZFFjMFyAJNYFF1TTi6dNS2k1qLqtEOaFNrSklUYwi1ZOcDskNQAJi0U/LkvIA1JWutRU2doZJDEevQLZqAku8dawKkhmRUBIvR16qSFQGtGiDpIG7ihap6BAEDQ08mRm4X62mKQA0dHjbFypiBkMJ+TIPmUQAtNE4EkNCysvOGxg7SUHNSdOSQvCMgkyJ7B75fl3FpDPqjT47fuH/eJ0tFmzh850e/+7XP8rTJw/jfZKl3Hxz9J7/xv7l2+Zm3PvrOz977NlQ4OGgvNuNytfrdP/nNV55tr5R8Zf+Fsj6fXH/h1e7VH/5scsDjNm9LmJUShnRxf/lQ2H/9lW8Slfd/9l+pIwVGhB1PQ82KUAVzRES4HcVcQCnCYGrDkHxgEakAxHyw1wWCEAKCa5v27iefmNjlG9cqALJHQBei84HJaRUDNAQiNgBGqqoiVWrulxfb7TpL+vjdD372/sf3Hp2dLrellBBw2rSTLgbGSdOUUn1sFKybtM8++8yNW7cWi312jh1737CPgASgiODbdnZ4lC742RdfGpJsV2cpjapapI41r5ar4dHj05NHw7BFNFFZJX14ul2t+4DQePIEuapnQNEaSEwRdcyS1SKCIRBTFWFA572piNnh0bWa87bKDilGzMRUS1VTU132WwJETo6dIJoKANpAIarBJmXvvSNAh4CGcGXS/JUvPGdWfvDOJ7eP+4JqDGZgAFVU9VOvt4KNFQ3A1DOPWg2tjAhiaFZFIBMYRueU5WJIJrpdCgDO9oNrJTSYEkgy2JHPGQUEHWLElE2QyIAIQ8Q2hn6TYrB+6zjUeXS+o22lLtrUQtUaF/jg4sOpO720/3rj4JkrX3/ngz/4J3/wp4/OHhRRNi8qHjHXuh0XUZ7P52cfrMqNy347nv6L7/z9ZyZLTPlwr/loYwbjtJlut8s74/DM1fdu3HplefqlB/e+u9c4AGIGqYXAYuOqVs/eexqTnq3HmYfSRM/StY0yqOCmCJuqlHYyPZhPay2nq+3LL79y6dqNxcHh5Vsvet/srjKSSq6j6pNd4k65QbADdJmIicjZ8eM79+7/9O2P3vnkQZ/zjre93dTj1YWpEprzTMBFJEsNTM89ePzqiycvPvfs0aWj4L2EMbZTDh2yd+y8byeHRy54Zn905dLB5SvDmPt+3fdbWF6M63E6X4SuWV8s1+v1crna9v1mOzJCdOwIPWKxyoAKQASmiIhjVTErYAwkYsU0Rm+jJkFum6FqWV4AmGNXRXYnDkBEEDNLKZtZjJGZd2HazF7BQoqGfRbQ6omcK1mkWHR2OA3Txez1Tx6VdalV0AhEgClngd00GHcjASBPobXNxnJSIgTB4KlUsYqghoje81g1FbGKRkYelxeJCb2DEGkzFB88OFRTYvAtEULbxe1YiMChjWmcTNv9IyOuk5lLmf1EpJqMsinSdV6gPnj88cX5mUO8dBEuzfSj47funv54NsNZG6rAZlvWq5q3tZ3RM08983iE80f3zlf8vU/+fv7TEerw2XlXvQ6FV0PftVMzI3IK+sb7v3v3+Nrl/UvDXa4iwQcgCOABKRBG5wkpOndyuhHDK4cTCM1y0/tg60HHmtYZAMg7F4LfpNRst+v1GtBxaIE9+8aHwERVJJUkWQyM2SloKUmSM6ulZpGCSMwhxPb9dz7+wdsfFTRUaJqIQMiMWHd34iQIWkyt1poSvv7e3fWqb7xDqw6t7bp2uj/bO/DdHpFzIRpOYtMBgtZELszni7br3MV5LTJO1xfbZarFTzpvJANs6ti0LQlHq57JO2qAUjEgBLAm+mVfRjEC2O0miigTlypd9BwRZ3MFMkAz6McMAMwMuru2OxVp2k5q2bnDAVCq1ZoULOdiALErACX17MwsUthW+eEHH4HwZkwhoBk5R7UFKWD2ZKNGQAhmCMTgo7XmwOHYi4ly69spb9alVvHBGar33jvIWo1QBdqJm0ydmRDymBXJsO7O5xgopDyaB+ewbVGLDVvNszqZRQIcUmoaCI1574rk5UbFEghX2ZzLhoP1Fs9XMY15zCJrmk2p5loFJ9HXIIXtB2//WWw4Ol6Plfhe52PX4odp2WlYpjEZTogMqoiYaBo35/jhMD7g+d758Xkr46QNLoTAMA0+5UIMxH5/0WWxjdjqdLXbMm+LAWE1dd7Pp5PttjdogXi7Xp+ens4W8xjYIYAEIlZ0ZgAEUqtqAQQzEVVQLZJrFftU+O5jS0wyjsEHH2KWOoyjVhETA/A+qEAtImJoCor3Hp3/+PW3NY83rl8JTSsiIupVHDNzYEbnPahAzSqitkWg6WRSta77JROO2+0n947vHS/PLvpa6yRQMGla7xzmWhDQB6+1RIL1kEeBquD5yf1ExCzgkCtFjNFnrbmIjQZgtQqz2wVU1VoJQNUIuYrA7udEQ3REoICA4Jjz1rXzqgYuEMVIroN3ztYXK3E+KJlj17Y8YJVSnGMwcwhZ1HQ3m0MDzKPlJEAAimlbu0kkKq5xzcSFhgBRVb3zeaNIgBU2F0XRYiADqdmBsmuNGMdaSlWVyt60WlU4OPS1qgqDq2enVSp1rc9VygDe4XyfGfHhJ4hkzvO40WYfT7alZhCtwbkYeLOp2370LZoCOfQOAaGN5ilYsbOhTILblpIETEjFXAiNAxfJR9+0kcmc+Ut70zqMqDJvAhH14zhpmkmEdT8K2iTw+ZDWQxFDx0DOoXPT6PcPD/OwZdcR4Wq5vrg4Pz19OJtNGh8ZHRgCCrEistaqUsAEiSSPaXOBquiDqJkUkZJTyQbjmLf90ON4ulqaWUqJiBAIFJgZCEXFANCACFT47oPjF5666mMHFJB4NzFlAuccAjaTGaDJ2Jc01pLGNG77zXpzsV0ef3z7kx+++cGjk+WYC4F2jquxIyxF1YVS1USHMoBzbFiAGjYAMoDAyGAiFY1mwWWpxYLVCsCOsKogaK3ZEhKhGWStuxkuou2AbEiEWHc0BFUVBAQcEkz2yPmGS63rQccKGB0ikJhvnKhqNQQQEAe4P5ss0zgMFQxcgH5r69NCE0QBUDPSvs8ANN9zviEpHDyPOffrUauZqSQxQAo0rgffkpk0HXPATV/QCBRFJLRuTNJ21E6wZE6laBJEmkyxSlmf8nTB7JyInjwsLvD8kNJgqejpcihAKNUMthuFORAAONyBgEENEHMVKdBvCjgkJHG47rNnP21aAGm8F3TOkQ+OiNkxqAUy8tSP9fhsCwDzSQxsVWSdZFukAxCApuFSDYh8DNw2kxgjQ9oxN5RKzqenD999y4KL+/uHvmmIHHuvIIRGaARWTTWVvDk/v/d+zWVx5SY2DaiiqXM8jOPZxXmqRc1yKaJKRLCDG++IuvaEighITOi96wJvxwSA3scQO/aeiEXFOa7VPg1YJ3JOcq6iYxrOTh798Ec/+aPv/fRklYAMCTySZ2TQyAwmYFar7mJXmd02lVIlIjKhmXlGRBiLTRQ4IJErlcdcax33upiGmoqYYbEdDNfMcKea3eHud1qCTxNFDMAAUVEZQs7iUq5mzAwmuEtaDRTUai5SRwNGArBq2+1IjtBh14WDPTw+yYaIAFIVGZoJM+Nk4mND/bbksS4mc60qSQGQdr9PU0QgJoNd8zMQmDWhFug3iaMDBRfVBzy+X5ilnXlA2Ntzbctd1+0fQCpjqeAEn3uliU0so95+70JJqwDqjmwFUiyPu28MsYKiEoJ3lJOhYewIENhQq6CRmY5lOwmxpQCTRg2889FH55i8qzVVg5KlVmgDW62rjRpBn+Aiw2Lhz9djUpw6jISxbdTHtulMivcOQNWgSO2HfH5+cbE8P18uJ/NFLtmBEbGAMQAzMfNujZnG7emD+8vtcHDjKe+cQ5hM22kXHdqoMqQ85ixqTRPjzmMNCLuAYVEzY89CRGAx+DY2IcSm7UJsiKjkXFUMCA1ySm3bFLWhH1Me0zhsN5v33rv9s7fuILjGlaoVARxRYIyEiNB4148peNoMVQCgZDIwg2IWHYnCk1Bwse0ojpgJk46OaNqGKnWseUd2YkRVZSJHaMCgYIhPPjDI+gQ2jruPmwJUTbmQywlC3JmujNXQGRGkpOOgYEZEVoyDH1FR1Zj2L3ETSUoOHRkJMYWW2o6J0cyW52MZdLGY1aJV1JwRoemOmQNGxoxt9KJaqgKCieYi4IkcAph37vRRLT0ClCPnOFh/obRHJikldcF5on4Yt2uTkpbnSRF8R7BTkxugAaKa0GzuESUPtu6BW9SiVnGxF5lxHLJUGHtzfsdD8oA6Sm1C18YWUb13nomQ3GTWD3kaaGOiYL2iryQA3kFTpRZzjPc3Oe61B9MOfFBAF+LYZ6TdRIVjM41NO9873GxW9+7cns/maNY0rWMGAFHdCcprzkAuzg4WAtwtwDWCQAgU2ulk1oX2bN0PuVYBMtSq5hGAAHfgAZW6W8ghkmPkKwf7zz379Gw2Y4eIYlIMiICriqnVsRBjiI2absft6fnpW++8+7N3PwbiSRO3w0BAROYQA3PjqJoCIrJPpcTAqaAYmgozo4gjFANRq6bEkFXXQ3YMglQRhqE82tasBmZ/nmK/I4j+HNu8Y6/S7vUOoreD3QMYVtfOuY5S+hqjc84hKzmsG2NENdCs3FDsYDrzVWrKNgylafxTz81OzocxQduCIVS1YSWliCpERzXJ42EsUtEzIky7NoQotRbJTBw8gcIoshkSghIAeTAUZOo3JuOTb3AcZN6ySun7evfOoIjgDRXZIbIgQZgFJFNQNjQGRUAEIKpiiPWpG85E3r/DvoHGx9AgkJadb8Nsf77YDmWvm3/u2dc+efj+pUtXH5585II2wXt2jokJYe7J++Xxo/Vm5VUF0KER0SbJUUvFYH8Spz6GLnT7+4OoUyWCGOKkbU1zCN6AEW29WqtUMLx27Wrw7JCMmXemvN0m3dQA4mSBvovdzLcTlR2sfGOYFouwTB35MKbsiYDRwAQUgRRM1AQQDKwaoTRt8/SNK/O9RVGRsa+1evbkg48tAhYTUYGhr0y1Fu/CMOTX33ivzzLZ3ysnZ4hGCIyICJ7ZeUgJxlIbJjFLSZwjDzAQpVo9YlXLortiip5qVQCr1RKAMK+LbAukIohgZmCGgAZmgE9wZPDz+rRdnojBk+rc5WYCoGsiFWWEWrNYVdxpwrOyJ0VwLZtTMbg4q4tFOLjE/Wq4/eEgZQQHzYzIcb+RYV0MlT2S2t5svlkPw5jJ72792OfcQ2aHzcQ1re/H1IbgVLWH4Llt3XpTfAegUPNu/4lqWJICuDih7VqNgMgsP6GrsXKtBlEJDQk5kHiwUXeRKcC06fXkpLaRYqSSdblOYYYmWHKtBUsv1566/s3PvfbxJ+8upk+1T11e7M/vn3407S45Eu8sRkJAQIhN47zLGsb1akyjMaMZM1MbZSzKYf/aPLSdomtKBtDGuawmUn2IsQ1pSI651qqKiHj37icAQJcpBucde++BCYl3Hl7yvvHRFMfNhhhNZDuktpsspjP/eDltY9uEtm1X2/W277VW5zwitm2jIogYm+gRn7p55dLlIwMuRTywGDhiE83jqIaKZAhVKgDUPG43q+PjczU/jBcXx6dIeOXSpe1yLSAeFFFNSQ2yIEh1zgXntaTFrLWhrJOEEIqI4u7zhQ4QkLZV91p2wAW5a5r1uE27gntiSTEAoJ+XJz4Jutn9hTsj888d0YAG4GYcbQrLQTeigrqTbAGAglFr7LF1sQy1qk5mnhj7UYSJnTYtS7HlRZFqaIaIWiGyjz48LktgtGqqSkyVBNQUIIAHpu221CJ782nXlctXmjHXVMg7zGM1IwhmBt45ETFVJBh7QQeakRCNlBzEhsa+1qJg5pgVquxsYGLOk6Ea0INHqkWbGWstZXSCGCM1MVaWFy5/nsWlvt44fF4qHe09Pcjjr37u15+5/uo7H/4bgRMXyLMjNEKcdLOjSzce3b+z3SxDCCcPHhPCkGotdXHpMMwWwQUzdURVqpkhgUMXgl8tV01scsl7i/3ZYt5NJqpydnYSfdjfX4hQKc45JhfMgIjRN2kca60i1e9OmYjz2fzqpYP3P7l/vtpWgOV282k3Atw9KEGCZ2ZG0/3F/JXPvHj15i2Kk3bSeeeJWM1KLoZVDYg8EBoBGiN6Qn/77v13b39CjjZDSWWowXWxLds1MjomJEdkfS6FHeXSBm6baGaNx1kbU66CZoBGJIZZdeJZgRPQrOWj6N99nNbDiEhPDpo7rhN82jY/baH4pJ/uWuefC2cCAADn2uADbq1OGcGMFGoVdeYJgFFE19sRFC9fi23rT0/HtDVHFtqQ+lJHQ0YMSBG1Khq1jT8fVm7KiOSMaq1myp4QnKrMp80wZKsAAUXkmRf2mevJh4OLVIqooqigJyIwqsQeGWoGA/CEk0PerlUdmTNkPTyI6015ImJXADQk8I45cMkGYmW0ZkJa4LXP3Pjp3eNtL5osBG2IXSxX58/32+1iugdCfb+aLQ739g4f3n/36Stfueg/WvbvhTYw7fCR6IifevrZVIaShr357OzsLI3j9PKl6d5CARDNsQ8hVKnOUQAPCNv12pHLtYYYAdF5r6ZmtaTt6eljdtQ2IThv4MmUyDkXDJFcMB29c6BWaybi2XR+sJjfuHS4HdM6FXsCMTVgMkJWUKlFtdY6bZuXnnvqhaeeWsznPnjnXWwbx05E+r6KCCCrFjA0paygJkPKPvjtdjNUi9HPu4mkYcx51k37zcUIal4jc6nYp3xpElrvVptx0XkjWLTxvEpRdcTFIBuIEoruTbBrGo/0aJU/WfaA9POqRPy0Xf65ovzz9frn3/z5a3fz8tHF0Ne05sYRGlTLtbpAkhUSiqpj2r9GfopFynadGF3bOOcpzvwaxjIWRvYOOXoENNCimQhBxMg4IBoTwZhLF30b3enp2oqOG0GFo0v48OEglXZQRVMgYDAABWBSEFUC1qbD2dRPZiym/ZAhWSoKsUz3/DDWOqpDQLcb9HKpSkR11LZjQf3Mzf2/+o2jO/9suV726Gyf2wH18fZD73xLl3PdRu8B4tjT7fN3pt1lGbXfnGUZUnazrmXvbAcIIOqazkKQtpvv75vswuUREPOYiHZz1g4NnHcXF2eOyTvfp7xc9203Xy2XpYz9pokx7u8LEC5m8/l0amo+AHiqJZsBqHVNo7VutmuzalbRbDFfPH3zWtPEO/cfnW+H7ZjrTkm5i7FhxwiLSffSs0+/+OxTe4tZzRk7SWkstcYYQvRt1/ZjqVXMVKTunO6l5gcP7r///of7Bwe43pQq2z4ddaHf9tg04OOoFUU6wshgwCqaoCqaSOlCVKTgORfwBABUDPsqSbA3J+txlaUfpYoSou6KD/DPV+H/1+s/10R3p9BP30FwEcfl6WnTeKGdzaCGAI44A1XCK/M2ZSHT6JoyGBLExjVd17R08nhdhoqAmrRpW3JgCv2QrRJ6EtCaFRV2mRfkQUQfPlzmKi4iIV69Hvpec4YQKXjnGDZ9GbYFFQ2smjikPKiKTmccp3p8DNXUFBgcEpeisLG2i6PmGJjImF0ppkMBA56RiDXeP/UCf//9u2cXcn3eXZkEqukMu8L+8fDx9QUUuz3C0uvlcfNq09xwvoiYd9NFdwCgZuCcc7twBBECs50EWi3nbKalVPZ+sVhILcMwSJW2aatIDBGxIcLNmGez2fHjx4+0zmbTy5euzGezUh+vLpZHR0dy9erR/iGzAyhZKhjGppFSxnFUlX7YbLbbWvqDgwV6vHTpcDGfPDq9uHPv5HS1yaoh8tFisT+ft95dPdh76umb165fi21DgP1mPZnPRXTYLJmo6WbgQ5VaUlKrQM4UH508+uD995bnp3m7PphON0POQttxiDGut33XTYf1mVdaJlGFWkvHbhrDwd6ULXuii0HUzAOZYQXqGl6o21ZbptqXWhRMDQF3co5/p1v+/2ucBob/9hhqP7/vI6D70e0Hl4x+7Uu6zvbtt2yLMXD1vswm9qtf++r/8n/yy//b/8M/+MnH5eIiObQ81q5rXYBtn7ebEXf3x2y7qX7JwIQpVxsUOyBCSYoVgZAQ+k1BB84xeNzbC8x27/7YxghURWsVqypgUEdBh64jHdQyN41rW9gOllW9Z+fZkyMyACCCIlpUQWziQimmYjWrgiJAHnB6Bd985/TxsY3Z3BSGYQxGL137irTTD8+/h+Sr3rl/dvr0pYeHi9k4XL84WxE5amG+WHhmMCFC710IAQBVK6KpViaaGKQ8ilQtxUCaJjZNHIcRFFClaTtAIKSDA/fJndvrzXa+mNciZxfL1WYTvDvc3x9yEgBmN5+q987MmF3fr3NOtdRSsiqWMZspEUy8r8EtJt3p2UXb+D2cpJyvHB0czmcvPPNUdLiYtNP9RdsEyYUD1FxDSW03LQVzSrWKeWcKKWUg86Gr1R7cv//JR++/ePPaU1cvnZwv521YpdyvSi0ZjAAIwAGCqanorOXpJIjpdkht4KoGhK2nJLJWWNcSxapYLjqKiJqBETEAqCobKNgTKPH/zxPcdieWnYn60+r8edWameMR/vI3y5degG1vjfnfezMnoO1IieCbr91640dvvPfhcbW2Fmym4eiyj41LuQ59AnkyZUVDS5ZNh7746Fx0eVtZCMMuSUCBUADJMajIqFAR9/T8TMdN1RFmh14KdtPQtHo6bOvuxicgBWfN9NJVeHi+Wa+Q2UxUFcXGmhQcTBdcRh03IK2S1ipQstYdjdag6cg1enJGiAw2XqzLSJyrTqXs4+TG4tUs90GxYRwzl2ZvvjgsVX1Q8p3YBz5OYggEJlLNdsJt3oVhgAoRdm1ba9ZaS6279eNsNh/HQUxEJcTWMV2cn4bg9/f3q9Tzi4vh4cPQNPv7e7WWxWLP+1DG8erlyweHB7HtREoaU8opjyMCEFBJRQWkKFMwgVk73ZvNldwlpJpl1jVHe/PFbIJmk+kkejftutSPtYBr4ma1AgB2nDNALTUnEVFVH0JO4+npxdnx8XxvxlWn0+bSlcOU61jqvfuPP/zoQyYIntdIoto5QMImcq4KQLnK2SYdTBpEDY73po4zThFPt/W034IBIf98REQAuyUR6m7sCU+m8p8W6e6BvvM3/7xwd93UPr0xuaeP6Oph3SRcbWAStW2x7y1wM6T0j377T/vl2cMTPFv2tWDJoWl9P2xLwZSKqZkhE3uPTLzdZkKQrM5TnDgxYXKC1Xb2UzGrFQR36T/f+vJn7jw6Pntwvwx1fV7LCLkvoWEfWXdKPFDXobGmXM/PtWZEUanVER9em5ydb3aF6Bi6CeciXn2FmkUIAA1VbTblYSlkpiZNcFXkfNS2nRcjMThon47x6WF7F+H1tn0G6ZXZZDabXzrffHK6fiugljJ6xtiGqd8jJkSFHQHbQLXuVqhMJFyebGtKAYAqBQkn0wmR77dbZndwcNj3fc51ndemsl1vtYInj9SDnTAiqJZa9g4PESnnjAC5Vs88DNtSi4EhoVaJMca2rWiHVVIRVWiiX0yarglm0HZdqeXi7HyxmJdaUh5B7fzkuOkmiJRyYeZaa61VDXIZPvrgvY8/er/r4mwyQUbUOp+0+94HH84enuQ6zheT0+VFqYaBJx2L2Ji1DcAAFbGksW3Y+ViLHgS9u9Xz7ejJwc83Q2DExIBVVcEQ5Eny05OyQ9j9J//8xv7n/iD8O1cl97Pb+ZVbfHVmj8/w4zNVQxegDIlE//C797XYLleSA+7C0bab8ukwFRCBGQ/3pyfLtdmTWWst4h2HhgDNRcw7Fr0iBAcE7KBb+Mm8xjN1LebeVJAQUpZxLOwZyYyMiL2PZ+s0JLFMtIstp50+jVGQinlD6nge4Mr8QC/w9sVF8VASGNj8wAnKdgAzQ6pt4xCobMtzN55uu/bR+e1nr32+DYez2eVx/ex0ejCbXH50fDzW7dGlg03qkNYmolLSWNtm0sTWuR0tSQ1MVHLOYCK1ACgYkHeggkBEVKuM4+CpMnNsJ2kYEZAIvHcATU6FEM8u1sen5y8895wcwZDzg0ePtkNPRI689469G6UO49YQqkiSKpqMBAgITWqq48DsEcDMjRnbthlScYQpDcsLZc+C5tkh0na7YfbswjgmVRWV1ebsozt3T48fqdX1OksZF7NFaHxJA4Ie7k2PrlzaDmfbzcYBKlqu2uqTCTyhBrAKKobELnr2nlPRi9QLMO2UI08s+vTp1NN2MDXCJ7vuT7dIT7xyALDbOPw7Jfrp494MnAX84/ds4XFd5WLD1Qy8eMYsWBOYEqLunILI0G+TJIuzIFoBFQ0P96ar9ZBGebLpRzBEZmqCEyizGMdSVxdFK4IKOwqNExl/7ztvP3qgMXqzWs2QDdmIPpVAVFCDXNN00ngPB3thzLUIlVJK1bGWyV5DRN1Mtn2adv76FXcxGJsHMzOdH5pnPDsVBDAPiGCqqsie3r3zozuP3vvLv/AfxS4st4+vT5+1CYU4K6W88OLn15vzu4/fqLhsfTtpJ+zAeYfkgGn3OWem3SEJA0nNhMziai2EHqEigPfRDFerZYFK7IlZVJAIiIAImSfT6JxfrVbV8GK1evTw4WTSTWeznBLuor84hOhUUsnFeb+8OEfEPA593wPQ2cVqux3UFMjatpXFvGk61bbkYKZt2wICG7kYcinBBzMA1ZqSqG63fcrDBx99/Pbb74LK3t68asm5bLabCU6ijymXSO75F2+8+eb52Jd+zF1Az5iSqBmAomESm7cBgE5GCEXM9Hy0PhvTk0MlotETRQjYz2XEu+61+xJ7chhFBH0ysVf8ed/cEb2fHEYBwVyq+t4DDISTBS5XVUeb7XHTaimwU5rsKp4da9UqlYlbz9tczWzSxu0wrvu8S8VRAQSYTH07Y0RzGsZ1jl1ovY5Fog8cMY2CZtDCYp8PD8In93S9rigIbFIVERFAqxFBLbqtfbzE7QwXbn5y2ndtgw5FsovkPSNggLJawk+WZ7Kh5SpXL/OFi47PTospeo8uEihosWGbiREIurY7X9//6bvv/cWv/wYo5zqk9Xa/W9y99yez7tqkmRRuJ03XNE3bNsxM+Km8xXRHryMgIAT2iAKAbOBZhWXHghlkCD4g0jCmYegRoYr2/VhVRaFpoohUEfJutV7WMXVdd+P6NSZw5JomYNmOm1JKj4iiNg49AGqtJWdA7KJz1FSppRSU0q9Wud8M3rVNi96NKcbQsufZYk/FRkrsHPtQcq6iy9X23Xffe/Tg0Xq58t6f2cX+/sLMqmoaEyITsfSb0NrR0Y2H9bHCGYATqcTOMQXnmuARhbxHs47x3lpvn644OGZHYrgTziH+fI/5b+fuBKa7BeeTCxDgDuzxqU3jz9XoroqffK2B0wFQFaeYE2o1MNysbL4gH2HsEaoRgBKCh5LUe25nMba+HwszXd6b3Dk+9x0cHbbLs9xvi5nlIkfTTkqVAqnY5vEAbOAhl4xCvnU+GnLdW8SDy7OzC+2XxQAsIyLgTgwBpqjkkByjwyJbRkpjTbV0E+fZlRGHTQ4N+tDVYkkMIsyPgmuVTWadX52BSPWNtwJVailCkZo21CSn54++ffLPn7/1XCRfdfDBfvruHxxM2mH54eHlW5dvvNA1k6bturZtYutDQCJTRUIiMDATESnMhOgN2EoxM+9MAwBYSqkUTaVMppOy3bJzaUxd16nherNVSUg0bLdhV6alXJQqxPePT8exn7TNlaNL82kDxLGZiNWSkm8aMLMQwqQzNCQioJpLLrnWQkilZgAc0qhJVc05p0azbU9ATETOIbOoXiw3J6fL13/6ttTaxai1QsGL5WYxm445EzGm3MROLMsILzz3TBvi45NT04rkwNAzhUDouHX+o9PheEiMblBn6EyAEXZiMkJ8stPEJx/pJ6UKyES7WzniLiRsN37aXZD0354+EXb+op//03HEkjD3plMwQARVofXWRMBFFhMTIEIgtGwcOOfSTagJOKg55ps3F6uy2o5jzooOzFCrESAy5yKyy/4xQgRyNl1EqYZcx+zPz/Tu3cdmiECmamCuC2BWs6Ij9IBovm1ERxTbprK/v3e+uShSSxJAmM5D05F32MQQYzRARFTAhml1uvUu5dGq1NDgWAwR2RkgmNgukWHImz99459+4dVf7cdHk6mUejHZuzo/vBwb770jz+y9bxofAjExETMTIBKaKYgRQrVSi4ZSSk0lZ4HNOAxIzjuPCKUIgdv0W3Z8dr4CpMlkMunaqmbEzhFUQQ6p3zL3pdbNdts1oR/SzRs39ucTJkujkI+7G69nVlX2XKS0sSupNFJVRVTHcRRVMy15rCnlNJaqqdZSxMAc+6Kw2fSn5+vlqh+rlJTbJgJYqcJYhjROutaMUs5q5IOyhTxsr1ze/8ZXX3vvrbfr0IcIk0k32d/D6eXNZoTxVPJJXwBBTUUMKuNOmmQGu1UXIDATIu2yQwAMCUGJCIlIVHatEwC6GKvUUuXJ0/3JwskIdmFNiGFOpDiftoONmzOBagCIhJM5DaMSUkkCDH7CngKY1SrtBIhgeSaLNh5dh+1W89Itx7EiEAoY7h22h4dhTGpKm9W4vii1mItkopJ07wqHSbd6PIybAsxkCgpA2Cw6tWoqO4GF43D92iJQb1iOT2psZkCy7YfgWfUJC2U+98O25Bz2DhvPGju/GWobQnB45+PHCthvdEyKDC5Q3QoK+uikahP9pf3FjStPLeaBmWLw7WTaxoZqWezvX7nxtPfR+xBDYOeYmZ70I0b0phuANXGrxZlxVSkllzT0/TAM/Xp5XPKy3+Rqvlq5f+8BGA7jkNLoA/sQUy4pjWYGwGDWtW3f9yklM2ja5vrVq/NpV8aBmCfTNsbYtG2MnMYewVIaurbTIkUKe1drBYCc85jS2A81ZWbKiinrZrsdi4DhejucXqxq0WrQtWEcxia4SduYWfTcdk3XdZGdCwyAbcuMHsCRY+e9Y85DQoddbF3X1cq5lPV2vV5vH58sx1xOz88BoW2brmmIqNZaSh2GIedcrIxjJjQFqqJg2kZ/db97cNaXXaCpGSNdvjw9OenVTFRw11lV1IwBFcjM3ByYkAif6Mlgdx8wDF2cH7jT44EJUHURw1ZEizrv+k2eTbntEJqxwVuny5OnDyc3afLG7dPJrEHGXOHxwxHMXMDY0WTS1qo5ifQwKGoFFTDcya52Rj9op4EdELChG4aclvLUtebZS9Nln09WCZE22z407JC3fQajpgUX8OI0rZbCTrebQbI1XdisCju4cqPJgqu1IKDzgERlq5oM0bCIDy4E750vsiG65Ihn7XQ6XXTd1KF10ymRizF6F0MIzvkQPDPTLnZG0SqLsHetb6dETsFMxVTGceyHbb+/v92er5bL1WYghs26X16sYtNlkXXfx1JV6tgnEQ0xVlNi2gUzjyWPOZ2cnT54eF9VYowh+K7r5tPZfG/OBFpyzqXUPno3jhlzVVNEqFXX67zejKUKOz+O5fx8OeQsagy0HvoiuhNjkikyVrGUq/dcRINYrWomzgzJXGz6cXTsm66FUhDARw/gDH3u0zAObTe9fvlKuBW3m/Hhowf+M8/P54sQYtO2IXgAFBFVKyWfXVzcvffg3r07j89PxhGco1u39h49WJZixAAIqrDYa1JOpgAEzGxmZAqEBqhAqHb5yhzbyN67ESsE0AEMAQg0S9c2125MNtvx+KT3De9P/XaoqjhuBBm6FpvGrTbp6YObqz6R5c+/vP9vXn8oIlLETKyQmlFD5IARfHAeMARA0L7XVKnxEQkvLtZo0Ex907AhOTZVqVVTr3tzN53T+XlOAkgEZh5dCE2WFFpwjs+OR63G6EopWrGduzGLqjHDtRstOXx8nMFsl12C5kmlVlMxz9S1zdH+/t5stjedH105PNq/Ml/Mmhi9i03T+uCD9855Zo4xeu8ds4r60CAjEzKzd56fLEsETMFATcZhVK1VtEoueRz6YbVaj+Pw4OHD7dCnNKwvLo6Pjx8fn+SafQhVJDhH7GKMVaoZzGezYexzLkjI7LzzzDyZtE3waCYi01k3X7SnJxfjMHrnmWC9GbdjNrXz1SqEmMay2fbEjADOuSo112pqiOAdKwgIEFIIHtCa4KaTaYzRVENLIbi8qTGEZtqBgiNGREMrtTjmxf5h181UCUDAqB/Wm9Wq7ZqDw0vT2X4IERGIHCEhAbGXku8/ePjeB7fvPvy4abfLczlbl37YlloB0bl46Sg8frhR5aplp2gmM9gFsgEdHrXRsRuSGqiLTlGJrYxCDRNyTnXoZbHXAJhJHVI2dbkXQjKQUrE1ef769KWn8c0Pyum5aIVxNRKzym5uoBzJRUcMVrEfshVAsdhiiJQ2ZS/Gycwtz2Gy18QJESk7qxXrYFYpTrSyPj4TRCRQQhpSHXKezhwQnp2P3pCEyIGCzqZxMoVasck8bgWU68CLK+5gDx/dHYMnDEBcYuNmISCACUy7SYiu7SZ7Bwf7+0fT6XzaRe+C87Ht2uCDd8F7996779y6dWt26erF6rjr2rbrHPMTU9CngxFmQgAiQoOu6djtDDcAVlWqqO7YWwikWjfb/vz84sHx4+98908ePz7uh6GmIUutqjHGlLIBEDGRiomUImLMlHPy3gUfTEoVqUUePF6vNksGnLZNn3IRNdGSZRzWparBLllERRUAeBdJDJBL3Q3KCLHW6gKLaEqZiB1zrgVRAUhUcypgVpFqlVpz0zRd05mBmhJhrSWnCmB7i4Vvgg9etZSs7Jxjdg6RwEyb6J5//qmnn7lxevGsp1aFVOHh8en3f/T9j+7cne7xcp0VGFh5F4GEaCaAzmy3DMdHD7cOyFKR2EDnSR2Mg9ggIbAyDqNeurog44vT8zFZ6QtUcFPqWo9iJcs86jzqpcN6cpo/fnwveNyftpsxrTdCjD46ZlK1vamP1OWxni77kg1Ym1lo5zDdlys6CxOPUNKoNWtKmgfZYRtyNufQTPMa9vYoEYrHzTCwh4B0wC4GOk5VW6oiFxfgmbRaSXL16vT8bNi/7Bd7bnNBy+PRRTebey2aRchZ2048xy5OG+cDO0c09MsYAtqsiU1gisE775n46vWbbbMYyvrPfva9v/6r/32/i6piAsSaEwG43Q6UiHdWVwQD2Q3/agFkLiU3TbDdYwxo0s2Ojo5eevmlr7z22snJyaPHj15//fXvfv/7Veo4pt1GSlURwbGTqrWWENqhH8xADJhIx7IZLjbbMaeqVdOYi5j3LqdUsyDhbt7odoJaMxFxzAhopgwIYGq6M5GraBFDSLEJVYxIzVEWFVTIVavsQv7btjHDWi0NvSNyLohqqWOMTeOjj42B1TICO+fAe4jBAaIZoFgqw8Xm5PrRrXayJ2oieuPmU8/ceuq7P/ijn33w5lnf7xbvomIGRESOTG1v4WOkk+ONoLodB/f61eaFW/s/fvth27hhW1OqyLTV4fi+T3m96aUiAljoeDoPmup2o7nYw2jXDkuu0nScQY6u4NEU8BTWG4wxGFk1qVnD1E87S+j6IV65Ornz8JQEnePZ1KcK1Woa5OxRUqHY4KRjAEjZGHfzMEYnm766iKUIBwyEaAgAycwhV8BSNW+kV5FqsaPVdi3i01qbGV++3liV9YXmAS5fnX1y+3TSxTboarNMfRq2m/39/ZJT23Rj7p0LuyxxZs9MRHrlyn4bFz9+9zvXrz7TtTPdKeMBETCEgADMvJv80S6tGJEAkZGQVJlMTR2iN1Rynpk1ZZJMzu3Nps70aG9+5fDw8uUrP/zRj+4/fCj6afMDc8TAICIARMiliiAimEo2wlRrytWzG1IGJLWSiwIgGRoimR7uTY5PtwpiALtIhV1yuEfnCNR054giJgAouRA7ImCzJBZJqxk7F32QWnIuTdMQEQHkcUg2FBWkGsLcew+qmgUcK4pW0ZotBAZWs2rpYnW8N9sPLqgk7310QUCvXLv0lde+cOXS9e/94Pt37t3NtTry0zb2aVSwrvWh4ZPjQdUxqiPG2YInLbz59iNv/trCzhE2g5QCRe3hg4vpIRRFYKGIBqxS9mZTxHyxrg/O9ewHp88/e0ns/PSx7F+2e6fj/gH4CwxNYA/rlJDoYj1sq21X7isvXrt5de/ju48z6N17+vyLh8cnJRIuL0zq7qYGZoREbQeIVqsgEk0xJ/PeUjYiRDAKmAXErKKmDeVBPx25oQ+Qk419/vC98YVXruRSgo8hJCm6N5+ny0O/hjSYD+SiExOxnaxudx1yIUwAUQ3J0cnJ/atXb6rlx8ePfvWb/57Uupsww871QcxMCGhkT/xgaog7yigCoA/RpHgkH4OIkg/eeyUGaIkJDNrJou+H6eLw8tXrzz7z3G/97u++/tM3XQySgXgHTiDvoZRqgFKFEGPgNNYxFRFlFwxAFGJ0aUw74ZoamEHT+lp3LjswQDXTHfDEoKoF53ewuV3TMtNdq/7cZ1588OiBSlVnKSVE8s5HH1IeV5uNAgRHAIZICtp0Hsye2Cp3yjggMKiliop3TkHOLu7tLS5N4sJMUE2KAqoxnZw9uHLl6Okbz3zp81/4kx/84A+/8+1Hy/XewcRtXKp1NqPHp7mbhOmEY0A3WbAU1BJVEiYZhYJz0yll02LcNKrFoJgJGnDC2gG2Dc5nR8vtvSGZIz+kTRkrR5o0uFkDObx2nYZRj468ndAw1lKgccBq165v3/rkvAJbsf3DZtpqO4F+ZZVw/3KLqICKhFpBioqJVmQCFy0XJU9tS1JRwaRYDNoEcuiwtzLqz90CzuF2YzsP62aziZEvjrOPLviwudg23GAnkxBD4EnXXT283IS4WwsjONGBSM0EQbabFRJ17fzO3XcOD6/NJ3u7Y5yZee+897tGamYIiIhEaMCIsFOWETEyKTkIteTkqHHsmTwwOOekVnaMTOSCmbEPL7/4Utd1m83mk/sPVNEIVTQ6570HIOddypkAhzGtNwMZllLart30PROWVJ+o2Z7ogIBcXfUZiXbUpl2krYgCooqi1d2lU02zWdNEVTUj79V7boM3kKKVEGoaS8nMVKSuNmvPDADee0MJniVlZYdIRLT7ODEzAKoaMa2WJ/uzo2m7Z8gAqrUaADlcbS68o+jj0K8a3/3i1782n07/6e/91mo5XL/eDMnQ/M1r3rSOm7q8MFov5XC/q5RHkw3Uc8k9WGY7OoyLzkK0FkP05rwjT56EGKrVywdxv+PQYGzgeJPXI4zV8shH+zT2enSZXExZeo9o2apAIBS1P3v3/HTo95yfx7CYRVfp80/fXJ7kfpvStmixJvh+q8vz/09Tf/Ys3ZZd92GrX7vf2Wee/utvX3WBAqpAEAAJgJQUdNCWg5IlywyGw9Y/5Gc/22FHWLQtMNgYFACKQIEACtXe9utPmyfbnbtf/fLDuXD4PGTEec9YOcecY/yGqivVt85Z3zZK9DaOaRpRBKBsvdWQQAwcEQJqjY1zHgNMIKEIYgAx/M7ZgGCx67erHn3Xkaq7Vg3y4Ww0nU6T0+Oz09nJ2elxHIWMc8aptVYpKfodAhZgdygPo8FIyv799ZunFx9Yqx4cMghj+jChUvJQRAUeus4hQphghDEm3oPv1AkmwGNMGCQEIQIBBB4igOEDWQJj+qAqMA2i+OmT5//9/+H/+OGL58YY4D1w3loHITLGAg8wxtoDpYxSshE94VRqZb13HkhjzN+5Kp33ADrCoTbu4e1EDjzkeZ1zxhjtrHJWaCmMdgAY57RznVLegdfv3wjTtF0LvGeEIgS1tW3bVXX9wExs2q7uO6k6C5Q1rmsbKXqthehbo3oEPIKQEGKNvb2/tMZRmjgPCCXsAeVnXdnsu6aYZEMpVRSFhCFG8acfffy/+MN/Ms4HQeRmswB60BZKVMBbjAgm42nAM7svBIQEEoAAMM6q3m+lmEyR8CgIubRK9gZiRBkG3p7Ok2HgOQJaWEUQ6DxASHu/reyTC9TsiEOGM+AsthY5D7X21sIog0rjbGjSITs7nS73228u5ebuXVELBDHjyHoPAOwL5QGCBEAIvAHI4641qtf1wXGKrbYAIGutpd46FwSIUSKxNcpC4MlDmBdDb4G3XvVGYcsISxLe1koIzThsa9N0hoddhlMLt0bHCGHRyTAOAVBtvw+DtGkKTAkl0Xa/JISOhxPR987bwWCKMIUP6NWH3TJA/uHsAQDECDiHIfLe+r9zPngPCQkQwhgT5yxh1ANAGCMPnckIewA5DxDGxtgnjx//i3/+v/u//t/+h5/98ueEYQs8gshYLaUmlHWd7HqlLYg4l1Z3fc8IldZ+d/P2AH03HzvnobXQW+ceBBf4zq+hrbUQeA8wgtZY4IFDCDuAEdbeKeM48V2vnHdBEBireym8MZgQjxDGiBIIITTWIQcgREpr0DahDxhlUvSUYIygNrqXhXVglC/atgmCAGFEg4AQ6nq3XN/NJxdNXTkEAMuckQDDXldBoj/4dCCMaVqNOR4veBhEGGKjLZmPed0rr0kWcyEVTehm09jet96nA+88qmVXt9ZDzBF+aN8bD+nqpu29hx6kCTHaCuUABMYhTpJBbL31g0G721njIQ4gIggRGBKfxMhoTAdGARAgUtaNNdwZcLLIf/jr+b/98/fb0noDAHEQISu9tw5RcDId3d0ejPfhCCCInXGUQA+cBb5r5WAUEYw8dAhDY9yDgwYRP0iCulUSGYrho/PhN9+uCSVSWq21M75uyjQa3G5vBuFjTlJjFfARxLhzrUG+q4rR5Nh7cHt/+fjRxxTCRotdsR1PFhAg9xBTMtoaC7FHDws8hB6suP5BfEAIAXQPXBdIGOMPZ2hMCKEEAPAd/BphCCCilGLsvDDWL6azf/7f/m+0kl9++y2hWBtrPWy6nhDd1r2DPgiYUKrt+ygMMQTWWOs9hMDa78YcQpGwthEPvScP0zmEGAEAnPMeAGMsJMg9vNIQEuM9cI54qQEPvfVeKG2c5xRjhL6bVb3DkEQs4IxKqyBQWlqNlAWWcgqRIZhIKQHwUvdKt8NshgHilBGEndFGI0TwoV6fnjzBnh/ardZF0296aQ/V7lDtlVPK7wiYIlQRpPoa93WL4EPvo/fOosVi5Kyd5TyNaZKEwAOMQK8gRKDtlTeeUJTkHDj/+BHq/f71shDGRhk6WsSE4pD5kAGEQJxlnOq+9vMZPR6mXSMxQs5bISxGDmG/WQJlPcR9L32vwLNHo//6P/vge5/N7g/r2YJK4xGDiEBnnTdwNo8BgjyAjAPgofcOE+AtcAoAi4CFzjkhNI9YENA8Zoxj63yA8JPFGFGjtEMQAeiVrimhYRDc3W2rqmGEUxJI0RAfSS2MMVqrtt8os1vWr4u2ct5FPDZGdkotpsfAQ86Zcdp7BCEA1lqtlVLfmccgRBAhiBBAAGIPMcQEYuzAA4AbUc4BQh5CTCnE2AHgnYcQEozxwzodE4IJZywMoiCIx6PBP/sv/5fnZ2fWgV4oD5zzTvSCEhiH3BitlOSMPZSARoxHhIaYBgQh9N1bCaHXBiirlNFS204qIaVUyllnpDHaSW2sBw55j5yBymAjvTJeOeQsNa3tOitKUUqvLfQPI4dzVhnbSyWt8B5YYC0AQkltrHNeqE4rIVTdmzLked2Wu8OVNiIMopCHUnav3vwKIa+tvineb8qvXl794ss3P7XWzEbnj08+9d4ycGzVgNmchxBBRBEFADjniPGGMtT3QiijuAOEp3lUbFsW4ocNRpRBY2AWMyMdguDoBO5Wfd146slw5hXodoUeTXzbgiyP378rKFIQAYqT41l8vyL7fT0MQowsQFIZ20td7kOCdmEyzIAcjMiPfuPo3/zP73/y0/6Hf5/fXjkhLAQAIugZBFQBB+6WRTrE1jhGIR3BugTeeNEbhNADTjIbMiutAyAfoFbZgEbnx+HhXff4Ynh5u9bAaquBB0pKayyiUAoB0rAXLQmgtkUNY8qmQpWtWUIuq2ZzOnuGMV1uLkf5DHqgrQMQWasgBN47/YCaedjSI4y+Iwl8l6ollD60AjirAQCEUoigB4AQAvzfRdohQBD93ejoCSEAYfQgNxAFEH744Sf/+//tP/+jf/dv/uonf9P2PSVUWYUQElIabcIg5IQCYHtooAecU2uMMN4aN8rjKOQEdWnAHLDeQ22tNQ4ToI1FFIUBCSIWhqSqhTJGqoemLgO0DaO4JY4RdNgJTAxGDhFNEYLYG2MpJNrYBAcIeS2NAoqzACDX9y3FCELQeyFUczydUITu9peci9PzJ5jRWhTXN682xfJ6C0fp2KIWOvPs4rfCiEyGT7pevLv56rDbnE0+3va1RwFhKMkhBonz2tqG1L2GgCllHQStNhTS4SCup7X1WmmgNWbc0BAsxnSzlvEYdbXsO/DkfCxrZMhhtRZJCgMCOsPiEJVrPZ9RDez1W/3iU62cBNCkSXi/6RLsMQJRhKMUpQPabkQ8MZ5v//1fNl+8PQQRXt6ZMDV97xGBYYCcB1KBIEOicYzgfIIBdIz7QCBAQVka5wymyFovextklBHorAqJwQ3MhuHhF+LRRX53T5x1iIIwZEoojDGhBBHsLSQhhRABr3pdGx9FJIUAUrqsu00Uft6L5tvrX/7u5/+FNQZQBBHqe2Gt8u5hnQQRQv//Ca8HAyTBGEL4MAMgiACCGCHvAcbf2WrRg+T9rs3IP4hi5xwhBCJICHXOEkq9B599//uLk5PxaPL/+lf/qhUdpVhqY63FGBtrMIBxzLMs7rqeYGwx1N5BhJBHZ+fDzabJIo5woLTSBkrrwggvpqOb5Vpr0LcmCoM8zdfrBiHHEAUAtLbXQK/WZjyItfbSWug8p4BQiJGHFmBAHPZlUyPqKCSUS+JQSOM8jcKAKe2NtyFNi2azK1+GoR6PH99t1u9X76umvJhMh8Pvb3c30G+J52l+rJXs+6pVLXCUEhSFx/WhD4ni1Po2l1nrJAg5hGZGqsYCKxEgkACcob5r29JYZxGBhEClgRRwMsCznHWN//h7rlqbQQpq5G/u6sUI4YZziouDCUJCsJqOkbGEMldUvZRb26KLs5gQ0wlGqPQOIgC19odSeuAD7XddtVt3h85nEQ05mZ+CugXGAED842naK4+43Gs6yEKD9aEAjJnZ46zp6qazOQ95iLaNbCspEXAOeAfm5+TsadS0vRT6UDaUISF9FLEDdBgjRBgPeMAYQpDTyKiGBshbVNUb4/HR7MO7+jKmPQb8avUV8CDkoXWWQgABJBRbrawHwANG2YMSMs4STMB34W5kVNvWdTocY4QBIhAi5wH2/qEpASHknXf+YTkFHooMnbVKKsIoghh4TxljPDTWYsZ5FP2Lf/EvhsPJv/wf/6XSQojeOguMgwAihLU2QUAIRdZAgBzCeBAGBKC6FTQisCRpHAFIqq5GxgFL+rafjpOmsprAOIHFro9CGAVB1xuhrdGAMdi3GhAgtXLeeQ+EUdbxLGTWGGo9M4iAyPtOSVO6ugvkPAI8i6xExgmKibTdfXUNMXGK7ZrmfAaICZ9+9KFqV5f3P0+iVOkEWwoNioJ03zTNfpvGi321jlKOu2S3aRmXwxwctAPUJXhEA0kQgqNk1HZdUTWQY+xhXxsUeiWd1ch5mA/5ZIzvmk2UxoSr5dJfvICvv6gWiyiP0NOPJ7d3WwhMMsZRqHDgj+cXRbfdt22cgdMnrKv9puiVRrQHQeTTmDikeokItQa6vsRZgvyE367FeEKlMlEGmg2MA3p6zDZrgGLtrBVKs0SHASeIVHVf1XI+T/7Brx/91c9u3cFnCZsO+Zt3pfWAejw/lc0tBwC1nQtC1EvdCimtiUIOESSYIIQoIxACB00QpdgzgI10XdVeO8A25bvr1Te36zcXpx897P+dc95bY5RW0iNsjcLYKWUwJnFCrbUYIe89wg/VM5ZQDDyG8CEp4x++n/+/TO3fLfU9gdg6C7ynjD68qZRS6wEmhDKOjSYEQwj/2//mv3n69MW/+Xf/+vrmXd1Vbd1IKbSzwEMhdZTQ6qC9pyfzlCB3KMV22588ylrW1bJiBI6yUdNIoTohRAjJZIy3W0EgyzMacFLsFHQwYDikmGOELUHAj+d8eSOiECmrq1ZA7DnFVCrnDCIkCo1rIcAYWgqoeHf1nvjhcMbqtt0dlqPJAGKbB8fEu6u7lxAPNuUK2BtE4nF4NhwObFd2ct3UIg1H1HOjO9V2GOB8wLUeAjypxZsQ+nRuOTaqi4no/ex4YAbofl0HApxOgxbDHoniAPrODnJmLWhETzhB1H71lfWQQWAnQ3p2jN++7Vb4JgjBcEqdkwQRFiOCwv2+nU6AMV4DfSi1ggQSBSz21htkMfcZCz1ppfIYYIowRG1EqTZAK0KQptzvaunQIJr65Qpg5JvaamAg0FpD7XwvYO8UZuX0ONg3NkywpcoRxCGYDOHr6/qCLdKQl6UajcO6UtBjxrEHnjPPCIvjOE0SxpkBD/0QSmkTclK1u1I3R8Pp7fIvDlX7g0//gTEOQAMh8kC1bQOAR9CX7V4qSklMKXfOeuit/e7nPohTaZVUMmARQsjY77zi3vsHO9nDv3+XvgLAe4AgwVgIgSAklHoAjDGEPiy9KSEEJOgf/6Pf/+STD378F3/9P//Fnx2K3c1yeXN/FwWhEBZhHKWkqUFZi/OzIQzQZtNbg1iCNuuOAOytieLYOEcC6r1RGk/PKGNMdXi9qrxDacI5o5SwOJMhwVGAwnNvBeYMae+8cb0SnhBNZZ5EwirlhPTICgkhRCQ6bCxL7rtKt3U/4snZ/Mm+uR6k87Y9SN2Nwl7I1WLyeLnvLq/f9V2XDzhCXRKcul4bv6u7W0Zmxb4LIuiQtnh9NEs5SetGLnfLCE1I37iqq44esTQNZwMynbHd60YpbwSIKDtasOsbkc9Ibz3mGmo4nsAABSwR75Z6W1pCyCwCVhNEcS160Kd9fxeGLs1RsXOMocEAbUudZEQpD5j78OzMeXN5f2AeZFFQNOp64+KETcZA1s4ZGEcYCtC0cL0XPhBFpc8G0cefTL9+v3O8YYQIAxMCm9ZhbiY8FKekNN1m30GPzx8RoaDwpALV87P5QbQ0tpjCXuggCKGGlCHggbNOyg4Az2iq5c4QRTHNg/mmudHaOMP21atWwbopxvnRgzC2zoZhAIBXSpXdLgnTKBg+2LcfQmGUYAAAxnw4nG73K0JDAr4DCDrnIEIPR3aIHxJNyHvorHnIQigprTZSawggosSBhxM89N5994HIYn70T//pf/HDH33+9u2bb7/59ic/+9lXX31lrDYG8ginCTYO3t3X8QDmU+u1OzvLlEgQcNBZJXUSpZhi5K1BSns8iJnuomHKtJVSdzwMnEHZICIZTSHZafToSWCEk61lnHsIN4caY4lCWa3ULKHaWYixxrWQQNmOYW96nwcphfby7mvpJQGbmAV5QJSuQjaWggQMGyPjkN6s3z09OeukNPB1W20drGzLq/YwzB2L9hBFHIyd0RxzIdGr1WtCAWJEIciPjiMe9YWqw8TLA1SdOf8gQsilmQOAGCdlC6MAlhXIM9x3TknAOWGB74WLY3UoAKUoCQnFOqSBkzVAwWKavG8LiJzzYJAGw3FoVOstfXQUHg69kRA4Giag6ZSHiFEjJWIaAuRD5r2xyNk0AlGma7m5vxezU5LNCK3V+TS727VVA4xTWjUEo9PZWCd6PDDGDA7l7n2z+XuPPrjsnIOiykFXoyjWvQJBxB+gAhgDbZs0CpIww4jW/RojxVBSmurd/ctBZCej4ZurLwHAJ/NHoAdCl1qUXbO5L/fOGU2gUK11hljAObPeYgQhdtZqiHASJ2V5GA7HzrkHwAbGD53vwFpjjcOIIEzdgzXE+weR9LBJtUa7h6uQ/zu0AQAASu8c8GAxH49H6eff/+Qf/eHv/fSnP/+LH/+n1+8uG1EFETS1NKYvdnY0yUYTDKCeT9O2lph5SjwnKo8jCGINotJuZQOmI2NTJ7pM6/G6vHO+RXDiHY1ztFz6Yc4OvpnmOXZB0aqLeUyCuijq4YgrpaM4ODrG2lqNWpS43gCK8KGtB1nIiYlcamXRWxPxUa9Ep0pdFCHPCbbK9QGNIBN5MK62U6P7fVsBu6+acr2tAxIeTTME8K5oo2QC4XA4qchwhJ1Wu3178Thq2t5b4pzdlyqfBIBYZZUx6NBpyqB1CFEnG7k9eIg8hCSIUF2bbAiKg4YAh7FJM6s65qwbDc1+p/MItcqVlYeQ/PonJ3u5v707MIROB3kUx5uqQwx1vUXcMRB1tUQeKusMNh6izUFE2DoHG2kAsAwjU4PkkYspjhPPlHv1tn98FHisI5r8xmfTq/vbX/xUnZypPEzrrjG4m2X827vm6JjdXSHKkOYKUp8EkGIcBiEJIMCKBX4UTxWoXt+8O5tdrA/OAA8RhYAGIbnbvtrsV4vpAtj+68ufCH04njxhQbQurqURs+EJAIpR4hERqhVtbbQaZrMsmygkm6bgLAbgoVf4ITUPtFbeGgsxod/NowBCzKg3FlOMMGKUQuCVVtYB4KGxznsPvffWOOcwoBhgxMnJ8flifvqbP/ytX/7iF//+T/7s1ftvGO875LUIbE/aGkYDf/wUgi6sGrXaaAeYbqTRYjiJA59XRlSNRqTJRkGMw7oLHLdN2wxDeLf1d/fikw+hHwOohBFwPOSLQbYqLBsFgOtDWYxOkjj0TV9rY6ej1BwMotRyW3QydvJoFLLAvL9bM9JQ2I+Hp8v1MgraJEisL5PBoO6arivaCgi7cg5ESZEDZ0BSGlPdreuuIICyausACFBKji4o9kgb47BRAiqty0pjiEcTuryvxxOQpTwILcAEWl8dPA9dWSkEvIOOIDRImfXSacC4DoMI+4AjGwckwsnbcgvofj7z27VjFI2y5OrVylvPGFqtuzTjEKCIEQ7Dsu5YAJAL7patQ2A4odbZVgCxg0kEV1vY5aAxqt2j5N4/eRyjREnpT+dplNhyQxendLtvqgNJp7yREnry9Cxf18WTo+O+tvkYCaFCkoUxR6Fq7CqEqGpYAiISqbYphykLKDBxsNxdEi/zPDW276Sw2gUUSbU7VKCTu323Wf3y/vPnHjGzGF8MsyMhtdFKm1I7gSBmNIjCxHuklY7jtDhsIESEBMhhqUSSBt5TJZRWbRDEGFNIsHXGSBHwEBLCKORxSChx0hilrTEQIwQ8JMQBjwkBxj4YAQFAGBHvwWmUHB0dv/jgwz/50z/521/95furu7ba0QQgFknZ1Y21TTIJH4NR3/bSszYb2q7RGAwZcvtunSUZRX46AcPWhGlstcsi8Iu3FeT2IJfWpJ30pqrnw4XHwcnk+K5bQxCNklD6A+ckHWTex1UrFxcRA5Z5JpQyqD20zapG4zzxRmDAoDYYglV56Hp5PBxL47zpq/KSYnYQtTbGtxmmDiPYtR3sgVE0Spl0+zDkw2BEsjQ0HVkvdzxVmOKu1NstnA1p02qIIMEIQGetBR47h7w3socIAsL8/UaPgvD0lN5XPaSoa1HDIEqcNFWxgUlBCCZGUhaZ0QQDA6KIUwwhBndbCTzgIaUUCmkxQYOMjTPeS1v2vquI6vxwAozzWqEodNgDa8HwCInaU0I5i3ZF1ZTow2fo5esKMJ6n1hkAmmDAoSF6v+kxCV0XfvP+NuVISxNy3/WAGjuOgoPRjnS9dyECPCEITDp1HwS41OpuvyWEaFTlqcujxdurvyFkioN+VYUhz7JggSj49t0XpWh+8IENgylCyPuesWgxPw9Y7AE02iJoLDfQkCROd/ttng0xplIoSiElSKp2tbo6O3mGiOy7TgqRJCkAgDEccIa8V22rem2MhRAgiBElhDPvnbMOMG6/w+98ZzklkHhGHz958t8dHf3+6g/+8i//4m9++j8V3Z3oXVuhLHbJEC0exZ/GF3er3eu7Vxa0GEei7vMkVIAz3iMQ7+4ltOjR6cA2/a5u46FcjM1mRY6nrNfKILreb+uuPp0nHONW1uN5dPNO6X3z5FFEHKiLOo8pRfjr69uU8jjECx7Yqmt6myC8PQhO10QFiyhqDi09T6kBu31zcTJfrTaBT5qmLmX96GQYMrreq9PxJKxZEkaIB98/H7551xJkUMrDLEcP5hHd2yezCGXk6n39+CkLMCyF4hxapXuJGQdV4dMMQYisdZOMf/Zi0P6q3naaMOShc6ivelPX3gd4OgspR0XfQuwJJNapppLblZ7MQu+g7F0UhSHynFNCSa8qGNJ8TDC03iKpYchBs/PAA++AqdB4Ap4u4sk4CCN6txOzabbatjeX/sMP0OOnsLofMNJq52TtOXNNp8Yzstzh51O6rKrJOEYMKifLXsVhNkgHxmGlZS+6PMdK2UZJadRiOnl7fctDCkmuNMyStBMbIBLgbK/qru8X2dliMfry8s0vX311dX//ez/6B1EwCNnAKdgrqa1yzg+G34UtCaGUkkO5Hg7n3ttit06T7PLq69X6cjgcuRaHUTQYDBFmSiunFfCZd31dHSiNCabfnaYw9sAh4B1wwPmH0oIHAx2EEGOEEEEowZidU3r8Xy7+we/87n/88/949W55OOxa83K/dVdRccc3Az749Pnscm/3pssGJEutFySOWdd1gFgSEAxo3dY8aOenTrTRMDQRDIYxCYAZZum+aWukx0EQ50i3csLiPgaewHLbpkFsBWxhhyQeDCLA0eVbeTKJ20CvGwUosD383ocnrZJvrKxkrXf7qup7pOI4C5AbWUgTAxyyRiVxRLno+03REbcz/TD9/MmELG+r42PAA6eEi1P65Hw4DIO/erXHFAzDYJAwt20otbU0BKG+As3eRBEUGseBmR37INJOeQiw1jZJuDaWWBYizakjkK7KyjowmVAEnJbACPL85BQH1e2yhSHjJKiKriiqviPpxAWhuJiN1ERKoa+vhGUeUWgkpAx6jfOQGSnzYOK8ZswEHLSt/sGvTeJQblbgdJzPxwNj9O3dYdd23gvh1Ytnw/1y64CjsQtCkaQAwzjgqlVrglKG2fbQEqaQw/uq21bq6dHs8Ty/Le7jMBPqNsZxSMcIhQoU0O7LSu/v9QfPBtCgECdlUb56+3o6OD09SpBvIYLKqJCHzmglWkep1ZhTul7dxmEklJS9ogS/u3l5u7p89viz0XQWRbG30Fp1OGyG+Zha4ywgLMaEAISU0AwjRICzVhsFPIAAAQs9ABAR7yyAwFkHPCYIc84QghqzxRH9r/7ZPzPG7nabL776m7/4y//P/fomGOB9fYMdHOUsjExT37YyCgnq+gZ6MJ1Ofvfxx9+8vS77Mo21OlAWBQbKpSh0D5IgigIdBMiapq7aOKFNo4dRlONUdQ1QDGLHHORBEKXgdtsmWXb2ySwgZughCVtT4bbpQp5s9+3x0Xgyza/KUsAgNHkvG9GZNE27vrOaAOIoQkKYcTyECB54f3Xb16Umv/lrp+/e3/cNzMYk4qRvqkMHiQfzY8iZu9tXjQIRBFLCQUyZxhXtjICzUZiEfrLQt6veYS8ECCPCA9isgbceYlBrGwhrNCbcG6OrEh/G3fOLxbOL7K++6EZJOs0CabpWKgjBs8eRUtaATojGaDKbDbJUHsqeYNG3KIrAi7NRXXdvb9RiYkLYcUAg1NZLgHZv3xMNYLG9tEBzbrHlUQB7aJTCUdRee88o8PhBFnsEsbbYWBTyPA5D7SWzBIVBKHBCYFm4LKQJCw/bDnMBB4SRobe9dXiUQJeyq6Le71VRdU17X7cmjoJybaJoaLSCHlrn8BBJpaRUhCDvbZKk1onbu9eT6TlE8O7+/d3yvYMkTdJit2coxQxeXX07yCZhlFrrESQ84A+5IYQcIJBADz1AmGCMAUDeWGOts8YZ75CBAD7E9QmhD0x5IJyyliB4NF0Mf/SfjfLxf/rxH5XlajSbFvuqkVVVa4rTXorOQRYbBMhyVT+7KJflPRub62sbgriXQjuXxnlpm/N5jqzCOlQoCqdxV25TggDpDq30xHuP285CVV2czx1qVst2PEp/9ZP3548m3phoHDktIg6++NU395U6/43pNzdv724b0YFnMekLkU+CQ7ctSn864AiKtoNpkmVZoDowykIvzH0piWir81PENsHqoC3wGBIl5LOnGBNSbez9TgccaQhmk8j0djqO5kO+71vj5GweB2l3vXQQgsUgCFLYt4pj4EPcHSxGZrv3owWsG+gdMNo7K+ME/9G/exkNPCUAEdiWfTjERloPZVORZBwaKxAC1bZPpmQ0iiNJZNzPB8PvPT/7f/7rr72FnfAss5Ai77UQ8L5nltmu7esKY4qToQ09G3OTx4kJ9L6qKKVG4xjnixN6u94rDXdLPJ0BpJhxPM4o6GU2SA2f4UwEVt4Xr+MQahRBSnopA1xAC1opkgjums5g0VapB1Zq5YAtiiKZH33z9S+BgWk6FMr8xm/8GoAAeEwJ9MA2XdF1NaNBWW6ub+8o9sABgHnblm1jzKj96qtXaZROxkdaW4QxJvSBNMww4lmCCADaIM4h4xATiLDTwitpOt31nTPOOW+8BQCwIOJhgCAEFHuHhRC66xwwR8dPXnzwo3/1x/8yQOjTpx99++YynNVtCSHlW1FYYwGMPOmXzRpT39cIQy6c6pTijPaiRx68vloxauaj2ZNHp9tN4WAAqdxuZXbCjBLLdcsTluQoxo4H/snHESY+S3B3aNOMU0ayi9nbb25b1aXHUbU/bG97TMhnL87ullckcIFOTsajUdrIvYeE9NKvYB+MqdFyyPKdaxvek84Jq/uqwd2BsMQnAUNUI2JW17qo9IBGiPSDmHupQ5YU+/5kEY0jVNQG0Xp/0G1H8ghlWbBqOkApRlr1nhIUBD4AznUIQx+nUUCA9vr9mwKHxhgCAJZeY+6BR3VF9ntNqQMiYGEIlUkwFgfbYhjScBjQX392Uvd1EOD//n/9uxp3t1XJOFndKKfocBDNpuHqfltsSAiHCbMnC2bMIWBDOtRFJZBFASODiOwPeylsXxejwTQOtFWVgXVX1LUIYdgbaaGvPOp5ZI2GgJWMcmBoWTfltsvHvuhQ3Zskjsc8R0EttD96hIFTo7FksKsaJ4zEfHC/XrZ1PRrOxqMBD/C2XDqr8xi/uvxryqKj8cIYvdq+v7t7j1n44799Px4eXZw9UdoSaiFAD5fPgHMWUMAxAA4GFPDA4/CBbYgoBZxjX1pDeqUB8A91tFr2WrAwyxFGyEMMQWe1VYIgfHLy9PHZ83EofvRbP7jfl/MwI5k57GtGUWUEhDQexrdX3ZQdX1dLbNFhXRmE4ow1og84ZpxaaEiC7/aXy43Bho0zd5KzTd0iFg1yJRUJKV/v6rbm0dhr65MRH0as7WvXuWQ0zAY4zwc2tLu9G7NkPEyFtkWpeQeztM3iODEsmdLbw1qX7sXJo0bsjYGzkcsD2DaQrPY9xogSkDBDADlahItxsloil/Qc6+NxvC08gqjpDWAm4uHt5pCNCcOQBEr1QR5iD6XWPoQh4GZ/QNaD8wvnJOlrYwSDVMeMJgGQnRYtQCCCVGAEiYUpHwiNQl72FczPnKlC1XRBwOpSBIQOh2a/0y8+PHt6sfijP//FB+f5b35/+Oe/KLBhEGpR+cVggCFq6844nY0dc+T4NGml2BTu5KTmCKL2JI6KmCRRLETLdauezsY8hI6QgHtjsKmScDaIRztfLnHPy53qlU0SioHGHjUA6s5NxuFwJOsOAYgM9m6kImEzBawGnxwNJlhsK3t/t+48lYZcr17ZXiMXPX98kuVYOIUJ7Zrby7v1h+fPi+1237d3m9t1uXx1dfn08cc/evzMOY8IQAAA67y1BEFCGPTGdwI6550BXQ9iBVgAcQAAAsBbqzxwCELjvXfOGE0wrsqDVCpOBgA47zxlXEmppIjj+POPPq02r8qiePp0cX2/vjlsJwyxXhEVTIZ5z7yrzX2xjUkYp+RivEDADZPgx99+E0b0yXz0B3/v+Z/89Rdv37TDaQqQPCihhSKJhSIQNYWpEb6NeMo6GTiapKzpcKNF16GtOOBoPZkEOyGrOy07NRqF42EkDch//cPNdj2YoK5tcI+vrncqtSCFlS6fz47fdXfvypswYBZoMgknn356RmhXFWJb1R9fDEbZYhLIwG/W28ZIoy3qhQ1pOMrigOJ1Z9quI8BnEU/cmYvNarfWPTmdJgdXBovoo2fB5fb+zVI5xYa5MwQaCutCDVA0yGhvlbTq0fjkaMq/2dyHOJUUe8aEkRhZajkhqUjaQ9k8yzCyfDFlP3t5gwNOCfi///GPURgMQnbYVRzF0zS5b0qpXBDmJ8fDzf3eNHUaLfwADqZuedsTwwlyCCkU8n/62adfT5vBAKz2ZSewJ/sAJcGRN+RwOjjaGvbV1d1mZzGDLGJx4pd7gQhUytA8wAhh5EcDF4YwziWiQcCjActN17y6XQqIDZIYWOrsrrhDAQppUPad1GTbHmiAjgZpTMab7bWyeH8oIPBvr76BOPzo8YdGasYhIfjBim+Bg9BLITCEwGjoPLQaUQWVcMDgdOghA1ooqbV1CCKEvrvceowJJW15cEazKAUQYEQwZbartXdBNHq164f366NRXtbiPVq+KndPksFm2XVtFyWht+G62XhjZ6MMOPn4eNKK9umLI92Ji+EgpnpXi+k4n43Cd1dNMohECPJMYZn27QFymNDIWUKzWgCTkMH9ZpnE8PhkuG+K29Xt2ek8jvmbtbDQqKm4u3p76On58ez4eIxjTU1QtzWnwWycOG9K095Wh3Qc3txuSSPHQ0Zmx3FxKONESt9nmXpze/3qvfYyePeuKJueUQwQmOVpGIai17uyLvoOInAyjWMKiruODe1kFr7bdn3jfAwI09ra/U5ohyKGnzzhPYhFGzjUDvMAU0EVGmbDT5+cdd3hyaP4+srzmEHGAkhPj3MtIYYDMsnfqSsF+2ePTmgCLt8cjk/zu80OD8Rmo0QgtfY8NJrgZDywfZWGMZbRIHTPzuaHtu5xtV/mVM/27XU64J6bz56cjmMi+/2l6Q6FnRyxRhnRlB5xTuhqXc/yBbeHri0vpsE4TJ5OMrHfspAuBiM+MELZxTwxPd6227sbo/v4tz7+9HSQ/5//L//Boujpo8jhfhjnve6Fg/uic1QssaY0ZFTt1/ZuWUyz1nhXCbPaH56dDatm9ej8832xCYJBOhh+FxYB3/EJtXL6AevqPPSIAgyMbvZrWhwMhJQGmMfeQ+csRAhRDDQEEBHKMRLNYRcjiFnsnOdhoASXTRWliUHxT37xzT/5h7/5ydnxz375xXXZcueSQUAC4LWDmhyl065XsPeQhFYC5ESu6EGAO7NOloU2DqSy8d4RXJR2MY8Zcoij+XzS2yZgodz320NhWfju9nWxls/Px20kEPYp57Voj4bzxZGv+t5KN5rktlQ37WYRJUACq2lj+/EJG2SBcV6XKqI8CuAkZ8SHwCFSrNtDIU+fIalKY2ijFXGb08lJlAQYOe+cVhYZd9i1RS2k1TREecxenM/e37xnFN/fNGfT0YsX+OZ6LXQCDfj2lYcijXw3GsDFSSrl8KY+YOQs09VKnI7nTy6Gd9X750+SUOLlmz5AMOFsFo4CCONxIrVqpZ1O0vkonUz9u5vCcXV1ueptwzEXpYaqFxZlaWyDGvU+h15K2aEuT2iSsber+15L3beM+CROoNOnx8FsSP74L951Yo+dV9DKA9SNRTG43u8inwyjnIPs4yfnRycbxauFobulfHoWvNorb1vRiPVKNnucBGBddr3mJ4Phb3zw4X/8819GeU442m6bcAiWao0xGgcD6UzvdNOLGJj1ezeIIxb2IWr2Fbxe3ceDYDTAXe+mw7MwGEZhCgGy3j1QiaH3ABIHvLMGQgyhdd5BAKwyh/0eAB1EqQ3TkAWIMtUbhDFlXGtNGUcI2CBUSighIh4+GAvDOO36ljh4dnb6n26++MWbd3/4ox98/Gh+u9q92fbDoVvk2arYOcGfn0+UU7XqMhitbtrxIMijZN327+93tkFpOIDcbapWA/jx48npsfv2pinr7vtPfnCzutrcLJXvBzgaz6dwivWcHM0HvelLzccjJqG6XV8PjnO7x2M7eXy8aPGrw0GuDk02jCj0zrli2yACsigcouh4kFlfP8qS+8p3vSAZtySOTsf2uoamgH0JA6albJ02BLPeqFoYg63oLIQeExcFbJoNb9YbHqVPjs7z+5XqQOeZY8xYPUsnbdljAwbTZHrB3l9LYHeI0DAMHqCHv/t7j+vucHnYv3xtZkdoNg1KYbWW95sbhH3aD4IE1o1Q1k+mk8bqq9sSIQyF8ti7nj17fLTcXOexTSlGVlkDAUPWCOhpeQD5b1C69Kwejhbx9d2WMzAbc0LVn/74bdXwQcZmM3yC0OE9GCVgGAx/vlM2Vm3f3LnNxZRr3MAeLXtjlEO7aLNXPdpS7olP1rU4NFoJfjof/ec//PU4QO8O95D1t8suyQbzLHRNM4pm62YJUxMj75TxIoZUtr6Czt6UoO6Mgx56e3NTSZXRH6TQIQy9d9IY75yHiDEWeOitgxBiB2zfNxhjzKh2ThlX7LZnT3ISRR4iAAAhxAGPMOVhhBCmGPnEKSWg8xgCT5BRmjAeBrHWejE7DvLF//TTr5KIH89ziGDXC2F8mHAj0d2yObmIV4eVdrFDZQgh5ymJEungkI1X27VnbURJjiIy5s+exs5tZQ+BU9I1uhVV58MRfzFY1K4lA3QyHzLMdvcloS7jsaZ4t617KQnG42m8rXbS2q5VrVdxQqZJEKbTDbw76JKbQBkvvF3td0B1tiQBt6SRza6IosKwECrn2oMLjxDFTnYqjFEnRXLEKPJSGQclBs72NImo4vCz0yfPnjwOv3fyZ3/108ttAyHA3iurgySwgT55lBmrLr+slPTHx/kwCSfJ5Hv/8BzQ4uX7t7LCLFMWUGnxoRGEOeFMyPjlapUqElA4SGMFu6+/LeMo3Fw1kynaVVoieT6PR2JQiV3TgQAjloaUkty71eaQBLkUvdc4xMS3MI9DwkwShsurbnOrHdZB7D2l9Y7sGp3yOMnIJEtacUAcG9K9KpZJgLIwOQB8tzsgGCYJQJ4FMWUg0NYgx8ZJGEVphPy//fOffPFuvYhHz58noyl31sx8yhD2EPKAcUReXZbDVONMSi+9wb32lNFJFEHvLm8q2crb5Q0iOCk4RyMeRBAi/xBS94QQamTXdXvKYgiBMQZjkoxGXd9q64wD0HkPjH+gwDlPMLPWaWAZ51EcdVXltEE0eGh+4XFK2oJTOxpM3r3+5s/+9Cff/97Tp2fT/1TVoDR1KR3Ap7OBkS4KxoQIvesQjRWSN6v3AXATmjSIBSG3lSLGZQv4dvemF4LJVBr5zc1LVcud6l4EZ0k+XC9bDPXB3l9MF9aq5eF+mLEBp8dpvCw7ivCbdikbmQYBAQhyUPdCFLeY4k51HsBG1Id9OzoZ3GzaYYgw4h4ZcnDm0PQngkaJu21929PzkKjW/fCjhU/7H3/b6M4d9kAbRwNIEbk4Gx+dcEoT4shyczc/dvHIpMLMzJH09bbpZ5PFpqju7vbOISs9Rda6ru7tP/y1D1DQ/9Gf/SwJmtERG+aZt53uCDQIhSAaZBmnnBAeeglsviBdI4WxYWQmE54kvgOcRlQYmSZ5J1qWBEGApNTHcUwjf7m1DpWvr952tY5D3zo3HLGERLpprMC97ymwR5Ph9rYvKrPv1EGalaohlhCwDx5lHhjtmS6F1kBL9+H5FPnA4t3Bu4iOTcM6Iipfbg7dhAaXV+uvXm7jlOFIaquITBGBs+Po0MhAAVtzpfGj06zTbdc6BKnHsBM9s2icsoAG63Xd9uavf/K3SZJNstQP80GWGweN8Q5ABIFSbXXYcE7DIHLWAAcRxTSM8/HYAa+1VrKkjAOEMOEQIvd3fwBCTDlASLR1MuKQYGA0IRhhuNmvx6N8nA83m8u/+uLlDz+6uBrEtfOut8bbMKdJHsz7dL2XSZbvCvPVl1fYw/FssFR7l6hKOVelsrZtcJdghTp2tzMC+Yw0J6f54iLqmup2J2jqX7/dZ0OmWTw61bGYDUcswLRrG0JIKw8M4V5CLY3BLg4NB3q96mTEOFbjKKnrAw5CZ7oYskNXIwqejkYEYky5n8+YAp2COs+DxZiu7+vjZ9lXr8rle60kZhhD4BJIXjxf/P4PnwwH2XL1/lffrBMgd1bvyyoJxr/15Nc4a39y+cX9Un30/BRhc/tuD+YChbJsu9PTyCTbP/4Pb3abKn+KHBH3231Iba0wwxFFLgpDD4VUPSY4DHEydEVZExJgG/7w8ydnx/kf/81P96Wpy/5oPFqcHG13++YAblaFnDTn54xbzxRZ3UAeAU8th6qt+vPz2baJla8cN0KY+50DMkHQN5Uc52QyCDYVSMcUMNFXlbEOunDI/TDnScA6gXoMseGy4mnEvdSFhK3pB1bt2iLMwWAPx0GMqAFWSKB3rdnuxe7e9/UhzUmUYmLCjHOIrDQaMd+qzhDf2g5ig5B/9/bV8umTEKqYuygO83zGCBFKVu1ea4cJ9l5ZaxDAHgBnnVHCaW2tY0PsCALeAIf9AxsIPEAutFAKGM0DtltvacAJJM2h8MgijIqm6vvaOVxKu7o9/Nqzk7Pj4a/6u84C5hJkUdm1V8vtrmiScbyvO0rDs+lwLxpRiXwg2j7EUJe2SLhfjE6XSmVT9nQKtd4d6k0rXABxmh4bIn70OxejNKABOOwj3MLlarMtRG9BliYJimwtBxmjGrJOS6cjxBIccOJPjmeEkWW/5bErDp1S2hhNQ1xrSI6nYXvvaCg3a3gyinFGaWR5DGzQ3N4KqLh2MqbkH//D71FeE+aZx5vb+i9+8U4D8uI0qTcgQqNHx2fAtnmEI0TyHJ8dBbc3uw8fP/ry5beQ4nhiZsf4Z1+9u3x/wAQUneUEyAIMjsLxGHmXNroSptZWtqbd7/DZSbze9cjGn52e/aPf/H7f6F+8eb0s6gjmxpib1fZ7n5zX2/pyW1riV4f+9DQIYmY0m4ynk4VyinmNNuVV0YHZ8eny1VvTd4TAQ6PqqjhejH/4m2dVtS0Oh83afu/TcyOLumyMIZwlEuMnId1I//X2Lsp7jFxrC2543eheWp6gui1vgB8MCKJgngRSCQuFxXbTiEE6qfrKkbYS9v6l+Se//ZR5uOoPnfKX7/Y0CAIIjYGPjiYF7u/uuz//27/eNcNO3SfpkFLqHBJSe4iTKO5FVVQVC2KKQoyws0KVTVOUSrTpcMjSDAEEAPTOGWAAAB5652xfHkRbhUnQi8PyRgynR219aNqahiwK45v7W4csx6zv9Lv3e5CoeMAYQrN4/OR08H53UwAzfMy7vjw05uI0DseU3qKIJxZ3onTOi2SahNwdzxeDBPzVV98eKnVYmyxhMcHTSeRMH0VBd6iZwo2uuxYYL9pOJcmAQe0lnMXjndhdbTfn0/mj0/Fa1AxxnLokd+eTgcZms6f7osOB0K0kAJyMadN2pKmNlZiFDiM3nzuK0deXilsCgf3tHz2fDOuyU0K22cQUh4Ot8B999UXTqUcf+MdP4pj2Zeut4hDUB9lGJh4PF8fnTLa1aUDyQfIiOKn2N0fzYa/du2UzTSMc+FFkdA/7JqhLU7aGQqO99UQaGao2CLn73vns44unEQu/er39y59/+fXLdeVkWarxMSxlddiZi3/w68///vj/9OrfJjEKIhzG4dMh3K39yUXT1bA/UJpsete5Sg6obMq+byXPHeIQYcJZrzpbFn67NidHWbevt1dt12PLUEn7ZDLqOmd1DbT0RtdWmy4WUr3b1IOUQag3lai2/WfxjIbg66vbYcYXszFxmACzmCS7Q7PbhokNHg/Zk0F2uVk3TbPed9SjMAytNKHnQuv5LAvjsBbFZlfcxODV23NlxGR8HARR3ynpaqN7TqnVAmPAeOCUwN4FFKtaEugpRM55RJCx3lnlvDfGWKO17FRXW91Y2a3u7rTWEGMpu151UZCELHTMJTlLE9oZhSUMEb1ZV4Mzd/FktK521brAGgaUDCeMMK+MJjk3WHSSSOh5gqQzu7L+yS9efnb2PNHBzbobDbOTE3w0TC+v95f3TRaPGWV54J8fP06joPKb9++3ooUU0rPF6WqzVF6z0AmvuEtHoYsBP9AGILbeddMzDZweTfL1ffHkbMqJJ44GAJF6pz79eBqx9nwexVQvN668Rn/4e1MAasr97/7esXX2drvZNHfLbfV4MZTj6uQif/FiwFBKME04iFBT+JVQGjZHhOfItZ7JdbGX3/gPPz9KUytFI9o+z1AY5ThQn36U1GXwRVlsD15qV/S1Ai1B+HiUPjsbLqbsxSifxsAyeLvb/+lfvzY9Op0lKceQq67pLen/3X/86X/3v/rBk7NR7w+zBT2fzwACJFwVXbFeMkxbVx/ut3qStJc3S9XLAeNl08uBHWSRkf7mam+UH0QRxXp51XnBRYfaWkUDPBnxbddThB7Pp9v2hmDLUpctwnzKutqWCj3/aFrtaugxAkGvFHeCMz5hKaKgqluEoe794xP+6y8my2L99f1KKosp0oHtlGi1GcbeeNfUEkIfh8g5dXV/n2WvQxqGOHSJFtLWTQ28OTk5RcC1VeHDUPUtC2icTTlxg2GGeFTXjXPGOqCkBM4prb1zQneF2Gco7vq6rLfOuyjLCfWb7QEQOspGR9PZ2hlj5LYsVGMMka10GONtvSG5iXIQeJYQP0mjfd29v1ppY/IxairlFSfIBwRy4q+Xpe9uP3k25bcwi7GT/v62f/daK8r/8e88f7pYQGwpJfvDLsA4Jow6tHfV7nDtOfz4o8+F2W3rVbWvNzvZUSedtwbt9l0wtrN8+PT8dJ3HYRjdLu8FBAOekWHCsrlxmj2ap5b2b67b06PB5x+dbOXmspEoaCYJy9L5ZJd8ch56ffj9f/g8Skfr9W4xnpwfnw//yefYq6/e/vjPf/ofDGS77TYiDQ3T8/P5ptiuluziWfr1t7frdTXPTw6yz6YeIZ3FwR/89nPR4X/9Fz9zwHzv9NHnz04H47ypO4pV6/nXRXPY3GrZnMzYfmuUcidnyWZXy1YFAdyX26Zr/t7vPf7bX/1cKe16dnLGV+27+5VDHBRtu9/byHPvyUHW0PuA8TCEEbbjbFBsq16ZPE3PTgZ1JddNR4GppZfKjzJ/u66Hoxhy6CoHYTTJwhweGQtmnLkJ6zXwFo1gmGQIp/5skBpyYD6/OJ7suvXddUspevI0TSPzurheLkFrTS/NbJjHqdy2ou99CN18EhnhkyTttLw/3C9vNoPkNo8C63wcZoyH2mnggZZ9FLLV8jaLEiMFD8nRURqlEU8zREKtTCekVcY7J7q+67q2KvfVanu4eTxbHIptWZXA4brruq69X205j1lEIxZEQVzv7CJODBWVR6MwQYFcdnpX9cKoOML7vfz1o/Dx0Lzpj7d2jZnC0EehHcQJIOajj9PIza2JbtblaEKbqu9aF8bBx8+P3i9FsS//5M1l3beGIMvE8+ezDuEwdB8Oz4pu84uv3x0i8MPPzz0o16v97VIw0p6dxG0Px0mGsJqehByD73/86PU32xgPlVVdp0kwRJPx6LPn8+3udVEVMLR/8AefPno8T0reNVdGF/f3qjqQMGFH8xOK+Yvjc81zKZz2+mZzc3W5JighAZkNZ7PpYpOIw+EXCFlKu11fbG+UI1OGw4Tb2Sg7zfiyufnZV+WA+Bfj6fPz8w+v7nnsfv83P04Z3Et3dHrBGex99cvLzTeXG+iBoxpnlgagt9605jgKyAQHnH3x8vLRo5PJcHq7vf7lm1W6OC4qCUzKA99WlqF4wCOl3KET2RQBK7UBRviOmIPoXMAn5wkOMBZIeSy0gQxGHCtvCtGeD5lXxDAwjCON2nLfy4amAYIM7Dux2q3zEBmPjofHceo15ByFFnWbqtAKJyTMphrT5t3bppYEhVZ0aleV4zHOYkC9k410EQsYbRq17oo8jE6nA2T19eY9YyEF+0NVs4QdH003W8QQ8ap4/fYVRGgyzVM2iCZPUT4C2oYht85ZD7RzECOMUN12Nzf3ot3eA8+jcHw8ayprhb6739/erRGA+SDDzFPONSbAcEaBa9qTBWG4tz29eVc1jdO9Ph+nd1r/9mmGbGKLTotDEgcmDj2Ck9MoH5L9u67Z4ld39eIEz/PYSGCAdon2gXqzukVg09Y2G50j7YCwsyjf+12tqw9fPNXQUCaq9t1+35CA/NbHx00tSts4i5BB/YEMH2FOIRV+e9eePToqiu3Zo2MymqvpGPdiexAVQ/T8hB+dYMAxZ6Ftd4yjRsrrpXxyMZfVenCa/esf/8emwyQ4xIQ7k9/e3Y7T+WIxvC7etr3J00k0iNtaN+oQhKgq1avX1XQGkziGrHe42y8FM9lvfvLxk9nTbX0ArhuO6cG+61TgowHBh7owkFbL260RgZaiOpgoA0mi8yQaPkmNxtGYAeev1918Ah8/Piuae287hQEPuO8EtvEwnRaV4GEScG+9ikJMIOqqHjlMHbcapwERLTRWOq2dtrJXnEPLvIuYhvjm/h7YECLsfCVsa1qHYFQc5O2NJkEAuAhDbxHwoJkdnZZVC23Ri6I4lO+uHKPR2QextDagGUvh/a7wArIAaq2th946JWxb63SSRiFPQhhjPEtPDq6+397L3m23oinkdD7cVwP96LzcqTyAUnQIBmOf4GiUnz/yFIO+R95gDBjnhLEOQQTh+aOL4XxeNavhcFBXh2K3wrmjBAdRFA/G5X693e4AtICJ6TiWFdwdDtqlm6q0a/3xcDaOKBJGisB6uFPiy5VZpEtVt/sGuA4iDBeLgEj38pfV3WUQY/fZk6RT3iMQRNrD2HhEyP5QyaMjfzxbjEcT62GjD4CmcRBQCmt9mB9z0TTlDljmJjRZDKY5D15eLn/+9n6FwREMGOUEg7Kmz5+eT+aAU/rbv/UhWUxR3W8PjWi7hkI2m8VXd7dBFMsaG4siQmEWnZ31J8P5zXp9ubreyVXf+cHYra9wtV0/fewuJudxlL281bebqyCGFMHj6cK6AUByFPuXb/ejUU7GUqKtPLhqjxeDvDfN5f7tvuhefMaSSX9TVLLOkC2Lpo0SrBV89W1bHQgD7tHi6MmHHAGvDTY+urmrDu+a87M5QdXr69tHT+Kzs0Ev4HK9ZSwkKfRNbFWNMcoXEECSxzOv4W5fnS6mRmgp0CQdAEodgBTCRvezQSwC7qFwuffSv3zVril6+n3bC7u97mkIbdmmoZnN8ixMrLe1ALOMKV9U/WZX5aNg9qv3LzlrgQGFPAwpH83B5VIwHs+HKaFBStnxDP7Nt3frXTebYehpI3xv1MUg7Wv19qrcRigbGlH2+9t3y7blnucxe/lmV7b3Gc22UBFHgA0Gs2MSDhCivulsU9X7befIYHFqlPReZ3nOwwQS/Pb92ygOmuYLo7XQK8rGRycncTZYMahUd7Ne1rt1EhAPMEZ8ECBtUByM6woYyaYZ5CHTBGvnC+Nd3UbWvG0tjVicaprZrgTLpc2j6NF5gjGIIrTaN9k0mKeDvvVhxipVP7qIxxm/vd5bYgeLpC33g0GCoVluLi9O4kCzLVCDKMrBIMXAyy7H4YtHMxtYFAGtfK314Waz37fDefj59zJm14Sn8rA7ZEGMscckwJF789VNGg+oIw1sTas3Bam3uOm2e7Evi8N4jk5PMUD627V+NGdhYpe3h0+nZ1EQaNqliey20Gn7eHpKzWbd9B9+7+iTpwPMtsVOrFYaEFq2vQJdZ/XwyFtiLq/Kqzu5vhODlGQjCFC0Lq0U4HsfzfKRYokLKFK92a4PwOFpDn0UU6wJhnlIofUeQQts0zYUkIiQDvgUB/MBEaLZN5YKjpxvhWYMQK8sglkOrUfK2rbTVeNUawjhk3nicb+ryMfzxfNnIRiVd3dtMGIK9OsSgtiIBueI7dtdRgNkGAApCsTb9bv5J4t4GL98XTnvTp6hJ+c0zz1ZslVTQ4BHcXqcp5jqEHJotZI6SSFF4SBJNk357du7usJHhLrKbrd9Ho4+PI+N7sa5bhFlLJaqW9Yb6EhAk+qXPzk5PRlPj+EgN0YVm10hrHBuOJnl4wUmAYDYOiuUaqpit7u9vPmm6bYx30Vs0zYtdOrjx6OTRfDmCi2bqyACk6MMUlsWCGh/qO1wMr5Zr7008wEbcY6lu9uKo/noBxGpZK8BjUk8PeGmt6fzBXR8v9skmf/w+JSERrl2moYmyj49PjZoo3UDQQCRYQBQqoF2u6olgQbAAhVZsbGKEAq4A73V2MNPHr8QpFwVLYEwpZYm+nyiF7MhAOT2riPbwijhBilTEDnbKYUIIet1YU3DUtMZta/RpgA3+9uzMzpfRHGKnr7I1ivx8bP8o09enC7OXWuL+hWEoqu1KDuv4Hq7lbpeXnYrobM5Lhs9HKLVCtXbYDSxszGlaQdhWynw7m375kukKZC4VZ6cDE8JzN2kj3F7fmQdM7tSJGEqoYkyQyHxEEUcrvbVobEfX6QYCFl5ZELnPAEkz9FNc7/do0TwQmiUoAFisjOytbVAGgoQoElGpBBa0zSKHj87e3O50c4cHQ2PByF4SiaZ28POmaNgJIdZI8H657TZ1bioGhwiRgi15NCKZKSdVel4AJmcxvnXcmW5OjtLswx99eVmtXe9Bi9fHx7NSIhBL2UQxp8+yYp2B0A/iI2wh7tte+h1kobjIauLKk3SH/3a46o6/Ory8L5YPT89S5D5+v6+tfWT07PTYf7F16/+xz/7H7aHzeef/zAKmEOGBvRQ1VUnHl28CGPmvHNGre6unJHvbt/+7a9+0XWHBOeTJKHYJYOh1M1uV03yFNLp5XJZqAMkPsox43h1X4e4Zor1VGzbfVlHgyBHATeM5QTLWoWQHN51KhPQEeyt1t3F7CiIZciDiNLbyryVd9OjEULYClnv1Pn0BEeu6Svbh0rCgI3ul31V1ItgfDp/vt3Vu3q77+AkG376/eckCL+87CJA+209mhATdQ3Ve9sjwfarnjRbzDBXQssOhAHerGWQjuOQC1sctroTXvcSWi4EsMpCqpTKvMw+mD3+rSfPB7Oj0/Mnbd9/+5L+Vjy5K+6RuS+atpZB2xyq2gcIhsxYp6RCnoDBBJ9dpPnI1VVRFM3yDrdNmMfsvuvSDGcxPxT6dACIBlUjlgctnYoDr7xtpcGeQxsY4asa4JaMcYisb/ZFX+iA5lmYcCu9VSyCQYgpoLFHEAAhW+Bd2Yq70pAQzhahsHJzrdLcRVnmQKmNmh0PHp0Ohik7HJqNqlaFKndlp/vJ2HBqsjGoernpQM5zZ72UvePO0QZj8Ph0sdqvJmzwvUfnf/nq9cnH8zBGP6nvagWiLOIEwEAWvTedJ4z1sDkZjkc5vDkUnRdl3zjjkgAxCsIg/uj4aJJ65FmOokJ3fdNiLyB1RgPZCzyCQQYvV9++/X+//X/8qz/+wadPxoP46NEPH314sbzZf/vlF7Oj+Xg2L7a37999O8z51fWb65tdhP3RjL57v+yUOZqqo0V2eXv3478tnz6fTNLo7q69v5U8t0+fkdOT4W7VYky9b7Yrc5qm2TAbzxNGtegaGLF6292vWkgQpT6J0ScfnP/h7/yukdtOFNp4vFGR1YspQaKXJcQ2jVhiiUBBtN5XBJBu1d/eq2dPBwFLsOOjxdCfNwGOlVbLcrO67rb7LSXOGj+aQE2JAADX/Ul81IqCEM8ZjWIGonGKXPzuffHph4+Oj+Gf//z1cu3zCWgbD3pynPKLGVbaMs9W7/rsCe+kvv7y57f3N06D3W7NGZgOcivUurQ0y2fjkfUFonZ6xtOECNsAonGA53OECbi5BWUVmT7+9Pz5YGR+/h5qJyH0yvtVteuhSAbGKBTyzKrdN1/UqieDEA0Zd8ICCCJGo2FibSV931g/yoJJSihEX9zct7YnnE6SLG5prVVroFbq6CQhj0Jo7Yfn03igDtjUVvWd+vKuQZh+umCtbbZXrfWwku3lV8p0SMWNJYhaOjrCk4C3sbht1kfZrJYtBg44yrkNub2pCwbhxXT+8/ebLJ/Uh2KWTURXYWjHJykWPoBBW3WDhP3a0w8+nA9+9fWbb8U2neBxQmUjOIPE+2HOFCxer/ZloZ+fLa73u7LrF7P4EQ++qZvVfaF6Zby1xhnQNu7Nt3eX4C15XILBbJznvLDtmzcvD/v75d2r9erdy6+Xr9+/lrod58OzI7Ta+W/f9fvu7nq18dyAxN5Xy1xxZWXbmyCJurqXGEaDhEGjt/FgzI/G3LsaSQwN+OrrO5rA8eOATaPVfa+k96ERfv/lza+0MVfLa9ObMAvPLlLUieJ+byGbTI+SJA6G8VmYnk7St5fbt7ct5VRJZwyZpMPBMLg/mG/fLTddORlPtWyzHFPK+85UXe2BdAAXGxvae6kNwYTxJOShCQnf7WAan02SqVX3wFPKpVYOiwBa+PF5/OKJ1HoIdfLl5fU3LzXitw6Ku31mK21tH6aYI5xSRCHORvFwGo6OQqFao/v1oSrrEhv//NEUwXZ3H3RlgqGaDfDRlPKYnZyMCUbWaYJBWx3aQ0MoIwYn0FcVRV0qxWHfqR4WCLvpKJyMEkjsQR0kUYiChLK61JMpkhbt9tJLG8BukGWqk7U2CafPj4bCut6IKFYzfLwAAA1DSURBVJIeCc/FJEikgAEafvzZuQb1l9/ed7ofTMhqqbViz08yzPFOlU3rz88H33v+0fFw9eX1q+kAekJFB53E3ovtatkUzSgaEUKenEyt61Z3h2ejo8hlt+1dwNUoyg9LzQgbJclnp8+mif33hXGGWKMQdcNxGEU4CSnn9Hq/P1SGomgauqdnk1/dXte6CwkKMLrbtq0QUcAYwQlP4lyzUHQd+5u/+qtnj54dH83bet/Uu5/+7GVZ72PmX331ZrkveKpaU922WhoxjCF2ADMkvPPAagtWB4mxiQJ+NB0AW3alCAZucDTClq1WRWequta/kw6dsUryIMMRTT76mOe/ze73TS/l9aZa9V+1SmDjfvjph2cngejVN7e7fbcbTcYRMdDWkUYMd8qhX3/6IiX8y8v3CpJ7IRu9joy/vivfLG+jjE+Jms+SdXvYHpq+0QJ54lXf8iyNpAHjYUgC4iMmayEMtBCMJqM8DqNlC09PX4TD/W63uzgfRBRcfGRBoJkdlLDrlLy/utMMkBjwIOhL+GvPjo9m4frmUEi12gq8xRjnfEDS6ZPmbtUB6Ew/n+JONC9fta9eFkKQOAXjhC/5enYejgaGk8CI2CLt3MQVvthbJfpewCRaEGao9Cej2PeIBuxkPpqn4Zv7feH2OGC2hd/uly+OR3V7iCI19jFBJFBGiHJ0FBaqZ4hVXbspWg+d6ARJwSBBzKOzxXD+/UkQh3/611dffLk7fcaKveVq8OJpPk3BtmoPFcISlI3+pr9WNcjQQCofMl9WfdXxUz96fXMvOwyC8N3doeir9rUqmpLD4PnjGb7rJSiow5zC0Sh+djb97KNHr16/jrN4QoKi74An4zxlCMYRPhtmojKdkbVuBElPhiNyBe+3XRJKgJBDrrM2pXSe51mcN3a/qWTG40Ksfvqzv4jpDw5le3fzpq02h7ZYtaU23XyQ02FdVGZfCxaaQRRuazumSHTGOmSox8QPaXrybBbG1soABKJrxaFgw2ke+dg2/fPjRUKDr969jwdgnkUXeX6CIyIx5dHL4p4wdOgajvGzk3lE4Bc/vyPYcRYEJGbUD2K6q7tXq30YtwEa/8aLT1O24zw4OYqklH3VFXt73xfpiA4yvGv2QTigiCBvpKh9751TpodpFt6u960C5LDqs4HolVkfdEbCDy9w25e395UwNAj5yTyLWUZ4VHpZvDMJErXZVA0vez6mKAK+Fo32QDrX9KCo2iBSFgAMjdGNKMTmtvUHevpono903VSrZZCHi2endXcAjsmzp8n5aRJFeL3FN6/r1oFCdAEnYRhr128PvZTu04+Si7HtOxA7fn/Zbld1xIIU5GdHT374/LPz0dRK+Ldf/MK57q9ebm8rM55nT8+Hnz9+8c2r5W19d7TgMRo0fb9ta1HaNMqmpwHM44jyxTTvuur2rqwPLIiDOEJUplE8rMtmu2rv+8NwEJxOeEgw5dZUAVZhgMgwDptQJoO4FbgVHjggLGApzyJ6/XY3HwVRarjrv/ds9vJeuYM/nweOtGW7rnZr1fSTmMMg4R3pesUDMB/lXskv3y6lgI/O40IS7dzdqitL4KhBzlrn4wgNs+zR8cL1jgAKDa0PTrmOcv3X3/yNRc3j+RFUYpCkdX+43qzDBHz2wZEGo7fvD1C3+QhIB0QoQIBEjaUyHtiTWfzJxUktYCsljUAAiD5AsesqDdOMC6yjmN/uNzzEHisYuOuivL4rI0iOFon2+ovLzXyYfPhkMUmGMY1LfV1U9aPJMXB4u6269qoU1mpPhD8982t9d9NuemvVri974QkVWqY5YyGA0sc8SCiv2rrfNkprWaEowYjaVre7ba0cI8UdfPaxBxgegGcEEdws7ztR631Tnz8OrJM3q10ccAs48mMYybaPN6VcpMksYZ3sBgP64Yd5TMX72/vdpprMgHUszWkYwTdX27v3mzzPpnQaeHWzBKRJsjE5fXE6naUG17WR1hnR+nYDvCRG29dfV2kUjseGDvzRSX57Wd283T19FsaDMA9GfDhM1hUD7mdXtx9/NEEWv79a52l+NBmvSy0sVNp1nVoV1d1gL7wfjybnU1+s5baWs/M0wlFfGuebbamOz5PKlsv1YXOPofFPngTHc5r4lCEqZHSoSXvrAuwkEvaQP3rM6NQqDbT2qSfnk1GHdFOrMCBaYhzg4wtKajQykw+PBu/qKqTIApuxdPZ8ZJu+bnkpzS+++jpB6PFx/igaSIOX26Ixy7NZfHNr7/tNOuSA6dCFOc2BgPPBVLj9BxexlLpszWQwzdL84MR2X1Si4j5IUBDjUGN0dX2nu84YVystZDuIorZSOUlPT58M+G1Ryd7tFWx8QDHwRydcSKeN/+BiPs3Zrm+s14jiJAqsVMC7cUgpJexkOo5S0ba+Q30XNrVuZYsI0EJ8K64J5KTlLA08wK+WV3/7rmUY5py9Xd0kOYQuKrX0GCwGeRTTUZq9fvsKKPA4Pm9L6R3qZNkIDS0IfBgQmkdD4HBT6ZVwABKkqHdamtZDO8gy7DIyDCPue5aGAALU629udpttM4nCkwmguKv29vVrpdzVZEgfHSGGSRqEgwmVEl83XS37ASFPKGGhlboLYsJj2peqE71zceDS8zOTzz0E/fbGbm/kINt3Nh5n0eAsvN1Ub7/dTdN0lBIetcxb0vGj4yFwoCy6lMDJOWNxXr5r72+0gBrR/XgSTRbD/bavYXtZ7r/985euH/7mZy+enyzWpWDBmNJbKSvR+5+/fOsa+oNPTrMpXi/fVRvEs+jp944IwVV1aKs9jZ3HZJQfZUHkqEOZcX0HhWUWut4dJeHOFattmcypaQTbunQYWeKqti63Ps7Iat/lWfzJr+U3spGgs9Srrh4GievkatMWsZ2O+GjIYgr6nnz+8QvGWHF/G4ej2dlAgv56W8S1Z+EgJCDP6PEiUlD0GjDGEoJ728aRBYY8mk10p368evvNoU+SvG3spthNx/EkzAgISEy01FXfvbu8VdZZYCBQCcXTxWQ2iENkUwR9wBgJW2sNIgyEIaaVxBiiJxfDQ1HeHwrvZUoRVXGSUeesUEop45SNB7moXdWpiOfHEd01daOrzltrXBaS6BhNp4HTcle3pRETnnBErdIhmBzN543YegC8cq1lbVke9t0imqUBb1lj2t5pBDrU9gBZewBiT53FuO5FHBEIYBqmWaY8bAZZruuoPDiCkLGAjbJFFsL1pZUtnk8x9WpVNgEwOR6lkXp1W4vKzBKyVlxDPMqN6q03IfQSEd8L6JDAoU5iOB0mnUS1lsbG89O8aHZdBycsvpg9pnatUXF6MRlk2XYlv/nFTrY1To2yPJ71h9VhfUshyhZHUcTiSZI2XoSBxCes2TeYmqKv1m/2o6yPguiz7+fpQLz59kBIZ21lRFrV2vZgMQ6CAJ7MB7PxkMMAYdyYimR+MYubEq+uutkROzkeZR8tkJKj8DhZjK03Usve14db1liz24vNzh1Ngslwuq5U3Ys0cPdrWeqeJyPZi66ps/CYNo5aPMpwt3AO1Z1GTuFRFt0v2/VBPx3GWZCYykBDYpaEnMi6zdngySfnGle7+70LJM+cqHXbQgDBLMq1jzdaGNNeVe2uUVnAhZbFrqEe3GzqsndPnkCCyCgOsiBghOy3FQCk7yTlrO2NIy4OYEQC6uk8iSaZ2m2L1y+3RdfMH4cjPoSm0dg3rdqs+7OjEafBTq07LRi0UuFtWUWcfP7xuRZyc1Ce+d3hIAzYiq5ebxazLIniCDGPtDPYaK0NKNeuZLj2XQSjYTCExgxYkJO574hreClF7UwvVxEwoyDe1O232wJiXBy8aW2cBD5UZakqpfKMxSGQrcyTWGtptclDdjEfBzjf+gjJjuxa2wq0gIANYm5CXXMcwPXm+m69ZlpDtetlOJ8myNUGuPf3apgmo7lxVLa98I3pSmS1hSCPKAlCYQGIY9LtxbaRwOpDrRBEGoHZaTD9+Nn6sM6iYQLw5e1hdyXysWpqb6yNgzoIetFJI4D32Bt6lJBNFfS1W5zEcB7pfQ3gsGqUODhoS0rjPBmdPcq370vd9Ze7uw406VCMpsF4kh9l04Rlrey3zWFT1pST00duu4adrIoSYxKdLI5RFENttW6LUhLsatm+e3XYla3sTCus9vp0Ovn7o08vizth9lqBXjVG2q6EJAjTNExoGAbMmvss5nEYOwNSdJyFqBp0ac/PonnWBzerct0U49HUHnWdhpwzkqni0O/KgqbJ6XxR7N/c7PY+AOoA61r0EITM1dLctibjcMz59fIwzaN5Es9jejwYYmsRBMt9s2r9+tCFhDFrn17MLtA8jiMItPXKQXQ6dRqKSloFiQagEzrG7jQcXpcH4GUUxNRS6EhEQgRB2WjgkTW+tjIf4jSOyb2/fl9ipxGCnhvDZGsaU/eOEOcUkJDbGGHfKIWwjcMsh4Om08CZyXSspb1fr6TSFbAgtBAZ7UiHmUMQSuI7aAVQTsckaCpQtcY5J6VhCAYBo5zleZ7FAQlrDXVdHN5eHjjn/183LVHURJZH5wAAAABJRU5ErkJggg==",
      "text/plain": [
       "PILImage mode=RGB size=224x149"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im  = PILImage.create('dog.jpg')\n",
    "im.thumbnail((224,224))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "categories = ('Dog', 'Cat')\n",
    "\n",
    "def classify_image(img):\n",
    "\tpred, idx, probs = learn.predict(img)\n",
    "\treturn dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
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       "        background: #F44336;\n",
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       "</style>\n"
      ],
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       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "{'Dog': 0.9999948740005493, 'Cat': 5.148555374034913e-06}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kejia/miniconda3/envs/fastbook/lib/python3.7/site-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
      "  \"Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\",\n",
      "/home/kejia/miniconda3/envs/fastbook/lib/python3.7/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
      "  warnings.warn(value)\n",
      "/home/kejia/miniconda3/envs/fastbook/lib/python3.7/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",
      "  \"Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\",\n",
      "/home/kejia/miniconda3/envs/fastbook/lib/python3.7/site-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
      "  warnings.warn(value)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7860\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "    }\n",
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       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "    }\n",
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       "</style>\n"
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       "<IPython.core.display.HTML object>"
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      "text/html": [
       "\n",
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       "    progress {\n",
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       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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       "    }\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",
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       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
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    },
    {
     "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: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",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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    {
     "data": {
      "text/html": [],
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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   ],
   "source": [
    "#|export\n",
    "image = gr.inputs.Image(shape=(224,224))\n",
    "label = gr.outputs.Label()\n",
    "examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n",
    "\n",
    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Expecting value: line 1 column 1 (char 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_25124/1586687238.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnbdev\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnbdev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb_export\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'app.ipynb'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'./'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.7/site-packages/nbdev/export.py\u001b[0m in \u001b[0;36mnb_export\u001b[0;34m(nbname, lib_path, procs, debug, mod_maker, name)\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlib_path\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlib_path\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_config\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlib_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m     \u001b[0mexp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExportModuleProc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m     \u001b[0mnb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNBProcessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m     \u001b[0mnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcells\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mexp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.7/site-packages/nbdev/process.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path, procs, nb, debug, rm_directives, process)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;34m\"Process cells and nbdev comments in a notebook\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnb\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrm_directives\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mread_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnb\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mnb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlang\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnb_lang\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcell\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcells\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdirectives_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_directives\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcell\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mremove\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrm_directives\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlang\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.7/site-packages/execnb/nbio.py\u001b[0m in \u001b[0;36mread_nb\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mread_nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0;34m\"Return notebook at `path`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict2nb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_read_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m     \u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'path_'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.7/site-packages/execnb/nbio.py\u001b[0m in \u001b[0;36m_read_json\u001b[0;34m(self, encoding, errors)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# %% ../nbs/01_nbio.ipynb 6\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_read_json\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mloads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;31m# %% ../nbs/01_nbio.ipynb 13\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.7/json/__init__.py\u001b[0m in \u001b[0;36mloads\u001b[0;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    346\u001b[0m             \u001b[0mparse_int\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mparse_float\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m             parse_constant is None and object_pairs_hook is None and not kw):\n\u001b[0;32m--> 348\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_default_decoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    349\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcls\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    350\u001b[0m         \u001b[0mcls\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mJSONDecoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.7/json/decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    335\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \"\"\"\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    339\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/fastbook/lib/python3.7/json/decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m    353\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    354\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 355\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Expecting value\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    356\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)"
     ]
    }
   ],
   "source": [
    "import nbdev\n",
    "nbdev.export.nb_export('app.ipynb', './')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'notebook2script' from 'nbdev.export' (/home/kejia/miniconda3/envs/fastbook/lib/python3.7/site-packages/nbdev/export.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_25124/27739807.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnbdev\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexport\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnotebook2script\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'notebook2script' from 'nbdev.export' (/home/kejia/miniconda3/envs/fastbook/lib/python3.7/site-packages/nbdev/export.py)"
     ]
    }
   ],
   "source": [
    "from nbdev.export import notebook2script"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fastbook",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "a6bcdb6a59911f3351684f415daee8e44db8213f3d27e54ce3de5aa84314ea80"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}