model2_GRU (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel('Google_stock.xlsx')\n",
    "\n",
    "#Sort the data by date\n",
    "data = data.sort_values('Date')\n",
    "\n",
    "data = data.drop('Date', axis=1)\n",
    "\n",
    "# Scaling\n",
    "scaler = MinMaxScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "data1 = pd.read_excel('Google_stock.xlsx')\n",
    "\n",
    "#Sort the data by date\n",
    "data1 = data1.sort_values('Date')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2194438715886437, 0.200152952498797, 0.23866175156389635, 0.14285591530899844, 0.1446174468962673, 0.1747697119681034, 0.1584175431360418, 0.1108089296762219, 0.24734910978208566, 0.3393483192410806, 0.17465800508695953, 0.19092424554891044, 0.27873444696500993, 0.3276491716505121, 0.5107195641713068, 0.29221231181686946, 0.19217020691551523, 0.14038117824981097, 0.15075702894067505, 0.149541142503609, 0.14099126967759676, 0.3236406131848491, 0.26262287756925823, 0.16144651818244313, 0.17938836186155221, 0.24529971815494606, 0.2608527531449783, 0.1871433972640407, 0.16043685983364267, 0.10328160445452671, 0.14973877775486355, 0.23574877981714443, 0.25322231387915034, 0.2387219014229738, 0.3029146903141541, 0.17729170963085172, 0.19891558396920328, 0.19014659379940882, 0.17489001168625834, 0.19562023097545886, 0.18780074929538737, 0.2397100776792466, 0.3494191242180518, 0.2481310579500928, 0.17718859558671893, 0.2514994500584313, 0.1375756169656974, 0.17335189386127725, 0.1416056575238881, 0.14246494122499484, 0.267739912009349, 0.1586452533168351, 0.18112411493778788, 0.12625885062212142, 0.12744895854815427, 0.12836409568983295, 0.1477237574757682, 0.10274025572282944, 0.18674383034302605, 0.1975192479549048, 0.15596858458788754, 0.1762219014229738, 0.15001374853921773, 0.211340826287207, 0.29664191929607475, 0.2445951055200385, 0.18623685295937306, 0.1707783391764625, 0.1751134254485461, 0.14120609060287345, 0.21181772874132124, 0.15031449783460507, 0.17582663092046472, 0.287009349006668, 0.5015595999175088, 0.2490333058362549, 0.19790592562040285, 0.15785041589331134, 0.17195555784697877, 0.21480803602117277, 0.19864920602186018, 0.19915618340551317, 0.1254940881281364, 0.174211177562384, 0.21696483811095071, 0.18484910978208566, 0.1690855502852822, 0.2131324328040146, 0.18997903347769302, 0.1637837698494535, 0.12493125730391146, 0.13624372722898193, 0.34438801814807174, 0.2772478861620953, 0.24112789578607274, 0.19621313672922253, 0.1711564240049495, 0.22111517838729636, 0.1964236612359937, 0.19830119612291194, 0.19490272908503473, 0.17379012854884168, 0.18307039252079468, 0.1699877981714443, 0.21377259916133912, 0.1612660686052107, 0.1818673953392452, 0.22271344607135493, 0.2309754588574964, 0.20184574138997732, 0.15166786966384824, 0.19034852546916892, 0.1786751563896336, 0.19382003162164022, 0.18527875163263904, 0.1870660617309411, 0.2518474599573795, 0.2912928782566852, 0.3440700831786622, 0.2605777823606242, 0.22047930844847735, 0.1580480511445659, 0.16627139616415756, 0.20283821406475563, 0.1488279370316904, 0.16324242111775625, 0.09020760294218738, 0.14685158451914485, 0.16999639100845537, 0.17084708187255104, 0.14741011892486425, 0.2231559771774249, 0.22177682683714856, 0.1692144428404482, 0.1711564240049495, 0.12991510277033066, 0.3139908572214202, 0.6361449096033546, 0.25074757681996285, 0.17658280057743866, 0.17942273320959648, 0.25921152127586444, 0.15340362274008387, 0.1632767924658005, 0.15149601292362688, 0.1876374853921771, 0.1845999175087647, 0.1478870213789785, 0.11292706399945006, 0.12991510277033066, 0.11893775348869182, 0.12834691001581083, 0.11413865401801059, 0.11874871107444833, 0.12245222382621845, 0.179740668179006, 0.1613176256272771, 0.14453151852615662, 0.15627792672028598, 0.10629339382690589, 0.15164209115281502, 0.07489087096995944, 0.09281123255654086, 0.09068450539630164, 0.14963566371073075, 0.1146757063312023, 0.12752629408125388, 0.160664570014436, 0.17674606448064895, 0.1761918264934351, 0.11133309273389702, 0.1347142022410119, 0.10643087921908297, 0.11101515776448752, 0.13895476730597375, 0.09440090740358836, 0.11428473224719873, 0.1152471299924383, 0.10857908847184986, 0.16698460163607617, 0.13785058775005157, 0.3754640131985976, 0.15337784422905068, 0.128243795971678, 0.14227160239224584, 0.1086220526569052, 0.10852753144978346, 0.14862170894342477, 0.1453693201347357, 0.1289784835361243, 0.18275245755138517, 0.11710747920533443, 0.1115350244036571, 0.16775795696707227, 0.22896043857840107, 0.1947093902522857, 0.12151130817350655, 0.10721282738709012, 0.1373350175293875, 0.17329174400219977, 0.36779920258472537, 0.9999999999999999, 0.313458101326734, 0.19067505327558948, 0.22999157901972916, 0.18059135904310167, 0.17526809651474531, 0.09987024816113288, 0.13846927201484843, 0.11417302536605485, 0.14149395064274423, 0.11064566577301162, 0.09814738434041383, 0.10182941499965628, 0.0786115693957517, 0.1449224926101602, 0.11129012854884168, 0.09588317178799752, 0.10500446827524575, 0.136252320065993, 0.10056197154052382, 0.11003987076373135, 0.15184831924108064, 0.09752440365711143, 0.08300250910840723, 0.09408297243417886, 0.10812796452876881, 0.13916099539423937, 0.1973903553997388, 0.09683268027772049, 0.10281759125592906, 0.11842218326802777, 0.1446174468962673, 0.10271447721179625, 0.09770485323434384, 0.12322987557572007, 0.12788289681721318, 0.15999432872757272, 0.14770657180174607, 0.13773028803189663, 0.18667508764693752, 0.1382114869045164, 0.1324757681996288, 0.1908211315047776, 0.1437194954286107, 0.28174623633738916, 0.14855296624733622, 0.06315305561284112, 0.11535883687358217, 0.13541881487591945, 0.10655977177424898, 0.11709888636832336, 0.15718447102495361, 0.14380542379872138, 0.1526818244311542, 0.22042775142641094, 0.19360521069636352, 0.17993830343026054, 0.18503815219632916, 0.20885749639100845, 0.2143698013336083, 0.16832508420980272, 0.1452919846016361, 0.23328693201347359, 0.17089004605760638, 0.13556489310510758, 0.16822626658417544, 0.33630645493916267, 0.37619010792603286, 0.19081253866776657, 0.20548480786416445, 0.4399962191517151, 0.4810699800646181, 0.3948279714030384, 0.24285075960679178, 0.20674795490479136, 0.16800714924039323, 0.22765003093421324, 0.16788684952223826, 0.17702533168350865, 0.1488021585206572, 0.15847769299511927, 0.18878033271464909, 0.18204784491647763, 0.18159242455489105, 0.1467570633120231, 0.16072471987351344, 0.14446277583006806, 0.1240848628583213, 0.17108768130886093, 0.10863064549391627, 0.1997576819962879, 0.18169553859902385, 0.12659397126555305, 0.10649102907816044, 0.10933096171031828, 0.1306970509383378, 0.10470371897985839, 0.1478096858458789, 0.16954097064686877, 0.2025159826768406, 0.19804770743108546, 0.18664930913590433, 0.15663023303773974, 0.1391524025572283, 0.14536072729772462, 0.2771533649549735, 0.26232212827387086, 0.20810991957104558, 0.22279078160445454, 0.00022341376228775695, 0.0014350037808482847, 0.00012459613666047985, 0.0, 0.005997800233725167, 0.2193407575445109, 0.2740642400494947, 0.1892916065168076, 0.13542311129442497, 0.14315666460438578, 0.17357530762356502, 0.16875042964185055, 0.11059840516945074, 0.1657429366879769, 0.21046865333058362, 0.29302863133292084, 0.1102375060149859, 0.10156303705231319, 0.08807657936344263, 0.08078985357805733, 0.09014745308310991, 0.14336289269265143, 0.11593885337182924, 0.07510139547673059, 0.0817522513232969, 0.07240324465525537, 0.04378050457138929, 0.07242472674778304, 0.13857238605898123, 0.08674039320822162, 0.10475527600192479, 0.0820572970371898, 0.07089090534130749, 0.05100278407919158, 0.07308637519763525, 0.06364714374097752, 0.054706296830961704, 0.07654928851309548, 0.0511961229119406, 0.06927115556472124, 0.08289939506427442, 0.0903107169863202, 0.07083075548223002, 0.05799735340620059, 0.07596497559634288, 0.06148174881418849, 0.08007664810613872, 0.07791554959785522, 0.07242902316628858, 0.07448271121193373, 0.0628823812469925, 0.05789423936206778, 0.04705437547260603, 0.06249140716298893, 0.05224444902729085, 0.07300903966453563, 0.06189420499071973, 0.07469753213721042, 0.10550285282188766, 0.19388877431772875, 0.06586839210833849, 0.0947489173025366, 0.08450195916683852, 0.07470612497422148, 0.09602925001718567, 0.0562573039114594, 0.06204028321990788, 0.0451682477486767, 0.03042723585619028, 0.045524850484636004, 0.08192410806351824, 0.04777187736303017, 0.058109060287344465, 0.06952894067505327, 0.07953529937444147, 0.06958479411562521, 0.05984910978208565, 0.12961864989344882, 0.1725957242043033, 0.08850192479549047, 0.07286296143534748, 0.052785797758988104, 0.044253110606998006, 0.039832095964803735, 0.04217364405031965, 0.05781260741046263, 0.04344968034646318, 0.09025056712724272, 0.08389616415755825, 0.061150924589262384, 0.06649137279164088, 0.0571466625421049, 0.04751838867120368, 0.06405530349900322, 0.051990960335464353, 0.06609180587062624, 0.06166649480992644, 0.042113494191242176, 0.065107926032859, 0.05498126761531587, 0.04182133773286588, 0.04432185330308654, 0.03907162988932426, 0.03365384615384615, 0.058310991957104555, 0.07029799958754382, 0.07304341101257991, 0.05535935244380284, 0.04634976283769849, 0.06765999862514607, 0.052008146009486486, 0.06247851790747233, 0.06998436103663985, 0.061309892073967136, 0.055114456588987414, 0.04176548429229394, 0.05226593111981852, 0.06866106413693544, 0.06848491097820857, 0.06343661923420636, 0.07258799065099333, 0.06189420499071973, 0.16064308792190832, 0.07244620884031071, 0.06304994156870831, 0.07410892280195229, 0.08263301711693131, 0.06185553722416993, 0.054908228500721794, 0.06952894067505327, 0.06193287275726954, 0.05042706399945005, 0.048845981989413624, 0.05198666391695882, 0.08200144359661786, 0.08542998556403382, 0.1084330102426617, 0.132437100433079, 0.11088196879081597, 0.0954105657523888, 0.15989551110194541, 0.1594314979033478, 0.23830514882793705, 0.11199903760225476, 0.10030848284869733, 0.12542534543204786, 0.10084983158039458, 0.0846652230700488, 0.05072781329483742, 0.05441414037258541, 0.07593919708530968, 0.062371107444833984, 0.08733329896198529, 0.059208943424761115, 0.05326270021310235, 0.08721299924383034, 0.05709940193854403, 0.07004451089571732, 0.048540936275520724, 0.041258506908640954, 0.04832611535024403, 0.05736148346738159, 0.055213274214614694, 0.07401869801333608, 0.08421409912696776, 0.059638585275314494, 0.06701123943081047, 0.09548360486698287, 0.07317230356774593, 0.07677270227538324, 0.06527118993606928, 0.049133842029284384, 0.09079191585893999, 0.06543015742077404, 0.05471488966797278, 0.05963428885680896, 0.11019024541142503, 0.10001202997181549, 0.0802785797758988, 0.07342579225957242, 0.06905633463944455, 0.08558895304873856, 0.12086684539767649, 0.1704475149515364, 0.12387004193304461, 0.12574757681996288, 0.15864095689832955, 0.11700436516120162, 0.09433646112600536, 0.030070633120230974, 0.04448941362480236, 0.09782515295249879, 0.03740891592768268, 0.05860744483398638, 0.06185553722416993, 0.08815821131504777, 0.12425242318003711, 0.08838592149584107, 0.14374527393964392, 0.08857066749157902, 0.0994406063105795, 0.10150718361174124, 0.0957156114662817, 0.11634271671134941, 0.09840516945074586, 0.09538049082285006, 0.09713342957310785, 0.11467140991269677, 0.09731817556884581, 0.0659843954079879, 0.08146439128342613, 0.07200367773424073, 0.1795258472537293, 0.24053499003230908, 0.12209991750876469, 0.08708410668866433, 0.07114009761462844, 0.07913573245342682, 0.0748221282738709, 0.054130576751220176, 0.07484361036639857, 0.05885663710730735, 0.06927115556472124, 0.08130542379872138, 0.0691250773355331, 0.06202739396439128, 0.04227246167594693, 0.061718051831992846, 0.0622980683302399, 0.04284388533718292, 0.07811318484910978, 0.09897229669347632, 0.10321286175843816, 0.09115711143191035, 0.07290592562040282, 0.07349453495566095, 0.05936361449096033, 0.07094246236337388, 0.04529284388533718, 0.07581030453014367, 0.07788977108682202, 0.05936361449096033, 0.07285436859833642, 0.07016481061387227, 0.07713360142984807, 0.09118288994294356, 0.05110160170481886, 0.11209355880937649, 0.06969650099676909, 0.11064996219151714, 0.09213239843266652, 0.06722606035608716, 0.08105193510689489, 0.05497697119681034, 0.06788770880593936, 0.08356533993263215, 0.07340431016704475, 0.0565623496253523, 0.055638619646662536, 0.050293875025778506, 0.0665944868357737, 0.06021860177356155, 0.07034955660961023, 0.11125146078229188, 0.09956090602873444, 0.05550972709149653, 0.09178009211521275, 0.07181033890149172, 0.07913573245342682, 0.06812830824224926, 0.1794614009761463, 0.21158142572351687, 0.10268869870076304, 0.06372018285557159, 0.0726481405100708, 0.08912060906028733, 0.07562985495291125, 0.05585773699044476, 0.059084347288100636, 0.06698546091977726, 0.06609610228913178, 0.06529267202859695, 0.0385560596686602, 0.06987265415549597, 0.05346463188286244, 0.05997800233725166, 0.08435588093765037, 0.08552880318966109, 0.08416683852340688, 0.06113373891524025, 0.06250429641850554, 0.05019505740015123, 0.10305389427373342, 0.06518096514745308, 0.04390510070804977, 0.11071870488760568, 0.08147728053894274, 0.082783391764625, 0.07343008867807795, 0.05758919364817488, 0.05930346463188286, 0.0649919227332096, 0.062186361449096034, 0.07764057881350106, 0.051969478242936684, 0.040725751013954765, 0.06980820787791296, 0.045709596480373954, 0.0552648312366811, 0.07841823056300268, 0.08101326734034509, 0.053378703512751764, 0.051110194541829926, 0.05493830343026053, 0.05704784491647762, 0.09027634563827593, 0.08291658073829655, 0.09571131504777616, 0.08391334983158039, 0.0527600192479549, 0.05467622190142297, 0.06828297930844847, 0.055372241699319445, 0.07881350106551178, 0.08372860383584244, 0.09446105726266583, 0.13904069567608443, 0.0763344675878188, 0.2045267065374304, 0.4793514126624046, 0.25146937512889256, 0.1447592287069499, 0.16847975527600192, 0.1298033958891868, 0.15543582869320136, 0.11460696363511376, 0.07387261978414793, 0.06733347081872551, 0.06299838454664192, 0.07296177906097477, 0.05570736234275108, 0.06371588643706606, 0.09995188011273802, 0.06722606035608716, 0.05997800233725166, 0.0773913865401801, 0.214520175981302, 0.12583350519007355, 0.07745583281776311, 0.045627964528768813, 0.044846016360761666, 0.0622207327971403, 0.09135045026465938, 0.12233622052656905, 0.18291142503608993, 0.24757681996287895, 0.15166786966384824, 0.18165257441396854, 0.14966144222176395, 0.08467381590705987, 0.09251477967965903, 0.15871829243142918, 0.11031913796659104, 0.07526036296143535, 0.08941276551866363, 0.0975974427717055, 0.07243761600329965, 0.08152024472399808, 0.05867189111156939, 0.07279851515776449, 0.08911631264178181, 0.054934007011755, 0.09739121468343988, 0.2182451708255998, 0.07650202790953461, 0.10977349281638825, 0.0628566027359593, 0.09590465388052519, 0.09306472124836736, 0.1340396645356431, 0.09888636832336564, 0.102937890974084, 0.07990049494741183, 0.11501082697463394, 0.0771507871038702, 0.09273389702344126, 0.08957173300336839, 0.09341273114731559, 0.07049563483879838, 0.05444851172062968, 0.0773269402625971, 0.06047638688389358, 0.08067814669691345, 0.06888018148071767, 0.06313157352031347, 0.10664140372585412, 0.07661373479067848, 0.1745677802983433, 0.28553997387777547, 0.11637708805939369, 0.09614954973534061, 0.09327524575513851, 0.06213480442702962, 0.08167061937169175, 0.08070392520794666, 0.06691671822368873, 0.07289733278339176, 0.0796427098370798, 0.06460524506771155, 0.08858785316560115, 0.06874699250704612, 0.05836684539767649, 0.07859438372172957, 0.08883274902041657, 0.08154602323503127, 0.06457517013817282, 0.0720251598267684, 0.056678352925001715, 0.09471024953598679, 0.060433422698838245, 0.09990032309067161, 0.04787069498865745, 0.03568605210696363, 0.08978225751013955, 0.09137193235718705, 0.09548790128548841, 0.1109636007424211, 0.11812573039114593, 0.07752457551385165, 0.07826355949680346, 0.11566388258747508, 0.08494019385440296, 0.09523011617515638, 0.10331167938406544, 0.11421169313260465, 0.0853010930088678, 0.06640114800302467, 0.1349419124218052, 0.06521104007699181, 0.05832388121262116, 0.0669382003162164, 0.022311301299236957, 0.0647642125524163, 0.07549236956073417, 0.0552261634701313, 0.06414552828761944, 0.14027376778717263, 0.0834708187255104, 0.08331185124080566, 0.1269935381865677, 0.10496150409019041, 0.08942565477418024, 0.08664157558259435, 0.10714408469100158, 0.09529026603423386, 0.1546796590362274, 0.09710765106207465, 0.1476722004537018, 0.10329879012854884, 0.08609593043239155, 0.07320237849728466, 0.056558053206846766, 0.09393259778648518, 0.1146499278201691, 0.1489310510758232, 0.22046212277445523, 0.2724015260878531, 0.26479256891455283, 0.22074998281432598, 0.2190228225751014, 0.1821466625421049, 0.15451639513301713, 0.11300010311404413, 0.12958427854540455, 0.09166408881556334, 0.10793032927751425, 0.106753110606998, 0.0805707362342751, 0.06794356224651131, 0.0834321509589606, 0.08598851996975321, 0.08402505671272427, 0.0697738365298687, 0.09605073210971334, 0.10625902247886161, 0.09209373066611672, 0.06964924039320822, 0.08373290025434797, 0.08439025228569465, 0.12791297174675192, 0.08886712036846085, 0.06073417199422561, 0.1212234481336358, 0.0843343988451227, 0.0734859421186499, 0.0736019454182993, 0.06945160514195366, 0.07960833848903554, 0.12772392933250842, 0.07856430879219083, 0.054212208702825324, 0.06118959235581219, 0.0681841616828212, 0.055569876950574, 0.08138275933182099, 0.07623994638069705, 0.07350742421117756, 0.06740221351481404, 0.04839056162782704, 0.0483089296762219, 0.04478157008317866, 0.06209613666047982, 0.05511875300749295, 0.05208118512408056, 0.05764934350725235, 0.07300474324603011, 0.04825307623564996, 0.07739568295868564, 0.06651285488416855, 0.08689936069292638, 0.0653614147246855, 0.13115247129992438, 0.25537911596892826, 0.08383601429848078, 0.11758008524094314, 0.13278940675053277, 0.1228517907472331, 0.10654258610022685, 0.07034955660961023, 0.06598869182649343, 0.07220990582250636, 0.07199078847872413, 0.0782549666597924, 0.06454939162713962, 0.06681790059806145, 0.07236028047020003, 0.05812194954286107, 0.05613700419330446, 0.056248711074448336, 0.08564051007080498, 0.07556970509383377, 0.07141936481748813, 0.0782162988932426, 0.05641197497765862, 0.08255997800233725, 0.06965783323021929, 0.06595002405994363, 0.08451484842235513, 0.09115281501340482, 0.05352048532343438, 0.057129476868082764, 0.05234756307142366, 0.0669124218051832, 0.05706932700900529, 0.06770296281020141, 0.04214786553928645, 0.051922217639375816, 0.05374819550422767, 0.05577610503883962, 0.05184058568777067, 0.08483707981027015, 0.14584192617034442, 0.08913349831580394, 0.06262889255516602, 0.050543067299099464, 0.09295301436722347, 0.19080824224926102, 0.11261342544854609, 0.09305612841135628, 0.0826931669760088, 0.06830446140097614, 0.057434522581975664, 0.0625, 0.06032171581769437, 0.0556472124836736, 0.06733776723723103, 0.04742816388258747, 0.05709940193854403, 0.03987076373135354, 0.04048515157764487, 0.05462466487935656, 0.05532068467725304, 0.05239912009349006, 0.055071492403932075, 0.04375472606035608, 0.053786863270777474, 0.04401680758919364, 0.050646181343232274, 0.06398226438440915, 0.1513327490204166, 0.16470749982814326, 0.11568536468000275, 0.07633017116931326, 0.0549726747783048, 0.0486139753901148, 0.07704767305973739, 0.04726060356087165, 0.05632604660754795, 0.033447618065580526, 0.041383103045301434, 0.03147556197154052, 0.03996958135698082, 0.043827765174950156, 0.03936808276620609, 0.030569017666872894, 0.0366742283632364, 0.04053670859971128, 0.03953993950642744, 0.04571818931738503, 0.039484086065855496, 0.04978689764212552, 0.035793462569601975, 0.04821011205059462, 0.05330566439815769, 0.03940675053275589, 0.04574826424692376, 0.06164930913590431, 0.038057675122018284, 0.053821234618821746, 0.08066955385990238, 0.055986629545610775, 0.059595621090259156, 0.046379837767237225, 0.05573314085378428, 0.08770708737196672, 0.04997594005636901, 0.041705334433216466, 0.04983415824568639, 0.06351395476730597, 0.06032171581769437, 0.06553327146490685, 0.04920688114387846, 0.047342235512476794, 0.056145597030315526, 0.06777170550628996, 0.054603182786828894, 0.051277754863545744, 0.06243985014092253, 0.045662335876813086, 0.03975476043170413, 0.04955918746133223, 0.05862892692651405, 0.039935210008936545, 0.05831958479411562, 0.03628755069773836, 0.04662043720354712, 0.08539991063449508, 0.07556970509383377, 0.07517013817281913, 0.05406183405513164, 0.07259228706949886, 0.06738932425929745, 0.07045267065374304, 0.1177777204921977, 0.18311335670585002, 0.10394754932288444, 0.10304959785522788, 0.08010672303567745, 0.08314858733759538, 0.0913805251941981, 0.06776311266927888, 0.057696604110813225, 0.1116725097958342, 0.2035342338626521, 0.10414088815563347, 0.1556936138035334, 0.10208720010998831, 0.06261170688114387, 0.05568588025022341, 0.06613047363717604, 0.0568287275726954, 0.06810682614972159, 0.06313586993881899, 0.024897745239568295, 0.09367481267615316, 0.06911648449852202, 0.10246958135698081, 0.129322197016567, 0.062066061730941084, 0.05956124974221488, 0.07230013061112256, 0.07532051282051282, 0.0635955867189111, 0.0779370316903829, 0.09006152471299923, 0.09182735271877362, 0.07288014710936963, 0.06954182993056987, 0.10399051350793978, 0.05259675534474462, 0.04051952292568914, 0.051703100295593586, 0.0414303636488623, 0.02644445590156046, 0.03356362136522994, 0.049232659654911665, 0.031548601086134595, 0.07570719048601085, 0.07086512683027428, 0.04576115350244036, 0.057026362823949954, 0.070130439265828, 0.054328212002474736, 0.05022083591118443, 0.045456107788547466, 0.0577954217364405, 0.046886815150890215, 0.05791142503608991, 0.055273424073692165, 0.039157558259434934, 0.07141077198047707, 0.08402505671272427, 0.06298979170963084, 0.06387055750326527, 0.12743177287413213, 0.1270837629751839, 0.13914810613872278, 0.09248900116862582, 0.0868649893448821, 0.06548601086134598, 0.06253437134804427, 0.05055166013611054, 0.052987729428748194, 0.049297105932494666, 0.04218223688733072, 0.04842493297587131, 0.05178902866570426, 0.04492764831236681, 0.05609833642675465, 0.05498126761531587, 0.06887588506221214, 0.05389427373341582, 0.04189008042895442, 0.06293823468756445, 0.05947532137210421, 0.04698563277651749, 0.09679401251117069, 0.06395648587337595, 0.04015432735271877, 0.038173678421667696, 0.04730786416443253, 0.0442402213514814, 0.04218653330583625, 0.05385990238537155, 0.07287155427235856, 0.052231559771774246, 0.03316835086272083, 0.05901560459201209, 0.04166237024816113, 0.07322815700831786, 0.05257097683371141, 0.10550285282188766, 0.05987488829311885, 0.14948099264453152, 0.08477263353268714, 0.08104763868838935, 0.06928404482023784, 0.07640750670241286, 0.04500068742696088, 0.05989207396714099, 0.07147521825806008, 0.04457534199491304, 0.06648277995462981, 0.05355485667147865, 0.04508661579707156, 0.04171822368873307, 0.046049013542311126, 0.03834983158039458, 0.047883584244174056, 0.03811352856259022, 0.035608716573864024, 0.04065700831786622, 0.04086323640613185, 0.05652797827730803, 0.05862892692651405, 0.07149670035058775, 0.05281587268852684, 0.08674039320822162, 0.1379837767237231, 0.09057279851515776, 0.0678533374578951, 0.06408537842854196, 0.060759950505258814, 0.0817780298343301, 0.05679435622465113, 0.06762133085859627, 0.05009623977452395, 0.03553567745926995, 0.04479875575720079, 0.04726919639788272, 0.04131436034921289, 0.10114628445727641, 0.06827009005293187, 0.05950969272014848, 0.04779765587406338, 0.05425946930638619, 0.043690279782773074, 0.07100261222245136, 0.05345174262734584, 0.06267615315872688, 0.10484120437203547, 0.06026156595861689, 0.07489087096995944, 0.053456039045851375, 0.07762339313947893, 0.062126211590018555, 0.0628823812469925, 0.14184625696019798, 0.16135629339382693, 0.08616037670997456, 0.06366432941499965, 0.09130748607960404, 0.13262184642881694, 0.06553756788341239, 0.04803825531037327, 0.051312126211590016, 0.0401070667491579, 0.06530556128411356, 0.06833453633051488, 0.11050388396232899, 0.11658331614765931, 0.14140802227263355, 0.08946432254073004, 0.07312074654567952, 0.07759331820994018, 0.060863064549391624, 0.07002732522169519, 0.050908262872069836, 0.04748831374166494, 0.06548601086134598, 0.05528631332920877, 0.04493624114937787, 0.04973534062005912, 0.04924125249192273, 0.05227022753832405, 0.0694559015604592, 0.07317230356774593, 0.13922114525331683, 0.19991664948099266, 0.0893826905891249, 0.1377045095208634, 0.07898965422423868, 0.08430432391558397, 0.054555922183268026, 0.07804444215302124, 0.05132501546710662, 0.046160720423455, 0.04400821475218258, 0.04527136179280951, 0.0508781879425311, 0.07802725647899911, 0.05150976146284457, 0.04539595792946999, 0.0376151440159483, 0.04291262803327146, 0.054534440090740356, 0.05734859421186499, 0.040192995119268576, 0.04978689764212552, 0.046500137485392175, 0.05423798721385852, 0.04491905547535574, 0.04634117000068742, 0.050598920739671406, 0.05556558053206846, 0.06765570220664054, 0.040364851859489924, 0.05664827799546298, 0.06529696844710249, 0.051763250154671064, 0.04311885612153708, 0.05409620540317591, 0.048399154464838105, 0.047032893380078364, 0.05970732797140303, 0.10730305217570632, 0.05581047638688389, 0.03990513507939781, 0.071402179143466, 0.055110160170481884, 0.044888980545817005, 0.07943648174881418, 0.07127758300680552, 0.09587457895098646, 0.04349694095002406, 0.067586959510552, 0.05480081803808345, 0.03782566852271946, 0.04057967278476662, 0.05181051075823193, 0.05009623977452395, 0.037958857496391, 0.04126709974565202, 0.07241183749226644, 0.05389857015192136, 0.04992008661579707, 0.03877947343094796, 0.055092974496459744, 0.04509950505258816, 0.05609833642675465, 0.050495806695538596, 0.06317883412387433, 0.051741768062143395, 0.051703100295593586, 0.0873977452395683, 0.22168660204853236, 0.08924520519694781, 0.05683302399120093, 0.05866759469306386, 0.04473001306111225, 0.04590723173162851, 0.048003883962329, 0.04744964597511514, 0.04643569120780917, 0.05315958616896954, 0.030594796177906097, 0.03771825806008111, 0.040871829243142914, 0.03635199697532137, 0.04819722279507802, 0.05968584587887536, 0.040626933388327485, 0.04679229394376847, 0.03172475424486148, 0.022732350312779266, 0.05585344057193923, 0.06085876813088609, 0.10532669966316079, 0.07373083797346532, 0.08170499071973603, 0.08156750532755894, 0.0884804427029628, 0.0543110263284526, 0.06231095758575651, 0.05511875300749295, 0.050908262872069836, 0.05463325771636763, 0.05477074310854471, 0.06662885818381796, 0.14040695676084416, 0.06645270502509108, 0.05717673747164363, 0.05416494809926445, 0.042440021997662745, 0.03210283907334845, 0.03233914209115281, 0.05430672990994707, 0.03562590224788616, 0.037791297174675186, 0.05283305836254897, 0.061107960404207046, 0.04282240324465525, 0.0546160720423455, 0.04466986320203478, 0.03843575995050526, 0.044463635113769155, 0.04169244517769986, 0.07358046332577163, 0.06734206365573657, 0.05116175156389633, 0.05114026947136866, 0.07605949680346463, 0.0691766343575995, 0.05693613803533374, 0.06052364748745445, 0.06326905891249054, 0.08639667972777892, 0.058903897710868215, 0.06652574413968515, 0.0657695744827112, 0.1032944937100433, 0.20837629751838868, 0.16285144703375268, 0.14780109300886782, 0.10145133017116931, 0.12249948442977933, 0.15028871932357188, 0.08806798652643157, 0.05401457345157077, 0.06650426204715748, 0.07886076166907266, 0.07190915652711899, 0.06080291469031415, 0.06466109850828348, 0.05625300749295387, 0.05383842029284388, 0.0666460438578401, 0.07588334364473774, 0.0805449577232419, 0.10775417611878738, 0.09725802570976834, 0.051312126211590016, 0.06551608579088472, 0.055191792122087024, 0.057881350106551176, 0.09108836873582181, 0.09299168213377328, 0.08160187667560322, 0.05514453151852615, 0.05961280676428129, 0.13250584312916752, 0.12021378978483535, 0.07836667354093627, 0.08038599023853715, 0.11424606448064893, 0.09232144084691, 0.11416443252904378, 0.13264762493985016, 0.14441981164501272, 0.11681532274695813, 0.11482178456039045, 0.09740840035746201, 0.10641369354506083, 0.05822076716848834, 0.07469323571870488, 0.07491664948099264, 0.08449336632982746, 0.06341513714167869, 0.057962982058156316, 0.05220578126074104, 0.051698803877088056, 0.0993503815219633, 0.05740874407094246, 0.07474908915927682, 0.0808500034371348, 0.10025262940812538, 0.2041829930569877, 0.1023922458238812, 0.08900460576063793, 0.06925396989069911, 0.07146662542104901, 0.06100914277857977, 0.06853646800027496, 0.0776448752320066, 0.08295524850484635, 0.06265037464769369, 0.05197807107994775, 0.08699817831855365, 0.061657901972915374, 0.05352478174193991, 0.06488451227057125, 0.0638877431772874, 0.046805183199285075, 0.044506599298824494, 0.06690812538667766, 0.043621537086684536, 0.04649154464838111, 0.04391369354506083, 0.03217587818794253, 0.038302570976833705, 0.07944077816731972, 0.04857530762356499, 0.1323468756444628, 0.10329019729153777, 0.08043325084209803, 0.07175448546091977, 0.0726223619990376, 0.06496614422217639, 0.05511875300749295, 0.046031827868288994, 0.03884391970853097, 0.0643818313054238, 0.05737866914140372, 0.09085206571801746, 0.06039905135079397, 0.06930552691276551, 0.0704870420017873, 0.05285454045507664, 0.055986629545610775, 0.09234721935794321, 0.06682219701656698, 0.055251941981164496, 0.04573537499140716, 0.05616278270433766]\n"
     ]
    }
   ],
   "source": [
    "to5=[]\n",
    "just6=[]\n",
    "for i in data:\n",
    "    to5.append(i[0:5])\n",
    "    just6.append(i[5])\n",
    "print(just6)\n",
    "\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(to5, just6, test_size=0.1, random_state=32)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "X_train1, X_test1, Y_train1, Y_test1 = train_test_split(data1[['Open', 'High', 'Low', 'Close', 'Adj Close']], data1['Volume'], test_size=0.1, random_state=32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train=np.array(X_train)\n",
    "X_test=np.array(X_test)\n",
    "Y_train=np.array(Y_train)\n",
    "Y_test=np.array(Y_test)\n",
    "\n",
    "\n",
    "X_train1=np.array(X_train1)\n",
    "X_test1=np.array(X_test1)\n",
    "Y_train1=np.array(Y_train1)\n",
    "Y_test1=np.array(Y_test1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape the input data to be 3D\n",
    "\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.GRU(units=80, return_sequences=True, input_shape=(5, 1)),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.GRU(units=80),\n",
    "    tf.keras.layers.Dropout(0.3),\n",
    "    tf.keras.layers.Dense(6)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "39/39 [==============================] - 6s 33ms/step - loss: 0.0066 - val_loss: 0.0032\n",
      "Epoch 2/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0045 - val_loss: 0.0036\n",
      "Epoch 3/100\n",
      "39/39 [==============================] - 1s 25ms/step - loss: 0.0044 - val_loss: 0.0031\n",
      "Epoch 4/100\n",
      "39/39 [==============================] - 1s 15ms/step - loss: 0.0044 - val_loss: 0.0031\n",
      "Epoch 5/100\n",
      "39/39 [==============================] - 1s 15ms/step - loss: 0.0042 - val_loss: 0.0032\n",
      "Epoch 6/100\n",
      "39/39 [==============================] - 1s 13ms/step - loss: 0.0042 - val_loss: 0.0031\n",
      "Epoch 7/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0042 - val_loss: 0.0031\n",
      "Epoch 8/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0041 - val_loss: 0.0031\n",
      "Epoch 9/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0041 - val_loss: 0.0033\n",
      "Epoch 10/100\n",
      "39/39 [==============================] - 1s 27ms/step - loss: 0.0042 - val_loss: 0.0031\n",
      "Epoch 11/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0041 - val_loss: 0.0031\n",
      "Epoch 12/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0041 - val_loss: 0.0030\n",
      "Epoch 13/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0041 - val_loss: 0.0030\n",
      "Epoch 14/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0040 - val_loss: 0.0030\n",
      "Epoch 15/100\n",
      "39/39 [==============================] - 1s 22ms/step - loss: 0.0040 - val_loss: 0.0029\n",
      "Epoch 16/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0039 - val_loss: 0.0029\n",
      "Epoch 17/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0038 - val_loss: 0.0028\n",
      "Epoch 18/100\n",
      "39/39 [==============================] - 1s 13ms/step - loss: 0.0039 - val_loss: 0.0027\n",
      "Epoch 19/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0038 - val_loss: 0.0026\n",
      "Epoch 20/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0037 - val_loss: 0.0028\n",
      "Epoch 21/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 22/100\n",
      "39/39 [==============================] - 1s 14ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 23/100\n",
      "39/39 [==============================] - 1s 18ms/step - loss: 0.0038 - val_loss: 0.0027\n",
      "Epoch 24/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0038 - val_loss: 0.0025\n",
      "Epoch 25/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 26/100\n",
      "39/39 [==============================] - 1s 15ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 27/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 28/100\n",
      "39/39 [==============================] - 1s 24ms/step - loss: 0.0037 - val_loss: 0.0026\n",
      "Epoch 29/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0037 - val_loss: 0.0026\n",
      "Epoch 30/100\n",
      "39/39 [==============================] - 0s 12ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 31/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0036 - val_loss: 0.0027\n",
      "Epoch 32/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0037 - val_loss: 0.0027\n",
      "Epoch 33/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 34/100\n",
      "39/39 [==============================] - 1s 27ms/step - loss: 0.0038 - val_loss: 0.0025\n",
      "Epoch 35/100\n",
      "39/39 [==============================] - 1s 14ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 36/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 37/100\n",
      "39/39 [==============================] - 0s 12ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 38/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0037 - val_loss: 0.0028\n",
      "Epoch 39/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 40/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 41/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 42/100\n",
      "39/39 [==============================] - 0s 11ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 43/100\n",
      "39/39 [==============================] - 0s 10ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 44/100\n",
      "39/39 [==============================] - 1s 14ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 45/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 46/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 47/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 48/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 49/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 50/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 51/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0037 - val_loss: 0.0025\n",
      "Epoch 52/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 53/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 54/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 55/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0035 - val_loss: 0.0024\n",
      "Epoch 56/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 57/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 58/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 59/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 60/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 61/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0037 - val_loss: 0.0024\n",
      "Epoch 62/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 63/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0027\n",
      "Epoch 64/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0037 - val_loss: 0.0026\n",
      "Epoch 65/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 66/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 67/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 68/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 69/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 70/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 71/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 72/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 73/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 74/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0035 - val_loss: 0.0025\n",
      "Epoch 75/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 76/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0035 - val_loss: 0.0025\n",
      "Epoch 77/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 78/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 79/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 80/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 81/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 82/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 83/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 84/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 85/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 86/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0026\n",
      "Epoch 87/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 88/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 89/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 90/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 91/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0035 - val_loss: 0.0025\n",
      "Epoch 92/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 93/100\n",
      "39/39 [==============================] - 1s 23ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 94/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 95/100\n",
      "39/39 [==============================] - 1s 20ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 96/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0035 - val_loss: 0.0024\n",
      "Epoch 97/100\n",
      "39/39 [==============================] - 1s 21ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 98/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0024\n",
      "Epoch 99/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0036 - val_loss: 0.0025\n",
      "Epoch 100/100\n",
      "39/39 [==============================] - 1s 19ms/step - loss: 0.0035 - val_loss: 0.0024\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1db87e832b0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(X_train, Y_train, epochs=100, batch_size=32, validation_data=(X_test, Y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 1s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "# Make predictions on the test set\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "# Scale the predictions back to the original range\n",
    "predictions = scaler.inverse_transform(predictions)\n",
    "predictions = predictions[:, -1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAIICAYAAACigUubAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd5gkVdn2787dk3dm02zeZZeFBRaJiisCApJUjCiiLEFQMirKiwkVEQwoRnzlJYOCgkjOH3FhScsG2GUTm3OaPNPxfH+cOlWnqquqq7urOtQ8v+uaq2e6e7qqq06dOve5n+c5AcYYA0EQBEEQBEEQhA8IVnsHCIIgCIIgCIIg3IIEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvoEEDkEQBEEQBEEQvqHmBc5LL72ET3/60xg3bhwCgQD++9//Fv0ZjDH89re/xd57741YLIbx48fj2muvdX9nCYIgCIIgCIKoKuFq70Ah+vv7ceCBB+Kcc87B5z//+ZI+47LLLsPTTz+N3/72tzjggAOwe/du7N692+U9JQiCIAiCIAii2gQYY6zaO+GUQCCABx98EJ/97GfV55LJJH74wx/in//8J7q6urD//vvjV7/6FY4++mgAwLJlyzB79my8++67mDlzZnV2nCAIgiAIgiCIilDzIWqFuPjii/Haa6/h3nvvxeLFi/GlL30JJ554IlauXAkAeOSRRzBt2jQ8+uijmDp1KqZMmYJvfOMb5OAQBEEQBEEQhA+pa4Gzfv163Hbbbfj3v/+NI488EnvttReuuOIKfOxjH8Ntt90GAPjggw+wbt06/Pvf/8add96J22+/HW+//Ta++MUvVnnvCYIgCIIgCIJwm5rPwbFjyZIlyGaz2HvvvXXPJ5NJdHR0AAByuRySySTuvPNO9X233HILDjnkECxfvpzC1giCIAiCIAjCR9S1wOnr60MoFMLbb7+NUCike62pqQkA0NnZiXA4rBNB++67LwDuAJHAIQiCIAiCIAj/UNcC56CDDkI2m8X27dtx5JFHmr5nzpw5yGQyWL16Nfbaay8AwIoVKwAAkydPrti+EgRBEARBEAThPTVfRa2vrw+rVq0CwAXN7373OxxzzDFob2/HpEmT8LWvfQ3z5s3DDTfcgIMOOgg7duzAc889h9mzZ+OUU05BLpfDYYcdhqamJtx4443I5XK46KKL0NLSgqeffrrK344gCIIgCIIgCDepeYHzwgsv4Jhjjsl7fu7cubj99tuRTqfxi1/8AnfeeSc2bdqEkSNH4iMf+Qh+9rOf4YADDgAAbN68GZdccgmefvppNDY24qSTTsINN9yA9vb2Sn8dgiAIgiAIgiA8pOYFDkEQBEEQBEEQhFM8KROdzWbx4x//GFOnTkUikcBee+2Fa665BqSlCIIgCIIgCILwEk+KDPzqV7/CTTfdhDvuuAP77bcf3nrrLZx99tlobW3FpZde6ugzcrkcNm/ejObmZgQCAS92kyAIgiAIgiCIOoAxht7eXowbNw7BoL1H40mI2qc+9SmMGTMGt9xyi/rcF77wBSQSCdx9992m/5NMJpFMJtW/N23ahFmzZrm9awRBEARBEARB1CkbNmzAhAkTbN/jiYPz0Y9+FH//+9+xYsUK7L333li0aBFeeeUV/O53v7P8n+uuuw4/+9nP8p7fsGEDWlpavNhNgiAIgiAIgiDqgJ6eHkycOBHNzc0F3+uJg5PL5fCDH/wAv/71rxEKhZDNZnHttdfiqquusvwfo4MjvkR3dzcJHIIgCIIgCIIYxvT09KC1tdWRNvDEwfnXv/6Fe+65B//4xz+w3377YeHChbj88ssxbtw4zJ071/R/YrEYYrGYF7tDEARBEARBEMQwwROB873vfQ//8z//g6985SsAgAMOOADr1q3DddddZylwCIIgCIIgCIIgysWTMtEDAwN51Q1CoRByuZwXmyMIgiAIgiAIggDgkYPz6U9/Gtdeey0mTZqE/fbbD++88w5+97vf4ZxzznF9W9lsFul02vXPJQiiekQiEYRCoWrvBkEQBEEQdYgnRQZ6e3vx4x//GA8++CC2b9+OcePG4fTTT8dPfvITRKNRR59RKJGIMYatW7eiq6vL5b0nCKIWaGtrw9ixY2kdLIIgCIIgiioy4InAcYNCX2LLli3o6urC6NGj0dDQQIMggvAJjDEMDAxg+/btaGtrQ2dnZ7V3iSAIgiCIKlP1Kmpek81mVXHT0dFR7d0hCMJlEokEAGD79u0YPXo0hasRBEEQBOEYT4oMeI3IuWloaKjynhAE4RXi+qYcO4IgCIIgiqEuBY6AwtIIwr/Q9U0QBEEQRCnUtcAhCIIgCIIgCIKQIYFDEARBEARBEIRvIIFDqAQCAfz3v/+t9m44Zu3atQgEAli4cGG1d6UkKnW8zzrrLHz2s5/1fDsEQRAEQRC1AAmcKvDaa68hFArhlFNOKfp/p0yZghtvvNH9nXLI1q1bcdlll2H69OmIx+MYM2YM5syZg5tuugkDAwNV269yuPnmm3HggQeiqakJbW1tOOigg3Ddddepr9eKQLj99tsRCAQQCAQQDAYxYcIEnH322di+fbvt//3hD3/A7bffXpmdJAiCIAiCqDJ1WSa63rnllltwySWX4JZbbsHmzZsxbty4au+SIz744APMmTMHbW1t+OUvf4kDDjgAsVgMS5Yswd///neMHz8en/nMZ6q9m0Vx66234vLLL8cf//hHHHXUUUgmk1i8eDHefffdau+aKS0tLVi+fDlyuRwWLVqEs88+G5s3b8ZTTz2V995sNotAIIDW1tYq7ClBEARBEER18IWDwxjQ31+dn2KXSe3r68N9992HCy64AKeccorpzPojjzyCww47DPF4HCNHjsTnPvc5AMDRRx+NdevW4dvf/rY6kw8AP/3pT/GhD31I9xk33ngjpkyZov795ptv4vjjj8fIkSPR2tqKo446CgsWLChq3y+88EKEw2G89dZbOO2007Dvvvti2rRpOPXUU/HYY4/h05/+tPre9evX49RTT0VTUxNaWlpw2mmnYdu2bbrPu+mmm7DXXnshGo1i5syZuOuuu3Svv//++/jYxz6GeDyOWbNm4dlnny0Y1vXuu+/ipJNOQlNTE8aMGYOvf/3r2Llzp+X7H374YZx22mk499xzMX36dOy33344/fTTce211wLgx/aOO+7AQw89pB7zF154AQCwZMkSfOITn0AikUBHRwfOP/989PX16T7/1ltvxX777YdYLIbOzk5cfPHFlvty9dVXo7OzE4sXL7Z8TyAQwNixYzFu3DicdNJJuPTSS/Hss89icHAQt99+O9ra2vDwww9j1qxZiMViWL9+fZ4Dlcvl8Otf/xrTp09HLBbDpEmT1O8LABs2bMBpp52GtrY2tLe349RTT8XatWst94kgCIIgCKKW8IXAGRgAmpqq81NsVNa//vUv7LPPPpg5cya+9rWv4dZbbwWTVNJjjz2Gz33uczj55JPxzjvv4LnnnsPhhx8OAPjPf/6DCRMm4Oc//zm2bNmCLVu2ON5ub28v5s6di1deeQXz58/HjBkzcPLJJ6O3t9fR/+/atQtPP/00LrroIjQ2Npq+RwiuXC6HU089Fbt378aLL76IZ555Bh988AG+/OUvq+998MEHcdlll+G73/0u3n33XXzzm9/E2Wefjeeffx4Adx8++9nPoqGhAa+//jr+/ve/44c//KHtPnZ1deETn/gEDjroILz11lt48sknsW3bNpx22mmW/zN27FjMnz8f69atM339iiuuwGmnnYYTTzxRPeYf/ehH0d/fjxNOOAEjRozAm2++iX//+9949tlndQLmpptuwkUXXYTzzz8fS5YswcMPP4zp06fnbYMxhksuuQR33nknXn75ZcyePdv2e8okEgnkcjlkMhkAwMDAAH71q1/h//7v//Dee+9h9OjRef9z1VVX4frrr8ePf/xjLF26FP/4xz8wZswYAHzNmRNOOAHNzc14+eWXMW/ePDQ1NeHEE09EKpVyvF8EQRAEQRBVg9Uo3d3dDADr7u7Oe21wcJAtXbqUDQ4OMsYY6+tjjHsplf/p6yvue330ox9lN954I2OMsXQ6zUaOHMmef/559fUjjjiCnXHGGZb/P3nyZPb73/9e99zVV1/NDjzwQN1zv//979nkyZMtPyebzbLm5mb2yCOPqM8BYA8++KDp++fPn88AsP/85z+65zs6OlhjYyNrbGxk3//+9xljjD399NMsFAqx9evXq+977733GAD2xhtvMMb4cTjvvPN0n/WlL32JnXzyyYwxxp544gkWDofZli1b1NefeeYZ3T6uWbOGAWDvvPMOY4yxa665hn3yk5/UfeaGDRsYALZ8+XLT77V582b2kY98hAFge++9N5s7dy677777WDabVd8zd+5cduqpp+r+7+9//zsbMWIE65MawGOPPcaCwSDbunUrY4yxcePGsR/+8Iem22WMH+9///vf7Ktf/Srbd9992caNGy3fyxhjt912G2ttbVX/XrFiBdt7773ZoYceqr4OgC1cuFD3f/L+9/T0sFgsxm6++WbTbdx1111s5syZLJfLqc8lk0mWSCTYU089Zbt/bmO8zgmCIAiCGL7YaQMjvnBwGhqAvr7q/CiLrTti+fLleOONN3D66acDAMLhML785S/jlltuUd+zcOFCHHvssW4fImzbtg3nnXceZsyYgdbWVrS0tKCvrw/r168v63PfeOMNLFy4EPvttx+SySQAYNmyZZg4cSImTpyovm/WrFloa2vDsmXL1PfMmTNH91lz5sxRX1++fDkmTpyIsWPHqq8LJ8uKRYsW4fnnn0dTU5P6s88++wAAVq9ebfo/nZ2deO2117BkyRJcdtllyGQymDt3Lk488UTkcjnLbS1btgwHHnigzs2aM2cOcrkcli9fju3bt2Pz5s0Fz+W3v/1tvP7663jppZcwfvx42/cCQHd3N5qamtDQ0ICZM2dizJgxuOeee9TXo9GorQO0bNkyJJNJy/1atGgRVq1ahebmZvUYtre3Y2hoyPIYEgRBEAThD377W+B736v2XpSPL4oMBAKARdRUTXHLLbcgk8noigowxhCLxfDnP/8Zra2tSCQSRX9uMBjUhbkBPNRIZu7cudi1axf+8Ic/YPLkyYjFYjjiiCMchx1Nnz4dgUAAy5cv1z0/bdo0AChpv92mr68Pn/70p/GrX/0q77XOzk7b/91///2x//7748ILL8S3vvUtHHnkkXjxxRdxzDHHlLQvTo/H8ccfj3/+85946qmncMYZZxR8f3NzMxYsWIBgMIjOzs687SQSCTVUsJT96uvrwyGHHKITTYJRo0YV3D+CIAiCIOqXH/4QSKWA734XkOaY6w5fODj1QCaTwZ133okbbrgBCxcuVH8WLVqEcePG4Z///CcAYPbs2XjuuecsPycajSKbzeqeGzVqFLZu3aoTOca1YebNm4dLL70UJ598spr0bpd8b6SjowPHH388/vznP6O/v9/2vfvuuy82bNiADRs2qM8tXboUXV1dmDVrlvqeefPm5e2jeH3mzJnYsGGDrjDBm2++abvdgw8+GO+99x6mTJmC6dOn636s8obMEPsgvqfZMd93332xaNEi3bGYN28egsEgZs6ciebmZkyZMsX2XALAZz7zGfzjH//AN77xDdx7770F9y0YDGL69OmYNm1aSaJyxowZSCQSlvt18MEHY+XKlRg9enTeMaRqbARBEAThXxjj4gbQHusVEjgV4tFHH8WePXtw7rnnqm6B+PnCF76ghqldffXV+Oc//4mrr74ay5Ytw5IlS3SOxJQpU/DSSy9h06ZNqkA5+uijsWPHDvz617/G6tWr8Ze//AVPPPGEbvszZszAXXfdhWXLluH111/HGWecUfQA+a9//SsymQwOPfRQ3HfffVi2bBmWL1+Ou+++G++//z5CoRAA4LjjjsMBBxyAM844AwsWLMAbb7yBM888E0cddRQOPfRQAMD3vvc93H777bjpppuwcuVK/O53v8N//vMfXHHFFQC4s7HXXnth7ty5WLx4MebNm4cf/ehHAGDpUFx00UXYvXs3Tj/9dLz55ptYvXo1nnrqKZx99tl5AkVwwQUX4JprrsG8efOwbt06zJ8/H2eeeSZGjRqFI444Qj3mixcvxvLly7Fz506k02mcccYZiMfjmDt3Lt599108//zzuOSSS/D1r39dTdj/6U9/ihtuuAF//OMfsXLlSixYsAB/+tOf8vbhc5/7HO666y6cffbZuP/++4s6J8USj8dx5ZVX4vvf/z7uvPNOrF69GvPnz1fb3xlnnIGRI0fi1FNPxcsvv4w1a9bghRdewKWXXoqNGzd6um8EQRAEQVQPOTLfJkq/PvA6IahUiikyUA986lOfUhPojbz++usMAFu0aBFjjLEHHniAfehDH2LRaJSNHDmSff7zn1ff+9prr7HZs2ezWCzG5NN30003sYkTJ7LGxkZ25plnsmuvvVZXZGDBggXs0EMPZfF4nM2YMYP9+9//zitYAJsiA4LNmzeziy++mE2dOpVFIhHW1NTEDj/8cPab3/yG9ff3q+9bt24d+8xnPsMaGxtZc3Mz+9KXvqQm3wv++te/smnTprFIJML23ntvduedd+peX7ZsGZszZw6LRqNsn332YY888ggDwJ588knGWH6RAcZ44v3nPvc51tbWxhKJBNtnn33Y5Zdfrkual7n//vvZySefzDo7O1k0GmXjxo1jX/jCF9jixYvV92zfvp0df/zxrKmpiQFQi0IsXryYHXPMMSwej7P29nZ23nnnsd7eXt3n/+1vf2MzZ85kkUiEdXZ2sksuucTyeN93330sHo+zBx54wHRfjUUGnL5uLJKQzWbZL37xCzZ58mQWiUTYpEmT2C9/+Uv19S1btrAzzzyTjRw5ksViMTZt2jR23nnnOUrqc5N6vM4JgiAIol5JJrUiWqtWVXtv8immyECAsWJXcqkMPT09aG1tRXd3N1paWnSvDQ0NYc2aNZg6dSri8XiV9pCoNPPmzcPHPvYxrFq1CnvttVe1d4fwGLrOCYIgCKJyDA5qxbNWrABmzKju/hix0wZGfFFkgPAnDz74IJqamjBjxgysWrUKl112GebMmUPihiAIgiAIwmXkaP56D1EjgUPULL29vbjyyiuxfv16jBw5EscddxxuuOGGau8WQRAEQRCE75AFjkXqct1AAoeoWc4880yceeaZ1d4NgiAIgiAI3+MnB4eqqBEEQRAEQRDEMMdPDg4JHIIgCIIgCIIY5pCDQxAEQRAEQRCEbyAHhyAIgiAIgiAI30AODkEQBEEQBEEQvoEEDkEQBEEQBEEQvoFC1Iia56yzzsJnP/tZ9e+jjz4al19+ecX344UXXkAgEEBXV1fFt10qt99+O9ra2qq9GyVRyeM9ZcoU3HjjjZ5vhyAIgiAI7yEHhyiJs846C4FAAIFAANFoFNOnT8fPf/5zZDIZz7f9n//8B9dcc42j91ZDlLzzzjv48pe/jM7OTsRiMUyePBmf+tSn8Mgjj4AxVrH9cIuBgQFcddVV2GuvvRCPxzFq1CgcddRReOihh9T31IpAKLVdvvnmmzj//PMrtJcEQRAEQXiJnxwcWuizwpx44om47bbbkEwm8fjjj+Oiiy5CJBLBVVddlffeVCqFaDTqynbb29td+RwveOihh3DaaafhuOOOwx133IHp06cjmUzi1VdfxY9+9CMceeSRdeeofOtb38Lrr7+OP/3pT5g1axZ27dqFV199Fbt27ar2rplSSrscNWpUFfaUIAiCIAgvkOc1ycGpBRgD+vur81OkuxCLxTB27FhMnjwZF1xwAY477jg8/PDDALSwsmuvvRbjxo3DzJkzAQAbNmzAaaedhra2NrS3t+PUU0/F2rVr1c/MZrP4zne+g7a2NnR0dOD73/9+nuthDFFLJpO48sorMXHiRMRiMUyfPh233HIL1q5di2OOOQYAMGLECAQCAZx11lkAgFwuh+uuuw5Tp05FIpHAgQceiPvvv1+3nccffxx77703EokEjjnmGN1+mtHf349zzz0Xp5xyCh577DF88pOfxLRp07Dvvvvi3HPPxaJFi9Da2qq+/8UXX8Thhx+OWCyGzs5O/M///I/OaUgmk7j00ksxevRoxONxfOxjH8Obb76p2+bDDz+MGTNmIB6P45hjjsEdd9xR0LF66KGHcPDBByMej2PatGn42c9+ZutwPPzww/jBD36Ak08+GVOmTMEhhxyCSy65BOeccw4Afj7WrVuHb3/726p7InjggQew3377IRaLYcqUKbjhhht0n2117swYGBjASSedhDlz5th+v1LapdGB6urqwje/+U2MGTMG8Xgc+++/Px599FH19VdeeQVHHnkkEokEJk6ciEsvvRT9/f2W+0QQBEEQROUgB6fWGBgAmpqqs+2+PqCxseR/TyQSuln95557Di0tLXjmmWcAAOl0GieccAKOOOIIvPzyywiHw/jFL36BE088EYsXL0Y0GsUNN9yA22+/Hbfeeiv23Xdf3HDDDXjwwQfxiU98wnK7Z555Jl577TX88Y9/xIEHHog1a9Zg586dmDhxIh544AF84QtfwPLly9HS0oJEIgEAuO6663D33Xfjb3/7G2bMmIGXXnoJX/va19Twqw0bNuDzn/88LrroIpx//vl466238N3vftf2+z/99NPYtWsXvv/971u+Rwz+N23ahJNPPhlnnXUW7rzzTrz//vs477zzEI/H8dOf/hQA8P3vfx8PPPAA7rjjDkyePBm//vWvccIJJ2DVqlVob2/HmjVr8MUvfhGXXXYZvvGNb+Cdd97BFVdcYbuPL7/8Ms4880z88Y9/xJFHHonVq1eroVlXX3216f+MHTsWjz/+OD7/+c+jubk57/X//Oc/OPDAA3H++efjvPPOU59/++23cdppp+GnP/0pvvzlL+PVV1/FhRdeiI6ODlVoWp07I11dXTjllFPQ1NSEZ555Bg0NDbbfU6ZQuzSSy+Vw0kknobe3F3fffTf22msvLF26FKFQCACwevVqnHjiifjFL36BW2+9FTt27MDFF1+Miy++GLfddpvj/SIIgiAIwhv8lIMDVqN0d3czAKy7uzvvtcHBQbZ06VI2ODjIn+jrY4x7KZX/6etz/J3mzp3LTj31VMYYY7lcjj3zzDMsFouxK664Qn19zJgxLJlMqv9z1113sZkzZ7JcLqc+l0wmWSKRYE899RRjjLHOzk7261//Wn09nU6zCRMmqNtijLGjjjqKXXbZZYwxxpYvX84AsGeeecZ0P59//nkGgO3Zs0d9bmhoiDU0NLBXX31V995zzz2XnX766Ywxxq666io2a9Ys3etXXnll3mfJXH/99QwA2717t/rcG2+8wRobG9WfRx55hDHG2A9+8IO8Y/GXv/yFNTU1sWw2y/r6+lgkEmH33HOP+noqlWLjxo1Tj8+VV17J9t9/f90+/PCHP9Tt42233cZaW1vV14899lj2y1/+Uvc/d911F+vs7DT9Towx9uKLL7IJEyawSCTCDj30UHb55ZezV155RfeeyZMns9///ve657761a+y448/Xvfc9773PfW4Oj13y5YtY7Nnz2Zf+MIXdO3JjFLapXH/n3rqKRYMBtny5ctNt3Huueey888/X/fcyy+/zILBoHYdG8i7zgmCIAiC8Iz587Xh7eOPV3tv8rHTBkb84eA0NHAnpVrbLoJHH30UTU1NSKfTyOVy+OpXv6q6DwBwwAEH6PJuFi1ahFWrVuW5AENDQ1i9ejW6u7uxZcsWfPjDH1ZfC4fDOPTQQy2T8xcuXIhQKISjjjrK8X6vWrUKAwMDOP7443XPp1IpHHTQQQCAZcuW6fYDAI444gjH2xDMnj0bCxcuBADMmDFDDQVbtmwZjjjiCF0415w5c9DX14eNGzeiq6sL6XQac+bMUV+PRCI4/PDDsWzZMgDA8uXLcdhhh+m2d/jhh9vuz6JFizBv3jxce+216nPZbBZDQ0MYGBgwdUY+/vGP44MPPsD8+fPx6quv4rnnnsMf/vAH/OxnP8OPf/xjy20tW7YMp556qu65OXPm4MYbb0Q2m3V87o4//ngcfvjhuO+++1QXxY5i26WRhQsXYsKECdh7771NX1+0aBEWL16Me+65R32OMYZcLoc1a9Zg3333LbiPBEEQBEF4B4Wo1RqBQFlhYpXkmGOOwU033YRoNIpx48YhHNafgkbD9+jr68MhhxyiGxgKSk3yFiFnxdCnCMjHHnsM48eP170Wi8VK2g+ACxiAC4+PfOQj6udNnz695M90m76+PvzsZz/D5z//+bzX4vG45f9FIhEceeSROPLII3HllVfiF7/4BX7+85/jyiuvLLl4hNNzd8opp+CBBx7A0qVLccABBxR8f7Htstj96uvrwze/+U1ceumlea9NmjSp4P4RBEEQBOEtfgpR84fAqSMaGxuLGrwffPDBuO+++zB69Gi0tLSYvqezsxOvv/46Pv7xjwMAMpkM3n77bRx88MGm7z/ggAOQy+Xw4osv4rjjjst7XQy+s1JLnzVrFmKxGNavX2/pHuy7775qYrpg/vz5tt/vk5/8JNrb2/GrX/0KDz74oO179913XzzwwANgjKkuzrx589Dc3IwJEyago6MD0WgU8+bNw+TJkwHwHKY333xTLbAwc+ZMPP7447rPNRYhMHLwwQdj+fLlZYuuWbNmIZPJYGhoCNFoFNFoVHeMxXecN2+e7rl58+Zh7733RigUKnjuBNdffz2amppw7LHH4oUXXsCsWbNs963Ydmlk9uzZ2LhxI1asWGHq4hx88MFYunRpTQlXgiAIgiA0/OTg+KOKmo8544wzMHLkSJx66ql4+eWXsWbNGrzwwgu49NJLsXHjRgDAZZddhuuvvx7//e9/8f777+PCCy+0rZg1ZcoUzJ07F+eccw7++9//qp/5r3/9CwAwefJkBAIBPProo9ixYwf6+vrQ3NyMK664At/+9rdxxx13YPXq1ViwYAH+9Kc/4Y477gDASyOvXLkS3/ve97B8+XL84x//wO233277/ZqamvB///d/eOyxx3DKKafgqaeewgcffIDFixfj17/+NQCoIVYXXnghNmzYgEsuuQTvv/8+HnroIVx99dX4zne+g2AwiMbGRlxwwQX43ve+hyeffBJLly7Feeedh4GBAZx77rkAgG9+85t4//33ceWVV2LFihX417/+pe6jHPom85Of/AR33nknfvazn+G9997DsmXLcO+99+JHP/qR5fc6+uij8b//+794++23sXbtWjz++OP4wQ9+gGOOOUYVqlOmTMFLL72ETZs2qUUCvvvd7+K5557DNddcgxUrVuCOO+7An//8Z7UQQqFzJ/Pb3/4WZ5xxBj7xiU/g/ffftz0P5XLUUUfh4x//OL7whS/gmWeewZo1a/DEE0/gySefBABceeWVePXVV3HxxRdj4cKFWLlyJR566CFcfPHFnu4XQRAEQRDO8JOD448iA3WCnMxdzOtbtmxhZ555Jhs5ciSLxWJs2rRp7LzzzlOPTTqdZpdddhlraWlhbW1t7Dvf+Q4788wzLYsMMMaP4be//W3W2dnJotEomz59Orv11lvV13/+85+zsWPHskAgwObOncsY4wnoN954I5s5cyaLRCJs1KhR7IQTTmAvvvii+n+PPPIImz59OovFYuzII49kt956q22RAcGbb77JvvjFL7LRo0ezcDjMOjo62AknnMDuvfdeXVGBF154gR122GEsGo2ysWPHsiuvvJKl02nd97rkkkvUYzVnzhz2xhtv6Lb10EMPqft49NFHs5tuuokBUNuTscgAY4w9+eST7KMf/ShLJBKspaWFHX744ezvf/+75ff55S9/yY444gjW3t7O4vE4mzZtGrv00kvZzp071fe89tprbPbs2SwWizH5Urz//vvZrFmzWCQSYZMmTWK/+c1vdJ9td+7MCkRccsklrLOz07IAQKnt0lgkYdeuXezss89mHR0dLB6Ps/333589+uij6utvvPEGO/7441lTUxNrbGxks2fPZtdee63lduv1OicIgiCIeuSpp7QiA/fdV+29yaeYIgMBxmpzmfienh60traiu7s7LzRraGgIa9aswdSpU21zIAjCCddeey3+9re/YcOGDdXeFUKCrnOCIAiCqBxPPAGcfDL//Z//BL7ylerujxE7bWCEcnCIYcdf//pXHHbYYejo6MC8efPwm9/8hkKlCIIgCIIY1vgpRI0EDjHsWLlyJX7xi19g9+7dmDRpEr773e/iqquuqvZuEQRBEARBVA0/FRkggUMMO37/+9/j97//fbV3gyAIgiAIombwk4NDVdQIgiAIgiAIYpjjJwenrgVOrt7lJUEQltD1TRAEQRCVw08OTl2GqEWjUQSDQWzevBmjRo1CNBq1XMOEIIj6gjGGVCqFHTt2IBgMqgvPEgRBEAThHX5ycDwROFOmTMG6devynr/wwgvxl7/8pezPDwaDmDp1KrZs2YLNmzeX/XkEQdQeDQ0NmDRpEoLBujaaCYIgCKIuIAenAG+++Say0lF69913cfzxx+NLX/qSa9uIRqOYNGkSMpmMblsEQdQ/oVAI4XCYnFmCIAiCqBDk4BRg1KhRur+vv/567LXXXjjqqKMs/yeZTCKZTKp/9/T0FNxOIBBAJBJBJBIpfWcJgiAIgiAIYpiTyWi/17uD43nsRyqVwt13341zzjnHdjb2uuuuQ2trq/ozceJEr3eNIAiCIAiCIAj4K0TNc4Hz3//+F11dXTjrrLNs33fVVVehu7tb/dmwYYPXu0YQBEEQBEEQBChErShuueUWnHTSSRg3bpzt+2KxGGKxmNe7QxAEQRAEQRCEAT85OJ4KnHXr1uHZZ5/Ff/7zHy83QxAEQRAEQRBEGfjJwfE0RO22227D6NGjccopp3i5GYIgCIIgCIIgysBPDo5nAieXy+G2227D3LlzEQ7X5XqiBEEQBEEQBDEsIAfHAc8++yzWr1+Pc845x6tNEARBEARBEAThAn5ycDyzVj75yU+CMebVxxMEQRAEQRAE4RJ+Ejiel4kmCIIgCIIgCKK2oRA1giAIgiAIgiB8Azk4BEEQBEGURDYL9PRUey8IgiD0kINDEARBEERJfOpTwPjxwK5d1d4TgiAIDXJwCIIgCIIoiTfeAPr6gDVrqr0nBEEQGuTgEARBEARREr29/LHeBxAEQfgLcnAIgiAIgiiaZBJIp/nv9T6AIAjCX5DAIQiCIAiiaOTiAuTgEARRS1CIGkEQBEEQRSPC04D6H0AQBOEvyMEhCIIgCKJoSOAQBFGrkINDEARBEETRkMAhCKJWyWS038nBIQiCIAjCEZSDQxBErUIODkEQBEEQRUMODkEQtQrl4BAEQRAEUTSywKn3AQRBEP6CBA5BEARBEEVDIWoEQdQqFKJGEARBEETRUIgaQRC1Cjk4BEEQBEEUDQkcgiBqFXJwCIIgCIIoGhI4BEHUKuTgEARBEARRNJSDQxBErUIODkEQBEEQRUMODkEQtQo5OARBEARBFA2ViSYIolYhgUMQBEEQRNGQg0MQRK1CIWoEQRAEQRQN5eAQBFGrkINDEARBEETRkINDEEStQg4OQRAEQRBFQwKHIIhahRwcgiAIgiCKIpMBBge1v0ngEARRS5CDQxAEQRBEUcjuDVD/M6QEQfgLcnAIgiAIgigKo8CppxnSXA446STgrLOqvScEQXgFCRyCIAiCIIqingXO1q3Ak08Cd9wBMFbtvSEIwgsyGe33euqfzCCBQxAEQRAVQC4RDdTXAEIe+NT7zC5BEOaQg0MQBEEQRFHUs4Mj76ssdgiC8A9UZIAgCIIgiKKoZ4EjixoSOAThT8jBIQiCIAiiKOo5RI0cHILwP+TgEARBEARRFPVcJpocHILwP+TgEARBEARRFPUcokYODkH4H3JwCIIgCIIoinoWOOTgEIT/IQeHIAiCIIiioBwcgiBqGRI4BEEQBEEUhXBwYjH+SAKHIIhagkLUCIIgCIIoCiFw2tr4Yz0NIChEjSD8Dzk4BEEQBEEURT0LHHJwCML/kINDEARBEERRiBycESP4Yz3NkJKDQxD+hxwcgiAIgiCKghwcgtAYGKC2VGtks8DvcTn+hS8hm2HV3p2yIIFDEARBEBWgngUOOTiEmwwMAFOnAsceW+09IQSMcdfmYvwZX8L9GJPZVO1dKotwtXeAIAiCIIYDxhC1ehI45OAQbrJxI7B9O9DfX+09IQQ8JI0hDH6xB3P1faF75uBs2rQJX/va19DR0YFEIoEDDjgAb731llebIwiCIIiaJZcD+vr4762t/LGeBA45OISbiDZUT9eA38lmgRD8U2XAEwdnz549mDNnDo455hg88cQTGDVqFFauXIkRYtqKIAiCIIYR8kx1PYaokYNDuAkJnNojmwXC0C7uendwPBE4v/rVrzBx4kTcdttt6nNTp061/Z9kMolkMqn+3WNc8pkgCIIg6hSRfxMKAU1N/Pd6GtyRg0O4iWhD1JZqB78JHE9C1B5++GEceuih+NKXvoTRo0fjoIMOws0332z7P9dddx1aW1vVn4kTJ3qxawRBEARRccScXXMzFzlAfZVhJQeHcBPRhhjjP0T1yWT0IWqBXB3NwJjgicD54IMPcNNNN2HGjBl46qmncMEFF+DSSy/FHXfcYfk/V111Fbq7u9WfDRs2eLFrBEEQBFFxhIMjCxxycIjhityG6uk68DNGByeQre8L3ZMQtVwuh0MPPRS//OUvAQAHHXQQ3n33Xfztb3/D3LlzTf8nFoshFot5sTsEQRAEUVWEwGlpqU+BQw4O4SbG9hSmmr5VJy9EjdVRB2WCJw5OZ2cnZs2apXtu3333xfr1673YHEEQBEHUNGYhavUkcMjBIdyEHJzaw1hFjXJwTJgzZw6WL1+ue27FihWYPHmyF5sjCIIgiJqm3kPUyMEh3IQETu2R7+Bk6jo/yhOB8+1vfxvz58/HL3/5S6xatQr/+Mc/8Pe//x0XXXSRF5sjCIIgiJpGFjhB5c5bTwM7cnAIN6H2VHsYBU4IWRI4Rg477DA8+OCD+Oc//4n9998f11xzDW688UacccYZXmyOIAiCIGoaP+Xg1NN+E7UJOTi1hzFELYxMXZ8bz9K6PvWpT+FTn/qUVx9PEARBEHUDlYkmCA0SOLWH0cEJI1NXfZQRTxwcgiAIgiA06j0Hh0KKCDchgVN7GB2cELJ1fW5I4BAEQRCEx9S7wCEHh3ATEsy1Bzk4BEEQBEEURb3n4NCAlHATcnBqDxI4BEFUhGXLgOOPB15+udp7QhBEucg5OPVYRY0cHMJNSODUHn4LUaO1YwmiRvnPf4BnnwWmTAGOPLLae0MQRDnUe4gaOTiEm1B7qj385uCQwCGIGiWZ1D8SBFG/yCFqYm2Jeho8kINDuAmVHa89zNbBqedzQyFqBFGjpNP6R4Ig6hezMtH1NHigGXfCTShErfYwWwenniZhjJDAIYgahQQOQfiHeg9RIweHcBMSOLWHWYhaPZ8bEjgEUaOIG0AqVd39IAiiPBirf4FDDg7hJtSeag+zEDVycAiCcB1ycAjCHwwNaWKmXstEk4NDuAk5OLUHhagRBFERSOAQhD8Q+TcA0NhYn2WiacadcBMSOLUHhagRBFERSOAQhD8Q4WlNTVzckINDDHdIMNcemQyFqBEEUQEoB4cg/IGcfwNoAqeeBg80ICXchByc2sMsRK2ezw0JHIKoUcjBIQh/IELUWlr4Izk4xHCHBE7t4beFPkngEESNQgKHIPyBlYNTTwM7EjiEm5AjWHvQQp8EQVQEIWwoRI0g6hs/CBwakBJuQg5O7UFV1AiCqAjiBkAODkHUN34QOOTgEG5CAqf2oHVwCIKoCBSiRhD+YGiIPyYS/JHKRBPDHbnt19N14GeoTDRBEBWBBA5B+ANxDUej/JEcHGK4Q4K59qAQNYIgKgKViSYIfyCu4UiEP1KZaGK4QyFqtQcVGSAIoiKQg0MQ/kAIHHJwCIJDAqf2oDLRBEFUBBI4BOEPxDVsdHAY4z/1ADk4hJtQe6o9aKFPgiAqAgkcgvAHVg4OUD+z1+TgEG5CDk7tQVXUCIKoCOIGkMnUzywvQRD5WDk4QP0M7mjGnXATEji1B1VRIwiiIsjODbk4BFG/GB2coHTnrZcBBDk4hJuQwKk9jCFq5OAQBOEJJHAIwh9YlYkG6mdwRw4O4SbUnmoPKjJAEERFkEUNlYr2H0uXAtdfDwwMVHtPCK+xKhMN1E+paHJwCDchB6f2oBA1giAqgnwDIAfHf1x9NXDVVcCjj1Z7Twiv8UORAZpxJ9yEBE7tQSFqBEFUBApR8zfd3fyxp6e6+0F4jx+KDJCDQ7gJCebagxwcgiAqAgkcfyNXySP8jdHBCQT4D1A/AocGpISbyO2+Xq4Bv5PJ5K+DQw4OQRCuQzk4/kacX7q5+x+jgwNoLk69nP9h4eDcdRfwkY8AmzdXe098D4Wo1R5m6+DU87khgUMQNQrl4PgbcnCGD0YHB9BKRdfLAGJYCJzbbwdefx146qlq74nvIYFTe1AVNYIgPCeX01dXIoHjP0jgDB+MZaKB+nNwhkWIWjLJH/fsqe5+DAOGRXuqM4xFBkjgEAThOkZBQwLHf4hzSjd3/2MsEw1oAqdeBhDDwsERFyUJHM8hB6f2oBA1giA8xyhoKAfHf5CDM3wwC1GrZwenXva5aMSJIoHjOcOiPdUZFKJGEITnGAe95OD4DxI4wwcqMlAnCIHT1VXV3RgOUIha7WG2Dk699E9mkMAhiBqEQtT8Dwmc4YPfHBzftllycCoGOTi1Bzk4BEF4DoWo+Z/hUCZ6yxZ/fz+nmDk4VEWtBiGBUzFI4NQetNAnQRCeQw6O//G7g/PWW8C4ccBFF1V7T6qP3xwcY5VH30BFBirGsHAE6wyzELV6vs5J4BBEDUI5OP7H7wLnnXf446JF1d2PWsBO4NTDAIKxfCFWL8KsKMjBqRjk4NQeFKJGEITnkIPjf/wucHbv5o89PdXdj1qg3osMmA1yfNluZYHDWHX3xeeQwKk9KESNIAjPoRwc/+P3dXBI4GjUe4ia2T76st2KE5VKAYOD1d0XnyO3qXq4BoYDFKJGEITnkIPjf8jBGT7Uu4Nj1kZ92W7lmSQqFe0plINTe5CDQxCE51AOjv8ZLgKnt7c+8ky8gjFycOqCbFYflkZ5OJ5CIWq1B+XgOOCnP/0pAoGA7mefffbxYlME4UvIwfE3jA2fEDXGgP7+6u5LNZEHb/VaJnpYODjGOGASOJ5hrMJXD9fAcMBvC32Gvfrg/fbbD88++6y2obBnmyII30E5OP5mONzchcABeJhac3P19qWayNeuHxyccJiLGxI4RKkY27zv2lKd4jcHxzPVEQ6HMXbsWMfvTyaTSCaT6t89FLhNDGPIwfE3wyH+3Chwxo+v3r5Uk0ICp5gBxH//Czz0EPDXvwKJhCu75wi5jUajJHCI8jC2nXoQ+cMBo8ChIgMWrFy5EuPGjcO0adNwxhlnYP369bbvv+6669Da2qr+TJw40atdI4iah3Jw/M1wFDjDFfnaLbfIwDXXALffDrz8siu75hixj+Ew/wF82G5J4FQMEji1iTFEjYoMmPDhD38Yt99+O5588kncdNNNWLNmDY488kj09vZa/s9VV12F7u5u9WfDhg1e7BpB1AUUouZv5PPru4EigKEhYGBA+3s4Cxxx7YbDQCCgPV+KwOnu5o9DQ+7sm1PEPoZCPhY4xk6Xqqh5Bgmc2iSToRC1gpx00knq77Nnz8aHP/xhTJ48Gf/6179w7rnnmv5PLBZDLBbzYncIou6gEDV/43cHxzj5PZwFjlmJaKA0gSPmCCvdH4g2Sg4O4QbGtuO7tlSnmIWo1bP4rEiZ6La2Nuy9995YtWpVJTZHEHUPCRx/43eBI4enAcNb4JiViAZKEzh9ffyx0v2BXx2cPXuAmTOBH/wAJHAqCDk4tUl+FbUcWLZ+LZyKCJy+vj6sXr0anZ2dldgcQdQ9fs3BWbcO+O1vtZno4QoJnOGDuHaNAqfYMtHZrBb2V+k241cHZ8ECYMUK4LbbQAKngpDAqU2MDg4A5NL1e3I8EThXXHEFXnzxRaxduxavvvoqPve5zyEUCuH000/3YnME4Tv8moPzy18C3/secPfd1d6T6uL3HBwSOBri2i03RE1eS4gcHHcQ32HrVmCgiwROpchmgSCyeAqfxO9xuS/akh8wEzj1rD49ETgbN27E6aefjpkzZ+K0005DR0cH5s+fj1GjRnmxOYLwHX4NURN5u1u3VnU3qo7fV/EmgaNRKETNaRKv7HpSDo47yMdx81oSOJUikwH2wmp8Es/gG/g/X/aB9YgxRA1AXV/onhQZuPfee734WIIYNvg1RE18D1ENarhCIWrDB7eKDIj8G6DybcbvDg4AbF6XxnT5RRI4npHJAHHwUoARpEng1AhmDg5L1++FXpEcHIIgisOvIWokcDjDJURN5JkMZ4HjVpEBWeCQg+MO8nHcul45UW1t/JHKRHtGJgPEwBd2jyDti7bkB0wdnDpWnyRwCKIG8WuImriRDecBLzB8HJwJE/jjcD7fbjk4cogaOTjuIH+H7RsVgTNmDH8cGPDPzFKNIQucIBhYpn4H0X7CNAenji90EjgEUYP4VeCQg8MZLgJnyhT+OJwFjp8cHL8JHPk47tisnKhRo7QVWSlMzRNkgQMAgYxPbnB1xOCgfjFmgIoMEARRAcTgoaGBP5LA8RckcIYPbpWJrmaRAbGPfgtRk7/D7i2KwInFgNZW/jsJHE8wCpxg1ic3uDohlwM+9CFg1ixDwZsMQwj6qieUg0MQhKuIAYwQOH6JlCCBwxkuOTgkcNwrE13NIgPDwcHp3SMp0REj+O81KnD6+4Gbb67fapRGgeObGbw6oa+Pr/+0bp3hXmzSGQWy9Xuhk8AhiBpE9PeJhP7veocEDoccnOGDH8pEDwcHJwrpRNW4wLn7buD88/m6YvUIOTjVRV5TKynpTNOLmkLUCIJwE6ODQwLHXwyXdXBkgcNY1XanqnhRJppycNxBPo46gSMqqdWowNm1S/9Yb5DAqS6ywNFFh0idUToc57/U8YVOAocgahDRpzQ28ke/CZxk0jBzNMzws4OTTmuOjRA4mQwwNFS1XaoqXhQZqFYVtWHn4NRoqWjRj9Zr6HLVigysXQssWlSZbdUwVg6OHI6WDcfynqs3SOAQRA3i1xwceUAxnMOW/JyDI48JJ0zQClIN1/PtRZlocnDcwdTBiURqPkRNHPt6vS/kCZxKOTif+ATw4Q/Xr/XlElYOjpnAqecS3iRwCKIG8XsODjC8w9T87OCI8LTWVj5WbG7mfw9XgWPl4BRbRa2aIWp+d3AaG/mCkwDqIgen3h2cbFYvcEKVEjjr13PLYvVqdz/XaSJdjWCZgyN1Rrkw77DIwSEIwlX8noMDkMAx+90PCIHT3s4fW1r4Iwkc/fO00Gf1Ef3RPvvUV5EBvzk4FcnByWa1hrx5s3ufe/nlQGcnsG2be5/pMYUcHBYKIRes/wudBA5B1CDGHJx6vZEZIYHD8XOIGgkcPX4qMuBXB0cWOOlg7QucendwMhkgDi0pryI5OLJVsWWLe5/79NPA9u3AO++495keU6iKGguFwUL8Qg/kKESNIAgXIQfH35CDM3zwokw0OTjuIL7D2LFAc5SfqK7+2hc4fnNwQrk6FjjipjY46N5nekzBKmrhMJji4FCIGkEQrkICx9+QwBk+kINTu8jnZlQrH+nt6qv9MtF+cHD0OTgV+CKSwMltdDFETZyEOhU4plXUQiGwYjuoGoQEDkHUIMNB4AzXAS+QPzissxxVW0jg6PGiTHS1igzIDk4dj3tUxHUYiQDtzfyg7uyO1HyZaL85OMEKOzj9q8jBEchtKJiTQtTIwSEIwgvEDczPZaKHs4NjHKD6YTZcQAJHjxdloivdXvzu4ITDwIgm3slu31P7IWp+c3AiSHs/yaMLUXPRwalzgWNaRS0cVh0cEjgEQbiKHx0cxkjgCIyDQz8MFgUkcPT4qUy0X3NwIhGgrYGfqK27JYHT21uTX9RvDk4E6dIdwSVLgFNOARYssH1bdkDbXniHiw5OnYeoid1nDAhBDlGrf6uWBA5B1CBGgcNYXfczAPLHCSRwrP+uBL293kxQk8DR40aIWjKpFzWUg+MOsoPTGucnavMuKQcHqMkwNT86OCW3pzvuAB5/HLj7btu3pfu07cV7trvXgMXJGBhw5/MqgJmDk80CYWgXulpFjRwcgiDcxChw5OfqFeP+k8Cx/ttrGAMOPhiYOdMQouACJHD0uBGiJrs3AFVRcwvZwWlSqqht2x1FMhvWVqitwTA1cnAkdu7kj0NDtm+THZwAY+6tW+MTByebBUKQVvQN8g5K5OXUIyRwCKIGMebgAPV7MxOQwNGodg5OOg2sWgXs2AFs3eruZ5PA0eNGmWg5/waonoPjN4EjOzjRIP8jhQjWrEFN5+H40cEpWeCIDqfARZHpN8zkuLHYZy6nqf86FThmDk6AQtQIgvCK4eDgDNcBL1B9B0ee7HT7PJDA0eMnB8dvIWqygxNQ1EIKUaxciZouFU0CR2LXLv5Y4GDIDg4Ad9bCkW9qdSpwZAdHDVGLhMGUC50cHIIgXEX0m/E4EAjon6tXKAdHo5YEjpvnIZfTxoMkcDhu5OCQg+MNsoMDSeBs346aLhUth6gxVt19KYVs1sUcHDGjUkDgeOLg+EDgyA6OGqIWCqkhagESOARBuIl84xUzv/U6WyegEDUN47GodBSAnHfj5nno7tYGXGJ8ONwFjhcOTrWqqPnZwZEFzp49qIsQNaA+z4MnDk6BiyI3ZLiBkoMDwNzBCUgOToBC1AiCcBP5xisGRvXu4JDA0fCrgyMmUxsbgViM/z7cBY4bZaKrHaI23Bycri7UtMCRj309Tny5JnAYc+zgGEPUcptdEDjyNutU4FhVUUOIQtQIgvAAedZXDIz8JnD6+/0xSCqFWhI4bgoPY/4NQALHzRA1cSzJwXEHMwcnjUjNCxz5/PtF4JTUnnp6tMZZyMEZ1Auc9FoKUQPMq6gFQiGpCgo5OARBuIgscPzm4LS2as8N10FvtQWOVyFqdgJnaKg+B2PlUihEzUkVNeHgiONKOTjuoHNw0qKKGjk4XuOagyM6HKDggRACZwAJAEB2k7sODqtTgWO5Dg4VGSCqTT0mGBKF8XMOTkMDkOD3mGErcKpdJtrrEDVZ4IjlRID8ZPnhgJsOjhhzUxU1d7DNwamDKmpAfd4XXBM4Iv8GcJCDw7e3FlMAAKFt7jo4gzvrY6HPXE6/JqnlOjgiRI0W+iSqwc6dwKRJwHe+U+09IdzGzzk4kYjm4gzXPJxqOziVDFELh7Vy58NR0LpZZEAInGqFqPnawaEcnIpRDQeHKQ7OGkzl29yzvfxGLG0zN1AfDo7RaDJ1cKQQtQCFqBHVYNEiYONG4P77q70nhNv4MQdHFm0kcOz/9ppKOjjA8M7D8YODk9EiV9T99oPAsXJwdAKnBstEy/eCZNL6fbWKazk4soNTSOAoDs5GTEAGIQRZDrweeBlIJyKYrA+BI4enARbr4EhWbZDV74VOAqeOEQ1zyxZncdxE/eDnHBwSOPmDQ7+UiSaBk49o9+UIHHJwvMHMwamHIgP17uBk0zlEoTViVxycAheFEDiDSGAbxvAnyy0V7QOBY7oOjjSTQSFqRFUQ11Ymw8PVCP+QTgNxDKLp8X9hRKALQH3eyGRI4GgMpxwcYHgLHHHdGkPUSikTXS2Bk8mf2PWFwNE5OHVUZKDec3CMtpMrOTiFDoSyzSRi2Ixx/LlyF/uUthlK1afAEV8hkzGEqEWEg0MhakQVkDs5NxblJWqDXI4Xjzgbt2HEt76M83ZdB8BfDo4Y8A5XgVNLIWpe5+AAJHCA+g5RG04OTgpRDAwAqUblYHd311yIRL07OIFUvsApqT0V4eDIAmcLOvlzLjo44fRgXVR9snNwTEPUqIoaUQ1I4PgTcV7HYisAYGR2m+75ekUeTAx3B6eWBA45ON7iZploIXAYq2xYo+8dnGBWPRFpcCXahTb+ImM111HVu4MTTFfPwUkHvHFwAozVxcmwy8ExDVEjgUNUAxI4/kScV5GEGYMys1j7factZiFqw3HAC1QuRO0vfwFuuin/ea9zcMRAXDCcBY4XDg5Q2QkPvzs4EaZ1rvEWReAMxrR69jUWpuZHB8frHBwo24y3euPgAKiLxT4dOTihEALKhR6gEDWiGnglcDZvrrn+fFghbl5RRdiIx3p3cKiKmkYlHJz+fuDSS4GLL86/7xodHLciK8T6Ck1N+ueHq8BhzJsy0UBlBYZfF/o09rUA0DhCEThdqNlKavXu4LgWolaEgyO22diuCZzcRvccHAB1JXBEDmChKmohcnCIauCFwOnrA/bZBzjiCHc+jyiefAcnqXu+XqEiAxqVEDgDAzzqxriwG6AXONmse/dlq8H8cBU48nn1g4PjtxA1NWyWaQezsY033j17ULOFBurdwXEtRK2IdXCEwEm0xbAtyEPUMhvLc3BYqn4dHLGOrVUVtUBYCVEjB4eoBl4InG3b+M105Up3Po8oHnFe4wHe80R84uCQwNGohMCR7/dWi7sJ3DoPJHD0yOfAzTLRADk4bqC6yiJELRRC6wg+LKrVSmpi0kJQjwIn4EUOToEbZFC4RrEYku3uhKhlhwzbNM4k1SBC4Ig8SVMHR7rQKQeHqApeCBzR2HM5f9zA6hFN4PCTIW6+9XgjkyGBo2G8F3uRMC6LGLsQNcC982BVEnm4Chz5PJdaJjqX0wYlzc2awCAHp3zyBE40qs5s16rAMZ73erwvuOLg5HL685JO28baClGVi8TAOrmDE9m1tazONztUvyFqomlbVVELKGWiQ7TQJ1ENvBA48qCoHldI9gPiphsP8hMQZf5ycMJhKhMtV6WS/3aTaggccnD0yIPPUnNwBga0cVtTU3UEjh8dHDk/KpyrH4FjPO71KHBCJgKn6PbU1ZUvaGw+RBVVsRgi40cjiyACuRywY0eRG9bIGR2cOhI4Zg6OroqaEqJGRQaIqiDf4LZtc+eGI3eWJHCqg5qDY3Bw/CJwqIqaJGLj+r/dpBiBU8x5uO8+4Cc/MZ8sJYGjRxb1gYD+NadlokV4WiAANDRox7aSAsOPDo583GUHR1dXQKidGhI45OAoiPwbcSEBtgdDiCoWjWF0ZwjbMIa/UMbscHbQpw6OVEWNHByiKsgdXS4HbN9e/meSg1N9jDk4YR8LnOHu4HgpcLzKwfnOd4BrrgFWrNA/z5hUlcqQbzJcBY5ViWjAuYMjCgw0NXGRQw6OO8jHL5TTOiedpiEHxxNCGRcEjsi/GT1ae87mYASVbQbiMYwdC1dKReeS/nJwdCFqioNDVdSIqmC8wbkRpkYOTvVRBY5SPS2S80cOjlmZ6J6emlskvCKo57hGHJxiBI64QRrXU5C/Azk4HCtHC3AucISDI0pvi8+iHJzykPe/YIhaDZWJ9oODYyZwim5PwsEZM0Z7zuaiULcZi2HMGLiy2GfOBzk4It86r4qakoMTBIWoEVXAC4FDDk71UWfBlRA1Pzs4jGkDuOFEPYeoiQGVsT3aJdQPV4HjpoPT3MwfqxGi5ncHh3JwLHBrgSwDQmwwpdJGFKnSHZyODq1R2oWoeeHg1HGZaOHgAPyw5YWoUZEBopoYr2USOBXmz38GHn7Y9Y81roMTzvpvHZx4XBv0DccwtVoROB0d/LGYcyDOo7H/cSJw+vq8qRhXq9gJHKdV1IwODlVRcwd5/0PZ+hE4FXNwfvADYNw4d1cRVxBiI5vgjbqsHJyODu0Cs7koxH0UMYPAKcfBqeMQNbnkfDJpE6JGAsee66+/HoFAAJdffnklNjdsoBC1yqNOaG3YAFxyCXDOOa5vI0/g+CREzRiuM5wrqVUiRE1uL0bHRlzbInzd6TmQ82xKcXCA4eXYuRmiVk0HR+yjHx2cUAgIpPOLDAz7HJzHHwe2bgUWLHD9o4XYKEvgCAenvV0TODYHQ2wzmOAhalsxlr+wbVuRG9ZgdRyiJoQ8oDk4aoiazsGp3xkpzwXOm2++if/93//F7Nmzvd7UsMM4SCIHx1tuuQUYO1bp78UNb/du15NIxHmNKgt8hrP+ClETA6ThXGhADFISCf5YrXVwRPi603Mgt0GrmeRgUHMnBLGYNgYZTmFqbhcZAKqTgyOXNfeLwJFzAmUlSg6OguhAPBgICLGRa9AETsk5OB0dji4KsU0RorYTI/k+7Nhl+T+FYMYQtTpa6LOxkffLgIWDQyFq9vT19eGMM87AzTffjBGyH2ZCMplET0+P7oewR1zLkybxR3JwvOWxx3iluqeegtaRMeb6rI2ag8P4CQjl/CVwxL1oOJeKrpUQNeHgOD0Hcv9gFaJm5lYAwzMPx4mD47RMdC2EqPnRwQmHoVOissBhrfIf3uSjFEvFHBzxwRUSOJ46OIzpHJzWVqAnwgVOZtvOIjcsfWxSv71cf/04OI2N+sOWL3B4B0VFBiy46KKLcMopp+C4444r+N7rrrsOra2t6s/EiRO93DVfIDroyZP5Izk43iIGiVu2QD9idDnmRnVwhMDxmYNjFDjD2cGpVploo8Bxeg7kz7QKUSOBo+G3IgOyg5PN1syYvyR0Do6JwEmlgKGEMjGbzWonospU3MHxYAOuCBwzB8dqXzMZBMEbazARQyAAsA4ucAK7yhA4Bgcn21dfAsfo4JiFqIWRqdtKp54JnHvvvRcLFizAdddd5+j9V111Fbq7u9WfDRs2eLVrvsELgUMOjjU6gSNb0R4JHLH4XDCXRRDZus/B0Q0oMLwFTrXLRIvX3AxRU4W5yWAeGJ4Cx29lomUHR36+HrFycJqatHPTlUxoDbpGSkVXzMHxMEQtkuUzLKyxjBA1MwfH6qKQvkOogY/qw2O5wAl37yo5zJwpB78fDQCATG99CRx7B4cEjikbNmzAZZddhnvuuQdxcQcvQCwWQ0tLi+6HsMcocHbsKL+zIwfHmmo5OAC/AZCD4w/kRP1qh6gVK3AoRK04vHBwqrnQp+zgyM/XI1YOTiCgJWDv6QqUnYfz/vu8Hs2mTWXtrkqlHJyskkCfS7q/gUhO6ZwaXXJwCoWoSZ1hMMEFTmICLyEZyOVKF6+Kg9MNfjPL1niIGmPWDk4moxc4QVFFDVkSODJvv/02tm/fjoMPPhjhcBjhcBgvvvgi/vjHPyIcDiNbz9M+NYTo6MaO1QYVW7eW95kkcKyplMBRb7w57QREkfKdwBmuVdTkm0W1BU6xOThOHBwSOBpelImulSpq8vP1iM7BMTReNwsN/PGPfEWBu+4qdU/1VMrByfbzDmTdcg9ycJR7G2tyKQenkK2pdIZZBBFJ8Abc0RlFN1r0n1UkLK0XOLWeg5NMavcfMwdHF6IW1Rycer3Ow4XfUjzHHnsslixZonvu7LPPxj777IMrr7wSITF1RZSFHBIybhywbh0PUxNFB0qBQtSsEYPEzZsB1j+AgHjBEweHIcK0zjqGpO8EznB1cOTzWI0cHMZKLxNNOTjF4UaIWi05OEaB40cHB3BX4OzYwR/duk1UxMFhTI0gGOzyIERNTN4pAieKNDJpBmh3VXsyGa3TKsLBSSKmXotjxwK70IFW9AA7dwIzZhT9PQLK9noUocRqXOAI9wYoXEUt6IMQNU8ETnNzM/bff3/dc42Njejo6Mh7nigd+eYpC5xyIAfHGjFIHBoChvYMIiFe8EDgiBLRgihSdZ+DY1UmejgNeAH9oFCUia6kg5PJaLN4IkRtcJCfHytxIpAHVxSiVhg3QtRqycHxU4iaVQ4O4K7AEdFPbt1PK+LgSBvJGdd6AfjaMQsWACeeCAQcihKJqCJwAqJRA8ils3A8JJXPxYgRjh2cJGLqtTh6NC8VPQ1ruMApBYODw2p8HRwhcKJR3u7tcnCCES1ErV4dnIos9El4g1HgAOULnGo4OP39vPxyrSP3Xb3bvC0yIBb5FPgxRG24OjjyAEU4OJVcB0eEp92KszHx0s8iAK52nJwHOwdHvEYCR8PNMtHGKmrVcnCCQW1MW88Cx87B0WkaNSGnPIHjlhCpiIMjdR5syGQgcOGFwMknK2smFI/q4DRrAqeoBi3yb1pb80fqZpg4OCNHamvhlC5w9A5OrS/0KeffAPZV1IJRcnAc88ILL1RqU8MGLwSOPCiqlGNw1FE8EXPDBu3GUovIfVf/Lm9zcMwcnHoXOFRFjWMmcCrp4AwNAVEkcTZuBx4DZsbX4/2hKejp4Td9OyhErTi8WOizmuvgiG2Hw3z79SxwKuXgiH9z635aEQenkMARA41XXuEuThEwxvsfAAjkCZyE+T8ZkfNvgMJlok0cnI4OYEuZAidgcHACg7W90KdR4NhWUVOKDNRzDg45OHWMXxycZcv4hbd6dWW2VwrG9TyHdlXewfFLiNqwEjg33QQcdJDuwhTHIRj0NtxIbi/CtQH4dd0K7aBPb9wCwNl5oCIDxeFlmWjPxMXNN/NVjSVkBwfwx2KfRefglFhpy+0QtYo4OHblEgHtyyxYUPRHZ7Pa/U0XopYswcHp4JXQnJaJlh2cjo7yHZyAwcEJDPnEwQmHVQeHqqgRVcFrB6dSAkdsp8RCJhXBeCySXd6WifajgzMsBc7NNwMLFwIvvaQ+ZVZyt9IOTgs0lTE17lzgOCkTTevgaDhxcAD7MDWrhT496Q9WrADOPx84+2zd02YODlDfAqcSVdQY8y5ETVTh89rBMRsI9O/iMya5t8sTOMGmBuREYYFiGrTRwSkiRE12cITAYTtKFDgZvYMTTNaXwBHHIq/IAC30SVQbOea9XgVONqvdPEsNg60ExtDadDfl4BTLsCwTvX49f5QslFoQOLKDMzHMBY4T4UEOTnE4KRMN2Ls4RgfH0xC1hQv5o2EwTw4OShI4/f3auXW7yEADX1vScweHmWyge7siULZvU9ZNcE4mo93fQg0x5EIlKHajg1NEkQGzHJzstlIFjt7BCaXqS+AIB8csRE1c7FRkgKgKfghRk7dXyw6OHOIDAJk+b3Nw/CxwzKqoMVadffKUgQGtUVdB4FiViTY6OOODvNMot8gACZx8LI/J0BDCu7apf1oNINJprR82OjieiIt339U+XNkAY5rD5FsHx67IQBkCR45qc9vBEYNULwSOnHcTSOUPBEJZ6bkiw9Tk+1swEUM2WILAEf2qMUStCAenoQHoDnOBk9le2uBDODj1KnCMDo4coiYucnJwCM/IZIBf/hJ4443818wEzp495RXyqLSDI2+jlgWO8ZjqFvSqQIhaDEnf5uBkMjVffKY0Nm7UfpcaunwcquHgGHNwxrLScnCoTHRhLB2cE09E8wGTMQq8fKTVAELuWiri4Lz3nva7WBxREl/D0sEpo4qaFwKnEg5OdkDrPIImAkdehLocgVO2g2MsMmD1GcpBkh2cQADItCoCqcQQtWBWH6IWTg/W9GydYwdHWvCKigwQnvHCC8APfwh873v5r8kDitZWbU2NIh1jHZV2cOpV4OiqpVCImiOMA+DGRm3A5MswNRGeBtR0iNqoTGk5OFQmujCWou+ddxBIJjENHwCwdnBE1xKNaiKpIg4OoDYaed+Gi4PjVoia/C9uFxnw0sFJ92sfKhLpZcQioADKdnByVXJwACDXzh2c4J4SBY4Soqbm4LBcZcsbFonjHBxDiBo5OIQniA7SbFAgJ/UGAu6EqZGDY45R4OiSCUngOMJYJjoQ8HkezoYN2u8FBE6l18GRQ9Tak85zcIoKUctmgXvvVYWeLHBqeJLTVUwdnExGPdhx8HZhdf6NJaIBD4sMDA4Cq1Zpfw/l75sfHZyiigwU2XDr1cHJ9EsOTrqAg/POO8V9thyCHY8jqzg4ItzLEVYOTpECJzCKC5xI7+6SOmGjgwPA9XAExoArrgBuu638zyrk4FiFqJGDQ3iCGKSYiQ3jgMINgUMOjjmizxIrv8eykoMjRiEuYbUOjt9C1ACfV1KzEDjyzLEYMHqdgyOlVOSFqLUOeBSi9vTTwOmnA5dcAkATOLkcT08aDpgKHGnUm0C+SyJjLDAAeBii9v77+lg5pc3KbdNPDo6ThT67ugDWpvyRThc9eJUFTj05ODqBkzHsOGOIyfendeuKunnrBE6sxBA1KwfH4jNETpEcogYA0bFcIAUYK8mhCykOTj8atWpwLgucFSuAG24Arryy/M8qVCbaKkSNHBzCE0SnaNaJeSFwKu3g1EuRAVngJBLawAQAOTgOMRM4vnZwCoSoVTIHB9DasNHBaRzYgTDS7hcZ2LSJPy5fDoDPOIvKYcMlTM00RE0aSDl1cESBAfmzXG8zcv4NYBqi5icHx0mIWjYL9KFJ++JFDoLr1sEZ0D5UDOJVpI5lO0bxX4pwcYwChwmBU46DUyBETeQUGR2ctlER7EEb/6OEMq7BHN/nbDCKQbFIqcsCRwwvhDgph2IW+qQqaoTn+F3g1JuDk0gAnZ1AAygHp1jIweFUIwcH0Asc2cEJMIYx2ObaQp/qAELcTTduBBjThSQOF4Fj6uCIwRmARAGBY+bgeBaiJuffAKYOjp8Ejp2DE49r56yrO1ByHk4lqqjJSy24RUYqMqCrmAboOpbXcAT/pYg8HCsHJ1COg1PgosgOmjs45S72GcrykxprimgCx2V72i6Kp1gcL/RJDg5RCcS4yNg5MkYhapVEFjjjxnnv4JiFqGUy9Z27YCwTDehLRfsOWeBIDb1aAkf0JUYHBwA6saXoHJyCIWribtrfr57g4SZwCjk4jUH7EDUzB8ezEDULgSP2LRDQHDgvQysrhZ2DEwi4U2jAiyIDRgcHcL8tZAe1iztsCFGTK6zNx0f4L8UInDRDXBY44SJzcFIp7Z7r0MHJWTg4ZQscxcGJNXvn4Ih244aQLcXBCSOLXLY+Bx4kcGocKwdHbuh+cXD6+jxatMwFjA5OnsBxUXlYrYMD1HSBloIMKweHMcsQtUqViTZeS6ING3NwAC5w3HJw8gQOoIarDTeBU9DBCSorwhcoE23m4LjeZowCR2kwxkU+AR86OCadkxuloivh4Lj52YKc7ODk9B+e6hViIYq3cQh/sogQNVk86RwcpwJHXD+yCi3g4OQkB0duxzqBU+wMK2MI5XgjamiNeC5wjL+XguMcHHn2DUA2XZ8WDgmcGsfKnpSvY784OEDtujg6gTOW6UPUsllXD5ZZiJr4mwROndDVpR/g12qImjIl71TgFJWDQwKnsIMTcFZkwHMHp7eXJ4sDwPTp/NHg4MjOqx8Ejp2DA7iz2KcXRQbMHBy3BY7s0oQNIWrJHk0svIOD+JMrVji+qIXYAMBzcIp1cITAGTFCsxQLOThKkYFMMIZAQHt+5EhgF5Qwt2IdHOkCjLd47+AYfy8FOwcnkzEPUQMAlq7PC50ETo1j5eB4JXCq6eAA9SFwJoxOIQiDY+NimJpViJp4rV4xlokGfCxw5PA0oKplogOG4j66ELWpUwE4D1Gzq6KWtw6OfE0oi54ON4FT0MEJlF4m2lVxsXQpf+zsBMaP578bcnB87eCYnCg3QtS8dHBiMe2cuO7gDEkhahYOzhDi2IlR2BqewF9YtMjZZxsFjnBwsg5vbsb8G6BgmWimbDMTiumeLytETeoMfengSBd8Ll2fVQZI4NQ4okFnMvowBjOB09nJH3t7S69cXGkHx9gf1YXAaZfcG3Gnd1Hg+DFEjTFtEDcsBI4cngZU1cERosLUwdlnHwCawCmUTEoOTnGYChxpkNxQgoPjSZEBEZ62//48wx7Iq6I23BwcU4EjKxYHeFlFLRwuvL5lqQjHAzAs6gk5RI2Pjt9iB/MXHObhCIGTRhgIBjUHx2mDNlZQAxyXic6EXRQ40kFvaItiAIqlVkcCxzYHhxwcwmukcZFp/HsgoAnt5mbtRrhlS/HbyuX0NyxycDTEeUgkgHEjlNh0hLS7oMsOjlHgxAO8M63VHKVCmAlywMdlooWDIy5Oixwcr5K15SIkQkTKOTiqg6MInHHYDMYKN2MSOMVhGqImOTjxEhwcT0LUhMDZbz9N4JCDA8DdIgNupGrKbcorgcMkl0W3qCeAdC9vF+kAD/d6M1uawEkFuNgQAidYjoNT4EAIgZO1cXBy20t3cJrawr5wcKxC1HKp+rzQSeDUOHKDNqtgpLtxorwwNePFQwJHQ3ZwxrZwB2cICU1RehyiFg/Wt4NjJXB8W0VNEThsyhT+d4WrqMl9hRioqQJnMJcvcALOFvssaqFPWeBQiJqG7OCUsNCnJyFqYg2c/ffnnRwwvHJwbIoMuJWDI7vY5SALM88ETlL7wBhSOmWW7lMcmFAMEycCC6AIHIeFBlQHJ2gQOMb1dqwwc3AK2JpM6X+zBgenrQ3YHeACJ7OtNAcnhQhaWgN1KXCcVFEDKESN8Airxm06M4jyBI6xkySBoyELnNHN/I8BJJBrVEYeXjs4PhI4ZmWi/ergvJucAQAY7KpsiJp8XRkFDuvr13LIZs4EAIwDFziFhIcTBydvHRyAHJwyHZxqhqgNVwdHF5VWgsDJ5fL7NTfuqbIw80zgDBl2VGpsmX4tn2XaNEngLF3qaHAvPjstHJyI9w4OhswFTjAIpFq4wGE7SnNw0oigtRWeCRw5iqeiOTiBALJCItTphU4Cp8axcnC8EDhmDo7X667Uo8BpiwmB04B01H2Bk8lIDo6SIS4Ejt9C1HwrcJQcnLd79gYAZPoqWyZabifGELVgLz/Y2WAYmDYNADCKbUMQ2aIcnIIhavI1MUwFTiEHRyz0WUyZaNdD1Hbt0mKaZ83KC1EbFg5OoRC1EspE9/by++eReAnP42jsjyWu9N8VcXBsZjtVgRPkAmczxqG/cRRvKEuWFP5sRWykjA6OU4FTgoMDCwcHALIjuMAJ7nZH4GT7vFno0/h7saTT+SXGdQ5OhiEEpSNSZjOyAX6hU4ga4QmVFDhmnaTXjoFXAue114Brr3XvBiwLnMAg78AGkcBQ2GMHR5m6jQXq28ER5yEY1Cp7Aj4WOIqD83Yvd3CCqeo4OOGwdjMTbTjczw92KtEKjBkDBAIII4uR2FnwPJScg7N9O5BMDjuBU8jBSRQIUbNzcFxrMyI8bfJkviFDiNpwdXDKzcER4WnnB2/B0XgRp+FfdePgGHdUDlnLDOgdHCCAD1qd5+EwqWQzALVBe+rgpPg2c5F8gRMcxT8n0tdVXINWQ9SiaGnRBE6mtzZD1OTu2MzBYRmpE1Iu8Cz4Ra97rY4ggVPjyPak2eBCNzMIdxwc+UbmdZia+E5i0OuWwLniCuBHPwJeftmdzxODw3hc+2MADegPVEbgxIP1vQ6OlSD3pcDJ5dSckxWMC5xQujoCJxrNm5BHqJ+ri0xDK9+BUaMAOFsLp+QcHADYsmXYCZxCDk4c9iFqFXFw5PwboKaqqA0N6fSgq3hdRU2c5slh3heMwB5XhEgligwEDDd+4doAQFY4OOG4MICxKFS8wBE5OBBV1MpxcAocCPF9zAROZPQI5BDQf7YTJAenuVnJyUXtC5xQSDtcusMmX8xC4JCDQ3hJNRwcebbQa4EjPn/sWP7olsAR96Eiq3paIjs4GNAcnF7mjcBRQ9SUkU29OzhW7VUMeJPJyuR8VYRt24B0GiwYxBrwdWZCBRwct9fBEccyFtMm5EUbjgxwFZNpVA6+Ul9+HDaXlYOTV/hE3FHFl9y0iQTO4KBu1irusMiApw6OnH8D1FQVtWOOASZN8kbkiH2PhrJajKCLC32Ke88EuCtwKlEm2viByV7tb1EkIBuOqQLn5X7nhQZEwn86pM/BCZXh4Axm7EPUAjYOzohRYeyBcn6LKRUtOTiRCJCOKCFqNS5wGhu1tdEsHRxDiJonC7VVABI4NU4liwyIz08ktJtZpQSO2O9iS9EX+ly39l8ncCQHpzvrTQ5OXoga/JGDY2yv8sDNNy6OEp7W2zwOfeDtIyStBl7JHBwzgRMd4uoi26jYZ8rF58TBcRyilsupEwHqKGjjxmEncPLavWGAHGOlL/Tp2mSHUeDUSBW1DRuA+fP5wMy4rJQbqFEQAfMEQVMHZ3DQ8U2FCxyGMRmefzYCe1wNUfPUwUkZSkP3aX/nBpS8sYgmcJ7drQicxYsLN0wRohYyhKjlSnNw7rsP+MSJBRyctGRpGxg5ssS1cCQHJxIBsorAyfXXvsAR6NpPNt/ByQX4QJAcHMITynFwii0QIM/6ysreS4wCxy0HR0ySyiF+5WDl4OxOeRyi5jMHRx4kAVxIC5Hjm0GvInB2xCZiCHw2PJjLqqNBeQbWq3Vw7Byc2BBXMbkmvYPjaojagJRoq1RqIwcHeVZEglk7OPK6RJ6FqDGmXwMHsAxRq7SD8/zz2u9e3IfEvou+FYBpiFpPj3KtiGlvhy5OVxdfb6ohx0eW9eTgqIJAQSzuCWgOTi4Sw8iRvG1+gKnINrfyHVm61P7Dk+Y5OKU6OC++CCSZQwcnmu/glLzYp8HByUYVgTNQPwJHHucFbELU6jXZjgROjVOswFHGKhgcLH5GXL4ZV0vg7N5deDX1Yj7XSwdnEAnsHPI4RE0Z/Yu/613gGNsr4MM8HGW6eR3TBA6AvJCfSuXg5AmcJFcXuRblwBchcBw7OHL+zQyehzQcBU45Ds7goNYXehaitm0b73SDQXVNJKsQtUo7OLLA8cK5Vh0cec0xqYMS/RJjQE9fUHuiCIEzHpvUv+vKwUnrPzDVp/0tcmhyEb7Qpyg00DX1IP6GAnk4QmzkOThOBM7goNaRKQ7Otm1cZPAdNT8QQdXBcVHgGB2cWAMAgNWRwJHbjy5ETUmKFg4OFRkgPKFQkQHjgDGR0Nz0YsPUzBwcr0OixDaFMDNbO6Ccz/XSwRlAA7YNVKbIAAmcOkJxcJYPTEIS0g3VRuAw5o6wF9g5OPGUcqBNBE4h4SG3v2xWv8+6wifibtrQAEyYwH+XQtRSKR/lXFnAmEkxGOHgKHaIEDhm517uUuRBiashasK9mT5daygWIWq+d3BCId2XlK+dUvJwurq0/BugvhycoMHBkUPUhMBhilgQYWobRjosNCAcHKVkc0A4OE5C1OTrR+lMtm3jIgNAQYHDPBQ4ubjSWOpI4IhxXiYDsDRvWLlgSHUrhYMjXqs3SODUOMU6OEDpeTjVcHDENlta+FgIcCdMTQgbrx2cLb3K1KrHOTiROs/B0ZVkNeBXgbOsfyJyCCElbr5KozTLwQHcHSza5eA0pPmBDrQWH6JmFZYm/65zcBobNYGzaZMu1MrvLo58PvMcHKWqSswmRE3k3zQ26kuruxqiZgxPA2rCwVmzBli3Tvvbi/uQ2l6ZWak7TjmLfe7Zk+/gpJLlLyxXCQcnaHBw5Cpq4mQYBc57MWeFBlQHJ6y0s2gJAqe9XR2E6xwci4silJFmfAy4FaLGYmJioHiBMzQEHHccX9rCSCUcHABIDfJOiIW0C51C1AhPKbbIAFC6wKlmDk4sphVFKVfg5HLa8XHLwRGfYywysGOQQtScYNdexay+bwSOEqK2ARMBQHNxbBwc+Xk3sAtRa0hzZRFo0zs447C5qBwc49+mAqepCRg/nv++aRNCIS2fxO8CRx505jk4SidtF6Jmln8DuByiZiwwANREDo7s3gDeTOyoVdQC1p1TOaWijSFqUaSR6Sl/EchKODiqIBDbHJA2kNSLBSFw5qcUgbNwoX3VLbHoZqgEB8ekglpRIWomAmfkSGAXShh8GBycQAPvaMU6ecXw+uvAc88Bf/tb/mvyGMYrBwcA0oO8YbGgdqGrRQbSFKJGeEA1HJx6Fzgpk764XKyKDHhRRc00RC2XVF+rR4ZjiNp6TAIALQ9HhGZUUOCYOTiNWX6gg22KslQ6jLHYip5u+xlmxw6OuB4aGzWBs3kzkMsNmzwcU4EjZv+FwMlZOzhmJaIBlx0c4xo4QE0s9GkUOJ46OLB2cMpZ7NMocACA7XZeZtqKSjg4eQJHdnBEhTVFCAuB88r2vXkYRn8/sHKl5WcLBycbNhQZKNbBAe/TenulELVMJr+6EmMIZ/kBCsTtHRy2o3QHR1w3wWTxDs62bfzRbELWSwdHvh+rAkdycHJBcnAID6mkwJFnfashcEYqLnG5AkfuJNxwcHI5fQltXZnoXGWqqIkwinoNUXMicHwx4E2lgK1bAXAHJxyWBE6BEDU3lxqwFzj8QIfalQMvwqWQQmCP/YIjxvYn/61bB0e+m3Z28nCSVArYuXPYCBxZgKjn2eDgRFgaQWRtQ9Q8c3DkCmpmDk6VykQzpgkcEbbsqYNjE6JWrsCRc3CK+V87KpKDowiCjLKSfXZAGwgEk8pN1eDgrFoTAjvwQP6HTR6OqNCWVdakCcQUgcOKd3CEMFAdHMB6gS5pn2Xa2zWBk91Weg6OcHBKETjKLUPto2W8FDjBoJTTN2QdokY5OITryKFWQGUdHNFx1qOD41aHIJBFktHBEeucuCVwcjl+gzeGqIWZP0LUjGWiAZ85OEp99lQwhh0YhX32yRc48gBFzq3wKgdHHq9mMkAr+IEOt7eob8q08hnRRNcW288tOkStsZF3JqNH87+HUSU1WfCJCsNGBwcA4hgqysFxrcjA+vV8I5GIVukOyAtRq7SDs2oVsGkT3605c/hz1crBEQKn1CIDRgcn0FUfDk5YcXB6wRtfdlDbgHBgAgkuFiZP5u27rw8Y2rdwoYGgsuhxzlBkIFyCgyMEjurgAPkXhtR4gol8gRONAgMNXODkynBwQk1c4IRS9ePgAFobygzlX+hMCVGjhT4J1zE2ZsvZUgP16uC4JXDcdnDkWZV4HLoiA24LHNE35xUZ8InA8X2ImpJ/sy0yEUAAs2fbC5xAwJu1cKxycIaG+NocABAZ2aq+n43heThNvVts188qusiAsB+kPJzhInBM27wYoImykbAWOFYOjmshasK9mTlTv5NFLPTpxbhHuDcf+YgmMDx1cGxC1HRpNzq1Uxi5yEAypMzu99SHgxPO6gWOWPsG0PJZgkq4VzyuXd5bOgsXGgiKEDXh4BRTZMCJg2M8GNIgxixEDQBYOxc4wd2lOzjBRn6Ow+nSHZxs1laflTUeE12PaMYCMdZLD+WHqGUpRI3wCmNj9mORAblyW606OELgqDkTUplorwSOetNVRjfhXH0LnGFTRU3Jv1mT5QUGCgkc+bESOThDQ5qDIwscjOMD7lHZLbbXjF30h6WDA5iWiva7wMlb5BPQBsejRqknPoFB2zLRViFquVyZpcXN8m8AyypqlXJwhMA55hhvIwnU65BZ30zLCVHr35PCGGwHAGxsmQUACHXXh4MTUu43Zg5OUHF3ZDdEhKktb5QcHIuZEiGQcorACSohamEnIWoWDk4Gkvq2EDhphBGJWQx5lfj4cH+P44OZS+kFjnBwShE44nsA+ZOybo1nduzgj6NG6Z8XbSgEZbZCLjKgCBxaB4dwHTsHx6nAsZuNNVLNhT5rOURNV2BAeiITlhycZNIV9WHl4AiB4+ccHF8JnAwXOPvvn19FzXgsvBgsWpWJTiY1gRMa0aK+PzzBWSU1Jw5ONAp9kQFgWDo4tgKnvV09MYUcHKsQNaDMNmOWfwPoQ9QYq2gOjpx/c8wx3t6H1LDZnPs5OJkM0NzHZxhZLIbtLdMBAKHe+nBwIjlrByeUthY4C1Oz+E51dQFr15p+tlHgBGL8SzgSOBYODhDQyvFbWCBJxEzvPwAQHd2GrBgOOxyA5Ab1IWrhZiVEjZnYMAWopsAR11gYioMjXehqiBo5OITbOBE4Jn2yyBlGOl2cWPBLmWivQtSMAicdaUA/pIBWefX2ElEXn7MQOPXq4AybMtFSieiJE/k41q6KmvxYEQenJ4UElIuiVXNwAuP5rEihtXDEeRQ5JY5ycIBhKXBsQ9RGjFCFRKEcHKsQNXkbJWG2Bg6gNZhcDshkKurgvP8+H+zFYjxEzcsFp1VXudgcHAdlont6pPybceMwmOCOQ8QFgSO3K6+OjyZw+MUqFvcEtAproYZ8gbNqfVQTzBZ5OKrAETk4IkStDAcHANIBC7UnCRyz8RIAtI8MYjf4ZzpdCyeb1Ds44ZYG7UWzagE2iBA1s3+tlIMjBA4oRI2oBMbBuVMHJxrVGnExYWrk4JiTJ3CUELVMJIE0oshFlB7ChTC1dBoIIosQlNgTReAEWQ5BZH0pcPzo4KzHJOy9Nx/DVjNEzZiDk9opqQrZGnCw2Cdj2j4aq1vJr1GIGifPwcnlTB2cBAZLKjIAlNFmsllg2TL+u5WDAwBDQxV1cF58kT9+9KN8N7wMUXPi4JS60KdcYCAwfjySCf6/kT73HBzPQtRyOYQZ38hghDc+ltQ2EFLyc0KNWjsRAueDDwAcbF9oQAgksVCoEDjlOThSoYESHBzdYp8lOjjR5hhyUGZ+ihA4jAHbt2t/e+XgCN1m5eCIEDUmFxkIUpEBwiNKDVEDSsvD8YuDI++zlw5ONsZHebkG9/JwdCWiAd3oJoakrwWOLwa8isDZgIkFBY6XIWpmDk4uBwxt5we5P9CoH7FKAsfqPMhtTwgc8Zz8GhUZ4OS1+d5eLWnGgYNTqMiAvI2i+eAD3h4TCWDqVN1LL78hJWIPDlbUwRFj4iOO4I+VcHC8CFGTCwxgwgQkG/n/Rgfcc3A8C1GTPiwZVe4/0k01kuX9WLgx38HRCRyLQgNC4OQUgROM8wskUozAMXFwLBf7dODgjBwpCRyHDk5OcnDCYaChMaD19UUInK4u/S4b/9WNhT7TaX36n0yegyN1MLQODuEZpRYZAEoTOH5Z6NOtlX8FVg5ONsafyMY9FDjS6CaKVN3n4Pi+TLQUomYlcIzHwouKVGY5OAAwsIUf5L5gi/4fHDg4ctsTzbKgwBnGIWp5Do4YYSQSvGEoAqdYBycY1MqLlzzuEOFps2bplMuWLcAxxwZ1eWOVdHDEmPhDH+KPlXBw1OpdhYoMiD/6+goqS90aOOPHI60InFh/eQLH6JR6LXBSMSVETSdw+O9mAmfDBiB9gCJw3n7bNAk4aHBwgsLBQdo+Z5gxLUTNzMFhLjk4DgWOcLUygSgCAX5ZD0LpbJUxghPk8DRAP34xLhVS6nUgxlSBgKoNVYw5OCRwiIpQLQenkiFq8kBMCJyBgfKcl0o5OEwROBkXBU4mI1VQA7SpcvDn/ezg9PfXbT/K6e9XB7HrMQkzZvA2IwQOGzQPUfOyTLRY00rkzAxuUxycUKv+H2SB02U+ypD7H6FbLAWOsciACFHr7kZ7lL/md4GT1+bl/BtAV2TArBqalYMjf2bJ/YFF/s3773OhPSiJ8ko5OJkMsGQJ//2gg/ijl/ehYhycPXugr69bIA9HtwbO+PFIN/FzHh8sT+DIQtgzB0c62JlEU94Gwkp+TqRJEzijR/NbFWPAutbZvLFs384VswGxxg6EwFGqqEWQtq8K2N+v7YeJg5Msw8EpReAIBycX4vvf0CAJnCIcHPk7GP/VpuJ1UYj8m/Z2/XUMmFRRoxA1ohL4PUSNMb2oam3Vrq1yXByvFvpMJMB3WnVwuPhIe+XgxGK6pYbrWeDYlYlukcyEuh70KuFp3WhBL1ryHJzcQHVycMTsIgAkt3N7pj9iLnAaMaCKICNygQGRpiH6pIIOTnOzakWMSvGBX12fawdYOjhiCrVEBwdwYS0ciwpqovCVHGpTKQfn/fd5u21u1hwBL0PUVAcnW1jg9PcDaRbWTkaBMDWjwMk0KwJnqMuVfQY8dHCkssoBpY0GpBtplOULnEBAO2erNyeAffbhf5jk4YgcHmYMUUPafhwtJgiiUaCxEcmkXmeqOTg2AsdVB0cpE511WeDYRaCUK3CM4WkAOThElSi1yABQXohapRwcubOOxfT2aTkCx9Mqaum0GkfP4rwzS8c8Ejji7qU8+iFEzay9RiLaALyuw9Sk8LRwGJgyhY9hRbhPpl8ZOFSgTLQ8WQFoxzezix/gwYghRK2xUX2Obc6fdQX0iwuLpml0cNTwKbNls5UwtfbBYSpwbBycYnJwAK3tlNxmLNbAEQJHHahJIWpeOzgLF/LHAw/UQvAqsg6OjYMjFRrkfZPDPBydwJkwAdkW/n+JZHkOjny8vc7BSSKmVkoTlc8AIGri4ADO83BEDo7onGQHx1bgyPk3gYAuMR+QcnBsQtTcdXD4ccqG+IfqQtSKEDh2IWqVEDh2OThMETiBLAkcokS6uoCvfAV4/HH98353cOTPFttzIw/H0ypqUsfFEtzBSUWVEYgYkZRBOi2FqImDIgmcenVwCrVXX+ThSBXUpk3TyriK2fBsf+UcnJShCQnHJdfFVcWg0cEB0NvMXZzAVnOBI5emN4ZI5ZWtNxYZANQwtbY+npvgd4GT1+bFoFgMkh06OK6HqKVSwPLl/PdCDo4Uoua1g2PMvwG8c3CyWS09JGjj4IQl00ZXaKBAiNqe3Uzn4AiB01CmwHHbwZk3D/j97w2pMpIgUEtBp7UNRJUJuGizA4Fj4uCEs3qBE5IEjm17ssi/EctiOCkyYHX/kYsMsCIdHC9D1Crp4IgQtYA0k5GjEDWiXB5+GLjvPuCGG/TPV6vIQKUcHDuB47CPMcVTB0ckDwYCCMZ5h6oKHJdycHQhatLjcBA4dT3oNVRQA7grmQkpAqdKIWqA5uCwLq4gk7GWvP8ZaOUCJ7yzsIMjzqMxRE09vzYOTlP3MHdwhE1dwMHxLERtxQre2FpatNwohXXr+KNZiFqlHByRfwN45+DI+60WGbCY3i+lklpm607ExERVZydYm1JFLZcseo0U3efK+x0qX+Cccw7wne8Ab70lPakc7BSiCDfyDYjFPeX7U6w1rvusUgVOSQ4ONGEwbhx3/CzLREuOlJ2Dswt88JHb4XB21SUHp5ZD1ISDA3JwiFIRIsRoALjh4Gzd6lx8V8vBCYe1kIS6cXAaGhCN8cztZKRyIWr1LnDMqqgB/nJwZIEDAJlI9XJwjCFqgV6uKpLxfAcn2c4FTnx3YQfHKkRN7Y+MRQYAVeA07OECZ2CgbkO7HeHUwal4iJpcYEBUn1AwC1GrRJEBxirr4Mj9aCgjKXcTTBf7LCBwglt4G+9vGg1Eowi0NCODkKP/tUPuRwOB8gROfz/XuoCmveUPSyKGiFIpLaAco+RgDlHwnbB1cMRJXL8+b7Yyz8EpNgfH4OCMGcM/qhwHp6EB6IkoDs52hw6OcjJYmQ6OMUSt0g5OXohaRMrBEYt+ZsjBUbnpppswe/ZstLS0oKWlBUcccQSeeOIJLzblC0QDN1YWLEfgjB7NRUM2qzXuQlS6TLQxjAZwX+C46eDE49BOUiKh7vdQ2F2BYxWiFkPSlzk4gE8EjpKDIxb5FOQUgcMG9GWiq5GDE+rnBzgVz3dwMqO4wGnotndwzELUUsYxopmDo7gF0e0b1adciOqsWZw6OGYhatms1u+47uBY5N9kMsBG5dTIIWqVKDKwYQMf94fD+sJulXBwghnjidJjWiq6gEiJ7uACZ7BDafOxALrg7H/tME6OlCNwxDqvgH5QzYY0ByfSpHdwhrq1ExFrsRY4rLkFmDGDP2HIw4nkrB0c2/ZkcHDEuEkIHCcLfVo5OIEAkB3BBU5gt8PwEeWg58KagzMAHrbOBop3cMQ90AsHx2qRT8A+RE1UUaMcHIkJEybg+uuvx9tvv4233noLn/jEJ3DqqafiPdGxEjqsBI5o6MLdkDuxvAGFgXCYX/iA8zC1SpeJNobRAO4IHGMd+XJvwqYOjixwQh5WUQN85eD4WuBYODi5qLMy0V6tgwNoAicyoAichnwHh43lAqe5z7zDcByils1qF6GJgxPcsknNCfJzmFoxDo6xPK7clXjq4Ehs3Ki1QTlErRIOjhgDz5qln/Dy6j4k96OFBI4u7cahg9O4hyvF9Cje5mMxYA+cLxRqhbFNlSNw5OGYLHByQ5qDE1NcGlH5LNmjnQh5HRyAF1UB+DW9eze0WEODwDE6OIGoViW0Wg4OAJ6IAyA00OdsZlTJwWHhfAcn21e8gyOOn5mDI663Sjs4LERFBvL49Kc/jZNPPhkzZszA3nvvjWuvvRZNTU2YP3++5f8kk0n09PTofoYLolS8lYMjZvCKcXCA4vNwqhWi5qWDY/Z3sZjm4DQ0eOLg6HJwfBSiZlcmGtBKRdetwGEMTBI4YvIS0MqhsqHq5+BEh3i/mjEROMHxvMNoGywzRE3uyOTR+TBb7LOgg2NTZEB0JeGwvn8UlFVkoECJaMC8ipqXDo5Z/g3gXYiaLNoC6SIcHIcCp6mHOzjZzvHqR7shcNx0cKwETrpPcjwUl0aE8aV6pZupoTNPJLQxh10ejnBwAom47nNKzcEpRuBYOTgAEBvdqoUROhmAZPQCR87BSfc4W+iTMajV4ITAMcshFhOAyaTp2qkFKSYHJ2Cag0MhaqZks1nce++96O/vxxFHHGH5vuuuuw6tra3qz8SJE73etZqhUIiaGAwUU2QAKF7gVKvIgHwDVyZRXBU45YapFXJwBoKVCVGrZ4Hjewdn924ElAt4d2KCeu0BmoODIS1RF6hOiFpDih/gbGN+iFp4IndwOlLOiwyYChwRniYvmANoCe1bt6K9mf/DsBI4RgfHpsiAnH9jSJMBUEaI2uAgsHo1/90gcESBAcC8iprs4Li9OK1Z/g3gXYiaLicwrwSgnlJycNr6ucAJTqgPB0cee4hy9ilEEW/mGxCLewqBM4SYacMsWGggk0EI3K4MxGO6LxNBBtmMzei9gIPjJETNbrzUMTJQVKnogCFELRoFhoSD0+vMwdmzR9vdSZP4o1mImrxWXCljACFwxPhKxrjQZyCcf6EHcuTg6FiyZAmampoQi8XwrW99Cw8++CBmzZpl+f6rrroK3d3d6s8GZTa0rnj3XX7TuP/+ov5NCJz+fr06NzZuMwfHbkZiuDo4RkHjlYMjjr3bAscuRM3vOTh1O+BV+qttGI1Je8fVsFIAYDFlsJjU5+BUsky0KnCy/ABnm/IdnPhULnBGZZyXiTYNUZMLDMiDoFGj+BsYw9QE7/Tq9nw7IK/Nl+DgmIWnyZ9ZdJtZtozfZEaO5ImaErKDU+mFPqvl4EQiKBjvbergFCgTPXKIC5zwFCUHp44cnMyAFKKmODgirEwInFTAxFaEQeCIk7lypXahSzdno8ABgMyQTYPy2MEpei2cjP4CDwSATIR3tBmHAkeM/dratHZmFqImC5xixzO5nHboHFVRk4sMiHVw6rQajGcCZ+bMmVi4cCFef/11XHDBBZg7dy6WLl1q+f5YLKYWJRA/dccTT/Be4957Hf/LwIB27edy+muz0iFqteDgeBGi5rWD0x8oUuAsW8aX7TYhk5EcHB+FqPnewbHIvwGgDmQDyepXUWsFP8CsOb9/bZzeqbynBxmTEAsnC31GozAvMADwZMJOvo0pEf+XinbDwTErMACUEaLmoIIaUNkqart3a+7RgQfqX6uIg5N3ovQUm4OTSgFjc7x9J6a7G6LmloPT16d37ORBdXaAH+x0IKbm2YjFUNO9yiRN0IHAGTlSsyWEgpVOZDCRL3BySZsG7bWDU6TAEaGNLKK1GyFwsv3OBI78HRLaJWfcdV0/UOy1sGePFmFm5+CoIWqSwNEcHApR0xGNRjF9+nQccsghuO6663DggQfiD3/4g1ebqw3EALeIu7axRKBsFYuG7tccnEpUUTP7u1isykSXJHDmz+d38TlzTEcnpg6Oj9bB8W2ZaKWCmp3ACRoETsuCF4CzzkJbbrfueTewysFpAe+bWEu+g9M8vgUDysC2b2W+i+NkoU9diJpR4ABqmNrkkP8X+9Qdk3RaUy0GB8dM4BRycEoOUbPIvwE0gdPQUNkqaosW8cepU7VZbIFX9yFTB8elHJzubmACePtumOFuiJpbDo5xrtlM4GSCUUSUdXCiSoiayM9JBfVr4Ah0AgfILzSgnMgsggjFlC/hVOBIDk46rekdNx0csRaOkwFIwGQQlo3y/jNXpMAZO1aL5jVzcBKJ0gsNiPC0lhbzfL68KmpSiBoVGXBILpdD0svRci0g7kpF1D61EzhG9V6pHBxZ4HgZEmVXRU2edSgWo2NTroMj/l8XoiY5OL1QTlAhgbNjB/ClL0HtnU1G88M9RK1uBY7i4BhLRANaMm0wpQ9R67jtN8Add+CjOx4C4K2Dw2+eTBU4MHHIo7EAtga4wzL4Qb7AcVxFTQgcs9G5UmhgHIaZgyOHNIkRs02ZaLtFPoEyQtQsSkQDmsDZd9/KVlETY19jeBpQ/kKWVhTj4BRbJrpr8wBGoAsAEJpUOQenmORzY0FbedyRHVTcmlAMkSbFwWH8OZGf48jBAfLzcCSxoQpmWSAMWQgcxnQOjkjMD4X4mMENB2fkyCIdnEy+gyMEjtMy0XKpazsHJx4vXezbFRgA7B0cEjgmXHXVVXjppZewdu1aLFmyBFdddRVeeOEFnHHGGV5srnYQN/YyHBzxEYCzHBwvHJxqhqiJyU3GCoY5F/xcq7+LpVCIWi9z4OBks8BXv6otNAGA9eQLYV2RAUOIWgzJunVwClVR84vAMXNwRChGMK13cEI9fKDTnOWPXpeJbkQ/wmKWri3fwQGAHWEucJJr8jsNeRxoDFHTpTHYOTiKwBmb8b/A0fXRYnDW2qopBZsy0XaLfALuOzjyGjizZlW2ipqIXjIWGABqy8HRFRno7ra8YNPPvwIA2B3sUG/eXjs4jBXXfxgFjq5M9KBwcGKag8OU5xSBkwnZC5z165W26UTghELIgYdLWjo4PT3aF2xvV52PUaN45Gs1cnACSg6OKHMNACxenMCRQ9TsHJxyomoKCRy7dXAoRM2E7du348wzz8TMmTNx7LHH4s0338RTTz2F448/3ovN1Q4lhKhtMUyUmjk45Qqc7dud3QjF9lqfvBdtbzyte84LzARONKrNWpYapua2g1OoyEBPzoHAufpq4NlnkUs0qGFAvZvzBY6uTPQwqqJW72Wis2u0EDW5RDQABBv4nUsslqcKnH7eTzRlunXPlwtj5gJHuDcZhBBuaTD9390x3mlkNpYRoiYXGTCiCJxRKf+HqOnGzcb8G6DyDk5PjxpKaVwDZ/Nm/lmRCLDXXoWrqFXKwfEqksC0iloxRQYAy86q7T+3AgCe7fiKmucUjcKVhT6tHByguGMkBM7MmfzRbB2cbCiKqLIOTgxJLqIG7AWOCLXK5ZSmJgTO0qX83mkmcKC5LyxlcYMTEwSJBJBI6EK7AL5NK4HDPBI46vpJsgMVU/rVweIEjhyiZubglCNwxFcZMzLLhaZhNoUcnCK55ZZbsHbtWiSTSWzfvh3PPvus/8UN4HoOTrlFBkaO5B04Y9qFZEU2y39m4n20X3g6Oi/9IgLIVVzgAOXn4VTawVEFzsCA+TTao48C114LAFj7w//DFvBZ8oFt5g6OH9fB8XuIWnYtd3C6miep7VegCpzMEBjTBoXBPt5PNGbdFThyPyHn4IgCAz1oQTxhUnsYQHcDb5vY7EKImk0OTvuA/x0cncAxVlADbHNwCjk4JRUZEKPaceP0A3Vo4WmTJ/P7TSWrqK1cyR/NiqyK9ivuT25RjIOjKzIQjfIkJcBcqOzahTGvPQgAeH7auerTXldRA0oTOIceyh/1AkfJkwnHVIEj7j1C4GTD5gInEDCEqXV2cnsilwOWLLEUOJkAb9CWIWo2FdQA+xA1NuRNkYFg1qSUrTJpERhyN0TNDQfnK9v/CBxyCPCXv+hez6uiFjYROOTgELocHGPMgQXFFhkQcbZOBI5UtKhgmJroHI/HM/x/+3oxDpuLju0thkoJHK8cnDyBA+QvZvTBB8DXv85/v+QSvDf7dDVnZ2hnvuNTaB0cv+fgFHHp1A7ZLMLb+YA9Nj1//S4hcCKZId06D4FeReBkFGfFJYEjt38zB6cbrabJpgDQ18w7jOA2ewfHdqFPByFqrX3+Fzi6Y+Kyg1NSiJqD/JspU/Qrsnvt4CSTWns1TgwA+nuDm31fKTk4Q0PKvcSuVPQ99yCUSWEBDsKeKZolJYeoMRcdHFkkOD0+PT1qRK2pwGGDQsREEVPWwYkgg6GBXEGBAxgETiCgLzRQQOAUdHBMKqgB9iFqssBx6uDkdjgXOLoQtQbe5wUHnFVULRSiJsYubgicg3bwqBw89ZTudeM6OGYLXtE6OIR2V2JMn0xjQzEODmAxoLDBaR6O6BOOxXPqc1OwVvea25hVUQPKFziiUxBVUN1ycOJxmDo4femY1inIYWqDg8AXv8hvhB/5CPDb32LzZq0oQXJnAQfHhyFqhaqoMebKckKVZds2BLMZZBBC+36deS+HGrWKQ2KNCYCpAqfB5RA1Rw6OeREkDLbx/Y/sdObg2AocmyIDTd0bATBfCxzduHkTF3S6tWdccHCKajMOKqhNnsx1aaWqqMnn3+y7ygNSN6MJinFwmpu1e4ltJTXGgFtuAQDcinN0gs1tB0ec/0Cg+EIMooLauHHaBKg87sglRYia5uAAQLInidwAv7nmnAocQJ+HIwkceRwtBI5lDk4FHJy2NmB3gAsctt15iFogJhUZUNYXiww4C0WQQ9S8dXAYJuxQ8qDkhVdh7+CI34MUokboRmYO79zGHBy7IgOARUiIDU4FTjIJBJHF0XhBfU4IHK/C1MyqqAHuOTjiuHlVJlpdoyEV0O7Ochu4+GI+azVyJPDvfwPRKLZs0QROaneBHJxhFKIWj2tft+7C1JS8hk0Yjxn7hPJejjRLAqeP370a0Y+AYo02pt0VOKK9h0Ka7jY6OFYCJ9XBRzyxPaXl4NiugwOoHVIonUQ7dvta4OjavEg0mT1be4MLC31a9gfJJPC5zwG//a32nLwGjgGxJsqUKQaBI4WomTk4jJXnuIprvblZ//kCub/wQuA4cXCCQW0CxraS2ttvA4sXIx2K4R/4qi4aUXZwAqoVVDxmE0XFChy5GYhBtS5lRBEEuUhMW6sGQLI3pYqFbNSiA4FzgVMNB6dQxEu2jX9+YHdhgRMycXByzbyhRAcL38RyucoVGejEFjT2KaXntmzRDTyNOTgUojac6O8H7rkH+MlPCr+3BIEjHByRLGdXZADwTuCkUsDBWIA2aBfmVKzR7YfbeB2iJo5bOSFqmYx2M7QqE51MIl/g3HILcOutvNe8914190AWOBkTgWMaouajdXDs2mvd5uHYLfIJqJWIACDbzxujWq4ZQCLtTYiafF3JDo5diFpmFBc4Td3WVdTkhT5N+yO7IgPxuLra3Hhs8rXA0Y2bRakwOZPeZqHPskPU5s8H/vtf4HvfAx5/nD/nwMGxC1Ezc3CA8tqtOP9W63oHAt4UGjAtMmATv+RoLZxbeXGBN8d/DnvQrhM40Sh3TkW1sFJdHLN+tFiBIyIVLQVOUgicqG5D6b6kmp/DIiU4OEuWqA3bSuAUdHBsBI5lmeghrSpcsMCIl3Xwvik4NJgfbq57I0MopwgcycERDTk61FMwtn/PHu3aGT3auyIDO3bwsZ0OycUxVlEzC1ELUoiaT8lkeA7FNddALb5uhSxwHKyFIyt40SmYCZyGBqgXpujEdGVZbSjGwZHD0wBgWmCtbj/cxiuBIzoIMWAuZ//ljt+qyEAqBb3AWbAAuOgi/vc11wDHHqt+xubNQB/4e7NdxYeo5XLuJttWikJlooE6rqRWQODEEwEMgZ9LUWZVL3C8cXCsBI5diBrr5B1Gw9DuvAvHlYU+ATVMbQI2DguBE8/2A8uX8z/kWsiqgzOEXFY/GCo7RE0eQJ97Lt++uNmYZPPLAscqRM3MwbHdBweIa1301WaoTrnXIWo2nZMu7cZM4AwOAv/4BwDgkdG8uIAscEIhAIFg2ZXUzMSm2wKHKR+Ui8SAQABJxRlJ9SZVscCiRQicKVO4QkylVCfTKHCyTh0cmxC1QmWi7fKGBPFRzUgJoWQ3AJEafTCmtRtRfj/EsvYCCdp3GDGC77+XIWoH4R39k5LAsXNwxO+Ug+NXWluBffbhv7/+uv17i3Rwdu3SrpMpU/ijWZGBWKzAjKkNJQmcww4DAEwtQ+CsWgWcfrq2SrXVNgGXBM7gIJ+tfPllrdy1ctMsx8GRO/54HKZFBnQOzvr1wBe+wJ/89KeB//kf3efJDg7rNS8yYBWiJp6vRxfHzw7O4AqtRPT06fmvx+PagFE4OK0BTdwmkvwLuyVczXLbnIaoRce2qwMaY4KgXQ6O43VwANXNrCUH5+mngZdecvczxbEZtWUxn80VFaUEYkQDIJjWd7JlOzhyAvzWrcCpp/Lfp07NU03ZrFY9Wjg4Thf6BLx1cIAKODgFQtQABw7OAw/wjmvyZPw/fAKAXuAIJ6rcPBy3HRxREE4edwSSepcmFVAe+1NqyWU7gTN1Kn/s6lK+ZiCguTivvQbAxMEJFoi5dODgWAqclHOBM3JUwFklNWkbssAJtTQiA+VCKXAjkyuoAdYhakFk8cX7voiLV1+OMNJFjccYMzg4kyfzRxMHx1TgqA5OHc6qggSOMz78Yf5oJ3DSab0ScHDnFg185EitAzXLwZEFTjLJnR8R9+yWwEn3JfEx8AXKcC6fgZpcRg7Or3/NI7Nuvtn6Pa4KnKef5vHmV12VJ3DccHBiMcVFM3FwdALn8sv5dOi0acAdd8DoiesFjnkOjl0VNcD/Aqdag95f/AI4+eTiB1IDy7mD09M6yTIqSwwYRZLuiJD2JWMpb0LUjNVLx4F3Atsx2lLgtLYFsBVKvKwhQdBsoU/bMtFW9oPi4IzHppqomtfXx+ciTj65jGtr82bgggv4anp33QVAOzYdGywWepFOQjCln4Up28ERAueAA/iARThIJvk3Yg2ccJhrsMZG5wt92u6DA2rKwXEgcHSLfcoi8uGH+eOZZ2LXHt7nG6vC6QoNlLiKdbkOTleXNhaYNcsiRE35IBbhH5wOKI+9SeubtkRDgxZyr7o4ou2/+SYAEwcnWCBETXJwMhlpbRcnIWoORJnAcaloaRvBuNZuGhoD6IGzUATjWj7iXKRSWp+YTAL74T3svfgBfHbtH/AAvqDmcTqht5d/nurgnHMOf3z7bfU9tlXURJEBcnB8jBOBY6yaVoTAGTtWm+w0C1GTE7BTKf31a1f2EHAucCJvvYYEhrA9NJbf6QFMyK1HENmibyyMaWHfdhWxRId8+OL/A668Uo1ZFTcGB6XoNZRaiGzDBvUm4KaDo062miz0qRM4O3fyE/bAA3lrTWSzvFMTAifYV3yIGuBdVTsvqXUHJ5XiSxU98QSweHFx/8vWKTVXJ+aXiAbMHRxZ4EQzgwgj7WmIWjwOTAMfbazGXpbjk5YWqOs0GQWOFyFqRRSc9IyuLn7++/u1cZRjdu0Cvv99vjrm3/7Gr/8//AGAdkxGrF3If5HD0wAgEkEuwG/B4fSg7iWnC30WdHDmzAF++lPteZP8G1FgYNIkPrbROTgWZaJ1FbDKFDizsQgf73/C8oNKDc2xw3UHZ8kS/jhnjumSR0BtODjCvZkwgfe3ZgInkNILgnRQCa8dSKnujp3AAWzycJQNWYWoOXFwdu7kQ4VgUE3ns3VwAkU4OCUJHMnBSSTgWOBYOTiANmZJJrXQYgD4DB7BiX86xXGp0R07gHbswhQoF/nZZ/PHDRvUMZPtOjgkcIYBQuC88Yb1dKOxwRUpcMysYrP4S6PAcerg7Nplf4Noep2Hp73e8An+T5EIIshgPDYVfWNZskSrimq3oC//XIbjHrucWz6KbVqSgyNu6Fu2AOBCyQ0HR3Q0qsAp5OAAwF//mj+YAe9PsllJ4AwUv9CneE/NsWyZ7U27UJlooLoCZ/Fi7Vw7XIRaJbadx/ck9i4scISD0xbU9w+t6Ha9TLQxRG0vrAYAfIBp1g5Oq7XAcVwm2q7IAKCGqE0I1MZaOPL5dtzn9Pby/Lpp04Df/IY3no98hL+2YAGwY4d6vFrXWDg4gQCyEWUR2HRxDo7jELW2Nj559NGP8r8/9rG8t8r5N4A+B4dZLPQZCGgip9wQtSdwEn7wysnAjBlcHBqc7WJDsJygc3DKLTIwNKSuVpqddYB66I0Cx41S0eU6OHJ4GgA0pLsxHSsxOKjlxAfSehGTCSoOTl9SdRoD8RIFjoKVg+MkB0c4HyNHam3QzsExCjY7HAsc5WCnEUYkqi2a3NDAQ4ABFOzYjGF2VgJHDS2Oj0YvmjBx5f8Djj/eUZ73zp3Ah7CQ/zFtGp+EE4miSj6UXQ5OQKyDwyhEzb8ccIAizXs0q99ICQ6OGD90dhYWOFYOTiGBIxLY5O2BMR5G8cQT6vta3uIC582WY3mvMWkSAF4quliBIH1sQYHTgV2IppRjpwgcMSuza1cRi4wqd5VAOo0O8FGKG1XU8hwcqUy0LjZc9Ojf+IY2S2JAHH8hcCKDDquoKSc/HqgxgZPJAPffzwdPs2YBn/qU5Vtr3cGRzdmi2ksyieZ+fqcaceAk07ckEvkCpzWg7x9a0ONtkYFwGpPAhZhjgWOwfc0W+rQNUSvg4EwM1qHAGRoCbryROzY/+Qnf+QMPBB57DHj1Vf47Y8CzzyKdBsJIo2mNMrtvFDgAslHesYRS2k7Ia0GVHKImLqK2Nj5geeYZ4JVXVHdexkzgiBC1wNAQMmneCRvLOLuxFk7vngzGYYu2I5dfzgdhIgQAFXRwnBYZMJaJXraMz1y1t6Mr0aneswwGvisCxy0HRwic9rM/g5XYG3thlXp8A2m9o5UJ8ROQHUiqYqFogTNjhq4/sBI4ThwcozAAnDk4XoSopRHRnYtEQhI4RYaohcPaNSX6JNnB2dK+P47FcxhMjOBVEu++u+D30RUYECJTLtuNAlXUyMEZBoTDwCGH8N+twtRcdnAYsy4yIPcBdjPiAJ9pywtTu/lm4Mwz+c3u618HNm5Ey/tvAAAWjFAqfimZglOxpugbi3Rvsi0kkkxCHXTxjesdnGSyYCESDSmmeRw2IxjUBgdu5ODkhagZHZwf/hD4f/+Ph6lYIASOqKIWTubbzLp1cAxlohvCvOMutbqcqzzzDL9pfelLauIo3n/f8u2uCJxirZUimD9f+70ogaNYlYOIY9JBJkuxg8/MJZUqajlllXAvHRyzHJyGnesRQg6DiGMrxlpGmLS2ApuhdBglODgF18EBtBwcthFABQXOo4/yQb4BuY+xHNNkMrwM8N57A9/+Nh85TJ8O/POfvN86+WTe2X7yk/z9Tz+NVArYB+8jmEry2RaRfS0hHJxwRmt0qZQmGsoOURMXVUMDD1cLBPLeahQ4sRiQhKaAxYKGxnvNhwNv4EmcgMB771rsRGFSu6RJnj//mfcp3d28CqVyELwoMiCObzSU1ap7FJuDI0SKVH579x5+fJub8/s6N0LUXHVw9uxB6NWXAQAzsFK9DoJGByekhaiJ1wIJ63VwAE3grF6tPBEK6aIainJwcjnteEkOjlHgqA6OfCByOXWRSicCZ+RIYBccxMgr20ghqjvPOgenyBA1IL9U9NCQ5uAk4y14E4fjnf2/zl9UqnfaoSswICZYhMBR8nBsq6hFhMAhB8ffFMrDcUngiLFBJqO5F8YiA/Lsk8n9Kg+dwFm+nN+gBXffDcyahWAui9WYhj0tSpUN5W5XrIPT3Q3Mm6f9XcjBmQjpIlUETlOTdnNwPJg3CBw5rM8TByeR0J0T1tAIHHOM+Wp1CkYHJ5Yq4OAYQtSa4/x5MSCpKj/+Md+Rjg7gkkv4c3v2WIZwll0m+p57+Kjh3ntL3mU7ShU4bJ1WQW3vmeYXoy5EbTB/HRzAG4Gjc3C28KnUDzANoVDAcmJEzsFh5ebgWNkPSohaW24P4hisjMBZvx74zGe0amIStg5OLscX6d1/f158ZcMGLtD+/ne+JPxXvqIvJCILnCTTZk8PPDCv4AgA5BSBE8loOyFHnlhpxKJC1Aog+hNRYCkQAEKN2gBW5AcZu7Yzc7fhBDyNxv/cVXAbVqR3KiXSI3EuahYt4ouCrF0L/OtfALwpMiCOWzzkLKHVNkRN5N8ccIBxPUodNefgvPKKuthwK7rV6yAkRIyyvks2pDwOJBHM8NfkBUDNyHNwAJ2DOYS4XuCEbBR7V5c2GGpvNxUGOgdH/gyp0RTt4NgNPiwcnMZG5zk4ZkLNWCpaDlFLx/nn9kYdOEwKOgfHKHAMDo5diFqQkYPjb4oVOA7iI8X4wazIgNyZWxUZKBSeJhACZ+v6FHDGGXwjxx3HZzOnTVP39Tkcqw2KShQ4zzyjL3dbSODoHJzFi4FMBoFACXk4ksDpxBbE49psiGsOTjqtjdSlEDXGnIVpCAetYTQXOPF0cUUGmiL8+aoLnHRaW7jw1Vd5/hTAD4TJaNVp1T9bB+eVV3jDMpmBRzLJq+g5tvv07NrFy5oLijGK9iwWa+BMMpugB6AXOBACx8MQNbMcnPB6TeBYhacB+hC13KbCDk5JIWqtreqMTsVKRb/+Om+fu3fnnWD5T3XMwBjw5JO8ZP5pp/GJoY4OXqlx5UrgvPPMG/PHPsY7i82bsdfQe1r8u0l4GgBkY0qImpSDI24liYS1Q++4ipoDgSOKDAgHBwDCDVF1UUrhLhn3ZST4wQru3FZwG1awLn6xpxLKxZ9IAJdeyn//9a8Bxjx1cOJB6UOLFThdXbxjkx0ciwIDQPUdnN27tUnVWbMAvPii+poscIJZ/kEiDC0b0txnIX6CDc4Ezvr1kt6Q8nDyHRwTcSLvOMAnTKLR4kLU5Jt/gcIIQCk5OPkCp9gcHBGiBuSXipZD1NIN/HO7I84FTvemPsyEklZhDFH74ANgzx6qokZAEziLF5sPpMpwcMxycIzXpVmRgWIFzn7/vprbku3twO2385CFhQuBb3wDycYRuBNnan18iSFqIv/GUDTFlDwHZ2hIDXMqR+B44eDE49B/GSlEDXB28xWCdvReXOA0ZIsTOI3hGnFwli5Vet5WHlIiq0mTG7fTnDHbMtHic80GBjffDJxwgia0isQ4Z1FMe+lawttvV/NEy+8mCxwmBA7Ld3DcWgfHtNDRB84ETkMDsD1oX2RAzsExroMTDWa0HbASOIFA5Rf7VMrTAsjrVPIcnLVrgaOPBk46ic9yNjUBV1/Nj+F3v6tbvyaPeBw46igAwEf7n86fPTWQi+aHqBUqMAAUEaJWQODkcuYCp7EpoLZZsW9GB6eD8eMY2rnddhu22+/iJz/bJNWJvvBC/uUXLQKeespTBycWdNY5mS70mcvx+77k4IimZSZwqu3gCPdm0iQl9NFC4IQy+jybrLIeTnYwhVCWvxYq4OB0dvL+J5uVIqkMAkduT8LBYWYNusAaOIBNkQGp0QSihQdMssBhDnJwjCFqTU3OQtTkRd6dOjiZBu7g7Ak7FziJlYsRBENvc6e2oREjtHDZd95BKMSvbbuFPoNUZMDnTJzIpXY2q6shruJyDo5o4KLxlevgfBwv4qj5v+JP3HyzOsBAczNw882483e7MA8fK8vBkctDf/7z/NFO4KRSBgcHKL2SmonAcd3BkYVtPK4bQDrZhhgvjpvJBU5jrjevioJuHRxDiFoixJ9fs6aor+A+b73FHw85RIuRNCudqqATOEjz6i0moWy2Do44v2Z1fJUKRvp4COfI4WlAcQJncCW/e6fGmFdQAwwCR/nw5gqEqOkmpCWBYzeRGQgAfc1c4AR3btdZBE5C1BqYVGzFSuAAlV/sU7RZIK8NyZf1rl3guSAvvcRHTd/5Dj92P/2p/WqUMkqY2idSTxZ0cHJKkQE5RK1QiWjAQYK/Q4GzZQs/d+GwNhEG6EtFi30zOjjtQuDsKl3gBHv5xZ5rlgTOiBHA+efz33/1K0+KDOQ5OMGgbXixzsFJJLTZgzVrgI08lwz77Wfr4FS7ipouPK2nR7fYYyu61etACByxvksuzB/ZUBJh5bVwo73ACQa1MbTaLc+aBabsrNHBySk5OAE7B0c5qKU4OEOIIRorHM/f3q4JnNz24osMOBU4u3drkS6jR2vPmzk4QuBkGxWBE3QucDrW8XO8Z4q+ip0xTC0atQhRC/NrIkQOjs8JBOzD1MRdSQz0Cty1h4a0e5BZDo5xFrYcgTOldQ/uwtcRBONx5EJ9SKTSAd12hMCZgI1IDThr3AsXctHW2Mgn1IEiQtTEGiIuCBwRoubGjVEncOQ/AjyPQYTVFyNwJu7Lp2YjyIAl9XclOwcnFqgRB0cI/EMP1Z5zKHBi1/yId64muTSOBI7ZwEDcAEtcPE9czmJ8U4zACW7g7TcwxbyCGmCooqasg9MsHBxl0Ox1FTWR7VvIwQGAdNsoZBHk8fliNAFnIWqJnNKBhUL2ISHSYp+eC5xcTj8pZePg7NwJrcb9L34B3HADX7yzGJTO71g8hxHo4gdr333Nd80LByeX0+4/BQSO6EsmTtQPmuVKalYOzghF4IR3lR6iFupTLnajeLz8cr5DL7yAWX28AI6bIWqqgxMovAYOoC8ywBi0/k6EzE6cCLS1eR6i5oaDs99+4Imy0iSTzsExhKjlIlqIWiSrtIUCIWqASR5OJILMrNkA8h2cnF0OjgMHJx63d3CSiDkaL0WjQLKJC4jArp3WZVwtigzIIWrMRuCI79Derm96xiIDssDJNfFrZHcRAmf8du4gD+5jmGAxycMxC1ELiCIDlIMzDBDrHNgJHDENVuCuLdybWIx3nlY5OKLBmxUZcCRwGMNR916ASdiAdZHpvMSpCXmDorFjkQrGEEYW0W2Fq3UAWnjascdqg1XHIWoi+dcDB8e1IgPSIp+CYsInRA7OtNnayCW5Ux+mZrcOTgSawHFcPtsLZAdH4FDgBFcs4788+2ze+2wFjl2IWhkCJ5fTLmeh14rJwUns5O23aR9nDk6mn5/bppzSPyhOhqfr4DCmCpzV2KugwGluC2ErlMBwKUzNrEy00cGJZ6X8G7sKKJUMUVu5Ut8fGxycvBA1MfqQLY1i2Hdf9LWNRwjKAHL//S0H0DklByeSLd7B6cBO/PH/7ceFmExfnzZ4dShw5PA0QL8WjhjUGh2cETlF4OzZXnKHFBngF3uwrVX/wsSJPF8UwOdW8MgDTx0chwInk1FuA+KJl17ijwccACDPbNBRUw6OCE9TrlFZ4IRFGJoQMRHFwUmmEM4pDk5TCQIHwOCJn0MaYSwOHqTrHmwFjgsOThKxgouiC1gHFxDBVNJ6FWIbB0cUGcjtse7YzAolAPkLr8o5OMLl3BWQiiBYrcuosFcPH0+xg0pzcChEbTjhxMHpVOLXHQqcsWN5H6MLUXviCXT88rsIIVO+g3P33Rjzwn3IIISzo/dYTgvmLeYcDGJXEy+rk9i21sGGtPC0k082XyHZSGYog3FQRv1C4CxcCORyxQmcdFrXEY3DZu8dHAWn22BMO+fT9wljQJkd7d+aL3Cs1sGJ5PjzPT0l3x/LJ5XieWiAYwdHXgk9IF6XcyIU5CpqeeMluxA18ZklCJwVK/j2Egng5OkrcDSeL0oQj+jnAmfkQdYCh5fdVeLYFQdHFTiKc+lpiNqePWp/tBZTCgocq8U+nZSJ1gkcOyoZoiaHpwHOBY5x9OGUQADvjf+k9rdFeBoA5GL8ZESz+UUGCjk4x+B5TO5bytc0kxHXQTSKQidb5N+ICmoCOUQtmjWpojYwgDjj+xxMp0qq9c0YEBni/xdqb81/w/e/DwA4ZMOD6MRmTxycvHBgCxobte+vy8MRAmf//QHAtopaLAZsx2jkAkF+o7daU8/Bfpft4Ij9VhaA1Qkc5T4TSighakr1MZZMIqIInEhTgU4E5gKn+8IfoBXdeDVylO69QuAEMvYOTjbLK4MBxQscpxEvDSMbMCiKwli5JA7KROe6Cjs4coEBwN7BYc38BrmTKY0rl7O/5yWTmJHiJz32EQsHZ8UKYGAAsViBEDVycIYBhx7K1ciGDXkJuKU6OKKBywKHXXklRt/9OxyFF41LoRQncNas4aU3AVyNn+H5/sMtJyTMwlp2t0zh+7ZjbYEN8TGUWA7lpJM0DZDJWCfCjhjcjBByyIUjwMc/zq/u3l5g9eriBI5hyr8TWxCP5tx3cGwETqGby65d2nEYNw7oC/Dp2YFteoGTSTPEjAJHeQykU2qnXrUwtffe441FTlQEHDk4kYj0+tKlebNjwsHJZg11PBjTh6gZ1U8ZDo7Ivzn0EIaLHz0Bz+I4NG5dbf9PCpk9vWjN8W1OOMJa4ITDQCrAG6MQOI1ZvYPjaYiaMsLYEenEIBpsI8cAfaloKwdHruLFmBSillX6wUICp5IhakaBY+hU8tbBKVfgAHg+coL2h43AYcLBMSkTXUjgTIaiToyDsBJKRJs5OCJEzdTBMU40bC8+D6e/H2hhvO+OdJjkN82aBUybhiBYSQtO2yGuNbXIQAGBEwhYFBoQN3LFwSlUZKAfTViz7yn8ib/+teT9LtbB2blTO0X7TurXJpg+/WkA5g6OWgpalFdOpiSBU5qDk8kAg2jIcwNtBY7k4MimhRw5WqjIQDEOTsfIQOG1cCwcnGAQSMWVEDUHAsfKwRka4v1qKiUtKaDMAPano9psoFB7JiQXvIco0tiNERjxIcMMxqhRvBNhDNi1C9GofYga5eAMB5qbtSWAjS6O0cFJpWyn9a0ETjYLtScaje2mDo6DhZd5T/K1rwG9vWAf+xj+1PA/APJ1mcCstGxX6xQAQPOOwlntTz/NO5799uNVWuRCQ1Yuzughnr+Q6ZzIv+CBB/IXFiwoTuCIG7qy0QgyGBXc5b6DYxKi5nQbIjxt5EjlRhfkAmdwu6GSmnyXMoSoIZVSByJVEzhmBQYA5wJH3KxyOV2SK6CfJdUNegcHteOSzeYX9HBB4Hx2ryVo716LEHJo2eFM4Gyaz92bPWhTC0dYkQkrOTgD5gLH0xA1ZYSxtYGPONxwcOTBQjot5TRkHDo4lQxRE4M5kc1r4+D07k5rnU4ZAuffe45TyyzLCxwaYTF9GBjgPERNzV/cvVtfbcAFgaNzcHImOTjGjlnK1XJKd7cWfhPuMHFwANUKGYmdnlRRUx0cB9P7pot9CgwOjpXAAYC3PswnHXH77fl9WQFKdXCEezN1KtC05DXeXiZOVO+5apGBTEYNrVRD1JQN5AaTavh0tLl0gQPkhzvmwqLIgMmXkBwc0cw6OvTHQOfgyIsIluDgOFoLx6JMNKCVcw70WAscqxA1uciAaO/iGhEzgMkk+EACsM3D6X+F598sDByE1jZDuHAgoDXS3bstHRxx0QeF+KkzSOAUi1WYmlHgALZr4Yhxg3i7NmZmai/Zjt2lh6hddx1fo6SlBYG77sLY8byhioG2EbPKS93tfIa+Zc9amw1x5PA0QD+IshI4Y1P8Bp0bp8x+S3GhJTk4HR0YbOaDmE4lTA3w1sFxmoNjPN9DYT49O7RTf4PTdfDGk59MqqZJ1SqpiWRtOf8GKN7BAfJm1gMBi8U+jcJF/gymXS/o7XW2IJGEuIyPzz2lPhfvcTYbve0tLnB2xCeareGoQ6xYzwaHEEUSUaacZw9D1IwCZ3tTeQLHzMERz6uiSggcO/sBUIXdWGxFX7eHN89MhlftA7RFOG0ETgdTZkRDIfM4IwcMDQHvrO/Atfghhk79snbPMIHFeV8SlXJwinZwAH1H6ZKDYxuiZuyYS3Bwenq02emAMQdHoAzkOrDLk3VwnBYZACzWwgH4gdlnHwD2Akdcj8snHQ9Mn84PwD33FLXf5QocXf7NUUepg+YW9PDrQPqQcENUt+PZIgWOuFft2aN12VYCh4kcnAIOjpXzoXNwAO1AleDgjBzpYC0cizLRgFbtLNRnPXNjFaImOziiHxfXSLCNf65TgZN9k08gLm842DwdUhI4uhwc6UIPRhUHh0LUhgmiGs56Q3ljReC8v6UVTMxe2kxNGh2caJRf9E3oU0sldmCXbZEBywt2/nzgZz/jv//1r8CUKWrkXCGBIzs4vR1TAABtXWstvwfAJ+OffJL/ftJJ/DEYzC95aGRsmg8QcxOUClSlChzpht7fwgdmY7ObXXFwhDgq18FRS0Qr5yEZ4dOz6d16ERxISR9k5uBM5jNTVXdw5PwbwJHAaQoN6tWmSR6OaaEBo8CRB6i9vfqVZQusHi3T36+lE81Y+7T6fLzX2WCt5z3eB/S2WYenCYTAwdCQFnIA6Bwct9fBUfsHReDsauECp1CImk7gSB2GWQ4OoHdwommHDs6YMcgFQwgji8ju0qtwFeT99/l129QEHHEEf86mitoYKPsyahQKqlYLVq3iuvuG1msQe/Be25ko1cHJFefg5AkcOVRFXAMlroED8C5OhKgJB0c3KHVB4MgOjnrhG/HYwYnnlD69kOqHjcARa4HBmYOTTAf5Wj8A8Je/FFWgodQQtUICRw1Rkw6yWgo6JqqopVSB4yREralJM03FhJylg6MInGCBHBw7gaM6OIB2MMp1cIoMUQO0YgDB1JDlSbH6HkYHJ4IUElBy3YoUOOF3+cTOunaLEFnpnq2rokZFBoYxYuRtjEFW8gl+9KsmtYpGMQIH4DeVdmifW5KD09vLq89ks8BXv6pWoikkcPKKDADoGzmF70e3vV2wYAG/vzU387VDBXaFBrJZYAJTROIkZYAoYtXfeQcd7bzTL1bg9DbzLzoqs8XVhT7LzcExOjjJmLnACSorRTOxABKgOynTJvE7RFUETjKpKYISHJyRIcNrTgWO8TPlv43XYRFham+9xQd508cNIP7Gy+rzDf3Wcc0yqdVcoGc6rUtEC0Q5YJZMagKnsVE9bpXIwdk9wpmD09ICbIbSYZiEqBkdHLlPcixwQiGkO3jn19S10f695SDa2CGHaIMCGwdHFThlhKcpaxVjn33sC8kBAIvnFxlw4uDoQtQAvcBx6OBs28bPXSikLYsm0Dk4zEGIWokOjipwrNYY8tjBaUrqSxDboRM48rFV8m9yOa1rsioyACjX0Vln8fvIkiVaqWkHlOvgzJ4xqNnWRoEzwHQCJ5Lgg9uAWA9nKIm4MtgutA6OwBimZungiBC1GnBwHAkciyIDAPRt2WLCrVCImnBwmqGND8Ij+JjBkcDJZtG8ehHf1riDzd9j5eBIJycYoSIDwwvRKAwdfLaHT7v1oQm9gcICxzjgBbjA6YD2uR3YVbzAuewy3ptMnsxnhxRKcXAGxnCPua1/k23vKcpDH3ecXiDZCRy5RHRQrCGy//784tq1C6OGlBXiuxxEHUk39O4mReCkN3u30GcZOTjifGfivLPK7DE4OEqImlh7AIBe4Eyo4mKf777LG197e/6Ur7gubAROR1B5TZyYVavy3l90iFoZAkfc5+dOfUl3ApsHnA3WQpt4Gw1PK+zgMCVRN5gc0ieNSgMMuZ0z5miZA1PycnCUEtE9He6FqAUC2n1Q5+CkHBYZAJDtVAos9G4q+N6SkR1Hi8kpuciA2wKnICJELad1kk4cnHi6F+2QrgMzgWPliiiISZIJE/IHnLLASYDvm62DU2YOTrUcnMYh5XuIAaMNpkUGADX/Rq7+aEzRAQzhzCNGqJOP8n3aljVrML6LK5VSHZxDs6/zN44dy8PklOMeRhbp7gH1Q+SFMcV6OBhK5q/RVgBXBI6Jg2MM7YpEgAykD62ig9PQHEIvlNkJC4FTKERNODjq9dHQgGhjRPtKosKC1f6tWIFwagD9aEBy0gzz9xgcHLMQNbXIAAmcYYLFTbJnsyZw+oLuOziOqqjdfz9w2208tOKuu3SzTKU4ONmO0RhEnC8QusF6LRxj/o2gkMARM5DhqYrAicXUm8WINVoCesGSyLLASfCBWUdSC1FLpwuWi7fErTLRRkGbSfDRS67HwsGJmgucyZ1VXAtHzr8xTk07KBOtCpzx44G99tJ/poIjB0e+9soQOKLAwIlBnn+TiXHh2jzoTOA07ebtt2VWYYEjygEHU0ParJwkcFrQg2xaa6Q/+xm/hz3zjKNd0aGbrEin1XDa3lElCJxt29SLx1jcRC4VrfZJKYcODqDaBiMGNuW35Ucf1Wcnl4oQOIcdZjk55baDI6r/zpzp4M3KyYjlinNwGnYaQqRLcHCs8m8AfYiamLXXOTjKddcPZbKnzBwcS4HjsYPTOLhTtx07LIsMGCqoNTWZh4/nCRGlwikeeMC6+o8gmwU++lH8af6haEF3UQ7O9u18HBwIAFM2KOWhjzqKP9HYiGxAObHd3aaOR1AROMHUIMIijMlBSB9gLXCMi8aqAidrEDiZjHYzsHFwAgEgFgsgaSwVrTxW0sGR18IxGwPmctrlUsjBkSfDdBEphRwcJe9wIT6EkWNC5u8xFBkwC1HTBA6FqA0PTG6SQ0NAcpfk4BQIUZPXRDEKHNnBKSpEbeNG4Pzz+e9XXQUceaRum6U4OLF4AGsxhf9hYRns3KnNhJ94ov41pwInNEUaICp5OKFFC9R7XsEwNemGvifBv2jb0BZdH1zq7J/Y93gcrhQZEOch16iMXnr1RQZCGZNqD1KHM2E071j7+4tYBNUtrPJvAEchau2BPdp7DzuM/24IUxPnXHfpeODgMKYJnH038PybrUd8HgDQkiw8WBscBEYluegfdUjhEDUIgZM2ODiKZRUE08QBNN33wguFP9qILgdn/Xp+R43HERzHOxs7ZwDg52AbxvAqYJmMehM15v6Z9Ulh8R0KFRkAEJrEBU5nbqM+jPStt3j52q98peBn2JJK8XW1AN5mpRu6rKjka7ziDo7Sl8SKdHAad67TP+GywJEdHDUsycTBWQ5FxZWbg2MVouaxg5MYKF7g5Dk4Dhb5BEzCmT/0Ib4OTSYD3Hyz/cZXrAC2bkUsN4SJ2FCUgyPcm2nTgOirUv4NAAQCSMWVPqi3WzdwF/sbVELU4ilpMs4jBycvB0fu60eMsK3gbloqugQHZ+RIlFwmGuDXjlgLx8zB2bVLSxsVOUoCY5EBM4HjKERNqVD6Dg7SldPW4SBETV0HB+TgDA+Eg9PVpbbS224DGnKawOlm9gJnzx7t+pMv1MZGvYNTqMiAemHlcsDcufyDDzsMuPrqvG2W4uDEYsAaKKVQLJI+nn6ajxVmz1bzpVXsBE56T58aYhGYLA0QRR5OMYUGpBv6rhj/oiMGNuv64HIFjltFBtSQxCZl9NJncHAy/EToHBw+PQWAr7wtzmXFw9SsKqgB+vgNg12mChxII4ACAsfrELWNG/k5mRzcgMa1S4FgEDuO5QPqtnThHJxVK5kaYtm6v4MQNUXghI0CJ5FALsRvKA1p7UuLQe6qVY6+jg7dZIUYWUybhnPPC+Lb3wYuuMD+/1tagAwi2BNSbqJbtoAxbXBi6+AknTs4kakWi30u4rHjWLjQehEtJ7z7Lu/YRozgIy3RoSSTurg0cY1PmFC+wGFMc3CchajpSzEDzhb6TFTAwTGGqJnl4CyDUnSnFAenmxXl4HixDk5DvzJALDYHRwwwGxrUcmGFBI7pZJhwcf73f+3bulRSfyR2FuXgCIFz4L4pbbE6IXAApBJKUnyvuYMjykU3y8VRHAocYdQ7FjhGB0cc1NZWIBwuKHDyFvssMweHFXBwzAROU5O9wLEqdQ3kFxmQr4+iBI7i4NgKHKsiAyYhakGw0sNgqggJnGKRZ2727EEqBVx/HUMTJIGTUwavFgJHDHbb2/X9RMlFBn73O+D//T/+AffcY5qcIwscs9AmUwcnBqzA3vyP5583/S5W4WlAgSIDa/ngsBst+tk7UUntnXecFArhSDf0nRGuIFr6NyMS0SKpSi004EaRAcbyc3DQwttIqF8vcFQHx3gDqfZaOMkkT4gF7B2cXC6vPLporyMgOTjiM5wIHA9C1IR7c/YEJQbssMOQmc5HpCPShQdra9/epVa3CUwYX+DdUO9c4axB4AQCyDbxL51IaV9aHMKVKwt/tBFdDo4kcCZN4l2FvD6rGeIcbA1oldTksZdoimYCJ1yEwAlOsFjsU+xzOl3aARDIjqMSjqPutNRuhNZxQ+Bs2cLPXSikDe5sEQ4O0zpJJyFq8W3cwUmJGesSqqiJ/mPy5PzX5IU+7RycpZjF/y5B4AzuGtBCnhzk4HgRohYfKDEHZ9Ys4DvfAf70J3VAKJqUlVYyvVd84Qt8Gn/zZuChh6w3LgmcUdhRkoNz/Ig3Fet5lFYNFtq6LaE+C4GT4L+I0NocAvkKxQLh4Kxbx495wRwco8CR8m8A+zV43XJw9ALHYnbVpkx0IYFjVWAAyA9Rkx1OXU6x3eCIMbW9LMDBZTk4okw0ALhW5rOCkMAplkhEG5Dv3o277wZ2bBjkChdc4OzJKq9brINjFp4G5IeojcAexKNcNVsKnHfeAX7wA/7EH/7AS1aaIAbW/f3mu2UlcO7G1/gf99+fdxPLZoGnePqCWh5aRugA3ar0AiUvYFPIEN5z4IF8MLJ5M6Y38QNVjIOzI8KVXFPfVgRYruxS0W44OF1d2uviPASFwBk0d3DypptM1sKpqMBZsoQ3vJEj+UquRuJxrXc2CBLRXltzksA5+GCeK7Zxo3ZBoICDI/x8+fON4sehwBFhlZ+KKA34hBMQHMPvBI2sX62KaMXOBbz9dsXHOprNDDYIgZPMW5k618QfE2ltlC+uUVFyuBisHByniHOwKasVGpAHT5EwA1Ip08WHQ4POiwxYLva5Wlpo9d13He93HkI8CzFtWNxOIK7xiRPLFzgiPG3aNEdLq5jm4DgJUYtt4+1vaYiHR5Xi4FiViAb0Do5pDo7Rwdm9u2i3Lb2TX+S5YEjXp+pQBnLt2I3UkHszyGqZ6L4Sc3ACAeCGG4BzzlFfL8nBicWA887jv9sVG3DBwfnIkBKe9vGP63IoM42KwOnXh6gZHRwx2E4FYoXLAyqMG8f3L5PhXb0YI+fpo0gBB6e93TZ3BbBwcJRroRutjh2chgagN8rbQ2DXTtMOmKWsQ9R0OTg2Do7ZdzAWGTALUctmgewIG4Gzbh3Q1YUUIngP+zl2cOxC1AAUvcZcLUACpxSkPJzf/haqewMAA2jArrR9iJqdwJEdnBByaqdiVmQgEUzySizpNPC5zwHnnmu5y42N2sDFLEzNKkTtbRyKdxsP52/4v//T/c9bb/Hrq7VVW2LC+H0Ai3VwNnIHZ0vEMFhubFRjOz7EuM1ajMDZHuC5A6Eczx0od7FPNxb6FI7diBGaBhAlHyNDUtsZAKJKlRqR2Jm3IcnBqWiImpgNNyswILDIw1EFDpMETlOTNosoLfhpK3DEIN0sRE3cZYpwcILIYv+tz/InPvlJxEY2YwjKcZcHjCb0LuXtt7+jcHgaAAQSWihSvsDhX7oxk+/g9PYW3JU8dDk4JQgcMX+zkWmlouXBU/y0TwOTJmFEiH8P2cEJDRVRZGCCFKLWLQ0iZIEjRmelYJYzZhL36maIWlEFBgAEGoSDU1yRgegWrk4WBJXvVmQVNcbsBY4xBycYNFz2yvFbiRlcoBj3wQHZ3cqAOd5i3aco5yuMLKKDzte4KoS60GdviSFqJogmVUjg5AmRb36TT/a88IJ5e8/l8gSOUweHMSkHZ6MkcOSPV/qf6IDewRFjDlESWvRb6aCz8DSAfy0xIffBB9YOjui/QzYOzp492v8bc1cACwdHUUTbMdqxgxMIAKydt4dAOm06I5xLWhcZ0OXgmIwBrSqoAXoHZ2jIXOAAQLJZEThdXfkTC0pbWRbaH2lEHTs4ZiFqOgeHBM4wQaqktmqVJnBYQwMYgtidtRc4ZiWigXwHBwBGMD6AM3Nw9tv5IrBsGbecb7654KyKXR6OlYMDAP9sV+KE//Y3XSMX5aGPP968ZLVdiFpoI5+B3BY1GSAqeTizhviFWozAGUhHsAPKFb25/MU+dZpG3MClMEUnn292viMj+OglKiVubt4MtQxnMGHh4FQrRE3k35iFpwksBI5oMm1ZwxSnSR6OaZlo8XlikG4WoibibBwInHSaf51D8DZi/bv5Rj/8YcQTAWyHcucsMFjLKCGWufHOBE4wwRtKjJkInGZrgQMUH6Wlu5aFWChB4MilokWfE8MQAk8+AWzbhn2z3F1JpbSZ2aAQOA6KDAgHpxEDGNginXC5elqpDs7goPa/op0Btg7OpHEZjIJy3st0cBzl30ATvnElRC2X08xD24U+NysCB0o+XJEOzrZtfAAVDObnTgL6ELUEBvXujbTgy06MxGCj0t8WGabGuvg5zzTYlLOOxZBJ8LaUGCh0I3CO2p57i3dwrLqYoosMCCZOBE49lf/+17/m/+MHH+jGEqOww7GDs20b369IIIPmxfP4k1L+DaD1P9FB8xC1cIM+RC0Tci5wAH2hgaJD1EzWwBkxwtwdNXVwlDa5A6McOzgA0DS6QasQaOKS5JL2Dk6pIWrGIgNyGXWdwGkYoY33jGHaSv7NW1k+jnIicKwcHLEODgAKURs2KA0jt3M30mnJwVFu6oUW+rRycIxFBgCgLcs7dbMiA2P7lcHLEUc4moGyEzhWDg4APBI/jd8ANmzg5VsV7PJvAHuBE97CB4jbYybhTkoezrTu4gXO0JB+kcJyHBzGDALHZMrTSQ5OXv4NgGgHH73E03qBEwX/oIBNDo6YEauag2NFAQenJbtH/z6TPBxbB0d8cTMHR9xFHQicxYt5e/hsXAlPO/ZYIBxGPA5N4BQYrEW3cIEem+6gghq0ELU48gUOa+aPjVn+vDzIBYovNKDm4ERZSQInFOJdmSxwxGfuF16BgJJsOibARxxyCGpwsAgHJ5FAT5i3hdx6ZbHPri79DbtUB2fRIj6aGj1aP4I3EThi//dq3YkgGLII2owK7CmqwAA0gRNTwsDk826pEdNphLbxTuWNnHIN7drFGw5jjgSOvAaO2eSUMURNNyDt7lYTjnejHf1Nzq6ZPJT7Y7bZfr2eTAu/tzUM5g80SyWTARIYQCil3BiKEDjS19dRUoiaQBQbuPPO/HGD5N4AxTk44vL5zIQFCPT3875XqfomYEqnG092IzuYH6IWaeL3ogal2ESmCAcHcCZwnDg4dqFdgL3AKcbBUTZnWyo6N2RfJtpJkQE7B8csRC0c1jRNMhPSGppxMk7Kv5GjcvMQ9+GeHsTDmcI5OOTgDBMUMZHdzi8+WeCEw6ULHGOIGgC0ZKwdnNH9xYWflOrg9KbjwDe+wf9Q4oS3b9fGvMby0AI7gRNVYsh3NZjMgCsCZ9y2IkPUWluRTEoCp0wHJ5XSwm8TsZwmcKSs3GIcHHH8ASA2kgucREYTOJs2wXohNQsHpyJr4QwNabPhThwcw4ySaK/NRoEjOzjKF7EtE20XolaEwBEFBj6T4OWhccIJAPjNRbh/ua3Wg7U9e4COQecV1AAg1GgjcFoUByfLb4YDA/rzWqqD07x5OT+QoVDhygIGjIt9inN4QGip+p7ROROBM1CEwAGwp4GLD7ZRWexTCDLR/leuLG12wlhgQGAIUctmtfY5Mcq/z06MBAvKloVzhIPjNEQt2Kgk8isOjsi/CQZ1kbB6Nm1CIJdDElEsyu7Hn8tmedvv79dmWh0IHLMCA0B+iJpZ/s1QuBEpxNDfqAicIhf7DPTy9i7avxWZNj7QbBpy18EZCWXgGo06chzlOiriPMmUVGRA8IlPcFXc18fXsJMRAkfpL4pxcITA+XSLEp525JG8cUkEJIGT6TdxcBr196J02NkaOIJiBE4wV9jBsRM4diFqxTg4hQQOkxwcs0VynQgcJw6OfK+QiqnaFxqQKqi1t+evOaQiRaJMbO4yD1ELB3lRCYAcnGGDIomzO/jFJwROoKmp4CJPgLMiA91xftNoTusdHDmhd1SvMoXvcPBSqoOTTAL41rd4x/jss8D77+Opp/gg7KCD8kPtBHYCRyTJ7mwwmQFXQtRad61BG/bYC5xMRrvbtLUhmZQGZps3l+XgyP+T6N3OD0QgoJsRLjVELTGaC5zGrHmIWl5vLN0dJ07kuzE0VFLxouJZvJgf51GjzONZBAUcnOa0YYrzwAP5jW3nTlU82lZRk0WMmEIVN0BRsqrgqrC8wEALujGrSymZ+slPAoDOwclstj6wK1dCLREdm16cwIkirVWTU2KQxAxqsyJwjCHfxTo4oi2OfuJ2/suJJ1oncVvQ0qK/jkT/MCuwTH3PKEXgyK5DYI9ysRZIcBf0NPMwtdBWg8A5+GDennI5zRYpBuEKyuFpQJ6DI/dN40L8+2zDGLs1mi0ZGNDmQIoOUVMcHLlEtGXEsVKg5f+3d+ZhclRVG3+r19n3zExCNiAESIAEwpKwBhISEAggKiqyiQgIIiJ+CgJxDwgKLogiuyAgCsgmO4QgCUsWIBD2LJBkJslk9q17uuv749TtulVdVV3Vy3T35PyeZ56Z6enprq7l1nnve865GzAeEYQT5w+2btUFfiDgeMyd6m8A40Kfpei37KDWW0KRfE+ZFql5HIyCvXS+K9VVjs+L1dL7VA5m18FJCJz6eldF8yUl+jBsNcxk5OAoCvCd79DPN91knOEQ6cFz5gBIz8E5aFATOKb0NADw1WpdHKPWDk6owngvigXy4+A4pXYBdHwMDo6qZuTgOK2FI2pwhhBMEhCpYkA3XdSsHBzAhcBpaaG2/oqCtzDN2YgOBBL3oDOO3653NJQdHB8Qg/YB2cHZQdCmaOJb6eIrh5537kbg2NbglKoJB2dz+SQAQGWEfrdqMlDfNTwOzuAgaKrv+OPpgT//OWV6GuAgcFQVpdsoQNxeYSFwamoSn2lfrHQWOPI+rq5OSlHLxMER260oelEvdtrJID68NBmQj3d5syZw0JOI1eUUNScHJxTSdcawpKnZzYabSSFwKqImByccpgWUpPdIEjjxuP6LEPKqqj+WpoNzJF6ET41R10HtdWWBE2uxr8FZv15fpBbj3AmcQIU+65mo89BuWmIGtTxO53KmAicSoYXZah+7mx6QOj25JcnBiVDAtaeqOzgNJgcniAgUcbO1m/Uw0VNDAie0RUtRE/U3u+4KTNXciXTS1OwWpXUQOBW9usBJZxFd4bTV17vKeAKgOzil6AdU1VWDAaFONoDGTrVee7OtW40toh2uVac1cAAau1M5OELgdJeml6IW6KPzXQTYdqh19PnEZF82iEalele3BwvOdThpNxkQnHEGTf+vWaOv8Cu1/BVOs5cuau++S81Udtm4hB5wEDjl0U4M9SW3VRYpaoKcCpxsOji9vYkL3KuD09CQwsHRuqjF/Mkvmo0UNasaHMCFwNHcm+7Ru6MP5akzbbWTtaxXut9JB8fvB4ag/c4CZwdBOylU7eKr8evTbuXlQDec18Gxc3Bq/V0JFb2plARO+aB9ilpdhxYMZMHBSSlwgESesHrXXXjlKfrMVu2hBbYCZ+tW+KODiENBd5XNGiKai7MfVjgHG+JOo61xYZeilo6DI9ffKOvX0S+mnA43AsqqBqeiWWsygCh6t9M/u01RAzC8jQacFviUcRQ4arLAAZIaDQiBMzio7dOuLn02s6lJn5Vub6cDJA6sS4HT1kaB6Hzo7aEFgQCwzUfBmtpqH6x1d8QwBtpBtWqZbYGTwBHfK+N0MzOnv3z0kbdUxMFBYB6eQWDLZroJiokJDxgETiSC2DY6dpNjuoNTHzU6OGN82p07EHBVEwgAA/Wk1Eu3mxycXXcF9tqLfvbaaKCnh4JEIFngmFLUhDgLhwHf1swEjnhLt+lpgO7g+BGHGh1y1SJaODjrQWNRvF6LYmQHJ4NFPgGauVVDNjU42s4ZrKB9Ka4ZLwJnaIgcAwAI1qcQONoxq47myMHJksBx22TA9l5RXQ2cfjr9LFpGb9hALxwIALNnA9DWwfHrA4KdwFFVunSm4S2E+rvopJo2LeltA9r+r4h1IqalqEV94YQ+Njs4cY8CR4QnbW26CDS7HkrIJkUtkxoc7Xzs95WhD+Xp1+BYDAZC4MT9yS/qJHBiMb1kxmubaMC9wNk8htL83QocQx2PnKLmkwQOp6jtIJhukjUBXeAYHByL9oKDg/pAaBY4NXH6w4CvFK1BuvGXD1g3GahBO0oHTTPbKcgoRQ0A5s4FdtsNSlcXjuu4B7W1wEEH2b+frcD5jNybFjQnFhFLQqvDEQ6ObYBnuqHbpahl4uCUlMA2p8NNkwGrGpySBn2Ktmdzt9hc+xQ1k1U0rALHbjbcjIPAKUMfAuLmJQscU6MBeea6sxP68RXr7MjvIS4kv193Unp7HdfjeP11+n5cUKu/0dLTEu8ZTN0RKvb5ZgQQw5AScN1tK1weoOJ1SIGVcHBq6GZYjU7DOqkTJtAkfFeXi8VuNVRaogbfxO30wGmnuVyQxUh1NTCIEgyUaft782b4MYSdhz5MPKduyChwxgW0E725OSnP345Io9ZJrdMkcHbZJX0HZ8UK2hFjxyYPsjYOTmkpElOrrWhyvb8F7e3AT39KP0+f7v7/hIMDAPHe/rQcHEuB49AiGkgtcAAkBvCkLmrafU+00v084r0Gp6tLn50O1junqClaIFczlKManCwIHKmxXPoODqA3G3jkEVo4Rrg3e+2V6DpYgkGEonpeqN3rbtpEY+iRipaeduihlgt0Bhu0FFm1E0NaitqQTx8zFNOSBfGgN4FTWakH2sLlTNqMUA4cHG0M3x6g8zObNTjQUtSsHBynGpy2Nl0nWIkPs4PjKHDEC8jbp50v62tTdFATiPvpVnsHh1PUdjS0EczXThdftd9B4JjarYiLNBhMHgirtQG8O1iHdoX+WNpv7eDsAs29aW52nV8vCxyzYHBycIaGtI/h8yXyhC/ETZh3tOq4oLHYrKSFPrUZyM8wzn6NRE3g7IcViEatizoBJAkcuxS1dBwckfrf2AjbqtxUs3Kqap2ipgQD6NNy3PtadYHjJkUNwPB1Uuvv1wPMNB2coSGpeUYgYIzehIOzfDkQj8Pv12evDQJHRBZyIwN5ylQO6CzSAgTLlgG74mOMj35KF+GRRxr+3hmmm6GyzV7g+DfS+dtZvpNDBaeRkhI95ccPbUwwCZwqdCEW0wXOqFG6bnPbaCASAeqxDQvwKD1w9tnu/tGEMJe6y+mk9bVuxq74BCHoAUhtxJiitpPPJvfWgXgzBW3V3Q4pal4dHCdBLq1hBugCp6wMBoHjxcGJRoEvf5nGi7FjgSuvdP+/YhFFAIj1DrhzcBICh8aiWK03B0deA8euyQBg7PxnOM21687fQPvy017vNTiywPHXOYsxpYGEVG0shzU4LrEZ4tDVpd/qPbeJltlrL1qnJhYDbrlFFzj77QeUl6NfG0NCnXpAaidwxLB9TPnL9INFehqgC5xqdCLSRTcyQyto070oHvImcADdYP9Qmx8xxw2KXYpaFhycNs1hzGYXNeHgiPbWMnIMqJqyeMRnaGhwXlpDVek+kG6K2prSDBwcUw0Op6jtaGgDoq+DLj5bgQMkReZyepo5RVp0TOvw1ScK3Er6kpsMRKPAzvDWYADQ447BQeMAHYvpswpWDo74HwDAWWeh31eGvbEaZ+26xPH9bB0cNwJHS1HbHR+gHD32AYeFgyMLnNJw3Lj9HpDX+Unl4Ni9fne3PsNtjvt6fRTF9LV2Q1VNDk6hpKi99RadHE1NiRlEWxwcnERhfW2t8cSfMoVOlK6uRBRv6KTWLv2f+T1kgSMVTDqlqS1bRulbAICDD06aKhf1BP7t9jU44c3r6G1qHKJDE7LASaCpCF8Nfa9GJ4aGdIFTWQlMokxV13U4kQjwdfwDIUQRn7avZUqKG8Qx6CjVBc4UaPU32rVWM5i5wBHFZHX9G2njNXfXkKK2dq2xk0EqnASOtIYZYO/guBU4qgp897vA88/TzO1jj3n7+P6Akgha430D7hwcbfzc6CcHZ8ijwNm6lT63ojiXkIlFSO1S1EKjaV9+1Ok9Ra2zU5qdTuE2+ZsokKtDW9ZirGzX4IihqLw8eegWuKnXBKC7OLfcord83G8/QFESQXdi/R44CxwFccyMOAuccKMkcLpp42KywDFZH2oOBI6I9gOywIlE9PjJpYNjEDha0C5SKLPq4Gg7OxZwrsFRursNqV2pPkOJdIvo6PCYotbRkZggWqVMB5CmgyPNZhhqcDhFbQdBU73+ThrVKn1GgTOIMGIiN9Ok4O3qbwCgQmso0O6rQ5tK7xHutW4ykHBwPKxvEQ7r93c5TU0eGK0cHEAflFsGavD3+GkAgNnv3uT4frYCRxML6zHBftBpagLGjIEPKqbhLU8CpxVNUBUFiMXQoG41bL9bVNW0zo/NlGeqm5Zwbyorkzvn9gcoIB/Y1oOODtpPKVPUhlvgyPU3qToNuRU4MoFAwq0z1+FYOjhixqm9PTknJMVKfPE4pahZ1d8IhMAJbN9imxdZu5kC/Y7mPS3/boWjwKnVAwyzwNltN/rZrcAZHATOxh30S5ruDaAfg7YQReuBrZLA0VZCLxvqRgn6EwJnjOJd4AQmkGiuiW4jCyQeJzulqYnuzqNG0XEQBS5usOugBhhT1FQ14xS1P/wB+Otf6dL4xz+8pacBFECI8yLe25/awZHsl01BGouiNd4EjhgzTP1SkrdN6/xnl6JWPp5uJmvaJIHjslhMdnBSCpxGep8GbHN2PzyQ7RqcVPU3gMsUNQA4+WS6hlpbqWspAMyYgXhcb2Mf7Eh2cKJR4+5/911gKt6lRkXl5bYOvHDQqtGJSI8QODYznQDUsLc20YAepmzUMlHNAscX1hwcVRI4YqcqCtTqmoR+9pqitlXx7uCkbDKgpUGrFjU4paVAF6RzWipVSNUJLhTSb7M9bYMoEfGAG4GzahV9nzAB6zrpRHTt4MiTE6YaHE5R29HQTopAbxcCiKJKMQocQMFg2LqTmpPAKR+kG0cb6rEtTu8R6jGmqIkanHQcHMC6DkcecOUbnvyzCOCfegq4CTTDVPLkQ3oEb0EqgbMOE+0dHMCQpuZW4AwMADEEEK+nQa0pvjnxuBdWr6bBuKQEOOJwNWWKmt1Ny6r+RjAQpChmcFt34nhUhr2lqK1bZ73oXNZwW38DGMWHhKPAkV/bjcCxS1GTn2MjcD78EOjpiOIovEAPmOpvAKBPW5XdF43YNgkZtYVyP7rHTbH8uxVmgRMLhBLHWAQYVegydD2vqNAdHLcpavHlK7EvVmEQIfi+8XXX22dGpKhtCZBYCW7dhD2hiYyZMxPnYxNaE+ZKM7wLnNKd6jAA7VxfojnCu+yi3+WFi+O2Dqe9XVeDVgGdmOGJRIDe3oQ4S8fBeeIJ4NJL6efrrgMWLHC3iTJ+v96OOd43YDj2lrS1JQbUliDZL5FqSeDIXdRscFV/Az1FLYwIgn5pkNF2TsVE2pefiRqcwUHba8ZMZ6ckcMTJZkOgmQK5BmxLy4m3wlCD4yFFzW6ISdVBDTAGp446MBgEvv1t/XefD9hnHwwN6UF3oDPZwQGM5YfvvgscAa3+5uCD7SN8bcANYghqOx0TQ6e0pHtR+g6OIClFzaoGR4zvtbXo6PIl7rFeU9SEKPTq4IgsGtVC4CjaxqgW+1RRqPNcYlyTUqadOqiJ/xUuTnS7VMOtzXg4ChwpnVEYMp5T1Hw+w0Qmd1HbEZGCtFq0oyJJ4AADIWuBY9ciGtDrbdriddgSo4sr1GWdopaOgwNYCxz5piEPAoqS7FA8+STwNqZh/bhD6IS/5Rbb93Lj4LgROI6toqUAWFX17YyNodSXnfo/Nmy/W0R62lFHacdFRHGmrlmpUtScjnckRCfLULsucGrLbFLUTEpq7FgafCIRXTTnBLcd1AD9upDXqQGdr4kaHKsIwKaTWmcn3KeoASkFzmuvAbOwFJXooZuDlgYpo5SVohvaRWyTcjO6g5yMgV3cCxy57S4AREr0oM5fm70UteAj/wQAPOFf4ClwMyOOwWYt3TPUJjk4U6YkoowmtCZEQnPcu8CpqlbwObSe5y9rqTRiTSPAex2OuMnvsov15y8r0we17dsTY1N5aTxxk3fj4LzzDvDVr9Jp/q1v6ULHK2YHRz72lggnubk5kSpkEDgeHJxUAidQqTdAKPVJA5xIUWuuR10dMIBSxMq1DXaZptbV5T5FLdBEx7EebYgMemgn6EC6Do5dDY4XB0e8vyPf/rauAvbcEygrQzSqB+uBDmuBI0SAqgLvvScJHM11taSiItEAxb+djp9B4JiVQUn2BY5oMhCQHRyL+puqKmMal4ydg9MK7w5OdTWwXZG6qJlnEYWDY5GiBtivhZPKwQH0zxfTxGYkVJ7YYY4CR6u/wb77JvRKylPbnKJmOjDcRW1HxO9P3EDq0YYKNVng9AW8OzglvXRBb43VYcuQlgbX0wHEYokxRlVJMGTTwRHBeTCYnIUkX1BDQ8AzWvlC5FtanvBf/2rbtcpW4Gh3WLcCx62Dk2iGACB+ALV322MLBU1eHRyRnnbssfr2oqkpaXnxVALHqkW0IBqmoGCovTth3deUWrSzk3/X7mCBgL4WTs7S1Pr69JlzNw6OvNS3ZMundHCEwFm5EohGExO6KVPUPAocQ/3N0UdbdvqS18KxDNYGBzG6l9RGdPJUy/exoqSEUlcFQ6W6wBFNBkowiKHeQcsUNbetogMfkgh5NXRkimc6I2LOz2N04obbNmIPvE8P2gicpnQEThWwEVptl5PAcevgiPQ0u/NVUQxpamJsagro7Y22YpSjg9PaSp23e3qoR8VNN7laJ9ISn08XOGq/CwdHq7/BhAmJgG2wylsXNRszOgm5tXm5TxrEpcBT3E8Gq73V4RgcnBQCRzQZCGIIkW3uHKJU5KoGx42DA7hIUxszhlLVgMTkkuzg+NuSU9Tk1/38c6CrS8XhcK6/AQAoCnp8NB6FtNS3eFB60YDeARIAFMebtjWpBI7PSuB46KAG2Ds4LTHvNTg+n94lUInFkhvXDNk3GQDsO6mlcnAAKcTopHM9Kk2GWQocsd6PNrkTn75f4hL1nKJmIXBEipoaZQdnx0GbHazDdpRDuyuVlyfqLPr81mvhOAmcsJaO1hKtR0uETjxFVYGODsPF2d8Tw0Sso1+y4OCIQdFq3JIvqKVL6Vqtrwd2+eEpNNps3kwtLS2wFDi9vYkbpFuBMxXvoqPFRqFIAbAsMhStO9ZuG19MbL9burqA//2Pfj72WDgu++22Bscq5hsqpXMk1qk7OFVhd00GgGHopPbWWyRWmputc+zMiFbOgGGKM6XAmTSJgpyBAeC996wdHKcUNfGYC4HjVH8D0DkrZkkNhZeCDz6AH3G0owah8Q53KRPmFLVomZSWI03Xx9s7DQJHXN6dnZbLMSQRXEfi6/OSSa63zQpxDNZH6MStW7cCZehHVAnRiScJHGFujoplKHDExSILHK9r4bhJqZTa/IuxqVmhyCNSVY8hBG33dX8/cNJJpDN22w3417/S6sKdwOeTUtTctIkWY9H48YlYZKBSOl/N14vDS6RycEorAxjSgptSRRp/pcBTHOqeMm8Cx1CDkyJFDWVl6AW15IxtybxVdDwOqKqakxocJ9PUKuXbkd/+Fjj3XOCKKwDQOJoQOO26g+P363M14vbw7rvUoKcJW2jwOfBAx7fq8dMFX9pNx8+81k3Up/9ubhvthp12MjoodjU4AXVIn8nx0EENsHdwWuLeBQ4AVDSU6G6+aUBQoiJFzd7BcRI4bhwcETvK9wqDwKmq0nfkhg3A+zQB1bHzvgmzxbODY+oKKqeoxaPs4AAAFi1ahAMOOACVlZVobGzESSedhA9Ez92RgqZ867Ad5fFkB0fMiJjXwhH3cCuBE+imUXKbWof2niC6xIKh27cb4t3yjo3UJckfTN3ZyoSTg5NK4Ii0rWOOAa1fc+659IBYlMyEpcDR7q59oWp0osZZ4Iwdi97SegQxhPBHNgGOjcAJzJ0NAGjeuhoN2OrJwXnuOZotmzxZi7Uceqq6TVGz0gdxLa1D7epJHI+KUAqBI71RzhsNeKm/EVjkcBjaRFtNcfp8hjocyxqcDFPUenuBTW9vwwxoKXdHH225+SkdnPfIIXkPU1BZ5X7a3ixwhmSB4/cnbqRqZ5dB4JSW6k5dyjS1eByhz2kdmc9LMxM4IuZcO6A1GYjQRbyxYne6qZoEjg8x1Gvr4ngROJWV0FPUBPKkjXBwPvvMXX2HOGetGgwIJAdHuE+NKm17rIE+l1WKmqoC3/wmCeXaWuDxx51n7N2gKNYOjm2KmoWDkxA4kQhN3QNZSVErL9fFV5lvQH8PsZGSg9MR8iZwerZHUArtNVM4OADQ7iPlEN+SeatosS5X4v2zUIPjxsHx+3Wnz1WzhHHjKAVcWzl2aEiffFG2GSdfzPNfhvqbmTPtW7tp9AboGJT30fEzB+7yuji+Uu8Cx+83nm92NTj0ZppTkCUHZ9OQ9xQ1wLnRgKI5OHYvaidwvKSo+XpovBsq068PQ7yhKLqCeeEFUu5NTdjip/G3qirlYddPWJGF4+DgxCPs4AAAFi9ejAsvvBDLli3Ds88+i2g0innz5qHXS6vPQqdezwsusxA43Rb5l4B+glvFAIFOvclAJAJsh34jls+7+i6asu9rnOB6HQ6Bk4NjNcMhx9WGtC0AOO88ev/Fiy1nWMU6OFYCZ1s5iQXHC1BRsHUcuTi161ZYP0dKyRAiJhDQWovuvTcAYDZe8uTgJH1Oh5wOt00GrI63EDhKj56iVh50l6IGDIPAEfU3GQqclA6O/B6SwOnqgrcUNbsEedBHOTL+HHxQ6bywcaRSChwtVepdTE058Wx+XUOTgTLjP3cp9KHVjs6kNCU5Tc2RTZvgiwwiigDaysaneLIz4hh83Gs8cTdWa3VHphS1BmxDADG66bpc/BSg+GBL0DRJIzs4tbX6sdLEpS1bt+qTEaIznxXSWjhibBoVN0ZQVg7Oz38O3H8/jS///jdNgGSDAYVEhNrnzcFJpKj5y/TZJLGOkI3AUVX3AqesTD9nyxRtR4kd4/MBNTWJcU10qnK72Kch1czFhdTup0AuvjVzB2doSE9PU0OhFD25jdgJHBH/OgkcRUk9IeaE7OCYA24RFItLxCBwnNLTNPqDmsCJdABIXsxTdnDSETiAcd4iycEpseiUkIaDkxA4g4MJV2Jzmg6OU6to4eDYvahdDY6XFLXyGAmjoXIbBwfQBc6zz9L3fffFtjZS0SnT04DkE9Z0YGQHh1PUNJ566imcddZZmDp1KqZNm4Y777wTGzZswHIRMI0EJAen1ELgdKrJJ7eqOqeoKdrCoULYiC4eaGszFPw39tBNrH+0t/Q0IH0HZ+1aylhSFCm7Z+xYytcAgD//Oel/xYUajUpFldrddWspiYVUg073JApUmjettH6ChYOT+BxamtqReNG1g6OqulOVEDgOOR2Z1OAolXSyKL16ilq5v4BS1MRsuJsGA4J0BY7UaMBVippHB8dQf2PRPU3gycFxWozR4nUNAqfCGNR1+3SBYy40d91oQHvCOkxEoMRhBV4XiGPQ0lMBVQoAN9dorbG1AUw4OKNFB7VRoyxXS3eio0wSOD5f8kTCPvvQ99dfd34hcb7uvrtz0CythSMEjnCfAjvR5xoYMC5QfN99wE9/Sj//5S9J68NmxKCiOTgDKRycpUv1tsG77JLYzdEo9GhGjA82AmfbNvpcqdbAAcjBEedsIkVNBJ21tYDPl7ifbI5pkadLB2eoTSugDle4mqTrDNTrHyBDDB3UGho8FVDZzaGI8TtVJq/rVtEWyA6OeT+ccgp9P/tsijHeXa16Ezgho4ummu4/ctvobAgc8yE3ODhC4KTh4CRS1FpbEzV1QqR4dXAcBU4KB8eqBicW0zPB3Dg4oglH3I3AeUHrDOqlgxqQfD82HRi5yQCnqNnQqR3gOofpjcHBQXR1dRm+ChrJwSmJac6UJHA64skCp7NTPzEtBY528xACR3ZwAH1wbO6niHZwtLcGA4A+AG/erBfkOzk44oISZTYHHmjK6xSLkt19d1IhnlyPn3BxNLHQUjLR8Pp2RPYigTNxewoHJ4XAcTtj9vbbdLMqLZXuCy4cnHRS1HzVFMUE+nSBUyK6FeXbwent1dceGU6B8847qC2lYMoxRc1jm+jXlqkp62+A1DU46rskcLw6OOYuavJNCwB6FG3la1OKGuBhLRztCR9jUkZ1IYAxayjerJ+8rfXWDs7oNFpECzorpRS1ceOSz/3Zs+m7CO7tcJOeBlg2GaiNCoHTlHh7EdMsW6YvKfTDHwLnnOP88l4ZVEQur8NCny+8QGmVXV3AIYcARx+diK2GhpAczdgIHDFWjB6deuwtK9NT1JIEjnb/E+PaZ4PeUtTiWoeoaKm7i6gzIHW0ypB0O6gB+m7t7jZ2QhOZgWPHJv2LgVSOvxMGB8c0Nv3+95TN2dICnHoqMPDuJ9gJm6gI/qCDUr72YIkpTdCcoiYt/CnWR/KKo4MTthA4Fg6Ok/NhcHC0A6LW1CKqPZZNB8c3pB1ADylq27ZRzKUozuJDxE0JgVPpQuCI2EvqoOZK4FRUGA+GhYOTSFFjByeZeDyOSy65BIcccgj2EgWjFixatAjV1dWJr3GpppfyjeTglAwlOzjtsWSBI4LdmhqLVofxeCIoFM6N7OAA+gU6bogcnMGdvDs4TU10gQ0N6desGwdHuBpf+ILpCbNnU1el3l4SORLyZzQLnM0hFylqAHwzqJXvbn1vWXdrk9Z9EC5N4n2POAKqomBPvI/Sjs3Ob6Qht4dOvE6aTQb6+vTDbxX3+Ws0gdPfnTg3woq7dXDkzdmwIQcdHFetonNyzBjLjV+xArjySosbtY3AcazBASiwbWwEhoYwbvtbAFJ0Uevu1m/yKQSOqgJtS97DTtiEWKgEOPRQmw+dwsEZHAQ+pjyxTB0ckZ4o6PbrN0M7BydlipokcNJodGQgHJYWEGzQj//WBs3BMdXgZCJw+molB0dOTxMIx+3FF207NgJI3UFNIKWoCZemZpAiKKW5Se5BgPXrgRNPpEN/4onAokUpPkwaJBycPpuFPh9/nAbe3l4SOU8/DYRC1g6OwKauxW2DAcDk4MCUoqbtJHG4P+nxJnBUrUPUUHnq+hsA6A7R+/m2Z9fBUTy2Upd3qxjbVdW9wEnVlMYJg4PT3m5QWOXllDZZUUHNCPfvI/dGPfBAPVfcgcHSFA6O1HTAX5b9FDV/0Kd3ahM3lUwcHC3nO97QmPh7Og5OIgYzC5yYNg45pKiZBY74DA0Nzia3iDtEEw610qYGB0i+7r06OHJXScCxTbTKNTjJXHjhhVi9ejXuv/9+x+ddfvnl6OzsTHx99tlnud60zNAGxia0IhjTzjZJ4LRF7XugW85CdHUlLBU7B0ec3KJF9NA47w5OMEhxJKDb6oM2poH8nqJ8KpG2JVAU4DvfoZ///GdDL1ufT/9/s8DZGHAncMr32RVdqEQYg4kuIQliMX3/Wjk4tbVonzAdALBH62LnN9JISk/r7NQD5hQ1OOY2vkK0lJVZp5wEarUmAz09iGnlC6G4jdq0mPrbaScaj6JRY8phVkjRYOD884Ff/Yq6SBlI18FRlMR7jf6cAlXLdXDkmWkRnaYQOJ9/Duy3TXNvDj8iqdW3jKPA+egjKLEYOlGF9pIxnjKxgkEgIrWJjptS1EQXI6UruQbHdYraJ9RgIBsCB9CDuYEaimKH4Ed7g2YnSQInHs9M4ETqmhGHliZkJXCmTaO7dU8P2Sl2uG2KYZGiVtWvR1BiUnTtWmoHvWULMH06cM89nkseXTHg087HAYs20Q88QO2ChcJ67DGIVp22Do7PZ1tX4rb+BjDW4JSIgnyTayocnI86vNXgKF3JwZsT3WE6KL6O7NbgeHVwQiFdL4hhpqNDH4pS9fzJJEUtGpViAlXVj4XG7rsDd9xBP4v20L7ZqdPTAAsnzbSYZ1xa7yVQnn2BEwiYOqABmdXgaDfEmLbgt6J4v3admgwIB8eQWidhSFHTYhQ3nwFIdnDkVAFbBwegAXvnnb0JHMB4T3ZKURviFDUDF110ER5//HG8+OKLGJtiaiMcDqOqqsrwVdBoA/x4bNAfq6hItIn2LHC0i7nPV54IhLr81ilqYpHPofHeHRwguQ7HTZtogC4Yy2yl00+nG+r77+u5oBpJndS0O6xbgVM/yoeVIBdn6HVTmpqcxlhdnbjoZeeoba/ZAIC92150fiPQjcrQHhrQpzzr6y2DBnn7zZPLcv2NVZp3qJ4ETmiQpuybmgAl4j5Fze/X1x3NepqawwKf7e36n5Pqf6y6qEVV1KDD+HcrtNSiuk9J4PR1RPTIQYiXQCBZLYq/2Qic117T62/8X7BPTwNSCJz39PQ0Lx3UADr+Ub9+Yqqm8a3PT78r3ckpaiLmb29PkaGjKaBPsGtWBU5v1ejE6yby77W7dC06EMJgRgKnvCaIVmh3fSuB4/MBc+fSz2IhLjObNtGMgs9nuYCrAYsUtcpevb2R0D/nn0+9U0aPJl3hoRbdExGfdl70m5oM3H478PWvU0T+9a8DDz5oGHCCcjwoRzPV1ZZrPAHeBI7cRc0uRU3cyz6LeqvB8fdoAsdFBzUA6C2h9wu0Z9fB8SpwAH2YEUOccG8aGuwXoRRk0mRgaAiIIYAORRtDLeqRvvQl4Pvf99ZgADB26gIAJWy8/8gOTroCR16yL8nB8VsIHC3uUWvdOTglJZLA0dytoTq9g5rXtapsU9RUFX7NwTHvJ4GhyYDm4LjpoCY+B6ALHKXapcDZd19qzOR2kU+Bg4Mjp6hxkwENVVVx0UUX4eGHH8YLL7yAnT0uRlkUaAP8BGgBcDAIhEKJm+DWweR1cBwFjnYxdwX0ky1RWCmlqJWiD6NBLxSfkN5+NQscNylqAAX9lvfNqirgjDPoZ1PLaIPAGRxM2BrrlYm27ylTUwOsBNXhDC4zNRoQgay2OrlIUZNfs2NfqsOZ3p5a4Dz3HJlCu+8uzTY5tIg2v5f5puVUfwMA4Xo6WSrRrT/PTm3aTP2JSyvrAsdhNvzll/X6LdH9LYGFwAkOdFOHLfnvVmgCp/J9EjhilheAMTdEfo2aGn3WyUbgvLmkX7/hOzQYAEw1OCJpWqB1UHsPUzzV3wiGglL0U5nawRECp6xMnxm2dXFUNas1OIA+cdhZRenC72KqnuZRW4uYn35pxJaMBE5VFTVGAGDfmky09RbdgsyI9LSpU1On5Fh0USvvSXZwtm6l8+HRR1OnHmXCoObgxPoGEpd3wz/+QMU+8Ti147/77qQcG9sUtSy0iAaMKWolqnWKWkkJ7c7EpMD27a5cnEAvXdu+GncXUk+JVijeleUaHI8paoA+/IhhRiSbuDlHMnVwAGB7wGGdLgDXf3c9JmI9VL8fOPhgV68dqzAJHNNaN3JXtWBFegKnulrf3a4Ejnau9YTrE/d21ylqGtFa2lfpjIcGgSPPLEn54HYOjlOKmlMdEZAscORrJKXAATJzcEwHRlHkLmrs4ACgtLR77rkH//jHP1BZWYmWlha0tLSgP2lJ+yJGu0mOEgOlpmySmgxI6+A4tQwWF5DINQaAnlCygyMW+OxANXz1DsGiA3YOjlOKGmCRniYj0tT+8x99xIdJ4IjHS0vRGmuwfU8Zvx/4oFxr+brS5OCY6jOshFrPjMMRgw8TBj+yiMaNWNYZpRA4Tou3OR5vAKWNFMEaBI6d2rRJ3haBSlY7qfX06OmAFg6ObNKJ2csEFgKntJ/O31gw7JgeJgRO8JP3UYFuhPq016iqMlrn8oyT/LMI7Pr6DBHE4POvoBQD6K3dierFHCgpkW5q8bgxDSTNDmqCWEBycEwCR6xDEW/vTKQ6yu+RMk1t61agpweqomAtds6qg/P+jNOweJ+L8HNcrZ/vioKBKgpqm9CascD5AX6Llw69EjjuOOsnCYHzxhuWbcA9rdkkpaj19QEK4okFDmUHByBd4aVLejpENQdnqJvuj5fj1yi7/Hv0x0svBf76V8v8GtsUtSwJHDlFLaxaOzgAjVttqEfnZG1H/eQnjq+rqkBogII3f607B6e/jN4v2JW5g5NJihqQPI/itv4GyKzJgCi5ES2z7TrK+V6h9DRlxgzXtmPcnCpoGkDkdXHSFTiAPnGYMkWtvz+R9rFliMb4igrnuQtDippGtCa9NXAABwdHOnh2Do5VFzWvKWqiBscnXSOOAkdrje9Z4Mj3UItxJsZtoo3cfPPN6OzsxOzZszF69OjE1wMPPJCLt8sP5pkfbSARKWpWPdDdODi9Yf1kS4gdycER6WmfYhcEQx49V410HByfL8Xk99Sp1HAgHqcbsoZhLRxJLAxGFNv3NLOhnmYmwu+tNM6omwROUpMBAMH6aqzQHCC8aO/iWLaHBlJGBH6/PiaYb1peBc5OO8G+IMpm6i8nndRWrqQdMnas5WjsWeAM0M/RilrnPIHGRmD8eCiqiv2wQk9rMwds8oyTPDjLtop2U4lGgQnvU/3N0JHzUuYplJQAUYTQHdTeQ065SXMNHEFMdnBML9AfpN/jHVpagmK8madcC0dTPl014xFBOKsCZwsacf/Bf8RbmG4IFAaq9TqcTAXOUhyMh/f7hf2Mx9ixwJ570vVvSoMF4L6DGmBMUetTsTfegS82RBdyYyOOPJI247rrKOUn14gUtVh3P36Ny/FraAJh4ULg+uttz1nbFDWHNXBSzNcYkFPUEjU4FgKHDrmC/3359/TA7bfrx8OCgQGgIk7XZ7DBncAZqKBALtyTuYOTrRS1dAROJk0GhLHREbRuFZ3gtdfo+yGHuH5ttcp4HAzr0gBQs+DgAHrHd1EHLEhycMQNraQEm3tpbEwlDKwcnMHq9NbAAYwCR5X3tZSLbuj+JmFwcLQYMN0UNX9tjh0chxQ1AIgr3EXNgKqqll9nnXVWLt4uP5i7QWkCx+8nBe5Z4Gg3jv5S/XX7SpKbDIgGA2uxc1qzEkB6Ds7MmS5W7RYto//2t8QVaHBwpHbLTqLKTOfoPdCPEgT6e4xT2C4cnHAYeBHaohW33UbBkbzAhcZbb5EgKSsDDj9c+oOLiMAur9ppDRwAKG8mgVMByknKSYqaqgLf+hbdWUxFqbY41N+0thrXdLUVONJ7lQ2SwBmqcOE4atPlhwTf0BsTuBU4fr8uHLRz4+23gTkxEjiVX3KuvwH0m0vSyuyRSEJdpOvgxEO6wDGn5vQF9RQ1gIYTOa5N6eBof2ivpRqWbAqczk7rMSJSkz2BAyStiZyMmGEx1+GoqvsOaoBh9W6ltwc/wa/o91NOAUIhnHoqGe+XXeZq8zMm4qdBcswrD+ByXEMPXncdLbzjIMi9pqht345E+qOo3XOirAzoA6nsiV3U2dDOwQGAtysOBk47jY7HxRcnd13R6OrSZ6eDDe5mCgbKtZS47m22r+uWTNpEA9kROJk4OImW2TYpaolrwY3YF5hqocwpaqp04afbJhoArr0WeOih5ImDJIHz0kv088EHo3ULXQNuBI7ZwRmozpKDs327npomHTwngWOuwXGbomZuMhCocxA4Y8fS7HN1NbD77lDV7KaoAUBM0R7jJgM7ENXViMu7T7KCk1ax1QZkxxl9LSAcKNNvHAmxI6WoGRycLAkcJ7Ehcv+/+EUXL3ziifTiW7ZQz0qYBI7Uo9SLwKlpCOAtTKNfVkhpai4ETkkJ8Ay0wOill4A5c2gwOPhg4Ec/Ap54AujoSLg3c+aYtslFToedwElVgxOs05oMIIoQBt2lqNk4OLYparfcQsLunXfozuIGh3QfYYKJAGnrVtPntnBwygfp/B2qSqWQkbgpH+R/w74xgV2KGpAUfbzz9CbsjdWIQ4Fv3tyUby8ETrs5z/3jj4GhIQyGK/E5xqbl4KhSZyK5cBTQF9rzacXXZgGVci0c7Q/bakgJZbMGp6tLn7SUx5xILUUcu+MDlEA7CXIpcESa2jPPGIPc9esp8A4G9SliJ8rKEtfX1O1L8CVorQCvuirxlGzsP7cIBycY6UMcCn5Sd7MrdWWbomZTuC+vgZOqGB4gB+dBfBkAcNSntwF//7sucKTrThzyTZtAEWx5OS1Keu+9lq/b2SnXF7hzcAYrKdD0xyJ6S880MTg4GdTgmJsMeElRy8TB6Qw7ODiRCM3WAd5yK03HwV9qugDMs4ZpUl9PTQHNsUuSwBE3miOPdO18WDk4A5XpOzjBIBCtpPNDicf1eEM7EEPwIxCyDqGTanBU1bODIyYBAvUOKWqNjVQk+N//AoEAenr0ECFrKWrCweE20TsQPh96glLgJXLTYBI40WjibHSTojZYLjk4pdrg29EBDA0hFMqug/PJJ2RmOLWJ/slP6Nq5+GIXLxwMAuedRz9rzQaEwOnrg8ENcercZqa+Hnqa2Uqp0YCLFLVwGHgec3Be6d3UiWjsWIoIli4FfvMb6gNbV4dTfrUvbsT3cGHTv4xFshk4OKlS1ORzphLdRoFjPhg2ydtC4Hz2mXHhOQDAhx9SDr/g8cftPoIRBwdHZAedcoq+nw0tquUKXC2dsDxCkUCsyoWDowmc/WJveE9Rk5+rRR+RJ2i2f9OY/V0FM+J8bfObHBwtPW1rwxQASloOjhrWT8xkgUO/B/oo8DO/fsq1cLQW0Vuq6YnD4eBE6+lOPR2rAAB94Rp3kbMJ1wLniCNojFm3LvF5AeiCfJ993H1wae2HS1v/Dz6o2H7kKYDDOm25pC+gpScqfpyBu/HomPNd/Z/BwZGdiBSLfLqpvwFIBz6FY/ELXEkPnHuu/iIWDs6mTaAZMVGD83//Z6hBFcgOjp0Ys9qYAdFm3S41yyVDUTUnNThulu7LhoPTHXJwcFavpntITY0+aLjAXAuV6JYoyJLAscNQgxOJ6A7OkUe6rl2xcnD6KtJ3cACguiGIThHLifNOO3hRBG1f11CDE4sBfX1pNxkI1js4OADVLc6aBUA/JUpLDeGFM64dHBY4OxSdQSlgkhyc8nKgB1JxX1cXrUKsXR9OKWrRKv01I+XSidfRgZLAEA4B9TF+D1PSvmj33ptuch0dwI03OreJLi0FjjnGwwBx7rl0kbz6KrBqlbWD4zFFrb4eiVbRlg6OdpO0c3AABXfFT6cZxQ0byO646y7qUrTbboCqYnLvKnwPf8D8W79MB2iPPSi1S4wYDlGBXV51qhQ1BALo11Yxr0Q3JnUuJ1EQCiUHKTZ3xtGj6U+xmKmHQjRK7bv7+qgtHEAdqIQKtKO7G/jgA/rZQeDMmaPPWBrS1MRgGY8ngpuKqCZwql0IHO09x0XXYhI0u8KLwDG1OBr9NgmcgcOcu6cJxM1lq2ISOFqDgU011KQgLQdHEjhyXjUA9IdqAABlvVsBqEkCR3RP3r7dJtNQc3BaK3IjcMTssSxwhjSBsy9o0qG73Lt7A3gQOBUVelcouZual/Q0gXbe7B4l4dp2wZXu/zfLvFx5HP6Ii/C3k57AvfiGa/FscHCqq/UHsiRwRIC0ED/DW+OOpwFONAqyEDhiQgff/z6dsJs3A7/+ddLrdnZ6FzjhEsW6o1UaxLr7UCpqivLUZCATB0esCWQp9ORrwUNfZH+ds8BR5As/BwLH4OCsWkX33bIy4IADsiJw0nVkLRsNaAcigpBtXFRRAfSiPLF4aWx7Z+Lf3TQZCGEQYdC9PtSQQuBIeE5PA1zU4GhNBjhFbccisU4NkJSipsKHoRLtsXfeScRJgYDNRPL25DSeYGlAvwG0tWH/zucxCtvQika8gkPTFjjBIC3SCADXXKM3N8vKuDV6tJ5ge9NN1jU4Uoqam4HH4OCsWKGnp7iswRF/U1XQoD9xIrW1vvVW4MMP8egtm/Fl/BP31l5Es8CKQkH+bbfRP1dVOXYmsjJXBgb0FAa7FDUA6PPrjQbGPfU3evCUU5LbxdgIHJ9PN5cMaWq/+hXw+uu03c88QxvR16fPjNkhGgyMG5dUCbp+PU2c+/3AYYfZCJzSUinPi3ZAxRB9V90InJqaRKvguXiOHnNKUTP/TYo+2rbGMbOHAuFR30hdfwNIAke0il63jgp5XnkFALC+YioA64VbUyEEThwK/FXG6bWNVXtiECFUD2zBZHyY1PyovFw/jyzT1LQHN5aQEnJqVucWKwdHHnNiDXSnbgTdVXsqcyxwAL0OR+SUAt46qAmkQfgRnAjfftPd/2+W6Q9V42L8Ea/X0jnqdr0dQ5MBRdGDdZuxykuDAUAfglT4cPuce/SJEsCiyYDk5JaUAL/7Hf38u98lnbAGB8flTEEoZL+qvFd82+n/B5Wwh2luHVngdHXpJlWqRT6B7LSJ7i5xSFFLp/4GlC6dWGwXySlqhpqcXAscUWN36KFAKORJ4BhS1Px+9IXp/pBurOQkcJwcHLqGlUQmz/Z1XYjHjZepHSUl0iKfAHzV+s0m5wLHIUWNu6jtYHT4rR0c8eOW3bQuJscdh8hfbwdAF6nlWjLarFS8Vn/NcBiGjj+zN98HgPKiYwikfdECwFe/SpPl3d1kZgBZzDsXzQbuvRf1PgpuB3qGEpGwOt57itpq7IUhJUBCcIO2uKqLFDX5Z7ubysOvNuNf+DJWnP1Hyl9ua6OV/X74Q0qLWbjQcfusBh2RjhgOO2ojDARo8Brrb0HJQ1rO+re/nfxEhztjUie1hx8GfvlL+vnPf6aCmeOPp9+feMLpo7iqvzngAIpLxA09VSe1qiES7/FaFzU40nvvq6U+pZWi1tGB9+9biVHYhh5fJarnz3T11uJ8aY1r4u7uu4Fp02iRJACfhNN3cMSLd6EKgaBxdnUoVIYlOAwAMB9PWwoo20YD7e2JCZI1ERI4nm5wNsjCwypFTR1ljDh6qoZB4Ige7o89Rt264nFvHdQE0nnzC1yVFUGYLiKm0GqRXQscQ4oaoB/0LDk4fr8+tg2VVdMSAKNGUSqfNAEjOziJ0qgTTgDmz6cTR06ThbEGx7WDE7ZZkyQNlDYKVDsD9d5Xf4QxC1aMfbW17rRSNlLUeksdUtTSFDil5T50Qx90zIt55lrgGFLUXqY21ziSmgO5FTihkMnBGTUKkSFf4m/p0NBgcd5pB8/JwRHngkhT61jfKTbJyiQxUFqqTwB0o8IgOnIicFKkqMU5RW3HpF2xd3AA4Omz76eBfnAQO//iHPwV38b4RpsUIWnVXoFB4GzahAM2PgwAuB9fBZD+rARAIuv66+lncd5mbdw65BByQvr7cdRackGCWzdRHlUwiEi9Hgi5FTgRhPFpmZYjL9LUPDg48t9l4nHgqafo50R76NpaEgS/+Q05HqYbtBmrQUeuv3G6hw6G6KbynfCtUHp6KGXOavVph/6iopPa+rVx4Oc/p44QsRjwjW8AX/sa/VEInMcfd+5C5KL+5qij6LulgwNYCBzRDc3luk3mm7PZpXEpcAb+Q93TPhhzlOuLRQS6ryiH6fu8oQGYPh345jextGwOgPQcHF+ZJHAs1oF4GjSD71ngiHqU0aOxoY3urOY2rOlglaIm70a10Rhx9GVJ4MTjZJ7K5XYJpk8Hfvxj+vncc8kh6Ooi8ZhijSMD2g56DMdjBWYUhMARw1laKWqAfs3a1BJ5FTiAHqj5/SAH59NP9TFCQzg4g4NSbxFFofznQIDEqBhkkWaKWjh7Do6/gwLVzqD39DTAmAXrJT0NyE6KWm+ZjYPT15eoFfQscEqlmhEAgTKjImgcO4wpamKm0qPA8fkABKQBqrHRctzyQmYOjr5PI2++DSD1ZwCMDk6PYpxJcytwPGVepkhRi3GK2o5Jm+Ls4GyP1wCPPAL88pdQFQXfxt9w16eH6rkCMprAUepNAkekAtxzD8qiXdiAcXgVB0NRLN1ET8yerce9QBYdHEUBvkcL1R298lpUoBulW7TPPG4cBqP6aedW4ADAOwEtTe0//6GbrBj5TAJHdm3kz2RVfrJqFbkt5eWUdpUOVtojZf2NRjRMJ8uxfdR1Dueea62IUjg45ejBF+78su42fe97wB136E+aM4d2zLp1iXoSS2wcHFW1FzhJ66eaBU683fh4Ksw3Z/OMtMsuaqNWUqpDz8Hu6m8A/dxZEZtG29/fT3eNlSuB225Dey8dh3QcnG2NU9CBasv00kBA7/Y3Gy+htiz5Dma7Fo5QPJMmJVJhsy1wLFvJm+7W/TWZCZyeHtLlf/wjlb994Qs2JWO//jVw9tmkhH74Q3ps+nRvUczFFyPyjW/iIvwJQHZS+tIlXQfHkKIGUMfEzz+3nJxQ1fQEjjBqEnFPRUXSjSIcNszD6eyxh96d5pJLgEgES5cC1/w6nlj7y63ACYWy5+D42ylQ7UpT4Mgpal4FTjYcnL4ybbv7+40d5VatoguoqcldvpxEWZkejA/Bj2CJMbioqE8qbM0qBoEDkMrXzmO3AgcwLkiKUaMcl8Bwg6XAceHglJSQ4FoGyhyY+vtv47e4FGNHpVa2paWSwPGnJ3DSdnAsgkrVxylqOyTbVWcHp6cHdJb/5Cf4x+lPoQ112K1zOa04K6/lEIslgkHfKJsUtUcfBUDujQpfRu6NzLXX6ilzWZ2YOeMMYPJkVPRvww/wW1RsW0ePT5xoGNzdDDxiNuKNmCZw7rqLAnCxoJkpRU3+HD6f8+Jqtu2hPWBVg5OqRbQgWkrTtX7EKWI580zrJzrcGaeWfoqlmIUDNjxEz7v9dn32VFBWlpgRS3RTi8UokmxuJvvq6qup8xqQFCR99BEJmVBIr/O2dXCEItXEUk1Mq4qvN4kRO/bdF3FFGprSSFFTN3yGPdupIUfd193V3wD6vbu/H7TPTDdz4TKk4+BEaxsxGptxGu61dHDexj5oUZpRjj7s1fW/pP+3dXDEA7vumjOBYzUT6htVjyHoN8RMBQ5A2ls04WppAe680+IfFIWCeXl2xuOMNaZOxfbrbsMGTICi5GRS2jVi/PXq4CSlqAUChsD20UeB99+nn9vb9VoRN2vgCAwOjgNJjQYEV19NJ+MHH+CNM/+E2bOB3i098EFzkV3OFBgcnE2bKNjcuJEKD99/n1KLX38dWLIEeP55RP/zJPpeftPytQIdFKgmFtL2iCxwRP3qcDo4QyUV+gvJLo6cnuYx9U52cAYRTr4vD2cXNYBmG7W2x2LZOjcCx7AuTR4dHEWhOPAS3IiOr38HAHApbsDNKw9ynmCEycHxGycAciJwgkE9aHVycGLs4OxQbI07OzhiUTUAeLViHvbDCnw+en9ya445hgrB4/FEn3QA8DfowVtJCfRgUWu5ex8o5ShbbsuUKdT0BqBSg6wRCCQ6GfwAv8VOm7TBV+qgFgza1COZELvgtv6vQz3tNAq+R4+mf25sJMEI+yVknAYFIXBEan86pEpRc0SOZk480T4yFQc8GjWmmL3wAo5deAD2xmps8TVTSt3ZZ1u/hjlN7Xvfo1yg1lZKIfnFL+jvEyYkedzCvTn4YH2221bgnHYafb/+euCtt1CjauK9zqWDU1aGtuap+u9ppKipzz2HIIbwqbIL9vjCru7eF7qeiUQSl5wBESSm4+CUlAADKAWgWAocQMHTKrk4e298Oun/bdfC0VLUhnaelAiSsyFwUtXgBMM+vRkDgMG69AROOKwHIN/8Jk1Ki8viuutsUr8DAeCBB3S1LdbI8YBoCFZamlYpRtbI1MGx2j/Ll9NwctBBlLUkkgaamry5VUkOjg1JjQYE1dWI/2oRAGDy/T9DTaQVp87XPmgo5NoNMDg4t95KEdzYscAuuwB77kkO3kEH0SrNc+cieNJxKDviALTeltwaP9BJDlCiG5lHhMDp66OMPWB4HZxAUKpUl+tw0qy/AVwIHPGAz5f6ZEiDJAfHlJ5WWuryupA3vLExKw5OUmqkizbRAE0ODKAUay+7Cbed9Bi2ogET2t+i+OXPf7ZNFS8p0VM4RQt5QU4EDqDfRy2bDIiZFHZwdii2xq0dHDHrJQuclhZgAybg8f9bQmlIqgpceSVw0kl6Dn1lJcpq9CvR4OAA2FK3O1ZhOoDM6m/MXHcddcOd736i2x2nnILWcfujEj048v0/02OSwHE76AiBsy1Wi66b7iFnYNMmuso3bUoM9lZNBuTfzeku27fTcjiAVH+TBpkInF2nSQLHqrmA+U0AXeT88Y/AvHkIdm3H6zgAM9Q3Ed1/lv1rHHccfX/1VUrt0dYqwo03An/6E9XsyDUOEub0NECfLN682RRkffnLtJrb0BBw9tmo09ac8NW7FDgA2neVbtJmB6emhuq89t8/OZLXnusbpIP9VtN8T9eKHPxZ3UQycXDk89Ja4OhparutTRY4olX0tm36bD+AhOLpGkUWj9/vPhvQCeHgDA3pwbdB4ASBVujTqukKHEXRxdSbb9LrvvgiXdaffgo8+KDNP5aV0RNXrTK6OS6RBU4+MQuctB0cCTGudXXRZS/Mbi/paYB3B8cscDo7gQUPnYU3sD+q0YUnp1+Bv/7G4xo4oOFvMY5Af0CKcgMB2sC6OnKhJ0wAJk9GbMpe+Bh0sZT/+KKkhUGDXRSo9pSkJ3DkzV69mr4Ph8AxuBEigrVzcDwiC5wIQskmjXggR1ZnKoHT1ORuEkIJGwVOpg6OocmAhzbRgB4S9vYCL5Yfj73xDtZOnk+ByIUX0gyERaMIOUWt30HgWOkjsYmeBY64YViIV5Gixk0GdjC2DDk7OPK4KrpqjRpXQukVt95KZ+tjj+mtT+vqDN2BzQJn9V5fA7RWjtkUOIqSna5LVi/8xinXAAD8qmZvelwDB6DAUOwXQ/p1IGC483p1cJ59lmbpp0zxlrZhxur13dbglDZq0czOO1OenB1yZNndTallF18MxGJQTz8d88Iv43N1p0TKhCUTJtAiSPE48Nvf0mO//z05ORdeSCuVr1wJnG9caDAe1zuoyQKnsZEOQTxuXBsVikIzVHV1wMqVqNYGa9mdTEXvFAeBoyiUivL668kWoOm5HQe6r78BjCLEqv4jUwcHoFPWfLMW95VnQU5E46a39EFDo6JCX0PL4OJov2ypIoEzapQ7ZzQVFRX6doobpzzuhEJGgRNtSE/gAMb9+eMf0ySnVsaHa65x6IsRCpH1nIYFI1JfCkXgiKA3Gw6O3KBh/Xp9X6YrcFJN2lulqH38Ma0/+MR/ffhh6A8AgBmrbofvxefpCR4uonAYeAvT8dW5baRMYzEKNHt66KaweTMVGX3wAZ6+7h1Mw1tYj/Go2LZe7yipEcpQ4Pj9+qaLbKNcpaiJcxSQHJwAdAdHXJgdHXp6cS4dnGEQOPHqGppog7f6GyA5RS0nNTguUtQAYyZPayvQima8csWTwA030AY99hjdj582TmbJKWpiAWiBeZ7TTMYOjlWKmo9T1HZIWqMuanA0kmb0zzkH+N//KOgU07FWAkdab2DN9K8lfs6mwMkl7fvNwTOQ0kekNXC8jJX1JpfYCrvXtXNwspGeBljX+LitwcEhWivxK65wjkrlEXrOHKqz8fmA3/4Wyl13YfTO9CETraLtkGe6r7hCLwJ24J13aL+XlwMHHqg/7vfrny8pTa25mVwhicAo9wInOl26SVvZEYpiHdRKAieKAGpPOSr5OQ7ImlnM8AsiEf0YZ+LgWAWL4j23ohHLxZpP8mKWGiJN7dN3eoF//pPWnNKEkFgDJxvpaYDRWRHCzs7B6UUZlKo0doqGeJ/Jk+m0BEhzV1TQMkRPPpn2S9sijq95yanhxuyOeO2iZhXoCIGzaBFdPiLQ8ypwxL5JgwmTvAAANhpJREFU5eCYU9ReeIHGijVryOn97auzaOFhQC+y8uDgiPOuNxrSK7hteO45oA/luBgkqnD99Xp3sW3bUPv5OwCAvtL0anAAfZgR95RcODh//COdC3/TlkczuBHmFDXRWXTixLQWLy0pMTo4tjU4ORI4cg1OdNbhiRPOs8Apya6DIwscVQQfa9bQdrpIUQN0gQMATaN91HDj9ddpZrW1lcoVvv/9xMkkt4nuD1nX4ADWIjltgSPusRYXetzHbaJ3OFQVaEnh4AiBo6r6ZKyYgQVA05TLl+u5YZMmJQscbdFDzJyJnjGTE38rFoFTWgpcjkX6A7vskpHAcWqgY5eiZjVrFo/rAieT9DT59a2aDKSswfnSl8jq+9a3nJ/n9+s39bfeojvsf/9LLawVJRG4GBb7tOKss0iVXHJJ0symHSI97fDDk8872zocAPjqVxE9/iQAFAAHK9wfcP++++At7IM3QwenXGBicJBKiHp7YRA4SzELM470brXYCWIR5APpCRzhFFgJHPkx0S7aPLOHnh58zfcAHsSXcPJ5o4BTTwX+rXXfmz8fm/pqAGRP4ADJMah8/GWBsxmjEQylX8hyyCEUTP/tb/r+r63VzcRrrkn7pW0ptBQ1Qdrr4GhEIno8f+qp1MhTHLdcp6ht3kzm7bx51NjgwAMpc2rGDNBBLC/XUxs8pqgB7oTB85pB9ChOxEvVCygw+853qPvm1Kmoa1mDQYTwSaNDOm8KzKZyth2cZctoaI/H9XkOg4NjTlET6WleFruV8PmA3oCLJgM5Ejg+H7AOEwEAA/NOTDyeqYMjrvF0N9vg4HR0AH/4Q2IG5hnMc52ilhT/TZtG+bhizcAbb6QasmeeQelAe8LBGQzbOzjmc2hgQI85s+ngqNpCn1yDswMRiQDb4c7B6e7Wb6YGgQPQFfTEExSh/fnPBoFTUoLESY+HHkqaPS0GSkuBFZiBn0+4jazZ8eM9LfIpcCNwvDg4K1dS3VFFBS2YnAnmm1Y0qs+kpBQ4gPspZPG8PfekG5pIbYTFYp92TJ5M3YduuMF1Wo9V/Y3AUeAoCnp+czPewj64D1/zVJtaWRfEdKzC3PCSlNt5880kUg8/HGiL1SQeX1Y5z3XgIWMncET9TWlpenW24nWtrl359UQdDp55hgaPBx4gIdzYiAsWfxVfwr8RjPZTUc6Pf0yzt//9b1Y7qAnMMag8BskpapsxOqMx6U9/ouvx8MONj3//+/Q+r7yi15Fki2IXOHYpamvW0P2puprGhcMPBx56iJbE+spXvG3bMcfQ2Gs+LmbEOLdsGcVsYhmuxYulMXDMGOCqq/R/SsPBSSUMWlvJ8RN8J/oHqGVltHjkSScBW7ZgW/NUHIxX0V67i+v3NyMLnKoq99l2bhyc9nZaiFscVzFpZeng/OEPVPP4z3/S72mkpwn6g7rASbo35zhFDQB+6VuI/fEGer50VuIxrwLHsH5PY2MiZdtj1+wEZWVAX5jiPEU05gHwQPP3sAiXuxI4HR26DjV8jtJSGvgee4yO59tvA/Pno25SHc4BrR8YMQkcuceD+VoQMUcw6OnSIhyaDCQcHE5R23EYHAS6UYmIKIxzEDhiNr+qyiaW9fvJxWloMExWJ8aSo48GRo82jC3FInDE532g/JvkGsBeiDjhReC4cXCEezN3buYd6cyvL2ZrgkFDhmHmXHMNpZQtW6b3DNYQi32mFDgeGRqiIAWwFjjixmEpcABE6poxHW/hXNzqSRRQAKGgq8eX0hlftoy+r1gBzF5QhZg247Rtxvy0umOJgNfOwUmn/gZwTlGTH3sVByNWVkF3rIYGinb+/W+gvx89TbtiEX6Ms/ZZQb27Fy0C9t0XUJRhEThmB+c1HIQ4FCzFrIzGJEWxNurGjNF1vOXCnxlQKDU45myrTFPUxH6aPl2fGzj+eOAf/0huOpiK006j0zDVJJBwcOJxes9rrgHuvtuiSdoll+hjl8caHCC1wBGTMXvuSft1Td8EdF9yNT2oKMD//R/uvPBNrMCMjJqByVmzXiZRUgkcVSUzf/16/doTY7rBwVmwgJRjTw/wr3/pKWoZCJyBcP5S1ABACQawHPsjpuoXhGeBE/ZjOfZDd+MuwNixaa39ZNgmBahpCGA7pAN+9dW4bswNKZfrEOPZunV0XH0+m+zB44+nPPCzzkpsaBh0grRVJ4twu2tBXuTT833vhBOoI6FFs5ZibjKQ/X5/Owh0cin4FX6Cn57+KZRd9BPRLHAs09NsSEpRkyhWBwcw1jN47aIGeEtRc+PgiJz+TNPTgOTZRSFom5uzU+ydQNjZFrhOUfPI8uUU2NfWWrcRt13sU0O+KXsZdBsatLbKA3SzFx3ErBDpOKEQsPo9Hxbip2jEFjTMT17w0A2pHJx00tPk100lcKIIof+Qo1Hx7MMUCe26K83SfuUr+EidjitmKBi1GaLfSIJcCBxzDGoeg17BYWjEFrShHk/maEwSItrUc8EzAwM0NojzsFBrcDJNUVu1ir7vu29Gm5XAzXW700403vX0kJA64QSbJ4bD1Jr+/PM92Ulua1dEetpxx9HxXrsWWDX3Mhw+uRmYOhXYf38M0uoFGd1DZQfHi8BJJdRuvpnctmCQ5jTmzqXZ/54ek4MzfTrNKr3xBmWAPPkkqVfRNj0N3q86EL0dZViCw3Cq+d68//50kzn55LRfPxXiOpCNAq8CJ1yiYCaW4Zafqzg7FMpY4AAUe7y3cQoOxf+onusHP0D0EfqbGwdHNMkdNcoh1bO5WV+Ye/t2zGtchXCsFzU7JxcIh8OU9uYkcDxz6KH6hpqIJRwcFjg7DOLkWhRciJ/dbfxbJgInFKKgOB4f+QJnuFLUzDeVtjY93SUbAsecH+66/iaLuE5R84iYEZ0923pwdkxRg/UCkW7w+aig/p13gA8+sBc4Q0P0d4BcuXPOAX617koAwOI07/WGxT4lhsvBAYC+a/+IigVHUXGKNBU/SduGrVupBa/ssAiB4zYYcIOTg6MotN1tQw1Jf8smYtxMR+Bs3kylFw89RJ0AZ86kBnxA4aaoeXVwzBOrwsHJlsBxuy2rV9N1m7JF+eGHp1zs0IwbB0dVqcEAQH1Y3n6bBM5Hn/px+Dn6AspiTMrEwUlX4DgJtVWr9DXpfvMb+gw1NZTitG6dycEBaGcfdBB9/fznXjbfki3Vu6EW7YgihNPNAqexMfuzZyb8FkaBEDhuYieAzpMhBNEfoxhKrP+UqcBZgEfx92s347gf0Ppsbu5rZoHjelyuq8Nr5Uehqwv4psXYlMrByXZH3IdHnYfrN5+Gey4oQ6qeSYUGp6iliVOQbl4Hx4vAURR9RtFs77PASS9FzTwj/8wzNPjttRcwbpz7bbDDPOC4bRGdTUSKmlgeKFs41d8AuRM4ALD77vRdCBgrPv6Y3qOsjETYkiXAPvuQOEo3WyPXDk6qGhwAKNttJ+CiixLpZ4LKSv1GaV7wc7hrcIDklLVc4FXgfPopdUE/5BByFi64gAq1h4aolkekphWqwMnEwYnHdQdH67Q7bNTXZ2f9JSvcNBn49FMKaINB4LDD9P48H31kfJ4IoAvJwenuJkMrEqHsM9HWW4zra9dmNpa6obSU3GN5O4eTrDg40v7dvJn2md+ffg0OQOd1O+rwaam++LQXgSMWg/Uy8STuFVbHYbgFzkCwEi0YjcESr4U9+YcFTprYpUMB+okt2vV7ndEXAokdHJ1MUtTMA0K2uqfZvb7rFtFZpKGBgnxVBTZsyM5rDg5SQAikFjgbNyavVTI4qE8spuN6uBE4YiJ4yhSa0Bw7lmaw16xJP3DNVQ2OENNW44AscOzqUQSihIEFDqGqlKb4i1+QHtx1V+Cyy2g9W1WlCe5rr9UnjkQ6ZSEKHHmCKxVWDs66dSTEw2GqQxkpuGkyINybWbPo+hEt1cXyMIJCc3BUlUT4Rx/Ra91+uz6nITvzSQ5OlpGvg0zrUtNBfC4hcPr79TE3HYEjshnGjctsn4mULzn2cCNwxBgu7iNuXSigsASOGJ/i8ey+7nDAKWpp4hSkyzNwfX3eHBxAv8HZBepA8QmcSIQGLr8fw95FTR4Q4nFqWAdkvv6N1esD+UlRUxS6Gb73Hg3s4uaeCcuW0eDc1GQfLI0eTe8diVCuuBhct26ldO3//Y+O+aJF1v/vhBA4779v/xxRfzNliv5YpnVPuXJwJk8mwWiVLiEHufICm1bsthvtV3lmWlWHpwbHPO6Yu6rlAieB89xzVJomB7E+H3DEEcApp1DjLDF7e/vtJJY//5z2oXByCqkGp7zc/flr1WRApKfttVfx3CPc4CZFTQicuXPpuxgDc+HgZLPJwJ13AvfeS+fBffcZG9MMt4Nj3s7hxJyiJtybcNj9pJKVwMkkPQ2wjj28ODgCLw6OOBZeBI7o1JZtgSPGoyJsosYCJ12cBI5YhywepzQ1rwKntpYGtFQFvsWAPGj299NFP9zr4MgB6/LlFHxXVuprbGaKXZOB4RQ4AN0M33sve6nScnqaXcAdDNLA3dJCgeOoUSRIvvAF2o7qamryI4IOL3hxcKZOtX+OV3JVgwPYn3PyDGMqAWXl4PT06Od/Nm9whebgqKrxXPzd70jchELUbe2LX6QCd6tC27FjdYEDFKaD4zY9DbBOUZM7qI0kUjUZiMf18UqMNSJF7eOP6e8iUMung2MOTt97jzJRAXIgzd3qZAdHCPFcOTiy0M9HfGFOUZPT09w2qBkpAocdnOzAAidNnIJ0RaGTu6vLKHDcBrzXX08FseaGKCNF4Dil99mRSuCoqjsHR24Pna19aM4Pz0cNDpD9RgOp6m8EY8fqAmePPWjWfO1aShV6/HF6LB2EwGlpoWvJSlhYOTiZkisHx4lMBY5wb8rLU66L6gmzwDHXiwyHwBGBweAgNVaQg0uRjvmf/9CaLU6Y68UKReDIjo2Xc0vsbznQyXYHtUJBHsPNIhegz719O+0/UXs3YQJdVwMDdMzHj6fH81mDIwu1/n5aiLWvj1aC+NGPkp8vOzhCsOXawQkG02gznAXMKWpe628A43kiAv5sCRzhkADeUtQEXlLU0nFwciVwitnB4RqcNEnlQsid1OS2wW448kiqXTAHE8ORDpJtfD59W0VAIXLgvVzwYpCxao8IGGcxndpEC4GTrfQ0+f3yWYMDZHctnJ4efX0ZNwIHoCBi4UKaJW9uBpYuTV/cABRci3PEysWRO6jlwsHJdg2OE7LASTWLb5V6I4KBbKanAcbPGgolBz7DMelSWqoLLXOamljIz00QU6gCJ10HR3zmlSv1tNt8dFAbDsR5pqrWgZZIT5s9W7+WAgFa2gMwXivZqGUR6wmVlxvFTirke8Ull1DnuaYm4O9/t05NlCetsuE8OeEUVA8HZgdHXOvpCpzhcHCcjgU7OPmHBU6auBU4HR36iecloLeiGB0cQLe+RUCRzsBTXa3fAKxcHDkYtVvoc+NGvT10qtleL8gDTiymz6bny8HJRoraK69QIDBxoh4k2CECx4ceou5VAHDLLdkZaJ3S1D79lGZCy8potjZb2DUZKDQHZ8sWfZtyUX8DGB0cqzFnOBwcwLoOp6tL//xuuiGaBU4h1uB4ObemTAG++136+ayzyM3ctIlE6D77ZHUT8458n7Wa4BLr35hTYa06qWWjlmXqVODss4Ff/tKb2yHu4W1tNEYqCtXf2AW/Ykzv6NAdhFw7OPmaPLWrwcm3wEm3yYBZ4BS7g8MCZwcilcAR9uTatSlWsfVAMTYZAJI7qYmBx0tQ6vPps2ZioHnjDeDWW43paUDyAC0Ez1NP0XP33ttbWkEq5AFnyxY93zvbA00qspmi5jY9DdCLuJ97jj77N77hsNCfR5wEjkhPE6uWZ4t8OzipgtyqKl3IiDS14RA4VoFPPgWOcG9qa92l5Y00Bweg7nB77UXB4HHH0WO77eb9dQod+d5nrsMZHNTXNpozx/g3K7czGw6Oz0dNKy65xNv/ma+hn/wkeZtlKir0uEF8hpEqcLKZotbfn501cID81OCISUWRlSFjJXCiUaC9nX7ONM40wylqOyBuHRwRgDQ1Oaxi65JidXDMAifdgUeeSdm+HZg/Hzj3XOo8I46HWChVRhyjzk76ns30NPGeAN14Rf1Nc3Pmx9srYjBsaUkukPeKF4Eji8WmJuD3v8/svWWcBE4uGgwA9k0GCsXBAZLrcPLl4AzXmGQlcIRQcTtZUQwCx+u5VVoK/OMfNMaJcXWkpacBtI+EU2KeuV66lI5lc3NyLZ5Vq+hcdyNzQo4XDjuMUnpTIY/rQO6bDOTbwcmGwBEpfZmugQPoAqenR8/SEEsiuK3B8fmM3fFScd11wJtvWscqVhNwQnwpirf3cQOnqO2AeBU4maanASND4HR2kt0OeE8rkmdSfvlLfcZi0SI9WLHraieTrfVvBObFxYDhT08DaCZbBEgi2EmH9nZgxQr6+cgjUz9fDjD/+lfdacsGTq2ic9FgAMi/g+Nm9n24BI65BsdMITg4bhfrFefpli10rRaiwEnHedl7b3JyBCOtgxpAgZtdao6ov5kzJzldzCpFLdfryThRW0vncnMzCVM322CeCMy1g5PvGpxspKiJCbFM18ABktPj5Xpftw5OY6O3Cc/SUmDGDOv0R7E/xPgH6OlpdXXZn1hlB2cHhAWOe2SBIwLvhgbv3Z6EwHntNeBPf6KfQyEq1PzXv+h3p5xVgAI2c3e6TCkUgSPWwgEyS1NbvJhmqPbYw12jhFmzKD3mqquAE09M/32tEALno4+SZ5By5eDkowbH6yy+OfWmEAROLmd+syFw6uv1a3XTpsKpwUm3i5rMd79L3Qt9vuxP4BQKdq2izevfyIjr5NNP9cA518X6ToRCNFnz/vvunUdzmlKumwyMBAdH1Ctlmp4GJKfHpyNwvHyGVIhzWu6imav6G4AdnB0SIXDM7oCABY6OGDj7+jIr/BMC54YbaJA55hjg0kvpseuvp+9Wx0N+7Oijs7/vCkXgANnppOYlPQ2gz//449T5L9tMnEjn/cCA3hIYoJugcHVGmoNTSClqwaAuAAotRc2rwFEUY5raSHFwAArC/v1vCuymTcvOdhUaVg5OZyfVYgLWtSxjx9L1HI3qk2vZaBOdCdXVye3XnRhuB2ck1OAIsiFw5NdZvdq9wAmF9Gs7FwJHdiVztcgnwA7ODolbB0cERdkIeEdCk4F0GgwIhMAZGqKL7vrrqcizpIRqcoDUDk4uZjflhT5FDc5wt4gWZKOTmleBk0sCAT2Yl+twPv2U9ndpafZuYoJiqMExz+LlSuAAejBW7ClqwMgVOACNibW1mW9PoWJebwwAXnqJZpYnT7Y+D3w+ffwQAWE+HZx0GC4Hp5BqcAYG9JrZQhA4hx1G319+2ShwnI6FWA8RyM4Et0CM/Z98oosOdnCsYYGTJm4FjoAdnMw7m8jFc9/6FqUmNTXRz4JUNTi5EDjyjTffDk6mKWqtrXpty+zZWdigLGDVaCBXHdQAawdncFAPrArBwdl1V/re0kKTKLkUOOLzOrWJlovAc4GTwPHSEbHQBU4uxPNIQZ5IEjilpwnMM975dnC8MlwOjhBS2Wy57wW5Bmf1avq5vNybaDdncGRL4Bx+OH1fssQokFONeSIOzKaDM24cXQuRiD4G5lLgcJvoEczAALXhnDCBumgI3LaJFmRD4AzXbGm2kdfBycTBEV3UKiqM6VCXXaYHiFYpakJsHHhgbpyVkZSi9uKL9H369Ox3Y0kXK4GTq/obwLoGRzixQG5a8HptMlBTo18PH36opyjky8HJ9Xgkxs+tW/UuRqIbWroOjqjBKSSBM9LaO2cTqxQ1u/VvZITAeeUV4K67aOYbKB4Hx3yvzNV2z5wJvP46rc+TD+QUtf/8h34+5hhvEye5cnAOPZS+v/uuPsniZswTcWA2BY7fr7eRFg7+cAgcTlEbgYTDwJo1lP8vBzki+HHr4GQj4PX59EGgmAROthycE0+kGprbbjMOGBMm0NorgPXxmDGD1sARjQiyjXjPaJQWEwXy7+Ckm6JWSOlpAicHJ9v1N4C1gyOu/bKy3AQYXh0cQA/cli3T25Zmew0EQBc4TjU4uR6PRo2i8S8eJzHX0QH09tLf0nFwPvtMd3Dy3WSAHRx3mJsMbNxI92afz9ltFp3U/vlPWhBV1PJlM+jMJaWlxgnSXF1rigIccIC3+qBsIqeoCYHjtWmN+f5vtY5MOjQ06PcacY90cxxykaIGJLuSnKJmTZHMYeQPkUfZ1UVBjghc85GiBtAgPzRUvAInEwdn1CjgmWes/7ZwIc3qn3lm8t8UhdbMyRXyOSAcnHzX4GzdSgGg1051Yka0EAWO3Co6lw6OlcDJZf0NkJ7AmTSJ1gB59VX6vb4+N+KrEBwcv5+u/9ZWmkEVs7oNDd4cGCFwxCw+wA5OsWB2cESgOWOGcxrTnDmUZqmqtEbQfvtR+/tDDsnt9maTnXfO/To4+UZcBx9+CLzzDv0uFq91i3wvDgSyex8+/HC674h7pJsxb8ECiglEDU+2GE6BU8wOzgi9VLJLZaUucAT5FDh9fcUpcLZu1Rekynae78SJ1D46H8iBn6pS8JWv2cGaGvrq6CC3zIvDsW4dFe/7/dkfkDNBCJyNGylNdNUqXeDk0sGRmwzksoMakN4sviieFgInV+ec+Mz5FDgAfT4hcEQdhRf3Rn6+3GI13wInG22idwTMDo68/o0TEyfSfcfny3693nAxcSJNZgDFde/3ghgDH36Yvh9+uPc11eR4LBtr4Mgcfjjwl79QHQ7g7jgsXAhcfXX26xPNjTOEwMmFg1/MDk6RXu7Di7kjGuBN4JSXZ29mTrxfMQ1yIoAQM/C1tfmzwXOBOfAbNSq/s2zppqmJGdEDDshdIJ8OdXX6zNRVV5G7FImQCMtWCoJMsTg4YhZPuKK5qL8B3KWoDUfnJbnRQDod1ABd4Ij6G78//2MpOzjukB0cVXXXYEAQCBSvuAGM49xIdXDE53rrLfqezppqcjyW7e6aYtJPTHy5HTdy0XzF3EWTHRxriviSHz5EwJGuwMlmPcZwBhTZQgicNWvoe766tOQKn8842OUrPU2Qbic1LwHDcCNcnBtvpFqnL30J+O9/cxO0ODUZyJXw89pkANBn8QS5Fjj5dnCyIXAaG437Ot/1NwALHLfI3So/+IBa8peUFFeqWbrIwXq+BXmukK8DoPAEztixenE/kN/jIC9gG43qmTFcg2OEBY4LhMDx0kVNvlFls8BsuIp6s4kIGMXCXdkeeAoB+TzIV4MBQTqd1OJxXeAcfXTWNyljhMABgJ/+FHjgAe/1RW4pFgdnuASOOJ+t6hzyJXDS6aAGkCDeaSf993ynpwHcZMAtcptoMVYdcoj9YtsjiR3BwZGvg2nT0osTcilwAL1dNJDfGGzsWL1V9Ntv6+5KLlLUirlN9Ai9VLJLOg6OHHyxwDH+PtIcHMA4u51vgSMGduGYuWH1arK5y8qoXWih8a1vUfHpxReTe5NL8lGDI4IWRXEv3GprqbGAmL3LlcA57TQa76xmVIdzPJIFjpgs8SpwAAoORDfHQhM47ODYI6eoiXTaQnSbc8GO4ODIwi0d9wbIvcA57DDgzjvp53weB7+f1kJbs0avwayqso9HM6GYU9RY4LigEFPUimmQM6eBsIOTW444gr4/+yywfbu7Qk0xI3rEEYWZ/jhzJq0iPRwIgSNy/RVl+BycigpvOduTJuVe4JSXA9/9rvXf8u3geG0yYP6fQhI4wWBuApSRghiX+vqAl16in3cUgTN+PI0RQ0OFcc7mAlnoF6rAKRQHB6A0tTVr9OYTuUhPAzhFbcSTqcDJpoNTzE0GBCPRwZHPg3zX4EybRl+RCHD//e7+p5Drb4Yb+XwV13muHRyxqKqcPuUGkYsN5E7gOJEPgbN5c/opaoBR4BRSDQ67N86IMXbpUqCzk7pF7rtvXjdp2AiFgD/9CfjZz4pn/R6viOtg3Lj0j2s4rE8Q5ULg7LqrPoGZ7xhMjP25FjjF7OCwwHGBk8Cxy//NVYraqadSa9yDD87ea+Yas8BhByf3iPWA7ror9XMjEWDxYvqZBY7xmhZ1OLl2cCZOBB59lBYj9IJch5MPgZOPFLVPPtGPi1dBCBSegyMCCK6/cUaMsWIy5qijkgvTRzLnnUcth0cqYtw98cT0O4/5/VSj+YMfpDf5kQpF0V2cfAscMfaLWlt2cJJhgeOCdBwcOd0gmwLn0ktpFfdimsXZ0RycQhA4X/86DUyvv25cINOKZcso7aOxEdhrr+HZvkJGbikrAulcOzgAcMIJwN57e/uffAucfDg4Yg2cpqb0UroKTeCwg+MOOUUNSL3+DVNcXHAB1fv93/9l9jpXXw1cf31u2jMDusDJ99ghu/cAOzhWsMBxQTrr4ABkoQPpzTKOJOSBoLLSedXpYqWQmgwAFPwdeyz9nMrFkRfMK+a1IrKFoiQ3Gsi1g5MuhZKiNhx1WzU1xvdJd4a2UAVOoZ1bhYb5Xstu88hiv/2Ae+7JjfOSTU4/HTjnHODHP87vdgyXwGEHx4KXX34ZJ5xwAsaMGQNFUfDII4/k6q1yTjoODgD85jfAZZcB++yTu20rBuQgYuLE3M2s5BP5PMimY5cJZ5xB3//+d+fZF66/ScbcKno4HJx02HNPGp/Gjs3Ptg1nipqiGK+tdBoMmP+vEGpw5AYTjD1mcWsO8BhmOKisBG69Nf8O4tixxrgjFy2iAXZwLOnt7cW0adNw00035eothg2rdXBE4OMkcM44A7juupEZ0HtBFjgjMT0N0M+D+vrC6YR0wgk0671xo95W1UxnJ6WxASxwZMyLfRaqg1NZSesgvPFGfsaZww+nSYt0ux55RRY46c70NjfrN+1CcHBmz6bZ69NPz/eWFDbyuDpnDt9XmR0bn4+aHghynaJWjA5OztpEH3vssThW5MgUOek6OAxhdnBGIuI8KIT0NEFJCfDVrwJ/+Qtwxx2UGvjUU9R1Zc89qRHBp5/SzMzkydSKlCFkB2doiPYTkP8OeVbk85raYw9g7drhe79sCJxAgK7TjRsLQ+BMmAAsX57vrSh8ZAeHJ2MYhlzM996jnzlFLZmCWQdncHAQg0I1AOgSU6YFAAuczJDTQEaqgyNuvoUWAJ95Jgmc++6jL8GTTwK//a2e1sQBgxG5Bmf1anJvq6pIGDL5IxsCB6D0jkIROIw7zA4Ow+zoyGma3GQgmYIpKV60aBGqq6sTX+MKqNLMLHBUlQWOF9jByR8HHaSvKVBZCZx0Egmbk0+mugkxj3D00XnbxIJEdnDEOgMHHbRjtaUtRLIpcIDCqMFh3CHG2KlTC6fOkWHyyXAIHHZwssDll1+OSy+9NPF7V1dXwYgcs8CJRvW/scBJzY5Ug1NoAkdRgGefpTSiadP0YvBLLwW2baOFQHt7gQUL8rudhYZcgyMEzqxZ+dsehshGkwEAmDkT+Pe/KVhmioOjjgKmTwcuuSTfW8IwhYG8TAA7OMkUjMAJh8MIF6haEAKnt5dUrJRJxwLHBT4fCZu2Nqr1GInMmgX84x/AEUfke0uSqa+nLzMNDcBFFw3/9hQDVg4OC5z8IwSOomSWDvqDH9CiyQUyh8a4YNIkYOXKfG8FwxQOU6dSTWF9vXFx+WzCDs4IR27f2dNDK78LWOC4Y+lSWqCtujrfW5IbLrgAOOsszukfKQiBs2ED8PHH9PPMmfnbHoYQgmTcuMxaUysKixuGYYqbpibqkJrLuIodHAt6enrwsYgMAKxduxarVq1CXV0dxhdZu6bSUjrI8bix0YDfzzn5bim01K1cwOJm5CAEzksv0fcpU/SFe5n8MWMG8Itf0HeGYZgdncMOy+3rc5toC958800ceeSRid9Ffc2ZZ56JO++8M1dvmxMUhdLUOjvJwREzhyIIYhhmZCHE6pIl9J3T0woDRQGuvDLfW8EwDLNjwClqFsyePRuqqubq5YcdIXC6u/VcR05PY5iRiZi86Ouj7wcfnL9tYRiGYZh88N3vAuefn1lKcL7gGhyXyJ3UAtpeY4HDMCMTszvLDg7DMAyzoxEOF2+sWzDr4BQ6ssDhNXAYZmQjC5yaGmD33fO2KQzDMAzDeIQFjktY4DDMjoMscGbN0gstGYZhGIYpfPi27RJZ4AwM0M8scBhmZCJ3xOP0NIZhGIYpLljguESshcMODsOMfMwODsMwDMMwxQMLHJdwihrD7DgIgePzAQcemN9tYRiGYRjGGyxwXCIETk8PCxyGGemIFLW99gKqqvK7LQzDMAzDeIPbRLuEHRyG2XGYM4fWvrnggnxvCcMwDMMwXmGB4xIWOAyz4zBuHPC//+V7KxiGYRiGSQdOUXMJCxyGYRiGYRiGKXxY4LiEBQ7DMAzDMAzDFD4scFxiJXDkVrIMwzAMwzAMw+QfFjguYQeHYRiGYRiGYQofFjgu4YU+GYZhGIZhGKbwYYHjEl4Hh2EYhmEYhmEKHxY4LhECp6+PvgAWOAzDMAzDMAxTaLDAcYkQOADQ1kbfWeAwDMMwDMMwTGHBAscl4TAQ0JZF3bZNf4xhGIZhGIZhmMKBBY5LFEV3cVjgMAzDMAzDMExhwgLHAyxwGIZhGIZhGKawYYHjASFwtm+n7yxwGIZhGIZhGKawYIHjAbEWjqrSdxY4DMMwDMMwDFNYsMDxgNxJDWCBwzAMwzAMwzCFBgscD5gFTklJfraDYRiGYRiGYRhrWOB4gB0chmEYhmEYhilsWOB4gAUOwzAMwzAMwxQ2LHA8wAKHYRiGYRiGYQobFjgeYIHDMAzDMAzDMIUNCxwPsMBhGIZhGIZhmMKGBY4HxDo4AhY4DMMwDMMwDFNYsMDxADs4DMMwDMMwDFPYsMDxAAschmEYhmEYhilsWOB4gAUOwzAMwzAMwxQ2LHA8IAscnw8IBPK3LQzDMAzDMAzDJMMCxwOywGH3hmEYhmEYhmEKDxY4HmCBwzAMwzAMwzCFDQscD8gCp6Qkf9vBMAzDMAzDMIw1LHA8EAoBwSD9zA4OwzAMwzAMwxQeLHA8IlwcFjgMwzAMwzAMU3iwwPEICxyGYRiGYRiGKVxY4HiEBQ7DMAzDMAzDFC4scDzCAodhGIZhGIZhChcWOB5hgcMwDMMwDMMwhQsLHI+wwGEYhmEYhmGYwiWnAuemm27CxIkTUVJSgoMOOgivv/56Lt9uWGCBwzAMwzAMwzCFS84EzgMPPIBLL70UCxcuxIoVKzBt2jTMnz8fW7ZsydVbDgsscBiGYRiGYRimcMmZwPnd736Hc889F2effTamTJmCv/zlLygrK8Ptt9+eq7ccFioq6DsLHIZhGIZhGIYpPHIicCKRCJYvX465c+fqb+TzYe7cuVi6dKnl/wwODqKrq8vwVYjstht9nzgxr5vBMAzDMAzDMIwFORE427ZtQywWQ1NTk+HxpqYmtLS0WP7PokWLUF1dnfgaN25cLjYtY844A1i6FLjiinxvCcMwDMMwDMMwZgqmi9rll1+Ozs7OxNdnn32W702yxO8HZs7kFDWGYRiGYRiGKUQCuXjRhoYG+P1+tLa2Gh5vbW1Fc3Oz5f+Ew2GEWTUwDMMwDMMwDJMBOXFwQqEQZsyYgeeffz7xWDwex/PPP49Zs2bl4i0ZhmEYhmEYhmFy4+AAwKWXXoozzzwT+++/Pw488EDceOON6O3txdlnn52rt2QYhmEYhmEYZgcnZwLn1FNPxdatW3H11VejpaUF06dPx1NPPZXUeIBhGIZhGIZhGCZbKKqqqvneCCu6urpQXV2Nzs5OVFVV5XtzGIZhGIZhGIbJE160QcF0UWMYhmEYhmEYhskUFjgMwzAMwzAMw4wYWOAwDMMwDMMwDDNiYIHDMAzDMAzDMMyIgQUOwzAMwzAMwzAjBhY4DMMwDMMwDMOMGFjgMAzDMAzDMAwzYmCBwzAMwzAMwzDMiIEFDsMwDMMwDMMwIwYWOAzDMAzDMAzDjBhY4DAMwzAMwzAMM2JggcMwDMMwDMMwzIghkO8NsENVVQBAV1dXnreEYRiGYRiGYZh8IjSB0AhOFKzAaWtrAwCMGzcuz1vCMAzDMAzDMEwh0N3djerqasfnFKzAqaurAwBs2LAh5YdgCoOuri6MGzcOn332GaqqqvK9OYwL+JgVDnwsig8+ZsUBH6fig49ZcTDcx0lVVXR3d2PMmDEpn1uwAsfno/Kg6upqPrmLjKqqKj5mRQYfs8KBj0XxwcesOODjVHzwMSsOhvM4uTU9uMkAwzAMwzAMwzAjBhY4DMMwDMMwDMOMGApW4ITDYSxcuBDhcDjfm8K4hI9Z8cHHrHDgY1F88DErDvg4FR98zIqDQj5Oiuqm1xrDMAzDMAzDMEwRULAODsMwDMMwDMMwjFdY4DAMwzAMwzAMM2JggcMwDMMwDMMwzIiBBQ7DMAzDMAzDMCMGFjgMwzAMwzAMw4wYPAmcRYsW4YADDkBlZSUaGxtx0kkn4YMPPjA8Z2BgABdeeCHq6+tRUVGBU045Ba2trYbnXHzxxZgxYwbC4TCmT5+e9D4//elPoShK0ld5ebnj9r388ss44YQTMGbMGCiKgkceecTw92g0ih/96EfYe++9UV5ejjFjxuCMM87Apk2bvOyGomK4jhkAPP3005g5cyYqKysxatQonHLKKVi3bl3KbXzwwQexxx57oKSkBHvvvTeefPJJw98feughzJs3D/X19VAUBatWrfKyC4qOYj9mI+k6K/ZjAdB4uscee6C8vBy1tbWYO3cuXnvtNU/7oZgYCcdM5vzzz4eiKLjxxhtTvm4xMRKO01lnnZUUpxxzzDGe9kMxMRKOGQCsWbMGCxYsQHV1NcrLy3HAAQdgw4YNrvdDoTMSjpOVBlAUBdddd53r/eBJ4CxevBgXXnghli1bhmeffRbRaBTz5s1Db29v4jnf//738dhjj+HBBx/E4sWLsWnTJnzxi19Meq1vfvObOPXUUy3f57LLLsPmzZsNX1OmTMGXv/xlx+3r7e3FtGnTcNNNN1n+va+vDytWrMBVV12FFStW4KGHHsIHH3yABQsWeNgLxcVwHbO1a9fixBNPxFFHHYVVq1bh6aefxrZt2yxfR+bVV1/F1772NZxzzjlYuXIlTjrpJJx00klYvXp14jm9vb049NBDce2116a5F4qLYj9mI+k6K/ZjAQCTJ0/Gn/70J7zzzjt45ZVXMHHiRMybNw9bt25Nc68UNiPhmAkefvhhLFu2DGPGjPG4FwqfkXKcjjnmGEOsct9996WxN4qDkXDMPvnkExx66KHYY4898NJLL+Htt9/GVVddhZKSkjT3SuExEo6TWQPcfvvtUBQFp5xyivsdoWbAli1bVADq4sWLVVVV1Y6ODjUYDKoPPvhg4jlr1qxRAahLly5N+v+FCxeq06ZNS/k+q1atUgGoL7/8suttA6A+/PDDKZ/3+uuvqwDU9evXu37tYiZXx+zBBx9UA4GAGovFEo89+uijqqIoaiQSsd2er3zlK+pxxx1neOyggw5SzzvvvKTnrl27VgWgrly5MtXHHFEU8zETjJTrbCQci87OThWA+txzz9k+ZyRRrMfs888/V3faaSd19erV6oQJE9QbbrjBzcctWorxOJ155pnqiSee6PYjjjiK8Zideuqp6je+8Q3Xn3EkUIzHycyJJ56oHnXUUbZ/tyKjGpzOzk4AQF1dHQBg+fLliEajmDt3buI5e+yxB8aPH4+lS5em/T633norJk+ejMMOOyyTzbWks7MTiqKgpqYm669diOTqmM2YMQM+nw933HEHYrEYOjs78fe//x1z585FMBi0/b+lS5ca3hsA5s+fn9H5MtIYCcdspFxnxX4sIpEIbrnlFlRXV2PatGmut6+YKcZjFo/Hcfrpp+OHP/whpk6d6nqbipliPE4A8NJLL6GxsRG77747LrjgArS1tbnetmKn2I5ZPB7HE088gcmTJ2P+/PlobGzEQQcdlFTOMNIotuNkprW1FU888QTOOecc19sGZNBkIB6P45JLLsEhhxyCvfbaCwDQ0tKCUCiUFMQ0NTWhpaUlrfcZGBjAvffe6/mDuX3tH/3oR/ja176GqqqqrL9+oZHLY7bzzjvjmWeewRVXXIFwOIyamhp8/vnn+Oc//+n4fy0tLWhqasrovUcyI+GYjZTrrJiPxeOPP46KigqUlJTghhtuwLPPPouGhgbX21esFOsxu/baaxEIBHDxxRe73p5ipliP0zHHHIO7774bzz//PK699losXrwYxx57LGKxmOvtK1aK8Zht2bIFPT09uOaaa3DMMcfgmWeewcknn4wvfvGLWLx4sevtKyaK8TiZueuuu1BZWZky9c1M2gLnwgsvxOrVq3H//fen+xKuePjhh9Hd3Y0zzzwz8diSJUtQUVGR+Lr33ns9v240GsVXvvIVqKqKm2++OZubXLDk8pi1tLTg3HPPxZlnnok33ngDixcvRigUwpe+9CWoqooNGzYYjtmvf/3rrG/DSKTYj9lIus6K+VgceeSRWLVqFV599VUcc8wx+MpXvoItW7Zk/XMUGsV4zJYvX47f//73uPPOO6EoSta3uxApxuMEAF/96lexYMEC7L333jjppJPw+OOP44033sBLL72U9c9RaBTjMYvH4wCAE088Ed///vcxffp0/PjHP8bxxx+Pv/zlL1n/HIVAMR4nM7fffjtOO+00z3VSgXTe7KKLLsLjjz+Ol19+GWPHjk083tzcjEgkgo6ODoMybG1tRXNzczpvhVtvvRXHH3+8Qe3tv//+hk5aZiWYChF0rV+/Hi+88EJRzyq7JdfH7KabbkJ1dTV+85vfJB675557MG7cOLz22mtJx0xYpc3NzUmdOzI5X0YSxX7MRtJ1VuzHory8HJMmTcKkSZMwc+ZM7Lbbbrjttttw+eWXu97GYqNYj9mSJUuwZcsWjB8/PvH3WCyGH/zgB7jxxhtddSgqJor1OFmxyy67oKGhAR9//DHmzJnjehuLjWI9Zg0NDQgEApgyZYrhOXvuuSdeeeUV19tXLBTrcZJZsmQJPvjgAzzwwAOut0vgycFRVRUXXXQRHn74YbzwwgvYeeedDX+fMWMGgsEgnn/++cRjH3zwATZs2IBZs2Z53ri1a9fixRdfTEpPKy0tTdysJ02ahMrKStevKYKujz76CM899xzq6+s9b1cxMVzHrK+vDz6f8XTy+/0AaNYkEAgYjpk40WfNmmV4bwB49tln0zpfRgoj4ZiNlOtsJBwLK+LxOAYHB11vXzFR7Mfs9NNPx9tvv41Vq1YlvsaMGYMf/vCHePrpp93viAKn2I+TFZ9//jna2towevRo19tXTBT7MQuFQjjggAOSWiZ/+OGHmDBhguvtK3SK/TjJ3HbbbZgxY0Z6NaNeOhJccMEFanV1tfrSSy+pmzdvTnz19fUlnnP++eer48ePV1944QX1zTffVGfNmqXOmjXL8DofffSRunLlSvW8885TJ0+erK5cuVJduXKlOjg4aHjelVdeqY4ZM0YdGhpytX3d3d2J1wKg/u53v1NXrlyZ6NwUiUTUBQsWqGPHjlVXrVpl+Azm9x4pDNcxe/7551VFUdSf/exn6ocffqguX75cnT9/vjphwgTDe5n53//+pwYCAfX6669X16xZoy5cuFANBoPqO++8k3hOW1ubunLlSvWJJ55QAaj333+/unLlSnXz5s1Z3luFQbEfs5F0nRX7sejp6VEvv/xydenSpeq6devUN998Uz377LPVcDisrl69Ogd7LP8U+zGzYiR2USv249Td3a1edtll6tKlS9W1a9eqzz33nLrffvupu+22mzowMJCDPZZ/iv2YqaqqPvTQQ2owGFRvueUW9aOPPlL/+Mc/qn6/X12yZEmW91b+GAnHSVWp42dZWZl68803p7UfPAkcAJZfd9xxR+I5/f396ne+8x21trZWLSsrU08++eSkQPSII46wfJ21a9cmnhOLxdSxY8eqV1xxhevte/HFFy1f98wzz1RVVW8zbPX14osvetkVRcNwHrP77rtP3XfffdXy8nJ11KhR6oIFC9Q1a9ak3MZ//vOf6uTJk9VQKKROnTpVfeKJJwx/v+OOOyzfe+HChZnsmoKl2I/ZSLrOiv1Y9Pf3qyeffLI6ZswYNRQKqaNHj1YXLFigvv766xnvm0Kl2I+ZFSNR4BT7cerr61PnzZunjho1Sg0Gg+qECRPUc889V21pacl43xQqxX7MBLfddps6adIktaSkRJ02bZr6yCOPpL1PCpGRcpz++te/qqWlpWpHR0da+0HRdgbDMAzDMAzDMEzRk9E6OAzDMAzDMAzDMIUECxyGYRiGYRiGYUYMLHAYhmEYhmEYhhkxsMBhGIZhGIZhGGbEwAKHYRiGYRiGYZgRAwschmEYhmEYhmFGDCxwGIZhGIZhGIYZMbDAYRiGYRiGYRhmxMACh2EYhmEYhmGYEQMLHIZhGIZhGIZhRgwscBiGYRiGYRiGGTH8P/afwSyTI1xHAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(data1['Date'][len(data1)-len(Y_test1):], Y_test1, color='blue', label='Actual Google Stock Price')\n",
    "plt.plot(data1['Date'][len(data1)-len(predictions):], predictions, color='red', label='Predicted Google Stock Price')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.10"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}