diff --git a/docs/pages/performance/fashion-mnist/plot.b64 b/docs/pages/performance/fashion-mnist/plot.b64
index c8562d6d3..2df5f788d 100644
--- a/docs/pages/performance/fashion-mnist/plot.b64
+++ b/docs/pages/performance/fashion-mnist/plot.b64
@@ -1 +1 @@
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8vQzDMJ555hlDkjF69GhHW1HH4y+//GJIMl555RWnfiNGjDBMJpNx4MABR1vPnj2NVq1aFXhfAwcOzNd+pfK+n+W+Xu57zXm/CQsLM9LT0x3tr7/+uiHJmDVr1kWvU9Br/7///c+QZKxcudLRlvN+l/d9d9iwYUb16tUdx1u3bjUkGQ899JBTv1tvvbXQe8otKirKkGR89dVXTu3p6elGUFCQ0bp1ayMlJcXR/ttvvxmSjOeff/6iz1uUsZn7fmNiYgwvLy/j//7v/xxtRR1XhbnY90Der+vDDz9smEwmY8uWLY62s2fPGtWqVcv3c62o8V/sNShI3p9xRXk/LowkQ5KxceNGR9uRI0cMm81mDBs2zNF2zz33GLVr1zbOnDnj9Pibb77ZqFKlimO8FjZODMMwBg4c6PSzMkdRf1bkxGs2m41du3a5fK8AUJkwjQuoQPr06aOaNWuqfv36GjFihHx9fTV79mzVq1dPknTu3DktXbpUI0eOVEJCgs6cOaMzZ87o7Nmz6tevn/bv3+8ouZ85c6batWvnqPTJLefTZIvF4ljLxW6369y5c8rMzFR4eLg2b95cbPeVkJAgf3//i/bJOZ+QkODScy9atEixsbG65ZZbHK/HmTNnZLFY1KVLlwKnpBVlIejp06erSpUquu6665yeNywsTH5+fo7nzfm0+7ffflNGRoZLsUtSnTp1nL5GOVNEtmzZoujoaEcskZGRqlq1qlMsffr0UVZWVr5S/eHDhxepAiIn9lmzZhU6TWL69Olq2bKlWrRo4XTtnPVlcl6HX375RXa7Xc8//3y+qU85423x4sVKT0/Xo48+6tTnvvvuU0BAgObMmeP0OD8/P912222OY09PT3Xu3Fl//vmno23u3LmqXbu2RowY4Wjz8fHR/ffff8n7l7K3P/fw8HB8On255s6dq2uuucaxRo4r4yKnci9neuXlaty4sfr165evPfe6PXFxcTpz5ox69uypP//8U3FxcZIufG0efvhhp2qTghZhLep4nDt3riwWix555BGnx//f//2fDMPQvHnzruh+S9P999/vVHX24IMPysPDQ3Pnzr3o43K/9qmpqTpz5oxjXbKC3mMfeOABp+PIyEidPXtW8fHxkuS4Xt7X9EoXy924caNiYmL00EMPOVVWDhw4UC1atMj3vemq0NBQR4WIlF2h1bx5c6fvZVff5wpS2PdAXvPnz1dERIRT9Wq1atUc03ovJ/4rVZT344uJiIhQWFiY47hBgwYaMmSIFixYoKysLBmGoZkzZ2rQoEEyDMPpNe7Xr5/i4uKu6Od+UX9W5OjZsyfrcQHAJTCNC6hAPvzwQ1111VWKi4vTl19+qZUrVzqVlR84cECGYejf//53ods1x8TEqG7dujp48KCGDx9+yWt+/fXXeuutt/THH384/VHauHFjl2KPi4tTSkqK49jT09Mxfcjf3/+SSZyc80FBQS5dd//+/ZJU6OK2AQEBTsceHh6O5NmlnjcuLq7QeGJiYiRl/8I6fPhwTZgwQe+884569eqloUOH6tZbby3SoqAhISH5pnJcddVVkrKn8QUHB2v//v3avn17oQmcnFhyFPVrd9NNN+nzzz/Xvffeq6efflq9e/fWjTfeqBEjRjiSMfv379eePXsuee2DBw/KbDZf9Jf3I0eOSFK+qRWenp5q0qSJ43yOevXq5Xttqlatqu3btzs9Z0GvYWHTN0pCRkaGFi1a5JiCJLk2Loy/16+52HSgoijs675mzRq98MILWrduXb71X+Li4lSlShXHa9+sWTOn8zVr1nSaRiqpyOPxyJEjqlOnTr5Eb87Ulrxf7ytx7tw5p+lm3t7eqlKlSrE9f97Xxc/PT7Vr177kGkLnzp3ThAkT9P333+f7Ps1JtOXWoEEDp+Oc1/78+fMKCAjQkSNHZDab8619dqXjvbDvTUlq0aKFVq9efUXPn/e+pOx7y73ukavvcwUp6nvfkSNHFBERka89JCSkwP5Fif9KFeX9+GLyjlEp+2dJcnKyTp8+LbPZrNjYWH366af69NNPC3yOorzGhSnqz4ocrv6OAQCVEckeoALp3LmzYzeuoUOHqnv37rr11lu1d+9e+fn5OT7te+KJJwr99LKwX1YL8u233+rOO+/U0KFD9eSTTyooKEgWi0UTJ07Mtyj0pfzzn/90LLQsZf+xm7M4ZWhoqDZv3qy0tLRCEyDbt2+Xp6en6tatK6nwP3yzsrKcjnNek6lTpyo4ODhf/7y7nXh5eRXpF2e73a6goCB99913BZ7P+YXWZDJpxowZWr9+vX799VctWLBAd999t9566y2tX7/+iitGcmK57rrr9NRTTxV4Pic5lKOoOzB5e3tr5cqVWrZsmebMmaP58+frhx9+0LXXXquFCxfKYrHIbrerTZs2evvttwt8jvr167t2My6wWCwFthsXWcjYVdWrV1dmZmaRqs8Ks3r1asXHx2vAgAGONlfGRc4fjDVq1Liieyno637w4EH17t1bLVq00Ntvv6369evL09NTc+fO1TvvvHNZFQSujsfScOONN2rFihWO49GjRzstPFxUed9frtTIkSO1du1aPfnkk2rfvr3jfbx///4FvvalMebdoSj3VRzjqqR2nyuNr0tR3o+vRM54u+222zR69OgC+7Rt2/aKnt+VnxXlZadAAHAnkj1ABZWTdLnmmmv0wQcf6Omnn1aTJk0kSVartcDdg3Jr2rSpdu7cedE+M2bMUJMmTfTTTz85JVdeeOEFl+N96qmnnKbc5K4GGDRokNauXavp06c79clx+PBhrVq1SkOGDHH8Apjz+NjYWKeFQfNWA+R8wh0UFHTJ18QVTZs21eLFi3X11VcX6ZfSrl27qmvXrnr11Vc1bdo0jRo1St9//73uvffeiz4up1or9+u/b98+SXIsWNm0aVMlJiYW6/3lMJvN6t27t3r37q23335br732mp599lktW7ZMffr0UdOmTbVt2zb17t37opUnTZs2ld1u1+7duwtd2Lthw4aSshdRzRnLUvaucIcOHbqs+2vYsKF27tyZ7zXcu3dvkR7fokULSdm7cl3uHzpz5sxRaGhogQuMFmVcHDp0SNKlF3O9nMqfX3/9VWlpaZo9e7ZTdULeKRU5X5v9+/c7fW1Onz6dr3qhqOOxYcOGWrx4cb5E2h9//OF0zeLw1ltvOcVZp06di/avWrVqvh3g0tPTdfLkyQL779+/X9dcc43jODExUSdPnnRK8OV1/vx5LVmyRBMmTNDzzz/v9FyXq2HDhrLb7Tp48KBTFU5Rx3thYyj392beKsm9e/de8mt1pVVpUsm+z+XVsGFDHThwIF97QW1FVRyvwaXejy+moHG1b98++fj4OD6c8Pf3V1ZW1iWf62L3Uti5ov6sAAAUHWv2ABVYr1691LlzZ7377rtKTU1VUFCQevXqpU8++aTAP0pOnz7t+P/hw4dr27Zt+vnnn/P1y/k0MueTwtyfTv7+++9at26dy7GGhoaqT58+jn+51w4YM2aMgoOD9eSTT+Zb4yA1NdWxvWvuT3Rzkji512lISkpyqh6SpH79+ikgIECvvfZagWuj5H5NXDFy5EhlZWXp5ZdfzncuMzPT8Yfi+fPn8326m5PsKMr23ydOnHD6GsXHx+ubb75R+/btHZVKI0eO1Lp167RgwYJ8j4+NjVVmZmZRb8vJuXPn8rXljX3kyJE6fvy4Pvvss3x9U1JSlJSUJCm7Es1sNuull17KV7GQ8/r06dNHnp6eev/9951esy+++EJxcXFF2lEorwEDBujEiROaMWOGoy05ObnQaQp55Uzl2Lhxo8vXzjF37tx8sbsyLjZt2qQqVao4diUqjK+vryTlS1JcTEHf43Fxcfrqq6+c+vXp00dWq1X//e9/nfoWtANSUcfjgAEDlJWVpQ8++MCpzzvvvCOTyeTYUa04hIWFOb3/XGotkKZNm+ZbA+bTTz8ttLLn008/dXp/mTx5sjIzMy96DwW99lLBr2lR5Vzv/fffv6znLGwMhYeHKygoSB9//LHT+Jw3b5727Nlzye/NyxmbeZXU+1xB+vXrp3Xr1mnr1q2OtnPnzhVayVkUV/oaFOX9+GLWrVvntObOX3/9pVmzZqlv376yWCyyWCwaPny4Zs6cWeAHQbl/Vl7sXnx9fQucgljUnxUAgKKjsgeo4J588kn94x//0JQpU/TAAw/oww8/VPfu3dWmTRvdd999atKkiU6dOqV169bp2LFj2rZtm+NxM2bM0D/+8Q/dfffdCgsL07lz5zR79mx9/PHHateunW644Qb99NNPGjZsmAYOHKhDhw7p448/VmhoqBITE4vtHqpWraoZM2ZowIAB6tixo+69916FhoYqOjpaU6ZM0Z9//qkPPvhAXbp0cTymb9++atCgge655x49+eSTslgs+vLLL1WzZk0dPXrU0S8gIECTJ0/W7bffro4dO+rmm2929JkzZ46uvvrqfH9sFkXPnj01ZswYTZw4UVu3blXfvn1ltVq1f/9+TZ8+Xe+9955GjBihr7/+Wh999JGGDRumpk2bKiEhQZ999pkCAgIu+ql/jquuukr33HOPoqKiVKtWLX355Zc6deqU0x/jTz75pGbPnq0bbrjBsd1vUlKSduzYoRkzZujw4cOXNQXopZde0sqVKzVw4EA1bNhQMTEx+uijj1SvXj11795dknT77bfrxx9/1AMPPKBly5bp6quvVlZWlv744w/9+OOPWrBggcLDwxUSEqJnn31WL7/8siIjI3XjjTfKy8tLUVFRqlOnjiZOnKiaNWtq/PjxmjBhgvr376/Bgwdr7969+uijj9SpU6cCq74u5b777tMHH3ygO+64Q5s2bVLt2rU1depU+fj4FOnxTZo0UevWrbV48WLdfffdTudWrlzpSAicPn1aSUlJeuWVVyRlbzfdo0cPHTp0SHv27NHkyZOdHuvKuFi0aJEGDRp0yU/D27dvL4vFokmTJikuLk5eXl669tprL7rOVd++feXp6alBgwZpzJgxSkxM1GeffaagoCCnhHHNmjX1xBNPaOLEibrhhhs0YMAAbdmyRfPmzcs3too6HgcNGqRrrrlGzz77rA4fPqx27dpp4cKFmjVrlh599NF8686UpnvvvVcPPPCAhg8fruuuu07btm3TggULCv0+Sk9PV+/evTVy5EjHmO3evbsGDx5c6DUCAgLUo0cPvf7668rIyFDdunW1cOFCRyXX5Wjfvr1uueUWffTRR4qLi1O3bt20ZMmSIlekNG3aVIGBgfr444/l7+8vX19fdenSRY0bN9akSZN01113qWfPnrrlllscW683atRIjz322CXjcnVs5lVS73MFeeqpp/Ttt9/quuuu08MPP+zYer1BgwY6d+7cZVWmXOlrUJT344tp3bq1+vXr57T1uiRNmDDB0ec///mPli1bpi5duui+++5TaGiozp07p82bN2vx4sWOhNPFxklYWJh++OEHPf744+rUqZP8/Pw0aNCgIv+sAAC4oHQ3/wJQEnK29y1om+asrCyjadOmRtOmTY3MzEzDMAzj4MGDxh133GEEBwcbVqvVqFu3rnHDDTcYM2bMcHrs2bNnjXHjxhl169Y1PD09jXr16hmjR492bLtqt9uN1157zWjYsKHh5eVldOjQwfjtt9/ybUNuGFe29XqOw4cPG/fff7/RoEEDw8PDw7Fd7OLFiwvsv2nTJqNLly6Gp6en0aBBA+Ptt98u9LrLli0z+vXrZ1SpUsWw2WxG06ZNjTvvvNNpK9rRo0cbvr6+BV6roHs2DMP49NNPjbCwMMPb29vw9/c32rRpYzz11FPGiRMnDMMwjM2bNxu33HKL0aBBA8PLy8sICgoybrjhBqfrFiZnW+kFCxYYbdu2Nby8vIwWLVoY06dPz9c3ISHBGD9+vBESEmJ4enoaNWrUMLp162a8+eabji2hc7Zef+ONNy55bcMwjCVLlhhDhgwx6tSpY3h6ehp16tQxbrnllnxbH6enpxuTJk0yWrVqZXh5eRlVq1Y1wsLCjAkTJhhxcXFOfb/88kujQ4cOjn49e/Y0Fi1a5NTngw8+MFq0aGFYrVajVq1axoMPPmicP3/eqU9hW3EX9HU6cuSIMXjwYMPHx8eoUaOG8c9//tOYP39+kbdBfvvttw0/P79822TnbI1c0L+c74UPPvjAqFKlipGRkeH02KKOiz179lz0eyCvzz77zGjSpIlhsVic7u9iW5TPnj3baNu2rWGz2YxGjRoZkyZNMr788st830dZWVnGhAkTjNq1axve3t5Gr169jJ07d+bbttowijYec/o99thjRp06dQyr1Wo0a9bMeOONN5y2dzeM0t96PSsry/jXv/5l1KhRw/Dx8TH69etnHDhwoNCt11esWGHcf//9RtWqVQ0/Pz9j1KhRxtmzZy95nWPHjhnDhg0zAgMDjSpVqhj/+Mc/jBMnTuR7P80Za3m33S7o/S4lJcV45JFHjOrVqxu+vr7GoEGDjL/++qtIW68bhmHMmjXLCA0NdbwH595e+4cffnB8/1arVs0YNWqUcezYsUs+p2G4PjZ79uxp9OzZ06mtqOOqIBcbKwWN4S1bthiRkZGGl5eXUa9ePWPixInG+++/b0gyoqOjL/m8BcVf2GtQkLxbrxf1/bggkoyxY8ca3377rdGsWTPHz/OCrn/q1Clj7NixRv369Q2r1WoEBwcbvXv3Nj799FOnfoWNk8TEROPWW281AgMDDUlO78dF/VmREy8A4OJMhlHOV+0DUGktWbJEAwYMUPfu3TVv3jzHNvCVRaNGjdS6dWv99ttv7g6lUouLi1OTJk30+uuv65577nHpsQMGDJCfn59+/PHHy7r2o48+qpUrV2rTpk2sc1EGTZkyRXfddZeioqKoSqgEHn30UX3yySdKTEy84gWRAQC4UqzZA6Dc6t27t77++mstW7ZMd911V7nfcQblU5UqVfTUU0/pjTfecHl3ql69el1yikthzp49q88//1yvvPIKiR6glKWkpDgdnz17VlOnTlX37t1J9AAAygQqewCgnKKyByjbqOypuNq3b69evXqpZcuWOnXqlL744gudOHFCS5YsUY8ePdwdHgAALNAMAAAAuGLAgAGaMWOGPv30U5lMJnXs2FFffPEFiR4AQJlBZQ8AAAAAAEAFwpo9AAAAAAAAFQjJHgAAAAAAgAqENXsk2e12nThxQv7+/uxoAgAAAAAocYZhKCEhQXXq1JHZTB0GihfJHkknTpxQ/fr13R0GAAAAAKCS+euvv1SvXj13h4EKhmSPJH9/f0nSoUOHVK1aNTdHA5SMjIwM/fLLLxo6dKisVqu7wwFKBOMclQHjHJUB4xyVwblz59S4cWPH36NAcSLZIzmmbvn7+ysgIMDN0QAlIyMjQz4+PgoICOCXJlRYjHNUBoxzVAaMc1QGGRkZksRSIigRTAwEAAAAAACoQEj2AAAAAAAAVCAkewAAAAAAACoQkj0AAAAAAAAVCMkeAAAAAACACoRkDwAAAAAAQAVCsgcAAAAAAKACIdkDAAAAAABQgZDsAQAAAAAAqEBI9gAAAAAAAFQgJHsAAAAAAAAqEJI9AAAAAAAAFQjJHgAAAAAAgArErcmeyZMnq23btgoICFBAQIAiIiI0b968fP0Mw9D1118vk8mkX375xenc0aNHNXDgQPn4+CgoKEhPPvmkMjMzS+kOAAAAAAAAyhYPd168Xr16+s9//qNmzZrJMAx9/fXXGjJkiLZs2aJWrVo5+r377rsymUz5Hp+VlaWBAwcqODhYa9eu1cmTJ3XHHXfIarXqtddeK81bAQAAAAAAKBPcWtkzaNAgDRgwQM2aNdNVV12lV199VX5+flq/fr2jz9atW/XWW2/pyy+/zPf4hQsXavfu3fr222/Vvn17XX/99Xr55Zf14YcfKj09vTRvBQAAAAAAoExwa2VPbllZWZo+fbqSkpIUEREhSUpOTtatt96qDz/8UMHBwfkes27dOrVp00a1atVytPXr108PPvigdu3apQ4dOhR4rbS0NKWlpTmO4+PjJUkZGRnKyMgoztsCyoycsc0YR0XGOEdlwDhHZcA4R2XA+EZJcnuyZ8eOHYqIiFBqaqr8/Pz0888/KzQ0VJL02GOPqVu3bhoyZEiBj42OjnZK9EhyHEdHRxd6zYkTJ2rChAn52ufMmSMfH5/LvRWgXJg1a5a7QwBKHOMclQHjHJUB4xwVWXJysrtDQAXm9mRP8+bNtXXrVsXFxWnGjBkaPXq0VqxYoQMHDmjp0qXasmVLsV9z/Pjxevzxxx3H8fHxql+/vgYOHKjq1asX+/WAsiAjI0OzZs3SkCFDZLVa3R0OUCIY56gMGOeoDBjnqAzOnj3r7hBQgbk92ePp6amQkBBJUlhYmKKiovTee+/J29tbBw8eVGBgoFP/4cOHKzIyUsuXL1dwcLA2bNjgdP7UqVOSVOC0rxxeXl7y8vLK1261WvlhggqPcY7KgHGOyoBxjsqAcY6KjLGNkuTWBZoLYrfblZaWpqefflrbt2/X1q1bHf8k6Z133tFXX30lSYqIiNCOHTsUExPjePyiRYsUEBDgmAoGAAAAAABQmbi1smf8+PG6/vrr1aBBAyUkJGjatGlavny5FixYoODg4AKrcxo0aKDGjRtLkvr27avQ0FDdfvvtev311xUdHa3nnntOY8eOLbByBwAAAAAAoKJza7InJiZGd9xxh06ePKkqVaqobdu2WrBgga677roiPd5isei3337Tgw8+qIiICPn6+mr06NF66aWXSjhyAAAAAACAssmtyZ4vvvjCpf6GYeRra9iwoebOnVtcIQEASkFWQoJinnjCqS3ozTdl8fd3U0QAAABAxVHm1uwBAAAAAADA5XP7blwAgMojKyFBkmT/+7+55W6jwgcAAAC4fCR7AAClJu/UrdzOTJjg+P/an3xSGuEAAAAAFRLTuAAAAAAAACoQkj0AgDIn9quvlH7wYIEL8wMAAAC4OKZxAQDKnJT165Wyfr086tSRT2SkvLt2ldnHx91hAQAAAOUCyR4AQKkJevNNSdmLMedeo6cwmSdOKP6HHxT/00/yDg+XT2SkrE2ayGQylXSoAAAAQLlFsgcAUGoutsuWV9u2Stu5U7Lb85/MyFDKunVKWbdOHnXrZlf7dOlCtQ8AAABQAJI9AIAyocodd0iGoZS1a5W8apWyzpwpsF/m8eOK//57xc+cmV3t06OHrI0bU+0DAAAA/I1kDwCg1Fn8/QvdXt2vf3/59u2r9L17lbxypVK3bi1ytY9HWFjJBg4AAACUAyR7AABljslsllfLlvJq2VJZ8fFFrvbRzJlqXr26Mg4flkdICNU+AAAAqJRI9gAAyjRLQMCFap8//lDyqlUXrfapHR2tuDffVFK9ehfW9vH2LvW4AQAAAHch2QMAKBdMZrO8QkPlFRqqrLi47Gqf1asLr/Y5dkzx//ufEmbOlK1Tp+ydvBo1otoHAAAAFR7JHgBAuWOpUkV+118v3379sqt9Vq5U6rZtBVb7GOnpSlmzRilr1sijXj359Ogh786dqfYBAABAhUWyBwBQbuWt9klctUpnFy2Sd2pqgf0zjx1T/LRpSpgxI7vap0cPWRs2pNoHAAAAFQrJHgBAhWCpUkU+/fppbny8BrdqpbS1a5W2ffulq33q189e24dqHwAAAFQQJHsAABWLySTP0FD5tmunrNhYJa9dq5TVq5V19myB3TP/+iu72ifX2j6ejRqVbswAAABAMSLZAwCosCyBgfIfMEB+/fsrbfduJa9aVXi1T1qaUlavVsrq1fJo0OBCtY/N5obIAQAAgMtHsgcAUOGZzGbZWreWrXXr7GqfNWuyq33OnSuwf+bRo4r/7jvH2j6+f6/tAwAAAJQHJHsAAJWKJTBQ/gMHyu/667OrfVauVNqOHUWq9vGNjJSNah8AAACUcSR7AACVklO1z/nzSl67VsmrVsl+/nyB/TOPHlXcd98pfsYMeXfunL2TV4MGpRw1AAAAcGkkewAAlZ6latUL1T67dl1Y28cw8vU10tKUvGqVkletkrVhQ/lERsrWqRPVPgAAACgzSPYAAPA3k9ksW5s2srVpk13ts2aNklevLrTaJ+PIEcUdOaL46dPl3aWLfCIjqfYBAACA25HsAQCgAJaqVeV/ww3yGzAgu9onZ22fwqp9Vq5U8sqV2dU+PXrIFh5OtQ8AAADcgmQPAAAXka/aZ/VqJa9Zc/Fqn6lTs6t9ctb2qV+/lKMGAABAZUayBwCAIrJUrSr/QYOcq3127iy42ic19UK1T6NGF9b28fJyQ+QAAACoTEj2AADgIpPFIlvbtrK1bausc+curO0TG1tg/4zDhxV3+LDz2j5U+wAAAKCEkOwBAOAKWKpVu1Dts3Nn9k5eF6v2WbFCyStWZFf75KztQ7UPAAAAihHJHgAAioHJYpGtXTvZ2rXLrvbJWdvnUtU+P/6YXe3To4es9eqVbtAAAACokEj2AABQzCzVqsl/8GD5DRyYXe2zcqXSdu26dLVP48byiYyUd6dOMnl6uiFyAAAAVAQkewAAKCG5q30yz55Vypo1F6/2OXRIcYcOXVjbp0cPWevWLd2gAQAAUO6R7AEAoBR4VK9+odpnx47stX0Kq/ZJSVHy8uVKXr48u9qnRw95h4dT7QMAAIAiIdkDAEApMlkssrVvL1v79tnVPjlr+8TFFdjfUe2Te20fqn0AAABwESR7AABwE4/q1eU/ZIj8brghu9pn5Uql7d596Wqfpk2z1/YJC6PaBwAAAPmQ7AEAwM2cqn3OnLlQ7RMfX2D/jIMHFXfwoHO1T506pRw1AAAAyiqSPQAAlCEeNWrIf+hQ+Q0apLTt27OrffbsKbjaJzlZycuWKXnZMqp9AAAA4ECyBwCAMshkscjWoYNsHToo88wZJa9erZSiVvt07SqfyEiqfQAAACopkj0AAJRxHjVqKGDoUPkPGqTUbduUvGqV0nfvLrCvkZys5KVLlbx0qawhIdnVPh07Uu0DAABQiZDsAQCgnDBZLPLu2FHeHTsq8/Tp7GqftWsLr/Y5cEBxBw4o/ocfqPYBAACoREj2AABQDnnUrKmAYcPkP3hwdrXPypVK37OnwL5U+wAAAFQuJHsAACjHCqz2WbNG9oSEAvvnrvbxiYiQd2SkrLVrl3LUAAAAKEkkewAAqCAc1T651/a5SLVP0pIlSlqyRJ7NmsknMlK2jh1lslpLOWoAAAAUN5I9AABUMCYPD3mHhck7LCy72mfVquy1fQqp9knfv1/p+/fL9MMP8unaVT49esgjOLiUowYAAEBxIdkDAEAF5lGzpgJuvNF5bZ8//iiwr5GU5Fzt06OHbB06UO0DAABQzpDsAQCgEnCq9omJya72Wbfu0tU+vr7yiYiQT2Qk1T4AAADlBMkeAAAqGY+gIAUMHy7/IUOUunVrdrXP3r0F9jWSkpS0eLGSFi+W51VXZa/tQ7UPAABAmUayBwCASsrk4SHv8HB5h4cr89Sp7J281q6VPTGxwP7p+/Ypfd++7Gqfbt2yq31q1SrlqAEAAHApJHsAAIA8atXKrvYZPDi72mfVqotX+yxapKRFi7KrfXr0kK19e6p9AAAAygiSPQAAwMFktcq7Uyd5d+qUXe2Ts7bPJap9zH5+8s5Z24dqHwAAALci2QMAAArkUauWAkaMcF7bZ9++AvvaExMvVPs0b35hbR8PftUAAAAobfwGBgAALsqp2ic6WsmrVyt57VoZSUkF9k/fu1fpe/fK7O9/odonKKiUowYAAKi8SPYAAIAi8wgOvlDts2VL9to+hVX7JCQoaeFCJS1cmF3tk7O2D9U+AAAAJYrftgAAgMtMVqu8O3eWd+fO2dU+q1Yped06qn0AAADKAJI9AADgingEByvgH/+Q/9Ch2dU+K1cqff/+Avs6Vfu0bJm9tk+7dlT7AAAAFCN+swIAAMUiX7XPypVKXr++8GqfPXuUvmdPdrVPt27Z1T41a5Zy1AAAABUPyR4AAFDsPIKDFTBypPyHDVPq5s3Z1T4HDhTY156QoKQFC5S0YMGFap/27WWyWEo5agAAgIqBZA8AACgxJqtV3l26yLtLF2WcPKmUnLV9kpML7O+o9gkIyK726d6dah8AAAAXkewBAAClwlq7tqx/V/ukbNqklFWrCq/2iY9X0vz5Spo/X56hoRfW9qHaBwAA4JJI9gAAgFJlslrl07WrfLp2VcaJE0petUop69cXXu2ze7fSd+++UO0TGSmPGjVKOWoAAIDyg2QPAABwG2udOqpy000KGDZMKX+v7ZNx8GCBfR3VPrnX9qHaBwAAIB+SPQAAwO1Mnp5Fr/YxDOdqn6uvzl7bh2ofAAAASSR7AABAGeNU7bNpk5JXrbp4tc+8eUqaP19ef6/t49W2LdU+AACgUiPZAwAAyiSTp6d8IiLkExGhjOPHL1T7pKTk72wYStu1S2m7dslcpYp8unWTN9U+AACgkiLZAwAAyjxr3bqqcvPNCrjxxuxqn5UrlfHnnwX2tcfFKXHePCXmVPv06CGvNm2o9gEAAJUGyR4AAFBu5Kv2WblSKb//fulqn8DAC9U+1auXfuAAAACliGQPAAAol6x166rKLbcoYPhwpWzcmL22T2HVPrGxSpw7V4nz5smrVavstX2o9gEAABUUyR4AAFCumTw95dOtm3y6dVPGsWMX1vZJTc3f2TCUtnOn0nbuzK72+XsnL0u1aqUfOAAAQAkh2QMAACoMa716qnLLLfK/8Ual5lT7HDpUYF97bKwS58xR4ty52dU+PXrIq3Vrqn0AAEC5R7IHAABUOGYvr+yqnauvVsZff2VX+/z+O9U+AACgUiDZAwAAKjRr/fqqcuut8h8+3LVqn9ats9f2odoHAACUMyR7AABApeBytc+OHUrbsUPmqlUdj6PaBwAAlAckewAAQKXjqPbJvbbP4cMF9rWfP6/E335T4pw52dU+OWv7mM2lGzQAAEARkewBAACVltlmk0/37vLp3l0ZR49mV/ts2FD0ap/u3WWpWrX0AwcAALgIkj0AAACSrA0aqMqoUc5r+xSl2qdt2+y1fVq1otoHAACUCSR7AAAAcimw2uf332WkpeXvbBhK27ZNadu2ZVf7dO+evbYP1T4AAMCN3Prx0+TJk9W2bVsFBAQoICBAERERmjdvniTp3Llzevjhh9W8eXN5e3urQYMGeuSRRxQXF+f0HEePHtXAgQPl4+OjoKAgPfnkk8rMzHTH7QAAgAomp9on6PXXVeW222Rt0KDQvvbz55X466+KeeYZnfvoI6Xu2CHDbi/FaAEAALK5tbKnXr16+s9//qNmzZrJMAx9/fXXGjJkiLZs2SLDMHTixAm9+eabCg0N1ZEjR/TAAw/oxIkTmjFjhiQpKytLAwcOVHBwsNauXauTJ0/qjjvukNVq1WuvvebOWwMAABWI2WaTT2SkfCIjlXHkyIW1fQqq9rHbHdU+lmrV5J1T7RMYWOpxAwCAysmtyZ5BgwY5Hb/66quaPHmy1q9fr3vuuUczZ850nGvatKleffVV3XbbbcrMzJSHh4cWLlyo3bt3a/HixapVq5bat2+vl19+Wf/617/04osvytPTs7RvCQAAVHDWhg1VpWFD+Y8YodQNG5S0apUyjx4tsG/WuXNKnD1bib/9dmFtn9BQ1vYBAAAlqsys2ZOVlaXp06crKSlJERERBfaJi4tTQECAPDyyw163bp3atGmjWrVqOfr069dPDz74oHbt2qUOHToU+DxpaWlKy/VJXHx8vCQpIyNDGRkZxXVLQJmSM7YZ46jIGOcoVRaLrBERCoyIUObRo0pZvVppGzdK6en5+9rtStu6VWlbt8pcrZps3brJq2vXy6r2YZyjMmCcozJgfKMkuT3Zs2PHDkVERCg1NVV+fn76+eefFRoamq/fmTNn9PLLL+v+++93tEVHRzsleiQ5jqOjowu95sSJEzVhwoR87XPmzJGPj8/l3gpQLsyaNcvdIQAljnEOt/D0lKVzZwXFxKjOiRPyT0wssJv93Dkl//abkn77TWdq1NDJ2rV1rlo1yWRy6XKMc1QGjHNUZMnJye4OARWYyTAMw50BpKen6+jRo4qLi9OMGTP0+eefa8WKFU4Jn/j4eF133XWqVq2aZs+eLavVKkm6//77deTIES1YsMDRNzk5Wb6+vpo7d66uv/76Aq9ZUGVP/fr1dfLkSVWvXr2E7hRwr4yMDM2aNUtDhgxxfA8BFQ3jHGWFYRjKPHpUqWvWFF7tk4u5enXZIiJki4iQuUqVi/ZlnKMyYJyjMjh79qxq167tmMECFCe3V/Z4enoqJCREkhQWFqaoqCi99957+uSTTyRJCQkJ6t+/v/z9/fXzzz87vdkHBwdrw4YNTs936tQpx7nCeHl5ycvLK1+71WrlhwkqPMY5KgPGOcoCz5AQ+YSEyD5ypFI2bFDyqlXK/OuvAvvaz55V8m+/KXnuXNnatZNPZKQ8W7a86No+jHNUBoxzVGSMbZQktyd78rLb7Y6qm/j4ePXr109eXl6aPXu2bDabU9+IiAi9+uqriomJUVBQkCRp0aJFCggIKHAqGAAAQGkze3vLt2dP+fTokb2T18qVSo2KklHI2j6pW7YodcsWWapXl0/37vK++mpZLlHtAwAAkJtbkz3jx4/X9ddfrwYNGighIUHTpk3T8uXLtWDBAsXHx6tv375KTk7Wt99+q/j4eMdCyjVr1pTFYlHfvn0VGhqq22+/Xa+//rqio6P13HPPaezYsQVW7gAAALiLyWSSZ6NG8mzUSPZ//CO72mflSmUeO1Zg/6yzZ5Uwa5YSfv1Vtvbts6t9WrQo5agBAEB55NZkT0xMjO644w6dPHlSVapUUdu2bbVgwQJdd911Wr58uX7//XdJckzzynHo0CE1atRIFotFv/32mx588EFFRETI19dXo0eP1ksvveSO2wEAACgSp2qfw4eVvGrVxat9Nm9W6ubNstSoIa+ICHnmWnsQAAAgL7cme7744otCz/Xq1UtFWTu6YcOGmjt3bnGGBQAAUCpMJpM8GzeWZ+PG2dU+v/+evbZPYdU+Z84o+ddf1dVkUnxqqvx69JBnixYXXdsHAABUPmVuzR4AAIDKyOztLd9eveTTs2d2tc/KlUqJipIyMvL3NQylb9mic1u2yFKjhnwiI+XdrZss7OYCAABEsgcAAKBMyV3tEzBypFLWr8+u9jl+vMD+WWfOKOHnn5Uwe/aFtX2aN6faBwCASoxkDwAAQBll9vaW7zXXyKdXL2UcOpRd7bNxY4HVPsrKUuqmTUrdtEmWmjWzd/Ki2gcAgEqJZA8AAEAZZzKZ5NmkiTybNJH3sGH6/dNP1SIpSVknTxbYP+v0aap9AACoxEj2AAAAlCNmHx8dr1dPXYcPl/76S8mrVhWt2icoKLvaJyKCah8AACo4kj0AAADlkMlkkrVpU3k2baqA3Dt5nThRYP+smBgl/PSTEmbNkq1DhwvVPiZTKUcOAABKGskeAACAcs7s6yvfa6+VzzXXKOPPP7PX9tm0qfBqn40blbpx44Vqn27dZPH3L/3AAQBAiSDZAwAAUEGYTCZ55lT7jByZXe2zcqUyC1vbJ6faZ/bsC9U+V11FtQ8AAOUcyR4AAIAKyKna5+DBC2v7ZGbm75yZqdSoKKVGRWVX+0RGyqdbN5n9/Eo/cAAAcMVI9gAAAFRgJpNJniEh8gwJya72Wb8+e22fi1X7zJzpvLYP1T4AAJQrJHsAAAAqCbOvr3x795bPtddmV/vkrO1zqWqfWrWyq30iIqj2AQCgHCDZAwAAUMk4VfvcdJNS1q1T0qpVyoqOLrB/1qlTSpgxQwm//CJbx47Z1T7NmlHtAwBAGUWyBwAAoBIz+/rKt08f+fTurYwDB5S0cqVSN28uvNpnwwalbthAtQ8AAGUYyR4AAABkV/s0aybPZs1kv+kmJf+9tg/VPgAAlD8kewAAAODE7Ocnvz595Nu7t9L371fyqlVFq/YJDr5Q7ePrW/qBAwAASSR7AAAAUAiTySSvq66S11VXZVf7rFuXXe1z6lSB/bOio5UwfboSfv5Z3mFh8omMlDUkhGofAABKGckeAAAAXJLZz09+110n3z59sqt9Vq5U6pYthVb7pPz+u1J+/10etWvLJzJS3l27Uu0DAEApIdkDAACAInOq9klMvGS1T+bJk4r/8UfF5672adqUah8AAEoQyR4AAABcFqdqn337stf2KazaJyNDKevXK2X9eqp9AAAoYSR7AAAAcEVMJpO8mjeXV/PmykpIUEpOtU9MTIH9qfYBAKBkkewBAABAsbH4+8uvb1/5XndddrVPzto+WVn5O+eu9qlTJ7vap0sXqn0AALhCJHsAAABQ7PJV+6xdq+TVqwuv9jlxQvE//KD4n36Sd3h4drVPkyZU+wAAcBlI9gAAAKBEWfz95dev34Vqn5y1fQqr9lm3Tinr1l2o9unaVWYfn9IPHACAcopkDwAAAEqFyWyWV4sW8mrRQlnx8RfW9jl9usD+VPsAAHB5SPYAAACg1FkCAi5U++zde6Hax27P3zl3tU/duhfW9qHaBwCAApHsAQAAgNuYzGZ5tWwpr5Yts6t91q7NrvY5c6bA/pnHjyv+++8VP3OmvDt1yq72adyYah8AAHIh2QMAAIAywRIQIL/+/eXbt2/Rqn3WrlXK2rXyqFdPPt27Z6/t4+1d+oEDAFDGkOwBAABAmeJytc+xY4r//nsl/PSTbDnVPo0aUe0DAKi0SPYAAACgzHKq9vnjj+xqn61bC6z2MdLTlbJmjVLWrMmu9unRQ96dO1PtAwCodEj2AAAAoMwzmc3yCg2VV2iosuLisqt9Vq++eLXPtGlKmDGDah8AQKVDsgcAAADliqVKFfldf718+/Vzrdqnfv3snbyo9gEAVHAkewAAAFAu5av2WbMmu9rn7NkC+2f+9Vd2tc/MmReqfRo2pNoHAFDhkOwBAABAuWepUkV+AwbIt39/pe/Zo6SVK5W2fXvB1T5paUpZvVopq1dnV/vkrO1js7khcgAAih/JHgAAAFQYJrNZXq1ayatVK2XFxSl5zRqlXKra57vvnNb28WzUqHSDBgCgmJHsAQAAQIVkqVJF/gMGyK9/f6Xt3q3kVauKVu3ToMGFtX2o9gEAlEMkewAAAFChmcxm2Vq3lq11a2XFxl6o9jl3rsD+mUePOqp9vDt3dqztAwBAeUGyBwAAAJWGJTBQ/gMHyu/667OrfVauVNqOHYVW+ySvWqXkVatkbdBAPj16yNapE9U+AIAyj2QPAAAAKh2nap/z55W8du1Fq30yjh5V3LffKn76dHl36ZJd7dOgQSlHDQBA0ZDsAQAAQKVmqVr1QrXPrl0X1vYxjHx9jbQ0Ja9cqeSVK2Vt2FA+kZFU+wAAyhySPQAAAID+rvZp00a2Nm2yq33WrFHy6tWynz9fYP+MI0cUd+SI4nOv7UO1DwCgDCDZAwAAAORhqVpV/jfcIL8BA7KrfXLW9imo2ic19UK1T6NG2dU+4eFU+wAA3IZkDwAAAFCIfNU+q1crec2awqt9Dh9W3OHDzmv71K9fylEDACo7kj0AAABAEViqVpX/oEHyGzhQaTt3Zq/tc7FqnxUrlLxiRXa1T48e2dU+Xl5uiBwAUNmQ7AEAAABcYDKbZWvbVra2bZV17tyFtX1iYwvs76j2+fHH7GqfHj1krVevdIMGAFQqJHsAAACAy2SpVi272mfAgAvVPjt3Xrrap3HjC2v7UO0DAChmJHsAAACAK2SyWGRr1062du2yq31y1vYprNrn0CHFHTqUXe3TtWv22j5U+wAAignJHgAAAKAYWapVk//gwc5r+1ys2mf5ciUvX55d7dOjh7zDw2Xy9HRD5ACAioJkDwAAAFACrqjaJ2dtn7p1SzdoAECFQLIHAAAAKGFO1T47dmRX++zaVXC1T0rKhWqfJk2yq33Cwqj2AQAUGckeAAAAoJSYLBbZ2reXrX17ZZ49q5Scap+4uAL7Z/z5p+L+/PNCtU9kJNU+AIBLItkDAAAAuIFH9eryHzJEfjfccOlqn+RkJS9bpuRly2Rt2lQ+kZFU+wAACkWyBwAAAHAjp2qfM2cuVPvExxfYP+PgQcUdPOi8k1edOqUcNQCgLCPZAwAAAJQRHjVqyH/oUPkNGqS07duzq3127y682mfpUiUvXZpd7dOjh7w7dqTaBwBAsgcAAAAoa0wWi2wdOsjWoYMyz5xR8urVSilKtc8PP1DtAwAg2QMAAACUZR41aihg6FD5Dxqk1G3blLJqldL27Ll0tU9ISPbaPlT7AEClQ7IHAAAAKAdMFou8O3aUd8eO2dU+q1YpZe3awqt9DhxQ3IEDiv/hB/lERMg7MlLW2rVLOWoAgDuQ7AEAAADKGY8aNRQwbJj8Bw9W6rZtSl61Sum7dxfY10hOVtKSJUpaskSeISHy6dFDto4dZbJaSzlqAEBpIdkDAAAAlFNO1T6nT19Y2ychocD+6QcOKP3AAZl++EE+XbvKp0cPeQQHl3LUAICSRrIHAAAAqAA8atbMrvb5e22f5FWrlL5nT4F9jaSkC9U+zZplV/t06CCT1aqshATFPPGEU/+gN9+Uxd+/NG4DAFAMSPYAAAAAFYjJw0PeYWHyDgvLrvbJWdunsGqf/fuVvn+/TL6+8omIkK1jx1KOGABQ3Ej2AAAAABWUR82aCrjxRue1fS5W7bN4sZIWL853LneiiAofACj7SPYAAAAAFZxTtU9MTHa1z7p1hVb75HVmwgTH/9f+5JOSChMAUExI9gAAAACViEdQkAKGD5f/kCFK3bo1u9rnjz/cHRYAoBiR7AEAAAAqIZOHh7zDw+UdHq7MU6d0+vnn3R0SAKCYmN0dAAAAAAD38qhVy90hAACKEZU9AAAAABT05puSshdjzr1GT46qDz8sa8OGpR0WAOAyUNkDAAAAQBZ/f1n8/WUuZLettF272IkLAMoJkj0AAAAALil140YZdru7wwAAFAHJHgAAAAAOFn9/1f7kE9XMM5XLHh+v9H373BQVAMAVJHsAAAAA5OMRHCyP+vWd2lKiotwUDQDAFSR7AAAAABTIu1Mnp+PUzZtlZGa6KRoAQFGR7AEAAABQIFt4uNOxkZystN273RQNAKCoSPYAAAAAKJBH9eqyNm3q1MZULgAo+0j2AAAAAChU3qlcadu2yUhPd1M0AICiINkDAAAAoFC2sDDJZHIcG2lpSt2+3Y0RAQAuxa3JnsmTJ6tt27YKCAhQQECAIiIiNG/ePMf51NRUjR07VtWrV5efn5+GDx+uU6dOOT3H0aNHNXDgQPn4+CgoKEhPPvmkMlk0DgAAACgWloAAebZo4dTGVC4AKNvcmuypV6+e/vOf/2jTpk3auHGjrr32Wg0ZMkS7du2SJD322GP69ddfNX36dK1YsUInTpzQjTfe6Hh8VlaWBg4cqPT0dK1du1Zff/21pkyZoueff95dtwQAAABUOPmmcu3cKXtKipuiAQBciluTPYMGDdKAAQPUrFkzXXXVVXr11Vfl5+en9evXKy4uTl988YXefvttXXvttQoLC9NXX32ltWvXav369ZKkhQsXavfu3fr222/Vvn17XX/99Xr55Zf14YcfKp15xAAAAECxsHXoIFksFxoyM5W6ZYv7AgIAXJSHuwPIkZWVpenTpyspKUkRERHatGmTMjIy1KdPH0efFi1aqEGDBlq3bp26du2qdevWqU2bNqpVq5ajT79+/fTggw9q165d6tChQ4HXSktLU1pamuM4Pj5ekpSRkaGMjIwSukPAvXLGNmMcFRnjHJUB4xxuYbXKMzRU6Tt2OJqSN2yQNU/FT3FhnKMyYHyjJLk92bNjxw5FREQoNTVVfn5++vnnnxUaGqqtW7fK09NTgYGBTv1r1aql6OhoSVJ0dLRToifnfM65wkycOFETJkzI1z5nzhz5+Phc4R0BZdusWbPcHQJQ4hjnqAwY5yhtQZmZCs11nL5nj2ZNm6YMT88SuybjHBVZcnKyu0NABeb2ZE/z5s21detWxcXFacaMGRo9erRWrFhRotccP368Hn/8ccdxfHy86tevr4EDB6p69eolem3AXTIyMjRr1iwNGTJEVqvV3eEAJYJxjsqAcQ53MdLSdHb8eOnv5RJMkvrWqyfvHj2K/VqMc1QGZ8+edXcIqMDcnuzx9PRUSEiIJCksLExRUVF67733dNNNNyk9PV2xsbFO1T2nTp1ScHCwJCk4OFgbNmxwer6c3bpy+hTEy8tLXl5e+dqtVis/TFDhMc5RGTDOURkwzlHqrFbZ2rVTaq6duDI2b1ZA794leEnGOSouxjZKklsXaC6I3W5XWlqawsLCZLVatWTJEse5vXv36ujRo4qIiJAkRUREaMeOHYqJiXH0WbRokQICAhQaGprvuQEAAABcvry7cqUfOKCsc+fcFA0AoDBurewZP368rr/+ejVo0EAJCQmaNm2ali9frgULFqhKlSq655579Pjjj6tatWoKCAjQww8/rIiICHXt2lWS1LdvX4WGhur222/X66+/rujoaD333HMaO3ZsgZU7AAAAAC6fV2ioTD4+MnKtNZKycaP8+vZ1Y1QAgLzcmuyJiYnRHXfcoZMnT6pKlSpq27atFixYoOuuu06S9M4778hsNmv48OFKS0tTv3799NFHHzkeb7FY9Ntvv+nBBx9URESEfH19NXr0aL300kvuuiUAAACgwjJZrbJ16KCUNWscbalRUSR7AKCMcWuy54svvrjoeZvNpg8//FAffvhhoX0aNmyouXPnFndoAAAAAArg3amTU7In4+hRZZ46JY88u+QCANynzK3ZAwAAAKDs8rzqKpn9/Z3aUnIt2gwAcD+SPQAAAACKzGSxyBYW5tSWEhUlwzDcFBEAIC+SPQAAAABc4t25s9NxVnS0Mo8dc1M0AIC8SPYAAAAAcIm1cWNZqlVzamMqFwCUHSR7AAAAALjEZDbLFh7u1Ja6cSNTuQCgjCDZAwAAAMBl+aZynT2rjD//dFM0AIDcSPYAAAAAcJlHvXqy5NlunalcAFA2kOwBAAAA4DKTySTvTp2c2lI3bZJht7spIgBADpI9AAAAAC5L3mSPPT5e6Xv3uikaAEAOkj0AAAAALotHcLA86td3amMqFwC4H8keAAAAAJct31SuLVtkZGa6KRoAgESyBwAAAMAVyJvsMZKTlbZrl5uiAQBIJHsAAAAAXAFLtWqyNm3q1MZULgBwL5I9AAAAAK5I3uqetG3bZE9Lc1M0AACSPQAAAACuiC0sTDKZHMdGerrStm93Y0QAULmR7AEAAABwRSwBAfJs0cKpjalcAOA+JHsAAAAAXLF8U7l27ZI9OdlN0QBA5UayBwAAAMAVs3XoIHl4XGjIzFTqli3uCwgAKjGSPQAAAACumNnHR16tWjm1MZULANyDZA8AAACAYpF3Klf6H38oKz7eTdEAQOVFsgcAAABAsfBq21YmL68LDYah1E2b3BcQAFRSJHsAAAAAFAuzl5e82rZ1amMqFwCUPpI9AAAAAIpN3qlcGQcPKuvcOTdFAwCVE8keAAAAAMXGq1UrmXx8nNqo7gGA0kWyBwAAAECxMXl4ZG/DngvJHgAoXSR7AAAAABSrvFO5Mv/6S5nR0W6KBgAqH5I9AAAAAIqVZ/PmMgcEOLVR3QMApYdkDwAAAIBiZTKbZQsLc2pLiYqSYRhuiggAKheSPQAAAACKXd6pXFmnTinz2DE3RQMAlQvJHgAAAADFztqkiSzVqzu1pWzY4KZoAKByIdkDAAAAoNiZTCbZwsOd2lI3bmQqFwCUApI9AAAAAEpEvqlc584p488/3RQNAFQeJHsAAAAAlAiPevXkUbu2UxtTuQCg5JHsAQAAAFAiCpzKtWmTjKwsN0UEAJUDyR4AAAAAJSbvVC57QoLS9+1zUzQAUDmQ7AEAAABQYjxq1ZJHgwZObSlRUW6KBgAqB5I9AAAAAEpU3uqe1C1bZGRkuCkaAKj4SPYAAAAAKFHeedbtMZKTlbZ7t5uiAYCKj2QPAAAAgBJlqVZN1pAQpzamcgFAySHZAwAAAKDE5Z3KlbZtm+xpaW6KBgAqNpI9AAAAAEqcrWNHyXzhzw8jPV1p27e7MSIAqLhI9gAAAAAocZaAAHm2aOHUxlQuACgZJHsAAAAAlIp8U7l27pQ9KclN0QBAxUWyBwAAAECpsHXoIHl4XGjIylLq1q1uiwcAKiqSPQAAAABKhdnbW16tWzu1MZULAIofyR4AAAAApSbvVK70P/5QVny8m6IBgIqJZA8AAACAUmNr21YmL68LDYah1E2b3BcQAFRAJHsAAAAAlBqTp6e82rVzamMqFwAUL5I9AAAAAEpV3qlcGQcPKvPsWTdFAwAVD8keAAAAAKXKKzRUJh8fp7bUjRvdFA0AVDwkewAAAACUKpOHh2wdOzq1MZULAIoPyR4AAAAApS7vVK7Mv/5SZnS0m6IBgIqFZA8AAACAUud51VUyBwQ4tVHdAwDFg2QPAAAAgFJnMptlCw93akuJipJhGG6KCAAqDpI9AAAAANwi71SurFOnlPnXX26KBgAqDpI9AAAAANzC2rixLNWrO7UxlQsArhzJHgAAAABuYTKZZMtT3ZMSFSXDbndTRABQMZDsAQAAAOA2eady2c+fV+ahQ26KBgAqBpI9AAAAANzGo25dedSu7dSWtmmTm6IBgIqBZA8AAAAAtyloKlfali0yMZULAC4byR4AAAAAbpV3KpeRkKDA2Fj3BAMAFQDJHgAAAABu5REUJGvDhk5tQTExbooGAMo/kj0AAAAA3C7vVK6ap0/LyMhwUzQAUL6R7AEAAADgdt7h4ZLJ5Dj2yMpS+p49bowIAMovj8t50NGjR3XkyBElJyerZs2aatWqlby8vIo7NgAAAACVhKVqVXmGhCh9/35HW9rGjfILC3NjVABQPhU52XP48GFNnjxZ33//vY4dOybDMBznPD09FRkZqfvvv1/Dhw+X2UzBEAAAAADX2Dp1ckr2pO/YIXtqqsw2mxujAoDyp0hZmUceeUTt2rXToUOH9Morr2j37t2Ki4tTenq6oqOjNXfuXHXv3l3PP/+82rZtq6ioqJKOGwAAAEAFY+vYUcr9wXFGhtK2b3dfQABQThWpssfX11d//vmnqlevnu9cUFCQrr32Wl177bV64YUXNH/+fP3111/qlGeBNQAAAAC4GIu/v7xatlTarl2OtpSoKHl37uzGqACg/ClSsmfixIlFfsL+/ftfdjAAAAAAKjdbp05OyZ60XbtkT0qS2dfXjVEBQPni8uI6KSkpSk5OdhwfOXJE7777rhYsWFCsgQEAAACofGzt20seuT6TzspS6pYtbosHAMojl5M9Q4YM0TfffCNJio2NVZcuXfTWW29p6NChmjx5crEHCAAAAKDyMHt7y7NVK6e2FNYEBQCXuJzs2bx5syIjIyVJM2bMUK1atXTkyBF98803ev/994s9QAAAAACVi1ee7dbT9+5VVlycm6IBgPLH5WRPcnKy/P39JUkLFy7UjTfeKLPZrK5du+rIkSPFHiAAAACAysWzdWtlWiwXGgxDqZs2uS8gAChnXE72hISE6JdfftFff/2lBQsWqG/fvpKkmJgYBQQEFHuAAAAAACoXk6enztSo4dTGVC4AKDqXkz3PP/+8nnjiCTVq1EhdunRRRESEpOwqnw4dOhR7gAAAAAAqn5igIKfjjD//VOaZM26KBgDKF5eTPSNGjNDRo0e1ceNGzZ8/39Heu3dvvfPOOy4918SJE9WpUyf5+/srKChIQ4cO1d69e536REdH6/bbb1dwcLB8fX3VsWNHzZw506nPuXPnNGrUKAUEBCgwMFD33HOPEhMTXb01AAAAAGXE+apVZfLxcWpL3bjRTdEAQPlS5GRPgwYNNG7cOC1cuFA1atRQhw4dZDZfeHjnzp3VokULly6+YsUKjR07VuvXr9eiRYuUkZGhvn37KikpydHnjjvu0N69ezV79mzt2LFDN954o0aOHKktubZfHDVqlHbt2qVFixbpt99+08qVK3X//fe7FAsAAACAssMwm+XZvr1TG1O5AKBoipzsmTp1qry8vDR27FjVqFFDN910k7777jvFxsZe9sXnz5+vO++8U61atVK7du00ZcoUHT16VJtyLb62du1aPfzww+rcubOaNGmi5557ToGBgY4+e/bs0fz58/X555+rS5cu6t69u/773//q+++/14kTJy47NgAAAADu5RUe7nSceeyYMk6edFM0AFB+eBS1Y8+ePdWzZ0+99dZb2rVrl2bPnq3//ve/uueee9StWzcNHjxYgwcPVpMmTS47mLi/t1OsVq2ao61bt2764YcfNHDgQAUGBurHH39UamqqevXqJUlat26dAgMDFZ7rB0GfPn1kNpv1+++/a9iwYfmuk5aWprS0NMdxfHy8JCkjI0MZGRmXHT9QluWMbcY4KjLGOSoDxjkqA8f4bthQ5oAA2f/+fV2Skn7/Xb4DB7opMqD48D6OkmQyDMO4kieIjo7Wr7/+qtmzZ2vJkiVq0qSJJk2apIEuvgHb7XYNHjxYsbGxWr16taM9NjZWN910kxYuXCgPDw/5+Pho+vTpjl3AXnvtNX399df51voJCgrShAkT9OCDD+a71osvvqgJEybka//888/lk2deMAAAAAD3Cdm/X/WOH3ccJ3t7a0PnzpLJ5MaogCuXnJyse++9V3FxcexsjWJX5MqewgQHB+u+++7Tfffdp+TkZC1YsEBeXl4uP8/YsWO1c+dOp0SPJP373/9WbGysFi9erBo1auiXX37RyJEjtWrVKrVp0+ayYh4/frwef/xxx3F8fLzq16+vgQMHqnr16pf1nEBZl5GRoVmzZmnIkCGyWq3uDgcoEYxzVAaMc1QGuce5jh9X3JtvOs75pKRoaJcu8mjQwI0RAlfu7Nmz7g4BFdgVJ3tyZGZm6syZMwVOm7qUcePGORZWrlevnqP94MGD+uCDD7Rz5061atVKktSuXTutWrVKH374oT7++GMFBwcrJiYmXyznzp1TcHBwgdfz8vIqMCFltVr5pQkVHuMclQHjHJUB4xyVgdVqlUdIiBJr1FBWrm3XM7ZskXfTpm6MDLhyvIejJLm89Xphdu3apcaNG7v0GMMwNG7cOP38889aunRpvscnJydnB2l2DtNischut0uSIiIiFBsb67So89KlS2W329WlS5fLuRUAAAAAZYTJZJItz0LNKRs3yvj77wEAQH7Fluy5HGPHjtW3336radOmyd/fX9HR0YqOjlZKSookqUWLFgoJCdGYMWO0YcMGHTx4UG+99ZYWLVqkoUOHSpJatmyp/v3767777tOGDRu0Zs0ajRs3TjfffLPq1KnjxrsDAAAAUBy8O3d2OrafP6+MP/90UzQAUPYVeRpXx44dL3o+J0HjismTJ0uSY2etHF999ZXuvPNOWa1WzZ07V08//bQGDRqkxMREhYSE6Ouvv9aAAQMc/b/77juNGzdOvXv3ltls1vDhw/X++++7HA8AAACAssdat6486tRR5okTjraUDRvkGRLixqgAoOwqcrJn9+7duvnmmwudqnXy5Ent27fPpYsXZSOwZs2aaebMmRftU61aNU2bNs2lawMAAAAoP2zh4UqcPdtxnLp5swJuukkmi8WNUQFA2VTkZE/r1q3VpUuXArcyl6StW7fqs88+K7bAAAAAACCHd6dOTskee0KC0vfulVdoqBujAoCyqchr9lx99dXau3dvoef9/f3Vo0ePYgkKAAAAAHLzCAqStVEjp7aUDRvcEwwAlHFFrux57733Lnq+adOmWrZs2RUHBAAAAAAFsYWHK+PwYcdx6pYtMkaNkoktrAHAiVt34wIAAACAovIOD5dMJsexkZqqtF273BgRAJRNRUr2HD161KUnPX78+GUFAwAAAACFsVStKs9mzZzamMoFAPkVKdnTqVMnjRkzRlFRUYX2iYuL02effabWrVtfcvcsAAAAALgctvBwp+PU7dtlT011UzQAUDYVac2e3bt369VXX9V1110nm82msLAw1alTRzabTefPn9fu3bu1a9cudezYUa+//roGDBhQ0nEDAAAAqIS8w8IU//33kt2e3ZCRobTt2+XdubN7AwOAMqRIlT3Vq1fX22+/rZMnT+qDDz5Qs2bNdObMGe3fv1+SNGrUKG3atEnr1q0j0QMAAACgxJj9/PJtt85ULgBwVuTduCTJ29tbI0aM0IgRI0oqHgAAAAC4KFt4uNJ27nQcp+3eLXtSksy+vm6MCgDKDnbjAgAAAFCu2Nq3l3Jvt56VpdQtW9wWDwCUNSR7AAAAAJQrZm9v2dq0cWpjKhcAXECyBwAAAEC5k3dXrvR9+5QVF+emaACgbCHZAwAAAKDcsbVpI5PNdqHBMJS6aZP7AgKAMoRkDwAAAIByx+Tpmb12Ty5M5QKAbEXajWv27NlFfsLBgwdfdjAAAAAAUFS28HClrF/vOM44dEiZZ87Io0YNN0YFAO5XpGTP0KFDnY5NJpMMw3A6zpGVlVU8kQEAAADARXiFhsrk6ysjKcnRlrpxo/z693djVADgfkWaxmW32x3/Fi5cqPbt22vevHmKjY1VbGys5s6dq44dO2r+/PklHS8AAAAASJJMFou8w8Kc2lKiotwUDQCUHUWq7Mnt0Ucf1ccff6zu3bs72vr16ycfHx/df//92rNnT7EGCAAAAACFsYWHK3nlSsdx5rFjyjhxQtY6ddwYFQC4l8sLNB88eFCBgYH52qtUqaLDhw8XQ0gAAAAAUDSezZrJnOfvk9SNG90TDACUES4nezp16qTHH39cp06dcrSdOnVKTz75pDp37lyswQEAAADAxZjM5gKncuVeYxQAKhuXkz1ffvmlTp48qQYNGigkJEQhISFq0KCBjh8/ri+++KIkYgQAAACAQtk6dXI6zoqJUcaRI26KBgDcz+U1e0JCQrR9+3YtWrRIf/zxhySpZcuW6tOnj9OuXAAAAABQGqyNGslSs6ayTp92tKVu3CjPRo3cFxQAuJHLyR4pe6v1vn37qm/fvsUdDwAAAAC4xGQyyTs8XInz5jnaUjZulP+NN8pkdnkyAwCUe5eV7FmyZImWLFmimJgY2e12p3NffvllsQQGAAAAAEVl69TJKdljP39e6QcOyOuqq9wYFQC4h8tp7gkTJqhv375asmSJzpw5o/Pnzzv9AwAAAIDSZq1bVx55tltnVy4AlZXLlT0ff/yxpkyZottvv70k4gEAAACAy+LdqZMSZs1yHKdu2qSAm26SyWJxY1QAUPpcruxJT09Xt27dSiIWAAAAALhseXflsicmKm3PHjdFAwDu43Ky595779W0adNKIhYAAAAAuGweNWvKmmcHLqZyAaiMXJ7GlZqaqk8//VSLFy9W27ZtZbVanc6//fbbxRYcAAAAALjCu1MnZRw+7DhO3bJFxqhRMuX5uwUAKjKXkz3bt29X+/btJUk7d+50OmcymYolKAAAAAC4HLbwcMXPmCEZhiTJSE1V6o4d8u7Y0c2RAUDpcTnZs2zZspKIAwAAAACumCUwUJ7Nmil93z5HW+rGjSR7AFQqLq/Zk9uxY8d07Nix4ooFAAAAAK5Y3oWaU7dvlz011U3RAEDpcznZY7fb9dJLL6lKlSpq2LChGjZsqMDAQL388suy2+0lESMAAAAAFJl3x46SOdefOhkZSt261W3xAEBpc3ka17PPPqsvvvhC//nPf3T11VdLklavXq0XX3xRqampevXVV4s9SAAAAAAoKrOfn7xCQ5WWa43R1I0b5dO1qxujAoDS43Ky5+uvv9bnn3+uwYMHO9ratm2runXr6qGHHiLZAwAAAMDtbJ06OSV70nbtkj0xUWY/PzdGBQClw+VpXOfOnVOLFi3ytbdo0ULnzp0rlqAAAAAA4ErY2reXcm+3brcrdfNmt8UDAKXJ5WRPu3bt9MEHH+Rr/+CDD9SuXbtiCQoAAAAAroTZZpOtTRuntpSNG90UDQCULpencb3++usaOHCgFi9erIiICEnSunXr9Ndff2nu3LnFHiAAAAAAXA5bp05O1Tzp+/YpKzZWlsBA9wUFAKXA5cqenj17au/evRo2bJhiY2MVGxurG2+8UXv37lVkZGRJxAgAAAAALrO1bi2TzXahwTCUSnUPgErA5coeSapbty4LMQMAAAAo00yenrK1b6+U9esdbSkbN8q3Tx83RgUAJc/lyp6vvvpK06dPz9c+ffp0ff3118USFAAAAAAUB1unTk7HGYcOKfP0aTdFAwClw+Vkz8SJE1WjRo187UFBQXrttdeKJSgAAAAAKA5eLVvK5Ovr1JYaFeWmaACgdLic7Dl69KgaN26cr71hw4Y6evRosQQFAAAAAMXBZLHIOyzMqY1duQBUdC4ne4KCgrR9+/Z87du2bVP16tWLJSgAAAAAKC55p3JlHj+ujBMn3BQNAJQ8l5M9t9xyix555BEtW7ZMWVlZysrK0tKlS/XPf/5TN998c0nECAAAAACXzTMkROY8262nbtjgnmAAoBS4nOx5+eWX1aVLF/Xu3Vve3t7y9vZW3759de2117JmDwAAAIAyx2Q2yzs83KktZeNGGYbhpogAoGS5vPW6p6enfvjhB7388svatm2bvL291aZNGzVs2LAk4gMAAACAK2br1ElJixc7jrNOn1bGkSPybNTIfUEBQAlxOdmTo1GjRjIMQ02bNpWHx2U/DQAAAACUOGvDhrLUrKmsXNuup27YQLIHQIXk8jSu5ORk3XPPPfLx8VGrVq0cO3A9/PDD+s9//lPsAQIAAADAlTKZTPLOs1BzyqZNMux2N0UEACXH5ZKc8ePHa9u2bVq+fLn69+/vaO/Tp49efPFFPf3008UaIAAAAAAUB1unTkqcO9dxbI+NVfqBA/K66io3RgUULCsrSxkZGe4OA2WIxWKRh4eHTCbTJfu6nOz55Zdf9MMPP6hr165OF2jVqpUOHjzo6tMBAAAAQKmw1qkjj7p1lXn8uKMtNSqKZA/KnMTERB07doxFxJGPj4+PateuLU9Pz4v2cznZc/r0aQUFBeVrT0pKKlJ2CQAAAADcxbtTJyXkSvakbNqkgJtvlslicWNUwAVZWVk6duyYfHx8VLNmTf7OhiTJMAylp6fr9OnTOnTokJo1ayazufCVeVxO9oSHh2vOnDl6+OGHJckx8D7//HNFRERcZtgAAAAAUPJs4eFK+OUXx7GRlKS0PXtka93afUEBuWRkZMgwDNWsWVPe3t7uDgdliLe3t6xWq44cOaL09HTZbLZC+7qc7Hnttdd0/fXXa/fu3crMzNR7772n3bt3a+3atVqxYsUVBQ4AAAAAJcmjZk1ZGzdWxqFDjrbUqCiSPShzqOhBQS5WzePUz9Un7t69u7Zu3arMzEy1adNGCxcuVFBQkNatW6ewsDCXAwUAAACA0pR3V67UrVtlpKe7KRoAKH4uJ3skqWnTpvrss8+0YcMG7d69W99++63atGlT3LEBAAAAQLGzhYVJuaomjNRUpe7c6caIgOKRlZCgk2PGOP3LSkgo9TgOHz4sk8mk2NjYYn/u1157Tbfccovj2GQyaevWrcV+nYK8+OKLGjp0aJH6FvU1KKnXyuVkz+bNm7Vjxw7H8axZszR06FA988wzSicbDgAAAKCMswQGyjPPDlypUVFuigZAYaZMmaL27ds7tT3zzDP63//+556AcomJidGoUaNUr149BQQEqEOHDpo9e7a7w3JwOdkzZswY7du3T5L0559/6qabbpKPj4+mT5+up556qtgDBAAAAIDilm8q144dsqekuCkaoGCG3a6shIRL/ss4eVIZJ08qMzo633NkRkc7zhflubISEmTY7W642/IlMTFRHTp00Pr16xUbG6uXXnpJt9xyi3bv3u3u0CRdxgLN+/btc2TWpk+frp49e2ratGlas2aNbr75Zr377rvFHCIAAAAAFC9bhw6KmzZNyvmjNiNDqdu2yadrV/cGBuRiT0pSzBNPXNFznHvzTZcfE/Tmm7L4+1+yX2Jiop5++mnNnj1bqamp6t+/v/773//m67dw4UI988wz2r9/v3x8fDRs2DC99dZbjt3G3n77bb377rs6f/68qlevrueee05hYWF64IEHlJGRIT8/P0nS7t279eWXX2rr1q36Jdeuejn27dun66+/Xo899pjGjRunXr16KSIiQps3b9batWvVrFkzff31145laBo1aqSHHnpIP/30k3bt2qWOHTvq22+/Vf369S95702aNNETub42gwYNUvPmzbV+/XqFhobm679o0SL93//9nw4dOiQfHx/deOONmjx5suP8r7/+qgkTJujMmTMaOnSoPvvsM1mt1kvGURiXK3sMw5D97zfExYsXa8CAAZKk+vXr68yZM5cdCAAAAACUFrOfn7xatXJqYyoX4Jq7775b586d0/bt23Xo0CFlZGRo3Lhx+fp5e3vrs88+07lz57RmzRotW7ZMb7/9tqTsBM1zzz2nhQsXKiEhQb///rs6d+6sDh066OOPP1abNm2UmJioxMRENWjQoNBYNmzYoGuvvVYTJ050imHq1Kl6/fXXdf78eYWHh+vhhx92ety3336r//3vfzp9+rR8fX3173//+7Jei5iYGO3Zs0dt27Yt8Pzo0aP15JNPKiEhQX/++aduv/12p/Pz5s3Tli1btHv3bi1ZskTffffdZcWRw+VkT3h4uF555RVNnTpVK1as0MCBAyVJhw4dUq1ata4oGAAAAAAoLXmncqXt3i17YqKbogHKl9OnT2vmzJn68MMPFRgYKF9fX7300kv64YcflJWV5dQ3MjJSHTp0kMViUZMmTTRmzBgtX75ckmSxWGQYhnbt2qWUlBTVqlWr0IRJYebPn6+hQ4fqm2++0ciRI53O3XbbbWrXrp08PDw0evRobdq0yen8Qw89pMaNG8tms2nUqFH5zhdFenq6br75Zo0cOVLh4eEF9rFarTpw4IAjqdStWzen888//7z8/f1Vp04d9e/f/7LiyM3lZM+7776rzZs3a9y4cXr22WcVEhIiSZoxY0a+YAEAAACgrPJq107KPU3CblfK5s3uCwgoRw4fPiy73a7GjRsrMDBQgYGB6tSpk8xms6LzrB0UFRWlPn36qFatWgoICNAzzzzjmBnUtGlTff311/rggw9Uq1Yt9e3b1+Xdtd59911dc801uvbaa/OdCw4Odvy/r6+vEvMkdPOeT/h797LXXntNfn5+8vPz0/XXX1/otdPT0zVixAj5+Pjos88+K7Tfzz//rJ07d6p58+bq0KGDfvzxxyLFcblcXrOnbdu2Trtx5XjjjTdksViuKBgAAAAAKC1mm022tm2VmusT9NSoKPn26OHGqIALzL6+CirCmjs5FWn2xMR8a/RUe+IJmf9e8ybnv0W57qXUr19fZrNZJ06ckI+Pj9O5w4cPOx3fcsstuuuuuzRr1iz5+vrq3Xff1ZQpUxznR44cqZEjRyolJUXPP/+8br/9du3YsUNmc9HqU6ZNm6YnnnhCDz/8cIFrBl2OZ555Rs8888xF+6Snp+sf//iH0tPTNWvWLHl6ehbat2PHjpo5c6bsdrt++eUXjRw5Uj179iyWWAvicmVPYWw22xUtHgQAAAAApS3vVK70/fuVdf68m6IBnJnMZln8/S/5z1q7tqy1a8sjV3VIDo/gYMf5ojyXxd9fpiIkWYKDgzV06FCNGzfOUaUTHR2tn3/+OV/f+Ph4x1SvPXv2OC1MvHfvXi1atEgpKSny9PSUn5+fPDyy61Jq1aqlkydPKuUSO+VVq1ZNS5Ys0bp16/Tggw/KMIxLxn+lMjIyNHLkSCUlJemXX36Rl5dXoX3T09M1depUnT9/XmazWYGBgZLkuM+SUGzJHgAAAAAob7xat5bJZrvQYBhKucK1MoDKYsqUKY7pWwEBAYqMjCxwrZlPPvlEb775pvz8/PTAAw/o5ptvdpxLT0/Xv//9b9WqVUvVq1fX0qVLHVU/1157rbp27aq6desqMDBQR48eLTSWqlWravHixdq8ebPuv//+Ek/4rF27VrNmzdKaNWtUo0YNx5Sv1157rcD+06ZNU0hIiPz9/fXwww9r2rRpql69eonFZzJKI+VVxsXHx6tKlSo6c+ZMib7YgDtlZGRoxowZGjFiBFV4qLAY56gMGOeoDEp7nMdOmaKUdescx9ZGjVRj/PgSvy4qt7Nnz6pGjRqKi4tTQECAoz01NVWHDh1yLBoM5FbU8UFlDwAAAIBKLe9UrozDh5V5+rSbogGAK+dSsicjI0NNmzbVnj17SioeAAAAAChVni1a5Fu4NiUqyk3RAMCVcynZY7ValZqaWlKxAAAAAECpM1kssoWFObWlkuwBUI65PI1r7NixmjRpkjIzM0siHgAAAAAodXmncmWeOKGM48fdFA0AXBmX9/mKiorSkiVLtHDhQrVp00a+vr5O53/66adiCw4AAAAASoO1aVOZq1aVPde26ylRUbLWrevGqFCZsZcSClLUceFysicwMFDDhw93OSAAAAAAKKtMZrO8w8OVtGiRoy01Kkr+Q4bIZDK5MTJUNhaLRVL2luTe3t5ujgZlTXJysiRdcqdCl5M9X3311eVFBAAAAABlmC1PsifrzBllHD4sz8aN3RgVKhsPDw/5+Pjo9OnTslqtMpvZRBvZFT3JycmKiYlRYGCgIylYGJeTPZKUmZmp5cuX6+DBg7r11lvl7++vEydOKCAgQH55VrEHAAAAgPLA2rChLEFByoqJcbSlRkWR7EGpMplMql27tg4dOqQjR464OxyUMYGBgQoODr5kP5eTPUeOHFH//v119OhRpaWl6brrrpO/v78mTZqktLQ0ffzxx5cVMAAAAAC4k8lkknenTkqcM8fRlrJxo/xHjJCJ6gqUIk9PTzVr1kzp6enuDgVliNVqvWRFTw6Xkz3//Oc/FR4erm3btql69eqO9mHDhum+++5z6bkmTpyon376SX/88Ye8vb3VrVs3TZo0Sc2bN3fqt27dOj377LP6/fffZbFY1L59ey1YsMAxf/HcuXN6+OGH9euvv8psNmv48OF67733qDICAAAA4BJbeLhTssceF6f0/fvlledvFKCkmc1m2Ww2d4eBcsrl9PSqVav03HPPydPT06m9UaNGOu7i1oQrVqzQ2LFjtX79ei1atEgZGRnq27evkpKSHH3WrVun/v37q2/fvtqwYYOioqI0btw4p3mLo0aN0q5du7Ro0SL99ttvWrlype6//35Xbw0AAABAJWetU0ce9eo5taVGRbkpGgC4PC5X9tjtdmVlZeVrP3bsmPz9/V16rvnz5zsdT5kyRUFBQdq0aZN69OghSXrsscf0yCOP6Omnn3b0y135s2fPHs2fP19RUVEKDw+XJP33v//VgAED9Oabb6pOnTouxQQAAACgcvPu1EkJx445jlM2b1bALbfIVMTpEwDgbi4ne/r27at3331Xn376qaTsea2JiYl64YUXNGDAgCsKJi4uTpJUrVo1SVJMTIx+//13jRo1St26ddPBgwfVokULvfrqq+revbuk7MqfwMBAR6JHkvr06SOz2azff/9dw4YNy3edtLQ0paWlOY7j4+MlSRkZGcrIyLiiewDKqpyxzRhHRcY4R2XAOEdl4O5x7tGunfTzz45jIylJyTt2yLNVK7fEg4qJ93GUJJeTPW+99Zb69eun0NBQpaam6tZbb9X+/ftVo0YN/e9//7vsQOx2ux599FFdffXVat26tSTpzz//lCS9+OKLevPNN9W+fXt988036t27t3bu3KlmzZopOjpaQUFBzjfl4aFq1aopOjq6wGtNnDhREyZMyNc+Z84c+fj4XPY9AOXBrFmz3B0CUOIY56gMGOeoDNw5zjsEBKjK3x8KS9K+n37SH3v2uC0eVDzJycnuDgEVmMvJnnr16mnbtm36/vvvtX37diUmJuqee+7RqFGjHAsmX46xY8dq586dWr16taPNbrdLksaMGaO77rpLktShQwctWbJEX375pSZOnHhZ1xo/frwef/xxx3F8fLzq16+vgQMHOi06DVQkGRkZmjVrloYMGSKr1erucIASwThHZcA4R2VQFsZ5So0aSpoxw3FcOzZWrQYPlinP2qXA5Tp79qy7Q0AF5nKyR8qunLntttuKLYhx48Y5Flaul2sxtNq1a0uSQkNDnfq3bNlSR48elSQFBwcrJibG6XxmZqbOnTtX6N7zXl5e8vLyytdutVr5pQkVHuMclQHjHJUB4xyVgTvHublzZyXNnCkZhiTJSEtT1h9/yDsszC3xoOLhPRwlyeXduCRp7969GjdunHr37q3evXtr3Lhx+uOPP1x+HsMwNG7cOP38889aunSpGjdu7HS+UaNGqlOnjvbu3evUvm/fPjVs2FCSFBERodjYWG3atMlxfunSpbLb7erSpctl3B0AAACAys5SpYo8r7rKqY1duQCUFy4ne2bOnKnWrVtr06ZNateundq1a6fNmzerTZs2mjlzpkvPNXbsWH377beaNm2a/P39FR0drejoaKWkpEjKXvz5ySef1Pvvv68ZM2bowIED+ve//60//vhD99xzj6TsKp/+/fvrvvvu04YNG7RmzRqNGzdON998MztxAQAAALhs3p07Ox2n7tgh+99/qwBAWebyNK6nnnpK48eP10svveTU/sILL+ipp57S8OHDi/xckydPliT16tXLqf2rr77SnXfeKUl69NFHlZqaqscee0znzp1Tu3bttGjRIjVt2tTR/7vvvnNUGpnNZg0fPlzvv/++q7cGAAAAAA62Dh0UN22alJWV3ZCZqdStW+UTEeHewADgElxO9pw8eVJ33HFHvvbbbrtNb7zxhkvPZfw9//VSnn76aT399NOFnq9WrZqmTZvm0rUBAAAA4GLMvr7yCg1V2o4djrbUqCiSPQDKPJencfXq1UurVq3K17569WpFRkYWS1AAAAAAUBbkncqVtmeP7ImJbooGAIrG5cqewYMH61//+pc2bdqkrl27SpLWr1+v6dOna8KECZo9e7ZTXwAAAAAor7zatpWsVikjI7vBblfKpk3y7dnTvYEBwEW4nOx56KGHJEkfffSRPvroowLPSdmLK2flzG0FAAAAgHLIbLPJ1ratUnPt/psaFUWyB0CZ5vI0LrvdXqR/JHoAAAAAVAR5p3KlHzigrPPn3RQNAFyay8keAAAAAKhMvFq1kslmu9BgGErZuNF9AQHAJZDsAQAAAICLMFmtsnXo4NSWGhXlpmgA4NJI9gAAAADAJeSdypVx5IgyY2LcFA0AXBzJHgAAAAC4BM/mzWX293dqS6G6B0AZRbIHAAAAAC7BZLHI1rGjU1tqVJQMw3BTRABQOJeTPZs3b9aOHTscx7NmzdLQoUP1zDPPKD09vViDAwAAAICyIu9UrsyTJ5V54oSbogGAwrmc7BkzZoz27dsnSfrzzz918803y8fHR9OnT9dTTz1V7AECAAAAQFlgbdJE5qpVndpSNmxwUzQAUDiXkz379u1T+/btJUnTp09Xjx49NG3aNE2ZMkUzZ84s7vgAAAAAoEwwmc3yDg93akvduJGpXADKHJeTPYZhyG63S5IWL16sAQMGSJLq16+vM2fOFG90AAAAAFCG5J3KlXXmjDIOH3ZPMABQCJeTPeHh4XrllVc0depUrVixQgMHDpQkHTp0SLVq1Sr2AAEAAACgrPCoX1+WoCCntrP/+Y9OjhmjrIQEN0UFAM5cTva8++672rx5s8aNG6dnn31WISEhkqQZM2aoW7duxR4gAAAAAJQVJpNJ3p06uTsMALgoD1cf0LZtW6fduHK88cYbslgsxRIUAAAAAJRFWQkJ8gwNlebMyXfOnquyx+LvX5phAYATl5M9khQbG6sZM2bo4MGDevLJJ1WtWjXt3r1btWrVUt26dYs7RgAAAAAoE2KeeKLQc2cmTHD8f+1PPimNcACgQC4ne7Zv367evXsrMDBQhw8f1n333adq1arpp59+0tGjR/XNN9+URJwAAAAAAAAoApfX7Hn88cd11113af/+/bLZbI72AQMGaOXKlcUaHAAAAAAAAFzjcrInKipKY8aMyddet25dRUdHF0tQAAAAAAAAuDwuT+Py8vJSfHx8vvZ9+/apZs2axRIUAAAAAJRFQW++KSl7Mebca/TkqDpunKyNGpVyVADgzOXKnsGDB+ull15SRkaGpOytB48ePap//etfGj58eLEHCAAAAABlhcXfXxZ/f5kL2W0rZcMGduIC4HYuJ3veeustJSYmKigoSCkpKerZs6dCQkLk7++vV199tSRiBAAAAIByIXXTJmUVMBMCAEqTy9O4qlSpokWLFmn16tXavn27EhMT1bFjR/Xp06ck4gMAAACAMsfi76/an3wie2KiTj39tPT3zAdlZSl59Wr5Dxjg3gABVGouJ3tydO/eXd27dy/OWAAAAACgXDH7+ck7PFwp69Y52pJXrpRfv34yWSxujAxAZVakZM/777+v+++/XzabTe+///5F+z7yyCPFEhgAAAAAlAc+vXo5JXvs588rbft22Tp0cGNUACqzIiV73nnnHY0aNUo2m03vvPNOof1MJhPJHgAAAACVimejRrI2aqSMw4cdbUnLl5PsAeA2RUr2HDp0qMD/BwAAAABkV/fETZniOE7/4w9lnDwpa+3a7gsKQKXl0m5cGRkZatq0qfbs2VNS8QAAAABAueMdHi6zn59TW/Ly5e4JBkCl51Kyx2q1KjU1taRiAQAAAIByyWS1yjvPBjYp69fLzt9PANzApWSPJI0dO1aTJk1SZmZmScQDAAAAAOWST48eksnkODZSU5Xy++9ujAhAZeXy1utRUVFasmSJFi5cqDZt2sjX19fp/E8//VRswQEAAABAeeFRvbq82rZV2rZtjrbk5cvl06OHTLmSQABQ0lxO9gQGBmr48OElEQsAAAAAlGu+vXo5JXsyT5xQ+v798rrqKjdGBaCycTnZ89VXX5VEHAAAAABQ7nm2aCFLUJCyYmIcbcnLlpHsAVCqXF6zR5IyMzO1ePFiffLJJ0pISJAknThxQomJicUaHAAAAACUJyazWb49ezq1pW7dqqzz590UEYDKyOVkz5EjR9SmTRsNGTJEY8eO1enTpyVJkyZN0hNPPFHsAQIAAABAeeLdrZtMnp4XGux2Ja9a5b6AAFQ6Lid7/vnPfyo8PFznz5+Xt7e3o33YsGFasmRJsQYHAAAAAOWN2cdH3l26OLUlr1olgx2NAZQSl5M9q1at0nPPPSfP3JlqSY0aNdLx48eLLTAAAAAAKK98evVyOrbHxyt1yxb3BAOg0nE52WO325WVlZWv/dixY/L39y+WoAAAAACgPLPWqydrSIhTW/Ly5e4JBkCl43Kyp2/fvnr33XcdxyaTSYmJiXrhhRc0YMCA4owNAAAAAMot3zzVPekHDijj2DH3BAOgUnE52fPWW29pzZo1Cg0NVWpqqm699VbHFK5JkyaVRIwAAAAAUO7YOnSQOSDAqY3qHgClwcPVB9SrV0/btm3T999/r+3btysxMVH33HOPRo0a5bRgMwAAAABUZiYPD/lERipxzhxHW8rvv8v/xhtl9vFxY2QAKjqXkz2S5OHhodtuu624YwEAAACACsUnMlKJ8+ZJdrskyUhPV8q6dfLt3dvNkQGoyFxO9nzzzTcXPX/HHXdcdjAAAAAAUJFYqlaVrX17pW7e7GhLWrFCPtdcI5PZ5VU1AKBIXE72/POf/3Q6zsjIUHJysjw9PeXj40OyBwAAAABy8enVyynZk3XqlNL/+ENeoaFujApAReZyKvn8+fNO/xITE7V37151795d//vf/0oiRgAAAAAotzyvukoedeo4tSWxUDOAElQsdYPNmjXTf/7zn3xVPwAAAABQ2ZlMJvn07OnUlrZ9uzLPnnVTRAAqumKbJOrh4aETJ04U19MBAAAAQIXh3bWrTDbbhQbDUPLKle4LCECF5vKaPbNnz3Y6NgxDJ0+e1AcffKCrr7662AIDAAAAgIrCbLPJu2tXJeeavpWyerX8b7hBJqvVfYEBqJBcTvYMHTrU6dhkMqlmzZq69tpr9dZbbxVXXAAAAABQofj06uWU7LEnJipl0yb5dO3qvqAAVEguJ3vsdntJxAEAAAAAFZq1dm15Nm+u9L17HW3Jy5eT7AFQ7C57zZ4zZ84oPj6+OGMBAAAAgArNp1cvp+OMQ4eUceSIe4IBUGG5lOyJjY3V2LFjVaNGDdWqVUtVq1ZVcHCwxo8fr+Tk5JKKEQAAAAAqBFu7djJXrerUxjbsAIpbkadxnTt3ThERETp+/LhGjRqlli1bSpJ2796t//73v1q0aJFWr16t7du3a/369XrkkUdKLGgAAAAAKI9MFot8IiOVmGvjm5SoKAUMHy6zn58bIwNQkRQ52fPSSy/J09NTBw8eVK1atfKd69u3r26//XYtXLhQ77//frEHCgAAAAAVgU9kpBLnzJGysrIbMjKUvGaN/Pr1c29gACqMIk/j+uWXX/Tmm2/mS/RIUnBwsF5//XXNnDlTjz/+uEaPHl2sQQIAAABARWEJCJCtY0entuSVK2WwGQ6AYlLkZM/JkyfVqlWrQs+3bt1aZrNZL7zwQrEEBgAAAAAVle811zgdZ505o7Rdu9wUDYCKpsjJnho1aujw4cOFnj906JCCgoKKIyYAAAAAqNCsTZrIo359p7ZkFmoGUEyKnOzp16+fnn32WaWnp+c7l5aWpn//+9/q379/sQYHAAAAABWRyWSSb55t2NN27VLm6dPuCQhAheLSAs3h4eFq1qyZxo4dqxYtWsgwDO3Zs0cfffSR0tLS9M0335RkrAAAAABQYXh37qz4mTNlJCdnNxiGklesUMCIEe4NDEC5V+RkT7169bRu3To99NBDGj9+vAzDkJSdkb7uuuv0wQcfqEGDBiUWKAAAAABUJCZPT/lERChpyRJHW/KaNfIfPFgmT083RgagvCtyskeSGjdurHnz5un8+fPav3+/JCkkJETVqlUrkeAAAAAAoCLz6dnTKdljJCcrZcMG+XTv7saoAJR3RV6zJ7eqVauqc+fO6ty5M4keAAAAALhMHrVqySvPrsdJy5c7ZlIAwOW4rGQPAAAAAKB4+ORZqDnzr7+U8eef7gkGQIVAsgcAAAAA3MirdWtZqld3aktiG3YAV4BkDwAAAAC4kclslk/Pnk5tqZs3Kys+3k0RASjvSPYAAAAAgJv5XH215JFr/5zMTKWsXu2+gACUayR7AAAAAMDNzH5+8u7UyaktaeVKGVlZbooIQHlGsuf/27v3ICvr+37gn7P3XVxAMMtFUQleKQiiRlABdQymIalMbZubaCdmTNMlrVCtl1jx0oi1tkmmIcZYE2w6Vic2Sgr+iCgBi+BtAQVEoqLBqItBCrvZ69nd8/sjcclZSBTY3Wf3nNdrZmfyfM5zdt/PzFfIvnnO8wUAAOgDuj6oueP//i9aXnwxmTBAv6bsAQAA6ANKjj02io89NmvmQc3AwVD2AAAA9BFd7+5pffnlaKutTSYM0G8pewAAAPqI8tNPj4LDDsuaubsHOFDKHgAAgD4iVVwc5WefnTVrWrs2OpqbE0oE9EfKHgAAgD6kYtq0iFSq8zjT3BxNzzyTYCKgv1H2AAAA9CFFRxwRpePHZ80aV66MTCaTUCKgv0m07FmwYEGcccYZUVlZGVVVVTFr1qzYunXrfs/NZDLxx3/8x5FKpeKRRx7Jem379u0xc+bMqKioiKqqqrj66qujra2tF64AAACg+w3o8qDmtrffjtZXXkkmDNDvJFr2rFq1Kqqrq+Ppp5+O5cuXRzqdjhkzZkRDQ8M+537zm9+M1O/cyvi+9vb2mDlzZrS2tsaaNWvivvvui0WLFsWNN97YG5cAAADQ7UpOPjkKq6qyZo0e1Ax8SEVJ/vBly5ZlHS9atCiqqqqipqYmpk2b1jnfsGFD/Mu//Es8//zzMWLEiKz3PPbYY/HSSy/F448/HsOGDYuJEyfGrbfeGtdcc03cdNNNUVJS0ivXAgAA0F1SBQUxYPr0qPvRjzpnzevXR/vu3VE4eHBywYB+IdGyp6s9e/ZERMSQIUM6Z42NjfH5z38+Fi5cGMOHD9/nPWvXro3x48fHsGHDOmcXXnhhfOUrX4nNmzfHqaeeus97WlpaoqWlpfO4rq4uIiLS6XSk0+luux7oS95f29Y4ucw6Jx9Y5+QD6/w3is44I2Lx4ojW1t8MOjqifuXKGDBzZrLB6Bb5vr7pWX2m7Ono6Igrr7wyzj777Bg3blznfO7cuXHWWWfFRRddtN/31dbWZhU9EdF5XFtbu9/3LFiwIG6++eZ95kuXLo2KioqDvQToFxYvXpx0BOhx1jn5wDonH1jnEScMHRoj33mn83j3E0/EsoaGyBTYa6e/a2xsTDoCOazPlD3V1dWxadOmWL16defsJz/5SaxYsSLWr1/frT/ruuuui3nz5nUe19XVxahRo2LmzJkxdOjQbv1Z0Fek0+lYvHhxXHTRRVFcXJx0HOgR1jn5wDonH1jne7X98pex+/bbO49LW1vjU8cdF6WTJiWYiu7w3nvvJR2BHNYnyp45c+bEkiVL4sknn4yjjjqqc75ixYp47bXXYnCXz6RefPHFMXXq1Fi5cmUMHz48nn322azXd+zYERGx3499RUSUlpZGaWnpPvPi4uK8/8uE3Gedkw+sc/KBdU4+sM4jikePjoYxYyL92muds5Ynn4zDzjwzwVR0h3xf2/SsRO/9y2QyMWfOnHj44YdjxYoVMXr06KzXr7322njxxRdjw4YNnV8REd/4xjfiBz/4QURETJkyJTZu3Bjvvvtu5/uWL18eAwcOjLFjx/batQAAAPSErtuwt776aqTfeiuZMEC/kOidPdXV1XH//ffH4sWLo7KysvMZO4MGDYry8vIYPnz4fu/OOfroozuLoRkzZsTYsWNj9uzZcccdd0RtbW3ccMMNUV1dvd+7dwAAAPqTskmTouBHP4qO324sE/GbbdgHfeELCaYC+rJE7+y56667Ys+ePXHuuefGiBEjOr8efPDBD/09CgsLY8mSJVFYWBhTpkyJSy65JC699NK45ZZbejA5AABA70gVFUXF1KlZs6ZnnomOpqaEEgF9XaJ39mQymW55zzHHHBOPPvpod0QCAADocyqmTo1f/7//F9HRERERmZaWaFq7Ngacf37CyYC+yH59AAAAfVzh4YdH2cSJWbOGlSsP6h/Qgdyn7AEAAOgHKro8qLl9x45offnlZMIAfZqyBwAAoB8oOeGEKBoxImvW8LOfJZQG6MuUPQAAAP1AKpXa5+6elhdfjPZdu5IJBPRZyh4AAIB+onzy5EiVle0dZDLRsGpVcoGAPknZAwAA0E8UlJVF+eTJWbOmp56KTDqdUCKgL1L2AAAA9CMV06dnHXfU10dTTU1CaYC+SNkDAADQjxSPHBklJ56YNWtcuTKZMECfpOwBAADoZ7o+qDn9+uuR/sUvkgkD9DnKHgAAgH6mbMKEKBg8OGvW4O4e4LeUPQAAAP1MqrAwKqZNy5o1PfdcdPz61wklAvoSZQ8AAEA/VDF1akRh4d5BOh2Na9YkFwjoM5Q9AAAA/VDhwIFRNmlS1qxx1arIdHQklAjoK5Q9AAAA/dSALg9qbt+5M1o2b04mDNBnKHsAAAD6qeIxY6LoqKOyZrZhB5Q9AAAA/VQqldrn7p6WzZuj7Ve/SiYQ0CcoewAAAPqxso99LFLl5XsHmUw0rlqVXCAgccoeAACAfqygtDQqzjora9b41FORaW1NKBGQNGUPAABAP1cxfXrWcaaxMZqeey6hNEDSlD0AAAD9XNGwYVE6dmzWrGHlyshkMgklApKk7AEAAMgBFV0e1Ny2fXukX389mTBAopQ9AAAAOaB0/PgoHDo0a2YbdshPyh4AAIAckCooiIpp07JmTTU10V5Xl1AiICnKHgAAgBxRcc45EUVFewdtbdG0enVygYBEKHsAAAByRMFhh0X5GWdkzRqefDIy7e0JJQKSoOwBAADIIV0f1Nzxf/8XLRs3JhMGSISyBwAAIIeUHHtsFB97bNaswYOaIa8oewAAAHJM17t7Wrdsibba2mTCAL1O2QMAAJBjyk8/PVIDBmTNGlatSigN0NuUPQAAADkmVVwcFWefnTVrWrMmOpqbE0oE9CZlDwAAQA6qmD49IpXqPM40N0fTM88kmAjoLcoeAACAHFR0xBFROn581qxx5crIZDIJJQJ6i7IHAAAgRw3o8qDmtrffjtZXXkkmDNBrlD0AAAA5quTkk6Owqipr1mgbdsh5yh4AAIAclSoo+M2ze35H8/r10b5nT0KJgN6g7AEAAMhhFVOmRBQX7x10dETjk08mFwjoccoeAACAHFYwYECUn3lm1qzxf/83Mu3tCSUCepqyBwAAIMd1fVBzx5490bx+fTJhgB6n7AEAAMhxxaNGRfGYMVkzD2qG3KXsAQAAyANd7+5pfeWVSL/1VjJhgB6l7AEAAMgDZZMmRcHAgVkzd/dAblL2AAAA5IFUUVFUnHNO1qzpmWeio6kpoURAT1H2AAAA5ImKadMiCvb+GphpaYmmtWsTTAT0BGUPAABAnig8/PAomzAha9awcmVkMpmEEgE9QdkDAACQRyrOOy/ruH3Hjmh9+eWE0gA9QdkDAACQR0pOOCGKRozImjV4UDPkFGUPAABAHkmlUlExfXrWrOWFF6J9166EEgHdTdkDAACQZ8onT45UaeneQSYTDU8+mVwgoFspewAAAPJMQXl5lE+enDVrWr06Mul0QomA7qTsAQAAyEMV556bddxRXx/N69YlEwboVsoeAACAPFQ8cmSUnHBC1qzhZz9LKA3QnZQ9AAAAearrNuzp11+P9PbtCaUBuouyBwAAIE+VTZgQBYMHZ81sww79n7IHAAAgT6UKC6Ni2rSsWdOzz0ZHQ0NCiYDuoOwBAADIYxXnnBNRWLh3kE5H45o1yQUCDpmyBwAAII8VDhoUZZMmZc0aV66MTEdHQomAQ6XsAQAAyHMDumzD3r5zZ7Rs3pxMGOCQKXsAAADyXPGYMVF01FFZs0YPaoZ+S9kDAACQ51Kp1D5397Rs3hxtv/pVMoGAQ6LsAQAAIMo+9rFIlZfvHWQy0bhqVXKBgIOm7AEAACAKSkuj4qyzsmaNa9ZEprU1oUTAwVL2AAAAEBERFdOnZx1nGhqi6bnnEkoDHCxlDwAAABERUTRsWJSMHZs1a1i5MjKZTEKJgIOh7AEAAKBT1wc1t23fHunXX08mDHBQlD0AAAB0Kh0/PgqHDs2a2YYd+hdlDwAAAJ1SBQVRMW1a1qyppiba6+oSSgQcKGUPAAAAWSrOOSeiqGjvoK0tmp56KrlAwAFR9gAAAJCl4LDDovz007NmjU8+GZmOjoQSAQdC2QMAAMA+Kro8qLl9165oefHFZMIAB0TZAwAAwD5KRo+O4mOOyZo1eFAz9AvKHgAAAPar6909rVu2RFttbTJhgA9N2QMAAMB+lZ9+eqQGDMiaNaxalVAa4MNS9gAAALBfqZKSqDj77KxZ05o10dHcnFAi4MNQ9gAAAPB7VUyfHpFKdR5nmpuj6dlnE0wEfBBlDwAAAL9X0RFHROm4cVmzxpUrI5PJJJQI+CCJlj0LFiyIM844IyorK6OqqipmzZoVW7du7Xx9165d8dWvfjVOPPHEKC8vj6OPPjr+5m/+Jvbs2ZP1fbZv3x4zZ86MioqKqKqqiquvvjra2tp6+3IAAAByUtcHNbe99VakX301mTDAB0q07Fm1alVUV1fH008/HcuXL490Oh0zZsyIhoaGiIh4++234+23344777wzNm3aFIsWLYply5bF5Zdf3vk92tvbY+bMmdHa2hpr1qyJ++67LxYtWhQ33nhjUpcFAACQU0rHjo3CqqqsmW3Yoe8qSvKHL1u2LOt40aJFUVVVFTU1NTFt2rQYN25c/Pd//3fn62PGjImvf/3rcckll0RbW1sUFRXFY489Fi+99FI8/vjjMWzYsJg4cWLceuutcc0118RNN90UJSUl+/zclpaWaGlp6Tyuq6uLiIh0Oh3pdLqHrhaS9f7atsbJZdY5+cA6Jx9Y531T2TnnRMOPf9x53LxuXbTs3BkFgwYlmKr/sr7pSYmWPV29//GsIUOG/MFzBg4cGEVFv4m+du3aGD9+fAwbNqzznAsvvDC+8pWvxObNm+PUU0/d53ssWLAgbr755n3mS5cujYqKikO9DOjTFi9enHQE6HHWOfnAOicfWOd9S1E6HVMKCqKwo+M3g46OqLnnnvjFsccmmqu/amxsTDoCOazPlD0dHR1x5ZVXxtlnnx3jujz86307d+6MW2+9Na644orOWW1tbVbRExGdx7W1tfv9Ptddd13Mmzev87iuri5GjRoVM2fOjKFDhx7qpUCflE6nY/HixXHRRRdFcXFx0nGgR1jn5APrnHxgnfdd9ZlMtKxZ03k8ZteuOH3u3EgVFiaYqn967733ko5ADuszZU91dXVs2rQpVq9evd/X6+rqYubMmTF27Ni46aabDulnlZaWRmlp6T7z4uJif5mQ86xz8oF1Tj6wzskH1nnfU3n++VllT0ddXbRv3hzlp52WYKr+ydqmJ/WJrdfnzJkTS5YsiZ/97Gdx1FFH7fN6fX19fOITn4jKysp4+OGHs/6jGD58eOzYsSPr/PePhw8f3rPBAQAA8kjxqFFRPGZM1qzRg5qhz0m07MlkMjFnzpx4+OGHY8WKFTF69Oh9zqmrq4sZM2ZESUlJ/OQnP4mysrKs16dMmRIbN26Md999t3O2fPnyGDhwYIwdO7bHrwEAACCfDOiyDXvrz38e6bfeSiYMsF+Jlj3V1dXxn//5n3H//fdHZWVl1NbWRm1tbTQ1NUXE3qKnoaEh7r333qirq+s8p729PSIiZsyYEWPHjo3Zs2fHCy+8ED/96U/jhhtuiOrq6v1+VAsAAICDV3bqqVFQWZk1a1y1KqE0wP4kWvbcddddsWfPnjj33HNjxIgRnV8PPvhgRESsW7cunnnmmdi4cWMcd9xxWee8+eabERFRWFgYS5YsicLCwpgyZUpccsklcemll8Ytt9yS5KUBAADkpFRxcVRMnZo1a3r66ej47T/aA8lL9AHNmUzmD75+7rnnfuA5ERHHHHNMPProo90VCwAAgD+gYtq0+PWyZRG/3YY909ISTWvXxoDzz084GRDRRx7QDAAAQP9RePjhUTZhQtasYdWqD/WP9UDPU/YAAABwwCq6PKi5vbY2Wl9+OZkwQBZlDwAAAAes5MQTo2jEiKxZg23YoU9Q9gAAAHDAUqlUVEyfnjVreeGFaN+1K6FEwPuUPQAAAByU8smTI1VauneQyUTDk08mFwiICGUPAAAAB6mgvDzKJ0/OmjWtXh2ZdDqhRECEsgcAAIBD0PVBzR319dG8bl0yYYCIUPYAAABwCIpHjoySE07ImnlQMyRL2QMAAMAh6Xp3T3rbtnjny1+Od7785Wivr08mFOQxZQ8AAACHpGzixCgYPDjpGMBvKXsAAAA4JB2NjVH+sY/t/7X6+mj/7RfQO4qSDgAAAED/9u5VV/3e13befHPn/x5x9929EQfynjt7AAAAAHKIsgcAAAAghyh7AAAAAHKIZ/YAAABwSKruvDMifvMw5t99Rk9ERMGQIXHEdddFpFJJRIO8pOwBAADgkBRWVv7e1zp27YqO3buj+OijezER5Dcf4wIAAKBHNT33XNIRIK8oewAAAOgWhZWVMeLuu2PAJz6RNW+uqYlMJpNQKsg/yh4AAAC6Vfnpp2cdt7/3XqTfeCOZMJCHlD0AAAB0q6KjjorCYcOyZs0+ygW9RtkDAABAt0qlUvvc3dNUUxOZjo6EEkF+UfYAAADQ7cq6lD0du3dHetu2hNJAflH2AAAA0O2KR46MopEjs2Z25YLeoewBAACgR3S9u6d53Tof5YJeoOwBAACgR3R9bk9HXV20vvJKQmkgfyh7AAAA6BFFw4ZF0ahRWbPm559PKA3kD2UPAAAAPabr3T3N69ZFpr09oTSQH5Q9AAAA9Jiy007LOu749a+jdevWhNJAflD2AAAA0GOKPvKRKD7mmKxZk49yQY9S9gAAANCj9tmVa/36yLS1JZQGcp+yBwAAgB7V9bk9mcbGaNmyJaE0kPuUPQAAAPSowiFDovijH82a2ZULeo6yBwAAgB63z65cGzZEJp1OKA3kNmUPAAAAPa7stNMiUqnO40xzc7Rs3pxgIshdyh4AAAB6XOHgwVFy3HFZM7tyQc9Q9gAAANAruu7K1fLii5FpbU0oDeQuZQ8AAAC9omzSpOyPcrW0RPPGjQkmgtyk7AEAAKBXFA4cGCUnnpg1sysXdD9lDwAAAL1mn125Nm6MjubmhNJAblL2AAAA0GvKTj01ouB3fhVNp6PlxReTCwQ5SNkDAABAryk47LAoPfnkrJlduaB7KXsAAADoVfvsyrV5c3Q0NiaUBnKPsgcAAIBeVTZxYkRR0d5BW1s0v/BCYnkg1yh7AAAA6FUFFRVROnZs1syuXNB9lD0AAAD0uq67crW89FJ0/PrXCaWB3KLsAQAAoNeVTpgQUVy8d9DREc0bNiSWB3KJsgcAAIBeV1BWFmXjxmXN7MoF3UPZAwAAQCK67srV+vLL0V5fn1AayB3KHgAAABJROn58pEpK9g4ymWhety65QJAjlD0AAAAkoqC0NEpPOSVrZlcuOHTKHgAAABLTdVeu1ldeifY9exJKA7lB2QMAAEBiSseNi1RZ2d5BJhPNNTXJBYIcoOwBAAAgMani4iibMCFrZlcuODTKHgAAABLVdVeu9GuvRfuuXQmlgf5P2QMAAECiSseOjVRFRdasyUe54KApewAAAEhUqqgoyiZOzJrZlQsOnrIHAACAxHXdlSv9xhvRtnNnQmmgf1P2AAAAkLiSk06K1IABWTN398DBUfYAAACQuFRhYZRPmpQ1sysXHBxlDwAAAH1C11252t58M9p27EgoDfRfyh4AAAD6hJITToiCgQOzZu7ugQOn7AEAAKBPSBUURFmXj3J5bg8cOGUPAAAAfUbXXbna3n470m+/nVAa6J+UPQAAAPQZxWPGRMHgwVkzd/fAgVH2AAAA0GekCgqi/LTTsmZNzz8fmUwmoUTQ/yh7AAAA6FO67srVvmNHtP3ylwmlgf5H2QMAAECfUjx6dBQOHZo1sysXfHjKHgAAAPqUVCoVZV0+ytXso1zwoSl7AAAA6HO67srVvnNnpH/xi4TSQP+i7AEAAKDPKTr66Cj8yEeyZnblgg9H2QMAAECfk0ql9nlQc3NNjY9ywYeg7AEAAKBP2uejXLt2RXrbtoTSQP+h7AEAAKBPKjryyCgcNixrZlcu+GCJlj0LFiyIM844IyorK6OqqipmzZoVW7duzTqnubk5qqurY+jQoXHYYYfFxRdfHDt27Mg6Z/v27TFz5syoqKiIqqqquPrqq6Otra03LwUAAIBulkql9rm7p3ndush0dCSUCPqHRMueVatWRXV1dTz99NOxfPnySKfTMWPGjGhoaOg8Z+7cufE///M/8aMf/ShWrVoVb7/9dvzpn/5p5+vt7e0xc+bMaG1tjTVr1sR9990XixYtihtvvDGJSwIAAKAbdX1uT8fu3dH66qsJpYH+oSjJH75s2bKs40WLFkVVVVXU1NTEtGnTYs+ePXHvvffG/fffH+eff35ERPzgBz+Ik08+OZ5++umYPHlyPPbYY/HSSy/F448/HsOGDYuJEyfGrbfeGtdcc03cdNNNUVJSksSlAQAA0A2KR46MopEjo+3ttztnzc8/H6UnnJBgKujbEi17utqzZ09ERAwZMiQiImpqaiKdTscFF1zQec5JJ50URx99dKxduzYmT54ca9eujfHjx8ew3/kc54UXXhhf+cpXYvPmzXHqqafu83NaWlqipaWl87iuri4iItLpdKTT6R65Nkja+2vbGieXWefkA+ucfGCd01XJpElZZU/TunVR/qd/GqnCwgRTHRrrm57UZ8qejo6OuPLKK+Pss8+OcePGRUREbW1tlJSUxODBg7POHTZsWNTW1naeM6zLA7veP37/nK4WLFgQN9988z7zpUuXRkVFxaFeCvRpixcvTjoC9DjrnHxgnZMPrHPeV97YGGf+znGmvj6e+P73Y/fhhyeW6VA1NjYmHYEc1mfKnurq6ti0aVOsXr26x3/WddddF/Pmzes8rquri1GjRsXMmTNj6NChPf7zIQnpdDoWL14cF110URQXFycdB3qEdU4+sM7JB9Y5+/N/b78d7b/8ZefxmQMGROWf/VmCiQ7Ne++9l3QEclifKHvmzJkTS5YsiSeffDKOOuqozvnw4cOjtbU1du/enXV3z44dO2L48OGd5zz77LNZ3+/93breP6er0tLSKC0t3WdeXFzsLxNynnVOPrDOyQfWOfnAOud3VZxxRtT/TtnT+sILUXTJJf32o1zWNj0p0d24MplMzJkzJx5++OFYsWJFjB49Ouv10047LYqLi+OJJ57onG3dujW2b98eU6ZMiYiIKVOmxMaNG+Pdd9/tPGf58uUxcODAGDt2bO9cCAAAAD2q665cmYaGaNmyJaE00LclemdPdXV13H///bF48eKorKzsfMbOoEGDory8PAYNGhSXX355zJs3L4YMGRIDBw6Mr371qzFlypSYPHlyRETMmDEjxo4dG7Nnz4477rgjamtr44Ybbojq6ur93r0DAABA/1N0xBFRfOyxkX7jjc5Z8/PPR9lvn/kK7JXonT133XVX7NmzJ84999wYMWJE59eDDz7Yec43vvGN+NSnPhUXX3xxTJs2LYYPHx4//vGPO18vLCyMJUuWRGFhYUyZMiUuueSSuPTSS+OWW25J4pIAAADoIV3v7mnesCEydrWCfSR6Z08mk/nAc8rKymLhwoWxcOHC33vOMcccE48++mh3RgMAAKCPKT/ttKh/6KHO40xTU7S89FKUTZiQYCroexK9swcAAAA+rMIhQ6J4zJisWdPzzyeUBvouZQ8AAAD9RnmXj3K1vPBCZFpbE0oDfZOyBwAAgH6j7LTTIlKpzuNMS0s0b9qUYCLoe5Q9AAAA9BuFgwZFyfHHZ82afZQLsih7AAAA6Fe67srVsnFjdLS0JJQG+h5lDwAAAP1K2aRJEQV7f53NtLZGy4svJpgI+hZlDwAAAP1KYWVllJx4YtbMrlywl7IHAACAfmefXbk2bYqOpqaE0kDfouwBAACg3yk79dSsj3JFW1s0v/BCcoGgD1H2AAAA0O8UDBgQpWPHZs2aa2oSSgN9i7IHAACAfmmfXbk2b46OxsaE0kDfoewBAACgXyqbODGiqGjvoL09mjdsSCoO9BnKHgAAAPqlgvLyKP2jP8qaNduVC5Q9AAAA9F/77Mq1ZUt0/PrXCaWBvkHZAwAAQL9VesopEcXFewcdHdG8fn1ygaAPUPYAAADQbxWUlUXZ+PFZsyYf5SLPKXsAAADo17ruytW6dWu019UllAaSp+wBAACgXysbPz5SpaV7B5lMNK9bl1wgSJiyBwAAgH4tVVLym2f3/A67cpHPlD0AAAD0e1135Wp99dVo3707mTCQMGUPAAAA/V7pH/1RpMrK9g4ymWiuqUkuECRI2QMAAEC/lyoujrKJE7NmduUiXyl7AAAAyAldd+VKb9sW7bt2JZQGkqPsAQAAICeUnnxypCoqsmbu7iEfKXsAAADICamioig79dSsmV25yEfKHgAAAHJG11250r/4RbT96lcJpYFkKHsAAADIGSUnnhgFlZVZM3f3kG+UPQAAAOSMVGHhPh/l8twe8o2yBwAAgJzSdVeutl/+Mt758pfjnS9/Odrr6xNKBb1H2QMAAEBOKTn++CgYODDpGJAYZQ8AAAA5paOhIUrHj9//a/X10f7bL8hVRUkHAAAAgO707lVX/d7Xdt58c+f/HnH33b0RB3qdO3sAAAAAcoiyBwAAACCHKHsAAAAAcohn9gAAAJBTqu68MyIiOurqYuctt2S9dsT8+VFQWZlELOg1yh4AAABySuEfKHMKKiv/4OuQC3yMCwAAACCHuLMHAACAnFRYWWl7dfKSO3sAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAcoiyBwAAACCHKHsAAAAAckhR0gH6gkwmExER9fX1UVxcnHAa6BnpdDoaGxujrq7OOidnWefkA+ucfGCdkw/q6+sjYu/vo9CdUhkrK7Zt2xZjxoxJOgYAAAB55rXXXouPfvSjSccgx7izJyKGDBkSERHbt2+PQYMGJZwGekZdXV2MGjUq3nzzzRg4cGDScaBHWOfkA+ucfGCdkw/27NkTRx99dOfvo9CdlD0RUVDwm0cXDRo0yF8m5LyBAwda5+Q865x8YJ2TD6xz8sH7v49Cd7KqAAAAAHKIsgcAAAAghyh7IqK0tDTmz58fpaWlSUeBHmOdkw+sc/KBdU4+sM7JB9Y5PcluXAAAAAA5xJ09AAAAADlE2QMAAACQQ5Q9AAAAADlE2QMAAACQQ/Km7Fm4cGEce+yxUVZWFmeeeWY8++yzf/D8H/3oR3HSSSdFWVlZjB8/Ph599NFeSgoH70DW+T333BNTp06Nww8/PA4//PC44IILPvC/C+gLDvTP8/c98MADkUqlYtasWT0bELrBga7z3bt3R3V1dYwYMSJKS0vjhBNO8P9d6PMOdJ1/85vfjBNPPDHKy8tj1KhRMXfu3Ghubu6ltHBgnnzyyfj0pz8dI0eOjFQqFY888sgHvmflypUxadKkKC0tjeOOOy4WLVrU4znJXXlR9jz44IMxb968mD9/fqxbty4mTJgQF154Ybz77rv7PX/NmjXxuc99Li6//PJYv359zJo1K2bNmhWbNm3q5eTw4R3oOl+5cmV87nOfi5/97Gexdu3aGDVqVMyYMSPeeuutXk4OH96BrvP3vfHGG3HVVVfF1KlTeykpHLwDXeetra3x8Y9/PN5444146KGHYuvWrXHPPffEkUce2cvJ4cM70HV+//33x7XXXhvz58+PLVu2xL333hsPPvhgXH/99b2cHD6choaGmDBhQixcuPBDnf/666/HzJkz47zzzosNGzbElVdeGV/60pfipz/9aQ8nJVflxdbrZ555Zpxxxhnx7W9/OyIiOjo6YtSoUfHVr341rr322n3O/8xnPhMNDQ2xZMmSztnkyZNj4sSJ8d3vfrfXcsOBONB13lV7e3scfvjh8e1vfzsuvfTSno4LB+Vg1nl7e3tMmzYtvvjFL8b//u//xu7duz/Uv65BUg50nX/3u9+Nf/7nf46XX345iouLezsuHJQDXedz5syJLVu2xBNPPNE5+7u/+7t45plnYvXq1b2WGw5GKpWKhx9++A/eXXzNNdfE0qVLs24w+OxnPxu7d++OZcuW9UJKck3O39nT2toaNTU1ccEFF3TOCgoK4oILLoi1a9fu9z1r167NOj8i4sILL/y950PSDmadd9XY2BjpdDqGDBnSUzHhkBzsOr/llluiqqoqLr/88t6ICYfkYNb5T37yk5gyZUpUV1fHsGHDYty4cXHbbbdFe3t7b8WGA3Iw6/yss86Kmpqazo96bdu2LR599NH45Cc/2SuZoaf5HZTuVpR0gJ62c+fOaG9vj2HDhmXNhw0bFi+//PJ+31NbW7vf82tra3ssJxyKg1nnXV1zzTUxcuTIff6Sgb7iYNb56tWr4957740NGzb0QkI4dAezzrdt2xYrVqyIL3zhC/Hoo4/Gq6++Gn/9138d6XQ65s+f3xux4YAczDr//Oc/Hzt37oxzzjknMplMtLW1xV/91V/5GBc54/f9DlpXVxdNTU1RXl6eUDL6q5y/swf4YLfffns88MAD8fDDD0dZWVnScaBb1NfXx+zZs+Oee+6JI444Iuk40GM6Ojqiqqoqvve978Vpp50Wn/nMZ+JrX/uaj56TU1auXBm33XZbfOc734l169bFj3/841i6dGnceuutSUcD6JNy/s6eI444IgoLC2PHjh1Z8x07dsTw4cP3+57hw4cf0PmQtINZ5++788474/bbb4/HH388TjnllJ6MCYfkQNf5a6+9Fm+88UZ8+tOf7px1dHRERERRUVFs3bo1xowZ07Oh4QAdzJ/nI0aMiOLi4igsLOycnXzyyVFbWxutra1RUlLSo5nhQB3MOv+Hf/iHmD17dnzpS1+KiIjx48dHQ0NDXHHFFfG1r30tCgr8Gzb92+/7HXTgwIHu6uGg5PyfiiUlJXHaaadlPcyto6MjnnjiiZgyZcp+3zNlypSs8yMili9f/nvPh6QdzDqPiLjjjjvi1ltvjWXLlsXpp5/eG1HhoB3oOj/ppJNi48aNsWHDhs6vP/mTP+nc5WLUqFG9GR8+lIP58/zss8+OV199tbPMjIj4+c9/HiNGjFD00CcdzDpvbGzcp9B5v+DMg/1myAN+B6XbZfLAAw88kCktLc0sWrQo89JLL2WuuOKKzODBgzO1tbWZTCaTmT17dubaa6/tPP+pp57KFBUVZe68887Mli1bMvPnz88UFxdnNm7cmNQlwAc60HV+++23Z0pKSjIPPfRQ5p133un8qq+vT+oS4AMd6Drv6rLLLstcdNFFvZQWDs6BrvPt27dnKisrM3PmzMls3bo1s2TJkkxVVVXmH//xH5O6BPhAB7rO58+fn6msrMz813/9V2bbtm2Zxx57LDNmzJjMX/zFXyR1CfAH1dfXZ9avX59Zv359JiIy//qv/5pZv3595he/+EUmk8lkrr322szs2bM7z9+2bVumoqIic/XVV2e2bNmSWbhwYaawsDCzbNmypC6Bfi7nP8YV8Zut1H/1q1/FjTfeGLW1tTFx4sRYtmxZ5wOwtm/fnvUvBWeddVbcf//9ccMNN8T1118fxx9/fDzyyCMxbty4pC4BPtCBrvO77rorWltb48/+7M+yvs/8+fPjpptu6s3o8KEd6DqH/uhA1/moUaPipz/9acydOzdOOeWUOPLII+Nv//Zv45prrknqEuADHeg6v+GGGyKVSsUNN9wQb731VnzkIx+JT3/60/H1r389qUuAP+j555+P8847r/N43rx5ERFx2WWXxaJFi+Kdd96J7du3d74+evToWLp0acydOze+9a1vxVFHHRX//u//HhdeeGGvZyc3pDIZ9z0CAAAA5Ar//AkAAACQQ5Q9AAAAADlE2QMAAACQQ5Q9AAAAADlE2QMAAACQQ5Q9AAAAADlE2QMAAACQQ5Q9AAAAADlE2QMA9LpUKhWPPPJIRES88cYbkUqlYsOGDYlmAgDIFcoeAMgzf/mXfxmpVCpSqVQUFxfH6NGj4+///u+jubk56WgAAHSDoqQDAAC97xOf+ET84Ac/iHQ6HTU1NXHZZZdFKpWKf/qnf0o6GgAAh8idPQCQh0pLS2P48OExatSomDVrVlxwwQWxfPnyiIjo6OiIBQsWxOjRo6O8vDwmTJgQDz30UNb7N2/eHJ/61Kdi4MCBUVlZGVOnTo3XXnstIiKee+65+PjHPx5HHHFEDBo0KKZPnx7r1q3r9WsEAMhXyh4AyHObNm2KNWvWRElJSURELFiwIP7jP/4jvvvd78bmzZtj7ty5cckll8SqVasiIuKtt96KadOmRWlpaaxYsSJqamrii1/8YrS1tUVERH19fVx22WWxevXqePrpp+P444+PT37yk1FfX5/YNQIA5BMf4wKAPLRkyZI47LDDoq2tLVpaWqKgoCC+/e1vR0tLS9x2223x+OOPx5QpUyIi4qMf/WisXr067r777pg+fXosXLgwBg0aFA888EAUFxdHRMQJJ5zQ+b3PP//8rJ/1ve99LwYPHhyrVq2KT33qU713kQAAeUrZAwB56Lzzzou77rorGhoa4hvf+EYUFRXFxRdfHJs3b47Gxsb4+Mc/nnV+a2trnHrqqRERsWHDhpg6dWpn0dPVjh074oYbboiVK1fGu+++G+3t7dHY2Bjbt2/v8esCAEDZAwB5acCAAXHcccdFRMT3v//9mDBhQtx7770xbty4iIhYunRpHHnkkVnvKS0tjYiI8vLyP/i9L7vssnjvvffiW9/6VhxzzDFRWloaU6ZMidbW1h64EgAAulL2AECeKygoiOuvvz7mzZsXP//5z6O0tDS2b98e06dP3+/5p5xyStx3332RTqf3e3fPU089Fd/5znfik5/8ZEREvPnmm7Fz584evQYAAPbygGYAIP78z/88CgsL4+67746rrroq5s6dG/fdd1+89tprsW7duvi3f/u3uO+++yIiYs6cOVFXVxef/exn4/nnn49XXnklfvjDH8bWrVsjIuL444+PH/7wh7Fly5Z45pln4gtf+MIH3g0EAED3cWcPABBFRUUxZ86cuOOOO+L111+Pj3zkI7FgwYLYtm1bDB48OCZNmhTXX399REQMHTo0VqxYEVdffXVMnz49CgsLY+LEiXH22WdHRMS9994bV1xxRUyaNClGjRoVt912W1x11VVJXh4AQF5JZTKZTNIhAAAAAOgePsYFAAAAkEOUPQAAAAA5RNkDAAAAkEOUPQAAAAA5RNkDAAAAkEOUPQAAAAA5RNkDAAAAkEOUPQAAAAA5RNkDAAAAkEOUPQAAAAA5RNkDAAAAkEP+P0OrBDRSY9PDAAAAAElFTkSuQmCC
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
\ No newline at end of file
diff --git a/docs/pages/performance/fashion-mnist/plot.png b/docs/pages/performance/fashion-mnist/plot.png
index d645724eb..0d3a1ef88 100644
Binary files a/docs/pages/performance/fashion-mnist/plot.png and b/docs/pages/performance/fashion-mnist/plot.png differ
diff --git a/docs/pages/performance/fashion-mnist/results.md b/docs/pages/performance/fashion-mnist/results.md
index ec02e82bd..7ab7c6371 100644
--- a/docs/pages/performance/fashion-mnist/results.md
+++ b/docs/pages/performance/fashion-mnist/results.md
@@ -1,10 +1,10 @@
 |Model|Parameters|Recall|Queries per Second|
 |---|---|---|---|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=0|0.378|337.457|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=0|0.446|281.828|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=3|0.634|272.814|
-|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=3|0.716|232.698|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=0|0.767|303.686|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=0|0.846|254.121|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=3|0.922|215.233|
-|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=3|0.960|190.689|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=0|0.379|353.162|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=0|0.447|295.007|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=500 probes=3|0.634|286.531|
+|eknn-l2lsh|L=100 k=4 w=1024 candidates=1000 probes=3|0.716|245.690|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=0|0.767|312.826|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=0|0.846|265.204|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=500 probes=3|0.921|221.817|
+|eknn-l2lsh|L=100 k=4 w=2048 candidates=1000 probes=3|0.960|195.653|
diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java
index 1802120d1..f3355f7ee 100644
--- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java
+++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/ArrayHitCounter.java
@@ -1,7 +1,5 @@
 package com.klibisz.elastiknn.search;
 
-import org.apache.lucene.search.KthGreatest;
-
 /**
  * Use an array of counts to count hits. The index of the array is the doc id.
  * Hopefully there's a way to do this that doesn't require O(num docs in segment) time and memory,
@@ -14,29 +12,36 @@ public class ArrayHitCounter implements HitCounter {
     private int minKey;
     private int maxKey;
 
+    private short maxValue;
+
     public ArrayHitCounter(int capacity) {
         counts = new short[capacity];
         numHits = 0;
         minKey = capacity;
         maxKey = 0;
+        maxValue = 0;
     }
 
     @Override
     public void increment(int key) {
-        if (counts[key]++ == 0) {
+        short after = ++counts[key];
+        if (after == 1) {
             numHits++;
             minKey = Math.min(key, minKey);
             maxKey = Math.max(key, maxKey);
         }
+        if (after > maxValue) maxValue = after;
     }
 
     @Override
     public void increment(int key, short count) {
-        if ((counts[key] += count) == count) {
+        short after = (counts[key] += count);
+        if (after == count) {
             numHits++;
             minKey = Math.min(key, minKey);
             maxKey = Math.max(key, maxKey);
         }
+        if (after > maxValue) maxValue = after;
     }
 
     @Override
@@ -70,8 +75,34 @@ public int maxKey() {
     }
 
     @Override
-    public KthGreatest.Result kthGreatest(int k) {
-        return KthGreatest.kthGreatest(counts, Math.min(k, counts.length - 1));
-    }
+    public KthGreatestResult kthGreatest(int k) {
+        // Find the kth greatest document hit count in O(n) time and O(n) space.
+        // Though the space is typically negligibly small in practice.
+        // This implementation exploits the fact that we're specifically counting document hit counts.
+        // Counts are integers, and they're likely to be pretty small, since we're unlikely to match
+        // the same document many times.
+
+        // Start by building a histogram of all counts.
+        // e.g., if the counts are [0, 4, 1, 1, 2],
+        // then the histogram is [1, 2, 1, 0, 1],
+        // because 0 occurs once, 1 occurs twice, 2 occurs once, 3 occurs zero times, and 4 occurs once.
+        short[] hist = new short[maxValue + 1];
+        for (short c: counts) hist[c]++;
 
+        // Now we start at the max value and iterate backwards through the histogram,
+        // accumulating counts of counts until we've exceeded k.
+        int numGreaterEqual = 0;
+        short kthGreatest = maxValue;
+        while (kthGreatest > 0) {
+            numGreaterEqual += hist[kthGreatest];
+            if (numGreaterEqual > k) break;
+            else kthGreatest--;
+        }
+
+        // Finally we find the number that were greater than the kth greatest count.
+        // There's a special case if kthGreatest is zero, then the number that were greater is the number of hits.
+        int numGreater = numGreaterEqual - hist[kthGreatest];
+        if (kthGreatest == 0) numGreater = numHits;
+        return new KthGreatestResult(kthGreatest, numGreater, numHits);
+    }
 }
diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/EmptyHitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/EmptyHitCounter.java
index f40bc17e3..efa3f081c 100644
--- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/EmptyHitCounter.java
+++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/EmptyHitCounter.java
@@ -1,7 +1,5 @@
 package com.klibisz.elastiknn.search;
 
-import org.apache.lucene.search.KthGreatest;
-
 public final class EmptyHitCounter implements HitCounter {
 
     @Override
@@ -41,7 +39,7 @@ public int maxKey() {
     }
 
     @Override
-    public KthGreatest.Result kthGreatest(int k) {
-        return new KthGreatest.Result((short) 0, 0, 0);
+    public KthGreatestResult kthGreatest(int k) {
+        return new KthGreatestResult((short) 0, 0, 0);
     }
 }
diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/HitCounter.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/HitCounter.java
index c895126e0..c2b3aa38b 100644
--- a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/HitCounter.java
+++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/HitCounter.java
@@ -1,7 +1,5 @@
 package com.klibisz.elastiknn.search;
 
-import org.apache.lucene.search.KthGreatest;
-
 /**
  * Abstraction for counting hits for a particular query.
  */
@@ -23,6 +21,6 @@ public interface HitCounter {
 
     int maxKey();
 
-    KthGreatest.Result kthGreatest(int k);
+    KthGreatestResult kthGreatest(int k);
 
 }
diff --git a/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/KthGreatestResult.java b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/KthGreatestResult.java
new file mode 100644
index 000000000..6645cc1e4
--- /dev/null
+++ b/elastiknn-lucene/src/main/java/com/klibisz/elastiknn/search/KthGreatestResult.java
@@ -0,0 +1,28 @@
+package com.klibisz.elastiknn.search;
+
+public class KthGreatestResult {
+    public final short kthGreatest;
+    public final int numGreaterThan;
+    public final int numNonZero;
+    public KthGreatestResult(short kthGreatest, int numGreaterThan, int numNonZero) {
+        this.kthGreatest = kthGreatest;
+        this.numGreaterThan = numGreaterThan;
+        this.numNonZero = numNonZero;
+    }
+
+    @Override
+    public boolean equals(Object o) {
+        if (o == this) {
+            return true;
+        } else if (!(o instanceof KthGreatestResult other)) {
+            return false;
+        } else {
+            return kthGreatest == other.kthGreatest && numGreaterThan == other.numGreaterThan && numNonZero == other.numNonZero;
+        }
+    }
+
+    @Override
+    public String toString() {
+        return String.format("KthGreatestResult(kthGreatest=%d, numGreaterThan=%d, numNonZero=%d)", kthGreatest, numGreaterThan, numNonZero);
+    }
+}
diff --git a/elastiknn-lucene/src/main/java/org/apache/lucene/search/KthGreatest.java b/elastiknn-lucene/src/main/java/org/apache/lucene/search/KthGreatest.java
deleted file mode 100644
index 9884198e8..000000000
--- a/elastiknn-lucene/src/main/java/org/apache/lucene/search/KthGreatest.java
+++ /dev/null
@@ -1,64 +0,0 @@
-package org.apache.lucene.search;
-
-public class KthGreatest {
-
-    public static class Result {
-        public final short kthGreatest;
-        public final int numGreaterThan;
-        public final int numNonZero;
-        public Result(short kthGreatest, int numGreaterThan, int numNonZero) {
-            this.kthGreatest = kthGreatest;
-            this.numGreaterThan = numGreaterThan;
-            this.numNonZero = numNonZero;
-        }
-    }
-
-    /**
-     * Find the kth greatest value in the given array of shorts in O(N) time and space.
-     * Works by creating a histogram of the array values and traversing the histogram in reverse order.
-     * Assumes the max value in the array is small enough that you can keep an array of that length in memory.
-     * This is generally true for term counts.
-     *
-     * @param arr array of non-negative shorts, presumably some type of count.
-     * @param k the desired largest value.
-     * @return the kth largest value.
-     */
-    public static Result kthGreatest(short[] arr, int k) {
-        if (arr.length == 0) {
-            throw new IllegalArgumentException("Array must be non-empty");
-        } else if (k < 0 || k >= arr.length) {
-            throw new IllegalArgumentException(String.format(
-                    "k [%d] must be >= 0 and less than length of array [%d]",
-                    k, arr.length
-            ));
-        } else {
-            // Find the min and max values.
-            short max = arr[0];
-            short min = arr[0];
-            for (short a: arr) {
-                if (a > max) max = a;
-                else if (a < min) min = a;
-            }
-
-            // Build and populate a histogram for non-zero values.
-            int[] hist = new int[max - min + 1];
-            int numNonZero = 0;
-            for (short a: arr) {
-                hist[a - min] += 1;
-                if (a > 0) numNonZero++;
-            }
-
-            // Find the kth largest value by iterating from the end of the histogram.
-            int numGreaterEqual = 0;
-            short kthGreatest = max;
-            while (kthGreatest >= min) {
-                numGreaterEqual += hist[kthGreatest - min];;
-                if (numGreaterEqual > k) break;
-                else kthGreatest--;
-            }
-            int numGreater = numGreaterEqual - hist[kthGreatest - min];
-
-            return new KthGreatest.Result(kthGreatest, numGreater, numNonZero);
-        }
-    }
-}
diff --git a/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java b/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java
index 4dabd9f57..448a1df9e 100644
--- a/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java
+++ b/elastiknn-lucene/src/main/java/org/apache/lucene/search/MatchHashesAndScoreQuery.java
@@ -4,6 +4,7 @@
 import com.klibisz.elastiknn.search.ArrayHitCounter;
 import com.klibisz.elastiknn.search.EmptyHitCounter;
 import com.klibisz.elastiknn.search.HitCounter;
+import com.klibisz.elastiknn.search.KthGreatestResult;
 import org.apache.lucene.index.*;
 import org.apache.lucene.util.BytesRef;
 
@@ -101,7 +102,7 @@ private DocIdSetIterator buildDocIdSetIterator(HitCounter counter) {
                 if (counter.isEmpty()) return DocIdSetIterator.empty();
                 else {
 
-                    KthGreatest.Result kgr = counter.kthGreatest(candidates);
+                    KthGreatestResult kgr = counter.kthGreatest(candidates);
 
                     // Return an iterator over the doc ids >= the min candidate count.
                     return new DocIdSetIterator() {
diff --git a/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/ArrayHitCounterSpec.scala b/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/ArrayHitCounterSpec.scala
new file mode 100644
index 000000000..333a4b888
--- /dev/null
+++ b/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/ArrayHitCounterSpec.scala
@@ -0,0 +1,103 @@
+package com.klibisz.elastiknn.search
+
+import org.scalatest.freespec.AnyFreeSpec
+import org.scalatest.matchers.should.Matchers
+
+import scala.util.Random
+
+final class ArrayHitCounterSpec extends AnyFreeSpec with Matchers {
+
+  final class Reference(capacity: Int) extends HitCounter {
+    private val counts = scala.collection.mutable.Map[Int, Short](
+      (0 until capacity).map(_ -> 0.toShort): _*
+    )
+
+    override def increment(key: Int): Unit = counts.update(key, (counts(key) + 1).toShort)
+
+    override def increment(key: Int, count: Short): Unit = counts.update(key, (counts(key) + count).toShort)
+
+    override def isEmpty: Boolean = !counts.values.exists(_ > 0)
+
+    override def get(key: Int): Short = counts(key)
+
+    override def numHits(): Int = counts.values.count(_ > 0)
+
+    override def capacity(): Int = capacity
+
+    override def minKey(): Int = counts.filter(_._2 > 0).keys.min
+
+    override def maxKey(): Int = counts.filter(_._2 > 0).keys.max
+
+    override def kthGreatest(k: Int): KthGreatestResult = {
+      val values = counts.values.toArray.sorted.reverse
+      val numGreaterThan = values.count(_ > values(k))
+      val numNonZero = values.count(_ != 0)
+      new KthGreatestResult(values(k), numGreaterThan, numNonZero)
+    }
+  }
+
+  "reference examples" - {
+    "example 1" in {
+      val c = new Reference(10)
+      c.isEmpty shouldBe true
+      c.capacity() shouldBe 10
+
+      c.get(0) shouldBe 0
+      c.increment(0)
+      c.get(0) shouldBe 1
+      c.numHits() shouldBe 1
+      c.minKey() shouldBe 0
+      c.maxKey() shouldBe 0
+
+      c.get(5) shouldBe 0
+      c.increment(5, 5)
+      c.get(5) shouldBe 5
+      c.numHits() shouldBe 2
+      c.minKey() shouldBe 0
+      c.maxKey() shouldBe 5
+
+      c.get(9) shouldBe 0
+      c.increment(9)
+      c.get(9) shouldBe 1
+      c.increment(9)
+      c.get(9) shouldBe 2
+      c.numHits() shouldBe 3
+      c.minKey() shouldBe 0
+      c.maxKey() shouldBe 9
+
+      val kgr = c.kthGreatest(2)
+      kgr.kthGreatest shouldBe 1
+      kgr.numGreaterThan shouldBe 2
+      kgr.numNonZero shouldBe 3
+    }
+  }
+
+  "randomized comparison to reference" in {
+    val seed = System.currentTimeMillis()
+    val rng = new Random(seed)
+    val numDocs = 60000
+    val numMatches = numDocs / 2
+    info(s"Using seed $seed")
+    for (_ <- 0 until 99) {
+      val matches = (0 until numMatches).map(_ => rng.nextInt(numDocs))
+      val ref = new Reference(numDocs)
+      val ahc = new ArrayHitCounter(numDocs)
+      matches.foreach { doc =>
+        ref.increment(doc)
+        ahc.increment(doc)
+        ahc.get(doc) shouldBe ref.get(doc)
+        val count = rng.nextInt(10).toShort
+        ref.increment(doc, count)
+        ahc.increment(doc, count)
+        ahc.get(doc) shouldBe ref.get(doc)
+      }
+      ahc.minKey() shouldBe ref.minKey()
+      ahc.maxKey() shouldBe ref.maxKey()
+      ahc.numHits() shouldBe ref.numHits()
+      val k = rng.nextInt(numDocs)
+      val ahcKgr = ahc.kthGreatest(k)
+      val refKgr = ref.kthGreatest(k)
+      ahcKgr shouldBe refKgr
+    }
+  }
+}
diff --git a/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/KthGreatestSuite.scala b/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/KthGreatestSuite.scala
deleted file mode 100644
index dcac7b0a6..000000000
--- a/elastiknn-lucene/src/test/scala/com/klibisz/elastiknn/search/KthGreatestSuite.scala
+++ /dev/null
@@ -1,61 +0,0 @@
-package com.klibisz.elastiknn.search
-
-import org.apache.lucene.search.KthGreatest
-import org.scalatest.funsuite.AnyFunSuite
-import org.scalatest.matchers.should.Matchers
-
-import scala.util.Random
-
-class KthGreatestSuite extends AnyFunSuite with Matchers {
-
-  test("bad args") {
-    an[IllegalArgumentException] shouldBe thrownBy {
-      KthGreatest.kthGreatest(Array.empty, 3)
-    }
-    an[IllegalArgumentException] shouldBe thrownBy {
-      KthGreatest.kthGreatest(Array(1, 2, 3), -1)
-    }
-    an[IllegalArgumentException] shouldBe thrownBy {
-      KthGreatest.kthGreatest(Array(1, 2, 3), 4)
-    }
-  }
-
-  test("example") {
-    val counts: Array[Short] = Array(2, 2, 8, 7, 4, 4)
-    val res = KthGreatest.kthGreatest(counts, 3)
-    res.kthGreatest shouldBe 4
-    res.numGreaterThan shouldBe 2
-    res.numNonZero shouldBe 6
-  }
-
-  test("randomized") {
-    val seed = System.currentTimeMillis()
-    val rng = new Random(seed)
-    info(s"Using seed $seed")
-    for (_ <- 0 until 999) {
-      val counts = (0 until (rng.nextInt(10000) + 1)).map(_ => rng.nextInt(Short.MaxValue).toShort).toArray
-      val k = rng.nextInt(counts.length)
-      val res = KthGreatest.kthGreatest(counts, k)
-      res.kthGreatest shouldBe counts.sorted.reverse(k)
-      res.numGreaterThan shouldBe counts.count(_ > res.kthGreatest)
-      res.numNonZero shouldBe counts.count(_ != 0)
-    }
-  }
-
-  test("all zero except one") {
-    val counts = Array[Short](50, 0, 0, 0, 0, 0, 0, 0, 0, 0)
-    val res = KthGreatest.kthGreatest(counts, 3)
-    res.kthGreatest shouldBe 0
-    res.numGreaterThan shouldBe 1
-    res.numNonZero shouldBe 1
-  }
-
-  test("all zero") {
-    val counts = Array[Short](0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
-    val res = KthGreatest.kthGreatest(counts, 3)
-    res.kthGreatest shouldBe 0
-    res.numGreaterThan shouldBe 0
-    res.numNonZero shouldBe 0
-  }
-
-}