From c9e8eb8efdf20608090bd93cbe725848f02f295b Mon Sep 17 00:00:00 2001
From: wassname <git@wassname.org>
Date: Mon, 28 Nov 2022 20:08:36 +0800
Subject: [PATCH] meh

---
 notebooks/001_001_run-S-Copy1.ipynb | 276 +++++++++++++++++++++++++---
 1 file changed, 254 insertions(+), 22 deletions(-)

diff --git a/notebooks/001_001_run-S-Copy1.ipynb b/notebooks/001_001_run-S-Copy1.ipynb
index 0f745c8..28bdfe9 100644
--- a/notebooks/001_001_run-S-Copy1.ipynb
+++ b/notebooks/001_001_run-S-Copy1.ipynb
@@ -98,11 +98,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "b3a40300",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2022-11-28T10:35:02.166Z"
+     "end_time": "2022-11-28T10:37:28.005707Z",
+     "start_time": "2022-11-28T10:35:02.162141Z"
     }
    },
    "outputs": [
@@ -320,7 +321,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b99b57bf3d3844c7a693f364d6d25a7b",
+       "model_id": "",
        "version_major": 2,
        "version_minor": 0
       },
@@ -330,6 +331,66 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 9, Steps: 147 | Train Loss: 0.2532325 Vali Loss: 0.1394154 Test Loss: 0.0949533\n",
+      "EarlyStopping counter: 5 out of 5\n",
+      "Early stopping\n",
+      ">>>>>>>testing : Exchange_ETSformer_custom_ftS_sl512_pl96_dm512_nh16_el2_dl2_df2048_K0_lr0.0003_'Exp'_0<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n",
+      "val 665\n",
+      "loading model\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/20 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test shape: (20, 32, 96, 1) (20, 32, 96, 1)\n",
+      "test shape: (640, 96, 1) (640, 96, 1)\n",
+      "mse:0.13797499239444733, mae:0.29500773549079895\n",
+      "test 1422\n",
+      "loading model\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/44 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test shape: (44, 32, 96, 1) (44, 32, 96, 1)\n",
+      "test shape: (1408, 96, 1) (1408, 96, 1)\n",
+      "mse:0.10001101344823837, mae:0.2315286099910736\n"
+     ]
     }
    ],
    "source": [
@@ -401,14 +462,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "563c1c4c",
+   "execution_count": 23,
+   "id": "a4576bab",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2022-11-28T10:35:02.332Z"
+     "end_time": "2022-11-28T10:40:35.984019Z",
+     "start_time": "2022-11-28T10:40:35.785399Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ETSformer(\n",
+       "  (enc_embedding): ETSEmbedding(\n",
+       "    (conv): Conv1d(1, 512, kernel_size=(3,), stride=(1,), padding=(2,), bias=False)\n",
+       "    (dropout): Dropout(p=0.2, inplace=False)\n",
+       "  )\n",
+       "  (encoder): Encoder(\n",
+       "    (layers): ModuleList(\n",
+       "      (0): EncoderLayer(\n",
+       "        (growth_layer): GrowthLayer(\n",
+       "          (in_proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "          (es): ExponentialSmoothing(\n",
+       "            (dropout): Dropout(p=0.2, inplace=False)\n",
+       "          )\n",
+       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        )\n",
+       "        (seasonal_layer): FourierLayer()\n",
+       "        (level_layer): LevelLayer(\n",
+       "          (es): ExponentialSmoothing(\n",
+       "            (dropout): Dropout(p=0.2, inplace=False)\n",
+       "            (aux_dropout): Dropout(p=0.2, inplace=False)\n",
+       "          )\n",
+       "          (growth_pred): Linear(in_features=512, out_features=1, bias=True)\n",
+       "          (season_pred): Linear(in_features=512, out_features=1, bias=True)\n",
+       "        )\n",
+       "        (ff): Feedforward(\n",
+       "          (linear1): Linear(in_features=512, out_features=2048, bias=False)\n",
+       "          (dropout1): Dropout(p=0.2, inplace=False)\n",
+       "          (linear2): Linear(in_features=2048, out_features=512, bias=False)\n",
+       "          (dropout2): Dropout(p=0.2, inplace=False)\n",
+       "        )\n",
+       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout1): Dropout(p=0.2, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.2, inplace=False)\n",
+       "      )\n",
+       "      (1): EncoderLayer(\n",
+       "        (growth_layer): GrowthLayer(\n",
+       "          (in_proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "          (es): ExponentialSmoothing(\n",
+       "            (dropout): Dropout(p=0.2, inplace=False)\n",
+       "          )\n",
+       "          (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
+       "        )\n",
+       "        (seasonal_layer): FourierLayer()\n",
+       "        (level_layer): LevelLayer(\n",
+       "          (es): ExponentialSmoothing(\n",
+       "            (dropout): Dropout(p=0.2, inplace=False)\n",
+       "            (aux_dropout): Dropout(p=0.2, inplace=False)\n",
+       "          )\n",
+       "          (growth_pred): Linear(in_features=512, out_features=1, bias=True)\n",
+       "          (season_pred): Linear(in_features=512, out_features=1, bias=True)\n",
+       "        )\n",
+       "        (ff): Feedforward(\n",
+       "          (linear1): Linear(in_features=512, out_features=2048, bias=False)\n",
+       "          (dropout1): Dropout(p=0.2, inplace=False)\n",
+       "          (linear2): Linear(in_features=2048, out_features=512, bias=False)\n",
+       "          (dropout2): Dropout(p=0.2, inplace=False)\n",
+       "        )\n",
+       "        (norm1): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "        (norm2): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
+       "        (dropout1): Dropout(p=0.2, inplace=False)\n",
+       "        (dropout2): Dropout(p=0.2, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "  )\n",
+       "  (decoder): Decoder(\n",
+       "    (layers): ModuleList(\n",
+       "      (0): DecoderLayer(\n",
+       "        (growth_damping): DampingLayer(\n",
+       "          (dropout): Dropout(p=0.2, inplace=False)\n",
+       "        )\n",
+       "        (dropout1): Dropout(p=0.2, inplace=False)\n",
+       "      )\n",
+       "      (1): DecoderLayer(\n",
+       "        (growth_damping): DampingLayer(\n",
+       "          (dropout): Dropout(p=0.2, inplace=False)\n",
+       "        )\n",
+       "        (dropout1): Dropout(p=0.2, inplace=False)\n",
+       "      )\n",
+       "    )\n",
+       "    (pred): Linear(in_features=512, out_features=1, bias=True)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "exp.model"
    ]
@@ -428,53 +583,108 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "92b67ed7",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2022-11-28T10:35:02.599Z"
+     "end_time": "2022-11-28T10:40:36.108906Z",
+     "start_time": "2022-11-28T10:40:36.094911Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Exchange_ETSformer_custom_ftS_sl512_pl96_dm512_nh16_el2_dl2_df2048_K0_lr0.0003_'Exp'_0\""
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "setting"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "ebea79f7",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2022-11-28T10:35:02.716Z"
+     "end_time": "2022-11-28T10:40:36.262065Z",
+     "start_time": "2022-11-28T10:40:36.238265Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 1422\n"
+     ]
+    }
+   ],
    "source": [
     "ds, dl = exp._get_data('test')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "9833f218",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2022-11-28T10:35:02.866Z"
+     "end_time": "2022-11-28T10:40:37.974021Z",
+     "start_time": "2022-11-28T10:40:36.394340Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test 1422\n",
+      "loading model\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/44 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "test shape: (44, 32, 96, 1) (44, 32, 96, 1)\n",
+      "test shape: (1408, 96, 1) (1408, 96, 1)\n",
+      "mse:0.09427478909492493, mae:0.23262830078601837\n"
+     ]
+    }
+   ],
    "source": [
     "preds, trues = exp.test(setting, data='test')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "658546f2",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2022-11-28T10:35:02.999Z"
+     "end_time": "2022-11-28T10:40:37.987615Z",
+     "start_time": "2022-11-28T10:40:37.975192Z"
     }
    },
    "outputs": [],
@@ -484,16 +694,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "5c88dbf7",
    "metadata": {
     "ExecuteTime": {
-     "start_time": "2022-11-28T10:35:03.149Z"
+     "end_time": "2022-11-28T10:40:38.098854Z",
+     "start_time": "2022-11-28T10:40:37.988519Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f66f8eaeee0>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Ply1/++23MWPGDABAZWUltJxMKysrw5dffom7774bQ4YMQefOnTF79mzcf//94e15EuMu7Q0wTOPq0uoUwhcjrVb3J7qxlcQIQRBEPJCZKe8tAgDz5wOsFqRXL2D6dPdjTIwA7sRXxttvA0OGuMM9/LqxIigxIgRwslu9erXHstGjR2PDhg3BvFRKI5X2xiBnpNXGiZE2SmIlCIKIV/i+IQcPyh/zJTDef18s82XOSMKJEaJ9sLLZNEHnjERYjJAzQhAEERccPCjmd3TvDowbJy7j0y6VTc39CYyOHeNLjITV9IyIDrZgwzSsz0gEckZa+DAN5YwQBEHEBd99J1bAPPGEexkvRjIy5Ov/+teAsgXYrFnA6NHi7z594itMQ2IkDrEGWdobyT4jbTJnhMI0BEEQ8QBLMmUCApCLEaWg6NcP4FMzf/c74LbbgO+/F6tqNBpyRgg/MGfEqA92Nk34pb18AmuThZwRgiBSl9dfF8MiO3fGek/cgoEXI3buetFi8dzmoovE3xoN8PLLYtIqzxtviEP07rwzsvsaCpQzEocEm8AaSWeklZwRgiAIAMDMmeLv++4Dli2L7b4wZ4S5GQCwf7/7NtdRA4AY1qmpAQYPBs4/Xz65l5GZGfn9DBUSI3GINejZNBGspqGcEYIgUhibTT4RFwB++9vY7AuPWpiG9QtR429/Az77DHjtNTE8E+9QmCYOkfqMBFhNo4uaM0JihCCIxMfpFPDVzip8u++05Dyr8cMPYldTliSamyv+Pv/8dthJP/Dt3RlPPy3+HjEisPWVfPUVcOONwAsvRGYfw4HESBwS6myacMWIIAgefUYC6S1DEAThjeP1rdh+rAHOGE4Vb7U5MPNfW3Djm5t8Vh0+9RTQ2gr85S9iC/WGBnF5aWk77agP1JwRNvLthx+Axx+Xrx+IGNm/X5xTs3Zt5PYzVEiMxCFMjJgC7TPiKu0NV4xY7E7w2sPhFCIS+iEIIjU5WteCsX9bhSkvrcMrq/f73yBK8BdZGqdWEhlKeNHB52Ps2xelHQuCuXPFZmWXXeZexv8d334rXz8QMULVNIRPrKHOpglTOPD5IoxI9C4hCCI12X68QbpI2nzkTMz2gx3b0gxa/N//aVBS4tmxFAC6dXPf/vxz9+2PPoryDgbAqFHAddcBffu6l73+uvs2G4bH4AfleYO5LPEwm4bESBxhsTvQZnMEPZuGVdNU1rXgaJ2PjCY/8FcPDFsEyoUJgkhNjtS6j0eHasw+1owurH+Spj4bn34qhmL4VuqMe+4BrrxSvL1uHaB3lXiolc3GA8XF7ttKd6PRNXQ9J8f79uSMEB4IgoCrXvkeE55ZI82ECTZnBABuXvhDyPvAxEh2mrvIipwRgiBCpZK7ODpa1yJdaLU3rLO0ebc7DrPfS9RozhzggguAZ58FHnxQXGa1RnkH/SAIogvy+efyffn4Y2DaNPE2LygEwe12+BIj8eSMUGlvnHCq0YKdJ0QpW9MsyvBgZ9MAwL7q0CUuszIzjXqYLXY4BfjMPCcIgvBFZZ3bDXEKwNEzLTir0EcSQ5RgF1pZHd0Wx7Zt6uuOHy8mdGo0gMkkLou1M9LQ4O55wpfzFhYCf/oT8OGH8jCNIADvvCO6IwUF3p+XhXCUIZ5YQM5InLCfExFttiCn9moCyy3xB/vCZhh1kitDYoQgiFCwO5zYfkyeKXrodGzOeuzY1vP8Gvzyi7jsp58AhyIyfc89ohj56ivxPmu3Hmtn5Phx8Xd+PpCeLn+MCQreGdFqgeuvFyfzpqV5f14K0xAeHDjt+WkINEzDJ66eVRh6Sz13kpdbjFCYhiCIUHj+m31obLPDqNfigl4dAcQub6TNdWxLN+jQuzdw7bVix1KlyNi6FVizBqirE+/HizPCxEjnzp6PMUFhMIhzZ4JJ8+vaFTh9WuzUGmsoTBMnKMVImkGLYV3zAtq2R0e3AMlOM/hY0zdmi5irkmHUSaGfSMy7IQgi9dh8WKyeuXlMDxh0GqzbX4NDtbF1RtIMOuh0wKJF6uuxcAU7wcebM6ImRjp1Ag4cEHNcxowRO65edZWYgFtUJE7p9YZOB3TsGJ19DhYSI3ECu2I4qzATF/Utwu9GlKE0L93PViJpBh3emjEcNy/cHJZ4qGsRv3H5mUboa1mYhpwRgiCCx2wVL25G9sjHGbPYzTnaYZofDtdh2faTuG9iX2QY3ac3JkbSDTqf2yt7c1x0kdgUrGvXqOyuT+6/H6iuFvNXWBmyWvM1rRbo0QN46SXx/t13A716AVOnAgMHAjt2tNsuhwWJkTihyVVB88Dk/rh0QKegtzfpxS+ZzR66eDhjFsVIQYYRRpczQjkjBEGEQrPLac006pGXIVoMh8NwRtpsDqT5ERPX/HM9ANEhnntpH2k5C0FnGMXtm5qAY8dE5+Oss9zbK52RXr3En1jwt795LvMmivbudd8uKHCX9fLdWr1x771iqKaiIradZilnJE4wS19c3182b0Qi4bTWzDkjOnJGCIIIHemYZtKjRwcxlHyyoU21uaI/fqw8g8H/70u8tDKwVqj7q+W1quw1013H1y+/BAYMAGbMcK/z5pvAkSPi7VhPsz19Wn159+7qy/v2Faf0AsDRo+6wjq+yXsYHHwDvviuWMdfWBr2rEYPESJzA6uAzTaGZVaxbazhNypgz0iHT6M4ZIWeEIIgQMFtc5bQmPfIzjVKrAhYODoY/Lf4ZNoeAp7/a63Udfo4WG5HB4HNGAHdiKp8Lcuut7tvMGTl9WmyOtmJF0LscFntV/swLLhArfbxx/vluZ6O8XPwdiBhhf+vbb8Nrm/z2gMI0cQKLr2aawnRGwgjT1LWIcd38TCMMri8zzaYhCCJYBEHgjmniaSbbpEet3YqmNhuAwPLhGM2uMDZPY5sNlbUt6FucDYNOK4WFAHnvJcAzZ8RfYipzRn76CfjNb4DBg4Gffw5ql8Pi1CnPZeef790ZYbz0klhRc+KE6HgEIkbYewGIpcOxgpyROKHFwmKaoToj4YdppJyRTAMMevHLbCVnhCCIIGmxOqShm1kuMZLl6uysJiz8YbZ4bvO7f67Hr15ch1ve2QwAqG5y199aFJ1e27yIEVayy4uSK68EOnQQb8eqtLe62nPZoUP+t/vNb4C//92dWxKIGLHZ3LdzcwPbv2hAYiQOsNqd0kk/M2QxEp54cDoFHHZV9ORnGCWbk/qMRA+r3Ylb3/kBC1YfiPWuEEREYeJBqxHbFADuMRNNKsLCH8ptGlps2F0l5oVsPCgmOlQ3uhUDu7BiKHNGlGEaPjxx113u27Eq7VXLGTl8OPDtGxvF8EsgCaz836aNoSIgMRIHtFjdX7SMMMM0oYqH+//zs/SF75BpksQN5YxEjuU7qnD5899KyXXf/HIKX/9SjaeW78a5j67AXR9ulcW9CSJR4StpNK4O0cwhCcUZUbL/tDtBlX1jqpvapGV1CjHSohAjSpHhcAAXXwxceKH4mxFPzgibQRMIN94IDBoklvz6g3dGYgmJkTjA7PqiGPXagLuuKgk3TPPxlmPS7fxMg+SM2ChnJGLc8d4W7DrZiDs/3AZA7mLVmq347KcTOF7fGqO9I4jIwZJX+YT8LJPYkLEpSDHCX6wBwI7jDbh6wXrpvtXuhM3hxGkuTFPfIj/DKnNGlM5IcTHwzTdiTw+eWDkjf/ubPCxz1VVi/5BAGTRIrI65/nr/68a6oRuDxEgc0BJmWS/gDtPYnUJIV9fdOmQAAM7rWYDsNAMMepYQS85IpDl2psXrYyy2TRCJjOSMcE4vC9M0W4K7FD/VKLcl/rnGM6zZYnHIckbqWqyy46AyZ6SoSDy5//GPvl87Vs5IerqYrHr//eL9YJuuZWUBV1whT071xp//LP4eOza414g0VE0TB5it4SWvApDEAyD2BjHqgxue53A5IPdP6ic+n5bawUcLFk9Xu0JkQxIJIpFhn/EszhnJDjGBtbFVLl7OqJQGm612VDe6wzRWuxMtVofkzEilva4Lvk6dgGee8f/asW4HX18v/o5mlcsNNwDDh3sO4GtvyBmJA1pUriKChZ/wG0qohmWfs06ueqkDK4VpIg2LfDW0el4hWuzkjBCJj7KsF3ALk2ATWJWi/UC1mGjfp1MWsl3PabbYZc4IIB/K12r13Q5+4UKxgobvNQKIy159FViwAPj2W6CqCrjtNuD994P6E4LmzjtFV2TbNvG+IfSRY37p2FHsYXLuudF7jUAgMRIjnE4BPx+rh8XukCzNcJwRvdbthIQiRqwuMcIaE+mlhFi6Ug+XM2arR0dIQOyToMRCzgiRBDRbVMRIiM5Ik+J7UuVyQO6e0Ac56eJZ2mx1h2nYofCnY/XSNsxxZGLE6RQ7lR44AAiC2Hm0rg5oc5srAICMDGDmTECvF8MYJSXAG2+ICaLR4vRpsV/I3/4GbNwoLvvll+i9XrxAYiRGvLP+MH790nd44D/bpUzvrBC7rwKATquBK2nda3mv0ylgd1WjFJLhYVfkJpcYMVI7+IhxXsU3mPDMWo/lja0qYRpyRogkgAkOWZiGOSNBixH19Yty0iQ32Wxxh2km9Bdne31/oBY2hxMvfLNPSgxns2mam8U8jF69xHwQVtqbl6e+Dzt3yu9fc01Qf4JPBAHYvt2dl8KSaAcNEifwnn22ODcm2SExEiOe/0acsbBk63HJ0swII4FVo9H4Le99de1BTHruW1Qsk8tsQRAkZ4SJEea0hNNenhBRNmACxBwdckaIZEWaAJ7hzqCUnJEgwzRq3xMAKMo2Sc7LDW9uRKNLtExwDRr9/OeT6P2XL/DMCndv9TRF0zNAzAdhuRlqTb9WrADeeku8PWwYsHkz8NFHQf0JPvnPf4AhQ4Dbbwd27QJ++1tx+bhxYkho61agc+fIvV68QmIkRvCCoUWlDC4UWNKptzDNU8t3AwDeWCdv5Wd3ClIegztnhJqehQPL5PdW2VRntkqJeTeP6YGRPQoAkDNCJAd1za45V1nus342K+0NM2eEUZhtkppE8l+zKUNKMXGg+uRzZZ8RQHQk9u8Xb3fs6LnN5MlATY14+847I59b8fjj4u933gGef969/Ne/juzrxDskRmIEH0ppk4Y4hffvkMpxg8zzsHJX7ixnhJqeBceGg7Woabag2WLHok2VGPLIV/huf42qKwIAL3yzD9/uE49wY3p1QI7rqpGcESIZqJNGS3BixPUZV1bH+IOJkd8N7yIt65BpRJpB55H0n59hQLpRh1dvHI7zehZ4PBfLGdFqxTwQQJzh8sUX4u1Jkzxfn5X3vv8+cOmlYv7Gjh1B/QmqnD4NvPKKGKJhvP66+PuZZ4DLLgv/NRIJKu2NEbxgYI3FlJMmg8UQYJ5HYbZJdt+iIkbYvljJGfHLun01uOHNjcgy6WUW9D3//gmf33WB6jb/2nBEup2TboDJdZCkPiNEMlCrIkY6ZInHndrm4Jp2sATWbh0y8eqN5+KbX05h0qBiAG4nFwBuu7AHLu7ndkQKs9M8niuNq6YxmQC73d1nY+hQoH9/z9cvKABaWoBu3UQHZsAA0VkJt/fIffeJbggPc3j89T9JRkiMxAjeVnS48jKUkyaDxeijCyuftFqaJy8oZ86IXquBzhXqYYPyyBnxz/KdJwF4xsINek1A8fEsk17K1fHmpBBEIsGckQ6cGOnoCtk0ttlhtTulCx9/MGckJ02PiQOLMXFgsfQYXx4/b3J/aLmqQmUTSYPOfXwDREFhNgNbtgBLlsjbwPOcfTZw7Bjw44/AWWeJyyLRQn3DBvn9r74CfvhBzB8xmdS3SWZIjMQAZR4Bq6bhy3NDwd0bxPOEdrLB3Wa8MEvelk9Z1gsABjYoj9rB+0QQBKzarTLVCmJFwbi/r/a5fdeCDPQuypKu2KjpGZEMqIVpctIM0Gs1sDsF1JmtKM71dC6UbK08g+U7qwAA2WmezTZqzW57Qqs4fipbJTgFAbC6e4/kZQCWZsDWAsy7x7VQpbnZwN7ASgPwwD3AddcAGa7dcLQCuhBrDpqbgWOH3M/FXufScd73o10wZEAqy2xnghIjFRUV+O9//4vdu3cjPT0d559/Pp566in07ds3oO0XLVqEadOm4corr8Qnn3wSyv4mBcoELvbF1Yc4l4bBwjRWu6eAONngLqBX6gtlWa+4L76TYQmRZdurvM6TOdPi//Lpf3deAL1OyzkjFKYhEhu+d1KHTPclvlarQUGmEdVNFtQ0WwISI/d8/JN0m+WcyF7Lh3hX5pMYnW3AE6XS/YOswdlnrh8vPJkDPOkK5eCfgJndfsrHjvshC0DzPMXChaE/X8T48wnAmBmTlw7q7LdmzRqUl5djw4YNWLFiBWw2Gy677DKYzWa/2x4+fBj33nsvLrzwwpB3NlmoUXQKrHVlnofrjEilvSrluHzjIKXbYVFzRqiaJiDeXX8YAHDFkJKgtzXptVLiKjkjRLIgXVxpNchJlwsIKW/EHNil/wlO6KepdE99dOogZBh1ePw3gzweC6eJJNH+BPXfWr58uez+woULUVRUhC1btmCsjyk7DocD119/PR555BF8++23qGdF3SlKTbP8i1grfXnDdUY83QxBEKDRaGTlcco8EHUxQs5IILDpoFcN64zPfz4Z1LadctKk8erkjBDJApuem59plD7fDJY34i2JtdXqwEur9uHSAcU4uywPnfPSceC0eLHbvyTHY/3zenbA9v83UZYLwlA6I60wiVf+LhwOIMfVV+TwYaBQpaxXidMJZLt248gRoGMH/9skFIaMmL10WNKxwdW2rqDAs4SK569//SuKiopwyy234Ntvv/X7vBaLBRYuVbmxsTGc3Yw7lF9Edj/cBFZlmKbZYsek59ZiVI8OOKdbnrSe0hmxKubSAG5hZKOcEZ8w8ZCbHvzwiCKuqomcESJZ2FMljj44q9DT7mcJrbXN6s7Ih5sq8fKqA3h51QEcfvIK6cLtf7MukOWf8KgJEUDNGdHIQhCVhwAWSc3tCCCACbdaAG0OUZTYENg2RGCELEacTifmzJmDMWPGYNAgT4uMsW7dOrz55pvYxib+BEBFRQUeeeSRUHct7qlRihFzpMI08km76/adxrEzrTh25hh6FWVJ63k6I+IJlR+2R31GAoMXcjeN7oalP5/EqJ4FWLa9yu+2fIk1OSNEoiMIAvZVN2PHcfEiVc3JYGEa5TGQUcVN3m2zOaRqmbKC4EfKZvkZPDpqlPu2MQhRce+9Yo5nOFNuBSFmeaJxS8hipLy8HDt27MC6deu8rtPU1IQbb7wRr7/+Ojqqtbbzwrx58zB37lzpfmNjI8rKykLd1bjjtOKqgJXdelP4gWJQlPZmmdxX6wdON3u8HkM6oRr4BFaaTRMIFu69e+TKQXh4ykCs218TkBjhu1MyV4qcESIRsdqduOmtTVh/sFZaNkBFjGT7aQlfmOUW6JsO1QEQL4xCcR795Yy0tAT9lACAp8JIXGWcfTbQ1AQsXgycc074z5cMhCRGZs2ahaVLl2Lt2rXo0qWL1/UOHDiAw4cPY8qUKdIyJ+upoddjz549OIsVbnOYTCaYkrjQ2ttVgSFC1TQ2V5iGT2TdecId6mJhGkEQMPNfW7Bi1ykASmdEvP31L6cw+flvsfiO0WG3q09GpHwb1/ul1WpUE+3UKODmdrDuu+SMEInIgdPNMiECAANKPcWIv3Ak3/RvzV6xZL5jlskj9yQQlDkjSkIVI+EiCOK0YLMZyPF8i1KWoM4ugiDgzjvvxJIlS7B69Wr06NHD5/r9+vXDdr7XLYAHH3wQTU1NeP7555PK7QgGliNSmG2Skr2ASDgj4vZ7T4kxW/4L/8tJToy43I4Wq0MSIgCkLqD8c7Fttx2tx5hegbtbqYJUFs25SukBipG8DE9nhNrBEwDw87F6fLXzFGZd3CtgcRtLWjkR8ashJehZmKXqjKS5wpHeZjCZre7lb7pmaHXMCu3C1J8z4mVslF+OHwfa2sThdWn+q5M9aGgQhQiQGgPwAiUoMVJeXo4PPvgAn376KbKzs1FVJVrRubm5SHcF0KZPn47OnTujoqICaWlpHvkkea4Zzb7yTJIdlpTVt1O2TIwYwkxgZSe0N9Ydwu8v6OH1Kps5Jq2K1uO8M9KrKAsGnUYK01DzM08cTkF6f/jk33Sjd4drUOcc7DguCkM+IY85IzQojwCA3/5zPax2J1ptDsz/1YBY745f2lwiok+nLLx0nfe4AxNWFi9jD1qsnuEbtUTYQMj0I0b0erEdfLCcfz5QWQls2gSMGBH89tXV4u+cnPDyTpKNoOICCxYsQENDA8aPH4+SkhLp5yNunnJlZSVOngyuxDHVYGGaPp2yZct1YZb2/t9It9P0/f4ar1fZTFi0WuUHBL7p2cDSXHx19zjpPjt4fLipEqv2VIe1n8kCP2CQf++UV7LTR3eTbvOzM/IzyRkh1GGfrf/8eCzGexIY7MLGnysoiREvYw/MFk+R8vCUgSHtU4YiTHPvZX1k96++Wvz9wgvBPa/Blb5iDbFLaqurdUpG7Kpo45KgwzT+WL16tc/HFy5cGMxLJiWsrK1vcZZsebjOyPlndcTt43ri1TUHseXIGdWYLeAO0yidEZNiVkSPjpk4r2cBNhysg9XhxO6qRsz7rxh2O/zkFWHtazIQqBjpV5yD/AwDOuWkoX+xW4Dmc72gmZtiVrkyJFKD7/fXYPGPxzD/CrcTUh9AF994gIWETX7ECPueeBsIqXRGRnYvkIn2YMjgZtP894/nY1hZnuxxVkET7MA7th0/n8ZmAyoqgIsuAvz19WxzFQyFEuJJZigjsZ2x2p1SJnmvIqUzEn6t1/BuBXgVB7H5yBn0dNmbOq1GVkHDbrconBG1wVVG7or9ZH2bx+OpDAuDaTXyVv7Kq8OCTAO+vf9imPRafLe/Rlqez+WMdMkXL5NONrTBYnfIwj5EanDdGxsBANkmPfIyDJIQOd1k8Zi0HW8E64x4S2A1K45JhTmh/90ZRj3um9gXFrsT53TN93h87FgxVDMwSONFzRl55BHg8ceBhx/2n4tCYkQdEiPtSG2zRbIhNRrReeAJtwMr4H7O000WyfIfVpaHzUfOSOtIOSM+wjTKZRa7Uxw05cLpFDwGU6UaFpVmcYCnM2Iy6JDlqkTiqwL4nJGibBMyjTqYrQ5U1ragtyKER6QOp5stsvytLUfOYNKgYh9bxJ5AxQhL9PbqjChKfjtlh3fGLr+ol9fHbr1V/AkWNWfklVcC3z49HRg5EujePfjXTmbCP/sRAVFntuKCp1bhsufWABA7duZnGGSNzsJtega4k1DtDqeUDKlsPiTljNjkX3y1rH2+GRefw2qlZmiqlTSA6ETxJ5M0TqzwApC3kTUaDc5yNaZj7a+J5OY/W46p5l/lphtlIdQtR+rac7dCguWUpRl8n1L85owoLpDUhuPFGiZGmDNitQJn3Nd6fp2RkSOBjRsBLtWSAImRdmN3VSNabQ7JnszPEOc28FcS4baD55/D5hCk18o06fHBraNwz6ViAhdf2svTKcfzKoSFbqwKZ4TECBcnV3GU+IMyf3tEd9EuLs1N8+id0NPlah2saQaR3Byta8E9H/+E37/9Az7YWClzCvIyDLJE5h8On1F7iriCiex0Y2RzRiJxgRZpWJiGOSNNTe7HbDbqrBoqJEbaiROKfAvWUVDW9TQCYRqp8ZnTKX3hTXotzu/VEVOHiUXt3sI0nfM968ykKg+7U5bAbPVyZZNKMEGmlt/BH5R5x6lDlglbHpyAlfeO99imu0uMHK2LUTcmot040+JOOFixq0pe4q/VyMT+zhMNXk/e8UKr5IwEmjPipc+IIkxTmhd/ta9TpwKzZgGPPQZccQXQ7Lp2MBrFHBQiNOitizLVTW249+OfpRkLDFZJIRtOFwFnhFXkCILb+WAHACZUHE71aprOKl98PkzDW6skRtxluGrOiNjjQDzBKA/QHbw0ccpOEz8TauWNRHLBf39ON1tQ3eS+WOGPFTlpejS22fHT0XqM6hm/I2KZSxiwGPFT2vunSX1xor4VV55dGsG9jAxz5oihGZMJ2L4dOHUK+OSTwEt933kHmD8f+PWvgZdeiuaeJhbkjESZJ7/YjbV7T+Ono/Wy5aySgq9giYQlybeUb2oTrzJYmIBV69gcAgRB8AjT+BIjVrtTdjVDYoQbMKgiRsoK3E0E/MXRGSyHRPl/IZIPXtgfqDbj6gXrpfu8GLmgt9j1mE9AjwSCIOCTrcex80RDRJ4v4Goa7niibBXhcArS8/xueBkemzpYVqUWTzi4r6hGA1x5JXDkiOiarFzpe9u6OuDoUaC+Ppp7mHjE5386jmi22LHtaH1APVbUOFHfqrqcJWbxV9WR+OLx7kqzRTyouZ0R92NOwTNMk5fhOYyKr6bhy/EoZ4SvpvH8v/GVUmkBlumyA7kysZhIPvjuyEqHkomRNIMWvV3l/96OI8Fgczjxh/e24NU1B7DxUB3mfLQNV7zgfdBpMLQFmMDK9yFRJrHy74O/7qmxpKlJbOnOqmGYqNiwAfj0U+CXX+Trm81i1U5xMXD77e6mZ1TaK4fEiB+mvbYBU1/+Dp9vD62rbE2zunfHRmWbIu2MaD2dEfYafB8Tu9PpcRBUG0Zlkto3kzOixFtpLwB04fJvAp0tkt7Ozkh9ixUXPb0aTy3f3S6vR7jx1WmXiZF0g076TERimvMPh+rwxY4qVHyxGz8fq5eWq7VgD5a2IJ0RfhvG8h3ieJGcNH3AbmIsuOMOoKQEOHxYvL91K/Dee8CuXeL9U6fk6z/zDPDmm+Ly114DWINyEiNy4vc/HidsPy7amB/9cDTobdtsDhw87a6M4Ac+9XX1kYh0zohWq5FEB2uuxk6GfIKs3SHIDkKvXK8+T4LPGeEPiN5K81IJVs6oLO0F5GJEzTlRg4VplI6VGmfMVvz+7U1YFqJIBoD//Hgch2rMWLD6QMjPQYSG2vdnylAxP6KeEyP+BssFAz9favWe09LtfafCr94KNIFVr9NKF11KgfXu+sMAgD+M7xXSlN72wqhoCLtsGXDjjW5HpEER+crMBMaPd9/fsEH8TWJEDomRAAklm72yrgVOQQzJbJ1/Kb574CJ8PXcs5kzojdvG9gQQ+ZwRwB2OaVbkjPBix+4U0GoVDwb3T+qHyweXqD6XVNrL9S0ByBkB+Goaz6/RyB5ismGWSR9wc7hgckYe+/wXrNpzGn98/8dAd9cD/urTTmG3dkVtiOW5XfMAAI2tru+tUcc5k+GLEb5S5fsDtdJtNuU7HFqtgYkRfh3le1BnFl3kUT0Lwt6faKIUI5WV8vstimK4uXOBVauA3/xGvL95s/ibhuTJITESIMqQRiDUuMr1irJNyM80wqTXoVdRNuZM6CNVTsjDNJH5d7BQTZOFhWmYM8KJEYdTyk3I8NEbgO3fifo2vLfhiLTcRicvyWo3qoRpCjKN+P6Bi7H2TxcF/HzpBjFOHogY2XE8/MRD1hUWECs6iPZD6YxkGnXSSZoN0kzT69zTnCMQpmmyqIdjIiFGWHWMvzANwPcaUeSMuD73vo5H8YBBkVo3dar8vpnrWfjjj+7wzTkK85mcETkkRgIklIMBO8B39FLKCcgt/kjMpgHcDghzL9gBTaNxh3AcTnc1ja9GRUzIbDtaL+Wg8M+dyvhKYAXEHgkFQQz5codp/MfwWc5ROPD/w5MNNHeoPVEOWfzXraM8qrLSjTop+TkSfUaa29Q/V2ciMIyvLcCmZ4D3XiPseJRhiN/kVUDujFx9tVgZw9PSAixdKlbZnHsuMG6cWH0zeLB7nQEDxIRWwk18/9fjiFAOBmw6b0cfQ674tuGGCJWxKZ+Hz0thQ/PsTgEtFv9JZ2plqwBV0wBcO/gAc0L8IYVpbA4IgqAaN1+79zTmf7rDo2+NL7zNEeKvzk/WtwFdQ9hpIiTYe3/t8DI8cdVg6LQaj4oZQRC4vhwRECNenJFICJ3WAKtpAPX5NE6urDcQQRNLeGfkP/9x387NFfNFzGb58poasRnajz8CVVVAURF1aVWDnJEACeULy+zWQh/OCH+yj5QzohQjfByXhWparA7scPUYUA7s4/F2oiVnxH1C8SbYgoUdhAXBe4Lwc1/vxZHawDu0vvHtQQx8+EtsU/S5AeSf6ZMN4ZeOEoHDJz+z772yKut4fat04vZVfRMoTIywkQTSvoT5XT5UY0alq2twINOm07iuzgxebMV7mEaZM8L4/e9FIfLVV0C158ghFBYCnTqREPEGiZEACSVMUyOFabxb9XqZMxLZBFYGf6XBxMiDn2xHi9WBjllGDFAM0uPxdnAhMeKeMBqpnggZ3PMcPG3G45/vQqVCeORlGBXb+D5wP/b5L2i1OfD7tzd5PMafDI6dITHSnqiF+JSi9lSjJSrOyAW9CrHg+nMwqoeYKBrIhdbpJgteXrUfu6saPR578Zt9AMSmiX2L/U+bZscjlrAKyPOkAsk7iSXnnqu+PD8fyMgQxcaTT4pdWXnhkp+vvh0hQmIkQJotdry57hDqWwLs+Qt3mMZb+29AnlQauZwR+b81QzaMT3xsw0FxEujY3oU+qz28XfVbUjxM8+QXu/HOejGhN1KTRXVajfR+z/rgR7z+7SFc/+YG2TrK5nuB9uJTywvgT0JqzgkRPdR61KgOXJRyRiLgjLhyRrLS9Jg8uATTR3eX7Ys3rHYnRj3xNf7+5R789X+7PB4/XCtmbN47sU9Aoebh3cSz8te/uBtyuKtxtAFXn8WKqVOB664Tb0+f7l5+5ZXu24MHi/c3bhTv6/WiUHnzTVGsaDT+O7WmGiRGABw704IZb2/Ct/tO+1zv0aW78Pq3BwN+3poAEli1nGcXrZwRNWeEcbdrkq83KEzjyRmzFf9c4+7NkRXBMefM6ThYIx7gj9bJHQt2dfvIrwcCEPNWfHUHLuYmMZ8xy4W0UowEI7SJ8FDLN1IK/zemD+eqaSLnjGS7qqjSpBCQ7+euM1vBWpTsOunpjFS5kp+7d/Ae7uW5YojYRuCbX6qlXkdS8mocd17lGTtWFCJXXeVeptcDM2aIXVYZOtehNz9fFCB8d1YLFbDJIDEC4LGlv2D1ntO48U25le10eh7ka710VFWj1sycEe9hGl0UnBE+TKPVeO/yesWQEtkMFTXUGnoBqS1Gjiim6vIlsuGS4ceiZhVNTOA6BXHWkDf4/x+r7tpf3Yzpb23Cd/trZesOf+zrlP6/tieSM8L9f/hk9tvH9cSEAZ2kPiNNbfawBQnvjADyidy+4F+3vsWG+xf/jJdX7QcgVuWdcrUwKMkNrHHG4M65KCtIR6vNgZW7xeQKJkriPUTDuP12ceDdlVcCHcXxQdixQ1z29tvAc88BH34InHGNFGIhmnHj3M9BfUbkkBgBUGtWl6hqFSPBHKwtAdTe8wIkck3P3P/WDKNeVpWh44SKr8RaBuWMeHKk1iy7n5PmOdMnVNQqCXjnwy1G3ALXVz4BPwGYnVTueG8L1u49LV3lsvJju1PABU+tlLUKrzNbMePtTfj859A7vRKeqIVp+EoUyb3gLiQueGqVz/4+R2rNkkuhBuszkqVwRvyJHOXn66PNR/H3L/fgjNmK2mYLHE4BOq0GhT6qBnk0Gg2uGCx2m2Wfq0TpMaLGww8Dr74K9Ool3rfZgLvvFtvGf/WVuGzvXvH3hRe6tyNnRA6JEQC56e4DO3/gV7tiCCbznB04fIVf+DBNpFog86JGeXLjZ9cEcvDwGqZxhG8bJyqHaxTOSETDNJ7P1dhqh9MpoKHVJlntvNvmq9KC77rJ8g4O18jFVPlFvTC4cy4AoLrJgrn//kl67Pmv92L1ntMo/yD0Tq+EJ+x/JgvT6NzfVbdgcC+rabbglJf+Ms0WOy5//lucV/ENGtvUy77Z4MxgnRFvn6/NR85I/WmKsk1BObtXuDo+r9l7Gm02hztME0GXsb2YNQuYOdM9OI/RqRMwbZrogNx/v7gsL090Uzp0AEaObO89jW9IjADISXd/AVgrZsCd8wEAV53TGUCQYoSVfvoQI9GYkM3HnpVXGvwBw1eVDyMnXf2qPxxn5OFPd6gmwiUKSmckkmEaNWfkcK0Zt727GUMf+UrqL5KdZuA6WbqFYVObDRXLfsHOEw2ykez8esqgTppBKyvv5nNLas2URxINpJwRg3rOSJbLbVO2V+ePTzw1TRaYXSf0f613d0r+dNtxPLZUrMpi2wbtjHh5fPPhOkmMFOcG1050UOccdMoxocXqwMZDdWixsYZnieeMMDIVKTMDB4o/Z86I1TWMJUuAEyeoukYJiRGIcU/GsXrxqvdEfSsu+ccaAKKYGHOWGBgMptkXi+Ub9N6vGHQRagHPI3NGDN7FSCDOSKbi5HjLBT0A+M5T8EVtswXvrD+Ct7475PUKLt45pmhOFUkxouZEXfnyd/hmt7xxQZZJz834cH8mZ767Ba+uPYi7P9rmMY2VredQ5EKl6XUo4U4mZxVmSbcjlVSdqHyy9Th+ikKlkdSjRqdeTcMuIpRuw6trD2D6W5vQpPju8KGU7w/USLcf+d8uvLHuEMb+fRUaWm0w6rUoy89wvV5glTptXi48th2tlyppOucFlwCh0Wgwvk8RAGDDwVqpTD4RwzQMk+Jw+sIL6ss1Gu+9SlKZ1D7SuOCt7OOufgtfuMZZA+IVC7tqCXRglSAIknDxdUDXRaEDjjxnROf1MV9VPgyNRiMTNyw/ItRGSfxpMFHzTpR/eyRzRgLp5qrViP9XpTPSZnNg/UExKXXvqWaPhGxvV7hpBp3MAePDTpHKY0pENhysxZyPtuHKl7+L+HP76zPizU39dNsJrN17Ggu/OyxbzgsK5oA4nYKslwcAjOpRILlvUjWNn4os9rnpXZQlW37gtFmaaj6wNNfr9t4odQmY42da8cB/twOI/+6rvuAP5ZmZQFlZ7PYlESExAnmbZDbzgz8IG/Va6aARqDPCj+v2KUai8B9QJrDy8AlwgSac8VYxs5VDFRJ8jow9RHcl1ign3GaaIncAVZZ3flI+xmOdLJOYlKycfqq8glf2DvEmRkx6LW4c3U26z4tzA7c/vk5YycgPh+qi9txWlWoaXpgY/IhSpauoDNUBQLPKjKMLe3fkXk/8/PiryGLCqWOWCY9NHYQJ/TsBEMPYK38RHTuWcxQMTPR+9tMJaVmiVNP4o7Q01nuQeJAYgbzioMHVHIq3R7UadzOqQE/CykFY3ohGgx+9znsC6+4q94ROvgeFL3gxwq7YQp1Nw4cIEnXyr1JEKZvMhQN/RZydplfN68mW8gnkrcJ/PuZ7kq83uz3NoENOmgFvzxgBQN4N08B9PgOZJpxM1HF9V5QCNFzcfUY8GxICvvPMAM8LHLkYEUVIg0qju0Gcg8ELIYuPiqw2rnX9Ded1wxs3DUeR60KG5SQN6uy9i7M3slXCm4cV+ViJxr33ir+Vk3wJ/5AYgfxKsL7VU4zYHE53mCZAMcKfaH05I9GwwX2FaRid89IDrt5JN3JXbwE2SvIGf3WdqMP2bM7o7TfvjORlGJCrkkCcraiGaLM70GZz4NW1BzzW5Wmx2HHsjOdcGyZq2GfFzF1R858Rb4PWkhU+xBHpv12tmgZw/28HBnly58M0khhRGabYq5M71MK/dpvNieqmNqzde9rDAWPf9TReOHHHrYv7FXmMKQiETBUxkp4gTc+88fe/A04n8Nhjsd6TxIPECOQHGvYF5p0Ni90hHfj3Vzdj9qKtfi1rdqLVanw3Mxvds6PXx0KFb3qmFCP3TeyL3HQDFv5+RMDPxx+E2FV5qMmnDl6MJGjOiDIBNJLwYiTTqFdNjs1QxvxtTjz39T7U+GnIV/HFblzw1CqP5cz5YieHFpXeJID7JJcKWO1OfLHdnTcWyb/d5nBKxxlltczGP1+CbQ9d6jcPif/+OZwC9nCOp9XhRJvNoSpG+N5CGo1GEiQWuwOXPrMW09/ahG9+kSdLswswvg/KbWN7Qq/V4InfDMabNw33ua/eUCuJf/CK/iE9VzxBCaqhkdgyNEz2nmpCTbMF1U3uEt56l7XJl0Ra7E7ZVcSn205g3uT+PsvZpEoaP3br4C65+O8fzw86G90X/GumG+T/4vKLeuGP488KqqcJf8Ds4GqQFUwnWh7+RB7utNBYwYdp+vsYMhgKfHVFmkGn+n9i/w/eGdl+vD7k1/RwRmS9STxzEVKB+Z/skDl3Da02RCofcfWe02i22NExy4SehfJ60AyjHoGYDBsO1mHWBz9i7qV98MHGSryx7pDs8cY2m6oYUX6eTHotLHYn2mxugbThYC0mDOgkrSOFabiLkt+P6YFpI7t6iKlgUArtx6YOQp9O/gftEclJSouRl1bulyVPAUCj6wvJx8cFwTOx0N+5PJAeI4xzuka24Fyv9R2mCba5Gp9Uxrp1KrP0A4U3lBLVGWEhuJevOwcX9yuK6HPznzNvuUZsHd4ZYSPcP5p5Hv7v9Q0BD9ATX0fujJitdgiCAI1GI7P/UyVMU9tswZKtx2XLIumMfLVTdFyuPLs0oNLpr+eOw7Nf75V1wd1f3Yz91c3YcbwBh2s9Q29NbXbpwsqo18Jqd+LOi3t5rJdm0KGxzY5arqcSu8iyOZzQwB0CSlOMhghHiACeAyYD6XtEJC8pHaYpUXE26lvFk2yrIhNdeWKwK6z6j36oxP84YSN1Xw2gVDPS8H1NIlEq96dJfQEAM87vLjkjZ1qsqrN7/ME7I4kqRtjf0KsoK+KliLwY8XawZ59FJiKaLXacqBerwLp1yPS44vSXlqQM0zgFt2vVmoJhmq9/OQWrw4khXXIxrGsegNDDkmowB0LpinijV1EWnv3d2aqPqQkRQPxfsdf51ZAS/Dj/UsxVGYrJcsD2nHKHeQw6LRxOAZc//y1+9eI66TMQrvhQoswZ8TXdnEh+UlqMzLu8P343vIts2d5Tzfh232mPygGlM2LjTqSnGttw/3+2484Pt0onKnePkfbv02Dw44wEy/DuBfjp4cvw8JQByHeJEafgTvYNBlnOSIK2lGdCUx+F/61JJka8OSOuMI3r8cO1ZjicAox6LYqyTbIqhYkDO+HeiX19viZLkuUdsBvf3IjPfz4pC9M0p4gYYSfxXkVZUo5UJIVYm9RjJPDvplGvxVNXDw54/SYuTJObbkBBplE95Ofah19OusVIq82BmmYL9lU3Y3dVk9R7KZAeOMGgFM3sQodITVJajADA+Wd5JpDe+OYmaXATQxlusXMVFWe4EkB28GZX/bHoYMm/ZqC9RPyRm26ARqOBQaeVTl51XgYM+kJIggRW5ooZotA9l/+ceTtZsXWY0NztOpF0yU+HVquRTqCAWL6d5uekx4S2TquRBMkPh8+g/IMfZeXATSkSpnFXuuiQ4wolRDJfRqpO8SI2vXHtiK5YeucFAa3LOyN56d5P8pIzUtUoLWuzOWTHP9aV2hRhZ8RDjJAzktKkvBj51ZAS/N+IMg8LUzkmXvlFtNrdJ1U+oZFZmiyBVemotAcC1+d0SOe8iD9/OEmsfDVvoiew6qLgjBgDcEbYCYR10P3Z1QWTJUHzVQrpRr2sn4TH6ynEsrKBG1/CHYr4TET47qhS9ZiXmTDhPX/wJ/dAZ8BsO1qPva7QCz97SwkTqpV17hEHbTanLD/oqOuxSIdplFWGOREcOEkkHikvRvQ6LZ68eohHcpeye6XyoM33EWlTGUbGHg8kgTXSHDjtbhxUVhC5Kh1GOEmsSVFN43LFDFHoEaOWMzJnQm/5Oq7PFHO9mMOU6erRwF9xZhh1Pp0RZc6LsmMvnzNyqCaxG1IFCguxmvRa6UQeSWfEXZ0S/LGhIMMoywHy1qfotbUHseXIGQCQOWVKmHDlh4K22RyysBRzWCIdpuEpyU2L2NRyIjFJeTHC0Gg0sjJNZS8JZe4HL0aaVUohA5lLEy3yuEZZ0fiCMzFSE4IYcSZ4mMbpFMA+GpHsvMqQh2nE27Mv6Y2rz3HnNjGnoyhbfpXMnBR+zky6QefzilaZU6S8zwvtg6dTQ4xYOLHA+n1EcnqxVerbEbzToNVqUJLrvsDY9ddJsj4ffVVKY5VVKzxd8j0vVtpsDtXKqUg7IzyBJvMSyUtQR9OKigqMGDEC2dnZKCoqwtSpU7Fnzx6f27z++uu48MILkZ+fj/z8fEyYMAGbNm3yuU2s+Pft5+H6UV09lr93yyiPkzrfg4BPdm21istt9tglsN55SS/89twu+M8fzo/K87Or6VDERKKLEb77ajQSWNWcEVEou08yJh0TI/IYO1u/E7c83ajzmZugFB8dFOWVfGnvoRpzSBVUiYYURjHopJkrq/ZU+2yZHtLzh+g08FWARr0WZQUZ0v1rhnfxGICp1nad0SU/w2NZq82BZounExRsjksgsJDvb8/t4mdNItkJ6tO1Zs0alJeXY8OGDVixYgVsNhsuu+wymM3er5hWr16NadOmYdWqVVi/fj3Kyspw2WWX4fjx4163iRXZaQbcckEPmfX50czzcEFvzyRXfrAUfxWhzBmJhTNSlJ2Gp68ZinO7RbZ/CYMNuwtlcFqih2n4/Y9GK39vpb3854jlLxXlqIuREq6BntIZ+Wjmedj114nSfWVYppPCbeHDExa7EycaWpHs8GJhTK+OKMlNQ32LDd/urYnI87eFWSr767PFKWwsHMeH5bJMenw6Sz5cUa3TKaNMRYy02RyqlVP+EqFDYckfx+Cl64Zh6tmdI/7cRGIRVMbQ8uXLZfcXLlyIoqIibNmyBWPHjlXd5v3335fdf+ONN/Cf//wH33zzDaZPnx7k7kafnoVZuOqczvj35mMAPA/WDL60t0VVjLhyRmKQwBptmEnkDEGMJLwz4uDFSBTCNF6anhlUhqh1zDJBo3E3kmMnt855bkGRYdTJQi39S3Nkn2llzkiRYngi017ZaXo0tdlx/Eyr6tV0MuEeYqeFTqvBOV3z8fn2k6pzfUJ7/vCckRtGdYNWo8F5PQsAyMMwGo2YyNwxyyiNB1AbKcBQC9O02pxotni6QL4SoUOla4cMdO2Q3J8nIjDC+nQ1NIhZ/AUFBQFv09LSApvN5nMbi8WCxsZG2U97MnlQiXSbHxLHw+eMmGVhmtiX9kYb5oyE4tjz2yRinxF+ems0nBGTigMCyMN97KRg0GlRwPUOZzY6n1OQbtShb3G29BzKmSdnKWL1nXLUyytZSCgVurCy0l4mDPMyxPfsjMoU3FDgp+CGglarwQ3ndUOvIvH/msmJS6tLLPMCxFcCq/ecEc+/NT+EYXgEESghnymdTifmzJmDMWPGYNCgQQFvd//996O0tBQTJkzwuk5FRQVyc3Oln7KySE2FCIzRZ3WQbnv7AvI5I2qzPGLZ9CzaaMNwRhK9AyvrMaLViCeFSCML03C3+eV8kmtBJi9GWJgmTbZul/wMrLh7LDbMu0Ra/vr04bhsQCfcP6mf7PU75aiXjrI8hJQQI4rSWyZG1Ga9MARBwHWvb8D0tzb5DF/aHU7pMxSpsAf/OWRimS+b9ZXAWqDSaMxbmEaZo0QQkSTkwu7y8nLs2LED69atC3ibJ598EosWLcLq1auRlua9Xn7evHmYO3eudL+xsbFdBUmaQYeld16Ahlab10Y8vF0vS2BVhGmS2RkJQYvIEiATWYxEo5IGUIRpOGdEVmXDXVHLeoq41u+Y6f7MMteut6LK4tIBnXApNwyNoeaM9OmUJTW6S4WW8HyYBnBfkPDNDZXUNFvx/YFaAMCJhjavgy/5i5hohD0ckljmnDQf4SCNRoNv7hmHuz/ahpw0A9btrxFLe1VEp5pwIYhIEZIYmTVrFpYuXYq1a9eiS5fAsqCffvppPPnkk/j6668xZMgQn+uaTCaYTLFV4YNcWfTesHsp7WVhmuTOGXGFaUKI0/CbJGICK/u/R6PHCODdGZHnjLhFCm/HszCNVqvB5EHF2Ha0XubyBUJBpuf37qK+RTjt6kOR7M7IhoO1+OGw2J+DiQUmxHyFafj35Uit2asY4auTQml65o1rh5fh619O4SpXCTgvRvyV959VmIXPZl2AHyvPYN3+GrGaRkV0RkuAEwQQpBgRBAF33nknlixZgtWrV6NHjx4Bbfe3v/0Njz/+OL788ksMHz7c/wYJgE1W2uu9miYWTc+ijTtME/y2jiRJYI2aM+ItZ8RLyIa34PnqjFeuPwdOwbPLpT8656XDqNPKruAv7F2IFbvESbPmJBcj//faBuk2EwvMGWnw4YzwVUeHasyqYyYAt+ti0GmC/t/44qnfDoHDKUjPGUp7IRY2UnZgJYj2IKgjanl5Od577z188MEHyM7ORlVVFaqqqtDa6i73mz59OubNmyfdf+qppzB//ny89dZb6N69u7RNc3Nz5P6KduLrue6KISsXpjFzmefxMJsm2rgTWENwRvjSXkfiiRFmg0cjeRXw5Yyo2+588iIvRjSa0E52Rr0WW+ZPQL9id1inX0m2FA5K5jBNi5dJ3fmZojPy07EGHDytftzi35dDPprD1bewbqaRL5MNV9ywyqo2qyOp/89EfBLUmXLBggVoaGjA+PHjUVJSIv189NFH0jqVlZU4efKkbBur1Yrf/va3sm2efvrpyP0V7USvomypOY/NSwLriyv34/Lnv8Xz3+wDkKxiRPwdSp+RxC/tjd7EXiCwnBF+nSwvzkg4ZKcZYOZOzB0yjcgyiSfkZL5iZtNpGUww5HKD5q7553rVbXln5KCXtvktVjsmP/8tgOg0EOPRhmCNsH1qsztQ3xq5jrMEEQhBh2n8sXr1atn9w4cPB/MScQ8TF3yfEeUBetdJdymyQZ981TSaMEp7E73pmZTAGoUeIwBg4vJBePfFoNImHpB310yLYH5SVUObdFuj0SDLNUBPLZcgWThWLxcjRimB1V0a660tfCP3vlTWqfcj2XGcb1EQ3ePCWUVZsuNQILAEaJtDQE2Tu0dJs8WOC1UaPxJEJKExiUHC7PITDa1Yf6AW5/UswIl6710pkzNnJIwwjcwZSbw+Iw5n+zkj/MWt0UvOiHxCb+Ssf75ajH+dZHZGjnk4I/IEVkaL1Y4nv9iNMb06YuLAYtQ0W/CPr9xjMY6faYUgCB6Jo+mcc1XvI/8kEjz0qwEAgOtGeo638AbvrLHct4/vGI0vdlThhvMCfx6CCAUSI0HCrlA/3HQUH246in7F2bKrIm/rJxPhJLDKmp4lmDPy6poDWLHrFID2yRnhrXa5M+I+aWSaIh+mAYCXrzsHf1r8E16YNgwAUjNM4wpb6HVaTBzYCV/uFP/3r609iHfXH8G764/g8JNX4N6Pf8KpRvfU21abA2dabB6lsPxsG3uUZ/wUZpvwout/FyhpBp2sc6tWIw7e4weIEkS0IDESJEpxsbuqSWUdTUxn00Qb1mQp3Nk00T4gRxK7w4mKL3ZL96P1f9VpNbikXxFqzFb04XqDqE3zBRSlvRFMirxiSAkmDyqW/tfsdZJajNSr54wAwKs3DseIx7/G6SYLdhxvkK23es9pz+c60+ohRviy3nilZ2EWaprrAAB5GcaoNPYjCDWS70wZZYwB2PP87A8Wa08mIjWbxu5IHDFiVszqiFaYBgDenDECn/zxfFl1BJ975FWMRDgpUqvSxTNZc0a+3FmF//10QrZM2SyMTZjl+43YvVSEKYUNANmMoHilV1GWdJvPlSGIaENiJEh8XRHfeXEvfFI+RjaWPdPHkKpEJbzZNLwzEv9XiozGNnnDK12UElgZynyDQEI2aRHMGVHCPsdNbZGZzxJPHK9vxe3/2uKx3EOMZIlipI5LYj3JJfoqn1NJWwLkSPUq5MUIdVwl2o/kO1NGGYPiADWkSy5+PtaAey7tgzsv6Q1Ankjoa2JmosIumB0hqBH+QjKRwjTKvgvR6sDqDV6M6DhXhndoojHinZHp+ky3JsDVfbB4SyZVNrbr4OpOe7jWXbq75cgZ1W2rmzxFCh+mOadrXrC72S7w/WV8zbQhiEhDn7YgUTojz/xuKI6eacUYruMi74xkJeEX2j2bJrymZ6GImVihzJWIZphGjcJsE343vAv0Oq1s8m6al8m+kYYl1joFMTSRTK3BA02kZjkg/Md+3f4a2Tqdckw41WhRDWexBFajXou3ZowIcW+jy4ge7mnq2xW5MQQRTZLvTBlllDkjWSYDLuorH0KWYeBzRpLvLQ6nz0ii5owowxPR6jPii7/9dqjHsiGdc3FxvyKU5qX5nUESDnw4yGJPLjESqNvTMcszbLFqdzUAYEBJDkaf1QEds0x4avlu1Q6mzBmZNLAYeXEaAjHotPjVkBIs/fkkbr4gsHEfBBEJku9MGWWUzojaRMxUCdOEksDqSNCcEeXJJV7KkrVaTbtcZfMlx1a7Eyrz9BIWPrH0/VtHYcnW4xigUs5anOs5/I41QZt1cS9cPrgE//7hKAD1qiP2OtHuvhouz117Nq4+twtG9wxuyCJBhEPynSmjjIcYUTmw8M2NkjlME5Iz4kxQZ0RxcknmElc1dFoN9FoN7E4hITvn+qLVKv49I3sUYEyvjhjTS73baElumtfnGNJFnPKdneY90dfiEiPRmEsTSfQ6LS7qWxTr3SBSjPiW6HGIMlfAX4fVZHZGUqnPiPLkkmpiBHC7I/HiCkUKFqZJ99M0zpsYuefSPuiSnwEAPgcKtrnet3h3RggiFiTfmTLK8OJDr9Woxs758IW/A1wiwvpPhNZnxH3bW4+GeER5cklFMWLSa9Fidcg6iSYDgYqRYoUYmdC/Ex6Y3Be9ivgKFDG5WD1nhIVpku+YQBDhQmIkSLwNLOPhT9LRTCqMFZHrM5I4zoiyOiI1xYgOgC3pwjQsfOJvtg/fzBAAFtxwjkfYNstHPxaLjTkjJEYIQgn5hUHC54iYvBxUEqlkNRTCSmBNkjBNsoUqAoGFaZJNjLRag08s7ZRjUm2AmMMNFFSGMVnTM28XMQSRytC3IkiyuR4P3p2R9tqb2ODuMxL8tvx743AKIeWdxAI12z3VMCV5zkggjsXvx3SHQafBK9efq/o4yxlxCkCLVR7OojANQXiHwjRBksNVxwQSpklG3H1GwptNA4iCpL0biIWCspomFXE7I6mZMwIAD14xAHMm9EFuuvrclnSDDjqtBg6ngKY2u2wcBOszQs4IQXhC34ogyeEOQt7m1JzbLb+9dicmuMM0wW+rDGElSqiGOSNzJvRG76Is/OuWkTHeo/bHFMdhmqqGNry8aj8WbzkW9LZtQYgRnVbjVYgAolCXhgpa5KE9ckYIwjvkjAQJP6/B5qUa5I5xZyHDqMPF/ZKzVl8bQWckccSIeGIZ26cQcyb0ifHexIZ4Lu2968Ot2HS4DoA4KXvSoJKAt2U5I/4SWAMly6RHfYsNjYrQnru0l8QIQSghZyRIlG2x1Ugz6DBz7Fmykr9kIpw+I06lM5Ig5b2seiYnCZvYBQr77MebM7K/ukkSIgCwYPWBoLYPJmckEDpkie1pj9a14N31h3Hnh1thczilqh3qM0IQnqTukTUCxNtBub2QckZC+PMdCeiMCIIghWmyTN4t+mQnXhNYv9x5CgDQIdOIWrNVatEeKK2uXI5I9QQaVpaHn47W47NtJ/CNa3bN/40ok44X5IwQhCck0cPAkoTj1AMhvDCN/H4itIRvtTmkXJdUHqserwmsB043A4DUxr3VGtz+tQXYZyRQRnQXJ98yIcJeQ8oZifN28AQRC0iMhIE1QUIMkSacBFaPME0CDMtjDc90Wg0yInTCSkRYmCZenJGvdlbhxjc3YuNBMUTT3zXcTllSy/PmukP46IdK2bJID7Ab1jXPY5nZ6hYjavOsCCLVSd3LvAhgS4Cr+mjg7jMSXtMzIDGckUYpRKNPyo66gRJvTc9m/muL7H7/EjFHq9XmgCAIHv+ro3UteHTpLgDAb4Z1kf4ed9OzyAjNwmzPkcZmi10q7SVnhCA8IYlOBI0mhA6sdocTL36zDz9wiYZAYuSMsEqaZBx6GAzxmjPCGOByRgB3Tw+eE/Wt0u0zLe68kkgnsBp0WmQqHDSzxS51YKUEVoLwhL4VRNCEMptm0+E6/GPFXvx0rEG2PBFa57NKmlTOFwH4PiPxkTNyVmGmdLtDplGqYgGAFqtnk7rjnBipbXaLESmxNIKOhbIXSX2LTepY7G2MBEGkMiRGQmDq2aWy36mG1vWpCcYZaWz1HBwGeO/VEk+wSpqctNStpAHizxnJzzBKt/80qS90Wo20j60qyeVH69xipI6ruGGfQaM+ciG4HIUYqTVbpNvkjBCEJ6l9qRciFVcNweTBJbiwd8dY70pMCGU2jdrJAUgMZ0QK06S6M2KIrz4jNtdn543pwzFhQCcAYkWMxe5Urag5dqZFus2LAyaujLroOSPMidFoAKOXzs0EkcrQtyIE0o06TBxY7DFSPFUIZTaNWgwfSIxqGuaMpHqYhp1E40WMOFyfHX62UYZLMKlV1BzlxMixM62SI8J+GyLojCirrpgTY9JrUzoJmiC8QWKECBptCAmsbV6ckUSopmE5IymfwGqIrzAN++zote7DGOsVoipGuDDN37/cg9EVK1FntkpVcZF0LJSCgzVio4ZnBKEOiREiaEJJYPUWpkmEahp2YstMcTHidkbiI4GVfXZ0Ws4ZcbmVSvFrczhxsqFVtqym2YIdx90J1cYITtNVeh81zWJYiMp6CUIdEiNE0IQym8Z7mCb+xYjZ5YykcsMzwLfrEAvYXCMDF6ZJV4RpmItzsr5NVTwz10t8nkg6I/L7LNRHyasEoQ59M4ig0QTojFjtTukK1XuYJj4sf19IzkiK5ggxWFJmg5fKqPZGzRlxCyY7lv58AoMe/hJLfz4hS17l4cVIZBNL1fNCKExDEOqQGCGCJtDZNBc9vRpDHvlKNpdDSUI5I6bUPpEwMVLf0n5i5Pmv9+G/Px5TfYxVYvGOBnOv/t9nOzHrg62wOpyY9cFWKXl1XJ9CLL5jNPp2Eru1sv+tXquBVhu5xNIhXXJVl1OPEYJQJ7Uv9YiQCGQ2jdMpSE2m9lc3J3QCKzkjInmuvh7eesZEmt1VjXj2670AgKlnd/YQCyzxVOaMuE72ZkUo6VCNKEbKCtIxvHsBOmYbseeUW4xEMl8EAGaO7Qm7U0DPjpmY89E2aXlahF+HIJKF1D66EiEhOSM+1AjveFjsDmlMu+d68R+mMVspZwQA8lzOSJPFDpvDGdEcCzUs3GfmREMruuRnyB6XSns5MWJW6byaZtDiPy535Zyu+QDcIZkmlxiJ9N+SZtBh7qV9cKqxTbacnBGCUCeob2BFRQVGjBiB7OxsFBUVYerUqdizZ4/f7T7++GP069cPaWlpGDx4MJYtWxbyDhOxJ5DZNA6ZGHF6dUYSoelZi4WqaQB5V9H2cEf4z8zhGs+cD6m0lxMS2xXjBsTnceJ0kwUluWn41RCxazJzQqLljDDyMuTNz8gZIQh1gvpmrFmzBuXl5diwYQNWrFgBm82Gyy67DGaz2es233//PaZNm4ZbbrkFW7duxdSpUzF16lTs2LEj7J0nYkMgpb02zvG44Y2N2HSoTnW9RAjTkDMiotNqpMZv9e0gRviqnUO1nscY5r7xzsgNo7t5fb7h3Qsk0cGckGZXlUu0uqKa9DrZ54YSWAlCnaC+gcuXL8eMGTMwcOBADB06FAsXLkRlZSW2bNnidZvnn38ekyZNwn333Yf+/fvj0UcfxTnnnIOXXnop7J0nYoO7HbwPZ4QTGU7BewVGIiSwUp8RN+xKf/PhuqhXQvEhl8M1amLEswPrrRf0xL9uGalaQtu1IF26zURJc5SdEcAd3gKotJcgvBHWN6OhQbRECwoKvK6zfv16TJgwQbZs4sSJWL9+fTgvTcSQQAblBSoyEiJnhPqMSLCKmvv/sx0z3v4hqq/FwmMAcLROJUyjUtpr1GtxYe9CnF2W57F+1wJ3zolJKUaimP+Sxw30I2eEINQJ+RvodDoxZ84cjBkzBoMGDfK6XlVVFTp16iRb1qlTJ1RVVXndxmKxoLGxUfZDxA+BhGkCFRl2h4DlO6owuuIbrN17OhK7FxF+rDyDE/WtsDuc0iyWVK+mAYCqBndC5rr9NVF9Ld4ZUTZaczoFaVAj3w6eMfuSPh7LyjgxwsQHEyORnEujJD+Td0ZIjBCEGiGLkfLycuzYsQOLFi2K5P4AEBNlc3NzpZ+ysrKIvwYROoH0GfGXC8Li/HanE899vRcnG9ow/a1N0tCyWHKivhVXL/ge5z+5EntONUnLU73PCOAZbotmAjIvQFoUVTJ8ThIfpmGMPqsDnrp6sGwZ74y4E1jF12gvZ8RECawEoUpI34xZs2Zh6dKlWLVqFbp06eJz3eLiYpw6dUq27NSpUyguLva6zbx589DQ0CD9HD16NJTdJKKEux2893X8hWnYycDuFGQD6LYf96yGaG+qGtukv+3pL8VqMb1WQ6PfAfzjd2cjh5te7K1KKhKYue6oytJwXgTpvTQrG1gqbzxWnJMm3VbmjESzTDk/g5wRgvBHUN9AQRAwa9YsLFmyBCtXrkSPHj38bjN69Gh88803smUrVqzA6NGjvW5jMpmQk5Mj+yHiBw3njGw8WIvZi7bidJNFto7DT5iGiRW7Q0AbN3iNPwHFCr6/xbaj9QDEfBEa/Q78emgptj10mXQ/mnNq+OduVTojDl6MqB/G+BN/tkkvKwE26sTHpGqaKDoW+Zwz0rNjZtRehyASmaCC4OXl5fjggw/w6aefIjs7W8r7yM3NRXq6mKk+ffp0dO7cGRUVFQCA2bNnY9y4cfjHP/6BK664AosWLcLmzZvx2muvRfhPIdoLLddn5NrXNgAQT+D/vPFcaR1/zggbYGZ3CqhrtkrLvQ3Ua0/4qbRnXK3PqZLGjVarQbpBh1Yfbf4jAS9MlaInEGeEr1zhe6QAbvHBpklHM3xSZ3Z/vi/sUxi11yGIRCaob+CCBQvQ0NCA8ePHo6SkRPr56KOPpHUqKytx8uRJ6f7555+PDz74AK+99hqGDh2KxYsX45NPPvGZ9ErEN+4OrO5lhxV9IALtH2KxO1DLHazjYTw9S1jlKc1LV1kzdWED6VqjKEZkzojidVhZsVYDrzNl0nlnJE0uJg2KPJNohmnGugRIWUG6LCRJEISboL4ZgYyMX716tceya665Btdcc00wL0XEMYH0GVE6I3Mv7QMNgH+s2CtbfqqhTXbyjw9nxHMfxvamK1oedqJvjWKYhq+mUb6Ou+GZdxHBh2mUbdiVTkg0wzSXDeiEhb8foVpuTBCECMl0Imjc7eDdy5SVNcqckTsv7gWNRoPKuhZ8+tMJTD+vG95YdwiHFM2s4sIZUbnaH9eXxAgPc0aimTPCh2nsTgFWu9Od+KwyJE+JTIz4ER/RdEY0Gg3G9y2K2vMTRDJAYoQIGrXSXqVJYlOEaVjy599+OwSPTh2EL3aIobyfFLNE4s0ZGViag/N6dsBQLyPhUxXmjEQ3Z0T+3K02B1eF5dl9VQkvVPyJkWg6IwRB+IfECBE07g6s7mWezoh6CEej0SDNoJM6eSqJC2fEJUZ+PbQUL0wbFuO9iU+kME1Uc0bkFTT7q5twbrcC7DjegFW7qwF4T15V4iFGdDrFfRIjBBFLSIwQQaOWM+LpjLjdhX/ecI7Hc3gTI/HhjES/wiLRaY8wTWObXIxcvWA9ls+5ELe9uxknXZ1g9QGKCJNeIT7IGSGIuIK+gUTQ8KW9DKUPwpyRoV1yMWlQicdz5Ka7ey+U5qbh5jFiz5q4cEZcgshEQ828Em1nxOEUcKbF6rF80nPfSkIECMMZ8XBK6H9NELGEvoFE0GhUZtMoK2ukagcvB3neGRlQmoN0o7ieJS6cEZcY0VO3TG8wZ6QtSs5IfYtVctt89ZrzlcDK45mw2n6lvQRB+IfCNETQqCWwKlNE/FU78GIk06RHmuvEHytnxOZwYvqbmzCkLFdqyEZhGu+kRdkZYY3CctMNHvNwePw1xc006mC2OnBJf/mwzvYs7SUIwj8kRoigUZtNI0DpjIgndOUVKIM/+GcYdVJIJFbOyJo9p7H+YC3WH6zFdaO6AiBnxBcZUc4ZYY3wOmQZfYoRq0pPGJ6V947HnqomXNi7o2y5MoE1K40OhQQRS+gbSARNIKW9LGdE56MpFSPNoJOutNti6IwwJGeEcka8Eu3SXuaMdMg04uBps9f11BrU8XTKSUMnbkAeQ+mE5JAYIYiYQkdbImg0agmsXsI0gSQYXtCro2Sbx8oZ4VuKt8e8kkRHagcfZWekINPoc71QPy9KMeKtuosgiPaBjrZE0GiDSWD1IUZW3TseC64/Bxf3K4q5M6Llkg8aXWEBCtN4hzkjLdFyRpqZGDFhvKv77ayLenmsF2qOkVJoKgfpEQTRvpA3SQSNap8RxTqBdMjs0TETPVwj1WPujHC72SCJEdLq3mA5FmfMnuW3kaDObAEghmn+ckV//HjkDM4/qwNeWrVftp6f4dBeyc+QOy45aSRGCCKW0NGWCBptALNp3NU0ATalinnOiHv/61tcYoRyRrzSuygLALDnVFNUnp8Jwtx0A7JMeoztUxhwg7NASDfqkGl0O18UpiGI2EJHWyJoNEEksBqCbEoVK2fEyiWwNlCYxi99i7Oh0QCnmyyoabZE/PlZJ950Y/T+BxkmtzGcTQmsBBFTSIwQQaNW2qu0y22uME2gTalYzkhNswXf/HLKb8lmpOFfj8I0/skw6lGWnwEAGP7Y12hq815+GwosiTjdED0xwv9/06L4OgRB+IeOtkTQaFU6TSkTWB0O3x1YlbATw5kWG255ZzOe/XpvmHsZHGrih8SIb84uy5Nur3QNrosUkhhROCO/GdY5Yq9B/1+CiB/o20gEjaoYUdy3BVBNw6O8Mv1wU2VI+xYqVpVcFRNdLfvkvol9JZds9Z7TEX1u1r8kTZG38/Q1Q/Htny6KyGuQG0IQ8QOJESJoNCqfGg9nJMgwjfIq1RFqmUSI8DkjDLpy9k1ZQQbev/U8AMD6A7URfW7Wv0QpGHRaDcoKMiLyv6H/L0HED/RtJIJGzRnxmE3DElh9lPbyKE86yoTYaKMWpqF5Jf7pkp8OAKoTdsOBVVV5yxmJhJBgIZ+zCjPDfi6CIMKDUsiJoFEzO3hnZE9VE15dcxBAEKW9ipOLslQ42qiJkTwq9/RLpqsixWJ3wu5wRqz8ttXqu5rmD+N74anluzF5UHHIr3HdqG4ozk3HsK55IT8HQRCRgcQIETT+ckZmL9oq3Q7VGWlvMWJRhGk0GiAvw3crcsI9MA8AzFYHctMjI0ba/FTT3D62J0b1LMDA0pyQX0On1eDSAZ38r0gQRNQhH5oIGvVqGvdt3rIPNGdEr9XIHBdnO7cbUTojeemGgPc9lTHptVKScovVHpHnFARBqqbxlmSq1WpwTtd86gVDEEkCiREiaPyFabK4ZlKBVtNoNBrZiSfWYRp/A9oIEY1GI4VqzJbIiBGbQ5ASmKnihSBSAxIjRND4C9NkcXM+gskh4PNGYi1GOmSa2vX1ExnWVt1siUwrf34kQDSbnhEEET+QGCGCRkWLyMRDdgjOCACFMxLavoWKsrQ3P5OSVwMl0s5Im6usV6fVBJxzRBBEYkNihAgajUbjIUh4I4MP02jUlIsXQi3XbLM5wj4ReoZpyBkJFDbjxWyNjDPCt4IP5vNDEETiQmKECAllqMbOWRmZnBhpCUIkhJIfIAgCJjyzBiMe/1qqwAgFzzAN5YwESpaJhWki44y0eum+ShBE8kLfdiIklNEXh1OAUyW20hzECSoUZ6TV5sCxM61osTqwv7o56O0ZnmEaEiOBkmFkzkiExIiX7qsEQSQv1GeECAnRPpeLD5vTCZNWBztXl9vYFvgJKpSGWWzCLhCc8FFicTkjXQsykJ9pxCX9ikJ+rlQjK9I5IzZXwzMSIwSRMpAYIUJCLS/V7hBg0ou/GcGMllc+pdMpQOsnAba+xf38Nc2WgF8LEEM8f1r8M3oWZklhmvm/GkCNsIIk0xThahovE3sJgkheSIwQIaFW3stECB/yuPrcLgE/p/IpLXan3xMS74zUNAUnRrYfb8DHW44BAPoVZwOgeTShkGmMrDMi5YxQQzOCSBnoyEuEhJoYsbnCM3aXGLl9bE9c1DfwcIdG4Y20BpCQyouR00E6I/zfUO0SMsYIzVZJJTIjXE3zzS/VAICcdLpWIohUgY68REioVVwyZ4RV1vR1uQ2BP6n8bkBihA/TNAU3OdbBJdzWmcVtyRkJngxj5KppTjW24T8/HoNGA/x+TI+wn48giMSAjrxESKhVzthcjgj7bQjTZWgN4Eo7HGdEWUEDRGY0farBQmmBiEd/VDW0AQCKc9IwplfHsJ+PIIjEgI68REioWfLMEWEOSbDdM5VrByJG6lvdbkiwCawWm6cYyUmjzqvBwnI7wunzwmD/w45Z1HSOIFIJEiNExGAVKTaXKNFrg/t4KUM/Vkdwzgg/LTgQlM/fIdOIsoL0oJ6DcDsjauIuWNxihPq8EEQqEbQYWbt2LaZMmYLS0lJoNBp88sknfrd5//33MXToUGRkZKCkpAQ333wzamtrQ9lfIk4YWJrjsYyJEZbAqg/aGZGvH8jJjS/tbQqip4na85/TLZ/aj4cA65TKD7gLlZpmUVB2IGeEIFKKoMWI2WzG0KFD8fLLLwe0/nfffYfp06fjlltuwc6dO/Hxxx9j06ZNuO2224LeWSJ++PiO0R7LmNPgDtOEZ7xZ7L7FyIHTzfh8+0npflObHUIQ036VOSMjuxcEt4MEAHeYJpCwmj8oTEMQqUnQtXOTJ0/G5MmTA15//fr16N69O+666y4AQI8ePXD77bfjqaeeCvaliTiCtQDnsUhhGpczEsTEXkC9z4g3rHYnZr67GYIA5GcYcKbFBodTQIvVIZuN4wveGZkzoTemjeoa1P4SIiZXp9RIOiMUpiGI1CLqOSOjR4/G0aNHsWzZMgiCgFOnTmHx4sW4/PLLvW5jsVjQ2Ngo+yHiH5tDnsAabHt3TzHi/eS240QDDpw2IztNjy/vHisJn2BCNRaXMzJpYDHmTOgjmzZMBI4UpolAzkgtOSMEkZJEXYyMGTMG77//Pq699loYjUYUFxcjNzfXZ5inoqICubm50k9ZWVm0d5OIAMqckWCraWaOPUt235czwlyNTjlpKMpOQ3aaKCQag2g/b3FVf5hoOmxYsIF2VE1DEESoRP0ovGvXLsyePRsPPfQQtmzZguXLl+Pw4cO44447vG4zb948NDQ0SD9Hjx6N9m4SESDcappxfQqxft7F0mwYX2KE9TJhjki2qyQ3mFk4LGeEuq6GBxMjkammcYVpsilMQxCpRNR96YqKCowZMwb33XcfAGDIkCHIzMzEhRdeiMceewwlJSUe25hMJphMdGWUaLAEVluIzggAlOSmSx09rT7ECJsMzJJkWevwxlbvYRpBEFDTbEXHLCM0Go108qSuq+GR5nr/rA4nHE4BuiBzhRh2h1Mqz+6QSd9/gkglon4UbmlpgVZxhazTiSebYCofiPjHHaYJLWeEwZwKXzkjNkVjtWyT6Iz4CtO89d1hjHj8a/x7s+i0MWfERAPZwoIfZujrf+aPuhYrBEGcCF2QSc4IQaQSQZ8tmpubsW3bNmzbtg0AcOjQIWzbtg2VlZUAxBDL9OnTpfWnTJmC//73v1iwYAEOHjyI7777DnfddRdGjhyJ0tLSyPwVRFwghWkcoVXTMFgOhy/bX3oNhTPiK4H10aW7AAD3/2e77PnJGQkPfrpuOEmsbLZQQaYxZHeFIIjEJOgwzebNm3HRRRdJ9+fOnQsAuOmmm7Bw4UKcPHlSEiYAMGPGDDQ1NeGll17CPffcg7y8PFx88cVU2psEaDUAP6KG5XiwtvChnuSZU+ErZ4S5L8xFYTkjvpyRswozceC0GQBw88IfUJqX5no9EiPhoNVqYNRpYXU4w0pipeRVgkhdghYj48eP9xleWbhwoceyO++8E3feeWewL0XEOWkGHVq4RldWhxOCIEjTcEN2RlgOQiAJrK4wDZsps2jTUVhsTvxqSAl6d5JPDe5akCGJkZW7q3H54GIA5IxEApNBFCPhDMurNYtipAP1GCGIlIOOwkTIsCoKhtXulHI5gDByRvSB54ywih02U6ayrgXPf7MPf/lkh8c2yhPlLyebAJAzEgkiUd7LwjTkjBBE6kFHYSJk0hQncavdKVW5AKFV0wABhmmcLN9DfI1pI7tidM8O0uNnzJ5D81oV+QyHasyu16OvQbhEovEZhWkIInWhozARMlcMkZdlezgjQfYZYZgkZ8R3O3j+NdIMOrx/6yi8NWM4AHcYh6fNFVLq0TFT8XpUTRMuLInVElbOCBuSR2Eagkg1qP81ETL3XNYXfTplY/PhM/ho81FYHU6p+yoQhjNiYDkj3k9sLEmWnwys1Wqkq2q1fJMWm1hpM6A0R3JFAMoZiQRpEZhPQ84IQaQudBQmQibNoMM1w8vQJV/M1xDDNKJI0Gk10CiHzQRIYNU06t1TjVwDLiWtVnFZX0ViK4VpwiddyhkJP0xTSGKEIFIOOgoTYWPkql/C7THCP5+vPiNWh6czAvAN01TCNDb1MA05I+FjknJGQnNGFm85hp0nxIGYFKYhiNSDjsJE2EjiweHkOqOG/tEyBVBNY3fIc0aU+6IM0wiCgBarGKahnJHIw8I0oZT2Vje14d6Pf5LuU5iGIFIPEiNE2PACwK7o/xEKgSSwemusxpwR1vOEYXU4pQZtXfLTZR0+yRkJnyyTmH7WYglejJyob5PdJ2eEIFIPOgoTYSMJAK6aJtRKGsDtVPhqeuauplGEaVzCQhAgNV8DgFauOVumSS/LS6CckfDJNIn/syaL93b83jhR3yq7T04VQaQedBQmwoYJAJvDyU3TjUDOSBBTe5XbAvIkVtYp1qDTwKDTorrJfTXeR5HQSgRPpssZMUdAjBAEkXqQGCHChm/fzgREOKGPwHJG5FN7GXx1DXNPnE4Bk55bC8DdD4NVf6QZtLKps0RoZLvESLOPQYXeONnQ5n8lgiCSGhIjRNjw5bRVrhNLUXboSYism+epRgs+3FSpuo5VMbWXoddpwSI3TIxUN1nQ6DpJsjDCS9edg4GlOVjyxzEh7yfhhjkjzdbQnZEMow5L/nh+RPeLIIjEgJqeEWFj1LlzPI67Tiyd89JDfr7iXPe2X+6swrSRXT3Wsfuo2jHotLBwLo1DZbDjRf2KcFG/opD3kZATVpjGJWCfvfZsDOuaH9H9IggiMSBnhAgbvpqGXeV2zg9djGSZ9HjiN4MBQDYVmMdXbgqfw8L2i4gu4YRpmttsAIC8dENE94kgiMSBxAgRNnzC6fEzohgpDcMZAYCSvDQAkHqDKLHavVftmBRdWNXm1BCRhTkjm4+cwdG6lqC2lXrTUFUTQaQs9O0nwobvCxKJMA0AZLgSTENyRrhSY/43AEwaWBzWfhHqMDECABf+bVVQ29q8tPYnCCJ1oG8/ETas+6bF5oiYGMn000TLV86IsgsrX+L7/LSzw9ovQp3stNDTz5gYCadrL0EQiQ0lsBJhw6pf+IZXhWFU0wCQym29hml8nMA8xIjr91mFmdRQK0rwzkiwSA3swuhNQxBEYkOXIkTYpClO8BoNkJ0WXjJihtH3rBNfbeeZQLEockboyjt6ZBnDcUZcrf3p/0MQKQt9+4mwYWEaRm66QTb7JRQyXCc3m0NQrYZhs2l8VtPY5WKE2r5HD9YOPhRILBIEQd9+ImyUJ/lIlGhmcF1RW1WSWJlAUQ3T6OTVNL7WJSKDsvlcoDidgk9hSRBEakBHZyJstFqNTJDkZoQ/ddWg00qiwqySN8JOYGqlvZ4JrN6TXYnIwTen44cU+sLmdLteVNpLEKkLffuJiMCHaiLVvMqdxOrpjNgd3kt7TV4SWMOZl0P458+X95NuB9rbheWLAJQzQhCpTFJV0zgcDthstljvRsJhMBig04VXZZJm0KLBNXw1LyMyYiTDqENDq021osbmpx08AGw4WItrR5RRTkI7wb+/NofTI5dIDRuXD0T/H4JIXZJCjAiCgKqqKtTX18d6VxKWvLw8FBcXQ6MJLW7Pn3jyIxCmAdx5I2rOiM1HNQ1zQD7ZdgI3nNcNba6KHEpgjS56LmnZ7ggwTOP6P2o1CDvpmSCIxCUpxAgTIkVFRcjIyAj5hJqKCIKAlpYWVFdXAwBKSkpCeh6+vDc3QmEaVlGj7ox4dzt03P//0aW78NOxBte69LmIJjqtBhoNIAiBh2l89YshCCJ1SHgx4nA4JCHSoUOHWO9OQpKeLnZLra6uRlFRUUghmzSu+iWSYRrAW86I9zBNJTcbhQkRgHJGoo1Go4FBq4XV4YQt0ARW6jFCEASSIIGV5YhkZGTEeE8SG/b+hZpzk8ad6CMuRlRawrMqDL2Ktb/nVJPq89HVd/RhYTOr3Ynnvt6L1Xuqfa4vOVwkFAkipUmaIwCFZsIj3PePzxnpmBVeK3hGhqvFuLK0VxAEtNmcHq/LmHp2Z9XnI2ck+jDBt2z7STz39T7MePsHn+u7e8DQ95cgUhk6OhMRgc2nAYAOmZERIzmu4WvNbXIxwoQIIG+Oxrh/cj+PZQCFAtoDJioqa1v8rClClU4EQQAkRogIYeQSWDtmR6aahs23aWyTh474hFY1ZyTLpMeUoaUey+mEF33UmtBZ7GKYbfmOKtzwxka8ue6Q9BjljBAEAZAYSUoOHz4MjUaDbdu2tdtrtnED7QoiVNrLnJHGVrkzwhJaTXqt13JQXzNriOhh0IvvO99Ztb5FFJOvrT2Adftr8OjSXZLAJGeEIAiAxAgRIcwWt2AIdU6Jkpx0dWeETfJVC9Ew1K606YQXfQwuZ4QJEACobbYCAMxcInJjq/i4VNqrp5wRgkhl6OgcI8aPH49Zs2Zh1qxZyM3NRceOHTF//nwIgmhb/+tf/8Lw4cORnZ2N4uJiXHfddVIvEAA4c+YMrr/+ehQWFiI9PR29e/fG22+/DQDo0aMHAGDYsGHQaDQYP3581P+eZotnL5BwyfESpmGD8zJ8jK1XHaBHzkjUYe97ndkqLTvTIt5usbk/I0yY+CrRJggidUj4PiNKBEGQrpzbm3SDLqiqlHfeeQe33HILNm3ahM2bN2PmzJno2rUrbrvtNthsNjz66KPo27cvqqurMXfuXMyYMQPLli0DAMyfPx+7du3CF198gY4dO2L//v1obRX7sW/atAkjR47E119/jYEDB8JojEzYxBdRESPpvsM06b6cERXhYaSKjajDSnuZAAHcwoSfvtxsoTANQRBukk6MtNocGPDQlzF57V1/nejzal1JWVkZnn32WWg0GvTt2xfbt2/Hs88+i9tuuw0333yztF7Pnj3xwgsvYMSIEWhubkZWVhYqKysxbNgwDB8+HADQvXt3af3CwkIAQIcOHVBcXByZP84PnbLTcPC0OaLP6dUZcV1hp/uYfaJ2cotU+Ijwjt6XM8KJkSZXhRQTI5TAShCpTdBHgLVr12LKlCkoLS2FRqPBJ5984ncbi8WCv/zlL+jWrRtMJhO6d++Ot956K5T9TSrOO+88mZMyevRo7Nu3Dw6HA1u2bMGUKVPQtWtXZGdnY9y4cQCAyspKAMAf/vAHLFq0CGeffTb+9Kc/4fvvv4/J38B48urBGN+3EItmnhex55RyRlqV1TQBOCMqLkigLcqJ0GHvexNXjl1ntno4lixMQ31GCIIAQnBGzGYzhg4diptvvhlXXXVVQNv87ne/w6lTp/Dmm2+iV69eOHnyJJzO6JwY0g067PrrxKg8dyCvHQna2towceJETJw4Ee+//z4KCwtRWVmJiRMnwmoVrzInT56MI0eOYNmyZVixYgUuueQSlJeX4+mnn47IPgRLtw6ZWPj7kRF9TrczYocgCJJwc+eMBBemsdpJjEQbtdLeOrMVbTYnBK5DvDtMQzkjBEGEIEYmT56MyZMnB7z+8uXLsWbNGhw8eBAFBQUA5CGFSKPRaIIKlcSSjRs3yu5v2LABvXv3xu7du1FbW4snn3wSZWVlAIDNmzd7bF9YWIibbroJN910Ey688ELcd999ePrpp6UcEYcjNrkzkYLljDic4lU1+78GUk2jdnKzkBiJOmpTlM0Wh8ewQ2WYhtrBE0RqE/UjwGeffYbhw4fjb3/7Gzp37ow+ffrg3nvvlZIt1bBYLGhsbJT9JCOVlZWYO3cu9uzZgw8//BAvvvgiZs+eja5du8JoNOLFF1/EwYMH8dlnn+HRRx+VbfvQQw/h008/xf79+7Fz504sXboU/fv3BwAUFRUhPT0dy5cvx6lTp9DQ0KD28nFPukEnzZ7hk1ilMI0huGoaCtNEH7Xcjza7wyOpnIVpKGeEIAigHcTIwYMHsW7dOuzYsQNLlizBc889h8WLF+OPf/yj120qKiqQm5sr/TB3INmYPn06WltbMXLkSJSXl2P27NmYOXMmCgsLsXDhQnz88ccYMGAAnnzySY/wi9FoxLx58zBkyBCMHTsWOp0OixYtAgDo9Xq88MILePXVV1FaWoorr7wyFn9e2Gg0Gmno3u4qtyB154x4//iqXWl375AZ4T0klKg5IxabU1ZJA7jDNFKfEcoZIYiUJurxDKfTCY1Gg/fffx+5ubkAgGeeeQa//e1v8corr0jj63nmzZuHuXPnSvcbGxuTUpAYDAY899xzWLBggcdj06ZNw7Rp02TLBC7o/uCDD+LBBx/0+ty33norbr311sjtbIy4YnAJ3ll/BC+u3I/xfYsAAK0uy99XOM6kuNL+06S++LVKi3gisqhVLFnsDlklDeAuBbfZKWeEIIh2cEZKSkrQuXNnSYgAQP/+/SEIAo4dO6a6jclkQk5OjuyHSE2uP68bAGDfqSZpGbP8fZb2ch09jXot/ji+F7ReWscTkYMPt3TJFy802mxqYkS8z+bWkBghiNQm6keAMWPG4MSJE2hubpaW7d27F1qtFl26dIn2yxMJTqZJdD/auOTTlgCqafiTWxolR7Ybek7wXTWsMwBxyvIXO07K1mtus+FIrRmvrD4AAAiiVyBBEElI0GGa5uZm7N+/X7p/6NAhbNu2DQUFBejatSvmzZuH48eP49133wUAXHfddXj00Ufx+9//Ho888ghqampw33334eabb1YN0aQKq1evjvUuJAQml5Cw2p1SeW9rAH1GeDFiilDJNeEfPkxTkid+v7cfb8D24/Ik6maLHct3VEn3TXr6HxFEKhP0JePmzZsxbNgwDBs2DAAwd+5cDBs2DA899BAA4OTJk1JjLgDIysrCihUrUF9fj+HDh+P666/HlClT8MILL0ToTyCSGRPnarDSXBamSfMhMvg+I2kGckbaCz6vqTg3zeNxNmX5RH0bfqw8Iy2/6pzO0d85giDilqCdkfHjx8sOOEoWLlzosaxfv35YsWJFsC9FEDLBYbE5kWbQSeWgJh/hF6MsTENX3e0FP6OoMMvk8Xh+hgF1ZiuO17fieL1Y3v/xHaPRp1N2u+0jQRDxB10yEnGNXqsBS0NgyY6BVGDIwzT0MW8veDGi5lzVNFvRr9idkK7XajC4c67HegRBpBZ0lCbiGo1GI+UTsDCNNYBJr3yYJjNBOvImA81tvBjx/P/07ZSNEd3zpfsDO+f6DLcRBJEakBgh4h7mbDBnxO703yiLf6xEJXeBiA7+nJGXrhuGiQPdk6QHd6ayfYIg2qHpGUGEi5jzYUObTRQhLEzjq4U4/1hxbupWbbU3TW3exchfLu+P3p2y0bMwS1o2pHNee+0aQRBxDDkjRNyjdEYCGa7Gh3DIGWk//jSpLwDgptHdPPq75KaLrf11Wg0+mnke7rqkN1XREAQBgJyRmDJ+/HicffbZeO6552K9K3ENq5qx2OQ5I3ofHVX5nBG1ElMiOlx5dmeM6tEBnXJM0Gg00Gk1cDhFJ4tNYQaAUT07YFTPDrHaTYIg4gxyRuIYQRBgt9v9r5jksATWNqUzEmA1DTkj7Utxbho0rpaqvDuS43JGCIIglJAYiREzZszAmjVr8Pzzz0Oj0UCj0WDhwoXQaDT44osvcO6558JkMmHdunWYMWMGpk6dKtt+zpw5GD9+vHTf6XSioqICPXr0QHp6OoYOHYrFixe37x8VJVhVBnNGbA5XzoiPMA3vmhRme/a7INoHPm8kl8QIQRBeSNowjdns/TGdDkhLC2xdrRbgu9Z7WzczyOn0zz//PPbu3YtBgwbhr3/9KwBg586dAIAHHngATz/9NHr27In8/HxfTyNRUVGB9957D//85z/Ru3dvrF27FjfccAMKCwsxbty44HYuzlCW9gbijORlGDCyewH0Og2Kc8gZiRW8GMlJIzFCEIQ6SStGsrK8P3b55cDnn7vvFxUBLS3q644bB/BjZLp3B2pqPNfz0ZRWldzcXBiNRmRkZKC4WCx13L17NwDgr3/9Ky699NKAn8tiseCJJ57A119/jdGjRwMAevbsiXXr1uHVV19NAjEiio42mzJM4z1nRKPR4KPbz5PCBURsON1kkW6TQ0UQhDeSVowkMsOHDw9q/f3796OlpcVDwFitVmmGUCLDrq7dzoj/0l4AJETiAJZsDPieJUQQRGqTtGKkudn7YzrFMbG62vu6WsX57vDhkHcpYDIVMR+tVusxD8hms0m3m11/7Oeff47OneWlkiZT4l+NStU0dgccTkGqztD7ESNE/EBJxARB+CJpxUgwORzRWtcfRqMRDofD73qFhYXYsWOHbNm2bdtgMIgx+AEDBsBkMqGysjLhQzJqmLgEVht3pe0rTEPEB6W5aTjR0IbrR3WN9a4QBBHHJK0YSQS6d++OjRs34vDhw8jKyoLT6VRd7+KLL8bf//53vPvuuxg9ejTee+897NixQwrBZGdn495778Xdd98Np9OJCy64AA0NDfjuu++Qk5ODm266qT3/rIjDl/bKxQg5I/HOhzPPw/cHavG74WWx3hWCIOIYOprHkHvvvRc6nQ4DBgxAYWEhKisrVdebOHEi5s+fjz/96U8YMWIEmpqaMH36dNk6jz76KObPn4+Kigr0798fkyZNwueff44ePXq0x58SVXhnxO5wh6tIjMQ/3TpkYtrIrtD5aFBHEARBzkgM6dOnD9avXy9bNmPGDNV1H3nkETzyyCNen0uj0WD27NmYPXt2JHcxLuBLe5kzotNq6ARHEASRJNClJRH38KW91gDKegmCIIjEgsQIEfdIYsTulMp6KURDEASRPNARnYh7SvPEFriHa8wBdV8lCIIgEgs6ohNxz+DOuQCA3VWNaLaIgwMpTEMQBJE8kBgh4p4u+enIzzDA5hCw/VgDAHJGCIIgkgk6ohNxj0ajwSCXO/KzS4z4awVPEARBJA50RCcSAjZk7XSzOHiNnBGCIIjkgY7oRELAxs+fMVsBAAY95YwQBEEkCyRGiIQgyyT256tziRG9coIhQRAEkbDQET0F6N69O5577rlY70ZYZKeJYqTWLIZpKGeEIAgieaAjOpEQZLvCNG02V58RCtMQBEEkDSRGEgSr1RrrXYgpzBlhUAIrQRBE8kBH9Bgxfvx4zJo1C7NmzUJubi46duyI+fPnQxDEdufdu3fHo48+iunTpyMnJwczZ84EAKxbtw4XXngh0tPTUVZWhrvuugtms1l63urqakyZMgXp6eno0aMH3n///Zj8fZEmi8QIQRBE0pJ8R3RBAKzm2PwIgv/943jnnXeg1+uxadMmPP/883jmmWfwxhtvSI8//fTTGDp0KLZu3Yr58+fjwIEDmDRpEq6++mr8/PPP+Oijj7Bu3TrMmjVL2mbGjBk4evQoVq1ahcWLF+OVV15BdXV1xN7eWJGjECOUM0IQBJE86P2vkmDYWoAnSmPz2n8+ARgzA169rKwMzz77LDQaDfr27Yvt27fj2WefxW233QYAuPjii3HPPfdI69966624/vrrMWfOHABA79698cILL2DcuHFYsGABKisr8cUXX2DTpk0YMWIEAODNN99E//79I/c3xgiWM8LQUzt4giCIpIEuL2PIeeedB43GfVIdPXo09u3bB4fDAQAYPny4bP2ffvoJCxcuRFZWlvQzceJEOJ1OHDp0CL/88gv0ej3OPfdcaZt+/fohLy+vXf6eaMJKexnj+hTGaE8IgiCISJN8zoghQ3QoYvXaESQzU+6yNDc34/bbb8ddd93lsW7Xrl2xd+/eiL5+PMEnsI7sXoCrzukSw70hCIIgIknyiRGNJqhQSSzZuHGj7P6GDRvQu3dv6HQ61fXPOecc7Nq1C7169VJ9vF+/frDb7diyZYsUptmzZw/q6+sjut+xINPIiZEeBTHcE4IgCCLSUJgmhlRWVmLu3LnYs2cPPvzwQ7z44ouYPXu21/Xvv/9+fP/995g1axa2bduGffv24dNPP5USWPv27YtJkybh9ttvx8aNG7FlyxbceuutSE9Pb68/KWpote5w1iX9i2K4JwRBEESkCVqMrF27FlOmTEFpaSk0Gg0++eSTgLf97rvvoNfrcfbZZwf7sknJ9OnT0draipEjR6K8vByzZ8+WSnjVGDJkCNasWYO9e/fiwgsvxLBhw/DQQw+htNSdsPv222+jtLQU48aNw1VXXYWZM2eiqCg5Tt7/vn00XrpuGIZ1zY/1rhAEQRARJOgwjdlsxtChQ3HzzTfjqquuCni7+vp6TJ8+HZdccglOnToV7MsmJQaDAc899xwWLFjg8djhw4dVtxkxYgS++uorr89ZXFyMpUuXypbdeOONYe1nvEDhGYIgiOQkaDEyefJkTJ48OegXuuOOO3DddddBp9MF5aYQBEEQBJHctEvOyNtvv42DBw/i4Ycfbo+XIwiCIAgigYh6Nc2+ffvwwAMP4Ntvv4VeH9jLWSwWWCwW6X5jY2O0di9mrF69Ota7QBAEQRBxQVSdEYfDgeuuuw6PPPII+vTpE/B2FRUVyM3NlX7KysqiuJcEQRAEQcSSqIqRpqYmbN68GbNmzYJer4der8df//pX/PTTT9Dr9Vi5cqXqdvPmzUNDQ4P0c/To0WjuJkEQBEEQMSSqYZqcnBxs375dtuyVV17BypUrsXjxYvTo0UN1O5PJBJPJFNRrOZ3OkPeToPePIAiCiB1Bi5Hm5mbs379fun/o0CFs27YNBQUF6Nq1K+bNm4fjx4/j3XffhVarxaBBg2TbFxUVIS0tzWN5qBiNRmi1Wpw4cQKFhYUwGo2yeS+EbwRBgNVqxenTp6HVamE0GmO9SwRBEESKEbQY2bx5My666CLp/ty5cwEAN910ExYuXIiTJ0+isrIycnvoB61Wix49euDkyZM4cSJGM2mSgIyMDHTt2hVaLTXlJQiCINoXjSAIQqx3wh+NjY3Izc1FQ0MDcnJyVNcRBAF2u12aeEsEjk6ng16vJ0eJIAiCiCiBnL+BJBqUp9FoYDAYYDAYYr0rBEEQBEEEAXnyBEEQBEHEFBIjBEEQBEHEFBIjBEEQBEHElITIGWE5tsnYFp4gCIIgkhV23vZXK5MQYqSpqQkAqC08QRAEQSQgTU1NyM3N9fp4QpT2Op1OnDhxAtnZ2REtP21sbERZWRmOHj3qs+SIoPcqUOh9Cgx6nwKH3qvAoPcpMNr7fRIEAU1NTSgtLfXZxyohnBGtVosuXbpE7flzcnLowxsg9F4FBr1PgUHvU+DQexUY9D4FRnu+T74cEQYlsBIEQRAEEVNIjBAEQRAEEVNSWoyYTCY8/PDDQU8ITkXovQoMep8Cg96nwKH3KjDofQqMeH2fEiKBlSAIgiCI5CWlnRGCIAiCIGIPiRGCIAiCIGIKiRGCIAiCIGIKiRGCIAiCIGJKSouRl19+Gd27d0daWhpGjRqFTZs2xXqX2pW1a9diypQpKC0thUajwSeffCJ7XBAEPPTQQygpKUF6ejomTJiAffv2ydapq6vD9ddfj5ycHOTl5eGWW25Bc3NzO/4V0aeiogIjRoxAdnY2ioqKMHXqVOzZs0e2TltbG8rLy9GhQwdkZWXh6quvxqlTp2TrVFZW4oorrkBGRgaKiopw3333wW63t+efElUWLFiAIUOGSM2URo8ejS+++EJ6nN4jdZ588kloNBrMmTNHWkbvlcj/+3//DxqNRvbTr18/6XF6n9wcP34cN9xwAzp06ID09HQMHjwYmzdvlh6P++O5kKIsWrRIMBqNwltvvSXs3LlTuO2224S8vDzh1KlTsd61dmPZsmXCX/7yF+G///2vAEBYsmSJ7PEnn3xSyM3NFT755BPhp59+En79618LPXr0EFpbW6V1Jk2aJAwdOlTYsGGD8O233wq9evUSpk2b1s5/SXSZOHGi8Pbbbws7duwQtm3bJlx++eVC165dhebmZmmdO+64QygrKxO++eYbYfPmzcJ5550nnH/++dLjdrtdGDRokDBhwgRh69atwrJly4SOHTsK8+bNi8WfFBU+++wz4fPPPxf27t0r7NmzR/jzn/8sGAwGYceOHYIg0HukxqZNm4Tu3bsLQ4YMEWbPni0tp/dK5OGHHxYGDhwonDx5Uvo5ffq09Di9TyJ1dXVCt27dhBkzZggbN24UDh48KHz55ZfC/v37pXXi/XiesmJk5MiRQnl5uXTf4XAIpaWlQkVFRQz3KnYoxYjT6RSKi4uFv//979Ky+vp6wWQyCR9++KEgCIKwa9cuAYDwww8/SOt88cUXgkajEY4fP95u+97eVFdXCwCENWvWCIIgvi8Gg0H4+OOPpXV++eUXAYCwfv16QRBE4afVaoWqqippnQULFgg5OTmCxWJp3z+gHcnPzxfeeOMNeo9UaGpqEnr37i2sWLFCGDdunCRG6L1y8/DDDwtDhw5VfYzeJzf333+/cMEFF3h9PBGO5ykZprFardiyZQsmTJggLdNqtZgwYQLWr18fwz2LHw4dOoSqqirZe5Sbm4tRo0ZJ79H69euRl5eH4cOHS+tMmDABWq0WGzdubPd9bi8aGhoAAAUFBQCALVu2wGazyd6rfv36oWvXrrL3avDgwejUqZO0zsSJE9HY2IidO3e24963Dw6HA4sWLYLZbMbo0aPpPVKhvLwcV1xxhew9AejzpGTfvn0oLS1Fz549cf3116OyshIAvU88n332GYYPH45rrrkGRUVFGDZsGF5//XXp8UQ4nqekGKmpqYHD4ZB9QAGgU6dOqKqqitFexRfsffD1HlVVVaGoqEj2uF6vR0FBQdK+j06nE3PmzMGYMWMwaNAgAOL7YDQakZeXJ1tX+V6pvZfssWRh+/btyMrKgslkwh133IElS5ZgwIAB9B4pWLRoEX788UdUVFR4PEbvlZtRo0Zh4cKFWL58ORYsWIBDhw7hwgsvRFNTE71PHAcPHsSCBQvQu3dvfPnll/jDH/6Au+66C++88w6AxDieJ8TUXoKIF8rLy7Fjxw6sW7cu1rsSl/Tt2xfbtm1DQ0MDFi9ejJtuuglr1qyJ9W7FFUePHsXs2bOxYsUKpKWlxXp34prJkydLt4cMGYJRo0ahW7du+Pe//4309PQY7ll84XQ6MXz4cDzxxBMAgGHDhmHHjh345z//iZtuuinGexcYKemMdOzYETqdziPr+tSpUyguLo7RXsUX7H3w9R4VFxejurpa9rjdbkddXV1Svo+zZs3C0qVLsWrVKnTp0kVaXlxcDKvVivr6etn6yvdK7b1kjyULRqMRvXr1wrnnnouKigoMHToUzz//PL1HHFu2bEF1dTXOOecc6PV66PV6rFmzBi+88AL0ej06depE75UX8vLy0KdPH+zfv58+UxwlJSUYMGCAbFn//v2lkFYiHM9TUowYjUace+65+Oabb6RlTqcT33zzDUaPHh3DPYsfevTogeLiYtl71NjYiI0bN0rv0ejRo1FfX48tW7ZI66xcuRJOpxOjRo1q932OFoIgYNasWViyZAlWrlyJHj16yB4/99xzYTAYZO/Vnj17UFlZKXuvtm/fLvuyr1ixAjk5OR4HkWTC6XTCYrHQe8RxySWXYPv27di2bZv0M3z4cFx//fXSbXqv1GlubsaBAwdQUlJCnymOMWPGeLQb2Lt3L7p16wYgQY7nUU+RjVMWLVokmEwmYeHChcKuXbuEmTNnCnl5ebKs62SnqalJ2Lp1q7B161YBgPDMM88IW7duFY4cOSIIglgKlpeXJ3z66afCzz//LFx55ZWqpWDDhg0TNm7cKKxbt07o3bt30pX2/uEPfxByc3OF1atXy0oMW1papHXuuOMOoWvXrsLKlSuFzZs3C6NHjxZGjx4tPc5KDC+77DJh27ZtwvLly4XCwsKkKjF84IEHhDVr1giHDh0Sfv75Z+GBBx4QNBqN8NVXXwmCQO+RL/hqGkGg94pxzz33CKtXrxYOHTokfPfdd8KECROEjh07CtXV1YIg0PvE2LRpk6DX64XHH39c2Ldvn/D+++8LGRkZwnvvvSetE+/H85QVI4IgCC+++KLQtWtXwWg0CiNHjhQ2bNgQ611qV1atWiUA8Pi56aabBEEQy8Hmz58vdOrUSTCZTMIll1wi7NmzR/YctbW1wrRp04SsrCwhJydH+P3vfy80NTXF4K+JHmrvEQDh7bffltZpbW0V/vjHPwr5+flCRkaG8Jvf/EY4efKk7HkOHz4sTJ48WUhPTxc6duwo3HPPPYLNZmvnvyZ63HzzzUK3bt0Eo9EoFBYWCpdccokkRASB3iNfKMUIvVci1157rVBSUiIYjUahc+fOwrXXXivrnUHvk5v//e9/wqBBgwSTyST069dPeO2112SPx/vxXCMIghB9/4UgCIIgCEKdlMwZIQiCIAgifiAxQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETCExQhAEQRBETPn/HOYnjsZOoWcAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "i=990\n",
+    "i=99\n",
     "(batch_x, batch_y, batch_x_mark, batch_y_mark)  = ds[i]\n",
     "l1 = batch_x.shape[0]\n",
     "l2 = batch_y.shape[0]\n",