From e26a65ea495608a1d8800920cd1befa42e8fd090 Mon Sep 17 00:00:00 2001
From: Mirko Birbaumer <mirko.birbaumer@hslu.ch>
Date: Sun, 13 Mar 2022 15:40:37 +0000
Subject: [PATCH] Changed Order of Chapters

---
 .../Solutions to Exercises - Block 2.ipynb    |   37 +-
 ...ining and Optimizing Neural Networks.ipynb | 2912 ++++++-----------
 2 files changed, 955 insertions(+), 1994 deletions(-)

diff --git a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb
index 4ed6fdc..e3c447c 100644
--- a/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
+++ b/notebooks/Block_2/Solutions to Exercises - Block 2.ipynb	
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 101,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 102,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 103,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 104,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 105,
    "metadata": {
     "colab": {},
     "colab_type": "code",
@@ -204,6 +204,33 @@
     "])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_11\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " dense_15 (Dense)            (None, 1)                 2         \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 2\n",
+      "Trainable params: 2\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
diff --git a/notebooks/Block_3/Jupyter Notebook Block 3 - Training and Optimizing Neural Networks.ipynb b/notebooks/Block_3/Jupyter Notebook Block 3 - Training and Optimizing Neural Networks.ipynb
index 063f8dd..1213a96 100644
--- a/notebooks/Block_3/Jupyter Notebook Block 3 - Training and Optimizing Neural Networks.ipynb	
+++ b/notebooks/Block_3/Jupyter Notebook Block 3 - Training and Optimizing Neural Networks.ipynb	
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "id": "dzLKpmZICaWN"
    },
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "id": "7MqDQO0KCaWS"
    },
@@ -87,8 +87,8 @@
      "output_type": "stream",
      "text": [
       "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
-      "170500096/170498071 [==============================] - 7s 0us/step\n",
-      "170508288/170498071 [==============================] - 7s 0us/step\n"
+      "170500096/170498071 [==============================] - 6s 0us/step\n",
+      "170508288/170498071 [==============================] - 6s 0us/step\n"
      ]
     }
    ],
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "id": "IjnLH5S2CaWx"
    },
@@ -186,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "id": "MaOTZxFzi48X"
    },
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "id": "ywVIEcXDvXW_"
    },
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 6,
    "metadata": {
     "id": "ug3dTdldvXXI"
    },
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,7 +425,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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xItuqXUuF7KMOMZwjPHEUivfeO+eO1D303r8I4EUAWFwo+Ep2THWe/9jN+JhCkVECgQTa33+btGY4TGpmlMoMvN9v8G8pJ9KhErrQEEr38AF3vvuJAAB+tyE7xpHQmpZoKQDAQU2iAYQCjsDveCcaGKJvUBWqXSjSFGFaohAqSt+T5ooiUrc3fvx23HYhb7xMlueqT6JCTiI7anadT7sCj7et2nWpWvKH585I5MhoxPE9kOrsT4sLpTQVXTSSzJd6g3ObL9B9kAl5TG2XBMLLd3sy/5Com3qNSTNlcG7b7WQkSFeimapS9k3Xi66jUoHHOHHlpET2VTVgSpIEdH/nARRZSTa6+bGPxu1I3Hpt0e05iT3fbxTKpnPuEgBM/r91zPGG2YDZdX5htp1DvN8H+FcBfGbS/gyAr5zOcAwfMsyu8wuz7RziWBeKc+4PAfwigBXn3F0A/xrAFwF8yTn3WQC3AXz6JJ2lHFCeUL9SkZQsI/KU1SXuaheE7uxus6IzAHz3ldfi9iCSCtZZUreVEnUk7rzD5IrtLb58dIekW7V92TkXSqxylvt7SX0DLWbe65JKFUukWyur1bjtRNCiK8kNKsPakYo1XvQhhoMpOtjj30ZCuwvFZETFIdKZMQV3gTO7Yj7tOh7L6djWBS4+d3SEM200ouvpjmjO5LJJ/7lWoOmKfG4osr0jieZYFXlXL4Zq9TiHLdENGXnO+TCSeR5NPeJEg0TXRVeyura3anE7K5EuuaxEyTRER6cneim7/Dw9VeXpozevxm2VMq7tMfmtJJWImlNuvUfh2Ae49/7Xj/jTLx97dsO5hdl1fmG2vTiwTEyDwWCYUZypFkomk45lJnVXelmKlqZEYyCzxs+vrK8mzvVf/uy/xu1IKpgsLZCf37/H3e5Ly6TUS4uk4/sPSLe2HjJ5YGmZdKlUIh1clM8BoFKia2BhkbSvVJZEDUkmeOP1H8XtVCgJByKr2Wuz3RcpzVQq+XvrIPRaNCJGErGhxXn7E/0MfxQffp8wu86nXX3k43NrlE1GikSnPMfnJELEIRlB0RftFnWV5AqSTCU6JSlxiTz9LCM20lKRp3kghZ33mVTVaNIlsb+bTORpNjmOvV1xrUkkydIq15GTcdy/x+ipVovXsFdn30HAz//JJ19I9O2kyLdWNSpvMDlpd58umOWqRvJ8B4+CvYEbDAbDjMIe4AaDwTCjONuixp47ylmlYbLz2pdEi1xI2uwlCQIARhKhEAQ8V+IXSQLkf+IjlIhUbYyn7kp/OdK5BaHjYYrjePDwncQ4/uHf/4W4ffnatbg99KRqtW1WKtnbIkXa2uMuczpFerWxVuUlCC2OhHoCwGKFY9zdp9ynDzjeXluoa39MB32UPM8Tw+w6l3b10Qij7jixxIlmSUEqIjmx1/Ii3WH9ZCYVglCSYOQ6ulr8WIRwlpfo0vrbP/183K6KzGzo6HK5f4cRMF//yz+L28++8ExiHEOpilTf5xrpivTr01c5/1tSZejBFiOkAlmRTtbpKOL15HLJijpuwLXQl4LYmTKThfT+GQyOL1Jtb+AGg8Ewo7AHuMFgMMwoztSF0uv38OMf3wYAlEUrodEg5VzKyU677GoPw6RgUlH0Jvqid3BpnREO2YA70M88cz1u52QHOMhwVzsrfRek9EogVNtPVfjoSpWUwSL7W71S5felsu1HbjzF/vI8V71J7YeMSG+mJXpjMFW5JQz5t6Ekm4R5Jnx40Ropr48/z377Nk4TZtf5tGs2k8bNaxsAgAOtFFPlOIYduiRWxXbDKYncdFpcAxLZ0c/TxgslViy6ss5zLUk0xlKVn2/fq8VttddGhcc8dbmaGEckLpS2FJbWZK21Kt091QW6i179Aee3IJWWauLO0mLMBwccHwCEQ43S4VrYrPG4lmihHIjb8SjYG7jBYDDMKOwBbjAYDDOKM3WhRJGPq6yM5LejJ9oRKxtMoFCZxc4Uzbxx40bcfuXbceUopCXC4eoV7lhvrGtSCXfrZdMXGYlWKBa5w63RCmgnqWFb6M/OQ8pH+oC0qpDn9/W81YrsiLeYfOClckshT6rm0kl9ib4IdlRF9nIkc7AgEQOHRUuC5Ob4E8PsOp92DRyQOzy39JdPs6OU4/gyYvtSISkn68E5qYkmDETVtlqpxG2dz4z0t/WAtth9WIvbaq8bl6/E7esb1cQ4NAqlJUW3+xKFIkEhGHlex9VrUsHnHl1Kql3zzMcZFdXp0B0CABUxUJDio3dPJJE7osnSlYpUR8HewA0Gg2FGYQ9wg8FgmFHYA9xgMBhmFGfqA3dwCCZhYz3x9eQkxKjbY3hULi/ZW/1kltlIdIHruxSTaUnoztM3Pxa3C5IVpdWvF5fpi+yLP3Yk/koVG1pbo58OAB4+5DjubtLf+T++8zdx+9lnP8LjN+nvunOX5agG4HVrqFRahI1yOfoFAWAo2W29jmTmiS+0KLrKtROEJb0fmF3n064AEKbGPuOVKvsbDUSIS8ZaEaGpMEy+G3ZFzKosYZ2b4v+9fZvZlNFoI267VykU9rqIhl27xNDNqmT0rq1xHKnU9MYAsyGXFmnzjKzVlKftDySs7+mb9K3fev0bcVvXqa7fVCVZlT6QMXY7XBde/PJ6/wTh8bVm7Q3cYDAYZhT2ADcYDIYZxZm6UNLpNC6vjWlILsPfjqKUXyoUSXmGI9KJdJgcajXPsK+PXWcl86Ui6dO1S6R9ZanivVAiZe0GPD4TcRyqL5wvyzGlJC26t0n6+vYO6darrzPc6f4DEUCqSYZfn+2f/EmWWypLtuCwKaFEU+WhvIj/5CULUbPgnFSD5+enqxttdp1PuwI+PndG+lPKr1mjXjIsE/4eAG7I6y2XRFPd0630yivU2777gHNYfYMuqZ16jV2kuQ6ev8TYv8UrDP1THXoAaDZoy5JkeIZS4T6nuuqS8arrTtejrtNqnus/HSbDQyNx5xQkw/OpkONoLUoYYV811d/Ao2Bv4AaDwTCjsAe4wWAwzCjOVg8cDn5SmikvGWYZ2bHO5EhlOg3Sl8FUtEJVBGt+9mdZkqiQIf1JS4ZbKO1RJNREMutyWU5HVCHNy2ZF9ChKTllGSk199/uvxu1mSzIMh5q1xYyvrNDSIOD4vGN/UYrXXZcSXgBQl0yytJTx6vWkQrfs/vcmkSDRKZfeMrvOp12jyMfn3m8wWiR0MofgNTV2Kdy1UExG1uha8I5rIZB5hud5myJkNpTK9zXJelS7/J0rPx+3C6Jd7qbSU53YP5flehlqpJKsI11fuu4uX2VESnWNbrLIcXyDfjKT0ombplBkKbqh6MFHkqHZGSYzOR8FewM3GAyGGcWxD3Dn3DXn3Nedc6855151zv3m5PMl59zXnHNvTP6/eNy5DOcHZtf5hNn1YuEkLpQhgH/lvf+Wc64M4G+cc18D8BsA/sJ7/0Xn3BcAfAHA5x93osh79CcCR40D0oNggbS7vccSUn2pFF0sJKuGh1LGaG+7Frd7QrX3D0hNr44oeuRFuCZTIsXKCMVpya65zlJvSmCmKEJJWrW66yUiIiX0WhMG8jyxVrkeCk3MSSTHfidJte9ts4yXh5Qm87wmJwJPhVw4+WzcDcyuPL/Z9V1wDkhNokw2tbSblzGJGJWT5KQoYHk1AFisMGqjUeMa6Q05D8NI3GxduotqYu/GiO63Rp1uJLVXryXl9ArJSBAv9s8EtGUkY+901X3Dd1xdd/tNKcfWp12WVqtx20255VqiOT8YMhoqDKmvrvdPf5AUWHsUjn0D994/8N5/a9I+AHALwBUAnwLw0uSwlwD86rG9Gc4NzK7zCbPrxcJ78oE7524A+BkA3wCw7r0/DIp9CGD9iO98zjn3snPuZd3oMZwfmF3nE09q13b3+DdAw4eLE0ehOOdKAL4M4Le89w0nO+ree++ce+QWuPf+RQAvAsBiOe83JzvV1zZIsRpN0obBiLRoZZUa0o16ckd2MBAaJtRUN+K///qbcTvluLOcEQ2Mj3yUFceDMulWp8nFOxKKNOgl6W5OzrUnmgivvUPNho9ucMd6pULXY7hC90GzSbfC7qAWt9MSQVGX0k0AsKeVyb1UyZZyTRnHh2uzNZ6ngex6m10n/ZpdD/8W23WlmvdbE7tGjnPYl2gRL+Xx1BbYZ0QKADR7vKaBRNNkJRmn2+P39/foothpcP7ffHgvbn/8Or+r9uq2aMvRMBmF0u+z76Lo6DQPuNZ+9CY1Wfojjun7osPSlrXZlsCkwR7Hmk4n348rCxzvzjb1ddKS4DOUJK7N3eQcPgonegN3zqUxXgx/4L3/k8PzO+cuTf5+CcDWSc5lOD8wu84nzK4XByeJQnEAfhfALe/978ifvgrgM5P2ZwB85fSHZ/igYHadT5hdLxZO4kL5ewD+BYDvOee+M/nstwF8EcCXnHOfBXAbwKePO1FvMMDbd8b0JCPSisM+Kc+NG6SlzRZ3nGsHSao9HJBqpI6IMvje6z+M22k55s7bjCpYW2EUQ3WRVbFv3Xo9bms5qH/+T/9BYhw50XJYXmK7UCe13BZaFQlNzGQ4plqDO9HNLhMXWjI3QSa5o94d8FyqjaGJCHuNWtxeW2AkAMyuZtcTYBiN6f2WaJDkKlLGTubfi1ZL1Ei6pLZ2OFfFDMdbEindeoPzsyWROLstRm8UFjifahcvZfo2Rdr3j7/8nxPjcJJ49Nxzz8btmrh8tnbY97UbTNLRdYcM+x562q8rkSphOum+CUR6N8wwkefHb9+Rzzk3h/fU43DsA9x7/1cA3BF//uVjezCcS5hd5xNm14sFy8Q0GAyGGcWZaqF47+Nd1m2pIr0gugBKqVWqMtKEBgDNtiSMqJxCRApTKfA7D0QS9K+/xd3kcmEzbnc7WiGddDUrFWReufUjKC4XqddREZnMy1f4+fZb3Dl3QqsePmTfTz3F6I1RxGOUTreaTKYAgKFIdo70ukUmsyfhG83eaHLs6WpmmF3n066jyMfn1v5CkY3d22W0SEp0Rw4r+RyimBUNklCifyRyRufNaQSHJC1dvsJKPZUS7Xf3Dm1xv8X92R++SSlaAOh1eK5XvnefY8ozEetA1mBYYuTIUDRcGnVetwskgUkCcSqSyAYk7wGVBo4cr1XvH41IOQr2Bm4wGAwzCnuAGwwGw4ziTF0oYTqN5dUxBV2Q3eR8hvRqR4qcFkSadNBLyo72hHqFQreyOUk4kAK2D3d43u6Qx69UGKHw1DOkx/0+d7Xrkkjw1u0kJcuui3SoUMtKkeMILnHXfqFQjdsHe7W4/eZbb8Xtj33iZtzuif5Ff5RM+NCf31aT13dzhX0U8rJDPtH78O50f7fNrvNpV+8CDMOxG2x1g1EXeZGK3doWCdmSFG2eGkpeok3KFRYT/sGrnJ9AJGcXlqpxOyVBNqUiXR2BZ2TSriRbvbVJWxaXkgmnlyXhKiPr8+FDul22G7SHri9VPg5kPfZ6dHUMh/Sh+KkIq7RUj2qLm6Yo84aM6LMEUtT4R0k3WzyOR35qMBgMhnMPe4AbDAbDjOJMXSijUYR6a0wdRhGp7LVL3FnOCr1udUmVS8Wk7KhLi+6D7HhnpMqGG/D3qdXm8ZkCaUpljTv7/UBkJNOkzfkljikKhdYgKf/43DMf4ffvSZFblaFsUAPhuWefi9u3377F4wdybWKiad2QSH5/y8WStEkzmxr9MZlDrQxyGjC7zqddXZCKzz0a8Vp1HOUCx9c6oOshkGgfAKgWaQ+dh1aX1/G0zNutNzlv+RKjmdYX6X55TooXq72ikD6X/FLSrkMnAl3y+qrrJbPHddSSqBVX4DgyUp0nknCpMEPXWDo9FWElCUk5uR803GpHim43E9FTj4a9gRsMBsOMwh7gBoPBMKM4UxdKkApQLI2pw0i0LXpSUDRMc0gZMrVE8sfkbHErpcelIzwK3Yh9ONEkKFZJheoNjZQgDdt8SHochqRwALAscp8l2Tkv50k5L2/w860H1FkoFUnvLokMa110J0T9EqmpBOlqlZEWlSrHWxcatrnF5AgfjMc+GJ6uzrPZdT7tOhgOsbk5vi4XMQpCi9ysrzHCp1Hj3BzUOLcA0OvQvVIfsK3zo/NWlvaazHNZkq9KObo0WhL5o3K89YNk9Mb6Bsfb7lB7ZaFKV56uIwkqQU7cJrqIsxL9IwFSCMPk+3FGvuMCrnu9T0aeHR7eU4+DvYEbDAbDjMIe4AaDwTCjsAe4wWAwzCjO1gceOOQnDrRASjRpeaLsiH68gmQ7OSTrLmZFcxlSTkm1nzvic+yl2Uc6Rz9Tu09/WZhifwNKVqPXZjjb3U6ykMnK9etxu3+XGWAFqViVr3Ca16uX4vbW9o95nsUqTyoZWA0ZyCeuskwYAERSHbzVoh+tJWW8VmQ+BpMpDIOj1EbfH8yu82nXMHCoFsbXmE6zPx1HUTJCr15hSGFwlaF4AFDbZ/hlJU3fNQq0/26N8/b8c5xPJwpRTrIvN8UuGQkDvXuftgxEqxtI2l/Xha4XXUcuTXtncxKeuFCN2/u7NZ4zw+9GUXJtZ8S33u5yIJp9XF7gXsxJtMnsDdxgMBhmFPYANxgMhhnF2bpQ4JCd0OJikSEyoxEpRAqk2qHoBg+HSTriPWmcl3CdekNoimjrhmAf+byE8Ii40aDNvlv7pDjZNKnTwgqp5PiPpIMDqYYdZiWLUFwGXsLpNEQsK/RqaYUCPL7OcCwXJMPEOg3S0naL15eTuQ2kGjkm+sKp1On+bptd59OuqVSAxcrkWqS/SHSq2y2ONRBt91Il6UIpVzj2SwsrcXtv50Hc1nkrFjifbsD56XfpQhn0OKaMZHpubND9Ut8RnwmAzfvb7GORNk5Lf7qOAllfNVl3fdEG74r7xUncoXNJl5au+2yWfYSy1lIpdZ8lM3QfBXsDNxgMhhmFPcANBoNhRnHmUSilCV1IS91VJZD5PKnXwYGUa0olhWGykoVVkIyljH4uP09tqTp9aYPiRB3pPVdi3+l1oceSjTVAkpINNLqiwl34tOhGa4nZgdCqtXXSvmzEXfQwLVleWY7Jyw48ABSL3LEuiMAQUjRru91+V9ufcukts+t82tVHHr1JyqhmsJalDRHbcqD7K3DJsWSznDedh8VFulMKIjoWerou0hmeq1gQUayWuFNGNGa5zKzKpXJSD1wl0wcy792IbpDlm/z+rmR4aiRUkOccZMR11W7S7dHrJtdUXwTFy2VeR6cj1y2Lyolr5SjYG7jBYDDMKI59gDvncs65v3bOfdc596pz7t9MPr/pnPuGc+5N59wfO+cyx53LcH5gdp1PmF0vFk7iQukB+CXvfdM5lwbwV865/w/A/wbg33nv/8g5938D+CyA//i4EzkAmckOdiAUNSPU0AmFCEQnN4qSYkYZoWEq4hO1KVCTk+8vLpCyaL5DPiua0FLeq1Tm531JSOmI/jQAeNkhD6XydiZD+tOU3eT8AulZu8fvtmV3PeOFdsvcBFOqRyPxPrTanJ+9PQorDSTKI5s5pLEOMLuaXY+xK+DgJtEWtTpFoVotuhuWJPGkKCpXqaknSyQX1RGda7VBukDb58Rl1qlTjKwkkTiDLuem1+N8jGStlQrJgWTElbPXoIsjn5MIHxmTrq+BmKkr61G8N4n12xE3F5Bc94GMsSCTNZL7x51GVXo/xuFo05P/PIBfAvD/TD5/CcCvHtub4dzA7DqfMLteLJzIB+6cSznnvgNgC8DXALwFoOZ9XO31LoArR3z3c865l51zL/f6w0cdYviQYHadT5yeXQePOsRwjnCiKBTv/QjATzvnqgD+M4BnT9qB9/5FAC8CwOpiyRcmdHQo+f8+kkrkIgJdFb2BUZSkuJrIsCvU0ov+QFV3zqUCtR+pJoFQFtnFjwbUU6iUSKOmWY0+upo9UrL0gP212/x8kOLnW/ukpQfbtbi9tETN4q0DRlnkC8nfW+9pvt0d0vmGUHuNHigUxnN46LYwu5pdp6F2rVYKvtUcuwEeFfUCAHVxaSyvsO1c8ke9I66gtTKjOfb2aI/yapXHSOm0gcyzamqrXUaSRISELZMuup1d9tcVHW7fFZeG43d0farLrCZz4ETjZrm6HLcrlaTGfEpK3mmQzkAiYEJZ/2H2+Mfze4pC8d7XAHwdwN8FUHXOHfZwFcC993Iuw/mB2XU+YXadf5wkCmV18ksO51wewK8AuIXxwvhnk8M+A+ArH9AYDR8AzK7zCbPrxYLzx+x0Oud+EuNNjxTGD/wvee//D+fcRwD8EYAlAN8G8L/46YyEd59rG0ALwM7jjptTrOD8XPdTAH4Zp2vX2zhf13hWOE/XbHY9PZy3a37Ke786/eGxD/DThnPuZe/9C2fa6TnARbjui3CN07gI13wRrnEas3LNlolpMBgMMwp7gBsMBsOM4sN4gL/4IfR5HnARrvsiXOM0LsI1X4RrnMZMXPOZ+8ANBoPBcDowF4rBYDDMKOwBbjAYDDOKM32AO+c+6Zz7wUTS8gtn2fdZwTl3zTn3defcaxM5z9+cfL7knPuac+6Nyf8XP+yxnhYugl2Bi2dbs+v5t+uZ+cDdWJfyhxhnht0F8E0Av+69f+1MBnBGcM5dAnDJe/8t51wZwN9grPz2GwD2vPdfnNwMi977z394Iz0dXBS7AhfLtmbX2bDrWb6B/xyAN733P/Lj0uN/BOBTZ9j/mcB7/8B7/61J+wDjNOYrGF/rS5PD5knO80LYFbhwtjW7zoBdz/IBfgXAHfn3kZKW8wLn3A0APwPgGwDWvfcPJn96CGD9qO/NGC6cXYELYVuz6wzY1TYxPyA450oAvgzgt7z3Df2bH/utLH5zRmG2nU/Mol3P8gF+D8A1+ffcSlpOSll9GcAfeO//ZPLx5sTXduhz2zrq+zOGC2NX4ELZ1uw6A3Y9ywf4NwE8PSmumgHwawC+eob9nwmccw7A7wK45b3/HfnTVzGW8QTmS87zQtgVuHC2NbvOgF3PNBPTOfePAfx7jKUuf897/2/PrPMzgnPuFwD8dwDfA8uB/DbGPrUvAbiOsUTnp733e488yYzhItgVuHi2Nbuef7taKr3BYDDMKGwT02AwGGYU9gA3GAyGGcUTPcAvSqrtRYPZdX5htp0vvG8f+PtJtQ0C58OUm7Qf/dsx3hB+N/y7QjB53Gg0Yh/yeSDnGkVR3HaBHCPj0HYqDOP2aDiM29GI53nXGI+YS70mvbxUiv1lpL/+YMC+5ZxhisdM96dzkMmk4nYg1+omc9Ps9NHrDR850e/Hrtls6Ev5zHhMYqcoYrvf5/hSKY5v2t7DEec6JX/LpNM8l9hjJPbQ6T+JLRRBKrkej7S/rCNte7nWlKyjSMYRydzoHEyHFzsccQ8ccU2H4xiOPKLIP/rLeO+2tfv1fNyvALBX7+w8qiZmOP3Be0CcagsAzrnDVNsjb/Qw5bC6PL4RC4UCByqzlA44JDXQIKJRxl/i3/ZrtbidD7JxuyTnqnfbcTtVzMXtQk6OL5XidnVxKW7v7bK2aa/ZTQxDl0C/15fxsRmmOY5MmuNeLOfj9tX15bj9zv37cftAHnzVBR4DAIMBF2fzoBa3n7q+wP4yfPCFk0X353/5QzwG79mupXwG/+gXnwEADOXm6fe5sG+/U+fx5WrcTqeTD4ZafTdul2VhX798OW7f3eQx+wcd9ifzMRzIehEjZbKZuK2PnmwpB8XS8grHtM/Ag2azGbfbXdYEHrW4LhZyXNtNWbediMcvVqsyvuRDRu8B/ZHQe0AfBu32eG1v73K+j8B7sq3dr+fjfgWAP/zKd2/jEXgSF8qJUm2dc59zzr3snHtZ38gM5xbv2a7d/nD6z4bziWNta/frbOFJ3sBPBO/9i5iUJ8pmAp+ZUMeR0I5I6ITL8hdW36JSaaWcSHCbxUoxblflV7nX4NtS1OavbTHDt4lqke1igb+wlSx/Cbfa/BWPfPIXPZ/n28H6+lrc3t3lW1u+wGOuXb3Ea5L3gY0NvvGl5fg33r4bt7OZJF1dWqpwvLxsrC5S9VIp6kGL8/GkULsuVwsew/HbxWKRY4qKvL7BgOPIyzGtZitxXrXBT9y4GreXq/zOUN70Vi/xvHfuPojbHbHZ8jLfzur1Go/p8JghX/gAAJXLtL+XMYVeXShcwwcHXF9peQPfqFTjdk3e3qOhvP1NUfn2gOPSt8F+j2/wgbhgDu8p5459Az8Wdr/O1v36JG/gFyrV9gLB7Dq/MNvOGZ7kAX5hUm0vGMyu8wuz7ZzhfbtQvPdD59y/BPDnYKrtq4/7jnMOmfQh3eNvx/I6N1ebbVLqSGjYYJj0swZCO69c2WBbzvXG69ysWwurPOYa3X7BUHa1heYtCD1bXST18SlSOABYXOR5iyVSw1TA8W5cJlXLyyZavbYftweelHFxiZTq+kB2tckSJ//m37Ip0rioR4pbWeAGSdQf0//AHf27/X7sGrgAxfR4Xko5zkGtzo3LaoXjyxW57Ir5auJcS7K5p/MwGHGz0kM3A3lMLk+Xy8P71B26fJlrYqlK++3v1+K2GyVvBbX/sEP/Sn6Ba2FpmTz4nngMCjna+OlnbvCYzW2236G7J5oK5MgW6JYIQp54ZZnXqpTfTwJPjooI4XHvzbZ2v56P+/VxeCIfuPf+TwH86ZOcw3D+YHadX5ht5wuWiWkwGAwzig88CiXRWSqFxQkFzQvl2dggpXq4TZqZz5Fm7AtlBIDLa/xONkfqViiQ8ly/wdhhjRkdSNhbFqSr2QzbrTZp841r3In2adKdcd/8Tk+iBNZXSavCgFSo2+XO8kKVFK7TY3/1/V05nv2trpEaAkChTI6Wdjwu7HMOOk2edziJe/XR8dTsvcBHUXxu7S+f5tykZUc+W+L42gdJql3fp47+PQkNWVjketF50/nUxInE/KdIXdfW+PlCRWKbB8moiWjEqIvrl7nWen3auCeunEvrfytua6y4rsdVcQ9VcuW4fX/rYaLvRYma6XQZRbGxSndDRtwVnfbYvXRvi+M5Ddj9ej7u18fB3sANBoNhRmEPcIPBYJhRnK0LJQyxtjamgZoi3BeaeOkK6U8xT4qbTSUp7pUN1hcd9LkTvi10tCIRA2lJy476knQRis6CUO12ixEUmmYb5JPj6AmV6gq9zglVO5BojFKZNGw4JI3a3iENy2VIHzWwoN9PUuTGfdJXTQDo13jenqSzlye0NDplDfjIe3QndG9bqHNW0oIzVZn/Ri1u57JJmqnXq+N88JBRJasrTFEOhIoe1Ol+yRU4h/UGowdyQrs10zDqJKm25lyXJYVa18twyDWs66sgqd+6HlcWGN2QFjoeIdn3yiW6G1oSAaOJLkVJbT+0a/hjXudpwO7X83G/Pg72Bm4wGAwzCnuAGwwGw4ziTF0oABBMSs71ukzMGPVIyQaB0IkOqVY6TFKh+h4Vx5xQUC8JBO/cpS7BYoUB8sVQgvO7pJ1K2bN50aAYktYMpmiRU+lQ0YIYSURELsP+lJq3RLMhKwkw2QwpeDFPqpUT3QkA2N/bkzavo5LnjroTKlucREGkpujtkyKVSsXnPujQrl1xMWzdIeXUxIcl0cUAgGCB8zYUe/iAbZ23fIbzr/M8GnBNqF36Ql01GiecWl8ZycLY3tvk+IQjhyIvurPNtbrfIAVfELdCR5UMwXmaXtt7e6TaA5HXHRxI0ozMjUaInDbsfmXzw7pfHwd7AzcYDIYZhT3ADQaDYUZhD3CDwWCYUZypD9zBw02cSlnR7/WisTwU/1VXQqiWC8xcA4C0lB5KBzxXp8dLyuboG+xJ9ZSeZFdlJRsvK74vl9ESTfxuIZcUx+lLiFOlygy6fJ59Owl1axww1G0g1TtcWsSeZNwY8PxdqfoCAKMef3+zaYZgLaxQq3ggYUn1iQ919JgyU+8Ho1EUnztfpu8yLWGEvYe8Vh339DWFEj4WpiVDTfyVg774wEOea2mR4YXeS6ai+OVbLfppM5I52OnxGABwWak0I6JCPbF3Y19ExHO8Vl13KbnF8uJXH2i5uamwzlrzIG6HUkoulOowKi51eE+5d5UxezLY/Xo+7tfHwd7ADQaDYUZhD3CDwWCYUZxtGKFzccVureJdkIyjjiM9q5T5+eggSUfgOPTLUvB2sC00ckAqVcqS8nTrpKiLl0lflF4r1i4xi6x7kAxLSgkdzAitygt167TZX07KQwVC+2pSfHWgpakkjKzTmRK3iST7T2hcWjSMOxJGtbk1DofTMKvTQH84wL1JRt36OueqIOMo5EgZR1JOrNNJzmcxJaGA8n6hLpFqla6SDKTQryQ05iVTcTSUjEupXayV6MtLyYzQppSzCvMc08IKQ772I4bG5RboMmhKmF1ZBL0urzJr8fZdFsIZDZKCXksSZtmTv+WzHG+7JUV/D0PjjtEDf8+w+/Vc3K+Pg72BGwwGw4zCHuAGg8EwozhTF0p/MMQ7m+MMJC8776UuaVh5UeiZ7PpWwmS22fWrpNG5omTHMeEPy0XS16WiVLCWkkldEcR57f4dHi+ll7pN0fttJeluOsXz9ntCn4RGR47UKSVVxrsHzNgbSEBDb8QxrS8y42tlgdcMALcaLEG1usy/SXeolkkBo8HYTRCGpy16lMLqxAWh/WnC5+oyXQzbm6SGT4kYEgD0JBpkc58UeSDayF3R4daol0i0mDsd2qzfFTeLZHR2JbtQKT4A5FOk2ntSeu2mjPe69N1okXbvtcRlI8tF1+n1q3QFvPGOLFoADVljXtw8D/bp1mlKpMphKbX+lCvmSWH36/m4X8dIasYfwt7ADQaDYUZhD3CDwWCYUZypC8V7Chzt7HAHv9gilV2RneiMJkGUk4kBnVYtbh8oTZKN+JQI5XQbFJJZl4iBV2+9GbfLIlBTFoEgySnAslBfAHBD7iAPJXA/LxWp6x2OIyfJI/cekAIiInWqLDLBoCtVv4eD5K52QbSOF8qknzuya98RN8FCeUzJUsEpi1kFqfjczQYTH/Ky074ic95qsP9WU3ScAQzFDVARTe9Wi3rgbSk7dXVVIg76UoItz3mWnByEotXtQ4mGaSfHkcty7IMOo3beep3r5YXnno7bW7K+UhIxE4lrZmuLYk2hrO1BNxkVVDug/foSZbOzxzG2RBhrZZIIcsoy73a/4nzcr4+DvYEbDAbDjOLYB7hz7vecc1vOue/LZ0vOua85596Y/H/xcecwnD+YXecXZtuLg5O4UH4fwH8A8J/ksy8A+Avv/Redc1+Y/Pvzx3YWprCxMt65H3ZIGyplobVCyVKi/aDVq4EkXWy1RTdBqourRvDzn2DV8Pv378ftblcqlksSylCqkkcgvypKsgIA9FuiKe1FAyMgDWvuMOqjJlRycYFRDAcSuTAckXrlRE9kMEzuqF+/eUPGyLnarTNJJBLdk6WV8fUF4/CQ38cp2TVIpZAvjune9m4t/rzd4xyurZLK6rjrtaTrIpTr7YpuRVaSKMpFztvDB7RlCNp+qcrIhZxEEkC0VjJFrimtag4AwxG/U13kunjnNt0gTXHlPP+Jn4rb33r19bjdEzoeSEmuSoHnn17bqQPaTO+HZblP0p5ze3hPvX07jr74fZyCbe1+PR/36+Nw7Bu49/6/Adib+vhTAF6atF8C8KvH9mQ4VzC7zi/MthcH73cTc917/2DSfgjgyJ8K59znAHwOAHKiaGY4l3hfdi0VskcdZjg/OJFt7X6dLTxxFIr33jvnjtz/9t6/COBFAFhcKPhKdkyZnv/YzfiYQpG7yYEkUNx/m3R1OExqK5TKTKjYb/BvKScSk7LF3RCq/vABIxr6iY1ifrchkQCR0NWWaGQAwEFNdo2F2o/A73gnWglS0qkqlKxQpCnCtOxWV5TmJc0VRaTkb/z47bjtQj5QM1meqz6JHjiJnOx7setSteQPz52RyJHRiON7INXHnxYqWZqKVhhJ5ku9wbnNF0jbMyGPqe3yRdPLd3sy/5Com3qNSR5lcG7b7WQkSFeiI6pS9k3Xi66jUoHHOHHlpET2VTVgSpIEdH/nARRZSTa6+bGPxu1I3ARt0QE5tGcqOJkWyuNsa/fr+btfH4f3G4Wy6Zy7BACT/28dc7xhNmB2nV+YbecQ7/cB/lUAn5m0PwPgK6czHMOHDLPr/MJsO4c41oXinPtDAL8IYMU5dxfAvwbwRQBfcs59FsBtAJ8+SWcpB5QnFKFUJNXOiJxidYnRCgVhhLvbrNQNAN995bW4PYikMnmWlHylRL2BO+8wCH97iy8f3SEpT21fIiKEOqlM6f5eUrdCi173uuR3xRKp0MpqNW47ET7oStKKynV2pLKJF92P4WCK5vf4t5HQs0IxufN+iHRmTNVc4E7Vri5w8bmjI5wuoxGp7B3RsJiu3K0VaLoixxmKDOhIojlWRd7Vi6FaPc5hS3RDRp5zPoxknkdTt4JokOi66EqWyPZWLW5nJdIll5UomYbocvREL2WXn6enqsZ89ObVuK3SqDWp7F6SSkSH0repyf1yWra1+/V83K+Pw7EPcO/9rx/xp18+9uyGcwuz6/zCbHtxYJmYBoPBMKM4Uy2UTCYdy4dqtMGyFKNNiXZEZo2fX1lfTZzrv/zZf43bkVS6WFogj7t/j7u4l5ZJvZYWSdv2H5D+bD1kwsDSMmlwqUQqsyifA0ClRAq5sEg6XypLQL8kibzx+o/idiqURBKRS+212e6LRGoqlfy9dRAaJtofI9nZ1yKu/YnOgj/Kz/E+4SMfn1t37TNSdDblOT4nO/4OyYiYvmhBqKskV5DkDNEpSYlL5OlnGbGRloo8zQMpFLvPJI1Gky6J/d1kIk+zyXHs7QpVl0iSpVWuIyfjuH+P0RitFq9hr86+g4Cf/5NPvpDo20nRYK1qVN5gctLuPl0Dy9XxODIZrq3TgN2v5+N+fRzsDdxgMBhmFPYANxgMhhnF2RY19owUyCq9lh31vgTk50LSKy/B8gAwkp3sIOC5Er9IkvjwEx+h9KdqKDx1V/rLkUYtCG0LUxzHg4fvJMbxD//+L8Tty9euxe2hJ/2pbbMCzd4Wqe/WHiMz0inS5o21Ki9B3B2RuBQAYLHCMe7uU8bVS0JHry0uif6YBvsoeZ4nhY9GGHXHiSVONEsKUmHFib2WF0mv+8nMDAShJMHIdXS1+LEIaywvkSL/7Z9+Pm5XRWY2dKTj9+8wuuHrf/lncfvZF55JjGMoVVbq+1wjXZF+ffoq539Lqgw92GLERSAr0sk6HUW8nlwumYDjBlwLfSmwmykzgUbvn8FhtMMpy8na/Xo+7tfHwd7ADQaDYUZhD3CDwWCYUZypC6XX7+HHP74NACiLBkajQWqylJMdWYlWGIZJYZ2i6BL0Rcfi0jp3wrMBd5OfeeZ63M7Jzn6QYbRCVvouSImOQCiZn6rc0pVqGoNF9rd6pcrvSwXUj9x4iv3lea56k5oeGZGkTMsu/2CqwkcY8m9DSUoI80wM8KJJUV4ff5799m2cJrKZNG5e2wAAHGilmCrHMeyQWq6K7YZTkpvptLgGJLKjn6eNF0qsgHJlnedaqpalzc+379Xittpro8JjnrpcTYwjEhdKWwrVavLHWpVuguoC3UWv/oDzW5DKLTWhx1qM+eCA4wOAcKhROlwLmzUe1xItlIOJG6OnWSqnALtfz8f9Osb38SjYG7jBYDDMKOwBbjAYDDOKM3WhRJGPq3GM5LejJxoDKxsMtFf5zM4UHblx40bcfuXbpBdp2Qm/eoW71xvrmnzA3WHZXEdGdrWLRUYu6K422knK3xZau/OQsqA+IF0u5Pl9PW+1IpEOLSaVeKnwUciTMrp0UhuhL5S5KnKmI5mDBYkEOSxGc0LV0RMjcEDu8NzSXz7NjlKO48uI7UuFpJysl1CKmmhMQNRPqxUWe9X5zEh/Ww9oi92Htbit9rpx+Urcvr5RTYxDo1BaIuvZlygUCQrByPM6rl6TCj736G5QLYxnPs4oi06H7hAAqIiBghRv0T2RWO2IJkt3ck9Fp5ygZffr+bhfHwd7AzcYDIYZhT3ADQaDYUZhD3CDwWCYUZypD9zBIZiEF/XEh5eT0LFuj2E0ubxk5fWTWU0j0Xuu71IkqCUhWU/f/FjcLki2m1Y1X1ymz6ovfruR+LVUlGZtjf5XAHj4kOO4u0m/2P/4zt/E7Wef/QiP36Qf885dlhkbgNetIXBpEcDJ5eiPA4ChZC32OpLBJS7Aouhl1yRr7rQRpsb+wZUq+xsNRNhHxloRoakwTL5DdEXApyxhYpvi/719m9mU0WgjbrtXKTz0uogQXbvEULCqZAiurXEcqdT0xoBUB1+kzTOyVlOetj+QsL6nb9K3fuv1b8RtXae6flOVZAX3QMbY7XBdePHL6/1zeE9pSbLTgN2v5/9+tTdwg8FgmFHYA9xgMBhmFGfqQkmn07i8NqaXuQx/O4pSVqtQJJ8Yjkjb0mFyqNU8w4M+dp0Vr5eKpFjXLpGOlKXa80KJ1KYb8PhMxHGobnS+LMeUknT33iZpzts7pNGvvs4QpfsPRCinJplgfbZ/8idZRqssWWXDpmTXTZX98iLqlJdsNc1udFI1nJ+fvurR4bkz0p9Sfs1C85JhmeCPANyQ11suiUazJ0195RXqbd99wDmsvkGKu1OvsYs018Hzlxi+tXiFoX+qaw0AzQZtWZIMz1Aq3Oc0bE8y6HTd6XrUdVrNc/2nw2S4WSTunIJkeD4VchytRQkj7I/dPel0srr9k8Lu1/Nyvx4NewM3GAyGGYU9wA0Gg2FGcbZ64HDwk5JbeclEykgkQiZH6tRpkJYOpna1qyJE9LM/y1JThQxpSloyoUJpjyIp4yUZWLkspyOqkMpksyKOEyWnLCMlxL77/VfjdrMlmWhDzcZjtlpW6FIQcHzesb8oxeuuS6knAKhLhmBayj31elJ5XaI6epOIgQ8iY+/w3PsN7tqHTuZQIiQauxQCWigmd+p1LXipCB7IPMPzvE0RRhpK5fuaZD2qXf7OlZ+P2wXRLndT6alO7J/Lcr0MNfJB1pGuL113l68yIqW6RtodOY5vMCVC5cRNUyiy9NZQ9KUjydDsDA9dAaecYmv367m4Xx+HY9/AnXPXnHNfd8695px71Tn3m5PPl5xzX3POvTH5/+Jx5zKcH5hd5xNm14uFk7hQhgD+lff+4wB+HsD/6pz7OIAvAPgL7/3TAP5i8m/D7MDsOp8wu14gHOtC8d4/APBg0j5wzt0CcAXApwD84uSwlwD8JYDPP+5ckffoT4RwGgfcAQ4WSM/aeyw11JcK4MVCsrp0KGWZ9rZrcbsnlGz/gBTm6ojiOF4EiTIl0p+MUNeWREPoLPXaSbpbFEEdrUbe9bJznhIapokgeZ5Yq5cPhf7nZMd/v5OkZPe2We7JQ5RvPK/JiRBQIRdOPjtduzoHpCZRB5taKsrLmESMSqtzR0GyevlihREEjRrXSG/IeRhKVfNOl/SzJvZujEg/G3XSUrVXryXluQrJSBAv9s8EtGUkY+901X3DdyFdd/tNKcfWp12WVqtx203R/JZoWA+GjK4IQ+pD6/1zeE9F3tv9Cszd/fo4vKdNTOfcDQA/A+AbANYniwUAHgJYP+I7n3POveyce1n9SYbzgye1a7t7fLiT4exh9+v848QPcOdcCcCXAfyW976hf/PjAMdH7ox571/03r/gvX8hl00/6hDDh4jTsOtJ3hQMZwu7Xy8GTnTnOefSGC+GP/De/8nk403n3CXv/QPn3CUAW8edZzgcYnMSgXBtg9S50SQ9G4xId1dWqTXcqCc1kwcDoddCYTTA4vuvvxm3U4472RnRSvjIR1mZOiiT/nSafKscCfUd9JK0KCfn2hONh9feoRbHRzcYibBS4d5RuEKa2WySfu4OanE7LTvtdSnJBQB7WsHaS/VzKcOVcXyLarbG8zSYRDOcll0HwxG2JnaNHOewL9EiXsptqS2wz4gUAGj2eE0D2Z3PSjJOt8fv7+/RRbHT4Py/+fBe3P74dX5X7dVt0ZajYTKCoy8VwYuiy9E84Fr70ZvUZOmPOKbviw5LW9ZmWwIdBnscazqdfI+qLHC8O9vU60hLgs9QkkIO76nDxA+7X+frfn0cThKF4gD8LoBb3vvfkT99FcBnJu3PAPjKsb0Zzg3MrvMJs+vFwknewP8egH8B4HvOue9MPvttAF8E8CXn3GcB3Abw6Q9khIYPCmbX+YTZ9QLhJFEof4WjMwR++b101hsM8PadMe3MiGTmsE+ac+MG6UuzxUiC2kGSkg0H5F6pI3ajv/f6D+N2Wo658zZ3n9dWuNtdXWS181u3Xo/bWubrn//Tf5AYR040OpaX2C7USYW2hS5HQv8zUjOp1mCEQbPLRIKWzE2QSUZKdAc8l2ooaOLDXqMWt9cWGOFxmnYFgGE0PtWWaJDkKlIWS+bfi8ZD1EhS3K0dzlVRKpCXRJqzLlXRt2Rnf7fF6I3CAudT7eKl7NemSIX+8Zf/c2IcKs363HPPxu2auHy2dtj3tRtM0tF1hwz7HnrarysRF2E6aYZAZEfDDBN5fvz2Hfmcc3N4T/UGA7tfMZ/361GwVHqDwWCYUdgD3GAwGGYUZxr/5b2Pd8+3pTr0gug9KPVSCdJIA98BNNuSWKAyGREpTKXA7zwQ6ci//hZ3nMuFzbjd7WglbdKarFQaeeXWj6C4XKSuQ0XkTy9f4efbbzEiwgldfviQfT/1FHf5RxGPUdrVaiaiwTAUKdaRXrfIn/Zkm7/ZG02OPV0tlFHk43Nrf6HIxu7tMlokJbojh5V8DlGU0LWcVOseiA6IzpvTCA5Jgrh8hZV6KiXa7+4d2uJ+i4EYP3yTUrQA0OvwXK987z7HlGdix4GswbDEaIWhaLg06rxuF0gCkwTiVCQxBkjeAyopGjleq94/h/eUypWeBux+PR/36+Ngb+AGg8Ewo7AHuMFgMMwoztSFEqbTWF4dU5UFiRLIZ0hldqR4bUEkLAdTdKInlDoUGp3NSSKJFDp9uMPzdoc8fqXCneynniGN6ve5K12XBJG3biepdnZdJCbFZVApchzBJe6cLxSqcftgrxa333zrrbj9sU/cjNs90Unoj5KJAfrz22ry+m6usI9CXiIfJroQ3p3u77Z3AYbhmFavbnBnPy9SsVvbIiFbkiKwU0PJS7RJucKCtD94lfMTiJzpwlI1bqdk075UpKsj8Ix02JXkjbc2acviUjKz/LIkcGRkfT58SLfLdoP20PWlSqqBrMdej/R4OCTV9lMRG2mpRtMW10NR5g0Z0WcJxuN7e/P4xI/3Artfz8f9+jjYG7jBYDDMKOwBbjAYDDOKM3WhjEYR6q0xJRxFpDzXLjFiICs0rNUlpSoVk/KULk1K5iSSISPVU9yAv0+tNo/PFEg/K2vcAe4HIg+aJr3KL3FMUZgU+FGZzeee+Qi/f0+Koaq8aIPaFs89+1zcvv32LR4/kGsTE03rS0Ty+1sulqRN90FTowQmc6gVX04DLkjF5x6NeK06jnKB42sdkFoGEj0AANUi7aHz0OryOp6Webv1JuctX2J0xPoi3S/PSfFitVcU0ueSX0radehEYVFec3S9ZPa4jloSteIKHEdGKstEEn4RZki10+mpiA1JSMrJ/aDhGztSxLc5icYYjZJz+aSw+/V83K+Pg72BGwwGw4zCHuAGg8EwozhTF0qQClAsjenNSDQQelIoNkxzSBkyi0SSwORscSulx6UfTSO7EftwojVRrJLi1hu6o056vfmQNCoMSc0BYFlkIUsSEVHOk4Zd3uDnWw+on1Eqkt5dErnOuuiJiKopUlMKF9Uqd+QrVY63LvR6c4vJBz4Yj30gySGngcFwiM3N8XW5iMkLWuRmfY0RA40a5+agxrkFgF6H7pX6gG2dH523srTXZJ7LksxRytGl0ZJIApX3rB8kky7WNzjedodaFwtV0lpdRxJUgpy4TXQRZyX6RwIuEIbJ96iMfMcFXPd6n4w8Ozy8p4JUUpr3SWH36/m4Xx8HewM3GAyGGYU9wA0Gg2FGYQ9wg8FgmFGcrQ88cMhPHKOBlN7SslPZEZ1IBcnSckgWWM2KNi+kTJJqBHfEN9VLs490jn63dp9+0DDF/gaUNkavzbCnu51kJaqV69fjdv8us74KUoU9X+E0r1cvxe2t7R/zPItVnjSgr60hA/nEVZaTAoBIqr63WvQZtqTc04rMx2AyhWFwlFz0+0MYOFQL42tMp9mfjqMoGWZXrzCEKrjKEDEAqO0znKuSpu8aBdp/t8Z5e/45zqcThSgn2ZebYpeMhJXdvU9bBj6p3az213Wh60XXkUvT3tmchCcuVOP2/m6N58zwu1GUXNsZ8fm2uxyIZjOWF+gfPdQ/Ck7Zrna/no/79XGwN3CDwWCYUdgD3GAwGGYUZ+tCgUN2Qp+KRWZLjUakhimQkoWiBz0cJvmE96QdXsKw6g2hn6JhHIJ95PMSmiUiOIM2+27tkwpl06TECyukOOM/kuYPpMp5mJVsM6GWXsKuNJQoK7R5aYXCSr7OkCgXJMP/Og26G9otXl9O5jZwQqsn2s6p1On+bqdSARYrk2uR/iLRp263ONZAtKJLlaQLpVzh2C8tsMr53s6DuK3zVixwPt2A89PvkoIPehxTRjI9NzZIj+s7wsEBbN7fZh+LtHFa+tN1FMj6qsm664s2eFfov5O4Q+eSrg9d91mpch7KWkullI63JmM4ZReK3a/n4n59HOwN3GAwGGYU9gA3GAyGGcWZR6GUJhQmLXRPiUY+T0p9cCBluFJJwZ+sZNcVSqQgGf1cfp7aUk380gZFbDrSe67EvtPrQqMkWWyAJNUe6C58hdEVadEXVmY7EIq0tk46n424kx2mJXsvyzF5n9QHLhYZiVAQQRykaNZ2u/2utj/lkmo+8uhNUtA0I64sbYh4jwPpdOCSY8lmOW86D4uLdKcURMQo9KSi6QzPVSyIKFZL3Cki+FQuM6tyqZzUA1fJ9IHMezeiG2T5Jr+/KxmeGlkR5DkHGXFdtZsULep1k2uqL4Li5TKvo9OR65ZF5Sb31AcRhWL364d/vz4Ox76BO+dyzrm/ds591zn3qnPu30w+v+mc+4Zz7k3n3B875zLHnctwfmB2nU+YXS8WTuJC6QH4Je/9TwH4aQCfdM79PID/C8C/895/FMA+gM9+YKM0fBAwu84nzK4XCMe6UPy41PWhmk968p8H8EsA/ufJ5y8B+N8B/MfHncsByEx2VgOhMhmhEE74SyD6x1GUFL3JCG1RcaaoTeGhnHx/cYH0R5lmPivawVIGqlTm531JXOiITjEAeIl8CKWieiZDathskS7nF0i72z1+ty1RExkv9EzmJphSxxkJS221OT97exTgGUg0QDZzSBPdqdoVcHCTaItanaJQrRbdDUuSeFIUlavU1AqM5KI6UnVcbZAu0PY5oeCdOsWNSrKzP+hybno9zsdI1lqpkBxIRlw5ew3S8HxOIgZkTLq+BmKmrqxHlevW9duZosq67jWypCCTNZL7x03uKQe7X4F5vF+Pxok2MZ1zKefcdwBsAfgagLcA1LyPi8rdBXDliO9+zjn3snPu5V5/+KhDDB8STs+uJ0gZM5wZ7H69ODjRA9x7P/Le/zSAqwB+DsCzJ+3Ae/+i9/4F7/0L2cyZ7pkajsHp2TV9/BcMZwa7Xy8O3pOFvPc159zXAfxdAFXnXDj5Vb8K4N5x3w+cQ2FCW4ai6+AjqVgtYsFV0ZEYRUkqpAHvu0JBvOhKVDUiQh4yfqRaE0JFJTojGlBDoVIinZuOrdd3lGaPVDs9YH/tNj8fpPj51j7dDQfbtbi9tEQt6q0D7sbnC8nfW+9pvt0d0r6GUECNCikUxnM4TW+f1K5RFKHVHLsBjtpFr4tLY3mFbeeSb3kdoZZrZUZz7O3RHuXVKo+R0mkDmWfV1Fa7jCSJCAlbJudkZ5f9dUX/2nfFpeH4HV2fSsFrMgdONDOWq6x8XqkkdZ9TUvJOg3QGEgETyvoPJ8k+wVRCkN2v83G/Pg4niUJZdc5VJ+08gF8BcAvA1wH8s8lhnwHwlWN7M5wbmF3nE2bXi4WTvIFfAvCSG+9SBQC+5L3/f51zrwH4I+fc/wng2wB+9wMcp+H0YXadT5hdLxCcP0G+/al15tw2gBaAneOOnUOs4Pxc91Pe+9XjDzsZJna9jfN1jWeF83TNZtfTw3m75kfa9kwf4ADgnHvZe//CmXZ6DnARrvsiXOM0LsI1X4RrnMasXLNpoRgMBsOMwh7gBoPBMKP4MB7gL34IfZ4HXITrvgjXOI2LcM0X4RqnMRPXfOY+cIPBYDCcDsyFYjAYDDMKe4AbDAbDjOJMH+DOuU86534w0ST+wln2fVZwzl1zzn3dOffaRI/5NyefLznnvuace2Py/8UPe6ynhYtgV+Di2dbsev7temY+8Elm2A8xTu29C+CbAH7de//amQzgjOCcuwTgkvf+W865MoC/AfCrAH4DwJ73/ouTm2HRe//5D2+kp4OLYlfgYtnW7Dobdj3LN/CfA/Cm9/5Hflyi+o8AfOoM+z8TeO8feO+/NWkfYKxDcQXja31pcthLGC+QecCFsCtw4Wxrdp0Bu57lA/wKgDvy7yM1iecFzrkbAH4GwDcArHvvH0z+9BDA+lHfmzFcOLsCF8K2ZtcZsKttYn5AcM6VAHwZwG957xv6t0nVFIvfnFGYbecTs2jXs3yA3wNwTf59Ik3iWYRzLo3xQvgD7/2fTD7enPjaDn1uW0d9f8ZwYewKXCjbml1nwK5n+QD/JoCn3bg6dgbArwH46hn2fyZwzjmMpTpvee9/R/70VYx1mIH50mO+EHYFLpxtza4zYNezlpP9xwD+PYAUgN/z3v/bM+v8jOCc+wUA/x3A98AyL7+NsU/tSwCuYyzR+Wnv/d4jTzJjuAh2BS6ebc2u59+ulkpvMBgMMwrbxDQYDIYZhT3ADQaDYUZhD3CDwWCYUdgD3GAwGGYU9gA3GAyGGYU9wA0Gg2FGYQ9wg8FgmFH8/3CU0U6NH/4CAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 6 Axes>"
       ]
@@ -469,7 +469,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 6 Axes>"
       ]
@@ -506,7 +506,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 6 Axes>"
       ]
@@ -543,7 +543,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 6 Axes>"
       ]
@@ -695,205 +695,205 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "125/125 [==============================] - 4s 29ms/step - loss: 29.2992 - accuracy: 0.1295 - val_loss: 2.6232 - val_accuracy: 0.1160\n",
+      "125/125 [==============================] - 5s 33ms/step - loss: 39.7156 - accuracy: 0.1335 - val_loss: 2.6489 - val_accuracy: 0.1350\n",
       "Epoch 2/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.4425 - accuracy: 0.1135 - val_loss: 2.5073 - val_accuracy: 0.1120\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.4483 - accuracy: 0.1350 - val_loss: 2.4473 - val_accuracy: 0.1270\n",
       "Epoch 3/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.3225 - accuracy: 0.1270 - val_loss: 2.4879 - val_accuracy: 0.0980\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.3218 - accuracy: 0.1445 - val_loss: 2.4431 - val_accuracy: 0.1370\n",
       "Epoch 4/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2730 - accuracy: 0.1310 - val_loss: 2.4898 - val_accuracy: 0.1000\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.2602 - accuracy: 0.1620 - val_loss: 2.4126 - val_accuracy: 0.1310 \n",
       "Epoch 5/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2527 - accuracy: 0.1357 - val_loss: 2.4923 - val_accuracy: 0.1020\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.2351 - accuracy: 0.1637 - val_loss: 2.4246 - val_accuracy: 0.1320\n",
       "Epoch 6/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.2337 - accuracy: 0.1395 - val_loss: 2.4835 - val_accuracy: 0.1120\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.2168 - accuracy: 0.1743 - val_loss: 2.4193 - val_accuracy: 0.1240\n",
       "Epoch 7/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.2233 - accuracy: 0.1445 - val_loss: 2.4874 - val_accuracy: 0.1010\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.1969 - accuracy: 0.1758 - val_loss: 2.4406 - val_accuracy: 0.1300\n",
       "Epoch 8/100\n",
-      "125/125 [==============================] - 3s 21ms/step - loss: 2.2184 - accuracy: 0.1452 - val_loss: 2.4832 - val_accuracy: 0.1020\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.1808 - accuracy: 0.1815 - val_loss: 2.4262 - val_accuracy: 0.1230\n",
       "Epoch 9/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.2100 - accuracy: 0.1500 - val_loss: 2.4880 - val_accuracy: 0.1040\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.1740 - accuracy: 0.1852 - val_loss: 2.4337 - val_accuracy: 0.1260\n",
       "Epoch 10/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.2022 - accuracy: 0.1530 - val_loss: 2.4859 - val_accuracy: 0.1000\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.1553 - accuracy: 0.1947 - val_loss: 2.4380 - val_accuracy: 0.1310\n",
       "Epoch 11/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.1953 - accuracy: 0.1532 - val_loss: 2.4888 - val_accuracy: 0.1010\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.1506 - accuracy: 0.1953 - val_loss: 2.4438 - val_accuracy: 0.1540\n",
       "Epoch 12/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.1894 - accuracy: 0.1538 - val_loss: 2.5027 - val_accuracy: 0.1020\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.1362 - accuracy: 0.2002 - val_loss: 2.4608 - val_accuracy: 0.1420\n",
       "Epoch 13/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.1854 - accuracy: 0.1587 - val_loss: 2.5122 - val_accuracy: 0.1040\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.1295 - accuracy: 0.2017 - val_loss: 2.4449 - val_accuracy: 0.1420\n",
       "Epoch 14/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.1750 - accuracy: 0.1632 - val_loss: 2.4948 - val_accuracy: 0.1010\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.1187 - accuracy: 0.2058 - val_loss: 2.4437 - val_accuracy: 0.1390\n",
       "Epoch 15/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.1735 - accuracy: 0.1632 - val_loss: 2.4909 - val_accuracy: 0.1230\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.1075 - accuracy: 0.2077 - val_loss: 2.4457 - val_accuracy: 0.1530\n",
       "Epoch 16/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.1700 - accuracy: 0.1667 - val_loss: 2.5037 - val_accuracy: 0.0990\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.1002 - accuracy: 0.2125 - val_loss: 2.4538 - val_accuracy: 0.1590\n",
       "Epoch 17/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.1627 - accuracy: 0.1663 - val_loss: 2.5082 - val_accuracy: 0.1140\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.0944 - accuracy: 0.2165 - val_loss: 2.4574 - val_accuracy: 0.1350\n",
       "Epoch 18/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.1568 - accuracy: 0.1710 - val_loss: 2.4986 - val_accuracy: 0.1170\n",
+      "125/125 [==============================] - 3s 28ms/step - loss: 2.0828 - accuracy: 0.2225 - val_loss: 2.4444 - val_accuracy: 0.1650\n",
       "Epoch 19/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.1562 - accuracy: 0.1685 - val_loss: 2.5115 - val_accuracy: 0.1250\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.0676 - accuracy: 0.2262 - val_loss: 2.4265 - val_accuracy: 0.1540\n",
       "Epoch 20/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.1547 - accuracy: 0.1725 - val_loss: 2.4900 - val_accuracy: 0.1080\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.0533 - accuracy: 0.2325 - val_loss: 2.4879 - val_accuracy: 0.1800\n",
       "Epoch 21/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.1421 - accuracy: 0.1743 - val_loss: 2.5250 - val_accuracy: 0.1330\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.0496 - accuracy: 0.2317 - val_loss: 2.4578 - val_accuracy: 0.1730\n",
       "Epoch 22/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.1404 - accuracy: 0.1800 - val_loss: 2.5074 - val_accuracy: 0.1170\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.0405 - accuracy: 0.2355 - val_loss: 2.4796 - val_accuracy: 0.1570\n",
       "Epoch 23/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.1289 - accuracy: 0.1780 - val_loss: 2.5015 - val_accuracy: 0.1080\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.0335 - accuracy: 0.2370 - val_loss: 2.5235 - val_accuracy: 0.1830\n",
       "Epoch 24/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.1291 - accuracy: 0.1807 - val_loss: 2.5162 - val_accuracy: 0.1160\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.0253 - accuracy: 0.2370 - val_loss: 2.5162 - val_accuracy: 0.1900\n",
       "Epoch 25/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.1236 - accuracy: 0.1865 - val_loss: 2.4957 - val_accuracy: 0.1230\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.0162 - accuracy: 0.2397 - val_loss: 2.5336 - val_accuracy: 0.1790\n",
       "Epoch 26/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.1152 - accuracy: 0.1957 - val_loss: 2.5331 - val_accuracy: 0.1630\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.0067 - accuracy: 0.2407 - val_loss: 2.4653 - val_accuracy: 0.1830\n",
       "Epoch 27/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.1078 - accuracy: 0.2015 - val_loss: 2.5095 - val_accuracy: 0.1440\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.9938 - accuracy: 0.2435 - val_loss: 2.4785 - val_accuracy: 0.1830\n",
       "Epoch 28/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.0992 - accuracy: 0.2072 - val_loss: 2.5087 - val_accuracy: 0.1550\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.9886 - accuracy: 0.2475 - val_loss: 2.4971 - val_accuracy: 0.1870\n",
       "Epoch 29/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.0940 - accuracy: 0.2068 - val_loss: 2.4933 - val_accuracy: 0.1600\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.9758 - accuracy: 0.2500 - val_loss: 2.5008 - val_accuracy: 0.1870\n",
       "Epoch 30/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.0920 - accuracy: 0.2140 - val_loss: 2.4880 - val_accuracy: 0.1600\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.9648 - accuracy: 0.2528 - val_loss: 2.4953 - val_accuracy: 0.1830\n",
       "Epoch 31/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.0796 - accuracy: 0.2190 - val_loss: 2.4703 - val_accuracy: 0.1580\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.9577 - accuracy: 0.2542 - val_loss: 2.4710 - val_accuracy: 0.1580\n",
       "Epoch 32/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.0632 - accuracy: 0.2257 - val_loss: 2.5121 - val_accuracy: 0.1640\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.9482 - accuracy: 0.2528 - val_loss: 2.4626 - val_accuracy: 0.1680\n",
       "Epoch 33/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.0498 - accuracy: 0.2303 - val_loss: 2.4610 - val_accuracy: 0.1760\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.9415 - accuracy: 0.2560 - val_loss: 2.5015 - val_accuracy: 0.1550\n",
       "Epoch 34/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.0442 - accuracy: 0.2342 - val_loss: 2.4584 - val_accuracy: 0.1660\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.9331 - accuracy: 0.2643 - val_loss: 2.4653 - val_accuracy: 0.1740\n",
       "Epoch 35/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.0400 - accuracy: 0.2365 - val_loss: 2.4692 - val_accuracy: 0.1550\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.9238 - accuracy: 0.2637 - val_loss: 2.5329 - val_accuracy: 0.1900\n",
       "Epoch 36/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.0286 - accuracy: 0.2430 - val_loss: 2.4842 - val_accuracy: 0.1770\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.9193 - accuracy: 0.2635 - val_loss: 2.5135 - val_accuracy: 0.1840\n",
       "Epoch 37/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.0188 - accuracy: 0.2407 - val_loss: 2.4958 - val_accuracy: 0.1810\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.9187 - accuracy: 0.2630 - val_loss: 2.4968 - val_accuracy: 0.1860\n",
       "Epoch 38/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.0040 - accuracy: 0.2455 - val_loss: 2.5405 - val_accuracy: 0.1880\n",
+      "125/125 [==============================] - 3s 28ms/step - loss: 1.9007 - accuracy: 0.2668 - val_loss: 2.5003 - val_accuracy: 0.1810\n",
       "Epoch 39/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.9926 - accuracy: 0.2503 - val_loss: 2.4944 - val_accuracy: 0.1750\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.8986 - accuracy: 0.2735 - val_loss: 2.4853 - val_accuracy: 0.1870\n",
       "Epoch 40/100\n",
-      "125/125 [==============================] - 3s 21ms/step - loss: 1.9908 - accuracy: 0.2473 - val_loss: 2.5124 - val_accuracy: 0.1890\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8862 - accuracy: 0.2750 - val_loss: 2.5039 - val_accuracy: 0.1890\n",
       "Epoch 41/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.9831 - accuracy: 0.2540 - val_loss: 2.4740 - val_accuracy: 0.1790\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8793 - accuracy: 0.2720 - val_loss: 2.5070 - val_accuracy: 0.1910\n",
       "Epoch 42/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.9727 - accuracy: 0.2525 - val_loss: 2.4678 - val_accuracy: 0.1780\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.8757 - accuracy: 0.2763 - val_loss: 2.5726 - val_accuracy: 0.1830\n",
       "Epoch 43/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.9611 - accuracy: 0.2542 - val_loss: 2.5043 - val_accuracy: 0.1940\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8755 - accuracy: 0.2783 - val_loss: 2.5209 - val_accuracy: 0.1920\n",
       "Epoch 44/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.9567 - accuracy: 0.2603 - val_loss: 2.5053 - val_accuracy: 0.1970\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8676 - accuracy: 0.2870 - val_loss: 2.4980 - val_accuracy: 0.1820\n",
       "Epoch 45/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.9530 - accuracy: 0.2615 - val_loss: 2.5082 - val_accuracy: 0.1740\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.8493 - accuracy: 0.2870 - val_loss: 2.5276 - val_accuracy: 0.1890\n",
       "Epoch 46/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.9427 - accuracy: 0.2595 - val_loss: 2.4811 - val_accuracy: 0.1840\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.8496 - accuracy: 0.2918 - val_loss: 2.5068 - val_accuracy: 0.1930\n",
       "Epoch 47/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.9305 - accuracy: 0.2610 - val_loss: 2.4980 - val_accuracy: 0.1960loss: 1 - ETA: 0s - loss: 1.9230 - accuracy\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8420 - accuracy: 0.2892 - val_loss: 2.5094 - val_accuracy: 0.1970\n",
       "Epoch 48/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.9268 - accuracy: 0.2668 - val_loss: 2.4961 - val_accuracy: 0.1940\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.8345 - accuracy: 0.3000 - val_loss: 2.5045 - val_accuracy: 0.1980\n",
       "Epoch 49/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.9163 - accuracy: 0.2702 - val_loss: 2.5397 - val_accuracy: 0.1970\n",
+      "125/125 [==============================] - 4s 29ms/step - loss: 1.8273 - accuracy: 0.3038 - val_loss: 2.5054 - val_accuracy: 0.1930\n",
       "Epoch 50/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.9175 - accuracy: 0.2632 - val_loss: 2.4814 - val_accuracy: 0.1820\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8240 - accuracy: 0.3075 - val_loss: 2.5058 - val_accuracy: 0.2010\n",
       "Epoch 51/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.9089 - accuracy: 0.2680 - val_loss: 2.4901 - val_accuracy: 0.1950\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.8186 - accuracy: 0.3088 - val_loss: 2.5290 - val_accuracy: 0.2040\n",
       "Epoch 52/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.8989 - accuracy: 0.2697 - val_loss: 2.4977 - val_accuracy: 0.2010\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8080 - accuracy: 0.3052 - val_loss: 2.5433 - val_accuracy: 0.2070\n",
       "Epoch 53/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.8867 - accuracy: 0.2772 - val_loss: 2.4967 - val_accuracy: 0.1960\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.8006 - accuracy: 0.3153 - val_loss: 2.5174 - val_accuracy: 0.2050\n",
       "Epoch 54/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.9229 - accuracy: 0.2695 - val_loss: 2.5073 - val_accuracy: 0.1930\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.7977 - accuracy: 0.3130 - val_loss: 2.5057 - val_accuracy: 0.2050\n",
       "Epoch 55/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.8804 - accuracy: 0.2777 - val_loss: 2.4827 - val_accuracy: 0.1990\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.7876 - accuracy: 0.3137 - val_loss: 2.5189 - val_accuracy: 0.1960\n",
       "Epoch 56/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.8777 - accuracy: 0.2803 - val_loss: 2.4874 - val_accuracy: 0.1970\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.7837 - accuracy: 0.3203 - val_loss: 2.5072 - val_accuracy: 0.2120\n",
       "Epoch 57/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.8784 - accuracy: 0.2780 - val_loss: 2.5325 - val_accuracy: 0.2040\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.7788 - accuracy: 0.3195 - val_loss: 2.5407 - val_accuracy: 0.2060\n",
       "Epoch 58/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.8740 - accuracy: 0.2792 - val_loss: 2.5915 - val_accuracy: 0.2060\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.7729 - accuracy: 0.3252 - val_loss: 2.5101 - val_accuracy: 0.2100\n",
       "Epoch 59/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.8610 - accuracy: 0.2845 - val_loss: 2.5330 - val_accuracy: 0.2100\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.7663 - accuracy: 0.3230 - val_loss: 2.5406 - val_accuracy: 0.2250\n",
       "Epoch 60/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.8525 - accuracy: 0.2875 - val_loss: 2.5400 - val_accuracy: 0.2020\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.7587 - accuracy: 0.3285 - val_loss: 2.5762 - val_accuracy: 0.2260\n",
       "Epoch 61/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.8514 - accuracy: 0.2907 - val_loss: 2.4904 - val_accuracy: 0.1900\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.7499 - accuracy: 0.3327 - val_loss: 2.5251 - val_accuracy: 0.2110\n",
       "Epoch 62/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.8390 - accuracy: 0.2907 - val_loss: 2.5369 - val_accuracy: 0.2180\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.7526 - accuracy: 0.3255 - val_loss: 2.5433 - val_accuracy: 0.2150\n",
       "Epoch 63/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.8372 - accuracy: 0.2943 - val_loss: 2.5329 - val_accuracy: 0.2040\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.7322 - accuracy: 0.3352 - val_loss: 2.6029 - val_accuracy: 0.2290\n",
       "Epoch 64/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.8336 - accuracy: 0.2977 - val_loss: 2.5637 - val_accuracy: 0.2080\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.7310 - accuracy: 0.3307 - val_loss: 2.5333 - val_accuracy: 0.2200\n",
       "Epoch 65/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.8239 - accuracy: 0.2965 - val_loss: 2.5447 - val_accuracy: 0.2080\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.7319 - accuracy: 0.3338 - val_loss: 2.5544 - val_accuracy: 0.2290\n",
       "Epoch 66/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.8190 - accuracy: 0.3013 - val_loss: 2.5832 - val_accuracy: 0.2160\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.7205 - accuracy: 0.3397 - val_loss: 2.5293 - val_accuracy: 0.2050\n",
       "Epoch 67/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.8129 - accuracy: 0.3022 - val_loss: 2.5346 - val_accuracy: 0.2100\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.7157 - accuracy: 0.3410 - val_loss: 2.5298 - val_accuracy: 0.2160\n",
       "Epoch 68/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.8064 - accuracy: 0.3013 - val_loss: 2.5378 - val_accuracy: 0.2100\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.7012 - accuracy: 0.3460 - val_loss: 2.5369 - val_accuracy: 0.2230\n",
       "Epoch 69/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.8012 - accuracy: 0.3052 - val_loss: 2.5590 - val_accuracy: 0.2210\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.7035 - accuracy: 0.3408 - val_loss: 2.5305 - val_accuracy: 0.2110\n",
       "Epoch 70/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.7980 - accuracy: 0.3058 - val_loss: 2.5898 - val_accuracy: 0.2030\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.6943 - accuracy: 0.3473 - val_loss: 2.5641 - val_accuracy: 0.2270\n",
       "Epoch 71/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.7928 - accuracy: 0.3052 - val_loss: 2.5300 - val_accuracy: 0.2090\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.6933 - accuracy: 0.3470 - val_loss: 2.5613 - val_accuracy: 0.2370\n",
       "Epoch 72/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.7860 - accuracy: 0.3100 - val_loss: 2.5580 - val_accuracy: 0.2210\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.6967 - accuracy: 0.3510 - val_loss: 2.5394 - val_accuracy: 0.2270\n",
       "Epoch 73/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.7922 - accuracy: 0.3133 - val_loss: 2.5843 - val_accuracy: 0.2160\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.6829 - accuracy: 0.3555 - val_loss: 2.5510 - val_accuracy: 0.2200\n",
       "Epoch 74/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.7802 - accuracy: 0.3167 - val_loss: 2.6474 - val_accuracy: 0.2270\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.6708 - accuracy: 0.3585 - val_loss: 2.5917 - val_accuracy: 0.2350\n",
       "Epoch 75/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.7768 - accuracy: 0.3137 - val_loss: 2.6039 - val_accuracy: 0.2200\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.6672 - accuracy: 0.3580 - val_loss: 2.5849 - val_accuracy: 0.2060\n",
       "Epoch 76/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.7647 - accuracy: 0.3153 - val_loss: 2.6303 - val_accuracy: 0.2240\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.6669 - accuracy: 0.3620 - val_loss: 2.5412 - val_accuracy: 0.2330\n",
       "Epoch 77/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.7644 - accuracy: 0.3200 - val_loss: 2.6154 - val_accuracy: 0.2220\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6631 - accuracy: 0.3602 - val_loss: 2.5888 - val_accuracy: 0.2230\n",
       "Epoch 78/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.7651 - accuracy: 0.3158 - val_loss: 2.6377 - val_accuracy: 0.2300\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.6519 - accuracy: 0.3672 - val_loss: 2.5782 - val_accuracy: 0.2320\n",
       "Epoch 79/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.7506 - accuracy: 0.3268 - val_loss: 2.5928 - val_accuracy: 0.2210\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6504 - accuracy: 0.3625 - val_loss: 2.5913 - val_accuracy: 0.2300\n",
       "Epoch 80/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.7528 - accuracy: 0.3252 - val_loss: 2.5811 - val_accuracy: 0.2260\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6440 - accuracy: 0.3675 - val_loss: 2.5698 - val_accuracy: 0.2280\n",
       "Epoch 81/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.7435 - accuracy: 0.3217 - val_loss: 2.6509 - val_accuracy: 0.2090\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.6370 - accuracy: 0.3730 - val_loss: 2.5609 - val_accuracy: 0.2180\n",
       "Epoch 82/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.7371 - accuracy: 0.3277 - val_loss: 2.6129 - val_accuracy: 0.2170\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6379 - accuracy: 0.3632 - val_loss: 2.6210 - val_accuracy: 0.2410\n",
       "Epoch 83/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.7316 - accuracy: 0.3260 - val_loss: 2.6233 - val_accuracy: 0.2110\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6245 - accuracy: 0.3688 - val_loss: 2.6740 - val_accuracy: 0.2350\n",
       "Epoch 84/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.7296 - accuracy: 0.3265 - val_loss: 2.6112 - val_accuracy: 0.2200\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6230 - accuracy: 0.3710 - val_loss: 2.5606 - val_accuracy: 0.2180\n",
       "Epoch 85/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.7231 - accuracy: 0.3300 - val_loss: 2.6130 - val_accuracy: 0.2280\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6160 - accuracy: 0.3778 - val_loss: 2.6207 - val_accuracy: 0.2280\n",
       "Epoch 86/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.7237 - accuracy: 0.3290 - val_loss: 2.6587 - val_accuracy: 0.2340\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.6111 - accuracy: 0.3825 - val_loss: 2.5788 - val_accuracy: 0.2130\n",
       "Epoch 87/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.7165 - accuracy: 0.3327 - val_loss: 2.6427 - val_accuracy: 0.2260\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6045 - accuracy: 0.3790 - val_loss: 2.6091 - val_accuracy: 0.2390\n",
       "Epoch 88/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.7071 - accuracy: 0.3352 - val_loss: 2.6491 - val_accuracy: 0.2270\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6016 - accuracy: 0.3832 - val_loss: 2.6235 - val_accuracy: 0.2400\n",
       "Epoch 89/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.7097 - accuracy: 0.3340 - val_loss: 2.6798 - val_accuracy: 0.2180\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.5908 - accuracy: 0.3842 - val_loss: 2.6353 - val_accuracy: 0.2380\n",
       "Epoch 90/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.7109 - accuracy: 0.3375 - val_loss: 2.6958 - val_accuracy: 0.2280\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.5932 - accuracy: 0.3885 - val_loss: 2.6077 - val_accuracy: 0.2260\n",
       "Epoch 91/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.7043 - accuracy: 0.3368 - val_loss: 2.6446 - val_accuracy: 0.2210\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5800 - accuracy: 0.3825 - val_loss: 2.6158 - val_accuracy: 0.2390\n",
       "Epoch 92/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.6963 - accuracy: 0.3408 - val_loss: 2.6865 - val_accuracy: 0.2260\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.5821 - accuracy: 0.3923 - val_loss: 2.6877 - val_accuracy: 0.2390\n",
       "Epoch 93/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.6938 - accuracy: 0.3440 - val_loss: 2.8336 - val_accuracy: 0.2290\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.5687 - accuracy: 0.3870 - val_loss: 2.6175 - val_accuracy: 0.2400\n",
       "Epoch 94/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.6840 - accuracy: 0.3515 - val_loss: 2.7079 - val_accuracy: 0.2340\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5729 - accuracy: 0.3913 - val_loss: 2.6666 - val_accuracy: 0.2460\n",
       "Epoch 95/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.6824 - accuracy: 0.3417 - val_loss: 2.6503 - val_accuracy: 0.2330\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.5721 - accuracy: 0.3850 - val_loss: 2.7200 - val_accuracy: 0.2430\n",
       "Epoch 96/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.6809 - accuracy: 0.3450 - val_loss: 2.6834 - val_accuracy: 0.2230\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.5583 - accuracy: 0.3915 - val_loss: 2.6243 - val_accuracy: 0.2480\n",
       "Epoch 97/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.6720 - accuracy: 0.3480 - val_loss: 2.7168 - val_accuracy: 0.2210\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5522 - accuracy: 0.3980 - val_loss: 2.6330 - val_accuracy: 0.2430\n",
       "Epoch 98/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.6628 - accuracy: 0.3540 - val_loss: 2.7528 - val_accuracy: 0.2230\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.5473 - accuracy: 0.3993 - val_loss: 2.6666 - val_accuracy: 0.2390\n",
       "Epoch 99/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.6666 - accuracy: 0.3550 - val_loss: 2.7556 - val_accuracy: 0.2340\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5468 - accuracy: 0.4015 - val_loss: 2.6816 - val_accuracy: 0.2360\n",
       "Epoch 100/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.6610 - accuracy: 0.3593 - val_loss: 2.7341 - val_accuracy: 0.2380\n"
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.5285 - accuracy: 0.4002 - val_loss: 2.6643 - val_accuracy: 0.2410\n"
      ]
     }
    ],
@@ -936,205 +936,205 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 35.2853 - accuracy: 0.1780 - val_loss: 27.6925 - val_accuracy: 0.1860\n",
+      "63/63 [==============================] - 5s 83ms/step - loss: 38.9929 - accuracy: 0.1730 - val_loss: 29.1064 - val_accuracy: 0.2150\n",
       "Epoch 2/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 22.8362 - accuracy: 0.2083 - val_loss: 21.2226 - val_accuracy: 0.2130\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 25.3772 - accuracy: 0.1838 - val_loss: 21.8548 - val_accuracy: 0.2160\n",
       "Epoch 3/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 18.8794 - accuracy: 0.2060 - val_loss: 17.9238 - val_accuracy: 0.2100\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 19.8298 - accuracy: 0.2025 - val_loss: 18.1778 - val_accuracy: 0.2210\n",
       "Epoch 4/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 15.3165 - accuracy: 0.2000 - val_loss: 15.5570 - val_accuracy: 0.2100\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 16.6457 - accuracy: 0.1900 - val_loss: 15.4868 - val_accuracy: 0.2240\n",
       "Epoch 5/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 13.6691 - accuracy: 0.1898 - val_loss: 13.4271 - val_accuracy: 0.2170\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 13.6000 - accuracy: 0.1865 - val_loss: 12.8108 - val_accuracy: 0.2150\n",
       "Epoch 6/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 11.4891 - accuracy: 0.1905 - val_loss: 11.7095 - val_accuracy: 0.1880\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 11.4166 - accuracy: 0.1947 - val_loss: 11.1794 - val_accuracy: 0.2070\n",
       "Epoch 7/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 10.0352 - accuracy: 0.2015 - val_loss: 10.2237 - val_accuracy: 0.1950\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 9.7131 - accuracy: 0.1848 - val_loss: 9.5409 - val_accuracy: 0.2000\n",
       "Epoch 8/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 8.8034 - accuracy: 0.2020 - val_loss: 8.7818 - val_accuracy: 0.1870\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 8.3732 - accuracy: 0.1762 - val_loss: 8.1889 - val_accuracy: 0.1950\n",
       "Epoch 9/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 7.3796 - accuracy: 0.1898 - val_loss: 7.4378 - val_accuracy: 0.1880\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 6.8537 - accuracy: 0.1663 - val_loss: 6.9958 - val_accuracy: 0.1860\n",
       "Epoch 10/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 5.9453 - accuracy: 0.1935 - val_loss: 6.3020 - val_accuracy: 0.1690\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 6.0754 - accuracy: 0.1540 - val_loss: 5.9209 - val_accuracy: 0.1860\n",
       "Epoch 11/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 5.1056 - accuracy: 0.1842 - val_loss: 5.5118 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 5.2544 - accuracy: 0.1740 - val_loss: 5.1466 - val_accuracy: 0.1810\n",
       "Epoch 12/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 4.5281 - accuracy: 0.1780 - val_loss: 4.9366 - val_accuracy: 0.1810\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 4.6252 - accuracy: 0.1663 - val_loss: 4.7123 - val_accuracy: 0.1830\n",
       "Epoch 13/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 4.1045 - accuracy: 0.1737 - val_loss: 4.5372 - val_accuracy: 0.1730\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 4.2262 - accuracy: 0.1698 - val_loss: 4.3589 - val_accuracy: 0.1850\n",
       "Epoch 14/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 3.8437 - accuracy: 0.1785 - val_loss: 4.2184 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 4s 68ms/step - loss: 3.8291 - accuracy: 0.1690 - val_loss: 4.1062 - val_accuracy: 0.1860\n",
       "Epoch 15/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 3.6531 - accuracy: 0.1727 - val_loss: 3.9902 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 4s 68ms/step - loss: 3.6995 - accuracy: 0.1675 - val_loss: 3.8875 - val_accuracy: 0.1790\n",
       "Epoch 16/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 3.4975 - accuracy: 0.1690 - val_loss: 3.7944 - val_accuracy: 0.1690\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 3.5367 - accuracy: 0.1615 - val_loss: 3.6899 - val_accuracy: 0.1900\n",
       "Epoch 17/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 3.2605 - accuracy: 0.1735 - val_loss: 3.6525 - val_accuracy: 0.1670\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 3.4015 - accuracy: 0.1653 - val_loss: 3.5415 - val_accuracy: 0.1820\n",
       "Epoch 18/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 3.1124 - accuracy: 0.1680 - val_loss: 3.5119 - val_accuracy: 0.1660\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 3.2796 - accuracy: 0.1675 - val_loss: 3.4191 - val_accuracy: 0.1870\n",
       "Epoch 19/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 3.1012 - accuracy: 0.1665 - val_loss: 3.4016 - val_accuracy: 0.1550\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 3.0924 - accuracy: 0.1615 - val_loss: 3.3142 - val_accuracy: 0.1820\n",
       "Epoch 20/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 2.9404 - accuracy: 0.1740 - val_loss: 3.3143 - val_accuracy: 0.1600\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.9879 - accuracy: 0.1708 - val_loss: 3.2390 - val_accuracy: 0.1830\n",
       "Epoch 21/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.8307 - accuracy: 0.1708 - val_loss: 3.2359 - val_accuracy: 0.1630\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 2.9895 - accuracy: 0.1685 - val_loss: 3.1509 - val_accuracy: 0.1840\n",
       "Epoch 22/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.8247 - accuracy: 0.1655 - val_loss: 3.1685 - val_accuracy: 0.1660\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 2.9009 - accuracy: 0.1610 - val_loss: 3.0775 - val_accuracy: 0.1850\n",
       "Epoch 23/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.8009 - accuracy: 0.1625 - val_loss: 3.1117 - val_accuracy: 0.1730\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.8509 - accuracy: 0.1737 - val_loss: 3.0101 - val_accuracy: 0.1840\n",
       "Epoch 24/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 2.7492 - accuracy: 0.1772 - val_loss: 3.0572 - val_accuracy: 0.1700\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.8392 - accuracy: 0.1752 - val_loss: 2.9604 - val_accuracy: 0.1840\n",
       "Epoch 25/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.7126 - accuracy: 0.1758 - val_loss: 2.9946 - val_accuracy: 0.1610\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 2.7752 - accuracy: 0.1653 - val_loss: 2.9164 - val_accuracy: 0.1790\n",
       "Epoch 26/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.6384 - accuracy: 0.1698 - val_loss: 2.9500 - val_accuracy: 0.1620\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.7326 - accuracy: 0.1650 - val_loss: 2.8776 - val_accuracy: 0.1820\n",
       "Epoch 27/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.5880 - accuracy: 0.1700 - val_loss: 2.9103 - val_accuracy: 0.1560\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 2.6034 - accuracy: 0.1710 - val_loss: 2.8376 - val_accuracy: 0.1800\n",
       "Epoch 28/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 2.6443 - accuracy: 0.1678 - val_loss: 2.8779 - val_accuracy: 0.1550\n",
+      "63/63 [==============================] - 4s 68ms/step - loss: 2.7180 - accuracy: 0.1602 - val_loss: 2.7931 - val_accuracy: 0.1790\n",
       "Epoch 29/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.6021 - accuracy: 0.1620 - val_loss: 2.8402 - val_accuracy: 0.1620\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 2.6530 - accuracy: 0.1720 - val_loss: 2.7540 - val_accuracy: 0.1810\n",
       "Epoch 30/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.5511 - accuracy: 0.1615 - val_loss: 2.8006 - val_accuracy: 0.1610\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.6300 - accuracy: 0.1758 - val_loss: 2.7233 - val_accuracy: 0.1830\n",
       "Epoch 31/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.5293 - accuracy: 0.1682 - val_loss: 2.7745 - val_accuracy: 0.1560\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 2.5393 - accuracy: 0.1745 - val_loss: 2.7036 - val_accuracy: 0.1800\n",
       "Epoch 32/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.5957 - accuracy: 0.1690 - val_loss: 2.7464 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 2.6100 - accuracy: 0.1710 - val_loss: 2.6807 - val_accuracy: 0.1830\n",
       "Epoch 33/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 2.5457 - accuracy: 0.1663 - val_loss: 2.7177 - val_accuracy: 0.1680\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.5735 - accuracy: 0.1735 - val_loss: 2.6536 - val_accuracy: 0.1860\n",
       "Epoch 34/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 2.4864 - accuracy: 0.1828 - val_loss: 2.6933 - val_accuracy: 0.1620\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.5137 - accuracy: 0.1710 - val_loss: 2.6243 - val_accuracy: 0.1840\n",
       "Epoch 35/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.4528 - accuracy: 0.1717 - val_loss: 2.6719 - val_accuracy: 0.1610\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 2.4839 - accuracy: 0.1730 - val_loss: 2.6052 - val_accuracy: 0.1810\n",
       "Epoch 36/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 2.4390 - accuracy: 0.1745 - val_loss: 2.6565 - val_accuracy: 0.1630\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.5209 - accuracy: 0.1715 - val_loss: 2.5853 - val_accuracy: 0.1850\n",
       "Epoch 37/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 2.4556 - accuracy: 0.1698 - val_loss: 2.6332 - val_accuracy: 0.1650\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 2.4970 - accuracy: 0.1680 - val_loss: 2.5662 - val_accuracy: 0.1800\n",
       "Epoch 38/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.4341 - accuracy: 0.1713 - val_loss: 2.6165 - val_accuracy: 0.1620\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.4675 - accuracy: 0.1782 - val_loss: 2.5524 - val_accuracy: 0.1870\n",
       "Epoch 39/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 2.4485 - accuracy: 0.1708 - val_loss: 2.6008 - val_accuracy: 0.1610\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 2.4922 - accuracy: 0.1748 - val_loss: 2.5387 - val_accuracy: 0.1830\n",
       "Epoch 40/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.4072 - accuracy: 0.1673 - val_loss: 2.5865 - val_accuracy: 0.1640\n",
+      "63/63 [==============================] - 4s 65ms/step - loss: 2.4425 - accuracy: 0.1745 - val_loss: 2.5275 - val_accuracy: 0.1870\n",
       "Epoch 41/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 2.4103 - accuracy: 0.1735 - val_loss: 2.5666 - val_accuracy: 0.1620\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 2.4266 - accuracy: 0.1698 - val_loss: 2.5165 - val_accuracy: 0.1840\n",
       "Epoch 42/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.4189 - accuracy: 0.1618 - val_loss: 2.5568 - val_accuracy: 0.1680\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.4228 - accuracy: 0.1770 - val_loss: 2.5076 - val_accuracy: 0.1840\n",
       "Epoch 43/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 2.4044 - accuracy: 0.1708 - val_loss: 2.5480 - val_accuracy: 0.1630\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.4093 - accuracy: 0.1730 - val_loss: 2.4953 - val_accuracy: 0.1850\n",
       "Epoch 44/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.4014 - accuracy: 0.1705 - val_loss: 2.5288 - val_accuracy: 0.1750\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.3991 - accuracy: 0.1758 - val_loss: 2.4861 - val_accuracy: 0.1860\n",
       "Epoch 45/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.3939 - accuracy: 0.1803 - val_loss: 2.5188 - val_accuracy: 0.1790\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 2.3871 - accuracy: 0.1817 - val_loss: 2.4766 - val_accuracy: 0.1810\n",
       "Epoch 46/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.3622 - accuracy: 0.1817 - val_loss: 2.5094 - val_accuracy: 0.1730\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.3806 - accuracy: 0.1815 - val_loss: 2.4659 - val_accuracy: 0.1820\n",
       "Epoch 47/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.3954 - accuracy: 0.1803 - val_loss: 2.4973 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.3835 - accuracy: 0.1790 - val_loss: 2.4595 - val_accuracy: 0.1820\n",
       "Epoch 48/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.3844 - accuracy: 0.1807 - val_loss: 2.4865 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 2.3727 - accuracy: 0.1737 - val_loss: 2.4505 - val_accuracy: 0.1800\n",
       "Epoch 49/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 2.3554 - accuracy: 0.1850 - val_loss: 2.4791 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.3783 - accuracy: 0.1807 - val_loss: 2.4462 - val_accuracy: 0.1790\n",
       "Epoch 50/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.3284 - accuracy: 0.1797 - val_loss: 2.4708 - val_accuracy: 0.1750\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.3763 - accuracy: 0.1780 - val_loss: 2.4387 - val_accuracy: 0.1790\n",
       "Epoch 51/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 2.3336 - accuracy: 0.1820 - val_loss: 2.4623 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.3534 - accuracy: 0.1805 - val_loss: 2.4282 - val_accuracy: 0.1790\n",
       "Epoch 52/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.3243 - accuracy: 0.1835 - val_loss: 2.4520 - val_accuracy: 0.1750\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.3711 - accuracy: 0.1813 - val_loss: 2.4220 - val_accuracy: 0.1810\n",
       "Epoch 53/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.3516 - accuracy: 0.1822 - val_loss: 2.4417 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.3147 - accuracy: 0.1825 - val_loss: 2.4144 - val_accuracy: 0.1850\n",
       "Epoch 54/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 2.2894 - accuracy: 0.1848 - val_loss: 2.4347 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.3456 - accuracy: 0.1778 - val_loss: 2.4077 - val_accuracy: 0.1850\n",
       "Epoch 55/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.2900 - accuracy: 0.1778 - val_loss: 2.4301 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.3252 - accuracy: 0.1793 - val_loss: 2.4017 - val_accuracy: 0.1860\n",
       "Epoch 56/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.3294 - accuracy: 0.1817 - val_loss: 2.4231 - val_accuracy: 0.1790\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 2.3513 - accuracy: 0.1750 - val_loss: 2.3958 - val_accuracy: 0.1860\n",
       "Epoch 57/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.3366 - accuracy: 0.1752 - val_loss: 2.4157 - val_accuracy: 0.1860\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 2.3232 - accuracy: 0.1807 - val_loss: 2.3887 - val_accuracy: 0.1850\n",
       "Epoch 58/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.3446 - accuracy: 0.1807 - val_loss: 2.4114 - val_accuracy: 0.1720\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 2.3277 - accuracy: 0.1782 - val_loss: 2.3811 - val_accuracy: 0.1860\n",
       "Epoch 59/100\n",
-      "63/63 [==============================] - 4s 67ms/step - loss: 2.3271 - accuracy: 0.1838 - val_loss: 2.3994 - val_accuracy: 0.1730\n",
+      "63/63 [==============================] - 4s 65ms/step - loss: 2.3428 - accuracy: 0.1787 - val_loss: 2.3761 - val_accuracy: 0.1810\n",
       "Epoch 60/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.3173 - accuracy: 0.1800 - val_loss: 2.3944 - val_accuracy: 0.1730\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 2.3032 - accuracy: 0.1842 - val_loss: 2.3688 - val_accuracy: 0.1800\n",
       "Epoch 61/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.3054 - accuracy: 0.1785 - val_loss: 2.3844 - val_accuracy: 0.1740\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 2.3350 - accuracy: 0.1803 - val_loss: 2.3622 - val_accuracy: 0.1810\n",
       "Epoch 62/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.2855 - accuracy: 0.1855 - val_loss: 2.3808 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.2887 - accuracy: 0.1883 - val_loss: 2.3575 - val_accuracy: 0.1820\n",
       "Epoch 63/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.3003 - accuracy: 0.1815 - val_loss: 2.3762 - val_accuracy: 0.1790\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.3158 - accuracy: 0.1770 - val_loss: 2.3536 - val_accuracy: 0.1810\n",
       "Epoch 64/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 2.2581 - accuracy: 0.1863 - val_loss: 2.3696 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 4s 66ms/step - loss: 2.3141 - accuracy: 0.1820 - val_loss: 2.3489 - val_accuracy: 0.1810\n",
       "Epoch 65/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2909 - accuracy: 0.1835 - val_loss: 2.3643 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.2764 - accuracy: 0.1803 - val_loss: 2.3446 - val_accuracy: 0.1830\n",
       "Epoch 66/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.2922 - accuracy: 0.1813 - val_loss: 2.3613 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 2.3017 - accuracy: 0.1832 - val_loss: 2.3407 - val_accuracy: 0.1810\n",
       "Epoch 67/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.2824 - accuracy: 0.1825 - val_loss: 2.3604 - val_accuracy: 0.1790\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.3019 - accuracy: 0.1810 - val_loss: 2.3350 - val_accuracy: 0.1790\n",
       "Epoch 68/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 2.2748 - accuracy: 0.1813 - val_loss: 2.3543 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.2773 - accuracy: 0.1855 - val_loss: 2.3321 - val_accuracy: 0.1780\n",
       "Epoch 69/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 2.2886 - accuracy: 0.1762 - val_loss: 2.3482 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.2658 - accuracy: 0.1840 - val_loss: 2.3270 - val_accuracy: 0.1800\n",
       "Epoch 70/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.3029 - accuracy: 0.1807 - val_loss: 2.3459 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.2564 - accuracy: 0.1905 - val_loss: 2.3245 - val_accuracy: 0.1760\n",
       "Epoch 71/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.2876 - accuracy: 0.1748 - val_loss: 2.3418 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 4s 59ms/step - loss: 2.2889 - accuracy: 0.1900 - val_loss: 2.3224 - val_accuracy: 0.1750\n",
       "Epoch 72/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 2.2897 - accuracy: 0.1750 - val_loss: 2.3383 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 2.2565 - accuracy: 0.1838 - val_loss: 2.3164 - val_accuracy: 0.1780\n",
       "Epoch 73/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 2.2948 - accuracy: 0.1782 - val_loss: 2.3343 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 2.2589 - accuracy: 0.1815 - val_loss: 2.3113 - val_accuracy: 0.1760\n",
       "Epoch 74/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 2.2457 - accuracy: 0.1813 - val_loss: 2.3315 - val_accuracy: 0.1830\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.2545 - accuracy: 0.1860 - val_loss: 2.3095 - val_accuracy: 0.1770\n",
       "Epoch 75/100\n",
-      "63/63 [==============================] - 4s 67ms/step - loss: 2.2500 - accuracy: 0.1842 - val_loss: 2.3255 - val_accuracy: 0.1830\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 2.2512 - accuracy: 0.1805 - val_loss: 2.3032 - val_accuracy: 0.1780\n",
       "Epoch 76/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.2510 - accuracy: 0.1850 - val_loss: 2.3211 - val_accuracy: 0.1760\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.2420 - accuracy: 0.1845 - val_loss: 2.3020 - val_accuracy: 0.1750\n",
       "Epoch 77/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 2.2719 - accuracy: 0.1863 - val_loss: 2.3194 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.2431 - accuracy: 0.1933 - val_loss: 2.2988 - val_accuracy: 0.1790\n",
       "Epoch 78/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2820 - accuracy: 0.1795 - val_loss: 2.3177 - val_accuracy: 0.1790\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.2895 - accuracy: 0.1873 - val_loss: 2.2969 - val_accuracy: 0.1750\n",
       "Epoch 79/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.2678 - accuracy: 0.1852 - val_loss: 2.3174 - val_accuracy: 0.1810\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 2.2677 - accuracy: 0.1865 - val_loss: 2.2929 - val_accuracy: 0.1760\n",
       "Epoch 80/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.2550 - accuracy: 0.1840 - val_loss: 2.3161 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.2354 - accuracy: 0.1910 - val_loss: 2.2906 - val_accuracy: 0.1770\n",
       "Epoch 81/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 2.2688 - accuracy: 0.1750 - val_loss: 2.3151 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.2603 - accuracy: 0.1863 - val_loss: 2.2851 - val_accuracy: 0.1770\n",
       "Epoch 82/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 2.2669 - accuracy: 0.1810 - val_loss: 2.3112 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.2570 - accuracy: 0.1852 - val_loss: 2.2833 - val_accuracy: 0.1750\n",
       "Epoch 83/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.2602 - accuracy: 0.1887 - val_loss: 2.3091 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 2.2521 - accuracy: 0.1850 - val_loss: 2.2821 - val_accuracy: 0.1800\n",
       "Epoch 84/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 2.2468 - accuracy: 0.1840 - val_loss: 2.3056 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.2345 - accuracy: 0.1870 - val_loss: 2.2794 - val_accuracy: 0.1760\n",
       "Epoch 85/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.2505 - accuracy: 0.1785 - val_loss: 2.3042 - val_accuracy: 0.1730\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.2333 - accuracy: 0.1877 - val_loss: 2.2753 - val_accuracy: 0.1750\n",
       "Epoch 86/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2740 - accuracy: 0.1795 - val_loss: 2.3016 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 2.2486 - accuracy: 0.1822 - val_loss: 2.2717 - val_accuracy: 0.1790\n",
       "Epoch 87/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.2601 - accuracy: 0.1730 - val_loss: 2.2994 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.2240 - accuracy: 0.1925 - val_loss: 2.2691 - val_accuracy: 0.1750\n",
       "Epoch 88/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2506 - accuracy: 0.1813 - val_loss: 2.2980 - val_accuracy: 0.1740\n",
+      "63/63 [==============================] - 5s 80ms/step - loss: 2.2164 - accuracy: 0.1865 - val_loss: 2.2671 - val_accuracy: 0.1740\n",
       "Epoch 89/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.2436 - accuracy: 0.1832 - val_loss: 2.2950 - val_accuracy: 0.1750\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 2.2183 - accuracy: 0.1895 - val_loss: 2.2639 - val_accuracy: 0.1760\n",
       "Epoch 90/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 2.2453 - accuracy: 0.1857 - val_loss: 2.2928 - val_accuracy: 0.1790\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.2185 - accuracy: 0.1908 - val_loss: 2.2599 - val_accuracy: 0.1750\n",
       "Epoch 91/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 2.2618 - accuracy: 0.1820 - val_loss: 2.2920 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 2.2281 - accuracy: 0.1895 - val_loss: 2.2579 - val_accuracy: 0.1750\n",
       "Epoch 92/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 2.2383 - accuracy: 0.1817 - val_loss: 2.2895 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 2.2232 - accuracy: 0.1848 - val_loss: 2.2563 - val_accuracy: 0.1780\n",
       "Epoch 93/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2282 - accuracy: 0.1838 - val_loss: 2.2856 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.2226 - accuracy: 0.1928 - val_loss: 2.2571 - val_accuracy: 0.1730\n",
       "Epoch 94/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 2.2556 - accuracy: 0.1828 - val_loss: 2.2826 - val_accuracy: 0.1750\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 2.1925 - accuracy: 0.1935 - val_loss: 2.2547 - val_accuracy: 0.1780\n",
       "Epoch 95/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2430 - accuracy: 0.1825 - val_loss: 2.2804 - val_accuracy: 0.1750\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 2.2210 - accuracy: 0.1910 - val_loss: 2.2541 - val_accuracy: 0.1790\n",
       "Epoch 96/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2424 - accuracy: 0.1860 - val_loss: 2.2779 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 2.2138 - accuracy: 0.1922 - val_loss: 2.2480 - val_accuracy: 0.1810\n",
       "Epoch 97/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 2.2644 - accuracy: 0.1782 - val_loss: 2.2741 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.2379 - accuracy: 0.1865 - val_loss: 2.2485 - val_accuracy: 0.1750\n",
       "Epoch 98/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 2.2469 - accuracy: 0.1855 - val_loss: 2.2724 - val_accuracy: 0.1750\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 2.2067 - accuracy: 0.1980 - val_loss: 2.2464 - val_accuracy: 0.1740\n",
       "Epoch 99/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 2.2493 - accuracy: 0.1883 - val_loss: 2.2691 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 2.2162 - accuracy: 0.1880 - val_loss: 2.2414 - val_accuracy: 0.1760\n",
       "Epoch 100/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 2.2231 - accuracy: 0.1805 - val_loss: 2.2679 - val_accuracy: 0.1780\n"
+      "63/63 [==============================] - 5s 72ms/step - loss: 2.2286 - accuracy: 0.1855 - val_loss: 2.2417 - val_accuracy: 0.1760\n"
      ]
     }
    ],
@@ -1176,7 +1176,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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f/epX5/z8J554Itu2bWP79u0AfPvb3275teeffz6/+MUvOHjwIJVKhW9961tccsklHDx4ENd1ecUrXsFHPvIR7r33XlzXZceOHVx22WX8/d//PcPDw4yNjc35+5kKtbAVRVGUw8J73/te3vCGN/CRj3yEF7/4xXN+/lQqxWc/+1kuv/xyMpkM5513XtNjf/azn7FmzZrg+X/913/x8Y9/nMsuuwxjDC9+8Yu58sor+d3vfscb3/hGXNdOgv7Yxz5GpVLhda97HcPDwxhjeNe73kVPT8+cv5+pED/TbqGwadMmo/NzFWVqROQeY8zUtTHziP49Hz4eeeQRTj755PlexrwzNjZGR0cHxhj++I//mBNOOIH3vOc9872sSWn0u2vl71ld4oqizBv5UoXhXGm+l6G0Mf/xH//BWWedxamnnsrw8DBve9vb5ntJh42jT7B/8Q/w9ZfO9yoUpW0RkaSI3CkivxORzSLyd972r4rIk3M5KvfDP36Y53zi1tmeRlnEvOc97+H+++/n4Ycf5tprryWdTs/3kg4bR18Me99mOPjEfK9CUdqZAvAcY8yYiMSAX4rIT7x9f2GM+e5cXciJCJUFFpZTlIXK0SfYpSwYd75XoShti7GJLX4KbMz7d1hUNSJCxVXBVpRWOPpc4qUcmMp8r0JR2hoRcUTkfmA/cJMx5rfero+KyAMi8ikRSTQ/Q2s4EcFVwVaUljj6BLs4Dq4KtqLMBmNMxRhzFrAGOF9ETgP+P+Ak4DygD/jLRq8VkbeKyN0icveBAwcmvU40IpRVsBWlJY4+wVaXuKLMGcaYIeDnwOXGmD3GUgC+Apzf5DVfMMZsMsZsWrp06aTnj0QEV2PYbclll13GjTfeWLPtn//5n3nHO97R9DWXXnopfpnf7/3e7zXsyf3BD36QT3ziE5Ne+wc/+AEPP/xw8Pxv//Zvufnmm6ex+sYs9DGcR6lgq4WtKDNFRJaKSI/3OAU8H3hURFZ62wR4KfDQbK/laAy7bbn66qu57rrrarZdd911Lffzvv7662fcfKResD/0oQ/xvOc9b0bnaieOPsEuZkHv2BVlNqwEfi4iDwB3YWPYPwauFZEHgQeBJcBHZnsha2HDQmvgpEzNVVddxf/+7/9SLBYB2L59O7t37+bZz34273jHO9i0aROnnnoqH/jABxq+fv369Rw8eBCAj370o2zcuJGLLrooGMEJtsb6vPPO48wzz+QVr3gF2WyWO+64gx/+8If8xV/8BWeddRZbt27lmmuu4bvftcULP/vZzzj77LM5/fTTedOb3kShUAiu94EPfIBzzjmH008/nUcffbTl97pQxnAenVniyHyvQlHaFmPMA8DZDbY/Z66v5Yj9W3UNOPpnO3N+8j7Y++DcnnPF6fCijzfd3dfXx/nnn89PfvITrrzySq677jpe9apXISJ89KMfpa+vj0qlwnOf+1weeOABzjjjjIbnueeee7juuuu4//77KZfLnHPOOZx77rkAvPzlL+ctb3kLAH/913/Nl770Jf7kT/6EK664gpe85CVcddVVNefK5/Ncc801/OxnP2Pjxo38wR/8AZ/73Od497vfDcCSJUu49957+exnP8snPvEJvvjFL075MSykMZxHl4VtjLrEFaWNiHoqXXY176QdCbvFw+7w73znO5xzzjmcffbZbN68ucZ9Xc/tt9/Oy172MtLpNF1dXVxxxRXBvoceeohnP/vZnH766Vx77bVNx3P6PPbYY2zYsIGNGzcC8IY3vIHbbrst2P/yl78cgHPPPTcYGDIVC2kM59FlYZdy9qcmnSlKWxDxLWz9k50dk1jCh5Mrr7yS97znPdx7771ks1nOPfdcnnzyST7xiU9w11130dvbyzXXXEM+n5/R+a+55hp+8IMfcOaZZ/LVr36VW2+9dVbr9Ud0zsV4zvkYw3l0WdilrP2pZV2K0hY43jeQdjtrTzo6Orjssst405veFFjXIyMjZDIZuru72bdvHz/5yU8mPcfFF1/MD37wA3K5HKOjo/zoRz8K9o2OjrJy5UpKpRLXXnttsL2zs5PR0dEJ5zrxxBPZvn07W7ZsAeAb3/gGl1xyyaze40Iaw3mUWdieYKuFrShtgW9ha6Z4+3L11Vfzspe9LHCNn3nmmZx99tmcdNJJrF27lmc961mTvv6cc87h1a9+NWeeeSbLli2rGZH54Q9/mAsuuIClS5dywQUXBCL9mte8hre85S18+tOfDpLNAJLJJF/5yld45StfSblc5rzzzuPtb3/7tN7PQh7DeXSN19z/KHz2Avv4A0MgmsWiHL0cDeM1v/KrJ/m7Hz3MfX/zfHoz8SO4svZHx2u2LzpeE6A0Xn2sbnFFWfBEI37S2cIyHBRlIXKUCXau+ljd4oqy4IlE/LIuFWxFmYqjS7CL2epjLe1SlAWPozFsRWmZo0uwwy5xtbAVZcHjW9gq2DNjoeUgKVMzm9/ZUSbYIZe4xrAVZcETVZf4jEkmkwwMDKhotxHGGAYGBkgmkzN6/dFV1lVUC1tR2glHk85mzJo1a9i5cydTjTBVFhbJZLKmbGw6HF2CXQrHsFWwFWWhU+10poI9XWKxGBs2bJjvZShHEHWJK4oyb/gWtnY6U5SpOboEW13iitJWaKczRWmdo0uwS1rWpSjtRJB0pvfXijIlR5lga+MURWknqkln+veqKFPRvoLtVuA3n4NSaGxbUVuTKko7oZ3OFKV12lewd98PN7wPttxc3aYucUVpK6qdzuZ5IYrSBrSvYFeK9ufonuq2mtakeseuKAudiD8PW5POFGVK2lew/Rj12L7qtrCFrS5xRVnwRD3FVpe4okxNGwu2J8ije6vbtHGKorQVjvcNpJ3OFGVq2lewfQs6bGEXsxBL28caw1aUBY92OlOU1mlfwW5mYSc6vf1qYSvKQsfRaV2K0jJtLNjeH3h9DDveYR9rDFtRFjxBpzONYSvKlLTv8A9fkMcPeI/Fs7A9wVaXuKIseKKOusQVpVXa2ML2BNm4VrTLXgOVuO8S1y8ARVno+HXYmnSmKFPT/hY22Dh2xHsrCXWJK0q7oJ3OFKV12tjCDiWVje2rtiX1Y9iadKYoCx5Hp3UpSsu0sWDXWdj+4I8gS1wtbEVZ6GiWuKK0TvsKtltnYZc8C9sXbHWJK8qCx1GXuKK0TPsKdr2F7fcR1zpsRWkbquM1VbAVZSraV7B9CzqW9ixszyUe17IuRWkXtNOZorRO+wq2b0F3rfJi2L5L3Bds/QJQlIWOxrAVpXVaEmwRuVxEHhORLSLyvgb7/0xEHhaRB0TkZyJyTGjfDSIyJCI/nsuFBxZ01yovS9xziWunM0VpG4IscdVrRZmSKQVbRBzgM8CLgFOAq0XklLrD7gM2GWPOAL4L/ENo3z8Cr5+b5YbwBbnTE+ySxrAVpd1wtNOZorRMKxb2+cAWY8w2Y0wRuA64MnyAMebnxhh/tuVvgDWhfT8DRudovaGLhlzilSI8foN9rmVditI2aKczRWmdVgR7NbAj9Hynt60ZbwZ+Mp1FiMhbReRuEbn7wIEDrb3IF+wVpwMCW26GzpUQz9jt6hJXlAVPxPsG0rIuRZmaOW1NKiKvAzYBl0zndcaYLwBfANi0aVNrf7m+IB93GbzvaXDLVqwHn/ROqi5xRVnoaKczRWmdVgR7F7A29HyNt60GEXke8FfAJcaYwtwsbxJ8l7c4kOwKLcS7ZVfBVpQFj2aJK0rrtOISvws4QUQ2iEgceA3ww/ABInI28O/AFcaY/XO/zAb4FrbUvYWIY3+qYCvKgkdEiIi6xBWlFaYUbGNMGXgncCPwCPAdY8xmEfmQiFzhHfaPQAfwXyJyv4gEgi4itwP/BTxXRHaKyAvnZOW+IPsCXb2g/akxbEVpC5yIaNKZorRASzFsY8z1wPV12/429Ph5k7z22TNe3aSLCrnEw/jPNUtcUdqCiIiWdSlKC7RvpzO3iYWtLnFFaSuciGgMW1FaoH0F2zSJYfvP1SWuKG2BI0JFY9iKMiXtK9huBZBqzNpH1MJWlHbCcdQlriit0L6CbdyJ7nDQsi5FaTMc0aQzRWmFNhbsCm59whloDFtR2oxIRLSsS1FaoG0F+9BYnnwZ7th6sHaHlnUpSlvhiCadKUortK1gF0slXITH99bNFdGyLkVpK2yW+HyvQlEWPm0r2Ma4uETYcShXu0Nd4orSVjjqEleUlmhbwcatUCHCjsFs7XYt61KUtkI7nSlKa7S9YD89QbDVwlaU2SAiSRG5U0R+JyKbReTvvO0bROS3IrJFRL7tzRaYNRFBy7oUpQXaV7BNBYOw81AOE3anaVmXosyWAvAcY8yZwFnA5SLyDODvgU8ZY44HDgFvnouLaaczRWmNthVsY1wqRBgrlBnKlqo7/Bi2usQVZUYYy5j3NOb9M8BzgO96278GvHQurhfRTmeK0hJtK9i+Sxxgx6GQW9wv61ILW1FmjIg4InI/sB+4CdgKDHnT+wB2Aqvn4lpR7XSmKC3R1oLtGrv8hnFsLetSlBljjKkYY84C1gDnAye1+loReauI3C0idx84cGDK47XTmaK0RvsKtucSB9gx2KC0Sy1sRZk1xpgh4OfAM4EeEfFH8q4BdjV5zReMMZuMMZuWLl065TW005mitEb7CrZbwcW6v2tc4mATzzSGrSgzQkSWikiP9zgFPB94BCvcV3mHvQH4n7m4nnY6U5TWiE59yALFa5ySiDaqxVYLW1FmwUrgayLiYG/qv2OM+bGIPAxcJyIfAe4DvjQXF4tolriitEQbC7ZNOlvXl27cPEUFW1FmhDHmAeDsBtu3YePZc0o0IpS0N6miTEn7usQ9C/uY/gy7hnK1d+gRdYkrSrugnc4UpTXaVrDFi2Gv709Tqhj2jeRDO9UlrijtQkS0rEtRWqFtBdvPEl/SmQCobZ4iES3rUpQ2wYlo4xRFaYU2FuwKLhE6kzYMny2Wq/u0rEtR2oaI6HhNRWmFthbsChE6kzEAxgohwdayLkVpG6IRdYkrSiu0rWCL5xKvWtghgdYYtqK0DTbpTP9eFWUq2lawMS4GocsT7AkWtgq2orQFttPZfK9CURY+bSzYFSqm6hLPhgVby7oUpW1wBG2coigt0LaCLW6tS3xcXeKK0pZopzNFaY32FWwvSzwZdYhGhPEJLnG1sBWlHYjq8A9FaYm2FWxb1iVEIkI67tQKtpZ1KUrboJ3OFKU12lawxRgqRIgIdCSidS5xjWErSrugnc4UpTXaVrD9xikREdKJaG3jFI1hK0rboJ3OFKU12law/TpsJyJkElHGCmELW1SwFaVNiOg8bEVpiTYWbNvpTAQycaeurMtRl7iitAna6UxRWqN9BRvbOMUR38JWl7iitCOadKYordG+gu25xCMi1sKuTzrTsi5FaQsiWtalKC3RxoJtXeIRL4Y9oaxLXeKK0hY4GsNWlJZoY8F2Md7yM4ko48X6xin6BaAo7YDfS9zo36yiTEp7C7Z4gh2Pki+5lP2huuoSV5S2IRoRAB0AoihT0L6CjYsbWNgOANmSJ9I6rUtR2gbHE2wdsakok9O+gm1cDFao03FvAIgfx9YYtqK0BwceY83Q3QCoXivK5LSxYFdwpdbCHvebp2hZl6K0B7/9PM9/+P8D0G5nijIFbSvYEVyMWFdaxrOwg/akGsNWlPbAieOYIqAzsRVlKtpWsDEurucSzySsYI/VuMTVwlaUBY8Tw3Ht360KtqJMTtsKtrWw65LOCpp0pihthZMgYkqACraiTEXbCnZ9HTZQrcVu4BL/1ZaDDGdLR3SNiqJMgRMnYipEcLXbmaJMQdsKdsRUcMVziQdZ4p5IR2qTzu7YepDXfvG3fOfuHUd8nYqiTIITAyBGWS1sRZmCthVswQD1WeIhC9sr6ypXXP7uhw8D1PYbVxRl/nHigAq2orRCewq2MTUx7KAOO3CJO4FL/D/vfJrH9o0CUKpoXFtRFhSeYMdVsBVlStpUsK3w+nXYTkRIxZxaC9s75ou3P8n5G/pIRCMq2Iqy0Ai7xDWGrSiT0p6C7bm7jRfDBusWHy+GYtheWddwrsRJKzqJRyMUVbAVZWERTQAQkzKuWtiKMintKdgmVL7lkY6HRmyKBBZ2qeISdyLEHbWwFWXBEXaJq4WtKJPSpoJthdeElm9nYodbk9rHxbJLPBoh5kQolfULQVEWFJolrigt056CHbjEQ4Idd6qtSb2yLtc1lF1jBTsqamErykJDk84UpWXaU7BNoxh2dEJZlx+z9i3sggq2oiws1MJWlJZpSbBF5HIReUxEtojI+xrs/zMReVhEHhCRn4nIMaF9bxCRJ7x/b5iTVXsJZU2TzjyXeKHsCbYfwy6rYCvKgsK3sKWsnc4UZQqmFGwRcYDPAC8CTgGuFpFT6g67D9hkjDkD+C7wD95r+4APABcA5wMfEJHeWa/aj2F707rAdjurLesygQs8iGGrha0oCwvHyxKnjP55KsrktGJhnw9sMcZsM8YUgeuAK8MHGGN+bozJek9/A6zxHr8QuMkYM2iMOQTcBFw+61WbkCXtUeMSjzjWJR6ysGOOUKroHbyiLCjUJa4oLdOKYK8Gwk24d3rbmvFm4CczfG1reElnbgOXuDEmKOsKBDsa0TpsRVmIaGtSRWmZ6FyeTEReB2wCLpnm694KvBVg3bp1U7+gQR12KuZQcQ2liiHuxbDrk86CedmKoiwMPMFOaB22okxJKxb2LmBt6Pkab1sNIvI84K+AK4wxhem81hjzBWPMJmPMpqVLl069In8SV0iw41H7uFhxJ7jEY9o4RVEWJiGXuHY6U5TJaUWw7wJOEJENIhIHXgP8MHyAiJwN/DtWrPeHdt0IvEBEer1ksxd422ZHgzrsRNS6xwulStBLvN7C1sYpirLA8F3ioi5xRZmKKV3ixpiyiLwTK7QO8GVjzGYR+RBwtzHmh8A/Ah3Af4nN3H7aGHOFMWZQRD6MFX2ADxljBme96sDCrsawayxscQBDsWSFPeFEiOnwD0VZeERDWeLqEleUSWkphm2MuR64vm7b34YeP2+S134Z+PJMF9gQ1x/yEXKJO55gl93AVV4s2Zi1tbBFk84UZaHhucS105miTE1bdzprZGEXym4g5KVSKdgXdyJBTFtRlAWCZokrSsu0qWD7jVPCMeyJFnapYoU95mjjFEVZkIR6iWunM0WZnPYUbHcKC9vbXi5XLWwr2PqFoCgLioiDkYgmnSlKC7SnYPsu8UiDpLOyG2wPYtiOndalMWxFWXiYSFxd4orSAu0p2G4jl7hX1lWuVF3i5bK3L0LCc4kbdbspysLCiWvSmaK0QHsKthfDlpBLvDaG7bnEa7LEIxiDfikoykLDiWlZl6K0QJsKttc4JdKk01mjpLOov02/FBRlMkRkrYj83BuZu1lE/tTb/kER2SUi93v/fm8urmecuHY6U5QWmNNe4keMBklnvoVdKFXLuip1SWdgBT2Fg6IoTSkD/9cYc6+IdAL3iMhN3r5PGWM+MadXc+LEpUxeBVtRJqU9BXuSOuyglzhQLpcRgWhEiDt2draWdinK5Bhj9gB7vMejIvIIczFlrxlOzMawVa8VZVLa1CXuxbCn6HRWKleIOxFEpGpha/MURWkZEVkPnA381tv0ThF5QES+7M0HmD2OnyWuf5uKMhntKdgNssSrddiVGgvb3+4LtlrYitIaItIBfA94tzFmBPgccBxwFtYC/6cmr3uriNwtIncfOHBg6utEE55gz9nSFeWopD0F23OJ12aJe7XXNZ3OyoHlXU06028FRZkKEYlhxfpaY8x/Axhj9hljKsYYF/gP4PxGr532uFwvS1w7nSnK5LSnYAfDP6oh+JgXow6XdbkhCzse7NcvBUWZDLEj974EPGKM+WRo+8rQYS8DHpqTC3pJZ1pyqSiT095JZ6EYtogQj0YohMq6ypVKVbDVwlaUVnkW8HrgQRG539v2fuBqETkLMMB24G1zcTGJauMURWmFNhVsfx52rYMgEY3UlHWVwy5xjWErSksYY34JSINd1zfYNmvE0dakitIKbe0SD8ewwQq2bZxit1caJJ1pP3FFWWD4LnGNYSvKpLSnYPtlXU6tYAczrz3Lu1KpBEJdtbD1S0FRFhTa6UxRWqI9BduzsE29hR1z7HhNr6yrUgknnfm12WphK8qCwokTp6IucUWZgvYUbM/CjtTFsK2FXalJOvNblsaiXpa4usQVZWGhwz8UpSXaVLC9LPF6l3g0UlvWValo0pmiLHScOHEpqYWtKFPQnoIdDP+oTXKPRyPWJe7HsMsTXeLamlRRFhiaJa4oLdGegu13OotMLOsqlkPTulxNOlOUBU80rp3OFKUF2lSwJw7/AM8lXqlziWvjFEVZ2Dhe4xT921SUSWlLwTYNWpNC47Kuah22jtdUlAWJEwNCf9eKojSkrQV7QuOUurIu153Y6UyzxBVlgeHEAZBKYZ4XoigLm/YW7MjUjVMS9eM1dfiHoiwsfMF2S/O8EEVZ2LSlYLuVMtCg01mQJe5b2BUu2fMlePxGnIjgRERd4oqy0PBc4pGKCraiTEZbCjZeNqk0Gv5RrgRZ4hHjcvae78DD/wPYOLa6xBVlgeEk7M9KcX7XoSgLnLYUbNdtbGEHZV2ekEcwxNw8lHKAdYtrHbaiLDB8l7hRC1tRJqMtBbtZ0plf1mU8wY5SIeoWAsGOOxF1iSvKQkNd4orSEm0p2FT8pLOJZV3GQNnYEq4OsUJNuWphq2ArygIjSDpTl7iiTEZbCrbxh39EpGZ7IuZlgrv2Z4a83VGyP+PRiHY6U5SFhifYjgq2okxKewq2W6FsIjh1gh30C/eM6AyehV3KApp0pigLEs8lrmVdijI5bSrYZSpEEKkT7KiNaZdcu70zcIlbCzvmRHQetqIsNKI2Szyigq0ok9KWgo3r4hIhUifYfpOUqoXtu8S9pLOoxrAVZcHhucRVsBVlctpSsF1ToUIEp271ft/woteSOONb2KVw0pmNYe8fyR+RtSqKMgXqEleUlmhLwcat4CINXOK+he1lifsWduAStzHs324b4IKP/Ywdg9kjt2ZFURrjWdjaOEVRJqctBdu4FVwiOE1c4gXfwg4nnRkTNE7ZPjCOMbB/VIcNKMq84wm2KatgK8pktKVg47pUiBBp6hL367A9C9u4UCkFjVMGx63rrVDWcX6KMu/44zXLegOtKJPRloJt3HJLSWcdvoUNUM4FjVMOZe2dfEEzxhVl/vF6iRt1iSvKpLSlYGNcXGSCYMe93uJBlriEEstKOWJe45TBcU+wSyrYijLveBY22ppUUSalLQXbuH6WeONOZxNi2AClXDAv+5Av2OoSV5T5J1TWVdayS0VpSlsKdrUOu3az3+nMF+wgSxygnCcetfOwB9UlrigLB0+w45TJ69+kojSlPQXbVKiYiTHs+qSziIT6hpey1Rj2uAq2oiwYPJd4XMrkiur1UpRmtKVgG1NpGMP2k87yjQZ8lPJB45RqDFu/HBRl3hHBlSgxyuT1b1JRmtKWgo1fh10//COow5aJr/GyxHOlCiP5sj1OLWxFWRC4TpwYZXIq2IrSlDYVbNcb/lG7ueoSD1nYfhelUo64I1Tc6j4VbEVZGJiIJ9jqEleUprSnYJvGWeJ+0lmuHNqY7rc/PZd4GM0SV5SFgXFixNXCVpRJaU/BbjKtS0SIRyMUwoZzINhZYtE6wdY6bEVZGETiNulMBVtRmtKegt0k6Qwg4dVau/5bS/fZn2W1sBVlweLEbNKZusQVpSltK9iVBnXYYOPYtYLtW9i5IMYN1n2uFraiLBCiNoadVcFWlKa0p2A3yRIHW9pVKLsY3/pOeRa2l3Tms7w7oUlnirJAkGhCs8QVZQraU7CNnyU+UbCrFrbjbcjY4QJeWRdAJu7QlYypS1xRFggSjdtOZyrYitKUthXsZhb2BJd4PAOxVE2WeG8mHljiiqLMP5GolnUpylS0p2C7FVzTOIadiDoUyjYpDYBY2hPsbCDYfZm4PU5j2IqyIIg4cRKaJa4ok9KSYIvI5SLymIhsEZH3Ndh/sYjcKyJlEbmqbt/fi8hD3r9Xz8mqTYVKkyzxeDRCsWJd5oAV62gyGP4B0JuOk4hF1CWuKAuFWJq0FFWwFWUSphRsEXGAzwAvAk4BrhaRU+oOexq4BvjPute+GDgHOAu4APhzEema7aLFNK7DBpv9vWX/GIHxHM9YK7uUq7Ow1SWuKAuGeIaM5DWGrSiT0IqFfT6wxRizzRhTBK4DrgwfYIzZbox5AKhXwFOA24wxZWPMOPAAcPmsV+0lnUUarD4ejbBvpFCNYcfSEEvWCHZv2nOJq2ArysIgniEjBY1hK8oktCLYq4Edoec7vW2t8DvgchFJi8gS4DJg7fSW2ADjlXU1apzi1VrHolG7IZaGaKqmcUpvOmYtbL2bV5SFQbyDlMmrS1xRJiF6OE9ujPmpiJwH3AEcAH4NTPiLFJG3Am8FWLdu3ZTnFa8Ou1lZF0AyHoMcEPeSzrIDgZj3ZuLsG82rha0oC4V4hiR58sXy1McqyiKlFQt7F7VW8RpvW0sYYz5qjDnLGPN8QIDHGxzzBWPMJmPMpqVLl7ZwUrfh8A+AZZ1J1valSMRjdkPIJd7fEceJCMcuyZCIOhovU5SFQjxDBINbyM33ShRlwdKKYN8FnCAiG0QkDrwG+GErJxcRR0T6vcdnAGcAP53pYoPzBlniE/f95YtO5Md/8mxEQnXY0RSUc6zsTvHb9z+XC49foklnirKQSHTYn6Wx+V2HoixgpnSJG2PKIvJO4EbAAb5sjNksIh8C7jbG/NBze38f6AV+X0T+zhhzKhADbvdc1yPA64wxs/d5GYNpkiWeiDokog5EvE5nsZRXh23v3Jd0JILjyq6hXHGJOu1Zjq4oRw1xT7CLKtiK0oyWYtjGmOuB6+u2/W3o8V1YV3n96/LYTPE5RfzhH41M7OAgP0u82uksTCJm9xdVsBVl/olnAHBK2XleiKIsXNpSqXzBbpQlXj3I7yWe9hqn1MbGkl4CmnY7U5QFgCfYkfL4PC9EURYubSnYBI1TJjlGIoBYsY6loVIEt5pklohZQdc4tqIsADyXuFrYitKcthRswcU1MrlLPOJYoRaxWeIQxLGhWq+t7UmVep4eyPKSf72dg2OF+V7KvCAia0Xk5yLysIhsFpE/9bb3ichNIvKE97N3zi7qWdjRShZjzJydVlGOJtpTsF2v09mkLvGIdYeDzRIHKFfj2ImoWthKYx7eM8JDu0Z4fN/ofC9lvigD/9cYcwrwDOCPvXbE7wN+Zow5AfiZ93xu8AQ7I9ofQVGa0Z6CTfNOZ9WDItbChpCFXXW3JTSGrTTBr88fyS3OJh7GmD3GmHu9x6PAI9juhlcCX/MO+xrw0jm7qOcST5PX9qSK0oT2FGyvcYpMtnrfJQ7Vn6FMcT9LXF3iSj1+e8zRfOmIXvehXcP8v+sfWVAuYRFZD5wN/BZYbozZ4+3aCyxv8pq3isjdInL3gQMHWruQb2FT0PakitKEthTsoNNZyy5xz8Iu56AwBsaoS1xpSmBh54+shf2jB3bzhdu2LRjBEpEO4HvAu40xI+F9xt5VNLyzmHbnQoBoEkOEtGg/cUVpRlsKthgX02QedkCiE9L99rHvEt/7IPzDBnj614FLXNuTKvXkApf4kbWw9w5bD1B+AYRpRCSGFetrjTH/7W3eJyIrvf0rgf1zeEHK0TQZdYkrSlMO6/CPw0UkaJwyyUG//y+h5imepf3gd2151+geEktOA9TCViaSL/oW9vwI9nxbmGJbE34JeMQY88nQrh8CbwA+7v38n7m8rhvLkM7rTGxFaUZbCrbQQpZ4T2jql+8Sf/I2+9OthFzi+uWg1JL3buJGj7BLfN+IJ9jzb2E+C3g98KCI3O9tez9WqL8jIm8GngJeNZcXdWMZOtQlrihNaU/BnmQedkNiXlmX8b4I3LJmiStN8QXzSLrEjTHsHfFd4vMrWMaYX2In6zXiuYftwvEMafJk5/+GRVEWJG0awza4CK3qdSDYPmHBVpe4UkcQwz6CLvGRXDmIXc+3YM8b8Q4yoi5xRWlGWwp2xKvDllYV22+cEvFmZLtlkjF1iSuNmY867D0j1S58i9UlLImM1mEryiS0n2C71gox01m6nyW+4dneOSrqElea4gv2aOHIWdh+wpm9/uL8PxlJdJDWOmxFaUr7CbYXh3b9aVytkOiCZ/wRPOvd9rlbJupEcCKiLnFlArl5sLD9hLPw9RcbkYR1iS/W968oU9F+SWfGs7AnbXNWhwhc/jEoeL2hXftFnIhG1CWuTMC3cEfzJVzXTD5kZo7YO1wdNLJYY7hOsoM0+aCsTlGUWtrPwvZGZE7LJe4T8e5PagRbLWylFj+G6hoYLx4ZK3vvSI6od2OwWAVb4h1e45TF2cNdUaai/QTbc4lPy8L2mSDYjsawlQmEBfNI1WLvHc6zts82+Fm0SVfxDI4YSsXc1McqyiKk/QTb9WPYM1i6H/f2zpGIqUt8ofDJmx7nvd/93WE7/w0P7eHFn76dijv1YI18qUJHwt7cHanSrr0jBY7pT3vXX6Q3kd7Erkp+bJ4XoigLk/YTbC+GzXSSznwiEduu1BfsaGTxfjkeTgafhIGt03rJXU8OctvjBw/TguBHv9vD5t0jjBWmtphzpQrLOhPA4Us8u3v7INmQ63ffSJ5VPSni0cjiTbpKWMGmuGjnkCvKpLStYM8ohg3WLR52iauFPfdc/+fw4/dM6yUj+RIHxgotWcDTxRjDXdsHAWpEshm5UoWlgWDPvYV9aLzIq/7913zv3l2AtegHx4us6EqSjEYWbQzbH7Fp8uPzvBBFWZi0n2CnevnYyf/N9c5lM3t9jWBr0tlhIXeompHfIqP5MhXXcHCsMPXB02TnoRz7R+15p2p7aYwhX3JZ1mVr91utxR4cL/L2b9zD4HhxymMPjBVwTfVmYP+IXduK7iSpuLPoBZuiCraiNKL9BDviMOQsJR9JTX1sI8Spi2GrYM85xSy4kwud6xoGQuLsx4r3hBqIzBW+dQ1TJ3T5/x+WT9MlfueTg9yweS/3PX1oymP9mxJ/LX4P8RVdSZIxZ/G6xL0YtpQ0hq0ojWg/wQZcY1of/FFPxFGX+OGmNA6VyQX7xw/u4aK//zmj+RLGmCAbe+9hEeyqiE5lYfsiuqxrei7x3UM2s7kVC9s/xrek94/a97y8K0kq5izqLHGASCk7zwtRlIVJWwp2xZjW+4jXE3KJJ2MRLes6HJRyUwr27qEcuVKF/aMFssVKELsOd/yaK+7ePkhP2vaR92PYtz1+gJ8/un/CsXnvBq4zGSMZi7ScJe4L9qFs64LtW9LZgv3ZkYySjDnBeM9FhyfYTlld4orSiLYUbGPAmWn3qQlJZ4v0y/Fw0oJL3Ld0h7LFGlHcO8eCfWi8yBP7x7jo+CVA1YL+11ue4IM/2jzheH9/KubQlYy1XIe9e9gX7KkF/uCYb2Hb/3u+cKdiDslYZPF2+vJc4k5ZLWxFaURbCnbFNcy4W2QkWlPWpS7xxnzm51t4+zfumf4LjYFSdkoL2+9mNZQt1cSJ59ol/tsnBwC4eONSoHqjMJov89RAlv11Nwi+iCZjDl2pWMsW9q4he55DLbnEC961KjVrSsUc6xJftDFsa2HHKirYitKIthRs18yiv3NNDFuTzppx39OHuGeKBKpSxeWWR/fVbcwBZmrB9kTpULZUa2HPsWB/795dLOlIBBZ21ruu33L07qdq36O/rmQsQmcy2nLS2Uxi2P61wtdc1Fni0RQGIWHylCr6d6ko9bSvYM9BDDsR09akzRjKlhibwh184+a9vOmrd7Nlf6iEq+S1lZyGS3zUE+w1vak5jWHvH8lzy6P7ecW5q+lK2Ri2b9mPe3HjcAY5VK1e3yXeioVdKFc44JWNtRLDrrrEK8HPVMxBREhGF7GFHYlQclJkyE+ZHKgoi5H2FGyXWWSJ19dhVzBm7pt1tDvDuRK5UmVSS+fpwWxwbEDJSxia0iXuW9jFwIo9YVkHe0fyc/b7+N69u6i4hldvWksqZjvj+ULgdzy7e3uthR0Idty6xFuJYfteAZGZWdjZYplU3K4vuZgtbKASTXsDQBbvZ6AozWhLwbZZ4jN8cZ1guwZKFRXseoY8ER6fpJWn7wbOFUOiXvTijy26xIdCLvGNyzvJFiuMttA+dCqMMXz7rqc5f0Mfxy7twIkIiWiEXNHehBTLLolohIf3jNS8x6p72qErGW2prGuX9zkcuyTTUtJZINieKOWKbnBDkYo5i7pdbiWaoVNyR2xKmqK0E20p2MaYWWSJhxqnRO2XZF4Tz2owxgRW82QW5m4v0aqm3adfQ+uWbAJaE6ou8VJwjROWdwJzE8d+dO8o2weyXHXummBbOu6QLVYCgX7Gsf1UXMP9O4aCY8JZ4p2eS3wqi9//HE5d1c1Qtjhpe9WKawK3uZ8/kS9VqhZ2bBH3EgfKiW66GFcLW1Ea0JaCbbPEZ+8STyfsl6R+OdSSL1kLFJh0WEZgYYcFJtz0wm3+2lqXeIlENMLaXtu9bi4E21/bRu8mACAdj5ItVoL39OwTlhAR26XMx6+BtlniUUoVM6XF61/rlFVdNS1HG3EoWwzuY/zPIFss11jYFdcs2qQrN9FNl4xrDFtRGtCWgu0aZpElXhXsTNyOUJzM7bsYGcpV47CTWdi7Apd46Mu1GBLsSdzivlXuZ4l3pWKs7PYEew4Sz/ze4f7ULbBx6VypHCScrehOsrI7FcTigaAG2maJ20S10SkSz3YP5VjSkWBlt+0/PjhJ4pnvDl/SEa/JEvcFO+n9XLRWdrKbbsZbGtKiKIuNNhXs2dZhexZ2vDYRSbGEk8jGmgy/GM1XXdk1n1+NhV2i4hqe80+38oP7dtW83rdah72ks65kNGgHum8OLGx/oMaSjqpg+y5x38LOJKKk4k7gTYDaGHanNxN7qpGcu4ZyrO5J0puOA7W12H9/w6O87ou/DZ77fcRX9aSC5LJcyQ25xL0wzSL9PympHrpFXeKK0oi2FezZ9RK3XwaZhFrYjRgKJU7VW9i3PLqP4WypZkhHU5d4pcTAeIFtB8Z5cNdwzXnqLWzbCtShNx2bIws7T18mTjxa/S9eH8PuSES9mejV9edLFWKOEHMiwf+PqQR791COVT0p+jJWsMOZ4ndsHeCXWw6yZ7i2TntNb4pC2cV1Dbk6l7hdx+J0iUdSvXSR1b9JRWlAWwr2rGPYxn5Bq4XdmLBgh8Vq30ieN331br74y22BOxzqXeKhPtCVUrU+ua7cyf/McyVbw+zXSa/oTgUx4dmwf7RQ4w4HP4ZdDsQgE7e9uwt1FnbSS0bsaEGwjTHsHsqzqidFryfY4Vrs7Qft5/HzRw8AVcFe3WPd/4Wya13icXWJAziZXqLiUsqNzPdSFGXB0ZaC7RpmUdZV7XTmW1Aq2LWEk6bCFvaDO62VfOeTgzWiWusSD4ltpRjEksMi5rqGQtllqSeoOwazdCXt72Jtb4odh+ZGsJfWCXaqziXuW9jh9rT5UoVkvE6wJ4njD2VtvfrK7iR9ad/Ctp/fofFiEF64xRs0cnCsiIi9MQErzLli1SWeikeC7YuRaKYPADc79ZhSRVlstKdgu7Mp65oYw9aaz1rCSWdhsXpotxXs3+0c4qmBLNGI0J+Jkys1KOsCcMsc8GLJgyGr3RejVZ6VOV6sBBb22r40Ow9lZ9c8Zf8jrBu6k/OcLbbLjkfaG10ZWNgJx3OJV4/Jl1ySMftn0ZGc2sJ+yktYW9eXJhW3wzv8m5NtnnW9ti/Fr7YcpFCuMDheoDcdpyNRtaTzDZLOFmvzlFhHLwBubmh+F6IoC5D2FOxZtyb1YthelnhW42U1DOdKOBGhMxmtEauHdlk3Zb7kctPD+1jelSSTiE7qEvdnPYdd4r5FvsrLqgbo9MRxXV+afMnlgJecNW3yI5jPX8S/lj7Iu7b/ETz5i2BXEMMuVnMYrEu8uv5csSqeHS3kOPgu72OX2sEVfel44Pb2911z4QZypQq/3TbI4HiRvky86vouVmrKuha9SzxlBVvyQ/O7EEVZgLSlYFdmVdblTKjDHm8Dl/i9Tx/i2t8+dUSuNZQt0ZOKTRgv+fDuYS48rh+AJw+Os7onFYhgQE3SWcglHhJs33r0y7gAupK+hW237RicoVt8ZDfilrmufKl9nh0IdqXi9uZirFAm6nU+qx8AEy6x8gV7ss5r2w6OExHrGQDoSceD9/rkwXGciPCqTWtIxiL86He7OThWK9gj+RKuoeoSX+RZ4iS7ARVsRWlEWwq2maOyrrgTIRqRtqj5/K+7d/KJGx87ItcaypXoTsXoTEaDGuSBsQK7h/NcduIyjum34rSqJ0myfhxkOIbtloLyqtFCOSifCizsnqqFHbjEe+25dx6a4YjFsb0A/NY92T4PWfzpuEOx4jKcK5FJRBEROxO9xiVeIRFYuxGciExpYa/qSQVd8/oy8aAO+8mBcdb0puhMxrjq3DX81z07uXv7IEs64oEw++I+IUt8sXbfS/UA4BSGJz9OURYhbSnYsyvrqgq2iJCOO0EjjYVMueLW1AsfTkZyJbrTMToSVZf45t3WHX7q6i42HWMTg1Z5FnZzl3g5cImDncwF1ZIuP4YNBElnazzBfnogy6HxIqd/8EZuerhuhOdkjNpjnzLL7fPQDYSfs3BwtBBYz4lYpEYcw/FkESETdyZNOnvy4DgblmSC572ZkIV9YJz1/Xbfh644jbddciyugaUdicDC9t3nE7LEi4uzrItkDwDRomaJK0o9bSnYFdd+mc6IUAwbbByzHSzssmuO2JAS3yXeEYph+wlnp67q5rz1Ns64qkWXuC+AvuXpW+S96TgJr07ad4mn4g5LOxPsOJTlt08OMpovc/N0BNuzsLebFd56qjcQvigeGCuQSVQFMmxhh13iAJ3JGGNNbuiMMWyvE+y+dIzB8aLdN1DdF4kI/9+LTuYbbz6ft196XNXCzja2sBdrDJtEFy5CrDQ69bGKsshoS8G2wz9m+OJQDBus1dUOMeyyayhW3CMyCnTYc4l3JKKBdbl51whr+1J0p2I8e+NSlnYmOGttj40L17vExf5yTKXE/tECG5d3ABOnVKXjTtAdrCsVDU6xtjfFjsEcd3uzqu96qnZm9aSM7qMUSTJIJ0YiNa1SfQv7wGghKOmrH7EazhIHPC9D425vA+NFRgvlCRb2SL7M7mE70zm8D+DZJyxlZXcqKN/yS8D8m4mEd+3FmiVOJEJWMiTKamErSj1tKdhzNfwDPAu7DbLEy94wiOIRGAoxlC3Sk47TmYwykq9a2KeutAlBq3tS3PVXz+O01d2kYpGJLvFEFwDZXJ5i2Q0GcBzyxCkbEuyetLWsfQsbbALXjkNZ7nrK1uJuOzDOQKtZ42N7GY3105GIIbF0jUs8FbMivT/sEq8bsRpuYgK29KtZWdeTXhb4+rCF7TVP8Vux1gu2jx/zro9hJ6IRRBaxYANZp1MFW1Ea0JaC7RozJ8M/oL0sbOCwxLEf3TsSWJgV1zCSL9OVinnu4BKlisuOwSwneJZyGL97GNj+4qX8WJDpOzxuBe3EFVawA5d4MGCjKtidIcFe15dm91COzbuGOX+DjZff/VSLjTRG9zEY6bNdzmLpGpe4b2EXy25Q0ufHjP3SrnyxEmwD6JjEJe4L9rEhUX7+KctZ3ZPiH70EwWaC7d8U+J+J/1xESMWcRd1LO+90kKqMzfcyFGXB0aaCzZzUYYOtxW6LGLZvYc+xYN/z1CCX//Pt/GqLLX/ys8J7PJd4vuTy9GAW11RLl8LYCVj28/znm5/g4KGhINN3dNxat75g+9akf3xzl3ga19iblDc9awPxaIR7WhXssb0cMD22y1k83dAlDtS4xCE0m7pcJ9gJh7Em07q2HxwnGpGgzSjYUrXv//GFnLmmmyUd8ZrEujC+RT1UF8MGexOxaLPEgUK0i4yrMWxFqSc69SELD5slPsMX18ewE1GyBxf+l6NvYc914tm9Tw3Zn08f4qITlgR9xH3LF+CRPdY96ZdchUnFHEoVO79516EccTcfuMRHs1awV/Wk6ExGgxh21SUepScdJxqRGsFa49Vii8Azj+3njNXd3LW9xTj26D52uSexrCsJlXRNElzY1e13GvNd0/lShVLFpVQxNWvpSESbVhE8eXCcdX1ponUJFcs6k3zvHRcyVig37cjn3xQM1GWJA56FvUizxIFirIsOs2e+l6EoC462FOw5jWHHnbZoTVquHB6X+GYv+9v/6fe+7k7FgpuEQLD7JlqLvtWaK1U4lC2SooBJdiPAWNaK5bLOBH2ZeJARnfM+70Q0wuWnrSAdd2qy/td5lvzGZZ10p2NsWt/Hl365zXYhCwnbBIrjUBzlabfTusTHagU7Ha/+d8+EyrrAWth+3LhWsGOTxrCbubyjToQez3vQCCcixJ3IhBg22PrvxWxhl+NddJoxjDEzrwZRlKOQtnSJm1l1OquPYUfJtkMdtusnnc3tWh/y6qv9tqNDuaqF7ddGP7x7hGhEajqT+fgCmitWGBovkpYClYSNYY9n8yRjEToSUXpDLTv90qlIRLhk41L+5iWn1JxzZXeKZCwSxK/PPaaXUsUENxVNGbUlXbvK3Q1d4pmGLnEvhl1yg57itVniNunMdSd6Np4ezHJMf2PBboVkLBJ83hNc4os4hl2Od9NJluIivmlRlEa0pWBbC3uGL45EwbjBUIhMwqG/uAvzxM1zt8DDgG/tFubQws4Wy2w9MEZ3KsauoRyHxotBTNWWdVm3+CN7Rlndm2ro3g3qhosVslmbKFSK2uS08VyeZZ1JRKTGws4WKzXx5HqciPCttzyD9zx/IwArvZ7jA3UjOicwZuu1D9AdSjpr7BI/Jv8o7Lw7ZGFXAgu7NunM6zdfl7VdcQ3ZYqUm9j5dkjEHv0pvgkt8EWeJu8keElIml9XEM0UJ05aC7ZpZTOsS74vRVOOob3N+CN9/6xyt7vBwOFzij+wZwRh42dmrAdvNbCRwiccDsdo7km8Yv4bameLFvP2CLUZtDDuXzwUzqXvT8aCsK1eXid2Is9f1BiVS/mCQ0Uk6jtkDrIW93/SyrDM5QbDDLvELH/s43PC+YPZ1vuQGIplqYInXdzvzs8qneh+TUc0Mrya/+dsXc1mX8aoM8qPTqL9XlEVA2wr2zDudeV+w/sSuhMMKGaxtqbkAORxJZ74b/NXnrQVsHHvPsG0l6jdO8WkUvwY7UANsU5Ro2SaZ5aM2KzxfyLOsywp2XyZW4xKfzMKuxy/58jPYb3p4H3/4tbsmHjhmZ07vNz32unUucScixKMRwNA5vh1G9tRY2GPB2M3q+w5mYtfFsQP3eXTmf0Lh7mbh/8+JqEOutHiTzsRrT1pUwVaUGtoy6cw1zK6XOIRmYkdZJkNIOW/d5JGFeQ9zOMq6Hto1zJKOOCet6GR1T4rbnzjIAzuHePYJS4hHI0EMGxqXdEFVdHYP5UiJbW6Sc6xLvFwqBYlXvZk4uVLFGyc5XcGutbDv2HqQmx/ZT7HsegLsMbYXV6IcoqOhSxysR6CjPGRbX1byJLxyg0LZDXIZMiFLvLPJTOxG7vPp4g8Zqf8sFruFHUnb1relcRVsRQnTkjqJyOUi8piIbBGR9zXYf7GI3CsiZRG5qm7fP4jIZhF5REQ+LXOQ9jnrGDYEgp2JOyyTIbutnG/8mgVAxW+cModJZw/tHuGUVd2ICKet7uKXWw6SLVb4wO/bJLCOZG1tdCN8sdk9nCONFeys2EQsU652FOvzhPtQttiSSzxMzImQjEUCC3skZ393E+rnR/cxFusjFo3SnYo1FuyYwwbxSobcEqmyP+O7amHX1GvHG7vE50KwU551X3+OVCyyqAU76gl2ebzF2ntFWSRMKdgi4gCfAV4EnAJcLSKn1B32NHAN8J91r70QeBZwBnAacB5wyWwXPetOZxC4xNMx6Mdrg7iABbvkzq2FXShXeGLfKKetsvHm01bZuOEfPHM9xy+zLu1UzAlujJpa2PGqhZ32LOwsCYzYendfsHu9ePTgeHHaLnGwbnHfwvZLzyZ0qBvby6FIH0s7EtbFHM9ApQiVqtim4g4bInuD5+niAe/zcIMbgHAooKOphT0xo3y61A/88PnT523kG2++YMbnbXeiHVaw3awKtqKEacUlfj6wxRizDUBErgOuBB72DzDGbPf21auJAZJAHBAgBkxj9FJj3FnVYfsxbPsF3OMOEREvLhye5bzAqFTmNkt8cLxI2TXBOMsXnb6Sx/aN8qfPOyE4RkToSNh+4uumcInvGc6T9CzscTcBTpxoqULMt7AzVQs7WyyTjtedr+J1E3NiNGJdfIz4aAGyaxjxLO3xXAE6HIh69c6j+zhITxA3J+bF3UtZiHRCOU86Hq1a2EAyXxXsgpeynU7UNk6BBoLtJZ0lZmFhJ2MOCYrVmxdjoFyo6Zy2GIl12HI+N6czsRUlTCvmwWpgR+j5Tm/blBhjfg38HNjj/bvRGPPIdBdZj21NOsMX17nEO0sD1X0LWLBLc5x0VqizEI9f1sG//Z9zrCs5RGcyRibu0JtuLKTpsIXtCfaYiWMiUeKU6awT7AOjhcYu8f/5Y/jPVzde7K57+N74G/jQ9v8D/3QiMn4QgJ5f/h187SXV40b3sKfSE2SmE/NuCkpZuO+b8MlT6I0WWC97MVFbKpbwBbtUCSz2jgZJZ+NNYtj11vF0WG328WDizZzMNrvhvm/Cp06FcouDTo5Skhkr2OQ0hq0oYQ5rhpWIHA+cDKzBivxzROTZDY57q4jcLSJ3HzhwYMrzVubEJe65P8OCXV64gl2Z4+EfvqXuNw5pRmcyytq+dNOs/KpLPB+4xMfcOG4kSpRK4FJe15cm7kR4bO/oRJd4uQCP/AgGnmi8iEd+RBmH/0m9FCpFOnI7AYgffBh23Wtd3rlDkBvkifISW9IF1iUOtgLg4OOQG2ST+zuOlb2Y1ZsAiGX3B5/HeKGMSF2nsyYu8eoNz8wFe637NHGpcAy77YaBJyB7EA5tn/E5jwZSyTgjJo1TGJrvpSjKgqIVwd4FrA09X+Nta4WXAb8xxowZY8aAnwDPrD/IGPMFY8wmY8ympUuXTnlSY2bZmhQCwU4VDlb3lRZwDDvIEp+bZCRf+ONTlCU987h+nnPSsqb7404EJyLkShU6I15SWCWOKzGilIMSqZgT4aSVnTy0e3hilvj2X1oruFnM8vGfsjV1Oj/x7vXS3u8smt0PbgmGd8CAtVIfKSxtYGHnAmttU+FOjpF9RFaeAfFOnPF9iHgWdqFCJh6dUGIVc2RCDXguSDqb+T1vX8W+327xbhTzXi7FwNYZn/NoIJNwOGQ6cPJD870URVlQtPJtcxdwgohsEJE48Brghy2e/2ngEhGJikgMm3A2Jy7xmZd11dZhJwohi74uo3ghUc0Sn7ukM6ht2NGID/z+qbz38pOa7vfHQQIsSVhRG6lEcSVKXCo17uVTV3Xz4M5hCmW3tif4Ez+1P4ujUK7rZja0A/ZvZkv3hTxdtAly3RXrFYnnvN/dwFYYtCK3zawMxbBDLnHvZuCcsVutJ6DvWOhcjozvJxGNBBZ2o2Q4OwCkzCd/+hgf/OFmIJQlPoWHYjJ6Xfs+unzBLviCvWXG5zwaSEYdhuggqha2otQwpWAbY8rAO4EbsWL7HWPMZhH5kIhcASAi54nITuCVwL+LyGbv5d8FtgIPAr8DfmeM+dFsFz2XZV2+SxRY0Fnic93prOoSn31UxBffnqhnYZdjVCRKlHKNYJ+2uosRz1IN3M7GwOM3Vk+Wq7OyPTF/uv/ZPF3IYBCWyhAJisTL3gjGwa0wsBWDsMMsC7nEPcEujgfnTbqeOPYfDx0rYHSf7d1dqjBerF2vTyYRZThX4uu/eYpfbbHWfX4OXOLdFWv1d+DdKPoW9uD8Wtgi8mUR2S8iD4W2fVBEdonI/d6/3ztc149EhHGni2hx6HBdQlHakpYapxhjrgeur9v2t6HHd2Fd5fWvqwBvm+Ua688JzHL4BwSCLWP7KJsIUXEXdNJZdfjHHCWdeRb2VC7xVvCt0u5omQJxxopQltoYNlRLx8KvYWALHHoSjrkInvqlFdbO5dWTP/FT6DmGYs/xjJWeoNKxhGWjQyyTkLAPbIXsAPn0Kgr5uB38ARNd4uueCU//BjDQf5y9zu77ai3sRGML+46tAwxlS0Fddn4OXOJ+wmOH8brsFRaMS/yrwL8BX6/b/iljzCeOxAIKsW6Spb1TH6goi4iF2dZrEnzX8FzFsBndy17x4uYLVLBd1+APi5orC7tULPLR6JfoHNs+63P51nKnU6QgCXKlMmUc4nUW9okrOoMe8H5L08AdfqaXIR7ODC7lYdsvYOML6fSy13OJJSyVIZYSKvkZ2AKDWzmUsqkWjV3ig7BkI6w+B5wEdK0JLOxE1LGCXazUdDnz6UxGOTjm1Zh7tdr5+l7iuUPwvbfA+MEJr29GR8kemzF1FvY8C7Yx5jZgXlO0S/FeUpXR+VyCoiw42k6wfeGa8fCPusYpjO1jb2SFfbxAs8T9pikwd4IdGd3Fa6M/o2f3bbM+l+8S75UxxqWD8UKFMtbCDo+0TMYcTlhm25YGLvH9j0DHclhxhn2eDenE9l/a38kJLwxahI7E+lkmQ9XudEtOtII9sI39sTVEBPoznmDXu8RTvXDxe+Hiv7AtaDuXQ2mc3mjeusQL5Zo+4j7hbX7pl+8SD0IKO+6CB78DD3635c/NT55LBRa2J1Cjuxdqb/t3isgDnsu8t9lB0636aIRJ9VrPQ6U89cGKskhoQ8G2ij3jBqfhxinGwNg+9kdX2m0LNEu8EprFPGetST1hjFZmLwy+e7vXHWQ42keuWKFkHBIRl6hT+1/sVM8tHrjEx/ZB5wpIN6i9feJGayWvvygYADJAryfYnkv8mAth6CkoDPM0q1jSkajezPkW9riXTZ7ugxMvh0v+wm7vsDdqKyIjXqezSkPB9r0E3akYxbJLueJSKFVIRCPVjHJ/3U/cOOH1DTGGZMG6xJOVkEu80/u/OLittfMcOT4HHAeche2p8E/NDpxu1UcjIpl+ANzsvBr6irKgaFvBnn2WuFe7WykyGPcF27omb3l0Hw/tWjhdlsLNUubMws5bwYtVZp8Z71vLXaUBRqP9jBfLFHFIysSbi9NW20zvIEt8dK8VzpQv2J4Q+8loGy6BWDKwsPdUulnCMGujQ1RwYO35wbm3usuq8WuoCvawV4XoX8PHi5Uvl0MUyp6F3SRLHOCFp9rjsyU7O7sm4cwXlu2/hEILc5xzh4gYm6SXrIzZwTOFUVh1tt0//3HsGowx+4wxFWOMC/wHtgPiYcPvdjZyaGYWuqIcjbSdYM9pDNubnzycWIWLBFnif/s/m/n8LxbOF2bYwp6rTme+YEfLcyDYXtw3XRpgLL4kZGFPFOxLT1zGSSs6OW6pdY1bC3u5bXISiVWF7+Dj1nI+4flAdWrW06UuouJyemwXw06vjUt7bC4sq9ZgA0QTIBEYto1WAivex7Owl8oQ+ZLb1CV+/oY+nnPSMs5Y0wPYed75klubcObfaFSK8OQvpvzM/P97ORMnXh6zJW0YWHmW3b/ASrtEZGXo6cuAh5odOxekuq1lPjIw607GinLU0HaC7WvXnAz/GLNfmoXUEgrEg6Sz4VxpQqOM+aRcmfsYtt9Fai4EOx1zSJEnVh4nl1jCeLFMwXVINLCwNyzJcMO7L7aWsFuB8QNWOEWsoPquZb/U64QXANWZ2FtytnvZRncbhyK9tp4aIBLlofGeakkX2HPGMjDiW9h1YVfPwl5qDtmxn6VKQwv75ees4cvXnEfGyyAfL5TJl+ss7NwgJLog3llbptYM7//eNrOSWHmsmnDWtdJ+HvPoEheRbwG/Bk4UkZ0i8mbgH0TkQRF5ALgMeM/hXEPGE+yxof1THKkoi4e2m4ftBhb2DE/gu8RNBcZs0k8xuYw8CVKlHK5rGCuUJ/SOnk/KIQt7roZ/xDzBjhTr3Le5Q7Zr2JpzWz5XKjSitJBcRrZYoZBw6Gog2DWMHwDjVsu4Un1VC/uJn8KyU6HHZn77FvYT4xlIQJ87yBbnOI5L90GqD5Pu48CeSq1LHOwAkGYu8WQPOAn6OcRwroQxhlNGfgn33VU9pnMlHP9cAPpK+9gge8gWPZd4tM4lnlkKy0+FJ26yLv3JvECj1nLMrD6Z6N6fQt4LwSS6bI34PLrEjTFXN9j8pSO5hq5++38iN6IucUXxaT/B9mPYc1GH7ZXguOkl5IlBOc9ooWxz0RaSYIdj2HPU6SzuNaWQUl3S2R3/Br/6Z/i/j4OX+DMVqbjDMuz5SqmlVtBiEWJTCbbnFqbDF+xeyA3ZzOCnfwMXVEv4fcHeT0+wbb/xLOa151OIdlHZZVjSEa97o2mbdAYTXeIi0LOWlYUdDI4XOVu2cPlDH5jo7H3vk5Du49QHPsZXYg+wv/Cyxi7xVC9sfCE88kPY+yCsPKP5e/cs7PUbz4I9N1Q/i2QX9B8Lj17f/LWLgF5PsIujA1McqSiLh7Zzifek4/zqfc/hZWe3NDBsImHB9sq40ulOsm4cU8ox6o9uLC4gwa4p65qbLPFYybPo6hOkDjxqP5utP2v5XOlY1cKuZJZTcQ3ZSoQ4U3yGY1580oslBy7xoadsVveyk4NDE1GHeDTCAdMTbNvjeo1YXn0tT130DwAsmWBhZ6qPkz1M4NhLOSF7H5VSnuc49+GKA3/0G3j3g/DCj9lj/C5pxUHWR/bBwcfIlyq1ozVzg3b9x9uYe1Bf3ozRfdZ93um992FvIF6iCy77K/jjOyd//VFOR1cvJePgjqlgK4pP2wm2ExFW96SCmOa0CQt2KQ/i0NmRJk+ccn6ckZwVmfHCHJVPzQHlw5B0liwN2Qf19b5+7LSVOKzHC09bwctOsOLlZqxlNF4SorRoYXeGLOzsYHUN/cfXHN6VjFIgzrjYhLU9FU+wnSgHs/ZaSzoauMTBimO0zvoGOOGFJNwcF0Qe4TmR+xjqP9veKPSsg9719hivA1msZOuku3bcQr7s1mWJH7Iu987lNtN7KsEe22uPTdqs+SAxLtFlRbxF78bRikQijEoH5JsMhFGURUjbCfasCSedlfMQTdKTilnBLmYZ8SzsBesSn6MYdqrkJTkVQ92kXLcqlltubrlpxcblnTxvjYFIjGiHFZqicaYW7MDCDrvEB6sZ0n3H1Rzu36SNxuw1dpa7gla1fieyCYLtN09JN+nzsf4iSpLgaucWTo08xfCa51T3+WLqJYQ5JeuN6Nt9K/lihWS0gUscbKLczrtqm8DUM7rPehYS3k2HL9j+NRXbT1wndilKwCIU7FAddrkA0QTdqRg5E8ct5hjJWcEult1gpOVMKVVcPvPzLUE7y5niu8RjjsydYFc8l3jYwh7ZZW9ijr0U8kOw6+7WTzi2DzqWk0pYUS0RJUpp8teM7rUiF/VENt1ny6L2PmiFLLOk5nA/jj0et9v3uT3BmMsDo1awl06wsD2XeH3CmU88zY6eTfyeY13Q+Q3Pre5LeOLpWdiR4giuEZYM3odTGqla2OWivfHxY+QnvNAm0225ufl7n8zCVgDbTzzue4IURVmMgl0Xw46l6ElbC9stZmvKuWabKf7AziH+8cbHuOGh2Q0x8F3iqZgzZ0ln6YpnYYdj2L5le95b7Oc0Dbc4o1aA/LKoMg6OaSGG7cevoSqqO++yiVd1Wda+YBeStuRnv+kNPCEHx4rEnQhdqbo8St8lXl/SFWJH/0X2smYJzvJTqjsSnfZnfgTcCpHiGHe4pxAxZc4s3ldtr+rXYPvXWHW2zRif7PMLLGxfsHfYz9xfr0Ip0VP9f6ooymIX7KqFnScOpXzgEofZu8X9EqzNu2f3peO7xNPx6NxY2JUyaXecElGoFKDivWd/rOOqs+1kq7CF+PUr4Teft49H9sAnT4Fd91T3e+Kb9pqolIgSMS3EsMOTuXzBO/j4BHc4QKdnvRfTKzBEOEg3WS/X4OBYgf6OeLVVqE/gEm9iYQN7ll0MwM8rZ5EJ50YkPXd1YSTo8327OYN8tItN5fuqWeK+YPvXiERs8tlD34UPdsN/vrr2goVRKI3XWtgju614z7jn7tGHSfbSaUaDyXKKsthZxIJdjWF3p2PkSSDlfJB0BrNPPPOFdrZtTn2XeDoxRxa2JzADjtfn2a/FHtgK0ZStPV51Fhx4zMa1i1nYditsvcUet/te6z5/4L+q5/TE1+8RXsIh4k7hEq+3sMOiWpdwBlUL+6njX8d9z/oMJaIhC7swMX4NU7vEgVLXWv6o+C7+tfyy2sYpYQvbc4uPR7o4kDiG1e7e0KQuL1YdtuIv/Uu49P32xufJ22xdts/gk/ZnzzFVC9stafy6Dkn308sYB8eK870URVkQLELBDsWwS55gp2LkTYxIJTenFrYvtA/vHgkavszoPIGF7cyNhe0JzMGoZ936ceyBrXZOdCRiBbNSgJGd1UQ03wL3m3r4gy7KRXvOjhVBN7AyUSKmXCtUYbzBK7UWdliwG1jYnvUb711Ddr3tgJYtVi3sCTXYUHUxT2JhJ6MRrnefwX56Aw8BAE7M9iMvjASJZ6VoJ0PRfpZwqFrW5SeXhdffu96K9ulX2R71o3uq+/zQQ/9xtiWreOfR+HUNsY4+UlLk4KGF09dfUeaTRSjYYZe4FexE1KEcSeJUCkEdNsw+hu2XYI0Wyuw4NPMWoIGFHZsjl7hnYR+K1Qn24NZqq0/fJT2wpSowh7bbzHH/+eA2OLil2pikc3nQV7xkQjdGzdZQKdbFsEMWakPBrk7NCrcJBTg4WmxsYcd9C7t5DNsX3rgTIR6t+5NIdNkuZJ6FXYp1cJAelspQyCXuCXajm4Lw5+jj3/j0eXF635L3XfAKAEmvPemw9hNXFGDRC3YhyFA2sRRRt8BIthSEEWcr2OFyrId2zTyO7Z8nFZ8jl7hnEQ7HPbEsjFkhPrS96or2BXNga1Vg3LJtajK4DbrW2G1P3Bi02aRjReBSLvlN9CpN3JlBl7Nl1W1hwWsUw/YEuysVC4Z0jBfLGGMYGC9MbJoC1Yldk7jE/ZnW/k1ADcmuGgu7HOtkr9tDt2TJiHdzFySdNbhG+HP0GdgGnauqNxO+K1wt7BrSnmCPaz9xRQEWo2BLyPIr5yBqh0VILIlDhfF8LigNmiuXOMBDu2fu1vOzxDMJh4praqZ3zYhcnWAXx7zuYuWqwHSutGI3sLVObLZaa3HDxbD0JNsgZKzaACUVyhIHqglt9QSvCVnY0YSNOaf7IdUz4SUru1M4EWFZZyKIlY8XygznSpQqpkkMuwWXuGdh17jDfRJdNTFsN97JrpIV1h7XE+rsoJ00Fs9MfH3XGnAStRb2wJZaD4Jfi60x7BoyPfZmrqTdzhQFaMNe4rOmJumsADEr2JFYCvJQyI3zuuSvWZW/lzPu7YO1fwnhUp9p4LvEO5PR6See5UfgN5+DZ//fQLCDDOyKixNpYA22imcRjiW9iYnFsaoo+5atiH08uNWuZcmJcPAx2PuAjcf2H2vrpH/zOZsLANCxgrgTIRoRSkzhEg9Z5TWkeqG7cdvZy09bwU/fczHLupIMZa3lPl6ohJqmNIhht+IS9yzsjgajNUl6LnF/OEeyi+1D1oXdXfGExG9L2ijDOxKxru/w9K3BrXDy79deA9TCriPeaW+yyirYigIsSsGOAFITwwZwEhkYhVJ+nD8ofoNoZJyO3Xl48FhY/oEZXcofi3nW2h427x7BGDOx7KgZ226FW/8fbHwh5YptFOJblYX6tpjTJTtICYd80nNHF8ergtR7TPW4/uNsE5PCCGy83Lqx/VKvvuPsLOrHrrclSeufDR3LEBFScYdSaQqXuN8opGtl7fbTXwHdaxu+xIlIMEfbv3kZL5Q5MGqvMaFpCsCa8+zaQnOz6/EFO93IJZ7ogqEdgYVt4l08me+AOHSVfcE+NKnLnf7j4OAT1WOzA7Uuf1+o1cKuQTLWJS7Zg/O8EkVZGCw+wQZrZQdZ4vZLPpqwrtNKfowuc4jPulfyh/GbSJZmnixW8izjs9f2cPsTB9k3UmBFd3KKV3n4JVGlXLVxiifYs048yw0ybDpw416yU2EUxvYDAplQTLn/ODt5yrg2tt1/LOz4rbfveFhxGvzJPRNOn4lHibhePXMzl/jAlto4rs/zP9TSW4hHI8SdCOPFkIXdKIbdfxxc8+NJz+Xf/GQaucTDMexIjHgyzT63xx5f9IQke2hSC57+42zowK3Y+LW/LXwNUAu7nvQSXIRYTgVbUWAxxrChKtjlgq07BuJJKxzdxX1EcBl2+ilGkhOHY0yDkiesK3vsNQ5lp1FP6no14OVctawrZgVl1oln2UGG6EASnlgWx21MObMUnJBo9R1nxRqswISf+9nkDUjHHaIxzz3dzCU+uLVhJvh0SCccssVy8z7iLTJp0lk4hp3sIpWIMkgnZROpCrbvEm9G33HW0zC8o5rAF64zVwu7MU6UsUgXiYK6xBUFFrVgu55L3H7Jx1JWvFZjY6vjsT4KkoBSbsaX8ZPOelLW2pxWT3FfsEs5Kt55fEGZrYVtcoc4ZDJI3LqXKY7ZmHK4JhpqRaX/+OrzzpWQ6Gh6/nTCwYl54tnMJT4we8HOxG3jlINjBZyIBJ/zdElMamF32+TE7AAkusjEnaDLWrLgC/ZUFrb3ufklchKpTgKDalmXWtgTGHX6SJcmGaKiKIuIRSrYjhWSSiGIYSc9wV4ntoRkPL6EPEnb9GKG+Eln3Z6QjE2nc5pvmZZywXl8l/hsh5KY7CBDppN4LGqzsn0Luz4BLBBUgd4N1ecNSq7CpONRYr6F3cglnh20VmmDbmbTIZNwyBYqHBgt0J+JE4nMrK1nMrCwm2SJAwzvsha2J+r7TQ+J/AHbACY7hYUdlHZtszcq3WuqA0+galmrhT2BbLyPzrIKtqLAohXsaFWIvSzxVNpajOvEWti55FJyJGblEvdd2d1pz8KeTpmYL9jlfFDGlZ6rGHZ2kEOmw7qCEx02ht3Iwk73Wwuze639nHzhmcIyftvFx/KCM9Z576OBYPsZ01MI/1RkElHGi2UOjjVpmtIivoXdMOksPE3Ls7DBCnY8u9/+P6oUJrewO5ZDvAOe+hXse2jijYp/U5DQxin1FJP91fI5RVnkLN6kM79/drResK2FXU4tJTsSn7VLXAS6kr6FPQPBLuUoeS7xlBfDLsxSsCU/xAhpUrGITfoqjNhuZfUWtgisOKMqKP3H289rxemTnv+5Jy+H2FL4FY0tbL+EbA5c4o/tHSVbrPCs4/tnfJ5kNEJnIsrKrgYJgf57H9sHazYFN037TQ/R3IPVbHcvo7khIrDsFHj4B/b58c+r3d+zzrrJ6zPmFUqppfQzTL5UmV1lhKIcBSxewfbHSnquyUzGxhHXyX7K8W7iiTTjJmGnKs2QUsUQi0SCL3m/73VL+MldpRyVSrVxCsyBhV0pkidOtxOxlt+hp+z1OldMPPZVX6/WFye74Z132Rj2VDiTZIk3iuPOgHTcYf9ogY3LO/ibl8ysVh4g6kS46c8uoS/ToI47cFMbSHQF5WQH6CWSHbDZ3wDrL5r8Iq/+pp1CJmKnoYU5/nnwJ/daV7lSg0kvJSMF9g0PkVwy85syRTkaWKSC7VRd3V6WeEeHtbB7ZJxiZiMdyShjbhyKM2+LWK64RB0JYqMzsrDL+aA8LHCJzyaG7VYQU6FoYtYVHO+AfZvtvo7lE4+vj832rGvtOo6fJd7IJb7VutmjM3djA1x20jISMYePvuy0wIsxU5qW24UTwZJdNRa2YOC+a21TmaluPjqXTww5+IhA34bpL3oR4HTZz2xscDfLVbCVRc7ijWEHgm1Fo7Mz9MXcuZyORJQxNzZLl7ghGhES0QhORKaZJe67xLNUXBcnIsQdL+ms7PK6L/6WT970+PQX5WVtl4jaGHY8AwWvaUojC3umRKawsGeZcAZw9fnr+Nerz561WE9KOBEs0UU6UU06A+DAI7DxBYfv+oucqCfY2YE9UxypKEc/i9TCjkJx1D72YtgdmWqZktO1kkzCYaQSx5SyzCz32GZzx5wIIkIm7kxvvnZQ1pWnbKzw+5OkihWXe546FGSNTwtPsIs49nzh8qxGFvZM8eu56wXbGJstvfaCubvW4SScCJboDCzsQ5GQ5+EEFezDRbLHhl9KI3vneSWKMv+ohe33Eo+ng91O1wqbgWy8LPFmM52noFwxRB0r95lEdHrTv0KNU0oVK9gx71wHxwrkShVGck26iE2GJ6A1FrbPnAp2E5f4+AF7szQHFvYRwa+RhhqX+FDUE+xEF6x75jwsbHGQ7rOCXRnRiV2KskgFOxzD9mKXkQhFPNdq5wo6ElFyJoGYSvPmH8A3fvMUf3/Dow33lVyXaMTrUx13GJ+RS9w2TomGZjU/PWBL0kbyM5gmFljYMRJRB/z2pMme4OZlTmjmEvd7as+ypOuIEY1X/4+Eks7GfcE+7rJqgp0y53T3ewmO4yrYirJ4Bduvww4lPhXFswo7lpOJR20dNkzaPOXnj+7nf+7b1XBfuWICq7gjEZ2mSzxc1mXPEwj2oF3PaH4mFrYXwzZ1FvZcxq+huUt8++1Ag0zphUyodahfhx2NJ+BF/wgXv3ceF3b0k0wmGDSdOFkVbEVZpIIdCt17WeIAlYhnSXVal3jWF+xic8HOFSscHCtiGrjNy66NYYPt/jUtl7gJtSatGJyIkPCSznzBnpFLvFyXdObHsOfSHQ7NXeKP32gnaGXaKOM3WW1s4ucNJKMOXPBWOwBFOWyICIekh3he+4krigp2yMIOMsU7fJe4JzqlLGOFMn/3o83k6mqpc6UKxYrb0D1dqhiiTrXt5fh06rBryrqsaz0Wtdb6Dt/CLpRx3ebx9Qd2DvHF27fVbgxc4lHPJX6YBLuRS3xsP+y+t/2StEIWtu8S1yYeR44Rp5dUUQVbUVSwo9W4bSTuWdudy8kknBqX+G+3DfCVX23n3qdr2yTmS1aE/YlRYWyWuJ905sxi+IdNXot74u8LvzFMGhf/73t38Y83Pla7MSjrckjEIlXBblYjPFMaucSfuMn+bLcyqND4S8cr00vGFuefznwwHusjowNAFGWxCnbIOgoJNrEUxNIQ76AjESWLt6+YZcSLF9c3P8n5gj06UbDLXnY3zCRLvBrD9s8TdSLUz7eYLPFsvFCeOCjEE9AiMXsD4Mew69uSzhbfJR5O2HviRtslbcUZc3utw03d+MtMIqoW9hEkH++nu6L9xBVlkQp2yMIOZ0ZHU9Y1LLY7WdUlPs5w1gpdvej6LvKDY3WZ5A99j1hppOoSn2kddjlHOZRt7iee+d1CJ4tjZ4sVXEMwPASwgyrwYtixw5h05rvE3RL87ttw68dhyy1wwvOri28Xkl0gjr2ZA1Ixx4YTlCNCMbWUFPlZDeJRlKMBFWwn1B5z9Tmw4WLAWlFVl3iO4ZwV6qYWdtglPrYfvvsmnp29KeQSj5IrVWrFczICCztfU8/tu8XX9VnxmEywfXd5Te9xP4ZtovZcS0+ErtVzn7XtlzoNbofvvxVu/ZidP37aVXN7nSPB6k2w/lnBjcam9b2cuUYnax0p3PQS+2BMM8WVxc3i7XTm/3RCH8ELPhw8zMSdmizxZi7xhjHsgu2i1lM+GFjGGS9ZKVss09lKK80ghp0NWpxC1cI+YVknTw1kGZ3EJZ71LPpixSWFZxF6LnE3ErXWf/ca+LOHp17PdBGxn+/jP7HP/+i39uag3axrgE1vtP88/uU1bVSSdjTQsQyAwvAeEtpzXVnELFIL2xOvUElXPVEnQsR3F5fGGfYs2bGQQJYqLiVvklaNYHt12z2VwRoLG2jdLR7KEi97jVOgamGfsNwmi41MUovtW9g1cWzPwjbO7AZvtEQkBtkB6DmmfcVamXciXasAyB5s3O9AURYLi1SwPat6imlRCW9GtnWJT7Swfesa4MBoKIZdrAp2YGF7ozFb7nbm12GX81TKFZw6C3ujL9hTxLChziXu1WHLkejO5V9j4wtVrJUZE+9dDUDx0M55XomizC+LXLAnb8WZSnttO4vZQBjDgp0LCXYjC7vPDFZ7ifstLVvNFHerx4lbCCz1sEscmNQl7l+rkYVNtMHs57nGF+wTXnj4r6UctXT0LqNgYpSG1MJWFjeLW7Cn6J3d1ZGmjNPUJZ4vWiGMSBPBdg9VO535FvZ0XeKAUykElrp/vrW9aVIxZ1KXeEML2xPsyJFyiUdTsP6iw38t5ahlRXeKfaYHd3j3fC9FUeaVRSrYfgx7csHuS8dtpnjIwg67tH0Le2V3ioNjhWp7Us8l3sUYSaxATt/Crgp7pJKvSTpLxiJ0paJ0paKM5BqfzxhTzRKvsbDt+5ApwgFzQqLTDseYy6EiyqJjRXeSvfThjOmITWVxs7izxKcQrd5MnKxJ0FnKBg1KaixsT7DX9KbYNZRjrOBlgIeGhfSYISCUdNZqDDsk2NFyvqasa0VXEhGhKxlramHnS24wFdRPjAMCC1tiR8Al/qqvQbqNeoYrC5JkzOFQpI9j8xrDVhY3i9TC9gW7eZY4QF8mTtbEKRfGg9j1aIMY9lqvJjponhIS7D7XtlTMzMYl7uYDl/i5x/RyycalAHQmo01j2OEbg1qXuHXdO0cihr381LlvyKIsSsYTy+gsHpjxbHpFORpYnBa2+C7xKSzsdJwcSXLjI8G28UaC3esLdoENSzI1HZm6yr5gV+uwWyIk2FG3EFjY7738pOq5UzEGxxvP6s6GbgxKDVzizpFwiSvKHFFMLydRyEN+GFI9870cRZkXFqmF3WIMOxMjS4JCdtx7Hq9LOvMtbGupB/3ES7ngmB7XThlKe72n6xuvNMVURTZayQdlXWG6krEgtn7PU4dqhLnGwq7LEi/jEIstzns1pT1xO1baB6N75nchijKPLFLBbi1LvDdtY9jlwhgAq3tSjBcrwUjLiS5xX7CzEEtTMUKXN2Uo6thksWyrIzbdcjBJK2YKxCITf1VdqSgj+TJb9o/xis/dwSd+Wp3MlW3qEi9S9mdhK0qbEO22tdilQ1rapSxeFue3dot12H2ZOHnimIK1sFf12ON969UX7NU9KUTggB/DLo5j4h0cpJvOcnWObyYebd3CDgu2W8BxJlrYnckYo/kSv9sxBMCXf/kkTx60ax1v5hIvF73BHzq8QmkfUkvWADB64Ol5XomizB+LXLBbyBIngZSti3tVj3V9+6LrT+rqSEbpTcdDFnYOYmn2mx46SgeD82USUbLTEeyEbY4SdQvEmrjESxXD3U8dIhGNkIg6fPjHti942JL3LexcsUKlXKBENGhxqijtQNeydQDkBnbM80oUZf5YnN/aLWaJ96RiZE2CaMUK9mpPsP3EM7+sKxVzWNIRZyDkEjexNPtNL5li1cJOxx3GW3aJVyBhLey4KeA0cYkD/HrrQU5d1cU7n3M8tzy6n0f2jNS4xH0L+5qv3MlND+6gaLzRmorSJizv6+aQ6aA0pM1TlMXL4vzWjrSWJR51IrjRFAljhdi3sP1SqlypQjQixJwIXclYtcSqOI7rWdiZYtXC7khEp9c4JXCJ54PWpGH8qV/bB7KctrqbZx5ra553HcrV3BgUvTrs/aMFcvk8eaMxbKW9WNGVZK/pRTTpTFnEtPStLSKXi8hjIrJFRN7XYP/FInKviJRF5KrQ9stE5P7Qv7yIvHQO1z8zWoxhAxBLkyZP3BGWdFiB9+PDuaJLyosFdyZD8elSDjeaYj89JIuDQROUdCI6DQu76hJPmGKTLPFqpvepq7ro77C11QPjhRrXu+8Sz5cqrMwIkVic01frPGelfehOxTggfcSy++Z7KYoyb0wp2CLiAJ8BXgScAlwtIqfUHfY0cA3wn+GNxpifG2POMsacBTwHyAI/nf2yZ0mLMWwAiWdwxNCftBYywFjBllLlShWScSvYHWELuzSOG01xwPQQwYXxA4CdsT2t4R+xFAYhRiEYrxmmK1WduHXqqm76M/b9DIwXa24MfJd4oezSHYd1S3p4+TlrWluHoiwARITR2FIyhf3zvRRFmTdasbDPB7YYY7YZY4rAdcCV4QOMMduNMQ8AbqMTeFwF/MQYk53kmCNDUNY1eQwbwEnYkq1lqQqdnkXrC3O+VAks7I5EqOtYMUslal3i9gW2B7KfdLZvJM/WA2OTX9hU7DpjKVIUmyadAcQcYePyTlJxh3TcYWCsSLZQDiZ7lTwLu1CqEKNcnaKlKA0QkS+LyH4ReSi0rU9EbhKRJ7yfvUd6Xbnkcjorh4LmP4qy2GhFsFcD4dTMnd626fIa4FuNdojIW0XkbhG5+8CBAzM49TRpMYYN4HiJX0vjlcDCHg9lide6xL0vklIO10ly0Hhu53Ebx87EHQ6OF3nBp27jjV+5a/ILu1XBTlJsWNblu8RPXNEZiHNfJs6gZ2F3JWOIVBunFMquFWztcqZMzleBy+u2vQ/4mTHmBOBn3vMjSqVjBREMjKlbXFmcHJHMIxFZCZwO3NhovzHmC8aYTcaYTUuXLj38C5pGDDuWsoLdn6gE7UWDsq6QS7wzESVfcq37uTROJZoij9evu5wHrIVdLLuM5kvsOJSlUJ4knu2WIeJgokmSFJs0TrGW8qkrq/Ho/kycgfEi2WKZjoRDzIlQrLiUKy5l1xBVC1uZAmPMbcBg3eYrga95j78GvPRIrglAvOYpOmZTWay0Iti7gLWh52u8bdPhVcD3jTELw5c1DcFO+IIdty7meDTCmJ90VqqQ8sqjOjxrdyxfhmKWspOmgCeMnmA//5TlXH3+Ov7mJadgjM3mbopbhkgU4yRJSeOks0Q0wjsuPY6rL1gXbOvvSDAwVmC8UCEdt/XWxbJLwXOLx0wJnCMw+EM52lhujPFTtPcCy4/0AhK9VrC1eYqyWGlFsO8CThCRDSISx7q2fzjN61xNE3f4vDANwU5lrGD3xuy9Rkei6vquj2EDjGVz4JYoOwnyptbCPntdLx97+emcuspaxE8PThLOdysgji0ro9iwrEtE+MvLT+KstT3BNt8lni2WySQc4tEIpUpVsK2FrYKtzBxjB783HZt1uEJc3cvXAzC2/6k5O6eitBNTCrYxpgy8E+vOfgT4jjFms4h8SESuABCR80RkJ/BK4N9FZLP/ehFZj7XQf3EY1j8zphHDTmW6AOiJWTd4RyIaDADJFSuk4tUYNsD4+CgApUiaQp1L3McfFrJjUgvbxrB9l3ijximN6O+IMzBWZLxQJh2PEnOEUtkETV4ctbCVmbHPC235Ia6m6dqHK8S1fPlKCiZGflDnYiuLk5ZGNhljrgeur9v2t6HHd2Fd5Y1eu52ZJakdPnzBbiFLvKPDCna3Y/uEWwu76hJPBkln1v2d8wS77CSrLvFSrWAv70wSdyLsnNTCtjFs10mSkpFgvOZU9GfiFCsu+0cLrO5NEY/aGLZvYatgKzPkh8AbgI97P//nSC9gTV+aPaYPd1gHgCiLk8XZ7sp3hcfSUx7a1d0D1At2c5e4Pzu7KMkJSWc+kYiwpjfFjkPNBdt1S3zjzl3kiZOkSLRBDLsRfV4t9t6RvGdh+4LtWdiuCrYyOSLyLeDXwIkislNE3owV6ueLyBPA87znR5TOZIyDkX5i49rtTFmcLM6hyMc9B674N1hx+pSHrlm3AYCzumzddEcyygFv7nW4rMtPOivk7HFFJ0kJB4Mg5cLE8/alJ41hm0qF4bLLUClqBbvFYR1+tzNjbBlZ3IlQKrsUSp6F7ZY0S1yZFGPM1U12PfeILqQBY/FlrMs/Ot/LUJR5YZFa2Ak45/UgU1utEs9A12oSw08CvoVdxhhjs8RDZV0AhawV7FIkBQjGSUywsAHW9qbYMdg4hj04VsChQgWH4XKUpLRuYfdnqtZzOhENXOJ+DDtiSlqHrbQthfQKeioHwZ2sR5OiHJ0sTsGeLv3HwcAWwNZSjxXKFCsurmFCDLuc9yxssaLoRpMNBXtdX5rhXImR/MRKt+/fa8tWyibCcMmZlku8v6Mqxpm4rcMOZ4mLWthKO9O1ijhlTPbg1McqylGGCnYr9B0Hg1sBr6NZvky+aAXQF+xkLIITEUqeYBciXpw82sTC7rPx8x11bnFjDN+7y5atxOMxBotWsGOtusRDFnbKyxIP12FHKkWNYSttS6LP5q8e2qulXcriQwW7FfqPg9whyA6SiUfJlSqMefOm/Ri2iNCRiFLJjwNQECvYxklCgxj22t7Ggr3t4DjbDtjEtVQiwcGCQ4oCLSaJk4zZfuLgxbCjDsWKLeuK4CKmooKttC2ZpbZJ0OCe7fO7EEWZB1SwW6H/ePtzYGuQXDYwZkU4Fa9+hB2JKG7RE2xCFnZpYqx6XWBh1+4byhaJYuPNmVSCQ0UHRwwxaXHKF9XEs3QiStwRNuYeoG/nzbaPOKhLXGlb+lbaJNDxg9rtTFl8qGC3Qt9x9ufgVpZ22hjxlv3W9e1b2GDd5W7RWsx5L4ZNLNXQwu5Ox+hMRieUdmWLFRxv6FkmWS0Ni7sTz9F0uV5pl7WwI7wi/11Oe/iTIcHWpDOlPVm5ah1lE6F0SGuxlcWHCnYr9K4HicDAVk5dZRup3P3UIaAawwav21kg2JPHsMG6xetd4mHB7kjFGcM2d4m7rU8lXeLFsf067KSbI1oaCwm2usSV9iSdTHBQepFRFWxl8aGC3QrROPSsg4EtbOjPkIk73L3dDjMKW9gdiSiRchbEoeB6se0mWeIAvZkYw7naLPFcsYLjucQ70kmyxlrD8cp4y8vt8wQ7403rSpks8fIocXWJK0cBQ9GlJLI6YlNZfKhgt4qXKR6JCKes6uLxfZ5LPB4S7GQMKecglqbsjUaQWKqpYGfiUcYLtSM2s8UKUc/C7kqlAgs7Vpmk73j9UjuqFnY8GiFp8sTcPGnx3OpqYSttTC65jM7i3A0VUZR2QQW7VfqPh4GtYEwwbQsmxrCdcg7iacoVr+451jhLHKpNWMJki2UcsSLemUmSNda1HqtMxyVurfK01+ksjRX7ZY69ydDGKUo7U86sZIl7ALeizVOUxcXibE06E/qPg+IYjO3ntNVVwa6JYSei1hKOpSlVrIktscZZ4mCbsIwXawU7F4ph96STjHnZ5rFy64L9otNXMJovsbI7ScwR0iYPAsujnmCrS1xpZ7pWk9lXYODQAfqXHPGx3Ioyb6iF3SpLTrA/92/mtNVdweYal3giStwUcGMpyq6LExEk2jhLHDzBrrewSxWSESvY6VSCkmNd4tFpWNhretP82QtORESIO5AR65IPLGx1iSttTLTfq8Xe/eQ8r0RRjiwq2K2y9gJbDvXEzRy3tIN41H50NUlnySgpCriOtbBjjtjJYE1i2B0Jh1LFBJO0wFrYGc8AlkiUTGcPANFy60lnYdJSDB4vFduQRS1spZ3pWLoegLH92+d1HYpypFHBbpV4BtZfBE/cSMyJcPKKTqC+rCtGWgqUoylKFZdYJOKVdTW3sIGaxLNssUwm6rU1i0Tp9ATbmYZLPEya6s1CfyDYGsNW2pder3lKcUDbkyqLCxXs6bDxhXYIyMBWTl/TTTru4ISGcnQkrIVdlATliiEaWNg5O++yjqpgV93i48UKad8Ajjj09PQAEC3NzMLOUI2f94m6xJX2p3f5WorGwQzvnO+lKMoRRQV7OpzwfPvziZ/yruecwBffsKlmd6fnEi9GkpRd186wjnkNVCpF6unwBDucKR52iROJsrw7xbhJEJmhSzxlqhZ2L+oSV9ofx3E4EFlCbGz3fC9FUY4oKtjToe9Y6D8BHr+RZV1JLjxuSc3ujkSUtBQoSNLGsCOehQ0N49iNLOxssUw66lnjEYdzj+klL+lpZYmHSYUs7B4zbB+oha20OUPRpaTze+d7GYpyRFHBni4bXwhP/SpoQRrGt7ALkqBc8Sxsv+a5NFGwOxI2/l1vYaf8YrtIlMtPW0l/Xy+RGbrEk271ut2uJ9hah620OePJlfSW9s/3MhTliKKCPV2WbLTu7dzghF0dMUO3ZBmNdFFy/Ri2Lcua3MIOJ51VSPt5bOI9iGdsDfgMSIR6kHe6Q/aBusSVNqfUsYol7kFMpfUpdorS7qhgTxffOm2Q+d1ZtgNBDkX6KIezxJscn4k3conXWtgAxDuhOEML21iXeN7EiBmvb7m6xJV2p3sNUXEZOahDQJTFgwr2dPHFrkESWTJv+xsPSm9tljjYTPE6GiadlSqkghi2L9gZKIzOaLkJr+HKHtM38T0oSpsS77PNUw7t3jbPK1GUI4cK9nSZxGKWMTtBaHelx3OJh7LEG1nYTZLOUk416QyARMeMLey4a28U9hEWbHWJK+1NZvl6AMYPbJ/XdSjKkUR7iU8Xv+lIpTRx35jNWt1e6KDsulNmicejEeJOhDGvn7jrGvIll2TgEp99DDteGadgogyazonvQVHalD6veUphYMc8r0RRjhxqYU8X3zqtNOheNroPF2FrNj3RJd4gSxzszGrfws6VbPJZ1cJuIYb9+I0w2nw2cLSSI0uSUZOe+B4UpU1Z0r+UUZNChlWwlcWDCvZ0mcQlzthexp0e9o6VKbkusXBZV7OZ2InqTOxs0f5MThBsz8Ku75ZWGIP/fDXc+YWmy42Vxxknyag3Vxtxqpa7orQpUSfCvshSYuPaPEVZPKhgT5dJks4Y3Uc2sYQDowWKZZdoJFzWNfVM7Jwn2ImIJ8zi/XoSHWDciWM6B7cCBkb3NF1utJJl3IQsbE04U44ShmPL6NDmKcoiQgV7ukxhYZdSyyi7hgOjhdrGKZNa2FawsyX7MzHBwu6wP+vj2ANb7c/R5l9aTsm3sD3BjqpgK0cHo6nVLCntadinX1GORlSwp8tkSWej+zAdywE4MFYg7kQmTTqDOsH2LezpCvZY8xi2U/YsbN8lrha2cpRQ6DyGDsYhd2i+l6IoRwQV7OnSLOnMdWF8P9HuFYC96Y86Eirraj4Te4JLXFy7MxzDhomJZ4OtWNhjjJNSl7hy1GF6bab4+N4n5nklinJkUMGeLs1c4tkBcMskelZWD420YGHHJyadxf2csHAdNtgkszADW7xrH6y1+I0Br2WjlLKMkwhZ2JohrhwdxJceC8DorsfmeSWKcmRQwZ4uzZLOvBrsTP+aYFPMEWslS2SSsq6wS9z+jAcWtl+H7bvE6yzsga1VF/1YaBDCY9fDPxwL+WEipXHGTdjC1hps5eigc+VGAPL7t87zShTlyKCCPV2aWdheLXSidxWdXgezqCMgXqZ4U5d4lPFiGWNM1cKO1LvEfcEOtSfNDtoBJGvOs8/HQm7xPQ9AYRgOPAbFMVuHjbrElaOLFf097DW9mENPzvdSFOWIoII9XZolnfmC2bGcpV32mGjE+3ijiaZlXZlEFNfYpinNBbtBDHvQ66F8zIX2Z7h5ir+W/Y8glSJjNWVd6hJXjg6WdSV4yiwnPvLUfC9FUY4IKtjTJeIAMjHpzE/86lzBsk4r2DFH7LZosuHwD6idiZ3zXOIx8euw61zi4Ri2nyHuC3bYwvbFe+8D9mWRlGaJK0cdiajDPmclnVntdqYsDlSwp4tIY4t5bB8kuiGWYlmnTTSLOq1Z2GBnYmeLFaIRIUqdhZ1oUNY1sMXGxteeD0hjC3vP7wDIS4oCcUwkrnXYylHFUHINXeUBKGanPlhR2hwV7JngJCYmnY3uhU5bgx1Y2BHPwo41j2GHJ3ZlixVScQfcMiDgu9SduBXvsGAPboXutdZdnu5vYmE/BEDRsda1m+hUC1s5qsh12DGbHNo+r+tQlCOBCvZMiMYbW9he05Rlfgw7bGE3yRIPz8TOFSukfcGOhAapiVi3eDiGPbAV+o+3jztXVEXaqwcHAjd8IWLj1ybRpYKtHFW4vevtA008UxYBKtgzwUlMTDob3WuFE0Iu8XAMuwULu1QhHY+CqUwc0BHvqMawjfEE+zj7vGN51cL26sHpPyF4adGxgl0+981w+lUzeceKsiCJLbF/A6WDWtqlHP3oPOyZ4MRqk86MqbWwA5e4b2Enm47HrE86S8UccCu1FjbYOLbvEh8/YEu8+jzB7lwBBx61j33hPuZCGLAdoMqOzTKXZ/4RRHVSl3L00Nu/nGGTRvZuQesflKMdtbBnQn0SWX7YWtC+hR24xKfOEq9POqu6xOst7ExVsP0OZ75LvGO5vWFw3aprfP1FwUtLTtp61R39dStHFyu6kzxpVmIOaLcz5ehHv8FnghOvTTrzu4x1WMFe05vmjDXdnLKyy25vKUu8Lums3sIOx7D9kq5+25qRzhX2NbnBqoW9+twgXl2OpUlEI4jIzN+zoixAVnQnechdT2pgs07tUo56VLBnQr0A+yLpZYknYw4/fOdFXHBsv90+WZZ4vFHSWaVag+0TjmEPbIFIDLq9DFnPFc/o3mo9eNcq8IYjVJw0CXWFK0chK7qSPGQ2EC+PauKZctSjgj0T6pPOfDe0Z2FPwBf4whjsvr/2VBHh9Nhu3LGDZEtlm3TWyMIOx7AHt0LvenC8YzxXPGN7a+rBrctccKMpElH9VStHH5lElCdjXmio7m9LUY429Ft8JtQnndVZ2BOIJm1Z160fgy+/0FrQIf4j+vdsevTvyRZ8l3iDpLN0v3W9lwswsK2aIQ62Hhvg4JaaenDWbILeY4jFoiRjamErRyfZ7o2UiQaNghTlaEUFeybUu8RH91pRTnRNcnweHvuJ/RlugAIsiYxxVv5OhsZzZIIYdt2vZsPFNnHtqV9ZC9tPOAPoXm2fb7mpJludZ/0p/NFviDsRtbCVo5b1K/rYIutgz/3zvRRFOazot/hMmJB05olks6SuaMpa5INesli4xMt1iVZydEuWc+QJUkEddp2FveFi64q/56tW9PuOrd1/wgvhydttxyffRR5xIJbilZvW8IYL18/iDSvKwuWUlV3cVzoGd/fvNPFMOapRwZ4JjSzszibxa//4MOEhHqFktOc499GbjjXJEs/AhmfDIz+yz8MWNsDGF9ibgrCF7XH5aSt53TOOmepdKUpbcsqqLh4yG4jkD8HQ0/O9HEU5bKhgz4T6pLMGIllDNFn7POwSL1WHFrxx6eO8+ry1jWPYACe8AIw3GCQcwwZYd2F1qtdkNw+KcpRx8spOHnRtRYTGsZWjGRXsmVCfdDa6b3KRjHmCveps+zMs2L57fOnJJA89Rjq7x1rY0uBXc8IL7M9oEjpX1e6LxuHYS+3jyW4eFOUoY1lnkoPp46ngwO5753s5inLYUMGeCdEElL0YdikHheHJRTJmW4Ny6svtz3AMu+R1QDv1pfbn1p81t7D7NsCSjbYlaX1SGsDGF9qfnStbfiuKcjRw3Kp+HotuhG2/mO+lKMphQ3uJzwQnXrWw/UYlk1nYJ14OV/wrrL0Abvqb2hh2yRPvFWfYnyN7Gsewfa78rDd+swGnv8omw617ZuvvRVGOAk5Z1cXN20/l5N3fQ7KDkO6b7yUpypyjFvZM8JPO/KEf0LxpCkCyG875A0h02uc1LnEvhp3ogHgnFEYmF+y158ExTQQ5loTz/rDaUEVR5hgR2S4iD4rI/SJy93yvx+eUlV3cWj4NwcCTamUrRyctCbaIXC4ij4nIFhF5X4P9F4vIvSJSFpGr6vatE5GfisgjIvKwiKyfo7XPH04CMNZ17Qt2s6YpYfyksJqkM88lHktDsgvyIzaxrH74h6IsHC4zxpxljNk03wvxOWVlF78zx1GKdsDWn8/3chTlsDClYIuIA3wGeBFwCnC1iJxSd9jTwDXAfzY4xdeBfzTGnAycD+yfzYIXBI43yK9SmLotaZi4F8uuiWF7j2Np23ilMNx4WpeiKE3ZsCRDLBZjS8c5VrC1Hls5CmnFwj4f2GKM2WaMKQLXAVeGDzDGbDfGPAC44e2esEeNMTd5x40ZY7K0O35ddblg25KKY1uHTkXEscJcGK1u813i8ZCFPZlLXFHmFwP8VETuEZG3zvdifKJOhE3H9HFz/hQYfhoGt833khRlzmlFsFcDO0LPd3rbWmEjMCQi/y0i94nIP3oWe3vjja2kUrQWdseyxlnbjYhn6ixsT7ADC1sFW1nQXGSMOQfrcftjEbm4/gAReauI3C0idx84cODILeyEJXx3eKN98vgNR+y6inKkONxJZ1Hg2cCfA+cBx2Jd5zXM1x/4jAlb2NmDkFnS+mvjHY0bp4Rj2K47cbymoiwAjDG7vJ/7ge9jPXD1x3zBGLPJGLNp6dKlR2xtFx2/hKfMCg71nAr3fVPd4spRRyuCvQtYG3q+xtvWCjuB+z13ehn4AXBO/UHz9Qc+YwILu2QFNtHd+mvjHbUWdrGZha2CrSwsRCQjIp3+Y+AFwEPzu6oqp6zsoj8T5+bk5bD/YdilTVSUo4tWBPsu4AQR2SAiceA1wA9bPP9dQI+I+Cr8HODh6S9zgREIdsEKbLLJlK5GxDO1MexS1nYui0Rs2ZfGsJWFy3LglyLyO+BO4H+NMQvG9xyJCM86fgmfOXgWJpaGe78230tSlDllSsH2LON3AjcCjwDfMcZsFpEPicgVACJynojsBF4J/LuIbPZeW8G6w38mIg8CAvzH4XkrR5CwSzw/0nysZiMSHRNj2LG0fZzssjcBpawKtrLg8DxlZ3r/TjXGfHS+11TPRScsYfuYw/CxL4aHvlfbpEhR2pyWVMEYcz1wfd22vw09vgvrKm/02puAM2axxoVHOOmsMDx9C3solMNXzFbLvXzXeu6QusQVZQY8+wSbT/Kz1It4RfG/4P5r4YK3zfOqFGVu0E5nMyGwsPPWvT0dCzveOTHpLJayj33hL2VVsBVlBqzsTnHe+l4+t7Ufs+6Z8Mt/rh2FqyhtjAr2TPAt7Nwh25Vsuhb2BMH2XOJh4VeXuKLMiJefs4YtB8Z58pQ/htHd1spWlKMAFeyZ4Av2+EH7c7ox7MJYteQk7BIPC7+WdSnKjPi901cSj0b4+r4NsHoT3P6p6nQ9RWljVLBngu8S9wV7uha2qVTddKVxtbAVZQ7pTsV4wSnL+Z/f7aZ08f9nO5/94u/ne1mKMmtUsGdCYGF7TV6mVYddN7GrlJsYwwYVbEWZBa84Zw2HsiW+P3IinPVa+OUnYeeCGS6mKDNCBXsmBBa2J9jTtbChKtg1WeJhwVaXuKLMlEs2LuXsdT38/Q2PMnzph6FrNfz3W20ZpqK0KSrYM2G2MWyo1oc2dYmrYCvKTIlEhA9feRqHskU+ddteeNnn4dB2+ME7bOtfRWlDVLBngi/Y2RnGsKHaPCXsEneiEPP2q0tcUWbFaau7ee0Fx/D1X2/n15WT4QUfgUd/DLf943wvTVFmhAr2TKh3iU+rDtuzsIuj9k6/FHKJQ1X8VbAVZda89/IT2bAkwx9dew87Nr4BzngN3Pr/4JaP6HAQpe1QVZgJjifYuUO2/CosuFMRCPY4lHP2se8SB0qZ1ew89V3ku8+BRx6ZowUr7UwymWTNmjXEYrH5Xkrb0ZmM8cU3nMeV//ZL/vDr9/Dtt3yKHidmreyx/fDiT1rPlqK0Afo/dSZEItYCdst2YIdI66/1xb0wZt3hUCPYO09+M50rj2f9irVI18o5XLTSjhhjGBgYYOfOnWzYsGG+l9OWbFiS4XOvO5c3fvUu/s+X7uHaN/8TvZmlNnM8OwCv+GI1LKUoCxh1ic8UP449nfg1WIEHa2H7cex4VbDz6dX0Z6JIRH81CogI/f395PP5+V5KW/Os45fwhdefy5YDY1zztbspX/Y38KJ/gEf/F770fDjw2HwvUVGmRFVhpviCPZ0abAglnY3a+DXUWNhIBBHBDjZTFLz/D8psufTEZXzyVWfyux1D/Ptt2+xQkKuvg5Hd8O8Xw22fqHq9FGUBooI9U/zEs+la2NGkjXsXx5sKtvdg1kucCQMDA5x11lmcddZZrFixgtWrVwfPi8XJ2zvefffdvOtd75ryGhdeeOFcLReAd7/73axevRpXy3WUKXjJGav4vdNX8C83P8GW/aNw4uXwjl/D8c+DWz4M/3Ye3Pt1qJTme6mKMgEV7JniJ55NJ0McbLw77vUTL3qCHW8g2PNkVPX393P//fdz//338/a3v533vOc9wfN4PE65XG762k2bNvHpT396ymvccccdc7Ze13X5/ve/z9q1a/nFL34xZ+etZ7L3rbQXf3fFaWQSDm//5r3sG8lD53J4zbXwhh9DZin88E/gX8+F33xOG60oCwoV7JkSnWEMG2zzlBoLO5RlHrg/F44b9JprruHtb387F1xwAe9973u58847eeYzn8nZZ5/NhRdeyGOP2fjfrbfeykte8hIAPvjBD/KmN72JSy+9lGOPPbZGyDs6OoLjL730Uq666ipOOukkXvva12K8Upvrr7+ek046iXPPPZd3vetdwXnrufXWWzn11FN5xzvewbe+9a1g+759+3jZy17GmWeeyZlnnhncJHz961/njDPO4Mwzz+T1r3998P6++93vNlzfs5/9bK644gpOOeUUAF760pdy7rnncuqpp/KFL3wheM0NN9zAOeecw5lnnslzn/tcXNflhBNO4MABW/rnui7HH3988FyZP5Z2Jvjc685lz1COV/37r7nhob3c89QhKsdcBG+5Bf7Pd6BzBdzwPvink+C/3wZbb4GK3rQp84tmic+UIIY9A8H2R2wGgh3KUA0sbOHvfrSZh3fP7R3+Kau6+MDvnzrt1+3cuZM77rgDx3EYGRnh9ttvJxqNcvPNN/P+97+f733vexNe8+ijj/Lzn/+c0dFRTjzxRN7xjndMKE2677772Lx5M6tWreJZz3oWv/rVr9i0aRNve9vbuO2229iwYQNXX31103V961vf4uqrr+bKK6/k/e9/P6VSiVgsxrve9S4uueQSvv/971OpVBgbG2Pz5s185CMf4Y477mDJkiUMDg5O+b7vvfdeHnrooSBD+8tf/jJ9fX3kcjnOO+88XvGKV+C6Lm95y1uC9Q4ODhKJRHjd617Htddey7vf/W5uvvlmzjzzTJYuXTrNT145HDzj2H6++YcXcM1X7uLt37wHgOedvIzPvvZc4htfCBtfCLvuhXu+Cpt/AA9cZ63v018J574Rlm6c1/UrixO1sGfKTLPEwbrEi1O4xBeQhQ3wyle+Esex7VKHh4d55StfyWmnncZ73vMeNm/e3PA1L37xi0kkEixZsoRly5axb9++Ccecf/75rFmzhkgkwllnncX27dt59NFHOfbYYwORbCbYxWKR66+/npe+9KV0dXVxwQUXcOONNwJwyy238I53vAMAx3Ho7u7mlltu4ZWvfCVLliwBoK+vb8r3ff7559eUU33605/mzDPP5BnPeAY7duzgiSee4De/+Q0XX3xxcJx/3je96U18/etfB6zQv/GNb5zyesqR4+x1vdz+l5fxo3dexHsvP5GbH9nPn153H6WKlwux+hy44tPw54/Dq74B654Bd/4HfOY8+PdLbPOV7b+qTt5TlMOMWtgzJTrDGDZ4FnYzl3j1HmomlvDhIpOprvFv/uZvuOyyy/j+97/P9u3bufTSSxu+JpFIBI8dx2kYB27lmGbceOONDA0NcfrppwOQzWZJpVJN3efNiEajQcKa67o1yXXh933rrbdy88038+tf/5p0Os2ll146abnV2rVrWb58Obfccgt33nkn11577bTWpRx+upIxTl/TzelruklEHT7844e56nN38KlXn8WxS70mR7EknHKF/Td2AH73n/DYT+D2f7INWKJJWHkWrD7XivoxF0Jmyby+L+XoRAV7pszWwh7e2cQlLrU/FyDDw8OsXr0agK9+9atzfv4TTzyRbdu2sX37dtavX8+3v/3thsd961vf4otf/GJggY+Pj7Nhwway2SzPfe5z+dznPse73/3uwCX+nOc8h5e97GX82Z/9Gf39/QwODtLX18f69eu55557eNWrXsUPf/hDSqXGGcLDw8P09vaSTqd59NFH+c1vfgPAM57xDP7oj/6IJ598MnCJ+1b2H/7hH/K6172O17/+9YGHQlmYvPmiDazsTvL//feDPPeTvyATj9KVjLJ+SYbTVnfzwlNXcPbaJUSe9afwrD+1nQ6fusNa2bvugbu/DL/5jD3Z0pOteC87BZafYn+mp/boKMpkqGDPlNlY2Mku2DNQdYnXlHU5QKXG0l5ovPe97+UNb3gDH/nIR3jxi1885+dPpVJ89rOf5fLLLyeTyXDeeedNOCabzXLDDTfw+c9/PtiWyWS46KKL+NGPfsS//Mu/8Na3vpUvfelLOI7D5z73OZ75zGfyV3/1V1xyySU4jsPZZ5/NV7/6Vd7ylrdw5ZVXcuaZZwbXbMTll1/O5z//eU4++WROPPFEnvGMZwCwdOlSvvCFL/Dyl78c13VZtmwZN910EwBXXHEFb3zjG9Ud3ib83ukrOWddL9fd9TSj+TKHxotsOzjOV3+1nS/cto2+TJxNx/Tye6ev5CVnrCR60ovhJO9voFyEPffD9l/C9tvhof+GwleqJ+9aDStO///bu/fgKKo9gePf093zyPsBESFBQSW8DDEJCl4ego9VWCusCCLeKqW4xarruvfG1a1Ya4mrRZVbUrveu6XU+li1LDVX3Fu5+GD3ehGFLZUVWEUEImDCS54J5DWZR8+c/aMnIYEEQgiZHvh9qqaS7p7p+c3JnPmlT5/pH+SNgcGFkHsV5I6EtMucqycKcRZKu+wC+BMnTtQbNyZBofn3FkLNJ/DLD2DUbef22A2vwOonYPRfOrNPnzrUsWn79u2MHZl/7pc8vci0tLSQnp6O1ppHHnmEUaNGUVFRkeiwztnGjRupqKhg/fr157Wf7du3M3bs2C7rlFKbtNYTz2vHF1jS9OezaApGWLP9MP+zs56vf6rnwIk2huemsGDicKZcM5hv6hrY8FMD44dlMr0wj5IrcjAV0HwQDm+j7cB3WEe24Tn6A9TvglinURxlOrPS2xN4Zj5kDoP0y52vnKUPcSa8Scndi1pv+rMcYfdVxyzxjHN/bOFfOAl715+7LxzSl2H2i8yrr77KW2+9RTgcpqSkhAcffDDRIZ2z559/nhUrVsi564tApt/DXSUF3FVSQCymWbPjCK+s283yP/3I8j/9CMAVuamsrTnC7z7bRV6GjxmFeYzMS2Pn4cF8vGUcMI4p1zzInMmXcUd+GH/zHqdGd/MhaDoA9budc+Ot3Xz1z/BAVoFzbtyX6XxG+LMhKx8yCyAl21luX586yDn3Li4qkrD76nyGxHNGwODRcKwGvJf1a1gXi4qKiqQ8ou6ssrKSysrKRIch+plhKG4bN4Tbxg3hUGOQr3+qZ0JBFlflpdMYiPDFzqOs/v4gn+04Qv2mMGlek4U3DMdrGXzy/SF+s/IoGT6L60fmMqHgJoZlpZCWZ3H0siBtkRhXZJvkRhsINBwgjxMUpgXwBw7Cib3owHFigQaME3tRbQ1O8ZKeeFKd5O3LcK794E2P/57p/PRndU303jSwUpzPNk+q8+0Vb7qz3vRe0iN+biEJu6/OZ9IZOEfZx2q6nr8WQiSVy7P8/FVJfsdyVqqH8uJhlBcPA6A5GMFjGvg9znD2k7PGsqG2gT9+e4CNe47z2Y4jZ9i7AeRiGoNI847Ajk0mEI4C4DEVOaleMlMiDDVOkGe1ke8PUZit8dtNNNYfwm83kataGRQLkRMJkxFtxd9y2PlKabAJQs0oen9KNGb5ISUXIyXbmRnvTXOSvjcNTI9z9UfL70yibb9ZPmd0wPQAyvnZsT3VWTYs5/O0y81z8qfhkXP8cZKw++p8jrABRt0OX/6blPUT4iKW4e96oSDDUNx49SBuvHoQAG3hKMdaQrSEbPIyfPgsg30NbbRFbAan+zhwoo2vd9fTFLTxmIpUr4XfY9LYFuF4a5hINEbIHkJD2Ob74228tLcFgMIhvyA9zaKhNczeQwHsmJOYlYL2aUt+E9JoJTXWQiZtTCrwMnawD6JhDtU3cPjoMTzRAKmE8KoIKXaY7GALuU0B0gybTOMYmWovKSqCT0UwYxHMWAhPLIRB/17XX1t+sHxo00dMmdjaIKoNohgYhsJUCsOyMJSBrSGKgTY8KNODMi1My4vH40WZFjEUAVsRsMGyPHg9HjweDx7LxLS8qPg/EZEYtIZjeDwWqX6/s970EFMe2qIKr8+Lx3Lua2PRENTYGOSkpeD1eokaHryW5UwgzhvtzFM4T5Kw+8r0AvHrgvfFFZOdZN/dOWwhxCUhxWsyPLfrKNu4YSeT/JWD0vjF1b3/Tncg7FzHINV78qM9ZEfZebiFmkPN7GkIYBkK01A0B20MBYVDMth/PMDvN+7jrQNBFHB1XiGTynIZnOkn1WuS7rOwTMXhphA1zSGa2iIcD0Q4HghzqDHIwcY2LMMgM8XDsUAQHxF8REgxbFLMKEbMxo5pPNj4CeMnTKoKkeGBHD/YkQjhUBsWNinKJsWIomIRDG3jUzY+O9yxT4sopophEMOM/2NgoAGNSQwFmMTwYGMRxFIxTKJ4sDGJYaIx4tsNYljEUEp3ekzUWUcMHxoDTQwbU+n4c8Gpn9oWcOrJzc5TBHdMep4xsx7u9d+xJ5Kw+2r0bGfWZl+HakwPzHhSErYQot90TtTtfJbJtflZXJt/5lLAf3vzqD4/rx2NYRoKpRQHG9v4bt8JrhyUxqjL0rFMA601ITtGUzBCayiKZSjyMnwdpwoAYjGNUifLybaEbD6vOcLOwy1ELYOQoYgoyE71kp+bSl6Gj3SfRXMwwtHmMA2tYZpDNjlpXjL8FlpDOBqjJWzT2BbhWEuYWEzjtQzyc1LIz06hNRSlvjVEU9CmORihLRwlHI1hKkW632JIhp/mYIS6+gANLW2Eg0GuyPFyZY6XYDBEazCEitmkWprhmR48Roz6pgC2HcGKhTnWEmR/Qyuzh07rc9t2Jgm7r0ZOc27n48a/6Z9Y+tHMmTOprKzk9ttv71j34osvUlNTw4oVK7p9zIwZM1i+fDkTJ05k9uzZvPvuu2RnZ3e5zzPPPEN6ejqPP/54j89dXV1NYWFhR6GNp59+munTp3Prrbee/wvDKcO5cuVK9u3bhyHnxIToN5Z5sj8NzUphaFbXU31KKfwe00nQPXyxxjC6TmpL91ncOWHYWZ97SKafay6RubvyqSW6WLhwIVVVVV3WVVVVnbEAR2effPLJacm6t6qrq9m2bVvH8rPPPttvyVrKcAohkp0kbNHFvHnz+Pjjjzuup11XV8fPP//MtGnTePjhh5k4cSLjx49n6dKl3T5+xIgRHDt2DIBly5ZRWFjI1KlTO0pwgvMd6+uvv57i4mLuvvtuAoEAX375JatWreKJJ57guuuuY/fu3V3KXq5Zs4aSkhKKiopYvHgxoVCo4/mWLl1KaWkpRUVF7Nixo9u4pAynECLZyZC4m62uhEPf9+8+Ly+CWc/3uDk3N5cbbriB1atXM2fOHKqqqrjnnntQSrFs2TJyc3OJRqPccsstbNmyhQkTJnS7n02bNlFVVcW3336LbduUlpZSVlYGwNy5c1myZAkATz31FK+//jqPPvoo5eXl3HnnncybN6/LvoLBIIsWLWLNmjUUFhZy//33d1wnHGDw4MFs3ryZl19+meXLl/Paa6+dFo+U4RRCJDs5whan6Tws3nk4/P3336e0tJSSkhJ++OGHLsPXp1q/fj133XUXqampZGZmUl5e3rFt69atTJs2jaKiIt55550ey3O2q6mpYeTIkRQWOjWIH3jgAdatW9exfe7cuQCUlZVRV1d32uOlDKcQ4mIgR9hudoYj4Qtpzpw5VFRUsHnzZgKBAGVlZdTW1rJ8+XK++eYbcnJyWLRo0RlLS57JokWLqK6upri4mDfffJPPP//8vOJtL9HZU3lOKcMphLgYyBG2OE16ejozZ85k8eLFHUfXTU1NpKWlkZWVxeHDh1m9evUZ9zF9+nSqq6tpa2ujubmZDz/8sGNbc3MzQ4cOJRKJdElOGRkZNDc3n7av0aNHU1dXx65duwB4++23uemmm3r9etrLcNbV1VFXV0dtbS2ffvpplzKcANFolMbGRm6++WZWrlxJfb1z2cf2IfH2MpxAn8twrlu3jtra2i77hZNlOOfPny9lOIUQ3ZKELbq1cOFCvvvuu46EXVxcTElJCWPGjOG+++5jypQpZ3x8aWkpCxYsoLi4mFmzZnUpkfncc88xadIkpkyZwpgxYzrW33vvvbzwwguUlJSwe/fujvV+v5833niD+fPnU1RUhGEYPPTQQ716He1lODuXAT21DOfatWspKiqirKyMbdu2MX78+I4ynMXFxTz22GMALFmyhC+++ILi4mK++uqrM5bhtG2bsWPHUllZ2W0ZzuLiYhYsWNDxmPLyclpaWmQ4XAjRIymv6TLdlVEUF7+zleGU8ppCXNykvKYQSUDKcAohekOGxIVIsMrKSvbs2cPUqVMTHYoQwsUkYQshhBBJQBK2C7ltXoFILHk/CCFAErbr+P1+6uvr5UNaAE6yrq+vx+/3JzoUIUSCyaQzlykoKGD//v1yLWnRwe/3U1BQkOgwhBAJJgnbZTweT5dLXAohhBAgQ+JCCCFEUpCELYQQQiQBSdhCCCFEEnDdpUmVUkeBPb2462Dg2AUO51xJTL3jxpjAnXGdKaYrtdauLpzdy/6cbO2eSG6MS2LqnbPFdNb+7LqE3VtKqY1uu46yxNQ7bowJ3BmXG2Pqb258jW6MCdwZl8TUO/0RkwyJCyGEEElAErYQQgiRBJI5Yb+S6AC6ITH1jhtjAnfG5caY+psbX6MbYwJ3xiUx9c55x5S057CFEEKIS0kyH2ELIYQQl4ykS9hKqTuUUjVKqV1KqcoExTBcKbVWKbVNKfWDUurX8fW5SqlPlVI74z9zEhCbqZT6P6XUR/HlkUqpDfH2+r1SypuAmLKVUh8opXYopbYrpW5MdFsppSrif7utSqn3lFL+RLSVUuo/lFJHlFJbO63rtm2U43fx+LYopUovdHwXmvTns8bmqv7sxr4cjyvh/Xkg+nJSJWyllAm8BMwCxgELlVLjEhCKDfy91nocMBl4JB5HJbBGaz0KWBNfHmi/BrZ3Wv5n4F+11tcAx4FfJSCm3wL/pbUeAxTH40tYWyml8oG/AyZqra8FTOBeEtNWbwJ3nLKup7aZBYyK3/4aWDEA8V0w0p97xW392VV9GVzVn9/kQvdlrXXS3IAbgf/utPwk8KQL4vojcBtQAwyNrxsK1AxwHAXxN8XNwEeAwvmivtVd+w1QTFlALfH5Ep3WJ6ytgHxgH5CLUwDnI+D2RLUVMALYera2Af4dWNjd/ZLxJv35rHG4qj+7sS/Hn9M1/flC9+WkOsLm5B+m3f74uoRRSo0ASoANwBCt9cH4pkPAkAEO50XgH4BYfHkQcEJrbceXE9FeI4GjwBvxob3XlFJpJLCttNYHgOXAXuAg0AhsIvFt1a6ntnHd+/88ue71SH8+I9f1ZXB9f+7XvpxsCdtVlFLpwH8Cv9FaN3Xepp1/mwZsCr5S6k7giNZ600A9Zy9ZQCmwQmtdArRyypBZAtoqB5iD8wE0DEjj9KEsVxjotrmUSX8+K9f1ZUie/twfbZNsCfsAMLzTckF83YBTSnlwOvc7Wus/xFcfVkoNjW8fChwZwJCmAOVKqTqgCmcY7bdAtlKqve55ItprP7Bfa70hvvwBTqdPZFvdCtRqrY9qrSPAH3DaL9Ft1a6ntnHN+7+fuOb1SH/uFTf2ZXB3f+7XvpxsCfsbYFR89p8XZ2LBqoEOQimlgNeB7Vrrf+m0aRXwQPz3B3DOhQ0IrfWTWusCrfUInHb5TGv9S2AtMC8RMcXjOgTsU0qNjq+6BdhGAtsKZ+hsslIqNf63bI8poW3VSU9tswq4Pz7DdDLQ2Gm4LRlJf+6BG/uzS/syuLs/929fHsjJAf10Un828COwG/jHBMUwFWdoYwvwbfw2G+cc0xpgJ/BnIDdB8c0APor/fhXwv8AuYCXgS0A81wEb4+1VDeQkuq2AfwJ2AFuBtwFfItoKeA/nvFsE5wjmVz21Dc6ko5fi7/3vcWbFDvj7q59fv/Tns8fnmv7sxr4cjyvh/Xkg+rJc6UwIIYRIAsk2JC6EEEJckiRhCyGEEElAErYQQgiRBCRhCyGEEElAErYQQgiRBCRhCyGEEElAErYQQgiRBCRhCyGEEEng/wFjh2+je1eVDAAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 576x576 with 2 Axes>"
       ]
@@ -1233,8 +1233,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "313/313 [==============================] - 4s 12ms/step - loss: 2.3050 - accuracy: 0.1786\n",
-      "Accuracy on test dataset: 0.1785999983549118\n"
+      "313/313 [==============================] - 4s 12ms/step - loss: 2.2463 - accuracy: 0.1805\n",
+      "Accuracy on test dataset: 0.18050000071525574\n"
      ]
     }
    ],
@@ -1269,9 +1269,21 @@
    "metadata": {
     "id": "Ccoz4conNCpl"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9999.999"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "predictions = model.predict(X_test_zc)"
+    "predictions = model.predict(X_test_zc)\n",
+    "np.sum(predictions)"
    ]
   },
   {
@@ -1315,9 +1327,7 @@
     {
      "data": {
       "text/plain": [
-       "array([1.1986407e-04, 1.6519021e-02, 6.6521651e-01, 3.7694808e-02,\n",
-       "       4.0283995e-03, 7.5087927e-02, 9.1714531e-02, 9.4427909e-05,\n",
-       "       1.0516093e-01, 4.3635345e-03], dtype=float32)"
+       "0.99999994"
       ]
      },
      "execution_count": 22,
@@ -1326,7 +1336,7 @@
     }
    ],
    "source": [
-    "predictions[0]"
+    "np.sum(predictions[0])"
    ]
   },
   {
@@ -1348,7 +1358,7 @@
     {
      "data": {
       "text/plain": [
-       "2"
+       "5"
       ]
      },
      "execution_count": 23,
@@ -1468,7 +1478,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x216 with 2 Axes>"
       ]
@@ -1504,7 +1514,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x216 with 2 Axes>"
       ]
@@ -1549,7 +1559,7 @@
     },
     {
      "data": {
-      "image/png": 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l4frno7I55CJtKLobzfWq6jlMVMJ3aD1ep2tV12K/Wqhdla5KqZXLZWFZLsaVLZUgUGuz19zoAN+lxPPBOUb3nDjew/jO9MP7Mc4T++3bjvXTiY/jyAI2MJV3MLtFdAkrcYUcB2aiW2IKIewHqrLxbIzDMxb7ALNeqQxCyon7169589mvaLXy7puvOT89ImVB1mffBRrwFFckQDNooDXlsjaq82DP60LtcKnK8wK1R2KYyOmExEyQRAgTiO2IgzFgkTghcTIqQ13p5WC7yF6p3dxMJAUkZlSg0HiunehnhIZthOckZHdUCdv3pPuivFF9bvET4s+pEjtiFzL3dOXnhOG+11qn1k7rnWUpXJaVWgshRnKpxBiIvtG1Do5fv9hKA9sm8Q95X7e4xcdFMFabpzhxaS3dzvtWhWzslAOnBgxqgcLQjtVxXx1Qt6P+AiLbi3iXbygWNHxx7ddqSgbqzmK1We5qNROD2DDVViX14Sp7+wwFodY7pZuL5VqrXUq7ArpmyNDVrltno8tJ2FfiNZ99KBLo1oWx5zeX0LZx7AelQvT6eQbI/vM7B97ip8XHA1miC8h5wviARrOxVWRvD2jXbS3FbR/ZUZ82frF4vFK5TTUGYZoyn/385/zVv/13rMtK+KcJQkIuZ+T8TL8U279qsEEuDXTNoJFlrbx7rCxr5bIUvnlcWGqDkCGfICQO6cicX5PyBDGjcdAMIi1EFIilENcF7UpvK70WtDeWyzPr5Qx0khyIAZTOhTPruhBFqaocmkl1qQqajKCffOp0LNrte/pwj3CLW/yI+HMCs8Naci/7eIJundobvSulNpal0lrn+bLw9Hym1AohkOeJlCJTzuSUNhH1nSu/G6hsL/D9b8iu/kU+7S0+tRD28/0oV8TgTlyiNFEHpu1KYcBa6NJNyTxEIaqZ53Rt0B0sKmgXehCYbIbEUnFDcNCnjTEcpl29yDSm/jvaG7WsrKWgmmk504JYd2NwVLVvDmFIsg6ImGHRWgq1dy6lcl4Ky1JMQcHQpg9RO4jtSmuKBpAOQffC1V5R7aj6PIo2MybqndYrrQ9zI4ghmKOXPWijFv05nftu8fHxkRa1o8rhYhkCewPApETCNRIb1VauQOqLa7aBjnFPGb3GMSXpfNmYM3meUYSYMhIiEqMBzkEd0OAGBkLvAVVhbUYfuJTOuXSel8pSKiFHUhxadAEJiRAzGjISM0OqS0P09+knhN43+kTvEYkFlRUQNLiorHZ6X+jdlE9yh9Lt07U+1IKuHMBeWhndkukt/v8+bOnvR+p1IrkqtOx3GXJZV5y7lzBz6FzubcvWjVLQWqO2RvPqTOudrsb7G0Mx21DptVj11bX82FV1A7S3+COFXB2Do4G5L5mdAjBoANf0gu13jKHqUW21rqWB1XH886Lt/+I5/LX2guVLWoHJZwV636kAo8J53brfuD7+RE11X4++RnFe+ljHev2w7We/D3D6wLfs72F/L8Ng4eWqfHHO0T+s63KLP+/4KIvaTqYTaC15m2Hn4aSgTD6xb2xTdf7b4LjotoPaFt743SBsw1WFxnZjWmFZI19/8c/EeKSUyhdffMm790+s68Lz0jhXcQpBpValNljWhdKgts55qdSmlAKLRqoIKUxIOiApE/JMSJmQMoQEKTFUE8bUqBHxE6qdsna0BSRAnqbt84yJVO2N9RIpxT5Oa50ldFIUhATYkFgMkSRA6ITerX3jki23HeYt/tC45sn+SwKxD0Hs/trwYeVTedmqt4RmSbT25vqwSu1mblKbspZmILY2lrXY+qkNlYBEO/fUZu3TGpWmAwD7tUD4iOVzA7G3+NgQhkaqaYgrLqnl16KKtkZvjTEMJsNoSEeb3nVXMYpBcJqebNJcNjAmdC/8NFTbt1rtY3i4I9tsiWqjNRuUVu2cnwOlrNvaHIoG43ySdSLlhATopbEuC6VWnp6e+Orrr7icLxwPM/fHI+REU/V8ZiokXbsVoTRcPfcYhFNa8z7tcD8Tf0yv9G5DayJxQHp7n44nWm9ov6Y23OIvPT4KyFaZaBpNqopgB6IfQFPqHLLxgLLANGRGhKtWhaLO83lZnL3Wnd1Z8r2tdK30s/LPv/5HHt9fqA3ePy+cl0qplXfPlecVSlG+eVy5LJW1dN49Vi5rAwloSJgJAjQ1DitxZp6OhDwRpgNhMjArMRGS3V9FnAoBPURScBpEb7QaCB3m+cBhnlGUViu9VVqrrOfI42J6shfpJBpTEqIYp2nqgSknJATQ5tqYA4j0P5DVd4tPNfYipPxJTuQDxH4n2JPRSr2qvCib5J7q4L8qtTWel4Xaug1ylUrrVuUp1cFt65RS6d2uVSISbYCzVEvGKXVqV6JXiToGYscp5g8FpTcQe4s/ViQUHcoFXBkiiAHTYSagvRmlADMcUhU2Byuc9y3NZRt3owSzke2IjJy60w+utWINuAJqxvJKp2nbnLhCDbRafYYFW98yiil7oj4wEyTQW2W5nLmshXfffMPv/vl3PJ/PvHn1ipysm7kDWXHHzIYEMfofo+raHQ+oUSfUhttisO+q905vhd6qF5YEQrRujSriFeXWqm8IbjnzU4mPpBZYG7+pXXq3nZT1BCBGq8RKgOSLTXUfvcCrtxbD+OA7phJH+8TbKb1X1nUBeaZ1WJdGKZ1SrXLTVagdaoNSlbUol9K4rBVCJKTkRgXQt9ajcX0lBLv2YZHBtdtaoePdiPGb+tDCxX44fLP3hRmgu+yXcwdkcySy99gatA132N6da73d3yuyeYtb/L74juPlB07s3+tT/h2H4DWI/S5+/Af2BgxTgd2YZOjCGlBt3UXUW2etThnoptPcFVq13/duieta7r13pUmnNUt4IrKB5OvP9OIjXEvavfzFd3/+q5/fAO4t/pDYaAJby1tfXrxl/rJ6Oi6mbyNX+qhjpZqRwqhL2rV8+BrX8ybj5hWlYajmbLMZo4vj8pgSRi7aK7K9t63V38cgVmvU2ii1UErxtdqdGtR9PXbjyyqEblKbG21gvA9/He3d3bo+lNly7Vi5ojdcUSBeGDDd4pOInwxkO8K5CbUHzj3TeuR8OfPNu0dKWTkdhIc7ISfhYRY+uwvkTbQ58F0e6IP/aq5zV8LPANdLUzuX5cxSAq0Lz6tyKdBUqT2gYYbYkRSRXK3FPyWgEWIkTjMSouFt16WUKaMiNBSJQp4S05SIKZFSQiRQWqWUYguyFtpa6KpIq+RgCyeEQIzWLukVSrMKEpII+UjvjVLUErXC09IIKIccOE2RQ/KTnn44nHKLW/xwqLrUzdWJfXia73NU6pVR8aGQlyB0o2lvT3qF4/T6XiNBf/dxOoCk+htTNR3peJV6m4PY0hrnpbKWQqmN82LDIwZw7T23ptRqia/WurVBS2ssy0oIwnm68Hw+k0Lk/u5IaydSDBwPMzFNm0nKj3UY+SlV3Fvc4tuxD1xJL7Yumw929Y62SnM5uVYKWqtJQA7jA+foSY+uM96IW2VX3Lq2m7a7VltvraKtWAGls1k3v1zMZmrUB7Wgro4LHcwGIaZkxZnxUQQz6nl+JqXE5bxQ10IvFbqZIMUQ0K4spdCB949PfPH1l+ScSCGQohBC4O505O50RBDTgh8KBc26mUGEFKCLnSe0mfkRPgcjYqoLvRn9wIyKimm434o/n0z8dCCr8FyEqpFzm6ia+fLxmf/8T+94Pj/xcJf4/LPEPAV+8Wri7ngkxECUnS+0kcjZQSxAjDu3T731sRUrBVQbl/MzpVVaF84lsrRgg15phpgso+cJ6dZqkXlCQiWkRDzMxJhsCrJWS7Ax0gN20ohCnhPTPJFTYpoyQYTLpdHXYs4oZaG6sxAiZHfziklIyarT66AX9A4hEaYT2irrWihlpTRlkgoV6hx5OEzUyTjFiTFE119SL25xi+8JVaWUsoHHAViHTA0fJKnvBLMvce02xDksla+PRbkCtNePUnbKwAZkMd3kocNuUjwmp7XWxnlZOS+FtVaezos/Vrb829U6Pqq2rsq6mtKJV2AEmHJkypEYA2/XV6gqOSdCiszMm1j8Xoy9XljfDVlVvvs3N4B7ix8fivRqx2qroA3pxRQJ1Fwi67pSS6WvK7QC1c7/SjNprQjSIiGI5SCv0EbM/CeIErQhoyvqbXgrUsq+O93W+XDmbKhWalspZaE5hae1RoyRPE+EGDbe0tiAIoGYEstSKMtKqw16J0ogesv/vKyU1vj6/TtChJQiKQo5WsFnLa/o6s5ko5qsSquFXgtBhBahOSWj14rWZu+jK0iCaEBWRQ3k1tW/41tV9lOJj6YWtHHRQO3CUjqXtZOnxlIsaZXmrjyMWcxvP9OHaeF6UEWvEqheJcBam9EautC7uMBzgBCt5RIwLckgSLQFLiESQkJi3PXnrASMXg+sONcwBLOcNTK96f710SLyyU7TqxzcpRfNVPZ2jGwXS87ik9hQe6e24C0Yf6Rzk16Wxm5xi+8Pha2dPjCacP3Pq3a/+iF2dZ8Xy/A67/md5DtQnXxw7YS6fVDk+v192Op3YD2GNcaw17i97eFG1/CKjmADXWMt2tmlVnwIxkxOWuuEYO1N/fCzvfyavv0FXFexRb7HXO+2Rm/xwyGeCzYjoe+6DOlJpxeIjgvbbdO03J9HVNxVcyTJ68sohISrhe6gkWH7vOepXQarv6ANICObOYkhVMK6EltjXSrrakC2FqP+aFdaM5pB186yrDxfzqQYSUHIUYgxMOXMPE2bjFYUAZyG0BUCpg6k1zxac/3SYINw6ra/qs2lO03G7BafTvx0ICsRmV9BTbQ+USXa8Fc80GKnElhagiKsNVNaIvdAEtDQdjbMtmtyMrs4R5TgVRtPaAoFoTQTVj6XwFJsCXbJkCdCTMT5jpAPhNyZe0FDg9I49AixOrVgIsRoFVlJ23vQrjRttNpopdAMxTIl32GWSAkBCUoRtV1xb4ABZxWhFa/ydttVqo9Ot1ap1UnoCBIT0FlbMT0+Os9LY8qNFBTJppmrQXZ77lvc4gdCVVlqcwe8SBAxHrdrqqpcccmC/W7ssa7dor8brMp3VGS/530sF87LgoiQUiLGiCXJCIhz8qzyo92qMilaP6IjtDZAsPPZuzrvDloIpBBcb9lakeBazN62VKB0I6DX3u0MI0Kw3ihbiVkd0V9/Ir9S5Fu/unVHbvGHhrB7S+5DiLrpxIbeiWqjV1GVrDCpJegZO2ZTU2R1+/VulASryEIkmHpBbVZMccqb1ApA0IgZAw22nhiloSpdK1oLKVhHQzWScnZcbbSfUoz/el6LS97Z8GXvSlkL5+eFWhvv3r/nd19+zVrMqCTkRBDhMGeOh+yV10bQTgyBt29e8/bNG6ac+ezta968ekWMgRSEFG2mxuS8XJHAc7OdD0xVIcXEpAWmjPH5Fhs8uSXNTyZ+MpAViYTpNSJCq4HSoclMC0d6UKqIt/uFpWVKT9QWaKHTZd+d7tOUnlxQr26ataz24QoCiwqXDk3heVWW4oNkcyakAyFl8vGBNB8JpbG2FQ0VSY1VE5KqDXDljAQfTgtGLejNBKF765tHdAuCzJkpRnKKtBSZopHuA9bGGILRGiKIbAMq3dufww/anr/Y7lYFCQm0GYe2mhTJ09rIS2VKtiZzsIWM3tiyt/hxMYBsjNGQqUvGqYyBRauyoHqlcMBL/LZj26uQl2D298UYGNPOuiw8Pz0SQuBwOCJTRoi+cQzbumjV1kUMQo6RECIhJpPPunpz1RNq752WIjXGbe22WlDVzS1JvAVaW4fQNvAwPkOQUZeCHZl+0JH5vZ+R7VFy65jc4keEYMm2X62zgBJ6R3oneE6J2kkoWYWswqSyAdnQlKAGTGVUWwWSGkgWEWJtzhtVpBakVe+8KIhpoG+tiWY0vdYErY0owpSjnTNcO31ZC8v7J9a1cl5Wvn73xFIKz89nvvz6G9a1sC6Fy/OF1hpraVxWq8IObdkhNRbFgLfWlV6M0/6zt2/57O0bjocD/+7v/pZ/+zd/zZQz98cDx8NEjIGalNRsw9pKpZZq73twiXOihwI6WSW2OLWg34DspxIfUZEVG54Y+jaC6TpKNHOCEDwhCRIyvtSwauvvoX1upaCxzC0RD+7s0Jfc7e7UeKRiiU9isl1gMHK6BJMQGYltE0nfXsNA85A2GWEcnUaNddP1U3Wd3Gu+ob/pnY84RKVHFfk6DV+BA2F7H0bEH2LvSu2d2I16oKPvyxXguMUtvid2Z3bns11d2LRdZffcuKo8grfQr+Ddt2gD3/vaL6u51pa0Ft/eGtzbni/WhWDJ2gFoDIOT+xJSa1erMHuvU1V35RAdmpq2TmNMG+1ne56rNbVRLV4srg2u/15u7NWDv/8+t7iFh+FO6zyMLsJ229fGZrOqVw5VXTaHR6XRruTVt3XqhHPjiAraLDeqgz+L4MDX7jvaL9a2NxvYbds4cqWr9XQdiiKdUitrcWfMS2FdV9a1sCyrWUg75317TB92sp3mVrmtrPR1IQTh+XzhMJ/RrlzOF5aL8d7nZFx3Ri52u1yjC1W7XYxHKz6L0mKEIWF2G/b6pOInA9mUMm9//iviU+GxPbNqJeaZNB3ITbl7OPHzn7/iOGfeHhuHYyXFjqjQ1VoCVhIygWdCtB2jCMoE2I4wpANBJqQr5fHM02VxkJcIORJT5nj3lvl4j4REnI7ENKFUYuqE4rtRxRe22rZYdscRwHatDrLXZeXrr74ixYiWwpwS85SpTn5HhJwyOWeCNEptrHUxANx3KkR3bqxg5PZ5ivQeKGun+ommdkWL8Zgez4UgynEO3B1nuiQI1oq5rclb/JgQEcJ8sA1ajGiwDWF3SblRDTUANxKX0XzGdu4FZxZ2kuqPgGzjXgre8m/2KB0M+cHbs39PKdoAaLX2YZDdZR5cAi9GRIJXZKtx8LxLY2DWwQBmAxqiJeKcTXkkhMB0OKIh+PPuIPb6M8n4Mq54xGPr/a3v5QOKxS1u8X3Re+P9u3dWqXQgtpbC5flsXNJl8Z83yrry/PxEq4WcAkuyeQ7dqGyQYySn5Md5JCVb3yFCSLY+1lKp1c0DQrLZEWSzlAelad+ksUqr1NbddSxAh2UtPD4vPJ8vPF9Wvnm8sCyFy1JYK5QW6GTC5AC5dzR1X5+d7Dz3KEoSO8us5ycWtQ1zrZ3npzO1VH73uy/IKTJNmedXD7y6PxFj5DRPzFNGtVOXiykrdLvd6so8T/RWOJ2OaGvU9UJvldpuPNlPJX4ykI0p8/Znv0TzM797VEJdiGkiT0dqF+7v3/Lzn/8Vd6cDd+GZOb4jUQit0cuT+0lfLSpbgVgldgKdQSIp3xOne6iN+l54uhjHNM9H0jSR8szh9Ja7+1cgwZ5DIl0LMRZCaARpY0LMkrqUrWIlIfqaHnJXQllWvj4/W+tHO3fHmTrPxsFNVvVNOZPThDB2p8s22LIB2BC3inJKkRmr2NKi78YDpSlltRPJ46XYrlszb/tMF39vxH23fotbfF+EgEwHX1s+WOjV2G29bXSeQZizCunmtmfEH7zI+QF8fQn+XhyVV1SFoQPZWzOlAB1DLJ1hmRkEcoqkGBBptNoJNK8e24YxxmRueSHQamOtBmTFLaN3HoT4aSSQkgm5D1UGEefbSWDUncKH39sLOsVLMuyPg/C3uMXvj9YMyKrPSwwJueWy0HpjXVa6z1AYkH2mrCspBnIyG55aVkpZATgeZk6HAyEIOSVyNrAbQkeCgcfmmssghNg8H42cZ8PFpRZat1ppFWjgh72toaVUns4XHp8uPJ8X3j1euCyVUipLFVq3AlTIyTZ8XRHfaI6uqwApGlUO7TwR6MXAfGvK0/OZdVn54osv0d6YpsxyPrOcH0gxcjrMHKaM9k4pF6vo9kZZztSycDwcCLDNoKzLmVZ3EH+Lv/z4CI6sEFMmxuzXlZgyKWVSagb0ppk8zSSpBElGFujOfb1u4W23fMgrJEIwrdc8n0jTHRIb07wyTRXFaA0xTPb6MRmnB0El2nM7MJXr7PpdMRK3qrdZDGxKa5h25e45fZ22zUllGCbsCXNUiwwcGLbGW6ZDGzeE4Uri703s+2itUyuU2p1moG7ve01luMUtfn9oV5ZlRXAeqA83hQ3MOnj1w2+Y98RrEMogp+49+P3I/6AUec0XdQD8ElxeX7h+EXu4cxGGkcgwHxkaXTEIcRiVRCX1SBf1DWLy++7vI0Tzd3/x3lwJob6YBB+12avHXr3H7fb4XgVeMNVvG8tb/AGhqqzratXPtmshFwdftVYforL2/bKuxgWPgdasa1lr2YDsyCcxBDcZSK6yo0iwDelo8QuCdCUEdSCrPiNir1W75dQaxDi8gm08g2lS12ZGI82VBPYct3dUR59DglMkXi5BT3Pi793XM5YLU4QYo4Pvbt+DX0ApxWQ7DbyuNsvSq92uBQmBy7qS1oleK+tSNs78LT6N+OlANgTyfGQ+CfcPFdIRiZmlNE7LwtvP3vDqzVvujjNHEiftJF3pl5XW3kFT1MnftnVL0BMSEg8PP+f+1S9JaeZ4/zmH01ta6xw/+5rPv3lPrY3n84XLshJT4nR4xWG6o3dlKZVaO70CXQhEApEYook070vOdS6rLaBaKK4Lm0RJYsMnuzjJRu0DgZgSh+PBnIT8pNFaZ62VUkyV4VqnM6bI5CcPsIXbanVlg06gc17NIrDUysMpA5CjcDcHpjjdykK3+ME4X878z//L/3tLLsKVCYkIIVp1UoIwT5nTcSbFyN3xwOv7EymEKxrpld7qC16px9ZvHzXWHQArkHLicDwSQyBl33BubU0cMA7OvDDPkJItMPVhT6uwGue++f1V1Ts4rn4Am1RX18ayGj+uNq/KqG72n6BoaxjZULfPMagIOUVrZ56OzPNkG4Lw7WHLjb57i1v8iFjXlf/8X/7xis6m9NaprmNeysri+eeyXHg8PxFj2K1nMWpB7w0ROMwzx3kmBKvY5mTduyi6qY9YGHgNPj9incK0DTuXWm0gKwiaktlxikDIqASezwvvnxbOl5XLWqmuGqQhErPZsiNDlAtonc7gxV69CzWAarazwpQnBHj9cOL1w4kYAnO2zxFD3IB+7wKtsl6E1ipPj++5nM907ZRiCgrTPHEucDw+O9g1msZS6p/kb3uLf/34qIpsmg7Mh8Ddg5qDVkgspbMsC2/evObVqzecjhNzD5x6JejK2p9p52R8n62CA10jSiLKxHT/OW9/+Xfk+cjD61/x8Orn9K48vP2Gn79/z7qs/Pq3v+XLL78mxMhxfmDOR2ptLMuZVhqtKvSAS0W7SPPeWux+Rmluo1dKYbks9FbJKSDZ5Ir2yWmukrW1PA+Hg+8gu3vEN/Sy0JrzDT1pCwZcQ7SduUggxWQ78rJa1bcVLuczrdj05zenGRHhMEVynEgxftxf+hafRJwvF/4//+v/urMGYJPiMopLIE+JGIX7uzvevn7FNGV+9vY1p8OE5Dw8QYBrcMoLFKtOx3ECLLBv+sbjUs7MnmxTys51FVckgcGBFYREgMmSq17D8I0j69XZaI4+GxXJlUKqD5K11dz3Wm+s67pVtsq6sK6LDYuVSndZovElhRA4HmfmeWJyVZOYkm9mB0v2RjO4xU+LUir/+OvfwNZdkxcDkM1dI1X7C1vY6yaGDUTa9TxPHGYz+MgpkqNtVCPspkNh78SENDaRwYexowNZqwJLiMhhRtLociRUApel8HheWJbCWm2mowNIJKaxLkZWVbrUjSv7soPxEsjmnIkh8ObNG371888IIVBXow0Mi/fWGr1B7RV6o5TCV199xePjoxWh3PUv58zj0pnn2Z0zV6MY3IDsJxMfYYgg2zSwYUIDaDl7JXGanE+aCBIRItJ3BYKdTuAVzw6N0ZKIhDQT0kz0a+lKyjNTXtEOKWZTRZDo1SbT0bt2GhJGF9XbMNG4pq1fVYK3UpPJErXeSbpPbcZgoNNs+uy1ECEEJboWbAjDDGGnCojqPvk5nn98c14BGkk9hEDvJpGkaha9tXVKsapwdVWDW9zih0K7UrwiOdw1roFs7wGlE4Iw5UwpBRG3tXXNY5Uds4rqCwDr+zhQ2YbFttLsddXWj+8QTctWtjXiFdjB2eUqS3s7f8i1j6QvW7tz3HdQgBpgGrGlWRVoLcXBa/OJageyi01Xq3YHsu261+Lnhx08tD5AhmxdmBec2Stewm1l3uKHQlVZ1pWRN4fhz6CstVYpPgRmBj57EWQDtJhkXhCQ2pBQCBJcjs43hALR739NeQtd7TkJRjEINpBVquU8iRBKI2xqQQ0Vpx5c0Qs2o4LBrWdkcJfW2kwLBufeV8jgxovTImLcTBDEu0Aw1BOuvpur+7dNAcXlLdWkOKWb5bsGG5arZahB/Gn/xrf414uPkt+SmOm6cr6sPD6dQQJvP/scRHi4O3A43Zm3clsJJRN6B4mWHHx4elMKWDtPSyHlwNozYX5FPJzMdCE/QFdSrky50VskxZkUDMymmEkxgxroTNGSsuUeJQbheDyQp8xSCuvzs7d0nK8ToQClFGop5HQgTzNTTtzdP/Dms884HY/uJmaLNGV1H/jGWgry9IxI38C9Vb8yacqgUJr5aCt2gundwXXKpHygSSSshRaVRuDxrHRdOa6RHIXWPsqE7RafSChQ3JBDHdxZe9Jbj0FYF+d1qzIFmxJ+dTxuiUpQ6tAN0J1eEFxaz5Ccozu1RDYw5pY7REjTbG1MEW+Thn1z55mrX4FfGYTdjdtut4c8lgFsn8iuZmfbeudSVs7LYla3y8LlcjauYSlW5erdPexdM7o4/25QDrQTY+D+7sjxdOBwOHB3d8fd6WjAPgwYsVdjdwh/q9He4oej1MpvvvzaN0zZ8sTQUVfLI7WZmk/MiZxtwHFbu1h7XrupgKTayUvZOxXe2cgxkMb9R7lIQOLgpTq1wAclO2LSkrETS0QymyKIYsoHj49n1mJW67VdAcSrynJX2xiW2ljX4jxa4wMLMKXAIdmmNqWJLNn4sSlQy4qIsixn1uVsutNTpE6BHBP39yfujgcuy4Xz+czz5QxdKU1oGNWhngtysfegDmLbDch+MvFxFdmY6QQuy8rT+cLpdMfD6zdM08RhSkyHTIpCLBOhJ4QKEjauqajYMJTCWpSnp0LMkdISMt0Tpjsk30O6R3onpoWcCi0JKcxEMSAbQyaFBE4ajyHQQtiagSEIh3m2Tuj5zLvnJ1qr3mZJWxW3VgOlR51JOTPNM8e7O169esPd3YllWblcLps6gSL03nl8eraTRPOqkpHuSCkxTTOg6GKamr3j06Vm9hBDIkYFDUhckdBpwPNioLnWznG+0Qpu8SPDdReHscdwzNq7BrwYAptTopaJZTEzEO1umED7FpAlClH3LoxVb69k7K4queobuRitQxMcjG5dCwbv/CrbvBgI28ZEriqeewVoLYXH50dKrTxfLrx/eqa1ymVZjEPn4HVwEHvtJr83vh/fkLZWaK0SY6SWwlpWm8hezHNe/D0OuHqDrbf4KVFb54t374khkvNsXO9RyVR3lOw2oJTbxCzBc1vcFNh7020DFtzAYFRsg19PKZN9zak2X8uCRNkKRwZkx2B1BklI7KSWCGnoxjbn7lbO5wtla9Pvq3Gromqn9boB2VKKOXJWc8kEiIeZmCMxBg4pcMiB6O5drVmnZC0Ly3IhhkA5TvSWIQZOpwOfvX3N83niiy++IKboIii71XtZKq31rYiEz8Dc4tOIj+DIwua+pVy1SV62zUcFRkJANLxowwdPqt5MYHhHr+vK09MTtQkxL+TJXDqqT3WaFqZuiXmjEXjlJ+dE791bK+N3lgDHDnbwha6TpnprxBQZEjlncp7Ik1235m2f0W5kfG42A4SR9Oz5xn0GeDXpo2vb0OAcQNVuO+/oklvSUbHdpqkZ3KREbvHDEULgdDzRe7MKZO/bdPMYPBw8u+PhwGE+MOVMir6h0711J/gE8ph8doVXwKqUIlbVlEE3cHAq43m4Zu7Yca07tWa0Ho0Va+/tQ5B49RRXRAB7TB8amP5CKtfrzCehfU1GAhrsdiDQGGokzbojstOPouvWWsiL1/8QzN5S5S1+TChOaZNA6GomlrAlpyEHiYCESAzD3Cf48Sym0epHXu/7yohmZ4dZ9nheY7d33w5a9Y0sY8gyXum3R7pzwTfDITcf2t4bsCW+baF6SeeaIRSE0O0cIW5sklJgmjIpBg5T4jQlQoCcAjmbKVErieZVW/Vhrhit0jXOYTFFUopoUzDTMv+cbMD12nn6Fp9GfERFNhDSDCGZK1Vt7rrRabWjGdtRxkjUTMozISgtZaaUaD0Rg0mCqMIcOlNQuhZ+9+t/4lz+Z6bDPf/mbxZ++W/cM/78Dr08Gpm7V2Iakj2KSCdGuLs7cDjNnC9nnpcn1noxqzyX0RJRI8nHSOtKqR2X2jOuUWuklHj16pUNw7x9y9u3n3N3OvHNu29Y1pWixcwMXDKllMK6FuMTDe0+MSmRWqqBhxiZo6sWSEGLWQnm2fhILWZ6NWpCECWGhgSlS+e8FHpd/mh/9D9J/OpX8JvffPfvfvlL+PWv/7Tv5xOJw+HAv/9v/hsTBh9ANlqL7sON3zQlToeZGCOvH+5IThuwiuXC0H3Fj+cYEiGYzA8hOo+P3TkMXGDd5XnULJY3nisvFRR29oBgjlyDQ68brWDAXMB9gQY/TqnaqdohCGnKBE3klDgeDgzr2iF1FPw/7cq6FMpa6K1xvjxxuZxJKfL64TUPr+85zLMNqW3v5yUPduCCcfsWt/ihUGDtNoilKkT13OWDjGbVEUHgcDhx//DaZjCuqiHrstCb0tXlumoBYJ7MMIAQCdOReDhaa72stLraUwTbbIYQyPmApAkkEuPsVdmASqRKoEun9pXehY4S4kSSiNmtm+064DMpoITNejdEKyZZRbbSq22B376+55eff8acEw+nAw+nAzEIuHJR743HdzOPjxntjVZWvvnmS9blwPqzN4QIKUeOpwOvXj1wWSuP5UytRmNorRuVYEOvettkfkLxURzZEDMicT+QNr257hUYs4uNMZPUXLBSsuqP7TZN5qqrkgOkoNTe+PrLL/jNV43pcIfEV0ynn5FCIJRHwjh4tZmvesB086QTQzBukSsEzHPk+ewl32bVlyDKPGViipTa6A5KwYFs74QYOd3d8fDqFfevXvHw6hV3pyNrKXzz7p1pzHp1uDYD8MU1AYcc13i+2ppxgdx9pfvglnVBArkHwAbJzEPavOKDVISOSuGyLlTWP8Kf+08Yvw/E/tDvbvFRMU8T/+5v/9a0KR3IxhCYcvbKjlctML5sjnGrjEQHsr13Pxb7ZqsJEEIjBqPkhBQJ3Z34XJBWVSm9+TAlqAZG6elaT9Zso23AI/igh4igwZ0zR9HniqoAPgytDmgdzDbXu4w5EVSJ02TDZSr03ra1nSQRJdnQzXlhuay0Vv31lJQSd6c7Xt0/ME3Z5IFkVyvoY3jzW9/4Dcre4odDFYofv240SZRdkQPRjcc+HY4cT/fklM3yvHUvsiiIgdfWCsti7f4YJyaJRhHIM2G+M+CL0DYOu3ctQiLlA0wHG5ROB0KYUIXaoXdoNCrN81hEYrKqr+4bQ6vs7tXZFxrpwe7Tg9CDre2H+yOfv33NYZ5483DH21d3hGCSWq0bvSdFJQSrxH7z5Zn379/TWqHWQnDFksM8czodIRRCWEzZwCuyYy712z3RW/ylx0+nFjDag96KvPKPHmTrMYkMLwnk3j/BqNr2XEEguQDe0iu1L0hIlLJQyoqGQCgroZoIcqkrpa7EGMk6j6c0maucbMgsJVKOUCGECs3bHjEQxapFKYGETkp7GyeG6MYOedO061fWswoO3tsO3P17uV46o63ZO5vZwocizaZaoGgMxGitE0FNDxBh2NPexJ1v8ePiSg1DgoHDzbhjFy8XsONtaFVurTh1nqxdN+faWntPCdKREEh4S1NtU9a10rVzvixcymrV2FEGugaxPrwS/N/RZYMsUQ2Fg12WK8RIdCtOtY9n54tgskOgRFVicIvarW0K2iI9NkRNtWEAWfUTUWuBUmZaL6SUTHprmsg5bc8BYx3bFzQGv27Uglv8QSFjHQz6SrL1FyISBNPs6Ts953r638/9G3XNS6xDFzamRMoTMY68Zcd56pPrK9uGz9RK4rZB22Xu9tfsgyY41Al0v1y/fsAoRddDmU6q95SvxKgEkg14xeQufubkFzcjoUjoNpA65V1SLI1zAWJuZ6XQakXEnMxiaNC7VX0x+oP6xKlu3ZxbfCrxcaPwEm0nVyplLZRcqKUSQzSOqthCVQJrVaQptQkqyXaPokQaKsqchLtZqA2eny6cn0zY+PHdlzy++9ISXnlE6jO1Fr559w1PT4/kPJEOiUOYiClyvJuZj0diFh4e76h4218brZu8iIRER2hdyQd3r22Nw+FA78rheOTu7p77hwdSzpRqQySmT2nAdS2Fp+cLtVbWtfoJwAH6sAjsivriQzrKAMWjNWNTm7YzDWhvntg7kWqgoQGrGCfoFrf4wVA7VoZqh4137OBVbKo5YCAypWGDbJP8ik0ml2bDiefnM+fnZ3pXA5rBugen+xMHB7/PlwvnxTRb/+k3/8wXX37lm1ZrmBpgjZuuZbri/mW/HaNx6IyOtFeQj6cjb968MX5diuScCVE4zBnkznVn947i5mIGVslybnmQQBRbf+VYKKXSeuP+buZyuSemyOs3r3i4v7P3NWXoBp5rtdZnkECKtl79q72ly1v8qBCEGCfyNHM43ruOqp//BWpdWVcbzmrVtFC1O7DtQ2vWZLB6VyQlJteGvXt4xcOrB2JMzMcT83xEgdyKU90sX5VabG4lZFTNDr51sVkMpwe23pwut7qVbqf34rSCDtqAvs2aGEMoOOVIgE5Qc+SKKZNFiSFwfzpyOs7MOTPnZEPgwQfa4kzvnSzCaZ5Yl4V6PrM8PpJDZD0vfPPVN6BKULg7HGlV0Vq4PD1aWzbPEJOPz8kLCcFb/OXHR1ELxqBUb81dqpq1Cloy+SsXXzb7VdCqtnAwcrn4ZHRAyRGOWViDIn1lPS/U2ricHzk/vzfSd3kEB7JP5/c8PT8yzTOv+isIikSYDhPHuwNdOse7A5dyQIKQni2RIgGJyUfLhOy6eefns4HiVEx14XjgeDySUraFPfivXp2ttbGsC6UYpWBUau278YqQWkXLWqiKyL6z3kBGiNswnPZmE+UoySvWWiutip0/bnGLH4qriShxBttQbA2YxmQSWwspRJPqEaF1Ex63iWVv23flshbePT7Re/cqktGCJCfiZBWfp2dTDXh+PvMP//CP/MN/+SdbJxLpzjNNMW7V35ysGhWCMKW0DVgd5pmUIiklux0Dr16/Ik+ZrkdmRrXUXLiGOYK1/HcKw9ArsfORT3mPCrVCnau7BnUOc2JZjCf88HDP6e4EsG026UrHOzFiFa3ATXTrFn9giMlexTQxzQemPG1T+2MQmVW8EmpdEC9sboOTw3K2q1oV1tfOfDpxvHsgxmjPPc+AkPtM88JJOF+QdfU8FTz/2RZXO5uUpHUZ63bpvZkigRpDfejFBufG23ozu2gRgdBNjQglx8AhCSkGjvPMYcpMOVtl1i2pp5xs06hKEjjkxOVy4evDgZwyUQJlLTw/PvnmN3GYZi6poLVRLmckusZ7CKgPUl8Pid7iLz/+COKkfrioHdwxxD1peQWm+oBFLyu6VnozolsQSLK36iRA6Db4YUTyjrZKrcX4drVAM9ce8QGPlN16b6gARGu5WEsye6ulW0sjJbqKtz1tBntMao/WZroyPhgDKWMiclMmgA3QfqhWMBQcwJY8zidqHZvkBAzA7q0dxcwcehS0BwKd6Am5a0BiMKu+W9ziB6L1zrv3jxvFBzUd5Tw0HENkitkqlDGSs7ftpaGmyujJS30IUogpI70b1cbpNsPsRH2SunWlts6yVi6X1YCwBDpWcRqyeOK83KEcUnJybrsNtuSU0AnmbNI74lSBODQ1/WLUpo2l93KIejsnGccXhS6u2QmoNj/HuNe704qGLSawOf6BC8uHwecNtkkQn8q+Lctb/IgwKs/ILUMiygHsiyElNkoPbvVqUlPDhVKvu/0OcNXnPUBiQ0J1JYTrp/aejOyPc3r47jMyLh/w2ZWwP4cPYwYx0wZbQ762xahEBsDVXARzIAfvZMjoloyupNpAWZXtc+NFnhQTU55cc11ZlpUQInkyYDyMJMAoGK01mlQbPI2J/iHB/hZ/0fHTgex15Udtj5di5O5w4HQ6cjoctt3X01L47RffsJ4fieWZtDaCKvdJiFMwO8ygpGiAMAXjvIlUSnnm+dmEpKlntF0QgTxlpuNMzhPT8WAWfDmRppk8H5k6HE/3rLUj8cLxvHplWFlrd/6PbILsKSaOxwNgk98xZUIY7RdFrofZmi2cWu3SnIzf+y4HNCgEQ8Gg90Cr4oNfszugCUgEzNkr0mjRIHai2663RromNOWP+0vf4pOIy2Xh//u//kfj17XuG0y7iPACyJrO8USIgfmQOd7NpnCQnDsOSJo53FmKG9WeECKH40yeJ6iNxiOXtfF8qXz9zTO//d07G4TExbrEBsuCqxfEjSsbmCcDsvM88fr+gXmauL+/4zQfiDmSQ+KYJ455YkqJKZjd9GgdmgD64A/CkOZTVVop1HW11ixX8oBeDQJISQjB1lathfpoldplWc31LAjRgW5KkdPpaPSGEDZa0C1u8UMhIXA4nIwyE90u1pU1DNCNlp7JLS6XBQnBaQZ1m9MYDo+i0DWgXThfCsoTIQamxTqKIsE3nmnjeIeQbIDL+a/WIexW2FEDpzg4JZuNtfFmjYtun2Mf8DLQawA9p+wbTCVoQ1COU+J+TqQYOB0PTMnMfeiNsu75rfmgmOlYd0TheDjy5s1bp0VUfvfF16SUeHh4xekYzQa+2XRa68ranmn2ZgiT0wxucyWfTHxcRVZNZ3Ls5FIQ5snagsaDsQRQWuebxzPnpyfmvnDonYgyCV4VNV5pCN0s1AMEsTZGayvrYoYD2i7QFkKI3N3fcTgefaArI1slNrlbViNPB6a50DtM00wplRC6CU+PNr6fP0IITHmidyXnydqnwVow/eok0h2gDq6sTU3upPzri/1+GNN2ened28nMEmxXboR97SA9UIlWwcIqs0igt4jKzRThFj8cayn84z/9xo5Bb6tb78ES5jWQzW76EWPk/tWJ1/0VOUUOh8zp6IkpJPLBOhTz4cjheDIQlxMxRzqFTqBUZS2dp/PKu/fP1K5UVQYjxqUuNyqRYMNi05S21mNQoc4HcjRqUpBACiYdNqVEjtHUS0YZNBgvvYutzW240pCt0XJKudpguoJBNmcj4+6KUxSUda0Ud/16fj6zLKtVkHN2/Urr/ujWUUmuc3mLW3x/mH375B2/oYahuwNWH2rKQm+dgnUGzJzDHL8kmLOeiBsBYBqta230y0IIgdqUWk07ep5hGsNcau9B1DoSY62IXM9sGMdcgqBDzxynIqj6EOb1p7L8H5MBWaMZmGazAIcpcTxkcgzMk5sjCaAm0dkBbc001a8qqKIw5YnT6Y5aK+/ffcPT0xM5Z+b5xDzv6x0fiiutsipITCQJmD7LDch+KvFRQFZ8wjiE3SbPLvazYXKgCk2FpoHSBWlCxHT16pg0HG2Lrb/h05atspbVaAPaELWEGFNmmg/G20suA6bCslbC84XLZeGyVNa1UaqCBNPlo5GiWTDUrujmHz2UB75dYdGu1pp0cNpdeDbEaBPTA0z3DrXRtQJCDLg6A8RoVal4Neiye88HVJQWhBisziSbu1InxQF4b3GL74/eO0/PZzaZEJsWQTD9xxgiJRZLdHog5IQGLBGsK10jIp1A2wwCkmvQbsdtCG7WMQTXZVMDmaaJ47BzVgOzgmxANmzUAq9wHmZyThznmbevXnGYZ1493PPq4Z6749GmmJ3f02unavFEbu3X0XIttW1dkObWvMuysFwWA7H9GshGVwfx9SdmcFLWunVYLpcL62oV2Tzl3SBlypuRib2tG5C9xY+L7zQQupqtGBlzHI9Dw1hktNIHAHZXLrG1GEMiJe+yRDtWh4a7KRtACmwSVRKjdwqHdbUfw65woL0Te9wBbhjv329vnAQ7v8QYbWhTBNFOUGORT3koFcgmc6cMkxUz/FE3ThhMgX0Y2jV2R8W12ED1+6cnmiqPj8+s67ptAvoAtmIzO3jF+xafRnzEsJdVVFKMzDkxT9mrsZnDbJSCAKCdrsKqmUUzS4twMf5nCIHTpORo1IQYI9DRAF06ncrz+gyPXxNDYs6BKQWCRA53D7z+7Ge+mBOESO3Cl18/wTdnlrXw9dfveT4vaK9AYp4OphMbzPnreVl5Oj+zlEotxU8WA37LVo21dk43+721ml2fCPNsFAE7+QRPgAudC6rYwJaD8+iatyEEpskeZ+DAdui9CfRAVfvOqCv0hSCNeY4kmf8Yf+9b/IVHKZV//Kffegs/eM5p9GYn/eh6jCEEXr9+BVNkkgkW0MduigExMPtU8cPDA68eHjbZrJgSBHPlqq1RfQMYUiJPE2/evuZXf/VLt4g1ru3gotqQVubV/R2HeeYwz7x9+5rj4cA8ZV7f3zFnO3883N+Z3WZO5BDQ2ljbytktZ9dqlarWO5d15bxc6K1TW6UWG1obQFa103qjd7fLTJGwyX4NfizWruxm01uKmZ1IDOR5IqbE4TBTtXPXKjEG5jqR4m2DeYsfDhEDZiqB2kwTdnOzxDoL4kOYgqA6eKlumBCcmO39DAmZGK1zOM1HDoeju1pmppyQYLdTStv8iDlZArxEpiKyVTcZ8yDd1llw/nr0dsqY8dZNxcBnY9wcgV6hF0SVOQWOkxBFmFKw4ow7c/Zu9IMeAmYN6MNsrsOuIuRppitcSuebpzOq8OW7R0SEdS18/e4bai0m/9cM8ErvEIJpSPcbkP1U4iN1ZH1YIgZyii8uySuyqPsREChEegu0CkHh0mB1slsWO+BHVdb2bp1SV86XsxHlZSbFCSSQp5nj6R7EJLu6Qm3K5bKwlsZaKo9PC5elEOikEIgxI8F2e703YrGBjiGrtW/gxhCW7RS7A9nWdgMEEFLOqCqTL6LmvwvFZH62CW8Z/vbq3Lq02WCOCm2XTo2CRtCmdBqoUSFyFnK4Jcxb/HC01vjm3fsrLcZAb4XWDNCZ1rJLYk2J160QNLJWCIut5woUgRQD8zTDvctahTHgIVQfCGtbZcc0kI+nI69fP2y2yt2pNUOx4DDPfPb2LXenI6fjkV/8/Gfc3Z3M8efuyORak7MPgYGaKUPrtFJZLiaCfllXni8Xams8Xy48np9t/ZVqXu+9G5BdFueqm7QQYAOhwb8Dr14ZvWfwbPdqr4RAPkyknFjrkcPpRHCbTICW/wjzsrf4NMLLmX2TxlM3FdCtGDIUinXTjxrucvgjbFhZJCIuhZfSxDQdvEiStgHOlEeeEUJKGy0hDAvmbeDMo49B5THfYfrqh3l2WcjxkNHSb6h2bKSzeReootWs2KcgzAmCeHeS7q/RGDI82pXun7lVkxcb3NaYMlJss3xeyiYLNhw1z+fLNocyNqAKUOuNWvCJxUdTC4KTyvOVX/t+rKtTAQIpz0xTZSkTTSOtd4oGKoqNOzWCVLp0iIWYE6KRmILz2Kx1KcHaJSnPzIcjIgFj3Nou7by8M93YUt0y14Wmu42e9N7c1q6bTp5LiQTBiOgqROloW+l1oVehtYw5FFnFqnvbpdVqn6MW190btrV15yQ5H2pQMDYno6GboBDc+k96gbYiWkjSzKghmOtZHgTDW9zie0K7UpbFpohTQsN+3ArmMDfNmRgDx+NMTIEQzCnofF4RMO56EFoILMcDy/lIqxmi6TNKCC60jm3q1OgvU47c3x0pr19ZJ6PaRtH0kn2oa5p48/o1p9ORecocDhM5RSRALSvahSJCWQ34mrORJcziQLa3xlIKT5eLJbRl4en8bHSGat2V3pVSCsU7LTo4sgJBdQOySiCmIai1d1BSiBt3PueJmK19axvPaNx19IoDf4tb/P4QEXKe7PYYkhaIwW1eezXVGu1sys86eHYu54igXkGd5tkGL2PkuHHXTZbO1pNspgsigsSdXzvkHu3NbP9jTD53H0Izyb2wqSyMgoyVZDvanWuwKaR0ei3U9WJqJhGobrYSBUlj2BPL2AIqTtPRnapkheFhgOIAP0TwjXOpbbfexhz9WlWkq7U9o4H0W7r8dOKjgGwAcoqcjkfKWjgejpvYulivDu3m6nF3/0BImdZWvnn3Fa3BWSMXolVspaJS6KEjE8xH26/Oh4npYMkj5UxMEykfuXt4w5vPfm6LNc+EmHl8fOTLr5949+6J1hrLWijVd3/tAn0Az9WmIWsFt7qdknCaAz1F5ljpy3tqrBQ9UWKnxwS9MOVECEIpC5fLmVorz88Xns5n825fVi7LAr4QzbnLdsQ5mQZmFEwqXtVcTVTRuhLqo9nwSmMKq8mGiXLIxnG6xS1+KHpvPL37+kqLNZKnxOE4kVLg7u7E68/chnWeOB4SISjnpyee37+jt8ohRI7RaEOhFVKvpJTITyem08l4eSkTkhmiSC8cp8SUAvmvfs4vPnuzq2B5e3KabdAlJ6MNzPPsVR3nwrs2dKvF2oS12oBlraxr2Wxzl2WlN1u753WldavOni8Xb00O7diXFZkBXK3w5QoKITBlyH0A1uh8e9kGu0IIpCkT3PkrzzMxu8pBU1qv/yp/51v8eUUIkePdva0Jr/ybLJ4di6rVKHDajWKAcbjzNDPPR3OaC2Hr7aeUSXnyYcSJlIcSiV2s8mpyd8Ym2Cuw4sUUkCvprQH8rFtpm8FmXYsNxO4mCChme6vmXNma3X85P/H8/ht6q6QAk/NjpygcXOXjkBPzlDYJQJtdgVrNjEUxgKsS0BCRlAl5BhXW+sTzebHnmTPT6UBTZapK7SawV9TUUq4r2bf4y46P7ouN6ed5mg2oubWkKXt0aztKYJoONBUkThQSTRtFE5Xs4C5QRVCvyKZcUAmkbJZ2IdpBL8H0K6f5wPF0T0yJaT6R8mQasYglu26cVms5NHopaK8+GLKa7lwfk5sQooHZHqwiS1vpRWgp0OpkYFgbMfoOWZXqFZ9SVtZlMZDsrQ8RobeMJnX+k7jW3vCTt4lRG2DrhF6QtiB9IUgnp0YOSgqQww3I3uLHhfZOWS7QEi0IoomchOz8ueNx5vWrB+bD5NJSAqq0Vnh+fG/dihjBVQLO88T5MJNypmqjtIrESJ5m8jRtyTFH27DOOcMDXKfGGCOHw8EURlLi7nQi50ytlfP5TKkFbYWyLizLxcxGLheXt6ssl3WTu1uXQmvd3PacErQU2zw2rwzt+q/7UGX0c8jQx0RcF9YnwYNCyrINcqVpnzCPORNidDWT5NUsH0q5DZTc4kfEVpFVNovkGKyLYfkgoWpW0EIgSkLEJLvu3OzAxYwdpKbNunmnsJlT2KAOyQCd14DuxUC1vHh/w0ik94gArQ1HQN2phDKe06bH3C7E6T+VWlaWy5lWCylAC/aYHgXNZg0vx5kUQLeBZ1uvm3TleJ+4zbVEJCYIzTTpayWluLkBmn6u0hRqV6idpv1Wkf2E4qdzZMcUc4zENBHTRIimjaoOKIXdyecwzyC4xl0CqXSZqHIgilAlEUiodCQ28mwCz71VLuf3xJhJYSbMxu9ZLgvv3r2zAZR8JsbIu3ePXC7PqJppQi0rtVVfZKsB2d4p3jZpLn0CHdFOlEqgkaUyhcoUIklXtJ5pPdIbzqPr5oqXEyJQamXKadOXLVIHI94HTJTWEq0ZjaFp31QZRFeUirZC1Ao0ksAchMmlyJKoNZduK/MWPxC9d5bnJ3pOiDYTFk8PHObM8Xjk9cM9n719zeEwU+rKWhZa60SBnAKBuMldpRjcF92qKjYrbZcUhClGCPKSQrONNlsIu5rB4IbHFAhRkGYAuqwLa1lYyspSigHW1ky+TqFLwPKZECaxFmLOME2mO10qaVpNBs/dj1C2oTZ8ExlGgpc90c/TbFq6IXA4mC518EGZmEYrdm/VGqd+WHc2P3/c4hY/EA44h92sor7Ril7lDJiWjzliWkXW5inEuwni1LqN5xoGFWY/pvf5DrYKK+P66uZLcAsDko7h0N0cxU1FfGizh2Fq0F1yQGnFNJd7K5R1ZV0XWiloDIQU0CB0n/FQEVcxwge87HRhg9VXjly6MytSykzTTAjC69evmHJiyom3r++4O87UrpyXwlottz+dF9badpm+W/zFx0dVZE0IeWKeT6xHSHkGEr2bmlz0ieDT4cBnrx9Y6oHH9++IaaZUpYUH1vgKDYEuhS4rSCMcMncx0Vrl/eMTj998QUwz83RPzgckJL7+5h1N/zOA8XnUhju++fp3NLexvZwfWZYFcMCq0FRZmy2gfTF2gq7MXBCp3MWJh7RwTNC1Uc8rSqCS6GRUzVrw7jTTWkKc9FSrvY8hYK1q7dAQhDLcwoBIIdIQrbT2ROwLQiPrmYNUchTuU2SeonGRXAP0Frf4oWi18O6L35JS4jLPxBQ5zYHPXv0db96+4Re//Bl/91//Lae7I19++QX/9Ot/5NJWpmg+5z1FjilxlzIpBg5TJgcb1khjUyXKMSeOh9k1ZbPJz4mbB/gkv3pzX8TuY6oke9IttXNZn3l8erK1+2TrtXXTwhwyd6Zdl5AsTF4fOlyBZxuyrJucEZ7Ggzv9WQFpNy8YNIMBZIfBwTzPTFPeN+nBprmrG54oSimNdX3aOPZDCeEWt/i+sIrs7FKOJg+XUiBPaRuYnrIfo9ugl7iclnctc96oL1t1lmvO61UVU9k6gfYG+ADI2s2dfjM6DMPcx4pBvVXK5UKr1Tezss14jEG1VhbW5YneKk+P73l8/55aCocpoXMmhUAKM4opKHQVo+Wo2dKaa6W5WA4RTLX5aqcXHnj1+hWqyts3bxA685T5xeeveXV/Yi2Vr9898nxZOV8WfvflVzw9X/x8c4tPIT6qIjt2hmYFa2234eNsCSv49LRVZEMMTDl7RTahkmlyIIRoRgBmyorEQpYVqYHev+Fyfk/KxsEZ0/7LssK796h21nWltUKtheXyhGqht5VazpRy8e6fvd/WoXS7NvO9bq9JJ4oNnO0V2URRlxFC6DLRxT6fVWSjE+wzOZm+ZXI9PcunoyIbPPG55Io2oCK9Ql3QfibRibGSpTMhzDExBztd1N42B7Jb3OL7QntnPZ9pMUKvxBjRVjhMmfvTgVcPd3z25hWn+xPr8mwaj+w2tl0wB56cPAEFH4YUr8YqUawim6NpVU7T5Pqq+6Q04rqVsFUzx4BJH9PFotRWvDK8bhVZMx9hG5gcWpciwYZWZOjbWlLvLtvzYWxA9mpY5fq2TXlPV0B22mTxxoR2V2Vdi3vQN5bFknpXGxpt7QZkb/HDYVrK0YBjYJO2Mr1X01SeHNQa/cAeZ/KMe9cjRm/TDTQqQ4t8dENeatB+K218R5FSN+WEvikRdG0m29cbta7UUrZuzGAnRJ/qrltF1hRD1tXuH0WZYkDirg073lNXELfBFifqmiffeM+y0XZiSkzTjPi5KUXheJj4+c/f8vrhjmUthJSZny9Mz888ny+01m8V2U8oPqoia63C5BWZ6hqT0Q9GE+UQEePCxEjEzQP80hXTgqyROAtpPiDS0bpaq10iMc3ENBPCtFn31WhJ5XI+o6rOea2eaM6uImCXWoolw5hAgg2WdOPSBIEkJmWSUmaOJxINCdnaE6VyLp3HpdM6pMM909FajbXr1iYq68rlfKbUynK5UNaVrtYiGjp94gszCEhobsGrhGj84CBmC5qjLVQxBwXX6wvfPiHd4ha/J1qrnhgnpmni/v6et2/f8Pnnn/Hq4RXzfGBKE6fDidev3jDnmfVwYj2e0K4cUuLkKgN3pxP3d3fGUZ9n0uST0qc7DofTNgw19CoHJ1VRmho9IKjx3JxaSsd+LyEyH07UBiHNNKKDxk7ximwYgHXTwdz1X2MwjuCg+3xrkcg+8LF7s49ELN5SFRsSU6VfFpa1+IMNSajL89VmUmKmTtJ2XiA3asEtfjgs7/Sxs3M5xrCB0K6mXSxi9KBerQNg2rJ2nM+HowHXEAzg+oZRfIBxMFq3Y35/cTcLGJDVea3eqTSqQ0dbNSDbTX1AhzJPK/ReHTQD6iZIMXiXMUHP9Bg4zDOn04laK4cpczxMplZymMnT7NJ3NsiFWAnJ2zabg+b2pv1nIQRyMhpfTkZ3CiLUUrhcLpRSzQRBlRgCx4NVvkMI3OLTiI+qyOYpMx1mDscjrVsSIyS6CC1EqkTjxETfvbXINM8cDgeGd/T798/EGDjkB+bTK2IQWgn0KtS6kg8X8rwiEiml8fj+vR3grh+nLt48Wn9jR1lbY7k8U0ohxESOmRCSJaZaWVu34ascvW2TeDgemSO0uvC7r97RW+ObxzP//PUjpXU++9mv+MWvTMMWiYiYDNfl+czXX31FKcVVC16aK0gI1LKyTtauDYfINEdElCiWqHOEeYZjNvHrkLDtrzb0ykbwFrf4vlAfQjweDtzf33O6O/HzX/ycf/u3f8tf/ZtfcXd/4tX9A3nKvH3zmTlaFUta6nzuKUTmmDbd18M8WfJMiZCyUwUmUsqW3OI+QIU4/7t3ai2sZXUh+Ehynlz3hBvSxN2rN0zHe2rt3L9ebXK5Ni6LydnZMOfs1axR5bWkHZyLa2z88fnBqESm4NC7Jepa604/sHsav6831mri6aXaoKaqW0+77Fdbqwu1j7Zr90QuG13hFrf4vlAHqiEYB90m9sNGYemtci7LRkdb18UF/a0oJGLmJMqoUELYONyyOc0ZmA3ba1pl1zaVqt0k43qlOb+7dqfHaKe3xTZn2vGBEHO+9PNDDPtGNYiZIMUQaMnUF8Z9Ygz01phz4uD3mafE4TC5QoOdH0bBSxDoQm9scnajmKqqpBg5HGYEG8iObnVr3ZELvSmtNALKlBJvXj1wd7qzzfUtPon46GEvG96wqiy+iFRcJXVUIl0HTlSdimDtFPUJxNgCXcWktaKATqDmvR5iJgRLnqYNuW7Jtw5bSt2HLoZ5SevVWoCtOZvcrf6w6cbWQcTEmINYC3KaI1MSLs+Ny2JDKO8en/jq629Yaycf7nmzrjblHCBEq7LWWlmWC2uplLVQS9208JDRHsUTfKBPE6ZZqdaylUCIkHIgZvGqbUelG1VDgjlI3OIWPxiWJEQgT5l5njkdjzw83PPq1attoCnFyDzN3J3uac0kpEY7PYfIFGyDN+XMNGWXq0re2hef3k9bh3Nw7ZS2VXy6dlMS8IpT3zZ3ozAVvUqTSV2tKuuKBBIXam2knJkPBwPDznFX9YTtLckgNg0NnrzthtvNmssXYUXL7mA0KlH0ZlSG1ljW1Wx6HYT3ZtWpVirqQu3dq2ohysZvvMUtfkx0NXvWUYkNW0XWDX2qtfJLWVnWdaPLqOeuaZ6prTptxyTgBl1mgNhNPfUKCI7r3pWOud813+RVl83SPtz/TAKM7n2TbhrsqKJdTHJLPHc5aBVRBBuEnKaJXg/03piSy2y5O1hK2SrOrdmwF04l8HXcdEjZmv78dg+n7ImYmUsMdhZptdJr800rti5FmKeJnLmtzU8oPmLLoqANoZt2XY6o7gclQTZe5yiCDH3Gw2G2KsmyUuoKvVObUp1uppKReLAJ6vmB411xYBjcO90dvNbVuG0H47ZZeIIS49WklIhpYj6dSNMMS+VxUbQWVMU08DBAKyETUiCkiTQdUAkcTsLDa6G2zjQfqbXBstL6sllaPj0/UWozZxKXDFKFEJNbYAqJRqaTCBwCHJMSRTmGXWZrTo0p2ncbpA02kQ2G3VqYt/gREULkdP+KV28+51d/9de8fvOGtz/7hYFEFdaqPJ9t3SxrozSht7BVdUR2Fz5B0C60op4wuwsSiJsU6T44IoNnZ6up9cb5UlnXioRO6ZGYDMwO69ohkdda901qo/uaKqVZu3Vt9hwSKLWxuuTWdRs1hmjT3SJXqgVKqbap7N149MObfQezVp3qrfqQ5kqpxZP2fj8ZJvUMfGCAuLe+gelb3OKHIriSTS0FQSgoFzHA2Fql1cWKMr1v3Gtz7spb0Uh9Q7gpdGyMUt8kDm1mxSuwfes8dG1OcTCOtx3nzQm53WTAGK5hvsJUjeZmI2QbR3ZUgIFtViaEgPZMnyZ67+QU3TlvdxOzCrLzYj2nv1Cwk0E1sM805je7I1VtfcuL9MJwCOvdsYcIhLRRNm7xacRPBrLWHihIUKY5malBh1qNwK1udSnd2wR29DNNMw8PD0w58/6bRy5PC007a+lcSidrJIaZlCMSG8d7gTDTauX58cLj4xO1VZ4enzlfzkzTxC9+8XNOxyNDWL21BhKZpwxiagrHhzek6Uh8OvPVU4Wl04Hah6NIQNJMzIk8K9OxEXOB/EA8vqWpEiSxrIVlLTw9X3h6eqa2xuWy2rR16yxr4XJZQYTDwYZhokCWhVkLkwj3qfJmMi2904Tb+CkpNNOwVaVrQXU0YW9MvFv8uIgp8/Znv+Kv/qu/5v/w7/8DP/v5z/nZzz8nHe5Ye6RcGufL2RU8ug9VRWKORLGBrY5QMC3oSwWpltCg+cUF3Aes23LGALLNLWIvlFJAAjF1QoiU2ng6n1ndRnZTBGhqvMCum1C6OEeuOag8X868e//oVamdLztNk1Vtg/hQpVEF1rKyuqb05XJhuZw3kGsamJ4Qx7CLJ3oRc/YKwbh4U0g+9CauZW0Pa6XSbrnyFj8iBDt2em0s68VNPwrretkqomau0c04xGXg5pw4HI+WR3Iy4NqbG39Ughp4FbXKbqmV4hSaWq3yyiYVNwYTfaxq8Gv9EgUkmq5tDMm7hWOORKxq69SbaGVRVKzCnF0hIATrMKp2ohifVTCVn7FRZhjIqm4dFhejdB6ucE14H1J3Zoqy0GpxCsZCb4O7a+eDlDKn0z0pxxuO/VeOX/0KfvOb7/7dL38Jv/71H++1PopEYkDLWwwpoA2km2MVY3Kf60oGm3JBb83oBX5AW8KC0IdQ+QTSSHkmT0ckVMA4bLVWlnXlclnGOyGlZNUWP+BlkNGD8fmmaSbPM+e1bdqS9hl2SzwZFIhoXEBFyEE5xtlBevN2pUl9PT09u8FC36pKrVk71ZK9tT2DKFG6y24FV0Wwoa452UVEiZhvdffp0TG6KjKYBbeVeYvvDwmB+XDkdHfPq9dvefvZ55zuH5Bgw5UGGI2So9saFVDTsZTB696opKNv57f951YBGa+J0/JeAtl1bZRiQ41SxSqztfL0fOGyLJ5s3cCgK+pANsbI5Il8WEBb5+OZ9+/es5Ziw1+uqTnPM6XZuh7OXr0r67qwLIsB2fOZy+WymRiouoKIjAEwG4YRbHBmGpq3IRCT2PoLgRAUGXqXqhuX7xa3+KEYI1hWfTUKwcVVMHD79MHvjmlQB4Tox2II4ariOugxwfKED3S1blzw3q/t0hXt1el3PnA8wGuQrfoaXC96mPdE14wdyiW9C1U7venVEKWbhgWTuVPvjqgO2txLxRD7TIEhWWnykrpXZWVXKxgxKD3jMxXfBJey0qoPc7uyCQSnMN3iXzt+H4j9od/9lPiIimynrhd67QiNKIqKUqV5x3G044RrYY0YDMhqV1JMzls1asGyNno368dSrVrSWoR4IFA5nBqikXVdeXaKQXcJj+75tlZrV8YYTRYrJ6b5yOl4Yj7eUbtwOj6ajm2rlPVCaY11CSzrwdxIat+UDmIQcghEhaYL5VKprbOUxmWtLstjHtDmSmIDb0ECeTpwON6RQucUlFNQpiTcTZlTMiHsWczbHkZ3VgkK2k1v70XR6Ba3+IGYpon/6t/+W37+q7/i7uEV+XBEJfL4XLis6pPI1lbca5H4INeyyfYAL8CsF1z9OPSuy66oc7XHauDDibs8lYAUIFBr4f3zM4sD2ebmJE6AhW7KJnNuhBiNM1gr2jvP55XLuRq9J3TE2z2tCbVZQh6VKlMTKa4g0lmXQlnaBmDtA47kbRWgLcEjiAa7dFNF6J50+/gi5JqDd0Ozt/j+sGGvlbIWLmezNh9VRgQb+J1OhCBM88zxcCS4I97pdNpc6oacnFFhim+kZOOpr7VSet2oBYo9f4w+EungdVRboysAWBV2nzHZQC3sa7+zKfX07goD0sFlM/ddnVx9bnuwIsPo0zi/iG8cx4lkp0sYbccpP92KRqW4NfV6sdu9sy4XqgPZGI2LO1clzUckpA/x8C3+guOnA9neWc/vqd3UX1O0FkcU21WOZGAHtR2wipJj5HA42vTmZNPQNjHceb4UYmwEUat8COSYSDERQ+f+1cz9XeVyPvPN+0da71R36OodWleW0ljXwjQJd2nicDxxOB558/oNp9M9IWS+endGCDw/P3N+fM+yXHiOyvl5InS3CZREjCCSCCGjCktRlvrMulaeLytP54XaTCd2iFB3IhLN8Ww+3XP/8IYcOg8i3AtMEd4cMq/nRBDIoZNknBzYlBhsp22tVXq/Lcpb/Kg4HI/8+//wH3j1+g2vP/8Zx/t7WlO++vrsyce6B6oGzvpo63lrjjF84R0AG9/cQ3yYau9kvDwwZfBmR9XIN7Rjs1lr5fn5kbUsXmXxSpE6fUEhJxtSSyE6/cCGOZdl4fl5MSBuE5HWVowNiUbn6X1XJ+i10ooNr7Rq4u5gFdfhNy8xuKSKVV03vVwNhO7t06q00FCxTXnQIUIftyrTLW7xfdHVugLrsvD4/pFS1o33KkGYDgfevHlNzhPzPHM43VkxJiVzw/RNmlEETJ/8Ui+2tnp30KqU3ijafDAq+DCWWdcmH+BMMZJcfSDHsLX8h3sfsFVvR+UXt4+1rqOhWmmu3jGo8qOaKtvIGYPJMyrAsFd9LdrGW+9XQ2jL5WK2761zPj9vnZVlXajFJPouTl0KQchpIsbI8VjJhxOEvA263eIvP346tUBtalDVdORMNH2067rt/hhTmvaQQZWNIZhxwCbsbDxVk94QelCCqrc4BPUJz+iLs3kbcSwSo7zpdm1gEBebTrbbTaapOeXMlLNZULpO6xA7N+qAyaHEsR110WqzzxNaN85ebfvF1udgs+5JLsZETJkYOlkyWRI5ulZsGJSD7Zuy4bhBhr2iEqiT+G9xix+KGCMPr15xujuZ21aI9FpZ1upT0erJ0BQAhre5IpsslgHZMdbE5sHOxordN1xsvxkt+qvCzNVv+thstsLlUlk3m9dyBWS9f9NBJNKCbutzDIDVqvSmtk68IqtNIdrrm7SQt1Pr4PQp6rxZ+0xOoBBBA2xemAzoPnSfYVCEtCsaxvlmTFWHG5C9xY8LB5/Dha7W6i54xiG1yX4DsdN8YJ5nyx8xkrINMjYGwPQOZNtB7QC4lUaj+wAW2DHKpjI0XMRiGHluWORaHrI96NCYhR3M4mIGe0VWg61pCft9Gc8xPvb2nH5btjLXi3ttYPYFhcA6nmspNuTZrEhVfOBz8dtBAr0LMXZiypTaqGNY8xafRPxkINt64/H9IxIzYTpZG16UINbOsOpKY0iChGA12TEw0VonxUT2RZoHuEyJec4cDuajvpyfeH5+tunKtkCz6ePSlTgdkJRZaufx7HxZyeQ5MR0PnO7fcP/wwDS7GHMwusHpcKQ2a7OejndGxA+B5+czdbnYwvcWS9FA0Ujr8P7pma/fPVtVaWmsXWg9OK3BWqgxZULMaIisTXheKzkqhyzMMSMR1phYY0RQqpirmNIp2qg6RlBcwgylatumU29xi++LWitffvUFz+dnLstCniZaU9bVuG0m5L8PV2zQVFzmDbkCdr56R4vRuW52/73qIlfoNWycO3mBat2WHe2mQZkdpOqYnIIt8cRo+tMdS9htUHMlXNnfetuyj8/UHZw7tQCF2gzAenV4tDXs3wZmR3XaZIHMfjeI0MO+oQ0p+vnL2rPCVQXrhmNv8SNCQmCazcVSQjAgG2zIMkjgeDyS8oRE00sem06rUFZsQzVmJwZ4dTODbi15vIOZk3gVNpJcazbH4FVY60iMfZq183FqT98qvLWaLFdvzQ2HDISXspi01jRxOh1JMaEpWkEmvOS3DhBrt032UrYujYHyy3phWc+03lmHQ1jvrMvijp3NqrPrauB1sYrsALuDmlF9nZ8vF7788kuenp4opf7J/863+NeJnw5ka+OrL79iPp64fzOTc0JFSdKMu+ZVDFQJRGvDYULGKUZ6UnLOTNNECI1pnsiz+Z6/ef2K169f03rjH//LP/DNP//O+HbrE61caK2yNCUfToQYeF4b7d0TKSXu7u6Zp5nj3R0Pb3/Om9dvSTEwz9n9rGce7u9ttyvC+flsi7FeePfuHdqMWjBS/aUoj2undlhW58V2NSmgFtAuLgtUEYTDceYwHegxcm6Cnis5wSEFDmmGBJeUyMMDHp8q1cZF7XVUAioRxLynK40+PAtvcYvviVIKv/71P7ntpTtiaUA1GkCVUUWUbUjCpn5tjY4arAvteCXHW+8BMz/ANqOjbSnO37OfO9h03dnND37QjDSQMB1bW2cmM6eidBl+Q/jWbr8ooCKEZFa3vfVNQ1KbaWOatFDzSW0nzV9byPpwmxWcraqq2pEr62ihe/s1kIJtcMNsG+xtUy4GeGOAm537v378Kaejf2qEEDje3aFdOd0/+ObJjq8x0JUmG3BUhFKvtJ39OoTdgOOaZqDdJLskCFNOTIfZNV5Ns90bi/umy3OJotS2y1q1dTWOea1cnH9aa+Vyfjbt2VY3furd3R3wlnme0Z5Jwc8ZuoPXQd/dN51WjrH3Xmm98/T8yNP5kdZMTeVyuVh3ttatE2OWt8YpXi6LDXuy9U1QMdOGKsKyrjw+PgKwrGMY/BZ/6fFR8ltrKcTsnFi5JoePiUTfnWm3RDYKNFuxZk+UxheyS54mcwtrlRjTJqZeWzc7ut5QAiHlrVLSujp3LZrrUJ5I+UCe5peOJMF4R6MCnN0ut/XAWru1IumbU9h57TxfGrUra1PWuvNxOwEVNRkjJ7B3sWlqJNIRagfpStVAC5EmQiVSvaoz2pcNWIkUn6BRiUA0UXmFrje7vVv8cKh2Lpezr60hdxMRzLDEfhavAGjcf7ZN/gpgCE3cPhkxE4DgE1HJJ6lHMt6HUBJRuwHmCKIGjoNidKPRY1Rv4m8VWUHE+w5Xk9k7VejlcJrdTW0Qy9uRu/OWg9fWkKG4cEUBGBXZ8Voy+h86uiPGeu+DVKGD8zNIQFfSRfaMf/w/5C1+dPwpp6N/eljuIYyV5e3+LQcGV+IYVLu+teR3Ss9uoDDUfvYKqK2roTIgw6zIbVqDXCtsyEZRGN2MwUVvpdBadfBYKLVsXNTem1dqOznnrUrbXd9WfSj0uqO/c2X3Nd2cYtGGy2ap/pqFZV23IdDmVdbqdIztPfrm1CrMYevkDGrCqNT+Kdww/9v/9r/9vb/77/67/+5f/PVvYfGTgWxtnS/fXXglM/camEI2vpkUGJzOa6a3L8jWzIKvlMJaFtb14tzYzpQT82Hms7ev+et/80sAphx48+aedVn43e9+y9dffbEd0N25stM0b8D01avXHA9Hm/Z89RnpeP9ieCxOgXxozDJxaMLx/oLGmbpMJn1VTRP2fH6i1sa5wHM1QNp6oCLWec2RJHZKigeYOyDBfOzno4GEmNCY6BEuAd45lW8twjsCiBI8MbbejDJRTZ+zS9x4s711/gRr8hZ/AVFK4Te//setUoPiQDUjGHANMe1A1zsl4pbLQ35rjH2It9jBgKxVZHkJZD1hWvIcHvBmLCIxexUzEyRastXBysX59M4/NbS7JWlLspbUGMmtVgOvrW0yYqaHOygE3bsXzs/vDn19MAwBjQH1NmsKbE5BKZgcl/EIsUuAGEzRYFCntqEYDTcMe4sfHZ0dvFoxJ2w0HHGDgXE4jRrHqMZeXyMGWCUGH5LaRy57a9RSbVPZKm0A2aE4KXuhScEHiTt1LTw9vme9XKil8Pj4SFlXaqueo5sPihq3PQRhuTzY7ErYweQYKMXX4kb5qbZ2u1tBj8rueTlzvpyt2rqulO112suKrIPT2o1mN6hLIuL8eXPiq05FuAa8t/jLj58MZEvr/OarJ1o68jONkCZoJj+l+AHkB7S1N12CozXWspqxwLKwLOcNyB4OmdPpwC9+8Tn/9b/7t8QY+cUvPufx8W95Pp/53/7jf+R///u/94ViyzGGyHw4eHU18+BANqXE6XgyZQTtRK2INqJm5lOAXKmSubs0wnyhXI6kGGl1obx7x/n9hWUxIPu4Qu1WJVVvl+Y8kaeDEehTJsbJCfbZgALiU85CC8JjTKwkQoevLkpcLWlHPyl135221my6W6Gp+ADblWPaLW7xPbGuC//wD/+JXit1Xeit2+BhyA46MylPhFF59WvRQNhmlgeYdeA6qAXRwaxgSiIOZA3Uuu1mSsSYQCIhHwlxQiSQ4kwQfw8+JCVcVYoEOxsFYZfHMvesbajFN8E6Kq+D/+r0A64eZ1d2NrpqAeHTpuAV5DmPyW1IyTVjxUDslIwja6BWCQGi9J144XzFW9zix0THxTZicNUMq9LuPFJe3NpAq28krznq9hxWhayD54ppRFdWe6zbcA1agox/B+fJbooEyrJc+Oabb3h6/551WXn3zdfe5u+b8931UFYIwpvXr02vPQT6saMh0Lo7W3aTt6wunbd6vu/NeLGX5bJxcUsrW6W2O9936MWbUoHNxdiXaN9NcGQfQtgkutZloZTC09OTcWrrjSP7qcRHUAtgKZ3S1PRO8WERMS7bC9ilLx/XtzbDmCy0AYuUIjmbI9fhMJNSpPWTJ5nE6e6Ow/HkAxr2tCEE8nwgJ7OpzdNMmmZijBAS6o2cruJC5oIyBPOi8/gSEv3SO0iia6Cp0PBhLvCBGG+/hoQ4BzGm2Yj6o8IV/GtVN5YVoZEpZIMH2gndoEJ0vb7eldoDTQ3Ilu5DLiqbYsItbvFDoaomW1MLZbnQWyVItA0WgZiyOQK94MPa2hBGhXE3ShAf3hrUAitsmoTPBmQHF883cjEmCJGQGyHORvcJjRCya1ZGArINTFnSFcTYD3uyFrOmHJXXUW3ap5vt5zjnVcc798cH9krVJtUlwpBHsFxuXN1tqO1qiGtc7P3oVWXsqqL8L/8nBW4tzD/3MIqo6ZN7PXGj1m2yVS+IM3xrLRiQ9NtjgHFUV7eKqG3w7B8Mrp9x1/F1PJy1gCEI3btu4LFUUwlYvc3ftW1UhDGY2Vrf2vc7tUe3aupGCajVrKZruTIyKKxroatx203C8kpGTE0ZqPnz7iZD/nHUN6e6UxaGEUrz1yyl3FQLPqH4CNUC+GaNHEvi3DNHzVTtNJnREKC3rY3RJW5JckhvDKWC4/EAqrx9+4pf/uJz7u7uePVwIicbpDjOk5kS5Mwvf/VXNEks68pXX33Du/dPNIW2dqSsxLWz9sh8blYdis8GLkdFls5aG0+XYtfPz/zuqyfOy0IvC/XS6K3z1BJtegA5EjrkWYgqu9amCDFN1joNAmlC0wQiDnQNJPRt2lrokljVifdXfOLkt3tQqhQqDUWoImZ/ORgaf5y/9y3+wiOIcDpOlNXK+nXYHI+1F8R449GBnW/0tJplJiOJNH0BEuEKwIk51u0c97BxZ0NIvpmLxHRC0oxINEDrFVlkn/zfLs6RlyHW7hVT7Z3ezPFLRxWWMYrpt3YmxF59wq6jv8fkw2kQmIKYD3wUToeJw2R+8FMWslecY9CNShB9IG2ct0QCMUDOgZRu0163+OForfLNN18zTxN393fknDfaTxCnq42GAi83SOoJoGg1FQ6cWtMqdPWhrLJxZDfXLV8fIQQOp5l5tmGyPCWiJGRbQ3Zvy0tGFcgp0aeJ4XbH1WYOlMNhRoHWGpdl4d3790RXYyjFQGqrxqk1M4grR65Rhe0GcMd99ors1c+7zeJUr64arUfM/7IUgro29fm8VWGbnyNu8enET+fIqvDlkjmumceeOegEQA8VNKKhIMEnhwkbFcCGOqwNOeXM3d2RIMLPPn/LX/+bX3J3OvHm1R052sxUPE4cjzPH2igSObz5nKfnM+v/9p/48mm13drSaK0QQuFpUWJcjJfTrE0hmP2roNTeeV47tStP5zNffv3IZVnRXsB9pGvL1Ok1mpVIZCYx/IBURpsnbJxCUkbTKCe54oBuRkUANCJN3Xea4Na1wtBj710pUkwFUKD7sNho59xamLf4MRGCAdk1NNrCxoFT9UQQItMsxGxUAhFbm3VpaFstQbRKWYt3TrpXZAB1v3bnu78g9IGthWD8WJFIzHeEOINECLOJlINRdLw6JEPDUix57ok0u1a0AVl89HkkdSJ7OddvGwCVXQQ+ml61iDCT3T8e5ijcTZEUI/fHmbvjgRAgJ6MR2GsWeq/GrwdsIOzKBSkGphwMkNziFj8QrVa++vJ33N3dmYJOsspsEOOvbkVG/TaQBRt6LGVlHS35slJX45m2Wl3TXV8MSo6uZ0qJ129e0R/uXL/96KohbI+xCnHwoWszYTAQLJtpw/XGc55nwKq4vTXW5QIYqFyd/zo0c3Wr1A472n2As5RCLetWEW7NFBiKKyaodpci697B9OFTpxx0f8z5fDZZLq8E34o/n1b8dGoBULqwdigNiqM2cQ3K8Z/6DnFvjY/EJZtziQ1sZQ7ztC3ya2I7eNUn2qBKiMkUAZoNj5XaaNWqsF0LMahLeNhkpPlu2XXtyrnY9WWtLKWx1O5SPfbBugoqybl2CZG893g2OG7UhJGY8ZYREjGaxd4ospnnYPQGjK6wDdMwpKFtStqWujDeNZuC7G1Z3uKHY1Qkr/3UR0sSnP36gi7qHunV5LWsqmNDT+IWl6Pdp73aANloZX5rcyXOuTXlDtVESLYmzEHLqsLNuw6Dv2dt/oj2bok0RFTxIRLTv90VUMa1IGk/SezUgGBmKldfyFBGiV71zTGarubVbQk+2CVqm9U+6APDIuHld/yiEn2LW/xAqO5ArQ9Knb70Hv+uVvhwxts44tVkt0aFUx3IbnzxoS2Lup2sZZQBEPUDrVfR/XX343qoCA26UNwHPsO4r2yDXZ3R7udFW/9bQNaNT4arINcAt7+kJTTnxg8aotEyBBn0DL/voBR0pyIM84Zbvvy04qOoBV8tkB8rf//bb3i3NOYAd0lJAlmFibQJjBsnD/I0czyemKZKisJxTsQQ+Nnnb3n9+p7j4cA8Z0TsoCxVWWvnshb+4R//mf/9N1/w/HzhP/3DP/GPv/ndNgAyXMGGnJCqCZxvHNxhs4ewqjULS608XTq14b7qOyXAMS1dIk2ypbQBALhiyInVe1v7FqGJbfp7JFv1lNtHcxRUOirGG5amhG0nOdpNMmbmbnGLHw4FutFochRCDrRu0nWKQuh0CqIwTwfuTkdiiLRjoZ5mtFml43JZTB6nrKxbqxB62SV0ehv8vI1xxzBTkJ6IUknSiDlyvD8yHe5oqizNOiJdldKNQwedTqUjdLVkLdtuUL0Nu098j2RrA2xCygZYp5SYsvuuzxOHw0wMgbvjgXu/fT9nTjm5taUQo3+OWqha/TM1wGQDYwzEPKQBEylHck4c5olpnv91/s63+LMKowOs9LrSykJLAXq0coVP/et25t/pAYxNpHaez0+8f/+OVhtlXSjrsmm1D23YfXJyZ7oTZBui0m7lXxmtQq+0RIQpT+ihUaptKgdtRjfDkr6ZMNjwlbo7577R667jvEvnWSHGOK/tJaDvnbqslGXdK8gue9lq2ygCG0h3m9zi9+vrSnc5sLbd5wOe8S0+ifgoasFXi8D7wvybr3l4vPBwyPzy9ZFDjswCEhMR54EOuZtp5qQd7Y2744S+uiPGwM9/9hlv37xink0sXbylvpTC86Xw+Hzh7//LP/G//Mf/zPP5wj/9+p/53Rdf0Vv3g96t+zr7hP+VViRqxgMqkR5nNERPmuIKkcYXFBfE6l5N7kS6JEDGsPN2mhg2noqgXbYq0wZcQzTJof1eVuRtmBMLoxLrgyoKwYFAcFae5XjxZH+LW/xQKPRCoDMloUn0ll6nK75BrHSFNB15eHVkyhktDV1twviyLDw/m9vO5XLm+eKqGotSZLT+oOle/elbtcXehQQlUkmhMWd4+/rI/as31N55XFYu1YY8ntdOcfvY1oeNrCBtdHZGq9XWRAy7Bq4E181Mwpys4nqYEsfZOMCnu5NZ9cbIm/sTr+9OxCDcpcAxWvl58Au7Ni51oZYFXIKLgFEkQiZn6wjlHMmT6VDPh9lbrLe4xQ+EqoHYutDKQo0BjdEGfzdVH4txfNvD+qbz+vT0xDdff21VT5/S37itg6IT42ZysvFlg2xVT+2jc6ovNomBwJwzokqqCQFKyVb9HS5fXbchL3P5WrnKhs6hHx+CF1S82irFjRvW1SQutXfKZaVclm/xezfnMtgdzLTTilefu0llDue+fgVkb0Nen178ZCALJg+1ts55rYS4kgKc12w7vmgDS7iCwS4yAmPHZK255FZ6JuEzqiyKVSlrbSxr4bIUzpeF5/OF83lhWQqleEvBKQTot4EsDB6QA9lgRHFcCaD78JZLsm/V1u4gUjdaxAC88EGjcePj6Ae/G1p348e7uNF38aCuH3v9/j2xb4++xS1+f4w23GgxyotpaK9q9IZ02fjjloSAYAON0f3YxSsucWsrjoLPB6okvkl7mT5MUk/oiChTihzmTO2dFoTYkmkmByV6K7LWoR/rWfBDIBuEFIZSQiAn48tNKXHIiRiEQ04cJ+vyHLL9PMXIlCI5Or1AcFmu7hJazU8cfa9sMQDFFY83DDqBGUCM27e4xY+OK+6qqngFcvul/z9svNWXfNcr84+rCX+Jw8TEQ0b38EoVYTAKRjXzyo5W1YyA9ue9fs0P38NORbhu4Q8xEGud2NUAsua4t1MF2tVnGMNd45nEzyQvAOlWrd3Pb6gD8/G7f7E/2C3+HOInA9kOrBJ5LJ1ff/We6X3gqznx/v17Dinw5jTzq1d3lkymyJ0nkPOy8PjuHdorD6eZ+/sDc84cp2yTxcG4obUJa4XffPHIP/72S94/X/jPv/6a337xyLIWHi+N0iOqgS7BzBgUq6JslVh7r5bMnSwg7qAiwr589s3pAKx9LI5u1SzTAhIaexIfjxSGJuc4acjW8rTBE2vzhO9YbgZ22bhDLzbKChAIeUgl3eIW3x/aO5fH99biKwvaG03VhMTV1m1TJYRIQnhMmTlnooqJw6nCWgjV+HdJG8k5s82KO3QZwLh/cI0fy4rSaH2lVOHAxJtXJ/7ql59bVXM+ElKmlJX3T48sqw1tLqV4i9CGH0d512m+BqpdLSEGITsXeE6RY04EEQ7TxGGaiCGQciJP2UFoIy6PgHJphYtb2nY3UFABDRCzfciYTFIsxEieMmnKpBSZDzPTnMnZaAU3asEtfkwIkILZG2urtLoimtDgcpCwbRAtX/jmUWQ370iRacobBz5G2/ClFLehxpAiMmTxQiTEQHIVAhuc6jw9Ni5PTz40Zk5epj5w2ZQF1lpcIcRoNoOr232zd+0cKOMDOs2oO42ptEpx+a16xZcdMllWhQ3M03yd8HyzrZsFdb8CrQOAa//94FVEblS8Tyw+YthLKATq2nheF0Q7xyx8fYhMSfjlmwdyiNwdJu6ZTPIjGpB9//4d2gqn6S2n+RXHw8Rh3oFs6YHShUtp/ObLR/63v/8t758X/uHXX/HbL8xxaymN0kc1JO4tfRn1Gyd9b7tEd/vBKiy2+7NdZPc7dk/kXa+ALB1x29hOgO4DKhuX1Yenw77zlQ3ImjyPCEQf34LrNWsV4W005WqH2Z2kP0TmJdxkfm7xw9F74/L06O3IYu04hTqYbq1BKYgIWZWnEKk5M4dESKZzTK1IK4TeidpwjQHqGAITayNuQ2AveGnjutOa8ehE7njz6sS/+eVnHA9HPv/859zf3bOsK9988w2Xy9kkdC4Xt6I0jtymUMA1R9aG06KAY07mGDglA7JzzhyygVd1vdiunfPlmfNyprfK+fLM5fJsyTlG8OpqPh3I84REsfNVzoQYyLMDWXcenObJgOxhJk83IHuLHxFix6yAGQxUM0PWttlrjGkrL2qO9scAZuEFkE1BaNHyX86JnJK7SUbENZ2zm5YEMTtX7Z3aO0vZp/0Xn/avrbGUxZ0ldRtIM8ODYYoydJVtMDTI4PPCnsLMfUtdVeD5ct6cOIsDWTNDsf/mEJld+WMMbhnld5wD2Kq2NsB2dc7Rqy93VINE9nPGLT6Z+MlA1gChtUBaU9BOacJSFVVhKZWlVGvr5UppxjPdhZQtycTotpYhbI1Es7Ezp6vLUng6G6VgLZXaOq3rBgTtzXzQVmE4YelgF2ykAb2+r/3Er3lx/UPx4n5X3J6Nv3c1LT2g9Yc11Y1T663U3bzLXVhGFSqmjTN1i1v8YPgOTb1yAXacj42ZdpvGb61RSyEAMSrVeWqt1Z171jv0fSJ6DJZ8i4d2/W8Z/+5WzdHu7fuGaGcOgWNKxN5pOZH7RA2B1BulmVNPG7aXVwnL9qm2koKoVYqBOQRm15/N4lJ7vhntmCKJtmIcxWZSfa2bHNmwCNUB0IN8cAnbdbi6XK/xW9zih2I/dgGuVT9eriOjA41r3XPb6E4491U3Wotu3NLx3ON5VUdF0/nsTZxnum5KB6UWq8r2Rq02qGX7Rqc8qDrV4f/H3r8H25Jt6V3Yb8xH5lp773OqblU/1A2SGglkjBAoJCEjRNtCwSvAIAdYvGxACkNYxoHDGIQFVoAInrbANpIc4GjCViCBA0vIQkjYgB4NmMCSWkgtiYcebkuN1H27b91bt+qcvddamXPO4T/GmJm5dp2qOlWn+lbXPTlOrLPXXo9cmWvnyDHmN77xjZ4jipf6ZaUYLPvXJ/FZlaPzXFcKQVtzTIFNuGOh126S1Me0iv69ba892hNYjMaw3fbuma+PffpENgSOxxtqnZlnpdVAxfiysyhff3biy/nrHIfEm3dHaIUhRabzTGliTRRp5Hhzx/F4IA8HJCSUwPOHB776/gP3Dxd+8Id+hB/4wR/idJl59/0zpeEl0riSTnkcUGThAi1OsSSsK6fmauGm/TmWxNRWm6HPw6NP7gKXKnJ3MaK9a1emRErJy5++QqY3k/Xuz5UDaLJdYYVpPZnIKS6SY3kYbFLZbrt9jIUQuL09UOaZ00NZFn1dJgdwWo0wnU48U0ghcomJKeZlXHLz7uGpFianJ0xltlJhU1otS4fxNiBvvbA3ZUzne77+1R/hR8bMl+6e8h05caPKsVXeqAUVqEG4DJnagm0/tSXYi//c8uEMNeooTUGLJdm1VZ5XnybUqqsiNC515lJnFKiiaLDR2ekQSeOIxEA+DKQx21CGnJAUCSkSh4E8DKSciHkg5kxMyZtq9gXmbi9jm0EFjjiuYMymIgc0LYhP5zKamZ/3pdiAjxAQVWpvEmtKmYstXmenwinAKncjzrlFleZjY5cmqUVZQL0K2CuMDhJ7wXObRNbSmH0HV61p144tpuda66pvK6rWJ+MJqjVee5+KIzilFmoty9CE4u99vCBfvkPpkzqvB0oEr8TsqezrY586kQ0hcDgemedoY1WZqW3mPDekVQInMo0xReZ5Zogw5gTzjKoQJRLzyHi83SSypsv6/HThK197j/efP/Bnv/wV/vSf/RHTe22R0qKtBhcPwy8Qj07aR4FvfVg3zrA+t3BTHTkNHqCkT/NCvNvFUtKOyPTvYklkcyZ7aXPbhdlpDMt+ujqBuqasXXcsYAfn4uVsc+vHcSSmV+rL2+01sRCEm5sDlzNcLkItFmgscPmL/JyczmeeT4bILonso8rBrJW5y960Zp3H+LCRpTHqg0ksnmA2YLqc+PrXfpQxKOHpm3D3lEMayMAtkIEWhVkSTeMG/QXPwi1wt+pi541Z4eIUnHOZeZjO1NaYzieenx6M81dnTmUyjl6AEhRCIB1H4mFEkhDHyHBjE4/imImDLUIlm/ZtSJE0JNI4GBcxZ2LKhF4l2RHZ3V7a/JrfetPU+tiC0uL88K5q45JXqEIpNvvDE1nxqoWpCDj/tF1smIc2pwzMS7KL9gTXF4iyStgt49WJaPDpeH56W/hzQKiuPNhV/7VQynSVyH5AZ1r6DEGvlNSu2iM++MemnxVvOuujZu072KCwHu+t3tMRWbBR9J325New3TdfG3ul7GjbFdmTyZ4QtgZTsdGO01w4T3Zyx9aIrq0qwQYcSEz0gQI2waRwOl84XyYuc3GieKVp2PjGdW1iXe36CxwkejHpuy/fNsu4hVvT0Ve/wGyOrSOo/XlxMXmRtdS4LGPZSH+5qV9ETPJrLYd0ZxSnIcRgTSYxxmVU5+6Su72M2WIqUkr0EriTWip2Ahq0AnQ/rYApefRBBUuziZfm28KF7XJbrCjscnqvKMjGq8D5dnWemM5npuFMOT3QHu79s6wJVDxY1Ufoa09k8c+XWnw/GtU1aMs8MU8XaqtM04V5miiOxlZPprU3eQYbfxu8kSvEcEUhcIfeBHn37bA2ia6d4N3Xd9vt46wrhlj3fnWgoywTudrS4LgMS9De6LTqr/axrXXhkyq6SFWpI5rFP8voA9132MTGleIgC9pqIXFTrO8v71XNZjTCLuNVi6O6/fNV2aoewIYysL0ieC+IYEl7C23Z/nqt2YBNj4L41e+y1UtZfXGnFrxe9gocWZ+JHgIhREJwSQwfz3qpyvPTzDn4BJBSGFLgyTHzpVsr04V8SxpvSeMBDZmpQqmNr733nD/z5R/h2f2J954/Y26Vik0Gaa16MPmg9M1WnmRFY5c0ccGaRD7Y8Rhg5aH6CF17rSOyIhASBENseqK5NKCETkUQHwq0Xrh855YybFUbXysSiDkusmODd0abHqZNOAPQVimtfto/1W6vkcUUefrWU9LzxGW+mIrHXJhLW7Rel6KAmhJBQ6x83xylwW4AlUbp04G0dy1vOX7dtknt1RKRVmaev/8uqUyM9894XyPPvvouB4SjRAYJ1zw4DG1Bba5dxRh7kzbOrVBpPLTKs1qYtXGqhedlpqhy0cq5FZoAOaKDIafpeOBwc/D7jsiGQMqD0QgkLPpiEoLJAeZMSons1IIY49p4udAK9nC528dba8p0PlHniTZPhGiTs4Z7U9joE6563NDmigHNYp4qnryWhSq0SndtGy4LXaGnLzzh+iwNThug92Is1wPf3jJJT1H88zEUdZ7rMmioo7MialQ7T4gXNHRJnO21qy7sOnKW2mg1Qk/UZ6MClVLW65Wb9mtM319/XBAbUa0L5v1Z/dl2+4LYKyOy22RWWzNdVpS5Nu5LIajxd8r5Qo4Bffspb9zdQhyQfCQON8RhRENmbjDNja8/v+fLX/kqzx9OPHt4oGil+uq0uU5OSiyJ7HbV1ssSW1NYAnN34H6/0YGqLkItGzoBnsT2ABfBS4oxxoULu9WTXDos1VaspVp5xEbtefKgxkcKITJG09wNITKMJrAeRBhyIAWh1WozteueyO728RZi4O7pHQg8v3/uPgMik79CHZGRJTld0BkxYk3E9FkFoWqjev2gK2ksXPIXBAzZRJoePFsrnJ6/j5wfON7f86wEnn/t66gEimQ62X1BYXpTiQgVZRKlCZy18pxCUeVZK7xbZyZVS2pboQAlCnMSNAaGuxtytDG5483I4ekdkiJpHIjj4Kiq04VEkOgVGJffys6FTdlUC4yzniDE5Vqx224vY6qNeTpTRJiny9JDEVP0Bsu28Ek7msoCzKyNl33wzxaVXJJSUUJQA5Xo0+9kedEilxV6NXV7BnegxXVle/LarDmytUotjctl9oSbZcJu8iEhoY+/FV8Y914VxUfpVj+mugA8LTRC3SLO60jbjxps8Pi54FXQZejQnsy+VvbZEC97Sb6X1EVAVxJ3VSjNcJa5NKa5klIzBQKFpkJthsbOzSb9zKUwu4CyeZ4upbyO9mxPVZMoeVSC6EFb+kLuuvSwhFyBZUb8Uh5dy4yLssCGvtCT0vV321p/XNUndqmvazsvCl+o+naDeIkzrMmzlYgqNGuYKd5huttuH2eCLGXzuNzCph7htqW8ACprctpEXYu5n8Pb5khd3vU4VMjVFjcPduH3WtFabazkPKPi/iaGyIiu22xiDRwFuEijAmcqJyqFxrlVLq0wqTKjFDH0VmMwfmsw2bqYk30fqdMIXkQD2uzuxt8fU6eW4L9cD9ipBbu9nKlSy4wgtFCtrB4Crdq52Af7rBSedVLVkshqHwZwvemujrPGnvW07HJX/hvbQsqK1orHpQXfteYtjLZQ+yStTXLZPxPWqZ3bY+0Vmy0tQVu7eqwjyT2p/aBKAY+CPFePLTHc7/Sfqm1HZV8ze6VEdhl5tzBSAirJR7s2WrDkcm7Kw6xEga/fz+R37zmeZr7t2y7cTwpJmahcVJnmmWfniWfniYfLTFEh5gxR0RLRGhanq6WAmF5r8KAYWnDESJ1zo75nYsmwbvReN1xYXVnta+Lq6GwXmK4I1S8UTW2amOnFWsKAyILGokptdmvYaL/abDZY8GaRmBL5eMPhcDQELAR7fa08XB5oZbKrjneI77bbx5kEYTwcKHPleHuDSECbIOH0wtcviaNCFdNLDroqHBhHtpf728rt/ohtLfFmKQHaOV1VqTJRzmdmTZQQ0VARSda80iqipnd79uT0gca7NC4oD9L4ujRmlHOAhwBVQHOkjhlCIB5GhuMBiYHx9pbDzQ0hRYbDgex0AuKi6Gy+KitVSoIsSNaVDFfnxDtndivBtdtuH2e1Fp6/9zXgul6xTTivG5r88cWrdAVBWMe59vgjEpd12Xab25/dVikr1sRWrQqyenej0xOaN1iqL4djEFLKjPlAiHFJvFH1hi1XQmg98XaKRO3SXms2rV031sGbx5PF7Ov4oI8tjdb+u0VuH3DSysIr3u31sFdIZPuKiw082usWoATj3qHM2qAUAvDsVEjxzOlSeP808zArocBMJWljmmbuLzMP08zDZOXCkBLaWLh7ltvZCrEPHpAgBLUAs6zwWFFZvWqY6peSFQXtdAJdnpelHBN9dnVrxnWyS0qlNttO8ovAFhXWnsi6Q87VEGgJMEhA0kBIiTwcGA5H29tWfSxv4f7+nun8gABRm5dqdtvto00kkIeRYSwcDgdQmC7zWmL8EDOawVq9aC7hsyETfKAK8uHbesHWmyXEVQr1cqGQqBIhCQRFmjVySbNkefLbMxrvUDihPBfl3aBMAiUFpiGgIqQUSEdDXvPNgeHujpgSh+MNx+ONcV6HbAticF5w309Hh5xgtyCvG53YRRN6+/gyAGVPZHf7eNNWOT1/3+7rGjs7L3VlvAkhioMnONVl3U5P4GKIi+KAYShrxWD7uv5527i07SMxXff+3JrIivjYadUlSZUQTdNcIjkljjdHUsqUMjNPF6chWBWx+RjcXklUC56PvhSj3FV9caPY+jL9QDLboaZ+f2VQ9Ca3sieyr5G9GiL7whNlW4Lv9xUb0GrBciqNEBoP5wvvP3+gaWMcE4dDYpoLU6lMxegFta6IkKpcfUwPPL2T2MoMVupQVcKWRwSLOyheOpW+Muy1ib7//R3280OLqV7D6XJB6gT3pQS7+fwgwdW77GKQkk1dUXDeU0OrrSStC9tuyyQVedG6dLfdrk2Ea9H+DaK4vKb/tzmde4lOPaz2delSBfyU1iWTexWlqTKrctHGhWA/fUzspVVaq5xQ3ncU9hnKvVTOopxDYI6BGgTNkTDaiM94GA1tdf5rGqziYeM6H42kXuhC/Xuwxff1XHpZry8vIEys3+Vj/erdfrzar/gVv+JDn/sX/oV/4cd+BxTocnUbKNQqmurVC49PLSBhE6+0Ay9rh74NJLBHjXPqzcZhe36unvu4j+SqjG9Iz+L78Cj6OWVB8GbQHpP69aXH+F4F9SS2KyXodle215wt8vpxFxl5fHfL8VX/LBwJrq7Gsieyr4t9+kR2jU5rbQIsaLSVtGLoTqCJ6bxdqvL+aeY0FX7wh7/K4fgD3BxH3n77Kd/69huUWvna+/d8/dkD58vEuTSm0lAV49O6Gp2EYKPzxIcOxEhQNYCnmRZl8Akj9LnNvcvTKQfmCea8S9lww4/rZgjvmihvvwIwDlPRDc9nedK/E4GUE8n5sIebW4bx6Chv43Q60VplvpyoZaZMZ07vfY359EDOiSc3NwzD8Kn/VLu9RiaBlAZSmmw4Rx9TGYUYZeHQLSjkxtpyai9Dk68Wby8DcOjVfVkeKyiTwkkbX2+Fr9aZKShHTVwQLlp4rz4w1cI9ja+2mTPKKQrvpcAsguYRbo8QI+k4cnN3dBR2ZLyzMmcaB/LhYLqwIZm8n4jTnSxgWyHGFXMXioA1cpkkV9wktSuKJRtorHOO9+Xlbi9nimi5BjiaIl1ZQLaxZeXOapfPA9bVl1HbtPkCrRVqDQvXdUnvNnH5ujlMHt33hV2XlmQZmut0A3UoClKwBDalSEzW8NyHGHTt13maFyS2X2K2vrIqLjjvd8PNfRE49niCXkdjoyfVrbnsmEuSzfNlaZzb7fWwV2/26gmbbk5WRzJUZBmz1xHZuc3UUogBvvLu+wx/JnA8DMytkseBpo1nD2een85cppm5CUX7CtTnti4ntisG9CYO8FKMd23Gtq4OW1sWxGspxVeg0rk/LNeKRZeWTXK6Kd30J/uKsrI2yyxUpwXlCcSUkJh9wMGB8Xiktcb5MjH7yMDzwwPzdKZOF87PnjGf7jmMIzfDAF4W3W23jzLByo4xRpPFi8Hl3bxEqQJVrxPUTQLbw5duN7h9qsO0H2O6wiVWdVBr3JpUuW+V91ulITzRihJ50MI77cJDm3neKl9pEycaE5FTyhQJDGngeMg2aev2yPjmHSknhuPIeHe0Y82ZlAfzVZVNrr6iWcIGgQ0uBP8xo2cX5Io1Mu9o7G6fxMRlsVaUci23S/cvEXtcTVfdVpdhqTh6+KOpIOLgSROkBUdvPfl83DS1sUVlZ+HX+sKsl+dZu/8FFr58wLi5IRqIFGNYdM67Pm5z5YFWq1EBr3ypXxC6FJfaDCCur0dX39nm/Vtv63SC4NeZ0iqtGqWglnkZELHb62GfPpEVIYWA+oU/iDWMsLnYL6U7WJxXPcFVYJor96cztVbee3bP3XvPUG08nC6maFCNZ1r6kJ8Gqt6BLatzS2t4lGbx9E4U71NQJCDBEu4QdROY+3AGC/YsyedyBMtP3fy21dFcOE/brwcjxQcPkjElb/Jy3VgvYYZeyVxujgJtvsnWqQuf+o+122dln3uJ8iVt7X62gBE88LTeALnhwy3vuQo4q99uKo4fvP/YHvtNR5Cw87gAZxr3rSIivK8VWuGklWcCZ4FTFKaUKDRaToTDQIqRfBgYDpmYE8OQGJI3TKZEWoaHhGVh2v1oc4BLAtp1n0MIRj9wXeqVknGd2IYQrhpsrhj3e0K728faSifowM/6lF4tErVXOnkUa6T/jsc4WePrEh+u+/Ufn5vbiZN90M7ymsW3131dhjO0BsHR483wg9CnBjrvtm3ep8sxcXWtuU6wNwg16xL6o3zqat2N0xlqpZY+4nanFnxR7LOKp59+RK0Ix8OBMF8o9QKtIqJLKd+6gKOv7jYTgUQQiVSU9x/OTNOJFIX37+/5ytfeBeBHv/oOD2frfryUylRtvGarYqtPEWKy8a19NnxMzQPTynOTGNeQnExFAdYSh8ga+OzmygOsqfiV9JDfW1DatvJymlopJQQbaCBiI3mHcSSEQD7ckIejXTySzXNvEqgxoC0QNFBSpNWIOlVCQ6SJUGplKvOn/VPtxhcnAX1VU6C2ZpOtysQ0TzStpCEwaLLRkgVT12DNU0GcWiDL48sGOwip17+/qKq+HT+iLs+jmCKCKjyI8iOtcKlnjiTenxs3LTHReBYaUxBKyszjgRptbOzBKQTHw5Gnd3fkbGObb29uDXkeB+IwICHQxEZeKp48910VVvWBFKxpJQgxWZUEv6aEkAgxkFIm+cIzpezXm1XSLPTrxm67vaSpD7W5SuIWdQBYF4+ynLi972LzrN2XNYndTpmkT7CDpRlsec32tZvH1/N4k4T2rn+ttNl6N0ShRRt5W+bZpowB8zwxTZM9NhutQFvzASO2Xd1q4La2iau6UTf5kO/Nj11FFsqDcXc7bXBmupy4XM5GcZgnqk832+31sE8/2UuEnDNNKzEEk8EBHzXZQOKKXOi6SlQCKkYzeLicebg/Iyjn6cKzh3tEhIfziWk2eatprpxnn2ZS7dwVEXJTUtIFNVF8QILEpTFqWdUF49H2/Q6yOnoIPXntt46C+gVHOjyqa1Ksxm3qK9M+fQVYhOSDCClFxmzB8HA4MB6OWIHJpn9VsBKNI9shWBm4dU3ZYOWlql26a7fdPt76RLnaKqUWlEZMQmqBFrDSnrQ1kVWcOnqN5hgyJH6+v8wnrzjlEnp9YVh92xeB97RQFYbauAgcqCa1FaEIyBAJNwckR8JxJD+5IefEzThyd3tkiIlhGLgZR+Oz5oSkDCIUYKaXK1feby9B9ppkR29jNK5fn9Ynfk2I8XpMdEdrl9n0YWfH7vZJbasewFIxWZ9WT1wxBLQnsQs9z0xgAwq5j6ku5yX9vsh6vm4T2avk9QUJpMdwUUWrNU/RqQPVR1o7HxURai0uu1XWJNb3e7keqK4KBgsn1i4v+PXno7+51X23cVjVmqT7mGqjOMz+3b7s32W3L7q9umqBdzOGvojcPCayymb0E3bhAXUKgHNda1Om2aZgldI8kdzeVt27/tm9vNGngXSuzpXz+sWgrwIBkI1I9ManFx6SrCM67TMsMNYu6twPo5MlRF3+Q4hiJdwYAilGI8XHyJAiQ44oQmjGGUSaUQs8hTbxaSPOS3D+ksuJFf9udvugvS5o60uZqmu9+rkZbHxkjMH0Vr022dqWR4c3QwHIqhICvqLbJLPdhT7s43s9X8QWrKE3jxjvLkpAUqTFYBWHHGkhQRByiqbhOiTi4UDIkWEcOAwDKUeGnEgxEKMt/iTIMtGH5Yh1kda6imPCJqCvvr8+v+XMXisYXF1LWH+ux7xHzN0+2rbC/x94js4sWGqF7j+dIrNSdJZ6Rz83wcazSlctEK8ybpLWD/k8q8xvlGqXJrRmcnhdAcCT0yBCqw2oa8PWR3BxjYXgIpxXfSnLh9vdZWeufbbLWfbvZvs4PTFuZWn00lb76psXXAF2+ya2T53I2kCC2UndNuxAUaI2aNU4eajRCsSCy1o5cQeNCWEAlEtpzM9NtN3GahoRvHbt1uWnJaHbMbSlWJL3eBW6IChAcqc3sfO0cOjWsX1rELMmmeSJscuJKMzzjE6TN4s1esogYsgqIow5MgyZGCO3h5Gb44GYIje3R443R1SFqdqEs7lW5ouYzm6r1HliOp/RVk2e63gEbcxTYdbLp/1T7fYamYE5lUZFohIixCyMx0SqQquROvdKgi6Nj03Xcc0K1CWobZoXr5JZWWh9/uy6D0slJHoiyMIFTyJITrQQqDFRxwM1mrrA3XggxETKifHG/CYNgcMxE2NgyIHjkIghkFMgZ280CX5tEF28ssGSSPfEQLwxZdkZuFpMS+iNcV22a72GxEeoLJ2w+OPI9gXdj28rtX4AEV2oA76IAhYljTVh9cSUtXfjCmUNnRdu78X537Ld5uN+Dum0ubVJeeXPK1JmS2Rrpc0TWishVXSjElTKjHos1romtj3RRBu1WCK6Dk+6tg9LN7fJ8UKRULWKicflabpQpzOlzJT5Qq2zJ+Z7Evu62SshsqZH1xD1jkZAaARPYGVhzOkaN7b/hQDYeMpSK222oqCEXi6xIQTW5LVBjx6tbq+mgGzQk5XPZkEshoCpH6grKHdE1YNt7MEsknJeEtlaO7m9EjxhX4kI28MRUgzkZFqxQ7ZbSonDkDmOg12z5uIXIVsAWIdooxUjrIPaEIYg1gVaJ9rOkd3tpWwdL2ltx0qIQhoioYLWQA3VJ9BBq17ZgKUK0oCwNHqwUGtQr0Rs6oBXiE8Pzv3+ksgKHmuJIUCONvUvJdo40FIipoHxcEuOmZwTx5vR/TAwjIEYIUVhyGHxs+CLz6VLG64S2aUVvC9Ur5DVdX/73a4w8rghpvPuF/799Vu/ULYnu5+PdQ1lY7bIWlqEdTF1VQUIH7zPdVXhcdVg3Q4foA8s+7Hu0BpTnfJQm5fjWyPUasNJalk4sgpIKbbQ7ZXDtpnG1SkF60cs9LuP/nK4WhR+cCACC11iu/FaZuZpolZXKWj7GPfX1V4JkTVZK3XExTrwr0bGqemvXkH9HZXFErjOlrlaQ23oA1dVAk9iXyiNs0lkHws/BwGqBacYA6qNWg15UWysbYgRYkQahNCLk+u+62Yn+jUi+AhedO007Rcqy8X9tdjqVFvxharNnNfW5UKKy4XYKMB+dEHkSsbom81+wk+AH/mRFz/37d8OX/7yN3Z/vlnMSu5GJ0gpIqKgkRYFrWoLOkdjmyt4NL91SRzTXPUNLoisXFEL1hL9Bp3dJokdKfKKRU9kxxxJMTLEyPEwMKZMjplxzGQfFpL9NTF50hpt4hHBmsia30SgLsmrbCZ2cU0L4DqR/WBie50QbMLxhsb0gjKqbL6n3Xb7MJMXJJ4KnXy+XUSxOSf7P5bfuE78PpAkduRVFhTXBh5c83NxJLbpSg+orrsurVFLQTxO1Y7IqkJKBFVizvaaEK54sctwoCV267qfj3e1h1fRDxzGFTAFS9+LNhufXZ2ja/zczef1cPnNGzZ3e4G90ojaUorRCDAeHgELMArQoBUWRES2Ses6PEA6ytrxW59Y0n3A/EMWJHb59E2ppN8WTo3Iwp01jo4iPmYvxsA4DMZhTYlhPBhymzNDEEJMNBVUq++zKRLY51WC90JL6CtrFtTWmryUaOPcicIyqJc20+ZgxzQX2tyo80y5nJjOD8xlps4z6uWnEGTpjC7e+PXNZh+WxH7cc7t9uPX8MebIeMhIqLQaaVm8KVHRqkuVwxDZnsh2D93ER+nhswfRFZFcAuw2GeyThXyh51VRF1I3bus4ZFKK5BC5Gw+M0ag4h2QyWzF4k2SIxGgjaCV4EhvtY1pMTNE+tygUR5YqLOoLQVz+TuxzoycKXWdXZKMbu1U72dIk1Ljx4sopdVMeDn2owm67fYwJNmpdnMKy5Wl3mlvozkJHU9dktG9jMX30iwKuK9uT404bsHDcE1auuK11g6j2eKmtQZkdcGm02e7HPFCa2sh4hDTe0BqUafak0qqKrdQ1Ef2QRZ4s9UxdaQ9s83LLF7pKQXR9z+YasaXMTJczl/MZa26t6/fSFRx4MSf5G20vUwXZKyWvZq+AyILWCmwRREvebOnkHYVb97s6qzfOtnilbDg7/hlbRHb5bL36uX18i9ZeOWabQZuVNrWRYrQkN0b7/CDeZNWlQbYobF/xrZQCcfSp72OnUXi8XZq4+k9atRG0io+ibat4c7EhEc3J6kZdcOmwzUr+x5PA8+54P47Nk8iYIqlFNECTtAwFsTXdNbVAPZG9OsU6KiQ98G4S1+Ul101QRgu6YhYQBHIIjhJHxtES1yEmboeRISZLXlMiisnX5ZiWhi5rerQEtUbbL3Vk1paXHZVdaiYLOtwpAkEe6cBK2Ozv5sZymAtdYZ3sJcv9EMLqq19UnsEr2DfK/79prjO+qLK7H6QK9PsrHeD6nJLNf/ZMT17F7240dzZ0AfDzuK2obKt1OY/r5n4ps8dLp/k5R7aWYqoFCsRMVKXMxRHZaNvozV+dd7+5kLy44Uyvfl7F96t6iF9DAJW1IboWS2bXwQebz3vB97fbN7e9+mSvT2Cf36n14ycBfBV7HQPmbt9g+7BT7Bt16n3o5+zn/hfNvmmS0M/LPq/r/TdHuHzt7Bvpbz/efFs+LconIl8B/vRnuzu7vYT9ZFX91s9qY/vf8XO3/e/5zWH73/Gby/a/5xff9r/hN5d96N/zUyeyu+2222677bbbbrvt9nnaN18H0W677bbbbrvttttur4Xtiexuu+2222677bbbbl9I+7FJZEW+C5E/+iHP/WuI/EUvsY1fgsiv+wSf+ZMQ+Q8Q+a8Q+S8R+S5//D9B5A/57YcQ+a3++N+KyH/hz7/tj/1URP6tj/gMQeR3I/L0pfdrfe8vQOSv+JjX/AxEfv0n3vZuu30CE+FPifAtL3j8bxbhw1n8L7/9/5cI3y/CfyHCvypC9Mf/LRH+kN/+lAh/yB//+SL8YRG+T4S/wB97U4T/QOTDr1Ei/GYRfspHPP+9IvycFzz+c0T4NZ/y2H6nCF/6NO/dbbePsh9rv9xs77eJ8IH4LMI/7NLl3+K//63uw/+JCG/7Yz9VhA+NkS7C8LtF+NAY+VkfpwiDCP+xyDe2eX23Hz/2jUdkVf8+VP/LDzwuEl9xy/868KtR/W8DPxf4Uf+870b1Z6L6M4H/DPgt/vp/EPjLgP8z8Hf5Y/8M8Cs/4jP+BuD7UX3/U+zfLwA+OpFV/SPAn4vIT/oU299tt1cyVX6bKp9Fy+nfpspfCvzFwLcCv9i3/7er8jNV+ZnAv83qi/8w5lv/S+CX+WO/EvjnVF8sBinCTweiKj/wSXdOle9T5X/xSd/n9huAf+BTvne33T6xfYZ+iQh/C/D8BY//ROCvBX5w8/CnjpGqfOIY+WmPU5UJ+F3A3/5J37vbN4f9WCayCZF/wxHS34zIDQAi34vIz/H7zxH5lxD5fuDnIfJLEfnjiPw+4OcvWxL5xYj8UUS+H5H/+AOfZAhvQvU/BED1OaoPj17zFPiFwG/1RxowAjfAjMh3A19G9U98xDH9j4B/Z7PNvweRP+z79Rv8sb8Jkd+LyB9E5Hci8u2ODv8y4B9yZPi7P+KY/l3g7/iIfdhtt5cyEW5F+B2Ojv5RkasL/T8own8uwh8R4S/01/8SEX6d3//1jqZ+nwh/XIT/vj/+00X4fY6q/uGOoG5tE8QSMPBI0MdlaP824P/mD82YH94Aswg/FfiJqnzvRxze4osiRN/fP+rH8w9tXveLfX//uAjf7a//BSL8dr//q0T4DSL8ZyL8CRH+fn/8Oxzl+UO+3e/27f024O/8iP3abbePtM/LL0W4A/5XWDL62P4PwD/Kta9exUj3gS+r8lIx8ht8nL/VP3u319B+LKH4/xbwP0H1P0Xk/4KhGP/io9fcAr8X1X8Yke8A/k3gZwPvAb8H+IP+un8C+OtQ/bOIvPmCz/ppwNcR+S3Anwf8TuBXoLodvvw/AH7XBk395/11PwT8j4HfxMcnkD8f+J8CIPLTsZXpX4HqO4i85a/5fwN/uU9n+PuAf9SP718FnqP6L/r7/8iHHNP3Ab8C+N99zL7sttvH2V8P/JAqfyOACG9snntHlZ8lwj8A/CPA3/eC938XVt34qcDvEeHPxxZk/7Iq/4YIA/DCSooI/76/9/8J/OZHT3838CObgPjPYxWVE/B3Y9eJj0J9wHyxJ8I/E/hzVPmL/bPf3LwuqfJzRfgbgH8S+KtfsK2/BPjLsevRHxThd2DJ6r+vyj8rRo24AVDlXRFGEd5W5asfs4+77fYi+7z88p8G/iXgCuQR4RcBf1aV75dr6dpXi5Hf2OP8oxh6vNtraD+WiOx/g+p/6vd/I/BXvuA1FSsxAvx3gO9F9SuoTnDFw/lPgV+PyN/Pix00YcHxH8FO5p8C/JJHr/k7WQMfqP6HqP5sVP8m4BcB/x7w0xw9/p4FQb62t1B95vd/IfCbUH3Ht/c1f/zPBf59T1R/OfDTX7CdjzqmHwW+80Pes9tun8T+CPDXiPC/FeG7VXlv81wv6/8BLGC8yP7vqjRPOH8A+Asxes4/LsL/GvjJqpxe9EZV/jrgOzBE5xc+evrKF1X5Q6r85ar8VZjv/jDGSP+3RPiNInz7Cz7iO4Cv+P0fAH6KCL9WhL8ersqaL3Oc/44qJ1XewRbQPxf4/cAvFeFXAT9DlWeb1+8+utur2DfcL0X4mcBPVeX/8ejxG+Afx8CiK1PlP1TlZ6tyFSPFuOnf4+99bG9tfOUbdpxq06knEZ58yLZ2+ya2H8tE9rFA7YsEa8+PUNMP2ZL+Mgyh+YnAH6A3Z632Z4A/hOoPoFqwMsPPWp4V+RYsOP2OD2zbEtZfAvyfgH8K+HsxVPVFZYqCyMd9Z78W+HWo/gxsZXr4hMd0gBcnB7vt9klMlT+O+cEfAf4ZkatgdfGflQ+vzHzAh1X5N4G/GTtH/z2RDySp288/Y2XGX9QfE2vI+Fvggw0jTjn4lRhy9E9ipc7vgRfyWU+4b6nyLvCXAt+LITb/2uZ1n/Y4/2Pgvwv8WeDXi/D3bJ7ffXS3T22fk1/+PODniPCnsPj200T4Xgzt/POA7/fn/lzgPxfhJ/Q3esL6S3jJGCneoPk5HOcInD9kW7t9E9uPZSL7kxD5eX7/78JO/I+y3wv89xB5G5GMN4gApiag+ntR/ScwFOYnPnrv7wfeRKRPffiFwLah7H8I/HZUX3SS/3Lg16A6A0fMeRq8cLX5x2Dpkv7dwC9mVTzo1II3sOAH5vDdnsFmtfjhx/TT4IMdpbvt9klNhO8EHlT5jcCvZru4ezn7xSIE56z+FOCPiakE/IAqvwZLUv+SR595J8J3+P0E/I3Af715yV8N/Neq/JkXfN7fA/x7qnwN87/Gh/vifwX8+f453wIEVf5tLBH+pMf5i0Q4iHVm/wLg94vwkzH6w/dgifHP8s8S4CcAf+oTfsZuuwGfj1+q8q+o8p2qfBdWHf3jqvwCVf6IKt+mynf5c38G+FmqfHnz9l8O/BpVPlGM/EYep/vuO76Pu71m9mPJkf1jwP/c+bH/JfCvfOSrVX8YkV+FlQ6+DibN4/arEfkLsIHrvwv4/kfvrYj8I8DvQkSwcsX3bF7xd8ALuiFFvhP4uaj+U/7Ir8WS4q9jnNrH9juwQPcnUf0vEPlngf8IkYrxeX8J8KuA34TIu1iy++f5e/9d4Dcj8ouwbtB/6EOO6a/iRcjxbrt9cvsZwK8WoWENVf+zT/j+HwR+H/AU+GWqnEX424C/W4QZ+DLwzz16zy3w20QYsYXy7wH+1c3zfwdbio/bBvX5a/2h/z1WypxYO6a31n3xdwJ/DvB/lVWq6x/7REcJf9j381uAf1qVHxLh7wV+uR/nc1gQ2Z8N/H9UKZ/wM3bbrdvn4ZefyjwZ/bmqfPIY+Y09zj1uvsa2j6j9JGYNaf86qn/Nj9H2R+A/Av5Kp0jsttvnYiL8euC3q36gUevHhYlwxJLPn+/8uE+7nV8FPFf9QCPqh73+XwZ+myq/69N+5m67fVr7AvjldwD/uiqvFCM/6XGK8FuAX+F0ht1eM9sne30SU/1h4Hv4NAMRXs5+Eqa2sCexu+32EeZNHv8khsZ+I+2P7knsbru92FT5YeB75CMGInzW5uoFv3VPYl9f2xHZ3Xbbbbfddtttt92+kLYjsrvttttuu+222267fSFtT2R322233XbbbbfddvtC2p7I7rbbbrvttttuu+32hbQ9kd1tt91222233Xbb7Qtpn1pH9smTO3377bdBAiEkVwoXbFaz/RQJCKaerKrrz9Z48aAvQExYdf1VXBr2+vHrXz7EPuQj9EOekMcf/hImj+5Y79yHN9Atx7IcU9/Co5/9V22oFlQbAH/8T/zAO6raBz+8sn3Lt3yLftd3fddntbndPqH9gT/wBz7Tv+cwJD0cRjt/JICYz7XWUN3cR0FBm52rEoQQ3NdEIATYDF7fzmBfTnFVtCm1FFqp6wvFzu+YAiHaNeDarWTZUPcUkf75ti8xBiQ89o1rv+o+pA2a2sZaa8uxbj8u50ROyY7DD6A1ZbpMTLNpqMcYCTEs2xY7RGqty/fkFza7LgVZ/PfZu892v/wmss/aL9+4GfXbnt5QVSmt0VSptXGZC62pn1bmKSkGcooEPwcXtMkceOM3fk6GNUZuTZuyNHOLbHxYlner6nVs7u9ljVMidi1QVZqu76mt9YCHqrqfy0fGUUX9EHTd/mZ/+qu2rt59UUQIYtcFVait0twv2wua1h+mmamUTxjRP9x2n/x87aN88lMnsm+//Tb/m1/5jxGHW4bbbyWkA1EiOUREAjklxmEghEBpylybBY7pxOV0T2sVoYG0TTJ4fXILkEIiSkQEkkRi8EAT1ter/9umuxaw7US33Hp7Pq/O153XAukaxJYUU7hOOnuA27i8vWfd5iY8L4l9jJEQAiJCyokYI4L4sfVQH/sHggfyWh6Y779Cne4B+Kv++r/lT3/av9mL7Lu+67v4vu/7vs9yk7t9AhORz/TveTiO/GU/7y9CUiIdjkiMXKaJh4cHSilcLhP3Dw+UWqEqWm2BNN4MHJ8cCSlAymgezMkCa3IrAsECWj0XyqVSp5n3f/Sr3L/zLgikYyaOiTwk3vyWJ9w+ORJEiH5ThVoErWLjgVpDG+QxcnM3kIfAcMjcPBlJQ7r2OV8EqyohRmKMIMI8KfPZEvTT/Yn75889WQ8oQkqRP+c73uY7v+NtUgy0Umm1cjld+JN/8k/zgz/4Q4gIt09vOd7eEERIMRIlUmvldH/icp6QGInjgRAzEgI5JL9mCL/nN/3u3S+/ieyz9stve+OW/+Mv/Wt4fr7wo++/x2maeP/hwpfffc55KqQUGYdMCIE3bg5865MbhhRJDYbmoa42tFRLHgXUw0aIAUme7noO2VSZppl5ni0Z9vgDeJ4oNFXmWinNfOdcK6U1FKgEmgghJNJwRGJirsppLpSqXOaJ5w8nSi1oVXS2pDZLZIjJEs7l6C0++/KZ0vrnKBLCMvW9+cLYDsP+BRGGlMjRYubxcGDMA3MtPHt44HSZ/Dga1cGeHn6/97/+/36Wf8LdJz9n+yif/NSJrCIoCSXTZIRwRCRSYyZIoIWEhgENAYd+EFE76cpEqwIUg1PQBQTabh2EKo0olgCWANERnxANuVHMCZu2JWEVEVprFA9Y4o/1oNiRJ0NbLJkVsRsiG8jJk9fgK+MQ7GLgq1NzOuWDi0FHoTxxDUEYZCR6AIxxIKVkx7FZKavYUSNiF6YgIA1NI63tk/d2ewkTIQyJMAykm5GQMhoDpRSCBGr3ByDkQBptIXV8cuT2zTtCCswqzCq0ZTHnJ+jm3C/KkhC2Ws3/RAgpkMfEMGYOx4Hb2wGRQP/XqjIJVFF/j6KhEnIkHyPDmIgposKKrDbzSdVGq4b8hqi0bFWfVh1B0mAL2NpotRliGgLBF9bHcSTFQAkTtTTqDNoq86UjsgltlrjnmIghGOI8N9t7iaQ0EIexX9Ko9TMDfHb7ZjYJhOGAOAJbSyHHwLe+eUdtVjE4jpkYA2OK3OZIFJACYXZwJARIlr3GFHwhB32Ss1VZ1PNcJQUDRKAXShzyaQoO8GSEFALVyw8zllCW2qgKEoVQZqQp0hrMBVpDaiWi5qdA9SS1z5QGrtHkxdQSVEBVqHOlemzrCLKIkFMipUQQIXeEOgTfnhKAFOxxVSWEQF0CsSG6L0Kpd/vmtFccURtQIkoCSahE9OpntgDjSaKd6ommQmuCqiyrp7AuKIFt6cF+6yVBdYRUl+qFUqsupQXx8mRrUKrSql0EDA3teaoll60pda2OeDLrqOoCqirSLPENqsS+d2qlyaUKsiSzKwobtCfMAdUIko1uIRkJedkf8zp8Na2WwIZopdUYbTEQ4qv9qXZ7PUxAYrBbSkiKhBQNkanNy4T2uhCEmCIhCGlI5DETkiWGpeijILSistppBdoWhLRXRCQIIRqlIKVAyhEhEIiImm+E0mjuawA0hQAhiu+r+MMr/UGbJbIdQUaE0BQVteuIm+oaEFFbUgpCDIGUIikK2uxm1AVLfBUs2Z+spEtUml+UtHWEKBCCIcG60Bh2He7dPt6MMhMghKUSGINwHAZUhCEnjoeBGANJIIv7X7BksQc8O2eFEBMxW/hWLUsCu1IO3NeCbPZCEV8Q0qkA0pNhr5iIUNXRUbXkVVpDpNpPrZbIakO6zz/KFx3euaqQbp9c652WULda3V89CReLe33bQWS5iQVo/07Nr5taFiILNWKbO+z2OtirJbLaua5eohddTmKVhlKdAwAhmZelITAcR2qNUANUEFWiNGJfy7UV5TTuXrAVbUpINJ5bjEKMgCqxhYVD2lHQ1pqd5G3l7pljBC/zy4LeqPn9QiXoSalqvwCZR3REVrws01pzpGh9T9+IOaC/PgSGIZNTNEQ2BPr1RRx9Xh1QsXWsX4zYA+VuL28igqRESGlJKEOMxJRAlTRHUgyoCilHxjETYmAYMzF7woshLuYXG86bl02CB0KtlsiKNgKdEWPJcYwrP7cvS0UFbeZztTZUK7UVWiu0GtZg1qAVQ1RbUcpUN9UP84ekQoiKhDVoE8R5svYZUXRZLAYRol9LQghLYNTGksi2Ai35Ncc5tYgg2UNvSsScCTHSSnNU91NPx93tNTLFKm4SrDqQk51HIWUIwXmxwRaX2gjak05FW7VQ27FMWSOFc24WP+tcW1WMhrdN7jriIkJb6HUNVBBVB2r88Y3LR7+vAtlh1lYb7jy0anzZ1tR8Rzz5FnGAqtMEbNtBlRgiii4xclsBFSDnvCCyPV4KoK3RBF9Adl7u8g0/Tpt3e03sUyeyloBZEFMajYYQNlyYhkql0SBaEIkCGjKabmitIfMFpohoI9VCLjOitrXamjlsCJCTQbbDgObByw2QonOHqJsVZkeNGrXUtcnFHSVIcBQqsFnCcr2WZf2/Z7d+gQi+zGvtOpHV6zcvnNtOi8g5k7Jx/kRkKZFAo19W1mVA82/YX7N7524vayKEg1EKQjJeWUuJYcjUYBSDnBOCMh4yN3cHQyqPI3kcbKFFgdKg4TSc4H5l1ILWEdkyo6U4bciS2JQCQ46GxArU5kkuoBpotVJLpcyWwJZ6oWkhJaGVtjRuaa0IME+Fy/2FWo26IMH8e2xYUtlZD1F8Ia1WriyVENZAGKMlCzEGr3AEoyk1pRS7RtTUqFGRJKSQGceDfR+evCKCxohKYK4z9TIznabP72+92xfIhCYBiYlxPIAIwzBwvDkSU8Saeqv9LBWdJ2+oqtQ6W1UgGBCCGNumiXil0xI8wfiyMYRNldF+Nm/MUgWpa/LXvPFMFJIVRohiaHFTW9RFw5EIqtRgyW6ThtQZrZbIzsUrlFEQ8Ua1aBz14Gh0b44Up9OpQkuN6mBT9AVmj7PbnpUeL7UVqopXa5onsobGilrc7MntHjZfH3tFagFsUZKl6WpZMdrKLIhxbcACThwSoTWECq0gTcjaGHqSiLVp9ARRgyFBGgOaLECG6A6Gr+KM3EZXTmhqyKk6eb052hNCsJVej4C6AWEfeb+84P7aKd07wXVFZZcX2muj8/PEy5rR0VycTrCuJHvaukK7a2nmOrHebbePNBEkRqMWeNIXghBCRIN6oBOaKwOkHC2RTdax3ysa5kcd7dyoGThmIkvJv/WPtUDaqQXuX0tHNCxN1601Lyc2R5vq6kuwKgRgyGzxxFSCH5sIdVO1uXIU565r2y5SWY7BkNiFJeFJgK4N4V5kEomEYAh1ypmYEwpUPMAjhizviOxuL2lW4LDG3xQN8T+MAzlHW+BVq0bU1qjLO3ShIqCGXK4VEo8i26oHLIli0E5rU6s+LDvhxQt3SvEYZmCrXO2riLUgB5QqWBKLLvFKW/NYaIoGTT0xdi59h3UXxYGNH4LBOD2mJk/CtyoFjxtQtg3ats8dlV3j6VbpaLfXwz6DZq+ELqe6cVdVjGbQANROsbDUKgylVGFBSkSUeDoTn78PtaJzgalYAnh7S9BbQ2TrBZ2GhRvT5UmieOLLeqIHi5i2EkVIPUeNgThYORVV5IrjtiayupEfku6PrGJZ1cOaAcIVqb4azAPkwe5LhJSc09vdrCeqRhvoHL71YqSIBqxxJSD2hXaBht12+0gTEfI40puyqvPPJAYi1kiVckJFyWNmOAzEFJGczH+9XikiiLqfhbgksT3ArSVPR4JkI0fVQKtSpsYUCgElYY0atVbqXGiloDhlwBGh6NeGjsYEEaQJdWiUUDt3wQJhjM6Hl005RKHTCfy5hQ4ka0IeQiA613UYBg43R1Th5vaWw/FIyonbJ7ccbwyRDdm4xrU16lRozSgRtc7Uujdh7vYy1huZAjlbA+QwJFJXs0GBaKBNa2hHVaXDLLrK0knw0NT54izJXwjeBKZKa8Epfl7ZVPVglowD3pQghepgjyzVxUCKnikLViFsjahKEpNQSCiiFVGLsbU2SgNUCFJoatz4WI1S0SumKsbF7U0xIQhBjA+botGeNtBS/+acGWEUBm3NkuYNRz2GYP0rGzBrT2ZfH3sFRDbQGPC+R2yttiayTaCqJXCiwTug7QTNJjlAboUhB6Q0uNzD134YnWfaeYbzjITI8NabHNobSAwQMoglzU0STRIByMFWjarKXGeKlyVFdSlrRg222kyRcBwQL+dI3RLf7dZCpObo+w7B5U/Mh80p59TQZLSKOM2EeUIIyO0bhJDRCDUm6jDaHmh1ysDa1QkgjhyvjqvQAhIiaEQ0Elqg6e6Uu328hRAZb25ptTBPk8ncKVa+jJFcLXkNRRhvRsa7AykligpFxZuqul4jxGDBFsQ6kjshvDVaqU4BUGJc+eOiClWZz4VWmtF5gCimcDBfJuo8O7ZTASUoRCJZjA4xpOzUnESrQinVOqTFrjG93C+uLIBTnMRXnRYre9LaebF2i8EWmEPKHG+O3D29AwJPnj7l5uaWlBJ3T285Hg8QhJYMwZ7nwqU+p8yF0mbmemEup8/zz73bF8QUqNqQEDgcj6gqKQo5e79GgBDsPJ5bo4VoPZC91CFqogUprA2barit0JbYFePayW8Vj15Jsb0w1NVurTWmSailWKXEE1lbKCbEVU7meTJlEmBACaIkKkEraKW1yjRXSlU0KdYU2ZAWiRjNoFdbVplLR3uDVYMETKkgrNz63vfSvDrbtFFrodZiPSre5B0kkHO2BB6uFq27vR72itSCSNdq7Kut7nS+hlwL5NoL5LKktMabFSSAtgLTBZ0m5GKJLCEQpiOxTEgLiDSQSBcOUdegDS7L1VpDygS1O7jzYhGimm6rtGY0B/VuzE0iGzqxJjZjrQbrjA5ePQwNxOV2glbEg7CUiTBdbKVcC8G7sBuyqg08EmlvjsauJmsuK47CXn1vu+32EiZCTMmC2EL7cck5DMkMMRC0N4G5qkHD1PD6Ztj4qzdsdnW65WTUtuo+ygYdVZZGSsCVRiogrhvblv3blhp6dcIaQIyKE4PzfFmJN8aV3VRMPPAtNJ3NqlS2P1nvd9Q3pkTK2RbWeSAPAyknhmEgDxkNQnWpv4W3jycJftttt4819wmEJeEy7ulG5Ua8erkpwW8v/CK98uFnulNp+sKtn9fboTvbZG5BbWFpnoohOPqrhrCILsmmiKG41SuFwYGhKDhA5BQDTzqbqt+sgaxpozVbEPdGTtjel7Wa4/vedeJ1JRja77Lk7Su9oG9HdGnmXKpJsl7Ddvvmt8+AI8tyMou2hW9jpfFGa9ZgZd2TQlBoWCmlEqmSncidQZOhljEi42C8WEnU4nqT0wWKMXQqmSaenEZcz06ZW6GsZ7wVbFQIGoka0BTQS4IUjFZQfZ8xAr0ANQVqTrQQPJF1DlGTJaktg1IPzgq+XKgnS2SjjsRwa6LyaYBB1xWmkevwCowhXOIoL51Y4EsAWX8+up7tttuHWg8MUQJZhOacvCGNjoQUzlOGGWL25pEgK8+tI7IY/ScQVlqQIyPi3LpWTb95OxmolsJ8mRARWjYqg0ighd61bDSHFDN9nScC+XCgxUBBfZuVoMpMo8aweMY2GNa5oqErezikhAf2oIQozv9NRiWQaPSFPECKNAK3T255+qWngHA4HsgH0/KULHZ1FHU5PJAorpYijGPm6ZtPqMfDN/6PvNsX1NZlIlh1Y57rhiZgzcN14Zvaa2MMqNiQjpRsYambBaA07+r3BK7T1EKw83/5RC/8WZM25juxc2k9/izrMt+GwJCM8jA7ibwAhyFxczgQUqPJzMMsBiyF4JRCXZPtq6/ArhtzdZXqaLQFa8K24xZZP1+W3V7BphBkoUHQujwXV1XV3V4v+0wS2WsZ5A7KCtpM9L/JWiJRFb8pTTNWcBcCA6LZ9yq5JIFQJTDPDWqjPXuG3j9YmUESjWSk9WRKXiowSaNIn/BhztA0EFs0ma4glsy6VI8hstDxY4CaImVI5uhVCcW2FRqeyArlKJRbv6A8XODhDBIY2gjh1hQWhgMcjNvUV+Sq0KKRDAJCIywBfv2/631aIrvImey228eYCKQ+bjbYYnLImZvbW2JMSGic5ntkFtKYEZeEUzWeW60KzXhrqBB7IgvOU3MeXbNu41Ztep7igXmaadosiJZMTFaiDKkRQiVEYRgzKdnUvzyYVFcaEi0GLihBG3MzykITpbq/amuIa8RqU+OrSpddlhWlCRb0YgqkIZOHRIrZaBIhEmImRggp8+Zbb/L2pVqJMrr8WBTCGNCEJ8WOhkUTos+OYj85Hl4g+L7bbi8yz7p8HJeqOMdzdkrMOqK5NF2SWcSak1El50DOYUFuvT6BqBDadiplW9DWuOo8OoC7ajIHgdisgaw1RbVSO1nW43kMMOSEAEUboVWqKk0CTyUxNkXjheezoHM1JLZ1hNZltXpm6cltqxUtXq2JEXWecByMhyuOOq/IsvsfVsXVGFzn1lULgiWy2ybOntTu9nrYp09kO6q//L6unjq+uKxAteeKuqSLshQmrNihSwkzQkjY8AAWNBNVtFTaPNlFAF24uE2hRWiitNDW1Wp/nwbXpg3mxISFgK4dbV0xHysZilqyW5VWrBml2QgTAFoOaDHUijJbg5qEhTdIbcv0lB79N8wC+5xlVd0VGq5fsa5Md6fc7ZOZ+IW9ycoTjS4/FWIgtLDoI6/l8o1+cy/DA5vTkM0pe91BTD/NTV+2qaDRqxCh+cK1IVjibCNmg49rDoQ+zasjQxj6s6C9HpkWH23q3dEYBajHTLxjWrqmpV1XrDE0IARicB3qlEhDZhizNY04Oi2h1071iqKw8m4d8Y6RJHsqu9vLWU89OxfA5LUsIesydz1qaqeaPaIKbJM1D45WlQwfLKRb/Fx1CAQWH8PvEwx0Eq9kXG1E+8TNgLPxSI4apyjkJLQGKVVijMRqFR2WBsyVztDpPh0wbmqocPOqEbA0fq47tyYYokvavtmmrHSL7ffEmgTv9nrYKyGyK6FcF6h/YRbQE11dXtc7MI0zp8Rq00KCKhISjAdIlZCOpGxae8SChga1mNRPJ7LmEY2DIb4dkdUGpSCtXO2nEKgkwOS7dMi21FRs9B49gLpMdIrIkC2glYaW9RibX1zaGJCDk+7PE3KaQALhzS8hT29NveCQ0NCdsROblCiryHT08GpDzjzJ793YeIfqB64wu+32YtOmzNOE1rpM3ZrLzMPpgRAi0+WyjHcUhTYVmgh1Vhv12hbmmvlFM71YsIVdE1xyp7lsVVsqGeI8vxCNi5sPiXwwbdqUB0IypYDxMJqmspc+QxBr5vRAToNa/HrRpbSANlfqNNtn14pWI/UebgZu7o6WYObIeMy05g1uSxKwKhWkFMlDQAnGiR0HQ8fEaYJiTao9JqcYkRBNeeHYqMEqR6IstKTddvtIE/MJVTGkUqEqXOZCbYWcIqMrdSC22FyUblwDVnqT1KPtOsa7/N5jxfq6jdRjR5+0J8Zh0xxiTHRFKd5notEGOJg2rTA0MQmuKtxFYWgmdXkuymWulNnoEqpwM2SejKP1woRgjVwKcyyUyRL5FCIpxCUpbc31ojFUFu0N5HYkxoWNmPcGpyI4r96Pv2u37xHz9bFXkN/qZYqVeN2le+iryRU/oavPNS+V2wqrEloj1GZ0guMN2pRwvCMdn7gjX2jM0CoaA4zWmMHxFsaDjdQLSgsgtcL9iTC5JM5SZwjUlGghojnB7RFyMvqbWmmmoRS1RDamSBoGG2NbKjoVnyhigRxAckSyt6d0uTARwvGWcHOHxAhjXvi7qoISV54PXOHR9t34JUdcuktciXrRXthtt4+2po3pcrGM1ANgm2amqRj/WwsBIYeEtka5zI4MRVq1QCsS6FPNW5kos4v+p4CksGi/dvktvBxoJX6T/4k5Mt4OjLejJbXjSMzGP83jaNxZDDbuk4h6nG1NTYaPjgDb9aRMhfnhTKuVcpm4nM6gytO3nnC4GV3zNXG4OXhXeF6WgF2tIIXEkDPDkEAsqR4OA6U1838P9J1zH0VIPolJY+NIREeTHJrnmVp3HdndPt5ErALQVNBq53ttcJpm5jJzGAdSzp7AWRMizUcLuY+FDfq/LZ1Lh1oXHoyXJ3TlmVopcl109jeGEH07FQkFqlGESquOrtqEwJSTNXupRfKoAdHIrEJKGQ2Ry1y5XArn00Rryu048OR4JLlqSG/kupwunA1MdkWTuFSQrIHSkdeefi+JNouUHgoa2RZ9HcBdpTl3e33sM+LI9vK/LEjsNu9akdp1WEKHcPss595dbdllsEQwCKoRcLpASqiP9CNnGAyR1aCW65WApNnkvDxIem1keZ/mbDqvQ7L99UDZ58WrYojsmA0NDaVnovbDWeUSo2tagiGn3niWs0uFdeJ942rNLNelj+2FpbetXHWadmfeSbK7vaT1wCfueM3L/Sg0qpXd6X7pk+/a9jxc/+98WEUR705cuKg8ek8v8UUbihBTXMbepsHvh0DMrnXZF27YprR45UYflVb7h6iNxmylUXw6mLa2JpNiJdYQTYPzas5837cuwxWiSQQFGx4RxFRJmvPu1g7LzZQhFQgKQShbPsVuu72ESQjmQ46IKn1CpC56qB1RXQaQbErmPXaswbWffLIktWvxbhuA+1Pr0IQuLbmU7juS291xS/Hp2xRr1sYT0OQAS06RITmhvCl1DrSq5BjJMRgv3VFZBYpL9amuOuqdRqfO5unasX1f1sMO67XGH1suRcIj2sEeM18Xe+VEtiOzC/q6ILF69aqFcqAu2KVKmM6EZ/eEuaBfe4f21S+DNvTNmRaaJZ69kaw1l9+IqAakQpidMB6qjcybK+k0EXxspPhYSUt8scSzAWGC4oG+a0r3Dk8R4lyJ1UNsKaiXMyVHwuDbnMsy6u+qmFPvqZeTOVoekJwhRNp4QMeDJatXXJ4lZegZvo/bY52etAfL3V7SjEpQ3CctAS2XifPze1qp1gA1+ohm8Q5gbBjBEC0YLdrGPr2uTsUTWYU++rlU45c3XTVaU+Tm6YHD7UgaEjdfumF8YmoJKadF9zWmRAgW7ObJJhrVqXB+mKhzdfTUxssOw8jx5oYYIw+XxmlqzNPMfJmZzrNxZSvElMh56N8C6txBvPkreONXGrIjw5nUWCYsBac4LYNT8OaSmBjzgWEYKdPMaTozPZyZp5lnz58zXfYRtbt9vIkE0nBgnk3fuZTKVApFTW/d6ASRGJMvHk2uTmNAvNkrLJJzsiz4WIqOLlvVK3n0ip/5LJWlZ2OhFNiOmYsQaNlQ39oajWL7EIRZG+L708N6EMhBiAg6RLgdKE25DJFTDLTWGGJiTK6o4Dx2VaVGZY6maCTb3IH+Eda7sjwqa+XGJOgdcV6i7prIb5vmdnt97LMbUduRH1clAK5Xd70pxJs/RBvhfCZ+/T3CdKG+86Pwo3/GtkehZTXlAtwxHbFBMyBIEUIP1CbSQ5gL3F8IDxdbkXnHtMYARdEYLYElQnJaRPPJRzmTjiMSI6FVYp0QNTkhpslee0wQBnOsc4VTsQtMzoh3drZ2oTQLbiFnJGWT4nrzLUODfX90WXm3lWKgtuIVDB3q3x3X4Nduu32oqSrTPLu0mwWD0+nE++98lel85nBz4O6pif6nlKwjWQIpene0BFvcWQMyU/VpVjY7E1qwcZqlOsqrC+80DYm7N265e+uWOERu3rphvBttTvuj6VogzFNlmiplrkznC8/efcZ0mkgxcRwOxJDITyLHJyPjOFKeT5RL43KamS4z54fJrjsVUhrIQ/8suybVas0oBIg5kA4DOWdSHkgxU5uQcyZnS2QTYbl2dRnBFBOH8cg4jlwqvHeeef7+A+fTma++8zUe7h8+x7/2bl8UkyDk8UjRM1M9cZknplKoTX1wULDFlQ8zaLVX8WwYSQdGe9UTXeUcQ5IlyW0SLJnFKGvBOqq8ybk6daEPFmJJjKvEpdRfWmX2hasGmNVApEDnohqNaPSK5xASxyHRFKYpcx4StTZrItuiqcFygDIpc1SqKK3JMn1zU5yxCYDN9eAdmQ4heKeLo65bRYarhrg9kX3d7NUS2W3Nz390asGVcsBSQnTOjpc9pVakzMg8Lz+hQZmgWoOJQZNxLTX2JNk1YEER6aitURV8GLpxBANIs/KoiNh7SkOodjGo9lNCQKpPSKkNKRVaQ0qB2RJWKWLDFoJY89lcvDzjVAjBR+xOvcpiCenmc3QZFrH53nBaxoZnrM5x2iLZu+32cdY5pjYiWpegMJeZuRSyj6xdz79e2pMF3dmeabI4tCE76r7UufELPzYGQ3tzIo2JNES/WYBdg8uWNmOIb6uFWiplKpRpRhLUUJFo+rbLkATZRDq/dVxmGXIgwbjt6vqYOEC1KY+uCbUn18EbLj2hsKTBvoXtRDAQWm2UuTBPxVDhHZHd7aVsLXU3p8M0td6I3tzUX+evXjHHx1mZbl/n/2/O780Ttt1Hb99WEE29p/uOj3TGFD/EurKNAuHvDRaqNgwGWVFigBhoMVDFfLerAiG6KAWF7fvZXIt8w+o8XEtwDYEVcKAMVv6BrhSDq29vt9fNPnUiK+ATSHwCSHfFJfDZCdn9KgCoEFslt0KojXC6mC7s+UKYZuehgcwVeTiZxlzIaOj8m4C24E4wWY4bgMEUu9CKRqUNK6wprokXarPpYLUQSoUQWafzqKkZXE4QgyFNxeZI6zyj02Td2acBuQzG3Z0anK2BTfQIyTRly+lMOT1HMZ3KkDKSB8LxCeFmNqdMioY+Ts/n36rJg1V/TCUgoesJ6nIh2W23jzLrOC4+/FWX36s2qjY0CvE4ksfBO4aTJWniNB5dJ86pKCkIOUZaE+bWqJfZxkTOM7VURIQ8Zm7ujuRD4u7tW55+651prt4kQhZPHj321EadGq02zg8Tz955j4f3L8yXmfv37imXmTqO5BARUdfZrIYmRSEfRht3HX0+vSrjMPhxdAUEC7hxELKPwNShcW4XWjWaQZYMYsMicky0oKABUS+5moyJNcNoQJtQZuX++Zn3vv6cy8OZ+/fuOd3vI2p3exmza/k0F+4fHnh4eDA1j5QQbyjU5hXAZo1Z1ytKripzJgNnOu0xmcoBnpQ2x3+C2Oh2S1jDFQq75ab3eB19LDXAEJPnxWt8x9Fe8fvi4FKfxUVfIKdAa8plmrmUmdZsCIqkLi/W+puM4+6f3xV6tKlx4IupDwXXmRWv/nR+bU9wdUO7WEGfPaV9nezTI7IiayK7XV91FLF5cxQsZREBYlXyVAi1EB7O6LN72nQhzcU0GZ1/yv0DLQRaGmlx8Hcn0GhobplMaisK8S4jnpi21FzqVVG/FxSkCNJs5Rv04koF9k8tYtGyy4A0S2RpCvNMu1xQbYRjJlxGm15SQGf7HjSCHgZUoJ5PXN5/jqoSPZENw0h+ciI+mSAkpInrb9kVp69JHV9GRIzzi435q6rm7Lvt9jG2cGRRqo+NLKVQWqPSIAXSzUg+jiSNJKJP9rGGMANPXNNSlRQgp0itYojpPFGLIaelFKMVHBI3b9ww3mSevH3L02+7s3WnNJoPJ+m9JIpS60y5VC7PH3j/K1/n2VfvqaVwOU1GWbipHA4DIUJthaaFRkVSIB8OaLCZ7Nn5f4dhtKRcbNymcQ6VeAjEQzLu+9A4tQu1NQYdFg3NECI5JRuIoJa0dgQJNTQo0BPZxv2zE+997RnT+cLzrz9w3hPZ3V7CTKVAmaaZ5/f3PH/+nMPhyNOnB6O7RCuxV8X4qLoO6JGexNqWABYVABEb1BGSZ4Z1KYLixDx6Emo6tUJDFmCkD0hA1bSmfdFZ1BLsrqJje9OT4eCL01WjNrR1X2MMtKDUSbmfzeeCRku8rQ5D7xpr4lq6iMlwSkBbY66F2RPZuElg29IYvlZZTfVEXc1g252zx8zXxV6JWrB0VtJLIyvFYFkQLdwee6x3UveyYOsnYoyQsyV1MfkKMtCHJnROaSenL11SnXMnwaRag5U2tqZN6ECLyWD1vV3o8FflGydAeDmEJSFfiQBWJtHQ90NcIgTbz6U+20suPcnvpIFOwv+gqy1v0/5JvbK7O+VuL28LL90XluKTvkIfRpASUSPRE9lWm3VOdxfusyrFtBvVmy1EN4NPvCxvgw1MlSDmSExhKRHKpg7ZrwOtNupc7TYV4+B2pQXMlfsoWCvS2MhaK0t6M0eyJhihkVJYRdE7TWChO5iurVF92nJblRewa4j6IvdRadaz72VxXmuzhUHxqWZ1r5Xs9nJmk7z06txbmpNgTcJ01WX/qLL5Gn6vn1kizhKHdZGxWwkJ/bze+IGuW9gSHdoSxyx+Nlny0CVWLcgoFgOX3Vo2tCoxWGjvqgPqsw/6EJRHY4Gk5xnrQx+Kt3bKAXu8fN3sFagFQozJhqWH6KszBfVW/p799fvIMslj9mBTb4/M3/o2UgpDe4NSvg1Q5hCYg036qSHRerdmWBu/pJSFrxqOGRmMJF/qqu3YOUlBoVWxEbPY2E3nwDMHK9uLT/QRhNgatTVElTLPTNMFbWpjNA82KKGqOPIjhNsb4s2NfWYUxuFgxx6jDXrIiXZzy5yjS4uJLWrpA4RWHtJyfZB+EbHe8z1c7vYyJiKkIVNKYZ4LtVZCCByOR4Zh4PbJU56+8ZTDzZEcEqMMCMJ0vnB+ONFqZb5MTJfJNGIFxmN29KMSyZQq1ONAKwMxJ27fOHL31o0PQLBBBwuyi6zKIAh1Kjy8e+bh/RPnZ2fO9xfm80xMkbu7G2KK3Dy54e2f8BbjzYEUE3O9UE92PDFUxqgMNwOHu0wU4ebNWw6HRBgCISRSPKDSiEN0hYZISI3aLpTauExnHkiUWl34PVrwX6pLLFR8RSi1oa1wmWbOpzOn05lymSml7Yoiu72U1dp4/9k9l2lmSAO3N7cMw2CoqieUxccyR5YWr2URJUvi27fYJ+UJVrDv4I03D6v6GGmnC7VqzVsdFBJDZWtrVJ+o1QeoVG2U6kipv66H8lmrob0hkOPKd42oxywf/ACMw8DhYA1tcUikg6mKtBiZZSLUxtys18SuFz0KmgpJiEZpsKEp0YYhxLgktsE57rvt9ukTWYHoSWxHT4OXw5er+2ZG+9L7hVCCI6i3N0g0qkARYfTV3Vxmyjxb40rw7msxXpxN5FIbi+KlhZATIUWb9d4q1cYTLUEpYqhsUB9SKb18D3O0n4bG2D5GVYpfAGopzNPFZUMiLVvJpQahONITh4E4DIgKaTgwHJ/aBShYqVNjpN4eKCmu0xCCoVW5+f55yccvVwvq1afi7onsbi9jIkLKVirXyRomJAQOxxtElbsnT3jyxhscb48MceCQDgSE+2fPjYYwF6ZpZpovJqGTMsMhG+eOStJKKZbI1jqShsjd0yN3X7olDdFeG20RK83KGdYwZs7VLsr91888e+c5l4eJy/OJ+TKTYuTuyQ2HmwO3b9zy9nd8iePtkct55uHZiXKpSMEGFER4esy8dXsgp0i4PRAOCUmBNmR0lEVyK+Q+bUxpbaJo40ImtEhtjVKacWC9TmMoEUvlR4FSjN4zXWbO54nTw5nqi4TddnsZq63y7Nk9rVZyHogxkWK0RBargJRqo/XUpaoEWRoqYTktARYuqKJ+/rK8ZuGrehNlb9QM2rXQQSU4/QiKq4+0Wq3qoJ2WhHPtN59X+4jaBsmTS9lItkcbPIIIc24cDiNVlTQO5OMIAkUClwZSG2FWNBSjBHeuLDaUKIS88IdD/576cfYKzCNsdqEY7PZa2WdDLdhQDGApiHNVOPcl3VLGV4FOJ2g2/rKKo7ZqJTyjHqhN0xJMD7aXJzqh22kFSjC+jTgz1pNTcUQ4BC+lIIaKItQALdoK9QpY0d7EpdTQqDFa93QnnXsiW8P6+b0zmxiR7AcafMpKDI4my7rPXmtdmVDLl3r1lS10hh362e1lTLAmiM0wgD4IAN1M3vJxrTFG021NiZiTlQdjuKa0LGoafUSkkGJgyPaelI2HashJwIv7tGU+mAu+N2jFaAVlqrTSlv0L0XRehzGTR5PEijkSS13BJqccSOjT9+wzJfcqh/l5C4GuY7coNOh6TdJOMWgrP30Nfv06QZ+D4q8VQ69qtY7z9qgZZ7fdPso8rlm1LRA6La3zPTc0gqX8v9ACNinc0qW/6rZ3ekB/vJfseeH2PN6J9H7GzU1YC/sm9yN9qqSadmvz64H5gyFUIcgyvXJrqzKIa02HYMIEfl966ZHHruSVzq1kn1/PRLevXZUglu9gT2JfS3sFRFbIOUFKtBQtKWUNDD2ZXX86SiMgwUrsQSISB0MeW2P22e2XuXLBRmfqpsNSqy4TiPp4WRR0NkktRSnqJZfOPRLTZp1VbRytQPL9sLGwXdN1XfkaDFpdZ1apNjuWWCE5vNxEKCYxQJJICsbfkzy4NmfPvi3ASo4gm2S2R+eN321XnKvpsvLebbePMxEhHTINJZ2jISgofZZ5CokcB3KyWxoyQYTj3Q0SxLmfjfv371G18nmZz4aStEZOkRQDEu843h6IOfHWG095ene3TvPSiKIkb/YqpXJ6/sDlPHF6duL03omHZw9oU/KYSDly98YtX/q2L3H79JbxZuBwdyQfspUop5mYKlGEHEzH8ngcSXdHYgrIkJHRqh0tmtqB0pjPhVJmggjjzUi8sR5LpFGkWFCmEVwST4j9wmBcQKz0epkuzFPhfHrgfHrgcjp1iOpq4bnbbh9u6zXfYoNVFstkMpPJF4fBG65iFFf6adgUH+OfRs/k+qIKVvqZIER/nY3E9cEnlkUvQE6VSCHSRJkD1Bhs+In6loRlqEBT02JuiutH2/jZpjaiOggMMSApEkWgbShF2AhcUEJMhJRAhJgbcVQbqhJniu9/l9gzeoIpMaxJrOcA4ovqJbyKL7TXRHaf6vX62SsnshojLUUroWug9mWibG6wnHyKWCILNhrS+ypbKT5ysnIOM6c+Prav2hSqdmeDiE0MMaeuy2q3qXdeizWhhGDIalTno4oN+ZJeQxTxUbi4oyutKLW0ZY58X2nGBr2IY+ivbSiFRo5KDDCMA4w3jhpvvrCtlqXfWUpF13DwdWxUHr9gt90+1CSY8H9tzWRrxDhtEtS5bYkcMznabPc0pKUBLA8DtTROz0+EmKA05jpTJpO6u8mRQ4pIgEMcIAZijrzx9Cm3N7eG+kartlgqaexuaRPP7ivn98+cnp05vX/i9OxESpHxdiTnxO2bt7zxrW/y5M0npCEy3gzEbMoJ4zQQUyGlwDgkYhAOh4F0NxpnLsRlip8hwUJToV4qp/sLAeFJKITRCiOqzdUQ7H7no/cEQwENQqtQtTFNM5fzmdPpgfP5zPl8JoiQyAvmvNtuH2/BQ06wql9r1DKbIk7OVhUJgRSEGDog69VLbLhBF7AqTSmuCR21Q0XefOkAToyBlMSrCq5OgFBbYNKABqUEoWnwBNGoDRICIdvo5tqM32sVwZnCbNSDZnJ+QRXNiRSixbhm2ukWwwISojHpYrJrSk9ki9KkQjwv1LklwfcKUohxOabFtqFw0wSmbJrWdnvt7DOhFvRVU1OTuLLzaSloLBjt9Zs9lXu0cHqM4/aE7/Epaifu9Xu2H7jBQxcSet/nvqDrK7mOrjRdP33BQBdyryyU38f7bJtaj1u9pHKFogpeuHnE6vko39sdc7dPYVv+2GPvedS3vOkkNvREo5XtY0rE2qhzWZQPamuU3jCZTJ0gesCT0FEQ8eYUcVkv56Y3QZpVUaLY/HWbLJZJYyIP2RCkFCwhls53M3mhqI725rCoFhjfXCz2ddWQZkG2NUOCy1QJIjQfOd3Xkx+gPtHXzJ0MsSFIaXPZn82NwPVKdbfdPtwUA2K0NkMim+mUg/srq99ubUt5kR7YemVTZPOaTSzu9IMVyHS1AbU5QuL9GIpxZYP5jUsOQAymfx4j2hpBrDoptS7XC3XJPgOXLFFurrawzCPq8dBj9SbULr7V1LXSmyXqcUlMnS4gIE2XuL0qLHQahLwwhO6I7Otlr9TsFYKYLuWQIGZqU6QEmjai89WCyCphJR5MLIskuLCHIaHVb15K8VtwrbwuWbWEmbZ2DEcnnNuOreWbGDzoYTOhVxx0ZQPZlKJ1RdfDV4igEXMYpzOEDQk/AH00QyQQm/1EhfoC5wqLA+qS1ALL8fcfBsCuNAzWV+2228eaACkEqlito/ZKg88uX6dyresko/sEQrak8ebuhjff/hLTZeL9r32dy+lCrYX7OnM6V2IM3I1PuL09QI60IVADiChS3HWB3EyDNc3KpQ0oB1ISLk/eYBBLYG/evCEfMofbI+PNgZATBBviIGrXjPF2AFUbMzsm080MkRpNAi8FISTz7PN94fmzB0qp3D974PnzEzEE3jq+QXxLrPwpDaRP5Wurz0kCyR6UC00rrVnSoW1Ga9lUaRQNuk4w3G23j7DWmqldTBOX03NaKYxD5sntgZwSOWVSTK5iYGitYoldb3QSQFwVKMW4SMVJSIhEow6JEsMK7RSnCswKBWscPiNciEZ7TYI2853QlKBKTInh5khKidoqYb6Y+snlwlwrOglaC+VSraLRlFjsc5M2ChWRQMHH7yKUqrSpogLnqXKeCnOpnC6Fh4vRCEm2QAabRqja1mW4o099oRn65AZnTizSe9tEfrfXxl4NkQ3WPBJSNE3HWmkq0IInfdcrQ1t16lKeD5s+/d4fqb0cGVxXUtrmxFwxzkpzrctrUnhw9FVkLbGIGAoksOhBal8a+hg89U/G97fPZkCtzIha71aIjnKpc3R928GRpz5ZZfmOfOuiXZxar4Cg/v1sMTMj7n/4anO33T7M+rkepat0cN20tSSxnuF2f/HzOmjgcHPkyZtPrUv/4excUeUyT5RyIaZI5oa7Q0SGhGZrnBQgNoXJPjdpYEAIpXHQTGMgRWG+uWOIiXzI3Lx9R74ZSGNmOAwu7G6lf5oSY2A4evKaI3E0eS86dw9rADO/hDJX7t8/M08zz5+feP7+AylG5m+ZnWMoVkaVPvWvV3YE89CMSqNRPaE16S2bgGJjq7t/qkHPn8efebcvmKkq5/PEdD5x/+wZZbrw9O6Wp7dH0qJgYCoGfQw0AJtE1icX2Jkagw36QLAhBdEel0KQHtPaMhVyFpiAinBBONNjZlomZAVHdkPODLe3RjWqBaYHai1oENLlRKNRJjVuqxpieqlqaj+qZCxuN3EN2t5PMlcUrAdmbsylcpkr57kYX14Smuy7KroOWWgb/msIPhhButyX3a4aw3Y09rWzV0hk+0nDJon0zv3Acr+XBJoYxCi6FgQUWVAhkNWhYjQ+Df3klYW/utQlRNzZDXldnXE9oWPo5PH++HUiq1giqxgzrs+mx8UGeua7IrJYcuz72/VxbYReXEqiy1PLUrIfYb+/PrbQHOiXqM0jG0fd15i7vaxFsYYoczxY4NfNrdMFFr9ClgpAiIGUM00h5URMEW2V1oJNxwuW+JKME6sBGs2aPJpxyelSdxqIDWKnFMTEYRydy5s5Ho7kw0AcIjllYvQJfWpLy7Cp7LhqHdK8FFr7SrOh4ioCLiWEusJCsv0PMS5qDkYRaktC371xWXTrunCW7XObO72C0+fB77bbR9mKyF6Yfbxza80rmxaz+vmk1YZtoIoko9V0+sBCJ2BDKfBksXty84R34anrqh1rdcRMlMHOc43QTGVEm9MEsIQ3IjQr17jvbG99e+u2FzUEltBpzymmEFIbDVzjemYqpuPcB7Fso+Oin7ApHfWK6aLQ0BfkItd9l3sT5mtnr6YjG/v0HJujLBJpRLT56MeQFq5o81GsrXojleqa7ALERIzJhKBjIo2HTRm+23q2r/p6mwQaWboeRfBENmwSQlmco/9suia1SyIrQNAlGe1TjroELNjlYPknfSiEEFKiepObuLe5u29Q5RcEzyWpd+rDgmCvC4Xddvs4Cwi3klEisnBcvXER04qsdZ1K1VyL2c53C0jDYeTuS0KZC+eHe+6fvc88DZzPAbmYPmu+HUh3pt9cUqPpRKgwzkq4ACpEHRg0IbVxkEhLA4chcPfGG5AieUgc37w1dQJRWjR/rxQKFxrVEM/g/leFeO/Lvaow+yUgNUqaLY89z8bDDYHj3Q359oaUErdfekK+teawVoqP7YW5wVwBsc7xSECkEsRUF5BGCtbYmWTDsVUbvrIS53fb7cPtMk38//70f0OgEbUSpHF7Y2jsYRhotXI5ny2ZLYVSJlDlMGaCjDbpLph2+5LK9vjkRDfVZqX/crEYEzqAFGh5hDQQwsBx/BKHfGcFyVlp1Zqtp/sHyjQzV5PJm8RIchIjEoVQssntNZefS8niWExoGmyqphitSRCPrQYUzVNlnqxB7OvPHnjn2XPmWnn+MDH7DKXajEcc/NgCWJIKS3KqulZOW2tLQm9FGuOta+hjPL+hf+LdPkd7JWrBgpZER2gkkNRkPWKIJJ/4pdI7mJV5O6bPx9NZh2Vc+DEhJVJHOhbpgutCu2z+70klsElkV0T2Q23T7LVNZHWrXdtLiP75fcJKRHDci3VFvA5wWBbK9kE2R7p/Zk9lF+C1I7Fh+X1Zay+J7Cf72+z2elpAOEpkUoFq6E5vVgIbD9tvC182+Hnt51gaMyFnaqnc3N1yc3vDlC9oqFQphhIdE/EmIVGo2pi1EZuSiqKTggaiCtkpPJnAEBNpHDi++Qb55kgaEscnR/KYqa1yLmdqqxRmJhEaxlOtWmjaCLMQL4pUoCg6eyd2apTU7HpyqUbjEVNvkHEg5cThyZF4GIhBmFqhtGqBs4nJ6wlEX0gKQjB9P2IwnmIMq+i7iAXpLiC/224fZ/Nc+KEf/hHGHHlyyOQUKKUSYyTnzNwa52mi1kopM/M0ObVOGccEEr3vJC6xQL0y6bPSQYVaG/M8W4QxsBW8HC/DQEgj+e4J8fglWlPmS6XOjflyoZwnLjpTGlCUIq7E0wc3JJPQCrUQqktuNkVjQlOmj5SvmGJPo9l4eLW4f24zpVae3T/w9fcskZ2KJbAiYjOOvCC5FDS7SY+k9n9vMAsev1tYFdl3PdnXzz59Irtc1D3B6+ijeDKp64qpUalq/Nd5MidtzZLd4MluIFv3s5cnZFlVbfFLZZP/LUnkmvjJolDwMnyZXsTX5d6KkPbj2eLBPbHsv8l2T64+ZrOfsr7Ouk6v92e7f+IXo95xHsRw3P6d7rbby5h6F7C23m2/LsZ6+bLVVdwfFFJClhIm1jgRhTQkhuNIiIJKhdB8DPNA9iqEeKIcVEgYcpkQovamSBD1ZLk1Q4NLsWEkraItQmsGvKrVMKJaS6WN2rT7oSqxYKM2KzZjWm38dC/whwbZh6OIc4SSdP56lyDqfqdL57WoUFtFtHg3uY3fbNtyJe6j/RqXwj5yb7eXstaU81QQgcawlOtDCMQYqMF0U1WVFBO2AoScXSYvRkKEPhN2HfTBQtFTrD+jeuFStaMpLoMVjLIXYzLZOpQq1RHOngg2VCvTPFGqVSZK8J/ThXmulKbUri2L+I0lbkVxkbCKg0M2Qaz293X2jysP9OhuCgbNK0NcAUGP0dUlmWfBm5Y4rroOn9jt9bBPTy0A17Vr1pBFNbFmsekdtTam6YI2mNvEVE40tfLJ6f4ebc1E2fNoCe2TJ9ykp1Y6EUFCRKU7Sw/Cxp1b0kc/Ux8jsngy2xULeFEO6B6wHYu35K6I6eNtjtV+yto1efXMo++lB71N96Td7x2X61u7tm3/XFFZJhT1RFxFjA64224fY9oa0/nCfJ5s1Ow02RPWom/ctPOFEKNPp7KpO8Nh5BCscZLQgyzcvHHHt7ZvpZZCnS+U6YIEZbzNjDkDSikzdS7EBrcKh2ic2IMEsgpaG6EVmGeqVs7P4HJ5II8DQWZaGRCF1PWaFYRoYuwqRB+UEiYlnhtSLUkNjkTNZeZSC0rjKIFRsgt/ZBo2oCFVpU6F5s00QQISlNImprlgA3gbUSeCwBghZ9cmEefkNvUE3+hUQ8pEX3A/e+/hc/hr7/ZFsVIbP/reA28+ueHJ08w4HIjDgXEcOYyjqROojT0OrossIhzGgePNwaZntWLKGX0x6v0dnWXXVDk34VIsZoSQCBLt53DDcLgzRHY8kIbR5OnOF2gzojNCIYgNEXn/2XPm0mi1UOYL2goxmF8EUWqpXKoNTFDEpgMGk9WTmEzybp6ZWqOpcGlwLlAqzC3QSEsNtFP3LqWB2ACTIQrZ+1Esf+8pq9Cjb19awprYWzhvnhvsqezrYq+mWoCVPtY1mbrclo1zLNNsCe184jQ/p1abjvPw/H20NYY8chiMJ3Q7JOLtrUnpSCJEl+EAKp3Ebs1iqC4n6lYtc1uC36KxL0JllbW1aj0eWR5/VNd4lMzavcdbXFzLgdeeaG9nQi/75ahv7FuSdTOdUN8h7yq7S+72cqaqlMmaKcrss9Y7uiFQamWeC3EygqmgPkVHaHVYfSYKooHD7ZEohu5SZ6RMgBKCLWC1WYdxKY2ocINwDKbokUW80QukNajF0B4aOgVaGRgOxkmNBJJkokSCAi2YUHpTQgs0hTA14gVrKBMvsyIwF8qk3pwmxJzcbyLVSUCxQZsbEvz6EQLiHOG5TIDpbEatxCiM2XRtW3HPdbS4N+fEGDkcR4b0SpfQ3V4Tq0157+FCHkeqJCQPhDSQc7ZJkFrR0biyaRgYxiMSAsOQGccBEZiniWk6W6WFSm11oan1+Dg3uFRz9pQjUTJIRuJIGo72mWkkpWz0c1FQU+QQqUho1Gnm+fN7Hh4m5nnmdLqnzDOHQ+bNJzeMQ6JV05RWNfAliaASkBhJOa85gFh2MCtMFUpVaguoR77OoVW1IQ86Vx90ZI2kHZgy3+v0u0bbDCLpWLJ6TqBdd3YPmq+NveJAhPVnb6Ds5XScWrCUMEuh1EItM7XMtFaNX1MqMUTGnHz8ZSLnkZQtqGq0RirkutC/lu29ZL+kpZuMcEkf9QqZNd64LK8QWZ1AfN/XtJS1rOgHutAP6OzWR9SEfucRteED6bR/jmx3mY4SW9Rc9G13p9ztpczO0RBsgEBK0fuRVtoPyznlclyi5qfFaAZGDdKlqhFTguZFRNPHQbTQZ1eKWpNUwC4o5pPueX5diFFIKThtyJQDIoqUis6FhiWuUKk0qtZFucDpqsswhT673dBQcdmihLh+dfJBCaIC1a9PswVJQnDtV09o4YrCQ5fdW3lTa5AU+y7ykMkpMR5GR6V32+1jTLA+kN7177I4Nmq2eef+GmdCDIsSjoUmWQgAdl6GZVHWWqNpW4cTdHAnRKMRpOQUPntcW6WVmVZmtBS0ztAKAeODpyDklBgG+8TLJSLBkGJTAUn22WJUCPt9BV4IXrmUhazglIc+QGHVse5JLDjnlU6R6KpGawP2UovVx/SKnsjrEp/3ePl62SvDCV12S3xFpr3pSRvzPDHPlfPlzMPpnlJnLufnnM/PqLVQp5lyngH42lduubu5JabE7e1T7u6eEmPicHvH4ebWVBCGkdQDxzYr3DSE9ZPcR4LQ083uY1eJI905rlPMRXPTt309+u5acmzdzvr/x9rSYIY7ur13ebcExOWEWm2Uahe73Xb7WBMgR+Jh4ObuhpBsQElz1YKck517tdFCpRbrdp4uE32s5XBzZMwJETGh9qNxfNIUiRifVc+NdnZqgrq0VTOkNjSfiKBKVVMeGG8SYbTA1geGiICcztRp9uaNYPsmfX68NZQmL7NGAjlag1kIgRCSx3Qrnxq/UMjJfPTSCpdzhQAxFapckBjQHNBsxxRVbLymiE0VS65LHQNEG71tvS8KMXLz9I43S2XImTeePOEwDva9/4E/9vn8vXf7QlgMgbubG5ObywMxZRThfJ55Hs/Q1sZB8VgXHe23HFddkzUCAYlKkIg2ZS4XLsWGd8wNqjo1KB8YPabmfCCGDCqU85n5XKilMD+8Tz2f0NZIVEIORMno08ztDZxOZ2ptiATGw8Hi8Tisi2A24JWApEQcsiWy80xRm9w1tco0F0ptzLU5X1apHts6TcCYdUqSYHxZrNGyPy/0qX+WGIvHxeASXnY96Jrxezb7utirJbLyKKnrCa0nf6VUylyY54nLdKGUielyZppOtFo4Pb/n4dkztCrPh5H38kiMiTfffIvpzbdIeeCNVokhEJOJRocXICCWEG5OWrW6/koIWLsYr1LWhcP6uMtxPQb6ylG3b3P+aniUAG+2vnWiD3WoDQ/3+hVeHgnWtNNaH5+7224fYyIQbXTscBgMbW3NBM1ViTH6Oe26ka0CgToXZoEaI3HINjM94uNnB0QbuSm5KpRKrUK9mGPkaPPhEaVSfG67q5IAKkoeTMXA+r28GQ3QqaAUWlPrllYWPUpEjCs4dNRVSNESbGtciX7QwUqRKDkIY3Jfn0EnUzMIp4bGgkZBD9YF3tHkFIzXb/q4phe96GRKWIhTBAvmN3eFwzjw5I0nHA/j5/BH3u2LZiLWIDkMg03wiglFmObKZSqICVcuiVjMhnz2qqaqGh/V5SzNPaypsTFRHNmtTV0DNhDSQBoOjspmS3wV6jxTq1VG6+VMm86gSnSVgpAS3GTGaqo/z56PzKWSslEehoMv3joS2qzqqiiSjN9rXHtxTVpLWudmoExprRdzaJ7QAlRjDRgtwWMfYg1sS311U/JUtcFIoli89BBpYPEH6p+7fRPbK3Jk13trImtNIikmxnEkhgShojJR6sCchSEptdqkHS2maSkItc6oNs7nB54/T6SUbf47EFOmFOuyFgmkZDzahV/aS/1LRfDRfbiGYnvq6BuQzQH1qUedrPA4EbXfxYPn1dY2z3/wvj+wvHYhQ/SO8o2jSk82PmQfdtvthSbYpL3oqEzsQzoiqmrPha28my/lWqMWG8HaSsXGC11zXlQx/daqNvLWM8+6ke4yWa/uW3Y+h8XHfGEYZW1QaV4SbNCqLr6lXt4Pgk/TWlGplf5ji9VVUN2uPx2RiSlb87fgGtU2QroUpZ4LFWy85pI4r8NU1Eug6wJybb7sjZhW7tz9crePtxgCT44HbsaBnPoEL+V8mYgoKcKQ7Nxqy+RJ1kXnZvG54qCOgvbmTPycx4YKpZTXOClh4ci0UqnF6H5abPQysDYYx4CKJdK1NW7vbgkpcrwZOd4cOYyDT7xzfm7zyotCCpEQgyedm8SzA16PGrCtqdR8aCtvtx22FIJN6WTZpl+znH7Qv6OOyK49NLu9LvYZdCrYyRk6qkFENXBzkxniDa1BbU+Y2xs0LczTA/PlObXMvPfuu7x7fId5nnj2/ns8e+89Wq2cz/d87WtfIYbIze0b3Nw+IaWBN770Nk+evEnKmSdPnnK8uSXEwOCNGQZGhcVZpAecDySyH32Wd76NWiuoT/96lMyKWAOLdKbAomL3AW7tR397+LSz1UGR0IuvG0T2pf4Yu73mJiKkQya1TBojVaMlcO7q4zj4OEyT/umT8FoplLPJA+Wc0bs78LPQChzGMW2XCqXQzjP1PIM2iq66yNsglFMkeiIdXVtZQjB0KAS7NhRDZ7Q16lzXQO2JqqqJtTdV47eG4tsPjsgKNvTatTRDJObR9K31wHi00qM4eNtqY57OnMuFKoLkzCFlNAgtRpqP6q3eeDKXZioH/i/FQI6BGAW1lpvP4a+82xfNhpz4Sd/+LRzGzN3xwJAzWipf/ep7vBfg5jjy5pMbco7k2YYGBTG0suuu19oopaLKwqFFlBATaRhRbcQ8MKo1cN7e3nI83q4VU7VF6nx6YDqfabVSLifaPNk0v+FgWu4hccy3EA/c3t1yvLtlLoU8JG7vDqQUqbUyz/MyUKUv7OpcKNOMtmYUHUdmQzB/DVii3Lm0gUAOzowNroQk69jpKELOkSEaV7jLd0EfWug0QOcYC15R0b3Z63Wyz4Ajy3ISW/JoK7+YI0O0MXgqB5ocUBplvqFMB2otRKBOE9Plwvl0TymTSfmURpkNeX14OHF8/pycRyudlMYwjCQXko4tojFaMcVXfJ2zG7oW7SMO7KMj4BGeyjWfYJUHubIFLe0k/HUrj/3nQz9d17W1BW9Hz9BFd7OPDNx9creXMZsuF1dUNvYqiflEzNEHmYTFT0RMSL1cTJGgzWsj17ZKoc1R2NJoc0PnagmoVqq2BfmUaI1YygZZifYzxEges5VWG5TZOXK9w7lzwV3/ttbG5PzB1poNb/B9XqaSSVqRGglIzCaRtVkbqgnPGnJ0aczniRYEuU3kHFERSkdjnVpUtevw9gqPXVtiDN4Q5ovdvYq528dYCoG3ntySUmLMtpDTVrm/P4HauX5zsNHNlrz6WNdNIruU4VWX5qre1BViRH3JCtZYNgwjw2A0gFqr+3AzGb3zyTScpwvUGTQRBqtOhBhJh5GQDoyqDMcDpTVSjoxjJkShlMLlcnG5MFPxEBEu5wun1qiFRfNWetIagg0GWpBaHz9vd5dIuqKybOTu4opKS1/oAn2xq2siq0vT2B41Xxd7pUT2ak55h/sN219K+0v5w5FR63wcjDx+vOXJ0zeYLxPzPFGLy308nDmfzhZAApRi4yfP5xP54TnzPDEeRi+fJMRHwoYQGQQkrod1xX1V++/xgIWNSyzH9cG09AXp6cKxZSnDemF0Lf5syiAfMJErqgHLN4dvpUfhlwJ3d9sNZaMxuWlaXILA8vsqqL7warySobiKQajOGw0uQ+v3SRyOR0v6auM0nbnME6rKpRTq1IgxoBxAsiEsEohB0Ca0ooj2BNGqKCrBhNQVnzjWFp69TdHSZT8FPDDaATSt63SgmqitgNi1IebkwS2irSGhMqSBMU00EYpCLQWiIJoQR3Z7E3mtNsTlcj5zOZ25nM+cTyeGMaM6Lkjvbrt9lIkIB2+g7Is0fEw02oxWsxJ+HpXYey9/V8kxXqoWowQo6hQ8l5UTm5S5UAqwhqmmjRQjY87IMKCtMmulihKj9aDY4IXgU8SsbJ8IS9V1KRqKTc40BDQsdIIYTFNWpTnYZLE+VSXEQlD1Rs2A4tcA7UCQO12nFbkySU6ZYUieDzSktMU360Ir0oVq5OyC3V4je4VEVpdAuTpbWyCQbRJn3FmX/8gHUjYJn5SOPLl7k1YLb739Ft/+bd/KPF/4yo9+hXe+8hVKKZxOM6fzPeJdjM/v78k5cz6fePrsfYZhZH77bZ48fWKBS+5I7lQdoV14RpvEtSery08/Jlj5Nj2DFLFV36N+sBXtQVBdkdktRHOlbKDrp7wose0Xsm3T2HZyym67fZxpU6Zp1ZBt1ZDS5qhirdbd3EJAmsncSHPEJNvlQBWfvlet6URcVCtGwjgQVXh6e8fTEKml8s67X+Pd995jmifef/Z1vv7sGSkl3nrjKU/aDSkEDmNAckAazK1QxJAc4/FFD1gDMUZarS7RZ+M20yX6fqs3vuAyWRa6yjxzmc5eclXSEIktEceB4ebWKjM2j9aOHUusi1buW+F0OtNSIOQAMlgS680z86Xw/P1nvP/Vd7mczrz71Xe4f/6M27tb7t44kvLh8/xz7/YFsRSELx1H5lK4TLOfz9b8pKrMh+bDBUyVwPjawRdrfXFZ7b5i/SLN9I9DioQhG60oJWKyZqthtOZpBJaVWYiMtw0dMrUULg+BebogIZKGkZAykpyql02xIxDX+CNWoYwCMmRDP0WWSZytJFKOiCjj4cBd88WoBC5zBSmkXEg5GzWvrbQA9WuTxW5biOYYubm54cnNgdaUaSrMpVJq4/7hzFzmhfvrl4W9QPIa2mek5r0yujdp7PrUwqHps6KtSSvHDIejD0eIHA6J+XJhnmce7p8zTROXy8xcJuetCpepkLOR2AHG0Qjo4zhYo8qxrsl13wuxxLGX8NeRneaU14oHj+97arrxjquEduHffvA7eTyIoTeR9KxY/ee6gly/x05iX3ftMQVit91ebLVWk65ZUFl7XEWtSWP51887g2V7MFLwBkyQGKwLWNTRWZvaczzc8MbhSJlnnl/OxIcHqI3zXHj//oGcMsfDkXFotCgMGdTnvWonuUVjBQQxNYLDYSTFRKuVeQ60WgnBRseW4FJ06DKPvZcjlUqpE01hrgOlFns+CHEYTG+2NAiKtspYJloqlFa4tIKUGVFXMeiITrNFQa2V6TJxfjhxPp04Pzxwfngg5YjSkLXfc7fdPtREhEOKSGtMtdJmk5yrvjBrPlygc72XSCrWDLU0GHscaLUxlwIiDMlHvQcbBpKduhBiWhofJeiyH3EYkBioZUbKTNAGnR7QG8eiOGfVlBI6H9VUTgAx/urjoUOh04pCIOXEMIzEWMmXyTiyrfkAFuO8irYlnvbPsLAoC2Uh58w4jq5iEJBQYbZjb5vrW99GT7r3aPn62GfT7LUkbb3859iiVURsXOSS0K6JoDinTYPNlD4ebkkxc3f3lKdvvMnlPHE+V0I4L06k8wTaOJ/uSUGoZeT99zLaZoZhQNvMdLklpsh4OJJT9s/sItT98zuc6myabYlfO0a7RVnXhLarC6ziXS9ymoV08egR5wd1rpA/IfIoZ+0ftuyrXG1rt91eZJ0Hx6aEh+BDAHBZKYxrGiAka/gyLMMbJn2RuBQlnIdWfDpXI1BqnywEYx54cntHDIkhDwQfGm0BWp3XGgwdYq05LKLvTikoc7ESa+u8WXOMYRxILRlHsLR1AehJZEyFEDJNlWHIVr9ojTJNnB8ejJcbEilYaTcNI0dR5lo4tZk4na28WW37TrpbGi1Lrcy1UFtbmtV6cD0ej9/gv/BuX0QTsGEDIuQYEI2UqmizxEvosnTNObI+JGEDvGxvtVnjFwKhJoI2AtET37g2V/XFaa0L790GPHdNVh/DjPkgfRSzWveHYTk+VaQ149VuwZ6eyIZ1dHvypi5tUFOfSCK0Vn0ARHVaQDPfKp2/a5z7Tu+JIRBjWK5j1ogqNlUwBOLpbIAQukiUBacoOYn9G/TX3e3ztldIZGUpfxj6Gf0hnw6kdhMvjWvPamXt75fOudFAun3C7c3R+GpEcjpwPp+YZ+XZs3vXo50p5WQcnHrh8vAeMSXun32V8XBgGAa+9KW3ub27YxwPvPmlt7hxQejj8UhKeVOycb7ekkE6OquyBG6cdXOVgG6S4OvkdfvbBx1INz+vFApYE/4Fmb3yQblKwnfb7eOsy2ilmJBs3fVNDIMNKaAJNCmSA2kYiC6ZE5Pz61wHp0tQhdZowHme0POFLMLTlJhTJiA8vXvC7eGW5w8PvPPOu/xo+CpRItpgqg0JEQmZlA7Glw29KdOav4KYOsfDw3mpnHSJsJQjd8ejITiKlyFxzp45yTQXzpNTEabCNM1omTg9f8blciGExJOnb/LkyZuEELgZM1GeMM9W7bk8f27KC5dKCzagxeK5UItymWYezhdaKYSUORxuuLm944033+RLb731uf2dd/vimAAjGNKZMyVEpmoqq7UpgUAtNj66zIVS6lLB6yhlcapQbcbbPp3PgKDJKT8iEAIpDxvaTlq45qVUj8sCIUHAxsFL9ioM0BpBjLcbaKtyj9PzqHUp46PqQ0sMgRUREsLNeLCkMkRUhVBMaWSaZy7TxDTPzMXG158vE6fLhGI6tgGFZA2pfYJezsl7YQLDaMn66TLx7HRBzhOtNqZm31kKXc3hc/tT7/Y52CuOqJVl5XMFInopMjg663naVRIoS1ndktkQBmIYaK1xd3tmvkzkPDAeDl42MX5bmX0CkTS0TsQYqeVCfsgM40gQKGXieLxhPIzEGMg6MA6Dqxusq8cF8eylV11H4l2jrLI4xoqz9iPeUhE2Ka68hCfJVb764rf173dPZHd7CVN1Lp0aChpiX4zVtXIS1KAT7wi2pslI6s0o6kodnXvgqGxtlVKLdfg7x09CYMgDcbBBC0MaiI8RWe0lUgtQMXbFhA7WCDQL4l2JIKWOJgfyMJCSSW31C4k1tJiz5FJI2RLZBx6M36uNOk+0uRJi4nhzZ8E2RmseiUKMEzlmG9TQlODDHvpK0gAqpfTjVptwlHK2a9N44HDYObK7fbwJEFCSCC2a5qsqJG9UBh8OUNvSrNkVM67GSfvvW0S2VhtPa+FVnBoQ16Yqp/JoU9pVBHM01mOLubyiUTu3xnbNNWM7B2LZnm9pQWjFkN4+qKTURkwmnddVRgyJrY48m1/NZbbjCpACqOuzr6hyXKS8TLovUZWFOoEPDKrNhvO2TQzf7fWwV5ffYk1oBUtQRRZVxyVPVOnKqM5pEVkGAXi+uzhsTInxcEAVjocjx+ORGCPzNFmARWhaUTUkpxRQrahWnj17j1JmTqcHFOX+/jnDMHB6xSug3gABAABJREFUcs842pSTYTi4XEhEQqKLN3elhQ0z1hID2Hjt1YHbnRe1SPYh85t89wOv6ujrsklHgrdo7fVLdtvtY637pMlECQTIIYNAHJJpyMraDGlVQ5ff8gQzuAC5lkKjj0uuNFEqcJom3tN7UojcHm44DAmJiSdPn/Jt3/7tAAxDMp5cSp7YGs+odzzTk1hYhqhU2ehSYsF39uaYlfXu++ivqa0u8kISAsOY7XiaUJtRELRV5ulMDZEyKWeg1pnWlHE4EFAuKtSp0ERowcZtizaGIXO4PdBKZVK1kbrAPBemy/wN/uvu9kW1DugkEeeIBqoGSoNDHhiyIZDR9ZH7eW4/lRAjURVpgSAXX2fqUqK3MrsQky1I53lmulxotXE5nZjOF6IIbRg4JKPqNAKIobZVraEyzAU9nQjFeLyzg7IAizqPf26nyFUJyzqzNz7fny88ezgxz4Xn9/c8nE5cponz+cL5cqG2xjTPlpBjqHTzhHerrrJGY7tm9QQ9pUTK2Sq+CMUXwaUamrxLF7w+9sqTvfptSVxl/T0ugKdPzjEm0IZ8JwszW7VRfbLPkEeePHmTYTjzxptf4s033+R8PnM5n3j+zFRV28IjsmoHGNL68PCMkBIpDbzzzpcZhgPjOPLGm29Z88k48vSNNxnHkZwGDsdbUspGKk+Do7VGm1BcfHlZCa++IUEWasTqLy9QrrtiHLwo4e2cAthOUloQbta18267fZwtvhgESVYyjDkyjAlxh2zBzrkUDNWMQSjTzOVsElppGEjjYIGxVFCf/INJQ6oq7z7cc5nvGWLi29+K5OGGOBz4Cd/5ndw8fUothfPpxDRdbLRsCGg1Tm2UYWnW7Iu2IA2GQFqGj9it1sb9/QOoIzTOc7Wk2C8efceAmAI3+YgC01SZL7bvrVw43T8DTOWglAJYj/jt7RvMWrm/3FMezlSBluP/n71/D7Zt2876sF/rjzHmnGvtvc8590pXryAhwsNxIDG4UqZKYPmRlOOAXS5kbMoOFhSpECdQpmwcEhxHlZBEiY0LAyEYUUY8HeyQEGzwC2wVGCMeAoKEwNiFBUYgXd17z9l7rzXnHKM/Wv5ovY8x1j6vffa59x7uOaPtmnvNNR9jjjHX6KN//Wtf+xp4h6Nyuj1Q6hPSNfG82gReFS7nibun5y/9H3WPj0U4tcWXD8ZYxuCI0VMRxtPI6XAkRN/qOtoMIKsfehRni71aub9e6V7HqRSkLa5wQhxHtFae3d1x9+w5pRQud2emy5UYAm88eUI93TRyJYC3sWnyg4ymTJkuqOsyuGaP5zzBDzhxlE0GBVZWNldrQVuq8vxy5a27M3POfPZzX+DNt95imhP315n7iwHZVCopNx2tOlCPk01DIu063fYdiif4QIiV4XBgTBm9TtT7M6kx2F7y3tb9ExZfFGnBQk7qBtzKw58qW3fVDT3ZJyU6I2u6uWEYUFXGcWgVi9YpxBhZbTIA28R2BTenZMAvBLPuCZFDY3en47FpZT21ZOpYTHuDIkQIveVtKwpjxdw9xbpoJLTJCxY5AdujWx9ArdiNNUW7foHb19kdY6cfcrDy4DV77PHe0Vkc5200Bu+JQ8QFb56r2vqiy9oWUmslp2QpROeQIdqysxRqMdYxeEfzA2LKGb0UUojMuVjrWO9Mkx4jOSWeCU1rZ9cK2zYsdn3b09w5vGK95LVS2zgvJVNKXjR33rOwu9oqqMU7XHCreXo0oFCLUl2zxquVnGa7Dkwz0zzjRDgdmuyoZFAxy7I2p3Y9f4ie8TAamA6W4lSsK1lK+cvxJ93jKzz6AhO6E0CjJ1xAxTGE2HxcQwOuncLYOAN4b+OvFqsTWbL9lqZ31Zhc58xlIOfC9Xolp8z5bEB2iJH5eEMemxZdO2mjq7SBSiJT1JoS4azhiPpoFnWuWeS1zl7bIrRUK3POFFUulwvnswHZy+XCdZqYU2ZuOlnLmliTBwGqA3WCqltB7Ivfo/TGJCY78CHgfDZOTJVCkxi8UwZ0j49tfEhpgV3ol8Vje8xY126E0RMqnXnc0Jpv35xtp1mOeG+VziHYzTVB+RKLno+lknmBirVS8gyqTCh3z99ini5cLyM5zUvXk5ubR8SmeTscjtaT3UdCY2dVhdpkAuI8TowRkhiR1s1ks0OsxkZvH0jd8np59gVpgT0sLKOwf1/7kNzjJUOEpauWNp2q94EYB3wDst1CJzjzc9Zqvq09DVkF8GbnU0uilmRbG6I1NQAKSnI21u+mC8Ozp3hnVjmn0w05JdJkBVKtF62xoKrkPOBcWMdKG7q929i6YlRK9aTkWstLb53DDHmj1QOm+6uqUNQAaG0g2Zlez5Ie5lNp7icZrYUM3J0z58uFuRaenu94Pp2NGMoeorkziCghOrQGxtOIohxOB4bjyDCOH8nfeY+vvPDiHnAU5ldcKFRUAzYQLFXvfMB1XXjXsGpdCpKrdoeDSp0SExBTbR617blcyClRc8GLMITAECPDEJvDTyUXe35OibvzmevlbFmbAIj2Du2WqQkDhzHjfSDnwpSsYCt3dlaVVIo1RanK3fnCm3dnUso8f3bPdJmtYC1XXDsu73RZVPsG5IMP9ryaVNE7IQRr8OCCs6YswZtWfRhI2QB3bU4LudaeOvoI/sp7fBTxykDWVphWZdi7nS8AtoNYqWvqfGFh3wuYbSqWXaDWSowDwzBSSjHt0JJzf4Ce29vbPrQq6DwrRWbSJFzvnzcLIE8chuZPN3A6HQkxMA4Hbm4fE0JkHI8cT48IPiIu4L11JxmGkXE8mfhcBB8jvZVe359FTq9QaSC7/aub7+CdGiI86A+tFao0WcN7gP899tiEiBBjNKamnU9DHBgPdp5TK9Irj5s2VlHS9cr5+XNKLow5WwMF59CaqGVe2FtaWjQ7JbnKTObHnj/lfH9hHEa+4Wu/jjdee52cEpSMR8lp5v7ZM6brlRAjPo4goU3G1rkrhsDpZBpB1xwJRCCXwjTPlFqbHU+f5Pq1RpnSzCVNVK14F9DQCrZEzO9VzYO2pMk+MyVKzpRauLtcOE8TqRbenK88SzPiIBw8PhrwkACHU8RHoeoNw3HgdDxx8+iW083NR/r33uMrI5wI0YdFQaaY1VspyVjJ4oFqWtjgzP84Bqqa6w8KtWSUTEHJasVUqWSmXJjvr8Rh4I3XJ1JrHT1PM/NlwhoYmI3dMIwcjweOpyM5Z9KcjDG9Xvn8F77As6dPccETTwM+egOslys5F4Y4cjrdElsW5jIncq1c58Td5UoqxYBsMiB7Pl949vxMzoV5nrleZ7MU67ZavU6mZYXGMHAYIjF4gnNIrQgQgmc8DOZ+MkTwAScwHA4cVchVwXlKBRVFqGRd/WT3+PjHh5QW0OqZVpOqDRJ7qP/sj73UdrtcQR744b3YYGAbqltfVvscM3U3XV5tanURwV/N1D0MkTQf8cFzOBwppZgU4TijKk07GwmhpVhU8H6wVWQ1gfqLuJpe/NWI1c6urrKDF0CpLsTyA9L6AVu7Y9g9XjpaalFtHbk1Fg/egzhLJ6pSqK1osi7dtHIq+JwJuSBO0Zqt206TH/QTs2LrLNXKNc/WbrOd53EYcSJL8YpROtaoQZwzbXtnlIrd9765LCyFHL0dppC9LYr7cThxbZvNnSFLq+i2bYr6VdbkBGlAtr+mM1u1VqY0c3cxfd05zVxLQhwMzhPU5BI+BMQ7gnrCEFGEYYzEIRJi/Aj/1nt8JYVJ4/QBwLJ2zLoZW23+c86AW0O+pmhzy0xrC9XV5/haTLedGoik15CU5intpV0DHMF7fPBNptecOUphmmYu1ys+BoiCCqQ5cb1Oi8dzcJEarKnDdU7kUjlPM8/PZ1I2IHtN5kpwvr9wd3+m5ELJ1fS3Ci4I4k0ioCius9DOdLhePEtDCLXOYa75yTb/Pit4CyYtsO5l3WUEiuraDGaPT0R8OGmBNuPkrrODB0hMUMNxC3n6DsVQm8pMnFukB7VNrinNTNcL18uV69VuqFKLJ+emOmoXAUEa6DVtkXOO3iPFLzZWbYio2RRdpysuO2sdmLIJ2uMzxreeNtuvgHMREcc4njgebwkhcHN7y+PHj/HBt1RNtM4q3i1Vp71LCphcomuKe1Xn+g20r3M5il4cx7L/7w7h99hjDRHr7uOaJMYmjoCIgdhaEvl6bRICJbvWIrNkyyCIknNmulzM7saD96tnay3VJpbg8G6EoqTzzDxNzLXwhbfeYgyDAeVSGA8jzgnjcaTUTPARH6OlTj2Ewfa7Oyx0MJpzXX4PIVjjTnHLgrZk89vsOjvtRcpVFod5XS9IbWzZtaW7JtRqhSaXaSI3fZ00D8qc7RokriBaYbbxfDgeOd54DuOB06MbDntDhD1eMgQ7NUutVJTUUvq5VEKOlJJxxbXn20JRPLBxMUCXbGAcBhRhupx58/k9IUbefOuOt966a5l106yD4rHCRu9Da/08k+fEPE/M85Wck7HGIZoH+5M3OJyOpHnmLj4nzdYOVxRKyqSUuU4zqQHZu0siFWsdO7fF6VygVtO8OucYY9PwhmA6dhGmkqG1pQ0dyHZXlX7UXcvfOvW54QChMCQlqSNOM72pijZpwYJH9vhExIcAshuwSh9mS+PLtm40jaz0dLpsCdqH0MyJgPNWiZkt9Zdy4nq5cHd3x/l8z93dHXd3d83ZwBtrw2rF41paNXQw620EmO9s92Jdu3iVlJjT1NL+aztqBbS6pgIwAQUI42EFsq+9/hqf/tQbxBh59OiGx49vLAXSvCWdc8ThQIwmS3DBdIGG2k17a19jZ7LXz19yLe1bdcg+KPd4uRAhjGPTx9roDDHgvBmJ55S5f35PSYnioQRLx01pXoqcUpqZZzMxP92MDDcHA5qKaV6dYxisAUlNhad399zdPWXwgegD89mKSl5/csOjmxvSEM3/2VmL6jgc8GFoY8QcQ9ACJZmlDyDFriM+BMZhxPnuJmLjplRlytYlKGelFDteVwSKtMXz2szESCrbthPT3JVamHPi2flMAeo4QrT+8SlN1CkZIL5W1MHheORTX/spbh8/ZogDj28fcRh2jewe7x/dOqqoLpX9U0pcr5aS9wdPSjPisQJHNUrDGheM0OYvcQIuE4cD4+GIiuP+s2/xN3/kC3jvefLodW5Pjxli4CZ6Hj953SQ42aQ+zju0FqbrxdjWy5nL5UyaE955DsOB25vHfO1nvp7Hr73GfL3y7PgFI5OuE289fcY8TUwpc3+ZmHLh+TTz5v2FORcKQmnzWElKrpbJHLxnbO2tQwz4waNAmCdknkCE6EK7CUHEwHcHsWKL3fF4IJweEVJhqg51kWnOOB+aRVjTyL6LfG+Pj2d8SPutd+JXH/5cNbIs6fIHagPpT7f/25O1Wts5m6h6txPr3qFqznGqrlUw2s00dJW6VIQ2BobGiC573aoktVKqMVGlmI1JVaUUJee1B3Yv9hrHK9dpNqcDp8RobKzzhTgoMXhEKlZc6loq1JlfbS3g/PodLEUtL0gRYLEq20hvl1TLHnu8V1ihSNOFtsYEi6Uc3Xc5k+dEDUIRWsVvXZehtVJ7Z6HadeB2vlbtrKa5i3ST9bk5C1yuV+7cmeM48OSRdeTy1VKANm6saMNM291SyFmLUGpuiiRd0p5eWVKOPX1oaz0Dp9bOsz24LA5pacvlVxbXkDa5dSeF2jw4rc87bbHbxn5p2natqFTqaOzwcBgZ4kAYoqVh99jj/WK55i9JR3MJaC1brfq/SV4WOqi90bUW0s5DdUumzxoFeOuQdZ1xznO5TFwuE1orx3AwWczGVUBE2hxX1ltu8iHMDSCEwGE4cDre4HGkwwUH1NzcAKplS3u3sJytzay5l5gXrHZips1c3TbLiSzuDIoy9wLPzTxuWdMVFSwyoXZt8yFQMb9cH0pbpG+/205N7fFJiQ/tWrBoYcFYRjV5QK+YXl/VpxVhaZKuq95HmtVNrZXL+cz93TOulyvn+zvSfCWnGYcyBE+tNgFNV2ONQgh4b7pAX7RpTk30rU4XOxPfbHM6JrQVrrdCNbE2e6rN05LSdLWVmq2AK6Ureq7tAlKBRAiB8/2RZ0+PtmIcD41B8hyPtxwOJ7z3DOOJYRgR8YQ4mCtCS+X45o3Z079t5C9tfaX1w95jj/cNobkTYC2Y1Qqm6v09ANP9PdP1SkkJ8BA3+jLp9YWmXZUGFqV12JpToU5zS+1XcipmqRUcxye3SIW7dGV+c+YwDBCUVBJdOXQ4nQCHuACt8NJuztxAsErqWm1ytcnXxroBcnMOWRamzttw8YrzAdXmbNBkPI2XWS5TVeviL1mbxjAOA6ebW6pAGUZqHFAtWOGN7bcbPOKFw+mIjwN4TxWYNdvx77HHy4TYeeudR0UYhoHT0djZw3ggOG/tomma72LacCuI6kW/dfFHlXaCplQ4X6xBwt/60c/hnOMwDHzNp1/j0689sm56reWsiBBTxXtbzN6fJ66XRE2ZPBc0KXUq5MtEur+gKTGIx8eREhOHEKkhM6UCuaDJfGdJBSnFfKm7ZrX5z6IwNI2+Q/DBWWMG2oJymWcTCYHgqMWj1S/gu8uKvPPEJt0bx4GKcLweGI8jw2FocsRE2XHsJyo+JJCt9Cp9WwlJM4OT1g2r2U3JSpb0FAnSTc03aBfT4N09f86P/eiPcr1eePbWm8yXe/I84agcBrP+uL+YCN20q2rpCgfe2fTlxFr6OWkdjhbGt7FLbYXnCagYeevbcRRfEDGfuxmTOBiTNXM+34HAdH3G3fPPm+dt9AyDbynL2Cy8PLe3T7i5fUTwgZubxxyPN3gfuLm55XA82ePHW8bxaCnXOOJ9tJ2RTlX3+zvzs8f7h7QixlLN5xSFNE1M9/fUnMnTlel8Ty3FGhOMfmEvO5DNWkk5LeyNOJMlXKcrl3MDxOPEYbi2nugDt1/1GmXOvPk3P8fzzz3lECNzmbg733MYBr7q9dd5/Oh2YTprtWrkGP3SD957hzbGuNbJPGxVqLmCUwO9bbHnGiAWLzgFgqHV4J1VPHfmidXnMhe7X9QM24tWhuOBxwhFhOQ82fuluC3NxhqPpyNxHIiHSDwcDcg64Vpn5t1Gdo+XDTFv54BV64vzuBCpqgzHgRgiwVmhk7YWtVIX8XdjbLU5jWDEB8KcMs/vzo0d/W/47Oc+z2Ec+OZv/Hruv/arCcFzGCJjsLEeSXiEkhLXpxfm89Ws6aaMpkK5JOZnFyaJOIGDC7gxQCrcDyMUI5HItrDVKcE8Q6mE0RGCN615BJo9XQBikwF2uYCr4FRx1WzxCgm7cHnyMaAalyyJb7ZcsVmIOV+5uQE/DMzzxOnmyPHmQE7mRlKaxeAen4z4UBrZhWXVjR6lFZgsaoKNTkVboZWyrQxjBbNNKJpzbiL0iZKTVXSq+cnF1hGo93Lv6c5arTVuTy1UQCqYL9/D/ZCmm1WRZoViW1wBOTiXUV37wXfNTa02c6UkOGetPEtypLkZNTevS+9908EaY9ylDeaCYC4MNUTGaB3GUFBvDLItANbk0l7qtcfLRi9yrDYDtHNW7QI/z61AqlC14B6MzfXn1t9VN+O0p+FV1QozsC5F8TBaNb8qWSuX2Sx/ztPE5XpdCi+8DwurZJmG3h2vLTJdy0A4x2bgsfSZbxmTrdymG8bLkr3oj7bXqKysLGuKtf9zzvwoBajOUdt2XJM+dA/eOAz2ui5xECiN4d1jj5cJOzfVtOIqNn5MBLe4cSznbp9TNxY2SnfcWGUy2uQ1pXXIulyulFKZDwPP78/cXyZi8Mu86BBK+9yaMnMyaYCWipRqbgelUlKmzAmcIzSSxrFJ/UvfRwPYy8QJJmhaiBhpj5l0bpEJ0OR06CJ96NuzAjFFXxxay2cbQ+u9J6hloHyw7E5tbWpFt/PnHh/3+FA0n6XqyrpyrNpnwkVO0M73dkp18NvKijcqNq3FTMpzMiPnbKn9YTjw+PETSs4cD0feeOPTpJT5wltPefb8jlIr0zxZh51qlaBNeUovRBtiZDx4fBwYxpHHjx8xDIOl79ukmXNmmmZKLZzPtrotJbXGCSOrmHcV/Eo/orqazC/6PrV2gofDkRgjNze33N48MpnBEJuzgjkn5GT6JmkdVnAOcRXBUWtqK8w9V7LHS4RYdb9ooczWx3w6n7l/9ow8zYRgfpLOCX6MpqFzQhXHjI1V7xwa46Kvra3RgHdrx71SK/eXs2lGbw646PFEjo9veDQngjiyKM8uZwrKeZ44pXkp8BqaF7OZevSsTuvs5zzDeLDJrRZySaAVKYpv2l0b7wmnukgB0ErJhpMFWncuWcbkOlnqsji2HvbtGlUzuc2ewxgZxkiIgZvXHnM4HcALGgXFJAW6qBn32OO9Q0RaUaOdl1WBUpDeGauBR9VKSJlSKuQCZJAZxFHmiTJdKcU6dd3fnzlfruQ5LVrSXCo6z1RVPvu5N6lVCcFxGEfGIeJEOPhg6flS4TpDytbnfZqRZFrXGCPn8xkfHOMY8cFxvlx4ej5znScuKVFoi0tpuVmt5JKp84S8UJzpgdgWoYP3DN6hogTvGdsi2KRCXWrUMirNwSSlAi5Tc7H9RnECwQshOIYhMI4RQbk6YU+UfLLilYGstbQzdqfUaieXgtSuI7BJwl77ALLSgWwffKCtqGumpERKNgHXohwPR/zrn4LGnnjnmFPiR3/sc3z+zadcpys/+tnPMr31FlrVKidrn7CsMOwEPHKeMBy4uX3E13zd1/Po0SNjg1q65XK98uzZM+Y58eabX+CudSQZx5Gb042t/qJ1RAGYpiuXy4VabBV8vV5Mf+cUkQI4fBi4Od0wDAOPHz3i8eMni64JLL1SSjI2t9n+VM2I9zhfELFWurm0C9see7xPmI1NIGkmXyfmOXF5fsfzL7zJfL3y6Mktj568wTgOxgy1BgfFzVxbSkKcxw3RGBDnzP+1WnHJ8XCwxd7dHefz2YBefYyLgegdp9cfIyFALqT7K2/dPyfVzBvXJ5zmI2McON2cOB4OaG2Td+2FlR3IOg7DiHOO6Xrl/m4m52wsTmdaVBGteGycV7VCzVoKqS0qXXDNL7P5Pncw2xfdtI5CwVK5pdi1x3vP8eaGcTwQx4Enn36N06MbUs3cpzvmMlGxCbbuRSV7vEy0Wo7OTHbPZdcIGy3VAGkRwpypKTdLqYwyW43G9cJ0uaPkzPO75zx79pzLdWKaZ/sIIKfMnJRpSvyNkvncF9608TQODM0h5GY8MMaIRzgiRLAmKdcJSZnxciHNM8dxMNeQG+sKOOfM8+lCKoVLSmQH6h3VCYVKpkCZ0akhVidL4XLA1D9OBIaB4AdEIATHcbTFMc5kQ96ZO0P3mU45M88ZxFNzQUtZJAoaHEP0jGPkcBxBleCE9FH9nff4SOJDMbJLiq5XRLZ5Rjol2n8+eFP7KfrCw+t2oNlpOdcMx63YI8ZoDNCcuDtfuc4Z5xzDYAMOultA09/2JgiuGScH8947HE4cjzetR7tvml3HPCVEPMMwLvKAGCLDMNrnDpFhPCwVlKurggnMxRzoF4lB8MHa6LVbCFYBvq0g7RIJ2R5/T9fg1u9lZ372+CChLNXFtVUmWwFVbyzgFokNWDrQ6vVbCtStbEpVOxPtcQO+iCytYZcaa2lSg3Ggukw5T1aVXVuRVTXg183Puy53Xe7azz5+3OLDLGs1cm+CAEgHkT2tqV3+00BqS1Fu48XL0ZrmbAvsNu7s2hPsNkTCEKlFkYyxu7aUXyy99tjjvUIw6cySXm/nedezdfkMQmuS0G7OtLKAZeaa04DpYc3JR2tdKv5NQWPbS8kKJk1qZK48vjnnlFIJbZypOKQUJFvBlkvCNM8mhasFDYovnrkU5pRJ1RaLVXWpf1mcFtS69VkmpDsutGNWA7aljR+7ZMjiWqAiLRu5ioP6AreUfh2pUKvJFN3q7GPdAN0D7/hdkPfJiQ9fQaTLfwtuXRjYxR1gvd/O6BWWNf2pc2bL4UR4/OQ1nDiTLZRMLTYwQqtyzjkzPnrCa8+ec52u3Dx5jTfffBPQVrHctt18ecbxwBuvv8bxeOT29pav+szXcntzuwGyws00cbx5QkqJ0+0TwnBkmqbW0u+4SALGcUTEWaOGaabWwuVyMXZ2k/73PvBVX/XVfOpTnyKGyO3tIzOnlnV4CQ4voRWwmMn80r2kXQD2EbnHBwlVtdaU00yaZ9I8UUtu+nJHcNLaSTc20lkePkQYRocvlmmcUgUVS+2VgveOOASGMVqaPziGw4g4E/A8f9ZaQCOcjgc0FrRADQPHYcBHR6VStJKrZRlQG/deTL5ArTZkO8vaFqIuBII0f81sVdtaC5oTi2h20c6xmKI4Z78DBMtDUlVxFXItCErIhVBNmjA4ITd7n3EcGY5Ha9MphUu5kEtirjNJk4Fw3ZeXe7xsNBs8VShtpmygz+Y3RQqIOms4MCVjbVuBogJ5nklzIufEPE1crxfmaSZ64bWbI6VWxFt6XmHNlmKSvzJn1FUuQE4m/1EfGJ3DqRK1EgSyg+yE7IWklfvrBRW4psSz69U6eNXKtVjR5DknJpQkHci2Nu0iaLF20XNVnJohYG2ZEe/M3906DnZAu9px1WqA+3KZeP78jjFljjcXxvGAOqEGcxqSWohOGGOgBod3snv8fMLiQwPZFyXV20KvrQ52wW+ib38McF5wYlqZ119/gydPnmxJmnaiG9ArpfD6Vz/nfLlwnSY+/Zmv4a233lo2v8XMADFGbm9uGGLkeDzxxqc+xfF4NC++4BEnpJS4TpNt+9lzHj35FPM8E7wnxmGpzj6Mo/Wg7wNWtXUcuxgD1kX1znF784ibm1u8c4zj0WQJjY3qEgPRzeqTlv50QnUPTd33CXOPl4laK9PUOm1NE2maqDkRPLjo8F4QKtImFe9N+z1E4TB6ayxAYUq1zbmNfRE4DAOnRzeICMPpwG1K1Fq4ThNvvfWU4D2v3T7h5nREiiISYUzEEPAxULRQGohNebb+785SiUWqgdlWoW2sTgVRQhwQH6hpIhfT5tWSyZP5ZYYYGeJoQBazqjM7P5q8ScF7fMuGzKpILTiFSCXmhKCMXijN83Y8jIw3J/CQpZBLJpfEVK+kOptCXndbvD1eMgTEO6Tq4kRgEvBiTUbwzV5OyXO2cVsLGqNlAATm+co8T+ScuV4vXM9n5pQZnPDG4xOKMBwODOOBWpW7+zOXy4VSK9dpZk4ZBMseOgjOUeLAwXuCCKdmC+ackL0jeWEumefnC3PJnOfEm/dnppSttqS9fs6ZK0oRXXStqwKoVZKUapIAbBFZaiE4x2kcieO4MLPLYhRprXMr57PNrYfjzM3tYw7jwTS4g4fgoWSidxyGQJ2DMc0f7V97jy9zvDqQbYCt69WWVAG9eLGnG9fX9ye3+HTd2Lrppa/yAvEM9BnD4nC+MOYDxQR1HE83NkjRF4CsvT+EtehqGA+EMOC9tZR1bTXoA4SiOFcZxsThcMK50LSxASfOJAJNu9dTliYRcE3PtKY2nXPLZ1kFdABpxs3Nfmz7z6QExm71TKnpqJoTww5l93jJqC0F1/uNS2M9Kta0o2c2vLcFpBU8mU0dAi5VxFnbV7bSFmHxbg0aEKBWB5NZZZXGMvV0n/cOiWHptNezC73wql8Slkrmlubv1dnWAY+FbV1Mz6H5wLJUcANLVfiiv2+OJajgRJscoqd5bZA56TdLhISWnnTeZA3qlNqsj4qWvnfLd72Pyz0+aCzznz6c9xbGohV/VVdRX9Da6yda6/ZeINbm3+CNjQRhaDK2Witz8CRvvsu97Wv/EGmf0+dubYVZ3TGku/kUVeZSuObMlDPXZD9xrnXOFXKtFFXrjqctU7FCg8Y8NzsxsM5mreajqIFfx1aJ2B0abOFZSl2aIXWJlKjDhaZrUBu/3smShdkTmZ+s+FDFXjlnSBmXEp7Z2MXaVlNUqtTGyWpjF3VJyS1b6aC3n/TtV/upW+VC0/RZj/TrPDHnTK6VOIzcPHpkr1n9tJpqwRohhDjgvKeq53zJzOlq9lvYWb+1Fppnwfmbpoc1za35Ujrm1AB2kzCogsqRMMQF3GvT/iiBabbtT3NuGlra+5s2UR2u7YNISwM50CCoF2oupGumzLsv3h7vH6pKTiZ5MbmO43hz4vjkBi/C7eMbPvXp14ljpGgi65XaLLPCQci1QIQkmVIVJ5WiCWolV0spOhzeBYYYqCUT3GRMaFHOd2fKlAjO81o8cBwPRO85DiPHGM0RIWfmeiF6Tx0Gq+YuFXpBVq1kKqqttWxrYtAnflGbmAkB1BNCJAZrKqIVas0oLHZBC4CmIKJUKWRaO1xXCE3axDgyDBFiwAVHcZlKJeWZXJJpYgHp1ee9o9gee7xEGCgrJg8opl/33nxjXVtcinNIhTwnczWgWlWTiMlpmpJmHAYe3dxQSuF0KLxWSiM/jMmsohA8YRjQqjwKA+XYF6NYozARRmfNCYJznEIket9cTIRrLpznxFvnK+d55pISb14mA7LiMDeF5i7QwSvdK6jNxXaPopCbdl1TImvFiyNVsw3zzrIz0dl8LYwE75rW11xLVOHu2XNC068f9IbIgKuFQwjcHA7onIkt+7JD2U9OvDqQ1QZkXcLNiaIzTgWnRuo7KoXCAl5bYUaflFbQV5dCjhXYspC0+mInk5YeNFNzW5nG8WAdd4CNMSyoGEO7YT+rCveXBGTz16wdWHdBL6b1C7cEugTYjqFUJZeuL+zCchAXCXHTAreh75wLaW4m7CW1lpf9WA3we/Fm7i601rZq1NDgwIuZ2F8LNe2GInu8f9iFfzZdbEtnPro58tWvP+E4Djx6fMOnP/06cYhc5nueX98il8SYPG5Ss8/xhakKOevSwlKrkmphrhWPVWCPLlBcJsgZp9bM4Dydua+VQxx4/KmRw+2BwXkOw8gxBlDMv1IThEBtCzop1SyAakUrpGoaOat6du3Y2HhROlyIAMQQWiGl9XevjQUS7/BdMIsun1Ulk2S2hbYUgiiII4YB9SdqcFwHx+wKRQvXfCWpdTSTIEhrzbukTvbY432iEzilFOZ5JucCKgTnwQXEmS+qESZCmmZwLVsS2lxQC523HIbIo9sbK94EnBjreb63FrUF8NEzVpsXnQuLM45zIL6zMNY5zDvPYRiIIZBr4TLNzDlzPyfeur/wfLpySYWn14kp1+WoLGHSijLFrP9ca9SwFKAppArzpgjtmjNOZCkg8yKcgjOZg3fE4IFIVZhbe/pSKs+fPkNqNQvBICaZKoVD8Dw6jNRpJgaPd3tT909SfCggm1KiSoJpwtW2stQFMuLUCipq5zJa2qHWls6om7SlruzsknHR1gO+Vwa39KJp91oPdLB+7w2MWsqkncK6ShN6LaM23Y4BU8hFF/P4hSV1DlMcrA4D0O2BDHg7pwY6EZNCaK9CtcdUoRQl5w5kzUpkAe0tZRqkV4TTJAkYkBVF1BjZnIv55+2xx0tEr+L1rXhyiJHDYeQwjhzGgxUyDZFKYioR55VEIFYPTgnRE6Jbcv61sUBWpNn8U7W7bNiCzGHFXBVW9nTZoS4baPkZ1WaBtRqpP5QpNWmN0agtibFV3hvb0husdBcEJ4K6wjtNYX0B2ZbLD/5twWgncpbjbPXYWqsBC7o8qK+XdyC7x0vEkl7XZitVzO9ZQjt/TTZnxZPtXO162mp2jku71lYYTbSaEkfFSaVWx+zn7nWDF4iuyfK8a17lTU7kbJ+0CnQnk3azYq01C1L7cG33F6lbO/XVqTVX0SZJaJcOG0HtNfQxTWtW0KQLPRPqhFIFFUXdw+uH1kpBKc6RciLNM84ZyaNt8WsdPD3B2c17vxOyn6B4ZSCb5pkf/uG/Af6IhrdQN+JxBDFAazk+cyfvjKwqq8YHY1u1VzguMgJ9MBH2CkdYGVmwNEWtKzCsfcJrfiDGwhp8dc7YFt+aDjgfmkShZTRbSmQhZdvE2Idjj0UGQVuFLsC3pYRYuwzZYrc20L4F2103ZNtszTbbSrl57zpBomCl2pk631uF9h4ffXzN18CP/ug7P/eZz8CP/MiXd39eCOeEw80BhxCabOXJ7YnPfPo1juPA6XTk8eNbQvTEgyOMkGtimO6Qq5JKtokuWKHFfBXmSVAcwwhOEqgwXWdSscm2lsIQBkDRwVpSDiFQvXApiUrltsYFtHanK6pCqaiUjVVYpSKo+lUz1+x4nEhLGbKMKUUJPnA6HPHOMTlnCru6LiBVlblkUkkUColKxnanTEqeFKWQypXsKzV60lgoOpg/bclQrUGJ5U88Kor4ir5gI7jHHu8UVa0oeJ4T1+uVnDIxDoxjMOlbCMRhQJxjqoVUki2hpokqBZy1Pz8cDqZ5PRxwj20xWOYLZT5bU5/nBU0XpMJBAochtLHjFzmc91bZbwu0gtbWSa9UpCYDzrlaty+FIQ4cVFCfmavH+7LUgjwgnsBQbG3jExYSKgOldc2rbYGsArMCORPEMXjfHHtai1tnHtApF0pOpJQJzpGnifEw4gczDCyqVqw2jpRj5vHjR2hVwg//2Jf7z7zHRxSvDGTnlPjhH/5vyDow6w0Fs5GKLuJoep6SFmBaFmC6WoJYlX+7vwGJi1/csiq0Eg3npDkGWAeTUrZMKRgjaytQA6OhtbILHA9HYhisa9BwwPvQVpqb1rTL0a3sLHTsLO/ws3+Wo/e9NkcCv75ms83tElEX4L0+KliV9gJkvUBNSL6HugPZvy3i3UDs+z33ZQpxjvE0MvqB2+FI9IHHN8cFyA5j5Hg6WMeeHBgPjlIz8epgyMxlxg+CO9gYu54r17MVfpn2zcb0PCXyJYMKUQJDiLZIjB7XmODqHJc8oxqs97laO8ruBi/NCF7FugNqKW1h69rNFq5L605piz6kafJskgzeG5Btn6u1+zsnUrFtzzkzldn8NKk0ty/yrOSpUlHO5crkZ7R49LYzzorWbJN8yzh58cZIOUFl95Hd4/1Da+U6GZCdrldytszB8XBoqfTA2AqJS5rQbK4FGUjVZAY3t484jOZpfvCeY/CglevzN7neZeYZnkmBdIUKYzwx+KHlSxyiRr7EZlGFKkXsHC6qzLWSOwtcynLOxzAwigdXmKvDlYJWk0mgNl+X0r1hjUhapBSNyCkOk/ZIq6psqf+5WsYxOMeNxuYj65r+waFUUspM18n2uRSmc+BwHDkchiaf8oTxSBwDeqw8efQI0dZJbY9PRHwI14Kmn9NCqskmFqmIM52MloKWvEwGpa/cWvUvbSJaDMU7IwtL29slxdJQX61mT2JMi93W19BAYXc7ENSBNo1cyQUh4735UdLSHAZkZU07Kg9B6IP7LwDcNtGKWAdrs9byiJRFl/sA+CIPE5EdPTcQbzXVNkHaAYPUjJQZqfMr/6k+aPxtTjru8T6x9Dnf3LRJcapasryn95zz0FjNGAKIEkNqxVOFEoUcTbe6ZD+aTrxqMZNzHEjX31nxlWvyBgRw3bm2MbKsi7iqlVLFWjUXW+RmlIKjoPhNk/ZG5CwOBl1m1C0P5IUbNNa2uyTU1XFAGiSmtgIxKjnbxGqenmZRturZXwjdLsT32OO9o2cQ+gSwnMsspy9Ltm+dFOycLa2Cv0txqiLeJHCoufz47kQSPM47cBCCJ8bQCqAdUmXjDmKf4DCA2fOPrjl99FsvBIsKxSuxEUmV2hog9SxjZ2abb4j2T+j6A9tuz7I8pHU2BI+sR29g2GpTLLMiVncigs+FOSXmOeGCQrDCOQVrZBTjg7l6j493vDqQFSG4CBqIEnEabN0n1iNIXAN6DSB6Vu1PbbPiQ43ZKi3YAtwHtRRtcJnEbguCt6/bSAuaLVZnZc183S4UYmZBdL/J2nVwaheNFwdBJ4x7ReYCnmHzWdufDcT2/d6wvGsaRpeWe/Z7Xaja6qE6wZFwekb0y8fI/m1OOu7xXqFKThmKcp+VII5aJoQr4xA4HA/c5ltCDPhW+O9c4DAceeJeo2hu7SMduWQGlwluXrIXi2SvVEoqaDUteM0FHwLH4YZT806O3nxqo4KqMOWEQxgQvLPOYJdp5gqkkjlfJ1LJVPFkiag4hhC5ORwIzuO0EL03Pd2m85g4b1merufF4cQyNSnN1oShZGpjjaKLBB9bwWghp4lZK8/Tmacl4Q4Dh1vPcBPa5G5gQRrbW1tHpcv5YhXVe+zxPtFng+Adx8OI1mpWWR68FKiJkoQiQi0Zp+ZulYudbyrK7AJnafrP4wGv1mUS7wmnGxgGTq9PPK7GjB7HE4fhCFXJkznf1FrJc2ZOpZ3bNmcBRK8EwNVC8RBqIZRK9Y6pVq7JE4A5W6F17+6VSmHKshRyW7d6RSpQOthVmp8fHnNKsDWuadu9E8QH1HuKc0ylwjyjpXC+TlwbI1u1F44J7s3n3KdMiJHTbWY4jJRcW0t4vzOyn6B4ZSArCN4FlGCektq0OOobqWg6F6Gvsnqrya4V1YU5WpWofXW3KfBa0CvQGEsDxMWk5PowgW9G5RhDg1vf28CuZTXMBsupgtjkJlpBSxtsbb24lcjKug3QpW0edE2tLPf7heGhPOEdwHGtlDSbIbY2vWAD+NlZ+06RRJALe/foPV4mzBavmGeIGnBMWSj1jhCE0+mGmWzNQQ4jj/wR5x2jPzAMAaXixFMb+AvuivO94nq95akyXwu19ELGTHQQh2BNQLwjRPNj9aVQz1emaSYgRBcQmo1eStRSmHLm+fncrH086iOI5zSOHIMhbkGJ3ryDsji0eTeLcyZdailPy5DYYjfl1Fp75qV1ZnSR4CJZC/d6IWcltc//wnQmHAde+/QJn4+LO0lvYtKbnqRp5nx3z3S9ftR/8j2+UkIaW9gAVnBCcFaspZrJyeYeawltb5BS0WR62cSFi9o2vFZbKDqH8554OuFq4ZgKj8Tm2pvDicN4QEvl+vzCdLlSUmEqhWmyrOEggdDIl9AKnJ16aoCgjlgqeLPJmrzDK6RcyLUy1UJRZcoZ52hd+5TUipopqzyHZX41gB6aXMg3Ntg7h3hPbdKCqVbKbNeG+2nmep2brteRVZiqUp7e8fwyMYwDjwsc52zNh+LAcTyYRd8en4j4orSo7SerYCC0uwPI5jUsRRGbtEl/z5aNBeAhkF3w5JLqa+nCDfztn9k2Y5POYn/exhHGDCHmG7lWJlvhWM552SdkKy144VjppvMrIJfl9StgXYFrT+m8HcjW2aySUNMLUk0Mn51QHDgp4JMV2eyxx/uEqnULMjNxmxBF4HpN+GB+leP1Sq0V74VcB1uzuT5gTZNqk60SvDVQsCxKdyuwymcfrIOWZssgmOynUmsGHOJDc+MwBse1a0OpFSN1Cylbp69pTlznmSklsyPygCsM3jr5VWc9u/q4FGdWf0tRaKt8rn2go0sWxImjtuzM9nvqcoPaDd2btlY2RZpbMVBvjYs2S7Kyyabsscf7xCo2e/CfRU/Rt8YdrhE43Umgu7NqNWeQnDPzPFsh8+AQb5pyEbFCKSzL2OtNShu7dZn1+rXB7OS604k4k9r4NraqQnDVcpjOmbSgXStKkyhV70jVmY+8tMY+2sZfJ5AUpLWcDs4R2sKw9eFbmhnI8nVUtMrS9n3JdvZFpXPL74pQcmGek12vnH+7jG+Pj3V8CCBrRRC1Qi6OXDMOt6y0+pQhbQjWdorWzshqB6OlTSplAa82zB4Yd9AtcBbJQeUBcF3vb1L+LD4gixzVBmx3HLDPBzXWpvllLvB4kTTp8jkdbG87q3Tytn3oEgugtV/WQdpfUCuaMzSja+urXanOkaOneE8IyuEIMe7Dco/3D62Vy/1lFZgBkPgCV4TKze2J1853DMPAa08eUykMQyQGIUarag4ucHu4pdZCaAWTpVYubkbTZN30bgdcdNSiXO8z86XgpHBNT9G35rWzXRzwVRlTIpSCVyFVR1RrC31/f2aeZ67zzBeePec6z4QQGcYj3gXy7Q3H6BEdCc4zxgEnZj/kQuspXyp3lztQpTeNVRQXhPEwGmgvAZ9LKzytpHkm5cylGb1POTHNM9M0U4NrBakGh0ub0GvJTOcrOSVqLpRpwuXd33mPl40mrxMjNlSgtTwAeq7RrLLGEAzIlkAYIhUlVyt8KihP54lnT5/ivOPxk0c8fu2RZWPUUcVTa+Xp/YWa7+w6kDKaK7VUUq2mi/WecBg5tDHVwWQuGUmQSjIQq5BKxbMWWqdaGRojm0pkGGIrwFRKmzdzKeRUTAdfihFFqPk+Nw2ra6BdBIIXK/GshgVyI3iG4AjHEe89j043HA5W8HZzc2Jo4/tynbi7vyf6wHw4EEOwYrQ9PhHxoVrU0tiXnGxy8s0XzzXB9pqd3wDZWimNySw1U0tqjGhpTI624o5W+KS1AVsrLusrNK3ObEPoTRZsp2pt/nvLirfrUnUp5Oo3A84FqKvxe1+5vs1aZC0o65eclV3esMwvrAMfFoe1r64zz6q4Wqx6W9U0fH2FexgoIRBHTxkiQ9z1Pnu8f9RauZ6vyzmsqpR8YZ6fUWvi5v7EJV0ZxoFcE4fTwKEeGIfASWLzk/TchBNL22RVcs0UKnNN4CrjTSQcoslr3BVcRasyX58zne9Md3u4IQ4H0/opBBRfIWeIBaZ55unzZ1yvE5frxOfeesplmhjiwOl0QwwRR+WNmxNBQOIA0dwRgnPgrYjsWi5crmdqrUTvGWLTtnohDoOxQ6lY8VpV0nxlSpmUG4Dtt5SY5xmGxkC3EVtalialzP39HfP5gqgSVHH7+nKPDxSrtruX927oDsDS7DGaFVfQyqCRqsr99cp0PVNK4TpPXOepFXY5Djc3iAhFxTTmqtyfr1zu7w0M4ggY25qrog5wjjAMjIcjTsxz1ouQSkLJeFG82HztW2YDtfenWgldI1srQw3NmUjMXgusMCtn69SXc+vQpYTWRrcDWXEOVK0jXy0tU7JmbIN3+ODNZu904Hg4EmLk9tEt4+HANF25u7vj7u6OGDx1nhlipO5A9hMTH6JFbSXnmZwdKVXmYrY0uLykDNaVZq9EbO3zmrVWqYVaU5t0C6qN3ZBmQ0W32KnLax4CWel4ugFOXZosrAzoCmR7SMe3JuSxo2lAuncSW5s1sKQveeH+yq2+O5B9qODdvKID4lqRbkCNmiG02CQt0SOh3Xbh+h4vE8qyCOunYq1WAFZKYppm6/xTKufzhfPl2jRsA8EbK+Od2KSHLcS8N3Yo+kT0garaUog2Dfvg8FGoBao3Q3Oc6XRrG9OFNZvaOcxMpQh2c1C9UL2jOKGgOLWUaBcRvW3k6TraLM1okoMeldWxYPtu0/+aS8KcrbPQXAoKuBBwwTezevskLbYoqLmZTuvqvLCr8PZ42ehESm+L3ucuoI0nZ7K3JaPYSJcuSRBLwy8d6to4r7mQk827VvPYZDSNHBGFKs3Wbtn2QweDXozWf5pns6OILo8b9rW5PYijii3yEGtOEtRY5j7vO8WcP0RwauMZrBNfjOGhw0jT4FfphFEfZ0Jo+xKCI/h2c6vswnWyqLFOWqtJgHZxwScmXhnIlpJ59uxzXBI8PcOUzaoj+mA9zulOkGoG5xvNStfvGWhsKzAtqNoKyiakrhXoelltLWlbE4RmmwPtgrBK/JaQBzn/9thK0i6vF3rHsdJWhh1Yd8uwdxgQD4DyC1B1Rarv8PjD57QdPyLU6FAvuGFkeP0J4XQiBOFw8gxB3ra9PfZ4MRQrxnAii1Zszsrl7so8Xbnczzx/esZ5z/2zM2lOHI4HXnvthk996gkxmmXPMMRFb3c63KIoPoyM8UjRwmW+cElX68ZTA+Jt0syhUAbT01YuTDq1jls2+UgVkjXJImtlOjiyGyjRBAGuuQDMCJnK7LR5ULI07ANL8+fUrgUC4/HIksFpTFEuhWme26LUOoUVLVzTzN3lwpRmPv/sGZ998y2KQD4GjjePiaeROA54hFIq092VdJ1N/18qQ/DGyNI6Gu2xx/uEtgWmuNaoR+zcStlapVsRcqsHkcAwNmDZ26yr4pt23YmQcAZ4i3K9v/L0809x1uPcvNxFkOrQ1AkZR22ex06tCNoKrVq+VAVpGnavyiAe7xQqeBJZrVPY4D3e6rkZG7tcqjZZQbfTa81IfCEF1xjWSK0jIhDjsFxfYF2U5pKs7T0rmJWunxUDrcfDwDiYI8HohehMuzs4+26CiBFDOe849hMUrwxkay3c37/JeVKe3hWuSfHOMTjfGNmKa64CnY2186qr2ITuTqALc1KbhtXSggC6AbKL5Za27pYVtgLVdYXXfwea7qivPt/++laNvGheWVoIrobo6xK5g+PuiNDXqtbNy4QUHchuAe2L8117ZWvrqebsEDwaPfEYiY9vGR89JngYBwg7IfsVEb/qV/2q93z+O7/zO7+kn9+7WYmzdpderNPOdJ65Xq5NfW6rvjQnEDgcR+b0Gn4UxjEyDiMHrXjvOY5HxvHYJpTIEAZKzYgohdQ643loQHYWJTWZQUrG/DoEXKC0Fpm52qgpQUiDUFykhlbQkge0FPI8Q60kV42lcRsg25imnBJVFT9EhsOIOMgpGcAtBmTnebYUpfMgjooy58x5unKdZ57en/nC8zsIjsPta4yPb4iHgRBNIlWrki4Tl7uLpV+jFas4FE9d/Hj32OP9YimUFJszslYuyZoJOLFGjgKEaNX9C1Oq1g3PixCdt+IoMSCrqqTrzL3c47zncDoxHqP5uVdBszGblWoFj4BvZj6+T4VaG1Vs+QUHRHF48VSp+Kb2c1ia30Erugp0L+famyC0RiSqSvaZsthv0VQVwjAMDMPQ3ru2u07ZkxYgu44rL8a6OucYYiAGc0OJzlq8m/uDyY28iB1P0bdPunt8bONDamR7JRVLKmB1IliZyYeJPV1lA62X+Zpr7+BPXsjab7ah71CNqC1Vo0tt1/Kzg1j6/oiaWXN/fU/xsO5G18X299trbXW4ZYXWz+gPdE2tbo7pHUI223YecWqr6cOIjIEwHghDJARvVmF1LYTbY4/3i97KeeE7Nie0pdVt8ThdZ853F0op3NyOTNcJwfqWx2iXhlLr4kjnxBHF4xxEFxh8oIgje9smNHP2aClOh6C9lbMXut5I/TouXDQWKThhqBUJHs3WlIBS8SHYBKm9V31dnBBqtUWg036My7KytcFsH6Kmr1fMNmhOs5mpp0RuVeDSil98iDjvKbkwXa7UXHHamSjH8RiI0RnrO0/W9WuPPV4i1hbrlorvLK15ILfGBMIC7IxEsQKt2hZvtdbmd27tzbX5oDc0ScmZNCc736s20GzZmehaX7xGwBgD/GCiXX48fF3zfW0SAkEWSzrEMqPS5m5jlGWV9zX9K4uMQRgagyoiTanTgKu37mM25W+uEV1L2ySLPetSckYRSkq2APAeL2LFYUtB9x6fhPgQrgWC4BFtPnCYHY5o7xFinTiW9PtyUm0hpi4ArQ+cbl8lrS9zn4cFbdvuIHGzPXm4XVRpLlsLYN3A2nVfxPRGgpmzd0eEKjYB28foA0C+/Vx94ETQZfvvAl5bl6NFHyWCeCEcIm4I+CFweP0xw80RFyLD4USIo7UqvL9nTrtf5R7vHyJizQ4AodoiT+tSP1mSGYyXWsmpcL27EGIgXa+Mo3A8Hbh9fIM6JYSAiCfIgHeewXlOMVK14lEG391KPOiF0jMqg6BV8VmXNtKusUqqoN7srRyBw3FANHIscMo3xvCmTLlOaKmcwkgVYcoFp8LETBBHLsWaJxjFhcve0raqBO9QJyQx9iircp2NgZ1L5gtP3+Rzz54y58w1J4gBN0SG04mbx1b9fX76nGdf+ALRe56cbnn90S2H44FPfdUb3Dy64Xo58/nP/gjnu+cf6d97j6+MEBFCCGizggOhVKzoMGWCEzQYYMytwl9QK0hMs7VZTok5zSZRKIXR2/TtxSMVcyy5O3P3/B7VSpkSg4t457gZR45xBJTaPJVNDw/m7yqtu6zNYeYk4MwpJIQFaBfnmuuCmFc8Yk4IyRx/aiuCtE7xDlpth3M9+wkxxkUjW7Vb3SmlOnKMdEb27YRqk/zlTFWYrldKI7Y8ys3hwOA9t8cDYwyE8OHdRff4yogP95fW3vm8Fz90VzhammIFf6IP2cwXBQf2sk2LyWXLDR62iXDlWw2QKg82Sm+agC5ErX2aqnlesjYsYPMZimnojP2V1tpWF+D54qhaHn/w4Mqayub/h6p6QV37LoLHHUfCcSAeRm4/9RrHx4/aRcJM44smrmmmnM/v/nfYY48WVpzljKXs+pstI5sr8zSTciFdZ87P7vHecTh6PvXVt6Q8I0EYTgNRI4M/kH0BB34YOfrYRkXBOyWVbAVTJZPVZAAEY1ClgOspvlLQdr+6JtcRiBIIMoA6tHpQR0mZ+TJQc2FUj1Yx6yEKmQwNyOZidj6ueGotrbOgVX2r6qI5RK3D1/3ljjlnnp/veHZ3R6qFuVbUewiBOI6MxxM5zdw9fZO7529xczjw6eMNr52OnG5v+LrPfIYnr7/G82dPme/vKNP0Ef/F9/hKiM74G0FiLj5VlZSt4FC9dZ3EmXa21kIpUHImT9bUI6VETk0q02QGi7ytgmplnieu8wxqEpjozAf6NB65ORxBK2m+knOyRafY+7SnJjEGtc/AvmlPFW0d9Ty1ZWecwV1zIWoZk6VAmlYY5nvr6uaH2wB9aM4itQrFNBIU3xqb0Dyb231lZajn2cZ9KZVL61DmfWA8nBhiZBwij25OHIdhb4jwCYoPuWTpFEvXo9RN6r/LDDZ1/AtN2n7txVTomgZoKcLF4ErXTmA95bBRMtg+9MrHJVZm1GCvLun8hU+VtWXuoi/o71p1BeuWNq/pvz7ULNiLRdfPW7dgP9S1/YwBCeaDGQ8H4nEkDMFSNa1zkJnaQ5mu5Hmm7K0w93jJEPeCNKdV84t3uOAIMfQZzLrJVSXNmfP9FQTGw8H0swg5pFUTq56i5mdgbM0AeIY4M5ZC0GKG7sXGrOia9lS1ss8+QfUMhkqlUpZ0oVYx25yakZZC1eqXZW+Riki7Jlge0w5Tu4dlkyypaWRTLeRayFrJYibuVdo6u03FQZQQbAIvKVlL0GK2eKL2XBzs5r3iXMU5NUZrdxPZ4yXCtOsZxVFdMCJEdXUJ8Z4Y/FKJzzblDosUwDed99ZHUpwH71FVgvMLgPONUQ2tsr/lM1th2ZZbWQFj1Q25tPAwJiWo7T1OaNrwVf7Q27uLrnN5VxSZLGkjD2j7In1jtb14mee7jK+DWgPatdbN/E3zvrXvK/hW7OXW73CXFnxy4kM1ROh2WLUks6ixM9xOQjUngiZM7TMHvWts28IqLeiaGhGohd7Pa2ufUxuCNRArJl2Q5m+3tIXd7mEbGFv1wXLrLNXqWccD26JVSNB1Rg8YWFkvABZNI+gwDS4rEYZADQ71Dgme+OgWfzrgY+TmyS2H09EGplbqdSanzOX5HfN1gpzgegd5fvU/1Zcg3quo6Tu/8zvf9/k9vkQh4IeWWahtHI4Odwh4IuMgcHTUWpjPievzK6VUnr915of/+o9xOAzMcyGOgfEwGNAbPEED3ldCtfHm/MgpnkyOEw4cDhO5JJ5Pz7nM91RV5lTJrU1lCbXpAQuzXNGcUCDViaLJWt1ezLRdMrgZ8+VSs/sqeJLzTEHJfbyLyQlUHaU0P+mSqMVSsc+vF55PV3It3Gvh4pTkIUVHHQNUq84OYt2Nakqc33pKLYVynZFSCSLc3HievDEwHjxxSIi74P3MED2H4fBl+bO+zHj6SnvN13wN/OiPvutL+Mxn4Nu//cu3v1/KqLVwf3eHuICEaBNFLYzBXAbGGDgdD1a05J21L1dbFFqaX6A5Ai3kS0OjLkZcjFZI5gTvbSHrxbzdvXOM3rEY33R13WYOE1gKj4Ulr4qK2ethu4ItR21erq2BUMmFmlLrFuisWYk4nAdz7usdx+xnt9BqsyvqAZRczLNWl5m7g2tn0gOgiFAAxDFGRwhC8IHj4cgwDMTgOYTI4P3bEqZfivhyjZWXKSL+Yu3Le43Lz3wGfuRHvrzXkZeJD1XstV2NaTUfPHXNakvr4hdr4hvotKhJhLSl8Vcw2DHmlhbtzRDWns0sQBYamKX1D1s28GAn1403cPwA7HZHAt28VlfBwlbmsOzni5/RitdE+8rTjrntuQHpxohJCPjTgfj4EWEIHF97wvHmhOZMvr+nThPlOnF5+pTL3T2ihVBmvO7mznu8f4iA+LbQ6yRHEPMkLp4YBdfYUapyvTOm43KZePNzz4lD4Hgzcv7qR1QtjMeBKY9UCcw1kHTA4xndgTEebaj6SIwzqaY2Xq3xgMkPLMOQWyMUKdaSVqpVZheyOSnUTJ6v1FRwxRFTwFdnTK1aarM4JSHWrtYFfIjQUrXarkc5F1KaKdVsti7ZCrqutTCLkh0Ubw4hqBJ8bI4GQi2Z+VxMh5cyrlp92jh6TreBODhCLIjMOFcaC7Tr8F413gvEvszzX0lRqzJNE84XvGIpd1Wid+DhMESO40Boj9NIoqVlOs2pwK/Nhjrx40PEt8YfddMh04vDt/cF52gCAdPPP0jaNOazb1d7IseuIc51/9kmCVKrJ9FSqEWpOVNLbo2IhBCNwfUevG8Als3+dncBWR+3j+7VKg8LxEtzNpKNXMgJRO/xOEIwScEQI9F7ou/b/1L+RT++8V7j7m/XMfmhrsLaUugLgFxWefZ882ReomNJaXdUZGEvt/KANTUvi1ShVzwu98Xcai3tYStAe/NmeDaJgmw+04m1AOyWW7W6VeLgdEPI6ma/2l5tUj3rMNseuD1aGwVcvaDS7MgOA34YcDEwHsyn0nmPlkq+zmjOpMuVfDEpQZ0zFLP3cfXtSuA99nj3aIxJsfaQvcNOThkJ4KMxmmGIxKEbkztKUVyuXK+Ju+cXci4Mw8DhdCCXyCAD1xAJLiAuIs4uH7U5HXhxxBA51COlVkQLSUrTyxacWDFYyJ5ag8lwxHwya3XIYDIDQdG5kmuFCq44vGZiiOAEj4eqSDbbIOccfrIxV3IipQnVynVOJDUGSYInuhFXK4dSSA340my5lvHqsIl6CKhmbh8diePQUi1mAF9qsU6ApXcp3GOP9w+bq9ZWsA5ZbBVjaADMO3Mn6O+hkykPFG9bXYDd1dpkA9LYTmta0K2rzItVFmlBl9nVRYJnrWVrlaUwVNr8WTq41K3mvpE3Yt64zrUuZU42U6Is+/52+d/6nUgDytKyuX0ubTlZ24+W2RUxt4SVN3Z2bHTc2giyJR26xychXh3I2nnU9HftOm/ZPlrhI66DVCuHpJ9qixGyUZXtMZbntyxnWxfaa1zvcOIY4mBsirAA2s7cdqa3e9RCn4dsdeibqbTVwzysjuwgdhWat9+xJhAptQYGi20YVhjWLg6W/mgyiTFCHHDBc3r8mPH2Fh8C480Nw/GE1kq6XDg/P1PmmetbT0n3Z7MWmSYkZ/OrlKY12mOP9w0FnamlMk2JkiuX+wt3z89MlyvHRyOPn5wI0SM4aoI8G4szT4U8Vz7/Y88oVIZDMIcDMUCbHieSZrwP3AyZU8mmlQ3RrG+8w41POMUbqlZzCUjZGijkibkkcsk4rJWs4Ah4HI6aCtkf0JyZ7ifuLs9IaYYk3F3voQjj4cgNj/E+UObMPCVrdlBXr+l+XwENDg0OnBBPRx7fHKx5wuPHPJ5mkyzUstgfmXjQGKg4REL0DAfP7WtHiIHqPCkXlMR1SlynzHXatet7vH84JwxDxHvPMHizemvaWBtDgSFGxDlyykwNL3YGs0v01hlym2lURAsOGILgfQSat6pYQVZQm+y1ykImdU1sv11LJTepHcXY1wV4Lw4DxT67Wc9K05ozBJM2eBtv1gLXQGqf23tdyWLj1fS9XWduZNIK2LuNV214QgAfAuZrIPSWS9556/SFuaPUnCmlyxr3+CTEh8+LtZNZ2iRgJ6suC8YuAVjBbHuTroD2oXPBerezr/3BLhb3zjPEgSEOyGZwAy0d02y0liYLQi9gdM4Rgmcxcl56Oj9EiouP30bWkNRa/dWu+6WtGNugQoz9yYhZAgWPHCIuBMLtieOTR3gfOBxPDMNITon5+T3z3Zk8z1yf3TPfWW9sp60fmijO6bL/n7T4cmrbPmod3RcnGhtbMyXZwmueEtN1ZrrOjKdAGDzDIZJTZThmnC+kKVlFsFbu766oK8QhcDgN3L52ZDwk/BAIh0gIwVw1xOOdx3mHkwgC3vmWlah4JqJL5JrN79KBd0IpEQCPI0o0P0xXyNVZmjIZM5tKomSlTBXNkARkPOBR5nnicn82fV4plJKXiaszQOE4Ek4jzgXGIXK4vQER/HhgTJmqlZQTuRgY7WySD4HT7YnheMAHGE4VdVa1nWuFUsz1IRdy3v2d93i5sHS7JwZjXr13jDG2iv4G6ETQsrptAKvGFB7MUw+Eea2pQXCtVbyYq4FvTRBcMb9lZS3AKhsJX9VKqoVUrfizZvOP9k4YghWMLTLClra0gmkxH/RmKbZ45W6L0ZYULevzLYzNdcv342Srj6XZY1rHsZ599aHXn5i0sLeplbZfWq04bCtb3OPjHR8KyAqWhjCRWztpal0qorUnSLqf7IaVXbfRQ9ezV3rl4oNkyvrKtjKsvSisyQ66dqczqAsjq0rtNq9VKYVWJNIyJct+yGZXmkZXWRmeTdf3rYqn62C1pzmdUajhMOCPIz4EfPQNv1fSdKWmZDZD5zP5eqGkjOYEujb0fXD8+5jc4yVCVQ3ATonr+UKaC/NlaoCvNmLHWWce7whDAMwL0rXJqhZlvmZqUc53E3dvnUmHzBAHDscDsRSChNZtxwNW+CliE7U43wpSHN4Fm1Sr2XY5hOKH1qnIM7jBJl5fQSKUSlQhP7owhsB8SZzLRJkrGpRZTbs61Zlrna2tNIq6lmx0HucDzgnxdGC4OeBD4HA6cjgc2mSYcM5Ta7UGDtXUg70YxXlPHEZiHHAenBS6IwsEVD0ihRAHwrBr1/d4iWjnlTUS8A/sqLqDTlE7x3ItzE26olpbi2VdCreks5tuna+6ID5jRWIioK5aYwDAVcW37WS1wmnLYUjr9rfJUFYrvLLPldYtq8sJWBimngk1lYEuDPL21smnPrW++PwWmDfCFpZ9WwGza58XnW/worsOteYM7TVOWDKye3xy4kMVe7kqSBE768qa0m/tN5Z0X/eXbdAXWe4/3GB3JwA21ZTvcEK2/so9qbeu8lbtji02VwVQf0xE8HUzANunPACym7t9kEJjeJtiaEXdLQUixsL6MeDGiATP4ckjxse3iPPEGHHeesTfPX9OOk/UnJmf35EvV7RUdJ5xtfRDXKRG+7Dc42Wjlsr1/sL57sIXPvuU62Vinmams4HZUtXSfSEQD8rhkbEv4syzsmQhp8I8zThnBSN1zsbg5oJERxwCU7pyf3iOF8/oDwx+wLvA8XDLOBwtXeoiIYxULTgn5BrJJRPxpJwILnAMB6KLeISxFaecz895bRy5Xi88fXbHj/jPcblMFArP9UzNjilduMzPKaUQ/cAQR5zzHI4HxtMN3ntONydOtydC8Nw+esSjR48A4TJNTNeZqpVZM5m8LmbFwPB4OBKHwSROLoEWwEGNgMO5yOFYQONH+efe4yskun9qbBICH3xzGLBW0rVCKhlV5TzP5rZRKjllUrKWr6MPjCGaDjYGkweJIMX0rBXlmmamPINADIHgW1taBdcZ2NKcekTI0oGsTeGlWovrOSVyLgsT2y28jP10bdFqi1RVpVSaFoKlnsV7hwu+uf60zKesXcG2gLYD1tWGs5rkB2vNG4LRsJHQsqS0JkZtTm9aYJNZWCHrLi345MSHkxbUJmWtjU2tNO+3futsRQelbRXHOxUvNXp0w0A+tLdawZxWQUuhLKu9rr1dP2sLilffyvZ+3XrMbVeF24H0wt7pRnMrW30sK+IUzB92iLjoGY4jh5tjK0yT5TuZLxcuz55Tc6Hcn6nTbHKCunY5e5G57o4re7w9Ph6SgC9OqEKaEtNl5vL8zPneWtCWlBvbArjGyAZPHAM1VNJkEgGtSkqV+WrLxPt4sfTiIXL7+g1PrhdKDajLFDfjxJFdYpamWfcB5yPeeWIYCT6i6kAzoUIWh8baKqkDp3gkurh0DYviOASPlonrNaJUvvAsMGsiF2WaE7nCVK6cy9UK2rzg/Gi2Q2Mg3BwIITDeHjne3hCC53R7w+l0A7AUilZVPJlEebBUdM4xjiMhDFixi5jXLQJY8wYnEONIPfRrzR57vHc432Q4PjRtrGlne6au1EKt1mRkKtnkK8lkQd3CjtpcBLovdK/96GxrzkzzjIjJ7EpoGlkalaS0AmtLy9fGBlfcQrrWWlvDEdOZZhFzQpFuddlEDm4lhB76DHT9rDTQ2x+T5ecDEAsLHStNEqAbVlVai1yco7s2dwa7f6L18OtPvnMmd4+Pb+zeMa8aHwRUfggA+nHGrjsA3WMbX/Zz/e0JmFfbzL7C3ON9Yj9DPnxsv8Mdpu6xDXlVNkFEfgz4a1/c3dnjJeIbVfWrvlgb2/+OH3nsf8+PR+x/x49X7H/Pr/zY/4Yfr3jXv+crA9k99thjjz322GOPPfb4KOMTauq0xx577LHHHnvsscdXeuxAdo899thjjz322GOPr8h4NSAr8kOIfPodHv9HEHn3Cp6X2/YJkT+IyF9G5C8i8p0vPP/zEfnB9tzvaY/9ZES+D5G/gMjPbI8FRP4wIqf3+Kxfh8jPfp/9+T8g8g9+wGP4dkR+4wd6z6uGyL+KyN//ZfmsPb4iQ4RfLsJfEuF3f5k+738lwn/VXOk+vXlcRPj17bm/IMJPb4//ZBG+rz32M9tjQYQ/LMK7jl8Rfp0IP7vd/1ki/EUR/rwIxy/Rcf2rIuxjbY8PHfuY/KId1z4m9+i2Uh/wBj+k8OlXeu/7b/uk8Pe1+4PCH1P4H7fff6LCn1N4vf3+1e3nv6bwLQrfoPD72mO/TOHb3+NzPqXwvR9iP/17PPftCr/xS/L9vP2zvlHhP/qyfNZ++4q8gf5l0G94h8fDl+jz/i7QbwL9IdBPbx7/h0H//ebe/veA/sn2+L8G+i2g3wD6+9pjvwz029/jMz4F+r2b338z6D/9pTxG0G8E3cfafvvQt31MftGOax+T++19GFmRm8aO/v8Q+QFE/onNs78MkT+LyPcj8lPa61cmUuS7EfnNiPwZRP4KIj+nPf53IvKnEPnzjUH9iS8g6zOq/2m7PwN/FviG9uz/DPi/o/pme/6z7fEEnNotIfIa8HOB3/EeR/fzgP9gc6z/MiJ/uh3nb9mY3n03It/W7v8QIv9XRP4s8I8j8j2I/OvtWH4Akf/BO3yHPxeRP4nIn2sM8Wfa49+ByL/ZtvFXEfnlm/f805vv6N9AxLfbd7fP+X5EfkX7Dv4a8ClEvuY9jnWPT2iI8JuBbwb+fRF+hQjfIcLvFOGPA79ThG8S4T9pzMsfEeHHtff9BBG+V4TvF+HXiHDXHv9aEf5oY1l+QISf9eJnqvLnVPmhd9idfxT4He3a873AayJ8LS+MXxFe4wOMXxF+CfDzgf+jCL9bhG8V4Y+J8AeAHxThIMJva8fy50T4+9r7TiL82yL8oAj/HxH+pAh/t1gzo+9ux/f9IvyKdlx/DfiUCPtY2+OVYx+T+5jc44sc74l04ecpfNfm9yft5w8p/LJ2/59V+K3t/spEwncr/AetochPVPgbCgeF36DwT7XXDArH9/j81xT+qsI3t99/v8L/TeGPK3yvwj/UHv9xCt+j8CcUfprCr1X41vc5tt+u8HM3v7+xuf87l+fsOL5tc9z/4uZ137N8P/CzFX7gHb6H17W5Qyj8EoVf2+5/h8J/rjAqfFrh8wpR4e9Q+HcVYnvdb1L4hQo/Q+E/fvDdrPe/S+HnfdSrov32t+dty8KAfgfo94Ee2+//Lug/0+7/YtDf3+7/e6C/oN3/paB37f4/D/qr230P+uhlPnezzW/Z/P5HQP9u0B8H+j2gfwL0p4H+WtBvfZ9j+u2gP3fz+3eDflu7/62g96A/frPP/2a7/1NA/zroAfRfAP032uP/XdDc9udngP7Hm22/trn/XaD7WNtvH+q2j8l9TO63L97t/TSy3w/8DxsL+bNQfbp57v/dfn4f8E3v8v5/G9WK6n8J/FXgpwB/AvjfIvK/Br4R1cs7vlMkAP8W8OtR/avt0QD8ROBbgV8AfBcir6H611H9VlR/JnDGGNy/hMjvROT3IvKT3uETvhb4sc3vf19jTr8f+PuBv/Ndjun3vvD7vwWA6h8FHjc2eBvfAPyHbbu/8oXt/kFUJ1Q/B3wW+AzwDwA/A/jTiPz59vs3Y9/fNyPyGxD5h4Bnm+18Fvi6d9nfPfZ4Mf6AKn3c/Uzg97T7vxP4ls3j/067/3s27/3TwC8S4TuAn6rK8w+7M6r8dVW+VZUH47exVL9XhJcZvy/Gn1Llv273vwX4Xe2z/jLmBfmT2uP/z/b4DwB/ob3+rwLfLMJvEGEfa3t8OWIfk/uY3OMV472BrOpfAX46Bmh/DSL/8ubZqf0svHuHMH3b76q/B/hHgAvwh96jUOm3AP8lqr9u89jfAP4AqgnV/xr4Kxiw3cb/CfiXgF8O/FbgXwT+9++w/QtwAEDkAPwm4NtQ/anAdy3PvT3u3/cYH8ZvAH5j2+7//IXtTpv7/XsU4Lej+t9vt5+M6ndgcor/HvA9wC9tx9bj0I5njz1eJl48h186VPmjwM8Gfhj4bhF+4Qd4+w8D/63N79/QHtvGBx+/7xwf5hj3sbbHlzv2MfkesY/JPd4r3k8j+3XAGdXfBfwrGKj9IPGPI+IQ+QkYq/hfIGLsouqvB/6/wE97h8/9NcAT4J974Znfj7GxYK4JPwlbqfX3/b3A32wM8Amo7fZOVZZ/Cfhvt/t98H0OkVvg2z7AMf4T7bO/BXj6AmtNO45+YfhnXmJ7fwT4NkS+um33DUS+sR2vQ/X3YReV7d/iJwE/8AH2eY89evznwD/Z7v9TwB9r978X07yxeR4RvhH4UVW+C5tMPsg14Q8Av1CsUvrvAZ6q8rc22/57gb+pygcdv+8Xfww7NhqT9OOA/wL445iODxH+O8BPbfc/DThV9rG2x0cR+5hkH5N7vHy8G5Pa46cC/woiFRN//y8+4Pb/OvCngMfAL0X1isjPB/6niCTgR4D/84N3iHwD8KuBvwz8Wazm6jei+luB/xD4HyHygxiD+StR/Xx7n2AneC9I+y3A727H+E77/QcxhvS3ovoWIt+FDYYfwVI1LxtXRP4cEIFf/A7Pfwfw7yDyJvCfAD/+Pbem+oOI/EvAf4SIw773/yW24vxt7TGA/w0AIhG7ePyZD7DPe+zR45cBv02EX4mlBX9Re/yfA36XCL8aK+DoC7RvBX6lCAm4g7ezPyL8coy1+RrgL4jwh1T5JcAfAv5h4L/C0pW/aPOeVx+/7x+/Cfh/iPD9QAa+XZVJhN8E/HYRfhC73vzFdpxf376TB2NNhH2s7fHliH1M7mNyjw8QX7oWtSLfDfx7qP6/vjQf8EUIkf8M+DmovvWK7/8e4F9A9aMbRCL/GPDTUf3ffWT7sMfHLsS8Ii+qqAj/JPALVPlHP+r92oYI/xnwc1R56xXf74GoylWEnwD8YeAnqzK/y+v/MeCnq7KPtT2+7LGPyXd8/T4m93hfRvbjHv88ltJ46yPejw8TAfi1H/VO7PGxi58B/MbGyrzFO2cbPur4sOP3BPynjdUR4J99twmzxT7W9vgoYx+Tb499TO7xJWRk99hjjz322GOPPfbY40sYr9aido899thjjz322GOPPT7i2IHsHnvssccee+yxxx5fkbED2T322GOPPfbYY489viJjB7J77LHHHnvssccee3xFxiu7Fjzyop+ODhHwYuWF2/vQfyoiDTGLvcbJ9nlAtL1y83h7steiabu//K5vb6H1wlaX560f72YbvNN7HTgPODR4iBGcQ1OizjPUak7QYu+tKtT2IU4rohWAokKp7XPF2QH3L0fszUWViiIKgiKqttdq92mf8+KBCPD5op9T1a96x0N/hfj0pz+t3/RN3/TF2tweHzC+7/u+74v69xwPo55ub9YHXijmfDjS+mjZ/m/3zJb54asejBrRB+NXxAaWog/GqN0Re+fy9n5lWP+zsbkdmfrirr89tL9z+/vm180G3u1+/5CH+7c9Yt28bL0YPfhcIKe8j8uPUXyxx6VzTr3zAGgfW+36D4p3juAdToRxiJyOI945nIBzD8ffcuZtxpVuxg1om3KE7Sh+r3iZkm9VqNW2X6qSS0VVqbWSq/W8F+dwztP8321qF/DeEbzHibT7hh22k/qL+/riPr3nsWzHd/v5I59/ylt355f9Ct439jH50cZ7jclXBrKfjo7v+HEnRg+3gzJ4GB3cBkd04FACFSdKcDAGcA6GAIfB7gvVei2I0v8hII4F+dYKVZWqMCclFagKOUOpoCpouxz0CbifuQVrQVJq5TJXUmnbKSxgE0AFCAfq+AbqR/T116lf+7XoYST96GeZ/toPUa9XrgL3DgrCuTrOxVNVOaQrh3RGq/JscjydHFUddRjQOKDOGTCOkarKfUpcSsHVylgSsdp9P8+4UlCU2akBZRSnC9bnd3wh/bVX/Zu9U3zTN30Tf+bP7F7SH1WIyBf173m6veEf+J/8gxsA9hDA9YkHbGJwgCDtvo0f5wLeRwS3rCoF0NbUR0QRV+0mig8F521SyzlTSkUrlCJotVsprs1ZDq0ekGVflErVSiozqnb+11rQtjjsC9H1OLDJW9fnat0e33qctdb3vK92gbHvafMhC3RQpZRC7dsu5cH32L/fH/tbP7aPy49RfLHHpXeeN177FIpQxUYdWpGSEJQnp5Gveu3EcYj8hB/3dfxdf8dP4PHtDccBbg+Cc1CpVEo73z21elQhF6WUBmAlARknMAYhuhU0958rwbPu39uArOvEi9rYAnKuXK+ZXCr3l4nPP71nmhN308ybdxdSqcTDkfH0CO+DgWkVnAivP7nl068/YoiBN54ceePJDcEJ5BnyjABGI1nU7T514ktkuVYtB9CuAdTNtaK98xf/X7771f9g7xD7mPxo473G5CsD2eDgjQMMHm4iRGe3Y1CC0FhGmxy8WxZoQAOfFTtBOwOJAdbOXkobgKUa+OzgNed2v0Cu9v7atomosb3tw0o7pXO1W2m33MDwlv+pKFUy6oR8d2b+wlvUGCnPnlHnmZoTSaB20FwdlGK/lYy2DYp4gg8UHMUPzD6iIswKKWeqKnMppFLxWomqC0jtzHVtP1U6a/s+q9E99nghBEHRhVlVtfvvZLen6DI9PAC+7fGHOY5qo0oLqqVNVgXR9nhnZx1ImwDtJNa2YtS2ZdeWrn1FWVvCQto532G2AVVZRqq0+VXb/ukCaJdJ7QHSrZtUTE+V6Jr9aKBU+kJ6/RgejLr+ns335mTdqz32eKVoQ6KqknMhiZCzgcVSK+DxPuAdFMpyjisOtAFiUar0894gYAVcGwaCGLOLbCYTG2fbc365L9KZJnuxtzeJKE4LvlR8dfih4Am4LKgkKoVcQKeEc4XgPNF7xDm8c4zDwDhEhjgQ2jGhAcHGqBfwbaQv47F/R53h3VyPbM5tY7dhAVsgNFprnzQ/MfHKQHbw8OOfGKA9esE7A5HB20RWayXnukyg/QQUsLEmNiWidrqWWim1n4DSs/CUaoutCkyZhZFNDZxquwj0CcU5lvmnbrYxJXt9rTA3QKuygtmSC3m+UslcLolnT+/JTvDpSpjukWqsTN8XrWKUL7Tp2AaRk0iMR5w4LnHgEgcSyptp4tl8tQFbK74qA8qIcsDmeQ8G+sEuTD1FxMuniPbYY2EwVJbJoAPaVTKwpilXeNnXlhXV2l7bUyQdxBZAqZqgZJssWlbFGCFT6HRmlLpZpbWfKtIyKdvQhRVWwHWgq4pQ6WvcPhdXUaR2UK2NVeVBqpJ2HLoA2sbzaF1ftwG40kDt+iVuwXB98Pl2gdpH5R4fJtbRl0vhOs3UUrhOiTllUspwCAzDQPCOXDO5JpvviqeoAxUjeqqNiaqC4pCWMSht/h2CJ3jjO21+XbV7faGoPTMhgnpv414c+AjikKr4oSIForsypIDGhC8BdYmiM/OslOkCCKcxcns84ALEGLm9veUwDpxOkXEcjHRyDim2Lx7Bd+me9DnVCJ3lmtb/183+Nka2j13teGBHsp+YeGUg6wUej/bz4AyAIeC8TWi5KKqVWtdFoNDOUnqqcGVTtEIt6/b7OVgrlAZWS5MTVIVS7PE2h1HbgK1KY4BkSU90JrYUe75sgOzyGpRUC1VhSoXzdSYBkcxIwmlDsP0N/T52cNqZZHE4CVTnUR/JPpC0clHleSmgyqgwNHZns4lFRtuxeNcVr9KCfWDu8QGiI9R3irc9vkGK7/rixqZqZ1INSEpjN7dycMAkCE07K2gjZLf5hYefJZt7D7hPffs7pD+hm316d/3Bqr/VvkDsjI828P4C46orm933WPvzy6KgM8d77PE+sR0YsC62sMdNIlBxArmUJn+xM9Q5j/cOlUrF2blZ+wJTGvBrc2H7JzRJGrbNKoKKs3O637b70dlctf63Kt5YIeeREO1nUZwqOMVFxcUZVwUJMyqeiqfUwpxN/jB4h9aKqmljhxgZYiSEgHfegKwvGIS1/9damjZGRd+2XlzGq+i6+JTtQlTe/VK2x8cyXhnIOuAgahpYFFdbpqPaCdYZmaKKqLQnWeZEB8vko9pS/4bzHoz3XKVpZIW5wlxscM+dkcXAaR+PtojsEnqbN0uFuQilKkVXVrdugaxCrtpUSOvyT6kUsWlbtKVrWIFmx7RJbPK9OuHiHRnhHgOvc63c5cI5lTbpm4bYt/2TNiCdKl5tJepbmhYVNgvSPfZ46Xivrn0LH9vYC9fZ1/a4Nu1P15sCIMVu1PaztM+xidf0ta5tC7w39scm5MaStFR/n3DendncgGd9++PGxnSdawOhfbt9f2tZV8edVdU+4dUGUHXJpvY0kM3z6/fhgOrcC/vQJt19ZO7xAaOvL7fYVlFKtTqOOReuc+I6J3IZbLnYgarqUjNSekYSh4prw8ggoaIUnYFs53yR5TwPweHELZ9r4BUDsKjpY90KZHEmDwAhtPFa1XGbYRwzuQq391e8j1ymiZTOiw7fOWfFXTEQhkgcB3zwSC/2IizL1oXsAkQqssiNDNBCJ3TsdSo9Dyrt2Puc2RfP+9j8pMSrA1lRHvnCVmtWWXWptSipWGqD6pYJJjvTqAptMmopwVzU9DXwYOIqCqXKAl5TcwtIFXI7Z8tmrnNOFluEiqUlahWmurK5ucrynixtfyuteAMy0LU31VVmb3A3qBB1ZWm8tO2JkHFUcdwHz330JByfz5XPz5VUC1+4Jp5eU0uY2gTqRFBvQv4OYkOTKejCwjYw+6p/qD0+cfFAL9ofayOkA9OeCekpcxFn2pb2Wm2pxlISKSVAcV5xvk1QviDOZAalZlv+iRAlIt4jCM7bcq8WBcmQa2OaDAxb9bZVdgqWzVhAbTsG6ua+rCyqakFLboUeusxfy6pWrZBLa1l+X9jYJldYFtUNAHQJFALeubY/ioawfJcL27y8fo89Xj4MxC4w1s4xDJzOOVOqcJlm7i5XfPA8modWzyGtcNlea/OlUhGyeooMlmfwgLeFWk5nUr4iohSMCHIORu+JEugZFl4Ageoc+LAAWvEBnMcHh5eIiCMMR4bxllIqPh6YsnK+XHnr6VPu789oMTrIB0+IgWEcOZ6OHA4jMYDzTXPrxECtdmlRD3MC0j7mZR2vxsjSmOx2rWv3l8zMeyzi9/j4xSsDWQFiU7F0ZqTJ5NpjnZVlZU0wCrTbThmQtedzMYD7INOBNFlB08WqgdeK/cx1ZVNrZ1IQY4Abi2qMrVqRV5UGZJt9liilAeMms2GpetQ1/aiNhu3srWwGVNfyZqwQLDlh9o5ZhQnlWiupVKZSmUvFo2Sao4KznbZ/9nmtTsYA74OxuE+Ze7x8LK4Fm1VhT6/bj4dgV7cnNSwDsdZKKZkOb3vaT7WPrsaM1tpsgirgGqvplu05pzjX04CN2e1ZGlGWwhXW3VjlAJv7C5Dtt8r61Obi0UA6/drUv41FWrB+UIejbmFiwYnDObcw1tsd6wC2uzzssccHDZH1rOoMba8RybWSciHlTKl1Of+WYbuZE6tqY2O9ndWdyNFCFU/Xy+YmpTPyRej5iGU/eqq+T2rOtYIv+ynOIeLxLuCcX+arWpXDYeIwjtSqxBAaLrbt9nHkvMOHgA8B59QypyjidGVklwPtEp5+1HZN2SRK7XFx6xzdF6G0cb8Py09UvDKQVVXmZJNIaVqeKlC80fypCFmlJR9toNn7KqXYMKqt/kJVyGWVFtiAbdqfDeOaVBYg20Ftf80CZJvdh+lf23611Wgv1MraBrNCaYysNglD9+bb2oCUNsAMTNuAKdqncajiqOLJIkwKl1pIKqRc0GReX6EqowoeYRRlECUiBIQgrdCrgVirOO0V5ntl9B5f3FicDB6ARJZMP6pULW2MT1ynC6gyHjwhepvbPPiwTCl094GqJiUQEWNwxeOc4oNt3/kGJp1NOLUo2nSzVrxlK8P12sALq9sObvvoa0BcN+CUdUIW7zbv7c/216//wCZdaV+CE1nTrw/ArC7gdyvG2GOPDxqy+d9YWaAqKVvh1xAD05zIuZC9s59NP1v6fIkDL3jXfM+DswrsppVVJ7aYK4lcC1odWR0ebwsyZ+d6l9kIGBD2HpwxxmIaIZw4vEhzBloLkr1AdEL0whA9xzHiHZyOB063Jw6HkcPxSBxHwjDgndly2kCX5Svozj8mTVxG9obFZpHZLUWm7TlZHEQUdbKM7z0+GfHKQLZWuFwLdQGXgjqheoeKPZaWlZ/NfIpQU6Km1NhY84lFWYEsa7OBfr+vybIKuU1fSXUDZFew5zeOB7Xpa6rae2tncxszW0Ua2O0TOo2BscHpMKa11LatKhQTE7Ga3QoVT2pA9qLC85JICtNcqNeCViXmykkdAThK5QB2ExjblgIGaC3D0w5i5Wr3WXOPlw7pejd9++OL/HPN1pkUoFUzV23eqbVyvV64u3sGKOKPjMcjzgkhQIg2YeailNr9LZVMRpxj8I2FFRik2egVQBSXrRC0pNUJpI8nI1OFWtr0uhSILYN0Rbod1OqasO3Wmd4L2gpJepGLMT2rhMCJbwDWVK+dbe0yA10YbOis9CIz2FeYe7xCNGO8dt/+r2rneVXlOheen68gwvkyMaWMd0JKmTSba0FOSs6K4nHeE+KhGbVHiN7Aa/BojtRamC9nUr7gxeHV4zXgxBFCIHhvJOwGpLrgbIrbzDsOh+8yIGcFnwUlijIGoQbHzRh5dHMk5ciTJ7e89vprHA4jt08eMZ6OjEPEScFh9SJmYylNFlQXvTt9nm20kmi1z5WN/MDZa3sWdZEU9Kzqni35xMSrM7L0FL0aSGyPqTRA20DsAmSbAK/iLM3RbrV5ZHWfV22guDQQtxoFGIjt4DU3AE17vqqs+jVtjKzq4vu6SBSW+3Y56dvuB7Ws/Np/fSh1TFAbI4taqgWkpXYcVYyBTi2VU014a7YlarJ236ZW39I8HQ53Y/r+2SYx6JzTbsC1xxcvms850BdLPFgkbbVmpRZykxbUDhwbGHauESh1ZUpVrTDTqU2yCzvqWsUzTWbg7X51tTkeyGbO7OAVVPu1Q9/htuWz1vvb+asXnvVCtH5fGtvqxVsnImyifuBUsOjutt9L7b+8bZGwxx7vFctcwjvn2fp5Vmsll0LKhVyarlxt/JmMR63hTiustnPVIa0wC2f6VyWhDOZzLo6KNEs+m5dtMecRtwJZEYylddKA7LrvSxZCV2cdaePbieCd4J0jBhuvMQaGIRKHgRAiLnhc8A86Wtpi27X7jsXreZvvUJbxuozOJYtky81F17jQXvuM+UmKDwFkhQuBIkLC2amzDAqhdNDZgGmt1tmnZCHn3vFnTR+WsvGFRZpbJQsQVRo7qisj2yUHPcko7b0rI7uC1d7lqyoLq6u6Vjuqdr5lc7npY6UNCXURwoiKw/kBF0xgX7WSarV9qtW6NqgSauWghQqYOYkSRDk54cYLB4HozfmhJTfXz+3IG9nu0R57vHfoyhi+a1tWOmPLhuhcnQpMd9o7a/U0fZ98awO42rQ91pErlxnQldFxQq2Z7P3yuzkYVJQMUpAF0DYgW20xCEJxtphdx0WbSk37YNvrScc+sJWFbZW2Et3+XEFqB82CEzP9ecDUsp0G105hYAvvzvzo5vE99niZeKeFV39G27mfq3KZEt45LtPMNCe8wxol5GxuQBlKBsSKhJ0zIOt8QOJon+ABDZRinrQ5G0hMGapmfHMUid7m7OANwDonxMEsv5Zxs7lOoNZ5Lxfr4ldrK/EW8MExHAZ8DRxPJ463N4yHkXgcccFD22bHyFIdVG/jqVS0tMVyI4FArf27rnOyrojf3ifYNasvpoG3adv3+FjHKwPZgvCcSMExi6cieOeJLuKcM6urYtDQunDZhJBnR57X1pU9vWkdtxp7CmTaCcrGDxbZsKpijgibta0gC7uJSDNVNpa4sHrt5a0Z+zI+usxg1QoJ9nqnDkUocUAPt6gLuPGIO9za+64XpvOFVK2pgs4TUitDhqFtc0ZJogQnPA7Co+AYBEYpDciKWZyIawVxzQ0CFkCxxx7vF5Y1WCjE93ztdiI1z2cDfN1ZoLaJxLk+mVlDBFXrPlRqRrWSy5Vcpv7pSwGJ925hd0IMeN8N66zIAzGbOeeUWh2CQ6tNdDmJZWsERK15ZZ9UOxvsfWia3kUuZzKBBcyaBlAeHPEK8qVdMdwClNtrWgqmT4h1Y/VVGpDVWim0VOgee7xEvBdDaNZXBu/mXLk/T2it3J+vnK9XhEopiVJMlpeyklMFiQyqeOfMbzYO+PHQFnARUQOcaa6kbOftnGbqNOFEmOfcmFQhBk9wDh8cMKDRmNYQZNHOaymNkCrknCi5UkqhN0UJQ+B0e6KqcvPkltvXHjMeDhxujsgQkBBwrrYFZMu5tuxGLQXtFdzdOk9ZACtt3C0LyV50qjS22hbWtU30+p7f+B4fp/hw0gJxFFxjZB3VkuZ4hIJ5yKraz95Vq3T/1yYpMP2ZmLSgkZBFjDVFt16vssgCOmFZNqnRF6XdPcGwFmQ9ZHe3zKtsXtPjYRqos0HebEl8hDhAHEEETZnq5tXPtvVvd9r1UOBFqQIBJYgQnSNiWlzpt8ZadYPnNQe8D8g9Xj7e3hjgJd+HbthZpbduNfCo62TWJgytpQHgsgC9nvswxrci1bwkndMmxWEBut3wvFdLO9fe42QZE2ymI5MHNI7WCd631+kqJ7Aq6ZW9dQ/8XzffT9e5NtXfCmA3RZbaN9wmR2xir+3xPVOyx8tGX1gt+uoHT67XeVuI0qQFnlwKpRRKNYlBzwKYtZzDDNx1mS7s/Pd0ElXESBjnAyIBlYwlDddMoDqh9vPZJi2z1fLOzESaZnUpvGzA0TqHVQOR7TjEOfOJBfOOjZEQg7GxrQj7BaHrYjMpNlGyOpSwjtNNponuSy3mZqCoCeMr9NbX+5T5yYpXL/ZCOBOMkSVQEVwVpmTqm5orJZVWOGKeslWVmgvVbCnbzSYQa0jQALI2P9o2vjvArA3Mom26XGYR2fzP8pq6bv4BiN2yL0uKQ5omtU+MbWCJOARr1+cOJ9xrr6NhoMQD53ikAOeqXHPTEmoharELTc5UsVVlVMGpEJ1wGjy3MRBQRoHYPDi79lCcUrcWDjshu8dLx8pSQGMeH86aS7q928ytzUua80Cty02cMMSwaNBSnltDoQRipnNVc/OGNWpUenNJlSZnK1Yxnd2yHbPtYmFawCOueVRGz3iIxOIRiTgOQDBg2sZJL6xegKz2o1tlAdtWnEu6UVl0htB0sXR3AnvhwmrrFjSYPrE2zaK5r9g432OPlw1ZBeo8GJhLBaYVTF7nhKCcp4nzdUJEjSpavJT7gtDOz5oztKLF4HzLhIRme5eIcSTGkZwdk16ZU0FQ0ty6gAmExsyG6EnpQBwi3tv4d96x7Ww5p8zlOlFKZcoVdQEJQgwBxhEVGE831gBhiFRgyuacEDwE32pD2uJUlLW/vNr3YTKD/q10e8+1m5eW2q51xoBJl/rUYrUp77CI3ePjGR8KyN7J0BhZb9X8tSLJAFhJhTxntFRybRWW2iDlssoycNcZVgOyShFdGiv04kX7zS06oo1TB1sgW+lsqi4AeEtqatcGKmsKsr25F0c7ZS1MEWfWJuLRm0fUT30VOo7cSeROAknhTuE+F2o29e1IhVqY5ytq3DJeHIM4BhFux8DjGPEoB4TY4bQ4EKHkSso0xmv5wvfY4/1DaaDrITO7FEKKFVL1+XQ5vRo4g4epdOdgGKNtWDIpNwmBJFRsRSpimtclbyJ9cNIWlIpmK1Bu+RT6J7v+UwLeafspBOdBA05GvDshhLaobGWRoos7QW/UKdCqnuty0B3I9pafJhVwCwCV1hjTSOame4Ulhalqi0pjnZWSywpkm15xjz1eNoyQXAbjC/yEPZ5r5TLNlFK4P195fn+h1sIYhePg1gwJHcgWSk62wAOiN+DpgnXV8j4xjBfiNKM4ShGmOaO1UPPcmhcYUBYgDoFpOjEMER884xjx3m9IFSHl3PbRnAvUB8QHovfE1rnr9OgR8XgkxkAV4TxNpr/1whBMKhS9JzQrMCcOCS0TWx3qWkmaW8dxh6baSbFq+9zToaqKlKbv34HsJyY+nLQAs6PqP6ltIqlKLUpunXxKtS4kS6oSllufTIuu01t5kU3dAlFdP7+nYuxeT+Jvn98QmUuqvn2ubB+W5WlaOpOeVeyTYe87HSMaBrPcwpOqkkQoItRWtemdN9htuR5rP+sEL0Jw660p/1oDh7Y3i86A1X2ENR27xx7vFy8WeW1JIOhgdkM/LszjhuHpTMeS4gcDuRUTpBaTAXSaZplYbRG5DqM1RVhrZ2P7exbyBZMj1AUI+9a/2bUx00FsdwqRNpBtyDZ1oRo1u+LYFbAvY4o+lh7mHnuB6Nu+x/Y9PWzAsILctcp6jz3eO3qh4vZEk8293mJWlTZvCrlaNsMaI/h1Plp+0rapyyDvNnpOWiMD55HmztG9kWtbtJVSFwKmOwkgkFJGnMlonGsOCX1Cbh7pOTfJg4h1AxNBvMfHiPPemh9412o/LMuh1fS8tdlp9a5ldvytymUDEKzJg7N5dPtdap+j+6BevwNTAz24cO3xMY9XL/aqylvnZEC2usXMmcbI1mLSgm5fU+sKM7u6zGGMLGA6Wt2wsDycfBeGZ1t53Gaybf/lPrGpCBUrLtkCUhWWtKIs77HtdAZ35X0A70wP6wNziFxVKBXenGd+bC7MpXK+P3N/vqK1clQ4xgHRitNCtE7YHEQYRYgiHJ0QRXFacdVusAIQV6qVpGpnufYKzD0+QGj3VQSWFH57amFzWIArgJZKSRmtlZwTOSdUK+IV5yriwA8VPxjQVCmoaxOgFKT1PXcN0Fp2o8satDGgujxmc7CBVCfmG2BO063rl8ug4F0ghmJV2YBIANwih+jZFW3AeAHfbBapCubLbJILbXUk9lV1XxNMNtAmQ2Nbu4Sg3zf2ay042TU/e7x8bBd6y4nbV4q6asFVTYonYq3YJ7Ws3RgH4s0R7x0uCT7ZEi6MoXXT0VUbu86Q5lHuPTEEtBbTrYZAKZAma8Ag0uwgxeb2ac7mGiKZy3U2tlcE70zik3NhmmdKVbPVioOxrWHg5vYRIUZONzecDjf4YP0sVVObrB29k58Q7aZC93E2gKqoa5UvvcsYwGacC2Udg9oxfbf5011a8AmKVwayuSqfu0tobZ29FmsBXdObdTW06gBUYWEfnS7t3R8wslvCpHfy6G/r+r4N1FzA6wOmtz1hvQtkKSCx2Xa7Ku4vap396EC2sT7eo+MIIZJC5E4dc4XPXyb+1rMzcy5M1zPT5WKvHwLjeMCheC14Ch7lscCtmH/sjVh7X0HxVZHSVpKdza4VqQn7RmSZmPfY4/3CAGwDXX3i3KwKO5uzzKPtVnMlXxO1VqbpwvVypmolRPADiIPROdzowBmQrc78ZZ3UZsDHg1a3PYugKNSCatOCe1tUeiA6h3fOJjLtSvaMONMIeh8YhrTYd5ntliMXJSVdgGddjq9lOVjHcZfNLexzbQXR2r2mu064LoC1ltykPWY11O9rzYt0Yxev7/FBwgkbJl+XeQfYSOS0+cja41OpXKsQVHgUDwyPHhODt4YIc7UFmnsByEqfv6TVeQjBe4ZhQFWJMRKiyYWqKinnNi6ddcUqCnPGZfOuzW1R571nHAac85RSmFOmaiUOIwcXQBxDHHny+DWGceRwOnA83eCckMuVlC0TIwuQdaAR0UNbcG75aelKO+ss5prjibRsjiriDMguycz2DUpf9O5A9hMTH6JFLaRS26TQWJ+Nn+NikaGdfWl32sTSo2dGuo58+8wG+m4+mHXwN2Aqy6t79eZ6f8PdrqmdFeq2i8lGWtDe0RkjbbICdY4qkFVJtTKXypwSUy7m0VesU0mNftl+L04JQJB2Y6vp26SEjLZu399qN7L9onaL5z1eJt7dP1YfnlILmmWjEbWCptJ0ZsUBxc7ZjdoAXczqdPnXPuHhtjtLSr8W2Pjri9XGwyyZlT7mu/ygs7320y/aQOluB2yvEC8a7mwXrLr+vrl1aVMHtS8eUS+IW7ex/awdxO7xciECIXiT5mh54czpdE8bG2JuG5aW94j3iG/WVa2xgNSCNPlNr/vYjsMHn93ArGuLRucc3ntq8VijgZ6VFFol56Kxr6U2CYHVa3hX8N5cDUrdSmxoTKoQfCD4gHcB3wrPpLrtDA3NJs+JMyuuPgdvEOlyXRDX9lOX72hBrwvR0+dsc0nZFnru8fGPVy/2UuVyze0X6VnD5ed2QNnQaJXBrIB1CzfXqUKXk7q/pt/vHpFsTtrlp7ZJsQneVaAuFZ6rrkilp10NWHfNnWu6VXMtqEj1iCp5HEhjpPrIvSpvXi5M4nnr+TOePn1qq9I0UeYJD1Q94DQTUY4lcajmlXnSyrGJHZwYDBBVNBdqqbb/tVdkrpYqD5D7Hnu8Xyjo23Sb+o73bfHZFpzdqaDoonPvVfqaK65Cyg5XPIJSfUK1NUFQXYu2YBnpfcEG0Hv8Wc+FxspqZ2GatKAxKWbZNwOZXCo6g0jA+wPeV0QCtTrodl5OF/bXoQuIrrlS2mK75ErJzbWgyqoj2mDb7fezdhqT1q8etLURRdr93hxhjz3eJ4YY+fqv/2qmaeLu+T0pWfYj59J0qKs+9NGjI2+88YTDOPC13/C1fPXXfT03Nwee3I6Mtye8E6pMpHo1IkkVpYC6xRYLsLEtDtVqjQqGASdwe3uLEyHNM4J14AJjjBfs1xd1VcmlkrK1o3euWtdOreuysa/1KlbAjbNWuOIaUBVy25ZScT4QfcQ7z2E8cRwfNUlBXT4XJ0uv6V6EXWshJfOvtbFZF0zru45WxBpDyNYbeo+Pe7w6kK3K/ZRtAtK1+t/16/qyaGoT1WZ1tLI3wpZDWbIrm/dbeqSD287eyJI+hE3fH/n/s/cvO7ZtSZoe9tkYY851cfd9OycuGZGRmUWxWCSgBsEW2RAbgtiQXkCA2KmWBBEEBQGCUG+gaqhBUA3qASRAb6AO1SGgJgGJFxFgFatYmVGZEXEu++Lua605x8XUMBtzTt/nRJwdsSOjKs5edrCOL1973X2OaTZ+++3/hSSBiFvU9j2qD10RTBioejFru1TXtwtGVMcXlHQ+3W5HHkdKTF7IXrgovL5/x5s3XxspvmakFAYB1ULUwiDCrVSeufPK2AqD9zebX1BFqonrCiYJGLTTMbzNqt/Ema5xjV8f+rSQfX/Si21PwgvWZshLq/ZTq8lN4fxQyEiAUCKherEpmSbW/g9qGzBb/xtJO9+sWd1qklkqwXusvpq9mBURv4+YmUqbjS7QTAQeCcR4ZBhAwgAkEOM8mNSQfdYunYcqpRRyMQH5VqAVDMGqETTyjS9l+c5YWrQLAd+J++rXtSfcax17jQ+IcUz87Gc/4uH+gV9J5XyGnLPxRmu1QSw3D3l2d+AnP/kRx5sjP/nZn/CjP/0px8Oe/Sjsd4ZMZgXNs63ZZsoaqsGMTKrL31XrgKgqKUbCbiSlwF29YxgGK2TFqAbg/G+c2pCzbQKp5NKY54IqhJCI266OeA5vGKdVba1HictFxNWJmnVZIsEK2Zg47G64PT7DXP8qrRZbfzEg0bbHtVUaRnGY5jO5OKUp2roPWG6XYMhtiMm1dK9581OJj1It6Mozy95HN/jhtwCJoiyTmU9uf/KrvPfPuhyQKz1gc/0J1aAjr2EZaMEnl+kJFKUt7UnjziK2CDQ4lUDDquMl4oNj4sYOldywdkuttFoJtSGt2iBNqzbAJUIUJQUrZGNTQuvC0StBfTs9vu16srRw/VvQ66K8xgfGdk2ofqPWWik0LC0NazcGK0yjXbdB5drZr1i2kmV6f9vMfKLd2l9HNx0TX5vLBSPAKbIe5Ns2zNIsNYUEoylVmhYfEBUWO69eeS5tIXu88XU7j9XRnSdfxqYnJGtxL31NbmXElu/O3rOAIbVX0YJrfECEEDge97RWOBz2gBKjFW+1iheyiRAC+/3I4bDncNibves4ksaBaDRUluTgoQsK610VbYQmaFgHPju/XNVoBSklVBspDQzD4Fxws6cWEWqt1iHcrNlv5m158vPJGlvymiX9Dl31994d/+xnH/JqLB7XS/fVflUXh+8GLGDPYU//zaHuaxH7acXvXMga7tGxFy9lRXkf0Bc2iCmWG1b9xxVZ3bb3HFyx6xuEp9MG8J/L2vICVCXQxgFiWpEi7ICXFJBoiGyg0TucPdEuKgdikiNGS4AmiazC3ISpVM554lyVPGefIGlEaaQAoyh7bdyUwi7AkcrRVQtCq4g6g9/JgJ2HtP3sazG7KNxei9hr/FbxdCO5KSwdZVw4ZIFFt1HGEW6OoMp0OTNd9tRWOE/3nKeCilMD1HWgF+qLWVMnsZU8hEgMcek1PkVkbVNpEnU2TNloFN/49WFLBSS6QYm3Ta2knSj1AVoiyICE2fhzGqBtE5+/s1AJqVrH0uth6XQif7GALINitTsGeRGrm9W4RG8BA2hY6+ZrXOM3xG4/8q/963/B+XTmRz9+xXSZOJ8vvHnzlpyz2ctGK2R/8MMf8rM//zOOxwMvXj7neDQt1ijVKARtw12tjelSmabZ0NLhljjcEGNivx+JcfB9nrXcAcbdjhACwzAgqhz2O1qrzPNErYVaKkECJRREAqVUUjLr+ZSiu+V1lz1Z3rd47sw5Gzo6RFLNBA00a4cApk0dk5j6QsRakQgqzS59vTVzEJvLTC6ZUjOXyz3ny6N1X6vxiFNMpNQlxjANXZf8usanER9RyG6pAXZit9JrVQ/o9zKtVMsviya6dD7dWsgCyyDnQlUQa7cvSXjZca2IUovBrO1iRA8jOg6gkJoSmslwpGSk8+Zt++rvPvhP4/3YDq/2IhOoEskamBUuuXE+zV7Izp7UK1GUMSijwJ7KTc3sGtxQOaqJTat2ooMJwzdHkJpjTv1LWL8L6V/fNa7xu4Wvs20nI4ZIjHFZS9GRjyFGxjQgwHw5c7nsqKXw+l1lrg+mK7vRauwtBNN5jaQ4ECQwxoEhuoGC9illiKFvQvtGVUArWmaKmmNRCUrsRWj084JijmO+fkqxrWiIA1EyQiCoUQVs4MM4eg7qENKqUiBOng+ii+yfWX6awF3EZQI7j745Ktu9p0M/L8nynV5z5TU+JPb7HX/3X/tz5mleOLL39/f86ldfMk0TIcSlWPz8Bz/gpz/7Gfv9gd1uZH/YGTWvAc04ta0ptVZKrlwujcfHikhiHB8ZxxMpDaRBGEh2vIYVqdxhclzaGmOKlHKk1sL59EjOMyUXVHXRoW1NGQabh9misL14jTF6W19ozYagESGOkVIGYgo0NdAHUczpPRCiIBGzmhVB1ZRNFuUVDH2d85lpniglc57uuUwnO2e1RIwRHQb27MwZ0BFek+u7rs5PJT6qkF1x1n5Sf4IjLojNes+NIuomyS732rYhl1t1RViQJwtpEYZ22RBiMAmRuPo3g670gfdaIL2H/xQN9QlQ6ZOgwYpc6ZOchhKJ9OGUaE5gjk/3wqB3Orfxa2kD/l2pPEVge2cTvvFU17jGr43etWCbdMSKuxgjyROP6UJGgghjSuwGK2S1FWqxrkbs6K26HJ3HkxThVIHu8x5C9JUuhlpKp6j3DWhHXJy/vlCOfC084T6wiLR3dNZWQ0C1AHEZuLKzz0ZyZ+1SugQfiwylbO/DOiC2dEpkfU99By5+0rLPsBUAvMY1fnNIEHa7ARGopZBSpNTC8XggRiHEZJvBGIxSMI6M42AIaKfAOcqz5I5lX2kFn0hY7aW1uUPfmm+33XZxOasQA1GtgxJjpFYrAsOSxzyX8fTnNv/2WPoXrmSwvSydUVg+Twg94T/NbpYLraNjRW2ltULzQrdp9U6K+N46spqUGJXoyiz4tOKjClmRrlbH5iebVLJii4IsBVxfiFa0+nM9TUGspewqjRV8ClKCkIZESBEE6hCpyQpa9jsYkiXJqULt05ebBda5NiildaQ0oOKS7BIoIaIiZBKTBuYmNKyFMQbzNJKYTEdPM6MrFYwSrI0KNBWmZsNq2sriHW10n17uu4LBN/aP223ANa7xYSEiDC5OHhx5jSHaepFATJFxGP16YEiJEIQxDex3IwD3b1/zhsKcA8PJLGNxjp0ZHvQNqeIzygSxy5B27MZxs7YtqaRohbMhnYawtlooNENLXQC9o6FbSp70uSw1KS5L6NUpB4KQaGrIk0pExVGoFEkhmEqB4tR0Mb+FuppZ48iryobGoxFV5951BLp/x/28ptf1eY0PiyDCfj+SUjRnq9rY7UfGMZFzsTWaBiQEbm5uOd7sScmsYUMMy+YJMdfI7s7X9Y9rra7XmillRlFyGZizFcLBByIVTOZRLQPHEAnJitIhD66Yg7ly1Qa1kktlyjMxBHuPEnz20Y7+htFyBFMJqbVRqs2Q2LCooa/7YTQ96sOO3X4ghkSIQvOuDWG1nWbJi5WqM7meKaVQ2oxqpiJQG01td3qZRxQY0mDfm8Tr4vyE4qM4sq4hsEFVO0rxzR1Wv0/Xy/Mc9wSJ7UpxT1HctRAWCUvbIA0DaRzQKMgYkWT0grYbYYhQmsuCVe9y6lLMNt+9NW0UNTcfFZfZkUALiSIDTQIzgbkFJqBJIDn6KzGRdjugkdpM0pmkyqjrFHdtylxBtKHFWpWi2++jo774ztmLWdnCQf07uWKy1/juCCKMw7Bw4IzXlthteHG7cedrKLHb2X3GYWC/H53TWrlcHpELDIPZTGqTVRFHenclGDVIIkFsUnhII+O4t3OBG/JY+9F4sU3NbKC1Ri0z2jJKs0JWih3ljtIIssig2PnClDxsqQSb+Ea8iB2wvkgCqUAghIGQRjvPKEgTtLGgwtLVFgKsArm9et5uLL+hrdIXLe8rQlzjGt8WHZEdhsTow1WHmz03Nwdqayu1QISYBoZht7TrJfRsYVP5jdV2emvkAVY45pJRoORMzrZRjR0B5WnXM7jZgAgUHwDThk3+R1tHpVTmOZO6jq0jsR2MMvqPIg1qM33Z4FrUtVoHZRhg3I3EKOz2A+Nu8M4Ny4Y0hN6NcWhJG7UXsuXiDnszDXP901ptTdMY5gmw8nfHHsTpTdf4JOIjEVneK8z89uX/3zyQvnWTtBlM+eY/bfsh9qJ9sEtD8CGtPjzshaHLgTUvfsXpBd3X2f1+ECBKb3sMEHcgkRYTcRhRCVyqEqtxbWMNhvAoaLPWpmpDaoXiCKsITWxpmq+XI0itoa6mYFSE3ipdvyXxxy8Fvq7f4VWC6xofEiJiKGuMTwrZYRgWZHa5fUjOy7MOR4oGfQYx6OapvLqval/sXW2gWz43UYJsxNG9/bhMEQeXyVJFW/B2/6r92B0SdLG1FZ7OOG7eSVcjWM4ZbWkpgk1m27BWRBf5EfeCF3t859iHIOao1zePS4foybfKtydF/cY9r3GNXxedl67Bzv0xRGJKUNtK+ZHgJgJrEbtQ7fwY7d3NpW0vqwLAMj+yOSz7fqvfFzZ0tr5/2xzeXWd96V6+9zl0s8l7SiDcgjL+/lozEy/dDp1u1Us2j/f3vaiCsJ5vvrXQ6H0hVZrW1ZmvNbQPVl/jk4jfuZAVf3Bw0KQLoW95Y9sCrf/8xqL4lvtvr6+8VUN+xBd5CZEmkQZcaiO3ClEJIdnx3oRCXPg+MZpIsqRI2I/EFAlDIh32hBRJw4Hd8TkhjjAM6G6PSuCXDw/kr99wygXNSp2UXJXLfOFyOdNqIT8q+XKhqnIeIo9DIgnMDAxUM1g4TxAmQlOGUkmu2VeXU8VTHLubSyjdxOEa1/juSDHx2avPiDEyjF68psjoKGxMkcGHSmIMxMHoBylFUjKOnQpM88w0zZRSvd3evdYFAlS65qww50phtoI5FoZUPbEmG96S1alI1Vr4NBvmkBht+hFYNm1LEQx4MsVJgbJIbnWugIB68hIBKo3qiHAjdM0/SeA+8UEjUZ2UoEJUH1ZVWTL6onXtBf1S2DvNwDazf4i/6DW+D2HH42BT+T5nEYKQols8u2qBdS+SKwF0Lqk9h9ZGy95oVKU248HGGDkcTL5rvx8Zd52S0Oc51jxqlWsvFF07VhvFkdycM3nOzPPMNE3kXOzde95lKYTX6AVrVy2wwlIp1ZDc2AIhBYYWFvtYUyywGRMRG9REGiLNZ0M6L18IyS4qIFF5ImQLqFRyne0zSmPKIwTjCV/j04iPKmTfL163Mlvrveznr8ct+lCI/7bdSfrFpLJcXkvcVi8EWghUlEtVJm1IgxghiiWlKNEml2MkjSMhJuJuZPf8lrgbGQ97Di+ek3Y79vtbbp//gDTskXEkHA5oiPDFF3z1Vz8nXC60SyWfK6U20sMD4T5RS6ZezpSqSFOmMXAeB6IIQ1SSgLRGcOH30BraZpPkXKwRAk+X5joZvbj+XoGfa3xAxBh58fw5KSVz8tkgst2ask8ZhyCmDiDiCCkmrA7MpTDNmVKrixR0ak8EUapWT8hmV6m1kJJy2FXKrlqRnMBglrAmQtRak8H1OmLAfHCh69T2rot1KCzZLql4oxO7ptPg3D6hmuIzSHAXvQ0FKghoIAQlJqP5DAQG3CFsU8i2jnjBk6GZTktSuqHhdYt5jQ8IMa1YbQEN1QtQoTU7ppchSRGT0erKIlGI7npetbjajR2mrVknIsaB/T4RQmLcDQxD16T1crXnVoc4t6mkaaM5l7WUajzUUsh5Zs6ZUostydBtYtd4v5gVWIpYVaXWRi6FpkIqyZzBvJAO0exy7XmdBN99q3FVEbWVG6IVsjaXAhL0yYsrjVKzyegFZS4XQnzPGOYa3+v43akF8j61YOV4diL6Nx/z61t07xdqndC+vBhr6ur0NMNfBI0JUCQl0v5A2htHLzqHLwQvZFO0AvaZFbLDfk+6fWZi0/tbwv5IGPYYoWdvi363ZzgcGETYR+V2MMs+k0NplHlmHgYenYdbWuNSKjEIFUjeTh3EkDCaWX7awAtuc+mfWd77AjZw9dUK8xofEp1aEFNaitZ+6fzyrRf5++LhKu8vxN7GBJbtlXuk+wasPekm6JP/96drvtFUPNHRi8DeB+32r+trGM1mQw1YaALqn9Vf8WlGpakNnqiaLqXdfSUgdZME8ef4tu32wgDU919HWE9L193lNT48DIxhoQJYTRcWbujynx+Ti2LA5hhdn2yFO0zX1QrhGHydx/fX99N1DiybNLOm7gVtvxjHtdvdiktxLeYFPCH7LEoGy5vrr6G6INDa2mLrvGEgPf1wm7W20tX7JnoDk8n6DlYqRPPBt0Kp8ZozP6H4SGqBfgOVXfk80DuC4oNU7+OyvSBdrq/A7HqIyuoAVFSXyX9taoY/KcLxyLAbGfYH7n70JxxevCDGyG53sCnLGAnjiKRIHBLD8UgYEiElwm50J6MdpFtaSNQglGDcW+4qtz9qDDnzKu2Q4YAC7776mtdffMF0PvPP8pl3f/3Pybnw7rHwOF8QgXFIDCmRJPDZsOfF/hnSKhIf4Hy2ts6saKnLd6HCYvdpyV3ZSERf4xq/MUIM5qXuSGxHXlft2E0y6pWcx3oMBkQSEhKKUKu7+LVAYPDkKgSxZFFUqdoWEXLtxacYCqo0prkYlaAPr4j7tQdTF6CJc9ssQUdxGIpmULFr0prklqFYi3oAq9V1U5uaVjFbzOhFcOia0uoW1mq322utvaRNmbzZOPvqE7Xhkg13sF1X5jU+IHpujAFEkm+Q3AjAC7uN3tsiFyfCQi0wFk2zoWHpCgKREHaEMBq14LBz3VnTWg5xTfHf2KO2Rs6ZMs/kPHM6n5guF+Y5c76cuVwmz9+BlIxTP447YoqrLW4HsJYCPS6Fc9dvhkbJME1WHOdcqNXXtbBoR/cBMlTozREVU19Jgxk7hBj6/tKLB+uQ0LJp1UrjfIFcLosD2DW+//ERw142SiHSKSuyGBws91h2krIcpOvQ0jd5oU/Wsv+O+G0CFZuO1KamINJM/mfYH0m3d4y3t9z95M+4/cEPSePI8e4Zw35vvs3jCDEaH3a/J8RoM1gde9EIOtAI1NaYWjHk6EY5ijC2yvHmGXfPXiASeP2rX3F32HN+fOTNP/85oYHmymMpzFMFgXHcMQwju5S42d/B7TNCdUmwqlAymie6IPsTtFlX7GnbRL3GNX5TBAkcDgcWofJl2MpXohezduf31pz29blp+WmgNS9k1aS2+vO14JzXWtBgbXx7Gm/F+7pVVXfmKat0XnQ6gLf7AbR13ZKA0nlzAdFqW2atS0FrydNXhsjq1UCzcwQYZcclAvvAS9CAagQiihXs4UmCX7fbrQPGdLkxb0F1niz2ete4xoeEATxhNf3wDVRrltNqXTsNIrY0OuXHQpf/BJPOEgIxDaS0I4TEbmf6s4agpqVtv+q5bjKwKrUUZi9kp8vExQvZaZqY59k2wml0WlIiDSYhVmulYPSGp92dFdpSFcvVCKVU8mxrvJRKbQ1pas5ejnx1KyVdveyhd1TT4Hxfr+51mzEbVQuiAS0NmZRYwoImX+P7Hx/JkV1730+6A9Lb5cr2H1fTBBa9xoUVI0+fZGXLeLtTxKxnQ7LCdLeH/Y4wjqTDDel4Q9wf0WGkxUSVyKTG36NhUj2h2sIsDQn27qs2T3IBrQlUyK1yaYXWlKllLmWioRwOjSENbv+353A8msXf4cB+v0dbo9QztZozitSKSkEQ5lrJrS3TqiTbYeqUaBKWhblMffu30H+9YrLX+KAQ3kss23a5rzmfXl6OLS86Fd8c+sBJSmlBWETX9r84XzYG34AlR5tiIHUKQ1xpDAaqytLZV20LBxXWlivizy/GxxMMiVVHYG3oyqvvhSu7QbF6F6gXsroRhIcNjWCzPVTTrpXlOVaqj+dLlrORI2e9lDaHpd/XH+4a3/cQb4evqXEtXO34V99M9la53cWontvb+hqNzq9dqUKrKQD+QNscNs89sOwvbVjzW9rv26I0hM5ljcQUSanbwcqGdrC6fG2pB+vH3OY0nl7f/rJU2LJ8KV3xpHOIg59fTHbs6fpeCUv6rZ/rGt/f+Cj5Ld/rLQfuQinoXcsnzb91gGlbs3Y6wdZJ5wk6KYEq0egBx1vi4UgYBoaXLxme3SHDSHr5knhr19vNcx7DHm1Q7i80JmqrTDlTqk83e3umtkou2dqRRZnnRmuQa+VSjDy+Px64eX7LMA6M8cDxp0f2hz0JZTdEptOZr//5n/LFn/4pjw/3/PXXv+LN67O9ZmnAxJgGboYdKY3sRPhs2LEbd2jOtFzJ2ZEmLaB1s0lY25fXZXmND4tvL2Clt0caNK8oFWiyXWuGsISQOB5viSHxbveGIIMllhbRaqbOw5gYdpZI2bsVbRD2B5uaDjGYbuY4UFu1QRWs1de0orVYQnZElQXJwdulZtrQ3bxsg1eWFdFlA9YzTz952BS20u1mq6FWoqQhIBrMoKRmlEijUKsNkAZJvD/QAr0wdmSpVVqzzlAuphV9jWt8UEh92nIU9cEl43RLa0vh1z0LRPHNkqGnWivalJQSh8PBi7lkHQwRs3SdJ1tDsSHBgJpaimu6+krxgrduEFWj2EWaKsMworjj2LAjxMRuHLm5vWEYBuZ5BgmU2s0cbLhst7Nupw12Cq03XTzBKxFVu715Z7W5trMtY5POE42uWR0Y0s60bSWyG8+uJ1vJeVpQ7FVZxNQKVmfPa3wK8dGqBT2MS8OaQDfF7VO9OKMgqOjyuG/QCTbXW28vhkjY74m3d8Tdjv1nn7N/+dIK2RcvCDe3tJjI45EsA7lVHi8TUynknHl8eCTPM7VUyuVCK5VSMvN8obbKnCvnc7ZJy1q5ZCtkX33+ij/56Z9wPB758Y9+yn4YuTkcSQLjEJjOF1794HM++/xzxnHgq9M9pSm5mK1ebUpOhXeXC/vdxD4l7g5H2O9hnmjDAzWcrYCtLvi+NEbXIva6JK/xIbGst82aW6KjPW3FLurm+GpLIWv8ciEwDNay1GZoaKviXL/BjRcgxkaIptU8DJE4RDdfcPSmQSyB2AKN5rJBdqwHdDlvtGWyJS5tUWnNbu+yWFqh29Sqt/rZftb2BGmW1pY6OUTjzUron9yK6oYlvhDtG/y2GS67r9IUqkJtUOq1kL3Gh0anwfSCy27rKhymbdw3mHacCQLVjAbAOKkdhYzBJPW0KaUKtdnaadoopSDSCGrHvapSsqkR2AvoUsx2hxCT+eoIrBWmTc3ha3DFn3G/Y384MAzmQJaLGZgsCiliRkUim4Gw7kukvWIQpxysBmMrTLMt8lelkZgGBi8w0jAylJFaC3Wxre1faO/EuMTZ39Jf8hr/8sXHGSL4zw2d5SnH4L0jSTYC/50Lswoub9sRfhCKIGmAcbf8ZLejDSNZBK0NCRWZZkQuaIjkGWoq5FJ5dz4x5cw8Zx7v75nmGS2FOs1oqYYU1ZnWGnMunM8zpTZybUyl0IA839r5R6R3Ir3VYzIpISZ2uwPHuzuqKrvDkSENqEJ2hLVr6k0lI8ClNS7N2kQTQnEeU8D4gEEX8GzTBL0uy2t8dyybHrVN0CKjilN1VJeWYDdn3jTanUcnpBhpMTKkxJgGLAUKtVTQQM2NVgxuCU7ok02yWrgK/ptZ5gZomySKLt0bF+HbqCZ4q7RXodKRWzM8WDd86xb5m2tk1X0FkO15yhO8qisqiGlg9tNQ0/VSVanNbDhL06UQr80UTK5xje+M3i0HFsmdzbGJrtSBJ3rGauL+qkrNhZaLO0SqrVGxDVY/DFttKNkKwWoufapqslq1Ore2F5qb7KL9Je2NxpgYVKyQHUb76frTW959iO/RiMQL8aa+bDdenuJSeMtC7NyJ9TvoAur9XCBqQ6UhNGJIpDiQ0oiIkMsMhIVTrGqfLcXuGvYtO9JrfC/joyxqu/zOQglYSWh+j3XNhiXdrCy1AvRm4WqkYHpxDUNh080d6dlzZBjh+Uva7TNaCDxKIp8mmsyUh4kqNtjR0o4WElOe+erdO06XC/M08e7tGy7nC6KNUAvSlBiEIVkinnLm4TSZR7RCVqM13Bz3ntgHhEDOjZwrqpGYDoy7xMsf/gk/+1f/dR7eveP1ZeIXX37FebpwOj1Q8olWG/fnMw1hNwyEkChxQHKjSkLTDqmFQSpRKwFlR8MBomV45RrX+JAwlHDrBbee0hcZHF1R2SeFLEqUwHG/Z4iRu5s7nj97QZ4npnzmfH9aNnXShBgFboZVZaBFpPnQh01TIioMMZkkXSvolE1AHUNMwVCn4O86uNSO8dhlQV5t49sw3mzr6c6L4P5/FnKrITXVHosV3QKQoDUbEOudE9FGk0hsEUXM4bpZkTAXK1qrb3iLI2Nztg7ONa7xXSGYHqqh+r7+pB+91ubv6xKB0KwzkOeZ6XJ2M4RKywVUGMY9h/0BEB4vmdwyrSllnij1gqr45stevyO5IQQO+wPjOLIiM3a/pmJWIm6sAJDSwP54dPUfISZz4wspkMYEUUynelwtZ0v1ba8PqgURdmpIbx9A9R0ktIpo2ewyex0QfA40IEmIkkhhoB0bKY3kPFMr1CJee8vCGz7s9uZY6E6F1/j+x+8FkX3y23azRdfEk1Xr2O/UPdPbAtzoUuxawzGARMLuQLp9BsNIu72j3d5Rgcc58zDbQNYln5iLtUg0DmiInKeJL16/5v50YpouvP36ay7nExFlUHuFcUgc9yNDilzmmfvT2QpZjJeLRKbLGRBiSOCKBrUaOhTiQCJwc/eCz370J+yOtzz7+V+x398Y2jpdAGvXXuZM5cyuVA6HTCo2tWkT4gNBhcrEQCBqIyGE94qMa1zju6LnhwXlYL0ugC6+7GsRu72uautwHMzU47DbczwcmUMkzxP5YvJXQ4oMIdGGwDACO5PQEvUCVsUltSwxp+ijoVWJItbYV0OWeq9V/E3KgsK62oLiz2NrsiO+uqA5XqjS57lX5Gu7BZT+AkEgWmelitlbBkC0Ld9DaVCdy5c7EluVqTbX2GxeBF8L2Wt8WCw4jytfrEVsH2RsvS2yIJktF/L5QiuFVhqaG4Iwph27YURFmHIDMUgol8w0meJOzo1ctpnDCr2UEmlI3hmxS9NVXVlCYIwDQSLDOHI8HEnDgBFyGo1mKGyKxADRZS1DCLTavGvDwqQIAVTHZWhLerdmQaTdCKX/ulCjHG0NwVHZyK5VR38T6XQmhAyw6OYOaWDcHQw9/ha++zW+n/FRHFlBvpVWsPBjthOFT8HaBUNZppddJsfUCSLEEVKipYESE4TAXCr5cqEonOaZSy40tUTTGiCB5szd1prvfNuTNmZvJeI74w74EASJhgmlODCkHRITh9sbbu5uOd7dMu53GA9Jl6FpbbZrPRxvaE25ub3j7tkzYoxc5onH0+OapL2tm/PMebog1dFhbQQfXFGEJGad2dslV2LBNT48bIhxPWB6kdjbJj1x2r89SXN+UIcgOPmV3W7k5nhkSIlSJvI8oTRSHEAFrZCnCmEmBCg1kMbg9piRpD4QJqbxWGsx/cnahe9WPdi+8XWZWaMW0BOZ0sR0YJfTivatXt3skdfnfOIYqD4EAut5RkBpiJpmtLTm1pZQ1HiHzU1OSm3u5W7SQa0Xvdfp6Gt8SPQNmhqio86VhZ4vHenx3LIMaDal5UotFc2Gygp2e3CHS1MYKYgEhgaqwZVJbNgL1v5MiubyNwyD0+RclzYEH8KywbPcKkEaIrbe+nuU/n6FZa1tL5a+TVmkyzN3q92lS8LCErKuzNOz0LoJ96/FTCJcczZE6mL8EAnSHdBcLzsNC+XvW8nu1/hexkchsoQVQ4X1gAvON1sP0J42/d6eoGqD4gMUUayVLyEg4xE53iJDotzcUnZ7qsDb04n7dw/U1jhNM5e5ICKkYUdMAxoCumu0IVHqTOtkcNoy7EFtFC1QK0EFjcBgns+hDcQa2d/ccPP8FcO45yd/9jN++nf+nBcvX/Hs1SsIkVLNpSg0K8KP+zvCD4W7Z2f+9Fdf8PXrNzw83FMVHu8fqa0SCFCVqoW3D/dc5pmgkFojtkbSxgEYQyApNAJ5wablGya217jGt0VTZZozawW3bjqX0/oGoV03n64DK0C0LoG2xMuXLxhjIOfMzWHHflgHLawobTzMJ8qbggQY9pFhDMQUuLkd2B8SiC4T1E0rtcxos/ZjDD2huQi82IYyRHcSgoW7JDVAUHfFq6bFrI7E9p+b70K6XBFKrZU5zzbNrauwetVKaxXRwKCR1GzzWFqgNaE2ZZ4LpVjxmvv0t6oZmlzX5TU+NCT6dP7GMAQvYXsXQzHTDrU2PXOjPE6UOVPnQp2sYL09PmeIIyFG8k4oBJoq4846B6ow50ouzWtnOwvEGDkej+zGnc1uzGZOYIiskGtDq9oarY2bm8rtzQ0RG8bUbtJQccWFZjhtK/TOZRpGuiuZBCtuUwxWNNeKaCOKGSFEUQLdGKJ3cMTWtZ+gYogECTRptLRz2lJgHA7MqdqQ6WhKKSlFxp3Ror5NgeQa38/4uEJ2K7vFylPp//QUgV3vuGkoGA8NXJvSC+E0EvYHGAbquKOlRFblYb7w5vFEbY3LeWaeMyFGDgdbwMRISxENUFs1xLW3HTczKIbItsVZyFqNAUm2qxv2e26fP2O3P/Ds1UtefPaKFy9fcTjegPiudTM8PQ47hmeJcbfnxavPePXZDxjGHb/65S+JcWChUjRrY16miTlnRIRdiIbAepnfJDAAAwIEIkYz4Ir8XONDQpXS6tNile5hta7XNYFu0ZF1DQciRLg5HkkhUHOmlkyZZkopnM8nzheTorrkzDmfkQDjHEljYBgCQXagCQkQUyMEW4uW9NZEZQWsO/zIOkzC8l5C7zgiRKMQNYXWE1XrH/0b0TshJjVUPZ+nBVEqmPKAoNCKd3SEUqNxBquSa10Q2dJtO3065jobfY0PC1913amyJ0RhQWkN4HSdZnV1n6q0qVDnTJ0K+ZJNR7U0okRiHEhJGZwekJJ3HRvEVEmle8/ZkWqOl3vGcaQ19VZpJRTrbtZm2ut5mmml2uBy02WjqyLWGQnOG/CLSeQZFSC5aon4GhNHZReNW99ch37O2a6hTSep610LRi2QYLMqADU2YhiIcXAXw8EoDjHasFe8Dnt9SvFRw162IFfkh+XAXNsQ72OyKgEN0e4eI3EUTx2m8UiI6LCjDiOaIpemzNNMUWWqlYoXvkFIKbrrR2RIkRaDz5es3KPm4tAhBGKyBBaaobdpHEnjyLDbEffC6J/p2YtX/OBPfsL+cMNnn3/O4Xhk3Jk134bG4wJ5uMSI0vy2kAIxRcbdjuPxSMmFVme0GZ+nD9wQbKitSTArvmHPIMJAI7XMoA2pBZkvtHpNmNf47jDqmQuV69O5fkSfILOhc9E80cS4KXR1pRmkaB2Lw2HPs2d3lFKISQgRcinMOnPJ61roFyEQQ0JEu/COF3+BLgHU1aUDxs2TEJ4gsorYOcM/nYboHZ1GC2412zapsNcLdJ6sAzxO67FauS00Ci+tEaC0Yj0Qb7G2FnxIxnVyRYnCwtu1d31dl9f4wJDuMPct1J6lPbLdgJpcXBBz1KtqGrJmydrXdXe+Wg06TJdVSX1IUllofCGERaLLjvFuiiAMw2AFbqhIVaqYyUlrlZozOEU9Blkuqj7LUStNGqTk/xa8wHVKX2uUoqgG5nlmmi7UmoBhoR6oTYa9V0uof0/q6gyF4jqyIiyKCWbW0Ato6QD0NT6R+J0L2ScGBn3eoVNRO9Li69JyjUuZx0BzGoCMO/a7vSWqrisngXJ7S7m5pYrwdcm8ubxzCRybHAaIY2IcEiFGjscdu92OIsJJIOMtTDU+mwIxDYwi0AZkSKCNw/HI8fkLdvsdh+MNz16+Ytjt+PwHP+Jnf/GvcLy55cVnn/ODH/2QcbdHfNCkYZyi7h3YaqWIUqPCEAi7gVR2PHv+nB/+4EeUeeby8Jbp9EDTxtwKWc1drMRAC5EwDIzPnnF7ODBo41gyu1Zp05n8+gvqufyuf6prfFKhNoUPhmgut69lYy9grXhdHXlS6lax1S5qJgJBBlQj4/CKly+fUWvhzZvXvHv3lmmaKF9kHs8nS9DFDBeUQGJgFw+YK1cGLxSlKU2FELqZpSXYFM21L6SB4NqVDTEePaDBHqOtUQWjFzShUc29Fts+iwRz62rqYtfivNaMSGBMAyGY6kGVShUzhS9VkVq8xRlBo/MIZdHMTjEs32bn8l7jGh8U1h6gqQ9cdn3F/sOP1e6kJwhRIoOMSMDoPHMzya0K3TJ6GCI6Gp2vFhtGVpTUcyostrBdU3aeZnvdTmcIgcPxyDCM1FKY0ok6Z4YhUcvE+VxJo5kixBjRFtgPkRLUNNnzjDZlPwzshsiQzAilNHsvtRZqLoQAQ1AihZQSt3c3BMEQ1Bi947Kp61HUJSxbq8x5YpomaitIFMbdQIyBcTcujmOdT3uNTyc+ilrQE8emH9CPvfUmWWkEVtAKNUYjqe92xOMthECtoMWq4LY7UMYdGeVxmnhzmWiqxGD82yDCEAJJus6dXYx+tA5hLBecE0c0e9torf5ht2PY7Rn3e453z/jshz/icDzywx//hD/7i7/g5vaO3fGGm9tbS6oNatl8vGWozac+g6JRbJpzSOz2e25vbsnDBPOZdglmkLDh19UuwxID8XBgvLtjUGXMM0NrVBFyTN/4nq9xjV8fbeXdbeg/PTks/DXBJ4DFvdTD8uim5gIUo7X2IbDfDQQRaq2WXFqxbsibgYDrsDZoBTQZMSaFAdxtx5ISqBpZwAwROqXBtShjXOwwxeV8HMhdsRkRe/1iBSsimzXJAmzJZn3auaAiKC22Lc7jhTfG262mk2vVc/NWr71LESG6UHsQK6qv+fIav01YxwSjxnwjZAGBjGpgvYpIRImIivHCaUvHw7oeYVUF8aFKVZ8JwZ3BpCC1UmslNzMJ6mhuP7bHcSSlgTJnKIUiQgwO1NCQqAiDM/GEFAzNKoIhss1aMcnfD9Jo6pJjrVFzRkSZLpFzEoYhsdsN1HoAIEhgqUGXQbKuTmLnkForpealu9JR2JTsnLF+0deV+SnFx3Fkt8VVzyLeFnjy7yJoMIWDGiMlJWsRjjvksIcQaRVaNRvInBIXhALoODJgbRNtNpihCCV0wXTIogRRiioF03xEhN1uh4rZ7u3G0X3gYYxWEB+OR16+esluv+Pm7hmf/+hH7PYHXr76nBfPX7A/HEnjjhS6haW1VlCWAazWGufTA+/uX3O5nHn9xa948+UXzOcL9XxmRIgEZoTklKKg2NRpEHbjjuF45OZw4NnzFzx//oLUGvt5YiiFGZjTSFlLkWtc4zsjOKetI7Cxo7ASrBjzSd/kiKyEteBVrZRSjFdasrUVgd04MI6DcV6HwG4/gijP7m6Zppc+yW9t+GFM7Mc9QxzphbVq8AK5+XT0uhXGC9Xm0ng29NElwbwgff9DbpdE7yV68S49AbKepVozJ6Ut5ciqUn8amyGxNSqCqCX50ExSTKRzjf2hV+mta/wW0bnfyyaqKeqDWVoVZ7DYZtG7AagNUGlU44mqHaytKjkXo+K5rNVic+Ibt0Uqki2FgCebPZOPWymC4nSCcRiI2IYx58ns2xkZ9gEYaNpIQQiS0FKtO1LtfDFPF8vVrEOYrRRDbVV5qJnpfE+MkXmemPNMSon98cj+eERCIKaBmJKbIhg9oVZz4yw52+aza1Avm/K+cd0iadf4FOIjC1l8iyn0SQtdTvSyXDQEoxSEQB53TPs9LSZ2t3fIy1dISqbVWE38/6Eqb4sx19rxjuOzSGuN0+me6fxoKUqEEmzqWaVRqFSUqZkzFyHw7PlznoXI7e0tP/3pT3n+7Bm7ceD53S3jOLI/7Hn+/DnjaNZ7t89eMDhvdr8/EmKkVLVJTlUrxnuxrs3br4Wvv/wb/vv//r/j9PDAP/vv/jv+2T/6R7RcOMrAcxmoEmhqU9C5CVOD0JQkkWd3z7h99Yrbm1t++ud/hx99/gNCqaTTI2HOPH71Ky5f/JJJ3n30n+oa3/8QbE0EkaVIDRJIPvwQQ7CNGeJuW5FFFcg7GaUULpczrVUulxPT5QQIz5/fMezuIML+MEK45VD2xCHy8sVzam3M80QumRgDx5uR3ThgU9oZxZQOpknJrXNmC10/0wpNF4Zv1RHVxcj2SYdlRV9lLWLxKpRofOAO4+KbZOflVq0ENd97DYoMsraMsGJ1lESS0YraGhaASEwvcEGHWqt/wL/uNf5YwzaOEfHjRcEMNuaM1oZWoAjSQEUN8UfQJoxpT2LgEmeEaLa0uXE+T5RmklopJQJCzs2t0RvzlJlz11k15NIGqJwr24xmUGtbBqaSd0vHEKA1LqcHvnr7FZfzI4ebPYTMbj8amnrYWRcnZx5Lps6Z+RR4dIvbkJKZJgB5mjg/PlJr4cvTPafHdyDw8uULXr56wTDu+MGPf8xnP/iByVneHNgdDo6N2X+lFqbLifP5BM6PtUHRQIwwJDtH1NrZx9di9lOJj6MWLNnkCQzLmj86kiKGwAahxUiJiZYScRwZ9nvEfZ27DeR8mbmUmYZ7Kx/2tFqRfKFenOLeddcDFDHtuwZUbYbqBGHc7RjGkefPn/PjH/+Yzz//nMN+x2cvX3I47Nntdjx79pxxHBnGHYebW2Ia1vcMTHNGz5PtXJfEiisQKELlfHrk9Ze/4uH+ntdffsHbL7+E2tgfn7M7DlQCo5o2rKoYIuuozziOHI833NzccvfsOc9evEJKMZ7gPFMvJ2Iar1Ii1/jgWBAK574Gsfbjej2uPFmnE9gAom9Gm+m9llKY55nz5YII3JQ9SjNu7RAZGUgpQlPGNFBr5XK5ME0TEoTdEL3F2Kc3IgWhhEST4uito6jak49V1cv76Tz8TWpaR2UWbJSuHLugsdZnXRDYJa1tKEf95ST4cznt3TYDgSTBp8dl7XTSzGZXrRDRei1kr/FbxEJ38XZ7rdRico7UrlZgvO0OmsSQkCDunheARqtKKRWJlaiG1qrThnyvRamV7Oo4aaMo1KkET/i69H2hINHPD9qYp0DOM5fLmRCVPO3MBMGpBSlGYhDwzWctmTxPtFoZVInJ1AtaLZScKWXm/t09r7/+ElBqsc3tbr/ncLzh9u4ZbWgMu4Gxb3BpXqAWQ2RL9i6S2QaBbvjqsgxyXvuXn058pLNXlx1f23hmbiDOi3U6gQRKiNQgXBAemlJr4zzPPD6eIcVler8Bk4K6oHFz/k2thWnOXOYJVB11sp1lVmUoldqUKRdyaewPR17s99w+e87LV6/44Y9+xA9/8APGceTu9oZxHBiGkXG3t91jTMZ1rc05rz6cVqq5rDhiJa7UcHp85PH+HfPlwptf/II3f/NLTg/3zG/eEi6TcezCRMXkSyKBcdwTtXG7H4nS2B0OvHr5GS9fvuLmeMPxeMM47ggxEWpDYmLcG71BvMC+xjV+Y4hJ7ATpg1xGJQgbfvlWait0NFNAQkRR9vu90Qqq6TDP2QrZNA4Mu9GKvHFA255W2+Ld3moz/npMXnhWWrVp/zQkYkymMV13fsYoFHzaeVOwwiqRh4qZM3REdvM5t4hscEH3jiyv7knr3Raqgidw8SrWaBWCRPHBLqMRRRF/qgbVCvyWzdCh1so8z9R6HcK8xofFIinnnHSwdnmt1Y6vLIscXpZIJBDUTABCEgNbjjeoNuIwGKJaK6k1qtqmUEJgGAY3BDJLWkQYUiKmRGuNEGZyLsTYXIa52LR/n/jXlUHeVQc6PWGeJqDZ0GU9GG9WN1x3tXWiYhzWIUZ7TzHZplZNYWhI0TbMOXN6eCTPmTdfvyaNO4ZhYM7ZJSrXJy61cjmfmafJ3y+IJPo0aPCzivtIXOMTio+mFjzVapOOsRhTzlvxOSYucaCEwFsiX9VGbtAezrT8NepT+3EcLbEMO2S3B4SCormQS+bhfObN/YO1R3zcOYiQLhdStEU6XwolF159Jvyrz1/w0z/9M374wx/wr/8b/wY/+clPHM0Njli5Pp2jrLVApS47ZStoDYEJuhAl0Fp5/Oprfv4//BNODw/85X/9/+Ov/qv/hsvjI/O7e8LbB4KCzkI5G5I7SOTu5jktCDeHgbof2B8P/OTP/oJXP/oRu92Oz15+zu3tHVIbcdghpVAuF8bjHWl387F/qmt8AhFE2A02lBVDXIrV6GLsgWB8WefOdmecGM0hCMG6BDdHajWTgzlfAGV/PHC8PZJiYtwNjEOilsrrL79mSG/NnvJ4pJZKzoX7+3eczo8mUn7Ysz8M1DqTgjKXgdJmTnNxXdm1EajqSKeCSLSziiNIT9HYsBTggcEep4ZImfSYDYnY5rrR7fgajaoFs8AMhBRMwsulWCKBqIHUX7G6q1KpTOczZZ4ptTLN0+Irf41r/MYQzD3SHSQDAZ0h58w8Zy9kFRrUUNFgx+FIIg0DUYXDnRVuTZW4O5BLpSLEYnqxEgMp2YZRVRl3O2pthsgmox/UWjmfrWtSa0O4LJJV/SfebcSLYbyYLSXz+PjA5SIIjed3N0iKBLUZlYht+rQWGkqUPYfRTBs07yjTSBHYDwOHYaTUyny+cH48EWJknipv3z4wjCOvPn/Js5fPXVqrOwUqc5mptRJTNCALk+2SXsg62T2IbhDoa3zf43cvZN/b8SwTwstFHJE1R5ASIiXY0NO5wSzmKpJ1QiWQmpKwtsYu7RiDCZ+3WpbCMpfCnLMVl74FlBBIqoRgGnvzZaZmS6TDuOP29pa7u2e8fPmSV69eLST41nVJWm/zqIud95bPtuWysn6D2iLPlwuPb97y8O4d91+/5uGrr5lPJ/QyEeZMQECySf6EQNiNDGmAFOF4gOPI/njk2d0znt89YxhG9vs9yaWBIiApMex2RjNIw5Xxc43vDjH3HuPAhicDXyJdtbXrx26luIzXJmIWk0qk1sowjqa/jJpW4zDYYMZ+z+GwMz7t4yPTaaDVRguJlpQYZh4fAq0qGpQYIkO0iec6jFZYFpOgMwHmp4qsqh1RbWaHsKG8PUFldUW68DZj35iuq3b9YY93aoGotyHFigzxny5GL755rU0XV6KaM3nO1FbI83wtZK/xW8XW7APwgSxz09LiyGKrlFhRgeQbNUFIw8BufzAVgBR9ALotduxBZVH/AGwArOlSyMYYqbVRSvWp/0qMkRjbk/emsnZGfOLMefSNkmdaFUrOru1l6zRgHVLj2bdFss6e3yhGMQRT5/HbtCm5zFymCZFAGu5RCaaeMCZT/4ldkcAR7GaymqqJVio62EZVNrrUXTv3Gp9OfPywl4f2zOGtvRYCGn2icn9gd/eMlBJDM31HUWhESjMzAHX5rdAaIVWkWIKYpgvzPFFyZr5c7OD1wSu6BE6MpDDSaAwJhMow7hiGkWEYCSFRijJNRoLPpdiEdVNaXQdNavFFsflcW6mizY3ElBh3O/b7AzfHG57dPmNKA224oMNIIHA83HE83Jo+3vGIHA62g73dI4cd437Hze0zxmFnE5pqhPbghPwYBI0BHQZ0uFILrvHdYeWc+447F3YZ9pLg0jjJhzuSUVl80GMYR29FWoFXW3W/dHPF2u/35FK9NT8uLf04JIadUWisBe/e5ym4QsBT3ry4zFbQaJPGneuwoSfp5vN0utKSXp0rYDy54FPKEcE2pKIR0S4h5uWswdHL+UlpK0fWb9FmOrg0JVeBaja49VzQ2agWec7G63Od3Wtc40Nj2TwGk9QKS1HnOqnBctKyPsQMfwgRIRD94FeFmoSSAoSwDEKJNitYg29eg639bSEbQmO/3xFjpJRiHYg4byTqbP3WZnq1cbA8V2pGW6HWmVoaec5M00wIwZ4HL6IlPCmmtVlBrtVFbZsyxsTN/kAdeiFtZUiUSM2FrDCdLpzHEyEGxjGR3IyoNsvddUikGF27ujKPu6XrZO6AXGe9PqH4uEK24cMT0EGTboinIrRhhBhJz54z/OjHtHHH/TQTLxPURpsq86XSqhJRqlYkKBoyGhLaGvfv3vJw/45aC5fTiTIVe81uSJACcTcwpD2qSmRgSI39/sh+f2S3uyGmkctUuX+4GM9mnharyVrqgs72zzGkyDgMi7bmMBg67J1Js+IcR25u7ggEXr58xQ9/8CPmy4UyXSiXiSDC8dkLjrfPCcPA7sULxrtnhBQZjwfSwQqIcWcToIidoOZ5JvoQThiiFbG7Hbrff9Sf6hqfSphcj2lLdq5sYjdYkToOA/vdnhgDu92B480tMcZlU2bHvBBTQLXy/Pkzbp/dkfPM6XzP6fLAXIXDzcEtnWHcDxyPdnwGEkEip9OJL74aqKoEH8DsA1gxJSQ0mhRCjoS2ceRzpHShGdBLUS+Dl9lSmwI36gC2sVXtszC2IW2sep3dPQjx1zFN2dCtchVartSpIRV0mpln0+1s50Kb7TwxZ2tt6nvF8DWu8Z3hG6+YEjQxlYAhGYhCXRBZSYEwJoIkJA5IsE3jMA6M+z2gzLjCh6+by9wpAnvftIpz5X0D6xx1gGEYF3WSIZ2ZppnWGtkVDFotZJRahWE3cri9RaIwT2ce3p0p88TpNPL4cKLVtmjSxhhdHcFeE7VhLlqllWx0odrYjyPj8xe01rhME5dpNqOg3MjnmRoKD/EeLZUQTeZvHAdj3bdKbZWUEi1X5sNE3u8YY0KqEmI0Hn+Mv/FPcY3vV3xcIdsRib6bo7clwCFFiJEwjsTjEd3tjbdXQUJFnSbQnMJWq7UIalWaF5rznLlcLqYhl12qpL+2qOk+uhezjXiCSCPG5BdbVKa7V42XMxeyUxZytp/iE6IdnYnRitfAZlu3QY1CCOZeMozsdnv2hwNBhBICRYw4v7u5Yby7JQ4Dh+fP2T9/QYiR3fHAsB+for3q0uzeCtLoSLWpT6PXhXmNDwijEYQFHbHCNBKTF7duQxljdHrAkZTSegyHSEqBYTAry/N05nR5ZMoTc52op4a4yDlOUYghEAeTEUkhEUMi52GR+1nNDHoN6hu1FljbmZth0Q2FYMFhlc1Iqf2DzWLJMrls9w8LzUkWpJcVxV2eu+uSeHQ1A2/z1gwtW5eozsU4sq1LbrVlqOTKw7vGh8XaeejHvITenQg0NwNajtM+fBUixI5yQk8DJiHXVeNWg52mzVztnCNqxa0Pey6Ug2B7vhDIo82C2HENUAH1+xgyHFNiGEdKMfeuWt2YoJixQq0+aObnnYU+gQ9IIqYn61SEGCJpFLfbBdQoFqVM1nENah3YaSbGTo8CHHm2bmojz2ZvGyVQ5kIZC1FtsJRwVfn5lOL3VMg6ny1EakpoEMZnzzh8/kPibk/47HPST/4U3e14/foNsX6FzDN6UUq9mFJAR1xCo5ZCKaYduwgg17KKs4sYbyiANPXWqbcnhhGA3eFoslUxoQTmUpnmbEMaufhEtlJcIzZ4QhZfkE0xSZGiq6+z2KJBlbTb8eyzz9jf3nI6PTJNM3meuJzPTOczIsLu7o7dzR0hJYabW9Lxxk4o44AMfkbqjkf9a8TcvqY8UTKcpwuXPDNduXjX+IAIMXB7e0dyE5AYIsOCwlqn4bA/2PVxx/5ws7T3Yhy8+PUEq8J+f+D58xdMeeJ8OfE2JlQb0zxz/3gPajy3pqZYEKIPloyRkHyQKsCcM+GiSFBiqsuwpvQBmM0m0a6tk9O2abXbZdvOF5tTRhq04Dw+BYkLv9ZcudoT/l/vJDVP/N3ituZCmasjsorMQF2LW0u6fchsmf68xjU+OBZ+um8wh2H0rVhFqtBiYxhG0jgQQyKEZPQZQk+zi2HHIJEmJj+ZtSHOOZWL56pYiWFARKhtIMbS34HPn1g3IjqC25o6rx5qiVYsp8TucHDXP2W+nBeufC4FmeyzpGRdRUOZR+O+AznPCDBPM3nOtFaJjnGhXSLQjEeGNFAGMzYY48gQzX52CIkkdt4xIwSjMOVLto5JbgwxMV9mhjFxvL1h3I20qzTeJxMfMexlE8E+2m+obIqUcaQNiZvPf8Bnf/fvsbt7Rnz1OelPfkobR77+q58zXCrhdEJPhVwbNZuEiMbmIu1mI9taI08T83QxyZt5ouXZ25MjEkGiyf8MaTBry3EkpMTx9jnD7kCIIyqBy5yJZ0N2pzw7x824sQoMfeIzRAjdSUzJxaw4UTU7zpQQEcabG35wvKO1ynA4cHz+kpxnHh8feTw9okDc70n7PQShBXMzszTocl5qnrcLj2gZPDNP6VYK70733M8XHsr8e/qTX+P7HCkmXr38jHEYOR6ODCmZCsHhYAWmF7WG1CZitM6ALWdbCzE4OBQCt7fPGPcj8zzxeH7ki9dfUErmdDlzyWcEJWkj0hAxZHbYJYYykMbktpHW+pwLpEE43gTGQQhNrNhtAe3uX8CTglZ18/t63YCrDst2PVwrNoP2ErhhVrogQQnBXb1EXdnAuL+0ghYol0w+FygQzopMPnxTBGmd0+BKCTYmfXXCvMaHhwiEgKjLaqXE/rCnpkSJlaiRVhu7cc+421shKwlL09K5ewCkZBQgBU5lRstEazBNlTJNxouNu2V9xzRshkC99e9F9TAOSxFrVAEhl5milTSO3Nzd0eqBYRzQVpgvO4LAPNvg435/4ObuQEoDMSYvZAWthcv5jLbG5XRiupxprTGmSEi942lyfS0otRnZJ4TAftyzH+w8NcbEEJK7k7mjV1OmcuEikGJkPl98GG7H81fP2R8O1HItZD+V+HhEdoUSrRXubfCw2zPePWP//AXx2XPis2foMDIcjkgakJhAwqLZ2hqoNJtR7lPCrdFaNZpBrVbwdWOC1ujOAn0Se0nObm8nrnyg2G6zlGpToj4pCtC29pcSlqmu5tvfWgu1zN4iTT7gLMRhzzjsoSnHu2fcvryQ5xmGER0Hk0jZ7wjjiIpQFSrG45PWie+Y7I/Igm5be8j4S7VkcimUWilXB6FrfECIBMZxx24c2e8PSyHbKQTDMDCOu4VyE4IlyVobRcuilwz4IEhiDDsQXNXA1kepFa3Fhyusk6c+ULWirM5LVee2qTnuqQ4sU1bi9184Sb8uNgNgCyrr+gSdSuDcWecc0J3BpLcm1/+xkAs6paDZoJhW25i3qkh1SkMzY4ROmZLNW5Xf/KavcQ2LTkPxY1RUFqMSYqJFc99a1Ea2XNO+Y1pyhK+ZZLrPUtduXmumGmBHfEQ1LOvAXL3MkKfLU4WYnIIAGkzGqoawrN8QApIGNAZqyQzj6Hm40nKmy9uFEBc1guAIb63F+OSuA1+r5W9zyIy+jtf1GYObtYTw5HqQ3in19axmf1SrDclpNM3akguqyv5yMMmv6zDmJxMf5+zFWnipQhhH9q8+h+ORu5/+jFd/53/E8cUr8m7PdLiliCDjnmF/YChtQU/7lH6X1DKuqBWxtVRKLrRmC0Jd+LloJTSBEjidT7QYiWnggDBIMMqAgMRoiGhvI2qXK9G1zePJrZaKSmO6nHi4f0spmcvlzOnxntoq4ziw31kR8OzuOS+fvyKGyJwz6XBEdnv2KaG70b4TF6NWLPlK97vu8iZitrVuYEJtlVYL8zzx7s1rLo+P3L/+ivuHd5zOp4/8U1/jU4iUEp9/5jaP+73bVxrNYE02ie4U1weXTqcTDw8P5qAXFEmOfEbbMJYy83i5Zy4Xcs3UNlGbtQ1bjLb+JIAGci6czmcKEwxmbKCtoBQqgYISNVLV6DJ9HQaCU216eWgKIp0/u+3ke3qnF7NeytIQh0m3KO7m6jKZ7QVxUyhGNowECOZUptKMsoAXDW01kljezJaze41rfGesk/0t4Osx0Z26QghUB4QadpDGbjXn2I3ZDOHobJ/XsAFlRaGq3XE5Ps0hrNTstsygOlm+lsAwmmLOAkhh+8AUbdBYSIiMiCjjkIg0yjwZje7xgVqLOWimYaEXGF8X57may1eeM6VYxV2jd0Q6ZzhEgsIwRCQad3cc7Tl7kds3mrWYfNhSd2CzJSVXW9PMPN6f3Hr3Cv58KvHRhWxTdZ1FJe2OHP/kT4kvXvDqX/17/Ph//G9y++pz3kwzvzpfKLnA4ZbheMuuCcP+gTgOvtScJytre712W7t58rZCAfdIbxXQRtFGe7znVArDsKOGyEESU6k0THyaEKiqlLZFZKsNtnQSvGIcXIWvv/qKv/wf/imnxwfevnvDl1/9ipwz+/2Ow/HAMAz8yY9/yp/97M/Z7fYcb++4efaMUQLx5pYxZx8km21ATZVQC6XajrFVoYoR4aniTiRGZK955vz4yK9++Qvefv0Vp3dv+fr11zw8vPv9/MWv8b2OYRj5yU9+ZoNdafBhD5aBiXUgA4ofk61V3r17wy9++TfkeYKgaGhIgGEXGXeR2grvHl9zKY/kkpnmE/N8sQHHNFKTtTAv80QMkWmemNuZsKu0Vm2Qo2aQQNZKaImiGTB+uhKMnoANOfaK0YBaHyaBpWDthey2uO3XG9ZpkaXK9LK4F7GqTk03DrwWRaoQ1cwiNCg1FJqjVn2ALDiq1ZZn1e9Aka9xjR5exAIqJqelEdKghNDQCnMs1pmQPkzZbLMWxLuXlWoEb9CGVNvwBYHdaMVolYL7i/QSD5qZhKBYXvKiMsbI4XjDMO4WAxWT62vsBnPi6vJXIQXqPHN3c6SVzOnhgTdffcE8Tex2O9N+TYnaqhWa2pjnifP5TKuVMk8u92UUPaP72Ofq7oJpiOxCIgRx6czk79mK0lYrJVfybANpS70goLVSghW0tTViilY4X+OTiI8rZHW9NAz9jPs96XBDOt4y3Nwx3N4R5ITO2cjlIZgFa0rWguhtyA2KYi2S5lONHZ1trFlDl0vTRqkVSkGCCT7XDfLZW40rcrxeWP/Z2p/VTBUul4n7+3c83N/z5u0bvv7qa3KerZC9HBmGgePxlofHB0prDPuD7Sqj7SyTBGuhqOnUNjV93G+bcF6MAFUX68tcMtPlwul04nI5k69WmNf4wAhB2O/31raMkSCLeCqwFoL9t76+cp45n0/M84SGamhkgF1LVBKtVXKevUVol1IzglDElToQtDVKKOQyUykQmhWi0mjSkZRI095d2aKt8vT9LdCTGG9A+796SxKnEyyP0+XU8H7Lf20zrsWnrqcRf8yqoNB6t2ZlLqxtYf/lSo+9xm8fm6Pc0VmjHfQN5mbTRcf81/WwpMmew7yjGHtB1wcanbqzZNSFPtMo1dQGmiaGWojNkNS4tEUNLe0D0DEZ190kpSstJkrOVriW4vq0YUVPNzm757S1C7oyEvtq7nzdsNASjFoQJCw9D22uR938s2zOG/13W+KNPJte/CK9d43vffzOhaxiHTkl0GK00/p4IBzvSLfPmCTyq7fveNuEt+czXz88MhUTUe5C7MNg5PAaTUqk8/NMqWCilsI8XVxhoNG10xFrAUpMhM6JHUaiP19wf2dxGSzoKO+6wKyYbUute7mcePPV10zTxF///K/4J//oH3F//5bT6ZF392+ptZiO386cjvI0c348sd8f+OnP/pza1F2QRlIa+hu1k5ZzXufZpruLFwS0PsCWmeeJt199yenhnseHd/zyb/6GN19/SbmYIcQ1rvEhsZXcWTdOm1a7I4wIJi3VCrlmHk7v+OKrv+F8OVFbXtDSYR8Z9hHVxuv7r3jz7mvrljRztwoC51ao8+yv0oBGqZnT/I5JH1FtVJlpUikEpqy0Fq04nq2tamIA4ujsBmcVaCu06hteR2PVSwJt9MFTbXUVTW/F+PVqnNdWbdetFagYXaBBbLaRjiEShoAGJYxQ1RRKukkC2gfJQLfc+mtc40Oib5r8+A7oMrEfvBhswUq0UosTZQItNAK2UQzRhid7/kIViV2CDkIaGGIy/rdYd6M1JU+zrdnSyPPFpK1KIg3RBiElQI1EiXSnS2MoKK2awo46GNPNVLrudIpmRqJqEpnT+WRD1dPFnDh9viOmwYCjNIDPyFiOjgtf12yybW6k+nBXLoVSrMvZu6pbh7R182ufdbrMTsVoXOPTiN+9kFUozdokGgc0BHR/Q7x7SXr2iotEfv7la+TdmVOeuD+fybVyvkykYWSveOEXSTUaIqK2m8uXmelyopbCNJ0p1YetOg9PhBASIRrHNo07hp05eVkx60T5sCbzrQZkv8RgO+AocD498vO/+mfcv3vHX/4P/5T/+r/6//Lu7ZslaSvdccWI8F/88pf8/K/+iv1uz8Pjo2nFHm549vwFz569NBmjhahverXns01tWpGebZgtX6xV8/jI3/z1z/n6yy84PT7wi7/6S969eU3QxtiyubpcIaBrfGfIInz+9IDZsEuDD3+g5Gro6dv71/z1L/+Sh4d7pnzmMp9QlDiKaT6KMpcLcz0DSkxmFiJAqYFLM83Y0iZqyzStTPVEbpOjoQ0NhnS2OTFpsKGqUqGpceNSwOSgZXm3yjfRVfCBFe38OcyFS5VWC6XOSyFbddMZcRc/Chi3RwkVQjV+7pgGhjCgVQlNKJLRphSK6Xz6OaqDw9Kuo17X+MBYkP+16BQxhZAgSomN4KhnK1BzRlUIRMZoA1Ld3ASgFqME+agFEacQeUGMCC1AE6F2xZ8yU0tlvpy4nC/ElAiDoNIMjZVk3UQE1b6hTJQcbI5FvZB12bDDzQ3jbnD7ZlPfyfOF8+MDpWTmYvMeTdU4tKPRj8IwItHAHomuFCRmRRui2WG3aprvTStzzuQ8+xB0pTabqUkpLFz/fn6rpTHnyShT9VrIfirxUdSC5q22xWUrmCVtC4Falel8QUtjyrO5aXkx2XX0ukizBIHW2xIsjltWeNYnVIBFo6BPdm40MGNMhBD98p4wOjx5HnuufsVQ4PP5xOPjA4+PD5weHzg9PgINcxpSe5+hi8Bb23beTTw+PHA+nxEJHI833jINSxukdTS4+gCbt1u0VbO79IV6cTrB+XRiulyYLxeSwBB1bW9e4xrfFfKk2c7TK+sNzbnorVVKLUzzxDRfuMwXLtOZpo1YhejSU0UnSpu9xWnJRxC0Nlo1mkKuNgTWtFHINtCl/Q0oTYO5+jnCSbWWZwBDlpb3uR7wT4wQvMOxTnHL2mbdKBB0Piz6TQrB8n30whT7GVz6R1WJfh5T16Cl03Y7ZWlpj14X5jU+LPTJsd2HjcOqMtBRRtqimqPa2D5sm9NkOSg7f1wWExRcQcSWh9MM+vP5ILU0G8wynfRAC3UxOjHdWjEtdb+Aru6WTjsgRJuTscSNNrNyLqXQXKu90ySedEll83l792jpIglQF7pA2+TQX7dxNL66cYtrtSHx6yjmpxMfRS1orjYgKUFMTK3x1du3CMKjfs3r9lfMCHFIxHEwYecYUZfxWZyHgk1cau0IS6WUmebDUasTitEJYkrc3D3ncHtHGkZunr1gf7wlxsT+eMM47nj+/Dlj947naRHb2yNoY54uZIF3b17zN//853z91Ze8/uoLWi1uTGb8IJMRaosFbyuFh/t7LpcLv/zl3/Dsn/4TDscb5mlGCIQQmaaJecrUWng4PXC6nHyhz2gt5Dxz//VXnB7ecXp85Bd/89e8/uIL8jwxXc6g1blLwQWpr3GN747OrXtazHaumaGLinK6nHl4fGSeJy6XiVYt6YlEQhwQbQTzMzBuaxVytqHMkouZGqBIMetJpZmagVoHo1JoVEvYavqZ2ho6AcWcfVouaG2kMbA/JuIgZtFJg2glrLrMXk+sYGhoaJ70uiyfKlWBPpwlQsIGtIIGQotLUm2oSSA1Qaq5fAWBKAGNyjgORAk+ZKIL+rUUxeIb26vEzzU+KDZgzOaQMStZ6/TFaJuoWqwY1AY1WvcwEJAYSd6ejyFQg7X8zVTAS9mwopSd3y0KESGJAU4xhM2Qs7uCiW8KgyybQFWoFcgBaWbpLNoQzHrWhq2Epo1cMrUULtOF8/lEzuWJy2d0nfYQ4yKrhXNjn66gToN6ukHc5u7o9IotTdBAL/s5z5N/f9e1+anERyCySm2NgJCGARlGK2S/fk25zPzi3QP/5IvXPM6ZZ8+f89kPP2e323Hz/DnPPv98QWSjS/c0TD6jgiFE3dO81WWRBi9k07Dj7vlLXnz+A8Zxx7OXn3G8fWbGCONIjIlnz56x2+2WIrhzZIHltlaLF8yV119/yc//8p/yy7/5hQk3l5nUvekPO5NGaZXSbJd5uUw8Xh4QEf7650eCBA7HI602xmFHjIl5zsyzWeCeLicuk7VltRVoJhb9y1/8DV9/8SvOp0d+8fO/4u3XX2ITqTO0iki0z3W1qL3GB8YKOnZ0xfl0mNxWt2V+PD3y9t0983ThdL64BmVAiMQwojRCsAJPtdGKkKdG00rTQtOMSQdNaJ2s2GVG8cS4IJmRqCOBgZaV/KjUWUxfcp5ppTLuIwqM+2ie6YAkS2oaXNHEKlADRZubFDgdqRe4xrWNIBBFnfMHoVVitGHOJj4Mqlgx3AvZ5O5+CnEMMFjBXuZCyf5aTZdKRDrB/hrX+IDom6ht5bbktl7MqpLFdJpbbYypGIIq1hlILklFVDT5Bu/JMblFau12UXlSyA4hUBaN2rWQVel6tSz2sYrSZnU6klnIiz8GDMgyK/mJkl2u8vRIzpm4pRPEaLnZKX9mKQ+9cNX1I6zRO6mbr6wDU+b6WRbK4DzPSwfXbHPrlSP7CcVHIbLLRKGY8kBTJZfCNM+czxceHh55mGbSMHB7uQCwcwkO64j4YEoIZs/nq3xVFugHomxaEUYOj2kwK79hZBjGBX3tA2QppcV67321gO3vhrhkcs5Ml8k4uTn7/Xgi7C7aJ5lt1XWaxDxPnE6m83q5nJnniRSNF2sqCm7q0NaBlE4rmKaZ8/nMxYe6cs4IjaSezIVv/QzXuMa3xzrRq5v11Ck6pVTmPFvyyZl5npmzDYGoytLulMWu2RKX5ZvuAOb0n1Z9QjnTmhe1FFRcYWOZZJalha8qaBO0Cq0KpSitKKGYXXQo1qmhubyQdPzU6QR+SlDVpZB9kum8uFxXi9nJrkNkvRHrxABlMVhYRszECwyVhfokrnwiy/DZdT1e47cL3VSxJjiwpc+w5Jl+hzUP2sNW+oEVoBJ6r6W3/mFR3gBHUJ8e2319h83a/DXvlpW208AHH1nkv3QtPN8bpO6XwKa4lk0e/QbauqR3usvgskHc5r3Nut7OvnSQqnpnprWnFMJrfP/jI3VkTalO1KYL5zLzcHpgKpnLdEa1GprTDAWSaJOHq8+0uQ6hyiVPvquyorJ5ksTlPyQE4rgnDDvG3X65pDSgYDvAmBY9u2EY2O12HI/HpR3Rd3PBV83FebHz5czj4wOX6cw0TYgqKUZwp5WcM2R8gKQui8TsaiFPF9589SXnxwMvX7zi65evGIYdIQ6EONBlUmKM1FqYp4l5OnF6fOTdm9e8ef01eZqoeXIUSRhDIgWzzh2HgeFKLbjGB0RDmatJ3c0bPePL+UKphXmaeTzZVPH59MjD/TtKzry7fyBIYkgHgg7ENuCQJRLMSGQImaQzpRUul4nzNKFaaVxoXIAGoaJidAJzJopEb31KSDZUNYyoRMo8ky+FUirtUmlvzsTB0NDhmIhDMJmA2BaOqk1vQ5BIIi18eRE7lUkAiT3BV7rzkDZFqllbSgHNatdrMGqBOMdPqtMSohezdp5q42DfZcteTF+L2Wv8trHqGHeqzLZeSzEgKDEKZsbhlqzdyKcjuqyFof3a6Tcsm86uPGDHvRJUSBIJAQ7DaCpBMRDGgTDYvEcSl+tbNp2Wt7qxD0bf7Z9k4duWYucV000vRmoSowwOw0CI0WgFm7mVbys0RYx+0IdDUSvYoz9+pWasPxd6Qd/Jivh94xX8+YTiIwvZhrFGrZid8myDUtPEZcqoNoKY1mvOGQkmqdEt+IZkxaaocjnBNE82+FTygvaICHEYEImkcUfaHRh2e8b9gXHXregg5wK+gHohezgcOB6Py/vtOnfGz4HpbGoFjw8PPD7cc7mYyHuKiV0aTOzdnbb60FbXfAUYB/v65suFr+cvGceRly9e8uLlK8bdnuPNMw43d/7a9rqtCdM08fDuntPjPW/evOb1V1+hNVPniYiSgrAfE2MMC71hCNdC9hrfHapWyOY5czqdKKVwPp148/Ytc545nU68e/uOnDN5npnOZxtAzAVhYEiJhFEHcDQUMcmbQWaiTrQWyGfl8X4yikE8oeHivNGKROPnxjAQxI7nYbBiM0gijkdCGrhw4UHP5DyjpXKaLqhUhl1knwfSaARdGdqyflI00fYhDEjqAu7BOL0ifebUknkttFYQNTqEVEytICvMzRDe0ty1ywvZYIM33W4TbJ1bwq4m6r7orF+L2Wv8tuEFoA8NdgQVgZhceSAYsVU9tzZthIW3KmhwVNU5pqtls26KZEVqddc644gPEmlB0GFHCuZ4yRhhMLWE5NtEe2u6IqOue7m4UvoAVh8CK8UGRedpIhd361vAKlMRSjGZyokE14R9yhcOYZX1MsS3utyXFbKtuQTYewPbW54sOIc2fXsn9hrf3/g9WNTii0zd5astbczg1AFz7lgni+FpK2XRem1W9G4P1O2kY3SduRg30lrvKRL0Qa6tSPOT6FOfPgRSuw90q8u0M6gtLAk2DOI8w96QXJOXN3CatVeLGHo7zxMigXqo/p5gO3FqTkeZUoqpFtSCVrPv6+2V7Xe3tFM+5o91jU8iVNXoAvPMNF1M9m26cL6cmV0Z43y5kHOmZOt+aO3DUr2t6Qes01LX9r1JB4lrWq58OrfUlK4A6+uQ5WmWtmZge2x3vduAUmkKrSmhdR1Lf86mhrRuJqA3ZwhPWt4qXfgB6q3I3o5duYOyfCbd0BL6+amhS1PU37cn2RBWBYPl+35vVOUa1/i26KmvI7KquhyPPed0ynU/xNbU9dQEQHvXv5NkOt0NK4A3JHnLaV5wttr8ddeuJI62OpEIYJWXg28knU5ZWClLa8dj+RxbKsFyWZ/Mlt7TQvb9AtXWNQtq3edc1u9Tt3d9ur6XIvaaMT+V+CiO7IyYL3mxdsIUkxWiwDAkbsOOAgy7/SoJsrRDLEGkmGipLi4ei1tJjIgG4mAGAxIju8Mtw+Fm4caqZ8k+sZhiZLfbcXt7y/F4JLk6wrYFsSwsYJ6Nn3o6nZinGcCL4EQaBpuuTJE4DKiaY1hZ9CqN49qL4VLNxeTx4R1vX3/Nfn9g2O24ub3DZIrsW9PWuJzPPLy753x6YL6YjqxoIwmEFElB7CKrteg3mfDXuMY3Y5om/vF//4+Z55nHhwffWBmdoNRCnjOXy8UGKWujFdvA0SpafAK4ZWo15YHWCk0Lqo08V6RFIso+Hbndm8byuQpTdQRJMkpd1hExEUgkSYwxIUQiEGi0BoebAQl7aotMRakaSUNYTU2C4jKThCAL770boSxobPAhGFHUeb0SPGE3RRKEAbQafcDkgnCjAzvv1FLIbVNk+2bXziOBmM05ELECoTsBXuMaHxJbV8lemIrTVFTN/c66Gj54qIJE58BKXdBZVKD20tcevykR6XMYeZo9vxQu7x6ZTyejuA1mORswXfaQUodZnbC6Fqt2MYtoXzCWp1ul5uxSW3lFRUVMn1bVfvoMjCJL/q21UWvzl/Ktr5pCSCmFPkC28IM7ag0G/mwGuTrlIEhAkxf7K8vgGp9IfFQhm7EWQy2mODClcWkZpJS43R/QENEYbfBj82jFi8YUSaXLW/VdmSxac8NonNiQEvubO3aHW2JKpDRiaKhSm1n16DiyG8dfW8jWaj3Bvij6UNrpdGKaJ9tt9vc0DKSYSJudYi5u7dcaWWeqmsRHLVYgtFq8kP2K6XDk7sULWHQo1XeYjely4f7+HdPpxDzZiSagxCAMIRCDkIL5voflpHJdldf47rhMl6WQfbh/MCHx1tySch2MUHX5qa6RU5sVeaqUMrsAeaNWL2pVEWkEIqLCPh2RvVFv6qUyzQWotCY0zWaQMiYEoxMkSYwpIYTlmFYVDseBGCHXCJNSaiYMLie0oQpIgBhsult6skyJINGK2DhgQmGVnmxjECLGs21ViQM0qVSZya0n7oY275RooQiEEBedaLBzWUomIVRcdqhWLyquhew1fovYFrHLIBU4AKQgzY5195wNwW7rReyqL6s0rXhvkbVXaPJYrVbKNJPPF8qceXz7jtO7e2KMHO9u2B33LFJcyagzjYJWL7CX9qOrgSA+C6M+3GUuXqVkqqugWB278lx7Ifu+BKYVrN6txDsyqm59bTm6Px/0LmUwPr6fy4Bl9gVYfvZ6wDaY15z5qcRHOXs1NaWBuqEWtIXMvoodI1utuC5B0sWBNihtZx7Iph0Yw8b0IHkRm54Mb/Xoi6ajNvDNacq1XdEL5w3HZtOiWFuV62JoTalhLYafTn1ae6VVX+BDXuRBEKMTdN/pWo1SUKppaKLb3W+/ul7vC/qaMq/xXaFNTQFjmpimix+D1TsJbWlt4oWsqk3umztP12TcWDn7z7X1KZ4AIykOAKQwkOJA0+AtTiU69hqIVmw+oeM4yiOuUxusoA5BkBacHrC2JLcTzU8vYUF8RGzNbznsTnJYqA4sVKaN7mbbrF9Yzgm94DeqEsvrddnAZRDmmiuv8YGxmvqsdAL6LRukthdvLJc1PyzH98IveC93bNr+tVaTj8uFPM9mSxsju8PeCtbYlQ5YKXpLDnq/ja9P1kanAW75qU/b/d8kxPW6YItKu50nfTitI63b525t+/pPX+t9ysHydV6z5ScVv3Mh24CTCq1BrVbAXqqS/aKiaGLlzwR8J9eopSAh0GpdeEPNd1JVm3F20oBIYNgd2B1viGngeHfH8eY5MSWOhyP7/eEJyns8HtmNo3k/iw1VPT4+LMlZW/MkZv/eC03bIUvvX0K3t3VkJro6gYTZCvfWSNXsNRtGMbDzjaOt794w55mH+7c8PL4lhOh+0ZXL+cz9/T33796RpwvTfDGKgigViJ4gizaoxickNJscvcY1viNKyXz5q1+6XmxeilBtaxI0GTh842WoYqcZsCSrLrPTMKxDiNJ1HJUoe3ZpoLZKTIH97khrlTmfyflicyRJSFFIMRihoBrtqLg8V20NYiUOdn5IzddeghB1oROEIEi0n8HXdkoj425PDMnVQXaAUFum1OlJsqXTDIaABBgPO0IzmkE7m1yPOD8WtWK4ZLOnDSGQRuuQhCgMuwEJQs7FBkylfvsf4hrX+JZYQJxeHFIXhLO27NJRNjAZESTYfaxx4pvRBfjpMxcNVdN1RRtCo+bCw7t77r96TZ5m3vzqSx5ev2EYR3qhnOrIcLMDxifrXv18QFN/CXEwxnJ3UyXPE3meKaX4Jhnb2DUW+av+8/2O6DqgBV3eT7Uxz/P6PbVOH+jW8qu9fI+tAsIWrCqlbDi71/gU4iMLWUM3azWKwVyVXBuluhxI02XTuLYkjGcqpdgOzHeZ66BYMxTF7WbTuGPcH0nDyPHmjpu7Z6QYORyO7Mfdk0L2cDgs8lsCTM57tde2n53OEEKguoVep0OwQV1E7D7RZbxsICVQakNKRWOjxUIjUMJsgy2qzJPRBuaceXh4x+nxHgnREp8Xso8P98ZfnCfyNFNrsRMFunQqq/VfjasXFQ1XQ4RrfHeUUvjqyy+fDGpseeFP3HBK9bagorUuhayhkHb/GIToiGQU424DThOwY3YYdhx2mdoKl/MjUzgjoqRQiaERgpBw1Ne1ZhuVpk4dGKyFmpoPn0RFIhDMPSvEsBm46usyMY5mPBLiSEhWyOYitLl6q9ZczNBGCkJIwSym9wMJoZVGLjPZzxMm1SVQhYptAmKMxCHZWoxhkRNCBLkE41dd4xofGp1WgNHMmhegtZmta7+t4xYSFJVq7FSpXsz6U2FSWU0rrRaWQlYr2Tnyb796zXyZ+OpXX/Duq9fs9jv2xz27/Y6GcqjVhig3VAcbDKuLNm0vB2trFN8cz26r3juM62faFLDvFbL+iZ6aFSzAsixGBnb7upluPkAO/FqTg277vtCoWn/8NT6F+CjVgubsHMdu1o740vrgG7uiXtR2Tby1XRcJ0UjiBNsxhhgYdsaRTcPIbrdnv9+bFe1+z363X0juAuz2e4ZhJKboxapPYW7oAVtKwPKf0x+e0Bz8WUVsaKVLgyRHiluti8XfagtoJ4JOgC++2LeFrGntGUm+VU+43goxuoYVEk27F7c3Yq+Un2t8QNggYpeiw4/rDXrRbGixo0D9hG+i52ursT9m5WdvaC997kNwioJPQqtNP0eCJ1QxOUiUVgAaKuas1fx4Z0sfCEZzWI5zYX3dJTb/+B7VoD/X07ery8+G6cWa+5dRDAj2ul0fUzYJedvuXdq5neKwGKVcOyXX+LBYUqHnwOXIXI6xvhZ7sWuFqfbFph32sUd2VZD3XmVFe9uWIqSuXrD+XIa7PGfrssk1Pq5uikZlRUlba6Z0ohvVgvfa/v0z9C6QLPQIntx3S4l4SlNoLBrQuhalW0pBj29XSegngmt8CvERiKwwBYEgNB/MaAriiCXBF4ILLJtPVaQ1KLniGYRht0dC5HB7x+3zl9RaiWMiDdbSf/7iFXfPnzMMIy9efs6z5y9JMXF7PHLc7x21tEM2DYnbuzv2hz0xBsZxXEjgnWva5blAGLorWBpJcUCCMfsgoGL8ujQMHG9uSCmx2x843NyaOsG7d7xTNbK7VkrNViTUyuPpkVwyb998zeHLG0IIzNmoBZ16cHq8pxbf1TZL8KWrMIhCCDQx6j7B0LBrXOM7Q6HlsiSQJwUZjor67+uks62fkPxn6NKR7i7nG0WaUr1loEsRrOTiqIwqYa6k2gvqTGkZRJlTc6RVkBGIXsCGgCSTuouer1vo6JNiUlgCLollOdc2nSbf5ZaX0TU1vTjtibH48FfTSmwVFESamyYoIQXSEL2QlbUA9hYrTjOwZ7TzXIiGEqdxWLs517jGB4QdUV6wsvLPbVPZLVcLtc62VqXRxAxAStuhWlERguhSzIW+9VSjCpmUnSGtQU39wMYuI4lAVCH4BrTVtoAq2Ye3tDU7h9T3ENlazWSlVht8ni13ZZfx26KiS5Hbese1D2+xcHcBQkgL+lxcBciogPY6dh87V3QwaTsb07us28HuzmHvQNY1vv/xuw97CWRHfPqlgVtLmve5NkNXe6pUL3ZLaYhUEDEv5hjZHW843j2ntcq43zMc9qSUePnqM56/fMk4jLx69TnPn79kSIm72xtuDgc7wP1tGJ8tmW91CCT/iVMgnrx5YPACNqXBuHbi/tNiyVOx6ei9UxYWHk5tiCrT5ULIkVIzKQ9OmyiUy0wpmfv7dxxef70pZM3V6/HhHefzo0t4ZZdUAZeSpudiDZ2dsR2Wu8Y1fkOouhHARiqutwp7MevFbYy2RrqRQIxeIAaInZagnT/q6NBCSygLF626L7yqIq2SnCdXLoWcJ0Nho5kNhCGQNBFGcxmKnQeLFdLqwy3VxeAtCQbCBonyd7YMey2T0SJIlQVStmfoskWF2gqi9tlCdKQ6BeIQTc6oOlrbO0beKSklGxDtVCMJpmCQUqS1j2pqXeOTiw1iummfWyevI56FVi0vNMG1lwOtZStQDbbxAnA7TIXxWtUc7dC+ETVHvGSjl6YZ2+9bG62YY6WpEFgBXTeFbH8Nk++bF9WBPmPSzwX9/b/PhV0sc2tFwQv1gqqrM/jzm6W7UZ1Kthwq0lVD4hN9ePu86/mgD3irmoGCqj5Bba/x/Y6POwvLmjTUWweiinjrMiwJxRDclTD7dCL6fYu53g4FlgVegy20PM9oq8xTWuSpQm/3iZBqdA1KTEInyILGbhEXEM7nM3mebTdZypKk8eKxL4yub7edrKzLe/cdKJ0yYUVDFTHnpMsFEfFhr8I8zxvdvU0rhZVCIP59icpSxOp1UV7jA0JxfdNt2285rn1L2X9+20XUklx/NnMpsOu1oW52UvqacVRn5bw1Lzr7UvbjtnPmqtIaSPMBywVmtQFmr2NXdsB2YfRWpXQkqSIYd51gG+PaerHdFgpTR6RFe2Eubq6wUgnWL9ApD2xarv5dBvAukyfmpbtzjWt8YCyI/9rW792TfrB3tNZu67xynjhLLrjMlgy/6bEoLIVfSolxGNntd4y78T3VHxuybstAVbUN3WawqteLbYO4vn/paOwTKuH2s66/bpmH71ES2pJfnxTBqk+ub4vUnve3SGzrm+9rzvxk4ncvZAVIbnLQiek0QslezI5WRMZIjYESrNiVVmnzBWpiniemabIdXZ6XXWSrhTIHWm08vLunlEqMkfPjiTdff0UMkcNuxzgOPoQSFs5aHKINh9AHViwl2sa3J3A7wM+Pj7z5+kumy4V3b96Q59kQZXSZls5z5u2bNzbgsVl8D/fveDw9ULLZ85VqMkc5Z+Y8EUrm9ddfGkcW09ssbqLweH9Pmc9+MmtLFdHnn9tCMRCbXJVwLWSv8UGhrXE5nQzJdA6c7TfFaTi+Lvxn8ha/tSqtD2Fr0JuguaDFpqVbLsuGr7gz2DZExCxuUyQIaIhIHMzIunbuncJk2q0xNaIYV09UCdVcvKpjsUtRK0ZRatIoFEtedULqow9jjaT9DCKUNjOXGVWbXi7Fub/NNtjmRKu9Ll5arIZkN6gdifWiQJVKsS5MCDQgNEu40Ye+rnGND491Y6nNKAVWwNUniGa/LmLrQYiUOpNLQaMQpBnP2+k0gWCdPXF+qcC433Hz/I6WC2OIPLu7JQ2J55+/4ObZLUQzKbhcTrRaXa5vtg1nWZ0uq3dXa1uVUGptZB/YnuaJ8/lMqcWK52C68M1NhLYFLKzyfhZ1+Tdbr6bcUL2rJAIG8BrKaoYJ8g2aQUdhO/Bk1Knr2vxU4uMQ2ShPT+TakFqsbamNIZgObI6CBmsbilZaztAqZZ6Yp7MjnrMTvA1tKVKQ1tDTiTlnQghcHk+8GwaCCOMwMHSXn+7EE9xnOXbW0Kbt4otxq/xa8szl8YGSM4/3D9ZOWdBkk74qJfPwsAowBxdvPp1OnM8XarV2jC2eugx6Ady/e8d0vgBsbHCb8Wrdkzr0oRynMpjEtb13UywzflO4LsprfEC0pkzTxKYNsbbksGM6+rDSqkjQmytrm1Od81pzps7Gmytzpszzcr0Lk0ff9IUQGMKeiHH4WgygkarCXAStvhfMThNooKETY82a1mpKJTgg2nVpCd5+VbX7lxlKcK5qITWT9KtaKJS1k1N7IduQpv2p7Kvx85Q0K6DVtPX8mxTn95vKAi24SUvoQNqiYXuNa3xwrETvTdegLV2NpZj1ArfvuIRq5iO1WBci+jrog03BNJGteWHrPu0GDjdHtFb2aaDe3hJj4Pj8ht1xR6Vx0cw8GS1gnidqyQvlAEdCc1lR2IVO0Jq5WTYl52x22KUwDAMyCkHDUliaDJ4++b2jsKvxAZ47e+ezd1Wg1s2msg9ZY/l4mYFZvlYflvYi+BqfRnxEIbtyY3sbvm0oAThpXUqgtkZtioZAmSemywkJgct04XK2QnY6n5gvZ5oqIWVCTEgQSh5cx1Uow8CQEkECc4qksBay4oVsSCuHZoPHWpLaILKCFbLz+Uytmcv5kZwn3xGuWnvAk55I9dZq5xhCtIW2LNSVLtBqpYZi6Gqry2P7EMnT71IXFKi3VTsbAq7Ugmv89tFNQYwj1zdIHen09dC6qL8NiYChMV1+p+ZMzX0AJNM6BadWqJ4sJDgNZsPY62oEnmCD/+ybQfHNmTSQjppW61D092Y2s76CpU9uuzoIlSoFrV6JR1MQWCSKUKjqeutG00FXbmBrZg/aHKmlbb4T50Us7cmGr09dtXdFOlz8h/yTXuOPNvRpjtigsv1kv223L1QcfE2IeAHoYgP4Yfqtr2XzKBICIQVb30Oyrke0Sc6uKNB0pRQ057p3+p/6Oukg0DcoBbWabGanG2xy31bjtdMO32fxfOP72Vxf1Q6++Qm/7baVDfVUQeEan0Z8FLVAQkQkEoN5kVeJFIFGo+aJy/1bNEZKjOSYUBEuj5F3b80R6Hw583g62W7wMjFdLnbwBrO0tddwXVds9xX79eBmAeLar313Gr2o7e3UXhbqupAEv6lVmk+HdvOEWgopKPLsligJOlcWKLkwzxlVS+A3RxOBf9DC5eJk91rRZnqcRZXmyOtiQegnsuWs1QtVcIkS5x0H40Fplwu6xjU+JAQkshaKTidI0llAimhZCrpavHhVm5pGbdCjo7D9Os0ME9S1ZmmN0CzRJBlsaKwPk/jgZ4jBhsYUkkZU2toSTOYCFqa6IqY+MQ2N0EX9RCEqGnoZbj2LLI0SM4gQdol4sCEsiUJIfUOoayejBS9mvVAvVujWqcFUDBGelVBsDUawwU+wIlrElEVaRkP1IiHBFZG9xoeE5xtthVo2CgWdWlBbd1dGay8OK6rBHPOkkWulOP+sbtyRtec5sSNe8dw5upylKmmfoO7s/hFmMWBlykbFabWS58k0zbeocXvq9FccnS21Ms2zPcc8kUum1GprO8Z1+MpNSZxVYCFsuOVb2GZzeb8Y/VZ5reWrXTjG1d9be5+ve43vdXwkIhtWToxP+/fWeC2Z+XKiilBCpKSIiiz8t6bK+XLm4fFk3NLJhMlV1XaM7x2o39B/fVLchfXAXjRdWZDiFYllLSD9mcUFqdURZFXl5rjHhlY64GLPnVujuHd9jIH9bodq43J+8B1sXS6Gwiq1c4A2O84+YCLyZG3bJzXgyPjEnjyvrIJrfGjYwBYrhQCIYq5c76OxvXhVGq0WapeQmzNlsuO8LYWsrookWHteOlUhDU4n2Oi5YpvQIJZkUgookRiFwTektEabi6O7FXJdOOouPGdIqFWVlrDEErdSqH3J50io1sGJQyKN1sGxza5vaDUsybkVpeVmQy25obkiTYkVtLrQV0dct18sJlyvvrlWCVfKzzU+MDrK2RxA2Q4KN1p1BLaxoJ+dm9q0IRK9oFSa2PzlBpux9aF9s2dSdTIkG7hWJagVtcYzdeUBl40seXZr9WIa1AvGIvZ632JdXUpZpCNztiK2c1O7Ax/yLfaxvR/63rLp/Nn+4t+Kqi4cqPWyNkvtsd1yfsvNvcb3P37nQtYSVVwR094uFGd49mlDoLVKKeq6qIZUKmryQPj0sDtu9cWpmyIPb/H1Qnb7HtguioUK0Jbbl1bHZmEstxmjCOg6d5akay3M8+TcwmSe8s6ZjTG6fmVfNHXxhX6ivrDEty+nXnf3wmPdVG92nUG8KJc/uhbmP/gH/+DX/ts//If/8A9+H378Y/jlL7/9Tj/6EfziF7/2Of7YIohsqAQ8QTesldkcbSlWyOpayOJdBOssOFfO26BL6x1wnSrfcC7Q0FIs93JzxYvW4csu6bUoInR4qXNVNwW3OrVgWQLSyULWwVCA0iA3NApgOpuGoIq3UWVDG1DIDRyRpfb30b+b9Z1Db3KyfiLn7wkslrt/TPEv07r8TUsSvl/L0riextlehrl0a0jSq0dZH6BdMcNuaz5k2MLGhMDuzJM8I+/li96UhKVl3zuErdMHdC0CO1i0KCS4msBWZagPfBU/T9jLbhKZv62uNrBk602Os4/56/Lj0zxo9MGnSOz63N3xa1UVap1/8UcSv4+18vf//qebKz+CWiDEYTQh85iWQlbFmoJZzdKuokwKp9Y8bzRyMz255gWnhMgwRsbRWh+9PQCr5Ai4XeuWh/vr3hqw0An61d6qVNZE7i1VNgtWVblcTnz99ZcMw8jxcMPd3XNiNMmSm+MRFKbpzOVyotRMno1bW2tBW7VTwFJfb//f398G7emKD9LF3J1HOERCcCpFtNv/UPEhB/t3Het//+//Lbyxj4nfdBbwf/t9JOff9O/b5/nbChEYgrXtae5j3rUdXRqudc5rK9SSnbO3UmJarcuwh26ud0TTlpEnL1cDseLUfpcnzpBWlKYgptMMhKYGfZaKToWWDQWSqstjW+fKig2KLkhvpxx5ual2Z3uPApoimqo/zlBTexu6vvdqSgw0RS8V5manhAKt2nM3WTfI/TkaSmlOmQ3BeLV/IGrB721D9y9R/KYl2f/9D7rZ/VsMVaOvlZK5XC421LTJPwCdV2650Q09SmPODTSwSzN5zGgLlFioQ0PDItDI6sznG7nQN3KWd1EDXYorJNSupOOt+FLMplrEVBDwoalSDOGstZLn4nSCmfPlQnFFE9uobjVe7TVr7+A4KNNnWvqQ1vt81wVkCkIUA41SSmYNHcKTwrVr165oLBvVh28DlX7/8Yc69j5krfze4g+UKz/0Ph8SH1XIimvRhRB98QSTwgJTHKjrdONUKkUbpTVyLSisXJogpBhJfnDnUsCnF+VJIfsUkX0avWhtT3diCj7x4c+jqy6mNuBpIQswzxMPD/fEaBzZw+HGDRYSKY32Hn0wrF9M5Ll6cf5+4fptv8jK4+0b1m5RFmRxK+qI979sHcwPONav8S8gBIjLweLuepvitZZCmSe0mRRcLbOjs9WqONhYV/rlPXSj78E6woPi2q+uQat2wxadtbUbLOn2NVobWuyCKmGhxVpBu6XbIGrrwjgSTw0StKHOadKodlazSTPU+fK6FOFqNIbqhi2zo7P+tpra3rIt3Rp/sP/ohqHLd3LlyF7jA0IVb8dn5tlBj+UYYzHZWJKBsrTzczbL6Vys8BSpGwmr4DScvkA7auldUeP20M1tG2oFpm610Fejn47IEny1Nl3uV2u1wrdZ0ZvnQqnFN5gbapF94mVYzD5/sLGX91DVXxf9fr3o7Zzb3ukFFuOFLarbkeKuB3+NTyM+Sn5LxZZQg2XAo/ntGqygNVRRGaJpOAZtBOevxBQXRYIYgvHmFHSeacwuZt6W4jA4J3cN65mI89dWtHWBdTYt1bb8rtJ3cWHh5qijP6ihLE2tzVlKMa3b1ryQNf7rZbowublBcccSA1q3bZ1NS2X7lrfX3yexe+Ha3YqkI0Py/oOvcY1vhiobZYHi9IBKyzbU2IpxXs2QxPmpzRKqrNMjHmsb/8lrdNMEtbW+tEGbbwZbr0LXlqfx1Q01oa2T2/2x4sXwet0b/EIvf93IwJ6tedG8oLJebC50oYVTqwu1QFhfX7rXfFvq7g3a3NuqLJ+j0wo22+GVqHiNa3xHqDZyyV6MlkV+sbt/RKzoFEdlV7ON6goHuN56Bg3Usa5Fp2tAf5NX2ovG4APLlt9q6xrLZtAzT2YyVHKm1YINUysiwWlz1bWX17Z9VyjoygKLclBfd0vHsxey6/taaHXvcWj783Rzg37b+3SCTjV8P7YUpnXtXuNTiI8a9qrSW3eGkzbpt1khGwcb8NqFCCHRnLem3iZPw8DgpgYddGmt8fj4yMPjI6vvtLUK+84MWBULAHGISLQZquSClVtHo540u3yOLSDjKaGdzO4T2RIozQY7Hk4nalvbJiFEL2RPnM8P7js9bVoiYYMay1oUbCkRW/pSWIvY4Oh0DJHgVAbxAvaaLq/xIaG1Mj08orVSluJ1LWS1VlrJ3u1oS4EXBJ/UX1uTAEqg8c02XWMtQJs/r6iipaDezjRYVZdmAwS0VXI2NJjaoDRC83zeVkRXmo2sqAghwroh7K1FQVxnUosu2pkhNiT0+1Xc18HUGjbvP/jHaVW3TiSb4qCTIlyeTHBuIcu30aTxhEVxjWv8mmit8Xg6GbXgfKbUSghittAitK5/7uBGTGabjPSOn3KJFx7lkZQKQ9hx2N1AUEJoSOhSXR3Q8aLQjQQIq0b7PM+cH0+UnHl4947L6Wwby+LUOKcL2uPXpyy1kkvxQrgusppDDAzDaColMTk/tT0prJ92Uq3YBEE39AMRISUrSd7nxK4atG0BnbYA0ILYBiGFtDzfNT6N+J0LWcdwlpN9b3A0EZoENEYkjd6SH2AYaT4cRrKDbhgHhnFkURZw3/asQsq246QUNNjuNQ7DkxbDsntFlws1O1Ky2uACNrDiiGyr/fZI07KS7UNYdr/d2WieM7WdCL1gd77cPJ+Zp7MXwq5bueT/tT30DSR1e/X9wa6+eDsaGzYz0ddK9hofEKpKnWdqKeRpWlHYPEOri80saj7svUbsTl/BOwALF21Twm5/LkCK9p78Zp3VutAB+uEvLvdq9FgrrruEV3/C4N2Rjs5azwTQvmmVJ2+kdbc+N1MAkGqFbH9rXaPISQJeVK/PZV0iR1uXYa8+DLO2ac0YYU3qTZXKH9+w1zX+xURTZc6zOz9mF/YXlLhoLLe+BqTLU9n66ZzPkmemMNEKlEM2vjusnY7elVzCc0prtiF02l8tlXmeyfPM5XzhfLZCFgdyRAIpNQNtwJWAxHTR22ra0E0OEKMJJu+uqqobl7CcKN5HZNcCdS1StwYHvSPZH9uL2P7aYK+50A+Mt+Bd4M6jv8anEh+lI6vBijsVK2A1BIiDKxAMDLsdMURaGkm7PXR9WLcTitGoBQgurlwhgDgiqVKN7xcMmYwpmXMXsiCZ0KmljhDFiGhdd2/do72t7c8u9m6WmWVpc0ooLndib1WVRbVAlkLWCtTYEqFERMXNV+x15MkCEhYR2CcFrS63LQNsYsMwVkz0Nk1YnI2uWrLX+JBQVebLZEknZ9+0VacQrC5XeOKQTofxJLgybNryfNsktEVmm+oir7XcTy359t+bdAWCsKyd9WJJT3xtNqcUrM+zJkMxOuBGPHOlN9B/KL6TlOU9vF9p6nK3TolYb+wU2u19N4/YtEzXO/whBkqu8ccfquaAlZ0nW2olRjFtZTEqi7hDXm2rk1YHY7qGa5YZjULOmZrLcgyLZ/L+GMt11UGaRsk22JnnmWmamabpyXXt3ZTWtZ4Hm31xKS2RsAyGVS9iRTrvNSxUHtTOCysA8831oU/u8y0yW3THL31yfYvIbruYYQP+bNh81/iE4qOGvVpKnjK8yEsj7PaEmBh2e+LtHZoGwm5PONwgIaGBZSK4NSOeqyp5mmk6oVRDcoedtT17e8HbLSH6KMvCHZXFKdeQnGoUAzVe0sJ721AMzC/eJkb71LZJhPkEt4NMCsSQFlWGZaU6VKTO320tLzZ6snZll+LXfwP6zlSXgiFIWEjycRme84lN5z+h32ztXuMa3xa1VO7fvFmRUV256YCrC/hwVRCimNZrN0gA06lUWJoK6tQd27BiRWdni6qbfbRq99tw1NWnpEWCmxSI8VybXUxtQG3YC0NVl+HOntzsjeG7PrqbVpfwEn/b3Z7T3Lu2SgVtubpuIHsK9Y3mco5YgeWF39fRo/5dKKwc2uuavMaHhVELzpScOV9O1FKIMZJGO+enOZLz7O38CmIAS86FnAu1NC75Qg33xDCxHw/cHm4Z0kAbAtoCiNLKqudaq8notVaZzmfydGGeZ968ecvDu7fkOXP/9g2X85nWqnHnayXEyDCMlo9iZNztCDFidrVlURuKKRHBwSjxteEKIqw1pSDrcnbEtvl9uvTX+9ElypYNsUttmTb1KoWZBqMmJs+dTzbV1/hk4qOoBSqdeeYmBDES0oikhOz2xMMNMgzE/ZHheIekaANhnhRKtd2p2dw1cBkeOiKruiwmkY397Bb2FBd+95uCtgWdZXtAt/UAj7kPu1RCTItFHyHaT3riM25sVy9YM5kS60DMA9IqSEOJdETW3lanITydau6tlI42dUS2T2f2orZTGK5xjd8mVBvz5WIVmeudBrA60O6xoKbSvEnyHuK6BSr7E6ijRn0f15HJ5XrfAG5gzcVTXRpo3DzpmtwW6+iOttiH2Hwgx0KFlR/gTyLv3VW8UO7tC8OZN4VqR2rXt/EeQty/Q12FGhQj1Gpb0N71S/jt/z7X+DRDVZl9OLirFsRWURIhBs87zWWqFIldF3VVFChkmjO9WXgAAQAASURBVM7EoOR59sEvH6j2lnytzWdAGrXUZcZkng15naeJy+XC+Xwh55nL5cLlcnFnLzNGCCEwjtUKVXfkjJ6Pi9usi+errq6zdCw2G7zl3GNfwPpdwMJpb/rNRdTnVYorF7UNChvdTU9gHYpehs22YNE1PqX4KNWC3x7C3z7gD3OwbScj35+S/DWP+M5bf/0z/Lov5A//ua/xCccf84l8y6X74Adde4nXuMbvO64F4TX+WEJ+14NVRL4A/tnv9+1c4wPiz1X1B7+vJ7v+Hf+Fx/Xv+f2I69/x+xXXv+cff1z/ht+v+LV/z9+5kL3GNa5xjWtc4xrXuMY1/kXGlYh5jWtc4xrXuMY1rnGNP8q4FrLXuMY1rnGNa1zjGtf4o4y/nUJW5D9C5L9F5P/+t/L833y9/xCRf2zjnvL55nZB5D/xf/svEfm3/Pa/h8h/4bf9O35bQuQ/Q+T4G17nP0bk3/1b/Bz/D0T+7t/a81/jkwwR/iMR/lsR/iDrUYT/UIR/7EZ+n29uFxH+E/+3/1KEf8tv/3si/Bd+27/jtyUR/jMRfu16FOE/FuHf9ev/ExH+GxH+PyIc/pY+1/9ZhP/p38ZzX+MTjmu+fP9xf4HI/+o77jMi8p8j8nED69f4XsTfFiL7HwD/Hqr//pNb//YOuv838D/jm0Ts/znwd/3yvwb+U7/9fwP874D/BfB/8Nv+t8D/DdXTt76CyGfAv43qf/5R7/Q3fwf/KfB//Kjnv8Y1vhn/AfDvqfJkPYp8pGrJr4/f23pU5VvXowifAf+2Kn09/vvA/0mVf1OV8+Z+v8/P+H8B/sHv8fmucQ245sv34y+A31zIqs7A/wv4X/4Oz3+N71n8/gtZkf8r8K8A/09E/vf8/9n711jbti2vD/u1/hhjzLnW2nufc++tF5WqwhgcOwFLUEQmgbgsJYojmaAIFIGsGJnkA4oUpBJIQXESlxQixY74EtsfIkiMgeA4D0IwiSwRCEoZUzwLqArkIRdVRVXde+495+y911pzzjFGf7R8aH2MOdd+nLPP2efWvXX3+B/Ns+aea87xmGv00Vtv7d/+f5EfQ+RPIfJXgT/VVlt/ua3u/hIiP9A+92sQ+QlEfgqRP4LIfXv9e9vK6+8i8tOI/LaX9qn6k6j+7CuO5ncAf7IJXf4E8ASR7wUSsG+PhMgT4LcDf/ITzux3Av/RxXn+ZkT+E0T+HiJ/A5Gbdm4/jsjfaY//Ynvvj7TX/zzwDxC5QuT/2j7704gsg/HHgf/Ktsrc8EVBhHU8ivCjIvyYCH9KhL8K/CkRfkiEv9yyoX9JhB9on/s1IvyECD8lwh8R4b69/r0i/D9b5vOnRXhpPKryk6r87CsO53cAf7LJOf8E8ESEl8ajCE/4DONRhP8u8N8C/qci/G9F+BERflyEPw/8AxEGEf7ddi4/KcK/0D63F+F/L8I/EOH/LMJfF+GHmzT1n2jn91Mi/Gg7r58DviTC93y2v8KGDa/BNl++PF/C/xz4be0cfhSR/1z7zN9t38NStfxz8HBxvuEdxaUTxhf2gJ9V+HJ7/mMKf1th1/79Hyr83vb89yn8ufb8Lyj8nvb89yvct+d/UOFfa8+9ws0b7fe8zd968e+/pPDDCj+g8FcU/prCb1D4owo/8inn9O8p/Pb2vFP4GYXf3P79SCEo7BWG9tqvVfhb7fmPKBwUfnX79+9U+GMX23588fwvKvymb8rfZXu8kw/QnwX9cnv+Y6B/G3TX/v0fgv7e9vz3gf659vwvgP6e9vz3g963538Q9F9rzz3oa8fj5X4vtvlbL/79l0B/GPQHQP8K6F8D/Q2gfxT0Rz7lnP490N9+8e8/Afq72vMfAT2A/uqLY/7ftOf/WdCfBx1A/xDo/6q9/p8Hze14fhPoX7zY9pOL538M9Hd+q/+m2+M76LHNl6+aL//Cxbb+LYV/+WJbu4vz+8a3/O+3Pb7lj1+uZq8/j+pS7vstwJ9pz/8U8FsvXv8/tOd/5uKzfxP4VxH5MeDXo3r31kej+vOo/giqvwU4At8P/MO2Ev4PEPl1r/jU9wLfaM//KeCrqP7Ntr1bVDMQgT+GyE+1c/lnLj7/N1D9R+35TwH/VUT+DUR+G6rPL973deD73vocN2x4Pf68nsvvn2s8ivBjwK9X5a3Hoyo/r8qPqPJgPLbM8X8gwqeNx1fhb6iyjLffCvzptq//N1ZS/XXt9f9de/2ngb/f3v8zwD8hwr8lwr8I3F5sdxufG77Z2ObLh/hrwP8Qkf8B8IPrd6NagBmRm7c+xw2/ovHLFcgePvcnjWPzXwZ+EfgTiPwrn+HTvwj8Zy7+/f3ttUv8z4D/EfAHgD+OcVT/9Vds6wQMn7K/HwU+AP5Z4IeB7uJ35+9A9f8L/EYsoP0jiPxPLt43tH1t2PDNwucej2qc1HU8ivDtOh7f5hyfYmP4rwC/vx3Hgm18bvhmY5svL6H6Z4D/Rtvm/w2Ry4bLHhg/ZT8bvsPxrZDf+k+A392e/8sYLxTgJzBeDRe/B5EfBD5A9Y9hA+c3foZ9/XngX8G6Mf854DmqX73Y9j8P/BKq/z+M/1Pb41WdmP8Q+Cfb8/8P8L2I/Oa2nZvGa32MrTwr8N8G/CuPSuT7gCOqfxr4X7xwTr8O+OnPcI4bNrwNPtN4FOEHgQ9U+dzjsakX/HPAc1XW8SjCPw/8kiqfdTx+Gn6cxqVr2d0fwMbwX8W4tYjwzwC/vj3/MuBU+T9hk/Y2Pjd8q/Auzpd3wDnLKvJPAD+D6v8S+L8Av6G9/iXgQ1TTZzjHDd+J+KZwFl7m/Pyhi9/9oMJfVvj7jYPzA+31X6vw19vr/6bCL7bXf6/CTyv8pMKPrzzTh/v7Awq/oJAVfknhj7fXReHfUfhPFX5K4YcvPiONj/p++/c/rfB32v7/S6/Yx29T+NMX//7NCj+h8Pfaz+t2Dn+/vfZvXPCWXuT8/Nfa+/6uwt9cjwu+W+FvfKv5JtvjO+vxCo7sH7r43Q+C/mXQv984qz/QXv+1oH+9vf5vgv5ie/33gv406E+C/vjCQ31hf38A9Bca5/SXQP94e11A/x3Q/xT0p0B/+OIzAvoXQd9v//6nQf9O2/9L4xH0t4H+6Yt//4kXOLJ/4eJ3A+i/2/b5k6D/Qnv9CvT/CPoPQP8s6N9t5/3Ptn3/3fb4r7f3R9B/CBq+1X/T7fEd9Njmyxfny9jO+e8p/KjCH1b4f7X58j+6OIbfpfBHv+V/v+3xLX98+1jUmh7dyY5Kfjfwe1D9Hd/qw3oAkf8Y+JdQffZN2v6PAreo/q+/KdvfsOEN0fRbT6qoCL8b+D2qfFuNRxH+Y+BfUuXZ5/y8B6Iqowi/Bvi/A/+UKvNr3v/fBH6jKv/jz3vMGzZ8IdjmSxD5s8Afxqh6G95hfDvJPP0m4N9GRIBnwO/71h7OK/EHsbLks2/S9p9hhP4NG77V+E3Avy3Cd/J43AP/DxEiIMB/73VBbEMA/ujn3NeGDV8k3u35UqQD/twWxG4Avo0yshs2bNiwYcOGDRs2fAZ8K5q9NmzYsGHDhg0bNmx4a2yB7IYNGzZs2LBhw4ZfkdgC2Q0bNmzYsGHDhg2/IrEFshs2bNiwYcOGDRt+ReJzqxa89957+qt+1fcBivWLWdOYoqDaftorIg7nPCDL2+y9TQPMtlGptZ630TZp72nP6/J+25SIoChV9bxZZ+3HSns/D5vZnAjOe0SkPXd2fCJ451sTaNvB+SAujsuei7iL9168tZ23otRSKFpBlap1PXb7LAiCOFmPt5RC1bp+fu3Dq+dd/Ow/+vkPVfUrn/4XejN8+ctf1h/6oR/6oja34TPib//tv/2F/j2H/bVePfnS5/68vPYXcvH7VzeIyif86/UbXvBJTafy0rMXN6ev/dcrtvvgJXn9rx68rq9+Q/v4h7/0j7dx+R2EL3pc2t/zB9d/P7iMVF8zPuT8zs/Tk/3i/PSKrb8tLg/rbbb34veRUyLnAii1VqoqThyxi8QYbX+fcn4/+7M/x4cffvhFnCawjclvNT5pTH7uQPb7v//7+LN/9t+nVgvYVJVKpZKwi69QSkZVib6nD3uc+KZ77ixwyzMpzWitHE/3HI/3qFabNETbe6Bm0AplKpTZojrxFgRWUZJUslTECS56nHfUWsk5U2qxoDV4nAgxBvZXO2IMxBjZD3tCCHQxcjXs8d4jCE4cqKBaqDVZYFoLtWYAvAv4EFnC0MvguVZ77/3hnsPpQKmFlCdSnhEEHzzeO5xz9F0khEDKiWd3dxxPJg1YSkW1IlVwxSHVxuPv+p3/6s993r/Zq/BDP/RD/K2/9be+yE1u+AwQkS/073n15Ev8i/+dP9z+tSycXn0vl6at1f7x6vddvMfJ2XrHtWv+4m0P9iXIg5lNZHnP60POV87Wsv4Ph+DVykg2Ru13FShtjazY/UNRHIqjnhfUFwot53MSRFxbGHvEWZFKRVCgqpLapKoKpdrC+cFJA3/8X//vb+PyOwhf9Lj8oR/6Qf7G3/yJB0mNUgrzNFNrRbw7J1icwwe/zi1Uu97SNDGPkyVuakuMiNAPA93QN0OuiwXnK8azPPzfiyf9mmBUlyH0ZvgMAfRLAX218fbBL/wSH379G+SUOBwPnMaR3dWeX/1r/0m+5/u+t31HAb+M18ux3fb/X/jNv+UND/jNsI3Jby0+aUx+fh1ZARcdThVVd86sSrBppFQLxqri2tSjqjjn8S4gCEkrNWeKVLRU8pyotdhs6W1QhhBxXbSgMoKmdvG3QFec4HqPix5xFiQ6b/tb4Lwj9MF+Bk/XRwsmnSOGgHMWVAZ/zsguP7U6arVjryVTsjQNamlTKOsNxCEgHpyjqjL0ClhQXWqiasaOvoLYNroWyHrn6cNM8fX8l1Hw4okS8fJqt9sNG16EBWPK5bT0adkL+YRAFmw0iVwGqheB7Isfa0HgOkvJUtF44e0v7e6C6dQ+IxevOsCLtPOSiwn5IqOlbs2eqiqKB7GgVqS27VWcKCIQg+C9LVy7rieEDgRqO8OcC3eHIye9CGBr+wbO0fmGDZ8IWxS16lwLQsfTyPNnz5jnROwiw27Ae0/sOga3Q5xYVbFatfL2+S1Pv/GhJWhKoeSM854vf9dX+NJ3fRfee3CCOEsUqS7VRFkrgOeR+HBMLr99YchevPthFPv6q14eBJUPxoeex/QrN1RtsVlz5nR/z/MPP2KeZ54/f8794cD1oxu++3u/l1IrKoLUer5nbTKi7zTeyhDBeUFV1oveLvY2iKQC5VwdafGZCPgWaIq7GGBqmd1aq73JKYIjeEcIoWVHbfJSPQ8scULXd/gurCtZowvYylawbGwc4vq72Aecaytf51oAynn0Xgw+dSDFAnXB2+RYl1r/w9uATfYOnEeqEnwk+II6xatDl89rRrFMcfAB7z21Kt55vLOA1WHn4MXR+57gvp28KzZ8O0MuMi4vZk1fdb9/bZB7+brIRdb1YsvycAw8/Pz56RLYykN20cuflOWGIWuw/PBxGXBfBrMXO1huEiKtams3IMEhokYpQnEOvBOiF7xz9J2niwFE1kB2dsJpdHgntv5sC1Bt35uyBbMb3gxLYLlQ6krJjOPEPE3UWvHeo0HxPjwYqMucM88zh8M9aU6UnMk5473n5tEjaq023+nDQb6Eq6IXC76L+dOSQg/efCY0XLIa5OGo/bS4UV8TYMolDfCCOSHtvaqg1ZJa82lkmiZOhyPHu3uC9+SUqHqurti8/IrvecM7hc8dHYkI3hlX5XzdnDmpKhUnbUCqIC2L4X0geDPSqbWQU2oDxVFVqBW0tGyrVBwFoQCCFkctbt2/tKxTKYqUZaUrSFGcc8TgjIJg4SyC0QVqVtQBTvEWI19SYUEqKm3Vqlbit0FWV+qDUQgsoPXOssFLMOzsVC37q127cTmqeqCSKyzxulIbx7cQvKOL9t04seP14gguriWUDRs+L157f1d9dTB2+bpqC9ouX36RGLAEoMtEeJ4uL+e1y0SqbVLX/O6LZUdZny2LZG3bkAeTsl5mffUicFbWYNPCU6NIBAHnhD56ht4qIsPQ0fc9VWFOlVSq1ZJUbcBq2/Drvq8NGz4FlznJWippnpnHEQFyjFCVGMP6rpwz0zRScuZ4f8f98+ekVrmspRJCYBpPpHlGq1qlMfhWPmkBpVr/BW2+Os9VyyJMEDmP3vO4Ox/xi2exjO/zr5eI1NnjNWf/IOOry5OLsS6sldXYBbRWovcEJ3gRtBZKTqCK9+5cqdyC13cabxHIOrq4e7C6gyWUXZZyF8NWbWh4F/Atu6goKWc0CSKeWoVcodRKyS3zUQtaMyCUFKjFwlIfLJviFHK2QBAgWdqE4D1+bwPAqcNpwBPQquTZMjTqzWzdeyvh5GIBKq6gLoO0jFAbbFULSuM31UJOtk/12rLLgg+KOMv6dNFoA7AE2QXVypyP5DpZgxuF0jjGMQRk8C2ADWtD2bmcumHDNwvKwxnsMvI8p2z0/O7XbuccTJ6bM1+6enWZS2W5NdjzJUhtQae2xeSy3bpsTRbSgaBii0xEUZXznKbOqjjLGbWg2QtEb+P+ehe43vd4H9jv9/T9nlIqt4cTnApVKk4r1HJRkbXgQLchueEz45yGLKUwHo4cDwdqLgTnKDFaINtWfHOauLt7Tppnnn74IR997WukaYJWmQgx8vjJE8bDkdh1xL5DiTYfeY94h9ZKSYkyza1fxBn9zlm1UsRdBLFtvH5aXNgyyufFXTs773FhqZ9c3ifOmVPLML9wJ5HGV3cWyMYusBsGnMAQA5PzBIGaM/M4EmIkhvCWNeUN3yn4/IEsgnfhQRr/QRC7DIgH5QNZObIAznsrhzi3lgG12mRVWhanuEp1FdRRqlJKK++p5VirglOllgpIy5ha4Nfmv5aZEWiT3EK2d3IehNoywbW28g8VpeLcmaO3TMyLUsKqsiDnjBAXpQ7njB2sgGvnUGuhqKfijMNfcztmGk9X1u/WyUIz+ITy74YNL0BexyH4JKhdx+cF00VUexHM2tMXGXOv2eSD8uhycK96o1xQDuyGIcu+2gC1o7nY70Vqx5pe9MH+UFk/9YBegWVkTaVEiN7RRY/3Rivou0DOxRbAcjkdX57Aw5zVhg1vghfHjFYLBkteHgUnjlrqSkOopVjWdpqYpxPT8cg8TTZvigWpORnVYKHWafUoDufP+63VOLUiAs63JI1D1a052NfXRZZx//D1lWa3VCmwKseDTejFNtb/P1Q1Wse7uLXSYufi8NkbDVBsHtTWRO7cC1xcaDSDi1zylqR9Z/AWzV6C9xG4kIlaJ5qX8zbLBeakBa5L41eIlkUhknIgZ0hayNpWddWh1YLSec6k2bilwxDpujZSp5YZbQ0ZqlaeSRn6vsP5hI9GjLeDsAC06wIl7wjRU0slJ2tOk1BwXUZcBamIazIgRSm5Pgx6EdRdjpglOwTn/mpaJre2ALm07Gyh1EQpCXB43zUJMMvILtSMupQ1N2x4Y1ysID/lXecnnxycKawB50u8tE/4zOu2/LrQcAlol6lVL56/tAW1sVXV1FLU6qltC34dgV4geJPc6zvHEB0hCH3f08WIc8Zfn+eZlAvjOHE8nZiTcRHrwt/bAtgNbwVlYcXYT/uv1troAZXpdOR03+G95/ajj/nwa19jHkeef/gR4/09OSVCjIQYqVk4He559vFHlqUcemJvn93dXDNcXVFy5v72lsOzW5wThv3O+kpCYLje48JSIb3ABd1uSfysDaEipJQYjydyybDQ7BSG/Z6rEFnC48vF5HLOOTVKYa3WtFasl6Yfero+mppDmhmnkXkcmU5HpuM9IsrdRx8x7Hv6YUfwYZXiOn+7ehFwb3Pmu4LPHcg6cXSxZ+XKNDzIz+p5tfVA2kNsNRViJHY9Ih6lZ04d8yyk6kjFeLElelJw1KocDonTKeG85/pa2O8jqkqaTR6naiWXmaqFGAI315m+7y/vGjgnhGgljN2uZ56VvouUouRU0KqEodJfZZxXVBLIBFRKhmLzJaIewWgA1V1MvaqmTqDSGkNaTnalJWRUM1VTWyWbLJd3ga7rjF6Ax7keJ6HxqJIN9g0bPgXLxGgZyk957+fI8i8Bp77w2msOZv3lq99zLkGyBKCvCKj14j16+ZlWftWaqDqhVETd+h04idbYJRBa9tU7YT90XO87gndc7Tt2QwdAypWURqY5c384cHt3JJfKnJJRgy5DbnnTpcKGDbBmIV9I7izBoZbKPI5kJ6AFpxXnhG/84i/yCz/zM4zHE6fbW47PnlNrZdjv8Ps9VZW7p0+hSUzGviN0HbHr+K7v+x66PpKnmY+/8XW+8YtfxYfAk/fe4+r6iq7vTSFh2MFag7ygFYglh0rOJhHWmqNFhHmaeP78mcmHlUrJGRSevP8++6sbfPSrOgM0/fY275eUOR2OlFKYpolpmhAnPHr8CHHXzDkzjicOh3vm04nD7TOOz5+SpxMf/uJATieubh4xDDuG3X4Nrlm/T90qmO8Y3ophIuLWeWfl1sBaMtAHHc3woG1aaMoCHnE2OZTy8IEIuaWAarUGjGkuOKf0vZKyDbRpVlJaAtlMqYkclBAzphSwrCqNFxd7KyuKOKYhA1bOybMNPHUV31e8GPdWXQYKtQqlgOoSwvqXyUQtc3NuhrnMTrc1qiq020ZdGsi0mljDwrV1zrRs4fXSSBs2vAg5c01fDLM+SU/2s+5j3frSPfwp731VTvZFEsNyyK9+/SKYveQrNarPwj+3PFCT35NFe8CoCwudwHtPDAHvXVMNcZZt1UzOliFKrSs819oqLxfnePF9bcNywxvjFSvLJb2jWiklW59ISszTiBNhPFrH/ng8Mh9P5Hm23oocmyxXIc8T4+mI855cMiEnSsmkeYb2nnmaOB2P1hy229F1nVUh6sJFv0jBvjBWFxqdQ1c1glIKKSXmeW7VTOPL5mw/L5eoF2u/87k2CbGUEvM0Ic5Z5aMaTbCUQs6JlBM5J3JKOCdMxyPj/T3eB1MwaBJcctEMLfIy7WDDdza+EKr0qxlz8uDZItljdBoL9nIuTNPIPCUOhwO3t8+Z5sScYM529cfYEUK0jOxx4nQacc5TamScPFVry8hmG9RlopRECIVpdMRoE1yt9tN5IXaC9zAMA8dDoeu6pnubUFX6HeyOEAKErhCHjDhFC2gBVAgCwQWcGP3BEZqJgmniKsvK0GgMq0y7sppFmMGCmSyICLVlap0oSDFNQFV8cBslb8Nng5HO+LSc4efKyuqSPH1VJeaz4MxTeCCQwDlXW1umthGTWDO3gLHpKznP5PmIajHTBJHWjLondh7nPLs+cjV0eO+42vfshh7npAWxpnwyzpnjOJNSYZwLc1FKhaJnXdkH1IJtTG74PLiQjqq1oKVyPE2MpxOlFvoY2A89IvD8ow/JpwM6T2ga0WwGCtPJUbTivLeAcJpx3hO6Dt9F+t3AkyePmb/0hDzP1DSj2RSCtGR71LJq1EILWC8SUgIm+XV7yzzPeOdM1905TqcTeUqmA58LZbYAtqSMFtuu1gqltEWiUMW1ILaYFm7O5DmRZ5v/ptPIKQTmceTu2TOef/QRaRqp44lIxZdMPtxzdCClcPfxh3S7AR8CXb/Dx4iTJsG5qfy8U/im9vwt088D3UlYuxbTnDgeT0yjlSk++uhDxjExJsc0O8AR4o4QdlStnE5HxvGEc577o6fvZV0FlmIBYc4nSplxztPFgvejGRKUiVoLzrMGsl3Xc319Mp6NZlRn0Mqw91w/DoTouLqBm/eEEBrPR+28OucQr9Y5Ej1eOpwIpSRqzWeuTjttsQ6T9eZlgXemlESpGQRKnSlqwTASEC/NTcnh6maIsOENIVhz46fQCz5rEPtgU6qfs2v/4j6w/GvltJ5zsPLg3ZeWCAvrtdg40UqeJ6bjLbUmvBgf1jtH7yD6nhgcN/uOxzfX+OC5Gnp2Q2cForaYzKVwOCWe351IuXAYE6dkVZyiZ13ZZYqXRa92C2Y3fBashQQrM2o2U6D757d87WtfYxxHohP6YPd+nUd0OqKlUKeROtt8dsoz9WgGCKfbe4bYW+m/aaoPV3ve/9ITpu9+nzwnyjzBPNu4zYmaEyUHai7UbOFrRqmKJWDa4U7jxNOPP+Z0uCd4o79550kpM4/TasyQZpPEKrMFt9roCLUYd7aKUF1dqYDzNNvnxpl0mhARTs5BNo7w0w++zjd+6RfQnOlSoqcgRZmfPyUf7sinAx+9/x7VO7q+5+bJ+/T7KzMWkr7R/L5Vf+QNv9z4ZRKvuLjbtwq7KRRYV6aVETJzmplTYp58C2Q9pRZKLdSqTHNlTsYdclMBSuPw2KrSbGmtLOEEail47x4Ejs5DzrL+FCZirKAZmIFKxeNCJXYOFxzD3rI21jlpk1htQYINFmk0i9bYtfIC7Q1rqdfS0Wu3p15q0774EMs2iTjr0GRbYW54M5hQvzVCvQmd4PL560tySxaJh7X/N8rFvuYYzlt9QIc4v36hSftaioKNoVpyq2zY2rKqAy14UePIerOnDt4ToicEWxgWrdRGXSqlknIh5UKuNqmv6kKLasNl0XSl9G4z5oZPgbaxpbRsJTZnqc1bKWVOxxOn04nooLhmCV0ToXm0i4msY2oGSlJrfE5VcEVN8aAWSrGsaZrnVdGg5rxmSbUUs5Vvc28pBcUystaZcq6OLDSH6XSihAC14p0nl0JdgtVcqLnNxSWv26ylUkuTrXMCzQH0oVKDZXRFbF9p8k2hwR7kTESbNXZFsyV+8tQzjyem0xFVZUiJkDPioVbFuYd85A3f2fjlU2FbJwJttns2I3rnG09NCEHx3gLR49Hc0rs+0FVrKqs1IDKAOJRA1csyo/FkfABxESceH4wHJLVaIFgLgppObQWtpivrfWEpHoIyToVxqqYzeXSk5IidcLWP3Dzq2jGbYLN3NikuPFYRb9xZmqc2FwFrURuEybh4tRRTYsgJ5yuiI6UoPkTENR1ZPN5FxMVXfq0bNrwWr5Hhel0Qu/z7zflln/V9S1gq66tn3twFt+ACq27sCw/R2jwEwTnogqDVXhfNOHVEV+mj0EVh6AO7Ia78WJMuUuPdT4k5ZY5j5jhlclVSEXJbPL5uStySsRveFCkXPvz4GSUbX7WWyv3TZ9zd3hrv83DPdHfLfDoiwZsijxO8U3oHgjNN2GAukFQLZlG1gFQzVQR1po2e/MSzb3xE/3P/mJoLz7/xEafnd2aWoMp4OBK6nuNxZLi6tvkVVhvmRTv9dLjno69/jfF4sGxntCB5TUZhiaSUss1zaabOM13XG22htBHsHRI8CpwOBw73B2opTMeR6WSGELGLdH0gTRPT7VNiSQiV3juGRaJTbDHpS+L08cco0A078jQzXN/Q73a896Wv4PZX34K/8oZvFb6QQPahS/PLeHHCqtUeghB8pPhKDI4QFZ8qKc8c7meqeoYyUGrB3EIiuNgyTpFal4nGQcu8ONdBUwtwznirVSsqESn1IjtbSQjjyYTUhboOXucyzhdElOsbx+He0XWO7/oe4fpqh4+BECJ9b+YOITY9XBGjAEhowWtqrl1KqTNVsxkpTDN5LuSaGU+JaZ5x3lELzCmtvGDxjuA6uuiJrvsi/lQb3hEsWrIvjspPCmIvX//kYPYtMh1rFvcczF7cHLgkGLSjoUrjxiIsNAO3bkXxDnx0aHVozmhOOITOF/ad0HWOqyFwte+bvJ0HHJXCPBfuDzNzytwdE3enTFXICoXAWfTr5ca5F490w4bXIeXEL339G8zjzP3dnSnRHI/Mz55R54nj7TPG508Zj0dcH9H9YPf/LjAM1ovhosfVQKkV5kIpRn0hFzLaEjzWTIUqH/3S15imES2V6fk96e6IOOF0f8B3EecD4atfx3ddy8he9rtYYmcaTzx/+iHzNOJFiN5b06T3xNjhnKMUq5Sqwu1HH/HR1z7AewuYpfFvXQy4LqBI08U1rm86jaTTCGqW995bJXW6/ZhYE15gFxz7zgOyymtqSRy+8QH3z54RdjsO9we66xtuHj9ht9vTD8PW8PUO4YvLyH6Ga2bxmgagNWaIc2Yg0ASVS63Nstbceoxvap38IoIT36StoMWxtjnXEjtipgIiDmptpYaCisNMwKycovV8XAuntVZBmvHCPCnzqI2LtzgHiR23k6YywLLT9b+L+uhKJzg3d5lerRZbVddiOZ9SKq4UnC9rc5quXdfbhLnhs+PzUgte/pye4883phS8iJd4CRdP5ZXv1IVff+nQtX7axs25a9lRxRorLeRVMzRx5gLo2z1mOXxVKFXJpZJLbZUaoxRYvldaP9oitH5uhLk80o1ZsOHToFUZx4lpnDgeT8xTQqexNWFZiV2zNWFRHFKt3O7UbJKdgBdT3qBJWTmWioXppwtGU3BVTYt1mjgdjlAreZwoKVmfhgi5VMRnfAU3W5Pz2T8PUKteLrSCJZCt3jWqToRSrPG6KCnXNZsLYomdxgMGrBGrjyCQk1UhtSjzeCKNI6glosQBtVDSjGuvWYKqjbpKq8RWSpotmAfm0xH1gX4YVqfMDe8OvhjVgk+7aJZVlNpFPI22GsupYHqrnr674vrmSzg/MTy/xeg4jr4f2F3tcM4TQsCHgHOOvot00TKftSxGCLTO4sUZJOK8R7WSc24SJ80JpVYrz+S6BtbarMBEKk4sIxu7Qi4JneB4ctzdZtKsBFd4dKVIABzNFQnwgkiwEmf20FbNORWTKqmFnCo1KbVATUqZleoqItlcXaowDwnvZwgODUZ92LDh2wOvCEY/FQ+iYB6Egyux/FVBt1wErSBtAeqoLC1YwXvisEM0kh1kik3+DqjJMrVqY0gRUlJSsTF5d5x4fpjMBCEpWf0LVALBv9R8ZsezmSNseFPMKfHzv/QB8zhxf3dPmhMhz/TzCV8zmmeuo2PQyC569t5cHoO0XhI1N8vg7MrvnJK9o1ZI1UyEUKHiKFVwpaKHe+bcmrDGRJ2T0e9yxoWAeE8oFRcyGAlulYY8c3EzghKDmYv4JbCkmKh6zdSi5FQpanS6XItRd0pFs+mfh11HzAPiXEsAOSqVlGbub+/Q2vjsbQaPZEIL2Gs1ec0L+z9TOZlmRBO1VE7dc6aUCc6Tpqlxc7dg9l3BLwtHdg0Um1LBdBpbWcSmJHGBfrjm0aPvIsaZjz52hFCoKvS7PVdXV+ZC0vfmSOKFXd/R9RbIpnQus+Qq1Co474lDjw+hBbCLDJeu1IKaK3m24LFWy4hWBSeK9zZN1Xoi5XtSzhwOwvNnibEv7IYMDnufV3CNh+sEp3ZcohUpVubJc2E8TtSmoVcr5hQ2KXlSk/fSRPJCLTAMUyvPeGqXIWyB7IbPhsuw8dsD+lL8+uKzy98+SPzK8o9lqVoQKk4q0Qf2YY+jMgvMNRnlQEDrbOIGakoiaGXKhdNYmFPh+WHk6d2JUpVjVjKLOojty7JhFsxeHq0CRbdgdsObYZoTP/Pzv0iaZg73B3JK7KXyvhQ6qbg0cdM5nI9E7+mDVScFsWSICKIQxaNOqd7oeUWUTCHVZnOuYsaVCtNtQrhrDWY2Bztx+BgtyeMcMRV8tIysOU6aEghLg1mTqPMx4rTitbRgsyCtSaxkJc1WzUhzQscZwKS55gQK3dWePmd88PRDz7AbEIF5mrl9/oxaMkErXiveCTf7jqEpi9SqTMka27w4M1aoFVI2uuCcmFUoxxPRm3xXyfnb7N634ZuJLzCQfYmNZ/9vfLtlIGkLGEupD94pYk1NzilOlkYnWZupfLDmqr4LOG9NYEMXLPB0rslvgRSxFam3z/jgUW1Eca1IbaZ5VREpVqpw1cr7okgrZyyBLDlTpVn4VSFn+13JS9NYxS8OJotXvbz8PaheUiouaAiyaB3Y/UNl4RBb8F1dWXlBGza8DV5HM3iza6t99uXo8g0+uvB+LsUHlLPs1gvHs279Qqn2wZNlrNnD6APeNGQbfcDUQgAulEDUMrK1VHIp7bHQCpSqi8SWrp3bwuJK9PCIqrZTeG0mecOGM2qtjOPEPM2Mk6kJBKfkYMY7TivBCU7NtMO5hUbAqpjBCw5WTgR1IFUu+iBbyhTW+fYMa5ZCWltzo+sZvY9G4XNnqo9Kq2xaFdRR8Wra5pc6Ok4rzkMVMJ7fMkrkwT6XhzjX7OLtd4tMV9WCaEGausFyzLXdKkSXMbmosjTqQi2mWpLSWZ3hLCm04R3AFxDIPizEXUKcXci1KimbHMc0zYyjZWS9XwJNRdVRqxkfKH7lAfWD5+ZRR4yBm+udNVt5x9BHumjZE7OFVXKu3B4Kp8myuaWZJWh7j3Fiz1kU5z1+sIFZaiWls6e0c3ZzGCQS/B4RJYSJKR3JtfLxs5kYP6SLnkc3j3l0Dd57grMGMNSs/XITsHbe0zUCeqkZVZMVU18JfTN2KLNJjeXKeD9RZ6X0sAun5hq/YcMbQr/Z2djPkO9dZqHLj0JbOMr5tTWwlYs3teYVaBnYggBBKr0XHI7oFS8VYVnARmj+8POcqArTNHEaJxDH3XHm9jCTcuU4zcwrNcnuOdD4tZhs19XQ0bd7zdJAl3LlMM7UvFlHb/h0lFK5e3bLnJKZCeSMD0LqxCgEpbbSvfVeFOcozkGISNdZBFcK0mSupL0XVboQ2aui4vBdh++6lr1sTciI9YZgQWSIHS4EvA/srsyqVmj8VLBrvBoVzzUnPOeWALap+yxBpCqpmoFRXah9bUM1V2qy8eH7iB86xAkxeroYKDlzuL3l/mpPSQlfZqSk83fQbgNVPBXrjYnOEYxIi3Mmt2e+L9V0eUuxgDalLfnzDuGtA9nXXivC2oxlIsimLzeO5s5VSyF2Hb3rUJVm/xqpRVENSCvTD7vAzaOOvo986cmex4+u8U4YukCMvgWdlm2a5kL46MTz+0RKlftjZp7rcjAtK0tr1rIbQexcMzKo+Gh6tdb0Zdve9YHrK3MBGsdnHA9K1YRwIqcjXRDSVyrRRWIM1NDTBUAxi8tsA0q8o/MD5u6VqVjTl4vQ5UjOmftDpUyFkgvj/cgsibpXroZrgtsMETZ8u+GzZCLPWdmHm5DGOJCXY+M1+6uNkVpxNO66QOctexXESqYCTRovGo1IISVbSI7TxGkcUYTb+5FndyO5KIexMDXRdloWTADTNVA677jZ9VztetbskCrjnJiSac5u2PBpqKVw++yWlBOnaSSXQug8iUj0jq650gURqnNk51DvkBhgGMA5JCWcyxZAtkSPAL1zNtc6R+h3xN64qLHrCcGCWnwA540jGyMuREII3Dx6xLDbGX0meLxzrTE5o3VppFwqhwu1h1U/dul9sWoGF++wpBGlJZGcUC0Ji4Wl1qx1//QZu6s9Jc3ILDC3pi/fGjdFmMWTJCACvfNE5xApFtQ6bJHcXMRogWxp5g8b3g28VSD7SdfJ5Xy1ZkRfeLy4Lcusnre7FOCd8MAl67zNc+nPe0f29UJFoO2zUQZYLOvaaFuqJ4sP+6JAsO55SRQ5h2s6t84FVDxaC6VCmhv/NZkAtHNCkIq6JotyIX/knGsaeO3c9fy6WzRoOZcqTcmgmtlDE5jesOGN8E2/f3/WUnqrCZ57oh9u5aKJ4zXkB0Bxcu5i9s4yWd7ZxHhO+F64bqk2L/bm3V4yqq49N0pBrfXh/UZYM2NOhOAdIZj2rGIqB7W2wFm2uXLDm0HVejNyyqSUjNYiSslutS9/cDEtA2GR3XDeejGMS7cutpS2eHMenCN2HaHrEOcJsSfEaEGuD6bs4RwumvSWj5HQ3u9ECDFcBLKhTbCyzk/tTBAa/S2UVuG0wzr/tqFigSxQRTG1TG30Acv4eu+tF6T4lcuz7Eo5/6wLlQJTXbio16zBgFxkZUvOW0b2HcI3t9lruZDWTshzqUJEcN41r2jIuXI8TpxOs4krt03kXJmmQimJj+uJ46G21aPDO0eMjsc3Pft9xzQXxikxz4l5rkxjYpqMJO6Cb1SHdjgKxUsbXCCqBGeco1IhN4/oUr1Nhs6a0rruCtUOoZKmEU3KdFKm00xNBe1AizaSesU5b6vQ4HDeoVRymillptTMOB+ZR8vc5rFQpmKyJQgOJUlmPE442TKyG35l4syXe9jgtby+JGIvJ6fzb9ts2LKjuz4SnLDrPTdDwDvI04E0TtSS14wtmIRfmSviKu5woLrnKI7DsZDm0kxRmjKtgBfjK3rnuN71VvUJgSePrtnvBrOuPY2Umqk0ma5trtzwBqilcLy9Y5pn7g53zCnhdx23eUeNHufhUaTNi0uvhUN8RPo9EjxOWZsOhcbWEaH4SHbBgtRgQaqIza3ivSVrWjYWWDmri3KBzW0O74MFlSzBpCV5Fo10g+3/zL9dMrJ68ds21itGHVJzKqtiAXtNEyWNOKDvIkMXKVS0OBNwxhanXisVacke06+VdiwiDqcgVXEUXJ4twXS65/D0G8TOU3L65v5RN3zb4AsIZB9e4JfQy0C2RY/SgjopxmcT5xE1LcfT6SKQVRsEJVemsZA8jKcTZiNLK3dA3wfK99yAwJwK05RIcyLNlXlKTFO27kxVnF+oDmcuXnVL0xV4bzcSTbXpvSqlBIpWG5Q+EPs9aEHnRBrvKVKYTmqKBNHbnFtbiTLYzcQ5oes6YtehtTJzJCFQhJogHTO5ZPKYKXMLZNWm8CSZ6Tiu3L0NG37lYmmm+rSc7sNAVjBNyyE4boZIDJ6rXeTxVY93wvEucTtap/Wiu6kouVZSUZCC3h+Zi0dxTNkxF9d0qt3acBmkEkWIwfF433NztSOGwPXNNf0wGL9/ztQ5r0HsFsdueBPUWjne3XMaTzx//pxpmghXPbd6jfbWxFz3HXiHq03TkRaY9jskWpAZWkLG+0jwFryq76i+8WhXIfWHo2hxnkQrpWRqLRbItiDWeW+PYCZCPphOu4jRd9Y06eXIXTKn2izXH/4Wp4KYqTtVlCKgVObTgfmkZJQudgyxM6voWVBZjriZKThpTZyh9bEtgWy1xuxamzSZmSDV0z2Hpx8ZpTjnb+afdMO3Eb5YQ4SlHP8JQddCCVBYJa9yNpu7UuvZrWR9/7nOUOqi9crapemckFIh50rOtSkiaDNS+AQ6w0oxkNbgxVref/Hwl0YxEJzzTV3AoYvmZDWThKXrWVp9xImzbmon7aeJsTvnV0MHk7W2Ae/E49vrjoBTf+FEtAWyG36l4uVrd7EYeIkU2/rCLCHU5K8avSg4RwzenO6CJwTXOrzP/L1l+l7pTNWki0oxRz9w1BrQelkqNZqAd6bdGX3bT7DAYeEiAiafV2u7v2yB7IY3w0otyFb2LsV6IVLOzA6Sd5YDkZaB9AEJwQLZEBAfraq42KG311pmCPGBhVOnjaqjLVv6sFz/wlhcgt61Qm96spR6ZuOpPJjTjXGw0ADkwdx6+byqazbSFsjWRq2rtbbH8t4XR9H5tXZGsM6sTVHkJdqAcei1VNI8maNZ3SQr3xW8XSD74CK0LCIPeGPni3tptqoVpqSUXCinSqkzOVeePj9yPCXGKZNzbatBk9JZyiK1QtHaGibPVrfPbydAyLlyOFgWNjV5rGX/tZ4DYLdy6Gyl3GotBCe2oMU0+1AjrJtntAWb3nW2anQ7krvC3MJ6lA7EEeKOYb9vsiWmysCixec9WiuhgkgACXTDyJBM0SF4cyVx4oje7DRDF9ld74h9fKs/1YYN31q81OXFMq2udMAWhIqY8LqgRC/sYod3cH2140tPrulioAvCLgoiSvJiZcgm37N0MNdSGk1JUEZydSCeKh0qHYrRk7w4gnc82nVcDWa08t6jK26uBkAoVKZpZBxnjscT9/dH5mzyXVsgu+FNUErh7tZUC+Z5puTMaYaP7+EYPc5d88Rdk7uO/fUjrr/0ZULX466u8TePER/OMlZAEWHV8lBBSzk3WSmAKXHUpnCw8Fxtcbe4Ri4Omgq1Gnc3WxNyaq5ZC3VgpRBUe805T4iWvRVnMpm0LGia03lebZq21TV5LgEpCVdnSkqcjkemeabMs5knFBtRwSneWROm00RQ4wVLFduuFtPTDe5iDCpzGvnoww95djiS0vzL8Jfd8O2At8zIvriaWtKySwB7kc1sjVWlwpxM6mqcMqdjJuXC89uR05iYp0wuCjhELBvpvGUpVSq5reJyahnYqtzezSYnV5VxKsy5rkHuclz2vFqDhlsaNZRaXOtWbo1jS0Z1TfAotdTGzzGtW4DqdqjsqVKoElGJIJ4QB4bhCucdPoZWorn4hpydm3MRCPTdidSZC0nf2cD13tN3AyGYZq7vbDW+YcOvbLw6mLVni7SPBbHehiV9dFzvopX7r3d86fE1fRdxVIJYw8jJi3Fja0ZqtaYPtUA252wsW51wxVwEJQDeI07xEvHBMrCPrgaeXA3EGHhys+d635NK5e5k3vDTZPaih+OJXJVStjB2w5uh1sr9/T255FVJ4zRXPj4WOu/ohp4vO4/GnrC/Ibz/Ffphhxv2+KtrxHlKreSmkVprsURNa5BeEjV6keVcMp+L5qys2deyNnItgayZBdk2Ukrc3d01J8qlIfKsba5aiTHS98ParBWjGSxM08jpeKKWYs6VpVUuHI3GB32AwYOWzOl0skA2JSQXaNbwThWvjT5AwrcqjavtPJqQs3pnsXL7LuZ55v6jD8kukOaNI/uu4PMHsgvZW2CdjF5BKVhfaUFtKYU0Z+ZUmKfMPGfSSgvQRh+wDywd/loVdW1/K02gCZxXIbfMS21NWqXUFtgu7mHSBmrrfGzp1oVWUKuizlHq+QawlEiW8ofJci3H1s6sNWAp7qz+UWmcncteNzlrWcOaaXbeE2JH7Pozj1gV7zxd3xMah8nFsBL1N2z4VJzjwy9sc7ztJlu1Bj1vbxEiERY1Amtw9CJEb//ugm8ZWEeMnuA9wZuPu6w8pTMH/0G7yYslz0Yz8HoWdPdOGqXg4uGaAULbblnME3JptIJ6QSvYKD8bPh1LIHg5h4HNBeK8NWiESPUB9cHkshoHdlnm2XzVaHlLkkhfSB9djP3FFMD+cxfHwoNjsEpppTSt93meOBwOjOO4zn3LvLgEsl3XoyqEEIgxGOVOlZQy0zi2zK6aQRiKijVWiwgSHbFzlhkGm+OcX78HhzVceu9QcThxaAuC7R5xee+wSk5d6Adq5gplcfLb8E7gcweyqkrNJkjMBRl84YfCBddUtfGBCne3R7761Y8Yx0TOkBKUotzdjfZasqC0tLLdnBKncWzuXYsGs6ItasyaOd4X0uRRhbzwY4HSuKt2SC3X6kzxwDKsrM0epUBK0nSn1bK6Cj475jnjfSUEAN/UgjziB0Qqc1bujkqMldBlYkz4UOh7pVPFOWfSJktHqDOengsdj8Wzv3pEIzUBTU6suakg9v6XiLsbNnwShEaZ+fSb+Sdx2h+8Tz/f1PCqPOwSwC7+6t4JnXd4B9F79n0keMe+jzy5HuhCYDdErneR4D0lF/KUqCWjJZmAezVawUJPWLK8qqAlUXXJyPZEp3gHQxfoh94ayIbIvrdxpyUzTZUpFe7vj9wdJ8Y5M06JVOrZBWwblxveELkUai2WPayV4Ht2148Z+o7h0ZdwN+8j+z26f0QOO8R3eIJJMdYzp3bJyNZaQcAHT7DJyTKostDvoGIlfuv/XzKyLZPLeTE2z4kPv/Eh9/f3HI8HPvjaB9zf3a1qBiLSeloyVZXr6xu+8uUvMwwDu92eR48cIQRun9/z9Q8+YJ4mSjWTopW53ji1X3p8DY+vLGxwnqubazRnpPPIGHEoMTpCMMnKWYS0BLJKUyuoeEk4NfEya/qqmMlKMgWETX7rncFbZGRBc2krJcfibPeC3CuLCUEpFswe7k98+PVnHI8TSqCqp1bheD8zT5b1yLldlBVySkzTbEHdMv3VxvOpBa1wKolxPK9WtR2ItMapZUqzZi3LxTgnSBU7/qZxuVz4FVldSnLOpDlTvWscWSvVWCDbI1RSzRzHmZArfZfpu0QIi5OK4L2at7W3r1ucx6lChK4f1ljjwffmvvDE2oZ3DG+ic/qmQeyyQUtUfv6r0jIoBtNrtZ/RwRCNp953gUdXPTEGrvqO92+u6EKgi46hD3gnTBVyLdTSgtklQGj0obVJZM3YFmqdERdwWggowcGu8+x3HTF49l1k6BY76mKqJ3PmeDxyfxiZUmVOiVxaM5nbgtgNb4YlI2tZ2SZF6T3D/pr9fkd//Rh/9cQC2eGGHHrERWsqLhYK5mzVTNNbLyttwHszMhARCnbd16rn65/FzdI1qt9F70pbjM0589FHH/Hhhx9yd3vLz//cz/Ps2TO8D3R9j/eBWmsLxpX3338fcFztr3j0SOn7HarC4f7Ih9/4iNPpSC61LfpaVZSmm14zV10geKtMDvs9UgvOC+KdOeoFa7xUMQtcaVrRrqrZy3M2LVGtiBZK4wQbtkD2XcLnz8higan4xYfcglla1ubBBLmUONqF5ZxJeiiCLCRuWUolbbXZShgpzczTeG76ahFzLXXtStSleWvZFbAGrWu35jkQlpUiJMZAX+gRSxlyZesJJTtyNiH1SxWCush+ACVX5tnoDPNsEmCleGL01BoeJG2W4HR9aen+fDFvJQ9+bNjwxli9yHmzQPVN3nMZvC5NIw/3+cL7Xzome1Hak0tKwcKJNZMDm8CCd8T276X0v5qdrPtr22md3t47aNUYarXXLrqygdUn3guruYJrwfTlxF9axutsnqArpeB8AFtGdsObYzXCafd77z1d39H3PbEz21hnGpAtIfOQOrBSBlrj1drU1dR+rJrYsr6X1AFYttAkuFjvD7VxY3POjNPI8XhkmmYUVrmvLnb4YIGs5EytSghmnuDaw7eg1IdA7CKldLhqmdJ1LYkFsjFGxHvTkQ8Rp8UC2VLWIFWCQ7wzGqCWxntXyBWkrjyK9Tta6RJNWWQjFrxT+PyBbKmMdwdc9IShbxp0HmITT25BnkLr+i9oKXTR8/hmx9AFVD1VA6XC8VipdWyrzpHxdGdkbmZO4zOc8/T9FV23W2WrpIl7nAd1OzZtVAJ/mZG1nmgRkMSahV2kPVYOnWrT1jOZkzwH0myBdNd19F3fgmoWtRPyOHGUE95BGhPjcSRGz1e+8sgGdoR+sfvj85doN2x4U7xJNv8zZWM/42c/iU7gWOgEMATzT49BuB4C0Tv6LnDdm8zWrrdHF2wBubDSV/qAQAyBYdgRgm8c+bRmv0wzU8nFJnkR6JzSByV4pZdKh2VoqZ6crYQ6zYk5F6ZUOIwzpzlZgxcCzuTwVm3ODRveBEsm0QcEZX91xZe+8hUePXrE9fUV+92Oruvw3lujIoCqqexIW5wKZjIArU8EpnFinmeWoNaatposnZx10pdkiSydzYgFrQrPnz/na1/9Gr/wC7+Ac46r/Z6b62v6vufm5hFd35NyZppmSilcX13x5L3H9H3P9c0VV1d7YteRy3sUzaR5XukE7TQs8SPC9X5gvzdbXMqAlGQZ6pRxKbdjdKgzRWjRjFOzn+V0QufJHuMRyRayLlSLXGFMlWOxJu0N7wY+dyBba2U+nvBdBOfxsa24gn84iT7Islaid1xf9fTRgtiqnpyVLp7WzsmcZ+b5SNVCyieOR+PpXF2/x25XTPojdM1TXY0LtHZWwZIpcdWapIwkv2RkYRWq1Np4dbo2oqmqNWEF4wXl5EnJNGDT3JH71FaggRADAsxlRvOESKWkRJpm+t6z2/fcPNqvGazlhrJhwzcf3z7EFLl4OMydSFCCCJ1vj+jZd54uOPro2XeOGBxDdPTBdGNXrdilUQYLqH3w9H1nEnfZuPdLlikn6/Y2+0pr2IxO6Zz110SnRDETBWqmZOvkHmczP5hyMW5sKs0Eoa1g5aUayoYNr4daAgNpTllO6IeBx0+e8N777zH0Pf0wELzHi1BLWYPYl6g8LT27ZF1LXSzMz43KIs2EJ4Q1A2yxqzwYkCnN1FI43N/z0ccf8cEHH3B9dcX3fd/38ejmEcNux3vvvc8wDKSUOJ5O5JwZhoHr62tiDOz3A8NuoOs6lEeIN0qea2YLy/y3zNGuNViCorVf52CjDVgQLuJAjAohNeM0ozmj8hzlANraNdfG7LrO4fNk43XTkX138FYc2VIKUnzLuJqNq3kvn/VbAdOMTdn8j2uxUp6TFk8at9V5WV07wDhA2kSTpRirri76dyoXVAVZ5UXWDs0lpl318lrT10Uga30wRqJftmUk+DboRZsQem1zp1EHSlaqCBAQF22LdQbz6mpdqIq0zudLtYJPDCy2WXHDNwUvlxdffPpaLtnl29/y+lyWkUYlsPEavNCFQB9dUymwjK3WTJqhZsELlGFoHEB7D+u4kpVW4HzArxOaVVOCD3QxWCCLImL61KZRa7w6LZmaE3hPKVYirdU0qEs1FZXFweucYboICjZseCPYtelWbfSzSY5cuHHZOz8d1nhl85sZLWT0Irh1jTsbWoNxK1O2xWCrjVQllUzC+lByaoo/Ct4FYuxwzqh10zQxzTOHw5GcE7UqXdcBNEv4hC7PkzlVmsbsy4GszdfLeZS1+cyp4mo7OmdNz4qSVSkq1rwqDpxHXZPdWmiLzSxJzx1sG94hvIVqQWUerTOR4PGdcVadCOr9hVSHcrw/cn97T5oTeZqIruCCSXIonuyVvvPmj1w9IkqtiVozNZv2rK+RklOT7DhTF0QcLgw4H9vgnikltwE+UWtu1IPz1a3r/8+cONcmRLOqjPQh4j0gBWQCVcpcOE5W+ohdoOsj4oTglegrzguxv2Z31dP3jn5wdJ0jBGnSXfW1McOGDV8s2vX9ie84+2udP/NJeAO+7atea8GrcWEtyxq90EfPk+ueofemAVtGRBPpNHN4eqSWzOPHT+i8Q3d7QvAm9dMCWFMBUWLXs9vvras7jeRZqFoJ3rHrA1WVlCspLROpR4rdS7I4ppJx3qN1T6kDpcJpKpySNaskFQp+5eK3lreV77thw6dBscSP80ZRCzHQ931rAjaqymLg4z5JarElbHIzE6ilME2jOVldNFV577mpipfGLQ+h8cUXOowjp8zd3R3j6cTTp08tSE0FFPphx9X1DSlnvvHhx6ScOR6OfPzRR4zTxHvvvcf3f//3c7XfM46JVCHGyHEcuTvck3Np+1lGy5mXi57n4BdNG3xLZnU+EH2zpTXaME4rUTy+6yklM6kwZ7N0l1KQWpmrLTjd8qEN7wTeSn4rzcl0GVO21Z5C8cGMdYpJhdRamU4j4+HUnEMy3hlHTp01aokzf/MQPD64Fshmas0WsNrSqwWlTT/2opklhA4fdra6axnZUhOlJEqezsT3xk09M1QXdT5Q51fvaSeeGBTvQbVQdUa1kPNISgsFIqB0ZpHZewimgxe6Hf0gdJ1p5XkveC/t/mNlEzvu1yfCNmz45cNnuQgfdDq9hNdNG0Jr5hKzfx2aLuzQB26uBvZDpKSJdDxRcibPJ+6efcQ0TQjKe++93+SFIjFe8FOdw6nRgGI34EO2aghWzYneoV2we1XTqq4VUqrklAGhJEFrxrmAOI+Kp6i5D05ZjRerNEvqc3nFwtmtdLnhDdGqBQKEEFswG9es7KLlCjzIXNpHz6mX5WfOmXkcrUnrdOR0PNr7WnY3hEDfdQx9D96bEY9r2VhtV2+pHO4OPH/+nLu7O6ZxbhbxQux6ht2efH/P7d099/f33N3d8cHXPuB0PDGeJq6vbii5mRR5a/I6nkZuDwdyKTw46Mvj17NZUcUqHohlgb03S+hd7OhDwTmhi9YA6lE8nuAjuEACxlpNiisXXBurZqa0BbHvEt4ikLUBV6saMT3Z6sunDFWbg0mmlmqZ2JTIc2q8tWRZUhEzIihKLRkhIxRC8AzDQK0F06ESvA/EuMP7zga/2IS28tWWzmUXrQ0acC5SJWNLwHIxDUvjDTWKA7Ttx9ZV2RPjgA8mDVZqQVWa5qxivnvVtqny4FFLbvwgYW6ez7mYi5BqaeVQt+raOt8En3l489oyPRs+Lyz7fzntwQPO7HqPXzjbZw9z1p+sY8S2KcsV+vL+LsbTuov1g5Zp6b0nOJuQ+ujbI9A1/qurQpG2yGxct5zbfSPNpDSbCol2sDS9sAx/17JY5jKkIYJWUN/GKHhfycHuV06KBbtqMn65VMQVqnhcUSrCXD1VXdOtvvg+Won204L6DRtexGLVvtiV+5Z51UrTpFuSHK9pJHxhStCFt9Yam2ncWJE2r7SyviwD5YIcu7hXLpVTq56ej0Hr2QAhzTPzPLd53Oa3UsrqpgUPx/uC2uzdtZ37ck+qVU1eU6G2+4pY2aadjkMwbWnnTJIvemcNotUhatSCrDAVxSmEppBQWrxwpilueBfwFvJbpoRRU6EeJpxPhJDQlHHOM8+J4zhSSuVwf8/98ztySqSUmMfZVp2uQ3xntrXjPdQD3hVurnf0/fe1fGlHJeLEMwx7un63Trg2n/nGO1p4qz2oGK2gAOrNzk9HVEszQnBrabKLXfONPlvudX3P/mqP846Sj6R0i9bEKNls96p1UtZcwQnVF2oIFBzTeORwcExTIPhMyUd88AxDR993iDi62BNCxHvPbrej6/r15rNhw9vAiobV7MhZJhqL+i7rEMA6wYmYJmNwK3sO17KNC+WmkUIvwtzzNpyYwcjDrbPKZXnn2MVIF/xqctBHUyV4dN3Td57ZZeromDNQM9N04nQ8cBgGbu+ek0vh+vqKYYg4F+0YnZ1tCJ4YIrVaoEwXQU0VwZsFfDNTsUD2cH/idJzIOfP8+T339wdKhUk9SR3iI/H6PcLumoo1irrWeNLc5795f8AN37FQNXOcYdix29t9f+k1WXRgV0mrZv260AyWQNA01NtiytkF7rpIbG6VXpy51XlP18Umi9XUeySgIrhFbK4q85wYTxPzZMkl1xQ5Us7M08zpOHJ7e8uz5885HY6cDgemaSJNU2umWpQYWkXVe5tfa6XUwjzZXF9KoeTckl+1NadhykKtKWzoOqBDvCO4yD5a0D90kb5rgXpxUArqRu6K8PFU8bUy1EqoFfUCXSD6uAWy7xA+f7MXzSavVFQTIkINxQjbzjG2QZBy5nQ8MZ1GckrMUzLru6qIT+DNcSenEzDhpDIMHbv9FeAp7Kg6IGLOISH4lrGZqSXbanRdwXq8DEBEJOLDiMsFyC0zW1v2JqyUhK7bWTa26xh2V3YDGHqG/Q7vPTk5nJup1VFKYHKwMn5qbT7XSi0gOFKaGccTIXju7yvC3G5efbuxeIZ+Txd7YoztnCwTvPLwN2x4S6yycoB5ua4tStgK8GybLC1rGpyueqquKSmbVeT6qba9JbPDOvl6J+f9rvPs0tRlTl19MBH0/WBBbYyefR/ookdK4OSELIBWu1e0isbpdGxd2L41hxg3VlwT4XNuLUlapSM0WS5TPhAsntdmW+3UIQrT5NCaGY8HUqncTZXDXAndwCPXs4sD2uT7HK3JUx/ktjds+ExwzhG7SN8syIHWqLSo2ri1IWzJqK7VlVWQVc/j0DlcCPg2xqMzi2XvPD74i6ysid7JkpVtmzJeeWpa6boaD9VSybkwzzOn44nj4ch4OjFPE2meW+O2mRRIy4Ce9yWNKqGkXFp1JZNSutBmtl4TH4Lp57bjjsFRxeNF6bw1hA7R6EiqShGo4lAXGItwl5TQguNea4sBfDMf2ibTdwVvFciunZBrGUQa96WVJNJsdrM5XYg0F7SYc5dNQxlVwUmlC5ZFQQSV5ttRHLXaIGxppjYp2cUrKuScWtuKaxNzaRnZtDqA1SbPUauCmCNKyYXs83qjWOz4jEpQWsfmgTSP1DoznVIzPtC1g1rcmcfknTbnr0oIgm8lTO+VkjOpdzinpHmiC5UQM851oLaS7Xcd0b3dn2TDhnOkJWsQ+6DufxGKLWoCXi7sYkVbZz/EaNlOEWlBXasaLCVNWB16bBd13cOiYxmcsIuRGDzeCdFJc/SqaClUMWqRts+uh75MhmlmnsM64YbgV4khacGyc24tUV5SDqQdI9DcBwUXPCFYc2iMkRgjuIrPGe+bPFL7voxKYCF8s3B5wFXcsOFNIY1W0EXjyMYY10rGsqBccVGKXzOLF+wAL+aG51GC0zafKl6qNXiJEqltQfowoFvyJUtVckmoeO8fUBrOBkU2n9eySF1dZIi1JXSaBNj6aAHyOE5rIJtbz4y9J1tTWqm4UtZA1glo8eTUm2sfnlo8tVhFpLSqStHziDRaX4sHqunVi8tb5eQdwlvJb1l24iwjArQmCjgdT9zf3pqlY3vUWigpUdJkAaUr4OyC7l3iybVSVah4Ch21OuoxMk22uvJi8jeqSsmVlAtgAbNVTx3O9YhE62qcD+Q8Nx5capzbAq2jMjlrSFv07vzBr1xbFyyrU8tELUfbRh7NrlYrZ6FpJYRKiAXnlP0usxsSPhRu98J+Z2WXYah0nUmPeXcwb/ku8pXvSjx6fE3fd3z5K4+JcQtkN7wFpHHneF2gdVYzcEBomdkolV4qTqD30Adz39vtOnb7vXHSnUdcaLxXf6boXGZk9bz9hYjgxEwPvGthoJpFpyjkKVFmZZ4nKz1W4845Z1mVUot1Vk8jzguPH98gomv5VPAQ1By9tDYOe1gzwrLw5Zd4vip9P6BVCDFydW1ORilXkkxkybgYmzygUTREy0X4utB/lpz3lvXZ8OmQ1oA1DAOPHj/i0aPH7PY7fPQPzHXs5yIjaQkTXRdQ7YoT6L0SAlDBS8bLzNmPEhAPdKAdKlC0srQay7JFJwy7HdfX1wB0LUu8BLRgDcopJdI02RxezvSGNbuaEvNoc/o0TkZVSJm723uePn1OapzaRcVooRnYYXqkufdN+5790DN0gUdeSUFR7wkUnHaoCrlCqcKcK7kKVT1FlVwrrljwWqYjmv3aPLfhOx9fSNS0ZD7Q2roezVp2Gkem2bg0y0W82M/WWqFWcEYG966yHyzbmtWR8eTicaOn1ubnUzClg6XJrNamIJBRtcYw5xJOmi90k99aVotn+Stbk4rYcT2QO7mclwS0ZrROTaMuU8pDoTpb1SreK84pOVXmueCdkufMPDU/6QH6rrkRkXFU+j4SYgc49vvC4yfXS5J7w4a3gi4NSQ+i2XOACRaSOVlsYitBbKHVeccuWFnvaghc73uTunIR8aEt9nzj3j0MZJca/pnHrisH1xyszTSkVOy+kFN7zYTZV95dW1DWWpmmE7kkpmlPyjOxBEQCUexYtDXPrDZCcnb9sgXupRGJtq5x20ff9wzDgEuFmJSQwfnQ+LdLY2cLI3QpzJ6D2C3ns+FNYZavkWHYsb/a0/Xdyo1tE8N5/tGFGrTIV50rK2YmAtGbwUevlc7ntkBczWdJZAqlJYcuOfJteyImAzYMzCkRmkWuXFQkVK2Zew1Em6jykgGtalzYnE25KCdr8k5zZhpNd3ZOZ6e9lwNZB84CWdECNaMpMl8P5LlHQqAET2mW9lk9pQW0Vc+OnZalrdSiJIFavTWUbXgn8PkD2VZa986aRJwXSla0JJPemiaOxwPjNF1cxFBTXvlAPgZ8a3QqKJVKVeEwOsapKRxkoeSls9GC0aqFaTowL1JYS7ZVBCcdIh6zrbMAV5t0V1U9z+3aypLuhe7Gde5vNwTN1JqgBeFabdVnYs/eJjV1a0NIcooT83l3YhJi3gml9OQUbVJvJdxSHIf7RAzGwZ3GTE4FcZYNE3fxZW/Y8AZQjENmF7IsfV4WULLMl3Ju6Mq5jbxCkowTpafDxR6HtAnT4bxD/LlRcvFXB2ndxW7dz8qfbUckWCBr9Fdz2XIFqrPFZK1CLf4BL/DyjKqa48+S/UnJzEeiN26dTa7NCEX0oQV0pfm1t4Cg6UzSbKi7fmB3dYVPhV11zBrAedOVfXAveCHL/eDJNj43fAraNe2cEMLSoGhKOSGGNpa0UeDqet3ZtbdcY+25Kq5mfJ7N9Wo+4uY7aLKUooq6iIQdxD04MRlJjIrn2vHUWo0DexqZpomcy9khs9HmlsXgpQSYYtruqWQLgHMm5YyKNYktygYpmzFCaYmrWuz4lgXrSnHAZLhKKdScKU7a3G8NYW6e1/1mCRQ8qRQqzU5eFyteQ6lK1kWFYcO7gM8dyAoQA4Qg9NG6g6ecmcYDaZ65f/oRX//gaxyOJyqQ20W1Cx1XXUdwnt1+z83jR20VaBdjLsovfHDLh89umROcjolxTKgq83xCZKTWxOn4lHG8R6tRC0pJjTPnW4ZYCMFfZFuXUknj93AmqRuhvpHul1VokxapbUW6rE6XGSyEgI/xJaJ7mgPjKSBOuA+B0CbbYbii63at+SUQxNF1lZLvuXteePR45vHjG/ZXA8E7+l0gti6bbUBueHMIxUVETVJqydIsuUMv0HnjldZ5RMc7tGbmPJHyhEMZ3nuMH94jBqEPsOtaN7IPuJaRNfkg3xaPF1JBC+dvCWbbBOPUFm+1Vkp2VpXRSimB2tz+xilQSn4QzFatzUmoMk0Tx9PJxu8wEFyT22o8+CWQXTzp24EAS4KrjXME8ZEggZvHnmH/iDlnZH/E358oFcYCc62gYs0lsnFiN3x+LItIo5n17PcD/TBwdb0nxLAmaExhx5oZgXU+suu6moSjFnw+EcfnSJ6Jh48Ihw8RrabLiqKhRyRYIOsr1UWKhtZJAqiQ8sTt7R0ffvgx43hinOYmjWXZWuctW9VmxSY66agoqRQOpxEXA8WB7ztiiRyPI4fjiTlljqcTp5M1fGut1hhOowFVXYNliqkOJQeTFKQWxnHkNI445zjN1jCN89QwoCEyzxMZkOAR8bgiOIWsypQyo17Y1m/4jsfnD2QF46k5WpckJCo1zZR5YhpP3N/dcXc8UjGFWBBkp+xjh4gQY2C3t4HsWnkh5UL46J6UZ+a5kpMjZ2eNIOUAHCglcTw+ZTw9p1bTuSsl2XE1qSDvPX3XN77PWYLECONLQ4qsXZpWQjlb1S5UCFUoRV8691ojEaMl1KVbWoRaPTkvWas2yJwnJaHrLOjtghJcYJ4h+JmS7fPjKZHmDNHT6caV3fDZoS3XqmJOOII0nqf9fmm+cgIF431rnqlppE4nqxRc9ThqaySBGKxJxS2Z2da08qAs2ra9pHAWDvkS0rqW09RaLaB1jlpLG0tCKeFB1/O6KTWlAQVysQaS4C2bVatx1Zeu70sqxZKTvWj0Zs1St3KSiNDvAv0AcyocizMJrlJJxxltDSmW0b0Ijjds+BxYKhkhmDRW10W63hqtcsmU1Ax/WBtQGs4NVs1CACkJl0ZcmvDjHf7wHDSv+6pxgJuTlerFsRgJIUKLISmlME4Th+OReZ6bckEbQg84uxfjiSUjq8w5MaWZkDrmnFCBOZtF7bxQDJpaAU1Tds3KtGNY7OZRoRShZMjCqnIgzkHO9mbvobMVZSr5IiNrlr9SxTTsayXVcxZ5w3c+3srZq+QCtTKpWbxO40hKVhLIuTDnQkqFilAARM5c1YWvJ+dBlXNhTplpLqRUSVkpNaN1buX82gwMHEM/4LBtlX5RRVhMGprMSYytZHNx3DVT8nTWsVvPZ8nIsnZhGtne28p2LXfaSDdR6BFgbRYTzpJDIo4QB2Locc3M4VyF1NaUctkVanInKZV1cl4yxcuNbMOGN4WotuYPC0a7phjQe2HfebyDLD0pR2pWiKCdXcNXu6Fpvtp1mOdE9RXnFedb8Og96o1aI960nLVd92sUKu48bB4c3NmZy+NwArkFxr5xb70PqyblIrGntclyOUfwnnmeCSGgpaC5XEy1BuPvNbek2gTa4SIzawnrqpBy5XAcOY0TuSqp1GV+5cGM/kpsg3PDm8H0wpdr3BO8OdMpFcmXcd6Fqcnl3HO5LVpgqwWtCbSsE6pQrfGyPeQigLyc9LTpvZZaLvpOlh4SW7iac16HasL7vM6vy1KVZQ5blAtaHwwo3jtUfTsOD3pWSkFopg7nZrhFjqsgzezgosJSBVwGvNEXSqE0Zy/UjBGEtmjf6LHvFD5/IFuVeZxAK6dqAWWaM6fTSMmF4ylxOCXux7xKXyFGLci14vVc6nQC05Q4jhPTXLi7G7k/FOZUmWYoNSMCwWViAMGzD4/Rq5tlvgQnlFI5nWbmuXCW3zF+UKkTWgtTzozHO+b52MqcZZX9OZchtSkcQN/tGXaP8BLW4opqZTweGceD6VK2bLJxiawj2znP/up99rvHhDDQ+T0aL3bi2uq4Zkp25JQYTzOnw0Qtlf1V9+I3/nn/VBveKSjQZN+04LSwD5En1zu64Nl1jkdDJDhIh8oUTtQcW8OXTSjDMLAbzCiEmjkdDmYH6y3AFDFt2OCNQmMW0QGWysdCOVjVTGS97AUzUFAHDjEBc6DWQoyRXDJdjPRdbxkiWCeyWivH49H0KEttxxEs01PqSxmYOWVSTtSq5FLJreJi2s9WMi21ceqK8vw4cRhNyq+6iDZxeHMuawtifcj/3dK0G94UFhianF3XdXRdR9/3xC7CpExpXBupFlkrnHvILVsLIOYsKVrQMlPLCWpB2kKw1mwuILnRihTjkl4cT9XFgdMcu0qxZubakijOO3yI9Lsdu6s9ziXmURES0QeCGE1BqykbVFXmlJjTRMpmFx8b/9eJtIZQaw4N7b6Qa6U0x0vLUgeC9yQC97NaX8wy90mBIhAKZRoZp5kpJTvvqgQFX8FVwVVdq1AbvvPxdoYIpVJLNjmtUsi5tgFRrQyYK3MqbXVpF2upZeXcrN3FWEZ2mhLTnJnTRUa2nANNESW0kqAPsTVcCT56nBdyrggjTs4lFpsAE+TKolJZ8kSaT03rNrVOaZoMijQlBAtkvXOgeyubakUpQCXnkdPp1vh9SyALqOb2mse5SPBdoyeUpS7D+ck5I1tLbdp7ZdXJtHPeSiQb3hyWkbAMjFMr4wcn7DrP0EX2nefRLhK8kGrHOFpG1rfMixPBh4AP3gZEVXJK0Hix1ds4JgRo174sVAK5kPgRy6QuEkIPIlk5c2jNalqbi1Fz6WuuRr6Ng7Loy9Zq3dFACmaZWX1LqdZXBLJzYmrOQnO2+5FRi5qZi0Ku9u9cKscxcZqLBeSdb9WYS6vQh0HrFsxu+CwQWTSP2/XdrvHFwetBmLn0cZx5MbQ2RdvWOoc0q/SaEXPlATXpuMuMbJtu2++Xaeis/mOPszbsshejQlhGtiQbp9XVlQbkLHvTVIhKy+5adnbJyDq3KJu03hVnmWgAVwq5WhIoxGDzujP127mcz1vVDFBwlrau2TKyy7GLsvJ/7f73Tfszbvg2xFuoFog1O7nmPOIq3lW8BKRWQj8Qu56YLPBDKyLQxbgO4lor4zQhknj6/Bnf+PgZ05x5fjsZZ6eAEAk+4hx0Uek7HjSbiHN0Q0+IsXU5enyY18nV6Awz86zk7IAd0/7aVocXg9Y5wQcriy7lFlXl+voJjx9/mRA6Wm8ltVZub58y9JZBWviCYJmlJZC9vvoy+/37xNjz6NENNzd7nHN0nSdE6/re7Tr6LhK7QNXKPM34IE3GTC+C3w0b3gSK00J0yj56gjge7SNPrgd2XaRzSucaTxX7abSdpp8sDsSzGJAorAYg0rKoCwfWtfG3jOeFLmB0AHlAnynLRIo2N8C6bvNysbYYHPiW8b2cYJcu61KKceBTMlvKasYKqK4UnVorp3GyZtNGFUhtYvShw4WOVoA16pMK6gIuNCpDO7clZDh/u9uQ3PDZ4bzj6mpg2HWEYGNjWR+t15Ks67z2i7pWAqRRSVcOeHO3qj7iYo90e6gF9QFxHg0D6kNrUry8ah8uKG3cy1mhgDOX12xuOx4/foRzwjhMBInM08yjR9cMg7lTijjSnCBnxtPIeDo1imGmpNTiA2vWEhHUVWrL2F66fFEKNbXFcS7kmJYvArBAVqJJhGlOUApeLEg2q/qKE8WrSWK+mte04TsRnzuQdU7odjtKruA7SrEVoFQrWQyHmeHqmqQO13odncB+2NF3HTEGci48v72nauXnfuEf8zM///NMc2ZMA+M8gHj6/pqh7/AernaOfV8R59bVmw+R/fVjht2elDK73YHTaWSxonXOkdLE4d4zzyemIRCkMk8jwXv6Pq4r467vcM5RasvUAo8fv8dXvvK95ot9Ech++OHX+eDrXyWnhPeycnGX7KqIZxjeo+8fE3zHo0df4urqsa1wo8M3wfmhD8To6TtPKZn7+wNKIc1XlNKzaGBuQ3LDm0BUiXViHwPvX0WG4HnyaM/3f+URV0NHmUfK6b41MmVmS2VYuS9ExDlUfGv1omUvCzbreZPRauogvilyhBDbhCYmayVLdQKrYrQgdJXJatlT52zcmOOWrnJ4Pniz8aydianPVn5MOVOORxbbzugDIQST7Gmd0eM4cTyeyKXw/Pk9z27vLLg19gHiPdeP3uPq0WMLVoNp4yoO9QHvlxk+NCcz7HcXFMMNGz4rYvB85bue8OTJNX3v8R7ELWoA9nNNvoAFsWrpUyeWiV21YBWqC5TQmwLBcAN5NAMPZxav1XfU0K3VT133wXkyacoEthAtOG+SjyYR5okxcH19xa/6Vd/HPM+Mx5Hn790xjzNdH7na7wjBM+XM8Xgkl8rd/ZHnT2+ZU26ZUdOULi0LvWSd26lR8kxJCbDEmPd2H7lfs9QLPd0W274lgALKDZnBezqEoBGH4FXpukpVNdWFDe8E3oJaYOV9xfgpIka2tnII+K7Dh44QZitvYhmY2JxDvHMrpyaXwv39PR8//Zhpzqg8RvGIU+gqwYP3QghKjLZ6DJ1NeCEGhl3Hbr8jJtNhhSVrG1fFgJJ7jDtYSf0eLzZQ9/uBGIIJQ+9M5SBn08erqrz//pf48pe/TN/3bWVrGVfTsj2RUrKM7Mr10xbIOrruMV18hPeR/X7Hft9bEB7sBuKc+cfHYMFtbVyjnMN54m+8RdlWlxveAILiqQRRhuDZ9YGrPnKz69gPHYnEOFkmZKEeKJyzqS0be2n9WKrRevSinVnErdnXB/qvq00RLNlYLgJZVaMBgI0Vo5a7BwHiYrJw3m7bmuqZWtDsagErMzbe7DiOHI8nUs7c3d3z7NktpVSKQlExs4O4I+yucR6cBFybLVUcTjwqpvxgarvyUhD7cjC7hbcbPhniHLtdT9/bYsktlQguqhEsjOyzCoc1bbL+/py9dajzVBdQH5HYnzO44sB34F6WjXuxoiAtI+sWZ761T1PWhunr6ytKGehihxaY+9msdjuz2J1LMSOEnJnn2R4p49FVPxpn5X4RoTbahKpSU6KkGTBTg7pkUtfmF9bvSgRisiRQ50CjwweHRxupwBulyrU7yjZnvjN4C/ktwUXj1EmxAp0uXM+q1lFYCrkUfCsxuubOYTI+nlxbp35OHE4n7g8HppTXTKtTK+dbZlWATKkZtJBPR1ROOB9bx/GJWirTOJNSbryj5Vou680ixo73338PVIld4Go/EGJYS6TihFKN46sKj24esdvviTE2tQUTc765eUT68ncZtcAtMl4tI1sLIo6+e0QXrxHn6buB2NmEXUoiz40znO0mEoNDtSPNAQWmMZFzK7OI38bkhjeDmFtXcBCjp4sWqOU0k6RyOh65u70lzxOnw4G72wMlZ/q9sHMDziviHeI7K7Er+DYBuRDBh1YmjKiLqLMJFefXgNigrfGxTZtL9zRY0Iiu1J922OtEfpEyWuXylm7qxXYypcQ0m/rIPI6MxyOlFA6HE/f3B6v2PL/n/u7eGr0qpIo1r1w/Zl/Umtuwc1kCWZt6jY14NkE4s2HPQcHGjd3w5nACQ+/poyN6CKIEaTz2lc9Ks1rmLBNp+nTth9FsUEh4iu8RHLV/ZJqqTf9VxWgyqbuiuI7qm8nHEhi2Cp9rNs+hSdl1XWc/e2tEizGuvNZaClSYdpPREbwjNCm+fetTKW0b3gkpJZxWvBZEFe8t24o4VFwzbbG5sGSjEPgQEbfoVDdOLfZd+UbFCK2S6Z0weEfnHOSZcnrOcR5JosxA3mQL3il8/kDWOeLQQ8rkWtFcKEnJqazBacrmwlMcBAGvQhW7YH0MjKcT98cT0zzx9PlzPnz6MXPK7Haw2zli7IH38L7De0Gp5GqKAqfxyDyPgMPFjxDf4cQRg1EFzDFFCU5BMqBohd1uz5e/9ITd0Ft55GogRE+palJhVVenS4C+H9jtr/DOo2qZWFUl9h2PHz9eOzzP3tSlBbJC3+3pugFojidNluz2buRwOrUyqx1X8I7DoafvAtOU+O7veZ+bR3tC8K386l7xV9iw4SEEiA664Nh3kauhIzohTSdIwvPnT/nwg68xnkZOx5HbuwOlVG4eC4/9FSF6ul2kD1cW0CIEWRosPS7YYo/YoaEFsj6iLqyZJGHhd9eLAHZxKDpXGEQuA9/l6B8+X2T+Us7rIlFV8eKIPuCd4+72lmdPn5JT5nA4cn93JOfM/f3I7f2JUitTVqaihK6jv3mf6/eXxtGICz20wLUujTALN3bttdH1eBboFsdueEN4L9xcRa53gSEKvVezhdaCVMG1zntZrJtLXRdOvm1jpehopUqkhGvwFR92+N17iLYg1VKgZh7gB1TMXto1ObzlPc7ZPNn3Hc4JOe/x3nO137Pf7djtBrQqfbOm7UJnyaJ+PjebCVyFQNd1iHMc7u/5ypM9OSWkzLg8WpXI9/gwIOKM8uB7VISiuVGXWBVPnBOGGOiDtwUAmSjVcq7maEtFGDWS8IzHez7+auGYEwVlVtOtr9tC853BW1nUindIbVeWLGLL5r9sunLGVaFCdUuzx9nmUhUrRyQr5Y/TbJZ3caaviVqto3kpWy4TjQWdM+N4smzJnFExysJ+t6OLXVMtKdBUBhZzg+ADV/srbm6uLCN7bYFsSoVxShSro9pkjWVwY2ydlNpEl1s2KYbYJrYLJ6Jmi2s+7gN9N1BVOY0jTO3mpJmcm4tKNn/o4G3Q1lwY9z05FUqx7tCHma4NGz4ZIialdTYtgJIL4pQ0TZxOI+PpyPE0cTxNlKqEXWaXFXXg1VFdwIm/0EW2cbuUOWxy9G1WbK8hCIUHFueXWpZLFrMR9V53ScsLH6967qwuxZiCi11tdY5pmjkdT8zzzPFw4ng4knOx8xwnSq2MWZlyJRZlTpmi4LRlXcUKoItu7EMqwfL/8y+0Zb6WzvINGz4NIkIXjVYWXAvIpOX9LxZ8QFMVOCsI6MV1t5r0iCO7YLQd5yl0lmVFVwtaJKLim5X6he5r28llU1etlRA8tYam59r0bastGlXtHmKVSau2mJqQ0nWRq6udqTFQkTKRc0KywyU7rxCWQNZTY29Btkgzx14CWePXe+fYd4EhBrwoA5megoiucp0Zx22NnKo3a/sQmcXoilmUitsKJu8Q3tI+yobG0vF8tmutKwGoVuuOzsmavU7TxDjNlOoZ55lpnpnnRK2CDz1BPc7ZYakqKU2cTvfWVOJnvE+UmjmdCtNogWFlRsk4J5Q5ExrndZ5muq43Hcm5SXWomoC7D5QKd3cjCMzzzOFwIueyamaa+1hH1w848VQ1rT1zASvU5vxzWQ6tanaZzjtubkCvAqrKeJw5jSPznHj+/I5nT5+hyiIYYtxhMQGROVVSLpRc7bvc5Lc2vDGEimMucDjNlFoJVE4u41Du7k7cnRLTVJmykIhUgVk9xywEgTxVkk+IqxYQt47msJQTnaMvEIvindlKlxoQwLfGTtoELbos3s6ZzXP1gkYVuBRif1jCV7WgtTSB9UXKS9WkuESEeZ44nU6r3FbKiZIruWlimhynNZDFrsfHDudbk5fzlDZ+K3rOyF62knNhSKKL3z3bRLnhjSECPjpC9IToidHjgzX6OucpxSpviz1sbfQCM+lpnHK1/gtQSlULWcV+a3NJ8wxYL86m4axqGd+SVqWCharggykT+CaH1XUdV9dX7HZ7hmGwY1erT8ToEVFTDqmFnBOqlWHYcXN9TQie/dBxvetsbhwPcLqDWnGxx8cBxJFCTwoDFZjSzJSmVslZuLHS+L2OKkqpQmrnVxqDP6uSilKqQ1MiBs8w9FSU6Kq9b6tiftvje74HPvjg1b/77u+Gr33tzbbzBfigSltFNc04LziVldRdW4PGPE+gMOx67k8nYvYcTieOp5FpnqkqxHCFSCE4W11SK9N4RHhqQbJv+tC1MI6ZebIyY66WVRJZSg8mDWQD1CwA9/trutBZt7QP+NgzjRO3t/fMKXE6nXj+/JZ5TmsTmRNH7Ab6fm83m5zJxQZvLcVEp5fsjElDt2C5Erznu79bcGL7vLs7cTgcmMaRb3ztQ77x4TcAoYsdIUTLIuOBwNTObZ4tqK5bHLvhDWHtiJ4xK0/vT8STIDUjZYRaGI9H7u7npvcsJKzE52oHs+ArBC2EMuPEEZzxbQXWpkYnjr7PbXw5robCnMxUoXeV4Cwr5EXxC6FAxR5Lh4uwLgiXoHQJZvUc9VKbdmxKqY3rxQraJlSA4/HI3f098zSRZtOjro0qZCYI4GKkjz2xH4j9DtcNSIjW/d2a2+oDDc1GLrhIlKELteAhW3bDhk+DiBCHQDcE+j7SN2varg84F5qk3DmQLWpFzkJF6nks1FpZ1lE257Sfctkwtu7VnitISXYfaMkmG0OFECK7/Z5aK33fU2vlyZPHPHp0w/X1NU6EEMx9L6XE1dWw2s6eRtNiv9rvefz4kSmX1ILkgmgl3z2jPP8QzRnpelzfgziOvuPkOrLC7f09ei8PmkFdU0hRbzrSWQtaZVn5IrWYRW6yYFbzzBAD7vqKKlCCoi0e2fDtjdcFsZ/2uxfx+QPZixv8kiWx5t/FZ729rWVlczYt2dz4biIYZ7SUppkq1tSlrSO67aSWQk6TZUmrGSCYMHqllCVQPgswF9eGuHOtWSrR9x1dN5gL0JJDFUetMM+ZcZw5nWaOx5F5nvE+ELvYtiHUsqyabQCrVrTmRiNYvoCFWmAllxAC85xJyVbQKRXSnJnnzDRNjKdxpVfUaqvR5bsodRGq1pXcv3zHGzZ8MqzjvirMuVALUBOaEtTMNCfmbC5XVYWCdTlnHKmKWbkWpSTTZCzOHk4EXyu+OkSqTTRS8LUSvTn1eAdezVZyaTpbeKS6PuF8GTfagF5Kcy29YVyUUhfR8xcMCuqiiFBqy9ouTWEvFDGkuYwtFpguNNUFM32o693mTCGQ5fmruLFL+qtRpTZs+FQIrcn54uFcaxRuWq4XXBu9oK0s4v7LfHA2GbnY+FI5eKF5UrVRePTic42brnpu+DINaQsoY4yEZhHtnFj2uFVlSunx2QyISs2UYuoFfd+aw1Tx1ZrXcp5I44D6hHQd0neoOIqLZBctU+zPkgQrnUJYqyOoUlTRomsgS2t6K7lQi6KlNMmwYI6B0ShSGx3v3cHbZWTrwn07O5aEEEDAh7DK85Rixge1FG7v7tk17dRpNl5szhlVIcYe5yre+7XsmOZj4/c5QuzxIbJMLj4EpOoq52GwgFbVgXrAU4swjbb/EDwfffQx85xIqTJOhVLMD9r7vpV6LiY5FVJuXaOlkJuYuy5BJqw3BsPCl3WM48zz5/eAcjicGE8z05QpVdpkem56WTJUS7PYKgRft+zPhs+GipCrMmUl2wtm7aieTAfxCucLLGNEBEJPxtm1mRWP8by9VOsYpk16bcKdihJTNp3mOXHqPMEJ171nHy2o3TXNxzZAWmDYmLJtfJdsTnilqYSAUKoyzZnTlBjnzJQqc1Z67+m6nZVDQ6CPJv/jxcwTTJKrMierjNwdEnfHiVpBYo/EnhA7rm4eEfse16gFl3gQ/168ZlllPfNi9fIT24S54ZMhzhNurnD7AY2B6hxJlToloHAazW415YLWZIoF2gwLxD0MylbO+jJ32Pha3rH+lDMrdvm4PPzfKp9XSiG3huTj8cTHT5+SSyFGz37XEYI3PeaUKW3xuJbuayLfPwMn3I8zx/uRkjPz8cDp9rlVXVxTBRE4quNYhazK/eHI/eG49tVorQhiGtHeIyihPUxasOnqKkgLmCUIcefp9zvwgnagXnDh4dje8J2Lzx3IqoI2r0dpGU7nPaGLSG2GBY1nmkvhcDiScsLs9GazqGsRnAWEjqHft+ys2e7VWkjzHaU8x4mn393QDVdnknrscLVak4nkxqcD1WrcUw2ggZKt/GhZ4Ixzgd3uOc51hLDHuYBqJMYrvK8PupFrFebZmsaqFhuUtCxRSx858dbvgqzORIpwf5hI6SmqyjzNpNRUHAo43zUq0EJKX4LY3B4mZbbIDW3Y8CZQoKizzudsHFVQa8pUAemh71ox3eEWHp2PJPVoFZPxKVY1Ma3Zto2VwiZ4P+NaV/EuOPpgEnJfuh6o+65lae3neeJ0axC48P1SytSSKLU0Co1QinIaE/eHidOUOM6VlCrd4Bn21/Rdz27ouNnvCd7x5NETvvLlL9vCN0Mq1hD69PbI0+cnclXUBdQHnA9cPfkS/bADcRRxtvTVh0HsBSX2XH16MdO7YcMbQoKjf/8RobtCu47sAzUpZZqoVTlNM6dxMmv3OTGPozVgOU9sjlg+WCOW0ex8c6EzKazVTW/5T9p8dJGhtScX5AORZoTim4Z5toSTKl/96gc8e/acvu94/GhP35t0lkiwfYq2RjFB8kQ6PiWXzIcf3/GLX/2IcZo5zpnb0YLy3OY+rZVTyozJrGyneWac53MDWTWTa2nHaU6Y0XpInNCHQAyOKPDIKzun9Nc7vvz4K1y9f4MEQQaPRCHELZB9V/AWGdlzB+WCxUtaWyZ2yTgqmP9yqaScmaaZ7F3znA7rNpzzqEorn2Blw5opOVHFE8pgQu5uCRg5r07FAWZEsHYVt+Yp1aXb2Ry7xnFsGWAF+jUDbNaUrF2jyzlWXcqVS2B5lkGxAzczCEQRlfU+UXJmap9NzayhlLbSbt3kIufvadnfUgZaLGqtkikXd6MNGz4B2oolrWIgil2XgIhnSUJaJ7NbDQAqcu7Ob448tTVvnYvvNrSs679Z22pFK9TqSdmqFk7k4t6w1jNX+kAbXWt1YzlWa+SCUtSayEqj2CgoDucjIUZi7On7vql9WKdzrU0vNltGeq6eMTuj6oiJx4uzxbZrFZfyCYHpi93i59cv3rSVLze8CUSQGJHg1+pERcm1UnNtlLLaLJYT8zRZMqNxRZ3Y4hS1ANI0zYNR7ZpSgSVSaGOvzY8PDqGNbWRV5zhf20pplZGcE9M0ATbn7YaAiDZpLJp6ieDDss1KTTPkmfl04HB3x/E0cciV57NR5dI8rec0TYlxNtWeOSfmnFcK0TqntnuRc46+74mdSYTtYiAGT++gj5he9mDfQ4geCQ7XeyRYLLLh3cBbUQusHHgx0SzBIKxarl3XMfQ9+92eFMzONbWOYu8hqKycNt9KFVpZRdBLSeQ8gTjcbLw8EbPF9D5gE6RvJUxvmV4WjbwB7wK15qZDWUhp5Pb2Y+PBxoFhOBFCZ/tdGkzUsqGKnVup56B2mcyt/F8Qga7r6bveCPQrs045lkRtPtLG77Nji13kJjziLPy8OBn5tcxaiikklCZBtmHDZ4ElQZfGqqWP2ZZn55jxnCXVtuBcS+UXc8DKb5VVQKstOJfpRqnF1AHmouQKTo3isDoNsfxUTCzz4fFq26YiFBWyOrI6CgF1PfiAj3uG3TW73cDVfmcSesFbtmdnnc+pCHOxQDa7gVkGcqmkquT2XfgQLs71zEEUveDCvjL1+qLc1rlxbcOGT4asnfhn5zzWuTOlzHg6MafE/fNbnn7jQ9KcCM4RWrLDrNQtaLOMrAXFPhr3W8R4osE/fG6GO+f3mPGPY55mnj99yv39PSllDvcHpnlm6Ae6EEyXFmWeuhYUJ0RHAKMQDp1xXOeEn2ckz5TTkfnuOdNpZCqOuTqyilEScmnNaorHIU7pQiQ0MxW7N1x8X63CGUM0DWsRYjAKk5MmNdZoT9pkP0XBaasybXhn8BbUAlMjUIx/fVaJbCoGPrQgtiPvdlxf35DmRCkz0zwBlRB0LSeqWle0iJDzEsgWUp6Y08nmZMmkcmrE7s7UB1yg76/xYdfI8xHvQpPb6fE+kNJIORwpJTNNiePxFtVKjAO73SNCMPmR2AZ6KdaQplXXVfKS5V2s/GotlGZ8cH11TZBr05qteQ1yD4c7jsd7RITd7oZh2ON9YLd/RL9QJMQ1x5UKOlNyNreTksglUap/kA3asOHTIC880zYm9UH4xvq783v14uflBhf5c1l/oypQ7N2lFOZmVjLmylwtVq1I46DKOZjlwvn9kv6tQl0fjlw9uQYr+zsFKqG/Yn/9hKv9jkdXO957ckMMnlpykwJS5uqYirNMbjyQwh2pFKZkjW4K4I1nr/owiD1/KZfZV70458tfbRPlhs8AI5mDO1c+qpo6Qa2mbXw8HBmnkQ8/+Dq/8I9+lvF0wiH4xnQ198lGIfAeCU0isouElrHsup4u2nzW91a18N6z3+8YhmG1nfXecxpHPv7G13n67BklF6ZxIqXMvN8zdJ0pz2pl2nW2CK4FnWeohTgMwCOjOkwzOo6QJvL9PeOzjzgdR050nGQg49BFrrJlgL3YeXUxQOP6LlXche+7PpemPY8poTgWty+WJYFVXWvGqUfVb6PzHcNbZWSXyWhZXbb8xtoxuejLWoDpUV+tCYvzpFCbbNY5CNaVoH7eS23vzUgVVBfy+7Iaa57ty6rVeePrtlWoql8DUNVCznPTgxVinLC9B7yniT+XpkpgZgVL5tnctRZerw3MpbSy8AeN7mDHXEsizaZO0MWeUrrmYS0mVSLGUrQO0kLJpo+56gde0AvW7M+GDZ8CuWx+lAtSgLYg9aW7/Ktu+/rCJXcRxD4IaM/hcmmNl5WFOnDO5K4/HpBP1z2dny/3k/XhwNlU7pxxXL0P+GDyRSEEamtAs+qQo4iAAx9nfIxUcbgK4ut6PC9mZM/EhlcMM32YidUH39c2ZW54QyzjSV68fpoVcynmYpcS02jKNg4z7rBAVtaeCvHeHk4IcyR0s6ns9JnSWROmlooW04r17qz3rrXiQ2CeJqZpZBpHW4xOMzkXYgjklMgpUYJrUpMFckZLglrQEtBSUOfPTVpVoRZqztRGB6zStN5foOcs3F2jCbpzsOoWquD53iEXP893M2mcqeU+8dBAQvXF6smG72R8/owstBLJuVFCVVZx5lKUlBLzPFNrwXtAHSEM7PoOVEklt8ynyW+JE1x1rezfmUpAiPh8ls0yGZ5KSpYRDqEyDHV1KDEZEJMCub55xDAMHE/31HoAZqapMM8j8zQhe/ByRRdhv4+89+QxXdcxzzOn04lcinVBz4Wq2nypY5MhyU2QHd578pj333uv/R6CN6/pn/v5xPH41IJwnSllwnvoe8/NjYlN52w0hpIrpcykeWSahmZp2yTGTLlsw4Y3hix815du55evvSaAfWEp+fC3L0zCaxTYVnKcHw+k+NrH6nnawRapjUOvRktIWckqSOjxXSWGigbL5PS7PbEpD3gfzJfd+RZoVipKypXTXMjZGmjGOVkDTSltMsV4D23fdb13veKr+lRsQeyGN8NC4nGiOKmIKN4LfReoXgm+NWiWAiKErqOrBYoiTcHApCbNBYuqSKkgMI4TiyRcFzu6rtm1d3F1pez7ni5GC3yDJXrmeebjj41aoItFe6kcDwdqrex2ex49umIXKrHu8SiDFjxqurSp8XhTWpWDFGs+897jsWqj4nAKKmejo/Mt6Oyiucb3a9Br96oKy0p8bV7FCzk4cvB4L0xqpg+uKFNSnLqtUfodwltkZM1ByIp+2gZak3lTmgxOIqWZWsoqpN6Fjr4zTurt/T3T7XNyKdaEIh51EINxX6vP5DKR89TI7VbSB6hqk1unBVXbfoyeYdfR91ZG+fJ3vc/19TV3tx3j6WNyOZHzxDyfOB7vCUH4/7P359GXZFteH/bZ55yIuPf+hsysyqpX9V6/7tcDgywQ0AiE3WKQZFkYARqghQck95JlW8MSyFaLBWYQRpjBYBaTZQm0mASSsDDCLYFlMfVqwELMCBAgWND9TPebasjM3+93742Ic872H/uciLi/zKzKqqx6Va8yvu/dujfvEDfi/uLE2Wfv7/5+vU90rXJ50fLp1++x2+04HA88uroixkjfm/lCzlpcvjqcOMZxYBwHRIRX7t/j1VdfoQmB3a5lt2s59kdu9m/xxS/ZxSfngRg9IQjdxnPnzg7NaoYQfSJrLi5mB/p+wzCUQDZmUjL1hBUrPhi827n0OPHz8RhvpiRoaS7RGsxOJUFKw0UNjK2Wn7WszBaTX1IYotKPmTE7XNMROsGp4ssqbrM9o2lLIBtaxAXjHWYli42hISb2x4ExJg5HcxGMxS47VXJw1hLMLjPA7473FeuuWFEwcTrLzQchFKWQEJzR1WJEgLazOTLHRB4jFKm6WKqDJAsAsyrHw5HD8QAKoZmVDWp1ErH+E2dSBlOzdE6Jw/HIOIwl2WLjoWla9jc3dF3H/Zfu8PLOs5VI8MK2cXReSNkxDkeyq6ojZdlcv9c7PA4/LWw9iJ8CUl3c3653TInb2hhaFYLUKqq2k45x2xKDx3nHsVAfJCYYRkiQ1uzPC4PnpBZIPS8nlYEpQ1tOwFScrrS49ohYI5iVCt3MgRFXFAcoagb2Ha6UGyiSQlB4a6UEX9UDls+BFmkrsQDaz6WKSTpr6pAs+nROaNpA2zWkPNI21qmZM8SoJZANtqqdFAZyoQkE2ibQNE1xbWlRMj6U5pby3pxNwovyvUaZSAtebZ7uqyLELPuzBrIr3h2PnyXyTi++A25TfOZsrD7zhk4pBbL8ZFUEwM5tLRmnVKkJUigFWpx+sOuCFD/2KWCupVksmM5qnN2Y5mvPbfvb0wcssj9P+yWe7ThXrHgn1CI4mlEyooUmICcnI1VSy3kHWU2D1Rir05xgAgQCZQ6MY5w3kc0GOrk0OeEZ59Ret0DWdJwrncDK/7aXiEyuecMwMJZbE8ToAupI6hhlJHtFcmRMGZeVtKAYPk7Rme+muIF6KagvzgvM+tQ0fvXk0+Ac4h3qXDFQKO/Jlp1eW0teHDxHs5eQcmPZkGTZlaxKTKmQ1xN98T4/HvZcXz8i58zlxQVt2xRVg8Bue2Z0Adcg0liZcXLqiWQdGOORnE2wOSVTCpDC99Oc2R+ujUDuA8d+T9t0bDYbsg7c3Jyzv7ni0dVD9vs9fdHJq6M6l2DbnjIHL+cafGjKZSeDNLYIbDd03daa2frjVBIJTVO0/IoQ/TDSF8OFmCguSgNjcSD7yle+VH6nzPFobmI5KXEcyTERx4jF7EZcz8VxacWKrx7ea3BWV1ynW5gm0Zp7LRrRlAAzKySEUYXjqByGbEYO6orr2FxiVKnNYNZIlsWh4kkkYhZShn60jOwQE8MYC293QX8qRMUa1p7wfZ/vB1ux4h2gQCSlnuNwTYpHyA6JATLEdCR46NpA1zZ0XYcgJBcZkUlfVXFlrvOTpW2Mib4fCu1u7uOAmlRSci59HsxrQOPl1gzo6WfGmFAij673/P++8AYPr/Z0TrgIjsYJOTTEbod6k8I6D+BRHjzaczUqfRaOOdOngVQOvxJ6NM+c2fqc/X+W3zqhH6gZIDjv2G46ural2wTO755zcblBGgfBMWTLLKujJNU+HiP65//8n//U137Vr/pVX8U9+eTiuagFKbWW3SzdiCmbt7n5oGeOxbnr6uaGtx+8RYwjzsHl5QXOmzzI2e7MgkjXgmupPNgqvTWMe47HK1KWYtFXg04tGZjEYX9F3x+sI/NmQ/ANm82GmI6cX59xPB559PAB+/2eYejLgC9NW2rKBBlFxOGcNZOEYALQzkMINtm17Za221HNH+owCU2LOCPep5Q5DplDPzKMyfQwowIDysAwDvAV4er6ClWl73v7XcTRNhuCa0wqTCcfE1IWUnruv/WKFR8OpklqQX0rHNmq5SilJCgmOGsd0Drb5I5ZOMTMfkgcozKqkPCIFIVbUcxOFpJK6fx2ZHFkHFE5DWTHSD+O0/Uiz6F0yQJZgPzxmOpWPBdee+3pxuyf+hR88Ytf3f15Aiwba+fjcUgMODMpiQFUGOOB4IWu8XMgK47RjVaxyNlK82Ka502w6mDNqu79AZF5XqNUKAC0WMOnZK6Xc8hqeyalYap0Khc3y0TK8PDqgOobZkogsBEIAhoa2GxQ79l1DXfOOhrvON4c2I9KysIxZvqxtyyt6kTtSYt5vI5Fk7osTWVTGdL20KS2oG0bzs86ttuOza7l/N4ll/fOSJI4+IExjWZvK6BSMrQrXgg8V7NXzXTkUuIwZYHZE7oGnjWbWm8pRZIr3kL13gecb7AAuejNkQu1wCGTUsGtvVDjzdpA8AgDmq3k3/dHQnD0fV8ctep2KYNXS8CciDGZ9MgwMsY46diqFr4fUqouRSqkdIDW0WbbsUFKnEs21Ru7dlWKWFeqd5YZHseeGCPeeYJrwZ2uRq3sWsXl1xLminfGB3vpftbzTU/unvh6pR5NY6FcO5ipSDlDKhOeFu7tcsK1yuJMb6hTcp4+a9eeKi6fc834PPl45kzsOx9nVUR4+uGvE+ZHiqcFse/22lcZHjHjAq0Ug7ke4AS8CMk5gnemxpEVTZnobQHnvOKSce4sI+vnbv9lpz+cnK81Kzs/txgRi8C3mqPU57QkeoYxWt+LKCrgARobo+I8DmXTOHLwJFVcCCAOyRHVhYxlmQtnbfaSoZ32caYALUeklmpvLk1vMw2/2vS6xzK+6vigL4YrPsZ4/4FsVvo+FY6OTiuucSwZ2TETx0yMmXFMjNEav67317zx5ps0TcNmc852e4n3gc32gu3uEhAOhz37wx7EGqxCsNWp0Q2stBJTngZnziNgJ3aKIyKOYTiQUs+jh11p2rKAMecMovjgyZq5vr6m74eiDiBFa0+QomnrnIlQO3GFg2NyYia9ZSOl7488eGSDbL+/4ebGMr9vvPGWWXDmNHdt50zfH0gpguoUXGto0NayTognZUeMjhiFYYBitLJixTPio72K14VbLllYzdG0JHV28SprPvoEfTIN2sOQzWJWBRVPZuaxZzV/9ljoAlksIztm2A/RRN2HyGEwNZQxzdQBPclUzZnZ94J3DWg/RHwNJB1XvAOCOO6154hKkdMSnAY8DeAIozC2I0EFdmeQhRjN2t3Jtc0TSUmdJWJCkZfMKdFcN8AcvFrCRE7ul4mQamFbs7FQJDKbxiS9xOGDud+JwDhaoseh9FK0ioYBjgPiHGnoaMi0TeB8t+VTr7yC944vfOlNbr7/i4xxYBgGjv2wME+yiogUnquIsNl0tN3W5HbF4ZyQU+Z42BeJMGEYew7HAC5zPPZ0xxaVDD5TWmmQDBMracULgfcdyGaFvi9NS0VRICVlGM3lZxhNCsduloUc48j1zY19cQi8dM9zfn6Ppu04P7/kzt37iAgPHphvMyihaQlNawT4OOLcOK3MJp7NibWdQXDsb66m1aVIzagaLcAXf+mrmxucHNjvjzy6uiH4hu1uy+WdC0LT0LYNm02H99aIZplhD+TidmL0gGNvWnxf+fIbvPGVNxnHkeubR+YvrYorqg05J4Y+MfSHaU8rJq1aceQspCTEKIzjGsiueG/4OFzD57GpaIqQYpnELIjMwJiVIVsgexgzx9GMFWzaLAFvscjNhT6QcqUXeLJ4hgT73jixh37kMIxWGi1ygFNTmLjFvsGt9jPsmfd0hO/5E+8XXyNJxxVPgRfPvebMMoqlTOBdILgOJx43KMf2iM9CkEAXNqScaXxDirnMIzK58oVijpBiJDThsWzslNlcLL6m4LW2cS4Wd048IbSEplkEspY8MjpeKuMxTbx1KTnmPI60Xti0DS/du8dnvv6zbDYdQ4a/9wNfIMeB4Xjg5vrGzIUoVZDiPuabYNKV51vOz3d47wjBXDrNEWzgeDCqwzD01kTtM8d+oDsOlvtpTEseAS0eLB+Li+CKrwqeS7Ugl5pg5bnMVAMAEzAP3m4+NGTN5nHOIrgs3ZjOySQVMg/KyrMz+1ZXlA2kCCHPmFei8zMJTTrRAqpCgrng2hmvqib2TMaJDRrNsNFuakZrW1MhmCx3y0BnQUy/3RkdUyKmuFBUKKWTas2pOq2WZ63NWbmhHr/RNObbOjI/BvgaSI19UE0O77aVp4ZxE8WtqD/X64TqRJOxey3ndp7NFJZNGmLvlcVm63tSKVdKEmLKZcwVjn4db7aReVvLfy+Orwaz89w38XqecuC6CIC/esHsiq9diEDrjN9ao1EvC/vZQjtwmIJOCB7JjrCkD1ANdExOyxe3K+eWCzSdz1u53bq/IOosDAeqUkFVD6qa7DZXG40BbE61vu5y9tvB2GI1zXPg0olrrprmiWY4LSBlNgCComjkaizgipmSTI1pVokt1LzBMfYjYz/igtAGTyhmEWV6Pw0RVnyi8VzUgmGIC7K2PVc1T9t2x0svvcpms2O73dG0DeM4FJJ6i3OOXaEVuMJxMWkqKZzVWPRlA027xaVYmsryVI7POU4c3cLYedKe2iptEexas5ftr7mS2CDauh0+OO7cveQbv/lznF+cs912nJ+fmYi0BLwzHu84mNlDHYQKjONIjCNXV484HuF4VGK09ziV6YIzaWuKGSx4byYObbsptroNKcE4JLxPDEMmhK+ebMHaZfkOWFNjJ7gd6lWZuZQhJ9PFRC2NanqQQlJPUmFMmcMQ2Q/JmiNjZkwzmaCkjaamlZiVfow26e4PSFn8Prq+5q1HxS/+OFjjl7hyRVgGsKdtLjBzbaUG3U87ME4XymsAu+K9wKgFZydzDlAaTRIhjbjU49JA4xuabUdG2O/DpPzRdi3dxqzYQ+HSjuPIm290uAWfvG66DJ8SdM77MnlkiWVixRW5rxI4hiZwdr6jbbti0mCGB/1w5Or6EWMcbD6TKSVEjonkHIf9kbfefki3P/Lo6pp+GKw/JadpJ6pFtWWmHaIJUSEINN4CWacKKZk7mcmUEFPk0fCIRwJd15LHyPXbV1xcnPENn/sML53fNVpCsIRV65sP/e+64uOB55DfMomtKdsIMGUfhbbdcu/eK+x252w2W0LT2EpKyokq5gvtXSgZTqYMZ6pSWzEhztO2W2sQiyM5J5wzb3WXxiLdBZKmPpDHJiFdTGW13IkUj/jBtmElFfBBuLxzwTd87rPce+keZ2db7ty9IIQAWdBsWaLD/sDNzWFSQBARhnHg4cMHfOlLXwTJuEeQkvGCvArqy1LReXMkQgmhoW03hNDRtBuaZoP3LTkL45jwoQayazZ2xXvAc54u7xamPTmkWzZ4SuG0J6RkbSj8uITdYobjENkfZzpAnGbhRcZIjWaQsjIUZyMtaiMiwtX1nrcf3RBTZIyZmKXsU9GpZs66Vn7snH6dm8cm1qzOR3jaJMPTs7QrVrwDvHjutTvTfI1msJNTZtSIpkxIEZcGJPeExhM2pprTtcFkHkVo25bzi3Pjx3pH4z3jOLDdzIHsSUYWuD2KZUpVlvxuaaY22UnLfjbBc7bbsd1ty6dsXr++uaIfDsQ04kQISOH8Yu6UY+KwP/LgwSOapuHq+mZqns5pzrwCUyW3COkhZLzTKZDVXLj0ORWVE+sn2d/sGfqBtm3Ix8jN+TXplZf5Id/wTbx6dr8YIzm8lzWQfYHwfBa1y7J5ebbOP2YX2yECMY5sNmeEMIDqRMJ27vbXLyePuYRRyx3Oe5wLKExCz06KLe7JJFQnpxrA2qylT5idF0ZEJ52QtbxiJRZX1BXKtrOW95Q6Rv3cSedoNTwoZggqkCtnyZXfoNAJnC/HU2kFYqXZxe/wUTWZrPgaxFf9VLmV5ZzoM2UM6rxL5SUyTNJ3sSgMZD3d9duZJEvSZKII4hJujDiRSWUkLbZRg9U6HqUGsHXDKovdnkuwp0nXBVXpscf1GnNrJ1eseApuZ/trUmWmpRX75ippiSOmaI5e5RzXnMnOlVNTF3PvU75zyZF90tL01txXDYjmdaSUpi/js7pKaVDmps2cJ+vcfhi42R9ompHjcSjV0+X8dYu8OlGOlFSUg5yYbrqq2dDnNFcjfWlya0IgNIEmmA1vE+xmsp4WyD6ucrTik4r3z5HVTEqlYanYUzpXOT0OOd/RtK+Rc+Lm5prz8zuM40jfHzke9pO8Vm3lMD8fowpoNuMDzbmU31s0B3SjBB/MKKFozpl2bSQXodV5iJzygUwiz1ahIkUyzxthXdXRNmYf6wMIVk4Zx56+Fw57Uy7IKZOTDbqhjwylkauO0WHoGceBlEa7xZEYi25tKpadziHtzhzHnCeEjrbdEXwzd4q6opc5SZnZ/YoVH2coMi/cslhwmdLU3AJCUmFIypgzxzGx70duDj1jzKfe6MuIstRIY7HUHGoJtFB1jmPkONqEmTG1A/vc7WC1PlUztovk68THXfBjF/z/pTPYGryueK9Q7JxPKTGmaAuyGM3IICX6OJDJqMAwHOn3AzErb7/xJg/efJNhtMytE2dVvCYwtoE4RsZhtIxn1tNhU+kGNaB77JTVk/c2TaBpG4L3RTJzJIRA121pmkDOid1mC6qkMTIeeusxiZFxMLv268OBNx88xDnH9c0NVzcHUjEyWionlG812bwxoyny4O1H9McBgUndxHixEcg0jefi/B5tYzq7r7zyMpcXF9y9d4eXXr3P2Z1Loxv56l7mWPFi4DkyskrORyq72thooWQwhabdcn55AQhn+0s220viOHJ1dcXDByZLldKRcTyUjE3GCo6KaiorVTMpCL5BvSIOmtAQ48AwHIhjj8TRTvycnhrEooI6KaXG2d3ES/GfRgmNx5eVHKKkFInjyOAdx2PAexuMKZqQe0pKTFU7NxfOsHFkcx7Nf7oEszZBG1fXgtcNNRtr/NgN3jd435ixgjjb50UQuzp7rfh4Y54sl7qPKeUFO1XMxSsrQzLzguMwcujHqdnrsYarEnIKlr099sM0umvQGTOMWhtIAHETP3C5f3PvS3k8bf5WWmtRAZ1KTjypMvJuBIwVK2YkVWLOjCmRUmaMkePQG8Utjla9EGUYBh5d9Qxj4uGDt3n08IFlKp2jbTtC05DalpxaYoxF4rHauOpifCzvBRVd8O9O4ZwQSoOzOYblEsj6otyzYRxHNt3GAu+sHAsFcFDlkG3Q5EdXVBWj5SCcDUjKeC4DfdKYJZPGa26uTNUolxjAOUfbtTSNNY3fu3eHy8sLNpsNn3rtVe7cueTi4pw7L91le35mwbsvC9Y1kH1h8FyqBfWa7h4rz592DFrguCy9L0roCyw9p6twenmhxIG1/D6X4U+2Ow2W+rGZ1F53qJZPqkZeLT80ocE769TUrAz9wPHYo5nCJfJT5yWqk22swkTgTzFNDiopJ25Lgp0e62LfnjAZ1rm0Tp4rtWDFVxPvPTzTk/tTldZC7UHKwq+qDhRObVUrqEHmoilk2noZ31mtsxvmcr9VdGqWdcl5vb0Lj2do52D0dJvLQ6p0iOnpdSiueI+wpmQm8475vC/9XsyqAdV0x4niBRrvIDscSk6RJDAKgJX0Y4xU3Wb7rlMd2bIH8/lemqNP2/pnSh0i5JSIKDH6Eixbf8pEP1io61SzIBCcKIpfbNbeUxV86v5NS9bFWMpgCifMxgmIFlqDqQbtdtspkD0727HbbdlsNqascFKBWfEi4TmavSBGwXuHbywL671ZujqxFVUcUmkK60lxmNQGVAQVZxTvaTIxHTgbYNlWZGp0gTpgrOvfTzay3geWvtPTOIXpokC5QDjnLWccgq1qg32+6zq887Rtx3ZzRgiBoc984Qfe5O03b4yP03YTT9aXjGkIjVnTSrXUTYzDwPXVDfvra4790bT/oMSrbjoGXztEixB0DbTrhJ5vTfA1+7tixVcDz8Itq6FfDSGLDxCV51ffUycyLf3NiUw/mlLBcRgZoslm5ZopqgHncoarkyE20eW6beZtn37rzF9d1Gbmxex0jShft8y+Tp+sjxaBALcNEdZZc8WzQYEhQoxmAJISxCREdSRVVAI+dDR4ugi7JjIK3NltGO6eM44RnBAPV4wIN9mMQVKMXD18WEr3t927hLSYN5YmBMsxYnNUnRtba6o67Ikx0XUHUkq0nWV/xQndZkPO2eh2peHSl8A2NLYNcc6C4TLnD4OZIpjUXkbTsoJa9qa4aCrzwlYwo4SLizMuLi745m/5Jr7+6z9L0wbOz8/ouo6maei2nakeAZLq8X54f88VHy88V0Y2ZTGhf+8K59NhCzpFY/F3zkoch4nXaqsym2xslVqmIAFxteyfUU1kzWihLZRagclruISIL52Wpk3rvWeSAVMtK0Y/ZWunQNaH4hZmAe352TlN0xYSuQWscci8+ZUHJg3mPN43OJEikdXhvGO72bLdnZnLV06kbDyhw/7A8Xjk2B+M21PLK4WvVLPJzrmii7uYeJUiUTSvSPMic7Xi4493ki6Dj7982XtpkJjfOdUPHitrMj1rt6wwpEQ/RvoxWpk1V0EeN3/4Cae7Yja0NddaA+WakbVJkIkLq1Ml9ZaL160A9rGvm2d3lt0068T4tYuPWlJQFcYMMQtjqskJiCpkdeiUoBEan+gajwfONy3xfEscI/t+5Lrfk5Ky7wf2pZnq5ma/UAWYv8/mQb113tqgEDkNZEUE7wMhBFLO9Mej8dGHFkVpGjNICCEUtYQR562fI3hPE0wmbLPdcHZ2hi/v6XvrEXHOVEyg8F9P94hpAar1Pk0Bdtu2lom9c8HXfd2n+UE/+Jttvi40CDveUsHNK3v9RcRzBLJS+JxGFZgCU0ubWOkw5bkcTy7ZVuPj2M22VLVd48Q/zYvV5XzSz2X2msSsQepML1DVyY7SFR9mwdQNRNxJOWXyfU6pvN9KHzEl4mgSKd4pmmvJJ+F9xrRuzcGMkjUlS+G1WtlICy12/rXm/Z1RjyejxRy6FlWptId6wVln0RUfEp4WvJ6eqadZWF28R8SynNPjUhqdKT2mBaslkE3ZhM1TXkryzOVJpolunmyX+zPbEdziL508fDJd57F/FN7AFMwu6ASnj+ej52QiXrOyK54N1dFuCtoW9LJqIuCy4rwn+IAgk7Nk9IEsjogpd1QTnpSE1DVobBf0nFuVBWV+TUAKiVSKDazJYc5ToxPBh0DTWsJnOQellEBkmqMBnPd0GzMR2m63nJ3t8N4zDCO+NI7Va0xKmX6wJmotDV05z3P6tMMyGxk1TaDrWrrWEk6uXl9qjFDjAji1pV3nzBcG7zuQFRHarsM7AfEodnIPY55kM8ZSSohpJOUR1Yz3ma4zzkvOUlaMyvHY8/CBfXa/P1gzWM4l+DQdyZyTdUkWdxDvAuqV4Bua0E48oWqOUBunlq5ZzrnFgBzZHw945/E+0DQJ5zxNSMRRccWVrGk6nDgLaGnwHjMqCAnvLPvrxINmNDtSMgJ7HaAoRavPTe5k2NPWHZqM6N9oC2Sk8IJcGbQW8K7dXiveC57tIv5+JWpqG4UURpwXCCKT45CrWskuGA1JlVSsaPf9wM3xyDDWsXy64lNk4sQ++Yhm1QF7fq5q3JYYOg1e9dYLpRq04CQtA+jTz5f0ri6/eQ1iVzw7pnNLStOx1MqhBYNNsGDVGpEFzXnKRqaUOfQ9+8ORlDP7Q3mcMsdDz/Fomc+cspkPqDlOmkW0mQlUe9g8Wd3avqgITesny/UQPHfu3Jkyp7UokXJi7I/klBn6o6kvaGaz3fDqa6+x3WwmDqsFsj3H49Fs2YeRvh9IMfHg4UPefvsBMSaOhyOHQ1/oBDWYLfKW4mi6hss7F7zyyn0uLy/o2oDmaMeQ4mIkzsvXyYJ3DWRfGLz/QNZJMREog0FNLmosk1OMiXGIRhHIsbhwZcRlQmOUBD/MK71hGNFCPej70fi0qsUe3U7I2ebOgjrnHF7dpC1n3dLVJs/0WS2IXfJl3czBiQnVofBWEzkbBcHcyRzORQuQy/NOGkLIaBZiUJMNqXIf3iPqUZWJElDHUWEmWRDr3LzyVCVrIqVYmtWqJJkWLb9SLtU1I7vi44HbIZzVY4wU4MSyOZVPLq4uQiFpJiqmWBAjx2EwF6+lHMdio7cDSpkaOZfNYJT7E7LByaPbw6ZmbqYXazZ2STNQ5gzsSfB6+37FiveGWi6/fXNS+i9K9TCUhEvbtmy3xkk99j27Y2cc1v2R/cYkIftdR9+Pk9lCjJFqGJILd9YUdeJEcTA6j0zNZsE7C2Q1W+9I2+J8MMm7vi80wUTfD/Yd41Ayw0rTtdx96R7nZ2ecne24c+eC4D1D33M8HorUWHHrjAkfAuMYGQYLbA+Hvvw2Nh4RKRRCRwie3W7LnTuXnJ/vaBpvcQK1Qmu/q5sUiExDyZV5c8WLgefiyFqm0CYplCJKnq28XlZxWeeyohYOTLXIsyDTtpRTZshxyubmYraA6sx308zkzTydpHO2FVEk236J1O27qdwJwpKTOh/DosSfZdKoBUg5knKYH8cRfCbGgXE0gWjNAdU8XUBmVYQnZHZ0/sc8GOeOzhoALJUZLBu9ZmRXPAN0ScbhJJJ7KoXgmbOypwGcKw/nEV3Lj5kotqgdfDRebLTmlDFGYvVlz3oSGtatnX7b47ORvs8g8nFTgyc95hkePylju2LFO2OKW7EFn4rgXRk9tVm60ESzeFCdSv5auv41J1Iur2EUviZ42uDJZe6sjV9Tc3VWhnERyCb7nKrZPudsiZjgQDRTHTqzPbSbUty1Zov4Om5Sygx9T18qiMEbb3YcR4ahLza3uehK56IPX+l+M02gBgNO1NzLgqNrAk3wNMHhnaApEceh/qIzzdBZ83id31VOryorPtl4DkOEUurPSoo1A6nEVAeBErMrgWEml2DPAtzSnlFWolmyGSUcD+Sc6Ic4DTrnHQ5npZJs2UudBlHh+fhAaFpU50Fmq1y/CGRnBYPTBiuLLE0+K6KuUhPsMzmbV7xzflJScM4zxiPDcDAOT1EwqA1tXbcBgcPxpv5YgLl7iVbR+HphiLg42AXLgQ9mvmCqDC0iliEex/Rcf+gPGq+9Bl/60pNf+9Sn4Du+46NtrljBB1paux26eVdk9wCP4DHt1mGM7I893jk0JQ59ICvEMnkex5HDsWcYIklnX/iJN3j7EBYZ2OfBbVODuVxSVpZTFvb0OJdZn5LAvfWOFSveHTZODNkVTrkT8B51gprC1hQ1aqHcVC1mVImxZTzbnGZesxLjOD2uCjqqLGQg1ah60aqcYzQ99JyVYYzEmBlT5upoxgyoEgmoE1JOpGhjN8bE0A+MYz85kqHKcX/gK1/+Co/aliY0bLrWsqNL3q2zCqmqMg4DXkC9wzu7lixrLN4J59uO3a4zqsLZlrNNSxscsd9z9WB23qw8X/XBqkG1KZxa4VzxIuC5LWpTyozjbDFZ5bRMK89KF6qOnF2ZCKr0R82QGod1HCP7/X4qV6Qyw4lbzDW5GiWkKZAFKcoCJgWSdcGXcZUfW6kF3MrI1vJDsQWU+cSPjEXuSyfSuWV6KUoFo3F0qppB6qZMbgiNyZMsBJmnzGtRwax0Ac2JlEZ88IUr5cotGMdQ3Mnv8XHB04LYd3ttxVcJH2JdrWaWaiBblohQePL9MBp3XpP5rKsSS5VmGKNNnqkuat20zQ8LJ0HsrUzsqWNXfc+7/3wfr9FoeJbO/I+6e/9FhmViS0ArNZB1LIR8jH6HgtrEVxuh7T5Mj6dAEkuGpBRLcsleUyAvAtla1tesjONY9M4zx+PAOEYOfWSMN4xDIouYi6Vq6fewpqwUSwA9xhNaztAPXD18xCEEcx4r9vEmXRnKHNnQtA1gAXYNOr0r2rOLLi3vhE0bON9t2G07tl1D13iCd6Q4cNxTmsAaoxSK4EJCvC9ZWfdEjv2Kry6+mtea58rImipBXQVWYrjMJfP63tKRKQoZjxAwU4RgkSrOMrjFajbrnIOptASbZmowWKad8rhmT50Dp54qfO4W8ltTRrbsz5SdxZWLipt4qVPWVmrRdKYf5Gml7EhpnFaEMcok+jwrKZTVYl4KSNeV6rxfvqwum6al6za0jXWAVoqBEffXQbniWXBqnvHBxYcza1Xmf9ZvtGqLMxvZMUIqvINcmk1q9nWMs/d6pQioPEPO9T1Guk/ilL8rtUBPeXXPREV4T3u14kWGK5bsrkh8KNYDoiKmeiMyBbC1ATHnbHOUKlMHhWqhH9hjMVaoJXucorn0gTjBq0OzlqDRo5qLi1exj0YmW/mzzUjOEHEcsxIxa1inCc0JRyYIqDMSu5QKhi/cWk0JXCar8VUzShKbD0UzogkEchwQte01Xth2je1XmZODd+w2LduuZdM2NE4Q1bKNcg9FPTqX45/Iizh5v+SjFV+reN+BbFbo+zSVHPKUIQXLdM4TqpX/G0CRnHG5Iavi/ADSoKKMKRkxPEdE/CztNY9eaxrTWESVR1KOtk3n8K6x75OMOlvinujILpxI6j6WnQMowWRTmlW8ZURL2qmS0FOOSLQgVLM1sYkIMXnGGEqGeiR4h6pp8gXfkF2eJEPmRhjT3w2NyZxsug0Xl3e4e+8+5+eXtN3OLGul8Iw/XsyCTyQ+MdmqZXT1AV7R60Rhk4mC2OIyZSVpnrh8/VgmpCLtMzFd1Tqph5hIWgk3tu68HRB+EAHiswSip1/45OB3mc09EZ3/MNPIKz5REJSGkSyKc7nYxSrqKQsox9RgmItKjWqR2dKTLOzU1JwtkZOTkPKkZXnSzFhpNFUKU1VJJRubkxkVxBjph0jTNNwcBo5j4s2rnn0/ErGKISnT6MguCAkPNWuMSVS6OIBYWImY2152Qu7tWhC9Y/Q1WE+4EoxebBxnzQ4RTCHImyb8xfmO3dmWtmnYtR6fIy4rPkd8dsajJREw6bAgGX+S5V2H54eJj9tc+XzOXqlwePKs+Xq7dQPm7CNYllQFyBlxDUhAMc3WMZpxgveNlStFcKVZjEVTlGqa7uftF6KNZlTmQLZmXl2hGTwJs22tm+z3lhq0FqOXzHOhE6QyDZuzl1ENTA0hTlJbZnlrmegpbr6V9a3Z2NA0bDZbdrtzNpudKUI4N/+aL2jq5+M2YF5k1JBUyrk/aTgWrhyUDFFisYCU+bNiC9xUqzd1w2V81Oe+WkHs49SC+vzTq5KnFra6zpYrngmCUrwsEbHS/+l0VLq8VMFpocqqyUxJfiwLq2qBoqqSBbyeus7dbuCshgmaaxOYoikzBk9Mia4ZSckax/bHgZvDkXEYQRNeR1LOBE3Gm/WVMpcnegGlObouToV5kWoauZBcmWt9UfoRoW0cvnBq26albRq8d5zttmy3G0LwtF4sg5tBckY041SKWkrGAV7KY3F40ceOf8UnG8+lWmAVkGo0sOCSLqKvqfRfPpNzJqnZ0TpvTiGaG5z3hWYgUwlfREhp5q6lPHdLVp1VoBC8S7lDjKZgE6mfKAW1Q7KuUIFS5bcVpHcWTLqiNetrRpaZEuCLvSwUa97iS+1csf1TRVzAuYDzSmg6us1uWjlDkRRpWrO4DQ3dZst2e8Zuu2Oz2dB1HW3blG7Wqfi6lkpWPDO0Ti5lXqx0mqdNdE+TdjuZDLTGm8v3npbY6xk7jTPErGdlSUeQiY86B64zfah884eycHt3asFpAPs0mkF56+NveoHw1VpgfpIWslIyGs4tKSxljC2y/FqalWX+4GP0G2u0ss9JjRpvf9+tx5qNiyqAipJFQJuSvBF2uzzNyUPKbLqWMSbOjqM1iKXMOI5TkxdT8orHV39aefQlgeVqEocSyNrxee/x5fubpqEJAe8c2+2GTdfig2e36dh0Dd57ujbQNgHnHU2R3TSqhLfeEqkJJFnXmC8QniuQzYVf6gPU4C4EjyuBrRUYmMoiVRLEBgO0bcN2u7VVV9PhxJOwhikttIGYZkqALgaL8w4fupLpYfEey+AaJzXMTV5TPsnKMqBTJhYRmhDoiv2sd54QmpJBKhlhmLK19fuq/BCL/fMZXBaCa9idZ1woag25yo44QtsRfEvbdbz08stcXt5luznj5Zde5s7du7StdWg6YuH6roHsivcGKyKUbKNIoQI871lUQ1CYNHkWS1Vzl/Rkme0SajQtzJNeDXLnILbMg3X3dNYq+CAUCx47isfoAsujowS0S7UCPblfseL9wIENh8nScvFiHQTYODAVScVpCTixz9VzsPZj1GawnN20mDyZLBb0guk8Xjhnpta2k7Ky3W2JKRNT4pVX7jKOkZgSx6M1h1W1hFwtpYskZJ3foATLleNbE0VQdGGlzGeFciQ1IeSnxJQpEZijWRNM3rJtG0Jjj7u2mx43TYsPwTTtvQW3Im5y/3KfoEj2a936/MPG82VksVWPOCN3WyDb4I39XVagxYYuVaWBTLFcJng7GcFWZnXiMzu9aG8qJYspxQQlu+rxvgaV9RXBekItk+pcM2VkoV4njM+3DGSrh3TbdWWFWANZU1qoLiiVwlDHR82W1m2rKuKS3XA07cZ4hKrkFMtgF0Iwsem27dhuzzg/v2Cz2bHb7dhuNovfMJdA/ZMZyH5Q2ZZPUtbmg8EcMFomVC2YfdI79b2V4aYAWedKyYyaOVooy1r6B60powWXvgayy40vc7wf5Dl/m+c6P553aw5o5yNbg9cVHxTqOJuG27RwYxonNlZd4Z+Xsnw9ZxcJEydSNNp1mgNPN1o3fOscrtsv2/R+fk/bWp9J0sz52cbku4pGbO2DqQEtMFVHawV1epxOx0zVip2t5Gfqkfd+ygjLpKFuigc2v0vJurpJqaB+JjTB3C+rFFel9JVg+KuRkl3nsI8HntMQ4eOAZz9ZRb62K4GfoAXmik8abp+b67n6NYt1cv4QILf/+XjAefrow/nqd93+guHztc4z/dre+/eOD0p+72tx3Mr7zTiIyFeA7/tgd2fFM+AbVPWVD2pj69/xI8f69/xkYP07frKw/j2/9rH+DT9ZeOrf830HsitWrFixYsWKFStWfJRw7/6WFStWrFixYsWKFSs+flgD2RUrVqxYsWLFihVfk/jqBbIi34vI/Sc8/9MReWdtiXff9g6RP4jI30DkryHyOCNZ5GdY+7/8g+Xf34bIf4vIn0PkB5Xn7iLyX02aXU/+rt+HyDc91/6+E0T+CCL3PrTtr/jk4qMaYyLfgMgfLePpuxH5uvL8D0Hkz5fn/4fluVDO8d07fNevR+QnvMv+/DJE/sfv8Ri+A5Hf/J4+834h8msR+Ue/Kt+1YsWKFS8wPvqMrOp3ofpBtML9WlR/KPCjgG9D5H86vSJyAfxc4L9ZvP/fBH4K8G8A/3J57hcBvwLVJ8hLAyJ/P+BR/TvPtaci76QW8R8C/+pzbX/FiiU+/DH2a4Hfheo/APwy4FeW5/932Lj7KcB3luf+FeB3o7p/4jeIvAz8OFS/5x33RPWXoPpHnvB5/14O6EPEbwKeb/Gw4mseItwV+eCu5yL8JBH+i+f4vBfhLy63IcLvEeFvivBXRfhtIjTl+Z8hwl8T4U+I8HJ57ptF+L3vsH0R4Y+JcPlRHrsIr4jwX35Q373i440PPpAVOSuZm7+MyF9F5GctXv3XEfkLiPwVRH5oef+cJRH5HYj8eyVL+t8j8lPL838/In8Gkb9Usjs/6OQ7Vfeo/vHyeAD+AvB1i3f8O8CvBo6L50ZgV24jIt8MfBbV736Ho/tfAv+vxbH+5HI8fxmRP1qe+7GI/NeI/EVE/r+I/JDFcX4XIn8M+KOIvI7I95Rj+quI/Piy1e8C/ufvsA8rXnR8/MbY/wD4Y+XxHwf+qfL49hi7C/w04He9w9H9DFhMQCK/BJE/W47ztzCLcf4ORH5mefy9iPxqRP4C8O0lK/wbFmPrxz7hN/xpiPw3ZZz+EUQ+VZ7/pYj8trKNv4PIz1l85mcvfqN/H7MO9GVf/mr5zf/35Tf6PuBlRF57h2Nd8cnHXZ6SmBD5SOQvfy7w128993uAHwr8cGAL/Evl+X8d+DHAvw/8L8pzvxxL+DwNPwX4y6o84iM8dlW+AnxBhG/7ML9nxccDH0ZG9icDP4Dqj0D1h8HJqugNVL8V+L8zZ2hu43PAjwX+SeDfQ2SDZUx/A6o/EvgHgb/31G+fJ8saWH4rFqD+wVvv/JXYhPoLgN8M/J955wEK8G3Any/bfQX4rcDPQPVHAN9e3vM3gB+P6o8CfgnwKxaf/1bgZ6L6E7ELw/+nHNOPAP4SAKpvA13JTK1Y8SR8vMYY/GXgny2P/xngopy//zfg/wj8Tmwc/GLeqeJhmMeY4Tej+mPKcW6Bn/qUz72J6rei+p+Uf+/KsfyrwG97wvv/JJb5/VHAfwL8vMVrPxT4J7Df6N9GpEHk7wN+FvBtZbsJW9j+SOAzqP4wVH848NsX2/kL5XhWvLj4VcA3i/CXRPg1Jav4J0T4LuC/E+FzIvzV+mYRvlOEX1oef4sIf0SEvyzCXxDhm5cbFuHHlOzqN4vwE8t3/KXy3MXtHRHh67Ax/x8sn1flDxUvAwX+DPMCNQMdZSEqwo8HvqjK33qH410mez7qY/8DZX9WfMLxYayK/grwf0XkVwP/Bap/YvHa7y/3f5554ruN/0eZ6P4WIn8Hm1T+a+AXYty734/qkweSlez/Y+A3ovp3MK7rrwO+47H3qv4l4MeVz/0E4AuYfdfvxTJJ/yaqX7r1qdeBr5THPw74HlT/btneW+X5O8DvLBktBSvTFPzhxfv+LPDbEGmAP1D2p+LLwKeBN594nCtedHx8xpjhO4HfjMh3AN8DfD+QUP088JPK574FmyD/OiL/IdACvxjV//7WNyzHGMA/gsjPwybTl4C/BvznT9iz2+XO/xgA1e9B5LIE30t8HfB7EXm97MvfXbz2B1HtgR6RLwOfAv4x4EcDfxZLCm+xcfqfA9+EyG8C/iDwXy22U8fxihcXPx/4Yar8SLDyOJbQ+GGq/F0RPvcOn/09wK9S5T8TYYMlnj5btvM/wugr/5Qqnxfh1wP/mip/SoRzTquPFb8eW7A9FuSWbTbAP49lbcGSPX8E+AHgZwP/KfA/e5fj/TaMUvRxOPY/h2WQV3zC8cFnZG1i+lZssv3liPySxat9uU88PYi+LWyrqP5HwE8HDsAf4ulNFL8F+Fuo/vry7wvghwHfjcj3YsHnd1EbvqDal/wijH7wb2MD/bcCc0lxxgHYPOW7K/4d4I+XDNJPu/X+m/mo9HuAn4BN+r8DkX9h8b5N+a4VKx7Hx2uMgeoPoPrPluzmLyzPPbj1uVrx+DlYRujnYePtNuYxZpnifxerYvxwbFw+bfzd3Pr348d4it+EZXt/ODbxLrfbLx7X31GA34nqjyy3H4LqLy0VlB8BfDeW1V5mu9ZxvOJJ+DOqJwunx1Cyip9R5T8DUOWoSuWV/33YOPxpqny+PPengF8nws8B7qoSb23vpwJfVj2pdtzGvwt8jyp/onznH1blR6vy0zC60B8CfrAIv0+E3yrCkxo2X1Ll6mNy7OtC8gXBh8GR/TSwR/V3A78Gm3DfC74dEYdxVr8J+JuYSsDfQfU3YmWLf+AJ3/vLsWzovzE9p/oQ1fuofg7VzwF/GvjpqP65xSf/BeAPlUzpDiun5PL4Nv468C3l8Z8GfgIi31i+/6Xy/B0sOIUnZYLn/f0G4Euo/lZs8vvW8rwArwHf+9TPrnix8XEaY/b8fWalj1/A7VK+yE/EqBB/i/c2xmpw+QYi58DPfA/H+LPKd//DwENUH956fTlO/1fPsL0/CvxMRF4t230JU2u4DzhU/59YoL78W/xgmEunK1YULBddkdN5+N0SJWDVwyPWdAmAKr8K47ZugT8lwg+99ZlvA366CN+LUWn+URF+d31RhH8beAX4P9z+shKwfgdGFfo/YePlT/Lksn0Uece44qt57OtC8gXBh0Et+OHAr0EkYyX6f+U9fv7zGE/nEviXUT0i8s8B/zwiI/BFTnmnlHLoL8T4qX+hlP5+M6onXKDHYBJA3wH8T8ozvw5bdQ7M5PYl/iBWKv0jqH4Fkf8t8PvLJP5l4B8H/i8YteAXlfc/DT8J+LfKMV1jATVY+fJPoxqf9sEVLzw+bmPsJwG/EhHFqAX/2uJzteJRG9J+C1Y2DE/Z7z+IZUj/A1QfIPJbsWDwixgd51lxROQvYtSef/EJr/9S4D9F5G2sUe0b33Frqv9dGdNVnm/EjvMA/PZbgTyFMvQtWHlzxYuLK55Syi/4EvBqUQW4xjjg/6UqVyL8PRH+aVX+gAgdUBU5HgD/a+APi3CjyneL8M2q/BXgr4jwYzC60N+oX6LKL6Ccm6XE/52q/Ozy738J44T/Y6o8ib/+bwG/UZVRhC1W3XjaQvRvYovjv/0xOPZ1IfmC4ONlUSvyOzDO3+/7qHfliRDZYl3Z34Zq+pC+4zcA34XqH33X965Y8V7xcR9jACJ/EvipT6AnPOvnvxv4zluVl68uRP4Z4FtR/cUf2T6s+FhAhP8Iq3D8v7GF2neqzk2LpST+c7EKwd8BvleVXyrCD8IUA+5jC6dvB76+fl6Ery/b/BcxDus/ggWYfw34DtUTisxyf37Sch9EiMD3wUQJ+P2q/LLy2qeB36rKP1n+/e3YIvAB8E8XdYDltn8x8AVVo9h8lMcuwncCvSq/6Sl/mhWfEKyB7HuFyD8B/PXSyPJhbP9/U+gGK1Z88PjaGGP/EHBA9b99n5//bj76QPbbsebOBx/ZPqxY8VWGCK8Dv0uVf/xjsC/fgzWEvf1R78uKDxcfr0B2xYoVK1asWPE1CxH+OYwi8Ogj3IdXgG9T5Q98VPuw4quHNZBdsWLFihUrVqxY8TWJj96idsWKFStWrFixYsWK94E1kF2xYsWKFStWrFjxNYk1kF2xYsWKFStWrFjxNYn3rSO7abyebQIKTDRbEcR5QEAcOIcg9SVmc51yf8LPFRDQ8n4z0pHpX6DknFFVclbGOBJjBIX8GM9X3uHfj3OC9Ymv6a2Ht7dpL0g5NpPLfDpEZHqPVmNrtW3U3ZcnfsfpXqWc31DVV97xy94D7t+/r5/73Oc+qM2teI/483/+z3+gf0/vvTZNQETw3iEiqCopp+m8y9mkIp1zeO8RIGclp4zeGh9SxuXtM1MVe68ymbSzPK/FPmunfDn360Z0+s9ivM/bm79Pytgq23KCL9cUcYIrL2rKpJzsWpAzqnlxaVlef+bxd/Kdi/snQuddvnXFmpBU13H5CcIHPS7vXrb6+itbNCs52VgUJzjn6sm5OM/KgzLv6HTegmp5LAGkTN/ii7+JENqWEEIZb84+r8o4jqSYcM7RdhuatrWtPm3e0joupieecmRP+LwuRpPOI70+sYwZ3DQvyjPNo0/e1yfv3ec//7288cYb72ODT8Y6Jj9avNOYfN+B7Nkm8JN/9GtkhTHZiSlhg9/cQXwLYYN054j3OFG81zIkE6IRNNeRaSe2+HKTeZCK4LG0sapyPBzo+yN9P/ADX/wSb775Filn+iESUwIE57wF1Ej55OPncZ3Ib09oSi73irKQiVUBlSm0LpcShIwINEFoGruQyPId2SZ2EaFtW5qmQVXp+55xHMlZSSmRs83eDo/g5guM2D5mzdNAfetq/33v92/2JHzuc5/jz/25VbP9o4KIfKB/z9AEvu6zr9N1LXfunNM0DX1/5PrminEcGceBQ38g58zZbsfdyzsE79nfHLm+2pPTqTyyBbvusQkmxkRKmZyVGCMxZnLOjKMtMEWEEALee5xzNE1jkzZ1/GlRVbeJPOfMmGyh6gSCczgneCe0jcc7x6ZrudhtaUKgbRu2mw3OOQ43e26urkkx0R97DodDGdsy3aRM9gA55WlRHInkMu4RnfZrum4opHJ9s/FqQbKU36Zu8+3jYR2XnyB80OPy9Vd2/PZf8W2Mfc/h6oo4jjRdw+Z8hw8ezZCSolkhKjpkmyIJZA2oOsboGEZHVoc298jtSyANrjnHhXOapuVTn/k091/7FN4HxHfgWsZh5Is/8GXefPMtdrszvukH/VBe+8xn58WuWwbKdvbX8QHYvLicK1VPki7La4OWxFJd0OYyB6IZ1VTGXGZUm4O70NH6FhGhCYEQPEs8Sy/6cr+X+/kP//h/6L38id4V65j8aPFOY/K5nL1EZFo91ot7nTtCcLSbBgkNwVuwJwLkEU0DaCbFSIrRTniBLDWoVVSWAwdySgzDwGF/pB8GhmGwyTSXSSnbRJSzIOJQ9GSALadhV5+vGWOpWaWakVK0bAMbg7aNOtcJBO9oQkActMGVQBbIFsDaftuFyQJZT9N4m6gJNL5klkcLCgAk2w/pnOAbCwBUlZhPLyQrVjwNmjN93wPKfm8BZEwRELz3qAZabdGc6bqOrusIPqAZcrRAzQLTsUxGmZROJzGgBLG5TFb1/Cwh4K21Yw1q27Yl50xK0T4DUzZFRHDCZCuUNaMJUCEKZMl45xjGES0LUe89ToSUcqkGCT4E2q4rk+cTslnlQjXv7ZwAw75u8WPOk6RNyNPTi4vdihXvDgFCgJwE39h56YKbKg5ZlRRLIJvU5hyd0yqQEVG8yzh1ZI6QroCA5p403EBouHmgNKHH+QB+h/gNMWaOx2s0J1KKHIeBm8PRFmJOHluk2phOpZKTGWMklbFeqzrzIrEenz3O5TOqkLIuAtmE6giqxJwYc0ZEON9uOd/s8N6x227xvrjUPiXLens/58f6hDlynTNfFDxXIOtwJfTL5BLIigPnodsELu/sCG1H23i2m2CTztgT+z05J/rjkePBskMRSJpRBJVMlpKtzUpOEGPk5nrPwwcPGMaR/dWe46EvGUvIU00yATaxOXTKmNSA1YnDeY8rZR3vPCJC1myTp+oiYrWBmPM0oyElqt1tG87OWrx3tI3QtfY9aYjEMZ7UIkWEpm0IIYAqYwcpe3JKHA8wDmX1GhVNCd8EdruWtmvIWRliJpbJ+wfe/Mik+VZ8DSDnzM3NDX1/ZBiO+OAIIbDZWEXAe0doPKhyfn7OxcUFTQh0bUfXbEgpcXNzw9VVIqWadU3Ttpe0mPmek0DvNo1GRNhut+x2O1JK9ENPjJGcFUmJpCU3qyXCzNloDprJIuQUC0Uil+qOp0uRrBbc5hLIOu9pN56mbW1/F8HrOEbG0SZncrlmlffU4HkZ0NZIt07mOetJ8AsOladXZlesWEIcNBtAhJQ8PmScdzhv85IlNTIpKqJq0xggkhFJVp10ivN2HqackOEGVWFMMEQQ8ejw/dw8uIu4Bg2XaLgAPCm3ZG0YR8/1zR7/4Gqq+tn3yERPUM3ElMg5M4yRq5sDwxjJqsSkhconE82hbAGAuKisxGQ3VUV0AO0tkC0JLOeEV+/d5dV7d2nbhldevkfXNjiR6bpya/MTbgexy/v5hef7m6342sH7DmRnztnp0qmuML13tG2g7Rq6NrDbtjgnxEEZibbiS5E4CAk7cUvB3wJO1Cr6NeObMzGO9P3AMI6MYyTFtKASyQnHDqVkdeedm4NZC2IrR9A5Z5minJispstnHUqy5TG2h1LoBJ5NZ4FB1wptY5SAEcVV2gT2YzgRQnCE4GybzpOzlSyzpZvQrKRsK9rglK51bLpATAqS8Hnty1vx7tAyUahmkIyPjq7r2GzaUgqHUuGnaRrawqlTFStvxkTfDyeB6MxNzwtaDtP96fxxWgmpgaz3fqIXpMIRRDKiGZexjKoIDqvOaFlUZp0XgzEKY7Sqhhch+pHs3JQcFSn8WW/ZorpvFowqEuvYPr0q2F7X22m6VikB8eKYT6LXNZJd8YxwTvAevBfQmg211xTIyZImkrFgdooyCy1PFHG5TLk9Lo92bg6RPCRUHEcXiakHadBmIIcBcQ0+XOKCI+fEMI4ch3H63lq9nJM6WhaaieMw8uhmz7EfC40wT1Q4VSljYh4DMStDTFPQO06BbI/o0SqxYySOo82doeFs05FSYhyj0fHcfF05WVg+BWu1csVzZGSlTD4O8YLLEJrGSpVNy27TsusCbRssiBMLCkFRZxNU6yEFR3LgM7ishXlaJzMYNTOmRE5xumlOBG+cuRrE1iuCZXQtaK3cvtosQglgQwhlUi/NIyLkLMQkqDryNHmXDHPd8wyacimqFM6BQk6QxILXOI7EcQSFEDzBl4k2J+JYuIE6B8nBO2g8mpWololugqdtPG0TcC7bZJ7yE/8KK1Ys4b3n/OIc1UTONasaGcdxOu+UjCCkmKxhEkFzRsThPWw2HZeXl1PQKmVBOY4jwzCQc6bvB/p+oE6F9tZlZkSmILbSCYwXnqeypVBoPkY4pZGSIU2W2ck5nyRialMI5RoRc8Zr4a2LIGgpl1pAq3WIKoTQoNkqL4hDXLD9dYCry9RsmdqsjENtJlXEKTnpKZ2gUBlWdsGKZ4WqQwsnvJ4/4o1nHQK0GzvPNCkaS0WyVCVtgVWrCVb9qEMnNHYeZ4TsPUMOZAmMYyCmgDhPq0Ijyjgk0oM9j+KDxeLNxpBzDlcC2Zyt4tEPlpHtp4ws0/5MjWfKlESKWaeMbMpKLJxyzQOoUQptwowE7zheRmLKVvWo1VBkOkbgHWk8axC7Ap6TWgAeceAB8dB1HRfnO9puw/n5jrtnG9q2taYoEqA4STjJZJdxQXBdsEaPXFdvEHGkUq5MGsnjSBpHoyXEHk1K23q831KVEqSkmepU7UQIjeAKd7wGtJaFDVPjCeVTKQkxWvk0jpl+sNWxE2t2ASFHJZYJ26FIzkjJ2NqFJzMcB4ajZbSa3YYuNIAyDCPjMIKADx7vjYO7CQ71RiGILpFipm0Du01gu20YYyFNxNMmnBUrnoQQAq++8jKH44GHDx8yjlaWd+5Yqg+Cc4I4GMaRoR8tSFNKB7Xj/PyC8/OLqUmx66xUf319w83NDTFG3nrrLWJ8UILUDJzSDuqCTdUC174fJs53znOjpxPLoHoAMRWUFB3kbO2W00Q1VzisOUwZYyJi9IITNQMfSom0UgswPrwPqEKTbJ/FCU3X0HR2GUw5kbIF91dXVxz2B1LOSCrVmJI1npo63dwos2LFO0ERUm5sIeU84jMuOHxwiLNx6UOpzI2J8ZjKY4iDlJxJLhXDEn5Kxgl0Qemc1Qsf5cCNdsTccDO0HFKD84HznbBFyeNIv3+LkaMljLDFoyV1pJT1S7OzKmNKHPrB5qGyyFSVsmSVkuCpvFibM2PhxWZVa2BDydkW1qjSuEQrmbbx3LvsGcZICMF+G7BgVis3GEpmasLUTL0GsSsK3ncgO2VCp6xlKR+GQNsEyygGRxOsTqB5PjmdqBHcHTROyLJY2c1LRJgmxITmhOZsN3TiuCIO5zzi3ZKWinMQ2lLGoc5/Ula/AefqRGd0AefKSjA7y06VXXCU7mQAZw0pqjUjWybtrOQy8C0DlqaGsnqvJStlQYVtuE68giNLBq+I2j4H7/BeyDpfYFaseDc4J2y2G1KOUybVgk1bCNkk5HDI9Lw1Wjl71tk4rhI+2+2G7XZ7kmUdhpEQmikwffqpOX8m55r9La+USciCwmnnLQAtjSATRaHSdOwThUZkC91aHXFzsnaqFCEgat/v1JcFKSQxrqGIsNl0tJsWUGKKpJwYx5HD4YA4Z0Lbi4rPvF9rNnbFe0OuzYc1lTotjEC8TFUEFJIvVDuZ6TEKUxO0oIVyYMka57GF3yjE7Bmzp0+OQ3T4LDQJQqn6XQ0D+9LGkbQQ5kTwUueZqtpjGdV+HEtmVcip0gkKDQemxewUvBa+fNa52cuoe7aA7Xwm+VL5iJmUFzx0rdne+h0FC6rFO0rlrXgh8VzUAhXLSrShATEuXhs8jXNIigw3V+Sjt0A0WZkuxYE0HqfALo6jnfAqiNoE4cTjscYoyRHNI5ojkHDOyPBU6ZvS5OG8KzI/LT6EwidKSFU/KNeOmXdXJsuSUfHOE8rE7MS4gilmnAjelc8WrhIoOSbGYSwSQRC8fY8XTxOa8jmT/LHJ3gJuxWRWck4IFrCaJqajbRzq1ZrCgJQSMSaOfc9xiKxY8W4Q59hsNuSc2O12Cy54ldAyHcuc4HjscVxb4OoDjW8QcbRtA9gCru/7iSN7OOzp+74oGuQpMFxyUXOmNKiA9wFfxmXl7i7hnCM0Db5oaYqr498avGKUcp0oGdwSUKpCUiUl217bNKaHW6ozPtSMbCXDCt2mIYTWxlnb0rQd3ju2Z1s2W/u9rq6v2JdjTNmuSSklnAuMk4rDsjta1il1xTNBM4y9JTniUHpEtMxd4kpTcy3JZ3CFfhagacsCVC2xUTnj8wIQEEVVOETh4VEYs3A9KvuU8Q5csIXtmBxv3SQeHX2h09g5bIEstiBcBLKmWpDm4LQGshPNpwSsTwhkK72gbieWBNGuFWitX+Tqes9bbz3gsOnYtg2Nd4TgLZZomxLo29ieM7Snv+370p5d8YnCc1ELkgihadhsz/AhsO02bBs7GWUcOD7Yl2xsQlOZCNJIigNUHmq2hi1cQFyw4NQ3tspUxRW5Ls0jIhHvrdTug10ERAQfPM4HmtBweeeyZJAyMfXkXCdP47jFcWR/OBLHOAvCl0yQK4MzeCGNI9Ep1dJBAI2QMF3XPCp9TjiH0RxaPxHmfWtcI5tQPSKKcwHntFAXTN7IOcd2006amU0bbFIvF5dxjPT9yM1+z/44PM+fasULAu8cFxfnhOCIMU5BrTVYWQZ2GEayZsYxcrg5IiJ0bcd2s8U7z3a7mWg4w9BPwdvxeLQgr3Q0h+AXFB0pNAKbwGxRGaZFWc6ZYbBzuGY1m6aha63hTMqiWIBh8KCJcXSkmBjU5LpMVq+oUadEHMcyuQlN09pYcx4fGqMaFY6sc47Ly7tcXN6haRpeeull7t69RxMC55fn7M7OGIeBH/jC9/OVN7/CYX8oXEZPjJFDe2As3OBhHKZmtZTSE8xYVqx4HDlnjtcDOQ/GFdeBJmdcI3hxRlsbc9EeB5xVOryYdCUlG5piUeERowgBpJwts5qE697x5UeeMTtuonKII8G7wv32HAf4gTcSbzwqCkHOoaXJ0i/6WCDCLWUPzdaqoZWXXug8WedKac6zgkmekkYQNZvkFsLdsxbOWmL0vPn2Qzoim64hxZHj8UjXtbzyyj1eunfXFrvOglmwXVtWc1asgOekFtSVnPO+ZF9M19GJoCmRxr7QASwjq5rRFMnJMjpaBgAI4kGCWLlEPLhsmdcipIzm0rk59YZYptRZU5f3pgzQtQ2bTYdqYhgzs757Xc0Zr6hWC52T6X5qAPOWwVpSDFiQ66fMbmln1uymhFF1G3KlXFtXrRO3rvD7UirUhNrDVRvRvLdGlrL9lE0KZYxrRnbFu8M0W208hhAK1aUGmTIFpTlZa1NUqwwIjsY3qNfJ7GBJS6jNXtbNbGN3KouW81p1wSEVShViNkGoE1DN0NbHlcZQb5YFtc/mMqGfKCGw1Kuc5YDKDzCVbEHAgfMmybXdbmnblsvLS+7du0fTNFxcXnB2fkbf91zdXHFz2GMZ3A1t1+G8n2gZKRmHtjbBwcrTW/GMUDPiSMmk5eaye16U4fNElwHKnFCypMYrQN1MlatryIxMGnIxQx9hSHCMSh8zKcMQhTHCOCrHfuTmEKdAFnElkM2PBbLLrK3qbA6yHNuVCz9JYeb63GyOEFWLdiwM0RFTwDuhH0YOhyM5RW5uDlzv9sQUuRzOiTmZIZJT6nL5SbSCx557cuJ2xScYzxHIKkNKkBIh2qBwwF6tE1/jAMMRcsI7ITjBiZLiiMbBJtaUzBABcKHBhc5Khz6h3vQlNfaIRoSEd0oTbFB5L7hQMjuhyvtYQOtcKd8na9yq2SUzcPA4acrFQRn7RK2UTIFmymy6Bm3McUXLwCR7crTmtOAqLYCiMGD8Ay3fmxWGIZbSCiZ2rW5qQMmFW3vsR2LMOOcYm0zjvXWHqpJUGcbIGNfMz4pnQ8qJ6+srDodjoQJYJjHG2UnO9FQzk6ocgB7RlEsWdmAYjtOiqwahKUViLO48cZwm3qpEMImfT7BJUfU0EM15fk/TNGy6rlB/rPqRYjRKQYzFNrdsTWdXPhFH03YI4ENjjoAsAlyETdex2WwJTcNLL7/M/fuv0LYt9156ics7dwghsN3t6LotiGOzPWN3dk4ILZ/5TOby8o7pV9/cMPRH9vs9X/jiF+hLdjbmZBq2K1a8C0QgeDM1sMCzZFTLeiurZWSnsVE69cVU9ObMUXnZ2DhlTDmMQpeVMUX2w5E+Bm4G5TBGGu84axt2TaAfMvtDz83NaAs+b6oGDsVjOs2WPCqJJxaBLIJi7ptVj12qwkCex/rUy8nS4askrUQmx8ucM8d+4OF1ogmOrJnrm2s2XUs/9OwPB9q24d7du1yU5lO/aLZcZ8QVFe87kM2qHGMki8MPo2mgDiPpsMerQhxhOCA5selazncbnBNyHNChJ+fEOA6Mw4Ci+NDhmxHEoX4g+8bKGmNEcsSRaYLSthYwusotdULTWvdnE0w+yBe5nDgmhj7ixETgvTMheC8t6h1xjPTHY3E+gnql6NqG3a7DOyHFXDhNxs3VZOWfpvF0jUecEKo2IMqoyTo1Ven7SD6OhZBv5gu2SjW5r6Ra5IwUL46uaQjBAtmxkOOTKn1KxDWQXfEMSDHx4MHbHI89V1fXRiNYNmOUwJMy+WiRxxll4MDBJtzgaRq7NFSum2HOBtV7y9KkKWt7mgeZA1sTW6+qBXNWs+s6dmc7q76UTs0xDuX6MJ58v4msZ5wqITS0RZfWl/JoleQaY8R76LZbXr7/Cl234VOfeo1Pfep12qbh4vIO5+fnOGeZ2iY0uNBwfnGHYz+QNXPv5fuA0YCurq84Hg+8+eYbPLx+xFtvmzV2jHHK1q5Y8U4QUZoQkVxMPrLHBQtkFUt+jEMkxam0ZyMpZ7RI5DkxGoII+NL0ITXgVXtvnwYeHfb0o+f6OHA4BprgOWs27ELL4Zi4vtrz8GEP4mjaxlR8aiCLFl76aL0tyKSjbITdBpxRinwIxu+t6Voql5fyeB7/qcxnNVGUy1i+3h+Ix4QTeOPNN/Fi8+9bDx/w+ltvsdvt+MZv/HpC1+CdtznS++k7a/56JRm82Hj/HFmdT86UMy5nomYkZ5JmJI7IMEDOtKVMj7hZKDJntOjDmqakx4kH504dd3JGMOF0EaMTKDJ1KVduqy880xPajJbJ2hUKQCnP2IquSGrlohE5TbqKNpZVDsEjRHJyQMY7+56MmuTPJGdk5Q/rSE3T75NzJiYbvEFmxQMW+nuxZnBFy0RPCWSNYJ9REnmtk6x4JiwpAEYRSFMmtgaF0y3rnB2BEkwyZVgLiWaamKrzT+XnWWlRT7epUEhH0/e9E5etjl3Q6fudyMSFm4JYlhke216lH0hpFishMyBUW9yu2xRDiC3b7XY2gWgayyqJmz4nzrJMXsy8IQRvXHbv6LqWY3+kaZopkn+yLeaKFU9GLZFLmQplaqxanNtZZ5tkKXzTKl/gnGVMWSZnSxNU4b3lwoOP2WTsYrJKaJW/qnz5mKLRA5KbGqmklGgmOlHpX8lVbstbM+W0oM2KSqlIzOtQlutZuxzMDoDT8ZZ/p5wZc0RUGfKI5MjQNlxdXXN2tiFl5XCsEl1qtCld8PJLKLuOwhcbz5WRvTkMDEMmjhC8x2s2ArtqeRxN9D8EdsmcSWx1V6JQlTJQFZFEkhEQskSSG6aT0wOo0komeVOltQxMCWJ9KHJAnjgmDvlYVn+uNIFUztx8sUBNwqvr2sLRm3mzbWu+8MaTBXHZXGudR7w1b4mlfm3lLFqsepn4sVnKIB3LMeVMDqVEq6XBrUiwqLNjGZISU1pc2Cr1aR2qK54NOmX5M13X0DQ2Jvp+NImblOcmpUUgJovTa7arpPBeH3++KiHkrIUfO3cqWylRp2ylBb51obYITtUoD8PQ451VJLx3NMHsrIWyoM1G1VERs69VG5MuJbxC67yplXjPnTuXvHT3Hk3bcvfuS9y79zJN27I7PyO0Lc57hjESH12RUubho0dcX1+TYiqKBUe6TcdnPvNpXn75JXJKXFxcMAw9oQm89qnXuLm5pj/2PHj4gOPhCFhj5ooVT4Uo4os7V9Yi3qpTKtE5MWqcK4YHZT7JCnGxsEtWsyeOgqTKA7cmxyE70IZWggW9XUsXOjMPKpnX4KEJppAj4oyWF0wxyBdqjytJI1VvGdnSYCneQWgqrwFgohVULWoLUpd9IUzvq86YWvpepmyq8xN5AYSsjgeP9iR9g+3mCvGe6/3BmsBefok7F+eWne02NKGhKhJV1N9ulel6cfC+A9mUMo9uDnhxtO6IF4fXTMgZp9B6Yeu9cUlDYiwC5EalqZmcwinNGXSk2AMZPzRnm7gab93RCp2ABkdWYVTBQkSxMmPboVp4qWmYjA+6rpn4ODMp3W5OTKoISobJV/tZR9tahjdncINJpYjPiPemg2teg6UElGbxdsHcWrCA4VjUBkLjCUVBwSblwjlyAjiz/4uZnJSqNTslsm4N1BUrnoacM4fDoWQjTWKq70eTxYqJIY+k4qRTcpfTZ21dJbduSypBlfAyBzGT9KqT0zLbC6ClMSSX83mpcFBpCco4DPSHI13X4DcburaxQLZqN6PT/joFlZKVEoe4RHJK07a0XUfbttx/5VW+4eu/nm6zYVc5rz5wfn5B120AOByOHPuew+HI3/7bf5vPf9/nAdhuLXt7795dvuWbv4Wv+8xnrRegPzDGgd1ux+c//30cjnuur28s8z0FsGsgu+LpEMAFRVJt/c+LC7xR09qNg1RUcYr7XVRHjjJxTmM23mrKQlLLjroQkKZlSB5o2LhAUFMfQXY4L5xtzCI9NKays2m9UQvCrK9cVJMx1ZFQnLZszKmIpZGdL9WPudnSaDal4gM2uS2qNiWFNIeXmuyWHYTC0wX7PZIjobz51hVvvPmApg08vLnh733hS5yf7fiWb/oGPv2pV2m7lnt3reqiFAWykqHNawj7wuG5VAuskSkTsxHYsxq/RlSR7GlL+0bK8wR3W4Nx+mf5LGBKB4VojgfR2lddKAWUDNLJ2Wp1+7nL2uLM277vJ5+YmsCsacs0L7GAtvjSz6Ls5TuW0gWyIJzPh3Uy+c/rwlP9yROiYZnUTa6Euv4tQcZcMF2x4t0xL9rqROKWgSlz6a+el4Vmd4KlIcHUU+JkCmZrWb++t3xq2oc61kt/ByJLvmx927JRzLIypwF03d9T+kJGcNh4sWFoygchmEX2drdjs9mw2WxLZSXgQ4PzlaOujGNkGAb2N3sePnw4ZZVFhLFI87WtGSU4pzQpsN1u2WzMIGIcx0ItWjoErljxTpgpbFkVV+bEep2XUp0rVlvlE67cpNDtbM5J6om5BLIa8BrI6hE8XlxxsvOIDzhvfRxVScc7P+kuV4UephnHxgEiljkWIVcehEgJZIsawXIMUoPYMuOpNTejhSpRu6bLUdm1IKP4xQXBleuE2dLHcSTmzPXNAR88WTP7/YFj39tvEBNZrVq6rFzavq01zBcJz2WIIC6Yq1Xl32VTIhBVxGeCKskJQzvSD6ZUgKbS1phLyXDmplI7NlUnTqyo4nImIwR1BBWSglb+X8wc5MA4nnKHFKYmrlwaM+oEL2JBa101AmWyL1+flTiagPQwjvTDaAYJpUNZih1gTMk+o2bWANa01TQB9cpmY88p1kDjgi/7U7KympExMcZUaBcZRHHOtAODc6ULO80B/4oV7wBbHFrH/9D3OOfL+bXglNYAdfk5nQp/9ppYRcImOpv0Kt+00nSAwsGVSc2gKhlwe9vT2DuV2RqGvnDHlX4zYOyBRPCetm3RYQTiVE3JRcJHMO3YEAKXd+7w2uufZrvd8KnXXuPll+/Tdi3eNZMs4G635fz8AtXM8XicG9RQXJmcRapjWObY91zf3BRx9obNdsPl5SUvv/wyDx68gveBL3/py3jvP+w/6YpPAHJW9jeJYUwcjqZE026EnYBvhBgbhjGQsyMmk89ShRgbxtSiKiQcSR2qQqYGro6GhlZaYml8vtyZusBme0Hb7UAgkUiaabXh8k7D6K2MPy9IF2oAJdBWe8ukCOIWYXXWPGVkx5g5DsbFN6rQUMb7SB7L2M3GuUVAu4QmIePJTom5QbBGNucdoopXUDzi4GY/EtMVh8PI2faLDMPA2W5nNKPSYxNjmprJXKmUplWy8oXB+w5kLSvTgEZSGiElJGdkjBalBYfPieQcx9bT9w2aE060SHwY32cKZHXmC6HVQUtwOUM2TmxUoUFMPSAmxj6Dc4wJ8EYnCG1rBgll0tTSpV3lgqZGFZkHrx1PmcRVTko7Qz8UN6NkDWLlc2a4UAPvCGqBb2ha2iZMy8EQfKExmfsYlAavasvHYA7aWfEoOMU7oW1NhSHnzDiK6c6uWPFuWASNuWg015tJ07nThsjF53RK08rEgbXmKHPOuri44OLiYgpCzXkuMo5pkvkyXmyprBSNzEoPAEoWyDKZRr3pycl0ovttZxJ9KRZqRGucwEPJMiNocQ2zsmhL07bcu/sSn/3sZzk7O+OVV+7zyquv0jQNKdn3Ouc4Ozvn8vKSnDMPHjwipmR8dGUyREFkakA5HI9cXV+z2XScne+4uDjn2Pe88sqr3OxvcM7z+d2OEJoP/2+64mseKcHNVaIfIjc3I2NMbM8doYVGhT523Aw7Yg70yXOIwZzl8oaYt2jJwEYNxkGVAGL6yztp2EqDOmg2PXfOe5w47t694OJiR8rK2/uBq8MIznHv3oZw3gKnWUuRSgOAeUmr5FL+9Jpo84DT+fqiqvRj5qY3567jYc+NDsSYGMcj/XBjFdaU0GwNZroBHQOqgegoZkFljiwVDo9DxWQCr/c9D65u6Frb1sMHD7i8ODfjpXJt2ZeGMO8dTdfhvS3gV7wYeC5nLycWYE4RaI1Ha8OH2sqtZlJyzpb5cMvhoyePbhcpizt1KbkXfnzhJJi2ay5ajhZ4upxx6kpHtvFsq1jz7As/Z6Xqt552XM8loCX3z8o/5f1lX5C55FnLoDV96gpdwR5bd7UqOGcT/CkXsQq/zxxZ54w4f0pVWLHiHVDpMGX8zaOqOF3V8v7iA/N5/4TNyXyemlZzsY10MmUwa5Z3eYPa9DFXSk6/t2aPdeLZzQ5kOh2HLK8vtfhZS6TF9KFpW7puw2azoW072qY2cNp1oTr4VYOIWoVZHvJMXcjFfc8yS6FUUYwT7GmKE1nTWHPp6i604llRFXJysaPNUUkRxAtjFProicnT50AfG5I6Um5I2qLqiDkwqgd1xQWzwTkhakOiKbW/hJOId8aJbRqHZC2ulZRqpKdhIa9XrhGz7e1MmbNeDmOdBoUmOXyhwGUx6kBWoc2Cy448eobgjPwTYSSDRlQjpGjzcR6hxAUUisKyQUzrjk5yfUqMGe+SVUj7gb41h7QULbvd9z3HfsQHTwJ8CI9RCVd8cvG+A1mzV91CimY9UswRcCa55VGcZsgmcD6OI1IkrNRbVijrzAUSJ1OnMhQpnkrey4pT8GqDSbOVHjyLrmaML0MUkubSqJWsyaRMwtUuk/IduZDUq1NRXWG2rWWD/GICFCwrGssqz3nLcIkYNzjGolodj4zHwUKHRaYna4YE1X86LfQ8pwl7os3qFLx6p2jhN61Y8W5w4thtzsz4oJT4cjUXUCXGWMTLb0Omu1yykgpITCARr0rSshD1ghezdlaxjGZtsPChITRSyn1VZ3VuDFFKBrRIXYk3i74scOgHomb63rhxlffmXMB7wfuG0HY45zk/u8PLL73CdrPl/r1XuHfnZXa7HWfbC5rQEXwgeEUb8M5ztjvjbLcz296uo2sCY/DGxxsL7YnMMPRkzXz5K1+h23Scn59zcXHO+fk5KHTthvOzCx7trmlCg8jKkV3xbBDset5448fGpLzxwHixV73nrX1DnxqiBvrcmqqrOrKOKELSkZStKrjptmw6c8cCSw5pVq7HzNWQ7XuGTIgmAXndH7k67El4o+kV2aySslns4a15pgS6iuI1ItpD4aU6BBXomkBoOhRH7IQ7OyGnyPWV8PDtA+OYubk+crx+ACh545B4Zi5ivoUs1jRd2IXWsGVPq1gzmFfFh0DTtIVS1OAAzYmh7/nyG2/z4NE1PoTJBGVYlUReGLz/QFaEXQlkxQuSEzpGstSgNkLsC2/WM44D4Aneg/g5e1kbO4pGK4UXO60JC63WBpIFs5opgawNupjNMECyyV65lBBxJJ/LyjTQNA1NYwoG1Ss+Z52841OyYDvnzNnZlq5ri/WupymBbM6zs1GDn2S7sjI9P8YE0bI+m03HdtsB1hiXi+xWKt+9bH6RWxq4UigY6iCEWWR6xYp3gnOOs905Q9+ThkhOiRwTQ1+MEfLcVDlBFg+kGAsU4wHjyEFQ04fGAU6sYYSi4+iLY51IaboK09imVERSzXgaWdeCWOeQ4JFgtsz7ocfHgVgm3wymUuAbvPpC29nifeDi/C6vvPwaZ7sd91/6lAWy2y277Y42bPDOFxMSW4yeLwPZTUfXNvSNB82Mo9Ebxmj0pJhGvvSlLyEC9+7d47XXPsXLL0dUodtsOb+4ZPfwEaFpi67mihXvhuKLJUrrM4nMTVTeuIE+C2/vHV941HIcW5IEkrSmFkACRiY5RlWcOC4vPHcuNgQPOJP2ylm56jMP+0Twjm5MtKNZnFsge42Kx7VCE6qeq5asKEbhK3s7NYDqHOhKjkjuQQvNjoBJeAWadoNzATRAbtGceLuJuOEB/XFkvNqTrr9s8+75BsZ7FjY3G/uObBbXqUx0SeyGs0A2iCOUgLlpu1IZAk2Jvj/ypa+8yRe+/HZx6zujaVqGYfxq/oFXfIR4/9QCKRlU53DBI7nkUnO2DEsEs7njJNs430udN0/ec6oSUDmolXWuC71LG2WPta3oKUVh3t3qB/94RHhbUeGk1HqrXFopB7fqktP9REPIi8flGJc0hZnKUPZNS+FUpp/m9hE89syKFe+KWyXzx0//J5xX5VzNujhvoTSBZNC5BFk57tMEWJQSTDXBGkmyLsZ1+cbbtAGbqDMpF446dVhJaTyD4ANt0+JDoGs7y6x2G6MS+NLY5czQwBy/TLrPe3P1m22qZ8tMLY2nqsW5b0EjYvm4XKzsmPxEK1ipBSueBXbOz5W2Kk4wRDgmpR+VYbRmsOyELAmVqrf6eCBblXlgHqtZLZOpVFpfvS+SXpqxPGpGJJcZ1P4n2EKUuUg67fjcAGoWu8LpGJFCubPki0ntoSZj2QRPDh5vUWfpdLZbpRbU4TUdz+LrJyLULerSUt1E1eRAx2jxRhgj6JPn+hWfTLz/Zi+URpSmCWy3rbleoTSAU9NePF5fkdPIpgtst03JjljmdWquYuauzrFi5c1osdEs8l6TCw9UCSCw7JAW4VUXTBzae8em7SybWsp/lX83Z0KlZFXtvm0bVM0QoZZGdeqEdIuJd17NLoNRXHkfTIOsisLHpMRc+YITgWLqCjcLXIrbEJCZ9ANTWgflimeDqnI89qRxLK54ZZxpnXSYuHBaubPzpwGKUkZGVGiaQGis838cI1dX18iCYzrGyLHvTee1yGB1mw2ghCaYBmyK3Oz3do5PskC+OHLVIHnmwadcupCTNXh1mw2qwuXFHe7ff5Wu23D//it8+tOfYbvdcvfuS3RtR+NNT3qz2U42u00TcM5bhaXMeof9DW++8QbX19ccj3tUM+Kg61ratuX84pxX7r/M659+ncuLC87PzmxboZluwYcTBYgVK94RAqFJIIrPJjkVB8+jY+ZqSByGG3I+YioEDicBEIaUGIbSmZ89SZ1RbS4DZ9u7NEFoGiWTUDLiFB8wyoFXsisSWR58MJnJplFCY2X3U4MSmeYmu5uYsvbPnCEJop6YhGE0nfUcR7JcmWRdgKa1no/truHOnUuGTcfN9QO6tiXGETB1A8ThmojLGVNLmL+/xrfTXmide51VdJ2naTu67Zb2OFpMURbD4zCSiyX3ihcDzxHI2oc3wXN5cUbXBNrgOWsbvDgON1c8ejswjgMhCG0wDdhJ7F8rO6dkQ6iZj5IdrdaZpZlLEbLzqNj6sVrTUvivWjMkZYIMIVjzx4JOcNsXfdlBXQPj2nilWh2QzMjB4abyf83qLAeKBeYm/eEX2pI1eB6TlUvBbP6qTq13NtHmbHxih6ekjCZHo9LUvWLFuyJnpT/21iWc8qJBcs4sOpk0QTAxHabX6virUjaIK655jjFGrq6v7d3l3I+xKA/kbNzZEOg2RqdpqjzPODIMo/HkxZ1ay06LuluBbDL+uvemDetc4N69e3zd132Ws7Nz7t17idc+9Tpdt+Fsu6VrjBfbNR3bzWZSPei6FnFC2zRlsoP9zQ1vvfkmN/trjocDqqZs0HUNu92Oy4tz7t9/mU+//prxbs/PCjUpFO5tOLlurFjxbhBRfDDeuUsBFTM7eHRUHhwSMR3I+YgjERBaKfYEMTEcksWQuWFILT40+HzJ2UZpG1NjNbKCToGs84BnDmSD4BubN9uQaZoiDTlVVEoQWZut0CmYreGlKmgUyEIehTSIWeHqSNLeMrKuwfkW74XtrkHjJWM/8vDtM9q2gZK5TeMIOHxM+JSNb6vuNB3LIiML9rq4cg0JhLal225o90fTgAfImTgOxOgep1Ct+MTiOQJZMdcu7wjeuK9NIWMH54ijSeMgxcnVqwWyVOUAJg5dHSh1EXh7HVUqjCVQdTiVybgAqeYFsw7e3H3JTCcogeft0sSsZiCTXmaVDwItMl2lKWvay7qCnUOA6XeZFrSnK8taop3qtpUusfg9l6L19lvIZPywri5XPBt0wb+u59lyOrDkhrDkw8mCyiMW+c7V9LmErrbtuSZhrzVNw3Zr3NWzszNrjGI+Z4dhsOoGFG3aGgQat9ZsbqfLwYS6uAyhKS59HdvtdjImaNuuKAiEyWmsBsn1Zt81j3WTCIuM48A4jKRcF7e6+A0e+0lvUYKmwbxixTNjMvQo5l7VqTaX885JRiXhnRB8xgHepUIDUAQ3PZ41nqtCz1JZpyaNFuMUFvPeTBfQErDKNODLzk7jcPH+LFNS9JR/AHWOdE5pGkfwjtwExqYB1eICOM/xWRWpOvKcXKbmEbgYYlOmuE6f0yvy2L7U32EdoS8O3ncgG5zj5fMdTdOwK7aSm67l7OyMEAI+WNkwjiPkEbJ1BmuO5DTOJ5lM/2ESWtdsRiBAdqC+ZIY2HW23I6mQskOSIylIzEjRmbSypwWDOSXGIshcSw0hmC2t937K1GoZaKFYY5qG7Iiqcjj0xFhE3qUaKLjJHcj2N83HokviQGng0sV1ASbKhEguk3jpCvWFd6yUlk1bbaeoT+k0X7HiFDUDKmpjgkp/qdf74uxjTVjmo24fnBdodsLWRZ+bA08387ir0YD3ntde+wxnu3PatuP+/fvcu3fvZLF4PPZ86Utf5MGDhxyPR958801ubq6LasjAOCYbe6Wa4nygaWxS3G3PuHv3Zdq247XXXuczn/kM5+fnnJ9dcPfuXZqmZdO2bDed+a9vOrv+FEOFzXZD1b21zHDP9fU1b7/9FvvDnuPhUAxdhGHoEWc0owcP3uYrX/kK5+fnXF5esN1uOB4OHA579vv9ZKqwYsWzIGflcIgMA9zsYRxhvxczNXCBJmQ2frSMaQPbtizsAoxJGaMQtCXoDhc6tmdn7HbnhKZhfzgyDkerigBNUxZvMMl94RwuBJtf/ILSw+xDVBV27K66fTEFoIqSvE3M4uwaAYrzStOaxfvlxYbXX71L1wauHx15FG7ojz1f3rbWvKylsToOZBFCjhYXGDFg+r3mnhKjPFA4v2OCISrDqAxjpi/yW2qdofZbqzIno1a8CHjfgaz3jntnO3zwNG2Lc45NZwOsaRp8sPpGjCNpPBL7azRF4gi5nrw1iJ0mzfpvKfp1oEWWQ7wjbFqa3Y4sjpw9LnvGlEnHgdRbh+K0FlNI0TTsYM4OiTi64su+FHUOwdO2TQlkIzEOpUEG9jfH8tmS/S3E+ZQiWlbSThZKC5zE59OKsYYKS03balUIlrkWhw3I5KxMojXQWAPZFe8OVYgxYt4+M2WnnoFOZOGXzpThkMUJq2UxOVvR1gxqnfEgBFMB2Ww2fO4bvoHPfOazbLdbXnvtde7fvz9lQ7337Pd7Pv/5z/Pmm2/y4MED/vpf/xvknBmGgb4/0vcDbUtxDXOWkQpYIHu246WXX2K33fGpT73K66+/xvn5BZvNlvPduQWsTUPXNIUe0BK8L9cl2z+A4/FA35s27M3NNQ8ePOB4PHA8Hsg5ISrmSFSa1B48eMDZ2RnjOPDqq69weXnJse85Ho9lW/0ayK54ZuQMh0Oi74WrK+OX7gcha8BJoGkS243JzHWtst2Yw2NW5XB0uBECDZ4t3m/Y7s7YnO3wvuEwjJOMJGJNVq7Q4GZHPdNdnow/FtOKVTwXSSAphkGlwunqdcFZ81ku6iW1+ct5CK0QgnBxseHVV+6w61oeNTe0KhwOgd22tfkvZ1KOjHEki6Op9D113K6GLINZgKxCTMo4KkPMDDExjokx5kJLktKgqk8trqz4ZOI55LegbQrXc/H81AAlxgV1XtFkXb6KImnu9pVSZ1BlDmoXBU+Eoi8L4oORuzcbsjhSDqg6fMr0GaLaiq4WTEwWpJkMCepgaNuWtu3ounbKyKJVF7ZSC2oDlhhlommgktEzxZUrmqafWmYVkjVrqeLy7F+/9HC37OxcCjmlQMxcJAHEizkiACE8PshXrHgyqjmHonVxVccRtiCUsmJKRZZgpsPM2+AWBccCWsoE6dgUA4LNZlO0Vi+mx7vdbtJfrsHs3bt3J2e9l156iXEc6fse54Tj8UDTNuzOdjRNICedqiBV/3W73bHZbGiaZpL4MkUCu6/VFO+8TchSbaeXBg2zekPOaZrkKybN3ZK9PR6PtG1bgtcj/fHIOA62OK/GDesCc8WzopT6cw246pPLkn+9uaVJjs2BKp4gwTKrhZqzVOHIWXETRa3QdrTqlS8zrsWKdsGLn3axNjTf5vncPs0XdBv7nN2cE5pCMwwhTLTDqndbNzUzCRb0pyWV4Nb36a3/1ubQvNiPeSzfpj2s+KTjOTKynpfvXDKmxHE0rtk4jhyOPUM0WRvxjTUzCQQxWoETgZzIOZJTJimlbC6IB1RMP680mjRNi2safNtx79XXuHz5UyCOQ/YM6hli5OzRNdf740JFwLitoWkmL/Q6mXVdx+XlJW3bFqF4m5DMI7onZdOA9U5QMk48bbspbiyUyR9iGqes7Tj0DMPRup+jGUNMDWY5GX9JRhhrznZuErHkmPGevKv3jjYE4xklZTs2k8UnX+nf759sxYuAemGXOQtr1IAyDnzAhQ7EMYyRYz/MHNWyCZOWYuKnGkfVs91u2G4tmLz/yiu8/NJLbDZbvv7rv57XX/80bdty7949Li4uLTvadoQmEGPk5Zdf5ng8cnNzwzd+4zfx4MHb7Pd7vvjFL3B1dTVN2EiZpEqyc7c746V792nbjsuLO5yd7YwysOnYne1MbaRp2LRdCbaLBbVz+GDyW7ZtJedISqc3M0LQ6RqQckIE3nzzDVQz19eP2Gw6+uOB/X7PG2+8waNHj7i5uTba1BrIrngGiEDTwJjMACFJUeGRUG5jaWROU4NvFstCivOIF9pmg7QX+LCh22xMIccJKWeGYSRnpWta2tBAqfb1wzBR2bwYT9Wp4ErJfg5mZeoBuU1/zYWAVF06J9v3lIhR8UFQXKnUBDbdht1mw7hJHDb2/U1oy3WlWLxzyhGutHy9FczaUryMUXQKzM26PdEPiWHMxe7dpAF1DWRfODwXR/buxRmHvmeMkZgjKdrE6GK2TExoitasgiRMXDaT4kCKgrhxJrsriM7sUspq03cdzW5Hu9ly95VXuP/ap8F5evWM2SbjZvuA7upmmsQp5cEayNYVqojQtu0cyKZMjJGcM4fDnkePHjIMAyDGy8WI62dnNviceBN9RhjjYELqObHfX7O/uTbx+TGi40hWK52O40DKJgifSx1nWkmXo4VSoim3EJTtNtC1wRoDklEcDG+93z/ZihcGlS9g//LVgEAEFxpcswHxIOZPrppmLlzhgbvSOGVZVcuu7HZn3LlzSdd1fPbrPstnPvN1bDYbPv3pT/Pqq6/SNA1nZ+dst9vJ+a9t23m3BPq+5xu/8RvZ7/dcXT3i+77v+3j77bdJRdg8pVTKglaN2HRbzs8vC5WhY9NtS+NXy2Zrwuhd07LtNsXNzyZbESlmKLNhQa4L6OmWTgLZnLIxkVR5+PABOScOhz3bzaYsVgcePHibm5s9x8OelApHfsWKd4EI+AbcaIGslkAW8eBCyX5mEFM2yLlaQEvpzXCEtqPdneHDlqYs3ERMYWQYzbGvC60lbwRiHompUuvmCoVjordSc7M1mC17e5qNRef/lWRRzkoqSjyNumnO9t4XjecNfRfpup6cMmFywbO5tCqUTNnZRXGjkqGUhezkxOnVIg9oVZthSIwxnTgHrnjx8HyGCE6KFqSd+BnTXpVcOaCWIUHVBJGdCZMH3yCY5aTzDYhZypZFXXH2soDPNx1Nt6VpN4R2Q2g7cJ6UHZqtWaVpzP+cRUexc9bsMQWyziEITfFJDyGQXbHpy5mxZJ2C92V1aa855/HOtGiXgWyTPDGabJaTInCdMmkcycOI5oz3R5z35JyoVIn54mFQnXl2s94nVI/pGvj61UBoxbOgBKNVRs6VzOo8DozvWjlxtZRYM5kiQpj0V2c+eXXHq9zYtjEzgk23KcoBzaLcP99mm2bbvcqrrRWLi4uLqZzf990k9F6915vQlubMgPfNtM26r6c3oz9Yhnk+volwNFVs6m91u35ZJ2lbhB4OBwR49OgRIQTGcWR/c0N/PNIPQ2kSW6fOFc+CGobpRC+wFIYWU5HTgK5WFtHluW2NkL5SC8qW6yJ0ohAVfWZyaXyaqG1F1afOQDUN+qQMZiHO6wkR4DRQvG3sU7E0OqmbWuzpfDvlGJxu/1SaAKMLlsqRWxzvIh80qwhpyTaveFHw/gNZQLzHeY9rPE4zY0r0Vw9JWenalrONceU6LzShwTnwncO7tuhOblG/LeX3VAI+JRTfc+c95/fus7t7j7bbcH7vNXaX9822MmZ8yvgYucgZ1wRAcWWl532wjFBoK3kHxMqsoWlw3hvxfIzkbKoFeRwZwmAbKIOl6zZst2dTedZ7b6TynMxyNitDydbknInDQBxGckpc31yx398QU2R/fcXhcGPadnFEizhsihFNtqI89r0R1zMMjKTBMtvtZmPNcytWvAtMDqvFCzTeFlh2zltDpoq31BBCjMkso4G2bei6Fu8cZ6VT33tP123oug3euSJ9taHbdFzeucvLL71Mt9lwfn5B13VFu9V4s865KbitmRywfdntjB6w3W4nDqpqnhpWcsqMZVzmknmpC2MpAzM0Zg+tag0uzldFEU8o1J0QjC8rCkjNKNUFqhQXorIIxRaiOWXGFHnrjYG3xQwh3nrjDbbbbckU22R7c3PDYX9TsrIrVrwzLJM4kvBEbRmBUZUxJ2J2+FyUekTMca5QyYRQGqcDTbtls73ENxuapityllYdbYJHFbpCu8maGVLPGEcEaHxD8LWyuDREyRNHdSknKTBzVyWX8r4Uyp+Nl5TN6Cfl06BTi3lBzpjddKwVRY8QQC0JVW1pTxUK8hzQT/s0b9x7aBqhaYRQxrwrfSg5lYUoqdAg1lD2RcH715GtWU5fypbBspE3+xvGIRI3G4JC4wNt1xKaDcE5fBMIvjOuizREaa25opTqLZuqeAEfAtuLlzi/+yptt2F3+RLd2V1TNBhGiBGfjNbgvNT40zi5vuF8d07XGB8QV3m3i47NnEmNBaOiyrDpCc4Xf2d779nZOXfu3DlpXJk0YssASylOXNuhHxiHgRgTjx69zdXVI8Y4cP2o4+aqRXNE+yM6mIh8GgbSODLGROyFMRkHaNRIdJmmhW5rXMUVK94NgslHeSdTIBtCmEqRiiOL6Rn4shD1WCC73Zoj1r17d7l//37hxwaroIhMpgCbzYbzs3MuL+8Wbded6UeHUJopWyvtB4/zp656rgTEVTHk/Px8arrSklWJMXI8DKRkZgtXV9eFAjRr5Abvp/qoSNXONImwWkKtGrJTyVLzlGE60WwWij1tRnMipsSxPzIMAyLCl/2XJprFbmtBeF8UDFbR9RXPCuO/Cgk1CUk1WkDMiSYrWQWvWECbqtmsGeYggdB2NN2O0GwJvp3UBEx3ttrCGmc8lfMypmjXBB9sfCwoAxas1n2r/5W5simFnyqFfiMK4iZFoaQ66eHq6VZRlUKLU1JSo0pgDWqoMy5r3VDJzs7mDDWIPf39pCxYQyP4UIPYUuiszreFdlAXnSteDHwgaT5rjnLlJmQn+MLFmdwsVWfv9sL9caGh6RSfMxJt0lOMhhAcuBDoNlvablMm4rAov+jklb5ctU1d1rJk/cyU9gklqHXiUKc4b5aWqFrXcyHSz2LrpUzKolxZxokTB0HQbPxWzSASabuObtzgoyf2B9LYk1MgZdPScyKoc6hzFryX70FmyoYixkWKq9TPindHtVsOTmgahy8BZds0iHNkhKSOrBbgdm1LytnkrM5NA3q73dJ1XdFaNg3WmuGso8qqE3VshOnx3H29LC3qyf1yX504cPOYRs2NyHtH1Xe2/TAeea1FOu+mgJVCBxAowaycfD+U5pQYJ078Scd12Zd6rYAiMJ/zlCFDjH8bvF0yx9GqLnkNZFc8I1IqLq+pOGJNydAaeDlUbYxljBqEmFMWNBO1xhdnyCXsdJ/HXv13nfik1uHLsBBY0Ojn2dEqFjMN7mTSrNlTljGiRd6aLeuaUyLGRIzRKq26zKpK2VeZ6HhLtQZgsX0tv4su9rEWVwXnWaio2Gdzns0VbiWJV3zC8b4DWTvFEs5B2wSbPHIibTpG59m0DZvgLQuLlRiSgyxCKiSXZnfJvTuNre7SSEqmBdsERwiC94HdnZfYXd41kfRuY+U/tRL+OJiWYx4HNMbSzGLf6QQr2TOAOMQrhTCIhDlYlGCDfbPpkHxpk3YNZEVou7ZYZLopi2Tco7l04b23TlHMcjYEo0744Nmd7UhxZLdpuDnbkMaB/QPHUZScEi5nRs2IOHY7wbeWrR6TNYglhOtDD8fxuf/YKz75cM5xdnZG2wTONi1N8KWDPyDOMcbEcUikrISmZbs7A+Du3bu88srLE9+860x/9dHDKx5ePQKFi4sLQmhRFUJo2Gy2ph6w3bHbneG90QmkdEdP8nbMGVm4FdjKrFSZ68Ql1ZXLKEJV3/lU99l4vCIOJTMMR5w44+uGzsxFHFDoBMNw5Pr6iuvrK479kZjipLvpjFg77ZNlhfL0fXGMpJTw3hPHSFOUGI6HI2Ncx+WKd0fO0A/C4ei4OXpujp5jEnLKRZ5AUO1QbczkpwR42uwI/gylod3eod3sCKEzukFZxLnisIlaY+fc3OyAsvh0Dgn2Hqm8WZaOmmpUuSnQXfBcFUuqqKn21MynYPrpmhNxTAjCYb/n+uoKHSOHw4GURttuiZqtUiK0wbSe21AqR6UHpGZTc1bTq6UG1zYufXA0nadtPW3naFuhCWXfomWJo1be8RrKvih4roysTToQiv2cNoFN0xCALgRa7yZ/85jVjAScJxWuzqbbsj2/wHlz00ppRMT0aZvGyp6bszt0ZxeISJEjsW7JFEfiMJByIsdoej2uKAsIxXc52VCVXGw5fRmefmLIV3vOpmlxO/NnFlfdT0p2q7HSaozxZDKt2ZhQMlJ2AfF4b683jSflTcm+Kk0QxqGH4Yj2R1KM5DCgySOidOKRoCYlMkZyTGRVjn2cSkUrVrwTxJlz3aZruDjblUWmm5qujkMk0xNTpnXG+xbneOWV+7z++uuTvrKqcUYfPbrisD+YtE+3KfGe4FwoFrGdVR7KYi8EN2eDmOkCj2s9LjNCp40ZVvUwA4bquLe0mDVqQSi2lwJZieM482KnTE/dBy26sGaAMI7GZ88Lviw1A1a7T2rJNVf1kRHvHJoTYwiknIvF7TouV7w7VM3NaxiFfhCOg2PMUhp6c+GKNnaO40g1EA1neC5AGkK3o2k7gm/x3pqOK03GOwtSJ+1kygJRyxgpZHABTPNy2rM6Um+V8+dG0DKblspH4aLmmdZjvR4ZhzD2PYf9AclGs0tFRWSyyhUK7ckC2uAF7439h+giq6ozxWFBfPDeKk2hcTTB0QTbjqplvJNarDF/44oXAc8RyBYyD1rEmo103jaFcxcCTWmQilnNyUMhOUeSCM7jusRGBBVvzRpFNiQ0nqYtzVWhupGUskVOpJQY+p6xPxYt2JEcowWfOSOlFJliNI6Pc7YKdcm2p2bVR1n5TmUUX3i0JZCtQvBLUfh6vywp3p6EpxKPczisXGSZrg4HtG3H0HY450nDiCa7CKgKASFlRYaRMWXGGBnzgTGtE+aKd4fU5i5vfNWubSY+O5gu5ZAUlzI+NLaAKw1Ym43JWaVSHgROuWsnM8PcpDFPeqclR3PIPc101sfL7dTS4FweXbqKuSkbq5qn76tTr12GrEkLoE0lEOd0wTkMA/u92csOg/FvqzFC1cXMydQTcrKm0ypFFoIHNTqDNbB5JCaSW6kFK54RArXdPqsnq6njuFKPMEqMQzxoCKTQWvCpG3LuEAlGOyi181zoZqrKGBPDaOM1LmgzVW91ak7OllSqqkA2AdbdK6YLYJS7haJJbZTWnOuoAyxodjX7W+LNGBPH4xFRpe9HxqE3Ccqi2XzKg2W+rui8sJ30YivPoDynWUkxEcc40YRiMk57XZjWTGxeLEZXfPLxfKoFJDzQiBIEmjawkTM0YzJbLiAIb+/3vPHoEX2MRBUGFXCeT/nA5t7LtN64em1XbCZbT9uWALaIKKecOB72HPY3xBjZ35gKAIqR0FE0BNR5C2STchgO5JjtAtE04BztZsN5a0YNNnyteUudoi5QXbjwczBadfmQeUKuHCDDaabJldUxzjLWTjxnZxd0XUsceiQmPI40jgTf0rd7xHn8ZoNrWpIqxzESU+bq+obj93+R6/7qef5UK14QSCmvb3c77t69y267mScOVfoxETYjKSu73RkXl3domobz8zPu3r3Ee8/NzZ6rq+tpMqkNVha4njrcae1Jqdzx6f2ndILTfazEu3myFIqRXZnUvFdETIqrbS3ATGnBb0WLygmMw8hwtEZR74XtrgNRUnLE6BjHgQcP3uL7v//v8ejRIx48eJu+P1qFpTgi5Ww6tv3xWCZUpQnWlNJ4X/i6nm7T0YSGYRh4qFdrs9eKZ4SA96hrSLTE3KA4gnicCE3TEbY7fAjEbsewuURdQAZFehAVlA1SbNd7mAK4B1d7HlzvEYSm2bDZJHLKDONAP/Q2B4WMc0a/82L88kqFdQClOZLi3OeDnyoVVT8BF+mnKoQQnPW1BCk261k57Hu+/OUv03pnigZjNqOkwzUxDkY1yImkGTRbkitZszVZwWWoFIZppWwXmXEU059+mCFGrl+65nzXsj8eOI4DQ4qWLKvUgjWSfWHwnM1exrPxAipC453pwqpNqK50R+v+hqvjgf0wMCToE4h4zu6+TFJBXcC1HZvdGc47ujaUQJYygSXIiXEYONxcE+PI/uoRh/31xIt13iNZYZOQbPzTsT8yDiM4h4sN4r2VMCYB+NJsUke0lFXhxE2woFT8zBVSP69268Q9vzhnYwEE4+qaUPSWTlti2zLu96RhJI4jOdmK2YXA9uKCZrshlYAj5ow0Df4rb7JOlyueBQJFlLzl7OyMs9220HEsqPRNBh/Jqlxe3uHl++aatdl0nJ1tERHGsUpK6SK7UzOydo5rKcjUBs4laiY2LZqhJj3Zup8nzVjMNB8soPVF49l7PaEWVH3cJbUnxpG+N2e/7bgt2ScpdCVrPLm5ueGtt97i6uoRNzc3xDiWQFYnLm8cTXGkKi74hQ2ocfsC2+2GEGzBfXOzZ7h1GCtWPBFlpabiyQQyAXBTUBl8i2/OcG2LdpeM2/tk1+AY8GOPy0rWprgHZaKODEW2cX/suTkMOBH60WhoWW3hF6PR9VKEmLJlfr1xy5G5kiJOkMbUfUw6LxSVk3mMx6jAOPeGiJsUhoSi/NNHHj58iLmqW6k2xsgwHMkpmsymzk1gdaFcyLvUxre6eJ7StWipxA4c90rjhOOx59j39MPAmCIx57L4XpOxLxrev/xWvZXBoK6UJ3CFYiZlZWS6jP//9v482JZsW+vDfmPOmZmr2fucqltVt3ktfRMOYwyWQlggnrBwIAzGMrYsjABDyLLABpsQ2MiSxcOBZWQshQWyMQIkBAh1bjCtQghCAcLCSHTvgQA/wo/3Hu/eW7eqTrP3Xmtl5myG/xhz5lr7VHdunbp1762T34l11tq5mszVzJxjjvGN75tSZIozc4EpC+JK/fHV0oCyyGPV1kRrJyuFlK2MEOPMPE3kFMlxpuRUM561LFmcdRLnTMnmNJZTRJxDnTmYNBHz+8ffjtnGEk4s4MUGuBO30A98zc4ULQS1DG6z3muKBpWQ2xhMy/8YgYC+3zBstnjnmfuJHBOuC3R9T9/1FEzqswBzTOz3ew6n8SN/ySteIlxQYBa0MqNahaDvOxRh2JipgRkahPOkdlnWbwu9tl0uPN5hoR5YplRqFfLMZ021CRPeHczWA74YM/f33yogzjUJr+ZSdJbPejaKviyp5qpNbcFsZJ5npmk2K9oaZJvySXvOOXCXUgNrxwXFQZb3S+0PaMe2YsUHwQJBSEmIOTCX3hREMFcsjQGZHK4IUSE7RV3BRXDqcJjFrBePF4dZ7Bgtoe869tstTuy2E7N5F/E48XUcOXKu1DnasNHKdjAVHslnll0V6licN1WrvFWBXIRcr7V6/aRitrfRQyzFEi+lQE6klJemTzsWk+YSS8raeKuTb+Pp6mWAW5+YC0xj5ODMivbx4xu8czx5euR0moxqATWG+OS+2xVff7xQRta3VV2VpWpDC4U5ZcY4k0rhZjrx6HjDYRxJxUju4gN345HTNIMPDKVUyRFPcUYRUFXmnJnGkThP3N0+5ebpO5SUSNORPE9W+h8GBHP2yuNI1JqlOR6I82TGDSUtEltaEmYL62rDSO3sLJf8VpYuS/HVyM+fJ7oud8QQ7QRQuU7nU0tL57IExK7SDEqKaCx0vqvZH1+97AO76z39drO8JxcCu6srbsaRfmdd5H/hL//Ai3xlKz7lEGHRPG5BY8658kIL/WbLw4cP8KGzjOxrr9J1ParGMTNHOl+tZStvvaJpqXZVAaFlZlNMTNNc+aQ2phovdZ7nxSHsMlBeGlLaQlLAVsNtWwtiLVjNNSC1l2i6sdzLKoHZ0MZqEV3lo5nnicPhwNOnT7i9veVwODBNpnjSiL4mcReXCVdRithn0VU3M1cn/JIzWsrimLZixYehZDjcFe6Ontt5x828s0C2WNbTl46Qvc01gyLHhHjwYs3EQYReBvqwwTsh1YWaquMzV9c83F9XakFnigYp0/mePhjf/xenAABp5ElEQVS1qGTPPNlkZPzX1qhd5ybncEVxXimuZUBbAGvBZZxhjo4YlRyVKSolKa44ZrXzTRJFfcE7hfmETkdyjEzTDFilVgvEaJnTPtcoWU2+S+u4T6mQmylEXThPufD2O3c8fRQZ+o6bp0eu91umOfP24yPHca7W9OHeeWvFpx8vyJFthO+zvuRSk6+NWXPOjGnmOI/cTSNZHUkdPmemFJlzIqRkshtiK0a79iiFXBvF5jgzTSPj8WATSRwh2Q8XX0XlsEAxiSOnRIozKc5I8XgnqAZKitYQthy/WyZlpEpMC8tKtVldPouWXWral7WnZQlklwDYtUYxO8TiA5vtjBQldhPTOJJTxgfPsKn6nV3HsN8ResvOvvrqK8yrg9CK54Jx3VpzVyvftazIIMJms6UfBvb7Hbv9nq7rztUOpAZoAe/LvYyjyWJVpQNaIGtVk5wS6gQnRgm61G1tLl/3Mqn2gufgtg062sTFwoX1wUN+d/bzHBiftzUaBUItrTpyTovl7Ol0Yp5n05NtMzXV1atmchtUzsfbZI2s9NmcxpriwYoVHwxVmGaYojCmgbFsKEUsq4kg6nHFNJBDhD4XnC9o7/BbTxDBS8C7rs5lEa3W55thIPQ9wNnu1ulir160Zk8XbS1dCp+o4p0Fs9QAVqoCgKOqFNSsaU6mgZuzkDKkLJRsfSZZqgRnNl14j6JJYYrkOBFr86jNmRbYm0oB56Rra+hc9qv1OfZfViWeZkhHvHeMp5nN0JELzNl0eb03fu+KlwsvQC0QvJhuRtOsa8p0qpAR5lKYcyZpoVRJOy2K5kIhk7LRBWIISyOH19rxsVQuLzTh1ByqXSuJeCv5BWlqefW1Xe08LqZALc6E4W2CtourntTiaqCq56wyFwyHSy/3SzSOrNB4sgpcdHHWTLWrzgzOVR5SMfMF5wPiMuCs1JIhzebC4osSeusmR5UQPMOwOnuteB5YIBdj5FStX3Ou3bxiGdZh6Bk2G7q+OwuKL+oEptJ+WbpvAV5K0cZr7MglWQb3gkdr/RoFp3Iu71/oyLbspbsMCkUXbqxIFWO3oWQLwCKLcUgLbpdObLFrwZQFStFlvEGbDLPJ9V0Equ12thl6KWMuDSZgMnxG0mee53ZISwOMZXNXasGK54MqpNkaluYkzMmTVUjZUVRwWfCFZXHUOUfw3iQsxVWtWE8IlpQRUZzYPBk6b42JIkvgKcB2GEibRCrKmJQp2Xkg5ULOJp3VBQjObrsAzjvEFXwniFe0OpBZRlaZZyUlc+xa5mTx+C4gztP1wrARvFdSnol4Cjbf9UN1/Ou6ZUHsvFvm8YXwpyDu3HOibULWFl84KEJMBSFVGqPY5+jawnNdYL5MeKFANjj7YSJGHi+AFe1hZuIuRcY4c9JMDqC9o8RCyhkphWk+cTjcoiWz3+8pJVGKle+a+1drxCglgxacFkQKfTB/acSZywfVqGCeyanYa8UIOeG8s8cPHZu+o+8CfbCB57yrmVgH1VVFRG0gUTmyrmWKzoPjnjamnDskvQt4VzVlvauvz3IRHKHbUAalqAPp6iq3kNIRJ9BteuP1omhJbLc9D8vVR/2qVrxE0KJM81ylaPJCBWhOXf1mwyuvvsJuZ5nYzvsa9xVKTov0lK9ufcq55H4aj9webigUxmkkl0QupiiSq2azazy6GkxP07S44gH3KA8igtdLPltdTAqLHzzeUYLH5RrQZqWkYukitSDYOWEYBgAzZKglEqNUKPMciTGSogm3x5iY51Qb4EpVQKi+9y2jVV2JRIR5jrb4FAsmzsGrLE5fK1Z8EEqB051yGOHu1HE796TiGHNnAZgWgla9cefYf2ZgO/R0QdgEC8yGbmC7GQjeUUpCs1U/Qh8IXTB2XLLMZClKh+dq2DGlzJuPT9yNMzEVnt7OHE4ZQQku46UsAbQTEGeW8+KsWbvUSimt+ghIVlyOQMGHgX63w3c9+33Hw4cbgoM7hOn2lqxKN2x5+ODa1H78luI6xHeEzi7ifH35lhgSkLPcnqmuCFkDRTtcVsoxEyRZpSjYa5iJir/H+V/x6ccLnYWd1EnJNWpB6y+0gHYqhSlnohbUCeqxiUgzIkrKkXke8c5VWY4mrbO8XM341JKfFsxkUwle6MW3ukMt+Wlt9KouJTkvWVzvHaFevLMA874Diq36mpWfLOWX+xnZ92xckfPxOufMQrAGstIC2YriwPmADx3OJ8BW5FqUkiNoNueyOJNTQLXQd57tpl/lRFZ8KBQsqIwFpeCrQULoe5wIPgQ22w27/Y6mkwogqovl8/2MLDWrmUg1MO26jpTjhW5jFT1Xy5aUKoK+KI7Avdul8kvP1QxLRbnaQAUWyLZrL1LF3OW8eKz2sdSMrikbmLPR8lmUYr72OVeNWMvCNr3YnPMi07NkZGGhSzRprVzLos45+hAWbmxTL1ix4sOgBeYJ4izM0TFFTyyeUw7k4vAlEbIFlTkKvfNsQiB4zGa6ZmS7YEYgRqy1qSV0gdDbHJxlKUQiG0fne7o58faNaSbHBMeT4+bOlHs8BSd1jlzmvIIEhzij+xWHjU8HXTDdeKdKV+dj54XQd/h+oN9s2Ox2BO8Yb59SJFDweN+x2fTk5JnpmPE10VMVh5zDLY1ltdnaotcWP5srKE2Ht6AlkjTbuBRfNdtZzmtrGPvy4KNnZKV18zu0BoKKmq2qKkkLUTOzZoqA6zxeAkWFkBTrhFKbIHPzQM9VoLwsEbE1mHRQMsNmQ97tEc0MZDrqJFkELVXvrnJsjAdbyQ6CnQS6bmmEacd+7z0t188fMC5dlhfPp3J67kW4yw4aAbdSMpruLUqeMzlOIMrpcESlmNLDeCLF6av6fla8nChaGKcJJzBP56ap/f5qCU4vF2El5zpf6BIESnXjY1lEWsZ1mkYOhzsU5ebmhsdPHrPdbOgCbDYdqq7yaq3C8GxGtlnLtqys1gbI1hxpYWQrJ17aU9ZesGKvOU9zYwAhCH3fMQw9vsoGdV2oZdayyG/N0VQL5mkmpnSmFyyB+JlCcSna3nYuC3fYE2ow23Xd2uy14vlRGxQ7lxlcwqFW0XBKCI7BWSPXdrdh6ANdcKQ0M46TJW8ksukzwUtV7rE5Ydj09BurSOSs5FSbs2ImpULMyhCUVx5sOI2Z20NBjkaNyerMgAejA4ponRdd5dYZ3xYRk8QrGecKvuotINa0Vdqi2DXFkxpIFlt0ChCcN8MHMTMI8Y7Om0MXzswepAaz1N6UpqCgqsbBz4FCRtThi+JUaoxQNW29I/hq2btmZF8avEBGVnB+sC5DLOuYS2Yuxok9lcQxJ445Er0Stj1DcbiQbKKsPJhpNlmpeTZlAhEYcl8zqVYq3Gw3lM7j0itsPVAyPo74bFaT0xiJMddlW65lwYJoxpEJHjZDz263ZdhsCF2PD6GxbS/e0fsEsM+Mhya/dWlzCSzyYU2j78yo5xzcOrVB6wP4Dtd1+K6naGGeJsbjLePoSXmiu+2IJXFIJ+a8erqv+HDknHn69OnCaVUtvPH6G7zy6mcqlcYvwVhOyTr8a/DWhYBXs3NtVj1Fz1z2pzdPuT0c2Gw3vP5Dr7O/2rHf73HuO9jtNjVgtZ95jLEqBTzFe888z/R9X9UQLgLnrjPOOuesq9TMiwWvVAqBKZGcDubOlbMdF8BnXn2VB9dXdH1XA1nbx+l0sjF1OnE8HLi5ueHu7o7TaSRGE3ZvY7hlYUupC+EayNra08a0855+GOj7vmr1Diu1YMXzQQRcwDth7xLqJ2bxBFWyeobtjv2Dh4Su47WHA9f7gaFzvP3OU778pTdJKTEetmjcE4IZAxwPRwCurvbsr64QMZMEc9EyJY6SM873XF1/ltdee5XbY+LmqDy6Mw3aaS6kallrQ742WztLUNm0ZeYkQTKDn/GS6bwiXUYdeJTiHMV7CB7feUI1FCopU1LGI2y6HvWZWToLap1ntwnsBjMrSgipzsKpVkiaaoKZr2SKL5RBEC344nGabXHujSrY94Gh8/ShariveCnw0c/CIoizbITQvDeEjGVjU1GiGq3AMrIOX7yV0JPxWRHrLM451mavmpGtDRfWXGGSP0VgGDb4tIOSEK9IrLa10byeG/ncUkwFk9gyndkQbPIOIdQO5Noe9mzsKpcb3j0SFgMEtWzshczdvee1f+2zWm5eZGSblIHU2T/lRJxncnZIUGLyZAqxTGQ17uKKFR8ELbpIS03Vwvnq6tqamC4yss4JGS5sLCtdBqp2LMDZeCDnTEyZohMpp+qQ9YScE+M4VmksWSadplrwbEYWMJ5cpRkELTZkpTZxcNnkwb3xqcWC7KY6MM8m6ZOur/He0dXxHWrXsvVq5apBHRc5sHQvI3s/kG1Na/dH/gWnt+3DB+MYh7UJc8XzQatBTucyvZiUXJZMBobOMrHdMLDZmSFQ56GkxN2dyUjuhsTxqhC8cHt7x+3tHcDicCcipBxJOVb6jVU3u37Dg1ff4JWrAREL9px3Vj1FiJaNMvlJteMsRZocetVpNzk6VzIqGadK8UoRFg34Zu/u3IXFbZX0axlZ80iwQFkusqfi/TJjVklZfG1AzbkFsqDqURdAC74oonWmdUZPCLWZO/h3z90rPr14sXSCVNMCNQHkWBJjnJhLZsozUTOZTBE9Z1ucyVIhUKQw5xlJQszmtON9MN/0Guh6H5Bhg+aEy3uCKJoz6h3qPOSMiyBFcLngNOEwa0uKlVrl3vEKpYlQaw1kq+rAUtqs2VOVWmYtzkouLW6tGdnzJHvm0V66gi19LHVyRFj4g6XyCo0/cZG8FTWOUikmyq520nDvirhXrHg3tGZU7Xdm2848bwsm59l0HXM6W76Kc7W5yzqXmxnCWfOVJcuTUuLu7o5Hjx6ZVevTG+4OBxNjd01BwHRkx3G0DGu2xrNhGOg6C/5C8EhV5XBO8M7XJq9zVeNsjUu1ujxWZy4LkgEePnxAypmuclq9M6rOPM/c3Nxwe3tbr+84HA7M03y/WbONT969VJS6CG1UjPZ4sID/kpO7YsX7IWV4cqccEkwRUrGLKWUYL92LSWGh1UikWKJHS6KoJXlKzmTEqil1IVdStmhSlDTPjPO4LCRzSgxD4jOvjQQyvctcbYSHe88UIWbHnGojprOCoUjBuakmqBxJA6XK7RXMjCFRSMUaJX2GnAuu8tBjKYgTkipWE3WkIsyxLhJ7O0e44Ol7z9D7Os9e0PS09ZZJ1ZaVuuANFuGiuCJIybUfxUyJQjhng1dmwcuDF8rIEoIRyKtV3inN3JzuGHPkdjoylomJSJKM+MrNKw6XLTObyBzjiaSZ02T6jk6cNVfkqoPXbXCbrXUhbwZKvEZTYr59Sjzd4WIiaiDJEckZzwnTeYTaqVJLIy0T6inqTIi6JnBBkcoFbO+tuLPZg/PeuIYX/s/lHCXgqr6mYAMKJxcOKlXO54KHl+vnlbWgVZbMLgVxVdarRCQamT5oMS/qFSs+BKUUDodT7UJuXNjq8uM8OReOx9O56lEbu7z31j2M0IWO4D3Jm4tQzUdSsmU2tRTeeusrqCrX11e8+soDXnl4zWa7wXvHMPTknDkcDjx58sSeXQPi7XZbn3dN33eUfbJSfeO5iZy1aqvjXqucjOPI48ePefr0xly6Rgtkr6+viDEuJg4+mJnK8Xjky1/+Mk+fPuVLX/oSb775JqfTiRhnSj5zYRfJrWVM15rKcr0QchcXMFVdePcrVnwY5gQ/8CYkgdlbU1Yqar0h6tCSCQ56B6KFeZrJUqqRyUzJ1vwbY4cWYRpHToejUW52e1zV3BpPJ57e3ZBTYjyNjNPEfrfnC5/9PAMTJShvXAsx9tyNmdNcOEzgROlF6UQJLrF3JzoXmUrPk3TFVEyhKLtAEUU143LCl4KLSpoz4hJzTEwpURCmoszqiRo4JuEwWnb2qhOuNqZWsN/1XO87EMcpJiQmWj22yWE210zzxAY0WJRbUpX/atrZpmzivFuaRVe8HHixjKwTUDERci2kkplytaLNiayFTEGr3h0qZ91WseaOVBKSpUr5VBmfUn+wzrIrvvOYJBBo8EZ0n2tGiYiEERe6qpPpcLla4NUuTCPktvYVW92VVk5pGRYj4wBnYwZ7wjljc7bifCaovFA2kHtLwXMW95z5KUundPvXJkmpJRwASs3MalmzsSueGy0j6y9UOeAcSJY6ec5zxDu36C0atcCfKQbNhlZk6VtUNZ3mpJnT6cTNzQ2qhcPhwGk8VY5eWsZJa/bSi0BRVRnHkb7vUVU2/WC0g0prUNfoPo5FVxasMSVbFraZGrRAdp7mhSpAPf62/8PhwN3dgePxuBgiPJuNbcd2DxesoGc+YbSauVvD6zphrvhw5AK3R4vBygB4WWSySq3MiagZE2gzAyjVRc4uRW1uLLXCkVMExNR5AFTJKTFPEyml+nsf8eIoKeLJdAKbXthvPUWxpqhqne5dwQt0Ttn4mUEsKxukMNemacWhohSUXOz8sLyPC7qOqLMmMISCkIowJRtnO8TK/8EufTCqXcyCr9NyowSCqTY4EdCzRKYuQe05kHX+fK7gXvfLik87XrBTQchamKtD12me7BInpjyTKJRasL+cJpqIesyJnE9Enzgcj9wd7sg5s99dWSOHgFdzEbLfcaC1YPh+Q0gJ8R19yiDm4KOAeG9yXlMxHbCqlmAOJ5CyIlXlWauRtKiV89Fixg1tEodzEHwRyL7LbnNp7tLl3Z67n+0EU6oebqyWu3GeKPMEKSIlmQ1hzUr13hOco6hDSiZfNJWtWPHhkOrOZTSBximdponj8Ugphb7r2AxD5bR5QlUTaJxWt5gQsDQ/qQiC1tL+SNcFjkcr96sq42lcXLQeP37Mm2++ec8zfbvdAnA4HNhsBuP+bbcE79kM1kTlfaDrhnNAjasB8MThYPtaaAsIp/FEnGdi7CsX1prcYoyVD5so5Syndzku2yITWinzYgIUWbZZH2khJWtD886bc9mqWrDiuSBkAt73bLY7XH+FccpMQnJ3tWM3OEKwZMfdMaGaOcVUEzRnbrv3ju12QB8+QES4vtqx7TuKKqHKWFU2n5knAClHxnlijHA6zZxOiXEqpFTq2KjGQ6JL5bG0bRfj1/nKgzXdAQRTYghaCJoJJRNSIiD0omwHjy8deepI245SHH3vCcHscYcOtps6ddaqTFskLkYp9aJqY9AczbQ6kdWEVa1ktkbsOqI/6S95xdcJL6RaYKuxwmEaOc0TN+MdT463nOLELIVZElnUwtk2SOqzFWWaJ+Ix4cTzaPeIB/sHbDc7ttsdrzx4BVTNfajp1QYBDRSf6bfmKJJzQkJHt9uTU8T1PfN4NB1WjMyO78jqiBk0KSEW1DUj6UbGyUipPZOl6lhecAORs1uRqtYJ1y+8WMsynzPNwCIlVkohTiNpnsk5Mx4PzONIipF8OqDTiKRI7wRf9T4H7wniyFqISdZAdsVzQbHsSAgmD9V1HSJuCWCd8wzDwOk0cn11xdD3i3xN3/fmKtR1hC7gk194taXqy7Z9NH6qqvLk6RMevfOIaZy4vrpit9vy9OlTfuiHfoi/9bf+1jJRAmw2Wx4/ecz19TX73Y4vfP7zXF9d0fc91/s9fd8RQsd2u8P7UCfvAAq3t7c8fvyYd955VDOyIwjc3DzleDqax3p172uZ32mamKaJUrIpD9RsbWm8+IusLBfT35kz26oqdj1X/jHAPE9rRnbFc0FViGWgC3uuH77KsHtI3we2u96anaqiCCiHY+TxzUhMmXiKqKt87CD44Aid55WH17x6fYUTx9Vuz/VuR86ZJ8HXnoqCB4ITnNhce3u4Y4xwc5t5elM4Tso4mUQXYoFr5iyf6TSTqJlgMuIdvjMNdq+KLxY0B4FeM70mhjTTR0+nws4VHu57YlCCbnB5S8mZ3TYwdNB1sN/CwysL0vcJYnKXg8+uSqnNXoUYMzmZWkkTB7LxetaojsWoe2sg+/LghbVjzPIuEWtW1rKzkeRqV2PLetTMjl6oAuSSmWPEkZnmuepfepPGqZaWWjlylgGtTl4Izne4kMA547ZWFQXfT/iSrW2rSnKonJu7Si2D5Oa71wTg2wVz9WpZ10VPUs86k/bnWTT+bKLQ6Au6XBs3NlNyMkpESkaLmGdyjJSUTMFaM76S1p0IwVlGVopQXC2jrFjxnDCO7Hmx1br3U7JGLxBSnRAaD61xy6zRq5mFwGWAB/bbz6UQmxrAZJneEMKSBbWy/pGnT2+qnbM9e5om+sqhTTFytd/jREgxWgWiZLqu4H0gBLUGDm9VmJZRnqbJMrKTNZLNc6zvLVUVlHzmotfb2qgAFxq6eu//c8MXwjLmLf9cH1P5sRkW29umvrBixQdDUDy4QNcPDJsNmyFwfTXQdX6Zk5rByBgz85wouWUdzwkTJ0LoAp3zeBE2Q08XPILWErwubLpGC8ol2xwdhTkW5rkQo1Jypc0J9xZvtf/sHv1N6nE4jzVaVcUAIyeoBbda8CXjsxDANGKLp+vsUhyEKo3lnNEFOw/O21jzTbagHYuqGTzUQFtzATE+sKufR+O5W7Ks+ovqGsi+TPjIgayqMkUrVxzGkcN84jRPi45sUsiqVdHj3HksGI+1CYxL5cSdxhPvPH7EcThyff2AVx68wjAMuC7Qbwa0uKpR2QildlIQBAn98noh7YxvFzrr/PSB0G9xYQAXQDwpFzSle9QCh+LF2ELN0xnFAlEuy4/3B4cs/50D2PpASo7kGpRPxwPj8UBJmfl4JI4jpSRcivSYJW8JnkKPA4JzNEGS7Mo6Jlc8F5rLVdf1DMNA35uv+TwnREacCwzDiZwy281mCfBa9aFRC7quo8+Zvh8Yhg05F6Zo/DuFJRAV4O7ujpubG4oWpmk8mxDUwLPREFJKDENPTIndbsduu2M8nbja7xmGgVcePGAYerquZ7+7qsYDRjUA4Yd+6If4yle+wuPHTxYLXOeEp0+f8vjx4xq4piVY7fue119/ne12y3d8x7fz5MkTTqcTT58+5eb2lpwTp3FknKclYFUqO8j+q1SDM7UoZ6uyOBHGcVzcv1as+CA4J+x3PdttR9c7QhC6Ttj0Qt8JMSlTOTcEj7EwxYIv0AWPq8Ho6XQkeWeJDrGkR+Oi55wZ58nU65zgqvuciOd4Srz95MgYHbdH4TRBKsKDnbDdBLzAEMSCyuIJUXFloGigzz1FPS4YxeGy+RFqFnSakVKYfWEeQIOQ4wwlI2Q0zaT5RE4JLTNxOpqZSI6Mh1vECTkVUrrMxtZqyCUVqJwpQQvkfG2Br1Vcck6seDnwkQPZooXjeOL2dOTJ8Za78cixzJxSld0ScwqpogCVZwMiDu9tBeh9skA2Kzd3N8yjdR4Pmw273Y7tdkvoWpnROHzOO1SdBbGhXyYVDcai753HpR1hnsEF+nFEfIcfduB7Ct4cT8pcVQts6RmcYwiu8n8a45wqY2K2sS2j0+AWfizLxAfnwDelmTiP5Bg53Dzh8OSJZWZPJ/LUiPRKT+Xkdj2EAKjRf7TajWphHZIrngfiLIDbDBt22x3DMOC9ZxyNu1qqGkjf92y3u8pfdUt3vogFwsNmQDFO626/Q4HbgzVM5VJMKst7Ykw8evyIt95+m2meuPuWLyx6reM4cjweF7WBw+GOEDquvvIWwzAwDD0/9PAhm2Fgs9nw6sOHbDYDfTew3++rBew5kP3iF7/E93//93N3d7e8X+c8X/nKV/jSl77E3d0d0/Ta8h42mw3f/u3fXju/LSg/Ho/8wA/8AD/8wz/MNE88evyYOc5nFRLOc+j9hhFzG0ux0oZyxgHztKoWrPhweO945ZUdw35gu/F0g7AZHFcbx9A5TnMhJ1skpZw5jInTnNkH2AyBINYQfXt7Mpku8fhKuTPJKbMzP4wnc+JC8GpBrPiOJ3eRQ7lhTp63bzuenjx953nj4Yb9rrMsb/BmF58y87QhpYTLwmkSk1twULM9NvepmBNXzkzHkeIdvQZGn8hBiPNodD3NlDQyn25JMXK6qGy+/aarDWeQUiZVO2i5/L9mi8QJXd8Tuq5WhawprvGGW7bae3tsiquJ0MuCF8rIppxJORudIEWSJoqav7nZETxTlNRzMNsksUQEFaMnnNLJVp3jycqGzhFTqr7qjqaX3gTTzVSA2skIIhkJ3RKIhm4wDqwLiAvQtPBqR2jLyKoqDqyxSu9VNmxlV00amqh7K03aPi85cpeTYXX/quXNFGfiNKI5k+cJjbOVfrw/e2c7O76Wvpa6GnXicPeMGlaseG8IpvRxaacK51J4jIk4R8v0p/xMhcHGlVQJLO89PvjaNObr69jvWZ0ipRBCqNSCkXnuSTEtFJz22JRSDWpPeG8artM00fcdmjNDDWQ1ZzabDX3fM03zuzKyT58+4XAwBYLGd21B+vF4IoSwBOyNx77ZbAghcH19zauvvkrf97zzzjtsNhsAfPBnaQLVyyjW/r9QILEFqlVHMmKT7josVzwHRKDrrEu/qfZUPxzTbwWaQk5phkJZKYHKE9eaHImVy5pxWCCbksN7M0GIqcpX1eYpZ2LoxFyIYyLmwhwdKYvZ4nbC9bYGxD4gzpGyOXVKDMSkhFzwqNEPXKPayFKOtCxotgpsEnJMODU1BRsv1eY6JetdyRaw2rmnVPoddv8SyDbZkBYvgPOezXZLN/T1s6iNak4sa+1d1bG267Va8vLgIweyWQt304nDPDKmyFQSsxay3A8EgXdtEKkZ2c4zbAbjsEUlx0zMkdvjLW+98xV22x3XV9c8fPAKXRcYdADLX54nmUWNwFZnKatRBwoU58GbRl2pHRxaMqQZpJkm1AmqOKBYl2cRXHGLnFCMdpIIIVhDibglQKjv6N710mWZCylGYm1MOR2PaMn4lHCacTiCEzrfsrzN2YhqOK1LQ92KFc8DEUfXDXRdX7VYq+TWXH/DPjBNVo0YK980+EAXfDUkcXXR1rqkLy7PyMw1DvnxeOTxo8eUXLi9veV4PDJNEyLCMPRG8anNkoBlhkuxpg2F7jSy3ViFYjOOeO+5vbnD+cYdLGhRnj59ypMnj5mm2VQWggXYb731Ft/3fd/Hbrfltdde45133rbmsetrrq+vKcWOaxxHUopsd1ve+OwbjOPIGGeOp5PRf+ZITGmhC51D2Hq7RQg0KaJ3FTlXrHhvCKgUTjFx92REvWfTzzx5Gum8Y86ZcU7kojw+FJKaSYALha7LdD7jteDKDJo5nEZOx6mW2rHEhwj90NP1HeLEKpidR/FMBGISYnbE4sjqERfYbXteeTDgnTAEITghZTiNjpSUm0mZKnc3AxGT02pT+jlo1nopuJDwXpimkfF4IMaJ26c3PH70mDjP+Oq+BVZxNLqOMs0z82y1x+CrlnTNtnrvCc7Tbzbsrq4saJ/Noc85R9fZoltLpuRYlQ1WvCz46IFsKTw53nE7HTnEiWOaSaJkV3mxF+Txe23AUH/4Qtd3eAloUU6HkTmeKDnz+Ok7CMp2u+P6+gGfeeUzpjuJrTLtJbW2LZqkVsyFkgtzMkkRLZiVXWeBblELLFumdOHA1uvkHTn7SqgHmYzg3hpXQK1kuzVP+S539zM2epG8adzWlJmnmThNHO/uuL15iqiy9cLgBPEQvDB0z3wNjROk4FymPJMFXrHi/eCcY7PZmsmAD4h4SomM40QuJlPXdSdSypyOJw6HE2BlxXlrWdBSyjkje3FxtRGxSC3jOdNzvL295ctfFsbxxOPHj5Zg1jkzQMg5n9UCKg/QOTMSOZ1OOHGLFNdmM9QeTPv9z9PM4XCqzVy5SmspIXTGoXeOH/zBxOPHj+i6wKuvvsobb7zOMAx867d+K9/2bd+Gc45Hjx5xPB7IOZtiwn7P6TRyGkdu726tSS0Xe31MtojWeMJZlMuC3DNn9l36sytWvCcU9ZnDPPPWzZFTUrwIvVjDFk5bwZBEIGoH3uE7GDZC5zOSChJHSi7c3Bz48pfftgpLvfjg+dzn3uCNz75m9KC+Y7MZyOo5jh2H2ZOKYy6eTEB8x4PrDZ97bUfnlH2n9N70Yacpk3Lh7VvhdrTq66yOWEDVDH+0BbStOOoU7zM+KN5DTkcON0+YxolHb73Nm1/6MnGe2V/tuL7eL42aU4yUXCw4P02IQD8M9J2pqPR9T+gUCR2b3Z5XXnsNzco0zqRoi+9+6AjBE+eJu9uni3vfipcDL0QtiDmZFqyWxabWfthy+UDO1ILzZmvOsmBOxcoD9pBCTJHTeAIR5nkipmgTYStVcPaBtuc07TvLzOYauV6qeBStfs2VWlDaUnIJti0b6oqA2OryHMjOC7WgdSkXva9esOxo0b+rigeXZdaUEFWKmPqCNZnJkum6/GxbIFvqfi+5uStWfBB8tWu8tFUtxRZ6pXbxX5b+L7v7y2X2X5pd7Nmm1sqVei9Tm3OuagLGjW3KBe1YGi2hTSwWzIrJ8pXGPTdVAhGWLGwpph17e3NHjKm+xv336b1jHE+UkpYsj/eOzWbDgwcPGMcR5xzzPJuLUil47xdDhq7vCCGQc6lSY/baSxaWFsu+t8D6OlmueC7U5I0CUyycYsapMFVam3jwXZ3UvIfFZrWNM+4toGJKnMaJOKfKSY9G85ljzVJaZsV7jxZfK3uQi5kBaZ2jnBOCt0sfCoO3xBDFKA99ULxTbF2nC19VlwFyYQdbe0VEFMTUF1oDZkqm7xznyCYlk8Nri9vc5sdsFRERvM9kl1FcNRAyOO/pup7iS1UeMk5sCFZVKiXdU1pZ8XLghZq97uaRU4pEjFJQXKWvVriLHzvtaumSrpsCaBH6oUPLZpHQOIxHshZu7m64uX3CZtgQgmcYTBez8Yya+0muk+fpcGCaRkrOxHki53gvw2OD0NECzVa2vG9soDRjg9Z9rapcXe158PAhXc2g2jEFW0y7yivCRNxtge0RvLmcZCWmjGghYhp8qtYaZkH8had9/fwUwDuKgAsvrJS24iXAYtcoxuFsTl6laTHWSUGp8nfzRAiOOQ4LBaB15mspVvpLJmnVdR1XV1cA7K+u2O/3gJgXfJ2sbm/veOeddzidLItq5f+wLMTapGengPN5oHHufUr1mMtZJeA9gkXjyCVKMcmunG2x67wHgc0w8OqrrzJWqsLd4cDjJ08opVQ1h565etW3ao8/XCwWn4lkn2USt2NYqQUrngd91/Ot3/JtXI89bK85TJ3R36qcuZi5FYhJ2+V8NJ53zuQ5IS6TxxNxPJFTZB4jZJOMdAhBpDYfztzeHBiGnv12S98NuOIIk8dUau05VspPvPnOEdVI52AXlM7VALRYv8vTk3J7KowREs16XfDi6L3DK3TOTBGcVB5vzEhRvBeurnb0feDhwwe88uorxHmmH8Iy14Vglu6lmHKPC7bo7ULABztvdH2g64zWZwYPihMleJAgxjUWM0kQXVrMV7xEeCFqwdPxyFQiM4VUg9jLTKktjGTJVN6bHGDRxVOFgb5yXArTaeb2eMsUJ57cPObR47fZbnZshp6r/da82MXhxIMWtFrWzvPM4eaWu8OBlCLH8chcs6ntIjjzjxfLJJlWnzmQLXyjpWhClTYZUYVXXn2FN8aJYejxznN9dQ3YArqtTL0THAEz5wsIAdSTM8TZ9O1CKUhwleag4C7kyFpmthoseFWk8/czZStWvA9axz5QKTGtQatUI4DzY1PO5o4lsN1slqxn0bNmsmVJIjll+r7nlVce4n3gs5/7PG+88QY5Z95880s8evst5jny5MkTvvzlLy8OW13X01WjgvbTvtRfPgeFxbqkK5f2Mgi/DGTb+LD7zKKzsfVEYJ4jp9OJoe957bXXOR5N5ufp06e89dbbaCkWhO92NWssbDabugCop8PL2FQv9GXbMbRqy0otWPGc6IeBH/UjfyRPD45+HzicrAFrjIVcWmbfFG/m8Y7xcKCUiE+FPJp26ul4x/HujpwS42mGXCxg1NoQpTAdR57IUzabDa+/+hk2/UAqjs4roVrLUquT45T4wS/f8vYTm24GpwQ5B9UiwpSFJ6NjSg71UILN2V6EHkcn0EkVMxCgWF8IRQnB8+DhtcmCnQ4cDrdM80RJsdrrQug6+srL7/rAJg5cBgoijn4wpYK+78ypUOpcGOw4REBcxmw8L1m8K14WfHRqAZCqdWppHY3LfboEsffKce/D8xSoHchNd7ZOvs6MFmKMhDAv6gFGLzAqQFtdtjK+dUZGYopM88w0T+cJp9IAvFgg3Dh3OdfsTxVOt0nR0jCXfvGbzca0I1Hmqg9rfMKLILMF7VT6hJx969vwXAIF1aVsssjjLhp9bgn6nXP3Mt0rVnwQ7tmw6tlW+Vlo0TO1oJh9cinOuo9rIFtqZla11DEaFmmr/dUVKUW6YBJUrau6acdqlaw7GyucA9H7yh9Y1nPZr9ZLqWP73e8PlNK0/Yw8BGA0pMkyq5cUh5zykt1tn4cpgsiZA+zuV0RaJvZy6MnFMn2lra94Xjhx7Lc7UhGutnZuj0kRn8nlrFaAKpIdxRcKhSAFR7VOv6imtN9tMyQothMEscRMs1Jf/tkYqctAoFAKzNH27QXiEshWvVgnzFmIyRndYBnDLPtthpbAxRhWiti+fPCm2NAHUxtAiVSNV23SWZaFvaQQ2Nivya57DabtMGzh6pyNWLm4tLhgpRe8PHiBQFaZSqUVqMltXVrQPovLkLY9Rpp8gYB4wWFOVubnrCiFeZ64O95RtFSuah1Rev7Roub4UapIe6wB7M3dDcfxuPAB28QanDWt5HzO/JRGM6jBbpvUWrOXqiK3SsEyU13neXB9xWbYsN3t2G+ty1K9LhI9Xdex3e1MDPv6mutXXqHkBHFizhEUZpRZC04cvTvr1Da36IIut1es+DBYMJkrP60s48U5myycmNSbFq3uW4cajAa2m4Gu60wep1jjkzVuneoCThbqwn6/49VXXyXGyDtvv4UPVi48nUaePH6CYuOp7zv6rqML3ZIpvgxsl+OGZZxaENvGZak0n0ZJOAeS90eFjVfj646UUrg73PH06U1tFgl89o03UCz7vNkMxJQ4TifGeaomEGEJku+FsLUM6sTRd8HOH07o+4Cvnyt3p4/1e1zx6ULXdXzujc/x4AE8eABTtAblUyzW11Fl6kopzCfP6aCUNENJkCZQJV11xHhlBgSnkflkjV/NMbKoEoGo5vwlznF3OJKKMJ0iZcpocYQ8M2iPqIMyULSr9DthlhbyAgJZhblArr1oXmuehWIXzTYSs1nQ55iZfSI7K/133pqnu75nf31F1/cc7yDOMypK1/dLRWRTqkxXLtaIeRqXZJirQazDzItUFOfsOEWwzCyQXeuZe29O+4pPJ16AI6ucSiRTSNa2AdTMZ50LXMsy3isiVmjjyNn2VlYvpSAecAWVwjifuL29oeRsNIFLBqliQWwpplmXMmmOzOPIaTrx5OYJN4dbs+eLM7mkxf7ViSxNYtZUVUy2S023tfEMW4OMOZmduL27IYSOrve88uABu90OFDbdFoLgFlNe4955J3Rd4MErD5mmIynOHG6ecDqaEsJUCmMpBDFfbDp3bhJrWbWVibfiOaGqxKrlmrNdhxDoe5Pjcs4vWclpmrm9u2OaAt45+t7ThY6YMzEm5hh5+vQpd3d31kzSdXRdjw+Bq6srXnvtNWKMfPlLe7quQ4Dj8cSjR49wzkqCfd+bJFANZrWO22UxWxeiqDnyZM7NXvdK94IFlK3hZfmv3l1PL8ZpN9ev25tbHj9+zGazYbPZ8PnPf+4iq2xW2KeqO+t9oOt74ylepmFbgQTBB2/82s5KnK3UCcBX3vmafacrvvnRdx3f+vkvUApL5S/lwpgskI0xMk4TORfmsWc8eHKOnA4n7m6P1ozoPc5b9UNywdWFapon0jyTcubR4cST01gVbxxPbw/kUpiOE3mcKXi87kzKUjtUPaUMqApZPQVZVICy6nnMCQRZhBWwLGhGSFAKqglFSVqYyHgH9I7gPeJg2AxcP3xAnGdyThyOR6Qo/bDh6urqHoc+pkTJjzkdRxoXSkSWYFZqtrcdjMMoBl4gO8Fbi9gn/h2v+PrhhTKylVRg/+4J9j9bkHvXk98FAZYuadrEVMWhL/zT277vvZDq8tfCcq2TdS723JSNCuBEwJezalZ9CXtsnTydWwJtVSvxgJk2oFBKZp4mpprJiZVmICIUQnVWaSyDWhJxHucDrmTUOTthKCSFpAVUlhBYqXJhLYjVtTt6xfPjklJwphVcBIEXZfycsmVxKqfVOqIzc7RKRPtt55LxGmoAqUtmtlRprQbjt6ZaLpRasj8rHKg2nt45p3rOst4/h5xHQytnVjxb/19OGPacRt1J2ahJIQUrb3bdYqLgnKeo8fh8CPhgi9t7uNhhG8fNZMJ5VxvZVjWRFR8OQRYKTqu3pVzwyeQV51lxYvSXjoDXjpygxMip9pF453C18uG94usCMHkhe0cqhVFhRiwJUs4LweCFLlTbc7WJX51QgkODo6gDdZalFTVXzlKzmnKex87VCqs83rtQUFcVhGhzVh2/lUJQauOnYMpDTs561Y060OT53vNzrM+BRmGUZds5c3vucVnxcuCFWuGz5CWUXWaW5ffXhJrPs85lk4TB0X6vLfQ13kudbMRZCXROzF0iZ0XNSBpVkxEpKouLGE7oh4Htfk/xsD1tmHQyNxSXIVX5kAtXMeddHTyOrjZ22Aq0bi9S5RiUEgspjpQiPHn6iC/+8N9lO2yJU0KK+dMPmx2bzQ4EUozkHElx5hQzUR1RPbMGJg2kLDw5RcYCffAkhf2SncoYCVCrRdo6MFd8OCwjG20BdsEFXSRyGj8Nc9dyzuSqjOVjbljzHE1GKyUeP3nM6XQiplgXe9YdfDweONzdWOZ2mmzSW4jeljnabHbs9ztyhmGzJXTV/KTyZy1ArJw275DgzII6l+U3b9J87eev92gGS2a37lYrc8/V14spcbg7kFNmu9kQnBkoSJ04i1O6fmC72ZJzqfQIzueAiwBWpGZktxt7rRDYbjd0XQtOvv8T/JZXfLNhaeylSVhZxTKILgFgcooH/KajC3tyTtzeHnny5CnjOOF8QFyHiNA5sbI9VTGgNk6/8dnX+PywuQg8bXF5miamOaLqSNqRCSiexJZMhyLEKs2VFWI+Z2aPUyYlo8CdcqwLxJlDHHEl0klmcBnf5tXB4YPgXKEKMeBQvHMUX6Uk60CzaqjNeUu2tyaRFtqO2ntAlb7r2G43KBBzJhdr7GrNZr4UQi90ufa5rHgp8NEzsqJkyTVjePmDqaslqfyV5Qn1quhiHScOwC0ctPqXEcCdqRKUorX72ZqyQFA9O4sUtQms1BfsNxu2QPGwOW3Z6EhMjkREXa6lQgsQnRe63ibPe+8NlsKEKIilRxkPI9N4Qgs8efI2Lit9P1CS0slAP2zYXydSsZJMnGdiNP3K45yYcCQ8owZOGpAC8ykRpsTQBcQ7ilSiejZdFtHalbqOyRXPAQtk5+V2u2780qYOUGoDVy4JV62gpzjjnDBNc3XBSjx+/Jjj6bgoH6RsQejhcMvd7Y01TE4zpTrsqFpzl/ehGpo8JGfYbHZ03VBf51w7acclVTrLVTUPTXWBLArO7JovSAVLyd9uy1m7ui5AxVml5PbujpQSrzx8Be/8YsOJOLyHoduw2e5IuVxweNsO7CI1W+RDYLPdsN9f0fUd+/2efhg+ia91xacAWcvC5wQQUYIrVpp3hVybnQkBt92TS+ZLP/wmjx8/4e7ObJkRa/Aaes/Qh6qZbBbP/dDz2c+9wee/5QvWwFh7LlQLc62uKEIqQlFnc2tUUrZ5NBnVlaJCzJ6iwnHMvPVk4jhlbk4TX3w8E6fah3I8QZ4ZfKGEhHdKFzqc782Ny9n8JSgeJTiHOtO4bizWgi6VVpOhrHruYo3SDS1m6LrAbrsx5/lkJiZwbjoLJdENRglcmzFfHrxQRlblgux23lqvL8qFreV+mYNkuW4yPO0pKiaPZR2ZF3JUevmaC62ururOJUqp2VwbyA7vPMXlarl5Jr6ZuLM8s/1iV9qyxFr5tC1Ta3fmbPQCCozjxOk0kgu40COhN+mSKgmWU+I0RaY5kWJmToWYrWxTskmXoTDGRB+97bOUqhEIrUN0xYrnwWUA265bma9RCsDWSpbt1KWpUUSWSc/ctNJCUShazl3/i6pBrqXFS5UEMxxpAW0r5Xvvl2bKSr87dyIvY9FRXDmPNb04j9znFFzcaueP6sonLevDIriul7pjS6bVLRSBEOx8cVZVaNz+Zz9blsW7XnzGK1Z8EEwTOdX5zi2LSuqCsqkSoOVelcQqhhal2ZxXEIRczLpcqbqzamofzjvjcIdACI5Qpa18cHTB1+DR0YyB5tmcMC8DWVUhFgtkvRMOJ5PAG2NV6rhUQ8mmUFBUcRcZZ1fL/cHdfx9tvF9+LksgWys0pakHyf3HnBuxK/XvXqR6JhPUguoayL5EeMFA1q7vZV0b902pEhjg6r9aCFnMDCxD4s8Zmcqh8+oIBARh22/oQmcCya17DKpYe+FZR6K+6/HOoRR2/ZYpjAQVkpuXMqblcMGLMz4SrcPDcMmXvXgKPR4NA8UppMLh9sjJzQhvMh4hhJ5+f82we4CCebmPEyUXTuNp6aaepok4W7OXk4KTwtA5DseR621HFzxXQ8/QeYJz7LtAVyVKVqz4ILQJs40TEcg5Lc5WtsizgDWEQJcC4mQJSM02crLGk2oG0u5DmwrCBW9VLnVnldPpRFFlV8dlCNZEtdvtuLq6qpSFuATNzXe96zxDVRfwzldpMFdVRUzB4F5UebE+tqytnVN635tCQnXvkrrYNUUT+1z6PtTMkedqf41z5uO+3+7Y9L1p7l5SoIpSRInzzM3NDdM0EkLgeDxeUAtWrHh/pJR4+62vLOog3jsoGU0zqKnnxJjQonSbnjBsIAQePHzA57/181wfR+Y5MY6pcld16ak4zYWYR6ahcDyOjNNIXwLBbwg+YAvYmiBxnn6zo+sHtBRSTNbMLKA1wLXA0c4lTw8zXjI3B4ssv/ROgZzQXMjF16qOUoq1fItA5x1D77nebXjl2lR7nL9hnKyasyjzFLOZ1aJL4OucqQmdZTOhpAQFYuoRcXRdTy4F5rmqEUHjx5sdttAvCiQrXga8uF3UOUlpK6DWZahlqc+LWEZRRGqW1IJUy4QE+wF7jw8eFIJ6OjpA2IQNfejofDBNVrCJqU40LZDVWrbp+w7oKFrYdhvGsMEXIcoEi1ByDbDF0YvHLYGsBeBZMy61iUzO/FTxSOjRosxj4XA8oiqMR+XROyPiAn64xg1XKMLhcOR4GtGitWEt1Y9MFo6QkIBMHxx3d4GrrWcz9Hz21Qdc7TZsAvTBEWQNZFd8ONqYgHODhhl/zEs2o20PXaDP3WIzaw2LMMeZaZrseTHe49pelELONRfVZeF3Gk/M1YihqFYh857tbsv+ao8fPYfDYTm+EDwhmPRVPwx0nSkolJJJqVnGyrIfe14drUvWRhe9yW7ouNruFxtawRa9JRdzHMLBIISux6NcAcNgnu773Y6h60nOgt60aHeanF6MkZubm4XDv9kc6FbHvRXPgZQi77z9FTbDQLne03edlUSSSUrm+vssqnTBuv2dt0D2c9/6ebNqvh15enO0oHeOxClVWcqEjjN9zBxPJ6ZxBO3Ybjq60Ajk3tywuo4HD6/Y7a9tYZpnyAmWfhFXs8Nm3vPoxpPSxLbPTNHRObMj06Lk4iglkKW6BtZzQh8cQ+e5vtrx+uufwTtPTIXHN7eVytSyy8bTn6cJxEgF7aQS62fR3DdLKmyq82DX9ZASqlSpQWsKLxb1WvNY59eM7EuEj+ksLEuAeVm8bw0ZHlnctLx4vPiLoLbyz2pWRBWTx/GWPQmLi9dF2Y9zea9Nqrbq5N5jav4XX58fxNUQ1h7vcfURbbDbdqeVh6ttArX35DDejrbGmUqtyNl4vDhFmHE6UxRO48w4RhtkjZguVvpsHaBn4ebCNGe8U8AxzokuZEQhpkLnVzmRFR8NrTTnqrf5MkackLNbuoRTqoFvNQ9oFIJnX+uyuC+cKQJgWRZ45vmqONe6/P27xuiZYlS3tbK/07OSQA1a33duqlwF249xYUM1ObAu6TNcox6gS8d0O0cYN+98PG3XNEpBUYpmBMgpnU8OK1Z8AEoptfEwEZyQug40IzkhLZBNpmYQ5pk4R1wwSkwXPLkL9H2grzatrYnKpBodWkwOzigy9TfM5fxYaIYBZkIgsMxxl43PlcRWG5y9F7zDLmJUt9aU5Ro3/SJiPJ8PagJLnDWiLRTCc6WIxo9VO8eUc37nvvRku/2MSQt6oSVPM0SgOmWezzMrPv14wUBWaP0Y1owhJu+BWbV6sSDRu47gOtNndcEaLuqk451fOoKbw0cojk7t0Hq/oXMDw9DTh0BotrZJbWWoGS3VG161uqQUcoy4AkEdENj5DV3VaF0cVETwxS0nBCudgiuCzywDifq/U0WcuZhJb6R9LeZ8MmXQVIhxIh6NIxhjIqa8zHXWCGOf29JQogLqmIuid5m7MTN0hTkK+83EbgjMc+Jq27/YV7XipcBlt/KlLFbLuF4+LqVIjPOSGe06v2Rw04ULVgjhgttadZsv3MKsXLoxKa8YmWNkCoHT8cjxcGCaJrrQcbW/wiH0nZX+vW8TriwUAIouj1FvFAMvk5UtYQlma570nJotdv8QAg+uruhCx7bfsKvUpH7o7T2GQN93bIeBgtEwUpyxzmeh8x5RKJLPtRs1zWlptCkEzYU4zWS3stdXfDiOhxN/+S9+D5tNz4PrK4YuELzQB4d3siyWALb7PVePH+JDIOZMR8H3nv56YD/4upjSyvsW02juAl3wvP6Zh3zmlT3OCZ3PlPnWXrRUFRzXo3lEc29jOc9oTuBsESeVf0qlEuWSzXAhRrRkgoPeO3wXcKVQsqOTVDXjCyZ9ZWO0FNN+d9mRcrqn2uCcXeY4M1fnTC4WxHKRuDKqn6mxHA9Hnj65sbk6TvRqDec4U1NxAbphh2M4qx6s+NTjhQNZ6+oHY9g4ArKYDjQ6QHAdnR+q0UALZN05O4lZ2YUWyC7UAvC+x7uOTdfT1dWmCGTKEsS2QadVF7PkTI4RyUpQh8ODH+g01DJOPpcpa8LJJui6vchZ/oezs5bDsliq4PtA1wVKEfIxLVa3hzRxTLGqKdhnRH3e4mR2CbVtkmGeE45MFxzTmNn0fglgxzm92Fe14qXCZUD7rC3rZYme2nzhvdQGk/tlfFVdFpjPvlabeIMPbIaB6Kps1zQxh8B4PHI8Hkkx0nnP1W6PlmLNKEs1xp3zNDUoFYS+am7mVBZK0TmfA3buqdnhYhknxALgq/2eoe8ZQs8QBjofjH9baQx91zEMNpHP48n2qRbIBufBKVlkUS45k5HkrB5SDVRWrHgenE4nvuev/hdsNh0Pr/f0XWAzdFztB0LVMHa1WrHZ7dg/eULoAvv9nusH14TgkX7AXQ+18uBN5cM59vsrdrsrM+nwjj4IaGGeT6R4WhIygi34yBMlGzc354jmDOpR152buYr96lO1fc8pQs4EEXrvyCiiAS0OX2ZcsTYyoQWzWgPZiHNuoQAo1OYvwSVIaeZ0uquB7Dk73HUBX2k7OVsDaYqR4/HI7c0t5h2U6aQqELmMSMF7x7bf0Xm3xBMrPv14gUD2HJSJgKjU0nsdlOLt4s60gHv/9KIruFIQlpqlnvexyO60AfYepbx7HdM181Sy8XjODWgWYKs6ipR37csyQpdNX+fbVRDootwoNC3bJv1VFLPqXfg69sEsNL77hcqLm+e/s1qBxBWIWXGxMIXMOMfKdVqx4sNxGcQ+G5he3q65RavK3yv58a7Hvt/frjZsBe8Xrcdc1Q5itYvOKSMIwVvJvymKiJwzsVqqs5c0gfMmy9cKkmejFKPi67tGVDs2d+9cUysqpZiNtS9Lo1upC9cUIzGaccoiIr+cd6Senp4dxS0vvGLFh2NRBpmFeWpW60oIQpf9YhggNV3pQsDPZirSFD8si9loAGbkYfcFvO8ss+sdxZsiQpxPpDjaAdTpOqgQ40yIs427lCwZ5BRcqqOsqSicKy9N9aRZxIpgNvKieDH92yad6S7OP6ZnXakCjeogVdXAtQW0LBWfhb8fhBBsbDsnlAJdEJuLNdfXMP1YcTVR5IRQpTu99+8+ma341OKFMrJOAw4xfQEROucYfPeujKwgOK2ro9x4dLoEtzUuxB6ixJhJdfKTYHwXC1ATOcdqlFAbt7RQciJFE2o+HU+m3zpH4jzVgLbUsoknU5Ds6omEe0GzE185NsU+GtWaMG0TlrTqJ3NUpmRNLmNSpqykokQKpZWKMA6RxbRlCRaW6a8uAOwacGGZRA9TYYqRKWVySWz7tYS54vnRXKgaHQDenZltpTwBUslobFwzloitSf8INlE0kxIvbml02m93PHzwgNPpxJNHj7i7uyOnxKN33qlqI9ZpfH21By3sNxtOm03lvEVSLGjJiBZi5bhuNkPN2trkVKpMnvNVVitXS2mMxpNz0182SSNRs602y01lGk8cDrcMacPrb7zO9fUVMUW+9KUv8tZXvsLt7Q23T28YT6clIA/Ozgfi3HnsXvD2Si7vCuxXrHhvCE6CNQdPkbm656US8d6dudwC7u4Oefy4BoPeJsa2wHPUjGVH19ui8Gp/xdV+j/eOXd+zGUJd/CUQc5zsh47QBULXcxoLu6ujVUJrQOh8oNuM+M6yssHZdk21GSxnXMn0GtkScT7TdQmRag8fFVFh3ztTBek6BCHGCJXGVLIFzd4Vhl4r79az7XvAdN19FYT13uF8W8JamLwZeq52heAmgnNses8QTAXJh0pXELdw41eO7MuDjx7IqgWnntoogRBcoA/9ot/aOVMk0KJQOacUqZavLRtrpUR1BVcfk1O2LE7N7KozCkHLtqo0ojdQs7Apme7lOJ4Yx5EcLbgtOS8ZWWlBpWmN3CvzC26xzhMaX0Ix574WgDbrWLMOnHMiZWXKhakUsr1NSos55ZyPvZ9wbiR8qaVVayCjeESFooUcE6KZ05wYpxOdWyfMFc+PS4WC+xqvZy3YRi1QjM9WcrpPO8B4toshQPVNb/zyZiCw3Wy43u+thlEKp8OBkhJPHz+m857NZsPrr7/OfrulpMR2szGZq5QYx5mSEpoFrTbPw9CzGQbL9rp2sXJkoz8kLmSCsNVlu14uGD+9eCVOE6fjcWls21/tqwzezKN33uHu7o7D3R3TOFkQe9GYFmoJ99mFQCrNrWnFig+GVQQdWmCaEwjMyYwHnG8mQBasplKIVb95PGUOh0TJ1p/RmqiGoWPYmPHA9X7P1X5P8I6r3Zb9doN3QtdZVtN7z26/ZbMdCF1PjMrpNOGcMARHCFWFI0dC39txdB7xDk2zBaql1EA2MRDpfWbXJ4JXcirE6r636W2BG0IAoRqoWGOk1p4WL0rfWQPZEDy67RBR070NjYrXGsYc3nU419GFnt1GCTLTec/QObaDLQJC8NUuugWw76UCveIbDZ//PLz55nvf97nPwZe//Hyv85EDWVMEMEUBX+kE3hmdoAVnC6tNW/mfiybfSgpXQUVxpTlQn6W1rIej3LtoyajzS6kDmuh5tpJmLWeW6g9fqoD0IryuNbNUj68pE1gjWi3tlItA1nqxKgtAF5mRonbCSUXNDcXutsdJEyKBc/h6iVa3PGfKLHyuwe3y6VTKQnm/11mx4t24DFpbAPZe1ILzSf/+81rnsNRt0igw9Qd+meF1tVGs73viPC9ZTNQaqWKczWN9kQQzsXfL5lZb2+X1zvs8j/ezCPpCURJZnH/EGW2gUQ60FEpKZGfOXeqtelP03O3czhclN43asqg5NF5d13XVJtscvfzF59guzeABgCo5tmLFe0IgBE/rjBbRmjU803oswcHSTFmKkksmJbNnp3JPbfFp86QlXyxADt5RUibN0Zq9OiF0tgCdUmIYZ7quI2XHaTSZqk3vCb5WQaZI6I2/PvSB4B03NxOHw4HTcWI8jaT5RJ4ncikUyaYhWxvGVMu9xmu9mMcv5y9776B1Lhb1iChd5wmh9c6cKVLe9TWQNT35rho9eH9eZDe3sPuXFR8Fv+E3/Ib3ve+3/Jbf8rHu6/2C2A+771l85EDWiWMjOyOYh65ayta0vljwWpplejYHEK18uIto9h6Xz7kqapwatQDLTlaZm3kamaepirrb/tBMnCdOxwPzPHPz9AmHw13djZ0YnPN0XY9zHkqh866KShvBRioPKXhTVii1bIiaEHo14kVnC5JTKRxz4XZKpFQYY6k+1ZCX8fNBgedZmmyhGahahlhLJeZbSaVJ/riyDswVH47GxbsMztr2cyb2YszVCFCzcbzRJmPH0khVyBecU7N/bIvGEAL73Y7dsOV2GLi+2jP0Hc4J4/HEEx6z20+88uCavN+CKrvNhvTwmnEcmccjsS5KpXLUS0pMp5E0OzNxEIcE85gXV0dGcHQ+oCgxJcRcecnzzN3NDV3o2G22uM2W4AMlWsNKioFxPHF3d8s8N71cO79sNxsePnxopdi+pwsB5yzobpy7RsWwLPaZWvDFv/E3PuFvesU3E7x3XD/cYjOiXYwXe3b5KqXakotlaKkVk4ySVckxk5L1dxwkghsRIPhbQn2dvvP0VUPVByvPOxG6vjPddu/Z7fYMw1AbozoLIL1nu93Q953Z3g49nQ8cp8Q7TydOU+buOPP2O3ecxkjvYR4sqyo1MBcRpt4zjzvTke8DznV2ftGCVPOfEGDooHghVPMS54Sh7xj6nmaQJE020/d43xF84Gr3gM2wtfdUNacr/4cmJablnAha8XLghTKyvQxnOkEtw/mFS1YWmatSrAS4pCzvleNaKVNwxcK6Jv8DteSZEk6EOE+kOIMGnHTgpbqTzMzTyDiOHA633N7eWnalM/ceD3QOJDicmge0U60DJQCC94EuDFVrU41/p1rVEdpFiHMiqnFij3Mi5ULMQmpv7Xk+uzbG2ozYwlktGFm4URKaXJhjcZdYseIDcN8kRN/FEztTB2Rx0gET6rhcZFowW6sEuWaBxPqSzXXLKiDeObabDUN11NttNpZtrQvPkk1YfZ7GuiAtDEPPVTEqwo234NWyuEY5KikTmUjSfNdtQr58L80JEGyk5GS0iJIi492BFAJBhU0wrl7OaeHZz9PE6XQ0hYVavQGlH3qur65q6Xag6+09NctPd8E7hrPWJQBrILviA+C8sL8a6jySMF3Xlkmsc56aCUCzaz7nOJSshZgK05SqwU5Z5qhSkikPCFV72carc4JUnql3Fhx6V4PU0BG8Yzv0i3TXbjPQd2bXvK3jOBXlGItR6ObE7e2JGDNzEFLnarOWt0yu98SpJ06TNYQ5JRRfi7JnRQPvlC5YNXQYAn1vDVrb7YbtsLGKrgSkash3vid4e/3dsKfvB5s6l9OBUIqcQ4tFiWgNZl8WvJBqga8/tEXouAZzls2RZV5c5sfLy3ugtCyl1edppUepnZCGi8ySE0oppJyJtVM6pURMyaRJ0KpvZ22S4sRUeqogwblL0jzXnW8e7Sb8bEEkVbfPmkZiynbJpm2Xq698k2NeBpMsR9tu1E+t8nfqZyTc/ziUc2m39oHVEbs2e614PrTg6jL7ennfWbVAKcUtv7f3eh3jsb+bZxun2exoS2HbD/S98Vq3uy1X+x25TrJay/hNxaCUYrauauoGXQjVj12WRpbmnOWaOPtF49liblAnar04VlVl6PolgxqqpJ+v3PfL4zFJofQuWbKiBUejEdSO61KQYvJeUvU2V6rPiq8GArWcfrletN8Y1MRPpROUapBwuVA6T2OC1oytV6l0HHfWaL3YZ9G6CK0UOilKcQWRmqyppiApZ+ZKwZk7y9qmVAjBkwvW1KxqzWmxqpCoEPGULJROwcmyiD4bGNjcibN5zollcIN3i6Z7UydwzuZfWc4FDhG/NHi62hCHs0j1Mh+manruLW64pC+ueDnwAtQCofeDBYLqIdemEbCu4mIOfG2FVJOzzwSysqQnRc8ajapmkyfi6Lyn7z390OG8q5neTMxKKplxnjgcj9ze3TFNE3fHE4fTia7rCEOPBG8DxFokwZnhgVbKQQjdIm/ShWrXWRTJ1R4vJtJUSArHKfH4dmSOkcMxMsbKEVJHqYHm5Srw8rQil/8tZHTujzXTIuKsXtuck/xZv3LFig9BC2Dfmxd7vm0ZWTMSWJoi9b6k1MJvEyFrRp0yTxPvvP0OPzD8AFf7PQ9+/BWfefVVtpsN3/nt306aZ8bTyFtvvcXTp0+Zx5G7m1t6H+j6jofX12yGDU+fPuFw85Q8G79U6o88hLCUPpfFJuBDoO/6JRPbjtRMGExUfQg9m94es9/u2W/3OOcR78kpEZ0wjSPH45EYZ6Z5WhbCc4xM1coXEQrGAU45LxNt3/X3dXU/9m9vxacRItB3pYr7W4Wv5Fr5qwmSabaFXs4WXJYCaY6gGUEJHtxgDRtFa/C6cGrrrFH54KpKLJlUbB7J+RwQx1QWiaxwdNXdTpZFX6PTXBqqQE3s1G5n5xynSiPs+o5hM+CDZ7PbkFIyjfVcyNEys06VoROCc3g6hrAFLbigS7Nb6LpqLWvGSc4ZncjMkmryx8VqHY1lYYucb7e513HRc7LiEp8k//WTxAtRCzpnouUtQF1iVKHSCbTqMkJzCLB58aK2vrhdcdHsYatMwTourfQRagamOnNlBSnEFJnmidM4Mk4Tp2linGeKwA61FZ53VmJx93VrvXeEPtQMUKjORs76vLKR6aWYp1AqMMbM3XFimiPjnJlT04utx7/cuCCbnyPYe++ZZ4NcwDQPKv9OauMN1pi2SomseB682/RA7v1977HYAlOERc6u/ZzPj6/LKQXEKhAC3NzcEIJnevCQ8mMKV1dX9H3PG2+8wel45O7ujtubGx7lTIqR8Xjk0HXs93v2n93x6quv4oD9bsfh9vbyiOi6ju1mYyLwF1nYvu/ZbLZLk1Wp2ayUIinOqCqdDyb75xzbYct2szGVA210CGcB7DQSYySlaO5FtakmVj9372Ndcwou56UsC0K417iyjssVHw5rclREjB9bKvfcmpL1omKhpGR2tXbbDH+MC4ulNRVM3byiZj4VSCmTitmhp6g1qG3juZ0b0sVxnX+/riVYGi1BxNzunFTdV1n4rIjDuQAi9ClRMAfAZgxUqi50yYoUy9F03l4viNER0VL1aysvOJhSgohDvDfTBxFcsPlboPL1qxpJdqZ2olByDWQFe6x7nzLTNyk+KACFTz4I/UYLiF9IR/aC4rkEsMDyh2AZUClS6yrnx59fQZ65ZWV9Gkm9C/Rdt7gB2UNMv7WoVpmS5hhi+nrDsKnNGr0JsLuAE187j6k01LNun9TUaCnZtPO0BQSW3Uq1+SwlWy3n6vanelHGaOnVJu21fEBy8bnI+UNr1+faaP3YzuLTKpUzyyXd4psDz/ND/7ge81wSHs/xoG+0wflRccmNfb+sLNQAti0qL+/Ty2u597Nr815OjWt64unTp7z99tukGDnc3VUThGQNJBsbi31vTR1dZy5bm2Fgu91yfXXFdDqRcmKeJ3LOz/jEW5AN1IkxV/rROfPUOMFWZrnQp4ZaunU102Wvczodub29IcbEaRzPtKTKzRcRYkrLOaVlaL2zLJhPdtp81xr1mwCf5Lj80Md80JgE+Nzn+Dxf/ljkeb4R0GhuRcS0W8UCRJVWHbFJwVfdZBHwpfJbGxHtXtKo0vfEsq6WoC00swVov1F73cZ7v8TS+ElLKMkyfi7lqxa1kEXvtlIBREz1wJv6QTtWbSVYLZWPb7qxRpITHAEoduzV9ejs4KfWLKb5gqLYJs0Ljfbat2LGRPUxF+/v0xPGrvgwvKBFbcVlNNey+yKI94AiziMaatmyhqwqiyZr+4m2NWZwdvFeuL4auNoPdF1gsxksK6vKOI2M08w4Tswpo+Lw3cD1w1fZXT8keM9+t2Xo+kVC536ppMn4gNUmEqmk+nZkkaKM08zxeGKaM8fjzPFYmGIx3XX1y7FTtXHflX195u+F29NKPXo+XSgtgFWKFJRSJ229d1L5WuPjmsg+KTyXhMfHpPPxYe/7G2Xl/EGOXAuHVsHat4xS8F7B7EXt5PxrrlqxOSYOtwf++vd+L4/fegtUOR6PTONILoX9bkv/hS/QdYHr6ys2my0PHlzz2mde5Y03XmO7GShp5o3XX+Pu9o4vfemLHO7ujBublaL53vHmZIGsE2dZ1Nzk9SpDXcycxYUO7+w4Sw1I55iYarY1/nDirUePyCXz6PETbquCwWE8cZpGFBjn6bwYsIOgOSo5+eTGYsM325h8LnzYuHvzTT7oEe3p3wyfjYhlJLPY3KcWuaFezJ2qzoC5QPaKD5Zldc7O/aW0ILbONlq9P1SJqiStLnWalttIWXjkqu6Cb1szly1oXYqH5+C1BcBOrHLp3bk6an/bbeccfd+z3Q5GCeq8BaAlgjqcepw6OldwnR2/agfsgFITUrm+tUYGUFBrzhSBgodiNdpSTZa02GfSKAVFm4umKRDVD3XFS4IXDmTlIhBtK6JlYMh5aPhlEJ5D1kUjVTlPRpgV3RBMmmS32bLf2SDpaudwLsWyKaeReZ7JxTq4nBe2/bCUAbddZ7qWF+VJy67cfw+t9JqLdS8361lVK1vO08w0ZaYpMc+FGJUioPW1VJcQtQYDz6x6L//WJUZ4Jj19Ts+qaF1lL+2XL/QdrXi5can7erHxPGnUH2Vb08FlnaQGsNLGs/09T+aeN51O/F2U28ePcc4tEj/OOTabDddXV/gq7dN1HbvtluurPQ+ur+m8J82f5Wq/49GjRzx5/IjxeKzyfQq10SrXjKw21y4n5JSJKWIGBxB8FZzvOrPKBMz1L9eSayROMwU4TCP5yWNKKRyniXGaiZUfO8W4VETaZ1bKJVXj8oP9OL+lFZ92uHMOhyLW+FRaBhMLxlyR6lDVEj1Cya5yU1kqfqkW/+zHbYu+okrWTCEvD22NzVyEqJdWsIu+erv/8jet4OVcuXTOGV+1cmiHqlTQ9x2bobeKi60gq56sufWJKl4KzrdAOtRFoi5VEFMIasdugSy000A2l6EqSabO1/nZWQCrUJbYQlD5xqLifVKLqK/3Yu3riRezqG0dlEtCtnJTlhJFC1vtMfbTdQvnLpdluqBWF3BQBZpN8Lit/pw4KLX5KmfyHMkxVgcs6JzpPPpQJbfEbHJDtQhxzwSTrbTfTBia5izUyavGpDlnYrRmEOvEvijatLKM6FKWsZKHXcu9x1zuv9EIWJq4hKadV6rwu7l9SXHNm4F15lzxsUEEUT1Pbg487iJQO/9um9ZsawSx59qjvDdFgJRSNS2w0qnFolUlAIi1kWUaTSbvdDoRUzS9192O8XRiMwxshsFGZA1en6VILIvjelxKLcvKWWO2Pb7kQtSIKkzTxGmcbLIXKGLhg5Zihg1q18579KIhx8b7OehvPP6LD/Jr8vWs+HRBEHOokkqNcYqTgtRMqc8Z56SaIBSyt9+el4STaut+QWdzqUA2VY3kxZqh1ObXRk8TkbOu+VIKrHN0m5/duZkStQrh5TE7J3TBxnXofA1eayA7mLNY3wWG3hRCuiAWtJIRTWiZa9CZjI9HJcy2+VJbgsmOrSmVNBvoJbhWQJ3RMmrQv8T2z1AOREz9YB2aLw8+erNX5cbAObwy/oxfShKtlO/lnOk3Gau6kMyKq8Fh48U4MW25bXUW2fQ9fW3eKHNkrJzV0+0dp7sDuSidwtWwQZyj7wdC6KqByjOuRNQJrk5IRdXs91RrxaY+pmD2k0UZp8jheOI0RaYpsrhv1WDTSjSKua5gq0FtskHnlWEr1aCKymVHec2GOeg6V52FitniSqEkiJmlsWXFig/DewV/79XshZwnsc57uuCXbmaRswyOd+Zn3ncdXd/ba7csZW2IHMfRMjdaOe7qyDGSsFE1jicUUxh48PAhJWe6ruNqv+fVV18lOM87b72FVN77OI6kVJtSYrTO7Gq6skhw1XFtTSgXwW0xjp5ZSFtQ+vT2jid3d9bV7RzqjCawu75is9vhU6DfHOnG3hq/ppmY9SJzXT+yZz/CC3LRihXvByeOTb+zhV3IdZGkiwtWKYVU0rIt1+2myDHXAFerEpByHCPHMVrzpXOIC0YtyEKqNASTpWrplQvOq5gmtGDzzpK11WVZi3Ae/32wCkvXeXa7Dh88fRfYbvuq9uPZ1qbpYePp3YwnISWS58mql5rRYmcDuRzDEvA1qHZecL6NbKtIlqKkagRRBHIJqLNkWClV15pzQOvF41yH9wH5BCQrX+Ys6DcSXjAjy7nWVicRX7VYpTZHiFT9uCWQhdSkuKRUa9e2pnI4ga4OFFv52UpPsNJiyuZDHceJeZzqcXh6b9qRQzcsOpWazC4PtUDQiPCWii0osqgq3KunogVKNu08k0WZmaZIrEH30iF2j1/Urs/3XWa1zomuxkUQLsuVIhCcWyZkG4Ni9reNzP8iX9aKlw7PBrDy7G+ORhuQe5I7y0QjVezce8QJm2FDP5jzTsnNRjMznU7MY1wmvVLMWShX2SpVZY7z0sh1d3tLX4PY1159levra+ZxZL/fczoemeb5njvZorqgXDSdOHxdMDpH9amXhZZgdAKT1EopczgeuL29tQDBO3AO33UM+x2hM1943wVc8Gg22lB5j+B/DVtXfCSIEFwPrlhQtyQmqhW6FnIOy29dK881Js8cm166krIuWrOmUFAoxZHr88TpuYJXaQg1LLWqpJxlMkVAfJvCpWZ8oTlqCTb++9C4sIHttquW1IHdbiAEoxMMncc7MV1Yl2u2OaM51mRwle6ixg2LXqyvQa3QOb8kx0oLZCkUjZBTpSO6muRpQew5kK0ftPXkyFkib8WnHy+UkTW/dKAGWZYtabR1Y8JaM9M5I3rm6tQA19dSvFaxWQTVTC42/FJypFnOk3DtUiYXfMuH1AySE4evGSJK9WCvgarUhqlSj8lVboF3LXMlC4U3ZtP0i9l4sTGaAUIpeg5el2C1BQPLJ8NSsF3oBtTM7VnAvTWPtM/PThQdm6E2yGkCMpFCEnMYWyfQFR+GFui9p9zWvYav81qrTQSlZTirMxA1YHSV8+qDJwTjt6mr2pXFI6XgsYrMpbVrMw4wD/ZqjFCVDYIPlJwXJ63T8WjNHZwD7EYZaG5ardHzzFvFjry6+iDNRdA6pYu2SV+XhbVVM92SkcoxMo2jBQVzrMF5uThf3ft0aee6xitcseJ54MTRD9XZq7RA9pxLVFW8z0tJvWVsXZW7aiYJqRolFKyKmHOh6zN9zNacnAoxN045NO57m5NBaHu25sWakQXQlrWVGsg2fVlrsOp6R997QnD0vSxmBt4r3hfj9jpB5DzXq9Yx975lDT1PpZdhwuURi8dV/qJzRhtojzsXNi9e1FyMPuI39fXBmtl9MXzkQNY74Wo3mOtVaeL95wKGtJI7CqUOXqiZW5tIgjdlA8UyoGZja45Z85zITnAlIXPVq61ZIK3Z1o6W+Q2LSLnkgpTZJuoqNt2yTkuBpXHrnBBqmcUWsMY/OowHnt6eOE0zT29PHE4TU8zE4sBVHT3ccgZ4VyC7/CH3gtX2WC5v1cxY13leud7z4GpTuQ0TWhKnMZJOR/KcP+pXteIlhIhlcYBzRvMZXGYyMkoqirhCJ2Y9KU7wfaDvzbq532wYhqHKY1XpIFU0btCcEIxH66uFkYJNKqWQYyTGyLEUvvzFL/L4nUdcX11RYuThw4ccDwemySos5yC2Um2GwSorImZZXWzR20TgzZRFKicwE7Fu59j49MUoS33fUdSqQLnSmcbD0eTCqhJDnKYqNF8W+957n9wztpfnYHYdnyveH8579g9esfkrXQayZ/3mhYmtZoiDQs4TOY2WsS2JXCJFlet5YJzttzrHqmleYI7KFBtH3HjiaJOzstcv2FwnNZHUHPV8DV4veeiLwoIKoXNstoEQrIel34ipGTgl+FQlxSpdoc7+RV0dQL5easTa5sNzPmg5ZvvAamVIFO8dqmZ1a5QnowWWpoPJeRxKUcgJIwyvNcyXBS+ckdX6g1qaM+C+C1UrldRgt5UUoOrGOVMwyLQskg3iXMziLqlxRIGFKtBePtSiSRCHr0FlKaWOVEXO3WRWf2xZUa3ZJoRqK3JxSgEtwjhGjuPEaZzriaLmRKWtFGtgfFGibWWbdvv+0vMyoG2UhPPK1XvHZtNztdtAyeisaHaQjHLwKdN3XvEJ4T25sZf3U9kuKBnFaf1V1tWZ87VT2Vs2NtRsq6/ZUgEkeFMTuMz4lrLIY7UsZ8mZuRRub245uAMpzlxfX4HCPI/kyom9XBA7Z4qT6qxz2yww76t5WNOWHXPJZsMJZ8WDUoPg4L1x9GtlRxVSnIlxNvvpaaZk6/5uGasmGNg+q3NUu+ZjVzw/RBz9sLHEj2uNT7XIf+74XdAys6V4SnKoZopGcvGoFrpe6JNpqc6xEGsgO87UQBZSzEYBUoWSlixlCyPFyRLIOmlN1ZWic6FfnmuTmQ/CZvD44PBB6DrBe6pGbG5t3PfGxeUWWZpJ331OkloZKqUtTBftkRrMyvmBVN0jLe9+KYueTeFkxUuDj0dHFnj25/uxvMzHjQuC24vsRu49+cNf6fLxX00w+uwryzpzrvhU4uu0Qnuu8fQc43sNaVd8GN51Mr+8ef7ja9EJ8WIz81fx2/6wh36iw2TN+ny98UnSJeTDMjbv+0SRt4Af+FiPZsXz4DtV9Y2P68XW7/HrjvX7/HRg/R4/XVi/z29+rN/hpwvv+31+5EB2xYoVK1asWLFixYqvJ772QmsrVqxYsWLFihUrVnwNsAayK1asWLFixYoVK74p8fEHsiK/BpG/gci/9bG/9nvv73+GyN/GfGJfv9guiPy2et/3IPJT6vYfj8hfrNt+Wt0WEPmPENl9wH7+T4j8A++x/bsQ+aMf83v6dxD5sR/ra654efAyjEGRv3NvX18PiPwfEflZX9djWPENDRFeEeFXfYyv910ifKT5RoR/XYSviPDXntn+k0X48yL8FRH+cxH+3rr9F4rw10X4syK8Vrf9aBH+3Q/Yh4jwp0V48PV87yK8IcJ/8HHte8U3Nr4WGdlfBfxsVH/xva0iH6NCwj38OeAf4t0k7H8Y+LH18k8Cv6Nu/58A/3Pg5wK/rm77lcAfQPX4nnsQeQ34+1D9Mx/rkb8/fgfwv/yE9rXi04d1DH4QPr7P4bcD79+au2IFvALvHcyJfJyqQc+F3wv8nPfY/n8AfpMqPxn45+vfAL8a+HuA3wn8D+u23wz8cx+wj58L/FVVbvg6vndV3gK+JMLf/7Xcz4pvDHy8gazI/xX4UcCfQOTXIvLdiPx+RP4c8PsR+RGI/OmaiflTiHxHfd6PRuTPI/K9iPxmRO7q9i8g8mcQ+SuI/DVEfsa79qn6l1H9O+9xNL8A+H1VDf7PA68g8gUgArt6iYi8Avx84Pd9wDv7hXCxuhP5OYj8TUT+EvDfvdj+GUT+UH1/fx6Rn1S3v4HIn0TkryPyuxH5AUReR2SPyB9D5K/W9/c/qK/0Z4F/6GsYeKz4tOJlGYOGX43IX6rH/BPq8b7fGHz2c/gvIfIX6vv6nqUCIvKPX2z/nYj4evm99f1/LyK/tr7vHwBeQ+TzH3DcK15u/BbgR9ds52+tWcU/K8IfBv4LEX7EZYZUhF8nwnfX2z9GhP9IhL8qwl8S4UdfvrAIf48If7lmSX9m3cdfqduunz0QVf4M8Og9jlGBB/X2Q+CL9XYBBuo4FeFnAF9W5fs+4P3+YuD/9Q3y3v9QPZ4Vn3acvZ0/pgv8HYXX6+3vVviLCtv69x9R+GX19q9Q+EP19h9V+EX19j+lcFdv/9MK/2y97RWun2u/59f86Rd//ymF/5rCdyj8xwr/qcJPUviXFL7rQ97Tv6nw8+vtjcIPKfxYNSnrf0/hj9b7frvCb6y3f5bCX6m3/1WFf6be/jnVc+R1hV+o8Lsu9vPw4vafVPipH/v3s14+/ZdP+xg87+tX19u/SuF319vvNwaf/Rx+u8Ivrrd7ha3CT6yfT1e3/18UfqnCT1X4kxf7fuXi9u9S+IVf9+98vXxDXkB/BOhfu/j7u0APoD/yfe7/daDfXW//f0D/kXp7A7qrz/+joP910L8I+h31/j8C+vfX21eg4XmOp277iaA/CPpDoD8M+p11+8+u+/gjoA9B/0PQz3zI+/0B0OtvhPcO+q2g3/v1/g2sl6/95ZNo9vrDqJ7q7Z8G/MF6+/cDP/1i+79fb//Bi+f+Z8AvR+S7gf8yqrcvfDSqP4jqd6H604Aj8G3A36jZmn8XkR/3Hs/6AvBWvf0TgO9H9fvq6PsDF4/76fV9geqfxrI1D+r2f6du/w+Ax/Xx3wv8bET+RUR+BqpPL17rK8C3vPD7XbHi0zcGG/4f9fovAj+i3n6/MQj3P4f/FPhfI/K/Ar6zbv9vAD8V+M8Q+Sv17x8F/P+AH4XIb0fk5wA3F8ewjtMVXy3+girf/0EPqFnFb1Xl/wmgyqhKo938ROBfA36+Kj9Yt/054F8W4dcAr6iSvorj+ZXAr1Xl24FfC/yeus8/qcpPVeXnY9WVPw78OBH+byL8LhHei8/+GVU+6BzxSb73dWy+JPgkAtnDR36m8eH+AeCHgd+LyC/9Kp79w8C3X/z9bXXbJf53GN/n1wC/G+Ol/sb3eK0TsPkq9v18UP3/Aj8FC2h/MyL//MW9m7rfFSteFJ/WMTjV68zzuRSePwfVPwj8t+vr/nGsaUuAfxPVn1wvPx7V70b1MfBfAf5j4J+qx9mwjtMVXy0ux2Pi/jz8PPPMl4AR+K+2Dar8FuCfALbAnxPhJ3wVx/PLOC8K/32wZq+GGrD+j4D/M/Cb6uP/E967bJ9EPjCu+CTf+zo2XxJ80vJb/2/gH6u3fzHGBQX48xgHjov7QeQ7gTdR/V3Y5PFTvop9/WHgl2Kd038f8BTVL1289s8Evojq92EcoFIv77XK/BvAj6m3/ybwIxBpnJ1fdPG4P0sb3CLfBbyN6g22YvxH6/b/JvBqvf0twBHVPwD81mfe34+D+92lK1Z8DPg0jMEPwvuNwfsQsUyr6m/DOH0/CfhTwH8Pkc/Wx3wGke/E1BEcqv93LOhex+mK58UtvJuveoE3gc+K8JoIA/DzAGpW8++K8N8BEGG4yIA+Af5bwP9ehO+q9/9oVb5XlX8Rq6J8NYHsF4GfWW//LHgXB/bXA79NlYgFi8r7j9O/hVUx4Ov/3tex+ZLgk24m+tXAv4HIr8fKhL+8bv9fAH8AkX8Wa+hoJfbvAn49IhG4A96dDRL5NVgW5/PA9yDyx1H9J7AyyM8F/jZWvvzlF88RbEJqzVX/GvBvYZ/Hr3yP4/5jWKf170Z1ROSfBP4YIkds4myD9buBfx2R76n7/GV1+28C/m1EfglW0vwyNsi/C/itiBSsAeZX1uP7HHBC9cvv9SGuWPEC+OYfgx+M7+a9x+Cz+EeBX1Lf15eBfwHVR4j8c8B/iIjDxuT/FMvq/Bt1G8A/U99DhwXX//mHHNOKlxSqvCPCn6tNTX8C+x1f3h9F+N8CfwGrVvzNi7t/CfA76/0R+O9fPO9NEX4e8CdE+BXAPy7CP4gFmH+97useRPi3sfH8ugh/F/iNqvwe4H8M/CtVSWDEFEbac74F+HtV+U1102/HgsUnYIHmM/hjdR9/+xvgvf+Dz+5zxacT3xgWtaYdeUJVEfnHgF+E6i/4eh/WPYj8J8DPQ/XJR3juAGRUE6ab+TtQ/ckf8PhfC9yg+ns+2sGuWPFV4tM+Br8WEPlHgJ+C6v/m630oK1Z8I0CELwC/T5Wf/Q1wLH8G+AWqS0/Kik8pvlHknX4q8K/WLM0T4Fd8fQ/nPfFPA9+BHd9Xi+8A/r2a0ZmxFfAH4QmtYWXFik8Gn/Yx+LVAAP6lr/dBrFjxjQJVvlQbwR6o8m5KzycEEd4A/uU1iH058I2RkV2xYsWKFStWrFix4qvEJ93stWLFihUrVqxYsWLFx4I1kF2xYsWKFStWrFjxTYk1kF2xYsWKFStWrFjxTYk1kF2xYsWKFStWrFjxTYk1kF2xYsWKFStWrFjxTYk1kF2xYsWKFStWrFjxTYn/Pwc9hoKKr+cWAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 864x720 with 30 Axes>"
       ]
@@ -1654,8 +1664,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[0.11279975 0.10304905 0.10780791 0.09079217 0.10205003 0.09319472\n",
-      "  0.07174336 0.09929511 0.11256534 0.10670257]]\n"
+      "[[0.09959157 0.09409852 0.10286462 0.10174965 0.10386666 0.10095541\n",
+      "  0.10075674 0.10259376 0.09819926 0.09532377]]\n"
      ]
     }
    ],
@@ -1681,7 +1691,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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cDu0LPLbnZfhj633x6H7P6z2fGFQ6gjjNv4o43Z7eXnNcW7Zp9Njn2dbxcGK64vrEgW0X4iB4cguh1Yn+/hcSZ1g7EIOdk575wMK+1JnEmcVfTli2W4AtO4/tQvSF9h3+3SB8ZAvBdYizkCOI4J3bAukJwDqTrLdS5/bT2rZ9Wuf+R1g4y+dPgbPb/t57n3/bp1YFvszCAe3RAPt6bXsMdrB7yGXLKLoYG2414sg0mtc7OmodTbRU1mYJvqFDdGncTPylUohv3VzRPiDziVHxXkOo1Rl9GGcA2xMDfaNW33XARQNvz7Gvd+c9O6J9AC4lWlp3Ase05+YTLcPH912baGF9jJhitQXwofbctPbvB4ixhNH/2Y44E9hykrXn0KYwEa3dS4iZBt+Z8Lr30L7pRhx4t6Hn02EWDd8j2ufnImIWwsVEH/z5RF9vH/VmEq3azYlQvw14H3GwH3U/7EZ0P5zY7p9KnCX1Ot2QGE8Z7WfnA5/vPDefaHQM/u3SB12+rMIPtaMQp+Fbth1yJaLFdCadrwEDG0yiztG0U+1OAJ5IhP01tK9DDriejwE2IlqBr+kswzeAKwasm7LeLQT/kRg4fT9xyv8uojX6oVb7CQOu94lEi++VxDf8Xtx57rRRCLT7s5lkf3sL3x+0/fgJRB/71u25zxBnPHOJ+dW3ANsPub91lmtH4NLO/QNYOAtmT+LMqJcAJPq0byb6d5/eHtuGCP7RLJNn0JnqRQ9z2zsZMq3lx0uI1vYhRBfDX7fP2TuJeb6DTzV7yOXNLP4gG27/tmE+3jbcNsRcxI8TLZklDt5OrX2Ifq5NO4/tRxwRVxlg3XYiRlYhBj5uBv6q82E8uROIl/WxjlNhvTs1TgNe3W6vSEwBuqAF4yw6U64GqL0XcaC7mmgBvYeYe3sqMfBzIz1O9ida3QcTB5i5wK+AnwDzO695b9uXv86AX7KYsEzziOtrfIYJp/nE3Peb6GGOOwu7lNbqbOsjRs8RB+HLgT/v/J/erzPBwmtYPILo4ngfcHh77CBiutvGQ2/7P7qc6QsQfTOjfpmtiaPTLGIg6kcthLcnWsLn08PINDH16c3EwMd+xHSYa4d6Q4BnEoNpb27rsBHR4ng5MRvgCuDdDDwAMO717tQ9kDj9nNt57FoGnmFC9HFeTWtdE7MvTms/5xMzLnr/kkULnzuIQbaDiMG9M+hc4Ifohlq93e59nu8D/U6iH/Zq2rzn9tgKbZ/YvOf3+waif/UYontpbqfeNnS+vdr3+ra6twH7tPurEi3hrxJdUdOH2ucWe7lTi8OaRNfCocTpwePbm7M70Qp+MtFP9DXia5l9Ds48hjgl/zLRKhj0VIT4htEtwHnt/kpEH9m7iJbYp5nkwMdUXO9Wcy3gre3nGS38v8bAk92JvshvA09t92cQsxsuJ77oMciV1ojT30uJ1t9ziMG/NxPTJp8+9PaesCzPB97YgnANYurb5UTf6IwB6m1FnFVs1nnsvBb8g+xrE8L35LbNR91ee3aeu5I4EK41zvfgIZc9fQHi9OdjREth5fbYa1l4unAscWWm3o7QE+qvSBsRHcO6HkBcXOeQzmMXj/tDOe71bvXWI2Y6XEF0s4yl742YbvUXLJxutRfRGu91at+D1J5NXNznxcSlFd9B9D32ch2Ph1H/2HYAOoHo9vgWMXD9gvb4jgPU3JyYUfHStt0vJ6b83UL0i68+4PruSzQsRt0Ph7e6zyS6Nr8MPGYc2/5hL3Na4T+8+My5REtwRtth7yZGbL9Hm+e7LPwQrb+ftFbJgUS/XHpf1BjX//4upzHV24DoariMaIH/kAG/XvwA9UfXuX0JcfbR2xcbHqDWqFth1LL/MJ0BPqLf++x2+1gGuMAQMXPpOGIWxLOJQciXEFcZ67Ue0cU06tqY3wL+kgmvObgdeK4Y10F/sdYhpejCHWSDzmPPJU5VDmr3jyL6f5foqmZT+acF7wISv4GzPP0Q83z3JFrDuybU35YYAxhkcPUB6m3SGjKX0L7d2B6fR7v2xBiWYTTPdrvWiOr9G25El+VlRDfllUQf9/W0+cWd163JgNcUmcxPyrUgar3/z4+8s5RyPfA7YkDqPuCgdh2Gc4BP1FrvG/JC3BlqrV8spTwduK0OcI0JLarW+mvig3pZUv3rSynz2nL0rpSyE9G6/Gz7E1onEFPKbgKOL6XcWeNaE1sAc9r1d+8e+DO1oJSyLTHt8LW11q/3XaDW+qNSys1EC/s1Na5lcifw0hYZZ7bX3d137b6kBHB7Y55JnKrcQZw+fJE4PX8k0Vd6Va315zD4hbhT1Fqvyl4GjdVvBvzdM4m/YbcZ0eWyF9HiX4MY7HxLKWVr4htoh9RafzXgsgBQa11QSvkBMf3ypwM2oj5MHGheVUq5q9b6uVLKfwIfbAeezwxQszdj+5NEnStBzSQuPv29WutT2l9kqMSVmC6ttX6+lLJhrfVnY1kwaRlQStmDmFHz7Vrrn7WrBz6HuMj5TGL2x921xyvbTSWllGcRffyvJq4tchJxTeOfpi7YHzGWP0nUCd/9iP7P/YGtSylH1loX1LhU3C+JP7CJ4Sstnlrr5cTF+w8opRxaa72HuNbGL4jLLt61rIYvQK31S8QUtDOIWVRHT/XwhTF1QbTw3ZGYg/jxWuvV7bqvXy+lzCXmTO5HfFVU0hKotV5USrmP6I6g9Ql/ghiAGqT/eSqptX6llHJD3Ky/yF6eh2OwLohSyuOI+a1nt9OhC4i5mBt1XrMTMTDyLeLriv9aOn+hV9Liaxf3Pwt4Za3189nLowc3ZBfEPcAtpZR12unQy4H/LqV8aPSCWus3iasizSO+IQVxuiRpCdVaLyXm0N+QvSx6aIO0gEet2DbA9l3ib32dUkrZgJhe9v1a6wmd1+9GfHtmK4afHiNJU0LvAdwZcNuI+DbbI4gpZl+otb61hfDngJtqrceUUqa3ub4r11r/r9eFkaQprPdBuBa+zyK+/nkrMeXsVODdpZQFtdbTSymHEl/LpNZ6X/uv9/S9LJI0lfUewKWUHYiLcOzRfs4CfkvMcDirtXjfQlwp6n52O0ha3gzRBbEB0bqdSbSCDyeu6XAH8Sdv7vJbYJI0wCyIWuvttdZrib8se16t9cfExTI2A66vtV5VSil915Wkpc2QX8T4LnFRjBnEZemOH33Dze4GSRo2gL9M/NWH/YG31lqvGbCWJC11Br8YT2ea2TJ1SUlJmqxxXIxnAdjtIEkTje1ylJKkRY3lcpSSpD9kAEtSEgNYkpIYwJKUxACWpCQGsCQl+X/OGOhsFOtSRAAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1716,7 +1726,7 @@
     {
      "data": {
       "text/plain": [
-       "0"
+       "4"
       ]
      },
      "execution_count": 33,
@@ -1758,205 +1768,205 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "125/125 [==============================] - 3s 21ms/step - loss: 60.2776 - accuracy: 0.1605 - lr: 1.0000e-06802 - acc\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 58.1451 - accuracy: 0.1558 - lr: 1.0000e-06\n",
       "Epoch 2/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 29.3304 - accuracy: 0.2040 - lr: 1.0798e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 28.4254 - accuracy: 0.2005 - lr: 1.0798e-06\n",
       "Epoch 3/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 22.7748 - accuracy: 0.2260 - lr: 1.1659e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 21.5144 - accuracy: 0.2097 - lr: 1.1659e-06\n",
       "Epoch 4/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 20.0149 - accuracy: 0.2488 - lr: 1.2589e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 17.0293 - accuracy: 0.2295 - lr: 1.2589e-06\n",
       "Epoch 5/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 17.4151 - accuracy: 0.2610 - lr: 1.3594e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 14.0017 - accuracy: 0.2225 - lr: 1.3594e-06\n",
       "Epoch 6/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 15.5394 - accuracy: 0.2690 - lr: 1.4678e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 11.8071 - accuracy: 0.2222 - lr: 1.4678e-06\n",
       "Epoch 7/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 14.0803 - accuracy: 0.2887 - lr: 1.5849e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 10.4193 - accuracy: 0.2348 - lr: 1.5849e-06\n",
       "Epoch 8/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 12.6668 - accuracy: 0.2945 - lr: 1.7113e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 9.3120 - accuracy: 0.2482 - lr: 1.7113e-06\n",
       "Epoch 9/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 11.8588 - accuracy: 0.3090 - lr: 1.8478e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 8.4738 - accuracy: 0.2625 - lr: 1.8478e-06\n",
       "Epoch 10/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 10.7524 - accuracy: 0.3147 - lr: 1.9953e-06370 - ac\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 7.4389 - accuracy: 0.2797 - lr: 1.9953e-06\n",
       "Epoch 11/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 10.0094 - accuracy: 0.3198 - lr: 2.1544e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 6.8308 - accuracy: 0.2643 - lr: 2.1544e-06\n",
       "Epoch 12/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 9.4757 - accuracy: 0.3110 - lr: 2.3263e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 5.9198 - accuracy: 0.2750 - lr: 2.3263e-06\n",
       "Epoch 13/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 8.6934 - accuracy: 0.3310 - lr: 2.5119e-06\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 5.3732 - accuracy: 0.2745 - lr: 2.5119e-06\n",
       "Epoch 14/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 7.5893 - accuracy: 0.3485 - lr: 2.7123e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 4.9192 - accuracy: 0.2747 - lr: 2.7123e-06\n",
       "Epoch 15/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 7.1017 - accuracy: 0.3668 - lr: 2.9286e-06\n",
+      "125/125 [==============================] - 4s 30ms/step - loss: 4.5268 - accuracy: 0.2810 - lr: 2.9286e-06\n",
       "Epoch 16/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 6.4868 - accuracy: 0.3663 - lr: 3.1623e-06\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 3.9997 - accuracy: 0.2940 - lr: 3.1623e-06\n",
       "Epoch 17/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 6.0443 - accuracy: 0.3803 - lr: 3.4145e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 3.6343 - accuracy: 0.3085 - lr: 3.4145e-06\n",
       "Epoch 18/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 5.7332 - accuracy: 0.3820 - lr: 3.6869e-06\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 3.4587 - accuracy: 0.3090 - lr: 3.6869e-06\n",
       "Epoch 19/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 5.5397 - accuracy: 0.3898 - lr: 3.9811e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 3.1796 - accuracy: 0.3142 - lr: 3.9811e-06\n",
       "Epoch 20/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 5.1825 - accuracy: 0.3970 - lr: 4.2987e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.8950 - accuracy: 0.3235 - lr: 4.2987e-06\n",
       "Epoch 21/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 4.9526 - accuracy: 0.4100 - lr: 4.6416e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.7645 - accuracy: 0.3392 - lr: 4.6416e-06\n",
       "Epoch 22/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 4.4758 - accuracy: 0.4252 - lr: 5.0119e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.5338 - accuracy: 0.3450 - lr: 5.0119e-06\n",
       "Epoch 23/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 4.3288 - accuracy: 0.4280 - lr: 5.4117e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.3537 - accuracy: 0.3627 - lr: 5.4117e-06s: 2.3589 - ac\n",
       "Epoch 24/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 4.0896 - accuracy: 0.4322 - lr: 5.8434e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.2710 - accuracy: 0.3735 - lr: 5.8434e-06\n",
       "Epoch 25/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 3.7686 - accuracy: 0.4435 - lr: 6.3096e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.1130 - accuracy: 0.3812 - lr: 6.3096e-06\n",
       "Epoch 26/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 3.7501 - accuracy: 0.4363 - lr: 6.8129e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.0246 - accuracy: 0.3855 - lr: 6.8129e-06\n",
       "Epoch 27/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 3.2985 - accuracy: 0.4633 - lr: 7.3564e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.9256 - accuracy: 0.3972 - lr: 7.3564e-06\n",
       "Epoch 28/100\n",
-      "125/125 [==============================] - 3s 21ms/step - loss: 3.1863 - accuracy: 0.4652 - lr: 7.9433e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.8877 - accuracy: 0.4078 - lr: 7.9433e-064 - accuracy: 0.\n",
       "Epoch 29/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.8219 - accuracy: 0.4888 - lr: 8.5770e-069 - accuracy: 0.50 - ETA: \n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.7927 - accuracy: 0.4087 - lr: 8.5770e-06\n",
       "Epoch 30/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.7807 - accuracy: 0.4875 - lr: 9.2612e-067 - accuracy: 0.48\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.7646 - accuracy: 0.4130 - lr: 9.2612e-06\n",
       "Epoch 31/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.4144 - accuracy: 0.5095 - lr: 1.0000e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.6831 - accuracy: 0.4257 - lr: 1.0000e-05\n",
       "Epoch 32/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.5267 - accuracy: 0.4922 - lr: 1.0798e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6677 - accuracy: 0.4330 - lr: 1.0798e-05\n",
       "Epoch 33/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2568 - accuracy: 0.5225 - lr: 1.1659e-05\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.5967 - accuracy: 0.4585 - lr: 1.1659e-054 - accura\n",
       "Epoch 34/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.2646 - accuracy: 0.4990 - lr: 1.2589e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5875 - accuracy: 0.4430 - lr: 1.2589e-050 - accuracy: 0. - ETA: 0s - loss: 1.5891 - accuracy: 0.\n",
       "Epoch 35/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.1021 - accuracy: 0.5095 - lr: 1.3594e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.5627 - accuracy: 0.4590 - lr: 1.3594e-05\n",
       "Epoch 36/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.8296 - accuracy: 0.5493 - lr: 1.4678e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5594 - accuracy: 0.4525 - lr: 1.4678e-05\n",
       "Epoch 37/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.7501 - accuracy: 0.5435 - lr: 1.5849e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5173 - accuracy: 0.4665 - lr: 1.5849e-05\n",
       "Epoch 38/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.8584 - accuracy: 0.5130 - lr: 1.7113e-05\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.5107 - accuracy: 0.4690 - lr: 1.7113e-05\n",
       "Epoch 39/100\n",
-      "125/125 [==============================] - 3s 21ms/step - loss: 1.7223 - accuracy: 0.5355 - lr: 1.8478e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.4870 - accuracy: 0.4697 - lr: 1.8478e-05\n",
       "Epoch 40/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.6808 - accuracy: 0.5400 - lr: 1.9953e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.4646 - accuracy: 0.4795 - lr: 1.9953e-05\n",
       "Epoch 41/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.4447 - accuracy: 0.5723 - lr: 2.1544e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.4927 - accuracy: 0.4700 - lr: 2.1544e-055 - \n",
       "Epoch 42/100\n",
-      "125/125 [==============================] - 3s 21ms/step - loss: 1.5951 - accuracy: 0.5450 - lr: 2.3263e-05\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.5002 - accuracy: 0.4638 - lr: 2.3263e-05\n",
       "Epoch 43/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.4580 - accuracy: 0.5548 - lr: 2.5119e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.5031 - accuracy: 0.4613 - lr: 2.5119e-05\n",
       "Epoch 44/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3944 - accuracy: 0.5660 - lr: 2.7123e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.4972 - accuracy: 0.4690 - lr: 2.7123e-05\n",
       "Epoch 45/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3392 - accuracy: 0.5695 - lr: 2.9286e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.4938 - accuracy: 0.4642 - lr: 2.9286e-05\n",
       "Epoch 46/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3687 - accuracy: 0.5518 - lr: 3.1623e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.5846 - accuracy: 0.4430 - lr: 3.1623e-05\n",
       "Epoch 47/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3353 - accuracy: 0.5667 - lr: 3.4145e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5091 - accuracy: 0.4568 - lr: 3.4145e-05\n",
       "Epoch 48/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3154 - accuracy: 0.5665 - lr: 3.6869e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.5310 - accuracy: 0.4502 - lr: 3.6869e-05\n",
       "Epoch 49/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3372 - accuracy: 0.5598 - lr: 3.9811e-054 - accuracy: \n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.5338 - accuracy: 0.4527 - lr: 3.9811e-05\n",
       "Epoch 50/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3050 - accuracy: 0.5627 - lr: 4.2987e-05\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.4778 - accuracy: 0.4733 - lr: 4.2987e-05\n",
       "Epoch 51/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3123 - accuracy: 0.5535 - lr: 4.6416e-054 - accu\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5517 - accuracy: 0.4478 - lr: 4.6416e-05\n",
       "Epoch 52/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3686 - accuracy: 0.5293 - lr: 5.0119e-05\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.5429 - accuracy: 0.4505 - lr: 5.0119e-05\n",
       "Epoch 53/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.4103 - accuracy: 0.5225 - lr: 5.4117e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5791 - accuracy: 0.4420 - lr: 5.4117e-05\n",
       "Epoch 54/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3974 - accuracy: 0.5320 - lr: 5.8434e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6217 - accuracy: 0.4205 - lr: 5.8434e-05\n",
       "Epoch 55/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3539 - accuracy: 0.5360 - lr: 6.3096e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5550 - accuracy: 0.4412 - lr: 6.3096e-05\n",
       "Epoch 56/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3960 - accuracy: 0.5285 - lr: 6.8129e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6207 - accuracy: 0.4243 - lr: 6.8129e-05\n",
       "Epoch 57/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.4210 - accuracy: 0.5080 - lr: 7.3564e-05\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.5623 - accuracy: 0.4505 - lr: 7.3564e-05\n",
       "Epoch 58/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.3646 - accuracy: 0.5250 - lr: 7.9433e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5867 - accuracy: 0.4367 - lr: 7.9433e-05\n",
       "Epoch 59/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.4185 - accuracy: 0.5102 - lr: 8.5770e-05\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.6819 - accuracy: 0.4027 - lr: 8.5770e-050 - \n",
       "Epoch 60/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.4707 - accuracy: 0.4803 - lr: 9.2612e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.6531 - accuracy: 0.4117 - lr: 9.2612e-05\n",
       "Epoch 61/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.4065 - accuracy: 0.5042 - lr: 1.0000e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.6331 - accuracy: 0.4210 - lr: 1.0000e-04\n",
       "Epoch 62/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.4203 - accuracy: 0.4963 - lr: 1.0798e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.6858 - accuracy: 0.3957 - lr: 1.0798e-04\n",
       "Epoch 63/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.5497 - accuracy: 0.4678 - lr: 1.1659e-04\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 1.7017 - accuracy: 0.4005 - lr: 1.1659e-045 - ac\n",
       "Epoch 64/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.6588 - accuracy: 0.4310 - lr: 1.2589e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.6921 - accuracy: 0.3960 - lr: 1.2589e-04\n",
       "Epoch 65/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.5678 - accuracy: 0.4390 - lr: 1.3594e-04\n",
+      "125/125 [==============================] - 4s 30ms/step - loss: 1.8322 - accuracy: 0.3363 - lr: 1.3594e-04\n",
       "Epoch 66/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 1.6629 - accuracy: 0.4123 - lr: 1.4678e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.7568 - accuracy: 0.3655 - lr: 1.4678e-04\n",
       "Epoch 67/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.6737 - accuracy: 0.4055 - lr: 1.5849e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8597 - accuracy: 0.3265 - lr: 1.5849e-04\n",
       "Epoch 68/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.6150 - accuracy: 0.4123 - lr: 1.7113e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.7671 - accuracy: 0.3435 - lr: 1.7113e-04\n",
       "Epoch 69/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.6676 - accuracy: 0.4150 - lr: 1.8478e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.8698 - accuracy: 0.3207 - lr: 1.8478e-04\n",
       "Epoch 70/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.7472 - accuracy: 0.3823 - lr: 1.9953e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.0889 - accuracy: 0.2025 - lr: 1.9953e-04\n",
       "Epoch 71/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.8559 - accuracy: 0.3315 - lr: 2.1544e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.1929 - accuracy: 0.1367 - lr: 2.1544e-04\n",
       "Epoch 72/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.9034 - accuracy: 0.3115 - lr: 2.3263e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.2795 - accuracy: 0.1135 - lr: 2.3263e-04\n",
       "Epoch 73/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.9967 - accuracy: 0.2560 - lr: 2.5119e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.2763 - accuracy: 0.1280 - lr: 2.5119e-04\n",
       "Epoch 74/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.1371 - accuracy: 0.1885 - lr: 2.7123e-048 - \n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.2561 - accuracy: 0.1377 - lr: 2.7123e-04\n",
       "Epoch 75/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.2103 - accuracy: 0.1462 - lr: 2.9286e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.2388 - accuracy: 0.1375 - lr: 2.9286e-04\n",
       "Epoch 76/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.2582 - accuracy: 0.1395 - lr: 3.1623e-040 - \n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.2863 - accuracy: 0.1080 - lr: 3.1623e-04\n",
       "Epoch 77/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.2285 - accuracy: 0.1430 - lr: 3.4145e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.2824 - accuracy: 0.1112 - lr: 3.4145e-04\n",
       "Epoch 78/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.2112 - accuracy: 0.1485 - lr: 3.6869e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.2705 - accuracy: 0.1308 - lr: 3.6869e-04\n",
       "Epoch 79/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2830 - accuracy: 0.1195 - lr: 3.9811e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.2849 - accuracy: 0.1140 - lr: 3.9811e-04\n",
       "Epoch 80/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2711 - accuracy: 0.1268 - lr: 4.2987e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.2887 - accuracy: 0.1157 - lr: 4.2987e-04\n",
       "Epoch 81/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.2832 - accuracy: 0.1163 - lr: 4.6416e-040 - accuracy\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.2914 - accuracy: 0.1105 - lr: 4.6416e-04\n",
       "Epoch 82/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2898 - accuracy: 0.1140 - lr: 5.0119e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.2878 - accuracy: 0.1125 - lr: 5.0119e-04\n",
       "Epoch 83/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.2965 - accuracy: 0.1098 - lr: 5.4117e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.2833 - accuracy: 0.1152 - lr: 5.4117e-04\n",
       "Epoch 84/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.2946 - accuracy: 0.1088 - lr: 5.8434e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.2810 - accuracy: 0.1190 - lr: 5.8434e-04\n",
       "Epoch 85/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2930 - accuracy: 0.1085 - lr: 6.3096e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.2884 - accuracy: 0.1107 - lr: 6.3096e-04\n",
       "Epoch 86/100\n",
-      "125/125 [==============================] - 4s 29ms/step - loss: 2.2923 - accuracy: 0.1095 - lr: 6.8129e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.2889 - accuracy: 0.1110 - lr: 6.8129e-04\n",
       "Epoch 87/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.2958 - accuracy: 0.1055 - lr: 7.3564e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.7234 - accuracy: 0.1082 - lr: 7.3564e-04\n",
       "Epoch 88/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.2978 - accuracy: 0.1095 - lr: 7.9433e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 3.4707 - accuracy: 0.1058 - lr: 7.9433e-04\n",
       "Epoch 89/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.3043 - accuracy: 0.1047 - lr: 8.5770e-046 \n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.3014 - accuracy: 0.1053 - lr: 8.5770e-04\n",
       "Epoch 90/100\n",
-      "125/125 [==============================] - 3s 21ms/step - loss: 2.3001 - accuracy: 0.1032 - lr: 9.2612e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.3014 - accuracy: 0.1053 - lr: 9.2612e-04\n",
       "Epoch 91/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.2994 - accuracy: 0.0997 - lr: 0.0010\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.3014 - accuracy: 0.1053 - lr: 0.0010.301\n",
       "Epoch 92/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.3023 - accuracy: 0.0980 - lr: 0.0011\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 2.3014 - accuracy: 0.1002 - lr: 0.0011\n",
       "Epoch 93/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.3040 - accuracy: 0.1037 - lr: 0.0012\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.3014 - accuracy: 0.1053 - lr: 0.0012\n",
       "Epoch 94/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.3002 - accuracy: 0.0948 - lr: 0.0013\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.3014 - accuracy: 0.1053 - lr: 0.0013\n",
       "Epoch 95/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.3002 - accuracy: 0.0988 - lr: 0.0014\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.3014 - accuracy: 0.1028 - lr: 0.0014\n",
       "Epoch 96/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.3002 - accuracy: 0.1025 - lr: 0.0015.3000 \n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.3014 - accuracy: 0.0980 - lr: 0.0015\n",
       "Epoch 97/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.3002 - accuracy: 0.0988 - lr: 0.0016\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.3014 - accuracy: 0.1040 - lr: 0.0016\n",
       "Epoch 98/100\n",
-      "125/125 [==============================] - 3s 22ms/step - loss: 2.3002 - accuracy: 0.1018 - lr: 0.0017\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.3014 - accuracy: 0.1053 - lr: 0.0017\n",
       "Epoch 99/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 2.3002 - accuracy: 0.1020 - lr: 0.0018\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.3014 - accuracy: 0.1005 - lr: 0.0018\n",
       "Epoch 100/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 2.3002 - accuracy: 0.1042 - lr: 0.0020\n"
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.3014 - accuracy: 0.1053 - lr: 0.0020\n"
      ]
     }
    ],
@@ -2002,7 +2012,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2065,205 +2075,205 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "63/63 [==============================] - 6s 81ms/step - loss: 87.3796 - accuracy: 0.1150 - val_loss: 63.4311 - val_accuracy: 0.1440\n",
+      "63/63 [==============================] - 6s 84ms/step - loss: 101.2536 - accuracy: 0.1265 - val_loss: 66.8154 - val_accuracy: 0.1490\n",
       "Epoch 2/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 51.3846 - accuracy: 0.1505 - val_loss: 48.1949 - val_accuracy: 0.1500\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 57.1182 - accuracy: 0.1590 - val_loss: 54.2139 - val_accuracy: 0.1760\n",
       "Epoch 3/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 41.1413 - accuracy: 0.1573 - val_loss: 40.6570 - val_accuracy: 0.1600\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 50.6808 - accuracy: 0.1602 - val_loss: 47.4573 - val_accuracy: 0.1710\n",
       "Epoch 4/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 35.6244 - accuracy: 0.1663 - val_loss: 36.0196 - val_accuracy: 0.1730\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 43.1654 - accuracy: 0.1675 - val_loss: 43.4377 - val_accuracy: 0.1850\n",
       "Epoch 5/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 32.3886 - accuracy: 0.1657 - val_loss: 33.1440 - val_accuracy: 0.1710\n",
+      "63/63 [==============================] - 4s 68ms/step - loss: 40.4142 - accuracy: 0.1643 - val_loss: 39.9637 - val_accuracy: 0.1920\n",
       "Epoch 6/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 28.9966 - accuracy: 0.1650 - val_loss: 30.4703 - val_accuracy: 0.1570\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 36.3001 - accuracy: 0.1867 - val_loss: 37.2917 - val_accuracy: 0.1800\n",
       "Epoch 7/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 27.2292 - accuracy: 0.1698 - val_loss: 28.9967 - val_accuracy: 0.1640\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 34.0930 - accuracy: 0.1690 - val_loss: 34.8942 - val_accuracy: 0.1840\n",
       "Epoch 8/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 26.0068 - accuracy: 0.1717 - val_loss: 26.4203 - val_accuracy: 0.1790\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 32.4781 - accuracy: 0.1860 - val_loss: 34.5795 - val_accuracy: 0.1780\n",
       "Epoch 9/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 24.0249 - accuracy: 0.1737 - val_loss: 25.5074 - val_accuracy: 0.1690\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 30.2826 - accuracy: 0.1793 - val_loss: 32.6315 - val_accuracy: 0.1940\n",
       "Epoch 10/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 22.6697 - accuracy: 0.1743 - val_loss: 24.8954 - val_accuracy: 0.1720\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 28.2433 - accuracy: 0.1852 - val_loss: 29.9914 - val_accuracy: 0.1990\n",
       "Epoch 11/100\n",
-      "63/63 [==============================] - 4s 65ms/step - loss: 21.3804 - accuracy: 0.1850 - val_loss: 23.4098 - val_accuracy: 0.1780\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 27.7199 - accuracy: 0.1875 - val_loss: 29.1113 - val_accuracy: 0.1880\n",
       "Epoch 12/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 20.5605 - accuracy: 0.1772 - val_loss: 22.9010 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 26.4905 - accuracy: 0.1920 - val_loss: 27.9231 - val_accuracy: 0.1950\n",
       "Epoch 13/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 19.8422 - accuracy: 0.1840 - val_loss: 21.5172 - val_accuracy: 0.1800\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 25.2349 - accuracy: 0.1865 - val_loss: 26.4574 - val_accuracy: 0.2060\n",
       "Epoch 14/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 19.0382 - accuracy: 0.1950 - val_loss: 20.8096 - val_accuracy: 0.1810\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 24.4556 - accuracy: 0.1972 - val_loss: 25.7006 - val_accuracy: 0.2050\n",
       "Epoch 15/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 18.4138 - accuracy: 0.1822 - val_loss: 20.0728 - val_accuracy: 0.1740\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 23.8386 - accuracy: 0.1912 - val_loss: 24.9988 - val_accuracy: 0.2140\n",
       "Epoch 16/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 17.7271 - accuracy: 0.1885 - val_loss: 19.4107 - val_accuracy: 0.1810\n",
+      "63/63 [==============================] - 6s 91ms/step - loss: 22.4274 - accuracy: 0.1960 - val_loss: 24.6765 - val_accuracy: 0.1970\n",
       "Epoch 17/100\n",
-      "63/63 [==============================] - 4s 65ms/step - loss: 17.6251 - accuracy: 0.1760 - val_loss: 18.9971 - val_accuracy: 0.1980\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 22.5474 - accuracy: 0.1988 - val_loss: 24.5952 - val_accuracy: 0.1960\n",
       "Epoch 18/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 16.2954 - accuracy: 0.1970 - val_loss: 18.3570 - val_accuracy: 0.1950\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 21.5807 - accuracy: 0.1990 - val_loss: 23.5175 - val_accuracy: 0.2010\n",
       "Epoch 19/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 16.0583 - accuracy: 0.1935 - val_loss: 17.7447 - val_accuracy: 0.1880\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 21.4346 - accuracy: 0.2005 - val_loss: 22.9050 - val_accuracy: 0.1960\n",
       "Epoch 20/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 15.8284 - accuracy: 0.1805 - val_loss: 17.1217 - val_accuracy: 0.1910\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 20.2885 - accuracy: 0.2075 - val_loss: 22.6272 - val_accuracy: 0.1980\n",
       "Epoch 21/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 14.8200 - accuracy: 0.1975 - val_loss: 16.7020 - val_accuracy: 0.2060\n",
+      "63/63 [==============================] - 4s 66ms/step - loss: 19.9793 - accuracy: 0.1980 - val_loss: 22.6117 - val_accuracy: 0.2080\n",
       "Epoch 22/100\n",
-      "63/63 [==============================] - 4s 67ms/step - loss: 14.8035 - accuracy: 0.1877 - val_loss: 16.4884 - val_accuracy: 0.1930\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 19.8646 - accuracy: 0.1978 - val_loss: 21.5507 - val_accuracy: 0.2110\n",
       "Epoch 23/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 14.0777 - accuracy: 0.2013 - val_loss: 15.8489 - val_accuracy: 0.1890\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 19.1136 - accuracy: 0.2007 - val_loss: 21.4854 - val_accuracy: 0.2080\n",
       "Epoch 24/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 14.2543 - accuracy: 0.1830 - val_loss: 15.5276 - val_accuracy: 0.2040\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 19.5056 - accuracy: 0.1955 - val_loss: 21.2948 - val_accuracy: 0.1870\n",
       "Epoch 25/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 13.4636 - accuracy: 0.1875 - val_loss: 14.6891 - val_accuracy: 0.1890\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 18.0079 - accuracy: 0.2017 - val_loss: 20.8069 - val_accuracy: 0.1820oss: 17.9966 - accuracy: 0.2\n",
       "Epoch 26/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 12.8913 - accuracy: 0.2037 - val_loss: 14.7856 - val_accuracy: 0.2010\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 17.9079 - accuracy: 0.2015 - val_loss: 20.0801 - val_accuracy: 0.2050\n",
       "Epoch 27/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 12.9247 - accuracy: 0.1965 - val_loss: 14.4304 - val_accuracy: 0.2000\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 17.7762 - accuracy: 0.2070 - val_loss: 20.5192 - val_accuracy: 0.1900\n",
       "Epoch 28/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 12.4494 - accuracy: 0.1972 - val_loss: 14.0287 - val_accuracy: 0.2110\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 17.3173 - accuracy: 0.2000 - val_loss: 19.4631 - val_accuracy: 0.2030\n",
       "Epoch 29/100\n",
-      "63/63 [==============================] - 4s 66ms/step - loss: 12.5309 - accuracy: 0.2058 - val_loss: 13.7324 - val_accuracy: 0.2090\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 16.8269 - accuracy: 0.2062 - val_loss: 19.0743 - val_accuracy: 0.1870\n",
       "Epoch 30/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 11.9367 - accuracy: 0.2002 - val_loss: 13.4195 - val_accuracy: 0.2140\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 17.0093 - accuracy: 0.2052 - val_loss: 19.0601 - val_accuracy: 0.1950\n",
       "Epoch 31/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 11.7203 - accuracy: 0.2035 - val_loss: 13.0633 - val_accuracy: 0.2100\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 16.4424 - accuracy: 0.2025 - val_loss: 19.1624 - val_accuracy: 0.1980\n",
       "Epoch 32/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 11.4939 - accuracy: 0.2030 - val_loss: 12.9967 - val_accuracy: 0.2120\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 16.0956 - accuracy: 0.2055 - val_loss: 18.4349 - val_accuracy: 0.2070\n",
       "Epoch 33/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 11.5324 - accuracy: 0.1910 - val_loss: 12.5197 - val_accuracy: 0.2280\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 16.0498 - accuracy: 0.2138 - val_loss: 17.8711 - val_accuracy: 0.2090\n",
       "Epoch 34/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 11.2674 - accuracy: 0.2002 - val_loss: 12.1535 - val_accuracy: 0.2180\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 15.9901 - accuracy: 0.2103 - val_loss: 18.1456 - val_accuracy: 0.2050\n",
       "Epoch 35/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 10.8380 - accuracy: 0.2093 - val_loss: 12.2492 - val_accuracy: 0.2010\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 15.3767 - accuracy: 0.2110 - val_loss: 17.8431 - val_accuracy: 0.2030\n",
       "Epoch 36/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 10.7348 - accuracy: 0.2025 - val_loss: 11.8892 - val_accuracy: 0.2110\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 15.3764 - accuracy: 0.2035 - val_loss: 17.3440 - val_accuracy: 0.2060\n",
       "Epoch 37/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 10.5163 - accuracy: 0.1928 - val_loss: 11.7170 - val_accuracy: 0.2050\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 15.3016 - accuracy: 0.2055 - val_loss: 16.9546 - val_accuracy: 0.1900\n",
       "Epoch 38/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 10.1681 - accuracy: 0.2023 - val_loss: 11.5104 - val_accuracy: 0.2070\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 14.6513 - accuracy: 0.2095 - val_loss: 17.6995 - val_accuracy: 0.1910\n",
       "Epoch 39/100\n",
-      "63/63 [==============================] - 4s 67ms/step - loss: 10.2094 - accuracy: 0.2007 - val_loss: 11.7209 - val_accuracy: 0.2120\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 15.0839 - accuracy: 0.2107 - val_loss: 17.6919 - val_accuracy: 0.1960\n",
       "Epoch 40/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 10.2874 - accuracy: 0.2030 - val_loss: 11.1544 - val_accuracy: 0.2140\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 14.2011 - accuracy: 0.2218 - val_loss: 16.6889 - val_accuracy: 0.1920\n",
       "Epoch 41/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 9.8548 - accuracy: 0.2000 - val_loss: 10.9128 - val_accuracy: 0.2240\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 14.6521 - accuracy: 0.2005 - val_loss: 16.9077 - val_accuracy: 0.1970\n",
       "Epoch 42/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 9.5805 - accuracy: 0.2013 - val_loss: 10.6923 - val_accuracy: 0.2160\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 14.4696 - accuracy: 0.2045 - val_loss: 16.0069 - val_accuracy: 0.2080\n",
       "Epoch 43/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 9.8389 - accuracy: 0.1980 - val_loss: 10.8398 - val_accuracy: 0.2050\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 14.2405 - accuracy: 0.2083 - val_loss: 16.1471 - val_accuracy: 0.2080\n",
       "Epoch 44/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 9.4249 - accuracy: 0.2060 - val_loss: 10.5591 - val_accuracy: 0.2190\n",
+      "63/63 [==============================] - 4s 68ms/step - loss: 13.6228 - accuracy: 0.2180 - val_loss: 15.8585 - val_accuracy: 0.2010\n",
       "Epoch 45/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 9.3991 - accuracy: 0.2105 - val_loss: 10.4659 - val_accuracy: 0.2060\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 13.6520 - accuracy: 0.2165 - val_loss: 15.7997 - val_accuracy: 0.2220\n",
       "Epoch 46/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 9.1690 - accuracy: 0.1988 - val_loss: 10.2464 - val_accuracy: 0.2140\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 13.8401 - accuracy: 0.2173 - val_loss: 15.8596 - val_accuracy: 0.1970\n",
       "Epoch 47/100\n",
-      "63/63 [==============================] - 4s 67ms/step - loss: 9.2827 - accuracy: 0.2035 - val_loss: 10.1138 - val_accuracy: 0.2190\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 13.4902 - accuracy: 0.2128 - val_loss: 15.6035 - val_accuracy: 0.2220\n",
       "Epoch 48/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 8.9197 - accuracy: 0.2155 - val_loss: 9.9359 - val_accuracy: 0.2200\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 13.2370 - accuracy: 0.2138 - val_loss: 15.3923 - val_accuracy: 0.2070\n",
       "Epoch 49/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 8.6668 - accuracy: 0.2205 - val_loss: 10.3965 - val_accuracy: 0.2140\n",
+      "63/63 [==============================] - 5s 82ms/step - loss: 13.1424 - accuracy: 0.2083 - val_loss: 15.7848 - val_accuracy: 0.2090\n",
       "Epoch 50/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 9.1509 - accuracy: 0.1980 - val_loss: 9.7671 - val_accuracy: 0.2210\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 13.4115 - accuracy: 0.2072 - val_loss: 15.3643 - val_accuracy: 0.2170\n",
       "Epoch 51/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 8.8055 - accuracy: 0.2120 - val_loss: 9.7981 - val_accuracy: 0.2210\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 12.8290 - accuracy: 0.2130 - val_loss: 14.9256 - val_accuracy: 0.2190\n",
       "Epoch 52/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 8.6455 - accuracy: 0.2058 - val_loss: 9.5791 - val_accuracy: 0.2190\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 12.9609 - accuracy: 0.2110 - val_loss: 14.7627 - val_accuracy: 0.2170\n",
       "Epoch 53/100\n",
-      "63/63 [==============================] - 4s 67ms/step - loss: 8.4916 - accuracy: 0.2097 - val_loss: 9.4260 - val_accuracy: 0.2280\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 12.5448 - accuracy: 0.2225 - val_loss: 14.5454 - val_accuracy: 0.2190\n",
       "Epoch 54/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 8.2758 - accuracy: 0.2107 - val_loss: 9.3284 - val_accuracy: 0.2210\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 12.6640 - accuracy: 0.2215 - val_loss: 15.0825 - val_accuracy: 0.2130\n",
       "Epoch 55/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 8.1894 - accuracy: 0.2090 - val_loss: 9.3506 - val_accuracy: 0.1980\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 12.4627 - accuracy: 0.2157 - val_loss: 14.8911 - val_accuracy: 0.2090\n",
       "Epoch 56/100\n",
-      "63/63 [==============================] - 4s 63ms/step - loss: 8.2820 - accuracy: 0.2128 - val_loss: 9.7846 - val_accuracy: 0.2130\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 12.4683 - accuracy: 0.2072 - val_loss: 14.6111 - val_accuracy: 0.2090\n",
       "Epoch 57/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 8.3598 - accuracy: 0.2090 - val_loss: 9.3707 - val_accuracy: 0.2040\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 12.3918 - accuracy: 0.2268 - val_loss: 14.1859 - val_accuracy: 0.2050\n",
       "Epoch 58/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 8.0148 - accuracy: 0.2160 - val_loss: 9.1976 - val_accuracy: 0.2070\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 12.2508 - accuracy: 0.2255 - val_loss: 13.8157 - val_accuracy: 0.2300\n",
       "Epoch 59/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 8.0729 - accuracy: 0.1995 - val_loss: 9.1469 - val_accuracy: 0.1970\n",
+      "63/63 [==============================] - 5s 82ms/step - loss: 12.1926 - accuracy: 0.2165 - val_loss: 14.1347 - val_accuracy: 0.2210\n",
       "Epoch 60/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 8.0123 - accuracy: 0.2175 - val_loss: 8.9841 - val_accuracy: 0.2120\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 11.7872 - accuracy: 0.2290 - val_loss: 14.4491 - val_accuracy: 0.2160\n",
       "Epoch 61/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 8.0255 - accuracy: 0.2075 - val_loss: 9.0406 - val_accuracy: 0.1970\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 11.8999 - accuracy: 0.2205 - val_loss: 13.5799 - val_accuracy: 0.2330\n",
       "Epoch 62/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 7.7979 - accuracy: 0.2138 - val_loss: 8.7216 - val_accuracy: 0.2030\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 11.7848 - accuracy: 0.2245 - val_loss: 13.8123 - val_accuracy: 0.2330\n",
       "Epoch 63/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 7.6717 - accuracy: 0.2048 - val_loss: 8.7191 - val_accuracy: 0.2020\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 11.6306 - accuracy: 0.2230 - val_loss: 13.8273 - val_accuracy: 0.2080\n",
       "Epoch 64/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 7.9337 - accuracy: 0.2080 - val_loss: 8.6152 - val_accuracy: 0.2200\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 11.5581 - accuracy: 0.2180 - val_loss: 14.5994 - val_accuracy: 0.2000\n",
       "Epoch 65/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 7.5857 - accuracy: 0.2095 - val_loss: 8.5533 - val_accuracy: 0.2170\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 11.6465 - accuracy: 0.2165 - val_loss: 13.3711 - val_accuracy: 0.2230\n",
       "Epoch 66/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 7.4746 - accuracy: 0.2150 - val_loss: 8.4395 - val_accuracy: 0.2160\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 11.6538 - accuracy: 0.2157 - val_loss: 13.0544 - val_accuracy: 0.2250\n",
       "Epoch 67/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 7.5529 - accuracy: 0.2085 - val_loss: 8.3611 - val_accuracy: 0.2250\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 11.4656 - accuracy: 0.2205 - val_loss: 13.4857 - val_accuracy: 0.2190\n",
       "Epoch 68/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 7.4024 - accuracy: 0.2135 - val_loss: 8.4891 - val_accuracy: 0.2080\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 10.8744 - accuracy: 0.2327 - val_loss: 13.1932 - val_accuracy: 0.2170\n",
       "Epoch 69/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 7.2022 - accuracy: 0.2135 - val_loss: 8.5370 - val_accuracy: 0.2220\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 10.9635 - accuracy: 0.2215 - val_loss: 12.8626 - val_accuracy: 0.2160\n",
       "Epoch 70/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 7.2330 - accuracy: 0.2157 - val_loss: 8.3368 - val_accuracy: 0.2150\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 10.8797 - accuracy: 0.2278 - val_loss: 13.0499 - val_accuracy: 0.2180\n",
       "Epoch 71/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 7.1719 - accuracy: 0.2163 - val_loss: 8.0086 - val_accuracy: 0.2210\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 11.1380 - accuracy: 0.2192 - val_loss: 12.6542 - val_accuracy: 0.2210\n",
       "Epoch 72/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 7.1011 - accuracy: 0.2190 - val_loss: 8.0636 - val_accuracy: 0.2130\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 10.8626 - accuracy: 0.2270 - val_loss: 12.7885 - val_accuracy: 0.2330\n",
       "Epoch 73/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 6.9326 - accuracy: 0.2268 - val_loss: 8.1927 - val_accuracy: 0.2320\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 11.1037 - accuracy: 0.2185 - val_loss: 12.6186 - val_accuracy: 0.2190\n",
       "Epoch 74/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 6.9288 - accuracy: 0.2260 - val_loss: 8.1182 - val_accuracy: 0.2160\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 10.7163 - accuracy: 0.2235 - val_loss: 12.6609 - val_accuracy: 0.2320\n",
       "Epoch 75/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 6.8918 - accuracy: 0.2338 - val_loss: 7.9932 - val_accuracy: 0.2310 0s - loss: 6.8640 - accuracy\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 11.0662 - accuracy: 0.2175 - val_loss: 12.9894 - val_accuracy: 0.2430\n",
       "Epoch 76/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 7.2133 - accuracy: 0.2115 - val_loss: 8.0421 - val_accuracy: 0.2040\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 11.0275 - accuracy: 0.2177 - val_loss: 12.6780 - val_accuracy: 0.2140\n",
       "Epoch 77/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 6.9548 - accuracy: 0.2142 - val_loss: 7.9215 - val_accuracy: 0.2200\n",
+      "63/63 [==============================] - 4s 67ms/step - loss: 10.8218 - accuracy: 0.2148 - val_loss: 12.2545 - val_accuracy: 0.2230\n",
       "Epoch 78/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 6.9369 - accuracy: 0.2185 - val_loss: 7.8404 - val_accuracy: 0.2120\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 10.5594 - accuracy: 0.2265 - val_loss: 12.3488 - val_accuracy: 0.2340\n",
       "Epoch 79/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 7.0997 - accuracy: 0.2120 - val_loss: 7.7585 - val_accuracy: 0.2230\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 10.4114 - accuracy: 0.2188 - val_loss: 12.6185 - val_accuracy: 0.2280\n",
       "Epoch 80/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 6.9580 - accuracy: 0.2150 - val_loss: 7.8094 - val_accuracy: 0.2220\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 10.4084 - accuracy: 0.2115 - val_loss: 12.0554 - val_accuracy: 0.2080\n",
       "Epoch 81/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 6.7121 - accuracy: 0.2202 - val_loss: 7.7070 - val_accuracy: 0.2130\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 10.3107 - accuracy: 0.2198 - val_loss: 11.9778 - val_accuracy: 0.2250\n",
       "Epoch 82/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 6.8451 - accuracy: 0.2180 - val_loss: 7.8513 - val_accuracy: 0.2430\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 10.2979 - accuracy: 0.2153 - val_loss: 12.3735 - val_accuracy: 0.2380\n",
       "Epoch 83/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 6.6499 - accuracy: 0.2310 - val_loss: 7.6191 - val_accuracy: 0.2230\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 10.3183 - accuracy: 0.2245 - val_loss: 12.3425 - val_accuracy: 0.2160\n",
       "Epoch 84/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 6.7305 - accuracy: 0.2198 - val_loss: 7.6156 - val_accuracy: 0.2000\n",
+      "63/63 [==============================] - 5s 72ms/step - loss: 10.1175 - accuracy: 0.2170 - val_loss: 11.6930 - val_accuracy: 0.2360\n",
       "Epoch 85/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 6.5236 - accuracy: 0.2210 - val_loss: 7.4502 - val_accuracy: 0.2270\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 10.1336 - accuracy: 0.2257 - val_loss: 12.3353 - val_accuracy: 0.2160\n",
       "Epoch 86/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 6.4832 - accuracy: 0.2313 - val_loss: 7.4636 - val_accuracy: 0.2400\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 10.1948 - accuracy: 0.2183 - val_loss: 11.6356 - val_accuracy: 0.2380\n",
       "Epoch 87/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 6.3933 - accuracy: 0.2150 - val_loss: 7.4960 - val_accuracy: 0.2260\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 9.7016 - accuracy: 0.2130 - val_loss: 12.1281 - val_accuracy: 0.2230\n",
       "Epoch 88/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 6.6315 - accuracy: 0.2118 - val_loss: 7.4906 - val_accuracy: 0.2370\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 9.8889 - accuracy: 0.2257 - val_loss: 12.0176 - val_accuracy: 0.2260\n",
       "Epoch 89/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 6.3671 - accuracy: 0.2307 - val_loss: 7.6169 - val_accuracy: 0.2300\n",
+      "63/63 [==============================] - 4s 68ms/step - loss: 10.0794 - accuracy: 0.2097 - val_loss: 11.8407 - val_accuracy: 0.2100\n",
       "Epoch 90/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 6.6556 - accuracy: 0.2170 - val_loss: 7.4098 - val_accuracy: 0.2310\n",
+      "63/63 [==============================] - 4s 66ms/step - loss: 9.9900 - accuracy: 0.2215 - val_loss: 11.5346 - val_accuracy: 0.2200\n",
       "Epoch 91/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 6.4345 - accuracy: 0.2222 - val_loss: 7.4402 - val_accuracy: 0.2240\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 9.6329 - accuracy: 0.2205 - val_loss: 11.3403 - val_accuracy: 0.2260\n",
       "Epoch 92/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 6.4228 - accuracy: 0.2205 - val_loss: 7.2602 - val_accuracy: 0.2320\n",
+      "63/63 [==============================] - 5s 82ms/step - loss: 9.6988 - accuracy: 0.2265 - val_loss: 11.5776 - val_accuracy: 0.2310\n",
       "Epoch 93/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 6.1637 - accuracy: 0.2202 - val_loss: 7.0793 - val_accuracy: 0.2170\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 9.9098 - accuracy: 0.2180 - val_loss: 11.3503 - val_accuracy: 0.2270\n",
       "Epoch 94/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 6.3256 - accuracy: 0.2062 - val_loss: 7.1821 - val_accuracy: 0.2160\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 9.5347 - accuracy: 0.2220 - val_loss: 11.4480 - val_accuracy: 0.2180\n",
       "Epoch 95/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 6.1190 - accuracy: 0.2250 - val_loss: 7.0112 - val_accuracy: 0.2360\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 9.4278 - accuracy: 0.2278 - val_loss: 11.3241 - val_accuracy: 0.2230\n",
       "Epoch 96/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 6.1672 - accuracy: 0.2188 - val_loss: 7.0114 - val_accuracy: 0.2250\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 9.4777 - accuracy: 0.2215 - val_loss: 11.4358 - val_accuracy: 0.2270\n",
       "Epoch 97/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 6.0777 - accuracy: 0.2145 - val_loss: 7.5616 - val_accuracy: 0.2170\n",
+      "63/63 [==============================] - 5s 81ms/step - loss: 9.6729 - accuracy: 0.2205 - val_loss: 11.6833 - val_accuracy: 0.2230\n",
       "Epoch 98/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 6.0525 - accuracy: 0.2177 - val_loss: 7.2203 - val_accuracy: 0.2060\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 9.6368 - accuracy: 0.2155 - val_loss: 11.0716 - val_accuracy: 0.2350\n",
       "Epoch 99/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 6.0467 - accuracy: 0.2227 - val_loss: 7.1099 - val_accuracy: 0.2290\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 9.2199 - accuracy: 0.2195 - val_loss: 11.2743 - val_accuracy: 0.2240\n",
       "Epoch 100/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 6.2256 - accuracy: 0.2235 - val_loss: 7.1370 - val_accuracy: 0.2340\n"
+      "63/63 [==============================] - 4s 71ms/step - loss: 9.5714 - accuracy: 0.2255 - val_loss: 11.2973 - val_accuracy: 0.2170\n"
      ]
     }
    ],
@@ -2308,14 +2318,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {
     "id": "Q6_cz3hgvXZ7"
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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cZHg3fH457H2i1iMRJuAz//c05//nXbUehlDHvO997+PRRx/l6aef5rrrriMSidR6SAcMEWxhbtK/BVJD5lGYtXiUIpefXWk5QZitiGALc5O0reDMS7h1PJRSP1RKdSulnnRta1NK3a6Ues4+ttrtSin1daXUJqXU40qpE2diDF6PQvRaEKpDBFuYmziCnTswczXnCD8GLirb9hHgDq31YcAd9jXAxcBh9s9bge/MxACUgrwotiBUhQi2MDdJOQ5bBHs8tNZ3A/1lmy8FfmKf/wR4hWv7T7Xh70CLUmrx/o7BqxT5WTZTRRBmKyLYwtxEQuLTZaHWeo99vhdYaJ93ATtcx+202/YLj0eRE8GuS84991xuu+22km1f+9rXeMc73jHuOeeccw7ONL+XvOQlFXtyf+pTn+LLX/7yhPf+zW9+w9NPP114/YlPfII//elPUxh9ZWb7Mpwi2MLcxHHYORHs6aJNk4Ypq6lS6q1KqfVKqfU9PT0THutRksOuV6688kpuuOGGkm033HBD1f28b7311mk3HykX7E9/+tNccMEF07pWPVGVYCulLlJKPWMLTj5SYf/7lVJP22KUO5RSK8r2NymldiqlvjlTAxeECUlLSHya7HNC3fax227fBSxzHbfUbhuD1vq7Wut1Wut1nZ2dE97MIznsuuXyyy/nd7/7Hel0GoCtW7eye/duzjrrLN7xjnewbt06jjnmGD75yU9WPH/lypX09vYC8LnPfY7DDz+cM888s7AEJ5g51ieffDLHHXccr3rVq4jH49x7773cfPPNfPCDH+T444/n+eef55prruEXv/gFYDqanXDCCaxdu5Y3vvGNpFKpwv0++clPcuKJJ7J27Vo2btxY9XudLctwTtqaVCnlBb4FXIgJgz2olLpZa/2067BHgHVa67hS6h3AF4HXuvZ/Brh75oYtCJOQFoc9TW4Grga+YB9/69r+LqXUDcCpwJArdD5tTJW4CPZ+8/uPzHzPgUVr4eIvjLu7ra2NU045hd///vdceuml3HDDDbzmNa9BKcXnPvc52trayOVynH/++Tz++OMce+yxFa/z0EMPccMNN/Doo4+SzWY58cQTOemkkwC47LLLeMtb3gLAxz/+cX7wgx/w7ne/m5e//OW89KUv5fLLLy+5VjKZ5JprruGOO+7g8MMP5/Wvfz3f+c53eO973wtAR0cHDz/8MN/+9rf58pe/zPe///1JP4bZtAxnNQ77FGCT1nqz1joN3IApQCmgtb5Tax23L/+O+fUNgFLqJEwe7I8zM2RBqIKU5LAnQyl1PXAfcISNgL0JI9QXKqWeAy6wrwFuBTYDm4DvAf80Q2Mgr2G2tUgWqsMdFneHw2+88UZOPPFETjjhBJ566qmS8HU5f/3rX3nlK19JJBKhqamJl7/85YV9Tz75JGeddRZr167luuuuG3d5TodnnnmGVatWcfjhhwNw9dVXc/fdRa942WWXAXDSSScVFgyZjNm0DGc1V6tUbHLqBMe/Cfg9gFLKA/wn8DrMf35BODjItK5J0VqPl2w8v8KxGnjnTI/Bq5S9vpniJUyTCZzwgeTSSy/lfe97Hw8//DDxeJyTTjqJLVu28OUvf5kHH3yQ1tZWrrnmGpLJ5LSuf8011/Cb3/yG4447jh//+Mfcdddd+zVeZ4nOmViesxbLcM5o0ZlS6nXAOuBLdtM/AbdqrXdOcl7VRSqCUBWpEfMoOexZjceKtFSK1ycNDQ2ce+65vPGNbyy46+HhYaLRKM3Nzezbt4/f//73E17j7LPP5je/+Q2JRIKRkRFuueWWwr6RkREWL15MJpPhuuuuK2xvbGxkZGRkzLWOOOIItm7dyqZNmwD43//9X174whfu13ucTctwViP7VRWbKKUuAD4GvFBrnbKbTwPOUkr9E9AABJRSMa11SeGa1vq7wHfBrO4z5XchCOWkR82jhMRnNR6r2JLHrl+uvPJKXvnKVxZC48cddxwnnHACRx55JMuWLeOMM86Y8PwTTzyR1772tRx33HEsWLCgZInMz3zmM5x66ql0dnZy6qmnFkT6iiuu4C1veQtf//rXC8VmAKFQiB/96Ee8+tWvJpvNcvLJJ/P2t799Su9nNi/DOenymkopH/AsJky2C3gQ+Aet9VOuY04AfgFcpLV+bpzrXIMpTHvXRPeT5fiEGeGbJ0Pvs3Dau+DFn6v1aA4Ic2F5ze/c9Tz/8YeNbPj0RYQD3oM4svpHltesXw7Y8ppa6yzwLuA2YANwo9b6KaXUp5VSTnXAlzAO+ial1KNKqZun8yYEYcaQTmd1gdd+A4nDFoTJqSoTrrW+FVMl6t72CdfzSQvKtNY/xvQuFoQDj0zrqgs8ttJMctiCMDnS6UyYe2gtrUnrBEewdb7GAxGEOkAEW5h7ZBJFBcjnqj9vtBc+txi2/73y/t/9C9xw1f6PTyggVeL7h8xfrz/25+9MBFuYe6Rd0yimEhIf3gWZOPRvqby/fzP0bdq/sQkleKVKfNqEQiH6+vpEtOsIrTV9fX2EQqFpnT+zbVgEYTaQcs3PnEpIPG2b9eVSlfdn08a9CzOGsiFx6Sc+dZYuXcrOnTuR3hX1RSgUKpk2NhVEsIW5x3QddmZ04nNyItgzjeOwJSQ+dfx+P6tWrar1MISDiITEhbmH0zQFpjatq+Cw05X359KQnV6LRaEyTg5bDLYgTI4ItjD3cOZgo6bosK1gZ8cJiYvDnnE8EhIXhKoRwRbmHmmbww41Ty2H7Qi2I/LZFPQU1+YllzbXkwVFZgwpOhOE6hHBFuYejsMOt05tWld50dkTN8F3zoDksHmdtaHyrLjsmaLQOEUctiBMigi2MPdwctjhlumFxJ0c9mivcdSFrml2e0by2DNFcfGPGg9EEOoAEWxh7uEIbKhlitO6yqrEHYF2ctoFwY7v9xAFQ7HoTBRbECZDBFuYe6RGwBsEf3hq+ebyorNyoS4I+Aw77O6N8L3zi6H3eYRXSQ5bEKpFBFuYe6RjEGwAj2//is5y4wj3TDvsXQ/BrvWmk9o8Q0kOWxCqRgRbmHukYhBoAK9/ajns8nnYWZez1vrA5bCdULy74cs8wakSF4MtCJMjgi3MPdKjEGwEj39s45SeZ2HTnyqflymrEnc7bLfwz3SVuNNhzd3wZZ5QWPxDHLYgTIoItjD3SI9AIApe31jBvve/4FdvG+e8sqKzgsNOlXY/m+nmKc593T3Q5wkemYctCFUjgi3MPZyQuKdCSDw5BPHeyqHy8qKzgsNOHxzBnochcY8UnQlC1YhgCwefWDcMbIXEwMxcL5ctbZAyUdGZI46jFVY4GpPDdgm3W7Bnukq84LDnn2AXq8RrPBBBqANEsIWDy66H4MuHwX8dB185uuq87abuCcTspqvhln8uvk6PQqDRFp2VhcQdUYx1j73OmCpxV/HZQXHYksMWBGF8RLCFg8vwHvO46mwjkFXMPX5y1xAXfOUvPLpjsPIBfc9D/5bi61TM5LArOuxqBLt8OleqmM+GAyjYksMWBGF8RLCFg4sTTl5ygnmsYp50z4gRzn3D44Si06NFsdXaCF9wnGldjsMerSDY6fEc9oEOicdKxzaPKK7WVeOBCEIdIIItHFwcsQs0mscq5knH0yY/PZoap2tZOlZ0vZkE6Hyx6EznSif5Oi623GFrPXGns5xryc2ZbpziXG8eFp157TeQOGxBmBwRbOHg4gh20BHs9PjHWkbTWfs4zspbmbhL9Gx4OdBgpnVB6dQuZ3+5YGcSgC4dU4nDdv2wOGCNU+ZfDrvQ6UwEWxAmRQRbOLg4rjXUZB6rcNiJiRx2Lmt+BBQcthXuQMTksN33cBePlYfE3Xlp93rYYB22OyQu87BnCqdKXItgC8KkiGALB5dCSLzBPFYh2I7DjlcSbKdLWLlg+yMmJA7FPLk75DzGYdvr+EKVO51l3SHxKgQ7OQz7np78OJB52EBOctiCMCki2MLBxQknB6LmsYqiM8dhx1IVQuKO2GXituDMJdheK9jO1C63gy0XbOe8UPM4nc7cIfEqBPveb8D3z6+ul/k8noftkRy2IFSNCLZwcMkmjYv1BszranLYVqjj6QoO2xFanTPi6Dhld0i84LDtvmgnxPaVXsc5L9zqymFX6HTmDVRXJT6wxfyIGNox8XG5TPE+8zCHXawSF8EWhMkQwRYOLtnUlAU7kTFCHasUEneHkTNxl8OOFh22U3TmHNu2GpKDpWHugsNuMdu1rtxLPNRcncMe2mUe3fPDK1EQaTUvQ+Jej3Q6E4RqEcEWDhy/fgfc/93SbdmEFeyycPUEFB32BCFxMEJaUnTm3MM6bCck3naIvXBP6bkA4RZAm1anlRx2sKk6wR62gj0wiWA74412GMGeZ6HhQqezefa+BWE6iGALB47Nd8HWv5Zuy6bAF3QJ9uQOO17IYVcqOouXPi8pOiub1lVw2KvMozuP7Q6Jg/lh4ZyXcxWdVeOw83kYsR3dqnXYDYvM/PGZnuM9y/FIlbggVI0ItnDgyGfM6lhuppHDdnLXlXPY7pB4oqzorGxaV8oVEodSh+0OibuPhdJ52KHmyad1xfuK72tg68THOuNvWGBfz688drFKXARbECZDBFs4cOTSkDK9wrXW/PS+rWRTCfCHxuaXJ6DY6ayakLi76Kx8Wpfd5wi2u/DMcbbhFnusS7Dd87BDzZM3TnHC4d4A9G+e+Fjnh0LjIvM4z+ZiSw5bEKpHBFs4cOSKDvv5nlE+8dun6BsaNg7bM5WQuO10VrHorCwk7rz2hcfmyZ22pAXBdoXEHTEvOGyXcGZTxXx2qHnysPXwbvPYdZJx2BOFewshccdhz6/CM2uwpUpcEKpABFs4cLgE2xFbnUnaHHageMwkTNhLvDwknonb/LVn7LSuVMxsCzWb4rGSHLZrHjaUCnYu7QqJN00+rctx2CvPNNctn0JWafwNC4tjnEc4DluKzgRhckSwhQOD1jaHPQxaFyu8CznssgruCXDOjWdyY51YSUg8XhRsqDytKxA1tq5hQWl70kzcuHJfsHisg9PpzOMz08WyyYmXlxrebY5deop5PVHhWcFhLxz7fuYBhXnYItiCMCki2MKBwRHJfAYyicJcak8uVSbY1YXElTK/AZLZHPF0ls09VlDLc9jpuMlfw9hpXenR4iph0U6IlRWdBSJFwR7jsNPgDZr8O0zssod3Q+MSaLfTx1xTu7b1jTKcdHdNK8thz7M1saVxiiBUjwi2cGBwC3FquFAwVhRsGxKfpDVpLq9JZvK0R42QxlJZfvDXLbzsG38zX/KZ0WLoOxM3rx2HXT6tK2XXyQbwh0urvR1n7ozLEWxfuFgl7vWb1zCJYO+CpiXQvAyUp8RhX/7f9/GtP28qHjvPQ+LOPGzRa0GYHBFs4cDgFuzkUKEfuDdv52GXu99xSGTMeZ2NRrDjqRw7BxKMpnPE0lnjmqOd5mDHYRdC4mXTutKx4qIj3mCxkxmY67gF2xHSYKPtdJYy+/zh4r3GwxFsXwCalxYcdiKdo2ckxc4B17npUVBeiLSV3neeUMhhi2ILwqSIYAsHBncHs+RQodLbl08b0fN4jFBNEhJ3VuhyBDuWytIbMxXbQ/EMpEfpxy7VmUmYP87CIuXTulKx4j5foFj5DcZhByo47GCj7XSWMT80JhNsrU1IvGmJed26qjC1q2fE3K93JA43vxv2PmlD8Q3FUP18y2F7JIctCNXiq/UAhLnHV29/Fj20i/c7G5LDxDPGQfp1qpgn9vonddhOwVlnQ7DwunfUiPxw0gj2rriXBvwEnJB4w6Li9aHUYTvTp7zBsUtm+qMuwXY57ES/zWH7TTgfxm+ekhgw4fKmLvO6aQlsvQeAnpgJo4dGtsHDPzVh8HTM/lDwmWvPs3nYUnQmCNUjDluYce57vo/fPOSqjE4OEk/lAE2ATFH0vIFJBdtZC9tx2KOpLL3WqQ4ljGAP54OkVLBC0ZmTw7YV6ukYGwc0r/nv+6zDLguJByJmO4x12NmULTqz1x6veYqd0vXX7iDn/eddZpqYndrWPWzGHR3daY7t32Lva11/oGH+hcSVNE4RhGoRwRZmnFQujw9XV7LUMPF0Dj85POhShz1J0ZmT+3aHxPtGjfANJ7Lo9CjDuQAJHZxgWlcxJN6T8vPU7qEKDru86Mx0aCNo5107RWdOlXgmDnseM47ajW2a8sRIhM09o2T9DeZa+Tw9NpTfkXEtDOIW7GDDvCs6cxqnSA5bECZHBFuYcdLZPH5Kc9iJTJYgxtFmPVb0PP5Jc9ijZYLdM5IimTFzoIcTGXQ6RiwfJK4D1mFXqBJ3hcRjOshoOkfOU+6wHcH2F44FjMPWOfqHBkuLzpKD5L53IcN3faN0wFawt6RMA5aEtxHQkB4pOOwVys7/7rdrZvvdDnt+5bCdojNZ/EMQJkcEW5hx0tkcK1oCxQ3JIeLpHCGMcCa1FdIqQuIJJyRuc9jb+4ttQU0OO84oIeI6QD4dt0VnZfOw89nCEpkj+ZAdg7fMYduQuLdsHnbQFIPt3NtjIgN2Wpfe/SjefIpnt+0oHbDtnrYlYcYQ91gxTg4Xis6WK9v5LNFvBL4kJD7CT+/bykd++fiEn8tcobj4R40HIgh1gAi2MOOksnmOWRgqbkiakLjjsON5K6RVFJ0587c7G80PgG19RQfq5LATBEkQIJccMZXfjmN1T+uyjnkobwQ5mfeZULnTsSyTKAuJuxw2ENYJch5fISSe3/EgAKo8lz3aDeFW9sTMdWPYHw/JIbpHkigFK9U+8h57n/7NY0Li92/p5/4t/RN+LnOF4jxscdiCMBki2MKMk87mCXlcOWw7rSuojDjH847DHickns/DD14Ej15P3M7Dbgr7CXg9bHM57Fg8gSefZlSHSOgAerTX7BjjsF2CnTOCnXBcfi5titKySSOcTtFZulSwoypBBn8h3O7Z/RAAKlcm2LF96IaFhalnI9px2EP0xFKsbguzXHXT07bOnqDHFJ31jqQKEYW5jlIKpUSwBaEaRLCFGSedyxP0uGKctuisxW/EN5bzkc7mSWlvZYe993HYcT9sv68QEo8GfESDXnb2m+lUQZ+H5KgpDIsTIkEQFbeC7eSZ3dO6UqWCHc957b5UsWuZqwNbLukUnRnBbiBBBl+hwl3ZlqIe91xugFgPuXAHqax5/4P5cOEz6B5O8YLODEGVYUvzycVzAqU57J5Yio7GAPMFr1Ii2IJQBSLYwoyTzuYJKrtgB6FCp7PFDSb+Gcv5+MVDO9nYnSSerDA96vk/m8dYdyEkHvZ7iQR8pG2yc2V7lHQiZu8RJEkAb9JWbDsh8YLDzhUcc3/WCGEsawXbmbIFpS1Ty0LiUZKk8r7ijwHLWMHeRzLYUXjZbwU7nxikbzTNmkgfAJt9qyFq54SXhcR7R1J0zBOHDSaPLTlsQZgcEWxhxjGCbZzxAE2ForPFNlI9nPXy7L4R0njpHarQKMQR7NFuEpkcIb8Hj0fREDRh7Oawn/aGAJmEOTeuQyR0EI+2lemFkLgHUCYkbovI+jNGkEcrOWx/CDxe8sqLV2nyHn9hCppXaVOo5g2Y/uAWX748h93DiK+18LIvawR7dKifXF4XCs42ZxdA2yp736LD1ukRhpOZeRMSB/PXJFXigjA5ItjCjJLLa7J5XQiJ9+pGW3SWZUHEOOzhjJdtfaNktI/+kXjpSk2pGGz/u3ke62Y0lSUaMEIdCRqRbW8I0Bz2k00aFzxqi84KOAIIxcI2O11q0IbERwoOO1VsM2rD3XllnHlO+YtV49hCNaUKleJxHcTndtjpUUjHGPIUBbsnbXPmI6aIbFF2N1m8bEq3mralUHTYoSaUzhMlSUfjPBJspWQetiBUgQi2MKOkbe7WCYn35JvQ1mG3+I0DHsp42dYfJ+/xobNp1m9zNR/Zdo9xxIuPg1g3iVSWcMCIq+OwOxqCNIX85Kxg5/1REhQF7rO3b+MBp8ra4zfTutKOuIfsGJyOHe6QuLlGTpn7ZD1B0la8ARJ5+9/FhsWf0KvwalfRnJ3S1YuZgx30eRhMa/BHSMXMe2xN7aLPt5DuWLbosB3BjrSbY1RsXjlsk8Ou9SgEYfYjgi3MKI5g+61g9+smVGaUdCZN1GMEuz+t2NmfoLO5kaDK8quHdxYv8PyfjYM96uWQS5FPDhUdthXujoYAzRF/QYSjDU2kVVHg7tuR4J5NtgDN6yspOhvVRrCHM/afftZddGaEOGtFOqv8xYp2XIVq/jDxSBc9uhl/3uWwrWDvyzXj9SiWtUXM1LNgE5lRI9jR+A4GQ0tNt7Zyh+0INiPzymFLlbggVIcItjCjpHJGqAM2h+2spNWg44StYD/blyWdyxMOh2kJKf7w1N7iBbbdC8tfAC3LAfAnewsOO1risH34cyaU7Q83on3FYrAkgUIbUOOwM5A2OewY5rihtP2nn0u7BNuIZMYKdgYfo9nif5HRnH0eaae3eQ0pAgTcDnvUCPaubCNt0QCtET/DiSyEmsnbfuKB4e3Eo8voi6XJdx5pznMWJLGC3aZGCp3d5gNej1SJC0I1iGALUyaf1/z8we2MprJj9hVC4raXeJ82VdZNKk7ECvaT3UZMw6EgEU+ewXiGkaTTPnQUoh2FNa5DqV6iNnftOO32aJDmsJ+wMtcJRBqL7UgxRWjOAiGFHHZymLzHTwo/Xo9iIG1D4tmyaV0YoQZI4S9Wk+OqLL/iOu494qMkdYCAHuuwtyWjdDaYMQ4lMhBqRiWH6QxmUclBso1dZPOa4Zaj4G1/hZVnmfMdwWaY9uj8mdYlOWxBqA4RbGHKPL1nmA//8gm+evuzY/alKoTEARqJE1LGjcayNsQdDuO3TnzfsBXNXNpUYjcsBCCc7ifsLy0662gM0BT2E8WcE440ogJFhx0nWOawc5AcIudvBBSLmkIMJJ0cdmpMDjvtCLb2MeJ22Fl7TvNSBmgkhd+sPuYQ6wYUWxJhOhpNnt0IdhO+9DBHR43L9zSbtbJ7YylYfGxxBQwr2IsDo4T8xR8Kcx2PR3LYglANItjzlVwWfvVW6N5gXvc9D794Y2l/7XEYSRqR/el929jh6jwGrhy2Xfyjz4bEm1S80Es8hR+/VxEOhfBbJ75nyC3Y/kKYuCHTV3DYDS6H3RT2E3EEu6EZb7DosBMEC53G8HjttK5h0n4zliUtIQZSjsN2hcRtMVnadkFLal8x1w2MuJ4n0lmSBAi6HfZoN0Ta2BvL0dlgxjicNA7bn41xSMg0Ywm0mXB/b6ysy1uomRwelgRKP9O5jkdROlNAEISKiGDPQR7c2s+l3/wbqWxu/INie+Hxn8PWv5nX2+6FJ38Jg9snvX7cdh9L5/J86bZnSvaNEWzrsJuIE1IZ8ijS+FjWGsHjC+DVRsT3DJY57HAbKC8N2f5CsVnE5rA7GwM0hfxEVIqM9tIQieANmsKtHB7S+OgdSZu5vYWQ+BBpXwMAXS3hYkg8lyqube1z9RkHEnlvMdeNq7IciKdzJHXARBLsetv5EactaboQBRhJZskHmwjnRjgkaPLY0Q5HsMt+HCnFiKeZhb55tmKXdDoThKoQwZ6DPLp9kMd2DtE/OsHSlU4Pb6c1qLNmdGZydxezueuLjlnEzY/tLnHZTicyX6FxipPDHiWg02ZZSxTL281CG06zk6LDzhjB9ngg2klTboCIddZtUVMMtrApRLMNiScI0hIN4A8ZMU6pIKBIZHJmaU5nWldymJTXHLOkJVwIe5c4bJvDTmnzAyGR8zHoEuzhtCo0+IhnciSx1eSpUTZ1j/Dohmfp082kc/lCDhsg4YkS0XFW+gcBaFm0AoDn9o1d+3qARjo8821NbEVOBFsQJkUEew7iCGomO8GXYNYR7DLhLl99qgJxu0b1RWsWAZQKtuOwdRbt8TNsV6tqIk6ANDmPcbEr2iLg8aFyGToaguwdThTHY3uA64YFtOYHCw77JWsX87O3nMrS1ghNYR8RUowSoiVcFOy4DhK2+d/ekVRxWldyiIRd6nJJS5i0dvqMp0pbk2KcNUA876UnUfwMk9pX+GwT6RxJ26wlmYyzoz9BB4M80OtEAUwlO8DedIigyrJU74FIO23NTZx7RCc/vGcLA2U/qnpzDbRSofvbHMbrUYheC8LkVCXYSqmLlFLPKKU2KaU+UmH/+5VSTyulHldK3aGUWmG3H6+Uuk8p9ZTd99qZfgPCWJzq7XRugpC406HLEWpHuLOJqq+/ssMIYPdIMbTrhOF95FDeAAQbyeKhRcXw6wzadg5b3h6162GnWdwcMg5b62JIHMhHOulQQwWHHfR5Of0Q06e7KeSnTQ0zpKO0RPyEIkawY/kgRy8xYfieWKo4rSs5RNzjOOwQaeuOTZV4sdNZLq9JWsFO42frYLGoLK19DMbtimPpLCkr2OlEnNFUhg41zK6MiSh0uBz29pidjhbfDE2m4OwjFx/FaCrLN+/cVLh+MpOjJ99AU3540r+DuYRHIVXiglAFkwq2UsoLfAu4GDgauFIpdXTZYY8A67TWxwK/AL5ot8eB12utjwEuAr6mlGqZobEL4zDq5JgnctjlofCCw55csB2HvbLduOcel2A7Dturs+D10xgK0q1bWEQ/fp1G+1wO2xuAfJZFTUH2DiVN6BoKDjsT7qRTFR22m5Dfy1Ge7Tyrl1rBNj8eEgRZ22U6jRmHbUPiqWFiKkrQ56E9GiyGxMs6ncWS2cK0rjQ+NvcXBTuDj4F4uvAZJK1LTydHScdHiKgU6ZD5QdHZaIrOAJ4btjn4oeehqQuAIxY18uqTlvHT+7aycyBe+BwHdCOR7OCkfwdzCY/MwxaEqqjGYZ8CbNJab9Zap4EbgEvdB2it79RaO3HRvwNL7fZntdbP2ee7gW6gc6YGL1TGWeEqPdESSI5IjQmJV+ewgz4PzWE/Ib+H7pFiGN2Z1uXTGfD6aQr72avbWKT68eWTBEJRLl6ziJNXtZlwNbC0yWcctjMW67DjgTY6GKIl7GMMiUGWql425FfQEgkQiRpnG3cLdiwFHp95T5k4MSJEAl5aImY+duFzyCaNE/d4GU5mCu47rf1sH0i4BNzPgHXY7pB4JhUnHzOLelxw8louWbuYFe2RgsPeYDuvqmy84LABrjljJZmc5iHbmrU3lqKfRgKZIbMm+DzBI0VnglAV1Qh2F7DD9Xqn3TYebwJ+X75RKXUKEACen8oAhalTCIlnJ/jSHzckPnkOezSdJRr0oZSiszFY2WGTA2+AxpCPPbqNxaofbz6NLxDiO687yYiZFebFjV6GEhniCftjwW7vp4WAyrE8UqF4bt9TAGzQy2kJ+wuCndABjl7ShEdZ5+/xQdwsaTlEhEjAR0s4MMZha1+QXF5bwS467Hg6R1ZZYcbHoMthOyHxTDKOGu0BYNXKlXzrqhMJ+rwFh71ttNiP3C3Yy9pMhMIpuOuNpRnQjXh0DlJDk/49zBW80jhFEKpiRovOlFKvA9YBXyrbvhj4X+ANWusxKqKUeqtSar1San1PT89MDmleUig6m8hh58pC4VOoEo+ncoUw9YLGULFJCa4qccdhh/zs1e0sVn14cslCYRdQWK96SaO5Vs+Azd1awe7Wxikv8Vcowtr3JADPqRVEAl4aGqxgE6KrNUxbNEhPzBawjVrBzkeIBr00hnygPOSUF7Ip8uk4/Wkv1z+wnZFklowuOmqArB1nmmIOO5HJ4bPNWjKpBDoxCIA/2lYYouOwncI7oBASB7OYSWPQZ9IBmB8Y/bYzHPH+se95jmJ6idd6FIIw+6lGsHcBy1yvl9ptJSilLgA+Brxc62I3CaVUE/A74GNa679XuoHW+rta63Va63WdnRIx31+KOezphMSrc9jOylmdDUG6h11FZxlzT08+Ax4/TWHjsKMqhYr1lAq2zVUvilrBHrLTmaxg78yYIrEOPTh2EPueZMTTTCq0EKUUjdEoOa1Ie0I0Bn10NASsw/YX3OpgPkwk4MPjUWZ5TmWK3tKpBAnt56ndw4wksy6HbcaX99jiMu3OYWcJhIwQ51LxwnrbKthUGGI04MXrUYxot2AXHTbAouYQe4ZMZGHfcLIwDY7R3sof/hzE61HSOEUQqqAawX4QOEwptUopFQCuAG52H6CUOgH4H4xYd7u2B4BfAz/VWv9i5oYtTES8mhy2I9TVFp099BPY+ZC5birBG9LXQ2KQBU3Big7bo7PgDViHbV3nwJYywTZCuLDBTsMaGinZvjVpxMsbrxB12fskA42HccIKs/Z0cyRAgiAqEC2E6ntjqUKeHKA/Fy50TWuNBMjih2yKbCpBSvvZORBnJJkp5KydVbscwfYHg64q8RzBsG3Wkk6grGATbCzcTylFU8g3rsMGI9iOw97RH8cbNe1JnTD+fEBy2IJQHZMKttY6C7wLuA3YANyotX5KKfVppdTL7WFfAhqAm5RSjyqlHEF/DXA2cI3d/qhS6vgZfxdCCbFqctjZKUzrSgzC/70P7vo8AEeM/J3Xxn8Gm/5EZ4MRMWc6l3NPj86A10dTyMdubUUomyx0EwMKDntBxPwz7B8eLdn+9GgzWbyF8HeBfA66N7D8qFP53uvXAdAU8nFL7jSebzoZMM6/15nWZenLhgpTxJrDfuOkcymy6TgpAuwaTFiHbc7x+M1Ytf0BEQpFCjnsRDpH2E4ly6cTeDI2OhAqOmyAprCfBEG0spXujYtL9hemtAHb+uNEW+3KXfNJsD2KnOi1IExKhfLbsWitbwVuLdv2CdfzC8Y571rg2v0ZoDB1qis6m0LjlC13g86ZNqaZJGsT6832WDcLmoyo9cbSdLWESWfzeBR48llbdOZy2FDRYQdVjtaIn/7h0pD41hHN1vAxHPr8n+GCTxXP63ve/LBYtKZ4Wa+Hz3nfwcWLTDOXDlsMpz1enIaivdkQS2zuvSXiJ93nh2yafDpJCj+7BhJmsQ7MMb5AyExMtOOJhEIMxDOks3myeU04ah12Jo43EyOHB69r1TBw8tiKfLAJL3kINpTsX9wcpieWIpPLs61vlKMOX2LmUswnwVYUOsgJgjA+0ulsjpHPa9OSk/GLzrpHkmQzZQ57oqKz5/9sHrMJ2H4fJ2QeNq9j+wrrNnfb1bbSuTxBn7fQYrQp7KObFvKObPrH5rDJZ1jUHGZwxHHYAbTW7B5Ksqv9NNjzWGlOd98T5nFhUbABPn/ZWt581mrAOOxUNl8IbwPsS4cK/cjbIgGS2jjsfMYIdiqbZ2vfKNqGwn3OWG1UIBKJMBBPk7Cfb2PUcdhJfJkYCRUprrxlaQ778XkUnnDLmHA4GIetNWzpHaU3lmZxR5v5UTOPBFuqxAWhOkSw5xjxTLG72Xg57Eu+/jfufNLO1JtsWpfW8PwdsOqFJry8/gcsccoURntY0GhEzZnalcrkCPg85noeH00hP1l8DHlNrrlSlTi5NEtbw+zpd6rE/fSNpkln8wwvsWtFb76reN7uR825nUeUDPVlxy3hiEW201ijccVDKUcIFL0ZP9GCww6Yjma2l3jKNkHZuGcEZYXaHzSPHivYTQ1RuodTxDMmghGxgq0zCfzZUVKeUncNRpAPXdCAal4K7YeO2b+o2dzj/i2mKnxFR9QsszmPqsSlcYogVEdVIXGhfojbcDhUDolncnl6RlJszPZxIUzeOKV/s1nB6/T3gM7DhlsAGPW1EnU5bKfwLJ3LW8HOQLCJxpARwiFfJ625/rIcdqBw7wuPWsyvN46amfreALsHzTiCy0+EJ1ph0x2w9nJz/OY7YfkLSq9VRmeDEcJbn+7lai/kAo2MDutCDrs14iehfeSzScgmSdICwKbuGN6GAKQgEDTTtpxcdntTAz2xfmJ2edHmRltglkkSzI2S8kbHjONjlxxNKpMDz4/MUp9lLG4297h/s3HUK9qiEGmbVw7bLK9Z61EIwuxHHPYcI+YW7AoO2xGbvA2JpzNpPvHbJ8k4IfJywXbC4YeeD4ecB8C2/AK6m46BWDft0QBKUZjalcrmCXitYHvNtC6A4YAtpirJYTvNSzJcvHYRUa+NDngD7LbLbS5pbYDV55pxaA2xbtj7BBxy7oSfwxGLGlnRHqGrzRSBOQt/FKrEowHS+Mmmk6hsqtjdLJfHawU6GHYE24y5vbmBXF6zw7YSbY6GSWsvZBME86OkfWMFuznsZ0FTCBo6jRCXUe6wl7dHrMOeP4LtFYctCFUhgj3HcNqSQmWH7Qh6AOOot+4b4Kf3bWNoxOauy6vEn78TWldC2+qCYP81v5Z0sB1i3fi8HtqjgaLDzuYJOiFx2zgFYDS40FyvQtEZuTSNIT+nLDMh5ozyFxz2kpawuW9sL+x93IwH4JDzJ/wcOhuD/OWD53L+MSZvPJg34lt02AHS2kc2ncKTT6H8IdNQBfBZwQ5Zh+3ksjtbjPhv6TWfVUPQZ7qdZVOE83GyvtKCsmpoCvmIBLz0jKRojfhNkdosEGyl1Pvsoj1PKqWuV0qF7NTO++0iQD+30zb3G48srykIVSGCPcdwO+xKRWfDSSPUbSFTHBVPGidbdNhlOeyBrcXirkXHMnLq+/hR7iKykQUw2gP5PB2u5inprA2J5zOF1qQAifAEgm0X/Th9lRHE+7ePsGcoQcjvoTXihyNeAoFG+MsXjdOOtMOiY6v6PJQtbOvOmPsWHbafNH7ymSS+XAp8IbparEAHjGCfdMgi/vn8wwpOu7PFCPKWXlPNHg54SasAKpskouPk/FMXbKVUwWUvb7cOfflpsOrsKV9rplBKdQHvwSzoswZTNn8F8B/AV7XWhwIDmDbE+42Zhz0TVxKEuY0I9hwjnp44h+2ExNcstGHekKIhaJwmMDYknhyEcIt57vGw76QP8LzuIh/tNFO9Ev0saCq2J00VHLbT6cxPwOch32A7fLnzzh5XP2/gqE6z74aH9rKjP8GS5jBKKYi2w5nvhY3/BxtuNiFyT5X/dO09+rLm/ZY4bPzobAqfToMvxNJWI8z+gCPQjbzvwsNR3iB4fCxqNkVlW3pH7bW8pAigMwkaVIJcoJHpsNgK9grbW5xT3gIv+9q0rjWD+ICwUsoHRIA9wHmY1fgAfgK8YiZuZHLYotiCMBki2HOM2CRFZ87+RVHjsLuavCxtDZPPjtM4JTEAoZbCy8IPgqjNSce66WwI0uNM68q6is68fvxeD796x+mcfsJac/w4IXGwS3ICj+wa5Y9P7zXhcIcX/BM0LjHTzmxovipsntzpNhYtEWwfOpvGr9Mof9Fh+63Dxq7djS8A3gDtDUG8HsWWnqJgp1WAfCZBI3F0YOoOG4qFZyvax1aZ1wKt9S7gy8B2jFAPAQ8Bg7aREky+CFDVSA5bEKpDBHuOUZLDrtA+aiRZmsP25LN0tYTJZytUiWdTRiDDrYVNjuB7Gm2IO7av0J5Ua+2qEk8XBHlNVzORrmMg3AYdhxWvXxBsqwFWuC9Ys4y8LjpPM+AIvOgzEGyGQyv26amMnTo2rE24ORJ0NU7BjyebwEcObyDM0lYjmOnmVRBqhpbl5hqdR0LnkXg9igWNQXbbzmThgJeMCuLNjBJVKXD1EZ8Kzvtc3jY7BFsp1YpZQncVsASIYtazr/b8KS3mo2QetiBUhUzrmmM4Xc6aQr6KDnvE7vfjiGSGrtYwbLUO2y3YdgWqQkicYp9yX5MV7NEe2qMryOQ0w8ks6WyeppCv4LALRDvgw1tKB+MtDYk7U8s+eMlano1v5uzDyxaCWXs5HHNZ9eFwKIxhBONiHYcd8nvJe/z4syYf7Q2EzecAZNqPgI9sL17j1LeZP8CCpmIr0UjAR58nQDQ7CIAnNL2QuJPDXtkxtsq8RlwAbNFa9wAopX4FnAG0KKV81mVXXAQIzGI+wHcB1q1bN6kSe5VCDLYgTI447DmG44BbIoGKRWcjtuisKNimpajX6XTmbpySGDCPLoftrAQWaFlkb9hdWPd5OJGxVeJeW3TmEuxKlIXEnceGSJjr3/oCXnbckrHnTEWsoeCwvfZHh7MsKIDyhQjmTcW3PxguhMSdueOVWNRUzMGH/V6yniDN+UFzq/D0HPY5RyzgshO7WNvVPK3zDwDbgRcopSJKKQWcDzwN3AnYyfBcDfx2Jm7m8SBV4oJQBSLYc4x4OkvY7yXk94xbdOb1KDz5oqvtag3jUzaUnolTsDvJQfPoEuy4bcsZbmgzghvbV1j3eShhFgEpdjqbRLCd/fnSkHhByGcC6+IbW8wcaLdgOw1RwAj2mq5mPvjiI7jwqIXjXm5Rk3HDQZ8Hr0eR9QRpx3Ro801TsLtawnzlNccT8o9trFILtNb3Y4rLHgaewHxPfBf4MPB+pdQmoB34wUzcT1brEoTqkJD4HCOWyhEN+gj4PJUbp6SyNIZ8KFcYemlrpOi4weSu/aGiw3YVnTkh90jIBw0LYbSnMNd6OGkdtlebrmiTCa+32JrUGQswudBPBVslvrBzEcE9HhpCxX/yvkAQbEAhGI7g9Sjeee7Y9qFuFjY71eZGXHOeEEFlxu0NzxqHvN9orT8JfLJs82bglJm+l0fJetiCUA0i2HOM0VSWaNCL3+sZJySepSHoMz20AfIZulrC+CkWq5FNWMEeNK/dIXGbw474vRDtLHHYw4kM6VyesNfet+qQuBOOTxmBnWrYeyKs+J97/KH87pxTTbje4vMXq9Cdta0nY2Fj6fSwnLfo0oORuSPYBxNTJV7rUQjC7EdC4hNx37ehe2OtRzElRlNZogEfAa+HVKWis2TW5GhzzmpdaToaAvjJFhevcArPCjnslsL58XSWoM+Dz+sxDjvWU2w/msiSyuYJe6oVbMdhuxYgcQngjGDvEYi2cOiC0mlXzuIeAOFwdRXaToFY2DrsvGu8gQYR7OmgFFIlLghVIII9HrkM3Pav8MSNNR3GYzsG+ZcbH6v6C200bRx0wFc5hz2SzNAY9BVFUudROk9A5Ri1/bY/f/MjZn3ixACgzFSqsusDpj92WQ47nXU77ElC4p4KVeKTifxU6TrJTAPrOGLMLn+w6LDD1TrsptKQuHbNKw83tFY8R5gYUyUugi0IkyGCPR5Ju9RjNlXTYdz1TA+/fHgnO/orrFNdgdFUjmjQS2CckHgslTV5XPf7yqXxk2XY9tu+66ntpto8OWjmI7tC1PFUrjCXmYaFEO8l6lN4lFN0lifscRbxmER8lTKinnc77BksOANoWwWv+yUExzY1CYWKgh2JVCfYBYftdxx2UbBDUXHY00F6iQtCdYhgj4dTIV3eqvMgM5QwYvZ8T6yq400Oe3yH7RSdFULiUHiPvbZ9Z5g03SMp47Bt/nrvUJJ4OkvMhtwB0+1M5/Ek+2kK++kbNdcMOYJdTfGYx+8KiWdmXrAnIOgS7GhDdV3KGoI+ogFvsdrclQef7jzs+Y5HctiCUBUi2OORHDKPNXbYjmBv6q5OsB1BDfgmKTpzRBIgbVptxrQRn5BKs284aYrObP76Vd+5l8/9bgPxdK4oVg22PenIXppCfnpGTGg76CkukzkpXn/pPOyZDolPQCRSzFsHgtV3GTt0QUPBaTutVkcJVVzvWpgc6SUuCNUhVeLjkXJC4smJjzvATNVhx9NmWldO63HnYTeG/OaHiPKaBTwyJtwetznsECl6XA47nc2zazDBHRu6WdRcXIaS1pXmcWArzeG2wgIgoULRWRX/vLz+sqKzg+ewI+5CM1/1xW4/esMpZq45oOzSm3EVYdb0KaszpJe4IFSHOOzxKDjs2gr2cEGwRyc9Vmtti8K8FedhJzM50rm8DYmnwVmswjrsF590OAAhMtZhm4U/eq0Q7x1O8szekWJIvG2VeRzYQlPYR++IOW5qDjtQs5C422GXLEoyCW3RQKHwTtmQeELNjj7g9YhHeokLQlWIYI9HcnY47MGECRdv6o5NWkkbT+fQGpPD9o7NYTttS8087FSxEMs6bG/IdOpq9mXN+tbJQQi3GrdtSWRcRWehZrOgR/9mmsP+wnFBNcWQeKHoLHVQQ+IN0ekJthvHYac84q+ni0d6iQtCVYhgj8csymErW4HdP5qe8NhCF7JxOp05a2E3Bm2v74AVGeuwsYLdGcq5ctitpgAN8HvNkpwFhw3GZfdvoSnkL9wv4Ai2p4qQuKc8h30Qi85c07qmK9jegBH9lE8Ee7p4lPQSF4RqEMEej1kSEh9KZDjMNvyYLCxedNDOtK7SL0Fnac1Gv91eLth2vnV7SDM81G/y2+GWgnM+70hTZFZw2ACtq2BgS2EuNrgEezohcd/BE2zlFulpC7Y5L+MVwZ4uksMWhOoQwR4Pp+gsM0XBfv7PMLRzZoaQzZHM5DlxuZla9XxPjMd3DvLojsGKxzsLc0QDPvxeD7m8LskNjqSMMDb7rfMOlIbEHYfdFsiRGukz21wh8VeesLRw/QJtq2BoJy2umq2gsn3Jqwlve301Kzpzfhxob2Da7VA91mFnfNVNCxPGopQiP7Y+UhCEMkSwx2M6DjubguteA3/54owMwakQP2pxEyG/h7ue6eaq793Pp25+quLx7hy1U8XszmMXHHbAcdhO0Vm85HWrP0sm1m+2hVroHknSGvFz1mEdLG+LcMQi13zjttWg8yymp7Cp6LCrEexAzULiThtUNU13DeCz08FyARHs6eKV5TUFoSpkWtd4TCeH3fOMyQ3ve3JGhuBUiLdE/KzuaOC2p/YBFBqUlHPvpl6UguXtEZ7eYyIE6VyeMCaEXchh+6ygOiHxjA2J+4LgC9PsyxLMDkOAgsNe0BgiGvRx94fOLb1pq6kUX5jbAxjRmnJIvLC85gFoTToRTvh9ClO6xlzC5sHzfhHs6SLLawpCdYjDHo/pVIk7Qr3vacjnJj62ChyH3Rz2c4jNY6/ujDIYz4w5NpnJce392zn/yIUsbY0QrOCwHQce9dqxBUundeH1gz9Moy9HC3bed7iF7pEUnY3jiJqd2tWe2lXY5NdWgKvqdOarucOebv4awB80P3p0ULqcTRenSlz6iQvCxIhgj8d0QuJ7rWBnE9C/eb+H4Bbst79wNV+8/FheftwSRpJZsmUV4L99dBf9o2neeOZKgGJIPOcOiZvrRX1lOWwnJO4xgh31pGlWVsQLDnscwW5YCP4ILcli3t4/pRx2oGzxj4OZw54BwY6YvH8+3DYTI5qXeJSZfSBTsQVhYkSwx2M6gr3vCQhYp7X3if0egluwj1nSzGvWLaM1YgRtMFF02VprfvC3LRy5qJHTVrcD4Peav9qMO4edyhLweQhgBbU8JO4NgC9ERGVowWzToWZ6JnLYSkHrSqKjOwqbCmtrV10l7oTED25r0sL49kOwGzuXcU3mXxk59NIZGtT8w/5TleYpgjAJItjjkbKCnUtTVQmr1sZhH/kS0/JzBvLYQ/GiYDu0RMzzwXhxTvbzPaM8uy/G616wAmXdSmWHnTVLa2btuYHykLgP/BFCpGhWo2Q9AYYzZn71uIIN0LqK4Mj2wksfU60St+PJpmrksKefw26LBvjU+97NRcevmqFBzT9UwWGLYAvCRIhgVyKfNzlsRzyqcdkjeyDRb9Zf7ji8GB7fD4YSWQ5VO2m57ws4raAKDtuVx97aawT3mCVNhW0Bb4UcdjJbbEsKrnnYNiTuDYA/hC+fot0zSsLbSLdtSzqhYLetwjvwPP/j/wpv996Mb8oOu0YhcY8PUPvlsAFWdkTxetTMjGke4nx2ItiCMDEi2JVIxwBt8rNQnWA7Ar1wDSxaMzMOO5HhbYHb8N7zFRg2RV2Owx5wCfY2u1b2ivZi8w5/BYddWAvbWVqzMA+7NCSusknW+HbQ7VtcmIO9oHECUTvyEtTCoznR8xzv9f0Sn7bXr7bTWd4dEj+Igq2Ucdf+/RNsYf/wSg5bEKpCBLsSTv7aWT6ymqldjkAvPMaI9vAuiPfv1zCG4mnOVI+bF/1bgKLDHnCFxLf3jdIY9NEaKYaggxUc9kgyQ2PQ7wqJlzlsjwmJM7SLI/KbeMh7QqEt6YQOe8Xp8La7+aHvNYRUBt/IbrN9Kstram2mxB1MwQZTKb6fDlvYP6xeSw5bECZBBLsSBcGegsPe9yQ0LzfrRy9aY7dVbnBSLaHhLcWGJANGsB2HPeQOiffFWd4eKeQCoeiwM7k8jPbCs3/k8OH7WOyPFR122eIfTkicoe140NyaOIqdA2bfgqbJ87yDQdMJzdv3rL1etVXimWK3s4NZdAZmLvZ+5LCF/ccJicu0LkGYGGmcUgmnLWm00zxWI9i9z8KCI83zzqOK21adNe1hHDJ8v32mCg67IejD51GlDrs/zlGLS+cBl+Swf/+v8OQv+Rzw5PAFkPsHe5BTdGbnXHsDxmED2UATdw8v5/F7thL0eUyx2iQMh5dCHFTvs6A84PFOeg6+oPl8nTz2wXbYjYvNH6FmONO6xGELwsSIYFdiCg77oW39DIxmuCA5ZELhUHSu+7lwyNGJ9ezzd7GwMViY162UoiXiL+Swc3nNzoE4F61ZVHJuwO2wY91kOo/hqX0JFtM3fkjc6yuEh32HnsvZowu565kelrWFS9z7eGQausj2evCN7Kk+zBxqNg7f7fIPJlffIiHxGuPxSA5bEKpBQuKVmEIO+1t3Ps//+/0GSI2A0+2qUF2+H0tzZtMcm32C5xtPLqyI5dASCRSmde0eTJDJaVa0RUpOd+Zhp7J5SI8S87ezU3fSmBuuUHTmDonbJScPOY+PXHwkHgWdDdWFjKORMHuU/cyq6XIGEGoxj7FuO4aDHBIPt0jRWY1xCuylSlwQJkYEuxJOW1LHYWcS4x7aN5pmJJEpE2wrcLmxLUQBrn9gOx//zSSNVXatJ0KSXW0vsGtOb3VN7fIXpnVttxXiy9tLBbukNWk6xkg+yIBuJJgZKI7LHzaha2cetu10BsAh53Hkoib+9eKjuOKU5ROP1fKKE7rQrSvtZ1Cl8IbNSmTE9tnzDrLDFmqOV+ZhC0JVSEi8ElNw2AOjadKpOHizRcH2eGyP7Mrn3f70Ph7ZPsBnX7F23Otm+rfjB1Ith0FrzjRySQxApI2WSIAdVqi39Y2d0gWuTmc5DakYAwQY9gTxJAeLP0C8fiOQTuje64fj/gGalkCLEem3nL163DGW88LDO+HZY2D936sX3oJgOw5bBHu+ITlsQagOcdiVSA2BL1wU4Aly0QOjafzOPGb3AhDewLhCv284yWAiU/iC2rh3mP7RdMkxyREzJSzY0GqWsIRC4VlLuOiwt/WPEvB6WNRUGtYtLq+Zg3SMnnQAIh0onXe52WBRID1+M7+m41A4+c3jvt9JsYuBVO+wW8xjYUwHOSQu1BxPoUq8xgMRhFmOCLabXQ/D1nuMww41F4uRxhHedDbPSCpLg7I54GCx01hhulIFukdSaF3sFX71Dx/g7dc+VDKtJR0bACDU2FYUQVt41hoNcETiEeh5hu19cZa2hcd02ioX7L1JH8EmW/U+sqc4Rqe5yUwJZetUBds67NGe4piEeYVH5mELQlWIYLu549Pws9dC3+Yywa6cw3YKvxqw+90O2xesGBLP5vL02naf/aNpsrk8+4ZTPLClnzs2dBeOy8QHSWo/jQ1RcPLCzlzssI+ver5K7u7/ZGtfnJVl4XAAv9e6lkwCdJ49CS8NbTYnP7zHCLXHUxTImRLsgsOuUngLRWfWYcuc6HmHtCYVhOoQwXaTGob0CGz7G4SaJnXY/VawG1UFwfYGitOnXPSNpguhv4F4uiQU/vnfbygsm5mLDzJM1Cz84Q+bucI2JL7EM0CbipGOj7C9b5TlZRXiUJyHrVNmjvVwPkRrh536NbK7WBjnCPVMOVvnx0XVVeLN5rFWVeJCzZHFPwShOkSw3VhxA6zDtqI2Tg7bEduKDtsbGOuwtWbf4GjJ+b0xc43LTuji+Z5Rbn7MtPVMxwYY1hG6Wm3Vtmtq19LU8wAMjQwzms6VLPrhoJQi4PWgMuY9jeoQCxd1mZ0je02HLygKZLUCOxmBqKmur1Z4vT6TSpCis3mL9BIXhOoQwXaTjsGhF4I/CpGOSR32wKjJQY8fEi9z2I9cy+HXnw5oe36avlFz7StOWU5bNMDfN/eZoYwOkvBEi4tutK2G3udAazrjmwAYHDLTz84+vLPi+PxehbJTtkYJs2TJEvt+kq5Q+AyHxMGsVuY0j6mGcAuMimDPVySHLQjVIdO63KRiRhjP/wRE2oz78/jGnYfthMQbCiFxd9GZf2xIfGALocRegmRIEaA/niboN7+ZOhoCrOlq5olddg54agjthIsBlr8AHr0Wup+mdeQZABKJOEcsbGRhU+XGHwGfB49tO5rzR+lsbTWtRzPxYkjccdYzKdgv/wboKtYQdwi3wuD2mR+HUBd4JIctCFUhDttBa+Owgw2w+FhoNgtZ4AtN4LBtDrtiSLxC0Zl13BGVIuDzGIdtQ+IdjUHWdjXx3L4RRlNZ/JkRfGGXYB9yrnl8/s9EBjYCECLNWYd1jPuW/F4PXjvlrLm5xeQKI+32fZU565l0tm2roP2Q6o93Cs9mehxCXeDMw85P4TeeIMxHRLAdsknQuWK7TgdncYoK9I+mCfu9NKgEOeUvrXD2BcdO67KvF0cUnQ1B+kcz9MRSBLxmcY21Xc1k85pbn9hDI3HCTW3Fc5uXQscRsPFWvAMmhx0kPW44HIzD9maNYLe22GtF7OOBDIlPFWdqF4hgz0NsfaQ4bEGYBBFsB6fgbIxghycU7IVNQZo9CVLesqlVXv9YZ24d9pKopjXqZyBuHHZHQwClFGu6jKO+af1OmojT2FLmng89H7bfi9J5hnSUsEpzyqo2xiPg86Csw25rt87acdjlQj1TRWfTwWmeAhISn4c4VeI5EWxBmBARbIf0iHksL5aawGEPxNO0RgO0epMkPGVTqyYIiS+JalojAVslnqLdLq7R1RKmNeLnsa17CaoMLa3tpecfcl7h6UbvYTR4s4T84y9hGfB6iI+YNquLOssE23eApnVNB3HY8xqnSlzWwxaEiRHBdnAWwBjjsMfPYfePpmmLBGj2JImrMoftq9DpzL5eGIG2aABvbA+54b10NBiRcly2kxP3RVpKz19xuhG0QAPLjziBqCc74VsK+DwkY4MALOm0ofNyh30gis6miuSw5zXFXuI1HoggzHJEsB2ckPhUHPaocdhNKsko4dKdFXqJ5zPm9YJQntZIgA8kvsabh75RcNgAa7uaaVL2x4NbyMDMcT7kfFh2Cos72vHkkhM2YPZ7PQR1gpT2s7zTFrCNFxKXHLZQIzySwxaEqhDBdkg7OezG0u3+MGTGyWHH07RFAzSoBCO6XLDHzsNOp811OoJ52qIBWvKDNGQH6XAJ9rFLm2nE9iYPjW2IwuU/hNdea9Zw1vlx+5WDCYk3kGCUUHFxkDEh8bLis1pQksMWwZ5vFKvERbAFYSJEsB1SNocdKA9tV3bYiXSOZMY45aiOM6zL5kL7AmMEO5MyDrstmKM1GiBCigDpQkgc4AWr2zl5sc1Lu+dhOwQiZow++wNhnD7nYELiEZUi6YkU5rqOX3RWwyn5JQ5bis7mG04vcSk6E4SJEcF2cHLYY0LilXPYTtOUtqifiI4zmCsTbG+wpHGK1rrgsFv9OdoiAcIqTYhMicNuiQT4+Hm2I1mwgsMujMueM477BxMSbyBBzuf6ETJuSHwWFJ05S3wK8wrnt6QYbEGYGBFsh/R407pCFV2s0zSlNRIglI8zMEaw/YUq8fue7+Owj/2eXX2mYrvZn6U16idMihBp2hvKxDJlu51VctgOfsdhjy/YQZ+HKEl0oIJg+8rnYddQsJ1cvYTD5yUSEheE6hDBdhh3HvY4DtsKdntI49Np+rPB0i8cX9CcpzVP7xkmm9do67gbvFnaIwEiJAmpdInDBsx63DCxYBf6nI8v2AGfh6hK4A258vIFh13emnQWhMQlHD4v8chqXYJQFSLYDukR4/B8ZS6vPIcd74eH/5cBJyTuN0VfMcLE0q5pVt4goCGfoy+WwudRrFlkXLE3m6A1lMerNEEyYx12cgiUd2w+3Y3jsMfpcw5m8Y8oKfwRV2h9TKezWRASD0RNDl0c9rykkMMWhy0IE1KVYCulLlJKPaOU2qSU+kiF/e9XSj2tlHpcKXWHUmqFa9/VSqnn7J+rZ3LwM0p6dKy7hrEO+7Eb4OZ3ke41S122eY2Yx3SYkaQR7Fxe8+dNA+b4XMo2RwngzVtBzyZp9ZnnQcxc7hKSw6ZCfKJ87hQcdijqcupev5ka1nVi8TXUViyVMi5bBHteoiSHLQhVMalgK6W8wLeAi4GjgSuVUkeXHfYIsE5rfSzwC+CL9tw24JPAqcApwCeVUq3MRlKxyktC+kOlLnZ4FwDZgR0oVVypK0aYkaRx28/sHeGvm20eOpuiL5amPeqa5pVJ4M8boQ2qLD5V9k2VHJo4HA7VCbbXSwNJIo1l1/rHX8GxrzHPCw1UarxwW6hFQuLzFK+s1iUIVVGNwz4F2KS13qy1TgM3AJe6D9Ba36m1tpOH+Ttgl7rixcDtWut+rfUAcDtw0cwMfYZJx8bOwQYjjPkM5HPmtRXsHVs3sWZJM15brDZChJh12Bv3DpPGik8uQ28sRUejazGQTALS8eI9ykU3OTRxhTiYHxIwYZX4ZScsoUEl8YUqvC+H2eCwwThsX3Dy44Q5h+SwBaE6qhHsLmCH6/VOu2083gT8fprn1o7USOWcsSMiTlh8eDcAucFdXHZiV2H+tjskvmHPMGlnqfFcit5Ymo5ooNhbPJsEuygHMFZ0U8NVOOzJ52GvWRjEQ75yqN9hNrQmBWhcNPmPFGFOUmxNKoItCBMxo3FQpdTrgHXAC6d43luBtwIsX758JodUPenRyp3F3KHnQKQg2Es8/Vxy3BLYvB4wIfFhGxLfsGeEdm0+Wp1NuRy2ExKPT+6w21ZPPN4qHHax3epEDnsWLK8JcPF/jNuzXZjDxPsJxrqBCbvsCoJAdQ57F7DM9Xqp3VaCUuoC4GPAy7XWqamcq7X+rtZ6ndZ6XaezSMXBJh0bv+gMjKjmc+iRPQCsaRw107HsnGnHYWutrcM2AphIJkll87RHXYuBZJJGtB0qCXbVOezxHXZhBbKJqs1nS0i8aQm0rartGISDzx2fZslNlwDisAVhMqoR7AeBw5RSq5RSAeAK4Gb3AUqpE4D/wYh1t2vXbcCLlFKtttjsRXZbbRnZB79+R6nLTcUqO1GXYI8O7EHZSu9DgnautA2Jj2AEuyeWom80jS9gQun9Q2Z/R4PLYWcTpYJdPjUrWU1I3BnXBK50vLnlbmbDetjC/MUfRtlUkeSwBWFiJhVsrXUWeBdGaDcAN2qtn1JKfVop9XJ72JeABuAmpdSjSqmb7bn9wGcwov8g8Gm7rbY8fwc89jPY91RxW3pkHIdthHdP3xBv/ab5nTLkaaE5bX+XpIbRykvWEySWyrBhjxHow5eYBiU7e8z0rvaov6RKfNyQeD5nxjJp0dnk87DHbbfqZraExIX5iS+Esv/+RbAFYWKqymFrrW8Fbi3b9gnX8wsmOPeHwA+nO8ADgq30LoSMtbbzsCsVnRkn+/Dzu2lIdUMAmg87HZ75vekVnhpBBRtpUH5Gklk27DEh8jXLO2E3bO8ZBFroiHqL18wkyorOXKJbTVtS17gmmtY17gpkbmZL0ZkwP/GHUfkMHvIyD1sQJmF+djqzhWMFB5pNQT47/jxsYNu+flY7YfCl6wANsb0mJB5sojHkYzCeYcOeYZY0h+jqMIK7o9ec0xl2NUHJJssctiusXU1bUjDdJnyhiR22swLZhA57luSwhfmJ/eEZIi05bEGYhBp3y6gRjmA7Od6JnKj9QtnRM8iZDTGIB2DRscXrDO2ESBtLCHPzY7sJeD2cdVgHbU15wITSAdpCLsHOJEqF1l04Fusxj04L0YkYZ+nPAoX3NVHRmYTEhRriEmwtIXFBmJB56rCdkLgVtImcqM1h9w4MsSowaKqZm+xU8r5NsON+WHkm3/3HdXzwxUewsDnIeUctoKXRiGQ8Hqcl4seP02dcVQiJu0R335PmsfPIyd+HLzyJYNt7SNGZMFvxi8MWhGqZ5w7bCnVB2IzIfu1Pz7KqI8qlx3cVGpT4dYpFqh+alkKzFezHbzSFZIecR3PEzzvPPZR3nnuo2de7yZxH1kzpcsLewaYKRWcuh73vSeP0W1YwKf7QDMzDlpC4UEPs/6+gykgOWxAmYf457EwS4n3muSPUZWth//zBHdz8qBV16wDa1AhN6W7jsINN5tgtfzEhvRWnj72PXfUroLKlU7pCTcVpXZWmZu19EhYeA54q/mp84cnnYXuDE4e7nTFIW1ChFrgctlSJC8LEzD/BHtldfF4IiZc60VgqS2/MimjLCvYGV/IW/x/wju41gq2UeQQj1s4UKzd2vekgGSvYtmlKqBl0HhKDxbWpnXx2Pm+mmi1aU9178QUndtixbmhYMPE1lp4ML/kyrDyzunsKwkxiHbYItiBMzvwT7GGXYBeKzpyOYA1orRlNZemNWUfs8fId3+tZwR5ULl3MXzuCfch5le9jHbafLB0NgaLDduZXx/sgbAvLnDz00HYzloVVCrZ/khz28K7iOMfD44VT3iJFZ0JtsJGdkEqTy9d4LIIwy5m/gu3xF4U6VaymTmbMfNCekRRaa5KZHNcNHMn2Jrt+tCOAjnCPJ9g2JxwgS3uJw7aCneg3jt7jKzrsvbbgbNHa6t6LL1Qq2Jkk3PuN4r2GqhBsQaglNjoVFIctCJMyDwXbVoi3H+py2E5HsEZiKVPNnc7lGU5m2dYXJ5uHzSd9HBYcDUuON8cech4ccr7ZVgkbEg8UQuJODtvOrx7tNYuJuEV335OAggVHVfde/OHSkPimP8EfP25y61qbHydNs3NxNEEAXNO6MuSl6kwQJmT+VYkP7YJQCzR0uorOiiHx0cFM4dCekRS7B437bVx5IrzwvuJ11l5u/oyH14dWHvwqS7s7JO4IdmIA/GWCvfcJs0rXRPOm3fhCpUVnzo+R/i2wZMDsE4ctzGZKHHaNxyIIs5x56LCt6ww0lBadeQPgCxQcNkBvLMUuK9hLWkJTv5c3wGnLGzjrsI5imLrQI1wbYXa75H1PVl9wBrbTmcthO4I9sLUY+hfBFmYzjsNWGXISEheECZmHgm3zuoEG1zzs4tKa5YK9ZyiB16NY0Dh1wVbeICcvayAS8I112OBy2Lbz2cBWWHBM9Tfwl+WwHZHu3+IS7KVTHrcgHDSsYIel05kgTMo8FOzddi61y2HH+wutQEddgm1C4kkWNYXwelSlq02Mz9UwxS4hWCg6A+OuHZcct4uYNS6cwvXDlQV7YEvRbYvDFmYzdh52WEmnM0GYjPkl2Nk0jHYXQ+JO0Vm8rzAnulJIfFrhcDBhdicUPiYkjg2JW5ecHDTbQi3VX99ftviHO4c9vAuUBxqm8ANAEA42dh52WDqdCcKkzC/BHtljHpuWmClVuZQR0nh/QbBHUzkAvB5Fz4gJiS9pqdAYpRq8gaKznjAknjRFaADh1uqv7wuDzpn34FSFB20ntV0PQ8Mi8M6/ukKhjvB4wBsgpDIyrUsQJmF+CbbjYsOtxQUx0jHrsEtD4ktbw3SPpNg7lGRx8zQF2xd0hcQrCHah6CwxTcG27UQzCTNNLJeG5S8w23bcL+FwoT7whSUkLghVML8E26mo9oeKU6dSsYoh8RXtUTbsGSaT03RNOyTuHxsSH9dhD5pt4Zbqr++0RM2miuHwFWeYx3RMBFuoD3xBMw9bHLYgTMj8EmxnzrIvXFxKM7bPhK0LIfEsYb+XhY1B9g0bdzz9kHhwkpC4U3Q2XYftLB6SKBacLX8BKK95Lk1ThHrAHyKk0tI4RRAmYZ4JthVPf8gsYQlmKhUUBTudpSHko6OxuHrV/oXErVAXeom7lrosFJ2ljGAr78RrV5fjOOxMsuiwW1dCs53KJQ5bqAd8Ybv4R60HIgizm/kl2E5FtS9UdNiD28xjpAOAWCpHQ9Bn2olauqbtsP1FoXZC4t5goW2pCYnbJTKTg8ZdqylMH3Ny2I7D9vgg2gltq8z2ZnHYQh1gHbY0ThGEiZlfgu3MWfaFik52wBFsm8NOZogGvXRahx0JeGkKT7PSujwk7vGZqljHGQcidmqWrRKfSv4aClNiTA57NzQuMatvtVrBlpC4UA/4wgTJSOMUQZiEuSfY934Dfv6Plfc5DtsfLhadWYf9gVt3AmZaVzTgM0tiYvLXaiqu140vUBoStyt4FQTbHzWim0uZqWVTyV9DoekEmUTpUprth5hHEWyhHvAFCSFV4oIwGXNvku62+2DnA5X3uR22x75167Bv35ohl9fEUlkWN4fotCHxxc3TrBCH0nnY2XRxzWmnWMwfLoa1R/ZCy/KpXb/gsJPGYS8+zrw+4XWmYUrLsumPXZjXKKVagO8DawANvBF4Bvg5sBLYCrxGaz2w3zfzh2XxD0GogrnnsON9kI5X3ucItj9cDIkP7SSHhyEdoW80xWg6SzToK4TEp52/BhsSd6Z1uR12xDw687DBCPZMOexwKxz7mumPWxDgv4A/aK2PBI4DNgAfAe7QWh8G3GFf7z++kBFsUWxBmJC5KdiZUcjnx+5z5mF7g0YolQfyGQZoBBTdwylGU0awm8N+VndGOXH5FEXUTUkv8YxLsB2HHSm67dTQ1AXbOXd4t/kxIiFwYQZQSjUDZwM/ANBap7XWg8ClwE/sYT8BXjEjNyw4bBFsQZiIuRcSj/eZx0y8WAnukE0YsfbY3ymBRkgN0Zc3U626R5LEUlkagl6UUvz5X87Zv7GUtyYthMTDgDI/GvwuBz/lojMr2I9cax6dkLgg7B+rgB7gR0qp44CHgH8GFmqtbX9f9gIz06jeFyJIhpzotSBMyNxy2PlcsQFJenTs/kyy6G6hUHhmHDbsHkySzOSJBmfod0zJ4h9p13SusHHXShVz2DCNkLgV++6nYPW5sPKM/R+zIJgf8icC39FanwCMUhb+1qaku6LEKqXeqpRar5Ra39PTU8XdQgS0OGxBmIy5JdiJQQrfIZkKgp1NFAu1oODA+7UR7C295pyGmRLskl7iZSFxR2zd45nKSl1QdNgouPDT+zNSQXCzE9iptb7fvv4FRsD3KaUWA9jH7kona62/q7Vep7Ve19nZOfnd/JLDFoRqmFuC7YTDobLDzqbKHLYR7AHdiM+j2NxjltucOYcdNKtp5XOlIfHoguKyl+7xTCeH7Y/AcVfC4mNnZszCvEdrvRfYoZQ6wm46H3gauBm42m67GvjtjNzQF8ZPFp3PTn6sIMxj5lYOezLBziRcrpSCwx5STRzS2cDmmXbYjkDn0qVV4ud/ojg+337ksD0eeOtfpj4dTBAm593AdUqpALAZeAPmB/6NSqk3AduAmZmKYH+0ep2ugIIgVGR+CXY2WSrYtp94PtzKouYQf33O5NtmNCQOxtnnMkUBD7cUxXl/HDZA5+H7M0JBqIjW+lFgXYVd58/4zeyPVl8+OeOXFoS5xPwKiWcSpVXZ1mF7oh0saAwWGjfMaNEZWIedKr524/4BMdUctiDMBewPW28+VeOBCMLsZn4J9hiHbarEA02dLGwqbo8GvTMzHkegs6nSkLgb93imGhIXhLmA33HYEhIXhImYw4IdG7s/myxx2FmfEexo6yIWNhWnV814SDyXLg2JuynpKx4cu18Q5jr2R6svJyFxQZiIOSbY/RBuM88zFdqTZpIlojiizRdFc/siOhvdDvtAhMTTlQXZcdjiroX5iuOwtYTEBWEi5l7RWfNS0zxl3JB40WHvDawko1toX7SUQPAAOOySkHimckjccdjTKTgThLlAwWGLYAvCRMwxh90L0U4zN3ncorOik36s8WxOSX2bJe0tLLA5bK9HEfTN0MdSCIlnSudhu/H4TE9zKTgT5iuOYGvJYQvCRMw9h912iCkmq8JhP7l7iGjAy+LmULFCPOCd/vrX5RRC4hMUnSllxiQhcWG+Yn9E+6RKXBAmZI4Jdj9E2iEQJZeKkcnkCPldFd/Z0hz2A1v6OWllGz6vcdRt0QChmXLX4JqHnRw/JA4QbIRox8zdVxDqCfsj2iNFZ4IwIXMnJJ5NQ2q4INhPbN7Nh37xeHF/Lgv5bCFn3D+a5tl9MU5d1VY4ZEFjcOYKzsCE5wFiPeOHxAEu/wGc+f6Zu68g1BPWYefTiRoPRBBmN3PHYSf6zWOkDQJRVCbG7kHXF0DWPrf5sge2mOPdgn3EokZiyRnsZ9y42DwO7xw/JA6w8syZu6cg1BvWYYtgC8LEzB3BduZgW4cdyA+SyuYLu6/92zO8DgoO+4Et/QR9Ho5d2lI45j9eNcMLaAQipvp7YJt5PZ7DFoT5TCF1lCCf13g8M1RDIghzjLkTEncLtj9CSCdJZXMAxFJZvnP7k2a/ddj3b+njxOWtBFw565DfW5rzngmaumBgq3nulcYogjAG+yM6oDOMpGTFLkEYj7kp2IEGQjpRcNipTI6gypj9vhDDyQxP7xnm1NVt41xsBmla4nLY44TEBWE+4/GSVz5CKs1wIlPr0QjCrGWOCnbUOOyMEexkNk8IO8fTH+KR7YNoDaesPEiCPbzTPJeQuCBUJOcNESLNYFwEWxDGYw4J9oB5DLdCIEKYYkg8lckVBdsXZo8tRlvRET3w42paCtrm0sVhC0JFtM8I9pA4bEEYl7kj2JlRI4i+gAmJkyaTNfmwVDZfDIn7Q/TGTIOG9uhBENCmJcXnItiCUBHlCxFUItiCMBFzSLBda13bZTN9WbMASModEveF6RlJ0RTyzXyBWSVKBFtC4oJQCRUIm5B4QtqTCsJ4zJ1pXZm46SEOBcH255Pkb/0Q0eAxBDG/3PPeAL2xNB2NB6liu6mr+FwctiBUxOMPEyQjDlsQJmBOOuy8zwh3k4qj1v+Q5m23FRx2LOejZyRFZ8PBEuzFxeci2IJQERUIE1Ei2IIwEXNMsI1QZ61gH6p2o/IZfIkeQsoIdn/GS28sdfAcdrARgs3muU8EWxAqoXwhop6MTOsShAmYQ4IdLzhsR7CP8pj5z/5Eb8Fh96e89MQOosOGYh5bHLYgVCbYSJMnIdO6BGEC5pBgF0PiWY95PEptByCYKgr2nlEYSWbpPFgOG0SwBWEyQi00MSohcUGYgDkk2MWis7TXEWzjsAOZYRqVqRjf2GuEu6PhIIpnsy08kypxQahMuIWoFsEWhImYQ4JddNhpjxHu5Z6ewu4u1UtK+9i4bxTgIDtsR7DFYQtCRULNhHSSWFxW7BKE8Zhjgm0dtidU2Jz3GJFcpnpIEeDZfSMAdBzMHHbLcvPoTDsTBKGUUAsAOjFU23EIwixmjs3DNg47ZXPYALG2Y2jqfYSlqoe0CrK934TGD6rDPuYy84XUuuLg3VMQ6omQmUnhTQ+Ry2u8ssSmIIxhjjlsK9gEyGvzH36w/QQAFqpBcp4AWpvD26MHUbD9ITjyJQfvfoJQb4RbAGhiVKZ2CcI4zA3B1rqk6CyT18QxgtzXdnzhsKxdj7ol4i9ZB1sQhBpjHXaTikvhmSCMQ1WqpZS6SCn1jFJqk1LqIxX2n62UelgplVVKXV6274tKqaeUUhuUUl9XSs18rCubNI9ODjuriWPy2P2R1SS8DQDkvWbbQc1fC4IwOTaH3cwogyLYglCRSQVbKeUFvgVcDBwNXKmUOrrssO3ANcDPys49HTgDOBZYA5wMvHC/R11OxlaWOg47l2dUB8lrxYB/CcNeu+61zwj2QW2aIgjC5IjDFoRJqcZhnwJs0lpv1lqngRuAS90HaK23aq0fB/Jl52ogBASAIOAH9u33qMvJmEKywrSubJ44IfbQRkJ7GfK2luw/aG1JBUGoDivYzdI8RRDGpRrB7gJ2uF7vtNsmRWt9H3AnsMf+uU1rvaH8OKXUW5VS65VS63t6esp3T07BYRtBzuTy7NFtPJ1fQSqbZ8jTAoDX74TEZT60IMwq/GG0N0CTEsEWhPE4oJVXSqlDgaOApRiRP08pdVb5cVrr72qt12mt13V2dk79RmnTDKWQw87leV/mnbw/80+ksnn6VQsA3oDZf1CndAmCMDlKQajZOOy4rIktCJWoRrB3Actcr5fabdXwSuDvWuuY1joG/B44bWpDrIIxDlszQoQRIqQyOfpoAcAXsiFxyWELwqxDhVpo8UgOWxDGoxrBfhA4TCm1SikVAK4Abq7y+tuBFyqlfEopP6bgbExIfL8p5LCdKvFiKj2VzdOHzY81NvHy45Zw5qEdMz4EQRD2k1Azbd6ECLYgjMOkgq21zgLvAm7DiO2NWuunlFKfVkq9HEApdbJSaifwauB/lFJP2dN/ATwPPAE8Bjymtb5lxt9FhRy2QyqbpzvfAoAvGOHrV57AkpZw+RUEQag14RZaVJzhRLbWIxGEWUlVrUm11rcCt5Zt+4Tr+YOYUHn5eTngbfs5xsmpMK0LIOz3ksrm6Ms3mf0+CYULwqwl1EyTGmUkJQ5bECoxN9p9lU/rsoLdGPKRyuTZk3MEW5y1IMxaQi006FFGkuKwBaESc0SwS0PiTg67Iegjlc2zN9vIvtBqWHhMrUYoCMJkhJqJ5mPEJIctCBWZI4JdWnSWyeXxehThgJdUNk8s6+H7a6+Do15aw0EKgjAh4Ra85EgnR2s9EkGYlcwRwU4AqpCjzuQ0fq8i4POQyuZIZXMEfd7ajlEQhImx3c48qcHajkMQZilzRLDtSl12XZF0Nk/A6yHo8zCaypLXEJTVuQRhdmMFO5SLlUzNFATBMDdUzLUWNpiis4DPQ9DnZdgWsIT84rAFYVbjWrErlpLCM0EoZw4JdqT4MpvHbx2204Qh6J8bb1UQ5iyFFbtGGUlK4ZkglFO/KpZJwK0fhMSADYkXHXYmZwXb72XYEWwJiQvC7CbcAhiHLVO7BGEsVTVOmZXseRwe+C4sP21MSDyT0zYk7iFlc2FSdCYIsxwbEm9ScYbFYQvCGOrXdubsij7Du4pFZ5aUKyTuEJKQuCDMboKmwVETcWLisAVhDPWrYnn7C3x4dwWHnSfgVSWuWhy2IMxyvD7y/gaalYTEBaES9SvYOfsfenhXRcE2Oezi25MctiDMfnSoxQq2hMQFoZz6VbESh10aEs8UpnW5BFtC4oIw61GNC+hkUBy2IFSgflUsN35IPJ3TNoctIXFBqCc8TUtY7OmXediCUIH6F+yRPZAagUC0sCtti84CUnQmCPVFUxeLVX+h4ZEgCEXqV8WckLjOQ2Z0bNGZT5WGxMVhC8Lsp2kxjcRJxYdrPRJBmHXUr2DnyopSxlSJSw5bEOqOpi4A/KN7azwQQZh91K+K5csFu0JrUr/ksAWhrmhcDEA4IYItCOXUr2DnynJcZYt/+MurxGValyDMfpqWABBJddd4IIIw+6hfFXM6nWGW1HQ7bPfymg4i2IJQB1jBbkqLYAtCOfWrYk5I3P4Hr9xL3ITBgz4Pyq6VLQjCLMYfJu5toiXbW+uRCMKso34F2wmJt6wwj2WNU/xeVSg0E3ctCPXDaHAh7fk+8nld66EIwqyifpUsnwEUtCwzr63Dzuc12bwuWfzDXXwmCMLsJhFewCLVRywtc7EFwU39CnYuA15/YRqII9jpnFlOszwkLghCfZCJLGKR6pf2pIJQRv0qWS4D3oArh21C4hlHsF0OOyQOWxDqhlzDYjrVMCOjo7UeiiDMKupXsPMZ8PjgsBfBsa+FttWAKTgDSkPi4rAFoX6wP8JT/btrPBBBmF3Ur5I5IfHWFXDZd8EXBMyULqBk8Q8RbEGoHzzNJs2VG9xZ45EIwuyifpUsnwGPf8zmjDuH7ZeQuCDUG4FWK9hD4rAFwU39CnYuC17fmM1O0Znfqwh4JSQuCPVGqH05AJ6RXTUeiSDMLupXyXJpU3RWhrvozONRtvhMHLYg1AsNzW3EdAh/bE+thyIIs4r6FexxQuLuHDYYdy0rdQlC/RAO+NhNJ8GY5LAFwU39Ktk4IXF3DhsgFPASlhy2INQNSikGAosIxyUkLghuxipevTCuwy5O6wL4f69cy4r2yJjjBEGYvSSjS2kbfAq0BlkHQBCAehZsZ1pXGUWHbf6TX3j0woM6LEEQ9h/VsoKGwTiJ4X7Cze21Ho4gzArqOCSeqVh0Vp7DFgSh/oguXAXA7m0bazwSQZg91K+qOZ3OyijPYQuCUH+0LT0UgL5dm2o8EkGYPdSvqpWFxO/d1Ms7f/YwKXHYglD3LF5+BADx7i01HokgzB7qV9Xy2ZKis78828PvHt/D1j6zYEBABFsQ6pZQUwdxQuQHttd6KIIwa6hfVculS6Z1DcTTADy5axgQhy0IdY1S9PsXEZK52IJQoH5VrazobCCeAeCp3UOA5LAFod5JRLtozewll9e1HoogzArqV9XKQuIDo8Zh7xlKAqaXuCAI00cp5VVKPaKU+j/7epVS6n6l1Cal1M+VUmOnacwkzcvpooddA4kDehtBqBfqV7BzmYohcQcJiQvCfvPPwAbX6/8Avqq1PhQYAN50IG8eWrCKJhVn6y7peCYIUM+CXdbpzAmJO0jRmSBMH6XUUuAS4Pv2tQLOA35hD/kJ8IoDOYamRYcAMLTn+QN5G0GoG+pX1Vw57HxeMxhPs6AxCIDPo/B4JCQuCPvB14APAXn7uh0Y1Fpn7eudQNeBHEDjQiPY2b6tB/I2glA31Llgm5D4SDJLXsMpq9oACYcLwv6glHop0K21fmia579VKbVeKbW+p6dn2uPwdKwGwDe4edrXEIS5RP0qmysk3m/z16cWBFvctSDsB2cAL1dKbQVuwITC/wtoUUo5hSNLgYrJZa31d7XW67TW6zo7O6c/ilAzfZ52mmMi2IIA9SrYWpsqcdvpzCk4W9oaoaslLFO6BGE/0Fr/q9Z6qdZ6JXAF8Get9VXAncDl9rCrgd8e6LH0BFfQmdx6oG8jCHVBfSpb3qbRrMN2pnS1RgMctrBBQuKCcGD4MPB+pdQmTE77Bwf6hkMNq1mW22l+pAvCPKc+l9fM2SlcBYdtKsRbI37eevZqtvbGazUyQZhTaK3vAu6yzzcDpxzM+6dbD6OhJ8Fo73ainSsO5q0FYdZRp4Jtp3BZwR6MFx32ivYopx9Sq4EJgjCTqAVHwrMwuO1JEWxh3lOfseOykHj/aBqfR9EYrM/fH4IgVCa0+CgAUnuervFIBKH21KdgFxy2EeiBeIaWiB/T20EQhLlCx4Iu+nUD9DxT66EIQs2pT8HOW8F2FZ21Rg5sW2NBEA4+i1rCPKeXEhp8rtZDEYSaU5+CXXDYRqQH4iLYgjAXCfm97PAspWV0s1SKC/OeOhdsJySepiXin+AEQRDqlZ7QKiK5YRjtrfVQBKGm1Kdgl4fE4xnaouKwBWEuMtxoWpTSs2HiAwVhjlOfgu2a1qW1WfijRULigjAnSbUeaZ7sk0pxYX5Tn4LtmtYVS2XJ5DRtUQmJC8JcJNq+hF7dRH7vE7UeiiDUlPoUbFens0Hb5UwctiDMTRY2h9mQX05uz5O1Hoog1JSqBFspdZFS6hml1Cal1Ecq7D9bKfWwUiqrlLq8bN9ypdQflVIblFJPK6VW7veoXSHxfqePuAi2IMxJFjeH2KiX4+3dAPlcrYcjCDVjUsFWSnmBbwEXA0cDVyqlji47bDtwDfCzCpf4KfAlrfVRmD7E3fszYKAkJO6s1CUhcUGYmxy+sJGN+eV4cinoe77WwxGEmlGNwz4F2KS13qy1TmPWx73UfYDWeqvW+nEg795uhd2ntb7dHhfTWu//yhyuaV2bumMAdDaE9vuygiDMPpa2htkRsJXi+yQsLsxfqhHsLmCH6/VOu60aDgcGlVK/Uko9opT6knXsJSil3qqUWq+UWt/T0zP5Ve20rrzyce3ft3H8shaWt0eqHJIgCPWEUopw11Hk8MC+p2o9HEGoGQe66MwHnAV8ADgZWI0JnZegtf6u1nqd1npdZ2fn5Fe1Dvvv20bY2hfnTWeumsEhC4Iw2ziyq5Pn9RKpFBfmNdUI9i5gmev1UrutGnYCj9pwehb4DXDilEZYCSvYNz6yl8XNIS5as2i/LykIwuzlmK5mns4vJyuV4sI8phrBfhA4TCm1SikVAK4Abq7y+g8CLUopxzafB+x/9wMbEn9g2wivP20lfm99zk4TBKE61ixpYkN+BYHYLnjiF7UejiDUhEmVzjrjdwG3ARuAG7XWTymlPq2UejmAUupkpdRO4NXA/yilnrLn5jDh8DuUUk8ACvjefo/aOuwMXo5Z0rTflxMEYXazsj3K73znsz26Bn75Jrj1Q7UekiAcdHzVHKS1vhW4tWzbJ1zPH8SEyiudeztw7H6McSwFwfbh88ga2IIw1/F4FEuWLOX92c/zi0Ovhwf+B875CETaaj00QTho1Gcs2YbEs3jxSThcEOYFa5Y08+TeUXLHXmE27HqotgMShINMfaqdy2F7xWELwrzguGXNJDN5NngOBeWBHffXekiCcFCpT8G2nc6yeCUkLgjzhDMO7QDgL1sTsPAY2PFAjUckCAeX+hRs67BzePB5RbAFYT7Q0RDkqMVN/O25Xlh6igmJS29xYR5Rp4KdJufxAwqfpz7fgiAIU+fMQ9t5aNsA6cXrIB2D7g21HpIgHDTqU+3yWbQyBe6SwxaE+cOZh3WSzuV5hMPMhp0SFhfmD/Up2LkMeY9ZncsvIXFBmDecsrKNgNfDn/ZEINIOOx6s9ZAE4aBRn4Kdz5AXhy0I845wwMu6la38dVMfLD1ZpnYJ84r6FOxcUbAlhy0I84sXrG5n494RUu1HQv/zkE3XekiCcFCoT7XLZch5rGBLSFwQ5hVru5oB2OFdbqZ49m+u8YgE4eBQn4KddztsEWxBmE+ssYL9RMqu0tezsYajEYSDR30Kdk5y2IIwX+lsDLKwKch9w+2Agp5naj0kQTgo1Kdg57Pk7LolsrSmIMw/1nY18/CeFLQsF4ctzBvqU+1yGXLisAVh3rKmq5nne2LkOo4Qhy3MG+pUsNNFwVYi2IIw31izpBmtoSe0Evqeg1y21kMShANOfQp2PktO+fAos06uIAjzi7VLTeHZc7oLcmkY2FrbAQnCQaA+BTuXkbWwBWEes6AxSEdDkIfiC80GyWML84D6VLx8hhw+mdIlCPMUpRRru5q4s6/FbBDBFuYB9SnYuSxZ5ZWCM0GYxxy7tIXHe/Lkm5bCnkdrPRxBOODUqWCnyeKTKV2CMI85fnkLWsO+JRfAxlsljy3MeepT8fIZssonDlsQ5jHHLW0B4Pa2K8Hjg79+pbYDEoQDTH0Kdi5LVnslhy0I85i2aIAV7RHu3eeHE18Pj/4MBrfXeliCcMCoT8HOZ8jik4U/BGGec9zSFh7bOQhnvtds+Pt3ajkcQTig1Kdg5zJklE+W1hSEec7xy1rYM5Rkn+qAIy6GJ26CXKbWwxKEA0J9Kp6dhy05bEGY3xy3rAWAR3cMwnFXwmgPPP/nmo5JEA4U9SnY+QxpyWELwrznmCVN+DzKCPahF0C4DR67odbDEoQDQn0KdqHTmQi2IMxnQn4vJ61o5foHtrNtKANrL4eNv4PkUK2HJggzTv0JttaQz5DRXrySwxaEec8XLz8WgDf9ZD2jR14OuRRsuKXGoxKEmaf+FC9vVuVJax9+CYkLwrxnRXuU71x1Elt7R/nPJxugaSk88/taD0sQZpz6E2xbAZqRojNBECynHdLOhUcv5ObHd5M/7EXw/J2QTdV6WIIwo9SfYOetYGvJYQuCUOTS47vojaV5uuE0yIzC1r/VekiCMKPUn2DnnJC4R+ZhC4JQ4JwjOmkM+fjffSvBF4bn/ljrIQnCjFJ/iucPw8Vf5HHvMTKtSxCEAiG/l4vXLOJ3GwbIrTzb5LG1rvWwBGHGqD/BDkTg1LexybNKctiCIJTwiuO7iKWyJiw+uA26n671kARhxqg/wbZkcnlZXlMQhBJOXd3OgsYgPxlYA/4o3Pn/aj0kQZgx6lbxcnktDlsQhBK8HsUlxy7m5uezJE97L2z8P9j8l1oPSxBmhLoV7GxeSw5bEIQxvOy4JaSzeW5tuAyal8NtHy0UqwpCPVO/gp3TMq1LEIQxnLCshaWtYX77ZD+86DOw70l48Pu1HpYg7Df1K9h5La1JBUEYg1KKlx23hL9t6qVv+UVmUZA/fxaGd9d6aIKwX9St4uXyeQmJC4JQkVee0EUur/n+PVvhJV8yDZdu/SDkc7UemiBMm7oVbAmJC4IwHocvbOSVJ3Txg79tYZdnMZz7UVOA9tNLYWhnrYcnCNOifgVbis4EQZiAD7z4CBTwpT9shNPfA5d+G3Y9BF89Br52LKz/Ua2HKAhTom4FOyc5bEEQJqCrJcybzlzFbx7dzb2b++CEq+Dtf4PzPwGhJvjjv0EqVuthCkLV1K3iZfJ5/BISFwRhAt513qGs6ojyLzc+xlA8A+2HwFn/Ai/5T0iPwJO/qPUQBaFq6lKw83mN1kjjFEEQJiQS8PG11x5P90iKf/vtk8Udy06BBUdLWFyoK+pSsLN509BfctiCIEzGcctaeNvZq7n5sd3sGkyYjUrBSW+APY/C7kdqOj5BqJa6FOycI9jSS1wQhCp41UlLAfjT0/uKG497Lfgj8JcvyapeQl1Ql4qXyecBcdiCIFTHIZ0NrO6McrtbsEPNcM5H4JnfwSP/W7vBCUKV1KVg53Lm17DksAVBqJYLj17I3zf3MZTIFDee9m5YeRb8/iOw94naDU4QqqAuBTsrIXFBEKbIhUctJJvX3PVMd3GjxwOv/B8IROB758GfPwfP/Qn2PFa7gQrCOPhqPYDpkJWQuCAIU+SE5a20RwN89fZn+crtz3LmoR187pVrobkL3nEf/OEjcPcXiye88TZY/oLaDVgQyqhLi5qVkLggCFPE61FcdmIXPSMpfB7F9Q9sZ3tf3Oxs6ITLfwDvftgIdbBZVvgSZh11KdhOlbg0ThEEYSp87JKjefLfX8zP3vICvB7FD+/ZUnpA+yHGVR93BTz9Wxjtrc1ABaECdSnYTkhcWpMKgjBVlFIsbArxsuOWcOP6HaYDWjnr3gi5NDxy7cEfoCCMQ10qnjROEQRhf3nzmauJp3O854ZH+NPT+8jnXXOxFxwJK86Av38b7vgMbLwVctnaDVYQqFfBzolgC4Kwfxy9pIn3XnAYj+4Y5M0/Xc/3/7a59IDzPwHBJvjbV+GGK+HrJ8DjN9VmsIJAvQp2YVqXCLYgCNPnvRcczoMfu4CTV7bys/u3o90dz5a/AN69Hj62F157LUQ74Fdvgad+U7PxCvObuhTsnOSwBUGYIQI+D689eTlb++I8uHVg7AG+ABz1MnjDrWbRkF+9Fbbde/AHKsx76lLxnJC4X0LigjDjKKWWKaXuVEo9rZR6Sin1z3Z7m1LqdqXUc/axtdZjnSlesnYR0YCXm9bvGP8gfxiuvAGal8Kv3wYZu5DIwFbpRS4cFKoSbKXURUqpZ5RSm5RSH6mw/2yl1MNKqaxS6vIK+5uUUjuVUt+ciUE7IXGZhy0IB4Qs8C9a66OBFwDvVEodDXwEuENrfRhwh309J4gEfFxy7GJ+98QeRlMTFJdF2uBlX4PB7XDPf8E9X4f/Og4ekmU6hQPPpIKtlPIC3wIuBo4GrrT/ed1sB64BfjbOZT4D3D39YZYiOWxBOHBorfdorR+2z0eADUAXcCnwE3vYT4BX1GSAB4jXnryceDrHNT96gJ0D8fEPXHU2HHMZ3P0luP3fwOOD+/9HXLZwwKnGYZ8CbNJab9Zap4EbMP9xC2itt2qtHwfy5ScrpU4CFgJ/nIHxAsUctk9y2IJwQFFKrQROAO4HFmqt99hdezH/r+cMJ61o5auvPY4Ne0Z40Vfv5m3/u57fPrqr8sEv+iwEG41wX/Kf0LNR8trCAaeaXuJdgDuxsxM4tZqLK6U8wH8CrwMumOC4twJvBVi+fPmk181Ia1JBOOAopRqAXwLv1VoPK1X8/6a11kqpipZyqv+fZxOvPGEpJy1v4zt/2cTdz/Zy21P7aIsGOOuwztIDm7vg/RtMXjsdh9s/Aet/ACvPqM3AhXnBgbao/wTcqrXeOdFBWuvvaq3Xaa3XdXZ2TnQoUGxNKiFxQTgwKKX8GLG+Tmv9K7t5n1Jqsd2/GOiudO5U/z/PNpa3R/j8Zcfy5w+8kBXtET59y9PEUlk+eNNj/McfNhYP9IfNYyACx/0DPH0z9D5Xm0EL84JqBHsXsMz1eqndVg2nAe9SSm0Fvgy8Xin1hSmNsALFTmcSEheEmUYZK/0DYIPW+iuuXTcDV9vnVwO/PdhjO5gEfV4+9pKjeK47xvn/eRc3PbSTH9+zlWQmN/bgF7wdQk3wwxfDjgchn4NYD2y4BXauP/iDF+Yk1YTEHwQOU0qtwgj1FcA/VHNxrfVVznOl1DXAOq31fleWZnOyvKYgHEDOAP4ReEIp9ajd9lHgC8CNSqk3AduA19RmeAePC49eyFmHdfD3zX38w6nL+dn927l/Sz8vPLwsctC6Et74R7j2lfCDsuxfoNE0YGlcdNDGLcxNJhVsrXVWKfUu4DbAC/xQa/2UUurTwHqt9c1KqZOBXwOtwMuUUv+utT7mQA1apnUJwoFDa/03YLz/XOcfzLHUGqUU//26k+gfTdPZGOSXD+3kzo3dYwUboONQePMd8MRNkB4FXwjaVsEv3mhy3Jd99+C/AWFOUY3DRmt9K3Br2bZPuJ4/iAmVT3SNHwM/nvIIK1BcXlNC4oIgHFiiQR/RoPmqPP2Qdu56phsYx480LIDT3lm67fT3wF+/DCddAytOP6BjFeY2dal4TkhcHLYgCAeTc49cwNa+OFt6R6s/6az3Q/My+O07ITlktuUr5MEFYRLqU7BleU1BEGrAOYcvAOAn925l12CiupMCUbjsezCwDX7zT/DXr8Dnl5qe5I6AC0IVVBUSn23ItC5BEGrB8vYIp61u58f3buXH925ldWeUC49ayDvOOYSWSGD8E1ecBhd+Gv74Mdj4f7DsBfDEL0yzldf9CjoPLx6bz8PgVtNBrXExeP0H/H0J9UFdCnYmJ9O6BEGoDf/7plPYuHeEB7b0c+cz3fzgb1u49/k+rnvLqTSFJhDX094JOg8dh8ERF5vpXtdfAdddborVGmwh201Xw4abzfPDXgxX3Xjg35RQF9Sl4hWX1xSHLQjCwcXn9bCmq5k3nrmK/33TqXz39SexYc8wb/rxgyTSE+SmlYIz3mPEGmDpOrjy5xDrhutfa8Ljz91uxHrdm0wzludug55nDs4bE2Y9dSnYksMWBGG2cN6RC/naFcezftsA77nhEXJ5TTKTY2A0PfnJS0+Cy38Aex6DH10Cv/8wtB8KF30BXvQZ8AbNwiKCQJ2GxLM5jUeBRwRbEIRZwEuPXULvSIpP3fI0V33/7zyzd4RsTnPzu89kVUd04pOPvAT+4Ub4+T9CZhT+4SbwBcDXAce+Gh67Hg6/CJ64EV7wT9B14sF5U8Kso24dtuSvBUGYTVxzxire/sJDeGBLPyevbMPnVbzj2ocqtzIt59Dz4U1/hJd/Ew5/UXH7qW+HTBx+9mrTkOUP/yrLeM5j6tJh5/J5qRAXBGHW8ZGLj+Q95x9KJODjrme6ecOPH+RfbnqMr7zmOII+78QnL1pj/pRsWwvnfRx8Ychn4U+fhG33wMozD9ybEGYtdSnY2byWgjNBEGYlkYD5Wj3niAV8+KIj+cLvN7J7MMEnXno0Xa1hOqLBqaXzzv6gecwk4L5vwd1fMrntno2w5jIzz1uYF9SnYOe0FJwJgjDrefsLD2FFW4R/uekxXvntewEI+jys6ojymVes4eSVbdVfzB82U8P+9EnYfJfZ9tcvw0X/AUtPBn8IBrZCdEFxipgwp6hPwc5rfNJHXBCEOuDitYs5fnkLj+8cYt9wkh39cW59Yi/v/tkj/OG9Z03ccKWcU94C8T5YuAai7fC7D5gpYW58YTjrX+D0dxsR334//O79cOb7YO3lM/vmhINKXQp2Lp8Xhy0IQt2wuDnM4uZw4fXLj+vild++h3/91RO8/8LD8Xs9LG0NT25EAlEz3cvhHffClruhfzNkE9CywszjvvOzsP4HsPbV8MD3IJ+BX74Zskk44XUH6F0KB5q6FOxsTnLYgiDUL2uXNvP+Fx3OF//wDL9/ci8AAa+HMw5t5zuvO4mQf5ICNYdABI64qOzilxsRv+s/4N6vw5IT4DU/hVv+2SxAks/BSVfP8DsSDgb1Kdh5LUtrCoJQ17z97ENY29XMUCJDPJ3j6d3D/PjerXz19md534WH8+FfPk5XS5gPXXTk1C++6mzzZ9/TZk1ufxiuuB5+fpURboATX2+6rwHkMmbd7mzSTCU75LziPmHWUJeCnZMqcUEQ6hyPR3HWYaXFYalsju/9dTP3b+nn0R2DeBS8Zt0yVnZEyef11JtFLTy6+NwfgtdeBzdcCbe8B+77phHtk94Af/qUCaVH2uHay2DFGfCyr0PHofv/RoUZoy5taiYnOWxBEOYeH33JUSxqCvHYzkE+cvGR+L0evnXnJh7ZPsDxn/4jr/z2PfzmkV1kcvnp3cAfgitvMGIcboM/fhy+cjQ8+D047V3w/g3w0q/CvifhO6ebFcUqkU1BcrjyvqFd8NNXQN/z0xujMC7isAVBEGYJjSE/1775VPYNpzjtkHb2DiW59u/b+MNTe2kK+RmKZ3jvzx/lv+54jg+86AguOXbx1G/iC5oc9klXw44HzbzuQBQu+Hfw+mDdG+GIl5gQ+a/eYhq2HHdF8fwdD8Av32SW//yn+00bVTd3/DtsvhPW/xBe/Ln9+0CEEurSYcu0LkEQ5iqrOxs47ZB2wMzj9ihFY9DHz9/2Av70/hfyvdevI+jz8M6fPcyvHt65fzdbdrJZvvPVPzJi7dC4CK76Baw8C379drjv22ad7r/+J/zwIkjHTWX6Yz8rvd6uh+Dxn5vGLk/+0hS4CTNGXapeVqZ1CYIwD1jUHOJX/3Q6v3nXGSxtjeDxKC48eiG/e89ZnLyylU/+9il29McBGE1lue7+bdzy2O6ZuXkgAv/wczjqpXDbv8I3ToQ7Pg1HXwrveRi61sHd/2k6sD31ayPmt/wzRDvhJV+CkT2mjaowY9RlSFymdQmCMF9Y09U8ZpvXo/jKa47n4v/6K1f/8AGWtkV4ZPsAI8ksAA9u7effXnr0/s+m8Yfh1T+FOz4F638EL/8GnPCPpoL8nH+F614FX1sLoz12YAF4xXdMSP22j5oFS1adbfZpbf7Iwk3Tpi4/uVxe45fFPwRBmMcsa4vw5Vcfh8+rGIqnueCohdz09tN469mr+el927j0m/fw4Nb+wvH3bOrl/Tc+Svdwcmo38njgwk/Dh7eVTgU79HwjxqEWePVP4GN74ePdZh54IAJHvhSe/i0kBkyR2s9fZ8T9udtn7kOYZ9Slw87kNZE5+istk8mwc+dOkskp/qcS5iyhUIilS5fi9/trPRRhlnHRmkVctGZRybaTV7Zx4vIW/v2Wp3n1f9/HEQsb6WwM8rdNvQDsGUxy7ZtPnXqUsvw7Vyl4/c3jz9de90Z48hfw32ebueBb/gLNy+G6y81Usou+YKrWY91mOpmnymYx85i6FOy53Jp0586dNDY2snLlSpQ0Lpj3aK3p6+tj586drFq1qtbDEeqEi9Ys5uzDO7n279u47/k+NvXEeM95h7KgKcTHf/MkH/3VE2hMavFjlxxNQ3CaUjDRd9TyU+GNt8Ev3mDE+pKvwPFXwZ2fMx3Ydq2HhkWw6XY49ALTjS0QNWFz93UTA/B/74fUCLzsa9C8dHpjnQPUpWDP5dW6ksmkiLVQQClFe3s7PT09tR6KUGdEAj7eevYhvPXsQwrbtNY8vG2An6/fQWPIRzydY+PeET5z6Roe2T7AwqYQ5x+1cOZqhJaug7ffY1YRW3ys2faiz5jGLL9+G4zsg+NfZ6rNf/hi8Phh7+Nw6IVw5EuMSN//PzC8y1Sef+cMuOy7cPiLi/fIZWDXw2bFsjkaeXWoT8HOa3xzOIctYi24kX8PwkyhlOILrzqW15++kmOWNHHHhn28+/pHeOk3/lY4ZnVHlKOWNIGGpW1hjl7cxFmHddIWncKqYm5CTUWxdjjiIvjQZtB58PrN61v+GVpXmTz5xt/Bs783x7asgDf8ASJtxq1ffyVc/gM4+hWw/e9w6wdMo5dz/hXO+UjxHolB6N4A4RZoP6w4bS2XMfesQ+pSsE3jlLn9S6pW9PX1cf755wOwd+9evF4vnZ2mfeIDDzxAIDD+f9r169fz05/+lK9//esT3uP000/n3nvvnbExv/e97+Wmm25ix44deOTfhSBMSMDn4fhlLYAJnd/09jAb9wzzgtXtPLl7iJ/cu5WNe4bRGm5/eh/pXB6vR3HS8lYOXdjAictbedWJXfv/Q9LjBWze+qiXmT8OF3/RuPL/3959R0dV7Qsc/24mvZJCaEETCEkghiEFCDUJcJV2E0CKkY4iIhbwKRf1Ktb3fEueV3z3EkRAxIdguyAo5dICKB0EJZRLyVAVYyIhECBlzvvjTIaEJBAwyczA77NWFjPnnDnzm83s/LL32WdvjwBw97vWRT7qG1g4WJ/UxW2y3l3u3QSaJ0PG23qLPqwnZB+GTwboLXPQ7ycf+TX8dkRvySf+BTo+oXe/5xzV/1Aw2H86tP8IK1FsNuN8h3aJ21pAQAB79+4F4NVXX8XLy4vnnnvOur+4uBgnp8q/NvHx8cTHx9/0PWoyWZvNZpYsWUKzZs3YuHEjycnJNXbusm70uYVwZG2b1bcm8JBAT/q1aWLdV1Ri5uDPF1id+QvfHc1h5U8/8+n2k+w7dZ7XUqI4/ftlnAyKJvXdqzj7bTI4Q2DLitvdfGD4V7DmFX3J0OB2EDUAlAHm9ITPR8G9neD0Tr17ffB8PXln/Jc+d3rmErhyXr+fPLIPbEuH7bPAxRuaJ+qt+7CedjsAziGbI3Ifdt0aPXo0jz/+OB06dGDKlCns2LGDjh07EhMTQ6dOnTh8+DAAGRkZ9OvXD9CT/dixY0lKSqJ58+blWt1eXl7W45OSkhg0aBCRkZEMGzYMTdMAWLFiBZGRkcTFxfH0009bz3u9jIwMoqKimDBhAosWLbJuP3fuHAMGDMBoNGI0Gq1/JCxYsIA2bdpgNBoZMWKE9fN9+eW1OZPLxte1a1dSUlJo3VpfRKF///7ExcURFRXF7Nmzra9ZtWoVsbGxGI1GevTogdlspmXLltZrz2azmbCwMLkWLRyKs6EebYLr8/wDkXw9sTN7Xv4T47s155NtJ4h9cw3d3tlAp7fXkzw9g/9ccZDMs3nWOlxcYuZkTgFHf83nl7wavOvF1Qv6vavfEx47Ely99dvI0j7VW+kXzoJ/c3jkX3oyT/wLhPfWk/zZH+D+t/SE/FFfPVm3GarfinZqB3w6BGYm6MfdqrzT+k8tcsgmw51+DbvUa8szOXC2ign2b1PrJj5M+3PULb/u9OnTbNmyBYPBwIULF9i8eTNOTk6sXbuWF198ka+++qrCaw4dOsSGDRvIz88nIiKCCRMmVLg16YcffiAzM5MmTZrQuXNnvv/+e+Lj4xk/fjybNm0iNDSUtLS0KuNatGgRaWlppKam8uKLL1JUVISzszNPP/00iYmJLFmyhJKSEi5evEhmZiZvvvkmW7ZsITAwkNzc3CrPW2rPnj3s37/fOkJ73rx5+Pv7c/nyZdq1a8eDDz6I2Wxm3Lhx1nhzc3OpV68ew4cPZ+HChUyaNIm1a9diNBqtlxeEcERKKab2jiTYz53tWbm0D/WnqERj85Fs5n2XxexNx/FydSLYzx1TziWuFF1bpCQpogFPJocRH+JfO8H5hcCAWZUFrY8uT98JLZKh05P6ttUvQqsU6D9LH6zW5x04uFxfEGVOT+j0FCQ8AT/vg20zoVE0dJ4EJYX6tKyN2uh/POSf01vv22eBmy9M2ApetVPPHTJhl5g1nORaZZ0aPHgwBoPeTZSXl8eoUaM4cuQISimKiooqfU3fvn1xdXXF1dWVoKAgzp07R3Bw+Vsy2rdvb93Wtm1bTCYTXl5eNG/e3Jok09LSyrVmSxUWFrJixQreffddvL296dChA6tXr6Zfv36sX7+eBQsWAGAwGPD19WXBggUMHjyYwMBAAPz9b/6Lo3379uVup3r//fdZsmQJAKdOneLIkSNkZ2fTrVs363Gl5x07diypqalMmjSJefPmMWbMmJu+nxD2TinFiI4hjOgYYt32SJdQfr9UyJoD5zjw8wVO5hbQqUUgkY28cXcxcCz7Igu3n+ThD7ezZGInoppUnL2tVnk3gmf26beNgb7md4MIuLfLtZHlBme4b6Ce1FdMge/eg+9n6APjvBrBsQ2WOdUtv+/qOYNvU/1aO0p/7cFvYNmTkDoT9v4fnD+pj3SPG6131f9BDpmwiyyDIO50t9MSri2enp7Wxy+//DLJycksWbIEk8lEUlJSpa9xdXW1PjYYDBQXF9/WMVVZvXo158+fJzo6GoCCggLc3d2r7D6vipOTE2az3hIwm80UFhZa95X93BkZGaxdu5atW7fi4eFBUlLSDSe4adasGQ0bNmT9+vXs2LGDhQsX3lJcQjgSP08XhrRrVuX+EQn30nvGZp5a9APLnuzCb/lXcXcxEODpwoKtJ5j7XRapbZswvlsLPt1xkp2mXN7ofx9NfN34YvdpdplyCfBypUOoP4nhDW590Jur17XH9Qz6terKuPvBgx/qXel7Pob690DsKH1w2p4FUL+ZPkjt5BbIzdIngQl/AIJawbZZsOov8D8RemL3CAAXL32q1hrgkAlbb2Hf+QnbXuXl5dG0aVMA5s+fX+Pnj4iI4Pjx45hMJkJCQvjss88qPW7RokXMmTPH2mV+6dIlQkNDKSgooEePHqSnpzNp0iRrl3j37t0ZMGAAzz77LAEBAeTm5uLv709ISAi7d+9myJAhLFu2rMoeg7y8PPz8/PDw8ODQoUNs27YNgISEBJ544gmysrKsXeKlrexHH32U4cOHM2LECGsPhRB3owAvV957qC3D5mwn5vV/UVSiX+t2dzZwuaiE8IZezMw4xgebjlNi1nAx1GPgzO9pHxrA8n1nqe/hzMUrxaRnHKN1Yx+e7xVBckRQ7QUcGKbfM16qYWvo/fa155GVJOEO4yH7IJQU613qQZE1GpJDJmxZXtO2pkyZwqhRo3jzzTfp27dvjZ/f3d2dmTNn0qtXLzw9PWnXrl2FYwoKCli1ahWzZl27ZuXp6UmXLl1Yvnw5M2bM4LHHHmPu3LkYDAbS09Pp2LEjL730EomJiRgMBmJiYpg/fz7jxo0jNTUVo9Fofc/K9OrVi1mzZtGqVSsiIiJISEgAoEGDBsyePZuBAwdiNpsJCgpizRp9vuSUlBTGjBkj3eFCAJ1aBPL2wGh+OpNHdFNfCgpLOPLrRRLDG3B/64ZsOZbD13vP0L9tU/y9XBg9byfL953lmR4teaZHS4rNGkv3niE94xhjPtrJ/a0bMj6xBbH31LeP+QqUgj/PqL3Tl47osxfx8fHarl27bnhM8xe+5YmkMJ57IKKOoqo7Bw8epFWrVrYOw+YuXryIl5cXmqYxceJEWrZsyeTJk20d1i3btWsXkydPZvPmzX/oPJV9L5RSuzVNu/l9dDZUnfosRFWy869y5vxl621npQqLzcz57jj/u+4ol4tKaODtSn13Z9xdDEQ39SWysQ8ezgY04HJhMWFB3iQ097ePpF6F6tRnh2thm80aZo274hr23ezDDz/k448/prCwkJiYGMaPH2/rkG7Z22+/TXp6uly7FuI2NfB2pYG3a4XtLk71eCIpjOEJ97Im8xybj2RTWGIm73IRX+89y8LtJyu8JrqpL/c19eHS1RLOnL9M/pUiXv1zFJ3CAq3H/HQ6D1POJaKa+BAS4Ek9O8szDtfCLiox0/KllTx3fzhPdq/kxnoHJy1sURlpYQtRPSVmjez8qxQW6wNJ3Vzqse7gr8z/3sTvBYW4uxho5OPGz3lX+P1SIYseS+BkbgHzvsti14nfrecxBvsya0QcjX31SWG2H89hyQ9nGNkxhNZNfGo87juyhV1sGaggU5MKIYS4nqGeopGvW7ltae3vIa39PeW2nT1/mYEzt1jnUW/m787L/VrTPsSf3SdyeWf1YVL+/j09WwWx/8wFfjqTB8DyfWf5+7DYcgPeci5e5dPtJ8nKuUSApwt9ohsTc48fAJv+nU3HFgE418C4K8dL2Jbbb2SUuBBCiNvVpL4788e244ONx+l9X6Nyq5RFB/vSKSyQiQv3sDrzHKGBnrzSrzXdI4OY+Okexny0k7AgLyIaepN98Sr7Tp3narGZxr5u5Fwq5OOtJ0gfFssOUy4fbDzOy/1a80iXP748rsMl7BKz3sK+G2Y6E0IIUXsiG/nwt6FtK90X3tCbNc8mVtj++fiO+jrjx3PYfzaPht5uDG3XjJEd7yUsyJvcS4WMnLedRz7WLwUN63APIxLurZF4HS5hKxRtm9UnyNvt5gcLIYQQNcjT1YnxiS0Yn9ii0v3+ni4sfDSBaV/vJ6F5AA9d1xX/RzjchWBfD2eWTuxM3zaNbR3KHSk5OZnVq1eX2/bee+8xYcKEKl+TlJRE6cCiPn36cP78+QrHvPrqq0yfPv2G77106VIOHDhgff7KK6+wdu3aW4j+xiZNmkTTpk2ts5oJIURt8HV35r2HYmo0WYMDJmxRu9LS0li8eHG5bYsXL77hAhxlrVixgvr169/We1+fsF9//XV69qxi+sBbdP0ynLXlVqZWFUKIWyEJW5QzaNAgvv32W+t82iaTibNnz9K1a1cmTJhAfHw8UVFRTJs2rdLXh4SE8NtvvwHw1ltvER4eTpcuXaxLcIJ+j3W7du0wGo08+OCDFBQUsGXLFpYtW8bzzz9P27ZtOXbsWLllL9etW0dMTAzR0dGMHTuWq1evWt9v2rRpxMbGEh0dzaFDhyqNS5bhFEI4Ooe7hn1XWTkVfvmpZs/ZKLr8fLjX8ff3p3379qxcuZLU1FQWL17MkCFDUErx1ltv4e/vT0lJCT169ODHH3+kTZs2lZ5n9+7dLF68mL1791JcXExsbCxxcXEADBw4kHHjxgHw17/+lblz5/LUU0+RkpJCv379GDRoULlzXblyhdGjR7Nu3TrCw8MZOXKkdZ5wgMDAQPbs2cPMmTOZPn06c+bMqRCPLMMphHB00sIWFZTtFi/bHf75558TGxtLTEwMmZmZ5bqvr7d582YGDBiAh4cHPj4+pKSkWPft37+frl27Eh0dzcKFC8nMzLxhPIcPHyY0NJTw8HAARo0axaZNm6z7Bw4cCEBcXBwmk6nC60uX4ezfvz8+Pj7WZTgB1q9fb70+X7oM5/r162tkGU6j0UhCQoJ1Gc5t27ZVuQxn6VKgsgynEKIq0sK2ZzdoCdem1NRUJk+ezJ49eygoKCAuLo6srCymT5/Ozp078fPzY/To0TdcWvJGRo8ezdKlSzEajcyfP5+MjIw/FG/pEp1VLc8py3AKIe4E0sIWFXh5eZGcnMzYsWOtresLFy7g6emJr68v586dY+XKlTc8R7du3Vi6dCmXL18mPz+f5cuXW/fl5+fTuHFjioqKyiUnb29v8vPzK5wrIiICk8nE0aNHAfjkk09ITKx4f2RVSpfhNJlMmEwmsrKyWLNmTbllOAFKSkrIy8uje/fufPHFF+Tk5ABYu8RLl+EEbnsZzk2bNpGVlVXuvHBtGc7BgwfLMpxCiEpJwhaVSktLY9++fdaEbTQaiYmJITIykocffpjOnTvf8PWxsbEMHToUo9FI7969yy2R+cYbb9ChQwc6d+5MZOS19WIfeugh3nnnHWJiYjh27Jh1u5ubGx999BGDBw8mOjqaevXq8fjjj1frc5Quw1l2GdDrl+HcsGED0dHRxMXFceDAAaKioqzLcBqNRp599lkAxo0bx8aNGzEajWzduvWGy3AWFxfTqlUrpk6dWukynEajkaFDh1pfk5KSwsWLF6U7XAhRJYdb/ONOJ4t/3J1utgynLP4hxJ3tjlz8Q4g7jSzDKYSoDukSF8LGpk6dyokTJ+jSpYutQxFC2DFJ2EIIIYQDkIRth+xtXIGwLfk+CCFAErbdcXNzIycnR35JC0BP1jk5Obi5yep0QtztZNCZnQkODub06dMyl7SwcnNzIzg42NZhCCFsTBK2nXF2di43xaUQQggB0iUuhBBCOARJ2EIIIYQDkIQthBBCOAC7m5pUKZUNnKjGoYHAb7Uczq2SmKrHHmMC+4zrRjHdq2maXS+cXc367Gjlbkv2GJfEVD03i+mm9dnuEnZ1KaV22ds8yhJT9dhjTGCfcdljTDXNHj+jPcYE9hmXxFQ9NRGTdIkLIYQQDkASthBCCOEAHDlhz7Z1AJWQmKrHHmMC+4zLHmOqafb4Ge0xJrDPuCSm6vnDMTnsNWwhhBDibuLILWwhhBDiruFwCVsp1UspdVgpdVQpNdVGMTRTSm1QSh1QSmUqpZ6xbPdXSq1RSh2x/Otng9gMSqkflFLfWJ6HKqW2W8rrM6WUiw1iqq+U+lIpdUgpdVAp1dHWZaWUmmz5v9uvlFqklHKzRVkppeYppX5VSu0vs63SslG69y3x/aiUiq3t+Gqb1OebxmZX9dke67IlLpvX57qoyw6VsJVSBuAfQG+gNZCmlGptg1CKgf/QNK01kABMtMQxFVinaVpLYJ3leV17BjhY5vl/A3/TNC0M+B14xAYxzQBWaZoWCRgt8dmsrJRSTYGngXhN0+4DDMBD2Kas5gO9rttWVdn0Blpafh4D0usgvloj9bla7K0+21VdBruqz/Op7bqsaZrD/AAdgdVlnr8AvGAHcX0N/Ak4DDS2bGsMHK7jOIItX4ruwDeAQr9R36my8qujmHyBLCzjJcpst1lZAU2BU4A/+gI43wAP2KqsgBBg/83KBvgASKvsOEf8kfp80zjsqj7bY122vKfd1OfarssO1cLm2n9MqdOWbTajlAoBYoDtQENN03627PoFaFjH4bwHTAHMlucBwHlN04otz21RXqFANvCRpWtvjlLKExuWlaZpZ4DpwEngZyAP2I3ty6pUVWVjd9//P8juPo/U5xuyu7oMdl+fa7QuO1rCtitKKS/gK2CSpmkXyu7T9D+b6mwIvlKqH/Crpmm76+o9q8kJiAXSNU2LAS5xXZeZDcrKD0hF/wXUBPCkYleWXajrsrmbSX2+Kbury+A49bkmysbREvYZoFmZ58GWbXVOKeWMXrkXapr2T8vmc0qpxpb9jYFf6zCkzkCKUsoELEbvRpsB1FdKla57bovyOg2c1jRtu+X5l+iV3pZl1RPI0jQtW9O0IuCf6OVn67IqVVXZ2M33v4bYzeeR+lwt9liXwb7rc43WZUdL2DuBlpbRfy7oAwuW1XUQSikFzAUOapr2bpldy4BRlsej0K+F1QlN017QNC1Y07QQ9HJZr2naMGADMMgWMVni+gU4pZSKsGzqARzAhmWF3nWWoJTysPxflsZk07Iqo6qyWQaMtIwwTQDyynS3OSKpz1Wwx/psp3UZ7Ls+12xdrsvBATV0Ub8P8G/gGPCSjWLogt618SOw1/LTB/0a0zrgCLAW8LdRfEnAN5bHzYEdwFHgC8DVBvG0BXZZymsp4GfrsgJeAw4B+4FPAFdblBWwCP26WxF6C+aRqsoGfdDRPyzf/Z/QR8XW+ferhj+/1Oebx2c39dke67IlLpvX57qoyzLTmRBCCOEAHK1LXAghhLgrScIWQgghHIAkbCGEEMIBSMIWQgghHIAkbCGEEMIBSMIWQgghHIAkbCGEEMIBSMIWQgghHMD/AwsxitYBNcIaAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 576x576 with 2 Axes>"
       ]
@@ -2353,7 +2363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {
     "id": "f2H8GEeevXZ_"
    },
@@ -2362,8 +2372,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "313/313 [==============================] - 4s 12ms/step - loss: 7.1838 - accuracy: 0.2212 0s - loss: 7.159\n",
-      "Accuracy on test dataset: 0.22120000422000885\n"
+      "313/313 [==============================] - 4s 12ms/step - loss: 11.0567 - accuracy: 0.2313\n",
+      "Accuracy on test dataset: 0.2312999963760376\n"
      ]
     }
    ],
@@ -2391,6 +2401,13 @@
     "# 5. Batch Normalization"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we will try to find an appropriate learning rate."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 39,
@@ -2403,205 +2420,205 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "125/125 [==============================] - 4s 29ms/step - loss: 2.7341 - accuracy: 0.0967 - lr: 1.0000e-06\n",
+      "125/125 [==============================] - 5s 33ms/step - loss: 2.6610 - accuracy: 0.1205 - lr: 1.0000e-06\n",
       "Epoch 2/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.7231 - accuracy: 0.1023 - lr: 1.0798e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.6515 - accuracy: 0.1180 - lr: 1.0798e-065 - accuracy: 0. - ETA: 0s - loss: 2.6483 - accuracy: \n",
       "Epoch 3/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.7117 - accuracy: 0.0960 - lr: 1.1659e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.6337 - accuracy: 0.1293 - lr: 1.1659e-060 - accuracy: 0. - ETA: 1s - loss: 2.6184 -  - ETA: 0s - loss:\n",
       "Epoch 4/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.7018 - accuracy: 0.0997 - lr: 1.2589e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.6296 - accuracy: 0.1310 - lr: 1.2589e-06\n",
       "Epoch 5/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.6871 - accuracy: 0.1042 - lr: 1.3594e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.6068 - accuracy: 0.1338 - lr: 1.3594e-06\n",
       "Epoch 6/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.6773 - accuracy: 0.1007 - lr: 1.4678e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.6023 - accuracy: 0.1300 - lr: 1.4678e-06\n",
       "Epoch 7/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.6678 - accuracy: 0.1047 - lr: 1.5849e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.5883 - accuracy: 0.1345 - lr: 1.5849e-06\n",
       "Epoch 8/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.6484 - accuracy: 0.1067 - lr: 1.7113e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.5785 - accuracy: 0.1425 - lr: 1.7113e-06\n",
       "Epoch 9/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.6282 - accuracy: 0.1135 - lr: 1.8478e-06\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 2.5576 - accuracy: 0.1412 - lr: 1.8478e-06\n",
       "Epoch 10/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.6128 - accuracy: 0.1070 - lr: 1.9953e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.5401 - accuracy: 0.1485 - lr: 1.9953e-06\n",
       "Epoch 11/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.5955 - accuracy: 0.1168 - lr: 2.1544e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.5266 - accuracy: 0.1478 - lr: 2.1544e-06\n",
       "Epoch 12/100\n",
-      "125/125 [==============================] - 4s 28ms/step - loss: 2.5668 - accuracy: 0.1185 - lr: 2.3263e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.5147 - accuracy: 0.1538 - lr: 2.3263e-06\n",
       "Epoch 13/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.5601 - accuracy: 0.1175 - lr: 2.5119e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.4966 - accuracy: 0.1560 - lr: 2.5119e-06\n",
       "Epoch 14/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.5297 - accuracy: 0.1222 - lr: 2.7123e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.4761 - accuracy: 0.1663 - lr: 2.7123e-06\n",
       "Epoch 15/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.5098 - accuracy: 0.1287 - lr: 2.9286e-063 - accuracy: 0.\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.4599 - accuracy: 0.1650 - lr: 2.9286e-06\n",
       "Epoch 16/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.4952 - accuracy: 0.1320 - lr: 3.1623e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.4455 - accuracy: 0.1670 - lr: 3.1623e-06\n",
       "Epoch 17/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.4701 - accuracy: 0.1320 - lr: 3.4145e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.4379 - accuracy: 0.1688 - lr: 3.4145e-06\n",
       "Epoch 18/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.4431 - accuracy: 0.1440 - lr: 3.6869e-06\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 2.4180 - accuracy: 0.1713 - lr: 3.6869e-06\n",
       "Epoch 19/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.4264 - accuracy: 0.1515 - lr: 3.9811e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.3842 - accuracy: 0.1772 - lr: 3.9811e-06\n",
       "Epoch 20/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.4027 - accuracy: 0.1480 - lr: 4.2987e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.3751 - accuracy: 0.1857 - lr: 4.2987e-06\n",
       "Epoch 21/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.3835 - accuracy: 0.1520 - lr: 4.6416e-06\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 2.3596 - accuracy: 0.1828 - lr: 4.6416e-06\n",
       "Epoch 22/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.3644 - accuracy: 0.1593 - lr: 5.0119e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.3400 - accuracy: 0.1953 - lr: 5.0119e-06\n",
       "Epoch 23/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.3339 - accuracy: 0.1752 - lr: 5.4117e-06\n",
+      "125/125 [==============================] - 3s 27ms/step - loss: 2.3225 - accuracy: 0.1975 - lr: 5.4117e-06\n",
       "Epoch 24/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.3023 - accuracy: 0.1822 - lr: 5.8434e-06\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.3039 - accuracy: 0.2025 - lr: 5.8434e-06\n",
       "Epoch 25/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.2856 - accuracy: 0.1918 - lr: 6.3096e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.2808 - accuracy: 0.2075 - lr: 6.3096e-06\n",
       "Epoch 26/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.2573 - accuracy: 0.1980 - lr: 6.8129e-06\n",
+      "125/125 [==============================] - 4s 28ms/step - loss: 2.2660 - accuracy: 0.2105 - lr: 6.8129e-06\n",
       "Epoch 27/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.2410 - accuracy: 0.2005 - lr: 7.3564e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.2398 - accuracy: 0.2153 - lr: 7.3564e-06\n",
       "Epoch 28/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.2158 - accuracy: 0.2177 - lr: 7.9433e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.2165 - accuracy: 0.2245 - lr: 7.9433e-06\n",
       "Epoch 29/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 2.1895 - accuracy: 0.2243 - lr: 8.5770e-06\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.1997 - accuracy: 0.2330 - lr: 8.5770e-06\n",
       "Epoch 30/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.1788 - accuracy: 0.2303 - lr: 9.2612e-06\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.1833 - accuracy: 0.2412 - lr: 9.2612e-06\n",
       "Epoch 31/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 2.1541 - accuracy: 0.2377 - lr: 1.0000e-05\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.1669 - accuracy: 0.2393 - lr: 1.0000e-05\n",
       "Epoch 32/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.1251 - accuracy: 0.2520 - lr: 1.0798e-05\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 2.1326 - accuracy: 0.2522 - lr: 1.0798e-05\n",
       "Epoch 33/100\n",
-      "125/125 [==============================] - 4s 29ms/step - loss: 2.1084 - accuracy: 0.2595 - lr: 1.1659e-05\n",
+      "125/125 [==============================] - 3s 21ms/step - loss: 2.1226 - accuracy: 0.2570 - lr: 1.1659e-05\n",
       "Epoch 34/100\n",
-      "125/125 [==============================] - 4s 32ms/step - loss: 2.0844 - accuracy: 0.2607 - lr: 1.2589e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.1070 - accuracy: 0.2600 - lr: 1.2589e-05\n",
       "Epoch 35/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 2.0649 - accuracy: 0.2693 - lr: 1.3594e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.0743 - accuracy: 0.2730 - lr: 1.3594e-05\n",
       "Epoch 36/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.0467 - accuracy: 0.2842 - lr: 1.4678e-05\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 2.0612 - accuracy: 0.2755 - lr: 1.4678e-05\n",
       "Epoch 37/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 2.0186 - accuracy: 0.2882 - lr: 1.5849e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 2.0427 - accuracy: 0.2750 - lr: 1.5849e-05\n",
       "Epoch 38/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.9956 - accuracy: 0.3060 - lr: 1.7113e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 2.0108 - accuracy: 0.2988 - lr: 1.7113e-05\n",
       "Epoch 39/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.9865 - accuracy: 0.2980 - lr: 1.8478e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.9928 - accuracy: 0.3035 - lr: 1.8478e-053 \n",
       "Epoch 40/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.9583 - accuracy: 0.3108 - lr: 1.9953e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.9722 - accuracy: 0.3105 - lr: 1.9953e-05\n",
       "Epoch 41/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.9443 - accuracy: 0.3175 - lr: 2.1544e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.9441 - accuracy: 0.3185 - lr: 2.1544e-05\n",
       "Epoch 42/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.9211 - accuracy: 0.3310 - lr: 2.3263e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.9302 - accuracy: 0.3175 - lr: 2.3263e-05\n",
       "Epoch 43/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.8980 - accuracy: 0.3383 - lr: 2.5119e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.9080 - accuracy: 0.3340 - lr: 2.5119e-05\n",
       "Epoch 44/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.8754 - accuracy: 0.3440 - lr: 2.7123e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.8839 - accuracy: 0.3350 - lr: 2.7123e-05\n",
       "Epoch 45/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.8504 - accuracy: 0.3557 - lr: 2.9286e-052 - accu\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.8557 - accuracy: 0.3465 - lr: 2.9286e-056 - accuracy: 0.\n",
       "Epoch 46/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.8324 - accuracy: 0.3557 - lr: 3.1623e-05\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.8285 - accuracy: 0.3620 - lr: 3.1623e-05\n",
       "Epoch 47/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.8225 - accuracy: 0.3627 - lr: 3.4145e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.8098 - accuracy: 0.3660 - lr: 3.4145e-05\n",
       "Epoch 48/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.7926 - accuracy: 0.3828 - lr: 3.6869e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.7975 - accuracy: 0.3778 - lr: 3.6869e-05\n",
       "Epoch 49/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.7666 - accuracy: 0.3910 - lr: 3.9811e-053 - \n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.7709 - accuracy: 0.3803 - lr: 3.9811e-05\n",
       "Epoch 50/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.7480 - accuracy: 0.3988 - lr: 4.2987e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.7515 - accuracy: 0.3877 - lr: 4.2987e-05\n",
       "Epoch 51/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.7273 - accuracy: 0.4132 - lr: 4.6416e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.7227 - accuracy: 0.4058 - lr: 4.6416e-05\n",
       "Epoch 52/100\n",
-      "125/125 [==============================] - 3s 23ms/step - loss: 1.7036 - accuracy: 0.4223 - lr: 5.0119e-05\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.7049 - accuracy: 0.4005 - lr: 5.0119e-05\n",
       "Epoch 53/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.6816 - accuracy: 0.4363 - lr: 5.4117e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.6827 - accuracy: 0.4195 - lr: 5.4117e-05\n",
       "Epoch 54/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.6528 - accuracy: 0.4335 - lr: 5.8434e-05\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.6608 - accuracy: 0.4238 - lr: 5.8434e-05\n",
       "Epoch 55/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.6421 - accuracy: 0.4423 - lr: 6.3096e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.6324 - accuracy: 0.4363 - lr: 6.3096e-05\n",
       "Epoch 56/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.6144 - accuracy: 0.4530 - lr: 6.8129e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.6163 - accuracy: 0.4363 - lr: 6.8129e-05\n",
       "Epoch 57/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.5863 - accuracy: 0.4622 - lr: 7.3564e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5870 - accuracy: 0.4512 - lr: 7.3564e-05\n",
       "Epoch 58/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.5684 - accuracy: 0.4782 - lr: 7.9433e-05\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.5643 - accuracy: 0.4688 - lr: 7.9433e-05\n",
       "Epoch 59/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.5419 - accuracy: 0.4947 - lr: 8.5770e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5403 - accuracy: 0.4793 - lr: 8.5770e-05\n",
       "Epoch 60/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.5241 - accuracy: 0.4955 - lr: 9.2612e-05\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.5232 - accuracy: 0.4798 - lr: 9.2612e-054 - accu\n",
       "Epoch 61/100\n",
-      "125/125 [==============================] - 4s 28ms/step - loss: 1.4922 - accuracy: 0.5145 - lr: 1.0000e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.4886 - accuracy: 0.4947 - lr: 1.0000e-04\n",
       "Epoch 62/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.4605 - accuracy: 0.5250 - lr: 1.0798e-040 - ac\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.4558 - accuracy: 0.5153 - lr: 1.0798e-040s - loss: 1.458\n",
       "Epoch 63/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.4500 - accuracy: 0.5310 - lr: 1.1659e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 1.4390 - accuracy: 0.5228 - lr: 1.1659e-04\n",
       "Epoch 64/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.4160 - accuracy: 0.5418 - lr: 1.2589e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.4083 - accuracy: 0.5347 - lr: 1.2589e-04\n",
       "Epoch 65/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.3833 - accuracy: 0.5598 - lr: 1.3594e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.3790 - accuracy: 0.5435 - lr: 1.3594e-04\n",
       "Epoch 66/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 1.3639 - accuracy: 0.5645 - lr: 1.4678e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.3547 - accuracy: 0.5615 - lr: 1.4678e-04\n",
       "Epoch 67/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.3266 - accuracy: 0.5820 - lr: 1.5849e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.3220 - accuracy: 0.5832 - lr: 1.5849e-04\n",
       "Epoch 68/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.3012 - accuracy: 0.5905 - lr: 1.7113e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.2962 - accuracy: 0.5847 - lr: 1.7113e-04\n",
       "Epoch 69/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.2574 - accuracy: 0.6165 - lr: 1.8478e-04\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.2609 - accuracy: 0.6072 - lr: 1.8478e-04\n",
       "Epoch 70/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.2467 - accuracy: 0.6192 - lr: 1.9953e-04\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 1.2386 - accuracy: 0.6200 - lr: 1.9953e-04\n",
       "Epoch 71/100\n",
-      "125/125 [==============================] - 4s 31ms/step - loss: 1.2082 - accuracy: 0.6360 - lr: 2.1544e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.1977 - accuracy: 0.6273 - lr: 2.1544e-04\n",
       "Epoch 72/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 1.1601 - accuracy: 0.6610 - lr: 2.3263e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.1699 - accuracy: 0.6503 - lr: 2.3263e-04\n",
       "Epoch 73/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 1.1304 - accuracy: 0.6618 - lr: 2.5119e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.1282 - accuracy: 0.6660 - lr: 2.5119e-04\n",
       "Epoch 74/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.0996 - accuracy: 0.6795 - lr: 2.7123e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 1.0946 - accuracy: 0.6827 - lr: 2.7123e-04\n",
       "Epoch 75/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 1.0548 - accuracy: 0.7020 - lr: 2.9286e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 1.0464 - accuracy: 0.6980 - lr: 2.9286e-04\n",
       "Epoch 76/100\n",
-      "125/125 [==============================] - 3s 24ms/step - loss: 1.0141 - accuracy: 0.7207 - lr: 3.1623e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 1.0119 - accuracy: 0.7138 - lr: 3.1623e-04\n",
       "Epoch 77/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.9744 - accuracy: 0.7345 - lr: 3.4145e-049 - \n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 0.9746 - accuracy: 0.7362 - lr: 3.4145e-04\n",
       "Epoch 78/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 0.9272 - accuracy: 0.7520 - lr: 3.6869e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 0.9250 - accuracy: 0.7502 - lr: 3.6869e-042 \n",
       "Epoch 79/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 0.8962 - accuracy: 0.7655 - lr: 3.9811e-04\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 0.8761 - accuracy: 0.7785 - lr: 3.9811e-04\n",
       "Epoch 80/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.8426 - accuracy: 0.7918 - lr: 4.2987e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 0.8317 - accuracy: 0.7915 - lr: 4.2987e-04\n",
       "Epoch 81/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 0.8049 - accuracy: 0.7987 - lr: 4.6416e-04\n",
+      "125/125 [==============================] - 4s 29ms/step - loss: 0.7896 - accuracy: 0.8133 - lr: 4.6416e-04\n",
       "Epoch 82/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.7616 - accuracy: 0.8205 - lr: 5.0119e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 0.7433 - accuracy: 0.8240 - lr: 5.0119e-04\n",
       "Epoch 83/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 0.7175 - accuracy: 0.8325 - lr: 5.4117e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 0.6914 - accuracy: 0.8428 - lr: 5.4117e-04\n",
       "Epoch 84/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.6672 - accuracy: 0.8510 - lr: 5.8434e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 0.6536 - accuracy: 0.8602 - lr: 5.8434e-04\n",
       "Epoch 85/100\n",
-      "125/125 [==============================] - 4s 28ms/step - loss: 0.6195 - accuracy: 0.8595 - lr: 6.3096e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 0.6093 - accuracy: 0.8727 - lr: 6.3096e-04\n",
       "Epoch 86/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 0.5772 - accuracy: 0.8842 - lr: 6.8129e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 0.5634 - accuracy: 0.8857 - lr: 6.8129e-04\n",
       "Epoch 87/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.5274 - accuracy: 0.8953 - lr: 7.3564e-04\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 0.5291 - accuracy: 0.8978 - lr: 7.3564e-04\n",
       "Epoch 88/100\n",
-      "125/125 [==============================] - 4s 29ms/step - loss: 0.4971 - accuracy: 0.9005 - lr: 7.9433e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 0.4716 - accuracy: 0.9112 - lr: 7.9433e-04\n",
       "Epoch 89/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.4486 - accuracy: 0.9190 - lr: 8.5770e-04\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 0.4349 - accuracy: 0.9190 - lr: 8.5770e-04\n",
       "Epoch 90/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 0.4084 - accuracy: 0.9287 - lr: 9.2612e-04\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 0.3955 - accuracy: 0.9312 - lr: 9.2612e-04\n",
       "Epoch 91/100\n",
-      "125/125 [==============================] - 4s 28ms/step - loss: 0.3708 - accuracy: 0.9352 - lr: 0.0010\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 0.3776 - accuracy: 0.9302 - lr: 0.0010\n",
       "Epoch 92/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.3318 - accuracy: 0.9517 - lr: 0.0011\n",
+      "125/125 [==============================] - 3s 23ms/step - loss: 0.3427 - accuracy: 0.9402 - lr: 0.0011\n",
       "Epoch 93/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 0.3165 - accuracy: 0.9457 - lr: 0.0012\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 0.3076 - accuracy: 0.9538 - lr: 0.0012\n",
       "Epoch 94/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.3029 - accuracy: 0.9435 - lr: 0.0013\n",
+      "125/125 [==============================] - 3s 26ms/step - loss: 0.2921 - accuracy: 0.9505 - lr: 0.0013\n",
       "Epoch 95/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.2939 - accuracy: 0.9485 - lr: 0.0014.2888 - accuracy: 0.\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 0.2879 - accuracy: 0.9457 - lr: 0.0014\n",
       "Epoch 96/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 0.2818 - accuracy: 0.9448 - lr: 0.0015\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 0.2518 - accuracy: 0.9582 - lr: 0.0015\n",
       "Epoch 97/100\n",
-      "125/125 [==============================] - 3s 26ms/step - loss: 0.2832 - accuracy: 0.9400 - lr: 0.0016\n",
+      "125/125 [==============================] - 3s 25ms/step - loss: 0.2542 - accuracy: 0.9503 - lr: 0.0016\n",
       "Epoch 98/100\n",
-      "125/125 [==============================] - 3s 28ms/step - loss: 0.2942 - accuracy: 0.9337 - lr: 0.0017\n",
+      "125/125 [==============================] - 3s 22ms/step - loss: 0.2827 - accuracy: 0.9362 - lr: 0.0017\n",
       "Epoch 99/100\n",
-      "125/125 [==============================] - 3s 27ms/step - loss: 0.2673 - accuracy: 0.9380 - lr: 0.0018\n",
+      "125/125 [==============================] - 3s 24ms/step - loss: 0.2776 - accuracy: 0.9358 - lr: 0.0018\n",
       "Epoch 100/100\n",
-      "125/125 [==============================] - 3s 25ms/step - loss: 0.2838 - accuracy: 0.9280 - lr: 0.0020\n"
+      "125/125 [==============================] - 3s 22ms/step - loss: 0.3275 - accuracy: 0.9133 - lr: 0.0020\n"
      ]
     }
    ],
@@ -2655,7 +2672,7 @@
     },
     {
      "data": {
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\n",
+      "image/png": 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NTVZ0Zqqis9OVxfqVJJRmauufVutdEhR9xeziWsxAd069yUimOxnZDPbkNdST055SXoM9eQ1055TPctEasFvYPhYRI504VssjBjfnOz4v6fHVQsH2FZJOR0TYvl7Nq6DOtXrO3air6+JoZ28HjhexEDTNEcvS0Uq52hzBlCt1Tc/VNV1paLpSWxzVjJerenqsrAszVZXXsW7TnWsu7Pd3N6fGehfWYPLN0UnPkvcL02b9xaz29Rd1eV9BQz15Ri3ALtTOVNKbJX1A0g9tP5KU/RdJhyUpIu6U9D5JH7FdlzQr6dZoZ4gC2V5cAN9Tyrd8nIjmGsxstaFytaELM1VdmKlpvFzV+ZmqJmaa6zYXZmuaXhI6Z6bmNFNtLAZTpT6/6jlyGau3kG0u+idTYc0LAZqjld5iVsO9F6fNhnryzXWYfHOhv5TPLk6R5TKMXoCt0tZU0mZ5KU0lvdQtTIeVK3XNVOu6MFPTmamKzkzO6fRURdNz9WSxv6FKfV6ztYZmkjWcqbm6xqYqmq2tvQ7Tnctob2++GSK9Be3tzS+5jDm7uIC/cAHAnlJee0p59eQzLObjZWFHTCUBUvNekYHuLg1051raPyJUrjY0NlXRhZmq5moXL1EuVxemxOqanK3pXLmqsamKnj03o4d/dkHlSn3NUClkuzTUk1dPIbN42XJP/uJ6S393VoPdeQ305DTYndNgT157SjntLRU00J1jKgwvSwQDtpXtxUVyqbTh/euNeZWXLeBPztY0PlPVeLn5Ol+uaqbW0Fy1odlaQ+dnqnr2XFkTszVNJpc4ryTTZQ315DTUk18cgSws3PcWm22+eI9LM3QGe5qhMlRq1mO0gt2IYMCulk2NWLo3vH9EaLrSnAKbmK3p/JJAGS9Xda5c1fh0VeMzVT15ZloTs7Vk2mzt6a9MlxenuXoLWQ315LW3N6+9pbz2lAqL5aUkaPqLS0cyOdZVsG0IBrys2U5uLszp0Ab2qzfmVa40RyDNe1waKlcampitarxc0/lyVRdmq5qeqyc3UNZ1Yaaqx56f1Nnpiibn6mueoyefUX+xGRSDPTld1lvQnlJeQ6W8epM79BcW6Pf2FrS31Ayenjx/1mgP/wUBLchmujTQ06UBtba2UmvML94gOZ1cXjw51xy1TMzUFu/oXyg7X67piRcmda7cvHrsxeSzXal7Vga7c8kaSl793RdHKaVCVkM9Oe3rL2pfX1H93Ux9oYlgALZBLtOlwZ7mzYQb1ZiP5Oqu5p31E7M1jZcrOjtd1dnpyuKlxguvFybn9MQLU5qYbd7TsppCtnnX/eVL7rzvLeRUSu6y7ytmdVlvQZcllxjvKeW5CfIlimAAdpmFtYvmgv3GLCzWl5MbIc/P1HRmak6nJys6PTmnsamKzkzN6emxsr7303GVKw1VG6vfq9KfhMXCFNdgMu012JNPh0xvQYM9BMluQTAALyPZFi4vriaPdJmYrensdEVjU82RyXi5qnPTFZ1Nfj43PqPjyQL+XG3lMCnlMxoqNUNjX19R+/oLury/qMGei4/O31PK68rBbg315Jja2iYEA4AXlc92KZ9tTntdtXd9lxTPJvemjE3P6cxkMzwulJtXd50vV3V2uqqnxqb1Tz85q6lVFuK7cxldOVjUvv6i9pTyyeJ6QVcMFHVwsFsHhrq1f6CbUcgmIBgAdFx3PqPDe3t0eG/PmnVnk3WShcX2c9MVnbwwp5PnZ3XywozGpio6fnJC58rVFUNkTymvy/uaI4/9/UVddVmPrt5b0lV7e3RwsIdF9RYQDAC21cJDJq8YKK5Zt1qf1wsTczpxfkYnLszq+QuzySNYKhqbmtNjz0/o7HQ1tU8h29W88qq/oH39Re0fKOqKgW5dOVDU1ZeVdPXe0qY/0n+3IRgA7Br5bNeaI5HpSl3Pnivr2XMzi8FxenJOL0zM6fjJCR197PQlD3+8or+oq5ORRjMsenRoT48O7+lRX7G1S5J3M4IBwEtKbyGr1145oNdeObDi9ojQxGxNJ87P6plzZf10rKyfJkFy9LHTOldOjzgGe3I6NNSjg0PdOjjUrQOD3To41KOrkvAo5l56ow2CAcDLiu3Fe0hed+DS8Jicq+nZszP62fiMnjs/o+fGm+9/dHpK/++JM5eMNvb1F3T13pJeMVzSKy7r1SuGS82F8f7uXbu+QTAAwBL9xZxef3BArz94aWhEhM5OVy8GxrkZPTs+o5+eLeuBR09rvPxcqn53LqP9g0UdGurRoT3dOjTUnJ46vHdnT1MRDACwTrYX7wp/w+GhS7ZfmKnq6bNlnbowp1MTszo1MafnL8zqufMzeuS5C5qYTT/OZKgnp/0D3bq8P7mvY6Conx8u6Rf29ekVwyUVstszTUUwAECHDPbk9YbD+eR7LC81MVtbnJpaeL0wMaczU3N69PlJnZuuaOEp8Jku6+BQd/Mqqv7mlVT7B4q6cvDiz4HunDKb8J0hBAMAbJGB7pwGDgysuLYhSZV6Q0+PlfXj01N68vS0fnqurNMTc3romfM6M3VKtcal3x3SnWt+TW4nEQwAsEMUshm9en+/Xr2//5Jt8/Ohs+WKTl1oTk+dmpjT5FwteUpvQ538MuS2gsH2TZL+p6SMpM9FxKeXbS9I+pKkN0o6J+nXI+KZds4JAC9HXV3W5X1FXd5X1LWHBi/Z/ulLd2n9XK3uaDsj6Y8lvUvSayTdZvs1y6p9SNL5iPgXkv5I0n9v9XwAgK3RztOnrpf0VEQ8HRFVSV+WdMuyOrdI+mLy/quS3ubdeFEvALyMtDOVdEDS0ot2T0h602p1IqJue0LSXklnlx/M9hFJR5KPFdvH22hbJwxImtgBx9vIfmvVbXX7Rsov0wr9uw062X87oe/WqtPKtp3afy/Fv7216mx020pl16xx/vWLiJZekt6n5rrCwucPSLpjWZ3jkg4u+fwTSZet49ijrbarUy9Jd+2E421kv7Xqtrp9I+U7oe863X87oe/WqtPKtp3afy/Fv71O999m9107U0knpdT3px9MylasYzurZsqda+OcW+n/7pDjbWS/teq2un2j5TtBJ9u2E/purTqtbNup/fdS/Ntbq85Gt21q3zlJmo3v2PyH/seS3qZmADwk6d9ExKNL6nxU0usj4sO2b5X03oh4/zqOPRoRIy01DNuKvtvd6L/dq5N91/IaQzTXDD4m6QE1L1e9OyIetf0pNYc090n6vKQ/s/2UpHFJt67z8He12i5sO/pud6P/dq+O9V3LIwYAwEsTX5YKAEghGAAAKQQDACBlVwWD7S7b/832Z23fvt3twcbYvsH2t23fafuG7W4PNsZ2yfao7Xdvd1uwMbZfnfzdfdX2R9aqv2XBYPtu22eW39Fs+ybbP7L9lO1PrHGYW9S8X6Km5p3W2CId6r+QNC2pKPpvy3So7yTpdyR9ZXNaidV0ov8i4vGI+LCk90t685rn3Kqrkmz/ipr/KHwpIl6XlGXUvBfiHWr+Q/GQpNvUvPz1D5Yd4jeT1/mI+FPbX42I921J49Gp/jsbEfO290n6TET8xla1/+WsQ313rZqPsymq2Y9/uzWtRyf6LyLO2H6PpI9I+rOI+IsXO+eWfR9DRDxo++plxYsP4pMk21+WdEtE/IGkS4artk9IqiYfG5vYXCzTif5b4rykwqY0FJfo0N/eDZJKaj5Jedb2/RExv5ntRlOn/vaSe8vus/11STsjGFaxngfxLfU1SZ+1/cuSHtzMhmFdNtR/tt8r6Z2SBiXdsaktw1o21HcR8buSZPuDSkZ+m9o6rGWjf3s3SHqvmv9Ddv9aB9/uYNiQiJhR8zsesAtFxNfUDHfsUhHxhe1uAzYuIr4l6Vvrrb/dVyWt50F82Lnov92LvtvdNrX/tjsYHpL0Sts/Zzuv5rOU7tvmNmH96L/di77b3Ta1/7byctV7JH1H0jW2T9j+UETUJS08iO9xSV9Z+nRW7Bz03+5F3+1u29F/PEQPAJCy3VNJAIAdhmAAAKQQDACAFIIBAJBCMAAAUggGAEAKwQAASCEYAAApBAMAIOX/A8bQk8m8rWL6AAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -2671,6 +2688,13 @@
     "plt.axis([1e-6, 1e-3, 0, 20])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We thus choose as learning rate $0.001$."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 41,
@@ -2690,205 +2714,205 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "63/63 [==============================] - 6s 83ms/step - loss: 2.2542 - accuracy: 0.2598 - val_loss: 4.9940 - val_accuracy: 0.1770\n",
+      "63/63 [==============================] - 6s 79ms/step - loss: 2.2254 - accuracy: 0.2600 - val_loss: 4.4723 - val_accuracy: 0.1860\n",
       "Epoch 2/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.9629 - accuracy: 0.3025 - val_loss: 2.8735 - val_accuracy: 0.1970\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.9340 - accuracy: 0.3005 - val_loss: 2.3719 - val_accuracy: 0.2670\n",
       "Epoch 3/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.9255 - accuracy: 0.3198 - val_loss: 2.1162 - val_accuracy: 0.2780\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.9001 - accuracy: 0.3183 - val_loss: 2.6717 - val_accuracy: 0.2340\n",
       "Epoch 4/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 1.8948 - accuracy: 0.3192 - val_loss: 1.9912 - val_accuracy: 0.2830\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.8929 - accuracy: 0.3220 - val_loss: 1.9975 - val_accuracy: 0.2900\n",
       "Epoch 5/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 1.8634 - accuracy: 0.3410 - val_loss: 2.1961 - val_accuracy: 0.2630\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.8611 - accuracy: 0.3372 - val_loss: 2.0876 - val_accuracy: 0.2790\n",
       "Epoch 6/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 1.8725 - accuracy: 0.3250 - val_loss: 1.9157 - val_accuracy: 0.3180\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 1.8415 - accuracy: 0.3392 - val_loss: 1.9267 - val_accuracy: 0.3450\n",
       "Epoch 7/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 1.8330 - accuracy: 0.3363 - val_loss: 2.0854 - val_accuracy: 0.2960\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.8311 - accuracy: 0.3335 - val_loss: 2.2161 - val_accuracy: 0.2850\n",
       "Epoch 8/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 1.8108 - accuracy: 0.3473 - val_loss: 1.9207 - val_accuracy: 0.3270\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 1.8227 - accuracy: 0.3470 - val_loss: 1.8352 - val_accuracy: 0.3470\n",
       "Epoch 9/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 1.7873 - accuracy: 0.3625 - val_loss: 1.8341 - val_accuracy: 0.3500\n",
+      "63/63 [==============================] - 5s 81ms/step - loss: 1.8005 - accuracy: 0.3555 - val_loss: 1.9612 - val_accuracy: 0.3170\n",
       "Epoch 10/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 1.7856 - accuracy: 0.3535 - val_loss: 1.7954 - val_accuracy: 0.3380\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.7873 - accuracy: 0.3565 - val_loss: 1.9092 - val_accuracy: 0.3330\n",
       "Epoch 11/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.7792 - accuracy: 0.3602 - val_loss: 2.3248 - val_accuracy: 0.2330\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.7787 - accuracy: 0.3668 - val_loss: 1.8393 - val_accuracy: 0.3430\n",
       "Epoch 12/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 1.7624 - accuracy: 0.3758 - val_loss: 1.8582 - val_accuracy: 0.3520\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.7713 - accuracy: 0.3625 - val_loss: 2.3659 - val_accuracy: 0.2420\n",
       "Epoch 13/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.7585 - accuracy: 0.3735 - val_loss: 1.8053 - val_accuracy: 0.3600\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.7579 - accuracy: 0.3688 - val_loss: 1.8774 - val_accuracy: 0.3300\n",
       "Epoch 14/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.7528 - accuracy: 0.3710 - val_loss: 1.9468 - val_accuracy: 0.3100\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 1.7349 - accuracy: 0.3800 - val_loss: 1.9507 - val_accuracy: 0.3340\n",
       "Epoch 15/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 1.7250 - accuracy: 0.3755 - val_loss: 1.9362 - val_accuracy: 0.3190\n",
+      "63/63 [==============================] - 6s 87ms/step - loss: 1.7358 - accuracy: 0.3765 - val_loss: 1.9685 - val_accuracy: 0.3110\n",
       "Epoch 16/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 1.7323 - accuracy: 0.3677 - val_loss: 2.1602 - val_accuracy: 0.2850\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.7369 - accuracy: 0.3860 - val_loss: 1.9346 - val_accuracy: 0.3060\n",
       "Epoch 17/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 1.7348 - accuracy: 0.3780 - val_loss: 1.8932 - val_accuracy: 0.3490\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.7235 - accuracy: 0.3823 - val_loss: 1.9151 - val_accuracy: 0.3290\n",
       "Epoch 18/100\n",
-      "63/63 [==============================] - 5s 75ms/step - loss: 1.7111 - accuracy: 0.3913 - val_loss: 1.8995 - val_accuracy: 0.3230\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.7189 - accuracy: 0.3845 - val_loss: 1.8515 - val_accuracy: 0.3420\n",
       "Epoch 19/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 1.7216 - accuracy: 0.3857 - val_loss: 1.7782 - val_accuracy: 0.3610\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.7137 - accuracy: 0.3860 - val_loss: 1.9302 - val_accuracy: 0.3070\n",
       "Epoch 20/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.7007 - accuracy: 0.3842 - val_loss: 1.8050 - val_accuracy: 0.3580\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.7074 - accuracy: 0.3873 - val_loss: 1.8100 - val_accuracy: 0.3590\n",
       "Epoch 21/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 1.6877 - accuracy: 0.3963 - val_loss: 1.8479 - val_accuracy: 0.3460\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.7262 - accuracy: 0.3910 - val_loss: 1.9742 - val_accuracy: 0.3100\n",
       "Epoch 22/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 1.6840 - accuracy: 0.3988 - val_loss: 1.8476 - val_accuracy: 0.3380\n",
+      "63/63 [==============================] - 5s 81ms/step - loss: 1.6946 - accuracy: 0.3882 - val_loss: 1.7584 - val_accuracy: 0.3720\n",
       "Epoch 23/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 1.6892 - accuracy: 0.3950 - val_loss: 1.8558 - val_accuracy: 0.3540\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.6944 - accuracy: 0.3945 - val_loss: 1.7250 - val_accuracy: 0.3690\n",
       "Epoch 24/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.6756 - accuracy: 0.4095 - val_loss: 1.9144 - val_accuracy: 0.3250\n",
+      "63/63 [==============================] - 4s 69ms/step - loss: 1.6891 - accuracy: 0.3947 - val_loss: 1.7670 - val_accuracy: 0.3660\n",
       "Epoch 25/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 1.6849 - accuracy: 0.3960 - val_loss: 1.7634 - val_accuracy: 0.3690\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.6727 - accuracy: 0.3972 - val_loss: 2.0516 - val_accuracy: 0.3140acy: 0. - ETA: 0s - loss: 1.6733 - accuracy\n",
       "Epoch 26/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.6741 - accuracy: 0.4072 - val_loss: 1.8039 - val_accuracy: 0.3450\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.6786 - accuracy: 0.3932 - val_loss: 1.7616 - val_accuracy: 0.3650\n",
       "Epoch 27/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 1.6633 - accuracy: 0.4050 - val_loss: 1.7362 - val_accuracy: 0.3850\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.6730 - accuracy: 0.4020 - val_loss: 1.7922 - val_accuracy: 0.3470\n",
       "Epoch 28/100\n",
-      "63/63 [==============================] - 5s 87ms/step - loss: 1.6675 - accuracy: 0.3915 - val_loss: 1.7573 - val_accuracy: 0.3850\n",
+      "63/63 [==============================] - 4s 70ms/step - loss: 1.6591 - accuracy: 0.4085 - val_loss: 2.1079 - val_accuracy: 0.2810\n",
       "Epoch 29/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 1.6411 - accuracy: 0.4047 - val_loss: 1.8315 - val_accuracy: 0.3630\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 1.6675 - accuracy: 0.4015 - val_loss: 1.8121 - val_accuracy: 0.3580\n",
       "Epoch 30/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.6543 - accuracy: 0.4065 - val_loss: 1.6689 - val_accuracy: 0.4140\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.6416 - accuracy: 0.4117 - val_loss: 1.8404 - val_accuracy: 0.3530\n",
       "Epoch 31/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.6376 - accuracy: 0.4195 - val_loss: 1.8304 - val_accuracy: 0.3600\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.6388 - accuracy: 0.4235 - val_loss: 1.8563 - val_accuracy: 0.3560\n",
       "Epoch 32/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 1.6322 - accuracy: 0.4135 - val_loss: 1.6550 - val_accuracy: 0.4090\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.6468 - accuracy: 0.4027 - val_loss: 1.8881 - val_accuracy: 0.3300\n",
       "Epoch 33/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 1.6259 - accuracy: 0.4165 - val_loss: 1.8499 - val_accuracy: 0.3470\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.6381 - accuracy: 0.4000 - val_loss: 1.7084 - val_accuracy: 0.3960 1s - los\n",
       "Epoch 34/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.6312 - accuracy: 0.4227 - val_loss: 1.7315 - val_accuracy: 0.3900\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.6259 - accuracy: 0.4178 - val_loss: 2.0066 - val_accuracy: 0.3260\n",
       "Epoch 35/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.6230 - accuracy: 0.4165 - val_loss: 1.6394 - val_accuracy: 0.4170\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.6329 - accuracy: 0.4117 - val_loss: 1.8665 - val_accuracy: 0.3560\n",
       "Epoch 36/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 1.6310 - accuracy: 0.4123 - val_loss: 1.6955 - val_accuracy: 0.4020 - accuracy: 0.\n",
+      "63/63 [==============================] - 5s 80ms/step - loss: 1.6259 - accuracy: 0.4065 - val_loss: 1.9742 - val_accuracy: 0.3290\n",
       "Epoch 37/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.6011 - accuracy: 0.4227 - val_loss: 1.6961 - val_accuracy: 0.3850\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.6109 - accuracy: 0.4132 - val_loss: 1.7001 - val_accuracy: 0.3900\n",
       "Epoch 38/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 1.6102 - accuracy: 0.4198 - val_loss: 1.7158 - val_accuracy: 0.3780\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 1.6213 - accuracy: 0.4137 - val_loss: 1.7414 - val_accuracy: 0.3810\n",
       "Epoch 39/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.6313 - accuracy: 0.4103 - val_loss: 1.6456 - val_accuracy: 0.3980\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.6084 - accuracy: 0.4263 - val_loss: 1.8025 - val_accuracy: 0.3610\n",
       "Epoch 40/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 1.5908 - accuracy: 0.4227 - val_loss: 1.8407 - val_accuracy: 0.3590\n",
+      "63/63 [==============================] - 5s 81ms/step - loss: 1.6024 - accuracy: 0.4272 - val_loss: 1.8944 - val_accuracy: 0.3500\n",
       "Epoch 41/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.6066 - accuracy: 0.4300 - val_loss: 1.9521 - val_accuracy: 0.3190\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.5979 - accuracy: 0.4240 - val_loss: 1.7995 - val_accuracy: 0.3660\n",
       "Epoch 42/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5782 - accuracy: 0.4408 - val_loss: 1.8885 - val_accuracy: 0.3470\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 1.6170 - accuracy: 0.4157 - val_loss: 1.7806 - val_accuracy: 0.3670\n",
       "Epoch 43/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5978 - accuracy: 0.4280 - val_loss: 1.7635 - val_accuracy: 0.3650\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.5955 - accuracy: 0.4215 - val_loss: 1.9363 - val_accuracy: 0.3470\n",
       "Epoch 44/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5855 - accuracy: 0.4290 - val_loss: 1.8491 - val_accuracy: 0.3240\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 1.5921 - accuracy: 0.4320 - val_loss: 1.7226 - val_accuracy: 0.3860\n",
       "Epoch 45/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 1.5950 - accuracy: 0.4240 - val_loss: 1.8440 - val_accuracy: 0.3330\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.5860 - accuracy: 0.4210 - val_loss: 1.7848 - val_accuracy: 0.3900\n",
       "Epoch 46/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.5726 - accuracy: 0.4310 - val_loss: 1.7390 - val_accuracy: 0.3790\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.5827 - accuracy: 0.4215 - val_loss: 1.7021 - val_accuracy: 0.3910\n",
       "Epoch 47/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 1.5843 - accuracy: 0.4415 - val_loss: 1.7202 - val_accuracy: 0.3730\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.5870 - accuracy: 0.4342 - val_loss: 1.8957 - val_accuracy: 0.3300\n",
       "Epoch 48/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5732 - accuracy: 0.4227 - val_loss: 1.6525 - val_accuracy: 0.4120\n",
+      "63/63 [==============================] - 5s 87ms/step - loss: 1.5912 - accuracy: 0.4235 - val_loss: 1.7864 - val_accuracy: 0.3640\n",
       "Epoch 49/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5691 - accuracy: 0.4360 - val_loss: 1.6301 - val_accuracy: 0.4300\n",
+      "63/63 [==============================] - 5s 80ms/step - loss: 1.5770 - accuracy: 0.4310 - val_loss: 1.8316 - val_accuracy: 0.3280\n",
       "Epoch 50/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5723 - accuracy: 0.4365 - val_loss: 1.6796 - val_accuracy: 0.3970\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.5571 - accuracy: 0.4410 - val_loss: 1.6629 - val_accuracy: 0.4030\n",
       "Epoch 51/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 1.5554 - accuracy: 0.4450 - val_loss: 1.8034 - val_accuracy: 0.3440\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.5665 - accuracy: 0.4387 - val_loss: 1.8981 - val_accuracy: 0.3180\n",
       "Epoch 52/100\n",
-      "63/63 [==============================] - 4s 67ms/step - loss: 1.5702 - accuracy: 0.4317 - val_loss: 1.7770 - val_accuracy: 0.3750\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.5724 - accuracy: 0.4440 - val_loss: 1.6325 - val_accuracy: 0.4190\n",
       "Epoch 53/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 1.5511 - accuracy: 0.4395 - val_loss: 1.7265 - val_accuracy: 0.3730\n",
+      "63/63 [==============================] - 5s 82ms/step - loss: 1.5585 - accuracy: 0.4470 - val_loss: 1.6321 - val_accuracy: 0.4110\n",
       "Epoch 54/100\n",
-      "63/63 [==============================] - 5s 76ms/step - loss: 1.5601 - accuracy: 0.4295 - val_loss: 1.8740 - val_accuracy: 0.3330\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.5561 - accuracy: 0.4293 - val_loss: 1.7318 - val_accuracy: 0.4010\n",
       "Epoch 55/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 1.5442 - accuracy: 0.4465 - val_loss: 1.7168 - val_accuracy: 0.3680\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.5445 - accuracy: 0.4420 - val_loss: 1.9078 - val_accuracy: 0.3690\n",
       "Epoch 56/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 1.5656 - accuracy: 0.4442 - val_loss: 1.7108 - val_accuracy: 0.4040\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 1.5589 - accuracy: 0.4500 - val_loss: 1.7896 - val_accuracy: 0.3680\n",
       "Epoch 57/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 1.5292 - accuracy: 0.4618 - val_loss: 1.8155 - val_accuracy: 0.3510\n",
+      "63/63 [==============================] - 5s 80ms/step - loss: 1.5530 - accuracy: 0.4415 - val_loss: 1.7042 - val_accuracy: 0.3890\n",
       "Epoch 58/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 1.5433 - accuracy: 0.4512 - val_loss: 1.6529 - val_accuracy: 0.4000\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.5470 - accuracy: 0.4403 - val_loss: 1.6300 - val_accuracy: 0.4160\n",
       "Epoch 59/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 1.5463 - accuracy: 0.4372 - val_loss: 1.7713 - val_accuracy: 0.3760\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.5514 - accuracy: 0.4397 - val_loss: 1.8705 - val_accuracy: 0.3600\n",
       "Epoch 60/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5269 - accuracy: 0.4532 - val_loss: 1.8304 - val_accuracy: 0.3500\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 1.5235 - accuracy: 0.4618 - val_loss: 1.7476 - val_accuracy: 0.3580\n",
       "Epoch 61/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5375 - accuracy: 0.4467 - val_loss: 1.6323 - val_accuracy: 0.4320\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.5442 - accuracy: 0.4403 - val_loss: 1.6304 - val_accuracy: 0.4240\n",
       "Epoch 62/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5391 - accuracy: 0.4470 - val_loss: 1.9411 - val_accuracy: 0.3150\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.5377 - accuracy: 0.4467 - val_loss: 1.6612 - val_accuracy: 0.4200\n",
       "Epoch 63/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5302 - accuracy: 0.4532 - val_loss: 1.9294 - val_accuracy: 0.3500\n",
+      "63/63 [==============================] - 4s 66ms/step - loss: 1.5294 - accuracy: 0.4480 - val_loss: 1.6210 - val_accuracy: 0.4100\n",
       "Epoch 64/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5218 - accuracy: 0.4552 - val_loss: 1.6804 - val_accuracy: 0.3910\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.5152 - accuracy: 0.4527 - val_loss: 1.7838 - val_accuracy: 0.3790\n",
       "Epoch 65/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5208 - accuracy: 0.4543 - val_loss: 1.7296 - val_accuracy: 0.4080\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 1.5269 - accuracy: 0.4605 - val_loss: 1.8113 - val_accuracy: 0.3610\n",
       "Epoch 66/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 1.5332 - accuracy: 0.4535 - val_loss: 1.5908 - val_accuracy: 0.4440\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.5587 - accuracy: 0.4448 - val_loss: 1.6794 - val_accuracy: 0.4330\n",
       "Epoch 67/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5037 - accuracy: 0.4540 - val_loss: 1.6887 - val_accuracy: 0.4250\n",
+      "63/63 [==============================] - 5s 71ms/step - loss: 1.5275 - accuracy: 0.4597 - val_loss: 1.6817 - val_accuracy: 0.4160\n",
       "Epoch 68/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5166 - accuracy: 0.4502 - val_loss: 1.6828 - val_accuracy: 0.4060\n",
+      "63/63 [==============================] - 5s 81ms/step - loss: 1.5257 - accuracy: 0.4532 - val_loss: 1.6274 - val_accuracy: 0.4140\n",
       "Epoch 69/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.5191 - accuracy: 0.4535 - val_loss: 1.7525 - val_accuracy: 0.3870\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.5149 - accuracy: 0.4572 - val_loss: 1.7517 - val_accuracy: 0.3870\n",
       "Epoch 70/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 1.5154 - accuracy: 0.4535 - val_loss: 1.6676 - val_accuracy: 0.4090\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.5048 - accuracy: 0.4593 - val_loss: 1.7174 - val_accuracy: 0.4160\n",
       "Epoch 71/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.5056 - accuracy: 0.4615 - val_loss: 1.8401 - val_accuracy: 0.3630\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.5041 - accuracy: 0.4600 - val_loss: 1.7637 - val_accuracy: 0.3880\n",
       "Epoch 72/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.4962 - accuracy: 0.4563 - val_loss: 1.7432 - val_accuracy: 0.3920\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.4997 - accuracy: 0.4635 - val_loss: 1.7541 - val_accuracy: 0.3800\n",
       "Epoch 73/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 1.4923 - accuracy: 0.4620 - val_loss: 1.7786 - val_accuracy: 0.3890\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.4926 - accuracy: 0.4608 - val_loss: 1.7850 - val_accuracy: 0.3680\n",
       "Epoch 74/100\n",
-      "63/63 [==============================] - 4s 69ms/step - loss: 1.5093 - accuracy: 0.4557 - val_loss: 1.6356 - val_accuracy: 0.4230\n",
+      "63/63 [==============================] - 5s 82ms/step - loss: 1.4933 - accuracy: 0.4690 - val_loss: 1.9646 - val_accuracy: 0.3410\n",
       "Epoch 75/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 1.4964 - accuracy: 0.4563 - val_loss: 1.7209 - val_accuracy: 0.3780\n",
+      "63/63 [==============================] - 5s 83ms/step - loss: 1.5224 - accuracy: 0.4460 - val_loss: 1.9222 - val_accuracy: 0.3830\n",
       "Epoch 76/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 1.4922 - accuracy: 0.4667 - val_loss: 1.8775 - val_accuracy: 0.3690\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.4925 - accuracy: 0.4642 - val_loss: 1.8548 - val_accuracy: 0.3320\n",
       "Epoch 77/100\n",
-      "63/63 [==============================] - 5s 71ms/step - loss: 1.5045 - accuracy: 0.4487 - val_loss: 1.8763 - val_accuracy: 0.3370\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.4916 - accuracy: 0.4667 - val_loss: 1.6134 - val_accuracy: 0.4320\n",
       "Epoch 78/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 1.4811 - accuracy: 0.4782 - val_loss: 1.6409 - val_accuracy: 0.4320\n",
+      "63/63 [==============================] - 5s 74ms/step - loss: 1.4864 - accuracy: 0.4650 - val_loss: 1.5904 - val_accuracy: 0.4430\n",
       "Epoch 79/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 1.4890 - accuracy: 0.4667 - val_loss: 1.7575 - val_accuracy: 0.3660\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.4939 - accuracy: 0.4595 - val_loss: 1.6587 - val_accuracy: 0.4260\n",
       "Epoch 80/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 1.4737 - accuracy: 0.4602 - val_loss: 1.5995 - val_accuracy: 0.4320\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.4904 - accuracy: 0.4625 - val_loss: 1.6674 - val_accuracy: 0.4100\n",
       "Epoch 81/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.4876 - accuracy: 0.4615 - val_loss: 1.6411 - val_accuracy: 0.4120\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.4880 - accuracy: 0.4695 - val_loss: 1.5580 - val_accuracy: 0.4550\n",
       "Epoch 82/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 1.4904 - accuracy: 0.4645 - val_loss: 1.6765 - val_accuracy: 0.3890\n",
+      "63/63 [==============================] - 4s 71ms/step - loss: 1.4668 - accuracy: 0.4760 - val_loss: 1.7285 - val_accuracy: 0.3970\n",
       "Epoch 83/100\n",
-      "63/63 [==============================] - 5s 72ms/step - loss: 1.4744 - accuracy: 0.4712 - val_loss: 1.6622 - val_accuracy: 0.4140\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.4755 - accuracy: 0.4638 - val_loss: 1.7850 - val_accuracy: 0.4000\n",
       "Epoch 84/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.4704 - accuracy: 0.4645 - val_loss: 1.6428 - val_accuracy: 0.4290\n",
+      "63/63 [==============================] - 5s 80ms/step - loss: 1.4671 - accuracy: 0.4745 - val_loss: 1.6592 - val_accuracy: 0.4250\n",
       "Epoch 85/100\n",
-      "63/63 [==============================] - 4s 68ms/step - loss: 1.4688 - accuracy: 0.4737 - val_loss: 1.7446 - val_accuracy: 0.4040\n",
+      "63/63 [==============================] - 5s 79ms/step - loss: 1.4668 - accuracy: 0.4633 - val_loss: 1.6160 - val_accuracy: 0.4220\n",
       "Epoch 86/100\n",
-      "63/63 [==============================] - 4s 71ms/step - loss: 1.4813 - accuracy: 0.4798 - val_loss: 1.6792 - val_accuracy: 0.3770\n",
+      "63/63 [==============================] - 5s 77ms/step - loss: 1.4656 - accuracy: 0.4615 - val_loss: 1.5644 - val_accuracy: 0.4450\n",
       "Epoch 87/100\n",
-      "63/63 [==============================] - 5s 73ms/step - loss: 1.4582 - accuracy: 0.4723 - val_loss: 1.6408 - val_accuracy: 0.4060\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.4756 - accuracy: 0.4717 - val_loss: 1.7007 - val_accuracy: 0.4190\n",
       "Epoch 88/100\n",
-      "63/63 [==============================] - 4s 70ms/step - loss: 1.4584 - accuracy: 0.4795 - val_loss: 1.8675 - val_accuracy: 0.3500\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.4686 - accuracy: 0.4733 - val_loss: 1.5786 - val_accuracy: 0.4410\n",
       "Epoch 89/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 1.4595 - accuracy: 0.4692 - val_loss: 1.7522 - val_accuracy: 0.3830\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.4297 - accuracy: 0.4860 - val_loss: 1.9454 - val_accuracy: 0.3450\n",
       "Epoch 90/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 1.4523 - accuracy: 0.4735 - val_loss: 1.7044 - val_accuracy: 0.3960\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.4629 - accuracy: 0.4742 - val_loss: 1.9222 - val_accuracy: 0.3480\n",
       "Epoch 91/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 1.4731 - accuracy: 0.4730 - val_loss: 1.7716 - val_accuracy: 0.3750\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.4546 - accuracy: 0.4767 - val_loss: 1.6141 - val_accuracy: 0.4430\n",
       "Epoch 92/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 1.4691 - accuracy: 0.4733 - val_loss: 1.8304 - val_accuracy: 0.3660\n",
+      "63/63 [==============================] - 5s 73ms/step - loss: 1.4495 - accuracy: 0.4750 - val_loss: 1.6185 - val_accuracy: 0.4370\n",
       "Epoch 93/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.4652 - accuracy: 0.4720 - val_loss: 1.6646 - val_accuracy: 0.4140\n",
+      "63/63 [==============================] - 5s 75ms/step - loss: 1.4623 - accuracy: 0.4737 - val_loss: 1.5608 - val_accuracy: 0.4510\n",
       "Epoch 94/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.4573 - accuracy: 0.4770 - val_loss: 1.6571 - val_accuracy: 0.4120\n",
+      "63/63 [==============================] - 5s 81ms/step - loss: 1.4479 - accuracy: 0.4840 - val_loss: 1.6304 - val_accuracy: 0.4420\n",
       "Epoch 95/100\n",
-      "63/63 [==============================] - 5s 78ms/step - loss: 1.4467 - accuracy: 0.4770 - val_loss: 1.8122 - val_accuracy: 0.3780\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.4594 - accuracy: 0.4790 - val_loss: 1.6251 - val_accuracy: 0.4270\n",
       "Epoch 96/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 1.4353 - accuracy: 0.4835 - val_loss: 1.6656 - val_accuracy: 0.4270\n",
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.4662 - accuracy: 0.4753 - val_loss: 1.6795 - val_accuracy: 0.4110\n",
       "Epoch 97/100\n",
-      "63/63 [==============================] - 5s 74ms/step - loss: 1.4453 - accuracy: 0.4755 - val_loss: 1.6765 - val_accuracy: 0.4040\n",
+      "63/63 [==============================] - 5s 76ms/step - loss: 1.4567 - accuracy: 0.4778 - val_loss: 1.5898 - val_accuracy: 0.4490\n",
       "Epoch 98/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 1.4494 - accuracy: 0.4818 - val_loss: 1.6135 - val_accuracy: 0.4310\n",
+      "63/63 [==============================] - 5s 80ms/step - loss: 1.4432 - accuracy: 0.4820 - val_loss: 1.6326 - val_accuracy: 0.4090\n",
       "Epoch 99/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 1.4442 - accuracy: 0.4832 - val_loss: 1.7504 - val_accuracy: 0.4080\n",
+      "63/63 [==============================] - 5s 80ms/step - loss: 1.4450 - accuracy: 0.4770 - val_loss: 1.7315 - val_accuracy: 0.3960\n",
       "Epoch 100/100\n",
-      "63/63 [==============================] - 5s 77ms/step - loss: 1.4388 - accuracy: 0.4793 - val_loss: 1.6360 - val_accuracy: 0.4080\n"
+      "63/63 [==============================] - 5s 78ms/step - loss: 1.4335 - accuracy: 0.4875 - val_loss: 1.8231 - val_accuracy: 0.3770\n"
      ]
     }
    ],
@@ -2922,7 +2946,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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Xo7Xm/e9/PytWrODDH/7wZA8rK+n+7vz8f5aQuCAIFg38VSn1uFLq6jTvzwcOeF63OtvGTmQQhrrHdQhhZvODH/yAdevWsWbNGnp6enj3u9892UOaMESwBeE40zc8wsYv/I1bn0k2pvftaOPzf95KPD5pUa8Xaq03YELf71dKvWgsB1FKXa2U2qSU2tTW1pZ957/+O3yrYEECYQby4Q9/mM2bN7N161Z++ctfUllZOdlDmjBEsAXhOLP5QDft/REe3OlGnu/b0cY7b9jEjx7Yw6N7OydlXFrrg87jMeAPwBkpuxwEFnpeL3C2pR7neq31Rq31xpaWluwnVQHQ8fEMWxBmDCLYgnCceWJfNwDbjvQBsONoH1f/fBPLZ1VTVRrkj0+O0sAJRylVpZSqsc+BC4FnU3a7BXibUy3+AqAnW/7a34lFsAXBLyLYgnCceWJ/FwDbDvcSj2tue+YI4WicG95xOi8/eQ5/eeYwwyOx4z2s2cADSqmngEeBv2itb1dKvUcp9R5nn1uB3cBO4AfA+8Z9VhFsQfCNTOsSBJ/0h6PEYpq6ypLcO2cgHtc8ub+LqtIgA5EYrV1DbNrXycrZNcyqLee16+fz+ycOcte2Y7zylLkFHH12tNa7gbVptn/P81wD7y/oiVUApthMFUGYqojDFgSf/OtvNnPVDY8BoLXmnTds4vZn84sI727vp3c4yqXrTXH1lkM9PLm/m9MWNwBw9vJmZtWU8fsnjn9YfFIQh120nH/++dxxxx1J277+9a/z3ve+N+NnzjvvPOw0v1e+8pVpe3Jfe+21fPWrX8167j/+8Y9s3bo18fozn/kMd955Zx6jT89UX4ZTBFuYUfQOj3Cweyjvz0VjcR7c2c7Trd1EonEOdg9x53NH+dvWY3kd54n93QBcdvpClIL/23yI/nCUjUuMYAcDikvXzeNg9xDR2AwQMqVEsIuUyy+/nBtvvDFp24033ui7n/ett9465uYjqYL9uc99jpe+9KVjOlYxIYItzCi+9rcdXHb9P/L+3HOH+xiIxBiJaXYc7ePZgz0A7OsYyOs4T+7vorY8xMnz6ljSVMXfnjOLHGxc3JjY56MvX8mt17yQUHAG/PcUh120vOENb+Avf/kLkUgEgL1793Lo0CHOPfdc3vve97Jx40bWrFnDZz/72bSfX7JkCe3tZqbEddddx4knnsgLX/jCxBKcYOZYn3766axdu5bXv/71DA4O8tBDD3HLLbfwsY99jHXr1rFr1y6uvPJKbr75ZsB0NFu/fj2nnHIKV111FeFwOHG+z372s2zYsIFTTjmFbdu2+f6uU2UZTslhCzOKY31hDnQOMTwSo7wk6Ptzj+zpSDzfcqiH/Z2DAOztGPT1+V89sp97dxzj0T2drF/UQCCgWDWnhj3tA8yuLWNBQ0Vi37KQ/3EVPSLYheG2T8CRZwp7zDmnwCu+lPHtxsZGzjjjDG677TYuvfRSbrzxRt70pjehlOK6666jsbGRWCzGBRdcwNNPP82pp56a9jiPP/44N954I5s3byYajbJhwwZOO+00AF73utfxrne9C4BPf/rT/OhHP+Jf/uVfuOSSS7j44ot5wxvekHSs4eFhrrzySv7+979z4okn8ra3vY3vfve7fOhDHwKgubmZJ554gu985zt89atf5Yc//GHOn2EqLcM5A27hBcFlIGxW+Mk3LP7onk4WNFRQXRZiy6Fenjlo1h5u7w/TH86+alA0FueLtz3H4/u6mF1bzptPN1OZV80xy/ZtXNyIUirfrzI9EMEuarxhcW84/KabbmLDhg2sX7+eLVu2JIWvU7n//vt57WtfS2VlJbW1tVxyySWJ95599lnOPfdcTjnlFH75y19mXJ7Tsn37dpYuXcqJJ54IwNvf/nbuu+++xPuve93rADjttNMSC4bkYiotwykOW5hRDIbNdKnWriGWt1T7+ozWmsf2dnLBSbPZ3zHIswd72NsxSENlCV2DI+zrGGDNvLqMn39ifzd9w1H+6y0buOhkt/L7pLk1AImCsxmJCHZhyOKEJ5JLL72UD3/4wzzxxBMMDg5y2mmnsWfPHr761a/y2GOP0dDQwJVXXsnw8PCYjn/llVfyxz/+kbVr1/LTn/6Ue+65Z1zjtUt0FmJ5zslYhlMctjCjsG74QKe/UDbAzmP9dA2OcMaSRlbPq+Wp1h46ByJcdPIcAPblCIvfvf0YoYDinBOak7aftbyJ16ybx6tOPX7Tt6YcIthFTXV1Neeffz5XXXVVwl339vZSVVVFXV0dR48e5bbbbst6jBe96EX88Y9/ZGhoiL6+Pv70pz8l3uvr62Pu3LmMjIzwy1/+MrG9pqaGvr6+UcdauXIle/fuZefOnQD8/Oc/58UvfvG4vuNUWoZTHLYwoxiMGMFu7fIfEn9kj2kVesbSRpSCmNPr++JT5/HrRw+w1yk8a+8P01RVOiq8ffe2Y5y+pJGa8uT52zXlJXz9svVj/i7TAuV4Bq1NxbhQdFx++eW89rWvTYTG165dy/r161m1ahULFy7knHPOyfr5DRs28OY3v5m1a9cya9aspCUyP//5z3PmmWfS0tLCmWeemRDpyy67jHe961184xvfSBSbAZSXl/OTn/yEN77xjUSjUU4//XTe8573jDpnNqbyMpyyvKYwo9j4hTtp7w9z8alz+dY/bfD1mY/f/BR/f+4Ymz79UrYd6eMV/3s/wYBiy3+8nBd++W4uWDWL15+2gDd9/x8saqzk7OVNtHYNURYK8C8XrOA1336QT71yFVe/aHlBv0uhl9ecCHL+f773K3D3dfCZTgjMoGK7AiDLaxYvY11eUxy2MKMYi8Pe1TbACbOqUUpxwqxqSkMBljVXUV4SZGlzJXs7Bvj9E61UlgZZ3FTJX545zNLmKnYd6+e+581qVeetnDUh36fosa5axwERbEHIhgi2MGOIxzWDEbfoLBNaa3a3D7C8pRqtNTuP9SfyzCXBAK8+dR7LWqoAWNxUxT3b29h+tI+XnjSbb1zuhri3HOrhqp8+RkVJkBWz/BW4zTgSIXHJYwtCLkSwhRnDoLOgRl1FCe394Yxzse/d0caVP3mMWz5wDvPrK+gZGkmqKP/vN7ktt5c0VdLebxozXJxSPLZmXh1/+9cXMzwSm7nTtnIhgi0IvpEqcWHGYOdgr5xjplO1dqWv7rZFZo/u6WRXmykoW+446lQWN5ntNeUhXrxy9NrPteUlzKopH9/ApzMi2ONiqtUgCbkZz9+ZCLYwY0gI9mwj2AcyhMU3O/2+Nx/oZlebmZKRac72EkewL1w9Z2Z1KCsUIthjpry8nI6ODhHtIkJrTUdHB+XlY7uJl5C4MGMYcJqmuA57tGDH4ppnnD7hmw90M6e2nLJQgPn1FaP2BVgxu5oLV8/mqhcumZhBT3dEsMfMggULaG1tpa2tbbKHIuRBeXl50rSxfBDBFmYMA06F+JKmKkqDgbQh8V1t/fSHo6yaU8O2I308ureTpc1VBALpc9DlJUGuf9uUnlk1tRHBHjMlJSUsXbp0sochHEckJC5Ma8LRGLc+cxitdSIkXlMeYn5DRVqHbcPhV569BICnW3tYLhXeE4e3cYogCFkRwRamNbc+c5j3/fIJdhztZ8CZ0lVVFmJxUyU7j45uGfjkgW5qykO8eu08go6r9ttzXBgD4rAFwTci2MK0Zo9T5d3WF0447KqyIGsX1LPjWB99wyNJ+28+0M26hfVUlYUSxWmZKsSFApDUOEUQhGyIYAvTmn3OIh8dA17BDrFhcQNaw1MHehL7Dkai7Djax7qF9QCsW2QexWFPIOKwBcE3ItjCtMaupNU1EElUiVeWBBOi/MT+rsS+f3rqELG45qxlTQC88uS5rF1QxwmSw544RLAFwTdSJS5Ma/Y5K2l1DkQIR+OUlwQIBQPUVQRYMas6IdjRWJzv3rOLU+bXcdZyI9gvXNHMC1e8cNLGPiMQwRYE34jDFqYNO4/10zPo5qR7h0focl53DkboD0epKnXvUTcsauDJ/d3E45q/PHOYvR2DvP/85dJG9Hgigi0IvhHBFgrOf/xpC5/8/TMTfp67tx/j5w/vA2AkFud133mQL92+LfH+/g53nnXnQISBcJSqMo9gL66nZ2iEZw728M27drJiVjUXrp4z4eMWPIhgC4JvJCQuFJzH9nbSNTCSe8dxcv29u3lifxdvPG0BWw/30jsc5ZHdHYn3bf66pjxER3+E2ooSKkvd9qEbFjUAcMUPH2FoJMb1bz0tY4MUYYIQwRYE34jDFgpOZ3+Ewz1DhKOxCTuH1pptR3oJR+M8uqeThx2h3t0+QIezetZeJ3+9bmE9XYPGYVd7HPbylmrqK0sA+PGVp3PBSbMnbLxCBqRxiiD4Rhy2UFC01rQPRIhr06t7oqZEHesLJ/LT9+1oY8exfkpDASLROI/v6+LCNXPY3zFIc3UZCxoqee5wLxWlIeoqShLHCAQUP7/qTGorQolVt4TjjBXs+MTd3AnCdEEctlBQBiIxIlET3vTmkAvNc4d7AbO29V3bj7FpbyevWz+f0mCAx/eZyu99nQMsbqqkqaqUrsER+odHqC5LXlHrlAV1ItaTiTROEQTfiGALBaWzP5J4bqdUTQTPHe4D4IozF7G7bYDBSIwXn9jCKQvqeGyvWc96f8cgixsraagqJRbXHO4ZprJUgkpTCuXcQIlgC0JORLCFgtIxEE48t13GJoJtR3qZV1fOxafOS2w7c1kTGxc38OzBXnqGRjjcO8wix2EDDEZiSTlsYQogRWeC4BsRbKGgdA4Yhx0MqESV9kSw7XAfJ82t5aS5NTRXl7FqTg2NVaWctriBSCzOv/z6SbSGxU3GYVu8VeLCFEAEWxB8I3ZDKCgdjmCfNLcmr5D4nVuPcqBrkLe+YDGhYPb7yHA0xq62fl66ehZKKb78+lOoKDFCvHFJI6WhAI/t6eTSdfM4f+WspGU0q8RhTy1EsAXBN3L1EgpKh5PDXr+wgd88doBYXCeWqczEtiO9vO9XTxCJxrnlqUN89Y1rs1aX7zo2QDSuWTWnFiBpOlZjVSl3feTFNFaVJvLVdllNgCpx2FMLEWxB8I0ItlBQOgfClIUCrJpbQyQW50jvMPPrKzLuPzwS45pfP0lteQkfftkKvnzbNi782n28dv18hkZi3L+jjf9+0zpetno2wyMx7thyJFEFftLcmrTHXNBQmfS6sdINiYvDnmLIPGxB8I3ksIWC0jEQobm6jMWNZqqUDYt3DUQ47fN/SzQ4sVx/3252HO3nf960livOXMzfP3IebztrMbdsPsTDuzoIBBQ/emA3AN+7dxcfvHEzP/vHPhqrSlniczpWRWkwETIXwZ5iiMMWBN/4Emyl1EVKqe1KqZ1KqU9k2e/1SimtlNrovF6ilBpSSm12/nyvUAMXpiadAxEaq0pZ3GRcrp2Lvbt9gI6BCHduPZq0//3Pt7FuYT0vOrEFgJaaMj776jVs/uzLeORTF/DOFy7l4d2d7Djax8//sY8Xn9jCfR87n3s+dl7OXLeXRqfwTAR7iiHzsAXBNzmveEqpIPBt4BXAauBypdTqNPvVAB8EHkl5a5fWep3z5z0FGLMwhbGCPbeunFBAJaZ2tTvtQp880J3YNxyN8VRrDxsXN4w6TmVpiFAwwOtPW0BAwXt+8TgdAxHe/eJlLGqqpLa8ZNRnstFUbQQ7tXGKMMmIwxYE3/ixKGcAO7XWu7XWEeBG4NI0+30e+DIwXMDxCUVGR3+EpqpSQsEA8xsqOJAi2M8c7El0Qnv2YC+RaJyNSxozHm9uXQUvOrGF3W0DrJ5by1nLmsY0rgYnjy2NU6YYItiC4Bs/gj0fOOB53epsS6CU2gAs1Fr/Jc3nlyqlnlRK3auUOnfsQxWKgY6BcCL8PLu2nGO9Rqjb+0z1eCQaZ8uhHgAe32c6kp2WxmF7uez0RQC860VLx7xWtW2eIo1Tphgi2ILgm3EXnSmlAsD/AB9J8/ZhYJHWej3wr8CvlFK1aY5xtVJqk1JqU1tb23iHJEwSg5EowyNxmqrLACPYR/tMwKW9P0ypk3N+Yn83AJv2drGkqZKWmrKsx335mtn87r1n8Zp187Pulw3bPEUap0wxRLAFwTd+BPsgsNDzeoGzzVIDnAzco5TaC7wAuEUptVFrHdZadwBorR8HdgEnpp5Aa3291nqj1npjS0vL2L6JMOnYOdjWzc6pLeNIzzBaa9r6wixqqmR+fQVP7O9Ca83j+7o4bXHmcLhFKcVpixvH7K7BdDyrKg1SXS4Oe0ohgi0IvvFz9XoMWKGUWooR6suAf7Jvaq17gGb7Wil1D/BRrfUmpVQL0Km1jimllgErgN0FHL8whbBtSb0h8XA0Tu9QlPb+MC3VZTRVl/LEvi72OFXjG5dkD4cXistOX8QFJ82mLCQOe0ohgi0IvsnpsLXWUeADwB3Ac8BNWustSqnPKaUuyfHxFwFPK6U2AzcD79Fad45zzMIUYzAS5ZnWHlewq13BBjjaN0x7f5jmmjI2LGrgUM8wb/3RowBpK8QngtJQIGsDF2GSkMYpguAbX/FBrfWtwK0p2z6TYd/zPM9/B/xuHOMTioBfPbKfL/zlOd60cQHghsStYB/pGaa9P0JzdSkvWz2bPz19iDm15Vz1wqWcMCtzC1JhBiAOWxB8Iwk9wRfPtPYwu66MWTXlo9471G0Ky27a1Aq4IfE5jmDv6xykPxylubqMhY2V/OF95xynUQtTHmmcIgi+kdakQk601lzxw4f53zufT/u+7R8OJvRsp07NqjXV31sOmmlcLdXZq8GTiMcgLhfxaY84bEHwjQi2kJOD3UP0DkeTlqn00jEQ4aS5tVxx5iJOmlubqOYuLwlSV1HCs8686+aa0rSfT8s3T4NHrx/32IUpjgi2IPhGQuJCTnYc7QNMLjod7f0R5teX84XXnDyqdmhObTk7jvQD0OzXYcfj0LUHjm0d85iFseG0It4EHNRaX5zy3pXAf+FO6/yW1vqH4zuhCLYg+EUctpCTHUeN4B7uyeCw+8M0VZWhlCKQsvb1rNoyIjFzMfYt2FHnPEMyoWAS+CBmNkgmfuNZG2B8Yg0i2IKQByLYAgBbD/Xy5u//g8FIdNR7O44Yh907HGUgnPy+1prOgUhicY1UbOEZkHGfUYw4gj0ogn08UUotAF4FjF+IfZ9UBFsQ/CKCPQP43r27uPaWLVn3uXdHG4/s6WRv++Co93Yc60s8P9KbHBbvHYoSjetEO9JU7NSuuooS/01LImYNbRHs487XgY8D2dTz9Uqpp5VSNyulFmbZzx8i2ILgGxHsGcDtzx7h1mcOZ91nf6cRya7BSNL2WFzz/NF+TplfB7h57GcP9qC1pn3ALO5h516nMrvOCHazX3cNHofd4f8zwrhQSl0MHHNaCGfiT8ASrfWpwN+AGzIcy//aANI4RRB8I4I9A9jfOUhbfzixrGU69nUYZ227lVkOdA4SjsZ50Ymm++zhnmGePdjDxd98gLu2HXP7h2cQ5NnOwh6+89cAI47LH+qUC/nx4xzgEmc9gBuBlyilfuHdQWvdobUOOy9/CJyW7kB5rQ0g87AFwTci2NOcvuEROgciaA1HezMvVW4FuzvFYW93KsTPXWEuvEd7h3mqtRuAbUf66Ew47Owh8eYcK3IlYR12PArhvuz7CgVBa/1JrfUCrfUSzHoBd2mt3+LdRyk11/PyErIXp/lDQuKC4BuZ1jXNOdDpVnYf7hlmYWPlqH3C0RiHnArwzoGRpPdswdkp8+toqCzhcM9Qolp8b/sAdRUlQOaQ9xwnJJ5X05QRTx59sAPKR63IKhwnlFKfAzZprW8BrnHWD4gCncCV4z+BCLYg+EUEe5pjc9OQeVpWa9dQIvLszWEPj8R4Yn8XCxoqqCoLMaeugiM9w4mw+d6OARY0mBuAhgw57ObqMubUlrN6bh6i6xXsoU5gqf/PCuNGa30PcI/z/DOe7Z8EPlnQk4lgC4JvRLCnOfs7XfGzPb9H7dPh7mPF+LebDvAff9pKfzjKP525CIC5deUc7B5mX4e5CdjTPshJc8PUVZRQEkyfXQkGFP/45EvyG/SI58ZCKsWnNyLYguAbEexpzr6OQeorS4jFdUaHbQV4QUNFwmHf+sxhaspDfO8tp/GCZY2ACW/fs/0YcQ1Lm6vY0z7Avo7BnPOrbatS30TcqIAI9jRHBFsQfCNFZ9Oc/Z2DLGqsZF5dRUaHvbdjkKrSICfMqk4I9uGeYdbMq+WFK5oJOe55Tm05cSd0/spT5gDw5P4umjMUnI2ZJIctU7umNSLYguAbEexpzgFHsOfWl2d02Ps7B1nUVEVjVSldTtHZkd7hRMGYxb4OBRQXrjaC3Tsc9d/BzC8JwVbSnnS6I4ItCL4RwZ7GRGNxWruGjGDXVXA4w+Id+zoGWNxYSWNlKV2DEYYiMboHR5hbV5G031xHsJe3VHPi7JrE9sIL9iAESqCyURz2dEcapwiCb0SwpzGHe4aJxrUTEi+ncyDC8EgsaZ9YXHOgc4jFzZU0VJUyGImxz6ks9/YBB1ewV8+rpaI0mHi/MZ+QeGQA7roOopHM+4wMQkklVDYVPofd+jg8daP7+qnfwIHHCnsOwT/SOEUQfCOCPY054FSIL2qqZG69ccupLvtI7zCRWJzFjVU0VBqn/NzhXsAVaMu8+gqqy0KcvsQUoS1pNlO68mo7uvdBuO8rcHhz5n1GBqGkAiomwGE//hP422fc13deC498r7DnyJd4DFo3Te4YJgsJiQuCb0SwpzH7rGA7DhvgcHdyHnt3m1k6c0lzJY1VpgnKc4dNs5TUHHZlaYj7P34+l51u1nxY2lwFZO5ylpa405glHsu8z8gQlDoOe6jL/7F9nT8KMY+7j0UKf4582X4b/PAC6NwzueOYDBKCneXfgyAIgAj2tGZvxwAlQcXcuoqEwz6U4rCfbu0BYM28uoTD3nqolw+Hbmbx7W8fdcyGqtLEmtdLmhzBzsdhxxzBznaBjtiQeEPhHXY8CjHPEqHxkckvbBs4Zh77j03uOCYDcdiC4BsR7GnM1kO9rJhVQzCgEuHtzQe6+Nhvn+LBne0APN3azdLmKuoqShLdyrYe7mVDaA/BY9mX5Ny4pIHykkDCafsiHk1+TIcNidscdiELkkY57JHMDvvoVvjTB7NHAwqB7Zc+3DOx55mKBJwlV0WwBSEnItjTFK01zxzsSSyLWV4SpLGqlF88vJ/fPt7K9+7dBRiHfeoCs4912J0DEWYF+43TzcJpixt57nMXJRb48EVCsHOExG0OOxZOblU6XuJRNywPRrAHMwj29r/A4z+F/qOFO386ZrJgi8MWBN9Ip7NpSmvXEN2DI5zsiDHAOSc0c7R3mJbqMv629Sj7OgY43DPMqQvqAaivLEns20gfRPqNu83SqSzvLmZWsLNdoEcGoaLBOGwwYfHSPFx81vPHzLnjMSMW8REI95gweTDlv0OfI9TDvVA7rzDntwx1QVkdBAIQ7nfO013YcxQDItiC4Btx2NOUZw8at2YdNsA3L1/PTe8+i386cxGRWJzv3bsbIOGwS4IBasuNaNXqHpNnjmWZfjUWYn6KzmxI3FSjF3Rqlz1vLOKOBdKLZf8R8xjuLdz5wUQuvnYyPPs75/jisGUetiDkRgR7mvLMwR5CAcWqOTWj3tu4pIGq0iA3P36AgII189yVtBqqSikjQlncqSb39vUuBAmHnSskXpnssAt9/thIcmg8XR7bFoENF1iww70metG1x30NM1ywxWELQi5EsKcpzxzsYcXsGspLgqPeKwsFOeeEZkZimhNn11BZ6oaCGypLaaDP3bmQ+WPwmcP2FJ1BYaddeQXbGz1I5+L7JshhR51KfSvQkf7k1zMJaZwiCL4RwZ6GaK159mAPp8zPvAb1+atmAW443NJYVUqT8gh2jsKzvPFVJe7Mw66wIfFCOmznRiE+kjy9K/WmQGu32Kzggu3cKNgwfCIk3l3Y8xQLKiCCLQg+kKKzacjB7iG6BkeS8tepnL9yFmWhAC9Y1pS0vaGylBHlESjr/gpFYh52hgt0PGYcaEklVNQDqsA5bOuwI6A80YfUudjhXo8TniCHPdTtnGsG57BBBFsQfCIOu0h4aGd7YiqWl6/cvo0n9ye7Q1twdnIWwZ5TV84/PnkBr10/P2l7Q2XJBIfEY8mPqdiVukoqzBzdivr8HHY0At88DXbckeH8GULiqQ67zzOVq9AO257XCrQItgi2IPhABLtI+N59u/nK7dvoD7th3O7BCN+5Zxf/7/+eRXuqbHe1mUIx74pa6WisKh01LevCNXO4cInHeRY8JJ6j01lCsE2fcioa8+tENtQJHTvh2NYM5/cWnXlC4qku3laIgyuohSI1hx2ewTlsEMEWBJ+IYBcBsbjmyX1dxDVs3t+d2L63w4jpswd7uXdHW2L74Z4h6itLqCrLP+NxxtJGLj7B0xt8ZIKqxDM6bOd8VrArm/Jz2LaqPRrOcH7vtK4sDtvbJrTgIXFnbMM9Jlc+k6vEQQRbEHwigl0E7DjaR5/jrDftc53gvg4jThUlQb51186Eyz7UPTxqLeu8GGh3nxd6WleuXuLekDg4a2Ln4bDt50eG0r+fuGEYSZ6HneribYV47fwJKDrzCHZkANBm/W8r4DMNFZiZ31sQ8kQEe4oRj2tufHQ/QxFX0DbtM+6vqaqUx/e5TnC/47A/+NIVbNrXlXjvUPcQ8+vzaBeaymAHVDab5wUPidscdoYqcZszT3LY+Qi28/lMDtveKMRSBTvVYR+BYBnULSy88405Ywv3ujcDtfPMb1LomoFiQBy2IPhCBHuK8fTBHj7x+2f43ROtiW2P7+2kpaaMV5wyhyf2dRGNmYvbvs5BZteWcfkZiwBX2A91DzGvfhwOe7AD6s0SmoUPidtOZxku0KkOu6Ihvxx2IiQ+nP59b5W4DYmHKkbfFPQdhZrZUF47cQ5bx6HvsHle5/zeMzEsrpQItiD4QAR7inHIWa/6sb2ugGza18XpSxo4fUkjA5EY246YIqj9HYMsbjQrbTVXl7G7rZ/+cJTe4ej4Q+K18wE1cfOwM4XE7fls7/DKJuM6M4W4U8nlsNN1Oque5U6xsvQfheo5UFY7AUVnnrH1ODdm9TNZsMVhC4IfRLCnGIed9aof2d2J1pojPcO0dg1x2uJGTlvcQAO9bNmxEzDrXS9qMqHjZS1V7G4b4LAj+PPGGxKvajFh6UKHaHP1Ek+ExD05bPAfFreCn9FhpwmJV88e7eL7jxohL6uZuKIzcAW71pleJ4ItCEIGRLCnGFZwj/QaobZFZhsXNzC/voKvVfyYdY9+hKFIjGN9YZY4gr28pYrd7QMcTAj2GB12PG7Eq7LJuNxCN06xgum76My2J/Up2CM+Q+LeorPqWeZ7Rj1V431HoGbOBIXEPWPrOWge6xaYRxFsQRAyIII9xTjcO0xpyPy1PLqnk988doDm6jJWz6tFKcXSsj6qh1rZ1WaEdFGTCR0va66mcyDCc4dN+HbMgj3cbS6eVc2mPehEzcPO6bA987DB/9SunA47TQ67erZ5tIVn0bD5HWxIPBaBkQzHGwsxr8M+YB5ndA5bBFsQ/CCCPcU40jPMhkX11FWU8NOH9nL/8+2869yllATNX1VzSZhm3cWvHtkHwOJGI2xLm41wP7iznYCC2TVl6U+QCzulq7IZSqqO/+If6arEwX9IPJ8ctjckDq5g2x7i1bOg3OkWV0iXnS4kXueExFNz6TMBEWxB8IUI9lRi7wP0drUzr76C05c08MzBHuoqSrjiBYsTu1QyRJmK8tfHtwOwxDrsFvP42N5OZteWEwqO8a/WOtnKRsdhT4F52N5x5WIkjxy2t+gM3LC7bUtaM8fksGHshWede+Dg48nbvILd64TEJYc92aMQhCmPCPZUITKIvuESXjT4N+bWlXP6EiNU7zhnCdWejmXKaWNZH++krqKEusoSABY2VhIKKMLR+DindDkOu6p5YorOcvYSHzRNRILme1HRYB79LrEZ8euws4TEB5yucVXNJiQOYxfSu6+Dn7wSjnpapUbDUOrcCPQfNd+3rMb83jNxxS5pnCIIvhDBniqMDKF0jEo9yNy6Ci5ZN483nLaAd5yz1N0nHoeIcXqzVDeLnYIzgJJgIFExPrduHBXiiZC4LTqboHnY2aZ1lbjfi2AJlNXl4bCd8WbsdOZp3JIaErdhdxv+Lq83RWfebfkS7jdu/+ar3DHFwiZ6YUW7rNrMRS6vm6EOW+ZhC4IfRLCnCk4It1RFmVtXzty6Cr76xrXUVZS4+3gqtpeV9XNCS3XSIZY1m9fzx9s0BSZQsH3ksEsrk7dVNoxhWpcfh50hJG6ncZXVehz2GAU7FjZC3PYc3PMld2zBMjc/bsPuM1awJSQuCH6Q9bCnCk7lcCkjmZueePKoHz6rDn32SUlvL2+p4s7nxumww30mRFtSMUHzsHM0ThkZcvPXlnwWAMmWw47HASf06l0Pu6LBPE+snmUddq3r2MfqsKNhmHOq+V6HnnS3hcogWAq9re5NgQi2IAhZEMGeKjhzgEuJZhZcj8Nu0l1QnVwJbgvPxpXDjg67IenSqonrdJZtPeySFIdd0ejm1nORbbUub//yWBQCjkgES5PFMtxrHHCozBXTsRadRYdNaD0QTHb/3mOXOpGS8rrkVcJmCiLYguALCYlPIOFojK/fuYPBSIaFLrw4DrsiEKO+siT9Pl7RsFOPPJy1rJk182pZt7B+DKN1GBmEEueGoaTSOMxCFgTlnIc9MIEO2yvYnpB4sCRZsId73dy1DVePNSQeDUOoPDlaER02gi0hcYMItiD4QgR7Atm0t4uv3/k893nWqs6I4wjrSuMopaBtO3zrjOTcrQ3LqqC7/KOHRU2V/OWac5lVO46Q+MiwK5illeZCmmmK1FjI1Us8ncOubIRBT5X4lj/ATy/O/HkwNwapNwVewbadzgIlowu+wr2uiAZLzHjGExIPlZljWPcfixgHX1FvXotgi2ALgg9EsCeQo71G6I70+BA8R7BrS5wL19FnoX07dO9z93GmdNGwJK3DLggjg2b1KnBDtYUMi8d8FJ2lE+xIn9s6dP/DsPf+9MfwFsml3mgkOewRRzidaEaqw7bhajDPxyqkVrBLszlsT0h8qAt+8xa45Zqxna8YEcEWBF+IYE8gR3uNCB92hPupA9184c9bicXThJidkHhNyLlw2Rysd81mGxJvOmHiBDvqcdhWOAu5xGYuhz3c4zpOi21Paqu4bTewdHlqb5Fc6vtegbch8XSCHe5zQ+Iwvn7iVpxLqlJy2OUewXbOtex8mH0ydOyCrr1jO18xIvOwBcEXUnQ2gRzrM0J91HHYf9x8kJ88uJc5deW889xlSfuGh4coA6qCjqgkBNuzIIUV7OYV8Pwd6Suqx4v3mHZ6VSEddq71sAfazUphXrztSWvmuMIaHR49BSwy6LRUHcjtsANxExIHE572hsSrPH8/ZTXJ9QNWXJTK+DXd8zjiXOqpB4iGIVQ6Ooe99Fx47wO5jzndkHnYguALcdgTyDHrsB3BPtBp8qv//dcdHOh0RTAe1/zs/h0ANFv9TSfYTtMUGh0xmQiX7RXsEmdN6olw2PE0hXiRAeOQq5qTt6e2J7XdwFIFWWszVrt/avMUr6tPhMRLzevyOte5pw2Jexz2L14Hf/10pm+YjDeHrePmdSzFYZdWZz/GdEdC4oLgCxHsCSThsJ2QeGvXIKfMryOg4D/+tCWx37fu3snWA6Ywrc7Rj4QYpYbEg2VQv8i8nogpQCNDRkzA47ALKNjZ5mHbLmuZHPaokHiKYEfD5sJv25mOComnFp1Fk0Pi0SHzmXBv9pB42w7TIzwXWpsxBsvMFDkwNyTRsDuVDEanAGYaItiC4AsR7AnkqMdha6050DnIaYsbeM36+Ty21616/uH9u1k3z3G1qc46NSReVu220kxTKT5uokPJ87DBf0i8fSfs/Hv2fbLNw84k2KlLbCYcdoog2/y1FfhRIfHUHLa36KzePA51O79zFocd6Uv+e8mE3cc6bDvGUTlsEWwRbEHIjQj2BKG15lifWds6HI2zp32AgUiMhY2VzKuvoGdohKFIjP5wlN7hKEsbHOGwayUnHHaqYNeYPC5MUEh82DMPO8+Q+EPfgD++L/s+2XqJexfd8JIIidvWoZ4ctpeEYDv7Z3PY6ULi4KyepZMddpnHYWttqvVjaQreUrHnD5Un3/zYMHnzSpPemH1y7mNNZ0SwBcEXUnQ2QfQORxkeibNuYT2bD3SzyXHUCxsq6Bs2wnGkdzhRMd5YaqvDHYFOWyXebwS7sslc5CYsh20ddp5FZ5H+zItuWLKt1uVdKcxLqMzkeQc7ze+Tac1rO85ESDxX0VkQAs5/ASvYPQfMo9f1ltWYc8Zj5nM6lvz3komEYJe5dQG2GC5UBjWz4Zoncx9nuiOCLQi+8OWwlVIXKaW2K6V2KqU+kWW/1yultFJqo2fbJ53PbVdKvbwQgy4G2pz89doFRgg27TPucGFjZaL16JGe4cQc7Vor2AmHnaFKvKzWCE3VrIkLiYdSHbZPwR4Zyu08E+thp7lAJxx2y+j3KhpNDtu7/OQohz3g7pvu/VGdztI47G4r2F6H7Yh3pN9tD5tpcREv9vzekHi4zwh+sCzz52YaItiC4IucDlspFQS+DbwMaAUeU0rdorXemrJfDfBB4BHPttXAZcAaYB5wp1LqRK0zTcKdPtj89akL6oF9iZz1goYKSkPmPulI7xDRmHHYtaEUhx1LJ9i9UDPXPK9ucXO+hSIeM+cb5bD7M3/GS2TAKfzSmac8ZasSH2g357bhYy+VjSaHbQvOILPDzpjDjiY/1/HRgt3T6rxOI9jhPveGw4/DTuSwPSFxu+Z2SAQ7gQpknuYnCEICPw77DGCn1nq31joC3Ahcmma/zwNfBrxXyUuBG7XWYa31HmCnc7xpj60QP3m+EYI97QPUV5ZQU17CnFrrsMMJh11tBTuWJSQe6XfFo6rFdaSFwoazbQ47VG4upn5D4iNDgHaFsf355Ep2rbP3Eh9og8rm0dvBEexcDjtXDjtd45RMIfE6d1/biSzc5968+MphO+MLlro3QTYPP0UFWykVVEo9qZT6c5r3ypRSv3EiZo8opZYU5qTisAXBD34Eez5wwPO61dmWQCm1AViotf5Lvp91Pn+1UmqTUmpTW1uBRWiSsA57fkMFzc6qWgsbzEW7qixETXmIIz1DHO4dprGqlFA8pSo8Y0jcEezK5gkUbEdclHKakOQj2Lhjv/Gf4K7Pu+97L8qZpnWl5q8tdgEQb4vQUQ7bZ0g8VJ4lJL7feZ1SdAamhiDbamCpJELi5W60Yuo77A8Cz2V475+BLq31CcDXMDfo40cEWxB8Me4qcaVUAPgf4CNjPYbW+nqt9Uat9caWljT5yyJBa81//GkL9+5o41hvmKrSINVlIebUOYLd6HYlm1NbzpFek8OeU1s+OnedUbAdt1fV4n8FK79EHcG1OWwwQuN3HrYVdjvmoe5kh+2NFmRy2Ony1+DmsJNC4pkcdqZ52M45Q+VmDnZ8xO10VlJpCtAyFZ2BSUnYfu6+is6807pSQ+LjWKBlglBKLQBeBfwwwy6XAjc4z28GLlDKT7u3XCcWwRYEP/gR7IPAQs/rBc42Sw1wMnCPUmov8ALgFqfwLNdnpxW3PHWInzy4l//+63aO9g0z2wl9z6k1Qm0dNsCcuvJE0dncuvLk3LXWnhy24wpjI0agrNuranaKoDzud6AdfvuOsS9UkXDYnnan3mUhc34+pXo7Gk6ev+zNIactOkvTltRS2WS+l3dd7FwOO7Vi3Z6/pHJ0L3G7YpcVVG/RWak3JO50m8snJJ7OYVtnP7X4OvBxIJN6JiJmWuso0AM0jfusItiC4As/gv0YsEIptVQpVYopIrvFvqm17tFaN2utl2itlwAPA5dorTc5+13m5L6WAiuARwv+LaYAg5EoX7x1G6WhAE+39vDI7k5aaoyztg57QaNHsK3D7h1mTl15sgDERkY7bNvL2pvDhmQB2/8P2PJ7OPLM2L5EOsEurRqDww67j94OYfEsDltrx2FnuP7bvLS3w9goh+2MP2OnM+ecJeXu8ppe4bTNU1QwufDNWyWel8O207o8Oewp6rCVUhcDx7TWjxfgWPmluESwBcEXOQXbuZP+AHAHJrd1k9Z6i1Lqc0qpS3J8dgtwE7AVuB14/3StEP/ePbs40jvMd/5pA6XBAO39YY/DNo8LGjwh8bpyjvWF6RyIOA7bIz6x8OjGKVawrduzuV5vHtuKQa650JlI67ArxpDDjrhtOb1u3yvSqVXi4V4johkdthXs3W4YO1NIvKzG7JMph11S6TRO8ThsSO485o30eqvEs+Ww+1PEyeuwA0HzOHWLzs4BLnGiZDcCL1FK/SJln0TETCkVAuqAUXmZvFNcItiC4AtfOWyt9a1a6xO11su11tc52z6jtb4lzb7nOe7avr7O+dxKrfVthRv61OLPTx/mvJUtvHT1bF62xrQOneU47KXN1SgFJ7S4izzMqStPLPo0p67CDYmDeZ4IkTtOzlYnpzrsAc/10orBWAU7kcP2CHawLHlsmYhFPe1Uw+64vSFxrytNvW/L1JbUUuER7KoWQKUPiXvFMVOns5KK0a1JYfRyl5YkwfaExL1LQh5+Cr56QnJ0wzuty553ihadaa0/qbVe4ETJLgPu0lq/JWW3W4C3O8/f4Owz/nUxRbAFwRfSmrQAxOOa1u4hVs42F/Y3bzRpe+uwLzp5Drd98FwWpoTELXNHhcQjmR12QrCzOOxUZ+mXdA47VOovX5u0DrVn/OFed45t0jzoVMHO0JbUYudWd+8zS2GWVKR32Db0HCrLUSU+Yhx9UkjcEezyFMEOlpjPhPvckHjq9zm2zTx617H2TusCU3hmFzApksYpKZG0HwFNSqmdwL8CGZso5XcSWQ9bEPwgrUkLQPtAmEg0znwn5P3CE5r59KtO4tVr5wEQDChWzUkWgdkewZ7jLToDx6GmzMNOCLYtOrMO2yvY1mGPcf3qdILt12F7XX0s7Klu18aVltcl57BTHZX9HtnmYYMRyfI6R5DTNE6xuedQeZaQeIXTYtSzHjZkdtjgrokdCLrbomHXofc6DVfsTZN9344FTOGZFfQp5rC9aK3vAe5xnn/Gs30YeGPBTyjrYQuCL0SwC8DBLiNW8+qM0AUCineeuyzrZ2x7UnDctldcopE0RWdOaNlO6yqtMqHrtDnsyXbY4eTvM9zrCLY3hz3GkDiY4rB0gjwy4DrsknSC7ZnWFXcEO11IPNVhgyvY3v29U+56D5nHtILtiLOtTvduEyQkLgg+kZB4ATjUbYRhvqeoLBeNVaWUBgPUlIeoKgslX/xj4TSCnZLDhtFzsQetYI/RYSeKpFIddp6CHYskf8YWniVy2CpLDjuDwy6tdMdVUT/aYWvtOGwbEs+Ww67MkcNOs9xlabVTJd6X/D0tCcHudrd5e4lDcuW5CLaLCLYg+EIc9hjQWuPtF3Gw24hVPoKtlGJ2XRkVJU6INRp2RSHJYaeGxL2C3VzgHLYjuqMcdp4h8Wg4WSxtdMCbQ06tEh9oM+1AswlZZZMJPac67N+9E1DJK41ly2Hb76dj6XPYaUPitaMdtvc79mQJiSdy2G4NQ7HksI8LItiC4Atx2Hly17ajbPj83+gccEXsYNcQNeUhastLsnxyNCfPq+PkeY5IxMKuGCflsDNM64LR/cTHncN2BG5UDnssDtsbEnccts1hh0rTF51lmoNtsR3MUnPYR56FZ26CY1s8gp3GYVtXn/T9vA673nnMFBLvTS4681a9W4ft7XUeCzv92J2bu1KPYE+xediTigi2IPhCHHaePHWgh67BER7c2Z4oKjvYPcT8ev/u2vLtf9rgvoiGjRD1HTahXXsBs4Id6TdVxt6ip6pmOPqs+3q8OezokCnC8p4jVDY2h+39jJ3a5c0hp16gw72uw82EzWNX1Cc7bDvlbajLExIvS55S5j2/V7B9F51VG7EORQEFeLrRjQy7DWxSHbY3YlDiDYlPyU5nk4MItiD4Qhx2ntjVtR7a5XYYa+0am2AHAopAwHFf0XByz2qLNySeuuxkVbPJ/dr8rRWw8TRO8YZtwYRz/Thsbzc0b+MXcL+P/S6hstEOe2Q4OXeeDju1q7w+2WGH+zw9wW2VeEX2edgWP9O6wC06i/SbGwZwb0r6Drn7JQn2cHLoOynVIA47gQi2IPhCBDtPjvQaIXpwp1vsdbB7KK/8dVpiEc+qUL3J28GIT0nKRb6qxWn/2ZciFOMR7JRzhMpMKDvXesVJDtvT+AXcMLE3h51adBZNc+5UKtM4bK2NiK59s7nZsPtknYftFWxPkKnGNLyhevboc1vBDvd5VgNzvmOP0x6/es7o9bpTF1IBI1ABCW4lkHnYguALuWrkiXXY+zsHOdA5SF1lCX3D0TE57CSi4eR1ly0JwR4a7UC9c7G9OeRxOeyUc1gHGotAIIugJuWw00zrAjeHHczgsKvnZB9fOocdDRshblgKV98L1c5vkrVK3PM9vA67YQlcfQ/MOXX0uUtrzPeKj0DzidC5yw2J2/z17DXQusn9TKaQeLAsufXpTEcctiD4Qhx2nhzuGeLs5UY4HtzZzqFuI47jctixqHGc1mGna+c5MjzagdomIwPtyQ57PK1JU28KrODkymOnOuykHLYtOrM57LLRVeJ+HLZ1tuV1rsO2ofiyGmg50V34I1Q2OtKQ1mGn5JLnrU/O4VtsukLHXRdvv6NtmjJ7DYR73O+ZKtje/LrgIo1TBMEXIth5MBCO0jsc5YUrmplVU8aDuzoSTVPG5bCtU/P2rAbjPBIOe3h03tPOWR5sd/uIVzYX2GH7FewMDjtYlj6HnXqB9pPDXnwWLHwBNCx2HXYkTfU8OK1Ls6zWZfEbmvZOp0sNifceMq6/dr55bW9QosMpDlsEOy3isAXBFxISzwObv55XV8E5JzRz17ZjzKs3F/+8BduKhwq4wpLIYXvmXFuRSyvYnpC4zQHWzh1HDnt4tGDbauZchWcjg05uWifPw66e5QmJW4ebJiTux2HPXQv/fIdzDMdh22lWqQV5mXLYKpD8O/pdl7rMc0OQ6rB7DkLdAtfdD3WZfWKRlBy2LYgTwU5CBFsQfCEOOw9s/npOXTlXv2gZca35/r27KQ0GaK7O4yJ873/B5xrNn1s/6l74U6vEy2r9Oey+o+4c7Nr543DYg1kcdi7Bdty5nQZmBbuqxRMSz1J0NpLm+2XD5qgTq5hVj34/FkkulotHjaP2TuUK+pw773XYo0LiB6F2nls97m1gk85hS9OUZESwBcEXIth5cNgR7Ll15Zw0t5YbrjqDqtIgCxoq3OlZfjj6rAldNy6HQ5tdJ5gq2KXVriiky2GHykyh1NFnTUg8VGFc3pjnYacRzYTDzhESjzgrZdlpYDGPwx7V6SzFYWvtOOw8ohTWQVvBLq0Z/T4k32jEY0awg2MRbM9UL1v8liTY8z0Ou9s8pk7r8i5MIriIYAuCLyQkngdHeoxztSttbVjUwM3vPZuBcDTbx0YTHTaOrGm5WT/ZiqHNw9oQclmN2/IyXUEYwLwN0PqYEZSKBiN6Y+50NphmHrZfh+1x50kOuxkOPels91SJex126qpWfrDtTa17T+ewITkvn1aw/YbE0+Www07TlA4j2LZTWsJhRzLksKVpShIi2ILgCxHsPDjSO0xDZQnlJW4V8Ulz0zTZyIUVxvJ648asGIbKjIAkctgeh51acWyZvwG2/B7at5tQbSjNOtG+x5XBxUNuh22brsRjbg47UGJuItLlsHXcOGul3Jx7vg4bYMCZDz8qh+18D2/uPR41FeBekQ74dNilGXLYA8fM85rZrsO2885TIxb2+4nDTkbmYQuCLyQkngdHeoaZUzfO+dbgCmNFvbm4e5dh9FZVl9UYUdA6fUEYGIcNcPDxZIc9lgtgpk5n4NNhVzo57LB7g1FWZwQ5GkkWbHDD4jaEn6/DBrcl6KiQuBVsz81LPAoqWDiHHYu408pKq9PksMPJbtreVPg950xBHLYg+EIEOw8O9wwnrWM9Zmx4u7zeiIi9wAdLzQU+sdBHDaAd1zqc3mHPXYtZrjLuCLbTp9u7MEVe48rksH2GxIOlzjxsR7DLPd3bUgXbhsXH47Dt8qKjQuJpxp226MxnkCnJYTs57GjE5O7BiHGwxOyXVHTmddiehUkEF5mHLQi+EMHOA+OwC3CxtQ7b9q7uP2oeQ+XGYdsctHV10WFHANMIWlk1tKw0zysaXFHIN48djxnHmNFh+wyJJxy2U3Blv+Nwj2cedrl7Thifwx5oNwKcejOT1mHbHLbH4fp1u4GAK9rldYAyv8mI47Dt71bR4BadxVJy2ImiM3HYSYjDFgRfiGD7ZHgkRsdAhLm1hRBsR9xsCLXfyYOGSpMv5tY12kroTPOUbVi8oiG9UOWibYc7FSxjDtuHwy6tdJbjjHhC4rZ7W8/EOezU/LX3e6SGxANjDImDuYEKhMy57Y1JwmE7gl1ebxy21mkW/xCHnRYRbEHwhQi2T471GsGaXbCQeLlbVWwddrAs+QJvHbYt2sp0oZ/vCHZloyt6fh32kWfh26fD9tvM64xV4n4cdoW54UjksMvTh8TtMQvlsMtqMr8/SrBTqsTzWYSjrMa4bKXc0H/iZsq5aaioN4IdjxoR8n6nUJnTuEXmYSchgi0IvhDB9slhZ0rXnII4bKeALOGwvSFxb5GSXQzEr2A3eQTbp8MeaDOP229Nfw6/nc4S87C9Drs0OSSeEGznmPYiPW6HXZ35fW8TmYRgjyEkDuY89uYgWOqExD05bHCq4ruTCwktSpnPp94UzXREsAXBFzKtyyf7O82FeVHjOC+2tklIksNOExJXQffCnkuw522AS74FJ10C+x4y2/x2O7PCsvtu8zjmTmeDyQ475jhs74ImsRGTb7aLa1gBH5PDdsY52GF6i6dSu8A8du1zt2knh+111fmGxO3vGixNCYmnOOxMc8tf90NoPsH/OWcCItiC4AsRbIdoLE4omDngsK9jkFBAsaAQ617reHqH7Q2Jh8rd0K0NiWfKYSsFG97q7OOMz28/cRsytpXNGXuJZwmJx0bMspMlVY7DdkLiwdLRIfFAyCPYNoftjCEf5+nNg6fLYVfPMu6+fbu7LR4bPQ/bb5U4wClvdFvAhkrN985UdGa/U2qB2YkX+j/fTEEFAO3OyxcEIS0SEge+ffdOXvLf99I77E6F0lpz7S1beGyvuUDv6RhgQUNFVlH3xYgn/GvdZzqHHSp1hcVO88q1mpU9rvc8uUgNdedy2OlC7TYsnHDYETeHnVp0Fiwx0QNwi84yFbxlI2lRjTQhcaWgZZUpqLOMNyS+4a1wzgedz9nVwgaT89Ll9ea3ss1TpMAsN8r5PyXNUwQhKyLYwNZDvezvHOTLt21LbNtyqJefPrSX3246AMC+jgEWN6VxcvkS9YR/A0HTWMQb8k7nsBPv+yhWyluwUwQ403rYNi/93yfCXdcl7+O9CfE67FCZ+Y6l1eamw1ZpZ3LYfm5IUscF6YvOAJpPhDb37zRtlbjfTmep2Bx2ZMBEFqwztF3Qeg6OHqeQnoRgS1hcELIhgg209Rn3+MtH9vPoHuOob3/2CABbD/eitWZf+yBLmgpQLDSSUmBVUee+FyxzHXaw1CPYfcmfyYYVPb/TuqzDtjnf1HMEgsYRx8Im1DvcA/f9F+y+190n4bDtPGxP4xQwghr25LAL7rAz3Ei1rExeK9w6bKWcPLZybx7yxUYSRgaSz9+w1Dwe22IeZWWu3NibHRFsQciKCDbQ1h/mglWzWNhYwaf+8AwjsTi3bzGCveNIP8f6wvSFo4Vx2KmCbQvPAiHTnMOGaL0V44mQeD4O2+e0Livsy89L/ryXkA3/OlOYlILfX+0Kof1OpZ7VuqJhV6zKaj0O25vDtlXiY3HYOULiAM1OQ5k2J49tG6eAuXEIlo49Z2q/Z2TQnYMNbhObI8844xTBzok4bEHwhQg2xmEvaqrksxevYeexfq69ZQs7j/WzYVE9kVicvz9ncsxLmgvgsG0xmBUnW3hmxS0REvfksO2KVHnlsPN02Gv/yQhc7bzR+3jDvwCnvwv6j8DeB8zrSIrDjo8YEU9y2I5gB0PuBTpRJT7k5JbzKABLColnEOyWE82jLTyzIXH7nfwurZmOoC06G3TnYANUzzZpjsNPO+OUHHZOEoIdy76fIMxwZrxgD0Vi9IejNFeXccFJszh3RTO/fGQ/AB96qbng3/bsYYACOWxbEe1cyK3DDnmEGlJy2H3Jn8nGWBy2CsKSc+ADj2ZoQmIdtiPYVgits/YWndmbjEh/smAPp6kS154cdj7uGlIcdoYcdt0ic1xbeGbPD+a3HY9gJzqd9Sc7bKXM79Ox091PyI44bEHwxYwX7PZ+4zBbaspQSvGZi1cTDCjWL6rn7OVNlIYCPLSrg4CChQ0T6LBDqQ7bGxLPMQ/bS76tSVMXqEhHsCzZYVc2O5+1gu0J8yeK1IZHO+zEPGxHNOOeHHY++WtwnLoj/Jly2IEANK9IcdgewR5rwRl4Op2lWUO8eSXgVDyLYOcmcQMngi0I2Zjx87CP9bmCDbBidg3f/qf1zK0zU7hWzanh6dYeFjZWUBoqwP3NqBy2U3SWyF17i85Sc9g+RE0pczPg22FnWGfbS6g0OYdtV6uy0YKEw65KLrKy4y1PyWGnFp2NxWHb448MZA6Jg8kp73/YPI/HkwVbjbHgDNw0ARpq5qSc88TkMQrZEYctCL6Y8Q7bVoi3VLtCc9HJc1m7sB6Ak+aYecRLxhMOj43Avn+Y54mQeErRWVqHndI4xe/Fv6Q8jxz2GBx2leOwrVCnzsNOfM557i06C6YpOhuLwwb3N8sUEgfjdnsOQLh/AnLYTpog1eHbYjfvGIXMyDxsQfCFCLYTEp9Vk/7CunqeEezF45nSte3P8JOLTJvMREjcEahRIfF0jVN6AeX/4l9SmV/jFN8O24bE7XrQ1mHbqEFleoddVgORPnOMjA57LIJtj5/FYTctM4/d+5ND4oHx5rC9RWcp/za8DlumdeVGHLYg+EIEuy+MUtBYlb7j1UlzC+Cw7frIA+2ZHba3OhxGT+sKlfufghQqz681qS+H7QmJl9U44eg0RWdeh+3NYYPp/hUoMbllSK4Sz2fhj9TjZ8phg9tpLTJgzmdvFoIl+XU5SyXR6SyNw65f7Pn7FMHOiczDFgRfiGD3hWmqKs3YcnTtwjouO30hF66ek/Z9X1gnGu5xxS2jw/Y4besAvRXXfpgQh+2ExFXAjD1U7n4v77SuJIedIthDXckOO14gh51pHja4YhrpT56HHfQUv40FOw87ncMOBE2xm3eMQmbEYQuCL2a8YLf3h2muzixYZaEgX3r9qSwaT0jcCttwj2ehC+uwG8xjatFZ6lKb+TjQkvI8BDsfhz3grgddUpGcww6VG+fsFX9v4xSAwS4nh+0IpbfT2XgcdqbWpOCK6chgmhz2OBx2qNRd+KM0zb+N5hPd8wjZEcEWBF/M+Crxtr5wokJ8wrBh8OFeI07BUlc4bJW4FU1vKDWYJrzsh5KK9II93GPE0hvC9eWwnTWuvW04SyqSq8StMKYbsxXscE/m1bomzGE779mQuL1ZmH+ae/6xkG7dci8rLoSeVjf8L2RGBFsQfCGC3RdmWXMBGqJkIxES7x09hSkRErfO2iPY3pBtXm07K9zuaF5+fTk0LoNLv5U8tvK60ft6SVcRHapIDolnFWyPA07qJW6rxIfH7rBVMPsNRyIkniLYL78u82f8kBT9SOOw111u/gi5EcEWBF/MaMHWWtPWfxwctm3/OdwzegpTYh62zV07eetQmQk92/m++Ux7yuSwO3aOFsZ8HLZXsEtSis5sWDhb0Rk4q3XZojPrsIfG7rDLqrMX49lxRQaSc9jjxfubpQuJC/4RwRYEX8zoeF3vcJRINH4cBNsRNhsS94pmsMSEVEcVnaVO88pXsFPmYWsNgx2jt/vKYZcm57DBac7iEeyEw87QOCVxLI/DTlSJj8NhZ5uDDW6f75FBkzMf6+pcqSQ57AmO0Ex3RLAFwRczSrA7+sO86Ct38+xBEy5uS+lyNmFYhx3uddxkijitvQyWn2+eh1IEOuG48xXslE5nwz1GIFOne/l12LGI0zfbk8P2tia1gp226MzrsNP0ErdFa/my4kI4+XXZ9wmGzDgi/ckh8fGSlMMWhz0upHGKIPhiRoXEnz/Wz/7OQR7c2c7J8+vSdjnLC639zY32VonHRkaHt1/13+7z1PnYY3HY3vyyZbDDPI7VYduQeP1is83bTS0yAJWNyeMFzzxpT1FWai/x2IgR7rE47A1v9bdfaZUnhz0BDjvbPHAhN+KwBcEXM8ph277hO4+ZBiDehT/y5tEfwDc3uHOQs+GtEs/VNztRdGYdtiMMeeewB5MdixXsUQ57OPexM4XEkxx2RfL4vc8DQfdzgWBy0ZkNq0/kfOUkwZ6AHLaExMeHNE4RBF/MKMG2jnpnmxHsIz1GSBOC/eQv4bEf+jtY+w7o3A1P/Cz3vl6HPTKY3U0GU5x1IiSe5zxsHTfu1ZLOYWvtz2GHyozYhfsyFJ0NuKKVaSqaDYsHS5KndaXOS58ISqvcBVQkJD71EIctCL6YmYJ9rB+tNc8e6mF2bRn1lc7F96FvwhM/93cwK1YPfcOEi7OR1OksR4FVyyrY8DZY8kLzOhESz7PTGSTnsQfanbF4HHY8ai6SuY7t7WmeEOxKzzzsDA7bW4Bm52IHQp4LdOz4OOySSnea24QUnYlgjwsRbEHwxYwU7L7hKG19YTYf6GadsyoX0bBxzbEc4msZGTKh3d6D8NSvs+8b9YbEc0xhKimHS74J1bPMa+uw83Gg6dbEHnQE2+uw7ft+HLYlMQ/b0688MuhuT9eaFFyHHfA67Ojxc9gJwS5USFxy2AVDBFsQfDGjBPtY3zABJ1322N4u9nUMsm6h0xq0bbtxfKnFWpkYGTJueO5a2PSj7Pt6q8TzXUoy4JmX7Rcrft652N4cts1t23H5KTqz2Fx0SYW5uYlFk8P8QY+DTivYweRe4scrhx12ligtdEg8EJL2o+NFBFsQfDGjBLutL8yaeaZRye+eaAVwHfbRLeYxV3jbYkVq4ZnQuSfHvo4o6bgJTecTQk2ExMfgsL2CPdDhPrdCnXDYPqZ1WbwOG8wKXOjk75Q6lxxSctieXuIJhz3Bgj1caMF2vltJlf9V1KYwSqlypdSjSqmnlFJblFL/kWafK5VSbUqpzc6fdxbm5CLYguCHGSXY7f1hTp5fS01ZiHt3tKEUnLLA6TR29FnzGAv7O1jUyUXXLTTuzS6hmXbfMOBc1OMj+bnJREg8nypxRzyjaRy2d7tvh51GsO05BjuTt4O7lre3j3a5J4edCIl7q8QnuujMCnaBctg2JD59Cs7CwEu01muBdcBFSqkXpNnvN1rrdc4fnxWaORDBFgRfzBjBjsbidAxEaKkpZ/msamJxzYmzaqgucxyXFeyoT8G23b3qF5rXPQeynHwIqprd1/nka8fU6cw67DQ5bO923w47XUjcOYe9EUjq3laWLPLgKTorSS46Ox4Ou6TK7apW6JD4NCk40wZnwXNKnD/Hp5OJNE4RBF/MGMHuGIigNcyqKeOEWUZ0EuFw8ITE/Qq2Uxldt8i87s4m2GGonu2+zsthj7FxCox22PbCmHDYPovO0jlse46EYHuEK1Q2+ibAm8MOpMthT7DDtqgCV4lPH4eNUiqolNoMHAP+prV+JM1ur1dKPa2UulkptbBAJzaP4rAFISszRrC9bUgTgr2o3rzZfwwG2qCszoTE/dzpW8FO57Ajg/C/a2Hn3925zrbqG/LMYY+lNWmGHHb1HGe7ddg2JJ6Pw/bMw4b0gh0szSzYqb3Ej0sO2zO2gjvs6VMhrrWOaa3XAQuAM5RSJ6fs8idgidb6VOBvwA3pjqOUuloptUkptamtrS33iSUkLgi+mJGCvXFxA6XBAC9Y1mTetOHw+evNo5+pXVawK5uNA/UKds8B6NprXHtsxFyIvA47H3EaU6czOw/bI8yRPqhb4Lwej8P2VIkDDNkctl+HndJL/Lg4bG9r1AJ3OpuGU7q01t3A3cBFKds7tNY2BPVD4LQMn79ea71Ra72xpaUl9wlFsAXBF74EWyl1kVJqu1Jqp1LqE2nef49S6hmncvQBpdRqZ/sSpdSQp6r0e4X+An7x9g3fuKSRp6+9kKV2HewjVrCd64+fsLhd8CIQMELoDYn3HzWPkX5XFL0OOx9xGk+VuBVm64Lr5jtjz9dhp1lKMltIPFiaJYcd8vQSjx+nHLbXYUtIPB1KqRalVL3zvAJ4GbAtZZ+5npeXAM8V5uQi2ILgh5x2QykVBL6N+Q/cCjymlLpFa73Vs9uvtNbfc/a/BPgf3LvzXU6YbVI51pfchrS8xHPh7t4H5fVQ41yPcgm21slzj+sXJjvs/mPmMewR7CpvSHwMVeJjmoftnDsh2GN12NlC4o7DTs1hp5LU6ex4O2yPC5aQeCbmAjc4/98DwE1a6z8rpT4HbNJa3wJc4/z/jgKdwJUFObMItiD4ws/V6wxgp9Z6N4BS6kbgUiAh2FrrXs/+VRyv6tI8aOsLU1seShZqS2wkOYyba2pXLAJoV+jqFsKOO9z3B5y8XaTPFcXyOuM6Y+GxOeyxdDqzrUltW9JaR7DH47BLMhSdeZ1mRcPom56kedjOBToec6q3VX43JPkyEYKdCIlPD4ettX4aWJ9m+2c8zz8JfLLgJxfBFgRf+AmJzwe8JdCtzrYklFLvV0rtAr4CXON5a6lS6kml1L1KqXPHNdpx0NYfZnF1DG54NXTsSn5Tx4zrs2HcXA7bCqF1lfWLYOCYK4TWYUcGkuc627nIY5rWNQaHHc3ksFOndfl02KFy08nMe450DvtV/2Paq3rxtiYF83tbhx0qn9jmIxMh2LZj2zSZ1jWpiGALgi8KVnSmtf621no58G/Ap53Nh4FFWuv1wL8Cv1JK1aZ+Nu+q0jFwrDfMqeXHYM99sPvu5DfjcXMBDmUR7GjYLA4SG3HDuFa06pxK8d6D5nHAExJP7FtuXLb3c34Yy2pdgaAR2ZFMOew8G6ekK7AqyZLDrpvvVs9bWlbBeZ+CFS9zxxiPJi8cMlFMRA4b4JVfgfVvKdzxZioi2ILgCz+CfRDwXn0XONsycSPwGgCtdVhr3eE8fxzYBZyY+oG8q0rHQFt/mPnlTvV3T2vKAGLmopEtJL7vQfjrp2H/wx4RdoTAOtfu/eax34bE+5NF0eZxxzIPO9+irFBFisNWUDPPvB7lsHOt1pVGsO13SCfY6QgE4Lx/g8pG81oF3XnYE11pPRFV4gCnvxNmnVS4481UpHGKIPjCj2A/BqxQSi1VSpUClwG3eHdQSq3wvHwV8LyzvcUpYkEptQxYAewuxMDzpa0vzOxSR6BSm5zEY64rhQwO2xF7u4AHuCKaOhc74bD7ksPO4wqJ5ynYJeXJOezKRjffmuqwUyu6U0m04fQIn/0Ow91OVXieQhgIGUc1MjDxYeXSCXLYQmGQximC4IucV1mtdVQp9QHgDiAI/FhrvSWlevQDSqmXAiNAF/B25+MvAj6nlBoB4sB7tNadE/FFstE3PMJgJEZLiSOeqW1EbQ47MR0qjWDbudnhvtEOu3a+cQn2RiDhsAdSBHsMIXF7U5Bv2LikIrlKvKLRDauPeKrEA6HcYpvOYQdLzXfW8bGFtAMB12FPdEh8InLYQuGQkLgg+MLX1UtrfStwa8o2b/XoBzN87nfA78YzwEJwoNMIVEuJI8SZHHYiJJ6mcUqSYNuiM7ukZImZEtZzwIT1rMP2zsMuGWNIfO3lpqjNFm35JVThTt8K9zpV6s4caO/iH37Gkphz7G3xqcw5RgbGNrXJFp1519KeKEpEsKc0ItiC4IsZ0elsf6cR2KagI7R9h5OX0dRxx2HborM0a2LHRszjcM/oojOAxmXQ/rx5PxYxwhDud13uWB12VTOsvtT//paScvfc4T5X8EMe5x0d9ld9HgiY6m5vSNyeA8Y2tSlgc9iDE++wQ6VudbqExKceItiC4IsZIdgHHMGuU45go92KbjDVyoFA9mldXodtHaq3cnv2yXBsq9vlrH6RcZ/WjYfKYNZqs1hIvvnosRCqcG8swv1Q5llly9s4xe9YQmWjnbD9/mMRXGWrxAePz9QoO3Zx2FMPEWxB8MXMEOyuQWrKQ5RF+9yN3krxeMxcyENZis7ijsNOymF7BXuNEZ8DzgJHjcvMo+21HaqA9VfAh5+Z2DnHlhJPSDzSD6XpHHbY//zu8jqobBp9DhhbSDwQdIrORLBnPFaw47HJHYcgTHFmxNVrf+cgixorTbi6vN5UNnsLz1KLztJN64p5BTulcQoYwQbYdZd5tIJtu4xNZCevdJRUQN8R8zzcN36HfcXNyf3Q7bHsufLFhsQjg8enW5gI9tRFHLYg+GJmOOzOQRY2OIJthdVbeDZqWleuojNbSOYRqpZV5sKz+17zOlWwJzpPm0rIEWatzZht/jlUPjaHPXu1yacnncP5TmMpGvN2OjseDtueQ3LYU49Eb3mZhy0I2Zh2gj0QjiZW5gKIxzUHuoZY1OQIdvUss9Rlz373Q76KztLNw/aIcGklNC43IXAVdLufDbYbIT/ezq6k3IwzOmyE0RadeUPl+TjsTOewx8yXwPHOYTs3LEoEe8oh87AFwRfTTrD/647tXPHDhxOv2/rDRKJxFjZUOCHxOiOmaR12tmldUfMY7jUiEwi5bUMtc042j1XNbpOUgfaJ75WdjpJKI9jhfvM6USU+Roed6Rzex3xQQRMORx+nkLh12BISn3JISFwQfDHtBLu1a5C97YNoJ7xmp3QtTOSw60wrUW/RmW1NGnSWfszqsPsyh3FtuL1qluvoBjuOT1V4KqFy8z0iTqGdHU8hHbb97FgEOxA0v+VYP58vksOeuohgC4Ivpp1g9wyNEInF6R02jnh/hxHsxbUBI1Dldc761a1m0Q9wHTY4QudjWle6MPBsx2FXt7gCaR328abE6SU+7Kx8WpYphz2ekLjNYY/RYR9PwS4RwZ6yiGALgi+mpWADtPcb0T3QNYhSMK/CqfIurzNzoWNhd91qWyUOZmpX2pB4yrSudEJnHXb1bFcgo0P5L9xRCBKLczhFb0kO29s4ZbIcdsB1/8ejIC/hsCWHPeUQwRYEX0xfwXYKz/Z3DjKntpyyqJPLLa+Hmjnmeb8z7SkedS/kwbLsIfFYBIa60otU3UJoWGIapHi7gk2Kw3bGZ6vUvW1RvYt/jCuHbedhj0WwQ67DnujWpN5ziMOeeohgC4Ivpt3Vy3XYRmBbO4fcKV1gHLZt0BB3CsnicfdCHipLP63LNk4B080snStUCj6wyV2JyjIpgu2c00YRyibAYRcsJH4cHHZlo2lPmlooKEw+ItiC4Itp5bDD0RjDI+Y/vQ2J7+8cZEFjhWmWAkawrTjbHLYtOgNHsLP0EgezGlcmkQmWGOEOBF3nOSlFZ874+p2FSJLmYRfIYYfG47CDbtRiLJ3S8mXD2+DKPx//BjZCbkSwBcEX00qwrbsGI9jhaIyjfcOmy9lQt3mjvM7kT8HjsL1FZ2XZV+sCsxqXH1eYEMlJEIlsDlvHzA1IweZhj9FhW47HtK7yOlj0gok/j5A/CcGWximCkI1pJdi9HsFu6wtzsGsIrRkdErcOW8fcR+XNYWdpTQpG6P0ItlckjzeJHLYj2F6HDSYcrePjdNjjaZzi+ac3Gb+PMHWQximC4ItpJdipDvtAlwn9JuZggxFsK86ZHHYmwVZekfHhCifTYYc8Drukyv1+VhyHupL3Gwv2Nxhra9LEcY5DSFyYukhIXBB8MS0Fu6Y8RFt/hNYuMwd7ge1yFiw1ApXIYVuHHfdM6yrLsPhHBCoa3dd+hC4h2JPhsG0Ou811+uCOOyHY47iZKB2HYHurtcVhz2xEsAXBF9NSsJe3VNPeF+ZA5xAlQcXs2nJ3pS5bEAaeavGYG6LNOK1rJHl5ST8Ou2yKOGzvFDMrju07zGPtvLGfY8WFcNGXYdaa/D/rnQ99PBqnCFMXEWxB8MX0EuxBI9gnzKp2QuKDzK+vIBhQbltScMUiXQ47VJp5ta4kwc6n6GySOp2BmY6WzmEffso8Nq8c+zlKq+AF70nOR/slUTNQalrCCjMXEWxB8MX0Euwhk5Ne2lxFOBpn2+Fek7+GZMHOmsMuzxwSL6t2l+DMx2FPRqcz7w2FbZriHcvhp8x3aVhyXIeVwIq8uGtBBFsQfDHNBHuE6rIQc2qNKO1qG2BBQxrBTs1hx6PJji9d0Vk8at5LLFOZTw57EluTescBbj798NPQdMLkuVv7ex+PLmfC1EYEWxB8Me0Eu66ihOYaN2e8sNERqHQhceuwtbfTWZbFP4IlrlstlpA4JIfE7Y3GyAA0n3h8x+QltWpdmLnIPGxB8MW0E+zaihKaq0sT2xb6ctg+G6cESjwOO5+isynosAFaxpG/Hi/270BC4oI4bEHwxbQS7N6hEeoqQrRUex12uhy2vUB4i85slXhp5irxYGmeDtsJ905GDlspV5zTOWyYXIdtQ+Ii2II0ThEEX0wrwbYh8caq0sQ1YEFDhVn/ORaGckdsEw47Q9FZPOq6b0tsxAmJOw7bz9zqUrvvJAg2uOLsLTqbMg7b+ad3PNqSClMbpQAlgi0IOZiWgh0KBmisLKWiJEhTVSlEBswOVkBT52GnTuuC0XnsWMQ47PI8HPZkzsMGV5xL0zlsZYrOJgtx2IIXFRDBFoQcTKsJsFawAZqry9BolFIQcdbCTl0TOR41hS46nuywwZna5RGTVIedV2vSSSqsSjjsNDnshsWTW/AVEMEWPIhgC0JOpo1gh6MxhkZiCcF+1alzTcMU8DhsR7Ctu9Nx9yLhndYFo5un2Cpxu56yH7GbfTIsOhvmnDKGb1QAEr2+vYJdBqjxNUwpBEqqxAUPItiCkJNpI9i2LakV7GsuWOG+mRBsR7i807psWNzmVG342lt4prXpGBYsdd/3IzRVTXDVbfl+lcJhowU2KgAmX1i3EBaePjljstgoh8zDFkAEWxB8MG0E2y6tWesIdhIZQ+Ixt/BMpYbEPQ7b7hMscRcA8YrgVMXeVKSO9X0PTV6Y3iIhccGLCLYg5GTaCHZPVsFOCYl7Hbad2mVFPBES9zhsK97BUjjlDVA9G6pnFXD0E4S9+fCGxGFq3GzYaXQSEhfAEWxpnCII2Zg2VeKpIfEkRgm2I8465gmJexqnQHIO2wq2bZyy6pUFHPkEUpJmHvZUISCtSQUP4rAFISczRLBtSNwRLuWZ1pVadGYF27sASMwcO1FwVixYwS6dAo46FSk6E7womYctCLko+pD4zmN9fPL3z5jpW/h12AFAOTnsFIcdTFN05g2JFxOhNNO6pgqJHLY4bAFx2ILgg6IX7Id3d/LY3i4AKkqC2QXbW+AUCCbnsFVqlXgYfvNW2PA2t8FIsQl2SWVyZftUItFLXBy2gAi2IPig6AW7c8C434c+8RIGIzFKgo7w7rrbiPLSF5mQeEmVO3ULjGBky2F37YPnboHGpVC/2GwrtpD4hrfB7NWTPYr0KGlNKngQwRaEnBS9YHf0h6ktDzGvPsWp3fNFcxFY+iLjsFOLm1TQyWHH3NfghsSPPGMeI4OekHiRCfbs1VNXsCUkLngRwRaEnBS/YA9EaKpOE/KNDruV3pGB0U4uEEqfw7YO+8hT5nFksHhz2FMZKToTvIhgC0JOir5KvKM/Yhb4SCUageFu83xkcPRc5EAgudNZapX4sW3mMTKQ3DhFKAwyrUvwIvOwBSEnRS/YnQMRGtMJdiwCQ93meaR/tDDYHLbO4LDjzlQucdgTgzhswYsKuP8XBUFIS9ELdsaQeCwC0SFT7Z0xhx3NPK3LMjKU3DhFKAzLXgzr32q6xgmTjlKqXCn1qFLqKaXUFqXUf6TZp0wp9Rul1E6l1CNKqSUFHICExAUhB0Ut2PG4pmswU0jcaXwy1J1esAMhiMfTFJ2lHCsy4GmcIg67YMw6CS79lnujJEw2YeAlWuu1wDrgIqXUC1L2+WegS2t9AvA14MsFO3sgKIItCDkoasHuGRohFtc0VacLiTuCPdzjhMRz5LCtcAQCrjCXVKWExMVhC9MTbXBaAlLi/ElNKl8K3OA8vxm4QNmOReNFis4EISdFLdgdA0aU0+ewHVc83J3ZYes007rADYvPXetM6xKHLUx/lFJBpdRm4BjwN631Iym7zAcOAGito0AP0FSYk4tgC0Iuiluw+43zbapKN60rR0g8kcN2LhLe0Gyo1LT1bFkJIwPF20tcEPJAax3TWq8DFgBnKKVOHstxlFJXK6U2KaU2tbW1+fyQCLYg5KK4BdvpcjYqJO5tiDLYYeZkjwqJh9K3JgUj1k0rTA/uYm6cIghjQGvdDdwNXJTy1kFgIYBSKgTUAR1pPn+91nqj1npjS0uLv5OKYAtCTqaHYKeGxGOepTF7W83jqJB40Ljr1Bw2mMrl+etNDjs65C4EIiFxYZqilGpRStU7zyuAlwHbUna7BXi78/wNwF1aF2jytMzDFoScFHWns04nJN6QKthRz9KYvYfMY1rBjqbPYb/ld2Y+9qM/MK/DfeZRBFuYvswFblBKBTE38jdprf+slPocsElrfQvwI+DnSqmdQCdwWcHOLtO6BCEnRS3YHQNh6ipK3AU/LF6H3XPQPKaGxFXQWfzD6WIW8PwUlY3OZxyRtx3TJCQuTFO01k8D69Ns/4zn+TDwxgkZgITEBSEnRR8STzulK8lhW8FONw87Q9GZxS7HaTumSeMUQZgYRLAFISfFLdj94fRNU5IcdrYcdoZpXRa7YIg4bEGYWESwBSEnRS3YWfuIW6zYpi7jOGq1rjQ/hf3McI8RdOnKJQgTgwi2IOSk6AU7/dKaTkjcO1UrncPO1Dgl8RlPSFwKzgRh4hDBFoScFK1gx+PaCHa2LmeVze42v4t/eCnxhMRFsAVh4hDBFoScFK1gdw+NENdp5mCD20e8epa7LW3RWQ6HnRDsHggWdUG9IExtZB62IOSkaAW7o9/pI54tJF7l6bI0qtOZU3SWrUq81CvY4rAFYcIQhy0IOfEl2Eqpi5RS2511cD+R5v33KKWeUUptVko9oJRa7Xnvk87ntiulXl6ogfcOm7B3XUWaym1bdGbXWg6UmP7gXkblsLMUnem4VIgLwkQijVMEISc5BdvpfPRt4BXAauByryA7/EprfYqzcMBXgP9xPrsa0w1pDaYv8Xec442b4RHzn7s8lOYrJATbCYmnhsPBk8NO0zjFYh02iMMWhIlEHLYg5MSPwz4D2Km13q21jgA3YtbFTaC17vW8rMJdR/dS4EatdVhrvQfY6Rxv3AyPGGdcXpJG/6Opgl09ep9R07rSHCdUDjjL/UrTFEGYOOwNtCAIGfEj2Ik1cB1anW1JKKXer5TahXHY1+T52byX4wtHHYedTrBt0VlVFoedrZe4OzD3sxISF4SJI1jizu4QBCEtBSs601p/W2u9HPg34NN5fjbv5fiswy4ba0g8EDIhuGwOG9xKcQmJC8LEESwRhy0IOfAj2Ik1cB0WONsycSPwmjF+1jdZHfaokHi6HHbAcdhO3ixTar1UBFsQJpxgaXKHQkEQRuFHsB8DViilliqlSjFFZLd4d1BKrfC8fBXwvPP8FuAypVSZUmopsAJ4dPzD9uaw0zlsOw/bqRL3lcPO8FMkHLaExAVhwgiUiGALQg5ydgPRWkeVUh8A7gCCwI+11ltS1sn9gFLqpcAI0IWzyL2z303AViAKvF9rmzQeH7ZKvCyUxWGX1UKwbOw5bBDBFoTjQbAEYhISF4Rs+GrfpbW+Fbg1ZZt3ndwPZvnsdcB1Yx1gJsJRHznsYAnUzXedtpdAyFkPO0cOW0LigjDxSEhcEHJStP02h0filIYCBAJq9JuxsHHWSsHb/2ScdirKx/Ka4DZPEYctCBOHVIkLQk6KWLBj6d01mJC4dcR1C9Lvk2hNKg5bECadYAnERbAFIRtF20s8HI2nrxAHE1pLbUWaSiBlta5cOWxpnCIIE4eExAUhJ8Ur2COx9BXi4IbEsxEIeYrOVOYqcWmcIggTT8CZhx2X9qSCkImiFezhaCx9hTiYkHguh62CgDYXiUzhcICSCvMoIXFBmDjsDbGExQUhI0Ur2OGReA6HnSsk7qTvYyOZw+HgKToTwRaECcP+/5LCM0HISNEKdlaHHRvxERJ3vno0nN1hl8o8bEGYcOz/L8ljC0JGilewsznsaNhH0Zl12JEcDlsEWxAmnIRgi8MWhEwUrWCHozHKMzrsiL+iM7tvpoIz8BSdSUhcECYM+/9LctiCkJGiFezhkThlGXPYkdyO2LrqaFgctiBMNgEJiQtCLopYsLM47GgYQrkctvPZWMRnDlsctiBMGBISF4ScFK1gh6NxyrI1TslZJe4V7CwN32yVuDROEYSJQ6rEBSEnRSvY2VuT+nHYIXffrCFxOw9bBFsQJgypEheEnBStYJt52NmmdflpnOLsm63orLzOPNpctiAIhUdC4oKQk6Jc/CMe10RihWqcksNhNyyGN94AK142tsEKgpAbqRIXhJwUpcMOR02/4aTGKYOdsPPv5nkhi84A1rzGnd4lCELhkSpxQchJUQr28IhZYSvJYd/zRfjlG2Fk2F9I3Ip0NEfjFEEQJh4pOhOEnBSlYFuHnchhaw3P/9WsvDXU5S8krvJw2IIgTCySwxaEnBSlYFuHnagS79gJXXvN88EOswKX3yrxXK1JBUGYeKRKXBByUpyCHbUhcUdon/+r+2b/EfPoOyQezl4lLgjCxCMhcUHISVEqVXjEFp05w3/+rxAqN8/7jprHQjVOEQRh4pH1sAUhJ0Up2G7RWRDC/bD3QVj5CvOmddi5QuLeHLaExAVhcpEqcUHISXEKdqLoLAD7HjR35ae8ybzZ5zck7ul0JkVngjC5SEhcEHJSlIIdThSdBc38a4BZq8x/+j6fDjsh0loctiBMNlIlLgg5KUrBTnLYNucVKIGKBui3Oewcvb+9eWspOhOEyUWqxAUhJ0WpVMNehx2Pmo2BEFQ0ekLiPnPYqc8FQTj+SEhcEHJSlIKdaE1aEoCYI9hBx2HnHRJHctiCMNkEgqACUiUuCFkoTsH2VoknQuJBqGw0Xc7A/7QuEIctCFOBQImExAUhC0Up2IlpXUkh8RKoqHd38lslDuKwBWEqECyVkLggZKEoBTscjaMUlASV+x88WGJy2JaQz17iIIItCFOBYIkItiBkoSgFe3gkRnkoiFIK4sZtm6KzBnenXEVnEhIXhKlFUELigpCNohTscDTuLq0ZHwGUm8O2+F38A8RhCzMepdRCpdTdSqmtSqktSqkPptnnPKVUj1Jqs/PnMwUdhITEBSErRdlEe3gkZqZ0gclhW/FNcti55mGLwxYED1HgI1rrJ5RSNcDjSqm/aa23pux3v9b64gkZQbBEqsQFIQtF6bCHRzwOOzbiirM3h50zJC4OWxAsWuvDWusnnOd9wHPA/OM6CKkSF4SsFKVgh6Mxd2nNeCy9w/a7+Efqc0GY4SillgDrgUfSvH2WUuoppdRtSqk1BT2xhMQFIStFGhKPu0trxkdcwfbmsPMJiUtrUkEAQClVDfwO+JDWujfl7SeAxVrrfqXUK4E/AivSHONq4GqARYsW+T+5VIkLQlaKUqmGR2KUWYedFBKXKnFBGCtKqRKMWP9Sa/371Pe11r1a637n+a1AiVKqOc1+12utN2qtN7a0tPgfgFSJC0JWilKww1Gvw/aExEsqIFRhnue1+IcItjCzUUop4EfAc1rr/8mwzxxnP5RSZ2CuHx0FG4SExAUhK0UaEo/RUuM4aG9IHExYfKAdzHUlM0mNU4ryZxCEQnIO8FbgGaXUZmfbp4BFAFrr7wFvAN6rlIoCQ8BlWmtdsBEESyA6XLDDCcJ0oyiVyszDTjOtC0xYfDg19ZYG72ckJC7McLTWDwBZ73K11t8CvjVhg5AqcUHISnGGxEdibkjcm8MGI9i52pKCFJ0JwlRDis4EIStFqVTDSZ3OYqMddq6CMzAhc+UcQxy2IEw+ksMWhKwUZUjc9hIHRuewT3o1NCz2dyAVBB2XojNBmApIlbggZKUoBTscjVPm7XTmFexT32T++CEQMoIvDlsQJh8JiQtCVoouJD4SixOLa4/DjuaewpUJ66zFYQvC5BMslV7igpCFohPs4RGznGbGKvF8sEItDlsQJh+pEheErBSdYIejcQA3JD4ewbZCLVXigjD5SEhcELJSdDnspqpSnvvcRa7Gpk7rygcr9NI4RRAmH6kSF4SsFJ1SKaWoKPWEsFOndeWDhMQFYepg18PWOnenQkGYgRR/LDh1Wlc+JBy2CLYgTDo2UiYuWxDSUvyCnTqtKx+kcYogTB2CTodCqRQXhLQUv2CPa1qXddjF/zMIQtETsA5bKsUFIR3Fr1TxqPsfPV+sYIvDFoTJR0LigpCVaSLYYxRcaZwiCFMHGxIfq8N+7s/Quadw4xGEKUbxC/a4pnVJlbggTBnG67B//y547IeFG48gTDF8CbZS6iKl1Hal1E6l1CfSvP+vSqmtSqmnlVJ/V0ot9rwXU0ptdv7cUsjBA+Ob1qXEYQvClCHhsMcg2FrDyCBEBgo7JkGYQuRUOqVUEPg28DKgFXhMKXWL1nqrZ7cngY1a60Gl1HuBrwBvdt4b0lqvK+ywPRRiWpc4bEGYfILjKDqzn4kOF248gjDF8OOwzwB2aq13a60jwI3Apd4dtNZ3a60HnZcPAwsKO8wsFKKXuDhsQZh8xjOtywr1yFDhxiMIUww/gj0fOOB53epsy8Q/A7d5XpcrpTYppR5WSr0m/yHmoCCtSUWwBWHSCYwjhz0igi1MfwramlQp9RZgI/Biz+bFWuuDSqllwF1KqWe01rtSPnc1cDXAokWL/J8wHgP02Kd1SeMUQZg6jCckbh12VARbmL74cdgHgYWe1wucbUkopV4K/DtwidY6bLdrrQ86j7uBe4D1qZ/VWl+vtd6otd7Y0tLif/TxqHkc87QucdiCMGUYT9FZ1LnkjEgOW5i++BHsx4AVSqmlSqlS4DIgqdpbKbUe+D5GrI95tjcopcqc583AOYC3WG182P/YMq1LEIqSnz+8jw//ZrN5MZ5pXeKwhRlAzpC41jqqlPoAcAcQBH6std6ilPocsElrfQvwX0A18FtlVtnZr7W+BDgJ+L5SKo65OfhSSnX5+Eg47PEu/lH809EFoRjZdayfO7ceNS/GFRIXhy1Mf3wpndb6VuDWlG2f8Tx/aYbPPQScMp4BZiUh2GPNYYvDFoTJpLY8RH8kSjyuCUiVuCBkpbit5bhz2DKtSxAmk5ryErSG/kh0fFXi1mFLSFyYxhS3YEsOWxCKmupyE+TrH44WpkpcQuLCNKa4BduGzsa7WtdYc+CCIIyLGkew+4aj46wS9xSdaV2g0QnC1KLIBTtmHqWXuCAUJTXl5ma7b3gkfZV4TyvE47kPZAVbx2V5TmHaUtyCnQiJj7eXeHH/DIJQrCQ77JSQeN8R+N+1sOO2DJ/24O0hPjKYeT9BKGKKW6nGPa3L+frisAVhUqgpcwQ7HB3dS7z7gPk/3n0gw6c9RMOe55LHFqYnRS7YBcphS9GZIEwKSSHx1CrxwXbzGO7NfaAkhy2V4sL0pMgFW3LYglDMJIXEA0FAuSHxAUewh3tyH8ivww73u8cVhCKjuAW7YDlsEWxBmAwqS4MElDOtSykTFk912L4E26fD/ttn4OevGfN4BWEyKW7BHndIXBy2IEwmSimqy0ImJA6m8MwK9kA+IXGPw84m2F17TeW5IBQhRS7Y4y06s41TivtnEIRipqa8xITEwRFsJyQ+2GEeh/PMYWfrdjbUaRy7zNUWipDiVqqY/U8+3sU/pHGKIEwWNeUhehOCXepGzsbssLPksAc7zVztSP/YBisIk0hxC/Z4HbYUnQnCpFNTHqI/7ElvjSWHPTIEKPM8W9HZYKf/YwrCFKPIBVumdQlCsZMUEi+pgHCfeT6QT0g8DOV15nmmHHY0AhHn2EPdYx6vIEwWRS7Y45zWJY1TBGHSqSkPuYJdvwi695vn+c7DrmgwzzMJ9lCX+1wctlCEFLdgS2tSQSh6jGA7/5cbl5pK7sigaTFaWmPE2JujTkc07Ap2pqKzoU73uQi2UIQUt1KNNyRuWyGOdXlOQRDGTXVZCf3hKFpraFgCw93QsdO82bjUPA73wr5/wPXnpXfQ0WGoqDfPMxWdDYpgC8VNcZdHj7fo7OQ3QEUjlNUUbkyCIORFTXmIkZgmHI1T3rDEbDy4yTw2LoMjT5uw+L4H4NCTxoHPOin5INEwlFSam3dx2MI0pbgddswzd3Ms1MyGdZcXbjyCIORNrdOetHd4BBocR936uHlsWm4eh3vM6l0AfYdHHyQ6DKFyU7SW0WF3uM9FsIUipLgFO+GwpWhMEIoVdwGQKDQsNhu9DhtSBPvI6INEw0awQ+WZl9e0IfFAiQi2UJQUuWCPM4ctCMKkU+0ssdk/HDXpqcpmaNtu3rSCHe6F/qPmeUaHXWYcdqZ52EOdRtCrZ4lgC0VJkQv2OHPYgiBMOkkrdoFTaKbNjXjtfLNtuDeHw/aGxDPksAe7TM1KeZ0pbBOEIqO4BXu8OWxBECadpDWxwVSKA1Q1u81Qhrv9OexQeXaHXWkFWxy2UHwUt2DHo4CSHLYgjBOl1EKl1N1Kqa1KqS1KqQ+m2Ucppb6hlNqplHpaKbWhEOce5bCtYFc2uzM4uva5i4KkOuxY1FwLcjrsTjNXWwRbKFKKXLBHJBwuCIUhCnxEa70aeAHwfqXU6pR9XgGscP5cDXy3ECdOCHbYCrZTKV7VZG7Gy2qh3clpl1SmEWynqYp12BkFuwMqm0SwhaKluAU7NiLhcEEoAFrrw1rrJ5znfcBzwPyU3S4FfqYNDwP1Sqm54z23LTobFRKvbDaPZbXQ/rx5PudUI9jxuHsA2wWtpMIIuoTEhWlKcQt2PCYOWxAKjFJqCbAeeCTlrfnAAc/rVkaLOkqpq5VSm5RSm9ra2nKeLxQMUFkaTCk6w+SwAcpr3bz1vHUmsuZtgmIFOlQGJRkcdjxueonborNwb7LoC0IRUOSCLSFxQSgkSqlq4HfAh7TWPlbdGI3W+nqt9Uat9caWlhZfn0nqJ149B2afAvNPM69t4RnA3HXm0Vt4lhDscghlmNYV7jHrYFuHLWtiC0VIcatdPCqCLQgFQilVghHrX2qtf59ml4PAQs/rBc62cVNdFqLf5rADAXjvA+6bZbXuo+181ncE5pxinkc9OexMDts2TalodPs3DPcY9y4IRUJxO+xYVHLYglAAlFIK+BHwnNb6fzLsdgvwNqda/AVAj9Y6zRyr/ElaEzsVK6rVs6HGSZlndNgZBNsurVnZBOX15rnksYUio7jtaTwqU7oEoTCcA7wVeEYptdnZ9ilgEYDW+nvArcArgZ3AIPCOQp28paaMZw/2EI3FCQVTfIQNidfMMaINyZXiI94cdqVZ/ENrUMrdx/YRr2yEyIB5XmzNUyKDprDO+72EGUWRC/aItCUVhAKgtX4AyKoEWmsNvH8izv+G0xbwt61HuX3LES4+dV7ymzYkXjMHQqWmerz3kPu+12GXlDvbwub5cC88+Qt334oGN41WTA473A//sxpefh1seOtkj0aYJIo8JC7TugRhOvDSk2azpKmSH9y/x6yL7cUbEgcTFvc6bG8OO1ThbHPC4s/eDHd80vwBt+gMikuwO543hXPb/jzZI0nP76+GP/+rubEQJoziFmyZ1iUI04JgQPHPL1zKUwe6eXxfV/KbCYft5K9r5mTOYVuHbcPk3fvNNWLeBvP5srriFOz2neZx7wPGqEw1tv4fbPoRfO8c05VOmBCKXLBlWpcgTBdef9oC6itL+PbdO5Pf8Oaw7WOSYFuHXe46bLvEZk8r1M6Dd90FH3zKVKDbG4BiEuwO5zeJ9MPBJyZ3LKlEI+am6cRXQNde2PKHyR7RtKXIBVumdQnCdKGyNMR7Xrycu7e38dCudveNKmcud90C89iw2CwEMuxME09y2BXJ23paoW6RKdQKlZltwRCU1uQv2H/9NNz+qfy/WCHoeN7p/KZg9z2TM4ZM2Pnsy17srDXePanDmc4Ut2BLDlsQphVXnr2EeXXlfOm2bcTjTi57yblwxc2w8Ezzes5a83j0WfPoddhWsBMh8QOu0Hspr4Ohbji61RX+bEQjsOmnsOP2sXyt8dOx03R5m7sW9tw7OWPIRNj5/cpqoaLenUInFJziFmzJYQvCtKK8JMhHLlzJ0609/OlppxI8EIAVL3OnM8091Twefso8eluThmyV+JDp09B3KLNgP30jfPcsuPu63AM78AhE+kx1empR3ESjNXTsgqYTYNl5cODRqVXcFe4zj2U1Zo77UPdkjmZaU+SCLTlsQZhuvGb9fFbNqeG//7qDSDRNv287H/vw0+Z1Jofdd9i0IK1fOPoYJ74cFp0NdQvh2Nbcg9r5N+dcQ8ffQfYdNmHnphNM2Dk+Ym4gpgpewa6ol5D4BFLcgi0hcUGYdgQDin+7aBX7Owf5zWP7GYnFefZgSr55zqkehz0EKmhy09ZhjwxCj7NOSTqH/dLPwjv+AovPhs49uQf1/J2uOegtSDdW/9iCs6YTYNYaZ9uuwp+n9XG3Gj0fEoJda+a5T6TDvuPf4Xvnws8uhf1T6KblOFHcgi0hcUGYlpy3soUzljby9Tuf58Kv3cfF33yAv27xzL2euxbathknHQ27Ql1SaR6jw6bgDIyLzkTDUrOfdenp6DkIx7bAylea196mLccDu7Ro8wqonmW+Y5ePm4x8+f274M7P5v+5USHxCYxAPP0bc/y9D5ipZIXi0GZzM3C80x15UuSCLSFxQZiOKKX4xCtW0TUYIRRQzKop46cP7XV3mHsq6JgR0uiwWwFe4tNhWxqXATr73OHn/mQeT7vSPNobgeNFxy4zXa1mnsnjNywx06cKSTxu5qz3HMi9byqJorMJDolHwzDQBuvfaubUF/LGYOv/wT++ZWYfTGGKW+1kWpcgTFs2LGrg/n97CbNryrj+/t185fbtPH+0jxWza4zDBhMWjw67DruqBUqq3HB5RSOUVmU+iV17u2sPtJxong92woP/a/Lfx54z+etZa0zBlwqOz2Hf/ik48UJzLL90PG/C4QHHX3kF+8gz8Mj3TSX9iS83gjkWBtqMAeodw1outgDOOuxhZ63xQIH9oO1uVzvXCb13Zt8/H+yxOna58/2nIMXtsCWHLQjTmvn1FYSCAS47fRGloQA3/GOveaN+san0Pvy0ExJ3HHaoDE64ALbdalxzNncNJiQO0Lnb3fbI9+HBr8OjPzDCf8Fn4KrbzUJDNXPHnsOOjcDD34anb8rvcx073WVF7Zi79prw7RM/hyd/Dn+4Gn70srGNC6DXiRoMHDNT2PIh3Acoc2NU0QBo00Y1H6IRuO0TyS1nU7HNcmrnOYJdQIdtl1/tnIDagAJS3IItOWxBmBE0VpVyydp5/P6JgxzuGTKh4TmnmilOI0NudTjASZdA/xGT56xflP3AVc2miYotPIvHzGIhyy+ATx+Bjz0P537E7WdeNz+7YLdthz9/2BwHYM/98JzT/9uuGOa9OciF1iaH7q10b1hiQv79x8wNxcIz4ZwPQvsON5+cLz2e79SfRTQtex+EZ39nnof7TMGZUq7Dz1dMjz4Dj3wXtvwx8z72d6+ZAMG2x+pIU3SnNfzjO/n9vU0QRS7YksMWhJnC+88/AQVc8+snGYnFYfWlJofd+pjrsMGEnAMlEAvndthKQeMSt4hr193GbW54W/r9a+dlD4k/8TPY9GM3ZH3ff7mFXANt5jGfC3+4z3yPqlnutoYlznF2mZD4vPWw4HSzzRao5Yv3JsRPWPyvn4a/fdYdY1mNeW7XGs+3UrzPyR1nm2LXexwcdrrq+7btZvGYzb8q3PnGSHELtoTEBWHGsLS5iv983Sk8treLr96xHdZebhbz6D/q5rDBhMqXvsg8zyXYYArPrIg+cQNUNrkV4anUzjduNFM1cetj5tGGdvuOuGJkBbv/qP/GJ/Yztj0ruHn3nXfCyIDJ5zevNNvad/g7bireQro+zw1J1z741hnJRXkD7XDoSTM2rU3RWVm1ec867HwLz6yrP/Zc5n16D5kK+fI6V7ALVdVtc9jpbqbsHPy+lBuZnoPwyPXHtbK8uAVbQuKCMKO4dN18rjhzEd+/bzf37x9y14b2OmyAky42j9mmdFkalhpB6mmF7beaG4FQafp9a+e7zVPuus4tbgOTSz/0pHluL+59h02HtMiAETqLX5dtP+MV7LqFgHLDx3PXGhEPhIwbzEX3fuP8Y1F3W+9Bc6MCyRGEfQ9C+3Y45FlwZOffAW2K/SIDyQ67osE85uuw+4+Zx2PPZRbAvkOmhkAps0xqPDr2FIAXrT057N2mYM7L81awU1IFT/0KbvvYaCGfQIpcsCUkLggzjf938WpOmFXNR256iu5T3gEqkOywAU5+A5z5Xn/V2I1LzbXk5qtMFfjp78y8b+088/j0TXDfV0yBmuXwUxBzCrb6DhsXbac89R913TLkIdjWYTe720rKzTg6d5nv3bzSRBobl/tz2E/9Bu76AjzyPXdbTyvMXmOO5xVsewPgDZPvvDN5fGlD4nmGq60YRvoyTy3rPeT+/okbgwKExUcGTdqhfpG5CfFGGMJ9sO8hZ4wpU77s79QzxiLEMVDkgi3TugRhplFeEuR/L1tH12CES35xgN/XX8nfAueYYrTETrXwii/5m+bUuMw8HngEXvQxN+ScDhtiv/+r5nHv/e57tl1oIOSEwj2OrP+YETcVNK/zFuyW5O22un32GtPhDUxjFT8O2+bX777Ofd5zEGoXGEH0OkZ7A2C3xeOw6+/ueAbaUxx2vXnMOyR+1Nx4QeaweO/hiRFs664XnGEevYVne+4zN3NNJ4x20lawe4/fvPziFmzJYQvCjGTNvDq+/ub1LG6q5Hvx1/Cuzcs5+0t38ZXbt6HzzSla8WteCedck31fKxgDbUa0uvebFcHACHbDEuPU+g4nO7W+I+Yz1bNNAVneIfHm5O228MzORwdoWWmOm2taVtceI0AqAH/5qAmN9x8xFfA189I7bCtWh5801e6nvNG8HkwR7JIKCJaNoejsCMw/zTw/thV23ws/f525xoO5UeibIMG2+WtbuOctPHv+b1BaDatfY76r97cVh50H8TigxWELwgzlVafO5ef/fCZ//fCLue9j5/O69Qv4zj27+FK+ol23AM75ELz+h6Nz4alUz3Zd8qv+2zzue9DkQQ88aqZY1cw1bnCUw243wtu4zF//cjAiX1Y3elwJwV7nbmteabq/5boZ6NxjxOmFHzIFVQceMU1iauebpiRWiKJht3refpdddwPKFexESLzWPX5Fw9gcdvOJxuUf3Wqq0Hf93dwQgRHL+Ii5oQDTEAcK0zzFOmybEvD+frv+Dktf7E6r83ZCSwi2OOzcxJ07LxFsQZjxLGqq5KtvPJW3vGAR3793N2d98S7+5ddPcstThxiMRLN/WCl42X+4y3ZmIxA0Yrn8Alj1apOz3Xu/CS33H4WFZxjB7jvscarKOFjryhuX+W/QMdA22l2DcdPgulJwO7W1ZwmLjzg52oalrug+/B3zWLfQDYnbJT11HIKlrsNu32H2a1llXvcfMyuJWYcN+a+JHY+b3656Nsw6Cbb9GY44K7HZfLb9LWvnOufwOOzYCGz6SXIRXT5Y0a9sMnUA1mF37TM3DMvOM3+n4N64jAybmwg4riHx4lW7uPOXIyFxQRAw/cc/f+nJnLqgnvufb+fh3R386alD1FeW8Mf3ncOS5iwtSvPhit8aoQ4EYPE5pkHLULdx3kvPMw6274gRudIaM+XJFp01rYCmZabCODKQvW0quCKfyqqL4ep7YM7J7rZmK9hZCs+6nelZDUvMn3nrYdtfzDYbEo9FTNjbCv+CM0z1uxXxxqVQWmlawHbvA3SyYPtdE/vGK8zvd+qbzPW8ejbMihrXX1ptbgSse00ItnXY9eZxqAt23QV//pBxwSe8NPd5tXbXVgfXYVc2mr+bo1vM670PmMclL3QNorf635IpJP73z8PKV8CCjbnH5JPiddgxcdiCICSjlOJNGxfyzcvX88gnL+BX7zqTaExz7Z+25J/bzkTTcqhypkAtOce4621/hgu/AM0nGFGJDpniKbt2d9/R5JA4+FvAw34mlUDAiK2X0irjfrMVntlQvC2sW/0awPldbEgcjEC27QAULD3XzPcO95lwsR1/VbN7vFSHnSsk3t9mfrOtf3Rda81smLXaPH/Rx8y5rWDbegAbEg+VmRuGoW63WYyfHu/hPvjKUrdLG7jRgIpGcwPRuRuObTOCXdlkogmpDtsKdt2i9J3vhntNYeLmX+YeUx4Ur2Bbhx0Qhy0IwmgCAcXZy5v58MtO5J7tbfzq0f3891+385pvP8ip197Bu3++afwibhu0nPJGeMF7zXO7eMShzUYAq2ebXPDIoBsSB3+FZ5kcdiYWnA5bb4F9/0j/vr1JsIV2a15jHstqTWW9FcS+w8Zh1y80YWIwy5kOdXoEu8X9DkmC3QBDPaZAa/vt6cex3xnfkWdcoa2eY+bPv/RaOPPd5nfzhsRV0CwvaqlsNO7Yphf8CPax54xAb73F3TbUZRx9qBTWvNYU4z17sxHsxeeYm6PKZnN+2+DFnmvh6SZ6kro8qx13gdct9yXYSqmLlFLblVI7lVKfSPP+vyqltiqlnlZK/V0ptdjz3tuVUs87f95esJEnBDtYsEMKgjD9ePtZi1k5u4Z//8OzfOvunZSGApyxtIk7thzlZ//Yx2Akyg0P7aW1azD/g885Ba78C1z6bTfMakUv3GOcWc1s98Jd1WIqtMEIYDbiMROazkewX/lVI7K/frNxial07THiZF27DYvXO5dsG3LuPWgcdvNK9wZk34Pm0S5EUtXsuktv0ZldE/uxH5pxHE3TbtQK9sigOzWuZrYR/hd+2FSb1y3whMQPm3F4r/c2V25vGvwItv3N997vNkgZ7HSL2GrmmJuwTT+Gnv0mHA5GtGvmuA7bfm87FSz13N2TJNhKqSDwbeAVwGrgcqXU6pTdngQ2aq1PBW4GvuJ8thH4LHAmcAbwWaVUQ0FGbkPiksMWBCELoWCAb1y+ng9esIJ7P3o+N737LH7wttM4f2UL/3nrc1z4tfv47C1buPInj9E3PJL/CZa8MLmK27s8Y43jsG3YuarFiFLDEjjybPrj/f1z8H8fcHKrOj/BrmqCt/zeuMG7rxv9fucec25vDvd1P4TXftc8t1Xw//iOyYW3rHRFfK8j2N6QuCU1JB7pg6d/Y17bSnMv+x50w8zP/9U5d8qyll7B7tozumudbU/a4Qi2n45jNl0w2OH2LR/qhEqPLJ3yRnehFivY4Ai2c45epz7BFv+lhsVtdXtvq1mcpkD4cdhnADu11ru11hHgRuBS7w5a67u11vb29GHANvB9OfA3rXWn1roL+BtwUUFGnnDYksMWBCE7K+fU8OGXnciipkrA5Lq/8oa11FaUUBYK8OlXncSe9gE+ctNTxOPjDJN7Bbt2niPYDlbkZp/sFjd5eeT7cP9/m4UmbPg6XQ47Gw2L4cSLTEg3tc1m1153Spil+QQTKQDThOXV/2u6qcXCpgrdjn//w87xnc97byRSi84ADm82j9ZtWoZ7TSh83RUQqjCut6zWFLJ5sYIdj5n9U6v4KxqMgCbC5n4Ee5u7kIp19l6HDXDSq81c8opGaDnJ3V6d4rBr57k3EalTu3r2u88LuMqXH7WbD3h/8VaMY87EPwO3Zfns/NQPKKWuBq4GWLQox3J4FslhC4IwDlpqyrjno+dRFgoQCgZMlfmft/LeXz7Of71xLbXlY7y2lFS47q9mDuBxs1bkZp9s+pZHBo3Du+dLZv/tt5qQecdO89z7mXxY8kJTid62DWY7AdF43Aj2iRdm/+yGt5o/A+2m6EopI6jhXlOYZpcyrfTcSJRWu88rPG41EBrdavTAo2a62NJzTSex1keTb2osdQtNq9ADj5qKcW+TGDCCaqvey+qSG9Vkom07LHsxtG4y537Be83v37DY3ae8Ds56v4neBjyetmYO7HfalPYeMvUJNvqQKtjdB0ykQsdMWHz2mtxj80FB7alS6i3ARuDF+XxOa309cD3Axo0b/d3eJqZ1icMWBGFsVJW514+rzlmC1pov3raNV33jfi5aM4eGqlLu2dZGMKD4yhtOZX59Bfc+38bsmnJWz6vNfOCauY5gzyMRDgfXLc852YhW23NmWtWTvzDh1dWXwkVfhv9ZBc85hVFjEuxzzOPeB1zB7jtsXHOqw86E19nXzDWCbcPhqeNKDYmDcefDva5gP/FzUzUdCJk/C043Itz6aHJUwmLbwG5z1hP3NomB5BuDxWfDjttM8Vem5jdhp095y5Vm1a8tf3TqBFIcNsBLPzv68/bvdGTY/JYt55moQEVj+pD4/NPMd0u3xvYY8aN2BwFv8mCBsy0JpdRLgX8HXqy1Dns+e17KZ+8Zy0BHIdO6BEEoIEop3nnuMk5dUM9/3bGNG/6xj0g0zklzaznYNcirv/UACxsqeeZgDwEF7zhnKR+58EQqS9Ncg2rmmhxpzRzjssDkPK07tY7ryLOmknrxOfCOv7ifn3OKuxLYWAS7frFxqPsegDOvNttsaNZWiOdDzRxTNZ4k2Jly2I74nfx6s1CIDYk/81uz/Gg8CovONtPQ5q0z73mrvxPfwZGdbX82IWqbL06cxyPYS84xgt132L0hOfyUmSO9ylkqtc2Zn96yyuzzxA1w8HEY7jEV535+AzDi3HfEddc2dL/zTjPVbPFZ5sZg5StMBMBvkxwf+FG7x4AVSqmlGAG+DPgn7w5KqfXA94GLtNbHPG/dAfynp9DsQuCT4x41eDqdSUhcEITCccbSRn77nrOJROP0Do/QXF3G3vYBrv75Jtr6wnzl9afy9MFufvTAHu7YcoTPXbqGeBz2tA/w1rMWU14SdAuqaua40UCvwNUvMWHk7bfBsS3wss8nD2LR2UZwVCBZmPyilLkJ2Hmn2yjk+b8ag5M6f9sPVpzSCXaoIrn4d/4GePkXYcPbTAj6yDNme9s2OPUyOPdfXYG3Ye7UgjNw88Nde41bTS0wtr9LRaM7f7vXEexYFG56uylWO+sD8LLPuRXiLauMQIcq4MH/BfRoh50O+3d65GlzE+YV7Of/av7ULoAPPGam49UvctIbx1GwtdZRpdQHMOIbBH6std6ilPocsElrfQvwX0A18Ftlqg/3a60v0Vp3KqU+jxF9gM9prQvQ/BV33lumdWsFQRDGQWkoQHO1Ca8uaa7i1mvOBUzV+ZtOX8gla+fzid89zVU/3ZT4TMdAhE+8YhWsvoR4sJRAsMQITVldslMOBIzL3uGU+6x8RfLJF58Fj3zX5IkDfmqD07DkHHj6RiOaLStNCHjZ+f7cZCrWXaYLiXvdNZipV2e9zzyvX2jEq+egma88a5U7LQyMeM7bYELaqVQ0mND1yODo/LV9347JOx0NTEOWrj2w5Fz4x7eMI66ZY9qsNiwxqdS1l8HjPzH7+/lNbLOZWz9mHu30vdlrzI3R0heZvPjue8z2ukVmbDsyzEUfA77iyVrrW4FbU7Z9xvM8Yz84rfWPgR+PdYAZse3k/NwZCYIgjJNQMFk4z1jayK0fPJdbnznM3LoKbn68lR/cv5sXn9jCj/7RyLMHL+YXZ/ZxwqwaI1x1KfW2s9eYhTcal5ulMb0sOss8jiUcbrFTknbc7rT53A/njzHAWZPGYduis1TB9lLnFBHbqVuzTkp+P1gCV9+d/rNKGffaviO9YFuRbVru6UTm9EF/4GtmDvnbboEH/gfu+rxx1M0nunVPZ77HFWw/OtK0HN78C1PFP9jphuhf9HHj4gc74Jsb4Mmfm+3WYQ+0mbB7eV3uc+SgeBPAiXZyhZnWLQiCkC/lJUFet8EUR500t4Z7dxzj8h88TElQUV0W4rLrH+EX7zyDVW+8YfS0pdlOH/AT08x0rZ5lxCVdMZZfGpaahSvu+ZKpjA6WwspXju1Ya15r2pPa0DOY6GZZXXbBtnnohGCntvDIQUKw141+L+GwlxsxLKk0IfHn/wpHn4XXfNdEJ879iMkxP/6T5Dz4rFVmEZddf0+eh52Nk15t+rhHBkyPeDC/Q6jUjKF+Eey4w/3udoGQjl0mVTBOirc1aWKFFXHYgiBMPvWVpXzpdady8vxabrz6Bfz2PWejFFz09ft53U1H+fHTYTr6wzx/tI+f/2Mve6vXQrCUQwtewf9tPjhq/veWF36D50/7f2MfkFLw2uuNsOy43YiTreDOl+oW04EsNTxf1ZzDYTuCvfseI2jWCfulbqGpU0p15mDEcd4GOOEC811r5pqpXY98z0QE7GpkSpkOcC94H6x/a/IxXvxxcxPRuHz08TOhlCvWqduXnWfy24GQGY/talegudjF7bADoeT5f4IgCJPIS1fP5qWr3TnFt3zgHG7e1Mptzx7hc3/eyuf/shVv+/LTFvyWJ3/ZS1xvBuDSdSZs/uT+Lt78225m1ZZx7ypNMKAYEzWz4XU/gF++AdZdPtavlZk1r8ke5ayZa+YjjwzCnBckd1jzwzkfNBGIdFO1SquSw+m188z86p4DcN4nk4vUgiG46Iujj7HoBfC+DH3Xx8Ky8+CJn5n56oGgiXK86OOjK9zHSPEKtp07l+8/AEEQhOPE3LoK/uWCFfzLBSvYfqSPvzx9iJaaMl6wrIlbnjrEbc8e4V3nLuOBne18+bZtvHzNHHqHR3jvL54gFFS0dg1x745jvGRVmsYifll+Pnx8d3K/70JxwWeyvx8MGfHq2Z/eJeeiaXlykVo2aueZ7mUqMNpJHy+WOi1I6p3cfUk5vOTfC3b44hXsoS7JXwuCUDSsnFPDyjmu0/rIhSv5yIXm9T92dXD5Dx7mgzc+yeYD3fQORbnp3Wdx1Q2P8fN/7GPdwgb+89bnuOLMRaxfNIbrXgEKnsZM3QJHsPPMX+eLDbefeNHoAr/jRVWzyXOny7kXgCLOYXdJ/loQhGnBWcubuHD1bO7YcpR59RX86l1ncsqCOi4/YxH37Gjj9d99iJsfb+Wjv32KSDTOM609XP2zTTx7sAeAp1u7uW9H2yR/iwzYwrOxOOx8qHVE+rR3TOx5cvHmX8CLPjohhy5uh23DDtOIkZERWltbGR4enuyhCFOE8vJyFixYQEmJNAmaznz1TWvZeqiXM5c24vSz4PIzFvLtu3dyrHeYa15yAt+4aydfvn0btzx1iLa+MPdsb+Os5U3cu6ONgIJfvPNMzl7ezL6OAWrLS2iomgJ9KuzSnRMt2Ce/3uSNT8g4y7joKW7BnqCww2TS2tpKTU0NS5YsSfynFWYuWms6OjpobW1l6dIxtJQUioba8hJesKwpadvcugp++LaNzG+o4MTZNWw51MuPHthDTXmIm959Ftfft4v7nm/nfect544tR/jgjZt588aFfO/eXdSUh7j2kjVcsnbe5F5LTv9nM+c831XH8qWqyZxrGlO8gj3YOfYpClOY4eFhEWshgVKKpqYm2tqmaLhTmHDOX+X22f7sq9fQPTTCv77sRM5Y2sjpSxoIR+OUlwS5dN18Lv32A3zr7p286pS5HOwe4oM3buYrt29n3cJ6DvcM0T04whdeezJnL2/mL08f5umD3XzswpWjmsIAdA9GqK8sgEOvmWOqyYVxU5yCPTIE0aFpm8MWsRa8yL8HwbKoqZLfvddt46mUMr3LMUVtP77ydAbCMV62ejaxuOZ3j7dyz45jPNXazby6CuJac9VPH+N1Gxbwq0fMms39w1E+/arV/PrR/SxtqeL8lbO4d0cbV/30MT776tW87awlk/FVhTQUp2BLl7MJo6OjgwsuuACAI0eOEAwGaWkx7REfffRRSksz33Fv2rSJn/3sZ3zjG9/Ieo6zzz6bhx56qGBj/tCHPsRvf/tbDhw4QGCsfZcFYRpw9nI37BwMKN50+kLedLq72GJ7f5jLr3+YXz2yn0vWzmNOXTnX37ebW546RN9wlICCj758Jdfft5tYXPPde3Zx+RmLKHEcuNYarSEw1nnhwrgocsGeng57MmlqamLz5s0AXHvttVRXV/PRj7oVj9FolFAo/T+bjRs3snHjxpznKKRYx+Nx/vCHP7Bw4ULuvfdezj///IId20u27y0IxUJzdRm/efdZPLqngwtXm7anA+Eou9sGeN/5y/n+vbv5yu3bTf771au59k9buWXzIRY0VPC1O3ew9VAvNeUl/OpdZ7K4qYqRWJzD3cO0D4RZ3lJNXYUURk4kxWlHEgt/iMM+Hlx55ZW85z3v4cwzz+TjH/84jz76KGeddRbr16/n7LPPZvv27QDcc889XHzxxYAR+6uuuorzzjuPZcuWJbnu6urqxP7nnXceb3jDG1i1ahVXXHEF2mkDdeutt7Jq1SpOO+00rrnmmsRxU7nnnntYs2YN733ve/n1r3+d2H706FFe+9rXsnbtWtauXZu4SfjZz37Gqaeeytq1a3nrW9+a+H4333xz2vGde+65XHLJJaxebeaQvuY1r+H/t3fnYVVX+QPH34dFkEWEcMdEE1CRrqwyuQDZjOto7lH+0izH8efUqPOzR63Gmcpn7Mlf26SUuWU/B6opHa3UR0TUyX1JA8QUwSTHDRMhVLbz++NebqCAmMD9Xvy8nodH7nfjc4/3w+F7zvmeEx4eTnBwMEuXLrWes2nTJsLCwjCZTAwYMIDy8nICAgKsfc/l5eV07dpV+qKFzfm4N2NQz3Y4OCgcHBQLRoaQ+Lto+gW0YtnECKbGdOGDJyOY+JA/QW08WbgpkwnL95L74zWGmdpTVFzKUyv3s/7IWWJfT6X/69sYtWQX/V5LISE1i2vFZVV+3qHvf2TZzlPW3Ba/nH3eMlTcYTfRPuwKf92QTsbZq/V6zR7tWzD/t8F3fF5ubi67du3C0dGRq1evsnPnTpycnEhOTmbevHl89tlnt5yTmZnJtm3bKCgoICgoiGnTpt3yaNLhw4dJT0+nffv29OnTh6+//pqIiAimTp3Kjh076Ny5M/HxNU+pmJiYSHx8PCNGjGDevHmUlJTg7OzMc889R0xMDGvXrqWsrIzCwkLS09N59dVX2bVrF76+vly+fPuVXg8dOkRaWpp1hPaKFSvw8fHh2rVrREZGMnr0aMrLy5kyZYo13suXL+Pg4MCECRNYs2YNM2bMIDk5GZPJZO1eMBql1ApgGHBBa92zmv2xwL+AbMumz7XWLzdagKJRuDo7Mnfwz49fTY3pwqxPjtAvwJd3Hw/Dq7kzI0M78MQHe3ku8TABrT14bXQILd2akbTve17blMnyf2fzTL/OdG/Xgm9zr/Bm8gnKyjVtvVwZ9mB767XLyzVKyRiNO2GnFbbcYTe2sWPH4uhoHtySn5/PxIkTOXHiBEopSkpKqj1n6NChuLi44OLiQuvWrTl//jx+fn5VjomKirJu69WrFzk5OXh4eNClSxdrJRkfH1/lbrZCcXExX331FW+88Qaenp707t2bzZs3M2zYMFJSUli9ejUAjo6OeHl5sXr1asaOHYuvr7mfz8fn9n/wRUVFVXmc6p133mHt2rUAnDlzhhMnTnDx4kX69+9vPa7iupMnT2bEiBHMmDGDFStW8NRTNp7QoXargHeB1bUcs1NrXX1Th2iSRoZ2oNN9bpj8WlpHkkf6+7BsYgQnLxQyIboTzZzM2wcGt2V/zmXeSv6OhRszrdcYGtKOrIuFLNyYSUxgKxZvy2JLxjm+v1yEV3NnenX05one91cZDS+qZ6cV9r3Rh/1L7oQbiru7u/X7l156ibi4ONauXUtOTg6xsbHVnuPi8vOE/Y6OjpSWlv6iY2qyefNmrly5QkhICABFRUU0b968xubzmjg5OVFeXg6Ym66Li4ut+yq/79TUVJKTk9m9ezdubm7ExsbWOsFNx44dadOmDSkpKezbt481a9bcUVyNSWu9Qynlb+s4hLEopQjvdOvv2f6BregfeGtrUaS/D2ueieZ03k9cKryBo4MDJj8vdmfl8fiyvcQtSuVSYTFxQa14pEcbLhUUszvrEk+t2s/D3VrzwtDuPNDK3CWltWbhpkx2Z+Xx+hgTQW09rdt3nLhEj3YtaOVZzaIgTZj99mE7uoBzc1tHck/Kz8+nQwfzNICrVq2q9+sHBQVx6tQpcnJyAPj444+rPS4xMZFly5aRk5NDTk4O2dnZbNmyhaKiIgYMGEBCQgIAZWVl5Ofn8/DDD/Ppp5+Sl5cHYG0S9/f35+DBgwCsX7++xhaD/Px8vL29cXNzIzMzkz179gAQHR3Njh07yM7OrnJdgGeeeYYJEyZUaaGwY79SSh1RSm1UShnnr0lhOJ3ucye8kw+9OrZEKcVDXX0Z3LMt14rLWPx4GCufimLu4O787zgTqbPjeGFId/ZlX2bgmzt4cd23fH3yEq9+eYz3t5/iu/MFPLr4a97bnsXR3Cv8IfEwE1fs4+kP91NcWm7rt9qo7LPCrphHXPo+bOL5559n7ty5hIaG3tEdcV01b96cJUuWMGjQIMLDw/H09MTLq+riBUVFRWzatImhQ4dat7m7u9O3b182bNjA22+/zbZt2wgJCSE8PJyMjAyCg4N54YUXiImJwWQyMWvWLACmTJnC9u3bMZlM7N69u8pddWWDBg2itLSU7t27M2fOHKKjowFo1aoVS5cuZdSoUZhMJsaPH289Z/jw4RQWFhq9ObwuDgGdtNYm4O/AupoOVEr9Til1QCl1QAbZiQpvPxbKrrkDGPpg1TWxmzk5MKV/F1JnxzI+siOJ+87wxLK9LP93NpMe8mf77Dh6dWzJwo2ZDH/3azannWNUaAeO5ubzxpbvSM44z/P/PMLpvJ8AyDh7lZfWpTHu/d3M/vQI10vKqgvHLimjjdyLiIjQBw4cqP2gpCfMC4LX5zqmBnHs2DG6d2/gOXftQGFhIR4eHmitmT59OgEBAcycOdPWYd2xAwcOMHPmTHbu3HlX16nuc6GUOqi1vv1zdHVkaRL/orpBZ9UcmwNEaK0v1XZcnfJZiEquXi/h8PdXuFZcxsDgNtZBaWcuF7E/5zJBbT0Jbu/F3M+PkrjvjPU8Xw8XpvTrzJvJ3+GoFF3beHI09wpR/j4snxSJh8vPPcC7Tl5icepJ5g3pTnB7G65kVkld8tl++7CbeP/1ve6DDz7gww8/pLi4mNDQUKZOnWrrkO7YwoULSUhIMHTfdV0ppdoC57XWWikVhbl1Ls/GYYkmqIWrMzHV9I939HGjo4+b9fVLw3rg5OBAWKeWdG/Xgskr9/O3jZmEd/LmvQnhtPJ04V/f/MCsT47Qe0EyYZ28eaCVByVl5fxj3/doDc/+4zAbnu2Lu8utVeG14jKe/nA/hTdKifQ3TwMb3snHpv3m9nmHvTjavKj5Y/b/i/BmcoctqtPQd9hKqUQgFvAFzgPzAWcArfV7Sqk/ANOAUuAaMEtrfdsZcOQOWzSWc/nX2ZJxjnGRHXFx+nm8yP6cy6z/5iz7cy6T++M1Cm+UMibcjyEhbXn6wwP89sH2jA7340ZJGfnXSvBxb0ZMYCtmfnKEL46eJex+b9J+yOeGpb/8ke6tmRbblfBO9fuUUtO+w27iz2AL0Zi01jU/7G7e/y7mx76EMKS2Xq78VzXznpvvjn+uL0rKyq1TrU6P7cq7206y/sjZKue0dHPmSlEJswcGMT2uK8Wl5aSdzWdb5gU+2nOa0Qm7GBnagReHduc+D5cq1844exWv5s74+1Y/FuZu2F+FrbX5OWx5BlsIIcQdcq60MtmffhPIoJ5tuVFaTjNHB7yaO3Ps3FX+b89pOvq48d+xDwDmgXFh93sTdr8302If4L3ULBK2Z5F6/AKvPNqTKH8fXtt0nI1p/6HIMtNb19YeDAxuw+Ce7Qhu36JeJoixvwq7pAjKiqUPWwghxF1RStGzQ9VBZ/ff58bA4LY1nuPWzIlZvwnit6b2/M+nR/jDPw6bJ4/RMDrcj75dfblYcJ3N6ed5b/spFm/L4q/Dg5n4kP9dx2uHFfZ18IsCb39bRyKEEOIeFdDGk8+mPcT7O06Rea6AWb8OpHOlZvBJfTpz+adikjPO0yfAt5Yr1Z39PYftfh88s0UWRG8gcXFxbN68ucq2t956i2nTptV4TmxsLBUDi4YMGcKVK1duOeYvf/kLixYtqvVnr1u3joyMDOvrP//5zyQnJ99B9LWbMWMGHTp0sM5qJoQQd8PJ0YHpcV35e3xolcq6go97M8ZFdqRDy/qZ5Mv+KmzRoOLj40lKSqqyLSkpqdYFOCr76quvaNmy5S/62TdX2C+//DKPPPLIL7rWzW5ehrOhNMREMkIIAVJhi5uMGTOGL7/80jqfdk5ODmfPnqVfv35MmzaNiIgIgoODmT9/frXn+/v7c+mSeS6NBQsWEBgYSN++fa1LcIL5GevIyEhMJhOjR4+mqKiIXbt2sX79embPnk2vXr3Iysqqsuzl1q1bCQ0NJSQkhMmTJ3Pjxg3rz5s/fz5hYWGEhISQmZl5a1DIMpxCCPtnf33Y95KNc+Dct/V7zbYhMHhhjbt9fHyIiopi48aNjBgxgqSkJMaNG4dSigULFuDj40NZWRkDBgzg6NGjPPjgg9Ve5+DBgyQlJfHNN99QWlpKWFgY4eHhAIwaNYopU6YA8OKLL7J8+XKeffZZhg8fzrBhwxgzZkyVa12/fp1JkyaxdetWAgMDefLJJ0lISGDGjBkA+Pr6cujQIZYsWcKiRYtYtmzZLfHIMpxCCHsnd9jiFpWbxSs3h3/yySeEhYURGhpKenp6lebrm+3cuZORI0fi5uZGixYtGD58uHVfWloa/fr1IyQkhDVr1pCenl5rPMePH6dz584EBgYCMHHiRHbs2GHdP2rUKADCw8OtC4ZUVrEM56OPPkqLFi2sy3ACpKSkWPvnK5bhTElJqZdlOE0mE9HR0dZlOPfs2VPjMpwVS4HawTKcQggbkTtsI6vlTrghjRgxgpkzZ3Lo0CGKiooIDw8nOzubRYsWsX//fry9vZk0aVKtS0vWZtKkSaxbtw6TycSqVatITU29q3grluisaXlOWYZTCNEUyB22uIWHhwdxcXFMnjzZend99epV3N3d8fLy4vz582zcuLHWa/Tv359169Zx7do1CgoK2LBhg3VfQUEB7dq1o6SkpErl5OnpSUFBwS3XCgoKIicnh5MnTwLw0UcfERMTU+f3I8twCiGaAqmwRbXi4+M5cuSItcI2mUyEhobSrVs3Hn/8cfr06VPr+WFhYYwfPx6TycTgwYOJjIy07nvllVfo3bs3ffr0oVu3btbtjz32GK+//jqhoaFkZWVZt7u6urJy5UrGjh1LSEgIDg4O/P73v6/T+5BlOIUQTYV9Lv7RhMniH/em2y3D2RjLazaEez2fhairprv4hxBNSFNahlMI0XCkSVwIG5szZw6nT5+mb9++tg5FCGFgUmELIYQQdkAqbAMy2rgCYVvyeRBCgFTYhuPq6kpeXp78khaAubLOy8vD1dXV1qEIIWxMBp0ZjJ+fH7m5uTKXtLBydXXFz8/P1mEIIWxMKmyDcXZ2rjLFpRBCCAHSJC6EEELYBamwhRBCCDsgFbYQQghhBww3NalS6iJwug6H+gKXGjicOyUx1Y0RYwJjxlVbTJ201oZeOLuO+Wxv5W5LRoxLYqqb28V023w2XIVdV0qpA0abR1liqhsjxgTGjMuIMdU3I75HI8YExoxLYqqb+ohJmsSFEEIIOyAVthBCCGEH7LnCXmrrAKohMdWNEWMCY8ZlxJjqmxHfoxFjAmPGJTHVzV3HZLd92EIIIcS9xJ7vsIUQQoh7ht1V2EqpQUqp40qpk0qpOTaKoaNSaptSKkMpla6U+qNlu49SaotS6oTlX28bxOaolDqslPrC8rqzUmqvpbw+Vko1s0FMLZVS/1RKZSqljimlfmXrslJKzbT836UppRKVUq62KCul1Aql1AWlVFqlbdWWjTJ7xxLfUaVUWEPH19Akn28bm6Hy2Yi5bInL5vncGLlsVxW2UsoRWAwMBnoA8UqpHjYIpRT4k9a6BxANTLfEMQfYqrUOALZaXje2PwLHKr1+DXhTa90V+BF42gYxvQ1s0lp3A0yW+GxWVkqpDsBzQITWuifgCDyGbcpqFTDopm01lc1gIMDy9TsgoRHiazCSz3VitHw2VC6DofJ5FQ2dy1pru/kCfgVsrvR6LjDXAHH9C/g1cBxoZ9nWDjjeyHH4WT4UDwNfAArzg/pO1ZVfI8XkBWRjGS9RabvNygroAJwBfDAvgPMFMNBWZQX4A2m3KxvgfSC+uuPs8Uvy+bZxGCqfjZjLlp9pmHxu6Fy2qztsfv6PqZBr2WYzSil/IBTYC7TRWv/Hsusc0KaRw3kLeB4ot7y+D7iitS61vLZFeXUGLgIrLU17y5RS7tiwrLTWPwCLgO+B/wD5wEFsX1YVaiobw33+75Lh3o/kc60Ml8tg+Hyu11y2twrbUJRSHsBnwAyt9dXK+7T5z6ZGG4KvlBoGXNBaH2ysn1lHTkAYkKC1DgV+4qYmMxuUlTcwAvMvoPaAO7c2ZRlCY5fNvUzy+bYMl8tgP/lcH2VjbxX2D0DHSq/9LNsanVLKGXNyr9Faf27ZfF4p1c6yvx1woRFD6gMMV0rlAEmYm9HeBloqpSrWPbdFeeUCuVrrvZbX/8Sc9LYsq0eAbK31Ra11CfA55vKzdVlVqKlsDPP5ryeGeT+Sz3VixFwGY+dzveayvVXY+4EAy+i/ZpgHFqxv7CCUUgpYDhzTWr9Radd6YKLl+4mY+8IahdZ6rtbaT2vtj7lcUrTWTwDbgDG2iMkS1zngjFIqyLJpAJCBDcsKc9NZtFLKzfJ/WRGTTcuqkprKZj3wpGWEaTSQX6m5zR5JPtfAiPls0FwGY+dz/eZyYw4OqKdO/SHAd0AW8IKNYuiLuWnjKPCN5WsI5j6mrcAJIBnwsVF8scAXlu+7APuAk8CngIsN4ukFHLCU1zrA29ZlBfwVyATSgI8AF1uUFZCIud+tBPMdzNM1lQ3mQUeLLZ/9bzGPim30z1c9v3/J59vHZ5h8NmIuW+KyeT43Ri7LTGdCCCGEHbC3JnEhhBDiniQVthBCCGEHpMIWQggh7IBU2EIIIYQdkApbCCGEsANSYQshhBB2QCpsIYQQwg5IhS2EEELYgf8H0ErCOXQxCP0AAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 576x576 with 2 Axes>"
       ]
@@ -2969,8 +2993,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "313/313 [==============================] - 4s 13ms/step - loss: 1.6613 - accuracy: 0.4116\n",
-      "Accuracy on test dataset: 0.4115999937057495\n"
+      "313/313 [==============================] - 4s 13ms/step - loss: 1.8141 - accuracy: 0.3872\n",
+      "Accuracy on test dataset: 0.3871999979019165\n"
      ]
     }
    ],
@@ -3084,205 +3108,205 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "1/1 [==============================] - 1s 856ms/step - loss: 2.3026 - accuracy: 0.0500\n",
+      "1/1 [==============================] - 1s 919ms/step - loss: 2.3026 - accuracy: 0.0500\n",
       "Epoch 2/100\n",
-      "1/1 [==============================] - 0s 46ms/step - loss: 2.3025 - accuracy: 0.1500\n",
-      "Epoch 3/100\n",
       "1/1 [==============================] - 0s 14ms/step - loss: 2.3025 - accuracy: 0.1500\n",
+      "Epoch 3/100\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 2.3025 - accuracy: 0.1500\n",
       "Epoch 4/100\n",
-      "1/1 [==============================] - 0s 71ms/step - loss: 2.3024 - accuracy: 0.1500\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.3024 - accuracy: 0.1500\n",
       "Epoch 5/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.3023 - accuracy: 0.1500\n",
+      "1/1 [==============================] - 0s 13ms/step - loss: 2.3023 - accuracy: 0.1500\n",
       "Epoch 6/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.3023 - accuracy: 0.1500\n",
+      "1/1 [==============================] - 0s 12ms/step - loss: 2.3023 - accuracy: 0.1500\n",
       "Epoch 7/100\n",
-      "1/1 [==============================] - 0s 62ms/step - loss: 2.3022 - accuracy: 0.1500\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 2.3022 - accuracy: 0.1500\n",
       "Epoch 8/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.3022 - accuracy: 0.1500\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.3022 - accuracy: 0.1500\n",
       "Epoch 9/100\n",
-      "1/1 [==============================] - 0s 66ms/step - loss: 2.3021 - accuracy: 0.1500\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.3021 - accuracy: 0.1500\n",
       "Epoch 10/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.3020 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 23ms/step - loss: 2.3020 - accuracy: 0.2000\n",
       "Epoch 11/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.3020 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.3020 - accuracy: 0.2000\n",
       "Epoch 12/100\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 2.3019 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.3019 - accuracy: 0.2000\n",
       "Epoch 13/100\n",
       "1/1 [==============================] - 0s 16ms/step - loss: 2.3019 - accuracy: 0.2000\n",
       "Epoch 14/100\n",
-      "1/1 [==============================] - 0s 12ms/step - loss: 2.3018 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 2.3018 - accuracy: 0.2000\n",
       "Epoch 15/100\n",
-      "1/1 [==============================] - 0s 53ms/step - loss: 2.3017 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 18ms/step - loss: 2.3017 - accuracy: 0.2000\n",
       "Epoch 16/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.3017 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 27ms/step - loss: 2.3017 - accuracy: 0.2000\n",
       "Epoch 17/100\n",
-      "1/1 [==============================] - 0s 80ms/step - loss: 2.3016 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 2.3016 - accuracy: 0.2000\n",
       "Epoch 18/100\n",
-      "1/1 [==============================] - 0s 17ms/step - loss: 2.3016 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.3016 - accuracy: 0.2000\n",
       "Epoch 19/100\n",
-      "1/1 [==============================] - 0s 68ms/step - loss: 2.3015 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 15ms/step - loss: 2.3015 - accuracy: 0.2000\n",
       "Epoch 20/100\n",
-      "1/1 [==============================] - 0s 18ms/step - loss: 2.3014 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 2.3014 - accuracy: 0.2000\n",
       "Epoch 21/100\n",
-      "1/1 [==============================] - 0s 17ms/step - loss: 2.3014 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 16ms/step - loss: 2.3014 - accuracy: 0.2000\n",
       "Epoch 22/100\n",
-      "1/1 [==============================] - 0s 44ms/step - loss: 2.3013 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 15ms/step - loss: 2.3013 - accuracy: 0.2000\n",
       "Epoch 23/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.3013 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 15ms/step - loss: 2.3013 - accuracy: 0.2000\n",
       "Epoch 24/100\n",
-      "1/1 [==============================] - 0s 18ms/step - loss: 2.3012 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 2.3012 - accuracy: 0.2000\n",
       "Epoch 25/100\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 2.3011 - accuracy: 0.2000\n",
-      "Epoch 26/100\n",
       "1/1 [==============================] - 0s 17ms/step - loss: 2.3011 - accuracy: 0.2000\n",
+      "Epoch 26/100\n",
+      "1/1 [==============================] - 0s 16ms/step - loss: 2.3011 - accuracy: 0.2000\n",
       "Epoch 27/100\n",
-      "1/1 [==============================] - 0s 19ms/step - loss: 2.3010 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 2.3010 - accuracy: 0.2000\n",
       "Epoch 28/100\n",
-      "1/1 [==============================] - 0s 48ms/step - loss: 2.3010 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.3010 - accuracy: 0.2000\n",
       "Epoch 29/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.3009 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 16ms/step - loss: 2.3009 - accuracy: 0.2000\n",
       "Epoch 30/100\n",
-      "1/1 [==============================] - 0s 20ms/step - loss: 2.3008 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 2.3008 - accuracy: 0.2000\n",
       "Epoch 31/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.3008 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 15ms/step - loss: 2.3008 - accuracy: 0.2000\n",
       "Epoch 32/100\n",
       "1/1 [==============================] - 0s 14ms/step - loss: 2.3007 - accuracy: 0.2000\n",
       "Epoch 33/100\n",
-      "1/1 [==============================] - 0s 22ms/step - loss: 2.3007 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 21ms/step - loss: 2.3007 - accuracy: 0.2000\n",
       "Epoch 34/100\n",
-      "1/1 [==============================] - 0s 42ms/step - loss: 2.3006 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 2.3006 - accuracy: 0.2000\n",
       "Epoch 35/100\n",
-      "1/1 [==============================] - 0s 19ms/step - loss: 2.3006 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.3006 - accuracy: 0.2000\n",
       "Epoch 36/100\n",
-      "1/1 [==============================] - 0s 54ms/step - loss: 2.3005 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 15ms/step - loss: 2.3005 - accuracy: 0.2000\n",
       "Epoch 37/100\n",
-      "1/1 [==============================] - 0s 18ms/step - loss: 2.3004 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 2.3004 - accuracy: 0.2000\n",
       "Epoch 38/100\n",
-      "1/1 [==============================] - 0s 13ms/step - loss: 2.3004 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.3004 - accuracy: 0.2000\n",
       "Epoch 39/100\n",
-      "1/1 [==============================] - 0s 52ms/step - loss: 2.3003 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.3003 - accuracy: 0.2000\n",
       "Epoch 40/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.3003 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.3003 - accuracy: 0.2000\n",
       "Epoch 41/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.3002 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 2.3002 - accuracy: 0.2000\n",
       "Epoch 42/100\n",
-      "1/1 [==============================] - 0s 56ms/step - loss: 2.3001 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 13ms/step - loss: 2.3001 - accuracy: 0.2000\n",
       "Epoch 43/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.3001 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.3001 - accuracy: 0.2000\n",
       "Epoch 44/100\n",
-      "1/1 [==============================] - 0s 18ms/step - loss: 2.3000 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 2.3000 - accuracy: 0.2000\n",
       "Epoch 45/100\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 2.3000 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 13ms/step - loss: 2.3000 - accuracy: 0.2000\n",
       "Epoch 46/100\n",
-      "1/1 [==============================] - 0s 17ms/step - loss: 2.2999 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 12ms/step - loss: 2.2999 - accuracy: 0.2000\n",
       "Epoch 47/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.2998 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 18ms/step - loss: 2.2998 - accuracy: 0.2000\n",
       "Epoch 48/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2998 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 2.2998 - accuracy: 0.2000\n",
       "Epoch 49/100\n",
-      "1/1 [==============================] - 0s 25ms/step - loss: 2.2997 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 21ms/step - loss: 2.2997 - accuracy: 0.2000\n",
       "Epoch 50/100\n",
-      "1/1 [==============================] - 0s 17ms/step - loss: 2.2997 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 13ms/step - loss: 2.2997 - accuracy: 0.2000\n",
       "Epoch 51/100\n",
-      "1/1 [==============================] - 0s 12ms/step - loss: 2.2996 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 2.2996 - accuracy: 0.2000\n",
       "Epoch 52/100\n",
-      "1/1 [==============================] - 0s 41ms/step - loss: 2.2995 - accuracy: 0.2000\n",
-      "Epoch 53/100\n",
       "1/1 [==============================] - 0s 14ms/step - loss: 2.2995 - accuracy: 0.2000\n",
+      "Epoch 53/100\n",
+      "1/1 [==============================] - 0s 16ms/step - loss: 2.2995 - accuracy: 0.2000\n",
       "Epoch 54/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2994 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 19ms/step - loss: 2.2994 - accuracy: 0.2000\n",
       "Epoch 55/100\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 2.2994 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.2994 - accuracy: 0.2000\n",
       "Epoch 56/100\n",
       "1/1 [==============================] - 0s 14ms/step - loss: 2.2993 - accuracy: 0.2000\n",
       "Epoch 57/100\n",
-      "1/1 [==============================] - 0s 19ms/step - loss: 2.2992 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 15ms/step - loss: 2.2992 - accuracy: 0.2000\n",
       "Epoch 58/100\n",
-      "1/1 [==============================] - 0s 49ms/step - loss: 2.2992 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 2.2992 - accuracy: 0.2000\n",
       "Epoch 59/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.2991 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 18ms/step - loss: 2.2991 - accuracy: 0.2000\n",
       "Epoch 60/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.2991 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2991 - accuracy: 0.2000\n",
       "Epoch 61/100\n",
-      "1/1 [==============================] - 0s 51ms/step - loss: 2.2990 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 19ms/step - loss: 2.2990 - accuracy: 0.2000\n",
       "Epoch 62/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.2989 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 2.2989 - accuracy: 0.2000\n",
       "Epoch 63/100\n",
-      "1/1 [==============================] - 0s 13ms/step - loss: 2.2989 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 22ms/step - loss: 2.2989 - accuracy: 0.2000\n",
       "Epoch 64/100\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 2.2988 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 16ms/step - loss: 2.2988 - accuracy: 0.2000\n",
       "Epoch 65/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2988 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 2.2988 - accuracy: 0.2000\n",
       "Epoch 66/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2987 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2987 - accuracy: 0.2000\n",
       "Epoch 67/100\n",
-      "1/1 [==============================] - 0s 51ms/step - loss: 2.2986 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.2987 - accuracy: 0.2000\n",
       "Epoch 68/100\n",
-      "1/1 [==============================] - 0s 19ms/step - loss: 2.2986 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 2.2986 - accuracy: 0.2000\n",
       "Epoch 69/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.2985 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 19ms/step - loss: 2.2985 - accuracy: 0.2000\n",
       "Epoch 70/100\n",
-      "1/1 [==============================] - 0s 51ms/step - loss: 2.2985 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 21ms/step - loss: 2.2985 - accuracy: 0.2000\n",
       "Epoch 71/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2984 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 2.2984 - accuracy: 0.2000\n",
       "Epoch 72/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.2984 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 16ms/step - loss: 2.2984 - accuracy: 0.2000\n",
       "Epoch 73/100\n",
-      "1/1 [==============================] - 0s 52ms/step - loss: 2.2983 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 13ms/step - loss: 2.2983 - accuracy: 0.2000\n",
       "Epoch 74/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.2982 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 54ms/step - loss: 2.2982 - accuracy: 0.2000\n",
       "Epoch 75/100\n",
-      "1/1 [==============================] - 0s 13ms/step - loss: 2.2982 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2982 - accuracy: 0.2000\n",
       "Epoch 76/100\n",
-      "1/1 [==============================] - 0s 13ms/step - loss: 2.2981 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2981 - accuracy: 0.2000\n",
       "Epoch 77/100\n",
-      "1/1 [==============================] - 0s 18ms/step - loss: 2.2981 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.2981 - accuracy: 0.2000\n",
       "Epoch 78/100\n",
-      "1/1 [==============================] - 0s 13ms/step - loss: 2.2980 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 20ms/step - loss: 2.2980 - accuracy: 0.2000\n",
       "Epoch 79/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.2979 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2979 - accuracy: 0.2000\n",
       "Epoch 80/100\n",
-      "1/1 [==============================] - 0s 18ms/step - loss: 2.2979 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 2.2979 - accuracy: 0.2000\n",
       "Epoch 81/100\n",
-      "1/1 [==============================] - 0s 20ms/step - loss: 2.2978 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 18ms/step - loss: 2.2978 - accuracy: 0.2000\n",
       "Epoch 82/100\n",
-      "1/1 [==============================] - 0s 17ms/step - loss: 2.2978 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 18ms/step - loss: 2.2978 - accuracy: 0.2000\n",
       "Epoch 83/100\n",
-      "1/1 [==============================] - 0s 39ms/step - loss: 2.2977 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 2.2977 - accuracy: 0.2000\n",
       "Epoch 84/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.2976 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 19ms/step - loss: 2.2976 - accuracy: 0.2000\n",
       "Epoch 85/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2976 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 18ms/step - loss: 2.2976 - accuracy: 0.2000\n",
       "Epoch 86/100\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 2.2975 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 2.2975 - accuracy: 0.2000\n",
       "Epoch 87/100\n",
-      "1/1 [==============================] - 0s 16ms/step - loss: 2.2975 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 20ms/step - loss: 2.2975 - accuracy: 0.2000\n",
       "Epoch 88/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2974 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 20ms/step - loss: 2.2974 - accuracy: 0.2000\n",
       "Epoch 89/100\n",
-      "1/1 [==============================] - 0s 47ms/step - loss: 2.2974 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 2.2974 - accuracy: 0.2000\n",
       "Epoch 90/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.2973 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2973 - accuracy: 0.2000\n",
       "Epoch 91/100\n",
-      "1/1 [==============================] - 0s 17ms/step - loss: 2.2972 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 12ms/step - loss: 2.2972 - accuracy: 0.2000\n",
       "Epoch 92/100\n",
-      "1/1 [==============================] - 0s 48ms/step - loss: 2.2972 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 54ms/step - loss: 2.2972 - accuracy: 0.2000\n",
       "Epoch 93/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2971 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2971 - accuracy: 0.2000\n",
       "Epoch 94/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2971 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 17ms/step - loss: 2.2971 - accuracy: 0.2000\n",
       "Epoch 95/100\n",
-      "1/1 [==============================] - 0s 56ms/step - loss: 2.2970 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 2.2970 - accuracy: 0.2000\n",
       "Epoch 96/100\n",
-      "1/1 [==============================] - 0s 15ms/step - loss: 2.2969 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 16ms/step - loss: 2.2969 - accuracy: 0.2000\n",
       "Epoch 97/100\n",
       "1/1 [==============================] - 0s 17ms/step - loss: 2.2969 - accuracy: 0.2000\n",
       "Epoch 98/100\n",
-      "1/1 [==============================] - 0s 50ms/step - loss: 2.2968 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 2.2968 - accuracy: 0.2000\n",
       "Epoch 99/100\n",
-      "1/1 [==============================] - 0s 14ms/step - loss: 2.2968 - accuracy: 0.2000\n",
+      "1/1 [==============================] - 0s 15ms/step - loss: 2.2968 - accuracy: 0.2000\n",
       "Epoch 100/100\n",
-      "1/1 [==============================] - 0s 71ms/step - loss: 2.2967 - accuracy: 0.2000\n"
+      "1/1 [==============================] - 0s 14ms/step - loss: 2.2967 - accuracy: 0.2000\n"
      ]
     }
    ],
@@ -3295,408 +3319,61 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "H56FtQxwvXaW"
+    "id": "5zgbDpH0sH7u"
    },
    "source": [
-    "# 7. Regularization\n",
+    "# 7. TensorBoard\n",
     "\n",
-    "## 7.1 L2 Regularization"
+    "TensorBoard is a great tool to observe variables during training (especially useful for models training a long time, like above model).\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
-    "id": "rScnNsBHhLka"
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "id": "FP5b7DGle7to",
+    "outputId": "9be8f4da-e6a9-45ab-aa2c-0c5a4e994055"
    },
+   "outputs": [],
    "source": [
-    "It is most common to use a single, global $ L2 $ regularization strength that is cross-validated."
+    "# Load the TensorBoard notebook extension\n",
+    "%load_ext tensorboard"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {
-    "id": "vhDjE0kQvXaX"
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:36: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n"
-     ]
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
     },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100\n",
-      "63/63 [==============================] - 7s 96ms/step - loss: 13.0181 - accuracy: 0.2160 - val_loss: 13.2011 - val_accuracy: 0.1950\n",
-      "Epoch 2/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 12.1518 - accuracy: 0.2840 - val_loss: 12.0171 - val_accuracy: 0.2470\n",
-      "Epoch 3/100\n",
-      "63/63 [==============================] - 6s 97ms/step - loss: 11.4058 - accuracy: 0.3178 - val_loss: 11.2302 - val_accuracy: 0.2820\n",
-      "Epoch 4/100\n",
-      "63/63 [==============================] - 6s 99ms/step - loss: 10.7203 - accuracy: 0.3207 - val_loss: 10.8606 - val_accuracy: 0.2100\n",
-      "Epoch 5/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 10.0897 - accuracy: 0.3298 - val_loss: 9.8507 - val_accuracy: 0.3080\n",
-      "Epoch 6/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 9.4855 - accuracy: 0.3495 - val_loss: 9.2743 - val_accuracy: 0.3090\n",
-      "Epoch 7/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 8.9464 - accuracy: 0.3347 - val_loss: 8.7243 - val_accuracy: 0.3240\n",
-      "Epoch 8/100\n",
-      "63/63 [==============================] - 5s 86ms/step - loss: 8.4246 - accuracy: 0.3483 - val_loss: 8.3198 - val_accuracy: 0.3170\n",
-      "Epoch 9/100\n",
-      "63/63 [==============================] - 6s 101ms/step - loss: 7.9513 - accuracy: 0.3485 - val_loss: 7.8701 - val_accuracy: 0.3100\n",
-      "Epoch 10/100\n",
-      "63/63 [==============================] - 5s 87ms/step - loss: 7.4930 - accuracy: 0.3705 - val_loss: 7.3292 - val_accuracy: 0.3430\n",
-      "Epoch 11/100\n",
-      "63/63 [==============================] - 6s 95ms/step - loss: 7.0981 - accuracy: 0.3647 - val_loss: 7.0211 - val_accuracy: 0.3320\n",
-      "Epoch 12/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 6.7173 - accuracy: 0.3765 - val_loss: 6.7382 - val_accuracy: 0.3330\n",
-      "Epoch 13/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 6.3762 - accuracy: 0.3663 - val_loss: 6.3951 - val_accuracy: 0.2830\n",
-      "Epoch 14/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 6.0660 - accuracy: 0.3645 - val_loss: 6.0316 - val_accuracy: 0.3210\n",
-      "Epoch 15/100\n",
-      "63/63 [==============================] - 5s 87ms/step - loss: 5.7584 - accuracy: 0.3750 - val_loss: 5.9498 - val_accuracy: 0.2510\n",
-      "Epoch 16/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 5.4748 - accuracy: 0.3808 - val_loss: 5.4712 - val_accuracy: 0.3390\n",
-      "Epoch 17/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 5.2459 - accuracy: 0.3745 - val_loss: 5.2130 - val_accuracy: 0.3630\n",
-      "Epoch 18/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 5.0009 - accuracy: 0.3790 - val_loss: 5.0301 - val_accuracy: 0.3470\n",
-      "Epoch 19/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 4.7974 - accuracy: 0.3778 - val_loss: 5.0356 - val_accuracy: 0.3010\n",
-      "Epoch 20/100\n",
-      "63/63 [==============================] - 6s 87ms/step - loss: 4.5918 - accuracy: 0.3850 - val_loss: 4.8138 - val_accuracy: 0.2510\n",
-      "Epoch 21/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 4.4131 - accuracy: 0.3860 - val_loss: 4.5613 - val_accuracy: 0.3380\n",
-      "Epoch 22/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 4.2511 - accuracy: 0.3877 - val_loss: 4.2133 - val_accuracy: 0.3590\n",
-      "Epoch 23/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 4.0962 - accuracy: 0.3840 - val_loss: 4.3263 - val_accuracy: 0.3050\n",
-      "Epoch 24/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 3.9479 - accuracy: 0.3837 - val_loss: 4.1604 - val_accuracy: 0.3210\n",
-      "Epoch 25/100\n",
-      "63/63 [==============================] - 6s 101ms/step - loss: 3.8269 - accuracy: 0.3913 - val_loss: 3.8741 - val_accuracy: 0.3390\n",
-      "Epoch 26/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 3.6984 - accuracy: 0.3842 - val_loss: 4.0194 - val_accuracy: 0.2720\n",
-      "Epoch 27/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 3.6033 - accuracy: 0.3762 - val_loss: 3.7121 - val_accuracy: 0.3350\n",
-      "Epoch 28/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 3.5039 - accuracy: 0.3873 - val_loss: 3.6310 - val_accuracy: 0.3140\n",
-      "Epoch 29/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 3.3895 - accuracy: 0.3873 - val_loss: 3.6758 - val_accuracy: 0.2780\n",
-      "Epoch 30/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 3.3008 - accuracy: 0.3965 - val_loss: 3.4139 - val_accuracy: 0.3260\n",
-      "Epoch 31/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 3.2209 - accuracy: 0.3960 - val_loss: 3.6432 - val_accuracy: 0.2830\n",
-      "Epoch 32/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 3.1354 - accuracy: 0.4013 - val_loss: 3.2672 - val_accuracy: 0.3400\n",
-      "Epoch 33/100\n",
-      "63/63 [==============================] - 6s 95ms/step - loss: 3.0679 - accuracy: 0.3873 - val_loss: 3.4932 - val_accuracy: 0.2670\n",
-      "Epoch 34/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 3.0092 - accuracy: 0.3842 - val_loss: 3.4149 - val_accuracy: 0.2750\n",
-      "Epoch 35/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 2.9387 - accuracy: 0.3963 - val_loss: 3.0612 - val_accuracy: 0.3430\n",
-      "Epoch 36/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 2.8899 - accuracy: 0.4025 - val_loss: 2.9881 - val_accuracy: 0.3310\n",
-      "Epoch 37/100\n",
-      "63/63 [==============================] - 5s 87ms/step - loss: 2.8424 - accuracy: 0.3947 - val_loss: 2.9952 - val_accuracy: 0.3140\n",
-      "Epoch 38/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.7795 - accuracy: 0.3913 - val_loss: 3.1798 - val_accuracy: 0.2870\n",
-      "Epoch 39/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 2.7378 - accuracy: 0.3915 - val_loss: 2.8324 - val_accuracy: 0.3590\n",
-      "Epoch 40/100\n",
-      "63/63 [==============================] - 6s 96ms/step - loss: 2.7003 - accuracy: 0.3923 - val_loss: 2.7913 - val_accuracy: 0.3480\n",
-      "Epoch 41/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 2.6558 - accuracy: 0.3938 - val_loss: 2.9557 - val_accuracy: 0.2860\n",
-      "Epoch 42/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 2.6065 - accuracy: 0.3930 - val_loss: 2.8128 - val_accuracy: 0.3180\n",
-      "Epoch 43/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 2.5631 - accuracy: 0.4095 - val_loss: 2.7912 - val_accuracy: 0.3540\n",
-      "Epoch 44/100\n",
-      "63/63 [==============================] - 9s 138ms/step - loss: 2.5446 - accuracy: 0.3968 - val_loss: 2.7181 - val_accuracy: 0.3350\n",
-      "Epoch 45/100\n",
-      "63/63 [==============================] - 11s 175ms/step - loss: 2.5097 - accuracy: 0.4035 - val_loss: 2.5991 - val_accuracy: 0.3520\n",
-      "Epoch 46/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 2.4774 - accuracy: 0.3980 - val_loss: 2.6282 - val_accuracy: 0.3430\n",
-      "Epoch 47/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.4469 - accuracy: 0.3997 - val_loss: 2.8327 - val_accuracy: 0.2810\n",
-      "Epoch 48/100\n",
-      "63/63 [==============================] - 5s 87ms/step - loss: 2.4187 - accuracy: 0.4000 - val_loss: 2.8206 - val_accuracy: 0.2760\n",
-      "Epoch 49/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.4080 - accuracy: 0.4035 - val_loss: 2.6159 - val_accuracy: 0.3110\n",
-      "Epoch 50/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 2.3828 - accuracy: 0.4055 - val_loss: 2.6431 - val_accuracy: 0.2970\n",
-      "Epoch 51/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 2.3392 - accuracy: 0.4065 - val_loss: 2.4633 - val_accuracy: 0.3610\n",
-      "Epoch 52/100\n",
-      "63/63 [==============================] - 6s 87ms/step - loss: 2.3308 - accuracy: 0.4117 - val_loss: 2.6086 - val_accuracy: 0.3030\n",
-      "Epoch 53/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 2.3193 - accuracy: 0.4030 - val_loss: 2.5730 - val_accuracy: 0.3050\n",
-      "Epoch 54/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 2.2961 - accuracy: 0.4095 - val_loss: 2.5999 - val_accuracy: 0.3080\n",
-      "Epoch 55/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 2.2812 - accuracy: 0.4033 - val_loss: 2.4191 - val_accuracy: 0.3650\n",
-      "Epoch 56/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 2.2537 - accuracy: 0.4072 - val_loss: 2.5827 - val_accuracy: 0.2960\n",
-      "Epoch 57/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 2.2567 - accuracy: 0.4017 - val_loss: 2.8786 - val_accuracy: 0.2270\n",
-      "Epoch 58/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.2384 - accuracy: 0.4042 - val_loss: 2.3259 - val_accuracy: 0.3660\n",
-      "Epoch 59/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 2.2352 - accuracy: 0.3960 - val_loss: 2.4744 - val_accuracy: 0.3210\n",
-      "Epoch 60/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 2.2107 - accuracy: 0.4008 - val_loss: 2.5989 - val_accuracy: 0.3060\n",
-      "Epoch 61/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 2.1988 - accuracy: 0.4002 - val_loss: 2.8597 - val_accuracy: 0.2500\n",
-      "Epoch 62/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.1779 - accuracy: 0.4100 - val_loss: 2.3780 - val_accuracy: 0.3270\n",
-      "Epoch 63/100\n",
-      "63/63 [==============================] - 6s 95ms/step - loss: 2.1578 - accuracy: 0.4025 - val_loss: 2.5887 - val_accuracy: 0.2920\n",
-      "Epoch 64/100\n",
-      "63/63 [==============================] - 6s 98ms/step - loss: 2.1611 - accuracy: 0.4022 - val_loss: 2.2919 - val_accuracy: 0.3620\n",
-      "Epoch 65/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 2.1421 - accuracy: 0.4065 - val_loss: 2.1934 - val_accuracy: 0.3840\n",
-      "Epoch 66/100\n",
-      "63/63 [==============================] - 6s 98ms/step - loss: 2.1225 - accuracy: 0.4137 - val_loss: 2.2906 - val_accuracy: 0.3780\n",
-      "Epoch 67/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 2.1114 - accuracy: 0.4103 - val_loss: 2.5732 - val_accuracy: 0.2620\n",
-      "Epoch 68/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.1030 - accuracy: 0.4078 - val_loss: 2.3567 - val_accuracy: 0.3350\n",
-      "Epoch 69/100\n",
-      "63/63 [==============================] - 6s 96ms/step - loss: 2.0863 - accuracy: 0.4157 - val_loss: 2.1938 - val_accuracy: 0.3840\n",
-      "Epoch 70/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 2.0790 - accuracy: 0.4108 - val_loss: 2.5503 - val_accuracy: 0.3050\n",
-      "Epoch 71/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 2.0817 - accuracy: 0.4100 - val_loss: 2.4150 - val_accuracy: 0.3000\n",
-      "Epoch 72/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 2.0706 - accuracy: 0.4108 - val_loss: 2.2823 - val_accuracy: 0.3300\n",
-      "Epoch 73/100\n",
-      "63/63 [==============================] - 6s 87ms/step - loss: 2.0592 - accuracy: 0.4148 - val_loss: 2.2665 - val_accuracy: 0.3340\n",
-      "Epoch 74/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 2.0342 - accuracy: 0.4223 - val_loss: 2.1628 - val_accuracy: 0.3720\n",
-      "Epoch 75/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.0361 - accuracy: 0.4162 - val_loss: 2.1333 - val_accuracy: 0.3660\n",
-      "Epoch 76/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.0346 - accuracy: 0.4135 - val_loss: 2.1840 - val_accuracy: 0.3620\n",
-      "Epoch 77/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.0472 - accuracy: 0.4125 - val_loss: 2.2439 - val_accuracy: 0.3370\n",
-      "Epoch 78/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 2.0369 - accuracy: 0.4078 - val_loss: 2.1270 - val_accuracy: 0.3920\n",
-      "Epoch 79/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 2.0165 - accuracy: 0.4182 - val_loss: 2.2412 - val_accuracy: 0.3310\n",
-      "Epoch 80/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 2.0148 - accuracy: 0.4078 - val_loss: 2.3163 - val_accuracy: 0.3190\n",
-      "Epoch 81/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 2.0013 - accuracy: 0.4105 - val_loss: 2.2562 - val_accuracy: 0.3210\n",
-      "Epoch 82/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 2.0057 - accuracy: 0.4103 - val_loss: 2.1052 - val_accuracy: 0.3600\n",
-      "Epoch 83/100\n",
-      "63/63 [==============================] - 6s 92ms/step - loss: 1.9984 - accuracy: 0.4065 - val_loss: 2.3521 - val_accuracy: 0.3040\n",
-      "Epoch 84/100\n",
-      "63/63 [==============================] - 6s 103ms/step - loss: 2.0071 - accuracy: 0.4095 - val_loss: 2.8214 - val_accuracy: 0.2440\n",
-      "Epoch 85/100\n",
-      "63/63 [==============================] - 5s 86ms/step - loss: 1.9809 - accuracy: 0.4112 - val_loss: 2.1729 - val_accuracy: 0.3370\n",
-      "Epoch 86/100\n",
-      "63/63 [==============================] - 6s 96ms/step - loss: 1.9611 - accuracy: 0.4227 - val_loss: 2.1808 - val_accuracy: 0.3400\n",
-      "Epoch 87/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 1.9549 - accuracy: 0.4238 - val_loss: 2.1579 - val_accuracy: 0.3220\n",
-      "Epoch 88/100\n",
-      "63/63 [==============================] - 6s 89ms/step - loss: 1.9649 - accuracy: 0.4145 - val_loss: 2.1404 - val_accuracy: 0.3790\n",
-      "Epoch 89/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 1.9651 - accuracy: 0.4190 - val_loss: 2.0501 - val_accuracy: 0.3910\n",
-      "Epoch 90/100\n",
-      "63/63 [==============================] - 6s 91ms/step - loss: 1.9717 - accuracy: 0.4140 - val_loss: 2.3215 - val_accuracy: 0.2900\n",
-      "Epoch 91/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 1.9553 - accuracy: 0.4182 - val_loss: 2.1125 - val_accuracy: 0.3660\n",
-      "Epoch 92/100\n",
-      "63/63 [==============================] - 6s 87ms/step - loss: 1.9436 - accuracy: 0.4155 - val_loss: 2.1085 - val_accuracy: 0.3620\n",
-      "Epoch 93/100\n",
-      "63/63 [==============================] - 6s 95ms/step - loss: 1.9441 - accuracy: 0.4112 - val_loss: 2.1820 - val_accuracy: 0.3260\n",
-      "Epoch 94/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 1.9377 - accuracy: 0.4255 - val_loss: 2.1216 - val_accuracy: 0.3670\n",
-      "Epoch 95/100\n",
-      "63/63 [==============================] - 6s 88ms/step - loss: 1.9356 - accuracy: 0.4285 - val_loss: 2.1735 - val_accuracy: 0.3260\n",
-      "Epoch 96/100\n",
-      "63/63 [==============================] - 6s 96ms/step - loss: 1.9493 - accuracy: 0.4078 - val_loss: 2.5470 - val_accuracy: 0.2460\n",
-      "Epoch 97/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 1.9324 - accuracy: 0.4090 - val_loss: 2.0640 - val_accuracy: 0.3690\n",
-      "Epoch 98/100\n",
-      "63/63 [==============================] - 6s 90ms/step - loss: 1.9279 - accuracy: 0.4257 - val_loss: 2.2967 - val_accuracy: 0.2970\n",
-      "Epoch 99/100\n",
-      "63/63 [==============================] - 5s 85ms/step - loss: 1.9366 - accuracy: 0.4130 - val_loss: 2.2642 - val_accuracy: 0.3260\n",
-      "Epoch 100/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 1.9155 - accuracy: 0.4162 - val_loss: 2.1090 - val_accuracy: 0.3570\n"
-     ]
-    }
-   ],
+    "id": "EuDr-lyAe9YL",
+    "outputId": "7be105fe-ee50-4101-9640-e2220e16aa96"
+   },
+   "outputs": [],
    "source": [
-    "# Define Network\n",
-    "model = tf.keras.Sequential([\n",
-    "        tf.keras.layers.Flatten(input_shape=(32, 32, 3)),\n",
-    "        tf.keras.layers.Dense(512, \n",
-    "                              kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
-    "        tf.keras.layers.BatchNormalization(),\n",
-    "        tf.keras.layers.Activation(tf.nn.relu),\n",
-    "        tf.keras.layers.Dense(128, \n",
-    "                              kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
-    "        tf.keras.layers.BatchNormalization(),\n",
-    "        tf.keras.layers.Activation(tf.nn.relu),\n",
-    "        tf.keras.layers.Dense(10,  \n",
-    "                              activation=tf.nn.softmax,  \n",
-    "                              kernel_regularizer=tf.keras.regularizers.l2(0.01))\n",
-    "])\n",
+    "import tensorflow as tf\n",
+    "import datetime, os\n",
     "\n",
-    "# Compile Network\n",
-    "model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n",
-    "              loss='categorical_crossentropy',\n",
-    "              metrics=['accuracy'])\n",
+    "# Then let's create a new model and train it again.\n",
+    "# Check out TensorBoard during training (click on the \"refresh\" button to see\n",
+    "# new data).\n",
     "\n",
     "# Fit Network\n",
-    "num_validation_examples = 1000\n",
-    "num_train_examples = 5000\n",
-    "\n",
+    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
+    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
     "\n",
-    "train_iterator = train_datagen.flow(X_train_zc[num_validation_examples:num_train_examples], \n",
-    "                                    y_train_cat[num_validation_examples:num_train_examples], \n",
-    "                                    batch_size=64)\n",
-    "validation_iterator = validation_datagen.flow(X_train_zc[:num_validation_examples:], \n",
-    "                                              y_train_cat[:num_validation_examples:], \n",
-    "                                              batch_size=64)\n",
-    "history = model.fit_generator(generator= train_iterator,  \n",
-    "                              validation_data = validation_iterator, \n",
-    "                              epochs=100, \n",
-    "                              steps_per_epoch=len(train_iterator))"
+    "history = model.fit_generator(generator= train_iterator,  validation_data = validation_iterator, epochs=100, steps_per_epoch=len(train_iterator), callbacks=[tensorboard_callback])\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "id": "PSMyg4Q_vXac"
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot training and validation accuracy\n",
-    "acc = history.history['accuracy']\n",
-    "val_acc = history.history['val_accuracy']\n",
-    "\n",
-    "loss = history.history['loss']\n",
-    "val_loss = history.history['val_loss']\n",
-    "\n",
-    "epochs_range = range(100)\n",
-    "\n",
-    "plt.figure(figsize=(8, 8))\n",
-    "plt.subplot(1, 2, 1)\n",
-    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
-    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
-    "plt.legend(loc='lower right')\n",
-    "plt.title('Training and Validation Accuracy')\n",
-    "\n",
-    "plt.subplot(1, 2, 2)\n",
-    "plt.plot(epochs_range, loss, label='Training Loss')\n",
-    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
-    "plt.legend(loc='upper right')\n",
-    "plt.title('Training and Validation Loss')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "id": "UI8zctRKvXaf"
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "313/313 [==============================] - 5s 14ms/step - loss: 2.0938 - accuracy: 0.3669\n",
-      "Accuracy on test dataset: 0.3668999969959259\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Evaluate test accuracy\n",
-    "test_loss, test_accuracy = model.evaluate(X_test_zc, y_test_cat)\n",
-    "print('Accuracy on test dataset:', test_accuracy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5zgbDpH0sH7u"
-   },
-   "source": [
-    "# 7. TensorBoard\n",
-    "\n",
-    "TensorBoard is a great tool to observe variables during training (especially useful for models training a long time, like above model).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 51
-    },
-    "id": "FP5b7DGle7to",
-    "outputId": "9be8f4da-e6a9-45ab-aa2c-0c5a4e994055"
-   },
-   "outputs": [],
-   "source": [
-    "# Load the TensorBoard notebook extension\n",
-    "%load_ext tensorboard"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 1000
-    },
-    "id": "EuDr-lyAe9YL",
-    "outputId": "7be105fe-ee50-4101-9640-e2220e16aa96"
-   },
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "import datetime, os\n",
-    "\n",
-    "# Then let's create a new model and train it again.\n",
-    "# Check out TensorBoard during training (click on the \"refresh\" button to see\n",
-    "# new data).\n",
-    "\n",
-    "# Fit Network\n",
-    "logdir = os.path.join(\"logs\", datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\"))\n",
-    "tensorboard_callback = tf.keras.callbacks.TensorBoard(logdir, histogram_freq=1)\n",
-    "\n",
-    "history = model.fit_generator(generator= train_iterator,  validation_data = validation_iterator, epochs=100, steps_per_epoch=len(train_iterator), callbacks=[tensorboard_callback])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": null,
    "metadata": {
     "colab": {
      "base_uri": "https://localhost:8080/",
@@ -4159,19 +3836,28 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "id": "XBZuaco_vXaj"
+    "id": "H56FtQxwvXaW"
    },
    "source": [
     "# 8. Regularization\n",
     "\n",
-    "## 8.1 L1 Regularization"
+    "## 8.1 L2 Regularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rScnNsBHhLka"
+   },
+   "source": [
+    "It is most common to use a single, global $ L2 $ regularization strength that is cross-validated."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {
-    "id": "mIGlhfVQvXak"
+    "id": "vhDjE0kQvXaX"
    },
    "outputs": [
     {
@@ -4186,208 +3872,120 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/100\n",
-      "63/63 [==============================] - 6s 85ms/step - loss: 327.1515 - accuracy: 0.0960 - val_loss: 327.0746 - val_accuracy: 0.1080\n",
+      "63/63 [==============================] - 8s 108ms/step - loss: 12.9485 - accuracy: 0.2275 - val_loss: 13.6787 - val_accuracy: 0.1450\n",
       "Epoch 2/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 326.1475 - accuracy: 0.1002 - val_loss: 325.6567 - val_accuracy: 0.1100\n",
+      "63/63 [==============================] - 6s 90ms/step - loss: 12.1168 - accuracy: 0.2892 - val_loss: 11.8690 - val_accuracy: 0.2570\n",
       "Epoch 3/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 325.1220 - accuracy: 0.1063 - val_loss: 324.5860 - val_accuracy: 0.1250\n",
+      "63/63 [==============================] - 6s 93ms/step - loss: 11.3794 - accuracy: 0.3228 - val_loss: 11.1403 - val_accuracy: 0.2840\n",
       "Epoch 4/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 324.0782 - accuracy: 0.1168 - val_loss: 323.5659 - val_accuracy: 0.1260\n",
+      "63/63 [==============================] - 5s 84ms/step - loss: 10.7096 - accuracy: 0.3250 - val_loss: 10.5221 - val_accuracy: 0.3070\n",
       "Epoch 5/100\n",
-      "63/63 [==============================] - 5s 85ms/step - loss: 323.1131 - accuracy: 0.1072 - val_loss: 322.5623 - val_accuracy: 0.1300\n",
+      "63/63 [==============================] - 6s 88ms/step - loss: 10.0838 - accuracy: 0.3313 - val_loss: 9.8429 - val_accuracy: 0.3080\n",
       "Epoch 6/100\n",
-      "63/63 [==============================] - 5s 85ms/step - loss: 322.0774 - accuracy: 0.1260 - val_loss: 321.5606 - val_accuracy: 0.1380\n",
-      "Epoch 7/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 321.0746 - accuracy: 0.1195 - val_loss: 320.5585 - val_accuracy: 0.1440\n",
-      "Epoch 8/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 320.0763 - accuracy: 0.1325 - val_loss: 319.5595 - val_accuracy: 0.1530\n",
-      "Epoch 9/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 319.1026 - accuracy: 0.1322 - val_loss: 318.5633 - val_accuracy: 0.1550\n",
-      "Epoch 10/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 318.0901 - accuracy: 0.1482 - val_loss: 317.5669 - val_accuracy: 0.1590\n",
-      "Epoch 11/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 317.0896 - accuracy: 0.1462 - val_loss: 316.5726 - val_accuracy: 0.1630\n",
-      "Epoch 12/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 316.0988 - accuracy: 0.1583 - val_loss: 315.5802 - val_accuracy: 0.1620\n",
-      "Epoch 13/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 315.1066 - accuracy: 0.1567 - val_loss: 314.5958 - val_accuracy: 0.1670\n",
-      "Epoch 14/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 314.1442 - accuracy: 0.1462 - val_loss: 313.6080 - val_accuracy: 0.1690\n",
-      "Epoch 15/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 313.1559 - accuracy: 0.1695 - val_loss: 312.6242 - val_accuracy: 0.1690\n",
-      "Epoch 16/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 312.1642 - accuracy: 0.1758 - val_loss: 311.6449 - val_accuracy: 0.1730\n",
-      "Epoch 17/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 311.1918 - accuracy: 0.1663 - val_loss: 310.6667 - val_accuracy: 0.1800\n",
-      "Epoch 18/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 310.1902 - accuracy: 0.1840 - val_loss: 309.6874 - val_accuracy: 0.1810\n",
-      "Epoch 19/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 309.2360 - accuracy: 0.1688 - val_loss: 308.7123 - val_accuracy: 0.1860\n",
-      "Epoch 20/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 308.2580 - accuracy: 0.1877 - val_loss: 307.7401 - val_accuracy: 0.1950\n",
-      "Epoch 21/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 307.2866 - accuracy: 0.1785 - val_loss: 306.7669 - val_accuracy: 0.1980\n",
-      "Epoch 22/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 306.3173 - accuracy: 0.1887 - val_loss: 305.7998 - val_accuracy: 0.1990\n",
-      "Epoch 23/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 305.3628 - accuracy: 0.1883 - val_loss: 304.8331 - val_accuracy: 0.1990\n",
-      "Epoch 24/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 304.4003 - accuracy: 0.1898 - val_loss: 303.8682 - val_accuracy: 0.2000\n",
-      "Epoch 25/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 303.4365 - accuracy: 0.1935 - val_loss: 302.9048 - val_accuracy: 0.2000\n",
-      "Epoch 26/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 302.4757 - accuracy: 0.2015 - val_loss: 301.9421 - val_accuracy: 0.2070\n",
-      "Epoch 27/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 301.4982 - accuracy: 0.2060 - val_loss: 300.9833 - val_accuracy: 0.2120\n",
-      "Epoch 28/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 300.5380 - accuracy: 0.2023 - val_loss: 300.0252 - val_accuracy: 0.2160\n",
-      "Epoch 29/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 299.5988 - accuracy: 0.2048 - val_loss: 299.0702 - val_accuracy: 0.2160\n",
-      "Epoch 30/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 298.6487 - accuracy: 0.1965 - val_loss: 298.1189 - val_accuracy: 0.2200\n",
-      "Epoch 31/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 297.6784 - accuracy: 0.2107 - val_loss: 297.1659 - val_accuracy: 0.2220\n",
-      "Epoch 32/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 296.7291 - accuracy: 0.2093 - val_loss: 296.2149 - val_accuracy: 0.2280\n",
-      "Epoch 33/100\n",
-      "63/63 [==============================] - 6s 94ms/step - loss: 295.7881 - accuracy: 0.2128 - val_loss: 295.2669 - val_accuracy: 0.2320\n",
-      "Epoch 34/100\n",
-      "63/63 [==============================] - 5s 85ms/step - loss: 294.8321 - accuracy: 0.2130 - val_loss: 294.3218 - val_accuracy: 0.2350\n",
-      "Epoch 35/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 293.8979 - accuracy: 0.2233 - val_loss: 293.3750 - val_accuracy: 0.2420\n",
-      "Epoch 36/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 292.9487 - accuracy: 0.2188 - val_loss: 292.4310 - val_accuracy: 0.2410\n",
-      "Epoch 37/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 292.0022 - accuracy: 0.2165 - val_loss: 291.4884 - val_accuracy: 0.2420\n",
-      "Epoch 38/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 291.0607 - accuracy: 0.2265 - val_loss: 290.5476 - val_accuracy: 0.2450\n",
-      "Epoch 39/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 290.1371 - accuracy: 0.2265 - val_loss: 289.6096 - val_accuracy: 0.2460\n",
-      "Epoch 40/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 289.1928 - accuracy: 0.2247 - val_loss: 288.6717 - val_accuracy: 0.2480\n",
-      "Epoch 41/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 288.2564 - accuracy: 0.2237 - val_loss: 287.7351 - val_accuracy: 0.2490\n",
-      "Epoch 42/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 287.3262 - accuracy: 0.2233 - val_loss: 286.8005 - val_accuracy: 0.2520\n",
-      "Epoch 43/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 286.4016 - accuracy: 0.2202 - val_loss: 285.8700 - val_accuracy: 0.2520\n",
-      "Epoch 44/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 285.4873 - accuracy: 0.2153 - val_loss: 284.9394 - val_accuracy: 0.2540\n",
-      "Epoch 45/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 284.5251 - accuracy: 0.2323 - val_loss: 284.0109 - val_accuracy: 0.2590\n",
-      "Epoch 46/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 283.6101 - accuracy: 0.2225 - val_loss: 283.0848 - val_accuracy: 0.2600\n",
-      "Epoch 47/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 282.6856 - accuracy: 0.2325 - val_loss: 282.1608 - val_accuracy: 0.2640\n",
-      "Epoch 48/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 281.7592 - accuracy: 0.2380 - val_loss: 281.2372 - val_accuracy: 0.2650\n",
-      "Epoch 49/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 280.8422 - accuracy: 0.2350 - val_loss: 280.3164 - val_accuracy: 0.2640\n",
-      "Epoch 50/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 279.9146 - accuracy: 0.2407 - val_loss: 279.3953 - val_accuracy: 0.2650\n",
-      "Epoch 51/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 278.9896 - accuracy: 0.2327 - val_loss: 278.4753 - val_accuracy: 0.2670\n",
-      "Epoch 52/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 278.0770 - accuracy: 0.2395 - val_loss: 277.5576 - val_accuracy: 0.2660\n",
-      "Epoch 53/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 277.1502 - accuracy: 0.2457 - val_loss: 276.6424 - val_accuracy: 0.2720\n",
-      "Epoch 54/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 276.2343 - accuracy: 0.2425 - val_loss: 275.7294 - val_accuracy: 0.2710\n",
-      "Epoch 55/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 275.3232 - accuracy: 0.2545 - val_loss: 274.8169 - val_accuracy: 0.2720\n",
-      "Epoch 56/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 274.4250 - accuracy: 0.2455 - val_loss: 273.9061 - val_accuracy: 0.2730\n",
-      "Epoch 57/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 273.5044 - accuracy: 0.2512 - val_loss: 272.9958 - val_accuracy: 0.2710\n",
-      "Epoch 58/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 272.6038 - accuracy: 0.2463 - val_loss: 272.0880 - val_accuracy: 0.2700\n",
-      "Epoch 59/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 271.6734 - accuracy: 0.2515 - val_loss: 271.1808 - val_accuracy: 0.2740\n",
-      "Epoch 60/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 270.7823 - accuracy: 0.2498 - val_loss: 270.2771 - val_accuracy: 0.2740\n",
-      "Epoch 61/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 269.8884 - accuracy: 0.2477 - val_loss: 269.3727 - val_accuracy: 0.2760\n",
-      "Epoch 62/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 269.0027 - accuracy: 0.2435 - val_loss: 268.4718 - val_accuracy: 0.2790\n",
-      "Epoch 63/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 268.0920 - accuracy: 0.2503 - val_loss: 267.5706 - val_accuracy: 0.2780\n",
-      "Epoch 64/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 267.1882 - accuracy: 0.2528 - val_loss: 266.6737 - val_accuracy: 0.2790\n",
-      "Epoch 65/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 266.2816 - accuracy: 0.2537 - val_loss: 265.7779 - val_accuracy: 0.2780\n",
-      "Epoch 66/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 265.3962 - accuracy: 0.2492 - val_loss: 264.8836 - val_accuracy: 0.2810\n",
-      "Epoch 67/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 264.5002 - accuracy: 0.2562 - val_loss: 263.9904 - val_accuracy: 0.2810\n",
-      "Epoch 68/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 263.6219 - accuracy: 0.2465 - val_loss: 263.0982 - val_accuracy: 0.2810\n",
-      "Epoch 69/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 262.7169 - accuracy: 0.2623 - val_loss: 262.2080 - val_accuracy: 0.2800\n",
-      "Epoch 70/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 261.8205 - accuracy: 0.2620 - val_loss: 261.3180 - val_accuracy: 0.2870\n",
-      "Epoch 71/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 260.9346 - accuracy: 0.2650 - val_loss: 260.4309 - val_accuracy: 0.2830\n",
-      "Epoch 72/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 260.0492 - accuracy: 0.2580 - val_loss: 259.5452 - val_accuracy: 0.2820\n",
-      "Epoch 73/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 259.1770 - accuracy: 0.2675 - val_loss: 258.6613 - val_accuracy: 0.2830\n",
-      "Epoch 74/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 258.3004 - accuracy: 0.2542 - val_loss: 257.7794 - val_accuracy: 0.2840\n",
-      "Epoch 75/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 257.4110 - accuracy: 0.2545 - val_loss: 256.8986 - val_accuracy: 0.2860\n",
-      "Epoch 76/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 256.5139 - accuracy: 0.2603 - val_loss: 256.0189 - val_accuracy: 0.2850\n",
-      "Epoch 77/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 255.6440 - accuracy: 0.2567 - val_loss: 255.1406 - val_accuracy: 0.2870\n",
-      "Epoch 78/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 254.7783 - accuracy: 0.2663 - val_loss: 254.2640 - val_accuracy: 0.2860\n",
-      "Epoch 79/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 253.9017 - accuracy: 0.2610 - val_loss: 253.3907 - val_accuracy: 0.2860\n",
-      "Epoch 80/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 253.0186 - accuracy: 0.2618 - val_loss: 252.5173 - val_accuracy: 0.2880\n",
-      "Epoch 81/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 252.1360 - accuracy: 0.2767 - val_loss: 251.6447 - val_accuracy: 0.2870\n",
-      "Epoch 82/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 251.2754 - accuracy: 0.2555 - val_loss: 250.7763 - val_accuracy: 0.2900\n",
-      "Epoch 83/100\n",
-      "63/63 [==============================] - 5s 84ms/step - loss: 250.4181 - accuracy: 0.2623 - val_loss: 249.9071 - val_accuracy: 0.2910\n",
-      "Epoch 84/100\n",
-      "63/63 [==============================] - 6s 93ms/step - loss: 249.5337 - accuracy: 0.2705 - val_loss: 249.0408 - val_accuracy: 0.2970\n",
-      "Epoch 85/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 248.6617 - accuracy: 0.2780 - val_loss: 248.1743 - val_accuracy: 0.2940\n",
-      "Epoch 86/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 247.8124 - accuracy: 0.2727 - val_loss: 247.3114 - val_accuracy: 0.2960\n",
-      "Epoch 87/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 246.9591 - accuracy: 0.2640 - val_loss: 246.4467 - val_accuracy: 0.2950\n",
-      "Epoch 88/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 246.0869 - accuracy: 0.2763 - val_loss: 245.5876 - val_accuracy: 0.2940\n",
-      "Epoch 89/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 245.2222 - accuracy: 0.2780 - val_loss: 244.7277 - val_accuracy: 0.2990\n",
-      "Epoch 90/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 244.3685 - accuracy: 0.2760 - val_loss: 243.8696 - val_accuracy: 0.2990\n",
-      "Epoch 91/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 243.5253 - accuracy: 0.2772 - val_loss: 243.0142 - val_accuracy: 0.2990\n",
-      "Epoch 92/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 242.6632 - accuracy: 0.2697 - val_loss: 242.1590 - val_accuracy: 0.3050\n",
-      "Epoch 93/100\n",
-      "63/63 [==============================] - 5s 79ms/step - loss: 241.8077 - accuracy: 0.2780 - val_loss: 241.3056 - val_accuracy: 0.3050\n",
-      "Epoch 94/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 240.9498 - accuracy: 0.2770 - val_loss: 240.4554 - val_accuracy: 0.3020\n",
-      "Epoch 95/100\n",
-      "63/63 [==============================] - 5s 80ms/step - loss: 240.1183 - accuracy: 0.2685 - val_loss: 239.6053 - val_accuracy: 0.3010\n",
-      "Epoch 96/100\n",
-      "63/63 [==============================] - 5s 82ms/step - loss: 239.2501 - accuracy: 0.2785 - val_loss: 238.7564 - val_accuracy: 0.3030\n",
-      "Epoch 97/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 238.4097 - accuracy: 0.2735 - val_loss: 237.9107 - val_accuracy: 0.3000\n",
-      "Epoch 98/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 237.5537 - accuracy: 0.2718 - val_loss: 237.0653 - val_accuracy: 0.3050\n",
-      "Epoch 99/100\n",
-      "63/63 [==============================] - 5s 83ms/step - loss: 236.7005 - accuracy: 0.2865 - val_loss: 236.2210 - val_accuracy: 0.3030\n",
-      "Epoch 100/100\n",
-      "63/63 [==============================] - 5s 81ms/step - loss: 235.8721 - accuracy: 0.2788 - val_loss: 235.3800 - val_accuracy: 0.3030\n"
+      " 3/63 [>.............................] - ETA: 6s - loss: 9.7428 - accuracy: 0.4010"
      ]
     }
    ],
+   "source": [
+    "# Define Network\n",
+    "model = tf.keras.Sequential([\n",
+    "        tf.keras.layers.Flatten(input_shape=(32, 32, 3)),\n",
+    "        tf.keras.layers.Dense(512, \n",
+    "                              kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "        tf.keras.layers.Activation(tf.nn.relu),\n",
+    "        tf.keras.layers.Dense(128, \n",
+    "                              kernel_regularizer=tf.keras.regularizers.l2(0.01)),\n",
+    "        tf.keras.layers.BatchNormalization(),\n",
+    "        tf.keras.layers.Activation(tf.nn.relu),\n",
+    "        tf.keras.layers.Dense(10,  \n",
+    "                              activation=tf.nn.softmax,  \n",
+    "                              kernel_regularizer=tf.keras.regularizers.l2(0.01))\n",
+    "])\n",
+    "\n",
+    "# Compile Network\n",
+    "model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0001),\n",
+    "              loss='categorical_crossentropy',\n",
+    "              metrics=['accuracy'])\n",
+    "\n",
+    "# Fit Network\n",
+    "num_validation_examples = 1000\n",
+    "num_train_examples = 5000\n",
+    "\n",
+    "\n",
+    "train_iterator = train_datagen.flow(X_train_zc[num_validation_examples:num_train_examples], \n",
+    "                                    y_train_cat[num_validation_examples:num_train_examples], \n",
+    "                                    batch_size=64)\n",
+    "validation_iterator = validation_datagen.flow(X_train_zc[:num_validation_examples:], \n",
+    "                                              y_train_cat[:num_validation_examples:], \n",
+    "                                              batch_size=64)\n",
+    "history = model.fit_generator(generator= train_iterator,  \n",
+    "                              validation_data = validation_iterator, \n",
+    "                              epochs=100, \n",
+    "                              steps_per_epoch=len(train_iterator))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "PSMyg4Q_vXac"
+   },
+   "outputs": [],
+   "source": [
+    "# Plot training and validation accuracy\n",
+    "acc = history.history['accuracy']\n",
+    "val_acc = history.history['val_accuracy']\n",
+    "\n",
+    "loss = history.history['loss']\n",
+    "val_loss = history.history['val_loss']\n",
+    "\n",
+    "epochs_range = range(100)\n",
+    "\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
+    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.title('Training and Validation Accuracy')\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.plot(epochs_range, loss, label='Training Loss')\n",
+    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.title('Training and Validation Loss')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "UI8zctRKvXaf"
+   },
+   "outputs": [],
+   "source": [
+    "# Evaluate test accuracy\n",
+    "test_loss, test_accuracy = model.evaluate(X_test_zc, y_test_cat)\n",
+    "print('Accuracy on test dataset:', test_accuracy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XBZuaco_vXaj"
+   },
+   "source": [
+    "## 8.2 L1 Regularization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "mIGlhfVQvXak"
+   },
+   "outputs": [],
    "source": [
     "# Define Network\n",
     "model = tf.keras.Sequential([\n",
@@ -4429,24 +4027,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {
     "id": "1sfknL8-vXao"
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot training and validation accuracy\n",
     "acc = history.history['accuracy']\n",
@@ -4474,20 +4059,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {
     "id": "uW4-afX7vXas"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "313/313 [==============================] - 5s 14ms/step - loss: 235.4020 - accuracy: 0.2974\n",
-      "Accuracy on test dataset: 0.29739999771118164\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Evaluate test accuracy\n",
     "test_loss, test_accuracy = model.evaluate(X_test_zc, y_test_cat)\n",
@@ -4512,393 +4088,7 @@
    "metadata": {
     "id": "cecEKPOYvXaz"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:38: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/200\n",
-      "32/32 [==============================] - 5s 126ms/step - loss: 8.2717 - accuracy: 0.1015 - val_loss: 8.9044 - val_accuracy: 0.0990\n",
-      "Epoch 2/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 8.2294 - accuracy: 0.1115 - val_loss: 8.3683 - val_accuracy: 0.0990\n",
-      "Epoch 3/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 8.2034 - accuracy: 0.1115 - val_loss: 8.1730 - val_accuracy: 0.1110\n",
-      "Epoch 4/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 8.2173 - accuracy: 0.1050 - val_loss: 8.0694 - val_accuracy: 0.1150\n",
-      "Epoch 5/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 8.1576 - accuracy: 0.1238 - val_loss: 8.0118 - val_accuracy: 0.1300\n",
-      "Epoch 6/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 8.1675 - accuracy: 0.1182 - val_loss: 7.9765 - val_accuracy: 0.1280\n",
-      "Epoch 7/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 8.1499 - accuracy: 0.1203 - val_loss: 7.9521 - val_accuracy: 0.1300\n",
-      "Epoch 8/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 8.1334 - accuracy: 0.1165 - val_loss: 7.9339 - val_accuracy: 0.1300\n",
-      "Epoch 9/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 8.1110 - accuracy: 0.1343 - val_loss: 7.9190 - val_accuracy: 0.1360\n",
-      "Epoch 10/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 8.1160 - accuracy: 0.1268 - val_loss: 7.9076 - val_accuracy: 0.1400\n",
-      "Epoch 11/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 8.1144 - accuracy: 0.1273 - val_loss: 7.8954 - val_accuracy: 0.1480\n",
-      "Epoch 12/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 8.0868 - accuracy: 0.1258 - val_loss: 7.8821 - val_accuracy: 0.1470\n",
-      "Epoch 13/200\n",
-      "32/32 [==============================] - 4s 143ms/step - loss: 8.0794 - accuracy: 0.1350 - val_loss: 7.8711 - val_accuracy: 0.1510\n",
-      "Epoch 14/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 8.0743 - accuracy: 0.1357 - val_loss: 7.8607 - val_accuracy: 0.1520\n",
-      "Epoch 15/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 8.0526 - accuracy: 0.1402 - val_loss: 7.8506 - val_accuracy: 0.1550\n",
-      "Epoch 16/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 8.0373 - accuracy: 0.1380 - val_loss: 7.8432 - val_accuracy: 0.1580\n",
-      "Epoch 17/200\n",
-      "32/32 [==============================] - 4s 113ms/step - loss: 8.0570 - accuracy: 0.1437 - val_loss: 7.8320 - val_accuracy: 0.1640\n",
-      "Epoch 18/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 8.0218 - accuracy: 0.1465 - val_loss: 7.8245 - val_accuracy: 0.1690\n",
-      "Epoch 19/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 8.0468 - accuracy: 0.1435 - val_loss: 7.8172 - val_accuracy: 0.1720\n",
-      "Epoch 20/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 8.0108 - accuracy: 0.1545 - val_loss: 7.8070 - val_accuracy: 0.1800\n",
-      "Epoch 21/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.9958 - accuracy: 0.1507 - val_loss: 7.7972 - val_accuracy: 0.1790\n",
-      "Epoch 22/200\n",
-      "32/32 [==============================] - 4s 114ms/step - loss: 7.9712 - accuracy: 0.1612 - val_loss: 7.7887 - val_accuracy: 0.1850\n",
-      "Epoch 23/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 8.0070 - accuracy: 0.1497 - val_loss: 7.7790 - val_accuracy: 0.1930\n",
-      "Epoch 24/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.9934 - accuracy: 0.1550 - val_loss: 7.7713 - val_accuracy: 0.1910\n",
-      "Epoch 25/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.9845 - accuracy: 0.1538 - val_loss: 7.7629 - val_accuracy: 0.1950\n",
-      "Epoch 26/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.9644 - accuracy: 0.1585 - val_loss: 7.7543 - val_accuracy: 0.1960\n",
-      "Epoch 27/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.9780 - accuracy: 0.1612 - val_loss: 7.7470 - val_accuracy: 0.1970\n",
-      "Epoch 28/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.9485 - accuracy: 0.1673 - val_loss: 7.7399 - val_accuracy: 0.1980\n",
-      "Epoch 29/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.9730 - accuracy: 0.1545 - val_loss: 7.7329 - val_accuracy: 0.1980\n",
-      "Epoch 30/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.9604 - accuracy: 0.1690 - val_loss: 7.7283 - val_accuracy: 0.1980\n",
-      "Epoch 31/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.9444 - accuracy: 0.1620 - val_loss: 7.7224 - val_accuracy: 0.2010\n",
-      "Epoch 32/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.9592 - accuracy: 0.1632 - val_loss: 7.7158 - val_accuracy: 0.1990\n",
-      "Epoch 33/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.9515 - accuracy: 0.1723 - val_loss: 7.7093 - val_accuracy: 0.2000\n",
-      "Epoch 34/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.9370 - accuracy: 0.1745 - val_loss: 7.7043 - val_accuracy: 0.2020\n",
-      "Epoch 35/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.9305 - accuracy: 0.1653 - val_loss: 7.6985 - val_accuracy: 0.2070\n",
-      "Epoch 36/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.9209 - accuracy: 0.1835 - val_loss: 7.6933 - val_accuracy: 0.2060\n",
-      "Epoch 37/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.9326 - accuracy: 0.1765 - val_loss: 7.6889 - val_accuracy: 0.2090\n",
-      "Epoch 38/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.9351 - accuracy: 0.1700 - val_loss: 7.6836 - val_accuracy: 0.2090\n",
-      "Epoch 39/200\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 7.9061 - accuracy: 0.1780 - val_loss: 7.6788 - val_accuracy: 0.2090\n",
-      "Epoch 40/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.9031 - accuracy: 0.1782 - val_loss: 7.6739 - val_accuracy: 0.2100\n",
-      "Epoch 41/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.8808 - accuracy: 0.1793 - val_loss: 7.6692 - val_accuracy: 0.2130\n",
-      "Epoch 42/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.8905 - accuracy: 0.1805 - val_loss: 7.6640 - val_accuracy: 0.2140\n",
-      "Epoch 43/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.8922 - accuracy: 0.1795 - val_loss: 7.6598 - val_accuracy: 0.2110\n",
-      "Epoch 44/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.9096 - accuracy: 0.1765 - val_loss: 7.6540 - val_accuracy: 0.2140\n",
-      "Epoch 45/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.8835 - accuracy: 0.1817 - val_loss: 7.6498 - val_accuracy: 0.2140\n",
-      "Epoch 46/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.8720 - accuracy: 0.1835 - val_loss: 7.6454 - val_accuracy: 0.2140\n",
-      "Epoch 47/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.8861 - accuracy: 0.1822 - val_loss: 7.6411 - val_accuracy: 0.2180\n",
-      "Epoch 48/200\n",
-      "32/32 [==============================] - 5s 141ms/step - loss: 7.8637 - accuracy: 0.1933 - val_loss: 7.6370 - val_accuracy: 0.2170\n",
-      "Epoch 49/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.8734 - accuracy: 0.1855 - val_loss: 7.6322 - val_accuracy: 0.2220\n",
-      "Epoch 50/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.8481 - accuracy: 0.1832 - val_loss: 7.6287 - val_accuracy: 0.2240\n",
-      "Epoch 51/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.8732 - accuracy: 0.1813 - val_loss: 7.6235 - val_accuracy: 0.2240\n",
-      "Epoch 52/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.8571 - accuracy: 0.1873 - val_loss: 7.6204 - val_accuracy: 0.2240\n",
-      "Epoch 53/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.8460 - accuracy: 0.1915 - val_loss: 7.6169 - val_accuracy: 0.2240\n",
-      "Epoch 54/200\n",
-      "32/32 [==============================] - 4s 113ms/step - loss: 7.8232 - accuracy: 0.1945 - val_loss: 7.6135 - val_accuracy: 0.2280\n",
-      "Epoch 55/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.8128 - accuracy: 0.2017 - val_loss: 7.6095 - val_accuracy: 0.2290\n",
-      "Epoch 56/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.8377 - accuracy: 0.1957 - val_loss: 7.6054 - val_accuracy: 0.2300\n",
-      "Epoch 57/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.8164 - accuracy: 0.2048 - val_loss: 7.6014 - val_accuracy: 0.2300\n",
-      "Epoch 58/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.8366 - accuracy: 0.1980 - val_loss: 7.5975 - val_accuracy: 0.2290\n",
-      "Epoch 59/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.8566 - accuracy: 0.1857 - val_loss: 7.5936 - val_accuracy: 0.2340\n",
-      "Epoch 60/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.8271 - accuracy: 0.1940 - val_loss: 7.5899 - val_accuracy: 0.2370\n",
-      "Epoch 61/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.8468 - accuracy: 0.2000 - val_loss: 7.5866 - val_accuracy: 0.2370\n",
-      "Epoch 62/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.8057 - accuracy: 0.2040 - val_loss: 7.5828 - val_accuracy: 0.2390\n",
-      "Epoch 63/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.8074 - accuracy: 0.2037 - val_loss: 7.5803 - val_accuracy: 0.2390\n",
-      "Epoch 64/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.8049 - accuracy: 0.2080 - val_loss: 7.5774 - val_accuracy: 0.2390\n",
-      "Epoch 65/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.8306 - accuracy: 0.1950 - val_loss: 7.5748 - val_accuracy: 0.2410\n",
-      "Epoch 66/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7938 - accuracy: 0.2050 - val_loss: 7.5715 - val_accuracy: 0.2400\n",
-      "Epoch 67/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.8254 - accuracy: 0.1912 - val_loss: 7.5689 - val_accuracy: 0.2420\n",
-      "Epoch 68/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.8172 - accuracy: 0.1980 - val_loss: 7.5662 - val_accuracy: 0.2380\n",
-      "Epoch 69/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.8092 - accuracy: 0.2020 - val_loss: 7.5634 - val_accuracy: 0.2380\n",
-      "Epoch 70/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.8169 - accuracy: 0.1947 - val_loss: 7.5605 - val_accuracy: 0.2400\n",
-      "Epoch 71/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.8063 - accuracy: 0.2083 - val_loss: 7.5579 - val_accuracy: 0.2430\n",
-      "Epoch 72/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.7876 - accuracy: 0.1998 - val_loss: 7.5548 - val_accuracy: 0.2430\n",
-      "Epoch 73/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.7891 - accuracy: 0.2000 - val_loss: 7.5528 - val_accuracy: 0.2440\n",
-      "Epoch 74/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7635 - accuracy: 0.2188 - val_loss: 7.5502 - val_accuracy: 0.2440\n",
-      "Epoch 75/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.7661 - accuracy: 0.2190 - val_loss: 7.5471 - val_accuracy: 0.2470\n",
-      "Epoch 76/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7862 - accuracy: 0.2050 - val_loss: 7.5440 - val_accuracy: 0.2480\n",
-      "Epoch 77/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.7641 - accuracy: 0.2138 - val_loss: 7.5414 - val_accuracy: 0.2480\n",
-      "Epoch 78/200\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 7.7802 - accuracy: 0.2030 - val_loss: 7.5387 - val_accuracy: 0.2470\n",
-      "Epoch 79/200\n",
-      "32/32 [==============================] - 5s 143ms/step - loss: 7.7838 - accuracy: 0.1990 - val_loss: 7.5362 - val_accuracy: 0.2480\n",
-      "Epoch 80/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.7977 - accuracy: 0.2058 - val_loss: 7.5336 - val_accuracy: 0.2470\n",
-      "Epoch 81/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.7726 - accuracy: 0.2040 - val_loss: 7.5306 - val_accuracy: 0.2490\n",
-      "Epoch 82/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7691 - accuracy: 0.2048 - val_loss: 7.5279 - val_accuracy: 0.2470\n",
-      "Epoch 83/200\n",
-      "32/32 [==============================] - 4s 114ms/step - loss: 7.7585 - accuracy: 0.2080 - val_loss: 7.5251 - val_accuracy: 0.2500\n",
-      "Epoch 84/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.7603 - accuracy: 0.2192 - val_loss: 7.5229 - val_accuracy: 0.2520\n",
-      "Epoch 85/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.7456 - accuracy: 0.2110 - val_loss: 7.5198 - val_accuracy: 0.2530\n",
-      "Epoch 86/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7311 - accuracy: 0.2253 - val_loss: 7.5171 - val_accuracy: 0.2540\n",
-      "Epoch 87/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7700 - accuracy: 0.2180 - val_loss: 7.5153 - val_accuracy: 0.2550\n",
-      "Epoch 88/200\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 7.7330 - accuracy: 0.2282 - val_loss: 7.5128 - val_accuracy: 0.2530\n",
-      "Epoch 89/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7535 - accuracy: 0.2145 - val_loss: 7.5103 - val_accuracy: 0.2550\n",
-      "Epoch 90/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7406 - accuracy: 0.2085 - val_loss: 7.5084 - val_accuracy: 0.2550\n",
-      "Epoch 91/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.7497 - accuracy: 0.2177 - val_loss: 7.5056 - val_accuracy: 0.2580\n",
-      "Epoch 92/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.7347 - accuracy: 0.2190 - val_loss: 7.5035 - val_accuracy: 0.2600\n",
-      "Epoch 93/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.7343 - accuracy: 0.2118 - val_loss: 7.5011 - val_accuracy: 0.2620\n",
-      "Epoch 94/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.7330 - accuracy: 0.2245 - val_loss: 7.4994 - val_accuracy: 0.2610\n",
-      "Epoch 95/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7581 - accuracy: 0.2128 - val_loss: 7.4974 - val_accuracy: 0.2620\n",
-      "Epoch 96/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.7483 - accuracy: 0.2050 - val_loss: 7.4945 - val_accuracy: 0.2660\n",
-      "Epoch 97/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7282 - accuracy: 0.2233 - val_loss: 7.4925 - val_accuracy: 0.2710\n",
-      "Epoch 98/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.7208 - accuracy: 0.2210 - val_loss: 7.4906 - val_accuracy: 0.2700\n",
-      "Epoch 99/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.7301 - accuracy: 0.2280 - val_loss: 7.4884 - val_accuracy: 0.2700\n",
-      "Epoch 100/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.7288 - accuracy: 0.2205 - val_loss: 7.4868 - val_accuracy: 0.2710\n",
-      "Epoch 101/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.7153 - accuracy: 0.2132 - val_loss: 7.4843 - val_accuracy: 0.2710\n",
-      "Epoch 102/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7427 - accuracy: 0.2180 - val_loss: 7.4820 - val_accuracy: 0.2720\n",
-      "Epoch 103/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7508 - accuracy: 0.2132 - val_loss: 7.4799 - val_accuracy: 0.2730\n",
-      "Epoch 104/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7374 - accuracy: 0.2195 - val_loss: 7.4784 - val_accuracy: 0.2710\n",
-      "Epoch 105/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7109 - accuracy: 0.2257 - val_loss: 7.4767 - val_accuracy: 0.2710\n",
-      "Epoch 106/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.7218 - accuracy: 0.2167 - val_loss: 7.4740 - val_accuracy: 0.2720\n",
-      "Epoch 107/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6929 - accuracy: 0.2285 - val_loss: 7.4725 - val_accuracy: 0.2690\n",
-      "Epoch 108/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6828 - accuracy: 0.2210 - val_loss: 7.4705 - val_accuracy: 0.2710\n",
-      "Epoch 109/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.7151 - accuracy: 0.2243 - val_loss: 7.4681 - val_accuracy: 0.2700\n",
-      "Epoch 110/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.7110 - accuracy: 0.2240 - val_loss: 7.4665 - val_accuracy: 0.2720\n",
-      "Epoch 111/200\n",
-      "32/32 [==============================] - 4s 135ms/step - loss: 7.7127 - accuracy: 0.2218 - val_loss: 7.4645 - val_accuracy: 0.2730\n",
-      "Epoch 112/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7181 - accuracy: 0.2135 - val_loss: 7.4618 - val_accuracy: 0.2750\n",
-      "Epoch 113/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.7345 - accuracy: 0.2160 - val_loss: 7.4600 - val_accuracy: 0.2740\n",
-      "Epoch 114/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.7043 - accuracy: 0.2270 - val_loss: 7.4582 - val_accuracy: 0.2730\n",
-      "Epoch 115/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6915 - accuracy: 0.2320 - val_loss: 7.4563 - val_accuracy: 0.2760\n",
-      "Epoch 116/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.7198 - accuracy: 0.2120 - val_loss: 7.4554 - val_accuracy: 0.2760\n",
-      "Epoch 117/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.7043 - accuracy: 0.2272 - val_loss: 7.4536 - val_accuracy: 0.2770\n",
-      "Epoch 118/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6905 - accuracy: 0.2310 - val_loss: 7.4519 - val_accuracy: 0.2760\n",
-      "Epoch 119/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6905 - accuracy: 0.2298 - val_loss: 7.4501 - val_accuracy: 0.2820\n",
-      "Epoch 120/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.6868 - accuracy: 0.2265 - val_loss: 7.4491 - val_accuracy: 0.2810\n",
-      "Epoch 121/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.7044 - accuracy: 0.2200 - val_loss: 7.4479 - val_accuracy: 0.2820\n",
-      "Epoch 122/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.6905 - accuracy: 0.2288 - val_loss: 7.4459 - val_accuracy: 0.2790\n",
-      "Epoch 123/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6706 - accuracy: 0.2280 - val_loss: 7.4444 - val_accuracy: 0.2800\n",
-      "Epoch 124/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6836 - accuracy: 0.2333 - val_loss: 7.4419 - val_accuracy: 0.2810\n",
-      "Epoch 125/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.6944 - accuracy: 0.2303 - val_loss: 7.4395 - val_accuracy: 0.2800\n",
-      "Epoch 126/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6784 - accuracy: 0.2342 - val_loss: 7.4382 - val_accuracy: 0.2830\n",
-      "Epoch 127/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.6786 - accuracy: 0.2257 - val_loss: 7.4366 - val_accuracy: 0.2810\n",
-      "Epoch 128/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6820 - accuracy: 0.2235 - val_loss: 7.4350 - val_accuracy: 0.2840\n",
-      "Epoch 129/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6736 - accuracy: 0.2240 - val_loss: 7.4330 - val_accuracy: 0.2860\n",
-      "Epoch 130/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.6846 - accuracy: 0.2253 - val_loss: 7.4314 - val_accuracy: 0.2830\n",
-      "Epoch 131/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.6891 - accuracy: 0.2255 - val_loss: 7.4297 - val_accuracy: 0.2870\n",
-      "Epoch 132/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6736 - accuracy: 0.2292 - val_loss: 7.4282 - val_accuracy: 0.2870\n",
-      "Epoch 133/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6661 - accuracy: 0.2235 - val_loss: 7.4273 - val_accuracy: 0.2830\n",
-      "Epoch 134/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6550 - accuracy: 0.2390 - val_loss: 7.4257 - val_accuracy: 0.2850\n",
-      "Epoch 135/200\n",
-      "32/32 [==============================] - 4s 114ms/step - loss: 7.6708 - accuracy: 0.2310 - val_loss: 7.4243 - val_accuracy: 0.2860\n",
-      "Epoch 136/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6671 - accuracy: 0.2393 - val_loss: 7.4221 - val_accuracy: 0.2850\n",
-      "Epoch 137/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.6570 - accuracy: 0.2410 - val_loss: 7.4202 - val_accuracy: 0.2850\n",
-      "Epoch 138/200\n",
-      "32/32 [==============================] - 4s 114ms/step - loss: 7.6506 - accuracy: 0.2415 - val_loss: 7.4185 - val_accuracy: 0.2880\n",
-      "Epoch 139/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6627 - accuracy: 0.2260 - val_loss: 7.4173 - val_accuracy: 0.2870\n",
-      "Epoch 140/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.6471 - accuracy: 0.2385 - val_loss: 7.4159 - val_accuracy: 0.2850\n",
-      "Epoch 141/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6434 - accuracy: 0.2440 - val_loss: 7.4143 - val_accuracy: 0.2870\n",
-      "Epoch 142/200\n",
-      "32/32 [==============================] - 4s 137ms/step - loss: 7.6576 - accuracy: 0.2233 - val_loss: 7.4128 - val_accuracy: 0.2870\n",
-      "Epoch 143/200\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 7.6684 - accuracy: 0.2295 - val_loss: 7.4106 - val_accuracy: 0.2910\n",
-      "Epoch 144/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6458 - accuracy: 0.2373 - val_loss: 7.4092 - val_accuracy: 0.2890\n",
-      "Epoch 145/200\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 7.6329 - accuracy: 0.2517 - val_loss: 7.4079 - val_accuracy: 0.2900\n",
-      "Epoch 146/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6422 - accuracy: 0.2315 - val_loss: 7.4073 - val_accuracy: 0.2930\n",
-      "Epoch 147/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.6533 - accuracy: 0.2358 - val_loss: 7.4056 - val_accuracy: 0.2920\n",
-      "Epoch 148/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.6501 - accuracy: 0.2430 - val_loss: 7.4039 - val_accuracy: 0.2940\n",
-      "Epoch 149/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.6542 - accuracy: 0.2362 - val_loss: 7.4019 - val_accuracy: 0.2960\n",
-      "Epoch 150/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.6436 - accuracy: 0.2365 - val_loss: 7.4001 - val_accuracy: 0.2950\n",
-      "Epoch 151/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6354 - accuracy: 0.2335 - val_loss: 7.3989 - val_accuracy: 0.2940\n",
-      "Epoch 152/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6617 - accuracy: 0.2380 - val_loss: 7.3975 - val_accuracy: 0.2940\n",
-      "Epoch 153/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6192 - accuracy: 0.2410 - val_loss: 7.3968 - val_accuracy: 0.2930\n",
-      "Epoch 154/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.6340 - accuracy: 0.2335 - val_loss: 7.3950 - val_accuracy: 0.2950\n",
-      "Epoch 155/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.6250 - accuracy: 0.2387 - val_loss: 7.3938 - val_accuracy: 0.2970\n",
-      "Epoch 156/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6438 - accuracy: 0.2370 - val_loss: 7.3924 - val_accuracy: 0.2960\n",
-      "Epoch 157/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.6198 - accuracy: 0.2453 - val_loss: 7.3913 - val_accuracy: 0.3000\n",
-      "Epoch 158/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.6583 - accuracy: 0.2342 - val_loss: 7.3901 - val_accuracy: 0.3030\n",
-      "Epoch 159/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6203 - accuracy: 0.2432 - val_loss: 7.3882 - val_accuracy: 0.3010\n",
-      "Epoch 160/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.6554 - accuracy: 0.2285 - val_loss: 7.3874 - val_accuracy: 0.3020\n",
-      "Epoch 161/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6492 - accuracy: 0.2365 - val_loss: 7.3856 - val_accuracy: 0.2970\n",
-      "Epoch 162/200\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 7.6338 - accuracy: 0.2438 - val_loss: 7.3842 - val_accuracy: 0.3000\n",
-      "Epoch 163/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6259 - accuracy: 0.2377 - val_loss: 7.3827 - val_accuracy: 0.3030\n",
-      "Epoch 164/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.6097 - accuracy: 0.2420 - val_loss: 7.3813 - val_accuracy: 0.3020\n",
-      "Epoch 165/200\n",
-      "32/32 [==============================] - 4s 114ms/step - loss: 7.6206 - accuracy: 0.2370 - val_loss: 7.3804 - val_accuracy: 0.3030\n",
-      "Epoch 166/200\n",
-      "32/32 [==============================] - 4s 119ms/step - loss: 7.6210 - accuracy: 0.2393 - val_loss: 7.3790 - val_accuracy: 0.3030\n",
-      "Epoch 167/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.6314 - accuracy: 0.2428 - val_loss: 7.3775 - val_accuracy: 0.3020\n",
-      "Epoch 168/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.6029 - accuracy: 0.2465 - val_loss: 7.3761 - val_accuracy: 0.3070\n",
-      "Epoch 169/200\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 7.5835 - accuracy: 0.2467 - val_loss: 7.3750 - val_accuracy: 0.3060\n",
-      "Epoch 170/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.6193 - accuracy: 0.2320 - val_loss: 7.3742 - val_accuracy: 0.3040\n",
-      "Epoch 171/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6305 - accuracy: 0.2362 - val_loss: 7.3725 - val_accuracy: 0.3050\n",
-      "Epoch 172/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.6199 - accuracy: 0.2432 - val_loss: 7.3713 - val_accuracy: 0.3060\n",
-      "Epoch 173/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.6162 - accuracy: 0.2553 - val_loss: 7.3701 - val_accuracy: 0.3030\n",
-      "Epoch 174/200\n",
-      "32/32 [==============================] - 5s 142ms/step - loss: 7.6056 - accuracy: 0.2400 - val_loss: 7.3687 - val_accuracy: 0.3060\n",
-      "Epoch 175/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.5935 - accuracy: 0.2498 - val_loss: 7.3676 - val_accuracy: 0.3080\n",
-      "Epoch 176/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.6068 - accuracy: 0.2490 - val_loss: 7.3660 - val_accuracy: 0.3090\n",
-      "Epoch 177/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.6315 - accuracy: 0.2330 - val_loss: 7.3653 - val_accuracy: 0.3070\n",
-      "Epoch 178/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.5982 - accuracy: 0.2445 - val_loss: 7.3636 - val_accuracy: 0.3080\n",
-      "Epoch 179/200\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 7.5611 - accuracy: 0.2500 - val_loss: 7.3620 - val_accuracy: 0.3080\n",
-      "Epoch 180/200\n",
-      "32/32 [==============================] - 4s 118ms/step - loss: 7.5995 - accuracy: 0.2453 - val_loss: 7.3610 - val_accuracy: 0.3090\n",
-      "Epoch 181/200\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 7.6195 - accuracy: 0.2380 - val_loss: 7.3603 - val_accuracy: 0.3090\n",
-      "Epoch 182/200\n",
-      "32/32 [==============================] - 4s 115ms/step - loss: 7.6183 - accuracy: 0.2453 - val_loss: 7.3591 - val_accuracy: 0.3080\n",
-      "Epoch 183/200\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 7.5992 - accuracy: 0.2420 - val_loss: 7.3584 - val_accuracy: 0.3120\n",
-      "Epoch 184/200\n",
-      "32/32 [==============================] - 4s 116ms/step - loss: 7.5978 - accuracy: 0.2368 - val_loss: 7.3572 - val_accuracy: 0.3130\n",
-      "Epoch 185/200\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 7.5972 - accuracy: 0.2453 - val_loss: 7.3563 - val_accuracy: 0.3110\n",
-      "Epoch 186/200\n",
-      " 8/32 [======>.......................] - ETA: 2s - loss: 7.6009 - accuracy: 0.2414"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define Network\n",
     "model = tf.keras.Sequential([\n",
@@ -5000,225 +4190,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {
     "id": "ZxI6XZxJvXa-"
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:39: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/100\n",
-      "32/32 [==============================] - 8s 194ms/step - loss: 7.9278 - accuracy: 0.1795 - val_loss: 8.7435 - val_accuracy: 0.1630\n",
-      "Epoch 2/100\n",
-      "32/32 [==============================] - 5s 146ms/step - loss: 7.5847 - accuracy: 0.2355 - val_loss: 7.9850 - val_accuracy: 0.1890\n",
-      "Epoch 3/100\n",
-      "32/32 [==============================] - 5s 140ms/step - loss: 7.4110 - accuracy: 0.2598 - val_loss: 7.3038 - val_accuracy: 0.2660\n",
-      "Epoch 4/100\n",
-      "32/32 [==============================] - 4s 128ms/step - loss: 7.2711 - accuracy: 0.2718 - val_loss: 7.1753 - val_accuracy: 0.2720\n",
-      "Epoch 5/100\n",
-      "32/32 [==============================] - 4s 129ms/step - loss: 7.1305 - accuracy: 0.2885 - val_loss: 6.9784 - val_accuracy: 0.3390\n",
-      "Epoch 6/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 7.0077 - accuracy: 0.2955 - val_loss: 6.9774 - val_accuracy: 0.2720\n",
-      "Epoch 7/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 6.8876 - accuracy: 0.3085 - val_loss: 6.9673 - val_accuracy: 0.2690\n",
-      "Epoch 8/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 6.7867 - accuracy: 0.2993 - val_loss: 6.7843 - val_accuracy: 0.2750\n",
-      "Epoch 9/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 6.6536 - accuracy: 0.3027 - val_loss: 6.5008 - val_accuracy: 0.3240\n",
-      "Epoch 10/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 6.5523 - accuracy: 0.3063 - val_loss: 6.6367 - val_accuracy: 0.2980\n",
-      "Epoch 11/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 6.4511 - accuracy: 0.3135 - val_loss: 6.2525 - val_accuracy: 0.3490\n",
-      "Epoch 12/100\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 6.3515 - accuracy: 0.3243 - val_loss: 6.2500 - val_accuracy: 0.3330\n",
-      "Epoch 13/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 6.2211 - accuracy: 0.3330 - val_loss: 6.1633 - val_accuracy: 0.3460\n",
-      "Epoch 14/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 6.1450 - accuracy: 0.3255 - val_loss: 6.0062 - val_accuracy: 0.3610\n",
-      "Epoch 15/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 6.0337 - accuracy: 0.3313 - val_loss: 6.0169 - val_accuracy: 0.2890\n",
-      "Epoch 16/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 5.9399 - accuracy: 0.3295 - val_loss: 5.9055 - val_accuracy: 0.3250\n",
-      "Epoch 17/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 5.8530 - accuracy: 0.3270 - val_loss: 5.8749 - val_accuracy: 0.2960\n",
-      "Epoch 18/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 5.7460 - accuracy: 0.3252 - val_loss: 5.7107 - val_accuracy: 0.3370\n",
-      "Epoch 19/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 5.6489 - accuracy: 0.3355 - val_loss: 5.5232 - val_accuracy: 0.3780\n",
-      "Epoch 20/100\n",
-      "32/32 [==============================] - 4s 129ms/step - loss: 5.5519 - accuracy: 0.3492 - val_loss: 5.5483 - val_accuracy: 0.3510\n",
-      "Epoch 21/100\n",
-      "32/32 [==============================] - 4s 131ms/step - loss: 5.4864 - accuracy: 0.3453 - val_loss: 5.6983 - val_accuracy: 0.2720\n",
-      "Epoch 22/100\n",
-      "32/32 [==============================] - 5s 145ms/step - loss: 5.3888 - accuracy: 0.3455 - val_loss: 5.2684 - val_accuracy: 0.3730\n",
-      "Epoch 23/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 5.3491 - accuracy: 0.3140 - val_loss: 5.4412 - val_accuracy: 0.2830\n",
-      "Epoch 24/100\n",
-      "32/32 [==============================] - 4s 117ms/step - loss: 5.1893 - accuracy: 0.3535 - val_loss: 5.1698 - val_accuracy: 0.3400\n",
-      "Epoch 25/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 5.1523 - accuracy: 0.3388 - val_loss: 5.2489 - val_accuracy: 0.2900\n",
-      "Epoch 26/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 5.0692 - accuracy: 0.3523 - val_loss: 5.0228 - val_accuracy: 0.3450\n",
-      "Epoch 27/100\n",
-      "32/32 [==============================] - 4s 128ms/step - loss: 4.9970 - accuracy: 0.3453 - val_loss: 4.9607 - val_accuracy: 0.3490\n",
-      "Epoch 28/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 4.9232 - accuracy: 0.3455 - val_loss: 4.9283 - val_accuracy: 0.3390\n",
-      "Epoch 29/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 4.8450 - accuracy: 0.3595 - val_loss: 4.7769 - val_accuracy: 0.3600\n",
-      "Epoch 30/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 4.7822 - accuracy: 0.3525 - val_loss: 4.9336 - val_accuracy: 0.3220\n",
-      "Epoch 31/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 4.7048 - accuracy: 0.3602 - val_loss: 4.6390 - val_accuracy: 0.3450\n",
-      "Epoch 32/100\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 4.6338 - accuracy: 0.3537 - val_loss: 4.8122 - val_accuracy: 0.3060\n",
-      "Epoch 33/100\n",
-      "32/32 [==============================] - 4s 130ms/step - loss: 4.5592 - accuracy: 0.3627 - val_loss: 4.4749 - val_accuracy: 0.3730\n",
-      "Epoch 34/100\n",
-      "32/32 [==============================] - 4s 130ms/step - loss: 4.4877 - accuracy: 0.3735 - val_loss: 4.6357 - val_accuracy: 0.3300\n",
-      "Epoch 35/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 4.4264 - accuracy: 0.3638 - val_loss: 4.3905 - val_accuracy: 0.3810\n",
-      "Epoch 36/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 4.3562 - accuracy: 0.3700 - val_loss: 4.2849 - val_accuracy: 0.3750\n",
-      "Epoch 37/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 4.3279 - accuracy: 0.3550 - val_loss: 4.3070 - val_accuracy: 0.3680\n",
-      "Epoch 38/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 4.2570 - accuracy: 0.3580 - val_loss: 4.1936 - val_accuracy: 0.3840\n",
-      "Epoch 39/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 4.2110 - accuracy: 0.3495 - val_loss: 4.2390 - val_accuracy: 0.3220\n",
-      "Epoch 40/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 4.1555 - accuracy: 0.3573 - val_loss: 4.1628 - val_accuracy: 0.3270\n",
-      "Epoch 41/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 4.1026 - accuracy: 0.3575 - val_loss: 4.1093 - val_accuracy: 0.3500\n",
-      "Epoch 42/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 4.0492 - accuracy: 0.3683 - val_loss: 4.0883 - val_accuracy: 0.3610\n",
-      "Epoch 43/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 3.9741 - accuracy: 0.3742 - val_loss: 3.9863 - val_accuracy: 0.3710\n",
-      "Epoch 44/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 3.9070 - accuracy: 0.3710 - val_loss: 3.8807 - val_accuracy: 0.3910\n",
-      "Epoch 45/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 3.9007 - accuracy: 0.3660 - val_loss: 4.0333 - val_accuracy: 0.3180\n",
-      "Epoch 46/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 3.8386 - accuracy: 0.3683 - val_loss: 3.8179 - val_accuracy: 0.3710\n",
-      "Epoch 47/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 3.8148 - accuracy: 0.3700 - val_loss: 3.7847 - val_accuracy: 0.3720\n",
-      "Epoch 48/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 3.7699 - accuracy: 0.3645 - val_loss: 3.9120 - val_accuracy: 0.2960\n",
-      "Epoch 49/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 3.7052 - accuracy: 0.3738 - val_loss: 3.7466 - val_accuracy: 0.3860\n",
-      "Epoch 50/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 3.6604 - accuracy: 0.3770 - val_loss: 3.6506 - val_accuracy: 0.3770\n",
-      "Epoch 51/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 3.6110 - accuracy: 0.3708 - val_loss: 3.7059 - val_accuracy: 0.3350\n",
-      "Epoch 52/100\n",
-      "32/32 [==============================] - 4s 131ms/step - loss: 3.5865 - accuracy: 0.3697 - val_loss: 3.6172 - val_accuracy: 0.3720\n",
-      "Epoch 53/100\n",
-      "32/32 [==============================] - 5s 145ms/step - loss: 3.5357 - accuracy: 0.3840 - val_loss: 3.5688 - val_accuracy: 0.3510\n",
-      "Epoch 54/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 3.4730 - accuracy: 0.3935 - val_loss: 3.5173 - val_accuracy: 0.3750\n",
-      "Epoch 55/100\n",
-      "32/32 [==============================] - 4s 129ms/step - loss: 3.4665 - accuracy: 0.3742 - val_loss: 3.5868 - val_accuracy: 0.3360\n",
-      "Epoch 56/100\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 3.4167 - accuracy: 0.3710 - val_loss: 3.3837 - val_accuracy: 0.3870\n",
-      "Epoch 57/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 3.3701 - accuracy: 0.3915 - val_loss: 3.4149 - val_accuracy: 0.3630\n",
-      "Epoch 58/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 3.3672 - accuracy: 0.3820 - val_loss: 3.3169 - val_accuracy: 0.4010\n",
-      "Epoch 59/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 3.3082 - accuracy: 0.3887 - val_loss: 3.3791 - val_accuracy: 0.3570\n",
-      "Epoch 60/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 3.3059 - accuracy: 0.3787 - val_loss: 3.4320 - val_accuracy: 0.3280\n",
-      "Epoch 61/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 3.2544 - accuracy: 0.3875 - val_loss: 3.3982 - val_accuracy: 0.3290\n",
-      "Epoch 62/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 3.2020 - accuracy: 0.3875 - val_loss: 3.4636 - val_accuracy: 0.3390\n",
-      "Epoch 63/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 3.1931 - accuracy: 0.3832 - val_loss: 3.2788 - val_accuracy: 0.3360\n",
-      "Epoch 64/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 3.1928 - accuracy: 0.3740 - val_loss: 3.1737 - val_accuracy: 0.3700\n",
-      "Epoch 65/100\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 3.1177 - accuracy: 0.3988 - val_loss: 3.1195 - val_accuracy: 0.3910\n",
-      "Epoch 66/100\n",
-      "32/32 [==============================] - 4s 125ms/step - loss: 3.0972 - accuracy: 0.3835 - val_loss: 3.3795 - val_accuracy: 0.2900\n",
-      "Epoch 67/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 3.0761 - accuracy: 0.3890 - val_loss: 3.1332 - val_accuracy: 0.3630\n",
-      "Epoch 68/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 3.0429 - accuracy: 0.3845 - val_loss: 3.0678 - val_accuracy: 0.3840\n",
-      "Epoch 69/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 3.0335 - accuracy: 0.3842 - val_loss: 3.2026 - val_accuracy: 0.3430\n",
-      "Epoch 70/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 3.0042 - accuracy: 0.3902 - val_loss: 3.0992 - val_accuracy: 0.3310\n",
-      "Epoch 71/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 2.9729 - accuracy: 0.3790 - val_loss: 2.9847 - val_accuracy: 0.3770\n",
-      "Epoch 72/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 2.9472 - accuracy: 0.3890 - val_loss: 3.0121 - val_accuracy: 0.3560\n",
-      "Epoch 73/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 2.9414 - accuracy: 0.3855 - val_loss: 2.8951 - val_accuracy: 0.4120\n",
-      "Epoch 74/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 2.9079 - accuracy: 0.3855 - val_loss: 2.9885 - val_accuracy: 0.3530\n",
-      "Epoch 75/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 2.8793 - accuracy: 0.3930 - val_loss: 2.9061 - val_accuracy: 0.3740\n",
-      "Epoch 76/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 2.8585 - accuracy: 0.3792 - val_loss: 2.8393 - val_accuracy: 0.3960\n",
-      "Epoch 77/100\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 2.8236 - accuracy: 0.3950 - val_loss: 2.8561 - val_accuracy: 0.3730\n",
-      "Epoch 78/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 2.8158 - accuracy: 0.3907 - val_loss: 2.8607 - val_accuracy: 0.3800\n",
-      "Epoch 79/100\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 2.7802 - accuracy: 0.4015 - val_loss: 2.8434 - val_accuracy: 0.3750\n",
-      "Epoch 80/100\n",
-      "32/32 [==============================] - 4s 122ms/step - loss: 2.7607 - accuracy: 0.3887 - val_loss: 2.8587 - val_accuracy: 0.3650\n",
-      "Epoch 81/100\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 2.7576 - accuracy: 0.3885 - val_loss: 2.7834 - val_accuracy: 0.3720\n",
-      "Epoch 82/100\n",
-      "32/32 [==============================] - 4s 128ms/step - loss: 2.7446 - accuracy: 0.3817 - val_loss: 2.8279 - val_accuracy: 0.3640\n",
-      "Epoch 83/100\n",
-      "32/32 [==============================] - 4s 121ms/step - loss: 2.7224 - accuracy: 0.3995 - val_loss: 2.8202 - val_accuracy: 0.3530\n",
-      "Epoch 84/100\n",
-      "32/32 [==============================] - 4s 128ms/step - loss: 2.7081 - accuracy: 0.3913 - val_loss: 2.8054 - val_accuracy: 0.3780\n",
-      "Epoch 85/100\n",
-      "32/32 [==============================] - 5s 144ms/step - loss: 2.6631 - accuracy: 0.4033 - val_loss: 3.2552 - val_accuracy: 0.2540\n",
-      "Epoch 86/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 2.6763 - accuracy: 0.4027 - val_loss: 2.8086 - val_accuracy: 0.3350\n",
-      "Epoch 87/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 2.6311 - accuracy: 0.4062 - val_loss: 2.7136 - val_accuracy: 0.3700\n",
-      "Epoch 88/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 2.6369 - accuracy: 0.3907 - val_loss: 2.6391 - val_accuracy: 0.3980\n",
-      "Epoch 89/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 2.6020 - accuracy: 0.4027 - val_loss: 2.6999 - val_accuracy: 0.3630\n",
-      "Epoch 90/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 2.5928 - accuracy: 0.4098 - val_loss: 2.5866 - val_accuracy: 0.4060\n",
-      "Epoch 91/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 2.5793 - accuracy: 0.4013 - val_loss: 2.8585 - val_accuracy: 0.3090\n",
-      "Epoch 92/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 2.5699 - accuracy: 0.3983 - val_loss: 2.8960 - val_accuracy: 0.3100\n",
-      "Epoch 93/100\n",
-      "32/32 [==============================] - 4s 131ms/step - loss: 2.5651 - accuracy: 0.3895 - val_loss: 2.9424 - val_accuracy: 0.2690\n",
-      "Epoch 94/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 2.5424 - accuracy: 0.3905 - val_loss: 2.6070 - val_accuracy: 0.3860\n",
-      "Epoch 95/100\n",
-      "32/32 [==============================] - 4s 123ms/step - loss: 2.5166 - accuracy: 0.4025 - val_loss: 2.7224 - val_accuracy: 0.3390\n",
-      "Epoch 96/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 2.5092 - accuracy: 0.4020 - val_loss: 2.6135 - val_accuracy: 0.3560\n",
-      "Epoch 97/100\n",
-      "32/32 [==============================] - 4s 127ms/step - loss: 2.4977 - accuracy: 0.4075 - val_loss: 2.5204 - val_accuracy: 0.3970\n",
-      "Epoch 98/100\n",
-      "32/32 [==============================] - 4s 120ms/step - loss: 2.4865 - accuracy: 0.4058 - val_loss: 2.5593 - val_accuracy: 0.3860\n",
-      "Epoch 99/100\n",
-      "32/32 [==============================] - 4s 126ms/step - loss: 2.4784 - accuracy: 0.3925 - val_loss: 2.4718 - val_accuracy: 0.3940\n",
-      "Epoch 100/100\n",
-      "32/32 [==============================] - 4s 124ms/step - loss: 2.4630 - accuracy: 0.3988 - val_loss: 2.5533 - val_accuracy: 0.3490\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Define Network\n",
     "model = tf.keras.Sequential([\n",
@@ -5263,24 +4239,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "metadata": {
     "id": "cixRpMMOvXbB"
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot training and validation accuracy\n",
     "acc = history.history['accuracy']\n",
@@ -5308,20 +4271,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {
     "id": "eCuOiek7vXbQ"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "313/313 [==============================] - 5s 16ms/step - loss: 2.5623 - accuracy: 0.3593 0s - loss: 2.5660 \n",
-      "Accuracy on test dataset: 0.35929998755455017\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Evaluate test accuracy\n",
     "test_loss, test_accuracy = model.evaluate(X_test_zc, y_test_cat)\n",
@@ -5339,31 +4293,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
    "metadata": {
     "id": "lZ_eBernxdYc"
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mWARNING: You are using pip version 20.2.4; however, version 22.0.4 is available.\n",
-      "You should consider upgrading via the '/opt/conda/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n"
-     ]
-    },
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'google.colab'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-62-61720729c72a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpydrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauth\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGoogleAuth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpydrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrive\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGoogleDrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mauth\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0moauth2client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGoogleCredentials\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Install the PyDrive wrapper & import libraries.\n",
     "# This only needs to be done once in a notebook.\n",
-- 
GitLab