From 72a9c57e0285e5ecd202163258877589e1585e02 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Timoth=C3=A9e=20Barry?= <timothee.barry1@ecl20.ec-lyon.fr>
Date: Fri, 10 Nov 2023 17:25:03 +0100
Subject: [PATCH] fix: change sigmoid with softmax and add some comments
---
be_image_classification.ipynb | 791 +++++++++++++++++++---------------
images/mlp_accuracy.png | Bin 0 -> 29142 bytes
images/mlp_accuracy_tf.png | Bin 0 -> 27057 bytes
images/mlp_loss.png | Bin 23034 -> 26384 bytes
images/mlp_loss_tf.png | Bin 21189 -> 25853 bytes
utils/forward_pass.py | 5 +-
utils/mlp_training.py | 13 +-
utils/process_image.py | 3 +-
8 files changed, 455 insertions(+), 357 deletions(-)
create mode 100644 images/mlp_accuracy.png
create mode 100644 images/mlp_accuracy_tf.png
diff --git a/be_image_classification.ipynb b/be_image_classification.ipynb
index 807001d..50ea54d 100644
--- a/be_image_classification.ipynb
+++ b/be_image_classification.ipynb
@@ -9,7 +9,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 7,
"metadata": {},
"outputs": [
{
@@ -18,7 +18,7 @@
"((60000, 3072), (60000,))"
]
},
- "execution_count": 1,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
@@ -32,12 +32,12 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -63,7 +63,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -73,7 +73,7 @@
" [2.82842712, 0. ]])"
]
},
- "execution_count": 3,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -99,6 +99,13 @@
" evaluate_knn(train_data, train_labels, test_data, test_labels, 1)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Get the accuracy of the knn model for each k value"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 6,
@@ -132,7 +139,7 @@
}
],
"source": [
- "if True:\n",
+ "if False:\n",
" data, labels = read_cifar('data/cifar-10-batches-py')\n",
" train_data, train_labels, test_data, test_labels = split_dataset(data, labels, 0.9)\n",
" k_values = list(np.arange(1, 21))\n",
@@ -162,35 +169,43 @@
"source": [
"from utils.process_image import save_plot_as_image\n",
"\n",
- "save_plot_as_image(k_values, accuracies, 'accuracy', 'k', 'images/knn_accuracy.png')"
+ "save_plot_as_image(k_values, accuracies, 'accuracy', 'k','Evolution de l\\'accuracy en fonction de k, 'images/knn_accuracy.png')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The model have the best result for k = 1 (about 35% accuracy), and the worst with k=2"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(array([[ 0.328055 , -0.09295718, -0.33842638],\n",
- " [-0.11653052, 0.58325438, -0.13258186],\n",
- " [ 0.18900546, 0.51515747, -0.76910745]]),\n",
- " array([[-0.00083548, -0.00088441, 0.00035065]]),\n",
- " array([[ 0.06636073, 0.91268095],\n",
- " [ 0.24104642, 0.93511262],\n",
- " [-0.10002242, -0.39107094]]),\n",
- " array([[-0.00142651, -0.0036116 ]]),\n",
- " 0.08808324100066224)"
+ "(array([[ 0.79362828, -0.36932403, -0.44283967],\n",
+ " [-0.97139098, -0.75715536, 0.59671452],\n",
+ " [ 0.94666291, -0.32683836, 0.47777268]]),\n",
+ " array([[-0.0014528 , -0.00076639, -0.00166222]]),\n",
+ " array([[0.75131074, 0.52740138],\n",
+ " [0.41564149, 0.30933499],\n",
+ " [0.66218606, 0.72875506]]),\n",
+ " array([[-0.00490338, -0.00496067]]),\n",
+ " 0.1383631074551818)"
]
},
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from utils.learn_once_mse import learn_once_mse\n",
+ "import numpy as np\n",
"\n",
"N = 30 # number of input data\n",
"d_in = 3 # input dimension\n",
@@ -218,7 +233,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -229,7 +244,7 @@
" [1., 0., 0.]])"
]
},
- "execution_count": 9,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -242,24 +257,24 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "(array([[ 0.3256724 , -0.09540915, -0.33746996],\n",
- " [-0.11805462, 0.58166566, -0.13193784],\n",
- " [ 0.18724356, 0.51319216, -0.76834097]]),\n",
- " array([[-0.00488305, -0.00512345, 0.00203958]]),\n",
- " array([[ 0.06319307, 0.90284858],\n",
- " [ 0.23713482, 0.92465898],\n",
- " [-0.1015984 , -0.39743855]]),\n",
- " array([[-0.00710872, -0.02124436]]),\n",
- " 0.7295273614523309)"
+ "(array([[ 0.78981139, -0.37130174, -0.44732111],\n",
+ " [-0.97530139, -0.75912051, 0.59234515],\n",
+ " [ 0.94351845, -0.32849849, 0.47405929]]),\n",
+ " array([[-0.00843903, -0.00445009, -0.00962447]]),\n",
+ " array([[0.73755152, 0.5135263 ],\n",
+ " [0.40785584, 0.30205767],\n",
+ " [0.64830103, 0.71534156]]),\n",
+ " array([[-0.02907245, -0.02827016]]),\n",
+ " 0.8159308284553612)"
]
},
- "execution_count": 10,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -271,1414 +286,1426 @@
" "
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Entrainement du modèle\n",
+ "On entraine le modèle contenant une couche cachée de 64 neurones avec 100 epochs, un learning rate de 0.1 et une taille de batch de 512.\n",
+ "\n",
+ "En effet, j'ai rajouté un paramètre `batch_size` pour améliorer les performances lors de l'entrainement."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.65it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.44it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=0, loss=0.32536565848357263\n"
+ "epoch=0, loss=0.33420675013485945, train_accuracy=0.18577777777777776\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.07it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.16it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=1, loss=0.3121928702945571\n"
+ "epoch=1, loss=0.3188549057785522, train_accuracy=0.21144444444444443\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.83it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.26it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=2, loss=0.30543033970759537\n"
+ "epoch=2, loss=0.3111488909632828, train_accuracy=0.2247962962962963\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.68it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.35it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=3, loss=0.30164690368706953\n"
+ "epoch=3, loss=0.30636305132753144, train_accuracy=0.23548148148148149\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.90it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.06it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=4, loss=0.2988765995063212\n"
+ "epoch=4, loss=0.3032836133855315, train_accuracy=0.24598148148148147\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 49.07it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.06it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=5, loss=0.29671164791713306\n"
+ "epoch=5, loss=0.3010618635390122, train_accuracy=0.2552962962962963\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.40it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.53it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=6, loss=0.29488462530706594\n"
+ "epoch=6, loss=0.29940237009013365, train_accuracy=0.26151851851851854\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.09it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.16it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=7, loss=0.29324902660111213\n"
+ "epoch=7, loss=0.29803387124328845, train_accuracy=0.26681481481481484\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.29it/s]\n"
+ "100%|██████████| 106/106 [00:01<00:00, 53.21it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=8, loss=0.29213942600333603\n"
+ "epoch=8, loss=0.2967996819608651, train_accuracy=0.27246296296296296\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.94it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.26it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=9, loss=0.29089870566858717\n"
+ "epoch=9, loss=0.29573798736004925, train_accuracy=0.2767222222222222\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.40it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.06it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=10, loss=0.2895862699722007\n"
+ "epoch=10, loss=0.29489263888888023, train_accuracy=0.2817222222222222\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.09it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.63it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=11, loss=0.2884022627687656\n"
+ "epoch=11, loss=0.2940597046766028, train_accuracy=0.28583333333333333\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.28it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.67it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=12, loss=0.28733885125291764\n"
+ "epoch=12, loss=0.2930951674728663, train_accuracy=0.2902592592592593\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.49it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.35it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=13, loss=0.28634791216387434\n"
+ "epoch=13, loss=0.2921363403651519, train_accuracy=0.29303703703703704\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.18it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 52.17it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=14, loss=0.2854148514424351\n"
+ "epoch=14, loss=0.2913191804120471, train_accuracy=0.2968148148148148\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.90it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.48it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=15, loss=0.28452080362101867\n"
+ "epoch=15, loss=0.2905481194409614, train_accuracy=0.3003148148148148\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.99it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.09it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=16, loss=0.2836357456217646\n"
+ "epoch=16, loss=0.2897327618107022, train_accuracy=0.30348148148148146\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.99it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.48it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=17, loss=0.28274151613860843\n"
+ "epoch=17, loss=0.2888923251604361, train_accuracy=0.3059074074074074\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.90it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.91it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=18, loss=0.281839854415075\n"
+ "epoch=18, loss=0.288066184871282, train_accuracy=0.3079074074074074\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 48.01it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.17it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=19, loss=0.28095542557069053\n"
+ "epoch=19, loss=0.287267772576149, train_accuracy=0.30994444444444447\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.96it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=20, loss=0.2800934496802053\n"
+ "epoch=20, loss=0.2865018793391632, train_accuracy=0.31192592592592594\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.45it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 52.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=21, loss=0.27931142585321705\n"
+ "epoch=21, loss=0.2857739017693884, train_accuracy=0.3141481481481481\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.86it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.92it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=22, loss=0.27862350963341603\n"
+ "epoch=22, loss=0.2850868818943626, train_accuracy=0.31633333333333336\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.84it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.55it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=23, loss=0.2780074274940075\n"
+ "epoch=23, loss=0.28443768606878783, train_accuracy=0.31833333333333336\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.73it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.77it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=24, loss=0.27742971683164996\n"
+ "epoch=24, loss=0.28381835941097344, train_accuracy=0.3201111111111111\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.90it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.01it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=25, loss=0.2768283124909597\n"
+ "epoch=25, loss=0.28322076144879466, train_accuracy=0.3212037037037037\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.22it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=26, loss=0.2762288972410761\n"
+ "epoch=26, loss=0.2826426077267652, train_accuracy=0.3226111111111111\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.30it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.69it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=27, loss=0.27566807003953936\n"
+ "epoch=27, loss=0.28209041457192024, train_accuracy=0.32401851851851854\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.90it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 44.84it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=28, loss=0.27515791265861705\n"
+ "epoch=28, loss=0.28157442411695865, train_accuracy=0.32587037037037037\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.43it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.93it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=29, loss=0.2746960225210329\n"
+ "epoch=29, loss=0.28110218763111233, train_accuracy=0.3269444444444444\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.30it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.82it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=30, loss=0.2742732930281749\n"
+ "epoch=30, loss=0.2806757866602613, train_accuracy=0.32805555555555554\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.47it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.58it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=31, loss=0.2738820269527825\n"
+ "epoch=31, loss=0.28029173232982685, train_accuracy=0.3296851851851852\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.39it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.90it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=32, loss=0.2735185848652638\n"
+ "epoch=32, loss=0.2799433389227303, train_accuracy=0.33135185185185184\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.34it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 43.73it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=33, loss=0.27318125599929644\n"
+ "epoch=33, loss=0.2796231932174876, train_accuracy=0.33331481481481484\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.03it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=34, loss=0.27286824262978615\n"
+ "epoch=34, loss=0.27932445663304367, train_accuracy=0.3351296296296296\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.17it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.61it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=35, loss=0.2725772806445788\n"
+ "epoch=35, loss=0.27904132143046423, train_accuracy=0.337\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.26it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 42.06it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=36, loss=0.27230585256038187\n"
+ "epoch=36, loss=0.27876895714230787, train_accuracy=0.33820370370370373\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.63it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 44.17it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=37, loss=0.27205124966222294\n"
+ "epoch=37, loss=0.27850322994212867, train_accuracy=0.3394259259259259\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.32it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.85it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=38, loss=0.2718105212711061\n"
+ "epoch=38, loss=0.27824050162221153, train_accuracy=0.3405925925925926\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.01it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=39, loss=0.2715805048412975\n"
+ "epoch=39, loss=0.2779776936949417, train_accuracy=0.3416111111111111\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.55it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.99it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=40, loss=0.2713579421724202\n"
+ "epoch=40, loss=0.2777125812293511, train_accuracy=0.3430185185185185\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.75it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.07it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=41, loss=0.2711396156588283\n"
+ "epoch=41, loss=0.27744409078201326, train_accuracy=0.34424074074074074\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.51it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 44.92it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=42, loss=0.2709225155028859\n"
+ "epoch=42, loss=0.27717235824576475, train_accuracy=0.3454814814814815\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.47it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 43.30it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=43, loss=0.2707040834217827\n"
+ "epoch=43, loss=0.2768984772303961, train_accuracy=0.3462037037037037\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.42it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 44.31it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=44, loss=0.27048249024427656\n"
+ "epoch=44, loss=0.27662407090256175, train_accuracy=0.3471296296296296\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.65it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.15it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=45, loss=0.2702568222700594\n"
+ "epoch=45, loss=0.27635086651437507, train_accuracy=0.34779629629629627\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.82it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 41.73it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=46, loss=0.27002708728127284\n"
+ "epoch=46, loss=0.27608035980482853, train_accuracy=0.34885185185185186\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.43it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 41.73it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=47, loss=0.2697940526835046\n"
+ "epoch=47, loss=0.2758135960703844, train_accuracy=0.3496111111111111\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.13it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.92it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=48, loss=0.2695589991244579\n"
+ "epoch=48, loss=0.27555109288329865, train_accuracy=0.35053703703703704\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.92it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.07it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=49, loss=0.26932347844910215\n"
+ "epoch=49, loss=0.27529289019660375, train_accuracy=0.351462962962963\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.84it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.18it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=50, loss=0.2690891216931801\n"
+ "epoch=50, loss=0.27503866505292973, train_accuracy=0.35224074074074074\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.17it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 42.67it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=51, loss=0.26885749099705014\n"
+ "epoch=51, loss=0.2747878517717082, train_accuracy=0.35288888888888886\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.09it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 43.23it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=52, loss=0.26862995216471686\n"
+ "epoch=52, loss=0.27453973893869543, train_accuracy=0.35385185185185186\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.32it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.49it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=53, loss=0.2684075698601837\n"
+ "epoch=53, loss=0.27429353642850424, train_accuracy=0.3544259259259259\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.03it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 44.46it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=54, loss=0.26819105416268263\n"
+ "epoch=54, loss=0.27404841735400715, train_accuracy=0.3552037037037037\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.66it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.74it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=55, loss=0.26798077591671593\n"
+ "epoch=55, loss=0.2738035469545913, train_accuracy=0.3562777777777778\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.89it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.99it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=56, loss=0.26777683118236534\n"
+ "epoch=56, loss=0.2735581114679576, train_accuracy=0.356962962962963\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.74it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.93it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=57, loss=0.26757911457047184\n"
+ "epoch=57, loss=0.2733113537174717, train_accuracy=0.35785185185185187\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.86it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.71it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=58, loss=0.2673873741424862\n"
+ "epoch=58, loss=0.27306261312855745, train_accuracy=0.3587037037037037\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 44.93it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 42.40it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=59, loss=0.26720124679735535\n"
+ "epoch=59, loss=0.27281136238697473, train_accuracy=0.3594074074074074\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.88it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=60, loss=0.2670202876161004\n"
+ "epoch=60, loss=0.272557233231127, train_accuracy=0.35983333333333334\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.89it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.01it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=61, loss=0.26684400316153295\n"
+ "epoch=61, loss=0.2723000279240422, train_accuracy=0.3605\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.07it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.45it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=62, loss=0.2666718871083877\n"
+ "epoch=62, loss=0.2720397174578739, train_accuracy=0.36077777777777775\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.75it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.18it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=63, loss=0.2665034496901216\n"
+ "epoch=63, loss=0.27177643015041403, train_accuracy=0.3617037037037037\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.97it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=64, loss=0.26633823551333574\n"
+ "epoch=64, loss=0.2715104341185179, train_accuracy=0.36244444444444446\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.96it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.17it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=65, loss=0.2661758317738733\n"
+ "epoch=65, loss=0.2712421148052744, train_accuracy=0.3631111111111111\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.86it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.53it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=66, loss=0.2660158723596366\n"
+ "epoch=66, loss=0.27097194657809753, train_accuracy=0.36362962962962964\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.93it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.75it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=67, loss=0.2658580417813423\n"
+ "epoch=67, loss=0.2707004579663997, train_accuracy=0.3644074074074074\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 43.28it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.33it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=68, loss=0.2657020813316263\n"
+ "epoch=68, loss=0.2704281935309972, train_accuracy=0.36464814814814817\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.51it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.53it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=69, loss=0.2655477993399877\n"
+ "epoch=69, loss=0.27015567870185625, train_accuracy=0.3655\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.39it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=70, loss=0.265395083591377\n"
+ "epoch=70, loss=0.2698833941302225, train_accuracy=0.36616666666666664\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 43.41it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.01it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=71, loss=0.26524390709925844\n"
+ "epoch=71, loss=0.26961176313561314, train_accuracy=0.3668148148148148\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.23it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 52.37it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=72, loss=0.26509431618523055\n"
+ "epoch=72, loss=0.2693411520253892, train_accuracy=0.36757407407407405\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.01it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.61it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=73, loss=0.2649463967492245\n"
+ "epoch=73, loss=0.26907188041709956, train_accuracy=0.36827777777777776\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.89it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.58it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=74, loss=0.2648002256355748\n"
+ "epoch=74, loss=0.2688042375904098, train_accuracy=0.36907407407407405\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.63it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.86it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=75, loss=0.26465582150033307\n"
+ "epoch=75, loss=0.26853850076553537, train_accuracy=0.3695925925925926\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.41it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.26it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=76, loss=0.2645131100486962\n"
+ "epoch=76, loss=0.26827495151944597, train_accuracy=0.3698148148148148\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.51it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.67it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=77, loss=0.26437191201743476\n"
+ "epoch=77, loss=0.26801388718172725, train_accuracy=0.3703518518518519\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.09it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.16it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=78, loss=0.26423195246053405\n"
+ "epoch=78, loss=0.2677556250379854, train_accuracy=0.37072222222222223\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.63it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.71it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=79, loss=0.2640928834531869\n"
+ "epoch=79, loss=0.26750049843025353, train_accuracy=0.37164814814814817\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.22it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.00it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=80, loss=0.2639543124721884\n"
+ "epoch=80, loss=0.2672488451959534, train_accuracy=0.37187037037037035\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.24it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 48.45it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=81, loss=0.263815831592953\n"
+ "epoch=81, loss=0.2670009902032938, train_accuracy=0.37212962962962964\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.97it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.96it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=82, loss=0.26367704366914124\n"
+ "epoch=82, loss=0.26675722494266635, train_accuracy=0.37287037037037035\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.65it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.86it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=83, loss=0.2635375811525412\n"
+ "epoch=83, loss=0.26651778797618153, train_accuracy=0.37357407407407406\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.35it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 47.66it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=84, loss=0.26339711410007394\n"
+ "epoch=84, loss=0.26628285003398494, train_accuracy=0.3737962962962963\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.96it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.09it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=85, loss=0.263255347272479\n"
+ "epoch=85, loss=0.2660525062566954, train_accuracy=0.3741111111111111\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 45.05it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 46.65it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=86, loss=0.26311201049702554\n"
+ "epoch=86, loss=0.26582677573838703, train_accuracy=0.37437037037037035\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.61it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.45it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=87, loss=0.26296684935829534\n"
+ "epoch=87, loss=0.26560560616964574, train_accuracy=0.37451851851851853\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.74it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 42.67it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=88, loss=0.2628196232828335\n"
+ "epoch=88, loss=0.26538888032876734, train_accuracy=0.37483333333333335\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.33it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 42.00it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=89, loss=0.26267011491754183\n"
+ "epoch=89, loss=0.2651764220974758, train_accuracy=0.37537037037037035\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.72it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 43.30it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=90, loss=0.26251814954775665\n"
+ "epoch=90, loss=0.2649680021029322, train_accuracy=0.3758148148148148\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.33it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.35it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=91, loss=0.2623636186728697\n"
+ "epoch=91, loss=0.26476334551407715, train_accuracy=0.3764074074074074\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.63it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.17it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=92, loss=0.26220650033019977\n"
+ "epoch=92, loss=0.264562145144911, train_accuracy=0.37687037037037036\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 43.80it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.28it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=93, loss=0.26204687100089963\n"
+ "epoch=93, loss=0.2643640809247775, train_accuracy=0.37762962962962965\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.25it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.86it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=94, loss=0.26188490805869413\n"
+ "epoch=94, loss=0.26416884304514154, train_accuracy=0.3778148148148148\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.99it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.35it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=95, loss=0.26172088492855883\n"
+ "epoch=95, loss=0.26397615328760526, train_accuracy=0.37827777777777777\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.23it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 51.36it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=96, loss=0.26155516191390676\n"
+ "epoch=96, loss=0.2637857791547724, train_accuracy=0.37872222222222224\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.11it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 50.57it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=97, loss=0.2613881745000343\n"
+ "epoch=97, loss=0.2635975381374024, train_accuracy=0.3793888888888889\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 47.19it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 49.07it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=98, loss=0.26122041931305423\n"
+ "epoch=98, loss=0.2634112927800681, train_accuracy=0.3798148148148148\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 106/106 [00:02<00:00, 46.72it/s]\n"
+ "100%|██████████| 106/106 [00:02<00:00, 45.85it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch=99, loss=0.26105243706027714\n"
+ "epoch=99, loss=0.26322693939818576, train_accuracy=0.3804444444444444\n"
]
}
],
"source": [
"from utils.mlp_training import run_mlp_training\n",
+ "from utils.read_cifar import read_cifar\n",
+ "from utils.split_dataset import split_dataset\n",
"\n",
"split_factor = 0.9\n",
"d_h = 64\n",
@@ -1688,24 +1715,24 @@
"\n",
"data, labels = read_cifar('data/cifar-10-batches-py')\n",
"data_train, labels_train, data_test, labels_test = split_dataset(data, labels, split_factor)\n",
- "losses, test_accuracy = run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epochs, batch_size)"
+ "losses, test_accuracy, train_accuracies = run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epochs, batch_size)"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Test accuracy: 0.3631666666666667\n"
+ "Test accuracy: 0.375\n"
]
},
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
@@ -1716,20 +1743,54 @@
],
"source": [
"from utils.process_image import save_plot_as_image\n",
+ "import numpy as np\n",
+ "\n",
"print('Test accuracy:', test_accuracy)\n",
- "save_plot_as_image(np.arange(1, len(losses)+1), losses, 'Loss', 'Epoch', 'images/mlp_loss.png')"
+ "save_plot_as_image(np.arange(1, len(losses)+1), losses, 'Loss', 'Epoch', 'Evolution de la loss', 'images/mlp_loss.png')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "save_plot_as_image(np.arange(1, len(train_accuracies)+1), train_accuracies, 'Accuracy', 'Epoch', 'Evolution de l\\'accuracy','images/mlp_accuracy.png')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "#### Bonus: comparaison / vérification avec le même modèle en utilisant la librairie Tensorflow"
+ "### Analyse des performances du modèle\n",
+ "On termine l'entrainement avec une accuracy de 37.5% sur le jeu de test.\n",
+ "\n",
+ "Comme j'ai rajouté un paramètre `batch_size`, les poids du modèle sont mis à jour à chaque `batch_size` images (pour un `batch_size` de 512, cela correspond à 106 batch par epoch), ce qui explique certainement la différence d'accuracy après 100 epochs pour un modèle sans batch_size (ce qui correspond à un seul batch contenant la totalité des images d'entraînement).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Bonus: comparaison / vérification avec la librairie Tensorflow\n",
+ "On implémente le même modèle avec la même architecture avec la librairie Tensorflow, ainsi qu'avec la même fonction de loss et le même optimiseur.\n",
+ "Cela permet de vérifier que le modèle implémenté manuellement donne des résultats cohérents."
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -1737,212 +1798,222 @@
"output_type": "stream",
"text": [
"Epoch 1/100\n",
- "95/95 [==============================] - 3s 12ms/step - loss: 0.3572 - accuracy: 0.1308 - val_loss: 0.3235 - val_accuracy: 0.1633\n",
+ "95/95 [==============================] - 2s 12ms/step - loss: 0.3570 - accuracy: 0.1407 - val_loss: 0.3220 - val_accuracy: 0.1719\n",
"Epoch 2/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3224 - accuracy: 0.1646 - val_loss: 0.3208 - val_accuracy: 0.1948\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3199 - accuracy: 0.1990 - val_loss: 0.3183 - val_accuracy: 0.2235\n",
"Epoch 3/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3197 - accuracy: 0.2022 - val_loss: 0.3179 - val_accuracy: 0.2209\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3166 - accuracy: 0.2252 - val_loss: 0.3154 - val_accuracy: 0.2409\n",
"Epoch 4/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3167 - accuracy: 0.2277 - val_loss: 0.3149 - val_accuracy: 0.2459\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3138 - accuracy: 0.2470 - val_loss: 0.3126 - val_accuracy: 0.2420\n",
"Epoch 5/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3138 - accuracy: 0.2442 - val_loss: 0.3121 - val_accuracy: 0.2604\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3111 - accuracy: 0.2543 - val_loss: 0.3102 - val_accuracy: 0.2533\n",
"Epoch 6/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3111 - accuracy: 0.2553 - val_loss: 0.3096 - val_accuracy: 0.2687\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3087 - accuracy: 0.2646 - val_loss: 0.3079 - val_accuracy: 0.2598\n",
"Epoch 7/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3088 - accuracy: 0.2634 - val_loss: 0.3073 - val_accuracy: 0.2761\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3065 - accuracy: 0.2730 - val_loss: 0.3060 - val_accuracy: 0.2757\n",
"Epoch 8/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3066 - accuracy: 0.2700 - val_loss: 0.3053 - val_accuracy: 0.2830\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3046 - accuracy: 0.2811 - val_loss: 0.3041 - val_accuracy: 0.2870\n",
"Epoch 9/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.3047 - accuracy: 0.2767 - val_loss: 0.3034 - val_accuracy: 0.2874\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.3028 - accuracy: 0.2894 - val_loss: 0.3024 - val_accuracy: 0.2850\n",
"Epoch 10/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.3028 - accuracy: 0.2827 - val_loss: 0.3015 - val_accuracy: 0.2946\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.3011 - accuracy: 0.2912 - val_loss: 0.3009 - val_accuracy: 0.2885\n",
"Epoch 11/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.3011 - accuracy: 0.2890 - val_loss: 0.2998 - val_accuracy: 0.3002\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2997 - accuracy: 0.2963 - val_loss: 0.2995 - val_accuracy: 0.2959\n",
"Epoch 12/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2994 - accuracy: 0.2943 - val_loss: 0.2982 - val_accuracy: 0.3063\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2983 - accuracy: 0.3004 - val_loss: 0.2982 - val_accuracy: 0.3026\n",
"Epoch 13/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2979 - accuracy: 0.3009 - val_loss: 0.2967 - val_accuracy: 0.3107\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2970 - accuracy: 0.3058 - val_loss: 0.2970 - val_accuracy: 0.3022\n",
"Epoch 14/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2965 - accuracy: 0.3026 - val_loss: 0.2954 - val_accuracy: 0.3191\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2957 - accuracy: 0.3105 - val_loss: 0.2958 - val_accuracy: 0.2994\n",
"Epoch 15/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2953 - accuracy: 0.3087 - val_loss: 0.2941 - val_accuracy: 0.3176\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2946 - accuracy: 0.3104 - val_loss: 0.2947 - val_accuracy: 0.3102\n",
"Epoch 16/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2941 - accuracy: 0.3121 - val_loss: 0.2929 - val_accuracy: 0.3213\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2935 - accuracy: 0.3137 - val_loss: 0.2937 - val_accuracy: 0.3163\n",
"Epoch 17/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2931 - accuracy: 0.3150 - val_loss: 0.2919 - val_accuracy: 0.3263\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2925 - accuracy: 0.3191 - val_loss: 0.2928 - val_accuracy: 0.3130\n",
"Epoch 18/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2920 - accuracy: 0.3173 - val_loss: 0.2910 - val_accuracy: 0.3283\n",
+ "95/95 [==============================] - 1s 11ms/step - loss: 0.2915 - accuracy: 0.3200 - val_loss: 0.2918 - val_accuracy: 0.3124\n",
"Epoch 19/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2911 - accuracy: 0.3198 - val_loss: 0.2900 - val_accuracy: 0.3278\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2906 - accuracy: 0.3205 - val_loss: 0.2910 - val_accuracy: 0.3176\n",
"Epoch 20/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2902 - accuracy: 0.3223 - val_loss: 0.2891 - val_accuracy: 0.3306\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2898 - accuracy: 0.3243 - val_loss: 0.2902 - val_accuracy: 0.3150\n",
"Epoch 21/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2894 - accuracy: 0.3255 - val_loss: 0.2882 - val_accuracy: 0.3367\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2890 - accuracy: 0.3259 - val_loss: 0.2895 - val_accuracy: 0.3189\n",
"Epoch 22/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2886 - accuracy: 0.3272 - val_loss: 0.2875 - val_accuracy: 0.3359\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2882 - accuracy: 0.3262 - val_loss: 0.2888 - val_accuracy: 0.3211\n",
"Epoch 23/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2879 - accuracy: 0.3292 - val_loss: 0.2866 - val_accuracy: 0.3404\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2875 - accuracy: 0.3291 - val_loss: 0.2881 - val_accuracy: 0.3231\n",
"Epoch 24/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2872 - accuracy: 0.3316 - val_loss: 0.2859 - val_accuracy: 0.3387\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2868 - accuracy: 0.3295 - val_loss: 0.2874 - val_accuracy: 0.3228\n",
"Epoch 25/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2865 - accuracy: 0.3333 - val_loss: 0.2853 - val_accuracy: 0.3426\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2861 - accuracy: 0.3310 - val_loss: 0.2868 - val_accuracy: 0.3270\n",
"Epoch 26/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2859 - accuracy: 0.3348 - val_loss: 0.2846 - val_accuracy: 0.3420\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2855 - accuracy: 0.3331 - val_loss: 0.2863 - val_accuracy: 0.3270\n",
"Epoch 27/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2853 - accuracy: 0.3363 - val_loss: 0.2840 - val_accuracy: 0.3478\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2849 - accuracy: 0.3351 - val_loss: 0.2857 - val_accuracy: 0.3274\n",
"Epoch 28/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2847 - accuracy: 0.3382 - val_loss: 0.2834 - val_accuracy: 0.3448\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2843 - accuracy: 0.3362 - val_loss: 0.2852 - val_accuracy: 0.3324\n",
"Epoch 29/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2842 - accuracy: 0.3391 - val_loss: 0.2829 - val_accuracy: 0.3491\n",
+ "95/95 [==============================] - 1s 11ms/step - loss: 0.2838 - accuracy: 0.3394 - val_loss: 0.2848 - val_accuracy: 0.3291\n",
"Epoch 30/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2837 - accuracy: 0.3415 - val_loss: 0.2824 - val_accuracy: 0.3465\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2833 - accuracy: 0.3379 - val_loss: 0.2843 - val_accuracy: 0.3331\n",
"Epoch 31/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2832 - accuracy: 0.3424 - val_loss: 0.2818 - val_accuracy: 0.3494\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2828 - accuracy: 0.3404 - val_loss: 0.2838 - val_accuracy: 0.3298\n",
"Epoch 32/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2827 - accuracy: 0.3446 - val_loss: 0.2813 - val_accuracy: 0.3489\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2823 - accuracy: 0.3414 - val_loss: 0.2834 - val_accuracy: 0.3330\n",
"Epoch 33/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2822 - accuracy: 0.3452 - val_loss: 0.2808 - val_accuracy: 0.3535\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2819 - accuracy: 0.3431 - val_loss: 0.2830 - val_accuracy: 0.3328\n",
"Epoch 34/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2818 - accuracy: 0.3465 - val_loss: 0.2804 - val_accuracy: 0.3524\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2814 - accuracy: 0.3435 - val_loss: 0.2826 - val_accuracy: 0.3339\n",
"Epoch 35/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2813 - accuracy: 0.3472 - val_loss: 0.2799 - val_accuracy: 0.3519\n",
+ "95/95 [==============================] - 1s 11ms/step - loss: 0.2810 - accuracy: 0.3444 - val_loss: 0.2822 - val_accuracy: 0.3354\n",
"Epoch 36/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2809 - accuracy: 0.3489 - val_loss: 0.2795 - val_accuracy: 0.3533\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2806 - accuracy: 0.3460 - val_loss: 0.2818 - val_accuracy: 0.3337\n",
"Epoch 37/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2805 - accuracy: 0.3501 - val_loss: 0.2791 - val_accuracy: 0.3530\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2802 - accuracy: 0.3470 - val_loss: 0.2815 - val_accuracy: 0.3346\n",
"Epoch 38/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2801 - accuracy: 0.3502 - val_loss: 0.2787 - val_accuracy: 0.3531\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2798 - accuracy: 0.3476 - val_loss: 0.2811 - val_accuracy: 0.3378\n",
"Epoch 39/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2797 - accuracy: 0.3513 - val_loss: 0.2784 - val_accuracy: 0.3569\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2794 - accuracy: 0.3490 - val_loss: 0.2808 - val_accuracy: 0.3365\n",
"Epoch 40/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2793 - accuracy: 0.3533 - val_loss: 0.2779 - val_accuracy: 0.3593\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2791 - accuracy: 0.3498 - val_loss: 0.2805 - val_accuracy: 0.3411\n",
"Epoch 41/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2790 - accuracy: 0.3531 - val_loss: 0.2776 - val_accuracy: 0.3578\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2787 - accuracy: 0.3502 - val_loss: 0.2802 - val_accuracy: 0.3383\n",
"Epoch 42/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2786 - accuracy: 0.3549 - val_loss: 0.2772 - val_accuracy: 0.3548\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2784 - accuracy: 0.3507 - val_loss: 0.2799 - val_accuracy: 0.3385\n",
"Epoch 43/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2783 - accuracy: 0.3555 - val_loss: 0.2768 - val_accuracy: 0.3580\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2781 - accuracy: 0.3526 - val_loss: 0.2796 - val_accuracy: 0.3396\n",
"Epoch 44/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2779 - accuracy: 0.3565 - val_loss: 0.2765 - val_accuracy: 0.3576\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2777 - accuracy: 0.3541 - val_loss: 0.2793 - val_accuracy: 0.3407\n",
"Epoch 45/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2776 - accuracy: 0.3575 - val_loss: 0.2762 - val_accuracy: 0.3593\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2774 - accuracy: 0.3537 - val_loss: 0.2790 - val_accuracy: 0.3441\n",
"Epoch 46/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2773 - accuracy: 0.3589 - val_loss: 0.2758 - val_accuracy: 0.3574\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2771 - accuracy: 0.3552 - val_loss: 0.2787 - val_accuracy: 0.3441\n",
"Epoch 47/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2770 - accuracy: 0.3592 - val_loss: 0.2756 - val_accuracy: 0.3598\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2768 - accuracy: 0.3554 - val_loss: 0.2785 - val_accuracy: 0.3469\n",
"Epoch 48/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2767 - accuracy: 0.3606 - val_loss: 0.2752 - val_accuracy: 0.3622\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2765 - accuracy: 0.3571 - val_loss: 0.2782 - val_accuracy: 0.3470\n",
"Epoch 49/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2764 - accuracy: 0.3623 - val_loss: 0.2750 - val_accuracy: 0.3578\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2762 - accuracy: 0.3571 - val_loss: 0.2780 - val_accuracy: 0.3450\n",
"Epoch 50/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2761 - accuracy: 0.3621 - val_loss: 0.2747 - val_accuracy: 0.3615\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2760 - accuracy: 0.3584 - val_loss: 0.2777 - val_accuracy: 0.3467\n",
"Epoch 51/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2758 - accuracy: 0.3628 - val_loss: 0.2744 - val_accuracy: 0.3620\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2757 - accuracy: 0.3592 - val_loss: 0.2774 - val_accuracy: 0.3457\n",
"Epoch 52/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2755 - accuracy: 0.3637 - val_loss: 0.2741 - val_accuracy: 0.3609\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2754 - accuracy: 0.3591 - val_loss: 0.2772 - val_accuracy: 0.3461\n",
"Epoch 53/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2752 - accuracy: 0.3647 - val_loss: 0.2739 - val_accuracy: 0.3619\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2751 - accuracy: 0.3605 - val_loss: 0.2769 - val_accuracy: 0.3491\n",
"Epoch 54/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2749 - accuracy: 0.3649 - val_loss: 0.2736 - val_accuracy: 0.3641\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2749 - accuracy: 0.3617 - val_loss: 0.2767 - val_accuracy: 0.3476\n",
"Epoch 55/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2747 - accuracy: 0.3650 - val_loss: 0.2733 - val_accuracy: 0.3652\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2746 - accuracy: 0.3624 - val_loss: 0.2765 - val_accuracy: 0.3480\n",
"Epoch 56/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2744 - accuracy: 0.3666 - val_loss: 0.2731 - val_accuracy: 0.3619\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2743 - accuracy: 0.3628 - val_loss: 0.2762 - val_accuracy: 0.3513\n",
"Epoch 57/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2742 - accuracy: 0.3658 - val_loss: 0.2728 - val_accuracy: 0.3670\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2741 - accuracy: 0.3638 - val_loss: 0.2760 - val_accuracy: 0.3513\n",
"Epoch 58/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2739 - accuracy: 0.3672 - val_loss: 0.2726 - val_accuracy: 0.3665\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2738 - accuracy: 0.3647 - val_loss: 0.2757 - val_accuracy: 0.3519\n",
"Epoch 59/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2736 - accuracy: 0.3671 - val_loss: 0.2724 - val_accuracy: 0.3656\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2736 - accuracy: 0.3659 - val_loss: 0.2755 - val_accuracy: 0.3509\n",
"Epoch 60/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2734 - accuracy: 0.3682 - val_loss: 0.2721 - val_accuracy: 0.3674\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2733 - accuracy: 0.3663 - val_loss: 0.2753 - val_accuracy: 0.3543\n",
"Epoch 61/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2732 - accuracy: 0.3694 - val_loss: 0.2719 - val_accuracy: 0.3665\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2731 - accuracy: 0.3674 - val_loss: 0.2751 - val_accuracy: 0.3531\n",
"Epoch 62/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2729 - accuracy: 0.3701 - val_loss: 0.2716 - val_accuracy: 0.3681\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2729 - accuracy: 0.3664 - val_loss: 0.2749 - val_accuracy: 0.3539\n",
"Epoch 63/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2727 - accuracy: 0.3694 - val_loss: 0.2715 - val_accuracy: 0.3656\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2727 - accuracy: 0.3682 - val_loss: 0.2746 - val_accuracy: 0.3556\n",
"Epoch 64/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2724 - accuracy: 0.3711 - val_loss: 0.2712 - val_accuracy: 0.3700\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2724 - accuracy: 0.3691 - val_loss: 0.2745 - val_accuracy: 0.3537\n",
"Epoch 65/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2722 - accuracy: 0.3710 - val_loss: 0.2710 - val_accuracy: 0.3717\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2722 - accuracy: 0.3686 - val_loss: 0.2742 - val_accuracy: 0.3541\n",
"Epoch 66/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2720 - accuracy: 0.3714 - val_loss: 0.2708 - val_accuracy: 0.3711\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2720 - accuracy: 0.3702 - val_loss: 0.2740 - val_accuracy: 0.3559\n",
"Epoch 67/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2718 - accuracy: 0.3712 - val_loss: 0.2706 - val_accuracy: 0.3706\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2717 - accuracy: 0.3703 - val_loss: 0.2739 - val_accuracy: 0.3543\n",
"Epoch 68/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2716 - accuracy: 0.3729 - val_loss: 0.2704 - val_accuracy: 0.3687\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2715 - accuracy: 0.3711 - val_loss: 0.2737 - val_accuracy: 0.3557\n",
"Epoch 69/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2714 - accuracy: 0.3725 - val_loss: 0.2703 - val_accuracy: 0.3715\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2713 - accuracy: 0.3717 - val_loss: 0.2735 - val_accuracy: 0.3550\n",
"Epoch 70/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2711 - accuracy: 0.3731 - val_loss: 0.2699 - val_accuracy: 0.3739\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2711 - accuracy: 0.3726 - val_loss: 0.2733 - val_accuracy: 0.3570\n",
"Epoch 71/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2709 - accuracy: 0.3743 - val_loss: 0.2699 - val_accuracy: 0.3719\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2709 - accuracy: 0.3730 - val_loss: 0.2731 - val_accuracy: 0.3587\n",
"Epoch 72/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2707 - accuracy: 0.3743 - val_loss: 0.2696 - val_accuracy: 0.3722\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2707 - accuracy: 0.3724 - val_loss: 0.2729 - val_accuracy: 0.3606\n",
"Epoch 73/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2705 - accuracy: 0.3745 - val_loss: 0.2694 - val_accuracy: 0.3733\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2705 - accuracy: 0.3748 - val_loss: 0.2727 - val_accuracy: 0.3589\n",
"Epoch 74/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2703 - accuracy: 0.3750 - val_loss: 0.2693 - val_accuracy: 0.3739\n",
+ "95/95 [==============================] - 1s 10ms/step - loss: 0.2703 - accuracy: 0.3748 - val_loss: 0.2725 - val_accuracy: 0.3596\n",
"Epoch 75/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2701 - accuracy: 0.3766 - val_loss: 0.2690 - val_accuracy: 0.3739\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2701 - accuracy: 0.3751 - val_loss: 0.2723 - val_accuracy: 0.3613\n",
"Epoch 76/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2699 - accuracy: 0.3772 - val_loss: 0.2689 - val_accuracy: 0.3731\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2699 - accuracy: 0.3755 - val_loss: 0.2722 - val_accuracy: 0.3615\n",
"Epoch 77/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2697 - accuracy: 0.3774 - val_loss: 0.2688 - val_accuracy: 0.3743\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2697 - accuracy: 0.3759 - val_loss: 0.2721 - val_accuracy: 0.3607\n",
"Epoch 78/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2695 - accuracy: 0.3771 - val_loss: 0.2686 - val_accuracy: 0.3743\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2695 - accuracy: 0.3775 - val_loss: 0.2718 - val_accuracy: 0.3630\n",
"Epoch 79/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2693 - accuracy: 0.3772 - val_loss: 0.2684 - val_accuracy: 0.3761\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2693 - accuracy: 0.3765 - val_loss: 0.2717 - val_accuracy: 0.3617\n",
"Epoch 80/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2692 - accuracy: 0.3785 - val_loss: 0.2682 - val_accuracy: 0.3752\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2691 - accuracy: 0.3779 - val_loss: 0.2716 - val_accuracy: 0.3643\n",
"Epoch 81/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2690 - accuracy: 0.3792 - val_loss: 0.2681 - val_accuracy: 0.3748\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2690 - accuracy: 0.3777 - val_loss: 0.2714 - val_accuracy: 0.3654\n",
"Epoch 82/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2688 - accuracy: 0.3798 - val_loss: 0.2678 - val_accuracy: 0.3783\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2688 - accuracy: 0.3791 - val_loss: 0.2712 - val_accuracy: 0.3667\n",
"Epoch 83/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2686 - accuracy: 0.3804 - val_loss: 0.2677 - val_accuracy: 0.3774\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2686 - accuracy: 0.3793 - val_loss: 0.2710 - val_accuracy: 0.3672\n",
"Epoch 84/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2684 - accuracy: 0.3801 - val_loss: 0.2675 - val_accuracy: 0.3776\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2684 - accuracy: 0.3796 - val_loss: 0.2709 - val_accuracy: 0.3641\n",
"Epoch 85/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2683 - accuracy: 0.3810 - val_loss: 0.2674 - val_accuracy: 0.3783\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2683 - accuracy: 0.3808 - val_loss: 0.2707 - val_accuracy: 0.3659\n",
"Epoch 86/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2681 - accuracy: 0.3808 - val_loss: 0.2672 - val_accuracy: 0.3772\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2681 - accuracy: 0.3806 - val_loss: 0.2705 - val_accuracy: 0.3676\n",
"Epoch 87/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2679 - accuracy: 0.3821 - val_loss: 0.2671 - val_accuracy: 0.3774\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2679 - accuracy: 0.3809 - val_loss: 0.2704 - val_accuracy: 0.3681\n",
"Epoch 88/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2678 - accuracy: 0.3817 - val_loss: 0.2670 - val_accuracy: 0.3798\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2678 - accuracy: 0.3811 - val_loss: 0.2702 - val_accuracy: 0.3691\n",
"Epoch 89/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2676 - accuracy: 0.3830 - val_loss: 0.2668 - val_accuracy: 0.3789\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2676 - accuracy: 0.3824 - val_loss: 0.2701 - val_accuracy: 0.3700\n",
"Epoch 90/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2674 - accuracy: 0.3829 - val_loss: 0.2666 - val_accuracy: 0.3819\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2674 - accuracy: 0.3833 - val_loss: 0.2700 - val_accuracy: 0.3700\n",
"Epoch 91/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2673 - accuracy: 0.3840 - val_loss: 0.2665 - val_accuracy: 0.3822\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2673 - accuracy: 0.3828 - val_loss: 0.2698 - val_accuracy: 0.3674\n",
"Epoch 92/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2671 - accuracy: 0.3848 - val_loss: 0.2664 - val_accuracy: 0.3813\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2671 - accuracy: 0.3844 - val_loss: 0.2697 - val_accuracy: 0.3691\n",
"Epoch 93/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2669 - accuracy: 0.3848 - val_loss: 0.2662 - val_accuracy: 0.3789\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2669 - accuracy: 0.3835 - val_loss: 0.2695 - val_accuracy: 0.3706\n",
"Epoch 94/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2668 - accuracy: 0.3856 - val_loss: 0.2660 - val_accuracy: 0.3824\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2668 - accuracy: 0.3844 - val_loss: 0.2694 - val_accuracy: 0.3720\n",
"Epoch 95/100\n",
- "95/95 [==============================] - 1s 8ms/step - loss: 0.2666 - accuracy: 0.3864 - val_loss: 0.2659 - val_accuracy: 0.3841\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2666 - accuracy: 0.3850 - val_loss: 0.2692 - val_accuracy: 0.3689\n",
"Epoch 96/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2665 - accuracy: 0.3864 - val_loss: 0.2658 - val_accuracy: 0.3839\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2665 - accuracy: 0.3851 - val_loss: 0.2691 - val_accuracy: 0.3719\n",
"Epoch 97/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2663 - accuracy: 0.3868 - val_loss: 0.2656 - val_accuracy: 0.3819\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2663 - accuracy: 0.3854 - val_loss: 0.2689 - val_accuracy: 0.3750\n",
"Epoch 98/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2662 - accuracy: 0.3868 - val_loss: 0.2655 - val_accuracy: 0.3819\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2662 - accuracy: 0.3863 - val_loss: 0.2688 - val_accuracy: 0.3717\n",
"Epoch 99/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2660 - accuracy: 0.3884 - val_loss: 0.2654 - val_accuracy: 0.3815\n",
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2660 - accuracy: 0.3858 - val_loss: 0.2687 - val_accuracy: 0.3743\n",
"Epoch 100/100\n",
- "95/95 [==============================] - 1s 9ms/step - loss: 0.2658 - accuracy: 0.3877 - val_loss: 0.2652 - val_accuracy: 0.3830\n",
- "188/188 [==============================] - 1s 3ms/step - loss: 0.2656 - accuracy: 0.4020\n",
- "test_accuracy=0.4020000100135803\n"
+ "95/95 [==============================] - 1s 9ms/step - loss: 0.2659 - accuracy: 0.3867 - val_loss: 0.2686 - val_accuracy: 0.3761\n",
+ "188/188 [==============================] - 1s 3ms/step - loss: 0.2653 - accuracy: 0.3880\n",
+ "test_accuracy=0.3880000114440918\n"
]
},
{
"data": {
- "image/png": "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",
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 1000x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAHWCAYAAACbsXOkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABmH0lEQVR4nO3dd3hUZfrG8XsmyUx67yGQUENvgYiioiJFFhdFV1hXioVdxbasa/0pllXQdS2IYllFFxtrXRsoVUURFKQoEOk1PaSXSWbO7w/I6JhQgpPMJPl+rmsuyJl3zjwnOdG5ec/7HJNhGIYAAAAAAL+J2dMFAAAAAEBrQLgCAAAAADcgXAEAAACAGxCuAAAAAMANCFcAAAAA4AaEKwAAAABwA8IVAAAAALgB4QoAAAAA3IBwBQAAAABuQLgCADcxmUy699573brPl19+WSaTSXv27HHrfn8Ldx9nSkqKpkyZ4rb94diuu+46nX/++Z4uo1ls375dI0aMUFhYmEwmk95///1m/30qKChQUFCQPvnkk2Z5PwCeR7gC0KrUfXg61uObb77xdIkNeuihh/T+++97ugyvsHLlSq8LlK3B7t279e9//1t33nmnJGnYsGHH/V2pe7j7Hwyay+TJk7V582Y9+OCDWrBggdLT05u9hqioKF199dW6++67m/29AXiGr6cLAICmcP/99ys1NbXe9s6dO3ugmhN76KGHdMkll2jcuHEu26+44gpNmDBBVqvVM4Wh1XjyySeVmpqqc845R5J011136eqrr3Y+/+2332rOnDm688471b17d+f2Pn36NHutv1VlZaVWr16tu+66S9dff71Ha/nLX/6iOXPmaPny5Tr33HM9WguApke4AtAqjR492iP/Uu1uPj4+8vHx8XQZaATDMFRVVaWAgABPl+JUU1Oj1157TX/5y1+c2359eaC/v7/mzJmj888/X8OGDWvmCt2jqqpKFotFeXl5kqTw8HDPFiSpe/fu6tWrl15++WXCFdAGcFkggDanpqZGkZGRmjp1ar3nSkpK5O/vr1tuucW5LTc3V1dddZXi4uLk7++vvn376pVXXjnh+0yZMkUpKSn1tt97770ymUzOr00mk8rLy/XKK684L8WqW4N0rDUizzzzjHr27Cmr1arExERNnz5dRUVFLmOGDRumXr16acuWLTrnnHMUGBiopKQkPfLIIyesXZKqq6v117/+VTExMQoJCdGFF16oAwcONDj24MGDuvLKKxUXFyer1aqePXvqpZdeOqn3ORlffvmlLr30UrVv315Wq1XJycn661//qsrKynpjt23bpj/84Q+KiYlRQECAunXrprvuuqtevVdddZUSExNltVqVmpqqa6+9VjabTVL9n1Gdhn4eKSkp+t3vfqdPP/1U6enpCggI0HPPPSdJmj9/vs4991zFxsbKarWqR48emjdvXoPHuGjRIp199tkKCQlRaGioBg0apNdff12SNHPmTPn5+TlDwy9NmzZN4eHhqqqqOub3b9WqVcrPz9fw4cOPOeZYFi1apDPPPFNBQUEKCQnRmDFj9OOPP7qMmTJlioKDg3Xw4EGNGzdOwcHBiomJ0S233CK73e4y9s0339TAgQOdx9m7d289+eSTLmN27dqlSy+9VJGRkQoMDNRpp52mjz/+2GVM3eWjb775pv7v//5PSUlJCgwM1IwZM9ShQwdJ0t///neZTKYGfw9/6US/T3PmzJGPj4/Ltn/9618ymUyaMWOGc5vdbldISIhuu+02l/2ff/75+vDDD2UYxnHrANDyEa4AtErFxcXKz893eRQUFEiS/Pz8dNFFF+n99993fpiu8/7776u6uloTJkyQdOTyomHDhmnBggW6/PLL9c9//lNhYWGaMmVKvQ+Ep2rBggWyWq0688wztWDBAi1YsEB//vOfjzn+3nvv1fTp05WYmKh//etfGj9+vJ577jmNGDFCNTU1LmMPHz6sUaNGqW/fvvrXv/6ltLQ03XbbbVq0aNEJ67r66qv1xBNPaMSIEZo9e7b8/Pw0ZsyYeuNycnJ02mmnaenSpbr++uv15JNPqnPnzrrqqqv0xBNPNPr70ZC33npLFRUVuvbaa/XUU09p5MiReuqppzRp0iSXcZs2bVJGRoaWL1+ua665Rk8++aTGjRunDz/80Dnm0KFDGjx4sN58801ddtllmjNnjq644gp9/vnnqqioOKX6MjMzNXHiRJ1//vl68skn1a9fP0nSvHnz1KFDB915553617/+peTkZF133XV6+umnXV7/8ssva8yYMSosLNQdd9yh2bNnq1+/flq8eLGkI5eH1tbWauHChS6vs9lsevvttzV+/Hj5+/sfs76vv/5aJpNJ/fv3b9RxLViwQGPGjFFwcLAefvhh3X333dqyZYuGDh1aL/Db7XaNHDlSUVFRevTRR3X22WfrX//6l55//nnnmCVLlmjixImKiIjQww8/rNmzZ2vYsGH66quvnGNycnJ0+umn69NPP9V1112nBx98UFVVVbrwwgv13nvv1avxgQce0Mcff6xbbrlFDz30kK688ko9/vjjkqSJEydqwYIFxz0PT+b36cwzz5TD4dCqVaucr/vyyy9lNpv15ZdfOrd9//33Kisr01lnneXyHgMHDlRRUVG9UAqgFTIAoBWZP3++IanBh9VqdY779NNPDUnGhx9+6PL6Cy64wOjYsaPz6yeeeMKQZLz66qvObTabzRgyZIgRHBxslJSUOLdLMmbOnOn8evLkyUaHDh3q1Thz5kzj1//5DQoKMiZPnnzM49m9e7dhGIaRm5trWCwWY8SIEYbdbneOmzt3riHJeOmll5zbzj77bEOS8Z///Me5rbq62oiPjzfGjx9f771+acOGDYYk47rrrnPZ/sc//rHecV511VVGQkKCkZ+f7zJ2woQJRlhYmFFRUXHc9+rQoUODx/5LDe1j1qxZhslkMvbu3evcdtZZZxkhISEu2wzDMBwOh/PvkyZNMsxms/Htt9/W22fduIZ+RoZR/+dRV78kY/HixSdV98iRI13OsaKiIiMkJMTIyMgwKisrj1n3kCFDjIyMDJfn3333XUOSsWLFinrv80t/+tOfjKioqOOOeeutt1z2VVpaaoSHhxvXXHONy7js7GwjLCzMZfvkyZMNScb999/vMrZ///7GwIEDnV/fdNNNRmhoqFFbW3vMOm6++WZDkvHll186t5WWlhqpqalGSkqK87xfsWKFIcno2LFjve/z7t27DUnGP//5T5ftp/r7ZLfbjdDQUOPWW281DOPIzyUqKsq49NJLDR8fH6O0tNQwDMN47LHHDLPZbBw+fNjlfb/++mtDkrFw4cJjHjeA1oGZKwCt0tNPP60lS5a4PH45W3PuuecqOjraZSbg8OHDWrJkiS677DLntk8++UTx8fGaOHGic5ufn59uvPFGlZWV6fPPP2+eAzpq6dKlstlsuvnmm2U2//yf8GuuuUahoaH1Lp0KDg7Wn/70J+fXFotFgwcP1q5du477PnWto2+88UaX7TfffLPL14Zh6J133tHYsWNlGIbLTOHIkSNVXFys9evXn8qhuvjl+qXy8nLl5+fr9NNPl2EY+v777yVJeXl5+uKLL3TllVeqffv2Lq+vu8TP4XDo/fff19ixYxtck9fQpYAnIzU1VSNHjjxu3XWzqWeffbZ27dql4uJiSUdmc0pLS3X77bfXm336ZT2TJk3SmjVrtHPnTue21157TcnJyTr77LOPW19BQYEiIiIadUxLlixRUVGRJk6c6PJz9fHxUUZGhlasWFHvNb9c0yUdmfH55bkWHh6u8vJyLVmy5Jjv+8knn2jw4MEaOnSoc1twcLCmTZumPXv2aMuWLS7jJ0+efMrr207298lsNuv000/XF198IUnaunWrCgoKdPvtt8swDK1evVrSkdmsXr161VvrVfe9z8/PP6U6AbQchCsArdLgwYM1fPhwl0ddlzRJ8vX11fjx4/W///1P1dXVkqR3331XNTU1LuFq79696tKli8sHL0nObmp79+5thqP5Wd37devWzWW7xWJRx44d69XTrl27eoEhIiJChw8fPuH7mM1mderUyWX7r983Ly9PRUVFev755xUTE+PyqFvTlpube/IHeAz79u3TlClTFBkZ6VzPUxco6kJK3Yf4Xr16HXM/eXl5KikpOe6YU9FQZ0pJ+uqrrzR8+HAFBQUpPDxcMTExzlbodXXXhaUT1XTZZZfJarXqtddec77+o48+0uWXX35SodBo5Hqf7du3SzryDxG//tl+9tln9X6u/v7+iomJcdn263PtuuuuU9euXTV69Gi1a9dOV155pfPSxzp79+6td55Jx/6dO9b3/mQ05vfpzDPP1Lp161RZWakvv/xSCQkJGjBggPr27eu8NHDVqlU688wz671P3ff+VMM7gJaDboEA2qwJEyboueee06JFizRu3Dj997//VVpamvr27euW/R/rg9SvF/g3pWN1GmzsB+1jcTgckqQ//elPmjx5coNjfmsrb7vdrvPPP1+FhYW67bbblJaWpqCgIB08eFBTpkxx1uBOjf3ZNTRzsnPnTp133nlKS0vTY489puTkZFksFn3yySd6/PHHG113RESEfve73+m1117TPffco7ffflvV1dUuM5PHEhUVdcJA/Wt19S1YsEDx8fH1nvf1df0IcTJdLWNjY7VhwwZ9+umnWrRokRYtWqT58+dr0qRJJ9UkpiHN1ZVx6NChqqmp0erVq/Xll186Q9SZZ56pL7/8Utu2bVNeXl6D4aruex8dHd0stQLwHMIVgDbrrLPOUkJCghYuXKihQ4dq+fLl9brKdejQQZs2bZLD4XCZvdq2bZvz+WOJiIio18FPani262T/Rbvu/TIzM9WxY0fndpvNpt27d59SN7hjvY/D4dDOnTtd/lU/MzPTZVxdJ0G73e629/61zZs366efftIrr7zi0sDi15eW1X0/fvjhh2PuKyYmRqGhoccdI/18GVdRUZHLJV6Nman88MMPVV1drQ8++MDlMsVfX05XNzv4ww8/nPA+bJMmTdLvf/97ffvtt3rttdfUv39/9ezZ84S1pKWl6bXXXlNxcbHCwsJOqv66umJjY936s7VYLBo7dqzGjh0rh8Oh6667Ts8995zuvvtude7cWR06dKh3nkkn9zvXWI35fRo8eLAsFou+/PJLffnll/r73/8u6ch/R1544QUtW7bM+fWv7d69W5Jc7h8GoHXiskAAbZbZbNYll1yiDz/8UAsWLFBtba3LJYGSdMEFFyg7O9tlbVZtba2eeuopBQcHH3etS6dOnVRcXKxNmzY5t2VlZTXY8SwoKKjBIPZrw4cPl8Vi0Zw5c1xmn1588UUVFxc32M3vVIwePVrSkRbUv/Trrms+Pj4aP3683nnnnQYDS0Otwxurbkbkl8drGEa9bo0xMTE666yz9NJLL2nfvn0uz9W91mw2O7sHfvfdd/Xeq25cXbCoW2Mjydku/7fUXVxcrPnz57uMGzFihEJCQjRr1qx67dR/PcM4evRoRUdH6+GHH9bnn39+UrNWkjRkyBAZhqF169addP0jR45UaGioHnrooXpdKKVT+9nWdeysYzabnTObdZfnXnDBBVq7dq1zHZN05Hv//PPPKyUlRT169Gj0+x5LY36f/P39NWjQIL3xxhvat2+fy8xVZWWl5syZo06dOikhIaHe+6xbt05hYWEnFYQBtGzMXAFolRYtWuT8l+5fOv30013+hfqyyy7TU089pZkzZ6p37971/mV52rRpeu655zRlyhStW7dOKSkpevvtt/XVV1/piSeeUEhIyDFrmDBhgm677TZddNFFuvHGG1VRUaF58+apa9eu9Zo8DBw4UEuXLtVjjz2mxMREpaamKiMjo94+Y2JidMcdd+i+++7TqFGjdOGFFyozM1PPPPOMBg0adNIftk+kX79+mjhxop555hkVFxfr9NNP17Jly7Rjx456Y2fPnq0VK1YoIyND11xzjXr06KHCwkKtX79eS5cuVWFh4W+qJS0tTZ06ddItt9yigwcPKjQ0VO+8806Dl7nNmTNHQ4cO1YABAzRt2jSlpqZqz549+vjjj7VhwwZJ0kMPPaTPPvtMZ599tqZNm6bu3bsrKytLb731llatWqXw8HCNGDFC7du311VXXaW///3v8vHx0UsvvaSYmJh6we1YRowY4Zyl+fOf/6yysjK98MILio2NVVZWlnNcaGioHn/8cV199dUaNGiQ/vjHPyoiIkIbN25URUWFS6Dz8/PThAkTNHfuXPn4+Lg0WjmeoUOHKioqSkuXLj3pG9mGhoZq3rx5uuKKKzRgwABNmDDBefwff/yxzjjjDM2dO/ek9lXn6quvVmFhoc4991y1a9dOe/fu1VNPPaV+/fo5f/duv/12vfHGGxo9erRuvPFGRUZG6pVXXtHu3bv1zjvv1Fv/+Fs09vfpzDPP1OzZsxUWFqbevXtLOjKz161bN2VmZjrvT/drS5Ys0dixY1lzBbQFzd+gEACazvFasUsy5s+f7zLe4XAYycnJhiTjH//4R4P7zMnJMaZOnWpER0cbFovF6N27d739GEb9VuyGYRifffaZ0atXL8NisRjdunUzXn311QbbfG/bts0466yzjICAAEOSszV5Q62/DeNIq+i0tDTDz8/PiIuLM6699tp67Z/PPvtso2fPnvXqPFaL+F+rrKw0brzxRiMqKsoICgoyxo4da+zfv7/B48zJyTGmT59uJCcnG35+fkZ8fLxx3nnnGc8///wJ3+dkWrFv2bLFGD58uBEcHGxER0cb11xzjbFx48YGf6Y//PCDcdFFFxnh4eGGv7+/0a1bN+Puu+92GbN3715j0qRJRkxMjGG1Wo2OHTsa06dPN6qrq51j1q1bZ2RkZBgWi8Vo37698dhjjx2zFfuYMWMarPuDDz4w+vTpY/j7+xspKSnGww8/bLz00ksN/kw/+OAD4/TTTzcCAgKM0NBQY/DgwcYbb7xRb59r1641JBkjRow47vfs12688Uajc+fOx3z+163Y66xYscIYOXKkERYWZvj7+xudOnUypkyZYnz33XfOMZMnTzaCgoLq7fPX5/rbb79tjBgxwoiNjXV+X//85z8bWVlZLq/buXOncckllzh/hoMHDzY++uijenVJMt56661673uyrdjrnMzvk2EYxscff2xIMkaPHu2y/eqrrzYkGS+++GK912zdutWQZCxdurTecwBaH5NhcLtwAABaio0bN6pfv376z3/+oyuuuOKkX7dr1y6lpaVp0aJFOu+885qwQvzSzTffrC+++ELr1q1j5gpoAwhXAAC0INdff71eeeUVZWdnKygoqFGvvfbaa7Vjx47j3mcK7lNQUKAOHTrov//9ry644AJPlwOgGRCuAABoAT788ENt2bJFd999t66//no99thjni4JAPArhCsAAFqAlJQU5eTkaOTIkVqwYMFxm6kAADyDcAUAAAAAbsB9rgAAAADADQhXAAAAAOAG3ES4AQ6HQ4cOHVJISAhtUwEAAIA2zDAMlZaWKjEx8YQ3MidcNeDQoUNKTk72dBkAAAAAvMT+/fvVrl27444hXDWgrgPT/v37FRoa6uFqAAAAAHhKSUmJkpOTT6pLK+GqAXWXAoaGhhKuAAAAAJzUciEaWgAAAACAGxCuAAAAAMANCFcAAAAA4AaEKwAAAABwA8IVAAAAALgB4QoAAAAA3IBwBQAAAABuQLgCAAAAADcgXAEAAACAGxCuAAAAAMANCFcAAAAA4AaEKwAAAABwA8IVAAAAALiBr6cLAAAAAND6GIah/DKbgq2+CrD4HHdspc2urdkl2nKoRFuySvTjoRLFhVj1/KT0ZqrWPQhXAAAAAH6T/LJqZWaXKjO7VD/llCozp1Tbc8pUVl0rSQqy+Cg6xKroYKtigq2KDrEoPMCifYUV2pJVol15ZXIYrvuMCrLIMAyZTCYPHNGpIVwBAAAAbZBhGDpcUaNDRZU6cLhShytsqnUYcjgM2R2GHMaRP+2GoZpaQ6VVNSqrrlVpVa1KqmpUWlWr0qoaFZbbdLii5rjvVW6zq7ygQnsLKo45JjrYqp6JoeqRGHrkz4RQdx9ykyNcAQAAAC3EgcMVWrY1VyaT1C0uRGnxoQoL9Dvua/JKq7U1q0Tbsku0O79cB4uqdPBwhQ4VVamyxu6WukwmqUNkoLrGhSgtPkRd40PULS5EKdFBqqqxK7/MpvyyauWXViu/rFp5pdUqrLApISzAGaZiQ/zdUosnEa4AAAAAL3bgcIUWbc7Wx5uztGF/Ub3nE8L81S0+RN3ijwQbh0Pall2irVml2pZdovwy23H3HxNiVWJ4gGKCLfI1m+VjNslsNsnHpKN/muTrY1aov6+Crb4K8fdViL+fQvx9Fezvq/AAi1Kjg465rsrPx6wQfz+lRge549vh1QhXAAAAQBOrtNmdgWdLVrG2ZpVqZ16Zgq2+SgwPUGKYvxLDA5QQHqCkcH9FBVm1dndhvUBlMkmDUyIVbPXVtuxSHSyqVFZxlbKKq7QyM6/B9zaZpNSoIKUlhKhzbIjahQcoKSJASeEBig/zl7/f8ZtN4OQRrgAAAAA3MgxDu/PL9dWOfK3ZXagtWUcuxzOM+mOLKmp04HDlcfdXF6jG9EnQqF7xLpfPlVTV6KfsUm3NLlVmdokys0tlNpnUPSFUafEh6p4Qqq5xISfs1gf3IFwBAAAAv1FuSZW+2pmvVdsL9PXOfGUVV9UbEx1sVY/EUHVPCFGPo6Gnwlarg0VVyiqq1KGiyiN/L65UTkmVOsUENxiofinU30/pKZFKT4ls6kPESSBcAQAAoNUrqarRur2HVVhmU5DVR4EWXwVZj6whCrT4KNjqK7PJpKJKm4oqalRUWaOiCptKKmtUVFGjkqoaVdc6VFVjd/mzusah3NIq7cwrd3k/i49ZAzqE6/RO0eqbHK7uCSHHDEgDOzTHdwDNgXAFAACAVqegrFrf7jmsNbsLtHZ3obZmldS7j5I7mUxSr8Qwnd45SkM7Ryu9QySX4rVBhCsAAAB4LVutQ+XVtSqrrlWFza6y6lqVH32U1T2qalVmO/JnaVWttmaVaHtuWb19pUQFqn1UkCp+sb/y6lqV22pVVeOQJAVafBQe4KewQIvCA/wUHuinsIAjnfEC/Hxk9fOR1dcsq5+P/I/+GWL1Vf/24QoPtDT3twdehnAFAAAAr1BUYdMX2/O1MjNXX+8oUEF5tWrspz7d1DUuWINTI5WRGqXBqZGKCz32fZRq7Q45DMniaz7l9wMIVwAAAGgSRRU2/XCwRBZf89F7Ix25P1Kw1Vc+ZpMMw9CPh0r0+U95WrEtV+v3HT7mpXsWX7OCrb4KsvooyHJkrVSw/5F1UyHWn9dPBVt9lRwZqMGpkYoMOvmZJF8fQhV+O8IVAAAAjmlvQbmWbMnRwaJKdYsLUa+kMHWNC2lwhscwDG3LLtXybbknDEtBFh+ZzSaVVtW6bO8SG6xz0mI1rGuMOkQHKdjiq0Crj/wIP2gBCFcAAABwsjsMbdhfpKVbc7R0S06Da5csPmZ1iw9Rr6RQ9UoKU2SgRV/uyNeKbbn1WpCnRAVKksqqa1VSVStb7ZG1TeU2uyQpwM9HZ3SO1rBuMRrWLUbtIgKb+AiBpkO4AgAAaGMcDkNFlTUqLK9WQZlNheU2FZTbtHF/kZZvy1VBuc051tdsUkbHSHWLC1VmTok2HyhWSVWtNh8s1uaDxZL2u+zb38+sMzpFa1harM5pICxV19pVerTxRKXNrk6xQbL60lUPrQPhCgAAoIWrqqkLLDUqrapVYYVN+aXVyi+zKb+s+udHqU0F5dUqLLcdty15qL+vhnWL1fAecTq7a4zCAvyczxmGoQOHK7X5YLF+OBqw8kqrNTg1UuekxWpIxyj5+x07LFl9fWQN9lF0sNWd3wLAKxCuAAAAvEiFrVZvrN2vt77br3Jbrcwm09GHnH83maTKo4GqrKpWNrvjlN4r1N9XUcFWRQZZFBlkUYfIQJ3bPVaDUiKPucbJZDIpOTJQyZGBuqB3wm85VKDVIVwBAAB4geLKGv3n6z166avdOlxRc0r7CDnaQS8i0KLoEKuigy2KCbYqOtiq6BDLkT+DrYoKsigiyEKTCMDNCFcAAABuZqt1qKjCpsMVNSq31Som2KrYUGuDa4vySqv10le7tWD1XpVVH+mc1z4yUH8+u6O6J4TKMAw5jCPrpOyGIcOQHIahAD8fBR9tbR7i76tgi6/MZlNzHyqAXyBcAQAANFJJVY1+yi7V1uxSZWaXaG9BhQrLbSqqqNHhCpsqjnbC+7WoIIviQv0VH3bkYbcben/DQVUf7aDXLS5E153TSWN6J3DfJaAFIlwBAAA0wOEwlFNapf2FldpXWKFdeWXall2qzOxSHSyqPOHrzSYpPNCiAD8f5ZVVy1brUMHRrnxbskpcxvZNDtf153TWeWmxzD4BLRjhCgAAtFkOh6GDRZXakVemnbll2ltQoX2FFdp/uEIHCiuP2ygiIcxfafEh6hYfqs6xwYoKsig80E8RgRZFBFoU4v/zZXqGYaiookZZxVXKKalSdkmVsoqrVFJZo/N7xOn0TlEymQhVQEtHuAIAAK1Gda1da3cXqrDc5lybVLdeqe7vOSVV2plXpp155dqVV+a8JK8hvmaTEsMD1D4yUO2jAtX9aJjqFheisEC/Y77u10wmkyKONpHokRjqjkMF4IUIVwAAoEUrr67V5z/lafEP2Vq+LdfZFOJkWXzMSo0OUqfYIKVEBalDVKCSI460Gk8I82ftE4CTRrgCAABeodJm1/bcUue6pt355Qqy+iom2KqYkJ8fsSFWhfj7au3uQi3+IVuf/5TnMvsUG2JV59hg+ZhNMrncH+roDFKgnzrHBqtTzJFHcmSgfFjnBMANCFcAAMAjtmaVaMmWHG3NKlFmdqn2FJTLYZzavtpHBmpUr3iN7Bmv/snhNIUA4BGEKwAA8JtkF1cpv6xanWOD5e9X/z5Ov5RXWq3/bTiod9cfrNcxT5IigyxHm0SEqHNssKpqHMotrVJeabXzkV9WrYJym7rEBmtUrwSN6hmv7gkhNIQA4HGEKwAAcNKqa+368VCJ1u89rO/3F+n7vYd1qLhKkuRjNqlLbLB6Joapd1KoeiWFqUdiqMwmk5Zvy9U76w5o5U95sh+dnrL4mHVOWowGpUSq29FAFRNsPamQZBgGYQqA1yFcAQAASVJuaZX2FVSouLKmwcee/HL9cKhEtl911zObpNAAPxVV1Ghb9pE1U++s//k5fz8fl5vq9ksO1/gBSRrbN1HhgZZTqpVgBcAbEa4AAGijCsqq9c2uQq3ela+vdxZoV175Sb0uMsii/snhGtAhQv3bh6tvu3AFWnyUU1KtzQeL9cPRx+aDxcotrVaFza6EMH9d1D9JFw9op86xwU18ZADgGYQrAADagFq7Q3sLK7Qtq1Tf7inUN7sKtC271GWMySQlRwQqPNBPYQF+Cg04+qf/kT/jw6zqnxyhDlGBDc4cxYf5Kz7MX+f3iHNuyy2pUl5ZtdLiQ+nIB6DVI1wBANCKGIahrOIqbcsuUWZ2mX7KOdLWfEdeWb3L+SQpLT5EQzpFaUjHKGWkRjXqxrgnIzbUX7Gh/m7dJwB4K8IVAAAtlGEYyi6p0uYDRy7B23TgyOV4BeW2BscH+Pmoa1ywercL0+mdopWRGqmoYGszVw0ArRfhCgAAL2MYhg5X1GhfYYUOl9tUXFmjogqbiiprVFRxpLlEflm1tmaVKr+sut7rfc0mdY4NVte4Ix34usaFqFtciNpFBHD/JwBoQoQrAAA8pKrGrq1ZJdpTUK7d+RXak1+uPQXl2pNfrpKq2pPaR1378z7twtQ7KUy924UrLT7khPebAgC4H+EKAIBmUlVj1/q9h/XN7kKt2VWg7/cXNbgOqk5CmL+igi0KD7AoLNBP4UcbTIQH+ik8wKLOccHqkRBKkAIAL0G4AgCgiVTX2rVu72F9vaNAa3YXaOP+YtnsrmEqOtiizrHBSo0OUkpUkFKO/tkhKpDQBAAtDOEKAAA3cTgMbc0u0Vc78vXl9nx9u6dQVTWuYSo+1F8ZHSOVkRqljI6R6hgdxA1xAaCVIFwBANAAwzCUU1KtnXllRx65ZTpcUSNfs0m+Pib5mM3Ov/uaTcouqdbXO/LrdeqLDbHqjM7ROq1jpE7rGKX2kQ3fIwoA0PIRrgAAbV7djNPXOwq0JavEGabKbfZG7yvQ4qPTOkbpjM7ROrNLtLrEBhOmAKCNIFwBANqkg0WV+mp7vr7ckd/gjJN0pBNfh8hAdYwJVqfYIMUEW+UwDNXYDdkdhmodhuwOh2rthgItvhrSKUr9ksNl8TV74IgAAJ5GuAIAtBpVNXYt25qrpVtzVFpVK7NJMptM8jGbZDr6d0PSjweLtSu/3OW1gRYfZaRGKj0lUp1igtU5NkjtI4MISgCAk0a4AgC0aLZah1btyNMHGw5pyZack76Uz8dsUt92YRraOVpndI5W//YRBCkAwG9CuAIAeD2Hw1BFjV0V1bWqsNlVbqtVbmm1PvsxW4t+yFZRRY1zbFJ4gH7XN0EpUUFyGIYcxpHmFHbHz39PjgzUkE5RCvX38+BRAQBaG8IVAMCr5JVWa2VmrlZk5uq7PYdVUlVTr535r8WEWDWmd4LG9k3UgPbhNJAAAHgE4QoA0GTsDkPr9h7WjtwyhQf6KTLIoqggiyKDLAoPtMjHbJLDYWjzwWIt33YkUG06UHzM/ZlNUqDFV4EWHwVbfTU4NVIX9k1URsco+ZgJVAAAzyJcAQDcqtJm15fb87RkS46Wb8ttsAufdCQoRQRa5DAMHf7FZX2S1DspTOd0i9FZXWMUF+qvQIuPgqy+svqamZUCAHgtwhUA4Dc7XG7Tki05+mxLjlbtyHO5jC/U31f920eorLpWheU2FZRVq6SqVg5DzuAVbPXV0M7ROjctVsO6xSg21N9ThwIAwCkjXAEATomt1qEVmbl6Z90BrcjMVY3dcD6XFB6g83vEaUSPOA1KjZSfj2sXvhq7Q4fLbSoot8lW61D3hFA69QEAWjyv+D/Z008/rZSUFPn7+ysjI0Nr16495th3331X6enpCg8PV1BQkPr166cFCxa4jJkyZYpMJpPLY9SoUU19GADQ6hmGoU0HijTzfz8o46Gl+vOCdfpsS45q7IZ6JITqr8O76pMbz9Sq287RvRf21Omdo+sFK0ny8zErNtRf3RNC1Zeb7gIAWgmPz1wtXLhQM2bM0LPPPquMjAw98cQTGjlypDIzMxUbG1tvfGRkpO666y6lpaXJYrHoo48+0tSpUxUbG6uRI0c6x40aNUrz5893fm21WpvleACgNaixO5RXWq2s4irllFQpu7hKWcWVWpmZp+25Zc5xsSFWXdQ/SRcPaKdu8SEerBgAAM8zGYZhnHhY08nIyNCgQYM0d+5cSZLD4VBycrJuuOEG3X777Se1jwEDBmjMmDF64IEHJB2ZuSoqKtL7779/SjWVlJQoLCxMxcXFCg0NPaV9AIC3MQxDheU27covV25JtQorbDpcblNhuU2HK37+M7ekWnll1TrW/x2svmaN6Bmv8QOSNLRztHwbmJkCAKC1aEw28OjMlc1m07p163THHXc4t5nNZg0fPlyrV68+4esNw9Dy5cuVmZmphx9+2OW5lStXKjY2VhERETr33HP1j3/8Q1FRUQ3up7q6WtXV1c6vS0pKTvGIAMDzDMPQ/sJK/ZRTqp15ZUcf5dqZV+Zys90T8TWbFBfqr/gwf8WH+isu1F9pCSEa1Suem+8CANAAj4ar/Px82e12xcXFuWyPi4vTtm3bjvm64uJiJSUlqbq6Wj4+PnrmmWd0/vnnO58fNWqULr74YqWmpmrnzp268847NXr0aK1evVo+Pj719jdr1izdd9997jswAGhmlTa7vtlVoBWZuVqZmad9hRUNjjOZpMSwACWFBygi6Mh9pyICLS5/RgdbFR/mr6ggi8zcOwoAgJPm8TVXpyIkJEQbNmxQWVmZli1bphkzZqhjx44aNmyYJGnChAnOsb1791afPn3UqVMnrVy5Uuedd169/d1xxx2aMWOG8+uSkhIlJyc3+XEAwKmyOwztK6zQFz/laUVmrlbvLFB17c/tz/18TOoSG6JOscHqFBOkTjHB6hQTrNToIAVY6v8jEwAA+O08Gq6io6Pl4+OjnJwcl+05OTmKj48/5uvMZrM6d+4sSerXr5+2bt2qWbNmOcPVr3Xs2FHR0dHasWNHg+HKarXS8AKA18ktrdK2rFIdOFypQ0WVOlj3OFyp7JIq2R2ui6ISw/w1LC1Ww7rG6IzO0Qqytsh/PwMAoMXy6P95LRaLBg4cqGXLlmncuHGSjjS0WLZsma6//vqT3o/D4XBZM/VrBw4cUEFBgRISEn5ryQDQJIorarTpYJE2HSjWpgNH/swqrjrua/x8TBrYIULDusXqnG6x6hoXLJOJy/gAAPAUj/+z5owZMzR58mSlp6dr8ODBeuKJJ1ReXq6pU6dKkiZNmqSkpCTNmjVL0pH1Uenp6erUqZOqq6v1ySefaMGCBZo3b54kqaysTPfdd5/Gjx+v+Ph47dy5U7feeqs6d+7s0qodADzJVuvQN7sK9NmWbH25PV97C+qvkTKZpI7RQeoQFaSk8AAlhgcoKSJASeH+SgoPVEyIVT6siQIAwGt4PFxddtllysvL0z333KPs7Gz169dPixcvdja52Ldvn8zmn9v8lpeX67rrrtOBAwcUEBCgtLQ0vfrqq7rsssskST4+Ptq0aZNeeeUVFRUVKTExUSNGjNADDzzApX8APKq0qkYrM/P02ZYcrdyWq9LqWpfnO0QFqndSmPq2C1efdmHqmRSmYC7tAwCgxfD4fa68Efe5AvBbGYahrOIqZWaXalt2qb7ZVaCvd+arxv7zf3JjQqw6v0echneP1YD2EQoPtHiwYgAA0JAWc58rAGjJauwOHS63Kb/MpoLyau0tqFBmdunRQFWikqraeq/pGB2kET3jNaJnnPq1C6fVOQAArQjhCgBOoMZ+ZH3U4h+ytSO3TAXlNhWUVevwCW7I62s2qWNMkLrFh6pXYqjO6x6nzrHBzVQ1AABoboQrAGiArdahr3fm65PNWfpsS46KjhGkzCYpMsiiqKAjN95Niw9RWkKIusWFqlNskKy+3FMKAIC2gnAFAEfllFTp+31FWro1R5/9mO1yWV9UkEUje8UrIzVSMSFWRQdbFRVkUXighY59AABAEuEKQBt1uNymTQeLtWl/kTYeKNbmg0XKKXG9X150sFWje8VrdO94DU6JlK+P+Rh7AwAAIFwBaENstQ69ve6AXly1Szvzyus9bzZJXeNCdFrHKI3uFa/0lEhmpQAAwEkjXAFokQrKqrUyM0+pMUHq2y78uCGoutau/353QPNW7NCh4irn9tToIPVpF6Y+dfeVSgxVoIX/LAIAgFPDpwgALYphGHp3/UE98PEWZ5OJyCCLzu4ao3PSYnV2lxiFBfpJkqpq7Hpz7T49+/kuZZccCVWxIVb95exOGj+gnXMcAACAOxCuALQY+wsrdOd7m/Xl9nxJUvvIQB2usKmw3Kb3vj+o974/KB+zSQPbR6hPuzB9sPGQckuPrKNKCPPXtcM66Q/pyfL3o4MfAABwP8IVAK9ndxh6+es9evTTTFXW2GXxNeum87po2lkdJUnr9h7Wim25WpGZq59yyrR2T6HW7imUJCWFB+jaYZ10aXo72qIDAIAmZTIMw/B0Ed6mpKREYWFhKi4uVmhoqKfLAdq0rVkluv2dTdp4oFiSlJEaqVkX91bHmIZvxru/sEIrM3O1YX+x0lMiNH5AO1l86fIHAABOTWOyAeGqAYQrwHNySqq0cX+RNh4o0sb9xfpmV4FqHYZC/H115wXddVl6ssx08AMAAM2kMdmAywIBeNT2nFIt2ZpzJFDtL3Y2nvilkT3jdP/veyku1N8DFQIAAJwcwhWAZldjd+izH3O04Js9+mZXoctzdfea6tsuXH2SwzSgfYS6JzCDDAAAvB/hCkCzyS6u0htr9+mNtfucXfzMJumcbrE6rWOU+iaHq2diqIKs/KcJAAC0PHyCAdCkDMPQ6l0FevWbvfr0xxzZHUeWeUYHWzVxcLImDm6vxPAAD1cJAADw2xGuADSJ4soavbPugF5bs1c788qd2wenROpPQzpoVM94uvgBAIBWhXAFwK02HyjWq9/s1f82HlRVjUOSFGTx0bj+SfrTaR1YPwUAAFotwhWA3+xQUaWWb8vVW+sOaOP+Iuf2bnEh+tNp7TWuf5JC/P08VyAAAEAzIFwBaLRau0Pf7y/S8m25WrEtV9uyS53PWXzMGt07Xn86rYPSO0TIZOKeVAAAoG0gXAE4pgpbrfJKq5VfVq28UpvySqv07Z7D+vynPBVX1jjHmUxS/+RwjegZr0sGtlN0sNWDVQMAAHgG4QqA06YDRXpy6XZtzy1Tflm1Kmz2Y44NC/DT2V1jdE5ajM7uGqvIIEszVgoAAOB9CFcAlFtapX8uztTb6w/IMFyf8/czKzrYqpgQq6KDreoSG6xz0mLVPzlcvj50+wMAAKhDuALaMFutQy9/vVtzlu1QWXWtJOmi/kmaOLi9YkOsig6xKsjiw7opAACAk0C4AtogwzC0fFuu/vHxVu3OP3IPqj7twjRzbE8N7BDh4eoAAABaJsIV0AqVVdfq292FOlxhU3l1rcptdpVX16qsulYV1XbtLijX2t2FkqToYKtuG9VN4we0k9nMDBUAAMCpIlwBrUSFrVbLt+Xq401ZWr4tV9W1juOO9/Mx6cqhqbr+nM7cgwoAAMANCFdAC1ZVY9eKbbn6aHOWlm/NVWXNz939kiMDlBIVpCCLr4Ksvgq2+ijQ6qvgo49h3WLUISrIg9UDAAC0LoQroAUyDEMvrtqtx5f8pHKba6Aa0ztRv+uToJ6JoTSiAAAAaEaEK6CFOVxu0y1vbdSybbmSpKTwAI3pk6Df9UlQ76QwAhUAAICHEK6AFmTd3sO64fX1OlRcJYuvWff8rocuz2hPoAIAAPAChCugBXA4DL3w5S7989NM1ToMpUYHae4f+6tnYpinSwMAAMBRhCvAyx0ut+lvb23U8qOXAY7tm6iHLupFhz8AAAAvQ7gCvITdYaigvFq5JdXKLq5STmmVcoqr9Na6A8o6ehngvWN7auLgZC4DBAAA8EKEK6CZlVTVaHtOqTKzy/RTTqkys0u1p6BcuaXVsjuMBl/TMTpIc/84QD0SQ5u5WgAAAJwswhXQDD7ceEjvrj+gn3LKdLCo8pjjzCYpOtiq+DB/xYb4Ky7UqpSoIE3MaK9gK7+uAAAA3oxPa0AT+++3+3XrO5tctiWE+atrXIi6xYeoW1yIOsUGKyHMX1FBFvn6mD1UKQAAAH4LwhXQhBb/kK3b3z0SrP6Y0V4X9U9S17gQhQXQjAIAAKC1IVwBTeTrHfm68Y3v5TCky9KT9eC4XjSiAAAAaMW4/ghoAhv3F+ma/3wnm92hUT3j9eBFBCsAAIDWjnAFuNmO3DJNmb9W5Ta7zugcpScn9mMdFQAAQBvAJz7AjQ4WVeqKF9focEWN+rYL03NXpMvq6+PpsgAAANAMCFeAmxSUVeuKF9coq7hKnWKCNH/qYNqnAwAAtCGEK8ANfsop1eX/XqNdeeVKDPPXgqsyFBlk8XRZAAAAaEb8szrwG9TaHXrui116cul22ewORQVZtODqDCWGB3i6NAAAADQzwhVwin7KKdXf39qojQeKJUnnpsXqoYt6Kz7M38OVAQAAwBMIV0Aj/Xq2KsTfVzPH9tT4AUm0WwcAAGjDCFfAr2w5VKLskkr5ms3yNZvkYzbJ18ckX7NZFTa7Zi/aymwVAAAA6iFcAUcZhqHHl27XnGXbTziW2SoAAAD8GuEKkORwGLr/oy16+es9kqQeCaGSJLvDUK3DoVqHoVq7IbvD0MAOEbr7dz2YrQIAAIALwhXavFq7Q7e+s0nvrj8oSbrvwp6afHqKZ4sCAABAi0O4QptWVWPXjW98r8+25MjHbNKjl/bRRf3bebosAAAAtECEK7RZZdW1mvaf7/T1zgJZfM16+o8DdH6POE+XBQAAgBaKcIU26XC5TVNe/lYb9xcpyOKjFyan6/RO0Z4uCwAAAC0Y4QptTl5ptS7/9zf6KadM4YF+emXqYPVNDvd0WQAAAGjhCFdoU4orazTppbX6KadMcaFWLbgqQ13jQjxdFgAAAFoBwhXajEqbXVe/8q22ZpUoOtiqhdOGKCU6yNNlAQAAoJUwe7oAoDnU2B2a/vp6fbvnsEL8ffWfKwcTrAAAAOBWhCu0eg6HoVve2qjl23Ll72fWS1MGqUdiqKfLAgAAQCtDuEKrZhiG7vvwR/1vwyH5mk2ad/lADUqJ9HRZAAAAaIW8Ilw9/fTTSklJkb+/vzIyMrR27dpjjn333XeVnp6u8PBwBQUFqV+/flqwYIHLGMMwdM899yghIUEBAQEaPny4tm/f3tSHAS/05LLtemX1XknSo5f21TlpsR6uCAAAAK2Vx8PVwoULNWPGDM2cOVPr169X3759NXLkSOXm5jY4PjIyUnfddZdWr16tTZs2aerUqZo6dao+/fRT55hHHnlEc+bM0bPPPqs1a9YoKChII0eOVFVVVXMdFrzAy1/t1hNLj4Tqe8f20Lj+SR6uCAAAAK2ZyTAMw5MFZGRkaNCgQZo7d64kyeFwKDk5WTfccINuv/32k9rHgAEDNGbMGD3wwAMyDEOJiYn629/+pltuuUWSVFxcrLi4OL388suaMGHCCfdXUlKisLAwFRcXKzSUtTktjWEY+s/qvZr5wY+SpJvO66K/nt/Vw1UBAACgJWpMNvDozJXNZtO6des0fPhw5zaz2azhw4dr9erVJ3y9YRhatmyZMjMzddZZZ0mSdu/erezsbJd9hoWFKSMj45j7rK6uVklJicsDLZOt1qG73v/BGaymnJ6im4d38XBVAAAAaAs8ep+r/Px82e12xcXFuWyPi4vTtm3bjvm64uJiJSUlqbq6Wj4+PnrmmWd0/vnnS5Kys7Od+/j1Puue+7VZs2bpvvvu+y2HAi9QWG7Tta+u05rdhTKZpNtGpenPZ3WUyWTydGkAAABoA1rkTYRDQkK0YcMGlZWVadmyZZoxY4Y6duyoYcOGndL+7rjjDs2YMcP5dUlJiZKTk91ULZrDtuwSXf3KdzpwuFLBVl89OaGfzused+IXAgAAAG7i0XAVHR0tHx8f5eTkuGzPyclRfHz8MV9nNpvVuXNnSVK/fv20detWzZo1S8OGDXO+LicnRwkJCS777NevX4P7s1qtslqtv/Fo4Cmf/Zitvy7coHKbXe0jA/XvyenqGhfi6bIAAADQxnh0zZXFYtHAgQO1bNky5zaHw6Fly5ZpyJAhJ70fh8Oh6upqSVJqaqri4+Nd9llSUqI1a9Y0ap/wfoZhaO7y7Zq2YJ3KbXYN6Ril/00/g2AFAAAAj/D4ZYEzZszQ5MmTlZ6ersGDB+uJJ55QeXm5pk6dKkmaNGmSkpKSNGvWLElH1kelp6erU6dOqq6u1ieffKIFCxZo3rx5kiSTyaSbb75Z//jHP9SlSxelpqbq7rvvVmJiosaNG+epw4SbORyG7np/s95Yu1+SNGlIB939ux7y8/H43QUAAADQRnk8XF122WXKy8vTPffco+zsbPXr10+LFy92NqTYt2+fzOafPzCXl5fruuuu04EDBxQQEKC0tDS9+uqruuyyy5xjbr31VpWXl2vatGkqKirS0KFDtXjxYvn7+zf78cH9DMPQPR/8oDfW7pfZJN3/+17602kdPF0WAAAA2jiP3+fKG3GfK+9lGIbu+3CLXv56j0wm6bE/9NVF/dt5uiwAAAC0Ui3mPldAYxiGoQc/3qqXv94jSXp4fB+CFQAAALwG4QotgmEYenhxpv69arckadbFvfWHdNrlAwAAwHsQrtAiPLbkJz37+U5J0gO/76mJg9t7uCIAAADAFeEKXu/Jpdv11PIdkqSZY3voiiEpni0IAAAAaIDHuwUCDam1O7R6V4He+u6APth4SJJ01wXdNfWMVA9XBgAAADSMcAWvYRiGNh4o1v82HNSHG7OUX1btfO7WUd10zVkdPVgdAAAAcHyEK3hcbmmVXv1mnz7YcFB7Ciqc28MD/TSmd4IuHtBOAztEeLBCAAAA4MQIV/Coqhq7/vDsameo8vcza0SPeP2+X6LO7BIjiy/LAgEAANAyEK7gUc+s3Kk9BRWKC7XqjtHddX6POAVZOS0BAADQ8vApFh6zJ7/c2V595tieuqB3gocrAgAAAE4d11zBIwzD0L0f/ihbrUNndonW6F7xni4JAAAA+E0IV/CIz7bkaGVmniw+Zt13YU+ZTCZPlwQAAAD8JoQrNLsKW63u/3CLJGnaWR3VMSbYwxUBAAAAvx3hCs3u6RU7dLCoUknhAZp+TmdPlwMAAAC4BeEKzWpnXpme/2KXJOmesT0UYPHxcEUAAACAexCu0GwMw9C9H/yoGruhc7rFaESPOE+XBAAAALhNo8NVSkqK7r//fu3bt68p6kEr9snmbH25PV8WX7PupYkFAAAAWplGh6ubb75Z7777rjp27Kjzzz9fb775pqqrq5uiNrQi5dW1euCjI00srj27kzpEBXm4IgAAAMC9TilcbdiwQWvXrlX37t11ww03KCEhQddff73Wr1/fFDWiFZizbLuyS6qUHBmga4d18nQ5AAAAgNud8pqrAQMGaM6cOTp06JBmzpypf//73xo0aJD69eunl156SYZhuLNOtGCZ2aV6cdVuSdJ9F/aUvx9NLAAAAND6+J7qC2tqavTee+9p/vz5WrJkiU477TRdddVVOnDggO68804tXbpUr7/+ujtrRQtkGIbufv8H1ToMnd8jTuem0cQCAAAArVOjw9X69es1f/58vfHGGzKbzZo0aZIef/xxpaWlOcdcdNFFGjRokFsLRcv09roDWrunUAF+Prr3wp6eLgcAAABoMo0OV4MGDdL555+vefPmady4cfLz86s3JjU1VRMmTHBLgWi5DpfbNGvRNknSTcO7KCk8wMMVAQAAAE2n0eFq165d6tChw3HHBAUFaf78+adcFFqHRz7dpsJym7rGBeuqoameLgcAAABoUo1uaJGbm6s1a9bU275mzRp99913bikKLd+6vYV6Y+1+SdKDF/WWnw/3qwYAAEDr1uhPvNOnT9f+/fvrbT948KCmT5/ulqLQstXaHbrrvR8kSZcObKdBKZEerggAAABoeo0OV1u2bNGAAQPqbe/fv7+2bNnilqLQsr389R5tyy5VeKCf7rigu6fLAQAAAJpFo8OV1WpVTk5Ove1ZWVny9T3lzu5oJbKKK/X4kp8kSbePSlNkkMXDFQEAAADNo9HhasSIEbrjjjtUXFzs3FZUVKQ777xT559/vluLQ8tz/4dbVG6za0D7cP0hPdnT5QAAAADNptFTTY8++qjOOussdejQQf3795ckbdiwQXFxcVqwYIHbC0TLsSIzV4t+yJaP2aQHL+ots9nk6ZIAAACAZtPocJWUlKRNmzbptdde08aNGxUQEKCpU6dq4sSJDd7zCm1DVY1dM//3oyTpyjNS1D0h1MMVAQAAAM3rlBZJBQUFadq0ae6uBS3YvJU7ta+wQvGh/rp5eFdPlwMAAAA0u1PuQLFlyxbt27dPNpvNZfuFF174m4tCy7KvoELzPt8pSbr7dz0UZKWxCQAAANqeRn8K3rVrly666CJt3rxZJpNJhmFIkkymI+tr7Ha7eyuE17v/ox9lq3XojM5RuqB3vKfLAQAAADyi0d0Cb7rpJqWmpio3N1eBgYH68ccf9cUXXyg9PV0rV65sghLhzZZtzdHSrbnyNZt034U9nSEbAAAAaGsaPXO1evVqLV++XNHR0TKbzTKbzRo6dKhmzZqlG2+8Ud9//31T1AkvVFVj130fHrlx9FVnpqpzbIiHKwIAAAA8p9EzV3a7XSEhRz5ER0dH69ChQ5KkDh06KDMz073Vwas99/kuZxOLG8/t4ulyAAAAAI9q9MxVr169tHHjRqWmpiojI0OPPPKILBaLnn/+eXXs2LEpaoQX2l9YoWdW7pAk3TWmO00sAAAA0OY1+hPx//3f/6m8vFySdP/99+t3v/udzjzzTEVFRWnhwoVuLxDe6b4Pt6i61qHTO0Xpd30SPF0OAAAA4HGNDlcjR450/r1z587atm2bCgsLFRERQTODNmLFtlwt3ZpDEwsAAADgFxq15qqmpka+vr764YcfXLZHRkbyAbuNqKqx694Pf5QkXTk0VV3iaGIBAAAASI0MV35+fmrfvj33smrDXvhil/YWVCgu1Kobz6OJBQAAAFCn0d0C77rrLt15550qLCxsinrgxbKKK/W0s4lFDwXTxAIAAABwavSn47lz52rHjh1KTExUhw4dFBQU5PL8+vXr3VYcvMtzn+9SVY1Dg1IiNJYmFgAAAICLRoercePGNUEZ8Ha5pVV6Y+0+SdLNw7uyxg4AAAD4lUaHq5kzZzZFHfByL365W9W1DvVvH67TO0V5uhwAAADA6zR6zRXansPlNi34Zq8k6YZzOzNrBQAAADSg0TNXZrP5uB+u6STY+sz/arcqbHb1TAzVOd1iPV0OAAAA4JUaHa7ee+89l69ramr0/fff65VXXtF9993ntsLgHUqqajT/6z2SmLUCAAAAjqfR4er3v/99vW2XXHKJevbsqYULF+qqq65yS2HwDv/5eo9Kq2rVJTZYI3rEe7ocAAAAwGu5bc3VaaedpmXLlrlrd/AC5dW1enHVbknS9ed2ltnMrBUAAABwLG4JV5WVlZozZ46SkpLcsTt4idfX7NPhihqlRAVqTG/uawUAAAAcT6MvC4yIiHBZd2MYhkpLSxUYGKhXX33VrcXBc6pq7Hr+y12SpOuGdZavD40lAQAAgONpdLh6/PHHXcKV2WxWTEyMMjIyFBER4dbi4Dn//W6/8kqrlRQeoIsGMCMJAAAAnEijw9WUKVOaoAx4E1utQ8+u3ClJ+suwTvJj1goAAAA4oUZ/ap4/f77eeuutetvfeustvfLKK24pCp717voDOlRcpdgQqy4d2M7T5QAAAAAtQqPD1axZsxQdHV1ve2xsrB566CG3FAXPqbU79MzRWatpZ3WUv5+PhysCAAAAWoZGh6t9+/YpNTW13vYOHTpo3759bikKnvPK6r3aV1ihyCCL/pjR3tPlAAAAAC1Go8NVbGysNm3aVG/7xo0bFRUV5Zai4Bk7ckv1yOJtkqS/jeiqQEujl+QBAAAAbVajw9XEiRN14403asWKFbLb7bLb7Vq+fLluuukmTZgwoSlqRDOosTv014UbVV3r0NldY/THwcxaAQAAAI3R6HD1wAMPKCMjQ+edd54CAgIUEBCgESNG6Nxzzz3lNVdPP/20UlJS5O/vr4yMDK1du/aYY1944QWdeeaZioiIUEREhIYPH15v/JQpU2QymVweo0aNOqXa2oq5y3do88FihQX46ZFL+ri02wcAAABwYo0OVxaLRQsXLlRmZqZee+01vfvuu9q5c6deeuklWSyWRhewcOFCzZgxQzNnztT69evVt29fjRw5Urm5uQ2OX7lypSZOnKgVK1Zo9erVSk5O1ogRI3Tw4EGXcaNGjVJWVpbz8cYbbzS6trZiw/4izV2xQ5L0j3G9FBfq7+GKAAAAgJbHZBiG4ckCMjIyNGjQIM2dO1eS5HA4lJycrBtuuEG33377CV9vt9sVERGhuXPnatKkSZKOzFwVFRXp/fffP6WaSkpKFBYWpuLiYoWGhp7SPlqKSptdY576UrvyynVh30TNmdjf0yUBAAAAXqMx2aDRM1fjx4/Xww8/XG/7I488oksvvbRR+7LZbFq3bp2GDx/+c0Fms4YPH67Vq1ef1D4qKipUU1OjyMhIl+0rV65UbGysunXrpmuvvVYFBQXH3Ed1dbVKSkpcHm3Fw4u3aVdeueJCrbr/9z09XQ4AAADQYjU6XH3xxRe64IIL6m0fPXq0vvjii0btKz8/X3a7XXFxcS7b4+LilJ2dfVL7uO2225SYmOgS0EaNGqX//Oc/WrZsmR5++GF9/vnnGj16tOx2e4P7mDVrlsLCwpyP5OTkRh1HS7Vqe75e/nqPJOmRS/oqPLDxl3UCAAAAOKLRvbbLysoaXFvl5+fX7DM+s2fP1ptvvqmVK1fK3//ndUK/7FrYu3dv9enTR506ddLKlSt13nnn1dvPHXfcoRkzZji/LikpafUBq7iyRn9/e6Mk6YrTOujsrjEerggAAABo2Ro9c9W7d28tXLiw3vY333xTPXr0aNS+oqOj5ePjo5ycHJftOTk5io+PP+5rH330Uc2ePVufffaZ+vTpc9yxHTt2VHR0tHbs2NHg81arVaGhoS6P1u7eD35UVnGVUqICdccFaZ4uBwAAAGjxGj1zdffdd+viiy/Wzp07de6550qSli1bptdff11vv/12o/ZlsVg0cOBALVu2TOPGjZN0pKHFsmXLdP311x/zdY888ogefPBBffrpp0pPTz/h+xw4cEAFBQVKSEhoVH2t1Rc/5em97w/KbJIeu6wfNwsGAAAA3KDRn6rHjh2r999/Xw899JDefvttBQQEqG/fvlq+fHm9phInY8aMGZo8ebLS09M1ePBgPfHEEyovL9fUqVMlSZMmTVJSUpJmzZolSXr44Yd1zz336PXXX1dKSopzbVZwcLCCg4NVVlam++67T+PHj1d8fLx27typW2+9VZ07d9bIkSMbXV9rtGTLkZnCywa114D2ER6uBgAAAGgdTmnKYsyYMRozZoykI+uT3njjDd1yyy1at27dMZtGHMtll12mvLw83XPPPcrOzla/fv20ePFiZ5OLffv2yWz++erFefPmyWaz6ZJLLnHZz8yZM3XvvffKx8dHmzZt0iuvvKKioiIlJiZqxIgReuCBB2S1Wk/lcFud7/YeliSd1SXaw5UAAAAArccp3+fqiy++0Isvvqh33nlHiYmJuvjiizV+/HgNGjTI3TU2u9Z8n6vSqhr1ve8zOQxp7Z3nKZYbBgMAAADH1Jhs0KiZq+zsbL388st68cUXVVJSoj/84Q+qrq7W+++/3+hmFvCM7/cVyWFIyZEBBCsAAADAjU66W+DYsWPVrVs3bdq0SU888YQOHTqkp556qilrQxOouyQwvUPj18cBAAAAOLaTnrlatGiRbrzxRl177bXq0qVLU9aEJrT+aLga2IFGFgAAAIA7nfTM1apVq1RaWqqBAwcqIyNDc+fOVX5+flPWBjertTv0/T7CFQAAANAUTjpcnXbaaXrhhReUlZWlP//5z3rzzTeVmJgoh8OhJUuWqLS0tCnrhBtsyy5Vuc2uEKuvusaFeLocAAAAoFU56XBVJygoSFdeeaVWrVqlzZs3629/+5tmz56t2NhYXXjhhU1RI9xk3dFLAvt3iJCP2eThagAAAIDWpdHh6pe6deumRx55RAcOHNAbb7zhrprQRNY5m1lwSSAAAADgbr8pXNXx8fHRuHHj9MEHH7hjd2gi62hmAQAAADQZt4QreL+s4kodLKqUj9mkfsnhni4HAAAAaHUIV23Ed3uOzFp1TwhRkLVR944GAAAAcBIIV23EOm4eDAAAADQpwlUbwXorAAAAoGkRrtqA8upabckqkUS4AgAAAJoK4aoN2Li/SHaHocQwfyWGB3i6HAAAAKBVIly1Ac5LAlNYbwUAAAA0FcJVG/AdNw8GAAAAmhzhqpVzOAyt30czCwAAAKCpEa5auZ9yS1VaVatAi4/S4kM8XQ4AAADQahGuWrm69Vb924fL14cfNwAAANBU+LTdyq3bU3dJIM0sAAAAgKZEuGrlvuPmwQAAAECzIFy1YrmlVdpXWCGT6chlgQAAAACaDuGqFau7JLBbXIhC/f08XA0AAADQuhGuWrG6ZhbpKVwSCAAAADQ1wlUrxnorAAAAoPkQrlqpqhq7fjxULElKp1MgAAAA0OQIV63Uxv1FqrEbig2xql1EgKfLAQAAAFo9wlUrtW7fz+utTCaTh6sBAAAAWj/CVSu1+cCRSwL7JYd7thAAAACgjSBctVI/HiqRJPVMDPNwJQAAAEDbQLhqhUqqarSvsEKS1DMx1MPVAAAAAG0D4aoV2np01iopPEDhgRYPVwMAAAC0DYSrVqjuksAezFoBAAAAzYZw1Qo5w1UC4QoAAABoLoSrVqju5sGstwIAAACaD+GqlamutWtHbpkkqWcSnQIBAACA5kK4amV+yi5TrcNQeKCfEsP8PV0OAAAA0GYQrlqZLVk/XxJoMpk8XA0AAADQdhCuWhmaWQAAAACeQbhqZerCVc9E1lsBAAAAzYlw1YrYHYa2ZtWFK2auAAAAgOZEuGpF9hSUq8Jml7+fWR1jgj1dDgAAANCmEK5akS1HLwlMiw+Vj5lmFgAAAEBzIly1Is5mFlwSCAAAADQ7wlUr8uOhn9uwAwAAAGhehKtWwjAM52WBdAoEAAAAmh/hqpXIKalWQblNPmaT0uJDPF0OAAAA0OYQrlqJuksCO8UEyd/Px8PVAAAAAG0P4aqVqLsksEcC660AAAAATyBctRI/st4KAAAA8CjCVSvxYxadAgEAAABPIly1AsWVNdpfWCmJe1wBAAAAnkK4agXq1lslhQcoPNDi4WoAAACAtolw1QpsyTrazIJZKwAAAMBjCFetQF0bdtZbAQAAAJ5DuGoFttApEAAAAPA4wlULV1Vj1/bcMknMXAEAAACeRLhq4X7KKZXdYSgi0E8JYf6eLgcAAABoswhXLVzdJYE9EkNlMpk8XA0AAADQdhGuWrgfWW8FAAAAeAWvCFdPP/20UlJS5O/vr4yMDK1du/aYY1944QWdeeaZioiIUEREhIYPH15vvGEYuueee5SQkKCAgAANHz5c27dvb+rD8Ag6BQIAAADewePhauHChZoxY4Zmzpyp9evXq2/fvho5cqRyc3MbHL9y5UpNnDhRK1as0OrVq5WcnKwRI0bo4MGDzjGPPPKI5syZo2effVZr1qxRUFCQRo4cqaqqquY6rGZhdxjamlUqiXAFAAAAeJrJMAzDkwVkZGRo0KBBmjt3riTJ4XAoOTlZN9xwg26//fYTvt5utysiIkJz587VpEmTZBiGEhMT9be//U233HKLJKm4uFhxcXF6+eWXNWHChBPus6SkRGFhYSouLlZoqPeGlh25ZRr+2Ofy9zPrx/tGycfMmisAAADAnRqTDTw6c2Wz2bRu3ToNHz7cuc1sNmv48OFavXr1Se2joqJCNTU1ioyMlCTt3r1b2dnZLvsMCwtTRkbGMfdZXV2tkpISl0dLUHdJYFp8KMEKAAAA8DCPhqv8/HzZ7XbFxcW5bI+Li1N2dvZJ7eO2225TYmKiM0zVva4x+5w1a5bCwsKcj+Tk5MYeikdsyaprZuG9s2sAAABAW+HxNVe/xezZs/Xmm2/qvffek7//qd/j6Y477lBxcbHzsX//fjdW2XR25pZLktLiQzxcCQAAAABfT755dHS0fHx8lJOT47I9JydH8fHxx33to48+qtmzZ2vp0qXq06ePc3vd63JycpSQkOCyz379+jW4L6vVKqvVeopH4TkF5dWSpJgQbh4MAAAAeJpHZ64sFosGDhyoZcuWObc5HA4tW7ZMQ4YMOebrHnnkET3wwANavHix0tPTXZ5LTU1VfHy8yz5LSkq0Zs2a4+6zJSoos0mSooMtHq4EAAAAgEdnriRpxowZmjx5stLT0zV48GA98cQTKi8v19SpUyVJkyZNUlJSkmbNmiVJevjhh3XPPffo9ddfV0pKinMdVXBwsIKDg2UymXTzzTfrH//4h7p06aLU1FTdfffdSkxM1Lhx4zx1mE2ioOzIzFVUcMubdQMAAABaG4+Hq8suu0x5eXm65557lJ2drX79+mnx4sXOhhT79u2T2fzzBNu8efNks9l0ySWXuOxn5syZuvfeeyVJt956q8rLyzVt2jQVFRVp6NChWrx48W9al+VtKm12ldvskqQoZq4AAAAAj/P4fa68UUu4z9WBwxUa+vAKWXzMyvzHKJlMtGIHAAAA3K3F3OcKp65uvVVUsIVgBQAAAHgBwlULVdcpkEsCAQAAAO9AuGqh8utmroJoZgEAAAB4A8JVC/XLywIBAAAAeB7hqoWqa8MeTRt2AAAAwCsQrlqogvK6ywKZuQIAAAC8AeGqhcrnBsIAAACAVyFctVCsuQIAAAC8C+GqhaprxR5Nt0AAAADAKxCuWiDDMJi5AgAAALwM4aoFKqmsVa3DkCRF0tACAAAA8AqEqxYo/+glgSFWX/n7+Xi4GgAAAAAS4apF4pJAAAAAwPsQrlqgAtqwAwAAAF6HcNUC5XMDYQAAAMDrEK5aIGauAAAAAO9DuGqB6tZcRbPmCgAAAPAahKsWqO4GwlwWCAAAAHgPwlULlO/sFshlgQAAAIC3IFy1QD+vuWLmCgAAAPAWhKsWqKC8bs0VM1cAAACAtyBctTA1doeKKmokseYKAAAA8CaEqxbm8NFZK7NJCg8kXAEAAADegnDVwtQ1s4gMssjHbPJwNQAAAADqEK5amMKjM1dRQay3AgAAALwJ4aqFcd7jik6BAAAAgFchXLUw3OMKAAAA8E6EqxbGeY8rOgUCAAAAXoVw1cIUlNXd44pwBQAAAHgTwlUL8/OaKy4LBAAAALwJ4aqFca654rJAAAAAwKsQrloYZq4AAAAA70S4amFYcwUAAAB4J8JVC1Jhq1WFzS6JmSsAAADA2xCuWpC6WSurr1lBFh8PVwMAAADglwhXLUhBed0lgVaZTCYPVwMAAADglwhXLYjzBsKstwIAAAC8DuGqBSmgDTsAAADgtQhXLUg+bdgBAAAAr0W4akGcM1dcFggAAAB4HcJVC1K35io6iJkrAAAAwNsQrlqQum6BzFwBAAAA3odw1YLkH70sMJKGFgAAAIDXIVy1IM7LAmloAQAAAHgdwlUL4XAYKuSyQAAAAMBrEa5aiJKqGtU6DElcFggAAAB4I8JVC1G33irE31dWXx8PVwMAAADg1whXLQTrrQAAAADvRrhqIZxt2LkkEAAAAPBKhKsWom7mimYWAAAAgHciXLUQdWuuorgsEAAAAPBKhKsWoqD86JorLgsEAAAAvBLhqoUoYOYKAAAA8GqEqxbi53DFzBUAAADgjQhXLUTdZYFRQcxcAQAAAN6IcNVC1LVij2bmCgAAAPBKhKsWoMbuUFFFjSTWXAEAAADeinDVAhw+OmtlNknhAX4ergYAAABAQwhXLUDdPa4ig6wym00ergYAAABAQwhXLYDzHlestwIAAAC8lsfD1dNPP62UlBT5+/srIyNDa9euPebYH3/8UePHj1dKSopMJpOeeOKJemPuvfdemUwml0daWloTHkHTow07AAAA4P08Gq4WLlyoGTNmaObMmVq/fr369u2rkSNHKjc3t8HxFRUV6tixo2bPnq34+Phj7rdnz57KyspyPlatWtVUh9As8stoww4AAAB4O4+Gq8cee0zXXHONpk6dqh49eujZZ59VYGCgXnrppQbHDxo0SP/85z81YcIEWa3HDhq+vr6Kj493PqKjo5vqEJpFXRt2Zq4AAAAA7+WxcGWz2bRu3ToNHz7852LMZg0fPlyrV6/+Tfvevn27EhMT1bFjR11++eXat2/fccdXV1erpKTE5eFNCsrq1lwxcwUAAAB4K4+Fq/z8fNntdsXFxblsj4uLU3Z29invNyMjQy+//LIWL16sefPmaffu3TrzzDNVWlp6zNfMmjVLYWFhzkdycvIpv39TcK65CmLmCgAAAPBWHm9o4W6jR4/WpZdeqj59+mjkyJH65JNPVFRUpP/+97/HfM0dd9yh4uJi52P//v3NWPGJ5TsvC2TmCgAAAPBWvp564+joaPn4+CgnJ8dle05OznGbVTRWeHi4unbtqh07dhxzjNVqPe4aLk+ruyyQNVcAAACA9/LYzJXFYtHAgQO1bNky5zaHw6Fly5ZpyJAhbnufsrIy7dy5UwkJCW7bZ3Oruywwmm6BAAAAgNfy2MyVJM2YMUOTJ09Wenq6Bg8erCeeeELl5eWaOnWqJGnSpElKSkrSrFmzJB1pgrFlyxbn3w8ePKgNGzYoODhYnTt3liTdcsstGjt2rDp06KBDhw5p5syZ8vHx0cSJEz1zkL9Rha1WlTV2ScxcAQAAAN7Mo+HqsssuU15enu655x5lZ2erX79+Wrx4sbPJxb59+2Q2/zy5dujQIfXv39/59aOPPqpHH31UZ599tlauXClJOnDggCZOnKiCggLFxMRo6NCh+uabbxQTE9Osx+YudbNW/n5mBVp8PFwNAAAAgGMxGYZheLoIb1NSUqKwsDAVFxcrNDTUo7V8v++wLnrmayWFB+ir28/1aC0AAABAW9OYbNDqugW2Ns71VlwSCAAAAHg1wpWXKyiv6xRIMwsAAADAmxGuvFw+NxAGAAAAWgTClZeruyyQmSsAAADAuxGuvFzdZYGsuQIAAAC8G+HKy/08c0W4AgAAALwZ4crL5ZcdbWgRxGWBAAAAgDfz6E2EcWKPX9ZP2SVV6p0U5ulSAAAAABwH4crLdU8IVfcEz97IGAAAAMCJcVkgAAAAALgB4QoAAAAA3IBwBQAAAABuQLgCAAAAADcgXAEAAACAGxCuAAAAAMANCFcAAAAA4AaEKwAAAABwA8IVAAAAALgB4QoAAAAA3IBwBQAAAABuQLgCAAAAADcgXAEAAACAGxCuAAAAAMANfD1dgDcyDEOSVFJS4uFKAAAAAHhSXSaoywjHQ7hqQGlpqSQpOTnZw5UAAAAA8AalpaUKCws77hiTcTIRrI1xOBw6dOiQQkJCZDKZmvz9SkpKlJycrP379ys0NLTJ3w+tA+cNThXnDk4F5w1OBecNTpU3nTuGYai0tFSJiYkym4+/qoqZqwaYzWa1a9eu2d83NDTU4ycPWh7OG5wqzh2cCs4bnArOG5wqbzl3TjRjVYeGFgAAAADgBoQrAAAAAHADwpUXsFqtmjlzpqxWq6dLQQvCeYNTxbmDU8F5g1PBeYNT1VLPHRpaAAAAAIAbMHMFAAAAAG5AuAIAAAAANyBcAQAAAIAbEK4AAAAAwA0IV17g6aefVkpKivz9/ZWRkaG1a9d6uiR4kVmzZmnQoEEKCQlRbGysxo0bp8zMTJcxVVVVmj59uqKiohQcHKzx48crJyfHQxXDG82ePVsmk0k333yzcxvnDRpy8OBB/elPf1JUVJQCAgLUu3dvfffdd87nDcPQPffco4SEBAUEBGj48OHavn27ByuGN7Db7br77ruVmpqqgIAAderUSQ888IB+2TeNcwdffPGFxo4dq8TERJlMJr3//vsuz5/MOVJYWKjLL79coaGhCg8P11VXXaWysrJmPIrjI1x52MKFCzVjxgzNnDlT69evV9++fTVy5Ejl5uZ6ujR4ic8//1zTp0/XN998oyVLlqimpkYjRoxQeXm5c8xf//pXffjhh3rrrbf0+eef69ChQ7r44os9WDW8ybfffqvnnntOffr0cdnOeYNfO3z4sM444wz5+flp0aJF2rJli/71r38pIiLCOeaRRx7RnDlz9Oyzz2rNmjUKCgrSyJEjVVVV5cHK4WkPP/yw5s2bp7lz52rr1q16+OGH9cgjj+ipp55yjuHcQXl5ufr27aunn366wedP5hy5/PLL9eOPP2rJkiX66KOP9MUXX2jatGnNdQgnZsCjBg8ebEyfPt35td1uNxITE41Zs2Z5sCp4s9zcXEOS8fnnnxuGYRhFRUWGn5+f8dZbbznHbN261ZBkrF692lNlwkuUlpYaXbp0MZYsWWKcffbZxk033WQYBucNGnbbbbcZQ4cOPebzDofDiI+PN/75z386txUVFRlWq9V44403mqNEeKkxY8YYV155pcu2iy++2Lj88ssNw+DcQX2SjPfee8/59cmcI1u2bDEkGd9++61zzKJFiwyTyWQcPHiw2Wo/HmauPMhms2ndunUaPny4c5vZbNbw4cO1evVqD1YGb1ZcXCxJioyMlCStW7dONTU1LudRWlqa2rdvz3kETZ8+XWPGjHE5PyTOGzTsgw8+UHp6ui699FLFxsaqf//+euGFF5zP7969W9nZ2S7nTVhYmDIyMjhv2rjTTz9dy5Yt008//SRJ2rhxo1atWqXRo0dL4tzBiZ3MObJ69WqFh4crPT3dOWb48OEym81as2ZNs9fcEF9PF9CW5efny263Ky4uzmV7XFyctm3b5qGq4M0cDoduvvlmnXHGGerVq5ckKTs7WxaLReHh4S5j4+LilJ2d7YEq4S3efPNNrV+/Xt9++2295zhv0JBdu3Zp3rx5mjFjhu688059++23uvHGG2WxWDR58mTnudHQ/7c4b9q222+/XSUlJUpLS5OPj4/sdrsefPBBXX755ZLEuYMTOplzJDs7W7GxsS7P+/r6KjIy0mvOI8IV0IJMnz5dP/zwg1atWuXpUuDl9u/fr5tuuklLliyRv7+/p8tBC+FwOJSenq6HHnpIktS/f3/98MMPevbZZzV58mQPVwdv9t///levvfaaXn/9dfXs2VMbNmzQzTffrMTERM4dtClcFuhB0dHR8vHxqdedKycnR/Hx8R6qCt7q+uuv10cffaQVK1aoXbt2zu3x8fGy2WwqKipyGc951LatW7dOubm5GjBggHx9feXr66vPP/9cc+bMka+vr+Li4jhvUE9CQoJ69Ojhsq179+7at2+fJDnPDf6/hV/7+9//rttvv10TJkxQ7969dcUVV+ivf/2rZs2aJYlzByd2MudIfHx8vaZvtbW1Kiws9JrziHDlQRaLRQMHDtSyZcuc2xwOh5YtW6YhQ4Z4sDJ4E8MwdP311+u9997T8uXLlZqa6vL8wIED5efn53IeZWZmat++fZxHbdh5552nzZs3a8OGDc5Henq6Lr/8cuffOW/wa2eccUa9Wz389NNP6tChgyQpNTVV8fHxLudNSUmJ1qxZw3nTxlVUVMhsdv1Y6ePjI4fDIYlzByd2MufIkCFDVFRUpHXr1jnHLF++XA6HQxkZGc1ec4M83VGjrXvzzTcNq9VqvPzyy8aWLVuMadOmGeHh4UZ2dranS4OXuPbaa42wsDBj5cqVRlZWlvNRUVHhHPOXv/zFaN++vbF8+XLju+++M4YMGWIMGTLEg1XDG/2yW6BhcN6gvrVr1xq+vr7Ggw8+aGzfvt147bXXjMDAQOPVV191jpk9e7YRHh5u/O9//zM2bdpk/P73vzdSU1ONyspKD1YOT5s8ebKRlJRkfPTRR8bu3buNd99914iOjjZuvfVW5xjOHZSWlhrff/+98f333xuSjMcee8z4/vvvjb179xqGcXLnyKhRo4z+/fsba9asMVatWmV06dLFmDhxoqcOqR7ClRd46qmnjPbt2xsWi8UYPHiw8c0333i6JHgRSQ0+5s+f7xxTWVlpXHfddUZERIQRGBhoXHTRRUZWVpbnioZX+nW44rxBQz788EOjV69ehtVqNdLS0oznn3/e5XmHw2HcfffdRlxcnGG1Wo3zzjvPyMzM9FC18BYlJSXGTTfdZLRv397w9/c3OnbsaNx1111GdXW1cwznDlasWNHgZ5rJkycbhnFy50hBQYExceJEIzg42AgNDTWmTp1qlJaWeuBoGmYyjF/cOhsAAAAAcEpYcwUAAAAAbkC4AgAAAAA3IFwBAAAAgBsQrgAAAADADQhXAAAAAOAGhCsAAAAAcAPCFQAAAAC4AeEKAAAAANyAcAUAgJuZTCa9//77ni4DANDMCFcAgFZlypQpMplM9R6jRo3ydGkAgFbO19MFAADgbqNGjdL8+fNdtlmtVg9VAwBoK5i5AgC0OlarVfHx8S6PiIgISUcu2Zs3b55Gjx6tgIAAdezYUW+//bbL6zdv3qxzzz1XAQEBioqK0rRp01RWVuYy5qWXXlLPnj1ltVqVkJCg66+/3uX5/Px8XXTRRQoMDFSXLl30wQcfNO1BAwA8jnAFAGhz7r77bo0fP14bN27U5ZdfrgkTJmjr1q2SpPLyco0cOVIRERH69ttv9dZbb2np0qUu4WnevHmaPn26pk2bps2bN+uDDz5Q586dXd7jvvvu0x/+8Adt2rRJF1xwgS6//HIVFhY263ECAJqXyTAMw9NFAADgLlOmTNGrr74qf39/l+133nmn7rzzTplMJv3lL3/RvHnznM+ddtppGjBggJ555hm98MILuu2227R//34FBQVJkj755BONHTtWhw4dUlxcnJKSkjR16lT94x//aLAGk8mk//u//9MDDzwg6UhgCw4O1qJFi1j7BQCtGGuuAACtzjnnnOMSniQpMjLS+fchQ4a4PDdkyBBt2LBBkrR161b17dvXGawk6YwzzpDD4VBmZqZMJpMOHTqk884777g19OnTx/n3oKAghYaGKjc391QPCQDQAhCuAACtTlBQUL3L9NwlICDgpMb5+fm5fG0ymeRwOJqiJACAl2DNFQCgzfnmm2/qfd29e3dJUvfu3bVx40aVl5c7n//qq69kNpvVrVs3hYSEKCUlRcuWLWvWmgEA3o+ZKwBAq1NdXa3s7GyXbb6+voqOjpYkvfXWW0pPT9fQoUP12muvae3atXrxxRclSZdffrlmzpypyZMn695771VeXp5uuOEGXXHFFYqLi5Mk3XvvvfrLX/6i2NhYjR49WqWlpfrqq690ww03NO+BAgC8CuEKANDqLF68WAkJCS7bunXrpm3btkk60snvzTff1HXXXaeEhAS98cYb6tGjhyQpMDBQn376qW666SYNGjRIgYGBGj9+vB577DHnviZPnqyqqio9/vjjuuWWWxQdHa1LLrmk+Q4QAOCV6BYIAGhTTCaT3nvvPY0bN87TpQAAWhnWXAEAAACAGxCuAAAAAMANWHMFAGhTuBoeANBUmLkCAAAAADcgXAEAAACAGxCuAAAAAMANCFcAAAAA4AaEKwAAAABwA8IVAAAAALgB4QoAAAAA3IBwBQAAAABu8P+UShixrExeXgAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
@@ -1955,6 +2026,7 @@
"import tensorflow as tf\n",
"from utils.read_cifar import read_cifar\n",
"from utils.split_dataset import split_dataset\n",
+ "from utils.process_image import save_plot_as_image\n",
"\n",
"split_factor = 0.9\n",
"d_h = 64\n",
@@ -1964,28 +2036,49 @@
"\n",
"data, labels = read_cifar('data/cifar-10-batches-py')\n",
"data_train, labels_train, data_test, labels_test = split_dataset(data, labels, split_factor)\n",
+ "# conversion des labels en one-hot\n",
"labels_train = tf.keras.utils.to_categorical(labels_train)\n",
"labels_test = tf.keras.utils.to_categorical(labels_test)\n",
"\n",
"model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Dense(d_h, activation='sigmoid'),\n",
- " tf.keras.layers.Dense(10, activation='sigmoid')\n",
+ " tf.keras.layers.Dense(10, activation='softmax')\n",
"])\n",
"model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),\n",
" loss=tf.keras.losses.BinaryCrossentropy(),\n",
" metrics=['accuracy'])\n",
+ "\n",
"history = model.fit(data_train, labels_train, epochs=num_epochs, batch_size=batch_size, validation_split=0.1)\n",
+ "\n",
"test_loss, test_accuracy = model.evaluate(data_test, labels_test)\n",
"\n",
"print(f'test_accuracy={test_accuracy}')\n",
"loss = history.history['loss']\n",
+ "accuracy = history.history['accuracy']\n",
"epochs = np.arange(1, len(loss)+1)\n",
- "save_plot_as_image(epochs, loss, 'Loss', 'Epoch', 'images/mlp_loss_tf.png')\n"
+ "save_plot_as_image(epochs, loss, 'Loss', 'Epoch', 'Evolution de la Loss (Tensorflow)','images/mlp_loss_tf.png')\n",
+ "save_plot_as_image(epochs, accuracy, 'Accuracy', 'Epoch', 'Evolution de l\\'accuracy (Tensorflow)','images/mlp_accuracy_tf.png')\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Analyse des résultats\n",
+ "On obtient à peu près les mêmes résultats avec Tensorflow qu'avec notre modèle implémenté manuellement, ce qui est plutôt rassurant."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Matrix of confusion\n",
+ "We plot the matrix of confusion for the model with d_h = 64, a learning rate of 0.1 and 100 epochs to assess the performance of the model."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -1993,7 +2086,7 @@
"output_type": "stream",
"text": [
"(6000, 10)\n",
- "[7 9 4 ... 3 9 0]\n",
+ "[1 2 8 ... 8 9 6]\n",
"188/188 [==============================] - 0s 2ms/step\n"
]
},
@@ -2003,13 +2096,13 @@
"<AxesSubplot: >"
]
},
- "execution_count": 14,
+ "execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": "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",
+ "image/png": "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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
diff --git a/images/mlp_accuracy.png b/images/mlp_accuracy.png
new file mode 100644
index 0000000000000000000000000000000000000000..89141b58146482adeed9491ec7118e76106365c0
GIT binary patch
literal 29142
zcmeAS@N?(olHy`uVBq!ia0y~yV1B{C!1#rOje&t-od&xu0|SF)iEBhjaDG}zd16s2
zgKuI<K~8>2PG*uqS!z*nW`3Tro|&GJjzUIBNkOrdzJ4xTfnI)5y1vE>i6jOF2F?PH
z$YKTt{zMRFTw%XFlYybo+0(@_q+-t7yOlK|SO5P1_}ypH%$7HbDvF8|HhA1%W>I?Q
z#d0Mpa7}|oN64R54Fb27ehCMxVa(AD6<HnXeRo0B)~u;rj7zduQrs2ZWS2P^MV>G#
zuCHe~*2mc-IK%VV*BYB=f<^CUTF>VW|1h`syrtS<10F^O5ZG?CdpC$?U~q6?2QdyP
z3tRw8ePCcI0SPeJFfhu38T>FM%%e&e7|_C{LUr=VB{w&x&%a#!?`?kl<dZ3v8kyP8
zy{xS7E3Tc($;r9v^}5~XZvJFoIPjh01p~u&OVJGf@4ueS&c73zw0K_CE6v&u2if20
z9adfwwe{5FIt@k!hvKKN-!U;TR6OcbpE6^{jRkv)pP$QH@$3vp_1&u1Yo+XJc6k4u
zTYhh4;9|FvSzC84H(+46utx9#BST*K-O}rts;WotrvAyXv$Om3@nd1R<PT1s!vUe8
zQ<<2U?!<C2FxWieWB8D>b?eryj*bgIK0cO~*?cqS)A#S!^XooKr$;+FHmdo|Sn%RS
z#?RlsSNqSmi`rB1QPTeTy4~-BoSdARzunxNemcd-a=-aZpH(YXTzGV}dv5=u`Lib1
z?y`KLzvn|!)t48FtFNxQaU;U^!vSV3P0hlW#aBbaSEiqzcWYnm?sfZqWo^y6>Qz`+
zILp5NpJev*C9hs(`OY@;&AqiHu%KW=-maHwvX(_lF79*hm%Dnm{QlY(FES$6hJAf;
z(Ruy8U#}u-&)xj-zW)E}_`k2hv+nQPyC!V)qsNa;b8nf<wJ2QVcz>>S`KxWW^MYra
z<%$RhELa!2`_)Bv`P_i_Eu6w4($d<iudX_B#N}@J{n}M4S5Cb0;rxp!$Nt#}2?>4a
zj{oEI|L6JoDU&8GYG`2i`&7SvqRQTHx3YIVo0TmhDyq8r>Z*5lcV}N*<k}nec=`Og
zRod(K1RXqhkc*S^Vgn=dmuu1ao|Dqf&Qi6vw-*%?Gpo3Nq*GW_Ol;YzRjW4tKFh*T
zA?gN-sPL$$Rf`rWO*fl-a>=!|(WlR!_cy;^v-w{2dt0!jN0SP_{d~~O9}*k8HgK_<
z=On*577JIe+vQdL?ajp{o|C7{n6aY#UZs2a-O}k^US3}wH1q#^6BS?oxAgC?>-(=A
zXk<1meB|O8wA^p5-|hNuZ*Ero`FMQlv}wzJzuWD9^XAQ{Eg2K9t&Lv3e%~*zy1Ke^
z`;Pa^U*C{;xa73%_7~f3=Uv|Ye&6bsmzS>&Umv&Y-LBVlTh_(@|8>3Uaj$vF@3-4u
zPx98g*d?l6a+ufL<i+&)^VfIl?~72I>^WJ@H)yt5?w6O#=Vu*i;oNjHXO>lI*5$h7
zf5)xg?^%56lou%2v+nNNdTm|o>gV&S{oMOxE?U3e^Z3p7)YD?Ibw8he`uzFwkxt>M
z6DBO!TmAjj;(ohJ@9Y0pdj|RW^|A5Ete7-O$ajuKpr@y&Rn?aj_iDe#hK7ahy2GEY
zZEYRB*uDSS-tYHTSHIio9=p42s)}yZ77yF1FBg2x@2bSd$Jc(j=)UX!zu!*_EWR9I
z=C`>2fBXKwzUB98w^x06u~5=DO+!b=N7_8^%BIxQV2_@&et$*S-)7-b@99~WmUy1~
ze)Y<g7kA6=U+oZ7e)9hP{VVt57#zIQ7#JR?t>61?mg35b)30Wge*1mC{$H|(k=?Hs
z%G1Bc*Z=+McDzq^?tT4;4F@Kq?D=?1`rNizv!s$QEO2xS2$+z#`R1IrlX@aIr&;co
zH_w}MaiMcNo9Ve(rl)^Aw4dJC*m!J5;bXsQ+uz^coAVqt;Ns%q={}moBWZMG!|k`{
z>bS-AOjIP-@B6iC^V?I}>rGT7>;M10KR5U4)vInkK0Ss>M>^Q%f8B4Edn?7m=-vMR
zb<@Ay&fmZHV5#2>gM%5jx2+8f33+m<{K|?zw~UMxfsv6v7tg=C(7D}h{oi`ivc8@k
zp2I0dlI;9)CEH>PkBYh#6m0OGohZ?E_+i1nsy8<_w%yDzyS6s^`hz*kf0=$uPm$pL
zZ{v4W|766roSTtXZA)GROs`(EWXTZ@K0dRc;NV_ib-y{=-Z?h2Nv>SEQt$Zv>i4$J
zlQtGTJ;gSknV}(3f~|iCGsB&CKcCOP{^{xI*tq$1zgB+!VR}7g@re^2y0N>i+${U^
zqfj?~UyQPQU&*%GPbby8Iy*1ExVU(3Ze3m7EThz}Q>RaVJt`g_GlSde<>&ME;l94T
z%lzhQZT|VYzUckV=W?wrEidl>|F_-y|AGYycMFfpo_nt=)(uKtwZFgpeet~J(@FJ7
zOEiO*?b!0az~qhr&)?7U|6l1fzo&87AS5K@Ne!>8Rmjq%OK)vRbPm?Ldi4Lf=j&Fi
z2nY<EsJHWpkYwA!UTJf$NnG6ATdTfi-L3t87gSE2{_StO?p=Y!mxJu`FOEs)=alp|
z9h1wpm+tKBl(H&$(SNP>)yh6liSql~+u+R1l`5G(KRwmb*1mk0-~LX5$e;SxlGbHw
zuC0k&ytn$h)$MyVpM8%WKmPiZ_WGQly9^D}`rYp^GUS;TJUAd9t8(u8^>Agko(s|W
zdzZd?l@+-*Z14Ab)vMO7E#-W{&My}-T`xA~&<2&#*VoQg{&BMUTDe?qq21A>jVqFm
z_wl~UyuL2B`GTaJ+&bOpZF4>rpGhljc7I#{V`pn?tCptbMQ{DROQy$Ft-M$)DkAdd
zMf%)c=XSo#XJ=;q`f!-P^v{pN>B-BR-mR3k`ts)H=Pl1Qb#$hjP5XV(&iCStKE_F3
zu7<~FURx7+>eQ*A)YPS?H(t6F)Y{f&#r%C{mXu}Dl4X8#SDD|h2{ukY7m}H|((d1n
zWZ&6lYfsuoFfeSXVJTs7*ldt>MFUh+UIdlG4-dD$mjC~webS^!SC;$FFE09bxm9iV
z-8#Q>^=5wCnf4?~Jb&_suibf4$#L28j5|9Dd%wNByZih3Up%|-uFGD(H|*%qqqFR4
zcTJi!Nkl?o#bWpVSL=4aE4o&o_xFnZ?qgv&_ji}CXXBS!vt)^i?dLPbUH$#jk0xDI
zpI@`c{_jhF`<?GTd?@()NPK?*+n4hDwc%H<Ue)vWU|{&*40db5QPJ=fxwp4P?J8NR
zxBHFJ+K7#d4rbk#wJNzVO*i^Ufklkl#qxJ|UfR@IJ$(3(H!N(rlzgfEZNUq{^QX_9
zyS7i(+Uj<8R@RpK|8+~3E}eU|^4zmx8NT*!@9w@f%9=NK?%PZ6uW-FD+x>S=?Kl41
zySqYnm%Y98=xDcT*_(*;-=IA7YwoYVH@4;8&OCp0nXhzfOUs`O`IZ09`%Uul@)8jf
zdzSNG)8D7&%SHE9>(*Usm#=&A>BQetQ?+A%&8_lEyuB^=)t#N6`{xzExv}xrQ~mmt
zrLV8OJZpYG<o>?etBc+HH?QMlcu+f|nU5hu>`dC`yuDw;Km~2p->=t0!^59{s@Sa(
zTmSd#sne&6XReM54_^){_#GV`?XA9V4_@x4rK?+7ImgD_JpBFs|8>p#8XFsX+kVP>
zd3#SjnsjmVdArv}T>nmKuV1lc%a)6w*JeMu!u9^V-S0KtznfUOWA=Uj`0-+gpz@dV
z_5U_sym0%*wgmgDE0;6;`(DL+_`>}Ef6}Md<=)zIQ8YYeVR(FPXgi;5k!9Jdy=j{x
z54ZDQcbBhSlE44&wu9+&UzKZ_tdHA!tMbj2unPO-?=D>m0@eL`f7^H@7d?28keZTW
zazpEFyT4UT`Q6g4o}MdvDnEZYDjsi8#m8Ww46fL}zg#}w%6;em>#EaZ9vOYIDSF~z
z`~6PwEYs|1RbO8{ofBPm>PV;X*XjHJxPt2LYwP3Jx3sYA{rztDuUD(rZ%H`F#LCV-
ze_LDNVz<m+UtaF|`E0gn>M4=Xu&}I)i(GfTSkx`0Gw0g=`uf^`Kc81Uo0%>mExr24
z5tpUQmYG?k&o;|7Nj}D7`|rnNDYKjzpq5l>div{D@wf{OjLhdE6TJQWuD!Up`0F8V
z{S{lbgiMbq>NGMo-kN@X9$W46b8}Zu)ehG(H4XiKr?`Jt`TKQ`9wp`8*x)$7{@>27
z?(X_9T~ANXS65a}uKPUu{)-nE7lWET_WysLXDhzA^q0IwOxexUSJ&6iSM!^*;>Zyf
zS&IUP|Np-4-<o+@&GS;QzwOfVcE5eX<0_Ye6y)9Ar3s4iJ)isb{rTkG+S;0VVuB*5
zoCGyL@BjO@{nN*f7gw*}x5_N{mI=rI<;E!|1R{0B?p8eR-Sz!mb!c4Ny6N$Ck%57M
zr_P=&{l?A7d2#-~FY4e1Py-_~sQT8nv5A>&o*xD(WB6JB{P|;&bwxurW=DXXot<8{
zI1|GK4fC~f{0s;B^ERuUta!Widdx3t89rq@yL%H-Y<@nO+&j<M-u}LXecc{GNy*Fz
z9kIpX>*ILOB}?#bk2K~v{Nv5$^F8xytEI%ckG{CIb@j(%()lL4TCbm*Yu&58ZpR@G
z4h{nq%k8q+InnxuSKJTN)zm!s;qzzXMT-~r{(iUnyhYug9p3Y`*YCOX;^JcWW-I^g
ze{b{8wJ1C^!zi`u;;zzcWdnl?J3bzh?mgVjZ(cR`=g*(VnE7oIjvPNOT~}8JuH7p>
zBs^X^J<jU!^EqeDvTtwCH@|f0QqO_~3d%-Cmv%g#SKV`Vwz>JQ^j*2P%^p9Soo{yN
z&>^1s`ufG$*Vi3O?zeq*>G^|$&BtzROzt(yy(JO0`s#}t8y9~(z|1dk_3G7gcCW9m
zHxCL9=IuV3v?c%ky^3cu)BSdzy~S&O=R(F^`MMtqFYYeSKYaiGeGd;0gJ)-Fr|<do
zYIX0wnRP!NwjWcS9+Skv!XlxlsCeu^BXjTe`*phyT3%llo9$uL#v}P@@&0rM28p)B
z89a|=*4i*LFffRN;;X1=)4zY;_rE@8{l28!l7WH$xJ(HHL&N7MPp8L+1q4jU+xb+i
zd^!_D1{<i*VJPSW6*r*HMO@WORo_`Ao{f!-@ANnsF7SNlux4O5U^#8Vgav>8fNSmb
z`~Usw*9+iaIG_pd>lS15e!GuUO12+fIA@LwD?9t?6r-KHWkB8Gyd;UXQ>RXWdJ&sv
zZjAPKaQJRyX=&Nj-(OxX2~uEl<l*7=tLtK|%cnC6Uf6qXWoQb&yxp1!69mk|LEbwM
zn`1Wn(c{NAGY>TjT-bYN<?&n2PEMe{Ap^sVU#}czWPnUzXkb@Z08-BIfDt0cP{F`t
z1(uR)Xy5}esHNmXX*sy-e_>MSwJVwNiLKv6reym&?sQ`QQ*vHZ_HL}0jQ}HfETE3<
zutCY5O<_Bis!s`zS~};F)?00z>-P?Ez17vYesPkp-e3JLKBWbV10msR!MywMg@ehW
zx2DFgyZbZsR&>O=IhVAaP7TvMAGUOs)$#s)`%4$N&Rx`W#Zt@TZc>wez}1XAh;J2E
zE<3ldCVFa-_v$IpRZi}G^DnIXWAyfv{`Gr@J{h-tIHAy;<n&im_|f{7YmKjJj3$(4
z2`~BJaGG@&mq|LrcHMc06IZNQ6lJLzdP>uB;hagrR#l%~uRMOK`_$@GiRJd~`{zf-
zE^4}}YUKf<^lT1Do>=U(X49`<F*~@Ox?R8nBNuc+uio0Kz2E0{an32twLz(+bG5SG
ztXi`&*!Anl^`>RA>sJKEWlqg{V{gZ#=P4Gy`BvMn)wwJ7Cpj};(bV#YUEEw6SW>HC
zKHFFP!KnvpA)d3Dl_;ULa?RCse~fZZZ+y9XPEhXojhX(AJ73Az8$J2UoHkiR_R;zq
z8yY6B|0QtGY|B1fwJY&!nRSh3ay%^HXZm&i!=KORU%y;FKP)b8-mfn&7lUe3W_G@u
zE$^$}?_IupUKOa97?_*8c2~*EMeTA`9_9Bcmv27*@}JTS`}%!#zi;37nj|D7RPy10
z<MhXpl9E+F9=4yq7z#=S;?oi(v~*Tq+qZ~I*7o1u#m|@Tdp0HHrOVpA7r(b{{}kMR
zBS6(<ThM~5;f-SPTQ~jCxKQ<J_0e!e#=0)v&*3E<`~TFO5&!Y3VY9nK{-UMc)3t1E
zWA*p_SQHc-yfxt<6R6dE^XAR2uCA;L_DV`l{dT`rOqw()Wn;vbkH_V+|NQva)zx+B
z!a`?N&z(DW>b?K@<+8tN;vtq^3ByB6|8}a+yI}kM&f`CJ7BfN>7#SEI-1IxkcwTqq
zrN(5@Tj3E4|GM7ae`@vXa}|9z0_scqc|-ee2z>2X{r{HPmHC4CTO`yD|Jax+P++xk
z<w_}oga$Fa7!A)N3z@F|{_9UpPCj2{vO9i%-L8ji(kAKW=D79q%iG1Irly`cbEagQ
zs)|a+t1Bx(z1hjHw%yF(;^hs^%v|Z@<TR@UlpP<mXT6fKiYjtq?+<-ZP`I?nYxR`q
zqDB9*UvfU39`^r*Ou=T)E^hvS_KpL;+|T>lwaJx#%Dx>?|MT}oj@vt3%s$xkEHHQU
z_3Z_<2qcZuEN-7k+kENLr72UVmLB^q-L_CvJ50mcI{Ndov)Nnx>+1IX`}@8=`{Sdd
zynScqT89@uI}`Z(+uPFLZ?|XP+LHO{Ywn-^O}lnQflBcAxn{Q|UxP{$KSLhpZL6$A
z!>_E^vB>SXf1R$*`fCp#dC6+6yK*pj>M32@g~q<@<};V9NLX#<!lGqp6wsN=bk)!(
zK=w9^mfa%J)e8>47Q8Tf!HN|gWxHc9Uc9LH_Tt5hw{{jk&%6&B(%7-XA~Zbw^zq~F
zpn;%ud%xXs`>hwZC*t72gS(zii;lUq+-K&Zy1&0Pm6V*~>;G;|J2PY9ojWn0CUx&T
zb!BDee*1qv{P#F9FfeRk3Ef&IyME2Zk6lqy=dGx}>38K+*w??`pN5uN1!iCGjM}{_
z>#bGb>wvE(-)paWtF>~C<?GfPOn+*w*e#m+NI7a{Y{lOd<+nF59Buohtv1E-!GW0q
z7xWiAc#x2LYm4X8r%z9vITKP<wacpP&5G6QcCB)}4{97HAM2U8fB$~(>Z+<;pRez&
z{yyz!(nZjKLQfA*<l1X&&u1DYx1Fwrl;P*^x`Gnno?;6gS<5=7)~BIbt5*d_hR!{;
z`gKTL=<-dgqNavkU%6#d*wLw`ddhc2O?&m%ToY*&lW%2@x?*)<(FccaB_`c0@q1pf
zN>MAFUx#Jg-!=6XOXN&`zCS!HU(`B!dZzUE_ka5QIri4)&!0twg^O=}pLsT|^xd7v
zNmJ&`StDy*rlG97*wNuXXguuAn;hNPU0-^(E?Ks0)w*?l?0hl-*Vn~L%WRKY+sjtY
zz`#(kY|<&Iw`a63pNpBj>GSC=%G-T*e>xp<^T>v`*=Cw+rn)X<{JMH(`@aXGB}H3{
zSI_Tf`cw1d{hzF`hNJF+jbg3aHyQkw-oe1}!Z%@K#FhQ^^{HuTSwB8Job*b(^YocB
zSC)8AKKILpul;ap-4D(6dp>pj`u={sUG1+qA7y5n<xaYplUMSz0^|<8_QMwzKJv;v
zzcsVE@9a<0^I=yr>bv_R4Hy37`u06^!qxDD|Mqcx`(FIdO67pRV56ApmQ5N;EB<9y
zEO1r)x34FQ`78_L-p)&xFPrAwu~@ofNydW%jZqsC8nv{vOzQqrcwV}(F<Dexe0fg~
zkLTCogTX;TS5^kAhlYp0p6qXT@%8%s>s-6VcFwt;w)y4U@_QHM|Nl77Hh<>SsY^|>
zuf2G+dVN?y!G=wnHkst!G64+_aPjlo{}<<FU}%sJ%`RWDXxBf%FTtPpdjEf(x_VV;
zq3hbR=hHLY*S+1+zhFBze?X8=$E2jDtEOrZcW<$2FDzY9Gjl)hmJF6JYM|aN7dN+Q
z@iU*NPoHX9T1K9prdzBhQ~u(D;>@%6|FcE~1}^;let*1spG=^GL&Ky=lfE32&d+#q
zVxr#j*i9*&Jclp5xVTulX4%!OtCLi{O{%};gocM}8yW^)UgldoGno(6PI_M9+PlxT
zBIdrF#<g!Tvx_Hg-Sq0$&Ih4em*00=V;B~{8RR-!Gnc!!+g3#17AWoI4BN2(e&sGE
zhIu`;7xwa0%Q7)A%#m$BeBt5aslUD*D6FmC5b>H%=K8#*Pn)aOU%mdu=)Cn4_Wkd!
zs0G};-KJqUDX?${|IGuR4VplO;rign;N{K7Uw!{@PSbMXp-r!Tg;#F<eEqmqR!V+K
z^+vy3CQ}7Pr;{HOyLdKTJJ0$3d+3RiKOZ!P#Yfg~-est>0=IN#*|r~EaldZOuCS--
z&l~=~U;LbPTb8Wex+@1CdCA&@tz7h}jr*(FvGd25#1$@-njo-nN(;x=_X6uW98Op6
zlVZ5n1!^WWq%B;t>YC-N+8sZhTz{-yvSUY(d;k1^-oMMw{^!eny8N5q+ADfC4uJ>%
zWeMl3t!Is#yT6ZP%ZYyX3yd)e;D$uSoK35uYNsb(zNWnNR@Ih&TlcSc)boDxk6EHM
zMYlI9)W@?%{}p-ie)Y9Rv90-cz1}KEt#o!-aBF+s(hCe?Nz4x-!Igf2^kIW3>8mfD
z+hg?hjPB)gHnWX;%(qP`Oqt;NQE;t@e4PBY$I47|?z^`}q<;On^Mg1e!*dRBt4rJ^
zbZzX-%G6u)vO&4F?8#J3>-9cM)w87E%1ggmANSVjs<vLp-dn9J({nna*dz4|t}+xn
z1kcqJoSQmt#no>zv-h3WwEcN?nQiOS-gRB?lFe2I&vq#7<t%*1c~4s}WMWGpLtGm;
zwA8d$uDLj`spzeq#`Sl9K-A@VO+`<qg_+(0wbkw>J73wG+p+5yi?*hk%iKjwl_s(b
z;Fi*XNR72?uC8kWwb3rW`}677de)~}I@W*7QVSCqUkOStlH0iOYy4v;5#dMrlTVf~
zYO^;yYX(`#ApUckR<2FRBHwoJFR^|r%brfxy#MX7<DTM>3A<caS9+IBuySWzT^u5?
zdI|FZf3RgAoZ1p!xU7xcCR}P&vBqgO-*k)k<<&N}W>?~47lB$_7f!DK>S%M~)c+u+
z13M4gU&g?|pc~*?I=Ae}tu1deR&INGqvXpE&Z(i-Pn9k5OAXz+DfFu8u7uetGkq_)
zUOf2fww=%3+pR0JeKzcJVb#1bU$BtDVm>$;&ql1hyAafr42fC!W{**>b=0c0?%rG9
zoBZzhKcW97sNVdwg*&+Dw}I;M_Lbo=E0`oj*&Dd~_E$18FxbSD&W-)ut+mB^e+;OV
z5Ky?3PyBlOr#qK~t<E~^SmeCsm8A5ld4&tTdU!%I8s<AO)rh`%zg(c+p7r<dRa1|)
zyWVCv;0%sg8<)9v7yfk>J#|k1^144oZ_lldH~o8H<EhJbeR`f@@tbe8vabl9t?;#L
zHUAUaM@}N5Z|WIWvorjO0Viqm6)V<(lHj)IH@4Vr|M&Xb9;49fmQru!bH0X7f1#l2
z#<sH7Mf<2b%bY*AymFh?7|oVsWp8-a2a2zPd6I1l_qK}u`sT3lnyke--uAxN2R0si
z-m!htj~9z3YcGu#k$n?mI`{8k#syE6L4BAx%58}&HXU0xNqFliZQF%k+55BaHa-UF
z?+d?X_Cj9x(fW5A6<^&mJMwdDt&6tXVa5aApvf$TbCcGt3fn!m`0(k{6+4`cYiEZB
zMJ~Q1T&iUoxHzi%aq-c$`_fd8w`V#ywfs_VSzR8p0+jqeh%zwPNP@D?1C{o}0fDYt
zKY6d78eQd7xlK#fDr{ve>(_-g+qb-0b1wO#p|;Y6Mfcu1fl4Dy&WEoArM0@(|Fnr`
zWLRJccKiAyi8ij&tF{`i_W@<WiRqUQCQm)3y*9`(@@l600sl_Ejn~fe_H%_TU*WKo
zZ;OV>fkhj_>b|aL=>GHf%o%ZZhCeBw5WkRQDbw~%bZYkFg+ibzFFkMOrq8ECVgieA
zeV^eIG+SZyt+rp<R!8CsA2PlY3f&(NJ$*$&>$Xq38^ReFKBz&A5SbdDeR-i!u2szc
zTQy5uQbS95f6ep_a!OgS>uAf0Y@ZEQTRDSn8`K|d`LWt}Luj3yO&1@-jt`)8yC8Da
zX6r4+>wTp8WUv2f{u(`9R?qI@g8BRBNA7)m=*sHY6_b*hriutJ(!1#R>HFtG=c^11
zbL2o_SYgwC_`=0W!nbCIFY|4G&HVG(;iXoA+2t?OjXdYxVp*AAGh@>2c1=q=pV-CC
zPfvGPFf5P+_gwpfVgeV>-D#Hk{JQ?5X>;S3#a5=STCvgmg>=r>(EEH3UtPEJ`D@*>
z+P`Lo)1R5jd<+@$KrTJ7*6+%Vt!e)aYj^JU_<uKc$(ui$+<yQ3Ipd0<k;mR!t*h$W
z3qXbZx!k4{v&~Uf3=2|0)-`ayx^m~^|95(q>dVDn-DW->nq0o)%gYP9rtWfMU!Co<
zLC@-^siDz?qqi9tbfrKZn`3TT%J=q^{^f0pm)@EdylSm`?yZ^G!KHJv75cfkFQm>@
zE{&Y-5qrxrNU%}NJd}r_;WsGf9XKBr6}kHFajlXCE1Z6JYn42RX#FNOP1}0C&(^ZV
zj{g%@uMe``cI|2GE9c7NCmd$_*sS7X_>c#R!4H!`F?jc1(%PHxYjoYcN*+Wk?b6Qj
zyKQfBz~A<<(-mXA5Zh#DP-*^FIm)nHhM_?o>^FH(>dp3N`(*mNFEc;Ub@AUtO>4b6
zctVn1ANaL?Vk4+{Eqln=)5pwEAO;FVhqlBQ5w5*u&!=bmC+__AWO`<~<IXbiDXTZE
z+2#0u$&7@hYHlmy+Y3tjxPqKsJFrImywAnJP{;RAm6hSZX$w$N4SfOXmO_fRsdK}Z
zJ)XL?_nZ0bC7{yDx97rvBiE!R2&~%>rt^r6;lNHehyRlE>i<>R{{3?K)alc~^X+QC
z+$lbv`QSif@3-3bd%u6V66~LOexB{BwQEDC>&1S#;LQILG`90?=j(O5w^V#gn*JTs
zH3T(<>f&Q!)|{NI{(5o0-K3j2*FYn?&+Y$5IyyQ&f3C#HaCTOrgjLa>mVLGnYtFWN
ze~tB9x$XIlkig{p3)fR7Jvtn{(mP~=l{@=N?~n~v?yM`lLj)E-W?+b6_>+Cx#e2G5
zXjRp&xSEfyOP4LnI@rW2WmU3b)8@^hqM}z9)VsK}$kl!cR8>_ynY8i6<9_>VKR!Md
z6%}2&(7D~}H?Opr4``0>bM@-0uZrdwCLhx<HxEBOT|YcGckQu$`SqQ`>aW&rzh_l1
z&drcv_J+^dx+oV^&RvtWS(nc*qJ96`2f?ol_VT6uyn*L$2z>3}4*kAO@zoVI4@NP0
z^AKJJ29=X35^jh0`WLui@1lPFaF~DoRm)#rUoV%=+u>OJ?99Xv`=+>{phf(4KNej4
zXI=It;QPC~Tl4N(EnTw2W70I;=w+|h?~emDCUbJ$NZMac+pK9~0-Cwv;^eekZN<ni
zZ}p~SezW<ezPR0deCs=hjb?IMft96mz2)uYCs!S7U6o(6qiQR6;Q1Q?r8&YX@?nAO
z4C~!U>8=)AZp*r=<?8CHX=4*}J*K#KUd<=Zt$BB&CQX?-b?Jr;2I&^lmu%T$GV^TO
z+Q`jr=J#ugo6oOWw=S&o>vv^!v)OOe<MkO16pKs=%{+YYaa8$&x{{jcKHswT*XM6F
zuif|Rx?&8o(4nc51Qy!8JtQH?4laSr9RmX=zA4+iXz}9MTZ<Pj-dg<p+~s}iw`>V1
zEG%5LZr!y0{{Fa%hpe&HZ?~R%e7KGGvhMafi;f&|d3$$vc)!i3j&-}=S*50?o;-fs
zy|S{BS8o3IuU|u5Tv|ST`Xu%DOivGZjNSfm79+#Ef~<Ex$}N}v(mP)EDfHHh$FFjo
z|AJ~1W}!n@_dB=jI>usb#mC@a?BcM$p{cnUG#Wm2`gCn$<KUMsU%tAraq+rcue3Zb
ztq5E^r?&d*tEKn$R<90T?iaN+YbqPR+?^%o=NKlpUAlaE?)}?2W=D@7|G(xn1H+u^
z(z13ldCjZg)m>lC?n}N^wk2fSs$Fb<ebiQFmuv_UIs|fpCZ}K*H+O;aQ3i$wG0F?f
z9aB=4)YQ~WnLq#irFrw`ub(kv#^rtIbIev3KR-8V$&)83Z*OdLmXwq{*YM9-*19Yt
zC1uICH#Zj_Y-ZP1Q*&cwW!0;F_3BkfOiazK^IN0V@`}x3X4o-5QQ}r~#KQI8L|&e@
zb^Vq0-f*g^QTd-mFA~0X@CNqZ2mn#rw<)e!!N=gheRTf^B07)!vX(_I$NOZ1Z*R+0
z^=xM24f6Et1PzV+`}^B_QrMaZ#rDGsuUxsZEM9=&!JbvCwt_}1%fzQ-hS%)`b;WK6
z&i<4Ws`nQ(h?)8P+O8$ROMY$R51zgvA+qLd-YO{uh8R$9%*)^Z`i{cKQzlJX)X2;Z
zDi2nzSOHoSqw1M_ye~8+W{#1Ok;>Kctp1*!fy@2of|g_W+x>J|zyIGZG2JK?Pc41@
z>yP{G*9j`SX{f2WReyhX^?luU>(Y03Ztmob-kx{YB<F^~-Qx4MRUeOvhek)Q_MEJi
zd1Xal`qn0Eh6jIEuiBdZ`Qfk7tLE~07uWqUlGR!_W${GYV4ZzwydE6amx5}c=N?}>
zcvIh5FfhdRG~9DhcJB*GOk9|DZqCX*du&wI)w6GIN<G*5m`~P9W%JM9f&E=wTzBjL
z|FtT5(!s>UG-b+^CFb`koR!`Crl|b<@gw8(G~K8@6&peGe$vvb6`k8M&dssZ>tC_|
z=d;<fOfo02@yqFKzPUzF+3m&6^!a-?i1RZPSj7b{&i8+3zVDsG#%*%Cp=<XhO7Uku
zUEU2HKTU2b4J<KOy_%VULHtegyVM)61sG->duUMdqhe{<lWCdm>wK+0=iHhYZEy9@
zIk#yosP3qHd-&?w+>Tu?>??gIgsKUH3hDEX;DUvj;Q~kd;S0wOtNzNVp104oAjY~l
z7u0yTwY@kb;P1T0vPx4X39S1jxR&EF1H%J#P*bfSsB~^@{CnGF=l7ky{^{*cEv@p{
z6PF()hfRN>P?{}W^htEhyIg0HYo{J9>tbYZ@OE-wXQ(iZ4qY3TD*kHo`%hc6b_YED
zzxit9hO6P>>aH(Wt;+>f$ej}#KYdONEoERRr~}piaq7#TwAkGbwD<aVc3-ky_byk~
zXwxO`TiQzcFCLYR-h5V)fni4is1jbk!T9{L|2e0xf0C`dRd(fB(VF6r3G3d<tyR3c
z`0Dc58E0}z*%%n^fClq3HulM^ztTRt_{;iU%Twp{i?_}83G!+$h`ntab-k=VFezn$
z^lBCch86A(><n8pRHvH$`hG3uZ+3FuH93v4ZP7Jnc0F2TCUwa+>Uv3cP|Ad>y`fAD
z3?`tyl1X-3Vn%q~&VBcym*|&^XMTO>^n6ayWY1j|8@IN7@4dQVUaqLrgXhn(7#SG+
z`WyHdGQxO3gZ5K@RlPT?-Mii6{u}FMi#|zBx4V0*Eu*4nO`ca@=<N#(=Am2+3?D9o
zf<*4UOK)uaTg^*dZ<yBJid-<~lGgOc|LW3sJvuI^&Q&tq5j{n(i?1wrDFXw8FUSWK
zOAa5M8Z|R^fv7e2m#_DdSFJ3z=#Q*91L}7*TW!6jI#+3`jQp{(3<ib;H$ic0({wm7
z<MTuz-TU*_pD%xs9bUKd^vAM?jJwn{y+CpG=*Ts$4L^3Q3<3>(fO?e$vsXp!-t<lG
za{BGhQ+F!Iv!-4>mu#0d`PX*->!x=XmG}lt+2z7|G9{_Lh=Jik5vaEze&u7A)Y>z8
z7e2lH`70@Wp;q>rbIFcj@v%3z)@g6i*3VkJAxz9XjEjMxAQ7bg`Q3eoifZ>M$Is8#
zzp$OX`>DY`dokfh>$kiM+_kN`NAI14;%fc=b<7M5??Le}M|}6E(_hYib>C9szVDl8
z*bbT3*UVfp?=C9I{hsl)i~H%tQU(SFu)#OloR5D!bZYOf8uxvh&z?WNq}zI1Sk(3m
z-gjb4%O0-16QRh!(2%aQfSIAr?6AR?`*&Td60geDZeAZTDaFY1dRg~1-;*0b34-h6
z_u}HI3=9nOdO;CuwEOUdO}Uj(UyjP<T4zmG`8msX*UtPo@Aht*Y~{|nbEgvn1Ef#>
z<K(KWx4)jZDgSz1|2?}t@b8~b4EFN9QWEQ{_1>?zv4O!noP&Yk!XHrNr>|YI$!&eQ
z?3G7rn6<RlPgH6B@%z;}Z=sUwOFO$bQld>57#fy=+<Fc)C?5B-X#c;DZxf&Ids>*t
zxa;>OmtGlx^$Obm=P)oZ{E7J^FUs&>%QulJnGd^ce`R>Dd!ufy|Nfqt%lEjOf!Duh
zY1_+ho~_Noz>s0d@q(d&t?c>qmp4VLzkHk~Bl&e_%=GfkD@U}gUTr!z@l7-z14Drv
z$c28^pL448iu>(uPJ7Ms%z2l4_NfPYuO!7^-Fw8qzz}7@WX0fc|LK%0kd-CT^ET&3
z^h@|k-f_K~?0hFel7XS&yTStIhUu%eT7Q}U{?nK5Ynr2W@4s=gu6DhI@4jW-ySA#%
zU}j*5@n9)o_+Z4E8d_C-HgWIPZwo%<UY(>OzuEKp*ERglVw0Qh#(?6jTxkJwgSY{Y
zGpHx_Xg&X{%egyOZN2Vq(9g5!#ka<{rdC_`-fCmpvD1rzf#JQI1ABwmnnk<(<a4Z6
zUEj=?l^!>9^WOUQf?nAxj)$v`op|;7@i8t&28IGIMp=fa1+hO<s~*p8uF5|gI5qsb
z=cG0J(*7D*h9$GBZcwsjU}#`hTEOhU9ayy}Z1pDYFVAK<>viud?cupHquct$wYs+=
zw@#h!6=h;zcyL_c0^@?2MHW1{r!}vN-)sft2s1goiFX6-x8FIQb7||{MWxRd@a<X>
zoITS=%{-i&fuX@zaRIY~aZo_mQvGuAm))_gzuv!75)HqyL*~7V=o0Cwtyf<(z7-XI
z^?sQaBLl+)k+gF03v+D$|M~3Y;W1(V{{5gSqO!NQE`vJ^pc;9@1_M?$HZ4obNKkeA
zxgs}r?K1!Q>*DrSX&M*=Y}~j}%DQY#QBhIMxychIC`>;2<kKwc@^_WH-5B1LE;_bO
zzfAPi_y70SC31&lKX`9E^<V$lU)Pt(h=yO$xbbTBLfb`Oubkcc<>{BzUn_r2omKYM
zwWXMWp&_k9@QWH~ozA7pm%l#h)(5SI;1<)laABeI)VXulu2`{x?XR+u(w7&D`?WMR
zJ;l0R<?H`!1hxKGtk3}UVWv)<dTT>svt0EX!>=zdyC3hDzuwGmw_<kQF3<aQzt^7L
zIj`aoC#Xem?tOFf&CL=So?JPhRr!wt|CX081dXW7vTFZ4<D|;h4&IAX#kXFZD(<>6
z|F^-<pC2}dF)%btJCGvL_VAt3ti@NZgz!ojIB<*WO;NFw;X8W#c=o3!C&6=huVnuI
z`g)ny{LTW);%6pRHFtNFN?8;v;FGgi@$Kzx?^W`S^Q}r(E%TduYss!$v)NA0&Y@9J
zt0qjCpf;EJz|W2QS2*dfy|=o4o&74Q-`^M7m|cmVUfOx(Y3!{Rm(ES>;@GldqZb3i
zg`f$%HvGyjd;@}m7k_<yJ^NUX<T<m=H`i?0Vv?GgI``w>w#1C1U81MXox8TD^0VLV
z^;@=ts7>}vOia{!Tm1ao)j5{MSN7Fb->v)oc9vQ0teRTs|BKgv+Wn<lKUdE_TXyUh
zLrg?;|CCop^}oHEoV(NNtMcW4vWH5O-*4OXXOY<}3CR;m`3wvUaSaXkSX^CQH{E{Q
z)z?>++27Z9?c7{z|KIyAT?#sS^ytc6yQ1vu?5ZBNikH0Idfn{S_EoFAE-&}L{^H`|
z*t%Ju<uzYlU-uUj6#Vt;^?K9fV?0lvJ$p4XeO~4l8_?|NpO4397ffY1r+(M~R9!`F
z$y&efPVkc)`8gs>|L<NknN7uI$41cH{0?Tuzf4?weAkxw&JKx-TX*J+&(h_~S0^9u
z+x2SI>X=_wuU!MJOP-u45w#^_;^~cFzLY#)U;X`^Zuike)$jL)OG-*^idxHSSA48T
zQt$o7n>k(m{r20%*&eKswFz6uZ}#j({OrE0_`udBzjQC=y%Jtsz5dOLB@%M_%nS@4
z!dQr&FFLHcU_RG#c7C}lU836OEW}kzO+!mdORWkXH0&ySd+WiDt68}wJD7JAbe&rL
z_2V;{+6|iSph4E#fB&A5nklqCKK@?dN5Qps!WBRZ;+Yuta_We6|N8!Z{h2d9yz+K2
zL4TDBXU?2C%Ovws<ox3g3wE60w--HE{{G(DsoLSIN?%_~w>iGdcXo=|Z0T4_xrW~%
zA=h7Sep~q~A-QkgonV#Si|dMZ-c*UbWf^pM@?(=N={-$r{+RMGFf>&CnV%@Ubouh^
zJ39)!JUuTSXk-S>Du;%IWc>N@anh2F$;Y+y^!y|xB`3Xl-n4wlk`=+r{Xi?)KnwM*
z$JgKe$x)q~yB4%Q4YaHYlyl#d?f&%Xlgicni)kApKpj$?^FQV1^H~^{N9}h0e>Zjo
zzuB{lpWkZcuGnhb|20#5RlM5qm*Tazw6tc}o&uF_0*re*-TGu!9y;W7c8+CmWo4z8
zpWn4jsi)^~AD6W%@ksmpy;?XtB;>`v-|w$aRCWigQ(v+~W$(vh(x4e4m6tCrF8=l5
zFu&?m`+xuThpmfQ30nLewmQ^%n$E>HH#djI#;!eb#Km{E+1b=S%LeY#20XbIB{A|j
zXD;sF{%qBbMaL?27M5!NHL~>E@6#80+{`g=jSvF^!&%1P3#ui*S+UgZd(<UWyK}k6
z>T=On`*(9s&Hr7u$}44p>;4zAs~^@rzOp=4<Mv`k28MoDNSpkC<ds{pw<=b+tS%SL
z`uff(*EVX^|GyX0794eVu0HZ&dhO$q%;ncUeKnl_FNJ}D;XtOq1;z&^ec@ML^yc$r
z6<%``of^Jd#=DF2nyC2Ji=wf&G*lI1{?*y#tpd4Amz7bLp)R35ur=;y(foU{^)8`X
z`8<PM7Pw9??X!_Qr7S9X$O)9$Ew*#KU|8_$=)G(2^sk-%e|P<vxj)aj->5#;`eNQ`
z>sP<xa-SYw{F;G*!J?nz1;d_Mi4sv$V^^@w+`aW@LFeY$f}kmNwUzF%cMO|a?=dhi
z6gUc8V60F*Y_O%OeB!??HPg-J@;noZ-|Y4!dpYNH)m6nJw-<rtcn>KpVE&-hmiS_|
z?eQ;XKIP<EgeZA_`fmLCT(@<`Bhk56)NZ`G#{^Qj6=dzP>(#H_&s>+a(DU5%YgXy;
zqp~IMr^<?oo>>I)LxDb%RfCwm^?D!4`R^{QdgJ8zwSUunyA6Ba?O!FldLuIf!+~Uh
z3yfPLes0sU{d`kz@8*9SWZIr&o4x#Y<=d|RtIl<Cf*TzNHiJ}Ih+ZxIb^29pRpnQ^
zxhuAMPg?h0bn$&&@W}9NP?wBB%H4teLeiC+vQfX@CjQO*nI}3m+<Vfmt^Ai4o0kOt
z=M&6Z$H&0H@S~8WghBVvsy9r3|J}FVvU@?yq$wA6O<l(CeQCoy!#CiW#k9r-z6Ft|
z&uL$NZ+~;))!nbe8@RKdF4w$Xa#2>ie8SP|Z=StkU|;~P|6sqMBRVbgwg3G0mnLm(
ze`26wFF*Mhuh)eq*}F8hOr5{`G-yRaoe`5&L)uf9-q`iEd*k)j+*$qUm6hkEQrqM&
zA4)x6U6{rCfPsPGLXF@BMiXxJtT$D~XC3$cIBNHtsbKHz)>r@UUWjEDj${Bi|ExTt
zY=e8Y&W!Ec{1=>^^)BiEK5{ZAjnM+Mn1F|+ghB3Q$-$aWrgKZZ_m_S8oCuN70ZA-f
zb#2}DYciJ>>3>_bp(g=k+IvR__6LGluVj85-`>8p;Hccwo2yg=&wye$mg5D(omJ=F
zUHC2C|LXkA-K9@{aC!#itk|y>d)alrmm;V=c%WL~0^^R>!-+2z>stQ${CxM;^(&kv
zT~WKS$}_zD+KR@Hn|}S;0cvtHJOCxF9Sdamoag7AzC7Q!95jTXqN#LY(f{3>7QfqL
z;m9b~WXZt5ut%6t_5ka{0try3SgdAWwDspxDxL@b?c4Ret8drqOD?RDKVNe&FfiQf
z{jbbc5GUET@cg#t*$1ER-l}|E&68`}uUGF*3zdFP?d0P9P+Z&!il%gh1<V%tVehrB
z{Q3Xp%C^N{589k~wff_9wHMc?mxg3$gDw8BpW_9?xt)71X`TI9vHRa%)9^`GPCZ(@
zThH@??ey}HiUmi{UInckn%CLD_h8P-&DL8iUN4yUQzu%~v}~sDr5lc%vub?PxAJY;
zwc7?%GAtInz&J-3)Bs-mKEL>!@%!6$KK^{~SITW%n759Hfq@}Ij^hPG-Ly%kq|Qc9
zTdVq-@1((!3k>$Y^3#?CGCxW+n|&u%4wNc#1THX|+-!3`UV2~p_0;^;(^T%>+CMif
zX8MuGZx|RD9E3sgCns%fH~AcYf90`v0msc6XH|RW^ZN7@lt+S8Y<6{E|1kN=70WL_
z-0#0UYuc{j>0Isdx_`T>_K8#R>LAyiPb@2EJbUx=d3(@8-JidIYnz&eW?x@tn|uHJ
zy=s3=O-(Lt?%;%k1(POE{`%|n`rTg)d7O>Y&xPFHmMbbCuwcs;lehQw-d^foSh(?6
zkL2PB69hm@T@STzYU%6S>tAR86VSNUYjONPxBPXMDw92bot@CVCBvuw5Gc+I_yjJ@
z&UkrgDQKW-P2}b+iHF%htxD&1zEDR;$HnlbsJ6Cu^tPOfKR!M-t@~5)_qqN5!f!L}
z>-Wv8{T8|R$D{73&1t<%%*>`GF9JeCLoI*zNE*A{ul+uEntuGce*1qBtHak{JJKn9
z{`XfVP{j0d@4xb^`M7A#*Q?r(gG^q|($ir9Z4{iN4BA9kE`Cu4G<<%bfiY@F!9taj
z4+}Kx?BXV?`(HcEZ(kGk{KfV4^YeB*WIH?4*u5=L!^|v<-}cJ{(4t|FNnYOGqO!7U
z_y2vHFJ+oFrR>d(g`gc(cXyR~d3sJ%(T(2bb3ML(Z=a0iq8m4E7}qU3n--jru>zz`
zNa)g$)$Q9OcfFe+_j^IzzRKq-6!w;+*O%r9KY6!_fq~(JF=~@kTUYn&%`~&wQ>RT^
zwr0&5<9+uHcv92Sw5+Y8!($3tRWkqo`wLo>Zke8uu_7oqc<%Evr%nY$M9eUr;~f+g
zHS0{;=4)#rFTY3!Eow^JymL>(=2VHcXZp8SzWje@mDFx$P-1Cj*HfJXD$AZT3cHiA
zG%Ct7C@yZ^XBm5&=PQ#dD=WLYx{55{y?(vAqk|)TzWk{ZCoVkhxBqupPaxoLQo-f_
zJ6+%B+h}>tGTPC-jPKdRG6n{Q2Q?pO34Up5VX^&qMA)k2#e~~g9FXScmb|-GfB${o
zA6`|pD{O64X`DT%Q3+aTzmHGet|rc&U)MP@bnUx;x2^W>|G1*&jM`cGi_tHy$GKR5
z;`yBNdd?TY%l)o`78Lf$TEDuLy&kmMBqTQW?vi3xH@8#AkGuc;`BU%h&!0cLIy)`j
z{yvko*{b~AnkP?EY`<J^-u3lb^wfzH6+O>=|9itSIVnj>$0<BKeEFkCk1p4(yP6g1
z;n8uL`-frW($HU5yCZ)U`A>|Btod@iP;*zV!=BxznLvGWoAq2TW}D?+y0ta?>e^^?
z-+4BXxwp4nUE(=;OXA_S`6YVYM;HD3`&--C`0~wX1r}3g&Rpr(%r^IHr3_#D=XRFP
z&d#pR&O*!OXVWgvGR>azT*f40f|r-qmb$-H>GMUk!%jRb&@eUqsvgUHVMgrF)Ll<o
z*MlZWCpw1d&b9CeFYmSbEDb8y{^Z07U+{l<ef@k;c6$2cNyfD`k-uKY|6j$(%%%bA
zpYHp$>Regi?=N3U_Wt>FI%-eFMo_v2?X?pX6_v8D+oO{C=ElZZhRJQ4>)qViK)J5=
z_qWXBeX_qE%l}_-J-$A+{7zxJ%E=U?%vV=dR((3D{`FzIeAer0YeAjdq~v7NnjZz8
zLE8}n%2B(^*6#cDO51mqiRWUsUaQrjcN*nCpWT<dtMuY^&|1~wW{p+<|9OLEcOl`q
zr|r6~P<V9o>hJgK<3STyAZK*z?~4#t^SLll+5O82<^C6+&)Z**&fi;l>~oKdWl(Et
z>nzLSWuHEM0<AVnJw44*ywbYl#e_4@-q-&N#JN)GoZbH4ci)Eu1}^LrR$sMbiOclZ
zvYFF#qt)u`>(l3hHug+b^S$)oU~{V3Y}2G89JasTY<~WsoZ}v-O>O((8Qawxvajwd
z#?;Hs2Cb2UI=}1so92CqhuJ`TGOB*R-44=R_9g<f-ajlTXwv5M+j4K4BphIvYf*Tp
zcj~<OH>Dl&zY2=`mg(MA*?suJgm)LK%1YvY{{FdRw+;gX!<_UK=JStDm0vKk@m$z?
zJ#MvYw^-(n4-Y?m{+xY5-Ow;_v0HDEb7@2MpX9rVzV-RtJkGmz=Fge8Z|nA&+C6#C
zxIr0EaF5!8_qVtE%UYHExM<hd*jV*;>vg>V*M@4{f4>^iYJbdC$^3HQ+poVL=H)B~
zZ7bg~@jA~`Q4x`hv$ITjZ(Uy#xj29S-)p?0jPEvQT#ZY&m|qaNXx^@w1)w&_j@j2)
zzOc(xIJEQ2=Uu4#@*)tlp*C*?$Bqv?hcC$6pT4#y)mqgvn63Qc|J@5``>28Ij0bsf
zpF4hkdwcup`uP29Z)<;lyZY_z?YjYj1&Nk2ZFT=o^6mZfw9fO=;p?g||F3)V<k>3F
zYVtXcOqE}Rtq%S9^C$1E;^N|`uV0(mFK?KBO4D+oVSVglp6C95HnBgH0hO`_al#kc
zc_bG}8mFz;vgOLnu%Mtt?sAnb*W;?!N^3KpThDL)JmW^w)l;|1eHiQPq6~~8li5`v
zq0V6cz|{G8{g1=)C7?}F&h31!Zf{?&l6iTVZ&zR6wb$$SzkBwop?Lilm$hmCvS<I>
zs_8yyij4d+`(B^0q?84-wLvBA1&Q^XFA56_Z*5BLzE|_vH}~G2NJd6RP)2j>m%IC7
z-V2A~X<NU~ko$F9?dg=TlgD!|Em>`y{mFFh&BtILB*Y0{So*8>oVwqf7w?Q3J}<lW
z?t=Wk$!n+o+wI?`SekLyt6pm6n!2}#@5V}j+DXPARyF8dUH`1^#RbKySFY^Xa=+xV
zZ{8{n3v(Gh=l#{?-S_TSgiJEYa@ZsH+qIlEYVSMyVg?3=1(Ba-{V%@1J%4`QzMpBi
zcXx%VP4;X{^q5rqKYjPzcjrDe%+I}7U-s&6ulv(vvzO-=Px{(@ZzE{q0t3SV{|BbR
zLGm>p997lSRxMcIur_MzrSki=%df5u*H%?^T^+WzsJ!yP`A@gPe}A2>|IKsKvwN4i
z!Rf}tInKCg-qfi}L2HJMjDpfON2YC#1m!^^BO{a4QzE%{c3ixx%H;d$<1`uDj~~lC
zFXgwFU0oma=x&T4$h*rv%n~fweRti12ML}*DJe^eie7D>_v*sJ=1rS7-;I>IkP=zl
zuUY#tO|s3TEP(xPeW-f*bQX|*JNKlq>`gyE@7A8m%|gP$Q)kUu_4D)d(@7g&ES(+~
zbi7aYZmisg$#2hW&V2Ox{;Ff|E|l6Po4h_Icg|E4ln71i<Ag8#zyJSt{i+o!CaBnc
zJR<z-sD9nT)B5{k)<$o?c4?{i-5A*qmt}M<7Z$Ex7aAElmrc#(g}?ITr^#yII{56&
zlNMX&>=f71(+jJp*b&}qbt}iLwXJQ{mMtNQii+>_*nI<nA{R%;pSd)zsVG)Nc1iiW
zy)Fya{<#w?#=yWZOL9}=^N(GYFSc&o`swS}tJ`vK$J|m@Qre?6XYo(%)vK=VxPA?^
zQa{P*&)4lAnK_?>8j;)&MH%m&Kkxtc_V)Dk#U|NPO-pzE&VRk-XF=zo=$|j&3$tmN
z=^VJ53aUyh-1jIg@V>OT-)<Es#~eL+^qn3nc))$v^Y{0&?lwN=`u6?mYvG{Mz>*D+
za<71|@~l!Jx47PwdA8M{-ClgMRvHEd7oPrT<CVS>BKhHQ)+?D^|IhON$~tez8dbC9
z$Gb+*_%^sj#4slrlv(-Z-@OxRn7`)Sy9-mcn`a$8$Mo~}%&(_nnXjy`-2t^muHviK
zyL%l|rDpT*J{<7N-t1-k?4p#9hOgH@4k|h7nhOb`C=O5v&9Z#qQ2eMsqUy=swZE<}
zlleKzw<^)c9$fxhI8=F7`CZ!PpYGd3l3U9wCRP5wxpL`!xoJ05{#J&>FD>maEB6G&
z_BrGAoG;$*|G)3__pMvE-r7;PSWG`|&2jnqJHDZeytP{_-RplQ%~yH(GWz$6wtKnI
zw8?!io9UN+{m;{Ex!dnmtp;t|Ug|v^wAnmw1y=!Rt;+qqyIm)JsShu^)ba1_9Y~7i
zKKlCkg7}(`M?s7E%OxAU{I9&acYXiW&ZymbuVq%==h=DUt|};kb~Nm<S}@<D&<V7`
z@X@13cVfBr%sHHx@oG}9Rl%B|!bHZZyps5zzbd}g&jV$Z>mO&i-#L8vaI9FTF^_X<
zYHHTiRa5U(JmxhIWAorPD$h<WTL>D4e|qj87yp(WyH!9w7oTyR<qK%zs2=0p9JAHF
zv(3DGeM2iMc1UYCZ~t9Wy>H&xJG!2yb}XG<b*y#QP9>0@cMnV%|E}BpuBe>Tf%!Mr
zY17=7YMz&3r>ne}R;R-SYP3~oub=tk*RAaJpcT!T7yg~IexLF2(b2pW9QW4k+2!|r
z-@dritEHg1SNHrE&)!V}nYv@*b(SwZJv^>%Ze|(%U0tAEdRLbe+hH4)|M_%!>CdOr
zL3`>RK78oq?VWw$eu|Oluc}`!m(Q&g5)zV<nQvX5H~Cfj?<+TDXKgE)TqZt6+v>=w
z=f}THmhV0U3f{Q0*+K^wI=4>&pKRgW&SzEf;)1{3{Yy)|r_P(VE^>2P=HWKpSC^Of
zxADoYYHDh7`@KGDtJh-p{%fbEYEPXyb?J{E6}*y0F7EwurQc4TJzHA2_t&e{Ml*e+
z43k<QgX`1lCae4ZJNfl+tqh+t|NiZvr?)>p{V4kyck(Z7tsD0t8L<GA_yPk1Z*5BT
zZvJ+5w)yJP*VpWS)||Kb-1BbdbGcr5`?$Ni%ddCq?^_YJHtOZo@c7&<|7Yj#TM1g6
zee&c<FE6i4@&A8?TgvcFx4n^^loS*9|JU{XC10<GgGPMyo^$c>mAw>~koYnAb$hi$
z+rs<xo1EXTvsaSe?8)u^J@h}HP7tWKexRG_*NcmbL8H)XV|E6W?T#(m9jhI-<^pKe
zAaJqU-zCLYuUyIa`|GQhw|DWjnP=0ME?ugrt^In<xxmQCmA7t1xw^QRRN2_u$Ak7-
znkF7<VLSfp+}zdI)<&<+zrPO@JE7s><+skST(@r8*|g%ygSGSGce<~y*%?2{q~0Z;
z_od+DXI-GgP{Fl6@@Vb3IhLT2V9+{%`1`+3ms!Y2S(m->pVu505~8yE?z$H*GNylf
z@gn2xot>Z8oHMNbRdV#$v0W{H{%@`Ln8eD?{(4$;USZ~Zqtq_YGNq0Vj@rLpuY;Pt
zMa9Km&lsOKshS5}K9e3Qd~W^q&3rHT=f9n#a`umFdGCw!U2j27+4tO(@ov&aP}jHW
z(@AyEni*CWmKEXa<6d1@c$js4>bpBTqc){<hOLcKt^a@Qx!nF=uU1PLr};ELe);m{
zx%ltz?naAsyMDh{z23;kC}nGu^qh3$QFZt5rp;g1`?{NcKKx$Alb`S1qTTx0JLEv!
z4u<#7Od0=9(Tm-+!h5=&{cD+t6DNN9_%X1qZl8B`UERK<q`E&7pW6R<$Un=ve4X9@
zKgD0)-Q9iB?*HHS^;750UAxqK`n-+Pu3QPZdiCnd1C7kDZf(tOejK)X>%|9s-04Tp
z)!2UNKWoI3d1nFNtXbbazq1GVS9kLDGmi3xNiEOL&c42&_TQhMzpk#Y`#SktDrBF<
zbk=)*x<AY^E-bjbFaO`4pR<h9`#_7krc9Y~`CIkVso|i_7++sre%`Y+=hha_w9TMx
zvGy;e3KG}cjbHQo-i-xT?F%2C+&HT=nSZ8_TKRNV(AWlZrLRN$-m0zZ_W%3E_V#*g
z`P{JeaepVDpFVFMcx!F9xc;?1S?iMfwcl4RTIBTjc>i>jURmof<Fqpc-{za=&jZbJ
zf%2c1hetqM+`LbpK83`^y(@~FeRf&-y-N4}|Guu5u1O0En<l0cvEb}%^X&WkYO6k<
zH3yy50@+rPu3F$991ynje|7TSRj*~F=7=m^UVqg+%^2L&ba=eync(+NpNb-Nrm?-P
ztE=;kx>@t&F}ohxT%8c&@*efg6oDZ0_y&g?9>v(ULc#jO7S=YH32v6ah~xq+&O
z)0JswXDz+CIo*1HJTaRm&L>E;)qQ^_2HGN3eOtiRe%bs#HG7^tPXtBMEa=>uUP~i0
z`z!JNKU&ky&3SpruKHUJX#Z){{<^)hwRUgFL7Gt$D4%{gY45yJhyK5>ts`sB{;K$t
zx=X|A2BfmlJHa;Zc(&UG`RX@@cZ*Kz-r8Nhe%;PzQl3F+Y0K_az19Wo3(s4}r+ac<
zQ<3e@_p+0gNM2VJJO?UC=ZLQ7d|`gCVzJ$i2h6r#uLM_pzgrF(aI&lY1)6s2>gXsa
zugp-)dL;uYidVgs*|o=e$uVB90!ZyUi*r5ai%FmZTwb(E=S>LtZ*lx~{{FT1|2#7X
zdE`ym?%AuIy+7nU%L9${ZwRaV#SAIJe{k-}ySnP?>Tu93OjmF3Rr&uv++&N+n*MqQ
zI@U-msv)0W<aKLkwTh?z-8<eF760X}0~K@*#g9alUsP6B{<?kt-?H$yN>{VlzT5AX
z&0e*7_1A;!@++#pzk8?0uj>#Qx^~&$<jFE^CX3>&WACqCyW1O-FE5<g@<Z^8bpD>e
z*xhAUr|Cw6Hp@SK^5n(N=ktooBQp}^{%7pw6Q3Wj`vb$-eVy{ZKvDMNWz-*zU!Wr*
zG=rDD*!TNg@N~V{JF$FG3;ee~dv*7#dy0|g`8(6rB-GnOo3N5uAC+Ik*Zo|2>eMMv
zrUDIgJv`K^rKMF=9+{z7<mH!f?02-P=l9r~p3K5GLG5J*hs#@jG}L`OD*hU@KRGXN
z-MhWt<8&i8xoq6HvB*LuYG2LHQ>RYdiB+1lGHSQ;<oowlOga_ywTt`el`Y+OVnOpC
z4QXqRvVRF?VrDM=ez*MfY5o0IuC0y!dcm1L^WB}DU0q$5e9iAZdH3l8&)<)mW25Rn
z>}XoMuj=2ox=lZ-&er>Zyn2p#y^jbeX#V{CT>9jM;NHLAZokuGKYQWS8>YQKj>dUj
z`V(Efy7qM#EIOV~W&A55FE0<8vIFho-tFyZJSn=$>7}>1=cHqNuNJ(%5yT5BCM+LZ
zWt9D~^3@wY-^%L6ec{(ux#x7q?c6sT8Zk}+7aHe301fo-Vm>ZfQgm<U{`vh#a+|*#
zUFWoS!>YP_Kko?cogyIl0#e^>QDG^0p!4MRsww+QCi6H;O}-Vk|Ap*pNXul40ZYjP
zo;Q5X!T0X1NES7Hb<NDB|L>yRP;;3CFEkogJf7VgwRwL;qnNyHeRce^xuDLWg+9lN
z1N|p7Ef@YgUFJ1OZl9i~>}{5vkXDqgD5I<e?_mRz&+)fI0+Yl4^F5TBeCr@2J7yf@
zcyS=JW|OJ^{P!9Y=J$IR=f70leDev^K?y7+3j~)=-8$>*!}3W>ey@AAM(^T5XkdD~
zJFrVl{P>bjbZYqO;Moqg1*Pic(?QNTz%6j0QT|8TeM8;I@`y<$<u3V=wUJvv8>$cd
zba!C?=N`H?wtvs|&`BlLS=GEA9R<a%py=Gg$|(Coo;&=_#eIvoTv?;Pekk>Prw3~1
zHHa%NU{-&7X7kIV`ro`J&HItc-}>V>)Pwh08u&amS@YQbIlp#QvBa)z)mz%WbreGK
z{2m@g*$G=}s^^)>>IF`?8eUv?GgwWydLGD~J2r5<Xjrnpw$@>i$-7B>U7TOb^9{ic
z5n_~$%TJ%@d;g90wEmk*3|GqDi3JT$FzCBFv|o?@^VQMw(u8*xyLh<^icP^fL>Xn{
z)^6DpCZA)Y<tbI}*;L&PDzYA2<#_QS>-`%0HNsoVRFAS~n;IUv`xK<(LlVoE`44|S
zpC9hz)b!`?Ur@(!Yxebd+uonn-@gXb{d@TEAs0V?cvMtW^FPqR89r*0PiNoQkf>MN
z)!l8Hc1EJLrA0$W$46MrC!n(OXXVa))6Ts!`0{<tuj99m$4)wM3f!!?)75Yfwg?<l
zG4M*8t?-|32U-Nq#l<D{c7N7YtzJ3XDDCidAz@+DK&OhOoDh(;t-7-G{)WWEQ)bV8
zT?5KjddH9VNG^WA@3-IS>H61?%h#^~m4}axbUuG;6}>6+CaYCQ%u0d!cwYa$V$k^o
z3=DhboHXD$eAm^)9nZ?{kAKT{$3A@c5HzZOtWQ>ZWgW}jB}-gDg8+-)-`tqo4my*g
z^z}7ORaMtXTXJq1#n%7*3R>uyl$?D2$4;}^z6%#F)Y8=r_3$_m8JBz7<lEJ)QF~Vh
ze2<y8!ae`RyLUyPE(>>8&Vxz`$NZ*Qv!vF>?~lvO%tT(dan({wTN|`gwS(h&<@wcN
zYlA{UrtB(vy9zX%dAv`ySnf2|LGtGz;o+x2gX9Wtb#<=4ifuKTxx`xEuK?nI2Z#Kw
zMT?YfzAbZgci+9m-Q8VVU;jOF`!Vrw+spU$|92Z18=L0ev#I@fR2+2jSjzU>s-Cdn
z@&5Je*DEP0`TgE0DJjXt%Uind(c{PUr<QJuSU-PGwVj2g*W2jrMUYge@N?I0hJ2Iq
zcQK%ncy-v?N&ENj-&}iTMc}#k@2{?U@2&nGw%l*-maMB<ymB@>Jm-T}ek@(S+<O1^
zn>lgvmrk#K{pH;+<w-Z9K!sq#^8}W?oJz{dPai*C{O3>2^lyLu)Eqr><j;%h{~y}z
zGv3|V`RmQ*^H+A4zn6b|dt<V@tD9Ta`FXaJmduzjgRT3*)vH%m?%45T%lz!EQKCXZ
zk3M}Wd!K8u=+i5!(j4Ix-qx<Wc7sNy7$&$l<O^XR*Zx-f`T6<lb1aKNF+aWX$@1bS
zCj@1!%Y1TgZ=0&ZD`n!Ls;UaAyEmWj64icEU@>LZtY60KPJlWeN8{xusPsxtQP@0l
z?Nv~Yt6*cQ0xhKg)mYaa9qrzle4Gz-G#m2z=c_%E#&iBYKRbK*ak*+A(D`CQLPArf
zPhY-gkIi$rXHTC#{q;7#es%u7pKhS7#%XEG>VBS%Use4494JGntc>2C2RelY)SX%z
zyE`nt_UlwN-&son7rV{9%Jpu-zB|E7_O^<0ZTgkv^1lpHwlU0^(M)iS^Za_9s4X6*
zrlz1-hS+n)+1GT=&a;iSv$F#Y2!aL?CQTALdhA%%i3y6JhVq)Q)tkXxH_+ni4-XC+
z%{~k2EzYa`c9XmKqV9o}7e7b;-X1(*>TP*Y{&=1sU!wo`Y_+VM-nuKI{_CeCdd%dH
zUFnzG1gWbZnBAMhYa`mf<8bz$$F~m~@x1(`HxaaWjDbO}y@79iLb=4R=O@=*b>sW+
zF83KYu+^BX_BdU;mkb)0@7TZo(k|ECkm$~6Xy9A_;oPsola|D*9Y1;h{PDBk=y7mn
zDcMr7R6T2JPV~GbzpmZ}m7fQM1utBD^tal39jG;@Vdb&+b}Ka5WQZ`z>fT#<{C57^
z`+jPZPeM(3%<&@QBIrDi#|wpi&hVAGXMPS+;2y{nyl`>R-s+<4t*iC27OzOCHwPb2
z!m!-k!M!`RT%xKzxz^!-C|CDkkj)=*SW31;tS%SVx*xXoLs;D|wa&fgK@t^aOjc2A
zg8R4sH?s5-i_ZhCVr7`ud!<Qi<&sTqr}v+pe&il#kc?p#JCjw^l76%2D~`Pj2=dub
zb);<vBnT{&K{Dmn=10ls&4ZX~!p%~$#R9bBu4qZfzO>10>c^q}w9p30ykBRp;|W^*
zyS8~v-a62*I>R|WMp<32wRabOH-F}lR0x{%XE@8vD64x){AR0`*7~s54&Cegx{4sB
zQNcBi7a4*7b}p<d-0`sX@ssC=--0svf}aWp7aY}CxhC*gACu>$jyjurpo(rmrQ(9c
zM=iE~uiU;(@zpt~ha{P-qI$~BWu~h1z7T)@476aA;lXm{1&dv81P81;+wT1&+3e-?
zlPuu#RL}A=%IY4fRu^yI{b`X|mhc&<A@WRCvut-CzVKVx-}c_QM+KnNGE1GwYS!L0
zdyh|vj#>(;MBW{Pss>90SI@tHLp=<%bOz$}S#1q{X>0fF@;kllspllOFV8=M<^~zg
ziZjZdy|p~LxOCCAb$t^XUrEb916KeH3r=#p$hhWbd(UoBz4c7c`b|&+fWg<@!F_Y-
zjvYauivFYu@B7nGYt5OgW~~j5Ud{XJMpR)UBQ(V8<d`5wk!UL_I{y3j&&${M>aDHW
z{<ptBogRPf>Gb$@A3hYcwzq>OVVBkuy?E0<Rif>l_16mP>iHm7WiWEQ@ZD1KG6=Nn
z>)M(~(AvshUtTU==-j?)!GZ-B|E&&N3pzY$Ytd7$)YMc>JG(gNHlB+Y7k^!R0BI>E
zXt~$>z2E0;+Pt~+vah+R#dXl3j(6_FJO?eBeEv1c_TsPR<E0NGmPSR^n5;?f*?k(6
zyc_19C1SbdkGkD=L7oL2ER}q`FL%rNU8S#03LZGf+EfI*y}f;Uh<%e?>8lXmxmH^}
zOK&3&Nc&o~tIzm&FKqALZxcXkj_mJUI}XXAXZ^fSvgvXn)>neU@g`z2d$W~$YU<Lg
z>}*j%!NOa1OP4M+n(4#E&0U;1-@1HV)b_l!`)Yr`0-X{Q8yh=$-Qu0AR&9O#SodrH
zzy25JAqg#tExug*f{duB=*hh8+E!M#mfBibMOl}>vsqp2Gv6+D<Hn78ivR0FLqkDF
zkxAKBZ8`0~c^RG)JeDJ!I1+jF`N?Vd=ffjI=RP{bR8p9f4QUTEJZE9N%L^KyQuUse
zadlPbr|;kM3-)Z=76$4I@*KXf$hAAB4^$7=-`!oFAE_gDxBC6wQzuSLxSEw)unszO
z^!%UFofnhW?wqvbVQq4%+2-${)e;TU8r|>ARxmOOva_>0b^d&L=K1sI!+p;5>qmTR
zxBue^T6CH|zjoQZz18Lxby{=eZ7KpzPuI7995VqiT+=Hz-!Qq&%iDYI`orz~|9>2<
z+%e0lJ?y>K6cygL2cZGS$5bU8p(FP7{r&ivGiRPNYd^dYwDwWT<i(cvv+e8mJv%dV
zaq8)5nyRWx4}J`OSo8he-Cut`pTD~6>rDCk_qJqCK9jb2(V|6Cwt3LjK-Rfy@ASi5
zdt-B(*1nRIeg(;>59T}+{H_LC`$2e<lIQ1VXD^?bY5etCbiSsdqGM%c<)kGG7AUZ?
zvaSRL?!BtjW;r(&fEIg{?Y?_cT}jDFtlKp*G7_{j1au}ZD?7Wkwzl`>WxkhX%kM1w
z@S))QAGer<nbxo8N9|aaEz-XooF4-@Uig|6J@F6~6U%ybW+rI6XHn6nX}ZzRVl3WW
zSm<mtdw;z{;>AU-t5&YOxOV%!S4Hzcqq^VT+youhYWL@Zvu99I(I)TtQ?<iity;Zq
z(p9YISWLe+NwRI>8QHJD&%|F|`%xWS$S>HLBwwO`dCqsMJHY|#wxsWyaP;_^UgPzl
z3#QK32jy4at8+FxdLNZ3F_G5xv%5F%mSGckMr^@R#RZFRT70ee{Xj)7AJmDu@JZmp
zMV%>O*IwOjKCbF{UBz|xPS8{zgT;4_7a3w<@AanKO<ds}GU3+tyr;0(P+qV&GGgOo
zNNv0&hoz*#X7}L>r``mAK3};<7Bm-h+Qp&$`0a`pJG`{AMWr4ruX}qK+F6<-&Qemb
z=gj2(?eSN)_SV$y0M7?)@mV5Uu}4&Wd-(3Qx8Rz8O9D$t#g@ZIr=B{eKl!FgY-OM{
zEaUJp%F4aH@~A*lRtdC`=~))2ws`PIalzrmsqYf1!F{iLjSYPLp@Ff>*X%v6;@Ld2
z9$bO7Dla&kT)TC-hiO^+BW0#5mRcturObg!SBLiFR;K@J3WB!ys7;=|8WfBTpSv6Q
z`cov1eTR9hL0n<MVddKC$-$11TcaXtrrd&c-40Z`JG8r7nO4<BKKF>Mt-TWis!18<
z)U%XSEZNHbDm;Jxrfg~RaL^DAgE$+btXyWkW2=g$rrMFSpv@@^2U;B++K+3lUl%-Y
zt=FV0D}5J09l_n)z}LTd|Ax9RdrB9$Ca%?iRy=w<Ojb7M?j0)f3|emHn70lz^T?nl
z0}|XfnQ^Z&B%yv#VkxPZvsYcdFr5VyH4Wy93l2YilM3O8D=j#@7_`$_+z@<X$${Sj
z7aj)oO`8)?I@h_*PHihUKeXdga1bQ(ZLW;1e}=3^ynLQNDAeyYv#|7U+TQRJ+_(WP
z&67)w7pw-Icfjz$kENty%3jv5{X5E4AR<N}k@xNwDvyAjQm(k*aPpMUt6KNNbUOvX
z4TO8`4gZX7h@EyZ-~p{F&b_@Y)WM<Q(xppVs=wz=w>x_DsFZP9kCT&=h?v+iPGPkb
zd-nX<6LY_{EbIU06AG)HnEyayCnkdB3v8EzY4$Z8Q1w{*>&wLp3!Os)0xo3y4_zIW
z`Q^pMsGUViH*MM^A}_yQ(YXzD;^R3xP$y*bdArr(@il?j*VY6kBrE{U)vQ{jl|J7v
z`Phry@AqB)^z`&s(1BiGUtOJLQTV8G|2^)+d`0aYpc3Rm91&~Cj(xxVwyd?iJ^SII
zR?rF<p2Gn-IcwyqUMT*3X1;%-%G7Dow5+V6>VMz94?5Bjv~3kMTeGY5^^{4Ilsu!h
z<;+~S|6dho7wN{08`J;9%s#v9!i4~kHWig8>3{g<RM-E}i9Wyf#^Q_2x3wQni-Yt(
zGA`O!N>^yRU6{RK*)q4+*Vl(fMa|lLHpfg`SJyW(GBUli@6;);tJkh+DJeNk0v*Wu
z^y$-`bBs+)LRPL^d9FAuHC0nb=ZxezRZU%8-?Yt<+1J)sicfzQw%qMPqyi`}Hkf}Z
zEoVF%h`B#`v(@o0FE5KqOTXrMQdwDfuK3S~Im`P%rvzzfoqD<h()FEFTekwWmgW1+
z_4@gO&Fr8Z2n_j0ICe>&nQI*mS||Sd{eJ!W`v0GQMI!Z<&%b_dI`wX1M*PK<wihHF
zL1p2CT8<aK0+N!N^78UepFiKd<@xjH+G=Xg-0%NJt2V7~`|o{!{G6n)@Z|)-UAuN!
ziHeKQ|5luSX2!xxmx7+Z{_yZ{dig~02)~n@SFc#6d^B7gTcHgtzQkovHVUNAKKw8s
zEo~VauT;sc?`P-R#}_|4b8%DZ>6m8|y@QgH79}Mm#hlCBd~-|MS*hoi?7UJ-CQTAr
z8^8bF&Ge&PqD2-mUcSD$UyRp=t#)#9iiw+-asK4b=bRJQK1nv)2`S2Vd;oPOU%q@P
zWu7-DC@2WjB-4%9;DEd)tE;;^*v+j?CG*~%O3+d#(5Snrii*Ya9(Gn%O$`kXwaK2K
zg>aMo?N;i=?$R(c4D7f4HUry=EX*ywyVaBbff^<Z`Jm(QCMvtH+OQ!2v^F#@Zr-HH
zlegyFG<vR--_9pH>E@rm%v{}Kx<S6ay`b$0(?6e?W$L{-{rohQzwgTT2fMnqg3e5x
zG)XA7>}Dz`d7rlVien+w|EBZC-@wNgK6rbB{qE_CS8?r&<4#>N|K0WVOm1z7_x>_y
zV*hAkDqUgxY{6mWIaNpYDua&QW?=Xb#8R>&HZpWA*Qr<Evz`lr&J6>frnKGKwCbMP
zja35m_R!AaJ4c81+jFB7)(J0$l!FqCvU$h<fR?^6C@eV4Tw~wl2XV+=ju!>H5e49P
zr3HuAzTtEJ-G7z+%efv%A(_Ndvg7K-$Lb-$&nFzczUIaT258F3b9QKto;$04-VLh&
zyEJGW#qeEm!Qr)YxR1CR{s)bmG?XhZIK1}TyeI{|NzhVRno%}yYd@rXo#5ioJ{wZj
zGkoQEQ84?G@K!CY_0x{Xy#STx4i4?N=Tz;n)w;1N0pUGYhxXfVtbbNiFE@kq1Q}RL
zcAVX}=$Bv#q!q!yWOXMapHUfX251vr-eC@KKCC%^{VwZ)>br%7>~GI#2M76d?DhuL
ztPI<g7aYz##^+T~&Iz*jfnf!>N?>3ZfO|H6?Apx`69HO}lw&q~wl>Ip5B!!J9bDPN
z@LhR9^~_D1j68#CYW6^-I8U8AB_bvD>Yb6o$&?R|h1NeRu$VG=^5O>%5@z}=`~0uc
zY_{*mj~}gSer#}Zays|>`NGr(`y7J{-!IVIk$*wtj(LWb|E_YL!vT?zGn0~&Q_YNj
z-xM}zdY5`8ZS$?I+2sfA%~e#6ysc+k`12vUzMbU~mfv;$%mCN*C}C9?7r8d<>?~97
zu+>-ZE?ppruJxVl9|^HeP{qS=fEjW;8G+MP`#!(I7>VAsTcu(`PY(}h$KAn$2fMnu
zE`4}-c<S7_Z!axhwaSa%=0gLh%e7#ELa&6O)6btj^HxdB0}Toq&GeaNRk~_(?{BtV
z%c?Ic-tBnI2Ri(Mk&*G$)z#DY{rwjGyzsDrN%1qEt+}_u7#SJ8yuB~KySw{tgrQC2
z-Ms6`{kF@F9B~27eg`l20}W^WE1#SCPIgV)-YC%YA1EY^W`d5C-yvf^yL!ov9TCaL
zdICK>Iu0E=WRiG@W$N_l@ds8ns4FaZyeD#3$x6_fc^FmxgLju6DDM05h#R!R5Hyju
zrRHbR)oa&4Lujj3tSI<4(=2xu=r)7BpU+wUdep5CiumZfokx2^4^Lhbz1^?==jr$>
zyUX*Zm%WqzbK&FT<E1Z`PCs@2yg%sh{%!g9*V+I3*w4k!Uyf&4`p%6-J8ti({JbUO
zq7tYtZC&=}!k)^{CiVYnKxb&5v%6!xV#SI8(CIRNKA$(Q`jhi+B90bE>n@fGE@lgV
zHa?jQn-2$=&%J+Jwp&nIIy)sLWyeOXicL*TOwN-&9u<$5XiJn(RaI@9KY#wmbJp)I
ze!sb~@$iI{l+@Int9>duHf=Im>^)uY*r%tbd$rf^xm5AtAUp5j2m2f!FY}+D2RiV2
zx$V0Gi=G)XBp$z9KEG_+)G1RyXDC<ve!E@YrSn68`JIC1!tZy>cW(jB@E)6Cn9Otd
z!97RkNj=~1*Wds7_vRgC(3#CDlCQ6=E&c{N@RWD*$rKsesxM7f`MxK!$Ub+4o(j5U
zi%I(In>k@oQL{E}+H@yE(k3!9bEOLCEX_%vHO*JAUE6iX-P6-E>;695?}DKFEkIMd
z1v~9k-btIhJIrq%;^oy<_jPr=rk0i$XkF-O{rzuj_SOHd12tmqm@izo(975N>AsIe
zuyet)uj_eUdUkg9*Qe9tU+sFmZt{`6&BoTVt5vkMy%Q4?y*xb&zj=ClPrjLRZI)@a
zUd7o0`S^12hG{D?4hUU7dsUNqv4spMGwAL867==W&EQE>rcYPzKDubb27~GQE?&F{
zT8i&H^@9kg?b608y(RDNF1F)4@-NH)t(CsG$Q9HPR_?c1RQmc_=BX)~n<8{T75nFV
z8E@|EzLS3c?ZaXI(`lPEH8nl$?Cdt(e*5e1`}*~U$;Ui|zVn@Xk@Ifio;^0MuC6;}
zAZNL-e4jX7aRKuO4p8phmV0}d{{BCg?k+uWSk|s40(73vH2rwJ`uh6v>CI_}9u{b*
zs=E5yex0JSGXMU*TibGH$JKmv?Ugn^clPSR$$uZq|GxlQ9kl&kl{e_Vk!8NKO-f&d
z96ffd=sknQ^NJ&~&s`yhuSl(#H3xk3iiCM6tG{XHC6(GgACH4t+PAi5KhNp!><skv
z?e+5ViuqM6U%CD3>+7o*EO7Yy>+9t8>(|%R)_y%I9uGR2W9s_#>lZCvyfgoT!|iRk
zSNB$zPv4ezch}1c3!NuD*<=2roZJ4(?fd`2`0f8}aO;&?8n?F!bmr2PEt!|6Oq{6L
zS<RkaX7=5~$H%9+b;@h-sV-+~pQLiUu+T?r=R|6yJvnyjfhW)53rjpFfBE<OefH5V
zQ7<2#63cu0>+661{JHav`^}p-tx8@9e4i*TZBwyfTi)GOzO&6N<25xkLEVzi*YBAB
zSm>?4cgsPuzTIVSgWCCIRXjT(i}ZKq=a|jDwkGm$?PJh|7pZ2u|LJe;@9&r4Ygc(E
zJLl%jJIaVdVBWvkoOG1SQigBovSo1}W<9jCdDQn=&DJ*d@v+`uFRw1pH6E<oVk;m^
zR?5pWKL~)L7}T`;`#S!AP(i_l;=a&~j1^2wOd?WJt2l+#G;D2S1qB7UI5{sqIXT%h
z<AOqL&Bvp8>*U@E$;iBkS^i+kfddX(^X}fdd9Jou9<*H<bk7H9*~PB6+iq_udmFX)
z%O&rsmrJLsc&g!RF@6{0-@fbPG3l?r@Bfc`dwY9$zntxs!pCl)BXD>9ez#k-@cn|g
z%F3PJ-rRirr|$p1zqhtzPF7M<y0WkKxA8sYySqv+gRbd0Z~OhlQStbY+}yR$DHTmK
zvunQLtlJB|8-2>SIj{Pi<>K1k-`b8Q8G_2dn$Ks=-|2D6xdbg<e?6}H?9Jy#I)#t@
z{eC~crM+ExZPZpH&<bT`bMx!9u7{QXzBkKmJ6PL%479Ap?fg7j-qlyLR8&<Z_4j_6
zbh@zpaH7q(8_8}ZC0i1=M)e+SW|zKs^Jd;Ev3ZBmHcNtX&c%JT)u1EcUw}@Mf3dh<
z3Um&!-5u+QokgjO|N9%~-LW`qaPFO)W!ak<hE-oOlr=O?aBy-O9_yFSpE7mo&WBkK
z{6M`#^S?QuRuTiloFkwE;6dAs$-bBDK&0_ZpI;A}`R~NCF)-)~G0HMDq&-@<``x6I
zDOn#t6IHvtnHd<KH>}?cTH2qUe`7-;Xf=P#tu<k*XRl&pV0bPBYUr6Lhpi4ZG&Iy(
z|KUS{=hy!E`cYdvKx?O%n3%3?Om+vY+0lD{e^=>h&{7^yy<PhH+ANF0MW;@mj{OBX
z9RJnD#qDdtR^Qr|3px&Hr;NUq)}^!N_t%82jaunFT@SSH_xyWTW`+l<94{CcwkUu)
z9Bw@l3qcF0&de}`Zktl{1kKf5Sm-P&C$|oCAqVIj+voG^-w7QM*Ns}jE?40otnLS1
zM)I&-9yBYbH~-_0$NjIj-Ojr_O*gva@7L?uXJ!~`>FMoTaGjIE!8^r>=Ws!}AOpjL
zGw*i2)&p%u{`CEOd1n2SiSD4yAtzFdUjG00{y(UGe!da3a|CqE0Jr|01)zHvKs)Lm
z9&Q(vkkIh_^<=Vt$>UygEp>JGix)3;b$4I>@$vE3CzJinqMA>pT>AgJ{=Yx?^fLKh
z%}q_9>!OUr<ro^~cUrz+U@%b*kFVY8%`kmQM+e9A_3XS-Pl|FbEN}#61r^Yy8PNJr
z<2l}!mQIh;YCpX2&6}K=K5C$SIUgPzw2yLre{b*H{d_z;As!wbTwGkUsyrDO9;kp0
zl|Ac`kdV;qe(wC#hK7dCubw}D{`}M4lPQx9KeUinWn_2|3c60ff&0^0^ZO=0_C;^c
zo3teF?yk(w&(4AxL300JtzMsEHv8(<>~PR%siLCe%$YOAx1=#J1c194vnIZJl?AFx
zxBUF|OUuY8XwsK+*6%?T2Was<Xr}^b9u#ygvP$Ne8HS)sL5}rGFTc4tef5S70igYJ
zbIb3oeDNZ~^U|A}o4?lo|6UJjX_j7(UA}3PQSH~O;j?TiKeg@+U|`q-I+MR4E#>B>
z)U4av)_O-@Ul+Tz__?3wSM4?Wk(*q;->=^fN?_aX*G1pmT|Ql9Yt~h-|3A;ye|gZ%
zpLL*t5wuPjl&I3@RUWHcSNQnY%2lhp>VI9H|7G_5KW-BzP6SP^>fd2yVt8;g;;1Y`
zgV>=IqnCfb-@pFh;o-ZnTnr2uOwYg_H{?4CKq42M46=C{8lLq+dR}N(=7AOzQRynd
YKlNIl_XU+iYk_+Op00i_>zopr0LOOLYXATM
literal 0
HcmV?d00001
diff --git a/images/mlp_accuracy_tf.png b/images/mlp_accuracy_tf.png
new file mode 100644
index 0000000000000000000000000000000000000000..0ec9606b2f14119fd7c2148f7b3e3083c76d3133
GIT binary patch
literal 27057
zcmeAS@N?(olHy`uVBq!ia0y~yV1B{C!1#rOje&t-od&xu0|SF)iEBhjaDG}zd16s2
zgKuI<K~8>2PG*uqS!z*nW`3Tro|&GJjzUIBNkOrdzJ4xTfnI)5y1vE>i6jOF2F?PH
z$YKTt{zMRFTw%XFlYt>q&eO#)q+-t7yX7?^rT>n9{QgdD_shNQ4o(v<SPQapE4lK!
zUKFmICvtmk(!&eZw-u|Z7R_rt-1Jn$HfPSfDzkuDto)9ST}+NuoG+K;{(AT4A4g=Q
z^Yj$6ldpePyiwl0*?9AOPqkCl_tOk{4ht}VfWg$OZ<s&~1_nl1Fg@SFfgQv+z%Fos
z5ybew$Wj8L8ERlk<VTe-FfgEoOM#DAx9j)2<@YshpKGt*qttzL(Om2Dd6z!_lUn}E
zDmXaUH1pDu;4(Xzo4w}uR(w8hfB%iqtRw#Rf0sNw+^%h76C>8``hNfay2=0TL`6h2
zEG#1C*L?DvTqDP@MU16{Aw%uef5y5c$Ib88B$suzf4$($|MKtm`{uiO=70G4eE#n{
z@~TlyvesoU=Kue*{N29ad7hUnpU)}2a)0Wt<17Cy4i27aQMkw~=f;Blf8XZMGS8p)
z>-+orPfT6h-Cy6zUVrt=%geL>Y-U`b`q_9l1H*yNpReQpuiCO@3)^k$vNst{rpLd5
zEZlTj@A3@8<UQNsORt7Xndi-s;cK^SU3m1zar=J@w`?()Tm5e5q$SnwcDi4Wt6uy3
zZ~p$jVVl#>U%Oj=f305Zu8M8n-`w1MGW+jVEiJ7rNk_Tv7N57>^>W#4pS4f<>wh$-
zou9XMsrU4(7Z(;TyT-(z%gQLrFl%PuVz-%>lFC-y%rU!}W48J90Zs!R=c!Yta`Et(
z>=x;tHA`ymzhAF^9hU$1;O4i}>)tUkGS0HC-geIZf6d(DbC$j_4_5|8Mr!W9yKc`O
z8_ysYmzJHZp06$~?cP=XeqDH6WorW;!*Y(;JB$lhSFT(c7!oo?&41pV2YV+?5@Kax
zdGYOb{`Z{IXU|?Wy&kjp$BznD7M2y^>*K1vUJdX6X*pTVHz+GBtNHEsyXEtB#JZnv
zJ(@J}^2;l$*YA7vGAzbk?YBpA+WgvYk)^M%`SP_pdwP1#GD_|0>gw9_4IDpvtIKom
z><D~5uX<fmQ`3|=bJn=a*M6C?)jKFObn3q!$L+Pu%)%BfT)6ASqV6r_@9!Ocw>$s7
zU2N^wt6ly5(+@uk*q(Ry%cs-&^4Fis^snFd%WHak-OgLva<$tJFPt((<nPD+`Vd!F
z*XCOD{CjKe|NFMRtG72aAz^`4`MWjM?{+SC+<QHyxOdW|Nlywarc9f*Oge9e<JGHI
zCrSP5c_sVq`NUbXR^9*e%>37v%l=pQ*VnVMvAz2FeExKmvvaM(o!j|BV`4ywU{~4O
zRhGriWWGs>>qIQreBLg4;lhPn+}xKN7@5Bulg{^;ly+u@VtswR{|B2ZU$4in@95wN
z4GWX|_<MWJ&!VT#pJ(6Lkm!Erxrdle!~#X<Hc;{hdHTa4?yryg?W^7zXI@fiZE4B4
zzpqxxIIYLY$w@?7diCx6{jqw}PiNoXey_^=@2{_)W77NOY`^r^|2dpJ|LyJVuRopE
z4-W~M($v&c^6TaD*N^+{zuj71r*R}YfA7-$f1c`l`T2zf1WZ`wJ9}B#+gqNK{(V_)
ze==#K%(w3!0*?Qx1*M}Lv)R|T9ha{M<<HB@d_U(D?~LK%<-ICfe&=E7zt89G<K^pq
zEaW+CktNH}@Yvaby&-McjvWy{fBuZ?Z)#%loV0)ce!ZPfg!cab_j{g0*!#cV?}vwm
zPCeEut-f;I>Nl3p=Pa)L{Osi^?e$mg|NmFs+TNahX^AJV)6tWlTq?uY-o_)jsPgl(
z%zJw(fBk)5Uw`E9``z#Dd}kWDdU|?R{rPy@wCqiU?f*ZYrR-{UWMyUT`DR=2puw$A
zW@X&os+S*+%ZFE0?RvN4F`sV4h6giBW2@h8on@Lmt&LxPUAuf;#J_+4c6~U+E&XQt
z<jIRK``gEMi|b$WHNU&0{?B9iS2s2;Ubpj^)Y)04uUq!Zw}1Y2JAd__Ju&8Y3YzCt
zz0&NJFm&>_{W?X>XU2oA_kVqTJ$2&5g-gAszuNVB-DUfKANhMFjom=ulRkgV8XaC4
zi-7#SU#G3x@rdi}Jlp8-*wU%{emvryI(_>5klL9uXG+=E?E&T6zmMhr2fV+xSJkul
z*_puIWp7Kq-z|R)@`{&NSKgkFZnmG#7{?vo|LK%A$nBvK5i54T+vUw~|7Szh_jhYs
zxy8L!Nxuq+j$Zxq^Yd!i@YvY3<@YMxlaKd>Iyf8<eEs|W|9|S;M;F=E{+e^4^z3YN
zfAP4Ag-%XRUrs3ZXZ-r|^45;R#ccd?cOLY+=}q@mcIyc!DcK@d{l;)^?YEntF#mdp
zTfgF4<d%$y$NJ^hpE%)R`{{&o)sKhmrROZ4zj(cV|F<`jCr@5%e9ppoe%-H?AUFSg
zn_nNCnz~eP*9)b+-|toPX4O1sWdHK<xcuvHxAU(%^V=?2y?$SmzunIzDw%(Ne4J&T
ze=qR;;m1E~rHoQK?&NKsrXT;#^4;q7`zBq@x*8r|`}F3wzu)h#UbDt0eQxQr>*-%!
zU-x%)b>;oHfsw(&lZ|0P<P7Wbb&4-H@V6g+c=P>be|u9ENl;{5|0o_)(0FlQZT0!r
zk(<*bC-q#9t3JDV?XF$7Bup|U2nq=$O_?&KVw<;7+L;*-D{TuOxh#&|UB;U=$1u6=
z<LUofS=ia7h1LCR9({dtW8+~1p2H{3o;7__w%f4gN5SEzMU~&Gf4yAJ>v`<7{{A;N
z%f8)Am!8y9|MzwLyxh>xP&X&1Cd0BfH`wNvuGpG=U20N~bpD=;*}wn)z5m~I$GPlv
zJCAwz_?Ue9{CV#;S@XO(f^u?sQ>IUs|GN3s*6i{d+4ff+o--_d=5sh{qr~pAx1VyX
zU$5ERbKye3`rNieiM;K%Ki3>;;cUB_WxDV0x7%#b*W7<!V6S{Q<HzR}>*FVFEO>b6
z@v7=09fEP+BLV{_3Q9?3rKF@th>D83uc^MUz|qakt?l~n_xpaYQ#={5Ehn=1yycbm
zb>FR(t*m~n_`T=vx7%^gwRCk&m6VivUa#AIE_?r*H#y2SHg~drAL$h4^*k2;|5y0>
z+TUMZCVzQ-{ru-&*P`=H4;?zxbL-YE<!gqeuR@$BeY_eTU%74Sv}sbAnVD``S*xD!
zi{F+rQ}HCId~1IHxlh)b$MYDdI9UHy{rB%DMLT;oY%q9yzW(3lM2S-S=`UVnJpOcA
z-#o``c3@OgmPA{k&Br6cATRUDSUmW;^Z)Pr|9L!*{rUO1zv3=K!((@DD+Uwe^ERJ*
z#B`&ce6`qewc^Evg<YMUAG0Rf6g+4U(~Vl9y>7>$&2K0B+XXr~H681he}D7Y`}gak
z^LDzXrl$US)U7}3SrcE>o{EjDR<AZKeH9Y9*8czdm%CoC3yz40XwEyJsHphs?)$pu
zmATt*n-)B9$i2Vs?~3`s`*p79Zol(rYwfEmE92Jl9==fj@3VZboNZKFV#U`83B#n8
zuI}!wX=i6m-Wj6b-uJxBYVN7?=dVkq&rv*V@a6CK``(j2efsp}>Gb$luU4-QyS*)U
zZ{Pa&>)w^^{`&9t`|K|-F1F8I{Gj0Ot$z2Ys9A135`|ZGy{;~uQ~T}at$nq-ySlqo
zuWr?vK5g2obJp*3E^yUl*b1xpTsSpVdu#4(v-8`o$JfVdYHC{jezUnhD&EntQAt_Z
z|HUj&h%8#9)GKd)@AdPn%{Qk^oVYOc^fXh8XBQ0L1;w8JyzqOJi%ZKKzvW`OQ6)#t
z|9vvq-=yGy1FxLTjm^3$DlXUK>-UzuyR))a+Pvzmec_`cljYvm@7eDkwmLL@PGQ@=
zKcBokFD>((9TE_*!2123;I_n!qg|qtUPWEqUH<-F!kg;9-|g(}|1WvP!cd{SfZ4&=
z7-YJ%*_qdY_SIWee*XNK`R2yPU+?SxuU@rE%k$Fi_xo1QnkBWqVPE!jz0y}#PQI3Z
zKgniu%==qgS97j=mi~4A^_|7(>))2`j(vN3dwzibY_nXcmUAagc*t55JeV;*d27_w
zh0g5j-|qi&XU`rRQBl#Ct>SS3IXP>tFRtsU&&zxFG5&$zn|phsK~0{JkSXak#kCr1
z`FDMF+)(}fox6>HT--b_FR#8`=TBPP>0+Gp<?Ho$P_53z#~0?}(vp;%Y<qgn)$6hH
zJNiQ}i-yN2wjW-2J-+^K@gH~j+9lfS_k7y%Z$rYI2S1+Aulwa0bXR_J-OtT`-)ufF
z^(TgziAh9S`t{bg)4$&WwVp5gS^F+_>kXQ1mRs`mYIyaokJoQ+%?@w=?PK-n{hf*X
zz8`<=Qa2w|ab8^=zIxFjr})~hQ-y?uuWrwe|N83c>C0zpe|~x@Wt8I4e17Hsd-LDO
zH!v{Vn{iZ@A!<S4<6|#>KA(TRiIv+V?Tp0V-}nEARaNb(`ueKW>s<Yk`kwzqm)0CB
zSt9ws-(-z!=k2%CRQ|pz-+%c?r!fES^Xp@moqk&M^3Uh<;faY0LACDE<;%4#EFyk?
zdwY3_=VTkxIbQ0&He1JVzuT63yX0Qwb4^>@Sbv+3EkQv+Dcf&<J*htb#Yy$~HV=0x
zD=UAUegBW!`u+cQz1#aeZtv%F)~i;n`t(Y%Q03yqi^u;gKBIs2x_SOR8&wq*lZyN2
z&;Oq9eth#ShnSc-*Ve_Z_P75V!Y!tA0pyYLcQL**3?A-Ki?f-tY?f~QhX)5&tz3Dr
zOH}&{s3>asF+tH8#LE2q?Ch!2r>%1w*8jhE{Kyd#&$SE}E(u;>T)=Aedd=o3GiI##
z`T2SFhX)6vc9mo{-`sb5YxeT5udkneTUNbwQU1L>H$VTI=;G#PHX}0Ds%G`?kK5K<
zt&dAfTXt)E{`;G8v9W7qt;^<I(YfpXOJ@7+wYj&qRc_TwOG~SI8*YDi_vxorsq_DO
zOPl3fn5G;3<<;u-RlZ!`Pv>3wdcR-3?#IK9=Uu(Mp%D=`0>4kJdp9X2B;?7}*q<&{
zJ~clcwy#>X>Jqzr&4a5?|4olQ{JL86&xgbOuWoF79C+R`@eoUF&Bvp^zTM9M`s?-j
z)&BGCUR_&z+c4YH-~amS_50U7dX&`K-Yy^h_iO3Vqetgnn!omL-t`w37n@dn$=G=}
z?{3B8-l~5;pPSbHDk*()Bk=95t=R?hJ&%94E??yL<a_3W1C5(*zy0-U^?FbszVxsE
zHF?Rd|9yLYJnD|xl+yY9tNz;?8=WWZxi+1hVUEqN9n22gDO;mTZ>COvd6?h+%AU&4
zA<@yVukMYVq_^V%Q)+s8b#LhUwQH{i``a#^Fk!;tzQ1oTENt%T?%rDWx9a@1zrVlB
zm#Mva@ZiDWwx97*W;rw7l<iJCKkw~Tu78)j^;hoL@ngg0HS>D{7rS|`ijI2~uzwEk
z?X=C{0^@xBzv3Ggs&}Lw^b4sy{bu3yYzepK$S=vs$)(pK(_cQF9)IoK-Q9cdt+v0G
zwY4g(^#8lv@1<(O{?>hcxTWsQMwRnH75kUHd-N#j>|E>X+516#R}+&E<MeYm8$xdB
zS6lT+nR*!-8dg1-=>FxXc)U#b>zyjRQYId4i5XvCT|FNAze7-2L`-a1yIj?ZgRdAa
zo&1~gIy`7a`qn60Tb)<!vtkMkvPNyqnwlQ7J>>h|`iHIJQ)bOtwJvt|ybsUiPImsW
zV`X8<IMBdY_4%y%*6Qzh>&3owb%9%xvQ{MlNl8h~=Og!4Z9R1OaR2LmO@@Z##jK!C
zBrCUAMbsU>(qoe86<epo79JI?dbM)7N%AqCuP-hxcI3Ve>Xba6UmrJF&G*u)tE;EZ
znzahlWQ?!>3#y!!Ubt{U`I<rA9gDk#$7OdtndDt^T6g=3J$qt`pPjjwxqR-mghyNI
z{{G7R_~>ZWqfYfHQ>HB0vc)7dCB?)dJtt?)yIrsK?$-bRD`i=<1l0aZO-p-qGkyNm
z21e#S>kYEr@9*t3t@~49`}@siEj_(!lIe36s(Me$cztax@3-0?58Joq+%!r}NqGUP
zZm+M41vPk0v#(`*d~{SxN9RxI)+NiAuXb+d+x7R`?XCIu?OIz~U)ulwc|LJ>&iQ$^
z(ar4qVfXjdmj3;EU0YYz*Sh@OmE-dDYqn-z_nIVE^+K`s+s$;V-)}a96s-21u6JvH
z{r-D(zjJlt_r(Yb3WE9-x3=Z3&R)0Et^U{L`6-)kf_j|VW@g(Qf7nlYc(}bfs=NOG
zZ*Wsof8UQqj~+eRQvbj1d|hH<V%4XU>ZNbDUe7wv!1!zb|KIkmZf;q3ca>`C>wo{X
zxMBbP{l|KxmoJ}R7iBj4Z1$%^izRI;HY{4acx%PSBwksoFFX1;xw$XTu`DjR8XCSb
z|NcJEu*36T_Wyn)&#inmv+CE&<^8W?zOLK*ElN~O3^a6*b$MBD+1p!7m(Q#6+FSko
z+n;xLE1%B=r5DqzD;jr;PV07cbzNG$e%~t4aEbl@pZ?8173zA0|AO1ua&<ovTie@L
zyZ6gYdQ~M+^6pOL%$YN1nPgr{v`@dd$W_X=$|U&$<AadQU!9K`8Pp$VoL=K;^z7W+
z*IW1BerpyK6vX3steM}g;zseaCr>0MtNWWJB_;9fzMJ>u#l^)3D|r?_G|FyEl$c-p
zZRW+j)#ZotwoA{oE;oy-e!KOs-ovexpVJ<%*?i83iJ6(#b6)CkzU^zKCtlHIo;q({
z-jCnEr9XcBc#PNl&V_``<(tpj-F|UxZFI9$|CT?u^+Q8KB(~ov>K2rh%>^~k7rXbH
zeS34W_{QrqCr=tK^O>2nWXTeVfB*i08sLTZDxXX0@A+`Z(EfSFw8+~#3KtvZ+%Qm9
zQ907l*=e|L_dBcPySqx;|IYmP;V}O()#))wE-o$-jEszK&(F>6eR+9#_1n$a*Y%PQ
zwQ#!S<g9sEVA1pK?QQAcWj=<hR;{x6y(Vt&trI6s7`!RleQ{fE^y5zTc@~e1vc=;n
z9wxs2_n?`-XP#}f6v(`+tW_URDECX~@Bi~Dv3{9+&4<RqW0L6-jg5_N5fL*C3m!N;
zK5KqIr{G-Z>M+SkJr57J%YUu@<_+@e#f{0wmG}KWoqFw+ZuGV@pyni~yTHQ2A|Y#4
zVxZ<bE2ZZ9-ST<0YJPJvSlHRkEuQPY|5|>p^0<Uu%??3v@#jDH{rmTQzxlWK_urS^
z*_L~|=d|ALGoOuRED9EUyyz}(y25+X$8Wdu&41i^9GkZG-p4<7l5L4Ke?A^>Q%SzE
zA~5du`n}(xoF{#}<gIUNk#>2RZ<|W;$45tb?=dsXk?BsHv*;#h?6j`6^!2rs<?rwP
zy1xJ4($(vBz1q=#dtdGDOP4QiO*qIjxBOmZZ-gSBY3Zww+MiFSNA0aL)qTNG&;=TS
zZSddma@lN9ZK!2!9ewcNLG_Y1yykZntc~6-S1MaH$BLDeHS7Aix&QvYuYcXdt!J=X
zh9Sd{;{^l5fe=t{&n)lGikFv{zg{*w?~<^;O=6iR1H+7_20jJ`A5bYbXO2v-grU>t
zXJ@Tnv9vNUY)OHS_P$=g3>x=+zzi~sfq~%%gZ*Z1hs#B~@18n!DkvjE!+Q}(e(j7z
z2`ydS(4e46Yr<~j)iN^foqF=rsUR<}uA8YK1s?>fzr6{ZZI*jRk6CHKcB`+(kKf(g
z?4FpIs1^>g;ed{+s;ZZ#XW~~Lc_)Yb)K|N=wzswlf=8bXzFciLxBxPRfq{O*&JWVd
zz(d~`CP}s}v~3mrWw<&*`ulVZodfaL=U%<{X48XaNSG9eY}MOdCT1~pX6&*vbAobD
z>Rmm@wA_jLkBIQ2fSeW1hKgVLcPll$xfS~0N`Iv4tsVPaW^eKei3)vf8NYmo0FxD8
zHz+0;797=DzwT<|C#jt=Z82?%({E4s{UCh$?SQSP*KRr$H8t$osx6z`QbV_DS^n6w
zU`<qXk=It+^DDEa{|>*JxIOZ!rD8~|@Zl?qeSc`~vf5$-iNR&YJkHji=5C7A_3GrA
zbT!Lz-Q!i+cW=#$UnX1W_f~t=ifWk^0bzxAuQNY2UA|<W!fxN?N848w*R1gK<_L}Q
znQ&@#>?!@VL3NQ^Kb>ARB|dEFo=sV@1!3BnN)EBYhp*~t`ovB?bj485<)7IyhQk*j
z@ipU+0Z;BZy{pGxe}6rh_xD!Y`<gGG8_by}u<9JU{7cTnRi(SPR@SdHOP`)``Cm}(
zt@uT`m8q)=CEfqZ?4BQ57uKC4*1aaIJ14ArjaYY1*vb28p&f_)6B++;@qP&QXqXQQ
zTB!%?KRDEv_V5NiZwXL!XOa6Z&)YEB6+CV6VYNit!oBQTuZ~5(*T1;UX89qeKT|{m
zS1jpZP;F(`(o_ogn8^5PdnBLqsinIVbUmM5)mwWvc*)l4uD;iwotgiD{Bu&U(W{SV
z&2Oo|?=w$MY~<qpkl%Bf&Cim_ih+Tl;c=QoTbbCD*SBR`LD4C_*=vjDZkMG~w;s2-
zmNd~xwoc13C4Tq((C2f+R&!UYWo1;&VfveUEAqjWXK#X1)23%#{uh+`Y)XcIV(S_)
zO;fdqSfRsLo+hlWs!ED|z3r&eYqMGQ5StdC(p`Jy|2DA~uh;(kb$=VvTkTaD8~XR3
zUF27|@YN)VWquzOi^8sE#Rs;26A#U*FKpe^r5zgkI?OXQw>d!WSGU-^zQTpCB*a(U
z|MI7D_U2V#W~*Hp|7|<AFXjLwi7c48dQIWVEt|p)^37)Yo9mwITeg#TYS^_EEB3g#
zikfPzT@#qv8y&h;FZAk;(8?3n?@ZGAn;*Sz+mk7ka}VE73!Fd2<7<xax^<q3Am{LH
zRGg>xsy$hvjcd-;Q<^J7Zm(Ftw(w6xldOyQmYO?H85kHGjDv$B7hkpJ&v@GBwDwlu
zg0I!xp|97?);OCqvFU9cSJ@4bHy?sG-1Qgh4v7gAKjp2Jog!1~x0OvtY-M)R47T!~
zi;}X*CJLrOj^Bj4g~Ym#ZVt0(IIIjVsO%0I@PuC35fT=;def@r{^s-ETG?9-SG%k(
z6U#^r>%8O6akEYF{KlZGg7(@s@`7C^#4cp4EBbL;<UxAw2UC>;{(o<~W(KrpxU$}s
ze!hsY0#x`iG^hmxge}#tKXth)msLw+^|Z~q!p>=3Ke&FYmi>=E9tVF<4_e%8Vxa5A
ze0kB-iDFBibU1`w+3{q}s;QfIy~~aaU3+SE_R})&i3dwhx3#k05|w*Vf92f^hBJ=f
z)auuiDB&*q`fZu?tqtoV=BGc)j7(d1?X9lWyLC^)x<wv+SY6HXL-WUj&t(@KrDlk&
zZCn}X#lfFZ5VPTM|HfMm#>evY85kI%R1X_$QC;p6c~sQPpCi;M;(#IZ_x?!Ls9l!f
zr;UoY*5yx>sWWS3`=M!az+aK^AD8uyJ4)UAZpJNOO5b|ja+gZm%Q@#ZW=V^d7?j-R
z>l0sJe?#Qa2kU1p`3zBN;G`JUD>XfOb(u)k#bYwMH|H$2ZMD}I7ZXkj-@c^as|R2B
z_a_CV{(Qj^3-<5(ry1{U{`}Qv-O5wiU*#^x20jjQ?e*Tdv|awS?4u8tAM_boncIe?
z&lfAd5th%e!W|S24&5mdZF8>biB1cB{o9s(OGWTHyYpv4GgH^5%eT)D%~fLFx_Lp&
z&(y2GlDy~byD_Q!NHN>(T&X*nZqAn<m$<b{NV?tad9{>Xy-3^k$DU2cmd?vny0Si2
zC3!ESj5*i?vwEYZN3)*GdO9^sbN?EzpQ%?%<GojhJXjOfxH+ozoR;XctG4S`m6}_)
zmu!fAUfOwigWgAw`Hp4wq1Se7$*k|JyAi%d{@c`y!~2qQPils)y&Vwcv*FxZndJ*#
z7D`PJ(`=uAwndx$f)qH_Tu3o{tABBK_P14gZmF(5sx<qmQn%}-D4k7Fudbb1x1y{v
zYLT~gc4(Ey>5@X%vz+y54f{jiE^0FUlA!T^u8`vFtDm}Sb6#CG3#z^4R<$kk>aLKO
zz{NWPb}ePjkOCL(Cc8{`^S`*68ojk5_}zND=~tCpS*=B+Uj_Jd#BNmNJKAC;BAXK)
zvLW`OVwo$uwaxkFH|pR0<J#x@7B1W}jXA>#VhYz`gD?AYS*xmEhONC3r{K9MVoBg#
z;gYDD6?fe3+T|UdaQg4(&kpsootXddpX+(|yi)Gfhv1YAu@fD?vFScwxF7`f?SrFl
z&9*#u*lCtNJ*(VtC)erKp&@Z=na@edYOD_nth{@@OKN(OvrHF1cYxE6gU1g(zq&Q=
zZ}CsQwYP&+JU?xYD#^T`^7r8zzASm^q<5@L4gE@>{JbToEm33r7O&f%=G>YSyXefD
zAW*$BZBEeC@ZEi{WtL9edTZW$n@7L->{lP-&sqKYT-N$09S4F#tr)g=fPB{=wr1D0
zwXv*zZ*?@TzdKa)*7C=V*obwu?59E_FBeQJ_T2P4@~Wnui)@m!h-}dHwKhg4;sqT`
z&nq|m5q*#!9G5w@$V+SM{vDqlT<1!i9<p-FCO75XsxMw0%<SSUH2E0H?C{wIlz21L
zR%OMmT2Xc~*W+FD<E?k|WMvNLYHZ&Ubh~Kot!d$9A0A$Lcc^IRonwOMGq-x=Dm7oR
zR1A5^xsZK-o2IFiN8PsM_4n@@_Pu+>P_POdoHaKX)3=J{p1OEQe$&=J%WFb+^WA?`
zYof?!zw{W(%Gi(%L5rHE2#K!P7U-BIExKe$hr>^KcL8<=6FX2+cMvw<akf+q1%=t%
zpMI>?e?-K^7TlPy>*=9IP4)9;+U(vDG(Y#uHIU&&?hgf{53HT{_3-O!Lj7FZRzE(N
z_fj^g`SreItr~h)6uVm^&odsl4zAh#Hbza4e*IlE`}03gEtU~nw$f;`cW6vt@sZ>;
zVwzS*7G7Ym&prQnUddYTq#15y-WNLFeS9S%uC>~gC66Ja5!_x{?vYlu^55pP_4oeQ
z%`fs^Ewv|qbJP_(wTQRL&7pyh!{Rche)7@|eXwTQ?F$F5mmdF`E4^^yU15_&(M`8p
z+0DN$Wnl0DuRX9h@z!jM>3W~+GT95W?y`UR`%X#JYW384606>MK72JV=6i;?sLJbu
z?_RK`hW>hgOK0!zzj1#ztUpq`?t?@9SFg^1Uy760mv-<5Htb(}@cFe}ceF~vw6|~Z
zYCV@VS4@6Y%ZCQF-FXZR`3eh|85+VC@4B}3QJ2)zkZUV;EP59h6u3CI(yw;^?}lsd
zG=nlzbDJOUyBD=!{dTd6xOS^QS4=g18p~!+`#)v<t>)w-e{;onefuxp*q2-4_uS&<
z-@g1$ruMT#3lkZ;xRs|cJ7l|oQju;@VCB|*H{(|1Zxhcrx{c|ro=&)H@9P=IS%2NW
zqOBTJd)sZ5|80+>tybW|=!Dj_wp!I&TlTvw4c!{GbMb{@pYvu9rEYKUb^GoW6tZEj
zp*}amg(gtUIdlia1TKDGCYE({8`IzX=ti^D=_f1>eY(Gpv5t%X%Xb^0Lrhne=eGRX
z(i>9cl+pZkfAvzAih0*}+_^O?dYSw;(bWec*M7eA_==|87QyvA3>Tt6snlU}P~_uF
z*X|gB3d1zNmD^<IDB7;xvFYGG*~d;MD{8l}m3D+(KVsm@@>AR7!2Qj0Zl3(GJ@Trm
zp--&P;kvV_3=9{*t4V(7q=&ApJEL{+*Irjp9Vq=xG&_A?uzQ49%*u<7QdbnCmT&LB
zw&2^vo|kM4CfcBkozN8(ezzqT)IiZ%A6V$Rwd+)NNI=+H-{0O_>f$$UdpcEi+w7`}
z1^cbkTUHg<+<o(6uCP;SRLqJ)&3|V9b7Ekya0MmC1%VQ63;%4&daG5C<-RZ2ntRTz
znepr9viiN%)3JY(^Rj)igxrF6Cm+6=DYNcf?xCx?hCUk?@<r;X-}jYeV2B6j{pBv9
zYrS887t4P7jcM(jz!h6}cZWokz7dd<da%4hq286{mZ)Hed1mzT9RXi+g10}b$XT(N
za}_(off|rU3W8Q;#YXMk?QuUx|H>&@)~L<D8?>_T#Ql!Y2@0xMa4D&2s+91OD=!k3
zy0Nd0Et!zyB*Vq~A=@C?ih*IRD5ER`gN*q0+ZUd_3DUhkYx&b??k!pFj?E5ovN_>y
zzco^qxMn+KO}TZPP5Z$8w4kF53?}*@TccJ+O^>eT3(1Jio7ttE^(1}siSt=g&t^@W
ze^qJr+IO%2E%MU6KWqK^?ax*ph<sbxH$$M^FJFFX&$o+GSM0oGlbj`_`4|p(g39O%
zhj!j=Gb;Alnpxku?djCa=yk!jKb_jL`M^r4>w?#`t(z2f`$}d8O5M>6Yqmc2VoL>+
zB?H59E=E~~1~HA*E1XW(pSu1D+_tTGIz3drY(GQ&>RZQKG}NvrZoeJSds=t;?SQo)
zv^HuwsCePcl3#0VeQbsQZI7FOC5-tP9Fjp9^n#Dbl+c&mx|Ky?S5GBvJdw5WMB3En
zJ8v6&IlM1P_rGTR?ay=Lj<em&eKk>LU0<Q%D*@?6d|+zLk`E4%YqvBoGd$o1rHLOK
zCY_S1{g&Kk7cqV5`t8q7KegMZ_cE;eingj#v-PnI1Ks-!3?|&5V65?R-RkXa_WZ@+
z(#WW3Gnc=*H9y_!{XCgvQ%)%C<aN$-HsW#CE5BR3JyFBdDx%r?=!-AkE^_gIG5@Fq
zT3`bXzLsy|;Tbod*{r=+>acXGsn+_km3wxDJ-w=DxjH*E@NH0`Ywz_Zxw4O)N&?rb
z{9Ts5=+^PJ84<P&3>iKkC-?=Gu8pot4ejFi5<cN>*^QfLV)LBuzqebkbawGp9b^C5
z-=@o4-+9}GEvsa`O6(PF&zGDF&rIQCaL@*op$}L@!mqsed?u!Lo1uGr%CnVgR)tM^
z@?o=<)s1y41a>cKlI`N#w))o7YuBSchla#Gw!D`#v1+-?i#2{5ySR%rZoFIWu>0-v
z^?kdZ=PEICK4oCoVgzzjUt)Y<t62V-i;H?Kf2CD7X7SHlaW-x1iKk{Ep|8Wj3YTu`
z(w3Hy7kjXLv-R=JjH)%~>~#GC4}M#wBhSEKBFyr|?b{jS^Do}l|KGi7^X99o!}V>y
zUI~`6D0q<gz4rUva?r}B%-7e}u3EV=aJk>ylH0l4U#?oc?$aC45|O>XUagKh4jRwk
z7T3$U@|vOH+OvkuQS$4~U#h?M=JGCE*SPnkJ=_<6#y&2syI%1%Pk3RM)O4=7SG5Yl
zR$qu*`e##?sMYGw;Mdn)$}YO9uIf~0mKMOyz~Jugkni&O`T6TxGB1C*Q+!@iS=o7W
z+SyATg3427&Rn@;#}2mN?yjyc=T*N8tg71erfj#}uNTU`^K2pu3k#(TlUmaEPgHi#
zdUt2%yz6}Ji#^w|Geqr@njU@H?D;GE>~C2wpT(Sa$qnnivOf07!GL)Sw#-Oa|F}uE
zj;rj4rmEJ7o=uDl4%~@`Jdb~fg&gHPed5Fg(88K8m;Jpb-O1Y?6ckkQ`K<ZtbJp)|
z))jwwdpjJooI$SQ0i$QB?h98}*Q)=2zx%IRHffR&XlQfCj)=s>MD<cWh73(F?d&PR
zK`YlR^3uIKXEpz|)Rh+^-&XF=PMUG-TlV7aDpgIhDfVql3{hg68s9Hu-8(TPJUsjB
ztE;<SuiI^NdrjEtHeTsvqS|3HpDPX<Y$<se1RB|%=q~rM>iy^Q_Te!xbCi^oO-o(`
z*#3CX+|}1-_w+gg!`a?M39Z$uLKkjc*Zs5cQ<kVzcFBRrwTC`sJ)IV!xqL~`?{4iW
z!9h=FK6o2osK^(4N!@e$x7@{goqv*985m4tnW~geo<BeR^wXf4nmyOQ{rFLldvDLr
zj^m=@;@Xy$k)ffXn{MV*eY=@n`tRrS>U;8_LHE61f|f2_nt6L$?yleOc7sNO*G6n~
zGMl~j;Lla7R)vIyKCO%mDyljCo1sB}^@_60>bjZRo?33z-R=V_(QMDJeETzX)tbWj
z7j`-^|Cu5nw&2SVgV<Z@k<L~}4dP_LBWmxGot&Mwmc5OdI(2I1$w{iS%yMVl+Md6@
z_?)HtB$K>57U%cbR(%OLKhG94(^~uQ=X0y~dp=9e$$xfc=C1$$e$QT2!;m3zZdDYh
zlAf0O?A7bFKW~Kw)Lr@8{Km|c?WU+`PI$_O*g(gBT^yUV&8{pw@_|9Mg;nF<gX;<G
z|Fu~d81^;RPc@k|V}``d9J8lSo>b(XKY23H$?1?_|K_N*ptU?IpfSi>TQVo7otyKL
z^Tqxv`)aF?9ytP<XlUnsQGT~{`ZC|y&njbsf)-7gGG+1(C58;Ew#1C+dBOA3pS}8g
z#%ArUz!h23%THH;s@G$!E1YvSSRHA*V*AKxiiF$(hW~B0T?`BiHL{F*C*F8l_Vx93
zfA48J7iSnIt6cRJ+MaS!=x)hn-{ZA%+i$<U>iT#3w|93}H#IS-s;S9+t8G7gQ0F~E
zO;GRYwQnAIrHY1T#0R!Mf4y&!SMF)u%lj5_t^XUmh^aL1xy9VemSKBuxoBwJ(Ca>$
z6!evWfnm8E<6c4E*=D}N>V6^r|NR9mK{z|h)Z1@)@MLxWX)3-m3>;6MJUPp<c-fL&
z?_T`<{XM*$Pc|epbg94HPnXZn&VuF`R6J8tQ$bMyT2iH|qLOiYTkfyV^Z&2uleMnW
zJ<P_iZ`rPEYtP<PjoQ9FLi(Fn=HGisf2TyNP5*s@yYohF7_U$FCCj<VrFm6zZXIV4
zKEcS~(Cy}s?-CwU*!t<yr;_)3zk|kb_f~(ucBqxxR`~eadwZk9UY{2hw4Cb)nq<7S
zyL>$pGib1X-SxO?UywePlWChX|Ni<4T5uQ|60&5;5*1!~yBL0(4-9LcFw}Temag4*
zXVUVg(Vw^0<!>~5ub+FtHFg#E!%jg)uRe~DB9{e|l$fT<NUkswY<x9U=-`h628Ii(
zHt<zxAC78gkn(nnoH{M%)YWw9&0nwj|6H}|E8i1A$A6%jGA>u?%4XjkyVTpY6?GiK
zwAVgjV3^UE^PoCo76ZeaDW{~SZ+kZF<?Oetzup{|solOhp#57!zO?9)&%2JS%)jmN
zHA{HmlobxURxmR#$bgn|7KG$&Us&rZ3Tm~^yt(w&oZwYwe|vA)ykO5kiBIoNCQ98i
zbv(TL=<4DcjYb&;h6gMH7Z?{zT`jM_Pxt)FrcGH-r-!aQ5Lx=6ax34IaQQ-`?HvmB
zrT$#kH|P~w?T>Dnq|7vXM->A@!?flGK8CduHm?fvHhZ3Ru<dWuw3(|<&1w_gA$uxs
z^D5_<j$JA(t3DreuX394=&;O|Nz4okbCf`9Y*oFqvbU53ud}<kAwE9m<mXvObXBzi
zn1dIKLVDJa&a{Yd(041X8+!J$ziOnjGcX)j@9MzbAhu%FwWYqly+NZb*WPMgYpZp=
zH7oj=X=ij+$ppO@6IXCosjP|)lF)s~z`&peTGGM2(RRJh|EP`L_up!Tq?N7Ais3!p
zTi(HYt<_e@bd~#}OTsgb@G>wQm@aUE@xXCNW7U=QcB*xs>7nh<roDQ(H2UkoT{`Q<
zKCMXDUA2uf{JfdqtE*};wzpce^-LBBYJ=Sd8fcQ=WPE?+?t6C^h~DNF5qz?Kvw!#1
z3tuee)<&&SYI+m*JMX0C)nM=KuNHreG~#3XoEZD4B=f%6tD9;uCk5A7^D!`7m;-VH
z`;}dHem(yrTUB&X?EI5l*QchSuGQc2+-YmXKruddw%qb}Cm+uG|2D67lj7~-KOfe8
z-1T&i->Z2tdDm1K7#JReD=lDdI0+g`vHrI?&OU9b$0X30UcCOhWq&qhfvTX?*~L?5
z2CsTwCVQcLH(!NpwPQca-I6@@QeQ6P#h|H_IZk4S4W!N~wTP`=vc%=#!-raWdSMX~
z{}-^I2?>0AX^m?1m-Fk(PCPXek$bUzf6NWn>0h%iFUbD2ZxNTSwZygS@3pTT*rIXQ
zXv<_P^_E#7pvKg@6Kwn18}6O@;LR~(^_o>-(%;0g|K3yDyD@8fndlVnNp+Wv1C8Cv
zgL76Ke!|7b;E?TT_QAHKkiEg}QIYhm8KH}|R(Efy^PhO_t?niNovBt|&M)FjWUNZ?
zDW7}a^xhsPMh1ol!W^LaoTpR6GXHJg{q@+XyQj3)<$S0Ms%WTM>arsLwnwk@j71k1
z>V;RaFfcT%S6sk+Kr=z2ZQt!uhyPI<y}9detd|OpHLlEPm|wNrWktDHOV!rSfb$~P
zlt8ikQ1AldgYbf|tFQiU{cVzeR4@0O-q)P>m(84n*|uI@@vf8W!}ggGiVO@4v$&b8
z80NI-S}v`6J3F?j{H)#7(5qgPnEtHYwIh7fI;kstUr#VHFccgDxp&U0?7LAvlDEbE
zPMaR~R^v;Ke>dl~_kQ`yPygB$SRwHEBuHfuNF~p1t~tNHmsgwozioR;fAut#n`^}8
z-dBy=Z@qW!xfcSutFIRIYzEEZxVt;BH^i@4SNeMQ+*VM=1hrLl&%dnHw>z`2koi{D
zp99;xIMO$nGcYjB0gY0vjjJqOTjafJOO|`A#{XB#YtHW|TDflPpB}!8yNnjEJ@;Bj
zzkc@BBUy|L3>Sg~FEDQ5dU|#1tp9K2a!+f625~-}UNtp7Z0Vm%T9&J0dB64ZZF@cE
zTp?%-(%I^$md%8XpmFJp7?u(S-C2nezY=1nrPXbUys0|X{m%J^=~pv9F0?YY^>e=b
z_=>URoI<eTAduo~zxKNB`hU1LYVYd?m6g6J3-n%2UUMU$9W>CzS*oFSWZ?{f-A*!f
zn<6-#FfcF}@H5IfbWe_sT6x^=?3LTAzn?N#;!r<3PwH0SbsoRqhW%&Xd;-lb99CMu
zoKdzaD^_dmnyH&tg@NXL)|ahZv+CW~Z#K*0eiZFapS0a@@8pu_EA-Cn;A3E5nD6eu
zenCfMN@(W8{+qul{?^rQ-s_O}_{Hz<Il}8C7w=vbzk9_pP0((SA5%GAFw|UQOy6qv
z*DzW>|Hk_Nx!Mz6i?6gfkr%w-u>ZlY@v?PXr9U!LzO(ecz1`+{kAZ>V!Ww}Kj6ddV
zt?v5rWRdQ#s_cDP{G3m1Cmk+LJnz!`YTkk=Clr`0K%>wiOjZo@6D8W>qV_rS>)&|8
z`7BoWa7p5M7jVzTm3?*$C;?um06D@buySi&ef3_k{8JNEVs9u1Zs)c4eVgp;vyO*>
zfdM?oxh#6~?}og($JKAOS53H^cJ<4}*sr^Lwyp8zV_;|qYi-~=VA7WOVt)MF3(DND
zcWqhj#{Tl<|JSSEoqU)!osEHk!NiiuszEI@@^MgH=G0BzT3`Q&-v*5=tGqO~6*m2R
zNsf#6gZWM;1_p)$GAtzw%ctDV1vNOfJ(-eKT(+`I)M9FI(9$`Ryq-=8dF4>qd;MRT
z{pz%ycdPzOdrU85U|`si$5O(u|J1vtBHde47VF+zdg_ekRe#H+MPa-9s(S98U)8dK
zVd*iJ*L@S`Ue)c?dkaeajIswjqxWz3cpU#@nWMt?+iPA4h%Y+#5!@qf`M_|r{i?kT
z7yp;#TV+7gt*lH|4U;#SZugn|tWG<9tM}bWhf3nA=2)q>tSolwxaG=z`hJ?}ZWjgy
zh6Ry|3z#hue|Kq>T=muteX%C&yxGI5$Ga|zK6+rU4)Wr9M`7^>;h=!9rK{tjR{lLY
z^_771ns7CKl{ugp*yRk2vIh#a*RHv^Was^?>UqJQ4;g2b`?f7=dYd+#735hCmJ){J
zOE(=``uhLYDCuva+FCc(RbEfi&{VqcW)mpkiZwUzJ(!Udn|Jrqv+q$G_c|Obn;kdT
zCO|gH`HUVnC{hDCUNH2n+H`EG{r=5y^K;KkynB9?xcay7ezP^vpoBap>&zRL2OVMW
zwXV#+7q|ZHPrpdrOTmwSzv$sJgt`JWu*(~AZO4}%=`%q!kr9uk-j#*!$G%<J`LYla
z?OP&PN*Ln!a!sc%@&2ureJp>Mrp<}_Pfmf{IIXvVZ;Mjic17n&SCt}l#MXc3yPjqD
z`I4N~dhb1!N0}HH7}oMI%4VqUyD@3`?SFgyex`;_Qn6p{YPQ<-pV{i1u<oqbJj>Ow
zlQK*}Tcpl7IIu@G`kTGEzBa#hz0~xFH^W|=g}erJbid^+z6y#%P`1x__^f_+bot)h
zj(N_zHn=bPS5;^(IVmR3*;H?P414pg)43~hLnbi&$xWLMO8i^ASV|VKR)0MG_SbFo
z*(#oo*_XXIXlR?VIjXNv1eA!31uis-oiO0B{dZH$c5lvVm6zYsw_l&Dd|BFu2jr{+
z<sb#e-kO;_Zk9KFKRb5P68ru)YoyP=%qR*48PM0-z_&%gRB!vgFW;>8?l<!m>;7`R
zd-v;Amv7IE6$1s-W021{w%`8HQf$B4wg3ON>3Z+x=9EQ(bqh1fW-#&`z7TJJ>jmfY
z;=1ep&&yL+nt+m_<;GdD3=Q%5-zKfCdRlL->e(NAue$&gxhBpmB@1T$zTP#rs_3Jb
z=cU@kyR&@ccWniwzO#aidnbaH^n3aGhB`Pj7#SID$-25q`Q7h#yWg*>|NpP6qa&c8
zU_;gScWWy@KdXG3EYbFE@Ao)WH8m{*gMh<ryqEp$|6aK<ol*Yl{jI-2-d6S8KJU8c
z<a0j8JC#6=R_|`O*YWK9{Pmfamt`L7k&N1$)?4-U)lxxaHw|<1?;BvfchHi^Ep>mZ
zKwbCA>i*Z3&#!xR^Z7-0`KxbkZvJ|w_<ZKmQ&YM4_^x#bDu+Zy-VB+}D!(zOcwOG_
z?^cp+CWQgkzOvU&zL^BF^ADHUVFRAc-5D#5jEqbY4lvmM{c?Gd%J$o9r%VyKTl4ws
zuFvPJ`(Mk{{{D8gRXlD5yIh4sTcU=hrswv%WwZ14|1I+jDlFXi=<(yPr?l5++}Tlh
z+$P@Bv-8m5!=W)TYc_1yFnPblj90tow*I>7tsNTq_}bCElfOL4uRfi%bp|*dX9=SA
z+CfVN(`R4Jy1F6p@Rp>bUCDC!5^d9TA{U)G<1@GPTI8f9veso9*4DRQ&w2Rp;i(fR
zEY=k-N{!gR-6MSer?pzycdWJ-uaVomNBkiuZD%k>q^+I!<oWZ{Pm46|?p2$;#orV6
zp0sK6X4~gKo?5=X|9k1GRa&8;p_Osdr%ai1<?8|SnjL!H^;PL}wX*L>O^{ldzW$AR
zy>xv#D1AgRG41N-<mJ73Yio9BSlBYq(zsh&GPCaQ+k4LP`JBmC_s`5UF8%-a`}|u<
z7oXh8bN1J{wP^YD+^H&{j^gIy-vbxiO)CN~Me!4zbx+FE$EPR9Y<5vm(VlJ3pFGh}
zRD9TRJaTOqXlW;?Z<f3L?y}Q*yM2t)&rMM|U#MPm4z#=E=+UF|j?c5Lt~;B0kOkCB
zf4KGPyn=tfH{W`_w3Asx>_I-%3RR{m<&C%BzPi4C{xqG)M>n69?cSPlQixZ|<i(Ej
zkIQ!3#$LI6>C%)56CRYFn>0yiZOqP39nVWFWVpDwKUXdIc_TPt-Nxd7%g>uVJCV2f
z)yLD-FV}JJI&o}9F(`)}UD#c%3>sNb^P98c-{0Ta4-Pbfw)4#KTdw9mPsekXL1NRS
z$&+n$pBSf~19gP2E_7xO4Gn!c+28Ksp32WA>E~oTCml_?cx7eq*9Xn~tAdyNd3k$Z
z{_^tj)G1Sz7$zT^Q93h2<$jL-h2Hh+0~c<N+PIL<wm#JUYp!%AqyUiOWZWwVT0(j1
z^y$lczu#N^=1tDmcXy+?#r3}UeA-t1Juh;teR)!W)!bblk4bMWe;>!n#-?R!8@qn*
zw^^XQKj-c0|9zhSw8RR$;BdtXk7jm$P={DlK;XiY>+Q!rzniu9*Jtb3p|8!ZC|kzp
z`@C3w<b%WJ)1dPCLPrGq-l>Ueo$r6Y_r2Qm?fus&5|{F;)Ly-x{x!)6)DD?bl)`-9
zncM1tvt7*erMLOoKL0<Rv3h^+OTF&akIz7h5V8-b6gKonY)IU{>|DI|cE#9>_OASK
zJ{F+DGfJ4r>H)LeZ^LNw{Xh4{<T+3O=6mgoclC)=KA;-hVYQP3yU(_g;=W~jHo9%i
za~A*Zcj><VYgdS7MJB6+Mf%&GyyX9UZN;(F7l-C`R{i-Dx26zO1a1lP5n`WlATo4y
z|MmCRzx@BVAZvQ`;$7+)*F^U|O@Ex926mgP1N$tG-qUN>*PXij`+j=n#bYv|S9Yj)
z9^Q52wfy5>C+35T(=AynB?|<1-fateZ@1w8y1UCkMmBzuI$!;EQFBS;eY0Kv-hA8i
zTksqs14F~Lz6L%Mp~D6xfA?IC+M{`Vv##2ag%|e<l@`8F`I;qs&L|61T-k8Ea8Ry%
z|GPG7kEVOs?9j#4s+m6XRd}=H&su<z9z(mU1G|)Cp=+<)uawCunXi7%EqQDy>${Ev
zRAa1PtjV9j;;(mW(VtCOTl;yhZEe0iI~L^ahO~wTzBx(uX`-*=wadIF$?e!-Eqnm9
zyvjmPh5dm@(QaPf`v2@Zc|q;6(zN3eo4vsi?IUoZG5Ez?)9S5h<rACUnEicvXkFTL
zP<gOGQgH$Ea*x2ut#WZ2A}5)YMyRW{u+NAQ1UckcOG919l`A2Czpk&p`tI)TvwEy&
zFC@=TU3DRHtzV(yteUi0H$km<hKw|h7wt2wN>|;h{T{nD|9;%_`SttmRlU{?4Go<c
zD>kd~YjyXRU#E82zxng^(YsISYr%n_-XT)hu`+mh*4tZKLF<%vmAqUuJAYqfXJ;p9
zC+n89vr?N+H}|iaFLBObNq4n6Xh8|oLp*{P_!sBj-v`=NxM|ZS(Bk92pXdL-(!j`k
zWp{c0-ansC8}S@|BlOVu-mkL$Yj1VKIu84PkGXL4O%W)5OoHpu#2<8LWo3Q2SN%To
z-JP9LTQVm4+5atxEk0`s8aV=O`_+xx6Oq5?W82NtL+m0}tG~WjUMhU}@5euvMZ}LC
zDFB({$H(|rAp6>yi+6Swe|<JP|5Vo2tV1oFpoO|q=goU}^Z6uiy^9Tu%sy*`_N6>i
zpB)ksxA;p-fUnGzb5MsQvwU&pkuY!o1=zDQGZz~sA3JgX{{Dk@nVFeV=6Q2CuO7U<
z=2qZ}?cwjIs{DOB>D{N~Bnyz$`<WR33f$hFAHKWHJ)f1AkMG)@oyD_PDE(L^H9fk%
zvc6d*^G<`v*KG0BB3F?5S=O`u@J*aOd-YW9aI+ixik^CbW&qSeSmM84`Xqa6Qt+}B
z3A^7(%&dC@N)`+cBtS80l-l*}&CSaRzxUPtHc34tBCOS<f2(>y%>Rf#lT}`>3w+td
zvu!pctxRiZsO#vJHeZ#0e;@C*?=LUAOPlAJEL8fj=(qiy6<t!(*_OGz+*Fr)Mh{dp
zIB+{U*tZ;h7~tpU*ZlVP_xIE1%OC!#sC#e5Vpr+nC+S;X9Q59GI%(sKSRqhC6l-dz
z>v(v${q?%t??Cl<dURmmL{JpXt^fB^EsQNbATD;br2HQZ&s}LAv;4aGO(9JcUCy(I
z+AH2{JYI4wGJR$2?y^^xm-nk=zP+`ztH1yHp32WY>-f@U?@d|#O*B-)QYm2jyz3_+
z5wpdB<%_e5x_b8CUtg>Ke!adm@2(Xq8(Y?=Cnu%s>;5EOcX5BcQFVF9|36u~RbKAv
z-lcis+<I^p4ep-uk^A7mgS-CydVN-p+3tt|&)=ti<wIVZnXK{NbMC!VK4{e_14EPo
z)33ywJ39h*m%k4yE!}$kTese>6HB@hmVLke?(!<xR<1AGK?cu)<b1KNhPn>WK3q_e
zYU7no*%*-`(WX+F_Nr`WOxiSd;VB{4UgXQ?PgA*TvSslt7q*$uc<G9~!(aG%?RG6)
zUEiHMcXDxYUFx_0w?cpapP;|Lzh8eozdo<5(%|44S9{QEo=n4oyUILZ+4^9Hqy3RC
zQEkv{#L**1PGoHbrSK0A4uZyh^L9QJtNs7?d)o94wJnyrUDmI=p}8_I$)bJ!**i_3
zB)27F%L4gBPm457w&dl$o-sorH8s_9(ygu8%g>zgG4^&AHs*2u{_nf{&O2?OH4ZnI
zADsn_?mn?E&L!{f#lF6_HaIG3mXVRsmluosPvvcY4cbWc``g>Idi=gG_GfS2lI6aT
z>Cf6(cC$J`V}YP`8^09)eVV@i#LE(|N&kLb-~Y;d|Ic2~_N5muGS1GozyIXbg%B_8
z>@VB>%{(XN$=&iiDcGnM&H`!=m?|ugPdd^eIPbZvb=j3?XJ>=<2AnwIao*=$#?OBn
zf2cp5YGep%tu-t=D)FT`D=X{C^XJRu>wX-}t_}$idALXP<QcuIcmBVfqH<Q^?o_CY
zB$<9W{`>bY>-IL??77>MKt+hc^xGFY@;+KEFP?K#6;yg>xN^K`-|>82HE470>J1w%
zya`HZ;=h)fnfmug!Tv-@j`!_0e9>O<>*eyTrLV(ORaHUhnTwC_TUGtpS*B{CZO5Li
zS#{N!UA=8~k)Qk4P97sj9$3rE_*WqLSdZaumKiDc%y&--4q94v(Q(&1ooQD<S^dHn
zP);{YZo8>^fJOAG-rvuw-%eT*-BuRVU@v?a?7FHRNB7m=uWjEwH9l<ZlobxKSFB;#
zBnTA6SzF~wJx$Xp@+((m#a8|6-gWJ>^tpE$_0sj5LDf$JAJZ?zd)4o4*Izd^HU0JP
z_xrD}R<D2c@ArH2m0Bjt%ER7=mU%7}I>cnMEq)WULOBWwkROk_rE_+_*>w8K+Gz8$
z^X=oW#}xN+hA!NAWyhUapMRQpUV48%PrqG0`8>#(Gur>{IsWj=W&i7wRK3sWF)zPS
ze?2YpeIwV=mK&yd4$+%WgH&BOC3vBK;i5%Oo72yS6&c-~A1yTf_Jz%-w|PxkCw(cd
zu`CK=ShwE0_Ku#OFSAt68SqS<Hcd-I!voal>YVy8<afp4y4>^ST1qG4!{^;Q2PrWc
z8tPW4&#$?py&!sR*wJIhw3L;dCr+HG7S<Mg@~zpIGnaN<`@H(RPjE+bJSf;1o+;0i
zNxHPeGi`c<+J=naKBHnU(N`ZXA2AL(3oarVR1Defo(5Gcpf&S1{(ZZh|N3fpyy-%v
zIazIqFJ3mUeJ*@1WBCV%`pMd$P;Rhd`Qm(Lj%Bc+p&{?K<42FaT-YvW1X3NJbMkUq
z?NLy9^2+!!qyT2H;ds%0V{i5LOP8eDww1lTmHGYMUA2&w;FAVCwxt`s{wZI+J+!3b
z@RKf3PJWQi^2Pbc(W9Ave|_cMwmy2h-^Gg;L8@PZ=CSUTI+WS3UMGF&9wa0Va5MfD
zcznG7y6*Nni&9TdJNdNe<kO<emzS3A`g$$eH1CeZ=F^AQ2ZY3}wZ6ah_v6zomv3w8
z=^TJqbwGOa4*5ro>~a^*&9yGQSNZ(p`StIjAD8VmO+3VMY=L<(sKj_WC1ho7&W1ec
zdp99gE?|thz@NDNwrTyp8ql=q*X!}uLEAWUZ*P0~bb9=>yLt92i|sz2F~;Z?KkC-c
zIx#`9tEVR<C1pt)uk^Kq|DKc8P9|-vuyyN`sSMh>RZM=}=}YXdH>GTj`f?<{TKhtt
z@op7Ruw*odeL4K%%F4-BrLVrMK5tzAuLd-2cHZ{;98fnk`<f1D`h4chnO)u8*&F^p
zpI^W3<KyG6dyLNoI5;%ORXz~}O+VTH`>`0*&G_9bV;NNZ{M^?)Wfn8uyzwaV()v36
zd;J6zU(4-_tCqXWhz1QjGRQFMzG#=QE?Xm4`D7y7ZTZ?Sfw6TzpQ_jFQ=eDibUn6w
zuA2Y6HS1z`zuNhHUT{Fbgt)q&PY=KQ|LgVo*T<yuL*C!pJ9XZ?b)arT(eAsTJ$2XX
z_}ZO0IXUOKr=&dLynaneqHQ5)T<NH&DQNC$c6*r##J?6wOurOC+ihbD4zluY>z6Y1
zim&_mlymOtUAv-Ihp)f3A@MNEO!jrlQ>Rad1_v)*u|i}0+xhkXc0PLa=*rIG^t%Oz
zd9^e&EFM{cW{iV^9(~Qwjf>wI9iMY@^4;^R)YrdIt(V?z4GQG@u1S_Ik&!nOkI(x3
z`}h2U4QEm3t5eg{<*!?Uwuh>!s#doCnW*gkD*pek>7c&2yIiG9e9gyJw_YjL>8DF?
z6oc}|!-o&&xqEq?;*51K-fDb*<^I||o|8(BpC~Eq;fA&wEUcJ*DOP=XvGC{T=jJ#5
zU5n0NnSFhoZ`A3-{Pt_M->dR|zxVq*CZ@VLp`1HA3PI<ITv_Z6IiR8T%fx+uzeR(l
zq}y%J$Clq+`sh*8`Q-ESY<){upB3)p_5J-lD)h<@(7G<Q`4h7sje!CmAEELN(AJVm
z-uhR!<=!?ac@f}wX_Bfp=*W*3bIb2pE-VJsz-gN`)zyz@SNr+(2?+`HRlP~>w_OH0
zd!k!hKkWQG+x}hozrVc|6&7B+HhTN3J)h5If7o~UVMgiX6Sm)vnytMVyCk4}zTEFz
z{m8XqkhXL~@*>v10*l@Iul;_%e?3UoG3op(pniMp?{A<?`|8HWBWe<xA-(YHEcsuB
z_ksG~9mj2dy;xlJppkt`!9%C7@9+PgF@JOU`#8{vJBEfITV7>@7B!k?e-!h))YH97
z(^BaIB!@rvA#kDo;URAQE7#-eb(ddW32N1B-~V?lXqe{ftE;bD&YR}mGTHm%QFqnX
ztKnNSFRR@xILupQHFuUx<tCNPLoJ-(B@@5xKb=sXWmmf^uI^{*(q+p))4?}y-qh08
zo*ZKC_7Ajkbl=Zs(r@qV3<gb{9~F-W%{^xw=@4B0O!~r^y_>80vtruLy_H$Lv37EQ
zEhw!q{NUKNL;l$G__~$T<ElLW|2$t0+J5M*zc=K(&F3?m^PhKVuM3Ean+F;)OHEH-
zUH<-F)Q*CM>5q2Y-Cce<>+jEe70@vorKMZ-cE2%N|M|=eL+9^z%jf5q&Azrietmd+
zZK$iOtCyG8r4J7ee?7p=zhzh68~0Cl*D+68a&hmZl#Lg*f=-Td_?&3SbJ$=vNBo4j
zbJvE)Rl4f${W8hR%j?VS`~Si~<;%6T(bt~{A2CH;h>|wF*emqfjw_G%`G-C>`to%C
zxzO7$6d>_j6Xt!6?W}0*olx^{dy1c*i`rkeH|^}KrEAvcfK~;iO>Y(6k{G`+E2b^U
zS=OsF>5V0*<^8Nx@7*NONiv|*3qVJeSe3p?DYLZjy#IF1>wEXs=c?Sj4XRd6thX%i
zj@nnV)63iY>iT$l&<@L&%jbvH)$O~P`jBPqy-*F$SM#MZi$HUi3<ZxkUQ7aYw?L5$
z+9Fx^|M&gZYq#GE3JL<P!T}u_aOqOeX73}05^W2wva5HzzTOAf-1Ad$fxME1MMQXf
z?bd1f@#}b{&0cJ~op%|OGk5H;h%LG3nl`<C@r`-!uQYy=vi!5=)ija3GLT=u6LIbr
zb{40D5-zBdcdzEN@8;774cFLN{M!@m)$*q9&NWanm?b~yjNy$OGw_KkH6I#pE`6Y}
z&)VYOoOhsc3x-+ROjc1X@A>-d-}{5sn=)MR5xj8mji}Y?t+wYiJH<d#s|;tw8D-Dj
zo7KGbx$I*n5y2Gmol+p9Ei73|Osc=$z5RRZ(yySlq{C-d2lvg}chxoR2m!4*W@vcU
z-O!h|cGb0|rPbZ4hK?Xb4bOTT`qJJWUn3NHeHUo$N|etANJHs>r}Bcuk<oiM=l!X?
zzir~C2pz~iFb4I0kdEEWkI(-${s%I{!PwoweRHZrTbTTugL6MV2f1dJHIvn>wRc;e
zg60|TriFfgQUGZsHN>|z^rgK9Y2U5hu6f{oT9GM8!-v-b7cS=1hTq?7YkKO57f9&A
zb>#(%Z${M?H0`MCu?KbTE-(pPxTqr<ekH@(Z>9>wQ_~t7`qDDbzT5Tg?aGUeU7Uq8
zVt7Hi|5UM*n0T+|_Y=6jUiLA#>9vIiBy?X^rv9G&R7i+Nu`yZAnkzLuI{Ukb=cHdy
z4{<v>xNqK^S>LIZeTR$xOL+SY0jTSPn;QDko`YQfdt2{>t67H7z&GW1kx_Q-t?ne1
zuesjeK{HPby6lXyXV3LVs!m$+F87e{I#5r5!B3XSYSz@Qtm)gHPxYL%OSNq##I>MV
zR1@vlo4lWxEtw(E{^$@Bv^C`?3DWTE-P^m9GHk(Nuu0&;#Wgu+w5EX<w=!J#1m=I)
zvcMP;qtieEy!hJFYtP;Uc?N|?T>#~xEe0$lCepV*%~`doRQt%o<z<16kYO5z<syu-
zXOH!>AD>_D$ukw<qpch-GR*2y{(usAgIG^PU)tijCW~Y9K~2nzPL3BDVmyaGloY<7
z<pD{lAZO^ju?<)_=~UFu+d5Xdc2^)Rs0UL(32B9V{XI>+kTUzcXi)6v@_VwMJ#?)3
z?(N-%|G<fe8<bi<eCBxvTA#?kpexKMd-l+=zh8}aY(4@?;0@C{8~EmjzgTna-y<*2
zpy@7Z9U!M%FcQ4*@bjzb;oiM`K^YDEPhJHX@9<b*!QtnBZD1KIN{`8^Ca%`u{VA40
zkbIN@i2qk88)}&(qpaQ9J-fngf1W!jA_<g!98SAAwEN%QvOlCv@o1Zsh`3Y&B(Gn{
z;CS&N^XuK)nULtXBzWQB<+^zX=R!)6ROJPSmsh40_@{J&)3vTNqpaQPV^71<{8p+!
z<D!P8<j0!a%G6csz;Uq+9v8h`4Se&zKHm51-P^=XQM%9!*W2B|H~*<*y&oi9t!2P9
z5rd`0S%267yr9X@FtD+)5j3?>_v_{IWBYi5|5OxonnJSBwFUz7F7^L@K9{nu+cQl!
z8nlj3LsixFa6A9?eYL-Be%t(du^2R1Hm~}f<=U8?K?e^W1g)ji($=2+YSXNiM2TBF
z=Bt7Wzb$gaOtn<*IsG&!C1uIHx?h=~^<ac1ThfsxTMX{=?yKfHZTjS5>Zk3IUmpKe
zPW}Ek-xD;oy7EfQCdX{2SD7kTuU?JXTeY=~SNhq_w5?I5IX4W}Mr?ev<6K}|+`3t_
zq+DHHJtvjDxv}ugo1FE<ZSC#a%F2&Bp08Q8D#+8b^ZH`T74PrWMb&74r&k<SJABFn
z9rRMPGp4J*|NAXDdHMBk-n@BibzHXm&cY>2RQwgz+rM);XIcErr?tJkT250_Q@u)a
z*^2oJeb6F^n`u`+=v1n&*P`=Z?)`o**umj|=lti;5vk`J)6dTfj!1ocb55+5=c`H3
z>}K_2W~_s^sIai|?6b?}%#m6D*3#0_%frLs)y96?Z!<tU<Uym1w$<Oh<gB;XQ&)HI
z?Cgwt{^suP?}_K17Fawfu<-k&b!KvSl&Ys!7l+ACAyB@^klM1qTxj>*b)aJsPEJyt
zmz%de8q~&F;Mm+(ac6PA-73%o7`M0{Xx3uyJ#TOCU?-=h>z3YLUR#Quo?1L_v!zzS
zHP$aP1a_aC3Nq2*@&eYqM6UT$c{$Nt?jmTi!u(!^^U9Sg=M_FxR8-{R<eW%gE~IzS
zf;CZ-L}c&$iTV#J?jEEFT#&vf8XmK7&mNn*)$jM7I(IJY{k^?g3m!V1SIOU9{(jok
zU&ZQemc`Fj?AjGIJ+^G-^~$s}GZy}Sx7&Zxm*@8XBmI^KgN_I4>gqD7_>iC*v*SZ}
zZ}4~1r&qUXtuOPOv?$XNTpD|U7U^A<xn{K{p84*(^w4L_%BdS8&ggN1CesUNIh$qV
zN;LD$4}P%b^*uS^9Ch$aeugQ>iw~Bpsine4T-yyG<=<;ZhxYRyeFTpG01siW<zSSx
zQ~&=!DErI<(CFU+Q^f^`pI_O&U^5$dgX^`H2EO^9KTDKAvv6|*-~3?vZtK`PApdVs
zV=4JDr$((4TtspSUU*piYWI%g8_s}B(lC(TM`hAlC6}7OW`9*&aCrHYkZUWh<$6q7
z)WrD$6mwDhOjb2se}8<wz+i9rCkI*>`L;Lk%|8TnqbQ@S-P7l)*IwKM8&e80=E`<h
zMRcvLfp7k&&r&Z+3c*3OOW?x8V6Hjr#}`~a3T}&PJ3F-dYc61ga9kbQ{eN06xD0U;
z*sLe=Zyw3X`fx*h+}gl5Kj?mwoHK+U1~NYIanb4>i`;~gA+?+xOUaKO=5xEQ6|Z*f
z;`jn>8f8R*gnyY{SllFwC{}$z#p=UIW@w8gN|DK`hRu5oDEKnYa=iE;`7>2#BWR7v
zg0D&o4yTJ)Kx4d0@WMl5u2Zj$!<-5Vz4VG>3)rDf2lJm)@7jUPzfxY9&I9pBM+4vd
z!23sS+ae)x<-}6*qvg{pP~T1EBGmUv3l6Ja**=f`N3>|lc4!;H#Fojb#;NY_kIxD1
zI%3_>n(nglg2U<+(;^mEgF3YhVW27`;K7=FM|IHtJcf+994|g_{!Crd2#F$)>I;zI
z&vtic_h0e;(K#tdd}}jV)iCKo*w#!|HBIR=O^-ngSqd_Q^<1;=i!+bF=1f&uaJVFI
z`@%!bjJ9o%cIjFXM%iWB>sCxW4N62A(Ci_2A#v9)$vcOc{-k_=JPp(ax?nZ2En(Lm
zqg~rUiIag*c3Co{s#I8TnEgt5VL#t8NQ!vv>d^l36C~6bn5@o(2b67Koofhjpdpi$
z&-c$#FSbA&xR>LF!QAvo7Et#d<#=HbTlZI`5GoDkJ4!+_M?(W&aOm}2p)rBVoqF%U
zN!^^u%BOU}o^@aEq;1u(xp4mpc(Uz)4NJ)kEl?f|JXjXFR&4TWP~u?tEN~%l)%^5=
zbV%FiT5rR>j(L^OB$qB*rlp|ZASo#swKeOi;P<~DkIRGBC@)>M>{QamjB9HmdB5#^
zzwdX-#)v&~FJHdwtzhk4|KI`7J3~ci^LCfu1?iK2etrh^QqRsXbk4rECUAY+UX!FF
z98aG;lPmlG=~L0x{QGgBGZaCc^oo0VcXws}|Mz#^@lQ`rOTQMH)v}y*t|TN`6|sDA
z`*cLuA9Nng<tHa6U)`D=Zu|XCF=(9rwEq5@8-Lk(Bo^%d|Mxv;I!LBZdAr*0pFcGf
z6%T&y=<Li~<DDV&bDrKiXb9frc+tB6`(VtURmK0mT=w_!@(TL>?QQAPso`1Q-`#b;
zyMNUxFI835IR5ycf`UmF(8f<0%NMr^^XKbNKfUzFkBaqgKYsjp>fAZIm!H#8QeI3A
zkGpuNmD@D^oDAqNCg(Puz|72-g8efsi<ccbeAu=hwBY^UpJM?J)?_>OD?;M_DaVVm
zCsqV5F8TR%`s(!a^FYlHyPr>lLC2%r|Nrm1VE@k7>vo64#Js6|b93`@PHj6ge{L&C
zTG&!lmnMEe2z3DDT%dY`b)cZ2pw-(gm$#I@zNY-{ae+mj)v~)eKYfHiWf{Yy499$t
zvfZ(Ne|^0?N!8otHt6U_oybLw&1`cwo!8XXzPv5>_LLbjDo&ldIfdtN!c1^u(4s@^
zi<?pIEfe247J;BSk^_y*rrFnYEa&=x*0M}8Nj$`&s;-{>?99wbuhgc24t#n3b6@T6
zEoE<`T3cFHBp>gav;=gX>SFi)u-#>Er%au?RMI%@MA~N1S(MLWo~#dvdmEJ68!ZIM
z#tshoDxl@*_?ETHN^IJ+>B@3{`SY)j%T@b;8V~2}e&>LOnI<jSTm3!j&ySB<8XBNO
z=Gge<*5vQ|>9%p>M&GL3<z9cKUNrtQW4*ak6qGNv<UyKu4wp4GH3KUuc64=ji;9V*
zW$9*@$zND{{+~Z+i};^D@Ug)R3|q|hP2mk_H+*5>+n5gPSb$1{mGMcsYBM0+NC$`Z
zOs+YQVwmA8#|s1Be+R`%cI|+c9BQ9>9G7>4$~^{w3yJfXo*y)X76=TCvd?~2ij_kW
z-GTFOm@ZVs8Y;N%<~lW1N;aoV6O`=}teC8PjDMan+3XGCGR$`rF9$Pfz`Gt97#Qe#
zm`O%dTcQVOFvbVmS!|g3%Gj~it>Gufi?u$PnJZN^RaK8dy2d}6E?o+0ZEv3)%cc^^
z^DpMosvURpQd3hkO-w?>x|d$GU%zCDi>9XLt<CBEhYlT*`uq8UQNjHODTVJJq=0A;
z{&{I{vP7GgxA)X3Q&d(@y?B0KgV>j_V_92Qfrgs@?MzHeT=t)x<1GJoUO%z#e12@-
zdBJ$v`a9NJ=KL1>=L4D?U})e6?d}7u8)xQtkrBez?rfZPX2Riz2D?Q}l<$~ZMBcHs
zki26K#vry7q*20ffc*lvu0u5d9Q;2Rz>OLP2D^raKDV!DjL&PBnuanmGM+kd;=-!X
z)o1iL?NXkdnJHzE(BS0kEGjO(+%Wl=hOzPG?D?lodHK#Xa=m!*A{Qs;#Ty%wr%s-n
z9CY-9E2zxW5$j&&H}}=eW3|nPZ*I?@U-y0YeN8K?sK&;|R~Hwz@B97E`uy*<#25Ff
z--jk8En;G32K5g9e!1+g7RD3*plGK_?XQxhOP6XI7zD^#m0V!vw^`u)Jo4GSgP?)E
zi;La0H8njUM}+UY{`+`wzlfOFt5e$RLn0z(C@CqGoHf0^V$GU0oa>qPfqVuV);CT&
zb77Wg_LbG)`k-O`ojZ4?O=pw;cvL(-<J1(*U(aUeZ^^i*boI)Wj2|B!7Fo@mR|`6G
z`W5I@y4(5t*Xr;4;S?Dex$Da%Z{Iai`<~p+-+%S<dHePAY^$qspYJ=UFKbcYkUp<+
z*`L3EK?fOuj<W8z|F=U)S=km(_sw^WOj`AmiSD4qNT5Y=pdCq|nFY`oSJkVP%WZxa
zyg%@=WEJR~?PoL7`F{WVct<%ru5xMJpO5akQCmFZK_k|GK6%I1|NZJ)%K1T*?Scmz
zpG=0&OrxWpZ#^xN^z`&Rc4nrr?;5UsI_BorC#2MTJSyInDB%_uIPu~8_xYf6T<6uA
z<=#s1FbZ1}kr+1fgW%GoOWl_H&FyjR7L!b$U%L%70WH;bu(tW|?#F%BJqMfF<v-SL
zzn!b1q9Or0?#%XfS63HL`{BerKcCI^4OFVR(kdR85mWp1YTRwTy<dVJpVD4$!qZ&M
z&g*Fenn^tW`teS8v%EVQ9!8+i_Vae!VmbyoV%;0_@9$GvE&FUSt8DKg?DgP`2+5kr
z!orOznp#?3leScS&ANK!O2u9GmoHy}=4ihQf)+Z&Ry=Ht``&(}w)yby2hIFf_Se^g
zCRw_=yMyQ3)qZ)@tzUKT{>J3?O`A9GeK-5Vhl13Ul!E(Pia_U)f+ikxqqliXy0$iY
z`P0+Wv#+cOoV267Am?~-|C)%6PJ)7hukP>PpFThFaNEhOtyxb`P4$g4KJz%ej4@;8
zr_Z0C7VQ*~mw&(2*3K?&wt2qUO06G8+i$O38NB?}8RPRI-{0L;^}KoW=BC?kyE-~P
z+%mVbi@SOArdkMR{Ksq2`I%>Dng05AJ70RwaZvWzSGyautR(aDvX}36zrVKqe%)%&
zNN(BgSYdU)FL$KdfBzTz?)$pz`P}kVOP092eED+I&77j0F~4r#|F=wg{hlC@%YBX)
z_ot+%YwPKqdtGa9Z|~~rS}4C2v_;h}5tQ^Fw##p+`B}8tyRrZF=Jfu&y<fvXDT|Sj
zF>QKt+M%~)tLy*&t9r4pUB$EZ*O!a@_J0;UpI7ZCtnO#B)9S}2Pyzt0vG{sSI)6p<
z_PnUwWowuD&0V!*iOM@!hUGG`cP?rqB_*Y7zA1HP)||<c7q_&qOkUNr9<)g4%SCtj
z%$u81K_}IvS35a1ty;Z0G$iE7R{QUPueViw&3gLm8E8q$r_Y}!&!0aZbVN4jxa_*R
zx~~5I=ldL)J3BjH-P*eP`Zm4TT^Zls-JSGgr}>ZnVs+q?C*SS)?AI%8?q^%|<w9t9
z>{9!GANxT|A5`AS%G~~VN12n8lUFTh-P*OGiHVBe1GSwU+!t?vOq8H4S3Gg#)B|sx
z!xuoOfq%bO?e9HZFElD@*7e_ie|??2fB*jLwV=(fcXyW;%WqA3KDT_HS<a0GTeGiM
z$$=UmQ^Vt4R-UVEj<@=J##mbC@s8BfVq3GX`%MBJGvCs}vi|q7B}-hi!`D68m-+P6
z>C>rOtNu^EnYQ`l+cNJ*_YQ89J#TB2aX~>;R5bJJtE=;Br|HFNZNI&?r>944z2vin
z-PTfPw`5*6DSYJe_5J;LW_G?SYa%y4DX}uG{FJiUySaacQEJz{ipRWvzwiGanvt<0
zy(hGyVh0m5v#6ZhI?!2$mzVhlhlETybm$Oh_iXls1&&sKzg+fRCzr;_%lq|AaDcvr
zMa0F67w5gbWBvmij8>0(jK6>`LIBPDgsqKQDjrwisK4h!lgi1YjTx`5tgQNQkp1e)
zV0F+|(_dd+g62$S7$%?5m0KodTeaoix9$5^=HA}+>e|}b`~Lr{w*7cSc-OC2t5pl%
zFYt?uoVhLU?yJhZ$;bKD#_kRa3=GV=z3r{x`-z}PesW^su6Mg$gZ3V;T(->3{BFr)
z(7B0AmZ(f#$F<C$TB>qO4(K3u&{=z+BL$h+`Ak3)Nt?Z!_$QX!IDThmvF-1WkPr#;
zdlicXB_%UiSXm{f#}svLtp8v4xvS0Js${0=;RpK;J_H>%>Gu8IUEcP?iA$C*mj-$7
z`tQ8$w`bVZ?h=%j&$p1_d%WlKIb$PZW8YO``xY%(a%9KXYtg;ia&Lpr^ZWhnZEy9v
zoyS+KTJ`D8;l0hNr>Dt&yKe%@I6RMkyqnn6#FYH!$4560j}AdGu{57~Ha9hNbxj{;
zC8%{LO0;>_OM`1_26jU>Q16|RmQukT9kK3v_5W)(pJrfK;0oSz@8dJe#8a$$>A~bD
zCnkco4EQnHZw75pIG+FH#Kc+F<?F7SwI5ClGG$<R5VD9>mZ2f-$io5+Ma6@!1tldl
zSFZd0bB<-PN!Aq&RyMX(wZFerJ)c{?r|<rT#O5|W*;NM)IJmmIzy9@leRyQ#%<Fdg
z`~Pg(Rr*@3{qVvgM_fSLU*q0CfByXSByYXIfPe{BrLUIo+x=Kjs4vCP5C%Gy&w<;h
z_E$--taaGyYilo04Ubz{`1n}n<z>E;RF+>}30kmjWE6D2?)O^o5uI^+ZfG~0pK0tK
zyv*k!=nzxT+)^{YUBK}^*}Zm;|NVY{J-_~U^xF9SapExrjqCP&@)8vjGkaOe#Bf0<
zlIO6&89gQj2ASJSJSTs-mAzh5LE%95Z_s@myWj7N7VCDc|8ZCzR860+UOvBW*ROAH
zSEtXf4FjF>37Wlrdvmk<!Gi}UEdgz!1@#=l!lpfX{CMh|IcrW%R)2kvUEU^#7i7xM
z)A3&pF!Ot?;{L0ss2H^&fid|ZBg2oW63u)J4#q|Ac0R9SE%5B>>iT3OZ;~<LdUZ1!
z@1<K?vsFE3o8<<rkK1eWFlN#@o6kNw@8+eZrJYLJsG+Ux{d{iuy};}jH#aYTURhAE
z;n1N&Q)bVu-ZY(sfq@Sk**%~$A72#Jtzuzgle=E`@87?;^}1r+ck;@Ur?4<MtUe@B
z!oUzUA%EY`Wd~pD#qRQ$1X|Bo`uf_*mnBvAK+6wOw?<vvS)AV5)~01@8tOewC-c%Q
zMg|92Q0qU#ZPlt(Q|8WnyY=k(^Ztg0hLe_Tzh4&(D*PTCYzCb)oPBjwXw}Q5(^Wh{
ztryTJV5hM9t9`%U1;@qB%iH%eEw=P(DCi;&mAmElYe9FIEZDKbqF2h)Yw6OZzka=5
zU;6p1x%t~xCWZ@KptD>u+{E-^RvbCvvOagNb@{btXJ@NkJ-Y!k*Z&f<Od=_1(Y>0_
zzNK$(g-#L?5m^E{0($$ss?}^f5(^eAPynSVf18gj&tpKFq*GIu&a3(4dAIKO+pf;e
zi>cFNua$^%FgR!*(|O0hutfmW*xdVl-mxCZWKf^xfr<e}egug$h&2UfGccIsLwfTJ
u512s*X@IsbAvLu@13Ls-;{=sF_|Lfg^3AVzJypO<N<3ZtT-G@yGywp=*IfVr
literal 0
HcmV?d00001
diff --git a/images/mlp_loss.png b/images/mlp_loss.png
index 1a9f7dcd4d8b63c61c21e45d46d4d9cc82c4d835..ce7e5b93c797eadf82485143bef53cdb32fbfd24 100644
GIT binary patch
literal 26384
zcmeAS@N?(olHy`uVBq!ia0y~yV1B{C!1#rOje&t-od&xu0|SF)iEBhjaDG}zd16s2
zgKuI<K~8>2PG*uqS!z*nW`3Tro|&GJjzUIBNkOrdzJ4xTfnI)5y1vE>i6jOF2F?PH
z$YKTt{zMRFTw%XFlYt?v*3-o?q+-t7yOk9oPw)Q!c;08)$$gWjI^0mnX%gtrX}YGN
zb#>9A1zQrXe99Kx9wNWQj_I0elDQnC<J!`LQ7f~rYH<jz@fKkbY!z^rwjo7TWisog
zGjs0Cb1rUVVVZQt^V#2B>vk%nzq9-)XKVi9<X)NXM2R*A5NL>wyc-K*Ffb@AU<NS~
zxL8U+G=on=10RUdz$tKn5yUWHWU>O$3~CMz>>$Pg7LFGnnqdY5qb!&q3^RmjL>ZEP
zAZcU9y*-ubU+3uW`Ou^z*8T0x&El~0zk}zzeeL7rb;;NKZpy9Cj0_L_SV|Zi+DmmV
zZ2R)-QMdk`*rd&J)o%><{{43QogOm-!;GHoySW({9xVTLSpMGvIoql^1$)Zh-^*L^
z><UPo?<^BfVRgTpqCesLe@$KHGgE2y*=O?(F)%bl3tnJkkiGYGdi=U6Q$)(Af1F%d
zS$XpG>C<;p85kJM6T7S#76h+ey*k*%rRCD)%l;MT^R}Npf8PK5z3TO^BLV{_=Iwkc
zcJ#=R6X(zSH?#9!>(<{_aq4~XWnXhmP0g1rk3T;<JNal*VQuclh>+0GrQ-257fnU~
z-;jDW?b-tSuUCSl3=$f0%w|7&{J8Y@+wH5@tXbnZn_GWR05dz^m1}FGOE0>LYbq!>
zaB^~1J?d2Vi`=bp^~#kO^QzwkPS=YSk&#)mdfhIs-{0ToZ+qLw%wF>Othu(Dnw!~d
zUuHI*3-|y3`yN(1|MApm(_Y=Het&g=W3z~e$dU;Y1Y+xcKK=D<cK*2+HIuevU)PH*
zzgybX(GlR_&~VQFf6d?L_Wvyx-aXXHZJKa^ft7_t!^S2?Q&Y3*>D2I;_u(NSFW&8b
z9~KleDaUN~qQ#3#PpVGOczmokJw}>=LH#g?6~ltV+WPw8o}QkQlf}ASCr+IB>Ep+X
zSHt7in%}Pp_P77LrKYwvHuqlL@7$|bu4J5=qB&`avU}f)L)`jT&ds$pEq><n^~FW!
z^?Sd~%G>o)ZR*siFW2pU_v_?Vz36Q|+wWDacI%N?C~2JLF{$e7tE)-BD?dMbxv*U>
zC@N}J-S6A?wam@Ky{GA1{Pp$q*PH3{PaZ#h{OY$44-fBpv*~onS<~w;e!X6Q{q_3&
z>moO&WnNg|xa-fS)BaJ{*KWVJYI=NKr1`yy#csV)OZ)A9c?hfdSS*Z9Ok4;GmPj43
zvvVwiV|SHYWS6g55S_QvwQP56`Tg4OnSa0E*qS{Z<RMT{zPh-$or|0M>zB*^*>`pn
z-r7~V`qAUZ{;z9VTU(_}v!<+Cy}I=I+;S}=qagi#KNhXq^-62+zhAF+{d%=p)$_dF
z?=|oCeD(t++VZ=l)BEIXSDl@0{`$23{wuxa_cT0zoz1UXd_BHC);RrK$p3$TOJ7`2
zto`+J`Kgm9FJ4&a?7v!%fg$QB1H*#ms((M9^Ln1$mt!`2_1#lbwRt^{?S8-S_LI-y
zF@>!cH>dkwzs@bDW1u3bzyHss*Acn7YYmH@cqnUVoY-*t?YhU4dUh5+-?eX^UG1)m
zd#k?-XU(xLUsw1ietTnM<FPL<FGug0cXxNWbX%f?tE+3<`t|DzUoM?4#n*oLYTm^~
zu5Buh-`D@&{rbj^!pA(G$Gr9TKCuc54o*ID?3mQ`xazl@_g^13%e|H2VU)M~?KZXJ
z`FlPd6JGauT6CV_qeqW=CQX{8mb=_%W>QOAo7B~-SJnT<-Q8WDU-Z_!UoN*Ow=Gd(
zclmp>9JARU+wK26T=G2YT}}D@+U<%be>|D&A9GwwORGoDwo1a&({tVXuif!~4oMg!
z9Pm6ZD<ze6r|$RL<!cxj8aB6TzGG<M%m4W3Xw=rM(8<@?<!c^P{ZF_5J#*2bMO$iq
z7G2+XxSc=y>Z;ILHkF%R-}wE0|N0v@BJLI(=3SR;XJ@zT^EvC#$jFub_WyR&>aDo=
z>gwvy=;+nE-|veKd(B^;`v2GU{b3#+Cpa%}Yi?@#aytIsrLSApuU`*Jzp=Z^=6?CT
z=l{Rola^S&-?RDT$H(r6c@JNZ&fl|8d;OlEw!{@nmK^yiWnZ`F)aldy)xWl%O)HL_
zefZ&ph0g7ue7vvr_q>ZCpT6I(k5_i<xiG^pIp%m%Q<F*ZF`l<KH@in~&zq{!+S;1=
z|KH!J{dKk7&$+m{FHeuJTiMLcKd(%dfng5kvK`C}R(k}M-R6``w#d)VkF9>Y6_m+C
zf`gT3pIrvZt)-=<zD&RF?XAAL+<(4WV#K6hUtd43+HWiSyRP>4x7?yT*RO}$R)4cu
zIB#9-ZnMbudY%s-KD@Q3^0Vgt{;OHGiL3s8yuW?Nj)>IM)T(DQ)4!ZCK5tUJZqcGe
zlk3($ww&wt_x}IC^XFB)(tLYwZ!}-~;jeKiDNi!zdS~sJcIC<y;lBSr{y#j>$ZVQ@
zO=oS~-Y8|ao`CoF_Lja}I{no#>HI4%E-sFpU-_t0-K6$ciK?1f){PB`yB>9Eo8;ay
z`TFMO=Pj?97#5gHII=TTv}gt|Td`=-A=6F&*4O{_K6&z_lugBlNX!2p5B``T&wg&(
zwM%>-+7jnX{cJtg&-0Rg{m;|FX*x31e=QztJC@vUyR4^&r&rcG43t^I*2Sz0T^*Kr
zpn-AS`|D!AzP?_5=T6MtU$0h68K?E!t9Z=YD{1Uj{QO+$)}8-;z22IAT~9Y=N5IFA
zAAdcT|G$EfnayP8Tt<ciH;-^wF&t3tk+lvhEG&%LSF`h2kK|%d$-{5+!C|u5-QDHa
zpPilkw8Uz5&!YU>+s=Ni^FDO=u>Xz7*sQuMdzTv2?@7L|Z*IQ*<ci$f+_h!z@2%ak
zWlPxH<)-(Kzbtw6`~CiWqpW$eXWw3Occbh*S-#_uzwfQz|8JMxt`|zS-|rOr_D!EU
zRZ~mr)t2+uL0SCOv|0b_B_t$PoSdwl{prccRcqH?HN76Q*j>IhWOMrYY4hjLSJBkd
z^V@#6Y<Ax6w`QWEqM0u)EUfx+(cORNOF0IH^d`X)h6eWyQEO+JWKOzQ`#n}SdYjMJ
zuU~zaZTG$;8XmK-si{dV`|9i0ueTOFbYf*?&AhXtP<P(`@bz(8`uh1sb8YPH<Ma1^
zy~df#%*;HS_w#XYZ|}+L*RP*f^Xa78;s@t#zt1^!`t<C(segTBxM%y@{Y;tsm|ILI
z<I~pZ)2FZ7`z>nk$79mJ-tB(xXW?I1_%X9SUtfQ{u-lLL>bIt{76lKsoL4pdV;{3U
zZ|<hen`4i!TD1z4CN(rQJ^%jxK3zrDs$|8<=QXvpSNGS~OSUcCwady<hHvWh>E@Zj
zva)M`{HVBk<;tAETn2_YT*@yP8u;Ef^V=2lPWX3YMd0FDJvRRHY$CbE^{(96S!|kd
zLE-wYC&_7NW-MGjzb?xBZpmb`oEr<4EKva!_rKn3KEEa5AQLM)`}T;#Q#6B3vajjH
z)_%PTE>&V<*Q$C?doic@oMqzX&54KGGQYmM`s;N3zePWORD6ALaq-R4%dxvkF21?B
z`RfH|{+F9h>s<~Fk6n7_PK+(6VPRKOF`4&aU6-PB+lw~oybHnpwo5;KD(aOo^@@y)
zytT9VIcKhX{hz|Mk(=FCu3Y)+>-ze&|Nj17y=6;?w0YhYP)QaQRTXvr_jCLIJO6!X
zx7V<>ja}^4dujXqy4Cf69?PfiIrsbD@Aud3|Nry{wI@KX78Mj+C>~!ExV!xQHR=34
z4`)e&sst@Fv#`bP{b4>nJw-)DTQV*x`OYwKJb3V+mbP~A^>wkPnU_>}<!mAx9UY_g
zR&BkuK7PIZ|DWg6L!=oPmOnYC`-G9<ho;}R8K$1cKA*S0zhv*_mnK5O!n~fxl>2Qi
zy|Lux=I;ISr9@dv>(qy@UrpI~rAiJ}@;p9re2rvVV$F|-?Z*x@GV{jA$1l#mzwg*-
z{rz{|BwyQ>dpoU1*819q@88W;cFGBt{Yk#o7r7;4;>0;~a(?{yAz^4}cr3sEcl6^A
z4-fm^>X9~Ick_7L)-OM{KbEyDI<n*QIqTjNCp?r53@+^WbV|Fo_V+i_>bb40t;gPO
zzn`a~rY2=)XXloav!?LlQE}<>HlNRE?)NY5kNkc2eO>yIqemtG{rh+9#l^+FZ{Fl2
z-`P=kY)9c^zuz@&i4yng|L---yriP6qjToN=g-D&Jrap~zTd0%y=C+F%jIJ?HYS5C
zKAg5$a&OgFqj$UC+b!O+=Z-;7oBNf!<@alkJvi9h``|$W$Yl~X6&pTY3HG16NsWQw
z+?1mR=PsQy1a~<e^lW4!z4P(l?K`Fm`{wv9=aaKp@!&y1q>fnmbWs22fG^MC09V)6
zM~@$;ZZ#^)W@9PgFaGerQB_TC*KQt=jcOb7@7tBWy><0&ssl?&zp#vq&dP<tZ7nT3
zc1nTd-z9CoJ$1&67pFK1?3s48Tbix4RZ(ea0QIFBmabPj<OH&Yfu7x~g4z4)cSRTf
z|2y~F)Ue&VML@1Bux7Hl^DI98?d9&9)mwKjIdyj_NVws);Dv`~u}!&?mOSc`G7o10
z3BGf8Xun;;nZ@&BDx_Pdu;6ems3u}yXpk4U@X!n*`h$^CHqRN{HG~MSYd9wb?n^2x
zI9$6((h=Ng6lb!!v&?(Ke3g}dJmzM-lFeHO>S8n8>2BcL&iV2CwDsYKbt8fzUq8zN
zS$$wH$BP2#?xTm?3PIcp9SwZjyOh8^)dm$Nt2=AF1t48d5a-Pek?)JT7U!*F0jUL>
zn8XY2vYIF?IJ_3pS-sHEz_-0<t>bzXuqhvMSiYETtN56ts;YYO{CWSCD_2Hs&x`HO
zy<hX$cj=NP8ag^Yjg5^`MkyWBbfd2=IlHIw^OQMr)*LwC(AwUvt*h%BU-vULI^Jz|
zA`3VfN(o%Jec{v7(^Dr-T)4CNdDi1&y;3$68;<qMuV1uiQQB>H7nc`}>~aBRW!ud1
z@2&arrDW;y<=Ib9O<ia9d`@wnm~PaP{QZB!=GXmNIcd_Ql9x-TuiCL=$6meHYr6gg
zI+*huK6@49yN2HpJVAl8J$-$zet39zYsN(-&q<eGh9o5|dbi^--`)EEf7e<azp&8R
zH1QCN?Vk^a&+g0Lep^&ZYSq=%;jbTc>)Sp3*|$|+Uq9T#!{fF7vR;|B#Tq{kicVT`
z_Oo7S$Nc`;+MuYnX}-CMQNl6bc<a`!w>Bg;U%GTD=J=Yh)t4?^nlg88?Wyz9Z41||
z(RuppS=O5y8+GgY)8|#XEnK)z%BG@Vb8c2vR?7C<v-g}ia{jpY!Mb~sW;_Gsf&-fc
zFWmO<^z4-3Yfnl_nkCcU*%|2Q=-53U)Yl3Km{3z&3vxE7oiRP8$aAvU`@P@iH8nLo
zdH#I){(oQBM{UiTx@gg&S+?<MY0I3PonzM@IdWuvU17T9Yw$=#Lqnd`!Gi~HZONPr
zDuO{(zNDmN)b6s}$!6lQB@@kZZ!I}*|37AWY}w3p`~UqCyT0}OJlp7}PoJ*4zOVN8
zt%q8E{n_f}kkJnQXK&vzS*>2NLZh;>a^;2%8%};abja!Gv18kQSgyXh>fYXJ?@4Vu
zl8ffdkx5NUdv%Cgzho;X4^PPZdwZ|$F3+D^|L^Ck%gg)cRll=5JJ0ra%jcUpW;b)T
zZ7vSXZ4m_Ljs%vyXMg<q1#0k}I(4c@w%BU!sZ*yeotUT`T`RZz^2%AWCbM6^w#Cpm
z+~Ij7c)-O*Lit6mhO)Br<>mh2US3_GRyZrS*osxFyk5S1IcZ7e<z<?RijFg9&YUE5
zt$X>BB`d<$$E{kq@*=3&pL%*)=E+H_la{PnrDgl;#o}GBR;?Bh7hgVUl8|oPo(Oh6
znG2=YW1nw({bwa4k25e;%?9-vy1Khf)6dC>iil*Kn4lQ7F{w5C@s2%vY$Dg%`y{Pc
zwdzvt_Pfje{{DXY{{8rqCr?)Wd^+8<@>7cEr0LVAZ_B;C3{;=0s;YMN^j!Jy@bK4@
z>hm)WG%)7vJhr*f6zr`7#TRV*B<GjzFx&27Zo5D3?_;Mg|6;{=?3Q6*U^t*W!%(`y
z>e_<CZzGyP$%uiaWX6Gw4RtFvx$&tBgR^;s8k5x>J);Y1t6htvE-ln@y9gS%0*jtI
zofYV!Abk`h@F9n#WCl}z6sRD6@RH+&0V^b`{oNhfHFwNwJ?Hlhl*MdB7-g3U$R6SV
zn|<C5B<Xg66YNy5+yO}GHm|RNFSvlU<Qk+v2>=<c3{fS^D7&mcXF<5k+Upms+Gno@
z+1~ICWDU#hJ*l7bcF0P)f=a~)ogi0qA3bCRP7!jw4SbgqWWo0OyE(LHe$WAn&+7q+
zgUgu)b>#(zO&}q0V6xzaM5*IURTJletC<6z1ui5?tuuYm196c9OUaC58wI}zL0t&q
zyOngzU()9Li05!Yu_?&67xr<yFz8*O5bqhZ_ORyfiB;y|pzvXM+ugu-c|&Ej=9T;I
zlR{!DuRzKlhu=yI4u`~8@7&A>D)t)M6(L+<a1N}HV6yUgdV|9XTsSu@2TL-}O<xEy
z@`C|O$qZ1FlYxQ3MwC%@*&%2+b~W$?LrT$x<sj4hSm90vX*mUo>{Vf3`If^1(%GSX
zWefX{Q>U{gCVDIl-TF=s6qpRRco}7vJxblGVRRvUQpgc#n6@<VUGC&dTx<dI?Y#B|
zzRM2-vq5YdLB_qkprr5V>G|pN=iscYRoB)=Ur)LX>NRR=YI1RN2PY&f0A=#j)6;g|
zTfchs<u#F;O)@Shu(Gjf+1bTiUgmrGU^Dye$3eGxUk7Htc>+qG2b#&P2_`Lh^(rg(
z&W=ESyB`b8^6snvIb`R~ouHh0>+_~tGUuH@{(R7>)0QalPH)j>FXC%~{dIq<dL;~<
zs=vPr-JW;XuhMS!-E|i(1WcViU0X$E$yf2qeQ`f>wm;juJ5-qMXy(T&3n78}Vv3cM
z*$0!J1=}ayx)qgsV}oO6X6Cx&u+^cNnVF}~owIxTocC}*SlG0p;$qXXH#fv?Pn|yf
zbkar*9i2BhQ*yatoBpa8Hi5%sbH(o64d+%ZS>nRi?rdmi=-YSv$dQcO+j4d5*6rC7
z!^z3HYURp_)2B}db*5vBPO6^WW?S`TMcUa}OQrL6EIc|N)Nxs{>{<JQ?CrPDKghZz
zo()NKN=&;>vaqvfUtZ?BYR#IEu&`-HMn+dQr~AiNJZ$y7b@l4im%m=GpPncYwJF83
z`}yfJXRbK&+g>>qIzM&|xN!o?hPf(QT3&H+aauY$IYoOmYzWx6aiebCxvZ_L%HQ9c
zv_#RlO+#JXedf%WzD&P17VIm3A6NV3qPu#mzESnJoYa(*9s9Q3e(UAqV{>?Qmi%$1
zs#M>-tFm8<J~*8Vnp{}$IhnUg`s3HHS2rXco-%dnQ>$ksR#T@=T{=<OecLY^zvY`x
zihNCeSaZBj7Sw}KQdai=RXO|YvI!F=sOfL!{_=Iw{{HDIE0-*~2Fr#C8Jl=hQ&KX1
zeR=8S>3Ok3P#M$}3k?m`w6%?$1R6b1RZ-C}HVzItT=Heg6p^p5u6moAnpXY)`+aN9
zO{41~-*)Y?0u9lDI!o2>_o}xaUf9&c<jLEg@%ii5((|qJQpLJmW1DhMX|D|{d>UBV
z%~MeB3CcGYo^ia$&G_`>q?VrEwK<l>Aprpk6rI~Lj&_Nz>wWy`)2EcJ{|q`8u3F^<
z>STX=e_y}8zFt*b{q@u7@z<W5oUG~zs%xy@?@?Zv`0IE7+Q`k%T1sEZ&eUE2D^ng=
zzK~nC*yeS?Gi~Xw_ny_<t*_ivXS{Cu?$e-x)M{?uBwicN><fure;-NxYdCSz{D1O8
zkjozAC@eTU<=1g@mM?4Bp#?TGqwF%11%)hse?Rh)U0V!otY=iSl*|a4o-lh+Q_WM8
zC3l**Al(3lTu`CDDMDvm>5J&dc5u377PycY_4jcEgjWpWt?PXRsUsgSa=b8Do2>le
z_NTW+R=m)%GZ$2Lz7iDGn7utTbSvLeQ0p61moa1nvy{x3wtaf&$&Cl0t)c~=l@}bo
z67%}PO%0H34(jd>?JK`NhZa#RjIzs)K`J7DN03A>@5G}@qBr_q1*G;uf}_C<R2;J2
z+%r`~Qp5Aq$@Q>;y^Q09!Cv`m3#aeiDFh0g3~QE>8FG-OQUw!}l~4cNJD~6cv)|v~
z`1Sp0s8!T+n07Oe_7@4Wm6&Uv7D(NSI{|5mTd6Zy`J7MMxB*=LJ1lp1aBn{VDQf4+
zGRn#_L8?G2YbGnJmbzZ$7nVQEEa$dCi|)<+4SjqI(?b^2>|MKRRjJ1MEw}DI1v&0O
zB*>r#Z&O2yayF=316LR?=5V}tVespJ#w=@)k`gnPk`jp=5l|Vpz*u>~;=>Ohp=4#u
zWM$R!<XFSJ__~m#p<AI{i*tG)VTIY-Cn+(jY=k)XxwC_N`yr@vMHyvfxgg<D62?+e
zq5-KlU##JH@gm^Y@m8-%Wn$s4GUh^?-Un`iGz$qQ1-mR*FXQKI&82-e78GX;b4)?H
z#k!Af<*Pk?un3w8()k%>WzQ{G$9wtC{yVX+o_T>h@!*Z(g6#{Bc8hCkYI@ey)q#re
zU8S$1a(+LXov)>;>YA9C$i>5RWrCvfmYkcLgm1-Ey<FPW(=+90(#2)Ivv2+2I`eYT
zzKfHV^luYgdGBEfq!KSE6S#2u1bMxW(9lrOkl(ZO^Y6z^PxNx$-Kwc`!2PNFt1XG|
z6QQ{xz4fR8&*8hSC2lKMu9UK?*&(JIrQ%s+HP_3>=Zb9korS;O@82K#T*9hkg__Td
z1?}>6J5GMIt^T&cy<hIvuh;8MQ%+2{yy|q`c5M@rkf%?da&dB6u021?^Vlcfs?V>P
zXBlo=URUpG53#MpVy>!miCWKs?TtiLIoou$9^SPhzDFgq)NkQzZBUYK*nIf*UDgse
zRaMm@3z@FY&deekH8r=}+j6r%7C(LZ)XT#oz}L5T&z?PBt_1tPoaC+d({t;UD_2%5
zTXyX0*1LKCJ)awQZ+z}@^7!dRu}4F1g~qI$7u$3v7Mzd%sPrtD?NLy$0o0%P`uh6o
zi|+EFZf<SY*2Vswd~W8<nJX_`2)M3oX!!As$m_I&b#`8F1vNcSoyx3>wFf1E5*3y&
zW&)z3s`~o+qGD!q>tZ7!R@}I8L(D#XYZR#Oz$<Cwvj6wp_a>Q_R8rH@{w;}0J3nvj
zr%y%Kv+wV#wSQTYR#dpZ?e3kK;BM6dV}%9V8RO&Qd8N&KA|oT$CHpN8j*N_yvMfqj
zeg1KY)odSsdttNLzKV*9zHxJ1qaIs*S^rHgtg>`2G*ixRGkuY(La1AD(9~((%$X}O
zs}kMFO-D-4UAh$X^y$-=4-Pg@T5{)3jAu|z&KpnnXV$BabTEAF<qXS9Y`vDJe@VYh
zWyfw6P%u~|vwSf#s{E89DlVRVZjPmvhQ<X@s!uw~b^TajWo6~2sQMWP0zyNV-p<?Y
z`}_O*>HGKZpEP;$*H5SQK_k2C%--+$+^7A(`_dH6U=`2e=jX2Osr-EF`5~WYkLoU-
z(p_`ml?=Fj-_Y0ld_nb$bKlk+=B?&`eM@z|_Kow=((-#;?Bdqk0y!mW@#fU#a}6fV
ze7PP;GnANXLkqXWy)K`=3)}-?ut;VpDanWn>(=wK+cw*}KN{4Uy-+B4;o`%krgnL{
z+0hFlHiDAfg2hS;79ZZdz+e1lRpi$10bBR!uPZ6H1X)qw#_{6C2d3P6Qa81OCT+>m
zz6>sVOMF;LO1Rc#zO!tF4qu$(VU(SF==?8JoA|oqdx=xSuI_@2FIm)rlyWBidnGQe
zv|MrR&0{9zAd_YJ8D-}l>n|?`m5U91Z4G^X%4-W}{XV|)=&F_F(E858mZhXbs#(bC
zs_KnptD@TX-3fj5t_Y<4!4`oF7c(Q)ip9>kV{rCw^ZQSCV|YOlbL5z;tfqE8SM`{5
z??#2gQBhN9xf<2q(C4SR)xG81Yq4dws+Q0DpR*lW6@@7;SRA}DLg&}p9dVPEq~5Gf
zwU2uKtP5n^f>MqbFG?=`I=Z5~WP(=KtFNb~S3;VY3{kBOeSXKYw!Sd$UHsyF?XGC|
z*A-8|V``s4=IzQV*z?HwZ%9nwVsmr30%&-8v6Pf}?YkFtx60Aq^U~%2f8+R;KZIEI
z;0ecz7h9I~c!WeW^na6E^=v1&SLOq9xAuYS(dNBglT>sgfA8gK4g~kFp1C@>FE^`A
zne?Z2&z7ePV&}inD1=yZL5a!AYU>Aw_`3#+W-n=jC^yh%va;G6t^YzXf3@q)-Fz=!
z?>P+h$}Esc;_}bdgTrlF-5CvVXRcUj!C~b;X)%YDuZQJnUOBgj3tC60gCxH6@m*W`
zH(AA#+y2%|$oSU-7nYKWo|*Qqb-P`EeP7o6>;1PRNa@JnD{$eV;kma`&#Lr(y)oY(
z8tS{Znj17|(;%<7;PB!*)tT|7yx*@kZc_gB_*4v3lO9V+#T19x+l|&ss(4lzR`>6_
z75oYkS_>E%W#yhX*~OOBdWB22eX-BaxpvX2eYZErOa>+^o6F4MU&5y+%q|lR&wS`^
zeFswK30!zM(}}s}%LjcnuSwJD_IOA0P20Uw2^7_!j&XnK%>xm>5+dsDmoNO^voErg
zZ#l%o2M0M`e8~8`PHI6wy?m}^#`Vqf-xNUt+eU&>R&K5L2H)bkd-0RDBz)zojgNT=
zab<-Tla<XqQ)gk5cXRYstu5C|{;5`03QF=1#8|#Oesg=f|L^bb%WL=M-L<+~`~5Cx
zs_ssN=&eadlP)G-=LuS|zklD&_;uCO@+V!++_75+<W9M^hPpY@`FjGhudSJ=A}S*C
zVv@Js#a*SZzZ_uZf5Bh><8bDD(A27zm)E5E^XCTz2XD>0yG!i$zMs!#dwF^WMnueD
zVrDi?IU%4My={%B^kD;&^m8&tj~+dF|9*UCX6CJp&Bt5*hHxhSt9llG_{r<ktL*nh
zUu^td46R7`ogM7YmEW&jE~xD0G3npe_4Sz#4mAGy^ZESO{`x=8`g=Yc%AA}1^V8E)
zCr(_rv$J?>;bS+@G{fWL{o#Ruft%kdD=C4Ts$p#%J=;7#tg33)rOTJM7CrR}pRGQx
z!pYz6=aOl9v8(j={|N$(W!l&6>65dqI(EH>ZKbX9*W<6xO;!2(_VV4TkDqPd>2feI
zFf_PtlxRB`b5EX=i|Z1|WlNT*#FkzSm9nkc0_unD*<<td?d@>)KAFs~JA;CQO>2G>
z=tgbvnDnH?YU+#`E3Dt`aQ=S3zJB#N!;%*Pph*&b`#&3a4hPiK?5X>{`~J&&)$gaO
zfcl(jYHpw<Gs}Et7M=Qh^KDscTidEVdtww771!<)^WRi)*6(j&>bf;zFK5Ot)mU5K
z14%sRtTr|NG-#@u!^q5*@$%BrUr#3cZ^^o<6<c;Qb<&b%c781*qfJ+zuUxzKY8$Wg
zlxfqRS*2}_nmTRTvNvzu1lQd&;7LtQ)igH`Pd?r^RRuI*%gV~?drneHYSoo1SH#Xo
z1_vwezI*RU>*U>U55>(gvI+Ea-t~BXeD>#mM&;8%!^;Pl1upa(*Z-@r{rlzet`CQ}
zOAqs!XMA{YaMF_UdzHsczwR~l^7498U?C!Wx#WwfnwlFcE2}QJgX`$%`04A{t1X<u
zTM8aJfjYR8p8UG=a*naFaqz;03)h7wB_*w1e=s;wN6glyaKW*8mtEao$?W>sx_%3&
znVbr$VO~7hb2ql(W=zGyRxM4<iFfnDD=K!p{?Q|8EFvIq;n{tA3yX-^X1PJBsY^F)
z+B9Y2#D(*0t3iX4Hj6)>oo5>@DJi*b{o68Y#l506d)7qNJg(f8m$_}5Xo$w&)~{8M
z%@>PEJ$U@?5-4|+1obSCe|}=3@~V|93tK<$t^S^Mu!;55*|VV~B_*rR3&R>PVrG_c
z_!}@@lU^4uFZP3VXSeumnBPCoUw+*U0sFT)tFEk%|K&B+^y@o4ZUzPh2X$8m`*Zqr
zAG!B_x#T@*$)-(4OP4Ksb-w=JW;MS#D^$IwZTV%hZ{I#Cvz!^5Hf@SIKF4qQrcIkn
zik_SZn}7Ue$*N_`+$LRF?9PAm*fBH9<#+S0PmiywjC$_!`e^H~*YC}F!zY}Le*JCA
ztMK!+D?h!mDxc2Ez`(FTSZRTL`r~80yB;)gt9XJYn3Ix{wv@k*1G%E=(@Ayz*D;ck
zl2zYsrhk3Zt*@o7?!N!;+x*bb(4}9#lz3j+l6l#r`dd!;?=xq7)<$jh+FkZG$j`6u
z-=F99**7<({(7okuemZY_siY#`)l3hDqTQLmwkV~Mf=V+^Zoeo<1CB9M>l`+Hwb^}
z=f7y5=Nk$tuFh#+o|3*lz!TKb1C_-WPHtfPYjO4JRW3fhX)3ip9=7j#HY+=2YgFmK
zpU<=3-`l&qblu6x>Z^C`h$wz`CeYEb5j1oqsvWk&tn&2f)040MI(*(^)v8q`w{y3H
z)>M4@{yn^?Xj5F>&s5)eHaD+6tN-`B9<<0y(l~9!nl(Pj$9gVyiE2N2{=8glot&=Z
z9EoL5|G%&K`zinRl*#$mzxt=YmfyZiOYh44yGy}oQ>B9MSK&F^?=g3)UatkM1u`vo
z;IKAgqm#0G-<39AX_Lf5EtB6JxF`5w&EffJ<(^ZQ+5K+in<gK6y`cW@<7*+M{X8Y*
zk)Sc(Ol9dA{K0qnjnmJ`+^v4U*UQW6Qiq^&O!jX3KOdaumfx$)TgUhAnzB~F`oCpU
zCMhv*`Ts#McDqTmHJA3CSTRsZ=GP$jB~3&`M8n=bzL}lB%-1n~d){0pC#NZM=Dd0K
zs!poTtE@~UROagXf7|?awu*A`f4I0QI>^*O@ycCrR$J?4W+2b>%k$0s{qd8oY|Rcg
z4=?(5f%E*lO|PPS|NpkC-Ml}>(JWtgo>j=>b1xsg+XS*TOi>!_@W&-qU)$~fI4)ec
z@YTJ&yLXkpU$<d{0cb*|$ZGC8J$^MIu?MT`|NN?%d*9e;s_EBVKU=?6{p`Mc+d}}9
z_xcnU$Ukpnm%FgYwfoDD$NjHguit;|dVKxb<8swLpvn1We!Ca%J|!4An&s~;DPFZs
za+U(9qTTm*u3TmQY8gMZWgufLY<&vt{c39VfNFu&>vpX=`LX{0@B6#nZo6Ib-~gj}
zXjz#-@6ud}wuMKt%U=1*=S^3My=3q9-#oR0sitz0A_D`%na-R9yPgH|%eQRVa<lir
zq{)**qoQ85bzKmQ&=EWPCsN+F@^Iy+uiLV&UUTxT`^7DKtAE#S6OiSyER26GKuZc2
zxpw<m?8~^QG`ITQ&b$>IvL{2EAAda`zg$b{Lil|-jq68_&d+@9xO?|$a4KgsIQrK@
zzW&cfP){=N?yjBl8t2WOySDcCx0ipv-@nc(9`oQCSH0A-ja&Nnwy&D<?@-lSt#v^S
z>!-`@u9~1WZ~6mpOfZN;Yl_0h$1;C^dkadAp<!XmHf=JBEk0{{Yg20XG~MWD?>=2{
zEa~UD61`t`?Zv&yp|P(o+&yjn^4|4*fu*^7cYA|c`Uycj3$|Y}zhAR>p>zAGhXp%!
zPnq8`f6`X{_RALz^_RUkxkk1~D(JjkV^qoEfUo_}_uDcsFx=?|hZ}S5iPKpZ?`A%K
zF*Sbv`WXV?M%1I=`I(X5<UVc&&t%)XIk4Y4_|sPT>%rLMubZD_T)k%f^}+t_FShIF
zhg6o%g*rlrrDVZe7nWbIcD~fC`tbbk)5ob7yL4H1Jv!;XB`aHT@k>ycKY9C(>CB^9
z+3PG{{!UFzeH+YSW!imoQS`Ad7d|X0xcl+^dQh*V?(d~=scJv*vp*~AzxMGJm3M;l
z-f8iDB(-$;^6jhcf)*(po4Q5UtjVB%)vPMrxXr=!zf-nn$@*1B>w3x0U1F{OZgGOW
zeED>C28IT|zMKR*mJ)tvb(8tFs-aa+?4!3@E%%rtBARk}&FNpSZY_*W-5PZ#1~gw_
zrM;=~r@#eUp1BwHTzew+@?rV*tZ>h%DwzdIGoHoXjDKFrU0w`Yx%#5#1lu!bhwla@
zPQ|O&mcME}U;g@k*!zIcwa$}D&Q@~HDy`g<<n#w>sxZvd4apjt<gW^A7k~ZL_IJ_}
zhIswEb%FVJjh40lso4Wn%#2X{GcA6mTvd6u*|VvhlXkWJe%+}3>*c1#jhiAO=JPQy
zG#I;s%79(%3x&&U+7e&P&F9nl{5Mx?gZxw#P{!@|ky&x?;=#Oipiv-M(VGWq*1XeW
zIB+sjN9=EsN0Z&I@;UGJ?C8(A7goD*bx63)&y~~a{_K_Ut2+jo`z~Q)DOu3W@!@OO
zG1cc9ugf2o1l|WVZC`!l-}dsB>iq2Vz@@uSgHqrJVZjTHe9N*c8vfN?F0eIx8N+&e
z&(_TNnd>S(3EQ5{0QE60ECi`Jk+ku}uPH9IGYrEXpUaf+J^v?R_PXd_JNN0QnSjFo
zMF+<V2X-;>SL?+uwZHDnmUg>r{%q>YuK&MX&i^YH2d5$jc4r56D~s-<i(a>@XFat|
zHvKk7Z`I23R~Nq*n`CNcUuI-rXy|Kc;42aOnE0>Cuy)6@cx#V?X8E;qkKY3IrxqwH
zE?}P9aWv`TlZSWxB4*#)aJjk_;*$bUG8Qgc>pQ<>UDMybw_pCPoVMm~+*iKk5cvx{
zOjZ{>ca~M{PhazT=gZIGIhW6i*R48VK5fTt8&E1e4l+6>X2pI`Nc?@d>mb+1&)2p!
zaozo~UBC2ocYLOJe+nqbd=4i`u<drQ6hNNlICA&tg1NHmR$Xnndi(3A>6Jwmyp_?q
zv&@V<%VY1jFJ!cPr^nC0!0@&MmY`cy-s-Hnu=xMUi?i+|c?Lx^^uOD;qkq}F`>}R$
zK5G~m7(TG1Fn?BDP%UAlSodcC?e4E9>bGT8Cms&@J}<CwS9wi*teruaSohJhR~Z=?
z7U)joJ=5OsF7ZW+Mcm%QE1$-{y&P|MXPtBC)=5i>ceiC-OFb!d*ZLGh@!AP6#TlNd
zzI*5WeXsZT?c~^1t4cj4>AetM8aZ7xJHcti?A6Q+3<qw4Dl4(s+pvx-zS(^KRTH;f
zKt;ulLx&Gfoi?q^>Gz>SPIpT#`*!vBhdVel=<R-E^!CQa#cTDM<&;i5TU)JX=`R2O
zyz8#akV!Rrbbh6{ch>Fu5PY^Kb@8_;Av<@wfZ9yK$_utne0qAiwz9G_X!PY+ue52!
zhlE~v`#4uu*R$K=cbBcr+wqVMv_R|T&6{36K3A4bk6Xpe&gU_S=kSFiox-41g@1qF
z{|~O(-re1;t*h%B8p@(R$2QQ>?CkEW2D@K(TX+51!xE`;se1P3so$pee{C<{zG}~|
zb9YrimBn14;5)ZHva(jS@yoAs?H2Q#G{<kbkg#y+aoO@0|9-!>fBIaOul>+r@Inbt
zTkH1r{P6qxYQNqszYkveSbXf*u~!?9%Uxc({oburyJj3~x;%Aq{`Ehix3^4_z0b(N
zU?sY#@#ly2ugwCYqgV4vn`!9ko-I9-wN*=3*Z1bln`^DyOG~%Ddi6>~P*BkmG=HV?
z^XE@f%jIX&E-#;7ck59lY%xT`<%{}yF8WLQFL;~x2i%{LFg5gg=Dpp~rgaDFr%nyl
zs5=Jg%+8Tz`C=9j6r|LCc;Sp0Gt`bJCnbdh1YB5Ed}fxZ_s5SP*G-r|XU>|uySp;K
zy}4;s^J7C+cD8B#znZuA_s9SI`BQhEI-)o6XlA^G&8-{7yI$Cr_g!2Z&L5JpV11cr
z_{(YH`!B7Z7rlH=P2C4jX89AsQX*XS=i~9OuU4;rwRZcxqTU(5fBblH*8KjJZ*Omd
z7R5C*Fo5E5dVJl^HP@#7H$R8GaN@Pm1<uOqNK@0TyRY3VUkIA2soA^cS4n!On$6G7
zf8XEchu-=z!wi&vm-jc^V_URn(UYf7mmWHFNbNW`H@B#?boHh4|6cmnUwYhczmDJj
zPr&ne)p?V@xA96Z^PO$Ba?Ki_pFe+sRvz%lS}mDpTfMEq4%VA^u$=4L=Zk-*t82e5
ze{7N%FlSENmrvrAyI%Nbe|_}4I^=!9E=Y1ctcY`%w0nN_qfYfN9}e?pAMFzL^7PEy
z7@Lu?Vpqw_MY7gqFIKPLcS*nQWB2AL`wLN>f{al%r7Pm*7Vevr+#FiB#IN}N%|)F4
zNQKk_=S1=EW}9k$7WvM%iv{f#fG*T{5&!=ecuB;J84{q>$jQlh>dcubDre{0#}_|4
zb5VQ!o<$cIyYD_HEF!XG!2$)(OWEu9F4K$MwIX_Z-mh=B^GmPCmS^7BkXZG&*E}j#
z+}d=}+AG#`em(zH`|ET2POa=3k=v=vNna1N)Oq!A6cm>-Ffdri-8@jU=WeV+I%rV_
zuCZ3%Ykm5Wn_RYT-8#!8GidVXGjpxOKR-J=S>@^T=b-%E+S=OH-F<mW=H)LBn)$cg
zHf2<Q{5JLK`g8BsX`k=C93P&0{dD}Z%%jtoYNtdmiI2bFygl|tbfek76QG_!)FF;t
z?GKlyWa#f?6|H)4SNGSS-_Dbk=p8isd;Z71U1b^Jk!!`w!$BDURyPa0J!!yGyJKrW
z|K9nF_qMN6^=uY$GJSsPpVT~&htFPtmJmqaJW#{&Laxn^qkP56@)xb=zhyjhw+@XA
z*7yANb7$eM|DRGydDCmH7#JF?;bm#T@<&}#e^W!(#l`OTZ&SRvXYH;^AO64J4#jbO
z{9IUG$-uxM*9tHGC37qz*0tZiy-L4qs@EhXrrOf-D0$FOV9lP2U&;&&4%G_MGx!<r
z9=^btDAtykQR+9*$o|B|b&s?>gU+vTIJ?Uxa{9OaiAN8)fyP>ML2YSpL#gHF70WqQ
zrGM>oBeur2ugX&ORGy^t<u~VF6BWZVdqIm;N|N9e%b8V=GUm?vS22Cw-<0WDujWo#
z!rA<2O5sb(S65X}fa{79JA`7r{eL?CZ7JG)?@!f!9_LS+qCmrXcPk6!kGB5W4XQ&M
z{F-6@b8+9e_P{#(wP&urnxo`7Y5n$4^Ioq@`NzKoRV-K!u2^3DfH%WROyb{aUKQ3Z
z+M4HoxbOWzv;4CXw}Z|5UoLc*y&a->2STw+TVlrBZ%MWrmBKB5mU;5_r_DCpwtQLd
zn{^EFO;!vH2VN?{Lhj;3%eig$?$!j<$8L?S<_q<nq{LkNb#~$2FQ-56K7IfC>9e9t
z3=AbQa6ei#SG5P{=Uc5%-(04#ev9G?ch;L<W*6Fi1*t#ZzJQN`p+F0vda_k}aDJ}M
z0(JXa3;(wFdraEJa{J5wW8bdyuDm<r7f7)XLh)jd;+z}nTjk$gY24JdKu+$#;x89%
zO=?pnIWgDViBV)=X!r?FO)t)5y`EcD{J2i{=a1^QxiO37Uc6o!-}h!+^sz4iB^&0?
z)@ET~FyMzL>Uj(9J-qV!zUYgUrq)~6E^rDuTzqA{|I1bX&9_Y#Qf7h}{~O*)_@L63
z_+oLcYt){!<=`gj8Zl7o^w(Eb+tRr01>eOu7#JS7!5wciF;U`IQTa--^*2}gmrdEx
zx4eY^`daZXS5*HdKS|xXW1|xTL&G<CllIW%T<s07^XI?0w%=SfVD93F{rlfdWZaeN
z!|y9~ZR*_nj0_FO;mNy^&Gp-2(7<urKPCUC?ypol7yh%GWn&Y#-)g`0>nn^54$I*N
zGx>&IzxMho@9(d7mhX<W-XE<S6t!RCu8EbWa<$v3q{Ls=3=A)7;Av$+CExOeTX(uv
zJ^XCzdu`?SUe0U2izk~DdNlp1-FqiO5!AeKg|)+(mT%j<!b!hwUwD4bo%OGx+Ep}<
zoR7Y~R3$4b%lzvbp(oF{7#SGqK;wkqZiYa{-cQP<Rc}|<f*k*Cs$8M${ssP1>MHBX
zW9P`|=IgJP2E~y@F+3Cte#!Vb|GyizCN=)e)me9xWPb-6>s|3T?|ZTAq_U8x?3?HJ
zlo%Np{Dk4f&mWWDUaP<M>wo^Te*L$VTULcldXl^K#U)0=U5PH{z3)U-wa<sJGcd?9
zz;(=#HT}w0`ysb6?$o`-y_Ko!9^X}BGD&q=v`%{33cZ77-$gkX7z(%$Zu<80$?dPN
zcG>Lx`IRwLX6^JZhgbaSRo;~yvfjh(DkDR~OL&rWxNRBq`a<<={#R2=D}ViRU;K(Q
z@!u}JrK{C=U$0vCknx?i00YAVcSPvdK6!Hc>-o~ks@Lv|Ux{^_><XW<&im!6kIYF=
zpC2v;8U7n#_;0JI=NC@tvR1ua{TEcfzpmIYe|w#wN?iE->!HrOTTdn>a@jC2WW>Xr
z{=g0tILD6t1!=Jg_;$GX>TXw-j7JkH{yy5n^7WO_lVe<r3=Qg_ffH~+%J{w5iZ}JW
z)spMxZ?2!xWnHzdbjLmG?b$2T_&hI6ZGL={RgQsSizYlz8BBk5t7`47s1@7y+b#J2
zPGjQXn;+aaT~(TU-#BPGf7j(7jD}r&--?TuGB7OIjIiai*2*;(>&>3OUb~(@<MKYE
z>xJE}Qul0~rt^1QO=V6N6@T@9pAslpJHnHm?BRWvvT8rX?z{JY+XBys=}SKEYJEBB
zpW!YGOSk7oST-J%VPLROhWq@&M;RUKMWXjbUo^k}^!4h!!@oYxzr~sO?^k5embz>D
zQOebBpI(?TFkHw=Vg9^v*Y1XMIUgS#MW1Z2`~7A!XjD=}PHx?Y4+Tq?E}eT}-=<AL
zpuKa~*Tp`4|9<_yzrV9zURs)4E6H$2$L>r#s7$_IXY3V!ZeIkyzs!pHs_v(s<z8*v
zq#X6_Xxvi!{c+C!J}k_uE!;aT_5H<lkGwv94hMDVS_QvAS}n89e7&dZg=S@~64QxT
za6PWtcjwNXU44Ca3;$kO8EjhhB?Ghzqpq%QmVN!cdsVM>b8l{P<>cg?v_#T4?Zx7L
zyG!@~eKS{8RegDw-~I|{d8qmQn&9x*($Lkf9xYFCcwV^V+QReu^#iYMQ>vX3yoBlZ
zMYZ?G+ka)2yw~V`DZDgr@1h%`atsV6;IOZf#<SXa`JX+99|lxZfL0`c*1vjAV&jtm
zZT@+EkX^oHtF&3p488q-iflifP+q&L+mEBfuPsqSeZNz7QTLaZeL}f6sw1}YrB60H
zx$z)qW_G2ml8KF`(?my>G6n`4Nm$b<1k(7=`1a-|(%QGYtCp))uMQ0lU*6HdaeZUp
zVz-xb%kN!0Ykq&tj~^9lV|Rz$-kyIw{{OG*o9lQPti-yHF8cRT@s+&%qTBkf)+%r1
zTYEcr)%mh%FD}cf-kOzveOnV(uJ(!xpXa}wsPeUs?^1`H;JO2H^2gqNU|?W)=i-ph
zjAcE;RZGys%#R-xOP4O)*{2(`BfvQAOaLflynU;?`s%9m`L$um$NO?W+L)S}f`;;I
zbGP62|D458;_`1_oK(4=c(3*HZk3k-sl9gp|4q2|R`2SryGudS=jRrAfrjU`<DYqE
z-6(jsCofUzmTrvm?)KI7F&#B~Z?0ltU|6tM;DYaqkH_V&uU@}z)z<9mr_P^0Ut06<
zP%CJg^Otkh?^jfRfA{Ns{r}as^Y{O4QTU%cbLPyd7Yo~OtuA3u6O~N4Jhl08>C*=l
zo|hi3iduU=c-7ZxDW>1$>~F0C%3Rn_^}X18_U9y(`25vusx4U=+YRPk*Nu5z#vAH1
z!@j?inSmigd5vQ}^VY3fxi~m3yk5V5U8k`6u0H?ycCo3cslUGO|9|&U`u{J>?O#rG
zmji8YoH}XJBdfY6Cj?vD+CW{MOD&wjF~{@wehmY4p|r!+WPGbR+q*zrO>NUT76;*}
zreAOES??qLnJ+szZsxvo)@u%5Emhyr&GY5z&RFNtyz2(MemOr(ySBbzU?}le<7h7L
zcHwrz`t|EUeU~+Bbiy6VA<MeY%ruVvb4S?UW+7-@x_0=w9HDcnE7z^_D=jVE6gAt=
z{OhMrlP=#ZQC=ax`DV_hn?H^lH%#W<RGA&}UZeMwtm?|d%R6n`S8XkPpO|~bdQJYf
z>6y`Wt$d+ZvwZV5UV9?8+fG{k)$&Hpg}v(qczrlROE$!N-v=!{)KyrpePT}!52!Dq
zsi`StnA8Hw)vH!%EnU9cd(txB*~?m5SW?r{ye2(6KYzVt@v|3G!{Y*jf+lfsajAGZ
zIy!Q3aRqsJbR6rIUjF;tZvTJ({(;7{?f(5p2F;Fuwsbw(_L}8_Wu%VS*AA|b{uK_f
z)@xR)yv&&EZ27Y+_g?%ukZV8xE2@o;S=s#5y>!E$6?S)`Ui_*l*|1h|!}0bN^))Nl
zj<m1Hwz&KlRJFEgzRS(HxX2Z})v&3F3A7*vG?^I`9IWao9#^q&(xgdW4sq*yOqw)l
z(w6e~_k_<GmcNTRJKNlU5@<Ez&CThr5A)lHWM-}ejl6>jrFqrwGQZka%{<2RAZL|D
z)bh3LZ-0Gr*ttz+%j@0DybJH%Jab>?VBNh}^4B+9uKB-1fVYD`^!*D3vqj7d2Ylg`
zB}4AoUEAU;f0osjFHqc&e|CFzbz<u)8TEgqj~}N7uGe#XB`?0HZe6&+`3!c31+wsZ
zli|+7ZTD8b@7CV>bU|!1|E;a2`(vW{Z>8Et-Ou^`A|ZP6fqVLTF1CxAp1wVrVa>ph
z&JAxW9MIu8e4%lZ@~M;S1N`>g{d%i-#jcy*_T3AcD=fU^$&F(V<ZcVxIm}#pduI^?
zgB3fxT5Px(dhLq#_5<&@-reTX+M;`Z-Kr?}&2N*Pq}*;xH(4)!%fPTj3~tK;<svKI
zxArwI(bDcWw~LyveBY}snfqQ^_zl+^UgdX+T)ZDLpPyo6Fwlov;E-PQC*$`<!SAoc
z+-?gW`g8UC*2y&+ejRUJmAzyCnlQQlYz!Z4;FV#+xpOZ~ri4em<$4n>efW3dCgro+
z_%~Iqj*t1!FUKgp%FfX7)8~&_AUlv64-EdHak0ziG#$H8cVXQerrq1bLsngTXtQwb
z_N$BTU3B!Fcl(~bol7EPjt4u#3mZf<{cxOncxCL-&~?Z6<(}Q1ecEyNsa3CM<<1v)
zw*KEoMX3uCx7bx$wCt|<i*W}(VPufyhX(|Mdb&g#*Bke(UlW`o?iX!{vHU4|N;|jC
zdDjMp`E48I<S$9wbH3>+$8bRr5xWIxr~0q#Ty|WZ`<->SR_J@1kLv?xZwD>%d?hdc
zN~+Mmk2kDg{dQ?Bc7_F&@E~EBV{7`A@2$p~E7O{vu6r==@*(5fYsFT&hb;JYynWZQ
z6W{y!14Aa9<(<mR;9zE;%mf;SW^%X<iYVQ+*QPZ;y;XGKUDWNkn83?9zh?+UPnLc1
z`*{0`Y8Qt7I%b9~mhhNjxU+c0qm0SDFSf~T$v(eO%k=y9f(7fR|2oFDd*01^`hA+8
z7#cSB!#ag544+r5S_!U<&i+qaQX9Q5*7U2o-LJDr6DKJ#U)9%(_?sl{TphDP=+GZf
zKoqzk!rfqdReSJm?^Rn&@2{Klr|VdJ^gJc`H_z`%+-ROEb6w!h`A*KHEwT&+UWh;_
z@i{7LdMcCW^=&S#DU<W_3&npXFMeB_+%#22e9e<%4D%(<-6~@^z>l!xg-zD$xwj%i
zR_3yPJN6-NVJ@qv*2<i^Ew%@)d)Fp4O_dQ|vu~q)%l8h4`(^877!uf!EUN=qrn}<8
zwx+g=-*Q>Ya`o4Q<Qiw+R$b}6!T03GgI3QY3i^4&=D$ez`MYXs@l%F|Z!YlmUqkw;
za*M6c14<`9m=_(eaL=VzX}6<wg1&b!%oV;YaN~R@Z`l1A397EFr;eY#b?z!N!!0I6
zy85wk)vT&q{WTZ99pVblxE~j>aMGz;X}5n@L|$3#Jt2F`-#If9<_ZfR`Bld7q5<Jq
zhwLdK*D|Udoxxh}9OBB=Ul9<xcJa+`|Lo#k$=2%ZU|;F)bGV~AW`%rzBQwKfc(<C7
zA^nsAkF19EC5uOKFS$NgcSpzFm&^GaHh+e~*W8%v_sq>AYLl9-sGe{uU}rD~&A39^
z-wf%q4`*h)U#JycU~hJ7-|i)=ZXQ428C22G|I5U$A5<nt74oOMS2ARnBSLn;-YeIv
zLHh}}9<$xPz{$ExYwPoXsZqbKyN66zo7h$JWuxgk-pfy&|J2;tUcvBz0ipB2-M(v<
zTQ;xw)V}glFJs;RhswHjYI;}BZ&kdzk@r=VJG<tA>$_#O*&EKm$7NU;W`LGGm5G?H
z$pwYv+ur^98{~7h?LDfb+Qz=aY*{wPhl8(fss@<l&wI$nu-pkAg$!1+J}H;p`nY)R
zlkMVRS?+<S`<p&*i@Kt$b>ZuqZ^_E<?kX`oIlA}(!vQ4AO2kyBn(`{YW#wJ`_V~Pq
zafMs<-TYQ(7xuL?kK@hBjl2u@=I3l!)O<5gt|1*M_~*_{l+fBBU)uUL`F!F1z=)Nz
z#N=4T#U3o)x9)=6Bd&LjX4keezqo^l>jw2A3*M(w!?d>_Sa+w%>#5i+&=^6r*QzUr
zCO^1hdMEzm#)DpYJa7L#Y67kG&ezZWEymA~+k(is57>{FO*yZd5%GB6+67KKS<6Ik
zWzWie|2j8x{)+`|pt?Zul~7Lh^HYoumLo;D#n!{2TTkWIl>OeuRGnO~KA;qozyclj
zdG+yTN)+~U2kzg|cKpD`*)|Nu$l8qkmV-uAG?pLu=Ui}p{e^eVp;_<ZgX6-wt4cXT
z_U{l-J#N<W?O*TWw>e(y2Y3)ZWjMD^d28P>-j{E{)mq)*j_RKicctBSXZf`%KIX8<
z&$>czJ2@`4>B;M}x5^mab|XBw;H_la!atuD`#M$o?Tg(VePhbG<EIzhyLfP2;J1H;
zybo<{E^PnX&Hlg;$-X<!J=Qu~cWG_;x?r#27W0<xU9+CezbhyH>N(fzzmKJxs<(GA
zM6UIj$o}CneDsBZVMceN$(r2zv+~P(w>*CmuCad0uAAQ)_r-bT$)wtg-THlu?ezI}
z-j@t><Pc?TL*K$j8FROZ{xP_<&vbuc@vd1_&pC6~imeQvt`MGJ|1CR3pue8^f)TQg
z)%PA=c{fQ5R8_rp-2M8xbLiHy+xmGwW((b7KhmyYI7jmngA};Bxpz!VKW>fvzmNT(
z<+2YBHiOO}=#?;BbaNZ%*p;}dm#Vej@0L$ml6bi7<;LT3*_ZY$J-*52;}PLq@Av)o
zi{zgxz|e5+r~!{?=oJ&rH__7OOS1hYoQ?kbsL52p?uzK0-$y>2DLWtx4upGc)AZxl
zWnNyE`SQ|I&_eYlR_-sive$zaP+h!uQGLsNtI}0&{c>vq7rTM>(0+b?{(5x&-le+H
z+cXpv9YIa83r~#YYd$oB4jFoTXJ;_zoQ+i95+?=*8|PHty+zz_ZWUcvr_284lx9Z7
zpXglU;3s?6ihb?l%aTduFMX>bFZJMY+bPB^pvioJ3%3J^pXph9(@a?1FT~Zgb<?Iz
zQK7<f1sE94hdwXJ3NM_x?b-C~(~WHtzv;5S)wSEaH+x6@WYf3ILQbOcudYApv15=0
zHNU`P=Agq+c%{wobo@Hf?(6I5T&e%}S$^-2N8P&f7B5@oW@>5*T4q(Py`F)Ap~TEC
zF6?VBZ`l1C39+?thYb%Klx+BuarnxQ=CWMNh;?g!mc6yFIr8;aLH5GeG7JCN9dG?6
z@b#6DioE{n+3UC%toWI#q%}1)Cmwzn;N#;n`LUs);jL}C(W}oHW?$1edi=PyuI}2K
z-{WgOy0Wsfzn+!7F7u-dw3ihXZ^6LuK|`$Dl{4|57su!8s(<T$lzUf}#y;k#-dXfX
z8&p%vwu)-m2Q7aS+dgIQ>j|sZuAd(I{0Q65WyknEI6&Jmn*PpE;%8Xi+>jU4Z~t#c
zT<zCTQ0KO;uI}u%pTF<_U$-)N`L-YXRFD>!?G_RiK3}z$fnh;0C+~-gC*meb-PFER
zx@OtEg|e+v?Q`6&|KQlsHLI$&{J^^Rd?s^OUbi(mAbRWfaq~S%&LG=#6&Gxuh`IT8
z-j~hwzb?-Qt%=G0`s%9H_dCV2b3dO{pRZwV9)5bdzWvLcIcBqS%;q(6fkpvmZ=cSl
z){>RrwBq%QM2TDBRW8w=PtSg%QorHPif?kOuHAFK`s=}Nd9$6o{2wgeRlZ&L>Za-h
z%})#pOjWZ#!Z&Lz?G#q`%DSPVr>*U6oPI9nW3j)@#}>KTFM(@gb_Qu`YU*yQez)_v
zNzM%eUOAf|In((-gF0f}u6LD~ujm>%Oy}(>mbH%wT|MWR?~A_HZ*z}MwTg*de!fiZ
zQo41$iR-=FtBRBAW+>g)*K_&%$XVo&NF{?@Tf@CJBO@cwsW3~IEXg?1AsDqGp>fr!
zRa558)AO8VmOD#GNNCE;nVO!UL6~P}W-bnot8@jO7UaH)m6cUfL*qng-0r*UK%+PH
z|31&Z{eKYy14ERe&4uvARqmM~?=`M(TwKZ<{6zZN*4we!x3!~ZDBU-`6aTkP<ks)w
ztr~`h=4*apu+n9!k|wkX_<7W(lupn*WagzMYR_j{m#=$vcJ}jMzpjSIPfe8A^{`F)
z)>F_Dg>!87^7_25q*gh`*3VFSUm|s@en(+fO<n6Yvplcz1#jA8pM?L-5dWDld;6rv
zP10^RrJbwq-$?lRvtsAtNemCxJhFTt*Oo5J#L&>UV9l;DP|N&e{tTu0lk>0l9rC(r
zHAj8R)~F?NfsN}f+*@yWV6&oZK@p;z!SG_mD;Yn~isD&v^Dpn%_BS9jck|oBOf^$v
zejm=L-hV^jE4X!<foSkCG@LtPz#|%dB_n%Z>}@VBsl&Tt?~8|@d}#A={?4%OE1SJ9
z^fy&+n3U`+B7Q2v+6Ji`!C-UnqE&mD*k6NN`_?W{+#$biUG}!xj``asJ$AaHZx(T~
z_w}vc$5^y49Gm9*Vh>VH^<X8>;ebF_-^myD>F#&3?$(|yn>&5UyNQgiq=nZ!k!StB
z<b*?Iq-3u9am08#1A|Fu@cRqjT|;>r_ZH6jv#p6sH2jLm?R(F1L+8&>P(9wZBHw4j
zuVZE`KlBcoEjzi`88i-BT*kn_@Ze(3y*SX2)W46u%%>Kfl6oo@k`e8AIHNkxv2~kx
z*ej2@i*G*P*3SB42h;D3nxATunnYi{zdUa`^8)5%mb-^P1RH_Y$viKSI;Fj~to7U5
zR$j(Bf6u8=GgQ~_2(X`fS>n$5&P<MXdlxr9d4D?ZQvsp_$-wa7jrBIo*gVJ9B5CK}
zeTTT7PKPw&O1n7%=Dz?nhnMWzc<`05?3#J8OqDz`w|EilX@(1Rr~0pCx+gkk+`lJr
zZ@YNdsx7VQ+p8mWU&#qBurqXgB`o{q*VG4n+Z+(BKZYIgo~gat%-g=bb)CwqxNm{e
zx5Z!2ZrdKU<lV%^SF*B8>@GU??q^#aUb29x=4KdIdK;qI!C<gFxxevtV8qI|t)jOA
zLsrgp<yC(7)O}Wc-EI}tmY=KreI`_uXL7td9+$|t?`?#wl@g+kXIPMI$m4u;D(}JC
z^uVQMBB49#>Q}U`{4_~K^vUXZ$DOJluiv|<Sw#Giy7u|P_7iMrE)JljPYetV=N4~S
zbxrhExtM9oxA)yzr5jq$zEnAR|G2m9ZkAQy(-lA?=)Y?FS2&bS`)+`g+8v&UhX${I
z->qHG@%HG{U;B1=Rr80gJa_Sv{eMtreY5w1DmT_u;UyEQ+)P=1M7Z6xRz-5|f#R!{
zb8dx4t$p9UTFR+9x#E6k(9%t>rd{9p`)9;dk)MY>s{2<QIHn-K<O0L|vOP?5e37hX
zm=zoqx;pmg)LW%Gd&I*t!#$UN;tO4U?`Ba72mgmmp0|##1VtCf1v+|laRu~il+7@=
zMzWe=`Qm#Iuk1VQzGe3k#Xaw}Ru-}TT+}sda-Esgjb%$#IDGBk$@yWb<=`3E_)6|z
zRk<;Ij*5|ip<r9c)hnPr?pwW;m*!1s`>=2K$I?xjpC^fkzgqroBBNIqPssNZZ6EI0
z8U@@{W|pYTS4VdEE$7J6wPn*oGT$$J)y7c2Lh;CZy_J*xX8iTEmzK>5{(hqEz&sJj
z1$n*u%ldgjR@i6XJ1>Up^mi!|ZKqbf-ts%(@VZaCy;p7h9x(Nr%&KeiCTUHTxBPjc
zdd2>J?Jec~90A);FqHOZaJ;{G(5!5>Juk8&&a7FpE3BG7H2eI*wu|4}!kruRSK9oX
zRX%%r=v^fyskq*Qd!thtcDb^E23r2qM1233oWlG$u@E$U&TyH7`$K3%!+JLLwz(hT
zLVfps@{)F{j>~k;IBy-YcJI<qtBA4?)31JSHCA2eTXag;E_<WLM#VZW8?gt{@yGY|
zaebP4^w10IJKcgM{D=KdGB7Y)ki2R+$10+1iQyjeeSD$W`I%ck@rABjb*=Ey`QN`Q
zOxeon!!!JO@+BJIOTD|VX}(TbdWQe=-KRmTJt}16^|K%AeP~qQlP=N57k)kS{X(s$
zQ^T_3lN;>Co^ii%FI{qO;hjTVPsPGas{gG}m@TCIC-$JN;i3H>uQMEN{dMy8>08&P
zbMQk>9=)(KQb){{<riq-=<`x;`-gvLRkia?lP&Gt^Ire&MU9%wty+E8RxjD)raIO1
zm(}_=E$6Evb-TE~S}X35|6Lm*F#Gz#Qpj;IHnYy&_O@NbbXED)k&ES?TI<(c-go%w
zx{Kf2?#o~NcPR@r7Pggd`8RpHMSt7-HLAmlpKsSv+@rXrocDY9{1*y$O?J6U$BSAi
zfw!13%yG$R=yzgvGBr^A((-+dpR=o|>8<h|hI^*V75)YFX;10fFPc@g9JGffyl|?D
zrlr}8hQ2h=;hhW&4|pQiiXCnHrD=A>|L|lbzl#0m9){H2418;U$Kcbx{~rWffBf9B
zFsOtDa^6mcvR&M@w)BneY&&*~lrZF&f_82&Q0=(Z+ZP@k?VdViii*9x{nBO2vhMCG
z)!kP6?PhxEK~`}M8=IJi4<CAYduMN}_4Dgnx9gQwYg?O^fkD9GcK-0VxVY#z(Aq|Z
z1+oNoq^^(M9cF%~p!wRG$i;8o<fy8ty}IPBf3=aBJtlYG|9{n><L6GEJQ=mSY%S=#
z9CrDdfV#T6x22%<;S3FCNrpU!3(5uO1`^*T@SS9mdP?Nyj~^K~H>IY>Ouw5Ko{_QQ
z)alcrLPAByo`V*uT-XTe_y~R>X0_K!#RvbN+yB34`FzgekB^UQTUkY|T)DF9_uK9M
zRr6-elDhf!o0>i-6Eyr3xNzG6^V}PF`+c`lU0~ouEp6?uprziRbI{u5>sG8@zc1=~
zOmVN>zaNjq&QC_#R(eJsd?3|@v^{rY8T>$P;n!Eg<FDS@njQQ4^jyE?Dlf(N|7d-;
z^SPYor3s48Uw%HH51Jp8GS7?I{Ct*e^)@3TqnP;V)2FYF-k#U91hfK7?p=gh!qv@{
zAeV-OOxd$%Pe??>jVBd}i3^jGll_0)wEz3kAGAVjYt`2*&^m74b1&b%1)aBMo_T+L
zyglgnPThIu(>9+>+q`q1-8D{0Pf&5B;3+C9%EilDS}rNTxYrkP+RddSox-Z7Jq_up
zshQ8t%+#%W*Qq}5!s33rRa3RYwNzD~wiNTp+r{kMxzo2V|Ng$%ySvNtOLISd{CI17
z{`{uq=G2W5QT2i8C-Syy8yW`w{`NNcG3OSk@4V+kzVn@9`OXW*Q)a%CJ!}vX7`X7q
zkBaTr8s!Bp-1cyDYg^_&A9PqQ=r{~+F`a<W(5aU$Usm<Jyv#S)$*IZ6*jUvQ)RmoM
zS^VXc_IeFX&68WhY&A4I9334eEdkAdOjh^5c4K2Q=y2Q2qg|p^-|v=hEqi+_?0V|f
zsH<DEuWzp2xK=<&=+QU(h6^7H?mzG;eE+})M1%0mr}q+{+==0Mk(+}5422Eu_a~`(
zgQgxmC##uSo(CPsmw(U3^AhMB+NIvpwTz8}%kNb#|Ml(dYV&&)&dJAmGPl15EsJE>
z!aJ|I&+O=C(L%Ky`31{%lox<#5C*Z0Y|?mhPo-_ne0OK3uh}YQzt-Pu>0Q<A=}o`c
zz__cL9efHdDD|oxfE+)7|Mcg@z8D9oJbyPSL22g9nV{|BP0h`sva)MCh1Ipp&9{5b
zUhLlg>f7!7@RXD#O-)TDZ?|4wwPC{rU(v%oa`k@-BXz_;Tlm%`-!acP*T~EcI(9S&
zbO!JD@7EVNHh;Mq9>4RRIgc}FmrG2{oIij6f_8YxT9@tEm^7pD@WTtu{B|qkY^zS(
zzaPK1>g%P${Pt_)-)}y%xB36S@B4RsI;AZtFR$NybkX+vb-PdgUc1xX%iG(u^i@b}
zON)k%j?eZxMcrE3+P6PyC9tuSY?=8K*BaM%dc0~MKOUEV{q1)Cb@BL`g`um%KvR0t
z<EmD!TD2-B7nBG0{(iT+>h0F+UqA{U9Bc+{+PM>9dM4%Xudh|VUM??vxAVD~<+<AC
z$Fg=c5sTgXr}g*u+x>XJoO^dyD5&QqDkt|Y>+=y|{}(6K=U=(B)cfnx>G7Zv_wn)m
z>lYWh-;J=mll$+_Pf#xmbd1=XIWnLHg`hsFm|o0_tiyYoSFO_Gm9vTH7T3@F_V?Ny
z<>2LhSEuPlgSO7?{eG_+JU=UGoVMk<>H!^|1-(5IhKF|i{dPMx-dcuF+0O3Xi8E(P
zl0ywPFIuGJJPG7u(3!cZsi|%K{rw+rWv}0P>&cTR5|et~yvZq_$~k$(iWLUGzPwCc
z6Seh}gn8ba!t1f+l5GcTo1dMXWqMk|IL)W~z4cr_WizvDKOW2fe_<6C7M6A+$L#U^
z|9{e}r*KT3WsulZ`1!24xn<c|)9Vt!%l%AwnycB>C#CH9dM$e0`sm{RoV&ZOPDoi3
zzyIFVXU+U}20CKhg%1uimaUe3cbvoOmP>B#TF_CITeg^7H(Y&n)s`(Lsp;vvHyX`|
ztgPIrqN$|hG-*rXVYaJRubS@cpEP;$)wR*)@04GFVzPx(c-9?xq3^u2zMr#xuc4~y
zdOfy$?xMwuOYc=ae|a-~e(B!*)!*|(MMO+?<~un%Pn|XERPE!3ZoN`V*R0XWy}vJZ
z(wDR5_g`H0x4-&ydc0m|HT&{wv#M3Bt)mkY6TLh=3%7cDdr!Wab#;1t-OYEVhigFv
zxfSRnE=w7{qeqUISjq_tFV4NaZRbj@88a`xyaL)bc5$(L_JaeBla@3!G5!4YOG`&*
zjpywdGbBVsM0PCnnsF2~0pBZap7r<FSM@!|Ynz|FxwX~Xdzwz5n_FAm|KIns|Ni>A
z>+?D5r>|eHetCKM?svV<{@r`D?_<vQ`}O<Zl<l50dGgbzPgUCwgD0qim-|gsc_%w3
zDEr;SrY0s=cX#v3`?qgL3knL}ahGdr;5#6axBqV$Xt43um&^WlW4V0t?(eJJ^?KcI
zP^$X*^XJapG9?0j%Y#A3Zok=hT*dSG-12$*e!sKUjolS;dAWaiajC)P`oFK^e;v2~
zw-7XV!OX^UVVUpjlJ9rRwe9TYRkI(6JT6kcr4up?G<QRG5u`f08zOop>)Dx^RqywH
z-;#D#3UnxAsg0Rg*w(FEwY0UfFU|XXVsq||4UVp^u2NPdEB5T&`}FPGwf*-0B0#gq
zd-m=X6&5~R+bn$K=+Tw+|Nr^+eSLl1zp}D&(vw~0KdQNp?|#4Ucgg?1-?L9m(UdaD
zm{9k1b^J@vndNC|%Rqq=mHlqwjT<+F&4SjgUmu>Csrh|kyW)byNc9`?X>NBTB+nc%
z-~pYyoxk_%G`n9fltIUzp8S2djdyZ?zyIXlpuwxk%E~+La&I1W>t6#MQ0hC|Y-gON
zrsgcG(p9g&-!Zqi_u}GW|B9Z+-`?K7y0^OAGpJwARzzC*_LF@ZZ@>NZ>9qdNe4Cik
z(yeQ*E&HGH4YZcv<&-0Pn}f5@+8Px-@t8VwYUbfK-gRd3wO=OA@mn4g9DI9k+7ABU
zI~UV%4KuxacPT*#R7S>CJ{1M6(+?{s*pS^Dnvt>M&)>hGLj^%6h)YUJf=*ud@aO=Y
zK-eo|8T5R9{l3|&+jq0Dv+w?<X`yCd5HNA##C4BBzOem%XEA7@3~0^$*X!}?OJ84m
zIWv9UMdR}}%AT7dbawrIxBKe}<^C1^^X)*VkjvG4a6Eeac=n?so$qwz@=Bjh4S#jf
zT^@9Xl}W+@hT6YhuY(pKESjCaFH-0`pHxU#Sl07%bJwjee}8YS?`$(rM!&nO)cUFI
zrS<XlpnbyNgJPF@PhYieou6^~xhqpNgGJ=!-!EI;6uqNby0RpXTU^g%SLy4tAHRM{
zDZBL;?ECSEyL>u}@Wd-`UjKYPzkI7@(UXqC+qv5%A3l8O=HSp!`1NYI^v|C^-)T!N
z`||ks8c8-#(eZBgdnsmiK9d}?*`O0yZ9bnd_PtkbHTPIEzg@<WBS$36W}n^h<573-
zojWn+q1>A{+<tp3w)}2t$?I!pL8qoxeS4E>^Wgxqo3C&0YToaB*VaT{ezQMwng9Ge
z3AfkpCOWtCNrD>Cf+8X*EiEk)FJHcNb9HT%UdQ$>1=Kjcd>-5oXK+xy20jjsA%O|f
ze_$Z_hKDV)jwW5aoxlI>J0k{$7gInzs1gCtVZb_Kr#aW3*4upsv;(wZ^MTuUSs54_
zuI+xa>GYRBpU<0Hrft4?Cxn-Q!D2S(yrc!p8*k=x_4ej|;^OC@e#PK#&B;lspu_7y
ztJpvrmA_t(&;Rr@fB)Yw``<UuKY9Ot{fQGEXJ;5XPn|l|_tu}k@9VEOFfylXz6m<G
zaHes(S>^sEOI*U^Yqv5nF<n{iFTeNmIqTf&iwq1E9H5Tb+!LUtK=%5*VfSl3_b&6B
zyGk>78R#6ZNuYhMpjNw$P0Z`->#u{>*<W29ZqE6@dz#M0x7+Wp11%c4oxlI8_4_@G
z@9nKN|0&0B`(?tq{r{?R@9l{Uk11>g8F%T@rLw(#3=CGv-H8%yySo_}7?zh^_BDU`
zbb9=>`Sar^Ki>cU@B6R6@Bfbz>vrA$^KAZ>l#@c&t3g?$jZb#f@_AKWpotsM8m`*k
z-!u&j0w#U=_xt_pQ`+mV9BgJ66&7CH(!#QI`SRBj-Q_a3v2`C^^#AYu|5p$5+v}`U
z{KC%4`m&kdE<x9Zfx%u7bROCZ2fbY{7O64DOj1%(irFu3S(GyQyIY^kO5fRLo|B4-
zicBg#B!pW`UvhD=`{|b@TIS~AV%@In{Bl>0ipSSnk^=4Dz5bq=nORgwXwjlYi;VZG
zFff45UuAG$7m|>uI5g$+=A-xT?+<-jQ&TgmCU;}RlM<`ji&PjGY`DPoyX^b%s9Sj7
zyX))ctE`OQUkAFTAtW^PY1RMD=j|q6%?iE0uU1q>X3df%DygZdTeHtHFf3pM9U4`l
zU}|crrJ<2gbmsQ$=)l0hNlQS3*Pt~^#%VqOKK9pV+}%}L^={{L6;DtD2Gky|{Z#^5
z^>g&-(Or-GtiP;||LbLFXgFyJ=t2ea{Xcs_mG;l4(^oHA<TStb+st=69`}9Q8^FL|
z13EsofiLA~muOdacXrX5{QLWEZOfg#q)ql(eC^lJyJfd?K^q=GXG4E|e?NYbh=9O?
zsoLSM*6n_G>BGarq5=X7K*_59=jr&E=d<i;clF8Ht^yrWy6gMBYEe<qrJj@3w%%uD
zVt8=y#!*>@2ERi|8(*y5eh<{~xf{#Hz+k})IeU%)={6N`_^Cu}Wn*wSevFuAH>m4I
dUcNr?pZ(EMCiN|epRR*i`JS$RF6*2UngFvUlVSh>
literal 23034
zcmeAS@N?(olHy`uVBq!ia0y~yV1B{C!1#rOje&t-od&xu0|SF)iEBhjaDG}zd16s2
zgKuI<K~8>2PG*uqS!z*nW`3Tro|&GJjzUIBNkOrdzJ4xTfnI)5y1vE>i6jOF2F?PH
z$YKTt{zMRFTw%XFlYt>J-P6S}q+-t7yOlK|U-$n1_}ypP%r%o+CY*Fq$l25(q$&{T
zu(x-?(_PoZbag#)b5{qMt_@#le=vIbjw^Ai3mo@GZI4<RrX6)uRrF4P21i$G!zR_1
zlTIFLzm1CP>V1qPJXj_^nUfNC{&=#S=eaqSKj*YRSID=!|M~ANp2G$#3?Sf>YQ7sp
zGcY(fu!9%}gg9P+Xod&|Mp-a}TVVk+h>^g^QUanGbQ&7?K#T@ffeVZv#s&r^D-g{f
z1~Y_d)R4g(CWp@IG3-74{`=}JTS5v83pd@&Nt+Ie;{z&fi7U2k3w!zU<)+&&??y2)
z+8&uQZJL&`@#Qy0pcsyDDJ<N`#w(RlCMm#Z`{c;cqn=!MT0VaKs1^>^uM@XEeCEuV
z2YVPk3tn*cIlkfBj~^9~#35I^RY1-JWC+6nIwp_HHvj(l>DE+k?piH%MvoO_zmK~^
zd*-zD+Sh`!%d*o`_ugCz5^Z?N@xs8@Ymf7!kht49MXDgN1Un`xpUFbcr>IP=ob9s?
zoMp~{bX!@y5G?^G{)Ps=&pRGlwe7g<2+luX4fhR?%;f`T34sfVQsp+X;EeOa!J$2q
z&rj&O&S3+eb)XW4;f$+8dnVg9`{sL>o=xAr@$8c<khKS5SW0FblN3}74{1?p+X)I;
z21eOsUYYL~83)QRFfcR(xI473yz|z0C&X5PR~#=4L?O0tC@eS}!W@~!3o=PX@Is<d
z`R}0Yrx4e+STI@nOm$)h=WLMVrsl~<E`nVyaEs%G!PSGFk7lJvw4J;P(%n$W@xma~
z>$mX>-?epD&9={s0UJ7_y@4;-)wNCYoTQ$o*7r{`AfGl|aCK;ZY1OvVc(OIvg>DY*
zFK1|g3kr@G8yeR8iGagrVOIm+Wr&RoOjbGx3J_N-Ecm`f)q9$Twzjw5^5C62cSh|l
z%iaEa-;YP#r%s*>baHA+N=lkCckbHd^XsgB)MaO{&fE1;ZEndW&#unSz^bZU&(6$T
zeCg7qdlpmWoFU%hcww9M^V8E)=g(hX<l4O@@i1GjynUQ;+L?gL%F5}#)m2qrPK(aF
zILkEq%gg2Sv+nJwoMoOr?_R}YUe6$B=Vr5<8w;ZI_l8QF<y`32-?w7XBBj`ZgRFZi
zqmJJ_7<uO<xKQMXH9Ks;v$^}l`l8}u)51qCx^a6VCO!H7d+N-YD}R1|{`%SMe7jZ2
zbE;mgoMl<OEN}ncZ+iFRx981O^PRQi?(XvJ4-XEy+swQEdg|$?LB77dj~+kn|CdsA
z;oPaJxqp9$cJ=XG-d|t7?&ecabSNI|x~XvGqT_o-U0vO%{dIdkefrdN`%T&IPoF+*
zNjp1Bx$d3;kEoc~sue3dE-&+aUABMLEUBA0W~wSGGX0x;FJ5%a&-l1<-A-64lX;jX
zejz?IEbP?t&nMrOmEU?-wtH*o>uY)T;laU+dwO_G&CRuSbbS8*-v57f@j1)or)y`=
znpJYy*Zk$D)B5kf#qXIld-l~kH6L!~t-oH)lYV(~lFY-d(w5chcKW8?Q#;DnW4c=f
z<nR}zB^Pwn^z{6s&GWA8Dt%q@d~Ugxy?y-Zu(eh>`>$OK`}=_1enO%|)UJ}u?Z>q>
zG$x#W%C*nP*tmMbujJ`M_dm*52_FU3G#tJxU*smunX_iY27{?nr<UG|3kzFz<;s;j
z`}FO%O_Pprq^768zP|t8R=vGnf<#3`N^UJzp7Z|R-st?jU$0%a)8CYEkm>4`D=hIx
zj~>1M?nsqK!J-{eYF(fVCNPWRg{_K_QIN2jPe5W~V!`bD@52)l7pnQstGROg<L|$7
z+g6mfe13a-`|G#c?_X<RWZw7fGhe&&$B!T9#)XN*Wc7kGe1pJI;}^CX_V)3`&(DSW
z`SpP!nq98K;p^A0pyZ{Jd2Wv7sS_tS%1&@|aZQ;#dGW);?W=e0j9kC>+pKwYzg|8N
z_;&4DSZvkHrMEUDHpkU|4gFbTS7{^X*{FI;Nm==<<;BMh5Ss$r9o{=`PCFaq>e?!%
z7o*XCeDRw%IcsBf8d((HnP*#_vRz*0NI-o2`qk_AMX9Q)c6D`KdU0{_*E7cFGp?<P
zoU~+b_4ll!U80kgym|A+Z~yDL<@YW|rq8{q{rswM&?6?<FGg12mVlGU>jmu+tJjtO
z_;hUVu6H|ot#TtK&D;Izg*+%q7z%RM%;I&DJip+ukd@U7n`TgsR|pZjkjVM2FZEg$
zC&Ub4M%if-F4=Wg#}?0iHTyQS_^Xm<l$|E<nj_pNWroTwgvyCRG2C;PUIV3_7C9y>
zo#ceyQ7=-VMVJDUmCj*ba2|KyP+oA@DCp6Qlb{04;f2zI!$wWsYazvpKoiG{4LwtK
zd{cafq*Hn6-sU(rsBVz@7m$pc%2E>H=FYx%+e+VFUAxJvL4Iqv=jza&dc^XOsHmWt
zr`Gz^8Q`Mrf|Emg>W!Rj3F31T-+|od#LFl<?NMGm-`vheAbyJ!la)?tLV<1xGz7UB
zWv5LNy1ws<)Om<)hAbr!UDkF6NA@;BB2&QGp?zsd$%UCJpzw28p}631kd@U7A0beo
zsc=Q$LZT+r6Ix7GI;tQ~T)wetS=f9?N$v0+q~X!rhea*ydZ)RdLCL`=J1uGR%_*1m
ziCLXr2ukG~sw^cDeb#ygXXZW!S6?kcOjbI}7dOZGL4!nb!QrByTh{Q<7GSc{d7i*N
z7u@7iC=s}jXvsd`F}(v45{?e-PcJ~Kk1mcE8|H{WJuJW|JMD4a`vvkD8KK3q&qJ!U
z1?3>!>sD~Mwz2D-<w`38`AtBH<HZJ<*=Ltc+2J6*w+HGf4kjy|mC6gkKS1gh0Y`^+
z)l?NX?@480W;0?qL4o{*kI70W(<f?<%FEA}q$X=aA`GOk*Sp+vlAZq7Sx5?B9{Ez?
zdFkA#s;g#OXF;m5FE$_p9M(8z>Md8vJiRXp8tL=g9okb%OD<OOffJR6!h*w_{Br8~
zYCp%#a|Sv2!hgXFiILFg5Mrw0kJ^;dxoOj;CnZ)>XU|@}HT!zm?)RW7W6m5IRTUKt
zH8r=JH*Z$`dbynccKWX`FQp6;8gk5Lo8{fPaX39;U7=(n->+|<tW3B5f@KRa#=Y%2
z@%v(a{`?uWC1WC}D*E{Nc=oX#NiBW-_eKAgEK#{zbXqrRb6T(6-!H*uXPf(fes=b<
zbp9T}JInX&iwj=nbJ6zuoy*b76IYo`5AM7u_~|Eau6FVw3sA`Yoqg7T=kOb)UyJYD
ziSeCd5!fxRKTYN5-@4Saw5)%Be(w7DY<B-EiRJUER;8Vrv+{QS{=JvK#qKW4d~spn
zuP2lJr_7!ExA)bjs=Y-!V^*zP8CX;E$NbkC$+|x`j#uSZFMYS`hss}r@(qwm>#tk<
zX|{z8d{z7znVA=VeSJN3`gHL-Y1?m4oj!fJY4$alkDuETS8Ui2;Ogo+%PjZS_Ek%F
z?u?9Ezy0!ReaX|skTQXVaW8uaN<H=TW5uq&F}}XNNy*8gl9DTD8mGTn*e-X;^7)+4
zYqK;FRmlFUAFFQf=$m=R`}Z|_9gz1An7cY`pRi+xMedy)fv>Nvy*#)4-pW6JYSu<=
zeHATRe6Wdi)ykEe`?|Wj`~5fWU6QQ!eeLf(S)YIULYiz<emicmesMo}@+22GcW`86
z<np(vsi|JRzHh%3m+g*KcI%m-lA4~LeR`U%mWIZK$n?3TXSQ0%@L9_ACI8!Va}(PU
z4K*X5uex@%k%8+2R~DvC2RAEioA|2uJ9>Jqv~UWCL_|p3dG`HxXhg({latluzZU+f
z+dpge?CM2F><;H`e|h9v;yasmP(iW4esS|X=TDzMo0h+edHUo@#-$~mRSz23L*nDt
zSAKrxISJG(>gnMT6%p~^veBN0QQu_h#_lqyIJ0V%*4vw#-Ip$1I%&zGMM|DQSy`{H
zmp%KvYvaq0QM*@r&Hi3hdU9nNxRK9b+uU%^IqmGMrCnWIx-mNf<oexj-n<#LvnaLT
z(GJ_{ZzuBXEgBZ?+ZPueU%S=I%S%L5bm_&#?yqO2&%3xLa&yz~H4z(?BtEFV+;m!R
zvWjlh7LV<B%c9@UnZbE16;ha7_`i{_s^evMwrt+b=T|H1Yy1oQ3UZ@NV#{`)eKQHv
zytG@dpVs`ZFa1JdrE*Y`sAOJvZt?6cev=uopkQJ60xCfCmCmfY_;&O5{ClUX!0G6h
zI+N8e7D%~s;lAL7i|TV`$W^GN6>CBop?`%LWow-Z)V}XEO^R~{rIo*ejIy<dUR<$g
zy_tI}{{AAP{LPSLeW1|Y!F~RN9*)Z$jT<LbK#Ge0E)MSV1#9@`&b$LEjlMXulzfp?
zPcwkDi2rpr^w}+5-2Bhd7~G_P(a!PWh4Ha%>|gd??X?QHpHyTDva+B?@WRFPBi70$
zDGpnpmEtdXCaYgl3SUAay<Ta-V*i&C&-TrPR33G04Sja0GEifebG&#FY?W4YKQeOt
zuV-e^QtfejL!X_h<+X)ZDwgv&-`dm<sk(mgGg<wb)2H`qxhhXFxNxf%xNz~gkIXC;
z&#G$gnJ1@$+;rfhvxED5<IhV>JTJ|X{T=!)*J!5{NW#L7<Hd_&qsfa(Pd#9fZ2QuG
z{@YAQ49JMHlzj0N@A03mvUk(UD^I*Y${+ktSg_cCR%OtvH)ffK=Vgh8n?fV3CL5$&
z^P}Hfvz!K%mskBIpMfHB!F%Nei_cr_ubuIm3(^sK!OrpG#pZL;(^nPj_Kp5l`PH>@
zEi_8DJ2|+|pDG}`GE<#ripts>M-0kAmexu$%GO35&v~~#PitzT1f-|RaJ;Xf&rUag
zO>|$u?y`_;uRjN^hZ<w3ykN0^cWH}ueowSe_99TV`QkRmix;2I$<J5Q6u9uW4(gKG
zU_q&=iz3{2cxBJpl6anR^J!4THzb2x^1O%R@)u#|!*?Qu!4*lotAqRgg1X;1@7C9D
z^Ut;052?Bjn7cc;?>9(w*(9`BIVAG>qW-cFXc*Qcvy^<{7ECOgTOO3KX@}_SZ_ui%
zrkJJVi?sL_{^IzZHy1xMgSG+=m^(YT?>98m_Iy{-zQ;GWsyuor)Klh84(|H})3(2~
z_vb6QIDP%?WJqfJa8}^L#r1y-$~WYgy?m?tRO{1~S<nbHWwQD;^&xX|>ATLAla{oH
z*Jnbs6nx=$@uGa4U3QDg%cs6ewbo@BKof%%OUV~&b>5=PS0_?_{%Mn|&2NWVAE>zC
z`vzkBce^gzwIyaeJk;vt?HwEvG6mGVsr~(Jo?Wlq^Xcil(^Yii!`F1(N`t0sW#t9m
z7d$;ZUEAJ1zFSQ9QX8+dN!_1{voj2xpFVvW`)>Y}DNCaBcDiO?TXS*w{JK?@pPyyk
z+f!NfxYxYt_nqqZd#gSiWZ#-|)2Q~($Kzd{ofmr~jjvp}awU5CRdpXZGl!cqIkWOU
zzxJ1ZW3n66`DU<Cnw==ocGLAs_|Ba>XIT_364Q^<@%;4r@2N9quKfA=x%Bf{bNQ==
zezspj&d#w6-hRJsH_zdKs;XUee_zMHHsAlVS7qh0Wo~)fqy23@w&?ACvq@R*{Ohl$
zo)&?GKYsjpysoJBfb{(8FQ1oR*FGn>j$iCg`=hynkeVXGO(V5=AE>s-B&I^0rIPvl
z+}uwUHgo%47VVredv<lmaURp>J%!mRUTd;u-~MvOefL>CP|j!A2WoZ@UCk<c9z0(=
zIoZtI{PgRuWmT<QyYDWGpCefz_V?rW@|7#ErWd(_a^D48rd>ym96g%(_*ie$_Pn|8
z_I&nx`uutJ)m5R#YxXoXG3|_5_dwg&`123Z+w3tD=C12$(Fna}`ugWrRmhkW!(F8X
z-zPkIlG57Rx^m5$HJ86_+GMnJ*|M_Vmb>q+6I6E7u(pog|L^Pi(w9r8uUfFcVN%ol
z-)hHWHY7A=Wo0$3fAr{4esNT-pUaF```K0@lfInT9lz@AbMu`_Am_c1V%*#A;^vlh
zsD)EYM~5fx^Y6b}IyzULoSfYM%c5?7{MoaOsfXJ5WLI6ea>eiXm#V$Lemw5qZM*F7
z!+@Han&sguW||gmU_0WnYR9U;^>w?>zbOJ4yTF^}i`<m?^Vhq}*M|7p{amsw=jNjL
zf1jqWTEG7Kp32WEp2g43Tx??HZu%`QBJyJG_IsE5WUaTPo))`X|Nk$jsrYrV{9lhr
zDVuMW{QY|Ub)WUS3tghxUv8$)&pbEB(#zlfx~q6>$}!DvoPiS)XO+86Q^|a}O8@Ei
z%>|$Wh~YJ-`R;~)IoH-iu3E9;!l72~rynaoqbF0f!`aj;Z*NZTfAg*RzgrN_-r>R8
zf3Nrd^Zxqv>#kR;R)@sKuGI`)mhtq|)FP|7d+X|IPxh;)XWo1ztEIL2s{Qt9+WBjK
zpO9w))y#26tX?RpO}TERQ<2*(S6AMCY}wJ*Q-21Fq-~!WE5yLSkT5IY99!5?f%2A~
z)ysH;_ZY|gzxVv=eEYOm@F>|eCkOWv5(2L;^cuT)PI_0p*h6&dS+#I*BZ*z`f?!bH
z3~QIiMvp>oUs=|4eLJ-NU;)V-dSiCC?xDraoJ-x(-?HAG)7tC}auS0lOG(RuU^ewv
zn>uGsTGFbYA2$1ZJ|uZMIJkHGS}(bHs(|dmwezBEKR(ohl;I3aR!+f5K1=ef%D1Sz
zY}~$m^_kl#5Is(^jIxT^VSdXt84HJ8zq8Ex|G}0*1_lPuXrB9tO}BG*Jzi9}>Gl%Q
z*<N2CPM-?NwhAqR7X&XIOWpBh@#Qv;Nn-o{?2NiK!vIvtHCS@I;Hcbhmp+B<h|8<)
zPgb`74*Ee{A+QJ}weAIn?vHm8o|okB-CCyPzxJdkD3Uo`SxOu(efK+i;pSqtRZGvV
zkN^8r*mUc!GkT!@kwb^#0_I6MX0zY@{#T)T+-=p0HDTrZ_gw`ypBP#sn5-`7SzcTC
zd!K#Wq%TL>cYpB<+zB;Tm8Il?_Bpw!i?sdDPg1%2<BwYC6|>pV0-)HP;OM~4SNOSd
zv8k1*-_%mwT_0P0jd%`2O=V!RD&R<WnengUre;yg?X9a;IQiG?IE|3K&>Fb1a93(t
z!pp5q*SBY_KAsH;(qGn0zZAQ=yF-J5g#J{%y|s1MzhAGvo>1<e5g~f!!{f_sFRT{_
zM^u4^A05^!ERcWHZvSWDy~^jZo|BG0zG(S;&f=}v*I$8#^WN8ex1PM(Ie*j5oL$c^
zO`a<A`ogwNR(HSN5`!d$3;lu@_%~L5PV=2*;yLNd_5J@sA0O+z+%8|Y!v6oy^Hq;J
z)z9ei?FENTy+B0O+m)ufzg|*Tb^ifQfiluezZ9dQqn{R7sCee@|GQ0Z#{(u$(74Lh
z{QGvs-p!upY!Vl&v#oAXdHHOA?Xt7g**@zy7#J8F>RlY{k94ZfTakKt+RMG)?_G}0
z-@CN<ylwcTkf^9tjLd8^Vr0)09kx_|@g|V@>YZoT&(Durzv>sv+2;i=@E4x9{r=)%
zyZp7t^tnq<>+g?we4M?_e0{`5C5tbz3)Ro$U1pv>chMTgi?^chuE?H$cY)db`)@XZ
zQsx(B$T;N&srl#C*I!?K<j9c++hTW@X<AuDX=-Y!g?)Qlxi@ZSjNa8NS2XPG?!77c
zH77T9>#mBE?5pxWzFZ#M_UixN=Mj(~?Bl$7pz6f}`9&Ky2A(~8_F%!j>hF2B9}X~U
zX=%Os{7~lpJ<fMK9xN&R|NWoe-aUouA<626KF5pp9Ul&Hf4vgy?>TAJ>eZmm^6&Ti
z<Hci3CQj3f%_=J0&{TYU>C0K0+r1~{{kpx}M+#IgJ=meJKtAbk8}BTu(p4%iTgBrh
z+<trQ-QC?FgI*qFmp9pI72*2Na&gJE6^&V0sh7Z=+~e$we+77XdAqv1C#&rJ`E2&C
zCzHHWw%;zjmAO3g`@6ep;q0q9HsAhoe7%oU`Pcc^S1(+878+KGEMJ^^Bn+ME|Np)p
z8WXd|{9c9g<>mg<Ri;j#Zd}M@Rp{#aVxpxbXmHludexV|so76fW*Y4VcP?v182<`z
zi|bv<ulp>Wnwpw<X^H17o61c~9L)bt3HCGGX%%sn^PPolZR$EuX8#<tUN7d8-T5V;
zR;GoT&gN^Z{l^#A{{EKv=f}rQx0(O9Z7h2mb@cf0>@P1as)c`lyJ3pcd5v{h7yRc%
zEPwm7w5yBbvb_H3YhQ$e&+2hAFfhCjyLq6B<3;@9B}-f;nPgm0n5=DCw}Z2~Z~My7
z>whYn@7<2mob4M`a4+L;&MSkPIcA%^L00oQn{AM5+aZ7G{rA`9`+v`k(VO1JBe_V?
zxlKb+(Gk>w`BS(5=F@|R9?cc}by-yS>*LF9Ck&SCj=Q~zRljaonEZ86)@)GbHs8&#
zdgs$=(O+&P_rH9%`~5Y^^f`*2Rsa8fFTI_+edWrPp5^x{m!A|pbn4LIH^1(-s%PCe
zAaVEKtNmZ^>`D@q&$zHj-Zx;5@u4@LK*=^j!ubyW#Ch}9f$~ffx892_f1gf|zjie|
ze(m1s@3TT~emrO}?cV3}Yg1?af1|!C|6}GKh3WHVn0s~_PE4B)n(XmVl8z8`zQZ3F
z8hZ7qOTl9I{;<%{shpw<*Z=Cjn5X;yXNszudq%KluU_>N<*C;<@koP8xpjgU_zmm+
zROrU-`LW?`x48bblj`$xoLpa=1{L7f=lh3w&2e^3-TLd`CevHdcQeW&L9W&5P<+=O
zvA1d~Xw>9jGdufrhxpBDy<OeiUvFiv&wO=drCRupw+1~&WvaiKzi9veeWDkiiI%dW
z*ZDst;m(tj&wyi4u_q_Nw&VhT;j@|PS+}?4?)velJ2W<S?W<Q=u|+3UfBpG<{_B^^
z{<CA{eyupjyy)v6eYNZx2PEFE&Aak_`r3<O{nsJMID$j=%i=AWm(}K0y;}L{XHDAl
zPisG{PkFX#P4=~Z`RLWUzxQ}`^j(<zSuL|Bt?6beBLjm1%S7Ha?R-`jx+f*eR=rDK
zu4!%Tc$IhUtiSd0uj6;mr~y?042*l-bt5;qL`FsyS<O9l_UzSLTeElH{eID1{_2&L
z!Bc0<SaIZt%hRV%Ww+HwM$TNf;}O@=<;%6LtfHRJt6sM)|Ngzp%Nrk`5tUt-UN>v?
z$-`_b*6s8ymXBZkbvNfrP(isudBOJyc-r&(v#x5z*8O~1^?Yvmmb9}{pi!$|UtS(8
zz0Q01f~$D!5?SlA7ocRKKEDQ>g7x+NzkdC?w=&CuFG==c)t|0TtJ)t7SGWH#`Tnsq
zYX9#Bu;&>J1kW$nzWd1}?|t9?zIv6lHg4~)74sd>pFMl^NT+bK_cpWvvLBW6&o4i6
z1U%ydYRqlRy={_pMI-m#o=Ek16-ggvtX#Qr*Xwn=zg(XG@5_o;k1U0=Gjl$^Jf(j9
z<zfBr+Sdfvsn;8M3b(1uiWOsEU~pmp7pmKfcgDp3mNH7|cy?xHamev3>}~w3hEh^e
zS8m-}wPcBkXHaqRW|fmKOUkU49e#M>_xt_zTb6TncXshyPA*n2lX|x=k$*0zX{8|2
zIy<%@^v_HGdaJzStJbWUa{l?`+i%}(x%;*(`}MW8?xj7m^LBZ@eEIU=1pBHl8N6~f
z5!_-r7Y_5=ubDAJ;_vzYfBL<=ytd@u-<Q{a`OX~~p6@{q;!h^%22M<@debHI_ep2z
zuMGF8&uq4Z`Nvv%F6Ub|8PvXM5wdzAR`UIB`Rkp}=Uw*J-z)NFhP#W)i=*Q4SGMKe
zo-$?1k{2&B_I^HRZS{W7=alW`|L1R9uwcPukA^d6&fK%&tzo`6u`&IUzw|Vb7fbB7
zh0VX<391*IxES}ctEj7krl?X=Q|E4*K6R?5sp;2j``^Fs|BoxbU%UNY&1c`aHJ?t-
zy*Jw+vB@m&&WcyBvfkd_?ynub?#jQvzfE&)Y{={1jx;NDye{cQpsb>8dB&uy*|$xe
zZ``VL5HwTUBEW=ec&_~S&g%Dj!$E~)*t(dNbLPy+`(C^=M#`dK!M6PS>+1i$j^Fj+
z5clr8*B=&W*xBv7%zaH%R&t&F+1V>R-zwIA`(L|e`~TRoC;Xtc>Wj~yX()U9_<p-z
zE9TXF^31)t$#wsqr~0ADBYLddVl3bHKc81!@_Oy|S09hdhlhntvytn!k?Vi;>Q&Qk
zoBw}4M{UgtEm*m=_?)FXsDfSQKmXqA=k4=^WhK|CKli;<-{TipJUiaLK5g&6U40Yz
zK{>DfxJsdI3ieJz-|fxm{>#&kPuGiGHEo(0Xo6~P-{y>qN_Wd{=T2Hu`}>=wy?s1r
zB-Gpc@*K-zkjuW^vRwGx#kK9_LwWxTP2%>KxnFyKiLd`1B3gQO*RM4X!Rrn{QMrJ9
z17B4~;@ZRejvDaX{p~h!+PYsslS+=hjEwp*KRI=~@FhkDh7WHi@UCfBeAm!=Gso<1
z$-9iZzvd^adivMw@ZMkbPS|w&PoH%>3=9khKDs%u|7y@)&T~~>|8$tk4Bz#2yZr4d
zljdg4j)K&Z2MS#r*nf3!Z#Mq&LsVE)SaKQvz8mY`>;4Vc{r&oEaDHrf+|t1JMKRc&
z?@DpFFQ~k~ed<-o!AJaG?tSChd>Ry21u_B`8ttAfan##uRNuA9SUBYJja57Q*I)g=
zcf0eM@1R!Smt2;T1^h+Ft-sz0UH<YCXY&*DCD#8oy6&&5n>;IOrVS{2yjacg!a@FN
z569)&>GRgK{<jQEESnv7XG8dAxt|{MW~o7H=mH*)s<h2Fzf9J2{`zE(XOXLGMsVTQ
zbMb%nU3<UIXg7Fi#eD%r*%$1~dVH>RWuL#m`7R>#idpux!}1?vKy&mLjtg99tV=r4
zAhEaNtK_W8qFJ~8w7vUrT<q_kmtR+9rtAfk5)1Sd7BK(ySfd=%t#9wTJ-%X*(&0B%
zpYNysI^TZpa=NN%+H^Js28IV594{Q~lQ-Y|^7v$A)x+tNO*IU?j)wOBx_62H>lgRk
zlAl0+i;!THeX-n6ZpwoCGxJv!AKP{H(dXCK_UB()KkxR6UC*Gcj|;3!Ru}qrE>iw-
zqF?>RwD*01Gwv=6H=Fv(UcSbEcGv2&dZ5<D0Y#9F>6>qU*_?U!%k}y5mz7Kp-u=z|
zMYXs6l}Y9MCY(P%0W{!uK#SvrL&ppQ70>nY=Dx=MTxUMj-IsR#^>DHL$}PJtzPSXd
zt2-L_T13K2TEzZ;5V>3a@+7D+cieh)$vfuvude!gLu}-zWGPWNawEsA)?Ph${okxr
z$I{hK+`h6bd;VXS{grjX!Y4reFg2wG%!*ptmTvlgk~V=_{yAnZTfeIQdMAJX@~+rW
zNN}~NFj)y4QabeJ*V
Mh};=vBQ+>PvI`^%v`|Z(FhQYI<2EC=+@pE?^FLw}G+h
z@2{1={<Sz7@w}W^UjCx#==<!?_oN_+!HJDgwn0Vno+P;c)_?9~U{TJJZBwtlT%;=h
z;^3jTS)YHN)dOYe7vDKvFj!fpuZhmTSFmhfb<nC?f7<?ixMo(H|FZqpqCMBZq0vy+
z+ran0z|(MI)ThJ2Uyq;OB>JA``m#w<XKOyx{=Hvq{p7QB+H_Dx{o>71!VnAUTNzk8
zgN$4@*C60*Zr$CF{kpr}Of<c<cmF+b)2*Sdy@BsR_R?K*s*3Kph{hC6N>Vz!CGR@p
z+}{VPtE$fG{d~C$)bKpc%P4z5@Mex#tX<}YusD0)<-FcO$*cXh@B6c}?$3|o;MNs5
zT=qLSuoql0n!M=i8-M@HxzGEj8XsB*%C?gKKd+m%{<iYr1+zg(u11=rgyDY2-Mq`9
z={#3oU-!MdvKib>pZ)LK%yl)N;<dh;&5V^}U|_hAE_i`aX7a+5hE@N>muDZWTr4V?
zcHMsawCrEVso=$Aju#Alku&Ztn?K(&X8oD@i-a#f4!wI}6(|{e{{N|Sm2}h@NN5Lh
zykOY3HE?BN)yr4LUsnH@SZJ`@cYTi5)i3`m!{6uVnuBxcg7XRsm@V{_4!=D$-_EQ4
z_tTeBb_Q-wx1OE#{fL!r#mC^QrrRMdOsHfjVK^RIme%y|<LZ^aPD(nfde%oqulfA%
z$GU58R~hXz0+n~SK<=|ncbOqJ&pxdF=grJh9vwl2p^NK2e2@~|v(NQxuHEL-pupDY
zY~WjP+(*vNVST)P=yX2`mBSO{;`Ta=>-}?x{#FS|^c!TDtQzD*-}hX2zu!LYbe)N#
zr{Tm~uU1}V?Y@8gQfU0C=jIUKZs1|EYKSX*w<Bb4;a`)dy`F)q&tI_B-kx>saQ2qf
zo9=;=7K69K0_GQ{%#UTNO8olX?fsH+)auaJ^>^7z?#bW39GqQt(%&33I<DB)z_%d$
zW{%lk1@++d@#dk>bif_|FUG#=P4ZNk41?V^Am5s>lrSti;nQ_(v%RhF_PO?9=QwuT
z{VCdiuJ(bg?e?;Mp5{DIS?9#hDBF<vrfl{)i{uq?`}1b0@py*B7SGPB`)GW&I%)2y
zJ-*N+t;J-;aGBS)?_zwtUF811?=oE5z#|vW|0Hhxw{2ew#5honQ*F7KyDRHf!QbS6
zB6Dp{sOAUHuKW8(4b<+r6@EDbl7IU_!_ioVy6t|wSPbgH3JD8OoiSs@o;`aGe_OwL
z_2sSC<5oL1vuSE+c_kn3ySgd$bk#4DhB(tpvdhKmD|hA1G4z_4ShhDfWZ}#Dis|W>
z85tNZycfJsed5#8(^KcqU+>y2mU+01SIWL_PuiIo3qO7Or1w33Yt~e~{eOx;Z7R?t
zv5~Q{Y3;9)w|94&@9?jy+Xrf=q|d8#i{6%V@$2>Y_1Ww9hG~bbd9n5~Q<ZDQjM(dQ
zY@Po9xGIvGuC`*^-<w}7``4!~1C^~`qRpD8h4i0*G>W%Yd`$A3bpL<H3N_=jX4V
zoxksA$NcX<YrK4Xt~@&0ZJK^g#`DsS!pA1r*L3byy<U6pubR({1)tAZ`<LG-JRW%U
z>DQ{KMLR=+gBSn!@gv#7ctJU6+MvFEdENiCiL-A1>FVUUeD=QV;+vD%PUk`Lgu{GS
zvkh`qFKibqUF!P!`ugy=xVVDZck{yI;^tjj6ImQ`JR>6`AUk{Yq)C&SZu{B)Em<3}
z(W(0TyQzs1;2uNnZ8P8bc6)C>Hi3>@GuC>7^0u$^vg_+-uj-8K1dVur>ZtGXaf{DZ
zXG2oEMIOfs+Xb681=-E_&)dHHa@qFVy8XwCLyqrwxoq~7dGqdRKC%CD!CA_>Yz-T)
z)RGe?Jmyxv+X-43u*`S%wYP;_TwG6zb~dd)bLLF4wdR48!)(X0euhpiz5O#;R914?
z*WLV}A|6yXhWR?LzWEeXv)q?ryxa6{-|sxz?{|u;UM`*fq{OQEwtUqK#o8|y-J>=p
zwO(5vzyA08{qfghihJ+X{eGL*zZI!5ySI?bq5nmM`QQ2b?Yy7&`%k|=Vcx%t?dSH@
zPX!g2`{%2dfr`QMk38Vn`M{ErEpZhOSz}8sx*mKBTEA3Ux;3uu=hHlBi~3Gs`@Oo~
zxlf-wVcBkOW_Idn(aWXN<1U?<Y219f{@3OCtClQr(GFi{v+7pGIhMaC+vCHsE}ovB
zU0pd>1=N$=9&h2N&RaAyMv{S{;VZbSNnD%yrsf>)pzQ3`QPI(<+sps?i;0P;?Z01N
z<InIy*-w7nn(OOkuZrGh?PfSJ@m564TJC9k)=SU7zhv*#HFNnG7#jAe-g(3HYxSQ$
zHD~8qhg(`&&N9iIBv<i(@#^*K+4uHTPFiAF{7gex*_oA<b<&dRcRSrd12~({+pS)+
zX3c}muQqPn*wxz`T2r&<oXuySyA_XnK})G7C^~Pc`kH0?^-A!s!}9+UZdEd+b#rWf
zoqoCPWz(Ay(1_#SJsU&Luh+kLTH4$6_m+dZp1l$D>MNZQtH!{<P~gH-_}&pTv{3u~
zt~zAxmhF!R&8N<uovJdo^jhSpQ>QKo``ajbf*9}a?&h~Xw#c=+<aX|M&q+3N{bD*1
z3(W7=1c!!(f_jVVcD>TNTX2|n)6KrTotkYM7wnJT|GVK`b@FPvTY;*cpytig;I>!k
zm)kV&NhTJRGB7Y)b2Qr^$5hqvP|ZcdfXDfLec;O9_cSgoS8ZH+Luirmm(#}W+3#LP
z7P+;|j8SA@X!v=8?V7U#zu}!04%N2A7rp0gfBl_(`-|&um!gv!C(bHA7Vx*?lYrGL
z1_p&CpvY~IOWMIH*i}>KtY5n+oPW<=UsZOd$;Xdc9y0yCMe*0kDLPA!SoRmNFfcUy
zYtKorW%(j-*ta9#+N;m6uljp$Ex9`VR+!j}74B`fzy9TH{_;8VuxW+EmXo4P3=Ci7
zHZ^_~xZuoVuBh<yZyVS4X|L<UYZv-|3-O-x#w_#M$(Or|P6ec<tIdd#XJBy1SCEe2
zXS6+1Ak4%r*|xC1JS_9&D*dfh(XIu*TNFD%YJKHjhgHnDdxVRTfuV{IJcg#&He2Au
zt>;TktKR&bTa_BTGOD)GUd&i%k;(fWoxcf5YQm=&85-)~PUooETa-T;l)>ix%-GIn
zvc+pslI+7@j(*G5&9MrR<P1~`=U`wcr~&n2z+uz4`0|xkm%F9Ez52*s#x^~Jd-Ls*
zgFi}QO><`)TT=KRlyZAv;lfd4T(^JW)!W)%UhKC1@^Jd^=G)8X8U*a0VdgWroqxjF
zG~<m<3=9p)pm98KoU-)YU9Nhy>c?H#s=u3SKW+bivNEX6JKR@#+2><RjTXu<FuVwc
zN9cj=+tcft|Gob!`|I`ouB%sGO;<@(bA7oyxb5X{%VrT-$-JUg1_lOqH&}{c>6^dm
zmzeF(hkCYK)4~g!cDB6WI6HIB(w&prGaS|&egX={UG50g`;UpukBf+3<9`44O50H0
zNh<C;VyF8_ESc{syKK!9iOJe53=9@Bh+wz0+rMz?Y}=|QclEcfT;et<WwYmf$+?TP
z{p42}yBUC7zkv^)1TK8$IeY;$5_@8I{HwW(x*mL+sJU@gb=5RIOVj4(R~Q)@Uc!Cq
z@c!4PMSlLZJG}MR=m*wz`%e;i&U1au93PPD4>B+ET_?c6(4qx5JE8B^var1y_PO}i
z?eO0HjeTD5_M1yqDzk&8l1@oK4ok5(y@ipX;T|H~&piDu9kq9@Q-A%2;P<)fdGh?b
zI<KfMPszA;#HY99_{<1J1_ln%6z1283)&^}oDNu(y<dMl{(tD2=l??&eY*AR!8cB4
zA=B(6P-}fwlB~^YJ_d#=Hh9o%m}fRWe)aay7duOj{(63`=;xnC`)RTd%bxa@zCQHv
zrB!wam-7Qgh69D5Y7&wX)~vmAdx^N&loze_Q#0ICYYTR3YdPJw)eIHyIUn%ug38Ue
zI@j4481~7+eSN`PqHW>X>b+H!kK@+f2)UKXQIoYN`1#x^t3DrFnt5n~ORL~DMur21
zh<sJse?I@kW7*$dKOGC&yRCA5*6dqN?^TamnLKvceolJ&vO9^MT^z|0HVh0FR&c*B
z$j{FEoLL&!8kYC%#ai9exreHpN?JZ0+hJO}COX(nP1|Y`=Q9Qdhwq5!`+xD$v+JOW
z<!M^~y+7qMSG|gEdhgnHyY$xw#$V?ow(m2xa(&4es20Y>z_6wRo_oH4)>~YAYqh}K
z?)!_kx~W>07jC@#Ynb`*l=S39o86XbSQ|G!zsAVGaPvSD#|v=I|Ic&y!naRWd-pE&
z`F|&99d9+?iT_jWUp}7uaF$ikf@O0jtjSd837OUX&WeFyA;PAXd8>Yjt<};Be|W8E
z@AkD$_tx8N*Yi{kPBMMl(m2b?C{lRyB$3AtZk94II5EOwl_NLTZok>oD=+TmidL0Z
zx0;EDm%m!qemmDHwV+9j-~Zan!|X4n9ONu&X`dNxz`*dr5FUsO@u7DwEaKmHV}1Gd
z&=-I8wYF~G^?CE_zJlFqX{wo@J}}OjF?Gpg_MK096gDt3T#$zsKa4eVAFX1m{gJxV
z?oZKv5Z20?ZM|1sGB5nw0v2DHSt~jtJ!cuH1Qwp-n7oRWf#E(P6&|=~#N!+vcI9QW
z{<rM6=d^0~Z1$R3I+uC(U9lO8|E?VDv?}#z@{^sj;-KfnO*dz#$}upkR)!bC7GZ6P
z8Q1@@t-T$&bnEU_TguX>ehV>`*%n`vvt*MobK~DBXI$o*oLCy6HjkO%KoLCl8T!=y
zeJ5XgZ?}NAnrZ#{?^RbHeJ;OsJZ-WYPtVnp%|cV=zPxm@S;)jnx9{Y01_lF3c=$T_
zXJvhT`8jC4T_*pV`EOQU+c#_3;cwj>n=^f)_8d#yVWM|;sqp2;rdp<bpash>ToJ*$
zckP~Sp7X`7zqmXpD_4uN#`eO6svC6=%VxVRIkK(&TSc#D_w^ZHDvla+$}%w6Ho=qJ
zft<F)6;B)%+v%>n`0SEYZQQG?&-R+GT9yB@=)YrUVB>~)V(T|%7D`PPTYu(A#?7}r
zF6;~kw!`aWmIT+f#1oHmFFo7axZz)AX79E)t8H^)zwuq1m}qrScKOAXg;H1lNzIj7
zzhuUVxOFlN4g0#`b}!IhnVE4_tUo;ZTj`#x(2%gz)6D9p|K{&0&X{8Dr@rHHNt1P$
z`jTUrDPB6+TbUUyu)?!`!@hNApI=}1msji4l~pGmUlsFT+hwygR`2IEMP)@Tfg5)V
zr6v}A$mTh{pOL{y0ujLVq4)1B%T3*SD=uvLqD@h`+4WN^XYc-dX2B7S-W>in%6A*{
ztPjdE2xuW<)5*8gxqI8z_HX*9vbL^W<9GXXc4*}FuS-fU9qjvkVtvUSccD9s41amx
zmM}1_dKJB8@0zgNr(SQ_yZ7_Hy?gwomfkJD1FDoS)f#Lt4v}NHFdf|ZxOaG(PUNEB
z?{@pk^}9bl-hbVh-*$=TWVO<r?-%#mty(^>%4@cH{xlWe83vB!cT2;U|IOXDZo25M
z@As-pANQKeZIsJ%V)#+T4zh5M-|bUbUtND+fBUp_)r!Br7JgE%DJ|Od?ZLYqj@L&|
zF*5u}1N-W|<LT-8*XLLkZ^^l7^z_-YSB>m)7e1f2U%zOP($XbMWVZc(b8~a)G0F57
zUoQJkzx?uw?e{y2r^i)!&aeNs)5yq3#Z#_-`Q`Mu`oE>Il~1Ssdep6Nl5=B&^0gzr
z91O>oDeuVF=P$Y4FLEc>EYl-si(i3!3|HP1Yrn{ohFu(+zZZRFV36qpCjz-CvuCgF
z6jsmr_~_^)mHqeEpE=`mcD{YQ`Td&Dg7)t}|C};o#)_le;@RisSjN69zUV5xa@)4B
z*49?{P+@aL2AO#FSJ6zum!@9Noc;X4lM;5RsNHw$mCh`C$ee7dWxDQi)H_Mez`K2u
zm>3qQg9?=PXH_2F-rb<?2xx42`^~pyPoF-`JUh$Oy|kyTjcx0;ZCaX|D^Gtved^Sg
z{`x=8{&qi~6t0wH_;6PBn2T2V4Th(D(gJtVB-*rIGacN!!gh=J)T>#umxq1Twafi_
zC2RHaZI=rl$vm`LXuGE0ulMqcD;2$7y_2=OOz$u<aEP*ekqe59)SQ1_ef#aZnY(Yl
zy>@MFbpI;}^SdRJFI~Pob^7${Pk)1!;XHcusN|&T^pfhHXV211&E;k4UVg3We^t)R
z&^PyN+T{Zl?QF*^OOACsc~ExPV9WMhfwxaFG45UQ_iL)_+^pHRx72-STzkKE!R3vY
zV*Psky7}3@8)kl#I%QDF!0=0zX_xEW+V6L(-tBz8CF`n|<=k_6-z)!oJZ@V3?M>eO
z=}VfLnErmR|6lGum6PEITaMXmHdVLmfTS(|lD{0RKaiXG8MGN~Th$(|Pcl63t5-{h
zMqOTVX_ngG;_S+T|0fJ~w=F-C<&!nXZ8__%w~H@7GBLPz5NxplQx(6bpI=x+#Ees?
zPFdyZYikE*Wxcvy_wD=s|9e6G$Gv~Q-M0GqWb*Di&-Z@67aSNkahiVozpclkHY7BD
z`uw@szItz5YFe7y(?|w}D(93PwiXlbgkLq;#<uJ2?>z?-Kf2C~x@x*T^R(UiZErq%
z&6l}T@8I*~;DLMha?{N~Ugu+~;_rm7U0%_xx630aVbYF6hn!-o-)_BI_wHWx`>UX3
z)}X0$OH0eU@4nwi?$12b!YO56_viYxhXoc-3M~3w$}liI1C47Zg-udRlbzFj<UK#n
z<omBICGKpm*mEm1a{b@0y1yQD{{`WLe-FPg<Mr;l_+m@n1v!onnR@DX%NQ7R+8XXP
zfBH<!g6K)_c0QK_Enq%x_j}EzO`Dp$R~<ceOv}v7tRiOp_0`Yk*T;RoUtj-zz6isC
zkh0yrGd(Y!^69!}nas~OIX~wktNPO0k3MF--gn^otF~+3o<4l_<<zp~<DKPg?9SE4
z1%3v;vp8z;1GJGO?cx7JwxB&Xt1uU1f#y{}i?JsE(TUv^qN}UB>&qo?YuWURi(GfT
z-FCal-v0ZYVj~{s>G5@y>*ugD9Qd_J>9C2NvEyT7;ilU>hXZDAkBqiyeE7k2UR3T~
z@flOj-M+GHtM%$FW$B^IAE&6fvbLWn_wku-s^Ip}$BUi8p+8yni@@Qic7}#$X7l&y
ztPc;2TyXi{`)?oO;%?u0`fuXHvR(;^)fZP5&Yjle-J3qchT%&pqBe9WU-c@Q@0`m1
z^<mMsujl3Gd@bEszsERcanNIvOR^Gj_LpQ=2fnl5?MQtm%<zI6Tr7gRj0_F=hu)iq
z)GTm}wr$*SPw-ZG$hFhY-~U=KaB|~BshL8{h3{;Xdf9iG^PNSnPiJV7Pl|pxKf@11
zTan?w#|w9!UGD3u60HQ)7LP-h+lKPqty`wNqrc>MR*KgeQ1(dinlmX`SyGFiK@HJ1
zWvB{r_MW{@V}0<%<z@!eHPr?8qwn5$n;sJ=C^b=NwIFjcbNt=L_r^ktQnm}<VO+2r
zRHA`<Xp9VLEAshRz8{#B53bnsPrU|t;qG7GBZ7I?FFcXp_3ym;p>ZQ<{Pr;WG0<8D
zSNBZ<ybLwbh#+g2YO<BB?NohW;Bx&_S<LaLUvF8x$x+v?e*1jy-g1y0UTKNN3r{v)
zu(PoYlk5!STKt-i!Mzn+kP964<zRThT~p_*$}aUK)phPZo%Q@RYgb&JF8USJmiSgY
zF|mwSPGIdC#>NlxOcmUmCo}7p?`clI%lJSWDfIWP+qKZuTI==J#f##swO%pBYro!-
zUEdsaYuEc-?yfDFCmJ$9L!c8F4W(wu-2G?jrdilh&ae>Cxn+2<zNXH3>DAs_TldFV
zYxD0b3%$1bhePuA(}z48KiFAl?%MlXZtX$lBoh_Q3!VP{1zI*|?sca#G3;$dc!a}M
zH@9x8OvZ(Ge|c|3My|j6`}Nl1`sT9t>%&h99;%*kcUe!12Ct-mEXPOBi&91kQ8PUo
z`D)fQbG~Ny;+w+!`r&^K28IRqyLq-}-#8G#D=o2lGk@j!rDxagz1n+g@BX;m-`So1
z%v)9U_SXFcwxNxQpQ~3%?TUSDtEm<Q&b&QbY(LI?Tc4@7>permVt3GvcSYSzwU*l#
zo-}OV`~17~r;6E4jJ5vN8TVtaF0lRmx^%(9(9o~Ct5)S-T=wYIVLSP>?V4wwhNLBJ
zF`B%{WX0d?2hTHjdrCK4vu04}P<*G@mZ-zPAY;GPcdgdCtSj?Yv5AJ8uK3~LY;b0J
zh-uy1pu(UB^6YG?E}F_86Ds7VNUU}243wH8v9xicPaOM#=b(fKTGD^Ou&dZDBd4n6
z*fRNl;?eu>2UO;6?&3FD@x-CIYqeCf;=UcL0)J;oUA^>-|BOd`<o%}~WgmV^sCduu
zR+3XTe(_WxcXm03U&5fB*ADy}Zs&THT26gcl$8{ADe?XGUw=MbG1<!g!)<B^>pn60
zj0^Xedv86@4Jw|!zt(c+I_4Ue8NM$$8!ybec(3#Q4=WADsFixll@3?3$ua!m0vXxB
zsdVT~<H@I!7VWOtzf@P?fj6JYiY2=~CUmUNyK;}Y_fy5}E3b^3gm0G~O`CkD&@WZR
zO_t|VXW%K10xf05sF$36M=h1BKAA8w+y@^raG*))@LMSxg$-ua;kRn)D);!_s$LLS
zeVe)F_;=a$)3zP3lgkG+`!75#d^Gpr!4LE9$vt4Ux1KsrVCfW#<%cBP_!;&#fi|dJ
zU~g@|d3(O^d#eM|Z3|EBn&uE;e__SmtGrrk^MXz`x=r22{$tj$u*%uDo<JHEukT;B
z3$ec|y7bGp3;(V<_>0Ri^vQ#~=wP3+`KF1%xrL2|e5vVbB|H6Ni)VieHD9&FO5hgr
z`sUcm{_UQ*b;<^N{y8LX->-d6P>$zy=G%w1XRpdViw!K=EmPFe{`JC4CWrrypk&4o
zxUsLGx8!){66<xRzt`3|r`}V0yE^mAy;sZDxBO_fzizT^)wZ3!uBltKv|jJx*7rQQ
zkuO!nV(PQ5peUyW%JZ-NE3RsqyzmQy!Zk;J%QdAO3=DnCe?2pMyPC7)o;RqiP;7AG
zefIx-JKw&(-aLJY!EWDo7VVdA?S9`FuCQnMu0>Pc6=_*cOMTk#xD&j-h>?Mz;7pRt
zL#c^E%LO%K%OCO{zR=iq?XXjIrtjo$TmQfLXm;|sIn%>^%U0gLFDjT8>@~+&;@0ID
zzoo~@|5;jIb5xgenC}YmnnVA_n>k)xogqO!OOB=P*mdXlbMuhE+iPk~*_r?Bo26En
zH7g|Q@`~E4zFO;fb+oS=I`6&mb<G#qxj(La<EzQ4Hoqq;|6|rGE}rG-wG|9uE+8`-
zRvx;Q-sN9vS?Xo}d&|#1TI+N9*uM7}{J;ACboOlimq)_Oe_KyZmh~#NoFXN^G-SGS
z<~og?kC|nE_C+Ph{t1<Ic>l;BG_Mp9S24qO$C2prnmXsHrLv0uR>%HOn|jqGbN=?g
zRMWFrAAg2MUSD%+S6c49*Kfa76!sPDyxo4WyySs(-8+s?%t=a>dU6K}ok4DL*e{T_
z{p4M_WmnH$Dr!qyv1*^|@2pw3(pT`eRP=v;aDCG(wX0^^S>v9T{#vwW-R=7~KgR5_
zwli3>FIarv-__RlG}Bp5pJB8x0wpg7r?#_cmm4D5*^XH4s#S~QS|j-Wr&opkXSsj1
zuCbpL&mS$<SF}>P(dpmIU-IbkzDslcOD(5L$STh6&9f0W1wLKlfMSv?DEd|lYHqyy
z?((b8s~2svyixkSg1_v6wbd87{K>PT{;tZ)2rYG9y{&3jytbXu9li2B&Y2$r-#WjP
z^(u8Ot<tkoJ-4vKKy3%}FKw{93V4+I9__3+Z15yE=e5W^>wWKET$o-td&~ZPprTte
z+;r8BRloE0oV{ju@UG(F7s2~K_0-Hdz4C>}zghfWT<`1z&0zCyyqQzv>V860c46Sk
z*!80No@aCIrdH3kX8rDL{YlpQZ0HrU)r+=8&VRf1<+qBD=RQ0zDwb8WJNq;w$|<B^
z=S~ZE3+Gpi-HPC?vTDUG?V84#Q8PU+-hKD@yY#B83{JKmweg(q<?@pYs%yLb?=3T{
zoShXKa_#SgUo%c@@Oyo9`R+MhrLLN~3vJIFch|J*65#)$2KL<r_8V`_RF7LtscGSS
zmo+=;_Vspqiz#*SzRAmTukx-1P5hkhs;Z3)y#IFNqT9|jyR=sYvx6K~_4|9_xsG`j
zu?i}JpnbCqbt>m17h0sZxb504A9Vl5qshl_FR8uS+gSQ&udmixJBdf%ubRAaa9%AH
zRdXvaaJ_82@T13b9yT(}7kF`Od3nosmgPo5Om{2YEl!_dbZ-WgT?{QVR-7;ZRl*mZ
z%*gwmne~<RP*ruu_bV*>-mT8NvTv5dg70QqS8iMB>*_AyXK!#$a7Ms-+da$GT{M*s
zuIy>aSgLL@RYdq?K$QdJoR-)EHyO(#*Uh(Q#``YzTFu<J*F@rl^w#yeJgZkrU%m5;
z-!EtT${nvCTO4|Oii7R@xAmQk@7Yz^c74#%Zx4VR<Wk^+C?WWloAWrcOGSl7-CeQw
zYHy=Jye!8TS^MeN%~tO`yIywc*IR)%zXnvz_ANSYId!50#~aDFjCBD?B?d>Vl_lKz
z_w|E4xxoEKj+yFF7cEV-O}9JW&bnRm=?V+e?|aO$5BDy#J;1(g`<9>0r_ZguU%TM+
zW0m7>TFQn|&9|6;FX&^q|Iar2@M8T3TA+2E2~zGAGun7ME>2*K<zM>!H*e@Q^X&JH
z#}Yc;|A@NuuQs-Lw$|$?&~_@{qgP|))HT&M1sB+Vsb;cS)36$}jIkm0=3BF+M=Ylb
zJ-?#aEX-eh-+-r5`gTar-361rU5G1MpeO;V)<DZWR{atSy}JFy<w>*j>d!aK@Zk00
zYWaF*J<E?XOAZ)RwOv?j4mk(sOQhLs-*qoIb}ovwGTi$*@r!Bi+P95+i#zl!FRXaP
z%)2T#>+-)}VsF>K-^cvEuRJ3lX^YWq=W6$k^FCc+vrexpP}_L85EAeg*6&=T^rWln
z!i_IK^zN_E%n3c&$R-+IYkGvePS5Vbf;O@Fr}muH`fOJIlc#3J-DM`zjc?SR_py?=
zXDqyUs?hTZch4CWL5rG(31S={V#^;`r!CT7mlaUxetqvM$D{00p+TZQb{>7sU-ryC
z=xXBe4xa7oYU{nZN*=f`JMyZ_xb54ql?(eJnb5&}pOx_6A7@$)-vb5p6;pYh4{u|?
z_r3eQZ<g9tnN2-+UR<$Yvf2eMyBQjiQ?}oJ(pP>;_I-#SxW)1F&#mywS5`8=@9HvG
z81#kpOv;63(D2NfH9D4a{hmI1mi6}5*1PY1zgXO#_3Fw>FCU+fq@+ckK7HDCr~dRb
z-CYlwxStg56cH9)3>wooKhM^-61@GeFR)@p>`c##R<$mh(tKss#QV!Ho$n``Ru;KI
zgQ<!?a$n8PPoF<;&A6zPdt-wmXcVYN(l{h8?%vaXtHah_dOAIRUHQFA_v~wH0@Ko#
zE%TqhZg2H>w(mbboz^!^JtfjBXB!1lw|suxD$r3Ne#<v+_u~dFAG+Y2)0QZaCVo*K
z<oHKNI-~YfY*ab<{=2rZaq#Eo=dYhNzh86Y{O=k&RW-G&kB^SdGEVPP0d05<c5!I|
zo%4{q?ljVtuh{cRpiLVK{wqsI@XLOYyKw2!qOY&7YwPRl?>L*cU0Yw@Kl|Dm%T>uH
zCMGMkZVmPHY<wQ^r*8khkNx#8T7>;B^hg>{nKWt9ojWmq|GuyPe+x0Y4RY?9_J(`R
zTH4xAKmUAEw%a<leD~c?6*kE0J>%oz*6rG51scDw`hKVQ>$BPUuO4;lUsIhPv*_yT
z@bc<8NL#gT_=DEjF<eNVaTGB>4O(yb;b+nQ`}&@s<&LF?dCfg0ZQ8u~>%(^Wtecxs
ztv;VI-feTOiIscG{Q2^KZod911X|U{02*+&Rj{;-OiWDF($;>xW$&6bJ}XzQjQ@S}
z{{Mg9cm001`|G#c`LCZ$_J3D$=EH}AURmpHm;WUn>$&**{r>xJKg#g6%kUjHoC7Kr
z7#5ft9@)!|eM8sozjyY2zqk5!{{C3)@O2@6e&-U8&#@?6)Ya8xm3x0%?(8-m$)Xy6
zP=GO9c>Q9*_X!kE8BbLKO%~5G&7QW*clNT)=k21k!`EHWUcYBi=<2YUMSUQf8XmV3
zv70*a<)x*drNB$4$K85q0ovu5yZx@3CunUrXy>A+h{%iZ{lBKV^-3+>|MzYFETdGf
z^>f8R<}XNxEUg4@XBN77psRun)GlIR;KqHdmqB-R$IBVQ7hc1TP%th{VS2{9osX%i
zW98blQ&m#a(x8VcXs=kicIxS;M!R`BQm;An-G3jRnYr@Oqem&5Z&v;JUI`vnTD4=x
zjHS{AS9atTtl3dkptB<njMuCwc`MPj@Yb!UrOTI}&U^kcU3)>ZT+*Ae-E6#4S6=To
zx3jzV#lGRf_kz0*A`0I=hyc+b9QpLt!s0yLKgOUe!+>(=*o#2EcIR#}-ARWZ8tfMN
zq8MXt5f)=@Ar@l}#$hqmKdc~UYA`ggLiU;yIF0Jd3iP!+Yj3I^bmHRTGAVrIa`fm?
zO;uG_&;nafDtdeQ%9SPA>vp;^v+)Gv<gD5Eeee5~Yu21u(v|26T92G#HhbOvf4?4>
zt&=WEeSdH7uZ#V4OBk8iG_<t5)aRB=`uBBxJ?njQ9%pWGy^yG=S&J4enlgR*^0&9Q
z&yFx$bKv>sm)D~6uU=T_EGjCx^x@(5*WYgEUk`sC`Rvxg-S79U{(Rm(9&~iq&75Dq
z@BhE|^6%oEZmU+UGO7NSbM*LeZAC@L@VLsQuU@^X`lyq@%2M)W2F_*nY17%dL0cKt
z?R+K$T5MAFdhK@5dh?f;m#^Np&u+(gUh_K(!s9Ak&)fgs6Sh8X?WIdWpzR52VZ7Rr
z+w<ni)%{2Wue{z?eD^^BJD<!2$@Do3J32V_{`>X%*Z2MZ*MgR<bar+gtbMoldmL!x
zL)5mMnRfqvBp*F;B;#0*WL8#|(Z-?~Z|7CNyLf4-H)yR9Xf5>jJH`EA9bYauAFSO`
z20GvdbT-DxNvg5?>P@bncyn{}*PqYlzh1lj-lf&+_pK_wUmG4CTN;`_mubx*hN=@w
zJSX>LuiL5iWApjv#+NQ%?rmyf5?<Xjb?Wr#(vy0wt`3)OOO&{J^{ShTOUuVUpU<;@
zzqu*3Tk&MX=Ct0()I&;H+1b4lmEEQC_kNuQ+M^+LJ+^#qqC|}Oi(9_tcT;?38ZEv3
z{cqj=iL+*9-6_9cdocIv)vIlX9~%7oG=2XUx6TbKW*VnU+5bH0Z<Pz$ZP0UbbGmff
z!L7}QJ&oe3UM_ub-o3c@&+q&H%~T{`Ut7!l?(?tL>!r<RpS`iWJb(RFzRwpO-TSeh
zr7<Hya?P4$%iJbSnKDJiGira`-l*tk>sa^2?)}#u9Bkg%w*ZtjjnmJ)DXH}UIk-zy
z8*~KG`}+UCy*xcHYA&C%$X&kH<j22rbFH6#tgwnT*PHIWbLY+&bH#UGF8hPdL)w;m
zTg~&<mdwfb>i^fyEx%VeX-8Q>ihFVIn#j#=oSdAePM+j^H+lMW_1$;Z_1pj3aXq`O
z5_H_$-{92Lr8Rc*@8p#)f19^GI(mDa=}N7LnfKpc2dzLeK4;<l_*m~`l}Z~qS2s5;
zMa6}e_vLMmmg_&>qLnCm-uC+(&?zXTudm5|le^BlYg6)ZKHb<|AwPfq{Pi}!es$^V
zYcD}1ppvq(Y5BW554K*s`hWY5Jg3`d&F|~1zrOn7Vt3F!0lWGBp#4W_=jZ8q##mb{
zTD;TEMy@|7D9CEByqw&+B}-J+Ef#QgU{Bx#t*A1~zqjW5{rdZFf)Wak9zDA2-LBVP
zu0`jcd|5IpmhTtG{`>3a%#pcUdOdd1l3+he*V*Rz*EXe|HYtA>GkJCM)W2WX*I%9g
z|4+K@?>C#RJ{}R?TKL#4w*K!|FMt2%w-^iV7#@+eRf1G}?Pq7jfRC~YnY^kg6f|gF
z_4R7_*K^kIGcGJ}Jot9y%9UA}nSrIHrOW^B>~p&P>+9>#xVUw}{<fhHA3o&b=fD2y
z>gul#n)$VCY+~}Z&wkC;?&<A)`Nqa%>s)E$w4QHoZYp~o%x?al|1iM(Ud7_PeLvG;
z%WkGxy<9T+%VYWf5$1Oanz^{RR0`idIMMe^%G1}^Hb?2{wrysip`nGctrARDRW2zh
zOF+x_4jnr5z-<5h^@k2Q`OdSsdE``rYFpxqTiNTcdh739QvdJsd?~AvjOE|eeP%4^
z=;-jf9vB!n@!Ol5hf75c?fX3U{fo`#?Jghdm2L)w-r0G!)<0~o^YVeF#bmbiJO*u}
zNjV|ldFjGJ=c%)1-Fms}BV^C@&OE0WXXoZO-_F&i@^N!dzW;vuk*&?j*0ZuxK0P_P
zYTdePzrMb1{{83k{Qpz#zYovL%Uge)=d-~PSz9%nr*Yj}dQb^e!hvF_>gUtxqT=G>
zHOEdpEds5^)SK=NDlR{L{ycTclqElYRDdSXCae2j%UnJ;t>~+PcT7x7!Mr9uLqkI;
zg9L{HYmhGv^O}Pu_pZm5-(4#I=K*`v?y|LVl}`k1Kb=sXq%!;Lve)bP$CckHY<KIE
zS-H@;{nfST{LreZU3$A-C>3|NuRLe}zvgb`^SM$s6&w6)KDyL?I;sBau>8LT^Zz_i
zpR}V)ARs(^x&FQ%N<TiIn`;e9QL3t{nV?hT_P8DEkz5SgP-_4C=6TQz<koH5UV)+|
zb$aZwFJDR~pW|9)P%TyYC7<8+%LK!U4++XfMwe!oWKQ~c(OrIKtdNdN(De28e_#3^
ztd)|M?p?ip-z^UxACpCk7x#AS@4Inhd;a`N*Ta+c?K(3nvEc54?Dcy-bs6T}u{iv&
zKw`0bznNU+lZk%sfB&s}d@VZP^iJOPhXocr?`xi0y9EYL^t^gVYfs$z!n>u{C7ap#
z&F1(mH_W+V@OWBup5d(7vzvbxynQgY{NBq??@fxIpEKoo{9@h2zrVjrPmizLDJU(S
zedPFY>Cey38fRr^`&vlPIGHBVmUwADXkP#WLqi$l*f9q3Pb4k4l4Cae-@otsXUDQJ
zF#Hu_lx1kJd(@@9Zo=6#>o=wEc0QjKE5*QI!3QeK7#L*sf&v<}=Fuv**g{4vl$n8{
zPaSml)fYv+cITBVSI&*=?ChMxboe}I!whJ}YhYlYlv&P<xQd6Ze)Ipo*?j(s{Qr;g
zpp}FTjLaohL&I0DTet4A9jMF%MZBd9-_oT^U*5`I|Mp8=YU)zGeLs?1-QBaFoR}E3
zG0FA(JQD^6MbMz9gMATb6HxX2-}B6JZ!NjGIsNsf(|VVu=|-z~e*OA&mSJ+6kg)L9
zyt`Jm|Nnfh`u%phHS33^-qT;r%3gOdG(2|cdAr{}+F@%hbO<V2{q6hz@BRO4{B<9g
zYk$35{_9=&{?#`(r>|bMYE}4oVTOi(CqUs|Cdk0>-~?!ApY7)}#zj_h=WhG|ZTtSK
z`Srh}K})zFiSNH~X{mSf`?+PeGOfN|3I4kO|L^@(UoN_rUiLL#xnoC!p`qcVCD-Gs
zeV@;--{<A+{j_ZN)QJ-pR(^i=vd{Y6i%o2^&o2A_{r|uDdHesCc{W~<mzG|AJ-&W!
z`a?#BAJafh*ao{NZ#JKQCvrelQ&Us($KNL>CtL3|ul$q(I@M{?mekW?z4G>U66aK3
z_86a=aQ}UHVBo|Yv)NwW-e2dwuX&!GZSnKT<mUUwj~vmkvx|HB^y%>`F9wDMvfzkU
zQB!-iLGk%<?~sryfp5hH1uwo#pM6$s|NZ;Is*DT*L0|{eJz3muXZ7Z}Ve&DLNua#v
zJKJpKzI}E-{;|u~DD1wwE^x7%rk<W(WMrh4j?SB_vltm17(fRN{hILXSz4vdJiptA
z4j-O6XO50%)xV$5r%s-{STlIpi}UsWHeXv8yZX3Xwa+9_(+zY$Qt|V1S97=DUDnmb
z1uBK?|9$L#TDDu&bNk)0*>xYg<26)OU7yb_pO=-L{q-Qbe8#^&KlfHIVqj=d0F8Uq
zo;WpCd+M}l*D`Of2wc3S^mUkLqvW;u)$c50YrkIg^7g(Q|L>D{udH?0BoSHJwV+8_
zP@_i8Z_bJrFEVQ1SKq%Z|L;TlgN@J5&0W20nHqQ}VBP20_cbgnBd5nzWxk)Qz`&r`
z6M2V`;e~pR+3dKgm#Uyu#A(x+7#R8(A&1^EARQo_egUMiB8!)yVISn+MAUXXsOQ2!
dofC);{Ad5d7qm1j+GhjER8Lnwmvv4FO#oGa(5wIe
diff --git a/images/mlp_loss_tf.png b/images/mlp_loss_tf.png
index 5a0165fd98fd248e70c795ffac2edfb979b19830..3a81c0356400fc60a67332cd8031fad421009d28 100644
GIT binary patch
literal 25853
zcmeAS@N?(olHy`uVBq!ia0y~yV1B{C!1#rOje&t-od&xu0|SF)iEBhjaDG}zd16s2
zgKuI<K~8>2PG*uqS!z*nW`3Tro|&GJjzUIBNkOrdzJ4xTfnI)5y1vE>i6jOF2F?PH
z$YKTt{zMRFTw%XFlYt>6%hSa%q+-t7yX7SzrT_kaynaT-_~l#`7AL34Y644LcCFLU
zN=ju7XWh6#ORS}o_3usYZ%?%~R_r#rvb8%kwB?3CW>W{N)B7b8=S?sxuCG^-<WMp3
ze8>B_W{$D?$-U+8_UPYPz2{S=*<k}7Mg|ZFzIj#;L^Ci5TmXw~U}3TX3y3*5u!9%}
zm^fa5Xod&|Mp-a}TVVk+h>^g^QUanGbQ&7?K#T@fm>~=!$&d>fIcBrp?f?Jpan-r-
z_}Wmj*}l=+axB+9|7+a$y;MzI{q?KW>+SNMXZvl-zrU`bfk8KF%ZWEZvyM#mw_92E
z_SVUV1sbZVu0fao|Mj0~<hpX@O5bml3=M697Z@L~giB{gSLsG>I`T&7_171R`?KEN
z*_k%I(8<Hk@7mAj^YhCj-Jcv}mk-IxTE)gAu^@l{-)|GXzx%iT*^AZd_g%98|I?pW
z!oXquo=;tJwO;~*U(K*6TvYe-sd(+Dlj_TA>=+W{?Kg8XFqrhR%hv?t-ri=LQQgib
z>$Bq72ax(+8OtE?*pk4xZ^L6tr@Hma=}teL>X(>da`WcRs<&IOf4P;t-gDBfl9!7t
zi=SPwOnY@@B^M{>#k=MAbKN?RU%eh*ue<y1y8nNk+ZS2QU3OJaUVc5d{+<AT+pkmf
zc06FZ{0f}z4y4biez)`0-QC-7rvAy~Yj@^rKm5k%0>{0I$Gks({>(hmAvkHtjvW@U
zl~1RZ_Eo;wcw9tSxcI`iXG^wh32}6EJay_+P(s3jl@Iq^OPwCOZ2!Nn>u+tzoP29*
zc6ym5!-LH)9`P{*q^2%iv}ln@-klxI-$cd4v=kH^{{J{`zvuR*O`A&IZoRIhr{~AW
z$oT5!=H=V3ehyf-?^l+py1M+9zyE%}Uw_&6X4>YJOP08--}`OWcJAW0Gw!q<TVVhH
z&*xXy*3OR6n=T|IWRiG@<>~Y1*DarYx$J-ar270d_Wyo3r_U*DYieq`vMN;D_Tv%Z
z_O-E-PMtno`upwn)vn!QT)ezjS;b=($Xb_OseW^Au65~I)9V^~dVYG-z3=WSy?k1K
ze~ruh;stl^#Qc3M|3BdSySt)7LW?$RFqm8YZs)J3)8qSI)_h&LYSkrYe%mGA-rhd_
zv`Ev^GP3+`>GV^lPrsJ=e%aqX^Tvk6uFlTDgoFjRw&kwQUbl1EIm7(@f45bAeYNz$
zg@D%9)|J)Y-~HPE|M&jG*9^+v#VlR6OiM-O$%?G==g(h1(kVQ3#te<=r<d}{TD|yq
zTz>lT#~<Z3r=AwOnPX=A^-6Hn$D`tVuisqmFRvTFZ_l}(Mn*;^d3P+f=G~3bo9?X}
zz3ob?c-)WW?{<o6hh=<taFC0O>r!m_UDerVmxaewx~i(G-mCs|H9S7|gOqvhttFu7
z>+0$X3J93sXZ_aX@B8}ypWm7DI2#%o&N9iIR6hIo<3Jae78ySGxx3`*{}lfG`E%ut
z9XEF8zPPY((vp7re?JQ9?=UpDxo}%C{OD+EVtV@I$(5UFn{SH9%IenF*T>d;Jo@Xy
zVg9d&xb?4OUYu=RzV6iN)23BlGCVKc+gn}w>*ez7!)?5~em<Lhbz5%q-tYIScl~;`
zdg}c7>u=}ne(QhTD*c>HZ28^NSGTvXKh`g=@0oRfUv2a8`}P0-Ry}BBHz|1$aJS-d
zub037^)->3x77SB(v8~U@$cWid&!?aeJVL;`TWHsZ@r74r274SeSG=7%H_Mt->)k^
zZ~J{hDahce$GzrXw(tMz`}g<v>(}G!*FHSlE?*LpmbMHOAUAW&?$&%hn{{zf>%Je4
zxMQo|Zq?G!xpJgaxc}Ap<jc!^XIYo8`}gbm{#CiRx1Bn3<_fp|o&}1|Z7=%m{{=WX
z9SWSgeqna?+pX74ik^7*&a;X1p00OwOXlS*X=kNszg%>mw5r~)_}k5NQ2Lx@l-i}F
zq_idVv{>zrhwb-1pP6qTKRv!~=Q*3tKDPgUJf5_~H2Ye{#YL{|d(5@8wYS#%ECTu6
zJ+wSCb7dQk<f6IO<*(k?|KELUOXgzv`agle%l)ptySv-8_SYAy<NLo}i`LT8x^(~l
zzj9Dw`glZm*ON)!TQV=Jy{|2Lxpexg@AvDk>u$fZ=*g3m)YR0>e}8`7+Lk-}ySR^g
z@%gtmH@iQdSG~@yUv91T`aMB~g@vFpB(CP8t8Vl*pa1{9?^pHw`}_O)?^4Fb##i^(
z*H4``?bWsD{Lqq;EpgRvO=AlVvR3{7`@QtzQSsAhn==nIF#h`Y`~BYc(R-`5a&d8`
zY`?uV<D%00y}Jq?I?XLQrCC}hC?qGh?%Uhj*%ueN?)v-fcK@sQ3=9P_k_-*Px3t&q
zN%C0r{b|w8J;%?^Ha|ZhrRLYm<)!!P?^QmRe0yu_=YKY}ze=1ZeLN<eU$H$jBt+ux
zudl{VPEI|C4mrIq^E_7j{qFO>m2YlrbPEc)<Y;=Y{C@4R#{b@2e0*ld`ebiE_?mNX
z&rJiK!zWIiGMeMJyztx2bm_e9x2x}*nQ6@HdF-_Q{x^5cQcegsPx|=l_4?}VQ>Rat
z{`m3Zv8B`FsxG9z&A+-Tv`yvlve|j7Hvi49|9#u>dga@#*QIo$x0w_b6}^(NuKJRp
zY-4jrLs!@KNxG`4>eZd!Hf`Eubv;d@ZDZx<w8Kw}B>C-rJWxCzTD|Y#;db*K`@h{<
zxBJ~LtK0uS_SZ{H>RG@4-z@{4!#`du?&tA5_UY+qX`Ac0+wZ2ew6;oyhK9DU&90X=
z%P~-qG)_BX@ucwXu2SPR9?3_4e=cm7GiqvX?k#<N?W$$osVSOmhaVbj%e|c@;r4a@
zbQNVQt6L||p8YEK{o`@@>pS0n{`85*^Vnp6yPFBT$BWLNooD-d!RH=X>uVf*e0CYX
z8|(iItNWR#NOFtoeaX|-(lWZWEjL?5UA;QH;?qfW-uA-}YuV=4ev4Gr)I9m(_V)FK
z&*zp)wIxdAZNJ^o(P5Cc|L?bEyYFu{pEui8^77JyQf3Az`Pe&*2i$GG-zlDDUtc%(
zx_Ere!@K{d@Bia!XlPjVd~W%k+n+vtGO7Pp(<^0qYWLgE=k3F5YWC>u|5NndtiHbf
zZq@6xT6%id%J0`+HxUm>KG?*%YQ>5RM>>U5x4-{=|JK**@#{Bi*kE<HK~+;lWy!f8
zsmm{~+*kWM>+&++_CIsZ+y9T5RPuW5_G>rm60&zBO8k1z%x{u(ghR4z;hZ@#@3U`g
zNYv8MxNvH!cK<7>SiR}rZ{5k;{_V}p#S<n7l)k$1a>e=3`M0uf@2}pX0?PE0_SClt
z3JOkIa^CK@&(x_?zs}QEQgTvNRh?yD|1a?BhV=7to<S)oPXhP8-~0XE<pqw-B9fAk
z&(BxAxUjHj_ubN{DXN)QRs@#XC0|(JIO)mX-|J<C|GnLQUrS$q{k`)xx2n%QExKx5
z^Tea}-_Pful9G*2=EYqxA)!SdA0N*?Jx%x5<9_=+*B_Pb{`$Dz-fLB!ro2hU1U6o&
zC9T}zUX$v6zcqjQ{CV~I>iVtqZ43;b1uifiaR2i5cDQpp-`dYsk9&;w{QmprPYI}y
zc-(LQt<U=3hr|4#k&%(=3-a&o3YE1kTO;hV<dx+1?C<7FZ=HXAWo7W(w|>im-`?74
zy<*<AwbAlhbiz|oQkoNU{-57?`z?FvR{Iadn>TL;74+iqHG#TOTULBt#hV!!8R=fv
zxJu6Z&-(hm-m=zZQ%*k(^6}}3+LH0`?V&&ESL1Fs8vi<e{J6G~(xKn!7AYqLVyj**
z1yuzi!orI|?Va^|K6%Y8d}V&OWU`;tOBGPvoppED)^$6dN!9**I^8{{J5j<t^!Kl?
zufIN>9xwMLJ=S{JuLJh4&2n#D`MJ|<&GmnOcgN^~%G+O$y7fiG#g`v!W`EtOJ}<z{
zt?j$5d-eIPSyx~A`?72=eSNLj&fDt8OzW>#f<;9{p71{2%2@m1!NFfIm(LFg3ww5V
zd;N{|sS<9RV>j_J_=)s5vNKfl%GpNg@A=RKu1-oWx>kL^TYhzCar)OcH$U%~vwvaz
zpWS!gJudpEeDMO$2X|>6=^$&vn^VK%R&Go_ey!T4U(UAg)tr92Un}12d@dJTeAcw;
z#lm(GQBl?1ch_|at6!VR@%Fq`jK$`gIiS*ZYsyKX+Fvi1&oa-S2MPh(zh5p#ZA$5M
za&i)pmR=pYIxO?vo=T8MzZ?~he=#*Y?&1u?WIoG@2_+?4RAj%2tFnKpu(5i*X7iWx
z_5U{C`>Q^;BxvsZ{wMG2A06qGGE8cDc5d$K%FoYU&Z~ZRaZBdqFJG_6zn&88XSwRv
zl2>6rmpyp@e*KjzA+`Vie!sOLu^H4{$+)0!^ytx*n>Gc3D#Jsq+#v67&A9kz!T0A+
zpK7Y9E%VG`Ww_Gcz{l`G%BEsN)V7?J<@YMxnc4ZST#v7>y-;=8)6+9*Z`D>CIsds~
zU$$mnuljDTVr2AbLFBrnzoPH<Z&6+U?enF}mtX7fhXw{NJUiRGIy&h8nk7qADsAL)
z@9fy<mz`EsRW<i<R(eJG?z_j|ZoXgtzjkinG09)&>;DC(SJ&13E94B>dVO2n->Ur8
zYeH--BkSt^Io2;<KDTTZs9j+D>4b9C>$TfUj*5oAc)R`nwbJXc>MM7tRvzgP1ci!?
zTz}rKmuh=I9OB;fd|owQ*3$J?m-$Mwvaw}-es<P9wEFkAx1ljHHFy54`ELLI$;rv4
zm7h}9#_x~Q)YPo{^>X>wW77F1H*LeUH8o!@`0cam|4eC<j0vC?m~O-d2SY={S9f-9
z&M}+4tMv7<R&MdEhlg6N{{Q*>`tO{xv(4qV#~)*4=;MvO!^jX<wmVkYz3<BM`E{!-
zi=VAnwQ7~$_cBkb_j^81nK&^peN9zp=+v|`GZs#mAiyhWwB+FD?fLiPSXo&?;ah(D
z-QC@LH@Cdkx3Ji8&!B$6Lg)5VFH2tCOrO8@GS{#9|NpqNva)_Xx2aAiE4`xr+s*XX
zIrat93fd1Z^w!@SB46{NvF`WV?ak*V&z)O)W&Ya&3%;y}_3!t7-%|Yi++w@%+H=<L
zZ9LiIPaB`NQTDuNe|XQHJ<YE?JUu6><ePuLweiW;H)XrGA3xCOz3uJ!`}gDbR)4?d
zZ~wO>WNUoc&D5wp6&s(Oon3xm-=9A<p`oF&SATkWdxwUFg{4RT{`OW>O6t|x{VU(?
zjCww|eBLzO=w(N{#jCeZoigRcQStbYqM}W^%HF=ZYnO3BVe0hh*}uNLl(HyT@LfE%
znT_|-9LwS@+1J-q9iJ~QB9ig=STFB66@G>csVxhb8~T4d>ek<*oA>+m{r&ZG!wv*4
zcFR20Bf0DSzTa2&R)4qBT{X$1?oS0c?ti^ny*245m#Vt@>%ZUcUti?fZBqNIq_wTh
zEaLEsi;GL2&n<uTs9XQq+U@sNUAh!>c9yBP`rMLBj+5tpeRb8FTTJJI@p+rYEiEit
zb8m;OT)C2c?q=6+u~}B7tK=#kFwQMLXSwS^6Ze#P^VU6|UmsWd>&r@G#XIvSPMWmn
zwEq5>^Y;JuaB*>6*<WA3bm`K}!)?6mvcGR6_rIK-zb`N=Yt_2_|Efyg-U{8hb7$1f
zqNQRw5ex3zxwH7!o6qO%<N57=EI8IHz5L(b-><L7*I%8Tzc2Fn`?m#VIX4!x%h&x_
z@OJr}qOLZ6`E?tUkG}#1X<gkuP%AX`^t6{-ug6_>6_0%~cl84h^YeN8_1*gWBHrHK
z9v&DtF|O*RYHMq2rSrei>#@s0{jI0^^?|Rit=(GsI_&HWLuXA*P4>Iq?7Kc3;x7Gu
zw|w=64FQRXiS8w_<#!6(?S4KHo?HL#C#a)vb#=HtC`LgJ{QZ9adfn)4GNJm-rLV8O
zJY#(R3aHt?E_U~+^XL8LYrjl1GB)1p&1-hmHaz>(6wP1n>;JDVK5rYo{a)4TMT-`J
z`q!DCpPdC&_OHIAyu7}C{+~a8rp%l7E_PACs&}%++1GSDSD7!+kJ#YQmiXcjx4zA~
zt;`I4lRP&cXJ(jnV&SyN$tin2ozgDt`(9z=mXx$e@npr1hwZOz)6>$H8Rp%wP*zhr
zR$w*P?fbjC{8s&mGJDR<N|ZPj9$%X}r{>eiw#zR~tV&;{?D=?1`m5gNZDntxl0Q5+
z=$4SMK=I`6H&+@@uemzQvUu6WoyF;g@87@Q(cNvFx96jqvcCTMbB4}sJcheUUzY{U
z<<<RPwqS4dcPWs+1Z8D&S=ia7mEC#_w&mZCdtaKCmL?^v=3^ivEX-S9U$1OuchBPU
z8RK5@_?nL!<_F(5%ATz5Zx*#JCzFMZO^TU~$3RR!E{8|bXvw)r$?aD0&h`JV$5pRQ
zlxPc#jLfw8aDdtE_O@KGQHF*WXIPc43JeQ-W_$kBRPEl=`up!>Twd1uuxMw`jT;e<
z|9n1gUia&z`g_Co`~U9)x2ZR7+z9gctu2|!fBwF&H!pjCul9zOv9U3)y}f<%xjB}+
za>WmfvfBdpKDlvaWpFQ_td)e{^2;5aorb@@zt6Y%dL`J+)wMNI;`g-3)%icS{Qdv0
zsHmuiQ&>%6u64N?sGa-aLxHlX>D7wwcgy*!?$6)(oagb0>5;}fhi~jCTx?kM#6ww0
z=@178hrzFJZ?kK@-Aw;FZ@d3|yEc{NmzS2_%e5|hW1u40+1dGZU;335fyWLsGQYmd
z&X8eccG$vi^CJcZhCiO55V^KC`uW^knO=QmW#`=6+pgZu-|x$9#W14>G+?Kbe0y82
z)ypN5r%ao+jFFjbMr@z|45QSpegA%C>qc)o^CpPlz;i+9upmQPA;ICmx-{{IWluj=
zY{|Z^*V@*0DsA)3Sn$B$WQn$gixw%Vs;HdETmJSM7sm^`wCn3)y?lI3cJqKn4#hUs
z{w~{^clXvCBL$|a#Z~WiKL7G$vcHzHva@aVw<|r8##grI->>Tb3+lInM!^oZ@vd64
zCggaZEU1=>Exj6A+84b!t@quY&wjPv@0PDxwJPZPy4bG=+2vRG&Ng!o3C_q^vE)^|
zfsW3b^RWsH3=9ovpe)?k*?F&r;j`cc>66y)_bm3Dtd{xh%}p;~->Y9<UN+6SVX$=h
za&0rSZwnqTUbLtLl<$k5pDVJO`|Hi-^PoYFqsNYA-Pn*gX-TiNdDepijk~_x%KmzQ
znO{Rk$7lclf4iBOnEH0CyOsAgF)<M`TFO(qRbY0aMB7c*IUPa4!B?02%b%TN865QW
z#`_~jT<#W~)}3XPdP;Ea<14}bS3f*F{Plt}zvm>-&|7_d{n?qu&u6^W($NX2sMx{9
z#RVRkZQxgyj^H<DG->MU;#wQA(aE`;?`z)X+i#_;N>+&J$JN~UF5R|p%NCQR%a><=
zcyMsiD&~Lx|9!WgTXM-$$}DF_QgZUtS+iE{EPifwEfo|S46_6n_X<9F^5n$ZGHpG*
zb?3hA*kO^Hl49~>UsDrPYfH<CmnB(0K0KUdnA~>G>a~t%P*~WsuI}!ypmKap@wtoP
z@wH1qW$W~qqRxH4-|c2Q9~l?7?$Dt_R{9|G8@?K_-F0$ucHWwGRZCP{Tw7Pyce0vq
z(C)IgCBI%SKmGWl$0WTyADr&i{eC-X$&@J~e}6pgpJiLUt&K-A=)L7~-`UI9<ti4O
zTn8SpF38Dn%x5t)G%T`^Id$gDn$5Px#=##ye&pSEK5zSK=XO3XPtS{><_u`KW477Z
zYCn5>`_i|!ww|j74P55l-ey{HZe{TDRSOmz_<d_%?eF<Jj6kk*NLE~6&b0jU%DBB%
zn!37Ycb|RyQA1bP_wTQ-){j0{*vL(tKE3)pXxMA%^5xep)9&sn_44w{`XHk>{j_zm
zwefb)AbCUS#iM&C`ph!%Og`Qh`u^VDtK0MAedpW7s;a6=ndi+>$viW|Fzf27soPs?
zZf(hYb$9pnTif&3fBIAe>Nm|YPVY-QJL{?KbJ_Ac3qh6ohX)5ieV;E+r^mm#>~DW{
z`~AA?W2#Ib_x33>RVhoFW=+|&d2{Lez28BZM^jVt*Qe9^dxekZ-rnY0_In>6kE*Kb
zu9wSZZ%IDR$I8aGs`&Xi&~TGU))kHShT`!x8`I9uTbp@#*-Owk$#TEBUk<X%XFNF2
z=v!9{vZUd0cj<*%o@`l=l@m^|g&mO~I0tY*=bgdX`nnryWGk2M+Eu@4(xT?Gdf>vu
z$Cca4=kq=P+h0#D(!Dh;I;%_*WJ!V*la-I~qgC$?1O~;W7O8^8>_B2qcwgjA)&@uW
zXTb}JvjnC)PYMZg<OWY!2wX^<m5^S_U1bPrWil`{H1K_9F=hi#TI}I?Vc=VkD_(vK
zGIzl!`)naASmvAJg2N{FQm@TW0T)#qFAUD_^LzvvGG<_4U@4iAc3+|-3S0;_H1J(E
zfEWkXbP&{eVqjp<XR`8nJXfX95Ij%f;Lu)~6@8)m<s-0pEG0A4Z$#R9#<)&(pPJWy
z@+!zP4Ld>3$>rXqGWF6~kSiMcl@}Z~`Ny|=PTke*35LQ)L0<e&$Wk&xt7YjmkQN6a
zMp-rPRg&%JTna(_8d*l!Wvg->(|N#tk9T)yUm20b3zAwTcp-6B0>q;YDoj>BVvknI
zHOC#&c?XiMkztfw##MA}+X6_?G~_EUI2@w088pAeaFyeQLFk6=REU*I3l69F2!Ue?
zEK%@QqC~9|l&l{-=6GRnb)OiFdz<5h!PN4Zrn`24WWf$!wT*SIC#WW3_yLN}r*9vf
zgCrHZz6QSFtA0Y^r;zv?`M}-?0l7udH1^Kv)i$e&s(sdhU3JFUp?zgj)~+4iI$J>k
z2eu1dNYq+BKi1FvYi`6DXn;sC$}U?i+a7ny0-Pf4dK&mHZxjO?D{vu^ajWoALvZ9Z
z=qoQcT;k|AEehmT1|}<?@Y_fCPJz1BfXT{d`}%HcMsT#eS6Xnm<hNR9G9;AmyE?SL
z%y0yU2$Pjg14K_l10Q$7BnwdBDl9n66u}PC%E0krLqqN4BNv-M$>2b_;Dy9p4`vy@
zfQ09P=^*|su`iRjpg|VPQZhqt1|;A?2^Sh5AeSH1y9Tq;NBqs2JV*WYpH`fDYq(Pi
zWTgQ+*x${1hJi95KG=<O>dGu-uWna*3{JQ|w3w`P7M96aK|HlyX~AKkrmSNNjUhVX
zT^-t0ujp+JgGESJ!@U#ps^3|vs;g@&C^)QKxiV^7PGs}(x*reQK_!%ul9Gsw%$g}v
zM9$7MUViX#{*H%iH*?H<=iBWS)XI;7M$&b`3(^;%mFmlVzu#RxHC6lS=5&8vX)~Y5
z$jGm9@}i<kcfa2k-7T(v?bX%Qrg?WPdZo?%K0iBa{o+Qy-7gRCX*z*UPEN`3Zj)Ec
zJvkSWziLDo_X^s6K4ZM=->=soJ)qvg=JfMn0Ra=@>V7`0>bL*<<uYhUceVF)y{!BD
z_U7$=YxecUMQ8gzADrhtE_-)pWpv(7*Zh4ym;L$sclM(oso1wsFCdlkzOIIQ9$H#j
zTau1)@yc3V*?l%|d-jV93#0Z_d`!HbFVS}C(j`#O=3p~>_4aBTIZ(r6*W*6xD{G^-
zyRE#-b5E=UlJ7nQ)}@ITEIM`a<iy)=!(w7$=DsZ~ER5P+mfQSZTSFsYb=caFxH!G`
z!wW&3r1ZI^)5`xp?zh*ox3>ok6uO7FS61#kb^7#PdvP%_xu;W2dIhFMg3>D}=ZOhR
zNv#T99kz1Ej)=_6Owg>zt*zP1FZ<iaYKN`4@c-}q|CvuuP2F@e$Gv19s4Q<}m&<sv
z>i?cw@C>{9#iM&3Nl8gh3M{6~oEe#3otC!j(c{O`HS_Q0g}3v`PMI`m(aFi`*-uVP
zoMo0fOC|H+p;pi|+ibJkC(oa&+uPgc-rW^CS=~RZsHkY~^VwFVtGKwh_N;$<d%OJh
zY@2zI(zIc{@&a=qJXL7E_}A6zc6qH1Uw>^?=;}S|TUuD|79N-VdhUy!r`~k$#KgqX
zxG<47Yv6^)zD~V&y&0F5cuHB9t*QF{ZY^kp^w*b{TAG>{cN9KW@k~C}6Sz8T?H=7H
zmzVhlgDTv8wbfInPk;USy#4h#mc=HZ9#c?B$(BWn7r(A)+rNMRqsNc0t_)tjZ10Z8
zIrmixw}2~%556p4+(4CLYI^$Xd)4nl-Q3!Of`UNxZk2l9?QOZU-+Zf`E8*qkRdU(a
zJo9*;EEf+?$n$e^L8BxYS5^e3#X3#qT-a?5%98Tw#yp1&c5|4_J#@&awY`1yf&~YH
z?%v%WKYgvCzJBoZX^FocZF5YU4vOD7AJ)v`t&vQ=kjQxuQYardE_fl4b8GSiZ^>qG
zS>g`jAM}G13J-2`yx7ok%k$AGEn7s1IZKXFc3Qygqp_2`v?91c<x#_X#RZ3j=G2w_
zw9c6{Ljz<`L%!02!$wdxs8BTe<}2bZUJi2dzV-&b(<|m`^yxt=4M9fPX@XCRJd#$L
zf~7kf_)c>qLP|w>H;49ANW};)TaB8a4s92_kSM8WntXv1Tr<`{3|aN|kso+z!Gmg!
z7aMrqs}w@&(Fe^OFE;cfq@Oj)J_D{h53qyuBNdpE&`jFXz<2tF_ywpf-vurtO5RJo
zCIJZ@u%;4lXXU|Zu!-ugAyFF3QWD{o%Uu<@)IG01u?Xyu3tK=+Kn1f9w5*h4veG&H
z^!bGSht)a@A@1g9l$~}6TFZzs%1+yqzsTa66Q~w=5Y6#o!<1WvFP1`+jUh`(gjcS3
zIizxVkPVWaaLe!o)JMk!E+lF?o_nSD4(eyHf?KFuSWaII(p)?F$i<`J2+UVpa5xB3
z`8-(7@nVC>dzC_DW1#^RE_fkP^Y^YDu-YA@>zl8TJEV<t-^rnUDa-X9M}1i2IfDc)
zL#*gmT5vchA`R4vXjl(oXGLH59=7Z0Hwm?HP~v3pZExW7<}9<^bKjT`>?nPZ@pJfg
z|9NBPSyBuS5|P<wk3j=Wh*5UhVJI7v0D`Ug=7M^k3<stQTu8KZJoifMOA=(ch`f_S
z`_n6Bh5Bm~ASvs<BSc_1xT)3vvaG0SCb*&2z^}C6a8c7-Xp#UW*N^Zf4Zq@o!$nR|
zkAM|sMPGpG0~-P@p7cRxeDf7L4rz((Yi{5>y)u@4?~*1la2o<F0MErP4((6h%vOPA
z33f)=X`AzTtaKrX1jG%tW~<F#8l4AET_zkaHq5!D_ij?iDQFO|Fv?DQ{Au&)^Ldkx
z1Z9KrQVl!f-ifx~?-YCa`Gq++Gz0|)Z_T;6i4EM@y}G+R-_^xM!@?p$Q&SVvA1JR+
zO<fA==9a#{7klvFK`#%F3o{Ipw`5;mw`|80wdBn=zs=U@gSx-5;a<nR%IC7avrIg{
z-z}dXwk~GnJlkqem-W=CQ>(uJSg}ImZteHGpmEEv^>J$pA0NxSv!f6+G_SI9%a#!B
z@O4*gzu#FrRXhCEVSf9Ns;XUjJ03JG+p|VZ@XeY?ZfH!p-{3i%AhynJ(yUpl>}r2y
zJUcUUl1l&a#W*{(S9jO{{PYwwEwnA~?kcm~TQc8jT3cJW`1!*f9UBiFI@I?kY1WO%
z^EJ!AMjowt{VU{azI<|-B?AM)0nX(+Q<&qOxT};`u2^xQOH>;)-~Qfg_uX|1o!hfM
zK04Z7yFXFF%iBA+s%jS-zucV%g{-Wsn{MWGb#z$BrAE8KI{_a=>)P#Ejw`9FyXS3>
zE-Wl8-FE!w(ag8Ew({;<zhHsG+NiBTm6bbX_}b^y|Eru^dM)y+Ty$92GVAwyf~UvV
zRW|ScsHLwTURk;G`}0GG4khoHwM;xn_yYI$uvsyjpqMq`jk=(#W?~YuI&AHwPft&O
zJ)ztWs=I!DyPeN(wQkd<py{z?GkFeQSQEKf%I5x-%*!Hja_?$Y)zr+Az8?4AEp~9y
zlK*<Q)!Y(6rhQ;#`Qj!dEUesqc;T5dXRK};8ykzt%in+S`%T$ytCANBrc4n5&Fq1O
z*0*F{ekBtueQtZ+-Bmw7KbJ4sbK=U%;Lw182erBP_x(K|8zH>&<Ckw-U&>!BUt9Sr
z=WE{P)1WR#gPXfUzKY-S;JsB}gQB8lffrjuMa}y4^>z7?=N~I<N_VZe-uC(H>+9JE
zn^?R0`s|+kj@6rf=+L28duBCHd)T-u(WBb)QgZy7&`8zfiy$NC+~9ZtuCc3LE}ae@
z&pbQJbl1mY(xE{?iwYkf^PF_7M{@DX;N?~Q^UZQ+Jv%>tz3*(Z%(JshL6Z)qptTm2
zPp3{=b@=CriOOFu``f=fYkq&q?YCiWZf!<JMj?TLiKk4>mVIo#t*L0^VEy^~tE2DD
zq2o>_T56ryE{olIgUZUbz1#aeE;S`ZLsiu^zV7GJ@<%&zZ*M!9cmKQfq)C&6?v`Bk
zy|pp9or#$lH21Uj<1uL|)2t~fna|J7{q<zBzlx`(re>+#>)HAHF1}no|DMYAt07Et
z`Ft(ccjx_me}2~2w{Ef5tOGwjWK5gR3W~1YrqT<wKE=_mFBtIrE&Z1CcZ;e2^f{Nj
z&i8m3?p9%7V0h4S;m)Q;{RQ3DI%m^1zq}_HtLiELZ%?p&{%PZ#N+2ULWLQcf;`*-%
zO5I$&RpsTu^ZycVf@>{x6(*}$t@V7bmn1BAw$DF({rKuKsF!8RSxQW}HzqRfdfCG_
zY1Q;t?X@e6AjVnj<9Lx_wodj&SZ844DGi;=Z{A0gfy(*=ovjUhY03xgFWi0Zt&(Te
z!`;2NX70cKqzmMT2R{@SERM8%{-k2*w%(x1sqWwFKUoVO14XSxqQHfVYi{J2*_K>9
z`KkPd$^U+z+Mm&FH&sEh2MQe>+&32o#jMD)*;{pJN45RWSk|Re)xudo#@5R*%AUQ{
zVti|LZFfSd_|(@I&tIPb87=&i$x>qCt!LjZw|`5!%FDU)x4k~jYYEfG$0&O??EMps
z*wUy;Dw(g`_pe%WDYwWKWM;t|ju#o%{#E*^A8lvNcs(!3H-5vtH$@<+2QvgOTwK%2
z_T%-!Zog?Nd$$x!uDczX0M(bn@gn2fxmQxlAKFHpdTVI%^zhYHMb&5Yz`3|jit#VQ
z?d|#3`(&;6*y~4c^AT3}yV9+{FC!_`V$N^(lHiI3`|EB;u9z;KYqlHQ2eZgx`NI6?
zZGQb_^ZPZ*o~KTnxDc5>cPVHV;@$4|*P8k5GKxwwJh}s>zmvP?I_b-qKc%;JEk6Pc
z@D~CX_&?mv-@h{d{=P{|%J0{PtIx05RQKnj``(|=W><Z^8h%!nul>vQxV2aJ*KRqb
zW&7*-@6}H~N~BE(hpc-~LtVqEQ(ik`^gJ&$v2vFjWEJ<AWL5rd&E4JQvm>SW4ySE?
zxzW2y<HXAgm(K1iDFxNq7uE<|;J>go+WhQ1+h}fay(@oyel9&{`P^et+1p!7U%bee
zy!!CNEuDOq{!8Bro%H3%o|3iabzterh~*2j&A%Uys~)zBg9h+ke>$xnetw><s^|Qg
zPo3LeTd#ThX<w(+-eB&~kjj$ZyH|&tTmx=a&SIRMD8X~Zw&C{Mu*AfLO3KQ6Z*R-F
zxoE)xg`>xg&5F1=KRhC0#fJ|Cq9P(1Iyz_G6iNApCC)rI@88}}-rA+NXUdlTj#Qr+
zE5yLSkf6D567MYGs0;iX%HPL<#^w(HytX#lwDwoY(&fv|e;2&-PCCr=<wC2v_oTGC
z&5rwX&YN!s4Foe-@G$*i1dTWD{rBtjq$Pj;)GS@L?A6QV^RKyz$1aK7oHjE?w(nTS
zJWi>L#*TUY3vb_#UAoC@J0z7mG;_RQkJwqXl#N$P#S=8YEh;X)d~NjhS6{EkU*CM*
zF1x7IB8oruhVRCOdby|cE<TQ5X9`Ir83K%d8I~?x%Eik&Ri*ap)o{?-r<CotOK)W^
z&wPDtty*~dVo|{-SA><TJtoCPZgI8GJ7EaP$}8j<|1!v0m0Y;9vv_Om@3OPA&HdBo
z*KSkM)Yk6q6rQ#6vh~qdU02K|EwR2AzxL23ugTh=0lS7~!3+Ea@ArOxCIA0N`=rT}
zL8FmhUtjkRD$V|-xqMDiQK>~(`P}QSxr>^%7}u|VH~07Kh&`bE({OdenJ8N$Bcm<V
z-}An{xfxtm{q?;8Xa;k`27}UfcWyp;^-m@~&g<cF-JMcj9|`TWZIj#gA|<vmY7!)z
zmVJDhlMu@C#rej@<aU*nrLV84g%|z#@yq$gqwmsRUp&u#T5{v-JKn!jCP^fgd4k60
zHEV38BLrjb@IUxi@#R4?zm|<njHjokmzP(Ni%W}K)eFT+8@bJ=56>_8yP(W!dGsEw
zJ92lw9OHUAEo25bMKE|MEs%c#%5Z098iU8CpU<zqcHaK~8h82H5aYBn7i_=ZNiM7W
zF*(O<_PjsY{9AiDudQn`y8G?h`J(C<E}#%QP*YN9@8IOr1R56Q*4we*+_!%Fe-WUS
z9A)qBWCo=kXTKY<YH7O2%SF${GV_;4Z=ZKFaPm2;lW!)0{GjG)wm~lH0{?{Bv!ma;
z6wI|Q56jD2$0@qdu%wIs>K5-^-jn9}Mz7DmmNqj+5ERbw?hf`2=jYj)7C$@FyzTrP
z%V688F9r*hOr~}FWiDNvl~y$W$2qOs^SY*xgs|_-`8P~!o^`6vyYS)R;jIY=nchpi
z01c>odwcuq&*$@J$H?|=|6g7wzV=?^g7|IEUmr6MfALrPm-d1CiDi)>8*2n~U$9FU
zrF2|d8@(JfX#ex)&r|2mUss(Tv*>>P|JdSZXAF1S$fWDpx2MGxu2O3L^Y!*Qr@O}A
z%?)+IeF9?x&pU#krKlyZRxUsFwrqCnsriRo1inV^u!~NPk5lVcylc2-vf7-B2O(kN
z1_={J&t|J_7iyJvc}|))XZpJSaMSlUe}U=^6ML4D1;=juJGm<_A=>kj?EAIXCizzT
ztm6O$sJ`L?=HEMC@6ogR{-h#whpp8AU;eiygr<O#3_}ge<{j5}JelOpx6|I<et#xc
z<H|Q>v;UcDxz23$-cl63Z(UvLPP<*14N)z-&gg-wKF&U|FH#R5K5Va1RaFHI#-~m1
zR1<jc{=)v_-vWzDCX_vSdv)u2-SAA;sW($W&a*gZ*uA?yaoI8D?W)UN#O?QcR4$z=
zbxSo&KjvyQ-|n+-Hi6vvxG5*WcFO{0UXMP`Yx|FXn|As9I@at>CI8j;Z*~KXNjA7S
zJFxRD3=f*Jtlw+mS+%o~n?n6-r{+OC;upYD^1$HS>w7``aRGbXeih%(xl(*T#{d%P
z>Pk#j1uW}cO<%L}dD%1-TTA^|{r?+oLaL8B85}PT2>$v#;oGjaXZj{BVYmP7oUea7
z4N}$lsj!qh(D0GD75JEa`(zd0zc0dT_hc<UDGK&lpumO3#R;2lntZ);Zr7(RW=1@j
zmv-;Zxc6QX(x`dB;^4sEH|cEJ<!13YYrOq(Rkt6%pqu}9&HHPqvtz|T9#89Q;FDqA
zeRr9<-rI!_??$gW^!DP9>-CooeQg1Esg8r>mF?SO|4U6>v@kX)H16ret?U0T$-lh$
zj2<@w1A~LHs{{K#mgVzf&&L+70#(XgTK&iN?{m7pMs*1&!C6$Xlsxb+`f2&)&V9eD
zmUkbm-o5+$PfzZDCrozhfKv2X9!6OUzBzSeXMfz*l)ATfsn*@Lyqe<HeSdmy-Uazn
z#*)dZV9pQgH^25@)C3jkQ}dk7_iqiF|9{tQaP%>l2(y$tu=rGb<Lmm<Rj(FJVF|6|
zx%=t<{I4(dcf*_s3JPQU_E`V;X^&#n|8Cn}e{IguXozgXQNat1ZVxux{_^C+yj^?p
z7umFxoIk$*^+x?`ryd(Zl3s%=NX?@Sx4*nNde`*jn#n4joA=*d(fNK&Q03IyPfmfn
z5+TSao8e+M+ZR-<`2XFy{LdS++E1tTdViib5}gt@D;7Ki+^x8P**76&{k5vp82Q#6
zf3}nquX8*1FVOmv*I96wHna*{XjC&=k<7if+}R$~II0cXs8{*V{qMHT2OvI<U}2Qa
zaEgs;d%2kN?^R8`kVx03QeO`x)qbsRgCzV73`|wZ)$evbpE768nuZ33rOTFO-Q1MQ
zEBE_hyZoy1_xHLwIsys`HUtF)nWUeaV|84jZDIBMz2Umi+d>i&7PRrnuKN1=y7{-%
z2Nqe9ax1>)y;;sEyC^j1;k6%@+Mp)TnGc{*sUH?hRm!0ClqQ*%RPL7FuZ`MSw6yBm
zo0W-&+cdScSKoup#AbhdbQH8A2(%ys)Nj4Fr}AF%yoyJhw!hzO2F(^u(~Dj8;6Vat
zyg4;B)weX#g6|O1pRccW>;uh0sl2?o<?F70U+0HJ9D4$qQfXMfNwUpxbF8wbuW#sl
zyIPatXFi^jjz11eN?Me+^QoBcOrxhA=L%n~Tn?K11TAq(k!TYV5;Cd$l=Ann{Qn6m
zpjL{Wp5OX?zgD^R%hlfa4BBU4V-v&5%F4yfZT+qLaN*~JU9z)&U0lCv#itdqJ{#g>
zE_=%+IYVMe-C|SY*9%R=4nTWPGRwcWrl+UJ>NyWTe|T0_R`YWmef{vn#KgJcYo1<;
zTl@7O@878catnMdRd*FUQ#zX*11fnQ6mz`jRYC3aYAP>&_#rePV8Mh50+w_Ac;)S4
zK=or}`dn4dd*5Zxg6CX6d?<LYZD{!Me2kcs=l5$5e|?$OXtg$WikH@%U%%&wR{dIC
za%?FmhuXC?<QXnpxG-vK*3@bG@$1~>DqSY4`Ci(7zpnZW>R9pqq{0~2_8lhAZ-w+q
zPtft4_3<lPZ*A(#P2H!?>VlRAI80VpU_NQa3<*mazSOj|Yd7c3oVoJHj~{IJ)3@Kg
zy3m<@>5?TGpPrnQvMgG%%y+gL#JR9OX+rEjzTNMnFZ!O3xfaa$uS_>){^HN`VpqtU
zKbry0I}L7b_&dB>`s?!?b8l~3`{?oG*K;;+*bv}7UC(w^@u6ROmRq;l?X6s3b*xQe
z_llVCf7L6O=|`xW$KGRPU}z8)W85p~JIlmVfA5z`an)~4*G6u3Grw1{*vrccv~a-l
z(hS37lhRiq?>}B%=6m_i&(B*kFROtR`P={9^6bpa#h@kalTtR{G%0-KV*BUAVNfsh
z%j@;~v(C&g1P%JQipQoLQ(Yuj+QWTS-JJhQ>fY$n$sU)**IW<%AGvCc(zGj}xbSlU
zO++Y3W1knEyazfje1GDUDN|l7o1J&5Pu5yQLSn_axz?|H&F@`U5x98I@jsvE{}1u>
z>^#;lzy8mk8eSQT08kg2iHT{}pI;Y#FwNz&HQcn^FI#-=fAjC=e|Hr2pZmSPb(Y;L
z5#Bcv$z_$GqO4E(n&WrFHKjLda!+VoJDb1u%B>!yn>m+woo~JVwov`#OLun(1_lNh
z+f9vMKj>F4nE66LdeKf{wiloF<)%nnT9a@7>d|xYOy{tPH<yA6aSJ|<7Y@ck58hwc
z;=L>T+tlS+N+;s?zt_6*A-(>>>+bCx=L|r#$!9kQ_E`&y{qj~;3bSVi9G93HURm<~
z`2OrWt545#02MeH$3O;&ii<6{e(dYjTIF4*UZ!8){_>_(`Y$JN=?AfnjiqFP<mTJC
zQsxWR+U-tVuxkrXaGq!DvGb7&>-YWd<~pr)R*xSvKozL4fLXm^@*^kHa;N&gwFTBU
zb1v<+e|up=eeL@9*HdRh(w(x41G}Gqo_+hWdDmUv_q<=e<b}dpJ&nsVrccj&)xJ8^
zB^#7i6DnCs9<W^Ao;zjVj@$|B-p$wX{Plj*TaeTKZm>Ik=MX4lWrP@IEf`rX|Can`
zz5C_<YyaS+kOgJW-d~;iKXTP>`(L3)pN6JQ2let6$bpQzw!U`DzCF1c)>-L#PWtlf
zQm)nSneS)3lx1LG*r32<RnWv;_VeY!Zog~M*O#s;O<7PTIwkYZ98pkNxkbBv{mrMK
z;9ueBz+U6>pU-es{cnN06;;(gZ|2VW@4l?f?(^J#dkZG7x+AywG$;Yq$T7-(P;B&T
ze`m9N^S|=f3Sa%YLNZ&U`Tytlx}AwxGJpHinXz)9@sP_LFB<q?iO8=i|9{WFKlW1W
z!^VG6n|B9HKl3ZFy#CATB5$oT;2{3s$x?E_{pMS<-gmd%GAFmcm=qj!(qNVPMC->Z
z^S{1Ybhe}#<l9*@&KmF>exr1ugo~S7R8DRkXz@yG>&#d_os^TyY^xqzw%?LgzEI`m
zk{tqf%@@sPE9o%Y30nGL!nUdLtH6a?9)3l>qi+qr+)usz<xBX>Q`&2%sPMiPTmIjC
z&uXE?O+M>D$>+H6%>z{&FYMad1>BYPZ*c-;z&WbXPrpeN{kmEDcfmH$kXS3*kJ(^P
z_A$ea?sKbhn<)3^O}Xx^4epafWZzslvQXA^SHAR5>lufb7#J2<f|m6#-aY<7;8BKT
z+rppqDX%u_U&{*i>r`18>$BlsrC&PlYcciNXVZ#W85kHW#8Q}FD=zpgA?GBs+H1Re
zzW(iHFS)N}`=1TU**jUO^!f)qtF69EPF`hXV0e%*k#|jd!@J}Pj*sc0Q$t_1zBhY$
z-##ZOGWBav&hyg#>q|CUt#r=mfT;F>t1jG=A~NOW%%3l-@{jUKwxw+M6rX=7O1Ql&
z<ybjrO;tlqf-Osl{9)&g$GN#~3+MgIn)~ekL`_f7=+G{I>ARn%^L6oZ8|(xnZj%<c
z;$xe#s`t(NrxnY6{kg{^ray0`ZtF*y^JK0+k+2-%1P8e1Otu}B{{CX+`b%4rvgfI2
z9(lT4+u!?AY4a^DwIiuTr3?%VD?mLArd|CH6<vZ2c%1JqOMiW`^5w7O@`X!YiG4l&
zd)vkP?Aya@EMo67GBBKRGut2sRcyrLoSD7J<ne6YU#ZpmR8GF!ke%}W+O9&6YPHGC
z3=9T>@Tg8R;BmIkIdSpKdh?8z&tg0$9nQPls>un8>$xK0S>OoS*ADX^$7bEzb5_s1
zpL5~+>rH!apL-*-dOv^Wdb^VoH{Hxp3u9+scwl{k?V7X0cSD<&4PEiuo@ak6m;U<n
zz2vID>n~{LDt-BW@NH?>bqmWkETs$#4aZzyZf?1nb58T3H@ErAzoOP5flnu@Z0+W}
z_O<!;%SXXcng{MDf?cAn22XWsw05tES^sy3>w5iriw<ql@(c>u5M2NIwp;jD@1Ph7
z<NeGG3^M-kz>bJ5teko;a$l_e{|%AWpTB!e`t!zY?)}9}*Gb+8vyF0}5v>3klLz&7
zAkqG8(W$o=|JWOWR`f(|Q@y@>Z+Os>b&?mt4(~d8@_eHCMkfY_hG)I7km-2#;<_wr
zRqDmG+Fe!CweDX1f3l08`@+-O+b=%t>+D+k+nRylKp;FLEV4YUrE^)@d)uqCOK)#k
zzQif0^!xY3yN^nrW_nt!jTJe)g^__lMiK5?KbEJVd7vT5dl8#LPj7!xxxKuD_u|x*
zxh0<KuW@mI06T}_HQfA1E}xIDbK1MHWc9OudxDpW=6MD+?2mn3n!ob&?`?tamv}^h
za?dMASnwa|T(x0MP;vdem6qMxvueXOmRIi<J1BKmJ#hM*TTzCJdcxW)3=A8@5DqG7
zOU!t>t!D4$n%Q=LQ@4K$3){Zxw{?!y>g+lHZdnI5TOBcAwqaoSPzjHMACnes@~ZuH
zIuE3$?q=YFyZ4l?EbR^wD#=`b?Ul^=Vg?3=8bl$q?^S<nYt{GVZ+}%>^?N!stZI9C
zuH-WB>aEvUZH2EWP0{<nz;K{^0xbJKe66+0=9ihRxBR+0E4OT&d9U=UY}xF#Iai#-
zm#Ms1v#yY<=-12`O$G*s$4b%>{NUn<>GzYVAul#x-Tmd@sku>GZSU{4J@N9whP;ov
z3{)MNCD-#YF!%+)-M^qtdUN;JkLRsT%dhI?p4R=E*Uwq_>W>`fE(KM`<tlC)QluFe
zEE3@^YII&bm-XM{Q0uQZU)?>WyYj=kY*E1#4{f7f?Rlqrb#tjjqAUZ0NjBX42PZ;z
z+5Sqb?R&QF)=Jj;lvPU(y}f(g$|&IQ?(J7T$mw3)D+<<OkI->rO6abyxmDZ#eSe*|
zbnVHImyC(E5!)+xUyH4e^PE_8gYz{5!+~G$9C={=_U(t(=3m~Nb*O6Ex3G8IuMHLb
zc;5;wPrUo+>VD@fF?D>x+N=x=84~dF!D8RHupM9T?#cnRAjH<33`vZfS{}dQ^1{p3
z$Fm}~SIoV)vh@(tAKx`13=9kO;K}1ch1Oj;-Mdp(SME=LvD)5fN@T>lyU*WmSQ-CY
zV(wj2zx2}H;FySp&Z$fc3=??Qbbw3n#$^vbO8iQ7-?#6Va&Y|Lc?)}Qs+NBH?$dMO
zX6$2=E#7ndy0{Z3Su!wOcmnrQgQZ!Tcy^s{MEp+O{pa>wo7iZzxp>XLOR9d$w{|Dp
zv0`Az5Q2x-fttwH)Tj+=-k<-UxUi_VIwbJv$9LRCRwgTwd(OR)S$<G`Ye0=f>_bKd
zhsO%=L~<=CDsZX1`J2F7w|thaJsA`hSULBv`q9>mfa79PR}9@&<}Ma}v7}U%fk8$d
zVbw04!x!G~=GvOzyKG<N?4?(&xwrhUUpSX<ca^$(6?66VX^ERQKeJ+BuyBLxS@83O
z{@M$Rc&oP*)NT`7cmCqJKYL5RxwHLTUH@Bx_l3aXM%kn*YE~zfcrh|K6vK;7mRahD
z4YpV=cj3PNJmcZ79Nn8!RzLc*%Idg^8*5fVyTsRg`NjX<E*6!2WVAAhf#HG-B83V&
zFTK6^s`aO@hkw~dZBcaB-u`suso?MS&ts$7G97+rfGpjYyIADSCu5M-1Xs9Q9~=n@
zid&jGyLf9sZC~A;8H=|{x|V+XeKXg~vnOExw~JP5W4HL_3ZHB#W?=XM>JLJiArGXB
zx=w$6e9ZP&p>to|ogMDCi@aaI`CfkG%Hq<NpxbVmTVuCWsk>e}#K+LkuLzHVeVNsH
zD?zrX&Mw+oa#`-Jmd5JM_Y=3je9~g@)w3_?{S%F^etjW4$u}4oEEd8mc}9u0g|d~^
zTT?E}>E4~Qdg<)%8P)IZHTPv-SBj{ewCZrx{3jZ!uIwk%k|WI-82m)wzGL`$bLReV
z``nXPH=W)2E7N=4CF5>BD>bzvD}!<ST>BQl;wp>PgtcqMwF+vJ4H+x?t4+#{-Wi
z)59{3UbE4?J!$pR*~OqS#?T$MrG14~Z(iNAirG6!snjc}CFqvRiqwh)o}$bQ4U56G
ze3dfJ#p1P~3CCZr*T3F=zizS$X!A<H-LH(fkGs3OO;b;aoSkbOUj6M&;O%X>rLWg+
zf7K?PXYpuO1qY){_a?9Hb+;oIh(>?jv))!!lwDR_UTndXBOG%tsX1wO3W6Nu=%+TH
znc+YeI7j8Hyx;S=4>XT?b91`3scGot<^I>J-|t=i<x9!YW5?u5{)mRhEM#P6TT%c2
z9~TG5g~$E&>p&~qKug4<wq~h%O4--#0j<0L`}_X?u%e<(`+mLB22DwVHma8%WEGbQ
zjJ0WD=wm-@u%*<0W!<e=%ja)@`to`1jfE10JxO+TD>*|TsVC>41n?FbT5zN%>E
z!e^a)laawd8eA&%s+gLF*8ci(@ytwPRZqEo_s-7FT~DV)o1~taa(LhUw`JN!MnT5u
z=dR2!Ojhv(%{aOBN-eE^xAS;)%=);!CGU1VfAz3k-fq#Z3Jyk-X_vaR{_a}rV4r{f
z`l{TYQ9E`0BVC`e%Zk4#SyBDE;HythK!2Fvt9x28oq>%$4(tpL!k|j_FQ2rdt7~i6
znuvv<8Q<+U-<I*pT7`6r>)S0Vo-%FPs!f}M0s{l@<^KKs{kr=6nnl^`cDiwLa)M^e
zx8~io`ujTmf7S<^r%#`1>EHj(|Kt#8+FgmkVvoY|`9Zf`wYSD@S@J6E+g;XFPy;UP
z?TPF9w?A*Ka&Pp~x)ZfYGu+$kY1Mscxfko@)*db`T3zrpLv-D{wTG{2HC^CiV5s3{
z#8~#eHg@;7j&q>>!CH!nj^LH`vkjBmKt17WYokw}Kkr|*I~LT(2QM|KTn1U2{@{il
z3&TH_*=LuvbU3KGu)MnSPFK}4uYVzL<k6_Dw*ITPiNDxBmlYIwO?#(C?XYv_zW%)8
zzP#w0kndp+UtP4?^DMjN(P8E@yJQ#`8r<9+wl6p?SM38jv|-=hZ_%tQZ+;(t(IaWR
zCFP`0?WdFKUS3|8zTdB3&nc`XBl6_CdFiVV-&rOne-|evE))_Lmi{Znu)vOQx9{9X
z&Q~?<LKgoD$=6<AlpX7~G3@=B>-pB)TdLd}FBx}Di4I!${#xqm-K970eLvo|>&o8H
zQt#^REe3P%XvOFrX3ofDXJAlgVX9K@=<J+$_+da=TH4&VGiT21>hHIIQa!)wm8R{t
z8_7`{5*n+%ym)wbU-|pEyQSA-f4x}TZ&LH4z%!^{&UQ=IRV~oD5xi_A(@(2TKmApG
zJqLqS|LOHrK2_hZJ-nBjIyG-;x=7~Nb4-7CE#8&=XV)2}RMDy0xw+FWT}!L%@8$V)
z{p8w1S4|at)YU`Szdw1AaC2q{9|MDlG0PXX2PIaZ9au}3E`7PETW?Z`t!GSB)U3RH
zKcBJP|9(W+|HVXixr=)$KVR8h{@&`hmbP|iWTfWqyK(8e4?mo6Jh(hJi6P@1(;wMa
zGbI+z>lB<cNyW2T`_8ZA@>1^K?{{;3y?u^p?Rl-RxR(d-K5m}FSDGogERQqs)h#2R
zyF!Pq8tS<){^Kjw<6vNTz#(u!`s5PN$tGoQBEG)68(sVR8)#QaDDt|8u(eT{x3*+X
zT5?>zKIZ$q>h<@kUh9I^-6|_PPZALlS~OvTKx=Dj=7|Z4pmnC8k(nh+RBFH9EwB3f
z_4?N{#^-00&1JX%T8i-1t1D>zlG@4X(?WM`)!Xc`|E<oI_q(~i++OFj_HOKoOI+K_
z_kN42E1P|5J4fL2X$xOnGV+=G*!k+#)EQANyIv^qGcf2hre2c*6%b#cn{Pl{Kfm2f
zpE_%nmZ#O<FPEpznX?9T#LXno81UNY?Q$i3zu)cl|9-!Izlx@^vNPy(2hiq^J9lD0
z(<y&`d;}ko@I!vO&QgX4F5yqVWfc2${<{&o;(od4tNz%l4{g_P*>vym;-<N;E*XW)
zeSCP&!M)BpYu_?72+M<uEl|I@LEnhSS+X@1G#HRwCVsU>_sX+5LY1$6?q6^?FL?Ki
zgwl1KZ(^g`Rt4A05SlT8kD-7GQ3oEd+Zy{MyZQI6o>%Kxt6%5m^7DQOUcTd1<csXX
zzdZ{Va`Au3e{_nG;m2oiQ4Z>XF%)F9C2DNm5R+}hwRPLFt+uv{J)}2xhla)%?_RRQ
zV7k?jwiU@a8E3Bxly>k2mT;`jU|?9&4X?`>jxSquYH{z)qh9AVF5T);TACih{ymm8
zm3N=I){T{Y8x=ug9DRigUx|pXn*7K~MDR(@p;L?uD-a%L=v#kilUMEut#A9TrHf@c
zmu=kk{9Wz`+l>qL=8A}~`lj1-)y~Z2ZFI|u*zSa*?S{u>87`C{oPWW@OMBH8!__XY
zjk>q!t@e0r)U`!#bA<J0X})&{{n~F`ZTZTl>vY9%-LBO&K_wgh$bM5>BggQ78(~7i
zx<{Y1)}Gh7^y^Gg?itN%$JPktp3=N_&30*#xxul!=ktP#&g{EzZ{N{<Vc!#`#|y_Z
zGCXjB_lj5;vRAG6lo6e`64b~{E-Nkn*=u_DnYY#*-hKLZIu37>o3HArxx9^Lsg8ai
ze26)9`{h_Uh6@JpW-x<^ep_NjxntzlAKzvj*k`yqV)iEQDZw%K9{gtd^VX}6@7uO5
z+p_~@AKl){5thMl`Vb?-3AQj7aQB&^;E{&L+BGlL;@9|Es@_{4{e8-cOLsr~zZc!I
z>x$m8yo~(oN-k@anI&KIG1$RJAUGJ*_i1f+zy0}JR<N)4?`3n6a?j{l=B(a5|L!ug
zK*ua;;WgU~4|?@+hV7n_ur;-<lEFd|(Ox>RU36M#So^m(NxA1%^1YSb+`To`zw?xq
zE$=@46E6d9&-lChOnXK2!{#|YA?yri1>q^1Awa(Wcz|2tOta@xD&nKF_T`<;t!S%U
zx$AUIP{@Q^F6^3`S`nTXuijU8XV*45F@IV)!v+mRFg?f!-L*P>#`@gc^>3~A8}5#%
zTspPACO@MgzO?-3iv(47cFmp3Uf0}yk>JW^J^KY01H)HD#$f1M{^t@`?%m*3xjV0}
zUt8_9$Ns&ZhV_%`!_0rD%E&HR^7l~n+wXw~{i5zME|`e$EQ9aTHJ8?|t@hfQ>R(#^
zQ@1*6_qpj>i$7d=XnW-8a<f3kd+$EwF8WqFKVMQ#!9Hy^dxJf^yU)R}p=qyZUhZk#
zwQ`U5Eq96j7P5U`{z=V?>ke<(^V>QnOEyyW-sDBAq907oih3lxnCa=KVv~!Z3=W?`
zqcqE6Ss567^)9>&SpFlRw2R+1BR+CzwDl*it)>1ex9|RQ&Gzc#t8Y)-UCp)qxb?)l
zjX7qTb~+B76CJ&JR^?UZ3(KxJa^ltd=$2hCtoRu==tNzp<=L;w%wUrKZr06b+pnF`
z+n?&+x$Ujy)oHd%Z*5!dqAL2%dEfn~>EW+WIH<a@UTyi|kUzt}hbO3FLcMS-C&P?>
z$RsaAL)ziQ?JGlF7DTzStoqORo9A#qo@Z<Mp<SR>bguu(ZO`A%-m-2Fs8CS7HKVrR
z_3jYc*MA%j?tS+A+C)dLl1sI63>PXg9KRd>na0ELV71m=xwq%^FMrz;l)IkuBPdZ;
z=dCPzGF5B&jz=4wX9&IuN=jL9%jIo$X}+lBik1$Ctvvhz4fc$34xd4p5VT#X!T&?~
zjn(Nb{d}v+>oZ-amR(`__V#z{_qUbRTa~wmxIPU%rKc5M7PvDk@AUfbGKPwJTe~;{
zoP4$G-&*`Qvch3&ACJ-jW(K}a&{|yu%@Z$|Pk!W78g%EN@O8<yg-v^>US0ccXLWYe
z{ngEX*8SOgYij(my;Jj^icHsDz9VM;8;up;jgy>X?_K+TZQ@nMeW7Qsw{$o#*)aUD
z04?=Bka#1<%$3DbRPaei|20AOx8^*~KbKCuHFJOX?oZ#d(*MQ1J##zS_}|`Ls{3~A
z@rrbPdP+;{x_|WNSAXZs5Lh1N8nS)c_CB69l~-GSo^W8b{JC73Va|yk>uneq4$M#4
zd{ad7O-RayVD;@>_8IlL4eRG_$cxTfS@!HL_b2Pm-&bupw06!TuBRg5CO4SmW#5E2
zxhxP;V!C2f6Mwq;bO*!ME{>3Hf$49`rcGpSc-939fD0$S@qcK~ZoD5Te53qH=y{j@
z$@>jIRC{TG#-R4UTVsE~TDDBph2>3lX+KBE>scS)uT^3am3}p8{^5Hk!nqkN!ocg4
zC(Ev5jhn>vEiB7DZe`ii>6zt@r5*R@#xINgS-o}l0w2lN)Kvx5Tb8E9{`hqxHz+5g
z;n88{DFT8Eer$Mk^Lexz%Zk*BhDV22JMXbqlw<I51TCY_U^~S8S5!`7T^{FG_8(f?
z-DkQ!y)|)n{OM|6?LXi41f_~hS$Qg1WAO&7GwW}~{eONVC#YK>+&5wUJFj@zN6c4O
zEz7pm{m5{^0=$=0ox#3+Z&lcj4u_@JHs612I3+k{-CfpH)Af5#-T43ekP*nVOSu*5
zo3k@?_8iU&j#=TYn|-a`zk?&BCZeG{r{;7A!%-GvIXQ+tG0<+K1MD~6njLLp)iBd>
zuoZr7f9$d0l<263yzeAgtGBLR;Ilh>+N9f|!mn1_w1K7>!W|=<=RbeHK~pa${`yar
z{O+2s=RYPgW(mHU)GWZy5C;xLk5zmh4Hfl{w(O9S;eNj^vvYmy|Jw3{arxSR*8O=E
z_G8VWSJQsnTK6sJ@99%l`#Sr%`2%WZcz;a1`p{PI%+ehj7>>5?6p<EN5HNjyj#-mU
zgRnfvr3~(861QJ%dCoBR(z1WaT6g6@P0qz?d){YHyf6M@bJwY-_ws%I=(qnk<51sj
zzUFv!^waNKk8S7S5Afkw`;lRmJt$!_^lAV9d+f0Jk++6hmhXs>Y`yx>c5@m}ja%Z(
zO<bo}*X!v72S!f)7PgDu(t7Wf{Fs`zN6NM`?)&z8$A(w)7#aWd9ovz@V4(+&ij1PZ
zzsHVz&$#=caH(i$DN}Xn1pRQghx@MmJafos=j)#C^Z6fY7Bbdl2|fz)S+V+8c1Q3=
zQ(LB}w)elK-aMLrl3|wIf6!D-Ln^D~-zhSWmp_^DfUSRJQT3GIn85V3U$>_H?3uOk
z<4xD6s|u<^Lc*3V+N70xO4IVm^R``oMB*<`-RGL|b;gey%=@-+EOnJT@X-~N+#EJP
zd%mIiaEUX|;ef=*shhe_O%0BTd!8K@SjzqPU6WC&*p!tk_N@9fHFV9_$fNCl-fUp7
z-^x+C(shOR#)XW(YvaqKQzqC8-(|GW0SB00h1qQ1hmHTZ_`ig=-<aSm7abC|Gd}#+
ztH_s|xlT_F4Ow>P@4m2*%8-b#rH3|Y<({!C>E5${aaG%&TMzCjnI!BNi2rP=<Dlyl
zQ}pX*|MR8H8E)XvJ5cAu{6|#k!Fs-p3wh;|CEB>YhiUHKQRVjQ4Rc)Nz4fW5wAY3#
zy}cr`Z{5SKkF#A_ey(=y@ZOks_3;z_oVoogC9UNppG=uM^L3*z7dUxXc!3=1_>YU<
zs$|0SC0$zmMP0AkzO6Y_lqx>GdViYuG|k;RVy5PS3bIrBYi)AASGC_=_99_<lG8Wt
zcMJXUSFDe{JkNmNL>`=o7YJJZedE=^5pqaz+T6*jqMv^D(q4VICUNJc?$zqsZ`+@F
zE)u@~lb80>ZxW|gWv;#c&iQ(P^t0^d-;HZOZZ*Dryk&=o;0ym5>^@GQSYgmPkhWP<
z>&D6z3CsPutV>qcEWN$h_NeH+&Uc%#o2vZ+UB7;JZRhyh{xE(0I_0$gPhWh?o_T+N
ztKA#}ewlEHgJyoTZ<_f!TUO**Hq+mK@!!Ekxf5%(c4+Ao-Oqss{ao3tMP(m_glt&-
z>vzKPMcGl$lbvF_IM?z0bo6_~UZ7#42%7%ne3Nmg#&xQDwb!Yw%Xb`-zV|(U=6OA>
z)i=XrpWT%^Ed8tBWx;-D!-anDe^lg8|2?7h?)$~gF<o3`S~>^%m%aq8P5@16s)Y7W
z&zF>2G3{ZyoW8#2)6l%9Q$zY^p4VLos#?GQwmm9(D>!J~n@3%<uKg^U8d|kw`=zyS
zcUEt;Eq|YQ{(bs(%}vWp_icM!U$P;1qUn8iHrWrRH{$i|kAM7;%;4h;4zz+RF?D=L
z+pYUnq~0^&nHsWVZ=&t)jQHulW!`UlHSOn}NnE1S!&ko2h<>iBX>#Da^~czf4c-?O
zfA6~G&UUrsufXy}O{<#s?Qb@Ho~R2BLx$c{X`4YaQUcTGPg>N>UtJTqG`e!>Ro#y}
zRYmW;|LpbsZ_Mqi`O!fu_gv!IY1`IyY85EsowZbT4+)QGn7_z5=APk~i@$Y!!=*oD
zN552V`osApqXc61G@%FYEgr4=7F5bz`|zID(<ve66ZS7(w!7BPR~!@^r|#-sc|JAl
zgu#+So3upbQWx~wEU2#iQn&W=#NVaI(`9V?SiuR|VKe9~M7byHKPc31%@-`X@%XNs
zGi$YW_Oa99{j<+&-ZYP^^DivzuJ!ZW`N>Ob%Kq(}{1)8bwfsxRt54-O_9WK*k9;?I
zQL{;&!)^QOn-+|a6C!?0x%KqnqNcg81gc9K?bdH`s@!__wYT<^-SMw$;yhcwU3sIu
zwqW(8*#Eyi&wcF7`C^4Q*f9)ziT@qymz>hNdr$A{)rEWSSohf(y*P26`936FH$2uk
zaDO3q5Jl#uMU}&(8Epcf<iSAqLuenQmoa8UwIzB?;^OA^T?blf-7quD*fH0w;U~un
zyJag@cublyV@3vKyZV|1D^_?seE2YJI-{pqbNuulBK^k~$L=oEG%*P=o4xj;{rU|X
z0y;Z8Z|$qyedy33slT5Kk{;|kxbe}wgBw9K2yeQyH(8>stE+3ugb50}=UzM?=D@wH
zf8*`9S$B7pPOi_3h?w#6Kcmy<!?n%HNB1@-AFPGo)T!0&(~7enUI*<mU_d#ycX8m(
z7(LLUs-m4SY15n1T7I(~@2X}$-t?OdjJvAY+YhOLRzrdGL3TEw7{Ck)eB^_F``o_X
zDL$`hX&K4L$Ot+&V^iwsGkTn2M!C04zP`NdzHs5fuHN3Opq12LzI?GdK4t3E%tI}l
zU44CF9v&Tfd%pywrlrjaF<kRv<8iskXVWep=@j03dq;l2{JGZUuTE*N4=E|x0@{?Z
zx4QiAm&^Y5{<bB)0By)TJxy0sLSn^2=k~0>zrL!4@n{$9z6(0o>dLm<Xwb<9wZFer
zy;#`3<-g78JJvt`{eEwnbc91yMdd`==FE3@c9wpVKWRH@_UzT#>vlLLCMI@ubX?ea
zJ#O`*M~~F@a78dO%Jx2+TYm5263@vc_iMj{Hq^elxp{dTuk^AtYjieyH@nt+zgs@b
zAhBthPUNDWpP#SZv?&O*2HVTaYtQjJb-&;K`f}O->%HptFL%G+cX?gx?vj(L(`Q7O
zu1Wd%=_zQb{FTM-{Z_Yk<`*p3v&RN>O7Fja|G>wd9ha|v@pAe6u#}V~-)E@Lt8hAR
z_j?U!0rFDs=~@;R5r^A&gCiqnc1}H<({KN8N7~t0OJ^FVXMwiN{P}qN>k(o9jAK2L
z_mX#%2LuHznrB=6YUlHLtIPjSyrUc*Q`lPf|8M!(`S$ViYroCh_y1pYue|-fm3oX}
zi446<^6&3E*1{>g$A0(SJY_}2gEP!>XLW`iJhf@lCLYgY<@amTCE607Jby0z@87>;
z)$jK{w@o`c%d|};`QxLbX;V47HMF#htjgYGoSAQbU&1J*qwsd_cFDGbwavfoRz9B#
zI;TeZ&ijuQJwJX_JU*#D|4ZKK^XJWD^rnBj|Nq~1O|KSLUU|DaJD$zTewDNP$t3R{
z?R7g28SuQ5mGPPM@uItYWxd^`>L>m7{|r<lPfyqXF8BGUc)Wz!?6Ws^7N<|X%J=zV
zQ=eO4VBkVg?XZj&7Z%>jE!!RI+|GCP(a~<-H9TQq0Ral0U0q$1R6ysXOr1Kl^xfif
zbFEkV&NjQ_exZd^*revi2dnSxM{1jYfyPx|-QB(Y->>WYwam=IKu1e~PKkQ|@9b=I
zZDr-f=W2!J<knre67o)3{yOwLtGl~OCoPGu`{`=`=R@;3+wU=+cdRY;DDQL^(~n!z
z(ZTWc-QC;OXD%#sPThWcYst$?YU?GReKg+9FlR<lQPGpK-J;^+;px>eF>|i1i_H!S
z-C)#zd~v6+de(skMysDsCaZc%N=g=4&F$*wSW%o46Enxi$jE1fP}s+BxAU`~o|+o9
zujZ%YZ&2>>oo$wRZA~O-Z}_`iul01}_r-wDUYg^#{8*pt>%Hc$e*YKy-kU$$D7A}=
zn;W#{$x?=I=iNNeDRR3Wb!n?~R<pO4s(klwb8AaVPJaF7`{&QWAt55)C-y5YVBXNe
zDXjKl`~AAh{Puqm$}~6Zczb*M>utC5F8}%YSyVtEq0BNv%|@;tv_l)T*nU#UDb3|z
z^>1!&HZ6M-F?n_K)V;so?XLQK*8J<W==_z@+w-EfW=&P|o3o;&h2@<r!*SWzI~O$|
zTh#exnOYiy4|P!sWnI1N=d;;gZ>G-&9~t)g+S<LhA3uH!I#ln^pFdW|i{*cA6V(pW
z(AM@|KCf!kpFe+|Jb%9Y{l4FRpj`Cl&mR$S@#D44&0BJA2F30!Tf2F!_jJ8m+j3{C
zRKDl^x4)Gcv;iG-?k(t8*>`t$zh1R^-KDqN@2|_eyv*~_y@L;BPudE}%hz9UITacj
zdg|=ir*@wVSxQW}ux@iV-e?rjnJ58v@<n&~m#^3F54*iB_w}3iS65H}^Y`!TJMJ%E
zzPz<9SNi)ze(TR?jK6|b_+4EUdN2I_y}hMJMZ+uhKi_w-k&Q>9;DS-#>uYO+gMuch
ztjxW=t*gI(eerz}F|n+dmzLf!w_CSmOGsJu-FhQy8NNyzIatE`_4W1gKYwc0Mr=G(
zeFk(OcmMIlSFT(ECA@{*)>3D24p7gGmC{K%)WQimQ}D~j<MP@jCRePFfp%duHa2qc
z^Ir#*mY$Q<G_|z65)%_ab4gd$$J@^>yOpUH&cC^(t*z?0<_b|6nKdt7WV|nXCoS{h
z+wJ`D|Ns7i&M|+r>9pSEO{u59JnGhebyPfliVElii8&U5{Puq~Se3n5u`T!Zt9jM$
z0-c<i^me^aD(-Gy>DD8$&|R+5CHK~r!0mZ=zkEJ#|9WQnyo;vSW0X6q*{^nWcW+HR
z%=Z5LZ1enUt3p?ch>9)+?=6*kd1-0)Im_oVvGsqyf|dqrX=|5$K5L%+<;6uW4-bQv
zs}i_2FQ0h4Gxu1Jq}yz>T%P5ZGrzpOeLZ4RO6N`0gH9?}e^))78ouZFo4dQO?|8Xv
zcF&0u9?BXTCwBaJ)ZGi(R~2relledO8c*|Ywy(dxT=wq;9Zp-YGsf-txw)Wo%r>5@
z>pz}+r}(_BTTILx(BWgo=WQ-~`1_l$jEZngOk4;qDRXaaF=S$5>M47BOS1fa?e@U9
zxOcW~)$I3cKKt(dzf97)Y)ztsds+XRo12gC`19#>?}P~g$~rn{B+T>X1crp99J+eI
z2Xwgq<T`UuMa{sF1=*L+Kz@7aLC4J;v$J!pufH*3V6adGwe~X9K&PYM%-Pl)_k2!q
zA80*h!{Y<z-!L&S9GGbRd`|I~Q`+mV+<aEF(`OYMXgSfA1<VW=dUnL<9X)>hdZnwI
zo7bv$b#~d;bmmsS+X*^2VRzZvFaLhOzh3!&{r-Qe_WyaR4-zdY*#eqpd-Uj$)$u)l
zKAnc1wGKLbb9Vl|o#+0|nIi)_&6|shE9iKi?AMd(^G)8lGBZ3-26X}FIDn5Mt$Mi>
zygB8lXn4k#7Z;;8r}e6UPI3oTqA4j$K<ksg->dfL7SmZFd?5Yotfid7Y8iiieEjwG
zdOXO)70&H^rFM@S+2t;Pj_$j<I{fvX&*v_KCUo!Ixudq%kAcC@1r+9+yBQc5+RMLQ
z4F@fp4RCO9nEMuV4m+s$1)bSlaF}<NY4)`8`<ct<E(`Xz4Fw&A2&zy(H6y4U*woyt
z>N&su-%e0E2elRT^!(OFZN0Rk@bQ-qhxxBZH=j+r{Qr0Te}8`4FB6u$x*#tkv`By7
zkEV?uK<B-IP83)mY5BOv_!Ub5sFM3scYl5SeyiW05+HPSnCGNxYa$oVv#qW=yi*0#
z+>q-Bl|7qo=G@wvJ^h@`=QGaNE-ZHEfB*g9!Gm1f+`(aC*EYrpF))CZdNM4K<mBVC
zTXFj5M<EG`3YX(QfB(Mz^Jd!SlaD`|xOp-#{17>|fSI9yDRNWFNk{wgmzP{8U0Lca
z{`b>qeJxGRkMH8QWK0C@Q!ahA<idpj&<vNZ?pn`mHU@^Ppfe2?NNVcqhr7AC&CRW=
ztLy6QoT#$*`@L#V{maHDv%>s-P4MYyx|i$!eU|sU1PXi5v{Ul&zN>$~-(UacO%CYX
zv|X=Otrig%SNGiY|KIP@!@TAiW@cfa1H!wyy0&CoR065-tzO8$@IwJK^l<jX^!U1!
z({!V+-MqFkSbc5m?y#V*Jg-4>te|sJx8~do`v3QR{nf46;hvzw=%1aPz5I6GZr|Ep
zUjhRHCV-l*pkrw675B<ohoz>b{<^;Z-_rU&kL9~MJ1?G?s66`}6C=Zi#50fh7!GhA
zO56N$alhRq(4uY7Ay*G1Ff%470w1s(Tw%(<Fv}b?h6`GdOQV}K{@Ks4{&(7Le%EhM
OIpOK*=d#Wzp$Pz(#ji5}
literal 21189
zcmeAS@N?(olHy`uVBq!ia0y~yV1B{C!1#rOje&t-od&xu0|SF)iEBhjaDG}zd16s2
zgKuI<K~8>2PG*uqS!z*nW`3Tro|&GJjzUIBNkOrdzJ4xTfnI)5y1vE>i6jOF2F?PH
z$YKTt{zMRFTw%XFlYt?~$kW9!q+-t7yOkv&U;qC9c-?2xOiwjqPR|o;jV-QXH>WTx
z3fq_zxboWCByZN_{a@2vR~`FRs(tI4s@~eAP4O9TlU#2{y^bqiyeLeBg;AX2gcQe3
zj*}{rQzo6cb6<Wkk6YNpC-+k3^%p<$DSAJ5_IKOoaVNCrpG}i!Q)B>v1g+gSSwRd2
z1|};oJ<Y*^9mF`m#PI?|Gej^j%7Pi(3JaJ)j08rO5)jRx)6l>NVl=P{Twnw-Hoy!K
z8#QDwhlxOT8RLt%g#jL^si~hTY&PA@Nt+Ie+5;+Wi7WQ)i@SOA=BC@3ySFkiRZY><
z)(-ab>${l>j^K_<mx8{&zkmOYkpffIm5&uRlMeAOy#CsBrxZv!?c>jyQzuV$rm)QD
zZ@AYgSM@^iZvFqiTKf9eZ){B7nsAWGcaBBj{^NFkzg*t+``zxZN5$h;Y~32_+|GCP
z>-G5c-uipDTrPZlZSCb=^Ls0ndQV@qcCD|nd*78{f7_*u%xp4YzOr|3?Tyoiq)6v@
z-IhkS*e9Lp^8ykR7v}B%Tedc8t5@>zzN=d@FNZ`%?!5mvI4G#(_1f)OH#en5?Jir(
z#l;m85U?OTzSgwp%rf8EC7({JXaD%{u<G~Q?OV&<MxC8)?yom}^=>_Gkbf??=CmbB
zq={dY_MK(oDXivm;ZQ5LYO8ws>60ff-q~4PdfC@p_L^m%{l5y|`F648cM988UQY7X
z3yg`GGfgM5=t$|$8aq{0)s^ek^@)`*Ffb@|P2^qEF7`$5!lg@#rfP?0y}Gh;Vs6>)
z*x==US2v}e=KEQB*dQb(X3eBYLUW5wX-+!C`2VPQ{1wCFGK(`WFVnQOjh(FKduj7|
zyW64D!CCHw8_O5HfY{iz2?i^AdV2hB8ygvg#KqMeIDhr(RZT;~K)!b8%gg<*H?eY;
z9Ft7<n53nxef7eH1=a6%x~I>pTozaT)^u+Dzn{AvG;wb!d3lL>p0NPOiRYh7tH8#u
zY4?1jbL8>IjIXb*W@Tgq6c%pe;^sEZxS-%W)5vvUfXAdSd*9dkUym)HtCD$VM`6|f
zzu&i3d`y~K^=jpB+w{P|iFIFB$CutV1qCNVACK;fSOr~OUqeH~RV!A!*s^!gBBz-%
zXR`m9b2sn$1Vv|)ygL@L^?$$ada<ZGBqT({GCe0}ja#40&6mc?Zao*iy}iv}ac*}0
zzR1$j(uv`jnJ>+E7=cW72v=NC&e(o<;m60vSMS(iu}N2iYvqm|5tG&Z?cRKDaZ+Su
zYCK`7?lWV-q)C(fj_2Ii;K<C*XY;^vj$e9;r|GmXQ0ixRy{NldTvJEq%A1>;r_P_h
zzEfB|>))TBpML+<($JWoa(0%f_upS%ySGdb7ZugCw~udT=MPg5xZth7cZsU^w2X_3
zTqhRaD>%$M%P_gk&+@6r+_GDlRZpjee>udh|KjcT`(}@Yz=7&6$hf!r%nU>4(9qCb
zZ@1kJiHloT`1sh%so`;*J~G>Df0x~STWxLd<8NJT>DAC%8<X1~J$`Ij^P|A_+l}N|
zhRJSEW_&(xAKxvm9|j8GZ=2_5-q?^>^=jpEP;xH+{S|CWpWZdcexo&|91IK$4ocDy
zg2^}w=@}|~%YJ^Yj^2{&zgW-OFq}E~E~G$UShk2&c3I`8#X*0+?vLKOciF`^L7?zH
zP{L9&L+zsEy8{pTx6F*;1c}Y)Z{WMEvg;7z-hDG6^#V)DjAe^i_cnr)K7+!7!!{*_
zpt_}DFUJc5Uw4P~>W~VBQTEwOiROC;BtTgNT^;Y_BOvb_h+`?4(Uy7*WK)7Zla<fo
zcP}2@bAr@WAa0?NrwmB4L}9^UlZlacB%8r?6HCdA<8L7qmjgRUU1q*xeje{iQwbwT
znPni(D7)<C3>Cj5U6}PIg@$F8$%nTq+`R*`+Qpj5%I9g~A`6i56I>kHPYOk5@q#!i
zf)^4u^%cI@0`^Bo8B56wt%(r7PvUrC5ShYb6$>%KlF7=)l^a~cwg_HG)H(*%;UK{%
zyUc4zw>8Kx52Xc%Q|5gWE2-xO7jF(F$_oyM$j07zo#_Y)HG!ubFAPK>p#j$TDkANy
zKR5s&QV^>YdK&nGFCGO~*@r+nkc4;cztWT^(RT7G$i9Y3ju!@@?&9CCEm-Ifed0+L
zNbrCSOUaB=Z##Nxe9g;20vAEX-F$1dQ`T_O3=L3_g56cpsW&YOoN1V>e6Abv&DDDc
zQoTr$QFd7}JRq5@cI^-br#cs7CMzFzNB{_2NMwB1*Kkgy5Ud3h2A?l9fsAHgDVfo4
z2yS*bIJC1~fcS#r#Ri65JHVDP%8EINgWaXD;4sr2K1iEI;6maq4{j?+`gSp7vhsOe
zc+7LsA852Bv6Rd>_qJ!E%2lXaKq{_ZJbG6?V{)Q|S~%F`X9^1rm&nH6ac;YEMal%4
zG}swsmlYqqnYz7oe;OqEFLpKXUA~!PrfPNg?j6Z?kfRl%1TQ3V%848A1R2zk$5ImE
zblm41B&iFm<#@4y1>$;8`iO8+g2ac5C6kqo;>N|@){w}aqO{<!kWwr(*(!K}%<S}o
zYRqH#qWA5BGry*}dAQzmZ%`HR>FH_yiZ_@2?brVKQ=_V`e)?@$_MIJtQJYduGWPHP
zbV@sQ^G%cbe}D8=?5T#N28S)m3(6O&&#Q21ZEfB4_uK8QSy#1sWv#<5FY^sfO?_I|
z@9*h(aqaedt5Q!-%Y1ultCogFfTLq$T<zDZwSBeEW~P5R$S$v8ZM}Q7{^DqrU58@t
zM5ckVfx{N11?3Cb<tiNPf1dOQRj(73-LpPEI_l-&aUuTSr|Ey?>{CyPfNB{j!=x5A
zez`T->vp;&AM3eT{eJKDmpkTKmuJ1Zv$N{Y$K#^n;@4%TUwUe_YsY19l2C{eys+ED
z+q*k#UChdr!OPvY-F#c-JJ%}o^fX=TC(n5ggQ_GiFRzkQn#*0bwL2-sR=r%h>tUO8
z$(xPG<v!UoIyl(PUw`>@*BxktJmq*1yJXj{sDlXsl9G~hx230~tk|>XPs9Axt5*jn
zB`uQSYhUI+e;q5g*oz+Ha~*H?f4^5PDlNTw{@*w0?V;|0ffFZ9nxwRT&YU^QyBFv^
zd->v#A0!VhO**T`Ft^f1Zf)G&C|+J(Ej_)k#KeVcyi!XvgO_DIJvH^$-}m+FLsy5L
zOxrAb?PuP;pJ}3EVs87s|E!tD^AhU3w;V5GRq)i1DxQyz^<Lgp`Z^>e1Z3`uyXE&o
zQ&X3+@kj_*+DY)d`t^GK^*=v9Z!LX&t@hmW604^rR{iH(OBZ^Gm#cMxQmDYv7mx0_
z%J8)x>z7~u=+UDS+X6I}JbIMmJJ06lhV#N)ts)#ODxTBhs#acG7rXlE>TvhCn>TNQ
zg7lm9<!b*&3t2&Bi^G(~tb4gd#l*6{y}5bn{Q2ugI)zQ^|JD5bTPG?a;xQ@h?5w4A
ze}Ao9vBD#$Voya~-9AvfJ18ue9#`e*Jx%B0BG+ye&y_1zdinak{bKy+(W5CdXRb^<
z+*bPjsBEh2={2U?SVVawyEe+liPwS3i5I#YFJd!}^+>K-wdzvt_PfhIeJbjeHuqb<
z=aW~;qaAN=Z$F)P|90oW_upTyTD|U4pRBcry!`ryhudFoJT7;+N7DGru9m#Z7mvnH
z`lFlsX;;<pPvGLGU<ag$*ZA)7=buxiOj)vPmsRE7xXq_uKA906ALeba<a_?FZJQ=P
zKC1^RgkM};GlzGPX!3<bN!h!1{`NS71O-|-UTo;;%X{|~(lH9`Xy7~Dv8MH0P&UZr
ziv$>Dr#&oe+r4A+6;Ly|A;ZO?J+-8y5K{ehxUrN(xGn02mZ0K{vePDdKnl6Qo(8_t
z9y#wHoil=%B*-W`?V@3~_31UBiV3Q7_1<o4XxXR8WTn##HAs?CcA6u^98e*CdI7ka
zX~=MMXis(7wPQP^&*mb}WTmrsaksTDxY%6;w&CK@xsXyA%$@jxgZ<02Z4;tyIYA4{
zyWI_Zrwg|CJ5G-PSA-5<6c-#eN=kmy)z97jg$-OPH)uFJv@hK;_pSF@K}Z!K$z-K-
z^sy4S=IiidDT(k(a_6@4UJLe$5r}`>r4U+Xn}N8CjQxZlr6b7vAf@CBFCT$R+*1M<
z5;dV222{X`K+DN6mXZiAX!)uQvh8igdsrhuAeQ6BhAA%L)#Bw*2=}O7ym=4qSsq5&
zX_o{q{DsD1TLYiBXU=?;b@QMh3X*q%hG<s<pZCuq@$$n4KI=du1q{3RLAoy<-K(3T
zaX}2)xOia)@`qDQPtLXv*P)5Um*d3-k==LIwl~j&v<*Np(BAn7lvG@lnXGi$mx5b4
zVD|C1kk&!N3ulM+rxh<C{^t_BkZ38JeBm`GB+@t-Wv4kq9R^B!AGi1mT|WS=UqO<G
zk<!m&HAoCNv6Mvk!D=H=!fA)JuE6%TL%j@24u_!zf!Rm~UA}nqu0Oa+Z_sdZXnzVy
zR9iGfj9>x6&L}$#q{d*$v1<z;Z4QU;3JVSw&DmIX^hWNa7tg?vStW2G(b8<T?xnMP
z=Rre(lgUb_6B-KAOjbIY6QMx`N<x|!U7=oWZQ%2MHREJ$aA@<%si5Ek8$aos#7src
zs1MKFoIvfZh7}GD?W%th4l&HNQSm*yR#Eg{cJHkw#m(MK3=9kndae%bsyE-7-TnX9
z@YAlOt;@EA@$cPw^35cWc?-M+FC<1rdVbvXcu(M@CG7X_tk`OubygSDq-NL!%GPD?
zZshpNPhOgE;zFtany_H+3oxAm7k1xp=C@sP=T6Md7(Gz`@$__k`v)8S?f-81^!fAD
zIdgQj-(Fkz_*mx2Nvae7u3fu!*T-Yhrd3}ucHYfPO;69hwkA?bTl;mNw_)qqw9B#2
zdxLfzU+EiKclY<HDwyLP9o{?n+k9-{;^r>>{dRly)m5QV)@5s!`OaQ;>eQ*fa`Pum
zS`?kX7gSkZzO^+wG$3F>QxntPU$0hAT5{}|8>qMZ`Ptdat3p?Q`EuER^^zqn&*xR=
zmB)o`_x3Az@$Y^AR2AD#|HAg}-gxRs7brv?yx1(+mN+BUCEqmjl8WyP14nLgy(ucK
zPKr;TJjr->XXmb$%VzVvmN_n8zXw!}TEE+oy#IIjnuvv<&eZK~xl#rR2NcggM=ERY
z9o{d&J>&bE<x!swnx$67R(-t{JY|YNdYLDv<I;b1@@B_;SMDnD%*@P-r>1I~mcF`j
z!ff~5bt{9HgL?JtfA%Cwfa+RL&rUzfr&E~a!o$PWXP;g6<jIr2@Az4mKK-nj8)tj<
zY&w5<+^p!Q`_k@y`?DO{y>S<1+}j-z9<D9Hqou68`0}@&9-gVwr^|mRE-fwf^6{B+
z_~C^wFE4{eF6I^<ll*PFZRgHN^LrJG>pnEg%Y4v}b9Hs~^7881Q&d#c_h*sMv<Dqm
znZ^4<d+siK9ya~uQvb4<u|lA5lQCr}>Hb(@6ScQ$YuK8Ig}dMF@@{Qy1r0lVw_LY%
zYp8g9%|@QX7Z$m8^HuCa>fvUJhu#0$Cw;*^+*eY^^Ve1G`70GKq!qb>3@s=*GAq_0
zf9kYp%Vx}w2n`7-xfK@}xbV!GGt718^0sH6n4mam2`Gi#-BqfkrFChFX0X!nl9tc8
zx3|4KYkuG6MTOw<%bLqC|2(UAEa}-CGne!0H7{?w+dJ$3FSfsrb}OIN19$B98Ae^$
z-EsKgg&!XuhsMOn+!W(#1&vXhoUAVYt<q`X1g1uZC%^j&_7y&M+xzjDw3n~1?W)r6
zEbDei{C#yNS=F<?wtn@sx36|Seg!J(7cet|8xF4?w##3;|L>dm+UV_m_J3dcKmGnY
zG$261bCzlLw7h*kpPl&V>grnc_v`hom7mi<iuV6~o4+;Xq|o0N?)H;J_@0Z$mQ1w!
z^+MV9-;c*!Y|UEw`r(z8JENkae0I8R_K}>nZnImemV}YX`<wovTpJ}omN(4n)q6*<
z8S&d(X#f57tJm%FvMzfQP*by~jbDD<ak*+A<FqpqRNmg%8GPRU|DGiU-@ofuzh1lD
zs<gSE?ejkCAaC=Kmy2_&zg$@*`>U?*{9FcpP&7rb+n)NeZ7bjZ4JDJi;^NkeY<JF&
zt6w!qSSD>c3j+hg7mmHU(qGt03f=q5ex9t9nl*2QRDJim`mIOa6oK?V=n%Ycaq-ck
zH%gCJE>`iZkKY%Y?UfBJh57^-Wo_?qHV2g+Ec}}yaY=vP-w;Q&9#BK?0<++Si`sW;
zzHwB>Z2XmyKKI|hhi02kgM(pxZ$n?)!u7{jN-yJ`taA5zR_Cf$%<Vrw&S8k)W|ZA~
z?2IR~>8knRU(e?5dVTafr1itFPmIZ`s#jvSZ~0vFsIu8#-n=r}8yb`e&Z-OrH#lCr
z2tD>pW%Yw4HlCN(`qwXNbC`Wn6lCFnhl&dpU*Gx9`nZO=YFujR*<JsqeuZYj_SS~J
zIQ4^qs~3Gf?lDR3Z^G=byw!#~l|X*CFk!N)S~|IX*~>*A|E5Y@>h-T(<PZ%lWFIWx
zc=2LuondXuC84>$7Hx6_m)wvr14~U3micvc=W(w|VtfC#dYyXhyAE7(^Yt|J#l3xV
zai&#rN?TNOTV74p+9t)rkdixLKgWysz^JHIvesoT`{MW2>|D3^Th!l={q-grwPZAB
zpIv6X&)iEceNEW(Yp*7L-#!bH!QY58{?)GgaajJ<+U@rysZ5<d{dJG=xeK43o-Td8
zcKfUA`~Ph{dG(=3irUVS&o`A-Joi^_by;fKs}>Fp^504e>W?m;U$?4LSlw&Vzpv}-
zGq0=&1P!Kr{qeZ}^+b0$!|fI_8@<(@WCX=^&5CN?^>O7^u8UVeE%An<0vGl#+PXE=
zZ+Y;fFUO?wU)(7^KUIZS-Y#Z$*;}K{CN<1uyM4dMSh#&GG_0z>oVRNiD0OU*Vfqy=
zQ~hRR)Sils_iDe#?)~@cwbkb{#;TsFsi|JRzNR~^YJ@8@^X?ki-nsgBUih`_WY*0P
zt$a+s!tYeS-}~#Z{J#a7!OLE-+y7`h)+ej&S@rMdbLH6k7mNR9Tw<78<rBYkOKZtn
z;ni0!i5cxy0mbM9m76?=6Aa#kvot&Ytg-w0^0NA-+}GFEdU<#RR8{TjoLXCKH{ajW
z({t78)swHkHr*`~9P(?{*SSC6uh_ORwAfF2jr1~JbFf(pZbL>>*Jos91t|!0fJ?T_
ze}8^zX==Xw_)sSCW!&_xbMm^LQD45Pt-TYL3@J7p9PS_gzVExO?XMS$CoQ>kD+*LO
zgUXiDdzH^KFD`Ob3vXZibj`)cT@QZLaY<e(^<Oj5{z4igkuQ>A{Hq<dHfm{p?d#}C
zOX~mszJKb(i3{6uZ-05Qxc}8bc6pPXRx-Z(ZN5i*_3pd=ZI;{JpO2nHQo6z^!3+Br
zZQK|r*Y7^*%dPD7FPpgaCfv=t&Z$0ULGJBsGh<}?)YfbJ?cZCmCdcfh{Aus6$DWHq
z3JHZS!3+D7PE1g&`gSutG%9LUczkW>{h#N`RXnFoo$52^0{2`4jZpia*AzT2`TDO9
zEp<Kssj*$OL6KE<JNMVm^Y!cQ+==m>V-Yx6-G7=$p0EA89glt1@x?y;^<HN2!<llS
z=38DIt6v|gapVxBO)GzT&0F`%%AI9zZ!O)JeEd`%|9{@(pP!z7s@f}Mw<lfy<k29m
z-`n%1``@pAu|F%5`{gg=zKeG*w?i_;g{3Fht~oo{cgR|oc})8C{e6Cssyjdb!^Xea
zt}Fk2U%7SD%_aL|%p8~VCP&(W3c|P)=GP0me}}g?DgOO+eScU`&?GiKnH7!9>{^zV
zk)Yvqr-c)4K3&KiYoq9Htj;?j(c}7`$H}?3-$D`uLtsn8Khf>?s#c5X#jF4gam&~L
z*=YCgNAlg;?{}j%Bs4xdJ3GCs@<r12xyC`qc|E4B-|725|D>1cwheaR#^O6i@$c(T
zoI7{z(b4YGcRQb#-THT@`20&ykMi7HYu{CGUM!G4y>RMv)7QJX?**4SJKUF^zV@f_
zv6E?=XU2*#FfbH^K-1)kl`C()cPTjB&L8gU+si4saCfTO&M)=G{o!jMjf3_7?`A>M
z6a(X5?PGnit6RCn-)*jac&Js%IIV|M^dOJ$=b)0;Uj!#D*&P?PoZsxd8KhL)8@uKv
zYsA^w?{}BKyuAE%ulc=>HI4gnZW>LUHtp5Y>2XFotxA@k_B(TR)}<)h-Mf6k|JP29
zn^C=K)1*gd!802U`mWr+!sk@IT6t?jV)Hrs|20>yU0bznTbOuEL8IO8H^#b=n~uEs
zbfGuLY<8EP#A3n7I8fR+rK)}Pijc`}9Z*KzD>C_r>@w5rYcGC0?hiM(v6)#)*s-Se
zcDebhJMH&^bCXx^Gd6PWt}}@<EK}=cW?*nQ?`F0^F6u&~v&4j13)<74o)*l$o;^SO
zMc@A#y?^UK(~u7`lov3!CpzVI)s=s=@Vun_KL6_c`?r(w96>f#$ue0L%vjcc^3vM$
z_Vlv-pO2PS6~5J*tPN_(UzlysUA>!IZ^wfLzq7NmcP|%+xb?qA+uu5S_LmdCqf77Z
z57WN;`bx_kk<F*U>bV6j+*Vapl`Z<eCUWzxl~NH?&!%18^S&=6GIw&^?>$cTm0y%?
zzg`wL-I2fO=2B24_Tu4;qqe6Ef*)VJctz+6|Nk$IcK6~|&zVwT`}gwXE7u=G0_MR6
zPzo@7aX_%6{KyOc&7VvSOdXe+`u_TOKWv5Kg|xCruwM+AtO{C!gy&W*Nmvwghi~oO
zz~%G*yj3>c&IXMlepd(feT{oew*Lz=2<$u;p?iBzbg^9g(rXDP-fRLH@x_6q<bkx*
zU3<^^ojFZ0g=M1a*M~gZe@#OoDGM|#!4S{SC~Lu`_H1d!$Npp5ddHTDUz_ssTDqO!
zXMIoz{IKG9aUjqrHDZ_Co+lPJb1&J4PkFUY{JP*JkY{Dgn5+s!=9*f0e~+_poAhP*
z_iZl@KGOFASD_C6jt=a7o8CWv{$l#`o+&DS-}UnTy1aWfq=I$ucW_|ebs$y!>C5N$
zE!`%SJnw$}<;AA8&%{8L-GNUWFC4^Y7T*&1TXHJF^HT2lS~m}ndkX?sN)~kA$T9o-
zeoNx6Pa%-*o3ru)=GYTyn_nI?=6nhAHeYW8Ux^_^^01==`z}UBP&Ig9p}>X4JST`r
z1}r5Dq~Gc<+3W|()eM~td?ma4Ce(%g7Ro8#x5JNLEPo1U<QG)P9q?f(Ily8z+t=Jj
zdfDsqAg>;HEnRZ{^lPwn4kF46nAJAk%$ZgFE8uU^FAmR3^7n48xjpZ<&q4vP#h_7`
z11bMMuXWVgm9QnpEb}D$?UKX$udmXTFoNWiyFCqj5eN9y_%6KHlUpva+t+{Xt%dXd
zeoo%H1sv858BPxDI*;}|{`GR9yqvy!Ppp1z(7ONIpCF|MbHxSa8{6foJa+EfS+p}|
zmPzKMY5MVX2a@O4{mOj${JFNJWn|v=Xwcx`Bvo%K*<^{fyxnijQd3hk?d;+}(~HM?
zrFZ`_y5M_GX6nlJ^yjBV)2~OXhR*uAoPX=_`$>>gxr>o;FSqT-Bf?rbI#=FqzrQYY
zby((`8yjbt<<8R5*8W~y<K@+rxA$w9ZuB;v?Ca}79UU9FxVXN&TD{(_Z|D1czrQ?a
z<_8V^{`zp3|7qE7)7)Dorsn45*HagEgIWW&RX<fKV>d?a%<b2U`LoX}TMC-k#Wzc?
zd3_~S&3D!kPGPl-hlg4xEeX(AGG~s=*}2x?=66djC(eER<8lA>FE1~b9+xdYVZf7`
zp8opR>-E>q*Z(W_42q4NYh+~f<%Dwoi%H&kmQQ}x*v+3bX;Mgdxcd6*)lbCR^|miu
z6gT~E>dsTIW=);;;)*h>u*s_UNjFm&85kB!SCEe2*L@MYWa&~@?eKM1uB;4JzWt_b
z_cFh^pwV#IPoLWoSM1mkp{lBC^>j+Gif8n;oWRP;%H_pss;ZfnmUy<8`hC$ko>KAU
zOQ?Eg#WMG+yo_gm95&Lu8;}YOSq_qVnv+C+-nnj>t{^bs{PWjeoX$_2`Q+06b#vBC
zZd-EdzR;DY{cA2vmY+HcT;(+AIXG;W=s&*r>gw?9qg|rAUa#975)rXt`~AA#9p}SC
zLY6F9vgCxey881MqTP89OOL)>Ki&6A<>O7FJd(Q}-p~G;@X^iC3RL{Qh$S%kj5a^3
z;yLkr?cCyfHJ{I}TD`h^kC%65?b}Y7jb*k4A1xrcn|Xcyg|GhW5+%(T7#J3aD=sJ(
z#5+yP=ou6jH_yn}*m~~e=bv95=C`+bU^&-s`JTA^<>FOpyYDWOJ}<M*dSCgPH)bzi
zhFX_?l%JLv9y=LQLR)Ypoz+`lu1(1t?j)7jXP0HK-y2qbuX1@CpX{nVdu%|<0&d>C
zY4v{3=QF!}N;Z3|KiAUN4GjOEJoVbU6_+i!|E)f1Zx#1*r)Ju8P@(cbT46!?LP_Ja
z6{}WxZNFPK`_$>vrg?WP_I^60J<;>tw*32gyKD2+*>LtUJ7hlZ|Nndcr{8~fJ?hd9
ziH=@9+21a5{hm)<F?!+K<K@p3f4X2|YyUhYcH<M}wfgI?^UM8n*l+Va;_Mug3vVug
z%1IeT$o%h(-6q?%ylhjy{_3cIndhWBi_C_)%5xiwju=&e;+);xY=c}~H+$6v7WXw`
z|2|mUHOQTjWA^gBeBO0_`F{>mzn+EqDO+g)v+bO-X_x0*ZhLWl^XI39$6hVYtuFcY
z{{H3q{VU9Nn}Dp1XJ(Xrp}No3#`%0)?WDew9wDX94)(u3w^u#bDzuf=8ETk;z=g)R
zl!E&%w&u@MVvQ8t+Z1hoJQM%*aK2qgrEKgyP!+UVc>(iYpFPTMpw7$w{V8+q^hxO6
z4_drgz9u|>ddRF;@MwCo!UE>KSH2(ndYQY}|Jn(i^;v&(cfDD!4JyK~dQAt-79N<y
z@xnphY(@T}dHY_u#8x>~)P6bYZT@nR>-*Q;r>Z7vgE~YXLRm@{OfNoq^hMuix0nCZ
z<n;Ts!?Me+ynMrc|MH`yvqg9$)24&!sRgo(vK>O{{pT*dzCL%A=HXwfmg+94j*DC0
zEw+EXzLxYEJzmhrBF77dkdr<|pv-omR=jWGamm&F+poWR!+!tj;rBVqOhJ{Ffh41B
zhmqNAUvd8v7gt}OyC}GibNjOUUoTn3{%KFW72=u=s=pG%n5+brZkqh~>-HwjUC)~?
zdX*f@db-Wl>h<^cufFH!ge=g_D60gGmR(U?z#MocZS%`Xoy=BSm;Ha&%L`JJb#vKW
zh}&I6nXDMTX6O6Xe_%hZse5td+0eSb4<7QD{M~<T#j*QI5Mx3V7BEi$^$*H(wsozy
z54E>SY{)TtxiZxH%iH_=*4D4pn3`zN37T%dFg1s5f2q6Z{PW9~EK#|7_3FvjUuVVg
z>EvAfX1nX@OaCqV_QXvxQQY32Xa78+_to^2BG5V_3n_5Npx?-*<%d;Nvm$75!I$0F
z%~!27)tK%3>)(%mzkY5>^!mQzj2>tN<vzpB163R^0^8aJ+!w9c=J|c!rnvKK^(L#R
zS|yu4-=etd&yyI-<Yb?9pz&n)rkn&@mM<EIojdZAo^G@K^_6>m=Gi_g&r1zcSnd{m
z<M6dMb2giO_Q@*-28Iu%6L{COH^`+{aD4P_b!waUvts$X+SI*TubC$;5oTNZV%lc+
z44?lg#of#d3<naGr6c$mZBKm=c$D#x{nnO>mui1M&OOVurB%iAdC%Evy!R!SF|OoP
z3uk9wXmD0UD83zed&&KMVT;o3?r9#L@F*$g*F{cd)9<&Pr#>p15i7^Qz`)l4clEkY
zk&$!XRaWl3cQ-=U^OHq#>C-6(cl}vnbJTi<2Ok5&fh>67y~ui>w<hSK_v^1e_2#Q+
z9)5l0>T=$TM}>=Ke3WHiIN*d($hUC9!spkgy!;pLy=C(@&q-JGE#t!Lt2^|h?#CAz
zmQB_Mwann5d{^^=*cD-IudN@>>MI%Vd%onz>-76_t3LO?ULMh8#lX;z2{+lmxi~wZ
zzkWw-_q~5kS9x_OE!ktTeVt|Pihb5<-3#~_7!sH^HGUPiu$YHm@ywMQmzJrX-j@0C
zlJBj{H?%yZX3Sck?I*c}yZ^m@xJ>dDMh1pm+;CTfT@c8)eE;74RrY&#TMAvdEHwAm
z$CH(ypnFnybY_eq1B1hRP~pW?HRYjV%T!P)*D1QR>c`x(TC20CsZ_>n-1ViUQOiQp
zzt+|d?86s(;qKnBXxg?3^WuY-fy#mIQ*54-l&`Jh?tj1j|Ie^_Y~OuNb~-UIG~8>4
zMGJ>EcPr1{jq9BLfB&YoR&Q-@j(u&#uDYUA3cjbjC!S3+Uhlxb&=A)GQ{OS~fB_Gv
zuxh`5chRG-qOX30XO-+%`Fno*r;=|kbgl)lGcf#UgFEL%rdhb@i}UYuFYXV27qCEA
z#q*S3(aTFGcg`|Ybl1#lKPk$@!0^Q<h55DO0(VI{r!OI<|HN#+9W=Aum6M&KTk5_h
z_P*@orIC}1P4(1o-eF>7V5pLS2Yl78XV(_QMFcN%pPqYJ-TJlC?c-nkFReMb^Vho>
zI@f~P85k^h5Jro&C1#XnuAQcHd)farcb85}*zvo1ev3t{$-ez*J`xKQU!M8L$G}ja
zfY9N#Tbk+D^RA%3e-FRC6?t3ark&#UevsOii#LnBUl1zG!0_NS!mnqW6rJ<euD@a*
zo^yTnYi-f2|JTnx?z#HC@ANfQVYZBr{VK)XEDQ`AIN%;w<XRcK`QP46A^tTXt1ivj
zyY`jx<~_SC_V3?kzHa_617;frhK?k-dt7D*oVf6NcG>Is+S^`lJ+Gydd{x%4%vW~i
zy7&F(FK%yJra7(jDkFo#6nGX@n)UYM_7^*^T7P|VO7_>9GvYVrJuH3o=kc%Ci$4CH
z8NtWE;KB=cjKH_Z$hq_WWVFwV-Wyyk6|H>R{p;sf)ycnK&e?eLluyyj2vE|zt^~{f
zOmkUpHB}YIE}a*%)A@bQilFG<XNt^bmpH|(I%lmHo!)mUD5z%6$*D{X3^K8BPdqq~
z^+&hr<K)<?y03m~Z|qXvbXPF>`jo_4(^{Wte)1C+x}>k^3<c?tMd<l3<(kQ>)~%^k
z?;aaX4Y}gJ%{u9=)z=yOX4zSJFYou5e%$r66$8VIneezcaDDr}JIlqdzq`~u|IMXm
zTJ<Trm9MP}Zu@rii{VVGT9>vxEu{<$7n<S4WTSGrM4Ok^tFJ-T5%<45-5{5IeS6!o
zmuoIYS|z)*O%mdG&A@Qr7Q+0Ci4tvQAto=5`j>&GLtb{DYCExShs10@sY%N=yQjYF
znJD?{aoLOrO$LSo|3DpQaADlIZsW#9aiCt%bo;wYzdo%#zUKIi+*9&5+|zr`TzIK$
zG-diTvyEO13=N1J?Qs2e$kheY?Qbp>x7WPBD|VmXZ?^BH*{@FU_{vUP_?Z1mV9(vO
zps5TD3bzpUybF!Gyy8)*XjO9TQo9>_tT&z8rSjFY<8tQVOJCm#zWjCV!oD?I`4|{>
ziNcE|gZ}k9L*M7DxFkJ4{KeU?Q$Lw*cl~zsM((T$x80ZXpTDr(eMxq}tY|aowXYc%
z7QBXs9AkC{ciWeX+ia_{YnOIi4=<aQ;x~zHnOepv!&=kSh+TdXQ&v^D_jpcaVraP6
z1uI85A}(*cTeo-TzM%Yd{N=6ryzl-lzbTk~eaRjXP>0G_@>+QFZ?#Li_!t=W8N!oj
zLM?Bj+1~9Nqu%GNxpZlg*4s6?ueR)uFWTd0sphNB|8n)@wk2AYjj4AS7!F*Am!T|G
zVuua3tX$=oKRqP#Wt&#*j-_t8xeHI!&GwbLtM7SVN{a8zaekj^#?#(?U|@J~0UpZ^
z=dazkw2a^U`ir~0SF7H4YvrzZVN+&nr{@M5z<Tr`WUhsUukz--I#1ac7`}DG{q{xg
zY3bRj_uW~0w{P@%UmLjgRogVb-+R|Z%r($3mATQrBs0V6@pVQ92MKuSzu4=f==}Qh
z>#aGT^K@^AtmQYqzQXRpwU;xtxU{SBdR=|uxN??_#nh*FP)Y=asx7R}zqXuNo;thi
z^<UqsQ5(0qon5=o?|4eZT&sn)8|E8mgfa&UbG%_-aN&f9qQKs$TTW4%7l*yC-xz(`
zEIj+_w_AR~=R83Zv7D8fvu4a*)LE%1HSOWVxrT2T7`}MHGa`fRtmx*bt+oAjH?~Bd
zHhX_<)+MX0EY|F0>$Ssv?$n$$VfG@<owX*r>V-BRVP?4CjYuRi9>v)KtG%<gCVBTx
z%U^r(RBrW^8$xeNdX%rN+xT~~NnL`)hJN`Q?WH#u8D1=e#{dIgc(lQi^W{Ic)Oh#W
z-P+|YpS${I`m_p9D>dc7pF1DseH8u_bVc9tUP~4u!-4CrW*g+d)h0{95}v~suHEhZ
zm62_+Vf~G;#b-nR)%KnCE6Q|=bKm|$gxAwQMYoKBL8k*8YkFTUIP<^!_xn9)rVTV?
z$;vI3@#skBZ`*GV+vTq<?zdazZ~r%Bl1bg43ecGA+`jybj0?WzcbBBkuMOK>{yr=&
zZeHED>QL(q3=#W>y6Uql_=8_Q<#`xAsQRV$taAFPSs_4+cE{A0{++BPiJ=7f&+P
zQ~xvXWvOAb4FiK3xDmFu+vejDVbDyWn10+E&&g__VgIU6C)GoPf);)G@};(KzGd+;
zKdYB2b1NQo7VV7L_5EJ;*E7cFUu-^a7rejjuZm}QOkwN2n$Ny}U;5Vv<>jpdt%L@x
zx2^j1aye+|S1v5priEeGB>M}?`nO-t`gu(X<iM*-w`OJWzAKK|b^NB>jl=6N{+;}!
z^yth0Lk5P6kc6#gglA^_=g$O#6?V11KqDL=*Q<c$d1e_Vw>>*MTii15&ySB*uh(oY
zc|NxsYJG6D!4mydQd^d730v*`8bqmze*L{=llP?u`|^5EzZA|)m7n)=s`9lwr>hJM
zF6^5czk(-)uRs=<zj`)1zb|L=?YFb6N>@$OjV_CLo{^IilAXOeC@4tjwx7*M7u)}T
zKJWVT>GagObJuRaUl%QHmUH2K-S^$h^V9`6Gz2(YKnv8|3tSlrQj(J2NX>hB^@Gp)
zG#`%q>@3b#n>Sgl)mwY@)upJlHv?B+nx&Sz>Qi>`)Oi23w-hyPlK*VH8Q1%E{+h>Z
zD%m;zQ+R!R7i8NoFud5x@gjD?E|f)h8GnC$Z7=PU&fgPw^XAQ2R;5`fw?T{6kBWw`
zsQ>@3tG9RR-Ms6E`R&&P`&mA1c&@3Xb!mOw*VW3q1sGm1ee9EX`h1y&effKNh1Jqb
zQM;AzoZ7c*OIWs7cIfpht3VyE$8Wb*J^A~g(DP%V)a=P?HvXNg^!S$X9=*xySQr={
zyii&YeZltooyGP4KF_cE{dW7)qPKsK79VV4{q>-k|I44x=d&LiXsmiZx7;NC+??8`
zKhy8ld_JqCqtjFK{`1c%lP6z(dYyqG-aTqj@O`;?3-kH;|0iBH;K{vx_r}Vpe>WS=
ze>E%W>$x_qy_*+meY#oFADsM4J+owgO7F}0O9KB+0*xNO1}&}blVIG-y=3W9O*J()
zQBl#k+rmRaG>nZuFPQ)LY<}Hh(40p8o{w$EdL)H!-n(*TNp#*$*Yh@?dzSgn*YlJz
zPU{I<AGh}YpJ(RmJTLr~2m7h-U%~VuGRJIo(d9d*e2ZW2d3;DXb=#KKkf6BrSC>|A
zDRZA|cQbN1ujtoRYxrOEmQJloj!k{?d1l;nx3ApWH}$`Ie)UD2r`PhOvJ4CnGK_n<
zEoAt5<?Z8+9z6;g4N+-T@6^`QyLL4^zV^WRyXE(5L8DKg@jxwm`~8>qUAq>xb=$U6
zdGBo!gM55?oEBaP+|S0aLCSqi?Bd_*nI-?1oY=N(+5)@#q03E|K0L6m%zdp{`0vnn
z*<m}^N9M1&{k#3rKDp&?snw=R+JB^Gz75S=HQV$SBSS-~z=ho&iHQr-&d&p_fCWuz
zeSUs^`u+Fej*g8gnb+1tzPi4CepgqQis$!x)$2iHp@09ruMf}5TX*X8X;sh8&Q8$!
zj_a?lu3SEEm2}<?$B!RBN?8;vn5GxI%3Z!Tgx}^vL*{i(hP1BoBN=}Vi1f<uD7UG0
zUf?@_`p+*f&so*(GM2ex9%{N`mED5vUaP)@oxJz%<2sAoZK+kJMyD4~e0#9AVp@2o
z-@j@W28O%+sn>RaHjkA4|NDLQo;@+3aoT2nyA__3)jTIH^O?Em&Yc+BPbZWofz}J<
z-rklwzxYnw@3+66>ep*}g7(jxHNU^+dR(=ysi`T*mFspq;)*T5TiR~B+R$5*p~J0L
zH#chg>E-87*Zisp-+S%tr<F_g)a}pS;vJNi<+Nk!U+=YVBR%!fCa^Pn(Lz*H9NcX@
zxp&`!T1(%;_uA=yUU}#Gp8Jm{KYDDUbb9ml?wV=g&c;GddwO3!JH^OgVTGt~3JOgB
zP4me9y(`wPcHimc`dZRg#BP1xe>!5fZ}YERrZ$&XC(e}PJ^v-6Vp{mkE!&tGsu<w)
zDnm_e<F*B6VJ0q7x30halo`9ze{1jB-?sN2cU|4U<FB$s{_?-lqNc^Owb>aS96++O
zM51kBZ0Xsk_;;6oeX5!|<$Cttbyc~mZq;Y+R!Oax`*6d&hZhasKCI2y<^GXx*GXoE
zUGngT1H-N`?`V(NtECS$?%fk7ed={+P~dvm>RI2a_ncLGx@?#A)*bw(zho$QA7EtI
zg=lXuoC&;tXI1Z1-iZg|F5P>z@Amyo_aDm|mhrxNeeJ;)7v7h%S17*hl$teh#U2xL
z;meE+eOw5CMtFzTF4()ZHb4Al@6@kZdAx7GSM2tcn(>N{{oA(-b|!ZhAFLIAsm0Im
zBMRZmgxZ4>7S64n^~E)G@4gi|7sRLX-n#KG?*5%^_s`$R3HopMQR<R;VDs(hJJwP6
zfA5$%hmT={KElo~F>Q$}HrWaAy%GI->xSGN^RO$~=NZ$cOkX0hU0LSN<-4c6i?aiw
z7Jd4)%Tz(tvC!~a-X11~hN*}q$b#})cLh4OY|3B7wLgE|r|k5%|J0XHULvvEmpMqt
zMB}Wi-1~dxcV5};$X^tcQOe#B*AH$QXdHItV7PcxKI8JVta^(L;lH=;T;&+O>hG<f
zx8cd&9~$Pro-DB3u~yi0hwTpjSF=|*ZrR1`u-*yY5@GOo`0kVFz6P^!(~RJ)@zJaP
z+zJuRtGyq2dkOR3vJY3ozB|?mPo4cLEHA3b$VTsGq!b@R6%WGW4+3UIHQfr@YrY|W
z)veo~>NbRxr>}`+4i+{sxVrYvx9@vQwu8#RPeS(?!9ys**=39j3<shf$r?({e0%l8
zn{)fuNVY93ir;Luo&B3{=)-lNZkPQI-L>4aaoYs55R(jN*TZ(tOOD(!HCU>=MP0*O
zSLPo3ci)g*$2Dj7e&AzZ(0viuwto^M!!D<6gC(18spa0iyCQe@-&;|4SFGLr_tvi4
z>o3i6+selO=XKj{!Nq&_<vm@h>bC0~zlUdlpI1}RUA{xs3?+h_8oyout$g3Nafifi
zwq?6a4X*00%wJxcdvL<3tl7$qwccCfqi?IP(!P6HrQSPW{<|+PFVEdqdv5lFjzGWp
zEKipl(N<S=w5$~}QBZZ<naTO;zPjVO9W(NFIWaKol7qQ%&61_MOWvCH-ukpu@|k(~
z&(K}V1E21D#_SiS?rAoA%FEJL=IT|^%t68?2Bq(>$A!mykL6(a;(#bQ9Q*|{xU-(m
zQ_EeS8S-+T+S?VGSH8`1%e^gIm(Q_9_RD9@JooAf{|gNv+u632&z7GsX+cT<WY5<O
zF18>af_8R9OfoiGR{Qtsk=k#PZ40I9ySH6!Ild?V_T3e4cT3;ipAQ;1HvK1d^+)uU
zU0cfLpSMnW`)dB3d%1S1b~exU`a3O=t_@;Z`<|iUojYg+zHQ&xwBW2o!7DrcWbGH6
zxBD&4l>6|&zT5JgAD5awy!K=6R^O~l?#$w?@v~R|{cXGdxajYt7v$Fes};Jk|NOo3
z?UUmdGBCKy$KF}Yb2oy4;o>@7iA(EtrA@tNvZ};?$L<>Uiu)Uv2VUjXZMSis-?GCa
zLjJ?jb)T7ogr>}u(RW|7S9%=_!#>GPjbA@Jk7r;I2u(_U^EURj@VDLjH!k(H{$|q3
zTyy-#ciY}u8<%@pe=}LRZe?h-RP@&68~x_LS#$gO=d#&Tri$#pKkfU!+DB7gmTH={
zG4M0o5C<)Qe&8)6v(ZZJ@Zw}6o8NceXXjl#_ie+yi(K)y>>BTWo8@M@y$v*~XPrG;
z<?5TyKP7hi&WxMBX>#l-udeX0P2M*>Kc4d8+MlkvJL11A!y*x|k=Y-1J$iDs>vG4M
z+TG1kZ3|bmIjC$XXZb1ncI}7Mt!J}1>nsnvzxn=r!O@#n^)2K6-ip5WTlKhm)T82G
zM^>*t?%BZJ5Z3|P>r^o1MvmFi>KL!@J51^lPTBo-URcoZp5yiISF@DfKP?3f#)XB}
zI-Fgr2yPo*{inXM_}Hzh`~7QDuKk=drQ+!q&w%}QA3<(ib^D-E5d&!58fYZ2^KM>n
z+x4Y9>%AYdzxzMKAv)k`>DgNwm#^H*x-ZZ=Yqrw;_p7pl7QB7HXs4(4r`T%Ox$=_5
z=e>$Ftu5~;&7ULSu5H5*!2|N_0jD_|4W(whx^^Jsd)@c#Y|ghrZ_CW9tvi3O^Nm-w
zRA|`kC9B#RqMF^7WdCj4@#xJdzrOIYpgo`v^yvyOo8%pN@}a`~sZS;yKU(#H;lf<-
z&S+bYn4VZr4pAumzxl#;Hs@7q_%p(DCzo9{-CjO7|I)f|>%LfL&knsS@Xl@j&Qt5p
zvS+kTSLrw2wwKi|E;26s$Ll41-wR&vxi0GMZNng=3R-pE5T~NIthQ<AlON1-m+wf0
zw62e|&c1#8z;&~gYt}_REqxdeA9h<MzIgU4=G;~5b1ob^)y6#U^xKY3mTzl38tz+L
z*M@EKj*EK4_+WzoXp#G(9_@Dz?!HhxDW7q9-LAdDH>O@U&7Qu#GXM0R^^se9*M8d0
z!fz!P$F==P`c{2y@pobJd9CMtiZn034sx61EvK&gkpaAu`+(h@a}m6r{#Pb^Il?S=
z`HE1;&*@IT*X`0TU(H`1fBS}1Ug!FZ3vFG@+mEPk*?uIw>hSuK_1=<k=_x^bdDaKN
zl$$QI@!Pr`F$^16z?%d5JoYHNRc7WrT`Idnb>GINzNuRu=6#kueQN4evz2$Qui=Wf
zIP-hf?JGBgY@W=h@boIRR$BWp(o=Trr-uRar;4mESs%P^$Bf0bD;X~6f+ORF5oe$C
z_9N!owyZoP9#9!uosrM~{$aR-=)SbjUrbq?ZCh^NUv&4MSnaOM<p(}j#%`P__T$5@
zFMdm_4?liveCz)2sySWV3r*!3wt~YgVHJz}nopOK_-_g2OSEPE(R~~H>U&>%|Cyls
za_?qGJAX3We)9UA1^RqOJLjHNTdHcMywG-Y?X+Jd{l=HB+TA^^^Oa#2IOj4<OW%BR
z%B&Y5S&M>?r>VdFE^*l4Np@avXzh-tdpGSX=B}x%p8ks~e)BD-wpFi~SFPgrSY`d-
zd~bhu;H={d_jUR$RkgZ$ee?Id>yJxH%HKD?xH2N@31f{jD6uen;WC@;>-n+JtE>O=
zgp%`&{oJiQZ@+C{ap=0t%!&Ip+3uNMuX*OW>DE;%O;=o+r8f1NiR_p3+qa{i9A5iD
zFICO*s`<sGCjH6z3-bAYe&joLg87C$ICd6w-OcOX($N2E`~Pbdnl|3GskNI|Ni(fl
zl^+=DyyNKJU4Qp(vRmL4zgg=wGgn*Bhwtqf6Q?h6NzYj>_M@bKbM2fz(~q60j`?)#
z@?N<PGq6(=>>tUt$LYsSYEpFG{g*NH3agcG!F9nWtEHksV&wxuogH*@6-`-gT@lE*
zJndEU`H1&DTUY11SA5@bi@D~<>wRzI(<@@VJPS_bsh?op%LvNU3_sYC-fa6eEq}V(
z>jS!<+7egTT@aJ}{qL#Rj}z6uukU+i#(K*MQrODusjgjfci9$~<(F*umhPJCRcbwD
z){B6y+bgfQv<Kbo^Z5QdWP9_*PYtc$ttt(937c;|DLs1fb=Tzw``F{k{3gwcZhot1
zXQQ^SX#RTURjcx^<<)oJa&oI+vWfq!nEgURTYlc1y!SyRvdK>`3mtyHveR#;tYOJU
z_Fa--r*4o4kLf825}Go3Qo)7CQO$~P^D;VnrmoM+2<+YOx?7s*q3Pq>``2f^Px}9K
z<EQy=Y<@&khR?CRqcs0TK$rC&uhQ+?&urg*sk~>YzjsP(<%0~!j#k@{m>%0bdDW9%
zPdHvzxu&LdeWa-D>5D&Sh2Fie=v7-o*>C1m>+%DGvjfhqReZZ9_tLpr&E*^4Pk6+q
z?>_zBzBe(mj!V9bn?GyEciW?HCm2tg`KIx&;sVfQXyAi_<EOkqV;^^UAI^OJ{m1R-
zr+;-<ePq9N|M1$+vYT&hZ{@kVC0~E#3i}k@xbU3ircbSPU7z2)eb!EQqCx))$j00Y
zowp^mw;P8s2l3xK8ppL>d;fAnnP=Ly)#?Xczeu}(@$0_FzY|VOSijSE-!rq^RaQTI
zzD;^kc+~CK@#VYbzIt4yaa4A>$oAs*)lJT|cM82qH=nbqG6N^k2IaKvx2N2?_sG`f
z^Mm7UPHkDUqqbUaVu?L|>r-{~<JHnkx9(n8boXCFRCC*_*|$UP*Stu5X;soQ_q{OZ
z`$Dh3LEN>w%HLnFjabAd<_u~*FkH}mQx=_aXy-!7dChX!5^b+$MJYdiy!!U;+wZ?^
ze^Z;E6WF^y)bw9N)*sz0Ua9!(H#KYSibYP+d+Ob|&;NHzMeVI@U+)sFqi+M|v$Q91
ze0kvnPWTMdk~iO+GUq`+QIoUtCH{Rnb4B>R@HU#g{kT1AyZr*&-iL*ss->cpbiY(z
zUyy%o-m$Q)tvsPWm{xsY<=d>b(%<QM<Xh$JbM=+We)qlm!CAP*x_^BusM=*%aD4O4
zoKHI*Jt;gYd+o;0Y1<~0{XX2dciCRkeaaW=cZTju4%&0;SeRs+(&UG-alQ{7?h8IS
zocwaG)PzY3PWzwxy=;09qylG9cx93F7F7R+1^r2sy)VVHwf(!;Rg*0%ZrxurtL>cP
zzQ=o&Kh#@Z$X|1(dfmgBa!gj=y1`ChXcrJq?$-$}w`h91`F6Qa)%T|--%olh8yllr
zr3H>B2ANdDvgjq=)&)CeYy>S2U|^v8F(Q!DF4_`3Ce_%@hinuw2zFO{ae~7@p3(Ny
zmFw5NClwVH`GCfO7#c*cT)(cZsd@5^k;2Ke56>s95aMbL2@6x}KdwCc?6s%%=bwMp
zR9AOjx^(HMs=cxQK3}jdxc|VT@cjc15DmhSFYg)hIDh^6_0+j@)9#*ok*>Xf)%Mhl
zw`E`7-`{_`e%<xgrhnxXzS!I`w+OmpZ6S2W9E^j)c9v&YZWsIK16rKNfO4qHt|^ya
zg4Sc{Ex(*uruo8gM}EPQ9pwcoJMzI8#5V8|Vh06q11q@uz<_E1NQMFVRF)S({I*{v
z6cragE!t_4c}Yb#e&3xplM)nNU0r7xBsST|^<TPt8MGjJ<&q^!E*qXbdv=y_dS6g*
z@YESIR&eX@2@n+(RSV_O-t~N5b;{<OC2zN0ciUEYKcT+t-JO;4e_yzJ`TB;|)a-HV
zms{I!_e*2beFL7mWw&!rojMg%Q?m!Ou&P(ud{%_v8U+cStb2PZPn|v;TvM|Lv`f_b
z-45sYdFeB&+28N^+;`6MxlC$$`suXIFK?#Lf4k*>!MlmGW=Wl$V;LN`KK$g#lfORp
z*RN@7Ys=fi6~V|T8~Y6Bc_5i(nHxHmdQZ<fH^&k@toQ5n`qf*vg4a<$`Tkq^cue`-
z(yE88;$NOj_Ro5GX{naB_GMP_n1rOO8zzZrhrQsh|IvJ`NAhx}^>^NVS?A~3TD@E{
zSwviX`Skd@$ocjEcGmsAeSekze7lLs?{>epv;Fa)8MM6K?#~D3op<wmXPfz+J$qIy
zjBoSZ>i2u2wq#5Mty3<2eeLDF>i1Vc%XwM3#a#Lx-fO)7{`!rL$zLxx^S>?s|L~5o
zar(I{>GNxsU5~Gi{r~fP{g-pr?_YFjuY0l1yCGD<L42k~;UdHIb8}97KbUag#JO{3
zU0q#1Yq-L~A|o?BjHc<uW=XUqUb%Kns<5!|*kpe@Nz1gmyGq+sl5cKGO`FQuy<+`(
z^EN)&tUdpJy*{>b`Mj){;<Ki0i3Rr){^#xa==S)@WPiH{{{l2pQqt0-zTdB}Pl?XW
zUHh=as<-~nWBI#*N;+A;zPvp4bb9=~%-f(4J2S`fvH{OK**D&kKAtf?@3PN+PW6+|
z=k3i^B(=lVbjW=EbXs59Z1&k3JB!n|U*-FJv8hk*B4o@u3++%8pEW#TVd3HGo?f1w
z6IIs6><n7FcJ17Eiyt0t&pyz=c*p(19Lr*p+FxJ%zPBH#ZT?&R@8@&S@g48$|Nr*#
z_rG3vTy}Z8e4WLn`qR^Nwe<Dhm)uu@tlGO{9{BEvuzyHl;YPEZ8w#FLo6~ylRlm2L
zTY4>W(vI?ijP~Mwo#<^oCr_R{b^d(6jB)9!kehGIYJWUzzZ++K1~h{3V(zNdt3`R(
zrcRuAF|*put?k<S`23*I4VwZqmVg$Ksn0E$<kl;t3L3<le)wTPQBl$IWXKsUc}`O|
zbb*GOqqb&E4O<hj@x0wT={M(}ot?ck<D!zNnAodn(RmlUM76(cp8wZ}uibfb`uV;)
z(!YP#cmGz~b+_cQ@28(NQg$^vl9G}{c-Ts>$ChV4J2P|Ak-g2%R&%PC?Aa6Z^5x69
z-;N(UrlqRd`dj0azy-#LLvt*Pzr0*N|JB~__l$RoM8srfu3WeCnbh8Yzg`#Zj7gi`
zET<&GrD<;;Uw*%KyUNQQkNYlzcBUNd7SI0n=B8RWyY}zv`~NLHE?*x5+Wk=aX5;a!
zudlA|`gSXO>fE_!_cA|NvAElMmrGRCtfZu*Z!=AIE&?C&q87@!de^U4tG~|w|HnOl
z@7HO+zQ32hIdA67mD8q)UA=a#%xd0mja_SFb_PwII@RjmkH?^0_cv}t=*I2};kWy-
zASpR{>coi$Yny9RQd2XZoR~OwTd#zn)9vl~)0dpsd+>ks5AZOq`Q4JqdOM#8^~%{s
zfi|(P+x^Z;R8-XS(Y=EUWzX48nmKdld6!eUxw)%Wu6$_MWyDfa!ijZ7qS;2Hh|WX_
zu#-U(M`z9Nhs4FjE&u!Ycz<|IOw97%E0!#AxxGFAxm{P%^7(bU?0!BGUK_X9>Jw-?
z{F#}?%JIebAFTGXdby-_TH&1yiOp<$G8&$saxN_`ZTatG&%w(ZpX}=_>h0~_bldv>
zDH&nm#Q_>Boz?8^WftF0JUKZzG&*|qRPAuLzRJ&M%|*FdO)@Vn$y+b^Y+<+cE+1UO
z-#3>YRC;!1=HmVT|Guw!xAXavj}=PEYbH&a1lke5WQoe(AII%MM-v1E1wHv%Wm^6&
z#&@1gr23qKrju6>>&W%18~Ho3Z@<0v%9Sf8%HB!WTrIsGyIk1cCJ>Y&^!EKo(v9C2
z1IqVbkK6wXoCGcYU!Ajlf91kL=c&`DFR%RkOw-yr`uW`Qc{`6D4tYMOxbNMr*Lu7X
z1`fyNs@H&aWY~T<zzmvpQZ0P1ux8;x$M~9$u1|jJMsEWhHRR>#d9i_!nXj_HN5aqv
zbWTy-r^)j(K0Z2{m79C@YIyuwZ@rx^H*ellixu@r{ATj$#a%I-hy)%9g98N?GRKZ|
z3itBc{Wt)s&?bHR{p)r7e<{mlD_5S}@oLrT9=qCK5}cfz$9_Japa168*3)|yC3?ok
zrJXg{k-wo{f6s>|!>lVBhhLUR9d740|M%m#ec3H}uGXH)&(9<!B_-P~zclf)|68(n
z$&w>WvJyq@=AYm3Y*uy;BQu+X-t^NFCK(e7U$5OR)##v5#Pgl+Ue)WhfB*RzSAWYn
zY{2(k?&jX=?H`-v{~Yk}@iEyGw>~f+AmPgO>$3|?4JMy8;5ocxKWOnF14Bbu5BSs<
zh67}u!)B3VHrw~S-S0J^v%4;6gSs{^6!mt$F}j(vtu_u6sGvo&4T}}7y<uWtIPlQ=
z^BLo>pp9ym&6Z!z3^HY4c#yG(RhFSa?#Rm$Eo0-&msk7Gx0`h6?s;+Ds3n`vS$Tt|
zOiVH+{QLX9zWmo8_4zf6^6P)!?&|Lk&&pZ_nxZf@F)6WnreFWFyU+Gpgymen)YR0M
zuU4;rw`KpXT~W{H*Y6V&5;7@y5dfNmjIF*1ntlhh8~2{rQ~9~%VXJu7*;%HuEQ^<c
zax`cQ=%gp#e}fj4e|>q`T{~>eg~|SQD?$5lgBjwtWK0AldhK;PoaWbj>YP{quaZ~N
zXvyXCKd!E?TiUI^FCzQ;x~n(S=dYb<oW5$=vSrchg&7*|ok){tODq!vt!Ds*4d{ra
zqMb2wx7{x~t-I^#v}jQ3emXTg3v{|uEhvwGPW$?M|Nr0la&<ov?^ZnS)za0Sdi(7(
zmAzjsd0Rc7QydZ&wydXz=j-e1{;aI5yFQ<@{vO?YHtn*0-AC@bg~w$l9a?B_Xc(x!
z|Bq4fLq>)lZ4%9V3=aH7w=$QPay^(dWzr;}pH-ipoSgd&lr2|>ub-v@@_O>|zTTE%
zPjHKat2HDjNNM)jWt%n`&8_?Ol6`IBnHh!?*Wb<Cz9(+ImbP~Pmq`o^4%VQswC%Y4
z_TKW-KQ9W&%hyL7KYH|N>92cfn>9taN?kn}7+gd_XAxcKuK93~{dbLh(h&~NOC5sB
zThh)-{rvmy#J~UlzVF|3`>m8&&I}v5{-WaI(3qGzYfYIM8Vo?^D7<iFYHT=l`m}X!
zcW-ZKRMae$mnYTdht$>W^RxSz0@|4ywXbI9zdz6Iy(gueo3rwE{{Gm_>F2NM*Z=em
z4Gql%Z5jV^$$RR=iHe?}*{+q^>-Pk`eEAYI4IUC7zaC`$EbH>T?{OLo44{KG7##RN
zozh;fp{>38^0vgoZJB?6eVug3c+LOM^Z#E74Ub*AW{u9@$MXL#oS3Mr>dDE;S@rd5
zc<EWw>lueyI6)`Uw2H^AV3(^<C|Y-ObNcF?J0ti1zWe^m=lTC*GBYzl2aoM~&&0^k
zk#yz}AA^H_;^v!QK&Ahl&*zMHi!d-0uz+F<w7wT{z#ljOy$!N>85-vGLJqs4)!B#*
a|M?>yXUb{(UbY<6r1y07b6Mw<&;$TQyp_xV
diff --git a/utils/forward_pass.py b/utils/forward_pass.py
index b5dad28..b366175 100644
--- a/utils/forward_pass.py
+++ b/utils/forward_pass.py
@@ -1,10 +1,11 @@
from utils.sigmoid import sigmoid
import numpy as np
+from scipy.special import softmax
def forward_pass(w1, b1, w2, b2, data):
- # compute the forward pass of the MLP with sigmoid activations
+ # compute the forward pass of the MLP with sigmoid activations for the hidden layer and softmax for the output layer
z1 = np.matmul(data, w1) + b1
a1 = sigmoid(z1)
z2 = np.matmul(a1, w2) + b2
- a2 = sigmoid(z2)
+ a2 = softmax(z2, axis=1)
return a1, a2
\ No newline at end of file
diff --git a/utils/mlp_training.py b/utils/mlp_training.py
index 8d3da4a..299cbb6 100644
--- a/utils/mlp_training.py
+++ b/utils/mlp_training.py
@@ -6,16 +6,19 @@ from utils.learn_once_cross_entropy import learn_once_cross_entropy
def train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epochs, batch_size, n_classes):
- # train the MLP for num_epochs epochs, using batches of size batch_size
+ # train the MLP for num_epochs epochs, using batches of size batch_size and return the train accuracies, losses and weights
losses = []
+ train_accuracies = []
for epoch in range(num_epochs):
for i in tqdm.tqdm(range(0, data_train.shape[0], batch_size)):
data = data_train[i:i+batch_size]
targets = one_hot(labels_train[i:i+batch_size], n_classes)
w1, b1, w2, b2, loss = learn_once_cross_entropy(w1, b1, w2, b2, data, targets, learning_rate)
losses.append(loss)
- print(f'epoch={epoch}, loss={loss}')
- return losses, w1, b1, w2, b2
+ train_accuracy = test_mlp(w1, b1, w2, b2, data_train, labels_train)
+ train_accuracies.append(train_accuracy)
+ print(f'epoch={epoch}, loss={loss}, train_accuracy={train_accuracy}')
+ return train_accuracies, losses, w1, b1, w2, b2
def test_mlp(w1, b1, w2, b2, data_test, labels_test):
# test the MLP on data_test, and return the accuracy
@@ -37,6 +40,6 @@ def run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, lear
d_in = data_train.shape[1]
d_out = np.max(labels_train) + 1
w1, b1, w2, b2 = initialize_mlp(d_in, d_h, d_out)
- losses, w1, b1, w2, b2 = train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epochs, batch_size, n_classes)
+ train_accuracies, losses, w1, b1, w2, b2 = train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epochs, batch_size, n_classes)
test_accuracy = test_mlp(w1, b1, w2, b2, data_test, labels_test)
- return losses, test_accuracy
\ No newline at end of file
+ return losses, test_accuracy, train_accuracies
\ No newline at end of file
diff --git a/utils/process_image.py b/utils/process_image.py
index c553733..f7877d6 100644
--- a/utils/process_image.py
+++ b/utils/process_image.py
@@ -5,10 +5,11 @@ def plot_image_with_label(img, label):
plt.title(label)
plt.show()
-def save_plot_as_image(X, Y, y_label, x_label, save_path):
+def save_plot_as_image(X, Y, y_label, x_label, title, save_path):
# plot and save image as png
plt.figure(figsize=(10,5))
plt.plot(X, Y)
+ plt.title(title)
plt.ylabel(y_label)
plt.xlabel(x_label)
plt.savefig(save_path)
--
GitLab