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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Overview\n",
"This notebook is a 2 part hands-on demonstration of working with Amazon review data for text analytics. The first part uses LDA and Kmeans algorithms to automatically group together reviews based on topics discussed within the text. The second part covers predicting review scores using SVM and Logistic Regression based on contents of the review. The end shows how to predict the rating of a new review based on the models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"import matplotlib\n",
"\n",
"from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
"from sklearn.decomposition import LatentDirichletAllocation,TruncatedSVD\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.manifold import TSNE\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.ensemble import VotingClassifier\n",
"\n",
"from nltk.stem.snowball import SnowballStemmer\n",
"from nltk.tokenize import RegexpTokenizer"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import review data\n",
"This dataset was compiled Julian McAuley and can be downloaded here http://jmcauley.ucsd.edu/data/amazon/. The Home & Kitchen data file contains around 551,000 reviews by Amazon customers. We read these into a Pandas dataframe for further analysis.\n",
"\n",
"Amazon reviews can span from 1 star to 5 stars. The difference between a 1-2 or a 4-5 might be very noisy when we build predictions. Instead, we bucket reviews by:\n",
"- Low: 1-2 Stars\n",
"- Neutral: 3 Stars\n",
"- High: 4-5 Stars\n",
"\n",
"Reviews are filtered to those with at least 45 words to avoid short, uninformative reviews like \"This is great!\". After limiting, we take a stratified sample of 6000 reviews from each bucket to get reasonable performance from sklearn in Python. The stratification helps us overcome the bias in that 80% of the reviews have 4-5 star ratings."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>reviewText</th>\n",
" <th>overall</th>\n",
" <th>bucket</th>\n",
" </tr>\n",
" <tr>\n",
" <th>bucket</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>low</th>\n",
" <th>300523</th>\n",
" <td>If you're a side sleeper that needs some support without feeling like you're laying on a brick, avoid this pillow! There is almost NO fill above that blasted memory foam, so essentially you're laying on the memory foam. I have woken up with headaches EVERY night since I got this thing! Going back tomorrow...</td>\n",
" <td>1.0</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>high</th>\n",
" <th>477743</th>\n",
" <td>i gave 5 stars for quality , this flannel is soo soft and warm i do not want to get out of bed . 3 stars on color , looked yellow in pic , but has a lot of brown -- kind of a warm tan . looks dark in bedroom . washed and dried = no problem . fabulous flannel , great price , dislike color .</td>\n",
" <td>5.0</td>\n",
" <td>high</td>\n",
" </tr>\n",
" <tr>\n",
" <th>low</th>\n",
" <th>81920</th>\n",
" <td>I received my Venta yesterday. Unpacked it, carefully read the instructions and tried to start it up this morning and repeatedly got a red light warning instead of a start.The first time I called Venta's US customer service the phone was answered by a dimwit who sounded like he was either stoned or asleep. The only words I managed to get out of him were various mumbled forms of \"Huh?\" and \"Waitta minnit\". Dimwit finally transferred me to a number that left me on hold for 5 minutes. It was eventually answered by a woman who asked me a couple of questions then transferred me to another number that answered then promptly hung up on me.I called back and the phone was answered by the woman who had transferred me to the land of the dead on the previous call. For some reason she was now able and willing to help me. She had a remarkable (attractive) voice and accent so I'm sure she was the same person. I have no idea why she could not help me on the earlier call...Once I finally reached a person who was (a) not comatose and (b) was willing to spend literally two minutes of their time troubleshooting we discovered that the unit I received had a faulty motor, then told me that Venta would not service or replace it because I didn't buy it direct from them. (Thank you Amazon!)It may (or may not) be a nice humidifier but I won't replace it with another because I'd rather not support a company that treats their customers in such a shabby way.</td>\n",
" <td>2.0</td>\n",
" <td>low</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" reviewText \\\n",
"bucket \n",
"low 300523 If you're a side sleeper that needs some support without feeling like you're laying on a brick, avoid this pillow! There is almost NO fill above that blasted memory foam, so essentially you're laying on the memory foam. I have woken up with headaches EVERY night since I got this thing! Going back tomorrow... \n",
"high 477743 i gave 5 stars for quality , this flannel is soo soft and warm i do not want to get out of bed . 3 stars on color , looked yellow in pic , but has a lot of brown -- kind of a warm tan . looks dark in bedroom . washed and dried = no problem . fabulous flannel , great price , dislike color . \n",
"low 81920 I received my Venta yesterday. Unpacked it, carefully read the instructions and tried to start it up this morning and repeatedly got a red light warning instead of a start.The first time I called Venta's US customer service the phone was answered by a dimwit who sounded like he was either stoned or asleep. The only words I managed to get out of him were various mumbled forms of \"Huh?\" and \"Waitta minnit\". Dimwit finally transferred me to a number that left me on hold for 5 minutes. It was eventually answered by a woman who asked me a couple of questions then transferred me to another number that answered then promptly hung up on me.I called back and the phone was answered by the woman who had transferred me to the land of the dead on the previous call. For some reason she was now able and willing to help me. She had a remarkable (attractive) voice and accent so I'm sure she was the same person. I have no idea why she could not help me on the earlier call...Once I finally reached a person who was (a) not comatose and (b) was willing to spend literally two minutes of their time troubleshooting we discovered that the unit I received had a faulty motor, then told me that Venta would not service or replace it because I didn't buy it direct from them. (Thank you Amazon!)It may (or may not) be a nice humidifier but I won't replace it with another because I'd rather not support a company that treats their customers in such a shabby way. \n",
"\n",
" overall bucket \n",
"bucket \n",
"low 300523 1.0 low \n",
"high 477743 5.0 high \n",
"low 81920 2.0 low "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cat = ['low','neutral','high']\n",
"def cat_y(y):\n",
" if y<=2.0:\n",
" return cat[0]\n",
" elif y>=4.0:\n",
" return cat[2]\n",
" else:\n",
" return cat[1]\n",
"\n",
"def get_reviews(path, n_samples):\n",
" dt = {}\n",
" i=0\n",
" with open(path) as f:\n",
" for d in f.readlines():\n",
" dt[i] = eval(d)\n",
" i += 1\n",
" df = pd.DataFrame.from_dict(dt, orient='index')[['reviewText','overall']]\n",
" df = df[df['reviewText'].apply(lambda x: len(x.split())>=45)]\n",
" df['bucket'] = df['overall'].apply(cat_y)\n",
" \n",
" df = df.groupby('bucket').apply(lambda x: x.sample(n=n_samples))\n",
" return df\n",
"\n",
"data = get_reviews('Home_and_Kitchen_5.json', 6000)\n",
"pd.set_option('display.max_colwidth', -1)\n",
"data.sample(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate Term Frequencies\n",
"We calculate both the actual term frequency as well as the tfidf weighted term frequency. For both algorithms, we limit to words occuring in at most 90% of documents and in at least 10 documents. While the term-frequency matrix is just a word count, the IDF calculation adjusts for \"boring\" words that occur in many reviews.\n",
"\n",
"We perform two tokenizing operations. First, we tokenize only letters, ignoring special symbols & numbers. We use the NLTK Snowball stemmer to try and get the root of a word as best as possible. Stop words are removed in the vectorization step."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stemmer = SnowballStemmer(\"english\")\n",
"tokenizer = RegexpTokenizer(\"[a-z']+\")\n",
"\n",
"def tokenize(text):\n",
" tokens = tokenizer.tokenize(text)\n",
" return [stemmer.stem(t) for t in tokens] \n",
"\n",
"def get_tf(data, use_idf, max_df=1.0, min_df=1, ngram_range=(1,1)):\n",
" if use_idf:\n",
" m = TfidfVectorizer(max_df=max_df, min_df=min_df, stop_words='english', ngram_range=ngram_range, tokenizer=tokenize)\n",
" else:\n",
" m = CountVectorizer(max_df=max_df, min_df=min_df, stop_words='english', ngram_range=ngram_range, tokenizer=tokenize)\n",
" \n",
" d = m.fit_transform(data)\n",
" return m, d\n",
"\n",
"tf_m, tf_d = get_tf(data['reviewText'], use_idf=False, max_df=0.90, min_df=10)\n",
"tfidf_m, tfidf_d = get_tf(data['reviewText'], use_idf=True, max_df=0.90, min_df=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compute topics using Kmeans and LDA\n",
"We are using two approaches to extract topics from our document set. \n",
"\n",
"**Approach 1 (Kmeans)**: Using our TFIDF matrix, we cluster documents into N clusters based on their TFIDF similarity. Within each cluster, we count the top occuring terms.\n",
"\n",
"**Approach 2 (LDA)**: Using our TF matrix, we attempt to extact N topics from our collection of documents. \n",
"\n",
"It's a subtle but important difference. Kmeans forces each review to belong to only one cluster while LDA allows a review to have many topics associated with it. I pulled 10 topics out of my head as a nice starting number. Further testing would have to be done to see if it was the best choice."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_topics = 10\n",
"\n",
"def get_lda(data, topics):\n",
" m = LatentDirichletAllocation(n_topics=topics, n_jobs=-1, learning_method='online').fit(data)\n",
" d = m.transform(data)\n",
" return m, d\n",
"\n",
"def get_kmeans(data, k, scale=True):\n",
" if scale:\n",
" s = MinMaxScaler()\n",
" data = s.fit_transform(data)\n",
" \n",
" m = KMeans(n_clusters=k).fit(data)\n",
" d = m.predict(data)\n",
" return m, d \n",
"\n",
"lda_m, lda_d = get_lda(tf_d, n_topics)\n",
"kmean_m, kmean_d = get_kmeans(tfidf_d, n_topics, scale=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show cluster top 15 words per topic\n",
"Using the approach [outlined in the sklearn documentation](http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf_lda.html), we first extract the top 15 stemmed words per topic in our LDA model. As a second step, we do something similar for our kmeans clustered documents. Here we just count the top 15 most frequent stemmed words per cluster. Both show similar sets of results."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 15 stemmed words per topic in LDA model\n",
"\n",
"Topic #0:\n",
"use, work, time, just, unit, year, review, onli, thing, like, month, replac, i'm, veri, turn\n",
"Topic #1:\n",
"vacuum, clean, bag, floor, carpet, brush, suction, cleaner, hair, dust, attach, power, dirt, hose, cord\n",
"Topic #2:\n",
"pillow, knife, bottl, use, knive, cut, sharp, like, soap, wine, set, waffl, edg, veri, sharpen\n",
"Topic #3:\n",
"use, pot, lid, cook, set, heat, handl, hot, temperatur, stainless, water, measur, food, steel, scale\n",
"Topic #4:\n",
"use, make, clean, blade, food, cut, grill, slice, juic, work, time, blender, tri, just, like\n",
"Topic #5:\n",
"glass, ice, tea, iron, plastic, leak, make, design, use, tray, water, fix, cream, just, like\n",
"Topic #6:\n",
"use, like, veri, look, just, nice, hold, realli, don't, becaus, great, good, color, easi, hand\n",
"Topic #7:\n",
"coffe, cup, water, filter, machin, make, use, maker, grind, hot, brew, tast, ground, grinder, pour\n",
"Topic #8:\n",
"pan, use, like, oven, stick, veri, just, bread, sheet, mattress, time, oil, cook, bed, set\n",
"Topic #9:\n",
"product, amazon, box, item, order, plastic, purchas, return, lid, piec, contain, look, receiv, store, price\n",
"\n"
]
}
],
"source": [
"def show_topics(model, feature_names, n_words):\n",
" for topic_idx, topic in enumerate(model.components_):\n",
" print(\"Topic #%d:\" % topic_idx)\n",
" print(\", \".join([feature_names[i]\n",
" for i in topic.argsort()[:-n_words - 1:-1]]))\n",
" print()\n",
" \n",
"def show_cluster_topics(cluster_labels, tf_matrix, feature_names, n_words):\n",
" d = pd.DataFrame(tf_matrix.toarray())\n",
" d['c'] = cluster_labels\n",
" d = d.groupby('c').sum().T\n",
" \n",
" for col in d:\n",
" top_n = d[col].nlargest(n_words).index.tolist()\n",
" print(\"Cluster #%d:\" % col)\n",
" print(\", \".join([feature_names[i]\n",
" for i in top_n]))\n",
" print()\n",
" \n",
"print(\"Top 15 stemmed words per topic in LDA model\\n\")\n",
"show_topics(lda_m, tf_m.get_feature_names(), 15)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 15 stemmed words per cluster in Kmeans model\n",
"\n",
"Cluster #0:\n",
"cook, pot, use, grill, egg, cooker, rice, oven, heat, time, food, waffl, clean, make, like\n",
"Cluster #1:\n",
"water, filter, fan, unit, use, kettl, iron, air, steam, work, room, heat, just, heater, clean\n",
"Cluster #2:\n",
"use, like, veri, just, look, work, product, make, time, great, good, don't, becaus, realli, bought\n",
"Cluster #3:\n",
"vacuum, carpet, floor, clean, hair, suction, dirt, use, vac, power, cleaner, hose, brush, attach, dust\n",
"Cluster #4:\n",
"knife, knive, sharpen, cut, sharp, blade, use, set, edg, slice, chef, handl, veri, block, good\n",
"Cluster #5:\n",
"pan, stick, use, cook, non, bake, egg, fri, nonstick, heat, oven, cake, oil, like, veri\n",
"Cluster #6:\n",
"pillow, mattress, bed, sheet, sleep, foam, comfort, soft, like, memori, feel, cover, night, veri, fit\n",
"Cluster #7:\n",
"lid, open, contain, use, jar, just, work, seal, like, plastic, fit, glass, veri, close, handl\n",
"Cluster #8:\n",
"toaster, toast, oven, bread, bagel, set, slice, burn, use, work, just, slot, look, bake, like\n",
"Cluster #9:\n",
"coffe, cup, maker, brew, machin, filter, ground, water, grind, make, use, caraf, pot, hot, press\n",
"\n"
]
}
],
"source": [
"print(\"Top 15 stemmed words per cluster in Kmeans model\\n\")\n",
"show_cluster_topics(kmean_d, tfidf_d, tfidf_m.get_feature_names(), 15)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare data for plotting\n",
"Our TF/TFIDF matricies are thousands of attributes wide which makes it a challenge to graphically represent documents as we are limited to 3 dimensions. We could perform a heirarchical clustering, but the number of documents makes this approach very slow. Instead, we perform a SVD/LSA to reduce the dimensionality of the matrix to something more manageable (eg. 30 dimensions).\n",
"\n",
"We then use t-SNE to attempt to visually cluster and represent our data as best as possible in 2 dimensions. More information about t-SNE can be found at [Laurens van der Maaten's site](https://lvdmaaten.github.io/tsne/). In the end we will have a 2 dimensional matrix for our XY plot. Be sure to read the [caveats of t-SNE plots](http://distill.pub/2016/misread-tsne/) as well.\n",
"\n",
"Our Kmeans output already has cluster labels in it (eg. one cluster label per document). However LDA allows for a single document to cover multiple topics. One approach could be to perform a clustering on top of the LDA output. But for this example, I chose the dominant topic for each document as its label in the LDA model."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_svd(data, components):\n",
" svd = TruncatedSVD(n_components=components).fit(data)\n",
" o = pd.DataFrame(svd.transform(data), columns=range(0,components))\n",
" return svd,o\n",
"\n",
"def get_tsne(data, components, perplexity):\n",
" tsne = TSNE(n_components=components, perplexity=perplexity, n_iter=1000)\n",
" o = pd.DataFrame(tsne.fit_transform(data), columns=range(0,components))\n",
" return tsne,o\n",
"\n",
"svd_v, svd_m = get_svd(tfidf_d, 50)\n",
"tnse_v, tsne_m = get_tsne(svd_m, 2, 25)\n",
"\n",
"lda_c = lda_d.argmax(axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Plot Data\n",
"Here we use our LDA and Kmeans labels with our reduced dimensions to plot our documents. We create a rainbow color scheme which allows for a variable number of topics/clusters. You could choose any other color map which suits your needs. I chose to plot this in 2D, however you could create 3 TSNE dimensions in the step above and create a 3D scatterplot as well. \n",
"\n",
"The plot tends to overlap quite a bit. If you sample through some of the reviews you'll see many tend to use similar wording."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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wHvunjSGmTSeSx073lrHNMydqHxgMJxtGCDEYjhEVFRUU5PmGoBbkVfs4/HXK\nX71lgzmqAsTExDRqHo1o5enIyMhgcdEiPB4P06drwSM+ZwSulObeMrZ55pxzzjmmbasNdv8tWbLE\n5DsxGMIhIk12A7oCsn79ejEYjnfycwskJSZTBlEoE9glgyiUlJhMyc8t8JZp2aK1xJAgiWTIQGbL\nBHbJQGZLAukSQ7y0atVKAO+WX5AvFRUVDdK+8vJyyc8t8K0/t6DB6q8tl/TtJyo5XZLHTJNmUz+Q\n5DHTRCWnyyV9+0WlPeEoLy+X3Hyr/5TLpx9z86PXj4aTl/Xr19vPYFdpAmN6oM34hBgMxwCPx0Nx\nSRGDKPT19agUFpaM9Po4rF77Lj269aRiTzkLGek9XuGiRcsW7D96gNGzriMruz2eFVt5YUIhw0cM\np2hRUb3bOGLYSEqXrWIQhZyOm08ppWTZjQwfOoKixYvqXX+dOHKQ/dPGeP9UcQnRaUcE2HlOXG06\nUrX7UxJ+eSWx5+chFV/w+pyJDB0+gsVFUepHg6GJYoQQg+EYYIegBvP12L59O23btuXMM8/k24pv\nWLp0Kf/5z3+IiYnh3HPPpU2bNuTm5jJ61nX0Gqb9InoNuwgR4ZlRT9fbUTNSIamh8Xg87Nixg3PO\nOcenfo/HwxvLlpI8djoxZ3Wj6uuduFqfReWOdbzRBB1T7TwnCQPv4OjLD4BUcajocQ4VPa4da6+4\ni5KZtzS5dhsM0cYIIQbDMcAOQf2UUu8gD9W+Hv4+Dv5RKMXFxQBkZbf3KdfO3QGoFmLqSqRCUkNR\nUVHB8OEjWby4WoOTl1fA3LmFZGRk+OQNcbX4GTGn6v6zNSEN3Z76Yrf3yPrXIDGV5CsfJrZdb46W\nvcOB2RPh8EEANm7c2KTabTBEG+OYajAcA7KyssjPLaAk5kafpGRLYm4iP7cg5MC0Y8cOhgzRgotn\nxVaf38pKtwD1d9R0ufSnIJhDbEM7gg4fPpI331pFp56zyLlsJ516zuLNt1YxbJjOARIub0hTc0y1\n21u16wOSr3zYJ+tr0ogpHN2i+/XxJ56MZjMNhiaH0YQYDPUgmDkhEHPmFTJ86AgWllT7euT3LWDO\nvMKgx1RUVNChw3kICTTL7MycG2cjIrRzd6CsdAvzb5lDfkF+nWfXFRUVjBim848oXCxiHIJwBjl8\nwnItJPUNLSTVFo/Hw+LFRXTqOYufnD4MgKTThwHC4sWj2LZtW9C8IYfn3k5ufsO2pyHIysqi8/ld\n2LxpY9DZ3J7xAAAgAElEQVSsryq9NStLlxuTjMHgwAghBkMdWLNmDdePu4H169Z69+XmFzBvjjYn\nBCIjI4OixYvYtm0b27dvj0hwcbsv5siRg3TqOY2Wp+Xz3prf88yop72/97jgAuYUzqnzdTidUVvT\niYX83schNpyQVBds00VGy2yf/RkttSnINrXMm1PI0OEjKHE4ptp93BRJSkoCtPYmvvdg735beyN7\nvwZg6LDhLF1SEvQ5MRhOJowQYjDUgoqKCn49cBArVqzQtv+x0722/0gjINq2bRvRTNjj8fDhh+8D\nesCOi8+gW5/X2PfDNvaUr+KDNVfToX37gINZJBqaQM6oY9nI2zzMUm5jyZIljZId1TZd7Nm9wtKA\nYP2tTRa2qcXOG1Iboc1JbbRU9cXj8bDq3XdwtemofUAc2pv9z9+G6yftSL3tJY6WvcPmwolc/uuB\n3HH7xGPSNoOhKWOEEIMhQioqKsjqcC7le38EqfLa/gH9bwOnFF+wYIH3/84BOyWtLXsr1gRt47AR\nIykprnb4DKahCeaMeh6/Yym3cfTo0XpfQyCysrLIyyvgzbcmAEJGSzd7dpdStvkW8vJqmloiFdps\nAvVBH3cOr768sNG0D3Zfpox5mgMLJvuEFaNcxF88isrPtxBzVjfih09hxbQxrCjV/jbhNGgGw4mM\nEUIMhgi5fOBAyr/5moSC8Tr0MojtvyEiNyoqKnjmmeesv2L4aMONOAfsjzbcBMQwYoTvYm52ropI\nNDS1jdhpSObOLWTYsBEsXjzKu8+Ojqkvw0aMZNmKt3W+jl1ak7SydDlt23dg29YtjTLY231Z+dmH\npN66gMqvdlD19U4qv9jCwRfu5uDcO7xlYztooS/p2qdQrhiTQ8RwUmOEEIMhAjweDytLtbkgtnMu\nh4oeD2r7P+ecc+ptChg+fCRffvUdnXrOIjH5DDas/B/eWz3KUSKG1NQUH3OJnasieez0iDQ03oid\nZTcilYGdURvLpJGRkUFxcd1NLcGw+8DVpiNS/rmPMFY+6w/0z8tj7erVDXAFAVAuDsz+o9cUIz9W\ncPCVhyE2nuRr/uFtx/5ZfwDlwvXT9sSd2cUsymc4qTFCiMEQAba6HUD2/JfYzv1r2P4PFU6kV++L\nGH/TzRGZQ4JhR4+06/QAcfGZJCS2pO/Abyl77w4+3vowypWAopL33tsUsI210dAEi9j5x5OPk1cw\noF7XEQm1NbWEw+6Dql3vBxTG1k0bQ3bOxQ1umtmxYwdIFTFtOtUwxSSN+luNduyfNoaD8/6PuDsX\nNagGzWA43jBCiMEQAba63XY8TPztXXD4YI0BZ9U776CSm4U0h4TTLmzatAlwUfbeRO++lqfl07bj\nX/l468MkJcbxwQdbOPPMMwO2MZSGxp9gETt5BQMiNus0Bew+jYmJ8e4LJoy9s3Y9Q4eP4LG/T20w\nLY/d9/E5I0i6aipVX+/k6MebOPTv+4jr5Ovca7ejcuvbVH61g8od64Cml/vEYDgmRHvxmlAbZgE7\nQxMiN79AYlLTxdWmo8/iZLhiJWHQnZJyx38EkOSx06X58995t+Qx0wSQNWvWVC9wFmJhs+zsHImN\nayades6SnMt2SqeesyQuPlPSmncWQDweT8g2xqZl+Cz6FpuWIbn5BUGP8aesrCzkddjnLysrk6Ki\nopDtaWx8Fo2ztuaZLUK2P3HIX3zvXwMtMOff93HuESHbAUhC/vha3x+DIVKOhwXsot6AkI0zQoih\nCVFRUVFjwHMOMim3/ksAaTb1A59Bp9nUDwSQX5zXUWJSm0vy2OlaQBg7vcYAZAsAnXrOkrzBR7xb\np54zBZDs7Jxat9E5wEYiOBQVFYW8jvnz50ckTB0LvAO/o09jUtMlLiFJSGrmI4yR1ExQLont4NYC\nQMH4oPehLlRUVEgft9v3+VAuUSnNfVcCTmnus8pu1+7dZe3atQ3UIwZDNceDEGLMMQZDhPjnrfji\niy8YPXq0V+3vaqXNI8HMIR9+ENhPwemU6PVpkEr2/bCNlLS27PvBQ5VUAXDDDdfXqo22qaGiooKC\nvAEUl1T7eOTn6kRk/r4R4cw6/3jiSd7d+N4xN9X4m7FCOeLunzaG8zpl8YHDXBbbwU1cr4EcXDDZ\nGzZrp1b3vw+1xQ4Ltp2XAbLdOaAUK95+x9dsF5sArhiapaXz/Xd72LBuHT169DChuoaTEiOEGAy1\nxHam9Hg8QPVgHXPaOZbDanWExNGtb+u/Y2Kh8mhIp9EWLVpw//0PAPDBmqsBSEhqxaED33jLP/Hk\nE/Tr1y/sQOXv8OnMjHo6bj6llJJlNzJ86AiKFvsKDqFSpvdxu1lRujziCJyGIFjuk6tHXQkE9/24\n8YZxXHvttSQUjCf+4lHVi+DFJ/sKBdQ/vDpQaPS7cybi7tmdSy7O4Y1lS6sLVx4ho0ULfjh09Ljx\nuTEYGgsjhBgMdSTQYB3XJY/Kj5bXcFhNGDiRQ/++P6TT6PDhI1mz9n0dFZPYkk/KHqRSPmX009eR\nld0ez4qtvDChkOEjhlO0qMi/OUHxz4z6LR6SyKR35R0Ul9wWUHAIljL96lFXsrK0tFFzpPgTLPfJ\n/n37gOAaG6UUAAn9xuBq8bMaba36eqdXMKnPwnihNDJvTBvDmjVruOXwYVZaycmQKvZ8u/uYCnIG\nQ1PFCCEGQz0INljf+5fJ7N6922uyScgeTuWO9TXDeudMJDe/ABFh8eIi0pp39omKGT3rOnoN04Nm\nr2EXISI8M+rpWg1UtonnIHv5JxfzqZWMDEDhCri8fDCzjr/2x6axVrcNNcCvmDaGPu4cVgVZ5M7t\ndods6/65fyI18ydU7vqgXgvjhQuNvu76cWwu2+4Vog4tndboye4MhuMFI4QYmjwej4fly5ejlCIn\nJ6dJfaDDrW/iHLSTx05n/9PX+mhJst05zJtTyKpVqwAXB/d/Rqees6iSSj5YczVZ2e19ztfO3QGI\nfKCqqKhgyl+1iaeIcQCcyvkM5Hm+5j0WMY4n/vEkgwcPDni8v1knmKnm0JyJZLsb/t6EG+DHj7ue\nlJmzagiB11x9FS+88ALnderMlkI/81jh7Tqb6u5P+eH/skGqyM0v4J7Jf6a4uLjWIbsulwuAw6sX\nklgw3rvfFnbWr1tL4pB7qoWonCvDJrszGE4aou0ZG2rDRMec1JSXl8svL+3rE0kASK/eF8n8+fOj\nGhpaG/xDNxOH/EVcSanSx50jIvo6u3Xr7hMVk53/oQAyetZ18uyRQu92zcyxYcN0neTnFkhKTKYM\nolAmsEsGUShJZEpbCuTPiAxkdq3qEwkSJWTdo9z8AlmzZk2Dhe5GGi7s8XikqKhIli5dKi1atfZp\nk//zE9u5vzR76hNvHS+++GKdon0ChQe72nSUtPvelqQrHxEVlxDwvM2f/05UWouA0TstWrWud58Z\nDDbHQ3RM1BsQsnFGCDmpyc0vEBWXoEMcHSGYdqilc7AoKyuT6dOny4wZM5qccBIubDYvr0Bi41IF\nkJzLdnrDclv+JE+S01Plmplj5aGdj8o1M8dKWmaa5BfkR3ReewAfRKH8GfFutuAxHo9MYJcAUlRU\nVOvr6uPOEVdSqiQOuUeaTf1AkkZNrTHwNkb+jVC5T1q0ai0kNRNXm46iUnTobtK1TwkgSdc+JWkP\nrq8Rcty1e/caYb6RhOwGCg/2PpvKJSo53ec3lZIhsZ37S9oD67wCi7Ov1GlZtRYIDYZQHA9CiDHH\nGJokti8AQPL/Ph4wBDNxyD28vugRzs5qx57ycrDCWAEu6duPFxfMbxLhjqFMNs4U7WXvTfRZLbfT\nBc+ztrQvz4x62ltXfkE+cwrnRHTeYKvknkEOABVsZz/lQO1NAHotHd8omSMbiyE+ieT/fbxBIz6C\n+d3Mm+O72F1JSQnl33xN4pB7OPjCXd62VX65HQDlivE6okK1+WPDunW1dhINFx4MkPT7hwKna09v\nDUDKtU+zv3AilVtXAiBfekAF9tExGE5UXNFugMEQCOdaLcH8AWJ+2p74YVPYU16OStKp0ptN/YDk\nsdN58501DB3uu8JstGnbti35+fk+A4x9nae2+S0tT8tny8YJ/PfTORzY/xm7vyzi4L7PyM7Ooaio\nCI/HQ9GioogFK+cquU7sVXK/4UO9WF1u7R0y/X01Kr/cztHNS0ga+SDxvQd782/ED5tCSXER27Zt\nq1X9TmwhzuPxePthcdGiGv2w2lqYTjU7xadt1aHTEzn89nyqyj/n8NvzOTD7j3Tu0sWnrI3TSTSS\n6/c/LtRvR0pnA7Bv+liqPvvQ57klMZV/PPFkJN1iMJwQGE2IoUliD6AQPLrB1fosquKTQKpI+v2D\nQWeyItIoK8E2BPZ17tm9gk49Z/He6it9VsvNzs7hlVfqvtiawkUR4xGqV8kt5kYULpZyG/l9dcKy\nurbbvjdV33wM1G7xvNoSbrG7nj17AiDff+vTNoDksdP58f5f+TgFq7gEkhITa5SF8E6iwRK6HV71\n7+o6gtT53HPP8fAjj/BRkOR1K46DMN3GWl3ZcPJhhBBDk8SOwliy7PWayb8Kbye2c39iTj2bw2/O\nBIIPfkOGDWPDunXe/U0tK2VWVhZ5eQW8+dYEQDi32xN89dkCdnx0Lxf26kZp6Vt1rnvHjh0IVZxG\nFxZSvUrumVzCx7zBjBkzuOaaa+rcbmeUjMr8KXDsQncDkZubS4tWrSl/5SHvQoP2c3Nk0xKq/uvx\nKa9Oy2LVu++S7c7hXcd1HN1UwtHSWSFDdrOysujW4wI2zr4NRIj5+Xlas7HrfW+ZA8+ORw7+SFzn\n/lYE0R9p0ao1V199tbfM4eWFxHbujyulOdD0w3QDJY7LdufwSgOvSmw4iYi2U0qoDcsx1e12y69+\n9Sufbe7cuXXx0zEcR1RUVMglffvViG6wIxCSx0wTElJCRk/EpDSrtcPhsaaiokLy8nwdV/Py6u/Q\n6XRMHY9HhlMk4/HUKSImWLt9HG5tZ8x6LJ5XX3bu3FkzOsb+fwBHUZRL5s+fHzAK65K+/QLeg+3b\nt0vmKS1rRuAk+T5rKjndp84WrVrXcGRVKc0ltnP/oFE/TY1gzrgtWrWOytpBhmrmzp1bY5x0V69l\n1GQdU6PegJCNM9ExBtHhlzNmzJC///3v0sed4zNQtGjVWoiNr7lImDUAhAvtjIRjtVqsHWbakOex\nQ3QHMlsmsEsGMltSYjIlP7fhBAO73WvXrm2Qhe0aor+fe+45ASR+wM2SdNWjIQXVJUuW6ME1tWaE\nTLY7x6ctZWVlktqsmZCQ4i1LyzPCrpZrb8HKpNy5SAttqU1PSLYJFy5th5wbmg4mOsZgaACcvgA3\n3XSTT5TJKaecwm8G/443Xn/dx97fNqsd2zx7vWYCm0jU3ba9+5RTTuGuSX+usWZJY5hzGsvGPmde\nIcOHjmBhSbU5pq5+IMFw3p/FRYtYsmQJq1at4sILL6Rfv34R1xNsjZi69PdVV13F/H+9yOuls1B9\ntINyMJPdJ598EjIr6wor3XqLVq0p/+Zr7/GH330RV+tzYPcnIetPuvYpqj7/KGSW1H1/HQBAt+49\nakT9NBXCOeOuLC1l3bp1dO/e/Zi3zXD8YqJjDMcdziiTjIwMXl+6BE/ZVmbMmMHf//533Dk5bPOU\nAfrjvu+R31K17zsgtI9CRUUF+fkDaNeuHQUFBVzQsxdLlr/tE73w+sp3GzTqxv+cWVlZ5OcPYM+e\nPQ1Sf0ZGBkWLfSNLihbXjCxpCCoqKsgrGEBubi6TJk2if//+5BVEfi3ONWIaor/nzSnk0j4Xcqj4\ncUD7qzjxX2MmVKQLykX5Dwd82la5fS0Hnv9DdX1B6o895wLic64MWSbOrYXEeXPnNFnfCqczrhP7\nGkhIZux1oVd5NhhqEG1VTKgNY44x1JK8gnxJzGwmObPulsE7X5KcWXdLXPM0iT0325uVMjuI2jgv\nr0ASEjOlU89Z0uPiZRFl6qx3ex3nzLlsp3TqOUsSEjMlL6/pquSDmUoC+QtE6hMSaWbUuuDxeKRz\nly41/FVUcjPp1fuisOdOsv4NZW7xJkfzy4Aa076Pt3xsB3eNLKkqJUMfm5zeZM0wTrLdOQGvIbZz\n/ybvz3IyYswxBsMxROePKCZn1t2cPaw/AKnD+iMilI66h6MfrQDl4oZxNWdrdtKwTj1n8ZPTh7H7\ny8VA6JBTkfqF/vqfE7ASlQmLF49qUmGawaIibhh3PV26dEFEgpo0SqaNYenSpSFNM+FU/fPmzWPo\n0KF16o+2bdvSosUpcOQj39WNY+NZ9c473HjzBC7p249Sv/VwDhRO1JErqZkh26ZankHVN5/ganWG\nb/3J6aTcVG1aiWl/EUe3rqyxwrLs2wPKxb1/mVzrazvWvPLyQs4462y+d1xDbOf+OsfJwR8BTLI1\nQ60w5hjDCYM9kLXO7uyz/1T3+QDEuUeAVNHFSlAV6NiMltkAJKeeBQRXPf91ygM+JpTamB2CndMm\no6XOcGonyvJ4PBQXF9cr4Vd9CWQqWbF2I78bMpSsrCyGDh8OBB+ow5lmwqn6J02aVOd+9ng8vLFs\nKUn/+zhpD64n5dZ/kfbgepL/93FAWFaqM5Ze2udC9k8bw/cTzmP/tDHEtOlE8tjpuFqdGbJtsvsT\nOLzfJzy3eWYLYlxwdFOJNznakcWPg1SRcucikq56lIQr/o+kq/5Oyp2LQKrYvXt3ra4rGmRkZLBs\nSQkACQXjSXtwPam3LsCV0tzbHw89/HA0m2g4zjBCiOGEwR7Ivl6x2Wf/V6WbAKha/2rQ3A/OpGEA\nKWlZnHJaPgee/6NPls3Dc2+nRavWrNr4Xr19F/zP6W3vrgUA7N+/n4IBBT7CTsGAggbzF4kUO0V5\n/PAHfLKhJv/+IZAqEofcw+Yt1asFO7EHpsQh94TsIzvvyOE5vllN9z9/G642HevVz04tS8ypZxPX\nuR8xp57tFZBi3VfyxrKlPP7o3/F4PEyfPh2A+JwRuFKaOzKu/rFGxlWU/oRm9+nD2rVrvX43O7dv\no2/2RT5CTe8eXQE4UHg7B/55E4deupcDz93IgcLbAYiJiYm6sBkJPXr0INudw6E3Z1G5Y111f1ir\nE69bu5bsnIuP+XNqOE6Jtj0o1IbxCTHUkryCfEnKTBf3zLtk8M6XxD3zLolrliK4XGHDRav9M2ZK\nzmU75dxuT4gr1ndBtmwrRLiuvgv+PhXOc/buv0HSmneuXtDMpSQ5PVlGz7pOHtr5qIyedV2tFrBr\nKIqKigSQZlM/8LlmewG4lFv/pa9fuSTGP1Ta8heIpI8CLfTnatPRu/JsfUKsQ92zlDsX1VjE75K+\n/Xx8SJJGPSLExvsuOBebIJ3P7xKyLf5h1/YCe/55NuISkkKGNh+rMPFImT9/fsDVidPue1vft6TU\n48LH5UTnePAJiXoDQjbOCCEnFIsXL5bJkyfLkiVLGu0cFRUVkleQ7/Nx7N6jh6xduzayYwMkDVu7\ndq13AAg3IAdbjba8vFzy/dqVX5AvO3fudJzTJbFxzXwcY0fPuk6ePVLo3a6ZOfaYO/+FG8TTHlzv\nvf5AA5MtRNhlpk+fHnJA9Xg8Mnny5Dr1czC8eUAidKgMJBB5k6DVMQdKuH60VyN2OvSWl5c3SO6V\nhsa+lsQh90jKrf/yrk5cfS1/MU6qTYDjQQgxjqmGRmfHjh1ceFFvdn/9jXdfy9atWP3uKo4cOdKg\n+TEyMjIoXlQUcMXaiI4tDrzarY1o4bjW6ckHDhzI6nWryb4qhwtH9GHP5xW8MKGQcTeMo7i4iCVL\nlpCbm8u5XR/3cYzNym7vU087dwfg2Kb19k/RHjB9/tvzAYg5qxuVO9YGrMfuo2uvvda7r4/bzfhx\n4+jSpYv3etq2bcuQIUOYNGlSg6WBD7QSb2wHN3Fd8jg89/YaZrpgKx/X5bmyCed8G/PT9l5TF6Id\nen89cBDvWqa/hlyZuL7Yz8Sy//wN1/AHUD/r4DXJxHbuT3zPQRx84e4mm37e0ISIthQUasNoQk4I\nWrRsKXHNUnzCZmPTkiUuIa6GZiBaM7zaqLu9oagRpCcvLy+X3hf19lXju5Sc2/cXMvKJq7yzRVvD\nknPZTskbfESy8z9sMpoQkeCmEjt9vkppLqpZKx2m6p+WvIPbG7Jqa0pisi4U18/ODTnDr00/R8ra\ntWula/fuEWkWGtoEEolGyV/jE6p8Y2oUI6GiosJrnrQ3W/NlwnWbBseDJiTqDQjZOCOEHPcsXrxY\nD66z7parj6z0bpmdz5GkZklR93eoi7o70IAc7Jj8gvwafh3JGSkSmxAr7X95rte0YA9QnXrOkrzB\nRyRv8BFp+ZM8SUpPlWtmjpWHdj4q18wcG5U+cuLxeGT+/Pk10ufHnNU9olwaafe9rf0gYhNqrOXi\nL2DUpp/rch22gOEvbDSmCSSQYEVSM3G16Ri034KZpBqyXfWhjztHXEmpkjjkL/USFpua38uJgBFC\njBBy0jN+/HgBZPDOl7wCyBUfzm0ys/xQCbbKyspk+vTpMmPGjIBtCrfWiy1YBLtOe7MHwm7duktc\nfLqPY2yMnzNkNLVF/pSUlHjvb3zfa0MOmHHukd59aQ+sCymw+PdnY6ypIxJc2Likb786J10LRyDB\nKvOUljUTgCWnS68Le4f1IWkKCzLWV1iMROgzAkrdOB6EEOMTYmhUWrVqBeiw2VQrgdj3O78Aou/v\nYIeeOhNsxZzZFdcFv6Gk+BnaORJzoVxccumlvLhgvjettnPNlEDYPgDBrhPg/PPP56abb6K4qNi7\n7/21VyFV2vckL6+Ae+6ZzO7duxt8XZm6Eihx2eHXnwWC+8rE9xni3Vf1zcdA6ERwzusM1891XXfH\nmfvE9rdYNvsPVO77PmjStfomkAvka3LddeN4c3mpTxIzV2wCKckpIX1xEgvGczi9VYO0qz4E85+J\nlED3wfZ7+cufJ3HduHFsWLfOW76x1m8yRAcjhBgalcGDB3PXpEm8c9NURIRT3eez50M9CHlWbKXX\nsOr1OcpKtwC1dzqsK05Hwaof93Bo2mgObV7m/T2uzXkkjplG5WcfcmD2H3lzeWmtHALtPCDBrhMg\nOSWZlatWMnrWdWRlt8ezYivzJsym/dntmTtnbpMQOvwJNGjsn/UHOHJQ/+sYMPc/fxu4YpCKL7zH\nO5N/1cfptD4L3gUSQON7D6by0/epLPZdaK7yy+2IVAENJyDbgpXH4+H115fSqecs0jN7sP/HHSSn\nns03X7zK66/fzksvvQTg61BrZyglsgUZjxXhhMVABLsPcuBHSp7/g8+9je3gJq7XQJYtmETf/v25\n/777qKysbDLCuaFuGCHEUCcinX1mZWWRne1m5TvvUDrqHu9+V2wMhTfOQkRo5+5AWekWXrilkPyC\nfO/HuTFWlXXizNJZ+e4CErev5NmxPXG3a0lp2W7Gzd7MwQWTSLr1RRBh/7QxlBQXRTzrzMrKIr8g\nnzl+1zl3wmxiE2Lp3bM3paWljJ51nVdI6TXsIkSEZ0Y93SjXXF+CDRp2/7hanek7o2/Tkapd73No\ndrVwUrlzPcTG1xBYAkWphCLUDDqcoBg0UuX8XA4VP87RsneI7dSP/dPGcHTzEu/v9095gF69ejXI\nLNzj8fDCCy8AOmtuUvLPiYtvwXtrRvHtl1ozdsUVV5CbX8CLL77Ib37zGxKH3ENiwXhvHXWNFmoq\nBLsPR9a8DImpJF/5sPfeHpg9kcovtiL7vmfDunXk5ubqZHFSRdfu3Zn21FNmBd/jkWjbg0JtGJ+Q\nJkVZWZnMnz9fsnPcPvbbvDB+CoFsxupn50pc+141/B127NhxTPMi5OYXSExKMwGkcGxPkecHe7fZ\nY3p6oxaczoDhclQ47dcVFRXSt1/fGtExffv11QmfQB7a+aiPz8hDOx+N6DzRIFyelKRrn5K0B9d7\nc0fY+/0jUjqf30V69b6ozve5vgvehTxeuSQ2NaN6UbogviHO+1wbn4Xy8nIpyM/zufb0jE5y6a+/\nkVNOyxeVHPicjREtFG0C3YdwPkPOfCokp/vkpunavXtEOYFOFoxPiOGEYM2aNVw/bhzrHXbZzM5t\ncc/8P/a8t4PlEx5l2IjhFC8qCni802a8ceNGHn/iSVaWLueI9btzFpNXMCDs7LYhtSTz5hTSt39/\nNqxbh7tdS5/fctrrv6u+3on8WOHdH2zWWVFRwcgRwykqXuzdV5Cfx4L5C/j2229Zvny5rjcnx6vt\ngeibpWqDU3sUyJQi339LzKlnE3OqLmfnD3lh7lyAGj4DdfUjCJdzI5x5Iljuk8Nzb+eSSy/l8OFD\nrCwtDeobkp1zMStLl1dXaM3IgbCz8pEjhrNq5VsUOrVuszaw5o1sfvi+LOg5165dC3dP8jHN2Oan\n45VA9+HwW7OAyPOp7J82hpize1C5Yy0b1q2jR48exm/keCLaUlCoDaMJiSrl5eU1so+e9suu0vvJ\n2yQhs5n8LP9CufrISnHPvKvWUS2BIh7CzW7XrFkjfdzuGrPnNWvWhIxiCYd93mCakMQhf9F5MOIS\nQs46C/LzJDMtUQrH9pRdUy+TwrE9JTMtUQry84Iek1+QL2mZaQ0ShnusIgiCLedObHyN/Q01U/e/\ntmDPSuLvdKbOSHJohIrqCKfxcSWl+uZDSW4uKq1FWM1OuGct1DltzVhjRQtFi0D3IdR3IGA+lQTf\n+xGbenxriBqK40ETEvUGhGycEUKOOc6PfV5BviRmNvNJMmYLH7bgccVH82TwzpcaxHwQ7sOflt7c\n5yPlatNRJ8Byxfjsv6Rvv1qbb2wBYvYYLUDMHtNT0pPixKWsepUrZL3hBpdQ66UESudem/aXl5dL\njjvbp46C/LxGM2FVVFTUSGHuatNRXCnNpHnmKQ1qSgsVvuk0T6Td+7Z+Hupw7roIxIlD7gmfej3A\nQGg/47umXubznOyaelnYwbe2Zp/jDed9qG0+lWD340Tsp9pwPAghxhxjALQpYfjIESx2hIrictH7\nHxcnDQQAACAASURBVLdythVamzqsPyJC6ah76PiHYQB8v+NzDpV/D9TffBBO1f/DwSM+ZpoDsyfi\nank6VbveJ3HIPcT3HMjRsnd48/nbap3WunDOXEYMH8bIadWmlBx3NgMHXcEPP/zAqaeeSk5OTlD1\n7qZNeqXeYCadYOaB3bt3M/6G8Uy4eQJHjx6ttVmioqKCczu05+AP3/mo92+c8xYjhg9jkfN+NhAZ\nGRls27qF//n1QK9JomrX+14V+Lffflvn1Ob+hHI+9UnFrlzakbEOTqqBojqCmWsOzZkIykV8z4E+\n5UOlXnc6MtvPeGnZbob3Pt17/PKtuwHo485hVSATUd9+jL/p5jpFAh0vOO9DoDT7zZpnsK98F4ff\nnl8drmytZBzsfjSFqCFDGKItBYXaMJqQRseeWblzciQpM91H6xHXLEVO+2VXn0ynttbjvFuGCiA9\nplwvSZnpktdAWTwDOt+lZgjKFTIbZ8qt/wo5C4p0BumcjQVbdC7Q7NptmYki0YTYDr4Nob1wW3XU\nVgPTUERqGqjLDD5S59OSkpJ6OakGI5CZwHamrY2pwF9DGEjrZpvtgpmIGiuBWlPXrDifr4Bmm5i4\nRrn3JwrHgyYk6g0I2TgjhDQagfw9/FOrO00u/vtiU5IEl/ZKDxcdUxsCfWi6de8R0kwT6uMfSp0f\n7gNs+2uESy1vD5and24jGWkJNUw6Oe5sb5/bQo1LIelJcT7+I82T46Vf30u9dYYbHOzzEkK9H+0I\nm/qkQI90xeK6rGwcaf8WFRXJkiVLfMwEKi5BVErziE0F/ueoqKioER3jL4D6p5UPN9DWVphoqqvz\nRoLdNy+99JL+NihXo/kiHe8YIcQIIU2WXr0vFBXju+y6M7W6v9Zj8M6XxD3zLolrliK4XOLOyZH5\n8+c32kyjNh9hdVpW0I9zsLTs4ZZlD5dy3Xnd9iA4eeP90imvk6+vhELmz58vItVCzW8fGBpSexFM\nQ+I/0NjnjaYmJByh0uKHI5LBt7y83OusHMlsePXq1V6hNti9DzZAr1mzRgBJumqqxHbu7zsjVy4h\nPrlWA2GkWqRwQla462no+9LUWLt2bZ364GTACCFGCGmSlJWVCS4lMckJ0uOBcZK/7PGQmhDn1r1H\nj6jE4QdzVMMVIyTXXHfDXvsl1OBkOxEmDrlHXEmp0sed4z2f/eGPJIeHv8Dy148elptfu01+O2WI\nz0zVLnPza7eF1F6kJvppSFITpHWrlr5aK3e2d1A8v01zyUyJD6qBiRb1zeUhEn4lXft3b06PIOW8\ngoU9aw4x+AYboH9xXkcfYcDOh5Jy5yIB5Jy2bRtlIIwkp0lthImGuC9NkWBCXVM3OTUmx4MQYhxT\nTyI8Hg+bNm3ixptvgiqhcv8h1k58gp/lX8hP+vbg3ZurU6t/VbqJtbc8Rl5BPo/9/dEGczSsK4Ec\n1Xr1voi4uDhWlPquu3FJ337Mm1PIqlWrgOD5BlyZP+XQczdx6P3XAVhZupyL3W4WvvJK2JTrTifc\nrKws+vbr65MZ9cfyH1h0/6t06doFwJsjJCu7PYcPHAaCOyf++dfnevcP7306DxeV8fHu7/zySqzi\nVwMG0K/vpaxdtZIzWiQzctpqb12nZGaw8OVXfK77WGShdVLfXB4Q+L7bDpnO7K2xnfuz/+lrfZ6D\nbHeON4fGsBEjWVa6EqSK5CsfDroujIgEzQj7oVW37Tht50Oxc6EULVrkva5AfVzX/g+YS2PVvzn8\n8v0gVcSPeKBW69w0xH1pivg7GFdUVDBixAiKi6uds3Ny3Cxc+PIJ48x7ImCEkJMAn8gXl4u41CRy\nZt1N6+zOfL1iM6sm/J0WXdvR4vy2PqnV8wrymVs4h4yMjKh/lEItkrVt27YaicAgfLTNkaLHSP70\nPe4FWgG7gfvefpuB//M/TLzzTtw5bl6YUOgVLMpKtzD/ljne1PL+HDl4xCfdemx8LBs3biQrKwuU\n3mcLNZ3yOjF+ziZEdATN8q27GV+4EZeCwT1/7q3D8+UPbNqlBRCnYCICI6et5sd9B+id/UuKihej\n0FMegG8r9jDw15ez8OVXEJGASdQK58xt1I+xy+UC6rdGTLD77kx5HtuuN66U5qTeuoDKr3ZwdPsa\nDky/jjtun0hGRgYlJSWUFBeRUDCeyqLHQw6+3n3BBNefn6cjMiR4unn/ZyPcGjeRCCc1IoGsxGgA\nh5cXEtu5P66U5jWuJ1B94d6Lhk6Sd6yFX5sRI0bwzrulTHlkEN16nM76tZ9y76RFtG/fjq1by4wg\n0lSItiom1IYxxzQIeQX5kpSZLj0eGBfS7NJjyvUCyOWXX37CqC6DmnESU7Upw8/c5P93q1atfP4O\nFB1jq7fbdD7dp+zPO7fRvjYPDJXkjBRJbZkmyRkpcs3MsTJ54/3SplMbn/JxcTE1/DuKbs0OabrB\nUp/nuLMlIzXBx4yTnhQnrVu1lH59L611ErX64ONT0cBOg4H8NVxtOkqzpz4JmNzOWTbljv9E5OAZ\nqkzK5Ddr+IOEM7sENO+kZkjnLl2kjzunVnVlu3MkJrW5b6K0lOYS27l/rcwqdU0BX9v09NFyfrXv\n45RHBskHO/7s3e7/20ABxO12N3obmgLHgzkm6g0I2TgjhNQb+2U875ahctHTE/WgGMQBNSY5QYhx\nnTACiEjgaJu4hCQhLklcIJkghSC7rH8zQVwgv2aWDKJQUmIyxd0nJ+SHt6ioSJRLSUpmik8kTUpm\niiiXkptfu83r0NrO3d7PodFXwLEFBtu/48HfdaohmPhn2Jw+fXrYMsfScdU56KbdV/ckYuHq9q4f\nYkWm+A+mdtnEIfd4BYvYzv1r+I6Q1EyyHf5AgQZolZLhM9DbfiAzZswI2d5wQo0tRKVMmCcJBeMl\nJqVZUEEgrIB056KIhYlQ2WIDUV5erjPl1lf4OkbOr7ZP19IVE3yEkKUrJvgI7yc6x4MQYswxJygl\nJSW89dZbvPTvfwPwwSPzvL99vWIzqVYCMoCvSnWircpDR7iwV6+om14aEn91fkxMDLm5ucQX3MTh\nokd5DBhulR2OfltHAmmcxtn0QyqFhStH8sw5M4L2i8vlQqqEYVN/H3A1XFeMi3buDgBcPLYvRw4d\nYedqbZdHoHv37jxlrTWyZ8+eGknTEuLTGPf8ZsRpupm9mfSMjuzd8z5KaVtPsERpAJVVwravfqDt\nqWk+vzW0/T/QKrvN7l3BwaLHOPjC3SxZsoR+/fo1WN3O9UO+n3AeoE0d90z+MxdccIG37NEtKzgw\neyKJv70LDh/08R1Bubhh3PXePwP5objadCR57HTv31L+OaDNf8HaumPHDr744gsguHknvu+1HH5r\nFvumDvX+VrJ4MevWraux9kw4X459fx3gvf5w68mEMm/6U1FRQVaHcyn/4UDEyeBC3atQ/ioNhW1y\nWr/2Uy67vJN3/7o1n3j/f7z6vpxouKLdAEPDsmPHDlq0PIW8vDymTJnCNo+H+PRUCpY/Sc6su3El\nxPPujY+wfU4JP372NdvnlPDujY+Ay0Vebi6LXvtP1Nru8XgoLi5m27ZtDV5327Ztyc/Pp7KyEoCY\nn7UHwO1Xzh5SqjgKwBnWHqe/gD9VVdo+n5Xd3me/LXhUVVZ5HVrffHoZX3m+YvSs63ho56OMnnUd\nZTvLuHvS3YAeHBYVFVNSUqLr6PwAfQp2kJiew8hpq2kz4T+MnLaaytjTObD/M/LyCnC79VWUlu32\nOf+iTV/isnxRrpy+hqw/FjPgbyvYs++w1wm2oe3/wQbK+J6DADh69GjYOoI9B+EG4cmTJ+PxeFhc\ntIhvv/0WAJXxE45sXkrCoDtw/fxcDsy8haNbSgFwndmFhEF3gFTRpUsXb332AO3xeCgqKqKPOwfX\nns85uqmEqvLPObjoMQ4+fyt93DkB/T/yCgbQrl07CgoKGD16tL7usnd8ytn+F5Wff4hKSCJ57HSa\nTf1ACzqJqYy97nr8cfpyBKprxowZ3uuP1N/Bfi8CDcb2feifm0f5N197HXrtjLDxw6ZQUlwU8H21\n75Xd/5VfWfcugP9NY5CVlUVOjpt7Jy3itZc38+V/9/Lay5uZcs9i2p/bGmiaC0SelERbFRNqw5hj\nakV5ebkkJCVKXHqKb+bT9BRJbJ0pVx9ZKRc+8QdvkjF7S0lLjery1+Xl5VKQm+vTpoLc3EaxHdsq\nbVtFXwjaKmlts63zj8cjf0ZkILPDqm7D5RT57ZQhkpyRImf1PCdkuUC5R3Iu2yl5g49I3uAjkp3/\nkZx3wXPePsrLq1aH57izJT0pzidMNyHWVSMhWmZKvA7pbSSfkPqEf5aXl0t+rq+JID+3+hprU/fq\n1at9lnjXm6o2gfy8oySNeqT2pgu/Ov1NEoFMECouQVRyeg3zTkz7mtlX0x5Y9//svXl8FFXWPv5U\ndXd6S3ens4qjoAIJICRAEllMuoUkJN3yjsrMOCxB0RkFx2ETXN95RURnQBFcZkRgdIwEMKPzc97v\nDAlBUAigQAAFF0yzKerrSOzGhUW2nN8f1VWpvTohIQT7fD79Sbq6695bt6rrnjrnOc9D1sBkzblq\nLZZDy9QwHmpYDkCbp4TnwRGb2vybc0aQfcKC85YKiUQilC4rbe/VJ4PcbgcFAm3D8HyhW2dIx3T4\nAHQHF3dCWmS5eXncwqUBPB1RvUDAfyT1vYp6/W4UAehQB4SIKFhaSskmkxSbYTJRsLS0Xfrjb+SW\ny/qQhzXRsmi/ywDyANQF/Wg6DtFNWEZOUzIFSo1v8GpquHa3nRiWW/j4v0DLuEeyB1UITkjZzacp\ne9DLBIAee+wxyY08EokouESggwXp07t3u5331i6UgdIgOU3JNAqVNB2HBExOoDQoLJaFPn9MbZcG\ngsQ4PGSfsJASehdIMUFd+xJsLgLDUqHPHxN7LpE6KFTct5aTZJ+wQHVBdtz9N2Fxdz9/UAF4zc3L\nVzjiLcVyaFlMwoCTlpD9zkXC51rOn5hfRz7/cgCtkRJ1W1skEhEkFQTHNtB2DM8XusWdkLgTct5M\nTOGtBTwdMOs3kkoYW7K7zTRfznXcWhGJ9nhiEt/IWdminZEiVYEVP4kbtSnXmfH5fQKrbCgUEgCk\nRpGQcDhMZWVBAlgyW9yUPehl8o88QH1y/0ysySrpQxwN2bZtG/Xr21fyuV5VDdA+arutWSj562AU\nKukRkPC6HosUC3isbLeOSUvImlNMHqe0MsjjtJGl69UxtydvUysKY8RsunTpUhqYl0cmJ1ctJK7W\nEQCzMcrRx8q2qmVaoFEefMofp2vedu53okIGB7ub2EuzFL9To7nqiIeec52vzmqdwQmJA1MvEuNz\nsIA28PTMsRMC/qP+gecFHpCONH7cWtiM9gCPyUF5ZrNZomAbC1hPrc3qVdW6+/bs2RNv/PMNQ+6R\ncePG4+31W9An98/4z+evYffWCdEWWFgSXOibtwTetEIcadyIt9dPx69+9WskmC2oqW3moejbpy8+\n/PhDTUK0DQ9dh88jJ9pFbbcloEfe+Ougm+xK2M2s4NRxb50vACIjr9yLwUOH4uE//EG1bTEe4eSu\ntXhRg2MFAJi0bgj/cMQQcGmER1m5ciUGDx4MQMq/cfarfTi1vgIAB2L9xS9+IQW9MiyOv3wP8OPR\nFoE41ZR/YzU90OjG6Lj44zR16QFzzgic2bsFbGo3CaCXcafDefdL+OG/r5X8To3mqrFRil06H3Yu\n8xW39rW4E3KRGA9aS/Ak4p0pT4Gomfn03akLABOLD+avgM/vx92/+x0GDBhwQfwoBWlzNFepAMCG\n6F8ePNYehEdaN6ZzuWEZ7bu8cjnGlY+TkJoFggEsjzqDoVAIq1dXI3tQBS7tNhZdu9+BYz/sxYFP\nnsSXB/+G3gOexaXdxgIA7N3GAiCsWzcBdtaDUahEN/jwGepQ2zAFXo8Hd1fsBFFzVc2Uyp0I5nSB\nr1c6AEQX5NXtUq3Qknnkr4PPUIfs6JXwDUL4nDbCcatysdyyeKLmtSAAOHdx4F69qiFq/CymxZ9v\n89TWN2D6WS+wGVfBdEl3ARQ6a9YsAEBKega+rbwXdOIoTm/7pwCCBYDRY8di8aJFEgctLS0NE++6\nCzu3bz8vDKZykje1/gCpI+WYtARH55Si6dAHwuemXtfCOXU5zrzPzbEY5KlFhnZqC1epZzbHl524\nNVv8arhILDMzE2XBAN7etAlgIGE+TbBZ8eKSpSgsLLwgHA+xZWZmIlhaiilr14LOnoUfnAMy1WRC\nsLgYKSkpuL6sDNXRahEACJaWonLlyk7JeGgUMeGfIr1phcI2p6snvGmF+PLg3yTbue9xkYMBTXcK\ni3c2xnGlxd+NB8tAQuc+vHc6KicNEt6LS3WJqFWOXls4iJmZmQiUBlG7dgroLOEK+LEJ8wBoL5Yb\nNmxQ7Y+nOV+7gYtAaEWDYLEBp3+MafFPTU1FcmoaIq/+j/A9tms/NB3+FGzXfkicvhJnGt7Bd8vv\nR5LDhvArMwFbIuwTFuL01jdwZk8ddm7fjvz8fKGElm975fLlyMrKalcGUzXWVq3+3CnX4IdXpKyw\n7A+H4U5OwfcnTiLhhvuQMGgUzrxfq2CLBZQ086bL++LYkkmCEzNixAgJY2zcfuLW0fkgvRfimJAW\nWSQSoTIZLiEvXwluu9AsEoloVsecb9BqR5sWIDUre64uULUY84SKnkdANB2HBHCjyekmi69cF6ha\nWOjXxJpoWVszYkYiEUV1DHSwBVpEYQ0NDVRVVUWFPj+xDBRVQx67hViLlWPONcB5iAGxcqAl7G6C\nw6PK1sq3GyvWo62rXuQmx4CwXfspmGwZRxKldglQ0Y2HKbVLQHFeDxw4oDjfPLBX7VwqWHMvAsXe\nzmadARPCELfYX5DGMMxAADt8Ph88Ho/kszFjxmDMmDHqO/7ErTWYhgvB1PRBsrKyUAlpqqYSHKFY\nKBQ6p7x4R+hZxGKBwPV4e/0WZOUsgDfNhyONdWjYdQ/cLgu+/+G0ZPuenVNx+vQPADiukh4I4heo\nRAir8AbGo76+Hn94eBZqa6rBMoDLZsGfbxkopGemrtgFi82Fb384i6ychQLWpGHXdAy7bjBqapRE\nVLyVBa/Huk3vwnz9PWDcqaDvv8GZVQtQVDBEgqdo6VxLiOXKAmAcbtjL5wlP5Scq7wcd/x6hhk8U\ngmXyp/3BQ4bih++O4KOP9wjbLJf3wenPP4Zt9Byc2bMRZ/fVw14+t1kLZvkD8A3Og8VikbQlTtsA\nwKnNVTi+eCJcT+yA6RIuBdEU/kIgTXM++G8c+9NIzf3E1++RI0c4rIiGvozYQqEQNmzYgMOHDyMj\nI0Oil6Rm/O9IPI6mY9/i6ONBNH3xcfMXGRbJ6cMxYMgKWBK8CB+uQ/36IixduhQ+nw/79+9Hamoq\n7pl5LzbVbRB248fZ2NioOM9r1qxBaWlpTHMQt3OzlStXYuXKlZJt3333Herq6gAgl4h2dsjAjKyj\nvSC9F+KRkJ+08dUGh2SVM4eiT2HiktZYLRwOUzBQ1u4VIudikUgkWh0jjUwcOHBAsZ01WalP7vPk\nH3mAsgdVkMWSTBnorygtDoVCVFVVRX5foWR//r1WhEWrmoCP2Fi6Sqtx+KqTUCjUJpGS4cUlxFik\nFUGMxUrDi0skY5GU76o8cRf4/MTaHMR0yZS0Zbp6GJn7DpNsy83Lp+HFJYoyVa2qF+eM11QjIdbg\nZN39Zs+erZhfvSqOcDhMw4qKlfwnDEvDi0s057WqqkoyDte87eSc8RqZuucTrE6yBidzlO+TlhDj\n8FJql4DkGpDTtTMWK9lvW0juhR+SbfQcYu2JlJSconqejSqGWvMbjlvs1hkiIR0+AN3BxZ2Qn7S1\nR/luMFBGyYnnT8xNzWIVAdNakMTlvlrOg69APUwub1eNFK3s5tPkH3lAd5Gorq7mUh0q5a8sw+0X\nq3aI3nzolfuqOTl6ZaEp6RmKtACvCeN6YodAElZbWytpiy9T1WrbNvpRVd0ak1OZ7nE/f1Aoa43F\nMRPPTWkgyBGfOZMUaSE9/o2CKE+GfcJCBReJ1jFl5cwlqy2ZUtMylORrziTOeZO1xV6aRa7HN0vO\n87mQ151Pa4kwX2eyuBMSd0Lido7GY0LEhGKtxYRs3bpVFxfR3jegcDis4BLhVXlbehNsrfMgNyNS\nNK3xrF69WjGXDfMCNDPALbAvvfSS6uJj+/WjBIDWrFkj4kMxxqKoOWRigbqEsrt1n7h5p03PkeAX\nTn5unQ/+m5wzXiPXEzu4BVfOeupIIpjMqg6F4DyJVIRdj20mODwKRwi2RMrpP8CYtTQaAdETw6ut\nrZXME39+2a79COZmB8YougOACgt92v0xrMIZEqv5ip2M9sa7nIuFw2EKBGS/yYuIzCzuhMSdkLid\no+mBVltqeQM5RlktAq/2Dg3zrKpipd3EpEQF02ks6aHWOg9qVlYWJKstWSBFyx70MlltyVRWpr1I\nCKmyhSMp/PwNFMzpIjmGzJ49JYucGiNoaloGJViTKHtQhZBOMupXfvxyhV62az9VoOjSpUsNF13e\ngVizZo0i5WG6epgigsGnQerr6zUdyPr6esrNy4/uw0gWdbU5UWMtdT74by61Y3USGFbnGBhFW3wq\nxjl9ZYuiO2vWrNFMpYhJ1tT2dT2xQ5JuaSuW1/aw4uIicrltNHfBKHpz43Sau2AUudw2Ki4p7uih\ntYl1BickXqIbtwvK5CBGr9eLVatXnzPYNhQKYfvO7QC0SzbbU9AqFAqhproGd1TcJVHarX1qFb7b\n+xUqJw2CLysNdQ2NMRGIZWZmoqwsiLfXTwdAsNovQ+NX1fjy4IsoKwu2aI5WrKjE2LHlWL16grCt\nrCyIFSu0lVgFfpeGRqx49xC27AtLjmFy5ftgmeYy0OOLJ+LsvnoJKdg3FTOR6LhSwXuyevUEQ96S\n/fv3AwwLCn8hafN4xUwc/dN/cSWzn2wWSkizszklVa2yVAB47pmn4fV6MaZ8vIIg7cSy+0Anj6PA\n58et48sBQAEI5QXfxMDqxsZGrFyxHAcPHkRpaSmA5pJjtTlZt/x+/PzGG7Gprg72CQtx6t3XcWbX\nmuYDZ1ic3LgC9hvvUx6D1QHHbQslbR0/dkw4bnHfPAnZiWVcGa0A+l12H0oDQZSUlCAUCqnPWZQb\nRKu0uenrA6CjEQDcb6o15HXnw0KhENauXYe5C0YJSrsjb8gGEeHBGW+0u9Jv3DiLOyFxOy9mVCER\niUQw6oYbsGHTJmFb3z598Ns770QwGDQkvjJqf8MGDs1/FXMdfl+xBUTNBF6/f2Un8vPy2vWGw/N/\niJV2P1yzG4d2H0KlKqOnMYHYihWV+OUvb8Zbb90OvjoGAE6fPo0jR45Iqir05oeIQIy0bWI4noxv\nvvlGdZ/MzEwEA2W4e9lb+O7YKY1j2IrjL9+DpiP/wZlda2AbPQeM0ws6fVIgBTu6eCKO/bAXThfX\nPs97YkTQxbIsQE2wj5+nIBo7vniiUKHCV26MGVcOmK04sUzKf3F82X0w9/bhzJ467Nu3DwcOHOAU\nY1UIzI4vnojpU6dg1KhRkrGoVeWkpGcgfPhr4X1uXr7w/5mGd2C6ciDO7Fqj2s+mKCvp6a1v4Oyh\nDxRO1sn/fRKmtG7Nx/DKvQDDwlp0O0xX5QoqtyCOAbXA58e7Uc4UBQnZn/5LwoIKhsVjj84GwHGj\npKRnIFwxUzJnJ996SdEW0OwMnd6zCWfeflGhMnyhsZby94Tc/G6S7XnXXCF8fiGN96K1jg7F6L0Q\nT8d0eotFITccDlNqcjJZxaFuNOu6sIAm+t+ofbky60gspkxGWh3DMu2vZyFW2n3m6xeoXyBH6P9c\n0kPNqRT1lEYsuAs5gNQ+YSGZLBbDFFEkEqG8vFzdY2BYszDH4vasOcXkenwzAaDcwn+1OJ1kVHUh\nrjzh595+mxKYCYYl66gHhT5nz55t2K74nFZXV1OBrCpH4OCQcYOAYQUNFh4EK+7HNW+7RDAOOikP\nyYs1Sd6bc0aQe9GnQtqkV+/eNLy4RIJPkav5Ome8Rs6HVkmuu9JAkEyJHkXKCwxLpqwhqloyRirD\n8t9ER4JBeZzQ3AWj6MP9jwivPz11k5DC6+zWGdIxHT4A3cHFnZBOb7GQjRX4fMQC5I1+LnwPoP4A\neRiGTCazKpDNV1BAiSxL8zXaFyuzXonhZIWHMtBfcqNMS8k4L/lpHhNyeU5XciY76VfzxhDQeqAs\nDw7NypmniQsxclLUqhc40TdrTBVEQgWTxjFk5cyjpOQ88tgtmiJyWTlzY8aiyPvVwzTwJndYXE/s\nkCy6rD1RuLb4OdVrVw00ymNRjLAWsDoli7pj0hJyP3+QTL2ulbTndLt1naEpU6bQrbfeyuFAVNRq\n4UyStJeSnkHr1q0T4VOkDosaSZv4OPg5SwhO1XXoGHuiYTWUWpm831d43jEiDQ0NxLIMuT02+tNT\nN9GbG6fTn566idweG7Esc1FUysSdkLgT8pM2/kb2JEDVAIUAIkhLbMXqv1qluE+KblZipVl/gVSi\nPQhQRLQfX2rJK7Pejwg5kUFWeCRy8Q7WS/m517T7U5lYVpxX0s0uyyavyyph9DQqGVaLbqRFmS7F\nFTJGZbxqyq/8ItoSxygYKKNkl01yDN5EK7EMKP+6tbrtOZ2JulEaPVOruhA/jfNP4UYOS4GM9VMo\n5RW3a3WSy5PUXO2RKC9b5Up9nTNe03UecvoPkCzasLlUK2ZMiUkEhtUds1G0xDZ6jqSMt9DnJyKu\n0mhgXh6ZHG7NihXdSBPDkikxiVMCfmgVWQOTyeRw645XfN3w14vEKbVbKD097bw7IsXFxZRglVY4\nJVjNVFwcB6aer1ccExK3drPq6mowAO4VbQsCmBv9f9++fZLvaynppou2rVy5EmPGjMG0yZOx+513\nUBndrw7AFADlAHhpuC1btgAA3LgMO7AUR3EYx/A1RqFSqrPSRHhjx3gEg9cDIARKg1i+su115nbq\nGAAAIABJREFULbxeLx544AHU1dUJ2JDfvnI3/nrLXyT6LsFAGSqXr9Bsh1fZzR5UITCc7nlvOnZv\nvRW5hf8PRxo50TSG4YAeWnoz+/btU4iNNR0+CEBb9E0Nq1G5fAXKx43F+MWrm7/vK8SGuo1o/Kpa\nt71jUeBkbm4eXnhhEfLy8jSPW2481qNWhGlgu/aDc+ILOPv5RxI1XLGWiYBtWH4/Cn1+1G1YL2m3\nfsu7yB88BGGR0i2oCT+c5LAwYFjYJzylihmxjpgkmU/eeLzEk0/MwxVXXIENGzbg2LFjeHjWbHz/\n3RFNDMrJV6R4jFMrHkBKega2vLcb1uBknKx+ThMgavpZLwU+ZO/evUhJSUFSkhdnTxyV4EGGF5dg\n5XIOjKwlQnfmk80ANWHowBxBcRfgMC87ttcrxsIk/wxAM74iFAqhuma1Jobo5zfehI2y89Ge9ve/\n/x3jxo1DTU0zCLxoeAmWL+9YdfGfkrEdPYC4XXwWiURwfVkZpk+fDopuGw5gMYAtAG6JbuvRo4dw\nswM4R0JsPDH0p2i+UGfNmoXMzExU19biuaYmjANwOTha92cAVAOoin63T58+YAC8guvwL9yJt/EH\nAMBOPI8vUI+9qEEYe3FF1N0xwwovuuOtN+tw489vaoupUBh/vKGNnwAAnF4npv7rPvxq7mgAHM31\nquoaTQeIV9nNylmIS7uNhd1xOS7tNha9ByxA41c1ONjwFBp23YOysiB8Ps7ZONK4UdIG76T06NFD\nEBs7tfx+nNpcBSTYAXBVL2LTqyAiIhyNOhO8OZ1OFBWV4MuDL+q2BxCycubhw48O4H/+Z5bGrKkb\nX3VRGxU3tI2eA/djG2G6/GokDL0Z5uB01NZU480338TK5ZUoKhgigFaPL56I4sKh+N9/vqFo98or\nr8Q3X/8Ha9asQY+emTA53XBMWgL3wg/hmLQEsCXi9BbpfkJlyHdfg72sD45XzMSpzVVoCn/BUZRH\nwaMjRozA5KnT8Itf/AJDhgzB9999y+2fNRRnv9qH07vexNn/7Bfac1hYyZgTLSzCh79Gwrh5SPDf\nCqC58oU33uFhM65SjG/fvn0YWz4edVu2wzHxBTgfWgVrcDJMTjcsFgsaGxtRU1MDhmEk1wV/HHy1\nUd2G9ZxDUV2NUCiEFVHnhR9L09EjOPrUzTj2p5EAgDvuuANlwevx/vvvA9B2SjfVbcDevXtjvALO\n3bxer3AM4r9xYb3zaB0ditF7IZ6O6ZSmigOJpkv4VIm/oICIuJTNwLw8YlkTeaKfC6Rk4DAhVoA8\naMaL3BttQ4vOPZFlKVhaSsHSUrIC5I7usyHahgfNoFcA1AVS4B0DLpzvK9RmHT0X47Ehv315Ej15\n4Bn67cuTyJXsokAwYLivEUkZZCmNWDhA5DwOLANKciTElCIKh8MKnpP+XZPI5bDQgP45VFioISLn\ntFFCby6dllv4rxbxm8gBjfLUgR7/hh4tulo/0El3uJ7YoQEYZQjmBClewmwlU+aQKDW6mwYPvZbD\nZ1idAqZE/H3+PWN1UML108h+5yKBIl18rIJAniwlxXbtpzrm1157TfWY7LcuUIBKhxeXcIBWlXlU\nM3F6zNzbpyAzM7u8AnurVnoO6Fgq944Gy7a1dYZ0TIcPQHdwcSek05kR1fqG6N8XX3xRUdViEt+0\nAWJE/4vba1DZJu7DX1BA27Zt427msjaDAL0gGgvvlNjhEjAiNnjpEvQnB+uV6K+0lUUiEQVzal5+\nXkwVOkYkZWJAJt9XS1lJ6+vrY9bX8fsKJaDTFybkktUsXcxyBwyQnEuAq46xT1hAAKgw8HFMTK9a\nWjT8ueYXVrlyLb94F0QxEbGaUQWONTBZsvBf3S+b5syZI4yFB3O6ntghcVzkSruMO13Jomp3S6pe\neAApzzgrEJ4t+lThcDHudAW7qymq3DswL0/1mMy9fZpKt7E6bnJnVst5uyY/T9UpFesOnW+7WJlT\n405I3An5yZmR6NxM3lEoLFRES7wsSy67XXIjuKJrV9X2hkedB3HkxMuy5C8sFMbBQhpB4SMyw/kn\nLpnzMhkhegREN2EZAaASPNmuN8Vt27YJLK7Cja/UGJjZGobTlkQAYt1HrTImmNOFvE6LorImPT2N\nTE43WQNRsbSJi1XF0vTGp6dFI1C4ixZptYhIXv41VFVVJSnf1TpGo0iI5MWwEtCpntidmKXViH1U\nEkm5epgCGNrsBLmEl33CAs6p0HDW5P0ZVfS09Po3YqetqqqidFn0zNL1ajIlejqMyr2oqOiiBKjG\nnZC4E/KTMf5mzlekaEUpPCxL/fr00f1Ob4B2Rz/3sKzqd1+ASpRDxA8iaJto9AE0V+vwDtI4VNMj\nIJqOQwSAbkSF8HTeHmFacfkwH4VxmpLJV+DT7asl0Y32NDF1O68dI3dKxKH2Apkaq9Pdi4aOeC8m\nJ0pRMhpVgrWN5pyO+vp6yVO4e+GHioiIEGGIph1S0jMUC7V8DrUqcNiu/YTKENbhIotV6jzrpXBa\norRrv3NRc9ktwwrHK1e2NeeMINf8XcqyWTD03//935LzxaVKmo9JjbNEPAZxdCqW30EsonWRSERx\nPXQUlbu4VFdM334xlOrGnZC4E3LRm5wMDABlpKQqROd4HIbYcdDEdIBLm/BOAx/RkONF+IiGmiS6\nUUQmT8Ux0YqE+PzSp0pedO5cjL9R8+XDj4DoPoTpEjYn5r5aim+I5btq39PaVx4JqZ5RKHFK5MRl\nVVVVioWHdwiMnCj+fLoe26zKT7Fw4UKqrq4WhPNso5vTImoLIXtplmb6QWxquidy5yUpOUXSlkBW\nJiEFSyJzb59AEGbOGcFFK6KYECPcCf+esTqEMfIRB+dDq6TaLlH+E1jsBIaNjbTNwGnQSoXFgg9x\nPvhvwVkrlKXEWhOha2u7mEnL4k5I3Alpd+toIBX/NF+CJ+lGVNAIzCcHm0QZKamSG5bX7SY3w1Cl\naNE34gUJiZwGPWyHXihdq4/ZMgcpDZl0IyqoBE+SHcmUgf7kNCVTenq6QnQuVhCpnvE3vgnYIDgh\nPdkAOdyJbdpXrEq1agRSJcVFVFJcJNmWnyfFrog5QtY/eJ3EKVGLhMjTKabEJMXCpGZiwTpxdMM+\nYaECBJqSnkGszaH7dG+06MpNvljy7/nIn7gt96JPVVlGxVEL96JPOUwMw2oymfKKtOJx9+nbVzh3\nhukis4WGF5dIjkPiHPAcH043paRn6Crd6qXC1CwSidCwomIF2JUX/buQsBb8b/HNjdMlTsibG6fH\nnZC4ExJ3QrRMDUjVt29fYYE4H84JfxOUV5fw73klTnmKpjrqVHihjG4ERY5Htchp8BcWkodlaSY4\nQOkyKJlX5cZX6Uj6YFnKSEmRjNcuoyjnHR5fgZRYjH/99uVJmouVkak5BT0QpPFY2+Z9EREVFvrI\nbEmkrJx5ukq1agRSSY4EslpMClIplpFK1oudF5YBeRwWRWWN31fY4oVfbnxlhbgNLuWirMIwW6z6\nC7SOg6IHjjWqzFFzdq7q3p37P8FOttGPSqIjvPItk3yZ9BrUUAOWz5MeYZuchI1IPbJTGgjSgQMH\nNCMdsaRX1Oao0OcnRoXNlbFYJc5LRz9I8cenFQmJp2PiTkjcCVGxQCBAniQnPTxnJF0z5Erpomq3\nSd63F8qbB38mQQowTYKJWNHNXJ4a4atbeoufkADyA1SF5kjIkyJHIxKJGGrQyE1vH/5J1l9QoAqQ\n9RcWCuN+8sAzEsfgyQPPGC5WWqZGo26xeCkB7jbtKxwOK9JIaZeWUdGNhxUgUCPq9dATAcU2k9Mt\nWUiMKmt4SXkxM6uaXgk/HrVFSa0NvcWRSb+Kc1DkeI6rclvsEMVamaNwdqxOaTRA/D8rBUIyXTLJ\nesP9BDCKcfOYEPnY1JwKcR9aKROtNIjadiNHiz93anOk5wRu27atRSme9rKLmb497oTEnZB2MbHn\nXnhdT/Ik2WnuglH08qu3kcstdUAsCSZKTLRRIHBu6QM1MwJ/8uWiaqmRILhISG+AnCoOCV+uyzsN\n/MLER1faospDi1b+iWjf06ZNa9PohFZ5bVb2XOG4tfpqqcBeIBggp1ea2nEkuSjt0jJFOawcYKoQ\n0ptRqNjGgxm15kA+5wIeYYK65kh9fb3qIiZ+mpc/kRtRpNtuna/al+3W+aq4Db30gmo6IrG5MseU\n6FVNp/CLrvOhVVJgLJ+C0QDNyqtb+PdazqhAxe40xrm01GKNhIjniBfi04sQ5ebltyjF017GX//y\nhzn+fUfylpyrdQYnJE7b3gmNl4XPyHBj4/q9mLtgFEbekA3/oCdBRJi7YBRy87thR/1neGzWKpw8\neRo1NTWG0vAttaYmTj5ei279zJkzADiqa19BASa98w6+amrCrwEMArAGwBEAJgD/B0go2O8GYPJ4\nsGzFCtz485+jbtMmof1gaSkqV66MeZxaEuLvv/8+WEhp5TMA8ALsTz/9NFgAlZNfBhEhy9cbDXV7\nUHXPcgSCgRbPJX/erPafofGr1XAkdofT1RMWG0dMb+lTiMpplZK+lk+pAMMyCARHItSwJyYmx1Ao\nhJrqGtxRcRcGj+WYMgePvRZEhL9OeAGH3M8DaGY/5Vlc6xoaBSptQMSSmpGo2GYeUIaTNc+p0rgD\nyjnnmVnXLH8ASLBL5OlPvHIv/vDwLADA2o2bwXbth6ZDHwDgGDR79uqNvZ/sEdrg6dd5SnAtinSc\nPIHEGX/H2f/sR9PXB3D2yz348dWH8WPFTADgZOpl1OO8jL18PmtrqlWp1WsXT8S6devwTeMD2CFq\ny5wzgmNX/fEoAKDpq31gky+F7YaZ+PHVhwEAjlvnq1K1A0CCvxz22xai6esDYDOuwtn923FmT50q\nYy3APUzu3L5dc4xvvvkmSkpKVPc1Mn7e1y6bibOffQDzgDJQ+AuBPZWnYxfP0dmvOEkGzXMDYMf2\nes3xtvW9Ss/463/UrwZg1pyROPRZBF27JWP3ri+w7d2DmnMetzayjvaC9F6IR0JUjX8yue2OoQRw\ngKrFL48ToiNqeU20g0dvBP5cunQpbdu2zZCUTK8Nr8sl+V5/gJKijKhGYzOKmPgLChQ8Ip5oH83v\nWTJF8/b8q7XVMVu3blUA9VK7BKhHn4e5KMFtCymhv5ShkmEZso56SCJAZmRGaSQAlJqWIYkwsAzI\n45QK6XnsFrKaWQWplDWnWHgKXrp0acwRIa3UBc/tASiBp3LxNUX6gWE53IEsoqEFtCz0+SXXxbZt\n2wQCL/4lTwkYpSMG5uUJvwVrcLKESVWNiZR/r9Uee+UARTrGKEIQCzaltamOcDisYE6VA0zlqTIJ\naZw8rWROEDhVWoPLaQ8LBAKUlOSUpGOSkpztEkE+n9YZIiEdPgDdwcWdEE3z+33kcFoEx+PuadcJ\nDokawhs6ofNzMTXwZxLDCOBONvpea6H/TfR7WqW0DijJxvrrHE84HI4JO2LkQMkVf1u64KpZaSCo\nBOo5kog1Wyk1LUOhTAq7m+N/mLddkv4wcrD4Y9NK7dhGz1FVTOVp1PlXQtZgBdNpQu8Csk9YQIzD\nExP2QGxq9OrWnGJJ+yzDOWNGWA0xBqWlQEuxDSsqJiYKYhVeZgtl9eotONFqgFg5vqG2tpZziOQY\nFLNVM+2i1Z5z9tuqZGPitKRWalFPUVd8zlsCBtVLRfGmNkeaVULmBBo8ZKjueM83DiMSicQZU+NO\nSNwJaYlFIhFKT08jlmUo0WWl2+4cIjgkapGQfv36tds45Iu+FaDFAK2HfpQjBNBCg+/M19iu9rTU\n0NBAeQMHkodlpY6LShWNEY9Itey9Vn9tpUOybt06BQGV6ephZO47TLItyZskvVFqRGXU9GnsXhcl\n9C9R3OzFY1OjGv/HP/6hiBYwFivZb1vYoly+fA6sOcXkcSol3RN6F7T46bglQEveOEwTQ7C7uGN/\nbLNy0WRNxDg8zREaOe4j6izMnj2bACWWQ9d50SnLlUeaYuHpUKuUUWtTfp3pOZCx4EH478DhURwT\n7G6C1Un2258l++3PSq6r3Lx8Yu2JQsWQ7dePCvT6HVUxcyHwlrSlxZ2QuBPSrhaJRCgpyUMsy6UL\neIdEHFJMdFnJbDa1u0cfCoWEentxKa7RQu+POi3ycl2e3ExrX/HTkmr0A6CI3OmREXC1NBIi7k9O\n0GZEtx5rhUF+bj4BoITgVGKvHCB5ijZ360t2jz0mHhE1fZqE/iVC2ae8X7UFjHcs1I63tU+wcnp1\nrYocNXG4tloY1BZ0c84IMvcdptCc4Y9VTaPF1KtA0L8R84Xwjpz99md1zzmTdoWkPbZrP3I9vlk1\n/RILT4dapQzPSSL0y1O+xwgGNbpulyxZIjhg/DHIj0l+Pl2PbVamqEQ6OQBaHGWLm7rFnZC4E9Ku\nxi+kMx8cQbP/+F808W4fJVilP2arNYHee++98zIerVJcrYW+Nvp3cdRpkNy8DPblVXiJ9FV7JU6P\n7ElaLZUkThVx71kys2YJr4YW3bqe2F2sFQb79+8nq01a4ZTQv4QSZ71FQMsrdfjF0TZ6jm6/WvwR\nkUhEcrw8lX1rc/nyfrQqcsTicG1dMaG2oMPuVpwfteob1xM7hMoPi69cIownd+TkYnNqaZwCn59e\neuklXQpz/tqxjZ4jRKn0nDOtc240nnPWz4nOlTiaJtbM4fcVKpNk88927SfhEzH39nVYxczFYnEn\nJO6EtKvxi74cBzLjAe6Jbdq0aed1PHqluGoLfQW/EEW/GwIXHeGVdgdq7JuRkqK4QetFNNQiIUTc\ngugvkOIgMlScoaKiEkV/Q3GvQPP+CJqp3mMSYBNHGxKlefq8/DxKlJXW2r0uMnfngHyt4RHRi3LI\nz9/SpUsl2Bc5vfzv0TLiKi0TCOx0JN3b40lYa1FV008x4iGRj1ENh6IKkE30Um5efsypo6qqKkXk\nwJwzglyPb9Y892rnnLUntsqB1CNEA7gUlHXUg7pzlRCcQvY7F1FCcIru9+QOljh9c7GkSM6nxZ2Q\nuBPSrnYhMv3JowsvgEu3iG+gqUlSXIOWA1GPZn0Y/uUvLFStXKhAcwpFHP2YCX1mVbX9QyIHSUzZ\nHA6HKTcvXzKe7kyQ7kdEEL3TcwYikYiyygAMFRb6qaioebs42vH4R09S6T1B1c9i5SzRi3Lwx6X1\nOT8/03FIcLi6M0Fi7cZOjZbx/bEMFJLuyS4bBQNl7Zab10ovaKnZqurAODwEs1UzpSGmdK+qqtLF\nYIixD1o4iEKfXxE5YJxeIdWhNkdq59wIYKsnmChvS0gdRcdizhmhOlcmp0ch7qfnCPEREzXV4c7M\n19FRFndC4k5Iu9uFVlqmBlQtGT6chg4erLgRTQaHCUmGOn272CEBmsnPeAuHw4pIBo8FEe+nx6xq\nGEkR3ZhLA0EyJ0rD+KzdS92ZYEyRECKi4cOLiTFJtU7AmslscdMVWfcQH+145usXqF9AKmbH2hxk\nc9skYNOWaMtoLex6eAM1ob37EaF0pr9utEIPWMj3Z79toaIip6S4SLcK5FwsHA7rL8QqGi5weIhx\nSWn+wbC6FTxapGtVVVVUW1tL1dXVSrZQWaSjMErSZpQOKTAo25af81ijYmpmlNpzTH9VcRxCJCh6\nbSUEp+oejzwSYr/9WUE5OB4JabnFnZC4E9LudqGWlolvfsHSUkpiWaG0ln+lAXQg6oiIt/cHaDea\n0y/WqCMhNy0sSH9w0Y/8gQNjunGpaszIoic8O6zWDdjOenQxIUIbDEuMI0mRD0/05lBh4CMh2tEv\nkEPOZFlaxm0nq11aTurz+1p8ruVP30ZPxzwm5CYso+k4RDdhGTlNyeQr8CscBaMqDrX+XE/sIIuv\nnABQzoAB7UblzS/A4koXXuHV5HBTr959VNMePPiUT1XpPclXV1drOnUS9V2e32TSEjL39in0b2B3\nU3Jqmir/hri/qqqqFs2BUVRMz4xAqnxKS64ZpcDZMKyi0kiCCZGle3hnpqPvaZ3R4k5I3Ak5b3ah\nlpbxi04/gJKgJAa7Fs1VNH9RcUhYgPpnZ7eY58NfUBDzTUtPY0at8sbar0hRZZI3ME+zP7londaC\nXxj4mNIuLSObyyk4I2qplzxZSkhNFVftPFRVVSkAkHzprd6iyoNTJY6uRjVQoc+vW30RC1eIyWxu\ncfmvkYmdH/eiT8l09TCFwzG8uISGF5eQyekma2AyR7UuixQYOW1qC6/4c9voOZLUjxHuJDc/39BJ\nbI215n5hdOxyPSE1p4U/XkUptMMjfW9OaPNr4KdoncEJidO2XySmRU3e0cZTlX8AjpZ9XHT7OHC/\njPEAItFtHgDrAewFsA/AR+Ao1f/++usKunK+XS3K+PsfeigminMA8Hq9WLV6Nfbu3Yt9+/ahR48e\nwlxeX1aGLWvXSinlP1qPH5//Dez3/kOgoX78T49jy5Ytkn0BjvJ77Nhx2P3BXlyRdQ8+bVgAc9ZQ\nSf/mXhy1+vGj+5F9zSuorwvgR+xAZmEvyfeyfL0BAO/v2oPsQRXwphXiSONGvL1+OsaOLUdNzSrF\nsUUiEYwvH4fqmtXCNkvXvrBNXIyzn3+EXcs4CnMteu0ePXqgsbERk6f+HtNnTMOZM2cUx8j3c8NN\nN2FTXZ0mFfdf//pXdO3aVdLfycV3wLZvE16cNAi+rDTUNTTi7lfew487V4EddlubUXnz14s5ayhY\nZxIYswWMwwP7+HkCfXzd8vvhG5SHYl8Bamuew8ma5wAApYEgVi6vBAAFdby517U488lmgcL87Nmz\nQj9i488xY3fjTMM7wnfOfrFH9/s76utR6PPjXY3+WjsfrblfaB378VfuBRgWxb4CYZ6AZjp08bVl\n6tKDo+Vv/Ay20Y+CcaeBvm/EyX8tBLpkgr4KAQAcv/1zh9O5x+38WNwJiVu7Gn8jArQdhnIALIDf\ng3NM/ADCAB4DkJGSgtTUVM1269Ds2ADAhujf1ug9yG/MoVAI1bW1Suep6SzGf7AOTPWzOP3vp5CS\nnoHS0lJhv9y8fMyb+yc8+dQC1NZUC9u/O7IdgPaCf/T7j5DouRpdLvsVvo/sQGjjJ4L2CwA01HEL\nVvc+/4NLu40FANi7jQVAWL16guoNenz5OGzZtB6V4kV+2S78+PdZsM94HSDC8SV34VSlcpEbVlSM\nyVOnSY5BvCCLbWz5eLxTvxOA9oJ6xx13AACSklPwbcVMNB35D07uWosXJw0SNGvGDe0GImD84rVI\n+M9+mC7pLuyvpVMTi4kXRCYxBWd2rYFt9BzFQvfW4okIhULAM09j/fr1YBgGfr9f4tA+/+fncM2Q\noRLdmZT0DCz6y59x+vRpoR+1c3zib1ObtzW8A9OVA3W/DwC/v/t3cLxcgVpRf1rnQc1CoRD279+v\n6jzG8rnYVi6vxJhx5ZKx5Obl44VFzyMvL0/yXS2nhYkcgqXptKChAwBMl0ywP3yNnLw87Ny+XfMa\nOpdrIBZryVzErY2so0Mxei/E0zEXhfXt00c3dfLzn/+c/vGPf1BGihQAaKQTEwuW41zMiFUVUALv\nBM6JKNOmY9ISDncQnEywucliyyDG4VVWWzBSfhcnm04Ot1MCQnV6E4lhGfKPPCBR4ZWr4vImpKx0\nCMH4tIu86qc0EKThxSUxqZyKeSygE67nlWRNidKKCU2ukFEPtWmJphpFu4LMCxzOQgs30dDQIKjV\n2kbPIfudixSU6JolrdGKGueD/yY2/UoBBCtgQuQaK9F0EX/ceikUNSCvET4nFhZWLYs1naOFQdm5\ncyclJadKtqekZ9DatWvbJf1kZOFwmPx+KdvthYCtO1frDOmYDh+A7uDiTshFYdu2bSMW2qyo/I+e\nBehRcBgROWOpVhliLDoxrTUj3MlLL72kesPkSaHsExaStV+RZHwsQJ6UIZJtvAMwARtoHKppMkJ0\nPyJ0JSulbPdFb5LZgyokTkj2oJdV50hwojQWeeeM1yQ3d/HCEiu5mrgf98IP1UXLHB6BOlzcRtdu\n3XSdJP7FWKw0vLjknM9lTv8BBFuiotRVTmte4PMrnC9TokcKLBU5MHJtH7WFFwxLtjGPS1lXxZgU\nOYOoyUIwmQ1xEHqOhBHLaiwsrG1lmlU6Kn2fSwVPaywcDlN6eholuqw0d8EoenPjdJq7YFRcwC7u\nhMSdkIvJSoYPV/CF8BozYqDqcNmCr8V2Krb2BOXqRVu0qgV4Rs2EXteShzUpwLhORzeyJHgoNzdP\nsuCLy2AfQTMBmpg8rKwsSFZbMmUPepn8Iw9Q9qCXyWpLljC68mYUCbGNflTz5i4+Nte87QoGTPH5\nkIM+FfTm3fObow3PH5Toq7AMyOOwKNR7LV37ikT+PK12QrQo2vnxNANGubnIzb9G1flSZfl0eIhx\np0vavrpvP8EJ5kGqFv94rt/ePqVCsNVJAEM5AwZE25AqNouVatVMazE34gQxAtC2J8DdyMHVEiVs\nr6iELzpXFxLfUltZ3AmJOyFxi5pa1MJIt8UoEtJR4+ajLVo3U7E0vdYxFhb6JTdVrTJYedlvJBKR\nVNoA+tUxwUAZJbtsikWeZfRv7vyxKbRA0q9UPR8KyvLRjxJrSySAkcwPFylprp6x37aQTBaLpA9L\n176Ck3CuC6PaIi2Ofoil7lPTmiMdsTCnsl37EWwuxRyJy0lLA0EyOd3CZ1oL79KlS7nKIqd2ZZHW\nOdJjdNWqeuL1XlpLv38uFquO0vmo+BPE96CtQN6ZSdI6gxPSKYCp06dPh8fjkWwbM2YMxowZ00Ej\niltLTVyBsnLlSsyaNUsTqLoEwBRwINOpJhOCxcUdBhLTq5zxer0c8E4G6jz5r4UwWZJw9vS3msf4\n4IP3S8COy1dWYtyYcrxRO17YFigOYvlKKfjQ6/WipmaV6njUrHL5CpSPG4vxi5urY/y+Qvzu7t9j\nwIABmvtmZmYiJT0D4cbP4Ji0RKggOV4xE2BYTJ46DSuXVwrHoAZYLA0Ecfr0adRFgYmC11cgAAAg\nAElEQVRM8s9wZtcaSfWMddhtYBIcOL54IqZMmYJnn30W9umvgnUmCe3EAkpUAxSGQiHU1lSrVusc\nXzwRZ/+zH2f3c2DhvLx8fPDhflyZNRMHG+bj1NY3YAtOBgA0HT7IjUMEljz71T40HfoAbNd+oPAX\nkjkKV8zEDTfehLoN65vnpaYGAGkCLr/++mtsrNugWVmkBjoWV/yotQlAchxAM+B18ODB3HsNQGxa\nWprqPLeFqVXNiPvmQeXno+KPn0MA2FH/GUbekC28377tU8l4LnRbuXIlVq5cKdn23XffddBoWmAd\n7QXpvRCPhFyUZoS1EL/aEuPRHqaW//dmXEdX9rpPP9qj8XR3Lk9/DQ0NtGTJEoX2S3V1tUAgFWu7\nLeWE0Bq/2vzoKbLq9ak29lgo5/XItcwur0CrnuiVMdSmX0mO6a+qCr/x4naxjvf11183jITojVXt\naVztHLmfP6iqZKumzlsaCHLgaRkglrFY252T43zjPrSMn8NefS4hT5JdoUDu9/vO63ja2jpDJKTD\nB6A7uLgTctGaHtbiQiVe07P6+nrKzc2T3PwzUlMpmWXbrXqHt3A4TMUlUsIvMKDUdGn1QSAYO9o/\nVnbMWM9RKBSKyclo6eIUC+W8XrqiNBAUBOIYh0qVE8MSGJa8qWmqCrl6czR79myJMygAY2Wg3cFD\nr22VAyY5/mibWgq1PPBVnH7btm2bLkNse/7+zoW5ta0tEAiQ2+2gXn2kwOP09LQL+gEoFos7IXEn\nJG4a1t6VLR1lYgfqfFTvVFdXk8/vI4fHIaF4N1vNZHfbJdtaojNjtIA7H1qlWGhjMSMnoyWLUywV\nPFrKxWIVW56SX6sd1uGi4cUlAtiTfyUlpxg6OQAUVTVyOnLxZ4zFSvYJC2KODqjNl9Z45NpLvKPp\nfGiVADw+X7gQ3i6EBw416Qufr+VyCBeidQYnpFNgQuJ28Zke1qIzG3HOM4C2P0Ye95CamopZj8xC\nTXWN8FnXnG7oF+gPp9eJK/KuwpmTZ3DbkjsEsrPBY68FEeGvE16IiXUyMzMTw4tL8PYr9wLUjHc5\nUfkAzDkjQOEvAACzZs3CrFmzBPIsI5ZaNexIoc8vEG95vV6srm7GvJhMJpw9exbffPONpO1QKIRX\nX30VgDYmYuXKlXjs0dnAw7NUybX4OWhqatJtxzxkNN5a9yJATZIxV7z8N+QPHoJwxUzJHB1/5V6w\nXfshcfpKHF04RoGrOVV5P3J69YDd4cCW93ZLPjvxyr04UTETJ6J9GZGSieeLx1ppHceZM2ck23ls\nBkW+1MVmtKddCEzPXq8X1dXVF929qNNYR3tBei/EIyEdYu2hYHqxm1wfBohN0yXWtgNB6ZOaxWqh\nW56/XYhyOJOd1C+QQy+erqRp/7qXAE6NV6w98+SBZ2J+wuXTB4xZRu7V28eF60WCY7bRc4i1JyoU\nXbWuo3A4TPm5UnI0uRaNFtZj//79qrLyatU04v3Wrl0r6OSIt8eiVGvqnq9IcfARikgkImBK5OMx\n0oVR+4xP8zz22GMt/v0ZHYc8EtLQ0EC5efmcMnQHYzPOl/3U7m2dIRLS4QPQHVzcCTmv1pKFVA0E\nyW//Kf3IeWvm76iI8ndUaPJ3tNQCwQC5kl2S1IrD2+x0iMXt/vjxfHr8oycJ0BbAC4VCtHr1apo9\ne7ZiYVIT23POfpvYKwdIF9pLs8g1/30FJ0ihz0/79+/XFLwLh8OUliJNQVyC/uRgkyTlyHpKtGoM\ntXIFVuG9zn4K4i7ZYsw4vWTqdW1MWI1QKCSUvdrvXESuJ3YI4FUtzIj4M/fzBxVz2RqMhCZbqwgT\nonDkZLiQjsJmtKeFw+ELUm28vS3uhMSdkE5lsSyk4XCYiopKpDlusFRY6Fdsb6tIwIVu/BNorEym\nrWlby6H448fzJVGO37w0kX778iQBEyKmfU9MTqR+2f3I65XS46emZdCBAweIqPkauCLrHsUC6npi\nh0DEZr9zUTM7qoRZNIlS0zLIaUqmUaik6ThEo1ApcJ74CvxkhUfymR3JlIH+wlzxx2wNThZwCuIo\ngREGQx4ZMdpPi+XU3NtHCcV36DoSfFQpHA4rIiJGDgz/mWveds7J04i2xHqdVFdX09q1axU4E6E6\nRschMzndNDAv76J9eAgEApSU5LzoGFGNrDM4IXFMyEVmWnwJRqJMoVAIq1dXI3tQha442rhx41G3\ncZtExfXjndOwadNmmC3OmNVdLybjuQa8aYWS7d40jiXkXES3+La1FHUP7/8aGT0vEcTtXrx9MfcF\nBkjyJOGvE14Q9mFYBh9EPgAAmLv1hf1OTkk3XDET1wwags2b6oRrwJOch08bFkgVUC/pLvBqnP3i\nEwXnB89r8c3iiRiB+ciOyv5lYxzoLAkcKKNQKf0MhDfAffbee+/hiSfnAwBOVj+Hk9XPwZwzAo5J\nS8B4OO4KLcxDeXk5KisrkTh9pYRnxGg//vysrl6F7du3Y+Jdd2Hn9u04s6cO2FMHwJjTYmz5eLy7\nU4bvWHY/4PBw3CqkVMA9deoU1r84GXT6pNDuqXdfhzlnRMzKsZFIBGPLxzeLDDIsGLsLCddPw6lV\nT8M2eo7AE2K6/GqACOHFE1XF+3aKcDOd0eT3Of69yWRCTU0N5i4YJfCAjLwhG0SEB2e8EVfm7WCL\nOyEXiUUiEYwbNx6rVzcrng4ZMhQOhxPr1r0pbCsrC2LFCiWAMJaFlIg0HZXdWyfgqt4PxazuejEZ\nD/A70rgxetyIvucWMLO59T8zvm0tRd0ERwLeXb4JVfcsh8/vw/hybjH3+/3o2bMn9u7di7Fjx2LP\n3j0Y++wtyCzshdDGT1A5rRInX3sEjnteExyH1157DQB3DdgdlyO1SwDhV5TquinpGTjy9ovcsWks\n7A5wC/83COEI9sODrsJ3usko3K4QKNyAP//leez6ZJ9iMT/+wp0w9yoAoO0QjB8/HpWVlYrP6btG\n3f3EAMy8vDzsqK+XgBQnT52mUILlHQl+sdMjRYPTy/2NGg82/eXNvwYsNjh+85ziWBNn/D0mkrax\n5eOxbtO7nJPmvRTH/jQS9lueBOP04tSqp5Ew6CbV88O4U1W3t7dKbXtYJBJBeXk5amqagdopKckI\nhyPCe5ZlkNkrQ7Jf3jVXAOicx3wxWdwJuUhs3LjxeOvtd+BKysEP3+4CALz77jsAY0af3OeR1qVM\nNTohfloAtBfSHj16YN++fQC0HRWLNU11+8X+I8/MzERZWRBvr58OgOBN8+FIYx0+3jkFAIsRI0Zo\nOn+xtB0IBvDq9EoQEbJ8vdFQtwcrpr4ChmXwRNHjAACf34d/vvFPRftEhO3bt+OOirtUK2Ws/9kv\nYe0Emq+BnEEV2LX1VnwjW0AX/eXPuGXCBGyqq9Nc2L/Fp1jGXI/91OwUg2EBasJnqBMiIQDwAbgq\nl35X99NkDT2+eCJo3xakpGfgOw2HYMSIEarS8WeqF+rup3Ztiqs2tNhg+aoVI+ZSq68cJ2uew9Kl\nSwXnMBQK4a21b8bE5qpWpRIKhbBhwwaJ83N615vCOOjUj9z50Dg/Zz55B+Ye18B0SXfJ9s7CDiq2\n8vJyvPtuHeYuGIXc/G7YUf8ZHpu1CizLoKmJ0KvPJfj8UAQPzXwDr/9rkrBfZ2NEvWito/NBei/E\nMSExGZ9DdyXlkCVBiukwW9xkSUilohsPS3AK27ZtU4BQ3R4vmS1uTXE0I+xDVs68NsdEtNX8tDdY\nVk3TxZWUQ0NHvHfOINVIJKKojikqLqJrBw+WbFPjIOG5ILQqZcRKumvWrFEVyJOL7fFzWejzkykx\nSVFZkZqWQSbWSqxdqoPCODwCXuQmLKO7sJvSmf4yfJE2/mJgXh4dOHBAl0dEi2fEaL9YTIvTojXs\nsrGyufKka3y/alVDpqvyyH7338j+m+ck45ArGttvXUCMRb3aqT0qYs7H746fey3xuZkPjiBPkl0g\nIpvxQInAiBrHhFwYrw4fgO7g4k5ITMbf0PQcBLc3n8puPk3+kQcI4OTj5SBUs8VNABt9KcGlfEmf\nxeKRLFJmi5cYxkyWBE9M6q7ny/QovdvLeHVSLYdMXk3UEhMvgjzjrFihV42N1QjYahv9KMHuJovV\nTpFIRFMgb//+/RQMlEm2lxQX0fDiEsX8rl27VndR9hX4ue8zrASMaRs9xxBAKp5nteoetbmKZbue\nxbKY6lWllAaCtG3bNkkbsbC5Di8uUcxvSnoGmRI9HKD1sc0KinawJkKUit31uOxzhiVGg021LX8X\nar+7Ap+/XX53/L1PS3xu0YvjBIdE/opXx1wYrw4fgO7g4k5ITCZWgvSPPCBZ/HinAwB503zUJ/cv\nhg6LJcEjYZRUlu6ysh80Sz7fdRdcdUyBz09gzdKxmsxU6L/unNvWWpj4m6L4PAy/4WtKTh92znPD\n98k7Olq6NPX19ZL9+BJfcaWM3W0nhmWaFyc2gYqKSoR95Is1r8ZbOYlT462cNIiSXTYKBsoU341F\nJVVLSl6gHdfgrQiHwwpnKBgoa/PrrKGhgaqqqrhrKAYnVi0Ck5uXT2vXrtV0hI3YXNXKlPnyYz7S\nAbubrMHJ5HxoVbNT4fBI+mO6ZAr/t5QWvjVWGgiSKdGjcJDcSV7FtXmuZhQJWbV2suCQAGixhlJn\nt7gTEndCzpvxuiVajgUAYk2JxJqswne1HBa+PJP/oaqV7loSPHT11X0VT/btScPckvDu1q1buadC\ntSc/1tTq8elFV3juFPl5SOsSILPFS9mDKij/urX0sytvI5PZQYWF/pj7lEcnEI2AiJ2QQ9HtA/Py\nJPurpXNYk4Uu7/E7yr9uXTQK5iWAVZ0XQXBw0iCiV24WXssmDlJdwGKhU9dyVFyPb9blrdBzhvQs\n1msnHA4r6NnlJa5qaQstoUA9bRs9inqjOXT8vkJX9yXh+qlkv3MR2UY/SozTK/C8tEQgrzXGj5vt\n2k9Rvt3WURd+zv1+HyUlOSXic54kOxVe11PikPh8nVuMrjUWd0LiTsh5s6qqKgJYBabDkpBMriRO\nHTQrZy4BEBQ9tRyW/OvWCTem9uTAiNXC4XCLNFjC4TBZ7XYCQLbRc1Rv4o899lirxqK1qKSmybgZ\nTFbqk/sXyr+OS010v/phcnn6KxaOwUOvNbwhy53ArOx5upEQrfMSCoVo8uTJuudz6dKliv14h+HQ\nwpESJ+TQwpGa+xhpxMTC7il3GlrqDBG1LCUXDodVOTbg8JA5Z4Rq5MDIKY0l+qDmuBtFk9hLeigc\nbMbpJXNvqQPFOyfOR946L5EQcWpYM9VkS6ThxSXGjWmY2pynp6dL3vfqk0H/X/VdghruxSBG1xrr\nDE4Ii7hdFNa/f38ATQAY7N46ARv+fRV2b50Am+NyHD96AN40Hy65nEPJRyIR5ObmYc97U/B/ny3H\nieOf4/8+W449792DtC4BnDzxOQAONR5L6W572/ixY7Fl7VpUAjgEoBLAlrVrUVpcjL1790q+G4lE\ncFWPnjh54gQA4MdX/wdHn7oZTce+BdBcsXD48OEWj4MvxUwYNw8JQ28Gm3IZEobejISxc/FN49fI\nypkH/8gDyB5UAZPJho93TEb9+mIALPZ/9Ch++H43YEuEY9ISuBd+CMekJdj6/gcYM65ct8/Vq6uR\nlbMQl3YbC7vjclzZ6x44Hd1wd3QuPo/+/T1rQoKo1FJuPXv2RL9+/QBon08148uE6xoaJds3fMK9\nv+OOO1AWvB5HjhwRPlu5vBJFBUNwfPFEfD+9L44vnoiigiFCRUlmZibHlbH8fpzaXIWm8Bc4tblK\nqFgpKSlBIBCQVK7w16IvS1qF5e+VpnnM4hJWfs7XbXpXdc5vvGkUwj+ckHyXwl+ATe2KM7vW4Kyo\nkojvS699o6oZvo2ePXsqjpWf8zMN70j25atYmv6zD45b50uuQ3v5XI7bBEBufj5YeyJsox+FY8IC\n0Fd7wVisOPHKvarz3VbVa/y49Y7bOuw2vLX2TcVvN1YbWz4e6zZyc+588N+wBicjfOxHFPj8qKqq\ngs/nwycff41RwUV4cMYbyB04CJ980tDiyrS4nR+Ll+heJMaXib719jtwunrh2A+fAAB++HYXEqwZ\nGHjt6/jq878DYHHnnXcK++3eejs45wVITh+GtEtHomHXPSgr425MxEWkdEt329NCoRCqa2tRCQhF\nneMA0NmzGL9zp7CY8eJpN940Ct/9eEaVZyJxxt+Fm/jIkSNbPBajRSXR3Qd2x+US7pSePbNw8NOv\n0L33f6Nh9/3CwgEgJkIqLScwc8DT2LX5pijFF2fWq6+DKfd64JPNmufF7/cD0D6f/OeSvjIzEQyU\nYcry9SDiFv0NnzTi95XvI6F3Acz+W7Bu+f0YM64cq6u50m+5EJ0aUZ5R6avcxM7QuKHdhO28MyQ/\nZj3+Dn7OiUgoUdcrDwaApq8PgI5y3BNffvkl1qxZo9v+PdOmAoiNn0RtztXKjU8uvx+JiW4cPfq9\n5nU4MC8Pb9bWcnP76sP48dWHAQDDi0sAAG/FON+tsczMTBT4fLrl2+YBZThZ8xw2bNjQYudn27Zt\nqK2phn3CQpx693Wc2bVG+GzTxo1Y+NR8bNiwIS5G14ks7oRcRLZiRSXGji2XEJY5Xb2QM2QlGr+q\nxifvz4AlwYXeA54VWE0bdk2D1Qp8/90RRA6/jcjhtwVOC0CbA0PsqLSnCU+/su38UmkNTsa6jdxi\n9ugjs3QXkh+rn8WP/zsfnuQUlJSUtHgs4qdTtZurI7H5KZCPLOzd24DsQRWwJCQDMGbt1OpT7jQc\n/6EBTQwL1u6ExT8B5gFloPAXhk+2mZmZKCoqQd3GKRCfzz3vTUVRUYnmfpXLV6B83FiMX7xa2GbN\nKYZt0l85dlINZ0pPJTUWR0VuLAPcvex9hTPEMsrvGjmNv/r1r7HrvfeaP2BYjlVU5bsAcPbLPTj5\n/54CGBZ33HFH83c02v/2229VHYlYow9qThoYFkejCrta1+FDDzyALVu24LlnngaeeVoxt3v37sX6\n9evBMAz8fn+bRwj+3z//iZ69eivUheUKzK2xu+6+GwBweusbOHvoA8nDxvGKmZh01++wvX7bBaHO\nG7cYraPzQXovxDEhrbJQKERVVVVUWOhX5Ie1sAB6qHGtss3zkWMVcAAa+AfXEzuEXPPl3brp5tEB\nEMuaad26da0ej1YpZqI3RxMQ7B95gAoDH7U6Jy/m7hg6YqeA8QHQKvGxlpxPOaBz6dKlBICcD61q\nV4CjmvF4g4TeBZKx8+/lfetiMhiWGIdHs/JEWTLLVRIxFivZb1sYU0kxX5Z6rmXiBT4/sfZEso2e\n0zxWc4KiisiUmKTAtMj7Ol/VRZFIhPLyr5FiU6KcJIwzicCog6D1TFwF2Nrf0k/NOgMmpMMHoDu4\nuBNyzsaD3vjFQ6sixmjxaGhooKVLl6ryXLQ3KRHPibEMXAXIMoA8rIms/YrI/fxBSogKhfEvy2V9\nJAJm9lsXtJlSqNqikpqWQQnWJAVHSmGhT+L4pXYJEOOQKbU6PIYkUVKngQcfSyuVeDKxlpgcECkn\nxVJTwt22bVuHLQD8ImQbPYfstz9L9tuflTihan0X+vyKxRp2t+4x2EY/2vxdq5MSE9300EMPqe7D\nXpql2j6TdoUh+LQlxyzv1z5BeU1brHaBQ0RLBK+11UWttcFDhxJM0jJ5xmJtFTCVd0LZy/vqPmy0\npyPc2SzuhMSdkAvGWlvlolYeyj81h8NhYaHlX4WFbU9KFIlEFNUx1n5F5F70KVn7FZGHYSWkXR7e\nEeEXBbP1nBRK1YxfVLZt2xblR5EuCEVFJYLz0BzFeI8SvTmS7yWnpgmVFEaLFM+t0daVSmoOR1pK\nBjnYJFUlXK3Kl0Kfv12dUa3qFVOitiNXVVWlWKyNylW1okxgWHI9vlmyj+3W+dLvyfbJzcs/p9+D\nUZWM/fZnyTnjNUoITjV0DltTXXSu1haRIN4EdeVRD8YjITFaZ3BC4piQn4i1Ftsxbtx4vL1+i0Id\n95e/vBkffPABGhu/Fn2bxcaNG5CV1RsNDXvaLNfs9XqxavVq7N27F6PHjsWuPSGYht6Ms4c+xMkP\n1uFFyECrAMZ/8TFOT+8rtOH47XMtAoTKjdfq4PPofM45ELgeGzfVw5XUT9DsAYDdu3cD4HA6N9xw\nEzZunCB85nT3xWVX3Q7QKTTseiD6+Qbhcy2dmbNnz3Lz0cZqveVjx6Nu7RaMQiW6wYfPUIdV4bvh\nQhfYkYwz+FGihFtfXw88PEuCVUhNy8DGug3YWMcdR6A0iOUrW66Vo2djy8fjuxOnFDiAFJddE1zZ\nv39/gJpgGz0Hpp/1AlgTmr75HCcOvqeJqXDe+wZOVNyDph/CcNw6X9LXscWT4H5sY3MHPx4X2j+9\n9Q00HT4I+/h5wj67KqWA3ZaaEQ7J3OtamC7pjqYfvuHeG1TiAPrVRW2No2gN7kfLBLDumy+A7doP\nJ5bdB1DLsTZxu8Cso70gvRfikZA2tZZiO/SjJ2ppAY6TxGxxx0zE1ZpjkD9ZaZF2JZRMooSSSTGH\nbtWiEeFwmIYPL1aNdPCpCS3Nnry8aySpsL7XvESFgY9lqTCWEhKSJPtq0d23JpplFGHh2xyK++g6\nzKbxWEP3IUwZkGq69ESQ7sJuyZzx0SBfgZ+cpmTVqEksY4jFYuXcUDNVBk+ewlycGnN6OU6Nmf+I\nOV1jdnk5KnWnfopHi15efHxac6QWeWKcSQJ3SdIr35Lt149ekJGQtjbJ77+NUqwXs3WGSEiHD0B3\ncHEnpF0s1vy0GgV52c2nBQIubSE7jhSttrZW6Ke1C5HWfqFQSGAn1QKtOh9aFdPNWS/lVFYWJNZk\nFRhPJTiMvHzh+3pMtWrfKQx8RD+74rYWOxVqAnNqTosWpkN+k37xxRc5ZllxuoK1kxVuiVNhR7Lg\nmKgRiI1CJT0CEl43YRmXntOhPW/JNRELFbyWRSIRLo0jSsnZb1uoFHO7ehiZrm6m1zdM16BZHG9g\nXp7hPmqLpLrOio+qqqqEeVFzvGFOIPuEBRIHCg6PLuU9UTMmZNlEDhOybGL7YkLay/h72E+Nhr2l\nFndC4k7IebPVq1dTeXk53XLLLYZPXbGa1pM3T+uuBXLte81Lspu19IkllsoaPcdAbKqgVYBY2Q1b\n7+asRksvBpbG4mToafZwLxOZzC7qk/tnhY5McvowQeXYCCwcazQrUBrUjU7w5k1OVaW2tzAuGodq\nmoyQxKnIz81X1YmZjkMSJ2Q6DhEYllPZlWFxhhUVtxgncC6REKN9nYkugtUpUI0bVb2oLXxbt241\niJ7MUcUhiRl41QTpxPPCL7x//OMfVSnbE/9QYxgdiEQi56U6Jm4XhsWdkLgT0u62b98+SvEmkUn2\nhJbodJxTKSpvWtLusURCsnLmUXL6MEUUQe3JXf5UrOUYyPdTA62WDB9OOQMGSLZplS4apTiMnIyr\nr+6ru79Ym8ViTSO1NJbZ4qW0LoEWAU31ollG0Ql+n9WrVysWTvfzBxULYXemORUjj6zwKSl5X8V4\nQntRZlgyJyqp7wt8Pt2nWiMqeC0ziqJUVVUJUS1+vOacEZz2iUxcTquv0kCQGIuVGGeSaopHzWGS\nO0dCnwYAal7bxjZ6DjlnvEauJ3aQa952sgYmazpJLbl+4nbxWGdwQuLA1E5u1w4ZjO++/RYOM4Pu\niVa8/+2PAICjx45jRFER+uVk4/bf/BZHjhzBkCFDWkzS1UyANkHYVlYWxOnTp7FpsxTkuue9e+BK\nysH+j/8IgMXh/1uFI411yB5UgUujRFs8m+jq1ROwd+9epKSkYNy48RKCtcJCPzZu3KC7Hw8+40Gr\na9aswZYtWyTHKAfDqYHjtmzZwrVjQGOuxTD68st/QzA4Eh/vnCyZi493TkdalwBS0vl2OAZVAOgz\n8DnFce3eOgHhw3U4eeJzfPL+dEMiOD0yJp6kq5uM4u2KKMUbD0DcunUrACmY8fjiiaDwFxLw58GK\n+/GPH28BCLgW9yMNfXAcjahb+0fMwiMIlAZRu3YK6CzhCvjxKTZgI/so0KQESjLJPwOoCQnl8wSg\npTm7BD9WP4dNdXUI1nHzKmbB5a2lDKu8GYE7BwwYgDmPzkYwGBTG65i0BMdfuFNgS9Xri2dmtd+2\nEKd31kj2MeeMgGPSEu7/KEh0/fr12LdvH7788kth/s9+tQ9ndq2BY9ISmK4ciLNf7IHpqlwkjJ0r\nAVCHQiFsqqsD0q7Aj68/DsuwW0BffCLQtQPAUwufVsyd3OJkXnG7UCzuhHRiq62txdeNHCq+e6IV\nh46fRuWgy+BLc6Cu8Tju3vl/2L1rN6ZMmQIWHDl7iteLJ596CgUFBTHdhLxeL2pqlOj2I0eOKJwT\ngMXpUxEkpw/DJZf/Cg277uPa0KnmmDJlmqL6Zuu2aQDYmKpAIpEIysvLUVNTI3wvEAhg+fLlihut\n2o1Xi5GUdzIKC/3Y/M4WfLxzGqQO11SUlQWRl5eHhoY9igqY5PRhyB5UoRi73nzUry8S+uQZa1tj\n/DF9hjpkC3VDwKfgKld4uvBBgwYBaF6cxQuhvJLo8OKJsMCFzZgntJfe1B81tdV4+umncezYMbyx\nqZlE3jfUh7pNKtTd79cCMHZ85DTwQOsrLbQo0MXVFAcOHJDMBetMQuKMv3Msu68+jOeeew7du3fH\nN998A6/Xi1AohP3790v0lSzZJbDk34ij83+BpgM7YRs9B7bg5OZjjzo9YtkEvk/GyTkMpzZU4swL\nzZ+be0uv+U2bNoFlGTQ1fgoAOP3mErBmM+y3LYQluwRnGt7BmlfuxS9v/jXWvbkGcYvbBW8dHYrR\neyGejtG12bNnS8LjlYMuI7q5r/BaNugyAkDzczLIazFRkkWaLw6WaSvRxmpigOgVWfdIqj+uzJqh\nm6p46aWXDNI68wwBm8XFReRy22juglH05sbpNHfBKHK5bVRcUhzzMeiBPSORiICzkh4AACAASURB\nVC4PiNZcZA+qoMLAR5Rb+C8qDHwsSe9oHa/J7GizqiIeE3ITltF0HKKbsEwVEyIANicuJvudi3TT\nFkxCoiRVwNq9EgyCr9AvAVSqpU9Yu1OSgnDN295qrEdLTIuvYv/+/dJqCwULqVeRypO/58G3jklL\nhJSKIGUvI6ZjLFap8q3FSozDwwGoGZZL50iUcaXsoinJyeSxWyRkYx6nlaw5xXG+jLgpLJ6OiVu7\nGv8ky5svzSF574++7+O24dmBXTB+6xeYn5OBmy/3oK7xOKasfxvlY8dgVc1qtNZ69uwpcBB06/l7\n2B2XC585PVcDYLHnPWXaBmDx+eecWq9WZGD/x3NgtWVocpqEQiGsXbsOcxeMwsgbsgEAI2/IBhHh\nwRlv4K//P3tnHhdVvf//15wBhk1hRMBdSVk0FWURUQEXEMHK9Nu1FL3ZquYtK+um3V+5VXpvqS23\nW2qLG5pZt7Irgjtgbgilt1RGJZdSUxm1m7jBvH9/DOc4Z+bMBgMD+H4+HueRc+Ysn3MOzed1Pp/3\n+/X+6CPJ08MaOp0OjzzyMK5evSobyTD16tiyZROOHj2K/HzjSIK1Y4ojLatWrcbOXY/DUHlL+k7w\n8EJgywGoqvwfDpXI67YYPwtISx1YqxEQU7LXrELWmHH4Ku/26ERGqtG7w5SiPbsR3zcR5SZTCNam\nLTSjXlKsyTMK2QAIebuewTLf5Rida9xGafokJi4OJcUluLbSOCpB1XVQnK2pYzoS4eiIntIoyrDM\n4VIVXHX7u3F18STZdEpQSCguV1yXRmn+WDQG5RdOykZtdme/ZNxu5Quouvq78bvooRbTOVAJ8Jmw\nQHYP6fofuLb8BVxfaywy5zP+H4r3+MqVK8jLy0O5Xo9VkxKkAn5Z/TqCCBi/eAu8zh2HulVn6d7V\npEAcw9Q3LEIaMenp6QgNbomLFy6iCkDBhQpkdfSSvs+/UAEA6OLvhe5qY4Wvbs290d7XC1kdvUAA\nxufmOWzYZQ1rUxq3rp8HYIC3b3spHgIAmgVG49ZNPfr27au4nzgVktg3VlEYSNdXLQpi4zvK2hPX\npxMASEXGlMy/9Ho9xo4bj7yNJrEoySn4y5Sn0Lt3b4v74cgcutgxVlbdhLefJ7LefRwRSVHQFR7B\nqqeX4Yr+O5CBEBwcKrsfsXHx+PCDfyEuLs7m8Z1Bq9UiJ3eDYqyMKWFhYbj42zls3rwZu3fvxrcb\nNuAHs2mLayteBFQCvBJGyvYVOzsfaBGODMnMTPx7Uur4iQiRkZFQBbWTddCOVppVem5K8SPWMH2O\nSlV2m79WKE3BzJs3DzNmzJC+rzp7DIbqomnmQqF88UR0694dh378ER6R/aTpnKpzx1F5bB+uLZkM\nkAGePeXPwDN6KK6RAc888wzeffddq2Js8sTJuHfEvQCsm40ZfiuDulVn6d4xTGNAcHcDmNqxe+8+\nBGoDoQYwpeQMVp28jNMVN7Hq5GVM/f4sMlv7I7yZRiZIRMSRElM3xZogurGWHngOZ05m41rFaZw5\nmY0Tun+geYAWN6+fRmT0fHTv8wkio+fj5vXTGDYsE0OHDlXcTxzxKCjYAZ1Oh5ycHOh0OmzcuEGx\noykuOin7vH/fCQBAeM956JmwHNt37MHYseNk24wdN156A26+6Ef4TlqC3d8fxCfLljstyPR6PTIy\nhiMyMhKZmZnY9d0uZL37MPqO7Y8W7YPQd2x/ZL37MMhA2LRpE86fPye7rv1F+2osQIz3ZSOOHj0q\nW/f5558jJWkg0tPTMXPmTAwdOhSZw4bj0qVLisdJS0vDq6++ik25uRgyIBEViyfi9+e6o2LxRLS4\nbgwmrSzdJdtH7OxawCgUTANfTTE6y2YgPDxcis8QLv0C74fmwOfJDyC0iUTF8hdw87u1MJT/gpvf\nrbXqfqn03Lbu3I0xWfLn68j9slZl1ythFABgxowZssq6hvM/K24vCoVnn3nGeF9M7pO6VWeoVLd/\nZq3dw+HDh9v8fn/JfrRr1w4AUFB6QbZN/pHqzxpf3PxurdFJVCUgJSXFxp1gmAaCu+eDbC3gmBCH\n+fLLL6lt61ayuepegd50cGhnWpnQjgI8BeoV6K0YM+KKuWNr/hVlZWU2fS1qU6G3tLSUBEFFzZr5\n0LwFI2lz4XM0b8FIatbMhwRBJcWnmMeS1MZzQgnTdGLRI+XNsnfo41urpOXNsncIcF1xLSWTq8Gp\naZQ6OE22rhV60WQctOoTonRcc5OzMAymMFUaCT7m8R2B1FmVaTUF2BpK8Rn2qr8S1e65WTMFUzqe\naHDnNfxZWWVdR+JXbKUR20sxjo2Lt4hJEXy01FE1WPrbCQkJpgAfT5nZWICvFwkquS9OTQrEMU2P\nxhAT4vYG2GwcixCn0el09Pbbb1OP7nfLfnDVADX3UNHKhHZ06p4IWpnQjlp4e1HmsHSXn9+aw6kt\nX4Ka+hYMGZJGHh5e8mv18KKg0FSr5l+1cd80x9RnJCnjJ7o77kMCQE8snywTIY8vM9rHK1UhFo/j\nzPWbmlxJQYy+AaQWNBZOp+HIdFgkKJmceUNLdyGNOqvknbggaGg4PrQZ+GoL02deWlpKc+fOpWee\necaq2V5tnpvS/RIt10VhoGQWJn4WrdqFDj1sGt/ZKthm7bt9+/ZRTk4OffHFFxZmY51VmTQcH0rP\nraysjEJCgmXbeHUdYCzq5uVDgMrt9uV1XVWbcRwWISxC3Ir4I//pp5/S9OnTqU98nOzHyxXZMe5G\naSSlWWC0zIG0LkdCxI6xZbC8mrCnh5rGv/8IvVn2Dj2+bBL5BviSSlBJ34sW6o46w5pir/2iy6mp\n8HgaOqOLqY3O2p7J2QTkUxreJI3gT/369nPIFt4e5eXlNGhIqvxNHqC01CEuGwmxt59kLS9mx8iy\nU7QWwsCRURtbotq0ArO5KGkZHEreqmaUiBdoAvKtirsvv/ySuoSHy/aNjYunoqIip+6/K1EabXK3\nILrTYRHCIqTB0VSdEsXrSkpKcai2Sk3dN4nkb3qlpaUkABQAgVbBaB2/CpbW8R5qL7oHiy0s1K1b\nxqdYfU5r1661OSKQhRy5fXr1OnsjITYt2K2Ijdr+PaVnZJLa05MC/DSytNNAXy+pnonp/a7Jc3Nk\nBOXjjz+2KVQ+/fRT2XXm5eXR7Nmza1QiQbye2Ph4Enz8yfuhubLRmZbBoVbvtzkN6f9n09Emvxn/\nIU3m0yT4+FFMXFyDaN+dCIsQFiFMPeNojImtYXNrKMVLJA8wjoBYK6I3d+5cm6ML4lSOLct4805I\nPKczIyFpeNPudIm9kRBrU0k1RTwfYL2y6wCzAniDU9NocGqaU8/NkREUR6d6avO2r7SvOMriET2U\nmn9wwmZ9GmvXZm+7+pgeEe+xz4RF5BE9VPEaeVSk/mkMIoRTdJkmhTWHV6XtlHwjdDodvvjiC6hU\nKgs/kHFjx6Ngyx6Mwip0RDJOogAbdk0CADODdFTniQAGg9EHw5qFOmDdJ+V+LIcANfK2PIOsMeOQ\nk7sBOp0OBTsLEKrqhQvL5am0FStehCBo8IthLzzgjRPIRw7+AhUEbMaLij4hpkRERChasG9ST0VG\naiYef/xxm/feWcTsFMB62umuomKZJ0dB9ksYMiAROp3OYddURxxTyfjSYzdV2DQ7x5a7qxJK+15b\n+RJUQe1QdawIFR8+Cd8JC43nraxERkaG1WM5kqpc23RmZxCf5a29X6GqOo3Z/Bq3Fu526D4xdxju\nVkG2FlSPhCQnJ9O9994rW1avXl0TYcjUA2KQ4fjx4+m1116Tpi0ayrCxEuXl5TR4cCqZO6MmJvan\ntWvXUl5enuIogViozdpIiLX9HBkJMa9ga/rGPhkHLQJFoRKoV0954T5zF1N76PX6Gsd6OPuMHRkJ\n8X5orksymBwZ+bI31VOXlXy9H5oj+6+967MWaGs6LeXINq7C9FmaXqNpYT3x2twZt9KUWb16tUU/\nmZwsxao12JEQtzfAZuN4OqZRUV5eTslJA0ll1pEbP1sGZTYkhg3LJEGtsaj46+HZ3ESYCDQZBy3i\nJQSAtIJAK2GMCVkJUAu1mjLTjZlHtizUFasUe7agLsi0iMnIycmxmDJ5GjrKQg6l4U2p83JFnIAz\nx6jNFIVpTIg87dSTBJVrMpgcvS57QqU22Tn29hUt8wUff5vVhEtLS6XSALbEkKvT0B0hJi5Ousbm\n//qZNNGpsnvp2cGYsRcTF+fyczPKNIbpGLc3wGbjWIQ0KjLSM0kNDWkQYJHiqYaGwjDYYb+KusT8\njd30Lc5WHRsPz+YUil6KIxopSUmyH9zM9NuZR7ZGF5RiWELRi16C3qr/hqN1YeqL2rxx6/V6Gpya\nZpEd079fYr13oiLWhEp9jIQEtmipKIKUhJ4tMeSoYHLlCOW+ffuka9REp1KAn7e8xo2Jn0lDHRFt\narAIYRFyx2DakduaengaOodNrVyNtXRYMdsEAKXcUyYTIaLHSGzSt5IgScObip1/bbxQxO+SB6TY\nFRi1mTJxNa5649bpdLR06VJZ8GttMphcjdhZJyWn1LhNStcjFrtT+weStkVLq2LOPPOktiMhSunB\nrggcDQoJJWj8bE6x1WYki3EOFiEsQu4YxDcvwHaKZxZy7PpV1BXW0mGjorrZHQlJyjgkCZK67Pyd\nERjmosYdcTf23rjXrl1b42PXJIPJ1SiNQDjiE0Jk+TyUrkfMHLHm3iqKBvPvjNV6A22KIYfcW10Y\nLyIKH1VwJwJApxbdIxMhpxbdI10Lj4TUD41BhHB2DOMSxCJ2AHASBeiJLOnzCeRL/26BLtJn8+Jk\ndYlOp0Nubg56JixHm+piecaieYSDeydAgBcgqHCo5FkAphVupyK4dQb8moXjzMlsAMCmTZtQWVnp\ncAVXZxALz9nL7gFuF2PT6/XIHDYcG/NuZ0FkpBszYVydBQHIK9iKz91aVsl77/8Lo0ePVjyOPaxl\nMNUnShktV7JfwoDkFLw8/SXFNun1eowYORI7CwqkdWJWiun1eHh4SH9Hx44dQ2ZBgdW6NIC8Zo3v\npCWoePfPsiKA4jlElKoYJyWnYO7sWejTp49iIb68xROxadMmVFVVOX2/xQwZ38f/iavz7kFB6QWp\n2i9wu8ZNbHw8V/dlJFiEMC5BTO/clLcVGzAFhNspnhvxDNTQoAP64xfslVI+Ha1KK1ZfPX78ONRq\nda1+IK2lw8ZhIi4YjuBnw1ZZhVv/gO4I7/GGrLCeUjVaV+NI1V4RpdRh07ReV6HX65GVNR65ubfF\nzrBhmejbrz/2LH8BIJPKu6umQ+jQAzsL8mtdpdmZe+FKlKrsip31zsUT0eWjpYoCJKJrN5Sf/01a\nJ3TogS0F30npqWQc5UWnTp2k/cV11sSc+XeCXyC8krNQebgAS5cutUgnF4958+ZNACoYX4aBwoJ8\nPPGkUZRYEzzp6enSOmdSekVBSpfOQBOdiikrd4LImG6df+QC/rLqBwgq4K8vvGD3WMwdhLuHYmwt\n4OmYRoVer6eUJPvZMXGxfWj69OlW3SYtTcEEs//ejucQ6244YuoEG9MtE5AvZZvci6WUgpmyNovn\na2hZPfYMxlxhdiUiTmdF9vw7de/zCUVG/5003i0oOrqXhbW5R/RQavb6d416/r8m2TBJySmK1u9i\nDZq+/fpbncqpafE7pWdYWlpKd3fvQVCpLdoDb3+7Uz/S/7ueGqeK4aVnZJKHv5Z8Jiwkz86xsmOJ\n2TE8FVN/NIbpGLc3wGbjWIQ0Kvbu3UtxMfL6NOFdwqmoqIh0Oh19/PHHpA0Mkn0fHBRKZWVlsuOY\nFlHrhMHk6amlZoHR5Oklj+fw9AqQCRNzkWD+42wtHRYQrHbijjpXugt7VuvmHaV4T/bt2+dUcKso\ndvy10bJ9TD97PzSX/Kato2b/KK63TBbTa3L1eZwNurW3PaAilW9z8n5oLvk8+QF5PzS3VsXvBg1J\ntXCPVVondOhBzT84IW+PSiAPf7mogU9zgofGTEAFElSCUz4zg4akWohSVWhnUvsHuCWo+E6GRQiL\nkDsCa+Xf78FiWWZHcFCoRfquBgEUHBQqHcv0zf4vMP47suffbY5ixA/cKgWZDhuWqdiejPRMKisr\ns8iO6YJMugtp5IOGk/Iq3gdHgk4dHQkxvycqCOStCpQ9C1vXnJOTQ1AJpPI1r9xrLPAWGxfvskwW\nR0VFfRRMcyZDx97IiSgIzAWCuaBxJItKVkfH9Hl4akjlG2AxEuMRPdSiPbFx8fKRD5VAPo8sUhRQ\nS5cudeqeqf3l7YJPcwoKCW1wI4lNHRYhLELuCDLSM8nXrPy7WEJe7AzFAmG2RhyI5G/2WTD+u3uf\nTwiwnT5rKkrENFdrHaxOp6O4mDjyEQJoJFbSZBykUPRyeFSgLqlJNoYjviGmo0sTsMPpKZzc3Fyb\nb/lffvllrQVBeXk5ZWRkyJ9DRobVYyhmePhrKTYunvLy8lwyOuJMho7dkRCVQCo/rYVAgEpwespK\n6VzN/r7f5vmVRqh0Oh0tWbKExo8fb1NAOSpC3GGSxlinMYgQDkxlaoVOp8PGvByMwiopI6YnskAg\nfIXx6I+XAADbt28HYL2Gyu7du5GWliYFt51EAdogHgBw67oxqv7ShcLqjBZUfzZmH/j6G/cRg0wL\nduZbtqeK8FXeeClIctOWTcgaMw5f5Y2Xjpc8IAVTnn4KvXv3dlv0vnk2xh+LxqD8wkmbtUqy16yy\nuBbTOjHmz+goNgKw/iyOHTtmcf1iDRxrwYw+Pj61zmQZN24cdu8uwPyFoxAb3xHFRScxf04esrKy\nkJOTI9vWWtAoXfsD3694XhZcmZkxDKuyV9coU8iZDB2xRs2WVZY1feAbCFRchs/4v1sEuVYsnggP\nD+d+isVAazJUoerccahbdYbh/M8ArD+jymP7UHV8PypWvIi4Pn0QFBSEocMyUFy0T9r2j0Vj4D/j\nWwh+gcZ9qgNjU1JSnGqXR2Q/VJ09BsP5nyGE3iW1Qelvi7mzYRHC1ArxR8dah1aKbwEAgwYNwqpV\nq6ym7yYmJgKQF1EbWvUOOmEwyg7PQ7PAaBz+/jlAlj77HABBOpYoSmy1R/wRdCYVtr4w71irzh6D\noboYmFIqpSio7F2L+TPS4rbQU3oWSqnT9lJxxX1qmsmi0+mwceNGzF84CveM6AkAuGdETxARZkz7\nyiLDxrSzM6Vq37/RzNsT7z8cg+TIYBSUXsAz2TswLmssNuRsdLpdIo5el1JarLpzPNRhvXBzy1Lr\nAqGyUpYNZutcer0e8+b/HQBwbclk43Gih0IzcrrxWFaekbStxhuLP/gArdq0wy3BUyZwK5a/gD/m\n3Qv/59YYs5xW/hWDU9McunadTodffvkFgFHMGE79V/pO6NADQP2m5TONAxYhTK0wHblQ6tB+EJYi\nIy0Tjz76KKb/9WVsKJen7+bgLwgOCpWlvVq82d8ScOvyAQCCLH22Rcgg6M9vx6XyPbii34fSA88j\nKSkFhYX5DnewdZn+6WinImLesdp7szV/q7R2LebPqCUiEI5M5OBp2bMQU6eJCBs3bpS125FKtLVB\nvPbY+I6y9XF9Oileq5Ioqjp7DDcP78QnkxIkf4qsfh1BBIxfnGs1VdjZ52RrH3HkZP/+/Zg4eTJK\n9u9H1fEiVB0vsmgvcFsgvDH/79hZcNtPx1Zq7Nhx47H7+4MWlWqvfTIVKk8Nrq140XIkRiUAZEBg\niyCU7C9CTFw8bt24Bt9J7yiOzPz+XHdZO2xhUa1XUMNgNnpXsfwFBIWEul3oMw0Qd88H2VrAMSGN\nAqWYBA0CSAVBFltRVlZGwUHy+Aal7BgRMQhPrETbKfJ5ikvOodikbykp45AUAyIuYnZMfddWMQ+k\nrGnApPl8ur05fmfiHszvyXB8SB7QyNo4ZHAapQ6WZ1aYPr+6dDAVr33+wlH04/FZ0jJvwUirsQTm\nQaNitVZrTp3mcRc1eU7l5eWUmTFMtk9mxjCr+ygGkpoFuQaFhDrsXmov5qJvYj+L7Ji4PvKUeDG+\nBzZiQGbPnu1w/IazlvJM/dEYYkLc3gCbjWMR0ihQshqPi4m3WrJ706ZNNHv2bKs+IUoopddqvFtQ\nUlKKRUdcX7VVrHVig1PTamyJbd5RCR16GH0ezDqulsGhVq9PKbvE2j0pKiqStjUNXrWVMeOKKr1K\nZGRkUGCgH81bMJI2Fz5H8xaMpMBAP8rIyFDcXtEGHdZrlpi3tybW5ZkZw6hFM3lhthbNvCkzY5jd\n61Nqb1JyilOdtqPeJbae0ezZs6Xz11YsmIsiv2nrHGofUz+wCGERckdRV50TESlWm7VnHlaX7SEi\nGpyaZpEOac8Iylp5drGdSh1V80Ct7HPL4FDyFSzTa4cMSrV7j+xViK2p6Zkr0Ov1lFxdQ0USSjay\nY0R0Oh3FxMWR2q85qX2bUYCPJ62caBQJKycmUICPJ4WGBMv2qUkWh7iPoyLHVnvFZ+CsIZorsk/E\nkRChQw9jto6ZV0hcnz4OXQeRpSiyN3rHIyH1S2MQIRwTwriMuoyv0Gq12LjRuUDSuo732LZls0XQ\naNXJ/+LGxvcciuOwmEvH7Tn4Y8eO4alJT2F/yX78fvkSACAuJg7TX56OBx54QDn7Z/vD0HgHomfC\ncmiDk3DpQiG273gOI0aMxPjxWfjtt9+gUqkQGhpqERtjL8C4rrMa9Ho9Ro8ejZ07C2Xrf//9it19\nw8PDsWXTJtx3//3YWVCA1l1DMX7xXun7qK6hOHL4N1lMSH6+Mf5C1aKt7Fi2sjjEe5QcGSxbnxIV\nbHUfa+111K7d/DlFRERgcGoatpvFfTgTQJqeno6gkFCUnz8BIaSTrP6Mp8YHm3Jz7R5DxDw2R926\nCzyih+Layr/WSewQ0/RgEcI0KtxZR8Q0EFHsxCzERq903Nj4nkOdilJxNDH9VjAAhw+UyevBHHgG\ns16dDQBojvay8wagPQADIqMXWRToKyycgMKdhQAZbu+gEjBk8BCsW7cWWq3WboBxXWc1jBs3DoU7\n8+HfTIOXZ2ZKKbqvzdyAqKgoHDlyxGaKrVarxcvTpyOzoADvLRmLmzcqceqkHh06toCXxgNpSYtw\n7NgxBAUFYfy4LORsNHa0V98YDk10KjSTPoLgF2i18wdud7jWCrPV5B7VOOD31nWZeFB5apw6b9Ge\n3Yjvm4hykwwWMWjVmVRmpfZ79h6GqkP5NovrMYyEu4dibC3g6RjGzViL+1i0aJH1YWeVQGo7Zdbt\nW3zLp0b+inIKUckN1TqrMukl6GkWiBLxAgG3Dd2SMn6i2KRvKX7gVqNJlnk9E99AEjw0NGyYpaFZ\nfTvHmgZKWgtMHZCcYvc4jgS3KsV0BPhpyKvrAIdcXsX9Tad7HI0JsUZNDdGa/aNYssmv6XRHTeKz\nHG2/abwR4x54OoZhGjnWRiuuXv0DUAkWw87Xqo2q+sVGo9DGm6A1nwvT0u2mUyNfqsbjovdJ+D58\nux0/L/8rPrv+AGLoEfwgLAUMwIWzG3H+zH9w8azcE8Pn4bcUUzFzc3OkaQp7pmeuRq/XY+TIkSgw\nKXlvLUXXkWq8ERERyMjIwPw5eSAixPXphP37TuDvczchIyMDRIScjblYpZjCuxM3D++0+8a+Kns1\nxmWNxfjFt6csRDO0muKMIZrp340Q1A4gguG3MqiC2gFwftosLS2t1lWhzdsvVroOCAhAXFxcrY7N\nNH1YhDCMFeyVcu+b2A979xdbDIsPTk3F1s2bbHYq9sy/gNtTIxehw3HKge/Dlu04uXgiTmIbMtIy\ncbPyFrbnPw/y8pFE043Ni3Ejx3qMCuAeAze9Xo+uXaNw/vwFaZ0gqFCYfxSjx9zuuPbvO2HRTltk\nZ2cjKysLM6Z9Ja3LyMhAdnY29uzZA8B6TMfSpUvx+OOP2zy+VqvFhpyNdXKP7E016vV6vDF/PgDg\n1oHNuPX9RlQe2HR7A5WA4OBgK3vXPZcuXcIrr85E8f4iaZ0tvxOGAViEMIxV7I1WPPfsVHyybLks\nsHRo6hDpTdpWp2IvFkAwAHlbngFVEQi2LdPFzrOoqAh9+vSB7+Pv3RYrKQ/jRo71GBWgfg3cREaN\nGomKa7/LLNpfn5mD+XM2wsfHUxrFeH3WRnh07I7Kkz86FHOh1WqRk5OjKBLsxXQ4ak0OGKexf/nl\nF/z6668AUC9xSmPHjcee7/8LoUMPXFs1HSqNj9ywbMWL+H+vzpTs/OsLvV6PcWPHY+OmXMDb32aJ\nAYYxh0UIw1jB3mhF7969kTt6dI3fipUsvk2nA8ynRqy1Q+w8L168CEAuVtStu8CjazIqlr9glk3x\nEgQPDYamDqn3QF+dTof8/AI88kQ/9OjZFq3bBMgs2k1HMTzadwWVn3Y6s0JJSEVERCAzYxieyd4B\nIuMISP6RC5i6+gAyM4Y5dHy9Xo8HRj+I/G1bYKDb69NSh2Dt5+vq7I3fdFRO1aoLrs4aDB9TsWli\n57958+ZaT7E4w7ix47Fj83cAGeCrMO1nWmKAYSxwd1CKrQUcmMrUAkdLwtvCmVLuNW2bLe+OpUuX\n0tKlSykpOcVuO6wFu/pMWEhQCXJTL5VA0dG9ZIZyrrhf9igvL6f4mBhZW5KTOtN3JS/R5sLnLIzH\nYCNIsybo9XqnHE/NSc/IJLWnJwX4aWTBrYG+XrUKTrWHqR+HPUMwV98zW4h/c/3wIpuUNUAaQ2Cq\n2xtgs3EsQhgnKS0tpbVr10pOlLX9UXa1VXl5eTllDkuXd4LD0mXHU8rIGZyaZmHHrdQORdHkr6W7\ne/Sk1157jV5++WW6u3sP2XEGDUmlIWnmx7ZvElYTMtPTqYVaTasAOgXQKoBaqFWUnNRZymJJTk6u\nc6O5mhxf7HDhAsMyZ5FlxdgxBPN+aK5LhLIjbRLdVydgB5uUNUBYhLAIYeqJvXv3UkxcnPSWD7OU\n1Nr+KLuqU8wclk4tvL1oVUI7OnVPBK1KaEctvL0oc1i6tI0tO3F77bAnYNMAdQAAIABJREFUmpSO\nrfLSkLp5c2q7cCGFFxZS24ULySswkNKt2KXXFMlxFDD+9FQvK6vb6evnSSEhwfXyBl8TxNEIwPH6\nNK5kQHIyCT7+5P3QHPLomkwqszRwlZ+WPKKH1nnnrySSQ1W9KEyVRoKPpQCuazHEWKcxiBCOCWEa\nHabGYUFBQRauo3UxN+2KYE2dToec3DysSmiHrI6BAICsjl4gAONz83D06FEQkdWMnLzFE4F33kZG\nRobVc9hK9xTjCjSZT0N9VyyEoHZQ+QeBbt5Ay2nPI2DECABAwIgRxnZMm+bSuXzJcdRsvRgOeldY\nJAoKCtyeSWGtQq4YIwS41rDMHqKz7s7qVObrn70KQAWoVLLMLI/oofCdtMT4bxvOr7VFKW39/PIX\nEHStEu2v98ZJNiljnIBFCNNgMe0MiAg//PAD/vn+v1BoUvI8KCQUlytuOpySWtf247aQOuFgX9n6\nlOrPx44dk9bVpv1Knaher8e4cUYn1Bs57xnvUUBLVF4xBrP+Nm8eru3ehdYLF0EdEAC/Pn0cPp+j\nSNkpgIknKyA+zX//+9+1EiB5eXnYu3cvEhMTaxSYqdfrZW6qwG0PEK1WK2U0bdmyGVNW/gAyCW59\neuX3Dge3OotSp39j1V/RLyYaEx7+Mx577DF4DX8Wvg/Okvax5fxaG2ylrV9cPBEXq4N142Li8MHi\nD9gnhLGPu4dibC3g6Zg7EqXhXmNgpUo2zeL90FyLeeiGWkCrvLyckgcYi7OtSmhHNLq7tKxMaEcA\nqKioqFYFysrLyykjI0N238QCcBkZGRQQ6EvzF46izYXP0fyFo8i/mYaiurWSPjcL9KXmA1Oo2/Hj\n1GbBAovzuSJwVYwJWVkdE7ISoBZqNWWmp9vf2QrHjh2joBB5VeHAFkH08ccfO9VWRyrk6vV6Gpya\nRoJKHjybljqkTqaRrAYbP2wZbCx06EHNXv/OZcHT5u3IycmhpUuX2gxAnT17Nsd/NCAaw3SM2xtg\ns3EsQpoc5h2ZUsemGLfgG0hQCbIfY2tZAh5dk41ixcUZLY5cjzVES/TW6EkBnmpaWR0TsjKhHWk9\nBdKoBSkupKYZORkZGRQY6CcTGoGBflJlWmt25hu2PC37HDJ9uiwmpLy83OkKxtbQ6/WUmW4WmJue\nXqsOPCgk1CIGCD7NpU7akUBiZyrkillLr732Gi1durROO11rVXalv3EHr7mmAlLxhaABinxGGRYh\nLEKYapR+zMzfXmPi4uiLL76w+SPn9/IGu6MeSimprk5ZVMpySUlKslnvYxRW0ePYR4LZD3pma3/6\nMLaN9CNek4wce3VTANDmwudk34kpsR98nCX7DLPsmGHDMknj3YJ6JiynlHvKqGfCctJ4t5DVnXEW\nVwX6inVnapspInb2tgJOy8vLLdJ7U5KVn7mrUBoJsTfaZ1oHxlrtI2dSki1eCDw1pPINqBeRz9SO\nxiBCOCaEqRfM57X/WDQG5RdOwmfCItza929UHipEyf79eOCBBwBBgLr93bL9xZiIyh9y4Vn9b3Xr\nLhA69LA04lo7CwAwIDkFT095Cr1793b5XP34rLHYs2M7ViW0Q3KwLwouVGDKnu8Q0aUz3v/gQ9k5\nxViQUPTEetWjMBCQP7ATrlYRuvh7IbyZBqcrbhrv00NjsWnLJodriYiI57BWewUAiotO4p4RPaXP\noiV6h44tZJ83bdokxVTodDrk5uagZ8Jyi+q8ubkTXBroay0g1BZ79+4FYD2GRt02CsLY+XaDku25\nqf59/jx4eXmhaM9O9OoQiB9OXTZ+X1CIrlGROHyktE4CapWcda9veBsAoGrRVrat9P9IZaW0zlal\nZnsupnl5eYrxH3T9D1xb/gJXyWVcAosQps4xD2arOnsMhlP/he+kJbi5ex0Mv/4XbRcuhG98PCqK\ninB25kxULJmEZnMLpWOIgXY3ti+Hun13SXCo9KcQ1MwH5SY/iLFx8fjwg3/VKijOVodoM8tl7y94\n6KEHQXS7ZonYwX2hGo3fvcqAG8Dpa5XSvgCQf6ECAPDTD0eRNWYccnI3OJWRI57DmtDw9PLHazM3\ngIhMLNFzENUtFF4aD3z79QGp0JtpUKcobrTBSbLzaYONOS6uCFwVsz9MM5wcrTmSkJAAwLqbrBB6\nFwxePgCA/Px8mzb6im6qq75Hrw6B+G9JES5fvY6e7QNxqrwCqyYlIDkyGAWlFzBleQlG3j8CO/IL\nFI9dW0yddQWg2sQfuPrGcGh6DIHmqY8h+AVaBKPaCiK1JcrMn4e5wPOMHoprZMDSpUvRtm3bOq0x\nxNwBuHsoxtYCno5pEpjPa4uxHH4z/kMAqO3ChdTt+HFpEYMivR+aY+KBEEjqyEQSOvRQHFp21fC+\nreBO8+s5dU+ELMD01D0R0j4vzBhKgYF+lJGRQeXl5dRSq5UdU6NW0YexbaS4kEBPDxIASsObNZ5b\nF2NC5i0YSZsLn6N5C0ZSs+a+FKDtSR6egeSpaS5rQ0hIsM3rJLo9HdAzYTkNG31LWnomLHNZDIAt\nXxRHkGJCzDwz1HcPIo9ox03Y9Ho9pSQnyafKoluT/oP7pdgQOBg3UhekJCWRVhBkRm8BKoE8w2JI\nk/E0qf2ay+6ZtXgSey6m4vNQCvzm+I/GRWOYjnF7A2w2jkVIk8B8Xluc09ZkPk0AKLywUCZCwgsL\nLQLhTGM8BiSn0Nq1a+vkR9BacGeGiXGXFMRoJcsF1XEWYjxGSlISaTWeMoOyAE+1LDakNYzi6n4s\nt9lB2EKv11NKSrJcaLRJpyH3n5dEAyDPYHBEvN2OCVlWHROyrNYxISK1yQYSKSsrs4gvEjr0II/I\nRBKaBzhlwiZ23Muf7EO6f2RYxIbATtxIXWHP6E1c0gYPJr1eT+Xl5TSgOiDZmXtr/jw8ooeSys/1\nZQuY+qExiBCejmHqHKV5baFDD9zY+gkAoKKoSDLKAoCr+/YBMMYmVFZWSsPLdV1eXqfTYePGjZi/\ncJQ0pWFaWE0cvo6IiEBKUhKm7PkOBKPPR/6FCkw9cA5RUaE4cuQ3dOjYAl0iQgAA+YWFVqduRv2p\nF/697gd0x59xFi/iKs4DqJm/g1arxUsvTUd+fgG69/kE2qC+8GtmvFfi9AkAjBkzRrqHjkz5rF69\nCmPHjkNu7gRp3bBhmVi92vkYAPNpLnuVih2Z7gkLC8PF385h8+bN2LZtG7Zs3Yr9RUUwAGi7cKFT\nJmzitJZaUCG8VTNpvRgbAtSvUZmIPaO35QDUAJ7Jz8e4MWNQJahvV9xdKa/UXLHiRUAl4Ompz1pM\neZk/D99JS1Dx4ZMc/8HUGSxCmHpBqWJsUEgoyi9ew9mZM0FE8OvTB1f37cPFuXORbhabANR9uXR7\nwZ2mHeJX33yDrpERGL/3F2m7qKhQnDlzBUkDw9ExLAjffn1A+s6aQdnG/xxGayEaOw3zEIpe2KWe\nh4xU5yrGiuh0Ovzyi7E9gkotCRAAuHTBGK+QlJTi9LG1Wi02bnQuUNYcY5zBOORt3CitS8/IwNzZ\nswFYj+lwpmNPS0tDWloa5gH46KOP8MQTT8A3Pl62jT0TNluVdtNSh+DgwYOYsrzE4ru6MioTsWf0\nlgggHABVVWF8Xh4Ao4DwiB5qISKEDj2gGfIotn4xxyJA1bxytOAXCP9pn+N6zru4/tmrsqBlhnEJ\n7h6KsbWAp2OaHObD/0VFRRQbH+/wvH1dYi/N1Xz4Wq/XSx4c4hLVLZT+nTOZ5i0YKfPosDZ1o6o2\nvVLBON2Uke58KrFSGqZKraFuse9L0ycenloCBFnV3PokPSODvAIDZVMjns2aUUxcHA1woEKws4jP\n0lq8ka1pHluVdhXjRpyowlsbFI3eAMo0mZ45ZdIuMRak+b9+JnVUf1mbPaKHGlPZFe5FXVWOZuqf\nxjAd4/YG2GxctQhJTk6me++9V7asXr26Rg+FaZjUddVUR1EK7jSPCTFHp9PR2rVrLQSJGOgpFq2T\nGZRpPKlPfBzl5OTQpk2banXtil4OPgGyOJoWIYPqPG7BGuaCILK4mPwHDpTdq5ZmMR2u8HURhU+b\nBQsovLCQ2ixY4FRhPlt/k0rfucJR1hZKRm+9ANJbiRGxiOsw/fvw0xoNzxT+JlxdOZqpH1avXm3R\nT5r8JrEIqVHjeCSEqWeUgjuVskasodQ5iUJE9vY8rHYuoSL2AjvvjltMSRmHXJrN4ixisKcYgOw/\ncCCpzUZFvAIDKSklxaWduLEzzTDrTF0/ylZbQzBnycvLM45mhHamAEE+MhIAYyC0lOHy4Bybfx+2\n/iYayosBU3N4JIRFCNOIMHYm8k5rQHKyyzqTuvhRt5eG2b3PJ9XTMc2pS3iEy87rDKYjIZ23bKnx\nNElNqevOdNCQVFJ5auTTYZ4aGpyaVifnIzKOfqn9A8izXTfZeUNatpSmjUyFkbW/j5i4uDprI+N+\nGoMIEZQjRRjmzmPsuHHYvns32i5ciPDCQrRduBD7Dh7EmKws+ztXI2bYHD161OK78PBwZGRkuDSA\n0TSQ0BQxsPPHfY/i4N4JqLz1B/4+f57LzusMxuyoDFyYMweXPvsMAGwGjLqaurjvIjqdDtu3bQO8\nfOA7aQmaL/oRvpOWAF4+2LZ1q+LfgStYk70KqUn9ceuXQ9K6AckpOKLTQavVQqvVIjdnA/Kqg1St\n/X0s/uCDOmkfwzgKZ8cwDER3yY1Op3SK6PV6jM8ai5zcPGld5rB0rFq9pk7svEWU0p8rj3yHaytf\nQvOgBLRo2R+njy9BC60fRo0aVWftsMea7GyMycpC3kcfAbCelu1INkxN7N3rivz8fIAM8Bn/DwtX\n0orFE226tNYGUWTYy1gaOnSo4t/HzezpSM/IrJWrMMO4AhYhDIPb6bnOpnSKKNWSeWbHdowbOwYb\nNubWXcOhnP4MlYDfK/bi9/K9CA4Oxd69u+u0DfYwdpo5OHr0KMZkZeHgnDkgskzLtif0lNJ812Rn\n16nQcwRrPid1jSM+L0p/H+z1wTQUWIQwDG5Pa9TkDd1mLZncvBoXeXMUpbfiEydOYPfu3UhMTGxQ\nvg7h4eHYnJdnHBWZNk1aPyA5GY9OsF0Qz3S6TKwztH3OHIzJykJuTo7iPnVNhw4dAFj3OenYsaPi\nfvWJo6MmDOMOWIQwDG7HLWyvwRu65GZpxZDMFUXeHMH0rTg8PLxBiQ9TTEdFvv/+e/zzX/9CYX4+\ndhYYDdVi4uKw+IMPZFMFtZ0uqysMBgOgEixcSa+tmg6oBFlFW3fjTEFEhqkvODCVYapZk52NQYmJ\nODNtGo4mJeHMtGkYlJiINdnZNveT3CyrK+GKiJVxzUdRbAWv3kmEh4fjk2XLsPfAAbSeOxe+iYkA\ngJL9+xEfH49hmZm4dOkSAMemy9xB586dATJAFdQOFYsn4vfnuqNi8USogtoBZKhTK3eGaQrwSAjD\nVGP6hm4+bG0rGDIiIgKZw9LxzI7tZrVkziNzWLq0fW1K1jdFTEc3rqxfjxuHD8umWraZTLXUZrqs\nLpECg3fuhtdDc6BqHgz6/QIqNyxCWkbN7PcZ5o7C3TnCthawTwjjZhw1onLEkKy2JeubGqLHScfs\nbJveIXl5eZSTk0NJKSm1ckCtK9hhlGmoNAafEB4JYRgbjB03Hlt37jYWA4vsh8rSXdia/ZJF4S+t\nVosNG3OtBv8Z3/pz4DtpiUUqZ97iiW6LaXAn4ujGHzt2ALA+1ZKeni6taxkSgjMmAa1idow74cBP\nhqk5LEIYxgo1EQ7Wgv9cUbK+seCoj4cYDLyt2sDM2lRL6IwZaJ6ZiYqiIlyYMwcDkpPx8vTpDa6z\n58BPhnEeDkxlGCs4IhwcxZ6zaVMIYNTr9RiWmYnIyEhkZmYiIiJCFlyqxJrsbAweMAAQBJydOROX\nv/4at86cweWvv8a5WbOg6dYN/kOG4LpOB++ePdHylVews6CgwQkQhmFqBo+EMIwVTIWDkgdEcHAw\nAMfe/K05m95cbXSubAodqi0fj3ffftviHon37b133sGVK1cw6amnUGwy1QJBgODnh+OpqdIqMYOm\nKY0cMcydDIsQhrGCKBw2rXjRzAPiJag8NHhpxsvw9PR0ONulKTtX2vPxiDRxOe3bvz98fXywbcsW\naV16RgY25+Xh4sWLOHbsGNRqNdIzMnBDp0PbhQvhERqKP3bsgH7NGkAQmsTIEcMwLEIYxiZzZ89C\nXkJfVJgIB4/oofDsPQzblj0PtV9zu0GrIk05gNGej0fQE0+gxZ//jIqiIuybOROGa9fQ+rXX4J+S\nYjFiImEwIPSFF3Bl/XopeBUAIAi4cuVKXV8SwzD1AIsQhrHBxYsXjQXKJi6B4cIJeHSOh2ePQbh1\neCcAQIgb4XS2S1MMYLTn4xH44IPwbNNGGh05M20arqxfD+2YMVZHTADgSk6OhX/I2ZkzMempp7C/\n+tgMwzReWIQwjA2CgoKMttyLnwQA3AAAb3/g+h8AgFv5K/HH5d/gO2kJBL/AJpnt4gjWbO/PzZoF\n38REaMLCpG3F0ZGKfftw4+efoQkLg2erVgCAwNGjEfz007iyYQPOz5+PiuoYE/MpnmI3WrUzDOM6\nODuGYWzw6qzZUPn4w3fSEjRf9CN8Jy0BVAKEDj2kz1XHilDxoVGkNKVsF2f51z//CT8PD5ntveHq\nVTQbPFi23VWTEYxrP/2EU489hpNZWQCAy59/jjMzZkA7ejQ82rcH0PCs2hmGcR08EsIwVrDlE1Kx\neCLo1g3Z5+s576Jyw6Imk+3iDHq9HnF9+uDKzZsInTED6hYtUKXX48I//4nf3nwT6hYtpNGR3+bO\nhWfnzrh1/DguvPUWqq5csZhu+fX559HqlVfwy5NPNjirdoZhXAeLEIaxgj2fEMNvZVC36ix9vv7Z\nq00m28UZ9Ho9uoSH45JeL5s6AQB1y5Y4M22azOVUaNYMt6rv7a3Tp41eIAMHQh0QIIsZ8e3bFxAE\nnJ892+nKxgzDNA5YhDCMFez5hAihd8k+b9q0CWlpafXcSvdz/6hRuHztGgDrUyfqVq1Qde4cIBhn\ngE1HPs7Nno1fn38eHT7+WLaP/r33MHjwYGMadAOzamcYxjWwCGEYK0RERCA2vg+Kl78g8wmpWPEi\nhA49oPLU4OZ3a3Ez22g4dqcIENFkTK1W4+TJkyjMz0fw1Km48M47uLJhAzTh4fDq2BGasDBp6qTq\n3DnjzgYDWs+ebRFoembaNClIVdynb2wsvvj8c2i12iaZ1swwDKAiY7XaBolKpYoBUFxcXIyYmBh3\nN4e5A7l06RJCW7fFrRvXbq/0DQAqbvtU2DIoa8yYO8Hq9XqMHTcOeaZptIIAGAwQAgNh+N//gKoq\n6StNt264cfIkVJWVoBs3pPXhhYXwbNNG+nzrzBkcTUpCmzffBAQBv82ahX4xMSgw9QZhGMZpSkpK\nEBsbCwCxRFTi7vYowSMhDGMDrVaL0sM/oXdsLK6INVAqrgAqAXfffTeWffoJ4uLi3NtIF6MkNmLj\n4+Hl5YX9Bw8i6Ikn4D9oECrPncO52bPh0bYtbp0+DajVaPvmm7IAU1y7BpW/P9rMmweP0FCczMqy\nGmh65sUXAQBD0tKwbu3a+r1ohmHcAosQhrFDWFgYLuv12Lx5M/7zn/8gJCQEo0ePblLTAqajHk9P\nnSrVgBHt0otXrgRu3gQMBpQvXYrypUvhP3Aggl94AedeeQUh06fj/Pz58O7ZUzIlu3X+PM7Pn4/W\ns2ZJosN/4ECcNQs0PTdnDlSenugWGYmv/v3vJnVfGYaxDYsQhnGQtLS0RhH3odPpkJ+fD5VKhZSU\nFJudul6vx/2jRqEwP1+2vvXcuXK79Opicq1nz74dUDpnDgzV0yweQUEAgJsnT0rGZOI602DVtgsX\n4vSUKbJsGQgCBg8eLMV/MAxz58AihGEaMaYjGEFBQXhg9Ghs374dMBikbcTpDfMOXq/XI7JrV+iv\nXZOyVfQrVqB86VKZXbo4jWItoBQAKsvLAQBeHTtKxxfXmU6/qAMCEPjAA6jYvRuvvfYaQkND7Qol\nhmGaLixCGKYRohS30TIkBOWXL0No1gytZ86URix2zJqFMVlZyM3JkR1jxMiRuHj+vMzbI/DBB1G+\ndKnMLv1/1aMh1tJvPdq3x8X33we8vHDtwAEIGo3Rz+P99yH4++PszJmKPh9/+9vf6vAOMQzTGGAR\nwjCNkLHjxklxG6aBoFRZiTYzZ1qMWOSZ1VrR6XTYWVAAQC4uNGFh0ERF4caRI9J6cXTDWkBp5enT\nGJKWhv3FxbJpFk23bqg6fRqBPj6y9ezzwTCMCNeOYZhGhtFOfiOCX30VASNGSIGgrWbNAgwGeFQX\ngxNRqrUiusECRnFhSrP0dNl6TViYFFB6+euvcevMGVz++mtcmDMHsfHx0Ol02LJpE34+dgwDkpOl\n49w4dAhDBgyA7sgR6HQ65OTkQKfTITcnh2M/GIYBwCMhDNPoEAWEtemRP7Zvh19CgrReqdaK6Aar\n6dYN58wq3+o/+QTali1x3mS9/5Ah+GPXLsURDVFQaLVaFObnKxqLabVajvtgGMYCFiEM08gQBYS1\n6RH9mjXQREXdLhg3e7ZFrZWIiAikZ2Rg23ffwaNdO5m4aBkSgn179mDylCkyu/SklBQsfOstXLhw\nwaZzaXh4OAsOhmEcgkUIwzQyRAGx3WwE48LcuWgRHAx9eblMVAxJS1OMwViTnY0xWVmy4NYByclY\n//XX0Gq1yM3JYbt0hmHqFBYhDNMIkQSEwvTIxYsXkV/t+2Er/dURocGjGgzD1CUsQhimEWJLQDgb\nf8FCg2EYd8EihGEaMSwgGIZpzHCKLsMwDMMwbqFRjIQ899xzCAgIkK0bM2YMxowZ46YWMQzDMEzD\nYc2aNVizZo1s3ZUrV9zUGsdREZG722AVlUoVA6C4uLgYMTEx7m4OwzAMwzQaSkpKEBsbCwCxRFTi\n7vYowdMxDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4\nBRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYh\nDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMw\nDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4\nBRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDMMwDMO4BRYhDYQ1a9a4uwn1\nxp1yrXydTQu+zqbFnXKdDR0WIQ2EO+l/iDvlWvk6mxZ8nU2LO+U6GzosQhiGYRiGcQssQhiGYRiG\ncQssQhiGYRiGcQse7m6AHbwB4PDhw+5uR51z5coVlJSUuLsZ9cKdcq18nU0Lvs6mxZ1wnSZ9p7c7\n22ELFRG5uw1WUalUYwFku7sdDMMwDNOIySKi1e5uhBINXYQEAUgHcALAdfe2hmEYhmEaFd4AOgHI\nI6JyN7dFkQYtQhiGYRiGabpwYCrDMAzDMG6BRQjDMAzDMG6hoWfHWEWlUnUA0NLd7WAYK1wkolPu\nbgTDMExDplGKEJVK1UEQhFKDwdBg046YOxtBEK6rVKpIFiIMwzDWaZQiBEBLg8HgvWrVKnTt2tXd\nbWEYGYcPH8a4ceO8YRypYxHCMAxjhcYqQgAAXbt2RUxMjLubwTAMwzBMDeDAVIZhGIZh3AKLEIZh\nGIZh3AKLkEZM69atsWTJkjo9x40bNyAIAjZt2lSn52EYhmHuPFiEuBhBEKBWqyEIgsWiVqsxZ84c\nl53rxx9/xMMPP+zUPnl5eQgPD5c+nz17Fk899RTCwsLg7e2NTp06YdSoUSgsLHRZO83PLwgCbt68\nWSfHZxiGYRoPjTowtSFy7tw56d+fffYZZs6cCZ1OB9Ee39/f32XnCgoKcnqf9evXY8SIEQCAY8eO\nISkpCaGhoXjnnXdw991348aNG8jJycHTTz+NH374wWVtFSEiqFQquKJcQFVVFdRqtQtaxTAMw7gD\nHglxMSEhIdISEBAAlUqF4OBgaZ2vry8AYMuWLYiLi4O3tzfatm2LV199VdYxJyYmYtq0aZg0aRIC\nAgIQEhKC1157TXYu8+kYvV6Pxx57DKGhofD19UWvXr2wefNm2T6mIuTJJ5+Er68vioqKcN9996Fz\n587o1q0bXnjhBRQUFCheX25ursVIxt69eyEIAs6fPw8AKCsrw/Dhw6HVauHv74/o6Ghs27YNpaWl\nyMzMBAD4+PhArVbjqaeeAgAYDAbMmTMHYWFh8PPzQ2xsLNavXy+dQxxB2bx5M3r37g2NRoPi4mLn\nHg7DMAzToOCREDdw8uRJ3HvvvZgyZQpWr16Nn376CY8//jj8/f3x17/+Vdruo48+wuTJk7F//37s\n2bMHEydORFhYGLKysiyOaTAYkJaWBgBYu3YtOnXqhEOHDkEQbuvM4uJiXL9+Hf3798e5c+ewY8cO\nvP322/D09LQ4XvPmzRXbrlKpoFKpFNeLiOJm165d8Pb2xk8//QQfHx9ERERg9erVyMrKwqlTp+Dl\n5SWJspkzZ2L9+vX45JNPEBYWhq1bt+LBBx9Efn4++vTpIx375Zdfxttvv4327dujZUs2zGUYhmnM\nsAhxA++99x6ioqLw1ltvAQAiIiJw4sQJzJs3TyZCunTpgvnz5wMAwsPDUVJSgkWLFimKkP/85z/4\n8ccfcfToUXTo0AEA0KlTJ9k269evR2ZmJgRBwNGjR6FSqRAZGeny6zt9+jQef/xxyUguLCxM+k6r\n1QIwjhh5eXkBAK5evYoFCxZg9+7diI6OBgA89thj2LFjB5YsWSKJEJVKhXnz5iElJcXlbWYYhmHq\nH56OcQOHDx9Gv379ZOv69++P8vJyXLx4UVqXmJgo2yYxMRFHjhw0sxkMAAAgAElEQVRRPOaBAwdw\n1113SQJEiW+++Qb33XcfAGNshiviMpR49tln8be//Q3JycmYM2cODh06ZHP70tJSXL9+HUlJSWjW\nrJm0rFu3DsePH5dtGxsbWydtZhiGYeofFiFNBB8fH5vfnzx5EqWlpRg2bBgA4+gLAKuixhri9I6p\ngLl165Zsm8mTJ+P48eMYO3YsSkpK0Lt3b3z00UdWj/nHH39ApVJh69atOHDggLQcOnQI2dnZsm39\n/Pycai/DMAzTcGER4ga6du2KXbt2ydbt3LkTQUFBsjiHPXv2yLbZvXs3oqKiFI/Zs2dPlJWV4dQp\n5VIl69evx6BBg6ROvFWrVhg4cCDeffddxXTZK1euKB4nODgYgDG1V+T777+32K59+/aYNGkSvv76\na0yZMkUSIeIUTFVVlbRtjx494OHhgVOnTuGuu+6SLW3atFFsB8MwDNP4YRHiBp5++mmUlpZi2rRp\n0Ol0+PLLL/H666/jxRdflG139OhRvPzyyzh69ChWrFiBJUuW4Nlnn1U85tChQxEXF4eRI0di+/bt\nOHHiBHJycrBt2zYARhEiTsWILF68GFevXkVCQgK+/vprHD9+HIcOHcKiRYswcOBAxfN069YNoaGh\nePXVV3Hs2DF88803ePfddy2ub8uWLThx4gT279+PgoICdOvWDcDtOJVvv/0WFy9eREVFBbRaLZ55\n5hn85S9/QXZ2NsrKylBSUoJ3330Xn332mbO3l2EYhmksiLEBjWkBEAOAiouLqSGzbNky0mq1it9t\n3bqV4uLiyNvbm9q2bUszZ84kg8Egfd+3b1+aNm0aPfHEE9SsWTMKDg6muXPnyo7RunVrWrx4sfT5\n4sWLNGHCBGrZsiX5+vpSr169aMuWLXT58mXy8vKiX3/91aIdv/76K02ePJk6depEGo2GOnToQPfd\ndx9t2bKFiIiuX79OgiBQXl6etE9+fj716NGDfH19afDgwbR27VoSBIF+++03IiKaOHEide7cmXx8\nfKhVq1b02GOP0ZUrV6T9X3nlFQoNDSW1Wk2TJ08mIiKDwUALFy6kqKgo0mg01KpVKxo+fDjt3r2b\niIhyc3NJEAS6ceOGU8/AHRQXFxMAAhBDDeD/F1544YWXhrqoiOomOLEuUalUMQCKi4uLm2wV3cTE\nRAwaNAhvvPFGrY+1Zs0aLFiwAPv373dByxh7lJSUiAG0sURU4u72MAzDNFR4OuYOQKvV4vXXX3d3\nMxiGYRhGBvuENFCUDMFqipgRwzAMwzANCRYhDRTz7BmGYRiGaWrwdAzDMAzDMG6BRQjDMAzDMG6B\nRQjDMAzDMG6BRQjDMAzDMG7hjhIhGzduxP0jRiI+JgETJ07E4cOH3d2kOuXs2bOYMWMG+iT2RerQ\nNCxbtkxml97UICJ88cUXGJ6Zgb594jF16lT8/PPP7m4WwzAMY4UmJUL279+PsWOz0L1bNO695z7k\n5uZK373xxhvIzMxE8YZfUPn93fj8kw2I6R2LwsJCaZsbN24gLy8PX331layaraMcO3YM/fv3R2Rk\nJBISEupc5Fy9ehX/+Mc/EN83ATHxcZg9ezYuXboEADhx4gRi4mKx6F//hD4sAEcMV/DII48ga1wW\nTA3qjhw5gi+++AJ79+5FTY3rPv30UwiCgPXr17vkumxRUFCAB/7v/xDdozse+L//Q0FBgfTdc88+\niz/96U/4vawEUZqLWL1sKWJ698KBAwekbSoqKrBhwwZ88803VuvjWEOv16N3796IiYlBTEwMIiMj\n4eXlhcuXL7vs+hiGYe4o3G3ZWpMFCrbt69evJw+1BwV7hFMcJlM7dTwBoAULFtDp06dJLahpAGbQ\nLBDNAtHfUEEdhH7U4+5oMhgMtHHjRmrZIkS02yZPDy8LK3V7DB48mFasWEFERF988QXFx8c7vK+z\nXL16leL6xJOHlyeFPTCIOo8ZSp6+PhTZNYrKy8tp3Pjx1KxtCD10+ht69NZOevTWTkpZOZMA0KZN\nm+j333+ne0fcJ10vAIru3Yt+/vlnp9px4sQJ6tevH/Xr14+++eaburnYapYtW0YAqHt7LU0e3Jm6\nt9eSSqWiZcuW0cGDBwkALRwbTbRiNNGK0XT5w/vp7naBlJY6hIiI1qxZQ4EBzaXr9fXxoXfeeafG\n7Xnrrbfovvvus1jPtu288MILL44tbm9AjRptJkIqKyupQ7tOFK7KoFdwi2aBaCYM1BfPkpenhhYs\nWEAqCDQdVyQRMgtEo/ElAaDCwkLSeHlTuCqDJuMgPY9fKAkvEwBavnw5OcL58+cpICCAqqqqpHWt\nWrWi48ePO7S/s7z33nskqNV0356PJJHxfz+tJi8/H5o5cyb5N29OvV55RPru0Vs76ZGbhRTYuR1N\nmTKFxo0fT5pmfpT86f+jsec2UPrGRRRwV1vq1qO77BpsYTAYKDU1lUpKSmjgwIF1KkKuXr1K2sAA\nGtevI1Ut+xPRitFUtexPNK5fR2qhDaSZM2dSgJ+Gbn7ygCRCaMVo+nBCLAGg7777jtRqNT2Y0J6O\n/H0Y/bxgOE1J7UIAKCcnp0Zt6tq1K61fv95iPYsQXnjhhRfHliYxHfPTTz/h1C8n0I9ehLraf00F\nFQZgOm7euoFDhw7Z3H/t2rVQV3njT7QOoeiB5miLIXgdkap78M6i9xxqw+nTp9G6dWsIwu1b2qFD\nB5w6darmF2aD9d+uR5vUeLSMjZLWBUR0QPv7k/HV+m9s7nvt2jWsWbMGvWY9hi7jhsE7KABtU+PR\n/6MZOPTfH5Gfn+9QGxYuXIikpCT07t27VtfiCLt378aly1fw0j1REASjm6wgqPDSPVHQX7ps9z4v\nXboUbVv4YtWkBES2bo5OwX54b3xvJHQJxnvvvuN0e3bt2oXLly9j+PDhNboehmEYponEhIgdvwGV\nsvXi55iYGAiCCjsxX/ruFq5hj7AAPe6ORnl5OUIpGl7wk+3flhJRVlZWx62vGYIggJSCTKsMEAQB\n948YgeOfbEDFuXLpq7K1W3D5+C+Ii4tDVWUlQvp2l+0akmj87Egw508//YQvv/wSf/vb32p3IQ4i\nPuPKKoNsvfi5f//+uHL1Bv655aj03ZWKm3h3y3GkpQ7BL6dPoU+nAHiob//Jq1QqJHYORNnxY063\n55NPPsGf//xnmehkGIZhnKNJ/IJ269YNncPCsVOYh1u4DgAwwIAdmA1vjQ/Gjh2LOXPnYCfmYak6\nDl/jUfzTowvOe36P9z94D1FRUTirKsY13A4wJBBOCFvRtWtXh9rQvn17nD17FgbD7U7y1KlT6NCh\ng2svtppRI0fhzLZinCv8QVpXfuAoTn1dgAdGjsLcOXPgR2p8dXcW8v88B3npzyJ//Gw8+NCDePDB\nB+Gl0eDM9mLZMc9sNVbZjYqKgj0KCwtx8uRJhIeHIywsDHv27MGTTz6JxYsXu/ZCq+nXrx+CWwbh\ntfWHJeFRWWXA3G8OI7hlEMaNG4epzzyD51cfwIDXd+DhxXvR5a95OPM/A95asBBRXbth57FLuH7z\ntnAzGAjbDpeja7fu1k6ryNWrV/H555/j0Ucfdek1MgzD3HG4ez6oJgsUAlM3b95MGi9vCvBoQz0w\nloI9IgkALV68WNomJyeHRtx3P8X17kNPPvkkHTp0iIiIfv31V2rm15w6CIn0Z2yhiSihGDxBAOjL\nL78kRxk0aBAtW7aMiIjWrVtXp4Gp169fp+SBKaQSBGo3NIHaD+9HgocH9YrpTb///jsREZ05c4am\nT59O8X0TaEhaKn366adUWVlJRESTJk0iD40X9fnHX2jUf7MpZfmr5NcqiPr0TXAqGFekrmNCiIz3\nVK1WU6eQ5pTVrwN1CmlOarWa1q1bR0TGGJV169bR8MxMSoiPo6lTp1JZWRkRER0+fJg0Gi9K696K\nCv42iPbNSqUHE9qTSqWiHTt2ONWOjz76iJKSkqx+zzEhvPDCCy+OLW5vQI0arSBCiIgOHTpEkyZN\nov6JSZQ1Not27txJjrJr1y4K7xwpZU4ENNfS+++/7/D+RESlpaWUmJhIERERFB8fTz/++KNT+zvL\n9evX6YMPPqDUtFQaOHgQLVq0iP73v/85vO/jjz9Oag8P6ZrT0ofSuXPnatSWQYMG1bkIITJ28I9M\nmEBJA/rTIxMmWPwN2CI3N5c6dmgnXW9oSDBlZ2c73Yb+/fvbDFhmEcILL7zw4tiiIqqZN4Q7UalU\nMQCKi4uLERMT47LjEhF++OEHXL16FTExMfD19XXZsRsqv/32G44cOYL27dvjrrvucndz6pyqqiqU\nlJSgsrISsbGx8PLycvk5SkpKEBsbCwCxRFTi8hMwDMM0ETzc3YCGhEqlqpdMj4ZEaGgoQkND3d2M\nekOtViM+Pt7dzWAYhmHQRAJTGYZhGIZpfLAIYRiGYRjGLbAIYRiGYRjGLbAIYRiGYRjGLdxxIqSs\nrAzfffcdysvL7W/cBKioqMDu3bvx448/ojFmQtWE0tJS7Nq1y+kquQzDMEz90uREyK+//opt27bh\n6NGjsvVnzpzBkCFp6Ny5MwYMGIA2bdpi6tSpqKystHIk55k6dSrCwsIgCAIOHjzosuPaorKyErt2\n7UJhYSFu3Lgh++6dd95Bm7Zt0K9fP/To0QPRvaLx/fffu/T8nTp1QteuXaUS9+vWrXPp8ZU4ceIE\ntm3bhhMnTsjWHz9+HP0SEhAVFYX+/fujTetWePXVV10qvnJychAbG4vevXujZ8+eWLFihcuOzTAM\nc8fhbqOSmixQMCurqKig8eP/TIIgSGZUQ4b8//buPKyqeusD+HefiRlkUlQQUAglVHBK0gRFIdES\nSrNU0vL1ltk1ekwbbLA0uxTetEQrNV+HFKcbmlM5h5HmiKIig2NiiuhBmeGc7/sHeq4nrfeAFIrr\n8zzrwT389lnLow8Lzm//dh/+9ttvNBgMbNs2mLb2zdnuoYXsFnWA/kEfUKXScMKECSTJyspKfv31\n1+zdO5KhD3fnpEmTmJ+fz5pITU3luXPn6Ovry/T09BqNrY3vvvuOzZo3M9Xr5u7GxYsXkyQXL15M\nAAz/RwTf3T2Z8d+Np3ewL13dXHnp0iWSZEZGBkeNGsWHQrty0FODuHXr1hrn4Ovry0OHDtVpXX9E\nr9czdsAAU70AGDtgAPV6PUtLS+nTwov+TjZc9bAX0yP9+EZrNwJgYmIiSbK0tJQzZ85kr/Bw9uje\njQkJCSwsLKxRDi4uLqZF6E6dOkVra2sWFRWZnSOLlUlISEhYFvWeQK2Svk0TMnLkSGq0NmwTMp2P\n9D3Gdl0X0cbWg10eCuUPP/xAAOzScysffarSFC1bv047Owdeu3aNA2Jiq7+Re0TQw2sQtVpbtmjh\ny7y8PNaUj4/PX96EHDp0iFqtlu2jQ/hW6iS+u3syuwwOpaIo3LFjB9uHBLN9dAjnVS42xb/PzqRW\np+W0adO4efNmWllZ0dXTjQ8P606vB1sQAD/77LMa5fF31HrD4/37s5G1lvM6NWd2X3/O69Scjay1\nfLx/fy5ZsoQAeCTKj3wqyBT/4+vMZk2asKSkhOE9elClKIxu5sjY5o7UqVVsGxjIK1euWJyDm5sb\nU1NTSZLp6en09PRkZWWl2TnShEhISEhYFg1isbJLly5hwYKFaBU4Bd7+YwAAdg5+0Gob4ZfUx7Bx\n40ao1To4u3U3G+fq0RsnMhOwaNEirE75FiHdVqJJ8wEAgNLi09i9tSumTJmCpKSkv72m/09SUhIc\nmzjipRWvQKOrfhtHLRyNvIxzmD5jOjKPHkPssEFmY5w8GsGrbQscPXoUX3z1JXy7tkL82tegtdaB\nJJa8shDjx4/HkCFD4OrqanEucXFxAIAuXbrgo48+gpubW90Vel1OTg7WrF2LBV2a41kfZwCAn4MV\nNCpg+Nq18PL2RnN7GwQ6WZuN6+Nhj7k/n8WcOXOwI/VHbA/3RQ/36qclHyksQ5etWZg+fTomTZpk\nUR7JycmIjY2FnZ0d9Ho9/vOf/0CjaRD/jYQQ4m/XIOaEnDx5ElVVlXBpEm6236Vx9bbBYIDBUIFr\n+oNmx/UFu6HTWSEtLQ1OLkGmBgQAbOy84eEVh1WrUv7q9Gsl83gmWnXzNzUgQPXj7gPCWyPzeCa8\nfb1xck+u2ZgSfTHOHTsHOzs7ZB/PwqPj+0FrXb1suaIoeOztGJSXl2Pjxo0W55Gamor09HTs378f\nrq6uGD58eN0U+DtZWVkAgJ6N7cz239jWarU4X1yG08UVZsd3FZTApZETvt+4ET0bO5gaEAB40Mka\nA5vZI2XVSotyMBgMmDJlClJSUnDq1Cls3rwZw4YNw+XLl++kNCGEuG81iCbE29sbarUa+ktpZvtv\nbMfExMDbpyUy9ozA5fxUVFboce7UQpw6/hGGD38WGo0Gym3+KhRFBfLuvKOkVctWOLn7BAxV/300\nPUnkpmXDr5Ufxr48FruTf8baj1bj6sVC/Hr4LL54ZiZUUBAbGwsAUFTmNd/YrknNnp6eAKqXQ4+P\nj8fOnTvvtLTbatWqFQDgp0slZvtvbMfFxcHVxRlP7c7DnssluFxehaTsAszMvYLRY14GAKiUW6+r\nUiyv9+DBgzh//jy6desGAOjUqRM8PT3rfLKvEELcN+r786DaBG4zJ+SZZ4ZSp3Ng2y7z2PPxX9mh\n+2raOXizffsONBqNzMzM5AMPtDGf1Bj7JIuLi7lq1SoCYMdH1pjmi4T3P0Ub28Z84YUXWFN/xzyJ\nffv2UaVSsfOghzg5PYFTjybykefDCYCbNm2i0Wjka6+9Rs1NT8lt3KQxN23aRIPBwFZ+rRgYEcQv\ni/+X8yoXc27FIvZ55VHqdDpevHjRohyKi4up1+tN29OmTWNYWNhfVDEZ2TuC7jZWXB7qxQuPt+by\nUC+621gxsncESXLv3r308fIy1asoCp8bMYIVFRX84osvqFIUpvVqaZovcryvP+11Gr7zzjsWvf6F\nCxfo6OjIY8eOkSSzs7Pp6urKs2fPmp0nc0IkJCQkLIt6T6BWSd+mCbl69SoHDIg1azI6dXqIZ86c\nMZ1jMBj4448/Mjk5mZmZmab9VVVVjI7uT0VRsXGzvmzmPYQ6nQObNfO65RvMn3nhhRfo6elJrVZL\nDw8P+vv7Wzy2NpYuXUpnF2dTvfYO9pw9e7bZOefOnePy5cu5YcMGlpeXm/Zv2LCBWq2W7j6N2WNk\nT/p2aGl2J4klTpw4wZCQELZv357t2rVjTEwMT58+XWf1/V5+fj4jeoabvccRPcPN7mKqqqrili1b\nmJyczBMnTpj2l5aWsltoV2pUKsY0d+TTLZxoo9WwzQMPsKCgwOIckpOT2bZtWwYHB7Ndu3ZMTk6+\n5RxpQiQkJCQsC4W8Oz9u+DOKonQAsG/fvn3o0KGD2bGsrCwcOXIE3t7eCAkJgaLc5nfwt1FZWYl5\n8+ZhyZJkFBeXICqqN1555ZW7/gmzpaWl2LFjBwwGA3r06AEHBweLxx48eBDTp09H+uF0eLfwxugX\nRyMqKuovzLZuZGRkICcnB35+fggKCrJ4XElJCb788kusWrEcVZVV6D9gAMaMGQNnZ+c6zW///v3o\n2LEjAHQkub9OLy6EEA1Ig2tChKhv0oQIIYRlGsTEVCGEEELce6QJEUIIIUS9kCZECCGEEPVCmhAh\nhBBC1AtpQoQQQghRL+6rh16kp6dj7ty5yMvLQ0hICEaNGnXX34J7J4qKirBw4UJs27YNDg4OGDJk\nCCIiIiy+bfletGvXLsyfPx8FBQXo2rUrnn/+ebi4uNR3WkIIIW6nvhcqqU3gNouVkeTJkyf5+uuv\nM7pfP44ePZoHDhwwHZs3bx4VRaHWtRm1QeFUW9nS2dXN7DH0RqORGRkZ/OWXX1hWVsaaKCsrY0xM\nDAMCAhgcHMzIyEjm5OTU6Bo1VVlZyW+++YYDBw5kTEwM586da8r74sWLbN0mgGq1ip26+LBlq8YE\nwFdffdXsGgUFBfz5559rtCjbzcrLy/nyyy/T39+f7dq1Y1xc3B3X9WcyMzMZHx/PftHRjI+PN1t0\nLiEhgQDoq9Gwl6JQp1LRq2lTs0XLDAYDDx48yH379t3y9FtLbNiwgZ06dWL79u0ZGhp625VxZbEy\nCQkJCcui3hOoVdK3aUJ27txJWzt7auydqQl5lFrX5lSpVFy8eDHz8/OptbKiLiyOTvMvsdFCPR1n\n5lDnFciuD3cjSe7Zs4dtgtqaVuJ0dnPnnDlzaKmysjJu2LDBtD1z5kyGh4dbPL6mKioqGB0dTQAM\n7tCCnR/yoaIofLhbKIuLizl69Gg2amTH1d+PYUbuJB7OeY/j34okAKalpbG8vJwvvfQSdTqtqebH\nHnvMbPVRS8THx3Ps2LGm7QsXLtR1qSbr1q2jTqNhY42GjwFsrNFQp9Fw3bp1zMnJoaIonADQAJAA\nzwD0VqsZO2AASXLLli308/Ex1du8SROuWLHC4te/cuUKXV1dTcu2p6amMigo6JbzpAmRkJCQsCzq\nPYFaJf27JsRoNPKBNoHU+Xeh05dn2Wihnk7zL1H38FO0tbPnrFmzCEWhY1IuGy3Um8L2xTkEwAMH\nDtDBqRF1LUNoN2457d/dRG23pwmAq1evZm3s3buXvr6+tRprifnz5xMAZ88byozcSczIncTFK0ZS\nq1Xz448/pqurM0e+2N10LCN3Eg9lv8umzZwZHx/Pf/7zn9Rq1XzltQiuXPsiJycMoIurPbt1f5hG\no9GiHIqLi+no6Mhr1679ZXXeUFFRwabu7oxWFJZebzJKAUYrCpu6u/PDDz+knVrN4uvHbsR0gCqV\niunp6bSxsmK4SsXNAFMBxgBUKQp37txpUQ579+5lQECA2T5HR0ez37iR0oRISEhIWBoNYmLqsWPH\nkHXsKLSPj4diU71suaLWwHrg2ygpLsL+/fuhKCooVrZm4xRbRwDAokWLUFJWAetxq6BtHwmNX2fY\n/mM2dG26IeGTxFrlNGPGDMTExNxZYX9i5coV6BLqi0fC/U37gjt4oVef1lixYjnKysphb29lNkal\nUsHWVge9Xo85c77CC2N6YNToR9C6jQdiB4bgg389hp92pmH37t0W5ZCbmwsXFxd8+OGH6Ny5M8LC\nwrB169Y6rfOGtLQ0nM/PxwckrK/vswbwPonz+fnIzs6G1fV9N3MEYDQaMXv2bDgYDFhvNCICQHcA\nKwEEqtX4d6Jl77G/vz8KCgqwa9cuAMCaNWtQVFSEU6dO1UGFQghx/2kQTUhFRQUA3NJkwMoOANCm\nTRvQaED51q9Nh2g0oGLzXPj6+SMvLw8a32CoHP47gVFRFKge7IWMI0dqnM/UqVORm5uLqVOn1qIa\ny5SXV8DGRnvLfhtbLcrLyxEZGYmUVekoLio3Hdv10wnk5lxAUFAQysrK0a1HK7Ox3R7xAwAcPXrU\nohyqqqpw+vRpBAUFYc+ePZgxYwYGDx6M/Pz8O6js9m68x797h2F3/WtwcDAuGwxYcvMYAF+qVAjt\n0gU5WVnoUVUFm5uOqwH0rqrCkUOHLMrB0dERK1euxBtvvIHOnTtj8+bNCAwMhEZzX83vFkKIOtMg\nmpCgoCB4NPdE+Q9fgEajaX/597Oh0WoxdOhQjBkzBmVLJqJkxhCUrvoQpZPCYcjYgk8TP4GPjw8M\n546B5SVm1zWe3AfvFt41yiUxMREpKSnYuHEjrK1//3N53YmOjkZaai5ysi6a9p3P02Pz98fRr19/\nfPDBZFwpKMcT/b7Apx9vwtsTUjBm1FL06hWOZ555Bmq1GofTz5ld88a2t7dlNbdo0QJqtRpDhgwB\nUN0I+Pr64vDhw3VS481CQ0PhaGeH6aj+nAPXv34KwNHODiNHjsTgQYMwQlHwjKLgbQDt1WocUKnw\nr08+gbevL/ZpNDDcdE0C+EWthk/LlhbnERYWhu3bt2PPnj1ITEzEuXPnEBgYWFdlCiHE/aW+Pw+q\nTeA2E1OTk5OpKAp1Pu1o1S+eugd7EADff/99ktV3RXz11VcM7tCJ7h7N+GjfaO7YsYMkmZ2dTa1O\nR12HaDok7KFjUi6tn3iTADh37lxaatq0aezYsSP1er3FY2rr6tWrDGr7IO3srPnEUyEcPLQTnZxs\n6ePrbZocevToUQ4bNozNmzdj6zYBnDx5MktKSkiSTz89mI5Otkz8bCB/OfQm5y8ZQZ+W7mzdJoAG\ng8HiPKKiorh+/XqS5IkTJ+ju7s68vLy6L5hkUlISATBUrebr178CYFJSEsnqu4U+/fRTtgsMZDN3\ndz4RE8M9e/aQrJ54rCgK4wCeBJgH8NXrE1TXrFljcQ7nz583/XnixIkcOHDgLefInBAJCQkJy6Le\nE6hV0n9wi+727dsZ3a8/m7fwYfceYVy2bJnFkyxXr15NJ2cX050Tao2Gb775psXjf/31VyqKQj8/\nP4aEhDA4OJhdu3a1aGxtXblyhRMnTmTr1gH092/FcePG8bfffrNorF6vZ1RUpKleAAx8sA2zs7Nr\nlMOJEyfYs2dPtm3blsHBwfz2229rU4rF1q5dyz69etHX05N9evXiunXrLB67YMEC2tvYmOq10mr5\n8ccf1+j1R40axdatW9Pf35/PPvssCwsLbzlHmhAJCQkJy0IhiXuNoigdAOzbt28fOnToUGfXLS0t\nxaZNm1BSUoKwsDA0bdq0zq59t0pPT0dGRga8vLzQvXt3qFQN4hO6P3Tt2jVs2rQJVVVViIiIgKur\na52/xv79+9GxY0cA6Ehyf52/gBBCNBDShAhRx6QJEUIIyzTsH3uFEEIIcdeSJkQIIYQQ9UKaECGE\nEELUC2lChBBCCFEv7rsmpKioCKdPn0ZlZWV9pyKEEELc1xpcE1JaWoqsrCwUFhaa7b969Sqee+45\nuLi6wsfHB009PZGYmIi6vDsoKioKwcHBCAkJQVhYGA4ePFhn1xZCCCEamgbz0AuDwYD33nsP0z//\nHMVXr0Kr02HokCH4/PPPYWdnh8djY5G2dy+c4+NhFRCAogyi5nEAAAOUSURBVM2bMX78eBiNRkyY\nMAEksWXLFixduhSlpaWIiIjA0KFDa7T0+ooVK+DoWP1QvJSUFIwYMUIaESGEEOIPNJgm5K233sIn\n06bBZeRIuPXogdLDh/FNUhIu5ufj7YkTsWPrVnh99RUcIiIAAA7h4YBajX998gnGjh2LcePGYdas\nWbBt2RJKo0ZYmpyMz2fNwo6tW+Hk5GRRDjcaEADQ6/UNfuEvIYQQ4k40iCbk6tWr+GzmTLiOHo3G\nr74KALALDYW2aVOsj49H26AgqLRa2PfsaTbOITISZxYvxrJlyzBr1ix4TJoE52HDoCgKSjMycHTo\nUEydOhUJCQkW5zJ8+HBs27YNiqJg/fr1dVqnEEII0ZA0iB/Vs7KyUFZSAofevc32O/TpA6B6Mqqx\nshIVp0+bHa/IyYFKrca2bdtg27KlqQEBAJugINjHxuKbZctqlMuCBQtw5swZTJkyBRMmTLiDqoQQ\nQoiGrUE0IR4eHgCA8uPHzfaXZWYCqH7svVuTJrjw+uuoOHMGJFH000+4PHMmnnzySRiNRij29qYG\n5Aa1oyNKS0pqlVNcXBy2bduGK1eu1Gq8EEII0dA1iCbE09MTfaOjUZCYiOK0NJBEWWYmLk6cCF8/\nP0RFReG7lBTozpxBTs+eyG7fHmeefRbt/Pwwe9Ys9O7dG8WHDqHkpkmkhmvXULR6NR6NjLQoh8LC\nQpw/f960nZKSAjc3Nzg7O9d5vUIIIURD0CDmhADA/K+/RnT//tgfFweVVgtjZSU8vb3x3bp1UKvV\n6Nq1K86eOoWUlBTk5eUhJCQEvXr1gqIoGDx4MD5PSsLBYcPgEBMDVaNGKF6zBrriYrz7zjsWvX5h\nYSEGDRqEsrIyKIqCxo0bY+3atX9x1UIIIcS9q0E9RZcktm/fjoyMDHh7e6Nv377QarUWXfPatWtI\nSEjA4uRklJSUIKp3b7w9cSICAgL+oipEQyVP0RVCCMs0qCZEiLuBNCFCCGGZBjEnRAghhBD3HmlC\nhBBCCFEvpAkRQgghRL24p++OOXbsWH2nIMQt5N+lEEJY5l6dmNpCpVIdNxqNlj9dToi/kUqlKjMa\njQEkz9R3LkIIcbe6J5sQoLoRAeBW33kI8QcuSQMihBB/7p5tQoQQQghxb5OJqUIIIYSoF9KECCGE\nEKJeSBMihBBCiHohTYgQQggh6oU0IUIIIYSoF9KECCGEEKJeSBMihBBCiHrxf/n/l0bCEu4vAAAA\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x55abe4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_scatter_2d(x, y, c, sample_size, title):\n",
" df = pd.DataFrame({'x': x, 'y': y, 'c': c}).sample(sample_size)\n",
" l = len(np.unique(c))\n",
" \n",
" ax = plt.subplot(111)\n",
" colors = cm.rainbow(np.linspace(0, 1, l))\n",
" \n",
" for c in range(0,l):\n",
" qq = df[df['c']==c]\n",
" ax.scatter(qq['x'], qq['y'],c=colors[c], label=c)\n",
" plt.legend(loc='upper left', numpoints=1, ncol=3, fontsize=8, bbox_to_anchor=(0, 0), title='Topic/Cluster')\n",
" ax.set_yticklabels([])\n",
" ax.set_xticklabels([])\n",
" ax.set_title(title)\n",
" plt.show()\n",
"\n",
"%matplotlib inline\n",
"plot_scatter_2d(tsne_m[0], tsne_m[1], kmean_d, 1000, 'KMeans Clustering of Amazon Reviews using TFIDF (t-SNE Plot)')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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cgrG4d7yv3BaMPYafQ1pWL0SEFTMWtFhIlG8ee+2LfhrqdW/spaiwoE200qlT\np/LnJX8mISmB0VeOYdDF5+PdXcuTc5cQGx/L+ecN7nDaMWjtPj09jS1b9gQPXdv0ATGnZbFyeUWb\nh7AZDJEwGrLhpKNk2nQq12xkMmXMZReTKaNyzUaKpzZOKwrl4DX7jT2cUTCMtKxeAJySpz2zWzIk\nKtg89vDRF1H25NLIO7cABQUFdO3SlWNfH2NlaQW/H3s7f5rxMEcOHsalXKxcsaJN6tEUZsz4ERDa\n5H/8I20xaesQNoMhEkYgG04qPB4Py1dWUHD8fgZSTBq9GEgxE44vYvnKikabr4MJxtjsMxi15Bbf\nNp9U6hzPzTHhBprX7Xlsj8dDRUUFHo+HZctXtOm8Z3V1NV3Tu/iVZaRnsOWdLR16/tUOFQtl8rfp\niCZ3wzcbI5ANJxW2Q1TvgEUKz7QWKWysVmQLxjVr1pCakg5KcfC9T9hdsYEvdu9he/lKan5+P4VF\nTQs/8nq9FBb5r65UWFTkC6vKysqKOrSppZdM7NOnD3v27GHVqlXMnz+fVatWUVtb2yJZwVoTrd1n\ncO2rHwUNXQPd8c344eUdOp7a8M3DzCEbTng8Hg/r1q1DKcUZZ5wBwPtUMpB6R6v3rEUKm6oVTf1+\nMV99LkzkYd7+4ikqZyzw/WZ7WTeFaSUlvLhhA6eXlpKUm8vhmhpevO02phYXs8Kx5KITj8fDjh07\n6Nu3L1lZWXi9XqaVTGfl8vrtC9xFLeZJPH78+CYvXBFY17ai5pXNDOiXzfRNH/jKzkvvxJ+Hns6b\nB79i9msfs7mmmpLiaSyrWN5m9TIYwiIiET/AYEA2b94sBkNHoba2VsaNGyuA7+NyKema0V2SXOly\nKUtkLrvkUpZI55gu4i4oatJ5VqxYIYBMpkxuReRWRGbjkRH8QgBZtWpVk467detWAeT00lI5e8cO\n36fHjTcGPW5tba24C4r8rtddUCRjxo2X2JQMSZq1WFLvfVuSZi2W2JQMKXA37XpbgtraWnG73X51\nzcrKkmeeeabN6lBdXe07992DMkWmnOP7LBnW0/ebx+NpszoZvpls3rzZft4GSxhZa0zWhhOWkpIS\nNlW/5JeRqXPnTnz22UE6ZyTwHNO5lzN4junkjRtO+dKyJp3Hjm11msG7ksVw5gCwYcOGJh3XNq8n\n5eYCcPzAAXb95CfsXbgQ0FnBnObrYM5qa1e/xAtrVhNffCfxI6fg6tqT+JFTiJ+20OdJ3B6UlJSw\nYUMl8xZbNd+GAAAgAElEQVRMZOgIbeLetm0bl112GZmZPdokjWVubi6Dc3KA0KFrYJy7DB0HI5AN\nJyT2fOlNtxYxcdJATj0tjYmTBvI/txZy9PjX7KvVc5+2Q1TFimVNNt/asa0rmMsO6hcqsM3gI0aM\naNJx7bCqwzU1AHx4/fUcef11Ti8tJauqitNLS3nh5ZeZdOmlIZ3VzqubCUBsv5F+x47tr9czbg9h\nY9+bG+cV8OLzW9n6zicBaSw/I3doTqtlOnPy8EMPAeEdvIxzl6GjYOaQDScktnbpXAEJIGfomb7/\njx07htvtbvZ5pv1Ah0u9wzO8wzMk0Z0LuZF13Eb3rplNnl/Nzs6mwO3mxdtu4+jevXyxdi2nl5aS\nNmkSAGmTJiEiVF1/PRd/+9sApKJDrT7Fw352cCo65OrY1peJHznFd+xjW14CWk/YOOeGRcRvnti+\nN5mZqVSt3cbC0slMnDQQgImTBiIi/Pr65ygqKgKaFiMeLbm5uRQVFnDt82v8s4699jEJMS4GDRli\nYpENHQYjkA0nJLZ2ubnmfV9nD/UrIEHzhZHX6+X/DTgXjsYzmTJ6k8f7VLKMa1nNL+nWtRubappm\nrrZZWl7O1OJiVlpmatt8bdN56FAAduzeDS4Xj9flk0w3vuBT3zYu4KslN4AIsf0v4NiWl/j6yRsp\ncBe1qLDxeDy8/vrrPPjAQ1Su19YB5VJInfi2cRe5mX/rfAAq13qA0IOmEfycUxnMyjVzKJ5aQsWK\nZS1WVxuv18uXx47z+dHjfg5eLvSEXnV1Dd0zM6neuLHDe48bTn6MQDackGRnZ+N2u7n91gpEhJyh\nZ/JK9Xv87tYVxMXEM27cuGYLo4IJhXx19AiTedTnsT2QYgThOaZTvnRJszvxjIwMVlRU+PJmH66p\n8WnIAIeqqwHoft117F24kMQ+fYh5913KgDygEvgpEJ8Yx95Hrqqvu+Vl3RJ4vV5KSkpYvrzeG/lU\n1yDiJIVPk1+n+IHpZI/qj6dqC0/NLeOWW2/B7Xbz9JPPA6EHTT04V7fnceG5ldNbJXPWtJIS1r/y\nCqeWlhJ7yil8MHcuxw8cIGXSJDh+nLrDh6mtrCRn2DBq9+5t0XMbDI3FCGTDCUt5eTlTvj+FX1//\nnK/M5VKMGT2uyQ5cNh6Ph1c267ndUDHNGzZsaLK5OpAJEyZQ4Hbzwvz5iAidhw7lUHU1exYsIHn0\naNIuvpi9Cxdy5N13eRR8AV3FaE1v+qefBhxRaClsB62FpZMZktubzTXv87tbVvDFoSP85IFZDJ+m\n56uHT7sAEeFPMx6mpqaGq6/ex6uvbuaO+cv9Bk0Lb1uBy6VIrssE/GPEW1qjX7l8uW8a4Kt33+X4\nnj3EJSVx8C9/8W0XA3gPHeLZZ59l8uTJLXZ+g6GxGIFsOGHJyMhg9arVbNu2jXXrtAk1Pz+/RTp1\nex4UQsc0N9WZKxRLy8uZdOmlVF1/va8sefRoTi8t5fMXX/SV5QXsl2/97TpzJl0uvzyqWOZosR20\nQs0DZ/T0z+TVL28AAPv27aO8vJx+/fpx6ulpfoOm/mdncvC/R+iCnlJobox4KAK92A9VVxMDJB0+\nzIPUWxiuBQ4p+MUvrjcC2dCuGIFsOOHJyspqcVOnPUd9CuexnDkIwpnk8x7rqOCndEnv1iTtOFyi\njIyMDCrXrmVUfj4bN2+my+zZpF18MZ+/+CJ7FiwgaehQDldXUwk415ZaZ/1N//73iTvtNJ8z2Mrr\nr2+2GTiS89yby17zCWGArZV6aUb7+txuNxs2VHL9jePp0rUz3tpDPPJgJS6X4t9cydl1P2BtzP/g\nHtey893g78WeNmkSX3k8HAceJIiFQeDdd98zC04Y2pdwQcpiEoMYvsG4C4okyZUumZznl+AiIS5R\ndu7c2ahj1dbWSlFhgd9xigoLpLq6WioqKvySU3i9XikISKqRcPbZ8q2KCkns10/SQJaA7LL+poGk\nDB3ql1wkq6pKAKmoqGhWG9jJSxaWTpa3d9zq+9xxz6UCSKfOSXLF47Pkrp2L5IrHZ0lKlxRxF7n9\nriUwQcjQEX1k3m8nSnJKgrhcStwFReL1eptVz1AUuN0Sn54up91zj6RddplgtZs4PrscdWtuexkM\nwYg2MYgRyAZDCLxeb4PMWLk5uU0SHkWFBdKlU7yUDespuyZmS9mwnpIeHyOugKxbzmN7PB55+umn\n5cK8PL869OjWzT87Gcipv/2tn0A+7Z57WiwLldvtlvT0znLHPZfK6qq5csc9l/qEqXIp/8xhRW7f\nNWzdulUqKipk5cqVAsiPZo6UZWtmNxDqrZkpK9jgpixAIC9x/Gaydhlag2gFsjFZG6KmvfIStxcZ\nGRlUrFjGtm3b2L59e5OvWycmWRlyfd4ZrOMzdjcI/7FN8VOmTGlQB+f32dddx4t3341KTPQ5g326\nYAEFUS5KEYny8nKKi4v95oHz8/O45pprOf/88wH86qYXzLjYL7c2wLTLh3HqafUZs2yzd0s7czmx\nvdhHj87n9ddfQY4d49pDX+uYZLS5/6dArILxhS3TXgZDUzEC2RARr9dL8fQSVjiS8BcWubnt1vl8\n+umnJ72Abu4ctT0PG2p93qMcihj+E1gH53dfLLPDGazA7WbB/Pk8+uijKKWa5eyWkZFBRUVF2IGJ\n8/u0kuk8v34DSbMWE9tvJF9veo4vn/pNyPCncM5cLTEI1IuPaC/xQYN7Mf3SxUzff8T3ewxwYV4+\n5eXlfvt8kwafhg5COPVZjMnaICKFRW5J7JIm+U/Mkyk7n5GRD94gMQnxfmbAQoep0uCPPQ9bNqxn\n0AUOZuORWxGZy65mzWN6PB6pqKiQ6upqGT9mTAOz9rhx41r9HtnXmjRrsaT/+YDvE9v7HElOSfAz\ne6endxa32x30OMEWp3C7m/aMVVRUCCCrq+b6zOWLH58ul/9khAAyZ84cn6k61AIe5tk2NAczh2xo\nEewONv+JefLjo+vlx0fXS0/3CInPSPEJ6Pwn5klilzQpLGrYudrziN/0uTl7DnmJNYe8ZFhPSY+L\nlWwKfCtIXcqSFpnHLCookHSlpMxyWCoDyXApSYh1hRSALYUt/FLvfdtPIKfc/pK4AuebwwhYe956\nYelkWV01VxaWTg4rwMMRzDHtpc2/lP5nZzaoz7gx46VzTBeZTJnMZZdMpqxZK4UZDCJmDtnQQvjy\nEo8aBMBBzy4+WL6B/Cfmcda0CQAkT5uAiLBixgKfuTWUmfvJsvJWyVnc0Sl7cikl06YyfcVKX1mc\niiOLSznIbt5jHatirmt2+I/H46Fi5UrKCAjtqROm14lvQYfWMsPaoUaBubWP73qbujph1apVHDt2\nLKwpOFLsc2Prb2d1W3jbSl+CktlXPckHuw/4JTu547YVfP7ZES6pW+Kfma0VM4kZDE7Mak+GsNgd\n7J6qNwD4bOeHQL2AtjklTy9yYK8uVDy9hHUbXyb/iXlM2fkM+U/MY93Gl5lWUsw3kYyMDJYtX6EF\nZkUFNTU1jLhgJMuY1SJLRNr45qsDyvMd/0ezApQtFBu7GpNeMKOIr8t/xdcvPU1d7Qd8/dLTvtza\n48eP56yzzmL79u0hjx0p9rkpK1iVl5czaNAQfn39c4wfdS9b/ruHm+df7LdS2I2/KaCuTnwLeNg4\nM4l1NJp6nwwdEyOQDWHJzs6msMhN9dxFbC9fSWxiJ6BeQNt8Uvk6oB10PB4PKyqWM/Te6zhr2gSS\ne2Vy1rQJ5JbOYUXFN7vzyMrKYtiwYcy7+RbfAg0AeRfmt8iKRy6XfqUrA8rXOf4P50SlPaSL6Nev\nH0VFRdb9r1+TORqWlpcx9sIRHH7kKj6bew6HH7mK4eefy5TvXkb+6Hy/Yxdd3PDYzoVDnARzAgsU\nSKEEVEZGBr/61Y0AdPnJT4DQAt/Dv/3KWyuTWHPwer0UBdynokbeJ0PHo1Em67lz55KW5r/Q99Sp\nU5k6dWqLVsrQsXiyrJxpJcWsmLFAF7hcbJhTiohwSt55fFL5OjU/v5/CIh02Yi9CEE6LbivTX0f0\nli2ZNp3KNRv9VpBauaF5Kx55vV6mlUxn5fIKXOh0kM7QnjkuRYJLMWZ8Qdh2mFZSwosbNnB6aSlJ\nublNSsOpQ410uNhrr73Ggw89ROW6dby0vopOyZ2Y+cTVfotRFJcUU7Gs/tjBTMyvVL/HnQtW4bZC\nuYJNiXTP7MG+PfULRAROkdiCPrZ7dyD0ohevuRaTWTfQl5mtJaYSWhpnfvHMzFQq13r421NrKS4u\npqKZ6VINzWPp0qUsXbrUr+zgwYPR7RxuglmMU5fBge3FW1NTI4VF/h6whQEJIQhwBPvx0fWS9/hv\nBJBHH320yY5LzmQT4ZzFQmXGagtv2WCObIFJMiZT5nPmiuTQFY1jXIG7SGJTMiRp1mJJuf0liTu9\nf6O9rO37dnppaYslGbE99HPvvFYAmfnE1fLY0TLf54rHZwU9drAMX04nsEDP//wn5klcWmfpMigr\nrKNhgdstcampEpuaHNTre9z4cR3ey9q+T/MWTJRRo7P877NLSU1NTXtX0RCA8bI2tDq2gA7WUY/K\nz5dOGamS9/hvZMrOZyTv8d9IfFqy4HI1qaMLJmBdQTpqm2CZsbp0ipeiwoKI52qqZ3htbW2DgcrY\n8eNk3JjxfmWAzGWXn0AOFvIU7HiFRe4G6TZDhRp1+sFtAsjPfvazqAZBtod0VlVVi6ThdA7Mxv/r\nLgHkrp2L/ATyXTsX+QZpwQj2jEUa8F3236V+3wPTkvbo0V06J8dL/7NP8c+A1qO77zkK92y3N/Z9\nGjqij6SlJ/p5oienJEhubk57V9EQgPGyNrQ6wRJm2KbTqnXrwOWi0jZzA7ExCUys+yNZuLWZthEL\n008vnsbGtS9SNqwned2TqNx3mNmvf0Jmn65s2FDpZ6oLmxlrxcqQ3rJOs6+Nva5wNHO7Tke2zFGD\n2FP1BlVzSqn74qjPPP02T7OaG0KuIOWcpww83gfLN7Lm5/f5mWlzc3MoLi7R7dtvpF99Yk7JAqW4\n7777fGXhPN0DF2OwsddkbuwcqtND//iRrwDwVG3xLdcI9YtRzJw5k2f/9lyDefTAZ8zr9VI8tdh3\nXCf2lMhnOz4gLatX0CmSffv2sXfvPp8H9/vv1rLrfS/bt+3lnoWr2bFjBzk5Oa2yYElLYd+n6g3v\ntpgnuqFjYJy6DC2KM0tT6j1v0ukHC3B1SgYU3zn+GDlcSRq9GEgxE44vYvnKiohOXraAvX9QD4p7\np9MrKZ7i3uncf94pbNmyh5/MGunnyBMpM1Yob1m/ut/7NkmzFvP8+g1MtQRepDoGc2Qbvmgux45/\nxekMJY1eXMAvyOQ8lnEtb1DGQXbzBmV6nrKgfp4y2PF2/6uKxE6xLCydzOqquSwsncw7W97i5z+f\nCy4XR99cDUDdF/s5fM93OXTfVJ2tGTht9PmMfOgG1m18mUmXXBLU8Ul7SLvZd9ttHPj73zn60Ucc\n+Pvf+XTBAi7MywvrGR0Mp4d+WvYZ9HIPp3zuEjaUr8e7u5YN5et5ck4ZfVwXMZkyKtdspHhq+LYu\nmTad/7y+zXdcJ7ZjYepZPf2+OwcSgR7cvft0ZdToLAovPgeAq66+Ourray+ys7PJyckBWtYT3dD+\nGA3Z0GLoBeErSJq12BeD2qloNq60Hhx+5Kqw4SThRvORBGyXrp39jmMLgsp9hynuHe/bft2+w0Bw\nTS9Y3eNHTgERVj5yVUSNIzBe28bW0rxspyt6/8n8mYc5j+eY7tvOPa7IL+QpWPz37hUbQ2pEccmJ\nfFV+Iyo+iWNVS4j/YDO3OWJsb79tBTu+OkpMWmeqKiupqtR+2IEac7A0nN0ze7C+spKiEPuEwvbQ\nXzd3ESLCkN9dTeWMBfxpxsO+bbJcbibXlZNIRsR4X4/Hw/KVFUymjDdd5WycvcjPsXDDdaV0GZRF\nTKd4tpev9HM0tHF6cAdz6Hr1lVdOCO3yoYceYujQoU1KR2rouBiBbGgxbCESaDqN7a9NlB7+zZmO\nCNlow0kiCVhv7SG/42RnZ1NUWMCctS9qT+PuSazbd5jr3thLUWFwL+NIdY80aHBqg8dz+vPZzg9J\nPasn+6r/C0AX6q/xE95AqAubJMN5vORpE3zx36E0on5XTuLt0qUcfuQqAG4LIbhtTr1oMH2+N5Z1\nNy9mWkkxyy0vZ3sxBjtv9cI776Tmrdf9zPDr5i7y2yccDTz0gXPOPZe333qLGazjzLr65yHSAM2+\nR73JI6uuiGe+KPabEknLSMf7xjb+8q3LgPp868uXL/e1cXZ2Nrm5Ofz2lmV+HtwLbllBbK+zObb7\nv20aBdBUcnNzcbvd3HHbipCe6IYTDyOQDS1GqCxNx7a8BDQ9nCSUgJ3z+if075/JYw+/3KATCpYZ\nq6iwgLInlwY5Q+S6Rxo0ZGdnM2bcWKqu+B1Hjx7zlcfGxhDjiueDuk3E0snvusePHx/2eE7tsnPP\nHkBoza7XxSN5u7T+2kIJ7lH/exOumBg2zr2PmE4JOjbckWHNJisrCxGhct26iFnZwpGRkcHyZf4L\nU4gI/fr14zN2+20baYBm3yN7/r2kroJatvEKi9nA3dRs0nPd27dvp1u3btxyyy0MHTrUt7/b7aa8\nvJwHH3yI4cOH+QYoLqAO4As9eLrzjjsYPnx4h88oF2wVLvsaDSco4Ty+xHhZGxqJL/zmqkck9d63\nJemqRyQ2JUPGjBvfrHASr9fbKC9rm8Z4y4aqe4E7ujzG48eOlfT4mAZrHnfv2rVJ1+31ev28rF0u\nJSkpnfxCdVLSk6RX4XCfR7H9ceZtdq49HOiB7H7+gZAe1LY375Sdz/h5M0/Z+UyTvK6duAuKpHNM\nF7mUJTKXXXIpS6LKGR3tfpFyYRe4iySmc6rEdjtD0pTLL+93l5gYKSqI7I3fUejIHuEGjQl7MrQL\nekF4f8Fb4C5qsXASe/9Vq1Y1+Tihwpoi1T3SMSH0ik7Nqa8z/jswNve00efLiAd/IXGpnaV7Zg8Z\nN2a8xMXES0pKYlDBHShUz/n51LDxz4QJL2qOAPB6vU0aoFVXV0vO4Nyw+wVbTMI5KPF4POL1euXC\nvDx9z7Trm++zxDquEXCGlsIIZENYWnsVprYYtTf2GqJNFtKUutva5K6J2X4CedfE7GZrk4HU1NRI\nanqa33V0GZQlndJTfXHPgSsrdR3UV4r3VjQQqglpyUFX6bKxE3A448lDrezVFDwejyxevDhinHSw\nZRFzBucETYIRbLnFt3fcKqur5vrdC989CxDIu6zjt+Q9M3yzMQLZEJRgQil/1KgOlYkoEk1dKzd4\nspA4yc0Z0uyBQyQNuSUHJva5cu+8Vsb/666giTAeffRRAWTU/94kmXnnSUIX/yQtcamdBZcr4jrW\ngWZzaLm1r0MlPgl2bNtUHc2yiNFoyM7tQmnIwbLBmeVEDU3BCGRDUIIJpeRYJempqe2Wcq+xnVxT\n1sqNJDCd2nJTO91gax5Hmx0sXL0D6xJpbnf+/Pm+FJ35T8yT4r0V0tM9wk/wnTPw3Ebd79aweARL\nfxlM+7bvXWPSjdrPSGBqzMBnpKigQLrExMgSSzNeYs0hZ3bt5tdeYy9qmHGtI6TUbI0Bghl0tDxG\nIBsaECiUaif1l6JTkyOacFuL2tpaKXIX+p/fXeg7/4oVK2T+/PmyatWqBtcQSfsJJJJJ+YZ+XSU9\nIU4yu3drcnsEczxranuG0x4jze3an+6ZPSQhLVnO+flUcT//gOQuvEbik5NkVH5eo+vT0jRmftq+\nd6HSjS5evLiBAImUC9u5XVGB/z3L7NpNklzpftp4LAnSSaVHpaG3BbW1teIOeD7czbRchJvSMUK6\neZjUmYYGBCbYmF79ARtrj/ilo5yz9gVKpk1l2fIVrV6f6SXFbFy/lrJZw8jr153KrfuYU7aW0fl5\nvLf7Qz47UL+UXGpaOs89+wxffaVTMIbLUBQsFCdSLPPMb3Vh9Z5DbN9fyw39ujLx1BR2HznGnLUv\nRt0e9prHzhCfpsaDBkvD6Yz/dYZE+RJjzClFdU4l+X+Wc2xbNbXlN1B39Bhvly7l7dKluFyKi8aO\n5a9P/6VJdWpJIiVScd7HwHCnT/Gwnx18SA0ul+LKK6/07e92u5k/fz6ffvopixYtYtGiRWHvRUZG\nBstW1N+zmJgYCgoKmEyZL7XpaeRyjK/4jjzmKxtIccREJq1JyfQS1m9cH3HlrMYQLD3tnLUvMqBf\nNnv2ferbLtrEMIbGYwTySUa45QadQim3i1Dx8ReNzvfckvWsWL6CslnDKB6phWvxyN6IwPRHNgGQ\nmJ2FSkvjcM0rfHbwAGPHjiUvTyeSWLHsbfpm9eCM3l0QhL89tRkIHcMaMlnIax9TdGoy+78+zpsH\nvqQOuGtrLXdtraXo1GR+9/+6MauR7dHcPMh22sxw8b9PlpWT1a+fX2IMXC5UbAyHy34JH20lqVMM\nN9/5HV+2rjtuW0F8bFybdaTRPIt24hObYOkus7OzcRcUsWL1T3mJu9lTp7dxuRRJneO5ef7F9RnJ\nbq1g+PAV1NUJAO6CoqjWmbbjrp966ilAJx+x2c+OBmUQfaa5lsbj8bC8Yjkzn7jalxd8+LQLEBH+\nNOPhJr27YfO/b/qA3Duvpc/3xvgNDBfde1+HW9r0RMcI5JOEaBZGyM7OJn/UKK7d+BIzv6XLwuV7\nDnzJWnJtYZ+23q+7//n76+83FPVj8dqdHH5P/LSA8jl/JjE+jnsWrvbt40vsAPzsujmUlT8ZtAMO\nmizk1GTKhvWiYN17pMS5eHDwafXawWsf8+VxCdkerUU02qOIUFtbS1xqZ/pd8R16XTySwx/sY8PP\n7uXrLS+DCDc3Y+GB5txrr9fL9GnTqFjpaOeCAsqWLvV7FoNp+cHSXQKULy0ju/8A9h3ZwenzS4nN\nzOT94mJunn9x0GucwTo+Y3dUC5h4vV5Kpk1n+cr6d2cp3+GHvEAiGWTgr6HbRJtprqWxn4/sUf39\nyvvlDQD0erxTp05t1H2LlJ42/ewzSe6VSfK0CXz9+WFW/fRu+jkWObGT7hituXmYxSVOUDwej98C\nAdEujPDcP/5Bp7Su3L21FtDaspNg+Z69Xi8Xuwvp168fRUVFZGdnc7G7kP3799NUfNr61n3+59+i\nv88c/S3+MH0wR78+Rp/cs+jSqyvDp11At14ZxH99lDJgF1AGpCkY0787ZbOGsXH9WkqKpwU9p21S\n9ng85AwZTFp8HFPPSOfNA0eo2X+EBwef5rd4xaLzT+WFvf5pOdsCp/boxKk9rlu3DurqGPnA9Qy9\n81pOzTtfL2Zx78+wF5RoysIDXq8X98VFfvfafXFRo+719GnT2Lhmjd892rhmDQXjxvmeV4/Hw49+\nOIOccwdROWMBf/nWZVTOWED+8JE8WdYw09S+ffv4dO8eTp0/n7RJk6j78suw13iUQ1EvYFIybTqV\nazYymTLmsovJlLGfd3mcMRxkNx9STSwJVKifhl0QpK2wnw9P1RZf2RfeL3jgknsAuOWWW/Q7WuSO\n+r45rWdO7P7AXrAD4P2/riElzkXZsJ7smphN2bCebLSmdgzNw2jIJwBObaVr164NNOEL8/JYX1kZ\n1cIIGRkZvOPZyiXfuZT169dx7asfRcz3HGpuqTlzzdnZ2RS5C5lTvhYRrRmv27KP68peo2jQqWSd\nkkKnuBgA9u7YQ2bWKXzi+Zhdb39AGfj0lGK0/Jm+ZR8P/ziHRdMGMf2RFWG1wKysLFatXtNAWw6l\nHeQMGdymnW402uPatWuB0Fo0hE6zGW5wccnkydS8+VqTc1d7PB4qVq5seI+OH2f6q6+SnZ1Nj8zu\n7N1TPxDLz8/jmmuu5fzzzw/ZzrYGl5SbC0B8795+1/jeu5+y+/39bN+2FwBFDNtYThpnAKEtHM4F\nK/zmhxGeYzr3WvuPGD6SI0eO8NwboRcEaSuys7NxF7l5am6ZTkOaN4AHLrmHg9s+9vfHKNeD02UO\nTTbcMYNN6fz01Y/oNvAs0rL0wjAHPbv4aN3rDUzbHx05yi9XrGT16tVhU8IaIhDO40uMl3W7Eize\ntkePHhKTnCZJsxbr9I6zFosrUXtKp977tt8C9an3vh02wUFNTY3k5gwJ6xXcmvG1Xq+3oZf1oFPF\n+8dLRP48RZZcNUwA+d1/75bHjpbJz/51g/aUDogb9SVyuH6U7Lp3YtBrtr1EA2NL7cQU4a6xJcPB\novVWjRT/G8lLOaN/b0lJT/IL+0lOSZD8EB7WtbW1MsrKXNWczFyRkm2k9DlVklMSGhWy5rze00tL\n5ewdO+TsHTskdXS+dE7pJP379fBrp3ji/L67wtzDSB7cv/rVryQ/P8/veLm5wROStCVer7eBl3XZ\nrGEif57i+9jvT7TvaLAogWTVTeJT6mPY7cxudrRCe0dqnCiYsKeTgGDxtskpCRLb+xw/wdvp+7cJ\nIEmzFvuVJ131SFQvZLgY07bIQOXxeCQnZ4ikdY6XJVcNk133TpQlVw2TtMQ4iYuLkSsenyV37Vwk\n31v4A93xBHT2vlSHv3c36ISChXK4gnQcwWOI45oVQ+wkWKapaOJYw92bsePHSXxactCEH3GpnaXr\noL7+g7nM7iHPV1jklvjkJIHm5a7etGlT2HsEjQ9ZsylwuyU+PV1Ou+ceyaqqklMWLJAYpSTNOt8u\nkPPA73sZSLpSQXNTb9261TcYCxXjnJ+f1+iY97bE4/HI/Pnz9Tt670Q/gRxqcBoJO4Z9AnfLr/BK\nX5e/4HcOXotOTZYuAfnbmxt7fzJiBPIJTqR425Tfb/bXhJVLYpLTm7wwQqR6tHYGqmDaskshySrT\nr6wTnSXN6uDtRA4ZChnTv7ssuWqYdEnuJEXuQt9xg2bnio+R89I7+XUcLRlDHIzGZJqKlmBadOcR\nwyXzppvElRDvVz4qPy/ktTgzfzVXQ3a73ZIQ65IMl/K7R2kK6XG+HsSFSmmZMzgnYtawAncQ4WAJ\n/UfBSOYAACAASURBVK0B3xsM2ByDtEDLU1xMvFzMw34LVuRdmNesAUSwdm6NWF7fO9pMDdlJ4CIe\n47lLElzJkndhvu+d+v3AzDbpG04GjEA+QbFfWjv1YajOq/P1f22gCY/Ky/frZKJdGCESrZGBKhTO\nxSNyBudKUkwXGc9dcglPyHjukiRXunSK9xc2Lsf/zsQikQYTdw3KbNBxtEZGqqZkmmrssbvOnCln\nrVnjM+eeds89AoTNER34rE3Z+Yz0dI9okGazU0ZqVLmr7brM++1EyRt1VoN7dNHTvw0r4BJUsuQM\nzonaomPX2zaPV1jnipSbOpjlKSWlk1/+b3dBkTz99NMCyP+Vz5A/PlYsy9bMDpoTOxJNTfXaGIrc\nhdIlpZOfhalLiv/gtDGEW/wjcPDaFvnbT3SMQD6B2Lp1qzz99NNyYYBADdd5dfrBbUE14dYQKK2t\nPYY7b7BOYefOnb6VeuzP4JyG83qRzO1PDD1dIHimp1Bs3bpVFixYIHPmzPHLIBaOSPOUzem47GNn\nVVX5hPHZO3ZIVlVVyGMHywJma8bB0mx26dY1qnsduKjDsjWz5Y+PFcv/PTlDDxj/9ybpOqivJKck\n+M1tp6Z0lmRXd79zBua0DqZdBuaijqQh19TURLQ83Xfffb5zbNq0SVzKv43y8vvKvAUXN2ogFTgA\nuP7G8dK5c4Lk5bVcxrSg/hiOwWlTCdef2KZtoyFHxgjkE4Da2toGy/25zjhXUm5/SZJmLZaY+HhJ\nSe3UIB9vjx7+TiwtpQmHY+vWrfLoo49GXJWnNQjVKUQafETSkOf/vx5+2nWwgYYtCKqrq/UgwOXy\n2z69SxfZuXNn2Pq3hYbsdHZyasjBjh0sh7QrIU7iUjv7NOPchddIbHKipPXvHXUdIwk7X5tlpPt9\nT3Z1l/iU1KA5rSPNvQfmorbnkJ3m8nRcEkesuAuKIq4ElZOT47ueInehpCfFS9ksrXWWzRomGZ3j\nJSE+Juo5ZGebvLT5lzJqdJb/ICjMVIK9f2MG2G25NnJtba1kdusuaXGuNrGencgYgdzB2bp1q/Qb\ncLYQnygJRbOl8/8sk6RZi0V1zpDYQRMk/c8HJHFGaYNl9GxTV1u9eJHyTXdktm7dKrk5Q6RLpzj/\nDsOaQ06IcUl6CIeUYLmCVYxLVHKynF5aKllVVXJ6aam4UlIkrlMnPzN5sPsSOCdnz1O2RC7kQGen\n0+65R+LT06UgiNAI5Z094JrLGgw2erpHyCWvPdEoLT7Uog75+Xm+drHrMJIbpISVQetjz1vnXZgf\ndO4978J83xrRgbmoYwIGWdkUyMU8LIBPq7v+xvF+ZmjnoMFZx1DzstF6WTsHAKNGZ0laeqKfqTwt\nLSmocG8LM3dzcRcUSZIrXU5loF89M7uFdh78pmIEcgeltrZWxoyzVo1R/h1g7KAJkjijVEA7bdlh\nS48++mi7JXa356acWkJz5qbagkDP6sAO2qkVh9Ke8/LzJKVLisx84mq5a+cimfnE1ZKYmiiJZw8I\nqomeN+j8Bg5HQ3JzfR13uDm5xhIo9IM5OxWE6LxDrRTlXvOAQPglHaMh2kUd7AHKCH4RtD62Z3c4\ny4KzHWtqaqSiokIWLFhg7VMuxVTIbDx+0wPnDTxP0tNT/fbvf3ampKZ2kqEj+vgGH77pjmZ6LtuC\n/fobx4e1HjQYwAWZ505LDy6824NAq89sPFJMhYznrmZbfU5GzOISHZS8/NH8Z8tWVI8+cOgAidPv\nJLbfSI5tfZkjS34FX+sMRHV7diJfeAHIz89vl1yx4fNNh0++0Z44E5mkxioueWm33+/npCUwMC2B\nsl2fhUwGUrmuMmSu4K/efZeEPn0A6Dx0KACvv/k6ce+ncXppKUm5uRyuqeHV3/yG3KFDybsgj7//\n8zkqVixr1sITwVI82rmaV1RURHXsUDmkD32wF1wu3rpzCUNLryPm/8WzvXxlyFSWocjIyKAiRF08\nHg/r1q1DKcVtv53PPG5h+cq7g9bHzkoGoXNIX8ITHGIvL6yez4ED15GRkUKFlejlWYrJpoBLWQrU\np7l8++3/kNg5hoWlk335r397yzISEmIpLDqb6g3v0rdvX1sRoXLrPt+zD/WZ5D788MOonv/s7Gzc\nbjcPP/ACEN2iKHYWvoUhUp++8sor5OTkhD1va2MnarHvTVey6EoWPTiH1dzQ5vm9TxaMQG4jNm/e\nzPCRF3Dsa71akex9F9cZ5xI7aAKuzum+zFqHH7kKgOMfvsOxZfdS4G771Hw2kfJNd8SXLjBJ/sCV\n2xrkqL721Y/44PBRIPTqT9AwV3CXnl0BOFRd7RPIh6qr9Y8i9Jg3j7RJkzh+4ACfP/ccckin3axc\nv44BWdm8s83TrIUnnCkee5PH+1T65WqO9thDcnLYOKcUEf8sYGPHjiEuLo4VjgUrzjn3XAonFDQQ\nPpFyXTvr4vV6mfLd7/Pii2t8OccBxo8dS01NDVdfcw2b5t7XoD6j8vOpWrcuZA7pN1yP8W5dJdTB\npk0vkxIX45dN7qevPs9fj17KIK5gBddxGjl8VPcKN83/dlBBd+/vX8Dt1oMPj8eDS8HsJa8hop/5\nZa9/zM+WvArAzJkzAZ2j+9YFC/j0009DtkV5eTmXXHIJlZWVUWVPs9+7UML76quvpqamJsidbTsC\nV+Cyaa/83icN4dRnMSbrFiMuIVFITPXLsOWcL3Zm1gJlmR3bdwH01ohvbG59IpnubVPjmxPOkrzu\nSWHN0i6QjIS4Bg4p+aNGCSAzn7haHjtaJov2PCwDCwf5OyKNGC6nLFggrpQU39yr7emcOmqUpMXE\n+CWnSAPJHzWqWddOGPNtpHsRLkEK+Hs1V1dXy6BB5/vPn+OScWPGy44dO8JmEAuGu6BI4lScpMfF\n+s3Xp8fH+Jzogh1zx44d0r1rpiSQ5jf3nkgXSXb1kNQUbdL9v/IZYe8zIJ2pj2cP59BlX4f9HA0b\nfqZfewVLOuIXdhcm+iA/P0/S0pIazK8HmqGjcY7rCCbh1vSLONkwc8gdhK1bt8rll18uEDqTlp3k\nw/4+oN+ADvHCifjHN6799Wj5hbufpHWOb9M55HALpwdid2bnpXeS5FgtKEOFPQUKJUDGjx3rS0uY\n0iVFrnh8lgwYfbakpCU2yJjmcilLGMcIaE/ns9as0cKB4KE3TfVSb27oVNAEKZ3iJDdnSFAHtE4q\n3c+RqhMZEkuCdO/Ro4GHtu0RHQz7foQTmM40ps4Bl+00dArn+d2jbvT3E1h/fKw46H1eO1oL0y5k\nSSJdfPOboRy6goVULSydLMvWzJZbb/922PtqfxJcSsaPHRO0LaKdXxcRyc3NaRAelpae6DfP3d5E\n4xfRWslQTjSMQG5namtr5aKx4/wct0Llmk688o9aGCemSrfumR3KQ9Hr9cq4sWMaxGOOHze2zeoZ\nXJiEDq3IG3WhAHJXhExCgNzQr6usG32m3NCvq6TFx0neqAt93rt5jhzGIbUVpWTcmPEydvx4iUtP\nly5XXKGFQ0DHvSug43YXuaW6urpR8c9N1ZAbk20t0nmgoUd0zsJrBAgal20PJMINjIIJl1BOQ10Z\nILEk+Wm6/17zU7/rC5Zf+VQGyhx2NIh31g5dwZ2lnB7jt991Sdj7+oQlrDOsQV64+xFNhER1dXWD\nCItRo7MaHQPdFgS7nhPBS7wtMQK5nbkwL0+IiRUSUyS+6LqwGnJ953B2h3xgi9yF0iW5fTytm5K6\n086wtGtiti/XrtMsnRGnw54A8bizfJ24XWZ/nAtvhDJz3nfffSLS0NM5lCb1y+dvkukP/kji42Kj\n0vidBDMRJrrSIma3akw+8kiaONR7RBfvqZBehcPDdrqN0ZCd+9j5mQPrcTVvij2l4xwk5eX1lYwE\nfZ/H9OgsGXEuf/N4XJykqG4+M7fT0tGjR/AwnerqasnNzfG7vpBpOQO+P/roo2HvZTS43W5JSUmU\nGVeMkP97coafibuja57BvMQ7Ug7wtsYI5HZCr5qT7/cSxw6aICq1m55DduSaVp0zJKb/hZLgnt3h\nRr3O1ZGg/eaRm7K4hVOIey8Z0EBbGpAaL6mxSsb06Ow7XvAk+XE+k3YoDTlQK6ypqZGM1NSGySlc\nSgZOOFceO1omAwsGSnpCbKMT8jcwEQbEDYcKdWotDblX4XD5/+x9eXxU9dX+M3cymUySycxkBZRN\nyMJiEknCEjIzMQvJDLRatGpI0hetUK2AVUDh7VsUcUFDUaSWrRTpm5DGV0t/rUwIppUElCUKQtGa\nYVGEukHGXWuFnN8fN/fm7ncmCatzPp/5wExm7v3e7Xu+55znPI/VHq076QpryMKFEVdD5kyJBKQf\nsul+BGTjGDs2T1SPXbRkMplNRl3nP2/hJN22IyUms5zcXJo4fryIhOR/AYoHyKsQMXMLNeF5DdWB\nKqW4S0qKqaSk5KKOPPVq4BfTPHe+LOyQL5CVebysyIMEvMVkFJAhTsywFTHCRZbpy/tEBKKvTIkI\nBOg7JZlQLVhnIp3wvOVlZBdExjVZKRRtNJAB3bXjNTkDiG4aTe2eVM19GAwgqwSMY7VFE8MYFI8/\nEAjwwDB+EeDKoKc/XkOPvFnD76vdk0o+52Dye1I1I37psfn9fsrJyyOT3S4iKVEjA+HOR7B85FwN\nWRiJR8FBRpgIDEORdiufpg5m0g0EAlRSVKparxfuV0oCYoaNUpAtAw2p1WOfeuopzQXcIzXXK2Y6\nhNex3Oshsy2WRt9bQZ6/reTr5MWlJTISkmyAAgoRM0eL2RepW04idMmSJZSamkoWi4nmLZzU68jz\nXEXZemxoF0P9+3xb2CFfAOOchxZ4yzTpZ3zKrTuyubBoaqFJiUBqbsm8oBEykbYzUWMSa25uljkA\nRvBvfoKFT2VznNZa4C/LyBGibVlGZugev1BrmUNsc5rORV0IcO7FvRdOVmrHtnfvXgK06TKVyEOC\nBcZxDlT4XQMYyu5CXsdM6E5ThzLp+v1+VfrVYCJzoBs0pOToXC4XPfnkk72KkPfs2SOr3Q4sH08T\nnpnHf4+rmY7JypJlQmwApQ1P4r/bk9St8Np1dHRQcXGxbEwMY6DxE6+iV/bdrxt5Su+Fc13fDUfI\ncgs75Atg3MpQDbxl9swmQ7SNyjze88o5G6yptTllD7KTzWLqMyWZUE3LmagxieV21X/Hx1soNoKh\nZVkpfHo4NsJAw2JM5JY4RbVJvMDlIpPdTskLFtCAmhpKXrBAMxqVmhCxfV/zL9l2K4jbZzgwkDAF\nrnhssVGUlspG9GqCEmNyxXVPYSo7lPtO6kCFvNkDn322Tyddvdr14sWLRdv0eDxk60qXv7DlTsoY\n2U+08LJLcAPxUZGUGO/QbDtqb2+ntLRUirWaxUpQ9mgaUHiNbKHR0NAgW/S5nMPoT747CQC/GAv2\nHCml7JOTkynSHEFWyZjibFEUaTaSszBVdRGk5njdbhdZ46LExxgXRSWlJSFdMy1To1AN15DDDvm8\nmV6EDICKSkovmmhYamp0gQcfmSRDWV8IPmupM9Hrk5Y6WiX07Yg4VspxbF6uahQeCjWlknFtVKIx\nQRkcxIGBgjm2qJEjKX3fPlmEbLJag05lh2pC3uzYCeMp1mYJqq9WbxGgFyE3NTXx29izZ4/I0Uk5\nohc9PEVUT+YWcMeOHVN0UEePHhV9Hmz/LzfmeQsnKbZR6UmoSh2oNGXPtWnpjYmj5dSi39xQN51u\nnZFPsV0yk+c6eg2lxev7YGHqzAtgaWlpKPN48be6+wEiRGRMxJm3X8G3tfdj1NWZ2PzC8xcds5XQ\nOPYdKV3ggfc+QycB27Ztw5kzZ3pE+9gXJmWi0mMSS00djsOHj/D0mNV7T2J3xzciNqe79r2PlKRE\nbG3ahqppFajuol4EAG95GWo31cPhcARNTalkDocDvi3s71euXImVK1dKyCDRRQbZbXrHNvreCrSv\n/wuOV1Vi4Jq1+GrvXpxasgRgGCQvXgzbddcBAGzXXQciQtPcuX1CdVpfV4eKyko0zZ0LAGAYAxbO\n3cz/3eVyoa6uDkAX1WdVFRobG/m/ezwe1NXVweFwiLablpYGT5kXTc1zQGcJQ+DGu2hBEzMbSYnJ\nKCsr479rs8cBYJms3n3nNHZsPyyimbypIhcWiwkL527GunXr4Ha7QUR4++23sWLFCqxYsUJ0Hb1e\nL3btasWtM/KxYd2rqgxZObm5svPHMAaseroFP64YgyiLCQcPnMSjD24FwxgwaNAgAAiKncvv96Ox\nyYepqOWZr5Ixiv+72pgAYPXKVp5hTLS9xkYsWjIFvj8fRGvrke4xA4iNNStur6WlpU+ebS0K1bCp\nW0gO+Z577oHNZhN9VlFRgYqKij4d1KVs9XW1qKisQlMXBSYAlHm8qK+rlU1CF5ulpaXB6ynHnLrt\nIGIn/5a3T+HuTQfg9ZSjtLQ06G3pUSv2haktIDi+4cceW4obb7wRrae+Rl48wffBlzylJgBUDo4E\nAajecxKnT5/GlsatmhNIb2gvud/PmjULK1euRCsgIBxEF+Egy1sezLGl3/5DxGcOR+v0JTjsdAIA\ncvLy8HpbG6Lz8kT75fi2pXzJPbk+0sVJYmIi5s+di5YdO9jxtraialoFajfVY+rUH+GNN14T8UY/\n9tBWVFZWwufzybZdV1+LyooqbG6q5j9LSkzGl2e+hXvjIqQ4s/DO//0dbfc/A4B1dDabpevYlR1W\nXFwcfjFnNs9xDQBupxM1v/41jhw5gnfeeYfnjR6dOQAb1r2q6kCrq6pEi5o33ngD6CR89dW3ePZ3\nu/Ds73aBAdDfkIMv6HWcPXsWHo8HSx9qAhEhd+wQvLb3XTy+ZJvMgUq5oQHAgWH8/9XGBAA5OeP4\nRZB0e00vHsLhAydli9AnljSisDhdcXt9ab19Zi5Fq6+vR319veizzz77LLgfa4XPFE5Z99guxhpx\nMNZbofO+lmvUS3cKmcSU6tssIMxE89IT2HS8CnBr7dq15+16lRYVkd1gkIGBJo4fr3tsdquZBpWP\nEyk0zZkzR1bjVQN79dX14a6L2+lUJG1JiHdoplq15AuFqGJATERS+lc2jTt2wlCy2S1074ISzf1k\njh5F8VEm0fhsJkZWguHSylz6W4So70rzct/lgGVup5NiIww8uQy/bbCgq7a2tqBTt2op+/5MFpki\njWSVsHbF2aIoMjKC3G6X6vXh9qeGjZi7oFS0PYYxKJK7hK33Fq4hh61X1tMFRV/JNQZLl6m3gJAC\nwrRYu7hXKPXhnvaXSttnOHBQ7phckWSj9NgGlY+jG9sbaKBHQsjRxfw1JjeXTFarqjay1vUJ5liU\ngEda51SLN1rp/Im2b2Cd4E3HXqAb3txEpX+toUk+Vp500cNTyFnIgtsYxiCjmbTGRZHBoD2+FEMW\nXYOfihz6K/vu57fLvYyMkSZhuUiPecK4CTJAlzclllbnDCAAFBMTKaqlKz1P0mNXIn6JZuzkcNgV\nUdYlJSWa92luF7hPq3uAe11MlJyXo4UdctjOu/WlGEWodJl6Cwi/3095uTkUH2WSkVNERERQwowZ\nNHjTpqBBUB0dHb0CehERuQrcZGZiqRCLaShzrczBCpHRObm5ZLbFkuvZX1H/a8dQtCNGpNUcbYsm\ng3DSNhhk4woWBCeMAqUmBB5dj426E74WGMkpoCYFWEGJkqJSsjA2cjDDyBQTQwAoIUvsIM22GN4B\nb9g0nSqq88gUKQZxmYyRNBazg3JISg7dYjGRHUNoIu6nKDgoFV4R0ExJaCLe0N2+Nv32Car3vNpi\n89ixY4ooa+H71NRUeuqppzQdO2dce5zagmTts9U8GO1iaUm62BnIemphhxy28259LeoeCl1mMKbU\nPmWUOKI4l4v6daVKtfbDoY17imYWpihTGQ/FxFlFDjY2PpY8AsEGqSIS19fMvap+M13skAGyO+yi\n1LD0+rQ/7iHfXCe1/HchAaB8zBdFgVLVHmladRa0r5Nj9FWaAgkRUWbKe/wuXqQiyhFHBgH7mHvj\nIrKkOMhis4jOTVRcFDFG8bG63S5qa2vjW43yMZ+q0KQ5PoNB7JSV/g+A+jOs0lcVmqgSPvoRatnt\nQhkpD4A2bJques/rLTa5xaXb7dLsYQ6mn5gr2UgXoWaT8aJqSQpFQOZStLBDDlufWCgr1r6KkHtC\nlxmKcTXKIYMHkU3CeWyLMpF13DjN/QRTqw32GKdju6KDvf3ZOxS3xbXS1BxbIfp+xrUjyRIndlyW\nOAu5BDVGbtyrp+eQN7OfaPJjALoJL2iKVij1CqehjOwm8YRvMzGUmDmMfvDqWplzmzDxKkpNFws8\nDCwfT5Uf+8j17K8IAF0DVqCDq5GrnRs7hom2U1RSTGOyxTKZJpgpxgixQ4oyUVRctKzfODrGRAxj\noJiYKLp1Rj499uupdOuMfIqJEdeQOUeuJjQxenR/1Ygz2MXm1q1bCdAmMgmGdERpEVpafPHRb4aa\nEbvULNz2FLZeWSAQQHVVJXyNW/nP3C4nNv/5/ymixTnUrtvlxJy6NkWUdrBoSx5hfOprVA6O5D9v\nOfU1gN6JnwcCARHqVhl1vUdzPxyCNRg0s5pxx+jHFgBAmjMDH/o/wKljHyN5WArSXSNE2+LOL9dK\n49/xNsZPmwgA+ND/Ad5++S3M2Hgn/9n4aRNBRPjd9NU8MphD0d9duw3RjEGGvH3+uxtwFePBDZ11\nGNLViHXkyBEQEY4ePQqj0QhALEr/I9TjD98VonrPQf7YYg2J+OadU/jMfwL9XNn47LW38ONbxsBd\nlI7HH27EB+9/LkJeP/LQVrT+5CFM+O18AMCVGIv9+B1O+F7lz43QuHPzpeUDuFez6OuPdhzAjtsf\nRTQIq3MG4LkTn+HvH3+F7/AtvjsL/Neek+js+n3c0P749zsfYN7CSTxyecp1mTh+vANrVrTgq6/+\njQ3r2H0zADrBtjdd/8ANeOtv/8DJ/e/g66++U0fKl6QrIqkBQTtbVyseZ+6u9/v378fdd9/Nt4ot\ne2wb9ux6B0uXT4XNZuHR49u3b+fR4cJjICIsnLuZv+YOh0O1e+BiaUny+/3wbW1Sfha3NvVJu96l\nYmGHHDZFq66qxKs7Xkb2IBveeI+F7Le07sCI9DT8s93PO2Ulx52SnITqNXv4915POWrrNvHv9Vpu\n0tLS4C0vw5ztL4PATlYtp77G3Qc+hre8rFcPZ3XlNOze/jLmpyegpr1DdWIco9BzyhnnTL9ua+P7\nfQHgq717AegvGLjjdxW40fbqWqAT+M0Pl+H4oRP8dwaNvhIAkJiYCLezEK07W/i/JScno/6eWhAR\n0l0j0Lr+ZQDqjku4QHhw8UPwNW7FepX2r+OWHXjhm0pc3TkNAPDY449jR0v3vhOTU7D19CxQZ3ev\n8OfGk8jLzkPb622YhGW4hm7DC19VonX6EgCsM+PaggCoOpF+z/0NADAEhRjOeNC+5q/s+RIsPgCg\nvfWfAIBrHrwVw6ZNAgCczc3Ad9+dwTPjrsSm9z7FG5/+W7bg+Py7ThgAfP7OBwBYZ7d351E8tuJG\n2GwWvLz1n7CaGDwzZgD/u9lvfIiUoQk4+a9P8doLe3Hy0AksXT4Vvs0HMOfVY6CzBDdYZzwLrAN/\n5qntfL+11PQWm8888wwOHnxd0irWiAX3/gmr1lfy7UkGgwGAeruXdFGo1H50sbQk6S1SglngXjam\nFT5TOGX9vTQurZY9yEbxZqOsZcTtdPLfVUPtul1OZY1UHUFzzkLhXg71uDhhB2ikDrXacojEjFVK\naGYlU2o5SklKUgQI2QBKSoinpIQUMsMmElyIZuwysA+CTHvrlQNSb2X1ds2GWIpPTCKjOVKc2jZH\nUnxikuI1lKKE88Hydk/CMpoNPxWClVR8pOZ6ntVKiLw2WsyUwmTTgyC6HwEaznjIwBjIEmeh25+9\ng2qOraDbn72DLPYYMjAGXgZS2A61vXCI6nVVBGIZDeRyDpNpKnOvJ7o0tW+bmc8f70s77qFX9t1P\nLqc4Zc4ANHPmTN2ShTI3u4muHj2KAHUg3NwFpSL5Ra3vXkqgKC49r/YsXg6tWOEacth6bEJRea1a\nV6g1YyU1HyXwkND6sp9b6oyUtJLjo0xB1a16QqeptHixxbAOTwsgpEYnuW3bNvL5fNTW1kZJKcky\nxxUbbxUBw4j0a5giB8MYKCouWlyX7nKGGzZskF0XmTwkQEkJKRRjjKfJWEX9DJmivzkLU0XiCI74\neIpmxCpTJsTIwGox48YSIO5PvuHNTQSA5qv0m/OOWuU8T/1xtuh3SjSrXJvTrTPy+QXFlubZPKKa\nX6Do3AdKi01hC5Vaq5h023p80ZcKYtnn87GLJbMEi2Bm5U8vh1assEMOGxH17KEUkgpoAav0UNVC\nQQBum/m4jwqxmKqxTRU8dK5M6oyUtJJDjcKDXTCoLV7medLZcyhxFO8JxjQdLTyquRI+mo4W/hpw\nFggEyCVpIxK2TgmNQ94+kZlCG8deQTVZKWSLNJI5ghEvFiwmGpw1SDHqBtg2JaXtNzU10ezZs+nh\nhx+mtrY2Kr62RIZmz0hPpri4KMoYmUJxNgtdffVoamtrkzn03DFsL+3gTZto4Pr1NKy5mUYePUox\nE8aTKS6GXM/+im469gK5nv0VRZgiKDbCoLjg4IlhNM6z8HdK+tgc6Qf33bEThtKiJZPJajWLQF+R\n5ggqKdEXavD7/ZSbm0tWq4UWLZlCV2dfqRn15uXkiX6vRjpy9OjRoDNRF4Nxz4Zp0CjRmE0DR11y\n0b6ahR3y99x620bgcjl7HSHz+/WU04oVK2STshWJdBt2ipwLt4AQigmoWU8WG0rpQlukiXJzxgSl\nRdxTU1u8bF9YqBshA6BYRpwmBsMoptWDWSAcPXqUEmw20faMAC2flqV4HR99axnvkGuOrSAANPre\nCrLE26hcEIF3dHRQ8bUlBEHrEgCKt9sVU8UZ6WzaXdh+VO71UFtbG38MXDpTimjvt2SJbD/FFgy6\nQAAAIABJREFUpSWUlJDARlsmRnSNeUetcp63bdvG3xtcmlrt3t+waTotXT6VYrscsSnSSIseniJS\nYmIYQ9CLtKXLp/IMYRkj+5FNItjBbU/NOUmveU8yURfayjxeirA6KOqWh8gycxVF3fLQRaUT31sL\nO+TvgWk5i962EQQCAUpJSpRNbNJtKNE72iwmyh5k5yMte3QkRUVEkA2gGoA2ArQMbD3PAjZC3bt3\nb/eqXjLRSlPBWrVoPQfKUR7qLVRC1YzV26/W4oUByA5GRqWZkZ7MT/KxVjNZRo7ge56NVmuPFJw6\nOjooJTFRsWZdlJGkmOn4xV/nyyLkG96q59uUhI7AaDST0Wrj+7OTFyzQXXCMvreC70WWOnmfz0dg\nGGJs4no9Y7OL7hNXgZsCgQAFAgEaNepqGTVmTLSJl73kzvMTAMUCZOg6BmkqWS07tGp9pShyVWtN\n4pS71IxbpG2om847ZiWmsKxrussJeulbPdWsizXaZEtA4mf6YtKJ762FHfJlbHrRb6jEGmrOJBjn\npUTvmD3IToFV1/MT+xM3s7XDbIgnSe59Xk4ev6pPYbIpwqpNuKEWASQlpCg6aeF5k9Z9C1wuZUaq\nIIXlQwGqKXJTW8w0DMWUBvH1zEhPplf23S+b5LmUbSg9z0JzulyaDtL/hEcWIf946S18XTo6PpYG\nesbTbd/tpJuOvcA7CWGZQxjNDly/nnVukv0JU8U3vFXP14I5Jy/VYDYOvloc0Xe9n44WWQTIObp8\nzKexmENXII8sFjMBoCLIa7YAKHPUKP6aNTVpE4pw9WOutvtIzfWKNV89h8wd260zWMCYsHa8pXk2\nPVJzPXuMghq11oLP5/PxferB6kpfbHapagDoWdghX8amF/0GS6yhJTQgdNJ6D0l7ezutW7eOfvKT\nn7D7laRlN84cSwxA8ZCkLdGNTAXA679qEW7oRQCTsEw1TRcsu1YoCNZQ0oOBQIDcLvEC5ypDId2P\nAD0Iotnw0wTcK5uchZP8wPXraeTRo5S6Y0dQEZPScWk5yHmedFGmw2QV19gHelgSD6Hz5O4P7jsD\namr4hcOw5mbNBUDy2BG8M678yEcD3NmyBWBRSaksnclY7DTM4FWMAKX3yP0I0BWGXH4cbrCRsjRD\nkJKYwDtlb3kZ2SWAP4fZSC7XcNn9oEXeoWcej4esVovm/RYTYyaTMVLxnlJanDMATcEa/tzchw6e\nbYxfNH6PtYkvhIWJQS5TC6aJPlhijeqqSuzeuR21d4yDKz0Jre2nMKduO0ZkpOOjj0/xv+P6iKWE\nIIFAAFXTqtHYJJbSa20/hbyh8Tj68ZcYnhKLQyc/QyeAp9FNpFAJ9u6sBhAdzfYbxiAZgDrhxo9v\nvhn/vWABALFMHQCeyCIJI2HDQGSiEnSWsLmpGocPHwYRoamxEVcsX66rFcz1Rer1eCpp2Er3y22T\nO1ctrTtE28ym22EBe14TkIoY9AOgLrcXOZgdk7DnmetrNhqNOHv2rGp/N3dcAFRJLZY1tmNZYzsA\nIHpEBq6q24QzgQD+dc89OPvuMQy9uRTfffkNTvh2oe3ep1HuZckvPvnkEzAGoJOA9+ezBB9xbhf6\nP/kULOnpuKu9HQSIenaNAIZPn8KPYcdPFuOr3YdE/cNztr+MnIlOFBdMQNMfF/HfHWwowo1Uy78X\nEpl4PB5eW/nbs1/gLfwf/kWvgQFwB4AvAdRC4V483YGySaWYMfNnyB6Tg61bm1C95yS/D7OJQWFp\nOj54/zO8tvddPPbQVsTFxWLNb1qRkBjDyys+urgRJaUlQfXO1tXVobKyEk1NW/HwA1tA1C3T+MiD\nPjCMAd988x8UFZagrr5W9nuur154zmbt/wBN392FDjqMdPwATcwcfBHzNpYu7u5tXvpQk6oMZtgu\noGl5awpHyBedBRv9Kvc6dkfReoCsZbdk6ao1SaPDUtSQASBzhLgGHNEFSFGLyjj+Yb0ImYmJIafb\nrRkhz4Zflqbz+Xz8PgZv2iTatlKkGWyErEQlKd2v2rmailqKgJmiDOI2nxhjPCUnJ8vaWdgacoao\n5/na4mI+BW+AOAXrLZPXxbnjShw9jOxGec3ahAhyFbhp8WK2Xzh1xw7+PA3dvJnMGemifQhR1l5P\nOdmjI8Uo7VgzWUZkEGO1UnJioui3bqeTiktLyBJvI9ezv+JpMrXKLBzlKXf9OdT5bPhlNVKuBcsA\nhu/jvhMHyYEhmvei8MUA9Ju1FbRqfSX98c8zZLVdDmiVnCzpy+5B9NnW1kZ5ebmi7Vx99WiZkITS\n9dRrYWMYAy16eMol3698KVs4ZX0JWG9akvTqw3rEGrpCEHOdmn3FaqljiyGWbBaTaGKONUdopi39\nfr+ohmy02kQAHqPdTrGFhbxjdhW4ZTJ1ZtioH7IVnbS0Dh7nclH6vn0iZy8lHxD2eG6om07Tb59A\nsdYoysvLlbVy6QFo1L43GavJAPHixVPmZVV/JLVu6aRf5vGQq7CQIiIiRJ8XAbQGoHijkbxlcgBf\nuddDUfY4ShwtJ7UoKSoVqUJdsXw5pb/+OsVJ2qkyu1qUZPekyuJuQn4+BQIBWelDJJjB8UMHwV9e\nfG0JmQwm0ZhMBhOVFJUqPiulqOEdNy+IoXIvLoM4jZ2RnixyZHMXlBIA+unPJoqwBS6Xq09qn6HU\nUPUW54/UXM8DArOyr+SVnYQlkMuhx/dSsLBDvkC2detWWrx4sSa7TG9bkvSiX6GpPeC6pB4CcI+S\nWpNSdMhPdipIYgcjjsqETmPv3r2UnTWGPR8SlHVsYSGl79vHR7MNDQ2KBBRSUokYYzylJCbJ6u22\nyAiKycpi0bpWK78/IRgrEAhQcXGxDPzDveeQ30oattIasl4kvW7dOsVrJL12wvft7e1se4/BIKvL\newUORtoaJVWNAkCjr75a9j2u3m4ZkUG2WLOmvnVvVb5cbhdZ4qKDWmgSEZUWF5Nd0iNsjzRSaXGx\naLsNDQ2y65eGMroC15ANkGUIslWc9NwFpaJMRcbIforR5sMPP3xeI069xTnnfIWSlwBLyLJoyWTR\n/XGpkIhcqhZ2yOfZjhw5QilJ4rRcSlIiNTc3y270vmhJCsWhqz1sWi1LesxbSlFfJbQnZunk6C4o\noPXr15OrgE1DG8CQCbG8aHzCjBk8OEgJWSx0UEosUa4Cl+aEZQAoyTCa7sRBRTAWR6AhcuZmE0Vn\nZPBgMKX9SlHWoUTSwU6KXOpWLdJr6fp3TG6u4u+VFmrt7e20du1aWrduHbW1tVEBh8jWYWLjqQ9V\nvqe1OOV+O2PjnZRZlkmOqAhN5rRQOggmThgvV/MyGSkJ6bKeeAagg5JzqZjGZgz0J9+dqkxaDGPg\nMw3n0rh7ZWxurqw10WGOEIHPhGhwrk/aFGkkkzGSiq8tCam973xaMMHNpWJhh3yejevZ5R7+1TkD\nyMxI6nrlZbqi4aGsUKWTqnRC10JREym3LKUkJ5E91ixy0no1ZC46LMETmhPzdLTQJCwjsyGGEhwO\n0X6TDekipzWc8VCEVdx7aoyL0+29FZ4TvZRejJGhNJRpInXVrhPXX6u0OFAyrUg61J5nom6HrIqW\nFmxL757iCD2kUonZ12TrRr4dHR2Ul5dLjAFks5hkLV2MQbn9R9ouVnNsBT398RrKLBPTa+bl5ojO\nQ7AYCu76zU9PIL8nVXb9JozPJ7ezQLQvrV7pdevW8e1QatiCeQsnkdVqUUVE94UptdlJF7oZGSmK\nLXPSiLkQi4lhDGSzR+u2951PUwtujh07dsHG1FsLO+TzaErk6N7+seSQrM7joyIpLzcnqAklFFNz\nvKUlxYrCD1LnKo00tZy40AKBAB/dci8jY5BNzDaLiaxMEu/4+hsyZXVmbvLm0rqcuIBw28WloUUe\nek61JovtW+aAYEIwlt7En3zfffxEHYxpRdJKPc82WzS5BVrGqsem4kRiGYasY8cq3lPShZunzEsm\nYyRZrRbRGGI4nm2NCNnj8ZDNHk2LHp5C48YPETsKATmHdIHBLVA4IJ9QGOPRt5ZR2b1excVEMBFy\nR0cHL9TA38P9Yylw/Qj++j366KNE1H3vu51OshsMojS2HQyZECFyrkr80Ta7hZyFqbL08LlI/yoB\nKbnFwKr1lZQ7djDZ7BYx9iHWzI/vxeZZfH9zCR7XXGBcqPS1NLjhaEtTkhIvyHj6wsIO+Twah0rl\nJm89JaG+ipA50xQtCFL4QWrSemUwyk18ZDXIrvheBKpRGdckLBOldbkJp6dpK295GTkkpPXxkUby\n9o/lJ+dK+GQR8p49exSv06qcAbKIJJT6v1JWQ2tSdKkQlxARlRYVyZyIrStiinM6WXpJwbXu6Ogg\nb5m41OEu6I4SlcbAGEDxsVGKGROlsW9pnk1XDrRTdIxJNeqSpvBTGQ/FxFlFwhhWBWEM4TXVwlCw\n1zxCvBjuuubcczYifYRom3v37qUxWeJeXSHQjTMl/mhOJEOavu5rwJRa6aM/k0WxVjM99usf0Z98\nd1JqepLsHs3LG0T5E4aKPrPjCgLUe94vBODrclV+Cvchn0cbN24cgO6+36Nf/geAur5nbs4YzDlw\nCITea/36/X74Grei9o5xqMwfDACozB+MN45/imWN7XClJ4nHkMG+lwrPS/tXU1NTkZCQINM69nrK\n8eDih/DzO36ON984jKmoxWC4cByt+Atm4gy+xl/uKcC/vzuLIx+xfchRJiMG3fMiAjjCb0dtXNvx\nIDrRiVik4Ct8jFeNj8FT4kVpaWlI54Wz2k31+NF116F6R3f/r7d/LGrHDcSWD74AAJgQgwOoxTbj\n3fCUeJGamgrPZC9Mpgjc9caHouv0i/0fwGoy4pkx/UW9slXTKrBFcJ7UTKpBq9fz/Prre1T7RRue\nfx5VFRWobmriPzNfeSUGLFqEzi++wOklS1Dm8fD7q542Dbubm1ELwAW2F3n2q6+CAdCpMoZOAoZl\njFbUt969e7ds7ATCyROfqmoeHz58mD9mrpd8amcd/vRlJX43fTW/HY/Xg7pauZ4wwF7TqmkVqN7a\nfdze8jLUbqrX7tPfcxI7T38Nk9GAt/3/xEsvvYScnBxZL/3okaNx+8zb4fV6Zc+jw+GAz+fDtm3b\nUFZWhnkLJ6GwOA0H95/EoMHxOHigu29ZTxc7VJOeN86u7/xfrPkqCwvnbgbAajJL79Gf7n0PUQTR\ntb8L/wID9Z73vh5/MLZnD3ufqc2du3bt6vFccClY2CH3gZWVlSElKRF37XsfBGCghT2tasQcq1av\nwQO/+h/FCSVU48W9JQ5uSnZ/LGtsR2v7Kd5RA0DL2yzhx+NLH0NL6w6ezIEfh4AEpLxsEv556ACW\n3ZKFm8YNRGv7Kcz6w98xftxW/jeHDLVIJS8yUYnP8T6acR+/z9R+VgBA7SvHAbCO7yTYB05tXAYQ\nmnEf/3lKfBKeWfWbkM8LZw6HA9tbW1HocuL13bvxwMgE3DzQhi0ffIHZ+z8AA+DZLlIJT4kXdfW1\n8Pv92OprRP5v5+Pk/2tFddMe0TbX5/VXJWUJdUHFkbioTYp3zHbh10sbRdvmiECGDx+OLVu34vDh\nw9i/fz9WPvMMdra24uTMmQCAMo8H9XV1/G98TU1yQozOTlR3vVcbQ92mTQDYRZxw0aY09hPHPwGg\nTarC/e44WpGJSljgQGWnD69gGV7CfOTl5OGpJ5/C888/D4PBALfbLTqvDocDWxrZ45aOiVskqE3o\nZDbhuy+/BQBMmjQJycnJ+PL0f1CKGsQgGV/jFF5pfxRNjdtw9913K14z7rfFxcV4+olmLHtsG/95\npJFBBGNCaWlpyPeCnknPG2cf4gA6Ownbtm3Du+++i5kzZ+KZMd33aF484VsC1kOZmOeRBxpB1E1I\n8viSbfAIFnLn06644goA6nPnoEGDzvuYzqtphc8UTlkHbceOHRMBEZQUZ6RI6r7gbdUULVBJNybG\nO8gRa6bsQXaKj4mU1ZgL3S6yxUlkCbP6U2DV9XxqueW/C7tqv1GUZijna7AsuCdSVkMW1hNTkpIU\n0d1GBrLashqgLFRTQ6YLlYU44+rHNx17gdXafaueSv9aQ7mP3qFZV+5pis/j8ZDNFq1YlxSmD4Np\nl1O6pzo6OngZQzUQmJGJIKvVIiMjYRiDpnQfW0Purqneu6AkqLqkq8BNZthEgMBIxFICRhAAWX92\n/viJQZUF9GrMsVYzLV0+lTbUTadbZ+RTdIyJLEycaF8czaTec1laVER2yCk4k+IT+hylzJWNXE55\nD74Sj7fwHvU5B2te+9xcMSHJhURZ+3w+MirMnTYTQ8YLlEbvCwvXkC+Qbdu2jRYvXkwvvPBCr3qN\nQzGl9qV4axSVlhTLAFqJ8fEEgO7zsqjmZbdkUfvjHvLNdZL/CQ/vyKWO0WYxUe5QB9X+jAUKrbst\nV1T7FbIlSZV2SkuKRY5PCTg2toulqKc172AtmEUQN6m7Ny7ieZZv+24n5S79ueZk39MxBgIBckvI\nN7i6pNCR9bRdzlPmJQvDyi2qgcDyx+fLUNYpzCi6Fg9pSvft3buXbBIpRwsTx9c0OecuRe42NDTw\nTle6X4YxkBlxIlYzM2yUlJAS1POjVGN2mCOIAWjRkinkcg0X7w+gqunjuuUTrdHEMAbNyV8PVNcX\n92t7ezs1NDTIgJNaIipKCxIe06IxVr2OjfNlvDayQUx4w72/VPukww75IrHzoV6ih4xuamqixYsX\nU15OHkUaYkQOU+o8xw+LFznGjt9eR96s/uIJzNAdNR98ZBIBoAmYx0/cfr+f1q1bxyv2BHNueksu\n0deWlJJMJlsMuZ79Fd107AVyPfsrMtliKMocGTQpSyjW3t5OBgMoKjpSTERhsxDDGHQViPToFaei\nltJQRnYYVclZuH3EGQZKHLM8YlSK1q9EDs1AG92PgK6YATeuJCaDrFZx202s1Uz9mSzFnm1XgVt2\nfELw4dq1a+mpp56SsbMNvJIFFo4bN4TioyJkCF5GYRGkBSDi71eJk+Oizp7cr9yx7N27VwYc689k\nifrlXQVuXelV4T1qZgwyAKASm1tHR4cI6AewTHfnM2JOTk6mWKuZfvqzifSTn06gn/5sIsVazZSc\nnHzextDXFnbI30OTOn+ldigLE8NHv0UjkskRI2k/imbTy5xj9Gb1l6e1YyL5dLcQUa2V2hSa0upb\nlzmshwua3tCTRo0cKTp3USNH8BNUX2c+uAk+dsJ40ba591Ikf7DpciFL2P0IyGQe88aMEdOpMgyZ\n4+LIvXERr1FsjosjMIxoH0rRut0Uyfd1ByP3xxG3qKW3lXjJuXthz549NHr0aFGUK416J4zPp9//\n/veUl5cn+ptWOltaJlCzvoyQldDv5giGFj08RRC1x1Aa4wlK21ipPFNaXESlRUXi+1bCd75nzx5y\nxMUpamUnOhznxSmHorR2KVkYZf09NCmCV6rm1LDnPcz/40Gs+69xyBsaj6rVe2TobCKges0ePLfn\nBH5wzQD4Dnyg+p2aW7Iw/48HkJ6Wir++uEUXBBIIBBRR27V1m5CWlgavpxxz6raDiEVdt7x9Cndv\nOgCvpzxkgEkgEMC0qio0NTbyn3EgJ6lqldQ4oNzANWvQ+e23+M/x44gcPBiM2YzDTifuX7gQ69av\nlwGKemMcYMf245uQsuRhfp/fHDiAL3ftxvjx4wGog12MRiMaGxtl45ECgaZhKzpwGK9hLXZhGer+\n+Ef+fDAMA3R2YvzKX2DYtEkAgNhpk0BEaJ2+BBER7HShjWRuQgcO8+C9iooKxfMTCATw+Rcsyl0N\nABbAESSA/e27vB4VcPPNN+HA/jfQ2fWeAWAF8Ay6EcSzwOC13Xtht9mxd+9eHD58GJMnT8bhw4dV\nAV833jwGz/5uF7ZuOQRAHWUcCARwz91zwAC4C+ws6warYnW30QhvSQmvBsaB77TuEUX0eydhe9M/\ncVNFrgil3oHDImUrpe1qgd62bduG3bt3Y8KECTxamVMi27rNByK5Etb7AO775BMMHToE77zzru7z\n0xsLVmntcjXmQg8gbOfGuHaopyuzUJk/GAMTojHqChsA4GwnobWdRTWrtR89uPlNrNt+TPM7yXFm\nAEC7/zB+cfccfPLJJ5pjEi4Q3ntyCmrvGIfdO7ejqnIaAKC2bhPGFxSies0eDLrnRVSv2YPxBYWo\nrdsU8vFPq6rCy7t24Yrly5G6YweuWL4cL+/ahYrKSt3fck7s67Y2mIcOhbWwEOahQ0WSh6mpqX2K\nRE1LS0OZx4NTDz2Ebw4cQFRaGr45cACnlyxBgcuFs2fPwu0swJwDH6P2+Kc48fV/UHv8U8za9z4Y\nsEh/r9fLLmzKJ/PXIi0tjZUiNM7BAdTiM5zASezBQePv4SkTt/V0drIuLsWZJRpbP1c2AODMmTMA\nBMh+Fcf2GtayLWSS7fv9fjQ2sojxqmnVaP8He3+93nZctB0O3f0x3sRnOIEDqEUj5sAABgxjwFv/\n+AdsZiNqx12J7YVD0AnWGVcCGNj170p04jucQWOTj0eoP/roowCA5058Jtoft6hxF6UDAFavbNW8\ntpzk4aqcAchJikY1gEFgEcvjS0qw8re/hbd8MtLT0xWvidA49PvTZ8+Kxv90J6F1x1Ecf6cDgHiR\nwi1O9NqShPdoIBCAt3wyysrK8MADD2DSpEn8mKqmVWP7S6+ATYaCb6oKAJgM8D0Pn332OdKGDdN9\nzntjQuS+0C5kK9Z5Na3wmcIp60vWpDXZjt9eR+4MsWIQNFLEI/pbdb9TcwtbJ1x2S5YuGjrYlHR7\ne3tQ9Wct48gF1GQcgyEX4MQVhNSdHH/1ubJAIMDzR3OvZAmFYHJCvCw1yyCSpmAND4CSgrCC4dsm\nUgezuZ79lewaQSP1K92+UkqWAWgK1lAa46E4q1xqMpoRk8sMhTjdyu17be4A9j6XpI6FPNQNDQ2y\n/WfbzHRw0rAuDADL/cylRfO71KmUTOnY/Z5UmpeewJ8jJalNNWCcXi161fpKUcq2FDWaIDs1UxsT\nBxjLx/zuc9s1Bi9YsRJp+trtdIa071CNqyFL74lwDTnskC9ZkzpApVqwOYJR5B82Md0IRyWOYmEN\n2ZvVP6harx5oq6GhIWjKTjWTsocJ9XyF2sdKDklaaw4EArzWMPcq60U7iF4tWwkklehwyJWqTEaK\ngJHyMZ+mo4Wmopai4KBUeFVFK5RY1dTq/eVeD69RzIHZLPE2KpewZimzZZkoLzdHLmJSVkbxRqNs\nYr8KbrauLaFI5VDXg+GmG9EgQvBzr4OThpG3f3drnhYPtdvpVNw/V3ceN24ILVoymeJsURRpjtDk\ncdajVBXqNXPXQ+maCO8LrfFv2DRd1IKmde3UTE/cBABNx3YCQMOGJpANoCd0zuu5quWyDHkGBcT/\n6HO633NtYYccNr4d6ombMxWj09XTc2Qo63jDUDLDRinIogxMpUjEUEqSOLIWoqwDq64PCg2tFyG7\nXc6geLe1rPjaEoqAmR+nWoQ8CctEwg5a7Wm9RckH6wylylI1mSmaUagQ8MRNrDPQJuPkJlKPjpSi\nLCV5xnKvfCESrOKYnsPhjmMw3GRCLE3CMn6MUXDQlRhPEzCPohgbDwIDQNn2KIrvkmAsSoomB6DI\nQ80rfmk4bO7lLEylu+5mI0a1LIoapSp3XdatW0eAutSm0vPhLihQkIM0iIBqbreLGhoaenQf6sl/\ncs46jfGQNTaaYi3dWtN9iSIPxnJy8wiWOIq+Yy3F/PcWMntnk8EcR4MNRed0v+fawg45bLJ2KLXo\ndN1tuTTPky6YnLpXp5zz4BzT73//ewLYNHWoaGi1fmmXy6nprIOZhNg0tYEiEUf9kE0MYyaj1SZW\ni7LG0XAJUtXtdPZKCpMztQhYyRlaGBvljsnlv6s0yfNkDiqRGMe/LZxYByBXVbUq2IiNs2AXInrf\n00vJTsA8PjoTjvE+dFB/iJWfJk4YTwYDyGQ0iM5X4PoR5E0RE9lwPNQNDQ2a+7/nvhJatb6S/vin\nGeRyDhMvMMrkCwyPx0Nmk5EcZqMoO2CPNJK3vKxH51tJt3konFSBF3kn3xvTG5OrgCUbmYzVNJQp\nFI3jfEbI3Dij71hL9j98yr+if7aGH084Qg475Eve+B5WjVowx6bFRb95ubky0XrO1ByrXjSr1i/N\nT5o96EHu6OiQtSExAE3CcplaFBhGFkVqRTvBPPxaEbZ0IrwPHbK2o9LiIoqzWmXOV0mgpF1Qq1SK\nkAF5jVEvOjrXEQcvFqAysQtfwjGmoYzsJrEWtcNsIgagJIxUXKy0FA4hAPTDH/6Qj3D1IvR5CyfR\noaMPkss5jOKNBlFam+vT5RZb3HO06GFlchHuedGS2lQyvicbaaJt9sfVfeaEtMakhDHIzswme5xV\nFrkr9S73lXH3atyTh0QOOe7JQwSAcscoa3tfChZ2yGETmddTTg6JzrGQ0rJoRDKtuTWHbBYT2aNN\nmg42FIlGJVNTPAo1Qu7o6FCVarsKbnoQRLPhp0r4aDpaRJGl0In1hgpT2I+7vXAIzU9PIFukibzl\nZTJnyDoZcTRuNXVTREoXBtn2KLKZGFqVM4CKkqJlk/WdOEg/wv+SBfEi0JMSc9NU1NIstFMlfKKa\n7LmKOIQazwygOLG7Cwpo3bp1tKRLlaoUNVQJH1WBdXxSLeMnutL4YzBDcyElPQ9cDVu6/5TERLLb\nY3i6z2AWDUC3OtKW5tm0an0lbdg0XRTJsrKkLsWxqFlKQqJi/29KQt9IDgYD7JM+l4FAQLbYdRcU\n9Dh1rmfcvWr2zibrE6/LImS1AOFSsLBDDpvIlJwoYwDNLh1O/ic8Mid43+QM3Qm7L1nIehJ1u7oE\n5kOps05HCx8d8PXFICNkaVqam0BW5wwQAYw4J/T888+LnKHSvriItyg5huIjxWnQuAgDmSMieIem\nBkpKhZcmYzUByrXP4mtLyGSMFI3PZIykkqLSXl83NRNqPP/JdydlpCeL9p+SkCB6bzHJ8ye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n+uuH0r0biGaoFAAFXVVWj0NfKf5ebmYtWqVYo12p7QrALBU63ytfvmZtDZszzr3N1GI7wlJd/f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\nQrQCUpCFEEKIVuB/AT7RnQQsAKNKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x50a3ca90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plot_scatter_2d(tsne_m[0], tsne_m[1], lda_c, 1000, 'LDA Topics of Amazon Reviews using TF (t-SNE Plot)')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Prepare data for rating prediction\n",
"The previous steps allowed us to understand how the data is structured, but it doesn't let us understand what drives positive or negative reviews. In the next steps, we look at the words within reviews to build a predictive scoring model. \n",
"\n",
"When training these types of models, overfitting can occur where we become very good at predicting our sample data, but fail to predict on new data. To avoid this, we split the data 70%/30% where we reserve the 30% for gauging our final accuracy.\n",
"\n",
"We will build 3 models. One that predicts low, one that predicts high, and one that predicts neutral. For each review, we run it against all three models. The model that scores the highest will tell us which kind of review it likely is."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(tfidf_d, data['bucket'], test_size=0.3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate model accuracies\n",
"We try 3 different approaches to building the review predictions: Logistic Regression, Naive Bayes, and Support Vector Machines. We also try a final approach that does a combined \"vote\" of all three models. This means we are actually building (4 approaches) x (3 ratings) = 12 total models. Since we have limited data, we will use cross-validation to split the data 10 ways and measure accuracy in an unbiased way.\n",
"\n",
"The accuracy % are printed below for each model."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model accuracy predictions\n",
"\n",
"SVM model (low rating): 74.1%\n",
"SVM model (neutral rating): 65.8%\n",
"SVM model (high rating): 77.0%\n",
"\n",
"NB model (low rating): 70.1%\n",
"NB model (neutral rating): 67.5%\n",
"NB model (high rating): 69.0%\n",
"\n",
"LR model (low rating): 75.6%\n",
"LR model (neutral rating): 68.9%\n",
"LR model (high rating): 78.4%\n",
"\n",
"COMBINED model (low rating): 75.3%\n",
"COMBINED model (neutral rating): 68.9%\n",
"COMBINED model (high rating): 78.0%\n",
"\n"
]
}
],
"source": [
"def calculate_cv(X, y):\n",
" results = {\n",
" 'lr': [],\n",
" 'svm': [],\n",
" 'nb': [],\n",
" 'combined': []\n",
" }\n",
" lm = LogisticRegression()\n",
" svm = LinearSVC()\n",
" nb = MultinomialNB()\n",
" vc = VotingClassifier([('lm', lm), ('svm', svm), ('nb', nb)])\n",
" \n",
" for c in cat:\n",
" y_adj = np.array(y==c)\n",
" results['lr'].append((cross_val_score(lm, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
" results['svm'].append((cross_val_score(svm, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
" results['nb'].append((cross_val_score(nb, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
" results['combined'].append((cross_val_score(vc, X, y_adj, cv=10, scoring='accuracy').mean(), c))\n",
" return results\n",
"\n",
"cv_scores = calculate_cv(X_test, y_test)\n",
"\n",
"print(\"Model accuracy predictions\\n\")\n",
"for m,s in cv_scores.items():\n",
" for ss in s:\n",
" print(\"{M} model ({R} rating): {S:.1%}\".format(M=m.upper(), R=ss[1], S=ss[0]))\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Training the model of choice\n",
"All models seem to have done roughly the same on low and neutral rating reviews. SVM seems to have given better performance for our 4-5 star reviews. There is definitely room for improvement. The distribution of the stratified raiting buckets were 33% each. \n",
"\n",
"If we guessed each review's status at random, that would be our % likelihood of guessing correct. Our three models show quite an improvement over random guessing, but still opportunity to improve. There are lots of ways to tweak the prior steps to get a better result. \n",
"- We didn't tweak any parameters in either the TF step or the modeling step\n",
"- Neg/Pos keywords might vary by topic so we might do this for one cluster at a time\n",
"- Maybe nouns don't provide much insight and we are better off removing them\n",
"- \"great\" and \"not great\" have opposite meanings so maybe we should have included 2-grams\n",
"\n",
"The list goes on, but I think you get the idea. The next steps just assume we are happy with our logistic regression model."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def get_lr(x, y):\n",
" models = []\n",
" for c in cat:\n",
" y_adj = np.array(y==c)\n",
" lm = LogisticRegression()\n",
" lm_f = lm.fit(x, y_adj)\n",
" models.append(lm_f)\n",
" return models\n",
"\n",
"lr_m = get_lr(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot the results\n",
"The following three charts show the result of our Logistic Regression model. We show the top 12 words **negatively associated (red)** with that review model, and the top 12 words **positively associated (green)** with that review model. Roughly speaking, the values indiciate how much more likely or unlikely a review is to be low, neutral, high given the # of times that word occurs in the review.\n",
"\n",
"In the first example, \"review\" has a very high likelihood of predicting a low review. Remember that words are stemmed so \"easi\" could refer to words like \"easy, easier, easiest\". We clearly see negative words associated with our negative model, and positive words with our positive model. Interestingly, the neutral reviews have words I'd consider as being unsure.\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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mehF/ATyFvxC/kss3AFdAZuFLnp/BR1QOzOTZmtxS7pS+Skrfr5p2VyCfM9P1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\nmERERERK+H/7EiUyZyFCtQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x543b7860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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X1m/OyJDlOtwlwTPp46K165XKnylpK1ypHZTq/RE+MngKcyohD+LPX/ZZmJiUuq9QuXJV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eAAAD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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x39702ba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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lz6/iIyqHZ/JsS24pd0rvl9IPKaffbZDPxam+1VrIc2bK83X8A3wLPmX2Ufr7\nB+CrmfxH4C/+gpyew322LJMrdwP8Y/E+roz8HdisSP3Lp3bOSHVOx30yrdCczIAv4cbib+Mv/HH4\ni30+PlWXLf//UtmfklvyjCtHD+B+gWane+diYM1cGUfh9htzcaPgb+NKxH0lXIO2uBWYD1zcynXa\nJpPW6r2Y8q2Cf8w/wJXcX+EjQq26Fejg+7Hoc5GOjUjHFnlGcQXpv6mPr+ErzZbN5emd7tW3UzlF\nXQzgH/35wIgW2tk35bkAX6Dx61T/7HS//ZOmbho2xKfxpqZ7ZCauhG9UpI0X4M/VPGAyOXcRufvm\niVTerHS/7ZA5PiF//+EK9UvpnCfw1Wxj8am6bL5v4QrnR6mfZ2aOrZbOeTW1cQa+EGGvXBlfS22a\nk/L8FP8hEW4F0qYkqCAIgqAE0sqlq82sZ7XbEgRB5QgbpiAIgiAIglYIhSkIgiAIgqAVQmEKgiAI\ngiBohbBhCoIgCIIgaIUYYQqCIAiCIGiF8MPUxUiBE3ehcdlxEARBEASl0Qt3tfA38wDDJRMKUxuQ\nNAL3XbFRq5k7j12ICNJBEARBUA4H4Y6SSyYUpoSkxc3s0xKydojRl6Se5iES2so0gOuuu47+/fu3\nkrX7M3ToUEaPHl3tZlSdkEMjIQsn5NBIyMIJOcCkSZMYNGgQZGIZlkrdKkySJgDP4m7xBwHPSNoH\n95g7EI8X9ATutfWZ5KxuBGCSFuCK02G4V+GpwIZm9kwqezngXdyj9IOStsW9uO6GB9z8OjAgRZDf\nK9X5c9wD7V+BI6xpMMcs8wD69+/PxhtvvMjBGTNmMGvWrLJkEwRdmSWWWKLos1FvLLfcciGHRMjC\nCTk0oc0mLXWrMCUOAX6DR7kGD6MxB5/2mo2HT7hX0trAjbiiswuwIx7P6n1gZUofdToHOAV3df8u\nHtfsq3hcs93wuFE34TGlzmhrZ2bMmEH/ddZh7rz6Mm3aZJNNqt2EmiDk4EhixowZ9O3bt/XM3ZjX\nX29z3NxuS8jCCTmUR70rTM+b2akAkr6NB0NcMTM1N0zS3sB+ZnaVpA+Bz8yjQJPOg0UDhDbHGWZ2\nX5FzB5vZ3JT2B1wha7PCNGvWLObOm8d1QL1M1u0C/K3ajagBQg7OJGCQGbNmzap7henVV5uLQ1t/\nhCyckEN51LvC1JD5fwM8IvU7SZEp0AsfBSoXy9VXYFpBWUrMxKNmt5v+QL0Mum5B/fS1JUIOQZ4Y\ncWwkZOHaR3rQAAAgAElEQVSEHMqj3v0wZe2ElsGjZn8DV54K2zrA+S2UsSD9zWpZi5dQX4G8oblR\nwnXZbbfdGDhwYJNt8ODBi+S7BzfIynMMHj4+y8SUN28BNQI4L5c2I+WdnEu/BPhJLm1uyvtwLn0c\nbgSW5wd4WPAszfVjHt2jH+Vej53pHv3oiOsBMHLkyEXSfvCDHzB+fNOe3HPPPQwcuGhPjjnmGMaM\nadqTiRMnMnDgwEVsBEeMGMF55zXtyYwZMxg4cCCTJzftySWXXMJPftK0J3PnzmXgwIE8/HDTnowb\nN47DDlv0ipTajwMOOKBb9APKvx4777xzt+hHudfjgAMO6Bb9gNKux7hx4xg4cCBbbLEFK6+8MgMH\nDmTo0KGLnFMqdevpOxl9P2lmJ6X9nYC7gDXNbEYz5/wU2N/MNsik9cLf3buZ2d0pbWfgbmD7jNH3\n/cAKZjY7c+4IYE8z2ziTdgJwgpmt0UwbNgYaGhoaFjHemzhxIptssgkNxGhDUJ9MBDYBij0fQRAE\nhe8ksImZTWzLufU+JbcQM7tX0qPAeEnDgeeAr+DG2LckwU4DVpe0AfAK8IGZzZP0GHCqpGnASviK\ntzyl2jmVzaRKVRQENUbc+0EQdBb1rDAVG1rbDfgFcDXwReB14EHgjXT8z8DeuIuA5fAZjGuBw4Gr\ncDcEU4Bh+KxFa/V1KH369KF3r14MqrNVckGQpWfPnvTp06fazag6hx12GGPHjq12M2qCkIUTciiP\nulWYzGyHImlzgBPTVuycT4DvF0mfDGyVS+6ZOf5Adj+TfhZwVi7tYuDi1nuwKH379mXSlCl15Yfp\n7rvv5jvf+U61m1F1Qg6NPPLII3W/Qg5gwIAB1W5CzRCycEIO5VG3NkxdlZZsmIIgCIIgaJ5ybJhq\ndpWcpAmSLkz/T5V0fLXb1FYkLZBUbDFREARBEARdiK4yJbcpxZfk1zor4x69SyKFX7nIzFbovCYF\nQRAEQdBWanaEKYuZvW1mXc6S2czeLDGgbwFRAePw7kTeN0e9EnJoJGThhBwaCVk4IYfyqIkRJkm9\ngSvwFWiz8WC02eNTgdFm9uu0PxJfobYS7g/vZjM7MR0bBJyAO5ycg/s/OrEQziQTCPe7eGy3tYGn\n8IC3/015BgMXAYfiTitXxYPsHmFmr2Ta9WPg5HT8JeAXZnZd5vgCYC8zu11SPzxI777AccC3gOeB\nH5nZY6ldV9M0uO9ZZvazdgu2SlQyAPBpp51W99G3IeSQ5ayzzuLvf/97tZtRdUaNGsVWW+XXotQn\nIQsn5FAmZlb1DbgcVya2A74G3I4Htr0wHZ8KHJ/+3w94DxgArIJP1w3JlHUoHlprNWAz3BHwXzLH\nt8W9cz8L7JCp70WgZ8ozGPgY+FcqYyPgMeChTDl7pzxHAWsCQ3Gv3dtm8iwABqb/+6X9/wLfSef8\nCVe0euDewY/Hp/C+iIdH6V1EVhsD1tDQYLXI9OnTrXevXoYrfLHFVvFtqSWXtOnTp1f7Uag6c+bM\nqXYTaoaQhRNyMGtoaCi8Kza2NuoqVR9hkrQ07sfoQDP7R0objDuGLMaqeLy1+8xsfsr3ROGgmV2T\nyTtN0onAvyT1tqYx20aa2f25+vYGbk7HFwOOMbMnMnkmSdo0pZ0MXG1mv035R0vaHDgFH41qjvOt\n0SP4CFxxW9PMnpP0vnehMbhvV6MeAwAHtcMkYNDHH0fwXaB3797VbkLNELJwQg7lUXWFCQ9suzjw\neCHBzN6VNKWZ/DfhfpKmSrobD2dyR1KekLQJHqZqA2AFGu20+tIYosrwEaN8fdlv/GcFZSnlmSLp\nvZTnifT3tzTlEXyUqCX+k/l/Jm63tCLuWbzbUE8BgIMgCILuT5cw+s5ibkO0NvBjPIbbZcCDknom\nW6i78Sm7A/Hpur3TqUtUobnFyBqBW/rb5utQLPjuFltsUfXgiTfccMMidXX3YK/Rj0ZqoR8QwXej\nH9GP6EfHB9+tBfulpXFboH0zaSsAH1LEhqnI+WvjtkEb4oMa84GvZI4PSmnfsKY2TPsVqW9fa7Rh\nmg9smsmzTjpvk7T/MHBFri03Ardn9vM2TAvbkdKWS3m2SfsHAO+3Iq+atmEqzA83gFkFtlMqVE+t\nbyEH3xpwO6ZafT4qySmnnFLtJtQMIQsn5NDFbZjMbI6kMcD5kt4B3gLOxpWLRUi2RD1xg+y5wMHp\n7/SU/glwvKQrgPWB05up+sxU35t4/Li3gNsyxz8DLpF0QmrLJcA/zawhHT8fuFHSU8C9+I/dvYEd\nW+huawF4pwHLSNoBeBqYa2YftXJOTVKpIKjCRzDqnZCDE8F3G6l3G64sIQsn5FAeVVeYEj/BR5pu\nBz7A3QosCwunrCyT9z3g1JSnJ24T9F0zexdA0qHAL/Gl+xNx4+zbc/VZKuNifLXak8AeZvZZJs8c\nfMbgeuDLeBDeIxYWYHZbUqZOwV0QTAUONbOHcvXk682zMM3MHk2K3o3A5/E4c13KrUA1AgCfX7Ga\napuQg9O7V68Ivgscd9xx1W5CzRCycEIO5VF3seSSv6P7gRXMbHYzeQbjfp8+X9HGlUBXiCVXST9M\nQZCnT58+8Us6CIKilBNLrlZGmCpNa1NjQRn07ds3PlhBEARBt6LLrZLrAMIdcjciv8qiXgk5NBKy\ncEIOjYQsnJBDedSjwvQ+HuC26HQcgJn9vhan44JFGTZsWLWbUBOEHBoJWTghh0ZCFk7IoTzqUWEK\nuhGXXnpptZtQE4QcGglZOCGHRkIWTsihPOrVhqmHpPPwVW+f4P6UzgKQtCpwKR5nbgHuCPM4M3tT\n0rLAO8BmZjZRkoC3gclmtmU6fxDwSzPrm/ZXwVf0DUjlPQScYGbTJe2Mr+BbKTviJeli4GtmtlOn\nS6JGaYvheBiYOyEHJ1bIOWFH2EjIwgk5lEe9KkyDgQvxwLpbAtdIehhfPXc7MBvYGg/Zcjm+zH97\nM5st6Uk8SPBE3M/TAmCjTKy6bYB/AEhaDPgbHjLl27g/p9OBuyWtD9yHB9vdFxibzukBfB/4aadK\noIaZMWMG/ddZh7kVdE0QdB969+rFpClT4uMQBEGHUq8K0zNm9vP0/4uSjsUdTgr4GrCamb0GIOkQ\n4L+SNklOKx/AFaYL0997gHWBrdL/29EY8WF/3HXDDwsVSxqCK0nbmdm9km7Ew7iMTVl2wj2A39Lx\n3e4aRADfoL1MAgbNmxfBd4Mg6HDqVmHK7c/EA+D2B14uKEsAZjYpE3S3oDAdnqbjtsVHkF4HtpP0\nH9wR5oR0+jeAtSR9kKtvSTzo8L3AH4FHJa1sZq/jytOdLRml1wulBPA9DxhegbbUOiGHIM95553H\n8OFxV0DIokDIoTzq1ej709y+UbosHgQ+B2xC4/TbA8D2uAL1qpm9lPIuAzyBK04bZLa1cQ/imNkT\nwEvA/pJ64eFVrmutEbUafLcjgieeeuqpi6Q1F+z1Zqof7LUWgtbOpHv0I4LvNlJukNS5c+d2i35A\n+ddj5syZ3aIf5V6PuXPndot+QHWC79ajp+8JwJNmdlIm7VZ8mux64K/4lNyr6dh6wLN4IN6JKW0i\nPkq1s5l9RdIK+DfrZgAzG5TyHQGcm8r7sIU2nYm/+88DfosbgeeVukLemvf0XS4FT6wNtD7CFARZ\nJuK/ZLrz8xEEQfsJT98dRLIp+g/wR0lDcaPvy4AJOcH+A49Vd1M6711Jk/Af40dn8v0RjzV3m6QR\nwCvAavgo0nmZqb8/AiOB04Cbm1OW6o0IpBq0lbhngiDoLOpRYWptSG0gPgPwAL4C7q/A8bk8DwAn\n0GirBK5EfSP99YrMPpK0DT5y9Gd8Ku9VfHXc7Ey+FyU9DnwzlVvXVCOAb9B9iOC7QRB0BnU3JdfV\nqYcpOSjdD9O7777LCiusUIEW1TYhh0Z69OjBhhtuWO1mVJ1Zs2aF4pgIWTghh5iSC7ohpQbwHThw\nILfffnsFWlTbhBwaCVk4hx9+eMghEbJwQg7lUa+r5GoGSdtKWpC8iAdtpNhqqHok5NBIyMIJOTQS\nsnBCDuURClNtEPOi7aQ7T0u2hZBDIyELJ+TQSMjCCTmUR0zJBTVJW2LJBUGWPn36hJfvIAg6nFCY\nKoCkJYALcLcDy+LOLIcmp5X5vEvhYVGWAXavR4/fEUsuKIeIJRcEQWcQClNlOB/3vXQw7sx4OB6A\nd81sJknLA3cC7wM7mdnHlW5oLdCWWHLjgb0q0KZaJ+TgRCy5RsaMGcOQIUOq3YyaIGThhBzKIxSm\nTkZSb+BHwCFmdk9KOxKYBgzBR5sAvgTcCEwBDjKzzyrf2tqilFhyY0rIUw+EHII8EydOjI9jImTh\nhBzKI4y+O5+v4orpPwsJSRl6nMYBFAF/B54H9g9lqXQuq3YDaoSQQ5DnssvirigQsnBCDuURClPt\n8Bc8mO/XSskcwXdTP6h+sNfuErS2u/QDIvhu9CP6Ef2I4LtdjjQl9w5wqJndkNIWA6YCo4EG4H5g\nBeAM4BBgOzMrGharHjx9R/DdoL1E8N0gCFoiPH3XMGY2V9JvgPMlvQu8DAwDlsJ/jG+IT8lhZj+R\n1BO4X9J2ZjalWu2uBSKQatBW4p4JgqCzCIWpMpyKK0XX4gF4nwAGmNn7kutKhYxmdlJSmu5LStML\n1WhwNYngu0E59OzRo+7jZUGEiMkSsnBCDuURClMFSO4BTkxb/tgDQM9c2gnACZVpXe3Rt29fJk2Z\nUpLjykcffZQtttiiAq2qbUIOjfzvf/+re5cCAMcee2y1m1AzhCyckEN5hA1TF6MebJiCIAiCoDMo\nx4YpVsnVGJJGSGrTRQyCIAiCoHMJhalCSJog6cISsp4P7NjZ7QmCIAiCoHTChqmGkNTTzObiLmaC\nEhg/fjwbb7xx3QfqnTBhAttvv321m1ETNDQ0cOSRR1a7GVVn/Pjx7LVXBMyBkEWBkEN5hMJUASSN\nBbYFtpF0Ir4q7nBgLLAbcDbwdWCApO2Bvcxso2q1tysxZswYDjrggAjUGyykZ48e7LLLLnVv+D1u\n3Lj4OCZCFk7IoTzC6LsCSFoW+CvwH9w5pXAF6V7gaeAU4CXgXXx13J5mVtSiO4y+m1Iw4CslUG/Q\n/ZkEDCIcVwZBUJxwXFnjmNlsSZ8Ac83sLQBJ89PhM8zsvkLe5JcpaCOlBOoNgiAIgvYSRt/VxfDQ\nKEEQBEEQ1DChMFWfOe05qTsH321LEMhzzz13kbQIWttIPfYDIvhu9CP6Ef3o+OC7mFlsFdiAvwEX\nZ/a3BeYDy+byjQAmtlDOxoA1NDRYYLbHHnsYYA1gVsfboTXQhlrYGsDi+XAOPfTQajehZghZOCEH\ns4aGBsPfExubte07HjZMlWMa8C1J/YAP8dG9MFgqk80335w77rij7oOuroWP5NQ79X4fZBkwYEC1\nm1AzhCyckEN5xCq5CiFpLeAaYEOgF+5W4GpgBTObnck3glglVzIzZsyg/zrrhFuBYCG9e/Vi0pQp\nde9WIAiCRYlVcl0AM3se+HYu+fdF8p0FnFWRRnUD2hKoN6gP+vTpE8pSEAQdTihMQZenb9++8YEM\ngiAIOpW6XSUnaUtJz0j6RNIt1W5P0D7yKyfqlZBDIyELJ+TQSMjCCTmUR90qTMCFuJ1sP+DQcgqS\ntEBSsVXWQSczatSoajehJgg5NBKycEIOjYQsnJBDedSt0bekt4BTzGwRO6I2lLG4mX0qaQEe/+32\njmths3WG0XeGuXPn0rt374rWOWPGjJqzmfroo49Yaqmlqt2MmmDppZdmnXXWqXYzqk41no1aJWTh\nhBzq0Ohb0lRgtJn9OpP2JHCrmf0sKTBHArsDuwCvAieb2R1pWf9U3A/DWElXA4eZ2bWStgVGARsA\n7+BG2aeZ2YJUxwTgWeAzPGTVM5LWSE0Yn8KaTDOzNSRdg/tY2ifTxtHAhma2faa8Z4B5wBHAJ8AV\nyfA7KIFqKEuxKq+2iVVyTr1/GLOELJyQQ3l0SYWpRM7EnQOfAhwP/FFSX9yZ8MrAc8DpwJ+A9yV9\nGbgTX+p/MLAucBXwEfCzTLmHAL8Btkz77wBvAYNx55SFGHHNDd3l0w/Bpwc3S2VeI+lhy8SXC2qH\nWbNmMXfevAj2W6NMAgbNm8esWbPqXmEKgqBj6c4K01gz+xOApP/DlabNzOwe4E1JBsw2szdTnmOA\nGWZ2fDr/ueQT6VyaKkzPm9mp2YrSyNL7hbLayDNm9vP0/4uSjgV2BEJhqmEi2G8QBEF90Z2Nvv9T\n+MfM5gKzgRVbyL8u8Ggu7RFgGUmrZNI6OljuM7n9mbTcziBDPu5QvRJSCPLEs9FIyMIJOZRHV1WY\nFrBoWJHFc/uf5vaNjulvqcFyS2kjtLOdEXzXeeyxxyraj2KBG2shaO0yRPDdLBF81/2TdYd+QPnX\nY5lllukW/Sj3evTt27db9AOqE3y3S66Sk/QY8I/C1JikZfGRmfMyRt9NVq1Jehc4wcyubWb/bGAf\nM1svc87RwC/NbPm0PwF40sxOyrXnY2B/M7s1k3YusJ2ZbZ5Jexj4xMx2aK48SbcC75rZ4c30PVbJ\nVZHCCosGYkquFpkIbALE8xEEQTHqbpUccD8wWNJfgPfxUCKflVnm5cAJki4BLsWn6EYCvyrh3GnA\njpL+CXxsZu+lNp4i6WB8qm8Q8HUiRmq3IIK81iZxXYIg6Cy6qsJ0DrAacAeuMJ2R9gvDZcWGzfJp\nTfbN7DVJuwHnA0/hq9+uBH7RQhkFTsYVqyNxFwZrmNk9kn6Ozzr0wlff/R5Yv4TyghqlT58+9O7V\ni0HhVqBm6d2rF3369Kl2M4Ig6GZ0ySm5eiam5JoyefJk1l133YrWWYuOK6dOncrqq69e7WbUBO+9\n9x477LBDtZtRdarxbNQqIQsn5FCfU3JBAMCwYcO4/fZOd7DehFoM9jty5MiKy6FWGThwYChMVOfZ\nqFVCFk7IoTy66iq5boGkwcn4PGgnl156abWbUBOEHBoJWTghh0ZCFk7IoTxCYao+MSdaBrU20lMt\nQg6NhCyckEMjIQsn5FAeMSUXBGVQi/ZM9U6fPn3iwxAEQYcTClMi+UT6Dx4LbjAeCPc03J/fpcB+\nwBvAcWZ2t6QewO+AHfDYdDOAywsBgSVtjYc3WSUbMkXSRcBGZrZtJm1PfHXeqsADwBFm9krn9jgo\nlwjEW5tE8N0gCDqDUJiacggwCvgm7uD4CmAf4BbcvcBJwB8krYr7fXoZ2Bd3QbAl8DtJr5nZzWb2\nkKQX8UC+vwKQtBhwIB4QuMDSwP/hfpo+xQP7jgO27tyudg/OO+88hg8fXpW6aykQ7zXAoVVuQy0Q\nwXcbqeazUWuELJyQQ3mEwtSUp83sl7DQU/dPgbfMbExK+xnwY+AbZvY47jCzwHRJWwLfB25OaVfj\n0SYKzi8HAksCN2XOWww4xsyeSHUMBiZJ2rSQFjTP3Llzq92EmgjEe1sNtCGoLWrh2agVQhZOyKE8\nwui7KQsD4ZrZAuBtmgbxfSP9uyKApGMkPSHpTUkfAD8Esj9rrwHWkrRZ2h8M/MnMPsrk+SyrGJnZ\nFOA9Whm0iFhyzqxZs6rej7upfgy2IUQsuSwRSw7OOuusbtEPKP96DBkypFv0o9zrcdZZZ3WLfkDE\nkqsqzcR1mwqMLtglpbQFwF7AUsBYYCjwGPABMAzYzMw2zuS/GXgL/z68AmxjZo+lY4OB35nZkrm2\nvIPHuftDkXaG48oaIeLK1R4RSy4IgpYIx5XV4dvAI2b220KCpK8WyXcV/uP9VeCFgrKUYbHs9Juk\ndYDlibBYQRAEQVAzhMLUfp4HDpY0AJiKG3d/E3gpl+9vwGx8xd0ZRcr5DLhE0gn4Cr1LgH+G/VJp\nzJo1q+pxw2pBs30XWKHajagBauFa1Aq18GzUCiELJ+RQHqEwNVJKwN5CmuEr6DYEbkj744DLgF2b\nZDYzSdfgBuSLTLEBc3BTjuuBLwMPAke0qwd1yOGHH141V/8RiLc26dmjR3wUqO6zUWuELJyQQ3mE\nDVMFkHQV0MfM9uqAssKGKcPEiROrKodacVw5adIk+vevtnOD2mDmzJnsvvvu1W5G1an2s1FLhCyc\nkEN5NkyhMHUikpYFvoEvKvqumd3fAWWGwhQEQRAE7aAchSncCrSDNgTNvQ1fdX55RyhLQRAEQRBU\nh7Bhaj+tDs2Z2faVaEgQBEEQBJ1LKExBl2bMmDEMGTKk2s0oSiXtm8aPH89ee5VtItctuPfeexk2\nbFi1m1F1avnZqDQhCyfkUCZm1i03fKn/8bm0J4Ez0/8jgenAPNyh5EWZfEsAF6T0D4FHgW0zxwcD\n7+TK3hNoAD4CXgDOBHpmjrdU39HAc+nc13Fv4M31a2PAGhoaLDA7+uijq92EokyfPt169+pVWFEZ\nWwW3xXr2tOnTp1f7Fqg6tfpsVIOQhRNyMGtoaCi8Kza2NuoVdTnCJGlf4EQ87tv/gJWBDTJZLgPW\nTcdnAnsDf5W0vpm9WKS8rYHfA8cCDwFrAr/DL8rPJe3XXH2SNgUuBg7CFbPPE4F3S+ayyy6rdhOK\nUkuBeeuJScCg+fMj+C61+2xUg5CFE3Ioj7pUmPB4bzOB+8xsPj7iU/C0vSoe+H1VM3s95b9Q0q54\nqK3Ti5R3JnCOmV2X9qdLOhMYBfwcWLW5+tKxD4E7zWwO8DLwdAf2NagitRCYNwiCICifel0ldxPQ\nG5gq6XeS9pLUMx1bH+gJPCfpg8IGbAMUC30CPlp0Zi7/lcBKknq1Ut/f8am6qZKulXSgpKVa60AE\n3639fgBMyO3XctDaCL7bSC3fV9GP6Ef0I4LvdiiSXgR+bWYXZ9Kexe2DfiZpSWAnYGd8quwlYFtg\nX+A6YD1gQa7YD83szRQ0d7SZfT6VOxcfZbol3w4zeynlydb3PdzGalszmy+pB7AdMCDVb8CmZja7\nSL/CD1MXIALzVocIvhsEQUuEH6bivAV8qbCTnEiuXtg3s4/N7E4zOxFXVrbER5eexEeYVjKzl3Lb\nm83UNRFYp0j+hXHlcvVtn6kPM1tgZveb2an4aNVqwA4dJYjuTLFfP/VISCHIE89GIyELJ+RQHt3Z\nhul+YLCkvwDvA2fhgW5JI0Q9gX/hI/sHp7/TzexdSdcD10o6BVegVsQVmKfN7K9F6voZcIekl4Gb\n8ZGpDYCvm9kZLdUnaXdgDTyG3LvA7oCAKR0sj27JscceW+0mtEilgsHugmvt9U4E322k1p+NShKy\ncEIO5dGdFaZz8JGaO3CF6Yy0D66Y/BT4Fa7I/AcPXVLw3n0obtx9AfAV3PzisVTWIpjZPZK+i0/L\nDQM+xc0wrkpZ3gNOLVafpPeAfXCTjl7A88D+Zhbv/hIYMGBAtZtQlAjMWz169+oVwXep3WejGoQs\nnJBDeXRbG6buStgwdR1qJTBvvdGnT5+6dykQBEFxyrFh6s4jTEFQVfr27Rsf7iAIgm5Cdzb6RtK2\nkhYkg+9qt6XUgL1BG8gvg61XQg6NhCyckEMjIQsn5FAe3UphkjRB0oW55Fqac2yxLZLGSlrENUHQ\nPOPGjat2E2qCkEMjIQsn5NBIyMIJOZRHt7JhkjQBeNLMTkr72+Kr5VYo5tOowm1r4rupmTxjgeXM\nbJ8W8oQNUxchbJiqQ9gwBUHQHGHDxEJlY1tgG0kn4qM5h6fDm0o6D3dG+RRwqJk9nzn3x8DJeJiS\nl4BfFMKcSOqHO5nc0MyeSWnL4SvttjOzB1PaQHxV3Sq48+E/4PHlls8qa5IGABeluh5ObXlD0gg8\nqK9JWpDav32h/KBrMWPGDPqvsw5zY5VcxendqxeTpkwJpSkIgg6l2yhMwAnA2viS/TNwX0ZfT3/P\nBobi7gF+C1xNCnAraW9cgTkeuA/YAxgr6WUzeyCV3dpU2up4+JPReGSIjXAXAvnzlsYVs4PSsT/i\nStbB6W9/4HO4WwMB77RVCEFtEMF3q8MkYNC8eRF8NwiCDqfbKExmNlvSJ8BcM3sLQNJ8XDH5PzN7\nOKWdC/xF0hJm9gmuwFxtZr9NRY2WtDlwClBQmNRK9UcBk5OnboDnJa0P/F8u32LAUWY2LbXlUly5\nw8zmSPoIWKLQ/qDrE8F3gyAIugfdyui7Bf6T+X9m+rti+tsf+Gcu/yO0bWBgbeDfubTHi+SbW1CW\nMm1ZsUi+Vongu07//v1rth9QueC73yOC72aJ4Ltw2GGHdYt+QPnX43vf+1636Ee51+Owww7rFv2A\n6gTfxcy6zYZ/ny7M7G8LzAeWzaRtkNL6pv23gYNz5RwPvJD+X5UU6iRzvE9K2ybt3wJclStjj2zd\nuH3SO7k8ewLzM/tjgVta6ePGgDU0NFhgdv3111e7CUVpaGgwwBrArALb9RWqp9a3Bh9RjufDavfZ\nqAYhCyfk0PhuBja2EnWLwtbdRpg+wUOPtIVJwLdzad8G/pf+L0yPfSlzfCOa2idNATbNlbFZG9sB\n7Wt/XXPAAQdUuwk1QUghyBPPRiMhCyfkUB7dxoYpMQ34VlrZ9iE+5VjM/iibdj5wo6SngHvxUf69\ngR0BzGyepMeAUyVNA1YCfp4r77fA0GQfVTD6HpyOtWgwXqT9AyStjY98vW9mn7Xh/KDGiICAlSXk\nHQRBZ9HdFKYLgGvw0aFeuFuBYgrLwjQzu03SCbiR90W4C4FDzeyhTP7D8UC6T+CjScNwM45CGdMk\n7YevjDseeBT4BXA58HEb2n8lPo34BL6ibnsg3Ap0QSL4bvWI4LtBEHQG3cpxZS0h6TTgh2bWr4PL\nDceVGR5++GG22mqrajejKJV0XPnkk0+y0UYbVaSuWmfq1Knsu+++1W5G1anlZ6PShCyckEM4rqwJ\nkvPLf+NTaVvhI1a/rmqj6oBRo0bV7AugksF3R44cyZAhQypSV60zcuTIUJio7Wej0oQsnJBDecQI\nUweRYtj9AFgBXx19LXCumS3o4HpihCnD3Llz6d27d7WbUXVCDo2ELJyQQyMhCyfkECNMNYF5/LqT\nOt8xTIwAACAASURBVKo8SYub2acdVV53pd4f/gIhh0ZCFk7IoZGQhRNyKI9QmEogBfV9Nu0eDHwK\n/MbMzkzHl8en374LLIl7CD/ezF7IlLEvcBawJu6w8hIzuzBzfCq+wm4tYC/gzzTGwgu6IBF8tzpE\n8N0gCDqDUJhK5xBcofkm7nPpSknTzWwMHmT3q7jC9AEwCrhT0npmNl/SJsCNwJnAn4Atgd9ImmVm\n12bqOBn4GTCyQn0KOokIvls9IvhuEASdQShMpfNymnYDjxX3Ddz30gO4V+8tzOxfAJIOAl6mcaRo\nKHCvmf0ynf+CpK/h0R6yCtN9Zja6An3pNvzkJz/h/PPPr3YzFqHSwXcvAk6sQD21TgTfbaRWn41q\nELJwQg7lEQpT6TyW238Ut1laD5+iWxg7zszekTSFxm9lfxYNx/UIcIIkWaPlfUOHt7qbU+sfxUoF\n3/1mheoJug61/mxUkpCFE3Ioj+4WGqWrM6fUjBF815k8eXLN9gMqF3x3TyL4bpYIvgvHHXdct+gH\nlH899txzz27Rj3Kvx3HHHdct+gHVCb4bbgVKIBl99zGz9TNp5+BTcXsBzwFbmtlj6dgX8Hf/IDO7\nVdJ16fzvZM4fBXzHzL6R9qcCo82sRd9N4Vaga1BYutpAjPxUkonAJkA8H0EQFCPcClSGvpIuAH6H\nv5OPBYaa2QuSbsONwH+Ex7A7F7dhuj2d+yvgcUmn48bfW+I/xn9U4T4EFSZim1WWkHcQBJ1FKEyl\ncy2wFG6r9Bk+GnRVOnYocDFwB7AE7lZgdzObD2BmT0r6Pr4C7nTcrcDpZvaHTPkx1NcOJk+ezLrr\nrlvtZixCxJKrHr2WWCJiyVG7z0Y1CFk4IYfyCIWpdD5Nq+SOyR8ws/dxpalZzOxW4NYWjq9RbgPr\nkWHDhnH77be3nrHC9O3bl0lTplTMD9PQoUMZPToWWAIMHz48jFup3WejGoQsnJBDeYQNUwkkG6Yn\nM24FqtmWsGHKMGPGjPg4EnLIErJwQg6NhCyckEN5NkyxSq40imqVkiakGHIdhqRtJS2QtGxHlttd\nqfeHv0DIoZGQhRNyaCRk4YQcyiOm5ErAzHaodJUVri8IgiAIghaIEaYgCIIgCIJWiBGm8llM0iUU\nD8o7CDgBWAd3Snk/cKKZvVU4WdJuwGhgVdx7+LUEJXPeeecxfPjwajejWSoVgPeaa67h0EMP7fR6\nugI33XQT55xzTrWbUXVq/dmoJCELJ+RQHqEwlc+hwFUUD8q7GO5GYAqwInAhMBYP0oukVfBYc5cA\nV6bzO9Qmqrszd+7cajehWSodgPeSSy6pSD21zmI9e/LjH/+47u01avnZqDQhCyfkUB6xSq4M0uq5\nL5rZ1zNp5wB7ZNMyxzYF/gV8zszmSvoFMLCIB/FhwApmNrtIGbFKrotQWI1RqQC8QQq+S3j6DoKg\nOOHpu7oUDcorSXhUjBHABsAKNNqM9cXDZfXHFaj8+UE3olIBeIMgCILOI4y+O4+lgLuB94AD8em2\nvdOxJcotPILv1n4/Tj311EXSajlobQTfbaSW76voR/Qj+hHBd7scrQTlPQR4AljVzF5NxwYBvwc2\nMrNn0pTcHoUAvJnzY0quRGbNmlWzYTAqGYB3FlCbUqgsEXy3kVp+NipNyMIJOcSUXLUpGpQX/7H8\nCXC8pCuA9XED8CxX4NN3o3DD8U2BwZVqeHfg8MMPr3lX/5UICDsUX2pZ70Tw3Ua6wrNRKUIWTsih\nPMpSmCQtRm5az8w+KatFXQujhaC8kgYDvwSOw3/8ngwsvFvN7GVJ++LfumNTGT8Frq5gH7o0xaZe\naoVKB+DdpCK11D4RfNep5Wej0oQsnJBDebR5Sk7SqsBFwPbAcvnjZtazY5oWFCOm5LoWlfLDFDTS\np0+funcpEARBcSo9JfdHfERlKPAGEcYjCJqlb9++8fEOgiDoBrRHYdoY+KaZ1b25gKRtgQnA8sUM\ntIMgCIIg6B60x63Ak8DKHd2QLkyMsFWR/NLWeiXk0EjIwgk5NBKycEIO5dEehekI4DRJP5D0NUlr\nZ7eObmAQtMTEiW2agu62hBwaCVk4IYdGQhZOyKE82mP0vQlux7Q2TUdXBFh3M/qWtARwAe6/b1nc\nt9JQM3siTcndT/KZJGkp4BZgGWD3lHYu7rByFeB1XHZnmdl8Sf2Al/ApzomZOk/Eg/SuVqQ9YfTd\nBQnj78oRRt9BEDRHpY2+rwFeBI6iPoy+z8cVnoNx30rDgbslrZnNJGl54E7gfWBnMyusJZ+NO7Gc\niftiujKlXWBm0yX9HXewnL1whxKuBboNlQ7CW+/07tWLSVOmhNIUBEGH0h6FaQ1gbzN7oaMbU2tI\n6g38CDjEzO5JaUcC04Ah+GgTwJeAG4EpwEFm9lmhDDP7ZabIGZJ+hY9WXZDSxgC/kXSSmX2aRpC+\nTvHIE0EXZNasWcydNy+C8FaAScCgefOYNWtWKExBEHQo7VGYHgS+Bv/f3p2HyVGWex///giEEFBA\nR/Q9ysgSJKjsoCKbBzhBRMPikc3DDkZfIhCQEHhZAii7hF0FIpvIcjRCQBBBQA2ySBLABZAlZEBE\nHAlrCEtyv3881ZlKzz49PdXL73NdfSVdVV391D1TPXdXPc9z0/AJE7AmKUZ/KC2IiPckPUj62/cQ\n6VbkHaQiuntE2T1OSbuTJq5ck3SrbmnSVaiSG4GLSFexbiBdXbo7Itqqc0hWFBfhNTOrXwPp9H0D\ncK6kSZJ2lDQm/xjsBtaJW4CtSInkYpI+B/wkW78jsAHwPXLFdyPiXdJs4ftLWgbYk871Sztx8d1k\n9dVXr6/joDpFa7fDxXfzXHwXxo4d2xDHAZX/PLbbbruGOI5Kfx5jx45tiOOAYorvEhH9egCLengs\n7O/+avkBjAQWkK4clZYtDTwHHAFsDSwkdQY/i9Sna53ctkcAT5bt8zLg5bJlo0llVQ4DXgaW7aFN\nGwExc+bMsIjbb7+96Cb0aubMmQHETIio0uP2Ku67nh4zU59Knx9RH+fGUHEsEseh4/MY2Cj6mRMM\n5JbccgN4TV2KiPmSfgCcJWkeKVGaSIrBVNIVI2XbHiVpGHCXpC9ExBPAk6TivLsDfwS+DOzcxfs8\nLul+0hfuyyLi7SE4vIYwZkz9XNSs5kyvLSw5aqBZNf1sujn1dG5Um2OROA6V6VfClN0ymgYcFk3Q\n6TsziZQUXQW8j9RvaUxEvCqlXKm0YUQckSVNv8mSppslTSHdUViWNIruZGByF+8zFdgMj45rOENd\nhLfZjRwxwsV3zWzQ9SthijSK63PVakwtyq72HJ49ytf9FhhWtuww0q210vNJpKQr7/wu3upjwJ+i\nn/NCWO1rbW3lsSee8DxMQ8TzMJlZNQzklty1pHmFThjktjQlScsDq5P6yx5bcHPqzo033sjOO3e6\ny1lzql2Et17iMBRuvPFGJ0z4dyLPsUgch8oMZJTc28Bhku6VdJ6kU/OPwW5gI5B0t6Rzull9Ial/\n013A5UPXqsZw7bXXFt2EmuA4dHAsEsehg2OROA6VGUhplPt6WB0R8fnKmtR4slnA342INyXdDVwe\nEVcNcF8ujWJmZjYAQ1oaJSI26+9rml1EvFJ0G8zMzGzgBtKHCQBJHyPNXv1AdNRNsy5kV5VmR8QR\nZcuvAYZFxB65ZUuT6s5NiIifDG1LzeqfCx1Xzh3nzTrrd8KU3V66BtiBNKR+LeAZSVOB9og4enCb\n2HDy90CvAW6QNDIi5mfLvkia5+kXQ94yszrX1tbG2qPXZsFb/g5XiRHLjeCJx13A2CxvIFeYvk/6\ng/4JYHZu+c9Is107YepBRGyTe3o7qcLDLqTkCVJplOkR8eZQt60e7b///lx+ufvKOw5Je3t7SpZ2\nJc3m2czuAb4wgNe1w4JpjVXA2OdH4jhUZiAJ0w7AjhHxVDZxY8kTwGqD0ahmERELJd0AfB24RtJI\nYCdgt2JbVj88c23iOJRpAf6j6EYUbF0cg4zPj8RxqMxAphV4P/B6F8tXBt6prDlN6RpgW0ktpCtN\n80lXnnrk4rvJjBkzGuI4Kv15bL755g1xHIPx8wDS1ZVy/0vn2ilPAT/tYttf0rnWzAvZtuXXfu+m\ncwXgV7Jt/1W2/AFS5eK8d7Jt55Yt/xOdqx5D349jXSo+Dp8ftXUclf489txzz4Y4Diim+O5AphW4\nHZgREadIeh1YLyLmSPoJsHxE7DLg1jSo7jp959Y/BZxLunr3bEQc0sO+PK2AWTcWDxn+Br66MlAv\nAJeAP2OsEQ3ptAKk4rN3ZX+4hwOnSPo0qbTH5gPYn6XZ079J6kD/nwW3xaz+eZDcwDl2Zl0ayDxM\nj0j6BKm22jDS97g7gfMi4rlBbl+j6O0y3jWksijPRsQfhqA9DWPGjBlsscUWRTejcI5D0tLSwvDh\nw3lnmnsHVGLEco1VwNjnR+I4VKbPCZOkE4CzI2J+RPwbOL56zWosZSPjulr/OGVFfK1vzjzzTH8A\n4DiUtLa2stVWW3XqM9GMJkyYwJQpUwb02kabh8nnR+I4VKbPfZgkLQT+T0S8VN0mWU/ch2lJ8+fP\nZ+TIkUU3o3COQwfHInEcOjgWieNQWR+m/oySU++bNLZeiuhaAZr95C9xHDo4Fonj0MGxSByHyvR3\nWoH+DamzPpO0taRFkt5fdFvMzMxsSf3t9P03ST0mTRHxgQra08xESkib/kqemZlZrelvwnQi8Go1\nGlJHlpZ0AbA38C7wg4g4AUDScOBUYA9gJdLUc5Mi4rfZ+lbgQmAL0pQMc4CjSNPQ3UVKmOZlSemV\nEXHAUB5YPTrqqKM466yzim5G4RyHDuPGjWPcuHFFN6Nw5557LocffviAXttonb59fiSOQ2X6mzBd\n507f7AdcBmwKbAJcKmluREwFLgJGk0qb/IM0c/dtktaNiKeBi0kx34I0o/cngTeANuCrpHp8a5Fm\nUn9rCI+pbjXSh3olHIekra2NH1/+Yy655JKim1ITrr766gG9rtGK7zbKcVTKcaiMR8n1QzZj94ci\n4tO5ZacBXyHN0v0MsGpEvJhbfwfwQEQcJ+kR4GcRcUoX+96adJVp5Yh4rYc2eJScWTcWj4Bx8d2B\nawemeaZva0xDNdO3+9Yk95c9vw84glS5aRipn1c+VsPpmDv3fOAHkrYnTfb584j4U5Xba9Z8XHzX\nzAZZn0fJRcRSzXx1qQ+WB94DNgLWzz3WAQ4DyG7brQ5cBXwaeEhSt3XjeuLiuz4OH4eL7y5WpePw\n75WPo56Po/Diu80suyXXEhHr5paVbsntDPwN2DIi7u3j/k4FvhQRG0jajPRx1RIR83p4jW/J5Tz+\n+OOMHj266GYUznFIXHw351/AhwbwugYsvuvzI3Echr74brNrlXQ2cAmwMTAemBART0m6BrhK0neA\n2cAqwDbAIxFxm6QpwG2kxOoDpEK7f832O5c0Su4rkm4F3oqI8u+AVmbixIlMnz696GYUznEo4wKy\n8CvgiwN4XQPGzudH4jhUxglT/wTpdtpywIOkW3BTIuKybP1+wHHA2cBHSR899wM3Z+uHkaYV+Bjw\nGil5OgIgIl6QdCJwOvDj7H08rUAvLrzwwqKbUBMch6SlpYVll12Wt6e9XXRTasMABws2WvFdnx+J\n41AZ35KrM74lZ9aztra2Tv0orH8abR4msxLfkjMzy7S2tvqPvZkNuv7Wkmt6kuZIOrTodpiZmdnQ\nccJkda18uGqzchw6OBaJ49DBsUgch8o4YbK6Nn/+/KKbUBMchw6OReI4dHAsEsehMu70XSaba+nP\n2dOuCuzOIY2MOz97viLwfWAssCzwR+CIiHg0W78GcA7wOdLklo8Bx0TEb3Lv+X+Bw4FVScWNfxcR\nu3XTPnf6NuuBO31XnzuFW71yp+/Btw8wla4L7Jb7GamA7vakqQLGAXdK+kREvAKsQJpv9xjSnL77\nANMlrR0Rz0vaGDgP+DqpzMoHgC2renRmDaqtrY21R6/NgrcWFN2UhtZoxXnN+sIJU9eei4gjsv8/\nKWk9YAIpiVpM0hakhGqViHg3WzxR0i7AfwOXZVeaHs297ERJu5KuSF0MtJISrl9mE1U+BzxSpeMy\na2jt7e0pWXLx3epphwXTFtDe3u6EyZqKE6audVlgt6yoLsB6wPuAl8tWjQDWBJC0PHAS8CXg/5Bi\nPoKUKAHcQZrle46kX5Hm5/1FRLw1aEfTwNrb2xtqgr2BchzKuPhuqhW3fNGNqA0+PxLHoTLu9F2Z\nFUiVl9ZjyYK7awNnZdt8H9gJmARska3/MzAcICLeIBXs3SPb10nAI5Le39Mbu/husummmzbEcVT6\n89hjjz0a4jhcfDen0uK7N1G94wDOPffcJZ7X8u+Vz4/kgAMOaIjjABffrQk9FdiNiE/nO31L2g64\nFRgVEW3d7O9R4PqI+F72fAXSbbfLc7f98tuPJH1M7RYRnT4u3el7SbNmzXIccBxKXHw35wWqE4M6\nLM7r8yNxHNzpuxq6LLBbvlFE3CnpPuBGSUeTiup+lHT7bVr2w3gS2FXSLdnLTgYW37+TtCOwBvA7\nYB6wY7b+iSodW0Np9pO/xHEo40FyyQtV2GcdxtbnR+I4VMYJU9d6KrBbfknuS8D3SAVzPwS8SEp+\n/pmtP4LUWfxe0kfNGaR+TyWvkLqonkjq2/QksEdElF90N7NetLS0MGK5ESyY5lFy1dRoxXnN+sIJ\nU9fezW6XHVK+IiLWKHv+JmkOpcO72lFEzAW2K1v8g9z6e4H/rLTBZpbqyD3x+BOeh6nKPA+TNSMn\nTFbXpk6dyoEHHlh0MwrnOHS44447HAv8O5HnWCSOQ2WacpScpLslndPN6kJ7wUs6UVK/OqI1s1mz\nHCpwHPIci8Rx6OBYJI5DZZpylFw2Em52V6PUhrgdi4CdI2J6btlIYNmImNfNazxKzszMbAA8Sq6B\nRMR8wBUSzQbIteSK475N1siaOWFaWtIFdF1gdzhwKmkyyZVIU8hNiojfZus/AFwIbAWsDDwNnBoR\n15V2Xl6kN1s2mzSL98nZ+iBNSQDwbESsIWkysFNEbFjVozdrQK4lVyzXmLNG1swJ037AZXRdYPci\nYDSwG/APYBfgNknrRsTTpOH/DwGnAa+T5k66StJTEfFQH99/U+AlYF/gdmBhtjwouB+VWb1yLbkC\nucacNbhmTpjauiqwK+nXpGRq1Yh4MVt/jqQdgP2B4yLiBSDfafwiSV8kJVh9Spgioj27svRqRLxU\n+eE0p7FjxzJ9+vTeN2xwjkMZ15JLJU32KroRtcHnR+I4VKaZE6YuC+wC6wLDgL+VFdsdTjbHraSl\ngP8HfI00s/fw7FFencmqbPz48UU3oSY4DtbJZ4puQO3w+ZE4DpVpymkFerE8aXbvjViyoO46wGHZ\nNhOBb5NuyX0hW/9rsoK6mUXkSqBklhmsRrr4bnLTTTc1xHFU+vMYPXp0QxyHi+/mVFp8dxRDexzv\npX9mz569xOJa+L3y+ZGMGTOmIY4DXHx3yPRUYBfYmVQTbstsFu6uXj8d+GdEHJw9F/A48JeI2DVb\ndj9wT0RMyp6/n9Qf6oyIODlb9japDMovcvs+kdTpu8s5AzytgFn3XHy3QHVYlNeaj6cVGJguC+xG\nxFOSriF14v4OMBtYBdgGeCQibiPVe/uqpM1I38EmAB8G/pLb/13AvlnR3VeBk1j8HWyxZ4FtJf0B\neDsiXqnOoZo1Gc8qMPQcc2twzZowBT0X2N0POA44m9RHqZ3U5+nmbP13gdWBX5HmTLoE+AWwYu49\nTgNWy17zKnB89jzvSOD7pO/DzwNrYP1y4403svPOOxfdjMI5DklLSwvLDF+Gd6e9W3RTmlKtFuX1\n+ZE4DpVpylty9cy35Ja0++67c/311xfdjMI5Dh2+8pWvcNJJJxXdjMJNmjSJ008/fUjfs1YnrvT5\nkTgOld2Sc8JUZ5wwmZmZDUwlCZNHyZmZmZn1wgmTmZmZWS+atdO3mTUoF9+tTbXav8mszyLCj9wD\n2B74PTCPNDruZmCNbN3HSRNSfg34HWmE3IPAWqTacH8k1Za7Ffhgbp+bkKZ5+xdpGoJ7gA1z6/fN\n9rsw+7f0OKGL9m0ExMyZM8Mi9ttvv6KbUBMch2Tu3LkxbNiwUj1GP2roMWK5ETF37txCfi98fiSO\nQ8TMmTNLv5MbRT/zA19h6mx50lD/R4D3ASeTpgxYP7fNZNKs388Bl5Pmwn2NNPv3W6S5eE8GDsm2\nfx9wRfZ8KdJ0ArdKGhURbwLXAbfl9v+fpGkPupjC2PLGjBlTdBNqguOQtLe3s3DhQhffhTT796ii\nG5EpuDCvz4/EcaiME6YyETEt/1zSQcBLkj5JRzGBsyLizmz9eaSEaZuIuD9bNpV01ai0z7vL9vlN\nYHdga+DWiHgbeClbtyZwEXBMRNw1+EfYWPbcc8+im1ATHIcyLr7r48/x+ZE4DpVxp+8ykkZJ+qmk\npyW9CswhXb7Lfy36U+7//8z+/XPZslVy+1xF0qWS/ibpFdJElsuX7bNUPuVm4OaIOGfQDsrMzMwq\n4oSps1uAlYGDSPW+P0sqopsvrJufRji6WZaP7VXAeqRbdpuRbu+9nN+npKWAG0h9nMb11kgX3/Vx\n+DhcfHexWj+Oezo3rd5+rxrl/Gim43Dx3SqS9AFSR+/FhXclbUHq4L0zqV/THGCDiHg0W781qW7c\nyhHxWrZsX1KplQ9kz18DvhUR12TPVyV9rBweEedny84HdiFNpvVSD230xJU5M2bMYIsttii6GYVz\nHBIX382ZSxqmUgsKLszr8yNxHFx8dzDNA/4NfEPSi6SPm9PouIrUHfWy/klgb0kzSfXmziSNsEsv\nlvYHvkVKyiTpw9mqN7JO4daNM888s+k/AMBx6MSzCsBvgC8W3YhMwT8Pnx+J41AZJ0w5ERGSdgfO\nJ11IfgI4lHRBuZQ0dZU89ZZQHUAq0DuTNLLuWFJh35KtSLfwppe97iTSaDvrxnXXXVd0E2qC45C0\ntLSw7IhleXva20U3pTZcUnQDOhRZmNfnR+I4VMa35OqMb8mZ9cwTV9YmT1xptcC35MzMMq2trf7D\nbGaDzqPkapCkOZIOLbodZmZmljhhsrpWPgS1WTkOHRyLxHHo4FgkjkNlfEvO6ppvvSSOQ4cVVliB\nWbP61TWhIUmqqzhUs4+Tz4/EcaiMO313QZKAScDBwEdIo+W+GxE/l3Q88E3g0xExL9v+l8CIiNg2\ne16aOmAn0jQCTwKTIuLWbP0WwKmkorz/Ik1Pd0xEzM/WzyHN43R+F21zp2+zbrS1tbH26LVZ8NaC\nopti/TRiuRE88fgT/qNuVeVO34PvWGAv0vR3T5GG/V8t6SXge8D2wGXAVyUdAnyONJN3Kdn6Fan0\nyV7AM8DapR1nteJuy95jP1IJlQuBC4ADq39oZo2rvb09JUsuvltfCi7Oa9YXTpjKSBoOHANsGxEP\nZIuflbQlMC4ifi9pb2C2pNNI8zQdEBF/z7b9L9KVo9ER8XTp9bm3mAT8JCIuyJ4/I+lw4B5J34qI\nd6p3dGZNwsV3zWyQudN3Z6OAkcAdkl4vPYC9gTUBImIOcBRwNHBTRFyfe/36wPO5ZKnc+sB+Zfv+\nVbZu9SocT0Mrr0XUrBwH66S8/lsT8/mROA6VccLU2QrZv18iJTelxyeB/85ttzXwHrBaVji35K0+\n7P9HpFt4pX2vB3wC6C7J6sTFd5MddtihIY6j0p/H+PHjG+I4XHw3p9Liu3dQd8dx3333+fzo4Tgq\nPT8mTpzYEMcBLr5bEyStQPp4OKhULLeLbXYHpgJjSKf9pRExOVu3FamK0zoR8VQXr/0JsEpEjOmh\nDe703UdtbW3u84DjUOLiuzmvACsV3Yg+qnJxXp8fiePgTt+DKiLekHQ2MEXSMNJ3rhWBzYFXSd/D\nLgYmRsQfssK5t0i6LSIeiIjfSfo98HNJR5K++41Ou47bgTOA+yRdQOo4/ibwKWC7iPj2EB9u3Wv2\nk7/EcSjjyijJ/N43qQlV/nn5/Egch8o4YepCRByfjYibBKxB+q42CzgNuBy4PyIuzrb9taSLSaPo\nNsimBtiVVFz3p6TRck9l+yIi/iRpa9Jou98BIt2Ky/eD8mU/swFoaWlhxHIjWDDN0wrUmyKL85r1\nhW/J1RnfkjPrmYvv1icX57Wh4Fty1rTOOOMMjj766KKbUTjHocO1117rWODfiTzHInEcKuNRcoNo\nMIrmStpX0rzBalOjmz+/XjppVJfj0MGxSByHDo5F4jhUxrfkBlFPo9v6sY99s318oJv1viVnZmY2\nAL4lZ2aWcR+m+uV+TFbLnDD1QzZH049IRXXnkQrs7grMjogjuth+ArA/aaTdy8DNwFGlIrvZNvsB\nJwEfBG4H7q3uUZg1LhffrW8uwGu1zAlT/0wBNgO+DLwEnAJsCMzuZvuFwLeBOaSk6WJSkjUeQNJn\nSXMxHQ3cBHwROLl6zW887e3tHoqM41Di4rs5C4ARRTeiH6pYgNfnR+I4VMYJUx9lV5f2AfaIiHuy\nZfuT5qjtUllfpjZJxwM/IEuYSIV7b4uI72fPL5S0ObD9IDe/YR1wwAFMnz696GYUznEo4+K7aRa4\nvYpuRG3w+ZE4DpXxKLm+W4OUYP6xtCAiXgOe6O4FkraTdKek5yW9BlwNfFBS6XvfOqSqTHn39aUx\nriWXjBgxoiGOo9Kfx7hx4xriOFxLLqfSWnJfoP6OA7qs9eXzI6n0/Jg8eXJDHAe4llxNk7Qe6dbb\nxyPi+dzymcBvI+KI/Cg5SR8HHgcuAm4g9WHaknQLbuWIeE3SLGBaRHw3t79DgckeJWfWf64lV8eq\nXE/ODCobJecrTH33DPAesGlpgaQVgU90s/3GpIT0OxHxYFaI96Nl2zwGfLZs2WaD1F4zMzMbJO7D\n1EdZUd4rgbOziSX/BUwmdezu6jLdU8Ay2RWjm4EtgHFl25wPzMiK9JY6fbv/klmlPKtA/fHPE7u1\nmQAAGxlJREFUzGqcE6b+mQD8kJQAvUYa8bYqaTwK5BKniHhU0hHAROBUUqHdScBVuW0ekHQwaVqB\nk4A7SSPvjq/6kTSIqVOncuCBBxbdjMI5DklLSwtLL7M07017r+im2ABUqwCvz4/EcaiME6Z+iIg3\ngb1LzyWNJF1l+lG2fo2y7c8DzivbzTVl21wBXFG2zZTBaG8zmDVrlj8AcBxKWltb2XOPPTn88MOL\nbkrhTj/9dCZNmlR0M/qlWhNX+vxIHIfKuNN3P0jaABgNPAisBJwAbAWMioiXh6gN7vRtZmY2AO70\nPQgk3S3pnD5s+h3gYdKg2uWALQYzWZJ0uaRpg7U/MzMzq5xvyfVDRDwMbFLltzkUUJXfw8zMzPrB\nCVONiYjXi26DWT1z8V3rjYv82kA4YVrSUpLOAA4izVv7w4g4CbotpDsx6wiOpH2Bc4H9gLNIo+d+\nCxxUmuhS0onAzqTyKMeRCu7eAhyczRqOpMuBFSNi16E44Ho3duxYT/WP41DS1tbG6quvzqJFi4pu\nitWwZi3y68+JyjhhWtK+wDnAZ4DPA1dImhERv6HrQrpn0FEXDmAkcCzwP8C7pMToWtIM3yWjgK8B\nOwIrAj8mzQa+N9Zv48eP732jJuA4JO3t7SlZcvFdeB74WNGNqBH5WFSxyG+t8+dEZZwwLenRiDgl\n+//TksYD2wK/6UMhXUjxPCQiHoLFV50ek7RJaRmwLLB3RLyYbfNt4BZJR0bES9U7tMY0ZsyYoptQ\nExyHMi6+6+PPcywAf05UyqPklvRo2fN/AKtAnwrpAryXS4yIiCdI5SvXyW3TVkqWMvcBw4C1+9NQ\nF9/1cfg4XHx3MR9Hh34cRzOeH810HC6+WyWS7gZmR8QRuWW/AOaRZuHurZDuvsAlEbFs2X5fBg6L\niKuzPkx7R8So3Pr3k07nrSPi9731YfI8TGbdc/Fd65WL/DY1z8NUfX0ppAuwtKTF0w5IWps0weVf\nc9u0SvpI7vlmpP5RT1Sh3Q2v/BtRs3IcrJPyqzfNzLEA/DlRKfdh6pu+FNIFeA+4QNJhpCToAuAP\nETEzt83bwJWSjiJ1+j4PuN79lwbm2muvZeeddy66GYVzHMp4VgH4I+kTxpaMRRP/bvhzojJOmDp0\ne2+yL4V0M2+SRs79lHRD4HekKQryngSmAbcCK5MSsEMGof1N6frrry+6CTXBcUhaWloYsdwIFkxb\n0PvGzeCSohtQQ3KxqFaR31rnz4nKOGHKRMQ2XSzbJff/XgvpZtvdSNddHPPb/IisYG8X6zr3YjOz\nPmltbeWJx5/wxJXWI09caQPhhMnMGkpra6v/GJrZoHOn7xoj6URJ/eq5b2ZmZtXlhGmQRMSVEfGB\nXrY5KSJ6G8d6FmmyTOuDrubhaEaOQwfHInEcOjgWieNQGd+SqzERMR+YX3Q76oVnrk0chw4bbbQR\ns2b5Iu1aa63lOGTqKRbV7F/lz4nKeOLKAZAk0ii5g4GPkOZQ+m5E/FzSUqTxGNtk69qAi/OlVSR9\ngTSa7lOkmnN/BvaKiOdKBXojYsNu3tsTV5p1o62tjbVHr82CtzxKzupTsxYGHiqVTFzpK0wDcyyw\nF2k+4aeArYCrJb1EKnXyHPBV0ozgnwcukfRCRPxM0jDgF6RRcruTast9hiWnNXAWazYA7e3tKVly\n8V2rR01cGLgeOGHqJ0nDgWOAbSPigWzxs5K2BMZFxO9JpVRK5kr6PLAb8DPg/dnjlxHxbLaNZ/k2\nG0wuvmtmg8ydvvtvFDASuEPS66UHsDewBoCkQyQ9JOmlbN03gFaAiJgHXAn8WtJ0SYeWlUrpExff\nTXbdddeGOI5Kfx4///nPG+I4XHw3p9KitXNpjOOAyo/jL9TdcVTj/JgxY0bDnOcuvlsHJH0GuJ90\nG+6FstVvk8qmXA5MyLZ7nTRD+GfyI+QkrQ98ERgLrAtsFxEPZn2YdupuNJ37MC1p7NixTJ8+vehm\nFM5xSFx8N+enpI4DVj+xqHJhYH9OuA/TUPsrKTH6eER0+moraXPg3mw279KyNcu3i4hHgEeAMyT9\ngXQ6P1i1Vjeo6667rugm1ATHwTr576IbUEMcC8CfE5VywtRPEfGGpLOBKVkH7hmkso6bA6+RasXt\nLWkMMId0q25T4BkASauRvv9OJ32fGA2sBVwxlMfRKEaOHFl0E2qC41DGlVGsHlX599afE5VxwjQA\nEXF8NiJuEqnf0iukO9Onkq4SbQBcRxrtdi1wEbBD9vL5pCRpH+CDwD+ACyLCZTLNKuTiu1bvmrUw\ncD1wH6Y64z5MZj1ra2tz8V2rWy4MXF3uw2RN66ijjuKss84quhmFcxw6XHDBBY4F/p3IcywSx6Ey\nnlaghkhaJKnzGE7rlr+JJY5DB8cicRw6OBaJ41AZ35KrkKRlIuLdQdrXIlJZlG7HffqWnJmZ2cD4\nltwgkrQCqWzJTsA84ExSoYXZEXGEpDnAVNLItp2BnwMHSPoY8H1gDLAI+D1wWETMzfa7CalT+IbA\nMsDDwISImJ2tn0PqJH5jKlXHsxGxxpActFkDcR8msyW5X9TgcMLU2RRgM+DLwEvAKaQkZ3ZumyOB\nk4HJAJKWBm4H7iVNL7AQOA74laR1I+I94H2kqQMOId0KPRK4VdKoiHiTNPXAS8C+2b4WVvMgzRqR\ni++adeaCvoPDCVNOdnVpH2CPiLgnW7Y/nWf0/k1ETMm97uuk25vfyC07kHSF6gvAnRFxd9l7fZNU\nfHdr4NaIaM+uLL0aES8N8qE1rMcff5zRo0cX3YzCOQ6Ji+/mvAKsVHQjakQzxyJX0Hf+/Pn+nKiA\nE6YlrUGKyR9LCyLiNUnlxXFnlj1fH1grqxuXtyywJnCnpFWA75ESpFWAYcByZDXmbGAmTpzY9FP9\ng+PQiYvvpnp69VAOZCjcg2OBPycq5VFyA1Ne7nEF4CFgPVLyVHp8go4SjFdl679NuuW3PvAyMHwg\nDXDx3WSllVZqiOOo9OdxzDHHNMRxuPhuTqXFXr9EYxwHVH4cW9AYxzGQn8eLHYsuvPDChjnPXXy3\nYNktuX+Tbsn9Ilu2IvA8cGmu0/eUiDg/97qDgNOB1SLijW72/RrwrYi4Jnu+KumUPLy0L0lv59+7\nm/14lJxZN1x816xMlQv61huPkhskWZ24K4GzJc0j5fGTSR2we8osrwG+A9wk6URSgrUasAtwRkS8\nQEeNuZmk2nNnksqk5D0LbJsV4307Il4ZpEMzay4eJGeW+FwYNE6YOpsA/BC4mVRM90xgVaA07KZT\n4hQRb0naCjiDNM3A+4C/A7/J9gFwAHAJqf/Tc8CxwNlluzqSNDXBwdnrPa2AWT+4lpxZZ65PNzic\nMJXJhvjvXXouaSTpKtOPsvVdJjHZyLbON1Q71j8CfLZs8bSybW4BbhlIu5vVGWecwdFHH110Mwrn\nOCStra0cftjhfO1rXyu6KYW74oor2G+//YpuRk1o9liU5mHy50RlnDCVkbQBMBp4kDQQ9QTSVaWb\nimyXdW3+/PK7ms3JcegwfPhw99UAbrrpJsch41gk/pyojDt9l8kSpstII9zeId1CmxARf83W3002\n63dB7XOnbzMzswFwp+9BFBEPA5sU3Q4zMzOrHZ6HyczMzKwXvsJUAUkrAeeT6s4tC/wWODQinpL0\nPuCfwC4RcXvuNbsAVwKrRMSC3or2Ws/a29s9+gPHIe/hhx9m0aJFRTejcPPmzWPllVcuuhk1wbFI\n5s2bx1prreWacgPkhKkyV5JKn3wZeJ00BcGtktaJiNcl3UKakP/23Gv2An6RJUt9KdprPTjggAM8\n1T+OQ0lbWxsbb7yxEyazbrgQ78A5YRogSaOArwCbRcQD2bKvk+ZY2pk0H9M1wFWSRmQJ0vuAHYGd\nst3sQS9Fe4focOrW5MmTi25CTXAckvb29pQsufhumrCw2WNQ4lgkT8KCu1MhXidM/eeEaeDWAd4l\nTT8AQES8nBXqXSdbdCvwHjAWuAH4b+BV0oSWkGrL9Vi0t2qtbxAeKZg4DmVcfNfHn+dYdLi76AbU\nL3f6rqKIeBf4GR11svcEro+I0v2CvhTt7ZKL7/o4fBwuvruYj6ODjyPp7jjo+op0vZ3nLr5bB0rz\nMAEXA38DPh8R92frPgi0AXtHxLRs2VakX+WNgEeBz0XEQ9m6Xov2dvH+nofJrBsuvmvWAxfi9TxM\nRchGwt0EXCrpm8AbpOTnOXKzgkfE7yT9k9Sf6ZlSspTpS9Fe68HUqVM58MADi25G4RyHMi44Co+T\nahaYY1HyUO+bWPecMPVf/pLc/sC5pEK9w0nTCuwYEQvLXnMtcBRw0hI76lvRXuvBrFmznCjgOJS0\ntLQwbNgwFk4rPwWb1O+KbkANcSwAF+KthG/J1RnfkjPrWVtbW6d+FGaWlArxNivfkjMzy7S2tjb1\nHwQzqw6PkhsgSZMlvShpoaTOwwjMzMysYThhGgBJo4ETgIOBjwC3DcI+T5Q0u9L9mJmZ2eBzwtQP\nkpaSJGAUEBFxc0T8K5tvaTC4Q1k/dTVHSDNyHDo4Fonj0MGxSByHyjR0H6ZszqQ/Z0/3Js3M/YOI\nOCFbPxw4lVSiZCXS1GSTIuK32fp9SaPg9iFNGbAWaSqAfYGQtIiUOA3Ltj8IOAJYHZgDXBARP8i1\n56PA2aRCu8sCfwUOAT4JnJjfJ7B/RFxVhbA0lPHjxxfdhJrgOHTYbbfdmDWrX305G9L222/vOGQc\ni6RacWiWjuQNnTBl9gGmApsCm5DmTZobEVOBi0izc+wG/IM0/9FtWeHbp7PXjwQmAgcC/862uwf4\nMfBhQLC4jtxkUgL0MLBh9l5vRMTVkpYnDWx9jlSs90VgA9JVvuuATwPbA9tm+3y1OuFoLGPGjCm6\nCTXBcUja2to4+BsHs+CtBUU3xaxpNEtB32ZImJ6LiCOy/z8paT1ggqRfA/sBq0bEi9n6cyTtQJpf\n6bhs2dLAtyKidKUKSa8ARER+gvrJwJERUZq0cq6kTwHjgKuBrwMfBDaKiFIyNCe3zzeA98r2aWb9\n0N7enpIlF981GxrtsGBacxT0bYaE6f6y5/eRbputCwwD/pb1SyoZzpLzBL+TT5a6ImkkqVjuVEmX\n5VYtDczL/r8+MDuXLJlZtbj4rpkNsmbu9L088B6pxlu+8O06wGG57d7qw75WyP49qGxfnwI268d+\n+szFd5MddtihIY6j0p/HpZde2hDH4eK7OZUWe32MxjgOqPw4HqIxjqPSn8djVO04HnvssZr73HXx\n3X7IOn23RMS6uWWnAV8BdiYVz90yIu7t5vX7AlMi4gNly3cCppU6e2fLnid1KP9eN/vaBzgPWD0i\nXuli/THAHhGxfi/H5Jm+czbbbDPuu+++optROMchcfHdnMtIX+HMsSipRhzqrKCvZ/ruWauks4FL\ngI2B8cCErHjuNcBVkr4DzAZWAbYBHomI/s6tdCJwnqTXgF+RRsFtAqwcEVNI9eSOBW6UdCyp8/iG\nwN8j4gHgWWB1SeuTivC+HhHvVHLgzeBDH/pQ0U2oCY5DGVdGSfcPXL47cSySasShic61ZkiYrgKW\nAx4k3YKbEhGlfkb7kTp3nw18lPSjv59UTLdfImKqpDdJI+rOJF1g/RNpWgIi4l1J/wV8n3RBc2k6\nphWAVHx3F9KF2BVJHc89rYBZP7S0tLDUUkuxaNqioptSGy4pugE1xLFIqhCHZino2wwJ07vZKLlD\nyldExELgpOzRSURcCVzZxfKbSB3Gy5dfR5oioEsR8RxpCoOu1r3T3Toz65vW1la22WabTn0mmtGE\nCROYMmVK0c2oCY5FUq04eB4mM7M6tNxyy9VFX4pqW3HFFR2HjGOROA6VafSEqRF7tI+ANCLB4MEH\nH/QMvjgOeY5F4jh0cCwSx2GJv50j+vvahh4l14gk7UUqz2JmZmYD8/WI6GrShG45Yaozkj5IKqHy\nLOD6D2ZmZn03AlgNuD0i/t2fFzphMjMzM+tFM8/0bWZmZtYnTpjMzMzMeuGEyczMzKwXTpgagKQd\nJd0vab6klyVNK7pNRZE0XNLDkhZJWq/o9gwlSR+XdJmkZ7LfhSclTZa0TNFtGwqSDpE0R9Jb2fmw\nadFtGmqSjpH0oKTXJP1T0i8kfaLodhVN0qTsM+GcottSBEn/IelqSe3ZZ8MjWV3SpiFpKUmn5D4f\nn5J0XH/20ejzMDU8SV8lTXY/CbgLWAb4dKGNKtaZpFp86/a2YQMaDQg4GHia9HtwGTCSVLKnYUna\nnVR26BukMkgTgNslfSIimqjaFVsCFwAPkT7fTwN+LWmdiHir0JYVJEucvwE8UnRbiiBpJeBe4Dek\nEdbtwFrAvCLbVYBJwDhgH1JZsk2AKyS9EhEX9mUHHiVXxyQNI00vcHxEXFFsa4onaQdSXcCvkk6I\nDSLi0WJbVayssPQ3I2JU0W2pJkn3Aw9ExGHZcwHPAedHxJmFNq5AklqAl4CtImJG0e0ZapJWAGYC\n3wKOB2ZnpbKahqTTgc0iYuui21IkSTcDL0bEwbllPwPmR8Q+fdmHb8nVt42A/wCQNEvSC5JulfSp\ngts15CR9mHSl7X+Apvwm3Y2VgJeLbkQ1ZbccNyZ9gwYg0jfBO4HNimpXjViJVPGgoX8HenARcHNE\n3FV0Qwr0FeAhSTdkt2lnSTqo6EYV4A/AtpLWApC0PrA5cGtfd+CEqb6tQboFcyJwMrAj6TLrPdll\n2GZyOXBxRMwuuiG1QtIoYDzww6LbUmUtpGLY/yxb/k/gI0PfnNqQXWU7F5gREX8tuj1DTdIewAbA\nMUW3pWBrkK6wPQGMAX4AnC9p70JbNfROB64HHpf0DunK47kRcV1fd+CEqQZJOi3roNjdY2HWkbP0\n8/tuRNyYJQv7k75Rfq2wAxgkfY2DpEOBFYBSiXoV2OxB14/fh/xrPgrcBlwfET8upuVWsIuBTwJ7\nFN2QoSbpY6Rk8esR8W7R7SnYUsDMiDg+Ih6JiEuBS4FvFtyuobY7sBfpfNgQ2Bc4qj+Jozt916az\nSVdMevIM2e04YHE1wYh4R9IzQGuV2jaU+hKHOcB/km69vJ2+VC/2kKRrImL/KrVvqPT19wFII2JI\nAwBmRMS4ajasRrQDC4EPly3/MPDi0DeneJIuBL4EbBkR/yi6PQXYGPgQMEsdHwrDgK0kjQeWjebp\nwPsPcn8jMo8BuxbQliKdCZwWEf+bPf+LpNVIVyCv7ssOnDDVoKy+Ta81biTNBN4G1ibdny3151gN\nmFvFJg6JfsTh28D/yy36D+B2YDfSiKm61tc4wOIrS3cBfwQOqGa7akVEvJudC9sC02Hx7ahtgfOL\nbFsRsmRpJ2DriGgruj0FuZPOI2WvICUKpzdRsgRphNzaZcvWpgH+RvTTSNIXq7xF9ONOmxOmOhYR\nr0v6IXCSpOdJJ8BE0i25/+3xxQ0kIp7PP5f0Jum23DMR8UIxrRp62ZWle0hX3SYCq5S+XEdEef+e\nRnMOaYjwTDqmFRhJ+iPZNCRdDOwJjAXezAZDALwaEU1TrDsi3iSNlF0s+1z4d0SUX21pdFOAeyUd\nA9wAfBY4iDT9SDO5GTgu+1v5F9KgqQmkqVf6xAlT/fsO8C5wFbAc8ACwTUS8WmiritdM3yBL/ovU\nwXMN0pB6SIljkG5HNKyIuCEbQn8y6Vbcw8D2EfGvYls25L5J+nnfU7Z8f9JnRDNrxs8EIuIhSbuQ\nOj0fT/pCdVh/Ojs3iPHAKaSRk6sAL5A6wJ/S1x14HiYzMzOzXniUnJmZmVkvnDCZmZmZ9cIJk5mZ\nmVkvnDCZmZmZ9cIJk5mZmVkvnDCZmZmZ9cIJk5mZmVkvnDCZmZmZ9cIJk5mZmVkvnDCZmfWRpA9L\nukPSG5Je7mHZIklj+7jPEyXNqma7zaxyTpjMrCFkicsFkp6WtEDSXEnTJW0ziG8zgVSrbj3gEz0s\n+whwWx/3eRaw7SC2EUn7Spo3mPs0a3YuvmtmdU/Sx4E/AC8DRwJ/BpYBvghcCHxykN5qTWBmRDzT\n07KIeKmvO4yI+cD8QWpfSanospkNEl9hMrNG8ANgIbBpRNwYEU9FxGMRMQX4HICkVSXdJOl1Sa9K\nul7SKvmdSNpJ0kxJb0l6StIJkpbK1s0BdgX2lbRQ0o+zZV/NL8u2XeKWnKSPSrpW0r+zW3cPSto0\nW3eipNll7ThI0l+zdvxV0rdy6z6e7X8XSXdJelPSw5JKx7k18GNgxWy7hZJOGOR4mzUdX2Eys7om\naWVge+CYiFhQvj4iXpMkYDrwGrAl6erTxcB1wDbZfrYErgTGA78HRgGXkK7UnAJsAlwNvAocCiwA\nhnexrLx9ywO/A54Dvgy8CGzAkl9YI7f914HJwCHAw8CGwKWS3oiIq3Ov+S7patpTwKnATyWNIl1p\nOxw4iXSLUMAbPcXQzHrnhMnM6t0oUlLwRA/bbAd8ClgtIl4AkLQP8BdJG0fETOAE4LSI+En2mrnZ\nlZkzgVMi4t+S3gbeioh/lXbc1bIyXwc+CGwUEa9my+b00NbJwJERcVOuHZ8CvklKzkrOiohfZW04\nkXQbclRE/E3Sq0D00CYz6ycnTGZW79SHbUYDz5WSJYCIeEzSK8A6wExgfeDzko7LvW4YMFzSiK6u\nXvXR+sDsXLLULUkjSX2ipkq6rKwdr5Rt/qfc//9BisMqwN8G2E4z64ETJjOrd0+SbmmNBm7qZdue\nrEC6yjStfEUFyRLAW/1sA8BBwINl6xaWPX839//SLT33SzWrEidMZlbXImKepNuBQySdHxFLJCiS\nVgQeA1aV9NGI+Hu2/JPASsBfsk1nAWuXjYAbDI8CB0paKSLKrxKVH8tLkl4A1oyI63ratJf3fId0\nVcrMBokTJjNrBIcAM4AHs/48j5I+38YA4yLiU5L+DFwjaQKp0/dFwN0RURqhdjJws6TngJ8Bi0i3\n0z4dEcdX0LZrgWOBGyUdS7p9tiHw94h4oIvtTwTOk/Qa8CtgWVKH85Ui4txsm95uQz4LrJDNQfUI\nML88kTSz/vHlWzOrexExB9gIuBs4m9S/59ekhOmIbLOxwDzgt9m6p4A9cvv4NWkU23+RbofdRxpt\n9uxAmpTb77vZPl8CfklK5o6m8y220vZTSbfk9s+2vQfYlyU7ind1hSn/nvcBPwSuz973qAEcg5nl\nKMJzm5mZmZn1xFeYzMzMzHrhhMnMzMysF06YzMzMzHrhhMnMzMysF06YzMzMzHrhhMnMzMysF06Y\nzMzMzHrhhMnMzMysF06YzMzMzHrhhMnMzMysF06YzMzMzHrhhMnMzMysF/8fEyHhGhV1wYQAAAAA\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x455d6208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"def plot_coef(title, model, feature_names, n_words):\n",
" v = []\n",
" for topic_idx, topic in enumerate(model.coef_):\n",
" [v.append([feature_names[i], model.coef_.item(i)]) for i in topic.argsort()[:-n_words - 1:-1]]\n",
" [v.append([feature_names[i], model.coef_.item(i)]) for i in topic.argsort()[0:n_words]]\n",
" df = pd.DataFrame(v, columns=['Term','Coefficient']).sort_values(by='Coefficient',ascending=False)\n",
" df['c'] = df['Coefficient']>0\n",
" ax = df.plot(x='Term', y='Coefficient', kind='barh', color=df['c'].map({True: 'g', False: 'r'}), grid=True, legend=False,\n",
" title=title)\n",
" ax.set_xlabel(\"Coefficient\")\n",
"\n",
"n_terms = 12\n",
"for c in range(0,len(cat)):\n",
" plot_coef('Top {N} words in ({R}) review model\\nGreen = Associated | Red = Not Associated'.format(N=n_terms*2, R=cat[c]), \n",
" lr_m[c], tfidf_m.get_feature_names(), n_terms)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Test output\n",
"To put it altogether, below is a test function which allows you to supply your own review to see how well the model will predict it's rating. For simplicity, I stuck with the logistic regression model and only allow for one review at a time. \n",
"\n",
"The program uses the stored TFIDF matrix to tokenize and transform our new review which is then fed to all three of our logistic regression models. Each model has an independent assessment of how likely it is that our review is a positive hit. You could set some sort of threshold or take the model with the higest likelihood to make your determination."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def test_review(text):\n",
" test_str = [text]\n",
" test_new = tfidf_m.transform(test_str)\n",
"\n",
" print('Review text: \"{R}\"\\n'.format(R=test_str[0]))\n",
" print('Model Predction')\n",
" for m in range(0,3):\n",
" print('Model ({M}): {P:.1%}'.format(M=cat[m], P=lr_m[m].predict_proba(test_new)[0][1]))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Review text: \"I bought these knives last week. I immediately returned these when they arrived damaged.\"\n",
"\n",
"Model Predction\n",
"Model (low): 93.1%\n",
"Model (neutral): 3.4%\n",
"Model (high): 10.6%\n"
]
}
],
"source": [
"test_review('I bought these knives last week. I immediately returned these when they arrived damaged.')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Review text: \"This is the best toaster oven I have ever owned! I am glad I bought it.\"\n",
"\n",
"Model Predction\n",
"Model (low): 10.5%\n",
"Model (neutral): 10.9%\n",
"Model (high): 84.0%\n"
]
}
],
"source": [
"test_review('This is the best toaster oven I have ever owned! I am glad I bought it.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@haisi
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haisi commented May 14, 2018

How would you go about predicting the whole X_train-matrix to measure the performance?
Essentially I want to do something like:

y_pred = predict(X_train, lr_m)
score = accuracy_score(y_test, y_pred)

I've already modified your test_review method to return the category with the highest probability.

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