diff --git a/pre-processing/data_preprocessing.ipynb b/pre-processing/data_preprocessing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9afa53d8a3b14a91fc681a9f5340c291745e8564
--- /dev/null
+++ b/pre-processing/data_preprocessing.ipynb
@@ -0,0 +1,415 @@
+{
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "orig_nbformat": 2,
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3.7.9 64-bit ('.venv': venv)"
+ },
+ "metadata": {
+ "interpreter": {
+ "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+ }
+ },
+ "interpreter": {
+ "hash": "f7bfe7bd0e1b2b0ad7f24d834ee1093564259268c66f76a41bb752c4f89e1758"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import os\n",
+ "import re\n",
+ "from transformers import GPT2Tokenizer\n",
+ "import matplotlib.pyplot as plt\n",
+ "from collections import defaultdict\n",
+ "\n",
+ "%cd /data-imperial"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Concatenate all message files into one file\n",
+ "list_files = os.listdir('Message/')\n",
+ "df = pd.concat([pd.read_csv('Message/'+file, sep='\\t') for file in list_files])\n",
+ "df.drop(['SALT','REMOTE_MESSAGE_ID','RETRIES','DELIVERY_ERROR','MEDIA_URI', 'MEDIA_MIMETYPE'], axis=1,inplace=True)\n",
+ "df.to_csv(\"all_messages.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "df = pd.read_csv('all_messages.csv', index_col=0)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "conversation_participation = pd.read_csv('CONVERSATION_PARTICIPATION.tsv', sep='\\t')"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Split the actor_id into volunteer/texter ids\n",
+ "volunteer_ids = conversation_participation[conversation_participation['INTERACTION'].isin(['counselor', 'observer'])]['ACTOR_ID'].unique()\n",
+ "texter_ids = conversation_participation[conversation_participation['INTERACTION'].isin(['texter'])]['ACTOR_ID'].unique()\n",
+ "\n",
+ "# Create a column 'INTERACTION' in the dataset to categorize the actor_id (texter/volunteer)\n",
+ "df.loc[df['ACTOR_ID'].isin(volunteer_ids), 'INTERACTION'] = 'volunteer'\n",
+ "df.loc[df['ACTOR_ID'].isin(texter_ids), 'INTERACTION'] = 'texter'\n",
+ "# The N/As should be messages generated by the bot\n",
+ "df.loc[df['INTERACTION'].isna(), 'INTERACTION'] = 'bot'"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Check if every messages categorized as 'bot' comes from the same actor_id \n",
+ "df[df['INTERACTION'] == 'bot']['ACTOR_ID'].unique()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Check if there is no missing interaction.\n",
+ "df['INTERACTION'].isnull().sum()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "df.to_csv(\"all_messages.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## A little bit of preprocessing"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "df = pd.read_csv('all_messages.csv', index_col=0)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Remove N/A messages from conversations\n",
+ "df = df[~df.MESSAGE.isna()]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Fix some strange messages and characters"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Remove messages which contains aes256\n",
+ "df = df[~df.MESSAGE.str.contains(\"aes256\")]\n",
+ "# Handle emojis\n",
+ "df.MESSAGE = df.MESSAGE.str.replace('<span class=\"?\\w+ (\\w+)\"?></span>',\"[\\\\1]\", regex=True)\n",
+ "df.MESSAGE = df.MESSAGE.str.replace('<span class=\"?[\\w*\\W*]* ([\\w*\\W*]*)\"?></span>',\"[\\\\1]\", regex=True)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Fix various symbols\n",
+ "df.MESSAGE = df.MESSAGE.str.strip().str.replace(\"[‘’]\", \"\\'\", regex=True)\n",
+ "df.MESSAGE = df.MESSAGE.str.replace('[”“]','\"', regex=True)\n",
+ "df.MESSAGE = df.MESSAGE.str.replace('͟','', regex=True)\n",
+ "df.MESSAGE = df.MESSAGE.str.replace(\"–\",\"-\", regex=True)\n",
+ "\n",
+ "# Remove empty and null messages\n",
+ "df.dropna(subset=['MESSAGE'],inplace=True)\n",
+ "df.drop(df[df.MESSAGE.str.len() == 0].index, inplace=True)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
+ "\n",
+ "# Replace non-english characters (chinese characters) with unk token\n",
+ "df.MESSAGE = df.MESSAGE.str.replace('[㔹㘵㔳㜴㙆㘱㙅㘹㘸㜲㜳㜷㘶㙂㙃㐹㘴㜹㘷㜵㜶㔴䑅㘳㘲䐸㍄㜰㑄㈰㙄㜸㍆㑅ㄹ㌱㙁㉃㑁㔷㐱]',tokenizer.unk_token)\n",
+ "df.MESSAGE = df.MESSAGE.str.replace('\\[emoji3030\\]\\w+','[emoji3030]'+tokenizer.unk_token, regex=True)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "freq = defaultdict(lambda:0)\n",
+ "def addfreq(l):\n",
+ " for j in l:\n",
+ " for i in j:\n",
+ " freq[i] += 1\n",
+ "\n",
+ "df.MESSAGE.apply(addfreq);"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Focus only on characters that are currently tokenized as more than one token (i.e. they're weird)\n",
+ "\n",
+ "freq2 = {i:j for i,j in freq.items() if j < 100000 and len(tokenizer.tokenize(i))>1}\n",
+ "freq2 = pd.Series(freq2)\n",
+ "\n",
+ "# Just replace with the unk token any that appear fewer than 100 times\n",
+ "df.MESSAGE = df.MESSAGE.str.replace(\"[\" + \"\".join(freq2[freq2<100].keys()) + \"]\", tokenizer.unk_token)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Let's see the rest...\n",
+ "freq2[freq2>=100]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Okay now let's just replace emojis with an [emoji] token as well as the actual emoji in bytes \n",
+ "# (which will hopefully help it recongize emojis that are too infrequent and were replaced with unk)\n",
+ "\n",
+ "def to_emoji(a):\n",
+ " if len(a) == 10:\n",
+ " return to_emoji(a[:5]) + to_emoji(a[5:])\n",
+ " return chr(int(\"0x\"+a,16))\n",
+ "df.MESSAGE = df.MESSAGE.str.replace(\"\\[emoji(\\w+)\\]\",lambda a:\"[emoji]\"+ to_emoji(a.groups()[0]))\n"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Remove bot messages\n",
+ "df = df[df.INTERACTION!='bot']\n",
+ "\n",
+ "texter_survey = pd.read_csv(\"texter_survey_collated.csv\", index_col = 0)\n",
+ "sv_survey = pd.read_csv(\"volunteer_survey_by_conversation.csv\")\n",
+ "\n",
+ "# Remove rows where every value is NA for the labels\n",
+ "texter_survey = texter_survey.dropna(how='all', subset=list(texter_survey.columns[9:])) \n",
+ "sv_survey = sv_survey.dropna(how='all', subset=list(sv_survey.columns[1:]))\n",
+ "\n",
+ "# Count the number of conversations with texter or SV survey\n",
+ "conv_with_texter_survey = df[df.CONVERSATION_ID.isin(texter_survey.CONVERSATION_ID)]\n",
+ "conv_with_sv_survey = df[df.CONVERSATION_ID.isin(sv_survey.CONVERSATION_ID)]\n",
+ "print('Number of conversations with texter survey :', len(conv_with_texter_survey.CONVERSATION_ID.unique()))\n",
+ "print('Number of conversations with SV survey :', len(conv_with_sv_survey.CONVERSATION_ID.unique()))\n",
+ "\n",
+ "# Select the conversations where we have texter and volunteer surveys\n",
+ "df = df[df.CONVERSATION_ID.isin(texter_survey.CONVERSATION_ID.values) & df.CONVERSATION_ID.isin(sv_survey.CONVERSATION_ID)]\n",
+ "print('Number of conversation with both surveys :', len(df.CONVERSATION_ID.unique()))\n",
+ "df.to_csv(\"messages_with_survey.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "df = pd.read_csv(\"messages_with_survey.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Conversation Lengths"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Consider conversations from the entire dataset\n",
+ "conversation_lengths = df[df.INTERACTION!='bot'].groupby('CONVERSATION_ID').MESSAGE.agg(lambda x : sum(len(i) for i in x))\n",
+ "\n",
+ "iqr = conversation_lengths.quantile(0.75) - conversation_lengths.quantile(0.25)\n",
+ "min_len = 1000\n",
+ "max_len = conversation_lengths.mean() + 2*iqr\n",
+ "print(f\"removing from below {(conversation_lengths<min_len).mean() * 100:.2f}%\")\n",
+ "print(f\"removing from above {(conversation_lengths>max_len).mean() * 100:.2f}%\")\n",
+ "plt.rcfont\n",
+ "conversation_lengths.hist(bins=120)\n",
+ "y = 13000\n",
+ "plt.plot([min_len, min_len, max_len,max_len, min_len],[y,0,0,y,y])\n",
+ "plt.grid()\n",
+ "plt.xlabel(\"Number of characters per conversation\")\n",
+ "plt.ylabel(\"Number of Conversations\")\n",
+ "plt.title(\"Conversation lengths\")\n",
+ "plt.savefig(\"plots/conversation_length_histogram.pdf\")\n",
+ "plt.show()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "conversation_lengths.mean()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Consider only the conversations where there is a corresponding texter survey and volunteer survey\n",
+ "conversation_lengths = df[df.INTERACTION!='bot'].groupby('CONVERSATION_ID').MESSAGE.agg(lambda x : sum(len(i) for i in x))\n",
+ "conversation_lengths = conversation_lengths.to_frame()\n",
+ "conversation_lengths.reset_index(inplace=True)\n",
+ "\n",
+ "texter_survey = pd.read_csv(\"texter_survey_collated.csv\", index_col = 0)\n",
+ "sv_survey = pd.read_csv(\"volunteer_survey_by_conversation.csv\")\n",
+ "\n",
+ "conversation_lengths = conversation_lengths[conversation_lengths.CONVERSATION_ID.isin(texter_survey.CONVERSATION_ID.values) & conversation_lengths.CONVERSATION_ID.isin(sv_survey.CONVERSATION_ID)]\n",
+ "conversation_lengths = conversation_lengths.MESSAGE\n",
+ "\n",
+ "iqr = conversation_lengths.quantile(0.75) - conversation_lengths.quantile(0.25)\n",
+ "min_len = 1000\n",
+ "max_len = conversation_lengths.mean() + 2*iqr\n",
+ "print(f\"removing from below {(conversation_lengths<min_len).mean() * 100:.2f}%\")\n",
+ "print(f\"removing from above {(conversation_lengths>max_len).mean() * 100:.2f}%\")\n",
+ "conversation_lengths.hist(bins=120)\n",
+ "y = 13000\n",
+ "plt.plot([min_len, min_len, max_len,max_len, min_len],[y,0,0,y,y])\n",
+ "plt.grid()\n",
+ "plt.xlabel(\"Number of characters per conversation\")\n",
+ "plt.ylabel(\"Number of Conversations\")\n",
+ "plt.title(\"Conversation lengths\")\n",
+ "plt.savefig(\"plots/conversation_length_histogram_with_survey_considered.pdf\")\n",
+ "plt.show()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "conversation_lengths.mean()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Create csv file where we include conversations between a mininum and maximum lengths\n",
+ "df = pd.read_csv(\"messages_with_survey.csv\", index_col=0)\n",
+ "\n",
+ "conversation_lengths = df[df.INTERACTION!='bot'].groupby('CONVERSATION_ID').MESSAGE.agg(lambda x : sum(len(i) for i in x))\n",
+ "conversation_lengths = conversation_lengths.to_frame()\n",
+ "conversation_lengths.reset_index(inplace=True)\n",
+ "\n",
+ "iqr = conversation_lengths.quantile(0.75) - conversation_lengths.quantile(0.25)\n",
+ "min_len = 1000\n",
+ "max_len = int(conversation_lengths.mean() + 2*iqr)\n",
+ "\n",
+ "texter_survey = pd.read_csv(\"texter_survey_collated.csv\", index_col = 0)\n",
+ "sv_survey = pd.read_csv(\"volunteer_survey_by_conversation.csv\")\n",
+ "\n",
+ "conversation_lengths = conversation_lengths[conversation_lengths.CONVERSATION_ID.isin(texter_survey.CONVERSATION_ID.values) & conversation_lengths.CONVERSATION_ID.isin(sv_survey.CONVERSATION_ID)]\n",
+ "\n",
+ "conversation_thres = (conversation_lengths.MESSAGE >= min_len) & (conversation_lengths.MESSAGE <= max_len) \n",
+ "df = df[df.CONVERSATION_ID.isin(conversation_lengths[conversation_thres].CONVERSATION_ID)]\n",
+ "df = df.sort_values(['CONVERSATION_ID', 'TIMESTAMP'])[['CONVERSATION_ID','MESSAGE','INTERACTION','TIMESTAMP']]\n",
+ "df.to_csv(\"messages_with_lengths.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [],
+ "outputs": [],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pre-processing/models_preprocessing.ipynb b/pre-processing/models_preprocessing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3b38617ba18b088aaf9a5e87bdc0dad40a6c95a1
--- /dev/null
+++ b/pre-processing/models_preprocessing.ipynb
@@ -0,0 +1,633 @@
+{
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "orig_nbformat": 4,
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3.7.9 64-bit ('.venv': venv)"
+ },
+ "interpreter": {
+ "hash": "f7bfe7bd0e1b2b0ad7f24d834ee1093564259268c66f76a41bb752c4f89e1758"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import re\n",
+ "import transformers\n",
+ "from transformers import GPT2Tokenizer, RobertaTokenizer\n",
+ "from collections import defaultdict\n",
+ "import torch\n",
+ "import copy\n",
+ "from itertools import chain\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "%cd /data-imperial"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Preprocessing with GPT-2 (Encoded conversations)"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "df2 = pd.read_csv(\"messages_with_lengths.csv\", index_col=0)\n",
+ "tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
+ "\n",
+ "special_tokens = {\n",
+ " 'bos_token': \"<bos>\",\n",
+ " 'eos_token': \"<eos>\",\n",
+ " 'pad_token': \"<pad>\",\n",
+ " }\n",
+ "num_new_tokens = tokenizer.add_special_tokens(special_tokens)\n",
+ "vocab = tokenizer.get_vocab()\n"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Replace some words by the scrubbed token\n",
+ "for i in [\"{{PHONE}}\",\"{{UK_MOBILE}}\",\"{{EMAIL}}\",\"{{URL}}\"]:\n",
+ " df2.MESSAGE = df2.MESSAGE.str.replace(i,\"[scrubbed]\",regex=False)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Add a bos token and a eos token respectively at the beginning and at the end of the encoded message \n",
+ "df2['MESSAGE'] = df2.MESSAGE.str.strip().apply(lambda x:\" \"+x)\n",
+ "df2['ENCODED_MESSAGE'] = \"[\" + df2.INTERACTION + \"]\" + df2.MESSAGE\n",
+ "encoded_conversations = df2.groupby('CONVERSATION_ID').ENCODED_MESSAGE.agg(\" \".join).apply(\n",
+ " lambda x : f\"{tokenizer.bos_token}{x}{tokenizer.eos_token}\"\n",
+ ")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "token_lambda = lambda x: re.findall(\"[\\[|\\{]+\\w+[\\]|\\}]+\",x) if type(x)==str else np.nan\n",
+ "a = encoded_conversations.apply(token_lambda)\n",
+ "\n",
+ "freq = defaultdict(lambda : 0)\n",
+ "\n",
+ "for l in a:\n",
+ " for i in l:\n",
+ " freq[i] +=1"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Add frequent tokens to the GPT2 Tokenizer\n",
+ "\n",
+ "new_tokens = [i for i,j in freq.items() if j>10000]\n",
+ "print(new_tokens)\n",
+ "tokenizer.add_tokens(new_tokens)\n",
+ "tokenizer.add_tokens([\"<unk>\"])"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "tokenizer.save_pretrained(f\"data/gpt-2\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "encoded_conversations = encoded_conversations.str.replace((b\"\\xcd\\x9f\").decode(),\"\")\n",
+ "encoded_conversations = encoded_conversations.str.replace(\"–\",\"-\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "encoded_conversations.to_csv(f\"data/gpt-2/encoded_conversations.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Perform some pre-processing on the encoded_conversations before creating the inputs"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "encoded_conversations = pd.read_csv(f\"data/gpt-2/encoded_conversations.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "tokenizer = GPT2Tokenizer.from_pretrained(f\"data/gpt-2\")\n",
+ "special_tokens = {\n",
+ " 'bos_token': \"<bos>\",\n",
+ " 'eos_token': \"<eos>\",\n",
+ " 'pad_token': \"<pad>\",\n",
+ " 'additional_special_tokens': [\"[texter]\", \"[volunteer]\"]\n",
+ "}\n",
+ "num_new_tokens = tokenizer.add_special_tokens(special_tokens)\n",
+ "vocab = tokenizer.get_vocab()\n",
+ "vocab_size = len(vocab)\n",
+ "bos_id = vocab[\"<bos>\"]\n",
+ "eos_id = vocab[\"<eos>\"]\n",
+ "pad_id = vocab[\"<pad>\"]\n",
+ "speaker1_id = vocab[\"[texter]\"]\n",
+ "speaker2_id = vocab[\"[volunteer]\"]\n",
+ "\n",
+ "# Save the tokenizer with the added tokens\n",
+ "tokenizer.save_pretrained(f\"data/gpt-2\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# For each conversation, we combine successive text messages from the same speaker into one text message. \n",
+ "# This is performed to make sure that each text message is from a different speaker each time for model training\n",
+ "\n",
+ "encoded_conversations['nb_utterances'] = \" \"\n",
+ "encoded_conversations['conversation'] = \" \"\n",
+ "encoded_conversations['pos_speaker_tok'] = \" \"\n",
+ "encoded_conversations['pos_texter_tok'] = \" \"\n",
+ "n = len(encoded_conversations)\n",
+ "\n",
+ "for idx in tqdm(range(n)):\n",
+ " old_conversation = encoded_conversations.ENCODED_MESSAGE.iloc[idx]\n",
+ " old_conversation = old_conversation.replace('<bos>', '<bos> ')\n",
+ " old_conversation = old_conversation.replace('<eos>', ' <eos>')\n",
+ " old_conversation = old_conversation.split()\n",
+ "\n",
+ " old_speaker_tok = [i for i,j in enumerate(old_conversation) if j in ['[texter]', '[volunteer]']]\n",
+ " pos_speaker_tok = [v for i, v in enumerate(old_speaker_tok) if i == 0 or old_conversation[v] != old_conversation[old_speaker_tok[i-1]]] \n",
+ " diff_list = list(set(old_speaker_tok) - set(pos_speaker_tok))\n",
+ " conversation = [i for j, i in enumerate(old_conversation) if j not in diff_list]\n",
+ " pos_speaker_tok = [i for i, x in enumerate(conversation) if x in [\"[texter]\",\"[volunteer]\"]]\n",
+ " pos_texter_tok = [i for i, x in enumerate(conversation) if x in [\"[texter]\"]]\n",
+ " len_speaker_tok = len(pos_speaker_tok)\n",
+ " conversation = \" \".join(conversation)\n",
+ " conversation = tokenizer.tokenize(conversation)\n",
+ " pos_speaker_tok = [i for i,j in enumerate(conversation) if j in ['[texter]', '[volunteer]']]\n",
+ " if len_speaker_tok > 2:\n",
+ " encoded_conversations['nb_utterances'].iloc[idx] = len_speaker_tok \n",
+ " encoded_conversations['conversation'].iloc[idx] = conversation\n",
+ " encoded_conversations['pos_speaker_tok'].iloc[idx] = pos_speaker_tok\n",
+ " encoded_conversations['pos_texter_tok'].iloc[idx] = pos_texter_tok"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Remove empty conversations \n",
+ "encoded_conversations = encoded_conversations[~encoded_conversations.nb_utterances.isin([\" \"])]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "encoded_conversations.to_pickle(f\"data/gpt-2/encoded_conversations.pkl\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Create the inputs for the GPT2LMHeadModel where we only consider a number of text messages equal to 2"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "**In this section, we present the way inputs of the GPT-2 model are created. To do that, we consider a number of text messages equal to 2 which means that each input will only consider the reply and the previous message. If we want more text messages, we juste need to modify the parameter max_len, which represent the number of messages we consider here. We can take max_len=4 instead of 2 to consider more context messages.** "
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "tokenizer = GPT2Tokenizer.from_pretrained(f\"data/gpt-2\")\n",
+ "special_tokens = {\n",
+ " 'bos_token': \"<bos>\",\n",
+ " 'eos_token': \"<eos>\",\n",
+ " 'pad_token': \"<pad>\",\n",
+ " 'additional_special_tokens': [\"[texter]\", \"[volunteer]\"]\n",
+ "}\n",
+ "num_new_tokens = tokenizer.add_special_tokens(special_tokens)\n",
+ "vocab = tokenizer.get_vocab()\n",
+ "vocab_size = len(vocab)\n",
+ "bos_id = vocab[\"<bos>\"]\n",
+ "eos_id = vocab[\"<eos>\"]\n",
+ "pad_id = vocab[\"<pad>\"]\n",
+ "speaker1_id = vocab[\"[texter]\"]\n",
+ "speaker2_id = vocab[\"[volunteer]\"]\n",
+ "\n",
+ "max_time = 2 # represents the number of text message we consider here\n",
+ "max_len = 512 # represents the maximum number of tokens we use\n",
+ "utter_len = (max_len-max_time-2) // max_time # represents the maximum length of a text message (number of tokens)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Pre-processing used for GPT2LMHeadModel\n",
+ "\n",
+ "# We only consider conversations with corresponding texter and SV survey\n",
+ "list_conv_ids = np.load(f\"data/gpt-2/gpt2_list_conv_ids_with_surveys.npy\", allow_pickle=True)\n",
+ "features = pd.DataFrame({'CONVERSATION_ID': list_conv_ids})"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Pre-processing used for StyleGPT2\n",
+ "\n",
+ "#gpt2_features = np.load(f\"data/gpt-2/gpt2_features_first512tokens_cluster.npy\", allow_pickle=True)\n",
+ "#list_conv_ids = np.load(f\"data/gpt-2/gpt2_list_conv_ids_first512tokens.npy\", allow_pickle=True)\n",
+ "#standard_embedding = np.load(f\"data/gpt-2/gpt2_standard_embedding_first512tokens.npy\", allow_pickle=True)\n",
+ "\n",
+ "#features = pd.DataFrame({'CONVERSATION_ID': list_conv_ids})\n",
+ "#features['GPT2'] = pd.Series(list(gpt2_features))\n",
+ "#features['UMAP_EMB'] = pd.Series(list(standard_embedding))\n",
+ "#features['CONVERSATION_ID'] = features['CONVERSATION_ID'].astype(object)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "from helpers.labels_processor import get_ages, get_genders, get_topics\n",
+ "df_age = get_ages(category=2)\n",
+ "df_gender = get_genders()\n",
+ "df_gender['gender'] = df_gender['gender'].str.lower()\n",
+ "topics = get_topics()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "topics = topics.groupby('CONVERSATION_ID').first().reset_index()[['CONVERSATION_ID', 'topic']]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# For the inputs, we only consider conversations with the corresponding texter and SV surveys. They have information on age, gender and topic \n",
+ "\n",
+ "encoded_conversations = pd.read_pickle(f\"data/gpt-2/encoded_conversations.pkl\")\n",
+ "encoded_conversations = features.merge(encoded_conversations, on='CONVERSATION_ID')\n",
+ "encoded_conversations = encoded_conversations.merge(df_age, on='CONVERSATION_ID')\n",
+ "encoded_conversations = encoded_conversations.merge(df_gender, on='CONVERSATION_ID')\n",
+ "encoded_conversations = encoded_conversations.merge(topics, on='CONVERSATION_ID')\n",
+ "#encoded_conversations = encoded_conversations[['CONVERSATION_ID','conversation', 'pos_speaker_tok', 'pos_texter_tok', 'age', 'gender', 'topic']]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Split the dataset into train and test sets\n",
+ "\n",
+ "ids = np.arange(len(encoded_conversations))\n",
+ "np.random.shuffle(ids)\n",
+ "l = len(ids)\n",
+ "test_size = int(l*0.05)\n",
+ "\n",
+ "ids_train = ids[test_size:]\n",
+ "ids_test = ids[:test_size]\n",
+ "np.save(f\"data/gpt-2/ids_train.npy\", ids_train)\n",
+ "np.save(f\"data/gpt-2/ids_test.npy\", ids_test)\n",
+ "\n",
+ "test_enc = encoded_conversations.iloc[ids_test]\n",
+ "train_enc = encoded_conversations.iloc[ids_train]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "test_enc.to_pickle(f\"data/gpt-2/test_encoded_conv.pkl\")\n",
+ "train_enc.to_pickle(f\"data/gpt-2/train_encoded_conv.pkl\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "test_enc = pd.read_pickle(f\"data/gpt-2/test_encoded_conv.pkl\")\n",
+ "train_enc = pd.read_pickle(f\"data/gpt-2/train_encoded_conv.pkl\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# To pad the sequence to the desired length (max_len is here the maximum number of tokens that can be used in the model)\n",
+ "def make_padding(input_id, token_type_id, lm_label, pad_id, max_len):\n",
+ " left = max_len - len(input_id)\n",
+ " \n",
+ " input_id += [pad_id] * left\n",
+ " token_type_id += [pad_id] * left\n",
+ " lm_label += [-100] * left\n",
+ " \n",
+ " return input_id, token_type_id, lm_label"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "def create_data_tensors(mode='train'):\n",
+ " '''\n",
+ " Create and save dictionary of input_ids, token_type_ids and labels tensors for the GPT2LMHeadModel\n",
+ " Inputs:\n",
+ " - mode : 'train' or 'test'\n",
+ " '''\n",
+ " if mode=='train':\n",
+ " enc = train_enc\n",
+ " elif mode=='test':\n",
+ " enc = test_enc\n",
+ "\n",
+ " for idx in tqdm(range(len(enc))):\n",
+ " input_ids = []\n",
+ " token_type_ids = []\n",
+ " labels = []\n",
+ "\n",
+ " dialogues = []\n",
+ " dialogue = []\n",
+ " cur_speaker = 1\n",
+ "\n",
+ " conversation = enc['conversation'].iloc[idx]\n",
+ " pos_speaker_tok = enc['pos_speaker_tok'].iloc[idx]\n",
+ " len_speaker_tok = len(pos_speaker_tok)\n",
+ "\n",
+ " for i in range(len_speaker_tok):\n",
+ " if i < len_speaker_tok-1:\n",
+ " token_ids = tokenizer.convert_tokens_to_ids(conversation[pos_speaker_tok[i]:pos_speaker_tok[i+1]])\n",
+ " else:\n",
+ " token_ids = tokenizer.convert_tokens_to_ids(conversation[pos_speaker_tok[-1]:])\n",
+ " \n",
+ " if len(dialogue) < max_time:\n",
+ " dialogue.append(token_ids)\n",
+ " else:\n",
+ " dialogue = dialogue[1:] + [token_ids]\n",
+ "\n",
+ " dialogues.append(copy.deepcopy(dialogue))\n",
+ "\n",
+ "\n",
+ " for d, dialogue in enumerate(dialogues):\n",
+ " if len(dialogue) > 1 and dialogue[-1][0] == speaker1_id:\n",
+ " dialogue[0] = [bos_id] + dialogue[0]\n",
+ " dialogue[-1] = dialogue[-1] + [eos_id]\n",
+ " \n",
+ " total_len = 0\n",
+ " for utter in dialogue:\n",
+ " total_len += len(utter)\n",
+ " \n",
+ " if total_len > max_len:\n",
+ " dialogue = [utter[:utter_len] for utter in dialogue]\n",
+ " dialogue[-1][-1] = eos_id\n",
+ " \n",
+ " token_type_id = [[utter[0]] * len(utter) if u != 0 else [utter[1]] * len(utter) for u, utter in enumerate(dialogue)]\n",
+ " lm_label = [[-100] * len(utter) if u != len(dialogue)-1 else utter for u, utter in enumerate(dialogue)]\n",
+ " input_id = list(chain.from_iterable(dialogue))\n",
+ " token_type_id = list(chain.from_iterable(token_type_id))\n",
+ " lm_label = list(chain.from_iterable(lm_label))\n",
+ " \n",
+ " assert len(input_id) == len(lm_label) and len(input_id) == len(token_type_id), \"There is something wrong in dialogue process.\"\n",
+ " \n",
+ " input_id, token_type_id, lm_label = make_padding(input_id, token_type_id, lm_label, pad_id, max_len)\n",
+ " \n",
+ " input_ids.append(input_id)\n",
+ " token_type_ids.append(token_type_id)\n",
+ " labels.append(lm_label)\n",
+ " \n",
+ " input_ids = torch.LongTensor(input_ids) # (N, L)\n",
+ " token_type_ids = torch.LongTensor(token_type_ids) # (N, L)\n",
+ " labels = torch.LongTensor(labels) # (N, L)\n",
+ " tensor_map = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'labels': labels}\n",
+ " torch.save(tensor_map, f\"data/gpt-2/{mode}_tensors/tensor_map_{idx}\")\n",
+ "\n",
+ "# Create tensors for the train set\n",
+ "create_data_tensors(mode='train')"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Create tensors for the test set\n",
+ "create_data_tensors(mode='test')"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "ids_train = np.load(f\"data/gpt-2/ids_train.npy\")\n",
+ "ids_test = np.load(f\"data/gpt-2/ids_test.npy\")\n",
+ "\n",
+ "gpt2 = encoded_conversations.GPT2.to_numpy()\n",
+ "gpt2_train = gpt2[ids_train]\n",
+ "gpt2_test = gpt2[ids_test]\n",
+ "\n",
+ "np.save(f\"data/gpt-2/gpt2_train.npy\", gpt2_train)\n",
+ "np.save(f\"data/gpt-2/gpt2_test.npy\", gpt2_test)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Create the inputs into the ConditionalGPT2 model, by incorporating labels (age,gender,topic) from surveys"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "tokenizer = GPT2Tokenizer.from_pretrained(f\"data/gpt-2\")\n",
+ "vocab = tokenizer.get_vocab()\n",
+ "vocab_size = len(vocab)\n",
+ "bos_id = vocab[\"<bos>\"]\n",
+ "eos_id = vocab[\"<eos>\"]\n",
+ "pad_id = vocab[\"<pad>\"]\n",
+ "speaker1_id = vocab[\"[texter]\"]\n",
+ "speaker2_id = vocab[\"[volunteer]\"]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Load encoded conversations from the train and test sets\n",
+ "test_enc = pd.read_pickle(f\"data/gpt-2/test_encoded_conv.pkl\")\n",
+ "train_enc = pd.read_pickle(f\"data/gpt-2/train_encoded_conv.pkl\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "list_age_train = train_enc.age.to_numpy()\n",
+ "list_gender_train = train_enc.gender.to_numpy()\n",
+ "list_topic_train = train_enc.topic.to_numpy()\n",
+ "\n",
+ "list_age_test = test_enc.age.to_numpy()\n",
+ "list_gender_test = test_enc.gender.to_numpy()\n",
+ "list_topic_test = test_enc.topic.to_numpy()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "def create_conditional_tensors(mode='train'):\n",
+ " '''\n",
+ " Create and save dictionary of input_ids, token_type_ids and labels tensors for conditional data\n",
+ " Inputs:\n",
+ " - mode : 'train' or 'test'\n",
+ " '''\n",
+ " if mode=='train':\n",
+ " enc = train_enc\n",
+ " list_gender_ = list_gender_train\n",
+ " list_age_ = list_age_train\n",
+ " list_topic_ = list_topic_train\n",
+ " elif mode=='test':\n",
+ " enc = test_enc\n",
+ " list_gender_ = list_gender_test\n",
+ " list_age_ = list_age_test\n",
+ " list_topic_ = list_topic_test\n",
+ "\n",
+ " nb_tokens = 512\n",
+ " for idx in tqdm(range(len(enc))):\n",
+ " tensor_map = torch.load(f\"data/gpt-2/{mode}_tensors/tensor_map_{idx}\")\n",
+ " input_ids, token_type_ids, labels = tensor_map['input_ids'], tensor_map['token_type_ids'], tensor_map['labels'] \n",
+ " cond_labels = f'I am a {list_gender_[idx]}. I am {list_age_[idx]}. I want to talk about {list_topic_[idx]}.'\n",
+ "\n",
+ " condition_inputs = tokenizer(cond_labels, return_tensors=\"pt\")['input_ids'].squeeze()\n",
+ " len_condition = len(condition_inputs)\n",
+ "\n",
+ " input_ids = torch.cat((input_ids[:,:1], condition_inputs.repeat(input_ids.shape[0],1), input_ids[:,1:512-len_condition]), dim=1)\n",
+ " token_type_ids = torch.cat((torch.tensor((len_condition+1)*[speaker1_id]).repeat(input_ids.shape[0],1), token_type_ids[:,1:nb_tokens-len_condition]), dim=1)\n",
+ " labels = torch.cat((labels[:,:1], torch.tensor(len_condition * [-100]).repeat(input_ids.shape[0],1), labels[:,1:nb_tokens-len_condition]), dim=1)\n",
+ " tensor_map = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'labels': labels}\n",
+ " torch.save(tensor_map, f\"data/gpt-2/conditional/{mode}_tensors/tensor_map_{idx}\")\n",
+ "\n",
+ "# Conditional training data\n",
+ "create_conditional_tensors(mode='train')"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Conditional test data\n",
+ "create_conditional_tensors(mode='test')"
+ ],
+ "outputs": [],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
diff --git a/pre-processing/survey_preprocessing.ipynb b/pre-processing/survey_preprocessing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7571d60362a7938762ec530c7440fcdec0585b0a
--- /dev/null
+++ b/pre-processing/survey_preprocessing.ipynb
@@ -0,0 +1,363 @@
+{
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "orig_nbformat": 2,
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3.7.9 64-bit ('.venv': venv)"
+ },
+ "metadata": {
+ "interpreter": {
+ "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+ }
+ },
+ "interpreter": {
+ "hash": "f7bfe7bd0e1b2b0ad7f24d834ee1093564259268c66f76a41bb752c4f89e1758"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Texter Survey and analysis"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%cd /data-imperial\n",
+ "from helpers.labels_processor import *"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Texter Survey preprocessing"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "messages = pd.read_csv('all_messages.csv', index_col=0)\n",
+ "texters = messages[messages['INTERACTION'] == 'texter'].groupby('CONVERSATION_ID')['ACTOR_ID'].first()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "texter_survey_value = pd.read_csv('TEXTER_SURVEY_RESPONSE_VALUE.tsv', sep='\\t')\n",
+ "texter_survey_res = pd.read_csv('TEXTER_SURVEY_RESPONSE.tsv', sep='\\t')\n",
+ "\n",
+ "# Remove rows where the value is N/A\n",
+ "texter_survey_value.dropna(subset=['VALUE'], inplace=True)\n",
+ "\n",
+ "texter_survey_value['QUESTION_ID'] = texter_survey_value['QUESTION_ID'].astype(str)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Find questions where a single survey has multiple answers, not counting the flag \"Other - Write In\"\n",
+ "\n",
+ "g = texter_survey_value[texter_survey_value['VALUE']!='Other - Write In']\\\n",
+ " .groupby(['RESPONSE_ID','QUESTION_ID'])\n",
+ "a = g.count().VALUE[g.count().VALUE>1].index.to_frame()['QUESTION_ID'].unique()\n",
+ "a.sort()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "texter_survey_collated = pd.DataFrame(\n",
+ " columns=texter_survey_value.QUESTION_ID.unique(), \n",
+ " index = texter_survey_value.RESPONSE_ID.unique()\n",
+ ")\n",
+ "\n",
+ "texter_survey_collated = texter_survey_collated.applymap(lambda x:[])"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# This generates the collated dataframe\n",
+ "\n",
+ "for _, _, response_id, question_id, value, _ in texter_survey_value.itertuples():\n",
+ " #response_id = int(response_id)\n",
+ " texter_survey_collated.loc[response_id, question_id].append(value)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "texter_survey_collated = texter_survey_collated.applymap(lambda a: a if len(a) > 0 else np.nan)\n",
+ "cols = [texter_survey_collated[i].dropna().apply(len).max() for i in texter_survey_collated]\n",
+ "cols = texter_survey_collated.columns[[i == 1 for i in cols]]\n",
+ "texter_survey_collated[cols] = texter_survey_collated[cols].applymap(lambda a: a[0] if type(a)==list else a)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# The ID from the texter_survey_res table corresponds to the RESPONSE_ID from the texter_survey_value (texter_survey_collated) table\n",
+ "texter_survey_res['RESPONSE_ID'] = texter_survey_res.ID"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "texter_survey_collated = texter_survey_res.drop([0]).join(texter_survey_collated, on=\"RESPONSE_ID\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "#a = [69, 71, 73, 74, 75, 151, 205, 72, 144, 145, 85, 86, 218, '221', '222', '223', '224', '227', '272', '273', '274', '275', '278', '269', 217, 70, 292, 270, '152', '225', 240, 220, 219, 289, 291, 268, 238, 241]\n",
+ "a = list(texter_survey_collated.columns)[9:]\n",
+ "texter_questions = [str(i) for i in sorted([int(i) for i in a])]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "def merge(xx):\n",
+ " xx = list(xx)\n",
+ " assert len(xx) != 0\n",
+ " if len(xx) == 1:\n",
+ " return xx[0]\n",
+ " if any(type(x) == list for x in xx):\n",
+ " xx = [i for i in xx if type(i) == list]\n",
+ " return list({i for j in xx for i in j if not pd.isna(i)}) #flatten list\n",
+ " xx = [i for i in xx if not pd.isna(i)]\n",
+ " if len(xx) == 0:\n",
+ " return np.nan\n",
+ " xx = list(set(xx))\n",
+ " if len(xx) == 1:\n",
+ " return xx[0]\n",
+ " else:\n",
+ " return None # CONFLICT"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "texter_survey_collated.index = range(len(texter_survey_collated))"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "to_add = []\n",
+ "actor_2_index = texter_survey_collated['ACTOR_ID'].to_frame().reset_index().groupby('ACTOR_ID').groups\n",
+ "\n",
+ "def f(i):\n",
+ " global to_add\n",
+ " actor_id = i[0]\n",
+ " convo_ids = list(texters[texters == actor_id].index)\n",
+ " vals = i[1].dropna()\n",
+ " index = actor_2_index[actor_id]\n",
+ " \n",
+ " for col,val in vals.iteritems():\n",
+ " if type(val) == list:\n",
+ " texter_survey_collated.loc[index.tolist(),col] = texter_survey_collated.loc[index.tolist(),col].apply(lambda _:val)\n",
+ " else:\n",
+ " texter_survey_collated.loc[index.tolist(),col] = val\n",
+ " \n",
+ " convo_ids = [i for i in convo_ids if i not in survey_convo_ids]\n",
+ " to_add += [{\"ACTOR_ID\":actor_id,\"CONVERSATION_ID\":c, **vals} for c in convo_ids]"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "a = texter_survey_collated[texter_questions+['ACTOR_ID']]\n",
+ "a = a.groupby('ACTOR_ID').apply(lambda a:a.apply(merge))\n",
+ "a.drop('ACTOR_ID',axis=1,inplace=True)\n",
+ "a.dropna(how='all',inplace=True)\n",
+ "\n",
+ "survey_convo_ids = set(texter_survey_collated.CONVERSATION_ID)\n",
+ "to_add = []\n",
+ "for i in a.iterrows():\n",
+ " f(i)"
+ ],
+ "outputs": [],
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "texter_survey_collated2 = texter_survey_collated.append(to_add,ignore_index=True)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "texter_survey_collated2.to_csv(\"texter_survey_collated.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Texter survey analysis"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "df = pd.read_csv(\"texter_survey_collated.csv\", index_col = 0)\n",
+ "df.info()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Texter Ages"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Plot the texter age\n",
+ "\n",
+ "df_age = get_ages(df)\n",
+ "\n",
+ "counts = df_age.age.value_counts()\n",
+ "labels = list(counts.index)\n",
+ "counts = list(counts)\n",
+ "tuples = zip(*sorted(zip(labels, counts)))\n",
+ "labels, counts = [list(tuple) for tuple in tuples]\n",
+ "\n",
+ "plt.figure(figsize=(10,5))\n",
+ "plt.bar(labels, counts)\n",
+ "plt.title('Texter Age')\n",
+ "plt.show()\n"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Texter helpfulness"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "# Plot the helpfulness of a conversation\n",
+ "\n",
+ "df_help = df.copy().dropna(subset=['65'])['65']\n",
+ "counts = df_help.value_counts()\n",
+ "labels = list(counts.index)\n",
+ "counts = list(counts)\n",
+ "\n",
+ "plt.figure(figsize=(10,5))\n",
+ "plt.pie(counts, labels=labels, autopct='%1.1f%%')\n",
+ "plt.title('Conversation helpfulness')\n",
+ "plt.show()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Texter Gender"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "source": [
+ "#Plot a chart on texter gender\n",
+ "\n",
+ "df_gender = get_genders()['gender']\n",
+ "counts = df_gender.value_counts()\n",
+ "labels = list(counts.index)\n",
+ "counts = list(counts)\n",
+ "\n",
+ "plt.figure(figsize=(10,5))\n",
+ "plt.pie(counts, labels=labels, autopct='%1.1f%%')\n",
+ "plt.title('Texter Gender')\n",
+ "plt.show()"
+ ],
+ "outputs": [],
+ "metadata": {}
+ }
+ ]
+}
diff --git a/pre-processing/sv_survey_preprocessing.ipynb b/pre-processing/sv_survey_preprocessing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..3a2f447ebcc13dd77cace2aaf91d21c60234a04b
--- /dev/null
+++ b/pre-processing/sv_survey_preprocessing.ipynb
@@ -0,0 +1,355 @@
+{
+ "metadata": {
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "orig_nbformat": 2,
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3.7.9 64-bit ('.venv': venv)"
+ },
+ "metadata": {
+ "interpreter": {
+ "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+ }
+ },
+ "interpreter": {
+ "hash": "f7bfe7bd0e1b2b0ad7f24d834ee1093564259268c66f76a41bb752c4f89e1758"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2,
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Shout Volunteer (SV) and analysis"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "source": [
+ "import numpy as np \n",
+ "import pandas as pd\n",
+ "import re\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "%cd /data-imperial\n",
+ "from helpers.labels_processor import get_topics"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "/data-imperial\n"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Shout Volunteer (SV) survey pre-processing"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "source": [
+ "survey = pd.read_csv('SURVEY_VALUE.tsv', sep='\\t')"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/data-imperial/.venv/lib64/python3.7/site-packages/IPython/core/interactiveshell.py:3441: DtypeWarning: Columns (3) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+ " exec(code_obj, self.user_global_ns, self.user_ns)\n"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "source": [
+ "nb_rows = survey.shape[0]\n",
+ "\n",
+ "survey.VALUE.replace([\"a:0:{}\",\n",
+ " \"N;\",\n",
+ " 'a:2:{i:0;N;i:1;s:0:\";}\"',\n",
+ " 'a:3:{i:0;N;i:1;N;i:2;s:0:\";}\"',\n",
+ " 'a:1:{i:0;s:0:\";}\"',\n",
+ " 'a:1:{i:0;s:0:\";}\"',\n",
+ " 'a:1:{i:0;s:3:n/a\";}\"',\n",
+ " 'a:1:{i:0;s:3:N/A\";}\"',\n",
+ " 'a:2:{i:0;s:0:\";i:1;s:0:\"\";}\"',\n",
+ " 's:1:\" \";',\n",
+ " 's:\\d+:\\s\\\"\\s\\\"\\s;',\n",
+ " 's:4:\"b:0;\";',\n",
+ " 'a:1:{i:0;s:0:\"\";}',\n",
+ " \"\"\n",
+ " ],np.nan,inplace=True,regex=True)\n",
+ "\n",
+ "nb_na = survey.VALUE.isna().sum()\n",
+ "ratio_na = 100*nb_na/nb_rows \n",
+ "print(f\"{ratio_na:3f}% of rows were N/A\")\n",
+ "\n",
+ "survey = survey.dropna(subset=[\"VALUE\"])"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "62.948353% of rows were N/A\n"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "source": [
+ "array_values = survey.VALUE.dropna()\n",
+ "array_values = array_values[array_values.str.contains(\"^a:\\d\",regex=True)]\n",
+ "\n",
+ "giant_regex = \"i:\\d+;s:\\d+:\\s\\\"*[\\w*\\s*\\W*\\d*]*\\\"*\"\n",
+ "\n",
+ "array_values = array_values.apply(lambda v: re.findall(giant_regex,v))\n",
+ "array_values = array_values.apply(lambda v: v if len(v)>0 else np.nan)\n",
+ "array_values = array_values.apply(lambda a:[i for i in a if re.match(\"\\w\",i)] if type(a) == list else a)\n",
+ "survey[\"array_value\"] = array_values\n",
+ "print(f\"failed: {array_values.isna().sum()}, or {array_values.isna().mean()*100:.3f}%\")"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "failed: 0, or 0.000%\n"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "source": [
+ "chars = \"\\w\\s\\'\\d\\,\\.\\-\\/\\&\\(\\):/!?\\+\\@=-—£\\[\\]%’‘’#\\*<>”“´…\"\n",
+ "\n",
+ "str_values = survey[(~survey.VALUE.isna()) & survey.array_value.isna()].VALUE\n",
+ "a = str_values.shape[0]\n",
+ "m = str_values.str.contains(\"^s:\\d+\",regex=True)\n",
+ "leftover = str_values[~m]\n",
+ "str_values = str_values[m]\n",
+ "b = str_values.shape[0]\n",
+ "print(\"Okay, all remaining non-N/A values are strings.\" if a==b \n",
+ " else f\"Hmm... {leftover.shape[0]} values are not N/A but not recognised as strings either\" )\n",
+ "str_values = str_values.str.replace('s:\\d+:\\\"(['+chars+']+)\\\";',r\"\\1\",regex=True)\n",
+ "\n",
+ "survey['str_value'] = str_values\n",
+ "f = ~str_values.str.match('\\w+')\n",
+ "print(f\"failed: {f.sum()}, or {f.mean()*100:.3f}%\")"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Okay, all remaining non-N/A values are strings.\n",
+ "failed: 0, or 0.000%\n"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "source": [
+ "q_ids = survey.QUESTION_ID.unique()\n",
+ "\n",
+ "# Question 23 has counselors' names. I thought it should be manually removed.\n",
+ "q_ids = [i for i in q_ids if i != 23]\n",
+ "for q in q_ids.copy():\n",
+ " s = survey[survey.QUESTION_ID==q]\n",
+ " a = s.str_value.isna().all()\n",
+ " b = s.array_value.isna().all()\n",
+ " if a and not b:\n",
+ " vals = s.array_value\n",
+ " elif b and not a:\n",
+ " vals = s.str_value\n",
+ " else:\n",
+ " raise ValueError(q)\n",
+ " survey.loc[survey.QUESTION_ID==q,q] = vals"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "source": [
+ "survey_conversation = pd.read_csv(\"SURVEY.tsv\", sep='\\t')\n",
+ "survey_conversation = survey_conversation.rename({\"ID\":\"SURVEY_ID\"},axis=1)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "source": [
+ "survey2 = survey.drop(['VALUE','array_value','str_value','LAST_EDIT_TIME'],axis=1).set_index(['SURVEY_ID'])\n",
+ "survey_conversation2 = survey_conversation.copy()\n",
+ "for q in q_ids:\n",
+ " i = survey2[q].dropna().index\n",
+ " assert i.nunique() == i.shape[0]\n",
+ " survey_conversation2 = survey_conversation2.join(survey2[q].dropna(),on=\"SURVEY_ID\",how=\"left\")\n",
+ "\n",
+ "survey_conversation = survey_conversation2"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "source": [
+ "survey_conversation = survey_conversation.set_index(\"CONVERSATION_ID\").drop(\"SURVEY_ID\",axis=1).dropna(how='all')"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "source": [
+ "survey_conversation[21].fillna(\"NA\",inplace=True)\n",
+ "survey_conversation[22].fillna(\"NA\",inplace=True)\n",
+ "survey_conversation[33].fillna(\"NA\",inplace=True)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "source": [
+ "survey_conversation[[18,26,27,19]] = survey_conversation[[18,26,27,19]].applymap(lambda a: [] if a is np.nan else a)"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "source": [
+ "survey_conversation.to_csv(\"volunteer_survey_by_conversation.csv\")"
+ ],
+ "outputs": [],
+ "metadata": {}
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Topic analysis"
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "source": [
+ "df = pd.read_csv(\"volunteer_survey_by_conversation.csv\")\n",
+ "texter_survey = pd.read_csv(\"texter_survey_collated.csv\", index_col = 0)"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/data-imperial/.venv/lib64/python3.7/site-packages/IPython/core/interactiveshell.py:3441: DtypeWarning: Columns (1,4,5,7,8,9,79,81,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,129,130,131,132) have mixed types.Specify dtype option on import or set low_memory=False.\n",
+ " exec(code_obj, self.user_global_ns, self.user_ns)\n"
+ ]
+ }
+ ],
+ "metadata": {}
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "source": [
+ "# Plot a figure representing the proportion of each conversation topic\n",
+ "topics = get_topics(df)\n",
+ "topics = topics[topics.CONVERSATION_ID.isin(texter_survey.CONVERSATION_ID.values)]\n",
+ "\n",
+ "counts = topics['topic'].value_counts()\n",
+ "labels = list(counts.index)\n",
+ "counts = list(counts)\n",
+ "colors = plt.cm.tab20.colors + plt.cm.tab20b.colors\n",
+ "\n",
+ "percent = [i/sum(counts)*100 for i in counts]\n",
+ "\n",
+ "fig = plt.figure(figsize=(15,15))\n",
+ "patches, texts = plt.pie(counts, colors=colors, startangle=0,)\n",
+ "labels = ['{0} - {1:1.2f} %'.format(i,j) for i,j in zip(labels, percent)]\n",
+ "\n",
+ "sort_legend = True\n",
+ "if sort_legend:\n",
+ " patches, labels, dummy = zip(*sorted(zip(patches, labels, percent),\n",
+ " key=lambda x: x[2],\n",
+ " reverse=True))\n",
+ "\n",
+ "plt.legend(patches, labels, loc='center right', fontsize=20, bbox_to_anchor=(1,0.5), bbox_transform=plt.gcf().transFigure)\n",
+ "plt.title('Conversation topics', fontsize=30)\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ],
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/data-imperial/.venv/lib64/python3.7/site-packages/pandas/core/frame.py:3607: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " self._set_item(key, value)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "<Figure size 1080x1080 with 1 Axes>"
+ ],
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dKwHcKX49UggxqXSB4gTG97g3bssJSZL21sCxH4kkSSm491lqVY1uHbX13RgphHiznLrMAfyBe11wfpEkKa2KusoTBKAkhoVCiBcrKiiEcBFCtCu1bqkQolkl+xgCGK+zKvwhYiQiIqqzmJ0nIqpBkiQlCSF6QXtz3wRACwDHhBDHAeyG9glpBgBbaJ/i9wXQvJL6/hJCDAXwErRjNZwWQqwEcAzaG6E2AMbh3qwHuwAsrvETqzi+PCHEuuIYfAA0LN50RZKkkCr2jRVCvARgEwATaG9EJxf/fBlALrQ3jE2gnY2hM7SzMOx/xLBdACwAMFsIsR/aWVauQftk3R5AWwAv4l5rgAWlZjwJhnZMDzdoZziJEEL8XFyHNYD+AAZBO4vFelQyfawkSalCiDBoB0LtIYT4CcBe6Aw+KUlStZ/2S5KUKYQYA+01VEB7kzwU2tlUEgF4QDtmSUkiKBNApcmjx2wftGM6eANYK4TYgHuJLgA4WPJe1NJ34wy07+GPQojnoH2v06D9DI7DvVZG1wFMf5gTlCQpRAjxfwDmQjsd7driZNMmADehTbh4QtuqqTe0yT7dLjDjAYwXQlyA9rtwHtoxMMyhbd3xEu51f4qC9r0nIiKqPyRJ4sKFCxcuNbxAeyO0GEA+AKkay20AEwEYllOXIYBl1ajjbwCmlcRUUu5ANeJ/kLLdyonlswe4Vh2gbelQneuUCaB5OXUcKClTjeO9Ws1jaaBNdhhUEHNKJfumQpvMCNJZ172CePoBKKqorlJlX9PZ9lol5zgE2pvvys7vBoDWldSxUqesVxXXtNplq6inFbTJpIpi9ipVvsa/G8UxxFZS1xUAT1VSV7U+i9B2JcmsRuyfVRBrVUv4o7wXXLhw4cKFS11d2BKDiKgWSNpm5hOFEF8DeAFALwB+0D7pN4O22fxNaLsmbAWwTZKkogrqKoL2yevP0N74dIP2ybMBgHgARwCskCRpX22eUyUOQXtD7KmzrqquJHdJ2ifTPtBO0zoY2pYQDtA+pc6AtoVDGLSzv2yVJCn7EeP9DdopcXtDm4xoCu31VELbdeQ6tAMw/iJJUlglMbcE8CG0SYgG0LYciYH2/VwsSdLN0l0BKqhruxCiM4BJ0HZBcoZ2mt6HJknSP0IIb2gTYwOgnVHDCtrkynkA/wBYJt3fwkR2kiSdEUIEQjsuxdPQXtcKpz2tje9GcQwB0L4fz0H7uRbQtg76G8BCSZJyKq6h2sdZLoT4B8Ab0LbIagLABtrEZwy0A8tugva90uVWXP5paFvUNIT2vS0AkADtd2UdgLXS4+tWRkRE9NgISZLkjoGIiIhINkKIkj+GDkraaXaJiIiojuLAnkRERERERESkF5jEICIiIiIiIiK9wCQGEREREREREekFJjGIiIiIiIiISC8wiUFEREREREREeoGzkxARERERERGRXmBLDCIiIiIiIiLSC0xiEBEREREREZFeYBKDiIiIiIiIiPQCkxhEREREREREpBeYxCAiIiIiIiIivcAkBhERERERERHpBSYxiIiIiIiIiEgvMIlBRERERERERHqBSQwiIiIiIiIi0gtMYhARERERERGRXmASg4iIiIiIiIj0ApMYRERERERERKQXmMQgIiIiIiIiIr3AJAYRERERERER6QUmMYiIiIiIiIhILzCJQURERERERER6gUkMIiIiIiIiItILTGIQERERERERkV5gEoOIiIiIiIiI9AKTGERERERERESkF5jEICIiIiIiIiK9wCQGEREREREREekFJjGIiIiIiIiISC8wiUFEREREREREeoFJDCIiIiIiIiLSC0xiEBEREREREZFeYBKDiIiIiIiIiPQCkxhEREREREREpBeYxCAiIiIiIiIivcAkBhERERERERHpBSYxiIiIiIiIiEgvMIlBRERERERERHqBSQwiIiIiIiIi0gtMYhARERERERGRXmASg4iIiIiIiIj0ApMYRERERERERKQXmMQgIiIiIiIiIr3AJAYRERERERER6QUmMYiIiIiIiIhILzCJQURERERERER6gUkMIiIiIiIiItILTGIQERERERERkV5gEoOIiIiIiIiI9AKTGERERERERESkF5jEICIiIiIiIiK9wCQGEREREREREekFJjGIiIiIiIiISC8wiUFEREREREREeoFJDCIiIiIiIiLSC0xiEBEREREREZFeYBKDiIiIiIiIiPQCkxhEREREREREpBeYxCAiIiIiIiIivcAkBhERERERERHpBSYxiIiIiIiIiEgvMIlBRERERERERHqBSQwiIiIiIiIi0gtMYhARERERERGRXmASg4iIiIiIiIj0ApMYRERERERERKQXmMQgIiIiIiIiIr3AJAYRERERERER6QUmMYiIiIiIiIhILzCJQURERERERER6gUkMIiIiIiIiItILTGIQERERERERkV5gEoOIiIiIiIiI9AKTGERERERERESkF5jEICIiIiIiIiK9wCQGEREREREREekFJjGIiIiIiIiISC8wiUFEREREREREeoFJDCIiIiIiIiLSC0xiEBEREREREZFeYBKDiIiIiIiIiPQCkxhEREREREREpBeYxCAiIiIiIiIivcAkBhERERERERHpBSYxiIiIiIiIiEgvMIlBRERERERERHqBSQwiIiIiIiIi0gtMYhARERERERGRXmASg4iIiIiIiIj0ApMYRERERERERKQXmMQgIiIiIiIiIr3AJAYRERERERER6QUmMYiIiIiIiIhILzCJQURERERERER6gUkMIiIiIiIiItILTGIQERERERERkV5gEoOIiIiIiIiI9AKTGERERERERESkF5jEICIiIiIiIiK9wCQGEREREREREekFQ7kDICIiqi+CQxLMAVgBUJX6t+S1BbQPECQAmuJ/dV+Xt+5BXhcCyChe0nX+TR/awamoNs+diIiI6HEQkiTJHQMREZHsgkMSDAC4AHBC+YmIihITJf9aom4/HMhF2eRGKoBkAEmllrvrhnZwypQlWiIiIqJyMIlBRERPhOJWEh4APIv/Lf3aHXU7CSGXfNxLasQBuF68XCt5PbSDU5ps0REREdEThUkMIiLSe8EhCQLaFhQVJSg8AdjKFmD9l4ZykhvFS/TQDk558oVGRERE9QmTGEREpBeCQxKUAPwBNAfQEPcnKNwBmMgXHVVCAhCPssmNkiVmaAcnjXzhERERkT5hEoOIiOqc4JCEBgBaAmhRvLQE0BiAQs64qFYUArgJ4AqAcABhAE4DuDy0gxP/SCEiIqL7MIlBRESyCQ5JMAPQDPcnK5oDsJEzLqoTsnB/UiMMwIWhHZwKZY2KiIiIZMUkBhERPRbBIQmeKNu6ohG0U44SVUcBgPPQJjRKkhvhQzs45cgaFRERET02TGIQEVGNCg5JMAIQAKAV7m9doZIxLKq/NACicH+LjbChHZxSZI2KiIiIagWTGERE9EiKB9xsD6Bb8dIBgJmsQREBN3CvxcYpAEc4FSwREZH+YxKDiIgeSPE4Fp0AdIU2adEenBmE6j4NtAmNfcXLv0M7OGXLGxIRERE9KCYxiIioUsEhCZYAOuNeS4s2AIxkDYro0RUCCMW9pMaxoR2c8uUNiYiIiKrCJAYREd0nOCTBGsDTuJe0CACnNqX6LxfAUQD7oU1qnBjawalI3pCIiIioNCYxiIiecMEhCfa41zWkK7SDcXLGEHrSZQL4F/daapwZ2sGJfzQRERHJjEkMIqInTHBIgjGAHgAGFv/rB0DIGhRR3ZcM4CC0CY39Qzs4RcgcD8nk1KlTXgqFYoKBgUE/SZJs5I6HiKi+EEKkajSa7Wq1emlgYGB0heWYxCAiqv+CQxKsAPQH8ByAfgCsZA2ISP/FQ9v1ZDeAzUM7OCXJHA89BqdOnfIyMjLa4OTkZG1tbZ1pbGxcKARzwEREj0qSJBQUFBilpaVZJiQkpBUWFj5fUSKDSQwionoqOCTBFcAQaBMX3QEYyxkPUT2mBnAIwHoAwUM7ON2SOR6qJWfOnPnaxcXlJScnpxS5YyEiqq8SEhJsb9++/WerVq0+Lm87kxhERPVIcEiCH7RJiyEA2oLdRIgeNwnAMWgTGhuGdnCKljccqklnz54N8/HxMTAxMSmUOxYiovoqPz/fKDIyUtOiRYuA8rYbPu6AiIio5gSHJBgA6IB7iYsmsgZERAJAp+Ll2+CQhFMANgBYP7SDU6SskdEjkyTJxtjYmF2HiIhqkbGxcaEkSfYVbWcSg4hIzwSHJJgA6A1t4mIQACdZAyKiygQWLzODQxIuQNtCY/3QDk5n5Q2LHhbHwCAiql1V/T/LJAYRkR4IDkmwhnY2kecA9AFgIWc8RPRQ/IuXz4JDEq7gXkLjhLxhERER6Q8mMYiI6qjiGUVeAvAigG7g/9lE9UkjAP8H4P+CQxJiUNzlBMCRoR2cNLJGRkREVIfxD2IiojokOCRBAOgBYAyAYQBM5Y2IiB6DBgDeLV7ig0MSNkKb0Ng/tIOTWs7AiIiI6homMYiI6oDgkARPAK8BeBVAQ3mjISIZOQN4s3iJCw5JWAHgZ85yoh+8pm0NlDuGykTPGnBK7hiqIoQIbNu2bVZoaOhDDYQ7efJk1/nz57ts3rw5auDAgZnV2addu3Y+J06csJAkqc5fHyICDOQOgIjoSRUckqAMDkl4OTgkYQ+A6wCCwAQGEd3jBuATAFeDQxJ2Bock/Cc4JMFI7qCI6PHYv3+/2dtvv+3WtWvXxvb29i2FEIFOTk4tqrPvP//8Y/nMM89429vbtzQ2Nm7t6OjYokuXLo3Xrl2rqu7xV6xYYfPqq682CAwM9LGwsAgQQgQOGTKkyr9Tdu/ebd6tW7dGKpWqlVKpbN2kSRO/L7/80rGoqKi6hwYA/Pjjj7ZNmjTxMzMzC2jSpInf0qVLbcorFxMTY2hjY9NywoQJ7g90ANJbbIlBRPSYBYcktIO2u8hLAKzljYaI9IABgGeLl8TgkITfACzjlK1UH50+ffqChYXFQ48LM3Xq1DujRo1KadSoUUFNxiWHVatW2a1YscLR0NBQ8vb2zktOTq7Wvdubb77pvmTJEicnJ6fCZ555Js3Ozq4oKSnJ8OzZs2b79++3HD58eHp16vnmm29cIiMjTc3MzDROTk4F169fV1a1z+rVq61fe+01b2NjY83AgQNTbGxs1Lt27VJ9/vnnDY4dO2axffv2a9U59u+//66aOHFiwxYtWmS/8sorifv27VO98cYbT1lZWV156aWX7ot//PjxHiqVSj1v3ry46tRN+o9JDCKixyA4JMERwChokxf+ModDRPrLAcAUAFOCQxL+BbAcwN9DOzjlyhsWUc0ICAjIe5T9XVxcilxcXB7skX8d9frrryeNGzcuKTAwME+pVEpCiCq7K3377bf2S5YscXr++eeTf//99xtKpVLS3Z6fn1/tOYLnzJkT4+XlVeDv75+/bds2y0GDBjWprHxKSorBpEmTPA0MDKQdO3ZEdu3aNQcAcnJy4jp37uyzY8cOm6VLl9pMmDAhtapjL1myxNHT0zP/5MmTl4yMjJCcnHy7YcOGzX/88UcH3STGihUrbHbt2mWzbdu2SAsLC6myOqn+YHcSIqJaEhySYBgckjCkeJC+WABzwQQGEdWcpwH8CuBWcEjC98EhCS3lDoieDL///ruqY8eOTRwcHFqUdFVo27atz6xZsxx0y7m5uTV3c3NrXl4dkydPdhVCBG7ZssVSd70QIrBdu3Y+pcsXFRVh9uzZDq1bt/a1tLRspVQqW3t4eDQbPny457lz50yqqhcAli5dauPv799UqVS2trW1bfncc881jI6OrrSL1vr16626devWyMbGpqWxsXHrBg0aNHvjjTfck5KSFFVdp0fVqVOn3M6dO+eWTkRUJDc3V8ycOdPNxcWloLwEBgCYmJhU+0Z/0KBBmc2bN883MKjeLeNvv/1mk5qaajhw4MCUkgQGAJiZmUlffPFFHAAsW7bMsTp1xcXFGTdr1izHyEj79tjZ2am9vLzy4uLi7r7XCQkJiqlTp3q88soriX379s2q7nmR/mNLDCKiGhYckuAHYCyAVwA4yRwOEdV/1gDeBvB2cEjCSQDLAPwxtINTtQY1JHoQc+fOtZ86daqnvb19Ye/evdPt7OyKEhMTDSMiIsxWr15tP23atMSaPmZeXp7o1atXo6NHj1o5OzsXDB48OMXKykp98+ZNk507d9p07tw5q3nz5vmV1fHFF184BgUFNbC0tFQ///zzySqVqmj//v2qzp07+1pYWJQ7C9CUKVNc5s2b56pSqdQ9e/ZMc3BwKLpw4YLp0qVLnfbu3asKDQ29aGtrW2emRN64caNVamqq4dixY+8oFArpzz//VJ07d85UqVRqOnbsmN27d+/s2jz+/v37rQCgT58+GaW39evXL1OpVGrCwsLMc3NzhampaaXJFFdX14ILFy6YqtVqKBQKpKSkGERHRysDAgLuJismTJjgYWJiovnuu+9ia/5sqC5jEoOIqAYEhyRYARgBbfKinczhENGTq03xMi84JGEttGNnhMgcE9UjK1ascDAyMpLOnDkT4ebmdl+3jdu3b9fKvcUHH3zgevToUasePXqkb9269aruDXBubq5ITU2ttFVEZGSk8YwZM9ytrKzUoaGhET4+PgUAoFar4/r37++9a9cu69L7bN682XLevHmurVq1yt69e/dle3v7u4mORYsW2b377rteU6dOdfv5559javBUH0loaKg5ACiVSo2/v7/f5cuX75umvU2bNln//PPPVVdX11rpbnP16lUlAPj5+ZXpEmRkZAR3d/eCK1euKC9evGjSunXrSrsNTZgwIXH06NHebdu29W3btm3Wvn37VJmZmYo33ngjEQD+/PNP1aZNm2yDg4OjVCpVnUkk0ePB7iRERI8gOCTBOzgkYRGAOAA/gQkMIqobzKFNqh4LDkk4FxyS8G5wSIKt3EFR/aBQKCRjY+MyT9JrYyyKoqIi/Prrrw5KpVLzyy+/3Cj9BN/U1FSq6qb8l19+sS0qKhJjx469U5LAAACFQoH58+fHlNddYtGiRY4AsGzZsmjdBAYATJo0KdnX1zc3ODi4Tn2n7ty5YwgAS5YscQaAHTt2RKalpYUdP348onPnzhknT560eO6557xr6/hZWVkKALCxsSm3ZYulpaUaAFJSUqrsijNq1Ki0RYsWRWdkZCh+++03ByGE9MMPP1wfOXJkenJysuLdd9/1fPHFF5Oee+65zJUrV1o3bNjQX6FQBLq5uTWfO3eufc2eGdU1bIlBRPQQgkMSugF4H8AgMCFMRHVbMwALAHwTHJKwHsDCoR2cQuUNifTVf/7zn5QvvvjC3c/Pz3/w4MGp3bt3z+zVq1dWbT3dP3PmjDIrK0vRokWLbC8vr8KHrMMcAHr06FGmi5Wfn1+Bs7Nzwa1bt4x114eFhVkYGhpKa9assV2zZk2ZOgsLC0VqaqphfHy8wtnZudybdgA4evSo6bp16+6bGtTa2rros88+u/Mw51IZSZIEoE0ybd68+UpJwqZdu3a527dvv9q4ceNmJ06csNizZ495bXctqQnvvPNO8jvvvJNcev3EiRPdAWDx4sWxhw8fNhs7dqx3nz59UhcsWHBz3bp1NlOnTvV0d3cvLD2LCdUfTGIQEVVTcEiCMbTTor4HIEDeaIiIHpgJgJEARgaHJBwA8M3QDk475A2J9E1QUFCCvb190bJlyxxWrlzp+MsvvzgKIdC2bdvMOXPmxOoO6FgTSp7aOzs7P1QCAwAyMzMVAODq6lpuHfb29oWlkxhpaWkKtVot5s+f71JZ3RkZGZUmMU6ePGlWug5XV9eC2khiqFSqIgBo2rRpjm6LEwCwtLTUdOvWLf2vv/6yP3bsWK0kMUrGFqmoe0/J+2Bra1vh9arKxo0bLf/66y/7NWvWXLGzs1PPnj3bydzcXL127dpoKysrzaBBgzIPHTpkNXfuXGcmMeovJjGIiKoQHJJgD+BNABMBVPrHDBGRnugOoHtwSMIZALMB/DW0g9ND31jQk+W///1v8n//+9/kpKQkxd69ey2Cg4Ot//77b/vBgwc3iYiIOF/SKkMIgcLCwnKn9ExLS6vW7B4lN7zx8fGVziJSmZJuDLdu3TICUGYshqSkpDJ1W1paqjUajUhPTz/zsMcFtF1PJk2aVKY1QW3w8fHJAwArK6tyv8vW1tZqAMjNza2VFqTe3t55Fy5cMIuIiFA+/fTT9yWzCgsLERsba6xQKKSmTZtWOghrRdLT0w3efvttr8GDB6eMGDEiHQCuXLmi9PLyyreystIAgIGBAfz9/XOOHj1q9ehnRHUVm0ATEVUgdV7nJsEhCUsB3ATwFZjAIKL6pxWANQCigkMSJgaHJJhWUZ7oLnt7e/Xw4cPT//zzzxvDhg1LSk9PV+zatcuiZLtKpSpKTk42zM/PL5PICA8PN6vOMVq1apVnaWmpjoqKMq1qOtRK6sgGgP3795eZdjUiIsI4Pj7euLx9MjIyFCdPnlQ+zDHl0L9//0whBK5cuaJUq8vmMS5evGgKAA0bNnyoJEJVevTokQEAO3fuLJNA2L59u2VeXp5BQEBAdlUzk1TknXfecc/PzzdYsmTJTd31BQUF932+8vLyeI9bz/ENJiIqLUjVEUGqjTYZ5y/ZpZzsAoB/1BNRffcUgB8ARAeHJEwPDkmwqWoHejJt3rzZUqMpOxlESWsGMzOzuxtbtWqVrVarxXfffWenW3bRokV2p0+ftihdR3kMDQ3x6quvJubl5RmMHTvWMzc3t/QNq7h161alrcvHjh2bYmhoKP3yyy+OkZGRdxMWarUa77//foPyzue9995LAIDx48d7lZc8ycjIMNi7d695dc7hcWnSpElBjx490m7fvm08Y8YMR91tGzZssDp8+LBV8RSzd6dAzc/PF2FhYcoLFy6YPOrxR48enWptbV20ZcsW20OHDt1NUuXk5IjPP//cDQDGjx//UN1oduzYYbF69WqHOXPm3NTtvtO4ceO8q1evmkZERBgDQHJysuLkyZMWjRo1yn3U86G6S0jSQyXCiIjqlyCVANAfwP8BeLpkdab5U0f3PL2jk2xxERHJIwvAUgDzhnZwipM7mLoiPDw8umXLlkkVbfeatjXwccbzoKJnDTj1qHVYWlq2MjMz0wQEBGR5eHgUSJKEkJAQy/Pnz5v5+/vnnDp16pKJiYkEAKdOnVJ26tTJr6ioSPTt2zfVzc2t4Pz582ZhYWHmHTt2zNy/f79q8+bNUQMHDrw74KYQIrBt27ZZoaGhkSXr8vLyRM+ePRsfO3bM0sXFpaBXr17plpaW6tjYWON///3X6osvvogt6bIxefJk1/nz57uUrjcoKMjpiy++cLe0tFQPHDgwRaVSqffv36/KzMxUWFhYqKOiokwlSbrv+kyfPt35f//7n5uJiYmme/fu6Z6engVZWVkGsbGxxqGhoZaBgYFZ//777+VHvaYVCQsLU86YMcO55OcNGzbYKZVKTf/+/VNL1n3//fexurPCXL161ahLly6+8fHxxh07dsxs3rx5zo0bN4z37NljA0Bavnz5tddeey2tpHxkZKSxr69vc1dX14K4uLhzusdftWqV9caNG60B4M6dO0aHDx+2cnd3z2/Xrl0WANjZ2RUtXbo0tvQ+Y8aM8TY2NtYMGjQoxcbGRr1z505VdHS0sm/fvqlbt269Vt5sMJXJysoSzZo18/f19c3ZsWPHNd1tR48eNX366af9nJ2dC/r165d66NAhq8uXL5v++eefV4YPH84xMfRYeHi4fcuWLb3K28YxMYjoyRakMgQwAsCH0I7gfx+L7GvtzHNuxmabebg/9tiIiORjAWAygP8GhyT8DmD20A5Ol2SOqc6riSRBXffpp5/G7d692+r8+fPmBw8eVBkbG0uurq4F06dPj50yZUpiSQIDAAIDA/M2bdoUNX36dLd9+/apFAoF2rRpk3ngwIFLa9eutdm/f7+qOsdUKpXSwYMHo+bMmePwxx9/2K9fv95OkiQ4OjoW9u3bN61nz55ZVdURFBSU4OLiUrhw4UKn9evX25uZmam7du2asXDhwtgXX3zxqfL2mTlzZnzXrl2zFi5c6Hjy5EmLPXv2GFpYWKidnJwKR44cmTh69OiU6l+5BxcXF2e0YcOG+1qx5OXlGeiu+/rrr2+5uNzr7ert7V146tSpix999JHL7t27rU+cOGFhYWGh7tGjR9rHH398u0ePHtUeeDUsLMys9PFjY2NNYmNjTQDtAKUA7ktijBo1Ks3JyenSzJkzXbZv325TUFBg4OHhkRcUFBQzffr0Ow+awACAyZMnu6WlpRkuW7bsZultnTp1yl25cuXVL7/80u3XX391dHZ2Lvj2229vMIFRv7ElBhE9mYJU5gBeh/aPdI/Kiibadjh4uN1v3R5LXEREdZMEYBOAWUM7OIXIHYxcqmqJQURENYMtMYiISgSprAC8D2ASANvq7GKfEhJoVJieXmikqtYTIyKiekgAGAJgSHBIwiFop2fdJnNMRET0BOLAnkT0ZAhSKRGk+gDANQBBqGYCAwAEYNH80qwztRQZEZG+6Qpga3BIQnhwSMLLwSEJfChGRESPDZMYRFS/BakMEaSaAOAKgDkA7KrYo1weccE+QlNYWKOxERHptxYAVgO4HByS8CaTGURE9DgwiUFE9VOQSiBI9RKACABLALg9SnUCGucm15eF1khsRET1ixeAHwFcCA5JGCZzLEREVM8xiUFE9U+Qqj+A0wD+ANC4pqptcvUnx6pLERE9sZoAWBccknAsOCTh6SpLExERPQQmMYio/ghSdUGQ6l8AWwG0qunqDTV5jd1vb6n30+cRET2iDgAOBYckbAoOSfCTOxgiIqpfmMQgIv0XpGqFINVWAP8C6FKbh2p+caaozfqJiOqRQQDOBockLA8OSXCVOxgiIqofmMQgIv0VpGqEINUf0HYd6f84DqksSG5tk3Ym8nEci4ioHlAAGAft4J8zg0MSrOQOiIiI9BuTGESkf4JUbghSLQFwEcBLAB5r64jW56cnPc7jERHVA2YAPgZwNTgk4d3gkARjuQMiIiL9xCQGEemPIJUdglRzoJ0udQIAWabzs8y63M40N+62HMcmItJz9gAWALgUHJIwIjgkgV30iIjogTCJQUR1X5DKAkGqTwFcA/ABAKWc4QjAKODCp1FyxkBEpOcaAlgD4ERwSEIvuYMhIiL9IctTTCKiaglSCQBjAXwNoE5Nb+qYdDjAsDAzo8jIkv27iYgeXiCAPcEhCTsB/N/QDk7hcgdERER1G1tiEFHdFKRqCeAIgOWoYwkMABCAVbOo2WFyx0FEVE/0AXA6OCTh1+CQBA+5gyEiorqLLTGIqG4JUlkB+ArA29COal9necauaxze9PMiycCQ/5cSET06AwCjAQwPDkn4HsCMoR2c0uQN6cEEhyQEyh1DZYZ2cDoldwy1ITIy0tjX17f5888/n7x+/fpoueMhotrFlhhEVHcEqV4GEAlgEup4AgMADCS1a6PoFaFyx0FEVM+YAJgCIDI4JGG03MHQk2PLli2WQojAyZMnu8odS3mSkpIUn376qdPgwYMbent7+xsaGgYKIQI3btxoWdE+kydPdhVCBFa0rFu37oG7xd68edNw3LhxDby8vJoplcrWVlZWrfz8/JpOnDjRrbzyO3futOjVq5e3m5tbcxMTk9YuLi7Nu3Xr1uhBj33o0CGztm3b+lhYWAS4u7s3f++991zz8vLKDA6s0WjQpk0bnxYtWvgWFRU96OmRHuDTQyKSX5DKD8APALrLHMkD8736ve3lp8bLHQYRUX3kCODX4JCEsQAmDu3gFCF3QFQ3eXl5FZ4+ffqCra2tWu5YalNUVJTxjBkz3AHAycmp0Nrauig5Obla93PPP/98sqenZ0Hp9b6+vvkPEsOuXbvMX3jhhcZ5eXkG3bp1S+/fv39qbm6uwfXr103++ecf28WLF8fplv/mm28cpk2b5mFqaqp59tln09zc3Ari4uKMd+7caf2f//xH9eGHH8Z988038VUd9/r160b9+/f3sbKyKhoxYkRiRESE6cKFC11yc3MNlixZEqtbdtasWQ5nz541P3bsWIQhG8vWS3xXiUg+QSpzAJ8BeB+AkczRPBRDda6va/zOsFvOfQLkjoWIqJ7qBuBMcEjCtwC+GtrBKUfugKhuMTExkQICAvLkjqO2NW7cuGDjxo1RHTp0yHFyclIPGzbMa8OGDXbV2XfMmDHJAwcOzHyU49+8edNw+PDhjSwtLdWHDx++2KJFi/sSIPn5+aL0zzNmzHAzMTGRjh07FtGyZcu75U+fPq3s2LGj36JFi1yCgoISTE1NpcqOvXz5crvc3FyDkydPRvr6+hYAQIcOHZr89ttvDj/++GOsgYG2g0FkZKTxzJkz3d97773bgYGB9f4z8aRidxIikkeQahiAiwA+hJ4mMEq0uPhVvX7yQ0RUBxgBmAYgIjgkYbDcwTzpFi1aZNenTx9vd3f35kqlsrWFhUVA69atfRcvXmxbXvl27dr5CCECCwsLMW3aNGdPT89mxsbGrZ2dnVu89dZbbqW7BIwZM6aBECLw9ddfdy9d1/z58+2FEIGdOnVqrFZrf/1GRkYaCyEChw0b5lW6fGZmpsFHH33k7Ovr62dqahpgZmYW0KpVK98lS5bcF+uwYcO8Bg0a1KT4GC66XS62bNliOWfOHHshROCUKVNcyjvHmzdvGhoaGrZu0qSJX3Wv44NycHBQDxkyJNPJyUmWvzs+++wzl7S0NMOFCxfeKJ3AALTJJN2f79y5o8jKylJ4enrm6SYwAKB169Z5np6eeXl5eQbp6elV3pPeuHHD2MbGpqgkgQEAAQEB2Xl5eQbx8fF3H8yPGTPG08PDI3/mzJm3H+4sSR+wJQYRPV5BqkYAvgPQV+5Qaopp/p02qvQLV9JV/o3kjoWIqJ7zBPBPcEjCZgDvDO3gdEPugJ5EH374oWejRo1y27dvn+ns7FyYkpJiuG/fPtXbb7/dMDIyUrlw4cJb5e03ZMiQp06cOGHRvXv3DEtLS/W+fftUP/30k3NiYqLRunXrokvK/fjjj7GhoaEWv/zyi1Pv3r0zX3rppXQAOHnypPLjjz9uYG9vX7h27drrCkXlw2clJSUpunbt2uTixYtmfn5+OS+++GKSRqMRBw8etHrzzTcbXrhwQblo0aJbAPDcc8+lAcCGDRvs2rZtm9WlS5e7rRYaN26c//TTT2d/+eWX7mvWrLH/5ptvbpfuprB48WJ7tVotXnvttcSHu6q169ChQxahoaFmarVaeHl55Q8cODDTxcXlgQaM+Oeff2ytrKzUzz//fMapU6eUO3bssMrJyTFo1KhR/vPPP5+uUqk0uuXd3NyKbGxsim7cuKE8d+6cSfPmze8mMs6ePWty8+ZNpa+vb66zs3OVSRkPD4+C1NRUw8uXLxs3bty4AADOnDljrlQqNc7OzkUAMG/ePPvQ0FDLgwcPXjQy0uvnY1QFJjGI6PEIUikBfATg/6AdtK1eaX3+o/j9nTcxiUFE9HgMAtArOCThKwDfDu3gVCh3QE+SU6dOXfD397/vyXpeXp7o0aNH4x9++MH5vffeS2zYsGGZ9+TGjRsm58+fv1DSkiAjIyOuWbNmfsHBwXY3b96M9fDwKAIApVIprV279lqHDh383nrrLa/27dtH2Nvbq0eMGOFdUFBgsHz58isNGjSo8gb8jTfeaHDx4kWz6dOnx86YMSOhZH1OTo7o06dPo++//97lpZdeSu3UqVPuqFGj0mxsbNQbNmyw69KlS+a8efPKJGKGDh2asmrVKoe///5bNWLEiPSS9RqNBqtXr3ZQKpWaN954I+XBrubjMWfOnPsGKzU2NpbefPPN+Pnz598q6YpRmUuXLhmnpaUZNmvWLOf1119vsGLFCkfd7ZMnTy766aefoocPH373uhgYGGDu3Lk333zzzYYdO3b0e/bZZ1NdXFwKb926ZbRr1y6bRo0a5f7555/XqhP/uHHjkhcuXOjSvXt3nwEDBqRGRESYhoSEWL7++usJBgYGuH79utHnn3/uPnHixPjOnTvnVvOykJ5idxIiqn1Bqv4ALkA7/kW9S2AAgCrzUjtlXnxC1SWJiKiGmGkk6f1Ptp055DVtaye5g3mSlE5gANrEw5tvvnlHrVaLrVu3ljvrxMyZM2N1u0JYWVlphg0blqLRaHDkyBFz3bLNmjXLnzdvXnRaWprhiy+++NTYsWM9rly5onz77bdvDxkypMqxHeLj4xX//POPnb+/f45uAgMAzMzMpNmzZ8dKkoTffvutWmNKAMCkSZPuAMCyZcscdNcHBwdbxcXFGQ8cODDVzs6uTnUxbdWqVc6CBQuiL168eC47O/t0VFTUuW+//faGpaWletGiRS6TJk0qd0aR0m7dumUEABcvXjT7448/7L/++uubcXFx4dHR0Wc/+eST2MzMTMWrr77qffr0aaXufmPHjk3dsmVLpKWlZVFwcLDd4sWLnTdu3Ghnamqqefnll5OaNm1arYFFvb29Czdv3hzl7Oxc8Pvvvztcv35d+d///jd+0aJFcQAwbtw4Tycnp8LZs2ffPn78uGm7du18jIyMWqtUqlZjxoxpUN4sJqS/2BKDiGpPkMoDwEIAz8kcSa0TgHGrC59dCglc6iR3LERET4pN52OvZhcUdQRw2Gva1iUA/i961oAMueOq7y5fvmz85ZdfOh8+fNgyPj7eOC8v774Ho3FxceW25e/SpUuZQVkbNGhQAAApKSll+oZMmDAhdd++fUlr1661P3nypEXr1q2zymshUZ7Dhw+bq9VqCCFQ3pSphYWFAgCioqKUZfcuX5s2bfLatGmTdejQIdWVK1eMGjVqVAgAS5cudQCAiRMn3qlOPeXF88YbbyT5+PiUmT3kUY0ePTpN9+fGjRsXTJ48Oaldu3Y5PXr08F2yZInT9OnTE6rqWqLRaHuKqNVqfPjhh7c/+uiju91mvvrqq4T4+Hij5cuXO82dO9dpzZo1d7t5LV682Hby5Mlezz77bOoXX3xxu3HjxvmXL182+fzzz10+/vhjj3///ddy27Zt1WqN0bNnz+xTp05Fll6/ePFi24MHD6p27959qbCwUAwaNKixSqVS//7771cuX76s/OKLL9yNjY2l0rOYkP5iEoOIal6QyhjAFACfADCTOZrHxjnxYEtFUXaW2tDcQu5YiIjqu/TcgpMHryZ0LP5RAHgTwGCvaVsnRc8asF7G0Oq1iIgI486dOzfNyMgwDAwMzOrWrVuGSqVSKxQK3Lhxw3jDhg12+fn55bb2tre3L9NKwdDQUAIAtVpd7pPy//znP6lr1661B4C33nrrTnWnzExMTDQEgPPnz5udP3++wr9FsrOzH6hl+oQJE+5MmDDB4ocffnCYP3/+rZs3bxru3btX5evrm9ujR49qzZwzf/78MoOD9uzZM7M2khgV6dKlS06LFi2yT58+bbF//37zkSNHpldWXnf62uHDh6eW3v7CCy+kLV++3CksLOxui5qzZ8+avPvuu14+Pj65wcHBd8cwCQgIyAsODr7esmVL5fbt2222bNli+bAzp8TExBhOnz69wbhx4xJ69uyZ/e2339onJiYarVq16lqfPn2yAGSEh4ebrly50nHu3Lm3LC0tNVVWSnUeu5MQUc0KUgUAOA3gazxBCQwAEJCs/aO+PS13HERE9Z0kSbnfH44sr+WbK4B1XtO2bvCatpUt42rBrFmznNPS0gznz58fHRoaGrly5cqYhQsX3po3b96tZ599tkZbwdy+fdtw4sSJXkqlUqNUKjUff/xxg1u3blUri2Ftba0GgHHjxiVIknSqouX48eNRDxLT6NGj0+zs7IrWrFljX1RUdHdAzzFjxlR7QM/y4njU6U8fhq2tbREAZGVlVXlP6Ofnl69QKKTi/coko+zs7IoA7dgoJeu2bNliVVRUJDp27JhZehBWhUKBDh06ZALAiRMnHvrvxfHjx3uoVCr1vHnz4gDg4sWLSgDo1KnT3YRS69atcwoKCsTFixfrZZfmJxGTGERUM4JUCgSppgM4DsBf7nDk0jDmj6cgqetUf1giovom9Gby8aTs/AaVFBkK4ILXtK0vPa6YnhTXr183AYBRo0aVeRp/8ODBGmuJqNFoMGLECK87d+4YzZw5M2bmzJkxiYmJRi+99FLDkq4NlenatWu2gYEBQkJCLKt7TJ1WIRWWMTExkUaOHJl4584doz/++MN69erVDmZmZprx48cnV/c4dUF+fr64cOGCGaCdfaWq8kqlUmrTpk0WAJw+fdq09PawsDBT4F73oOJjGABAUlJSuYmnpKQkI0A7yOjDnMOKFStsdu3aZfPTTz9FW1hY3FeHbjKldHcn0n98Q4no0WmnTf0XwAwAT/ScVgaS2t37xqrjcsdBRFRfFag1l/86E92lGkXtAPzhNW3r317TtjpUWZqqpUGDBvkAsH379vuSA+vXr7f666+/auw6f/HFF04HDx5UDRgwIHXy5MlJkydPThowYEDqv//+a/XZZ59V2crGzc2taPDgwckXLlwwmzp1qktRUdkhHy5cuGBy6dIl45KfHRwcigAgJibGuExhHe+8806SQqHABx984BEXF2c8ZMiQFBsbmzrXTSE1NdUgPDy8TOuDvLw88frrrze4ffu2ccOGDfO6du16XzeYy5cvG4eFhSkzMzPvu1csGfPjiy++cMvIyLi7LSkpSTF79mxXAHjxxRfvzs7SvXv3TADYsWOHzfHjx+9LfBw9etR0x44dNkII9OnT54Fb8CQkJCimTp3q8corryT27ds3q2S9n59fHgCsXbvWumTdjh07VMbGxlJ1BxGluo9jYhDRowlSvQVgDgDzqoo+KfwuL7C56vWa3GEQEdU7kiRJy0Mu52ukB/ob9gUA3bymbZ0YPWvAutqK7Unx7rvvJq5bt85+zJgx3mvXrk11cXEpvHjxovLff/9V9e/fP3Xr1q02j3qMgwcPmn399ddubm5uBb/++uvdQSJ/++236JYtW5rNmjXLrUePHlm9evXKrqyen3/++eb169eVc+fOdf3777/t2rZtm+Xo6Fh4+/Zto8uXL5ueP3/ebMmSJdd8fX0LAKBly5Z5jo6OhVu2bLEdPny45OHhUSCEwOuvv57cpEmTuy0MGjduXNCtW7e0ffv2WQPAxIkTq92V5FFNmDDBPTk52RAATpw4YQEAc+fOdV61apUdADz33HNpo0aNSgOAO3fuGAYEBDTz9/fPady4ca6zs3NhUlKS4dGjR63i4uKMra2ti1avXn2tdFePl19+ueGJEycsNm/eHKXbzWX06NFpmzdvTl63bp2dn5+ff48ePdLVajX27t1rfefOHaM+ffqkvvXWW3dbpPTo0SPnhRdeSF63bp3d008/3fTZZ59Na9CgQf7NmzdNdu/ebV1YWCjGjh17p02bNnkPcR08TExMNN99911sqfXJ33zzjevUqVM9jh8/bh4dHW1y/PhxyzfeeCOB42HUH0xiENHDCVK5APgFQF+5Q6lrDNU5TZ0T9oTHO/VuKXcsRET1SXRq9r+XEzO7PsSuDgD+9pq2dS2At6NnDaiVpv9DOzidqo1665L27dvnbt26NfLTTz91279/v0qtVgtfX9+cX3/99aqNjY36UZMYycnJipdfftkbAFatWnVVd8pSW1tbze+//36td+/evqNHj34qPDw8orzBQnXLh4SERM6bN8/+77//ttuxY4d1fn6+gZ2dXaGXl1f+F198ETN48OC7rQAMDQ3x119/XZk2bZr7tm3bbLKzsxWSJKFr165ZukkMABgzZkzyvn37rP39/XPKm3WltmzdutXm1q1b97UUOXLkyN0pbT09PQtKkhgODg5Fo0ePvhMWFmZ+6NAhVXp6usLIyEhq0KBB/ltvvRU/ffr0BDc3t0pnJSlt7dq10QsWLMj65ZdfHNatW2cHAN7e3nnvvffe7Q8//DCxdEJk7dq10d9//33m6tWr7Q8dOmSVk5OjMDc3VwcGBmaNGTMmccKECWW6JVXlzz//VG3atMk2ODg4SqVS3ZeYsLCwkLZs2RI1adIkj7///tve1NRUPXbs2DsLFiyIe9DjUN0lJOmhuiAR0ZMsSPUigB8B2ModSl2Vo3Q+sbP7obZyx0FEVF9oJClh+tYzyrwiteoRq0oA8Gb0rAEbH3TH8PDw6JYtWyY94vGpHpg8ebLr/PnzXebNm3fj/fff52eCqIaFh4fbt2zZ0qu8bRwTg4iqL0hljSDV7wDWggmMSpnmxbdRZVy8KnccRET1xcZzMddqIIEBAE4Agr2mbV3tNW0rf5fRA0tNTTX47bffHFQqlfr1119PqXoPIqpJTGIQUfUEqXoDOAdgpNyh6AMBiIDzH9+WOw4iovogLbfgxL/X7nSs4WpfBnDOa9rWXjVcL9VTf/75p2rq1KkuXbt29UlOTjZ89913b3OcBaLHj2NiEFHlglSmAL4B8F8AoorSpMM640Jbk7w7iflKR46KT0T0kCRJyvn+30iXWqreFcBur2lb5wKYHj1rQGEtHYfqgb///ttmw4YNdnZ2dkVvv/12/GeffZYgd0xETyK2xCCiigWp2gIIA/AOmMB4YAIwaRURFCF3HERE+izkRtKJ5Jx891o8hAAwFcBRr2lbG9ficUjPrV+/PlqSpFNJSUnh33//fVzpQSyJ6PFgEoOIygpSGSJIFQTgKAAfmaPRay539rZQqHMf26jlRET1SUGROnJd+I3Oj+lwbQCEeU3bOvYxHY+IiB4CkxhEdL8glQ+0yYvPwS5nj0xAsvGLmn9S7jiIiPSNJEmaZSFXijTSY/1dZA7gZ69pW//ymrbV+jEel4iIqolJDCLSClIJBKnegbb7CKcGrUFP3VztBUnDgb+IiB7A9ZSsw1eSMv1lOvx/AIR7Tdv6tEzHJyKiCjCJQURAkMoGwBYAiwCYyhxNvWMgFXk8dXNNqNxxEBHpC40kxS87dqWVzGF4ANjvNW3rV17TtrJlIhFRHcEkBtGTLkjVCsApAP1ljqRe87s8z1LuGIiI9MWGszej84rUVnLHAUAB4BMAB7ymbXWTOxgiImISg+jJFqQaDe34Fw3lDqW+MyrK8ndKPHhO7jiIiOq61Jz80CPXEzvIHUcpnQGEFaglpdyBEBE96ZjEIHoSBamMEaT6EcCvYPeRx6blhaBcuWMgIqrLJEnK/v5wZF1t8eCQnqdxup2W6yJJktyxEBE9sZjEIHrSBKncABwE8KbcoTxpzPLi2lhmRl2XOw4iorrqaHTiyZScgrqaxIAEIDEr3/VaUnajIrVGIXc8RERPIiYxiJ4kQaoeAE4DqGvNdJ8IAjBofX56rNxxEBHVRflF6kvrw292kTuO6sjOL1JdvpPll5VfZCZ3LERETxqOtEz0pAhSTQXwP2gHKSOZ2KSHtzHJT0rKN7G3lzsWIqK6QpIkzdJjlzWSHv2OKlRrjK8nZvs6qUxiHC2ViQCAIFWgzGFVLij9VG0fws3NrTkAxMXFcRyoWjZs2DCvDRs22F26dOmcj49PgdzxED0ubIlBVN8FqSwRpFoHYDb06I/D+koApi0vfnlB7jiIiOqSq8lZ/15LzvKTO44HJUES8el5HtFJ2Q3VGol/V1O9tGLFCptXX321QWBgoI+FhUWAECJwyJAhFQ4KHxkZaSyECKxoGThw4FPVPXZ+fr746quvHF944QUvX19fPyMjo9ZCiMB58+ZV+jDo/PnzJi+88IKXk5NTCyMjo9YODg4tnnvuuYYXLlwweZBzv3nzpuGgQYMa2tratrSzs2s5ZMiQhnFxceU2BJg0aZKrpaVlq+vXrxs9yDHowbElBlF9FqRqCmADAF+5Q6F7XON3+huo83I1CiUHVSWiJ55GI91eHnK5tdxxPIqMvELby3cyzfjLluqjb775xiUyMtLUzMxM4+TkVHD9+vVqzdLj4+OT279//7TS65s3b17tgc4zMzMNPvvsswYAYGdnV2Rvb18YHx9vXNk+hw4dMuvfv79Pdna2QceOHTOfe+65nJiYGOMtW7bY7N27V7Vjx47Izp07VxmDWq1G//79G1+9elU5bNiw5NzcXIN//vnHrn///ianT5++pFDcezZ49OhR08WLFzvPnj37ZsOGDQure370cJjEIKqvglQvAFgBwELuUOh+ApJ90yvf/XvBZ+rTcsdCRCS3dWdv3Mwv0rSXO45HVVCk4fSrVC/NmTMnxsvLq8Df3z9/27ZtloMGDWpSnf38/f1z5s2bd+tRjm1hYaFZu3bt5fbt2+d6enoWTp482XX+/Pkule0zfvx4r+zsbIOgoKCYzz///E7J+p07d1oMGDDAZ9y4cQ0jIiIiDAwqbzx18OBB8wsXLph999130f/973+TAWDKlCn58+bNcz106JBZjx49cgCgsLAQ48aNa9iuXbvMyZMnJz3K+VL1sNkbUX0TpFIgSDUXwN9gAqPO8o5e6Q5Jo5E7DiIiOaXk5B8/Fp2k9wmMJ41Go8HXX3/t0KhRI38TE5PWjo6OLUaPHu2RnJxcabfVJUuW2LZv376JpaVlKxMTk9ZPPfWU/4cffuiSm5srSpcVQgS2a9fOJzo62ui5555raGtr21KpVLb29/dv+tNPP9mWLr9lyxZLIUTg5MmTXffv32/WvXv3RiqVqpUQIjAyMvLuk/sHiWHHjh0WPXv2bOTk5NTC2Ni4tb29fcuWLVv6Tpky5b6b6JiYGMMJEya4e3l5NTM1NQ2wtLRs5eXl1WzYsGFeERERZVoNrF+/3qpbt26NbGxsWhobG7du0KBBszfeeMM9KSmp3Ou3ceNGy8DAQB9TU9MAlUrVqnfv3t5hYWGPLWk2aNCgzObNm+dXddNfG5RKpfTiiy9meHp6Vqt1Q0REhHFUVJSpra1t0SeffHJHd1ufPn2yunfvnhYZGWm6c+fOKv9GvnbtmjEAdO7cObtkXfv27bMB4Pr163e7pUyfPt3l5s2bJitWrLhR3fOiR8OWGET1SZDKEcBaAN1ljoSqoJAKG3rF/h0a3WB4O7ljISKSgyRJWT8cjmwgdxz04MaNG9dg5cqVjg4ODoUjRoxINDIyknbu3GndrVs388LCQmFkZCSV3uc///mP17p16+ycnJwK+/Xrl6pSqdSnTp2ymDNnjuvBgwctDx8+HGVkdP9QAunp6YrOnTv7WlpaqocPH56Unp5uuGXLFpu33nqrYVxcnNFXX32VUPo4oaGh5t9//71zYGBg1vDhw5OSk5MNTUxMpAeNYd26dVbDhw9vbG5uru7du3eaq6trYUpKiuLy5cumK1eudPz2229vA9ruDp07d/aNiYkx6dSpU8azzz6bJkkSYmJijHfv3m39wgsvpPr5+d0ddHPKlCku8+bNc1WpVOqePXumOTg4FF24cMF06dKlTnv37lWFhoZetLW1vfuQY8WKFTavv/76U0ZGRtKAAQNSnJ2dC0NCQiy6devm6+PjU+1uGXKIj483mjNnjn1ycrKhnZ1dUdeuXbPbt29fqzHHxcUZAYCbm1u+bnePEl5eXvkAsGvXLqt+/fplVVZXw4YNCwAgJCTELCAgIA8ATpw4YV68LR8ATp06pVywYIFLUFBQDAdXfXyYxCCqL4JUHQCsA+AmdyhUPf6Rc0yjGwyXOwwiIlkcvp54OiWnoKvccdCD2b17t/nKlSsdGzRokH/ixImLTk5OagDIycmJ69Spk09iYqKRq6vrfTdzixYtslu3bp3dM888k7Zhw4ZrFhYWd5McJd0DZs2a5fjpp5/e9+Q8KirKtF+/fqmbN2++VnJDeunSpdvt27f3mzVrltuIESPuSxAAwJEjR6xmz559Y+rUqfc163/QGJYtW+ag0Wiwc+fOyI4dO95343379u2791CbN2+2jImJMRk7duydn3/+OUa3XF5entBt4bF582bLefPmubZq1Sp79+7dl+3t7dW68b377rteU6dOdSupJz093eD999/3FEJIu3btutS1a9eckvLjxo1r8MsvvzhW+EbVAUePHrU6evSole66du3aZa5evTq6cePGtXLD7+TkVAQAt27dMtFoNCjdeiQ6OtoEAC5fvlxlS5Zu3bpl+/n55UyZMsXz6NGjFrm5uQYbN260bdasWU7Xrl1zioqKMHbsWK8WLVpkT5s2LbE2zofKx+4kRPVBkGoigINgAkOvGBdlNHdIOnpe7jiIiB63/CL1xeCzNzvLHQc9uJ9//tkeAKZMmXK7JIEBAGZmZtKMGTPiytvnxx9/dFIoFNKaNWuidZMHADB79uxb1tbWRX/99Zdd6f0UCgXmzZsXq/tE3dfXt2DcuHF3ioqKxM8//1xmH19f39zSCYxHicHc3LxM108XF5ei0utMTU3LlFMqlZKNjc3d9YsWLXIEgGXLlkXrJjAAYNKkScm+vr65wcHBd7vKrFmzxjo9PV0xePDgFN0ERknMFhYW99VRV1hYWGjefffd2//+++/FxMTEM4mJiWe2bdsW2b59+8zQ0FDL3r17N8nIyKiV+9AWLVrke3p65icnJxvOnDnzviTP7t27zQ8cOGANAGlpaVXO2GdoaIitW7de6dGjR/qWLVts9u3bp+rXr1/q1q1bLysUCnz55ZdOUVFRpitWrIhOTk5WDBkypKG5uXmAiYlJ6549ezbiLCW1hy0xiPRZkMoQwI8AXpc7FHo4rS58lrW72x65wyAiemwkSVIvOXoZEqf91kvnzp0zA4Bnnnkms/S2Pn36ZJZuwp+ZmWkQGRlpam1tXfT11187lVenkZGRdO3atTJPxp2dnQt8fX3LPLHv2bNn5vz5813Cw8PNSm8LCAjILr3uYWIYOXJk8q5du6y7dOnSdODAgSk9evTI7NmzZ5a3t/d9YzP07ds309HRsXDx4sXO4eHhZn369Env1q1bVseOHXMMDe+/1QoLC7MwNDSU1qxZY7tmzZoyMRQWForU1FTD+Ph4hbOzs/r06dNmANC1a9cy19rOzk7dtGnT3BMnTlRr/LNVq1ZZh4WF3Xe9AgICckaNGpVWnf0fhJubW9GCBQvuG9CzX79+Wb17945q06aN79mzZ80XLlxoX7rlTU1ZtGjRjRdeeKHxZ5991mD79u2q5s2b58bFxRnv3LnTunHjxrmXLl0yre74Hl5eXoVbt269Vnr9uXPnTObMmeM6derUW82bN8/v3bu39/Hjxy2/+eabmyqVSv3BBx94DBkyxPvMmTOX5BhLpL5jEoNIXwWpzKEdvLOf3KHQwzPPvdnOIvv6zSzzhh5yx0JE9DhcSco8fD0lq5vccdDDyczMVACAu7t7mdYIRkZGsLa2vm99UlKSQpIkpKamGlY1q0Rp9vb25Q7m6ObmVqgbiy4nJ6cy+zxMDK+++mqaiYnJlQULFjj9/fff9n/88YcDoJ1x46uvvoobOnRoBgDY2tpqjh49evGjjz5y3b17t/Xhw4etAMDa2rrotddeS5w1a9btkjE50tLSFGq1WlQVQ0ZGhsLZ2VmdkZGhAABnZ+cy1xoAHB0dqz2V58aNG603bNhwX0uT559/Prk2khgVMTIywquvvpo0ZcoU88OHD1sCqJUkxuDBgzP3799/8csvv3QNDQ21OHHihKW7u3v+J598Euvm5lY4fvz4pyr6bFWHRqPBmDFjvBo3bpz72WefJZw7d85k79691lOnTr1VMotJRkaGwX//+9+GW7ZssRw8eHCZJBQ9GiYxiPRRkMoJwFYAgXKHQo9GAAYB5z6O/rfDH0xiEFG9p9ZIt34OucLfXXrM0tJSDQCxsbGGpcejKCwsRFpamqGTk9Pd9ba2tmoAaNq0aU5ERMTFBzlWUlJSuc3xSwZvLIlFlxBlJhl56Bheeuml9Jdeeik9IyPD4MCBA+abNm1SrV692nH48OGNjh07FhEYGJgHAN7e3oV//fXXDY1Gc+P06dPKnTt3Wi1fvtxhwYIFLhqNBgsXLrxVEq9GoxHp6elnqnN8KysrNQDEx8eXe892586dandXWL9+fTSA6OqWry2Ojo5FAJCTk1OrzRM6d+6cu3Pnzqul17/33nuuANCmTZsyLXaqa9asWQ5nz541P3bsWIShoSHOnj2rBIDAwMC7XX46dOiQAwDnzp0zZRKj5rFtC5G+CVI1AXAMTGDUG3Zpp9oYF6Smyh0HEVFt+zv8Rmy+WsPpv/VY8+bNcwBg9+7dlqW37dy501Ktvj+voFKpNI0aNcq7cuWKaUJCwgN1IYqPjzfWnR61xL59+ywBoGXLljll9yrrUWIAACsrK83gwYMzly9fHvvOO+/cLiwsFJs2bVKVLmdgYIA2bdrkTZ8+/c7u3bujAGD79u3WJdtbtWqVnZGRoTh58mS1pkdt3bp1DgAcOnSozLVOTk5WXLx40fRBz0Vux44dMwcAT0/P/Md97Pz8fBEcHGxraGgovfLKKw/1d1dkZKTxzJkz3d97773bJUks3fpLXufm5vI+uxbx4hLpkyBVJwBHATSUOxSqOQIwa3Fxxlm54yAiqk3J2fnHj99I4rTSem7s2LFJAPDtt9+66CYEcnJyxCeffFLuAONvv/12fGFhoXj55Ze9kpKSyiQREhMTFYcPHy4zvoVarcbkyZPddRMjly5dMv75558dFQqFNHbs2JTqxv2gMWzfvt2isLBsj4OEhAQjADAzM9MAwMmTJ5UxMTFlWkrcunXLCACUSuXdgT3fe++9BAAYP368V3R0dJlWFBkZGQZ79+41L/l55MiRaVZWVupNmzbZHjp06L7r8+GHH7pmZWXVyXFlDh8+bFY6mQUA//zzj+Xy5cudAODVV19N1t2WnJysCAsLU964ceORB8PMyMgwKCq6vwdOYWEhxo4d2+DmzZsm48ePT/Dw8Ci3i05VxowZ4+nh4ZE/c+bM2yXrWrZsmQcAmzdvti5ZFxwcrAKA5s2b1+lpcPUVu5MQ6Ysg1fMAfgdQrew96Rf321v9Tjf7Ol+jMDGROxYiopomSVLm94cj2W2uHnj22WezX3311Tu//vqrY/Pmzf379++famRkJO3cudPayspK7eDgUObO/7333ks+deqU+erVqx0aNWrU/Omnn05v0KBBQWpqquGNGzeMT548afnCCy8kdenS5abufk2aNMk9c+aMebNmzfy6d++enp6ebrhlyxabzMxMxSeffBLr7+9f7af5DxrD+++/75GQkGAUGBiY5eHhUWBsbCyFh4ebhYSEWLq6uhaUJFC2bdtm9eWXX7q3atUq29vbO8/BwaEoLi7OaM+ePdYGBgZ4//33E0piGDJkSObHH38c97///c+tadOmzbp3757u6elZkJWVZRAbG2scGhpqGRgYmNWrV6/LgLYFyYIFC268/vrrTz377LO+AwYMSHF2di4MCQmxuHz5smmbNm2yTp48Westm1atWmW9ceNGa+BeF5bTp0+bDxs2zAsA7OzsipYuXRpbUn7y5MkNoqOjTQIDA7NcXV0LASAiIsI0JCTEEgCmTp1665lnnrmvO8fvv/9u/e6773o9//zzycVdX+76+OOPnSMjI5UAcOHCBTMAWL16tf2RI0csAKBz585ZkydPvjsjzdatWy3feecdr86dO2e4ubkVZGVlKQ4cOGAVExNj0rdv39T58+ffN+hodc2bN88+NDTU8uDBgxeNjO7lWpo1a5b/zDPPpK1bt86uf//+BpaWlur169fbN2/ePHvgwIHsSlILmMQg0gdBqncALABbT9VbAhoHn2s/Hr7Y+L0ucsdCRFTT/r12Jywtt6Cr3HHUtrOv33jgfQSE5GqtjLazMKl2qwK5/fLLLzFNmjTJW758ueOaNWscrK2ti/r06ZO2YMGCuBYtWviVt8+qVatu9u/fP33p0qUOR44cscrMzFSoVCq1i4tLwZtvvhk/ZsyYMuevUqnUO3fuvPzuu++6r1271j47O1vh7e2dO2nSpIQ333zzga/Xg8TwwQcf3P7nn3+sz549a3706FErAwMDuLi4FPz3v/+N/+ijjxIcHBzUADBw4MCMmzdv3gkJCbHctWuXdXZ2tsLBwaGwc+fOGVOmTEkofbM+c+bM+K5du2YtXLjQ8eTJkxZ79uwxtLCwUDs5ORWOHDkycfTo0fed15gxY1Ktra0vz5gxw2Xbtm02RkZGUtu2bTMPHjx4acaMGc6PI4kRFhZmVnpQ0NjYWJPY2FgTAHB1dS0AcDeJMWLEiORNmzZZnz171vzgwYOGRUVFws7OrrB///6p77zzzp2+fftmPcjx9+zZoyo9C0tYWJh5WFjY3VYrukkMf3//vMDAwKzjx49bpqSkGCqVSk3Tpk1zPvroo1tvvPFGysPMFnL9+nWjzz//3H3ixInxnTt3LtO6Ys2aNdETJkxosHv3buuioiLRvXv3tGXLlt3kzCS1Q0iSVHUpIpJHkEoAmANgityhUO1TGxhf3fTMuafKHZWMiEhP5RWqIz7eGuYr1YNE/LLBLnDyeKpW6nawNLnlojK9XXXJJ4MQIrBt27ZZoaGhkXLHQkSPX3h4uH3Lli29ytum979MiOqtIJUJgD/ABMYTQ6Ep8PaICz4pdxxERDVFkiT1T0ejFPUhgVHbEjPzXW8mZ3vyASMRUeX4C4WoLgpSWQPYCWC4zJHQY9YsclaZUdiJiPRVVGLm4Rup2T5yx6Ev0nIL7a8lZTdWayT+jU5EVAH+B0lU1wSpGgA4DKCb3KHQ42dSmNbSLiU0Qu44iIgelVojxf5y/EobuePQN9n5RVZX7mT5FKo1HLuOiKgcTGIQ1SVBqpYAQgD4yx0KySfg/KfpcsdARPSo1oZF3y5Qa8yrLkml5Repza7eyfLNL1I/sa3zJEk6xfEwiKg8TGIQ1RVBqt4ADgFwlTsUkpdFzvV25jk3Y6suSURUNyVl5YWciEluK3cc+qxArTG5eifbN7dAzanViYh0MIlBVBcEqUYB2AbASu5QSH4CUAScn35N7jiIiB6GJEkZPxyO9JI7jvqgSKMxupaU5ZOdX2QmdyxERHUFkxhEcgtSfQzgNwBGcodCdYd9yvHWRoXp7FZCRHrnwNWEM2l5hc5yx1FfqDWS4fWkbJ+M3EJLuWMhIqoLmMQgkkuQSiBI9R2AmXKHQnWPACyaX/r6jNxxEBE9iNxC9YXN52O7yB1HfaORJIMbKTmNU3MKVHLHQkQkNyYxiOQQpBIAfgDwX7lDobrLI+4fH6EpKJA7DiKi6pAkqeinI1FGEv++rBWSJInYlFzv5Kx8W7ljISKSE3/JED1u2gTGYgBvyR0K1W0CGucm15adkDsOIqLquHQn48jNtOwmcsdRn0mQRFxabsOEjDxHuWMhIpILkxhEj5M2gfETgDflDoX0g8+1n/iHKhHVeWqNFLsi9CpnI3lMEjLyGtxOy3WROw4iIjkwiUH0uGgTGEsATJA7FNIfCk1+Y/dbm0/JHQcRUWX+OH09vlCt4Qwaj1FiVr7rrbRcTstORE8cJjGIHod7CYzxcodC+qf5pa/5fzUR1Vl3svKOnYpNaSN3HE+ipKx8FyYyiOhJYyh3AET1njaBsRTA63KHQvpJWZAcYJMWFplqHeAjdyxERLokSUr/4XCkt9xx1BUtLn0jx2Fdipeq9f5fnWzZ5+bm1hwA4uLiztXWMSIjI419fX2bP//888nr16+Prq3jPIotW7ZYDho0qMn7779/e968ebeqs8/kyZNd58+f77J58+aogQMHZtZ2jER1AZ/uEdUmbQJjGZjAoEfU+vz0ZLljICIqbf+VhLMZeYUcu4fqBCFEYLt27ZjwryPmz59vP3LkSM8WLVr4mpqaBgghAidNmlRhy6GkpCTFp59+6jR48OCG3t7e/oaGhoFCiMCNGzdaPmosGo0GnTp1aiyECBRCBBYWFpZbbvPmzZbdunVrZG1t3crY2Li1h4dHs4kTJ7qlpqY+0H3zoUOHzNq2betjYWER4O7u3vy9995zzcvLE+XF1aZNG58WLVr4FhUVPeTZPXnYEoOotgSpDAAsBzBG7lBI/1lmXWlnmht3O9fUjQO5EVGdkFtYdG7zhdgucsdBVB1eXl6Fp0+fvmBra6uWO5aaNHXq1DujRo1KadSoUZ2bkv2zzz5zz8rKUlhZWakdHBwKY2JiTCorHxUVZTxjxgx3AHByciq0trYuSk5OrpH71f/973+OoaGhViYmJlJ+fn6ZZAIAzJ4922HatGkeCoVC6tOnT5qrq2tBeHi42Y8//ui8Z88e1bFjxyLt7Oyq/Pxcv37dqH///j5WVlZFI0aMSIyIiDBduHChS25ursGSJUtidcvOmjXL4ezZs+bHjh2LMDTkrXl1sSUGUW3QJjB+BhMYVEMEYBhw/pMoueMgIgIASZKKfjwSpQRQ7s0AUV1jYmIiBQQE5Hl6epb/CF5Pubi4FAUEBORZWlpq5I6ltBUrVlyLjIw8l56efuaDDz64XVX5xo0bF2zcuDEqPj7+THx8/Nlu3bql10Qc4eHhJjNmzHCbMGFCvJ2dXbnv/40bN4w+++yzBgYGBtKuXbsubdmy5drSpUtjjx8/HjVt2rS4y5cvm06ZMqVa488sX77cLjc312Dfvn2Ry5Ytiz1y5Mjl9u3bZ/72228OGs29tykyMtJ45syZ7u+9997twMDAvJo41ycFkxhENU2bwPgFwGsyR0L1jGPykQDDwswMueMgIrqYkH4kJi2nsdxx0OMXGRlpLIQIHDZsmNfZs2dNBgwY8JStrW1LAwODwC1bttxt9r9+/Xqrbt26NbKxsWlpbGzcukGDBs3eeOMN96SkJEV1jpOcnKz49NNPnTp06NDEycmphZGRUWsbG5uWPXv2bLRnzx5z3bKLFi2yE0IEAsCJEycsSroMCCECJ0+e7Fo67tLHunHjhtGoUaM83Nzcmpcc59lnn/X+999/y8y4U3KsRYsW2W3evNmyXbt2Pubm5gEWFhYB3bt3b3T69Gll6X1iYmIMJ0yY4O7l5dXM1NQ0wNLSspWXl1ezYcOGeUVERBiXd/5Hjx417d69eyNLS8tWpqamAW3btvXZvXu3eelykydPdhVC3HftgXtda6Kjo42ee+65hra2ti2VSmVrf3//pj/99JNtFZe/RrzwwgsZTZo0qXYLEQcHB/WQIUMynZycaqy1TGFhIV555ZWn3N3dC7799tsKxxkJDg62ys/PF88880xajx49cnS3ffXVV/EqlUr9119/2WdmZlZ5/3zjxg1jGxubIl9f37vnHhAQkJ2Xl2cQHx9/t7nFmDFjPD08PPJnzpxZZYKH7sckBlFN0iYwVgB4Ve5QqP4RgFWzyG/C5I6DiJ5sao3m5srQq+3kjoPkFR0dbdKlS5emMTExxkOHDk156aWXEq2trdUAMGXKFJcXXnihcXh4uEWPHj3Sx4wZc8fT0zN/6dKlTh06dPBNSUmp8h7kzJkzylmzZrkZGBigV69e6ePHj0/o0qVLRkhIiGXfvn191q1bZ1VStk2bNjnvv//+bQBwdXUteP/992+XLD179qx0sMtLly4Zt23btunq1asdPDw88idMmJDQtWvXjAMHDqh69erl+8cff6jK22/btm2qoUOHNrawsFC/8soriW3atMk8ePCgqnfv3j63b9++e6OamZlp0LlzZ99ly5Y5ubm55Y8aNSrxpZdeSvL19c3ZvXu3dXh4uGnpusPCwsx69erlm5+fbzBixIikHj16pJ8+fdpi0KBBPuHh4ZV2ydCVnp6u6Ny5s++lS5dMhw8fnvT8888nx8TEmLz11lsNP/30U6fq1qPP/u///s/14sWLpj///PN1U1NTqaJy8fHxRgDg5eWVX3qboaEhXF1d83Nzcw0OHDhQJpFUmoeHR0Fqaqrh5cuX7yaozpw5Y65UKjXOzs5FADBv3jz70NBQy+XLl183MjJ6uJN7grHjDVFNuZfAGC13KFR/ecaubxLu93mhZMDfeEQkj99PRd8p1EgecsdB8jp9+rTF22+/Hf/999/H6a7fvHmz5bx581xbtWqVvXv37sv29vZ3n6ovWrTI7t133/WaOnWq288//xxTWf2tWrXKu3nz5lkXF5f7Rju8evWqUceOHZt++OGHDV544YULANCpU6fcTp065c6fP9/Fzc2toLozewDA66+/7pmYmGj04Ycfxn3zzTfxJet37959p1+/fr5vvfVWw/79+59VqVT3ddfYs2ePzfr166OGDBlyN0ny9ttvuy1evNj5hx9+sJsxY0ZCyfWIiYkxGTt27J3S55yXlydyc3PLdMk6cOCAauHChdGTJk26O6j3nDlz7D/88EPPOXPmOK1evfpmdc4tKirKtF+/fqmbN2++plBoG8BcunTpdvv27f1mzZrlNmLEiFQ/P786N5ZGTTl48KDZokWLnN9+++34rl275lRW1t7evggAbty4USZJpFarcevWLRMAuHjxonLQoEGVJsbGjRuXvHDhQpfu3bv7DBgwIDUiIsI0JCTE8vXXX08wMDDA9evXjT7//HP3iRMnxnfu3Dn3Uc7xScWWGEQ1QZvA+BVMYFAtM4DapVH0ihNyx0FET6aEzNyjYXEpbeSOg+RnZ2dXNGfOnDLJgkWLFjkCwLJly6J1ExgAMGnSpGRfX9/c4ODgKrsz2NnZqUsnMADA29u7sH///qnXr19X6j7pfhhXr141OnLkiJWLi0vBl19+maC77ZlnnskeOHBgSnp6umLVqlU2pfcdMGBAim4CAwDeeeedRAA4efJkmaf1pqamZcasUCqVko2NTZn1rVu3ztJNYADaa6dQKKQzZ85U2RKghEKhwLx582JLEhgA4OvrWzBu3Lg7RUVF4ueff7arbl36JisrS7z22msNGzVqlDd79uwqu2sMHjw4Q6FQSLt377Y+dOjQfd2IPv/8c6f09HQFAKSmplbZHcrb27tw8+bNUc7OzgW///67w/Xr15X//e9/4xctWhQHAOPGjfN0cnIqnD179u3jx4+btmvXzsfIyKi1SqVqNWbMmAblzWJC92NLDKJHFaRSQJvAeFnuUOjJ4Hv1B7vLT02QOwwiesJIkpS++HAUx8EgAICvr29Oec3zw8LCLAwNDaU1a9bYrlmzpsx+hYWFIjU11TA+Pl7h7Oxc6dgHu3btMl+wYIHT6dOnLVJSUgwLCwvvu7m7ceOGUePGjR+6JcHx48fNAKBt27ZZJiYmZc6lR48eGf/8849tWFiYGYD7kgqBgYHZpct7e3sXAEB6evrde6y+fftmOjo6Fi5evNg5PDzcrE+fPundunXL6tixY05Fs1G0bNmyTKsBExMTyc7OrqjkZro6nJ2dC3THZSjRs2fPzPnz57uEh4eXGfOjtEWLFtlFR0ff1zqhZ8+emQMHDqy0NYLc3n77bffY2FiTQ4cOXSzvvS2tSZMmBZMnT749Z84c1969e/v26dMn1dXVtfDs2bNmx48ft2zSpEluVFSUqYFB9doA9OzZM/vUqVORpdcvXrzY9uDBg6rdu3dfKiwsFIMGDWqsUqnUv//++5XLly8rv/jiC3djY2Op9CwmdD8mMYgehTaB8RuAkXKHQk8OQ3Wuj2v8jrBbzn0D5I6FiJ4cey/Hn83IL3xa7jiobnB0dCx3loe0tDSFWq0W8+fPr3RK8IyMjEqTGL/99pv1mDFjvI2NjTWdO3fOaNiwYb65ubnGwMAAhw8ftjxx4oRFXl7eI7UqT0tLUwCAs7Nzuefi5uZWCGjHlii9rWT8D10lYxuo1eq7yRZbW1vN0aNHL3700Ueuu3fvtj58+LBV8f5Fr732WuKsWbNul77JLq9uADA0NJQ0Gk21n9Lb29tXel6ZmZlVJkRWr15tf+LECYvS6+tyEmPr1q0Wq1atcpw8efKtjh07Vru7xuzZs2/7+fnlfv/990779u2z1mg08PHxyf3jjz+ubN26VRUVFWVa0ee+OmJiYgynT5/eYNy4cQk9e/bM/vbbb+0TExONVq1ada1Pnz5ZADLCw8NNV65c6Th37txbdXHGmbqCSQyihxWkEtC2wGACgx67Fhe/0txy7it3GET0hMgpKDq7NSKui9xx0KNJyS6wsTU3Tq2JuoQo/17a0tJSrdFoRHp6+plHqf+rr75yMzIyko4cOXKxdevW900/OXLkSM/ybqwfVEmyICEhodxxpuLi4owAwMrK6pFmy/D29i7866+/bmg0mhunT59W7ty502r58uUOCxYscNFoNFi4cGG1x/B4EElJSZWel6WlZZXnFRoaWqY1QV136tQpM0mS8O2337p+++235U6LamxsHAgAR44ciejUqdPdRMdrr72W9tprr6WVLj9nzhxnAOjUqVOZFjjVNX78eA+VSqWeN29eHKAdX6O4zrstb1q3bp3z999/21+8eNGkXbt2HC+jAkxiED28b8EuJCQT0/zEQOv0c5fTVM3ZtJuIapUkSYU/HokyA8B+2nouLjW3oYGAxtrMOL22jtGqVavsAwcOqE6ePKls06ZNXtV7lO/mzZsmjRo1yi2dwFCr1QgNDS03gWFgYAC1uvr5hvbt2+cA2mlZCwsLUXqWiAMHDlgC2hvLB42/ovjatGmT16ZNm7zhw4enNm7cuMX27dutayuJER8fbxwZGWns4+NzX5eSffv2WQLld1upD1q0aJH74osvJpW3bcuWLbY5OTkG//nPf5KEEHB0dCwz7kppFy5cMAkLC7No3Lhxbtu2bR/qM71ixQqbXbt22Wzbti3SwsLivpY3eXl5wtLSsuQ1x6ysBl4koocRpJoM4H25w6AnW+vz0+/IHQMR1X8X4tOPxKbnNJI7Dnp0EiQRk5rrnZFbaFlbx3jvvfcSAGD8+PFe0dHRZVoCZGRkGOzdu7fKwSldXV3zb9y4odStQ6PRYMqUKa5Xr15VlrePSqUqio+Pr/Zgn97e3oWdOnXKuHXrlvFXX31135Sj+/btM9+8ebOdlZWV+uWXX37o1isnT55UxsTElHlwfOvWLSMAUCqVtdZlQK1WY/Lkye66iZ1Lly4Z//zzz44KhUIaO3ZsSm0d+3G5ffu2YVhYmFJ3Wtvnnnsuc+3atTfKW6ytrYsA4Pfff7+xdu3aG40aNbrbPaS8qX/j4+MVI0eObKjRaDBjxoyHGqciISFBMXXqVI9XXnklsW/fvlkl6/38/PIAYO3atdYl63bs2KEyNjaWmjZtWmaqV7qHLTGIHlSQajiAuXKHQWSVeamtae7t+FxTF2e5YyGi+qlIo7nx64mrHeSOg2qOJEniZkpOIy878ygLpeFDN42vyJAhQzI//vjjuP/9739uTZs2bda9e/d0T0/PgqysLIPY2Fjj0NBQy8DAwKxevXpdrqyeiRMnJnz44YeegYGBfv369Us1MjKSTpw4YXH16lVljx490vfv368qvU/nzp0ztmzZYtuzZ89GLVu2zDEyMpJ69OiR2a9fv6zyjgEAy5Ytu9GtWzffr776yn3v3r1WAQEBObGxscbbt2+3EUJIP/zwQ3R5M4hU17Zt26y+/PJL91atWmV7e3vnOTg4FMXFxRnt2bPH2sDAAO+//35C1bU8nCZNmuSeOXPGvFmzZn7du3dPT09PN9yyZYtNZmam4pNPPon19/ev1RvlefPm2R85csQCAEoGB925c6f1sGHDjAHAx8cn7+uvv47X3WfChAnuycnJhoC2hQwAzJ0713nVqlV2APDcc8+ljRo1Kq2k/Jw5cxznz5/v8v77799+kKl1yzNt2jTX/fv3qwIDA7McHByKbt26ZbRnzx7rzMxMxeeffx774osvZjxMvRMmTPAwMTHRfPfdd7Gl1id/8803rlOnTvU4fvy4eXR0tMnx48ct33jjjQSOh1E5JjGIHkSQqge0A3mySS3JTgDGrSI+izwWuIxJDCKqFb+fvJ5UpJE85Y5DX5z1/T+5Q6gWjSQZRCdnN25obx5pbmJY4/3uZ86cGd+1a9eshQsXOp48edJiz549hhYWFmonJ6fCkSNHJo4ePbrKFgBTp05NMjExkRYvXuy0fv16O6VSqWnTpk3WL7/8Ev3nn3/alJfE+Omnn2LefPNNHD161OrgwYMqjUaDoqKi25UlMfz8/ApCQ0MvfvLJJy779+9XhYaGWlpYWKiffvrp9E8//fR2t27dHqnLxcCBAzNu3rx5JyQkxHLXrl3W2dnZCgcHh8LOnTtnTJkyJeGZZ56p8URSCZVKpd65c+fld999133t2rX22dnZCm9v79xJkyYlvPnmm7XeCuPIkSMWGzZsuG8a16ioKNOoqChTQDsrTOkkxtatW21u3bplXKoeq5LXnp6eBbpJjJrUs2fPzLNnz5rt2rXLOjMzU6FSqdSdOnXK+OCDDxJ69er1UO/Tn3/+qdq0aZNtcHBwlEqlui8xYWFhIW3ZsiVq0qRJHn///be9qampeuzYsXcWLFgQVzNnVH8JSapyxhkiAoAgVQsAhwCU+aVJJBcJSN/cO0yhNjR/5AHOiIh0xWfkHvlm34XOcsdRlywb7AInj6fkDqPGGBoYFHo7mF8yMVI89DSlVDcJIQLbtm2bpY8DcxIBQHh4uH3Lli29ytvGMTGIqiNI5QFgO5jAoDpGAKpmUXNPyx0HEdUvkiSl/nAk0kfuOKh2FWk0RteTshsXqjVVTrVJRFRXMIlBVJUglQ20CYxyp2gikptXzJ9PQXqA4diJiKqwO+r2haz8Inu546DaV6DWKKOTshtrNBK7yhKRXmASg6gyQSolgE0A/OQOhagiBpLa3fvGb6Fyx0FE9UN2QVH49ou32I3kCZJbqDaPTs5+it3MiUgfMIlBVJEglQGA1QC6yB0KUVX8Li+0ljsGItJ/kiQVLD4caQEOYP3Eycovso5NzfWQOw6qGZIkneJ4GFRfMYlBVLGFAIbJHQRRdRiqc5o6J+wJlzsOItJv526nHbuVkestdxwkj9ScAofb6bmc8YqI6jQmMYjKE6T6PwD/lTsMogfR8uKXHF2eiB5akUYTverktfZyx0HySszMd0vKyreruiQRkTyYxCAqLUg1CsD/5A6D6EGZ5sW3UWVcvCp3HESkn347cS21SCMp5Y6D5Hc7Lc8zPbfQSu44iIjKwyQGka4g1TMAfgb7ApMeEoAIOP/xbbnjICL9cys958i522kBcsdBdYMEScSk5Hhn5xeZyR0LEVFpTGIQlQhSBQBYD8BI7lCIHpZ1xoW2Jnl3EuWOg4j0hyRJKT8eifKVOw6qWzSSZHAjOadRQZGGfxcRUZ3CJAYRAASpvABsA2ApcyREj0QAJq0igiLkjoOI9MfOS7cisgqKOAYClVGk0RhFJ2c30mgktlAlojqDSQyiIJUdgB0AOBo31Qsud/a2UKhzc+SOg4jqvuz8ojM7I29zKnGqUF6h2uxGSk5DSZLkDoWICACTGPSkC1IpAWwG4CN3KEQ1RUCy8Yuaf1LuOIiobpMkqeCHI5EqueOgui8zr9Dmdnqeq9xxEBEBTGIQLQXQUe4giGraUzdXe0HSaOSOg4jqrvBbqUdvZ+Q2lDsO0g9JWfkuKdkFNnLHQURkKHcARLIJUr0HYJTcYRDVBgOpyOOpm2tCrnm+0kHuWIio7ilSa67/fuo6k/g17OW9T8sdQqV+7/XvI+0fl5brdfP6VU1Ac79Gzz//fPL69eujayayygkhAtu2bZsVGhoa+TiOVxOGDRvmtWHDBrtLly6d8/HxKZA7HqL6hC0x6MkUpOoJYI7cYRDVJr/L8zhQLRGV69cT19KLNJKJ3HGQfpEkyeBOVpGn3HE8jMjISGMhROCwYcO85I6lpv3555+qzp07N3ZycmqhVCpbu7u7N+/Xr99Te/bsMa9uHfHx8Yp58+bZP/PMM94eHh7NlEpla0tLy1aBgYE+8+fPt1er1RXuu3v3bvNu3bo1UqlUrZRKZesmTZr4ffnll45FRUUPdB4//vijbZMmTfzMzMwCmjRp4rd06dJyW/7ExMQY2tjYtJwwYYL7Ax2A6g0mMejJo52JZC3YEonqOaOiLH+nxIPn5I6DiOqWuPScI+fj01rJHQfpJzsnZ6Oth0Jz53777S25YyHgrbfechsxYkSjiIgIs+7du6ePHTs2oVmzZtl79uyxfvbZZ30XL15sW516fvvtN9spU6Z4hoeHm7dq1Sp73LhxCX379k2LiooynTx5sueAAQOe0pTTS3X16tXW/fr18z1x4oRlnz59Ul999dU7hYWF4vPPP28waNCgp6p7Hr///rtq4sSJDU1NTTWvvPJKokajEW+88cZTf/75Z5lxe8aPH++hUqnU8+bNi6tu/VS/8CaOnixBKjMAwQDs5Q6F6HFoeSEod1f3/XKHQUR1hCRJyT8eiWoqdxykv4yMjODesLEplEbukiRdE4Kzr8rl5s2bhkuXLnW2s7MrCg8Pv+Dm5na36cPmzZstBw8e3OTrr792mzhxYkpVdfn6+ub9/vvvV4YPH56uUCjuO0b79u2b7ty50+a3336zfu2119JKtqWkpBhMmjTJ08DAQNqxY0dk165dcwAgJycnrnPnzj47duywWbp0qc2ECRNSqzr+kiVLHD09PfNPnjx5ycjICMnJybcbNmzY/Mcff3R46aWX0kvKrVixwmbXrl0227Zti7SwsOCUOU8otsSgJ83PAFrJHQTR42KWF9fGMjPqutxxEFHdsP3irUvZBUXVejJLVJ64mJto2cAG77013iY+I8+lZH1MTIzhhAkT3L28vJqZmpoGWFpatvLy8mo2bNgwr4iICGPdOtRqNWbPnu3QrFmzpmZmZgGmpqYBzZo1a/rNN984VNZtQVd0dLTRBx984NK6dWtfe3v7lkZGRq0dHR1bDBo0qOGpU6eUumUnT57s6uvr2xwANmzYYCeECCxZFi1aZKdbdv369VbdunVrZGNj09LY2Lh1gwYNmr3xxhvuSUlJCpRj48aNloGBgT6mpqYBKpWqVe/evb3DwsKU5ZWtaVeuXDHRaDRo2bJllm4CAwAGDRqUaW5urklNTa3WQ+vBgwdnjhw58r4EBgB4eHgUvfrqq4kAsH///vu6qf722282qamphgMHDkwpSWAAgJmZmfTFF1/EAcCyZcscq3P8uLg442bNmuUYGRkBAOzs7NReXl55cXFxd7u9JSQkKKZOnerxyiuvJPbt2zerOvVS/cSWGPTkCFJ9COAlucMgepwEYND6/PTYgx3/5gwERE+4rPzCsN1RtzvLHQfVH4mZ+a5mxobZBkV5WZ07d/aNiYkx6dSpU8azzz6bJkkSYmJijHfv3m39wgsvpPr5+d0d3HLo0KENN2/ebOvs7Fzw0ksvJQkhsGPHDutp06Z5HDlyxGLTpk1VJt937dpl8cMPPzi3b98+s1+/fjkWFhbqq1evKnfs2GGzd+9e6717917q2LFjLgD07NkzMy0tTbFixQpHHx+f3P79+6eV1NOmTZu7N99TpkxxmTdvnqtKpVL37NkzzcHBoejChQumS5cuddq7d68qNDT0oq2t7d0+FStWrLB5/fXXnzIyMpIGDBiQ4uzsXBgSEmLRrVs3Xx8fn9wau9AV8Pf3zzMyMpLOnj1rfvv2bUMXF5e7iYzt27dbZGdnG/Tu3TutkiqqxcjISAIAQ8P7bx33799vBQB9+vTJKL1Pv379MpVKpSYsLMw8NzdXmJqaVtpqwtXVteDChQumarUaCoUCKSkpBtHR0cqAgIC7yYoJEyZ4mJiYaL777rvYRz0n0m9MYtCTIUjVB8D/5A6DSA426eFtTPKTkvJN7NmNiugJJUlS/g+HI63ljoPqn9jUnKdO79saExMTYzJ27Ng7P//8c4zu9ry8PJGbm3u3z8mSJUtsN2/ebNu0adOcY8eORapUKg0AZGRkxHXq1Mln8+bNtj/99FP6m2++WWkXiAEDBmT+5z//CbexsblvoIZjx46Z9urVy/f//u//3A8dOnQZAAYOHJjZuHHj/BUrVjj6+/vnzJs3r8x4Hps3b7acN2+ea6tWrbJ379592d7e/m6TkEWLFtm9++67XlOnTnUrOb/09HSD999/31MIIe3ateuSbkuEcePGNfjll1+q1QLhUTg5Oak/+eST2KCgoAb+/v7+ffr0SbO1tS26fv26yd69e607deqUsXLlyhuPcozCwkKsXbvWDgD69++frrvt6tWrSgDw8/PLK72fkZER3N3dC65cuaK8ePGiSevWrcuU0TVhwoTE0aNHe7dt29a3bdu2Wfv27VNlZmYq3njjjURAO3jppk2bbIODg6NKPjP05GJ3Eqr/glTeAP4AP+/0hBKAacuLX16QOw4ikk9YXGpIfGYeW2RRjVNrJEVmkXABAFNT0zI3l0qlUtJNNPz66692ADBjxow43ZtRKysrzcyZM2MBYOXKlVUm3d3c3IpKJzAAoGPHjrkdOnTIPH78uGV+fn61B+xYtGiRIwAsW7YsWjeBAQCTJk1K9vX1zQ0ODr7bFWvNmjXW6enpisGDB9/XlQIAZs+efcvCwqJ6/WIe0WeffXbn119/vapWq8Wff/5pv3jxYuft27fbODs7F4waNSq5dDeTB/X222+7X7582bRbt27pw4YNu6/FRVZWlgIAbGxsyj1XS0tLNQCkpKSU2xVH16hRo9IWLVoUnZGRofjtt98chBDSDz/8cH3kyJHpycnJinfffdfzxRdfTHruuecyV65cad2wYUN/hUIR6Obm1nzu3Ll8SPOEYUsMqt+CVKYA1gMod4omoieFa/xOfwN1Xq5GoTSVOxYierwK1Zpra05d7yh3HFR/tWrf2cTR2VmzePFi5/DwcLM+ffqkd+vWLatjx445pbsgREREmBsYGKB///6Zpevp379/pkKhQEREhFl1jvvnn3+qli5d6nDu3Dmz1NRUQ7VafV/SIj4+3tDT07OwOnWFhYVZGBoaSmvWrLFds2ZNme2FhYUiNTXVMD4+XuHs7Kw+ffq0GQB07dq1zHnY2dmpmzZtmnvixAmL6hx71apV1mFhYfedc0BAQM6oUaPSqtr3k08+cZo1a5b7a6+9ljB58uQ7DRo0KAoPD1dOmzbN7a233mp45swZs59++umhul/MmDHDcdmyZU4NGzbMW7t2ba2Pr/XOO+8kv/POO8ml10+cONEdABYvXhx7+PBhs7Fjx3r36dMndcGCBTfXrVtnM3XqVE93d/dC3QFAqX5jEoPqux8BtJQ7CCK5CUj2Ta989+8Fn6lPyx0LET0+kiRJK0OvZqolybjq0kQPx8LSCqs37TFYMndG1t5dO8wOHz5sBQDW1tZFr732WuKsWbNum5iYSID26b2VlVWRUqksM0aCkZERrK2ti1JSUqq8R/nqq68cP/vsswZWVlbqLl26ZLi7uxeYmZlphBDYtm2bdWRkpGleXl61W2KkpaUp1Gq1mD9/vktl5TIyMhTOzs7qjIwMBQA4OzuX29LB0dGxWskTANi4caP1hg0b7htg9Pnnn0+uKomxZcsWy5kzZ7o/88wzacuXL7+bqOjSpUvO9u3brzZq1KjZsmXLnCZNmnRHd0yS6vj6668dPv300wbe3t55+/bti3RycirT2qKktUlqamq5LS0yMzMVAGBra/vQrVI2btxo+ddff9mvWbPmip2dnXr27NlO5ubm6rVr10ZbWVlpBg0alHno0CGruXPnOjOJ8eRgEoPqryDVBACvyh0GUV3hHb3S/UKTKRoIA3atInpCxKbnHIlISO8idxxU/zm5uOHzb38y/8XO9FLUhbOanTt3Wi1fvtxhwYIFLhqNBgsXLrwFaG98MzIyDPPz80VJYqNEYWEh0tLSDM3NzSu96S0sLMScOXNc7e3tC0+ePHmxdGuL0NBQ88jIyAdqeWhpaanWaDQiPT39THXKW1lZqQFta4/ytt+5c8eousdev359NIDo6pYvsWnTJhUAdOvWrUxrEEtLS03Lli2zd+/ebR0aGmr2IEmML7/80vHzzz9v0Lhx49z9+/dHVdQlxdvbO+/ChQtmERERyqeffvq+LjWFhYWIjY01VigUUtOmTfMf9NwA7bgjb7/9ttfgwYNTRowYkQ4AV65cUXp5eeVbWVlpAMDAwAD+/v45R48etXqYY5B+4h+yVD8FqQIBLJI7DKK6RCEVNvSK/fuk3HEQ0eOhkaSkn45G+csdBz05JEgiNi3Pu2VA66Lp06ff2b17dxQAbN++3bqkjJ+fX45Go8GOHTvKdLXYvn27pVqthr+/f07pbbpu375tmJmZqWjdunV26QRGenq6wYULF8p0R1EoFBIAlO5yUqJVq1bZGRkZipMnT1ZretTWrVvnAMChQ4csS29LTk5WXLx4sda7b5aM+ZGYmFhuIiU5OdkQAIyNjSudGUTX9OnTnT///PMGvr6+uQcPHqwwgQEAPXr0yACAnTt3lkkgbN++3TIvL88gICAgu6qZSSryzjvvuOfn5xssWbLkpu76goKC+97DvLw83tM+YfiGU/0TpLIFsA6ASVVFiZ40/pFzOCYG0RNia0RcZE6BmmNCUa27EnkRyYl3AABFao3xjeScpyRJwq1bt4wAQKlU3h2Ac/To0UkA8Mknn7hnZmbevRfJzMw0mD59ujsAvPrqq0mVHc/Nza1IqVRqzp8/b5aenn63jvz8fDF+/PgGaWlpZW7qHRwc1EIIxMXFldu16r333ksAgPHjx3tFR0eXaUWRkZFhsHfvXvOSn0eOHJlmZWWl3rRpk+2hQ4fuS5p8+OGHriWDXtamp59+OgsAVq9e7XD9+vX7Yv7rr7+swsLCLExMTKSePXvenaY0Pz9fhIWFKS9cuFDm7+SpU6e6fP31127+/v45Bw4ciNSdsrU8o0ePTrW2ti7asmXLfdcgJydHfP75524AMH78+DsPc247duywWL16tcOcOXNuOjs7322Z07hx47yrV6+aRkREGAPahNHJkyctGjVqVOtT2lLdwe4kVL8EqQSA1QC8ZI6EqE4yLspo7pB09HyifadmcsdCRLUnM6/w9L7L8Z3ljoOeDCH/HsD8mZ+hReu28HyqEWzt7C0Tb8X47du9w9jAwADvv/9+QknZN998M2Xz5s3W27Zts/H19fXv27dvmhBC2rFjh01cXJzxgAEDUt96661Kp1dVKBQYO3bsncWLFzv7+fn59+nTJ62goEAcPXrUMj093bB9+/aZx48fv6+FhEql0rRo0SL71KlTFoMHD27YuHHjPIVCgWHDhqW1b98+d8iQIZkff/xx3P/+9z+3pk2bNuvevXu6p6dnQVZWlkFsbKxxaGioZWBgYFavXr0ul9S3YMGCG6+//vpTzz77rO+AAQNSnJ2dC0NCQiwuX75s2qZNm6yTJ09Wa2DPhzVmzJjUX375JePYsWNWzZs3b/bss8+mOjk5FUZGRpoeOHBAJUkSpk+fHqubBIiOjjZq3bq1v6ura0FcXNy5kvXfffed3dy5c10VCgXat2+f9c033ziVPp6Xl1f+pEmT7g68aWtrq1m0aNGNMWPGePft29dn0KBBKTY2NuqdO3eqoqOjlX379k19/fXXUx/0vLKyssSbb77p9eyzz6aOGTPmvv3/7//+L37Lli22zzzzjE+/fv1SDx06ZJWZmamYOnVq/IMeh/QXkxhU33wKoJ/cQRDVZa0ufJa1u9seucMgoloiSVLe94cj7aouSTXt917/yh2CLDp164nbcbE4ffwoDuzahqysTDg4Opl26vJ01geT34995plnsnXLb9q06dqcOXMcVq9ebb9mzRp7AHjqqafy3n777fgPP/wwsTrHXLBgQZyDg0PRqlWr7NesWeNgYWGh7tKlS8bs2bPjPvroI9fy9lm9evX1SZMmNTh06JBqy5YttpIkwd3dvaB9+/a5ADBz5sz4rl27Zi1cuNDx5MmTFnv27DG0sLBQOzk5FY4cOTJx9OjR9yVXxowZk2ptbX15xowZLtu2bbMxMjKS2rZtm3nw4MFLM2bMcK7tJIZCocD+/fuvfPPNNw7r16+33bVrl01eXp6BlZVVUbdu3dLfeeedO88//3xG1TUB169fNwEAtVqNX375xbG8Mm3bts3STWIA2qlRnZycLs2cOdNl+/btNgUFBQYeHh55QUFBMdOnT79j8BDDcE2ePNktLS3NcNmyZTdLb+vUqVPuypUrr3755Zduv/76q6Ozs3PBt99+e2P48OEc1PMJIiTpobooEdU9QapnAWwHu0kRVUoCNHue3hmbZd7QQ+5YiKjmnYpJPrj61PVucsdRHy0b7AInj6fkDkNvKAxEUWNHywvGhgaVdksgIiotPDzcvmXLll7lbePNHtUPQSp3AGvAzzRRlQRgEHDu42i54yCimleo1lxdczq6k9xxEAGAWiMZ3kzJaciHpkRUk3jDR/pPOw7GbwDYdJaomuzSTrUxLkh94H6qRFR3SZIk/XL8SrZGkqo9tSNRbcspKLJKyMhzljsOIqo/mMSg+uB9AD3kDoJInwjArMXFGWfljoOIak5MWs7hS3cyWsgdB1FpiZkFrll5ReZVlyQiqhqTGKTfglTNAHwtdxhE+sj99lY/A3V+vtxxENGj0/w/e/cdFcXVhgH82QJLX3qvAqIiIiDYe4kVe43daCxRI4mJn0ZDEpOoscVYEmvsYq+xEY1dsaKigojSO7tL3T7fHwYigtIdFt7fOZ7EmTtzn8XGvHMLw6T/cT3Ki+0chJSGAcOJF+U3UKrUNb7tKCGk7qMiBtFcwUJtvN5OtcQ+14SQsnGgtvCI2XCb7RyEkKo7GZHwPF+hMmY7ByHvolCpteNFBU5s5yCEaD4qYhBN9gMAb7ZDEKLJ3F9utgGtuEaIRsuWKu5ejE6lxTxJrZcjVZik58jM2c5BCNFsVMQgmilY2B7Al2zHIETT8dRyV8fEI3fYzkEIqRyGYQrWXn1mwXYOQsorJVvqkC9X6rCdgxCiuaiIQTRPsNAIr3cjod+/hFSDppFLtNnOQAipnDvxmWHpuTJHtnMQUl4Mw3DjsvJd1WqGw3YWQohmoodAoonWAHBmOwQhdYVAIfY2ywp7wnYOQkjFyFXq5/vux9I0EqJx5Eq1TpKkwI7tHIQQzURFDKJZgoWDAIxjOwYhdY3P44UStjMQQsqPYRhm661oqZphtNjOQkhlZOXJrbKlCgO2cxBCNA8VMYjmCBZaA/iD7RiE1EUG+S8D9PPjEtjOQQgpnzhR3pXItGzaUpVotERRgYtKzdDzCCGkQugvDaJJtgCgFa0JqQEcgOfzeEEM2zkIIWVTM0zaHzee0+5cROMpVGrtRFG+A9s5CCGahYoYRDMEC6cB6M12DELqMvOsW75aCglNKyGkljv+OOFFgUIlZDsHIdVBXKAwF+fL6fczIaTc+GwHIKRMwUJ3AMvZjkFIXccBDLye/XTpntfSjmxnIYSUTlIgv3PpRWprtnOQ0t1QbmI7wnu15k9mO0KpksQFTvoCfsSGdWuNZ8+e7fzrr7++mjVrVuabbQ4fPmz0ww8/2EZHR+vk5ubyunbtKg4NDX1R3j4GDx7sfPjwYbNnz5498vDwkFf/pyCEfCg0EoPUbsFCPoBdAPTYjkJIfeCYeMyDo5bTN3eE1EIMw+SvvRppzXYOQqqbUs1oJYgKnN51PjIyUnvUqFFuCQkJ2sOHD8+YM2dO8rBhw7I+ZMb6IiwsTDcwMNDF0dGxqY6Ojq+lpWWzli1bNty0aZOJSqWq0L1yc3M5c+bMsXV2dm4qEAh8TU1NvXv37t3g3r17Ou+65uzZswZdu3Z1tbOz8xIIBL42NjZeHTt2dDt48KBRRfq+fPmynr+/v4eBgYGPvb291+eff24rlUpLbOurVqvRokULj2bNmjVSKpUV+nyEPTQSg9R2CwAEsB2CkPqCA7V1w5hN1yLdZrRlOwshpLiwuMzbGXkyGilF6qQcqcJEruaU+hR56tQpI5lMxlm9enXC1KlTqXhRQ/bs2SMcO3asK5fLRdeuXcV9+/YVZWZm8s+ePWsyZcqUBn///XfGvn37Ystzr4KCAk7Hjh0b3rt3z8DT0zN/4sSJqQkJCdqnT582uXjxovDUqVNRXbp0yXvzmqVLl1rMmzfPUVdXV92jRw+xnZ2dPDExUfvs2bPGQ4cOFX711VeJS5cuTSmr75cvX2r17t3bw8jISDly5Mj0J0+e6P766682BQUF3D/++KPYIuZLliyxePjwof6NGzee8Pn0aKwp6FeK1F7BwgAA37Adg5D6xiPmd8tItxlsxyCEvEGuVEXtf/CKioukTpOqeWalHU9KStICAHt7e8WHTVS/LFq0yF6lUnGOHTsW2adPn9zC43FxcYk+Pj6eISEh5j/88EOyu7t7mSM2v//+e6t79+4Z9OzZU3Ty5MkYHo8HANi1a5dozJgxrlOmTHGOjIyMKDwuk8k4ixcvthMIBMyNGzeeeHt7ywrvde/ePZ3WrVs3WbNmjU1wcHCqrq4u876+N2/ebFZQUMC9c+dOZKNGjeQA0KpVq4Y7duyw2LBhQwKX+3oyQmRkpPaPP/5o//nnnyf7+flJK/ElIyyh6SSkdgoW6gHYCSq0EfLB8dQyd/ukE3fZzkEIeY1hGPXmW9FyNUP/JpLa4Z9zf2HyiP7o6tcILVyt0M2vMSYO6YOQ7ZtLtJWIRPh1yXcY0LklAtxs0LaJIyaP6I/rly6UaMu89Wxy8uRJQw6H47dixQpbAOjXr19DDofjx+Fw/E6ePGlY2fy//PKLecOGDZsIBAJfMzMz75EjRzplZmby3m534sQJw5EjRzq5urp6GhgY+Ojo6Pi6u7t7fvHFFzb5+fklpiYEBQXZFmb7/fffTZs1a9ZIT0/Px87Ozuvt83/88Yepp6dnY11dXR9LS8tmn3zyiX1BQQEHAI4fP24YEBDgYWBg4GNkZNR8wIABLikpKSXyVbeEhASBgYGB6s0CBgA4Ojoqvb298wAgJSWlzL+H1Go1tm/fbgEAq1atSigsVADA6NGjxX5+frkvXrzQ+euvv4p+DdPS0ni5ubk8Jycn6ZsFDADw9fWVOjk5SaVSKVcikZT5/BobG6ttYmKiLCxgAICPj0+eVCrlvpl/woQJTo6OjrIff/wxuax7ktqFihiktloOoCHbIQipr7ye/UT/PhBSS7zKyrv6PD2nKds5CAGAg7v/xOxJHyPmeSQ6duuJsZNnoF2X7pBKpTi2f0+xtkkJcRjRpxO2rlsNE1MzDB09AR/1G4SXz6MwfcwQHNqzvdQ+5GqOPgC4u7vL5syZk+zv758LAIMGDcqcM2dO8pw5c5Ld3d1lpV5chjlz5th///339o0bNy4YO3ZsmpWVlXzfvn3mffr0cX277bJly6wvX75s1KRJk/yPP/44ffjw4elaWlrqlStX2nbu3Nn9XWsorFy50mr27NnOdnZ28vHjx6d16tSp2M5fv/32m+Xnn3/u1KBBA+no0aPTjY2NlVu2bLEaPXq0044dO4yHDBnibmxsrBw1alS6q6ur9NixY6bDhg1rUJnPWxFubm4Fubm5vLNnzxq8eTwxMZH/8OFDfQsLC4WPj09BWfd58uSJIDk5WdvJyUn2ZiGhUPfu3SUAEBoaWlTEsLOzU5qYmChjY2N1Hj16JHiz/cOHDwVxcXE6jRo1KrC2ti5zYQ5HR0e5SCTiP3/+XLvw2IMHD/R1dHTU1tbWSgBYuXKleVhYmOHmzZtfamlplXVLUstQRZ/UPsHCHgCmsR2DkPpMR57pYyK+Hyky9vFgOwsh9ZmaYVI33njuzXYOQgod3P0ntLS1sf/sFZiZWxQ7J8oqtqEIFs6ZjuSEeCxZuxm9+g8uOp4tkeCTYX2xdNE8dOreC2YWlsWukzJcU4VKneTh4SFfuXJlUlBQkO3t27cNJkyYkNm3b9+cquS/f/++/r17954UTolQKBRo3bq1x61btwwvXryo17lz5/zCtn/88Udso0aN5IXTDwrNnj3bds2aNTbbtm0zmTx5sujtPm7cuGF44cKFp23bti31gf/69euG165de+rr6ysFXq8f4eXl1eTo0aNmf//9t/GRI0eiCkdDqFQqtG/f3v3KlStG169f123Tpk2ZRYTKWr58efzQoUPdAgMDG3br1k3s7Ows+3dNDGMjIyPVjh07YgwMDN47lQMAHj9+rAMADRo0KHWKRsOGDWUAEB0dXbTAJ5fLxfLly+OmTp3q0rp16yY9evQQ2djYKJKSkrTOnTtn4ubmVrBv376Y8nyOSZMmZf766682nTp18ujTp4/oyZMnujdv3jT85JNPUrlcLl6+fKn17bff2k+fPj3lXb9GpHajN22kdgkW6gDYwHYMQgjg+3hBZtmtCCE16eij+JdSpUrIdg5C3sTj8aHFL/n22sT0vyUtIp88wp2b19Ctd2CxAgYAGAmFmBb0P8hkUoT+dbzEfRiGw0sUFzhUf3Jg7ty5xdZ00NLSwpgxYzIA4MaNG/pvtm3SpEmJAgYAzJ8/PxUAzp07V+qfzVGjRmW87+F44sSJaYUFDADQ1dVlBgwYkKVWq9G5c2fxm9M5eDweRo4cmQUAd+/erdHd+nr27Jn7zz//PHN0dJT99ddfJuvXr7cOCQkxVygU3JEjR2b4+/uX64FfLBbzAMDQ0LDUURPGxsYqAJBIJMWmyEycOFF08uTJSENDQ+WRI0fM1q9fb3306FEzXV1d9ccff5zRuHHjco2+cXV1VZw4cSLK2tpavnv3bouXL1/qfPbZZylr1qxJBIBJkyY5WVlZKZYtW5Z869Yt3YCAAA8tLS1foVDYfMKECQ6l7WJCahcaiUFqm/kAany4HCGkbIa50QG6BYnJBbp2NmxnIaQ+EhfIb1+JSWvFdg5C3tR7wFCs+OEbDOzaCj37DYJfq7Zo7t8SpmbmxdqF370NAMjJzsaGlUtK3EeUmQEAiImOKrWf7AKFqaRAkSnU1cquzvytWrXKe/uYk5OTHABEIlGxZ6Ps7GzuTz/9ZHny5EmT2NhYQV5eHo9h/huIkJycXOo8hICAgBJ9vMnf3z//7WO2trYKAPD19S1xzsHBQQ4ACQkJZc57uH79uu7BgwdN3jxmbGysXLRoUVpZ1x45csRowoQJDZo2bZq3bdu2l97e3tL4+Hj+ypUrLZcuXWp37tw541u3bj2rqekX69evNw0KCnLu0aOH6Lvvvkt2d3eXPX/+XPDtt9/azJ8/3/HKlSuGf/31V7lGY3Tp0iXv7t27kaX1cenSJeH58+efKRQKTr9+/dyFQqFq9+7d0c+fP9f57rvv7LW1tZm3dzEhtQsVMUjtESxsCOArtmMQQl7jAHyfx99EXfffRkUMQj4whmHy116JpD97pNYZO2UGTEzNELJjC/Zs+wO7tmwAh8OBX6u2CFrwPTy9fQAAEtHrnVBvXrmIm1cuvvN++Xnvft5PEhc4GQj4EdWZ38zMrMTogMKtNVUqVdEbeJlMxmnXrl3DR48e6bu7uxf07dtXZG5urtTS0mIAYNWqVTZyubzUUe2FBYl3KRyJ8FYGBgCEQuE78ykUijJH0d+5c0dv1apVxf7usLW1lZdVxEhNTeWNHz++gY6Ojvr06dMvDA0N1cDr0SibN29OiI2NFYSGhhpv2LDBbNasWe8dqVn4+XJyckpdjLRwpMabn/Xhw4eC2bNnO3t4eBQcOXLkZeFioD4+PtIjR4689Pb21jl9+rTJyZMnDSs7pSg+Pp6/YMECh0mTJqV26dIlb8WKFebp6elaO3fujPnoo49yAWSHh4fr/vnnn5bLly9PKvwakNqHihikNlkHQFBmK0LIB2OZec2Hr8jJVmoZGrGdhZD65GZsxu3MfFlHtnMQUpp+Q0ag35ARyJZIEH73Fi6cOYWjIbswbfRgHP0nDKZm5jAwev3PxlffLcHHEz+tVD8KlVo7WVJgC+CDP0zu2bPH+NGjR/qDBw/OPHjw4Ks3z8XGxmq9XSh4E4fD3myEWbNmZZZVZCjNhQsXDLKzs3mtWrXKKe3hvWPHjjmhoaHG/05pee/9mzZtKgWAmJgYndLOR0VFCQDAzc2taErNyZMnjZRKJad169Y5b+5mAryeUtOqVauciIgIvdu3b+tVtogxefJkR6FQqFq5cmUiADx9+lQHANq0aVM0+sXX1zf/wIED5k+fPhUEBATQehm1FK2JQWqHYOEIAN3YjkEIKY4DGDWNXHqf7RyE1CcypSryYHhsW7ZzEFIWI6EQ7bv0wLfLfkXg0FGQiEW4d+s6AKCZjz8A4H7YjSr1IcqTW6kYzgffPuL58+cCABg0aFCJhTvf3r2jLpDJZBwAyMzMLPUld3p6Oh8AtLW1y1zYs0mTJjIbGxt5bGys4NmzZ9pvnz9//rwQALp161ZUjJDJZFwAyMjIKLX/jIwMrfL2X5pt27aZnDt3zuT3339/9fbipG+ugSGVSun5WAPQLxJhX7DQCMBKtmMQQkrnlHCoIUeteO/QWEJI9WAYRr35ZrRSzdBoWVI7hV2/gjfXhSiUlZkOANDR1QUAeHr7wDegNf4+fQJH9u0q9V7Pn0YgMyP9vf0xABQcvnGVQleCs7OzDAAuXrxo+ObxJ0+eaAcHB9t/6Dw1rVOnTnk8Ho+5f/++weHDh4uNvoyOjtbauXOnBVC88AAAERERgvv37+sUFkGA1zuNjBs3Lh14vaWtSvXfDJldu3YZ371718DV1VXau3fvont16tQpBwDOnDljcuvWLd03+7h+/brumTNnTDgcDj766KMKr5GSmprKmzt3ruPo0aPTe/bsWbRoapMmTaQAEBISYlx47MyZM0JtbW2mvIuIEnbQP5CkNvgeAM37JaSW4kJl4/Zq2/XnDaa0YTsLIXVdTGbuleiMHJpGQmqtoMmjoatngGa+LWBr7wiGYXAv7AYiwu+hiVdztGzXqajtkrWb8Mnw/gieOxN7t/2Bpj5+MDQSIi05CVFPIxAd+RQ7j50rsVXr2xhwPvgzy4gRIyQ//vijbPPmzVZPnjzRbdasWX5CQoL2hQsXjDt37iw5depUiREGmszZ2Vnx+eefJ69YscJ26NCh7p06dRI3bNhQmpqaqnX27FmT/Px8bvfu3cXDhw+XvHldjx49GiYlJWk/e/bskYeHR9GuL4sWLUo9e/as8ZkzZ0y8vb0bd+jQITs+Pl779OnTJjo6OuqNGze+enPaSOfOnfOHDBmSefDgQbP27ds37tGjh9jBwUEWFxcnOH/+vLFCoeBMnDgxrUWLFqVu2/o+U6ZMcRQIBOrffvst4a3jmUuXLrWdO3eu461bt/RfvXoluHXrluGnn36aSuth1G5UxCDsChY2B/AZ2zEIIe/X6MU6s+cNprAdg5A6Ta1mkjffjPZhOwepvNb8yWxHqHGz5n2L65cu4OnjcFy9cB7aAgFs7B3w+fxgDBszEW/uXGFlY4d9f13E3m0bEXr6BP46chBqlQpmlpZwdffAyAlT4NaoSbn7VjL4YNNKjIyM1H///XdkUFCQ/c2bNw3v3r1raG9vL5szZ07St99+m6qtre33obJ8KMuXL09u3rx5wcaNGy3u3btncOnSJWMdHR11w4YNC0aMGJH5xRdfvH/YzBt0dXWZS5cuRS1cuND6yJEjpps3b7bS19dXde/eXbx48eIkPz+/EsWIkJCQV2vXrs3ZtWuX+eXLl43y8/N5+vr6Kj8/v9wJEyakT5kypcTUnrLs27dPePz4cdMjR45ECYXCYoUJAwMD5uTJk1GzZs1yPHDggLmurq5q4sSJaatXr06saD/kw+KUNhyMkA8iWMgBcB0AbR9HiAa41XzN/STrnvSARUgNORgee/Pay3T6N7EW2xRoAytH2gmeLfra/GxXS4PnbOcghNS88PBwc29vb+fSztGaGIRNk0EFDEI0RrOnP9DQSkJqiChfFkYFDELeL0+uNBLlyY3ZzkEIYRcVMQg7goUWAH5mOwYhpPx0Zel+xpJH9AaMkGrGMEzu2quRdW6hQEJqQkq21EGtZtjbw5QQwjpaE4OwZRkAU7ZDEEIqxvfxgrQLbY+7s52DkLrk+qv0u1n5clrMk5ByUKjU2inZUpuDOzbJXr16JSirvY+PT/6YMWPEHyAaIeQDoSIG+fCChe0AjGM7BiGk4oxynvnrFiSnFOjaWLOdhZC6QKZUPTsUHteO7RyEaJLMPLn1rl2782/fDtMvq+2gQYMyqYhBSN1CRQzyYQUL+QA2AKBhgIRoIA6g3fzJosgbfpuoiEFIFTEMo9p447maAXhltyaEFGIYhrPv1N/KBhYGd9nOQgj58GhNDPKhfQ6gKdshCCGVZ5V+qTlPmZfLdg5CNN2LzNxrMZm55d9fkhBSJFemFIrz5UK2cxBCPjwqYpAPJ1hoDyCY7RiEkKrhAMKmUcvvsZ2DEE2mVjPJm28+py2LCamCZInUQc3QIp+E1DdUxCAf0q8Aypy7SAip/Zzj9zUAo1KxnYMQTXUwPDZOplQbsp2DEE2mUKkFqRIpTW8kpJ6hIgb5MIKFvQEMYjsGIaR6cBmVvWvsjjC2cxCiibLyZWE3YjNasp2DkLogI09uLVOotNnOQQj5cKiIQWpesFAXwG9sxyCEVK8mz381ZjsDIZqGYZjctVciHdjOQUhdwTAMN0kitWc7ByHkw6EiBvkQ5gBowHYIQkj14qvyG1unhoaznYMQTXL1ZdpdUYHchu0chNQlOVKFSY5UQVOWCaknqIhBalaw0BTAV2zHIITUDO+n38vZzkCIppAqVE+OPIxvz3YOQuqiZInUgWEYtmMQQj4AKmKQmvY/ALT9FSF1lK40pYUw++kLtnMQUtsxDKPaeOM5h6HvvQipEVKFSj8rT27Kdg5CSM2jf0hJzQkW2gH4jO0YhJCawwE4Po/nJ7Odg5Da7nlGztWXWbmN2c5BSF2WliOzU6tpy1VC6jo+2wFInRYMQIftEISQmmWcHeEvkKaly3QsLdjOQkhtpFIziVtuRrdgOwepeVrdWrMd4b0UoTfYjlAqbwcTtGjVFlsOnKzSfRQqtXZajtTKWqibUk3RCCG1EI3EIDUjWOgBYALbMQghNY8DCJo/CX7Cdg5Caqv9D14lylVqWnSQ1Fu9WjdDr9bNPkhfGblya4VKzfqL2oCAAA8Oh+P3vh/Dhg1zqsg99+7dKwwICPAwNDRsrqen59OsWbNGv/32m1lpbc+dO6c/depU+6ZNmzY2MTHx1tbW9rWzs/MaPny40+PHjwUV6TcrK4s7evRoRysrq2bGxsbNu3Tp4hYREVHqPVauXGnO5/N9r127pluRPgipCNb/gJM660cAPLZDEEI+DJu0v5vxVAX5Kp6uHttZCKlNMvNkt8LiMluynYOQ+kLNMLwUidTGwVQvns0co0ePzmjXrl1Oaee2bt1qKZFIeL169cou7/1++ukniwULFjgaGxsrBwwYkKWtrc2cOnXKZNasWc6PHj3S3bhxY8Kb7UeNGuUmEon4zZs3zx04cGAWn89n7ty5Y7B//37zEydOmB4/fjyqW7dueeXpe8SIES4XLlww7t+/f6aurq760KFDZj169Gj47NmzCENDQ3Vhu5cvX2p9++239tOnT09p27ZtQXk/GyEVRUUMUv2ChS0ADGY7BiHkw+GAMWkSteryo8bzO7CdhZDagmGY7LVXIx3ZzkFIfSPOV1hYGKrSdLR4MrYyzJo1K7O04+Hh4YJVq1bZmJmZKUeNGiUuz70iIyO1v/vuOwehUKi6efPmUw8PDzkApKenJ/n5+TXetGmT1bBhw0RvFiU+/fTT1MmTJ2c6Ozsr3rzXvHnzrJcuXWo3ffp0p6ioqDJHUcbHx/PPnz9v/MUXXyQtX748GQBatmyZN2vWLOeQkBDhJ598IipsO2nSJCcrKyvFsmXLaK0sUqNoOgmpCUvYDkAI+fAaxO1yBqNWl92SkPrhckzaA3GB3IbtHITUhLMnjmDC4N5o28QRAW42GNytDbasXQm57L+6we0bV+HtYIKkhHgkJcTD28Gk6MfCOdNL3FOUlYnvv/4cXf0aoYWrFQZ2bY2jIbvfmeHaP39jxtih6NjMFS1crdCnrQ9WLl4IiUTMSZZI7d5sa2dn52VnZ+eVlZXF/eSTT+zt7Oy8+Hy+b1BQkG01flnKtHbtWgsAGD58eIZAICjXnrAbNmwwl8vlnAkTJqQVFjAAwMLCQhUUFJQMAOvXry+2LtWPP/6Y8nYBAwAWL16coqOjo37+/LluSkpKmaOmo6OjtQGgVatWRQWStm3b5gFAbGysduGx9evXm166dEm4cePGVzo6OrTXLalRNBKDVK9gYTcAXdmOQQj58LiM0rFB3J6bMU6jW7GdhRC2SRWqJ8cexbdjOwchNWHNku+xZd0qmJiaoVf/IdDT18fVi6FYs/QHXL90Ab/vPgwtbW3Y2jti6pyvsXvLBgDAx5OmFd3Do4lXsXvmZEswbmBPaGlpoVvvQCjkcpw7dRTffvkZuFwuAoeOLNb+91VLsWHlEgiNTdCh20cwNTNH1NMn2P7HWly5GIqdR8+aWBgK9AwE/PzCaxQKBadDhw4eYrGY36FDh2xDQ0OVi4vLBxutUVBQwDl48KAZh8PBjBkz0st73ZUrVwwBoE+fPpK3zw0cODB7zpw5uH79ulF57sXhcMDjva5d8PllPwq6urrKAeD27dt6Q4YMyQaAGzdu6AGAk5OTHHg9WmPBggUOkyZNSu3SpUu5pqgQUhVUxCDVJ1jIAfAz2zEIIexp8nylYYzTaLZjEMIqhmGUv1+P4jE04pXUQeF3w7Bl3SpY29ph94m/YW5pBQCYNe9bzJk8GpdDz2L7H7/hk5lfwM7BEdOC5uH4gT0AgGlB895538gnjzFwxBgsXLKq6CH740lTMbRHO2zb8GuxIkbY9SvYsHIJvP38sXb7ARgJhUXnju3fg0VfzMCGFUvw7U/L7NwsDZ4XnktPT9dyc3OTXr9+PdLIyOiDjxzcsWOHiVgs5rdp0ya7SZMm8rKveC0mJkYHAJo2bSp9+5yTk5NCV1dXnZqaqpWTk8N9c42K0mzdutUkLy+P6+3tnWdubq4qq29HR0dl165dxStXrrSNiYnREQgE6kOHDpnZ2NjIhw0bJgGAyZMnOwqFQtXKlSsTy/uZCKkK+seVVKchAGgLOULqMS1lrqdV+qVHbOcghE2R6dnXYkV5HmznIKQmFE7vmDzry6ICBvD6rf4XCxeDy+Xi8L6dFb6vjq4evly0uKiAAQCuDRuheYuWiHkeify83KLje7b+AQBYtPTXYgUMAOg/bBQ8PL1w6ugB5MuVRtlShcGb51esWBHPRgEDALZu3WoOAJ988klGRa7Lzc3lAYCZmVmpRQcDAwMVAGRlZb13esizZ8+0v/76a0cej8csX7683AufhoSEvBo6dGjmpUuXjE6cOGHasmXLnLNnz0YZGRmpt23bZnLu3DmT33///RWPx8O4ceMchEJhcy0tLd+AgACPu3fv6lTksxJSHjQSg1SPYCEfwGK2YxBC2OcdEVxwrtNFtmMQwgqVmknYdusFFfRJnfX0cTgAIKBNyXWcnRu4wcrGFolxscjJlsDQSFiizbs4uTSAgWHJGRFWtq+XtsiWiKGn/7oe8fDebfC1tHD+1DGcP3WsxDUKhQKizAyIRVlI1eLZGeloRQKAQCBgWrZsWaFdM0pbM+PTTz/NeHNtivJ49OiRICwszLAiC3pWp8TERH6vXr3cRSIR/+eff44r784kwOviyZ49e2LfPp6amsqbO3eu4+jRo9N79uyZO3HiRIe9e/daLFq0KKFRo0bS+fPn2/ft29f9xYsXj/X09GidDFJtqIhBqssEAA3ZDkEIYZ+eNLGFYU7UyxzDhi5sZyHkQwu5/ypZrlLbs52DkJqSm/16V1BzK6tSz5tbWiM5MaHCRYx3teXzXj+uqFT/DZ6QiLKgVCrx+6ql771nfl4uChSmBpIChREAmJqaKrjcig1EX7VqVYnFebt06ZJT0SJGZRb0LGRgYKASi8X8zMxMnrW1dYnRGIUjNUxNTUsdqZGYmMjv1KmTx6tXr3R++OGH+Hnz5pV7PY73mTJliqNAIFD/9ttvCdnZ2dxdu3ZZDBw4MPObb75J+zd3XK9evTw2btxo+vnnn5e6WwshlUFFDFJ1wUJdAN+yHYMQUjtwAK7v4wXxl1ofoCIGqVfSc6U3b8dn0sK2pE4zMHo9WiIzLQ16ziX/ms9IS3ndzrD8BYwKZzA0glqtxpXHL8vVPjVbagu8XtSyohiGuVvhi94ilUo5Bw4cqPCCnoUaNGggvXfvnsHjx491rK2ti42giI2N1SooKOBaWVkpSlsPIzY2VqtLly4NX758qfPzzz/HVVcBY9++fcLjx4+bHjlyJEooFKpv3bqlq1AoOD4+PkULqbZt2zYfACIiInSro09CCtGaGKQ6zARgV2YrQki9YSIJDxDIMio055cQTcYwTPa6q5FUuCN1XiPPZgCA2zevljgX9zIGqclJsHN0KrZWBZfLg0pV5hqS5ebl2wLZEjGiI5+Wq71UodIHOKw99+zcudNYJBLxW7duXaEFPQu1b98+BwBOnTpVojJ05MgRIwBo06ZN9tvnXrx4odWxY0ePly9f6ixbtiy2ugoYmZmZvNmzZzsNGzYsY8CAATlvnpPJZEWVovz8/IpXjQgpBypikKoJFhoDePdS04SQeokD6Hg/+S6C7RyEfCj/vEh9IJEqSh9fT0gdMmD4xwCATWuWIyvzv1q1SqXCysULoVarMXB48V2qhCamEGVlQlpQoeUo3mnMJ9MBAN9/PRtpKcklzufn5+HhvdvFjjEcLo9h2FmWYevWrRYAMHny5PcWEZKTk/n379/XSU5OLjZafurUqRna2trMtm3bLCMjI7ULj6enp/NWrlxpAwDTp08vdu+oqCjtjh07NoqPjxesXr361ZdfflltLxamT59uDwDr169PKDzWuHFjmZaWFnPmzBnjwmP79+83BgBPT8/q+YUn5F80nYRU1VcATNgOQQipfWxTzzXlqqQFap4ODSMldVqBQvn4xOOEdmznIORDaN6iJcZPm4U/N6zB4G5t0L13IHT19HD1YiiiI5/Cx78Vxk+dVeyalu06ICL8HqaPGQLflm2grS1Awyae6NS9V6UytGzXEbP/9y3WLPke/Tq0QPsu3WHn4Ij8vDwkJ8bjzs3r8PFvhQ27DhZdw+GAA04FF8SoBo8fPxbcunXL0MzMTDly5EjJ+9r+8ssvlqtWrbKZM2dO8sqVK5MKjzdq1Ei+aNGihG+++cahVatWjfv27SvS1tZmTp06ZZKamqo1efLk1LcX6uzcubNHUlKStqenZ/6rV68E1bVA6dGjRw33799vvmfPnug3d0sxMjJSjx07Nm3Lli1W7du3d3dxcZEdOHDAzNraWj5lypSsivRBSFmoiEEqL1hoA2A22zEIIbUTB4xZ4+g1VyI8vmrPdhZCagrDMMrfrz3XZmh0KwGgCL3BdoQPYs7879DIsxn2bd+EE4dCoFQqYO/kgs/mLsDYKZ9BS1u7WPvJs75EjkSCS6Fn8eDOLahUKgQOGVnpIgYATJz+OZq3aIm92zbifthNXDz3FwwNjWBpbYPBo8ah94AhJS/6dzRGZdbGqKx169aZMwxTqQU937RgwYI0FxcX2apVq6wPHz5splar4erqKl2wYEHizJkzSyyamZSUpA0AERERehEREXql3bOiC5RKJBLujBkznAMDA7NKK8j89ttviWq1mnPkyBHTsLAwQx8fn9x169bF0c4kpLpx2BpWReqAYOEGAFPZjkEIqb3UHK1Xx3o8cmTj7RchH8LTVMmljTeed2Q7B/kwNgXawMqxAdsxSBXYGeu+NDMQ0MgAQmq58PBwc29vb+fSztE3laRygoWOACaxHYMQUrtxGYWzc3zI7bJbEqJ5VGomYVvYC3+2cxBCyi89R2ZLL3EJ0WxUxCCVNQeAFtshCCG1n2fUilKHsRKi6fbce5miUKnp9zchGkSuUgtE+Qpaz40QDUZFDFJxwUITAJ+wHYMQohm0ldleFhnXHrGdg5DqlJYrvXEvIasF2zkIIRWXniOzYTsDIaTyqIhBKmMaAAO2QxBCNIdPxKK8slsRohkYhpGsuxrpynYOQkjlyJQqXXG+XMh2DkJI5VARg1RMsFAAYCbbMQghmkWvID7AIDcmlu0chFSHC89THmZLFZZs5yCEVF56jsya7QyEkMqhIgapqLEA6C99QkiFcACu7+P5VMQgGq9AoXx08kliO7ZzEEKqpkChMsiRKmhkMSEaiIoYpPyChRwAX7AdgxCimUzF91poy7NoWzuisRiGUay/FqUDgMN2FkJI1aXRaAxCNBIVMUhFBALwYDsEIUQzcQC9Zk8X0wKfRGM9SZVcTxDnu7OdgxBSPfJkSmGeTKnLdg5CSMVQEYNUxFdsByCEaDb75FNNuCqZjO0chFSUSq2O2x72IoDtHISQ6pVGO5UQonGoiEHKJ1jYBkAbtmMQQjQbB4yFx4t1YWznIKSidt99laZQM/TGlpA6JleqMJEqVAK2cxBCyo+KGKS85rIdgBBSN7i/2moHhmHYzkFIeaXmFFy/n5jVgu0chJDqxwBIy5bS2hiEaBAqYpCyBQsb4vV6GIQQUmU8tbyBY+KhO2znIKQ8GIYRr78aRetgEFKHSQqUZnKlWovtHISQ8qEiBimPL0G/Vwgh1ahp5DJttjMQUh6hUSmPsmUKC7ZzEEJqDgOGk54rs2Q7ByGkfPhsByC1XLDQCsBYtmMQQuoWgULsbZ5160mGacsmbGch5F3y5cqHfz1NbMd2DqI5mp3ay3aE93rYZyTbEWotcb7c3MZIJ4nL5dB0R0JqOXq7TsoyEwAtdkQIqXbNHy+UsJ2BkHdhGEax/lqkPgAO21kIITVPpWb4mXlys+q85+nTpw14PJ4fh8PxmzVrlm1Fr9+3b5+wbdu27lZWVs10dHR87e3tvXr16tUgNDRUvzzXDx8+3InD4fhxOBy/x48fl/v7ealUypk1a5atnZ2dl6GhYfOWLVs2vHr1ql5pbY8ePWrI4XD89u7dKyzv/QmpKipikHcLFuoDmM52DEJI3WSQ/ypAPy82ge0chJQmIkVyPVFS4Mp2DkLIh5OVV31TSkQiEfeTTz5x0dHRUVfm+mnTptmNHDnS7cmTJ3qdOnWSTJw4MbVp06Z5oaGhxj169Gi0fv160/ddv2fPHuH+/fvN9fT0Ktz/Z599Zvfbb7/ZeHl55Q0dOjQzMjJSr1evXg1jY2OLrRsikUi4M2bMcA4MDMwaOXIkvZggHwwVMcj7fALAhO0QhJC6iQPwfB4viGE7ByFvU6rVsdtvv2jJdg5CyIclU6p1JQUKw+q415QpUxxzc3N5M2fOTK7otXFxcfyNGzdam5mZKR8/fhwREhISu379+sQzZ87EHD58+DnDMPjpp5/s3nV9UlISf9asWc59+vQReXp65lekb7Vajd27d1sMGTIk88yZMzFbt26N3717d3Rubi5v06ZNxQonM2fOtJfJZNw//vgjrqKfkZCqoCIGKV2wkA9gDtsxCCF1m7kozFdLLhaznYOQN+268zJTqWZ02M5BSG2UGB8HbwcTLJwzHYnxcfhq+kR0bOYKfzdrjOzdGZdCz5R6nVwmw5Z1qzC4Wxu0dLdFm8aOGD+oF86eOFJtfQDA6aMHMWlYP7TzdIK/mzUGdG6Jjb8uh1wmK9fny8yVWZXvK/Fuu3btMj548KDZzz//HGdra6uo6PXR0dECtVoNb2/vXDs7O+Wb5/r165ejr6+vFolE71zbcNy4cU4AsHnz5tiK9p2cnMyXSqVcf3//vMJjHTt2zAOA2NjYoikpZ86cMdi1a5fFL7/8Emdtba2qaD+EVAUVMci7DAXgxHYIQkjdxgEMvJ79FM52DkIKpWQXXA9PEvmynYOQ2i4pMR4f9+uKpIR49B00HB/1G4joqKf4fNLHCLt+pVhbhVyOaaMHY82S76FSqTB83CT0HTQMsS9f4KvpE7FmyfdV7gMAFn3xGebNnIz4VzHo2isQw8d+AqGxMdYt/xHTxgyBUqkspZficmVKoVShqvR6cImJifxZs2Y5devWTTx9+vSsytzD09NTqqWlxTx8+FA/OTm5WLHi9OnTBnl5edw2bdpkl3btmjVrzEJDQ41Xr14dW5nigrW1tVJHR0d97969ojUwrl69qg8ATk5OMgDIzc3lTJ061blHjx6iCRMmiCraByFVRbuTkHeZy3YAQkj94Jh0vNH9povlDFebtl0lrGIYRrTuWmRDtnMQognu3LiKaUHzMHXO10XHevUfguljhmD772sQ0KZ90fEdG9fizs1raNe5G37duhd8/utHkE/nfI3R/bpiy7pV6NDtIzRv0bLSfRzbvwfH9u9Gl5598fOajdDR1S06t2HlEvy+ailCtm/Gx5OmlvnZ0nNklg6mevEV/6oAY8eOdWIYBlu3bq3wKIhCVlZWqm+++SYhODjYwdPT0/Ojjz4Sm5qaKl++fCn4+++/jdu0aZP9559/lrh/VFSU9oIFCxz69++fNXr0aHFl+ubxeBg5cmTGn3/+aZmdnc2zsbFRHD582NTAwEA1adKkLAAICgqyE4vF/E2bNtE0EsIKGolBSgoWdgPgw3YMQkj9wIHayiPmjzC2cxByLjL5ca5Mac52DkI0ga29AybP+rLYsbadusLGzh6PH9wrdvxoyG5wOBx8uejHogIGAJiZW2Dy7NfvzQ7v3VmlPnZv/R18Ph/fLV9brIABAFNmz4WxiSlOHTlQrs8mKVCYK9XqCj8nrV692uzChQvGv/zyS5yDg0PZwz7eY9GiRWnbt29/oVKpOPv27TNfv3699enTp02sra3lY8aMyXx7molKpcLo0aNddHV11VUtLqxbty5h2rRpKeHh4fohISHmbm5u0pMnT0a5uLgoLl68qLd161arn376Kc7W1lYZFBRka2Fh0YzP5/t5eno2PnfuXLl2TiGkKmgkBinNTLYDEELql4YxG62fudFfPYQ9eXJl+JlnSe3YzkGIpvBo4gUej1fiuJWtHR7evV3087zcHMS9ioGltS1c3EoOdApo0wEA8CziYaX7KCjIR9STxzA2NcPuLRtKzaulLcDL6KgyP9ftG1dx58ZVLh9KVy2o8wDA2dlZNmvWrMz3XRcZGan9zTffOPbq1Uv0ySefVHmKxTfffGO1ZMkS+/Hjx6cGBQWlOTg4KMPDw3XmzZtnN23aNJcHDx7o/f7770U7fH3//fdWt2/fNti3b1+0hYVFldao0NXVZdatW5e4bt26xDePS6VSzuTJk13at28vmT59etb3339vuWrVKps5c+Ykt2/fPvfnn3+2GThwYMNnz549qmoRh5D3oSIGKS5YaAOgN9sxCCH1C08tc3NIOnYn3rZ/C7azkPqHYRj5+quRBgA4bGchRFMYGglLPc7n8aFW/7erZ27O66UbLCxLXy+z8HhOdskdOsvbR7ZYDIZhIMrMwO+rlpbvA7zDnRtXC+9h9O8P+Pv755ZVxBg3bpyzQCBQb9mypcpTLE6ePGn4448/2nfv3l28efPmokJFu3bt8k+fPv3Czc2t6aZNm6xmzZqV1qRJE/nDhw8FS5YssRsyZEjm8OHDa2yr06+++somNTVV6/z581EAsHbtWuvWrVvnrFy5MgkAmjZtKvXw8PBasWKF5erVq5NqKgchVMQgbxsH+n1BCGGB17OfefG2/dmOQeqhR8ni60nZBZ3YzkFIXWRgaAQAyEhPLfV8etrr44b/tqsMQ6PX1zZq2gwhpy9V+j4AMC1oHqYFzQMAOJrqPTfW0y51Ac23RURE6OXm5vJsbW29Szv/22+/2fz22282Xbt2FYeGhr54372OHz8uBICOHTvmvH3O0NBQ7e3tnXf+/HnjsLAwvSZNmsjDw8N15XI55+DBg2YcDsestHt6eXk1BYAdO3a8GDNmjLg8n+lN169f112/fr31smXL4lxcXBRZWVnc9PR0rUGDBhUtXuru7i43NjZWPn36VPd99yKkquhhlbxtItsBCCH1k0Ce5WMquvcsy8S3EdtZSP2hVKtf7bwT05rtHITUVfoGhnBwckFC3CvEvnwBJxfXYudv/7vLSKOmpT77l4uevgFcGzbCi6hnkIhEEJqYVClzoaw8uUV5ixiDBw/OLCgoKLGORkxMjM6dO3cMGjVqVNCsWbO85s2b55d1L5lMxgGA9PT0Up/VMjMz+QCgra3NAICrq6ts2LBhGaW1vXDhgjAjI0OrV69eIkNDQ5Wrq2v59pp9g0KhwKRJk1wCAgJygoKCivVTmLWQXC6nNRdJjaMiBvlPsLADAHe2YxBC6i+fxwuy/m5/mu0YpB7ZcTtGpFQzzmznIKQuGzD8Y/y2bDFWLV6EFRt3FK1zIcrKxMY1vxS1qYoxk2cgeO5MfPvlZ/h+5XoYCYtPRckWi5EYH4vGXuUvluTJVEK5Uq2lzecqymr7559/lrqbyZo1a8zu3Llj0L17d/GaNWuKTbGQyWScJ0+eCLS1tRlPT8+i4kL79u1zd+zYYblr1y6L2bNnp7u4uBT1v3//fqP79+8bCAQCpkuXLrkA0KZNm4I2bdqUuhtKQECAR0ZGhtayZcsSmzZtWuECBgAsWLDAJi4uTnD06NHowmOmpqZqS0tLxcWLF4UKhSJeS0sLp06dMsjLy+M2bty4oDL9EFJeVMQgb/qE7QCEkPrNMO9FgF5+QlK+nr0t21lI3Zckyb/6KFlMi3kSUsPGfToTVy+G4uK5vzC0Rzu079IdBQUFOH/qGLIy0jF+2iz4BlRtQNTAEaPx9NEDhOzYgr7tfNCmYxdY29kXFS/u3rqO/sNGYeHPq8p9TwYMJzNPZmYj1E2pUrh3ePXqlZavr6+nra2tPDEx8VHh8QkTJoi2bt2afePGDSMvL6+mPXr0EFlZWSkiIyN1//nnHyHDMFiwYEGCtbV1lRbwLI+7d+/qrF692iY4ODjew8ND/ua5mTNnpixcuNDB39+/ka+vb96RI0dM9fT01F988UVaTeci9RsVMchrwUIhgCFsxyCE1G8cgO8T8c3za/5/UhGD1CiGYbI2XItqzHYOUrc87DOS7Qi1kpa2Nv7YcwQ7N63DX0cPYu+2TeDx+WjYxBNfffsTeg2onm9B5/+4HG07d8OBXdtw8+ol5GRLIDQ2gbWtPcZ/OhN9Bg2r8D3F+QoLayOdFA7nw637y+PxcPHixeilS5daHDp0yPTcuXMmUqmUa2RkpOzYsaNk5syZaYMGDSrXNJeqUCqVmDhxonOzZs3y5s2bl/72+fnz56dlZ2fztm/fbrFr1y49Dw+PghUrVlR5e1lCysJhGIbtDKQ2CBZOBVD6nlSEEPIBMUDOqa531AqtdyxLT0g1OP008eq5yGQahUEqZFOgDawcG7Adg3xgTmb6z4W6WjVeNCCE/Cc8PNzc29vbubRztPAKKURTSQghtQIHMGwaufQB2zlI3ZUnUz6gAgYhpLyy8mQWbGcghPyHihgECBZ6A/BjOwYhhBRyTDjckKNWlLmQGiEVxTCMbN21SBrlQwgpt1ypSihXqmkaPiG1BBUxCABMYjsAIYS8iQuVjfvLLWFs5yB1T3iS6GZydoEL2zkIIZrj3wU+zdnOQQh5jYoY9V2wUACgantaEUJIDfB4sZ6+YSTVSqlSv9x992UrtnMQQjSPOF9hTmsJElI7UBGDDAJgynYIQgh5G18t9bBL/use2zlI3bH9doxEqWYEbOcghGgehUotyJYqDdnOQQihIgahqSSEkFqs2dPF9NqLVItEcf7Vxyni5mznIIRoLlGenEYIElILUBGjPgsWugDownYMQgh5Fx15hp+x5NFztnMQzcYwTOaG61FN2M5BCNFsOTKlsUrN0PMTISyjP4T12wQAHLZDEELI+/g+mp/Gdgai2f56mvg0T66kqZOEkCphGIYrypebsJ2DkPqOihj1VbCQi9dFDEIIqdWMciMDdAuSktnOQTRTrkxxPzQqpR3bOQghdYM4X2HGdgZC6jsqYtRfHwGwZzsEIYSUhQNoNY9YFMV2DqJ5GIaRrb0aSW9NCSHVJl+uNJQr1Vps5yCkPqMiRv1FC3oSQjSGVcbl5nxlbg7bOYhmuZ+YdSM1R+rMdg5CSN2SlSen6WmEsIiKGPVRsNACQCDbMQghpLw4gNAzcjltt0rKTaFSv9hz91UbtnMQQuoeSYGcppQQwiIqYtRPYwHQMDhCiEZxTghx5aiVSrZzkNqPYRhmW9iLXBXDaLOdhRBS98iUat08mVKX7RyE1Fd8tgMQVoxnOwAhhFQUl1HZu8Zuvx7tMonerpP3SpDkX3uaKqHFPMkHd2Vx7R4w1v4bX7YjVDtvBxO0aNUWWw6cLFf72zeu4pNh/TB1zteYFjSv0v2K8uVm+gJ+QqVvQAipNBqJUd8ECz0ANGU7BiGEVEbj6N9okUbyXmqGyfj9epQn2zkIIXVTanIiNq1ZjomjR1o6Ojo25XK5fhwOx+/x48eC91134sQJw44dO7oZGxs319bW9nV0dGw6ffp0O5FIVOPPYy9fvtT68ccfLTt06OBuZ2fnpa2t7WtsbNy8TZs27tu3bzeuyL2ysrK4EydOdPDz8/OwtLRsJhAIfE1NTb29vLwaf//995bZ2dklPk9GRgZv4cKFVoGBgS6urq6efD7fj8Ph+B09etSwop9FKpVyZs2aZWtnZ+dlaGjYvGXLlg2vXr2qV1rbo0ePGnI4HL+9e/cKK9oPqd1oJEb9M4jtAIQQUll8VX5jm9TzD5KtujdnOwupnU49SYzKl6totA4h9cTRi7ego/vhZnZEhD/A2l9+BIfD4djZ23MNDAxUOTk5vPdds2zZMot58+Y58ng85qOPPhLb2trKw8PD9TZs2GAdGhoqvHHjRqSZmZmqpjL/8ssvlhs2bLC2s7OTt27dOsfKykoRFxenfe7cOZPx48cbXblyJXXz5s3lGlWSnp7O37t3r7mXl1d+ly5dJObm5kqJRMK7du2a4bfffuuwY8cOi7CwsKempqbqwmuioqK0Fy9ebA8AVlZWCmNjY2VmZmalnkM/++wzuy1btlh99NFHIltbW8Xhw4fNevXq1fDx48cRTk5OisJ2EomEO2PGDOfAwMCskSNHSirTF6m9qIhR/1ARgxCi0byffK9IturOdgxSC+VIFfcuPE+hAgYh9YiLW8MP2p+nd3NsO3gKDZs0hZ2lWfbQXp0Ft2/fNnhX+9jYWK1FixY5cLlc5ty5c886d+6cX3juf//7n/WSJUvsvvjiC9s///wzvqYyt2zZMq9Pnz6Rffr0yX3z+L1795I7duzYaMuWLVbjxo3Lat++ff677lHI1dVVLhaLHwgEAubtc/3793c5fvy46cqVKy0WL16cWnjc3d1dfvTo0ahWrVrlW1lZqQYPHux8+PDhCi+OqlarsXv3boshQ4ZkHjhw4BUADB48WNS3b1+PTZs2mb7Z58yZM+1lMhn3jz/+iKtoP6T2o+kk9Umw0AFAC7ZjEEJIVejIUlsIs5+8YDsHqV0YhpGuvRpJOwYQ8oE8un8Xc6dNRLcWTdDC1Qpd/Rrh01GDcPbEkRJtz544ggmDe6NtE0cEuNlgcLc22LJ2JeQyWVEbmVSKdp5O6NTcHcp3rOG8+H9B8HYwwaXQM0XHvB1MMGlo3xJtM9PT8O2XM9HZpyEC3Gww7KP2OH5gb5U/t5WNHXxbtoGBoRFypErjstofOXLESCaTcbp37y5+s4ABAD/88EOKUChU7d+/3zwnJ6fGnsvGjRsnfruAAQC+vr7Svn37igAgNDS0XFM7+Hw+SitgAMDQoUNFABAdHa3z5nELCwtV//79c6ysrKo02iQ5OZkvlUq5/v7+eYXHOnbsmAcAsbGxRdN5zpw5Y7Br1y6LX375Jc7a2rrGRrgQ9lARo36hURiEEI3HATi+j+cns52D1C53E7JupeVKndjOQUh9cGjPdowb+BEunjuF5i0CMHbyDLTv0gOizHTs37GlWNs1S77HV9Mn4mV0FHr1H4IR4z8BwzBYs/QHTBs9GAq5HAAg0NFBj34DIcrMwNWL50v0KZfJcPbEEZhZWKJtp27vzSfKysTYAR/haMguOLm44uNPpsKjiRcW/y8Iuzatr7avg5pheOBw3zuVJCUlRQsAnJ2dZW+f4/P5sLW1lRUUFHD/+ecf/WoLVgF8Pp95879VceLECSEAeHl5FVT1XqWxtrZW6ujoqO/du1e0BsbVq1f1AcDJyUkGALm5uZypU6c69+jRQzRhwgRRTeQg7KPpJPULFTEIIXWCMPuJv440NU2qY2XJdhbCPoVK/WLvvVc0jYSQD+BF1DP8tOBL6BsYYtuhv+Dm0bjY+dTkxKL/D78bhi3rVsHa1g67T/wNc0srAMCsed9izuTRuBx6Ftv/+A2fzPwCANB/6Cgc2r0dJw7uQ6fuvYrd95/zp5EtEWPM5Bng89//CPPb0h+QEPcKoydNw9zgn4qOjxg/GWMH9KjS538bw+Fqve+8ubm5Eig+UqCQSqVCUlKSAACePn2q069fv5xqDVeGrKws7unTp004HA769OmTXZFrFQoFvv76a9t/78O7deuW4bNnz3RbtmyZM2fOnPSayMvj8TBy5MiMP//80zI7O5tnY2OjOHz4sKmBgYFq0qRJWQAQFBRkJxaL+Zs2baJpJHUYjcSoL4KFlgBouzlCSJ3AAQTNnwQ/ZTsHYR/DMMzWW9F5aoZ574MEIaR67N+5FUqlElNmzy1RwABeT7codDRkNwBg8qwviwoYwOsRCF8sXAwul4vD+3YWHff2C4BTAzdcCj0Diaj4S/TjB19PBQkcOuK9+RQKBU4dOQB9A0NMDfq62DlPbx/0HjC0nJ+0nDjc91ZUAgMDs3k8HnP+/Hnjy5cvF9tF49tvv7WSSCQ8ABCJRO8d0VHd1Go1Ro8e7ZyZmcn/+OOP0319faUVuV6hUHBWrVpls2rVKpvt27dbPnv2THfAgAGZZ8+ejdbT06vyqI53WbduXcK0adNSwsPD9UNCQszd3NykJ0+ejHJxcVFcvHhRb+vWrVY//fRTnK2trTIoKMjWwsKiGZ/P9/P09Gx87tw5Vka7kOpHRYz6oz/o15sQUodYp11oxlPm55XdktRl8eL8q8/SspuxnYOQ+uLRvTsAgHad3z+lAwCePg4HAAS06VDinHMDN1jZ2CIxLhY52f9tHhE4ZAQUcjnOHD9UdCwzPQ03Ll1Ao6bN0LBx0/f2+So6CtKCfHg0aQpDo5I7a7Zo/WHf6TVs2FAeFBSUrFQqOd26dWvUr18/l08//dS+devWDX/66Sf7hg0bFgAAl1v2t+lr1qwxCwoKsn3zx8mTJyu8TSkATJkyxf706dMmfn5+uRs3bqzwoqJ6enoMwzB3VSrV3ZiYmIdr1qx5de3aNaPmzZs3joyM1K5MpvLQ1dVl1q1bl5iYmPgoNzf3/u3btyM7duyYL5VKOZMnT3Zp3769ZPr06Vk//vij5apVq2zGjBmTceDAgef6+vrqgQMHNoyPj6eZCHUA/SLWH4PZDkAIIdWJA8akyfNVlx81XlDyu2NSL6gZJv2P61FebOcgpD4pLDhYWtuU2TY3+/UMBXMrq1LPm1taIzkxATnZkqKCQ78hI7Bu+U84fnAfho/7BABw6sgBKJVKBA4ZWXa+nNd9mlmUPtvQ/B3Ha9KyZcuSmzRpUrB27VqrCxcuGKvVanh4eBTs3bs3+tSpU8KoqChdS0tLRVn32bVrl3lpO6H07du3QtNQpk6dar9lyxarFi1a5IaGhj7X1dWt9MgJLpcLFxcXxcyZMzObNGki7datW6OpU6c6Xrx4Mbqy96yMr776yiY1NVXr/PnzUQCwdu1a69atW+esXLkyCQCaNm0q9fDw8FqxYoXl6tWrkz5kNlL9qIhRHwQLhQC6sB2DEEKqW4O43c6PGs1TgcP7oMNwSe1wMiLheb5CRWthEPIBFRYb0lKS4eL2/kEABkZGAIDMtDToObuUOJ+RlvK6neF/IyasbOwQ0LYDbl75By+jo+Di1hAnDu4FX0sLvQYMKTuf4b99pqeVej7jHcerimHeXwcYP368ePz48eK3j//yyy/WANCmTZsyRxaGhYVFVjZfoUmTJjls3brVsmXLljnnz5+PNjQ0VFf1noW6du2aZ2hoqLp161alRodU1vXr13XXr19vvWzZsjgXFxdFVlYWNz09XWvQoEFZhW3c3d3lxsbGyqdPn+p+yGykZtD0gvqhHwCaK0wIqXO4jNKxQdzu22znIB9etlR+52J0KhUwCPnAvHxbAACuXgwts20jz9czvW7fvFriXNzLGKQmJ8HO0QlGwuLTPgKHvh5xcfzgXjyLeISopxFo16kbTM3My+zT2a0hdHT1EPnkcbFpKoXu3CiZpTpIlUyF11uIiIgQ3L9/38Dd3b3A39+/QmtSVJRarcaYMWMct27datmmTZvs0NDQ59VZwAAAkUjEzcvL4/F4vBpbE+NtCoUCkyZNcgkICMgJCgrKePOcTCbjvPlzuVxOz751BP1C1g+0KwkhpM5qErXqg77xIexjGKZg7dVI2pmGEBYMGzMRfD4fG3/9BS+inpU4/+buJAOGfwwA2LRmObIy/3u+VKlUWLl4IdRqNQYOH13iHl179oOBoSFOHd6P4wf2AAACh44qVz4tLS30GTgUebk5+H3l0mLnIsLv46+jB8p1n4rKk6uN3nUuKyurxDNXSkoKb9SoUS5qtRqLFy9OqJFQ/1Kr1Rg1apTTrl27LDp06CA5f/58tIGBwXsLDTk5Odz79+/rPH/+vNj6FmFhYbr5+fmct9tLpVLOpEmTHNVqNTp37lyyelRDFixYYBMXFyfYtm1bbOExU1NTtaWlpeLixYtCheL1LJ1Tp04Z5OXlcRs3blwj27+SD4umk9R1wUI9AB+xHYMQQmqKlirP0yr9n4epFp1occd64k58Zlh6rqwj2zkIqY9cGzbC/B+XY/H/gjC8V0d07tEbjs4NIBZnISL8PvQNDLFl/wkAQPMWLTF+2iz8uWENBndrg+69A6Grp4erF0MRHfkUPv6tMH7qrBJ96OjqonufATiybydCdmyBsYkpOnQt/9aoM79eiFtXL2HXlg2IeHgfPgGtkJGairMnjqBd5+745/zpKn0NFs6ZXvT/L188BwAs/m6h6W+6r5/3p0yZkvHRRx/lFraZN2+e7cWLF4V+fn65FhYWyqSkJK3Q0FDjnJwc3rfffpswbNiwCm1vWlFz5861CQkJMdfR0VF7eXkVfPPNNyUWNPHx8ckfM2aMuPDnly5d0u/Xr19Df3//3Densfz+++/mBw4cMPP19c21t7eXGxsbq5KTk7WuXLlilJGRoeXs7Cz97bffShRlpkyZYp+ZmckHgMJ1PZYvX269c+dOMwAYMGCA+M3+y+Pu3bs6q1evtgkODo738PCQv3lu5syZKQsXLnTw9/dv5Ovrm3fkyBFTPT099RdffFEz84nIB0VFjLqvJwC9MlsRQogGax4RLD3b6R+2Y5APQK5SP993P5amkZBaqf03vmxH+CAGjxoHN4/G2P7HWty+cRUXzp6CiYkZ3Bt7YtDIMcXazpn/HRp5NsO+7Ztw4lAIlEoF7J1c8NncBRg75TNoaZe+kUXg0JE4sm8nlAoFevYf/M52pTExNcP2I2ewZukPuBx6Bk8ePoCzqxsW/LQCtg6OVS5iFG73+qa/T5/kADADgI4dO+a8WcTo0qVLzsOHD/XOnTtnnJOTwxMKhao2bdpkf/nll6ldu3at8V22Xr16JQAAqVTKXbdunXVpbQYNGpRZniLCiBEjsvLy8rh37941uH//vkF+fj5PX19f5ebmVjBt2rTUuXPnppc2TeXUqVMmSUlJxX4Rr127VjR6xcnJSV6RIoZSqcTEiROdmzVrljdv3rz0t8/Pnz8/LTs7m7d9+3aLXbt26Xl4eBSsWLEizsHBQVnePkjtxSlrERqi4YKFuwB8zHYMQgipSQzA/N325Kscw4YlV44jdQbDMMzv158/jkrPph1JCCs2BdrAyrEB2zFILWWqr51qb6JXo1NDCKkvwsPDzb29vZ1LO0drYtRlwUJtAH3ZjkEIITWNA3B8H8+nbxzruFhR3lUqYBBCaqtcmVJYditCSFVREaNu6wqA/jIlhNQLJpKH/gJZRkbZLYkmUjNM2sYbz2ndE0JIrSVXqnVkClX5570QQiqF1sSo22hXEkJIvcEBdLyffHcrzOc3WvCxDjr2OP5FgULVmu0chJC65dj+PUhKiCuznUcTL3Tp2afMdhKpQmipxSuxRgMhpPpQEaOuChZyAfRnOwYhhHxItqnnmnJV0gI1T0eX7Syk+kgK5Hcuv0ijAgYhpNodP7AHd25eK7Nd4JCR5Spi5EiVQktDUBGDkBpERYy6qz0AC7ZDEELIh8QBY9Y4es2VCI+v2rOdhVQPhmHy116NLHU1fUIIqaotB05W6/3y5SpDtZrhcLkc2j2BkBpCa2LUXTSVhBBSL7m92u4ARl1iezeimW7FZdzOyJPZs52DEELKg2EYbrZUYVR2S0JIZVERo+6iXUkIIfUSl1E4O8eH3GY7B6k6uVIVdeBBbFu2cxBCSEVkS2mXEkJqEhUx6qJgoTMA2sScEFJveUat0GM7A6kahmHUm29Fy9UMTX0lhGiWPNpqlZAaRUWMuqkz2wEIIYRN2spsL4uMa4/YzkEq71VW3tXn6TlN2c5BCCEVpVCptfPlSh22cxBSV1ERo27qwnYAQghhm0/Eojy2M5DKUTNM6sYbz5uznYMQQioru4BGYxBSU6iIUTfRSAxCSL2nVxAfYJAbE8t2DlJxRx/Fv5QqVbQwHiFEY+XIFMZsZyCkrqIiRl0TLGwIwI7tGIQQwjYOwPV9PJ+KGBpGXCC/fSUmrRXbOQghpCqkcrW+Ss3QsxYhNYD+YNU9NAqDEEL+ZSq+10JbnpXFdg5SPgzD5K29EmnDdg5CCKkqBgwnV6Y0YDsHIXURFTHqHloPgxBC/sUB9Jo9XUwLfGqIm7EZdzLzZfZs5yCEkOqQK1UYsp2BkLqIti2rS4KFHACd2I5BCCG1iX3yqSb3mv4sU/MEArazkHeTKVWRB8Nj27Kdg5CqyL2bxHaE9zLws63yPRLj49C7jTcCh4zED6vWV0OquitPrjICkMh2DkLqGhqJUbd4ArBkOwQhhNQmHDAWHi/WhbGdg7wbwzDqTTeeq9QMvVwhhNQdMoVKT6lS89jOUV3WrFljxuFw/NasWWP2IfrLyMjgLVy40CowMNDF1dXVk8/n+3E4HL+jR49WaoRLfHw8f9y4cQ52dnZe2traviYmJt7du3d3vXr1qt7bbSMjI7U5HI5fWT/OnDlTrilDjx49EnTq1MlNKBQ2t7a2bjZ27FhHkUhU6rN4//79Xezs7LwkEgk9q78DfbNQt9BUEkIIKYX7q612T93nMOBwOGxnISXFZOZefZGZ24HtHIQQUp0YADkypaGJnraY7SyaKCoqSnvx4sX2AGBlZaUwNjZWZmZmVur5NTIyUrt9+/aN0tPTtby8vPJ69+4tysjI4J89e9akc+fOwn379kUPHjw4u7C9mZmZas6cOcml3SshIUHrwIED5sbGxsqOHTuWuZ17dnY2t0ePHg0LCgp4gwcPzkxKStLauXOnRXp6Ov/06dMxb7bdt2+f8Pjx46ZHjhyJEgqF6sp81vqAihh1Cy3qSQghpeCp5Q0cEw/djrMf4s92FlKcWs0kb74Z3ZztHIQQUhNypVTEqCx3d3f50aNHo1q1apVvZWWlGjx4sPPhw4crNQpk+vTpDunp6Vrjx49P27JlSzyX+3qQw8OHD5PbtGnTZNq0ac7du3d/bGRkpAYAc3Nz1cqVK0udHzZjxgw7ABg8eHCmrq4uU1bfISEhwpSUFO2//vorslevXrkAMGTIEOdDhw6ZJSYm8u3s7JQAkJmZyZs9e7bTsGHDMgYMGJBTmc9ZX9AQlboiWMgF0JHtGIQQUls1jVymzXYGUtKhh3GxUqXKiO0chJCKexkdhc8nfYz2TV3QsqEdxg3qieuXLryz/emjBzFpWD+083SCv5s1BnRuiY2/LodcJivR1tvBBJOG9kVGWiqC585CtxZN4ONkhmP79xS1eXj/Dr74dBy6+HrAr4ElegR44vt5nyMtpeQL9CcPH2Dpt/MwtEc7tG/qAn83a/Rr74fl33+DbLG4WNst61bB28EEu7f8XurnSEtJhq+zOUb2Lv7+UKlUImT7ZowO7I42jR3R0t0W3du3tPjpp58sVCpVsbaF0xUGDx7sHBERIejZs2cDY2Pj5vr6+j5t27Z1v337tg4AJCUl8UeOHOlkYWHRTCAQ+DZt2rTxiRMnSp1OoVAosGTJEgtvb+9GBgYGPrq6uj6NGzduUlb/kZGR2n379m1gYmLiXdjH3r17hW+2DwgI8Jg9e7YzAMyePdv5zSkVkZGRNfLvq4WFhap///45VlZWqrJbv1t+fj7n8uXLQi6Xi5UrVyYWFjAAoFmzZrJhw4ZlpKena+3YscOkrHvJZDLO/v37zQBgxowZGeXpPzY2VhsA3hy14e/vnwcA0dHRRV+76dOn2wPA+vXrE8r50eotKmLUHc0BlPkHjxBC6iuBQuxtnnXrCds5yH+y8mVh11+lt2I7ByGk4hLjYzFmQA9IxCIMGT0ePfr0x9OH4ZgxdijOHD9cov2iLz7DvJmTEf8qBl17BWL42E8gNDbGuuU/YtqYIVAqlSWukYhFGNO/Ox7eu42uvfpixPjJMLOwAAAc2bcL4wf2xNWLofBv3Q6jJ01Fk2Y+OLJ3J0b16YLkxPhi9zq0dzvOHD8MZ1d39B/2MYaNnghzSyvs3LQO4wb1RF7ufy+++w4aDi6XixOH9pX62U8d2Q+VSoXAYSOLjikUCswcPwI/fTMXOdkS9BowGIM/HgeGYTgLFixwHDx4sEtp94qPjxe0a9euUXp6utbQoUMz2rVrl33jxg2jHj16eDx69EjQsmXLRg8ePNDv16+fqHfv3lmRkZG6Q4YMcX/+/HmxwoFMJuN06dLF/X//+59jdnY2LzAwMHPkyJEZarUa7+s/ISFBu3Xr1o3j4+O1Bw0alNWnT5+s58+f644ePdrtzWLJ6NGjM7p27SoGgK5du4rnzJmTXPjDzMysSkWGmpaWlsZXKpUcY2NjpYmJSYkpGg0aNJABwIULF8pca2Pv3r3CjIwMrRYtWuT6+PhIy9O/o6OjHADeXHvj7t27egDg5uYmB4CjR48a7t+/33z16tWxtf3rWRvQdJK6g9bDIISQMjR/vFAS2uEc2zEIAIZhctddjaTtVAnRUHdvXce4Tz9D0Dc/FB0bMX4yxg7ogcXzg9CuczcYGL4eZHVs/x4c278bXXr2xc9rNkJHV7fomg0rl+D3VUsRsn0zPp40tVgfz589Qd/Bw/Hd8rXg8/97bHkVE43F84Nga++ILQdOwsrmv11Xbl29hKkfD8LSb/+H1Zt3FR2fNGMO5i9eDh6v+Dqbh/ftxHdzZyFkxxZMnP45AMDKxhYt23XCjcsX8PzZE7g3alLsmuMH90FLWxu9+g8pOrb5txW4fulvjBg/GV8F/1zUj0qlwo9fzcg5tD/EdNeuXaLRo0eL37zX7du3Db766qvEpUuXphQemzt3rs3y5ctt27Vr17hPnz5ZO3fujCu837p167I/++wzlyVLllht2bKlqFLzv//9z+bq1atGY8eOTduyZUt84ddLqVRi1KhRTgcOHDAvrf+wsDDDoKCgpBUrVhQNXzl06FDWkCFD3JcvX27Vr1+/HACYNWtWJgD8/fffxoGBgeLCn2sCc3NzJY/Hg1gs5kskEu7ba03ExMQIAODFixc6Zd1r8+bNFgAwadKk9PL2P3z4cMmiRYsUw4cPdxs0aFBWUlKS1pkzZ0x69OghtrOzU0okEu6MGTOcAwMDs0aOHCmp6Oerj2gkRt1BRQxCCCmDQf6rAP28WBqmWQtcf5V+NytfXvX9HgkhrDA0MsKnn39V7Jintw96DxiKHIkEF86cKjq+e+vv4PP5+G752mIFDACYMnsujE1McerIgRJ9aGlrI+ibH4oVMADgwI6tUCoU+Oq7n4sVMACgZbuO6Ni9Fy6Hnik2usLW3rFEAQMABg4fDQNDwxLTYAKHjAAAnDi4t9jxiPD7iIl6hvZdesDYxBQAoFarsXfbRphbWmHutz8V64fH42HRTyvkHA4He/bsMX27f1tbW/mPP/6Y8uaxKVOmZAKAXC7nrFu3LuHN+3366adZPB6Pefz4cdEXUqVSYevWrZbm5uaKzZs3x7/59eLz+Vi/fn3C+/pfunRpsfk3gwcPzraxsZE/fPhQv8QXTAMZGBgwAQEB2Wq1Gl988UWx3zCPHz8W7N+/3xwAJBLJe1/wR0ZGal+/ft3I2NhYOW7cOFF5+xcKheqzZ89Genl55R84cMDs7t27BqNGjUrfu3fvSwCYOXOmvUwm4/7xxx9xz58/1+7SpYubjo6Or76+vs/AgQOds7Ky6Jn9LTQSoy4IFvIBtGc7BiGE1HYcgOfzeEHM1Za7aAQAi2RK1dND4XHt2M5BCKm8Rk29oW9QcvR9i9btcPzgXjyLeIjAoSNRUJCPqCePYWxqht1bNpR6Ly1tAV5GR5U4bmvvCDNzixLHw+/dBgDcvXkNEeH3S5wXZaZDpVIhNuYFmjRrDuD1dI+Du7fhzPHDiImKRG5ONtTq/17Iv72ORpeefWFoZIS/jhzE7P8FFxUmjv9b1Og/9L+pJLEx0ZCIRXB0ccWmNctL5OGAMRYIBOro6OgSb/qbNGmS/3aRxtnZWf7vf2VvT3/g8/kwMzNTpqSkFE0nefjwoY5EIuE5OTkpv/7661KLw+/qv3HjxiX6BwAbGxv5gwcPyrV9aFmCgoJKZPr0008zPDw85NVx//JYs2ZNfOfOnRtt2bLF6s6dOwYBAQG5GRkZ/DNnzpg4OTnJnj17psvlct+7SOfatWstGIbBkCFDyrWg55u8vb1lly9ffv728TNnzhjs2rXLYsuWLTGWlpaqLl26NMzMzNTauHFjTE5ODnfevHmOH3/8MfftXUzqOypi1A3+AKrlLxlCCKnrzEVhvlpysVihbWzMdpb6iGEY1cYbz8EAJV+JEkI0RmnFBQAwt7AEAORkv96tMlssBsMwEGVm4PdVSyvUR+G93iYRZQEA/vz9t/den5/33+6XX02fiAtnTsLe0RmdevSGuaUltLUFAIDdWzZAIS++uKiOri569B2IQ3u248blC2jXuTsUcjnOHDsEEzNztO3cvait+N88cS9fvOsz8gAgLy+vxN97RkZGJdY/0NLSAgAYGhqWujYCn89nlEpl0Zbh6enpPACIjY0VrFq1yqb0r0bp/QuFwlL74PF4xYo8VVFapi5duuR8yCJGixYtpDdv3ny6cOFCm6tXrxpt27bN0tLSUjF58uTU3r17Z/fs2dPDzMys5MIs/1IoFAgJCTEDgM8++6zcU0neJzc3lzN16lTnHj16iCZMmCA6cuSI0dOnT/XWrVv3cuzYsWIAePXqlWD58uW2ERERAk9Pz5Ir4NZTVMSoG2hrVUIIKScOYOD17KdL95otox2dWBCdkXM1JjOXvvaEaLjMjNKf4zLS0wC8nm7y5n8bNW2GkNOXKtQHh8Mp9XjhWhvXnsQW/f/7RITfx4UzJ9GqfSes23Gg2PQUtVqNPzesKfW6fkNG4NCe7Th+YC/ade6Oy3+fg1iUhY8nTS0qNLyZp0vPvli1aWep93Iw1Ys20dOukfUOTExMVADQvXt38blz517URB9VwTDMXbYzAICnp6fs8OHDr94+vnr1ajMA8PHxyStx0b/27t1rnJ6eruXv75/r7e1dLcWEoKAgO7FYzN+0aVMcAEREROgAQMuWLfML27Ro0SIfAMLDw3WoiPEfml9TN9B6GIQQUgGOSccbcdTyD/YGiLymVjPJW25G+7GdgxBSdc8ehxdbc6LQnRtXAQCNPJsBAPT0DeDasBFeRD2DRFTuZQTey8u3BQDgXtiNcrWPe/V6JH7H7r1KrK/x+MFdSKUFpV7n498Kji6u+OfcaeRkS4rWx+g3ZGSxdi5uDWEoFOLRvTtQKBSl3itfpqqxUdPNmzeXGhoaqh48eKAvk8lKr/xUAx6PxwCASqWqsT7YsHfvXjMAGDt2bNa72hQu6Dlx4sRqGYVx8eJFva1bt1r99NNPcQ4ODsVGgEilUk5p/0/+Q0UMTRcsFABow3YMQgjRJByorTxi/ghjO0d9cyA8Nl6mUtP0R0LqgJzsbPyxelmxYxHh9/HX0QMwNDJCl559io6PmTwDCrkc3375GbIlJQcjZIvFePoovNx9jxw/GXwtLfzy3QK8iokucV4hl+PeretFP7dzcATwX4GlUGZGOn76Zu57+wocMgIymRT7d2zBlYvn0bCxJxo3bVasDZ/Px8jxU5CeloKli76GtKBkUeRlfLzh3bt3y9z9ojK0tLQwceLEtPT0dK2JEyc65ObmlnjwjY2N1apq/+bm5ioAiIuL0y6rLVuSk5P59+/f10lOTi5WrSooKOAUFBQU+7qo1Wp8/fXX1mFhYYZ9+vQRtWvXLh+liIqK0r527VqFF/R8F6lUypk8ebJL+/btJdOnTy8qnDRt2rQAAI4cOWJceOzEiRPGAODt7V2u7VzrC5pOovlaAdAtsxUhhJBiGsZstH7mNpPtGPVGZp7s1s3YjJZs5yCEVA+/lm1weN9OPLp/F839WyIjNRVnTxyBWq3GNz+vKjbNY+CI0Xj66AFCdmxB33Y+aNOxC6zt7JEtFiMxPhZ3b11H/2GjsPDnVeXq28WtIb5b/hu+/XImBndtjTadusLJxQ1KpQIpiQm4F3YDJmbmOPbP61q1p7cvmvu3xN+nT2DsgB7w8W+FzIx0XL0YCucGbrCweucyEug7eDjWr/gZ61cugVKhKDEKo9CU2XMR9eQxDuzahkuhZxHQtj0srW2QlZGBuJcv8ODOLf2vvvrK2M/PL6XUG1TR0qVLkx89eqS7Z88ei9DQUOM2bdpk29raKtLT0/kxMTE69+/fN/j6668Tq9J/ly5dcnV0dNSbN2+2zMzM5FtbWysA4Ouvv04zMzMrdW2NqpoyZYp9ZmYmH3i9HS0ALF++3Hrnzp1mADBgwADxmDFjxIXtf/nlF8tVq1bZzJkzJ3nlypVJhccfP34s6Nq1a6N27dplOzo6yhUKBefKlSuGz58/1/X19c3dsWPHq3dlWLdunblara7Ugp6l+eqrr2xSU1O1zp8/X2w128DAwBxPT8/81atX28bFxWnn5eXx/vrrL5NevXqJaCpJcVTE0Hw0r5gQQiqBp5a5OSQduxNv278F21nqOoZhctZdjXRkOwchNc3Ar/7sGmzn4IRvfl6JX3/+Dgd2bYNCJkdjr2aYMvsrtO3UtUT7+T8uR9vO3XBg1zbcvHoJOdkSCI1NYG1rj/GfzkSfQcMq1H/fQcPh0bgpdmxch9s3ruDG5YvQ1dWDhZU1uvUJxEf9BhW15fF4WLNlL9b+shhXL57Hnm0bYWllg0EjxmDy7C8xqEurd/ZjY+cA/zbtcevqJfD5fPQZOLTUdlpaWli9ZTdOHg7B8QN7cTn0LPLz82Biag47R0fM+HI+Ro0bU+qb/uogEAiY8+fPv9iwYYPprl27zC9cuGCcn5/PNTExUTo4OMjmzp2bOHHixMyq9GFhYaHauXPni8WLF9sePHjQrKCggAsAkyZNyqypIsapU6dMkpKSio38uHbtWlGFzMnJSf5mEeNd7OzslJ06dZLcu3fP4MKFC8Z8Pp9xdXUt+Omnn+K+/PLL9DfXOHmTUqnE3r17zYHqWdDz+vXruuvXr7detmxZnIuLS7G5R1wuF8eOHYv+9NNPHU+ePGnK4/GYQYMGZRaumUH+w2GYKheTCJuChScB9CmzHSGEkBJk2qb3/+py04ftHHXdlReplw8/iu/Adg5CqmpToA2sHBuwHYNoKCsjnXgrI500tnMQognCw8PNvb29nUs7R2tiaD5aII0QQipJIM/yMRXde8Z2jrpMqlA9OfIovh3bOQghhG358ppb3JOQ+oSKGJosWGgPwJrtGIQQosl8Hi9452rkpGoYhlH9cSOKy9D3G4QQAqlCpc92BkLqAvqmQrPRPG5CCKkiw7wXAXr5CUlltyQV9Tw95+qrrLxGbOcghJDaQKFSa8uV6tIXXyCElBsVMTSbP9sBCCFE03EAvk/EN8/ZzlHXqNRM4pZb0VRsJ4SQN+TKlDSlhJAqoiKGZqNvDgkhpBpYZF731VJkS9jOUZfsf/AqUa5S09BpQgh5Q75cSX8vElJFVMTQbLSoJyGEVAMOYNg0cukDtnPUFRl5spthcZkBbOcghJDaRqpQ67GdgRBNR0UMTRUsdAFgxnYMQgipKxwTDjfkqBWKsluS92EYJnvdlWfObOcghJDaSKZQURGDkCqiIobmoqkkhBBSjbhQ2bi/3BLGdg5Nd/lF2n2xVEE7ZxFCSClUDMOTKVTabOcgRJNREUNzURGDEEKqmceL9eZsZ9BkUoUq4tjj+PZs5yCEkNosX06jMQipCipiaCpTZ2vwBWK2YxBCSF3CV0s97JL/usd2Dk3EMIxyw/UoPkPfWxBCyHsV0JQSQqqEz3YAUklNevcFYAy1OgHy3ERkpxRAHK8PcYIj5HlWbMcjhBBN1ezpYibRpjfbMTROZHr2tThRXke2cxBCSG0npSIGIVVCRQxNFPo/BwCmAAAu1x46RvbQMQIsG74+zzDpkOfHIjctF+J4HYjjbVEgcWQvMCGEaA4deYafseTRc7HQy53tLJpCpWYStt16QdMcCSGkHGiHEkKqhooYmqn5e89yOBYQ6FtA4AKYubw+xjASKKUvkZcpgTieD1G8NfIynQGGV+NpCSFEw/g+mp92od0JKmKUU8j9V8lyldqe7RyEEKIJlGq1lkKl5mvxuEq2sxCiiaiIoZm8K3wFhyOElm5zGNsDxvaAc2uAYQqgkscgX5QJSQIHonhz5KQ2AKMW1EBmQgjRGEa5kQG6BUnJBbq2Nmxnqe3Sc6U3b8dntmI7ByG1wfkvRrMd4b26r9jFdoRqsWHlEvy+aik27z8B/9bt2I5TKflylZ5Ql5vNdg5CNBEtvqWZmlfLXTgcXfAFnjCy7gCHFu3RbGBjtPmUi9aTI9F82FW4tL0EE8dH4GrlVkt/hBCiITiAVvOIRVFs56jtGIaRrLsa6cJ2DkJI3XJs/x54O5jg2P49bEepMQX/7lCSkZHBW7hwoVVgYKCLq6urJ5/P9+NwOH5Hjx41rMx9s7KyuPPmzbNu1KhRE0NDw+aGhobNGzZs2GT27Nm2SUlJJV5gBwUF2XI4HL93/Th48KBRefuOi4vj9+vXz8XU1NTbzMzMu3///i6JiYmlvjSfNWuWraGhYfOXL19qVeZzkvqNRmJopoqPxCgvDkcLPC0PGJh7wMAcsPMGGEYNRv0S0uxkZKcoIIozhCTRGUqpaY3lIIQQllllXG7OV+bmKPkGlfpGsj74Jzo1XCJVdGA7ByGkfhkxfjJ6Bg6CtZ3mzmIr3KEkKipKe/HixfYAYGVlpTA2NlZmZmZW6hktMzOT5+fn1zg2Nlbg6emZP2zYsEwAuHnzpsGaNWtsQkJCzG/fvv3EwcGhxDSWQYMGZTo5OcnfPt6oUSNZefpWqVTo3bu3+4sXL3QGDx6cWVBQwD127JhZ7969Bffu3XvG4/03g/369eu669evt162bFmci4uLojKfldRvVMTQNKH/MwDg+kH75HC44PBcoGfiAj0TwLrx6+OMOhGy3ETkpOZDFK8PSYI9ZLk09JoQUidwAKFn5PJL4Z7BtONGKQoUyscnIhI0cxw3IUSjmZiawcTUjO0YVSJTqnQBwN3dXX706NGoVq1a5VtZWakGDx7sfPjw4Up9uNWrV5vHxsYKhgwZknngwIFXb54rvO+vv/5qsXz58uS3r50wYUJm3759cyr1YQBcunRJPyIiQu+333579dlnn2UCwBdffCFbuXKl7eXLl/U6d+6cDwAKhQKTJk1yCQgIyAkKCsqobH+kfqPpJJrHGwCH7RAAAA7XDjpGAbBw74SGXfzhP9YGbaelI2DcHTTpfQk2XjegaxwHgGE7KiGEVIZzQogrR62khdfewjCMcsO1KAFD30cQUm89vH8HX3w6Dl18PeDXwBI9Ajzx/bzPkZZS4vkYTx4+wNJv52Foj3Zo39QF/m7W6NfeD8u//wbZYnGxtpOG9sWiL2YAABZ9MQPeDiZFPxLj4wC8XhPD28EEt29cLXatt4MJJg3tC1FWJr7/+nN09WuEFq5WGNi1NY6G7C71c8hlMmxYuQS92zZHC1cr9GrjjbXLFkMukxXdryYolIyAYRhYWFio+vfvn2NlZaWq6j1jYmIEANCvXz/x2+cCAwPFAJCenl4jL7FjYmK0AaBt27Z5hcdatmyZBwAvX74sWm9vwYIFNnFxcYJt27bF1kQOUj/QSAzN05TtAO/F4VhAW98CpvqAqfPrYwyTDaXsJfIyxBAn8CGOt0RuRgPaGYUQUttxGZW9a+z269Euk9qwnaU2eZqafS1enE8jVAipp47s24Uf5n0OLW0BOnXvCWtbO8S+jMGRvTtx+fxZ7Dx+DjZ2DkXtD+3djgtnTqFFq7Zo2a4TGLUaTx49wM5N63Dtn1DsOn4e+v/O3AscOgqGRkJcPPcXOvfoDQ9Pr6L7GBoJy8yWky3BuIE9oaWlhW69A6GQy3Hu1FF8++Vn4HK5CBw6sqgtwzAI+nQsrvx9Do4urhgxbjKUSgWOHdiL6Khn1fgVK4kBw5Eq1Dq62jxpdd3T09OzAABOnTolHDt2rPjNcydPnjQGgO7du5c62uLy5csGYWFheiqViuPs7Czr27dvjo2NTbmL+C4uLnIAuHnzpp6Pj48UAG7fvq3/7zkZANy9e1dn9erVNsHBwfEeHh4lpq4QUl5UxNA8DdkOUGEcjhG0dLyLdkZBK4BhpFApIlEgyoA4kQNRnNnrnVFUOmzHJYSQNzWO/s0k2mUS2zFqDZVaHf/n7Rf+bOcghLDjVUw0Fs8Pgq29I7YcOAkrG9uic7euXsLUjwdh6bf/w+rN/+2EMmnGHMxfvBxvrosAAIf37cR3c2chZMcWTJz+OQCg/7BRAPC6iPFRn6Kfl1fkk8cYOGIMFi5ZVdTfx5OmYmiPdti24ddiRYyTh0Nw5e9z8A1ojY17j0JLWxsAMP2L+RjTv3uF+q0MqUJVrUWMzz//POPgwYNm+/fvN3/69KluQEBALgDcvHnT8MWLFzpff/114ujRo8WlXfvLL7/YvvlzbW1tZurUqSmrVq1K4nLLHnTXsWPHvCZNmuR/8cUXTtevXzcoKCjgHj161LRp06b5HTp0yFcqlZg4caJzs2bN8ubNm5deHZ+X1F80DFTzaF4RozQcjg742k1gaNUBDr7t0WxAE7SZwkfrKc/hM+waGrS/BBOnh+BpVXpuHiGEVAe+Kr+xTer5B2znqC323HuVqlCp9djOQQhhx4EdW6FUKPDVdz8XK2AAQMt2HdGxey9cDj2DvNz/voWztXcsUcAAgIHDR8PA0BDXL12otnw6unr4ctHiYv25NmyE5i1aIuZ5JPLz/tt078TBfQCAz+YuKCpgAICRUIgps7+stkzvIlVW78s7PT095tq1a5EjR45Mf/Tokf6WLVustmzZYhUREaHXqVMnydChQ8VvX9O8efP81atXv3r69OmjvLy8e1FRUY9WrFgRa2hoqFqzZo3NrFmz7MrTN5/Px6lTp6I7d+4sOXnypMmFCxeEvXr1Ep06deo5j8fD999/bxUVFaW7bdu2V5mZmbz+/fu76Ovr+wgEAt8uXbq40S4lpCJoJIbmcWc7QI3hcPjg8d2hb+4OfXPA1uv1OD9GHQtZThKyU+QQxRlAkuAMhVSzV3MihGgU7yffK5Ktav6tXG2XliO9cS8hqzXbOQgh7Am/dxsAcPfmNUSE3y9xXpSZDpVKhdiYF2jSrDmA14s5Hty9DWeOH0ZMVCRyc7KhVquLriltHY3KcnJpAAPDkruCWtm+fhbPloihp28AAHj2+CG4XC68W7Qs0d7Hv1WF+t2wckmJY4FDR8HOwfGd18iU6motYqSkpPACAwPdYmJidDZu3BjTr1+/bAA4ceKE0f/+9z/HDh06ND558mRk4SKbAPD2tBN3d3d5UFBQRkBAQH7nzp0b/fHHH1YLFixILc/UEmdnZ8WpU6di3j7+6NEjwS+//GI7d+7cJC8vL1m3bt1cb926Zbh06dI4oVCo+vLLLx379+/v+uDBg2flGfVBCBUxNEno/3gAGrAd44PicDjg8Jyga+wEXWPAqtHr44w6GbK8eOSm5kMUrwdxgj1kObbvvRchhFSSjiy1hTD7yQuJUZMPuztULcIwjGTdtch6+/kJIa9JRFkAgD9//+297fLzitZ3xFfTJ+LCmZOwd3RGpx69YW5pCW3t12s97t6yAQp5uXbxLJd3rZvB571+7FGp/iue5OZkQ2hsAj6/5CORmbllhfr9fdXSEsdatG733iKGXKnWrVAnZZgxY4bD7du3DXbt2hX98ccfSwqPT548WaSrq8uMGTPG9euvv3YICwuLLOte7dq1y2/WrFnevXv3DC5evKg/atQoSVnXlEatVmPChAnO7u7uBYsWLUp99OiR4O+//zaeO3duUuEuJtnZ2dzPPvvM5eTJk4aBgYE0CpuUiYoYmsUZAA21AgAO1wY6hjbQMQTM3V4fY5hMKApeITc9F+J4LYgTbJGf5YTaspsLIURjcQCO7+P5yRfbHK23D/EXnqc8zJYq2rOdgxDCrsJRDteexJY64uFtEeH3ceHMSbRq3wnrdhwoVjBQq9X4c8OaGstaFn0DQ0jEIiiVyhKFjMyMtArdKzxeVOH+5Uq1DsMw4HCq51vVCxcuCAGgd+/eJQoBvXv3zgaAiIiIck8HNDU1VQJAbm5upYdHLFmyxOLhw4f6N27ceMLn8/Hw4UMdAPDz8ysaDdKqVat8AHj06JEuFTFIeVARQ7PUjfUwagqHYwZtPTOYOgGmTq+PMUwOlLKXyM8UQZzAhyjeArnpDQCGfu8TQipEmP3EX0eamibVsarY67k6IF+ufHTySWI7tnMQQtjn5dsCTx49wL2wG+jQ9aMy28e9ej27oGP3XiUKBY8f3IVUWlDiGi7v9TOzSl3lXUffq1HTZgi7dhnhd27Br1XbYufu375Zo30DgJphuHKVWkvA5ymq435yuZwLAMnJyXwTE5Niu38kJydrAYCWlhZTnnvJZDJOYcHD3d29UkNlIiMjtX/88Uf7zz//PNnPz6/YAqYymayoclNQUEBzSEiF0G8YzVJ318OoKRyOIbR0mkFo1xFOLdui+ZCGaDtVhVafPIH34CtwbnUZQrsIcHkl/wUlhJA3cABB8yfBT9nO8aExDKPYcD1KBzSqjRACYOT4yeBraeGX7xbgVUx0ifMKuRz3bl0v+nnhdIo7N64Wa5eZkY6fvplbah/GJqYAgJTEhOqKXap+g0cAANb+8iMU8v+e+XOyJdj46/Ia7buQVFHxKSXJycn8+/fv6yQnJxerCrVo0SIHAL755htbleq/ApBSqcT8+fNtAaBNmzbZhcdFIhE3PDxcUCKTVMr55JNPHJKTk7VdXFykHTp0yH+7TXlMmDDBydHRUfbjjz8WLXri7e0tBYATJ04YFx47cuSIEAC8vLzo+3FSLvQ2WrPQSIzqwOEI/t0ZBTC0Aux9AYZRQa2KhlSSiuxkJURxQkiSXKCSl70hOSGk3rBOu9CMp8zPU/H19NnO8qE8SZVcTxDnd2Q7ByGkdnBxa4jvlv+Gb7+cicFdW6NNp65wcnGDUqlASmIC7oXdgImZOY79EwYA8PT2RXP/lvj79AmMHdADPv6tkJmRjqsXQ+HcwA0WVjYl+vD2DYCOrh52b9kAsSgL5hZWAICREya/c82Lyug3ZATOHD+Ea//8jUHd2qBT915QKhUI/esEPL198OrFc3BqeKFJqUKlM3f2DKPMzEw+ANy+fdsAAJYvX269c+dOMwAYMGCAeMyYMeLCa3755RfLVatW2cyZMyd55cqVSW8cT+zatavBkSNHzBo1aqTftm3bbAC4evWq0YsXL3SMjY2Vy5YtSyxsn5aWxvfx8Wnq6emZ7+7uXmBtba3IyMjgX79+3SgxMVHb2NhYuWvXrpjSdpYpy8qVK83DwsIML1269FRL67/Z8E2bNpV1795dfPDgQbPevXtzDQ0NVYcOHTL38vLK69u3L00lIeVCRQzNQkWMmsLh8MDju0HfzA36ZoBN0zd3RklGdooU4nhDiBMcoSiwYDsuIYQdHDAmTZ6vuvyo8YIObGf5EFRqddz2sBcBbOcgRFN0X7GL7QgfRN9Bw+HRuCl2bFyH2zeu4Mbli9DV1YOFlTW69QnER/0GFbXl8XhYs2Uv1v6yGFcvnseebRthaWWDQSPGYPLsLzGoS8ldQIyMjbHyj+34ffUyHD+wFwX5rxcJ7TNoWLUWMTgcDlZt2oXNa1fi5OEQ7P1zIywsrRA4ZASGjZ2Ei2dPwcDAsNr6K41MqdY5deqUMCkpSfvN49euXStacMTJyUn+ZhHjXQICAgpu37795Pvvv7e+cuWK0Z49eyw4HA6sra3lY8eOTQsODk5xcXEpmrpiYWGhHDt2bNr9+/f1L1++LJRIg/58VAABAABJREFUJDwtLS3GwcFBNm3atJQFCxak2tnZlbkrydtevnyp9e2339pPnz49pW3btiVGV+zZs+fVlClTHM6fP2+sVCo5nTp1Em/atCmOdiYh5cVhmHJNiyK1Qej/XuL14p6ETYw6BfK8OOSkFUAUrwNJgj2k2eXaQ5sQovnUHH7csR6P7MCpxKspDbP99ou7DxJFfmznIKS22BRoAyvH+rVRXH114/JFTP14ECbO+Byz531bY/3oafNz3CwNomqsA0I0VHh4uLm3t7dzaedoJIamCP2fAMC792giHw6Haw2BoTUEhoD5vxsVMIwIioJXyMuQQBSvDXG8zb87o1BJmZA6hssoHRvE7b4Z4zS25OvDOiQ1p+D6g0RRG7ZzEEJITUpLSYaldfEpLWJRFn5d8h0AoMtHfWu0f4VKrV12K0LIm6iIoTlcQQ/EtReHYwJtPRNoOwIm/9aaGCYXKvlL5GVmQZLIgyju9c4ojJq2ySVEwzWJWmUY4zSW7Rg1hmEY8bqrkbSYNCGkzlv+/QJEPY2At18ATMzMkJqchGsXQyERizBk9Hh4+dTsYDSlitGuzm1WCakPqIihOWg9DE3D4RiAL/CC0BYQ2gKO/gDDyKFWPkW+KBOSJDXEcabITmkAtbLce3YTQtinpcrztEr/52GqRadmbGepCaFRyY9yZMr2bOcghJCa1rVXP2RmpONS6BnkZEsgEOjAtWEjDBwxGgNHjKnx/hkwHLlSrS3Q4snLbk0IAaiIoUnojVhdwOFog6fVGIaWgKElYN/89c4ojOoFpNkpkCQrIYozgiSpAVQy2hmFkFqseUSw9Gynf9iOUe3y5cqHfz1Nasd2DkII+RA+6jcQH/UbyGoGGRUxCKkQKmJoDhqJUVdxODxw+K7QM3WFnilg4/n6uFoVD1luInJSpRDFGUCS4Ah5viW7YQkhhXSlSf6GOVEvcwwburCdpbowDKNYfy1SHwCNayaEkA9ErlILAOSynYMQTUFFDM1BRYz6hstzgK7QAbpCwPLfX35GnQZ5fixy0/IhihdAnGAPqcSe3aCE1E8cgOP7eH7CpdYH60wR43GK+FqipKAT2zkIIaQ+kSvVArYzEKJJqIihOVzZDkBqAQ7XEgIDSwgMALN/t3hjGDGU0lfIzZBAHK8FcbwV8jJdQAvBElLjTCQP/QWyjAyZwNyc7SxVpVSrY3fcjqnTO64QQkhtRDuUEFIxVMTQBKH/4wKwZjsGqaU4HGNo6TaHiQNg4vD6GMPkQSWPQX6WCOIEDkTxFshNawCG/pEkpDpxAB3vJ9/dCvP5rSPbWapq152XmUo148R2DkIIqW9oJAYhFUNFDM1gBYDHdgiiQTgcffAFXjCyAYxsCndGUUCtjESBOB2SJBVEcabITm4AtVKf7biEaDLb1HNNuSppgZqno8t2lspKzi64Fp4kast2DkIIqY8UaoZeMhFSAVTE0Aw2bAcgdQCHowWelgcMLDxgYAHYeQMMowajfglpdjKykxUQxRlCkuQCpdSE7biEaAoOGLPG0WuuRHh8pZFbkjIMI1p/LdKD7RyEEFJfKVVqbTXDcLgcDsN2FkI0ARUxNIMt2wFIHcXhcMHhuUDPxAV6JoB1k9fH1eoEyHMTkZNaAFGcPsQJDpDn0ZQmQt7B7dV2h4iGX6rB4WrcWjTnIpMjcmVK2lKVEEJYJFeqtXW0eDK2cxCiCaiIoRmoiEE+LC7XHjpG9tAxAizcXx9jmPR/d0bJgzheG+IEOxSIHdkNSkjtwGUUzs7xIbdeOY5syXaWisiTK8PPPEuiaSSEEMIyKmIQUn5UxNAMVMQg7ONwLCDQt4DABTD7d0dJhpFAKX2FvEwxxAl8iOL+3RmFoTVcSL3jGbVC75XjSLZjlBvDMPJ1VyMNAXDYzkIIIfWdQqXWYjsDIZqCihiagYoYpHbicITQ0vWGsT1gbA84twIYpuD1ziiiLEgSAVGcOXJSG4ChlbdJ3aatzPayyLj2KN28rRfbWcrjYbL4RnJ2gcbvqkJIbZJzdjfbEd7L8KOPWel30tC+uHPzGsLjRaz0rwmUakbjihgcDsfP398/NywsLJLtLKR+0bi5u/UULexJNAeHowu+wBNG1u3h4NcezQY2RptPuWg9OQrNh11Fg3aXYeL4EFytXLajElLdfCIW5bGdoTyUKvXLXXdiWrGdgxBCyGs0EqN0GRkZvIULF1oFBga6uLq6evL5fD8Oh+N39OhRw6reW61Wo02bNu4cDsePw+H4KRSKUtuFhYXpBgYGujg6OjbV0dHxtbS0bNayZcuGmzZtMlGpVOXu79GjR4JOnTq5CYXC5tbW1s3Gjh3rKBKJSn0e79+/v4udnZ2XRCKh5/VS0EgMzUAjMYhme70zSkMYmDeEgTlg2wxgGObfnVFSkJ0igzjOCOJEZyilpmzHJaSy9AriAwxyY2JzDRo4sZ3lfbbfiREr1YwL2zkIIYS8pokjMT6EqKgo7cWLF9sDgJWVlcLY2FiZmZlZLc+wP//8s2VYWJiRQCBgZDJZqVMr9+zZIxw7dqwrl8tF165dxX379hVlZmbyz549azJlypQGf//9d8a+fftiy+orOzub26NHj4YFBQW8wYMHZyYlJWnt3LnTIj09nX/69OmYN9vu27dPePz4cdMjR45ECYVCdXV81rqGihiagYoYpO7hcDjFd0Zp/Po4o06CLDceOWkFEMfpQZzgAFkujUYiGoEDcH0fz4+93GpfrS1iJEnyrz1OFtNinoQQUosoVQw9l5XC3d1dfvTo0ahWrVrlW1lZqQYPHux8+PBhs6reNzw8XLB48WK7KVOmpBw7dsw0KSlJu7R2ixYtslepVJxjx45F9unTp2gUcVxcXKKPj49nSEiI+Q8//JDs7u4uf19/ISEhwpSUFO2//vorslevXrkAMGTIEOdDhw6ZJSYm8u3s7JQAkJmZyZs9e7bTsGHDMgYMGJBT1c9ZV9HwlNou9H88AJZsxyDkg+FwbaFj1BIWbp3g3iUA/mNt0HZaBgLG3UWT3pdg63UDuiaxAGgvdVIrmYrvtdCWZ2WxnaM0DMNkbbgW1YjtHIQQzXZs/x4ETRmL3m2bI8DNBm0aO2LcwI9w8nDIe6+Ty2RYu2wxerXxRgtXK/Rp64PfVy2FQl78+S8xPg7eDiZYOGd6qfeZNLQvvB1Mih1jGAbHD+zF2AE90MnbDf5u1ugR4ImpHw/GmeOHS9wjNTkRP30zF73bNkcLVyt08GqAWRNG4vGDexX8avxHIZdj99Y/MLxXR7Rr6oyW7rbo1boZZk8chZtX/inR/mV0FBbOmY4eAZ5obG9qZGZm5t2vXz+X8PDwYuuITZgwwYHD4fh98skn9m/fY9WqVeYcDsevTZs27oVTG06ePGnI4XD8goKCSn0Ramdn52VnZ1ds/abMzEzewoULrVq1atXQysqqmZaWlq+JiYl3ly5d3EJDQ/Ur/UWpIgsLC1X//v1zrKysyj9vowwKhQKjR49uYG9vL1+xYkXS+9omJCQIDAwMVG8WMADA0dFR6e3tnQcAKSkpZRagYmNjtQGgY8eORdNO/f398wAgOjq6qIAyffp0ewBYv359QkU+U31DFb/azxpUbCL1HYdjDm19c5jqA6bOr48xTDaUslfIyxRBksCHKM4SuRkuAL3JIOziAHrNni6+fcd7Za1bNPPMs6QnuXJlO7ZzEEI024/zv4Brw0bwC2gDcysrSEQiXLlwHgtmT8WrF9H4bO6CUq+bO20CIsLvo1ufQPD5Wvjn3F/YsHIJIh7ex5qte8HhVH6zpN+W/oAt61bBztEJPfoOgIGhEdLTUhERfg/nTx1Dz8BBRW2fPgrH1I8HQSIWoU3HLujasx/EWZm4eO4Uxg/uhVWbdqJ9lx4VzrAwaDpOHzsEN4/G6Dd4BAQ6OkhPTcH92zdx7Z+/0ap9p6K21y6GImjKWCiVCnTo1hOOzi7qjISX2efOnTP5559/hKdPn45q165dPgBs2LAhISwszGDr1q1W3bp1yxkxYoQEAO7cuaMzf/58B3Nzc0VISMhLHq/ym8M9ePBAZ8mSJXb+/v65Xbt2lRgbGyvj4+O1//77b+OePXsa7du3L3rIkCHZle6gFvn6669tnz59qnvhwoVnurq6730p5ubmVhAREaF39uxZg48++qiokJGYmMh/+PChvoWFhcLHx6egrD4dHR3lAHD16lW9Hj165AHA3bt39f7tQw4AR48eNdy/f7/5nj17os3MzKqtaFMX0Tf7tR9NJSGkNByOEbR0msHYDjC2A5xaAgwjhUoRhQJRJsSJDMRx5shObQBGpcN2XFK/2CefanKv6c8yNU9Qa3blyZUpHpyLTKYCBiGkyg6FXoeDc/FldRRyOaaPHYpt61dj6OgJsLIp+S1sTHQUDv99A0bGxgCAmV99g0+GB+Jy6FmcPByCfoNHVDrTwd1/wtLaFodCr0NXV6/YOVFWZtH/K5VKzJ02Afn5edgccgItWv83uy4tJRmj+nZF8NxZOH09HNoV+Cs8J1uCM8cPo4lXc+w6EYq3Cwpi0X8D9LLFYnw98xPo6Opi68ELcG3YCAC4Te2Er+7euZPcsWPHxlOmTHF68uTJUwDQ0dFhQkJCYlq1atVk2rRpzi1btnxibm6uGjlypKtcLudu3rw52sHBQVmRr9fbmjdvLo2Li3toY2NT7D4vXrzQat26deOvvvrKYciQIRFV6aM2uHTpkt6aNWusZ8yYkdKhQ4f8stovX748fujQoW6BgYENu3XrJnZ2dpb9uyaGsZGRkWrHjh0xBgYGZY4OHj58uGTRokWK4cOHuw0aNCgrKSlJ68yZMyY9evQQ29nZKSUSCXfGjBnOgYGBWSNHjpRUz6etu+gNf+1HawEQUl4cjg742k1gaNUeDr4d4DWgCdpM4aP1lOfwGX4VDdpfholTOHjadeJNAqm9OGAsPF6sC2M7RyGGYWTrr0UJ2c5BCKkb3i5gAICWtjaGj/sESqUSYdcul3rdlNlfFhUwAECgo4NZ8xYBAI6GVH17Wr4WHzxuydEIJqb/LaFw5e9ziI99iZHjJxcrYACApbUNJkybhYy0VNy6dqlCfXM4HDAMA22BNrjcko9Yxib/rVt+4tA+5EgkmBY0r7CAAQBQqNR8f39/6ciRIzOePn2qd/fu3aKXME2bNpWtXLnylVgs5g8bNqzBxIkTHaOjo3VmzJiR3L9//yqvnWBmZqZ6u4ABAK6urorevXuLXr58qfP8+fNS143QFLm5uZzx48e7uLm5SZctW5Zcnmt69uyZ+88//zxzdHSU/fXXXybr16+3DgkJMVcoFNyRI0dm+Pv7lzkKAwCEQqH67NmzkV5eXvkHDhwwu3v3rsGoUaPS9+7d+xIAZs6caS+Tybh//PFH3PPnz7W7dOnipqOj46uvr+8zcOBA56ysLHpufwONxKj9aCQGIVXB4fDB47tD38wd+maArVfhziixkOUkITtFBlGcISSJTlAUmLMdl9Qd7q+22j11n8NUaXx0NXmQJLqRnF3Qie0chJC6ITkxHtvW/4pb1y4jJTEBUmnx57i0lNKXGWjRquSawj7+rcDj8RAZ8bBKmXoPHIq92zZiYJdW6NFvAFq0bItmfv4wNCpevw2/F/bvZ0jAhpVLStwn7uULAEDM86gKTSkxMDRCx249cSn0DIZ91B5dewfCN6A1vHz8SowMCb/7OkPU04hiGbQZhS2PwyhevHghAIBHjx7p+Pn5SQvPT5kyRXThwoWMkJAQ8zt37hj4+vrmrly58r1rOlTEuXPn9FevXm117949g6ysLL5CoSj271dsbKxWWQtYvktp63N8+umnGR4eHpW6X2XMmDHDPiEhQXD58uWnAoGgXGurHTlyxGjChAkNmjZtmrdt27aX3t7e0vj4eP7KlSstly5danfu3DnjW7duPdPSKntzGW9vb9nly5efv338zJkzBrt27bLYsmVLjKWlpapLly4NMzMztTZu3BiTk5PDnTdvnuPHH3/MfXsXk/qMihi1HxUxCKlur3dGcYKusRN0jQGrf9+CMOoUyPPikJNWAFGczuudUXLozyCpFJ5a3sAp8WBYrP3QADZzKFXql7vvvGzDZgZCSN2REPsKH/frimyJGL4BrdG6Q2cYGBqBx+MhKT4Oxw/uhVxe+nOpmXnJter5fD6MTc2QlZFepVxzv/0J9o7OOLZ/N7auW42t61aDz+ejXefu+GLhYji6NAAASEQiAMC5k0ffe7+C/Lz3ni/NsvVbsW3Dr/jr6EFsWPEzAEAg0EG3PoH44psfYGbx+vNLxK8zHNqz/e1bFHuZkpOTU2JYydChQ0UhISHmADBt2rQ0Pr96Hud27NhhPGHCBFdtbW1127Zts11cXGT6+vpqLpeLq1evGt6+fdtAKpVWejTAqlWrSowu79KlS86HKmKcOnXKYOfOnZZBQUFJrVu3LtfoidTUVN748eMb6OjoqE+fPv3C0NBQDQBNmjSRb968OSE2NlYQGhpqvGHDBrNZs2ZllnW/0uTm5nKmTp3q3KNHD9GECRNER44cMXr69KneunXrXo4dO1YMAK9evRIsX77cNiIiQuDp6SmrTD91DRUxaj9rtgMQUm9wuNYQGFpDYAiYu74+xjBZUBS8Qm56NsQJAojjrZGf5QyA9bfrpPZrGrlMJ9Z+KKsZ/rz9QqJimJJjvwkhpBJ2bFoHsSgL369Yh/7DRhU7d/roQRw/uPed12ZmpMHGzqHYMaVSCXFWJgwMDYuOcbmv/4lVqkpf5iEnu+SSATweD6M/mYbRn0xDZkY67t++ibPHD+PcyaN4EfUMh/++AW2BAAaGRgCAX7fsRqcevcv3octJR1cX04LmYVrQPKQkJeDures4fmAvTh3ej6T4OPx5+DQAFGU4cO4KGjZuWnS9jVA31sJQkPGu+ycnJ/OnT5/urKOjowaA+fPnO/Tu3TvH1ta22BeKx+MxwOuvbWmys7N5RkZGxRaO/OGHH+y0tLSYa9euPfX19ZW+eW7UqFFOt2/fNij/V6IkhmHuVuX6qrp7964ewzBYsWKF7YoVK0p9QaWtre0HANeuXfs/e+cdH1WVvvHn3HunZiZT0nsjIZQQem8CIoiA2HtX1FXWsq4/tuuq665l3V1XXRsKdldZFRQxgqHXUANJIKT3TGYyvd17fn9MEhISSCENPN/PJyQ599xz3pkMydznvu/zHps6dapr06ZNGqvVyk+ePNnWLGC0ZtasWbasrCx9k0Fnj0SMxx57LMZisQhvvfVWKQDk5uYqAWDSpEktfh3jx493AsChQ4eUTMQIwESMwY+x8ykMBqPPIMQIudoIYwJgTAiMUWqD31MEZ4MFlnIOltIw2OuTQaXOcwkZPyvkvsZRoaZdufUhk0cMxP7lFue23OpGZubJYDB6jbLiIgDAvMsXtzu2b/f2c567b9f2duadB/bugiiKGDpiVMtYsE4PAKiprGi3ht1mRcmpwnPuExIahnkLF2PewsWwmBuwZ/sWnMw/juGjRmPU2PEAgJw9O3tdxGhNZHQsFi27DguXXoOlsybgwN5dsJgboDcYMWrMeGR9+zVydu9sI2KIEj1rexFJknDjjTcm1tbWyl566aUSAHj88ccTbrjhhqSffvrpRGsfjubOFuXl5e08LI4ePaqw2+3tRIzS0lLFkCFDXGcKGKIoYs+ePeclYAwGRo0a5bruuus6FIjWrVtndDqd3LXXXltPCEF4eLgfADweDwEAk8nU4TVzXV2dAAByubxLpSlnsnnzZvW7774b8c9//rPoTGNWt9tNOvqaEYCJGIOf4IEOgMFgnAEhWsiUo6CLBnTRQMJEgFIPJN9xOC31aKygMJeGwFadDElUDXS4jIFlTO7vrT/M/KHf95UorX99R/6AiCcMBuPiJToukEmxd+c2zL50Ycv49p9+xNqP15zz3Df/8SJmzV3QYu7pcbvxz+efBgBced3NLfOCNFokDUnDwX27UViQ12J+KYoiXnz6d+08OLweD3IPH8CYCZPbjPt8vpbSDaUq8Od49vzLEZeQhE/ffwcTps7o0Pfi0P49SBs+sp2XxbloMNXDVFuD1GFtf+26nA44nQ4IgoBm34Sl19+Mt159CW+88jeMyByLjDHjAo+PBkQMURTx3Xffaa+44ooWw86nnnoqIjs7W7do0SLzY489Vg8AmzZtCl6/fr3hD3/4Q8QzzzxT0zw3MzPTrdFoxB9++EFfUVEhxMTE+IFA6cIvfvGLtqkwTURHR3tKSkqUxcXFssTERB8QEE4ef/zx6MLCwguqy1tVVZVQXV0tREZG+pvNSq+88krblVde2aEBakxMTLDT6ZR/+OGHJa29LWbPnu3geZ4eOHBA8+WXXwZfddVVLcbwJ0+elK1ZsyYMAObNm9dtY1W3203uvffepBkzZjQ++OCDLa1rRo4c6QKAtWvX6qdNm+YCgG+++UYPBH6u3d3nYoWJGIMfJmIwGBcChCjAy4dBGw5ow4HYMQClIqhYCFdjNaxVfphLdWisTILoZV0ifkYEOUsmBjmKyxxBiR2+cewrvjteke/0iu1d9BgMBuM8uP62u/HVZx/hiQfuxLzLlyA8IhIn849j+08/Yv4VV+L7b9ae9dzkIWm4au4UzFu0BIIgw08bv0VZSRFmzJ2PK66+vs3c25c/jD898TBuv2oB5i+6EnKFAnt3bIXf78fQ4SORf+xoy1y32407rlqI+MRkDMvIRFRsHLweD3Zt/QmnTuRj9qULkZw6FAAgk8nw8lur8cAt1+Ch26/H6PETMXR4BpQqFaorK5B76ADKS4vx4/68bokYtdVVuH7BTKSmD0fqsBGIjI6Bw2bDlh+/R31tDW66azmCNIGSGb3BiJfeeB+P3nsrbl16KSZNn4WUtHTwkPQ15SXKnJwcTWNjo+DxeHKAQFvQ5557LiYmJsb7/vvvlzTvuXr16uLMzEz1888/H3PJJZfY586d6wAAhUJB77777tp//OMfUWPGjBm+YMECi9/vx9atW4PDw8N9YWFhvjPjf/DBB2t+/etfJ4wbN274woULzTKZjO7du1dTWFiovOSSSxo3b948YO9d7rvvvtjmbIjmspYXX3wxcs2aNSEAcOWVV1puvfVWS/P8F154Ifzvf/971KOPPlp1PsaniYmJvkceeaTqpZdeir722mtTZ8+ebUlLS3PX1NTIvv/+e4PT6eQuvfRSy/XXX9/tlqi//vWvo2pqamQ//PBDQevxJUuW2EaMGOF85ZVXoktLS+UOh4P/9ttvDQsXLjSzUpLTMBFj8MNEDAbjQoUQHkRIQVBICoJCgKiRzZ1RSuGxV8JW7Ya5VANLRQJ8zrCBDpfRNxCAH3v0t0VbJ33YbyKG3eM7kFVQzQQMBqOf0V52c+eTLnDSho3E259+jVdfeBZbN22E6BeRNnwEXn5zDbQ63TlFjBdeX4U3//EC1v/vc9TVVCM8MgoPPPZ/uOvBR9o1clp2wy2goFjz1mv4+r8fI1inx+z5C7HiyT/g8ftuazNXpVbjkd/8CXt3bMWh/XuweeO3CArSIDYhCb997iUsu/6Wdo/hs++3Ys1b/8aWrO/x1WcfgXAcwsIjkD5yFB54/P+gb9WWtStEx8bjgcdXYt/Obdi7YxssZhN0egMSk4fgl//3RyxYenWb+ZOmz8LnG7fh/f/8CzuzNyFnz07IBJkyLCyUTJ061Xb11VebAcBkMvE333xzCgCsWbOmsLlUBACMRqP04Ycfnpo3b176bbfdlnzo0KFjoaGhIgC8/PLLlWq1WlqzZk3oxx9/HBoaGupbunSp+YUXXqhMT09vl6X3xBNP1CsUCvraa69FfPHFFyFKpVIaP368/d133y3+5JNPDAMpYqxfv95QWVnZpjRm+/btLddICQkJ3tYiRm/y4osvVo0ePdr15ptvhuXk5Giys7P1SqVSSktLc91www2mxx9/vNuOtDt27FC99tprkX/7299Kk5KS2ghKHMfhq6++Orl8+fL4devWGXmep1dddZWp2TODEYBQ2qMSHkZ/kbWyAqxDCYNx8UOlGnidpbDVOmEpVcBSHge3NWagw2L0DhRwrJ+zx+eTN+VQ9+VelLr/uim3usbmTuzrvRiMnxtvLYlCRHzyQIfBuEgJUgiNKWGakwMdB4MxGDh06FBoZmZmYkfHWCbG4IdlYjAYPwcIFwGFJgIKDRDa9AaZUjN87mI46qywlMtgLouE05QIoMctzhgDAwGCRuU9m71/1Auz+nqvAxUNu2ts7j7fh8FgMBi9iyRRdm3GYHQB9h9lMJO1kgMQNNBhMBiMAYIQA+QqA+TxgCEeSAJAqQOi9xQcDWY0lhOYy8Jhr2WdUS4A4iq/GZYz8lkv5eTt3OJ7C58oFX60v3hKX63PYDAYjL6j2diTwWCcGyZiDG60AFhLHQaDcRpCgiAoMqCLAnRRQPwEgFIvJH8eXOY6NFZSmEsNsFanQPJ33ZGM0ecQSOFDC9/Ylpe6ok9anlJK6ao9hXaR0j4TSRgMBuPnxN6d27Bv57ZO52mDdbjlngfOez9JAhMxGIwuwESMwQ0rJWEwGJ1DiBy8LB2a8HRowoGY0QClEqhYCLe1Bo1VPlhKdbBUJkH0sM4oA0ha0ZtReUMepu0c7HqBcotz+/Gaxj4RSBgMBuPnyL6d2/DG3//a6bzo2LheETFYJgaD0TWYiDG4YRcbDAajZxDCgQgpUBtToDYCUU1G5JJUDq+tHNYaD8ylajRWJMDrCB/YYH8+8JI3Ja7yq31lMVeO7811JUrr3thRMLI312QwGIyfOw889n944LH/67f9KKWcRCnhCGGdFxiMc8BEjMENy8RgMBi9C8fFQqmLhVIHhKcFxqhUC6+zFPY6B8xlCjSWxcDV2G/tQH9uZOT9RSiLubJX11x/rKLA6RNZS1UGg8G4wBElynM88Q90HAzGYIaJGIMbJmIwGIy+h3DhUGjCodAAIUmBMUob4XcXwVHf2NQZJQIOUyLAUl3PF4XPPNpo3n+8wTBuWG+sZ3X79m86Uc0EDAaDwbgIECXKy3gwEYPBOAdMxBjcMBGDwWAMDIToIFONhj4O0McBiVMASp0QvafgNDfAUk5gKQuDrSYZVGJGkt1k7NHfmrNmbDjvdSil7n9vyw/thZAYDAaDMQiQKGVt1BmMTmAixuCGeWIwGIzBAyFqCIqRCI4EgiOB+PEApT5I/gK4LLVorJRgLtXDWp0MyacZ6HAHMxrHqYlqZ1mFUx0Xcz7r7C9r2FVrd8/upbAYDAaDMcBIlHUmZDA6g4kYgxuWicFgMAY3hMjAy9KgCUuDJgyIyWzqjCIVwW2tgrXKC3OpDo2VifC7DQMd7mCBAMKYo787uX3i+z0WMXyidPLjA8WsjITBYDAuIijLxGAwOoWJGIMbJmIwGIwLD0I4ED4JakMS1AYgcnhgnEoV8NjLYatxw1yqgqUiHl575MAGO3CENewcK/NZG32y4G5n3VFK6bu7TzolSmV9ERuDwWAwBgaJgokYDEYnMBFjcKMd6AAYDAaj1yBcDJTBMVAGA2GpgTFK6+BzlsJWZ4OlTAlLWRRclnjg4k+nJYB2ZN7z2QcynpvV3XPLLM5tebXWGX0RF4PBYDAGDonSi/7vH4NxvjARY3CjGugAGAwGo08hJAzyoDCEBAEhiYGxls4opkZYygVYyiJgr0+6GDujxFd8mXZwxFM+ysm6nFEhUVr3xo6CUX0ZF4PBYDAGBsoyMRiMTmEixuCG/RJjMBg/P1o6o8QC+lgAkwFKXRB9p+BsMKGxgsBcGgJbTQqopBjocM8HDlJUatHb2wtSHuiyt8U3R8tPuHzi1L6Mi8FgMBgDA+tOwmB0DhMxBjcX3V1HBoPB6BGEqCDIR7R0RokbB1Dqh+QvgLuxuTOKDtaqZIi+C6oUb2jh62EFKQ90aa7V7d33U2ENEzAYjEGK8dUjAx3COWl4KOO816goK8XlUzOx5Job8ee/v9YLUXXM7x99EF//92N8u+MQYuLi+3XvgYSy7iQMRqcwpW9ww0QMBoPBOBuECOBlaQgKnY7oUTMx4opMTL5Hg6nLizHupp1IveQnhA7JgUxpGuhQz4UgudNiqtbndDaPUup6dVt+eH/ExGAwGIyuc+pEPl576S/45V034bJJI5EZZ0BmnAF+v7/ba0mUcn6/H0899VR4WlracKVSOVan042eNWvWkB9++CHobOft2bNHtWTJkqT4+PiRSqVybHh4+KhJkyalvfXWWwZRFLu8/5EjRxSzZ88eotPpRkdGRo667bbb4s1mc4fXjEuXLk2KiYnJaGxsZNeUjH6FZWIMbpiIwWAwGN2BEALCJ0KlT4RKD0QMC4xTqQoeRxnsNS6Yy1SwlMfBY4sa0FhbMer4s6iIWnTOOXtLTXvq7J5um4AyGAwGo2/Zkb0J/3nlb+B5HvFJKVAolPB43D1aSxQlsnjx4uQNGzYYEhMT3bfffnut2Wzm161bZ1y4cKHuvffeK7zlllssrc/56KOPdLfddlsKx3GYO3eu5YorrjCbTCbh+++/N9x3333JP/74Y/0nn3xS0tneVquVmz9/fprL5eKvvvpqU2VlpWzNmjVhdXV1wnfffXeq9dxPPvlE9/XXXxvXrl1boNPppB49WAajhzARY3DDVE0Gg8HoDQgXBaU2CkotEDokMEapCT5XCex1VljKFDCXRcFlTsAAdEZReuvHGiyHCsz6zLSOjntF6cSnB0u67JvBYDAYjP5j+iXzMGrcBKSlj4BSpcLCKaNQWV7Wo7U+//jDoA0bNhjGjBnj2LZtW75araYAkJ2dXXfppZemr1ixImHRokVWg8HQIhz84Q9/iBVFkXz11Vf5ixYtsjePl5aWVowZM2bEp59+GvrnP/+5KjU11XuuvT/99FNddXW1/Ntvv81fuHChHQCuueaaxC+++CKkoqJCiImJ8QOAyWTif/nLXyZcd9119VdeeaWtRw+UwTgP2EXy4IZlYjAYDEZfQUgI5OqxMCbMRvL0KRh3YyKmPWDHpLsOIWPpFsSN2wZN+AmAdD8fuAeMOfrbuo7GKaX0nV0n3RKl7MYDg8EYVBSdLMAjd9+MGSOTMCktBrdftQA7sje1m/f6y88jM86AvTu3tTtWUVaKzDgDfv/ogz2K4clf3I3MOAP27dze4fGsb79GZpwBz/3uiR6t3xUSU1Ixasx4KFXn31hwzXvvBAHAU089VdEsYADArFmznIsWLWowm83C6tWrDa3PKS8vV2g0GrG1gAEA8fHx/szMTAcAVFdXd/o3pKSkRN60l6N5bMKECQ4AOHnypLx57MEHH4wFgNdee628J4+RwThfmIgxuGEiBoPBYPQnhGghU2ZCFzMTCZOmY/Q1qZh2vx+T7zmGzKu3InHyVuiic0H4nuUJn4Nge8FElauy6szxErNjW0Gd9fzd+BgMBqMXqSgrwa1XzkejxYxrbrkD8xctxfHDh/CL267Fhq+/7Lc4rrv1LgDAFx+91+Hxzz8IjF97y539FFHP8bjdOJizT65UKqUFCxa0y3BYsGCBFQA2b94c3Hp8yJAhLrvdzn///fea1uMVFRXC4cOHg8LCwnxjxoxxdbZ/fHy8FwC2bdumbh7bv3+/umkPLwD873//03722Wehr7zySklISEjXzTYYjF6E3dUZ3DCRicFgMAYaQpQQ5MOhjQC0EUDs2KbOKOKJps4ofljK9GisTILoDe58wbNsA8jG5P6+YMf4d1q8OiRKa/+z48So3nkgDAaD0Xvs370Dty9/CI/97s8tYzfccS9uu3I+nvnNY5h+yTxotD3+ldhlxk2ehpS0dGR99w2eNDdAbzC2HCsvKcbubT9h9PiJSE0f3uexnC9lJUUQRRHJyckemUzW7viwYcPcAHDq1Kk27cVffPHFsmuvvXbIkiVL0ubNm2dJTEz0NHli6IODg8XVq1ef0mg0tN2CZ3D99dc3/uEPf/Bdf/31Q6666qqGyspK2YYNGwzz58+3xMTE+BsbG7lf/OIXiUuWLGm48cYbG3vtgTMY3YRdJA9uWCYGg8FgDEYIEcALqQgKmYbojFkYfnkmJt+txdTlpYHOKHOyETpkH2SqDktEzkZ4/dbRgt/ecvftq6NlhW6/qOv9B8BgMBjnhzY4GMsf+XWbsRGZY3D5ldfC1tiITRvW91ss1916F7weD7767KM24198/D4opbjm5sGfhQEAdpsVAKDVajvMcDAYDCIAWK3WNjeiFyxYYP/pp5/y4uPjPd9++63htddei/z0009DfT4fd+ONN9ZPmDCh0ywMANDpdNL333+fn5GR4fz8889D9u/fr7npppvqPv744yIAePjhh2M9Hg/3n//8p/TEiRPyOXPmDFEqlWODgoLGLFu2LLGhoYFdWzL6BZaJMbhhIgaDwWBcKAQ6o8RDpY8PdEZJD4xTqQpeRzlstQ6Yy1RoLI+F2xrT4RKAbkT+C9mHRjw1q9Hl3belsHZKPz4CBoPB6DLpIzMRpNG2Gx8/ZTq+/u/HyMs9jCXX3tgvsSy+5gb84/mn8cVH7+P25Q8BAHw+H77+/GME6/SYf8WVna5RUVaKrz//qN34A4/9X2+H2+usXbs2+M4770weOXKkY9WqVUWZmZnusrIy4eWXXw7/61//GrNx40b97t278zrK7jiTzMxMz5YtW06cOb5hwwbNBx98EPbOO++cCg8PF+fMmZNmMplkb7755imbzcb93//9X/zNN9/MndnFhMHoC5iIMbhhIgaDwWBc6BAuCgptFBRaIDQlMEZpA3yuEjjqG2Euk8NSFgVnQwIALrH8syGH0n9nfXVbfuSAxs1gMBjnICQ0rMPx0LBwAIDNau23WII0Wixadi0+/2AV9uzYiolTZyD7h+9QX1uDW+5+AAqlstM1KstL8cbf/9puvD9FjObyG5vN1uE1gNls5gEgODi4xXC6pqaGv+OOO5KVSqX03XffFWq1WgkAhg8f7n377bfLS0pKFFlZWfrXX389ZMWKFaaexGW328n999+fOH/+fPOdd95pXrt2bfDx48fV//73v4tuu+02CwAUFxcrXnzxxejc3FzFiBEjPD3Zh8HoKkzEGNywlCwGg8G4GCHECLnaCHk8YIgPjFFqh+g95bPVmEx73ynKUA6JdOt8uRInQSRqzicpqcfPEY8ooz6RhyQCMgKOFwkllFJeAgeJgkoSlSRQKlFCRQoqSpBAAFGioJSjlFAAhCMcBWkGEgfCITAGjpBAlIQAIIGes6Q5dAKQprGWY6fnNJ3UfIyePoW0LEkB2jIJoM2ntx8nJDAcOEoAAhKIH4G5p9vhBsJqWoa0Dvl07O1+DC1bgQBNCwdWbT2XgBAQSlufSNov33of0hwHAaVoeU7azG15FhkXDkEybmKoSuE723EJ0Jzt2GAgVKWwdz7r3DgVAgEQZKmvFUNVinZlCs6GOgGAMkQX7AtVKTwAoOI5OQC5CtQVqlK0KZWoctk5AGoZgT9UpWgxTZYRKAEIBoXgCFUpaOu9z5wLAPfefR/3+Qer1F99uMp/+dx57rUfrlIB4O+6825nqEohoRMWzpmLinpnt5+PjiBAEAASqpLbBaHz7IdmNOlDwfO8pqysTOHz+XBm5sTx48eVAJCcnNwiEmzatEljtVr5yZMn25oFjNbMmjXLlpWVpW8y6OyRiPHYY4/FWCwW4a233ioFgNzcXCUATJo0qeUJGz9+vBMADh06pGQiBqOvYSLG4IZlYjAYDMbPBUI067313te4KtUfi7mw+NB4m4KKkIXo4NPW8x5VDYHCSYKCwCmUkiASP5wggoOoiAta4oRakkgQLxGlzE8UnB8y0QeZ3w+56KMyyQeBSpBRH+Wo10eo30/g81H4RUJEPyV+kRDJTzmIFLwogUiSRESJ8iIlhFJwIgWRKIhIwVEKIkoEFBwRJUIoOCJRAgpIfkmSRIlKIqW06bMkSpAkSqkoSZJIKZUoIIFKEgWlFFSiEiRAEtH0PSilAREGAKhEAUoIlSgFbZITKCUSDSgPlIIAIFRCkxjRLMQ0Cy6EEkKapAoCQgACrrXC0vQVafV9QHJpkWkIh5Yvm0WewLaklSBBcPpoi3SDFg2DBISXwCBFi2hDaBuRKBA5CCFtBBCAUK5FPWolgQT2ogStHkorONJeLzlDbAII4dqE2iznkDaKEDn9JJwOEy2CTbNW1fomTMvppGkr0tF4i47VJEQ1HaSn128z1jynSYEitGkt2hIV2q3T+hBt/hmcoS7Rli9Jq69bncNTP2QS1+ZAawb7lZuCcGczd2x+ntu+LjpA3vT29NjRw7zP7oD2DPPO3du3CQAwZtQYUUkCc0MNRgoAdZUVpHmsmdyDB3gA4ECgJHzLtnzTi0xB+JZzFE3HedJ2LgCMGzWWTpgwWdq4Yb1wZN9eYfvWbH7y5GlS5rCMwPJnh6DNS631U9LR3LNDm142zc+egvAygZx167ZrUUChDMK4ceO9e/bslm/YsEG7ePHiNh1KNmzYEAwAl1xySUuai8fjIQBgMpk6vK6rq6sTAEAul3dq7NkRmzdvVr/77rsR//znP4vi4uLatBx3u92ko68ZjL6GiRiDGyZiMBgMxs+AIp+95D7Trtpq0T3h9klX7+Xe/N446ZKxIZvEERbHyWxEq0KQoptE1EHBpIw2kDLRzLvlEDm1XKYOotSoNiFcqBOAI5BoOaHULOc4KhN4uZwXNG5BCHYLQrAo8BrwfJDAqZQKjsi1hPA6gIQSQlreD3gp8TolweGgvNMuCW6HJLhtksxnkwSvVZL57ZIg2qgg2SQBdkkGhyQnLsrzLsrzXspxBDwh4HiOcISA53hCOJ4QTkY4SU6IKOeIX06I2PS1JCdEkhNQGSGQN33IOEJkgaCIQAgvEMLzBDwPIvAEAgci4wgEAsibPhRo+iCEEEoplUTJL4mSr+mzX/KLYuCzJEqi5BcD34uSX5REvyhKfqnpsyiJga+lpmM0cMwHyS9Kok+EKIqS5JMgiiKV/CKR/BIV/SKRRIlKfpGIogTJJxFJlAiVJCKJlEKikCglTSINCKVUkgIXT1QCCYgzNJCOAhBITZ9p00fglNY5L0AgW4UiINJICJzSPAZCOvweZxxr/l5qUSoCekSrhBPSWo1pfYw7Pb9ZoCBcYAhc09dc83hgrPUH4RB4r8O1Oj/w+Ai4psfKAeCaRCRKAKlJAJJAiNR09UlbjTXNAQWI1Pw4m79v2kFqEo4oOX3szO/R5vsmoWTeJVHE6/W2FpaaP/XoArG/8XikpivqZo2mjUzTGnLGcMv3Hq9EAMBqteK55/6s+sMfnvc3r3fg4H6ydu1nglYbjHnzlvBOp58HgJEjx3MA8OFHaxRLlt4kE/jAr5zKyjLy0kvPcwAgUSLY7f7m975ECmwDp1NS2+3+5q8BAJJEBLvd3+465uab78XevXfjjjtuVFJKcdNN93B2u1995rz+wmEXFYLQ8bV9RUUZXC4nYmLioFKdDvG22+9t3LNnt/yPf/xjzNy5c/PVajUFgOzsbPX69euNBoPBf+utt5qb58+ePdvB8zw9cOCA5ssvvwy+6qqrWgSOkydPytasWRMGAPPmzWvXsrUz3G43uffee5NmzJjR+OCDDzY0j48cOdIFAGvXrtVPmzbNBQDffPONHgAyMzN7vQU5g3EmTMQY3LByEgaDwbiIcUp+x6/NOXuz3bVTACSkhSft0yqDJhCR7nDs2SqbM1s76lv3gmJrcBl+yP1ImZQ2zBFBkmRjXDFebYhWLtcJsmqnJSi/QrScsrgFLz9Uq9NPchuNRqdOpxMFXqGRxIZgj7/C7KLFNkkqFSWpklBqVgAuA0DDAVCOU5p5PsgsCFqrwAe7BEHrNQhahPIajhdUCo5TqAgRIgHOSAjpUbq8W+JcDio4HBLvclDBbZcEj02Sea2S4LNLgt8myiSrJEh2SQYH5alDEohT4omb8pyHcryXcgofOIWfEqUEoqKAGiDtLk54wCsQ4pYR4pUReGWEeOWE+OQc8ckJ8ctkxCeXC5KcyEQ5R0Q5IZKCECojhMo5goCYAigJIQIhnIwQTiDgAgIK4XkCgUdASCEEcg6Q4bSQoiSEdD13/DygEqWSJPkkv+QPfD6rSCOKTWKN5JdEsUW0EanolyTJJ0qiGBBnmgQcGjgmomlOYFyUiBQQdIgoipD8EpFECZIoEVGUiCRKHJUkNH3mJJFyVJIIlSgvSZSjlPKBTBoK2izmUBBQyoGCp6A8KGSBHyFkCLxHPfN9kAgQEYBISOBzk1AhAkRsEnLEVqIMOS3iQGwSc6SmY61FDunsYlBTRkwgyUaSpHAqir5WpUUtl/4EIHSwv3FzOp1B57uGxxO4Rp0wYQo++eR9PufAHn7c2EmoravBt+v/B0mS8OdnXoJMLpO5PYFqk+EjMjBhwhTs3bODXLFoOj9lygzU19dh06bvMWPGJaisLIckifD6XC1X/KIUqDrx+93w+VwUBPCLHgAgkiRSv+huKZ1ozuRZsHARjE+HCtXVlTAYQjB//kKfRL0dZtWQM3WaNtIUaf0zbplHzpwY+JaaTPV4+qnftLQ9bWioFwDgV0/c529OW3rooce9aWnpUvPZjz56l2r37h38p5+uc02fPrulxOb662+0r/vmv9KGDRsMI0eOHD5//vzGhoYGft26dUZJksg///nPEqPR2PLYExMTfY888kjVSy+9FH3ttdemzp4925KWluauqamRff/99wan08ldeumlluuvv76xwx/oOfj1r38dVVNTI/vhhx8KWo8vWbLENmLECOcrr7wSXVpaKnc4HPy3335rWLhwoZmVkjD6AyZiDG5YJgaDwWBcpKyyFe54xXo8SQJmAwAhxD9n6FQjABBRIo3Hjo+PnHtJ0SW6k2HrS5OQPvdR38kdaxwnGg/GDsnILNGRkCR9ftTJIIdeNyk0VTUreYRb0PARFo/Dm9dQUXri1DGx1mnhJIKw4OBgl9E40mY0zoRWq1XK5fJIQkgYpR6nRKuqJKm0QZKKXR5PGXXTChWl1mjAF41AXXcbCBGcPB/UIPCaRl4IdghCsEcQtFIgy0Mt5zilknCyYAJeD8DYfA9fyUkqJbyqkF78yyZRSC7KO52S4HRQ3mWXBI9dEjxWSea1S4LPJgmiVZKJNipQuyRQhyQQhyRwVonn3ZQXPJRTeCkn91OiFNEijgQBRNH57h1DAEkg8MgI8QiEeGSE+AJCCnxy0iSkEOJvykIRFaQpG4UjVN4kpsgIIXJC0CSgEIEQroNsFIHjORnHc3K+KSMFAT8GJSFnz1+/kOhnoYaeS6gJfIgCCAUlUnPWDEGTGBNwTKGE3pfmBmizg0rLPAoayLYBgIAxDWn63L/4XM2FQpQEMkxaleG0lNc0Zei0XOy3vsinBH4CQBEbGys+88zffH/9659lH3+8ivd6vRg+fKT08MOPey+ZM09spe9QAHj7nTV45s9/UPz44/fC6tVvk8TEJOnJJ3/vmzVrjv/bb78KAhV9cjlpuotPAEgqAIIgwCGXB8pgBJ4SAEGAJMoE4m71/BEAUAlyLFlyFX3vvTdlV199vU+lkvuaijxaFQwFpCfaPE5BmvUJelqWOuP7pp9Vc1bUGU+r2WzBl19+0u7pXvvlpy3XWlctu0GIi0tqOdbssuPz+VVO1+nkBZlM4fj6669PPffcc+Effvhh6HvvvRcul8ulCRMm2H73u99VXXrppY4z93nxxRerRo8e7XrzzTfDcnJyNNnZ2XqlUimlpaW5brjhBtPjjz/erXbfALBjxw7Va6+9Fvm3v/2tNCkpqY0PDMdx+Oqrr04uX748ft26dUae5+lVV11lavbMYDD6msAvV8bgJGtlNoCZAx0Gg8FgMHqPw15z/gP1e9xW6stsPT5zyMTsUTHpswBg4m2f7VHW2ieGTZ2yNXTShBmFFk3OxrKYxGEzwgpdjcVBeT+9KxNkvGZIRuYJrd4wUeXSntTXR5rlHtUouV5WroxR1MsNQgzhyRCX32s5aa46kddQ7qywmQxu0TcUgEIul9cbDIaykJAQq16v59VqdQjP80mEkBYbf4la6qhUXitKxY2SVOqlUoUgUZMGcIQCUhQ6FduJyPNqE89rLIKgtQmC1iPwwT5B0ILng3iOUyo5Th5EiKAHSAghRN7rT3gP8FPid1Le6ZJ4h10S3HYqeGyS4LFLMq9NEvxWSSbaJEGyBz6InQrEKQmci/Kcm3IyL+XkAXGEaxJHoEZAHOmXm0cc4JcR4m4SU7yygHjS/NnfVNLTXNYjnS7rIVROCAIZKaBNmShERggnAM1CisCD8Nzpsh5Zc0kPCWRQKAnpuQg02BnpakBSWlrvLkpBAUopbSqNaVJEmsbRwTia5jdfCdPmQy3TW5xD6OkpgdIk2iSyBHx0KU4ve/ozaVk0IMAArYWa1p9PCzRoJ9Q0iQP9faVx+23XYt++3Vj/bTYSE5M6P6GHtPZxIQEBqJ0Y1CISdSgGnS5FasrwoCCAJkhpDgvTVvdZ4AzGBcKhQ4dCMzMzEzs6xjIxBjespozBYDAuEsyix/yQae/Rwz7LVJxx8a+SKeszooeObv6+uRi8fveeiSETx9el6O1jJ3nqtu/cTCcNmRi7fcK1T089ufPTHcf27p4iVygbhmSManDF2SZylJOCLWEW7fFQHSfxKYKKL1HGKIpHhMXpRobGTyOECKIkeUutdUePN5Sbis01qmM1NRkAjE1bixqNpshoNFaHhIR4g4ODVQrF8GiZMGL4aSPIAJT6fZTWVEhSWZ0kFTslqVSSaLWc0kY94I4CoAcoL4qOcFF0hHu9NZ0+RxxRWHkhyCTwWqsgBLt4QesVBC0VeA3h+EBZC0dkOoAzEEKCO12whwiECsHEHxzM+YMjetGq0UuJ1yXxDgcVXHZJaMkcsUkyv1USfLZA1ohklwTYJQEOSSBOynMuygseiRc84OQ+yin8lCgkEFUrcaRN5oUECB5KNR4KzZlGB/0EFQJlPR5Z4MMrI/DKOeJrEVKaRBQFR0RZQEyh8kBJD23xRiEEstOZKIEPgOcJEXgCnkOTfQog505nozR7o1w473FJ02XwGS6jHX93gdKJUAPaRo5png2ceaQToWbfvr383r271DNnXSJmZI509otQQymRKO21H5NaJet22QeD8XPjwvkF//OkXdsqBoPBYFxYiJSKf7ce377afiqDAjM6mrNo5CXHCSEtx4gYKK2noqiwFpw4phuaNmtsRMM0i1uWfXwPmRWVptmaOvXGqTHDZ5fmZr3hPLZvz0yZQlGbMnLUcckoTWg01qoFr6LEUB9Z7D8ZnOY4SaKIQCzKKHmuMlLOJerCRybpI0YCgcuKWmdjYV5DeeVJcxVnctji7Hb7lNLS01nBPM/b9Hp9SUhIiNlgMFCNRqMXBCGB42LiOS4mHpjc7jFR6miUpIoqSSqxSFKxR6IVhEp1QRT2EMAfjcAFZxsk6gmWfJ5gn6+h3XpnQsC7eT7IxAuaRkHQBspaeK2fF7SE59UCzynVhMg1hPAGBMpaBrzEQk6oXM775Tr4Db25rlviXE7KOx2S4LJTweWQBI9NEnzWgCGr3xYwZKU2SYCdCnA0ZY24JF7wNGWO+MDJxUBZjTrgNwIVQHp6YUb8gMJPqcI9QBm/BBBlBJ7WQoq8pbSHiK0yUlqZzEJqKulpYzIrgEBGwAuEcAAdRwjxo8WItGk7Cq6pJIHQ00aljGY6EWo6Huk6q978D6oqK/Hx6tXgOA6//fOfeaUhSHseS3afXhJqVEqZ/2xbMBiMAKycZDCTtfJjADcMdBgMBoPB6Blb3DWHHm/IUbqpOPRscyK0ofnXjFmY2voie/L1Hx2QN7rHAACvUplSl9+jJoSoAGDtibjsSrt6li5ccSBprD4JoMHF+7/ZVl2wfRyAIEEur08ZMeqoPjR0AiEkCBSS2q47oDdFegWffCwBUYDAKw+VHVHFKBxCMD+EEBLdOiab11VT0FBxKr+h0lflMIf5JH8aOigdUalUFUajscJoNLr0er1cqVSGcxyXSAg5Z5kJpZJEqalGkspqpdOGozJKzVrAFdFkONqbSBynNglCkEXgtbambi0+PlDWwvGcqrmsRddU1qLsfMmLG0pBXZR3OprEEUcgc8Tb1KXGZ6OC3ybJJJsk0ObMEScVOJfE866A34jMRzm5jxKFCKJqZcZ6QT+3q/UEESmpXZpLCCgHUI4QqUnhoBwhlANo4Fig7IBr8qdo6UFLTiskLUrJaSPK5sPAmdNoOxHlZyOkjB2ahsqKciQkJeHXv/09rr7hwn37rOL5iiAZz8pJGD97zlVOwkSMwUzWyncB3DnQYTAYDAaje1T5XVX3mXYVFfsdUzube/fU6w6qZMrRrcemXPvhIZnN0+KZkXD9tVvV0VEzAIBSSB8eS9rd6JVPUWr44qHTQinHkSS3zVR+9IfXqn1u23gAEGRyU8qIkUf1YeFjCSFaAOBE3qxrCD+iaQyJ4CjXIqwIwXyeKlZRIzfKIghP0s+M0Sv67KcsNSfyGsobS631wU6/Jw0BE8l2cBznCQ4OLjYajfVGo9Gv1Wq1crk8jhAS1rVnDwgYjlZWSVKpSZJK3JJUCkprlZRaDWczHO1NCJHbeD6oQRA0ViFgXuoTeK3ICxqO51QyjlMGcZxM29StRdeXsVxsiBSikwoOp8Q7m8pqPDZJ8NqazFitkiC2FkccVIBT4nknDZixelvEEU4ZEEegQqCkpl+6wnRHxBgMcAQBAaVJPGkrqAR8GppEFRCAcoHOLs1Ol836SYu40vLvmZkobYUUrslIc8Czny5EVAJfHiTwndffMRgXOUzEuFDJWvlvAA8OdBgMBoPB6BpeKnr+ZD686xtXxXh04UJ7VHT6zpmpE6ecOT716g+OCg7vyObv5UZjcfJtN8c3Z2v4JeJ572hKnkfkM3kZsQybEXpKpuDHAkD50R+3lR3+fgQAAwAIMpk5afjIw8bwiNGtL7jlLnW+wRRZo3AFZRCQlvIGTsFVKmPkJ5Xh8iAiJxkdmW1KlIoVNtOJvIbymlOWGrnFY0+mQMS5HqtcLq9rMhK1NRmJhvI8n9iTrIfzNxztTTgvzweZBEHTKPBae1O3Fh/PN5W18CoVIbKgQFkLMV5QXg0XED5KfE6Jdzip4LRLgtsuCe4mccRno4LPJglSK3GEOiWBczT7jQTEEXlzpxqprRlrm9fShSZiDDTN2SikSTxpElICIsrpr5vnoSUj5bRKclpMaZuNArRPWGndaeSCLetRC3yZWuBrBzoOBmOgYSLGhUrWyhcBPD7QYTAYDAajc9Y6Svc8bTkS4QdN6Mp8nuNdy6fdaOI4LvbMY1OXrTkmuHzDW4+l3HXHHrkueGLz924/1/je0ZQ6kXJDQOBPm2TcEWSQzwQAr8tWl5v1+km3rb5FIOEFoTF5+MiDxojITEKIvmVhiXi0VmNOcEO4nBeFMaSVSSThYVNEyHOV0QqRV3Mj2px3BiaXrSy/oaL0hLlSqnU2RotUSkbnFxCiRqMpDQkJqTYajZ7g4GC1QqGIJoTEnGkk2lWaDEerJKm0XpJK7JJUSiVaraDUogM8TYajAwblOJWZ54PMgW4twU5B0PoFXivxvIbneZWiqawlGOBCCCHqAYyVAcAtcW4X5R2OQJcaV5RvTELS0CE+CkIlSiCBUAkIfKaENH0NiYJIAJECJpBc09ecBHCUsgyF/qRJIJGaS3naZqaAEpBWmSldKutpnY0CtBNS2mSjdLusRy3wJWqBr++VB89gXMAwEeNCJWvlnwH8bqDDYDAYDMbZOemzFd1Xv8tUJ3nGd+e8+cNmZKeFJ83q6Ni0K1fn8W5/m7KOoMSEw/HLlo5qPWb1CJUfHEsmFCQKAGKGabPDEtTTmz0pak7u3n1q75cJoDSy+RxeEKyJ6cMPhEZFZxBCjK3X432yKr0psiDIpksk4NqKMQR+uVHIVcYoLDKdkEg4ck6xxunzmE9aqk7mmcqdFfYGg6eptWvnzwwgCIJVp9OVhISEWJqMRA2CICT0RkeSnhiODhSECA6e15gEQWMV+GCHIAR7BUEr8ryG43m1jOMUao6TaQDeCEDfU+GH0XWczmlIS+uSTnlOKDhJAmn64CgNfE2bxBAqBZIKaED8IOhAHAkII2g6GeCbO24wBh/NZT0t5TxtBBWAA6GkqdWqTuBrgmWCeaBjZjAGGiZiXKhkrfwtgGcGOgwGg8FgtMcu+Wy/asjZv91TNw1At+rxtYqgqtsmXaU72532aUveP8F7xXY562kPLs/lFYoRrcdqHcoTnxfEhwOBUhFDlHJfQqYurfmC3+91NR7b9NZhR0P5dLS6I8jxvD0xffj+sOiYkYSQkDYbUVCVQ3tYb4q0y7zKMQTt4+Q1fKEqRlEuD5WFEB4jOruA9kuip8RaV5BnKm8ottaqbF5XKppKXrpKk5FopdFodOj1ekVXjUS7SpPhaLUkldU1GY76JalSTqk5GHCF94HhaC/C+Xhe1cDzGosgBNsFQesWhGC/wGsozwfJOE6l4Di5hhBeB5BQQvrHQ+Jio7dEjL6BQAIRA+JHi0hC6WlhhEqBOQFRhLZ8Jk2CCKFtRRJOAjgwcaRfiVXKT4XImYjBYDAR40Ila+VjAF4a6DAYDAaDcRpKKX3bfnLHq9b8IVInPhBn46bxS3YYg/RnNf2cvvi9U5xPSj5z3DA6c2fkJbPaeWicsmgOflcUPQwgCgBQaYXCoVND5IQjcc1zGsqOHijY9qGRUrHNFRjH846Eoen7wmPihndkvkkkzhZsDjsUbAnVcxI/8szjAEDkpE4VrchXRMjlnIJkNHdSOReUUlrjtJzKM1VUnrRUcQ1uW7xEaVxn550Jx3HuJiNRU0+NRLvKQBuO9iKU45SNAfNSrU3gtU5BCPYJQktZi5zjFEGECNqmspYOTVx/jgxuEaOvILQpY0SSmipkTmeNcFQCmjNH0GHWSFNZjQRwLeU1tD99ay4s4lXykwaZ0DjQcTAYAw0TMS5UslY+AOC1gQ6DwWAwGAH2e0zHHzbt9duoP6Ona8Qbog8vGTVv1LnmTL/ivVLOL8W3O0CImP7wL6oI395H41Ctfue2ivDJzZ53gpwzDZsZWiHIuJa9RL/Xlb919Z7GqoLpOMP8kuM4V3xa+p6IuPh0QkiH4ozMozxlqI8sVTq1wwlIx1kJHJyKcPlRVbTcy2v4od0RE6weZ3WBubIov6HCV+0wh/kkscPWrl2hyUi0PCQkxHq+RqJdRaLmeipVVItSsVWSSnxUquQkatICjrAmw9EL0guBEN7F85qmLA+tM9CiVisJvBaBshalihCZlhBeD8DYul3wxcbPU8ToG5ozRmirshoJRKJtskaaBZGm0pomcYS2FkYuMr+RRJW8QCcTbAMdB4Mx0DAR40Ila+UdAFYNdBgMBoPxc6dB9JgeNO05lutrnIbzuxCV7pt+Y76clw0716Tpi1ZVcCKN6ehYxOxZW4xjMmd2dCy7LDz7aL2hxWeDEHiHTg3ZowqWTW89z1p76tjxze8IkuhLO3MNwnHu+NSheyLjE1IJCXhttIPCH2Q1HNA3REi8XzaWnL29pSQzCLmqGEWDzCDEEo6knO0xd4RX9NkLLTUFeQ3ltjJrvdbp9wzF+WU7iFqttsRoNNYYjUZvcHCw6nyNRLvKIDcc7UWI2FTWYhYErV3gg12CEOznBQ0V+CDhdFmLoANICCGkSz4pgwUmYgx+WvuNtCqtaeM3EsgcwWkz1hZT1jbiSLPfSL+KI8lqRZ5W4B39tR+DMVhhIsaFStbK6wF8MtBhMBgMxs8VkVLxb4252z9yFGcC0HV6QidMTMjcNjExc3pn82ZcvqqKSLRDAYGTyexpv7hfbN0utTVfnYzNLrcFtTEMjc8IzjbGqGa0vkMuSaKvcOenO+pLDk5GB4abhHCeuCGpe6ISEpMJx3UoqAAA5xfq9A0Rx4KshhiOckPO9bh4NVeijFEUK8JkeiKQkd31spCo5C8PtHatO2WpkTd6HMkUOG+fCkEQrHq9vsRoNJoNBgN600i0q7Q3HC3nqFSvorCHDjbD0d6EEHmjIGjMPK+xBrq1BHsFQUsFPohwnFreZF4aDHDG/vx5nA0mYvxcIWgSRaRO/EbamrE2iSNNJTRd8hsZEqQ4FsTzrv5/jAzG4IKJGBcqWSuXAPhqoMNgMBiMnyM/uqoPPNmQo/FAamew2RPkvMx677QbPF0pr5ix8N16QhF6tuNxSxdna5I77mxCKehHxxN3WjyKNp4bIbGq3XEjg0cSQtpkMjjMlYW5WW84RZ+7wxIZQogvNmXIrqjE5KSO2sG2RuEMOmYwRZrkbvUogo5FlpZ1BWJWRsmPKaPkPKfkRvbUd8HkspXmNZSXnTBXSnVOa4xIpSR0s6Xh2VCpVOVGo7EqJCTEqdPp5L1tJNpVLmzD0d6DgHfzvLqBF7QWQdA6BD7YIwhakRcCZS08p1QGyloEPQJlLb3+c2IiBqN3afYbOd2lRiMPPsFzgnegI2MwBhomYlyoZK28FMDGgQ6DwWAwfk6U+50V99XvKisTnZN7c90lGfOy443RHQoPZzJj4btmQs/euUPQaKqH3HNnyNk6TIgSvO8dTcl1i8KY1uNqvSw/bZJRSzgS3XqcUkkqzlm3rTp/21gAHYsJhPhjk1N2RSelxHEcd86rOCIRl9YSmhNsCVNzIj+aoJNSDQKPIkx2VBmtcAjB/NnLWLqA0+dpOGGuPJnXUOGqtDeEeAIlM72WxdDKSLTeaDSKfWkk2lUuIsPR3kTiOFWDIGgsPK+1CUKwSxC0PoHXgheCOJ5TKTlOoW4qawntqldKpyLG5rreir9vuGRgXqbz51+FrVt3wuWqGpD9LyQUirBDhPD+gY4DANatW6ddvHhx2qOPPlr18ssvVw50POfin//8Z8gvf/nLxH/84x/FK1asMA10PIzz51wihtDPsTC6B0slYzAYjH7CQ0X378yHdm9wVU4AcNbyiZ5gVOuL4gxRZ+1G0g567r/Pfrs90lNv2q4MC53W0XGeg/yW4UXJ7x1NKfBTrsX3wmnxDc39qa522MzQXF7gWlq1EsJxSeOWzIwaOq3i6A+v5flctvHtY6JCeeHJ6eWnCv0xicnbY1KGRHMcl9Rh+BxVWY1106zGOgheebneFFmotuuGEJCOn1cKhafWN85T6wMACMF8nipWUS03yqIJT9r5dpwLtUxhzAxPmpgZHgjNL4nuksbaw8cbys3FjbVqu8+dhvMoDZIkSWmxWNItFgtOnTrVMn6GkaigVquNPM8n9aWRaDOEKNQ8SUrhuaQOPUcuVsPRTuAkyRXq9bpCgc6FBUJktoCPh6ZREIKdAq/1CEIw5fkgwvNqOccpVRwnBANIwMX5fDEGDUTqr53y8/Pl6enpGVdddZXpiy++KO6vfS9kNm/erP7vf/9rOHLkiPrYsWNqk8kkhIeH+2pqag73ZD273U5+//vfR61du9ZYVVUlDwoKEidPnmx75plnKseOHetuPbdZVOpszRMnThweMmSIr7N5W7ZsUT/++ONxx48fV+v1ev8111xjev7556uUSmWbLAdJkjBx4sShXq+X5OTk5AnCwEsIAx8B41wwEYPBYDD6gc8cJbuesxyNFUG7lCnRXRZnzK0nhHR4wX8WOv37XPXDj2FJN11/1uMKQdLdNLzItSY3uYK2Eg98Hin8yI+1wenTQncoNUIbYUWpCYkZv+z3MeW5m7aXHdowDICx3cKUChVFhdMqigrFqMSkHXEpqREcz5/VsNMv98bWR5XGgkJS23U5elOEW/ApxhKc/eLebxXTbcec6QDAKbkKVYz8pCJcriUyknG27JOzIXC8MsUQNSrFENUUPqXVDsvJvIbyqkJLFd/gsidI6NhEtTt4vd6wmpqasJqamtbDolarPdVkJOoJDg5WKxSKGO4cHiN9AUcMoeANoXwHHXKbDEfLA4ajxQ5JKhMlWq2k1KIHPJG4aAxHzw2lPq3fb9b6/eZzzjMaxsLrraccJ/MTIoiECBIhvEQIT5s6A7F2tIzzgIAQrt9EjM6YNWuWIycnJzcyMnJQZIYMBtasWROyatWqcEEQaEpKittkMvX4etrlcpFZs2al5eTkaEaMGOG86667asrLy+XfffedYfPmzbr169cXzJkzp8XkNTU11fPoo492mM6Um5ur2rhxoz41NdXVFQGjqKhIdvnllw8NDg7233jjjXXHjh1T/eMf/4hyuVzcf/7zn/LWc59//vmww4cPB+3cufPYYBAwACZiDHacAx0Ag8FgXMzkeRsL7zfttpgkb6+WjrQmLTxpn1YZNKGbp3X699ldU5PmdzhyhKCgsWebo5X7I68dWlL4WX6CBSD65nEqQXl8a/3UxNG6nwxRqtlnnhc7Ys608OQJdblZr+902+qnnGV5vqq4aGpVcZEUGZ+wMz51aCjH82f3DyHgnNrGsU5tIziRbwxuCN+jbTSGcpQffq7HKbmlGEehO8ZR6AbhYVVEKvYpo+SUV3MjzmZuei4IISRKYxgSpTEMuSQ+YAPS6HFWFTRUFOebK3w1DkuETxJT0Tt323mbzZZss9mSS0pKWgZbGYlajEYjgoKCdIIgJA6EcSUhgoyQmHiOi4kH2v+oA4aj5VWSVPqzMhw9N5RIklcGeNsJaj/DJ4PRixBCxIGOoTVarVYaM2aMu/OZPx/uueee+rvvvrt+3LhxbqVSSQkh43q61tNPPx2Rk5OjWbBggXndunWneD5g4/PBBx+Yb7311pT77rsvMT8/P7d5fOjQod6zlfUsXrw4CQBuu+22+q7s/fbbb4e4XC5u3759+enp6V4AmDx5ctrq1avDXn/99XKOC/wJzM/Plz/77LOxjzzySNW4ceMGzWuBpcMNbhoGOgAGg8G4GLFJPus99Tuzr63bmmCSvD1+A9IZhBD/nKFT22czdE6XbjLU/LSlU2OrMLUn5YrkilKAtnvzUXywcXb5cesOStsfk6u0YWMW/3pK8qRr9oCQcxWyc9WlJVP2/LhxSNHx3F2iKOZ3FpPEizpLWNXMsiG5w6viTpxwqezZFLTTGmYqIthd4Zli2WebatraGGTNdRz0WnxbqETLOjv3XOgU6qgJUalTbhk+e+bjE64c+ui4JfYlQybuTzNEZ6sE+UH08k0Fv98fXF9fn1FQUDBj165dM3788cdR33//ffCWLVsqjhw5sqeysjLb4XDsFEXxFKV0QC9qCAnS8fzQdJns0skKxb2zVMqnZqjV/x4fpH4/Ua1aI6iUr1Qq5I8fksmu3sbzE34iJGYHoD4KkEFuDsE4H9as+RQ33HA3hg2bBIMhCeHhqbjkkiX4+OP/nvM8j8eDP/3peaSnT4ROl4Dhwyfj2Wdfgtfb1seypKQMKlUU7r33lx2uM3/+VVCp2lrnUErxwQefYfbsxYiLGwG9PhFDhozD4sU34PPP2/vkl5dX4pFHfoNhwyZBp0tATMxwXHPN7di372D3nowO+PTTtbjssqsRGTkUen0iRo+egeef/zs8Hk+7uSpVFObPvwo1NXW4995fkpCQkEyVSjVmzJgx6Rs2bNAAgNVq5ZYvXx4bHR2dIZfLxw4ZMmTEu+++26FvksvlIr/5zW8i09LShqtUqjEajWbMuHHjhr799ttt5j/22GPR6enpGQDw5ZdfhhBCxjV//POf/wwBAuULhJBxjz32WPSZ+xw5ckSxbNmyxPDw8FEymWxseHj4qGXLliUeOXKkXberxx57LJoQMm7dunXaVatWGTIyMoapVKoxOp1u9BVXXJFcVFTUThDcunWr+s4774wbOnTocJ1ON1qhUIxNSEgYee+998bW1dX1q7lya6ZOneqaNm2a68ySi+4iSRLef//9MAD4+9//Xt4sVADALbfcYhk3bpy9sLBQ+e2332o7W6uqqkrYuHGjQalUSvfff3+X/EBKSkrkBoPB3yxgAMCYMWMcbrebq66ubnkPcueddybEx8d7nn322UFlaMMyMQY3JgAUveSyzmAwGD93KKX0dVvB9jdsJ4ZSoE9KR1ozI2XCdoHju7ePKEmkizcZrAUnxkXNn3eCk8nO2UElQecYNTO2dteW8vCJAGmzdl2xc6rL6s8dMtEQRghp1+UiImXixJC4jMbjm9/eajeVTcfZ/yaRmrLSyTVlpTQ8Jm5PwtB0LS8Iwzp7DF6lK7U29lQqKHyaxpBdOnM4z/uFsQSddJagELz1vtHe+iYfDQ1/QhmrqJSHyEIJj+GEdGImeg4Ugix4eEjcuOEhcQACrV3LbPXH8kwVdUWNNYpGjyOFAr3ukOh0OmOcTmdMRUVFy1iTkeiJkJAQk9Fo9Gs0mgE3Em2GEI4jJCya48KigfYJQa0MRxskqdgpSWWEGY5eHKxY8X8YPjwN06dPRmRkOBoazNiwYRPuuuthFBQU4o9/fLLD8265ZTn27TuIZcuugEwmYN267/HMMy8iJ+cQ/vvf93Ee/23xxz/+BS+88C8kJsbj6quXIDhYi+rqWuzffxBffvkNrr12acvcAwcOY/HiG9DQYMGll87G0qWXw2RqwDffbMDcuUvx6afvYsGCuT2KY/nyR7F69SeIiYnGlVcugk4XjD17cvDUU3/D5s3bsH79pzgzJb+x0Yo5c5YgKCiILF26tMFsNvPr1q0zLlu2LHXTpk15999/f0JjY6Mwd+7cRr/fT77++mvjPffck5yQkJA3d+7clnIDt9tNZs2albZ3715NUlKS+7bbbqtzOp3ct99+a7j33nuTDx48WP3qq69WAMCcOXNsFouFX7VqVfjQoUNdl19+uaV5nfHjx59TuM3OzlZfccUVaQ6Hg58zZ44lPT3dXVBQoPzqq69CsrKy9OvWrSuYNWtWuzVee+21sB9//FE/d+5cy9SpU2379+8PWr9+veHYsWOq3NzcYyqVqkUYeP3110O///57w6RJk2wzZ860SpJEDh06pH777bcjNm3apNu3b99xg8EwaEpvusuxY8cUVVVV8oSEBE9rIaGZSy+9tHH//v2arKws7eLFi23nWuuNN94I8Xq9ZNmyZQ2hoaFdEr7j4+O9ZrNZOHHihDw1NdULAAcPHgxSKpVSc/nQyy+/HLpnzx5tdnb2cZmsW5WcfQ4TMQYz8/7iR9ZKC3B2h3oGg8FgdI09nvrcFaZ9cFD/9P7YTyVT1mdEDx3d3fOISH0A2t3JOhv1e/bVhE+b0mkb2Iwwy+RGjyz7UJ2xnahib/COyP2prmrYjNB8XuCGnnlckKt0GZc9PKOhPPdgwbYPDFQSz9WdhNRWlE2srShDWHTMnsT04RpeEM5ZMhI4CzK73jTZrjeB98mq9aaI/CCbPp6gY/PQM/HbxVR7njMVADg5qVHGKAoU4XIlpyAZ52uuyRFOSAgOH54QfFrjqXdaS/IbKspOWCrRqrVrr9PaSLSwsLBlXKFQ1BsMhlKj0WhrMhINaTIS7fJrp69pazjaXstrbzhawUvUpAGcF7Ph6IDi99u8HBfw8gA4SghPA4IfIQGBk/KUUgGg53zu9+/fjOTkxDZjXq8XS5fejBdffBX33HMbYmLaNxnKyzuBnJyfYDDoAQBPPfV/uOyya/Dttz/g44//i5tuurbHj+2ddz5AdHQU9u/fDLVa3eZYff3pm9N+vx+33LIcdrsT33//X8yYcdoaqLKyGtOnL8QDDzyGvLw9UCi6999pzZpPsXr1J1iyZCHee+/fUKlULceeeeZFPPvsS3jjjVV46KF725x3+HAu7rnnVvzzny85VKqwMgD497//bX3ooYeSFixYMHTcuHH2nTt35qvVagoAGzZsMC1cuHDo888/Hzl37tyWXwxPPfVUxN69ezUzZ85szMrKOtl84VlRUVE5ceLEYf/+978jly5darn00ksdV1xxhS01NdWzatWq8BEjRji72n1EkiTceeedSXa7nX/ttdeKHnjggZbM8bfeestw3333Jd9xxx1JJ0+ezG2dXQAAW7Zs0W3duvX4xIkTW3z/Fi9enLRu3Trjhx9+qL/nnntaTGn+9Kc/Va9evbr0TMHn73//e+hjjz2W8OKLL4Y/++yz1V2JeTBy9OhRJQAkJyd3WKKRlpbmAYCTJ092+vdr9erVoQDwwAMPdDkD7u677zb94x//iJo9e/bQRYsWmY8dO6batWuX9p577qnhOA5FRUWyP/7xj7EPPvhg9bRp0wadTyP74zD4YemYDAaDcR7Ui+66a2u3bLu7ftdwB/WP6PyM3mHRyEuO98SzgfNL3TJQM+3bP5FKUk3nM4HpsXWz4oPt2R0d87mlqKOb6mI9Tv/us51vjB0xesI1T4Xro4ZmA+j0bk9dZcXEvZt+GH7yyKF9fp/vaFdiBABR5os0RZbPKk09mlQTXXTEq3Bto6D2rp4veWmEs8g9w7zbOsG0rVG0Fzh3++3+bZTSLtUKd4VQdXDCtNhh0+8YOXf6ExOXJT08dpFpQdKYPUm68GwFLxwF0Kmx2vng8XhCq6urxx47dmzWjh07pmVlZaV///33wvbt208dP358Z01NTbbL5dojSVJF56sNDBwxhPL8yJFy2RVTlYpfzFKpnpsepP7P6CD1mhi16n1RpfxbiUL+UI5MuGIrz2X+REjELkCRB8Ay0LFfqEiSW+7321U+nyXI52vQeL11Wo+nVuPx1AR5PNUqj6dG7vXWcj6fSfL7G32i6HRLksdBqd8OUBtA7IRwjpSUZCchnAfgWn5fyeVyLF9+B/x+P376aWuH+69c+UiLgAEASqUSf/7zbwAA77//yXk/PplMwJkXzgAQGhrS8vV332Xh1KliPPDAXW0EDACIjo7EY489iOrqWmzevK3b+//7329DEAT85z9/byNgAMDKlY8iJMSATz75st15arUKzz33B/C80PJ8Ll++vIHneWq1WvlXX321tFnAAIAFCxbYo6OjvceOHWuj1nz00UehhBC88sor5a3vnMfExPh/9atfVQHAm2++eV5ZXFlZWUFFRUXK0aNHO1oLGABw7733mseOHWsvLi5Wbty4sZ3J7V133VXTWsAAgPvuu68eAPbs2dMmMystLc3bkYnkL3/5y3qNRiP++OOP/e4h1JtYLBYeALRabYd/S/V6vQgAjY2N58xKXL9+vaa4uFiZmprquvTSSx3nmtualJQU3zfffFMQGRnp/fDDD8OKioqUDz30UPU///nPCgC4++67EyIiInx/+9vfqnbv3q2aOHHiUJlMNlan042+884749xu94BWCrBMjMFPPYButZdjMBgMBuCnkv8vjbnbP3OUjAbQL9kXzURoQ/MjtB23P+0MInZPxIAkyRvz8vP0w4dFdGX6FckVMz/JS9ze4Fa0i08SadCx7PqJyeMMP+nCFbM7Op8X5Kphl9w9y1pbdPz45rc5SfS1y9w4k/qqyvH1VZUwRkTmJA8fKQgy2aiuxAoA7iBbRlWQDUTiHMHm0G1aS2gwLwldPh8SgtxV3knuKi8ASDKDcFgVqzDL9EI84brVMeacBMmUIaPDk0NGhycDAHyi31VsrT2WZ6owl1hrNXafOxXn0dq1i5zNSLRRr9eXhoSEmA0GAwkKCtILgpAwEEaiXaXJcDSB42ISum44WqemcIT8fA1Hew9KJY5SL9eReSkAlJVV4ZVX3kV29m6Ul1fD5XKfcbzcC8BDCEcAcJRSJQBu+vSp7cqkp06dCJ7ncehQl3XODrn++qvw+uvvYMyYWbj66sWYMWMKJk0aD52u7ct89+79AIDS0nI888yL7dY5eTLQOjkv70S3SkqcTicOH85FaKgRr776Vodz5HIF8vNPtBtPTU2BVqsBIaTl978gCAgJCfG7XC5u+PDh7coNIiMjvYcOHWq58DebzVxpaakiPDzc15EZ58KFC62PPfYYjh49qj7zWHfYu3dvEADMmDHD2tHxmTNn2nJycjT79u1TL1y4sI34PGHChHYlJklJSV7g9EV9Mx6Ph7z00kuhX3zxhfHkyZMqu93OS9Lp6pHq6uoe/x/fsWOH6r///W+bTHe9Xu//wx/+UNvTNQeK//znP2EAcPvtt3dbpJ8zZ45j//797XysXnvtNWN2drbuhx9+yPP5fGTx4sWpOp1O/PDDD0+eOHFC+dRTT8XK5XJ6ZheT/oSJGIOfXrtrxGAwGD8XNroqc1Y2HNR5IfW570VHXJExx0UI6VG2I+lmJgYA1GRvydQNS3cQQjr1GCAE5Lr04onvH03JcfmFjjqbkFP7zbMjUoK2RaVqJhJCOnyjGByeNGzitU/7T+76LLu++MBkdKEEpqGmemxDTTUM4eEHkodncDK5PLPzRxeAclJQY0jt9MaQWsg8imK9KapY5dCmE5DIrq4BgPOZ/aN85sBTzKu5IlWsolQeKjMQgYwgpBMfjm4g4wVVqiE6M9UQ8MOjlEpVDvOJPFN5VaGlmm9w2xNpL7R27Qp+v19XX1+fUV/f9i2FWq2uMBgMFSEhIS6dTidXKpURHMcl9Obz0Fc0GY7qeL69hkapJFFaXylJZbUSLbFLUokoSVUCpWYd4IoA6ID7iVzIFBWVY86cm2CxWDF16ljMmTMVwcEa8DyH0tJKfPTR1/B4bHKvt67V747A/zmDAcTnaxCb2tOKhBMknudpSIghqK7ORAjhHAA4BDxxunWR+sILTyEpKR5r1nyKF198FS+++CoEQcCCBXPx/PN/REpKQLNsaAgkD3z55TfnXM/h6PJNbQCA2dwISinq6kx49tmXunVucHCzdyPX5q68IAj0bHfqeZ6HKIotglBDQwMPAGFhYR1mgcXHx/sAwGq1ntf/7+bMgKioqA73aR4/U5QAAIPB0O6xyGQyCqDNYwGAxYsXJ//www/62NhYz6WXXmqJiIjwKRQKCgBvvfVWuM/n63EmwL59+9R///vf29Q7RUdHe/tTxGjOtLDZbB3+PJqfP51Od9asx5qaGv777783KJVKafny5V0y9OyMsrIy4be//W3c3XffXTNnzhzHSy+9FFpXVydbs2bNqcsuu8wOwHro0CHVe++9F/7iiy9WarXaAfElYSLG4IeVkzAYDEYXKfHby+6r311ZKbomDVQMo6LTd6pkyrO1Je0UIkrd7kYhuT16Z0XllqDYmJldmc8TyG4ZXpS66mhKvl9q74EBADWFjumuRt/h5PGGGEJISEdzCMcLqVNvnBU9bPap3Kw37KLP1aUMCXNt7Zj9tT9CHxp2KGVEhihTKM7aJrYjfApPYl10cSIoxCCbfp+uIcIv+ORjCToWXM6G6JSS7AWuJBS4QGTEpIySH1dGymWckhvZFUGoOxBCuGiNMTVaY0ydkxB4miweR2VBQ0VxQUOlv9ppifRL4hD0Y6nvOYxEC0JCQhqajESD5XJ57GAwEu0qAcPR8GiOC48G2jcfOsNw1CVJZZTSWhWlVmOT4eh53am+2Pn3v1ejocGC1177M26+eWmbY//977f46KOvz3puba0JcXFRPKUiD3gAMeBRYTKZERysgcdTEwQAfn9AaPB6HfD7G31NoodECC8RwsNstqgBcIRwTgA8Atf0wsMP34eHH74PtbX12LFjNz7//Ct8+eU3OHYsHzk5P0GhUCA4OJCZ8fnn7+GKKy7rteelOeNj9OiR2Lnzhx6t0ToTo7sYjUYRAOrr6zvMniktLZUBZy9f6CrNF9XV1dUd7lNVVSVrPa8nbNmyRf3DDz/op0yZYs3Ozj7RujRGFEW8/vrrXco8PBsrVqwwrVixolcu+nvKyJEj3QBw6tSpDj0vCgoKFAAwZMiQs7Y1bTb0vOqqq7ps6NkZ9957b7xOpxNffvnlCgA4fvy4EgCmTp3akkUzduxY5+effx56/PhxxZnlQf0FEzEGPywTg8FgMDrBTUXXyoYDe7Lc1ZMAxA1UHDzHu6anjD+v/XuSiQEAVT/8mJRyx61iV++iy3lJe/OwIv3q3ORyChLb0RxrvXfU8S31ZenTQy0cT1LOtlaQISp5wjV/oiU567ZU5W8dC6BdLXRHWOrrMvdnb4IuJPRwysgMn1yh7F67WwLeEWwZ7wi2gPPzJl1DxFGN1RjFUa7bZZjUR0NcpZ7prlIPQOBRhMn2KWMUTkHLpxHSrWyPLqNXBEVPjEqLnhgVCNfj91lPWqpO5DWU28ttJp3L7x0KQHXuVXqXJiPRYZ0YifJqtTp0sBmJdpWuGY6WV4tSSaMklfjbGo6iX7JnBjOnTpUCAJYsmdfu2LZt+8957vbt+3DDDYvbjO3ceQCiKGLUqPSWMb0+IAhUVFRDkryy1mUtVqsdhYVFAACPp6aN4ESI4Oc4mRgaqpGWLp0vLb1yIb1sfr1q69ad/NGjea7x48eSiRPHCQCE7dt396qIodEEYfjwoTh2rAANDWYYjT3x5ed6LGIYDAYpLi7OU15erjhy5IgiIyOjTT/XDRs2aAEgIyOj5WKU5/kOsyDORXPnkm3btnXY+nPr1q1aoOPSka6Sl5enAIBFixY1ntkV46effgpyu90XvK/j8OHDPVFRUd6SkhJFXl6e/MwOJT/88IMOAObNm3fWziSrV68OA4D777+/V256r1q1yrBx40bDt99+m6/RaNq0kHW73USr1TZ/PeDP/4AHwOgUlonBYDAY5+BDe9HOyZUbGrLc1bMAnFcXivNl7tCpeziO61AQ6Cqcv/uZGADgs1jifJbGvd05RyP3R1yfXuwDaMPZ5nicYtyRTbVhPre471xrEUJI4rjFM8csebJRpgo+59wzaTTVj8rJ3jzu2L7duR63u1uPoRlJEEPM4ZWzyoYcTauOPZnnVjq2UFBLT9YChcJT6xvfeMA+07SlMcJy0HbMU+fNpiIt6NF6XUQhyIJHhMaPuzpt6qxfjls8+okJy4Qb0mfkjg5PytYp1LsxgDc2zjASnZ6VlZW+ceNGfvv27YXHjx/f0WQkuleSpAGrke4tAoajGSPlsiumnWk4SkgI5fk4L89HOXk+zM7zBhvPaxwcp3ATwvXKndDBTnx8QMfZtq3tf9WsrO1Yvbq9aWVr/va3N2E2n7ZScLs9eOqpfwBAm6wOrTYIaWlJ2LXrIPLyTotpoijiN795oZ0Hh8fjxa5dB0CpXxBFl8Lvt6l8PkuQ01GtsVjMPADI5W6Vx1OtvOyysUJSUhz+859VWLfua78oOl2S5HVQKtoBageIfefOfS6n0+MKmJeSLv9cV6xYDq/Xi+XLH4XF0tjuuNlswYEDh896PiE9FzEA4KabbqqnlOLRRx+N9ftPL1VVVSW88MIL0QBwzz33tPweCQsLEwkhqKio6HIW26WXXmpPTEx05+TkaFatWtVGqVm1apVh//79moSEBM/8+fO7bMZ8JikpKV4A2LJlSxuhpKKiQlixYkV8T9cdKHJzcxUHDhxQejyeFrGI4zjcfvvtdQDw6KOPxori6ZfZBx98oN+/f78mJSXFffnll3coYmzYsEFz6tSpbht6no2amhr+iSeeiL/lllvqFixY0PKzGz58uBsAPv30U32rvXVyuZwOGzbM08FS/QLLxBj8sEwMBoPB6IBcr+XEA6Y9drPk7XHpRm+iVQRVpYYlTjjfdXqaiQEAVT9u0iZcc1W3zglReZMWp5Qf+aYwVgWQDu/6S34afHRz3ZghEw3Z2hDFOX1GlJqQmPHLfhdTkbt5e+mh74YBMHY1FmtDw4gDWzZDqzccH5KRaVOoVBO79WCa8Kic6TVxhelEIm5NY8gOnTlMyYnCaIIe+ZQQf6M43NYYuKnIKblyVYzilCJcpiEyMooQ0mfvpXiOkyXqwkck6k63dq1zNhbnNVSUnzRXot5l67PWrl2BUirYbLYUm82Wcg4jUQQFBRkEQUgkhHR45/bCghBKBTkgtLvoIwQAivo/pH7mnnuux4cf/g+33/4rLF16KSIjw3D8+ElkZW3HsmWX4csvN5z13KFDkzB58jIsXXopZDIB69dvRlFRGS67bGa7DI0VK+7AQw/9EfPn34Yrr5wPpVKBrVv3wOfzIyNjKI4cOe1H6HK5cdlltyM5OR6jRw9HXFwUPB4PNm/ehfz8U7j88tkYOjRguiuTyfDBB3/HVVfdj2uvXS5MmjRayMgYCpVKiYqKGuTkHEVxcTkKCjZBEEKbdiCU4wQ/ITIxUNIiSAHbIw5N9kccAO6OO27mc3IOy9588z2MGDEF8+bNRlxcDMxmM4qLy7Bt2y7cdtv1+Ne//tbh80MId15djf70pz/V/PDDD7off/xRP2zYsBFz585tdDqd3Pr16w0NDQ3C/fffX93kawAA0Ol00qhRoxz79+/XLFmyJCk1NdXN8zyuvvpqy6RJkzosE+A4Du+8807xkiVL0u65557kjz/+2JKWluY+ceKEMisrSx8UFCS9++67RR11iekqs2bNcowdO9a+ceNG/ZgxY9InTZpkr62tFX766SddUlKS+2y+H/3BgQMHlM8880yb7Dyr1cpfffXVic3fv/rqq+VRUVEtf8vnz5+fVllZKc/LyzsydOjQloyLP/zhDzXff/+9fsOGDYbMzMxhM2fOtJaVlcm/++47g1KplN58883isz2Pb7zxRo8NPTvivvvui1coFNK//vWv8jPGTX/961+jn3jiifjdu3cHFRcXK3bv3q1dvnx5zUD5YQBMxLgQYJkYDAaD0YpGyWtZYdp3OMfbMA2BWuhBweKMuUWEkKmdzzw3nF/s8ZsCZ1n5CNHtPsIrlRndOS8+2JkxO65m909lEeObDPU6gj+5xzwrOk2zJTw5aGpnF+8xIy6ZFpY8vj43640dbltdt54Xm8U87MDWn6DR6fOHZGRaFCrVREJIt03cKEeVNkP9VJuhHoJPXqGvjzyptuuSCUiPS34ktxTrKHTFOgpdIAJpVETKc5VRcvAqbkRPWup2lzC1LjFMrUucETscAGD3uutOmCtP5TdUuCsdDaFe0Z8GoMNa9f7iLEaiVK1WlxsMhsoL0Ui0q/hnnFtTIkT0AX4/iF8E9QHwE0p9HKU+GaXiBfG+fOTINKxb9w7+/Od/4fvvt0AURYwcORQffPB36HTac4oY7733Iv72t//gs8/Wo7q6DlFR4Vi58gE8+ujdOPO/+K23LgOlFP/+9xp8/PHX0OuDcfnll+CPf1yBW299rM3coCAVnnrqEWzduhd79hzE+vWboNEEISkpFi+//Dvceuuydo9h+/bP8eqra/D999n48MOvwHEEERFhGDUqHb/5zYMICdG3OoMSSfLJAF+n/7deeOExzJ07QXrnnc+waVM219hog16vo7Gx0XTFiuX+m2++zt/UtYWnlApokxl/fpkYSqWSbt26teDpp5+O+OKLL0Lee++9cJ7naXp6uvO5556rW758ebusuw8++KBoxYoVcVu2bNGtW7fOSClFbGys92wiBhDoarF9+/Zjf/zjH6O3b9+u3bRpk85gMPivuOKKhqeffroyMzPzvO7QC4KAb7/99uTjjz8es3nzZt2qVavCw8PDvTfddFP9X/7yl6r09PR+a5d+JhUVFbIvv/yyjU+U2+3mWo8999xzlVFRUe1PPgOVSkWzs7MLfv/730euXbvW+Pbbb0cEBQWJl156qeWZZ56pHDduXId+GHV1dfyGDRt6zdDzk08+0X399dfGtWvXFuh0ujbvQTQaDV23bl3BihUr4j///PNQlUol3nXXXbWvvPLKgLbvJpTSzmcxBo6slRMB7B7oMBgMBmOgkSiVXrXmb3/bfnI4BTo0mhwo4g3Rh5eMmtf1tp/nQJtXWzDmkXU9bq2tzxixO2re3B4Zm+6oCN1yoDakU3NQXYQiJ2mMPpkQou/KurWFe/cU7vlvHCjt/F1dBwQF604MycisV6rVk3ra9aUFCqpyBB/SmyIdMq9iLDlL9km3IfDJQ2RHVTEKmxDMJxOuY5+RvqaptWvBcVO5pcRap3EEWrsO2laqHMe5dTpdsdForDcajWKTkWgcISS087P7H6VSiSFDhvTR6lRqEjlEEJ8E6geFj4D6BIn6ZKCUlYFfhBDCSYEMD0FUqxOOE8IP2N1tBmMwcejQodDMzMzEjo4xEWOwk7UyGUBhp/MYDAbjImaHu+7Iow37BCcVhw10LB0g3Tf9xnw5L+uV2IKPVh8f/atvz2ctaeiKX5RyPJ/Yk5PXn4rJLm7UdNqaVqkRioZOCyEcR7q0j9/rth7f/NZBu6lsBoAetcZTa7WFqaNG1yjVQZPPW8wAQESuUWcOP6xtDDFyEt+rd/YELV+gilVUyYyyMMJjWE8ySXqDptauJ4+byqsLLVWC2e1IpKDRAxFLd1AoFHUGg6HMaDTaDAaDoFKpQgaDkWjfihjnhhDJD/h9rbI4QKmPp9QnUOof0OwbRm9AaHDwyJyBjoLBGCwwEeNCJmulFoC103kMBoNxEVIjumrur99z8qTfNhU9vPDtayYmZG6bmJg5vbfW0x+ozB21csN5XVCHz5i2JWT8uC61W+2IT44nbDO5lZ0+Jl5GzMNnhhULcm5MV9duKD92sGDbGj2VxMSexqfSaIpSM0ZXqjSayb1ViiBzKwsNpqhypVMznKB324lyClKljFacVETIVZycZAz0hbjF7ajMb6goLjBX+GucjVF+SUzBBWD2TgjxazSaEqPRWGs0Gj3BwcFBCoUimuO4fusYMpAiRidQQvyBUhX4JaClVIUPlKpIF03JzsUKIYJXqx12ZKDjYDAGC0zEuNDJWukGcMG1MGMwGIye4qWS9xnLkZ1rnWVjAQxaM0A5L7PeO+0GDyG9d9Fr2Ft+JOP3G7vlaXEmRBBcQx96wEkI6VHZjUThf/9oykGnXxjf+WbwpU0O2RWkl83o8vqiz52/ZfVuS1X+dJyHr4kyKKgkNWN0mVqrndxrBpsUfo3VmKNrCAfvl40l6GXjTh52ZYT8qDJKIfJB3DBCSJeNT/sKt9/bWGipPnHcVO4ot5v0br83Df3c2vV8aDISLQkJCbEYDAYSFBSk7ysj0UEsYpwTQqgI+AOlKvBJLV4c8AlU8ssAOihF4oFm69a97bqwdIROp8WDD956XntxvNKhCUrNO69FGIyLCCZiXOhkrSwH60vOYDB+Jqx3Vuz7vflgiA90wLoudJUlGfOy443RnZZedIeQnSUHRzz14+jzXSfmist/Ck4dMrun53tFYn/v6JAyn8R1qbQldrg2OzRePb07mRHWuqLjxze9zUmib2hP4wQApVpdOiRjdGlQcPAkQkivpdXzfqFOZ4o4prEZYgnlUnpr3VaIMqNwVBWjsMj0QgLpYmlOXyNKkrfMVl9w3FRuKmqsUVm9zhQMMh+aLkDVanWF0WisNBqNTp1Op1AqlZEcx8WfT/bOhSpidMbFYDjaF/zlL6/h+eff6HRefHw0jhw5u6FpVxAEjUWtTmIl5AxGE0zEuNDJWnkAwOiBDoPBYDD6kiKfveQ+067aatF93m1K+wOjWl904/jFsb150QwAoVuKcoY/t3ns+a4jBKnrhtx7d/D5lC44fHzd6twUl0RJfFfmG6KVexNG6YYSQrpsJEkl0X9y1+fb64tzJuM8sw4VKlX5kIzRRRqdbhIhpF0LzPNB6dQc1ddHmuUe1WiCvmkVygdxRaoYRYk8VBZCBDKiN3w/eotaZ2NRXkN5+UlzFal3WeMkShMGOqae0IGRqFYul8d31Uj0YhUxzg0zHO0PZDJ9nUoVVzrQcTAYgwUmYlzoZK1cB2DRQIfBYDAYfYFT8jt+bc7Zm+2unYILqHTu9klX79Uqg3pdcAn/8eS+9Be2dF7G0QUSb7p+myoi4rz8Ohpc8uKP8xK1QNdKU1TBwsmhU0KU3e3O4bBUFeVmvWETva7z7vIiVyqrhmRkntDqDZN623+CSMSptYQdCDaHajiJH0XQN4adREbqldHyPGWkQs4pyEhCiLov9ukpdq+rrsBcWZjfUOGpspvDvJI/DcAFe8e+2Ug0JCTEptfrBZVKFcrzfOKZr5+fp4hxbloMRwNeHFIrLw5mONoN5PKwCqUysnqg42AwBgtMxLjQyVr5LwAPDXQYDAaD0dusshXueMV6PEkCetR6c6BIC0/aN3/YjF4RGs4kYkPBnqGvbJvYG2spQkMLk265Mfl8O2OU29S5X52MTQK6diEtyLn6YTNDKwUZ1y1BglJKSw6s21qVt3UMesELRa5QVg/JGFWgNRgnEkKU57vemQheRYmhPrJY5QhOIyB99xrm4FaEyY8oo+VuQcsPJYSE99lePcQn+p2nGmsK8hrKLaXWOq3D50nDIPaz6QqtjURDQkK8Wq1WZTAYxg0ZMoSZZHYdZjjaRZTKqGK5PNQ00HEwGIMFJmJc6GStfBzAiwMdBoPBYPQWh73m/Afq97it1Jc50LF0F0KIf/n0m0oFjk/ui/Wj1h3fnfrqzkm9td6Qe+7aK9Nqzjtj5LgpeM+m0shxQNf8BAgHz9CpIftUWtm07u7ltjdU5v7wWoXXZe2VTBeZQlGbMnLUcZ0xZEKfZDRQSGq77oDeFOkVfPKxBH3afYTK9MIxZYyiXm4QoglPUvtwrx4jUSpW2htO5jWUV5+yVMubWrteUGJlR1x22WVISkoSZTKZVxAEked5EEJ4SqkCF0CHl8HGGYajtKltLBfI4vDJMEi7UvUFKlVCgUwWbBvoOBiMwcK5RIwLNu3vZ0bxQAfAYDAYvYFF9Jofath75JDXPA3n0ZViIJmRMmG7wPG9aubZGuKXpN5cr3rzT0LckivOe51hIdaJjR751v01IV3qQkIlKPK2mabFj9L9ZIxWzupONohSY4wet+x30RXHftpeevDbdJynqaTP4wnP2783XJDL61NGjNqjDw2dQAgJOp8120DAObWN45zaRnAib9Y1hO/WNBrDOcqn99oerXbzWfwjfBY/AIBXcaXKGEWxIkymJTKS0WtdWs4TjhA+VhsyNFYbMhQJAa3S7LZX5DVUlJwwV/prHZYoP5WG4AK8SJUkifd4PCqPx9NmXBAEr0wm8/E8TzmOI4QQGaW0V71ZLjYoJTwg4wEZWjfEIaT54+djOMpxMu9Ax8BgXChcVP/5L2KKBzoABoPBOB8kSqW/W49ve99+KoMCMwc6np6ikinrM6KHju7LPTif2KspkvbCU2Mkr/c4J5d3qcvIuZgcXT/D4pFnF1q0XRZxSg83znaavbtiRwSP6m4WRMzw2dPCksbV5/74xg63tW5q9yNui9/rDc0/sG+2IJObUkaM3KcPCx/b2204JV40mMOqZprDqiB3qfMNpsgahSsog4AYenOfZkSXFO846Yp3nHSBCMSijJIfU0bKCafiRnTHYLU/MCg1MVOih8ZMiQ40o3H5vZaT5qoT+Q3ljnKbyegWfWkAer3sp7/w+/1yv9/fRrQghEhNWRt+nufBcRwHQE4pZe/BuwClvAzgZaBtk5sCAgeVWkpViJ+C+kDhv2ANRzlOwUQMBqOLsHKSC4GslSEA6gc6DAaDwegJW9w1hx5vyFG6qXhebTQHA9eMWbg1MjisS5kIPSX+o4PbElfnnJcZ55kYx43dHjFzerfLOs7GZ3kJW+tcym49D0F6WV7qJKOOcD3zjqg9tW9P4e7P40B7ryRBkMnMycNHHjaER4wmhOh6a912SMSjtRpzghvCZbwojCXoh64jBF55qOyoKkZhF7R8CuHIoG/VLkqSt9RaV3C8odxU3FijsnpdQwAYBzqu1lx22WVISDj/xiw8z/ubxA2J4zgQQoSmkpQLLjNlsHIhGY4Swvm12hGHBjoOBmMwwTwxLgayVlpxgRtkMRiMnxdVflfVfaZdRcV+x3nfQR8MRGhD868ZszC1r9teJry/f1vCx4d6VcQAIf70Fb+oJRwX3RvLSRT+1bnJBxw+Wbc8K2QKrmbYzFATL3DDe7Kv3+u2Ht/89kG7qXQGevFijxeExuThIw8aIyIzCSH63lq3w718siq9KbIgyKZLJOD6rU2pEMznq2IU1fIQWQThSV+UufQ6lFLa1Nq14qS5ijO5bbED3dq1t0SMs0BlMplXJpP5m0pSOAAySumguuC+SGgxHCXEL1E6sIajHKdwaTRpx/pzTwZjsMNEjIuBrJVHAIwc6DAYDAajM7xU9PzJfHjXN66K8QB6z3dggLl76nUHVTLl6L7eJ+mdvVvjPj/S69kekXMvyTaMyug1Lw+fRJyrjqQU+SR+RHfOIxxc6dNDDyqDhCk93dtccexQ/tY1OiqJiT1doyN4QbAmpg8/EBoVnUEI6dsMAAqqcmgP602RdplXOZqgFz06OoFTkCpljOKEMlweROQkgxBywfg22Lyu2oKGylP5DRXeaoc5tL9bu/axiNEhHMcxI9F+hhBJBMR+MxwVBI1FrU4q7M01GYwLHSZiXAxkrfwGwPk7szEYDEYfstZRuudpy5EIPwb2bmlvMyo6fefM1Ik9vujuDsn/2Z0duza3141DObncmvbgcvSmT4LTx9e/n5vikCjp7s+bJo3RZ+sjlbN7urck+tz5W9fstlTmTUMvX8RyPG9PTB++Pyw6ZiQh5LxMRbsCkThbsDnsULAlVM9JfL/esCA8bIoI+VFltELi1dxwQvrGu6Ov8Ip+R1FjTcFxU7m1zFan6evWrgMhYpwNZiQ6cLQYjp4WObjzMRyVyQy1KlVsWV/EymBcqLDuJBcHRQMdAIPBYJyNkz5b0X31u0x1kmfiQMfS2/Ac75qeMj6uv/bjfGKfrCt5vcGO0rJsTUJ8rwkkapkYekN6seOj44l1AAnrxqmk6IBldniiekd0unYsIaTbZo4cL1MOm33XLGtd0fHjm97mJNHXa54rkihqTuUemVWcd8yRMDQ9Ozwmbjgh3Xp83YJykrYxpGZ6Y0gNZB7lKUN9ZJnSqR1GQML7as+WvUVo3ZXeKe5KLwCI8hDhoDJG0SjTCYmE67Y41e/IeSFoqDFmzFBjwPIj0NrVlHfcVF57ylIjs3jsSRSI7K943nzzzf7aCgDkTR9d5le/+lUfhdJ7aLVaTJ8+Hd99991Ah3JWWgxH0TuGoxwn95w5xmAwzg5LQ7twKB7oABgMBuNM7JLPdn/97p+W1WbH1kme8QMdT18wd+jUPRzHxfbXfsTfqx1W21Cd9WMqpdTfm2salN6EK1PL6gDq6O65tcXOqSf3mk9SSut6un9wWNKwidc+nRKWNC4bgLun63SEJIpBRcdyZ+39caOmurQkm1Ja05vrd4RP4U6ujSmeVTrkiLE+vGyvT/DuooHekv0B7zX5R1sPO2aZtjYmmPfZCt3V3p8kPz1KKe27F2YvEmjtGpp+aeLomctHXzblyUlXR96XOb98VtyI7VFBhq0C4U4C+NmmIUuSZAdgJ4S48TN8HkaMGIERI7pVAdcDCEepTEGpKohKWg2lRg1oeBAQo+BIIsfzCX6ej3HxfISD5402ng+2c5yqV3939TYff/yxbuLEiUO1Wu1otVo9ZtSoUen/+te/up2l5nK5yF/+8pewjIyMYQaDIVOtVo9JTk4ecccdd8QVFBR0Ksi5XC6Smpo6ghAyLiIiYlR39i4tLRUWL16cZDQaM0NCQjKXLl2aVFFR0eEN/RUrVkRrtdrRRUVFzI9mkMIyMS4cigc6AAaDwWiGUkrftp/c8ao1f4gEzB7oePoKrSKoKjUssVvmlecL55P67MLCZ7VFexvMOxQhxl41W43RuIbPS6jel1USORog3XpvYTd5Rx7Lrq8YNiO0gONJWk/2JxwvDJly/azoYbOKcrNet/q9rsyerHM2JElSFecdm1VSkOeOTx26JTI+IZWQnnVZ6TIEgkNnnuDQmcH5hXp9Q0RukNUQw1FuSJ/u2wrRIabY850pyAeInNSpohT5iki5nFOQDEKIqr/iOF+MSm3slOj02CnRAT9Tl99rOWGuPJHfUOGsON3aVXHuVS4O7Ha7ptW3VCaTeZqMRCWO43gwI9E+h1JOAORC6yQajlMN2vaqzz33XNhvf/vbeL1e77/yyisb5HI5Xb9+vWHFihWJR44cUb355pvlXVnH5/Nh+vTpaTk5OZqkpCT3kiVLGhQKBT1w4EDQ+++/H/7FF1+E/PTTT3njxo07q6CzYsWKmMrKym6XTImiiMsvvzy1sLBQefXVV5tcLhf31VdfhVx++eWKnJycPJ4/7eO6Y8cO1WuvvRb5t7/9rTQpKam/BGRGN2EixoVD8UAHwGAwGACQ42k4/pBpj99G/b3WsnOwsjhjbhEhpF+7qxC/2KctFquyfjQkXn9tr6871Ggd3+iRbdtbHdrtzipelxhz5Mda+7AZoXvkKr7HJUlqfWTS+Kv/REsOrN9SlbdlDHrZG4FKkrIk//jM0oJ8T9yQ1K1RCYnJhOP6vHWpJPhDG8IrZjWEV0DhDDpmMEXWy93qTII+bAt7BtRLw5wl7jBniRvg4FKEy/eoouUeXsOn92WpTV+gEuT6UWGJE0aFJQIA/JLoKbXWHTluKm8ottaqbYHWrheUN0gPIT6fT+Hz+doIOMxItP/hOG5QlpPk5+fLn3rqqTidTifu2rXr+NChQ70AUFdXVzlu3Lhhb731VsR1111nnjdvXqeZeGvWrDHk5ORopkyZYtu6dWtBa+Hg0UcfjX7llVeinn/++cjPP/+8uKPz161bp33nnXcinn/++dInn3wyvjuPIzs7Oyg3N1f9r3/9q/ihhx4yAcDjjz/uefnll6O3bNmivuSSS5xAQGi5++67kyZOnGh77LHH6ruzB6N/Yb+MLhyKBzoABoPx86ZB9JhurN269fb6HUNt1J8x0PH0NfGG6MPGIH2/t4fl/H2XiQEArsqqYX6X62BfrD0xyjQ91WDN7sm5kkg1uT/VjbfWeXp0fjOEEJI49oqZY5b8n02uCt57PmudDUolRemJ/Bl7ftwYXnHq5FZJkrp0J7I38Kgdw6vjCmeWpRyVm0Oqdoi8/wBFP7u0S1B5qr0TLTn2GaYtjSGNh+xHvSZfNhXpBdldQeB4RbI+MmNRyvhZvxhz+YQnJ16lv3Pk3MIp0UO3hqmCt3OE/KwMFyVJ4j0ej8rhcGisVqumsbFRZbVaOafT6fX5fA5JkmzffPONe9GiRXTIkCEICQlBamoqFixYgLfeeqvNWucq33juueeg1WqxdevWDo9XVVXh3nvvRVJSEsLCwjBjxgx89tln7eZRSvHhhx9i7ty5SExMRGhoKNLT03HllVfiiy++AABs3boVWq0WpaWlKC0thVarbflYvnx5y1rffPMN7rnnHowePRoRERGIiIjAjBkz8Prrr0OS2ldULV++HFqtFiUlJXj33XcxadIkhIaGIjk5GQ8//DAaGxs7fGwVFRV44oknMHr0aISFhUGv12dmZGQMe+KJJ9pleBUWFspuu+22+NjY2Ay5XD5Wr9ePnjNnzpDs7Gx1h4v3Iq+//nqo1+sld955Z22zgAEAYWFh4mOPPVYFAK+99lqXRMzCwkIFAFx22WWW1gIGAFx99dUWADCZTB3eYG9oaOCWL1+eOHnyZOuvf/3rbpcfnjp1Sg4A06ZNaxFbJk2a5ACAoqKiFhHvt7/9bVRpaali1apVJd3dg9G/sEyMC4V5f2lA1korgF5zlWcwGIyuIFIq/q0xd/tHjuJMAL3e+nOQIi0YMWtAUqqJT+rTTAwAqM3e6oteML9P1p6fWDXL6pFtqXGqZvbgdK5wn3lW5JCgbZFDNBPPp/WnUmOMHrfsd9EVx37aXnrw23QAvd5lhFIqKzt5YkZZ4Ul/bHLKtuiklDiO4/rFDJNyVGU11k21GusgeOXlelNkodquSyEg/ebf0gTns/hH+iwBqxVezZUoYxTFijCZnghkJCGE7+T8QQchhEQE6VMigvQps+ICzWKsHmd1gbmySCYIYwWep35R7LYZ7YWO3++X+/1++QcffIAnn3wS4eHhmDdvHg0JCRFNJhM9duwYv3r1atx3330SpfS8rjEsFgvmzZsHnU6HW265BY2NjVi7di3uvvtuVFZW4pFHHmmZ+9RTT+Gll15CYmIirrrqKgQHB6O6uho5OTlYu3Ytrr76asTHx2PlypV47bXXAAAPPvhgy/kZGac1+T/+8Y/gOA4TJkxAVFQUrFYrsrOz8etf/xo5OTntRJpmfv/73+PHH3/EwoULMWfOHGzduhXvvfceTp06hfXr17eZm5OTgyuvvBJmsxnTpk3DokWLvBaLxVJQUKB6+eWXo1944YWq5rnbtm1TL168OLWxsVGYPn269fLLLzebTCZh48aN+ksvvTR9zZo1hddff33HSkkvsHXrVi0ALFq0qN0ey5Ytsz766KPYsWNHl65NRo4c6QKAjRs36n73u9/VthYy/ve//+kA4JJLLrF2dO69994bb7Va+ffff79H4kJSUpIXAHbt2qUeM2aMGwD27t0b1HTMAwD79+9XvvLKK1F/+tOfyloLNozBCWuxeiGRtfIwgIv+7ieDwRg8/OiqPvBkQ47GAyl1oGPpTyYmZG6bmJjZ7bKI3mDkb7/PNu6v6PUWq2dAhz784ClOEFL6YnGJQlyTm7zf7pP1uDQkOExxKHmcPo4QYjzfeHxuuyk36408l7W2b0ugCPHHJCbvjkkZEs1xXFKf7tURFFRt1x3UmyJcgk8xlqD7XV96EyIQszJKfkwZJec5JTeCENJnrU/7i1MREtJTUkEByUtFt1fyS16/n/P5fUoAXD93J+k2991333mvsWDBAuTn52Pv3r0IDQ1tc6yhoQFGoxE8z/tlMpk3MzNTRQjB8ePHvU3tX1tE2ueeew5/+ctf8O2332LGjNP6uFYbeJksW7YM7733HjgukDheXFyMmTNnwm63Y//+/UhKCvwXi4+Ph0qlwoEDB6BWt01OqK+vbxNjc1ZIbm5uh4/t1KlTSE5ObjMmSRLuv/9+fPzxx9i0aRMmTDhtk7R8+XJ89NFHiI2NxcaNGxEXF2hk5ff7sWjRIuzYsQObN2/G+PEB32uv14uxY8eipKQE77zzDq677joIgmBSKpXFQCDrIiUlxQcEShtSUlJG1tTUyL/88suCRYsW2Zv3LS4ulk2cOHEYpRSlpaVHVCpVn1zQGQyGTIvFIlRVVR2MjIxs1zpLrVaPcblcnNVqPaDVas9p/itJEhYuXJiyceNGfUpKinvGjBlWuVxODx06pN6/f7/m5ptvrnvrrbfKZLK29w9Wr16tv/3221NefvnlkkcffbQeAAgh48LDw301NTWHu/I4/H4/MjMzhxUVFbV4Yvzvf/8zDhs2zHXw4MHjlFKMGzcuXSaT0T179uQ3v+YYA8u5Wqyyn9CFRfFAB8BgMH4elPudFZdXb9r1SMO+MT83AUPOy6wTEkb1WrvO7sL1YXeSVpCG/TmVfbU4R8DfPLwoQ86JR3u6hrXOk3l8S71d6oUSBZlSEzL6il9NS5l83V4QUtX5GT2EUqGiqHDanqzv40sK8nZIoti/5RUExKltHFOZWDC1PPmYp1Ffu0Ui4rF+jaEV1E8NrjLPNPMe22TT1kaF7bhjv6/Rv4VS2nc/g36CAJyC8Gotr9CEKILUkUF6EqIOHtQdJnoTnuchCO2TLYzGgOYoiqLgdrvVCNwwJY2NjQqr1QqXy+Xx+/0OSqldkqSzmibyPI+nn34arS8mExMTcf/998Pn8+GTTz5pM18mk+HMEgUA7USWzjhTwAAAjuNaMjd+/PHHDs978sknWwQMABAEAbfccgsAYP/+/S3j3333HUpKSnD55Zfjuuuua16/5XXTLGAAwKeffqovKytT3HHHHbWtBQwASExM9D388MPV9fX1sq+//rrPsrTtdjsPACEhIR32/tZoNCIANDQ0dJpxxXEcvvvuu8JHH320qri4WPnee++Fv/nmmxG7d+/WTpgwwX7rrbc2nClglJWVCb/85S8TZs6c2dgsYPQEQRCwfv36k5dccknjunXrDJs2bdItXLjQvH79+hNNr7WIgoIC1apVq4pNJhO/dOnSpKCgoDEKhWLsnDlzhrAuJYMPVk5yYVEw0AEwGIyLGw8V3b8zH9q9wVU5AUCfGxYORhYMn3WAENLXmRBnhfj7vpwEAOp3750YMmF8HeG4PjFlFDiqumVEUfR7R5OLJNqzrASPU4w/uqm2cdiM0H0yJX/eLXzDk8dPMMaNtB3f/M4We33JDLS6K9zL8FXFRVOrioukyPiEnfGpQ0M5nu9XMVDiRZ0lrHqmJawacrfqhL4+qlLpChpJQHq9rKZLUMg9tb5xntrANZoQzB9XxSpq5UZZJOHJgImGvQiREe5nUWKybNkyPP3007jkkkuwZMkSTJkyBRMmTEBISKcvrTZGoj5f4LXgdDolj8fjaTYSBaCJjY1FYmJiuwVmzJiBv/zlLzh06FDL2PXXX4833ngDEyZMwLJlyzB9+nRMnDgROl33fW9NJhP+8Y9/YOPGjSguLobD0davsrKyY+137Nix7cZiYwOVXRaLpWVsz549AID580+X8zW1u23Hjh07ggCgrKxM/thjj0WfefzkyZMKADh27JgSwDlLSjo6f/ny5fX9WTbhdDrJNddck5Sdna37y1/+UnL99ddbNBqNlJWVpXniiSfiFyxYMHTVqlWnbrnlFkvzObfffnuiKIqkp2UkrUlMTPStX7/+1JnjR44cUbzwwgvRTzzxRGVGRoZn3rx5Kbt379b+9a9/LdXpdOKvfvWr+KVLl6YcPHgwj2VoDB6YiHFhcXygA2AwGBcvnzlKdj1nORorgg7YBfxAY1Tri+IMUf1u5tka0j+ZGKCiqLAWnMjVpQ+d3Vd7qATReGN6seOj40m1FCS8J2uIfqo7urluTOokY7bGKD/v16YgU2oz5v9iprni2KH8rWuCqST2ZdkHV11aMqW6tIRGxMXvik9LN/A83+8X7F6lK7U29lQqKHyaRuMunTmc5/2ysQQD51fht4rDbMecwwCAU3IVqhh5oSJcriEyMvJ8vFAYfc/y5cthNBrx/vvv491338Xbb78NQggmT56M3//+98jM7F6HY0op5/F4VB7P6QYdISEhcDqdXplM5hMEgXIcRwDIwsPD5QBgtZ62Tnj++eeRmJiIDz74AC+//DJefvllCIKA+fPn47nnnkNKSteq5iwWC2bPno3i4mKMHz8eN954IwwGAwRBQGNjI1577TW0jrE1HQkmzZkqong6iaHZ6DMq6rR/Z+tMjNY0NDQIAPDdd98Zvvvuu7PGbbfbO72y/vvf/97OMHTOnDm2zkQMjUYjWiwWwWQy8R2VkzRnahiNxg4zNVrzu9/9Luq7774z/PnPfy574oknWrIqrrvuOmtcXFzh1KlThz/55JNxzSLGq6++GrJ582bdv/71r+LExMQ+aXUqSRLuvPPOxNTUVNcf/vCHmiNHjih+/PFH/RNPPFHZ3MXEarVyDz30UNK6deu0S5YssfVFHIzuw0SMC4sBSwllMBgXL3nexsL7TbstJsk7eaBjGWgWZ8ytJ4T0v5dBK4go9dutnpqfskcFD01zEUJUfbWHXumLW5ZamvfliXgV0GNPBP7E7oZZ0enaLeGJ6qmEkPN+/2KIGZ458dqn3flb1/xkqcybjr59T0Rqykon15SV0vCYuD0JQ9O1vCAM68P9zhIFZHZ9w2S7vgG8X6jR10fmBdn08QQD4N/RCsktxTgK3TGOQjcID6siUr5fGaWgvJobTgjRD2RsjI659tprce2116KxsRH79u3Dhg0b8Mknn+Cmm27Cli1bWrIyOI6D19vxdfLZOncAAS+LZiPR1uNFRUUSAC4oKMhPKXVzHMcLgiD7xS9+IfziF79AXV0dduzYgS+++AJr165FXl4e9uzZA4VCcZadTvP++++juLgYK1euxG9+85s2x3bv3t1iCno+NIsdVVWnK6rO1l41ODhYBIAPPvjg5M0333xe5p2U0v2dz2pPcnKyOycnR3P06FFlZGRkm7SUkpISmcvl4iIiInyd+WEAAUNPAJg/f347IWDKlCmu4OBgsbKyUl5dXc1HRkaKOTk5agB4+OGHEx9++OHEM8+pra2VEULGAUBdXd3B0NDQToWUM3n++efDDh8+HLRz585jgiDg8OHDSgAYN26cs3nO5MmTnQBw5MgRFRMxBg9MxLiwYCIGg8HoNWySz/pow74Duz2maQD6xODxQiItPGmfVhk0ofOZfQsR+6ecBABEl9voqqreoo6O6kknkS4TpXGnz0+s2r+xOGoUQHpcW1yZZ5vptHhzEkfrUwgh3c8VPwOOlymHzb5rtq2uOO/Yprchid70812zE0htRdnE2ooyhEXH7ElMH67hBWF4H+/ZIaLgjzBFlkeYIsuhdGgO602RVrlHNZqAaAYinmaoiGB3hXeKu8ILEPjlRtlBZYy8UaYTkglH4jpfgdGf6HQ6zJ07F3PnzoUkSfjkk0+wa9cuLFq0qOX48ePH4fP5cKbfweHDZ/dkrKioQFlZWRufCQDYuXMnBwAjRowQbDZby2u12Ug0JCREWrp0Ka688krZokWLFNnZ2Th27BjGjBnTPO+sosqpU4FKg6VLl7Y7tm3bts6fjC4wcWLA63jjxo24++67QQjxEEI6NOWcMmWK46233sKWLVu05yti9JQZM2bYcnJyNOvXr9fNmzevjYixdu3aYACYOnVqhx1FzsTr9RIAqK6ubnf96XK5iMPh4ABAoVBQAJgyZYq9eexMPvvss1ClUiktWbKkAQCUSmW3Uxjz8/Plzz77bOwjjzxSNW7cuDbZMB6Pp+XvsMvlYjUkgxD2Q7mQmPeXRgB9ZsTGYDB+HlBK6evWgm3Tqr737PaYZoEJ2iCE+OcMnXreXTB6g/4qJ2mm6ocf4ymlfb5pqsE2bnJU/Z7zXcdS7Rmbt83UIEn0vGukm9GGJaZPvPapIWFJ47IB9Is5Y11lxcS9m34YfvLIoX1+n6/HBqi9gTvIPqo6/uT0spRcYjFWbxM5f5cc//scCsFr8o22HnbMMm1tjLPst51013h/kvw0l7L2egPG9u3b0dHTX18fqBBQqU4ndo0ePRp+vx+ffvppm7mffvop9u7de9Y9RFHEs88+C0k6/auptLQU7777LgRBwFVXXQUA8Hg82Lt3b4uRqN1u11itVk19fb2iOR6ZTOaklNoJIU6DwUDr6+vhcrna7RkfHw8A2Lp1a5vxQ4cO4eWXXz7nc9JVFi5ciISEBHz77bf4/PPP25WSFBYWtig9N910kyUuLs6zevXqsE8//bRD0TYrKyvIZrP12fXc/fffXy+Xy+mqVavC8/PzW7Ji6urq+JdffjkKAB588MG61ueYTCb+wIEDypKSkjaq1cSJE+0A8Pzzz0e5XK42Yv2vfvWraFEUyciRI50Gg0ECgHvvvdf86aeflnT0AQQyVZq/12g03f59cOeddybEx8d7nn322Za0mMzMTDcAfPPNN/rmsbVr1+oAICMjo/2LhjFg/OzfuF6AHAPQzpyHwWAwusIeT33uCtM+OKh/QNqHDlZmpEzYLnD8oPACISLt1xsM3oaGRJ/VtluuC57U13uNi2yYZvbIs/MbdOf1XLvt/qSjm2rNw2eGHRTk3OjeiI1wvDBkyvWzoofNKsrNet3q97q6V9jfQ+qrKsfXV1XCGBGZkzx8pCDIZKP6Y9+OoJwU1BhSO70xpBYyj6JYb4oqUTm0QwlI5EDF1Bq/XRxiz3MOAQBOTmqV0Yp8RYRcwSnIKEIGtp3sz4l77rkHarUaY8eORVxcHCil2LNnDw4ePIhRo0a1aZd611134bPPPsPKlSuxbds2REdHIzc3F/v378e8efOQlZXV4R7Dhg3DgQMHsGDBAsyaNQuNjY1Yt24dGhsb8bvf/a7F9NPtduPKK69EYmIiRo0ahdjYWHg8HmzZsgUnTpzA/PnzER8fr7bZAlUAU6dORU5ODpYsWSJNnTrVr1QqMWrUKCxatEi46aabuH/84x948sknsWXLFqSkpKCwsBAbNmzAkiVL8MUXX5z3cyeXy7F69er/Z+++w6Motz+Af9/Z3tJ7L6QTQg0tEMCAKEVBrGABG2IFr14RK4jo9Sd4sQI2FFQsgAgKEkogIRB6CSFASEJIL7tJtu+U3x8YLiGBFJLMbjKf5+F5dHZ35uwmm905c95zcOedd2LWrFlYtWqVom/fvv5ms5k6e/asfP/+/U40TR8GLlck/PLLL3kTJ06MuO+++3q9//77hri4OKNSqWSLi4ulx48fV166dElWWFh4vDXLOdojOjra+sYbb1x67bXXAocMGRIzceJErVQq5bZs2eJaXl4uefzxx8uvrdBYu3aty/PPPx8yderU6t9++62gYfuiRYtKU1NTXTIzMzW9evXqPWrUqFqFQsFmZWWpT548qZLL5eyyZcsudsbzuNbSpUs9srKyNGlpaTlXVwj17t3bMnbsWN2vv/7qfvvtt1MajYb57bffPOLj4w0TJ04UlpLYESGJ4XhOA0jhOwiBQOBYqhhz5VPVWblnbHXD0XkTGRySQiKviveL6st3HA0I23U9MRqU7dylCJrStIS6M6QElyXXWSR7Sg3Km1rCwtg415M7K9RRQ933Kp0lI1p+ROsoXXxCB971Fnfx2J97S3LS+gJobx+PNqkpL+tfU14GVy+vo2Gx8ZREKu2SJMr12GSWkEq/ghBwYFT1Loeca7xtYpt0AIF9NN1krZyXscDsZSwwAxSMMm9plsJXahWpRdGEkLbN1exAb8x9uasPydk41mJladrK0MRG22Qsx3Xq9/v58+cjLS0Np06dws6dOyGXy+Hv748FCxbgoYcearRsJDIyEj/99BPee+89bN++HWKxGImJidi0aRP++uuv6yYxXFxcsGbNGixevBjr1q2DXq9HREQEFi9ejClTply5n1KpxIIFC7Bv3z4cOnQI27Ztg0qlQnBwMJYsWYL77ruv0X6ff/551NbWYvv27dTBgwelDMPg7rvvxogRI+Ds7GzdvHkzvXDhQllmZia1Y8cOEhkZiaVLl2L06NEdksQALk8yycjIwNKlS7F161bq4MGD3iqVig0KCjL/61//Kr76voMHDzYdO3bs9OLFi723b9/u8uuvv7oTQuDp6WmLi4szzp8/v8TX15fukMCuY8GCBRWhoaGWZcuW+axfv96dZVmEh4ebFyxYUPzss89Wt3Y/oaGhtiNHjpx+6623fFJTU51//fVXD5Zl4enpabvrrruqFyxYUNavX79Or4TLz8+XvPnmmwFz5swpGz58eJPqih9++KHgiSeeCNy+fbsLTdNk1KhRulWrVl0UJpPYFyJU4zmY1PlPAviC7zAEAoFjoDmWXlKbnfGzobAvgJvuIdAdTet3214fJ88OOwm+WYkPrsuSVxoSu/q4kXOezBbJZHFdcSyOA/v96dCsequ0Q5rJBsY5pbkHKkYQQjr0W6bFoC09tf2zIquxtst/Hi4ensfD4+IZiUzWdHYjTyhGVONc7X1KXefmQ3FUJN/xXAcrcRVnK/xlNRJXcSChSFhH7vyCN4vo8C6dlttmNMdZbRxttTI0rAwtYRim5a6WgmYRQliJRGKVSCQ0RVGgKEoEQMpxXIdM9lEoFDkikcjY8j0Fgp7n+PHjHgkJCSHN3SZUYjiebL4DEAgEjuFvU8mR+TXHnK1g7WKZhD3y1njkems8hvMdx9W6ejlJg8p9++t8RnfNrwohoB6IKej77anwExZGdNPLJ4qy65INOltWULxTDCHtnoDShEzl6jvgzgW+JTlp+wqPbokC4N5R+26Jrqoy4XDaTji7e5wI7x1vk8rkA7rq2NfDihg3rVfJSK1XCWQm5RmXKt8KmVnZh8CuJohQNi0db9NevjgtUlL5Cn/ZRamnxJWISRwh/I2V7SpiQqRiIpEqKAkgAVhwtJVjLFaG4ayMTUTTtBxCRV6rcBxHWa1W+bXNQBsaiYrFYpaiKBBCJBzHSdHG1/V641UFAsGNCZUYjiZ1vgsALd9hCAQC+1VI64ueqDpQUsKYOr3HgaN7dNg9xxQSeV++47jakPt+OCLVmbv+6jshTPSzc0qISNRlEyBMNKVdfSq8huGoDpmOo3QWn4sc4q4gFAnoiP1dzWbWV2enfpFrqqsY1tH7bg0nN7fs8N4JRplczvsEnUZYYtHUuh9x1nrKKEbcl6Bjq2E6EpGQGrmv9LTcRyqm5FRvQto+icURKjFawgGcjWPMVpZhrIyNstG0rKMqC3o4rqFqQyQSsf9UbUg4jmt2IhMhxKJSqXht6isQ2LMbVWIISQxHlDr/IgBhzJhAIGjEzDGm+TVHs1LNZYMBCE3uWtDHLzpzZETiUL7juNbQe9Yel9RZeOmH4D1qZJpbv75dWrlTa5EUrz0dKuI6qHmkWEpVxoz0KBNLqPiO2N+1KvMPHzy//xd/cCwvTbY1Lq45veIT6mUKRZcvcWmJ2CYtdqnyOafUO4cT2Pk4VAKLzFNyUu4vM4k1oghCWvf71x2SGM2hOdZi5RiblaGJlbZJWZZt9yhkQWMURTH/VG0wIpEIhBAxx3FSkUhUp1Ao8viOTyCwV8Jyku7nBIQkhkAguMpafX7mB7WngxhwwtKRVhBRIlNS+ED7/DvK8rOcBAAqMzIHuPZN0BHSdcsDnGU2/6mRF3N/OxukBIjTze6PtrKep3ZWOEUNc89QaCQdvlTIM3TAINeAuPozu77aU19VOAJdXJZfr9PGHN27G2pnl9xe8Qk6mUKRSAixi6UBtMTqX+V70R8cOIXB6ZhLtY9BYpX1JyCKlh/dxTjILBW2gZYKGwBwYmfRaYW/rFLqJvElImKv/T46jZhQMjGhZEpKAkgUYMDZrCxjtbI0Z6VtQl+Nm8CyrMhisSgsFkuj7Wq12nD1OFqBQNB6QhLDMZ0AMIHvIAQCAf+yrbpzT1Vn6bWs1e4qCuzZLVHDsiiKssuED+ExicHabGp9fsFuTVjoqK48ro/KHDU+tOTo1ny/OHTA9AuOhexMevXw4D7Ou1395MkdfZIvlsg1vcc9PVJbnHM8d+93ThzLhHbk/ltDX6uLOpaeBpWT87le8QlVcqVycEc3Nm03AmJS1/U1qetAGKrOWet1SFPr7kaxoi5pHNsOhK5lYutrL/dXpOTUJYW/LE/mJXEiEtKbENLjqhJEIBIFJZYoKDEgloMDGCvHWKwszVppWmSjbTIA9vH75qDEYrHQD0MgaCchieGYTvAdgEAg4Fcta619vvrQscPWmiQAwlrmNtDIVKURniH21VfgKoTld2162Y5d0erQECshXTtKM9xF32+YX2XGvhLPYUDHJB0KT9SOMuis+wNinfoQQpQdsc+rufrHJCTevchyNv37NG1xznDw8L3KUFcbcTxjT4RSo8mL6NO3XK5UDbGbZAYATsQ66TzKRug8yiAxy/Ncq30vyY3qWALiyXds18Oa2QBDninAkGcCEaFW5iM7LfeTciIFFYceOuWJACIZESllIhEgkoGTgaM51tyVo127G4lE0mS8p0AgaB3hj41jEpIYAkEPxXIc+0ldbsaX+vOxHGCXlQT2blL8LfmEEF6aM7YKx18lBgDQer2PpaoqXe7pmdTVx+7nrR2utUjTcqpdOux3u+qiaYipns6JGOzm2tq+B21BicSy6OSZyfVVhbmnd65iWdoa09HHaA1jfX348Yy94Qq1Oj8ivm+JQq0eYm+TOGxyc3iFf344ONDqOrcs5xoviGhJfwJit99HOQbO5mLLUHOxBSCwMVM0Vg6UlRBOhus0bOwJCEAkhJJLRFKoRFJAenm0q/Wf0a42hpYyDNOliVBHQghhxWKxpeV7CgSC5thNpl7QJrkAhD98AkEPs89ceXJo6dbcVfrzI7guHPXYnQS5+p1wU7nYbwIDAGH5r6wp3b7Dm69jjwkqT/ZTG9M6cp8GrS0me3clGJrN6cj9Xk3jERyVOO3tCM+wgWkAeCsTN+n1oScy04cf37f3kqGuLp3jOJqvWK6LQKx3rkksDj2TWByao613qk7jCGv/DQ45SEBzUqbepqbraAlj4SwcR/QgRFgWgMujXZWURO0iUag95Rqpl8qZdlGqDUqZXP/P0glhmsA/RCKRyU5a2QgEDklIYjiilCUMgNN8hyEQCLpGOWMqn1KelvFk9YHeRo7h5SpvN8GOj0u2/yundjDq0FxeEUEbDIf5Ov6dvYpGOMusmR25T5uZ9Tm5oyLYYqA7dL9XI5RI3GvIPckJE14sE0uVxzrrOK1hNhiCT+7PSDqesadEX1ubznGcjc94rocR05413sXJF3udCi/3v3DKIjPu5cDV8R1Xa3BWVsboaTVdR8sZI2vjWKIHoYwQTtYBABSIWE7EKiexXO0hU8u9VS6cm1JjUsuV9VKJxEgIYfiOkS8SicTIdwwCgSMTkhiO6wjfAQgEgs5l5VjrG9rjaSllO5Tn6frh6OIpCN1NYnDCPqlIYv9JIDtIYgBA2e49vP2+EQLqvuiC/jIRc7wj98uxUJ7eUzVEV2be3ZH7vZbS2Ttk4F1vJvjFjNoLgNcTcrPRGHTqwL6kY+lp5fU63R6O46x8xnMjZqW+d1nQ+RFF4afEWveyDIaij3PgHCIhwDGchDHQarrOpqT1LMfSMIBQeoDYXyUMTwhASYlIoRZJNW5SldJb6SxyVzpZnBQqvVwqM1AUZZeJts4gkUgMfMcgEDgyIYnhuA7wHYBAIOg8W4zFh4aU/FW8wViUDEDDdzyOTiqS1A0K7hPFdxytQTj76FdVf/Zcf9ZmO8vX8cUUJ5sReyFERNjzHbxrkn9UN6r4TH0Gx3GdtjSTEEKC+90+ov8d8w1SpXNWZx2ntSwmU0B2VubIY3vTquq12j2d+dxvFkdxyjq3iuGXwk8nlATnFhlVtWkcuBK+42o1jqNYE6Oi62xqup4Ws1aYOBA9CLHbBBJfJJfHuqpdJAqVl8JJ4ql0tjkr1AaFTK4XiUR2+zt6s6RSqVCJIRDcBCGJ4bj28x2AQCDoePk2feHYstSDr2iPDrSB6/Kxjd3V+Njko4TY7zSERjj+e2I0qMo6VMHn8eVi1vmB2HwVAVfa0fuuyDcMP39Qe47juMqO3vfVZCpX3wF3LkgM7jdxH0CqOvNYrWExm/yyD+4feXTPbm1dTfUejuPsup8DLbUGVfoVJl/sddKn0qfwsE1iyeRgvwmY5rAWRsHU02q6jpYyZs76Tx8NYTJFM0SESBSUWOUslqs95RqZl8qFcVVqjCq5Qi8RS0wAWL5j7ACsMJlEILg5QhLDcWUDqOc7CIFA0DGMLG14pjpr9+SK3T5ljNlux386IjelS36gq69dN/NszD6WkwBA9aHDiRzLlvEZg5OU9p0WVagHuNqO3re+2tr7dFqVlWW4cx2972v5xYwcNnDq65TC2Tujs4/VGlaL2ef0oayRR/bsqqutrkrjOM6+rwwTUEZN7YCSkNyhl8JOG+tcKvewhDnDd1htxdlY6T99NBS0kaVZhuhBKANAusPJeYej/hntqhHJ1O4ylcJb5ULclU5mjVypl0mkBkIcb7mOWCwWmnoKBDeJcI6x1FDQnNT5OwCM4TsMgUBwc76pz9v3UV1OKAv48h1Ld/Tw4LsOauQqh0kMjRj/dR0BnPiOo4HvuJTdLnGxo/iO44JOfeyvfL8YgMg6et+UiOhjRniclipEiR297+ZU5h85dH7/z77gWP+uOF5rSKTSyvDefU47u3sMJISo+I6ntaQmZa5rtU+ZzKSKJyBunX087WQ1ooJ6Xff2I5eyOzuEm5IY04fvEDpco9GuNC1l2OZHu/r7+2Po0KH49ddfuzrERhQKRYWrq2sRr0EIBA7g+PHjHgkJCSHN3SZUYjg2YUmJQODATli1ucNLth1fWpczTEhgdI5Ir9BDjpTA+IfdVGIAQPmevX05jtPzHUeYi75vkn/FEXRCo0eW4dTZuysH1lVZOnS06/V4hvYfOGjaW04aj+A9sJNJFjar1fPMkUPJh3fvNGsrytM4jnOIak+rwhhVHnAh+WKvU+oaz+L9tMh2iAMnVDX0II1Guyrsf7SrVCptc1NPmqbx9ttve0VGRsbK5fL+zs7OfZOTk3tt3769TQlHf3//eELIgBv9e+mllxp9H/noo4/cU1JSwoOCgnqr1ep+CoWiX1hYWNx9990XfPz48TYllU+ePCkbNWpUL2dn574+Pj59HnrooSCtVtvs+egdd9wR6u/vH19bWyucrwqasIvmYYJ2E5IYAoED0jFW7TM1B08et2qHw85OWLsTQgg9JmpYp1+Z7QR2NQaWNVtcjMXFaaqAgGS+Y0nw0g3VWaRpp6pcOyMWKu+gNtknQr3XJ1w1hBDSqT8HsUSu6T3u6ZHakjMncves1nAsYxc9cGib1T332JFksUSiDYvtnebq5d2XEOLMd1wtIpy03qV6SL1LNUQ2SalLtU+uqt45lIAK5js0Qdf6Z7SrWC4WA2KAk4G1cYwZgILjOJYQwnE8ToFqaxKDZVlMmjQpbOvWra4hISHmhx9+uEKr1Yo2b97sdttttzl/++23eTNmzNC1Zl9PPvlkuU6na3L+x3EcPv74Yx+GYcikSZMaLd376aef3CsqKiR9+/Y1eHt72yiKwpkzZ+S//vqrx/r1693XrFlz/p577mlxClNdXR01bty4SJPJJLrrrruqS0pKJN9//71nZWWl+K+//rpwzTGdN23a5LZhw4azzs7OQlJS0ISwnMSRpc73BMBr0zWBQNB6LMexy+py0lfrL8RzgCvf8XR3I3slpvXxj+b9xLutRoz/miV2VikpcXEpCn/kQT9CiF0k3X4/H5B2qV7VaT9bJy/ZsbD+LkGEdP7yBABgGdpyNn1Nprb4dBLs7AKTSCyuDYvtfczN2yeBEOLCdzxtpTBoTrhU+dRLrPK+BDe/TEZYTuK4RC4qJA8fgZ1btsLGsRYrx9isjI3YaFrKsmyXJI8JIYyPj8+xtvTEWLFihdvs2bND+/XrZ0hPT89VKpUcAKSlpSnHjh0brVarmby8vJOurq7tPtn/7bffnKZNmxYRExNjPH36dM7VtxmNRtJwzKtt2LDBaerUqRFhYWHmvLy8Fn/xV61a5frEE0+E/fnnn7m33XabHgCmTZsW8ttvv7lfunTpuL+/Pw0A1dXVotjY2LhRo0bVrlu3rrC9z0ng+ITlJN1VypJKABdavJ9AIODdHnP58cGlW899q78wUkhgdD6FRF4V7xfVl+842ozjOHtLYACATacLtOlqeR8T2mBy+KWRLjLLvs7af12FpW/O3qp6luG65DOWEoll0cmPjOo97pk8SizNafkRXYehaedzJ44lH9qVSlWWFKdxHFfDd0xtYVLV9ykNPje8KDyb1bmVp7MUc5LvmLoDjuOw/PNP0XvwACi93RAY0wvPvjQPtbW1CIuPQVh8TLOP+/HXn3HLxNvgFuQHpbcb4hL7Y/EH78NiaTpwRuSiwpgJ41FVXYUnn38G/lFhUHi5In7IQHyz5rtm92+1WvHOf95DRN/eUHi5IrxPLF5/5+0m+5cQSqaiJGpXiVLlJlFKfv3hJ2bypElsVFQUwsPDMW7cOHzzzTdg2cZ5gaKiIvj7++OFF15AXl4eZs+ejT59+iAgIAD79rX8J0ksFhvb2tRz1apVngDw9ttvF1+dTEhOTjZOmDChRqvVir/77rub+l6xcuVKTwCYOXNmk2lNzSUwAGDKlCl1Go2GuXjxYquWlBQWFkr/iftKJcqgQYMMAHD+/PkrfUzmzJkTAACfffbZpbY8B0HPYndflARtJiwpEQjsWCltKp1Uvmvf09UHE8wcE8V3PD3FhN6jcxyiBP4ahGbtttN+aeoOu2k2SgjIfdEFA+Ui+mhnHcNiYIJP7axwt1mYw511jGtpPIKiEqctjPQKG5QGwK5GMDI07ZR36kTywZ3bpRXFl9I4jqvmO6a24ChWU+tenlQUnh1fEnQ236SsS+PAlfMdl6N65l9zMXf+y6itq8XjD8/CfXfdje07d+DWKZNgo23NPubRp2djxmMzcT4/D1Mn34E5jz0BN1dXvLF4IW6/607QdNM/f7paHUbcmoL9Bw/grslT8ND901FSWorHnnkKq39Y0+i+HMfh3kcexJvvLgIhBE8//iQm3HobvlnzHe595MFmY7LZbJh07114/qUXRfp6PfXA3ffiiUdmgQLBa6+9hrkvvEBLxGITrumrUVhYiEmTJuHSpUuYMmUKpk+fDo1G0+LrJpVK29RfyGg0kmPHjqnlcjk7fvz4Jn1qxo8fXwcAu3btavff56KiIvHOnTudlUol+9hjj7U6Sblt2zZ1fX29KDIyslV/q4KCgqwAkJ6ermzYdvjwYSUA9OrVywoAGzdu1Pz8888eH330UaG7uzvTtmci6EnsqmRR0C77ATzAdxACgaAxK8dY3tKe2P+HqXggAAca7+n4vDUeud4aj+F8x9EehOFssLOeGA2Ml4rjGLP5hEgut4t6dBEF6YzY/LBvT4WfpTkqsjOOwdCc86ldlQkRiW571G7SkZ1xjGsRihKFD7k72TdmZEH29s91tNXYtyuO21osw6gvZJ9MLjhz2hAcFZ3m5R8YSwjx5DuutrDJzKEV/gWh4MCo6l0POld7sWJa2p+gc/ugdBd792Xgi69WIbJXBDJTd8PFxQUAsPiNtzHujokoKS1FcGBQo8d8u/Z7fLv2e9w5cTLWrPoaCoXiym1vL1mMhe+/i89WrcBzTz3d6HHHT53ErAcfxhcffQyR6PJqtuefehp9hw/GB/9dhocfmHHlvj/++jM2/bkZQwYlYscff0EulwMA3np1AQaPbv7t++7//Qd/70jF04/PxrL3/nPlGAzD4Mnnn8E3a74TPzD1HvHkCRM5G8eaqiUyFoAqKysLzzzzDObPn9+m166tSYzTp0/LGIZBWFiYRSJp+usZExNjBoALFy60e2rTZ5995kHTNJk2bVr1jZakfPPNN64nT55UmEwmcv78eXlaWpqzs7Mz89///vdia45z77331r7xxhu2e++9t9fUqVNrSkpKJFu3bnUdN26czt/fn66traWefvrpkMmTJ9fcf//9HT5SW9C9CJUYjk+oxBAI7MwGw8WswSVby/4wFScDcJhRhd3FxPgxJkKIQ36+EZqx20oMAKjYm2HmO4arycSs8wOx+U4EXHGnHYSD+NyBmpEV+YY9HMd12ZVBpbN3yMC73kzwix21F0CLTfO6GsswqvzT2ckHd/ytLrtYmMZxDljVQCAyOGkHlYTmDr4UmlNb71ydxhL2HN9hdS5y07/D3/24FgAw/8WXriQwAEAqlWLxm283+5iPv/gMYrEYX33yeaMEBgC89vIrcHdzxw+/rGvyOKVSiQ8Xv3cluQAAsdExGD54KHJyz0Cv/19O4Nu13wMA3nn9rSsJDABwc3XDgpf+3WTfLMvik5VfwMfbG0uXvN/oGCKRCP/3zhIQQvDDL+tAACIllEIllqkAwNvLC+++9rbVSaHSy6UyPUVR1uu/Yv/T1qaeWq1WBAAajabZn5urqysDAHV1de26MM2yLNasWeMBAE899VSTpSRX++2331yXLVvm+8UXX/ikpqa6+Pn5WTdt2nQ2JSWlVc/J2dmZ3bZtW258fLzxl19+cT98+LD6gQceqPzxxx/zAeDZZ58NsFgs1IoVKy6eO3dOOmbMmF5yuby/SqXqN2XKlJCamhqH/FwXdA6hEsPxHQNgBiBv4X4CgaCTnbfV5z9Rtb+6krUk8h1LT9XHLzpTIZEP5TuO9iJM150kt4fuVPZA7zGjCiiRKITvWBpopLTP3VGFeT/nBuuAzms8WXymfqRBZzsc0te5V1ctVSKEkOC+t4/wiRhaemr752esRp3d/W1hWVZRcOZ0cuHZM+agiKg9PkHBEYQQhxsZzYppjxqv4uQar2LIjKrTrtU+1VKzsg+B4y1LuxG6nhZRMpGJSAlDCKTgOGnLj2rs2InjAICkoU2LDIcMSoRY3Pj0wmg04vipk/Bwd8d/P/+02X3KZFLknM1tsj0iLBxOTk1XSgQE+AMAtDod1Go1AODo8eOgKKrZuEYlNa3EOHv+HGq0NYgI74XFH7zfbFwKhQJnzp5psr1P73io5HIpAKmSkgASBVhwtJVjLFaG5qw0LaYZWgaAAJd7aaxbt44G4H31fpYuXVrS7IG7yO+//+506dIlWWxsrHHkyJHGG9138+bNFwCgpqaGOnz4sOLtt9/2S0lJif6///u/wueee65Vy8sSEhIse/bsaZIo3Lp1q3rNmjWeX3311QUvLy9mzJgxkdXV1ZKVK1deqK+vp1555ZWg6dOnU9dOMRH0XEISw9GlLLEhdf4RCOXqAgFv9Kyt/l81Rw5nWCqHA7CLMYk9kYgSmZLCBwbyHcfNoOy4J8Y/KO3RYxfdBw4I4TuQq3kqLeETw4pPbL7gLwdIpyX1dWXmAbkZdF7kMHcdRZEuG90pU7n6DrjzVd+SM3syC49siQA4j646dmtxLCsvzM0ZefFsriWwV8Re3+CQMEJR/nzH1R4WpSG2TJkHwhKTRuexz0nnqaAYUV+CNnZktFOshVHgnz6XREJZKBllIxQRgWMVN37kZbV1lwuDvD29mtwmEong7tZ4qI9WpwPHcaisqsLC999tU6wuzi7NbheLLp/CMMz/8r61dbVwc3VDc8sufLy9m2yrrrnc/uFc3vkbxqXXNy008PFqur8mo10B1sYxJitLsyXFxeJly5bJATRK8LWUxGiotKivr292MlRDpYaTk1O7PjtWrlzpAQCPPPLIDaswrubm5saOHTvWMGLEiPN9+vSJefnll4MnTJhQFx4e3nwzlBbo9Xoye/bskHHjxmlnzpyp3bBhg1NOTo7y008/zX/ooYd0AFBQUCD7v//7P7/s7GxZXFxc0y6wgh5HKMvpHoQlJQIBDziO41bVn8sYXrrNmGGpHAU77WXQU9wSNSyLoqgAvuO4GYRm7boSAwAqMw8MssemjsHOhj4jAyqOAVy7xwy2hqmeDj+1s0JDW9ljnXmc5vhFjxw6cOrrlMLZO6Orj91aHMfKLp7LHZG142+v4gvn97Is67ATBjiKU9S5VQ67FHa6X0lwbrFBrUsDgd2/R9uCs7EyRk+r6TqbgjayNMcSPQhlAHDd95HTPw0syysrmtzGMMyV5EAD538qKfr1SQCjM9zw381wdnJGjbYGNlvTc+my8qarnRriunPi5BvGlHfidJPHtmbCCAEoKREp1SKZ+o5bxstZsy2f47jDV/9raR+xsbEWkUiEoqIiWXPPKycnRw4AYWFhbT6xLy4uFqemprq0taFnA7lczo0YMaLeYrGQPXv2qNv6+Abz5s3z1+l04lWrVl0EgOzsbDkADB48+EplyMCBA40AcPz4caHyXABASGJ0F0ISQyDoYkcsNTnDS7edWl6XO5y9pjxU0PU0MlVphGfIIL7juFmEsf8kBkfTivrzeXY5pjLeUzckwVO7t7OPw9g4t5M7K+KMtbZOP9a1JHK1W98JLw7vNez+QyBU5/UCuUkcx0mKzp8bkbXjb59LeefSWZYt5Dumm0FLrQFVvheTGbFNxMgZI6TEAIJmR086LIYTMwZaTdfZVLSeAUvDwBFKD0IaXeXv2ycBAJCe2XSk6P6DWU2mjKjVasTFxCD7TA5qtJ03obdfQgJYlm02rt3pe5psi46MgouzCw4cymo28dHhxFSbmnoCl8eb9u3bV282m6mtW7c2GX+ydetWJwAYPXp0m/vmNDT0nDx5cs2NGnreSGlpqQQAxGJxu94Lu3btUn799dfe77777sXAwMBGvzhms5k0998CASAkMbqLA3wHIBD0FDWMpfr+ir17H67aF1XP0fF8xyO4bFL8LfmEEGXL97Rv9jxi9WplO3fHcRxnlyW9SQGVyUFO+rROPxAHSe6+6hFVRcY0juvc6o/meIb0G5g47W1njWdIGmDHJ9McJ76Udz4pa8ff/kXnzmawLFvAd0g3i+GsSisxqmwyM8vJOT3ExK4a3nYIDhRrYlRMnU1N19FixgozB6oehFgevO/yULwlH36A2tr/DZGwWq14beFbze7uhTnPwmq14tGnn4JOp2tyu1anxZFjNzcx+ZHpl8eovv7O2zCb//cjqdHW4N3/+0+T+4vFYjzzxGyUlpXh+X//CyZT00mhpWWlOH0m56biAgAQYiWi1jX/vNbjjz9eCQBvvvmmv9FovHIyn5aWptyyZYubq6sr/eCDD2qvfkxhYaHk6NGj8urq6maXoVzd0HPOnDnXXUpSVlYmOn36dLN9U3788Ufnv//+20WpVDY7/rUlZrOZPP7446EjRoyonTNnzpXsVu/evU0AsGHDBpeGbX/88YcLACQkJHS/95qgXYSeGN1BypKLSJ1fCKDL1ucKBD0Nw3HMf2qzM34wFCQAGMF3PIL/CXL1O+GmcukWfYEoG2P3lRgAwBiNnuaKir0Kb2+7fC9MDCse+dOZkIwas6zTR+0WnapLNmhtWUHxTrGEkHaXVLeHSCJT9x47J1lbknsid8+3ao5lwrry+G3CceLi/Lzhxfl5jG9I6L7A8AhvSiQK5zusm8FxnMgGsxoigBKLLWJOagPQpb8DXYWzMHLGwsgBIGlAknXWjIfJ12tWS+KHDsTUSXdAIpFg89a/4OzkBD9fX1BU4+uksx58GEeOH8PnX65ERL94jLvlFgQFBKJGq0V+YQH27svAI9MfxOd9+7U7xvun3YOf1/+GP/7agj5DB2Hy7RNgs9H4bdMGDOw3AHn5TXtCvvbyKzh+6iRWfP0lNm/9E6NHJMPfzw8VlZU4n5eHjAOZeOf1NxEbHdPuuAAAorZXYTR4/PHHazZu3OiydetW1969e8eOGzeutqamRrR582Y3lmXJ8uXLC93c3BolUufNm+e/fv169//+978FzTXd/OOPPzQXL16UxcbGGkeMGHHdhp4XLlyQjhgxIjYuLs4QHh5u9vPzs+l0OlF2drby+PHjKrFYzH300UcFnp6ebf7sevnll33Ly8sl27dvP3v19smTJ9fHxcUZP/roI7+LFy9KDQaD6M8//3S97bbbtEI/DEEDIYnRfewAMIvvIASC7miHqezov2uOqC1gmx80L+ATOz4uudv0InGE5SQNSv/e4Rc6436OtGZxeBcjBOSe6ILE1afCj5hocf/OPl5NsSnRrKfPRg5xUxGKdHkzS1e/qD6Jdy+ynM1Ym6a9lD0c9v39TlRakD+stCCf9QkKzgyKiPKgRKIIvoO6WSxHy6ygZb17hXFiyIwUIyKwcQ5fHdYcjmalny7+L6LDovDl2m+w4puv4Obqxk2ZOBmL33iLBMVFIjykae/ZT/5vGcanjMOKb77Ejt27oKuthZurK4ICAvGv517A9Hvuu6m4CCH4efUavL/sQ6z+YQ0+XbUCvt4+eGT6g3j95flQers1eYxEIsGGH9Zhzbof8d0Pa7Bl21boDXp4enggNDgECxe8gQfuvrm4AICIqTZXKjSgKAqbNm268O6773qtXbvW49tvv/WSSqXsoEGD6l977bXSsWPHtrmZyMqVKz2Blht6RkREWJ9++unSjIwMTXp6upNOpxOLxWLO19fXev/991f+61//qujfv3+bqyP27dun+Oyzz3z+85//XAwNDW20loeiKPz+++/nn3zyyaDNmze7iUQiburUqdUNPTMEAgAgHGe/FYiCNkidfx+AH/kOQyDoTi7RxuInqvYXFTHGIXzHImheYnBCemJIQhLfcXQUp+zyM31f3BLNdxyt1euxmQclGo3d9iKxMlT9N6fCS2iWiuqK44llVGXsCI9ykYTq3RXHa0591cXc0ztXsixtvclLx12G8w4MOhAUGe0mEoki+Q6mJao+g9ArJKRV9yWEosWQmYiNyMC0fZSpwyHgzhddMMcN7au4Z+o09sevVwvL1v9B1PJsIqKEpRACQRscP37cIyEhIaS524Q/Lt3HDtjzmliBwIFYOMb8Us2RtNvKd7oKCQz7JRVJ6gYF9+mSk9OuQmi2y3sr3IyyXWl2XQUjFbGa6TH5LgRcl0zIoC2s58mdFb3Mepq36SEaj6CoxGkLI73CE9MANF3kb39IedHFIQd3/B1xIftUFkPTHdCAwD5wHCu2cSaNVWyUMnLaBBnRg1x/6ocjKasoB3vNnyuj0UheXPCyAgDuSJlIMRbOzHFED9INe4a0BSE0qB7+GggEHcyeyw0FbZGypBKp808ASOA7FIHAkf1sKNz/ru5UAAMume9YBDd2a+zIo4SQbvVzomjH6InRQJ93oS9rteZQUqndXvVXS2nve6ML8n86E1IDkKb15B2MYyHP2Vs1PDjBeberrzyZj+U2hKJE4YOnJftGjyjMTv1cS1uMfbs6hnYgFcVFiRXFRfD0888KiY5Vi8TiWL6D6igMZ1MwsAEywooh01M0JQLNKfiOq70+/uoz/Pz7rxg5NAk+Xj4oryjHrow0XCotxq2jx+KuiVPAWVk5Y72c6CBiykbJKAsREQocqwBgd8vQOo2IqrfDVXcCgUMTkhjdSyqEJIZA0C5nrLV5s6sP6KpZq1B54QBclc4FQa5+Q/mOo6MRm2NVYgBA5f4srfdI+17R466whk4Kv3Tyj7wABUC65MSx8HjtKIPOlhkQo+lLSNcc81pKZ+/ggVPfDCo6vnVv8eldfQA48xFHW1WWFCdWlhTDw9fvUEh0rFwskfC2PKfDcRxFX2kGKrKKOKmV2IgMLGfXVU3XumXEaJw4fRKpe3aiRqeFWCRGRFgvzJk1G889OgfXnrRzNCthaPbyc6QIQ8kpMyWmAI6TA1yzEzS6CyKm2jz+VCAQ3JiQxOheUgG8yHcQAoEjqWdtdXNrDh09YKkeDsChO+X3JJPjUyoJISF8x9HRKNpxGns2qDlyNNEraVgJoSg/vmO5kSAnY/yowPIDu4u8BwKkS06aqgqNQ011ttMRg93cCCE+XXHMaxFCSFDf20Z4RwwpPbX981yrUZfIRxztUVVaMrCqtARu3j5HwmJ7i8USSR++Y+pILMdIWZikkAAiIjWKWDEHK6eEA1QpjEkahTFJo9r3YJYTsUZGxYIBCDhKJjIRCWEIgRRcN+wdIhYJSQyBoIMJPTG6lz0AhNFDAkErcBzHfV53Nn146TbLAUt1MoSkrsOI9Ao9pJGr7LaZ5M0gNsbxehtxnFh3Kvsc32G0RpxH7eB+XjVd2q/CoLXFZu+u5Bia5bXXg0zl6jvgzlcTQ/pPygTIDScS2Jua8rL+h3al9sk9dviozWo9znc8nYHhrEorMapscgvLyTk9xD2khwIHwpoZBVNPq+k6WsqYOQvHkfpu00eDECsRUVa+wxAIuhshidGdpCwxAsjkOwyBwN5lWaqyh5ZuO/1Z/dkkDvDkOx5B6xFC6DFRwzq9rwFfKAdcTgIAFXsz+nEc5xBXG4f5V40Mda5P68pj2sys78kdFcEWI72/K4/bHN/oEUMH3vWGWOnsw1vz0fbSVlT0O7x7R8KZI4eO2yyWI3zH0xk4jhXZOLPaKjLKabnNfLkZKHG4Cq324mysjNHTGrqOljNGluZYogehjICDNkQVqjAEgk4hJDG6n1S+AxAI7FUVY668u2JP+qNV+2MNHB3HdzyCthsRPihDTInC+I6jsxCaccgv6qzV6mS4WHSU7zha6/awkmR3hTm9K4/JsVCeTqsaXFtu3t2Vx22ORKZyTZgwb3jEsPsPgVBdMrmlI+mqKhMOp+3sn3P44AmrxXyY73g6C8vRciuMaqvURLFy1gAJMfIdU1fiGE7MGGg1XWdT0noWLA0DCKUHITTfsbWW0A9DIOgcQhKj+xGSGALBNWiOpRfpTqaNLkuVnrHVJcEB1hsLmlJI5FXxflF9+Y6jM1E21vGWk/yjLHVHBMdxDnNycU9U4RClmD7UxYclF47oRhXn1mdwHMf78k+PkH4DE6e97aLxDN0DB7zSXVtd1edI2q4Bpw8dyLaYzQf5jqfzcITmLCorZVTa5FYbJ4ceItKzlihwHMWaGBVdZ1PTdbSYtcLEgehBCO/voxsSKjEEgk4hJDG6n0MAdHwHIRDYi79NJUcGl2wt/NlQmAwH6cwvaN6E3qNzCCHd+mfoqJUYAGCrq/ez1mgP8B1Ha1EE4hmxF2IkFHu6q49dccEwPO+Q9izHcbz3phBJZOreY58aGTP60WxCifP4jqc96mpq4o7u2TUoO2t/jsVkyuI7ns7EcYzExpnUVrFRyigYE6RED+J4CaibxVqu9NGQMWbOynFED0JMfMfVCEUZCdVzlgIJBF1JSGJ0NylLGAC7+A5DIOBbIa0vurVsx4EXa470t4IVpo44OG+NR663xmM433F0NkeuxACA0tQdDtWvRCLiVNNjL3hShLvY1ceur7LGn06rsrAMZxdNUV18o+IT714Y4BYQtxuAje942qNep405und34qkDmblmo/EAx3EO/X5qCcNaFVZiVFtlZq5HNQO9BmdjpYyeVtN1tII2sAzLED0IZQAIr8kdIqZq+Ty+QNCdCUmM7klYUiLoscwcY5pbfShtYvluzxLGNJjveAQdY2L8GBMhpNt/ZlGOOJ3kKqaS0hjaaHKY3hgAoJIwnvdGF3AAV9XVx7aamICTOyp8rCbGLpZCUCKxLGrkw6N6j3smnxJLu7xCpaPoa3VRx9LTBp86kHneZDBkchzXvSsVOO7qZqAWyIgelOP0jehQLCdijbSarrOpaD1NWBuMHCg9COn6xJywlEQg6DTd/gthDyUkMQQ90lp9fuaQkq01qeayZAByvuMRdIw+ftGZCom8L99xdAVCO3YlBgBU7NnrcCdPbnJr8B29isoBrssbJ7IMp8neXTmgvsrSpRNTbkTjERSZOG1hlFd4YhoA+yrRbwNDXW3E8Yw9Q0/uz8g3GfT7un0yAwDL0bLLzUCNon+agRoAOPzflXa5PL5VydTb1HQdLWEsnPmfZSdd0EeDMBBT+s4/jkDQMwlJjO4oZclZAF1eGisQ8CXbqjs3svTvo+/VZg9lwPnzHY+g44gokSkpfGAg33F0FUevxACA2pwzA1madrjeCgEaU9wtQWXZAMfHGnbq/EFtctl5/V6O4+xiKQehKFH44GnJfSf8q0IsUzlUdc21jPX14ccz9g47kZleaKyvz+A4Xn7GXYtDQzNQlU1uYf5pBmrfTTA7GWdl5f8sO5ExJs521fjWjv+7K6bqCRF6iAsEnUXMdwCCTpMKYBbfQQgEnamWtdY+X33o+GFrzTAIf8+6pVuihmVRFJXMdxxdhdDd4kIxqTl0pNRjSKLD9aKJdq8bpLNI9x4udx/Bx/FLz+lHGGttR0P7u4QQQlz5iOFaCmev4IFT3wgqOrFtb3H2zj5w4AbJJr0+9ERmeqhcpSqMiO9bpNRohhBCOvSz48DJoR25u44iBqAGgGEJh8wiVkLDCgU4TsRzXK1SUFSIqGHxeHDaA/hy2Rcdsk+OZiUMzUoAABRhKLnITIkJwHFy4OZfF6EfhkDQuYRKjO5rM98BCASdheU4dnntmb0jSv+mD1trRkJIYHRLGpmqNMIzZBDfcXQlR2/s2aAq6+AgjmV5n7zRHkP8qkaEu9TztrSjtsLSL2dvVR3LcPl8xXAtQggJShg/ov8dr5pkKheHmUBzPWaDIfjk/oyk4xl7SvS1ten2Uv3SFRjWJrfCqLbKTISVs3pI2jfR49V338Ct901CeGIMnHt5wad3EBLHJ+GdZUtQra3u6LA71+U+Gqp/+mhQHdJHQyJqksQghAxITEyMuqlYO9nhw4flt99+e5ibm1uCTCbrHxIS0nvu3Ll+er2+XWUlv//+u2bs2LHhHh4eCVKptL+Xl1efpKSkiHXr1jVJhmq1WurZZ5/1Dw0NjZPJZP2dnJz6JiUlRfz++++athzTbDaT5557zs/f3z9eo9H0HTx4cGR6erqyuftu3LhRQwgZ8OOPPzpscranEpIY3dc2OPA6VoHgevaZK08OLd2au0p/fgQHuPMdj6DzTIq/JZ8Q0uwXj+6K0N2jyp1jGFnd2XMO2xhyfGhJsqfCvJev41sMTPCpnRVuNgtzhK8YmiNTufj0v+PVwSEDJmcCxCGTVFczG41Bpw7sSzqWnlZer9Pt4TjOyndMXYbjKJqzqK2UUWGTW62cDHpQrT9pX/7lpzAaDbhlxGg88+hTuG/KPRCLRFi0dAkGjh2GopJLnRl957m2j4aZs3AcqQdpw+QXijISinK4xNjOnTtVSUlJMdu3b3cZPnx43axZs8rVajXz0Ucf+SYnJ0eaTKY2JTJmz54dcOedd0aePHlSNXbsWN0TTzxRPmbMmNqamhrxrl27GiUmKisrRYMGDYr55JNPfMRiMaZPn1552223abOzs5V33nln5LJlyzxae9xnnnnG/+OPP/aNj4833H333dW5ubnK2267LbKwsFBy9f1qa2upp59+OmTy5Mk1999/v1A542CEq5fdVcoSI1LnbwNwJ9+hCAQdoZwxlc+uyjp/nq4fBkBYaNrNBbn6nXBTuQzjO46u1l0qMQCgfHdavFNUpNFRE1HTogqHfZcddtBgk/BSDcTQnPOpXZV9Iga77VG7SkfyEcP1+EYlDfUI6ac9vWNFhlFX5vCjjy0mU0B2VmaATK4o6RWfcF7t4jKYECLjO66uwnGM1AaTFBJAREmNIkbMwcYpwV3/s7bqdDHk8qb9s994fyHe/+T/8J9PPsTH7y7r1Li7AmdjZYyNlQEAERGakovMREQocKwC1/kuQsSUritj7Ag0TePxxx8PMZvN1Jo1a85Pnz69FgAYhimeMGFC2LZt21wXLVrk/e6775a1Zn8ffvihx4oVK7ynTp1avXbt2kK5XN7os81isTR67V5++WW/vLw8+bhx43SbN2/Ok0gu5xuKi4uLBw4cGPPqq68GTp48uTY8PPyGySGWZbF27VrPadOmVf/yyy8FAHDXXXdpJ06cGLVq1Sq3d955p7zhvs8++2yAxWKhVqxYIfQRdEBCJUb3tpHvAASCm2XlWOsb2uNpKWU7lOfp+uEQEhg9ATs+LlnS8t26H0Kz3eb3mzGZ3UylZYf4jqO9KALR9Nj8OAnFZPMWBAfxuf01IysKDGn21oxSIlO5Jtw+b3jEsPsPgVAOetm9MYvZ5Jd9cP/Io3t2a+tqqvdwHNf6q+/dBMNalVZiVNlkFpaTc3qIm69AaC6BAQDTJk0BAJwvuNBo+3c/r4Us0Anf/bwW23Ztx9i7b4dnbABkgU5X7lOvr8dLb89H2KBoOPXyRPyoAfho5Sdg2ZvrFbRo6buQBTohLXMvvv9lLRLHJ8G5lxcC+obhiRfnoKyivMljjpw4inlvvoyB44bBp3cQnHp5ImZYgvjFf7+srrxYoaT1LMfSMIBQeoDQ3679HiIXFb5d+z02bN5EJyYmRmk0mr6EkAHLly93J4QMAICDBw+qCSEDGv7NmzfP7+jRo3JCyIDBgwdHXu85REZGxorF4v7XVhN0lD///FNz4cIF+cCBA/UNCQwAEIlEWLp06SUAWL16tWdrfhYmk4ksXrzY39fX19pcAgMAZDJZo21bt251AYDFixcXNyQwAMDf359+6qmnys1mM/X555+3WI1RWloqNpvN1KBBgwwN25KTkw0AUFhYeCUxuXXrVvWaNWs8P/jgg4s+Pj529bdV0DpCJUb39gcAGsLPWeCgthiLD72uPeZuA9djGjsKgMTghH1SkSSJ7zj40E0ae15Ruj01OOyhGSwhxCEvmkgoTjkjNt97dXZ4AcuREL7iKM6pTzbqbIeCE5wjCSFOLT+i63iE9Bvo6h+rP5P2TVpdxYUR6AYXyKwWs8/pQ1k+EpmsolfvPgec3NwHOWpFUXtxHCuywayGCKDEYrOYk9KwQQH2xk0vt2z/CwAQHx3X7O3r/9yIv3en4tbRY/H4jFm4eOnyRXCLxYLx903CoeNH0Cc2HvdNuQe6ulosWf4f7N2f3iHPafmqT5G6ZyfunjQV40alYF9WJlb/vAZp+9ORvmknPN3/d4781Y+rsWnrHxgxJAljkkaBZVkcPXkM/131Cbbt3o70TTspDadRsabL57+MjbUCkP6ycT2zfeeOoJEjR9bOmDGjsqioSDZw4EDj3LlzS5ctW+br5+dnvffee680DRkzZkx9v379zIMHD64/cOCA5sSJE7I+ffo0miKzfft21blz5xS33nqrNjg4uFOWqaSmpmoAICUlpcmyitjYWGtwcLClsLBQlpOTI4uLi7vhlJuNGzc6abVa8axZsypEIhH3008/OZ88eVIhl8vZoUOHGlJSUgzXPqaqqkoCANHR0U32HR4ebgGAtLQ0JwClNzq2j48PLZfL2SNHjlx5v6anp6sAIDg42AIAer2ezJ49O2TcuHHamTNnam+0P4H9Ek5uu7OUJTVInb8HwBi+QxEI2iLfpi98onp/RRlj7lFNHQWAVCSpGxTcx64bn3Umiu4+y0kAwFqjDbbV1R+QOjsN5juW9lJKGI/7ovONP+SEVgLEk684tKXmgWY9nRc5zF1HUSSIrziaI5LI1HEps5N1pWdPnkn7VsmxtMNNpmmOzWLxyjl80EsilVaG9+5z0NndYyAAFd9xdTWWo+VW0IAUnBgyA8WICGycEgCWfrEcBqMetXV1OHLiKDIOZiI+pjdeenpes/vauvNv/L76V9w6emyj7R+t/BiHjh/BnbdNxo9ffAeKupwLe2nOXAy9vWNWU/2TfEDf3glXtv3rrVfw8Vef4bUlb2LF/316ZfvLT8/D8nc+hEjUOGfzzU/fYfZLz2DFd1/iX3PmXtlOGCIFgG2p20U///zzuWnTptVd/bhhw4aZli1b5uvv729dunRpybWxPfnkk5UHDhzQfPLJJ54rV65sVNn0xRdfeP5zn6qbef43cu7cOTkAREVFNVt5Exoaai4sLJRlZ2e3mMTIyspSAYBcLmfj4uJiz507p7j69oEDB+p///33PD8/P7phm6urK11ZWSnJzc2VDRgwoFEMeXl5MgDIz89vvvznKiKRCPfff3/Vt99+61VXVyfy9fW1rV+/3k2tVjOPPvpoDQDMmzfPX6fTiVetWiUsI3FgDp8tF7RoA98BCAStZWRpwzPVWbsnV+z2ERIYPdOtsSOPEsLfiSLfCM10m+UkDcp27nL4K9iuclvQlIiiKoDT8xmHqZ4Oz95VqaKt7HE+47geF9/I+MS7Fwa6BfZOA+BwjQ2vx2a1ep45cij58O6dZpbjLCCke5VMtRYHQnMWlZUyKm1yi42TQ79s5XK8s+w9fPzVZ8g4mIlxo1KwZe3GRlUNV5s0bkKTBAYArP55DSiKwruvLrySwACA0KAQPD1rdoeEP33qfY0SGADw+rz5cHZyxk8bf4HF8r9z8+CAoCYJDAB45N4H4aRxwva0Hc0eY1zKWP21CYzWmDFjhtbT09P2yy+/uF/dQLOqqkr0559/ugYGBlruuOOONu+3terq6kQA4OLi0uzSCo1GwwCAVqtt8QJ4RUWFGABWrFjhAwBbt27N1el0Rw8cOHB6+PDhdYcOHVLfeeedjRKdY8aMqQWA1157zY+mr+Q2UFJSIv7iiy+8r46xJZ9++umlp556quz48eOqdevWefTq1cu8efPms6GhobZdu3Ypv/76a+933333op+fHz1v3jw/T0/PPmKxeEBcXFzM33//3eOSlI5KSGJ0fxsBdKsre4Lu6Zv6vH1DS7fWpZkrRgHoMQ3VBP/jqnQuCHL1G8p3HHzqTj0xGhgKCuMZi4W/vhIdxE9tihkbXHoG4OiW7915aCvrfmpnRYyxztYxNfYdjBKJpVEjHkqOv/XZAkosc9gJNc2hbVZ3m9ksM+rrOY5AD0J67Fp6jmMlNs6kzss+CWNVjanw5HnDulVr2PyLBRg8PglHTx5r9nGD+g5osq1eX4+8ggvw9/FDeEhYk9tHDh3RITGPGNK0B62zkzMSYuNhtphx5nzule02mw2ffbsCo6eOg0/vICiCXSALdII8yBl19XUoLmtSTAEA6Nu/X7sSDRKJBNOnT6/S6XTi1atXuzZsX7FihbvZbKYeeuihyquTO9ezfPly93nz5vld/W/z5s1tGlF6sziOIwAgEom4P/744/ytt96qd3Z2ZhMTE01//fVXnre3t+3gwYPq1NTUKwmD999/v8THx8e6detW19jY2NhZs2YF3nfffcG9e/eOc3Z2ZgCgNc8fABQKBffpp58WFxcXn9Tr9UcPHjyYm5ycbDSbzeTxxx8PHTFiRO2cOXNqFi9e7LVs2TLfBx98sOqXX345p1Kp2ClTpkQWFRUJKxUcgJDE6O5SllwCcJjvMASC6zlh1eYOL9l2fGldzjAW8OU7HgF/JsenVBJyuSS3p6KY7plzrtyXWc93DB0h0q1+YKJP9X6+4+A4SHMzqpOqioy7OY6zy6oAtXtgROK0t6O8eg1OA2DkO56OxDKMyFBbqzbq69HTkxkAwLA2hZu3RnX7neOw6Zf1hmpdDWa98GSz9/X29G6yrbb+8nm/l0fzRXjNPaY9vDy8rrP/y9tr6/6Xf5g+5xHMff0llJaXYdK4CXjxqRfw2txX8NrcV+Ds5AyrtflpvL6+vu0e0/vcc89VikQifPXVV1deiNWrV3tIJBJuzpw51Td6bIM1a9Z4LFu2zPfqfzt37mwxieHk5MQAgE6na7baob6+XgRcXvbR0r6cnZ1pAIiJiTFGRUU1ej00Gg2bnJxcCwCZmZlXkhjBwcG2rKysnAcffLDSYDCIvv/+e8+dO3c6T5o0qeaXX37JAwA3N7ebqu56+eWXfcvLyyVff/11IQB88sknPkOHDq1funRpyZQpU+rWrl2bbzKZqA8//LD5XxSBXREyTT3DBgAD+Q5CILiajrFqn6k5ePK4VTscQKtKBAXdV6RX6CGNXNXjlxB1x0oMANAePznIe+SIIiISBfIdy80a5FudpLVI085pnXhvOFx0qm6UUWc7ENjbqTchxO7KoAlFicIT70r2ix558dT2z6ppi6Ef3zF1pIZkBiWiGIVKoyeEKNCTP884jgoI9FZFR0bixKlTqKyvMng6e8rAclfONwhp+ifOWXO5V21FVWWzuy2vbDo9pD0qqiqus//L252dLsdx+PgR/L71D4wZMRp/fPcbxOL/nS6xLIsPP//vdY/R3PNrrdDQUNuYMWN027dvdzl69Ki8qqpKdO7cOcWECRO0V/ePuJGsrKzclu/VVEREhHnr1q3Izc1ttu9EQz+KlvphAP/rq9GQGLlWw5IVk8nU6GJ6YGAg/d133zXpU7Fp0yYNAPTp06dJQ9DW2rdvn+Kzzz7z+c9//nMxNDTUVlNTQ1VWVkqmTp1a03CfiIgIq4uLC52Tk6O40b4E9kGoxOgZhL4YArvBchz7Ye3pPSPL/sZxq3YkevIXPgEAgBBCj4ka5sZ3HPaAMN0ziQGOE2lPnMznO4yOMi6kNNlbadrDdxwAUH3JNPhsZs0ljuWar2+3Awonz6BBd73Zzz/ulnQATaYfODqWYUWGulq1ob6ue75/26i0rAwAwIptKqvEKGYUjJEh7HVPfjVqDcJDwlBcVoK8a0azAsCezL0dEtfe/RlNttXW1eL46ZOQy+SI7nW5p3RDDBPH3tYogQEAB48dgslsambvXItldBRFgWFuXLQzZ86cCgD45JNPPK9q6Nl8dqcDpaSk1ANAamqq87W3nT59WlpYWCjz8/OzxsTEtJjEuP322+sJITh//ry8uefbkCQIDQ1tcV8AsHr1ancAuP/++2taum9zbDYbHn300dDExMT6efPmNWqOarFYGr1nrVarcG7sIIQfVE+QsiQHQLsyswJBR9pjLj8+uHTruW/1F0ZygGvLjxD0BCPCB2WIKVHThdA9EKHZbvu5XJGxbwDHcTq+4+goUyMvDtdIbQf4jgMAjLW2qOzdlWLGxtp175GghFuT+t/xqkmmcrWL162jcWz3ff9e7VxeXqOlFw1YlsXb776LyqoqDB40CK4uLgAAhrUqOTEtAwCaYswQkSYnrw/fMwMsy2LBkjfBsv9bIZV/sQCffv1Fh8S9dv1POHaqcU/cRUuXoLauFvfeMQ0y2eV2XMGBl4f/7Mls3HamoqoSz7/2r2b3zRCu2akeV3N2dqbLyspuuGRy8uTJ9cHBwZZff/3V/c8//3QNCQkxT5o0qdOX491+++31YWFh5kOHDqnXrl17JZHBMAxefPHFAAB4+OGHG/XlsFgs5OjRo/Ls7OxGfcwiIyOto0eP1pWWlkrfeeedRksz1q9f75Senu6k0WiYqVOnXvklYhgGtbW1Td4/n376qduGDRvc+/XrZ5gxY4auPc9twYIFvhcvXpR98803hQ3b3NzcWC8vL9uuXbucbbbLq1S2bNmiNhgMVExMTHNZKoGdEZaT9BwbAfyb7yAEPVMpbSp9onp/fgFtGMZ3LAL7opDIq+L9ovryHYe9IGw3rcQAwNlolT6/IE0TFsr7MoyOQBGIHojJ7/PNqfCTVkYUz3c8NgvrdXJnhVP0cI99crXYbv/WylQuPv3vmO9Tmpuxv+DwpnCA67HTiBzV36mpeGvxuxiamIjg4CC4ubqiorISGfsykV9YCG8vL3yy9MPmHyxi5FaxEZREYhZzEhpWKMBxoheeeBabtm3Ghj9/x+DbRmBs8i3Q1dXit80bkJQ4DJu3/3nTcd86aixGTRmHaROnwMfbB/uyMpFxMBPBgcF4Z/7bV+43MGEAhg0ago1/bULynSkYNmgoKqoqsG3XdkSGRcDPu2n7LoawBgA3XIYwfPjwus2bN7uNGTOmV0JCglEikXCjR4+uv+22265MPaIoCjNnzqx46623AgHg4Ycf7rSxqlcTi8VYtWpVwYQJEyIfeeSR8J9++kkbGBho3bNnj1N2drayf//++tdff73Rup6CggJJ//794/z8/KzFxcUnr75t5cqVF5OSkpRvvfVW4LZt21zi4+ONhYWF0tTUVFeKorjly5cXuLu7XynT0Ov1lI+PT8Lw4cPrQkNDLRRFISsrS33s2DFVWFiYef369XnNTYtpyeHDh+UfffSR71tvvVV0bX+OZ599tuz1118PHDRoUHT//v0NGzZscFMqleyLL77Y/LojgV0Rkhg9xwYISQxBF7NyjOUt7Yn9f5iKBwKw2y/VAv5M6D06hxDSMa3nuwHCcN02iQEAZTt2RqtDZ1m7SwNXMcUpZsTm+397Kiyf5ahQvuPhWMhz9lYNC+nrnObiIx9JbmaBfifzjRo+xCOkr/b0jpUZRl1p07ERDioueGuTbYQinEKtMVAUJQPLSXgIq0ONHjkSF/ILkHngAI6fOoXa2lqolEr0Cg/HfXffjdmPPwY31xsXW7KcTW6FDZARVkykeplETv314ybloqVL8Osf6/HJ158jOCAIrzz7Eu4YP7FDkhjPPf407hg/CR9/9Rl++WM91CoVHrp7Ohb++81GTUVFIhF+++onvPnBImzbtR2ffvMF/Lx9MfO+hzH/+ZfQd0zitbvmaMK22Lz2iy++KJo9ezb27dvnlJaW5syyLGiaLr06iQEAc+bMqV64cGGgRCLhZs+e3SVJDAAYM2aMIT09Pee1117zS09PdzIYDCI/Pz/rCy+8ULpo0aJShULR6s7T4eHhtsOHD+fMnz/fd/v27S4HDx5Uq9VqZvTo0bpXX321dPTo0Y1eL7lczk2aNKkmKytLk5GR4QQAwcHBln//+9/FCxYsqNBoNG1uYEzTNGbNmhXSp08fwyuvvNJkSc6rr75aUVdXJ1q9erXnmjVrlFFRUaYPP/zwYmBgIK8TqAStQ7iWl3AJuoPU+QTAJQB+fIci6Bk2GC5mLdSd9KbBBfMdi8A+eWs8cqf1uy2CENIjSrBbI/GhdVnyCkOTb8jdSeiM+zPknp7d5qQVAHQWyaUfTodKORC76WrvGazM9I/R9P2n2aRdqyo8dvj8vp+8OY4N4DuWliQ9/RKC/dv3VYqQf5IZou6RzOhohIhsYkgtxEo69PVZtPRdvLPsPfz98xYkd9C41qsRmahW4qk831H727x5s2bSpEmRd9xxR83GjRu7TS8hgaCtjh8/7pGQkBDS3G3CF8eeImUJh8tLSgSCTnXeVp8/pnT7oTd0JxKFBIbgRibGjzEJCYzGunslBgCUbt9hNyf6HcVFZguYEnGxBuDsZpRsZaFx6PmsmnyO4zpmtEMn8gjuO2DQtLddnbzC9gCwy5GxHYHjOGKsr1MbamvFLMfpQZGbGhnZ3XAcI7FxJrVVYpQwCsYIKTGAwO6vtlIKcbsaTl7PBx984A0Azz33nLCsQSC4DuHLY8/yK98BCLovPWurn111YPeUirSAStYijPQV3FAfv+hMhUTel+847A1huW4/rcdcXhFBGwyH+Y6jo/mqzdHjQkrPApzdnJjqa2yx2bsrWYZmz/AdS0tEEpkqLmX2yJgxj58mlDiP73g609XJDIZj9aAoa8uP6lkY1qq0EqPKJjOznJzTQ0xabJzJCwKOUkp0N7ubrKwsxfz5833Gjx8ftmfPHufRo0fXjhkzpt0jRQWC7k7oidGzpOHykhK7L9cUOA6O47gv9ef3fVKX24sFRvEdj8D+iSiRKSl8YCDfcdgllusRFxfKdu9BwITb+A6jw0W41g+os0oy9pd4DAPsox+Fzcz6ntpZ6RSd5L5fphQP4Tuelrj4RPROvHuh9dy+H3fXFJ0cDqDbLrvgOI6Y6uvVhBBOrlLrRWKxFCzbLfrFdBSO40Q2mNUQAZRYbBFzUtvvm/5QnMg+0WLCNzggGA/dM71T4yNSUS2hyE1XD+3fv1/53nvv+avVaua2227TfvXVVxc7Ij6BoLsSemL0NKnz34PQ4FPQQY5YanKeqc6i6zma9878AscxLmZEWqRX95hQ0dGG3r3muKTemsB3HF0h6pmnzlISSSTfcXSGHYU+aWdqnO3td5wLG+CS5uwlH8V3IK2lry46d3rHSitDW+L4jqXBzfTEaBEBFCq1QSyWiDmWlbX8gJ7pyWefxQ/rfm7xfiOHJGH7LzffEPRGRK7yCyKVRNupBxEIeiihJ4bgat/xHYDA8dUwlur7K/bufbhqX5SQwBC0hUamKo3wDBnEdxz2qicsJ2lQlXWoSbf47uKW4LJkX5Uxje84rkEuHNaNKjlbn85xnEMsX1C7B0YMuvvtGO9eQ9IAtDj9weFxgEmvV9XrtDKGZQyEoix8h2SPVnz8MeorylFfUQ59ZSVtrqzTW0rqrZaiOlz9r7MTGCCEpRRiXeceRCAQNEdIYvQ0KUtOAzjCdxgCx8RwHLNEd2pPctl28Slb7QgIf0MEbTQp/pZ8QoiS7zjsFttz3lPVhw4ncixbxnccnWVKRNEIJ6l1P99xXKs8z5CUd0iby3Fcl41uvBmEUFRY4tTkvhNfqhLLVEf5jqerNCQzaIYxQEhmXBfHsWIbZ1JbxUYpI6dNkBE9SNc0h6XkIh2hiFDSLhDwoMd8WRI0IlRjCNpsh6ns6OCSvy78YCgYCcCZ73gEjifI1e+Em8plGN9x2LOeVIkBlpXU5pzJ5TuMzkIIqPtjCvrKRMwJvmO5Vn2VNT5nT5WJZbgOGwvZ2RROnkGD7nqzX0DvlHQAtXzH01XMBr1Kr9PKaIY2EIqyz+aWdoLhbAorjGqrzAz2cjNQU2cej1KIqztz/wKB4PqEJEbP9CMAmu8gBI7hEm0svr1s5/4Xag71s4CN4DsegcNix8cld9sGfR2G60FJDADlaXv7chyn5zuOziKmOPmM2AtBIsLa3bQNi5EJPLmzwstqZg7xHUtbBPYZl9T/zgVmmcrV7qpcOpPZYFDV67RyIZnRChxH0ZxZbRUZFbTcauVk6PhxthShiUJc16H7FAgErSYkMXqilCUVALbxHYbAvlk4xvxSzZG028p3uhYxRrvvaC+wb4nBCfukIkkM33HYO9LDkhisxeJsvFTcrZc4ysWsy/0xBXICzu6WzrA055S9q7JffbXF3vp33JBM6ezd/475Q0IH3rkfIBV8x9OVriQzaNpIKKpTKw26A5ZjpDaY1FaJUcLIGSOkxADgppeAUHKxltjHACKBoEcSkhg91/d8ByCwXz8bCvcPLtlatdVUkgxA6F8guClSkaRuUHCfKL7jcAgselQSAwBKt+8I4ziO4TuOzuQss/nfFXmxDuDs8cqt6HyWNrksT7+X4ziHqtL0iRw2ZOBdb0qVLr7pfMfS1cxGg7Jep1XYaJuRiIRkRmswnFVpJUaVTW5hOTn0EJN2V7RQKrFD9JQRCLorIYnRc/2OHrSmVNA6Z6y1eaNK/z68SHdyCAMugO94BN3DrbEjjxJCPPmOwzH0rEoMALDV1gbYdLVZfMfR2bxV5sjxoSV5gH1OBik9qx+Rf1R3kuM4Hd+xtIVEpnRJuH1uUsTw6YcJoYr4jqerWYxGZb1Wq7DZbEJlRitxHCuycSa1VWSU03Kb+XIzUNLqRCoRU2ZKJu7+03IEAjsmJDF6qpQlZgC/8h2GwD7Us7a6x6oy0+6u3BtczVoH8B2PoPtwVToXBLn6DeU7DkdBuJ5XiQEApak7nPiOoSuEu+j7DfOrPARwdjnRoLbc0u9MerWWZbkCvmNpK4/ghAGDpr3t5uQdngZ0zXQKe2IxGS9XZtisJiKihBPsVmI5Wm6FUW2VmihWzhogIS2+dpRCqMIQCPgmJDF6NmFKSQ/HcRz3ed3Z9OGl2ywHLNXJAMR8xyToXibHp1QSQqR8x+EwOK5HvgeNl4rjGLPZ7qZ4dIZ+3tphse61e/iO43rMejr01M4KF5uFcbhxpiKJTBV3y5PJMWMeP01EYoeZvNKRLCaTol6rVVqFZEYbcYTmLCorZVTa5FYbJ4ceItJs1RSlktR0dXQCgaAxIYnRs+0FUMB3EAJ+ZFmqsoeWbjv9Wf3ZJA4QSv0FHS7SK/SQRq4axHccDoXruYnEir0ZPaYUfnRQebK/2mC3zTQZG+dyaldlvEFntdtky424+ET0Tpy2MMgtMH43gI6dSuEgrA3JDKvFDCGZ0SYcx0hsnEltFRuljIIxQUr0IJere4hMVEvEVI/8nRII7EmP/bIkAJCyhEPq/DUAXuM7FEHXqWLMlU9VZ+WesdUNByC01hZ0CkIIPSZqmBvfcTigHvu5rDuVPch7zKgCSiQK4TuWrnBHr0sj1+aEZtZapPa53IqD+GxmzciAWE2aR5AyiRDiUEudKJFYGjXiwVH66kvnTu9YYWVoS1xXHFfP/dEVh2k9E+S4Kj3o4zyDlzBuu3MK0vftQ31FOS/Hby+GtSoYYgVkhJFAphcpO34pyV133RWyfv169zNnzpyMioritWcOH7EkJiZGHTx4UM1x3OGuOJ6gexAqMQTCkpIeguZYepHuZNroslTpGVtdEoQEhqATjQgflCGmRGF8x+GAemwSAwClPXqsxzRmJATkvuiC/nIRc4zvWG7k0un65MITtUc5zi4nq7RI7R4QMejut2O8I4akAejxFQkWi9kMijLYZVMWe8ZxIhuxyCmFRGiK78AOHz4sv/3228Pc3NwSZDJZ/5CQkN5z58710+v17fpO/Pvvv2vGjh0b7uHhkSCVSvt7eXn1SUpKili3bp3z1fe76667QgghA270b+jQoZGtOSbDMFi4cKFXSEhIb5VK1a9Pnz7RGzdu1Fzv+Uql0v7vvvtut6u47slflgQAkLLkHFLnHwAwmO9QBJ3nb1PJkfk1x5ytYJP5jkXQ/Skk8qp4v6i+fMfhoHr053Jl5oGBbgP6VxNC3PmOpSuIKU42PfZC6Lenws8xHBXBdzzXoy0xDzTr6fNRQ91rCUUC+Y6nrQihqLBBU5N9o0YUndr+2RnaYujPd0x8sVkscpvFAolMZpEplDTHsirhikbriKWyGkJIt87/LF26tPi1114rCwkJ6XZLZnbu3KmaMGFCJE3TZPz48dqAgADr3r17nT766CPfPXv2aNLT088qFIpW/3xnz54dsGLFCm9vb2/b2LFjde7u7nRVVZX4xIkTyl27dmnuvffeKwmvO++8UxccHNxsZcsvv/zidunSJdnYsWNblSBbsmSJ15tvvhk4bNiwurFjx+o2b97sOm3atIi9e/fmDB069ErdFU3TmDVrVkifPn0Mr7zySmVrn5ej6NFflgRXrIaQxOiWCml90RNVB0pKGJPw8xV0mQm9R+cQQkbwHYcjIj38c5mjaUX9+bwsp4hePSbhKhezzg/E5hvXZIeVciC+fMdzPaY6utepXZVVMSM9ToglVB++42kPhZNn4KC73gwsOvF3+qVTqfEAnFt8UDdls1hkNotFJpFKLTKlSkhmtIJEruh2J4LXCg4OtgUHB3e7BAZN03j88cdDzGYztWbNmvPTp0+vBQCGYYonTJgQtm3bNtdFixZ5v/vuu2Wt2d+HH37osWLFCu+pU6dWr127tlAulzdKflgslkZvpwcffFD34IMP6q7dT1VVlejzzz/3lkgk3FNPPVXdmmN//fXXnoMHD67PyMg4BwBnzpypiIuLi//00089hw4derHhfgsXLvQ+e/asIisr6zRFdb/FF93vGQnaYy0APd9BCDqOmWNMc6sPpU0s3+0pJDAEXclb45HrrfEYznccDolhGb5DsAdlO3fHchxn5juOruQkpX2nRRXqAc6uS9VpK+txakdFtKnels53LDcjsM+4pP53LjDLVG77+Y6FbzarVabXaVUWs8kKitK39jL0mp9+wvSZsxA/cBA8g4LhFxaOlAkT8dMvv97wcRaLBQuXLEHvgQPhHhCIPoMSseSD/4PV2vgideHFi9B4eePJZ59rdj+33TkFGi/vRts4jsPan9bhltsnICQmFh6BQYju2w933nMvftu4sck+iktK8OIr8xE/cBDcAwIRFBWNex58EIePNh3MQ4nFBpFE0uLfpeXLl7sTQgYsX77c/aeffnLu169ftEKh6Ofk5NR3/PjxYSdPnpTd6PEffPCBR2RkZKxMJuvv7u6ecP/99wdXV1df6UdD0zR8fHz6qNXqfrW1tc2exz388MOBhJAB33zzjWvDtq1bt6rHjBnTy9vbu49UKu3v4eGRkJCQEP3iiy82Spw2LHvIzc1tMlVs165dygkTJoR5eXn1kUql/T09PfsMHz484ssvv3S9+n7Lly93v/XWW8MDAgLi5XJ5f7Va3a9///7Rn332GW99sv7880/NhQsX5AMHDtQ3JDAAQCQSYenSpZcAYPXq1Z4s2/J0ZpPJRBYvXuzv6+trbS6BAQAymaxVb6UVK1a4m81m6tZbb9X6+vrSrXlMaWmptH///oaG/4+Ojra6uLjQly5duvIzO3nypOyDDz7we+mll0ri4+MtrdmvoxGSGAIgZUkdgO/5DkPQMdbq8zOHlGytSTWXJQOQ8x2PoGeZGD/GRAgRPlvagWLYbnf1qz0Yo9HTXF5xkO84upqX0hJxe1hxPsDZ9RdOjoP0THp1UvUlYxrHcQ5bWi9TOnv3v+OVIaED79wPkAq+4+EbbbVK9TqtuiGZAYIb/mznvvxvFF26hGFDh2LOE4/jril3oujSJTz+9NNY9N57133cQ48/ju9/+BG3jRuHJ2bNAiEE737wAabPmoWb/XV6+913Mfu551BRUYGpkyfjmdlPYtTIkSgpK8WGTY0brh47cQLDxtyCVd98g4hevfDko4/itnHjsC9zP8ZNmoxtqamN7i+Wy9vU0HPjxo2uM2bMCPf19bU++uijFf369dNv27bNdcSIETHHjx9vNpExd+7cgIULFwbExMSYHnrooQpvb2/rTz/95DFhwoTwK3GIxZgxY0alwWCgvvzyyyZJAb1eTzZs2ODu4eFhe+CBB3QA8OuvvzpNmDAh6tChQ+rhw4fXPfHEE+Xjxo3TSqVS7ttvv/VqzfP58MMPPcaOHRuzfft2lwEDBuifeOKJ8jFjxtRWV1dLVq5c2WgfL7/8cnBxcbF08ODB9Y8++mj5pEmTaoqLi6VPP/106PPPP+/Xltexo6SmpmoAICUlpUmiODY21hocHGwpKSmR5uTk3DDJBAAbN2500mq14ttuu00nEom4n376yXnBggU+ixYt8kpNTVW1Ja7Vq1d7AMATTzzR6t8vHx8f67Fjx5QN/3/27FmpTqcTBwQEWAGAZVnMnDkzJCIiwvTGG284VifdNujRZauCRj4F8BTfQQjaL9uqO/dUdZZey1rts9O9oNvr4xedqZDIhd+/diI026qrMD1B6fZU/9AZD3CEkB5V4R7qbOg7IqAic+8lryGAfT/3iyfrkg0624HAOKfehJA2fXG3Jz6Rw4a4B/fVnd65Mt2oLUniOx6+0VarVG+1SsUSiVWuUlvBsSpwTRuBH0hLQ1hoSKNtVqsVU++/H0uXf4xHH34Yfr5NV0flnj2HrL174OriAgB489X5uH3KVGz9ezt++uVX3H/P3e2O/evvvoefry8O7EmDUqlsdFtV9f8q9WmaxsOPPQ6DwYA/N6xH0rBhV24rLStD8rhb8fQLc5F9+BBkMhlACCuRyWvaEsuuXbucf/jhh/P333//lZPmRYsWeb3xxhuBs2fPDs7MzDx77WOOHj2qOnLkyOmIiAgrANhsNgwdOjTqwIEDml27dilHjx5tBIBnn3226qOPPvL95ptvPF988cVGJ79ff/21W319vejhhx+ubKgGWLVqlSfLsti2bVvu1T0TAKC0tLTFc8HDhw/L//3vfwepVCpmx44dZwYOHNioIiUvL09yzf2z4+LiGiVjzWYzGT16dMSnn37q88ILL1SGhoZ2adL+3LlzcgCIiopqtpomNDTUXFhYKMvOzpZdG/u1srKyVAAgl8vZuLi42HPnzimuvn3gwIH633//Pc/Pz++Gn+mpqamqc+fOKYKDgy2TJk2qb+1zmTlzZuWbb74ZOHLkyIjo6GjTli1bXAkhmDNnTiUAvPfee54nTpxQZWZmnhaLu++pvnC1THBZypJsAHY7s15wfbWstfaRyn177qtMD9Wy1n58xyPomUSUyJQUPtDhGv7ZE0JzQhLjH5aq6jBarz/Edxx86OOpGxrvodvDdxytUV1kGnx2f80ljuVK+Y7lZkhkSpeE215IikyacYQQqsdMyLkR2maT6nVatdlopJurzLg2gQEAUqkUj8+cBZqmsXvP3mb3++95864kMABALpfj7dcWAAC+//GHm45bIhFDJGo6DdjD/X+9grdu344LBQV48tFHGyUwAMDXxwcvPPM0yisqsHvv5ecglkprCEW1vM7gKkOGDKm/OoEBAPPnz68IDAy07N+/X3P27NkmyzVeeuml0oYExuXnIsGDDz5YBQCZmZlXEoXBwcG2sWPH6rKzs5V79+5tlK35+uuvPSmKwjPPPNOkf4dKpWryHFqzhGH58uWeDMOQefPmlVybwACA8PDwRgmJ5pIAcrmcmz17dgXDMGTLli1OLR2zo9XV1YkAwMXFpdllmxqNhgEArVbb4ll/RUWFGABWrFjhAwBbt27N1el0Rw8cOHB6+PDhdYcOHVLfeeed4TfeC/DFF194AsBDDz3Upl4rCxYsqHj99dcvFRYWytasWePp6upKr1u37tywYcNMubm50sWLFwe88MILpQMGDDC///77nv7+/rTGz08AAMUlSURBVPEikWhAaGho3Jo1a1zacix71n3TM4L2+BRAj2mm5uhYjmM/qcvN+FJ/PpYDRvIdj6BnuyVqWBZFUcLfj5tAaEZIYlylbOduSeAdk/gOgxcjAyuSdRZpWlG9yu7fU0adLSp7d2V5zEiP0yIxFct3PDfDPahPfxe/aOOZtG/S6srzRkC42AfaZpPodVqJSCKxKVRqS0NlRtGlS1j28SfYvXcvLhUXw2RqdIEfJWXN57WShjUt1hs6eDBEIhFOnDx1U7Hee9dUfPHlVxiUNAJT7piMpGHDkDhwIJydGp8zZx26nB8tunQJ7/7ngyb7ybtwAcDlqpFbU1IgbkdDz+HDhze5si4WizFo0CB9UVGR7MCBA8rIyMhGjUCGDBliuPYxDRMtrj25njNnTuXWrVtdP/vsM88RI0YUAkBWVpbi+PHjqpEjR9ZGRUVd2fcDDzxQ/ffff7skJSXFTJw4sWb06NH1Y8aM0V+bfLiew4cPqwFg8uTJrRqzfO7cOenChQt90tPTNWVlZVKz2dzofVRcXCy53mNbsnz5cveCgoJGSz7GjBlTP3HixFZXMtwsjuMIAIhEIu6PP/443/BaJyYmmv7666+8iIiI3gcPHlSnpqaqUlJSmvxMAaC6ulq0ZcsW17Y09GwgEomwcOHC8oULFzZZKjJz5szgoKAgy+LFi0u///57l1deeSVo+vTplXfffbf2iy++8HzkkUfCIyMjTycmJpqa27cjEZIYgqttAFACgJf1aoLW22euPDm35pDYyDHCBAgB7zQyVWmEZ8ggvuNwdIThhMaeV9FfyO/LWq05lFQaw3csfJgUfmnkDzkh+3QW2bCW780vm4X1Prmjwil6uMc+uVps9/HeiEgsVcbd8mRybdn57Jy0r2UcQ/fiOyZ7wDQkM8RiW2llpXX0rbepdDodhg0ZgltGJcPJyQkiSoTCoiL8sG4drJZmp0nCy9OzyTaxWAx3NzdUVrWp7UQT7y1ahJDgYKz58ScsXf4xli7/GGKxGONSbsG7b72N8LBQAECNVgsA2LBp0w33ZzAYQMQio1gqNbY1Fm9v72YTBA3bdTpdk3IRd3f3Jp8BDcsBGIZptKRn0qRJ9WFhYeZNmza5abXaIldXV/bTTz/1AIDHH3+80Qv58MMP62Qy2fmPPvrI+5dffvH48ccfPQEgLi7OuGjRouIpU6bcMDlRX18vAoCQkJDmf6hXOX36tHT48OExdXV14gEDBuiTk5PrnJ2dGZFIhMLCQun69evdLRZLu5ODa9as8Th48KD62u0tJTGcnJwYoPnXHfjfc3R1dW3xYoKzszMNADExMcark0UAoNFo2OTk5Nqff/7ZIzMz87pJjJUrV7qZzWZq4sSJNa1t6NmSpUuXemRlZWnS0tJyJBIJli1b5hMUFGT57rvvLlIUhdGjRxv8/f2dlixZ4r1hw4aCjjgmn4QkhuB/UpbQSJ2/EsBbfIciaF45YyqfXZV1/jxdPwxoukZVIODDpPhb8gkhDn3iYg8ooSdGE5WZB7TeyT0zV0sIyH3RBQO/zQ4/aqbFdr9UkGOhyNlbNTSkr8tuV1/5KL7juVnOPr3iEqcttJ7P/Gl39cUTwwA0Kf/viRialnz03+WSmpoafPLRMvPD0x+Qgvtfxcov69fjh3Xrrvv4ispKBAYENNpG0zSqa2rgpNFc2dYwEpKhm/+zWFvbdJCPSCTC008+iaeffBKVlZXYdyALv23ciA2bNuHMmVxk7d0DmUwGJ83lyoyfvluNCePH3/D5SuSKdjV9LS8vb7baoGH79ZY1tMXMmTMrX3/99cBVq1a5z5kzp2rDhg3uXl5etvvuu0937X3vu+++2vvuu6+2rq6O2r17t2rTpk3Oa9as8br33nt7ZWZmnh4wYMB1J680LLUoKCiQurq63nBCy3vvveej0+nE//3vfwuee+65RhUGK1ascFu/fr379R7bGllZWbnteVxERIR569atyM3NbbbhfX5+vhxofinMtRr6ajQkRq7V8LM1mUzXTdasXr3aEwCefPLJm8vc/SM/P1/y5ptvBsyZM6ds+PDhJgDIy8uTDx8+vK7hveTk5MSGhIRYzp49q7jhzhxEjy+TEzSxEoDQId/OWDnW+ob2eFpK2Q7lebp+OIQEhsBOBLn6nXBTuQgJjA5AaEaoxLhGzdFjiRzLFvMdB19EFKQzYvPDxYRt0gTQTpGCY7pRl3Lq9nWHMbmUSCyNTJoxKn78cxdFElk23/HYi/zCiwCAW0cly00GA8sRogcBCwDp+zJv+Njmbs88cAAMw6BPfO8r21z+6ZtxqaSkyf3r6utx/p8lH9fj6emJOyZOwHdfrkLyiCRcKCjA6TNnAACJAwcAAPbtP3DDfYAitESuaFNDzwYZGRmaa7fRNI2GKoLBgwe3ubrjWk899VS1XC5nv/32W4+Ghp4PPPBA1Y2aOTo5ObGTJ0+u//LLLy89++yzpTabjWzatMn5RscZMGCAHgA2bdrUYi+L/Px8GQA8+OCD2mtvS0tLa1JB0VVSUlLqASA1NbXJcz19+rS0sLBQ5ufnZ42JiWkxiXH77bfXE0Jw/vx5OdPMx3ZOTo4CAEJDQ5vd186dO1W5ubmK4OBgS0ctg3n00UeDvb29bf/5z38areOyWCzkRv/vyIQkhqCxlCWluLysRGAnthiLDw0p+at4g7EoGUCTD0WBgEfs+Ljkdq9tFTRGaFZIYlyL48S6U9nn+Q6DTzIR6/RAbL4TAecwyZzKAuOw81naPI7jusXoUrVbQK9B096O8Y4Yuqel0aM9QWCAPwBg34EsMDQtNtTq1Ea9nt22Y4dp9dq1N3zs+0uXQqvTXfl/s9mMN99ZDACYcd/9V7Zr1GpERkRgf1YWzuT+7+I7wzCY/8YbTXpwWCwWZB7IanI8m80Grfby8RSKyxegJ4wfj7CQEKz65psmo1QbHDh4EFaaqSKEtOvnvX//fs2PP/7Y6IR5yZIlXkVFRbLBgwfXX9sPoz3c3d2ZO+64oyYnJ0e5ePFif5FI1GxDz7/++kttszW9PtlQFaJUKm/YtPS5556rFIlE3NKlS/0OHz7cpJLh6ukkgYGBln+O2ej76m+//eb0888/N11L1EVuv/32+rCwMPOhQ4fUa9euvfJzYRgGL774YgAAPPzww5UNVQvA5RP+o0ePyrOzsxv14IiMjLSOHj1aV1paKn3nnXcajZddv369U3p6upNGo2GmTp3a7DKdL774wqPheB3x3D777DO3tLQ055UrVxbI5fIrv6+9evUyHTx4UFNTU0MBl5M1Fy5cUERGRjp8PwxAWE4iaN6nAO7hO4ieLt+mL3yien9FGWMWeg0I7FJicMI+qUjS40cSdhRKSGI0q2JvRj+X+N51hJAu72hvLzRS2ueeqMK8dbnBOoC48B1Pa+hrrHGn06pKopPcc0ViKorveG4WIRQVNmjKSKnCwkhkcovNYla2/Kju6ZHpD2Ddr7/hiWefw4Tx4+Hj7YUzZ8+Kd+3ZK558++3c71u2EA7g0EzVaFRkBBJHjMSdkyZCLJbgz61bcaGgALeOTWkyXvX5p+fg6RfmYuzESbhz8iTIZTLsyciAzUYjPi4OJ7P/VxxjMpsxbtIkhIeGom9CHwQGBMJiMWNn2h7knj2L28ffiujISACXp36s/eZr3HnvfZj2wHQMHjQIfXr3hkKhQHFJMY4cPYb8wkIU5OfXuLXzNRo9enTtgw8+GP7999/rwsLCLCdPnlTs2bPH2dnZmfn8888vtnO3TTz77LMV69at86ioqJCMHj26trlmnXPnzg0qLy+XDBgwQB8UFGSVSqXc8ePHlfv379f4+flZZ82adcNqk3+mXFx8+eWXg4cOHRqbkpKiCw8Pt1RXV4uOHz+uUqvVzIEDB84CwPPPP1/566+/esycOTN83bp1Wl9fX1tOTo587969zrfffrt2y5Ytrh313NtCLBZj1apVBRMmTIh85JFHwn/66SdtYGCgdc+ePU7Z2dnK/v37619//fVGjTILCgok/fv3j/Pz87MWFxefvPq2lStXXkxKSlK+9dZbgdu2bXOJj483FhYWSlNTU10piuKWL19e0FyPk5qaGmrz5s1uUqmUmz17dpsaejanqKhIvGDBgsBHH320fMyYMY36b8ybN69sxowZvQYPHhyTnJxc+9dff7kCwPz585s0BHVEQhJD0FTKkj1InX8KQO8W7yvocEaWNrysPXIwzVwxFEAw3/EIBM2RiiR1g4L7OPyJiT0hNNumEX49BWu1OhkuXkxTBwfb/aSOzuShtIRPDCs+sfmCvxwgza7rtjdWE+N3amelIXqExwGZQjSY73g6AiEikUztoRTLjHqzXqfgWLZRo0A16f4TdWKjo/Hr2u/x/tKPsGP3btAMg9joKHz12Sdw1jiR37dsgc1qAUegJyAKcNyV1+i7Vavw/tKl+Pm331BaVg4/Hx+8+tJLmPfcsyCkcc7joQceAMdx+OSLFfhh3c9wcXbGhPHj8eaCVzFj1qON7qtSKrHw9dexNyMDBw4ewua/tkKtViM0JBgf/ec/ePCB+xvdv3dcHDJ37cTHX3yBrdu3Y81PP4EiBD7e3ugTH49XXn7Z4Ofv3+4r1nfeeaf28ccfr3z//fd9d+3a5SwWi7lx48bpPvjgg0t9+vRpcclCaw0fPtwUHR1tOnPmjOKJJ55o9sr+v/71r9Lff//d5cSJE6p9+/Y5URQFX19f6zPPPFM2f/78ck9PzxYT6C+++GJVQkKC6YMPPvDZv3+/Zvv27S6urq50VFSUadasWVf6OgwePNi0ZcuW3Ndff91/165dzgzDkOjoaOPq1avzXF1dGb6SGAAwZswYQ3p6es5rr73ml56e7mQwGER+fn7WF154oXTRokWlCoWi1VU34eHhtsOHD+fMnz/fd/v27S4HDx5Uq9VqZvTo0bpXX321dPTo0c0uF1q1apW7yWTqsIaejz/+eJCzszOzdOnSJpV606dPry0tLS3873//67N69Wqv4OBgy+rVq893h8kkAEA4rsdXxQmakzp/NoDP+Q6jp/m2Pm/fsrqcUBbw5TsWgeBGJsXfkhbs5t+jTyo7mvPJstMJL/3p0CMqO4vESVMSPusRL0JIj7/4cqrSZX/aJa9EgDjSkmA2bKDrHmdP2Si+A7lZygAavcIjAAAcx9I2k85iNRlVPIdltygRxShUGhMhjZMZ9k7u7Jwrlsr0bX3c8uXL3Z9//vmQ5hpbdgatVksFBAQkODs700VFRSdFIod5iQWCFh0/ftwjISEhpLnbHOkDUNC11gBo1Txowc07YdXmDi/ZdvzDupxhQgJDYO9clc4FQa5+Q/mOo7shNkaoxLgOW129n7WmpumC9x6ot6duSIKnNp3vONqIunBIO6r0bH06x3E33QvAXhBCiaVKN5XS2cNAiUTCdKFmsAwrMtTVqg31dYQD9CDE7l8nIhaZ2pPA4MOHH37oaTQaqZkzZ1YKCQxBTyIkMQTNS1miB7Ca7zC6Ox1j1c6ozNgzvTKjVx1nS+A7HoGgNSbHp1QSQoRxgx2MEpaT3FDp9p3tXZ7e7SQFVI4MdtKn8R1HW5XlGZIuHNblcBzX6VeouxIllquUzt5EplQ7xIkvHziWpf5JZlAsoAdlv8kMiVxh1z0DqqurRa+++qrPww8/HPjBBx/4e3p62v71r391iya6AkFr9fiyTMENfQbgWb6D6I5YjmOX1eWkr9ZfiOeAkXzHIxC0VqRX6CGNXCU0m+0EwnSSGzOVlkbTRuMxsVLZl+9Y7MGEsOKRP50Jyagxy4bzHUtb1FVaEnL2VF2MTvLQUSISznc8HYZQIonCRS2SKo0WfY2YoWkh0dsMjmUpY12tmlCEVag1eoqi5GA5+zkfuWas6ubNmzU7d+5scTKci4sL/cYbb3RJIqGqqkq0ZMkSf6lUysXFxRmXL19+0dXVVUiCC3oUoSeG4MZS528HkMJ3GN3JHnP58RdrjsjNHCM0RRQ4FEII/WTSAxfFlCiM71i6I8+0C0diluzuz3cc9swpJvqg//hxQhLtHwwH2+pT4SdMtHgA37G0lUhM6mJGeJyVyEUD+Y6lLa7uiXFdHMfaLHVGi6Fe3TVROS5CEU6h1hgoipKB5Xgf2S1RKEtkanVpw//PmzfPb9myZS0u821ugoVAILg5Qk8Mwc34D98BdBeltKl0UvmufU9XH0wQEhgCRzQifFCGkMDoPMQmVGK0pC7nzECWpvP4jsNeiAgkM2LzI8UUe4bvWNqKoTmnU7sq+9VXWx1uWUyLCKEkcme1ytXbLJJIOmwSRXfEsRwx1tWpDbW1Ypbj9KBIkxGhXYaAkygVjSZ8LF26tITjuMMt/RMSGAJB1xKSGIIbS1myHcBhvsNwZFaOsbxaczRtXPkOpwLaMIzveASC9lBI5FXxflF9+Y6jO6NsjFAa2TJSfehwCd9B2BOpiNVMj8l3I+Au8R1LO4jOZ9Ukl1/Q7+E4zm57JLQXoSRyhcZbKlc71xNChPf3DXAcR4z1/CYzxFJZNUUJDVoFAkcgJDEErfEe3wE4qg2Gi1mDS7aW/WEqTgYgjGATOKwJvUfnEEKc+Y6jOyNCY89Wqc46lMixbGXL9+w51FLa697oAhvA1bR8b/tTkqsfWXBMd4LjuFq+Y+lwBEQs02iULt5WsVRm4jsce3d1MoPhWD0oqmum2RBAolTadUNPgUDwP0ISQ9Aa6wHk8h2EIzlvq88fU7r90Bu6E4k0uGC+4xEIboa3xiPXW+PhUM0DHRFFCyNWW4NjGFnd2XPZfMdhb9wV1tDJ4ZeKAc4hT5R1ZZb+Z9Kra1iWK+Q7ls5AKLFMrvZUKDSuekKI8F5vAcdxxFRfrzbU6iQM2/nJDEosqRWJJebOPIZAIOg4QhJD0LKUJSyE3hitomdt9bOrDuyeUpEWUMlaHKpZmUBwPRPjx5gIIcLnRScjNuG8prXKd6f14TjOyHcc9ibQyRg/KrD8BMA5ZH8Vs54OPbWzwom2skf5jqVTEEAkVamVrj6MRCYXfn9bgeM4YtLXq/W1WinDMgZCUZ3SY0SqEKowBAJHInwpFbTWGgCOuN62S3Acx31Zfz5jeOk2Y4alchQA3jtsCwQdoY9fdKZCIu/Ldxw9AWVjhTXzrcSYzG6m0tJDfMdhj+I8agf396rJ4DuO9mJsnOvJnRW9DTrbXr5j6SyEiCQytYdS4eSuJxTlkAmnLscBJr1eVa/Tyjo6mUHEIpNYJqvvqP0JBILOJyQxBK2TssQKYCnfYdijI5aanOGl2079t+7McBbw5jsegaCjiCiRKSl8YCDfcfQURFhO0ial23cEcxwnvGbNGOpfNTLUud5xp35wkJzNrB5RWWhM684/Y5FEoVa6+HBShdLAdyyOpCGZQTOMAR2QzJAqlKUt30sgENgTIYkhaIuVAKr5DsJe1DCW6vsr9u59uGpfVD1Hx/Mdj0DQ0W6JGpZFUVQA33H0FJRNuCDbFtYabbCtru4g33HYq9vDSpLdFeZ0vuO4GZdO1yUXnqg9zHFct71KTggllirdVEpnDwMlEiZjtIXZoFfpdVoZzdAGQlHt6mdBRCKzWCbXdnRsAoGgcwlJDEHrpSwxAPiY7zD4xnAcs0R3ak9y2XbxKVvtCAjvI0E3pJGpSiM8QwbxHUdPQtmE6SRtVbZjl4LvGOzZPVGFQ5Ri2qGX3WhLzINy91WXcaxDjpBtNUosVymdvYlMqdbzHYujMRsMqnqdVt6eZIZEoSglhHRWaAKBoJMIJ1+CtvoYQI8te9xhKjs6uOSvCz8YCkYCEMZNCrqtSfG35BNClHzH0ZMQWshhtJWh8GIfxmI5xXcc9ooiEM+IvRAjodjTfMdyM0x1dMSpXZVy2sae5DuWTkUokUThola6eBlFYnHXjBbtRq4kM2jaSCiqxSk9RERZJHKFQ44lFgh6OjHfAQgcTMqSGqTOXwlgLt+hdKVLtLH4iar9RUWMcQjfsQgEnS3I1e+Em8plGN9x9DQULSwnaY/KjMx6nzGj+A7DbklEnGp67AXP77LDL7IcCeI7nvairazHqZ0Vmqhh7hkKjcRuRz73OtohE+k7LYF8LEjVWbu+oakPzEBmVhZKz5/t9GOZjQal2WiATKk0SmUywjFssxVbErmyTKjC6FrLly93f/7550P++9//Fjz33HONlqivX7/eadGiRX7nz5+X6/V60S233KJLTU3NS0xMjDp48KCa47jDnRXXXXfdFbJ+/Xr3M2fOnIyKihISiA5AqMQQtMeHAHrEG9zCMeaXao6k3Va+01VIYAh6CHZ8XLIwXYcHRJhO0i7aEycTOYa5yHcc9kwlYTzvjS7gAK6K71huBsdCdia9enhNsSmN4zjh/SK4IYvRqKzXahU2m61JZQahKJtEoRD6vNmJ3Nxc6QMPPNDr0qVL0nvvvbdq7ty5pffcc0+3rJLJy8uT3H333SFeXl59pFJpf39///hZs2YFVlZWilq7j7q6Ourzzz93mzRpUmhoaGicQqHop1Kp+vXu3TvmzTff9Dabza3Kzr388su+hJABhJABGzdu1LT2+AzDYOHChV4hISG9VSpVvz59+kRf7/GHDx+WS6XS/u+++65na/ffGkISQ9B2KUuKAXzPdxid7WdD4f7BJVurtppKktGJV0UEAnuSGJywTyqSxPAdR09E0axwSbA9OE6kPX4yn+8w7J2b3Bp8R6+icoAz8h3LzSo8UZt8KbvuAMc5/nMRdD6Lyais12kVNpvVRESUEQAkCkUZIURIhHWx6dOn644cOZI9ffp03dXbt2zZ4mSxWMiiRYsuffnll5eWLl1a8sQTT2gBYO3atflHjhzJ5iXgDpadnS1LTEyM/fXXX9379OljeOyxx8oDAwMt33zzjVdiYmJ0WVlZqxIZf//9t3rOnDmhe/fudY6KijLNnDmz4o477qgpLy+XLFy4MGDYsGFRRqPxht8p0tPTlcuWLfNVKpVtXsu6ZMkSrzfffDPQ39/f8sADD1RWVlZKpk2bFpGZmdmo6ommacyaNSukT58+hldeeaWyrce5EWE5iaC9/gNgJrphIuyMtTZvdvUBXTVrFSovBD2KVCSpGxTcJ4rvOHoqQjPCF+p2qti3b6BrvwQdIcSF71jsWYDGFHdLUNnBHRd9+gOk1Vf97FFVkWmIqZ7OiRji5kII8eU7HoH9s5hMCovJBLlKVa9SKDv0hErQOu7u7oy7u3uTtZMlJSUSAAgICLBde1tERES3qf5+8skng2pqasTvvPNO0YIFCyoatj/22GMBX331lfe8efP8f/jhhxYrC/38/GyfffZZ/syZM7VyufzKdwetVkslJSVFHT16VPX+++97vf322+XNPd5oNJJHHnkkND4+3hgcHGzeuHGje1uex9dff+05ePDg+oyMjHMAcObMmYq4uLj4Tz/91HPo0KFX4l+4cKH32bNnFVlZWacpqmNPGbvdCaigi6QsOQtgPd9hdKR61lb3WNX+tLsr9wZVs9YBfMcjEHS1W2NHHiWEdGi5n6D1iE2oxGgvzkar9Pn5x/iOwxFEu9cNGuBds4/vODqCQWeLyd5VSTG0YzcudUTrfluPR59+BoNHj0FoXDwiEvph8j334deNv9/wcRaLFe8tXYbEUWMQHBOHIaNvwYfLP4bV2vg8tejSJfj2isTzL/+72f1MfWAGfHtFNtrGcRx+Xr8ek+6+F3GDBiMktjcGJI3EfY/Mwu9btly5n1gq0xFCuLy8PMlDDz0UFBAQEC+VSvu7uLj0HTNmTK+0tLR2V9/OmzfPjxAyYPPmzU1K63Nzc6WEkAF33XVXyNXb77rrrhBCyIDc3FzpBx984BEZGRkrk8n6u7u7J9x///3B1dXVTRKOBw4cUEyaNCnU398/XiqV9nd1dU2IjY2NmTVrVqDFYrnyWXJ1PB9//LF7TExMrFwu7+/m5pZw9913h1y8eLHZC9rl5eWip59+2j8sLCxOLpf312g0fYcOHRq5fv16p+s991WrVrkOHTo00tnZua9MJuvv7+8fP2nSpNA9e/ZceT2XL1/uTggZsHz5cncA2Lx5s4YQMuDDDz/0A4BJkyZFNixvaHgNExMTowghzX4v/+2335ySk5N7ubq6Jkil0v6BgYG9n3zyyYCqqqpmk7QbN27UDBgwIEqhUPRzdnbum5KSEn706FH59Z5TR8rOzpZlZGQ4+fn5WV955ZWKq2/74IMPShQKBbthwwb3urq6Fs/Phw0bZnrqqadqrk5gAICrqyv77LPPlgPA3r17r7s85Nlnnw0oLi6Wrl69Or89yYXS0lJp//79rwx6iI6Otrq4uNCXLl2SNmw7efKk7IMPPvB76aWXSuLj4y1tPkgLhEoMwc1YDOAuAA79xZvjOO6L+nMZn9efjeKAZL7jEQj44Kp0Lghy9RvKdxw9GcUI00luRtmOXTHq0FArIUTa8r17tiF+VSN0Fmlank7j8J95NgvrfXJHhVN0kkemXCUW/oZ1kVfeeBOREREYMmgQvDw9odXpsHN3Gp7910vIy8/Hv+e+0OzjnnzueRw7eRITx98KsViMbak78H/LP8bxk6eweuUXuJlGm0s+XIqPv1iBoMAATL79Nmg0GlRUVuLYiZP448+tuGPCBFAikU3l4lqZnp6unDRpUkRtba04KSmp7vbbb9dWV1eL//77b5exY8dGf//993n33ntvbbuDaYe5c+cG7N2712nMmDG1ycnJdRkZGZqffvrJIz8/X7Z///4rHVEPHDigSE5OjiGEcLfcckttcHCwpa6uTpSfny/7/vvvPZctW1Ysk8kandwuW7bMKyMjw3nChAk1Y8aMqd2/f7/6119/dc/MzNRkZWXl+Pn50Q33PXv2rHT06NFRJSUl0gEDBuhHjx5dZzQaqR07djhPmzYt4oMPPih88cUXr/TXYVkWd999d8j69evdXVxc6PHjx2s9PDzo4uJiaWZmpmbjxo2WkSNHNrv0KyIiwjJ37tzS9PR0zcGDB9VTp06tDg4OtjbcdqPX68UXX/RdunSpn7OzMzNmzBidp6cnnZ2drVi5cqX3jh07nLOysnLc3NyufLB+8803ro899liYRCLhJkyYUOPj42Pbv3+/Ojk5OToqKqrFaTY3a9u2bRoASE5OrhOJGudYXF1d2f79++szMjKcdu3apbrjjjvq23scqVTKAYBIJGq2unPTpk2ab775xuvtt98uam9ywcfHx3rs2LEryamzZ89KdTqdOCAgwApc/p2YOXNmSEREhOmNN95othrkZglJDEH7pSw5htT5PwO4l+9Q2ivLUpX9XPUhGDg6ie9YBAI+TY5PqSSEhPAdR08mjFi9ObTe4G2prEqXe3kKf89bYXxoSfLPZ4L3VprkI/iO5WZxLBQ5e6qGhPZz2e3iIx/Fdzw9wa4/tyAkuPGwG6vViumPPoZPVqzEQ/ffB18fnyaPO5eXh91/bYGL8+Up9a+8OA/Tpj+I7bt24deNv+PuKXe2O6Y1P62Dr7c3dv25BUpF44Ek1TWXe0QqnV3KaIbhHnjggTCj0Sj6448/cidMmKBvuF9BQYEkMTEx5plnngmePHnySYVC0WXL/I4ePao6cuTI6YblEzabDUOHDo06cOCAZteuXcrRo0cbAeDLL790t1gs5Pvvv78wY8YM3dX7qKysFKnV6iYfJnv27HHevXt3zvDhw6+crD/66KOBX3/9tdcLL7zg//PPPxc2bJ8xY0ZoaWmpdMWKFRca+lIAQFVVlSgpKSlqwYIFQffcc48uMDCQBoClS5d6rF+/3r13797G3bt3n716uQhN0yguLr5us/CoqCjr0qVLS+bNm+d38OBB9cyZM6snTpzY4gn8H3/8oVm6dKlf3759Ddu3bz/n4eFx5ZgNE1Beeukl/6+++qoIAGpra6m5c+cGE0K4v//++8zVSZWG16GlY96s3NxcOQBERESYm7s9LCzMkpGRgTNnzshvJonx7bffegDAuHHjmiThqqurRU8++WTIgAED9FcvZ2mrmTNnVr755puBI0eOjIiOjjZt2bLFlRCCOXPmVALAe++953nixAlVZmbmabG4c9INwnISwc16HQDd4r3sTBVjrry7Yk/6o1X7Yw0cHcd3PAIBnyK9Qg9p5KpBfMfR0xGhsedNK03d4c13DI5kWlThMJXEdpDvODoIyT+qG1V8pi6D47hmTxIEHefaBAYASKVSPDJjOmiaxt7MzGYf98LTc64kMABALpPh1X+9CAD46dffbjousUQCEdV0JYG7m9vlKgxnl8p169a5FBUVyR555JGKqxMYABASEmJ79tlny6qqqiSbNm267tKJzvDSSy+VXt3/QSKR4MEHH6wCgMzMzCazcZtryOjp6clce5UfAKZMmVJ9dQIDAP7zn/+UqNVqZtOmTe4mk4n8cxzFwYMH1bfeeqv26gQGAHh4eDCvvfZaicViIWvXrnVt2L5y5UovAPjiiy8Kru13IRaLERwc3KTPxc1avny5FwCsWrWq4OoEBgA899xz1dHR0aYNGza4NWz74YcfXGpra0WTJ0+uubYqpOF16OgYr1VXVycCAGdn52aP1bBdp9O1u1/Ru+++67l3716n6Oho07UjbAHgscceC6ytrRW3dxlJgwULFlS8/vrrlwoLC2Vr1qzxdHV1pdetW3du2LBhptzcXOnixYsDXnjhhdIBAwaY33//fU9/f/94kUg0IDQ0NG7NmjUu7T7wVYRKDMHNSVlyDqnzvwbwBN+htAbNsfSS2uyMnw2FfQEIV+sEPR4hhB4TNcyt5XsKOhthhCTGzTKXV0TQBsNhsUol9DVqBYpAND02P+6bk+HZNlbULRL6FfnG4cY6+lSvQa7eQo+fznOppASfrliJvZmZKC4phdncOG9UVtZ8BfnQwYlNtiUOHACRSIRTp2+utcnUyZPw1XffI3n8bZh0+20YmpiIgf37wUlzuTWA0tmllFAUt2/fPhUAFBUVSefNm+d37X7Onz8vA4DTp0/LAXTZkpIhQ4YYrt3WsLRCq9VeOWd74IEHar755hvv6dOnh48fP157yy231I8ePVofFxd33aUBI0eO1F+7zd3dnYmJiTEdPHhQffToUfmwYcNMe/fuVQOXT7ibe20qKyvFAJCTkyP/537UuXPnFO7u7vS1SZLOdPToUbVYLOZ++OEHtx9++KHJ7TabjWi1WnFZWZnIx8eHOXLkiBIARo4c2aTC4erXoTXH/v77712OHj3aqG9Kv379jA8++KCufc+mY6xevdrljTfeCPLw8LD99ttv569dUvTtt9+6bNy40X3JkiUXY2Njb6pZqkgkwsKFC8sXLlzY5I0+c+bM4KCgIMvixYtLv//+e5dXXnklaPr06ZV333239osvvvB85JFHwiMjI08nJibe1O+LkMQQdISFAB4C0CWNcdrrb1PJkfk1x5ytYB1+DbBA0FFGhA/aJ6ZEI/mOQwAQWhhO0hHKdu9BwITb+A7DYUgoTjkjNt97dXZ4Act1jyVl+mpr79NpVcUxIzzOUiIS2fIjBG1RePEibrtrGmpr6zB44ECMSkqCRqOBiKJQVFyMn9dvaNKos4Gnu0eTbWKxGG6urqiqbnLhuE3eXvAqggIDse633/DJipX4ZMVKiMVi3JKcjLcWvGobHBpeBQA1NTViAPjrr79c//rrr+vuT6/Xd2nFenNTOxpK8RmGuZLkHj16tHHr1q1nFi9e7Lt161bXhskSISEh5ldeeaX0ySefrLl2P76+vs1WQ3h5edkAQKvVioDLyw0AYN++fU779u27biWKwWBodH9vb+8unSCi0+lEDMOQZcuW3XAyUV1dncjHx4dpqILw8fFptnq84XVojY0bN7qsX7++0TSPqVOnVreUxHBycmIAoLa2ttlKi4btLi4uba4K+f77710ee+yxMDc3N9v27dtzr01SlJeXi+bOnRs8dOjQ+pdffrnTJvMsXbrUIysrS5OWlpYjkUiwbNkyn6CgIMt33313kaIojB492uDv7++0ZMkS7w0bNhTczLGEJIbg5qUsKUbq/E8A/IvvUJpTSOuLnqg6UFLCmAbzHYtAYE8UEll1vF9UAt9xCC6jGFZY4tkB6s+eG8COSzlLSSTCyWsrKSWMx33R+cYfckIrge5RvWA1Mf4nd1ToY0Z4ZEkVoqaX/wXttuLrb6DV6vDR++/h3rumNrptwx+b8fP6Ddd9bGV1FQL8Gl/gp2kaNVotNOr/XQgn/5S603Tz53N19XVNtolEIjwx8xE8MfMRVFVX48ChQ/h985/446+/kHPuLDlzJhcKheLKyeSaNWvOT58+vUMrLSiK4hqe07WamzLSXikpKYaUlJTzJpOJpKenK7ds2eL89ddfe82ePTvU29vbdueddzaqOCgtLW22L0VFRYUEAFxdXRngf0saFi1aVPTaa6+12DOhIfFSXl7epQ2VNRoNw7Isqa2tPdaa+zf8zMvKypo99214HVrjt99+KwBQ0Nr7N4iKijIDwLlz55q96HvhwgUZAERHR7dpOdzXX3/t+sQTT4R6eHjQ27dvz22uWWdeXp5Up9OJMzMzNSKRqNlKxSlTpkQCwNtvv130xhtvtLlfRn5+vuTNN98MmDNnTllDVU5eXp58+PDhdQ1LV5ycnNiQkBDL2bNnFTfcWSsIX5gEHeU9/D979x0Wxbn9AfzM9sqy9F6lCKwICHYQ7IAlmqYxwRJFjTERY24SjblJ1MSYoDFXjTH5RRJiYmKLgg0soGLFtgHFQu+9bt+d3x9euDSlCAzlfJ6H515nZ2e+LGTZOfO+5wVo+ReFQgpSK19VdiMhtOi8MRYwEGopxCMolSAIUdt7op6A00m6Tum1651uWDZQiTlqmxecckoByBbDzvsqnZYUpJwvGVZdojxPdZb+JCMrGwAAQiZPavHY5WvXnvncy1dbPn7tRjJotVrwcHNr2Kav92QQQH5BQYv9a2pq4XFG5jPPY2RoCCGTJ8MP330Lo0eOILOzcxg3btzgAgCMHDmyDgAgMTHxqUtQdlZ9MSArK6vFRf2VK1da9LV4Xlwul5w4cWLdtm3b8r/44otsAIDDhw/rN98vMTGxxVSJsrIy+r1797hsNpv08vJSAACMGTOmDgDg0qVL7Zpaoaenp3NycpKXlZUxLl269NwXpu01dOjQuurqavqNGzfaNQrc29tbBtD6z7z+dejqjM1Nnjy5BgAgISFBT6ttWpyrqKig3bx5U8DhcHSBgYEtphU9za5duwyWLFniYGJioj579uz9p602YmJionn55ZdLW/uytbVVAgD4+/tXvfzyy6VDhgzp1DSPRYsW2Zqamqq/+uqrJv/RNl7yt7V/dxYWMVDXmPBFGQB8TXWMer/VZlwekX+yPF5RGAC9fJoLQlQwFRqlmQqNRlOdA/0PgSMxukzZjZvDSZ2u5dUPeiYLgXzwRNuC+wBkn2vY/Qy0xzcqxhU8rL1AkmSXNxgciKytLAEAIKlZQeJc4gXY9+dfz3zuth07obLqf4MfFEolbPr6GwAAeOXF/43qEAgEMMjRAa7fvAlpDx81bNdqtfDvTZta9OBQKlVwLTm5xfnUajWUV1aqAQD4fL4OAGDu3LmV1tbWyl9++cV4//79rRby4+Pj+TU1NR1+T64vkPz6669GavX/ft0ePXrE3LJlyzOnPrRXXFwcv7a2tsWFYFFREROg9Yafhw8fNmxeZHj//fctamtr6dOmTSuvX4XF399f5uPjU3v69Gnxtm3bDJsfBwDg2rVr3Ly8vIYRDeHh4cUAAEuXLrVrPtpEq9VCVlZWu0c5tNe7775bBACwePFiu8zMzBbHr66upp05c6ahaDR37txKPT097dGjRw0SExOb9LOofx26OmNz7u7uytGjR1fn5+ezvvzyyyaroaxZs8ZCLpfTXnjhhTI9Pb0mP79bt25xbt261eJa5rvvvjNcsWKFvZmZmer8+fMtppA0NmjQIPX+/fuzWvvy8fGpBQBYtWpV0f79+7Oaj+Jpj507dxokJCSIfvjhh0wOh9MwN3bQoEHy69evC8vLy2kAAKmpqaz09HSus7Pzc/dPwekkqCttBYAVANDtyxQ9TYqq8uGysmu1FToVrhWP0DOESoLkBEHgRXMvQmhJHInRVXQ6ZtW9+2n67m5dctEwkDgb1AyrUrIuXis06lfNrwsf1Y6VVanvOPjoWxMEMWCbGRMEQZLk873XzH9tLuw/cBCWvL0SQqZMATNTE7j/4AGcS7wA04Onwt+xx5/6XCdHRxg3NQRCp0wGBoMBp+LPQGZ2NkwIHAcvzZzZZN/lb74JER9+BDNeeRVCp04BDpsNl65cBbVGDe6DXSHl3v2GfRVKBcx4ZQ7Y29rCEA93sLK0BIVSCYkXL5EPHz9mBQUFVXp7eysAANhsNvnXX389Dg0NdXr11VcHbd68uc7d3V3G4/F0eXl5rDt37vByc3PZWVlZd4RCYYfWvg4KCqobNmxY7Y0bNwSenp6Dx4wZU1NcXMw8c+aMyN/fv/r48ePPPe1i8+bNZleuXNEbNmxYjY2NjUogEGjv37/PTUxMFOnp6WlXrFhR2vw5/v7+VRMmTHANDg6uMDMzU1+5ckVw8+ZNgYWFhWrbtm25jffdv39/+vjx411WrVpl9/3335t6e3vXiUQiTV5eHuv+/fvchw8fcuPj4+9bWlpqAABWrVpVevHiRcGRI0cMnZycPCZNmlRpZGSkKSgoYCYlJQnnzJlTFhkZmf+833djM2bMqPnoo4/yvvjiC8vBgwd7jBs3rsrW1lZVW1tLy83NZV27dk3o4+NTO378+IcAACKRSLdt27asN99802HSpEmuISEh5fWvw8OHD7n1P7OuzNia3bt3Z/v7+7uuW7fO+ty5c0IXFxdFcnIy/+rVq0JbW1tlZGRkXvPneHt7uwMAkCTZUKU7duyY8N1337XT6XQwatSo6u+//75Fsxl9fX1NZ6aFdFROTg5j7dq11osWLSoKCgpqMookIiKicN68eYOGDx8+OCAgoOrEiRNiAIAPP/yw9c6/HYBFDNR1JnxRC/EfbgSAb3v61FU6VdU7ZTfuJKvKRwH+XiP0TEMsXC9zmRws9PU2WMToUkUJF7xEboNrCYLo9g+m/Y2vedmYCiUr4WGFXr9qhF1dovS8d6E0y3W0UQWNTjh2xzkeebl0x2G7DKnTqJR1FTqNStnp4fNurq5w4LdfYXPkNjhz/jxotFpwc3WBn3b+B0RCvWcWMXZv/xa27tgBh44eg6KiIjAzM4X3Vr4NK8LDgSCavgXOeelFIEkSdv/fz/DXocMgEolg8oTx8OHqCHjzrbeb7MvjcmHd+2vg0pUrcP3mLTgZFw98Ph+sra1VmzdvLlq5cmWTC/vhw4fLb9++nbpx40bTuLg4/QMHDhgSBAHGxsZqd3d32Ycffphvbm7eqRFJJ06ceLRixQqr06dP6+/du9fE1tZW+cknn+ROmzat+vjx4+K2j/Bsy5cvL9HX19feunWLn5ycLNBoNISpqal63rx5xR999FGRs7Nzizvyq1atKp4xY0blzp07TWNjYzlcLlc7e/bssm+++SavvhhRz9HRUX3r1q3UzZs3mxw7dkx85MgRA51ORxgaGqqdnJzkS5YsKfb19W24k06j0eDw4cOZu3btqv6///s/49jYWLFKpaIZGRmpfX19a2fNmlX5vN9zazZu3Fjo7+9f++2335rcuHFDEB8fzxAIBFpTU1P13LlzS954440mDU4XLFhQoa+v/3DDhg3mx48fFzOZTNLX17cmISHh/oYNG8x6oojh7u6uvHr1auoHH3xgmZCQoJeQkCAyNjZWL1iwoHjz5s35xsbG7WrqmZGRwdLpntTX/vrrr5bdcgHAwsJC1RNFjMWLF9uIRCJtawWY1157raqgoCDr22+/NYuKijKxtbVVRkVFPXrelUkAAAiSxG7oqAvFf8gCgAcAYNsTp9ORpO4/1WmXfqx95EYCtDrsDSH0P3QaXR4+ek4ZjUazojoLamr43N+T2eVyXBq0C9m8OCuBb23Vry7Ee9KBNJvEIhm3361eRGcQVYP9jR4x2a03uHsWnpUGBjk6dUesnkMCaDV1tYraSh6pI/vtiDwGm11nZGVzv+09+6+IiAiLrVu3mh87duxBaGhoh6cJIESlO3fuGHl6etq19li/feNCFJnwhQoA/t0Tp0pSlEhHFpxM21P7aCwWMBBqn/Euo65hAaN3wukkXa8g7owjSZIdXq4OPTHLOXu0kKW+SnWOrqbVkKJ/zpYMrS1XJVKdhRIEAJ3JF/D0zbRMNkdGdZzuIjQwyqE6A0Koe2ARA3WHXwHgXncdvEgrL3qhKOFSeNlVDxmpHdxd50GovxGy+QVOxna+VOdArSN0ZLc3Fhto1FVVVqrKymcvl4CeikYAfe7gjCEsulZKdZZuQH94tdy/OKMukST7VSPTdiMIOpMtMOJx9QxrCRqtXxX72DxeBZvHa/cqDwihvgV7B6CuN+ELLcR/uA4ADnblYVWkTrWhUnr5sCzHGwBwVQWEOmiaZHwGQRCjqM6BnqIfD+umUmH8WT3bl2ZTHaPPYtBI7jy3dKuofxzTtSTNgeo8XS3vfo1/XaU62W6oaNBAXXKazuQKePpsjVpeWaeSy7p8GdCeRhAEKTQ0ajE/vzNiYmKEZ8+ebXMp1p5qoogQegJ7YqDuE//hNQDokru+sbK8Gx9X3DZUA2nfFcdDaKCxEVvcnT5kwhCqc6CnGzXr1xSGTO1OdY7+yHnZkjt0DseT6hx9WZWSmftbqj2TBMKU6izdgSNkpLuMMqTTaMQze3r1i54Yz6DTKOoUtRUsnVbb5cti9hSuUK9YZGLaJVNJ6ntKtLWfhYWFKi8vrz+OWEKIMtgTA1Hlg+c9QIa6NmtiYfz1DypuDcMCBkKdppviHtBnP5AOFDidpPsUX7ikoDpDXydiq61mOWdXApD9sjmgokbj8M/ZYj2NSneb6ixUojE4fJ7IjMbmCWqpztIZBI2mFRgadtlynpGRkfkkSSa39YUFDIR6FhYxUPeZ8MVZADjWmafKdJq6FWXXEqYXnzcr1CpwDj9Cz8HP1jOJRWdi/5jeDosY3abynxRfnVabQXWOvs6Mr3CZbJf/EIBUU52lO2jVpFh6tthdVqW+QHUWShEEncnVF/D1TeR0BqPFcp29GV+kX0CnM/pVfw+EUEtYxEDdLQIAOvQHMKrmcdLIglPVCYriAABgd08shAYGFp1Z7Ws7xIXqHKhtBIk9MboRreLmbVypoAsMEtd6j7AovQbQT+cjk8BMSyobW5otSyBJUkd1HCoRdBaXo2fKYPP1aqEPrJ1EZzBUfH0x9qVAaADAD0yoe0344hEAfNueXe+qKtJG55+683X1vVE6INucf4gQattkN/9bBEEYU50DtQOJzba7U8mVq74kSZZRnaM/8DEtH+1qUN2vlyfNSakOyJZW3yBJsk9Oq+gqBEHQmBw9AV/fVEFnMpVU53kWvtggl6DR+mdxDSHUBBYxUE/4HACKnvZgpVZVMa/kUuJrJZcGVZNqbLyGUBcR80SZNmKLkVTnQO1E4nSS7kRqNNyaR49x3noXGW9bGGDOlyVQnaM7lefJ/R5cLisgdWQu1VmoRtCYHK7QlMUR6tcQBNHrCgVMNqeWpyeqoDoHQqhnYBEDdb8JX9QAwNrmm3UkqfumKjXRv/A03FFV+AMAfoBHqAtNl0woIQiCRXUO1D4EjsTodoVnz7uTJIlNPrvIC045Y/VYqitU5+hOsiqN0z/nStgatQ4LYAQQDJZAyNM3VTFYbDnVcRoQAEIjo2yqYyCEeg4WMVBP+RkAkuv/kagoujO84OTDvbXp/iSAmMJcCPVLzib2N4QcPjbF7UtwJEa308pkxoqi4utU5+gvCAJocwZnDmXTtXeoztKdNCqd8T9ni53lNZpLVGfpDQgag80RGHO5euJagkZQ3jeEwxeWsDjc3lNUQQh1OyxioJ4x4QsdALxToJEXTCs6l/RW2XVPBanFZoMIdQOCIDRBLqMMqM6BOgyLGD2gIC7ekiT7aVNKCjBoJGeeW7otndA9pjpLdyJ1wL5/sXS0TkcqAH97AAgAOpMv4OmbaZkcjoyyGDSaVmho1GVLqiKE+gYsYqCeM+GLS1OKzsZlaupGUR0Fof5srKNvEoNGd6A6B+ognE7SI5SlZQ6a2locjdGFOAyd/tzBGVwCyAKqs3Q3jVLH0WrIOgCgfARCb0AQdCabb8TjiQxraTRajy9tytcX59MZDE1PnxchRC0sYqAepQPyQwAY0J2+EepOXCa7TGLhgg1y+yYsYvSQwrPncfnuLqbH1ljMds6uBSCrqM7S3ZQyDV+t0KmAADXVWXoLGoMr4OqbkSwur66nzklnMhW4pCpCAxN+YEI9ShomzZdEST4DgK+ozoJQfxTiEZRKEMRYqnOgTmFSHWCgqE3P8NSpVPdoLNZgqrP0J6Z8hdNU+/xbJzIs3AH6d1NhtVLL0WpJNVfAUJAkyanf/ldJ714g4yXj7mtDRhA0BotnwGCw+HWK2nKWTqvt1vc0oYFRDkEQTbbNnj3b7tChQ4b379+Xuri4qLrz/Agh6uBIDESFbQBwn+oQCPU3pkKjNFOh0Wiqc6BOwxsLPajk8tXefbXZRzno13qNtiy5AdD/+47oNDqmrErNIgiix0Yf9AZZ2VkgFPMgfPmSVh+nMdh8nsiMxuYJunzk7dffbgfzQc5w+cbNWo5AUN3Vx++NvvvuO0OJRDKYx+N5CYXCoX5+fi6///67qCPHSEpK4kZERFh4e3u7GhsbD2Eymd4mJiZDpk2bZn/x4kVea8+5efMmZ9WqVRbjx493NDc3lxAE4UMQhI9a3fEBSNnZ2Yxp06bZGxgYeBoaGnrOmDHDPi8vr9W/eStXrrQQCoVDMzIysLCPngqLGKjHScOkagBYSXUOhPqbUEmQnCAIfF/vi3SkjgAg2t4RdZXyW7f9SJ0uj+oc/dFQk4pR7oZViVTn6AkkSdLqKlV8IIkaqrP0KgRBZ3L1BXx9EzmdwejyEREMLqekte2RkZF5N2/eTLGzs+sXU32WLFlitXLlSruSkhLmnDlzSmfOnFn+4MED7ty5cwdt2rTJuL3HWbZsme3WrVvNVSoVMWXKlMo333yzyNnZWR4TE2Mwbtw416ioKP3mz4mJidHbtm2beUJCgj6Xy9Wx2exOFSa1Wi0EBwc7xcfH6wcHB1eMGzeuKjY21iA4OHiQVtu0jUpSUhJ3586dZp9++mmuvb19v/gZou6BH3YRJaRh0jgAOEB1DoT6iyEWrpe5TM5QqnOgziE0OmxM19NIklH5T8ojqmP0V+NsigIsBXUJVOfoKbJqlVCnwZ5fzRF0FpejZ8rg8PVqu7JMS9DorV7g2traqr28vBSdveDuTeLi4vh79uwxtba2Vkql0pSffvop59dff82+evVqqkgk0n766afWaWlp7Zq29fLLL5dLpdJ//vnnn3u//vpr9q5du/IuXrz4cOfOnRlarZZ49913bRUKRZOf0IwZM6rOnDlzv7Ky8mZ6enqKoaFhp4oKCQkJ/JSUFN6WLVuyo6Ojsw8ePJj5zjvv5N+9e5efmJjYMApErVbDokWL7P38/GoiIiJKO3MuNHBgEQNR6R0A6PcNwBDqbnQaXT7GcZg11TlQ5xFaLGJQofjCRW+S7P+NKKkyY1Cuvz5blUR1jp6iqFMLqM7Q3TZ9uQE8PJ+0ktn3ezQIxbyGr+h9vzbZN/5MHMx+aSbYDbKhmdraCEYGTSQ//fIrTVV1y1kgqffvw7J3V4FvQCDYDnYHd9/hMHH6TPh4w0aon77gGxAI33z3HwAAmDZtmnP99AaCIHzqjzN79mw7giB8Gl/cp6WlsQiC8Jk9e7ZdWloaKzQ01EEsFnuy2WxvDw+PwU+bmlFWVkZfuHChtamp6RA2m+1tb2/v/u9//9s0NTW14XjP+3o+y86dO40BAFavXl1gbGzcMGTBxcVFNX/+/GKVSkV8//33Ru051tq1a4s9PDyUzbcvW7as3NbWVllZWcm4fv06t/Fjnp6eyqCgoDqBQPBcBaH09HQWAMDo0aMbpl0NHz68DgAgIyOjocny2rVrzbOzs9k///xz1vOcDw0MOP8WUea/TT4/BICdVGdBqC8b7zLqGo1GC6A6B+o8QqvDYbMU0KnUwrqs7ASBnS3+99MNCAKIV1wzfaL+cbit0DKGUp0HPb+xY/yhqqoKdn6/AyQeEggNmdbw2BDJkIb//8XmjbDpy40gFhvAlMlTwdjIGFJS/yG+//FHxtmEBF3sX/sJgUBAADwpYITMfgmAIGDy+CCwtrKC2tpayMjKhqjf9sEHq94FJpMJi+eHQezpOPm169e5s2bNKrO1te3QNJXc3FzWyJEjB1tbWytnzZpVXlFRQY+NjTWYN2/eIIFA8GDatGkNU4JkMhnh7+/vnJqayhs8eLBs1qxZZVVVVfRt27aZX7p0qUeKVUlJSXoAADNnzmxR9QkNDa369ttvzRMTE4XPex4Gg0E2/t+uZm9vrwIAuHLlCs/Ly0sBAHD9+nX+fx9TAgAkJydztm3bZv7vf/87BxuyovbAIgai2vcAMA8ARlEdBKG+SMjmFzgZ2/lSnQM9H5pGp217L9QdCuLPOg9aNF9NEAQ2kesGDBrJnueWYf/zP44PtSTNieo86PmMHeMPNja2T4oYEk/46IN1LfZJvJAAm77cCH6+w+HgX4dBX6Tf8Fj0vl9h2VvhtG92/qD9fN2HKo1Kyf3z0GFQKJXw866dMGXihCbHqqyqAi73yQCBlW+/XVopk6uvXb/OXbBgQVloaGiH+pBcu3ZNGBERkf/NN98U1G87ePBg+Ysvvuj09ddfmzYuYnzyySdmqampvNDQ0PK///47g0Z7Mnj90aNHBb6+vm4dOW9nVFdX04qLi5k8Hk9na2vbosjt7u6uBADIzMzktHx2+505c4b/+PFjjomJidrX11f+PMd6moCAgDo3NzfZ6tWrbZOSkgRyuZx25MgRAw8PD5m/v79Mo9HAwoUL7YYMGVL3wQcftNrrBKHmcDoJopQ0TEoCwBIAXGsdoc6YJhmfQRBEq53FUd+B00moo6mpMVeVl1+jOkd/xmboRK+5ZQgIIPOpzoK6367dTwbY/ufbHU0KGAAA8+a+DkMkQ+DAoYN0jsCYy9UT1wJBkAAAHE7L63F9kQhoNBrQ6HS1npFx7vPksrCwUG3evLmg8bbZs2dXm5ubq+7evctvvH3//v2GNBoNvv7667z6AgYAwKBBg9RLliwpep4c7VFeXk4HABAIBK0WuA0MDLQAADU1NfTOnqOoqIi+aNEiewCAL774IofB6J572wwGA2JjYx8FBgZWxcTEiM+ePSuaOnVqRWxs7EM6nQ6fffaZ6YMHD7g///xzZllZGX3GjBn2fD7fi81mewcFBQ3CVUpQa3AkBqKcNEyaIomSfAUAa6nOglBfYiO2uGvA18dRTP0AocaRGFQqiDtraPfqS1TH6NeELI35Sy5Zj/5Ms60CIDq0PCTqW65dvwpMJhMO/30IDv99qMXjKrUaSktLoKyiDAwNDAUvvzJP8+PeKMaCZcshdMpkGDt6FPh5+4CdrU3Dc4QGhjk0Ov253icHDx4sa+1C3dzcXHX79u2GKSLl5eW0nJwctpmZmaq1qQ3+/v61X331VbvOWVpaSt+0aZNp8+0fffRRkZGREWXv+9XV1bTg4OBBWVlZ7KVLlxYuXLiwW5ectrOzU8fGxqY33y6VStlbtmyxWLNmTb5EIlFOmDDB8erVq8LNmzdni0Qi7XvvvWczY8YMx9u3b99vXExCCIsYqLfYAAAvAwAONUWofXRT3APw7kQ/QdNiEYNK8oICV41MdovB43lRnaU/M+YpB4U45N2JTbd0BSDYbT8D9UXl5eWg0Wjgi82bnrlfXW0dGBoYgp/vCMap4/Gw+auN2piTp+gHjvwNAACODvaw+u234ZWXX6ri6ome+yJbJBK1+j5Lp9NBp9M1/LuiooIOAGBoaNjqCDkLC4t2j5wrKyujb9261bz59vDw8NJnFTHqR1rU1ta2OtKifqSGUCjs8N+O6upq2oQJE5xu3rwpePPNN4t27dpFyVLTOp0OFixYYOfk5CRfv359kVQqZZ85c0Z/zZo1+StWrCirz7pixQr7mJgY4fTp03EZY9QAS1qoV5CGSRUAEE51DoT6Cj9bzyQWnTmY6hyoa+ASq9QrSryIhaQeYCeq8xxrVXwTgNS1vTfqi/T0RCDWF0NNheyZXzY2/xtpMdxvOBw6cJSek5GniT18SL5qxVtQWloGy1dFwPkrV8t6Mr9YLNYCAJSVlbV6szc/P7/dN4FdXFxUJEkmN/9qq3mlnp6ezsTERC2TyWhZWVktblikpKSwAQDs7OwU7c0CAFBRUUELCgpyun79umDp0qWFe/bsea4pOs/jyy+/NL579y7/559/zmQwGHD37l0OAICPj4+sfp8RI0bIAACkUin3acdBAxMWMVCvIQ2TngOAvVTnQKi3Y9GZ1b62Q1yozoG6DoGNPSlXfe++j06jeUx1joFgiHHlSIlx5QWqc6DOodOeDA7QaVt/2/Id5gsVlRVw715qh4/N4XAZ/uOmcP/9yYa6Des/1gIA/P330YapHnQ6nQQA0D7l3F3BwMBAZ2VlpSwuLmY1Xqq1XmJiYo+sTjJq1KhqAIAjR47oNX8sJiZGBADg7+/f7tEJZWVl9MDAQOfk5GTB22+/XUDVCAyAJ8vebty40erdd98t8PHxaVKIUSqVRP3/l8vleK2KWoW/GKi3eQ8AsDMxQs8w2c3/FkEQxlTnQF0HVyfpFYiyG8kFbe+GuoK/VXGAtbAugeocqOP09fWBIAjIyc1p9fEVy99+8r/vvAUFBS17udbV1cG16//rpXvl6hWQy5sujEFjsPkVtQoNAACPx2sYtVM/xSMzM7NFcaErvfzyy2U6nQ7ee+89y8ZTTR49esT84YcfWvS46A7Lly8vAQD45ptvzEtKShqmlaSlpbH27t1rwmKxyKVLl5Y2fk5BQQHj1q1bnIKCgiajRUpKSugBAQHOd+7c4a9evTp/+/btlDbZXbBgga2NjY1y48aNDe+5np6eCgCAY8eO6ddvO3z4sAgAQCKRdMvKKajvwp4YqFeRhknLJFGSVQAQTXUWhHojMU+UaSO2GEl1DtS1CLUWh9b3AmXXbvga+fmWEDQaFgl7wDTHXP/f79ldqlCyR1OdBbWfQCCAYcN8IenyJVi0eAEMGjQI6HQ6BE8JAQ8PCYwLCIRPP/kc/v3Zehg6bAhMmjgZbG3soK6uFrJzsuFS0kUYOWIkHD5wFAAAtm2PhMQLCTByxCiws7UDPl8A9+6nQlz8abaenp52xYoVDRfqkyZNqlm7di18/vnnVv/88w+3furHV1991aUFyE8//bTw+PHj4piYGAMPDw/OuHHjqquqquixsbEGvr6+NfHx8frd3Why4sSJdW+++WbRjz/+aCqRSNxDQkIqVCoVERMTI66qqqJv3Lgxu/m0lC1btphs3brVfNWqVQWRkZENhYrQ0FDHlJQUnrW1tVKn0xEREREWzc/34osvVowaNaqhWFBQUMBYsWKFVf2/KyoqGAAAr7zyih1BPBkssW7dukIvL68OTWmJjIw0unbtmjAhIeEek/m/mTIeHh7KiRMnVh44cMAwODiYJhQKtQcPHjSSSCR1HV1OF/V/WMRAvY40TPqbJEryKgCEUp0Fod5mumRCCUEQdlTnQF0LR2L0DqRWy65Oe5gqGuwSQHWWgYAggHjFNdM3KsXxplzD8KY6T1cIYXVspgGLy5AxWAQbADq9VCYV9nz/E3zw0fsQdyYO/jr4J5AkCRYWluDhIQEAgIh3V8OI4SPh+x92wuUrSRB7PAb09ERgYW4O88MWwssvvtxwrMWLloC+vj7cSL4BV65cBo1WA2amZpp58+aVf/TRR0XOzs4NF+re3t6K7777LmP79u1mv/76q0n91IOuLmIIBALywoULae+9957F8ePHxT/++KOppaWl8p133ikYN25cTXx8vH5nmmp21J49e3IlEon8hx9+MNm3b58RjUYDNzc3WUREROGcOXOq2nuc3NxcNgBATk4Ou7VGowAAdnZ2ysZFjOrqatqhQ4cMm+93+PDhhm0LFiwo60gRIyMjg/nJJ59YLV++vHD06NEtRlfs27cvc8mSJdZxcXH6Go2GGDduXOWePXuycWUS1BxBkiTVGRBqQRIlMQOAFAAwoDoLQr2Fs4n9jUmDxw6jOgfqeuIbuXcl604PoToHAqBzOBVOSxezCYLgUZ1loFBqadV7pY4FGpLW63v9+M4Tg521Y5cek8GiKdk8BkGSZLdOkegraDRCxhWy79Xf7e9tvvnmG6P33nvP9quvvspas2ZNadvPQAh1xp07d4w8PT3tWnsMy1qoV5KGSQsBYAXVORDqLQiC0AS5jMKiXj9FU2vxjkIvoVUoxPKCghtU5xhI2HSd3mtuGfoEkJStlEAljUrHller6QRB4Lx/AJLNY2X0hgJGZmZmi1VBHj58yPr666/N6XQ6+dJLL7V7JARCqGvhdBLUa0nDpL9LoiSzAOBFqrMgRLWxjr5JDBrdn+ocqHsQGh32xOhFCuLO2Dq8MU9HEATe7OkhApbG9GXXrPT9920rAAgx1Xl6mk5H0uuqVByeHqsWgOyR1S96IyabkU9n0DrUY6G7zJw501Gj0RBDhgyRiUQiTXZ2Nvvs2bMihUJB+/DDD/Ps7OzUVGdEaKDCP86ot1sGAMVUh0CISlwmu0xi4eJJdQ7UfQg19sToTVTlFbbq6uprbe+JupIRV+kQ6pibA0D2iovYHkcCIatSCUgd1MIAHJtFoxEyFodRSHWOeq+++moZg8EgT5w4ob9nzx7TCxcu6Hl6etbt3bv38aZNm3pNToQGIixioF5NGiYtBYBwqnMgRKUQj6BUgiBEVOdA3Qenk/Q+hWfO8anOMBDZ6smGBFgX3QYgB+zoJHmNWqDVkHUAMJBeA5LNY2X2hmkk9T744IOS27dv36+oqLij0WhuVldX375y5cqDsLCwSqqzITTQYRED9XrSMOkRwCVX0QBlKjRKMxUa4fKD/RyuTtL71GVlS7RK5T9U5xiIPIyqRngaV1ykOgeVlDINXyXXqQiCGBBTFphsRgGdQcOeIAihdsEiBuor3gaAPKpDINTTQiVBcpyX3/8ROBKjVyq5dLmW6gwD1RirEn9bvdoEqnNQSaPScuQ1aqK/N/yk0Qh5b5pGghDq/fCDMeoTpGHSSgB4k+ocCPWkIRaul7lMzlCqc6DuR9PosIjRC1XcuetHarXZVOcYqEId8wIMOcoBPSJDpyUZsioVhyCI/lpQq1+NBN8DEULthkUM1GdIw6QnAeBHqnMg1BPoNLp8jOMwa6pzoJ5BqLUDae57X0KruHM3k+oQA9lLrpnDeQxNMtU5qESSQNRVqgQkCTX9reEni8PIxWkkCKGOwiIG6mtWAcBjqkMg1N3Gu4y6RqPRrKjOgXoGTY01jN6qOOmyD0mSlVTnGKjoBDBfc0t3YdB096jOQjV5tVqo1fafhp90Oq2KxWHiCnQIoQ7DIgbqU6Rh0loAmAMAA6LRFRqYhGx+gZOxnS/VOVDPITQ4EqO3ItUafm16xh2qcwxkLDopmOeWbkQDMofqLFRT1mn4aoVOBQAaqrM8D4Ig1Gw+K5PqHAihvgmLGKjPkYZJrwPAOqpzINRdpknGZxAEwaM6B+o5OBKjdys8e86VJEkV1TkGMj5Ta/yya6YGgCynOgvV1EotR1GnJQmCUFCdpbPYXGYmjUb06UIMQog6WMRAfdUWAIijOgRCXc1GbHHXgK8/iuocqGcRGixi9Gaa2jpTZUnpNapzDHSGXJX99EG5+QDkgO+hoNPomLIqNYsgiDqqs3QUg0UvYrDo1VTnQAj1XVjEQH2SNExKAsAbAFBCdRaEupBuinsAk+oQqOfRcInVXq8g7owp1RkQgLVQ5hFoXSQFILVUZ6EaSZK0ukoVH0joMyuX0GiEjM1l5lGdAyHUtzGoDoBQZ0nDpIWSKEkYAMQCAEF1HoSel5+tZxKLzhxDdQ7U82g4EqPXUxQXO6lr624wBfxhVGcZ6NyMqvyqVMwLN4sMx1KdpbmZr31JxWkF7d3xdMzH3ZnjqaaGToaLly6ARqNNH6jLqaalpbFcXV0ls2bNKjt48GAm1Xmohq8Heh44EgP1adIw6QkA+JbqHAg9LxadWe1rO8SF6hyIGjidpG8oOp+An5t6iZEWpWMdRDUJVOdAHUOn05RUZ0A9hyAIHz8/vz732ea7774zlEgkg3k8npdQKBzq5+fn8vvvv4s6c6zff/9d5Ofn5yIUCofyeDyvIUOGuH733XeGT9v/n3/+Yb/44ot2pqamQ5hMprexsfGQmTNn2qekpLA7ct7s7GzGtGnT7A0MDDwNDQ09Z8yYYZ+Xl9fqAIaVK1daCIXCoRkZGX1mNDD+MUb9wb8A4BbVIRB6HpPd/G8RBGFMdQ5EDUKD00n6gpqHj7x1anUa1TnQE1Md8gOMuIqLVOdAbdPptNjEEzVhZ2envnnzZkpkZGSvml60ZMkSq5UrV9qVlJQw58yZUzpz5szyBw8ecOfOnTto06ZNHfqctmnTJuO5c+cOevjwIWfmzJnlc+bMKS0uLmatXLnSbsmSJVbN909MTOSNGDHC7eDBg4aOjo6KhQsXFvv6+tbGxMSIR4wYMfjSpUvc9pxXq9VCcHCwU3x8vH5wcHDFuHHjqmJjYw2Cg4MHabVNZ+IlJSVxd+7cafbpp5/m2tvb95nVH7GIgfo8aZhUBQCvAkCfa26FEACAmCfKtBFbjKQ6B6IOTa3DKXF9ROm166VUZ0D/85JL1gg+U32D6hzo6QiCUKo16j67kgrqHmw2m/Ty8lLY2tr2mgvnuLg4/p49e0ytra2VUqk05aeffsr59ddfs69evZoqEom0n376qXVaWhqrPcdKS0tjffrpp9YikUh75cqVe7/++mv2Tz/9lCOVSlOsra2Ve/bsMY2Pj+c3fs7ixYvt6urqaP/+979zkpKSHuzevTv3+PHj6bGxsQ/kcjl90aJF9jpd2yM3ExIS+CkpKbwtW7ZkR0dHZx88eDDznXfeyb979y4/MTGxYfU7tVoNixYtsvfz86uJiIjoU3/bsIiB+gVpmPQBAKykOgdCnTFdMqGEIIh2/VFE/ROh0eFIjD6i7MZNP1KnK6A6B3qCRgDjtcEZg5k0XSrVWQaS6H2/wmtvzAHJUDcwNjcACxtTmDA5CP7Y/3vzXUkOn/W4/h9yuZxYuXKlhaWlpYTFYnlbW1t7rF692lyhUDQp5KalpbEIgvCZPXu2XWvn9/PzcyEIwqfxNp1OB999952hl5eXq1gs9mSz2d5mZmZDxowZ47Rnzx5x82M8fvyY+cYbb9hYWVlJWCyWt76+/tCgoKBBCQkJz7XEeUVFBe3NN9+0MjU1HcJms73t7e3d//3vf5s2vwPeWFZWFvP111+3sbS0lDCZTG+xWOw5adIkxwsXLrTIsn37dkOCIHy2b99uePjwYT0fHx8XHo/nJRaLPV988UW70tJSOgDApUuXuIGBgYP09PSG8ng8r6CgoEFPuwAvKiqiv/XWW5YODg7uHA7HWygUDh05cqTzoUOH9Jrvq1AoiA0bNpi4ubkN1tPTG8rlcr0sLS0l48ePdzxy5IiwcUYAgOvXrwsIgvCp/4qIiLAAePbPuKamhrZ27VozDw+PwXw+34vH43k5ODi4z58/3zonJ6fbejru3LnTGABg9erVBcbGxg0/MBcXF9X8+fOLVSoV8f333xu151i7du0yUqlUxIIFC4pdXFwalug2NjbWRkREFDQ+HwBAamoq68GDB1wDAwPNunXrihsfa/LkybXjxo2rTEtL4546darNHjjp6eksAIDRo0c33OAdPnx4HQBARkZGw7SUtWvXmmdnZ7N//vnnrPZ8T70JFjFQvyENk/4fAERTnQOhjnA2sb8h5PB9qc6BqEVocCRGn6HTMatS7z+gOgb6Hyad5L/mlm5MI8g+90G8r1q1+h3IycmGUaPGwPKlK2D2rBchJzcbFi9dBJ9v/LRhPxaHmU1n0BqWxA0NDXX4448/jCZOnFgZFhZWTBAEREZGWgQHBzu25w7zs6xcudKyfhpAaGhoxeLFi4vGjBlTXVRUxDp48GCTIsbFixd5w4YNc4uOjjZ2cHBQzJ8/v3j8+PGV169fF0ycONF1//79nep/IJfLCX9/f5effvrJVCwWaxYuXFg0cuTI2sjISPO3337burXn3L9/n+Xr6zs4Ojra2MbGRrlkyZIif3//6vPnz4vGjx/v+rReDDExMfovv/zyIENDQ828efNK7OzslAcPHjQMCQlxPHPmDH/ChAmuGo2GePXVV0u9vb1rz507JwoNDXVqXkx58OABy9vb223nzp1mBgYGmtdee60kNDS04vHjx5wXX3zR6Ztvvmly0f7SSy/Zffzxx9YajYaYPXt22YIFC4r9/Pxq0tLSeMePHxcBAAwbNky2atWqAgAACwsL1apVqwrqv4KCgmqe9RqWlJTQfX19XTdt2mRZV1dHe/nll0tff/31kkGDBin++usvozt37rRrSkVnJCUl6QEAzJw5s8USwKGhoVUAAImJicL2HOvChQtCAICQkJCq5o+98MIL1Y3PBwCQl5fHBACwtLRU0un0Fsezs7NTAgCcPn26RWGpOXt7exUAwJUrVxqKYNevX+f/9zElAEBycjJn27Zt5mvXrs1tXGTpK3B1EtTfhAOAJwBIqA6CUFsIgtAEuYwyoDoHoh5Ni0WMvqQo8cJQkfvgWoIg2r0qBOpefKbW+BXXzKzf79mVAhDtulOKOu9q0g1wsHdosk2lUsGsl2ZC5LZvYNGCN8HaxqacxWE0GaL++PFjbkpKSkr9XW6ZTJY3atQol3Pnzol27dpl8NZbb5V3NlN0dLSxiYmJ+t69eylCobBJRaSgoKDhmketVsPcuXMdZDIZ/dixY2khISENS9RmZmYy/fz8Bq9YscJ2+vTpUi6X26FRcp9++qnpP//8w5s0aVLl8ePHH9dfjN6/f79g+PDhbq09580337QtKSlhvv/++3mbN28urN8eFxdXPHXqVNdly5bZBwcH3xWJRE2+p7Nnz+r//fffDfm1Wi2MHTvW6fLly3qzZs1yioyMzFq2bFnD6/nyyy/b/vXXX0a///67/rx58yrrt8+bN8++oKCAtXv37vQlS5ZU1G8vLS2ljxkzxmXt2rU2L7/8cqW1tbWmrKyMHhsba+Du7i67ffv2PQaj6aVkYWEhHQBg1KhR8lGjRsm3bt1qbmlpqYqMjMxv72u4cOFCm7S0NO7cuXNLfvnll+zGF/RVVVU0jUbTLX8vq6uracXFxUwej6drbYqLu7u7EgAgMzOT057jpaencwAAPDw8WkylsrW1VXO5XF1RURGzpqaGJhQKdaamphoAgPz8fLZOpwMarelYg8zMTDYAwMOHD9s8f0BAQJ2bm5ts9erVtklJSQK5XE47cuSIgYeHh8zf31+m0Whg4cKFdkOGDKn74IMPStrz/fQ2OBID9SvSMKkMAGYDQIuqJ0K9zVhH3yQGje7Q9p6ov8PVSfoWnVIpkuXm3aQ6B2rKgKOynTkopxiAxB5Z3ax5AQMAgMViweI3l4BGo4FzCefUHD6zxciYNWvW5Dceps/j8cgNGzbkAQD88ssvz118YjAYJIPBaFF4MDc3b2gsun//fv2cnBz2/PnzixsXMACeNJt8++23C0tLS5lHjx5t8453c7///rsRjUaDyMjI3MYX366urqpFixYVN9//8ePHzEuXLumZm5urPvvss6LGj02cOLEuNDS0vKqqiv7rr7+2mA4TGhpa3jg/nU6HuXPnlgMAODk5yRsXMAAAwsLCygAAbt261TCS4fLly9zr168LJk+eXNG4gAEAYGRkpF23bl2+UqkkfvvtNzEAAEEQJEmSwGKxyOYX2QAAZmZmT58z0w55eXmM48ePGxgbG6t37dqV23xEgkgk0hkaGj7XOZ6mvLycDgAgEAhaPb6BgYEWAKCmpqblMIlW1NbW0gEAnpa3/jz15x0yZIjS1tZWWVZWxti4caNJ433j4uL458+f1wcAqKysbPP8DAYDYmNjHwUGBlbFxMSIz549K5o6dWpFbGzsQzqdDp999pnpgwcPuD///HNmWVkZfcaMGfZ8Pt+LzWZ7BwUFDeoLq5TgSAzU70jDpA8lUZL5AHCY6iwIPQ2XyS6TWLh4Up0D9Q6ERoc3FfqYgrgzDo4L3tASBNGuD7SoZ1gK5W7jbQuvn8ky8wIg8HNuMwRByEiSfK6eDwAAOTk5sPXbb+B84nnIzc0BuVze5PGs7IxygiBaVGcnTZpU23zb5MmTa+h0OqSmpj5XrpkzZ5ZFRUWZODs7u0+fPr0iMDCwJjAwsK75RWRSUhL/v98Dq74/Q2OPHj1iAwCkpqZyoAM3xSoqKmjZ2dlsMzMzVf1d+8aCgoJqtm7dat5429WrV3kAAL6+vrVsNrtF8SUwMLD677//Nrh16xYPAMoaP+bj49OiWGdlZaUCAPD09JQ1f6x+dEFeXl5DX4wLFy4IAACqq6vprb0WJSUlDACAe/fucQAADAwMdIGBgVXnzp0TDR482G3atGkVAQEBtePGjatrPvqlMy5cuMDX6XTg5+dXq6en16njlZaW0jdt2mTafPtHH31UZGRk1C0FkK6yffv2rBdffNFp/fr11idOnBBJJBJ5Xl4e69SpU/pOTk7y+/fvc1srHrXGzs5OHRsbm958u1QqZW/ZssVizZo1+RKJRDlhwgTHq1evCjdv3pwtEom07733ns2MGTMcb9++fb+956ICvrmjfkkaJj0iiZJ8BQDvU50FodaEeASlEgQxluocqHcgtDgSo69RV1VZqSorL7PFYlxZqJdxNaj2rVIyL9woNML32GbqKlVcnohVC0B2eipURmYGjBvvD5WVFTBq5GgYHzge9PT0gE6nQ1Z2Nuz7PRoUSkWrb2pWVlYthukzmUzQ19fXlJeXP9d1yY8//pjj4OCg/O2334x27txptnPnTjM6nU4GBARUffvtt7keHh5KAID685w4cUJ84sSJpx6vtra2Q1dw9XfUDQ0NW11O1tLSssX3Xn9X3czMrNUVOuqfU1VV1aJYKhKJWlyQ10/vaO0xJpNJAgCo1eqG6RhlZWV0gCe9GRr3Z2iurq6u4fxHjx59vH79evNDhw4ZfPPNNxbffPMNsNlscsqUKRXfffddjrW1daeX062oqKADAJibm3e6R0NZWRm9ebEIACA8PLz0WUWM+pEW9SMomqv/+QqFwnYVQgQCgbayspJRVlZGb22ESv156s8LADB9+vSac+fO3fvss88srl27Jrh+/brQyspKuW7dulxLS0v14sWLHYyMjDq9motOp4MFCxbYOTk5ydevX18klUrZZ86c0V+zZk3+ihUrygCeTKtZsWKFfUxMjHD69OnP7F9CJSxioP7sIwDwBYBAqoMg1Jip0CjNVGg0muocqPcgtGTvvd2Bnqow/qzI9qXZVMdArRhuXja2UsFKeFSpF0B1ll6GkFWpBFwhs5YgQACd6C7wnx3boby8DHbt2A3z5r7e5LH9f/1es+/36Kc2PszNzWU6OTk1uUBVq9VQWVnJ4PP5DRdz9XeAn9b/oLq6usWFJoPBgPXr1xevX7++OC8vjxEfHy/Yv3+/wYkTJ8TBwcHctLS0FC6XS+rp6WkBAKKjox+99tprXTb9uP5itKysrNXrq/rGjY3p6+trAQCKiopaHb5f/5z6zF2tvtjx+eef5zRfEeNpBAIBGRkZmR8ZGZn/6NEjZlxcnPDXX381/Pvvvw1ycnJYycnJaZ3NIxaLtQAABQUFnV6xzcXFRUWSZHJHn6enp6czMTFRFxcXM7OyspjN+2KkpKSwAQDs7OzatVywg4OD4ubNm4J//vmHY2Zm1mTUTFZWFlMul9NMTU3VzUewjB49Wn7q1KnH0My7775rAQAwbNiwTk+X+/LLL43v3r3Lv3z5ciqDwYC7d+9yAAB8fHwaRu6MGDFCBgAglUq5vbmIgR+aUL8lDZNqAeBVAGh3MyGEekKoJEhOEAS+/6IGBDb27JNkuXluWoXiDtU5UOsm2xcEmPDkF6jO0RvJa9QCjZqUAUCHL47TM55cX82YNrPJdoJGKM6eP9NiGkVjp0+fbjEC5NSpU0KtVgtubm4NF1JGRkb1TQ5bXMyWl5fTsrKy2M23N2ZpaakJCwurPH78ePqIESNqcnJy2Ddu3OACAIwcObIOoP2rTLSXWCzW2djYKIuLi1n1F7yNnT17tsX5hg8fLgN4sgypWt3yBvv58+eFAADe3t4tpod0hTFjxtQBAFy6dKlTI3MGDRqkXrZsWfmFCxce2tjYKG/evCmob+4J8KQY9aylZZvz9/evo9FocO3aNUF1dXWPf04aNWpUNQDAkSNHWoxKiYmJEf03Y7su7MeOHVsDABAbG9tidZnDhw/rNT5fW5RKJXH48GEDBoNBzps3r6LtZ7SUlpbG2rhxo9W7775b4OPj06QQo1QqGz6DyOXyPvH5tE+ERKizpGHSYgB4CQA6PfQKoa40xML1MpfJGUp1DtS7EFoSixh9VPGFS8+8aEPUmu2cPUrAVF+jOkdvpJJreCq5VkMQRIeG7ttY2wIAwIWLiY03a0+eji3av3//M5tzbtmyxaKkpKThIlcmkxHr1q2zBAB4/fXXG3o+iMVinb29veLWrVuC5OTkhtUYNBoNLFu2zFqhUDS5hpHL5cTp06f5zc+nVCqJyspKBgAAn8/XAQDMnTu30traWvnLL78YP20p1fj4eH5NTU2Hr5PmzJlTqtPpYPXq1ZaNL97v37/P+umnn0ya7+/o6KgeNWpUdX5+Puvzzz9v0sfh7Nmz/GPHjhnq6elpX3vttU5duLbF399f5uPjU3v69Gnxtm3bDFvb59q1a9y8vDwGAEB+fj7j2rVrLZY4ra6upslkMhqdTicb9/YQiUSawsLCdo+qsLCw0ISEhJSXlJQwly1bZtW8AFJVVUWrnwLTHZYvX14CAPDNN9+YN/49TUtLY+3du9eExWKRS5cubbLiTkFBAePWrVucxivgAAAsXbq0lMVikT///LNJWlpaw2tQUlJCj4yMNG98vnrV1dU0jabpbBy1Wg0LFy60zs7OZi9evLjIxsamU9N1FixYYGtjY6PcuHFjQf02T09PBQDAsWPH9Ou3HT58WAQAIJFI5C0O0ovgdBLU70nDpEmSKMl7APAt1VnQwEan0eVjHIe1uk48GtgILTb27Ksq/0nxNQ0al0Gj0+2pzoJaohFAn+uW4bFX6pii0tHdqc7T22hUOrZOq9ZwhUw5SZItLk5bs3jREoje9yu8sWAezJz+ApiZmcMd6W1lQsJ52+Dg4IrY2NgWK2nUc3R0lLu7u7sHBwdXMJlM8tSpU/o5OTnscePGVS1fvrxJ48qVK1cWrlq1yi4wMNA1JCSkgs1m65KSkoRqtZpwcXGRp6WlNeStq6ujTZ482dXGxkYpkUhk1tbWSoVCQUtMTNRLT0/nBAUFVXp7eysAANhsNvnXX389Dg0NdXr11VcHbd68uc7d3V3G4/F0eXl5rDt37vByc3PZWVlZdzrarPKTTz4pio2NFZ86dUrs7u7uFhgYWFVVVcWIiYkR+/r61pw9e1a/+XP27NmTFRAQ4Pr5559bnTlzRs/Ly0uWm5vLOnHihJggCHLHjh2ZYrG42xon7d+/P338+PEuq1atsvv+++9Nvb2960QikSYvL491//597sOHD7nx8fH3LS0tNZmZmczRo0e7OTk5yQcPHiy3srJSVVdX08+cOSMqLS1lzp8/v7hx1tGjR1fHxMQYBAUFDfL09JQxmUwyMDCwZurUqS0avNb76aefsseMGcPdt2+f8eXLl4Xjxo2rZrFYZFZWFuvChQuiP/7441FoaGi3THOYOHFi3Ztvvln0448/mkokEveQkJAKlUpFxMTEiKuqqugbN27MdnFxaVL027Jli8nWrVvNV61aVdB4KVlXV1fV+vXrc9etW2c9YsSIwaGhoRUsFouMjY0VFxUVMRcvXlw0YcKEJlNDYmNjhW+//bbd6NGjqy0tLVW1tbX08+fP6+Xk5LCnTJlSsXXr1k6NLo+MjDS6du2aMCEh4R6T+b+ZSx4eHsqJEydWHjhwwDA4OJgmFAq1Bw8eNJJIJHXd9Rp3FSxioAFBGibdLomSjIQn00sQosR4l1HXaDQazs9GLRA67InRhxHlN2/lGvkOwyJGL8WkkbzX3DLMolIcM3UkYded5zry2wfdefhuodOSjLoqFZ2n176Gnx4eEog9ehI+3/gpnDp9EtQaNeni4qKLiop6LBaLtc8qYsTExKT/61//Mj948KBhSUkJ08TERB0REZG/YcOGwuYrIbz77rtlJEkSO3bsMD148KChnp6edtKkSZWRkZG506dPH9R4X6FQqFu7dm1uYmKiXnJyMj8uLk6fx+NpbWxslJs3b85euXJlk7vnw4cPl9++fTt148aNpnFxcfoHDhwwJAgCjI2N1e7u7rIPP/wwv/GyrO3F5XLJhISEtPfff9/i6NGjBv/3f/9namFhoVy1alXBK6+8UuHu7q7f/Dlubm6qa9eu3Vu3bp35uXPnRNeuXRMKBALt2LFjqz7++OOCgICAbplKUs/R0VF969at1M2bN5scO3ZMfOTIEQOdTkcYGhqqnZyc5EuWLCn29fWVAwA4OzurVq9enX/x4kXh5cuXhZWVlQyRSKSxt7dXfvLJJ3mLFy9usqzr999/n7N06VJISkrSS0hIEOl0OtBoNAXPKmIYGxtrr1+/fn/jxo0mhw8fNqhfttbMzEz18ssvl3p5eXXrCIE9e/bkSiQS+Q8//GCyb98+IxqNBm5ubrKIiIjCOXPmdKiHytq1a4vt7e2VW7duNTt06JChTqcDR0dHxdq1a/Pefvvtsub7u7u7K3x8fGqvXr0qLC8vZ3A4HN3gwYNlH374YX54eHh5Z1YLycjIYH7yySdWy5cvLxw9enSL127fvn2ZS5YssY6Li9PXaDTEuHHjKvfs2ZPdm1cmAQAgSLLFaj4I9UuSKAkPAC4BwFCKo6ABSMjmF7wxfJaIIIjnXtoO9T8jXv09mVUp96E6B+ocgsGQu6xYJiMIotXh2Kh3qFAws/fds+cCEMadPYbvPDHYWTt2ZaxehStk1hAECNvb8JPOpJdx+azMbg2FEBqQ7ty5Y+Tp6WnX2mO9u8SCUBeShkllADAdAIqozoIGnmmS8RlYwEBPQ+h03TbHF3U/UqPh1jx89A/VOdCziTlqmxecckoByKfeBR7o5DVqoVZD1gFAm9MXaDRaHYfHzOqBWAgh1AQWMdCAIg2T5gDACwCAjdhQj7ERW9w14OuPojoH6sVwOkmfV3juvDtJku1aeg9Rx0IgHzzRtuA+ANmp5ngDgVKm4avkOhUBxFObohMEoeYImI8JgsAh3QihHoc9MdCAIw2TXpZESRYDwC9UZ0EDgm6Ke0Cr678jVI/QkTgSo4/TyuRGiqLiC1wz07FUZ0HP5mxQM6xaxbx4tcB4DNVZeiuNSsvRaXUarpCpIEmS0+xhksNnPqLRaP1+5beYmBhha0ujNqevr69Zv359cU9kQghhEQMNUNIw6a+SKIkHALxPdRbUv/nZeiax6Ez8oIyejcSRGP1Bwel4K/vX55IEQeCSub3cMLPyMRUKVsKDChE2W34KnZZkyKpUdJ6IVUeSZMPypWwuM5POoHdrs8ne4uzZs8KtW7eat7WfhYWFCosYCPUc/NCEBrIPAeAY1SFQ/8WiM6t9bYe4UJ0D9X64Okn/oCwrs9fU1t6gOgdqn4l2hQFmfHki1Tl6M5IEoq5SxQcSaoAEYLIYhUw2o7ztZ/YPkZGR+SRJJrf1lZeXJ6U6K0IDCX5oQgOWNEyqA4DXAACbsaFuMdnN/xZBdL4LPhpASBwZ2V8Unj2P08f6kFlO2WOELPVVqnP0drJqtRAIooTFZeRRnQUhhLCIgQY0aZi0Bp6sWFLa1r4IdYSYJ8q0EVuMpDoH6htwJEb/UZueMVSrUqVSnQO1D0EAbe7gjCEsuhbvpD8DnUmT8/TYuThTCiHUG+CHJjTgScOkGQAwGwD6fYMq1HOmSyaUEATBojoH6iNIEkdi9COll69UUp0BtR+DRnLnuaVb0QldOtVZeiMajVDrG/Me0mhEm8uuIoRQT8AiBkIAIA2TJgLAUqpzoP7B2cT+hpDD96U6B+o7CBJwdZJ+pPzWHT9Sp8Nh930Il6ETzxmcySKALKI6S29CEIROz5j7iM7s/yuRIIT6DixiIPRf0jDp/wHA51TnQH0bQRCaIJdRBlTnQH0OFjH6E5JkVEpTHlEdA3WMiK22muWcXQlA1lCdpbcQGnAyWBzGgFiJBCHUd2ARA6FGpGHS9QDwM9U5UN811tE3iUGjO1CdA/UxOJ2k3ym+eNGbJMkqqnOgjjHjK1wm2+U/BCAH/MgDnh47jyNgVlKdAyGEmsMiBkItLQGAU1SHQH0Pl8kuk1i4eFKdA/VBuDpJv6NTqYV1Wdm3qc6BOm6QuNZ7hEXpNQCSpDoLVTh8ZrFAzC6kOgdCCLUGixgINSMNk2oA4EUAuEl1FtS3hHgEpRIEIaI6B+qTcDpJP1QQf9aZJPGOfl/kY1o+2tWgOpHqHFRgcRgVQkNODtU5EELoabCIgVArpGHSWgAIAYBMiqOgPsJUaJRmKjQaTXUO1GcxqQ6Aup6mpsZcVV5+neocqHPG2xYGmPNlCVTn6EkMFr1GZMzNwKVUEUK9GQ5fRegppGHSQkmUZCoAXAIAbNSInilUEiQnCAILw6iz8O9xP1UQd8bQ7tWXqY6BOukFp5yx0an2V6pVrBHt2X/Ze0e7O9Jz2fX19Kc+RmfS5PomvEcEjei302gsLS0lAAB5eXlSqrMghDoPP3Aj9AzSMOl9AJgOAAqqs6Dea4iF6xUukzOU6hyojyJJksDpJP2WvKDQRSOT3aI6B+ocggDanMGZQ9l07R2qs3QnGp2m0jfhPaDRCR3VWfq75ORkTnBwsIOBgYEnm832trOz81i1apVFbW1th4e/PH78mPnSSy/ZmZiYDGGxWN6WlpaShQsXWpeUlLT6N0Wj0cCuXbsMfHx8XIyMjDy5XK6XnZ2dx4svvmh348YNTkfOvWvXLgNnZ2c3Ho/n5ezs7PbDDz+IW9svJyeHIRaLPZcsWWLV0e8PoafBIgZCbZCGSS8BwGsAgH/YUQt0Gl0+xnEY/mFGnUZoSS3VGVD3Kkq4gD/jPoxBIznz3NJt6YTuMdVZugNBIzT6JtwHdAZNQ3WW/u7s2bP8MWPGDI6Li9MfPXp09cKFC4sEAoF227Zt5gEBAc5yubzdhYyUlBS2n5+f24EDBwyHDBlS9+abbxZZW1srf/75ZxM/Pz/XwsLCFoWMGTNmOCxfvtw+Ly+PPWXKlIr58+cX29raKg4dOmQ4atQot6NHjwrbc+7ffvtNtHz5cnsul6ubN29eiU6nI8LDwx3++OOPFn3BFi9ebCMSibSRkZF57f3eEGoLFjEQagdpmPQQALxLdQ7U+4x3GXWNRqNhEQN1GqHVYePHfq76fpqPTqN5RHUO1Hkchk5/7uAMLhDQrwpSBEHoRMbchwwWXUl1lv5Oo9HA4sWL7RQKBW3v3r2Pjx07lrFr1668O3fu3Js8eXLFzZs3BZ9//rlpe48XHh5uU15eztiwYUNOfHz84507d+ZduXLlwaJFi4oyMzM5ERERlo33T0hI4B0/flw8aNAgxcOHD/+Jjo7O3rVrV15CQsKjrVu3ZqrVamLTpk3m7Tn37t27TWxtbZU3bty4/8MPP+RevXr1vlAo1O7atcu48X4///yz+PTp0+Lvv/8+UyAQ9NtpSqjnYREDoXaShkm/A4BPqc6Beg8hm1/gZGznS3UO1LcRGh3e/ez/iLLryQVUh0DPR4+tseAxtSRBEP2lkEEKDTmPWRyGrCNPSktLYxEE4TN79my7W7ducSZMmOAoEomGcrlcLx8fH5dDhw7pNX/O9u3bDQmC8Nm+fbvhgQMH9Pz8/FyEQuFQgiB86vf59ddf9WfMmGFvZ2fnweVyvbhcrpe7u/vgDRs2mGi1LV/y2bNn2xEE4ZOWlsbasmWLkbOzsxubzfY2NDT0nDNnjm1ZWVm7p+l9//33BiwWy9vBwcE9LS2N1ZHXo72OHz8uTE9P5wwbNqz2tddeq6rfTqfTITIyMhcAICoqylina3vgb0pKCvvSpUt6FhYWqg8++KC48WNbtmzJ53K5usOHDxtWV1c3XOs9ePCADQAwZsyYaqFQ2OQkr776aiUAQHl5ebv6M+Xl5bE8PDxkTOaTntSGhoZaOzs7RV5eHrt+n6KiIvqaNWts5s2bVzJlypTa9hwXofbCIgZCHSANk/4bALZSnQP1DtMk4zMIguBRnQP1bYQWixgDQdn1G36kTldCdQ70fOigY4i4WiUA9Pm7ykIDTiaHz6zu7POzs7PZAQEBrpWVlYx58+aVBAcHV6SkpPBfeuklpz179rTaH+Hw4cPiV1991UkgEGjnzZtXEhISUlH/2Pr16y3/+ecf3tChQ+sWLFhQPGvWrLK6ujr6xx9/bD179mz7p+VYtWqV1WeffWY1ePBg+RtvvFFsamqq+uOPP4xCQkIc2/N9rFu3znT58uX2Eomk7vLly/ddXFxUHX812hYfHy8EAJgwYUJV88fc3NxUtra2yvz8fNa9e/fYLZ/d1KlTp4QAAAEBAdV0etNajVgs1nl7e9cqFArauXPn+PXbhw4dKgcAuHTpkrB5/42//vpLHwBg7NixNe35XiwsLFQpKSnc+uJSeXk5LTMzk2NpadkwomfJkiU2bDZb99133+W255gIdQR2Q0eog6Rh0ghJlEQIAG9SnQVRx0ZscdeArz+K6hyo76Opdf3lri56BlKrZVenPUwVDXYJoDoLej4sUPH0eOy6ahnBb3vv3oknYudyhazy5znGjRs3BEuWLCnavXt3w0VqYmJicVBQkOvq1attZ8+eXWVgYNDkjn9CQoLozz//fPjiiy+2KJ7ExMQ8cnd3bzKtRavVwksvvWR3+PBhw7NnzxYHBQXVNX/erVu3+Ddv3kx1cnJSAQCo1WoYOXKky9WrV4Xnzp3jBQYGtjrSRKvVwsKFC61/+eUXk0mTJlUePnw4ncfjdVtx6uHDhxwAABcXl1abxdvb2yuysrLYKSkp7OavQ3NpaWkcAAAnJ6dWj+Xg4KC8dOkS3L9/nzNjxowaAABfX1/FokWLin766SdTZ2dnj/Hjx1cJhULt/fv3uYmJiXqhoaHlW7dubVffiiVLlpS88cYbjr6+vq6+vr61Z8+eFdXU1NDDw8NLAAD++OMP0dGjRw0OHz78QCQSYU851OVwJAZCnRMOAPupDoEoo5viHsCkOgTqH3AkxsBRdD5hCEmSHRq6j3onDij5fC70ySHyXAGrUKDPLnre4wgEAu2XX36Z33ibv7+/bMaMGeU1NTX03377rcVojPHjx1e2VsAAAGjtwp1Op0NEREQxAMDx48dbTFMBAFizZk1BfQEDAIDJZMLrr79eCgBw+fLlVgtNMpmMmDp1quMvv/xiEhYWVnz8+PHH3VnAAACorq6mAwDo6+u3WrgWCoVaAICKioo2bzLXH0skErV6rPrtlZWVTYZp/Pjjj7lfffVVVkVFBSM6Otp4165dZufOnRO5urrKw8LCyvT09NpVcHj99dcrt2/fnlldXU3/5ZdfjAmCIHfs2JExd+7cqrKyMvo777xj+/LLL5fOnDmzZu/evfr29vbudDrdx9LSUvL1118bteccCD0LFjEQ6gRpmFQHAK8DQCzVWVDP87P1TGLRmYOpzoH6B+yJMXBoFQqxPL/gBtU5UNfgEwoBl0P0qUIGh88sFhpyumSVCHd3d5lYLG5x0RsQEFADAHDr1q0W0y2HDRvWYiRFvcLCQvry5cst65ftJAjChyAIn7Fjxw4GAMjPz2+1V8WIESNaHNPW1lYF0HpBQKFQ0MaMGeMSHx+vv3bt2ty9e/fmNJ+S8Szbt283jIiIsGj8FRMT065VPaik0+lg/vz51h9++KHtqlWrCh49enS3srLy1smTJ9MAAF566SWnL774writ49R7++23yx49epQil8tvPXjwIHX58uXlAADLly+3AgDYuXNn7sWLF3kLFy50dHV1lR86dOhBUFBQ1Zo1a2xbW8UEoY7A6SQIdZI0TKqWREleBIATADCO4jioh7DozGpf2yEuVOdA/QehaUcXN9RvFMSdsXUIm6cjCAJvJPUDAppcoGPz6pRKXZ+YWiI05OR01bGMjY1bXVnJwsJCDfC/0QKNmZmZtfqc0tJS+rBhw9zy8vJYEomkbtasWWUGBgYaBoMBlZWV9J9//tlEqVS2uvyooaFhi9EIDMaTSxytVtviOTKZjJaamsrj8/nakJCQDvcEiY6ONrp+/bqg+fbQ0NBn9pPQ09NrdXREvZqaGjoAgFgsbrOwXX+sqqqqVo9Vv73xqI///Oc/hlFRUSaLFi0q2rRpU2H99smTJ9cOHjz4oYuLi2TDhg1Wy5cvL+vsFJAjR44I//zzT6N9+/Y9MjQ01H711VemfD5fu3///kw9PT3dtGnTahITE/W+/vprs1dffbVFbxCE2gv/gCL0HKRhUgUATAOAa1RnQT1jspv/LYIg2n2nAqG20DTYE2MgUVVU2Kqrq69TnQN1DQIA9OhyHpNJk1OdpT0IotU6QKeUlJS0Oq0yPz+fCfC/C+32nH/79u1GeXl5rFWrVhXcvXv3fnR0dPb27dvzIyMj8+fOnftcvTuaMzAw0Ozfv/+hRqMhpkyZ4pKYmNihBt3Xrl1LI0kyufFXZGRkflvPq+9fUd/PormMjAwOQOvTapqr76tR32ejufT0dDYAgKura0PPjOPHj4sAAIKCgloUW2xsbDT29vYKmUxGu3v3bqvHbEtVVRXtrbfesps+fXr5nDlzqgAAHj16xLGzs1PWT1Oh0Wjg7u4ue/ToEbcz50CoHhYxEHpO0jBpLQBMBQAp1VlQ9xLzRJk2YouRVOdA/QtOJxl4Cs+cw1WN+hECSELEVLDoDKLNi8/+JCUlhVdRUdHiWiIhIUEIAODl5dXu/i+PHj1iAwC8+uqrFc0fO3v2bJdP1ZgxY0bN4cOHH2q1WiIkJMQ5Pj6+20fSTJgwoQYAID4+vsVUitTUVFZWVhbbwsJCNXjw4DZ/jyZPnlwDAJCQkKDXfPnZiooK2s2bNwUcDkcXGBjYMNVGpVLRAACKi4tbHYlfXl7OBABgs9md6g3y9ttvWymVStru3buzG29XqVRNKlcKhQKvP9Fzw18ihLqANExaDgCTAOAh1VlQ95kumVBCEES3rB+PBi6aRovTSQaYuqxsiVapxMJ3P0IDHV2fpRpQn6tra2vpH3zwgUXjbYmJiby///7bQCAQaF977bUWBYmnsbOzUwEAxMXFNSlYXLp0ifvdd9+ZdU3ipqZMmVJ79OjRBwRBwIwZM5xjY2NbTBHpSsHBwTUODg6KGzduCH777beGQoZWq4XVq1dbAQCEhYWV0Gj/+zVSKpXErVu3OCkpKU2WXXV3d1eOHj26Oj8/n/Xll1+aNH5szZo1FnK5nPbCCy80adQ5atSoGgCAHTt2mJWVlTWZhvLVV18ZFxUVMY2MjNTe3t4dHlV08uRJQXR0tPGWLVuyzczMGqoqTk5OisePH3NTU1NZAABlZWX0GzduCAYNGtQnRi6h3gt7YiDURaRh0kJJlCQQAM4DwCCK46Au5mxif0PI4ftSnQP1PwROJxmQSi4l1ZkFBVIdA3UhOmgH1KpVw4YNq/3999+NkpOT+cOHD68tLCxkxsbGGpAkSURGRmY1X171WZYsWVK2c+dOs/Xr11snJCQIHR0dFY8fP+acO3dONHny5MrY2NgWK510haCgoLoTJ06khYaGOr/44otO+/bte/zCCy90uE9GezAYDNizZ09mSEiI8/z58x3/+OOPCmtra1ViYqJeSkoKz9vbu/bjjz9usmpMZmYm09vb293CwkKVl5fXpPC5e/fubH9/f9d169ZZnzt3Tuji4qJITk7mX716VWhra6uMjIxs0sB1zZo1JX/99ZfhgwcPuM7Ozh4TJ06sFIlE2rt37/KuXLkipNPp8PXXX2fX9xNpr9raWmLp0qV2kyZNqliwYEGTwtW//vWvwpiYGIOJEye6TJ06tSIxMVGvpqaGvmbNmsKnHQ+h9sAiBkJdSBomzWtUyHCkOA7qIgRBaIJcRhlQnQP1TzQ1NvYciCruSP1MA/yzCTrdhuosqOsc3DkJVMCUV8roHHjSMoMSTDa9Wt+E94igEd22bKiNjY1y9+7dWWvWrLH89ddfjdVqNeHm5iZbu3Zt/uzZsztUCLCzs1OfOXPm/po1a6xu3LghuHDhgp6Dg4Ni8+bN2SEhIdXdVcQAABg9erT89OnTaVOnTnV59dVXB0VFRT3urqaTQUFBdRcvXry3bt06i4sXL+rV1dXRLSwsVO+++27B559/XsDlctv983J3d1devXo19YMPPrBMSEjQS0hIEBkbG6sXLFhQvHnz5nxjY+MmBXKRSKS7du3a/c8//9w0JiZG/Pfffxuo1WrCwMBAM3Xq1Io1a9YUBgYGdngJ6IiICMvKykrGnj17sps/NmrUKPnevXsff/bZZ5ZRUVEmZmZmqm+++SbrlVdewaae6LkQJNmtSyIjNCBJoiRWgIWMfsN/kF/iEEtXf6pzoP7J4Gr2XY9P4odQnQP1PJOAsQmG3l4BVOdA7XfPywcG29u1uZ8C2HXVMoKSFUuYLHqNvinvYXcVMNLS0liurq6SWbNmlR08eDCzO86BEEJ37twx8vT0tGvtsQE1dw+hniINk+YCQCAApFOdBT0fLpNdJrFw8aQ6B+q/cDrJwFWSdHkYSZLt7huA+g4OKPkCLtT29HkZLHqtyLR7R2AghBDVsIiBUDeRhklzAGAcADyiOAp6DiEeQakEQbToJI5QV8HpJAMXqdbwa9Mz7lCdA3UPHqEQcDlEjxUyGP8dgUGjEfieghDq17CIgVA3alTIeEBxFNQJpkKjNFOh0Wiqc6D+jVDj6iQDWeHZc4NJklRRnQN1DwFNLmCzad1eyGCy6VViLGAghAYILGIg1M2kYdI8eFLIuE9xFNRBoZIgOUEQ+D6JuhVNrcVh3wOYprbOVFlSeo3qHKh7EACgR5fzmUxahxsmtheLw6jUN+U97qkpJC4uLiqSJJOxHwZCiCr44RyhHiANkxbAk0JGCsVRUDsNsXC9wmVyhlKdA/V/NA1OJxnoCuLOmFKdAXUfAkhCxFRw6AxC0dXHZnEZ5SIT7mOCwB4YCKGBA4sYCPUQaZi0CJ40+8T5z70cnUaXj3EcZkV1DjQwEFjEGPAUxcVO6tq6ZKpzoO5DAx1Nn6Vi0GiEuquOyeYxS0XG3AyCoGwlV4QQogQWMRDqQdIwaQkABADAJaqzoKcb7zLqGo1GwyIG6hE4nQQBABSdT8Ar0X6ODlqGPketIwjiuVck4vCZxXpGnCwsYCCEBiIsYiDUw6Rh0ioAmAQAJ6jOgloSsvkFTsZ2vlTnQAMHodZhEQNBzcNH3jq1Oo3qHKh7MUDDFnE1KgDo9H/3XCGrUM+Im4MFDITQQIVFDIQoIA2TygBgBgD8SXUW1NQ0yfgMgiB4VOdAAwdNg0UM9ETp1eulVGdA3Y8Faq4ej+xUo0+eHitfaMDJ6+pMCCHUl2ARAyGKSMOkagCYAwA/UJ0FPWEjtrhrwNcfRXUONLAQOJ0E/VdZ8k0/UqcroDoH6n4cUPIFPOjQ0qt8ETtHIObg7wdCaMDDIgZCFJKGSXXSMGk4AGymOgsC3RT3ACbVIdDAQ8PpJKieTsesSr2HU0oGCB4oBFwO0a5CBl+fnc3XZxd3dyaEEOoLsIiBUC8gDZN+AAAfUJ1jIPOz9Uxi0ZmDqc6BBh6a5rl7/KF+pCjxghdJkjVU50A9Q0iTC9jsZxYySKEhJ50vYpf0WCiEEOrlsIiBUC8hDZNuBoBwAMDlFnsYi86s9rUd4kJ1DjQwERr8Tx79j06pEslycm9SnQP1HD26gs9k0Vr0yCAIQisy5j7kClgVVORCCKHeikF1AITQ/0jDpD9IoiSVAPArALAojjNgTHbzv0UQRADVOdDAhKuToOYK4s84Oi4I0xIEQac6C+qYFRvyO/M0AgBaayhNBwDn5wrUzH++HZrclcdrLz8/P5fr168LSJKk5Py90fbt2w3feecdu2+//TZz5cqVZe15zuzZs+0OHTpkeP/+famLi4sKACAtLY3l6uoqmTVrVtnBgwczu+vcCPUmOBIDoV5GGib9EwCmAkAV1VkGAjFPlGkjthhJdQ40cNE0WlwnETWhrqq2UlVUXqU6B0IIPa/a2lpi1apVFnZ2dh5sNtvbwMDAMzg42OHmzZucjh5Lo9HAp59+auLs7OzG4XC8RSLR0ICAgEFxcXH85vumpaWxCILwaevr5MmTgvacWyqVsseNGzdIJBINNTMzG/LGG2/YVFRUtHotPWPGDHtLS0tJVVUVXmt3ExyJgVAvJA2TnpVESUYDwHEAsKE6T382XTKhhCAIO6pzoIGLwCVWUSsKz5zVt31pNtUxEEK9SGRkZN66desK7ezs1FRnaQ+5XE4EBAQ437x5U+Du7i5buHBhUW5uLuvEiRPic+fOiWJjYx8EBQXVtedYOp0Opk2b5nDy5EmxnZ2dIiwsrLiiooIeExNjMHXqVNHevXsfz5s3r7J+f0NDQ+2qVataXc0nNzeX+ddffxnp6+trAgIC2jx/dXU1bdKkSc5yuZw+e/bssvz8fOavv/5qXFJSwjhx4kR6433/+OMP0dGjRw0OHz78QCQS4XzRboJFDIR6KWmYNEUSJRkBALEA4EV1nv7I2cT+hpDD96U6BxrYaGodjsRALchy89y0csVdOpczhOosCKHewdbWVm1ra9snChgAAJ999pnpzZs3BVOmTKmIiYlJp9OfzJCLjo6ueP311x2XLFlil5aWllK//Vn27NljcPLkSbGXl1fdxYsX03g8HgkAkJCQUDJx4kTXlStX2oaEhFSLxWIdAICRkZE2MjKy1fldb731liUAwOzZs8u4XG6bNxL2798vKiwsZB0/fjxt6tSptQAAL774ot3BgwcN8/LyGJaWlhoAgLKyMvo777xj+/LLL5fOnDkTGzR3IxziglAvJg2TFgCAPwCcoDpLf0MQhCbIZZQB1TkQIrR4owa1rujCRQXVGRBqzfbt2w0nT57saGVlJeFwON4CgcDL29vbdefOnc/8uyqXy4mVK1daWFpaSlgslre1tbXH6tWrzRUKRZNibv1UgNmzZ9u1dhw/Pz8XgiB8Gm/T6XTw3XffGXp5ebmKxWJPNpvtbWZmNmTMmDFOe/bsETc/xuPHj5lvvPGGjZWVlYTFYnnr6+sPDQoKGpSQkNBaf5IO2bNnj3jkyJHOIpFoKJvN9ra0tJRMmzbNPjExsdVjHzt2TOjn5+fC5/O9BAKB17hx4wa1Nt1i9uzZdgRB+KSlpbWrb9o///zDnjp1qoOent5QLpfr5eXl5frHH3+Invf7aw+dTgdRUVHGAABbt27NbVyomDdvXqWPj0/t48ePOcePHxe253h79uwxBgD49NNP8+oLGAAAAQEBspCQkPKKigrGL7/80uLn3JxSqST+/PNPQwCAt956q7Q9587KymL991wNozZ8fX3rAAAePXrU8LNYvny5FQDAzp07c9tzXNR5WMRAqJeThklrAWA6AOyhOkt/MtbRN4lBoztQnQMhXJ0EPU1VSqqvTqvNoDoHQs29//77tnl5eazhw4fXLFq0qGjatGnleXl5rLfeesv+nXfesXja80JDQx3++OMPo4kTJ1aGhYUVEwQBkZGRFsHBwY463fO9F65cudJy5cqVdiUlJczQ0NCKxYsXF40ZM6a6qKiIdfDgwSYXtxcvXuQNGzbMLTo62tjBwUExf/784vHjx1dev35dMHHiRNf9+/d36kJfp9PB7Nmz7ZYsWeJw//597pQpUyrefPPNIl9f39pr164Jjxw5ot/8OcePHxe98MILTgKBQDtv3rySYcOG1SQkJIgmTJjgUlBQ0OlR81KplD127FjX/45eqF20aFGxubm5at68eY5Hjhxp82L/eaWmprILCgpYtra2SldXV1XzxydOnFgFABAfH99mEUMmkxG3b98WcDgc3ZQpU1qMcJgyZUo1AMC5c+f02jrW77//LiotLWUOGzas1svLq12FYhsbGxXAk9+b+m3Jyck8AIBBgwapAACOHDki/PPPP422bduWZWhoiGundzOcToJQHyANk2oAYIkkSpIBABvhSSdz1ElcJrtMYuHiSXUOhAAACA1OJ0FPRZTfvJVj5DvMnuogCDWWnJyc4u7urmy8TaFQEIGBgU47duwwe/fdd0vs7e1bTHt4/PgxNyUlJcXY2FgLACCTyfJGjRrlcu7cOdGuXbsM3nrrrfLOZoqOjjY2MTFR37t3L0UoFDapiDQuBqjVapg7d66DTCajHzt2LC0kJKS2/rHMzEymn5/f4BUrVthOnz5d2p6pBo1FRkYaHTp0yNDDw0N2/vz5B40vZjUaDeTl5TGbPyc+Pl588ODBBzNmzGi4OH/rrbcsd+7cabZjxw7DDRs2FHUkQ72lS5faVFZWMj777LOcjz/+uLh+e3R0tP7rr7/u2JljdsQ///zDAQBwcHBotVDg7OysBAB49OhRmw0+U1NT2VqtFhwcHJRMZouXEAYPHqwAAEhPT2e3dawff/zRGABg0aJFJW3tW++VV16pWr9+vfqVV14ZNGvWrPL8/HzmyZMnxZMmTaq0tLTUVFVV0d566y276dOnl8+ZMwcb8/cAHImBUB8iDZN+AQCvAUCLijZqvxCPoFSCIHpkOCVCbaFpsYiBnq70yjVfkiTbNeQZoZ7SvIABAMDhcMilS5cWa7VaIjY2ttU74mvWrMmvL2AAAPB4PHLDhg15AAC//PKL0fPmYjAYJIPBaFF4MDc319T///379+vn5OSw58+fX9y4gAEAYGdnp3777bcLS0tLmUePHm3zrn5zP/zwgwkAwPfff5/Z/G48g8GA1vpZhISElDcuYAAAvP322yUAADdu3Gix6kZ7PH78mJmUlKRnaWmp+vDDD4sbPzZv3rxKX1/f2qc9t6tUVlbSAQCEQmGroxL09fW1AABVVVVtNsSoqKh45rHEYrEWAKC6uvqZN+jT0tJYSUlJevr6+pqwsLCKts5bTyQS6U6dOpUmkUhkf/31l2FycrJg7ty5Jb///nsGAMDbb79tpVQqabt3785++PAhKygoaBCHw/Hm8/leL7zwgl15eTlec3cxHImBUB8jDZP+LomS5APAYQDo9uGA/Y2p0CjNVGg0muocCNUjsIiBnoHUaLg1Dx9d1XN2Gkd1FoTqPXz4kPXZZ5+ZXbx4UVhYWMhSKBRNLtJaG3EAADBp0qQWF8+TJ0+uodPpkJqa+ly9KGbOnFkWFRVl4uzs7D59+vSKwMDAmsDAwLrmxYSkpCQ+AEBOTg4rIiKixdSXR48esQEAUlNTOdCB5e6rq6tpDx8+5BoaGmpGjx4tb+/zfHx8WqyO4ejoqAIAqKqq6tS12tWrV3kAAL6+vjUMRstDjBkzpub69evtWlo0KSmJe+DAgSafN/X19TXr168vftpzeqv//Oc/xiRJwosvvtiuhp6NeXp6KhMTEx82337y5ElBdHS08U8//ZRuYmKiDQoKci4rK2P+8MMP6TU1NbQPPvjA5rXXXqM1X8UEPR8sYiDUB0nDpAn/XbnkKAC4UJ2nLwmVBMkJgsCKOOo1cDoJakvhufMeQqdBCoIg2hx2jVB3S01NZY0ePXpwdXU1w8fHpzYgIKBaJBJp6XQ6ZGVlsQ4dOmSoVCpb/TtrZWXVYiQCk8kEfX19TXl5+XNdl/z44485Dg4Oyt9++81o586dZjt37jSj0+lkQEBA1bfffpvr4eGhBACoP8+JEyfEJ048vW96bW1thz4rlJWV0QEATE1NOzRatn5EQmP1Uya0Wm2n/j7Uj4IwMTHRtPa4mZlZu1c4uXHjBm/r1q3mjbdZWFio2ipi1H9fNTU1rY60qM8oEona7B9RP9LiaceqH6mhp6fX6vcL8GQa0f79+w0BAFasWNHuqSTPUltbSyxdutRu0qRJFQsWLKg4fPiw3r1793g7duzIeOONNyoBADIzM9lff/21RUpKCru1EUyoc7CIgVAfJQ2TPpBESYYDwO8AMJXqPH3BEAvXK1wmZwTVORBqjNCSWMRAz6SVyY0URUUXuGZmY6nOgtCXX35pVllZyfj2228zV65cWdb4sd27dxscOnTI8GnPzc3NZTo5OTW5yFer1VBZWcng8/kNF7M02pP6gUajafX9sbq6usXFLIPBgPXr1xevX7++OC8vjxEfHy/Yv3+/wYkTJ8TBwcHctLS0FC6XS+rp6WkBAKKjox+99tprXda/oH7ER1FRUbtWDulO9QWE4uLiVq/1CgsLWx0p05qVK1eWNf85t4eHh0d9n4pWi68PHjxgAwAMGjSozeaabm5uSjqdDjk5OWy1Wg3N+2Lcu3evvv/GU4sEv//+u35JSQnT19e31tPTs0uKCREREZaVlZWMPXv2ZAMApKSkcAAAhg8fLqvfZ9iwYTIAgDt37nCwiNF18G4kQn2YNExaBQChALCF6iy9HZ1Gl49xHGZFdQ6EmiO0OvxbjNpUcPqMFUmSHRr+jFB3yMjIYAMAvP766y16CiQkJDxzisLp06dbPH7q1CmhVqsFNze3hgs/IyMjDQBAfn5+i4JAeXk5LSsr65kNHC0tLTVhYWGVx48fTx8xYkRNTk4O+8aNG1wAgJEjR9YBACQmJrZrac/20tPT0zk5OcnLysoYly5d4nblsTuq/iL6+vXrQo2m5eCEixcvdun33ho3Nzelubm5Kisri33//v0WP8e4uDgRAMCECRNarDbSHI/HI4cOHVqrUChoJ0+ebJH95MmTegAAgYGB1U87Rn1Dz4ULF3bJKIxz587x/u///s9006ZN2dbW1k1e5MZLBjdfPhh1DfzghFAfJw2T6qRh0vcB4HUAaNdSUQPReJdR12g0GhYxUK9DaEn8W4zapCwrs9fU1F6nOgdC1tbWSgCAEydONLmYPHjwoN6ff/5p/KznbtmyxaKkpKRhFIVMJiPWrVtnCQDw+uuvN9ztF4vFOnt7e8WtW7cEycnJDXfyNRoNLFu2zLp5Dw65XE6cPn26RRNMpVJJVFZWMgAA+Hy+DgBg7ty5ldbW1spffvnF+GlLqcbHx/Nramo6/N4cHh5eDACwdOlSu/rpJfW0Wi1kZWW1ewTE83B0dFSPGjWqOi8vj/XFF1+YNH4sOjpav739MJ4HjUaDsLCwEgCAVatWWWm1/5s1Eh0drZ+cnCxwdHRUBAcHNyliPHz4kHXr1i1O89d/8eLFJQAAn3zyiaVMJmsoDCQkJPBiY2MNxGKxprXCGgDAgwcPWJcuXepwQ8+nUSgUxOLFi+3Hjh1btXz58oYVdTw8POQAAIcPH9av33bs2DF9AABPT0/8jN6FcDoJQv2ENEwaLYmSPIAnDT+fukb7QCRk8wucjO18qc6BUGsIHfbEQO1TeO48y3rGNKpjoAHunXfeKTlw4IDRggULHPfv319hbm6uvnfvHufChQui4ODgitjY2Kc2HXd0dJS7u7u7BwcHVzCZTPLUqVP6OTk57HHjxlUtX768yZSFlStXFq5atcouMDDQNSQkpILNZuuSkpKEarWacHFxkaelpTWMdqirq6NNnjzZ1cbGRimRSGTW1tZKhUJBS0xM1EtPT+cEBQVVent7KwAA2Gw2+ddffz0ODQ11evXVVwdt3ry5zt3dXcbj8XR5eXmsO3fu8HJzc9lZWVl3mi/V2pZVq1aVXrx4UXDkyBFDJycnj0mTJlUaGRlpCgoKmElJScI5c+aURUZG5nf0Ne+M77//Ptvf3991/fr11mfOnNHz8PCQp6ens0+fPq0fGBhYde7cuW5fpW39+vVFp06d0j958qTY09NzsL+/f3VOTg7rxIkTYg6Ho/vhhx8y6fSmM4Nee+01++vXrwuOHTv2IDQ0tKHAsXjx4vIjR47onzx5Uuzh4eE2adKkqvLycnpMTIyBTqcjtm/fnmVgYNDqz2vHjh1GOp2uUw09W/P++++bFxUVMePi4h403j59+vQad3d32bZt2yyys7NZdXV19OPHj4unTp1agVNJuhYWMRDqR6Rh0muSKIkvPClk+FGdp7eYJhmfQRDEKKpzINQaHImB2qs2PWOoVqVKpbNYblRnQa37z7r/3kNgsctBTy8TCKLfTQEaPny4PDY2Nu3jjz+2PHfunEir1RKurq6yqKiox2KxWPusIkZMTEz6v/71L/ODBw8alpSUME1MTNQRERH5GzZsKKzvg1Hv3XffLSNJktixY4fpwYMHDfX09LSTJk2qjIyMzJ0+ffqgxvsKhULd2rVrcxMTE/WSk5P5cXFx+jweT2tjY6PcvHlz9sqVK5ssUzx8+HD57du3Uzdu3GgaFxenf+DAAUOCIMDY2Fjt7u4u+/DDD/MbL8vaXjQaDQ4fPpy5a9eu6v/7v/8zjo2NFatUKpqRkZHa19e3dtasWZUdPWZnSSQS5YULF+6/9957lpcuXdK7evWq0MXFRR4dHf24uLiY0RNFDC6XSyYkJDz4+OOPzQ4fPmzw448/mvL5fO3EiRMrN2zYkO/j49Pu0Qk0Gg2OHj2avmnTJpPffvvNaO/evSYsFkvn6+tbs27duoKJEye2WOUF4Mnond9//90IoGsaeiYlJXF37txp9tVXX2Xb29s3aZBKo9Hg77//fhQeHm4TExNjQKfTyVmzZpXV98xAXYfA6ZUI9T+SKAkHAH6AJ1NMBjQbscXd6UMmDKE6B0JPM+KVfbdYVQovqnOgvsHAe2iSaYA/FmUpcs/LBwbb2z17Jy4vDwSCwh4JhBBC/dSdO3eMPD097Vp7DO/+INQPScOkCmmY9A0AeA8A2ly6qh/TTXEP6JH5pwh1FqEjW10yDqHWlN+640fqdLlU50CtIAgtCPUeYQEDIYS6FxYxEOrHpGHSbwBgIgAUUZ2FCn62nkksOnMw1TkQehZCh9NJUAeQJKNS+s9jqmOgZugMOeiL7wGH02VLdiKEEGod9sRAqJ+ThknPSaIkXgCwHwDGUp2np7DozGpf2yEuVOdAqE1YxEAdVHzxkrf+EEkVQRDdPqcdtQObXQpCvez+2P9ioIuJiRGePXu2zeVI9fX1NevXry/uiUwIISxiIDQgSMOkBZIoSRAAbIInU0z6/WoIk938bxEEEUB1DoTagtNJUEfpVGphXVb2eYGd7TiqswxshA4Egmzgcsva3hf1RWfPnhVu3brVvK39LCwsVFjEQKjnYGNPhAYYSZRkBgDsBQB9apN0HzFPlDl32HQLgiBYVGdBqC2jp0c9pKu0TlTnQH0LQygsGLRovhFBENj3pwc1NPak0RWgp/cYmMx2r66AEEKo/bCxJ0KogTRM+jcA+ADALaqzdJfpkgklWMBAfQVB4kgM1HGamhpzVVn5NapzDEgsVgWIxfewgIEQQtTAIgZCA5A0TJoOAKMAYA/VWbqas4n9DSGH70t1DoTajcSpnahzCuLPGFGdYYBRaRmMchDppwONpqM6DEIIDVRYxEBogPrvMqxLACAMAGRU5+kKBEFoglxGGVCdA6EOIbGxJ+oceUGhi0Ymu0l1jgEiCwDGaun0GqqDIITQQIcfnBAa4KRh0l8AYDgApFCd5XmNdfRNYtDoDlTnQKgjCByJgZ5DUcIFHBHQ/WIBwBvCl+H0HYQQ6gWwiIEQAmmY9B8AGAYAO6jO0llcJrtMYuHiSXUOhDqMJLGIgTqt+n7aMJ1G84jqHP2UGgA+AIBpEL6snOowCCGEnsAiBkIIABqml6wAgGkAUEJ1no4K8QhKJQhCRHUOhDoBG3ui51J2PbmA6gz90H0AGAHhyzZD+DJcyg8hhHoRLGIghJqQhkljAGAIAJyiOkt7mQqN0kyFRqOpzoFQp+B0EvScyq7f8CN1umKqc/QjO+HJ9BHsN4IQQr0QFjEQQi1Iw6SFADAVACIAQElxnDaFSoLkBEHg+xnqq7CIgZ4LqdWyq9MepFKdox8oAoAQCF/2FoQvk1MdBiGEUOvwQz9CqFXSMCkpDZNuhSdNP+9Rnedphli4XuEyOUOpzoHQc2BSHQD1fUXnEz1JkuwXK01R5CgASCB82XGqgyCEEHo2vPuDEHomaZj0jiRK4gMAkQCwlOo8jdFpdPkYx2FWVOdA6DlhTwz03LQKhVieX5DIs7TwpzpLHyMDgFUQvuyHrjjYTz9n+HTFcbrLogX2yVSc18/Pz+X69esCkiQpOX9fFRMTI5w2bZrzqlWrCiIjI/OpzoNQb4EjMRBCbZKGSeXSMOkyAJgBAL1m3vV4l1HXaDQaFjFQ36XVaQkAguoYqH8oiDtjS5IkLrnaftcBwKurChgIdVRaWhqLIAif2bNn21GdpadpNBr49NNPTZydnd04HI63SCQaGhAQMCguLo7fmeN99913hhKJZDCPx/MSCoVD/fz8XH7//fdWG75HRERYEATh87SvAwcO6LX3vNnZ2Yxp06bZGxgYeBoaGnrOmDHDPi8vr9WBAitXrrQQCoVDMzIycATmc8KRGAihdpOGSY9KoiSXAOA/APAqlVmEbH6Bk7GdL5UZEHpehFanARyJgbqIqqLCVl1VfYWlLxpBdZZeTgsAXwDApxC+TEN1GIQGGp1OB9OmTXM4efKk2M7OThEWFlZcUVFBj4mJMZg6dapo7969j+fNm1fZ3uMtWbLEas+ePaampqbqOXPmlKpUKuLYsWMGc+fOHZSRkZH90Ucftbrq3qxZs8psbW1Vzbe7urq2qx+cVquF4OBgp8ePH3Nmz55dJpfLaX///bdhcHAw++bNm/fp9P/9eU9KSuLu3LnT7Kuvvsq2t7dXt/d7Q63DIgZCqEOkYdIyAJgjiZIcgCcd3E2oyDFNMj6DIIhRVJwboa5C05AaAGBTnQP1H4VnzwlsZs2kOkZvlgEAr0P4sktUB0FooNqzZ4/ByZMnxV5eXnUXL15M4/F4JABAQkJCycSJE11XrlxpGxISUi0Wi9scWRYXF8ffs2ePqbW1tTI5OfmesbGxFgAgLS2tcPjw4W6ffvqp9ezZs6tcXFxaFCsWLFhQFhoaWtPZ7yMhIYGfkpLC++677zJXrFhRBgCwevVqZWRkpEViYiIvMDBQBgCgVqth0aJF9n5+fjURERGlnT0f+h+cToIQ6hRpmPQgALgDwJ89fW4bscVdA74+FjBQn/ffkRgIdZm6rGwPrVIppTpHL0TCk1GEEixgPL/t27cbTp482dHKykrC4XC8BQKBl7e3t+vOnTsNnvU8uVxOrFy50sLS0lLCYrG8ra2tPVavXm2uUCiaTKtra5qFn5+fC0EQTfqP6HQ6+O677wy9vLxcxWKxJ5vN9jYzMxsyZswYpz179oibH+Px48fMN954w8bKykrCYrG89fX1hwYFBQ1KSEjgdeIlafI9fvTRR2bOzs5uXC7XSyAQePn4+Lj8+OOPTTJERERYuLq6SgAADh06ZNh4OsP27dsNmx83KSmJO27cuEFCoXAol8v18vX1dXna1Au1Wg1ffvmlsaenp6tAIPDicrlegwcPdtu0aZOxVqttsm/j1/ru3bvskJAQBwMDA08ajeYTExMjfJ7X4mn27NljDADw6aef5tUXMAAAAgICZCEhIeUVFRWMX375pcXPrDU7d+40BgBYvXp1QX0BAwDAxcVFNX/+/GKVSkV8//33Rl39PQAApKenswAARo8eXVe/bfjw4XUAABkZGQ03KNauXWuenZ3N/vnnn7O6I8dAhCMxEEKdJg2TlgLAK5IoyV/wZFSGcQ+cVjfFPQDnEqJ+gdBgEQN1vZKLSXVm4wOpjtGbPASARRC+7ALVQfqL999/33bQoEHy4cOH15iZmanLy8sZZ8+eFb311lv2aWlpnG+//bbVJpShoaEOUqmUHxwcXMFkMslTp07pR0ZGWty6dYsfHx//iEbr/P3VlStXWu7YscPM0tJSFRoaWiESibSFhYXMO3fu8A8ePChevHhxRf2+Fy9e5E2bNs2pqqqKMWbMmOrg4OCKsrIyxunTp/UnTpzo+uuvvz5+5ZVXqjqaQaFQEAEBAc7Xr18X2NvbK954440SmUxGO378uHjx4sUOt2/fLvzPf/6TBwAQFBRUU1lZSf/5559NXFxc5MHBwZX1xxk2bFiTlYZu3brF27Vrl+nQoUPr5syZU5qbm8s6deqUeNq0aS5Xr15N8fT0bJj+oFQqiQkTJgy6ePGinp2dnWL69OllHA6HvHTpknDt2rU2165dExw5ciSjefbMzEz2mDFjBtvZ2SleeOGFcrlcTujr62ub7/e8ZDIZcfv2bQGHw9FNmTKlxSiIKVOmVB85csTw3Llzeu+8805ZW8dLSkrSAwCYOXNmdfPHQkNDq7799lvzxMTEVosxiYmJgmvXrvG0Wi1hZ2enDA0NrTE3N2/332V7e3sVAMCVK1d4Xl5eCgCA69ev8//7mBIAIDk5mbNt2zbzf//73zmtjQZBnYNFDITQc5OGSQ9IoiTn4Ukh46XuPJefrWcSi84c053nQKin4EgM1B0q7kr9TMf5ZxF0ui3VWSimA4CtAPAxhC+TUx2mP0lOTk5xd3dv0jdAoVAQgYGBTjt27DB79913S1qb9//48WNuSkpKSv0dc5lMljdq1CiXc+fOiXbt2mXw1ltvlXc2U3R0tLGJiYn63r17KUKhsMk0hIKCgoZrHrVaDXPnznWQyWT0Y8eOpYWEhNTWP5aZmcn08/MbvGLFCtvp06dLuVwuCR3w6aefml6/fl3g7+9fFR8f/4jJfHLPJS8vL9/Pz2/wjh07zGbMmFE5ceLEutDQ0BonJyflzz//bOLu7i571uoj58+fF3377beZK1eubLio37Jli9H7779vu2XLFtPo6Ojs+u0ffvih+cWLF/XeeOON4p9++imHwXjyrWs0Gpg7d67tX3/9ZRQdHV3RvOfEzZs3BW+99VZDkaW7pKamsrVaLTg4OCjrX5/GBg8erAAASE9Pb3OqZXV1Na24uJjJ4/F0tra2LX7f6n9HMzMzOa09f8uWLRaN/81iscilS5cWbt26Nb89BbWAgIA6Nzc32erVq22TkpIEcrmcduTIEQMPDw+Zv7+/TKPRwMKFC+2GDBlS98EHH7TalwN1Dk4nQQh1CWmYtFQaJn0ZAF4GgG55o2bRmdW+tkNcuuPYCFGBptZ1+V0uhACAVn7n7kAftnwPAEZB+LL3sIDR9ZoXMAAAOBwOuXTp0mKtVkvExsa2urrDmjVr8hsP+efxeOSGDRvyAAB++eWX5x7yz2AwSAaD0aLw0Pju+v79+/VzcnLY8+fPL25cwAAAsLOzU7/99tuFpaWlzKNHj7Z7hYp6+/btMyIIArZt25bb+ALd0tJS89577xUAAPzwww8dHrXq7e1d27iAAQCwcuXKMjqdTt6+fbthSolWq4X/+7//MzEyMlL/+OOPDQUMAAAGgwE7d+7MJQgC9u3b12Laj6GhoWbLli3dvoxrRUUFHQBAKBS2+vdPLBZrAQCqq6vbvNleXl5OBwAQCAStHsvAwEALAFBTU9OkgfbQoUNl27Zty7x37560rq7u5oMHD6TffPNNllAo1G7fvt185cqVlu35XhgMBsTGxj4KDAysiomJEZ89e1Y0derUitjY2Id0Oh0+++wz0wcPHnB//vnnzLKyMvqMGTPs+Xy+F5vN9g4KChqEq5R0Ho7EQAh1KWmY9K//jsr4FgDmdOWxJ7v53yIIIqArj4kQlQgtFjFQ9yi5dHmYgdfQCoIg2jWvvB9RAcCXALARwpfh0O1u8vDhQ9Znn31mdvHiRWFhYSFLoVA0uTGal5fX6sXZpEmTaptvmzx5cg2dTofU1NTn6kUxc+bMsqioKBNnZ2f36dOnVwQGBtYEBgbWGRoaNnmfTUpK4gMA5OTksCIiIiyaH+fRo0dsAIDU1FQOALR7SklFRQUtOzubbWJioq6fWtDY1KlTqyMiIuCff/7p8Pfp6ekpa76NzWaThoaGmqqqqoYL9Lt373Kqqqrotra2mn/9618tvrf/Pk/36NGjFiMTXF1dZR0ZeZKWlsbavXt3i8LTs0aU9BZvvPFGZeN/Ozk5qSIiIkr9/PxkgYGBrrt37zZdu3ZtUXumltjZ2aljY2PTm2+XSqXsLVu2WKxZsyZfIpEoJ0yY4Hj16lXh5s2bs0Uikfa9996zmTFjhuPt27fvP880qoEKixgIoS4nDZOWAMBcSZRkLzyZYuL4vMcU80SZNmKLkc97HIR6E+yJgboLqdHwatMzrgkdHcZRnaUHXQaAxRC+LIXqIP1Zamoqa/To0YOrq6sZPj4+tQEBAdUikUhLp9MhKyuLdejQIUOlUtnqVZmVlVWLIf9MJhP09fU15eXlz3Vd8uOPP+Y4ODgof/vtN6OdO3ea7dy504xOp5MBAQFV3377ba6Hh4cSAKD+PCdOnBCfOHHiqcerra3t0JVl/agAY2PjVpfPtLGxUQMAVFdXd3hZ7af1pmAwGKROp2toilpSUkIHAMjKymJv3brV/GnHq6ura5HBxMSkQ8t+Pnz4sNVztFXEqB9p0Xx0RL36kRp6enpt/n2sH2lRW1vb6rHqfyZPG/XR3JgxY2RDhgypu3nzpuDcuXP8uXPndrgvCsCTJrMLFiywc3Jykq9fv75IKpWyz5w5o79mzZr8+lVMqquraStWrLCPiYkRTp8+vdMrpAxUWMRACHUbaZj0tCRK4gEAHwPAGgDo9LC56ZIJJQRB2HVVNoR6A5pG2+bycQh1VuGZc24CB3sVQRAsqrN0sxoA+AgAdkL4Mvxvqpt9+eWXZpWVlYzmPRoAAHbv3m1w6NChFitr1MvNzWU6OTk1GSGjVquhsrKSwefzGy406+9MazQaAlrRWiGAwWDA+vXri9evX1+cl5fHiI+PF+zfv9/gxIkT4uDgYG5aWloKl8sl9fT0tAAA0dHRj1577bVOXaS2pv6CurS0tNXPOtnZ2UyA9l9Qd0Z9gWDixImVp0+fftyR5xJEqy/1U4WGhtaQJJncoScBgJubm5JOp0NOTg5brVZD874Y9+7d4wAAODg4tJiy1Jyenp7OxMREXVxczMzKymI274uRkpLCBgCws7NrMTLmaQwMDDQAHS9iNfbll18a3717l3/58uVUBoMBd+/e5QAA+Pj4NIyoGTFihAwAQCqVcrGI0XE4dgUh1K2kYVKFNEy6FgCGAkCnOsM7m9jfEHL4vl0aDKFeAEdioO6kqaszUZaUXqM6RzeLAQB3CF/2Hyxg9Iz6pSNff/31iuaPJSQkCJ713NOnT7d4/NSpU0KtVgtubm4NF3hGRkYaAID8/PwWBbjy8nJaVlbWM5s+WlpaasLCwiqPHz+ePmLEiJqcnBz2jRs3uAAAI0eOrAMAeNqKFZ0lFot11tbWyuLiYqZUKm2R7+TJk0IAAIlE0vB90ul0EgBAq9V2rILwFEOHDlUIhULt7du3+UqlskuO2dV4PB45dOjQWoVCQat/TRo7efKkHgBAYGBgi9VGWjNq1KhqAIAjR4606GESExMjAgDw9/dvV5FAqVQSKSkpPAAAJyenNosorUlLS2Nt3LjR6t133y3w8fFpUjxp/DORy+V4Hf4c8MVDCPUIaZg0FQACAOBNAGh393GCIDRBLqOeue48Qn0VTa3Diy7UrQri4s2oztBNMgBgBoQvmwbhy3KoDjOQWFtbKwEATpw40eQC9ODBg3p//vnnM5tWbtmyxaJ+ygPAk+U2161bZwkA8PrrrzeM6hCLxTp7e3vFrVu3BMnJyQ39GzQaDSxbtsy6eQ8OuVxOnD59mg/NKJVKorKykgEAwOfzdQAAc+fOrbS2tlb+8ssvxvv37xe1ljM+Pp5fU1PT4eukuXPnlpIkCatWrbLSaP5Xoy4oKGDUr4Tx5ptvltZvNzY21hIEAXl5eV0yWorJZMLChQuLS0pKmAsXLrSura1tUcjIyspiNn5NqbB48eISAIBPPvnEUiaTNWRMSEjgxcbGGojFYk3zIllWVhbz1q1bnLKysiajcJYvX14CAPDNN9+YN/7dSktLY+3du9fkvyuONLzmFRUVtDt37rQoMikUCuLNN9+0LigoYNnb2yv8/f1b9CFpjwULFtja2NgoN27cWFC/zdPTUwEAcOzYMf36bYcPHxYBAEgkEmw83Ak4nQQh1GOkYVISAH6SREmOAsA3APB6W88Z6+ibxKDR/bs9HEIUIDRYxEDdS1FcMkhdW3uDKRAMozpLF1HAk8admyF8WbuHiKOu884775QcOHDAaMGCBY779++vMDc3V9+7d49z4cIFUXBwcEVsbOxTm8k6OjrK3d3d3YODgyuYTCZ56tQp/ZycHPa4ceOqli9f3nz1jcJVq1bZBQYGuoaEhFSw2WxdUlKSUK1WEy4uLvK0tDRu/b51dXW0yZMnu9rY2CglEonM2tpaqVAoaImJiXrp6emcoKCgSm9vbwXAk4aYf/311+PQ0FCnV199ddDmzZvr3N3dZTweT5eXl8e6c+cOLzc3l52VlXWn+VKtbfn3v/9dFBcXJzpz5oz+4MGD3cePH18lk8losbGx4vLycsbSpUsLJ0+e3NDcVCQS6YYMGVKXnJwsmD59ur2Tk5OCTqfD7NmzK4cPH96pi9vNmzcXSKVS7r59+4zj4+P1R40aVW1hYaEuKSlhpKenc27duiX417/+lefj41PYmeN3hcWLF5cfOXJE/+TJk2IPDw+3SZMmVZWXl9NjYmIMdDodsX379iwDA4Mmr31ERITloUOHDJtPY5o4cWLdm2++WfTjjz+aSiQS95CQkAqVSkXExMSIq6qq6Bs3bsx2cXFpmMJUXFzM8PLy8nB3d5c5OTnJzczM1KWlpYykpCS9vLw8lr6+viY6OjqdTu9w6xKIjIw0unbtmjAhIeFe42kyHh4eyokTJ1YeOHDAMDg4mCYUCrUHDx40kkgkdaGhoTiVpBOwiIEQ6nH/bfz5xn8bf+4CAOfW9uMy2WUSCxfPnsyGUE8iNFpcnQR1u6LziXSr0GCqY3SFvwFgFYQvy6A6yNMsWmDf4R4Bfc3w4cPlsbGxaR9//LHluXPnRFqtlnB1dZVFRUU9FovF2mcVMWJiYtL/9a9/mR88eNCwpKSEaWJioo6IiMjfsGFDYfMVGt59990ykiSJHTt2mB48eNBQT09PO2nSpMrIyMjc6dOnD2q8r1Ao1K1duzY3MTFRLzk5mR8XF6fP4/G0NjY2ys2bN2evXLmytPH+w4cPl9++fTt148aNpnFxcfoHDhwwJAgCjI2N1e7u7rIPP/wwvz0rUzTH4XDICxcuPPjss89MDx48aLh3714TOp1Ourq6yjZt2lQSHh7eYiRqdHR0xsqVK60TExNFMTExBiRJgpWVlaqzRQw2m03GxcU93rVrl0F0dLTR2bNn9WUyGU0sFmusra2Va9asyVu4cGFZ20fqPjQaDY4ePZq+adMmk99++83ovyMmdL6+vjXr1q0rmDhxYl1Hjrdnz55ciUQi/+GHH0z27dtnRKPRwM3NTRYREVE4Z86cJn1PjI2NNW+88UbxrVu3+ImJiaKqqio6k8kkra2tlcuWLStcu3ZtkaWlZYd/9hkZGcxPPvnEavny5YWjR49u8bPbt29f5pIlS6zj4uL0NRoNMW7cuMo9e/Zk48oknUOQZLtX0kEIoS4niZKwACACANYCQJO5si96Tb1gpmc8lpJgCPUAw6Ss2+6fnRlKdQ7U/7msWJZGYzJdqM7RSQ8B4B0IX/b0pSR6yJ07dzI9PT1L294TIYTQ87hz546Rp6enXWuPYekHIUQpaZhUJQ2TfgkALgDwW/12U6FRmqnQaDR1yRDqfjidBPWU0qvX+uKFtwyeFLglvaGAgRBCqHfAIgZCqFeQhknzpWHSeQAwBgBuhkqC5ARB4HsU6tdoalxiFfWMsuRbfqROV9D2nr3GAQBwhfBlmyB8WadWCUAIIdQ/YU8MhFCvIg2TXlp0ft4wLpOzCAA2AoAJ1ZkQ6i4Erk6CeopOx6xKvfdA38PdnOoobbgPAG9D+LJ4qoMgFBMTIzx79mybS7Hq6+tr1q9fX9wTmRBCWMRACPVCP42LJgHgx92ZW/6EJ0OJ3wGAZ64Jj1BfRNPgSAzUc4oSLwwVubvVEATR5kUZBcoBYAMA/AfCl6mpDoMQAMDZs2eFW7dubbPwZ2FhocIiBkI9B4sYCKFeK9xuTTUA/Gt35pbdAPA1ALxAcSSEuhRNrcPu2qjH6JQqkSwnN4FvYx1AdZZGFADwLQB8CeHLKinOglATkZGR+ZGRkflU50AINYXzzRFCvV643Zr0cLs1swDAHwAuU50Hoa5CaLRYxEA9qiD+jCNJkr1haV8dAEQBgDOEL/sACxgIIYTaC4sYCKE+I9xuzYVwuzWjAGAWPJk3jVCfRsOeGKiHqauqrVQVlVcpjnESALwgfNl8CF+WQ3EWhBBCfQwWMRBCfU643ZrDAOABAIsBII/iOAh1GqHuDTfE0UBTEH9Gn6JT3wSACRC+bCqEL7tLUYbnRpI4gAohhLpTW++zWMRACPVJ4XZrtOF2a34EACcA+AAAKqlNhFDH0TQ4EgP1PHlevptGLr/Tg6fMBIB5ADAMwped6cHzdjmCICpUKhWT6hwIIdSfqVQqJkEQFU97HIsYCKE+LdxujTzcbs1mAHAAgC3wpEkcQn0CjsRAVCm+cEnZA6cpB4DVAOAK4ct+g/BlfX4Ig06nO1FZWdkbV3dBCKF+o7KyUqjT6U487XEsYiCE+oVwuzUV4XZr3ocnIzP+DwDw6hD1ejQNrk6CqFGVkuqr02gyuunw1fBkuVRHCF8WCeHLeqJg0iO0Wu0PRUVFlUVFRQZKpZKJU0sQQqhrkCQJSqWSWVRUZFBUVFSp1Wp/eNq+BL75IoT6o92ZW5wAYC08GcJMpzgOQq1y2nrxvPmpB+OozoEGJuPRoy4Y+Q0b24WHrIIny6Vug/BlTx0G3NclJyfb0en0JTQabSpJkmKq8yCEUH9BEESFTqc7odVqf/Dx8cl86n5YxEAI9We7M7c4AsA6eFLMYFAcB6EmXLYkJJieeRxAdQ40MBF0usLl7eW1BEEYPeehqgBgGzwpXlQ+dzCEEPr/9u4uxvLyoOP4b2ZhWZbFCm3xpbRMpVBPUKq4vCzs1KRVs7Ql3TTVlqqpperJKsar8U6t1rtzoSY18WjYLVBI2xRraxM1LQVD05bSxdKkniPl5VARdqHLy7LMvszMjhf/M+yqy7Lszswzz5zPJ/lnZsgGfuFis3x5zvOH4/BxEmBVa09MPdyemPpIkrem+ZjJbOFJ8JKxWfd6Us783Ny6F77/0PdO4W/xXJKPJZlIe9vHBAwAloOIAYyE9sTUI+2JqY8muTjJTUlmCk8Cd2JQ3K677r5kfn7+1V6I/FySP00TL/5MvABgOYkYwEhpT0w92p6Y+u00MePvI2ZQ0NjM4bHSGxhtc9P7X3dg1+77TvCXP5vkT9LEiz9Pe9vzSzgNAI5JxABGUntiatCemPrdNG8z+USS6cKTGEE+TsJK8OSX7zx//viXpO1J8sdp4sXHxQsAShIxgJHWnph6rD0x9QdJ3pTm/zA+XXgSI2R8ds5JDIo7uGfPm2df2Hes0xiPJml+f2xv+4u0t+1d5mkA8P+IGABJ2hNTe9oTUx9PckGSbUkeKjyJETA250oMVoZdd919xlE/7kzywSQXpb3tE2lvc1INgBXD6wYBjtKemNqf5G+7g87fJdma5I+SXFl0FKuWj5OwUux75NFLZ/btu+X0DRtuTnvbV0vvAYCXM3b8j0AC0B10JpNMJXlPEsf/WTQ/f+MXvnb2Q3s2l97BSNuf5NYkf93q9/6j9BgAeCVOYgC8gvbE1D1J7ukOOq0kf5jkN5KcVXYVq4GTGBT0RJK/SdJt9Xt7So8BgBPlJAbAq9QddF6T5LeS/F6aV7XCSdn4O3d8ff1/PX916R2MlJ1J/irJZ1r9nldMA1AdEQPgJHUHnbEkv5zkxiTvjsuSeZU23vC5b6x/Yu+m0jtY9Q4n+UKSv2z1e/eUHgMAp0LEAFgE3UFnIs1bTT6a5LVl11CLyz/82W+euXvfVaV3sGo9lmRHkh2tfu8HpccAwGIQMQAWUXfQWZfm1YS/n2Rj4TmscFf85mfuW/f0i5eX3sGqcjDJ55NsT3Jnq99z8QoAq4qIAbBEuoPOxiQ3JLk+yY+WXcNKdOWHPv3tM56ZFrtYDA8kuSnJba1+75nSYwBgqYgYAEtseDrjfWmCxjviNa0MXfXB2+9f+9yBy0rvoFrPJ7k9yU2tfm9n6TEAsBxEDIBlNLw74yNp3m7ypqJjKG7Tr932wOl7D76t9A6qMp/k39Kcurij1e/tL7wHAJaViAFQQHfQGU/yzjSnM7YmWVd0EEVsev+nvnv6vkOXlt5BFf47ySfTXNL5cOEtAFCMiAFQWHfQOSfNvRkfSnJ1fNxkZFz9vlu/d9r0zCWld7BizST5pzSnLv611e/NFd4DAMWJGAArSHfQuSDJB9JEjZ8ru4alds3WW/prDsz+dOkdrDjfSfKpJLe2+r2nCm8BgBVFxABYobqDTivN61qvT3JR4TksgWvee/ODaw7OXVx6B8XNJ/lWkjuS/IOPiwDAyxMxACowfF3r9WlOabyh8BwWyebrbn54fGbuwtI7KOJwkq/lSLh4vPAeAKiCiAFQkeGFoJNJfjXJe5OcX3YRp2Lzez45GJ89PFF6B8tmNsldacLFP7b6vd2F9wBAdUQMgIoNT2hsHT4uiKzM5nfveHx8bl6IWt0OJvlymnDxxVa/90zhPQBQNREDYJXoDjpvyZGgsSnJeMk9vLLJd+14cuzw/E+U3sGim07yz2nCxZda/d4LhfcAwKohYgCsQt1B57wk16UJGr+UZF3RQRzT5LXbnxqbz3mld7Ao9ib5Uppw8S+tfm+68B4AWJVEDIBVrjvonJXkHUm2JLk2yZvLLmLB5LXbnxmbz7mld3BS5tO8CvXONB8XubvV7x0quggARoCIATBiuoPOW3MkaLw9yZllF42uyS3b944lP1J6ByfskSRfGT53tfq9HxbeAwAjR8QAGGHdQeeMJJuT/MrweVuSsaKjRsjklu0vjiVnld7By3o6yVczDBetfm9Qdg4AIGIA8JLhXRrvTPKLaU5ptMouWt0mt2w/NJasLb2Dl7yY5J4cOW3x3Va/5w9KALCCiBgAvKxh1JjMkahxaZzUWDSTW7YfHvMWmZJmk3wrzb0WX0nyjVa/N1N2EgBwPCIGACesO+ick+bjJwtR47Ika4qOqtXh+fm3v2uHILS8DiV5IMnX04SLu73+FADqImIAcNK6g86GJJuSXJnk8iRXJPnxoqMqMTYzNzN53c2nl96xyj2a5N4k3xx+/fdWv3ew7CQA4FSIGAAsqu6g88Y0MWPh+YUkZxcdtQKNH5id3rz1lvWld6wizyX5dppYcW+Se1v93lNFFwEAi07EAGBJdQed8TQXhB4dNS5JMtL/Ab9m38G917z/Nq9XPTl7ktyfZOfw6/2tfu/hspMAgOUgYgCw7IZh48IkP5vmstCFrz+VEbno8rTnDzx79QduP6f0jgo8keQ7GcaKJDtb/d4Pii4CAIoRMQBYMbqDzvokP5P/HTcuSXJeyV1LYe2e6aev+vVPv770jhVib5LvJ3kwyX8Ovz6Y5EEXbwIARxMxAFjxuoPOa5K8JcnFSS76P8+5BaedtDN279t15Yc/O0qXoB5K8kiOBIqXYkWr39tVchgAUA8RA4CqdQedc9PEjIXAcWGSNw6fNyRZkW8AWffE3sevuOFz55fescjmkzyeo05S5EisGLT6vbmC2wCAVUDEAGDVGt698WNJzs+RsLHwLPy1n0yyZrm3rX/s2cc2tj9/wXL/c0/S/iS7TuDZ7RWmAMBSOq30AABYKu2JqcNJnhw+9x3r1wxDxzlJXj98XnfU98f6+bVJzjzVbWOzh0ufSphN8lSOEyQWvm/1e3tLjQQAOJqTGADwKnUHndOSnJ1kwwl83ZBkXZK1aT7asjbJ2g0P/fCFy2784nlp3sZyvGfhlMj+JNPD5+jvT+rnVr93aNH/xQAALDERAwAAAKjCeOkBAAAAACdCxAAAAACqIGIAAAAAVRAxAAAAgCqIGAAAAEAVRAwAAACgCiIGAAAAUAURAwAAAKiCiAEAAABUQcQAAAAAqiBiAAAAAFUQMQAAAIAqiBgAAABAFUQMAAAAoAoiBgAAAFAFEQMAAACogogBAAAAVEHEAAAAAKogYgAAAABVEDEAAACAKogYAAAAQBVEDAAAAKAKIgYAAABQBREDAAAAqIKIAQAAAFRBxAAAAACqIGIAAAAAVRAxAAAAgCqIGAAAAEAVRAwAAACgCiIGAAAAUAURAwAAAKiCiAEAAABUQcQAAAAAqiBiAAAAAFUQMQAAAIAqiBgAAABAFUQMAAAAoAoiBgAAAFAFEQMAAACogogBAAAAVEHEAAAAAKogYgAAAABVEDEAAACAKogYAAAAQBVEDAAAAKAKIgYAAABQBREDAAAAqIKIAQAAAFRBxAAAAACqIGIAAAAAVRAxAAAAgCqIGAAAAEAVRAwAAACgCiIGAAAAUAURAwAAAKiCiAEAAABUQcQAAAAAqiBiAAAAAFUQMQAAAIAqiBgAAABAFUQMAAAAoAoiBgAAAFAFEQMAAACogogBAAAAVEHEAAAAAKogYgAAAABVEDEAAACAKogYAAAAQBVEDAAAAKAKIgYAAABQBREDAAAAqIKIAQAAAFRBxAAAAACqIGIAAAAAVRAxAAAAgCqIGAAAAEAVRAwAAACgCiIGAAAAUAURAwAAAKiCiAEAAABUQcQAAAAAqiBiAAAAAFUQMQAAAIAqiBgAAABAFUQMAAAAoAoiBgAAAFAFEQMAAACogogBAAAAVEHEAAAAAKogYgAAAABVEDEAAACAKogYAAAAQBVEDAAAAKAKIgYAAABQBREDAAAAqIKIAQAAAFRBxAAAAACqIGIAAAAAVRAxAAAAgCqIGAAAAEAVRAwAAACgCv8DGmfSjNvag9sAAAAASUVORK5CYII="
+ },
+ "metadata": {}
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file