Loan Default Payments_Archive

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from scipy.stats import boxcox\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the dataset\n",
    "\n",
    "There are 3 datasets present. They need to be joined by a common attribute.\n",
    "\n",
    "-\tBorrower Information: Information on the clients’ customers\n",
    "-\tLoan Classification Information: Information on the loan itself\n",
    "-\tLoan Payment Information: Information on the payments on the loans.\n",
    "\n",
    "Between Borrower and Loan Datasets, the common attribute is \"member_id\" whereas between Loan and Payment Datasets, the common attribute is \"id\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>member_id</th>\n",
       "      <th>emp_title</th>\n",
       "      <th>emp_length</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1296599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Jan-1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1314167</td>\n",
       "      <td>Ryder</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>RENT</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Apr-1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1313524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>RENT</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nov-2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  member_id emp_title emp_length home_ownership zip_code  \\\n",
       "0           1    1296599       NaN  10+ years           RENT    860xx   \n",
       "1           2    1314167     Ryder   < 1 year           RENT    309xx   \n",
       "2           3    1313524       NaN  10+ years           RENT    606xx   \n",
       "\n",
       "  addr_state  annual_inc  annual_inc_joint  open_acc  pub_rec  total_acc  \\\n",
       "0         AZ     24000.0               NaN       3.0      0.0        9.0   \n",
       "1         GA     30000.0               NaN       3.0      0.0        4.0   \n",
       "2         IL     12252.0               NaN       2.0      0.0       10.0   \n",
       "\n",
       "   delinq_2yrs earliest_cr_line  \n",
       "0          0.0         Jan-1985  \n",
       "1          0.0         Apr-1999  \n",
       "2          0.0         Nov-2001  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##Reading the dataset\n",
    "df_borrower = pd.read_csv('C:/Users/sarita.machado/OneDrive - Accenture/Sarita_BEL/myLearning/Projects/Assessment Test/OneDrive_2019-07-04/To be communicated to the candidate/Borrower Information.csv')\n",
    "df_borrower.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sarita.machado\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>purpose</th>\n",
       "      <th>application_type</th>\n",
       "      <th>issue_d</th>\n",
       "      <th>desc</th>\n",
       "      <th>title</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>installment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1077501</td>\n",
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       "      <td>36 months</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Borrower added on 12/22/11 &gt; I need to upgra...</td>\n",
       "      <td>Computer</td>\n",
       "      <td>5000</td>\n",
       "      <td>10.65</td>\n",
       "      <td>5000</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>162.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>60 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>Borrower added on 12/22/11 &gt; I plan to use t...</td>\n",
       "      <td>bike</td>\n",
       "      <td>2500</td>\n",
       "      <td>15.27</td>\n",
       "      <td>2500</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>59.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>36 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>Dec-2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>real estate business</td>\n",
       "      <td>2400</td>\n",
       "      <td>15.96</td>\n",
       "      <td>2400</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>84.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       id  member_id        term grade sub_grade  loan_status  \\\n",
       "0           1  1077501    1296599   36 months     B        B2   Fully Paid   \n",
       "1           2  1077430    1314167   60 months     C        C4  Charged Off   \n",
       "2           3  1077175    1313524   36 months     C        C5   Fully Paid   \n",
       "\n",
       "  pymnt_plan         purpose application_type   issue_d  \\\n",
       "0          n     credit_card       INDIVIDUAL  Dec-2011   \n",
       "1          n             car       INDIVIDUAL  Dec-2011   \n",
       "2          n  small_business       INDIVIDUAL  Dec-2011   \n",
       "\n",
       "                                                desc                 title  \\\n",
       "0    Borrower added on 12/22/11 > I need to upgra...              Computer   \n",
       "1    Borrower added on 12/22/11 > I plan to use t...                  bike   \n",
       "2                                                NaN  real estate business   \n",
       "\n",
       "   loan_amnt  int_rate  funded_amnt  funded_amnt_inv  installment  \n",
       "0       5000     10.65         5000           4975.0       162.87  \n",
       "1       2500     15.27         2500           2500.0        59.83  \n",
       "2       2400     15.96         2400           2400.0        84.33  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_loan = pd.read_csv('C:/Users/sarita.machado/OneDrive - Accenture/Sarita_BEL/myLearning/Projects/Assessment Test/OneDrive_2019-07-04/To be communicated to the candidate/Loan Classification Information.csv')\n",
    "df_loan.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>13648.0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1077175</td>\n",
       "      <td>8.72</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       id    dti  inq_last_6mths  mths_since_last_delinq  \\\n",
       "0           1  1077501  27.65             1.0                     NaN   \n",
       "1           2  1077430   1.00             5.0                     NaN   \n",
       "2           3  1077175   8.72             2.0                     NaN   \n",
       "\n",
       "   mths_since_last_record  revol_bal  revol_util  out_prncp  out_prncp_inv  \\\n",
       "0                     NaN    13648.0        83.7        0.0            0.0   \n",
       "1                     NaN     1687.0         9.4        0.0            0.0   \n",
       "2                     NaN     2956.0        98.5        0.0            0.0   \n",
       "\n",
       "      ...       mths_since_rcnt_il  total_bal_il  il_util  open_rv_12m  \\\n",
       "0     ...                      NaN           NaN      NaN          NaN   \n",
       "1     ...                      NaN           NaN      NaN          NaN   \n",
       "2     ...                      NaN           NaN      NaN          NaN   \n",
       "\n",
       "   open_rv_24m  max_bal_bc  all_util inq_fi  total_cu_tl inq_last_12m  \n",
       "0          NaN         NaN       NaN    NaN          NaN          NaN  \n",
       "1          NaN         NaN       NaN    NaN          NaN          NaN  \n",
       "2          NaN         NaN       NaN    NaN          NaN          NaN  \n",
       "\n",
       "[3 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pay = pd.read_csv('C:/Users/sarita.machado/OneDrive - Accenture/Sarita_BEL/myLearning/Projects/Assessment Test/OneDrive_2019-07-04/To be communicated to the candidate/Loan Payment Information.csv')\n",
    "df_pay.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining Borrower and Loan Datasets on \"member_id\"\n",
    "df_inter=pd.merge(df_loan,df_borrower, on=\"member_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0_x</th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>purpose</th>\n",
       "      <th>application_type</th>\n",
       "      <th>...</th>\n",
       "      <th>home_ownership</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>addr_state</th>\n",
       "      <th>annual_inc</th>\n",
       "      <th>annual_inc_joint</th>\n",
       "      <th>open_acc</th>\n",
       "      <th>pub_rec</th>\n",
       "      <th>total_acc</th>\n",
       "      <th>delinq_2yrs</th>\n",
       "      <th>earliest_cr_line</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>36 months</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>RENT</td>\n",
       "      <td>860xx</td>\n",
       "      <td>AZ</td>\n",
       "      <td>24000.0</td>\n",
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       "      <td>9.0</td>\n",
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       "      <td>Jan-1985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>60 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>RENT</td>\n",
       "      <td>309xx</td>\n",
       "      <td>GA</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Apr-1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>36 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>RENT</td>\n",
       "      <td>606xx</td>\n",
       "      <td>IL</td>\n",
       "      <td>12252.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nov-2001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0_x       id  member_id        term grade sub_grade  loan_status  \\\n",
       "0             1  1077501    1296599   36 months     B        B2   Fully Paid   \n",
       "1             2  1077430    1314167   60 months     C        C4  Charged Off   \n",
       "2             3  1077175    1313524   36 months     C        C5   Fully Paid   \n",
       "\n",
       "  pymnt_plan         purpose application_type       ...        home_ownership  \\\n",
       "0          n     credit_card       INDIVIDUAL       ...                  RENT   \n",
       "1          n             car       INDIVIDUAL       ...                  RENT   \n",
       "2          n  small_business       INDIVIDUAL       ...                  RENT   \n",
       "\n",
       "  zip_code addr_state  annual_inc  annual_inc_joint  open_acc  pub_rec  \\\n",
       "0    860xx         AZ     24000.0               NaN       3.0      0.0   \n",
       "1    309xx         GA     30000.0               NaN       3.0      0.0   \n",
       "2    606xx         IL     12252.0               NaN       2.0      0.0   \n",
       "\n",
       "   total_acc  delinq_2yrs earliest_cr_line  \n",
       "0        9.0          0.0         Jan-1985  \n",
       "1        4.0          0.0         Apr-1999  \n",
       "2       10.0          0.0         Nov-2001  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inter.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Joining Intermediate DataSet and Payment Datasets on \"id\"\n",
    "df=pd.merge(df_inter,df_pay, on=\"id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>dti</th>\n",
       "      <th>inq_last_6mths</th>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <th>revol_bal</th>\n",
       "      <th>revol_util</th>\n",
       "      <th>out_prncp</th>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <th>...</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1077501</td>\n",
       "      <td>27.65</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>13648.0</td>\n",
       "      <td>83.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1077430</td>\n",
       "      <td>1.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1687.0</td>\n",
       "      <td>9.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1077175</td>\n",
       "      <td>8.72</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       id    dti  inq_last_6mths  mths_since_last_delinq  \\\n",
       "0           1  1077501  27.65             1.0                     NaN   \n",
       "1           2  1077430   1.00             5.0                     NaN   \n",
       "2           3  1077175   8.72             2.0                     NaN   \n",
       "\n",
       "   mths_since_last_record  revol_bal  revol_util  out_prncp  out_prncp_inv  \\\n",
       "0                     NaN    13648.0        83.7        0.0            0.0   \n",
       "1                     NaN     1687.0         9.4        0.0            0.0   \n",
       "2                     NaN     2956.0        98.5        0.0            0.0   \n",
       "\n",
       "      ...       mths_since_rcnt_il  total_bal_il  il_util  open_rv_12m  \\\n",
       "0     ...                      NaN           NaN      NaN          NaN   \n",
       "1     ...                      NaN           NaN      NaN          NaN   \n",
       "2     ...                      NaN           NaN      NaN          NaN   \n",
       "\n",
       "   open_rv_24m  max_bal_bc  all_util inq_fi  total_cu_tl inq_last_12m  \n",
       "0          NaN         NaN       NaN    NaN          NaN          NaN  \n",
       "1          NaN         NaN       NaN    NaN          NaN          NaN  \n",
       "2          NaN         NaN       NaN    NaN          NaN          NaN  \n",
       "\n",
       "[3 rows x 41 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pay.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Unnamed: 0_x</th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>purpose</th>\n",
       "      <th>application_type</th>\n",
       "      <th>...</th>\n",
       "      <th>mths_since_rcnt_il</th>\n",
       "      <th>total_bal_il</th>\n",
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       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
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       "  <tbody>\n",
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       "      <td>1077501</td>\n",
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       "      <td>credit_card</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>60 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>36 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 71 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0_x       id  member_id        term grade sub_grade  loan_status  \\\n",
       "0             1  1077501    1296599   36 months     B        B2   Fully Paid   \n",
       "1             2  1077430    1314167   60 months     C        C4  Charged Off   \n",
       "2             3  1077175    1313524   36 months     C        C5   Fully Paid   \n",
       "\n",
       "  pymnt_plan         purpose application_type     ...      mths_since_rcnt_il  \\\n",
       "0          n     credit_card       INDIVIDUAL     ...                     NaN   \n",
       "1          n             car       INDIVIDUAL     ...                     NaN   \n",
       "2          n  small_business       INDIVIDUAL     ...                     NaN   \n",
       "\n",
       "  total_bal_il il_util  open_rv_12m  open_rv_24m  max_bal_bc  all_util  \\\n",
       "0          NaN     NaN          NaN          NaN         NaN       NaN   \n",
       "1          NaN     NaN          NaN          NaN         NaN       NaN   \n",
       "2          NaN     NaN          NaN          NaN         NaN       NaN   \n",
       "\n",
       "   inq_fi  total_cu_tl inq_last_12m  \n",
       "0     NaN          NaN          NaN  \n",
       "1     NaN          NaN          NaN  \n",
       "2     NaN          NaN          NaN  \n",
       "\n",
       "[3 rows x 71 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking the Size of the Merged Data set.\n",
    "\n",
    "***We are also able to make a very important observation here. Loan Status is our dependant variable and the other variables are independent variables***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887379, 71)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking for variables which have nul values and sorting it based on the maximum amout of null values present"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>dti_joint</th>\n",
       "      <td>886870</td>\n",
       "      <td>99.942640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>annual_inc_joint</th>\n",
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       "      <td>866569</td>\n",
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       "    <tr>\n",
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       "      <td>866007</td>\n",
       "      <td>97.591559</td>\n",
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       "    <tr>\n",
       "      <th>max_bal_bc</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.591559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>all_util</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.591559</td>\n",
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       "    <tr>\n",
       "      <th>inq_fi</th>\n",
       "      <td>866007</td>\n",
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       "    <tr>\n",
       "      <th>total_cu_tl</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.591559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inq_last_12m</th>\n",
       "      <td>866007</td>\n",
       "      <td>97.591559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desc</th>\n",
       "      <td>761351</td>\n",
       "      <td>85.797726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_record</th>\n",
       "      <td>750326</td>\n",
       "      <td>84.555303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_major_derog</th>\n",
       "      <td>665676</td>\n",
       "      <td>75.015974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mths_since_last_delinq</th>\n",
       "      <td>454312</td>\n",
       "      <td>51.197065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <td>252971</td>\n",
       "      <td>28.507661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <td>70276</td>\n",
       "      <td>7.919502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <td>70276</td>\n",
       "      <td>7.919502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_title</th>\n",
       "      <td>51462</td>\n",
       "      <td>5.799326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>44825</td>\n",
       "      <td>5.051393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <td>17659</td>\n",
       "      <td>1.990018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_util</th>\n",
       "      <td>502</td>\n",
       "      <td>0.056571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>152</td>\n",
       "      <td>0.017129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <td>145</td>\n",
       "      <td>0.016340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <td>53</td>\n",
       "      <td>0.005973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issue_d</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_amnt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>int_rate</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0_y</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dti</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revol_bal</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_prncp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>out_prncp_inv</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pymnt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_pymnt_inv</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_prncp</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_int</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>recoveries</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Count    Percent\n",
       "dti_joint                    886870  99.942640\n",
       "annual_inc_joint             886868  99.942415\n",
       "il_util                      868762  97.902024\n",
       "mths_since_rcnt_il           866569  97.654892\n",
       "open_acc_6m                  866007  97.591559\n",
       "open_il_6m                   866007  97.591559\n",
       "open_il_12m                  866007  97.591559\n",
       "open_il_24m                  866007  97.591559\n",
       "total_bal_il                 866007  97.591559\n",
       "open_rv_12m                  866007  97.591559\n",
       "open_rv_24m                  866007  97.591559\n",
       "max_bal_bc                   866007  97.591559\n",
       "all_util                     866007  97.591559\n",
       "inq_fi                       866007  97.591559\n",
       "total_cu_tl                  866007  97.591559\n",
       "inq_last_12m                 866007  97.591559\n",
       "desc                         761351  85.797726\n",
       "mths_since_last_record       750326  84.555303\n",
       "mths_since_last_major_derog  665676  75.015974\n",
       "mths_since_last_delinq       454312  51.197065\n",
       "next_pymnt_d                 252971  28.507661\n",
       "tot_coll_amt                  70276   7.919502\n",
       "tot_cur_bal                   70276   7.919502\n",
       "emp_title                     51462   5.799326\n",
       "emp_length                    44825   5.051393\n",
       "last_pymnt_d                  17659   1.990018\n",
       "revol_util                      502   0.056571\n",
       "title                           152   0.017129\n",
       "collections_12_mths_ex_med      145   0.016340\n",
       "last_credit_pull_d               53   0.005973\n",
       "...                             ...        ...\n",
       "term                              0   0.000000\n",
       "grade                             0   0.000000\n",
       "sub_grade                         0   0.000000\n",
       "loan_status                       0   0.000000\n",
       "pymnt_plan                        0   0.000000\n",
       "purpose                           0   0.000000\n",
       "application_type                  0   0.000000\n",
       "issue_d                           0   0.000000\n",
       "loan_amnt                         0   0.000000\n",
       "int_rate                          0   0.000000\n",
       "funded_amnt                       0   0.000000\n",
       "funded_amnt_inv                   0   0.000000\n",
       "installment                       0   0.000000\n",
       "Unnamed: 0_y                      0   0.000000\n",
       "home_ownership                    0   0.000000\n",
       "zip_code                          0   0.000000\n",
       "addr_state                        0   0.000000\n",
       "Unnamed: 0                        0   0.000000\n",
       "dti                               0   0.000000\n",
       "revol_bal                         0   0.000000\n",
       "out_prncp                         0   0.000000\n",
       "out_prncp_inv                     0   0.000000\n",
       "total_pymnt                       0   0.000000\n",
       "total_pymnt_inv                   0   0.000000\n",
       "total_rec_prncp                   0   0.000000\n",
       "total_rec_int                     0   0.000000\n",
       "total_rec_late_fee                0   0.000000\n",
       "recoveries                        0   0.000000\n",
       "collection_recovery_fee           0   0.000000\n",
       "last_pymnt_amnt                   0   0.000000\n",
       "\n",
       "[71 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_null=pd.DataFrame({'Count': df.isnull().sum(), 'Percent': 100*df.isnull().sum()/len(df)})\n",
    "df_null.sort_values(by=['Count','Percent'],ascending=False)\n",
    "#pd.DataFrame({'Count': df.isnull().sum(), 'Percent': 100*df.isnull().sum()/len(df)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 71 columns in the original combined dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you take a look, there are atleast 15 columns who have more than 80% of their data as 'NA'. Hence we can drop these rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis=1, thresh=int(0.80*len(df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887379, 50)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***21 columns are dropped owing to the fact that they have more than 80% of their data as null***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0_x</th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>term</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>pymnt_plan</th>\n",
       "      <th>purpose</th>\n",
       "      <th>application_type</th>\n",
       "      <th>...</th>\n",
       "      <th>total_rec_late_fee</th>\n",
       "      <th>recoveries</th>\n",
       "      <th>collection_recovery_fee</th>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <th>last_pymnt_amnt</th>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>36 months</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jan-2015</td>\n",
       "      <td>171.62</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>60 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>Charged Off</td>\n",
       "      <td>n</td>\n",
       "      <td>car</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>Apr-2013</td>\n",
       "      <td>119.66</td>\n",
       "      <td>Sep-2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>36 months</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>Fully Paid</td>\n",
       "      <td>n</td>\n",
       "      <td>small_business</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Jun-2014</td>\n",
       "      <td>649.91</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0_x       id  member_id        term grade sub_grade  loan_status  \\\n",
       "0             1  1077501    1296599   36 months     B        B2   Fully Paid   \n",
       "1             2  1077430    1314167   60 months     C        C4  Charged Off   \n",
       "2             3  1077175    1313524   36 months     C        C5   Fully Paid   \n",
       "\n",
       "  pymnt_plan         purpose application_type     ...     total_rec_late_fee  \\\n",
       "0          n     credit_card       INDIVIDUAL     ...                    0.0   \n",
       "1          n             car       INDIVIDUAL     ...                    0.0   \n",
       "2          n  small_business       INDIVIDUAL     ...                    0.0   \n",
       "\n",
       "  recoveries  collection_recovery_fee  last_pymnt_d  last_pymnt_amnt  \\\n",
       "0       0.00                     0.00      Jan-2015           171.62   \n",
       "1     117.08                     1.11      Apr-2013           119.66   \n",
       "2       0.00                     0.00      Jun-2014           649.91   \n",
       "\n",
       "   last_credit_pull_d  collections_12_mths_ex_med  acc_now_delinq  \\\n",
       "0            Jan-2016                         0.0             0.0   \n",
       "1            Sep-2013                         0.0             0.0   \n",
       "2            Jan-2016                         0.0             0.0   \n",
       "\n",
       "  tot_coll_amt tot_cur_bal  \n",
       "0          NaN         NaN  \n",
       "1          NaN         NaN  \n",
       "2          NaN         NaN  \n",
       "\n",
       "[3 rows x 50 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_numerical = df.select_dtypes(include ={'float64','int64'}) \n",
    "#df_numerical.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We try to remove features which are highly corelated. Variables which are highly correlated should be removed as they disturb in the process of creating predictive models. Here we remove variables that have correlation of 0.65 and above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create correlation matrix\n",
    "corr_matrix = df.corr().abs()\n",
    "# Select upper triangle of correlation matrix\n",
    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))\n",
    "\n",
    "# Find index of feature columns with correlation greater than 0.95\n",
    "to_drop = [column for column in upper.columns if any(upper[column] > 0.65)]\n",
    "df.drop(columns=to_drop, axis=1, inplace=True )\n",
    "#df.drop(df_numerical[to_drop], axis=1, inplace=True )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887379, 35)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***We managed to remove 15 more columns since these features were highly correlated(as much as 65% or more) to each other***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0_x                    int64\n",
       "term                           object\n",
       "grade                          object\n",
       "sub_grade                      object\n",
       "loan_status                    object\n",
       "pymnt_plan                     object\n",
       "purpose                        object\n",
       "application_type               object\n",
       "issue_d                        object\n",
       "title                          object\n",
       "loan_amnt                       int64\n",
       "int_rate                      float64\n",
       "emp_title                      object\n",
       "emp_length                     object\n",
       "home_ownership                 object\n",
       "zip_code                       object\n",
       "addr_state                     object\n",
       "annual_inc                    float64\n",
       "open_acc                      float64\n",
       "pub_rec                       float64\n",
       "delinq_2yrs                   float64\n",
       "earliest_cr_line               object\n",
       "dti                           float64\n",
       "inq_last_6mths                float64\n",
       "revol_bal                     float64\n",
       "revol_util                    float64\n",
       "total_pymnt                   float64\n",
       "total_rec_late_fee            float64\n",
       "recoveries                    float64\n",
       "last_pymnt_d                   object\n",
       "last_credit_pull_d             object\n",
       "collections_12_mths_ex_med    float64\n",
       "acc_now_delinq                float64\n",
       "tot_coll_amt                  float64\n",
       "tot_cur_bal                   float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking to drop columns which intuitively wont add value to the prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We remove the columns which wont add much information to our predictive modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Unnamed: 0_x','zip_code','emp_title','addr_state','earliest_cr_line','issue_d','purpose','collections_12_mths_ex_med','last_credit_pull_d','last_pymnt_d','earliest_cr_line','sub_grade','title','pymnt_plan','application_type'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dropping the null rows since we have sufficient amount of data and there is no need to fill the null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887379, 21)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the word \"months\" from the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(773968, 21)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['term'] = [x[:2] for x in df['term']]\n",
    "df['term'] = pd.to_numeric(df['term'],errors='coerce')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing the word \"years\" from the column data \"emp_length\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    274533\n",
       "1     114695\n",
       "2      71501\n",
       "3      63580\n",
       "5      49760\n",
       "4      46663\n",
       "7      41178\n",
       "8      41165\n",
       "6      38592\n",
       "9      32301\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['emp_length'] = df['emp_length'].replace({'10+ years':'10'})\n",
    "df['emp_length'] = df['emp_length'].replace({'< 1 year':'1'})\n",
    "df['emp_length'] = df['emp_length'].replace({'1 year':'1'})\n",
    "df['emp_length'] = df['emp_length'].replace({'2 years':'2'})\n",
    "df['emp_length'] = df['emp_length'].replace({'3 years':'3'})\n",
    "df['emp_length'] = df['emp_length'].replace({'4 years':'4'})\n",
    "df['emp_length'] = df['emp_length'].replace({'5 years':'5'})\n",
    "df['emp_length'] = df['emp_length'].replace({'6 years':'6'})\n",
    "df['emp_length'] = df['emp_length'].replace({'7 years':'7'})\n",
    "df['emp_length'] = df['emp_length'].replace({'8 years':'8'})\n",
    "df['emp_length'] = df['emp_length'].replace({'9 years':'9'})\n",
    "df.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the variable emp_length to an integer and replacing the NA values with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    274533\n",
       "1     114695\n",
       "2      71501\n",
       "3      63580\n",
       "5      49760\n",
       "4      46663\n",
       "7      41178\n",
       "8      41165\n",
       "6      38592\n",
       "9      32301\n",
       "Name: emp_length, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['emp_length'] = pd.to_numeric(df['emp_length'],errors='coerce')\n",
    "df.emp_length.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.emp_length=df.emp_length.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This heatmap helps us to validate the fact that our variables that will be used in further analysis are not correlated to each other by > 65%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tW7SwnOeP7QkJwb9tWwCaN23KkaPmTHtCQmjZogX58uXD28uLEj4+hJ05Q1JSEn+dOEGnDuYVLo6OjpYZfFs8b3/+JIUKFQLMg+PU1FQUG9v3Yy+irgCiY2LYf/Cgpb5y60X1B2/UqI6LRgNAtWpVLf18Tp6n7zx85DB16ryJRuOCRqOhTp03OXzkz1zXSV5gr7z6/14GGdDlbAJQVlGUk4qiTFYUpb+iKMcURTmtKMpIAEVRSimKEq4oylzgBOCjKEqSoigTFUX5S1GUXYqivKkoSoiiKFcURWn3osLqXPNz684jy8/Rd5LRueZH55qPW3eS07cnJKNzzYfONT/RCRm233mEzjXnZV4Z3U24jUuRYpafNa5FSUyIz1QmKvIqiXfiqVS1VqbtiQnxmfZ1cS3K3YTb6Dx9uHopnPtJ90hJSebC2RMk3omzOdO9xNu4uGbMVIy7iXcylbl14yp378Tz2uu1su5O5LWLzBr/PXMm/EDb9z/LdnF9J17PrRtX8S5VLtu+T6LXx6LTeVh+1mm1xOr12cp4pJVxcHCgUKFCJCQmEBFxHQWF3t/0pmv3bixZusSyz9x5c/mwRw+cnZxszmLt+QB0Oi362CyZYq1kSkjg4cOHLFm6hK+++DJT+XLlyvLX3ycsZQ6GHrT5De2xWL0enU6XnkurJTZbLj06rS5TrsTERJo0aYqzkxMtW79L23Zt6N69By4uLjYdMycxej0eWY4Rk+UCICY2NvPz6HTE6LM/z4ZNm6jna57ZvB4RgaIofPH113Tu1o1FS5fmLpNHxufTEqPPkkkfi06rzVLGnCk+Ph53d/OFrLu7O7dvp9+kOHU6jI7vd+Grr78xLwGzQWyG1wXAXavNdpGUW4mJiRQqVNgyGNa6P9sxX2Rd7dqzh7YdOtK777eMDgiwKY8+NjZTXWm1OisXlHq0OivtvHETnJydad2mJe3829K9e3dcXFwoU6Ysf5/8m8TEBB49esShQ4dydf69iHMvo51/BNOieQub81ieL8s5la2e9OnnXaa+87r53Pq6z9d069GNpb+Zzy1HB0cGDRxEl65daNGyBVeuXsHf39/2TFn7Ap0OfZZMer0eD4/sfac+NtayHcx1rNfruXHzJkWKFGFoQADvffABw0eO5MHDh7nI9Oz9OcDNmzfp0q0rn37+GSf+PpFpv/983ZvGzZpSoEBBmjZpanMmSz38j+sKYOLkyfT79lsUu2e7lH2R/cFj6zdutPTzOXmevjMuNhZtxr7EXUtchn3HjhvNRx/1YPHihTkO4EXeJAO6nA0ELquqWgP4AygPvAnUAGopitIgrdxrwG+qqr6hqup1oCAQoqpqLeAeMAZoBnQARr2osNbunKkqKNZuGavWy/9vcqQf2GQysWXdElp37GnzvloPb/ya+bNo9mgWzx5Lca9S2NnZvmbfWoeV8Vc1mUxs3/AbLdp/aHV/n1Ll6TNoKl9+P44DuzaSmppieSw5+RGrF/1Ey44f4eSUm2WOVjrRLC+AaqWMgoLRaOTvUycZO2YsixYuZE/IXo4cPcKFCxeIvBFJ40aNc5Ejw/NZq6esmZ5QZt7PP9O9W/dssyllSpeh10cf81Xv3nzd5xsqVKiQ689bWHu7yZ7LWiE4e/YMdvb2bN+6g6ANm1ixcjk3bt6w6Zg55nqO+spo/sKF2Nvb06ZlS8B85/vvkyeZOGYMvy1cyO69ezl81LYZaWv1kPXXstqucvjdK1esyB/btrI+cA3dPviAvt/1symP9Wb+fB2NLXVq23Gyb/tf1BWYP2O4ecN6Zv40ldlz5z1zoKx9tfXf3TxrZW9nx9Yt29mwPoiVK1dw8+YNSpcuTc8Pe9Knzzd8+9++lC9fHnuHXPSdVrY977n3WGpqKvsP7KdJ49wNCJ75vEPBYDRw8tRJxowew8IFC9kbspejR4+Sakhl7dq1rFi+guDtwZQvV57FSxZnO8YTM1nZlrWVPCn3k7YbDAbCz5+nS+fOrF29GmdnZxYuWmR7pufon9zd3NixdRtrVq7i+379GDRkCElJSZYy8+bMZVfwTlJTUzh6LPtql6fmsrLteesqZP9+ihYtSpXKlXOVJfNzWsn1P+oPAI4eO8b6jRvp921fGwNZy2Pbc1kdpKXtOyJgJMuXrWTu3PmcPHWSHTu225ZH5CkyoMud5mn//sY8E1cR8wAP4LqqqoczlE0BdqT9PwzYp6pqatr/Sz3pCRRF+UJRlOOKohy/e25LrgNG30mmeJH0mRqPIvnRJyYTnZBM8SLpM28ervmJSUwh+k4yHhlm5DyKOBGTYcbOFhrXoiTeSZ+Ru5twG41L+hrtlOSHxERF8sv0EUwc1pvIqxf5bf5Ebly/jItrsUz7JibcprCL+UPpdd5pQp+Bk/iy3ygKFChEMa3tS3Q0rsUyzRLeTYi3HNec6RH6W5Esnj2Kn0Z+w41rF1n562RuRmSehXD38MYxX370aZ8JNBoNrF40lWq161G5+ls25wHQarXExERbfo7R6y139x7TabVEp5UxGAwkJSXh4uKCVqujVs1aFHEtgrOTM/V863H+/HlOhZ3mXHg4rdq2ptdnn3A94jqfffG5zZl0uvTnA4iJ0WdbdvSkTGFnwpg+cwYt27RmxcqVLFy8iNVrVgPQoX17Vq9cyaIFC9FoNJTwKfEMdZU+qxCj1+NmJVeMPiZzLo0LO4KDeedt89LGokWLUr1adcLDw206pjWrAgPp1LUrnbp2RevuTnSWY2jd3DKV98j6PDExaDO8zkGbN7P/wAEmjhljebPW6XTUrlmTIkWK4OzsTH1fX86dP//kTGvW0KnLB3Tq8oE5U3TG59Nnej5LpgyzhBnLFCtWzHIXODY2lqJFzeduoUKFLIP1BvXrYTAYuHMn8yy3Ne4ZXhcwz/i4Zamj3HJ1dSUp6R4Gg3m5uT7W9mP+G3WVUe1atYi8ccOmutJmqSu9PgY3d7csZXToYzK3c43GheCdO3i77juWdl4trZ0DtGvnz2+/LWf+z7+g0Wjw8fbJMUumTP/jc++xQ4dCqfhaRYoVK0Zu6LS6bOdU1tdfq0vPnbGf0ul01HyjpqXv9H3Hl/MXzvPPhX8A8PH2QVEUmjVtxunTp3ORSZu5L4iJyd6f63RER2fvO3VarWU7pL8XeOh06LRaqlWtCpiXHj6tH8iW6Tn683z58uHq6gpA5UqV8fb2zvbZ5/z58+PXwC/bctccc72Auvr75ElC9u2jeatW9B84kKPHjjFgyBBy8m/1Bxf++Yfho0Yza9o0S73m5Hn6TnetFn3GviRDH+nubp5hLFiwIM2bteDcubM2HfNV9bKXU8qSy/8bFGC8qqo10v6VU1V1Ydpj97OUTVXTb5mYgGQAVVVNPOXbRVVV/UVV1dqqqtbWVG6T64C7w+Lo8JZ5eUKNUhruPTQSezeFA+fiqVepKBpnBzTODtSrVJQD5+KJvZvC/WQjNUqZ13t3eMuDXadtX9oI4F2yHHH6W9yOi8FgSOXUX6FUqlrb8riTc0GGTVrEgNFzGTB6Lj6ly9PzywF4lyxLpaq1OfVXKIbUVG7HxRCnv4VP2jLGpHuJACTcjuXsqSPUqG3bsgUArxJluR0bzZ14PQaDgbATh6j4esZMBRg4bgH9AmbTL2A23qXK0+3z/niVKMudeL3lQ9cJt2OJ19/Ctag7qqqycdXPuOu88G2U+9emSuUqRERGcvPmTVJTUwneGUzDBn6Zyvg18GPzFvNAftfu3dSpUwdFUXinbl0uXrzIw0fmz8z8deIvypQpw/vvdeaPHTvZtnkrixcsomSJkiz45dfnyuTnlyWTn/VMixcuYvuWrWzfspXu3brxaa9P+KDLBwCWpSe3bt1iz569tHz33VzVVeVKlc25osy5/vhjJw0aNMhUpn79Bmzdas61Z89u6tQ25/Lw0HHs+HFUVeXhw4ecOXOGUiVL2XRMa7q+/z7rVq1i3apVNG7YkE1bt6KqKqfCwihUqFC2CxN3d3cKFCzIqbAwVFVl09atNEqr04OHDrFw6VJmTZuGs7OzZR/funX55+JFHqZ9Jur4iROUfcoH/bt26WL5spLGjRqyacsWc6bTp5+cqUABTp0+bc60ZQuN/BoC0NCvAUGbzfUYtHkLjRqas8bFxVnu+oadOYNJVW26MKlUsRI3bkQSFRVFamoqu3b/Qb16Odfz0yiKQs2atdgbYv4s5vZtW6lf37Zj/ht1FRERYamrc+HhpKam2lZXlSoTGRlBlKVN/kGD+lnbeX22btsKwJ69e6j9uJ3rPDie9nm6x+28ZMlSQPr5Fx0dTUjIXprnYonjizj3HgveGZyrLJZMlSsTGZHeT+38Yyd+WfvO+n5sScu0e096P1X37bpcvJTed544cYLSpUuj1Wq5cvWKZeB9+MhhSpUqlfWpn+j1KlWIiIjgRlqm7cHBNGrYMFOZRn5+BG3eDMDOXbt4Ky1To4YN2R4cTEpKCjdu3iQiIoKqr7+Om5sbHh4eXL12zZzp6FHK5uJLUZ6nP799547lfe/GjRtERETg7eXFgwcPLIMWg8HAwdCDlM5FPb2ouvqub192Bwezc9s2Jk+YwJt16jBx7Ngcs/wb/cGtW7f47w8/MH70aEqlfWuoLZ6n73z7rbc5evQId+/e5e7duxw9eoS333obg8FgWVJrMBgIPXSQMmXK2pxJ5B3yZwtydg94/KnkYGC0oigrVFVNUhTFC0j9N8NM61WFtyq4UqSQIwfHvsOMrVdxSLsdsOpAFCFn4mlYpRh7RtblYYqRAcvMd0cTHxjMf6pggHlQM3vbNRIfmO92D191Ie3PFtiz72w8+87GW3/yJ7C3t6fd+5+yaM5YVJOJ2nUbofP04Y8tq/EqUZbK1eo8cV+dpw/VatZl2pjvsLOzw7/LZ5allSt+ncKD+/ews3eg3fuf4VygUK4yte70Cb/NG4fJZKLm2w3RFvdh97ZAvHzKUDHDgDOr61fOc2BXEPb29iiKQpvOn1KwkIbrl89z6tgBdMVLMHfSjwA0bd2VClVs+3pyBwcHBvQfQO8+X2MymvBv146yZcsy9+d5VK5UmYZ+frT3b8/Q4cNo174dGo0LE9K+dUuj0dCje3d69PwQBYV6vr7Ur1ff5vp4WqaBPw7gP9+kZfJvR7myZZk7bx6VK5szdfBvz5Bhw2jr3w6NiwsTn/BNYBl93/8HEhMTcXBwYNDAAWjSPiCem1w//tCfvn37YDQZade2HWXLlOXn+T9TqVIl/Br44d/On4ARw+nQqT0ajYaxY8YB0Pm99xk1eiRdunYBVaVtm7aUL2+eSLd2zNxoUK8eB0JDaenvj7OTE6NHjLA81qlrV9atWgXAsEGDGDpiBI8ePaK+ry/10z5DMXbiRFJSU/m8t/l7lqpVrUrA4MG4aDT07NGDD3r2RFEU6vv64lfftte3Qb16HDh4kJbtrGTq8gHr0mZNhw0enOFPKbxD/XrmTJ/16sX3AwawfuNGihf34KdJkwDzRdWa39dib2+Pk1N+Jo8fb9PyHwcHB/p99wPf9euL0WiiTZu2lClThl9/nU/FipWoX78B58LPMWjQj9y7d4+DoQdYuOBXVqww5/zPf77gesR1Hjx4iH/7NgwaNJS333qb3v/5huEBQ/nll/lUqFCBtm1y/1HkF1VXf+zew6YtW3BwcMApf36mTJxgc1398MOP9P22LyaTkbZt2lGmTFnm//IzlSpWokEDP9q19WfEyAA6vdcBjUbDmNHmi9b33uvM6DGj6NqtC6oKbTK084GDBljOv/4//Jir8+9FnXuPHj3i6NGjDB6U8yyK1Uw//sg3fb/BaDTi386fsmXLMi+t7/Tz88Pf359hAcPw7+CPi8aFcWPNmTQaDT269aBn2rnlm6Hv/OLzL/jsi89wcHCguEdxRgSMyFWmwQMG8GXv3hhNJjr4+1OubFlmz51LlcqVadSwIR3bt2fQ0KG0bNcOF42GyRMmAFCubFlaNG9Ou06dcLC3Z8jA9D8DMHjAAAYMHkyqwYCPlxejR47MVaZn7c9PnDjB3J/n4WBvj52dPUMHD8bFxYX4+Hi+7fcdqSkpGE0m3qxTh/c6vWdzphdZV8/rRfUH8375lcSERMakfQuyvb3LetIzAAAgAElEQVQ9gStX2FRPz9p3ajQu9Pr4Ez79rBcAvXp9ikbjwsOHD/muX18MBiMmo5HaderQrp3tnxUVeYciH47MmaIoK4FqwHbgBvD46wSTgB6AEdiiqurrGfZJUlW1UNr/RwBJqqpOyfrY05TrveeVenEmdXy+ZVMvQqrB9LIjWNXWt3zOhf5lL+rzks/jVXz9nB1ewYqyewUzAXcf/Kv3s2yiKZC7v033b7ifbMy50L/M7hXsEOxe0Xae3+HVW8xkML1SlwcAOLySr9+rV0+vYr8JUMzN9VV8AbOp8e3uV+9FzeLkjCb/el3KDJ0NVFXN+tdPZ1gp9nrGHzIO2FRVHfGkx4QQQgghhBDiWb16t52EEEIIIYQQQthEZuiEEEIIIYQQr7xXcmXvK0Bm6IQQQgghhBAij5IBnRBCCCGEEELkUTKgE0IIIYQQQog8SgZ0QgghhBBCCJFHyYBOCCGEEEIIIfIoGdAJIYQQQgghRB4lf7ZACCGEEEII8cqzlz9bYJXM0AkhhBBCCCFEHiUDOiGEEEIIIYTIo2TJpRBCCCGEEOKVZydLLq2SGTohhBBCCCGEyKNkQCeEEEIIIYQQeZQM6IQQQgghhBAij5IBnRBCCCGEEELkUTKgE0IIIYQQQog8Sr7l8hU2qaPby46QyY/r4152hGwODq/+siNYlWIwvuwI2Zz859bLjpCNj1bzsiNk41Xo1bvPZV+w0MuOYFXhAvledoTsTKaXnSCbE+ejXnaEbKqV073sCNlsP3z1ZUew6v3GFV92hGyMJvVlR8jGQb5+0CZhl2NedgSrGrq5vuwINpE/LG7dq3flIoQQQgghhBDCJjKgE0IIIYQQQog8SgZ0QgghhBBCCJFHyYBOCCGEEEIIIfIoGdAJIYQQQgghRB4l33IphBBCCCGEeOXJl6laJzN0QgghhBBCCJFHyYBOCCGEEEIIIfIoGdAJIYQQQgghRB4ln6ETQgghhBBCvPLkM3TWyQydEEIIIYQQQuRRMqATQgghhBBCiDxKBnRCCCGEEEIIkUfJgE4IIYQQQggh8igZ0AkhhBBCCCFEHiXfcimEEEIIIYR45cm3XFonM3RCCCGEEEIIkUfJDF0edOHs32xZuxiTyUQd3yY0bN7BarmwE3+ycuFPfP3jBLxLlgUgJHgDxw7txs7OjradP6FC5RoAhO7dyrHQ3aiqSh3fptRr3NrmPON7VKRxVTfi76XQasxRq2WGdS5PwyrFeJhqYsBv5zgbmQRAh7c8+LplKQDmbL/GhiPRAFTxKcyknpVwcrQj5Gw8o3+/aHOex44cOcysWdMxmYy0bt2W7t17Znr81Km/mTVrBleuXGb48JE0bNgYgOjoWwwbNhiTyYTBYKBjx/fw9+/Ao0ePCAgYQlTUTezs7HnnHV++/LJ3rjL9+echfpo2FZPJRLt2/nzU8+NMj6ekpDByZADnL5zHRePCmDHj8PT0JDU1lfETxnH+fDiKYke/776nVq1aAKSmpjJ5yiROnDiBnZ3CV1/2pnHjxjZnOnP6LwKXL8BkMlLPrznvtn0v0+P79mwnZNc27OzsyJ/fiR6ffI2nVwnOnfmbDYG/YTAYcHBwoNMHH1OxcnUApo4bTGLCHRzz5QPg2x9HotG45qqujh8/wvx5szGZjLR4tzXvd+me6fGwsFP88vNsrl69zMBBw6lXv6HlsYULfubY0cOoqok33qjNl//pg6Kk39YbGTCY6Ogo5s1fkqtMqqoyccZMDv55GCen/IwePIhKr72Wrdy58xcYNm4cyckp1Kv7NgO+7YuiKJy/eJExk6eSkpKCvb09g7//jqqVK3PsxN/8d9BgvIoXB6CxXwO+6vWxzZnGT5rMgdCDODk5MXbkSCpXqpSt3Nlz5xgaMIJHyY+o71uPQT/2R1EUEhMT+X7AQKKiovD09GTqpIm4aDTcu3ePgUOHcutWNEajkY97fkgHf3+bMh0MDWXi5MkYTSY6tm/PZ598kunxlJQUBg8bxrnwcFxdXJg8cSJenp6EnTnDyNGjLb9X76++oklaWx42YgT79++naNGibFi71qYc2epp8mQOhIaa62nECOv1FB7O0IAAHiUnU9/Xl0H9zfU0Zfp09u3fj4OjIz7e3owZMQJN4cJs2baNxcuWWfb/5+JFfl+xgopW2oU1Z0+fIHDlr6gmE74NmtGiTebzb/+e7ezbsx07xY78Tk50/7g3xb1KcO3KP6xYPNf8u6HSpv0H1KhV17KfyWRk/IjvcS1SjK+/G5arujp8+E+mz5iGyWSibZt2fPhh5r7z5Mm/mTFzGpcvX2bkiNE0apTe3/Tr91/OnjtDtWrVmTxpqmX78ePHmDN3FiaTSgFnZ4YMGYa3t4/NmS6fP0lw0G+oJhM13mqEb2PrbTH81BHWLZvOJ9+OwdPH/L4XE3WdbesWkvzoAYpix6ffjsFoNPLb3JGW/e4mxFO1Vj2a+39kc6aDoaFMnDIZk9FExw7t+bRX9nY+JK2du7i6MHlCejsfNSa9nf/ny/R2vmz5ctZv3ACKQvly5Rg9YiT58+e3OdOhQ4eYMnUKRpOR9v7t6fVxr2yZhgcMJ/x8OC4uLkwYNwFPT0+2bd/Gsgzt+OKli6xYtoLXXnuN8PBwAkYGkJycjK+vL/2/75+pP7W1riak9QmdntAnDMrQJ0xJ6xMAfl24kPVBQdjb2THoxx/xfecdAIZm6BM2Pmuf8AL6zsfCzp6le8+PmDJhAs2bNbUp04t4P54xOYC7CXcwmoyUr1CFrh99iZ2dfa7rS7zaZIYujzGZjGwKXEivr4fw3bBpnDoeSsytyGzlkh895FDIdnxKlbdsi7kVyam/Qvlu6DR6fT2EoDXmTiM6KoJjobvp/eN4+g6ewvkzfxGnv2VzpvWHo/lk9sknPu5XpRiltAVoMuIwQ1ecZ+QH5gsdlwIO9Gldmk6TjtNx4nH6tC6Nxtl8j2FU19cYuvI8TUYcppS2AA0qF7U5D4DRaGT69ClMmjSVpUtXsnv3Lq5du5qpjFbrwaBBQ2nSpFmm7cWKuTFnznwWLlzKvHm/snLlMuLiYgHo0qUby5atZsGCJYSFhXH48J+5yjR5yiSmT5vB6lWB7Ny5kytXr2Qqs2lTEIU1Gtat3cAHXbsxZ84sADYGbQBg5YrVzJo5mxkzp2MymQBYvGQRRYsUZe3v61i9KpCaNWvanMlkMrLqt/n0+SGAERPmcOzwfqJuRmQq82ZdPwLGzWLYmBm0aN2R31cuBKBQIQ1ffzeUgHGz+PiL/7J4/rRM+33yVT+GjZnBsDEzcj2YMxqNzJ0zg1FjJvLzL0vZF7KHiOvXMpXRumvp9/1AGjbK/EZ57twZzp07w5x5C5n782L++ec8YafT22fowf04OTvnKs9jBw8fJiLyBptXr2R4//6MmfKT1XJjpk5l+I/92bx6JRGRNwg9fASAaXPn8VWvjwlcsojen33C9Lk/W/Z5o3o1ApcsInDJIpsHcwAHDoYSERHBtqAgRgwdyuhx462WGz1uPAFDh7AtKIiIiAgOhh4CYMHixbz95pts2xTE22++ycLFiwFYFRhI2TJlWB+4hsW//srkn6aRmpqaYx6j0cjYCROYO3s2QevWsX3HDi5fvpypzPqNG9EULsy2TZv4sHt3ps2YAUC5smVZvWIFa9es4ec5cxg1ZgwGgwEA/7ZtmTdnjs31kq2eQkOJiIxk28aN5noa/4R6Gj+egKFD2bZxIxGRkRw8ZK6num+9xYbAQDasWUOpkiVZkFZPbVq1Yt2qVaxbtYrxo0bh5elp82DOZDKyetl8vukXwPBxszl25AC3spx/der6MWzMTIaMnk6zVh1Yu2oRAJ5eJRk4YipDRk+nz/cBrFgyD6PRaNlvz84teHjaPmB6zGg0MvWnKUydMo0Vy1exa9dOrl7N3HfqdDqGDB5Gs6bNs+3frVt3hg0NyLZ9ypRJBAwfydIly2jWrDlLli62OZPJZGL7hsV0/WwAX/Wfwtm/DxEbfSNbueRHDzl6cAdeJcql72s0ErRqDq06fcpX/afw4X+GYWfvQH4nZz7vN8Hyz6WIO6+9/qbNmYxGI+MmTmDerNlsfNzOr1hp55rCbE1r59MztPNVy1fw++o1zJs9h1Fjze08Rq9nxepVrFq+gg2/r8VkMrEjODhXmSZMmsDMGTNZG7iW4J3BXLmS+T1mY9BGNBoNQRuC6N6tOzNnzQSgVctWrFq5ilUrVzFq1Cg8i3vyWlo7Hj9hPEMHD2Xj+o1ERkRyKO2cyE2uMRMmMG/2bDatW8e2p/QJ29Pq6qe0urp8+TLbg4MJWruWn+fMYfT48ZZ23r5tW35+nj7hBfWdj3/naTNm4Fu3rtVjWvOi3o+/+GYAw8bOJGDcbO7dS+Svo6E2ZxJ5R54b0CmKkvSyMzwPRVHaK4pS+Vn3j7x2iWLuHhR10+Hg4Ej1Wr6Enz6erdzOLatp0MwfB0dHy7bw08epXssXB0dHirrpKObuQeS1S8RG38SndHny5cuPvb09pctX5uwp6zNt1hy7lEDCfcMTH29azc0y83by2l00BRxw1+SjfuVihIbfJvGBgbsPDYSG36ZBlWK4a/JRyMmev6/eBWDDkWiaVXe3OQ9AePg5vLy88fT0wtHRkcaNm3Lw4IFMZYoXL07ZsuWws8t8Gjg6OpIvbWYpNTUVk0kFwMnJiZo1a1nKVKhQgdhYvc2Zzp07i7e3D15e3jg6OtKsWTP279+Xqcz+A/tp3co8O9q4UWOOHT+GqqpcvXqVOrXrAFC0aFEKFy5EeHg4AJs3b+Kjjz4GwM7ODldX2wdPVy9fRKstjrvWAwcHR2q/XZ9TJ45kKuPsXMDy/+TkR5Y7syVKlcW1SDEAPL1KkJqSatMFvy3+uXAez+JeFC/uiaOjIw38GvPnn5nfhHQexSldpix2We4UKyikpqRgMBhITU3FYDTgWsR8Q+DhwwdsWB9I164fPlOuvQcO0vbdFiiKQrXXq3AvKYnYuLhMZWLj4rh//wHVX38dRVFo+24L9hwwtz1FUUh6cB+ApKT7uLu5PVOOTJn2hdCuTRsURaF6tWrcu3eP2NjYzJliY7l//z41qldHURTatWnDnpC95v1D9uHftg0A/m3bsGdviDkrCvfvP0BVVR48fICLiwZ7+5zv6oadOUMJHx98vM3tvGWLFuwNCcmcOSSEdm3bAtCsaVOOHD2Kqqo4Ozvj4GC+qZOckgIZXtvatWrh4uLyTHUEsHffPtq1bm2up6pVza+dtXpKSqJGtWrmemrdmj1p2X3r1rVkq/b668TExGR7jm3BwbRs0cLmTNeuXMRd55F+/r1Vn1N/Z+57M55/KcnJljrJlz+/5fVITU3NWFXcuR3HmVPH8W2Q+WaVLcLDz+Ht7Y2Xl7nvbNK0GQcO7s9UpnhxT8qVK49i5cMstWvXoUCBAtm2oyjcv5/W9u/fx83N9j49KuISRYt5UKSYDnsHB6rUqMs/Z7O/7+0LDuSdRm2xd0h/37vyz2m0xUug8ywJQIGChbP1+bdjb3E/KZESZSranOnMmTOU8PbBO62dv2ulnYeEhNCuTVo7b9KUI8est/OMs11Go5Hk5GQMBgOPHj7C3d32ejp79iw+PumZmjdrTsi+zJn27d9Hm9bm871J4yYcTcuUUXBwMC3S2nFsXCxJ95OolnZOtG7dOtsxc2KtT9iTpa72hITgn9YnNM/QJ+wJCaFlixbky5cPby8vSvj4EHbmDPC/6BNeTN8JsHL1apo1aULRorbfjH5R78eP9zEZjWk3yORDaP8X5bkB3f8B7YFnHtDdTbiNS9pJC6BxLUpiQnymMlGRV0m8E0+lqrUybU9MiM+0r4trUe4m3Ebn6cPVS+HcT7pHSkoyF86eIPFO5ovU56Fzzc+tO48sP0ffSUbnmh+daz5u3UlO356QjM41HzrX/EQnZNh+5xE6V9uXnADExcWi1eosP7u7u1tm2Wyh18fQq9eHdO7cnm7demS7+Lh37x6HDoVSq1Zt248ZG4suQyatVmflzUOPVmcu4+DgQKFChUhMTKR8+fLsP7Afg8FAVNRNzp8/T0xMDPfu3QNg/vyf6dmzB4MGDyQ+PnN7eJqEO/EUKZY+qChS1I2EO9n337trK0N++IL1a5bSpccX2R4/cewQPiXL4JjhBsLSBTMZPfRbtm5cne2CISfx8bG4ZbiQcXNzJz7ettevUuUqVKtegx7dOtKjWydq1XqTEiXMF3PLfltEx05dcrWEKSN9XBw6rdbys07rjj7LgE4fF4cuQ/aMZX7s24dpc+bRvGMnps6ZS9+v0uvy9JmzdP6oF72/78+lK5lnRJ4mRq/HwyO9Xel0WmL0sVnKxGbOrdMSozffjIiPj7dcNLq7u3P79m0Aun3QhStXr9KoeQs6dH6fgf37Z7sQtkav1+Ohy5hHR0yWdq7X6/Hw8ADS23lCQoK5HsLCaN+pEx07d2b4kCGWC9/nFZM1l1abLVdMbCy6rNn12W/abNi0iXq+vtm279i5k1a5GNAl3ImnSNEM51+RYlbPv5BdWxnW/0s2BC6hS/fPLduvXr7AqMHfMGZoX7p99B/LAO/3lQvo0OWjbDc7bBEbG4s2Q1vRumuz9VPPYuDAwfzQvx/tO7QlOHg7H/bomfNOae4l3kHjmv7eVdi1GPcS72QqE33zKncTblO+cuYVCvGxtwCFlb+MZ8G0QRzauynb8c/8fYgqNermahlhTKweXcbzTqtDn/W8i9Wje0o77/BeJzq935lhg83tXKfV8tGHPWneqiVNmjejUOFCvJOLGR59rD5b+832HqNPb+OWTIkJmcrs/GMnLZq3SC+vzfJ75uJGJljvE/Q29gn62FjLdvPza9FbOSefxYvqO2P0enbv2cv772VeLpmTF/l+PGNSAD988yFOTs7UevOdXOUSeUOeHdApZpMVRTmjKEqYoihd0rYXUhRlt6IoJ9K2+6dtL6UoSriiKL8qinJWUZSdiqI8cd2VoiifK4pyTFGUU4qirFMUpUDa9iWKosxTFGWvoihXFEXxUxRlUdqxl2TYP0lRlLFp+x9WFEWnKMo7QDtgsqIoJxVFKfs/qgvL/00mE1vWLaF1R9veLBVFQevhjV8zfxbNHs3i2WMp7lXqf7q+2tp7pKqaZwCyP2C9fG5ZHz/YfmCtVsfixctYuTKQHTu2WTpqAIPBwKhRAXTq1BlPT6/nCpW1DqwNfBQF2rZph1ar5eNePflp2k9UrVoNewd7jEYjer2eatWq89tvy6n6elVmzppheyasVVT2emrUtDVjp/xCx/c/YlvQmkyPRd2IYH3gUnr0Sv884SdffU/AuFn0HzKei/+c43Do3lxksv762XqxFRV1g8iICH5b/jvLVvzOqZMnCAs7xeXLF4mKusk7vvVzlSWnYDa9hmllAjcG0b/vN+xcv47+fb5hxPiJAFR6rQI71gby+9LFdH2vI98NHvw8kbKdQ6qV1zmn+gw99CcVX6vA3p3BrFu9inETJpKUlPMCCVtalPV2bi5VrWpVNq5bx+rly1mwaBHJycnZyj6Lpz1nbsrMX7gQe3t72rRsmWn76bAwnJ2cKF+uHLay+tpZOf8aNm3N6Mnzad/5I7ZtDrRsL132NYaPm82AgCns2LKO1JQUwk4eo7DGlZKlbM+ROZP1Puh5rVmziimTf2Ljhs20atWGmbOm257JWqvKkEk1mdgZtIymbXtkK2YymYi8eoH23b/mo69HcOHMca5ePJOpzLmTf1LljVxe5Npw3lntLzK08w1r17Fq2XIWLja387t377I3JITtW7awK3gnDx8+ZMvWrbZHetY2nqEyw86E4eTkRLm0dpxTeZtyWdlma59gy+/0rF5U3zlx8hS++7avTSsasj6blUTZtuT2/RjMn2OfNHMpBkMq58+dzmWuV4u98ur/exny7IAO6AjUAKoDTTEPkooDj4AOqqrWBBoBU5X0s688MEdV1SpAAtDpKcdfr6pqHVVVqwPhwKcZHisCNAa+AzYD04AqQFVFUWqklSkIHE7bfz/wuaqqh4BNQH9VVWuoqpp5ETmgKMoXiqIcVxTl+M6t2T/kq3EtSmKGOzZ3E26jcUmf0k9JfkhMVCS/TB/BxGG9ibx6kd/mT+TG9cu4uBbLtG9iwm0KuxQBoM47TegzcBJf9htFgQKFKKYt/pSqyZ3oO8kUL+Jk+dmjSH70iclEJyRTvEj6TImHa35iElOIvpOMR4YZOY8iTsQk5O6izt3dHb0+fUlUbGwsbs+wvM3NzZ1SpUpzOsNnsKZMmYi3tzedO3fJ1bG0Wi0xGTLp9TG4ubtlKaNDn7aUy2AwkJSUhEbjgoODA9/9tx/Ll61kyuSpJCXdw8fHBxcXF5ycnGjYsCEATZo04cKF8zZnci3ixp349BmmO7fjLMsTran9dn1OZlgCcud2HPNmjKPXF//FXZfeZooUNd9Nd3IuwJt1/bh25R+bM4G53uMy3MGNi4ulaFHbXr9DoQd5rWJlnJ0L4OxcgNp13uL8+XOcDz/HpYv/8HHPLvzwQx9u3rzBgP7f5ni81evW8/7Hn/D+x5/g7uaWacYmRh+Lu1uxTOV17u6ZZn4yltm8fQdN/PwAaN64EWfSls0WKljQskytft26GAxG7iRkvmue0ao1a+jU5QM6dfkArbs70dHp7SomRo82yzItD602c+4MZYoVK2a5ix8bG2tZIrRh0yaaNm6MoiiUKFECLy9Prl67lmN96bRaomMy5onJlken0xEdbV6G/bidZ106VaZMGZydnbl06VKOz/kkqwID6dS1K526djXXU8Zcej3aLH2Ch1abaSll1uxBmzez/8ABJo4Zk+2ibvvOnbR8991c5StStBh3bmc4/+7E4/K08++t7EuwAIp7+pA/f36ibl7n8sVwTv99lCHff87CeVO4EH6axfOtf9bTGm2WGRB9rD5XyyOtuXPnDpcuXaJKldcBaNK4KWfOhNm8v8alKHczrES5lxBPYU0Ry8/JyY+IjY5k2bxRzBrbh5sRlwhcPIWoyMtoXIpSsmwlChTU4JgvP+Uq1iD6RvoMeEzUdUwmI8W9y+Tqd9JptcRkPO/0MdmWR+q0OmJsbeeXL3H4yBG8vTwpWqSoeblr48acPH0qF5l02dpv1vc9rS69jVvLtHPnTt5t8W7m8vqn/54558reJ2Srqyf0CTqt1rLd/Pz6XD9/Rv9G33n23Dn6DxxE81at2blrF2PGj2f33pxvar6o9+PHHPPlo/obb1rtQ0Tel5cHdPWAVaqqGlVVjQH2AXUw384YpyjKaWAX4AU8nlO/qqrq4yvzv4BSTzn+64qiHFAUJQzojnnA9thm1XzbKAyIUVU1TFVVE3A2wzFTgC02PpeFqqq/qKpaW1XV2s1bZ5+u9y5Zjjj9LW7HxWAwpHLqr1AqVU1f9ufkXJBhkxYxYPRcBoyei0/p8vT8cgDeJctSqWptTv0ViiE1ldtxMcTpb+GTdhc36V4iAAm3Yzl76gg1amdfTvSsdofF0eEt85KJGqU03HtoJPZuCgfOxVOvUlE0zg5onB2oV6koB87FE3s3hfvJRmqUMn9bVIe3PNh1OndLQCtWrMSNGze4dSuK1NRU9uzZha9vPZv21ev1llmBe/fucuZMGD4+5iV7CxbM5/79+/Tp899c5QGoVKkykZERREXdJDU1lT/++IMG9RtkKlO/fn22bjPfkd2zdw+1a9dBURQePXrEw4cPAThy5Aj29g6UKV0GRVGoV68+J078BcCxY8coXdr2C5NSZcqjj4kiLjYagyGV44cPUP2NtzKViYmOsvw/7NRxtDrzN489uJ/E7Kmj6PB+T8pVSF9FbDQaSbpn/vyj0WAg7OQxPL1L2pwJoMJrrxEVdYPo6Fukpqayf98e3n7btjvo7lotZ8JOYjQaMBgMhIWdooRPSVq38Wf5ynUs+W0NU6bMwsvLm4mTc57N/KBTR8uXlTSqX5/NO4JRVZXTZ85SqFDBbJ+Dc3dzo2CBApw+cxZVVdm8I5hG9eulPVaM43+bu6Cjf52ghLc3AHHx8ZY70WHnzmEymXB9ymdDunbpwro1q1m3ZjWNGzVk05YtqKrKqdOnKVSoULYLHnd3dwoUKMCp06dRVZVNW7bQyK8hAA39GhC02dxVBW3eQqOG5gFncQ8PDh89asl37dp1vL1ynpF+vUoVrkdEcOOmuZ1vDw623HB4rKGfH5s2bwbgj127eLOOuZ3fuHnT8iUoUVFRXLt2Dc+0b7p7Fl3ff9/yhSWNGzZk09at5noKC3tyPRUsyKmwMHM9bd1Ko7QB+MFDh1i4dCmzpk3DOcuX6phMJnbu2kXL5tm/JORpSpYujz7mFnGx5v78+JEDVHsj8xdz6DOcf2dOHUebdqEWFxtj+XKI+Dg9MdE3Keamo33nnoyftoixU3/l0//8wGuVqtHry342Z6pYsRI3IiOJijL3nbt3/UG955nVBgoXLsz9+0lERJi/4OHY8aOULFnK5v09fcpyOy6aO/F6jAYDZ0/+SYUq6R8pcHIuwPejfqXPkFn0GTILrxLleL/XD3j6lKXMa9XQ34ogNSUZk9HI9SvhuOnS2/HZvw/lfnYOqFKlCtcj09v5juBgGqadU4819PNj05a0dr47h3Ze3BMPDw9Oh4Xx8OFDVFXlyNGjlCld2uZMlStXJjIikptpmXb+sRO/Bn6ZyvjV92PLVvP5vnvPbuqkZQJzO961exfNm6W3Y3c3dwoWKEhY2jmxdetW/PwyHzMnr1epQkSWPqFRlj6hkZ8fQWl9ws5du3grLVejhg3ZHhxMSkoKN27eJCIigqqvv56r58/o3+g7g7duYee2rezctpXmTZsydNAgmjRqlGO2F/F+/OjRQxITzCuMjEYjYaf+wqO4dy5rTeQFefnPFjxpUrM74A7UUlU1VVGUa8Dj6aGM0zxG4GlfdQdyBeIAACAASURBVLcEaK+q6ilFUT4GGmZ47PFxTFmOaSK9TlPV9LUCRv5HdW1vb0+79z9l0ZyxqCYTtes2Qufpwx9bVuNVoiyVq9V54r46Tx+q1azLtDHfYWdnh3+XzyxLK1f8OoUH9+9hZ+9Au/c/w7lAIZszTetVhbcquFKkkCMHx77DjK1XcUibc151IIqQM/E0rFKMPSPr8jDFyIBl5lmJxAcG858qGGAekM7edo3EB+Y3ueGrLqT92QJ79p2NZ99Z2z8XBuY1+P/9bz9++OE7TCYjrVq1oXTpMixc+CsVK1bE17c+4eHnGDZsUNrn4Q6yePFCli5dwfXr15g7d5ZluUeXLl0pW7Yser2eZcuWUqJEST7/3PxV0B06dKJNm3Y2Z/rhhx/p+21fTCYjbdu0o0yZssz/5WcqVaxEgwZ+tGvrz4iRAXR6rwMajYYxo8cCcPv2bb79bx/sFDvc3d3/H3v3HRXF9TZw/DsuKjZAhUUF7BrFmtgrWGIXReyaxJjmz8QUYwOx995716hoxN6wYm+JvcTEiqiwWEGj4O7O+8euCwurLkmM8Ob5nMM5sHNn5uHOzL1z5965y+BBidNtf/N1DwYPGcSkSRNxyelic4a5V9FoNLT/+CumjB2MUTVSo3Z98nnmZ2PocgoUKkq5D6oQvmsLly6cRqNxIGu27Hz6pakxu3fXFnTRd9myYRVbzMM+vuszhMyZHZkybhAGgx6j0UjJUuWp5Zu6G12NxoH/df+O4P69MRqNNGjQmAIFC7Fs6UKKFXuPqtVq8Pvl3xg2LJgncU84duwIPy1bzOy5i6lZ04ezp0/RvVtXUBQqVKhMFTsbg29Sq1pVDh45QrN2HXB0zMzQoEDLsrZdurJ6sWkGwv69ejJgxCjTNN9Vq1CzalUABvbpw9gpUzEYDGTKlImBfXoDsDM8nNXrNuCg0ZA5c2bGDBlk97Ci2jVrcuDgQRr7tSCLoyPDBg+2LAto157QVSEADAgKSjIdf3Vq1TQ9tPn800/5sW9f1q5fT968eZg4diwA3b74gv6DBuHfpi2qqvLDd9+SM2fOFPtPzsHBgaC+fenWvTsGoxH/Fi0oWqQI02fOpJS3N3V8fWnVsiWBwcE08fPD2cmJsaNHA3Dq1CkWLFqEg4MDGTJkoH9QkGWfffr148Svv/Lo0SPqNWzI19260crf9le2vDKfDh2icQsb+dShA6ErV5ryKTCQ4MGDef78ObVq1KCW+V25EWPGkPDiBV90Nw1lKlumDIPMQ2N/OXkSd60WL8/U3ShpNBrad/6SaeMHYzQaqV6rHvk88rNp7XLyFypKuferEL57C79dOGO+/rLxyRem6+/q7xcJ2xKKRuOAkkGh/UfdyJ7D6Q17fDMHBwd+6NmLnj2/w2A00qxpMwoXLsy8+XMpUaIEtWrW5tKliwQG9SUuLo5Dhw4yf8E8lv9kyr//df+KiIib/PnnM1r6NyewX3+qVKlK3z6B9A8OJIOikCNHDgIDg+2OKYNGQyP/LqycNwqjaqR8JV/c8ngRvv1n8nkVonipV7/LnCVrdqrUbsKCKf1RUChasrzVe3YXzxyl/ed9/lI+BfXty/++Np3nLf1M5/mMWTPx9vamjo8v/i1bEjQgmKZ+fjg7OzF2VOJ5vnCx6TxXMmSgf6DpPM+ZMyf169WnXaeOaDQaSr5XgtatXjeQKGVMffr04Ztvv8FgMNDCrwVFihRh1uxZeJf0xsfHhxYtWjBg0ABa+LfA2cmZkSNGWtY/eeokWq0Wz2TncWC/QAYPMU3bX6N6DWpUT90D35d59ZUdZUJjc5kwzlwmFC1ShIYNGuAXEICDRkP/fv0sQxl7JysTunfrRkBqy4S3UHb+VW+jPkZVmTFpOHr9C4xGI++VLEvtuo1fF4ZIp5TUTlbwrimK8kRV1eyKorQCvgKaALmAX4AqQDugqKqqPRRFqQPsAV4+4tqsqmpp83Z6AdlVVR38iv3cwzR5yUNgK3BbVdUu5vfkNququkZRlILJtpl02RNVVbObP28NNDOvPw04qarqG+dsXrvrbJo6OH3W/nMTpfxTDg4s965DsMkxc8Y3J/qXnf7d/q+i+Ld4af/+Deg/zSN72hu4oMlm/wOWf5OaBmdLU8xf55GWHDiXcor9d61sUfc3J/qXbTtq/2RA/6a2de2f/fLf8sKQ9s7zzA5pr+y0/V7au3UoDZYHAL5V3kt7BboNH/bbnfYOajI7R9f71/MyLV599loHnAXOYGq09VFVNQpYDlRUFOUXTL119r9QZG0AcAzY+Te2YUsI0FtRlFP/1KQoQgghhBBCiP+mdDfk8mWvl3k4Y2/zT9Ll94BXzfNbOkm68W/Yzyxglo3PuyT5/UaybSZdlj3J72uANebfD/E3vrZACCGEEEIIIV5Kzz10QgghhBBCCPGflu566P5piqLMAJK/4TvFnnfchBBCCCGEEOJd+s836FRV/fpdxyCEEEIIIYQQf8V/vkEnhBBCCCGESPsypIu5OP998g6dEEIIIYQQQqRT0qATQgghhBBCiHRKGnRCCCGEEEIIkU5Jg04IIYQQQggh0ilp0AkhhBBCCCFEOiWzXAohhBBCCCHSPJnl0jbpoRNCCCGEEEKIdEoadEIIIYQQQgiRTkmDTgghhBBCCCHSKXmHTgghhBBCCJHmZZCuKJskW4QQQgghhBAinZIGnRBCCCGEEEKkUzLkMg17oTe+6xCsHBxY7l2HkELNoWfedQg2XRpf9V2HkELVIrnedQgpaLJkedchpKBX096cyGmsKLCIefjkXYeQwsO4Z+86hBRqlc73rkNI4ULEw3cdQgo1yqa9fAIwGNV3HUIK8Qn6dx1CCpkcMr3rEFJQSHvledXCLu86BPH/kPTQCSGEEEIIIUQ6JQ06IYQQQgghhEinZMilEEIIIYQQIs3LkPZG0aYJ0kMnhBBCCCGEEOmUNOiEEEIIIYQQIp2SBp0QQgghhBBCpFPSoBNCCCGEEEKIdEoadEIIIYQQQgiRTsksl0IIIYQQQog0T2a5tE166IQQQgghhBAinZIGnRBCCCGEEEKkUzLkUgghhBBCCJHmyZBL26SHTgghhBBCCCHSKWnQCSGEEEIIIUQ6JQ06IYQQQgghhEinpEEnhBBCCCGEEOmUNOiEEEIIIYQQIp2SWS7ToT8unWbr2sWoRiMfVK1L7Q9b2kx34fRRVi2axFc/jsQjfxEib15h46q5AKiqSp1GbfAuV5nHD+8R+tMMnsQ9QlEyULFaPar5NklVTMeOHWXatMkYjQaaNm1Op04fWy0/c+YU06ZN4dq1qwwcOARf37oAREXdZcCAIIxGI3q9nlatWtOihT/Pnz9n0KD+3LlzmwwZNFSvXoOvvuqeqphGdS5B3TKu3I9LoMnw4zbTDGhTDN9SuXn2wkjfpRe5cOsJAP5V8vB144IAzNh2g3XHogAo5ZWDsR+XxDFjBsIv3GfYz3+kKqaDhw4xetw4DEYjAS1b8nnXrlbLExISCBwwgIuXLuHi7Mz4MWPwyJcPgHkLFrB2wwY0GTIQ2KcPNapXt6xnMBho16kTWq2WmVOnpiomMJ0PYyZP4eCRIzg6OjKsfxAl33svRbqLv/3GgBEjiY+Pp2a1avT9/jsUReG33/9g+LhxJCQkoNFoCOr1I2W8vdl74AAz5s0ng6Kg0Wjo/d23fFCunN0xjRo3jgMHD+Ho6MiIIYPxLlkyRboLFy8RPHgQz5/HU6tmDQJ790ZRFMJ27mTmnLlcu36dlcuWUtrbG4AXL14wZPgILly6iKJkoF/vXlSuWNGumA4dPsTY8eMxGgz4t/Sn66efWi1PSEggeOAALl26hLOzC2NGj8YjXz5u37lDq9YBFChQAICyZcoQHNTflKeXLjJw0GDi459Ts0ZN+pjjt9dfjemlu3fv0qpNa7p9+RWffGy6bpevWMHa9etQVZVW/v507tjJ7ngAThw/xsyZUzAajTRu3Iz2HTpbLT979jSzZk7l2rVr9A8eRO3adSzLGjbwoWChwgBote4MGzYagFOnfmXunBno9XqKFXuPH3v1RaOxvwo7ffIEixbMwmg0Uq9+I1oGtLdavnnDGnbv2o5Go8HJyZn/ffMjblp3y/I//3zKDz0+p3KVGnz25TdW644ZORBd1F0mTJ1ndzwvqarKqPETOHDIfJ4PHoR3iRIp0l24dIngwUN4Hh9PrRo1COz1o9V5smjZMiZMmcqBXTvJ6eICwPFffmXMxAno9XpyuriweO5cu2I6ffIEixea8qpu/Ua0bJUsrzauYU+SvOr2dcq86vmtKa+6fmHKq0MH9rIudCWKopAzZ26++b4vTk7OdufTLyeOMXvWNIxGI40aNaVte+tz8tzZM8yZPY3r167RL2ggtWr7AnDm9Enmzp5hSXfrVgT9ggZSvUYtJk0Ywx9/XEZVVTw8vPixdz+yZMlqd0yHDh9i3PjxGI0GWrb0p2uXlNfegEFJrr1Ro8mXLx937tyhVZvE8qBM6cTyIGxHGAsWLsBgNFKrRk2+/+57u+MBOHr0CJMnT8RgNNK8uR8ff/SJ1fJTp08xZcokrl69wpAhw6hbp55l2datW1i8ZCEAXT7pSpMmTQHYtWsnS5YuxmgwUL16Db7+ukeqYgJT3TfGXPe1ekXdF5Sk7htnrvsOHz3K5KlTefHiBRkzZuTH77+nSuXKPH36lE+SbCNap6NZkyb07d07VTH90/Vx8ODB7N+/n1y5crF+zZpU55OqqoyZMpWDR47i6JiZYUGBr6iLLzNg5Eji4xOoWa0qfb/7FkVRuPzHFYaPn8Cfz/4kX568jBo0gOzZsnHkxAmmzJrDC/0LMjpk5Iev/0eVChVSHZ9Iu6SHLp0xGo1s/nkhH30VyDeBEzl38hC6qMgU6eKfP+Po/m14Fihq+Uyb14uvfhxF9z5j+bhbEJtWz8NgMJAhg4ZGLT/i26BJfPnDcI4f3GFzm69iMBiYPHk8Y8dOYMmSFezevYsbN65bpdFq8xAYGEy9eh9afZ47tyszZsxhwYIlzJo1jxUrlnHvXgwA7dp1ZNmyEObPX8y5c+c4evRIarKKtUej6Dr99CuX+5TKTUFtVuoNPkrw8t8Y0t5UaDpndaBH00IEjP2FVmN+oUfTQjhlMd04Du3wHsErfqPe4KMU1Galtncuu+MxGAwMHz2aWdOnszE0lK3bt3P16lXrmNevxylHDrZt3MhHnToxccoUAK5evcq2sDA2rFnD7BkzGDZqFAaDwbLeTytWULhQIbtjSe7gkaNERN5i06oQBvbpzfDx422mGz5+AgP79mHTqhAiIm9x6OhRACbNnEm3rp+yesliun/+OZNnzgSgSoUK/LxkMauXLGZIUCBDRo+xO6YDhw4REXGLrRvWMzg4mGGjRtlMN2zUKAb1D2brhvVERNzi4OHDABQtUpTJ48dR4YMPrNKvWbsOgHWrVzNv1kzGT5yE0Wh8YzwGg4FRo8cwY+o01q4JZXvYdq5eu2aVZt369Tg5ObFpw0Y6d+rElKlTLMs8PT1ZvTKE1StDLDdvACNGjWJAcH82rt9AxK0IDpnjt8ffjQlg/MQJ1Khew/L3lStXWLt+HT8tWcrqlSEcOHCAmxERqYpp2rSJjBw5nvkLlrF37y5u3kxeHrjTu08QdevWT7F+pkyZmTNnEXPmLLI05oxGI+PGjqR/8GDmzV+K1t2dHTu22x2T0WBgwdzpBA0YwaSp8zh0MJzIWzet0hQsXJTR46czfvIcqlavxU9L51stX7ViCd6lyqbY9rEjB3F0zGJ3LMkdOHSYiFsRbF23lsH9gxg2arTNdMNGjWZQ/yC2rltLxK0Iy3kOcDcqiiPHjpM3Tx7LZ7FxcQwfM4bpEyeyYfVqJoy2vd3kjAYDC+dNJzB4BBOnzOPQARt5Vagoo8ZNZ9ykOVSpVovlyfJq9UrrvDIYDCxeMJOBQ8cxbtIc8hcsRNjWDXbF83L9GdMnM2zEWObMW0J4+G5u3rxhlUar1fJjr0Dq1K1n9Xm58h8wY/YCZsxewOixk8jsmJkPKlQC4Mtu3zBz9kJmzVmEVqtl04Z1qYpp9JgxTJ86jdCfbV976zesJ0cOJzau30injp2YMi1JeeDhyaoVIaxakVgePHr0iMlTpjB71hxCV6/h/oMHHDt+LFUxjZ8wjgkTJrNieQi7du3g+nXrmPK4uxPcfwAfftjA6vPY2McsXDSf+fMWMn/eIhYumk9sbCyPHz9mxsxpTJ0yneXLQ3jw4AG//HLC7phexjVi9GhmTp/OhtBQtr2m7ttqrvsmmeu+nC4uTJ88mXU//8yIoUMJCg4GIFu2bKxZtcrykzdvXurVrZuqmN5GfdyyeXNmz5iRYn/2Onj0KBG3ItkUsoKBvXszfPxEm+mGT5jAwD692RSygohbkRw6ajpPhowZy3fdviJ06RLq1q7F4hUrAXBxdmbq2NGELl3CsOAg+g8b8ZdjfNcUJe3/vAv/mQadoihvvEtSFOV7RVHsfzz3+m11URQl35tTpk7kzSvkcnMnl6s7Dg4OlPmgOr+dS1m47t66ipp1/XDImMnyWaZMmdFoNADo9S8A01mXwzkn+bxMT8QzO2bBzd2D2EcP7I7p0qWLeHh4ki+fBxkzZqRu3focPHjAKk3evHkpUqQoGTJYn3IZM2YkUyZTjC9evMBoVAFwdHTkgw8qWNIUL16cmBid3TEBnLjyiEdP9a9cXr+sq6Xn7fSNWJyyOuDmlIla3rk5dOkBj//UE/tMz6FLD6hdKjduTpnI7qjh1PVYANYdi+LDcm52x3Pu/Hnye3nh5elJxowZadywIXvCw63S7AkPp0Xz5gA0qF+fY8ePo6oqe8LDadywIZkyZcLTw4P8Xl6cO38egKjoaPYfPEiAv39qssfK3oMHaN6oEYqiULZ0aeLinhBz755Vmph793j69CnlSpdGURSaN2rEngOm46woCk+e/gnAk6dPcHN1BSBr1qyWXoRnz5+nqudpb/g+/Jo1RVEUypUtY4opJsY6ppgYnj59QvlyZVEUBb9mTdmzNxyAIoULUahgwRTbvXrtGlUqVwYgd65c5MiRgwsXL74xnvMXzuPl5Ymn+fg1bNCQ8GTHL3xfOM2bNQOgfr16HD9+AlVVX7nNmJgYnj55Srmy5VAUhWZNm7E3fO8bY/mnYtqzdy8eHh4UKVLYkv7a9euULV2GLFmy4ODgQIUPKrBn7x67Y7p8+RL58nmQN18+MmbMiK9vPQ4fOmiVJk+evBQuXBTFzi8Uio19TMaMGfH0zA9AhQqVOHBgn90xXfnjMnny5sM9T14cMmakek0fThy3rhJKlylP5syOABQrXpIH9xPPtWtXf+fx44eUK2/9RPv5s2ds3hhKQJuOdseS3N59+/BrYj7Py5QhLi7uldde+bLm87xJU/aEJ/7/YydOoue3Payur63bt1O/Th1LIy93LvsePl25chn3VObV/WR59ejRQ8qWS8wrVVVRgfjnz1FVlWd//knOXLntyyDg95fnVF7TOeXjU5ejh63PKfc8eSlUuAiK8urbmgMHwqlYsQqOjqbYs2XLZokvPiE+VXdiNq+9feFWaVJbHty+fZv8BfKTK2dOAKpUrszuPfZfexcvXcTT0xMPD1NdXL/ehxw4sN8qTd68+ShatBgZkuXT0WNHqVSpMk5Ozjg5OVGpUmWOHjvC7Tu38fLKT05zTBUrVUpVGQW26769ycqpveHh+Jnrvg+T1H0lS5RAq9UCULRIEeITEkhISLBa9+bNmzx48CDFw7vUxvRP1McVK1TA2dn+nufk9h44SPNGDc11cSninryqLv4zSV3c0FIX34iIoEJ50yiYapUqsnufqZwoWbw4WnO9XLRQIRJs5KNI3/4zDTpVVau/ORXfA3Y36BRF0bxmcRfgH2/QxT1+gLNLYkXo5JKb2McPrdLcjbxO7MP7vFc6ZXf6rRt/MG3Uj8wY3YvmbT+3NPBeenhfx93I63gWLJpi3Ve5dy8GbZLhNm5ubpZeNnvodNF8+ulHtGnTko4dO+Pqat1IiouL4/DhQ1SoYN+wOHu5u2Tm7sPnlr+jHsbj7pIZd5dM3H0Yn/j5o3jcXTLh7pKZqEdJPn/4HHeXzHbvT6fTkcc9MZ/c3d3RJWug6HQ68phvwBwcHMiePTuPHj1CFxNj+RzAXatFpzM1cMeMG0fP775DyfDXL2ddzD3czZWmZfsx92ykSTw27m6Jafp89y2TZs6ggX8rJkyfwbfdulnS7d63jxYdOvJNr94MCQq0O6bo5Pml1RKdLL+iY2Jw1yZN40607vUN//eKF2fvvnD0ej2Rt29z8dIloqKj3xiPThdDHvckx8Bdiy7ZQwZdTGKapMcPTDdr7Tp24LMvPufkqZOW9O7uWuttviH+fyqmZ8+esXjJYrp9+ZVV+qJFi/DrqZOWNAcPHSTajvx56d69GNySnEuubm7cu3/vNWtYS0hIoHv3z+nxzVccOmS6GXV2dkGv13P58m8A7N8fTkwq8unBg3vkTlKu5M7txoP791+Zfs+u7ZT/wNSLYzQaWbpoLp0/+SJFupCVi2neIoBMme0vB5KLjokhT56k5YI2xTkcrdOlOE9eXgt79+1Dq3WjRPHiVuvciIggNi6WLl9+RdvOH7Fh8xa74nlw/x65c1vn1cMHr86rvbut82rZ4pR55eDgwOdf9qD3D1/R7bMOREbepG69RnbFA3Dv3j3c3KzPqfupOKde2h++B9861j14E8ePomM7fyJvReDXopXd29LpYnB3ty6Tk5+TSa9Py7X32Fwe3LlN+44d+OzLxPLAy8uLGzducOfOHfR6PXvDw4mOjrI7ppgYnVV56KbVpngI9ir3Yqzrca2blnsxMXh6eHLz5g3u3jXFdGD/PnQ6+8sDsF33JS/LX1X3JbVz1y5KvPee5SHwS1u3b6dRgwapemD4turjv0t3L3ld7IYuWYNOd+8e7m5uNtMULVyI8IOmhx079oYTFZ0yrl3h+yhRrFiKfBTp23/mHTpFUZ6oqppdURRfYDBwDygN/Ap0BnpgaoDtVRTlnqqqdV61HWAi0BD4UVGUukBzIAtwGPgKCAAqAssVRXkGVAO8zetlN++7i6qqd1P7f9h6upe0CDMajWxbtxT/jv+zub5XwWL0CJxATFQka5fPpJh3eTKae/Hi458TsnAijVt9gqOj/R2Vth842l+warXuLFpkGmrZv38/fHzqkMv8NFmv1zN06CACAtqQL5+H3du0h62yX1VBsRW7+ve70W1lU/JN2jy+ivLKz8PNY/VLeXtz/Jdf/kZwtrafLImN/+BlmtXr1tO7x7fUr+NL2O7dDB41irnm4Sn1fHyo5+PDr6dPM2PePMvnbwzJ1v6S5dir8uV1/Fv4ce36ddp1/oh8efNSvly5FA82bMZjx75elcbN1ZXtW7bi4uLCxUsX+eHHHwld/fNfiv+fimnW7Nl06tiJrFmtr/XChQrz6Sdd6Na9O1mzZqF48eJ25U/i/lJ+lppLZ/mKNbi6unL3zh169/6OQoWKkC+fB/2DBzN71jRevHhBhQqV/n5Mr8jn/eG7uHb1dwYPNw073rF9E+9XqIyrq9Yq3Y3rV4m6e4cuXf+HTmf/TXfK2P7qMTT1es9duIi5M6anWG7QG7h46Tfmz5pJfHw8nT7tSrkypSlofm/rlfHY/NR2Xh3Yt4urV6zzqvwHKfNKr9ezM2wzoyfMxN09L4vmz2Dd2hAC2tj7bubfPKmAB/fvc/3GNSpUrGz1ec9egRgMBmbNmML+fXto0NDe98dtnlTJUtguw1xdXdm2ObE86NnrR9as+hknJyeC+gXSN7AfSgaFcmXLcfv2bXv/xVeEZF9G2ew5VBScnJzo3asvAwYGoygKZcqU5c6dVMRkOyy7676Xrly9yqSpU5lrHs6f1PawMEYOH/6vxfR3y+3XB/YX6z1zmiGB/Rg9eQpzFi3Bt2YNMmbMaJXuyrXrTJ41m9mTJvwz8Yo04z/ToEvmfaAUcAc4BNRQVXWqoig9gTqqqr7u0V824LyqqgMBFEW5qKrqUPPvy4BmqqquURTlG6CXqqq/KIqSEZgGtFBVNUZRlHbACKBr8o0rivIl8CXA5z2Cqd8kwGq5k0tuHj9KfFoa++g+OZxzWv5OiH+O7u4tFk0fCsCT2EesmDeOjl/0xiN/EUs6tzyeZMyUGd3dW3jkL4LBoCdk4QTKVqyJd7kqdmWiZVtublZP7GJiYnA1d+2nhqurGwULFuLs2dOWSVPGjx+Dp6cnbdq0S/X23iTqYTx5czoCjwHIkzMzusfxRD2Kp0oxF0u6PC6ZOfbHI6IexpMnSY9cnpyORCfpsXsTd63WqicoOjoaNzfr3kh3d3eioqLI4+6OXq/nyZMnODs7m9aNSrxpjNbpcHNzY+++fYTv28eBgweJT0jg6dOn9O3fnzEj3jw+PiQ0lLUbNwFQqmRJq16BaJ3OMmzSEpubG9G6xCeY0TGJaTZt20bf778DoEHdujbflatQvjy3bt/h4aNHlkkbklu5ajVr1pneYyldyts6v3Q6tG7WMeXRaonWJU0Tjdbt9cNgHRwc6NvrR8vfnbp8SoH8+V+7Dph6RaKSPC2Pjtbhlqw32XSMo3BPdvwURbE8DfUu6Y2npyc3IyJMvY5JnqBGR+tSnBNvK6Zz58+xc/cuJk+dQlxcHBkyZCBz5ky0b9ce/5Yt8W9pmmxp6vRpVk/938TNzc2qp+JeTAy5c9tfHrwsO/Lmy0fZcuW5cuV38uXzwNu7NJMmm95N+eWX40RG3rJ7m7lzu3I/yaiB+/djyGljCOLZMydZt2Ylg4ePtzzo+v3yRS5dPM+ObZt4/vwZer0eR8csuGm1XL/6B19/+REGo4HHjx8xOLiXpXHzOitXr2bN+vUAlPb2JioqabmgS3EO53F3T3GeaF3duBUZye07dwjoYBryLfhllAAAIABJREFUGa3T0aZTZ0KWLMbdXYuLiwtZs2Qha5YsVHj/fS7/8ccbG3S5c7taDaF8XV6tXbOSwcOs8+q3S+fZud06rypXrWn6P/KYBq1UrV6bDetWvTGfXnJ1dbMacn8vJobcuVJXx+zfv5fq1Wvh4JDytkej0VDbty6hP4fY3aDTarVWvWcvy+Sk7C4PPEzlQSlvb3xq++BT2weA0LWhaFIx8sItWXkYo9PZXRe7abWcMvcUAuhidLz/vmkIY82atahZsxYA6zesS1VMYLvuS36Ov6ruA9NrBd/37MnIYcPw8vKyWu/y5csYDAZKmSe9+jsx/d36+K8KCV3L2k2bAShVskSyujgGN1fr4cnubm5WPZxJ0xQqUIA5k0zv3d2IuMX+I0eSpNPxQ1B/hgf3x8vjn31ALt69/8yQy2SOq6oaqaqqETgNFEzFugYgNMnfdRRFOaYoyjmgLqaGYnLvYeoN3KkoymkgGPC0tXFVVeeqqlpRVdWKyRtzAB75i/AgJoqH93Xo9XrOnTxMidKJQxEds2Sl38j59Bw0nZ6DpuNZsJilMffwvs7y4u6jBzHc193FJZcbqqqyfuVs3Nw9qFGnWSqywqREiZJERkZy9+4dXrx4wZ49u6hRo6Zd6+p0OuLjTY2iuLhYzp8/h5eX6YZj/vw5PH36lB49UjfLl712n7uHfxXTsInyBZ2Ie2YgJjaBAxfvU7NkLpyyOOCUxYGaJXNx4OJ9YmITeBpvoHxBJ8A0E+aus/YP+yldqhQRERFE3r7Nixcv2BYWRh1fX6s0dXx82LDJ1MjasWsXVSpVQlEU6vj6si0sjISEBCJv3yYiIoIypUvzw7ffsjssjB1btzJu9GgqV6pkV2MOoH1AAKvNE5bUqV2LTdu3o6oqZ8+fJ3v27CkadG6urmTLmpWz58+jqiqbtm+njrmSd3N15ZdTpwA4/uuv5Pcynd4RkZGWp4mXLl/mxYsXuLzm/YIO7doSGrKS0JCV1PX1ZePmLaiqypmz50wxJas03dzcyJo1G2fOnkNVVTZu3kIdX5/X/t/Pnj3jz2fPADh89CgOGg1FChd+7ToApbxLEXHrFrfNxy9sRxg+Ptb78vHxYdNmU8W8a/duKpmP34OHDy3XXmRkJBEREXh6eJjiz5aVs+fOoqoqm7dsxtfH942x/BMxLVqwkG2bt7Bt8xY6dezIZ592pX0702yGDx6Y3qG9e/cue/bspXEj+4fHvfdeCW7fTiwPwsN3U626feVBXFyc5b2Ox48fceHCeQoUKAjAw4emoeUJCQmsWrWcZs1b2B1TkWLvcffubXTRd9G/eMHhg/uoWKmaVZrr164wb9YU+gQNxdkl8SHZtz8EMmvecmbMXcZHXb6ktm99On38GQ0aNWfOwhBmzF3G0JETyZfXw67GHECHtm0JXbGC0BUrTOf5VvN5fu7cK6+9rNmycuac+TzfuoU6Pj4UL1qU/Tt3sGPTRnZs2oi7VsvPy3/C1dWVOj4+nDx9Cr1ez7Pnzzl3/jyFbbxTmiKvir5HlB15NX/2FPoEpsyrmXOXM33OMjp/Ysqrjh99Rq7crkTeiiDWPNzw3JmTeHi8+SHKS8XfK8Gd25FE3b3Lixcv2LdvD1Wr1XjzikmE791tNdxSVVXu3I60/H7s6GE8veyPyda151s72bVX287y4JapPIDEay82NpbVa37Gv6X970aXLFGSyMhb3LljuvZ27d5JzZq17Vq3apWqHD9+jNjYWGJjYzl+/BhVq1Q1xfQwMaZ1a0NpnoprD0x1381kdZ9vsrrP18eHjea6b+euXVQ251VsXBxf9+jBdz168H758im2vXX79lSVT0lj+qfr47+qfUArVi9eyOrFC6lTqxabtoeZ6+ILZM+e7TV18QVzXRxGnVqmMva+uZw0Go3MW7KUNi1Mxyo2Lo5vevflu25f8n7ZMn85VpF2/Vd76JJ2qxhIXT48V1XVAKAoiiMwE6ioquotRVEGA4421lGAC6qqVrOxLFU0Gg1NA7qydNZIjEYjH1T1RZvXi91bV+PhVZgSZV79ntnNa79xYNcGNBqNaQKGNp+RLbsTN6/+xpkTB3DPm5+ZY/sAUL9pB4qXet+umBwcHPj++5706vUDRqOBJk2aUahQYRYsmEeJEiWoUaMWly5dZMCAQPP7cAdZtGgBS5Ys5+bNG8ycOc0yjKFduw4UKVIEnU7HsmVLyJ+/AF98YZoK2t8/gGbN/OzOq0mflqJKcRdyZs/IwRHVmbLlOg4a07CElQfuEH7+Pr6lcrNnSDWeJRjou+wSAI//1Ju+qqCvKS+nb73B4z9Nk6sMXHnZ/LUFGvZduM++C69+t8RWPgX17ctX3btjMBrxb9GCokWKMH3mTEp5e1PH15dWLVsSGBxMYz8/nJ2cGGeema5okSI0bNAAv4AAHDQa+vfrl6ohZ29Sq1o1Dh45QrO27XB0dGRoUJBlWdtPurB6yWIA+vfqxYARI4iPj6dG1arUrGaq8Af27cPYKVMwGAxkypSJgX1M59Gu8HA2bdtORgcHMmfOzNihQ+wemlK7Zk0OHDxE4xYtyOLoyLDBgy3LAtp3IDTENHvXgKBAggcN5nn8c2pVr0GtGqYbvV179jBq7DgePHxI92+/o0Tx4sydOYMHDx/y1dffoCgK7loto4YNsyseBwcH+vXpy/+++RqjwUiLFn4ULVKEmbNm4e3tja+PD/4tWtJ/wACat/DDydmZMSNNM3OePHmSmbNn4aDRkCGDhuCgIMvT5/6BQQwcPIj45/HUqFGdmjXsv1H9OzG9zo+9e/H48WMcHBwI7NcXJycnu2PSaBz4pscPBPb7EaPRSMNGTSlYsBCLF8+nePESVK9ek8u/XWLw4P48eRLH0SOHWbpkIfMXLCMi4gaTJ40nQwYFo1GlfftOFChgmr3159UrOHrsCKrRSPPmLXn/ffun3NZoNHT94htGDDF9RUqdeg3xyl+QVSuWUKRocSpWrsZPS+bx/PkzJo4znQ+ublr6Bg21ex9/Ve0aNThw6BCNW/qbzvNBAy3LAjp2JHTFCgAG9OuX+LUF1atTq8brXwsvUqgQNapVp1WHjmRQFAJatqBY0Te/G63RaOj6+TeMHGrKK19zXq1euYTCRcx5tdSUV5PGm/PKVUuf1+RVrly5ad2uM4OCf8TBwQFXNy3de9g/vbxG48D/vvme4KBeGIxGGjRsQoGChVi6ZAHFi5egarUaXL58iWFDBvAkLo5jRw/z07JFzJm3BIDoqLvci9FRpmxig0BVVSaMG8Wffz5FVaFQ4SJ8821Pu2NycHCgb+++dO9hvvb8/ChSpAgzZ8/Cu6Tp2mvZoiXBAwfg19IPJydnRicpD2bNmYVGo0GTQUP/wMTyYOz4cfz+x+8AfPn5l5avNrA3pp4/9OKHnt9iMBhp1qw5hQsXZt68OZQoUZJatWpz8dJFAgP7EBcXx8FDB1gwfx7Ll4fg5OTMp1268tnnpvr2008/s3ytxOTJE7ly5Q/L5/ntGNGQPK6gvn3pZkfd18Rc9401130rQ0K4desWc+bNY84809eCzJk1yzLJT9jOncycNi1V8SSN6Z+uj3v368eJX3/l0aNH1GvYkO7duqVqwrJa1aqa6uJ2HXB0zMzQJO+dt+3SldWLTV8r0b9XTwaMGGWui6tQs6qpLt6+cxch5pmc6/nUpmVTU49zSOhaIm7fZu7ipcxdvBSAWZMmkDtnTtIbO+fS+s9RXjfj0v8nyd6h66WqajPz59OBX1RVXWzuZfNTVfX6m7Zj/t0FuIyph08DHAXWqKo6WFGUTcBEVVX3KoqSCbgIfKSq6hHzEMziqqpeeF3Mq7afTlMHx6e815sT/ctqDj3zrkOw6dL4qu86hBQMfz591yGkoMny16d7f1v0qtQW9op5mPbOqYdxz951CCl4e6W9m6YLEQ/fnOhf5pT9r08u8zblyZXjXYeQwrP4F+86hBRyZE17k2ykxdLc8DTuXYdgk6Obe1rMrhRaD9mdpu6NbVkzqN6/npf/1SGXrzIX2KYoil1z8qqq+giYB5wD1gNJvz9gMTDbPMRSA7QGxiiKcgbTME97Zt0UQgghhBBCiFf6zwy5fNmrpqpqOBCe5PNvkvw+DdPkJW/cTpK/gzG9E5c8XSjW79qdBuwbzC6EEEIIIYSwIkMubZMeOiGEEEIIIYRIp/4zPXSppSjKMSD5gP6PVFU99y7iEUIIIYQQQojkpEH3Cqqqpu7L2IQQQgghhBDiXyZDLoUQQgghhBAinZIGnRBCCCGEEEKkU9KgE0IIIYQQQoh0St6hE0IIIYQQQqR58rUFtkkPnRBCCCGEEEKkU9KgE0IIIYQQQoh0Shp0QgghhBBCCJFOSYNOCCGEEEIIIdIpadAJIYQQQgghRDols1wKIYQQQggh0rwMikxzaYv00AkhhBBCCCFEOiUNOiGEEEIIIYRIp6RBJ4QQQgghhBD/AkVRGimKcllRlCuKovSzsTy/oih7FUU5pSjKWUVRmrxxm6qqvp1oxd/2Z9zTNHVwEvSGdx2CTdkyp71XQUv2OvquQ0jh7Jgq7zqEFBw0MhY+PUuL1UcGJe0F9SzB+K5DSCFzRs27DsEmgzHtHb+0mVVSdton7Z1PAE+e6991CCnkzOWcLk6qjsP3pM2DmsSK4LqvzEtFUTTA78CHQCRwAuigqurFJGnmAqdUVZ2lKIo3sFVV1YKv26f00Il0LS025oQQQqReWmzMCfFPS4uNOfGvqgxcUVX1mqqqCUAI0CJZGhVwMv/uDNx500blblgIIYQQQgiR5mVIF/2Ir+UB3ErydySQfAjVYGCHoig9gGxA/TdtVHrohBBCCCGEEOIfoCjKl4qi/JLk58uki22sknx4QgdgsaqqnkATYJmiKK9ts0kPnRBCCCGEEEL8A1RVnQvMfcXiSMAryd+epBxS+RnQyLytI4qiOAKugO5V+5QeOiGEEEIIIYR4+04AxRRFKaQoSiagPbAxWZoIoB6AoiglAUcg5nUblR46IYQQQgghRJqnpPN36FRV1SuK8g0QBmiAhaqqXlAUZSjwi6qqG4EfgXmKovyAaThmF/UNX0sgDTohhBBCCCGE+BeoqroV2Jrss4FJfr8I1EjNNmXIpRBCCCGEEEKkU9KgE0IIIYQQQoh0Shp0QgghhBBCCJFOSYNOCCGEEEIIIdIpmRRFCCGEEEIIkeZlSOezXL4t0kMnhBBCCCGEEOmUNOiEEEIIIYQQIp2SBp0QQgghhBBCpFPSoBNCCCGEEEKIdEoadEIIIYQQQgiRTsksl0IIIYQQQog0T2a5tE166NKhQ4cP0bKVP34t/Vi4eFGK5QkJCfQN7ItfSz8++uRj7ty5Y1n2+x+/8/GnnxDQtjVt2rUlPj7eat3vfvie1m3bpDqmI0cO06ZtAAGt/VmydLHNmPr3DySgtT9du3axxPTixQuGDhtCx07t6dS5I7/++qtlnRcvXjBy1AhatwmgbbvW7NmzJ1UxHTx0iGYtW9LYz4/5CxfajOnHvn1p7OdHh48+4naSfJq3YAGN/fxo1rIlhw4ftlrPYDDQun17un/7bariARjVuQTHxtRka3DlV6YZ0KYYuwdXZXP/ypTyym753L9KHnYNrsquwVXxr5LH8nkprxxs6V+Z3YOrMqBNsVTH9FfPpzt37lC1RjXadWxPu47tGT5yhGWdsB1htG3floC2rZk8ZXKqYwLT8WvesiVNXnP8evXtSxM/PzomOX6Hjx6lbceO+LdpQ9uOHTl2/Lhlne1hYbRq25aWAQFMnJz6uP7pmJ4+fUrrdu0sP7Xq1GHMuHHvNKZnz57RvUcPmvv70zIggElTpqQqHktM/i1p6ufHgkW2Y+rdty9N/fzo+HFiTOfOn6dN+3a0ad+O1u3astt8zcfHx9Pxo860btcW/9YBzJg1K9UxAaiqysgxY2ns54d/27ZcvHTJZroLFy/i36Ytjf38GDlmLKqqAvD48WM+7/Y/mvi14PNu/+NxbKzVeucuXKBshYrs2LnL7pgOHzlMQJtW+Ae0ZPGSxSmWJyQkENg/EP+AlnTp+onl+tPr9QweMoj2HdvRpl1rFpmv3fj4eD759GM6dupA2/ZtmTN3jt2xvJQWj98/XU49ffrU8lm7ju2pU68u4yak7tp7W+fTnr3h+LdtS0C79rTt2ImTp07ZHVNarPfeVlxLf/qJFgEBtGzdmt79+qW4n3mTt3X8rl2/TqePP+H9ylVYtHRpqmI6cuQIbdu1pnXrVixduiTF8oSEBPoHB9G6dSu6fvYpd+4m3ksNGz6UTp060Pmjjvx6MvFeaseOMDp16kCnzh35/vtvefToUapiEumDNOjSGYPBwOgxY5g+dRqhP4eyPWw7V69ds0qzfsN6cuRwYuP6jXTq2Ikp00w3ZHq9nuABwfQP7E/o6jXMmzMXB4fETtrde3aTNWvWvxTTuPFjmTxpCiErV7Njxw6uXbeOaePGDeRwciJ0zTrad+jIjBnTzLGuA2DF8hCmTZ3OlKmTMRqNACxavJBcOXOx5udQQlau5oMPPkhVTMNHj2bW9OlsDA1l6/btXL161SrN2vXrccqRg20bN/JRp05MNN+4Xr16lW1hYWxYs4bZM2YwbNQoDAaDZb2fVqygcKFCqc4ngLVHo+g6/fQrl/uUyk1BbVbqDT5K8PLfGNL+PQCcszrQo2khAsb+Qqsxv9CjaSGcspiO3dAO7xG84jfqDT5KQW1Wanvnsjuev3M+AXh6eLJqRQirVoQQHNQfgEePHjF5yhRmz5pD6Oo13H/wgGPHj9kd08u4Rowezczp09kQGsq21xy/rebj97LhkdPFhemTJ7Pu558ZMXQoQcHBlrgmTJ7M/NmzWR8ayv379zl6zP643kZM2bJlY82qVZafvHnzUq9u3XcaE0CXjz9m07p1/BwSwukzZzhw8GCqYho5ZjSzpk1n/cuYrtmIySkHW8wxTTbHVLRIEVb+tJyfQ1Yxa/oMho4Yjl6vJ1OmTMyfM5c1q1azemUIh44c5szZs3bH9NKBg4eIiIhg64YNDA4OZtjIUTbTDRs5ikHB/dm6YQMREREcPGS6iZy/aBFVK1dm68YNVK1cmQWLEhsWBoOBSVOmUKNaNbvjMRgMjB03himTp7I65Gd27AjjWrLrb8PGDTjlyMG60PV0bN+Raeayc9fuXSQkJBCyYhXLlvzEuvVruXPnDpkyZWLWjNmsWL6SFT+t4MjRw5w7dy5VMaW14/c2yqls2bJZPlu1IoS8efNQt4791x68vfOpapXKrF21itBVIQwbPIhBQ4fZnU9psd57G3FF63QsX7mSVcuXs37NGoxGI9vCwlIV19s6fs7OzvTr24cuH3+U6nwaP2EskyZOYeXKVezYGcb15PdSmzbilCMHa9aspUP7DsyYMR2ADRvWA7B8+UqmTpnO1KlTMBqN6PV6Jk2eyIwZs1j+0wqKFC3Kz2tWpyoukT785xt0iqI8eQvbLK8oSpMkfw9WFKXXP7Ht8xfO4+XliaenJxkzZqRhg4aE7wu3ShO+L5zmzZoBUL9ePY4fP4Gqqhw5epRixYrxXvHiALi4uKDRaAD4888/+Wn5cj7/7PNUx3Tx4gU8Pb3w8DDF9OGHH7J//z6rNPsP7Kdpk6YA1K1TlxO/mGK6fv06lSpWAiBXrlzkyJGdS+anZJs2beSTT7oAkCFDBlxcXOyO6dz58+T38sLLnE+NGzZkT3i4VZo94eG0aN4cgAb163Ps+HFUVWVPeDiNGzYkU6ZMeHp4kN/Li3PnzwMQFR3N/oMHCfD3T3U+AZy48ohHT/WvXF6/rCvrjkUBcPpGLE5ZHXBzykQt79wcuvSAx3/qiX2m59ClB9QulRs3p0xkd9Rw6rrpyeC6Y1F8WM7N7nj+zvn0Krdv3yZ/gfzkypkTgCqVK1ue1NvL1vHbm+z47Q0Px898/D5McvxKliiBVqsFTDeY8QkJJCQkEHn7NgXy5ydXLlODt2qVKuzavfudxpTUzZs3efDgARVS8eDibcSUJUsWKlcyXZMZM2akZIkSROt0dsd0/vx58nt6Wc6pRjZiCg8Px6+ZOaZ69Tl2whRTlixZLA+Z4hMSUBTT2BpFUSwPm/R6PXq93rIsNfbuC8evWTMURaFc2bLExcURExNjlSYmJoanT59Svlw5FEXBr1kz9oTvNa0fvo8WzU3XQovmzdizN/H/WhESwof16lnOL3tcuHgBL08vPC1lZwP2JS879++jaVPTPuvWrccJc14pwLPnz9Hr9TyPf05Gh4xky5btb+dVWjx+b6OcSupmRAQPHj7kg/ftv/bg7Z1PWbNmteTPs2fPwM6sSqv13tuKS28wEB8fj16v59nz57i52V/3wds7frlz5aJMqVJWD8ztYbqX8sTDw8NUHtRvwP79+63SHDiwjybme6k6deryS5J7qYpJ76WyJ95LqarKs2fPUFWVP58+xc01dfkk0of/fIPuLSkPNHljqr9Ap4vB3T1xuJ27VktMspstnS6GPOY0Dg4OZM+enUePHxERcRMFhe7fdKdDp45Ww3tmzprJR507k8XRMfUxxcTgrnW3/K3VutsoFHVo3d2tYnr8+DHFihVj/4H96PV67ty5zW+//UZ0dDRxcXEAzJkzm48/7kxgUD/u379vf0w6HXncE2Nyd3dHlywmnU5HnjzJ8unRI3QxMZbPwZTHOnMejxk3jp7ffYeS4e1cOu4umbn78Lnl76iH8bi7ZMbdJRN3HyYOJ4l6FI+7SybcXTIT9SjJ5w+f4+6S2e79/Z3zCeD2ndu079iBz778nJOnTgLg5eXFjRs3uHPnDnq9nr3h4URHR6UiF2wfv2g7j19SO3ftosR775EpUya8vLy4fuMGt81x7dm7l6jo6HcaU1Jbt2+nUYMGqbrRfdsxxcbFEb5/P1Uqv3qIcHLRMTrc8ySJSeuOThdjI43tmM6eO4d/6wAC2rZhQFB/y02RwWCgTft2+NavR7UqVSlbpozdMVn2q9ORJ2ls7lqik8emi8Hd3NBNTGO6Ju7fv2+5aXRzc+PBgweW7e7es5e2rVunKp4YnQ73pMdPqyUmJtn1F6OzlK9Jy8569eqTxdGRxk0b0dyvGZ06dcbZ2Rkw5VXHzh1p0OhDqlSuQunSpe2OKS0ev7dRTiW1PWw7DT5M3bUHb+98Ati1Zw/N/VvR/dvvGDZokF3xpNV6723E5a7V0uXjj6nfuDF1PvyQHNmzp6p3HN7u8fsrYmJi0FrdS2ltNjDdX3EvdWD/vsR7qcu/Ea2LxsHBgT69+9Kpc0eaNW/C9RvXad7c72/F+a5lUNL+zzvJl3ez2zdTFKWzoijHFUU5rSjKHEVRNIqiPFEUZYyiKL8qirJLUZTKiqKEK4pyTVEUP/N6XRRF2aAoynZFUS4rimJfSWhat7eiKCcURTmrKMoQ82cFFUW5pCjKPEVRLiiKskNRlCzmZZXMaY8oijJOUZTziqJkAoYC7cyxtzNv3jtJrH9tEDoANp44JquEVBtpFBQMBgOnzpxmxPARLFywgD3hezl2/BiXL1/mVuStVA83Sdyh7f1ZJ7GRRoHmzfzQarV0+fRjJk6aSJkyZdE4aDAYDOh0OsqWLcfSpT9RpnQZpk6z/10eW89lk19jtmNSXvl5+P795MqVi1Le3nbHkVq27idUNWV+mhbYTp86f/18cnV1ZdvmrYSsWMmPP/QkKLg/T548wcnJiaB+gfQN7EfXLz4jX758aDSpe1L5d47fS1euXmXS1KkMMg8ldHZyYkBQEL379uWTrl3NcWneaUxJbQ8Lo3GjRnbH87Zj0uv19OnXj04dOuDl6fm3gkpxnr4mprJlyrBuTSgrl/3EgkULLe/FaDQafg5Zxc7tYZy/cJ4/rlyxP6ZX7zZFbDbP9zdcaGPGjeeH775N1flk2peteJKXnbYSwYUL58mg0bBty3Y2rNvI8hU/EXk7EjDl1YqfVrBl01YuXLjAlaupyKs0efz++XIqqbAdYTRq2DAV8Zj3+ZbOJ4D6deuyad1apk6cwPSZ9r1zmFbrvbcR1+PYWPaGhxO2eTN7duzg2bNnbNqyJXVxvcXj91fY7FFOHs8r7qWaNWuOVqvl066fMGnyJMqUKYuDRoNer2ft2lCWLlnG5k1bKVqkmM15DkT6lyYbdIqilATaATVUVS0PGIBOQDYgXFXVCkAcMBz4EPDH1Ih6qbI5fXmgjaIoFe3YZwOgmHnd8kAFRVFqmxcXA2aoqloKeAQEmD9fBHRTVbWaOUZUVU0ABgKrVFUtr6rqKnPaEkBD8/YHKYqS8RVxfKkoyi+Kovyy0MbL6Fqt1qq3I1qnSzHMwF2rJcqcRq/X8+TJE5ydndFq3anwQQVyuuQki2MWataoyW+//caZc2e5eOkSTZo35dPPu3Iz4iaff/nFm7LMOiZdYk+HTheNq5trsjTu6My9IS9jcnJyxsHBgR++78lPy1YwftwEnjyJw8vLC2dnZxwdHfH19QWgXr16XL78m90xmfIgMabo6OiU+eTuTlRUynxy12otn0NiHp86fZrwffto0KQJvfv14/iJE/Tt39/umOwR9TCevDkTe0nz5MyM7nE8UY/iyZszsectj0tmoh8nEPUwnjxJeuTy5HQk+pH9L4b/nfMpU6ZMlmGw3iW98fTw5GZEBAA+tX1YtmQpSxctoWCBAuT38kpFLtg+flo7jx+Yhgh937MnI4cNwyvJvn19fFixbBnLly6lYMGCFMif/53HBHD58mUMBkOqb5reZkxDhg+nQP78fNSpU6pjio5KEpPOxrWndSf6FTG9VLhwYbJkyZKiMeKUIwcVK1RMMWnDq6xctYqAdu0JaNcerZsbUUlji9alyK88Wq3VENOkaXLnzm15Yh4TE2MZXnnh4kV69wukQZOm7Ni1i+GjRrF77943xma6/pLmlQ5X15TX38vy1ZKZj0w8AAAgAElEQVRXTs5sDwujetVqODg4kCtXLsqVLWcZYvVSjhw5qFChAkeOHHljLFb7S0PHD95eOQVw+fffMRgMeJe079r7N86npCpWqMCtyEgePnz4xtjSar33NuI6euwYHvnykStXLjJmzEi9unU5febMG2P5t49fami1WnRW91K6FMMjk5YZye+lvv++J8uWLmfc2PE8iTPdS/3+++8AeHp6oigK9erVS9U7tSL9SJMNOqAeUAE4oSjKafPfhYEEYLs5zTlgn6qqL8y/F0yy/k5VVe+rqvoMWAvUtGOfDcw/p4CTmBpgL6cMvK6q6suZLH4FCiqK4gLkUFX1Za204g3b36KqaryqqvcAHeBuK5GqqnNVVa2oqmrFrp92TbG8lHcpIm7d4vbt27x48YKwHWH41vaxSuNT24dNmzcDsGv3bipVqoSiKFSvVo0//viDZ8+fodfr+fXkrxQuXJi2rduwc/sOtm7awqL5CymQvwDz5857w7+TqGRJb27diuDOHVNMO3fupHat2lZpatWqxZatpqdne/buoWJFU0zPnz83vSMAHDt2DI3GgcKFCqMoCjVr1uKkeaamEydOUKhQYbtjKl2qFBEREUSa82lbWBh1zI3Dl+r4+LBh0yYAduzaRRVzPtXx9WVbWJjlvauIiAjKlC7ND99+y+6wMHZs3cq40aOpXKkSY0aMsLH3v273uXuWGSzLF3Qi7pmBmNgEDly8T82SuXDK4oBTFgdqlszFgYv3iYlN4Gm8gfIFnQDzTJhn79m9v79zPj14+NDy0nxkZCQRtyLw9PAAsAw9iY2NZfWan/Fvmbp3L0qXKsXNZMfPN9nx8/XxYaP5+O3ctYvK5rhi4+L4ukcPvuvRg/fLl7da5745rsexsaxavZpWqXgn5G3FBKbhlqntnXubMU2dMYMncXH07d071TGVKlWKm7cSY9oeFoavj42YNptj2p0YU+Tt2+j1pndM79y5w40bN8iXNx8PHj4g1jwM+/nz5xw9doxCBQvaFU+Hdu0IXRVC6KoQ6tbxZePmzaiqypmzZ8mePXuKG0s3NzeyZs3KmbNnUVWVjZs383/s3XdUFFf7wPHvUARR6ewiig2Nii2Jxq7YojEq1kSjpmj8WWPURMWCPRbs3WisMfZurIkFa9RoLGCJPbZIUyyIlN35/bHrwsIiS3yN8L7P5xzPkZm7M8/ee+fO3Ll3Zusa468TUJstPxuOhS0/b6NuHcOxsnv7Nn7ZsZ1fdmynYYMGBA8eTP26dTONzb+0v+H4M7Wdv1C7dtq2szbbtxv2uW/fXt4ztp3e3lp+P3nS9HxMeHg4RQoX4eHDh6Yp68+fP+fEiRMUsTKvIPuVH7y+dgoM0y2zMjr3b9SnW7dumUZiLly8SFJSklXPkGfX897riCu/tzfnwsJMz4YdP3HCqpe2/Bvl908ZrqVup1xL7fmFWrVqmaWpVbM2O4zXUvv376NSxUrpr6VOHMfWzpaiRYvh5eXFjZs3TDcEstoeiJwju/4OnQIsU1V1sNlCRemvpow364EEAFVV9YqipP4uacekrXkyWgHGq6pq9o5nRVGKvNiPkQ7IjdWPKZuk3cY/yns7OzuCBgTRs3cv9Do9zQMD8fPzY+738/Av7U+dgABaNG9B8PBhBLYIxNnZhQnGNzc5OzvTsUMHOn72KQoKNWvUoFbNWpns0bqY+vcfyNd9vkav19GsaSDFivkxf8H3lC5Vmtq1Awhs1pyRo0bQuk1LnJ2d+W6M4YTw4MED+vTtjY1ig5eXFyNHjDJt96tevRk5agTTpk3F1c2VYcFWz57Fzs6OIUFBdOvZE51eT8vmzSnu58fsuXMp4+9P3Tp1aNWiBYODg2kcGIiLszOTJkwADC+IaNSwIYGtW2Nna8vQQYOyPJUqI9M6laHKW6645bXn8NjqzNh+AztbQ1VadegeoeEx1Cnjwb5R1YhP1BG03HDH/dGzZObsvMmmIMNg8+wdN3n0zHDhNHzVn0z8rDSO9rYcOB/DgfPWP2v4KvXpjz/+YN78edja2mJrY8vQwUNMd+knTp7E5SuGO4Ndu3SlcOHCWcqnF+XX3Yry+9BYfhON5bdq9Wpu377N/B9+YP4PhhsT8+fNw8PdnZCJE/nTeMeye9euFMlCXK8rJoDdv/7K3FmzspRHryumpKQkfli4kKJFi/LxJ58Ahoug1q1aZSmmHr0MMbUINMQ0Z95c/P39qRtQh5YtWjBkWDBNAgNxcXFm4nhDTKdPn2bx0iXY2dmh2NgwdPAQ3NzcuHz5MsEjhqPT6dGrehq9/z4BaTo+1qhdsyaHDh+mcWBzcjs6MmbkSNO61m3bsWHNagCGDRlC8IgRPE9IoFaN6tSqWQOALp068W1QEBs3byZ/fm+mTpyY5RjS5tXA/gP4+uve6PQ6ApsF4lfMj+/nf0/p0qUJqB1A88DmjBg5nJatW+Ds7MzY78YB8FGbjxk9ZhRtP2kLqkqzps0oUaIEV65cYeToEej1evR6PQ3qv5+ldj47lt/raqcAft3zK7NmzLQ6ltReV336de8+tm7bhp2dHY4ODkwOmWDVNL/set57HXGVL1eO9xs04OP27bG1taVUqVJ81Lp1JpGYe13lFx0dTdsOHXkaF4eNovDTipVs2bCevHnzposhbT71/3YAffp+jV6vp2nTZhQr5seCBfMpVbo0tWvVplmzQEaNGkGbNq1wdnZmzItrqYcP6Nv3axTjtdSI4YZrKS8vL77s3IXuPbphZ2eHt7c3w4cNz1I+iZxBsfYtUP8mRVH8gS0YplxGKoriDuQDzquqmteYZiTwVFXVyca/n6qqmldRlC+AcUBZIB44DnRWVfVkBvt68bmGwBigvqqqTxVFKQAkAU7ANlVVyxrT9wfyqqo6UlGUcKCLqqrHFEUZBwSqqlpWUZTWxv9/nkGs4UBTVVVvviwfnj2Jy1aFk5isyzzRvyyPQ/a8J1G6/7E3HUI650KqvOkQ0nnRmRU5UzY8fWCjZL+g4hP1bzqEdBzs/zMX6/9JOn32KzuAbJhVZP2e8v+q7Fennj7P+E3Xb5Kbu0uOqFRfhuzLfoWaxqKgev96XmbLq2FVVS8oihIM/KIoig2GjlWvLGziMLAcKA6szKgzl2afvxif3fvNeCfsKdAR47NxGfgS+EFRlDggFHhkXL4fGGScLmr5h02EEEIIIYQQVrN5TS+lyemyZYcOwPgykTVpFudNtX5kmvSpx7IjVVX9ysr9pN7mDMDSqxTLpkozOdXy86qqlgdQFGUQcNKY5gHw3kv2af07pIUQQgghhBAiA9m2Q5dDNFEUZTCGfPwL+OLNhiOEEEIIIYT4X/Jf16FTVXUpsDT1MkVRPIC9FpLXV1XV+jdIpN+XpVFEIYQQQgghhPhX/Nd16CwxdtrSvytcCCGEEEIIIXKw7Po7dEIIIYQQQgghMvE/MUInhBBCCCGEyNnkJZeWyQidEEIIIYQQQuRQ0qETQgghhBBCiBxKOnRCCCGEEEIIkUPJM3RCCCGEEEKIbM9GnqGzSEbohBBCCCGEECKHkg6dEEIIIYQQQuRQ0qETQgghhBBCiBxKOnRCCCGEEEIIkUNJh04IIYQQQgghcih5y6UQQgghhBAi25O3XFomI3RCCCGEEEIIkUPJCF02pmSzuxBnLv/9pkNIp6qf+5sOwaJzIVXedAjplA86/qZDSOfw8ApvOoR0PJwd33QI6ejUNx2BZfG3/3rTIaTj5FvkTYeQTvj1iDcdQjoVS/q86RDSiXn07E2HYJG3e543HYL4x7LZhRSw9+TNNx2CRW0aZr/zsbCejNAJIYQQQgghRA4lHTohhBBCCCGEyKGkQyeEEEIIIYQQOZQ8QyeEEEIIIYTI9pTs9oKJbEJG6IQQQgghhBAih5IOnRBCCCGEEELkUDLlUgghhBBCCJHtyQ+LWyYjdEIIIYQQQgiRQ0mHTgghhBBCCCFyKOnQCSGEEEIIIUQOJR06IYQQQgghhMihpEMnhBBCCCGEEDmUdOiEEEIIIYQQIoeSny0QQgghhBBCZHs2MhRlkWSLEEIIIYQQQuRQ0qETQgghhBBCiBxKOnRCCCGEEEIIkUPJM3Q50JGjR5g4eTJ6nY6WLVrSuVMns/WJiYkEDx/GxYsXcXFxJWTCBAr4+JjW//3337T6qA3du3bj888+A2DFypVs3LwJVVVp1bIlHdt3yFJM4edOsfanhej1OmoGNOSDZm3M1h/Yt5PQPTuwsbHBwcGRjp174VOgEBfCT7Np7Y8kJydjZ2dH63ZfUMq/AgBTxg3hUexD7HPlAqDPwFE4O7taHZOqqoRMn8Hh337D0dGRMUOHULpkyXTpLly6xLCx40hISKBmtWoE9e2DoihcunyF7yZNIjExEVtbW4b0/5Zy/v7sP3SIOT8sxEZRsLW1ZUCfr3m3QgWrYjpy9AiTJk9Gr9fRokVLOn+RvuyGjUhVduMn4OPjw71792j1UWsKFy4MQLmy5QgeMhSA3b/sZtHiRej0emrVqEnfPn2tziOA8R1LUa+cJzFPEvnwuxMW0wz7qAR1yngQn6Qn6McLnL/9FICWVbzp1bgIAHN23mTT8fsAlPHNx8TPSuNob0Po+RjGrLuSpZgAjh8/xqxZ09HrdTRp0owOHT4zW3/27GlmzZrB9evXGD58FHXq1APgypXLTJ06iWfPnmFjY8Onn35OvXoNADh16iTz5s1GVVVy587NoEHBFCxY0OqYVFVl/MRJHDpyGEdHR8aOGoV/6dLp0p2/cIHgESN5nvCcWjVqMnjgABRF4dGjR3wbNIh79+7h4+PDlIkhuDg7c+LkSb7u943pOG1Qrx49unW1KqYjR44Q8qI9aNmSLy20B0OHGeuUqysTLbQHLdu0oUe3lPagcZMmOOXJg62NDba2tqxascLqPHqRT1N/WMRvp07h4ODAsD69KeXnly7dvOU/sXN/KE/i4ti/ZpVp+f2oKEZPn8nTuDh0ej29PvuU6pUqAnDl5k1C5s4j7lk8NjYKiydPwsHYRrzM4SNHCJk8Cb1OT6uWLfiyU2eL+XTh4kVcXF2YNCGEAj4+hIWHM/q7Mabv1aNbd+rXM9S15T/9xMbNm0BRKFG8OGNGjsLBwcHqfAo7c5KVPy5Ar9dTu25DmjT/2Gz97u2bOLh/NzY2tuRzdqFzt754emkAWLtyMedOnwSgWat2VKlWG4D5sydx8/oVbG3tKOr3Fp93+Qo7u6yd6l9HXn3Q5EOzOrV6xcosxXTixDHmzpmBXq+n8YdN+eSTT83Wnzt3hrlzZnL9+jWCg0dSO6CuaV3D92tTtGgxADQaLWO+CwFg3LhRXP7zEnZ2dpQsVZp+/QZmKa8OHznChEmT0On1tG7Rgi6d0+fTYGM+ubq4MDnEkE+xsbH0GzCA8PPnaREYyNBBg0yf+aJLF6Kjo031aMG8eXi4u7/2mAB+WLSIjVu2YGtjw+CBA6lRvboh/z78kDx58mBjLLu1K7NWdv9LeRU8ciQHDx7E3d2dzevXZzmfLl84w/YNS9Dr9VSqVp+Ahi0spgs/fYxVi6fSY8B4Chby41ncE1Yumsrdv67yTpU6BH78pSnt0rljefIoFr1eR2G/UgR+3AUbeRDtv4506HIYnU7H+AkhfD93Llqtlg6fdiQgIAC/YsVMaTZt3oyzszM/b9nKrt27mTFzBhMnhJjWT546hRrVa5j+vnr1Khs3b+KnZT9ib29Pr95fUatmLQoXKmRVTHq9jlU/zqfvwNG4uXswfsS3lH+3Mj4FUj5fuVoAAfUaA3D2j+OsW7mIPgNGkTevM736BePq5sHdO38xc9IIQmYsNX2uc/dvKFKsxD/Kq8O/HePWndv8vGY1YefP893kyaz44Yd06b6bPIXhQQMpX6YMvfr358ixY9SsVo1pc+fSvXMnalarxqGjvzF97lwWzZ5NlYoVqVOzJoqicPnqVQYMG86WVZmf4HQ6HRNCQpg3x1h2n3UkoLZ52W3espl8+ZzZutlYdrNmEDLeUHYFCxRkzcrVZtuMjY1l+owZrPhpBe5ubgwbMZzjJ45TpXIVq/Np47H7/HTgDpM+97e4PqCMB0U0TtQfeYy3izgzql1J2kw6hYuTHb2bFKXlhN9RVdg8+D32novmcXwyoz8pSfDKS5y+8ZhFvSpQ29+dgxceWB2TTqdj+vTJTJkyAy8vDd26fUmNGrUoUqSoKY1G483gwcGsXm2e946OjgwdOpyCBX2Jjo7i//6vM++9V4V8+fIxdeokxo4NoUiRImzatIHly5cyeHCw1XEdOnyEW7dusWPLFs6FhTFm3HhWLf8xXbox48YzIngoFcqXp8dXvTl85Ci1atZg4ZIlVK1cmS6dO7Fw8RIWLVnCN336APDuO28zd+ZMq2N5kU/jQkKYb2wP2nfsSJ0M2oNtW7eyc/dups+YwaSQlPZg0pQp1KxRI922F86fj5ubW5bieeG3U39w++97rPt+LucvX2bivPksnjwxXbpald/joyYf8lGPXmbLl6xdR/2aNWjd+ANu3LpNvzFj2FxpAck6HSOnTmdkvz6UKFqUR48fY2drm2k8hnyawIK589BqtXzSsYMxn1I6mRs3b8bZOR/bt25l5+5dpnwq7ufHqp9WYGdnR1RUFG3atSWgdm1iHjxgxepVbF6/AUdHR/oHDWTX7t00Dwy0Ko/0eh3Ll8yj/5DvcPfwZPTQfrxdsSoFCqa0m4WKFGP42Ok4ODiy79ftrF25mJ59BnH2jxP8deMaoybMIjkpiQmjgyhfoRK5nZyoWqMOXXv1B2D+rIkc3L+beu83sSqm15VXLzpJi+Yv+Ed1SqfTMWvmVEImTsPLS0Ovnl2oXq0mhc3aAy0DBw5h7bpV6T6fK5cD8xcsTbe8fv2GDB48HIBxY0eyY8fPBAa2tDqm7yZM4Id58/DWamnboQN1AwLw80uTT/nysXPrVnbs2sXUGTOYEhJCLgcHevfsyZWrV7l67Vq6bU8YO5ayZcpYFcd/KqZr166xc/dutqxfT2RUFF26d2f75s3YGo+vxQv+Wdm9alw5La9aNGtG+7ZtGTJsWJZj0uv1/LxuEZ16BePs6sG8SYMpXa4SmvzmNx0Tnsfz24Gd+BZJuTays7OnQZO2RPx9i4h7t83St+vUD8fcTqiqyqpFUwg//RvlK6Zv80XOJl10KyiKslRRlDYvWb9QURTLV8P/YeHnw/H1LUjBggWxt7enUcNGhIaGmqUJPRBKs6ZNAWhQvz4nTvyOqqoA7Nu/nwIFCuDnl3LBd/3GDcqXLUfu3Lmxs7Oj4rsV2bd/n9Ux3bh2BY0mP14ab+zs7KlUtRZn/zhuliZ3bifT/xMSnqMoCgCFivjh6uYBgE+BQiQlJpGUlGR9hrzE/sOHaPbBByiKQvmyZXny5ClR0dFmaaKio4mLi6NC2bIoikKzDz5g36FDACiKwtO4ZwA8jXuKl6cnAE5OTqb445+nfJfMWCy7A6FmaV5WdpbcvXuXQoUL4W480VapXJm9+6wvO4Dfr8YSG5ec4foG5T1NI29nbj7G2ckOL+dc1PL34MjFBzx6lszj+GSOXHxA7TIeeDnnIq+jLadvPAZg0/H7vF/BK0sxXbx4gQIFCuLjUwB7e3vq1WvA4cOHzNLkz58fP7/i6e40+voWomBBXwA8Pb1wc3Pj0aNYwFCmz57FARAXF4eHh2eW4tp/IJTApk1RFIUK5cvz5MkToqKizNJERUURFxfH2xUqoCgKgU2bsi90v+HzoQdo3sxQvs2bNWXf/tAs7T+t8PBwfAum1KkPGqVvD/aHGmIGeL9+fU78bt4eFCxQwKwD+J9w8MQJPqxbF0VRKFuyJE/j4oh+kL5DX7ZkSTwt3FFXFIW4Z8Zj71kcXm6GNCdOn6F4kcKUKGq4kHdxdjZdcL5MeHg4hQr6muXT/rTtZmgogU2bAfB+/QYc//2EaST3RYckITHR7HjX6XQkJCSQnJzM8/jneHlZX8+vX72MxtsHjTY/dnb2VK5Wm9Mnj5mlKV2mAg4OjgD4FS/FwweG9uve3duULF0OW1tbHBwd8S1clLCzpwCo8M57KIqCoigULf6W6TPWel159Sr+vHQRn1TtQZ26DThy9LBZGm/v/BTzK46NYv1lTZUq1Ux5VbKUP9FRkVZ/Niw8nEK+vvga86lxo0bsS5NP+0JDad7MkE8NGzTg+AlDPjnlzs2777yTpdHc1x3TvtBQGjdqRK5cuShYoACFfH0JCw9/43HltLyqVLEiLi4u/yimO39dxd3TG3dPLXZ2dpSvWJ2LYb+nS7dn+xpqNQjEzs7etCyXgyNF/Ephb5d+toKj8fpLr9eRrEsG/jPH5ZuiKNn/35sgHbr/AFVVu6iqeuHf2FdkZBTeWm/T31qthsg0J6HIqJQ0dnZ25M2bl9jYWOLj41m6bCndu3YzS1+8uB+nTv9hSnP4yGEiIiKsjin2YQxuqS6M3dw9iX0Yky7d/j3bGdq/KxvXLKNtx/TTyf74/Si+hYthb5/SSC1bOJMxwX3Yvnn1Szs2lkRGRaPVaEx/azUaIqOiLaRJuQjTeqWkGdjna6bNnUPDlq2YMnsOX3fvbkq398ABmn/Snq/6D2DUkMHWxRMZhTZ12Wk0REVGpkuTruyMnZG79+7Srv0nfNm1C3+c/gMAX19fbt68yb1790hOTmZ/aCgREfetisdaWlcH/n743PT3/YcJaF0d0Lrm4u+HCSnLYxPQuuZC6+rA/dhUyx8+R+uatZNxdHQUGo3W9LeXlxfR0VEv+YRlFy9eICkpCR+fAgAMGDCIoKBvadOmOb/8sosOHT7NZAvmIiIj8fZOiUur1RARGZUmTZR5vdNqiDCWc0xMjOmi38vLiwepOjlnz4XR6uO2dO/1lcU70ZZERkXh7Z1SpzSalH1ZSpO6PXgWH8+SpUvp3s28PQBAUejeqxft2rdn/YYNVsWSWlRMDBpPj5S4PD2IirF+hLZLu7bsPnCAZp278M3o7/i26/8BcOvePRRFoc+IUXzW71uWb9xk1fYioiLRpi43jZbItOUWFYnWQj4BnAsLo2Wb1rT++COGDRmKnZ0dWo2Gzz/9jIYfNqZ+w/fJmy8v1atVs/o7PnwYg3uqdtPdw5OHFtrNFw6G/kK5CpUAjB24kyQkPOfJ40dcunCOBzHm3yc5OZmjh/ZTrkJFq2OC15NXACgK3Xr1pO0/qFPR0VFovFKOKS8vL2Ky0B4kJibSs8eXfPVVV44cPphufXJyMnt+3c1771W1epuRkZF4a1O3BVoi09zciYyMtHjsZWbYyJG0btuW7xcsyNJ571ViStuWaDUaIo1tiaIodO3Zk4/bt2fdP2gP/pfy6lU8jn2Ai1tKu+ns6sGjWPN2897tGzx6GE2pslk7rpfMGcu4wf+Hg0Nuyr5jfT0XOUeOmnKpKMpmwBdwBGaoqrpAUZSnwAygKRAPNFdVNUJRlKXAY6AS4A0MVFV1vaIodYD+qqo2NW5zNnBSVdWliqIMB5oBuYGjQDfVihZCUZRQ4zZPviQeLfA98OJWeA9VVY9mNQ8shZP2LmhGaeZ9/z0d2nfAycnJbF2xosXo9PkXdO/ZEyen3Lz11ltW3fVOtUcLy9LfoqjboAl1GzThxNED7Niyhk7d+pnW3btzi41rl9F3wCjTss7dv8XN3YPn8c/4ftYEjh3ZT7Wa9bIQlqV8SBt5xmnWbtrMgN5f06BuHXbv3cvI8eNZMGMGAPUDAqgfEMCpM2eY88MPpuWZBJR+UdqysxQPCp6enuzctgNXV1cuXLzAN/2/Zf2adTg7OzNk0GCCBg9CsVGoUL4Cd+/etSIW61m626SqhrjSr/jP3J2yfNRlbcMxMdGMHTuawYODTaN469atISRkCv7+ZVi1agVz5sxk4EDrOuQZxWVdnXp57P6lSvHrju04OTlx8NBhvu73DTu2brEinldrDzp2SN8eACxbsgSNlxcxDx7QvUcPihYpQsWK1l9AWM4n68vvl0OH+LBePTq0aE7YpUuMnDadlbNmoNPpOHvhIkumTMLRwYGvhg2nlJ8f71Uon0lAluLJPOgXMZcvV45N6zdw/fp1gkcMp2aNGiQkJLA/NJSd27aRL28++gcNZNv27TRtYuX0Rkv7yyDp0UP7uHn9CoOGG6bKli3/LjeuXWbsiP7ky+eCX4nS2KRps5cvnkvJUmV5q1RZ6+IxxZV+0avmlYODAz8uWYLGS0PMgwd069GdIkWKUMnKOmXpmMpKQ7Ny1QY8PT25d+8uA/r3oWgxP9NNHoAZM6ZQvnwFypW37lloQ0wWQkqbxorjM62QcePQajTExcXRt39/tm7bZholep0xvSzW5UuWoNEYyu7/unenaBbK7lXjepnsmFevIrNzh16vZ8fGZbTu2DPL2+7UayhJSYmsWzaT65fDKV4qkzZT5Dg5bYSus6qqFTF00r5WFMUDyAMcU1W1AnAQ+L9U6fMDNTF0riZYsf3Zqqq+p6pqWQyduqb/IMaM4pkJHDAufxc4b+nDiqJ0VRTlpKIoJxctXpxuvVar4X6qEZiIiEi8PM2n+Wg1KWmSk5N5+vQpLi4uhIWHMX3mDBo3bcKKlStZtGQxq9cYnslq2aIFq1euZPHCRTg7O1PI17rn5wBc3Tx5GJMy8vXwQTSubhk/mFypai3OpJqS+fBBNPNmjKNT1754afOblru5G+5UOeZ2onK1AG5ev5xpLKs3bODjz7/g48+/wMvT02y0IiIy0jRt8gWtl5fZCEtEVEqan3fupH6dAAAa1qtH+IWL6fZX8e23uX33Hg+tuJOo0WjMRs8iIiPTTdHKqOxy5cqFq6vhhTD+pf0pWKAgf926BUBA7QCWL/uRH5cso0jhwhTy9c00lqy4/zCB/G6Opr+93RyIfJTA/dgE8ruljLx5uzoQ8SiR+w8T8E41Iuft5khEqhE7a3h5eREZmTJKHCVNo4cAACAASURBVBUVhaen9dMj4+LiCArqz5dfdqVMGcMFbWzsQ65du4K/v+F5i3r16hMeHpbptlatWUPrtu1o3bYdGi8v7t9PiSsiIhJNmjL0TjNKljqNh4eHaYpmVFQU7sbphnnz5jV1rGrXqklycjIPHz7MNDatRsP9+yl1KjIyfTyp05i1B2FhTJ8xg8ZNDO3BwsWLWbXa0B6Y4nV3p17duoSft9hcmVm/fQef9u3Hp3374enuRmR0ymhTZHQMnu7WP3/z8697aWB8rq9cqVIkJiUR+/gxGg8P3ilbBldnZxwdHKhesSJ/WjGaqdVoiEhdbpERFo49LREW8im1YsWKkTt3bq5eu8qx48cpWMAHdzd37O3tqV+vHmfOnbX6O7q5e/IgVbv5ICbaNP08tfNhp9m2eQ19+g83m73QrGU7Rk+YzYChY1FVFa13yotuNq9fyZMnj2j3aRer43nhdeQVYBphM9SpelbVqRe8PM1nokRFRWVpuvSLtsPHpwAVKrzD1Ssp55Iff1zMo9hYuvfobfX24EVbnbotsJBPWq3FYy+z7QLkyZOHJo0bZymfXiWmtG1J6vOTRpNSdvXr1SMsCzG9alyZbReyV169ChdXDx6lGqV/HBuDs0tKu5mY8JyIv2+zcOYoJo3oxe2bV/hp/kTu3LJuRoe9fS5KlavEhXPpp3HmJDaKku3/vZF8eSN7/ee+VhTlLHAMw0hdCSAR2GZcfwookir9ZlVV9cbpkFoyV1dRlOOKooQB9YCsP2mbcTz1gHkAqqrqVFV9ZOnDqqouUFW1kqqqlb5M88YlgDL+Zbh1+zZ3794lKSmJ3b/sJiAgwCxNQEAAP28zhLBn717ee8/wTMWSRYvZuW07O7dtp0P79nzZqTPt2rYDME39+vvvv9m3bz+NP/jA6i9cpFgJIiPuER11n+TkJE4eO0SFd8xfyhFx/57p/2FnT6LRGi4+nsU9ZfaU0bT8+DOKv5XyGKJOp+PpE8MzWLrkZMLO/I5PwcKZxtKudWvWLlvK2mVLqVu7Fj/v2oWqqpwLDydv3rzpOnRenp7kcXLiXHg4qqry865d1K1Zy7Tu5OnTAJw4dYpCvoYHk2/duWO6Q3fxzz9JSkrC1Yo585bKrk7tNGVX23LZPXj4EJ1OB8CdO3e4dfsWBQsY7jC/KLvHjx+zdv06Wraw7qF+a+0Ni6ZlFcP0kreLOPMkXkfU40QOXYihZml3nHPb4Zzbjpql3Tl0IYaox4nEJeh4u4gzYHgT5p5zWXuOp1Sp0ty5c4e//75HUlIS+/btoUaNmlZ9NikpieDgQTRq1Ji6dVNGdPPmzUdcXBy3bxs6widP/k7hwkUy3d4nbduyYc1qNqxZTb26ddi6bRuqqnL23DlDnUpzIvfy8sLJyYmz586hqipbt22jbkAdAOoE1GbLz4by3fLzNuoabxhER0eb6lRYeDh6VTV14F+mTBlDnbpjrFO7dqdvD+oEBLDVWKd+3buXysY6tXTxYnZu387O7Yb2oEvnznzSrh3P4uOJizM8Z/gsPp7fjh2juIU3VKbVpsmHLJ8+jeXTpxFQtQo79u9HVVXC//yTvHmcLD4rlxGtlye/nzsHwI3bt0lMTMTNxYUq777D1Zt/8TwhgWSdjj/Cz1O0UOY3MMqUKcNft2+Z5VMdY5mY59PPxnzaY8qnO3fvkpxseMb03r173Lx5E5/8Pnh7e3MuLIz4+HhUVeX4iRMUK1o07a4zVNTvLSLv3yUq0tBunvjtIO9UNG83/7pxjWULZ/N1/+E4u6TUB70+pX28/dcN7ty6Sdny7wJwYN9uws+donvvgf/oTXavI6/S16nfrKpTL5QsVYq7d2+b2oPQ/XuoXt26lzo8efKYxMREAB49iuX8+TDTcb9j+8+c/P0EQ4NHZjmvypYpw61bKfm0c/du6tapY5ambkAAW3425NMve/ZQxZhPGUl9IycpKYkDBw9mKZ9eJaa6deqwc/duEhMTuXP3Lrdu3aJc2bLpyu7ob79RIgsxvWpcGcmOefWqChTyIybqbx5ER5KcnMy5U0cpVa6Sab1jbieGTljEgFFzGDBqDr5FStCx20AKFsr4eyckPOfxI0M+6XQ6Lp8/jZe2QIbpRc6VY6ZcGqdKNgCqqar6zDjN0RFISjUtUof5d0o9LPCiZUjGvCPraNy+IzAXqKSq6m1FUUa+WJdFL4vnldnZ2TFoYBA9vuqFXqenefNAivv5MXfePPz9/akTEEDL5i0YOmwYzZoH4uziQsi48Zlu99sB/Xn06BF2dnYMHhSEs7Oz1THZ2trS7rNuzJg4Er2qp0btBvgULMTWDSsoXLQ4Fd6tQuie7Vw8fwZbWzuc8uSlU1fDq/X379lOZMTfbN+yhu1b1gCGnydwcHBkxqQR6HTJ6PV6Spd5m1p1GmYpr2pVq8bh336j6cdtcXR0ZPSQIaZ1H3/+BWuXLQVgaP/+DBs7loSEBGpUrUrNaob55cODBjJxhmGaV65cuRg+cCAAe0JD+XnnLuzt7HBwcGDi6FFWTbews7MjaEAQPXsbyy4wED8/P+Z+Pw//0oaya9G8BcHDhxHYIhBnZxcmGMvujz/+YN78edja2mJrY8vQwUNMdy8nTp7EZeMd565dupp+2sBa0zqVocpbrrjltefw2OrM2H4DO1vD91l16B6h4THUKePBvlHViE/UEbTcMFL56Fmy4acKggwnnNk7bvLomeFibviqP40/W2DLgfMxHDif8bNBGeVV377f0L9/P/R6HR9+2JSiRYuxaNEPlCpViho1anHx4gWGDRvMkydPOHr0MEuWLGLZshXs37+Xs2fP8PjxY3bt2gHAoEFDKVHiLfr3H8SwYUOwsbEhX758BAUNySQSc7Vr1uTQ4cM0DmxObkdHxowcaVrXum07NhhHvIcNGULwiBE8T0igVo3q1KppuPjs0qkT3wYFsXHzZvLn92bqRMObH3/Zs4c169Zja2uLo6MDk8aPt7pODQ4KokevXuj1eloEGtqDOfPmUeZFe9DC0B40DTS0BxPHv7w9eBATQ79vvwUgWafjww8+oIaFt2C+TPWKFTl68hRtuvfA0cGB4N4pox+f9u3H8unTAJi1dBm/HDzE84QEmnXuQuD7Dfi/T9rRp1Mnxs2Zy+qtP6MoMKzP1yiKgnPevHzSvBmdvh2AokC1ihWpUalSRmGY5dOQoCB69OqJTq+nRWBzYz7Nxd/fn7oBdWjZogVDhgXTJDAQFxdnJo43TOo4ffo0i5cuwc7ODsXGhqGDh+Dm5oabmxsN6jegbYf22NraUrpkKdq0am11Htna2tLhix5MGT8MvV5PrTrvU8C3MJvWLadI0RK8U6kqa1cuIuH5c+bOMJSZh4cXfQaMQJesY/woQ3vkmNuJrr2+NU2T/3HRbDw8NXw33FCGFd+rTvPW7a2O63Xk1Z07d+j77TeA4aKy8QeNLb5ZNeO8sqN3728YFPQNer2eDxo3oUiRYixdspC3SpaievWaXLp0kZEjhvD06RN+++0Iy5YtYtHin7h16y+mTZuEjaKgV1Xatetoejvm9OmT0Wq1fN3b8BxpzZoBfPpZp5eFki6fuvU05FPL5oZ8mj13LmX8/albpw6tWrRgcHAwjQMDcXF2ZtKElIlCDT/8kKdxcYYbVvv3s2DuXPL7+NCtVy+SkpPR63RUrVKFNq1aWZ1PrxJTcT8/GjVsSGDr1tjZ2jJ00CBsbW2JiYmhzzcpZfdh46yV3f9SXgEMGDSI30+dIjY2lvqNGtGze3dat7TuJqutrS3NPurM0rljUVU971atiza/L3u2r6FAIT9Kl3t5WzdpRC8Snj9Dl5zMxbDf6dQzGKc8eflpwUSSk5NQ9XqKvVWWyjXftzqfRM6hZPVFE2+KoijNgS6qqjZTFKUUcAb4ANimqmpeY5o2QFNVVb8wPkO3TVXV9cZ1T1VVzasoii9wCCiJocN2BhgFbAb+xDCiZothFHC9qqoj027LQmyhpHqGLoN4VmOYijldURRbII+qqo9f9p3jn8Zlq8I5fv7Omw4hnap+1t/1/zfpHdI/l/SmlQ86nnmif9nh4dY/s/Jv8XD+J/dxXi9dtmoJUsTf/utNh5COk2+RNx1COqf+vJd5on9ZxZI+mSf6l0U+jHvTIVjk7Z7nTYcg/otsOZz132X9N7RpWCFHvP7ym1kHsukZMcXU3gH/el7mpCmXuwA7RVHOAWMwdLiyTFXV28Ba4BywAjhtXB4L/ACEYejc/acnGffBMKUzDMNUzH8ynVMIIYQQQgghTHLMlEtVVROAxhZW5U2VZj2w3vj/L9J8PnW6gcBAC/sIBtL9ynDabVlYXyeD/aSOJwJo/rLtCCGEEEIIIURW5KQROiGEEEIIIYQQqeSYEbrsQFGUTUDa15gFqaq6+03EI4QQQgghxP8KmxzxpN+/Tzp0WaCq6n/2ffBCCCGEEEII8QpkyqUQQgghhBBC5FDSoRNCCCGEEEKIHEo6dEIIIYQQQgiRQ0mHTgghhBBCCCFyKHkpihBCCCGEECLbk7dcWiYjdEIIIYQQQgiRQ0mHTgghhBBCCCFyKJlyKYQQQgghhMj2FEXmXFoiI3RCCCGEEEIIkUNJh04IIYQQQgghcijp0AkhhBBCCCFEDiUdOiGEEEIIIYTIoaRDJ4QQQgghhBA5lLzlMhtLSta/6RDM+Gqc33QI6djmzv2mQ7DIJhu+henw8ApvOoR0ao4++6ZDSOfixPfedAjpKdnz3tvFZ7nedAjpFHsS/6ZDSMclj8ObDiGd53/ffdMhpOPp7fOmQ8iA+qYDsCD7nWOEdUoUdH3TIYj/QtKhE0IIIYQQQmR7NnIvw6LsedtXCCGEEEIIIUSmpEMnhBBCCCGEEDmUdOiEEEIIIYQQIoeSDp0QQgghhBBC5FDSoRNCCCGEEEKIHErecimEEEIIIYTI9rLhr0JlCzJCJ4QQQgghhBA5lHTohBBCCCGEECKHkimXQgghhBBCiGzPRuZcWiQjdEIIIYQQQgiRQ0mHTgghhBBCCCFyKOnQCSGEEEIIIUQOJR06IYQQQgghhMihpEMnhBBCCCGEEDmUdOiEEEIIIYQQIoeSny0QQgghhBBCZHs28qsFFkmHLgc6+ttRpkydjF6vp3lgC774/Auz9YmJiYwYNYJLly7i4uLCuO/G4+PjQ3JyMt+NHcOlPy+h0+n4sHETOn3RyaptZubkyePMnzcbvV5How+a8HHbDmbrw8LOsuD72dy4cY1Bg4dTs1Yd07pFC7/n9xPHUFU977xTiW49eqOk+p2RUSOGcP/+PebNX5qlmFRVZfykSRw6fARHR0fGjhqJf+nS6dKdv3CR4JEjeP48gVo1azB4wAAURWH3r78yd/4Crt+4warlP1LW3x+ApKQkRn03lvMXL6AoNgwa0J/KlSpZFdPhI0cImTQJnV5PqxYt6NK5s9n6xMREhgwbxoWLF3F1cWFSSAgFfHw4euwY02fOJCkpCXt7e77t25cqlSsDsGv3bhYsWoRep6N2rVp807dvlvLp+PFjzJo1Hb1eR5MmzejQ4TOz9WfPnmbWrBlcv36N4cNHUadOPQCuXLnM1KmTePbsGTY2Nnz66efUq9cAgFOnTjJv3mxUVSV37twMGhRMwYIFrY5pfMdS1CvnScyTRD787oTFNMM+KkGdMh7EJ+kJ+vEC528/BaBlFW96NS4CwJydN9l0/D4AZXzzMfGz0jja2xB6PoYx665kKZ/gRZ2azKEjxjo1ciT+pUulS3f+4kWCR4zkeUICtWrUYPCA/iiKwqy589h34AA2Nja4u7kxdtRINF5eXL9xk2GjRnHh0iW+7tmTTp99anVMR44cIWTyZPQ6HS1btuTLTp3M1icmJjJ02DAuXryIi6srEydMoICPj2n933//Tcs2bejRrRuff2Yo+8ZNmuCUJw+2NjbY2tqyasWKLOVT2JmTrPxxAXq9ntp1G9Kk+cdm63dv38TB/buxsbEln7MLnbv1xdNLA8DalYs5d/okAM1ataNKtdoALJw3lT8vhpPbyQmALt37UaiIn9UxvY56Pn78d5w5c5q8efMCMGjQUEqUeMvqmP44dYLFC+ei1+lp0LAxrdp8YrZ+6+b17Pl1B7Y2tji7uNLr6/5oNFoARo8YxOXLFylduixDh481fWbooL7Ex8cD8OhRLCVKlGTQ0NFWxwSGej5l/gKOnjyJo4MDw/v1pVTx4unSzV32Izv27ePJ06cc2LDetHzbr3uYuXgxXh4eAHzUrCktGjUC4H5kJGNnziIiKgpFUZg2aiQ+Wm2mMR05eoSJL+p5i5Z0tlDPg4cb67mLKyEW6nmrj9rQvWtKPV+xciUbN29CVVVatWxJx/bm5y1r8mn8xEkcOnLYeI4ZlcE55oKxPXhOrRo1GTzQcI559OgR3wYN4t69e/j4+DBlYgguzs7s2x/KrHlzsVEMx9+gAf159513rIrp8JEjTDCeY1pncI4ZnOocM9l4jgH4YdEiNm7Zgq2NDYMHDqRG9eokJCTw+ZdfkpiYiE6n4/0GDfiqR48s5dOrxBUbG0u/AQMIP3+eFoGBDB00yPSZpKQkxk6YwO8nT2JjY8PXvXrxfoMGrz2mjPLKmm1m5swfv7Nk0Tz0ej31G3xAi9btzNZv27KevXt2YWtri7OzCz2++hYvTcrx8+xZHP16d6FylRp82fUrAFb9tISDob/yNO4py1dtzVI8IueQKZc5jE6nY+KkEGZMn8na1ev45ZfdXL9+3SzNlq1bcM6Xj00bNtO+XXtmzZkFwJ69e0hMTGT1yjUsX/YTmzZv5N69e1ZtM7OY5s6ZwejvQvh+wTIOhO7j1l83zdJovDR88+0g6tQ1b2wvXAjnwoVw5sxbxNzvl3D58iXCzp0xrT9y+CCOuXNnMZcMDh05wq1bt9mxZTMjg4MZM368xXRjxo9nxNBgdmzZzK1btzl89CgAxf2KM33yJCq++65Z+vUbNwGwae1afpg3l8lTp6HX6zONR6fTMXbCBObOns2WDRvYuWsX165dM0uzcfNmnPPlY8fWrXzaoQPTZswAwM3VldnTp7Np3TrGjh7NkOBgAGJjY5kyfToLv/+ezRs2EBMTw7Hjx63OI51Ox/Tpk5k4cQrLlq1k79493Lx5wyyNRuPN4MHB1K//vtlyR0dHhg4dzrJlK5g0aSqzZs3gyZMnAEydOong4JEsWrSM+vXfZ/nypVbHBLDx2H06zz6T4fqAMh4U0ThRf+QxgldcYlS7kgC4ONnRu0lRWk88SauQk/RuUhTn3Ib7VqM/KUnwykvUH3mMIhonavu7ZykmMNap27fZsXkTI4OHvrxOBQ9lx+ZN3LqdUqc6ffYpm9asZsOqlQTUqsW8H34wxO3izKAB/fni045Ziken0zEuJIS5s2axacMGdu3axbU0x+6mzZtxdnZm29atdOzQgenGOvXCpClTqFmjRrptL5w/n7WrV2e5M6fX61i+ZB79gkYxdvI8jh89yN07t8zSFCpSjOFjpzNm4hwqVanB2pWLATj7xwn+unGNURNmMWzMVHb9vIH4Z89Mn/u4Q2dGT5jN6Amzs9SZe131HKBHj14sWrSMRYuWZakzp9Pp+GH+LIJHjGPGnEUcOrif27f+MktTtFhxJk2dy7RZP1Ctei1+XLrAtK5Fq4/p029Q2s0ydsJ0ps6Yz9QZ8ylZsjRVqtW0OqYXjp48ye1799jwwwIG9/6KkDlzLaarVaUyS6dNtbju/dq1WDF7FitmzzJ15gBGTp1Kx9atWDv/e5ZMm4q7i0um8eh0OsZPCGHOzFlsXL+BXbszruc/bzHU8xkzzev55KlTqFE9pZ5fvXqVjZs38dOyH1m7ajWHDh3ir1vm9TQzhw4f4datW+zYssVwjhmXQXswztgebNnCrVu3OHzE0B4sXLKEqpUrs2PrFqpWrsyiJUsAqFqlMhvXrGHDmtWMGTmCEaPHWBWPTqfjuwkTmDd7Nls3bGDHS84xO43nmKnG9uDatWvs3L2bLevX8/2cOYwZPx6dTkeuXLlYvGABG9euZf3q1Rw5epSz585lKZ9eJa5cDg707tmT/v36pdvu/IULcXd3Z/uWLWzZsIFKFSv+KzFllFfWbPNl9DodixbMZsiwsUyb+QNHDody57Z5m1CkWHEmTJ7N5OnzqVq9Fj/9uNBs/ZqVy/AvU95sWcX3qjJu4iyr4xA5k3TocpjzF87jW9CXggUKYm9vz/vvN+TAwQNmaQ4ePECTJk0BqFevPr//fgJVVVGA+OfPSU5O5nnCc+zt7MmTJ49V23yZy39ewid/AfLn98He3p7aAfX47bcjZmm03vkpWswPG8V8rFxBISkxkeTkZJKSkkjWJePqZrjQjo9/xqaNa/nkE+tHK1LbH3qAwKZNUBSFCuXL8eTJU6KioszSREVFERf3lLcrlEdRFAKbNmHf/lAA/IoVpWiRIum2e+36ddPomIe7O/ny5eP8hQuZxhMWHk4hX198CxryuXGjRuwPDU0TcyiBzZoB8H6DBhw/YSi70qVKodEYRjCK+/mRkJhIYmIid+7epXChQri7G/KsapUq7Nm71+o8unjxAgUKFMTHpwD29vbUq9eAw4cPmaXJnz8/fn7FsbExby58fQtRsKAvAJ6eXri5ufHoUSwAiqLw7FkcAHFxcXh4eFodE8DvV2OJjUvOcH2D8p6mkbczNx/j7GSHl3Muavl7cOTiAx49S+ZxfDJHLj6gdhkPvJxzkdfRltM3HgOw6fh93q/glaWYAPYfOEBgkw8NdapcOZ48fUJUVLRZmqioaOKexvF2eWOdavIh+4zl/GIkByA+Ph4Fw/Hg4e5OuTJlsLPL2qSJ8PBwfAsWpKCxTn3QqBGhlupUU0N78H79+pz4/XdUVQVg3/79FCxQAL9ixbK035e5fvUyGm8fNNr82NnZU7labU6fPGaWpnSZCjg4OALgV7wUDx8Y8vDe3duULF0OW1tbHBwd8S1clLCzp145ptdVz1/F1St/kj+/D97ehnazZq06nDhu3m6WK/+2KZ/eKlmamOiUula+wrvkfsnNrvhnzwg7d4YqVdN31jNz8NhxPqxXD0VRKFeqFE/i4oh+8CBdunKlSuHpbv2Nkeu3bqHT6aliHG1yyp0bR0fHTD8Xfj4cX9+Uet6oYfp6HnoglGbGet6gfn1OnDCv5wUKFMDPL6WeX79xg/Jly5E7d27s7Oyo+G5F9u3fZ/V3Adh/wHBsGc4x5Xny5EkG55g43q5QwXiOacq+0P2Gz4ceoHkzQ8zNmzU1nXucnJxMs1Ti4+PByilmls4x+9Lk077QUJobzzENU51j9oWG0rhRI3LlykXBAgUo5OtLWHg4iqLgZBwVT05OJjk52WwGzeuOyyl3bt595x0cHBzSbXfTli2mETAbGxvc3Nz+lZgyyitrtvkyV6/8iXd+H7Te+bGzt6d6zQB+P3HULE3ZciltQom3SvMgJqW+Xb92mUePHlLhbfOO7VslS+Pm7mF1HCJnkg5dDhMVGYk21fQUrUZDVFSkWZrIqEi0xiF4Ozs78ubNy6NHj6hfvwG5HR1p3OQDmgU2pUOHjri4uFi1zZeJiYnC0yvl4tjT04uYmKiXfCJFaf8ylK/wNh3bt6Jj+9ZUrFiZQoUKA7D8x8W0at3WYkNujYjISLzTfK+INCfbiKgoU14Z0miJiHz5dy/51lvsPxBKcnIyd+7e5cLFi9yPiMg0nsi08Wi16eKJjIzE29sbSCm72Fjzi8df9+yhVMmS5MqVC19fX27cvMnde/dITk5m3/79VsXyQnR0lGkKF4CXlxfR0daVXWoXL14gKSkJH58CAAwYMIigoG9p06Y5v/yyiw4d/lmnPCNaVwf+fvjc9Pf9hwloXR3Quubi74cJKctjE9C65kLr6sD92FTLHz5H65r1ehURGYW31jslDo2WiDTHSkRUmuNJqyUiMiVPZ8yZQ/0Pm7B9106+6tE9yzGkFhkVZaovABqNJl39TZ0mdZ16Fh/PkqVL6d6tW/oNKwrde/WiXfv2rN+wIUsxPXwYg3uqDry7hycPH8ZkmP5g6C+Uq2CYsmzowJ0kIeE5Tx4/4tKFc2YXLBvX/Miwgb1Y9eMCkpKSrI7pddVzgIULF9Cp06fMnj2DxMREq7cVExONh6fG9LeHpxcPYjLOp72/7uLdiu9Zvf1jxw5TrsI7ODnlsfozL0TGxKD1SilDjacHkS+JzZJ9R47SvtdXDBo3ztTO3bp7l7x58jDwu7F07P01MxctRqfTZR5P2uNOqyEy3XkvJU3qeh4fH8/SZUvp3tW8nhcv7sep03+Y0hw+cpiILLSdYDzHeKc+1jVmx7ohTRRajSZNGkPsMTExeBnPnV5eXjxI1Wnes28fzVq2oufXfRgzYoRV8Vg6x0RaeY5J25ZoNRoijXHqdDpat21L7fr1qVa1KuXLlbMqnv9EXBl5bBwlnz1nDh998gnfDBhAdBbq6OvIK2u2+TIPHkTj4ZlyLeXh8fI2Yd+eXbz9rqFN0Ov1/LhkAR0//z+r9yf+u/zXdOgURflGUZRw47++iqIUURTlkqIoyxRFOacoynpFUZyMaSsqinJAUZRTiqLsVhQlv3F5qKIoIYqinFAU5bKiKLVesr8iiqIcUhTlD+O/6qnWDVQUJUxRlLOKokwwLiuuKMoe47I/FEWxfr5QKqrlWMzTWEwE58+HY2Nry87tu9iyaSsrVv7Enbt3rNrmS2OysAFrP3/v3h1u37rFjz+tY/mKdZw98wdhYWe5du0K9+7dpXqNDIsg87gsfDOFtHllIU0msbdsHohWo6Vtx08JmTyFtytUwNbW1op40ku7p8ziuXrtGtNmzmSEccqli7Mzw4YMYUBQEJ937oyPj49VsaTsz9LSrN19jYmJZuzY0QwaNNQ0urFu3RpCQqawfv0WGjduwpw5M7O0zcxYKiJVTV++mHU53wAAIABJREFUhhWW0/8T1tSXzNL06dWLvTu20+SDxqxcs/aNxTPv++/p2KGD6e57asuWLGHNypXMmT2bNWvXcupUFkbJLO0vg6RHD+3j5vUrNG7WGoCy5d+l/NuVGDuiP9/PmohfidLYGOtzm3ZfMG7KfIaPnc7Tp0/ZsXXdq4T0kqgss1TPu3btzvLlq5g/fxGPHz9m5cqfrN+gxYbTctID+/dw9eqftGj1seUEFhw+uJ9atetaH08msVk8tjJQs0pltixZzMo5s3nv7bcZOXUaYOgYnDl/nj5ffsnS6dO4e/8+2/ZkPqPgVet5h/bp63mxosXo9PkXdO/Zk169v+Ktt97KUttp2Gf6ZWnbGovnISsapAb16vHzpo3MnDqF2XPnWRePhWXWnmNelse2trZsWLOGvbt3ExYezpWrV62K5z8RV0Z0yclERETwzttvs27VKiqUL8/kadP+lZgyXG7FNl8aUxaupQ6G7uH6tcsEtvgIgF92/cw7FSvjmeomkfjf8l/xUhRFUSoCnYAqGI6f48ABoCTwpaqqRxRFWQz0VBRlBjALaK6qapSiKG2BscCLJ1ftVFWtrCjKh8AIIKMnbCOB91VVfa4oSglgFVBJUZTGQAugiqqqzxRFeTEfZQUwQVXVTYqiOJJBZ1pRlK5AV4Dp02aYXlrygkajMbuLGBEZiaen+dQxrUZDRGQEWq2W5ORknj59iouzC7t276Z61WrY2dnh7u5OhfIVuHjxomEEIZNtvoynpxfRqe5CRUdH4e5u3RS7o0cOU7KUP7lzG062ld6rwqVLF3DK7cTVK5f54rO26PQ6HsXGEjSgDyGTZrx0e6vWrGX9JsMzbmXL+JuNVkVERqLxMo/L25hXKWki0Hi9/Lvb2dkR1P9b098dvuhE4UKFMv2uWo3GPJ6I9PvSarXcv38f79RlZ3zG5H5EBH2/+YZxY8bg6+tr+kydgADqBAQAsG7DhixdlHh5eRGZ6vtHRUXh6Wn99Mi4uDiCgvrz5ZddKVOmLACxsQ+5du0K/v5lAMO03wEDvrF6m9a4/zCB/G6OwCMAvN0ciHyUwP3YBKqUcDWl83Z14PiVWO4/TMA71Yict5sjEalG7F5m1dq1rN+0GYCy/v7cj7hvWhcRGYEmzbHirUlzPEVEpKt3AE0af0DPPn34qruFETIraTUa7t9PiScyMjJ9nTKm0aapU2FhYezZs4fpMwzPhCk2NuTKlYtP2rUzbcPD3Z16desSfv48Fa18PsXN3ZMHMSlTAx/EROPqln66z/mw02zbvIZBw0Owt7c3LW/Wsh3NWhpeBPD9rIlovQ0vIXgxFdve3p5adRqwa9tGq+KB11PPAdNU4ly5ctG4cRPWrFlp9TY9PL2IiU4ZZYqJjsLdwrSos2dOsX7dSsaMm4K9fS6rtv3k8SOuXLlE0JBRVsezbts2Nu/aDYD/WyWISDWVODI6Bi8P66dWujo7m/7folEjZi9ZCoDG05OSfsUokN8wuhFQrSrhl/7MdHtarcb8uIuIxMvCee9+hIV6Hh7Gr3v3MH2moZ7b2Njg4JCLdm3b0bLF/7N332FRHH0Ax78jqNgQC6BREw1qFNSYZgMFS+yCxNgwMTGvSYw1xS6WxF5S7L2liKCoIKhYUcGo8TV2TFVBI8UOiEjZ9487DjgOOBIL5P19nidP5G5273ezszM7O7Nz3fHs3h2ABYsWZputkRsfX1/Dc9QNnJyIjs56ruc8/6oYjZpnTVOpUiXi4uKwtbUlLi7OMHU+q1dfeYWoq5O5fft2vlMKTbUxtma2McZ1SUxsbI5trcuV47VXXyXsyBHqmFgk53HElRsbGxtKWVnRto1u8aL2r7/Olm3bnkhMeeVVfvvMS6VKlbmZZebAzZtxVDBRJs6cPsnWzT5MmTbPUCf8+ssFIi6cY/fO7Tx4kERqaipWVqXo1/8/Zn9+UfGobtD+2/xbRuhcgK2apiVqmpYAbAFaAlGapmU8lPC9Pt0LQANgj1LqFOANZF1+L+Mq4b9AzTw+sziwUil1FtgEOOpfbwes1TTtPoCmabeUUuWAapqmbdW/9iDjfWOapq3QNO1VTdNeNe7MATjWdyQyKoprf10jJSWFPXt206pVq2xpWrZsRXBwEAD79+/jtVdfQylFlSr2/HTiBJqmkZSUxLlz56j5XE2z9pmXui+8wF9/XSU6+jopKSkcOrifZs1a5L8hYGtnx7mzp0hL083NP3v2NM/WeI4uXT34foM/6771Zd68hVSrVj3fzhxA39698N/og/9GH9q4uREYFIymaZw+c5ayZcvmqFxtbW0pXboMp8+cRdM0AoOCae3mmudnJCUlcV+/ityRo0extLAw6xmkBk5OXImM5Oo1XT7vDAnBzc0tWxo3V1cCt28HdFMrm7ymO3b34uMZMmwYI4YN46XGjbNtc1M/TefuvXv4+vnxhqdnvrFkqFevPlevXuX69b9ISUlh//69ODubt4hCSkoK3t5j6dChE61btzG8XrZsORITE4mK0i0wcOLETzz3XE2zYzLHvrM38GyquyhsXNOa+KQ04u495PCFm7jUr4h1KUusS1niUr8ihy/cJO7eQxKT02hcU3eh6dm0CnvP3MjrIwz69uqFv88G/H026MpU8A5dmTqbUaaydwxsbStTukwZTp/Vl6ngHbTWd7izLrpw4OBBk89oFoSTkxORUVGGMrUrJARX1+zl183VlcAgXX2wZ98+Q5lat2YNO4OD2RkcTD8vLwa+9x59+/ThflISiYm65x/vJyXx49Gj1HYwf0JBLYe6xEZfIy42mtTUFI7/eIiXXmmaLc2VS3+wftUiho+chHX5zA54enoaCfG65xyjrlziauRlGjTSLUp057aunGuaxsmfjlKtxnNmx/Q4yjnoRu0yYgoLO0StWuY/i1i7zgtc/+saMfp6M+xwKK81zV5v/vnHbyxb8g3jvL/Axsb854OOhB/i1VebUaKEeR1AgJ5duxoWMXFt1pwd+/ejaRpnL16kbJnSBXpWLuvzdoeOHaOW/gaUY5063EtI4PZd3Y2YE6fPUOvZGib3kZWTo66cX9OX85DdOcu5q6sr2/XlfO++fbymL+drV69hZ1AwO4N05fw/A96jT2/dDYOMKY7Xr19n//4DdOrYMd9Y+vbujb/vRvx9N9KmtRuBQUH6NuZMHm1MaU6fOaNvY4Jo7eoGgJtrKwK262IO2B5kaHsiIyMNo0AXIiJISUnBxsaG/DRwciLSqI1pbdTGtHZ1JUDfxuzeu5em+nxq7ebGzpAQw7PZkZGRNGzQgFu3bhmmNz548ICjx44VuN76J3HlRimFa6tW/HRCtyLusePHC/Qs8OPIK3P2mReHOi9w/fo1YmOuk5qSwpGwg7z6WvNsaS79+Tsrl85n9PgvKJ+lThj+yTiWrvyBxSu+4+13P6CVW7t/ZWdO5O5fMUJH7qPaxgPYmj7teU3TmptID5Bx2z6NvPPnEyAGeBFdxzjjgR5l4nMf2f0ES0tLRo8cxfDhw0hLT8O9mzsOzzuwbPky6tevj2srVzzcPZg8ZRKePbpjbW3N9GkzAOj5Zi++mPo5vfv2Bk2jW9du1KlTB8DkPs1lYWHJR4NH4D1hFOnp6bRv34nnatbiu2/XUKfOCzRr7syvv1xk6lRvEuITOHbsR77/bh3LVqzDxcWVM6d+ZvCg90ApXnmlCU3N7Azmp5WLC4fDwunk4UEpKyumTplieK9Hn774b/QBYOL4cZlLSrdwpqV+xb+9+/czc85cbt2+zeDhI6hXty4rlizm1u3bfDhkKEop7O3smDnVvBXILC0tGT9mDIMGDyYtPR1PDw9qOziwaMkSnBwdae3mxhvduzPO25vO7u6Ut7ZmzqxZAPhs3EhUVBTLV65kuX5lxOVLl1KpYkVmz5nDL7/+CsCgDz6g5nPmX+haWlry8cefMnLkJ6Snp9G5c1dq1Xqe1atXUq9ePZydWxIRcYGJE8cRHx/PkSNhrF27mvXrf+DAgX2cPn2Ke/fusWvXDiBz2faRI8cyceJ4ihUrRrly5RgzZrzZMQF8PcCJpnVtqFC2OGHTWzA/+BKWFrrTyOfwX4Seu4mbUyX2f96cpIdpjPkuAoC791N1P1UwRvdM1qIdl7l7X7e4yiSfX/Q/W2DBwfM3OXi+YM8EAbRyceZweDidPLrry1Tmsy09+nrh76MboZk4bizeU6bofgrDuYWhTH29cCGXr1xBqWI8U7Uqk8aPA+DGjRv0frs/CYmJFFOK7318CNjkl20RFVMsLS0ZN2YMHw0ZQnp6Ot3d3ant4MDipUtxcnTEzdUVz+7dmTBxIl3d3bEuX545uazMmeHWzZt88pluBDo1LY3OHTvibGIVzNxYWFjQ792P+HLmRNLT02np9jrVajzH1k3fUbNWHV56tRl+G1aT/OABS+brYqlUyZYRoyaTlprGzM9HA2BVqjQfDPnMMOK8fNFc4uPvgqZ71u6dgUPNjulxlfOpU6fon/PRqF27Dp9+OrpA+TTww2F8MWWsYYnyZ5+tic8P63CoXZcmTVvw7boVPEhKYt5sXR1T2daO8d66f08Y+zHXrkbx4EESAwf0Yciwz3hJ/zxN2OEDeBotd14Qzq+9ypETJ3hj4PtYlSzJxE8yfwql39Bh/LBIt2LegjVr2B16kAfJyXTt/w7uHdrzQb9++AYGcujYcSwsilG+bDkm6be3sLBgxH/+w5DxE9A0jXq1a2dbATM3lpaWjB09ho+GDiE9LR0PD105X7J0KY4Z5dxDV867eejK+excVpzM6rNRI7l7967uPBo7BussI4vm0LUxYXRyN9HG9O6Dv+9GACaOH4/35Mn6nzFpQUsX3fk0cMAAPhszhi3btlG1ahW+mjMHgD379hMYFISlpSVWJUsyb/Yss6ZpZrQxH5rRxnTStzFz9W1MbQcHOrRvj3uPHlhaWDBh7FgsLCyIu3GDCZMmkZaejpaeTofXX8etADd8/2lcAO07dyYhMVF3M+bAAVYsWYKDgwOfjhjBOG9vZs2bR8UKFZiWJf+fRl4BJvdpLgsLC957fyjTPx9Peno6rdt2oMazNfHdsB6H2nV5tUlzvl+/kgcPkvhqbmadMGZ83j9L8v36lYQdPsDD5GQGDfSiTbuO9OrTP89tRNGjTM0FLmqUUi8D64BmZE65fBs4CbTQNO1HpdRK4CK66ZYXgLf1rxcH6mqadl4pFQqM1DTthFKqMnBC07SauXzm18BVTdO+VEoNANZomqaUUh2BSUC7jCmX+lG6o+imXG5TSpUELHIbpctw7058oTo4cbcTnnYIOTxrV+5ph2CSpgrf4Pete0lPO4QcXL44/bRDyCFijvkLTzwpaYWwPAGc/OX60w4hh+efMX8k60m5eTfPqv6pqG7xIP9ET1iJKs/kn+gpsCyUp5/MOyuqLlwu+IJMT8KLjs8ViUL1+aqwQnVtbMrkgS5PPC8LZTVVUJqmnUTXoTuOrjO3CrgNRADvKKXOABWBpZqmPQTeBGYrpU4Dp4C/MyS0RL/vo0BdIFEfyy4gEDihn9I5Up/+bWC4PpYjQJWcuxRCCCGEEEII8/1bplyiadpXgOEXTpVSNYF0TdNyrAuuadopIMecAU3T3LL8+wZ5PEOnadpvQNZfbxyX5b1ZwCwT6bM/gCGEEEIIIYQQ/8C/YoROCCGEEEIIIf4f/WtG6IxpmnYZ3WqW/4hSqgMw2+jlS5qmmb+UoBBCCCGEEEI8Bv/aDt2jomlaCBDytOMQQgghhBBCCGMy5VIIIYQQQgghiigZoRNCCCGEEEIUesWKxI8rPHkyQieEEEIIIYQQRZR06IQQQgghhBCiiJIOnRBCCCGEEEIUUdKhE0IIIYQQQogiSjp0QgghhBBCCFFEySqXQgghhBBCiEJPKVnm0hQZoRNCCCGEEEKIIko6dEIIIYQQQghRREmHTgghhBBCCCGKKOnQCSGEEEIIIUQRJR06IYQQQgghhCiiZJXLQqyUZeFayada2cLX/0/VClceZbAohGFVsrZ62iHkEDHntacdQg71R//0tEPIIWJuk6cdgkmvPl/paYeQw8NixZ92CDlYk/y0Q8ihmIX10w4hpwf3n3YEppUu/bQjMEF72gEUEYWvMa5XpczTDqFIK1b4DmmBKaU6AvMBC2CVpmmzTKTpBUxBd7Kf1jTNK699SodOCCGEEEIIIR4zpZQFsBh4HbgK/KSUCtQ07UKWNHWAcYCzpmm3lVJ2+e238A25CCGEEEIIIcS/TxPgd03T/tQ07SGwEfAwSvM+sFjTtNsAmqbF5rdTGaETQgghhBBCFHrFiv5QVDUgKsvfV4GmRmnqAiilwtFNy5yiadquvHYqHTohhBBCCCGEeASUUh8AH2R5aYWmaSsy3jaxifFDsZZAHcANqA4cVko10DTtTm6fKR06IYQQQgghhHgE9J23Fbm8fRWokeXv6sBfJtIc1TQtBbiklPoFXQcv11Xbiv7ApRBCCCGEEEIUfj8BdZRStZRSJYA+QKBRmm1AawClVGV0UzD/zGun0qETQgghhBBCiMdM07RUYCgQAkQAfpqmnVdKfaGUctcnCwFuKqUuAAeAUZqm3cxrvzLlUgghhBBCCCGeAE3TdgA7jF6blOXfGvCp/j+zyAidEEIIIYQQQhRRMkInhBBCCCGEKPSUMrVIpJAROiGEEEIIIYQooqRDJ4QQQgghhBBFlHTohBBCCCGEEKKIkg6dEEIIIYQQQhRR0qETQgghhBBCiCJKVrkUQgghhBBCFHrFZJFLk2SETgghhBBCCCGKKOnQCSGEEEIIIUQRJVMuiyBN05g5dy6Hw8OxsrJi+pQpONavnyPd+YgIvCdP5kFyMi2dnRk3ahRKKeZ98w0HDx3CsnhxalSvzrQpU7AuVw6AX377jS+mTychMZFiSrHxu+8oWbKkWTHNnr+AsB+PYmVVkqnjx1H/hRdypLtw8RcmzphBcvJDXJo3Y8yI4SiluPjbb0yb+yUPHz7EwsKC8Z99QkNHR346+TMfjxtPtapVAWjj2opBA941K5/Cj4QzZ9480tPS8OzuyXsDBmR7/+HDh3hPmkhERATly9swe9Ysqj3zDNf++os33uzBc889B0Cjhg3xHj9BF3/EBSZNnkJy8gNcnF0Yrc9Tc4WFhzN77lzS0tN5o3t3Br73Xo6Yxk+cyIWICGzKl2fu7NlUe+YZjhw9yjcLFpCSkkLx4sX57OOPadqkCYmJibyTZR8xsbF07dyZMaNGmR2TpmnMnDOXw+FhuvL0+eemy9OFC3hPnsKD5Ae0dHZh3Gjdd7979y6fjRnLX3/9xTPPPMOXc2ZT3tqa4ydOMPyTT6n2zDMAtGvTho8+/KBgcc2dZ1TO6+WMKyJCH1dGOR+JUoqFS5ay/+BBihUrRsUKFZj++RTsbG3589JlJn7+ORcuXmT44MEM6P+2WfHMfKsebRpW5mb8QzpPO24yzcSedXBzqkRSSjpjvr3A+agEADybVmFIp5oALN55ma3HogFwqlGOOf3rY1W8GKHnbzJ1029m50/2fJrL4TB9Pn2eS31wIQLvKZN58CCZli6Z9UHInj0sWb6CPy9dwue7b2ng6Jhtu+vXr+P+Zk8Gf/gBA/r3NzumWV99xeEjP2JlVZJpEyfiWM/UsbuI99SpJCcn07JFc8Z++qnhfPrBz4+NmzZjYWFBK+cWfDpsGHfu3uXTseM4FxGBR5cuTBg10ux8OnLkCPO+nEdaehrdPboz4N2c9cGkyZOIuBhB+fLlmTVjFs/oy+5vv/3G9JnTSUxIRBVTfLf+O1JTUxn4/kDD9jGxMXTu1JmRn5kfU0ZezVmwkPBjx7AqacXn48ZQv27dHOkWrVxFUMhu7iXEc2TXzmxxT5wxk4hff6W8tTWzJ0/mmapVOBcRwdR5Xxo+Y9C779KmVUuzY3oc9fm6DT7s2L0HgNS0NC5duUJoUCDlra3Ni+mb+YT9+CNWVlZMnTA+l5guMnH6DJKTk3Fp3pwxH49AKcWoiZO4EhkJQHxCAuXKlsVv/TquXb+Op1c/aj77LAANnZyYONq8+vNx1Z3x8fGM9fbm+vVo0tLSeLf/23h6eDyRmEL27GHJsuX6+uA7Gjjp6oPMNiiV4sUtDW2QuR5XXgXt2MHqdesAKF2qNBPHj6feCznPH1PCwsOZpW+Pe+TSHo/L0h7P07fHACtXr2ZLQAAWxYoxbvRonFu04NLly4wcM8aw/dVr1xj60Ue83a9fgfJp9tff6OtOK6ZOnIBjLuXce+p0Q9055pOPdeXceyKXM8p5fALlypVl07fruXP3Lp+Nn8C5iIt4dO7E+JGfmR1TYSM/LG6ajNAVQYfDw4mMimLHtm1M8fZm6syZJtNNnTmTyd7e7Ni2jcioKMKOHAGgedOmbPXzY6uvLzWfe45Va9cCkJqaylhvbyaOH0/Apk2sXbECS0vz+vxhR48SGXWV7Rs3MGnUKKbN+8pkumlffsmk0aPYvnEDkVFXCT96DICvlyxl0IB38Vu3hsED3+ObJcsM27z0YiP81q3Bb90asztzaWlpzJw1m8ULFrJlsz+7Qnbxx59/Zkuzdds2rK2t2R4QyFv9+jF/wXzDe9WrV8fPZyN+PhsNnTmA6TNnMtF7AoHbAoiMiiRcn6fmxjR91iyWLFpEgL8/O3ft4o8//siWZsu2bViXK8eOwEDe7tePr+frYqpgY8Oib75h66ZNTP/iC8Z7ewNQpkwZNvv6Gv6rWrUqbdu0MTsmgMNh4URGRrIjIEBXnmbkUp5mzGSy9wR2BAQQGRlJWLjuu69au5ZmTZqwIzCAZk2asFpfngBefqkx/r4b8ffdWKDOHGQt51uZ4j0hn3I+gR3btmYr5wP6v81W3434+2zAtWVLlq5cCUD58taMHTWSd99+q0DxbDkazXuLTuX6vqtTJWralabtlKN4/3CRz/voGuHypS0Z1qUWPeac4I3ZJxjWpRbWpXTn1Rd9X8B7w0XaTjlKTbvStHKsWKCYQJ9PkVHsCDCjPpjgzY6AbURGZuZTbYfafDNvLq+8/LLJ7WZ/+RUtnVsULKYjP3IlKorgzZuYPHYc0+bMMZlu2pw5TB43luDNm7gSFUXYjz8CcPzEfzlw6BD+P3zPto0+vKO/ICpRogRDP/yAkcOHFSietLQ0Zs2ZxYL5C9jst5mQ3SH8aVQfbAvQ1QcBWwPo59WPBQsXALp60XuSN+PHjmeT3yZWLNPVi2XKlMFng4/hv6pVq9KmdcHOPYCwY8eIvHqNgB++x3vkZ8z46muT6Vq1aMF3y5fmeH1b8A7KlStH4IYf6NezJ/OXLwfAoVYtfli+HN/Vq1g8dw7TvvyK1NQ082J6TPX5u159DXX58A8/4JXGL5rVmQMI+/EokVej2O67kUmjRzFt3jzTMc37kkljRrPddyORV6MIP3oUgLlTv8Bv/Tr81q+jrZsrbVxdDdtUr1bN8J65nTl4fHWnj58fDs8/zxY/X9auXMncr74mJSXlicRU28GBb76cl6M+0LVB89m6yY/pX3zBOO+JZsXzqOLKLa+qPVONdatWsdXPj0Hvv8/n06aZFU9aWhrTZs1i6aJFBPr7syOP9ninvj3+St8e//HHH+wMCSFg82aWLV7M1JkzSUtLo1bNmvj7+uLv64vfhg1YWVnRtnXrAuVT2I8/ciXqKkGbfJk0djTT5uRSzufMY/LYMQRt8uVK1FXCMsr5tKls+nY9m75dT7vWbrTVl/MSJUow5IP3+WzokALFI4oO6dCZoJSaopQq2G3WJ+jAwYO4d+mCUooXGzYkPiGBuLi4bGni4uJITEigcaNGKKVw79KF/aGhADg3b27oqDVq0ICYmBhAdweubp061NPfHbaxscHCwsK8mA6H0a1jB5RSNGrgpIvpxo3sMd24QWLifV5s0AClFN06dmD/4cOA7o5Lwv1EABISErGtXPnvZY7eufPnqFGjOtWrV6d48eJ0aN+BUP33zxB6MJRuXbsC0K5tW44f/wlN03Ldpy5PE3mx0YsopejapSsHQg+YHdPZc+d4tkYNauhj6tShAweMYjoQGop7t24AvN6uHceOH0fTNOrXq4ednR2ga3CTHz7k4cOH2ba9cuUKt27dyvXCPDcHDobi3rWrrjw1akR8fLzp8pSYSOMXdd/dvWtX9uu/+4HQg3h00+WjR7eu7D8QavwRf4uunHfOUs7jiYszKlNxN0hMSMxSzjsbynnZsmUN6ZKSklDo7upVqliRhk5OZt+syPDT73e4k5ia6/vtGlU2jLydunwP69KW2FqXoKVjJcIjbnH3fir3klIJj7hFK6dK2FqXoKyVBT9fugfA1mPRvP6ibYFiAl3+u3fV1weNGhIfn0t9kJhA4xf1+dS1i+E4OTxfi1o1a5rc974DB6herRoOzzsULKZDh3DvlHHsGuhiMlEfJCQm0rhhQ11MnTqz/+AhAHy3bOE//ftTokQJQHfMAEqXKsXLjRsbXjfX+fPnqVGjhqE+aP96e0IPhmZLc/DQQbp20ZXjtm3acvwn3bl39NhR6tSuQ9086sXIyEhu37rNSy+9VKC4AA6GhdO1Q3td3enkSHxCInE3b+ZI18jJEdtKlXK8HhoeTrcOHQBo5+rK8ZMn0TSNUlZWWFrq4nz48GGB7mo/ifp81959dGrXzvyYwg7TrWNHfUy5l6nExMQsMXU0xJRB0zR27z9Ap9fN/+xcY3pMdadCkZh4H03TuJ90n/Llrc1vi/9hTA7PP2+yPtC1Qbr6Kbc26Gnk1UtZbgo0atTQcD2TH1Pt8X6j9nh/aCge+va4fZb2eH9oKJ06dKBEiRJUr1aNZ2vU4Oy5c9m2PXr8ODWqVzeM8pudT4fC6NZJV85fbNBA1+7lUne+2FBfzjt15MDBnOU8ZN9+OrV/HdDXnS++SMmSBas7RdEhHbpHSCllXo37D8XExlLF3t7wt73Chs98AAAgAElEQVSdHTFGFWNMXBz2WdPY2xMTG5tjX1sDA3FxdgbgSmQkSik+GDKEnl5erFm/3uyYYm/cwF7f4dDFZEusUSUUe+MG9ra2JtOMHj6Mrxcvpf0bPfhy8RKGD8oczTlz7jw93xnA4M9G8fufl8yLJzaOKvZVMj/L3o7YuOzfPzYuM42lpSVly5blzp07AFy7do3eXn35z/sDOfnzSUN6e3u77Ps0kae5x2R03Oztcxy32NhYqlQxHVOGPXv3Uu+FF3Jc1O7YtYuO7dsXeDpCTGwsVapkjcuOmFij8hQbl/342tsZytPNmzex1R9XW1tbbt26ZUh3+sxZ3ujVm0FDhvK70d3P/OMyOoZ29sQYHcOYuFgT5Twz9vmLF9O2cxeCd+1k6EeDCvT5BWVvU5Lrtx8Y/o6+nYy9TUnsbUpw/XZy5ut3krG3KYG9TUmi72R5/fYD7G3yn95szOz6wC5rGtP1QVb3k5JYs249gws4sgoZ51bW+sCOWOOyHheHvZ2tyTRXIiM5eeo0Xu+9x7uDPuLchQsFjiH7Z+UsJzkuJmMz60zDuXf3DpFXdPXikGFD8HrLi/Xf5qwXd4Xs4vXXX/9bU4Fib9ygStZzy7YysUY3Lszd3tLSgrJlynLnru4mwdkLF+jxzrv0HPAeEz79xNDBM2efj6s+B0h68IDwY8do5+aKuWLjjGOyy5FPujRZYrLNmebk6dNUqlCB52rUMLx27fp1er07gPeGDOXkqdNmx/S46k6vPr3589IlWrfvgGfPXowdNYpixcy7XPunMZljz9591DfRBj3OuPJqZzJs2bbNcD2TH1PtcY46Kpf2ODYuzvA66MuiUf7tDAmhc8eOZsWS7TON607b3OpOo/PTKM1/T52mUsXs5Vz8u/1fdOiUUjWVUheVUuuVUmeUUpuVUqWVUpeVUpX1aV5VSoVm2exFpdR+pdRvSqn389i3m1LqgFJqA3BW/9pbSqnjSqlTSqnlGR09pVRHpdRJpdRppdS+XPb3gVLqhFLqxKo1a0x+pqlRJOMLCXPSLF+9GgsLC7p26gTophb9fOoUs6dN49vVq9l34ABHj5t+VshEUDk/DzNi0qfx2xbAqOFD2b3Fn1HDhjJl5mwA6r9Ql12b/di0fi1933yDT8aPNzOcv59HtpUrsyt4B74bfPjs008ZN2ECCQkJZu0zz5hMvGa8dX6f8fsff/D1ggVM1k+5zGpXSAid/kYDYmpQ0vhraSaiz++7O9arx54dwWzx88WrTx+Gf/JpAeP65+V8xJAh7NsRTJeOndjg61egzy8oU9mhaTnPA90bptP/HSaPjTnnXj4BLF62jLf7eVG6dOmCx2RypNucmHT/T0tL4178PX5YvZrPhg1l5PgJeY6e/514zCpLKFLTUjl1+hTTpk5j9arVHAg9wHGjenH3nt107FDwcy/32B7N9g0dHfFfv47vly1jzQ8bSE42c0TlMdXnGQ6Gh9O4YUOzp1vmGpNZ9VT2v3fu2UvHLKNztpUqEbLFH791axk5bChjP/+chMTEvxvSI6k7w4/8SL0X6nJgdwj+G32YMWs2CQkJTzWmDL//8QdfLVjAJO8J+Sd+gnEd/+kntmzbxqcjhpsXj4nXzG2P86tPUlJSCD14kPavv25WLNnjMqeuMrGhUZqde/bQ6W98vii6/p8WRXkB+I+maeFKqTXA4HzSNwKaAWWAn5VSwZqm/ZVL2iZAA03TLiml6gO9AWdN01KUUkuAfkqpncBKoJU+ncmHZTRNWwGsAEhJSDCctj5+fmzeuhWABo6ORGeZVhATG4ud0ZSWKnZ22aYexMTEYJflbmrA9u0cOnyYVUuXGioLe3t7Xn35ZSpUqABAS2dnLly8SLNcHnze6L+FLduDAHCqXy/bHb6Y2DhsK2efHmRva5tt5CBrmu07dzFGXxG3b9Oaz2frnrkpW6aMIX3L5s2Z8eXX3L5zhwo2NiZjMnyWvR3RMdFZvn8stpWzT2ezt9Olsbe3JzU1lYSEBMqXL49SynDn0bG+I9WrV+dKZKRu5CMmNvs+bc2fIqf7vNyPiS5ue6Kjo6liFBNAdEwMH3/6KTOmTqWG0V23X375hbS0NJyMFrTIjY+vL5u36MuTkxPR0Vnjis0RVxW77Hdws6apVKkScXFx2NraEhcXR0X99LisUx5btXRh2syZ3L5921C+TMbl58fmrdt0cTk6Zj+GsTHYVTaOy95EOc85vatLp44MHjGCoYM+zPWz/6no28lUrWAF3NXFVqEksXeTib6TTNM6meW1ik1Jjv12h+jbyVTJMiJXpYIVMVlG7PLi45ulPnAyUR/YmqgPYrOmyVn2jJ09e449e/fx1fwFxMfHo4oVo2SJknj16W06pk2b8Q8I0MXkWJ/orOeKiZjs7bLfoY+JzTxH7e3saOfmhlKKhk5OqGLFuH3nDhXzKDt5sTdRTiob1Zl29ro607g+sLe35+WXXqaCje6znVs4c/GXizTR14u//voraWlp1DexwENufLduZUtQMABOL9QjOuu5FXejQFPO7W1tiY6Nxd7OltTUNBISE3J0lJ6v+RylrKz4/dIlnOrlXGABnkx9nmHX3v10atc23++20d+fLYHb9THVN4opNkc+2dvaZi9TcdnTpKamsu/gQTauWW14rUSJEpn1fb161Kj2DFcio3AysQATPJm6c2tgIAMHvItSimeffZZq1Z7h0uXLNGzQ4LHHlJfomBhGfPoZM6Z+wbNmjPw8ibwC+OXXX5n0xVSWLVqITT7XBhlMtcfG7Xlu7bG9nR3R0VnbpuzXAofDwqhfrx6VTUyRNmXjZn/8AwMBXTnPVnfGmSjndrY5zk8743IeepCN60wPCoh/p/+LETq9KE3TwvX//h5wySd9gKZpSZqm3QAOoOu05ea4pmkZcwHbAq8APymlTun/fh5d5/BQRjpN03LOF8hD31698Pfxwd/HhzZubgQGB6NpGqfPnqVs2bI5KiJbW1tKlynD6bNn0TSNwOBgWusfjg07coTV69ez8OuvKVWqlGEb5+bN+fW330hKSiI1NZUTJ0/iUKtWrjH16fGG4QH31i1bsn1XCJqmcebcecqWLZOjErKtXJkypUtz5tx5NE1j+64QWrd00b9XiRM/6xacOP7fkzxbvToAN27eNNwNO3vhAunp6djoOzh5cXJ0IjIqimvXrpGSkkLI7hBcszwED+Dq6sr2IN0FzN59+3jttddQSnHr9m3S0nSLB1y9epXIyEiqV6umz9PSnDl7Bk3TCAoOws3VLd9YMjRwcuJKZCRX9THtDAnBzS379m6urgRu113A7Nm7lyb6mO7FxzNk2DBGDBvGS40b59j3jl27CjQ617d3b8NiJW1auxEYFKQrT2fO5F6eSpfm9Bnddw8MCqK1/ru7ubYiQH8hGLA9iNb6aVQ3btzIPHbnzpGuafk2trpyvgF/nw36cr7DqJwblSnbykblfIehnGesaAe65/Fye07sUdl39gaeTXXTcBrXtCY+KY24ew85fOEmLvUrYl3KEutSlrjUr8jhCzeJu/eQxOQ0GtfUXXx7Nq3C3jPmTbXr27sX/ht98N+orw+C9PXBmTzqg9JlOH1Gn09BwYbjlJtv16xmd3AQu4ODeMvLi/ffG5BrZw6gb8832fz9d2z+/jvatHIlcGfGsTuniymX+uD02XO6mHbuoHWrVoBuNdtjJ/4LwOXISFJSUvK9iZMXR0dHoiIz64Pde3bj2sqoPmjpSlCwrhzv259ZHzRv1pzffv+NpAe6evHkyZPUylIv7grZRYf2HQoUT29PT3xXr8J39Spat3QmKGS3ru48f4GyZcqYfFYuN67OLdgeEgLA3oMHee2ll1BKce36dcMiKH9FR3M5KopnskwTM/Yk6nPQrTD531OncGuZXxMMfXr0MCxW0rpVS7bv2qWPKe8ydebcOX1Mu2jtkrmy57ETJ6j13HPZpqtlq++vXeNK1FWqV8v9uacnUXdWrVLFMDvmxs2bXL58herVqj2RmHJzLz6ewcOG8/GwYbxsog16Wnl1/fp1Ph45kplTp1JTvzK1ORo4ORFp1B63NmqPW7u6EqBvj3fv3UtTfZ3Q2s2NnSEhPHz4kKvXrhEZGZmts71j164CTbfs82YPw0ImbVq1YvtOXTk/fe4c5crkUs7LlOZ0RjnfuYvWrTLPp6M/6cp51qnc4t/v/2mEzniQWgNSyezUWpmRPjdZ52coYL2maeOyJlBKueezD7O1cnHhcHg4nTw8KGVlxdQpUwzv9ejbF38fHwAmjhuH95QpPHjwgJbOzrTUzy2fPns2D1NSeH+wbpCyUcOGTB4/nvLW1vR/6y369O+PUoqWzs64tjRvmeuWzZsR9uOPdO3dFyurknwxPvPr93r3Pfz0d4omjPyUidNnkpycjHOzprg0awbApNGjmTN/AWlpaZQoUYJJ+pXG9oSG4rc1AEsLC0qWLMnszyebNQXD0tKSsaPH8NHQIaSnpePh4U5tBweWLF2Ko6Mjbq6ueHp0Z8LEiXTzcMe6fHlm61fdOnnyJEuWLcXSwoJixSzwHj/eMEo2Ydx4Jk2ZTPKDZJydW5g9Xz8jpvFjxjBo8GDS0tPx9PCgtoMDi5YswcnRkdZubrzRvTvjvL3p7O5OeWtr5syaBYDPxo1ERUWxfOVKlutXa1y+dKlhsYiQPXtYsnCh2bFk1crFhcNhYXRyN1GeevfB33cjABPHj8/yMxgtaOmi++4DBwzgszFj2LJtG1WrVuEr/YqGu/fuxVe/7LyVVUnmzpxZoCmqrVyc9eW8uz6uyZlx9fXC32eDLq5xY/XlXB+X/ph8vXAhl69cQaliPFO1KpP0ZfLGjRv0fru/4ac5vvfxIWCTX7YRRVO+HuBE07o2VChbnLDpLZgffAlLC9338Tn8F6HnbuLmVIn9nzcn6WEaY76LAODu/VTdTxWMeRWARTsuc/e+bnGVST6/6H+2wIKD529y8HzOxTDyzycXDoflUh/06Yv/Rn19MH5c5nLgLTLrg7379zNzzlxu3b7N4OEjqFe3LiuWLC5wHFm1dG7BoSNH6NzjTaysrJg2MXOK8Jtvvc3m77/TxTRmNN5fTOWBfon5li2aA+DZrRsTp03Ds68XxYtbMn3yJEPZ6dC9OwmJ90lJSWH/wYOsWLAAh+dzv/EEunNv9OjRDB0+lLS0NDzcPXBwcGDpsqU41nfE1dUVDw8PJk6eiIenB+WtyzNj+gwArK2tecvrLfrr60VnZ2daZukk7N27l/nz5+f20flyadaMsKPHcPd6C6uSJZkyNnPZ897/GYjv6lUAfLN0GTv37ePBg2Q6vNkTzy5dGDTgXbp37oL39Bm4e/XDupw1sybrVh/8+cxZ1m7YgKWlJcVUMcZ/8jEVbPK/GQaPrz4H2H/oMM2bvEbpLDcTzYupuS6mXr2xsrLiiyzT73u98y5+69fpYxrJxOnT9TE1w6V5M0O6XXv30dFoIZaTp06zeNUqLC319f2okWZPBX1cdeeg999nwuTJePbshaZpfDJieJ4zGx5lTHv372fm7Dn6+mA49V6oy4olS/DZ6EtUVBTLVq5kmb4NWrF0iaENelp5tXTFSu7eucs0/eq+FhYW+G34Id94MtrjD81ojzvp2+O5+va4toMDHdq3x71HDywtLJgwdqxh0ZqkpCR+PHbM5GMR5mjZojmHj/xIl569sCppxVTvzHLes/87bNI/w+s9aiTe03Tl3KVZM1yaNzek27V3r8lFfzp69iAhMZGU1FT2HzrM8vlf53nTvrAqJr9aYJL6J88lFBVKqZrAJaCFpmk/KqVWAheBTsCXmqbtVEp9DbykaZqbUmoK0J0sUy6BZqamXCql3ICRmqZ11f/tCASgm3IZq59aWQ64D5wky5TL/Ebpsk65LAzSksx7ruBJ0krlfRH+tFgUwhpHPZr7CY9WevrTjiCH+qN/etoh5BAx1/zfe3qSNDOXUn+SHhYr/rRDyKHY/XtPO4Qcipm5auITpQrnpCGLv/EcqSgsCl9bnP7g/tMOwaSSFSsXvswy4asffiyEFzPZfdqv+RPPy8JZez4eEcA7SqkzQEVgKfA5MF8pdRgw/oGe40AwcBSYmsfzc9lomnYB8AZ26z9rD1BV07Q44ANgi1LqNOD7CL6TEEIIIYQQ4v/Y/9OUy3RN04zXLT8M1DVOqGnaFHN3qmlaKBBq9JovJjpsmqbtBHaau28hhBBCCCGEyMv/0widEEIIIYQQQvyr/F+M0Gmadhkwvd6vmZRSDYHvjF5O1jSt6T/ZrxBCCCGEEEL8Xf8XHbpHQdO0s4B5a/UKIYQQQgghHqliBVgt+/+JTLkUQgghhBBCiCJKOnRCCCGEEEIIUUTJlEshhBBCCCFEoSczLk2TETohhBBCCCGEKKKkQyeEEEIIIYQQRZR06IQQQgghhBCiiJIOnRBCCCGEEEIUUdKhE0IIIYQQQogiSjp0QgghhBBCCFFEyc8WCCGEEEIIIQq9YvK7BSbJCJ0QQgghhBBCFFHSoRNCCCGEEEKIIko6dEIIIYQQQghRRMkzdIVZscI1T9iiTNmnHUIOqelPO4KiI0172hGYoArfPaWIuU2edgg51B91/GmHYFJhzKsShbBMqbLlnnYIORXG51AKYx0FaBS+vCp8EcHNe0lPO4QcKlmXetoh5JBWvPDFJIq+wtfyCSGEEEIIIYQwi4zQCSGEEEIIIQq9wji5oDCQETohhBBCCCGEKKKkQyeEEEIIIYQQRZRMuRRCCCGEEEIUeoVsvcBCQ0bohBBCCCGEEKKIkg6dEEIIIYQQQhRR0qETQgghhBBCiCJKOnRCCCGEEEIIUURJh04IIYQQQgghiijp0AkhhBBCCCFEESU/WyCEEEIIIYQo9IrJ7xaYJCN0QgghhBBCCFFESYdOCCGEEEIIIYoo6dAJIYQQQgghRBElHTohhBBCCCGEKKKkQyeEEEIIIYQQRZR06IogTdOYMXsOndzd8ezViwsRESbTnb9wAc+evejk7s6M2XPQNA2Au3fvMnDQR3R292DgoI+4e+8eAMdPnKBZy1b06N2HHr37sHT5iqceU3x8PENGjOCNXr3x6PEmWwMCzI4p/Eg4Hm940s3DnTVr1+Z4/+HDh4weO4ZuHu681b8/1/76K9v7169fp7mLM+u//dbw2g8bNtCjV0/e6Pkm32/4wexYMoSFh9Ote3c6u7uzas0akzGNHDOGzu7ueL39tiGmI0eP0svLC8+ePenl5cWx48cBSEpKYvCwYXTz9KR7jx58PX9+gWMKDw/H3dOTru7urM4ln0aNGUNXd3f65ZJPzZyz51OnLl3o0asXvfr0oW+/fgWOqbDGpWkaM+bMoZO7B569eudRziPw7NWLTu4ezJiTWc5D9uzB482eNHzlVc5duJBju+vXr/Oaswtrs8Scn5lv1ePYbBd2eDfJNc3EnnXYN6UZQROa4FSjrOF1z6ZV2DulGXunNMOzaRXD6041yhE8oQn7pjRjYs86ZseS4XHm0y+//ka/d97F482eePbqRXJyslkxPepzLzExkTd79zb817J1a2bPnWt2HmXQ5dVcOnl0x7N3Hy5EXDSZ7nxEBJ69etPJozsz5sw15NXCJUvx7N2HHn29eH/wEGLj4gB9fd7KlR59vejR14ulK1YWMKa/f/zmff0N3d54A89evRn+2Wfci483bLNyzRo6uXvQ1fMNwo8cKXhMHh549s4jpgh9TB5GMX2jj6l3zphAf+65FOzce9RlCmDBokW069iRJi1amB2HcUxdu3enUx4xfTZmDJ3c3embJSaAlatX08ndna7du2c7Nvnt0xzHjh3lrbf64OXVkx9+yJnHp0//zMCB79KmTUtCQ/cbXo+Ovs777w/gP/95h3fe6UdAwNbMeFcu4803u9OxY9u/FVNhzKvwI+F0f8MT9+7urFlnut0bM24M7t3defud/vyVJaZff/uV/gPeoUevN+nZO7NuHDJsCL369qZHrzeZNmM6aWlpBY6rMFGq8P/3NEiHrgg6HBZOZGQkOwICmOLtzdQZM02mmzpjJpO9J7AjIIDIyEjCwnWVzqq1a2nWpAk7AgNo1qRJtovll19qjL/vRvx9N/LRhx889Zh8/PxweP55tvj5snblSuZ+9TUpKSn5xpOWlsbMWbNZvGAhWzb7sytkF3/8+We2NFu3bcPa2prtAYG81a8f8xdk7wzN++pLnFs4G/7+/fff2bJtK9+v/xY/n40cPnyYK5GRZudRWloa02fNYsmiRQT4+7Nz1y7++OOPbGm2bNuGdbly7AgM5O1+/QwdtAo2Niz65hu2btrE9C++YLy3t2Gbd/v3Z/vWrWzauJFTp09zOCysQDHNmD2bJQsXstXfn127cs+noEBdPn1j1Gmc++WXuDg7Y2zV8uX4bdyIzw8F7/gW1rgOh4cTGRnFjoBtunI+M5dyPnMmkyd4syNgG5GRUYTpG/zaDrX5Zt5cXnn5ZZPbzf7yK1o6F+xCbsvRaN5bdCrX912dKlHTrjRtpxzF+4eLfN7nBQDKl7ZkWJda9Jhzgjdmn2BYl1pYl9L9ks0XfV/Ae8NF2k45Sk270rRyrFigmB5XPqWmpjLW25uJE8YTsHkTa1eswNIy/1/feRznXpkyZdjs62v4r2rVqrRt06ZA+WTIq6godmzbyhTvCXnnlfcEdmzbSmRUZl4N6P82W3034u+zAdeWLVm6MrPj9vJLL+HvswF/nw189MH7BYvpHxy/5s2astXPj61+vtR89jlWrdHV53/8+Sc7Q3YTsHkTyxYtZOqsWWZfXGbmkxkxeXuzY9u2bPnUvKk+Jl9faj73HKuMbhLN/uorWhagE/W46nPXVq3w+e47s+MwjmnarFksXbSIQH9/duQR0059TF/pY/rjjz/YGRJCwObNLFu8mKkzZ5KWlmbWPs2J65tv5jFnzpesX7+Bffv2cvnypWxp7OyqMG6cN23bvp7t9UqVKrN48XJWr17P0qUr2bDhO27c0N20aNHCheXLVxU0mwwxFba8SktLY9bs2SxasBD/TaavW7YFbKNcOWsCtwXSz6sf8xfqYkpNTcV7ojcTxk3A328zK5dn1o2zZ87Gz8eXzb6buH37Nnv27v1beSYKtyLToVNKTVFKjTTnfaXUF0qpdo/ws2sopQ4opSKUUueVUiMe1b7/jgMHQ3Hv2hWlFC82akR8fDxx+ruyGeLi4khMTKTxiy+ilMK9a1f2hx7QbR96EI9uXQHw6NaV/QdCC21MCkVi4n00TeN+0n3Kl7fGwsIi33jOnT9HjRrVqV69OsWLF6dD+w6Ehmb/nqEHQ+nWVfeZ7dq25fjxnwx3c/cfOEC1atVwcHjekP7PS5do1KAhpUqVwtLSkldefoX9B/ZjrrPnzvFsjRrU0MfUqUMHDhjFdCA0FPdu3QB4vV07jh0/jqZp1K9XDzs7OwBqOziQ/PAhDx8+pFSpUjR57TUAihcvTv169YiJjTU7pnPnzlGjemY+deyQM58OhOqOLcDrbdty/Kfs+VS9WjUcnn/eeNf/SGGN60DoQdy7dtGX84bExyfkUs4TaPxiI30572Iozw7P16JWzZom973PELNDgWL66fc73ElMzfX9do0qs/VYNACnLt/DurQlttYlaOlYifCIW9y9n8q9pFTCI27RyqkSttYlKGtlwc+XdKPkW49F8/qLtgWK6XHl05GjR6lbpw716tYFwMbGxqz64HGce1lduXKFW7du5dpRz8uBgwdx79JZl1cNGxKfEE9c3I1saeLibpCYkEjjRvq86tKZ/fr4y5bNHHFNSkpC8c9vD//T4+fcvLnhYrJRwwbExMYAsD80lE4d2lOiRAmqV6vGs9VrcPbcefNiOngQ9y5dsuRTLjElJGTJpy6GfMoWU4MGxMTEGLYznHsO5p97j6tMvdioEba2BTvf8oppv1FM+0ND8dDH1D5LTLpj0yHz2NSowdlz58zaZ34iIi5QrVp1nnmmGsWLF6dNm3aEhR3OlqZq1ao4ONSmWLHsl6XFixenRIkSAKSkpJCerhnec3JqQKVKlQsUS4bCmFcmr1sOZt8+t+uWH48epU6dOrxgom7MqCNS01JJTU15aiNI4vEqMh26gtA0bZKmaY/yFkQq8JmmafWBZsAQpZSjuRsrpfK/4iiAmNhYqlSxN/xtb29HTGycUZo47PUNRmYa3YX+zZs3DQ2Gra0tt27dMqQ7feYsb/TqzaAhQ/m9AHeWHldMXn168+elS7Ru3wHPnr0YO2pUjgrflNjYOKrYZ04hs7e3IzYue0cnNi4zjaWlJWXLluXOnTskJSWxbv06Bn3wYbb0tWs78N+fTxrShIWHZbsoyD+mWKrYZ80je2KMLkhiY2OpUiVnTFnt2buXei+8YGjkMtyLjyf00CGaNsl96l2OmOLiDJ8HYGdnl6NDmDVN1pjuJyWxdt06Bn2YPZ8AUIpBQ4bQx8uLzf7+ZsdT2OOKMT6GdnY5jmFMXBz2dlnT2Ofbyb6flMSadesZXIBRcXPZ25Tk+u0Hhr+jbydjb1MSe5sSXL+dOV0x+k4y9jYlsLcpSfSdLK/ffoC9TckCfebjyqcrVyJRSvHB4CH09PJizbr1ZsXzuM+9Hbt20bF9e9TfuFKKMa6r7OyJMaqrYuJisTeOP0v9On/xYtp27kLwrp0M/WiQ4fXTZ8/yRp++DBo2vOD1+SM6flsDAnHRz3TIWS/b56iX/3FMOfLJREyBgYbR+/tJSaxZv57BHxTs3HvcZervMBVTrJkxGde59nZ2xMbGmrXP/Ny4EYddlrJia2trGGUz73vFMGDA2/Ts2R0vr7eoXPnvdXiz77Pw5VVsbBz29tn3G2fc7sWauG65e4fIyCsoFIOHDqZvPy/WrV+XbbvBQwfT9vV2lC5dhnZtH9l4x1NRTKlC/9/TkP9cladIKTUB6A9EAXHAfzAylbQAACAASURBVJVSDsBiwBa4D7yvadpFo+3WAUGapm1WSl0G1gPdgOJAT03TLiqlKgE++v0cBzoCr2ialv3WKKBp2nXguv7f8UqpCKCaUioZ2KRp2sv6z60DbNQ07RX9564B2gOLlFJ2wCB0ncMLmqb1+bv5omk5XzMuPxo5E+V3seFYrx57dgRTunRpDh0OY/gnn7Ij0Lxn1h5XTOFHfqTeC3VZs2I5UVFRvP/RYF556aVsd6VNx5P/Z+WWZumyZfTz6kfp0qWzvfd8recZ8M67DBo8mNKlS1G3bl2zRgcMn2fiNeNvn1/cv//xB18vWMCKJUuypUlNTWX02LH069uXGtWrmx/TP8ynt/rlzCeA9WvXYmdry81btxj00UfUqlmTV155pejHZaoMY15ceVm8bBlv9/MyGfM/ZeqjNS1n3Lo3Hs38/8eVT6lpqfx86hQbv/sWKysrBg76CMf69WnWNO+bGI/z3APYFRLCjGnT8owh19j+QVnPMGLIEEYMGcLKNWvZ4OvH0EEf6urzoO26+jwsjOGfjWTHtq059mMypkd0/JavWo2FpQVdO3cye5tcY3oE+QSwfPVqLCws6NpJF9PiZct426vg597jLlN/xz+JKbfX0029XtC4TAVWgL3Y2dmzdq1uquWECWNxdW1NxYoFmwaeIyYzInryeZX/hVRu52ZaWho/nz7F999+h5WVFR9+NIj69evTtElTAJYsWkJycjLjvSfw008/0axZswJFJgq/QtuhU0q9AvQBXkIX50ngv8AKYJCmab8ppZoCS4D8Hly4oWnay0qpwcBIYCAwGQjTNO0LpVQXwKzbc0qpmvqYjmmadk8pdVcp1VjTtFPAAGBdluQPNE1z0W/3F1BL07RkpZRNHvv/ICOWJQsXMPC99wDw8fVl8xZdY9zAyYno6MyRoZiYWOyMpmhUMRrRyJqmUqVKxMXFYWtrS1xcnKFizNpJatXShWkzZ3L79m0qVKhgMtYnEdPWwEAGDngXpRTPPvss1ao9w6XLl2nYoEFuWQjoRuSiY6KzfZat0V09eztdGnt7e1JTU0lISKB8+fKcPXeWPfv28s2C+cTHx1OsWDFKlixBn9598OzeHc/u3QFYsGhhtjvU+dF9XtY8ismRR/b29kRHR1PFKCaA6JgYPv70U2ZMnUqNGjWybff5tGk89+yzvF3AhT7s7eyIjs7Mp9jYnMctI02OfDp7lr179/LNfF0+qWLFKFGiBH379Mk8rhUr0qZ1a86dP1+gjlNhisvH14/NWzPKuWP2Yxgbi51t9ik/unKeNU3O42zs7Nlz7Nm7j6/mLzDEXLJESbz69M5zO3NE306magUr4K4uvgolib2bTPSdZJrWyayKqtiU5Nhvd4i+nUyVLCNyVSpYEXMn/4VHnkQ+2dvb8+orLxvqpJYuzly4eDHfDt3jPPd++eUX0tLScHI0e9IGPn5+bN66DYAGjo7Z66rYGOwqG9ed9tlmA+jizznVrEunjgweMYKhgz7MXp+7uDBt1mxu375DhQqmm59HffwCtm/n0OHDrFq21NCJyVkvx+Sol7PF5JclJkcTMVU2EVMex9kQ09LMmM6eO8eeffv4akGWc69kSbx6533uPc4y9XeZisl4+mZuMRnXuTGxsYZt89tnfmxtbYnNUlbi4uKoXLngUyUrV7alZs1anDlzCje3gj+vmlVhzCs7OztiYkzvN3vcOds9Ozt7Xnn5FSrY6OpGF2cXLl68aOjQAZQsWRJXV1dCD4ZKh+5fqDBPuWwJbNU07b6mafeAQMAKaAFsUkqdApYDVc3Y1xb9//8L1NT/uxXwPYCmacHA7fx2opQqC/gDH+tjAlgFDNBPq+wNbMiyiW+Wf58BflBKvYVulM4kTdNWaJr2qqZpr2Z05gD69u5tWKykTWs3AoOC0DSN02fOULZs2Rwnva2tLaVLl+b0mTNomkZgUBCtXd0AcHNtRcD2IAACtgfR2s0VgBs3bhjuPJ09d450TcPGJte+5xOJqWqVKhzVrwB24+ZNLl++QvVq1XKNKYOToxORUVFcu3aNlJQUQnaH4Orqmi2Nq6sr24N0n7l33z5ee+01lFKsXb2GnUHB7AwKpp+XF/8Z8B59eusGVDOmgl6/fp39+w/QqWPHfGPJ0MDJiSuRkVzVx7QzJAQ3N7dsadxcXQncvh3QTcVpoo/pXnw8Q4YNY8SwYbzUuHG2bRYsXkxCfDxjRo0yO5YMTk66fMqIaVdIznxyc3UlUJ9Pe/btM8S0bs0adgYHszNYl08D33uPvn36cD8picTEREA3nenHo0epXYBnUwpbXH1798J/ow/+G31o4+ZGYFCwvpyfzaOcl+H0mbP6ch5sKM+5+XbNanYHB7E7OIi3vLx4/70Bj6QzB7Dv7A3DCpaNa1oTn5RG3L2HHL5wE5f6FbEuZYl1KUtc6lfk8IWbxN17SGJyGo1rWgP6lTDP5Ji4kMOTyCfn5s359bffSEpKIjU1lRP/PYnD87Xyje1xnXugm25ZkHoAoG+vXobFStq4uREYvEOXV2cz8ir7xa6tbWVKlynD6bP6vAreQWv9+ZB1YaYDBw8anj3MUZ+np2NjUz73mB7h8QsLP8LqdetZ+M3XlCpVyrBNa1dXdobs5uHDh1y9do3IqCgaNnDKJ5988PfRxxQcbJRPJmLKlk/BhnwKO3KE1evXs/Dr7DF9u3r1/9i777Aojv+B4+8RULAgioA1sTfUmGjsBXtsKPYSjUlMoklM1diwxN4Su8auMRZUVOwaCxaMGrsolhgVKyA2RKQc+/vjjoPjDjyMCnx/n9fz+CTszu5+bmZn9mZ3do5dW7awa4uh7n388Qs7c/B6z6mXVcHdneBkMTVIFlOD+vXxM8S0a/duqhtiauDhwfadOxPLJjiYihUqWLXPFylbthy3bt3i7t07xMbGsnfvbmrXrmPVtqGhocbZGiMinhAYeI4iRd5O0/EtyYh5Zel7i0e9ZN9b6ln+3lKrZk2uXLlC1HN923ji5AmKFy/Os2fPCDMMb42LiyMg4BBFU3iPW2RuGfYJnUHyZ8tZgEeapqW1BUy4vazD9DNbHAhgiVLKDn1nboWmaeuTrPJF/7RvL3BC07TwJOsik/x/S/SdSE9gmFLKXdO0lGcySEW9OnU4eOgQzT3b4GBvz+iRI43r2nfugq/PagCGDRmC94gRPI+Opm7tWtSto39noPfHH/PjwIGs37iRAgXy8+ukSYC+wfJZuw4bGxvs7bMxefx4q4fDvK6Y+nz2GUNHjMCrYyc0TeP7b79J8YlhUra2tgz6aSB9v/6KeF08bdp4UrJECebMnUv58uXxqF8frzZtGTpsGK3beOKYOzcTU5iZM6kfB/Tn8ePH2NraMnjQQBwdHa3Kn4SYhgwcSJ8vv0QXH49XmzaULFGCWXPm4F6+PA08PGjXti2Dvb1p4elJbkdHJk2YAMCq1au5efMm8xYsYJ5hFrt5c+cSGxvLgoULKVasGJ26dgX0He327dpZHdPggQPp+9VXxMfH09ZTn0+z587FPSGf2urzqZWnPp8mpTC7XIIH4eF8/+OPAMTpdLT44ANqW5htMjPGpT/PA2jexsJ53qUrvqtXATBsyGC8R4zkefRz6taqTV3DcXbv3cv4SZN58PAhX37zLWVLl2b+nNlpiiG5qR+7U720E3ly2nFobC2mb72GrY2+3q46eAf/wHA83J3Z+3NNomJ0DFyun+798bM4Zm+/zoaBVQGYte06j5/pm6Thqy4xqWc57O1s2H8+nP3nwy0f/A3nU25HR3p2/5AuPXqilKJu7drUr1v3hfG8jrrnbBhFsPPPP5kzc2aa8sc0r2pzMCCA5m3aGvJqRGJede2G7yr9PcJhgwfhPXIkz58b2k5DXk2dOZPrN26gVBYKFijA8CGDAdi1Zw8+63z17Xm2bEwePy6N7fnLl9/YiROJiY3ls75fAlCpYkVGDB1CyRIlaNakCZ4dOmBrY8vQQQOtHrZer04dQz5ZiKlrV3xXGWIaPNiQT8+pW9tCTF8miWnIEKuObcnrOqd+nTaNrdu38/z5cxo1a0Z7Ly++7NMntVDMYvrCipiaG2KabIipZIkSNGvaFM/27bG1sWHooEHGsrG0z7Tm1Xff/UD//t8TH6+jRYtWFCtWnEWLFlC2bFlq165LUNAFhg0bTEREBIcPH2LJkkUsW7aCGzeuM2fOTONQx86duxonr5k7dzZ79uzi+fPndOjQhpYtW/Pxx70zbV7Z2toycMBAvuxn+N7i6UmJEiWY89tcypfTX/fatmmL9/BheLb1xNExNxMM31scHR35sHt3PuzZA4WiTu3a1K1Tl/DwcL774XtiY2LQxcfzftX36dC+Q1qKT2QSytJY4IxAKfUe+uGL1UkccjkPaAdM1TRtrdJfnSppmnZGKTUSeKpp2hQL79BV1TTtvlKqKjBF0zQPpdQMIFTTtDFKqebANsDF0jt0huMsAx5omvadhfUzgfbAp5qmbTcsS3rcLMBbmqZdN3QMbwFlNE17lHxfScU+i8yYhZOBxMWndwSW2WTJeNNIWRrfL8zZWH+f540pN+DYixOlg6DJ1k/A86ZoKuMNPFHxGfB3nzLiVHcZr+oBoFkxEdeblgFLj/AnUekdghlnR4cXJ3rDYnUZ80TPnitHRjytzCxY/3fGzMAkPmv3/hvPy4zXShlomnYS/ZDF0+ifgiXMcdsd+FQpdQY4D7R5yUP8DNRTSp1EP3FJaj8oVhvoATRUSp02/GuRZP0K9JeiXSlsbwP8oZQ6B5xC3yFNtTMnhBBCCCGEEC+SoYdcapo2FhhrYZXZCwuapo1M8v+9kvx/0ST/fxzwMPx/OPqOHABKKa9U4jhE6jfE6gCLNU0z3oZNdtxYQxohhBBCCCHES3iZn4f5/yBDd+gyA6XUBqAEL55pUwghhBBCCCFeKenQGWiaVlQp5WyYPTO5RskmO0m6XYpP9oQQQgghhBDidZIOXRKGTturm0NYCCGEEEIIIV6jDDspihBCCCGEEEKI1EmHTgghhBBCCCEyKRlyKYQQQgghhMjwMuDP/GYI8oROCCGEEEIIITIp6dAJIYQQQgghRCYlQy6FEEIIIYQQGV4W+WFxi+QJnRBCCCGEEEJkUtKhE0IIIYQQQohMSjp0QgghhBBCCJFJSYdOCCGEEEIIITIp6dAJIYQQQgghRCYls1wKIYQQQgghMjyZ5NIyeUInhBBCCCGEEJmUPKHLwJ48i03vEEzkyp41vUMwE/bwaXqHYJFb3pzpHYKZqJs30jsEM0HPMt45VbW4c3qHYCZocrX0DsGicgOOpXcIZoKmVE/vEMzcj4hJ7xDMRMfGpXcIZgrmskvvECxS2bKldwgWZLzHFM6ODukdggVaegdgxs4mvSMQ/4vkCZ0QQgghhBBCZFLSoRNCCCGEEEKITEo6dEIIIYQQQgiRSUmHTgghhBBCCCEyKZkURQghhBBCCJHhZZHfLbBIntAJIYQQQgghRCYlHTohhBBCCCGEyKRkyKUQQgghhBAiw1PyKMoiyRYhhBBCCCGEyKSkQyeEEEIIIYQQmZR06IQQQgghhBAik5IOnRBCCCGEEEJkUtKhE0IIIYQQQohMSma5FEIIIYQQQmR48sPilskTOiGEEEIIIYTIpKRDJ4QQQgghhBCZlHTohBBCCCGEECKTknfoMqEjR/5i2rRf0cXH07q1Jz17fGSy/tTpU0yfPpWrV//h559H07BBI+O673/4lvPnA6lU6R2mTP7VuPzOnTsMH+HNkyePKVO6LMOHj8TOzs7qmA4FBDBx8mR08fG0a9uW3p98YrI+JiaGIcOGcSEoCKfcuZk8cSKFChbkXGAgP48eDYCmaXzZpw+NGjYEYNjIkRw4cIC8efOyYd26NOfT38eOMmfOdOLj42nevBVdun5osv7s2dPMnTODf//9l6HeI6hXr4FxXbOm9SlarDgArq5ujB49AYBTp04wf95s4uLiKFWqDD/2H4iNjfXV6FBAABOnTCZeF087r7Z8+rF5Pg015FNup9xMnpCYT6PGJOZT3y/0+RQdHc3HvT8lJiYGnU5H40aN+apv3zTlk6Zp/LpgEX+dOEG2bNkY9m0/ypYoYZZu7vI/2L7Pn4jISPb5rDIuvxcWxqhpM3gaGYkuPp6vevagVtUqAFy5fp2Jc+YS+SyKLFkUi6dMJlvWrC+M6dzp46z8fT7x8fHUa9CUlm06mazfuXUDB/btJEsWG3I55uaTL74jn4srAGtWLubsqeMAtG7Xheo16wGwcO6vXAoKxCF7dgB69/met4qaf84X5dWEX3/l4OG/sLfPxphhwyhftqxZuvNBF/EePZro6Gjq1qrJoB9+QBnG/a9Ys4bVa9dhY2NDvdq1+KFfPx49fswPgwYTGBREm5YtGTqgf5piGj95MgcPBWBvb8/Yn0dSvlw585guBOE9cgTPn0dTt05tBg8YgFKKnX/+yZx58/n32jVWLf+dCuXLG7e5dPkKo8aO5WlkJFmyKFYvX062bNleGNP4D8vSsGI+wiNiaDHmmMU0wzqWwsPdmajYeAb+foHzN58C4FU9P181LwrA7O3X2XD0HgDuRXIxqWc57O2y4H8+nNFrr1idR8Z8mjSZgwGHDPn0cwr5dAHvESN5Hv2curXrMPgnfT49fvyYHwcO4s6dOxQsWJBfJk0kt6MjAMeOH2fi5CnExcWRx8mJpYsWWh3X0aNHmDVrGjqdjpYtW9O9e0+T9WfOnGLWrOlcvXqV4cN/xsND3z7eu3eX4cOHoNPFo9PF4eXVgTZtvEy2HTLkJ+7cuc3SpSvSlFd//32U3+bMQBcfT/PmLencxbTtPHf2NL/Nncm///7LkKEjqFvPw7guNDSEqb9MJCwsFKUUo8dOIn/+Avzw/ddEPXsGwKNHDylTthwjfx5ndUz6ujeVg38dxj6bvaHulTFLd/5ikrpXsxaDfvg+Sd1by+p1hrpXqxY/9Pua2NhYfp4wkfMXg8iisjDo++95v8p7Vsf0Os6pLdu2sWjpUgCyO2Rn2JAhlC1T2qqYDgUEMMFwLW6fwrV4cJJr8RTDtRhgwaJFrPfzwyZLFgb/9BO1a9Xi2vXr9B840Lj9rdu3+bpvX3p0725VPK8rLoCmLVqQI0cOsmTJgo2NDWtWrkxTTK+zTQA4d/483Xt+xJQJE2japPEbiWnnn38y57d5hvZ8ORXc9e35o0eP+H7ATwSeP09bz9YMHTQoTXklMj55QvcaKaVGKqX6K6V6KaUKJlm+UClVPrVtU6LT6Zjyy2R++WUaK1esZvfuXVy79q9JmvxubngPHUaTJk3Ntu/e7UOGDxtptnzO3Fl07tyFNT6+5MqVi81bNqUpprETJjBn1iz8fH3ZvmMHV69eNUmzfuNGHHPlYtumTfTo3p2p06cDULJECVavWME6Hx9+mz2bUWPGEBcXB0Cb1q2ZO3u21XEkj2nmzF8ZN24KCxctZ9++3dy4cc0kjaurGwN+GkLDhuYNbdas2Zg3bwnz5i0xdubi4+OZPGkcQ71HsmDh77i6ubFr1440xTRu4gTmzpzFxoR8+tdCPjnmYqshn6YlyadVf6xg7Wof5s6azaix+nzKmjUrC+fNZ53PGtasWk3AX4c5c/ZsmvLqrxMnuXn3Dmt/m8Pgr/oyae48i+nqVnufxVMmmS1fsmYtjerU5vdpvzKm/49MmqffPk6nY+Sv0xjYtw+rZs1gzpjR2NrYvDCe+Hgdy5fM5fuBPzN2ylyOHj7A7VvBJmneKlqc4WOnMXrSbKpWr82alYsBOHPyGDeuXeXnCTMZNvpXdmz2NX6JBOjU/RNGTZjFqAmz0tyZAzh4+C9u3LzJ1nVrGTFoMGMmmecHwJhJkxgxeBBb163lxs2bHPrrLwCOHT/BvgMH8F3xBxtXr+Ijw5eirFmz8vUXn9P/m35pjykggODgm2zz28hIb29Gjx9vMd3o8eMZMdSbbX4bCQ6+yaHDhwEoWaIk06ZMpsp7pl9i4+LiGOTtzbChQ/Bbt5Yl8+dja2vdzYv1R+7xyazTKa6v7+5MUdfsNBp5BO8VF/m5i/6Lee7stvRrWYz2k47TbuJx+rUshqOD/pijupbBe+VFGo08QlHX7NQrn9eqWBIcPBRAcHAw2/z89Pk0LoV8GjeeEd5D2ebnR3BwMIcC9Pm0cMkSalSrxrZNftSoVo1FS5YA8CQigjHjxjNr2lT8fNfxy2TL54QlOp2O6dOnMHHiLyxbtpK9e3dz/Xrydio/gwZ507hxE5Plzs75mDVrHosWLWPOnAWsXLmc+/fDjOsPHPDHwcHB6liSxjR75lTGjJvMgoW/s2/fHm7cuG6SxsXVjR8HDKGBhbZz8sSxdOjUlYWL/2DGrHk4OeUB4Neps5g7bzFz5y2mXHl3atepl6a4Dv5lqHtr1zJi8KDU696gQWxdm1D3jgBw7ISh7v2xnI2rVvJR924ArPPzA2DDihXMnzGdyTNmEB8fb11Mr+mcKlSwEEsXLmTDmjX0+ewzfh4zxqp4dDodYyZMYO6sWWzy9WVbKtfi7YZrzK+Ga8zVq1fZvnMnfuvW8dvs2YwePx6dTkexokXx9fHB18eHNStXYm9vT6MGDSwd/o3GlWDx/PnG2NLqdZVfwmeeOn06tWvWfKMxlSxRgmm/TDFrz7Nmy0a/L/vS//vv0xSPyDykQ/dm9AKMHTpN03prmnbhZXZ0IegChQsXplChQtjZ2dG4URMOHjxgkqZAgYKULFmKLMq8eKtWfZ/shicUSeLhxInjNDDc+W3eoiUHDuy3OqZzgYG8VaQIRQoXxs7OjubNmrHP398kzT5/fzxbtwagSePGHD12DE3TcHBwMH5JjI6JgSSzF1WtUoXcuXNbHUdSly4FUbBgIQoULIidnR0eHo04HHDIJE3+/AUoXrwkKot1MyY9efIYOzs7Chd+C4AqVd7n4EHr8ykwMJC3ChehsCGfPrCQT/7+/ni2MuRTo8Yc/dtyPiXccVZKGcszLi6OuLg44zprHTh2jBYNGqCUokKZMjyNjOT+gwdm6SqUKUO+vOZfopVSRBo6TU+fReKSR5/m2KnTlCz6NqWKFQMgt6MjNlZ06P795zKu+Qvi6lYAW1s7qtWsx6njR0zSlHN/h2zZ7AEoUbIsDx/cB+DO7ZuUKVcRGxsbstnbU+TtYpw7cyINuZG6fQcO4Nm8BUop3qlYgYiIp4Tdv2+SJuz+fZ5GRlK5YkWUUng2b8He/fo66rN+PZ/27ElWw1NKZ0N+Zndw4L3KlY3L0xST/348W7XUx1Spoj6msDCTNGFhYURGPqXyO5X0MbVqyd59/gCUKF6MYkWLmu338JEjlC5VirKl9U8GnJycrCo/gL//ecSjyLgU1zeulM/45O309Sc4ZrfFxTErdcs7ExD0gMfP4ngSFUdA0APquTvj4piVnPY2nLr2BIANR+/R5B0Xq2JJsG+/P56tWhnyqRIREREp5FMkld95x5BPrdjrv0+/vf9+2rRuBUCb1q2M+bdt+3YaN2pIgQIFgMQytcbFixcoVKgwBQvq2/OGDRsTEHDQJE2BAgUoUaIkKll7bmdnZzxfYmNj0TTNuO7Zs2esWbOaHj16WR1LAmPbWSCx7fzrsKW2s4TZbHM3blxHp9NRpcr7ADg4ZMfe3t4kzbNnzzhz+iS1atVNU1z7DhzAs0VzfflVqEDEUyvqXovm7DVcy/R1r4dZ3bt67RrVq1Y1LnPMlZPzQUHWxfSazql3K79jfNJTqVJFQkJCrIrH0rV4b7JrzF5/f9oYrsVNk1yL9/r707xZM7JmzUrhQoV4q0gRzgUGmmx75NgxihQuTMGCBUmL1x3Xy3pd5QewcvVqmjRqRN40tAevIqYSxYtbbM+zOzjw3rvvki1b2q8xInOQDt0rppQaqpS6pJTaDSSMB6kKrFBKnVZKOSil/JVSVV9m/2Fhobi5uhn/dnF1NavsafX48WNy5sxl7DC4uqRtn6GhoeR3S4zJzc2NkGTbh4aGkj9/fgBsbW3JmTMnjx49AuDsuXO0bd+edh07MnzoUKufAqTm/v0wXFxdjX/nc3Hhfvj9VLYwFRMTw5df9qbf118QEKD/Mp47txNxcXFcunQR0N8BDwsNtXqfIWGhuOVPkk+uboSGhllIk3I+eXVoT/tOHRk2JDGfdDodHbt0xqNxI2pWr0GlihWtjgkgLDwc13zOxr9d8zkTFm7eoUtJ7y6d2bl/P60/6c0Po8bw4+efARB85w5KKb4d8TM9v/+R5es3WLW/hw/Dyeucz/h3Xud8PHwYnmL6A/67qPiOvjrpO3DHiY5+TsSTx1y8cJYH4Yl5vN7nd4b99BWrfp9PbGys1Z8xQWhYGPndEs8rN1dXQpOf62FhuLm6WExzIziYk6fP0O2TT+jVpy+BF17qvo6JkOT1z9XVrP6FhIWZtBturm6EvODcvXEjGKUUn3/5FR27dWPx0mX/OVbj8Z2ycffhc+Pf9x5G4+aUDTenrNx9GJ24/FE0bk5ZcXPKxr1HSZY/fI6b04uHfiYVEhpK/qT1z82VkOT1LzQMtyTthj6NPp/Cw8NxcdGXq4uLCw8MNz2u37jBkydP6NX7Mzp164bf5i1WxxQWFoaLS5L23MUljW1vCJ980oNOndrSteuH5Munj2/x4gV07tzVeNMjLcLv38fFJUnbmc/F5Mlfam7fukmOnDkZNXIoX/b5lAXz55g8TQEICDhA5XerkCNHjjTFFRoWRn6Tc9jFct1zsVw/bwTf5OSZM3T75FN69U2se2VKlWLfwQPExcVx684dLly8xL0Q69r113VOJbV+40bq1K5tVTyWrsVmeZTCtTg0LMy4HAx5l6yN2L5zJy0++MCqWN5EXPr26Us6devGWl/fNMf1usovJDSUPXv30alDhzce0/8HWVTG/5ce5B26V0gpVQXoAryLPm9P0dGpFQAAIABJREFUAieA40B/TdOOG9K9/EE080X/aX9gcmf3ZfZpISSSb53aMSpVrMhGX1/9u2zDh1Ondm2r3tNJNSZL+ZSG7VesXEe+fPm4e+cOAwZ8S7FiJShYsBBDvUfy29yZxMbGUqXK+1Y/sdAHZSEm84yykCYxnzas0+eT94jEfLKxsWHtah+eRETw/Y8/cOWffyhVsqT1Yf3Hc2rXwYO0aNiQ7m3bcO7iRUZOncbKmdPR6XScuRDEkl8mY58tG18PG07ZEiV4/51KaQ4opWgOH9zL9X+vMGj4RAAqVHqPa1cvM3ZEf3Llyk2JUuXIYiijDl16kdspD3FxcSxdMJNtm9bSpn03qz+nPrQXn+2Wz3X9f3U6HU8inrBi0SICL1yg/5ChbN+w/j/VYc3CiaWsiin1Y8bp4jh1+jSrl/+Ovb09vfv0pXy5ctSoXu2lY008tvkyTTOPW7/Ccvq0snyeJz9U2vNJp9NxISiIhfPmEf38Od0/6sU7lSpS9O23XyrOtJwLrq5uLF6sH2rp7T2I+vUbEB5+n9u3b/H1199y9+7dNB//v1wPdDodgefOMue3Rbi6ujJ2zEj+3LWdD5q3Mqbx37eHD5q3fIm4LCxUVpznhnNKp9Px5EkEKxYt1Ne9od5sX++LV6tW/Hv9Ol0+/oQC+fPzTsWK2Nha166/rnMqwbG//2b9xo0sX7zYungsLLP2Wvyico+NjcV//36+65f2YeGvK67lS5bg6upK+IMHfNanD8WKFqVqlSrWx/Waym/i5Cl8/+03aft+8JpjEv/7pEP3atUFNmia9gxAKWX9i2gGSqnPgc8BfvllKh/17GWy3sXVlZDQxOEXYaGh5MuXj//CycmJp08jiIuLw9bWltCwtO3TzdWVe0mGhISEhODqYjocys3NjXv37pHfzY24uDiePn1qNpyyePHiODg48M8//+Du7v6fPpOLi4vJ07P7YWE4O1v/mRI+f4GCBan0TmX++ecyBQsWonz5Ckydpn+v7/jxY9y6ddPqfbq5uhJyL0k+hYYY7+4lpnEjxNp8uvoP7uUT88kxVy6qVqlKwOHDL+zQrdu6Db8//wSgXMmShN5PfAIWej+cfHnzWP25Nv+5h2kjhgNQsWxZYmJjefTkCa7OzrxbwR0nw9ChWlWqcOnq1Rd26PLkzceDJE9TH4TfxymPs1m68+dOsWWjD4OGTzSZwKe1Vxdae3UB4LeZk3DLrx8e5GQYCmpnZ0ddj8bs2LLeqs+3au06fA3v2VQoX87k7n1IaCiuLqbnlZur6R3VkNBQXAxPTtxcXWns4YFSioru7qgsWXj46BF581if3wCrfNawboP+iWcF9/Km9c9CTPmTtRshoeZ1NDk3NzeqVnmPPIbY6tapzYWLF19Jh+7ew2gK5LEHHuvjy5ON0MfR3HsUTfVSTolxO2Xj6JVH3HsYTf4kT+Ty57EnJMkTu5Ss8vFh3fqEfHLnXtL6FxJqlgf6fAq1mMbZ2dnwRE3/FC1hKJWbqxtOTk5kd3Agu4MDVd57j0uXL1vVodPvK0l7Hhb2Uu15vnwuFC1ajLNnT/P48SMuX75E587t0Ol0PHr0kG+//Yrp0617HzmfiwthYUnazvvWt5358rlQsmQpChTQ17latepyMeg8NNevf/LkMZcuBjFipHXvhK1atw5fP/1ltEK5ctwzOYfDcM1noe6FmdZPF0NdcHN1SbHuDfzuO+M2H372GW8XKZJyTG/gnAK4dPkyw0eN5rdZM3FycsIalq7FZteYFK7Fbq6u3Lt3L3Hb0FCTbQ8eOkS5smXJ52zeFqdXXK6Gp1TOefPSqGFDzp0//8IO3Zsov/MXLjBg0GAAHj56xMFDh7CxtUnx3cNXGZP4/0uGXL56lm5GWb+xps3XNK2qpmlVk3fmAMqVLcetWze5c+cOsbGx7N7zJ3XS+HJ5ckop3nuvCvv89wKwfdtW6ta1fp8V3N25ERzMrdu3iY2NZfvOnXh4eJik8ahfn02bNwPw5+7dVHv/fZRS3Lp92zgJyp07d7h+/Xqax+dbUqZMWW7fvsXdu/p88vffQ81adazaNiIigpiYGAAeP37E+fOBvP12UQAePnwI6Idk+visoFXrNlbH5O7uzo2bifm0Y+dOPOp7mKTxqF+fTVsM+bTnBflUoCAPHj7gSUQEAM+fP+fI0aMWx88n16FlC5ZPm8ryaVOpX6M62/btQ9M0Ai9dImeO7BbflUuJm0s+/jZMxHLt5k1iYmLIkzs31d97l3+u3+B5dDRxOh0nA89T7K2UvyglKFaiNKH3bhMWeo+4uFiO/XWAd6tUN0lz49pVli2cxTf9h+OYO/HLTny8jqcR+vesbt64xq3g61SopH85/NFD/XAYTdM4+fcRChWx7glK144dWPfHctb9sZyG9eqzafs2NE3jzLlAcubMiUuyL5Uu+fKRI3t2zpwLRNM0Nm3fRoN6+vrUsH49jh7Xv9N3PTiY2NhY8lj5Zc0kps6d8F29Ct/Vq2jo4cGmLVv1MZ09p48p2cXdxcWF7NlzcObsOX1MW7bSwKN+qseoXbMml69cISoqiri4OI6fOEmJ4sXSHKsle87dx6u6fhhV5aKORETpCHsSw8EL4dQplxdHB1scHWypUy4vBy+EE/YkhshoHZWL6m8OeFXPz+6zLx5C3bVzZ3x9VuPrs5qGDTzYtGWLIZ/OppJP2Tlz9qwhn7bQwFBHPerXMw6n9Nu8xZh/DTzqc/LUKeLi4oiKiuJcYCDFi1mXT2XKlOPWrcR2au/e3dSysp0KDQ0lOlrfqY2IeEJg4Dneeutt2rRph6/vJnx81jNz5m8ULlzE6s6cPiZ923kvSdtZo6Z1Q/5KlylLxNMI4zDx06dP8pah7QQ4sN+f6jVqkjWrdSMwunbowLrlv7Nu+e80rF+PTdu268svMJCcOXOkUPdycCbQUPe2bU+se/XqcfSEfvbbpHUv6vlznkVFAXD46DFsbGwpkUr5vYlz6u7du3zXvz/jR49O05PeCu7uBCe7FjdIdi1uUL8+foZr8a7du6luuMY08PBg+86dxMTEcOv2bYKDg6lYoYJxu207drzUcMvXFdezqCgiIyMBeBYVxeG//qKUhdmZk3sT5bdz6xZ2bdvKrm1badq4Md6DB6c6kcyrjOn/A6VUhv+XLvlieQiReBlKqfeApUB1EodczgPqA79qmrbPkM6fJEMwUxJ+/5HFwjl8OIDpM6ai08XTqlVren30MQsWzKNs2XLUrVuPC0EXGDz4JyIiIsiaNSvOeZ1ZsWI1AH37fs6N4Bs8exZF7tyODB7sTY3qNbh9+7bhZwueULp0aUYM/9lsgoZc2VN+mfbAwYNMmjIFXXw8Xm3a8Hnv3syaMwf38uVp4OFBdHQ0g729uXjpErkdHZk0YQJFChdm85YtLFqyBFtbW7JkycIXn39ubPh+GjSIv0+c4NGjR+TNm5ev+vShnZfptNz3wp+mGNPRo38xd45+xrJmH7Ske/eeLF26kNKly1KrVh0uXQxi5MihPH0agZ1dVvLmzcvCRcs5f/4c06ZOIUsWRXy8Rrv2HWluGDI0f95sjhz9Cy0+ntat29KufSeLx3bLm9Pi8oOHEvOprac+n2bPnUP58uVpUF+fT0OGeXPx4iVy53Zk0vgJFDbk0+Kl+nxSWbLQ57PPadigAZcvX8Z7xHB0unjitXiaNWlCn8+/sHjsZzevW1yuaRpT5s3nyKlT2GfLhne/fpQrpX/C1+O771k+bSoAM5cuY9eBg9x/8IB8efPi2aQxn3XtwrXgm4ybPYeo589RCr7+6COqv1sZgO3+/vy+bj1KQc0qVejXy/QnNoKeWT6nzpz6m1WGny2o69GE1l5d2LB2OUWLleLdqjWYPHYIt4JvkNvw9MjZ2YVvB4wgNiaGkUO+AcDeITsfffqVcTbLiaMHExHxGDT9u3Yf9f4ae3vzWQCrFk/5DrSmaYydPIWAI0ewt7dnzDBv3A1TSnf4sAfr/lgOwPmgILxHjeZ5dDR1atZkSP8fUUoRGxvLsDFjuHT5CnZ2tvz4zTfGCRmatW3L08hnxMbGkitnTubPmGHsQKlUfkJE0zTGTpjIob8O42Bvz+iRI40/PdC+S1d8V+t/YiIw6TTXtWozZOBPKKXYvXcv4ydN5sHDh+TKlYuypUszf46+A7B56zYWLlmCUoq6tWvz43ffmhy73ADLP0kw9WN3qpd2Ik9OO8KfxDB96zVsbfQXuVUH7wAwsnNp6pV3JipGx8DlQQQG629MdKhZgL7N9F9i5+y4ge8R/bDBCm8l/GyBDfvPh/PzmssWjx00pbrF5fp8msChw38l5pNhSu/2nbvg66NvIwPPX8B7xAieR0dTt3YthgwciFKKR48e8ePAgdy9e48CBfLz66RJxqfni5ctY6PfJrJkyUJ7r7ZmU7rff/yclBw5cphZs6YTH6+jefNW9OjRi8WLF1CmTFlq167LxYsX8PYezNOn+vY8b15nli5dwfHjx5gzZ6ZxWJqXV3tat25rsu+7d+8yeHB/iz9bEB2b8qQ1x47+xW9zZxIfH0/TZi3o1r0ny5YuonTpMtSsVYdLl4IYNdKbiKcRZLXLSp68eVmw8HcATpz4mwXzZqNpGqVKleHb7wcYn6AP+PEbOnXpzvvvWy6jgrlecJ5PmULAkaP6nwzxTlL3evRk3XL98c8HBeE9eoyh7tVgyI9J695YLl25gp2tLT9+04/qVaty+85d+nz3HUopXF1cGDV0CAUNE9wkUCm8AvC6zqnhP49i9549xol29FPyJy9Dy18aDxw8yMQk1+IvUrgWBxmuxZMN12KAeQsXssHPD1sbGwb270/dOvqbC1FRUTRu3pwdmzeTK1euFMsoNa86rpu3bvHtDz8A+uG0LZo354vevS2VUooxvc42IcHQ4SOoX7dumn624L/EtHvvXsZPnJTYnpcpzfw5cwBo2qIlTyMjiY2NxTFXLubPmUOJEsWNx7bLniNTjNv02XE6w3dcOn9Q+Y3npXToXjGl1FCgJ3ADuAVcAK4B44AooCawnf/QoUsvqXXo0ktqHbr0lFKHLj2l1KFLTyl16NJTah269JJahy49pdShS08pdejSU2oduvSSWocuvaTWoUtPKXXo0lem+O6dAWSor1EZmnToXp306NDJO3SvmKZpY4GxFlYlnYLJ481EI4QQQgghhPhfJu/QCSGEEEIIIUQmJR06IYQQQgghhMikZMilEEIIIYQQIsPLIr+5Z5E8oRNCCCGEEEKITEo6dEIIIYQQQgiRSUmHTgghhBBCCCEyKenQCSGEEEIIIUQmJR06IYQQQgghhMikpEMnhBBCCCGEEJmU/GyBEEIIIYQQIsOTXy2wTJ7QCSGEEEIIIUQmJR06IYQQQgghhMikpEMnhBBCCCGEEJmUdOiEEEIIIYQQIpOSDp0QQgghhBBCZFIyy6UQQgghhBAiw8uSRaa5tESe0AkhhBBCCCFEJiVP6DIwx+x26R2Cqfj49I7AzMOIqPQOwaICzjnSOwQz2YsUTe8QzBTPgOUXkyWD1Tsgq8qY996CplRP7xDMlOt/NL1DMBM0uVp6h2BG9ywuvUMw81xlvLoHYE/GeyIQr2npHYIZm4yXTZAhyy69IxD/i6RDJ4QQQgghhMjwZMSlZRnztq8QQgghhBBC/I9RSn2glLqklPpHKTUolXQdlFKaUqrqi/YpHTohhBBCCCGEeM2UUjbAbKA5UB7oqpQqbyFdLuAbwKr3CKRDJ4QQQgghhBCvXzXgH03T/tU0LQZYDbSxkG40MAl4bs1OpUMnhBBCCCGEEK9fIeBmkr9vGZYZKaXeBYpomrbF2p1Kh04IIYQQQgghXgGl1OdKqeNJ/n2edLWFTbQk22YBpgI/puWYMsulEEIIIYQQQrwCmqbNB+ansPoWUCTJ34WBO0n+zgVUAPyVUgD5gU1KKU9N046ndEzp0AkhhBBCCCEyPEMnJzP7GyillCoG3Aa6AN0SVmqa9hjIl/C3Usof6J9aZw5kyKUQQgghhBBCvHaapsUBXwM7gSBgjaZp55VSo5RSni+7X3lCJ4QQQgghhBBvgKZp24BtyZYNTyGthzX7lCd0QgghhBBCCJFJSYdOCCGEEEIIITIp6dAJIYQQQgghRCYl79AJIYQQQgghMrwsmX+Wy9dCntAJIYQQQgghRCYlHTohhBBCCCGEyKRkyKUQQgghhBAiw5MRl5bJEzohhBBCCCGEyKSkQ5cJaZrGuImTaO7piVenTlwICrKY7vyFC3h17ERzT0/GTZyEpmkAPH78mN59+tLCsw29+/Tl8ZMnAOzd549Xp06079yFTt26c/LUqbTFNGkSzdu0watz55RjCgrCq1Mnmrdpw7hJiTFNmTaN1u3a4dW5M9/8+CNPIiIA2LJtG+27djX+q1i1KhcvXbIqptMn/+bbrz6hX99ebPRdbbZ+i986vu/Xm/7ffcGo4T8RFhpisv7Zs0i++LQri+bPMtt24rjh/PjNZ1bFkdTrKrsE586fp1KVquz6c7fVMR0KCKC1V1taenqyaMlis/UxMTEMGDiQlp6edOvZg9t37uiPFRhIxy6d6dilMx06d2LP3r3GbZb/8QdeHdrj1bEDPw0eRHR0tNXxABw9eoQPP+xCt24dWbHid7P1Z86confvXjRsWBd//8TjXrlymb59P+Ojj7rz8cc92Ls3MR/Gjx9D587t+fTTj/j004+4cuVymmICOHz4MO3at6ONVxuWLF1itj4mJoZBgwfRxqsNPXv15I4hr/SxXaHXJ73o2Kkjnbp0Ijo6msjISLp262r817BxQ6b8MiVNMR0KCKB127a08PRk4WLL5dd/4EBaeHrSrUdi+R0+coRO3brh1bEjnbp14+ixYwBERkbSoXNn47+6DRowcfLkNMX0Os/zY8eP075zF9q070CvT3tbHdP4D8tydGIdtnlXSzHNsI6l2DOyBluGVsO9SE7jcq/q+dk9sga7R9bAq3p+43L3IrnYOrQae0bWYFjHUlbHkpSx7fRsg1enVNrOC4a209O07dz555+06dCRilWqEnjhgsk2ly5foftHvWjToSNenTpZXQ81TWPCtOm06tyVDh/1IiiFNvfCxUu07/kRrTp3ZcK06caYLl65woef96FTr0/o+ulnnDPEFfH0Kf1+GkTHjz7G68OebNy6zeJ+LTn812Had2yHV/u2LF221Gx9TEwMg4cOxqt9W3p98pGx7sXFxTHy5xF06daZjp07mNTbF+3zRV513YOEOtGRFp6ejJ840Zin1goICMDTy4tWnp4sWmK5jRowcCCtPD3p3rOnMaYEd+/epUbt2iz73bTd1el0dOrala+/+SZN8STIqNe+Vm3b0jyV8vtx4ECae3rSNUn5ASxYtIjmnp60atuWgMOHAYiOjqbLhx/SrlMn2rRvz6y5c62OJWlMr/p6DAnl1+Wly09kfK+lQ6eUOvyK99dLKWX+rfrF2xVVSnWzIl0lpdRfSqnzSqlzSin7NByjslKqRZK/Ryql+qc11rQ4eCiA4OBgtvn5MdLbm9HjxltMN3rceEZ4D2Wbnx/BwcEcCtAXy8IlS6hRrRrbNvlRo1o1Y6Nfo3o11vv44OuzmtEjRzBi1GjrYwoIIPjmTbZt3KiPaXwKMY0fzwhvb7Zt3EjwzZscMjSENatXZ8OaNWzw8aHo22+z0BBTqxYt8F21Ct9Vqxg/ahSFChakbJkyL4wnXqdj0fxZDBk2lqkzFhBwyJ9bN2+YpClavCQTpsxiyrR51KhVlz9+X2iy3mflMsq7VzLb99G/DmFv72BVviT3usoO9A321OnTqV2zptXx6HQ6xk2cwNyZs9jo68v2HTu4+u9VkzTrN27E0TEXWzdtokf37kybPh2AkiVKsOqPFaxd7cPcWbMZNXYMcXFxhISGsmL1Klb9sYINa9cRHx/Pjp070xTTtGlTmDTpF5YtW8mePbu5fv2aSRpX1/wMHuxNo0ZNTJbb29szdOhwli1bweTJvzJz5nQiDDcHAPr2/YpFi5axaNEySpUqbXVMCXFNmDSBGdNnsG7NOnbu2sm///5rkmaj30YcHR3x2+BH927dmTFzBqD/Uuk93Jshg4awds1a5v82H1tbW3LkyMGqlauM/woUKEDDBg3TFNPYCROYM2sWfgnld9VC+eXKxTZD+U01lF8eJydmTZvGhrVrGTtqFEO8vQHIkSMH63x8jP8KFChAo4bWxwSv7zx/EhHBmHHjmTVtKn6+6/hl8iSrY1p/5B6fzDqd4vr67s4Udc1Oo5FH8F5xkZ+76NuZ3Nlt6deyGO0nHafdxOP0a1kMRwf92wqjupbBe+VFGo08QlHX7NQrn9fqeBIcDAggOPgm2/ysaDuHerPNbyPBwYltZ8kSJZk2ZTJV3nvPJH1cXByDvL0ZNnQIfuvWsmS+/pyzxqEjRwi+eYvNq1cyfMAAxkz51WK6Mb/8wvCfBrB59UqCb94i4MhRAKbOmUufj3uxZulivuz9CdPm/AaAz/oNFC/6NmuXLWHRzBn8Mms2sbGxL4xHp9MxafJEpk+bwZrVa9lloe75bfLDMVcuNvhupFuXbsycPROA3Xt2ExMTw+qVPixf9gcbNq7nzp07Vu3zRTG96roHMGbcOEZ4e7PVz48bwcEcCghIU0zjJk5kzsyZbPD1ZceOHVxN9pk2bNS3UVs2beLDJO15gsm//EKd2rXN9r1i1SqKFytmdSzJZcRr35gJE5g7axabfH3Zlkr5bTeU36+GvLp69Srbd+7Eb906fps9m9Hjx6PT6ciaNSuL589n/Zo1rFu9moDDhzlz9myaYnrV1+MEK1atpNh/KD+R8b2WDp2mabVex35fQlEg1Q6dUsoW+APoo2maO+ABvPgKk6gy0OKFqV6hffv98WzVCqUU71SqREREBGFhYSZpwsLCiIyMpPI776CUwrNVK/b679Nv77+fNq1bAdCmdSv27vMHIHv27CjD4OSoqChIwzjlffv349mypT6mihWJePrUckxPn1K5UiV9TC1bstdff+zaNWsav2xUqlCBkJCQ5Idg286dNG/WzKp4/rlyifwFCuKWvwC2dnbUqlOfv4+Z3meoULEy2bLp++6lSpfjQXhivP9evczjxw95p3IVk22eR0WxZZMv7Tu+8D6BRa+r7ABWrl5Nk0aNyJvX+i+VgYGBvFW4CIULF8bOzo4PmjVjn7+/SRp/f388W7UGoEmjxhz9+xiapuHg4GAss+iYGOO5A/oLU3R0NHFxcTyPeo6Li4vVMQUFXaBQocIULFgIOzs7GjZszKFDB03SFChQgBIlSpIli2kTVqTIWxQuXASAfPlcyJMnD48fP7L62Kk5f/48RYok5lXTJk3x3+9vkmb/gf20aqkvn0YNG3HMkFdHjh6hVMlSlC6t70Q6OTlhY2Njsm1wcDAPHzzk3XfftTqmc4GBvFWkCEUMMTW3UH77/P3xbG0ov8aNOXpMH1O5smVxdXUF9F8GomNiiImJMdn2xo0bPHjwwKyz8CKv6zzftn07jRs1pECBAgA4p+Fc//ufRzyKjEtxfeNK+dhw9B4Ap68/wTG7LS6OWalb3pmAoAc8fhbHk6g4AoIeUM/dGRfHrOS0t+HUNf2Tgg1H79HkHevP8wT7/Pfj2crQdlaqSERECm1n5FMqv2NoO1u1NOZJieLFKFa0qNl+Dx85QulSpSibyjmXYkwHD9H6g2YopahUwV3fnt+/bxrT/ftERj7jnQoVUErR+oNm7D2or6dKKZ4+iwTg6dNIXPLlMy5/9iwKTdN4FvWM3I6OVsV0/sJ5ihQuQuFC+vO8SZOm7D+w3yTNgQP7aWmoew0bNuJvQ91TQNTz5/q2KPo5drZ25MiRw6p9puZ11L2wsDCemtUJf6wVGBhIkcKFTdpzf0sxtdLnU5NGjTj299/Gp2B79+2jcKFClChe3GSbkJAQDh48iFfbtlbHklxGu/ZZKr/keb3X3582hvJrmqT89vr707xZM7JmzUrhQoV4q0gRzgUGopQie/bsgP6GSlxcnMl18UVe1/X4XkgIBw4eol1bL6tjEZnP63pC99TwXw+llL9Sap1S6qJSaoUynGVKqQ8Myw4ppWYopbZYue/WSqmjSqlTSqndSik3w/L6SqnThn+nlFK5gAlAXcOy71PYZVPgrKZpZwA0TQvXNE2X8DmUUhOVUicMx6pm+Dz/KqU8lVJZgVFAZ8MxOhv2WT5Jum8M+8qhlNqqlDqjlApMkjbNQkJDyZ/fzfi3m5srIaFhydKE4Wa4YCSmCQUgPDzc+AXbxcWFBw8eGNPt3ruX1l7t+PKbbxk9YkTaYnJLEpOrKyHJGuuQsDDckqZxczPGlNSGTZss3iHcsWsXLazs0D14cB/nfIlfrpydXXgQHp5i+r27d1D5vfcBiI+P5/cl8/nwI/MhlatXLaV1m/ZkzZbNqjiSe11lFxIayp69++jUoUPa4gkLxS1pPK5uhCaPJywUt/z6YWa2trbkzJmTR4/0naSz587h1aE97Tt1ZNiQodja2uLm6spHPXrStEVzGjVtQs5cOamVhjun9++H4eqaGJOLiwv374elsoVlQUEXiI2NpWDBQsZlCxfO5+OPezBr1nSzzsuLhIaFmp2/Zl9IQhPPcWNePX5E8I1glFJ81e8run3YjWW/LzPb/46dO2jSpEmavgCEJq93bm5m9S40NJT8KZRfgj9376ZsmTJkzZrVZPm2HTv4oGnTNMUEr+88v37jBk+ePKFX78/o1K0bfputumxYxc0pG3cfPjf+fe9hNG5O2XBzysrdh4lDFe89isbNKStuTtm49yjJ8ofPcXNKe7tgddvpalpPLbWdSd0wnHOff/kVHbt1Y/FS83MuJaH375uWjasLock6dKH37+OW5EZN0jQ/fdOPqbPn0rRde36ZPYdv+nwOQJf27fj3xg0at/VDZsLQAAAgAElEQVSiw0cf89O335jdlLEkLDRZ3XN1JSzM9POHhoUa8yjhPH/8+DGNGjXGwd6e5i0/oLVnK7p3/5DcuXNbtc/UvI66FxoamqxOuBH6gnI2zYMw4/EAXF1dzc6TpGmSxvQsKoolS5fS54svzPY7acoUvv/2W6vKKiUZ7dpnqfxCrSy/5Pns5upqLCedTkf7zp2p16gRNWvUoFLFilbH9DquxwCTpkzmh/9YfiLjexOl+y7wHVAeKA7UNgxpXAC0BuoC+VPe3MwhoIamae8Cq4GfDMv7A19pmlbZsM8oYBBwUNO0ypqmTU1hf6UBTSm1Uyl1Uin1U5J1OQB/TdOqABHAGKAJ4AWM0jQtBhgO+BiO4WPYrizQDKgGjFBK2QEfAHc0TXtH07QKwA5LwSilPldKHVdKHbc0phvA0pD65N+3NMwTWfOlrHHDhmzesJ4Zv/7CrDnWj/+2NM4/+fGsSTNv0SJsbGxo1by5yfKz587hYG9PqZIlrYzHfFlKn/+A/27+vXoZz7YdAdi1YzPvVqlGvnyuJumuX7vKvbt3qFajjlUxWB9XsjQvUXYTJ0/h+2+/sfoOfJKDvTAeS0EnxFOpYkU2rPNl1fI/WLRkMdHR0Tx58oR9/v5s37KF3Tt3ERUVxZatW60PyeIrI2nrUISH32fs2FEMGjTUeBH7/PM+LF++innzFvHkyRNWrvwjTft86XMcRZwujtNnTjNm9BgWLVzEPv99HEvy3gzArj938UGzD9IWk4Vl5sWXetz/XL3K1BkzGJFk2FeCHTt30vyDtMWkP6aFuF7Bea7T6bgQFMScmTOYN3s28xYs4PqNG6luYy1Lh9Y0ffmZr3h1M61ZzAfS3nYmF6eL49Tp00wcO4bfFy1iz759HDl6LNVtkhzw5WIypFmz0Y8B33zNrvW+DOj3NSPHTwTg8NFjlC1Vkt0bN7BmySLGT53K08jIF4djYZl53bOUCM6fDySLjQ3bt+7Ab8MmVqz8g1u3b1m1zzTHlDxNGuvef47pP1yH5/72Gx927258wpRg/4ED5M2bl/Lly1sdh+XYzJel57Xvv5RfavlsY2ODr48Pe3bu5FxgIFf++ec/BfVfr8evqvxExvcmfrbgmKZptwCUUqfRD4N8ClzTNO2KYfkfwOdW7q8w4KOUKgBkBRJergkAflVKrQDWa5p2y8qG0BaoA7wPPAP2KKVOaJq2B4ghseN1DojWNC1WKXXO8DlSslXTtGggWikVCrgZtp+ilJoIbNE07aClDTVNmw/MB4h9Fmmsuat8fFi3fgMAFdzduXcvcUhiSEgorsmGtOVPdmcuaRpnZ2fCwsJwcXEhLCzM4jCFqlWqcPPWCB4+fEiePHksfshVa9awboMhpvLluZdkmGRIaCiuhmE2JjElTRMSYhK33+bNHDh4kIVz55o14tt37UrTF0tn53yEJ3mqEx4eRh4Ln/PsmZNsWLeKkWOmYGenfzpx+dIFgi4Esmv7Zp4/jyIuLg57ewdcXF25dvUKX33eA128jsePHzHSuz8jx6Q+icWbKLvzFy4wYNBgAB4+esTBQ4ewsbWhUYMGqcbm5upKSNJ4QkPMhke6uboRcu8e+d3ciIuL4+nTp+TOndskTfHixXFwcOCfq/9w+/YdChcqSN48+tgaNWzI6bNnaNWyZaqxJHBxcSE0yQQ1YWFh5Et2LqUmMjKSgQP78+mnn+PuXsG43NlZv4+sWbPSvHlLfHxWWr1PMORDsvM3eVyubvpz3C1ZXrm5ufHeu++Rx0lfl2rXqs3FSxepVk0/Qcfly5fR6XSUK1cujTG5mta7ZHUK9Hee76VQfvdCQvjuhx8YN3o0RYoUMdnu0qVL6HQ63K38IvAmznM3VzecnJzI7uBAdgcHqrz3HpcuX6bo229bFWNq7j2MpkAee+CxPr482Qh9HM29R9FUL+WUGLdTNo5eecS9h9HkT/JELn8ee0IeWTfpyCqfJG2nu4W208VC2xlqWk+T52dybm5uVK3ynrH9rlunNhcuXqRGdcuTwqz2Xc96wxNP93JlTcsmNAyXfM6m+3dxMXkilTTN5u07GPitfvKFpg0b8PNE/buOftu28cmH3VFK8VbhwhQqUIBrN25Q8QXnmGvya0doKPnyJW+n9HlkUvccc7Nj505q1dAP6c+bNy/vVHqHoKAg/RO1F+wzNa+j7rmZ1Qnz9viFMd27Z/w7NNS83iWkSd5GnTt3jt27dzNtuv69Y5Uli/6pYVgY/vv3c+jQIaJjYoiMjGTw0KGMHzv2hfFk9Gtf8vIzu/alUH7J8zkkNNRsW8dcuXi/alUOHT5s9Y3o13E9Pn3m9EuXX0aVRX63wKI38YQu6RVOR2InMm1TNyWaCczSNK0i8AVgD6Bp2gSgN+AAHFFKlbVyf7eA/Zqm3dc07RmwDUh4YSRWS7wVE5/wWTRNiyf1zrDZZ9Y07TJQBX3HbrxSariV8QHQtXNnfH1W4+uzmoYNPNi0ZQuapnHm7Fly5sxpVuldXFzInj07Z86eRdM0Nm3ZQoP6HgB41K9nHKrkt3kLDTzqA/p3eBI+7oWgIGJjY3FyciIlXTt1Mk5Y0tDDg01bt+pjOncu5Zhy5ODMuXP6mLZupUF9/bEPHT7MomXLmDl1Kg4OphOOxMfHs2v3bpo3bWp1fpUoVYa7d28TGnKXuNhYDh/aT9X3TYf9Xfv3HxbMnc5PQ0aR2ymx0/rN94OZu2AFs+cvp0evz6nn0ZjuPT+l6Qetmbd4NbPnL2fUuF8pWKDQCztz8GbKbufWLezatpVd27bStHFjvAcPfuEFDcDd3Z0bN4O5dfs2sbGx7Ni5Ew/DsRJ41K/Ppi2bAfhzz26qvf8+Silu3b5tfOn6zp07XL9+nYIFCpI/f37OnjtHVJT+XZmjx46l6WX6smXLcevWLe7evUNsbCx79+6mdm3rnorGxsbi7T2IZs2a0yDZ5CLh4frhYJqmcejQAYoVK25pFykqX748N4NvctuQV7v+3EX9evVN0tSvW58tW/Xls2fvHt435FXNGjW58s8Vogw3CE6ePGnygvqOnTto1tS64cRJVXB350ZwYvlt37kTDw8PkzQe9euzabOh/HYnlt+TiAi+6tePb/v1493Klc32vW3HjjTdRHkT53kDj/qcPHWKuLg4oqKiOBcY+J8makhqz7n7xhksKxd1JCJKR9iTGA5eCKdOubw4Otji6GBLnXJ5OXghnLAnMURG66hc1BEwzIR59n5qhzDq2rkTvqtX4bva0HZuMbSdZ1NpO7Pn4MxZQ9u5ZasxT1JSu2ZNLl+5QlSU/pw7fuIkJYqnnFdd2rdjzdLFrFm6mAZ167J5x040TeNs4Hly5sxhfA/OGFO+fOTInp2zgefRNI3NO3bSoG4dwzpnjp/ST0Bz7MRJ3ipcGID8bm4cPX4CgPAHD7gefJPCBQu+ML/KlytP8M2b3L6jP8///HMX9erVM0lTt249thrq3t69e3i/qv48z5/fjb+PH0fTNKKioggMDKTo20Wt2mdqXkfdc3FxIYdZnUi9nJNyd3cn+OZNk/a8frLt9e25Pp/+3LPHGNPSxYvZvnUr27dupXu3bvT+5BO6dunCt/368eeOHWzfupWJ/8fefYdFcfwPHH8vB4INRKo1dsVuNDbEboyi2JJYYzQ9saTZRcHee9fYoqJixdgLYsHYvsYottix0myoFLnb3x93nhwccpgYIL/P63l8hNvZ3Q8zO7M7O7N748bxXo0aFncGsvK5r2KFCoSnKL9GKcqvUYMGBBnKb/fevdQy5FWjhg3ZsWsXiYmJ3L5zh/DwcCpVrMiDBw+Mb+iOj4/n6LFjZp9vTcvbOB9/36cve3fuYue27UwcN56aNd7L1p05kbbM+mLxi0BxRVFKqqp6FeicgXUdgDuGnz99+aFhW2eBs4qi1EE/7fEWkDed7e0CBiiKkgv9iFwDIK3pmebEWrAPFEUpCDxQVXWl4RnDHhnYh4n69epx6PBhWvi0IaedHaP8/Y3LOnTsxIa1+lf0DxsyBF8/P+ITEvDyrItXPf1zaV/07MnPAweycfNmChRwZ+pE/d3TPfuC2bJ1K9bW1tjZ2jJ5wniLp3vUr1ePQ6GhtGhjJqbOndmwerU+psGD8fX3Jz4+Hi9PT7wMz8qNmTCBxBcv+PK77wD91AG/IUMAOHnqFG6urhQxXBRYQqPR8NmXvRkzYgg6nY5GTZpTpGgx1gYsp2SpMtSoWYeVyxcRHx/H1En6t3k6u7gycMhIi/fxJt5W2b0pa2trhgwcyLe9vkOr09HWpw2lSpZkzry5lC9fnkYNGtKubVuGDPPF28cHBwd7Jo4bD8Aff/zBkmVLsba2RrGyYujgITg6OuLo6EjTJk3p2LULGo0Gj7Ll+LB9hwzF9MMPP9Gv34/odFpatmxF8eIlWLx4EeXKlcPT04sLF84zbNhgYmNjOXLkMEuXLmb58lXs37+PP/88zZMnT9i5U/9K9EGDhlK6dBlGjfI3PGugUqpUaX76acDrAzET14ABA+jdtzdarZY2Pm0oWbIk8+bPo7xHeRo0aECbNm0Y5jeMNu3a4GDvwNgxYwGwt7enW5dudO/eHUVR8PT0xKuel3Hbe/fuZUaKt81ZGtOQgQP55jt9+bVroy+/2XPnUqF8eRo1bEj7tm0Z7OtLSx8fHOztmTheX36r16zh1q1bLFi0iAWLFgGwYN4844tGdu3Zw9xZszIcE7y947xkiRJ41q1L+487YmVlRYd2bS2++z2tZwVqlcmHYx4bDo+py4xt17HW6Nu31YfuEhIWQ8MKTgSPqENcopaBK/SvVX/8PIk5O26waWANAGZvv8Hj5/oLp+GrLzGxuwd2NhoOnIvhwLm0n9N9fV6l0XZ26syGNYa2c8hgfP38iU+Ix6vuq7Zzb3Aw4yZO4sHDh3zX93vKlSnDwrlzcLC3p3vXbnT6RH/MeXl60sDLy1wIqXjVqc3h33+nVcfO2NnZMnLIYOOyj3t8RuAy/eMAQ/v9xLAx40hISMCzdi3q1a6tz5cBA5g4Y6bxzX/DB/QH4KsenzJszFg6dP8UVYUfvv0Gx9fcNHzJ2tqaAf3607dvH7Q6LT6tfShZoiTzF8zHw8ODBvUb0ManDX7+w2nXoS329vaMGa2vex99+DEjR42gY+eOoKq0btWa0qX1XzFhbpuWelt1L3mdqOfpiVc9y6f4W1tbM3jgQL7t1QudTkdbHx9Dez6PCuXL07BBA9q1bcvQYcNo5eODvYMDE9N4q+o/Laue+762oPxaGMpvkqH8SpUsSfP338enQwesNRqGDhqERqMhKjqaocOHo9XpUHU6mjdrRsMM3CR4G+dj8f+HktHvOLFoo4ryVFXVPIqiNAT6qarayvD5bOCkqqrLFEX5AJgORKN/Lq7iy3RmttcDqKGqam9FUdqg73DdAY4C76mq2lBRlFlAI/QjYufRd5h06KdMOgPL0nqOTlGUbsBg9KOG21VVHZD87zD87A88VVV1coq/MT/6TqENMA7wSJEuDGgFlAUmGWJ6AXyrqurJ1+Vj8imXWYIua4UDcD484xdQ/4byxSyfIvhv0alZb5rCw9i4zA4hlTy53uyFN29TDuus+TC78sYTLd4ej37HMjuEVC5MSvu77zKL9nn6z6792xJtcqWfKBPY5cjgs8n/At1buHb7uzRZ7xRDRp+//jdkwUspAGxz58p6mWXG9gPnsmgOvtKyQYV/PS/fSocuw0Gk6PgJPenQpU86dJaTDp1lpENnOenQWUY6dJaRDp3lpENnqawXVBa8lAKkQ/dPyowOXda8ShBCCCGEEEIIka7MeobOhKqqIUCIoig9ge9TLA5VVbXX392HoijNgQkpPr6uqqp806IQQgghhBBZnJVVthhI/NdliQ7dS6qqLgWWvqVt70L/rJsQQgghhBBC/CfIlEshhBBCCCGEyKay1AidEEIIIYQQQpgj3ytunozQCSGEEEIIIUQ2JR06IYQQQgghhMimpEMnhBBCCCGEENmUdOiEEEIIIYQQIpuSDp0QQgghhBBCZFPylkshhBBCCCFElmclr7k0S0bohBBCCCGEECKbkg6dEEIIIYQQQmRT0qETQgghhBBCiGxKOnRCCCGEEEIIkU1Jh04IIYQQQgghsinp0AkhhBBCCCFENiVfW5CFPUvQZnYIJk5dvJvZIaTiVbFgZodgVlyiLrNDSCXsWkRmh5CKQ27bzA4hFXsSMjuEVJQ8eTM7BLOiYxMzO4RULkyqmdkhpOLR/3hmh5DKlh/KZHYIqTjmzXrHE0DOHDkzO4RUNFny1e1qZgdgRtaLadPBvzI7BLM6taiW2SFYRMmSx37mkxE6IYQQQgghhMimpEMnhBBCCCGEENmUTLkUQgghhBBCZHlWMuPSLBmhE0IIIYQQQohsSjp0QgghhBBCCJFNSYdOCCGEEEIIIbIp6dAJIYQQQgghRDYlHTohhBBCCCGEyKbkLZdCCCGEEEKILM9KXnNplozQCSGEEEIIIUQ2JR06IYQQQgghhMimpEMnhBBCCCGEENmUdOiEEEIIIYQQIpuSDp0QQgghhBBCZFPSoRNCCCGEEEKIbEq+tkAIIYQQQgiR5SmKfG2BOTJCJ4QQQgghhBDZlIzQZUO//36EqdOmoNPp8PFpw6fde5gsT0xMZMQIPy5euoiDvQOjR4+lYMGCJCUlMWbsaC5duog2SUuLli3p8WlPANasXU1Q0GZUVaVNm7Z07tQlQzGdO3OKwIBFqDodnvWb0bzVhybLDwbv4EDwDqwUK2zt7Oja4zsKFCrKjWt/sWrpXABUVFq17UTV6nWM6+l0Wsb5/0w+Ryd6/TgsQzGpqsq4yVM4FBqKnZ0dY/z9KF+uXOrYL1zA138E8QkJeHl6MrjfzyZ3gJauWMGUGTM5tHcPjvnyAXD85P+YMHUKSUlJOObLx7KFCy2K6cjvR5gydTI6nY42Pm3p8WkPk+WJiYn4jfDj4sULODg4MHb0OGPZjR4ziouXLqLVamnZwpuePXqSkJDAV998yYvEFyRptTRp3ISvv/o6Q/l09vRJAn5diE6no36j9/Fu87HJ8l3bNnFw/y6srDTktXfgs69/wNnFFYDAgCWc+eMkAK3bd6JWnfoALJg9iRvXLqPRWFO8ZBk+/aI31taWNzen/necJb/MRafV0fT9FrT/sLPJ8i2b17N3z3Y0VhrsHfLRq28/XF3dABjpN4i//rqAh0dFhg4fY1xn6KAfiIuLA+Dx40eULl2WQUNHZiivVFVl4sxZhB47hp2tHSMGD8SjTJlU6WYv+oWtu3bz5GksR3buMH6emJjIsLHjuPDXXzjY2zPBz4+CBdwJu3CBUZOnGPfxTY8eNK7vZXFM4yZNTnac+1PeI43j3M//1XHevx+KojBr7jyCDxzAysqK/I6OjBnhj6uLC8dPnqTvTz9TqFAhAJo2asS3X31pUUzHjh1l9uzpaLVavL1b07Vrd5Plf/75B7Nnz+Dq1asMHz6Chg0bA3D//j2GDx+CVqtDq02iXbsPadOmncm6Q4YM4O7dOyxbtsqiWEzzaRKHDhvyaYQ/5T08UufT+Qv4+vsRH5+AVz1PBvfvj6Io7Nqzh7kLFnLt+nVWr/iViuXLG9e59NdlRo4Zw9Nnz7CyUlizYgW2trbpxjSuWzkaV3ImJjaRlqOPm00z7KPSNKzgRNwLHQN/Pc+5W08BaFfLnV4tigEwZ8cNNh27D0CFInmZ2N0DOxsrQs7FMGrd5QzlE+jr3+JFc9DpdDRt1pIOH5nWv6DN69i7ezsajQZ7+3z0/r6/Sf27dOk8Hh4V8fUbm2rbixbMInjvTlav25ahmP7pY+r582f06fOdcf2oqEiaNWtOnz4/WBzT4dBQxk+ahFano0Pbtnzx2WcmyxMTExk8bBjnL1wgn4MDkydMoFDBgvp8WLyYjUFBaKysGDxgAJ5165KQkMCnn39OYmIiWq2WZk2b0vvbbzOUT/90TAArAgLYsHEjqqryYfv2fNK1a4ZiAkP9mziJQ6GHDfVvRBr177yhnYrHy7Megwfo69/jx4/5eeAg7t69S8GCBZkycQIO9vZcu36dYX7+nL94kb69e9Gze3cze/93YwI4fvIkEyZNfnWNsPgXi2K6fOE0OzYuR1V1vFu7MV5N25hNd+70UQKXTeern8ZQqGhJ4+ePHkYzZ9zPNPzgQzwbt+bxw2g2rprL0yePUKysqF6nMXUatLQ4j0T2ISN02YxWq2XS5IlMnzaDNasD2b17N9euXzNJs2VLEHnt7dmwfhOdOndhzpxZAOzbt5fExEQCVq1h+fIVbN60ibt373L16hWCgjazdMlyVq4IIPTwYcLDwy2OSafTsmbFAnr/5MfwsbM5cewQ9+6Yrv9enQYMGz2ToaOm06xlO9avXgJAwULvMMh/CkNHTafPz36sWjYPrVZrXC9491bcCxZ5o7w6FHqE8FvhbN+0Ef+hQxg1brzZdKPGjcdv6BC2b9pI+K1wDh85Ylx27/59fj92nALu7sbPnsTGMnrCBGZPnUpQYCBTxpvfbkparZaJkyYwY/pMAtesY/fuXVy7Zlp2QVuCsM+bl00bNtOlUxdmGcpur6Hs1gSsZcXylWzavJG7d++SI0cO5s2ZT8Cq1QSsDOD3o0c4e/asxXmk02lZsXQePw4cwZjJ8zh25CB3bpuWXdFiJRg+ZjqjJs6hRi1PAgP0ZffnqePcvH6VEeNnMWzUVHb+toG4588BqO3ZkLFTFjBq4hxeJCZwcP8ui2PSarUsWjALX7+xzJizmEMH93Mr/KZJmuIlSjFp6lymzVpEnbpe/LrsVYe6bfuP+f7HQam2O2b8dKbOWMDUGQsoW9aDWnXqWRzTS4ePHSP89h2CVq3Et9/PjJ06zWy6+nXrsmLBvFSfb962nbx587IlYBVdP/qIGQsWAFCyeHFWLVjA2sW/MGfSREZPmUpSkjbV+uYcCg0l/NYttm/ehL/vUEaNG2c23ahx4/DzHcr2zZsIv3XLeJz37P4Jm9auYcPqABp4eTFv0SLjOu9Wq8aG1QFsWB1gcWdOq9UyY8ZkJkyYwvLlAQQH7+XGjesmaVxd3Rk0yJemTZuZfO7k5Mzs2QtYvHg5c+cuIiBgBdHRUcblBw+GkDNnToviSOlQaCjh4bfYHrQZf1/f1+fTUF+2B20mPPxVPpUqWYrpkydR/d13TdInJSUxyNeXYUOHELR+HUsXLrT45sXGo/f5bPbpNJc3qOBEMddcNPE/iu+qi4zoVBYAh1zW9PEuToeJJ2k/4SR9vItjn1O/z5Gdy+IbcJEm/kcp5pqL+uXzWxTLS1qtloXzZzLMfxwz5yzh8MFgboXfMElTokQpJk+dx/RZv1DXsz6/LjWtfz/8lLr+AVy5fIlnT59mKJ6XMf3Tx1SuXLlZvHi58Z+7uzv16zfIUEyjx49n3uzZbNmwge07d3L16lWTNBs3b8Y+b152bNnCJ127MnXGDACuXr3Kjl27CFq/nvlz5jBq3Di0Wi05cuRgycKFbAwMZP2aNYQeOcKfZ85kakyXr1xhw8aNrF6xgg1r13Lg4EFu3rxpbvevdehwKOHh4WwPCtLXv7Fp1L+xhnYqKIjw8HAOh+rr3y9Ll1K7Zk22bwmids2aLF66FAAHBwcGDRxAj+6fZJmYnsTGMnrsOGZPn0bQhvVMmTTRonh0Oh3b1i+h29eD6DVoCmdPhRJ5/3aqdAnxcRw7uJPC75RKtWznpl8p5VHV+LuVlYbmbT6hz5CpfPnDKE4c3m12m9mJlaJk+X+Zki+ZstdsRlGUG4qiOKexrJiiKGEZ3N4yRVE+TD9laufPn6Nw4SIUKlQYGxsbmjVrxsGDB0zSHDx0EO+W3gA0btSYEydPoKoqKArxcXEkJSWRkBCPtY0NuXPn5saNG1SsUAk7Ozusra2p9u67HDgQYnFMN65dxsXNHRdXd6ytbahRy4s//zC925wzZy7jz4kJCWA44HPY2qLRaAB48eIFyevBwwfRhP15Es/6pidoS+0/cACflt4oikKVSpWIjY0lKjraJE1UdDTPnj2jauXKKIqCT0tvgkNe5efEqdP4qW8fkxG77Tt30rRRI2Mnzym/ZRdM586fo0jhIhQ2lt37HEhZdgcP4O3dCoDGjZtw4sRxVFVFAeLi40lKSiI+IR4ba33ZKYpCrlz6vE1KSiIpKSlD88uvXfkLV/eCuLoVwNrahpp16vPHyaMmaTwqVMHW1g6AkqXK8fCBPg/v3rlFWY9KaDQabO3sKPJOcc7++T8AqlR7D0VRUBSF4qXKGNexxJXLlyhQoCDu7gWxsbGhnldDjh8LNUlTqXJVY0xlynoQk6xcK1d597UX/XHPn3P2zGlq1fa0OKaXDhwOpVXz91EUhcoVyhP79BlRMTGp0lWuUB4XJ6dUn4eEhtK6eXMAmjZowPFTp1BVlZx2dlhb6+tBYmJihspw/4ED+Hi3fHWcP40lKirFcR4VzbOnyY5z75YEh4QAkCdPHmO6uLg4FP7eyejixfMUKlSYggULYWNjQ+PGTQkNPWSSpkCBApQsWQpFMT0F2djYkCNHDkDfHqiqalz2/PlzAgPX8MknPd4orv0hB/BpZWgPKlciNvYpUVFRJmmioqJ49uwpVasY8qmVN8H7QwAoWaI4xYsVS7XdI0ePUqZ0acoZRmrz5ctnbNPSc+LKIx49S0pzedPKzsaRt9M3nmCfyxoX+xx4lXci9MIDHj9P4klcEqEXHlC/ghMu9jnIY6fhj+tPANh07D7NqrhYFMtLly9fpECBQq/qX/1GHD92xCRNpcrVsLVLVv9iXuWjvv7lIiWtVsvypQvo3vOrDMUDb++Yeun27Vs8fPiQypWrplqWlrNhYRQtUoQihfXteYvmzY116qXgkBDatG4NwPtNm3LsuL49Dw4JoUXz5uTIkYPChQpRtEgRzoaF/W9CVBoAACAASURBVO32/G3EdO36dSpXqkTOnDmxtramRvXq7Nu/3+KYXtp/IASfVq0M9a+y/nxstv49o2qVKob614rgEP2+9occoE1r/bmxTetWxnrplD8/lSpUyNAMkLcd0/YdO2japDEFChQwxmiJOzevkN/ZnfzOblhbW1OxWl0unj2ZKl3w9kA8m7TG2trG5PMLZ07g6OSKq3th42d5HRwpWKQ4ALZ2OXF2K0Ts4wcWxSOyl/9sh07R+8/9fZFRUbgZprYAuLq6mWmAInF106extrYmT548PH78mCaNm2CXMyferVrg06Y1Xbt2xcHBgRIlSvLH6T94/PgR8fHxHDlyhIiICItjevQwBsf8r/q7jo5OPHqY+iI3ZO82hvX/mk2By+jY9dXd/utXLzFySG9G+/aly6ffGi+G1gX8QruOn77x3Y6IqCjc3V/llZubKxGRkaZpIiNxc3M1TWPIz/0HDuDq6mK8UHvpRng4T2Kf0OOrr/m42ycEbbVs6lBUZCRubsnicXUlKso0nsioSGP5mpRdk6bktLOjhfcHtPZpRdeu3XBwcAD0F0pdunXh/Q+aUatmLSpWrGhRPAAPH8aQ3+lV2eV3cuahmbJ76WDIbipVqQFg6MCdJCEhntgnj7l4/gwPYkyPxaSkJI4c2k+lKtUtjikmJhon51dl4uTswgMznaaX9u3ZybvV37N4+0ePHqZSlWrkypXb4nVeioyOxt012fHi4kxklOWd1eTrW1tryJM7D48e6y++z54/T4dPe/BRz88Y+tOPxg5eeiIio3B3ezWC7ObqRkSK4yoiKsWx5+ZGROSrspoxZw5NWnqzbecOen/7jfHzP8+epX2nznzTpy9XUtzpT0tUVBQuLq/25eLikqqNep3IyAg+++wTPv64LZ07d8PZWd8hWbJkER07djZ25DMqIjIS9xT1LyJFXBEp2lc3V7dUbUZKN2+GoygKX33Xi4+6dGHJsuVvFJ85bvlsufcw3vj7/YcJuOWzxS1fDu49THj1+aME3PLlwC2fLfcfJfv8YTxu+dKf+pncg5hoY54DODm5EBOT9jG+d88O3q1eM93tbt+2mfdq1iV//tQ3OtLzto6pl/bt20OjRk0y1HmKTHk8ubkRmSKmyMhI3A03/l62548ePSIyKsr4OeiPxUjDcabVaunQsSP1mzShTu3aVK5UKVNjKlWyJP87dYpHjx4RFxfHocOHuX//vsUxvRQRGWnmfJyi/kVG4eaa4nxsyJeYmBhcXPTl5uLiwoMHf79T8rZiunHzJk+ePKHHF1/ycZcuBP221aJ4njx+gIPjq/rhkC9/qs7XvdvXefwohrIVTM+piQnxHN63hYYfpD1W8DAmkvu3b1DIzMieyP7+Ux0ew2jZBUVR5gKngE8URfldUZRTiqKsUxQlj6IoLRRFCUy2TkNFUX4z/NxZUZSziqKEKYoyIQO7tlYUZbmiKGcURVmvKEouw/aGK4pywrC9hco/8WoeM3cXU95RN3cHUlHg3LlzaKys2LZ1B5s2BhEQsIo7d25TvHhxun/SnT59evP9D30pXbo0GgsvJtMIyexd/oZNvRk1aQFtP/qU7b8Zi4DiJcsyfOxsBvpNZufWDbxITOTs6RPktc/HO8XevOExnw+W5VVcfDwLlyyl9zffpFquTdJy/sJF5s6YzoLZs1iweDE3LJiCYiabzMRjLhGcOxeGlUbDjm07Cdq0hVUBK7l9Rz9tQqPRELAygG2/befcuXNcuXol3Vhet8O0DtIjh4K5ce0yLVp3AKBi5XepXLUGY/z6MX/WREqW9sAqxcjEiiVzKVuuImXKWd7JTOOAMuvA/r1cuXKJtu0/Np/AjMMH9+NVv5Hl8ZiEZv54+SfWr1S+PBuWL2Pl/PksWRVAQkLi39imJcf5qzTf9+rFvu3b8P6gBQFr9XWzfLly7Nn6GxvXrKZLx4/p+3M/i+IxJyNNn6urG0uWrGDVqkB27drOgwcPuHz5L+7cuY2Xl+VT4lJSzdRAy9rO18eepE3ij9OnmTBmNL8uXsy+/fs5esz883AZZW7Xqmq+fUXN2LGYFrPVL40Nh+zfw9Urf6Vb/x7ERHPk8EG8W7d7bbqM+LvHVHLBwXtp0iRjM0HSaKpN06RxPL3uONNoNGxYu5Z9u3ZxNiyMy1csb8/fRkwlS5Tgsx49+PLbb/mmVy/KlCmD5g1Gw8wfVynSmKujb3H62tuKSavVcv7CBebOmsmCOXNYsGiRRdcIZiXbl06nY+emX2nepluqZPt3rqNOw5Zp3vBKSIhn7dJpfNDuU+zsUo+gi+zvP9WhMygL/Ao0Az4Hmqqq+i5wEvgJ2APUVhTl5e35jsBaRVEKAhOAxkBV4D1FUdpmYJ8LVVWtDDwBXj5pPVtV1fdUVa0I5ARapbchRVG+UhTlpKIoJ5ctW5pquaurKxGRr0bPIiMjcHZxTpHGjUjDCFtSUhJPnz7F3t6BXbt3UrtOXaytrcmfPz+VK1fhwoULAPj4tOHXX1eyYP5C7O3tKVLY8ufWHPM7mUype/gwBgfHtKcY1KjlxZ+njqX6vEDBItja2nL3zk2uXr7AmT+OM/TnL1k8bzKXLpxh6YKp6cayOjCQDl260KFLF1xdnLl//1VeRURE4upiemfW3c2NiIhI0zTOLty6fZs7d+/SoXMX3m/tQ0RkJB917UZ0dDRubq541qlDrpw5ccyXj+rVqnHpcvovHnB1dTUZ+YyIjEx1p9gtWfm+LDsHewd27tpF3dp1jGVXJVnZvZQ3b16qV6/O77//nm4sLznmd+ZBsrvvD2KiyeeY+g76ubN/sHXzWr7vNxwbm1fTPFq368TI8bPpP3QMqqri5l7QuGzz+gBiYx/T6ZMvLI4H9CNyMdGvyiQmOsrsXf0/T/+P9esCGOw7ChubHBZtO/bJYy5fvkj1GrUtjmftpk10/PwLOn7+BS5OztxPNmITERWNi7PZ2dhmubm4GNdPStLy9NlT40P0L5Uo9g457ey4cv26uU0AhuO8cxc6dO6Cq4sL9yNe3TGPiIzANcVx5e7qZnrsRUTg6pI6bu8WH7A3eB+gn4r5cvpX/Xr1SEpK4uHDR+n+jfrRk1f7ioqKwjkDefSSs7MLxYoV58yZ05w/H8Zff12iY8f29OnzDbdv3+L773ulu43VawPp0KkzHTp1NuSTaf1LmQfuKdrXiMiIVG1GSm5ubtSo/i6Ojo7kzJkTr3qenL94MYN/rXn3HyZQwPHVBZq7oy2RjxO4/yiBAo6vRt7c89kS8TiR+w8TcE82IufuaEdEshE7Szg5O5s8txgT85r6F2hZ/bt27Qr3793h268+4avPu5CQkMC3X1n+zNPbOKZeunLlMlqtlrJlU79I6HXcXF1Nj6eICONojTGNm5txNMvYnjs46Ne9n7zORqZa1z5vXt6rUcPkme7MiqlDu3asW72a5UuW4ODgwDtFi1oUz+q1a+nQsRMdOnbS17/0zseuprNokqdxcnIyjspGRUWR38JpjJkRk5urG5516+qvERwdqf7uu1z66690Y7N3yM/jZDNkHj96QF57R+PviQnxRN6/zbLZI5k2oje3b15h9S+TuRN+lds3r7BnyyqmjejN0QM7OLR3M8cO7QRAq01i7ZKpVK5ej/JV0h9NF9nTf7FDd1NV1aNAbaA8EKooymngU+AdVVWTgJ1Aa0VRrAFvIAh4DwhRVTXKkGYVUN/Cfd5SVfXlQz4rgZdvW2ikKMoxRVHOou8oVkhvQ6qqLlRVtYaqqjV69OiZarmHR3lu3Qrn7t07vHjxgj179lDfyzRMLy8vtm3XTwMM3h9MjRr655nc3dw5aXieLi4ujrCwMN55pxiA8Y7l/fv3CQnZz/vvN7fwT4d3ipcmMuIe0VERJCW94OSxQ1SuZtpoRN6/a/w57M+TuLrp55ZHR0UYX4ISEx1JxP07ODm70faj7oybtoQxUxbx+bf9KOtRmZ5f/5RuLJ0//pgNAQFsCAigccOGbNm+DVVV+fPsWfLkyZPq4tvF2ZlcuXPx59mzqKrKlu3baNSgAWVKleLgnt3s/m0Lu3/bgpurK+tWrcTZ2ZlGDRpw6vQfJCUlERcfz9mwMEqYebYmpfIe5Qm/dYs7xrLbTf36KcuuPtu26adnBAfv472XZefuxomTJ03Krtg7xXj48CGxsbEAxMfHc/z4cYpZEMtLxUuWIfL+HaIi75OU9ILjvx+kWvVaJmluXr/K8l9m07ffcOwd8hk/1+m0PI3VTxe8dfM6t8NvULGy/oURB4J3EXbmf3zTZwBWVhlrZkqVLsu9u3eIuH+PFy9ecPhQCO/VqmuS5trVy8yfO53BviPJl88xjS2ldiT0IDVq1DY+U2OJju3asXbxL6xd/AuNvDzZums3qqpy5tx58uTObfZZubQ08KzLb7v0L4jZe+AA71WrhqIo3Ll3z/gSlLv373Pj1i0KJpv+lFLnjz82vqykccOGbNm23fQ4T9FRcXFxJlfu3K+O823badRAP9p1M9kLkPYfOGB8Tiw6Otp41/5sWBg6nY58+RzS/RvLlvXg9u3b3Lt3lxcvXhAcvJe6dS17AU1kZCQJCfoOSGzsE8LCzlK06Du0adOeDRu2sHbtRmbNmk/hwkWYMWNOutvr3PFjNqxZzYY1q/X5tNXQHpx5mU+mF28uLi7kypWbP88Y8mnrNho1fP2ooGedOvx1+TJxhueTT/7vFCVLFLfo703PvrPRtKulPw6qFrMnNk5L1JNEDp2PoZ5HfuxzWmOf05p6Hvk5dD6GqCeJPEvQUrWY/iZBu1ru7D1j+ZRggNKly5nWv4P7ea9m6vo3b840hgwbZVH9q/FebZauWM/CxQEsXByAra0t8xausDimt3FMvbRv354Mj84BVKxQgfDwcG7f0bfnO3btolHDhiZpGjVoQNBvvwGwe+9ear2nb88bNWzIjl27SExM5PadO4SHh1OpYkUePHjAk2Tt+dFjx8w+t/lvxgQQY7g+uHfvHvuCg2nxwQcWxdO5Y0c2rF3DhrVraNyoIVu2bjXUvzOvqX+5+PPMGUP920qjBvr4Gzaob5y6GPTb1nTrZWbG1KhhA079YbhGiIvTXyMUT79NKFi0JA+i7/MwJpKkpCTC/jhCuYqvplba5czFwDGL+NFvNj/6zabwO6Xo/EU/ChUtyed9Rxg/r92gBV5N21LL6wNUVSVo9QJc3ApRt5H3G+WZyB7+i19b8MzwvwLsUVW1s5k0a4FewAPghKqqsX9zOmTKMXlVURQ7YC5QQ1XVW4qi+ANv9vBHMtbW1vTrN4C+3/dFp9PSupUPJUqUZMHC+XiU86B+/Qb4tG6D/wg/OnzYDnt7e0aP0r+6/cMPP2LU6JF07tIRVYVWrVpTunRpAAYNHsjjx4+xtramf78B2KcYNXgdjUZDp25fMWuyPzqdjrpeTShYqCi/bVxF0eKlqFKtFiH7tnHx3J9oNNbkyp2bT7/Uvxr66l/n2bVtAxqNNYqVQqdPviFPXsv3/Tr1PT05FBpKi7btyGlnxyi/4cZlHbp0YUNAAADDBg169bUFdevi5Vk3rU0C+jcSetapS/vOXbBSFDq0bUPpUulPDbW2tmZAv/707dsHrU6LT2sfSpYoyfwF8/Hw8KBB/Qa08WmDn/9w2nVoi729PWNG61/7/dGHHzNy1Ag6du4IqkprQ9ldvnwZ/5F+6HQ6/SvGmzTDq55lr7sHfdl17fEtU8YNQ6fT4dWwGYWKvMOmdSsoVrw01WrUJjBgMQnx8cydoX8DmJOTC9/390ObpGXciAGA/kTzVa+fjc8//rp4Nk7Orowe/jMA1d+rS5sOln0Vhkaj4Yuv+zDSfxA6nY4mTT+gaNFirF61jJKlylCzVl1+XbaQ+Lg4Jk8YBYCziytDfPU/Dx30A3du3yI+Po4venaiV5+fqfau/hm7w4f2065DJ4vzJ6V6tWtz+OgxfLp0w87WFv9BA43LOn7+BWsNr6aePm8+O/btIz4+geYffkQ7b2++6dmDti298R0zFp8uXbHPa894P/1Xcfxx5ixLAwKwtrbGSrFiyI8/4GhB5wmgfj3Dcd6mrf449/czLuvQuQsbVhuO88GD8PX317+O37MuXp76l8JMmzWLGzdvoihWFCxQgOFDBgOwe98+1q7fgEajwc7Wlknjxlo0/cna2prvv/+J/v1/RKfT0qJFK4oXL8GSJYsoW7Ycnp5eXLx4Hl/fwTx9Gsvvvx9m2bLFLFu2ivDwG8ydO8s4Baxjx86UKFEy3X1alk/1OHQ4lBZt2hjyyf9VPnXqzIY1q/X5NGTwq1eU1/U05tPe4GDGTZzEg4cP+a7v95QrU4aFc+fgYG9P967d6PRJdxRFwcvTkwZeltXBaT0rUKtMPhzz2HB4TF1mbLuOtUafx6sP3SUkLIaGFZwIHlGHuEQtA1foR+UfP0/Sf1XBQP3zrLO33+Dxc/3LVYavvmT42gINB87FcOBc2s+fmqPRaPjymz6M8BtoqH8tKPpOMQJWLqVU6bLUrFWX5UsXEh8fx6Tx+q/9cHFxZciw0QAMGfj9q/rXoyO9+vYz1r839TaPqZCQYMaPn/xGMQ0ZOJCvv/sOrU5HuzZtKFWyJLPnzqVC+fI0atiQ9m3bMtjXlxY+PjjY2zPJ8EbkUiVL0vz99/Hp0AFrjYahgwah0WiIio5m6PDhaHU6VJ2O5s2a0bC+pfeV305MAD/268ejR4+wtrZm6KBBqWYVWEJf/w7TwsdM/evYiQ1r1wAwbMgQfP38DF+vUhevevr690XPnvw8cCAbN2+mQAF3pk7UvzkyOjqajl276b8yRFFYuSqAoA3rTV729G/HVLJECTzr1qX9xx2xsrKiQ7u2Fl0jaDQaWnboyYr5Y9HpdFSr1QjXAkUI3h5IwaIlKFexRvoZnUL49Uv8efIQbgWKMm+i/nzVpFUnypSvluFtZRXyveLmKebmTWdXiqIUA7aqqlpRURQX4H9AY1VVrxieayusqupfiqJogKvACWCdqqqBiqIUAI4C1YGHwC5glqqqQYqi3EDfMUt1q9Owz+tAXVVVf1cUZRFwEVgMXAKKARrDtterquqvKMoyQ5zrX/f3PHr4JEsVzqmLd9NP9C/zqlgw/USZIE6b9VqcsGuWv+jm3+KQO2MvbPg3FHew/PnRf4tNnryZHYJZ0bGWPef3b3K2z3rHlEf/f+aZun/Slh9Sf39iZnPM+2ZfSfG2OTtkzbiynix1yZJlbTiQ/vTLzNCpRbWsd+FixuGTl7P8gVavRul/PS//i1MuAVBVNQroAaxWFOUM+g5VOcMyLbAVaGH4H1VV7wGDgf3An8ApVVWDLNzdBeBTw37yA/NUVX0ELALOApvRdx6FEEIIIYQQ4h/zn5pyqarqDaBist+D0T8bZy5tb6B3is8CgAAzaYuls8/yaSzzBXzNfN4jre0JIYQQQgghhKX+syN0QgghhBBCCPFf958aoXubFEVxAvaZWdREVdWMPXUuhBBCCCGEEP8A6dBZyNBpq5rZcQghhBBCCCHES9KhE0IIIYQQQmR5VlbZ4mWc/zp5hk4IIYQQQgghsinp0AkhhBBCCCFENiVTLoUQQgghhBBZnpUiUy7NkRE6IYQQQgghhMimpEMnhBBCCCGEENmUdOiEEEIIIYQQIpuSDp0QQgghhBBCZFPSoRNCCCGEEEKIbErecimEEEIIIYTI8hR5y6VZMkInhBBCCCGEENmUdOiEEEIIIYQQIpuSDp0QQgghhBBCZFOKqqqZHYNIw5NHsVmqcBJfJGV2CKncjnyS2SGY5VHMJbNDyBbi793J7BBSsXWwz+wQUtHkyp3ZIZh1J+ZZZoeQinvOzI4gtWsPX2R2CKn4TP8rs0NI5eSYGpkdglm5bOV1A5ZQdNrMDiE1q6w3bhGXqMvsEMyyz5c3Wzycduz0tSx1bWxOraol/vW8zHpHuhBCCCGEEEIIi0iHTgghhBBCCCGyKZlHIIQQQgghhMjyrLLFxNB/n4zQCSGEEEIIIUQ2JR06IYQQQgghhMimZMqlEEIIIYQQIstTFJlzaY6M0AkhhBBCCCFENiUdOiGEEEIIIYTIpqRDJ4QQQgghhBDZlHTohBBCCCGEECKbkg6dEEIIIYQQQmRT8pZLIYQQQgghRJZnJd8sbpaM0AkhhBBCCCFENiUdOiGEEEIIIYTIpqRDJ4QQQgghhBDZlHTohBBCCCGEECKbkg6dEEIIIYQQQmRT8pbLbOjI70eYMnUyOp2ONj5t6fFpD5PliYmJ+I3w4+LFCzg4ODB29DgKFixIUlISo8eM4uKli2i1Wlq28KZnj57cuHmDIUOHGNe/e+cOX331NV06d7E4pqNHf2f6jGnodDpat/Lhk0+6myw/ffoPZsycxtWrVxnhP4pGjRobl/300w+cOx9G5cpVmDRxivHzkydPMGfuLHQ6lVw5czJ06DAKFy5icUynT51g2ZJ56HQ6Gjf9gLbtO5ks37plPcF7d6LRaLC3d+CbXj/j4upmXP78+TN+6vsFNWt58tmXvQEIPbSfTRtWoygKjo5O9P5hIPb2DhbHdDg0lAmTJ6HT6mjfri2f9/zMZHliYiJDhw3j/IULOORzYNL4CRQqWJCzYWGMHD0KAFVV+fbrb2jSuDEJCQn0/OJzEhMT0Wq1NG3SlF7ffmtxPG8jJoAPvFuSK3duNFZWaDQa1qwKyFBML7c5ZcFCjpw8iZ2tLcN//IFypUqlSjd3+a9sDw4m9ulTDmxYb/x86569zFyyBBcnJwA+at2Kts2bA3A/MpIxM2cRERWFoihMG+FPQTe3VNs2F9OEGTM5/PtR7OxsGTVkMB5ly6ZKd/7iJYaNHUtCQiL16tRm4Pd9URSFi5cvM3rSFBITE9FoNAz5+UcqlS/PsoDVbN+9B4AkrZbrN28SsnULDvb2FsU0btIkDh0Oxc7OjjEj/Cnv4ZEq3bnzF/D19yM+PgGvep4M7t8fRVGYPG06Bw4dxNrahiJFCjPa3x/7vHkBWLRkCRs3B6HRaBjcvx+edeumGw/AiRPHmD93JlqdjhYtvOnYqZvJ8rNnTjN/3iyuXbvGkKF+eNVvaFwWGRnBtCkTiIqKRFEURo2ZiLt7AX76sTdxz58D8OjRQ8qW88B/xFiL4kmeV2+j/GKfPmXIyNHcj4ggSavl086daOvd0qKYTv3vOIsXzUGn09G0WUs6fNTZZHnQ5nXs3b3d0E7lo/f3/XE1tFMj/QZx6dJ5PDwq4uuXOi8WLZhF8N6drF63zeI8GtetHI0rORMTm0jL0cfNphn2UWkaVnAi7oWOgb+e59ytpwC0q+VOrxbFAJiz4wabjt0HoEKRvEzs7oGdjRUh52IYte6yxfG89PvvR5g6bQo6nQ4fnzZ82r2HyfLExERGjPDj4qWLONg7MHr0WON5b8zY0Vy6dBFtkpYWLVvS49OeAKxeHUDQls0oikLJkqUY5jscW1tbi2M6HBrKhEmT0Op0tG/bli8+S912DjG0nfkcHJg0Qd92Hjl6lOkzZ/LixQtsbGz4+YcfqFWzJgDf9OpFVFQUWq2Wd6tVY+jgwWg0mkyL6dmzZ3yabBsRkZG0atmSgf37WxwTvGynJnMo1NBO+ftT3qNcqnTnLlzA18+f+IQEvDw9Gdy/H4qiMGvuPIIPHMDKyor8jo6MGeGPq4uLxdtNM6aJkzgUetjQdo5Io+08b4gpHi/PegweoG87Hz9+zM8DB3H37l0KFizIlIkTcLC35/GTJwzzH8Gt27ewzWHLKH8/Sps5d5nzptd3O3buYMXKFcZ0V65cZsWvKylbpiy79+xm6bIlaLU66nl60rfP9xbFIrIXGaHLZrRaLRMnTWDG9JkErlnH7t27uHbtmkmaoC1B2OfNy6YNm+nSqQuz5swCYO++vSQmJrImYC0rlq9k0+aN3L17l2LvFCNgZQABKwNYsXwFtnZ2NGrYKEMxTZk6mSmTp7Fq5Wr27t3N9evXTdK4ubkxdMgwmjV9P9X6Xbp0ZZivX6rPJ0+eiN/wESxftoJmzd5n2fKlFsek02pZsmg2g33HMHXGIkIPhXD71k2TNMWKl2LcpNlMmraAWnW8WPXrLybLA1cvp3yFyiZ/57LFcxk+chKTpi2gaLHi7NoeZHFMWq2WsRPGM2/WbDZv2MCOnTu5eu2qSZqNmzdjb5+XbVu28EnXrkyfMQOAUiVLsnrlKtatWcu82XMYOWY0SUlJ5MiRg18WLGT92kACV68h9Pcj/HnmTKbG9NLiBQtZt2btG3XmAI6cPMmtu3fZsGghg/v0ZsKcuWbTedWqybJpU80ua1bfi1WzZ7Fq9ixjZw7Af+pUunVoT+CC+SydNpX8DpZ1yg8fPUr4rdv8tiaA4f37M3qy+f2OnjKF4QP689uaAMJv3Sb06DEAps2dxzc9exC4bAnfffEZ0+fOB6BHl84ELltC4LIl9P36K6pXrWJRZw7gUGgo4eG32B60GX9fX0aNG2c23ahx4/Ab6sv2oM2Eh9/i8JEjANSpXYtNgYFsClxLsaLv8MsSfT27eu0aO3btJmj9OubPnsWo8ePRarXpxqPVapkzaxqjx05i0S+/sn//Pm7evGGSxsXVjZ/7D6FR46ap1p80YQwfftyZX5asZObsBeTL5wjA1GmzmbdgCfMWLMGjfAU869W3KH+Se1vlt3bjJkoUe4d1y5eyeNZMpsyew4sXL9KNR6vVsnD+TIb5j2PmnCUcPhjMrfAbJmlKlCjF5KnzmD7rF+p61ufXpQuNy9q2/5gffhpkdttXLl/i2dOnlmSLiY1H7/PZ7NNpLm9QwYlirrlo4n8U31UXGdFJ3yF2yGVNH+/idJh4kvYTTtLHuzj2OfX3jEd2LotvwEWa+B+lmGsu6pfPn6GYtFotkyZPZPq0GaxZHcju3bu5dt30vLdlSxB57e3ZsH4TnTp3YY7hvLfPcN4LWLWGbsM/EwAAIABJREFU5ctXsHnTJu7evUtkZCRrA9eybOmvrA5Yi06nY8+e3RmKacz48cydPZugl23nVTNtZ968bDe0ndMMbadjvnzMnj6dTevWMWbkSIb4+hrXmTxhAhsCA9m0fj0PHz5k9549mRpT7ty5Wb92rfFfgQIFjDfuMuJQaCjht26xffMm/H2Hvr6d8h3K9s2bCL/1qp3q2f0TNq1dw4bVATTw8mLeokUZ2q7ZmA6HEh4ezvagIH3bOTaNmMYaYgoKIjw8nMOh+ph+WbqU2jVrsn1LELVr1mTxUn3buWjxYsqVLcOmwEDGjhrJ+EmTLIrn71zftfighfE6bqT/SAoUKEjZMmV59PgRM2fNYO7seQSuCeTBgwccP2H+Rk12YaUoWf5fpuRLpuw1G1EU5YaiKM5vsF4PRVEKJvv9F0VRyv+dbQKcO3+OIoWLULhQYWxsbGjW7H0OHDxgkubgwQN4e7cCoHHjJpw4cRxVVVGAuPh4kpKSiE+Ix8bahty5c5use+LECQoXLkSBAgUsjunChfMULlyYQoUKYWNjQ5OmzTh0+KBJmgIFClKqVGkUM98fUqPGe+TKlSv1hhWFZ8+eAfD02TOcnV0sjunKlUu4FSiIm3sBrG1sqFuvASeOHzFJU7FSVWxt7QAoXcaDmJgo47JrV//i0aOHVK5S3fiZqqqoQEJ8PKqqEvf8OY75nSyOKSwsjKKFi1C4sL7sPmjenP0hISZpQkJC8GnVGoBmTZpyzFB2OXPmxNpaf3GUkJiIYmgwFEUx5l1SUhJJSUnGZZkV0z/l4NFjtGzcGEVRqFSuHLHPnhH94EGqdJXKlcM5v+UXiNfCw9FqddSqVg2AXDlzYmdnZ9G6+w8dpvUHzVEUhcoVKxD79ClR0dEmaaKio3n27DlVKlZEURRaf9Cc4EOHAH15PX1uOKafPsPFOXUzsHPvPlo0Td3RSTOmkAP4tPJGURSqVK5EbOxToqKiTNJERUXx7NlTqlapjKIo+LTyJnh/CACedeoYy7FypYpEREYAEBwSQovm75MjRw4KFypE0cJFOBt2Lt14Ll26QMGChShQoCA2NjY0bNiE348cNknj7l6AEiVKpjrx3bx5A61WS/Xq7wGQM2euVGXz/Plz/jx9irp1vSzOo5feVvkpisLz53GoqsrzuOc42NtbNKpy+fJFChQohLu7Pq/q1W/E8WOm7VSlytWwNeRBmbKm7VTlKu+SM2fqtlOr1bJ86QK69/wqA7mjd+LKIx49S0pzedPKzsaRt9M3nmCfyxoX+xx4lXci9MIDHj9P4klcEqEXHlC/ghMu9jnIY6fhj+tPANh07D7NqljelgOcP3+OwoWLUMh43mvGwZTnvUMH8W7pDUDjRo05cfIEqqqCohAfF0dSUhIJCfFY27w672m1SSQkJOjPifHxOLtYHtfZsDCKFilCEUPb2cJM27k/JASf1oa2s2lTjh3Xt50e5crh6uoK6G+MJSQmkpiYCECePHkAfXv+IoPt+duK6aWbN2/y4MEDqr/7rsUxGfd74AA+3i317VSlSsQ+jSUqKkXdi4rm2dNnVK1saKe8WxJsiP9lvgDExcWhoFi83bRjCsGnVStD21mZ2NjYNNrOZ1StUsXQdrYiOGS/fv2QA7Rprb/WatO6lbFNvXrtOrUNI64lihfnzt17RMfEpBvP37m+S27X7l00f19/8/zOnTsULfoOjo76G2M136tJ8P5gi/JHZC//yQ6dopfZf1sPwNihU1X1C1VVz//djUZFRuKWbGqYm6srUVGRJmkioyJxM0zJsba2Jk+ePDx+/JgmTZqS086OFt4f0NqnFV27dsMhxcjE7j27aP5+czIiKirKeCIAcHVxTdUovolBg4bQr/9PtG3Xml27dvBJt+7pr2TwICYaJ6dXJ2cnJxcePki7Qd2/bydV39VfROp0OlYsW0i3T780SWNtbc0XX/Wh/49f883nnbl9+yaNm3xgcUwRUZG4uScvOzciI6PMpHE37i9Pnjw8evQIgDNnz9Luww50+Pgjhg0ZarwI12q1fNSpIw2bNqFOrdpUrlQp02NCUfi613d07NKF9Rs2WBxPcpExMbi5vOrwuDo7EWnBSTG54NAjdOnVm0FjxxJhOCbD79whT+7cDBg9hm59+jJz8RKLRp4AIqOjcUt2rLu5uhCZokMQGR2NW7ILw+RpBvTtw7Q583i/fQemzJlL329ML7jj4uMJPXaMpg0bWPw3RkRG4p6iTYiISlmGUcY2QZ/GjYhI03YDYFPQFurV9dT/HZFRuLu5v1rHzY3IqNTrpBQTHY2Ly6s8cnZ2ITrasvbgzu1b5M6Th5H+Q/num89ZtHBuqrIJDT1I1WrVU92MssTbKr9OHdpz7eZNmrZtx4ef9mTA932xskr/FPQgJtrkRpWTkwsxMWlfjO7ds4N3q9dMd7vbt23mvZp1yZ+BG06Wcstny72H8cbf7z9MwC2fLW75cnDvYcKrzx8l4JYvB275bLn/KNnnD+Nxy2f5tEaAyBTHr6urm5kL70hc3cyc9xo3wS5nTrxbtcCnTWu6du2Kg4MDrq6udO3ajTZtW+PdqgV5cuemdq3alseUst65uaWqd5GRkbin0Xa+tGfvXsqVLUuOHDmMn3393Xc0aNKEXLly0SwDN3feZkwA23fu5IP333+jm3cRKdsTVzciUrQnEVEprm/c3IhIdj6aMWcOTVp6s23nDnp/+43F2007pkjck5//3FxN9vdy+yZthpurse2MiYnBxdBWuLi48MBww7FsmdLs3afvNJ0NC+PevXtERESkG8/fub5Lbs/e3bxvuI4rUrgIN2/c4O7duyQlJRFyIMSiWET2k9mdnn+MoijFFEW5oCjKXOAU8ImiKL8rinJKUZR1iqLkURSlhaIogcnWaagoym+GnzsrinJWUZQwRVEmZGCfYcl+76coir+iKB8CNYBViqKcVhQlp6IoIYqi1Pi7f6dq5rOUjatqNhGcOxeGlUbDjm07Cdq0hVUBK7l957YxyYsXLzh46CBNzEyDem1MZnb4TwzWrF27msmTprJ502+0bNmKmbOmWx6T2U/NB3XowF6uXvkLn7YfAbB7529Ufbcmzs6uJumSkpLYs2sr46fMZf7i1bzzTgk2bVxjcUzmgkqVT2bzUp+ocqVKbFq/gdUrVrJ46RISEvQXSRqNhnVr1rJn5y7CzoVx+cqVTI/p16VLCQxYzdzZs1kTuJaT//uf5TG9br9plKE59WrVJGjpEgLmzOa9qlXxnzoN0HeAT587x/eff86y6dO4c/8+W/fu+8diMlsfDGkCNwfRv29vdm/cQP8+vfEfZ9rUHAgNpWqlShZPtwRQzRSiRTGlKOgFvyxGY62hVcsWFq9jNp43XA/0ZRN29gxfft2LWXMWcO/eXfbs3mGSJmT/Pho2amLR9swElzq2f6D8jhw7TrnSpdi7eROBSxczbto0nhpmF2QwnDTzKmT/Hq5e+Yu27T9+7TYfxERz5PBBvFu3S3f/b8JceKqaRt1U/5lzwRuXmwLnzp1DY2XFtq072LQxiICAVdy5c5snT55w8OBBNm0MYtvWHcTFx7Njx3bLQzLzWeqm8/V14crVq0ybORO/ZFMuARbMncv+PXt4kZjIsRMnskRMADt37aLFB5bfxMzIfi1J832vXuzbvg3vD1oQsDbQ4u2mHVPqz1KuarZ9TWf7X/TsyZPYJ3To2IlVa9ZQrmxZNJr0X1nxd67vXgoLC8POzo5SJfXP7Nnb2zNw4CCG+A7mq6+/pEDBglhn4JnMrEhRsv6/zPCf6dAZlAV+BZoBnwNNVVV9FzgJ/ATsAWorivLy1m5HYK1hauQEoDFQFXhPUZS2bxqEqqrrDfvsqqpqVVVV4yxdV1GUrxRFOakoysmly1I/M+bq6mpydyUiMjLVVEQ3V1fjtKmkpCSePn2Kg70DO3ftom5t/fSq/PnzU6VyFS5cuGBc78iRUMqVLYeTU8bu6rq6uhKZ7G5/ZFTqmDLq4cOHXLlyhQoVKgLQpHFTwsLOWry+k5OzydSkmJgoHM1Myzvz5yk2rl/NgMEjsLHR343869J5/o+9+46PongfOP6Z5KhCaGn0EjoCFpCehN5TKKGqYEERsCACoSMdsSCIlWKjCSIISKihhC41VJEeIAklldTL/P6448glB7moQPL9Pe/X616a29m9h9m9Z3d2ZueC/ljN4Dde5Kfvv2FH8GYW/zifixdMzyK4u5dCKUXDxp6cPWN/p6ubqyvhN9Lvu3DL3b37ZdwIv2EazmTZdxl6UStVqkSBAgU497d1w82pcGHqPV+PkN3WQ7aeREyu5h6aEsWL06J5C0JPZD1UD+CXtWvpM3gIfQYPwblEccLTDZ2JuHkLlxL2D60s6uRE3jx5APBr25bT5oauq7Mz1TwqUbqkOwZHR7waNeRMhudM0lu68lcC+r1CQL9XcHF2turZCo+IxMXZ+vvi5uJidVc8fZnf/9hASy9T71ubFs0JTff9A9iweSvtW2XdWFmybDlde/aia89euLq4cCNDTnB1sR7K6Z4uJ5jKhOOabj+v/v13duzcyYzJky0XEG5urtwIv3F/nfBwXOz4Xju7uFjdVb55M5ISJewbYe7s7ELlylUoWbIUjo4GGjduxrm/zlqWx8REc+b0KRo0aGTX9uDx7L/V69fT0ssTpRTlypShdMmSXLhk/cyuLSWcna16L2/dirTZq3b0yJ+sWL6YwDGTLHnqQc6fP8eN62EMHPAiA17tTVJSEgMHvJhlLPa6cSeJksXuD4N1L5aPiOgkbkQlUbLY/Z4396L5CI9O5sadJNzT9ci5F8tPeLoeO3u4Zjh+IyLCcc5wjLu6uhERbn3ec3IqQtDGDTRs1Nhy3qtjPu8dOLCfUqVKUaxYMQwGA829m3P8uP3PH7u5ulp/78Ktv1Ng6mG68YDceSM8nHeHDmXqpEmULZt5sq98+fLh7eWVacjkk4rpzJkzGI1GatWsaXc8S5Yvp2uv3nTt1ducp9Llk4hwXDPkE3dXN+vrm/DwTLkMoGP7dmzearoJ554xT9nYrlVMy5bRtUdPuvboaYop/fkvPCJTfZlyZ4TNMiVKlLD0FEdGRlLcfI1RqFAhJk+cyMplS5k2aRJ37tyhTOlSZOXfXN/dY2uUlWczTxYt+J4F8xdSvlx5m8ebyP3+1xp0l7TWe4GGQE0gRCl1BHgZKK+1TgU2AJ2VUgagI7AaqA8Ea60jzWV+BrL/xP1/QGv9jda6nta6Xv9+/TMtr1mjJpevXCHsWhgpKSls2rQRT0/rUJs182TdurUAbN26hfr16qOUwt3djQMHD5qe/0pIIDQ0lArlK1jWC9oYZOmmz47q1Wtw9coVrl27RkpKCls2b6Jpk+w/35Je4cKFiY+P4/LlywAcOLif8ulizYpH5WrcuB5GRPh1UlNS2L1rO/XqW18EXjh/ju++ms3wwA8pYp54AeDt9wKZ983PzP36R/q+PABP71b0fvFVipdw5uqVy8REm4anHD96iNKly9kdU61atbh05TJXw0z7bkNQEN5e3lZlvL28WLP2dwA2bdnMC/VN++5qWJhlwpFr165x8eJFSpUsxe07t4mJjQUgMTGRvfv2UbGC/fX0KGK6m5BgefbxbkICe/buobKHh13xdO/UyTKJiVfDRqzfuhWtNcdPn6bQUwWz9axc+uftduzbR0XzSaxmlSrExMVxxzxM5eDRY1Qs9+ATXM+uXSwTljRv1ozfNwShteZY6AkKFXoq03NwLs7OPFWwIMdCT6C15vcNQTRv1tS8rAQHD5smnNj/5yHKlSljWS82Lo4/jxzB21z2YXr1CGDl0iWsXLqEFt7erFm7Dq01R48dp1ChQpka5S4uLhQs+BRHjx1Ha82atetobh7WuStkN/MXfc+czz6lQIEClnWae3nxR9BGkpOTuRoWxuUrV6j9dK0sY6tWrTphYVe5cd2UD4KDt9CwUZMs1wOoWq06sXGxliFgR44coly67/2O7cE0aNiIvHntH7L3OPafu5sb+w6aeqFv3b7NxctXKFMq6wu4KlWqc/1aGOE3rpOSksKuHduo/4L1TKLn//6LL7/4lFFjJ1kmiHmYevUbsvDHFXwzfzHfzF9Mvnz5+PKbH7Ncz15bjt/Ev4FpiNszFZyITTASGZPMzpO3aFqjOE4FDDgVMNC0RnF2nrxFZEwy8UlGnqlg6nX2b+DO5mP2PeN0T40aNbly5TLXLOe9TXg2y3jea8a69abZPLdu20q9e+c9N3cOmp+nu3feK1++Am5u7oSGHifR/Ez0gYMHqFChot0xPV2rFpcu38+dfwQF4e3tbVXG28uLNb+bc+fm+7kzJjaWQUOG8M6QITz7zDOW8nfv3rU0EFJTU9kZEpKtfP4oYrpn/YYN2e6d6xUQwMoli1m5ZLEpT61bb8pTx+/lqQzfPRdnCj71FEePm/PUuvU0N99AuWS+FgDTc3P36sXb0yvL7VrF1KMHK5ctZeWypbRo7s2atWvNufPYQ3JnQY4eO2bOnWtpbj5Hent5svp307XW6t/XWnJqTGysZVKklatW8fxzz1k9A/gg/+b6DkyPi2zZsoXWra0nn7s3FDQmJoYVK1fg6/uP+ytEDva/9rMF98a4KGCT1rqXjTLLgEHAbeCA1jpW/fPZHFKxbhTbN7PCv2AwGBg+7APefnsIxjQjPp198KjkwVdff0WNGjXw8vTC18eX8RPG4d/VDycnJ6ZMNk1n3b1bAB9OmkiPXj1Aazp36kyVKlUAU2Ng//79jAoc/Y9iem/oMIYOfQdjWhqdOnaiUqVKfPvdN1SvXp1mTT05deokgaNGEBsbS0jILr6b/y0//7QEgIFvvcHly5e4ezcBP//OBI4cTYMGDRkxPJDRYwJxUIrChQsTGJh5CMiDODo68sprg5n64SjS0tLwbtmWsuUqsHzJ91TyqEq9Fxrx0w/fkpiYwKezTFPvOzu7MnzUhw/cZvHiJejWoy/jx7yPwWDA2cWVt4bYP3WzwWBg1IgRDBz0Fsa0NPx8fKns4cEXX86jZs2aNPfyxt/Pj1Fjx9DRx4ciRZyYOW06AIcPH2bBooUYDAaUgwOjA0dRrFgxzp49y5jx4zAa00jTabRt3RovT/vvRTyKmK5evcq77w8FTEPo2rdrT9Mm9l3Qp9ekfj12HzxIl9deJ3++fIx9713Lsj6Dh/DzXNPsXp8vWMDG4O0kJiXR6aWX8WnbhgF9+rBszRp27NuPo6MDRQoVZpx5fUdHR9559VUGjRqN1prqlStbzYD5MM0aNWTXnj106tGL/Pnz8eGoQMuygH6vsHzRAgBGDxvK2CnTSEpKoknDBjRtaHo2Z9zw4cyc/TlGo5G8efMybvj942frjp00eqE+BdM1quzh2bQpO3eF0N7XlwL58zNpwgTLsq49e7Fyqel7NnZU4P2ptxs3oZl5n0yZMYPklBReH/gWYBpGO370KCp7eNC2dWt8unXD4Ghg9MgRdk304ehoYNDgdxkVOIy0tDTatO1AhQoV+X7RfKpWrUajxk05c+YUH04YQ2xcLHv37uaHHxbw7Xc/4OjoyOsD3mLk8HfRWlOlSjXad+hs2fb24C0E9OyTrfpJ71HtvwH9XmbslKl0felltIZ3B75JsaJF7agrR15/cwgTx48gLS2Nlq3aU658BRb/tJDKVarxQoPGfL/wGxITE/houik3ubi4MmrsZABGjXiHsKtXSExM4LV+PRj09jCeNT8L/E992r8WDaoWpVihPOya0pjZ6y5gcDSdIpfsvEZw6C28a5Vg68RGJCQbGfGjqZcy+m6q6acKRpieLJi7/iLRd003fMYtOWP+2QJHtp+4xfYT2XsW1mAwMGzYcN5+523S0ox07uRDpUoefP3NV9SoXgNPTy98OvsyYeJ4unbzx8nJicmTpgDQrVt3Jk3+kF69e6A1dEp33mvRoiUvvdwXR0dHqlathp+f/cNU7+XON98y5U5/X1PunDtvHrVq1qS5tzdd/PwIHDOGDj4+FHFyYuZ0U+5csnQpV65c4etvv+Vr82yNX3/5JVprhrz7LskpKaQZjbxQvz4B3bo90ZhKmG+iBW3axLw5c+yOJSPPpk3YGRJCe18/c566P7N11169WbnENBvy2MCRjJkwwfTzKk0aW/LUp3PmcPHSJZRyoFTJkowzf3cftt2sY2rKzl27aO9jI3f26MnKZaZHKsaOGsWY8ePNP6XQmGZNTTG91r8/748Ywa+//UbJku58MnMmgOnnWMaOw9HRkUqVKvLhePti+jfXdwCHDx/C1dWVMqXLWG33409m8ddfpp8Kee3V1yhfrrzddSRyD2Vr/HFupJSqAKzVWj+tlHIB/gRaaK3PKaUKAmW01meVUo7A38AB4Bet9XKlVElgL/A8cAcIAuZorVcrpS4C9bTWmW4pKqXyANcxDfWMA7YDG7TWE8zP5n2itd5mLhsMDNNaH3zYNtOLiYrNUTsnOeXBM589KVcjYp50CDbVqPDvhpz+f5F4PexJh5BJviL2P7/2uDgWzP4EII9D2K2snxN73Nyz1x5+LM7fyfonDB43n8/OZl3oMTs45V8/Zv5IFMz3v3bv+9FQafZNLvVY2TE50eOWkJz2pEOwyalo4Sf09Ff2HD15KUddG9tSt2b5x16XOe9I/w9orSMxzTK5RCl1DFNjrbp5mRFYC7Q3/xet9XUgENgGHAUOaa2z/IExrXUK8CGwz7yt0+kWLwK+ujcpyn/yDxNCCCGEEEKIdP5nbjtprS8CT6f7eyumZ+NslR0MDM7w3mIg0y8ga60rZPG5nwOf23h/JZB+vnZve7cphBBCCCGEsOZg4/eMxf9oD50QQgghhBBC/H/wP9ND9ygppUoAtn6oqqXWOntPdwshhBBCCCHEf0QadHYwN9oyz+MrhBBCCCGEEE+QDLkUQgghhBBCiFxKGnRCCCGEEEIIkUvJkEshhBBCCCFEjuegZJZLW6SHTgghhBBCCCFyKWnQCSGEEEIIIUQuJQ06IYQQQgghhMilpEEnhBBCCCGEELmUNOiEEEIIIYQQIpeSBp0QQgghhBBC5FLyswVCCCGEEEKIHE/JzxbYJD10QgghhBBCCJFLSYNOCCGEEEIIIXIpGXIphBBCCCGEyPEcZMilTdKgy8EcHHLWQfvH3gtPOoRMmtQp9aRDsMmYpp90CJncir77pEPIxNk9B+6/xJxXT+S8wwmAUoXzPOkQMklUOS+mYoWTn3QImRycUu9Jh5BJvdEHn3QINh2f2eBJh5BJTryoVQ4y6MseUbEJTzoEm5yKFn7SIYh/Qb59QgghhBBCCJFLSYNOCCGEEEIIIXIpadAJIYQQQgghxGOglGqnlDqjlDqnlBppY/lQpdRJpdQxpdQWpVT5rLYpDTohhBBCCCGEeMSUUo7AF0B7oCbQSylVM0Oxw0A9rXUdYAUwM6vtyqQoQgghhBBCiBwvp00Y+A+8AJzTWp8HUEotBXyBk/cKaK23pSu/F+ib1Ualh04IIYQQQgghHr3SwJV0f181v/cgrwJ/ZLVR6aETQgghhBBCiP+AUmoAMCDdW99orb+5t9jGKjZ/nEgp1ReoB3hl9ZnSoBNCCCGEEEKI/4C58fbNAxZfBcqm+7sMcC1jIaVUK2A04KW1TsrqM2XIpRBCCCGEEEI8egeAKkqpikqpvEBPYE36AkqpZ4GvAR+tdYQ9G5UGnRBCCCGEEEI8YlrrVGAwEAScApZrrU8opT5USvmYi30EFAJ+UUodUUqtecDmLGTIpRBCCCGEEEI8Blrr9cD6DO+NS/f/rbK7TWnQCSGEEEIIIXI8let/teDRkCGXQgghhBBCCJFLSYNOCCGEEEIIIXIpGXIphBBCCCGEyPEcZMylTdJDJ4QQQgghhBC5lDTohBBCCCGEECKXkiGXudDu3buZ9fEsjGlG/Hz96N+vv9Xy5ORkxo0fx6nTpyhSpAjTp06nVKlSAPz1119MmTaF+Lh4lIPix+9/JF++fGwI2sCChQtQSuHi7MKkSZMoVrSY3TH9ffoIQat/QKel8UyD5jRp4Wuz3Kmj+1j542e88s5kSpX1ACD82iXWr5xPUuJdlHLg1XcmYzQa+WHeRMt6MVG3qP18U9r4vmx3TAcP7OOrL+eQlpZGu3YdCejZx2r58WNH+fqrOVw4f56Ro8bRzNMbgKNHDvHNV19Yyl25cpmRo8bRuEkzPv14Bn/9dQatNaVLl+X9D0ZSoEBBu2MK2R3CR7NmkZZmxM/Pn1ds7Lux48dy6tQpihQpyoxppn137do1unTvSvny5QGo/XRtxowaTXx8PK+8/qpl/YjwCDp0aM8H739gd0z79+9l3hezSUtLo32HTvTq9aLV8mPHjjDvi885f/5vxoyZgKdXc8uyNq09qVixEgCurm5MmjwDgKlTJ3L2zGkMBgPVqtfgvfeGYzBkL92E7A5h5qxZpBmN+Pv580r/zHU1Zly6upo+ndLm4xzg+vXrdOnejTcHvMHLL70EwM+LF/Prb6vQWtPF35++va2PiaxorZnx2Wx27dlD/vz5mTR6FDWqVctU7uTp04ydMpWkpCSaNmrEiHffQSnFB2PHcenyZQBi4+IoXKgQy79fRNj16/j37kOFcuUAqF2rFmOH27cPtdZM++gjdoaEkD9/fqZMmEDNGjUylTtx6hRjxo8nMSmJZk2aEPjBByilmPXZZ2zfsQNDnjyULVOGyRMm4FS4sFU9+nTvzlsDBtDfXI/2xDT9k0/ZuWc3+fPlZ/LYsdSsnrmeTpw+zZhJk0hKSqJZo8aMHPoeyjyc5uflv7B0xQocHR3xbNyYoUMGk5KSwsTpMzhx+hQOyoGR771H/eefsyum3Xt28/Ens0hLS8PXx49+L/ezWp6cnMz4ieM5bc6bUydPo1SpUqSmpjJ5yiROnzmN0WikQ/uOlpyb1TbtsW/HY78wAAAgAElEQVTfXubO/Qyj0UjHjp3p08e6jo8ePczcubP5+++/GTduIt7eLQC4ceM648aNwmhMw2hMxd+/G76+/ty9G8+QIW9Z1o+MjKB167YMGfKu3THt2bObTz79mLS0NHx8fHn5pcx1NXHieE6fOU0RpyJMnjzVUldTpk7mzJnTGFONtO/QgX4vm+pqyZLFrF7zG0opPDwqM3bMOPLly2d3TNP6VqdFbWduxSbTYfJ+m2XGdq+Cd60SJKSkMeKHk5y4EgeAfwN3BrWvAMAXf1xk1b4bANQqW5iZL9Ugfx4Hgk/cYtIvf9kdD/zzHBV27Rpdut3P53Vqm/I5wJwv5rJ23TpiYmLYsyskW/EA7AoJYcasj0gzptHF349X+7+SKabRY8dy8tQpihQtwkfTZ1C6VCmOh4by4eRJgOn7O/CNN2nZogVJSUn0f+1VkpOTMRqNtGrZikEDB2Y7Lq0102Z+xM6QXaY8NXGi7Tx18iRjxk8gMSmRZk2aEjjclKeio6N5f8RIrl27RqlSpfh45gyKODmx/+BB3n5vqCX3t2rRgoFvDHiiMd1z/MQJ+rz0MrOmT6dNa/tmoT+wfx/z5pnPx+070bNXX6vlx44d4ct5n3P+/HlGjxmPp+f983FEeDgffzKDyMgIFDBl6ke4u5fk41nTOXv2NFprypQpywfDR2XrukXkDtKgy2WMRiPTZ05n3tx5uLm58eLLL+Ll6UWlSpUsZX5b/RtOTk6sXrWaoI1BfD7nc6ZPm05qaipjxo1h0sRJVK1alaioKAwGA6mpqcz6eBa/LP+FYkWLMfvz2Sxfvpw3BrxhV0xpaWn8sWohfQaMwqlICebPHk3Vms/j4l7GqlxSYgL7d22gdLnK99c1Glm95At8ew3CrVR57sbH4uBowJAnL68PnW4p992no6j29AvZqqcv5n7G1Okf4+zswjtD3qBBoyaUL1/BUsbV1ZX3hwWycsVSq3XrPvMcX3w1H4DYmBhe6d+b556vD8CANwfz1FNPAfDNV3P5ffWqTA3Fh8U0fcYMvvzCtO/6vNQXL08vPDLsu8KFnVjz2xo2BAUxe85sZkwzNZLKlC7DssXWsT711FNW7/Xu25sWzVvYWUummOZ8/gkzZn6Ki4srg956jcaNmlK+QkVLGVdXN4YPH8XyX5ZkWj9v3nx8/c2iTO+3bNmGwEDTT6pMnTKB9et/x8fHP1txTZs+g6/mmevqxb54eVnX1arfTMf576vNdfX5bGZOn2FZPuuTj2nSuInl73PnzvHrb6v46fsfyJMnD4OGDKZZ02aUNzei7LFrz14uX73C78uWcvzECSbPmsXP336bqdzkWR8zbsRw6tSqxaBhwwjZu5emjRrx0aQP78c3Zw6Fnipk+btM6dIs/36R3bHcszMkhMtXrrD+t984FhrKpGnTWPLDD5nKTZo2jfFjxlC3dm0Gvv02u3bvplmTJjRq0IB3Bw/GYDDwyeef893ChQx9+23LejM++YRmjRtnL6Y9e7h05QrrfvmFYydOMHnmTBYvmJ+p3OSZMxk/ciR1n36age8NZdeevTRr3Ij9f/7Jth07WPnTj+TNm5dbt28DsGL1agBW/fwzt27fZuB7Q1m6cAEODg8fbGI0Gpn50QzmzvkCN1c3Xu73Ep7NPK3y5uo1q3EqXJhVK39j48Yg5nwxh2lTprF5y2aSk5NZungZiYmJBPTsTts2bXFzc8tym1kxGo3Mnj2LWbNm4+LiyptvvkqTJs2oYPX9c2fkyDEsW7bYat0SJZyZO/dr8ubNy927d+nfvy9NmjTF2dmF+fO/t5QbMKA/np5e2Yrpo1kzmfP5XFxd3ejX/2WaNfOkUsX7/641a1ZT2MmJlStWsXHTRr74Yg5Tpkxji7muFv+8lMTERHr2DKBN67YYDAaWLV/G0iXLyJ8/P6NGB7Jp00Y6depsd1y/7r3BT9uv8tHLNW0u96pVggquBWk5YS/PVHBiYs9qdPvoT4oUNDCkY0X8px9Aa/gtsD5bjt0kJiGVD3tVY8zi0xy+EMP8QXXxrFmcHSdv211P/yZHlSlThuVLlmbarpenJz0DeuDj72d33aSPaeqM6Xwz70vc3Nzo1bcP3l5eeFTysJT59bffcHIqzLo1a/gjaAOfzZ7NRzNmUNnDgyU//YzBYCAyMpJuPXvg5elJ3rx5+e7rbyhYsCApKSm8/OorNG3ShLp16mQrtp27Qrh8+TLrV6/m2PHjTJo6jSU/2shTU6cxfsxo6tapw8DBQ9gVsptmTZvw3cKFNHzhBV57pT/fLVjI/IULGfrOOwA89+wzzPv882zX16OMyWg08uns2TRp1MjueIxGI3PmfMKMGZ/i7OLC4EGv06hxE8qXtz4ffzB8FL8sz3zszJgxmd59XuL55+uTkGC6QQ7w5sAhluuWr76cw+rffs3UUBS5X44bcqmUKqqUeiuLMhWUUr3t2FYFpVTofxfdv2Nv3A9z4sQJypYtS5kyZciTJw9tWrcheHuwVZntO7bTqWMnAFq2aMn+A/vRWrN3316qVK5C1apVAShatCiOjo5oNFprEhMS0VoTHx+Pi7OL3TFdu3yO4iXcKVbCDUeDgVrPNOLsiYOZym0PWk7j5p1xNOSxvHf+7DFcS5bDrZTpTmXBpwpnuji7HXmd+LhoylWqbndMZ8+colSp0pQsWYo8efLg5dWCvbt3WZVxcy9JxUoelqRny86dwdSr14D8+fMDWJKi1pqk5KRs/SBK6IlQypYtY9l3bdu0zbTvgrcH07mTad+1atmS/fsPoLW2a/uXLl/m9p07PPesfT0WAGdOn6JU6TKUKlWaPHny4N28FSEZ6sndvSSVPCrj8JB6yqhBg0YopVBKUa16TW5GRti9LjygroKDrco8rK62bttG6dKl8fC4f3F1/sIF6jxdmwIFCmAwGHj+uefZum1rtuLatmsnndu1QylFnaefJjY2jsibN63KRN68SXx8PHWffhqlFJ3btWPrzp1WZbTWbNy6jfZ23rV9aEzbt+PTsSNKKerWrk1sXByRkZHWMUVGEh8XxzN16qCUwqdjR7aa67NJo0aW3tM6Tz9NeHi4Zb0t27ZRpnRpPDw8yI5tO3bg06G9KaannzbFZKOe4uLjeaZ2bVNMHdqzdcd2AJb9+iuvvvQiefPmBaBE8eIA/H3hAg3q1bO851S4ECdOncoynhMnT1C2TFnKlDYdT61bt2G7+bPu2bFjOx3NebNFi5YcMOdNBSQkJpKamkpiUiJ5DHl46qmn7NpmVk6fPknpdN+/Fi1aERJifayULFkSD4/KmfJUnjx5LPWTkpJiM09cvXqFO3fuUKfOM3bHdPLkCcqUKUtpy7+rNTsy1tXOHXTs0BGAFs1bcOCg+bunFIkJCaSmppKUlIghTx5LzjQaU0lKSjLVY2Iizi72n2MADpyLIio+9YHLW9VxtvS8HbkYg1NBAy5OeWlWswQhp24TfTeVmIRUQk7dxrNWCVyc8lIovyOHL8QAsGrfDVrXtT+mf5ujHqRO7Tq4ZLNuLDGFhlKuzP3rg3Zt27ItY0zBwfiYG9KtW7Zin/k4v5cXAZKSky095UopChY09eakpqaSmppqWZYd27YH49Opkykn1KlDbGys7TwVH88zdeuackKnTmwN3mZaP3g7vp1NdenbuRNbtwVn/IgcFdPipUtp3bIlxc25yx5n7l23lDJdt3h7t2R3iI3zcaXKKAfrfXDp0gWMRiPPm28+FyhQ0PZ1S1L2rltE7pHjGnRAUeChDTqgAvCvGkZPSAX+ZdwRkRG4ublZ/nZzc8ucgCIiLWUMBgOFChUiKjqKy5cuo5Ri0JBB9O7bm+9/MN3FzWPIQ+DIQHr06kHb9m05f+E8vr62h0zaEht9B6eiJSx/Fy5agtjoO1ZlboRdICbqNlVqWjc2bkVeBxSLv5nGd58GsnvbmkzbDz28m1rPNMrWSeTmzZu4uLha/nZ2ceHWrZsPWcO2HcFb8W7e0uq9T2ZNo3cPf65euYyPbxe7txUREYmbm7vlbzdXVyIjIjKVcTeXSb/vAMKuhdGzdy9eHfAahw4fyrT9DUEbaNO6TTbrKRLXdPXk4uLCrZuRD1nDWnJyMm8NfJXBgwcQsmtHpuWpqals3hRE/foN7d4mWNcDgJubKxEZGoURkTbqKiqKhIQEFn2/iDcz9DBXruzBn4cPWcrsCtll1XixK67Im7i53q8vN1dXIiJv2ihz/4LMzSVzmUNHj1KiWDHKly1reS/s+nUC+vXnlUGDOXTkqN0xhUdE4J4+J7i6Ep4hJ4RHRmbKG+ERmRvZq9asoWkTU6/m3YQEFnz/PW8NsG/4UnoRkZG4u6aPyYWIDDFFREbi5pKxLk1lLl2+wqGjR+n9yqv0GziQ0JMnAahWpQrbdu4gNTWVq9eucfL0GW6EZ32zIDIiQ950dSUy0/EUgZurdd6Mjo6mZctWFMifn/Yd29HZpxN9+vSlSJEidm0zy7giI3Fxub8NFxeXTPn8YSIiwnnllRcJCPCjV6++OGe4EbdlyyaaN2+ZrZwQERlpqQcw9QhkvsiNwNXNRl21aEn+AgXo2Kk9Pr6d6dOnD0WKFMHV1ZU+ffri69eZjp3aU+ipp2jYIHs5IStuRfNx/U6i5e8bd5JwK5oPt6J5uX4n6f77UUm4Fc2LW9F83IhK9/6dRNyK2j8E9N/kKICwsDB69O7Fq6/bzuf/RHhkBG7u6Y9JNyIiMuaCCNzcbcd07Phx/Lt1pWtAd8aOGm1p4BmNRrr37IF3q5Y0atCQOrVrZz+2iAjc08fm5kp4xtgiIq3zq5urJU/dunXL0tB1cXHh9u37PalHjx2nS0AP3hw0mHN///3EYwqPiGDL1m0EdOtmdyxgOh+7uFpft9y087rl6tUrFCpUiAkTRvPmG6/wzddfYDQaLcs/+mgqAd19uXLlMn5+XbMVV06jHFSOfz0JObFBNx3wUEodUUp9ZH6FKqWOK6V6pCvTzFzmPXPP106l1CHzy67xQUqpfkqp1UqpDUqpM0qp8eb3Jyml3klXbopS6m2llLdSartSarlS6qxSarpSqo9Sar85Pg9z+UVKqc+VUruVUueVUve+1VZx/5PKsXV3L+PJ2mYZFKnGVI4cPcLkSZOZ/918tgVvY//+/aSkprBixQp+/ulngv4IokrlKixctND+mLBxxzFdSDotjY2rf6RV58xd/GlpaVy5cAa/PoN4edAEzoQe5MJf1p2qJ4/sodaz2RvyRRYx2eP2rVtcuHie5+tZD/UcOiyQn5aspGzZ8uzYnp0eHlsxZdh3NsooFM7Ozvyxdj1LFy/h/feGMmrMaOLi4qzKBW0Mol3bttmI50H7zv6KWrxkJfO+nM+oUeOZN+9zrl0Ls1o+e/bH1KlTl9p16mYvrn96nCvFl199RZ/efSx3le+pVLES/V/ux5tvvcWgIYOpWrUqjo6O2YoLm5+ZoYitfZihzB+bNtMuXe+cS4kSBP26kuWLFjJsyGBGTpxIXHy8nSH987pK7+v583F0dKRT+/YAfPHVV7zYu3emerQvJhtv2pmnwHQBGRMTy8/zv+P9wYMZNnoMWmv8O3XCzdWVnv1fYcann1G3dm0cDVnvQ9vhZIzHViE4cSIUB0dH/li3gdWr1vDz4p+4GnbVrm3+E9nZhqurGwsW/MjPPy8nKGi91UUuwNatm2nZsnX2AnjIfrlfxPYxfuLECRwdHFi39g9W/bqaxYt/JizsKjExMezYsYNVv65m3do/SEhM5I8/1mcvrizYqjatM8duWvDvOyn+zffOxdmZDevWs2zxEt4fOpTA0Znz+T8LKvNbmf6dD4m7Tu3arFqxkiU//sT8hQtMvTmAo6MjvyxdxqYNQYSeCOWvc+eyH5odsdnOnQ/fUTWrV2fT+nX8unwZvXv25O33hj7xmGZ8NIv33nk72+cXm/HYua7RaOT48WO8MWAQX8z7huvXr7Nx4x+W5R98MIqly1ZRrlx5goO3ZCsukTvkxGfoRgJPa62fUUp1Bd4E6gLOwAGl1A5zmWFa604ASqmCQGutdaJSqgqwBKhn5+e9ADwN3DVvfx0wH/gVmK1M41x6msvVNsdSA7gNnAe+01q/YG4ADgHuPXleEmgKVAfWACsyxm2LUmoAMABg9mezeSXDA81urm5WvQrh4eE4OztblXF1cyU8PBw3NzdSU1OJi4ujSJEiuLm58dyzz1kmO2nSuAmnz5y2dMeXLWPqLWjdqjWLsvEsj1OR4sRE3bL8HRt1i8JO9ydUSUpKJPLGFX780vT8UFxsNMsXziKg/zCcihSnvEcNCj5lepC4cvVnuHH1AhWrPG369127RFqakZJl7H8uBcDZ2cXqbvnNyEhKFHd+yBqZ7dixjcaNm9mczMPR0RFP7xas/GUpbdp2sGt7rq6uhIffsPwdHhGRaWiNm6srN8JvZNp3SinL8KqaNWpSpnQZLl2+TK2apudJzpw9i9FopGYN28+XPIiLs/Vd5cjISEqUsL+e7h17pUqVpm7dZzn311lKlSoNwA8/LCA6Kor3Jk7JVkxgugt6I31dhUdkGgb8oLo6HnqcTVs289nns4mNjcXBwYF8+fLSs0dP/P388PczPZvy+dw5Vj0RD7J05Up+XfM7ALVq1LDq2QqPiMAlw/fPzcXF6i5veKR1mdTUVLZs387SdM+U5c2b9/7+rV6dsqVLcenyFWrVsD3MeMny5axYtQqAp2vW5Eb6nBARgWuGmNxdXTPlDdd0x97q339nx86dfPfll5aLleOhoWzasoVPPv+c2NhYlIMD+fLlo3ePHtiyZMUKVq429bA/XaMGNyLSxxSZKSZTT2KGunRxNi9zoZW3N0opateqhXJw4E5UFMWLFWPEu/cn9+j7+utWvZwP4prx3x8Rkak3y83VlfCIDHnTqQgbgoJo3NA0LLV48eLUrVOXU6dOmXo5s9hmVkw9cve3ERkZmSmf28PZ2YUKFSpy7NgRy6Qp5879hdFopFo1+4eqg7mu0u27iIhwnF0ynGNc3YgID8fN9X5dOTkVIWjjBho2amypqzrmulJKUapUKYoVM50Xmns35/jxY7Rvb1/utMeNO0mULJYfiAbAvVg+IqKTuBGVRIMqRS3l3IvmY99fUdy4k4R7uh4592L5CU/XY5eVf5OjMuXzMtb5/J9yc3Ul/Eb6YzLcxjnGjfAbN3DPEFN6lSpVokCBApz7+xy1atayvO9UuDD1nq9HyO7dVKlcmawsWbaMFb+a81StWtxIH1t4hFUOAnOeSp9f05UpUaKEuUfb1It9byhjoUL3n0P2bNaUydOmcefOHcux9iRiOnHyJB+MDATgTlQUO3ftwtHgSMvmzXkYFxcXq1E7N7NxPnZ2dqVy5SqUNE8O07hJU06dOon5/hxgum7x8m7BL8uX0K5dR7u2K3KPnNhDl15TYInW2qi1Dge2A/VtlMsDfKuUOg78AmQnK27SWt/SWidgasQ11VpfBG4ppZ4F2gCHtdb3WiwHtNbXtdZJwN/ARvP7xzENqbznN611mtb6JJD1VaOZ1vobrXU9rXW9jI05gJo1a3Ll8hXCwsJISUlh46aNeGV44N2rmRdr160FYMvWLdSvXx+lFI0aNuKvc3+RkGh6xuHQoUNUrFgRV1dXzl84z507pmGSe/ftpUKFChk/+oFKlfXg9s0b3LkVgTE1lRNH9lC11vOW5fkLFOT9D79lyOg5DBk9h9LlKhPQfxilynpQqVodIq5fJiU5iTSjkUvnT+HsVtqy7onDu/9B7xxUrVada2FXuXH9OikpKWzfvpWGjZpkvWI6wdu2WA231FpzLeyq5f/37d1NmbL2T6hRq2YtLl+5v++CNgbhnXHfeXrx+1rTvtu85f6+u33njmX4xNWrV7l85TJlSt+vpw1BG7LdOwdQrXp1wsKucP36NVJSUgjetpnGje2rp9jYGJKTkwGIjo7ixInjlkln1q/7nYMH9jN6zIQsJ6ywxVZdeXllqCsv23W1cP4C/li7jj/WrqNP79682v8VevboCWDpwbh+/Tpbt26jfbt2WcbSs2tXln+/iOXfL6K5ZzN+37ABrTXHQkMpVKhQpgadi7MzTxUsyLHQULTW/L5hA82bNrMs33fwIBXLl7caxmO1f8PCuHTlKmVKl+JBegUEsHLJElYuWUILb2/WrFuH1pqjx4+bYspwUeLi4kLBp57i6PHjaK1Zs24dzc31uWv3buZ//z1zPv2UAgUKWNb5Yf58Nq5dy8a1a+nbuzev9+//wMYcQK9u3Vjx4w+s+PEHWnh5smb9H6aYQkMpVOipB9TTUxw119Oa9X/Q3NMTgBaenuz70/Qc7sXLl0lJSaFY0aIkJCZyNyEBgN379uPoaMCjYkWyUrNGTdPxdM10PG3atBFP82fd06yZJ+vMeXPr1i3Ur2c6ntzd3Thw8CBaaxISEggNDaVC+Qp2bTMr1arV4OrVq5bv39atm2ncuKld60ZERFh6UGJjYwgNPU65cuUty7ds2ZT93jmgRo2aXLlymWuWf9cmPJtlrKtmrFu/DoCt27ZS715dublz0Pw83b26Kl++Am5u7oSGHicx0fSc9oGDB6wmfvkvbDl+E/8GpqGEz1RwIjbBSGRMMjtP3qJpjeI4FTDgVMBA0xrF2XnyFpExycQnGXmmgulmon8DdzYfs39Y/r/JUZny+WXrfP5P1apVi0tXLnPVHNOGoCC8vbytynh7ebFmrekG1aYtm3nBHNPVsDBSU03PKF67do2LFy9SqmQpbt+5TUxsLACJiYns3bePinZeH/Tq0YOVy5ayctlSWjT3Zs3ataaccOzYg/NUwYIcPXbMlBPWrqW5OX5vL09W/26qy9W/r6W5t6mub968aekJPR4aSprWFC1alAd5HDEFrVvLxvXr2Lh+HW1atWJMYGCWjTmAatWqExZ2Px8EB2+hkZ35oFq16sTFxRIVZbqOO3LkEOXLV0BrTVi665a9e3dTNl2eEP87cmIPXXr29ja/B4Rj6j1zABIfXtxKxk7ue39/B/QD3IEF6Zanv4WXlu7vNKzrM325/2xArcFgYPjw4Qx+ezBGoxFfH188PDz48qsvqVmjJl5eXvj6+jJ2/Fh8/X0p4lSEqVOmAuDk5ETf3n156aWXUErRpEkTmpkvNAe8PoDXBryGwWCgpHtJJoyfYHdMDo6OtPPvx5Jvp5Gm03imvjcu7mUJ3vALpcpWpGqtB3eWFihYiAaeHZg/ezQKReUaz1g9Z3fy6F56vjY82/Xk6Ghg4OB3GTNqGMa0NNq07UD5ChX54fv5VK1anYaNmnDmzCkmTRxLXGws+/bu5qcfF/L1t6bnCsNvXOdmZAS1000moLXm44+mcfduPFpDxUoeDH7b/uEdBoOBER+M4K0hg0gzpuHr44OHhwfzzPvO28sLP18/xowbi4+fD05ORZg+dRoAhw4d4suvv8TR0RFHB0dGB46yuqu6afMm5szO/ixfjo4GhgwZysgRQ00/79C+IxUqVGLRwu+oWq06jRs35fTpU0wYP4q4uFj27Anh++/nM3/BT1y+fIlPP/0IB6VI05qePftaZsf87LNZuLm58fYQ03NsTZt68eJL/R8WSqa6Gjl8BAMHm+vK14fKHh7M+/JLatY01ZW/rx+jx46ls68PTkWKMMNcVw/z/gfDiI6OxmAwEDhyBE7pppi2R7NGjdi1Zw+dAnqQP39+Phw1yrIs4OV+llkqRw8bxtgpU0hKSqJJw4Y0bXT/eaENm7fQrpX1ZCiHjhzli+++w2BwxMHBkTEfDLOa/vphPJs2ZWdICO19fSmQPz+TJkywLOvaqxcrl5hmJx0bGMiYCRNITEykWZMmNDM/KzdlxgySU1J4/S3To8t1atdmfLp/1z/RrHFjduzeTYdu3cmfPx+Tx4yxLOv24kusMM8kN3b4B4yZNJnEpCSaNmpIM/OMcP6dOzN28hT8e/chj8HAlHFjTRfCt+/w5rvvopTC1cWFaePH2RWPwWBg+LAPePvtIRjTjPh09sGjkgdfff0VNWrUwMvTC18fX8ZPGId/Vz+cnJyYMtmUN7t3C+DDSRPp0asHaE3nTp2pUqUKgM1tZofBYOCdd4bywQfvkZZmpH37TlSsWIkFC76lWrXqNGnSjNOnTzJmTKD5+7eLRYvms2jRz1y+fJF58+aglEJrTY8evaiU7vODg7cyffqsbMVzL6Zhw4bz9jtvk5ZmpHMnHypV8uDrb76iRvUaeHp64dPZlwkTx9O1mz9OTk5MnmTqhe/WrTuTJn9Ir9490Bo6paurFi1a8tLLfXF0dKRq1Wr4+dk/6y3Ap/1r0aBqUYoVysOuKY2Zve4CBkfTqXXJzmsEh97Cu1YJtk5sREKykRE/mibLib6bavqpghGm89Dc9ReJvmtquIxbcsb8swWObD9xi+0nbtn+8AfU0z/NUYcOHWLeV19icDR/30fdz+efzv6MPzZsIDExkTbt2+Hv58fAN960O6ZRI0YwcNBbGNPS8PPxpbKHB198OY+aNWvS3Msbfz8/Ro0dQ0cfH4oUcWLmNNNs0ocPH2bBooUYDAaUgwOjA0dRrFgxzp49y5jx4zAa00jTabRt3RqvbN64AHOe2rWL9j428lSPnqxcZpq1ceyoUel+XqUxzZqa8tRr/fvz/ogR/Prbb5Qs6c4nM2cCsHHzZpb9Yvp5k/z58/HRtGl2D1t+VDH9U46OBgYPeY/Ake+TlpZG23YdqVChIosWfUfVqqbz8ZnTp5gwYTRxcbHs3bObH75fwHfzf8TR0ZEBbwxi+AfvojVUqVqVDh06o7Vm5swp3I2/C2gqVarM2++8/6/iFDmTsncGvcdFKVUCOKS1Lq+U6gK8AXQAigMHgQZAaeATrbWXeZ1Pgata64+VUv2BBVprpZSqAKzVWj/9gM/qB0zFNOQyAdgHvKK1PqiUyoup1y0PUEVrbUcPmsYAACAASURBVFRKeWM91DPY/PfB9MuUUovMn7vCXC5Oa11IKfV8+rizEhcTl6N2zqrtZ590CJk0qfPgXownyb144awLPWa3ou8+6RAycS6a834LRyXmvHpyzF8g60JPgE5JftIhZJKo8mRd6DGLT8h59VQgf86rp3qjM8+OnBMcn9ngSYeQiUMOnKnQQeWoS5Yc6/ot+56PftzKlXXNeQeVDecv3cjxB1ql8u6PvS5zXA+d1vqWUirE/HMDfwDHgKOYes6Ga61vKKVuAalKqaPAImAesFIp1R3YBmTn27IL+BGoDCzWWh80x5GslNoGRGmtjQ/bQDYcSx+31vrT/2i7QgghhBBCiP+HclyDDkBrnXFq/w8yLE8BWmYok/5XLgPN5S5i6n17mAit9eCMb5onQ2kIdE/3ucFAcLq/vW0t01r3yxBvoYfELYQQQgghhMhCTuydzgly+qQoT4RSqiZwDtiitf7rSccjhBBCCCGEELbkyB66/5pSqi0wI8PbF7TW/piGbFoxz0yZvXnyhRBCCCGEEOIx+3/RoNNaBwFBTzoOIYQQQgghxD8jQy5tkyGXQgghhBBCCJFLSYNOCCGEEEIIIXIpadAJIYQQQgghRC4lDTohhBBCCCGEyKWkQSeEEEIIIYQQudT/i1kuhRBCCCGEELmbklkubZIeOiGEEEIIIYTIpaRBJ4QQQgghhBC5lDTohBBCCCGEECKXkgadEEIIIYQQQuRS0qATQgghhBBCiFxKGnRCCCGEEEIIkUvJzxYIIYQQQgghcjwHB/nZAluU1vpJxyAeIOXu3Ry1c9JyVDT3GXNgYHkcn3QEtuTEJJjz9l1OpXPg/lM5cP/lzHrKeXLenjPJifm89vB9TzqETI5Mf+FJh5BJPkNOHPSV844nk5yXFfIULJjzgrIh7NrNnLpTLUqXcn7sdZkTv31C2C0nnvyF+K/lxEaKEP81yefi/wfJ5+K/J0MuhRBCCCGEEDmeg5IGsS3SQyeEEEIIIYQQuZQ06IQQQgghhBAil5IGnRBCCCGEEELkUtKgE0IIIYQQQohcShp0QgghhBBCCJFLySyXQgghhBBCiBxPJrm0TXrohBBCCCGEECKXkgadEEIIIYQQQuRS0qATQgghhBBCiFxKGnRCCCGEEEIIkUtJg04IIYQQQgghcilp0AkhhBBCCCFELiU/WyCEEEIIIYTI8Rwc5HcLbJEeOiGEEEIIIYTIpaRBJ4QQQgghhBC5lAy5FEIIIYQQQuR4DkqGXNoiPXS50K6QEDr5+dHex4fvFizItDw5OZn3R4ygvY8PvV58kbBr1yzLvp0/n/Y+PnTy8yNk927L+zGxsbw3bBid/f3p3KULR44ezXZMnf396Ojjw/yFtmP6YMQIOvr40Pul+zEdDw2le88edO/Zg249AtiydatlnR9/+gn/bl3x796N4YEjSUpKylZMIbtD8Ovij4+fDwsWLbQZ04jAEfj4+fDiyy9xzRzTtWvXaNikET1696RH755MnjrFsk7QxiACegbQNaAbn83+LFvxAGitmTpjJu19fPAPCODkqVM2y504eRL/7gG09/Fh6oyZaK0BiI6O5rU3B9LBx5fX3hxIdEwMAFu3BeMfEEDXHj0J6N2HQ4cP2x3TPz2eoqKi6P/669Rv3Jgp06dbrdPvtdfo5OdH1x496NqjB7du37Y7nntyYl09qphiY2MZ9M47dAnogW/XbqxavdrumHaFhNDZz48OD9l/w0aMoIOPD73T7b/de/cS0Ls3/t27E9C7N/v277es8/ncubRq144XGje2O470HlU9rV2/Hv+AAPwDAujzcj9Onzlrd0yPop5M8Xeng48P02bMsMSfHf91Pk9KSqJn3750CQjAt2tX5n755T+K6b+uqzcHDaJrQAB+Xbvy4eTJGI3GbMUUsjsE3y7+dPb1YcFC2/l8+MgRdPb1oe9LL1liCrt2jQaNGxHQqycBvazz+Zwv5tK2Q3saNW2SrVjumda3OvtmNGX9mBceWGZs9ypsmdCQtaNfoFbZQpb3/Ru4s3lCQzZPaIh/A3fL+7XKFmbd6BfYMqEhY7tXyXZMu3fvpkvXLvj6+7LwAee9kYEj8fX35aV+9897AH/99Rf9XulH94DuBPQMsJxzU1JSmDxlMv5d/enSrQtbtm7JdlyP4rqlTYcO+HfvTtcePQjo3TvbMT2qPGXvdm15FOfj2XPn0rJdO+r/w3wucgmttbxy6Cs5Pl5nfCXExOgWLVrov8+c0fFRUbpTx4761LFjVmV+WLhQjw4M1Mnx8fq3lSv1kMGDdXJ8vD517Jju1LGjjrtzR58/e1a3aNFCJ8TE6OT4eD1s6FC9+McfdXJ8vI6PitI3b9zI9NmJcbZf8dGmmM6dPqNj75hiOnHsmFWZRQtMMSXGxetVK1fqIYMG68S4eH0n8qaOi4rWiXHx+sqFi7pBgwY6LipaXzp/QXt7e+uom7d0Yly8HjxokF62eEnmz46Js/mKuROtWzRvoc+cPK2jbt3RHTt21MeOHLMqs3D+Ah04MlDHx8Tplb+s1IMHDdLxMXH6r9Nndft27TNtM+zyVe3ZzFNfuXRFx8fE6aHvDdVbN2+x+fnJ8bZfmzds0K/066eT4mL1gT27ddcuXWyW6+Lvr/fvDtFJcbH6lX799JagIJ0cH6enTZms582Zo5Pj4/S8OXP09KlTdHJ8nI6KjNBJcbE6OT5OHz98SLdt08bGdv/b4ynq5k29d9cu/eOiRXr82LFW6/Tu1Usf2r/f5mdav3JqXT3emOZ+Ptvy/zeuXNH16tXT8VF3LNtLio+3+bpr3n/nzpzRceb9d/LYMasy35v3X1K6/ZcUH6+P/PmnvnLhgk6Kj9ehR4/qpk2aWNbZv2ePvnrxoq5bt+4DPzvpIfvvUdXTvpAQHXn9mk6Oj9NbgoJsbvdx1lMXf3+9b/dunRgXp/v366c3BwU9oJ5svx5FPk+Ki9NRkZGWXN61Sxd9YM+eTJ/9uI+pW+HhOik+XifGxem33nxT/7ZyZebPjo2z+YqNMuXzs6dO6+jbd3Snjh318aPHrMosnL9AjwoM1Hdj4/SvK0z5/G5snP7rzFndvn17m9vdu3u3vnT+gq5bt+4DP/tubJz2GLjF5qvnx39qn6n79JmwWJvLX5l7RAeH3tQeA7forjMO6MPno7THwC36ufe360uRd/Vz72/Xzw41/f+zQ7drj4Fb9JEL0brbzAPaY+AWHRx6U/efc9jmtmOjYzO9om5H6ebNm+tTJ07p2zdv644dO+qjh49alZn/3XwdODJQx0bH6hW/rNCD3hqkY6Nj9Z1bd3SHDh30nwf+1LHRsfrKpSs66naUjo2O1R/N/EjPmD5Dx0bH6ug70frypcs2P/9xHufJ8fHa28tLh1+9muPOMVlv97+vp4edjw/s2aPDzPn8YXX1pK957X1FRtzWOf31JOrloT10SqmiSqm3sihTQSmV5a0Rc7nQ7DY4/w2l1ASl1LAsyvgppWr+w+27KKX2KaUOK6Wa/bMos+d4aCjlypalbJky5MmTh/Zt27I1ONiqzNbgYHw7dwagTatW7Nu/H601W4ODad+2LXnz5qVM6dKUK1uW46GhxMXF8eehQ3T19wcgT548OBUubHdMoaGhlCtTljLmmNq1bcu2DDEFBwfj08kUU+uWrdh3wBRTgQIFMBhMI3+TkpNR6brSjUYjSUlJpKamkpiQiIuLi/0xnQilbNkylpjatmlL8PYMMW0PpnOnTgC0atmS/fsPoPWD77CHhYVRrnw5ihcrBkCDF16w6lG0x7btwfh06oRSirp16hAbG0tkZKRVmcjISOLj43mmbl2UUvh06sTW4G2m9YO349vZFLNv505s3Wb6NxUsWNBSdwkJCWDniIR/czwVLFCA5559lnz58mWrDuyV0+rqUcakUMTH30Vrzd2EuxQp4oSjo2OW8djafxm/e9uCg/Ex77/W6fZfjerVcXV1BaCyhwdJyckkJycDULdOnWx93x5XPT37TF2KODkBUKdObcLDw+2K51HUU2RkJHGZ4g8mOx5FPldKUbBgQQBSU1NJTU21yqtPoq4AChUqZIkpJZsx2cznGc8x2cznAHVq/7vj/MC5KKLiUx+4vFUdZ1btuwHAkYsxOBU04OKUl2Y1SxBy6jbRd1OJSUgl5NRtPGuVwMUpL4XyO3L4gqmnZ9W+G7Sua398J06coGzZ++fiNq3bZDrvbd+xnU4dTfXUskVL9pvPxXv37aVK5SpUrVoVgKJFi1py0Jo1a+jfrz8ADg4OFCtazO6Y4NEc5/+FR5Wn7NmuLY/qfPxv87nIHbIaclkUeGiDDqgAZL+v+yGUUo/z2T4/4B816ICWwGmt9bNa653/YUwPFBERgbubm+VvNzc3IjIkioiICNzdTUM4DAYDhQoVIioqiojISMv7AG6urkRERHA1LIxixYoxZvx4uvXsybiJE7mbkGB3TOGREbi5p4vJ1Y2IiEgbZTLHBHDs+HH8u3Wla0B3xo4ajcFgwM3VlZdffIk2HdrTsk1rChUuRONGjeyOKSIiEjc3639rZEREpjLubhliijbFFHYtjJ69e/HqgNc4dPgQAGXLluXixYtcu3aN1NRUtgUHEx5+w+6YAMIjInBPX1duroRnrKuISNzMF0X3y5hiv3XrliUxu7i4cDvdUMbN/8fencdFWfX/H38dwTUDXFhcc2lRSLNyKxdwDxcQUVAq07RFTS2z1NS023Ltbk8rU+82lcxKUxQFxVxS3HctTcWNxQVZRNbz+2MGZBlkRhkYvr/P8/GYh3LNuc71nnNdc645cy2zaRN9/PoxcsxYZkybZlaee9meijJ1+nT8AwP56ptvivxgZYqttZU1MwUNDOTfM2fo1L0HfgMCmPjWW5QrV/QZ8abWX8xdrL+NYWE0eeQRKlSoUOQyzWHNdZft199/p307806Vs0Y7xcbG5svvSmy+PuZuct1rfw6GL8P8AwPp2KULT7VtS/Nmze4pU3FtU6+MHIlnly5UqVKFbl27WpDpdl9tyORCbFy+/jzORH9uzHTx4kUCgwYx7KXb/XlJcHWqyOXrt3L+jr6eiqtTRVydKnD5+u1LCKLjU3F1qoCrU0Wi43NNv34LVyfzvzCLjYvFNd+6KzBAiY3LKZN7vxd1LgqlFKNGjyLouSC++/47wHA6OMCCrxYQ9FwQb098m6tXr1rQCtbbzpVSvDxyJAFBQaxYudKiTGC9fsqcek2x5v5Y/N9X1CeG2UBjpdQBpdQ84+OIUuqwUiowV5kOxjJvGI/EbVVK7TM+zDppVyk1RCm1Qin1B7DBOO0tpdRupdQhpdR7ucoONk47qJT6wcz6XzLWdVAptVIpVcWYzQeYZ8zf2PhYr5Taa3wdTQqprwUwF+hpnLeyUqq7Uuov4+teoZSqaiz7pFJqi7HOUKVULXMym2LqY3H+7zlNfXhWShU6PSMjg+MnThA4YAC/LF9O5cqVWWTi3G1LQhX48rWQZQM0b9aM335ZybIffmTRksWkpqaSkJDA5ogI1q1ZQ1joBlJSUlizdq35mcwIpU2UUShq1qzJujUhLF+6jDffGMc7UyaTlJSEg4MD70ycxIRJE3nxpWHUrl0bOzvLvnswNa7J31Ymc5nxbXbXzp3547df+eyj//LFfPOum7mX7elO5sycyW8rVvD94sXs3b+f1WvWmJUn73JNZCvFtrJmpu07/qLJIw+zeUMoK5cvY+bsOSQlJRWdx8Q0S9ffqdOn+fizz5g2ZUqRyzOXNdcdQOTu3fz6+++MGzvGvDwmpt1rO5ms08KL9a3RnwPY2dmxMjiY8NBQDh85wj+nTpVIpmyFbVNfz5/P5o0bSU9LY9fu3eZnMqMPKqyMc82arF8bQvDSZbw5bhyTJk82671VHExtDlob9jMFnzBd3hJ33U4oMjIzOHDwAO/PeJ9F3y5ic8RmIiMjycjMICY2hscee4ylPy6lebPmFl8/bq3t/IclS1ixbBkLvviCZcHB7Nm717JcVuqnzKnX5HwmphXH/lj8/6GoAd1E4LTWugWwE2gBPAZ0xTAIqmUss1Vr3UJr/TEQC3TTWj8BBAKfWZDnKeAFrXVnpVR34CGgtXG5TyqlOiqlPIDJQGet9WPAWDPr/lVr3co4z3FgmNZ6B7AaeMuY/zTwDTBaa/0kMB6Yb6oyrfUB4F0g2Ng+9wFTgK7G174HGKeUKg98DvQ31rkY+MBUnQBKqZeVUnuUUntMXRDr6uJCdK7TjGJiYgocSnd1dSU62nDkKCMjg6SkJBwdHQ3zRt8+ohQTG4uzszNurq64urjkfIvbvWtXjp04UWhDmsoUE50rU6yJTC6uxJjIlFujRo2oXLkyp06fYueuXdStU5vq1apTvnx5unTuzIFD5t+oxcXFJc/Rs+zXmj93dEzBTBUqVMDJyQkA96bu1K1Tl3NRUQB4dvTkh+++5/sl39HggQeoX69ekVmWBQfjHzgQ/8CBuDg7E527rWJiccmXy83l9jeA+cvUqFEj5xvXuLg4qlevXmB5LZ98kvMXLnD9+vUis93L9lRUvQD33Xcfvby9OXL0aJFZwDbbqiQy/bZ6NV07d0YpRf369alTpzZnzp4tvKGMTK2//HnutP6iY2J4fdw4Zs6YQT0ztuU7Kal1d/Lvv3n3PzP4/OOPc96nRbFGO7kWyF/wvXM3ue61P8/N4f77adWyJdty3UjibjIV5zZVsWJFvDw9C5zGecdMrrf7akOmWJxr3mV/Xvd2f25t0ddTqVWtUs7fbtUqEnsjlej4VGpVu33kzc2pIjE30oi+nopbriNybtUqERNv/s3AXF1c85yGHBMTQ82aNfOUcXF1ySmTZ3tydeWJx5+gmlM1KleqTLun23Hi5AmcHJ2oVKkSnbw6AdC1S1dOWPD5wJDLOtt59um9NapXp0vnzhw2Yz9TEv2Um6tLkfWaYq398f81Simbf5QGS+5y2R5YprXO1FrHAFuAVibKlQcWKqUOAyuw7HTGjVrr7HNruhsf+4F9QBMMA7zOwC9a6ysAucoX5VHjEbfDwLOAR/4CxiNqTwMrlFIHgK8Bc4+mtcXwWrcb530BeAB4BHgU2GicPgWoW1glWutvtNYttdYth7/4YsEX4eFBVFQUFy5eJD09nXWhoXTy8spTppOnJ6v++AOADWFhtGnVCqUUnby8WBcaSlpaGhcuXiQqKopmjz5KzZo1cXNzy/kAuTMyksaNGpn5ssHDw4Nz529nWh8aipdn3kxenp6sXmPItDE8jNbGTBcuXiQjw3ANwqVLlzh79iy1a9XGzc2NQ4cPk5KSgtaaXZGRNGrY0PxM7h5EnT/PRWOm0A2heHX0zFPGs6MnfxiPHIWFh9PKmOna9es5d2C7cOECUeejqFunDkDOKRUJCQn8/MsK/Pr6FZllUGAgK4OXszJ4OZ07ebF6zRq01hw8dIiqVasW6LCdnZ2pUqUKBw8dQmvN6jVr6GRsTy/Pjqz6w5B51R9r6ORleE1RUVE539wdO36c9PR0sz7s3sv2VJiMjIycAVJ6ejpb/vyTBxs3LjIL2GZblUSmWm5u7DTeEfDK1aucPXsuZ5u7k0c9PDiXb/155Vt/Xp6erDauv41ht997CYmJjBo9mrGjR/N4ixZFLqsoJdFOly9f5vXx45k1YwYNHnjA7GzWaCdnZ2fuK5A/bx9jTq7i7s+vXbtGgvE0uVu3brFz1y4aNmhQqm118+bNnA++GRkZbN2+3aJMpvpzz3xt7elpZn8eFWXWe6s4hB++knMHyxYNHEhMySQuIY2tx67Svml1HCrb41DZnvZNq7P12FXiEtJITs2kRQPDdaJ+bdwIO3TF7OW5u7tzPup2O23YuAHP/Pu9Dp6sWWtop/BNt9vpqbZP8c+pf0i5lUJGRgb79u2jYcOGKKXo2KEje/buASBydyQNG5m/LwbrbOc3U1JITk4G4GZKCjv++ouHzNjPlEQ/Zfi8c+d6i7udhFB3urZFKdUAWKO1flQp9QlwSGu92PjcDxgGbAnAeK11b+P06UBV4G0MA8ZbWmv73HUVsqwhQEut9WvGv/8L/K21/jpfuTGAi9a6yPODjFmStNYfKqXOAH211geNy/LSWg9RSv3PmOsXpZQDcFJrbdYgLndmpVQfIEhrPShfmWbAN1pr8y8AM0q/edPkyvlz61bmfPghmVlZ+Pn68srw4Xwxfz4e7u508vIiNTWVSVOmcPzkSRwdHJg3ezb16hrGkF9/+y2/rVqFvZ0dE8aPp0P79gCcOHmSd997j/SMDOrVqcOM997LuflAtqw7XAa1ddtW5hoz9fXx5eXhw/lywXzc3d3p5GnI9M7UKZw4cRJHRwfmzppN3bp1+WPNGhb/bwn29vaocuV49aWX6dzJ8E3glwsWELpxA3Z2djR9pAnT3323wDU+mXcItXXbNj786EOyMrPw9fFh+LDhzP9qAe5N3fHy9CQ1NZUp707l5MkTODg4MnvmLOrWrUtYeDgLvl6AnZ0dduXsePWVV3J2ihPfmcTf/xhulf7y8Jd5pkcPk8suX8j9LLTWfDB7Ntt2/EXlSpWYMX06j3oYvvPwDxzIyuDlABw5eowp06ZxKzWVDu2e5p0JE1BKER8fz5sTJnD5cjS1arnx0dy5ODo6smjJ/1i9Zg329vZUqliRN994nScefzzf0k13+veyPXXv2ZOk5GTS09NxuP9+vpk/n1q1azNk2DDSMzLIysykbZs2vP3mm4Xc5KPw9Ve6bVWymWJj45g8bRpXrlxBa82woUPo06tXrlYqfIf959bb7z0/X8N7z9T6O2Fcf3ON6+/rhQtZtHgx9evXz6nr6wULqFG9Oh998glr160jLi4OZ2dn/P38GPnqqwWWrQpZf9Zqp3ff+w9h4eHUqmXoou3s7Ph56U95l32H7by42+no0aM5+du3a5eTv2A7Fa64+/OTf//N5HffJTMrC52VRY9u3RjxyisF11ERmYqzrbTWvDZmDGnp6WRlZtK6VSveHj8+54ZY2Yrqz+f919if+/rw0rDhzF+wAHf32/355KnG/tzRkTm5+vP5Xy3A3s6OcuXsGPHq7f78408/Yd369TnbuV/fvox4peB23uztXSYzfTzUgzYPO1GtanmuJqTx6doz2NsZ1vayrYbbyU8PfJiO7jVISctkwg/HORJlGGz3f6oWI3oYvpSYv/4cK3deBuDR+vczd3BTKpW3Y8vRq7z3s+mf5jgw2/RPJWzbvo3/fvRfMjMz8fXxZdiLw1hg3O95Gttp6rSpnDx5EkcHR2Z+MJO6xu0pJCSEJf9bglKKdu3aMXaM4QSoy5cvM3XaVBITE6nmVI1p06ZRy63gx6SK9oUfIyju7fz8hQuMHTcOMFwz2tPbm1eGDzex5JLfx9ypXoM79+fFuT9u3Lgx//3kE0LWrSM2Lg4XZ2f6+fkxykR/Xr5KlTIxMrx6Jd7yi/JLWI2aTiXelkUN6GoA+7TWDyil+gGvAD2B6hhOKWwD1AE+0lp7Guf5GLigtf6vUmoosFhrre5iQNcdmAF00VonKaXqAOmAM/Ab8JTW+qpSqnphR+nyDeiuYDiCdh0IAS4aB3SfG1/jEuM8O4CPtdYrlGHP3FxrbfJcv3wDOmdgL4ZTQU8ppapgOBJ3FjgGPK+1/st4CubDWusizw0obEBXWu40oCstd/oAUJoKG9CVLlvsq21z/dmaOw3oSlNhA7rSZIttZXuJbPOdZ6v9eWEDutJU2ICuNN1pQFd6bHGbssUeQQZ0xak0BnR3fPdpra9iOIXwCIbr2w4BB4FNwNta62jjtAxluNnIGxiuOXtBKbUTeBhIvptgWusNwFLgL+Npkr8A9xsHQh8AW5RSB4GPzKxyKrAL2AjkPgF8OfCWMvz0QGMMp2MOM9Z9FPA1M28cMARYppQ6hOGawyZa6zSgPzDHWOcBDKd1CiGEEEIIIcQ9ueMROlG65Ahd0Wz1G105Qmcu21x/tsYWjzqBHKEzl+0lss13nq3253KEzjxyhM5cttgjyBG64lQaR+hK8vfehBBCCCGEEOKulCtXJsadJa7EB3RKqR7AnHyTz2iti75dYOF1TgYG5Ju8Qmtd6M8D2FL9QgghhBBCCHE3SnxAp7UOBUKLuc4PuMNvu9l6/UIIIYQQQghxN2zxhGchhBBCCCGEEGaQa+iEEEIIIYQQNk9+SN00OUInhBBCCCGEEGWUDOiEEEIIIYQQooySAZ0QQgghhBBClFEyoBNCCCGEEEKIMkoGdEIIIYQQQghRRsldLoUQQgghhBA2r5zc5dIkOUInhBBCCCGEEGWUDOiEEEIIIYQQooySAZ0QQgghhBBClFEyoBNCCCGEEEKIMkoGdEIIIYQQQghRRimtdWlnEIVISUq2qZWTmWVTcQBITcso7QgmOVSpUNoRxF2zve0c5K5e5sq0wX2anQ3elc32WglscNUBkJ6ZVdoRCmgxMbK0IxRw/MO2pR3BBFvcqGyvPwAoX6WKbQbLJ/FGoi2u1Dzud7y/xNtSjtAJIYQQQgghRBklAzohhBBCCCGEKKNkQCeEEEIIIYQQZZR9aQcQQgghhBBCiKIoG7wm2RbIETohhBBCCCGEKKNkQCeEEEIIIYQQZZQM6IQQQgghhBCijJIBnRBCCCGEEEKUUTKgE0IIIYQQQogySu5yKYQQQgghhLB55eQulybJETohhBBCCCGEKKNkQCeEEEIIIYQQZZQM6IQQQgghhBCijJIBnRBCCCGEEEKUUTKgE0IIIYQQQogySu5yKYQQQgghhLB5cpNL0+QInRBCCCGEEEKUUTKgK4O279iObz8/+vj6sHjJkgLPp6Wl8fbECfTx9eG5wYO5eOkSABcvXaLN008RMGggAYMG8v7MD3LmGfnaKAIGBtJvQH/en/kBmZmZFmXasWMH/fz74evny5L/mc40cdJEfP18GTxkIjZ7cAAAIABJREFUMJeMmULWhTAoaFDOo2Xrlpw8eRKA48ePEzAwAF8/X+Z+OBettUWZdu78i4EDBzAgwJ/vf/iuwPP7D+xnyNDBdOj4NJs2h+d5LiRkLQGB/gQE+hMSsjZneljYRp4f/CzPPjuQL7/83KI8ANu2b6d33754+/jw7eLFBZ5PS0vjzQkT8PbxYdDzz+esO4CFixbh7eND77592b5jR575MjMz6T9wICPHjLGJTN179sRvwAD8AwMJCAqyOJM1cqWmpjLwuefoFxCAr78/XyxYYHEmrTUz58zF28cHv4AAjh0/brLc0WPH8BsQgLePDzPn3N52QzduxNe/P82eeJIjR4/llN+xcycBQUH4DQggICiIXZGRZmeyxvorqs6SznTm7Fn8AwNzHm3at+eHn36yKNP27dvx8fOjt48PiwrpN9+aMIHePj48m6vfzHb58mXatmvHd99/n2d6ZmYmAYMG8dpdvPfAOuvvh6VL6du/P77+/ha3U3amPn370vMOmcZPmEBPHx+CcmW6vR0PyLMdJycn0z8wMOfRoVMn5sybZ3kmv7708vFh0RLTmd6aMIFePj4EDb6d6fCRIwwYGMiAgYH0DwwgfNMmwNAfBD3/HP0DA/Dr78+Xd9Ef3O1+D+Cff/5hyItDGBAwgICBAaSmpgKQnp7O+x+8j5+/H/369yN8U3iBeu9k1nNN2DWnPSFTWhdaZuqAhwif3pY1k1vjUa9qznS/Nm6ETW9L2PS2+LVxy5nuUe9+1k5uTfj0tkwd8JBFebIV93Z+OTqaoS+9RJ9+/Qzb+dKlFme61/78xo0bDH91BD19fBn+6ghuJCQA8O+ZMzw7+AUeb92GJfn6i6LY4n5PlBFaa3nc4wOoDfxS3PXeTEzS+R+J8Td0506d9d/HT+gb167r3r166cMHD+Ups2TRYv3OpEn6ZmKS/vWXlfq1UaP0zcQk/c/Jv7W3t3eBOm8mJunYy9H6ZmKSTk5I1CNefVX/+svKgsu+kWjyEX8tXnfq1EkfP3pcX7tyTffq1Usf3H8wT5lF3y7SkyZO0ok3EvUvK37Ro0aOKlDPvr37dCevTjl/+/X109u3btcJ8Ql6yAtD9PqQ9QXmuRJ33eQjJvqK9vLqpA8eOKIvX4rVPXv21Lsj9+Ypc/jQUb3zr0g9ZsxY/fPPv+RM//f0We3l5aX/PX1W/3v6nPH/5/TpU2d1hw4d9D9//6uvxF3XY8e8rtev22By+WnJyQUeKQkJunPnzvr0yZM6OT5e9+7VSx8/dChPme+XLNGTJ03SacnJ+veVK/Xo117TacnJ+vihQ7p3r1466fp1/e/ff+vOnTvrlISEnPkWfvWVfn3MGD182DCTyy7sYa1MXp6eOubCBYuyWDtXalKSjo+L02nJyTo5Pl779+und//1l4nlJxX6CFu/Xr84ZIhOTUrUu//aof379TNZrp+fn47csV2nJiXqF4cM0eGhoTotOUkfP3xInzx6RAcNGqT3RUbmlD+4d4++cOaMTktO0kcPHtDt27XLV2fJtZM5dZbGNpW7/qeeekqf/ecf08tPSirwSLpxQ3fu3Fn/c+KETrhu6DePHDqUp8z/Fhv6zZSkJP3rSkO/mfv5Ea++qkeNHKm/mj8/z/Svv/pKjx0zRg978UWTy05JKtn1d/TgQd3T21vfuHJF37xxQw9+7jn9z7FjBZadWsjjpjHTqZMndZIx07FDh/KU+c6YKTVXptTkZH1g7159/swZnZqcrI8cPKjbt2tnchm+vr56+59/Fph+K8n0I/mGMdOJkzrxuiHT0UOH8pT532JDpltJyfq3lSv16FGv6VtJyfp63BWdFH9D30pK1ufPnNVt2rTRSfE3dEpikr4WG6dvJSXrxOvxul+/fnrXjr9MLr+493vXr17XPXv21Ht379WJNxL1+XPndfy1eJ14I1HPmztPz5k9RyfeSNQ3rt/QUeeiTC6/8Yhwk4+B/92rfWbu0icvmi7z4hcHdMSRK7rxiHDtP2e33v9vvG48Ilw/8eYWfS7upn7izS368XGG/z8+botuPCJcHzhzQ/efu1s3HhGuI45c0UM/32+y7pLczi+ePasP7Nmj05KT9fXYWN2ta9dC+inr9eezPnhfz//8c52WnKTnf/65nj3zA52WnKQvn4/SeyN36XlzZuuvF8w3Uact7veSdWl/lr6Xz8a29iiNdinTR+iUQam+BqWUvdb6kta6f0ks78jRI9SrV5e6detSvnx5enTvQURERJ4yEVsi6NO7NwBdu3QhMnJ39sCzUFWrGr6ly8jIID093aJzlI8ePUq9evVyMnXv1p2ILXkzbflzC717GTJ16dyFyN2RBTKFhobSo0cPAOKuxJGUnETz5s1RStGrV68Cdd7JsePHqFu3LnXq1KF8+fJ07dKNrVv/zFOmVq3aPPjgQ5TLtwnt3LWTVq1a4+DgiIODA61atWbnrr+4eOki9erVp1q1agC0bNWKzRGbzc50+MgR6terRz1jO3n36MGmfOtuU0QEvn36ANC9a1d2RRraaVNEBN49elChQgXq1qlD/Xr1OHzkCADRMTH8uW0b/n5+ZmexdqZ7ZY1cSimqVKkCGLbzjIwMlIUn42/eEoFP794opXiseXMSExOJi4vLUyYuLo7k5GRaPPYYSil8evdmk3E7adyoEQ0bNChQb9MmTXBxcQbgwcaNSU1LIy0trVTayZw6SzpTbjsjI6lXty61a9c2O9ORI0eoV/d2v/lMj4L95uYIw7oF6NalC5G7b/ebmzZvpm6dOjRu1CjPPDExMWzduhW/vn3NzpKbNdrq3zNnaN6sGZUrV8be3p6WTz5J+OZ766c2m2orY6ZuuTIZtmMXoPDt+Ny5c1y7do0nn3jC7ExHjhyhft16edZf/kwRERH49DZm6tKVXcZ9THY7AKSmpeW85++1P7iX/d7OXTt56MGHePjhhwFwcnLCzs4OgNWrVzN0yFAAypUrRzWnamZnAth9Kp745IxCn+/avCa/7YoG4MDZBByq2OPsUIEO7jXYfvwaN25mkJCSwfbj1+joUQNnhwpUrWTH/jOGo0+/7Yqm22POFmWyxnbu7OyMe9OmANx33300atiQmHx9cVHutT/fHLEF3z6G9evbpzebNhteU43q1Wnm4ZGz3ZVmOxXHfk+UDWVuQKeUaqCUOq6Umg/sA55XSv2llNqnlFqhlKpqLNdKKbVDKXVQKRWplLpfKVVJKbVEKXVYKbVfKdXJWHaXUsoj1zIilFJPKqXuU0otVkrtNpb3NT4/xLisP4ANxkxHjM/ZKaXmGec5pJR6xTi9llLqT6XUAaXUEaVUh7t5/bGxcbi53j4VwtXVhdi42Lxl4m6Xsbe3p2rVqsTHxwNw8eJFAoMGMeyl4ezbvy/PfCNGjaRzt65UqXIfXbt0NT9TXCyurq65MrkW7BRj43LK5GS6EZ+nzIaNG+jRvcft8i656nRxLfA67yQuLjbP/M4uLgUyFeZKXBwuueZ1cXbhSlwcdevU5dy5s1y+fImMjAy2/rmF2NgYszPFxsbilq+dYvNlio2Nxc2t4LqLjYvLmQ7g6uJCbKyhPebMm8e4sWNR5Sx/O1srk1KKl0eOJCAoiBUrV9pMrszMTPwDA+nYpQtPtW1L82bNLMoVExuLm1vuXC7ExMblKxOHq/FD7e0y5m+7G8PCafrII1SoUKHIstZoJ3PqLOlMua0LDaXnM8+YnQcoUK+LS8F1krtM7kw3U1JY8r//8eorrxSod+6HH/LG2LGUu4v3HlinrR5s3Ji9+/YRHx9PSkoKW7dtIzo6+p4y5f+gXFim3DaGhdHExHYcsn49z3TvbtGHypi4WFzd8u0P8r/v4mJxLSTTocOH8evvj3/AAKa+Mznng3ZmZiYDBgbi1bULT7WxrD+4l/1e1LkolFKMGj2KoOeC+O57wyUBiYmJACz4agFBzwXx9sS3uXr1qtmZzOHqVJHL12/l/B19PRVXp4q4OlXg8vXU29PjU3F1qoCrU0Wi43NNv34LV6eKFi3T2n3CxUuXOH7yJM0ffdSiXPfan1+9ehVnZ8Pg1tnZmWvXrlm0/Pxsdb8nyoYyN6AzegT4HugGDAO6aq2fAPYA45RSFYBgYKzW+jGgK5ACjALQWjcDBgHfKaUqAcuBADAMvIDaWuu9wGRgk9a6FdAJmKeUus+Y4SngBa1153zZhgE3jPO0Al5SSjUEgoBQrXUL4DHgwN28cFNH2vLvGAsr41yzJuvXhhC8dBlvjhvHpMmTSUpKyimz4Mv5hIVuID09jcjdu62fidtlDh85TKVKlXjwwQfNKl90qIKTzP0AYfJoplI4ODjw1vgJTH13CiNGvoJbrdo536reZaQCr6iwtixsesSff1K9enU83N3NzmHtTAA/LFnCimXLWPDFFywLDmbP3r02kcvOzo6VwcGEh4Zy+MgR/jl1yrJcpjeNvGVMpDd32zt1+jQfffYZ706ZbF4eE9PutZ3MqbOkM2VLT08nYssWunfrZkGie+s3F3z1Fc89+2zOt9zZthjfe+53+d4D67RV40aNeHHIEF4aMYJXR43i4Ycfxs6CIwX3kinbqdOn+fizz5g2ZUqBcutDQ/G2cEBuuj8vEKrQTM2bNeO3X1ay7IcfWbRkcc71anZ2dqxYHszG9aEcOWpZf3Av+72MzAwOHDzA+zPeZ9G3i9gcsZnIyEgyMjOIiY3hscceY+mPS2nerDmffPqJ2ZnMYaor0rqQ/asunjsKWrNPuHnzJm+MH8+E8eNzzjQyO5eV+3NL2ep+T5QNZXVAd05rvRNoC7gD25VSB4AXgAcwDPgua613A2itE7TWGUB74AfjtBPAOeBh4GdggLHuAGCF8f/dgYnGuiOASkB943Mbtdamvo7pDgw2zrMLqAE8BOwGhiqlpgPNtNaJpl6YUuplpdQepdSeRSYuiHV1dSE65va3rTExsTjXzHv6g6vL7TIZGRkkJSXh6OhIhQoVcHJyAsC9qTt169blXFRUnnkrVqyIZ0dPi05vdHVxJSbm9pGqmJgYatasmaeMi6tLTpncmbJt2LCBZ3o8k7d8rqNfMbExOd+EmcPZJe/8cbGxBTLdad7cR95i427P2759B75duJiF3yyifv361Ktbz+xMhvWSt53yvyZXV9ecb9Nzt5Ori0ueb9ljYmNxdnZm/4EDhg+4PXvy1sSJRO7ezYTJ5g0GrJUJyDntqkb16nTp3JnDR4+ancmaubI53H8/rVq2ZFu+m8uYsiw4GP/AgfgHDsTF2Zno6Ny5YnHJV7dbvqM/psqYEh0Tw9hxbzJzxn+oX8+87coa7WROnSWdKdvWbdto2qQJNWvUMDtPTqZc9cbGFlwnucvkznT48GE++fRTvHv14qelS/l28WKWLV/OgYMHidiyBe9evZgwaRK79+xhkgXvvZxlWqGt/P38WLFsGd8tXoyjoyMP1K+PuUxlKtBWhWQCw3b8+rhxzJwxg3r5tuOTJ0+SmZlp8RdQri4uxETfeX/g6uJKTCGZsjVq1IjKlStz6nTeD7QO999PyydbFrjZ1J0z3f1+z9XVlScef4JqTtWoXKky7Z5ux4mTJ3BydKJSpUp08uoEQNcuXTlx4oTZmcwRfT2VWtUq5fztVq0isTdSiY5PpVa120fe3JwqEnMjjejrqbjlOiLnVq0SMbmO2JnDWtt5eno6r48fTy9vb7p16WJWluLsz2vUqJFzVDYuLo7q1aublaEwtrTfs2VK2f6jNJTVAV2y8V+FYWDVwvhw11oPM043+4tmrfVF4KpSqjkQiOGIXXZ5/1z119daZ98GKdlUXcZ5Rueap6HWeoPW+k+gI3AR+EEpNbiQLN9orVtqrVsOe/HFAs97uHsQdf48Fy9eJD09ndANoXh6euYp4+npyR9r1gAQFh5Oq1atUEpx7fr1nLtXXrhwgaioKOrWqcPNmzdzOqWMjAy2bd9m8jqfwri7u3M+6namDRs34NkxX6YOnqxZa8gUvul2JoCsrCzCwsPo3q17Tnnnms7cV+U+Dh8+jNaatWvXFnidd9K0SVMuXDjPpUuXSE9PJyx8I+3bdzRr3rZt2hIZuYuEhAQSEhKIjNxF2zZtAbh23TCGT0hI4LdfV9Knj6/ZmR718CAqKooLxnZaFxpKJy+vPGU6eXqy6o8/ANgQFkYbYzt18vJiXWgoaWlpXLh4kaioKJo9+ihvjBlDeGgoG0JCmDd7Nq1btWLOBx+YWHrJZbqZkkJysuHtcTMlhR1//cVDjRubnclaua5du0aC8ZSmW7dusXPXLrO280GBgawMXs7K4OV07uTF6jVr0Fpz8NAhqlatWmCn6ezsTJUqVTh46BBaa1avWUMnTy/TlRslJCYycvQYXh89midatCjVdjKnzpLOlC1k/XqLT7cE8PAw9JvZmdaHFuw3vTw9WW3sNzeGh9PamOl/ixezbu1a1q1dy7NBQQx/8UUGDRzI2NGj2bh+PevWrmXOrFm0atmSWRa896zZVleNp35dvnyZ8E2bLDoi9qiHB+fyZfLKl8nL05PVxkwbw8Jy2iohMZFRo0czdvRoHjexHYesX2/50TkM6+/c+ag8688r33vKsP6MmcJvZ7pw8SIZGYZryi5dusTZs2epXas2167fXX+Q7V72e0+1fYp/Tv1Dyq0UMjIy2LdvHw0bNkQpRccOHdmzdw8AkbsjadioocXtdSfhh6/k3MGyRQMHElMyiUtIY+uxq7RvWh2HyvY4VLanfdPqbD12lbiENJJTM2nRwAEw3gnz0BWLlmmN7VxrzbvvvUejhg154fnnzc5SnP25l2dHVv1hWL+r/lhDJy/zP6OYYkv7PVH2lPUfFt8JfKmUelBrfUopVQWoC5wAaiulWmmtdyul7sdwyuWfwLPAJqXUwxiOtp001rUceBtw1FofNk4LBUYrpUZrrbVS6nGt9f4iMoUCI5RSm7TW6cblXARqAhe11guNp20+geG0UYvY29sz8e0JjHhtFFmZWfj6+vBg48bMX7AAd3d3vDw98fPty+SpU+nj64ODoyNzZs4CYN++fcz/agH2dnaUK2fHlHfewdHRkatXrzJ23Bukp6WRmZVF61at6O9v/j1e7O3tefvtt3ltzGtkZmbi6+NL48aNWfDVAtybuuPp6Ymvry9Tp03F188XRwdHZn4wM2f+ffv34eLiQt26dfPUO2niJKa/N51bqbdo93Q72j3dzqJM494YzxvjxpCZmUXv3n1o1KgRCxd+TZMmTenQoSPHjh9j0qS3SUxMZNv2rSz6diE//bQcBwdHhg55kWHDDRemDx06DAcHwze9n3zyEadO/ZMzvb4F33zb29vzzoQJvDJyJJlZWfj5+vJg48Z8MX8+Hu7udPLyol/fvkyaMgVvHx8cHRyYN3s2YLjBQI/u3fHx98fezo7JEydadLpnSWYybE/jAMO5+z29vWnfzvx1Z61ccVeuMPndd8nMykJnZdGjWze8Opo3yM/WsX17tm7bhrePL5UrVWLG9Ok5z/kHDmRlsOG7oKnvvMOUadO4lZpKh3ZP06G94fWHbdrErDlzuXb9OiPHjKHJIw/zzfz5LFsezPnz5/lq4UK+WrgQgG8WzKdGEd/4WmubMlWnuayVKSUlhb927TJ5Gp85mSZNmMCIUaPIysqir4+h3/xywQI8svvNvoZ+s7ePod+cO2uWxcu5m1zWaKs3xo8nPj4ee3t7Jk+ciKODg8WZXjUjU09jprnGTMuWL+f8+fN8vXAhXxu3468XLMjZjkM3bmT+55b/3Et2phGjDJn6+vga19983N3d6eTphV/fvrwzdQq9fHxwdHRg7ixDpv3797P4f0uwt7dHlSvH5EnvUK1aNf7++2+mTHuXzMwssrShP/C0oD+4l/2eg4MDzwU9x+DBg1FK0a5dOzq0N1xWP2b0GKZOm8p/P/ov1ZyqMW3aNIva6uOhHrR52IlqVcuz7YOn+XTtGeztDF+eLtt6iYgjV/HyqMGm954iJS2TCT8YvqO+cTODL9ed5bcJLQH4IuQsN24aBsLvLjvJ3MFNqVTeji1Hr7LlqGXX9VljO9+3fz9/rF3LQw89hH9gIABjX3uNjh3Mvz3Bvfbnw4cO5c0JE/j199+pVcuNj+bOBeDKlSsEPvscScnJlFOKH39ayqqVvxR5Sqit7vdE2aCKuvuhrVFKNQDWaK0fNf7dGZgDZJ8TMEVrvVop1Qr4HKiMYTDXFcgAvgKeNP5/nNZ6s7EeVwwDrxla6/eM0yoDnwBPYzjydlZr3VspNQRoqbV+LX8mZbjr5vtAH+M8cUBf4+MtIB1IAgZrrc/c6bWmJCXb1MrJzLKpOACkphV+N6/S5FCl6BtaCFtle9u5ZVex/f8t0wb3aXY2eFc522sl09c02YL0zKzSjlBAi4nm/1ZlSTn+YdvSjmCCLW5UttcfAJSvUsU2g+WTmnzTFldqHhXvK/m2LHMDuv+fyICuaDKgE8XP9rZzW/0AYItkQGce22slGdBZQgZ05rLFjcr2+gOQAV1xKo0BXVm9hk4IIYQQQggh/r8nAzohhBBCCCGEKKNkQCeEEEIIIYQQZZQM6IQQQgghhBCijCrrP1sghBBCCCGE+P+ADd5jyibIETohhBBCCCGEKKNkQCeEEEIIIYQQZZQM6IQQQgghhBCijJIBnRBCCCGEEEKUUTKgE0IIIYQQQogySgZ0QgghhBBCCFFGyYBOCCGEEEIIIcooGdAJIYQQQgghRBklAzohhBBCCCGEKKPsSzuAEEIIIYQQQhRFlXYAGyVH6IQQQgghhBCijJIBnRBCCCGEEEKUUUprXdoZRCHSb96UlVMEW20gOSWgLLO9repqwq3SjmBSDYfKpR3BBNtbfzYpK6u0ExRUzla/Y5Ye3RxNx+8s7QgFHP+wbWlHKMBWe6gKVaqUiQ29LHw2Ll8KbWmrvacQQgghhBBCiCLIgE4IIYQQQgghyigZ0AkhhBBCCCFEGSUDOiGEEEIIIYQoo2RAJ4QQQgghhBAlQCn1jFLqpFLqlFJqoonnKyqlgo3P71JKNSiqThnQCSGEEEIIIYSVKaXsgC8Bb8AdGKSUcs9XbBhwXWv9IPAxMKeoemVAJ4QQQgghhBDW1xo4pbX+V2udBiwHfPOV8QW+M/7/F6CLUuqOP4UgAzohhBBCCCGEsL46wPlcf18wTjNZRmudAdwAatypUhnQCSGEEEIIIUQxUEq9rJTak+vxcu6nTcyS/8fSzSmTh72lIYUQQgghhBCi5N1xXGMTtNbfAN8U8vQFoF6uv+sClwopc0EpZQ84AtfutEw5QieEEEIIIYQQ1rcbeEgp1VApVQEYCKzOV2Y18ILx//2BTVprOUInhBBCCCGEEKVJa52hlHoNCAXsgMVa66NKqf8Ae7TWq4FFwA9KqVMYjswNLKpeGdAJIYQQQgghRAnQWocAIfmmvZvr/7eAAZbUKadcCiGEEEIIIUQZJQM6IYQQQgghhCijZEAnhBBCCCGEEGWUDOiEEEIIIYQQooySAV0ZtG37dnr37Yu3jw/fLl5c4Pm0tDTenDABbx8fBj3/PBcv3f55i4WLFuHt40Pvvn3ZvmNHnvkyMzPpP3AgI8eMsYlMU6ZPp2PnzvTt39/iPNmZ+vTtS887ZBo/YQI9fXwIypVpx86dBAQF4TdgAAFBQeyKjAQgOTmZ/oGBOY8OnToxZ948izMVdzt9/+OP+Pr707d/f96aOJHU1NQSyRQfH8/Ql16i1dNP88Hs2XnmGTJ8OL379sU/MBD/wECuXrvjz6eUWK709HSmz5hBL19f+vj5sTEszKJMWmtmzpmLt48PfgEBHDt+3GS5o8eO4TcgAG8fH2bOmUv23YZv3LjB8FdH0NPHl+GvjuBGQgIAa0JC8AsIwC8ggGdfGMKJk3+bnWnXrp0899xAgoIG8NNP3xd4/uDB/QwfPoTOnTsQEbEpZ3p09GVeemkow4a9wAsvPMuqVb/lPLdw4Vf079+XZ57pYnaO3KyxnXfv2RO/AQPwDwwkICjornJZa/1lO3z0KM2fbMmGjeZvV9bK9O+ZMzw7+AUeb92GJd8X3C6KzDR3Ht6+ffELHMix4ydMZzp+HL+AQLx9+zJz7rycTJ/PX4Bf4ED8BwXx0shRxMbFWVRvoZms0E6Re/bQtkNH/AMH4h84kAVfF/bTUQXZ4nZe3JkuR0cz9KWX6NOvH77+/vywdKnFmWY914Rdc9oTMqV1oWWmDniI8OltWTO5NR71quZM92vjRtj0toRNb4tfG7ec6R717mft5NaET2/L1AEPWZwJrLP+iqrTnEzF+bklt9Fjx+J3l5+nhO2TAV0Zk5mZyfuzZ7Pgiy9YvXIlIevXc/r06Txlfv39dxzuv591q1fz/LPP8tGnnwJw+vRp1oWGsuqXX/jqyy+ZMWsWmZmZOfP9uHQpjRo2tJlMffv04asvv7Q4T3amD2bPZv4XX7Bq5UrW3SFTiDHTx8ZM1Zyc+OKTT/htxQo++M9/eGfKFADuu+8+fgkOznnUqlWLLp07l2o7xcTG8tOyZQT/9BO///ILWVlZrAsNLZFMFSpWZPTIkYx/4w2Tdc/+4ANWBgezMjiYGtWrm53Jmrm+/vZbqlevztpVq1i1ciUtn3zSolxbt20nKiqKkFWrmD5lCjNmzjJZbsbMWUybMpmQVauIiopi23bDDv/bJUto27o1IatX0bZ1axYtWQJAndp1+N+33/Lbzz/z6ksv8d7775uVJzMzk08++ZC5c//Ld98tJTw8jLNnz+Qp4+LixqRJU+jSpVue6TVq1OTLL79m0aLvWLBgIUuX/sCVK4YP3k8/3Z6vv/7WorbJnclafdTib75hZXAwP9/Fh0qw3vrLft0ff/op7Z56yiYyOTo6MnHC2wwZ/LxFeQC2bt9O1PnzhPz+G9OnTGbGrEIyzTJm+v03os6fZ5vxg+3Qwc/zW/Axb2L4AAAgAElEQVRyVi5bimeHDixYuNCiek1msuK6e+LxFqwMXs7K4OWMeOVls/LY4nZujUz2dna8NW4cf/z6K0u//57lwcEF6izKrzujefGLA4U+7+lRgwYuVegyfSdTfjrBewMfAcCxij2jezXEf+4e+s3Zw+heDXGobLg5+38GPcKUpSfoMn0nDVyq0NG95PYxhbWVOXUWlam4P7dkCwsPp3KVKha1kc3S2vYfpUAGdCYopc4qpWqaW0YpteNOZYvT4SNHqF+vHvXq1qV8+fJ49+jBpoiIPGU2RUTg26cPAN27dmVXZCRaazZFRODdowcVKlSgbp061K9Xj8NHjgAQHRPDn9u24e/nZzOZWj75JI6OjhbnKSzT5nyZNkdE4GPM1C1XpqZNmuDi4gLAg40bk5qWRlpaWp55z507x7Vr13jyiSfuKVNxtFNGZiapqalkZGSQcusWzs7OJZKpSuXKPPH441SsWNHs5ZV2rt9WrWL4iy8CUK5cOapVq2ZRrs1bIvDp3RulFI81b05iYiJxxqMP2eLi4khOTqbFY4+hlMKnd282RWw2zB+xBd8+vQHw7dObTZsNr+nxFo/h6OAAQPPmzYiJiTErz/Hjx6hTpy61a9ehfPnydO7clW3btuYpU6tWLRo3fpBy5fJ29+XLl6dChQqA4chlVtbtnZCHx6PUqHHHLrBQ1trOi4O11h/A0uXL6dalC9Ut/PLCWplqVK9OMw8P7O0t/3WizVu24NOrpyFTs2YkJiUSF3clX6YrJCcl06J5c0OmXj1z1nPVqrePsKSkpKBQZtdb0u10t2xxO7dGJmdnZ9ybNgUMX2o2atiQmHztXpTdp+KJT84o9PmuzWvy265oAA6cTcChij3ODhXo4F6D7cevceNmBgkpGWw/fo2OHjVwdqhA1Up27D9jOMr6265ouj1m/n4PrNNW5tRpaabi+Nxy8+ZNvv/xR14ZPtyiNhJliwzoioHW+umSWlZsbCxurq45f7u6uuaczpKnjJvh1AR7e3uqVq1KfHw8sXFxOdMBXF1ciI2NBWDOvHmMGzsWVc7yTcJame6FqUz5d0KFZcptY1gYTR55JOeDb7aQ9et5pnt3lFL3lOle28nVxYUhgwfT1dubTt26cX/VqhYdIbiXTEWZOn06/oGBfPXNNzmnPZVmroTERAC++PJLBgwaxLi33uLK1asW5YqJjcXNLXcuF2Ji4/KVicPVuGO9XcawTV+9ejVnwO3s7Mw1E6ei/vr777Rv186sPFeuxOHicjuPs7NzzlE2c8TGxjB06PMMGNCXoKDnqFnTsg9Fpuu0Tn+glOLlkSMJCApixcqVd5XNWusvJjaW8E2bCbiL05lKYpuyPFMcbq6514MrMXF5++WYuFhc8/exuXJ/+uWXdOnZi7Xr1/HaiFfNrrfwTNZrp4OHDtMvIJBXR73GKTOPqNjidm7tffHFS5c4fvIkzR991KJcRXF1qsjl67dy/o6+noqrU0VcnSpw+frtSwii41NxdaqAq1NFouNzTb9+C1cny75YtEZbmVOnpZmK43PL5/Pn88Lzz1OpcmWzs4iyp1gHdEqp35VSe5VSR5VSLxunPaOU2qeUOqiUCjdOq6qUWqKUOqyUOqSU8r9DnUlKqQ+M8+9USrkapz+glAo3zh+ulKqvlLJTSv2rDJyUUllKqY7G8luVUg8WsowaSqkNSqn9SqmvAZXrueeUUpFKqQNKqa+VUnamMhr/9VJKRSilflFKnVBK/aSMn/iN7XBCKbVNKfWZUmrN3bSxqY/F+YcUpj48K6UKnR7x559Ur14dD3f3u4lklUz36l4yZTt1+jQff/YZ0/KdugCwPjQU72eeKbFMhU2/kZDA5ogIQtesYdOGDaSkpPDH2rUlkulO5sycyW8rVvD94sXs3b+f1Wss29ytkSszI4OYmBgeb9GCFcuW8Vjz5nz48ceW5TIRLP8itYn05m7Tkbt38+vvvzNurHnXsZoeJ5v//nFxcWXJkh9YuvRn1q8PKZbBgLX6gx+WLGHFsmUs+OILlgUHs2fvXsuzWWn9zZn3IW+MHYOdXYHdQ6lluhfmvLeKKjN21CjCQ9bS6xlvlgb/bHa9hWcqOK042sm9SRM2hqzl15+DCRo4kDFvjDMvj4lppb2dW3NffPPmTd4YP54J48fnOQJbHEytIq3JObKb9wnT5S1ljbYyp05rZcqW/3PLiZMnOX/+vEWXh4iyqbiP0L2otX4SaAmMMQ6+FgL+WuvHuP2r51OBG1rrZlrr5sAm09UBcB+w0zj/n8BLxulfAN8b5/8J+ExrnQn8DbgD7YG9QAelVEWgrtb6VCHLmAZs01o/DqwG6gMopZoCgUA7rXULIBN4tog2eBx43ZihEdBOKVXJ2A59gA6AW2EzK6VeVkrtUUrtMXVBrKuLC9G5TseKiYkpcIqdq6sr0dGG0xcyMjJISkrC0dHRMK9xOhi+8XR2dmb/gQNEbNlC9549eWviRCJ372bC5MlFvEzrZrpXpjK5mJkJDKegvj5uHDNnzKBevXp55jt58iSZmZkWD4Ct0U47d+2iTu3aVK9enfLly9Olc2cOHDxYIpmKqhcMp+j08vbmyNGjZmeyVi4nJycqV6qUs2Pr3q0bxwu5sUJuy4KDc26Y4OLsTHR07lyxBbYrN5fbRwXyl6lRo0bOaWJxcXF5Ts87+fffvPufGXz+8cc4OTkVmQsMRxpiY2/niYuLo2ZNy0+VrFnTmQYNGnLoUOHXuZjLWv1B9ulENapXp0vnzhw2c5sqifV39Ngx3po4ie49e7EhLIz3Z80ifPPmUs1kqWU//4z/oCD8BwUZMsXkXg8xuNTMn8k1z6nBhj624LbXy/sZwjaFG+ZxdSmy3jyZSqCdqlatShXjtUUdO7QnIyOD69evF5opm61t59bMlJ6ezuvjx9PL25tuXe7uRkl3En09lVrVKuX87VatIrE3UomOT6VWtdtH3tycKhJzI43o66m45Toi51atEjHxlt0MzBptZU6dlma6188tBw8e5NixY/To2ZPBQ4dy9tw5hsqpl/8nFfeAboxS6iCwE6gHvAz8qbU+A6C1zv76tyuQc7cLrfWdes80IPvr/b1AA+P/nwKyrxj+AcMADmAr0NH4mGWc3grYfYdldAR+NGZZC2Tn6QI8CexWSh0w/t3oDvUARGqtL2its4ADxrxNgDNa63+04euVHwubWWv9jda6pda6Zfa1Prk96uFBVFQUFy5eJD09nXWhoXTy8spTppOnJ6v++AOADWFhtGnVCqUUnby8WBcaSlpaGhcuXiQqKopmjz7KG2PGEB4ayoaQEObNnk3rVq2Y88EHRbxM62a6V496eHAuXyavfJm8PD1Zbcy0MSyM1sZMCYmJjBo9mrGjR/N4ixYF6g5Zv97io3PZmYq7nWq5uXHo8GFSUlLQWrMrMtKiG9vcS6bC5P5AlJ6ezpY//+TBxo3NzmStXEopPDt2ZPeePQDsioykcaOi3s4wKDAw54YJnTt5sXrNGrTWHDx0iKpVqxbYYTs7O1OlShUOHjqE1prVa9bQydOQ3cuzI6v+MHRnq/5YQycvTwAuX77M6+PHM2vGDBo88IBZbQTQpElTLly4wOXLl0hPT2fTpjDatWtf9IwYTt3JviNqYmICR44cpl4985ddGGts5zdTUkhOTgbgZkoKO/76i4fM3KZKYv2Frl3DhpC1bAhZS/euXZkyaRJdOnUq1UyWGhQQwMplS1m5bCmdvbxYvTbEkOnwYWOmvIM1Z+eaVLnvPg4ePmzItDaETp6GZZ+Lisopt3nLFho2aGDI2tGzyHpLup2uXLmSc9Tj8JEjZGlt1hcqtradWyuT1pp333uPRg0b8sLzlt9gxxzhh6/k3MGyRQMHElMyiUtIY+uxq7RvWh2HyvY4VLanfdPqbD12lbiENJJTM2nRwHDdsV8bN8IOmXctZjZrtJU5dRaVqbg/twQGBLBp40ZCQ0L4fskSGjzwAEu+vbsbXgnbZvnV0oVQSnlhGKg9pbW+qZSKAA4Cj5gqjumjy6ak69vHmDMpPHN2ma3Aq0Bt4F3gLcALw9G9OynsaPd3WutJZmYFyP01Ue68xXLbG3t7e96ZMIFXRo4kMysLP19fHmzcmC/mz8fD3Z1OXl7069uXSVOm4O3jg6ODA/OMt25/sHFjenTvjo+/P/Z2dkyeOPGuThEqqUxvTZzI7r17iY+Pp0uPHox89VWzb9qSnelVMzL1NGaaa8y0bPlyzp8/z9cLF/K18e5sXy9YkHOnxtCNG5n/+ec20U7NmzWjW9euBAQFYWdnR5MmTRjgX+gZzMWaCQy32E5KTjYMJjZv5pv586lVuzavjBpFekYGWZmZtG3Thv79+pVYWxWWq3HjxowbO5ZJU6Yw+8MPqV6tGu9Pn25Rro7t27N12za8fXypXKkSM3LN7x84kJXBywGY+s47TJk2jVupqXRo9zQd2huuiRs+dChvTpjAr7//Tq1abnw0dy4AC75ZyI34G7xvvOufnZ0dPy/9yax2ev31cYwf/wZZWZn07Nmbhg0bsWjRQpo0aUK7dh04fvwYU6dOIjExkR07trFkySK+++4nzp07y/z5n+ecQhQYOIjGxg+PCxZ8SXj4Bm7dukX//r706tWHoUPN+1bXGtv51atXGTvOcCpcZmYmPb29zb7OMDdrrb97Ya1MV65cIfDZ50hKTqacUvz401JWrfzFrNPlOrZvx9bt2/H27WvMNO12pkFBrFxm+C516qSJTJk+nVu3jJmM6+Tjzz/n7LlzKFWO2rVq8e47k4qst7TaaUNYGMErfsHOzo5KlSoyb9Yss04DtcXt3BqZ9u3fzx9r1/LQQw/hHxgIwNjXXqNjhw5m5/p4qAdtHnaiWtXybPvgaT5dewZ7O0MbL9t6iYgjV/HyqMGm954iJS2TCT8Yzpy4cTODL9ed5bcJLQH4IuQsN24abq7y7rKTzB3clErl7dhy9Cpbjlp2PbS1PreYqtPSTNb43PJ/SuncRNLmKUtvVlBoRUr5AsO11n2UUk0wHJ16HvgI6Ki1PqOUqq61vqaUmg1U0lq/bpy3WmFH6ZRSSVrrqsb/9wd6a62HKKVWAyu01j8opYYAvlprP+PplSeBf7XWnZVSC4DexvlMnoumlPoMiNVav6+U8gZCAGfABViF4ZTLWKVUdeB+rfU5pdRZoKXW+kp2RuOgdrzWurex3i+APcByDKeCdtJan1ZKLTPW0/tObZp+86ZstkWw1Qay3tUtwvpsb6u6mnCr6EKloIaDLV5kb3vrzyZlZZV2goLu4qZcJUN6dHM0Hb+ztCMUcPzDtqUdoQBb7aEqVKlSJjb09KQkW23CHOWrVi3xtizO3nM9YK+UOgTMwHDaZRyG0y5/NZ6KGWws+z5QTSl1xDi98HNTCjcGGGpc3vPAWACtdSpw3rh8MByxux84fIe63gM6KqX2Ad2BKGNdx4ApwAbjcjYCtSwNqrW+haEd1iqltgHnLK1DCCGEEEIIIfIrtiN0wnz5j+QVRo7QFc1WG6hMfM0lCmF7W5UcobOE7a0/myRH6CwgPbo55AideWy1h5IjdMWnNI7QFds1dEIIIYQQQghhPTY/nisVNjOgU0rtAvL/MuTzWus7nSpp6TKGYjw1M5ftWutRxbUMc2itI4CIklymEEIIIYQQ4v8emxnQaa3blMAylgBLrL0cIYQQQgghhCgJtnrCuhBCCCGEEEKIIsiATgghhBBCCCHKKBnQCSGEEEIIIUQZZTPX0AkhhBBCCCFEoeTn1kySI3RCCCGEEEII8f/Yu+/wKIo/juPvIQEChiSUFHqXKqh0aaFJEQKIAv6sKHZRURFpAtKLIkhRkCrSpAgivYRelV5UEKQnoYSEluTu5vfHXsIlOZILJXun39fz8Jjb3dv7ODe3t7MzO+ehpEEnhBBCCCGEEB5KGnRCCCGEEEII4aGkQSeEEEIIIYQQHkoadEIIIYQQQgjhoWSWSyGEEEIIIYT7s9nMTuCWpIdOCCGEEEIIITyUNOiEEEIIIYQQwkNJg04IIYQQQgghPJTS8ovrbivhxnV5c9KlzA5wB+741rlrWQnPJfXcNe5YTkLcb+732Sv3yXazI6RyZGQNsyM4lTXnQ+73BjoRH33F7Q+o2QJyZ3pZSg+dEEIIIYQQQngoadAJIYQQQgghhIeSBp0QQgghhBBCeChp0AkhhBBCCCGEh5IGnRBCCCGEEEJ4KG+zAwghhBBCCCFEumR2fqekh04IIYQQQgghPJQ06IQQQgghhBDCQ0mDTgghhBBCCCE8lDTohBBCCCGEEMJDSYNOCCGEEEIIITyUzHIphBBCCCGEcHta28yO4Jakh04IIYQQQgghPJQ06IQQQgghhBDCQ8mQSyGEEEIIIYTb0zb5YXFnpIdOCCGEEEIIITyUNOiEEEIIIYQQwkNJg04IIYQQQgghPJQ06DyQ1prBw4bTPCyMtu3bc/jIEafbHTp8mLbPtqd5WBiDhw1Ha2Pc8dWrV+n81tu0CGtN57fe5mpMDABLly2jbfv2tG3fnudffoWjf/xpeqZEBw4dolKVqqxavcblTJu3bKFlmzY0Dwvj+ylTUq2Pj4/n4+7daR4WxnMvvsjZc+eS1k2aPJnmYWG0bNOGLVu3Ji3v3a8f9Ro2pM0zz7icw9GDKqe/T5zg+Zde5rHqNZg6Y0aGMrljOT2oXOntM7MznTh5knYdOiT9q1GnDj/8+KOpmVzZZ3r+C8eDuLg4Or7wAk+3b0/rdu0YO2GCy1kcPciy2rl7N+06dKR1u2d45bXOpme6GhPD+x99TNv27en4wov8deyY6ZkS3U2delCZXN1vZma6l+8YcL/j1JAXyrJjWB2W9a5+x236PFuatf1qsrRXdSoU9k1a3rZGCGv61WRNv5q0rRGStLxC4Vz82qs6a/vVpM+zpTOUJ5E71inhGaRB54E2bd7CqVOnWLZ4Mf1692bA4CFOtxsweAh9e/di2eLFnDp1is1bjAPh91OnUrN6dZYtWUzN6tWZPHUqAAULFGTa99+zaN483nr9dfoPHGh6JgCr1cqo0aOpXauWy3msVisDhw5lwtixLFmwgGUrVnD8+PFk2yz8+Wf8cuVi+ZIlvPj883w1ejQAx48fZ/nKlSyeP59vx41jwJAhWK1WANq0asW348a5nCOlB1VO/v7+fNb9U1556cUM5XHXcnoQuVzZZ2ZnKl6sGAvmzmXB3LnMmzULHx8fGjVo4NHlBP+N40G2bNmYMnEiC+fNY/6cOWzZupV9+/dnqJzgwZVVTGwsAwcPYezXo1i8YD5fjhhueqZJkydTtszDLJo3j8EDvmDoiBGmZ4K7q1MPMpOr+83MTHf7HQPueZxauP0Cr47de8f19SvkpVhQThr1207vH4/Sv2MZoxxyetPlqeK0G76bp4ftpstTxfHLYcwv+MVzZeg96yiN+m2nWFBO6pXPk9Gicss6JTyDWzXolFIBSql30tmmmFLqf/f5dcOVUlXtf59USuW7n/t3eJ1HlVIt7nU/6zeEE9ayJUopKleqRGxsLFFRUcm2iYqK4vr16zxauTJKKcJatmRd+Hrj+eEbaN2qJQCtW7Vk3fpwAB57tDL+fn4AVKr0CBEREaZnApg1Zw5NGjUiTx7XD44HDh6kSOHCFC5UiKxZs9K8aVPWhYcn22ZdeDitW7UC4MnGjdmxcydaa9aFh9O8aVOyZctGoYIFKVK4MAcOHgSgapUq+Pv7u5wjpQdVTnnz5OGRChXw9s7YxLXuWk4PIpcr+zSjrBJt37mTwoUKUaBAAY8uJ/hvHA+UUuTMmRMAi8WCxWJBKZWhcoIHV1bLli+ncaOG5M+fHzCOEWZnOv73CWpWN3pEShQvztlz57l46ZKpmeDu6tSDzOTKfjM7091+x4B7Hqd2HYsm+rrljusbV8rHoh0XANh7Mga/nN4E+mWjbvm8bDlymas3LMTctLDlyGXqVchLoF82fH282HPC6BFbtOMCTSoHZqygcM86JTyDWzXogAAgzQYdUAy4rw26TPQocM8NuojISEJCgpMeBwcHEREZlWKbKIKDglJsEwnApUuXCAw0DjSBgYFcvnw51Wss/Pln6tSubXqmiMhI1q5bT/sMDt2LjIwkJNgxTzCRKQ5ekZGRhIQYwyW8vb3x9fUlOjqayKiopOUAwUFBRNpz3qvMeO8ywl3L6UHkcmWfmZ3J0fKVK2nRrJnLeR5UpnstJ/jvHA+sVivtOnSgXqNG1KpZk0qPPJKhXIn/Tw+irE7+8w8xMTG80vl12v/vfyz+Zanpmco8XJo1a9cBxkn++fPnXb5w6G516kFnSm+/mZ3pXrjrcSotwQHZOX/lVtLjC1fiCA7ITnBANs5fibu9PDqO4IBsBAdk50K0w/IrtwgOyJ7h13XHOuV2tM39/5nA3Rp0Q4GSSqm9SqkR9n8HlVIHlFIdHLapa9+mq7OdKKW8lFIj7c/br5TqYl/eSCm1x758ilIqw582pdTPSqnflFKHlFJvOCy/ppQaZl+3RilV3d7z97dSKkwplQ34Auhgz97hDvt/Qym1Wym1+05jwrWTn+BIeWFYk3ojV68e79y1i4U//8xHH7zv0vYPMtOwESPp+sH7eHl5uZzFeC0neVJu4yS0UuqOy++HB/3eZTiPk2VuUU4PIJcr+8zsTIkSEhII37CBJ5s0yUAi9ywn4zWd7ONfeDzw8vJiwdy5rF25kgMHD2bonrDbr+sk230oK6vVyuEjRxj/zRi+GzeO7yZN4uQ//5iaqXOnTsTExtCuQ0d+nDOHsmXK4OXlWo+Pu9WpB5nJlf1mdqZ74a7HqbQ4Kw6tQTl7Fe36+5Med6xTwjO42w+LfwZU1Fo/qpRqB7wFVAbyAbuUUhvt23yitW6Zxn7eAIoDj2mtLUqpPEopH2Aa0Ehr/adSagbwNvB1BjO+qrW+rJTKYc+0QGt9CXgICNdad1dKLQIGAk2A8sB0rfUSpdTnQFWt9Xt32rnWeiIwESDhxvWkj+DsuXOZv3ARABUrVODChdtXNSMiIgkKTN61HxJ0+4pNym3y5s1LVFQUgYGBREVFJRtm8seff/L5FwP4duw3BAQEpFkQmZHp0OHDdPusBwBXoqPZtHkzXt5e6d5nFBwUxIUIxzwRSVetkrYJDubChQuEBAdjsVi4du0a/v7+xnMvXLj93MjIVM/NiMx67+6GO5VTZuRKb59mZALYtHkz5cqWJV/evC7neZCZ7qac/svHA79cuahWtSqbt26ldKlSaRcUmVNWwUHBBAQEkDNHDnLmyEGVxx/njz//pFjRoqZl8vX1ZWD//oBxAt/0qZYUKnjnIcbuWKcyI1NIcFC6+83sTPfCnY5TrrpwJY78uX2AqwCE5M5O5NU4LkTHUaP07XOjkIDs7PgrmgtX4ghx6JELye1DhEOPXVrcsU4Jz+NuPXSO6gCztdZWrXUEsAGo5uJzGwPfaq0tAFrry0AZ4ITWOnHqxulAvbvI9b5Sah+wHSgMJE5lFA+ssP99ANigtU6w/13sLl4nmec6dGDB3DksmDuHhg1CWbJ0KVpr9u3fj6+vb6oDWWBgIDlz5mTf/v1orVmydCkN6ocCEFq/XtLwm8W/LKVBaH0Azp8/z4effMKQAQPu+KWf2ZlW/rqUVct+ZdWyX3mycWN69+jh0qQRFStU4NSpU5w5e5aEhASWr1xJg9DQZNs0qF+fxb/8AsCqNWuoUa0aSikahIayfOVK4uPjOXP2LKdOneKRihXTfU0zy+luuVM5PehcruwzszMlWrZiRYaHW7pbOf3XjgeXL18mJjYWgFu3brF9xw6KFyuWbpbMKqsGofX5fc8eLBYLN2/e5MDBg5QoXtzUTDGxsSQkJACwYNEiqjz+OL6+vtyJO9apzMgUWr9+uvvN7Ez3wp2OU65ae+Bi0gyWjxbzI/amlaiYeDYdvkSdcnnwy+GNXw5v6pTLw6bDl4iKied6nJVHixnzELStEcKa/Rddei13rFPC87hbD52je+kMVqTu5b/nzmWlVChGY7GW1vqGUioc8LGvTtC3xwbYgDgArbVNKXVfy7lenTps2ryZ5mGtyeHjw4B+/ZLWtevQkQVz5wDQp2dPevfty624OOrWfoK6dYx74jp36sTH3buz8OefyZ8/hK+GG7OfTZg4iavRVxk4xJj9yMvLi3mzXJtC/UFlulve3t707N6dN995B6vNRtvWrSlVsiRjx4+nQvnyNAgN5ek2bejRuzfNw8Lw9/NjxNChAJQqWZKmTz5JWLt2eHt50euzz5KG43T77DN2/fYb0dHRNGralHfeeot2bdu6nOtBldPFixfp8PwLXLt+nSxKMfPHWSxeMD/NkyV3LqcHlcvZPs3OdPPmTbbt2EHf3r1dzuLO5QT/jeNB1MWL9Pr8c6w2G9pmo2mTJoTWy/g1wgdVViVLlKD2E0/wdPsOZMmShXZt27jUe/ggM/3999/07PM5Xl5elChRnC/69jW9nO7Fg8qU1n7NynS33zHgnsepUZ0qUOPhAHL7ZmXzoCcY/esJvL2M08TZm84RfvASoRXysq5/LW7GW+n+gzHN/9UbFsYtP8mi7lUBGLvsJFdvGJOrfD77D4a/VA6frF5sOHSJDYdcm/DHkTvWKeEZlLPxyWZRSuUFftdaF1VKPQ28iTGJSB5gN1ADKAh8pbW+42UjpdRbGA2vjolDLoEbwJ9AQ631MaXUNGCP1nq0vWH2idZ6t1LqJMawyFSXVpRSrYHOWutWSqmywF6gmdY6XCl1TWvta9+uH3BNaz3S/via1trXPow0TGv9sivl4TjkUtyJuw4Cd8e3zl3LSnguqeeuccdyEuJ+c7/PXrlPtpsdIZUjI2uYHcGprDkfcr830IlbURFuf0D1CQzO9LJ0qyGX9nvRtiilDgK1gP3APmAd8KnW+oJ9mUUpte9Ok6IA3wOngBT8yVAAACAASURBVP324ZH/01rfAjoBPymlDmD0on2bwYgrAG+l1H5gAMawy4xYD5RPa1IUIYQQQgghRGrapt3+nxncqodOJCc9dK5w1wtK7vjWuWtZCc8l9dw17lhOQtxv7vfZkx4613lKD93NiAtuf0DNERzy3+6hE0IIIYQQQgjhOneeFCVdSqmmwLAUi09orV2ffcH5fvMCa52samQfFiqEEEIIIYTIRNqkH+52dx7doNNarwRWPoD9XgIevd/7FUIIIYQQQoj7SYZcCiGEEEIIIYSHkgadEEIIIYQQQngoadAJIYQQQgghhIeSBp0QQgghhBBCeChp0AkhhBBCCCGEh/LoWS6FEEIIIYQQ/xE2t/9dcVNID50QQgghhBBCeChp0AkhhBBCCCGEh5IGnRBCCCGEEEJ4KGnQCSGEEEIIIYSHkgadEEIIIYQQQngomeVSCCGEEEII4fa0zWZ2BLckPXRCCCGEEEII4aGU1vJ7Du7q0sVot3pzDhyPMDtCKjVLBJgdwambXj5mR0hl7e6TZkdIpXQh93v/yoY8ZHaEVKxZc5gdwamsXmYnSM2mldkRUlm08Q+zI6TSolYpsyOkEh170+wITuXP62t2BI/gVicsdsoNU5X7ZIfZEZw6Nr6h+x08nbh+5rT7vakpPFSocKaXpQy5FEIIIYQQQrg9rWXIpTMy5FIIIYQQQgghPJQ06IQQQgghhBDCQ0mDTgghhBBCCCE8lDTohBBCCCGEEMJDSYNOCCGEEEIIITyUNOiEEEIIIYQQwkPJzxYIIYQQQggh3J62uf3P0JlCeuiEEEIIIYQQwkNJg04IIYQQQgghPJQ06IQQQgghhBDCQ0mDTgghhBBCCCE8lDTohBBCCCGEEMJDySyXQgghhBBCCPenbWYncEvSQyeEEEIIIYQQHkoadEIIIYQQQgjhoaRBJ4QQQgghhBAeSu6h80Dbt2/j66+/wmqz0apVGC+9+HKy9Xv27mH06FEcP36M/v0H0LBBo6R1y5b9yrTpUwB45eVXadHiKQDefe9tLl28SPbs2QEY9fUY8uTO43Kmg/t/Y97M77HZrNSp/yTNWj2TbP2GdcsJX7OMLFmykD27Dy+8+i4FChbh8ME9LJo3A4vFgre3N+06vkLZ8pUBGD2iLzHRV7DarJR+uALPvfwmWbJ4uZxJa82w0WPYvG07Pj7ZGdCzB+XKlEm13eGjf9Bn8GDi4uKpU6sm3T94H6UUf/x1jIEjv+TGzRsUCMnPkL598H3oIbbt2sXoCd+RYEkgq3dWur77NjWqVHEp07Zt2xj19ZfYrDbCwlrz0kvJ37v4+Hj6f9GPP44exc/fn4EDB1EgfwESEhIYOmwIR48cQWVRdO36MVUeN15z1aqVTJ8+DZQiMF8++vX7goCAAJfL6c/De/l1wVRsNhtVazWi/pNtnG53cM92Zk/5ire7DaFQkZLcuB7LrMlfcfafYzxWI5Sw9q8lbTtt/CBir0Zjs1kpWrIsYe07kyVLxq4f7f19F1MnT8Bms9GocTPatOuYbP3SxfNZu2YFXl5e+Pn58/Z7HxMYFJy0/saN63Tt0pnqNWrz2hvvATB75lQ2hq/m2vVr/DB7SYbygL1OjfqaTVu34ePjw4A+vSjvtE4dpfeAQcTFxVH3iVp07/ohSim69e7DyVOnAIiNvUauXL78NGM60Vev8nHPXhw8cpTWLZrT85OPXc60ZesWRowcic1mpU2btrz6Sqdk6+Pj4+nTtw9HjhzB3z+AYUOGUqBAAQD+/OtPBg4exPXr18misjBzxg9kz56dd7u8S9TFi1itVh579DF6dP8ML6+MffaGDB/Bpi2b8fHxYVD//pQvVy7VdocOH6Z3337cirtF3dp16PFpN5RSrFy9mvHffsffJ04w+4cfqFihPADR0dF07fYpBw8dok1YK3p99pnLmTZv2cKwkSOwWW083bYNr3V6NVU59erTh8NHjuAf4M+IocMoWKAABw4e5IuBA5L+v95+8y0aNWyY9Dyr1cpzLzxPUGAQY8eMcTlPor+O7GX5wulobePxmg2p27i10+0O7d3OvGlf88ZHgyhYpGTS8ugrFxk35GNCmz1D7YatuHrlIgt/HM+1mGhUlixUqdWQWvVbZCjT1m1b+fKrkdhsNlqHteGVl19Jtj4+Pp6+/fty9OgR/P39GTxwCAUKFGD5iuX8MPOHpO2OHfuLH2bMpMzDZVi1ehVTp03BarVRp3Zt3u/yQYYy7dq5g/HjR2Oz2WjevCUdn3sh2fr9+/cyYfwY/v77b3r17ku9eg2S1kVGRPDlV8OIiopEAYMGjyAkJD9fjhzKn38eRWtNoUKF6fZpT3LkyOlyps1btjB0xAisNhvt2rSh86up61QPe50K8Pdn5DCjThn1uJu9Hoclq8ejx45lydKlxMTEsGvr1gyV0b1kApg0eTILFy/GK0sWenz6KbWfeIK4uDhefu014uPjsVqtNGncmPfefvuucg2z53r6Drl6OuQaYc+1dft2vh4zhoSEBLJmzcrHH35IjerVkz23ywcfcObsWRbNn5+hTPd6nLp69Sofd/+Mc+fOUaBAAb4cPgx/Pz+X9+vMkBfK0vCRfFyKjafFwJ1Ot+nzbGlCK+TlZoKN7jMOc+j0NQDa1gjh3ebFABi3/CSLdlwAoELhXAx/qRw+WbMQfugSA376K0PlJDyD9NB5GKvVysgvR/Dll18z68c5rFmzihMn/k62TUhwML179aFJkyeTLY+JucqUqd/z/aQpfD9pKlOmfk9MTEzS+r59v2D69JlMnz4zQ405m83K7Bnf0eWTvvQbOo5d2zdy7uypZNtUr1WfvoO/oc/A0TR96ml+mjUZAF9fP97t2pu+g7/hlTc+ZOp3o5Ke88Z73ekzaAx9B48lNvYqv+3c4nImgM3bt3Pq9Bl+mTOLz7t1Y+DIr5xuN/DLL/n80278MmcWp06fYcv2HQD0HzacD956kwUzptOwXl2mzZoNQIC/P2OGD2XBjOkM6N2TXgMGuZTHeO+GM+qr0cyePZdVq1emeu+W/LIEv1y5mD9/Ic91fI5x48YCsHjxzwD8+ONsxowey5gxxomNxWJh1NdfMW7cBH6cOYuSpUrx0/x5LpeRzWbjl58m8/LbPfmg1yj2/7aFyPNnUm0Xd+sm2zYsp3Cx0knLvL2z0vipDjRr+2Kq7Tt26kqXHiN4v+eX3LgWw8E921zOBGCzWpk8cSw9+wxi1JhJbNkczpnT/yTbpliJUgwdOZaRX39HzSfqMnPG98nWz501nfIVKiVbVqVaTQYP/yZDWRxt3raNf06fYelPc/n8s08ZOHyk0+0GDh9J38+6s/Snufxz+gybt28HYMTAAfw0Yzo/zZhO4wahNKpfH4Bs2bLx7huv8/F772Yoj9VqZeiwYYwd8w0LflrAipUrOP538jr18+KfyZXLjyU/L+H5/z3P6G9GA2CxWOjdpze9evRiwbz5TPpuIt7exjW+YUOGMW/2XObP/YkrV66wes2aDOXatHkLp06dYtnixfTr3ZsBg4c43W7A4CH07d2LZYsXc+rUKTZvMU5gS5UsyddfjqTK448n2z5b9ux0eedtPunaNUN5rFYrg4cNZcI3Y/l5wQKWr1jB8b+PJ9tm4c8/4+eXi1+XLOHF55/n69Gjk7LMnvkjP82Zy4Sx4/hi0EAsFkvS836cPYvixYtnKE8im83Gr/On8MKbn/HuZ19y4PctRF5w/vnbsXEFhYqWSrVuxaIZlCr3aNLjLFm8aNr6Rbr0/IrXPxzArs2rnO7zTqxWK8NHDGP012OYN+cnVq1ayd8p6tTiJYvxy5WLRQt+5n8d/8c344zPVPNmzZk1cxazZs7ii35fkD9/Aco8XIboq9GM+WY048dOYN6ceVy+fJmdu5yfrN4p0zfffMXgwSP5fvIPrF+/hn/+OZFsm6CgYLp92pOGDRunev6wYQNp3/45pkyZydhxEwkIyA3AW2934buJ05g4aTpBQcEs/nlhhjINHDqUCWPHsmTBApatWMHx407qVK5cLLfXqa/sdcqox+84rceh9eox54cfUi1/0JmOHz/O8pUrWTx/Pt+OG8eAIUOwWq1ky5aNKRMnsnDePObPmcOWrVvZt39/hnMNGjqU8WPHsjjx83eHXMvsuUbZc+UOCGDs11+z6KefGPTFF/Ts3TvZ89asXUuOnK43wh3d63Hq+6lTqVm9OsuWLKZm9epMnjo1Q/t1ZuH2C7w6du8d19evkJdiQTlp1G87vX88Sv+OxgVF/5zedHmqOO2G7+bpYbvp8lRx/HIYx/MvnitD71lHadRvO8WCclKvvOvnd8JzeESDTikVoJR6J51tiiml/pdZmRxe9xWl1NgMPuekUirf3bze4SOHKVSoEAULFiRr1qw0btSETZs2Jtsmf/4ClCpVmiwq+du7fcd2qlWrjp+fP35+flSrVp3tOzJ2ou3MieN/ERSUn8CgELy9s1K1Zl32/b4j2TaOVz3j4m6hlAKgSLGSBOTOC0CBgkVIiE8gISEh2XNsVqv9BEplKNf6TZtp1awpSikqVaxA7LVrRF28mGybqIsXuX79BpUrVkQpRatmTVm3aRMAJ0+dosqjRm9hrWpVWbthAwDlHn6YoHzG21eqeHHi4+OJj49PN8/hw4eSvXdNGj/Jxo3J37tNmzYk9Zo2aNCQ3bt3obXmxIkTVK1aDYA8efKQy9eXI0eOAMZVxps3b6K15sb16wTmC3S5jM78c4w8+ULIky8Yb29vKlV5giMHdqXabs2vc6nbOAxv76xJy7Jl96FYybJk9c6WanufxPfOZsVizfh7d+yvPwjJX4DgkPx4Z83KE3Xqs2tn8qvVFR95lOzZfQAo/XA5Ll+KSlr39/E/uXr1CpUfTd5z+nCZcuTOkzdDWRyt37iZVs2boZSicsWKxF6LdVqnrl2/TuVH7HWqeTPWb9iUbButNSvXrqP5k00AyJkjB49Xrkz27KnLMi0HDx2kcOFCFCpUiKxZs9L0yaaEbwhPtk34hnBatWwJQONGjdi506hT27Zvp3Tp0pR5+GEAAgICknrhfH19AbBYLVgsCaiMvX2s3xBOWMuWRjlVqkRsbCxRUVHJtomKiuL69es8WrkySinCWrZkXfh6AEqWKEHxYsVS7Tdnjhw8/thjGS+ngwcpUqhwUjk1a9qU9eHhybYJDw8nrGUrAJo0asyOXTvRWpMjR46khm5cfHzSsQvgQkQEGzdt5uk2bTOUJ9HZFJ+/io89wdEDu1Ntt27ZPGo3apXs8wdwZP8ucucNIiikUNKyXP65KVDYaGBm98lBvuCCxF697HKmQ4cPUbhQYQoVNMqqSZMn2bBxQ7JtNm7cwFNPGXWqYcNG7LKXlaOVq1bS9EnjouLZs2cpUqQouXMbDanq1aqzbv06lzP98ccRChQoSP4CBciaNSuhoY3YumVzsm1CQvJTokQpVJbklfWff05gtVqpUsU4fubIkRMfH+O48dBDDwHG5zEuLo6MVPQDBw9SpHBhCtvrVPOmTVmXok6tCw+ndSujTj3ZuDE7dhrldLseZ0+138qVKhEY6Pox/H5lWhceTvOmTcmWLRuFChakSOHCHDh4EKUUOe0NJovFgsViSfYZuNtcKT9/68PDCbPnauKQq1zZsgQFBQHGxZU4h+/bGzduMGPmTN7s3DmjRWW85j0ep9aHb6B1K+Nz0LpVS9atD3d5v3ey61g00dctd1zfuFK+pJ63vSdj8MvpTaBfNuqWz8uWI5e5esNCzE0LW45cpl6FvAT6ZcPXx4s9J4yL94t2XKBJ5burX+5C22xu/88MHtGgAwKANBt0QDHggTXolFJuMTw1KiqSYIdhZYFBQS4fKC5GRRHk8NygwCAuOjx30OABvPzyC0ydOjnVl3Naoq9cInfe2+3T3HnyEX3lUqrt1q/5lV6fvMHCudPp8MIbqdb/vmsrhYuWIGvW2ycto4f35ZP3XsTHJwdVqj/hciaAyIsXCbZ/EQAEBwUSmeLkO/LiRYIdvjwdtylVojjhm42ThlXrw7kQEZnqNdaEb6Bs6dJky5b+CWZUyvJ38t5FRUURHGxs4+3tja+vL1evXqV06dJs2rgBi8XCuXNnOfrHUSIiI/D29ubTbt15/oX/0bJVC06cPEGrVmHpZkkUE30Z/9y3Gzh+AXm5Gp385O/c6RNcvXKRshVdG1aaaOq4QQzu8TrZs+eg4mM1M/Tcy5cvktehYZo3byCXL6WuU4nWrVnBo48bJ2w2m40ZUyfywsuvZ+g1XREZFUVIsEOdCgwiMsV7GBkVlbrepdjmt737yJsnN0ULF763PJFRBAeHOLxWEFGRkam2CbFvk1inoq9Gc+rUPygU77z3Ds89/z+mTZ+W7HnvvPcOjZo0JmfOh2jcKHWvR1oiIiMJCbld14ODg4iIjEqxTYpyCg4iIjL1Z+x+iIiKJNgxT1AwkSnzREUSHJKinKKjAdh/4ABtn2lHu/bP0qdnr6QG3vCRI/jogw8yPJw4UczV5J8//4A8qRpf58+c4Gr0JcpUSP75i4+7xea1Swhtlnx4u6MrlyK5cOYkBZ307N1JVGRk0jEI7HUqKkWdcvgecjxOOVq9ZhVPPtkUgMKFCvPPyZOcO3cOi8VC+IZwIiIiXM508WIUgQ51JV9gIBcvXUzjGbedOXMaX19f+vXrxVtvvsrE78ZhtVqT1o8YMZj2z7bm9OlTtGnTzuVMkZGRhDiWU3Bw6mNBZCQhd6hTD8K9ZIqMikpaDsb7Hmn/PFqtVtp16EC9Ro2oVbMmlR555J5zRdxFWa1es4ayZcokfd9+M348L7/4Ij45cmQoT6J7PU5dunQpqfEdGBjI5cuXXd7v3QoOyM75K7eSHl+4EkdwQHaCA7Jx/krc7eXRcQQHZCM4IDsXoh2WX7lFcEDqCwnC83lKg24oUFIptVcpNcL+76BS6oBSqoPDNnXt2zgdj6OU8lJKjbQ/b79Sqot9eVKPmVKqqlIq3P53P6XURKXUKmBGGvkKK6VWKKX+UEr1dXi9n5VSvymlDimlUrdg7oaTdparV8ucNtLsz+3Xtz8zf5jF+PHfsXffXlasWH5voZz0yDRo/BSDRk7k6fYvs2zx3GTrzp05xcJ503mhU/J2+wef9mf4mOlYLAkcPZyxYR44+f9VKXI5K5PEbfr3+Iw5CxfR8dXO3LhxI1lDE+DY3yf4esK39Pn0ExfjOCv/9LdRClq2bEVQUBCdXn2ZUV+P4pFHKuHt5YXFYmHhwgXMmP4DS39ZRqmSpZk+Y5pLeQC0k/fOsT7ZbDaWLZxO87YvubzPRJ3e7cVng77Dakng7z8PZui5zquq83q+MXwNfx//k7A2zwKwasUvPFalOvnyBTnd/l6kV17gPHvKK//LV6+meZMm9yVReq/lNDMKq9XKnn17GTRwEFMmT2Zd+Hp27Lzdsz5+7HhWr1hFfHw8u3al7rVNM1X6sVwqy/vGhTxOjxf2jSo98giL5i9g9g8zmTx1CnFxcWzYuJE8efJQvnz5+5s1xedvxaIZNG39QqrN1q/4iVqhLZJ6qVOKi7vF3KmjaNb2ZXx8XB+S5rz6ulLHb/958OBBfHx8KFXSaEj6+fnRvftn9OzdgzfefJ38BQrgnaF7MtN8uTRZrVYOHNjPm2+8y7jxEzl//jyrVt3+fuvWrSdz5i6iSJGihIevdT2Tk2Wpq1Qm1nHuLVNaWb28vFgwdy5rV67kwMGD/HXsWKblSnTs+HFGjRlDX/uQy6N//MHp06eT3c+aUQ/qOOXKfu+Ws/1onfrcxlhx/15XuD+36HVywWdARa31o0qpdsBbQGUgH7BLKbXRvs0nWuuWaeznDaA48JjW2qKUcmUgcRWgjtb6ZhrbVAcqAjfseX7VWu8GXtVaX1ZK5bAvX6C1vnM3A2Bv+L0B8OWXo3j5pVeSrQ8MCiIi8vaVzajISPLlc230ZmBQEHv2/J70ODIqksceM+5RCQw0Tn4feughnmzSlMOHD9G8uWs30gfkzscVh6ulVy5fJCCNe/Cq1qzLj9MnJNt+wujBdHrjQwKD86faPmu2bFR+rDr7ft9B+YqPpZllzoKFLPxlKQAVypVNdsU/IjKKwHzJh9sFBwYmu1LouE3xokX5bpRx393JU6fZuG2bw3aRdO3Zi4G9e1G4YME0MyUKCgoi0uG9i4yMTDU8MigoiIiICIKCgrFYLFy7dg0/P3+UUnz44UdJ273++msULlyYP//8E4BChYwhV40aNWLGD2lde0jOPyAvVx16U2OiL+HnnzvpcXzcLSLOn+b7Mf0BuBYTzczvhvPCm59SyGFihjvJmjUbZR+pyuH9uyhVtlK62yfKmzcfly7efl8uXYoid57UdWr/vt9ZNH82/QaOJGtW46rtn38c5sjhg6xa/gu3bt3EYrHg45OD5196LdXzXTFn/gIWLDEmUKlQrlyyntqIqEgCU3z+goMCU9W7IIdtLBYLa8M3MGfalLvK48ioLxccXisy1XCt4KAgLkRcIDj4dp3y9/cnKCiYKo9XIbf9fqI6tetw9OhRalSvkfTc7NmzU79+fcI3hFOzZtq9rLPnzmX+wkUAVKxQgQsXbtf1iIhIglLkCglK3iPnbJv7JTgoiAjHPJERTsopmIgLFwhJUU6OSpQoQY4cOTh2/Bh79+0lfMMGNm/eTFx8PNevX6dHr14MGeTaPbUAfv55kn3+rkZfJpdf8s9f5IUzTBv7BQDXYq8y+/uRPNf5E878c4zDe3ewesmP3Lp5A5VF4Z01KzXqNsNqtTB3yldUqlKH8pWrp3rdtCQeg26XVST58qWuUxGREcnrlN/tslq1eiVN7b1zierVrUe9uvUAWLhoIV4Z6NUMDAxM1vN8MSqKvHld+97Lly+IUqVKk98+8ccTtetw5Mhhmje/vY2Xlxf1Qxvy07zZNGv2lEv7NT5XjnXcSZ0KDuZCOnXqfrqXTMFBQVy4kPaxxC9XLqpVrcrmrVspXcr1Xl9nuVJ+1tMqqwsREXz40UcMHjCAwvZRDfv27ePw4cM0bdECi9XK5cuX6dS5M1O/T34vdUr38ziVN29eoqKijPoZFUUe+3dUSHBQuvu9WxeuxJE/tw9g9IiH5M5O5NU4LkTHUaP07cnQQgKys+OvaC5ciSPEoUcuJLcPEQ49duLfw1N66BzVAWZrra1a6whgA1DNxec2Br7VWlsAtNau3FiwJJ3GHMBqrfUl+3YL7RkB3ldK7QO2A4WB0nfaQSKt9UStdVWtddWUjTmAcmXLcebMac6dO0dCQgJr1q6mTp16LvxvQM0aNdm5cwcxMTHExMSwc+cOataoicViSRraYLFY2LJ1MyVKpH+ynqhYidJERpzjYtQFLJYEdm/fROXHaiTbJuLCuaS/D+zbTVCw8eV64/o1xn75BW3bv0Sph29f6b5162bS0D+r1cqBfb8Rkr8Q6enY7mnmTZvCvGlTaFC3Lr+sWInWmv0HD+Hr+1Cqk+/AfPl4KGdO9h88hNaaX1aspEFd4+27dOUKYFwlnzR9Bs+2Nmafi4mN5b1u3fngrTd4rJLrQ0/KlSvP6dOnOXfuLAkJCaxes4q6desm26ZunXosW/YrAOvXr6Nqlaoopbh16xY3bxrVcMfOHXh5e1G8eAkCAwM5cfIEV+xZd+7cSTEn9x7dScEiJbkUdZ7LFyOxWCzs/20rZR+pmrTeJ0dOeg2dTLf+4+jWfxyFi5VOtzEXF3eLmKtGHqvVyp+H9hAY7FqjN1HJ0mU4f/4skRHnsSQksHXzBqpWq5VsmxN/H2PShNF82vML/ANunwS/37UHEyb9yLiJP/DiK29QL7TxXTfmADo+0y5pIpOG9erxy/IVaK3Zd/AguR7ydV6nHsrJvoMHjTq1fAUN6tVJWr99126KFy1KSNC99yBWKF+BU6dPc/asUadWrlpJaL36ybapX68+vyw1LnKsWbuWatWqoZTiiVq1+Ouvv7hpb/T+9vtvlChRghs3bhBlb0xbLBa2bNnsUp16rkMHFsydw4K5c2jYIJQlS5ca5bR/P76+vqlODgMDA8mZMyf79u9Ha82SpUtpUD/0nsvEmQoVKvDP6VOcsZfTipUrCU3xWqH167Nk6S8ArF67hur2cjpz9mzSJCjnzp3j5MmTFMhfgA+6vM+aFStZ8esyhg8ZSvWq1TLUmAMoUKQkly9e4Mol4/N3cM/WZEObfXLkpPugSXTtO5aufcdSqGgpnuv8CQWLlOS19/snLa9Zvzl1G7ehRt1maK1ZPPs7AoML8kQD1xonjsqXK2/UqcTj1OpV1KuX/Dumbt16/PqrUafWrVtLtarVknotbDYba9euTTUpV+JwtJiYGOYvmE/r1s5n03WmTJmynD17hvPnje+98PC11HqiTvpPtD/32rVYoqONY9Levb9TtGgxtNacPWtMFqO1Zvv2rRQuUtTlTBUrVODUqdt1avnKlTQIDU22TYP69Vn8i1GnVq1ZQ41q1R5oD929ZGoQGsrylSuJj4/nzNmznDp1ikcqVuTy5cvExMYCcOvWLbbv2OH0/tb0cv2TIldoilyh9euzxJ5r9Zrbn7+Y2Fje7dKFD7p04bFHb0/+06F9e9atXs3KZcuYMXUqxYoWTbcxB/f3OBVavx6L7ReQF/+ylAah9W//v6Sz37u19sBF2tYwhqY+WsyP2JtWomLi2XT4EnXK5cEvhzd+ObypUy4Pmw5fIiomnutxVh4t5gcYM2Gu2e/acGV3pW3a7f+ZwVN66Bzdy9FQ4bz338Ltxm3K8SvXXdhvyn1qpVQoRgOyltb6hn0Yp/OxMRng7e3NR10/oetH72O12mjZshUlSpRg0qTvKFu2HHXr1uPwkcP06PEpsbGxbN6yicnfT+LHH+fg5+dPp1de5bXOxrTmnTq9hp+fPzdv3qTrR+9jsVixWa1UrVaNsDDnU2c74+XlRceXSMIJCgAAIABJREFU3mT08H7YtI3a9RpToFARliz4kaLFS1H58RqEr/mVI4f24uXlTc6HfOn0xoeAcV9dZMR5fl08l1/twzA/+LQ/aM24UQOxWBKw2WyUKVeJeg2bpxUjlbq1arJ52zZadngOH5/sfNGzR9K69q+8yjx7D0mvTz6iz6AhxMXFUbtmDerYeyJWrF7DHPuVvEb169HmKaPHcs6ChZw6e5aJ02YwcZrRGzZh1JfkzZ2btHh7e/PJx9344MP3sdkS37uSTJz4HWXLlaNe3Xq0ahVG//59eeaZp/Hz82OAfQbNy1cu8+GH76NUFgIDA+n7udFjFhgYyGuvduatt9/E29ubkJAQPu/zuctl5OXlRatnX2Xa+EH2adMbEJy/MGt+nUvBIiUp59C4c2ZE33eJu3UDq8XCkQO76PROb3I+5MvMicOxWBLQNhslHq5I9ToZG17o5eXFq6+/x6D+PbHZbDRo1JTCRYoxd9Z0SpZ6mKrVazFz+iRu3brJVyOMKeXzBQbRvecXae535vRJbN60nvi4ON7q/D8aNm5G+46uDyet+0QtNm3dxlPPtscnuw8DevdMWvfsSy/z04zpAPTu9gm9Bxo/W1CnZk3q1LrdGF2xZg3Nm6S+J61Z23Zcu36dBIuFdRs38d3oUZRMZ/ZEb29vunfrzjtd3sVmtdE6LIySJUsy/tsJlC9XntD69WnTug29P+9DWJsw/Pz8GWqfcc3Pz48Xnn+eF156EYWiTu3a1K1Tl0uXLvHhR11JiI/HarNRrWo1nml35/u0nKlXpw6bNm+meVhrcvj4MKBfv6R17Tp0ZMHcOQD06dmT3n37cisujrq1n6BundoArFm3jiHDhnP5yhXeef99ypZ5mInjxwPwZIunjHJKSGDd+nAmjh9PyZIl0i2nnt278/a772C12WgT1ppSJUsybsJ4ypcvT4P6obRt04aefXrzVFgY/v5+DB8yFIA9e/YwZdpUvL29UVmy0KtHz6TJPe6Vl5cXLdp14odvB2Oz2XisRgOC8hdm3bJ5FChSgrIV0/78OXPqxB/s272J4PxFmDC8OwCNWnbk4fJpj25I5O3tzaefdOP997tgtVkJaxVGyRIl+fa7bylXrhz169WndVhr+vb7nLbt2uDn58eggYOTnr9nz+8EBQVRqGDyC3BffjWSv/4ypkvv/Fpnimag8eTl5c17XbrS47OPsdlsNG32FMWKFWfatO95+OGyPPFEHf44eoR+/Xpx7Vos27dtZcb0KXw/+Qe8vLx44813+bTbh2gNpR9+mBYtWqG1ZvjwQdy4fgPQlChRivc/cP3nQhLr1JvvGHWqbWujTo0dP54K5cvTIDSUp9u0oUfv3jQPC8Pfz48RQ4cmPf/JFi0c6vF6ez0uyZdff82y5cu5desWjZo25em2bXn3rbceeKZSJUvS9MknCWvXDm8vL3p9ZvxUSdTFi/T6/HOs9skemjZpQmg91y4ip8z1lgu5WthzDbfnmj1nDqdPn+a7SZP4btIkAL6bMIG8TkZsZNS9Hqc6d+rEx927s/Dnn8mfP4Svhg9Pd7/pGdWpAjUeDiC3b1Y2D3qC0b+ewNvLOO2dvekc4QcvEVohL+v61+JmvJXuPxiTo129YTF+qqC7ccwYu+wkV28YF6I+n/2H/WcLvNhw6BIbDqU5UEx4KJWRyS/MopTKC/yutS6qlHoaeBNoAeQBdgM1gILAV1rr+mns5y2MRlbHxCGX9iGRa4AvtdbLlVKjMIZkhiql+gHXtNbO5yY39vkKMBhjyOVNYAfwqj1PZ611K6VUWWAv0ExrHa6UOglU1VqneZnk0sVot3pzDhx3/Sb2zFKzhOu/t5aZbnrdc9v9vlu7+6TZEVIpXcj93r+yIQ+ZHSEVa9a7u+n/Qcvq+m1Qmcam3e+mkUUb/zA7Qiotark+ZC6zRMemNxjGHPnz+podwSO41QmLnXLDVOU+2ZH+RiY4Nr6h+x08nbh67C/3e1NT8C9VOtPL0iOGXNrvO9uilDoI1AL2A/uAdcCnWusL9mUWpdS+O02KAnwPnAL224dCJs6K2R8YrZTaBFjv8Ny0bAZ+wGi0LbDfP7cC8FZK7QcGYAy7FEIIIYQQQoj7xmOGXGqtU/4kQbcU6xOARunswwJ8ZP/nuHwT8LCT7fu5kGsaMM3J8jjA6RhBrXWx9PYrhBBCCCGEEOnxiB46IYQQQgghhBCpeUwPXUYopZoCw1IsPqG1butO+xRCCCGEEEK4Rmub2RHc0r+yQae1XgmsdPd9CiGEEEIIIcS9kCGXQgghhBBCCOGhpEEnhBBCCCGEEB5KGnRCCCGEEEII4aGkQSeEEEIIIYQQHupfOSmKEEIIIYQQ4l/GJrNcOiM9dEIIIYQQQgjhoaRBJ4QQQgghhBAeShp0QgghhBBCCOGh5B46IYQQQgghhNvTWpsdwS1JD50QQgghhBBCeChp0AkhhBBCCCGEh5IGnRBCCCGEEEJ4KGnQCSGEEEIIIYSHUnJz4b+fUuoNrfVEs3M4csdM4J65JJNrJJPr3DGXZHKNZHKdO+aSTK6RTK5z11wic0kP3X/DG2YHcMIdM4F75pJMrpFMrnPHXJLJNZLJde6YSzK5RjK5zl1ziUwkDTohhBBCCCGE8FDSoBNCCCGEEEIIDyUNuv8Gdxxb7Y6ZwD1zSSbXSCbXuWMuyeQayeQ6d8wlmVwjmVznrrlEJpJJUYQQQgghhBDCQ0kPnRBCCCGEEEJ4KGnQCSGEEEIIIYSHkgadEEIIIYQQQngoadCJTKWUKu7KMiFcoZQqrpTycXicQylVzLxEnkEplUUp5Wd2DiHuJ6XUB64sE8JVSqlnXVkmhNlkUpR/IaVUS2AAUBTwBhSgtdamn8AppX7XWj+eYtlvWusqJmYKBgYDBbTWzZVS5YFaWuvJZmWy5/ICgjHeQwC01qdMzFMTOKS1jrU/zgWU11rvMDHTbuAJrXW8/XE2YIvWuppZmew5VgPPaq2j7Y9zA3O01k1NzDQLeAuwAr8B/sBXWusRmZyjrNb6qFLqcWfrtda/Z2aelOwnayu01rFKqd7A48BAs3IppZ5Oa73WemFmZUmklMqT1nqt9eXMyuLoDt8ve7TWj5mRx/767vr9UhvYq7W+rpR6AaOej9Za/2Nipl+AO56Uaq3DMjEOcMc6lWpZJmVxy8+dcA/e6W8iPNDXwNPAAe0mLXalVFmgAuCf4gTFD/Bx/qxMMw2YCvSyP/4TmAuY9oWrlOoC9AUiAJt9sQYqmZUJmIDxpZ/oupNlmc07sTEHoLWOtzfqzJYvsTEHoLW+opQKMjMQRuM7Rin1PLAM6I7RsMvUBh3wEfAG8KWTdRpomLlxUumjtf5JKVUHaAqMxKjnNUzK0yqNdRrI9AYdRr3RGBcLU9JAicwMo5R6DvgfUFwptcRhVS7gUmZmcWIabvb9YjcBqKyUqgx8as8zA6hvYqa/gRBgpv3xc8BJYGVmB1FKNQdaAAWVUmMcVvkBlszOY+dWnzvhXqRB9+90GjjoLo05uzJASyCA5CcoscDrpiS6LZ/Wep5SqgeA1tqilLKanOkDoIzW2uyTEUfKsU5prW1KKbOPIVFKqTCt9RIApVRr4KLJmQBsSqkiiT2qSqmipHHlOZNkVUplBdoAY7XWCUqpTM+ktX7D/mdzrfUtx3WOw2dNlPjZfwqYoLVerJTqZ1YYrXUns177TrTW7jZMfitwHshH8gsFscB+UxLd5o7fLwAWrbW2HzNHa60nK6VeNjnTY1rreg6Pf1FKbdRa9zQhyzlgNxCG0ZBKFAt0NSGPO37uhBsx+2RMPBifAsuUUhuAuMSFWuuvzAqktV4MLFZK1dJabzMrxx1cV0rlxX7CbR9aeNXcSJx2gwwp/a2Ueh/jyi7AOxhXVM30FvCjUmqs/fEZ4CUT8yTqBWy2fwYB6mH0SpnpW4yr3fuAjfZGZoyJebaSunfX2bLMdlYp9R3QGBimlMqOm9xvrpR6CmOkQ1LDV2v9hQk53GrYrH2Y4D9Arcx8XRe54/cLQKy9kfkiUNc+xD+ryZkClVIltNZ/Q9L99YFmBNFa7wP2KaVmaa0TzMiQFvsw/tIkPxZsNC+RMJs06P6dBgHXMD7o7jD8zNExpVRPoBjJ7w171bRExhCwJUBJpdQWjC8QU256Vkp9ZP/zbyBcKfUrbtIox2g8jQF6Y5ycrMXkRorW+jhQUynli9GDGGtmnkRa6xX2k92aGMNjumqtTes5VEplASK01gUdlp0CGpiQJQQoCORI0SDwA3Jmdh4n2gPNgJFa62ilVH6gm8mZUEp9i1E+DYDvgWeAnSbFccths/bh/MOAIIzPnTvcP+7s++UZE/Mk6oAxTPVVrfUFpVQRMn/4dUpdMb73Ei8UFgPeNC8OAE2VUm41J4FSqjPGKJ5CwF6M75ltmD9cXZhIJkX5F1JK7dZaVzU7hzNKqa3AJowhDEnDTrTWC0zMlN2epQzGwfoPIIvWOi7NJz6YLH3TWK3NuBrvzpRSg4HhKSYf+Vhr3dvkXAp4Hiihtf7CfrIUorU26wQc+9Cleulv+cBzvAy8AlQFdjmsigWmaa0XmZHLkbtNSASglNqvta7k8F9fYKHW+kkTM/k4Gzabclkm5jkGtNJaHzHj9e/EPjQ96fvFXXp87L30pbXWa5RSOQEvsy+K2b+Py9ofHjXjezhFnmO435wEB4BqwHat9aP2OQr6a607mBxNmEh66P6d1iilntRarzI7iBM5tdbdzQ6Rwjb7jFWHEhcopX7HhKFfWuv+9td/Vmv9k+M6s6dKVkoFYtzvWAz36V1t7nh/hX3ykRYYvYhmGo8xmU1D4AuMxsoCjC9hs6xWSn2CMSHD9cSFJsyMlg9Yav/neIO/Bky/R8RNJyQCuGn/7w2lVAGMyT7MLi93GzYb4YaNuZwYvXRFtdavK6VKK6XKaK2XmpzrdYxe1jxASYxe82+BRmbmAqpw+zumslIKrfUME/O445wEt7TWt5RSKKWy24c/lzE7lDCXNOj+nd4FPlVKxQEJuMEQAQdLlVIttNbLzA6SYujXY9w+sXSHoV89gJ9cWJaZFmP0rq7BoXfVZF72L7Q4MH6HDshuciaAGlrrx5VSeyCpoWn28OfEhve7DsvMmBnN1/7fMhgN3MUYn71WgDvcA+KOExKBcewMwBgW9zvGezfJjCBufOzcrZSaC/xM8qHqZswEmmgqxoiUxPv7zmAcx01t0GEcB6oDOwC01n+ZPROvUuoHjMblXm5/x2iM2TfN4nZzEgBn7MeCnzEu1F3BmMRF/IdJg+5fxj7Uq4LZw4PS8AHQ000am00xhn4VAhwPzrGAGbNquetUyYncsXd1JrBWKTUV44v/VWC6uZEASLAP20ucCCGQ2709pnCXGdIceqFXAY/r279r2A9zL1gkcscJidBaD7D/uUAptRTw0VqbldPtjp12fsANwHEYqlk/7ZCopNa6gzJ+WgGt9U3797TZ4uw/8wIkDQs1uxeqKsbPq5idw5HbzUmgtW5r/7OfUmo9xm+KrjAxknAD0qD7l7FPQ7wIY9iC29Fa5zI7QyKt9XRgulKqnZn38KXgdlMlO3Cb3tVEWuvh9vsJGmFcHBigtc703yxyYgywCAhSSg3CmATB7Pv6dgNTgFna4TfyTFQEiHd4HI8x1MoUbj4hUeJPOrwD1ME48d6slJpgxv1qbnrsdMufeADi7SMHEi/ulMShXplog32CshxKqSYYdesXkzMdxPgduvMm53CUx8z7VO/EPqFU4rFgi3b4PVbx3ySTovwLKaXGYUwusCvdjU2glKpE6vuwzLyC6jbTgTvkyeouN84nUkrFAg9hnIyY3bvq9uw3qic2NNeafW+PUqoU0AljdrvdGEPBVpl1NVwp1QtjRslFGCclbYG5WushJuVx6wmJlFLzMC7sOP7ocm6ttWn31trLLFX9Maus7NPcdyH190uYGXnsmZpgXMwpD6wCagOvaK3Dzcpkz5UFeA2jN1Nh/Hj392b2jtl7mx7FmL3V8WKKme/fUGCdO81JoJT6HGMm7sTzpjbAT1rrgealEmaTBt2/kFLqMMb9KScxJj9IPPE2+6Z+lFJTMCYXOITDhANmTqxxp+nAtdavmZjpAKlPlK5inIgPdMP7e0yhjN90+gYohzEcxgu4bvKU0lmA/VrrimZlSIs9X0uM3xO0YfTajTZhcpTEq8x17Q83aq33ZHaGlO40IVHKZZlNKbVPa105vWWZnOljh4c+GPXqiFnHc6XUPmAycACHIc5a6w13fNKDy1Jba73FPmujL7d/wmS7NvEnTNyZUqq+s+VmvH+J3PFCplLqCMaPsN+yP84B/K61LmdWJmE+adD9C9mnIk5FGz++aiql1GGtdXmzczhy0+nAh2PcFD7LvqgjxhfJVaCO1rpVJmZxqx8RdmQfRtgR496rqhg/Kl5Ka93LrEz2XD8CPdztXlZ773gnjPs0VwI/YgzbeVFr/aiZ2dyFUup3+6y3aS7LbEqpacC3Wuvt9sc1gJe11u+YmcuRvfGyRGvd1KTX36G1rmHGa6eklPpNa13FHeqOI6XUPK11+ztcNMQdLvyKtCmllgPP6ds/1xMAzNRatzQ3mTCT3EP3L6S1/kcpVQfj92Wm2idk8E3veZlkm1KqvNb6sNlBHCTeg+JO04HX1lrXdnh8QCm1RWtdWyn1QiZnccsfEU4KoPUxpZSX1trK/9u78zC7qyrd4983tILMIBHlKqOAA7MoICiggKiIyNiIAipoO0CccLhOgDi2cC+iNqB0RAQUpAVFpiAyyxQIARXvVQGbVtqGRghDGN/+Y++TOlWpVBVDfmufyvo8T57knFN5znpOVZ3zW3uvvRZMV5l1GO0FwG8kXcPwEQGRpUMzgb9TdjA+5aH5TldL2nLB/3PR0GpDor4L72cB+6oMhDdl0HFL76NQKh267pra7+haBnoBw0v2IhadHq3Nmkb+PPViOjggJiiNyaDspjZB0uW2t6q7Yf1JZvhuGMybb7o2w49kdN6RV9IxlNfnYcrny4x6e3vg8q7jSW3JhG4Sqh9om1LKLqdTLgR+SKndj3YiJam7k/Km1EI56M/VSDvwPktL2sz21QCSXsVQUt7pxaXt99a/tx3r6yRtb3tGN1HN86DKOIBZdVfzr5TymGiHRQcwij1s/2m0B2zv2nUwDWq1IdGELrwlrWD7noUdzIjn7N/lWQyYSpm7GGV94J2URab+GYIRi047AdvV5545ztd2xvZfawfeE2xvFx0PgO2t6t/NNE3rkXQAJQl+IWWcwubAr4n5mbqu/j2Tcva45+LuQ0mtyZLLSUjSLGBjSk31xvW+2S2UUkj6A2XHZ+QZh5By0HqeaHPbV9bbixPbDrwX1yspZ5uWpiS99wEHUM4evtn2aYHhjSqitKiWF/8n5fzcRyjtm79j+w9dxvFkSfq17S3G/8pn7PleQpkbdrXt+/vu39F2trvu02JDookI/P3reYwy2Puxvsc7TTIl3QJs0FLHP0kb2r5xjMc/HdEISNLPKKXW4SM6JK041uMR53t76qLFKylnHzeq76WH2d4rKqbxSDrD9m7RcaRu5Q7d5PSIbUvqtUluYcei58+2fxYdRI/tJyQdSR36WsvQwltK1w6l60tajrLw0t9mvrlkrup8tlLfQsBcRtkVa/iDbYnxv+SZIelgyhDh3wEnSJpm+6z68JfJ+UUjvUHSFykljf9AI2VfExD5+7cgvwS6TDJvBJYH/tbhc45prGSu2gOI6Ow6l1LKP4PhZeERpaAzKTupo/0Mm9gy3rm250pC0uL1PPm6gfFMROTrlYJkQjc5nSbpOGB5SQdShi1HlxD23CLpFMq8m/4zDpFjCy6QtBulEUoTW9Z1p3A3avtt1eGvDm6dPo4mXrsRWv1g6/K1OhB4he37Ja0O/ETS6raPJiAJGAD/F9gVuKmV94MJajHWrn++VqZ8xlxLI23vJyDqd/AX9U8429Fn1sdyRz2ScSYwQ9I9lPLslrX4XpAWskzoJqepwE8oZXrrAp+n1PK34DmUD9r+DpJmaJ5KhI9Szl09JmkubazIn0XpaDmTBnYMB1h+sMFivTJL27dJ2oaS1K1GJnSj+Xfg5gFL5lrV9Ws41izBVoX8nNk+sba7X9X27yNiGEll5XIfYA3bX5S0KvB829dExWT7bfWfh6rMyVuOvqqGiLOrKY0mE7rJaXvbnwTmNaioZYWfjAupsP2u6BhGGu8gtqSX2/5NV/FUL7S9Y8fP+XTdFh3AAOkykbpT0ka2ZwHUnbqdKGc01+8wjkHxCeAcSZcwfJfnqLiQJiST8/LzfPKAXWCHfN8kvQX4BuX88RqSNgIOD97N/A7lbP3rgC9SGhKdQTnDFs6jz8Pruqx4IvK9YBGUCd0kIun9wAeANSXN7ntoGeCKmKiGk7QE8B7g5QxvARw2WHwCTqL7N+wrJa1v+6aOn3c+ksbsgNgrl220U2LYB5uk5wOvoqzAX2v7zr6H39lhKPsyojNqbVyxby3NBnKluc+XgPsp70/PDo7lyTSMeH0H4TxZXf/+PR+4VtL1lAWL8wdgpzVqYP2hlPeniwFsz5IUXfq4me1NJN1QY7qndjFuWac/47VD6Ym2xxpfFL54n7qXCd3kcgpwLuWA9af67p8T2SVqhJOAW4A3UNpb70Np1tCyiKRgK2B/SbcSP95hrCHm0eWy4wn5YKutrj8PXET53h0j6XDb/wpg++auYrF9xxiP9S/0tLjSHGFF2zuM/2WdmVDDiC7f41tNMm1/VtLnKCX97wK+Jek0Sov+P3YZS4+kdYB/AVa2vZ6kDYCdbR9RY/5yRFzAY7bv7Z3PrqKT30drwtJr6DaVvm7Yjer0NbP9uKSpkp69oG6uti/oMqbUhkzoJpHafvheYO/oWMbwYtt7SHprreE/BTg/OqhxRHzIvTHgOUfVYplsTx2IfSjzdyTsXeRGfbAdAmxs++4a53OBKym7Bq3KMp3iQkk7tHJR1GjDiOaSzHlPXjo83wncSdmZXoFyZnSG7U90HQ+lIdkhwHE1vtn1c++IgFj63Szp7cBiktYGDqa8R0X6JmW+2vMkfQnYHfhsbEhNug24oo6e6O9Q2npZeFqIMqFLXevNd/q7pPUoH7qrx4XTJtu3S9oKWNv29LpSufR4/29hk/Rm5i+Xjey8eQJl/txM4PHAOEa6g3L+o2cOpdlGy6JX51vxQeATkh5mqFQ1ukkSAJJ2Bl5bb15s++yIOBpNMnsjOvYD7gK+Bxxi+9E6b/T/U85Hdm1J29eM2Al7bEFf3KGDgM9QKkB6C6uhSabtkyXNpOzsCtjFdlbwzO8v9c8UypGalDKhS507XtIKlFW3n1GSlM/FhjSuzofUSvoCsCmlS+l04FnAD4Etu46lL6ZjgSWBbSkXS7sDYd3HqnttnxscwzySPlr/+R/A1ZLOoiRKbyX+tUoTMF6TpCiSvkppDnFyvWuapC1tfzowrGaSzGolYNeR8/HqvNGdgmK6S9JaDJUR7g78NSiWfuva/gwlqQs1ooT3b8Cp/Y9F7PS2WlZcn3u+maspqf3zwmlRImk/2yd2/Jy/tP368e7rOKZZwMbA9bY3rvfNDjpD14tptu0N+v5emjK7L+y8Ub3IXYxyjq+/I+H1QfGM2Ta95Q9iSTf0ftYWRbX5wqO9JhqStqWcKfyN7fDh67XR1Ua2n6i3FwNuCH5PGJlk7g1cF5lkStqEcgbZwBVR7wV98awJHA+8GrgHuBXYZwJD2Rd2XL8CXkBpyvKjgE7O/bHcylAJ76qU10mUIfF/jtgRHhHTSPPK+iPU7918F++2XxcQTmpE7tCl1kwDOknoasfNJYGV6q5h7417WWCVLmIYwyP1LEjv4nKp4HgAHqp/PyhpFeBuILr0arP696Z995nS9rpzjSdsY64402aXxC5dC2wD3CPpEOBtwDnAxyRtY/tTY/3njiwP9HYGlosMpHoTw5PME4EbgJCErjZE2ZOhRk3TJZ3ea0ASxLa3q+/hU2zPaaCbJLa3rZ1496RUziwL/DjiteolbLUK5Ge2z6m330jQDN1Wy4qrj/f9ewlgN9oo402BcocuNaXLXQJJ04APU5K3/2AoobsP+K7tb3URxwJi+ziwNrA9pWvpu4FTbB8TGNPngGMoF/7fpiRO37Pdesls51pcQZV0G/AiRqx+14dDV5xbIOlm2+vVf18HvMb2Q5L+gbJTHrYTVmPaG/gq8CvK9++1wKdt/ygwptnANr3ys7pocHHUayXpd5RmRHPr7edQvncvjYinxnC97U1G3DfT9iuiYhpJ0vqU84V72Q4bEzDa6yLpOtubLuj/LMRYxuz4G73zO5KkS2xvHR1HipM7dKk1na0w2D4aOFrSQZGJ0mhsf0PS9pTkcl3g87ZnjPPfFnZMX6z/PEPS2cAStbNqGEkrA18GVrH9RkkvA7awfUJkXLS5gnoeo6x+2/5YbFjNuE/SenWkxF2U79tDlM/JKaGRAbZPlXQxpcRRwCc9fLZhhK8AN9QFjHlJZmA8t1G+b3Pr7cWBqHEFL6E0kFpOw2d5LktfU6kokl4K7AXsQfl5/xEQ/V5wl6TPUs6LG3gHpRIkwpFjPBZWBQLzVVtMAV5BmcGYFmG5Q5eaEnWOR9KrKd025y1y2P5B13G0TNK+o90f+TpJOpfSNOYztjesuyk32F4/KqYFiV5BbWn1u0V1PthJwI31ri2BS4ANgKNsnxIVG0BtUX4qJSl/YLyv74qkFzCUZF4dmWRKOrPGMoNy0b09cDml0Qa2D+4wlrcCuwA7UxqA9cyhnFkLHREg6SrKz9Pptv8SGUtPTVS+QFkYMHApcHhEU5SWjTjf9xjlXObhti8PDSyFyoQuNUXSt2x/qOPnPAlYC5jFUOt7d/nh3xfLHEbfpezNVwtrnS6pfxdzCUrp5fW2dw8KCUnX2n5oMJugAAAS80lEQVRl/0KApFm2N4qKqcYwcgV1U+Bo2+sGhYSk84HLGL76/Vrbb4iKqTW10cgOwDqUxZ07gPNt/z00MEDS1pQdlTdTOqb+GDi7V14YFFNTSaak/cZ6vOuGWwCStrD9666fdyJqI6B16s3f2350rK+PJukY2wd1/JzPAt5PXydX4LjWX6u06MmELnVK0uKU8rPVGb4bFjbLrJ67eJkH6JdB0gq27wmOYTngJNs7B8ZwMeXnaYbtTSRtDnwt+ixB3woqlBXU2wheQR2x+g1l9fuwXP1+ciSdYXu3wOdfjFLudSCwY/AiT3NJ5lgivne1+dZ7mH9+57u7jGOk+r37AeW9SZTztfvZvjQyrrGMdh6xg+f8HmVsUG8x4J3A47YP6DKOETF9EDi5t9BUm7rtbfs7UTGleHmGLnXtLOBeyiDoh8f52q7cTKk/b2E20ET9ktJSPdKDlMYtkT5KKWdaS9IVwFTKfLxoLwM+wFD79MuA6yIDqonbtMgYJonIduXPAd5CSaI2oaOOwAti+xLgkhFJ5r9Szom1KOJ7dxJwC/AG4HBgH6CFYdlHATvY/j2ApHUou63NNGtpxCttb9h3+yJJNy7wq7txoO1v927YvkfSgUAmdIuwTOhS115oe8foIEZYCfitpGsYPsssbOdpAkabjbNwn1D6OUO7TlMoSctpXcfRz/b1daV5Xcpr0krZ0ImUhjbfrLf3plzY7REVUL1g+zjz747n7KInJ2QnX9KPKWM6zqN0mb24Ny4gUmtJ5jgivncvtr2HpLfaPlHSKcD5AXGM9KxeMgdg+//V8sI03OOS1rL9R5g3V/Dxcf7PwjZFknpVRXVBJaw7aWpDJnSpa1dKWt/2TdGB9Dk0OoCnIOLC5Bt9/34MuN32HQFxzFPLmYbthEk6toGSr3VHrOr+qoFV3dOBY4HvEX9Bkp686cDbbTfzvWs1yWxMb4Hp75LWA+6kLKpEu07SCZSFJig7hzMD45mIzhcygUMo799/qs+/GvCugDj6nQ+cVuf2Gfgnyu9gWoTlGbrUKUm/BV5M6cr0MEPNPkJnPA2aiLMELZJ0GqVr3A/rXXsDK9gO2wkDkPR94FjbV9Xbm1HOp3wgMKamZl8NqsBOvEtSSoxXtf1eSWtTFg7O7jqWvph2pJxfbSbJHEvE907SAcAZlG6p04GlKWNoju0yjlHiWhz4IGUxTJQztd+xHXYUQtKatv80xuP72/5+hyH1nndxhqpAbol8jWo8U4D3UoauC7iAMhN2IH4P08KRCV3qlKTVRrvf9u0BsVxue6tROkuGd5QcT9CFya7A14DnUV6j8NdJ0o0jdsJGva9rtdHOugwN7l6Vcm7mCYIWMCQdSmnf/lOGlxZnU5Q+kqa5zKgc9T5JO9i+ICCuH1N2UPa1vV4tdfx1ZEfXFpPMsUR971okaSlgbi8JqGV7i9t+MDCmS4H/BVxLSTAvi67mGcQGJNGNm1KMTOhSCEnPY3jHrz+P8eWLJEmbMFRKeIXt6/seW7HrC3FJfwDeYruFA/1AmzthNY5RFy56ghYwbh09FIc1+WjRaLvfUbtyI2K4zvamI0Z0hC5etJJkSrqJsce9RCygfHSsx20f1VUso6lz6LazfX+9vTRwge1XB8f1bMoswW2A9wFL215xzP+0cOOZbwxOC+8HY2k9vrRw5Bm61ClJOwNHAqtQdgtWo+xcvDwyrtZI+jylgca/1bumSzrd9hEQtqvyny0lc9VmwL6S/ky5oFsN+F3vAi+qlDciYRuP7TWiY2iZpL2BtwNr1PlqPcsCd8dENcwjNWHqNUJYi/hOwWvZ3qu+dth+SFLEOaedAp5zPMtEBzCOJXrJHIDt++uOaxhJWwGvqX+WB86mdAiONIgNSHKnZhGUCV3q2heBzYELbW8saVvKuac03N7Axr3mHpK+ClwPHBEY03V1Rf5Mhpfs/duC/8tCtyOwAuUCAEqZTvgQ6JZIep3ti2rJ7HyCv38tuZIyumQlyqJTzxxgdkhEVU2SjqU0PniRpJOBLYH9I+OikSSz0QWUwybydZI+bfsrCzueUTwgaZNe5YekVwAPBcTR7xLKeJevAOfYfiQ4HsgGJGlAZEKXuvao7bslTZE0xfavJH0tOqgG3UYpSe11a1wc+GNYNMWylNlzO/TdZ4Z2ESPsAhxQYxClY9t3bR8TGFNrtgYuorSWHyn6+9eMmhTcLmk74CHbT9RRDy8BQs/x2LakaZTfvc0pP+vTbN8VFVOLSaakzYFjgJdSdlEWAx5o+Tw0pRIjIqH7MHC6pL/U2y+gjJ6I9FzKz9BrgYMlPUEp4f1cYEyfpDQgeT99DUgC45mIiF3yFCzP0KVOSbqQchH+Vcqb998ogztD6/ZbI+lMyjmCGZSL7u2ByymvF7YPjouuHZJmA1vYfqDeXopyAZBdU9NTImkmZcd3BeAqyo7Bg7b3CY7r28D3bV8bGUe/+lr1J5lXBSeZ1wH/SBnRsSmwL2UO3GeiYhpP5Hknlblz/d0bw2d4SnopZRHqNcCrgT/b3jo2qgWLaEDSauOmFCsTutSpXmctygfIPsBylA5SLZxRaYak/cZ63Hbnw3slTQUOZP7B1O/uOpa+mG6iLAj0SlOXAK61vX5UTK1pvTlDa3pNUSQdBDzH9tdbaDKgMvJlHeB24AEaGPnSWpLZ1zhmdu91kXRlywuGozXh6eh5ex1KV7N9YAsdSiX9Efg9ZfHyMuDqRsouFyio43STjZtSrCy5TJ2y/YCklSm7T3cD52YyN7+IhG0CzqJ8yF5IO4OppwNXS/ppvb0LcEJgPC1qvTlDayRpC8qC03vqfS18Vr4xOoBRbAu8T1IrSeaDtUviLElfp5yJXCoolomKKo+bTulQukW9fQdlZzNy5MTaHrzB9J3tiozRuGkZ2mjclAK18CGVFiGS9gT+GbiY8kF2jKRDbP8kNLDGSNqJ0kBmNcrvafjMN2BJ258MfP752D5K0sUMDcd9l+0bYqNqy0SbM6R5pgGfBn5q+zeS1gR+FRxTk40/aC/JfCcwBfgQ8BHgRcCozYAacnrQ87bSobTfKpKOoZyjM2WnbprtO2LDakazjZtSvCy5TJ2SdCOwve2/1dtTKR0vQwdBt6bOfNsVuMmN/JJKOgK40vY50bGkJ682+PgXYOU6M2wDYOfeKIyUBt14Z4uCYjqRkpT0D6Y+MrJUvcZxJfB6yozTTWqH0lNtvyowphnAKZTmVgDvAPaxvX1UTOOJKnXsq3QCuKZ3TZUWXVOiA0iLnCkj3njuJn8OR/PvwM2tJHPVNOBsSQ9Juk/SHEn3RQeVJuy7lJ2nRwFsz6Y0kEh9JE2V9M+SzpF0Ue9PdFxpQkY7e7x/10GMsEEvmQOwfQ8QfR5ztA6lvwQ+ERkXMNX2dNuP1T/fB6ZGBlS7y451X+dVK5L2AK6hdEjdk3LsYPeu40htyZLL1LXzJJ0PnFpv7wXkjs/8PgGcI+kShs98C2tgYXsZSSsCa1NGKqTBsqTta0ZUVT0WFUzDTgZ+TBlW/U+UJOG/QiNKYxrjbFELQ+GnSFqhJnLU99DQa68Wx2BUd0l6B0PXB3sT//3bDxi5w7t/776gbpKfpTQDG1bpBOTRlUVYJnSpU7YPkbQbpUZewPG2fzrOf1sUfQm4n5I4PTs4FgAkHUDZpXshMItyIdAr20ntu6uWVfWGQO9OOY+Rhnuu7RNqqd4lwCV1YSW1q+WzRUcCV0r6CeV3b0/K+3u0q4A1bf8iOpA+7wa+Bfwfymt1Zb2vc403IMlKpzSfPEOXUoN67bej4+jXGxFAmTW1kaSXAIfZjh5GmyagNvc4njLb6R7gVsr5lBabbYSRdJXtzWslwTeBvwA/sb1WcGhpAlo8WyTpZcDrKIuYv7T92+CQmhyD0RJJqwFrUIa+f6rvoTnAbNth1Q21g+uGDK90mt1a07LUrdyhS52QNIfR2/u20L2xRRc2OBx0ru25kpC0uO1bJK0bHVQa24g5dOdQOjZOoVzE7QbkHLrhjpC0HPAx4BhK2d5HYkNKE1HPFn2D9roorwg8YHt6PaO5hu1bg2NqrUNpU7NO60LX7cAWIxYJfheZzFUGjmOou/PxlIqZtAjLHbqUGlQT4CWBRyhNLMIT3zrr7V3AhymrzfcAz7L9pqiY0vgkfaH+c13KRclZlJ+ntwCX2j4gKraUnkktdlGuv3+bUoZ2ryNpFeB021tGxdSq2nnzMsp8vHmzTm2fERjTyEWC1wChiwQLGCw+O3dXF22Z0KXUIElTKION17B9uKRVgRfYvjo4NAAkbQ0sB5xn+5HoeNL4JF0A7GZ7Tr29DOXCcsfYyNpQ518t8APR9sEdhpOeAkk32V6/7/YU4Mb++wJimkXpanl9r719XnyPTtIs2xtFx9GvpUUCSe8HPgCsCfyx76FlKOMn3tF1TKkdWXKZUpu+DTxB2Qk7nFK3fwZDZR+harOINFhWpez49jxCKW1KxXXRAaSn7dwGuyg/UrtK9poRLRUcT8vOlvSmxmadttSA5BTgXEY512f7v2NCSq3IhC6lNm1Wh73eAGV2kaQmul2mgXUScE0tnTXwNuDE2JDaYXtCr4WkY2wftLDjSU9Ji2eLTpN0HLC8pAMpXRu/GxxTq6YB/1vSwzRy1ICGFgls3wvcSxnnkNIwWXKZUoMkXU3pRnhtTeymAhf0SnZSeiokbUI5AwLl/NwNkfEMotHOr6Q2tHq2SNL2lJlvAs63PSMynkEl6eW2f9Pxc34NuJqhRYJLgc2zo2RqTSZ0KTVI0j6UlcBNKLsouwOftX16aGApLeIyoWtPq2eLJC1GSeC2i3j+ySbid6/VRYKURsqSy5QaZPtkSTMpQ7sF7GL7d8FhpZRSi5o8W2T7cUkPSlqulsulp0edPVHfIoGk/uH0ywBXdBVHShOVO3QppZTSBEm6IUuf00RJOo1yjm8GZfYjkF1Tn4oud+jqLMoVaGyRIKUFyR26lFJKaeKOjg4gDZRf1D9pgGQDkjRoMqFLKaWUKkk/Z+x5dDt3GE4acON1T5V0hu3duopnwOXM05QWIBO6lFJKacifgOcDP6y39wZuA86PCihNamtGB9AKSW8DLuqdN5S0PLCN7TMBbEePoEipWXmGLqWUUqokXWr7tePdl9IzIbumDpE0y/ZGI+7LM6spTUDUtPuUUkqpRVMlzds1kbQGMDUwnpQWFaNdk2YlWUoTkL8oKaWU0pCPABdL+lO9vTrwvrhw0iTXWSv+AXCdpKOAb1POsR4EzIwNKaXBkCWXKaWUUh9JiwMvqTdvsf1wZDxp8pK0g+0LouNogaSlgM8BvUHsFwBfsv3Agv9XSgkyoUsppZSGkfRqys7cvCoW2z8ICygNHEk3MXq3VAG2vUHHIaWUJrEsuUwppZQqSScBawGzgMfr3QYyoUtPxk7RAQwaSTOAPWz/vd5eAfiR7TfERpZS+zKhSymllIZsCrzMWb6Sngbbt0fHMIBW6iVzALbvkfS8yIBSGhTZ5TKllFIacjNlDl1KT5ukzSVdK+l+SY9IelzSfdFxNeoJSav2bkhandHLVlNKI+QOXUoppTRkJeC3kq4B5jVDsb1zXEhpgH0L+EfgdMru777Ai0MjatdngMslXVJvvxZ4b2A8KQ2MTOhSSimlIYdGB5AmF9t/kLSY7ceB6ZKujI6pRbbPk7QpJYmbBZwFPBQbVUqDIRO6lFJKqbJ9yfhfldKEPSjp2cAsSV8H/gosFRxTkyQdAEwDXkhJ6DYHfg28LjKulAZBnqFLKaW0yJN0ef17jqT7+v7MyTNP6Wl4J+Va60PAA8CLgF1DI2rXNOCVwO22twU2Bv4rNqSUBkMmdCmllBZ5treqfy9je9m+P8vYXjY6vjSwdrE91/Z9tg+z/VFypMGCzLU9F0DS4rZvAdYNjimlgZAJXUoppZTSwrHfKPft33UQA+IOScsDZwIzJJ0F/CU4ppQGgnLUTkoppZTSM0fS3sDbga2Ay/oeWhZ4zPZ2IYENCElbA8sB59l+JDqelFqXTVFSSimllJ5ZV1IaoKwEHNl3/xxgdkhEAySbE6X05OQOXUoppZTSQiJpZUqzD4BrbP8tMp6U0uSTZ+hSSimllBYCSXsA1wB7AHsCV0vaPTaqlNJkkzt0KaWUUkoLgaQbge17u3KSpgIX2t4wNrKU0mSSO3QppZRSSgvHlBEllneT114ppWdYNkVJKaWUUlo4zpV0PnBqvb0XcE5gPCmlSShXiVJKKaWUFg4DxwEbABsCx8eGk1KajPIMXUoppZTSQiDpetubjLhvtu0NomJKKU0+WXKZUkoppfQMkvR+4APAmpL6584tA1wRE1VKabLKHbqUUkoppWeQpOWAFYCvAJ/qe2iO7f+OiSqlNFllQpdSSimllFJKAyqboqSUUkoppZTSgMqELqWUUkoppZQGVCZ0KaWUUkoppTSgMqFLKaWUUkoppQGVCV1KKaWUUkopDaj/ARUid25RE2sdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15, 15])\n",
    "sns.heatmap(df.corr(), annot = True, fmt = '.3f',cmap = 'vlag_r', center = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Loan  Status\n",
    "\n",
    "Since Loan Status is a dependent variable. I would perform some operations on it to make it a categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current               564955\n",
       "Fully Paid            148065\n",
       "Charged Off            33110\n",
       "Late (31-120 days)     10745\n",
       "Issued                  7826\n",
       "In Grace Period         5949\n",
       "Late (16-30 days)       2188\n",
       "Default                 1130\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "loan_status=df['loan_status'].value_counts()\n",
    "sns.barplot(loan_status.index,loan_status,palette='Pastel1')\n",
    "plt.xlabel('Count')\n",
    "plt.title(\"Loan Value Status\")\n",
    "plt.xticks(rotation=90)\n",
    "loan_status"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***If you see the diagram, there are lots of loans which have status has \"Current\".With sizeable loans being \"Fully Paid\". We can remove the records with status as:-\n",
    "\"In Grace Period\"\n",
    "\"Does not meet the credit policy. Status:Charged Off\"\n",
    "\"Does not meet the credit policy. Status:Fully Paid\"\n",
    "etc.\n",
    "since they are not sizeable in number***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df[(df['loan_status'] == 'Late (16-30 days)')].index,inplace = True)\n",
    "df.drop(df[(df['loan_status'] == 'Late (31-120 days)')].index,inplace = True)\n",
    "df.drop(df[(df['loan_status'] == 'Issued')].index,inplace = True)\n",
    "df.drop(df[(df['loan_status'] == 'Does not meet the credit policy. Status:Fully Paid')].index,inplace = True)\n",
    "df.drop(df[(df['loan_status'] == 'Does not meet the credit policy. Status:Charged Off')].index,inplace = True)\n",
    "df.drop(df[(df['loan_status'] == 'In Grace Period')].index,inplace = True)\n",
    "df.drop(df[(df['loan_status'] == 'Current')].index,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fully Paid     148065\n",
       "Charged Off     33110\n",
       "Default          1130\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "loan_status=df['loan_status'].value_counts()\n",
    "sns.barplot(loan_status.index,loan_status,palette='Pastel1')\n",
    "plt.xlabel('Count')\n",
    "plt.title(\"Loan Value Status\")\n",
    "plt.xticks(rotation=90)\n",
    "loan_status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['loan_status'] = df['loan_status'].replace({'Charged Off':'Default'})\n",
    "df['loan_status'].value_counts()\n",
    "df.loan_status=df.loan_status.astype('category').cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182305, 21)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Box Cox Transformation\n",
    "\n",
    "We use Box-Cox Transformation inorder to transform the numerical variables into a normal shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical = df.columns[df.dtypes == {'float64','int64'}]\n",
    "for i in numerical:\n",
    "    if df[i].min() > 0:\n",
    "        transformed, lamb = boxcox(df.loc[df[i].notnull(), i])\n",
    "        if np.abs(1 - lamb) > 0.02:\n",
    "            df.loc[df[i].notnull(), i] = transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One-Hot Encoding\n",
    "\n",
    "We use one-hot encoding as it a very straight forward one-step procedure to get the dummy variables for categorical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_string = df.select_dtypes(include ='object') \n",
    "dummy_grade=pd.get_dummies(df[\"grade\"], prefix=\"grade\")\n",
    "dummy_ownership=pd.get_dummies(df[\"home_ownership\"], prefix=\"ownership\")\n",
    "df=df.join(dummy_grade.iloc[:,:])\n",
    "df=df.join(dummy_ownership.iloc[:,:])\n",
    "df.drop(columns={'grade','home_ownership'},inplace=True )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating the \"X\" and \"y\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, df.columns != \"loan_status\"]\n",
    "y = df[\"loan_status\"]\n",
    "X_dup = X.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building Predictive Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, precision_recall_curve,classification_report\n",
    "from sklearn.preprocessing import RobustScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Using Robus Scalar Method to scale the data***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "robust_scaler = RobustScaler()\n",
    "X = robust_scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Splitting the data using scikitlearn's train_test_split and using 70% data for training and 30% for testing***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=0.30, random_state=123, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Verifying that the data is split into train and test data***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train is (127613, 31) ,X_test is (54692, 31) ,Y_train is (127613,) ,Y_test is (54692,) \n"
     ]
    }
   ],
   "source": [
    "print(\"X_train is {} ,X_test is {} ,Y_train is {} ,Y_test is {} \".format(X_train.shape,X_test.shape,Y_train.shape,Y_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Defining the confusion matrix***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CMatrix(cm):\n",
    "    fig, axes = plt.subplots(figsize=(8,6))\n",
    "    cm = cm.astype('float')/cm.sum(axis=0)\n",
    "    ax = sns.heatmap(cm, annot=True, cmap='Blues');\n",
    "    ax.set_xlabel('True Label')\n",
    "    ax.set_ylabel('Predicted Label')\n",
    "    ax.axis('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression is used for binary classification. \n",
    "In our case, we want to predict if a person will be able to repay the loan or no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_regression = LogisticRegression(C=1)\n",
    "logistic_regression.fit(X_train, Y_train)\n",
    "Y_pred_test = logistic_regression.predict(X_test)\n",
    "CM = confusion_matrix(y_pred=Y_pred_test, y_true=Y_test)\n",
    "CMatrix(CM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Decision Tree Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.98      0.99      0.99     10272\n",
      "          1       1.00      1.00      1.00     44420\n",
      "\n",
      "avg / total       0.99      0.99      0.99     54692\n",
      "\n",
      "[[10135   137]\n",
      " [  166 44254]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "class_tree = DecisionTreeClassifier(min_samples_split=30, min_samples_leaf=10, random_state=10)\n",
    "class_tree.fit(X_train, Y_train)\n",
    "Y_pred_test = class_tree.predict(X_test)\n",
    "#CM = confusion_matrix(y_pred=Y_pred_test, y_true=Y_test)\n",
    "CM = confusion_matrix(Y_test, Y_pred_test)\n",
    "CMatrix(CM)\n",
    "print(classification_report(Y_test, Y_pred_test)) \n",
    "print(confusion_matrix(Y_test, Y_pred_test))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfrom sklearn.externals.six import StringIO  \\nfrom IPython.display import Image \\nfrom sklearn.tree import export_graphviz\\nimport pydotplus\\nfrom pydotplus.graphviz import graph_from_dot_data\\nfrom sklearn.tree import export_graphviz\\nfrom sklearn.datasets import load_iris\\n\\ndot_data = StringIO()\\nexport_graphviz(class_tree, out_file=dot_data,  \\n                filled=True, rounded=True,\\n                special_characters=True)\\ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \\nImage(graph.create_png())\\n'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "from sklearn.externals.six import StringIO  \n",
    "from IPython.display import Image \n",
    "from sklearn.tree import export_graphviz\n",
    "import pydotplus\n",
    "from pydotplus.graphviz import graph_from_dot_data\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "dot_data = StringIO()\n",
    "export_graphviz(class_tree, out_file=dot_data,  \n",
    "                filled=True, rounded=True,\n",
    "                special_characters=True)\n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "Image(graph.create_png())\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## C. Naive Bayes Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.93      0.50      0.65     10272\n",
      "          1       0.90      0.99      0.94     44420\n",
      "\n",
      "avg / total       0.90      0.90      0.89     54692\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "NBC = GaussianNB()\n",
    "NBC.fit(X_train, Y_train)\n",
    "Y_pred_test = NBC.predict(X_test)\n",
    "CM = confusion_matrix(y_pred=Y_pred_test, y_true=Y_test)\n",
    "CMatrix(CM)\n",
    "print(classification_report(Y_test, Y_pred_test)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## D. RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.94      0.96     10272\n",
      "          1       0.99      1.00      0.99     44420\n",
      "\n",
      "avg / total       0.99      0.99      0.99     54692\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_pymnt           33.708811\n",
       "recoveries            27.348634\n",
       "loan_amnt             17.929464\n",
       "int_rate               3.079147\n",
       "revol_bal              2.195526\n",
       "total_rec_late_fee     1.954705\n",
       "dti                    1.924199\n",
       "tot_cur_bal            1.740106\n",
       "revol_util             1.724699\n",
       "term                   1.688056\n",
       "annual_inc             1.680017\n",
       "open_acc               1.105784\n",
       "emp_length             0.712880\n",
       "inq_last_6mths         0.513839\n",
       "grade_A                0.354473\n",
       "tot_coll_amt           0.353227\n",
       "delinq_2yrs            0.332747\n",
       "grade_E                0.267971\n",
       "pub_rec                0.228134\n",
       "grade_B                0.216103\n",
       "ownership_RENT         0.179819\n",
       "grade_D                0.165829\n",
       "ownership_MORTGAGE     0.165126\n",
       "grade_C                0.146180\n",
       "grade_F                0.126710\n",
       "ownership_OWN          0.101801\n",
       "grade_G                0.031159\n",
       "acc_now_delinq         0.022620\n",
       "ownership_NONE         0.001356\n",
       "ownership_OTHER        0.000816\n",
       "ownership_ANY          0.000060\n",
       "dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#Create a Gaussian Classifier\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "clf.fit(X_train,Y_train)\n",
    "Y_pred_test= clf.predict(X_test)\n",
    "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)\n",
    "#clf.feature_importances_\n",
    "list(df.columns)\n",
    "feature_imp = pd.Series(clf.feature_importances_,index=X_dup.columns).sort_values(ascending=False)\n",
    "CM = confusion_matrix(y_pred=Y_pred_test, y_true=Y_test)\n",
    "CMatrix(CM)\n",
    "#print(\"Accuracy:\",metrics.accuracy_score(Y_test, y_pred=Y_pred_test))\n",
    "print(classification_report(Y_test, Y_pred_test)) \n",
    "\"\"\"sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "plt.xlabel('Feature Importance Score')\n",
    "plt.ylabel('Features')\n",
    "plt.title(\"Visualizing Important Features\")\n",
    "plt.legend()\n",
    "plt.show()\"\"\"\n",
    "feature_imp*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Computing the ROC curves for the models and finding the true positive and false positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createROC(models, X, y, Xte, yte):\n",
    "    false_p, true_p = [], [] ##false postives and true positives\n",
    "\n",
    "    for i in models.keys():  ##dict of models\n",
    "        models[i].fit(X, y)\n",
    "\n",
    "        fp, tp, threshold = roc_curve(yte, models[i].predict_proba(Xte)[:,1]) ##roc_curve function\n",
    "\n",
    "        true_p.append(tp)\n",
    "        false_p.append(fp)\n",
    "    return true_p, false_p ##returning the true postive and false positive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'NB': array([0.89856367, 0.89973435, 0.89935821]), 'DT': array([0.9940055 , 0.99393469, 0.99393469]), 'LR': array([0.98509603, 0.98692903, 0.9873757 ]), 'CLF': array([0.98516655, 0.98537744, 0.98434304])}\n"
     ]
    }
   ],
   "source": [
    "models = {'NB': GaussianNB(),\n",
    "          'DT': DecisionTreeClassifier(min_samples_split=30, min_samples_leaf=10, random_state=10),\n",
    "          'LR': LogisticRegression(C=1),\n",
    "        'CLF': RandomForestClassifier(n_estimators=100)}\n",
    "unbalset = {}\n",
    "for i in models.keys():\n",
    "    scores = cross_val_score(models[i], X_train - np.min(X_train) + 1,Y_train, cv=3)\n",
    "    unbalset[i] = scores\n",
    "    \n",
    "print(unbalset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_unbalset, fp_unbalset = createROC(models, X_train - np.min(X_train) + 1, Y_train, X_test - np.min(X_test) + 1, Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(18,5))\n",
    "#predict = model.predict(X_train)\n",
    "ax = pd.DataFrame(unbalset).boxplot(widths=(0.9,0.9,0.9,0.9), grid=False, vert=False, ax=axes[0])\n",
    "ax.set_ylabel('Classifier')\n",
    "ax.set_xlabel('Cross-Validation Score')\n",
    "\n",
    "for i in range(0, len(tp_unbalset)):\n",
    "    axes[1].plot(fp_unbalset[i], tp_unbalset[i], lw=1)\n",
    "\n",
    "axes[1].plot([0, 1], [0, 1], '--k', lw=1)\n",
    "axes[1].legend(models.keys())\n",
    "axes[1].set_ylabel('True Positive Rate')\n",
    "axes[1].set_xlabel('False Positive Rate')\n",
    "axes[1].set_xlim(0,1)\n",
    "axes[1].set_ylim(0,1)\n",
    "\n",
    "cm = confusion_matrix(y_pred=Y_pred_test, y_true=Y_test).T\n",
    "cm = cm.astype('float')/cm.sum(axis=0)\n",
    "\n",
    "ax = sns.heatmap(cm, annot=True, cmap='Blues', ax=axes[2]);\n",
    "ax.set_xlabel('True Value')\n",
    "ax.set_ylabel('Predicted Value')\n",
    "ax.axis('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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