{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Mixed Effects Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "from statsmodels.tools.sm_exceptions import ConvergenceWarning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: The R code and the results in this notebook has been converted to markdown so that R is not required to build the documents. The R results in the notebook were computed using R 3.5.1 and lme4 1.1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%load_ext rpy2.ipython\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%R library(lme4)\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "array(['lme4', 'Matrix', 'tools', 'stats', 'graphics', 'grDevices',\n",
    "       'utils', 'datasets', 'methods', 'base'], dtype='<U9')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comparing R lmer to statsmodels MixedLM\n",
    "=======================================\n",
    "\n",
    "The statsmodels implementation of linear mixed models (MixedLM) closely follows the approach outlined in Lindstrom and Bates (JASA 1988).  This is also the approach followed in the  R package LME4.  Other packages such as Stata, SAS, etc. should also be consistent with this approach, as the basic techniques in this area are mostly mature.\n",
    "\n",
    "Here we show how linear mixed models can be fit using the MixedLM procedure in statsmodels.  Results from R (LME4) are included for comparison.\n",
    "\n",
    "Here are our import statements:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Growth curves of pigs\n",
    "\n",
    "These are longitudinal data from a factorial experiment. The outcome variable is the weight of each pig, and the only predictor variable we will use here is \"time\".  First we fit a model that expresses the mean weight as a linear function of time, with a random intercept for each pig. The model is specified using formulas. Since the random effects structure is not specified, the default random effects structure (a random intercept for each group) is automatically used. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mixed Linear Model Regression Results\n",
      "========================================================\n",
      "Model:            MixedLM Dependent Variable: Weight    \n",
      "No. Observations: 861     Method:             REML      \n",
      "No. Groups:       72      Scale:              11.3669   \n",
      "Min. group size:  11      Log-Likelihood:     -2404.7753\n",
      "Max. group size:  12      Converged:          Yes       \n",
      "Mean group size:  12.0                                  \n",
      "--------------------------------------------------------\n",
      "             Coef.  Std.Err.    z    P>|z| [0.025 0.975]\n",
      "--------------------------------------------------------\n",
      "Intercept    15.724    0.788  19.952 0.000 14.179 17.268\n",
      "Time          6.943    0.033 207.939 0.000  6.877  7.008\n",
      "Group Var    40.395    2.149                            \n",
      "========================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = sm.datasets.get_rdataset(\"dietox\", \"geepack\", cache=True).data\n",
    "md = smf.mixedlm(\"Weight ~ Time\", data, groups=data[\"Pig\"])\n",
    "mdf = md.fit(method=[\"lbfgs\"])\n",
    "print(mdf.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the same model fit in R using LMER:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%%R\n",
    "data(dietox, package='geepack')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%R print(summary(lmer('Weight ~ Time + (1|Pig)', data=dietox)))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Linear mixed model fit by REML ['lmerMod']\n",
    "Formula: Weight ~ Time + (1 | Pig)\n",
    "   Data: dietox\n",
    "\n",
    "REML criterion at convergence: 4809.6\n",
    "\n",
    "Scaled residuals: \n",
    "    Min      1Q  Median      3Q     Max \n",
    "-4.7118 -0.5696 -0.0943  0.4877  4.7732 \n",
    "\n",
    "Random effects:\n",
    " Groups   Name        Variance Std.Dev.\n",
    " Pig      (Intercept) 40.39    6.356   \n",
    " Residual             11.37    3.371   \n",
    "Number of obs: 861, groups:  Pig, 72\n",
    "\n",
    "Fixed effects:\n",
    "            Estimate Std. Error t value\n",
    "(Intercept) 15.72352    0.78805   19.95\n",
    "Time         6.94251    0.03339  207.94\n",
    "\n",
    "Correlation of Fixed Effects:\n",
    "     (Intr)\n",
    "Time -0.275\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in the statsmodels summary of results, the fixed effects and random effects parameter estimates are shown in a single table.  The random effect for animal is labeled \"Intercept RE\" in the statsmodels output above.  In the LME4 output, this effect is the pig intercept under the random effects section.\n",
    "\n",
    "There has been a lot of debate about whether the standard errors for random effect variance and covariance parameters are useful.  In LME4, these standard errors are not displayed, because the authors of the package believe they are not very informative.  While there is good reason to question their utility, we elected to include the standard errors in the summary table, but do not show the corresponding Wald confidence intervals.\n",
    "\n",
    "Next we fit a model with two random effects for each animal: a random intercept, and a random slope (with respect to time).  This means that each pig may have a different baseline weight, as well as growing at a different rate. The formula specifies that \"Time\" is a covariate with a random coefficient.  By default, formulas always include an intercept (which could be suppressed here using \"0 + Time\" as the formula)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Mixed Linear Model Regression Results\n",
      "===========================================================\n",
      "Model:             MixedLM  Dependent Variable:  Weight    \n",
      "No. Observations:  861      Method:              REML      \n",
      "No. Groups:        72       Scale:               6.0372    \n",
      "Min. group size:   11       Log-Likelihood:      -2217.0475\n",
      "Max. group size:   12       Converged:           Yes       \n",
      "Mean group size:   12.0                                    \n",
      "-----------------------------------------------------------\n",
      "                 Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "-----------------------------------------------------------\n",
      "Intercept        15.739    0.550 28.603 0.000 14.660 16.817\n",
      "Time              6.939    0.080 86.925 0.000  6.783  7.095\n",
      "Group Var        19.503    1.561                           \n",
      "Group x Time Cov  0.294    0.153                           \n",
      "Time Var          0.416    0.033                           \n",
      "===========================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "md = smf.mixedlm(\"Weight ~ Time\", data, groups=data[\"Pig\"], re_formula=\"~Time\")\n",
    "mdf = md.fit(method=[\"lbfgs\"])\n",
    "print(mdf.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the same model fit using LMER in R:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%R print(summary(lmer(\"Weight ~ Time + (1 + Time | Pig)\", data=dietox)))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Linear mixed model fit by REML ['lmerMod']\n",
    "Formula: Weight ~ Time + (1 + Time | Pig)\n",
    "   Data: dietox\n",
    "\n",
    "REML criterion at convergence: 4434.1\n",
    "\n",
    "Scaled residuals: \n",
    "    Min      1Q  Median      3Q     Max \n",
    "-6.4286 -0.5529 -0.0416  0.4841  3.5624 \n",
    "\n",
    "Random effects:\n",
    " Groups   Name        Variance Std.Dev. Corr\n",
    " Pig      (Intercept) 19.493   4.415        \n",
    "          Time         0.416   0.645    0.10\n",
    " Residual              6.038   2.457        \n",
    "Number of obs: 861, groups:  Pig, 72\n",
    "\n",
    "Fixed effects:\n",
    "            Estimate Std. Error t value\n",
    "(Intercept) 15.73865    0.55012   28.61\n",
    "Time         6.93901    0.07982   86.93\n",
    "\n",
    "Correlation of Fixed Effects:\n",
    "     (Intr)\n",
    "Time 0.006 \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random intercept and random slope are only weakly correlated $(0.294 / \\sqrt{19.493 * 0.416} \\approx 0.1)$.  So next we fit a model in which the two random effects are constrained to be uncorrelated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10324316832591753"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.294 / (19.493 * 0.416) ** 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Mixed Linear Model Regression Results\n",
      "===========================================================\n",
      "Model:             MixedLM  Dependent Variable:  Weight    \n",
      "No. Observations:  861      Method:              REML      \n",
      "No. Groups:        72       Scale:               6.0283    \n",
      "Min. group size:   11       Log-Likelihood:      -2217.3481\n",
      "Max. group size:   12       Converged:           Yes       \n",
      "Mean group size:   12.0                                    \n",
      "-----------------------------------------------------------\n",
      "                 Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "-----------------------------------------------------------\n",
      "Intercept        15.739    0.554 28.388 0.000 14.652 16.825\n",
      "Time              6.939    0.080 86.248 0.000  6.781  7.097\n",
      "Group Var        19.837    1.571                           \n",
      "Group x Time Cov  0.000    0.000                           \n",
      "Time Var          0.423    0.033                           \n",
      "===========================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "md = smf.mixedlm(\"Weight ~ Time\", data, groups=data[\"Pig\"], re_formula=\"~Time\")\n",
    "free = sm.regression.mixed_linear_model.MixedLMParams.from_components(\n",
    "    np.ones(2), np.eye(2)\n",
    ")\n",
    "\n",
    "mdf = md.fit(free=free, method=[\"lbfgs\"])\n",
    "print(mdf.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The likelihood drops by 0.3 when we fix the correlation parameter to 0.  Comparing 2 x 0.3 = 0.6 to the chi^2 1 df reference distribution suggests that the data are very consistent with a model in which this parameter is equal to 0.\n",
    "\n",
    "Here is the same model fit using LMER in R (note that here R is reporting the REML criterion instead of the likelihood, where the REML criterion is twice the log likelihood):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%R print(summary(lmer(\"Weight ~ Time + (1 | Pig) + (0 + Time | Pig)\", data=dietox)))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Linear mixed model fit by REML ['lmerMod']\n",
    "Formula: Weight ~ Time + (1 | Pig) + (0 + Time | Pig)\n",
    "   Data: dietox\n",
    "\n",
    "REML criterion at convergence: 4434.7\n",
    "\n",
    "Scaled residuals: \n",
    "    Min      1Q  Median      3Q     Max \n",
    "-6.4281 -0.5527 -0.0405  0.4840  3.5661 \n",
    "\n",
    "Random effects:\n",
    " Groups   Name        Variance Std.Dev.\n",
    " Pig      (Intercept) 19.8404  4.4543  \n",
    " Pig.1    Time         0.4234  0.6507  \n",
    " Residual              6.0282  2.4552  \n",
    "Number of obs: 861, groups:  Pig, 72\n",
    "\n",
    "Fixed effects:\n",
    "            Estimate Std. Error t value\n",
    "(Intercept) 15.73875    0.55444   28.39\n",
    "Time         6.93899    0.08045   86.25\n",
    "\n",
    "Correlation of Fixed Effects:\n",
    "     (Intr)\n",
    "Time -0.086\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sitka growth data\n",
    "\n",
    "This is one of the example data sets provided in the LMER R library.  The outcome variable is the size of the tree, and the covariate used here is a time value.  The data are grouped by tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "data = sm.datasets.get_rdataset(\"Sitka\", \"MASS\", cache=True).data\n",
    "endog = data[\"size\"]\n",
    "data[\"Intercept\"] = 1\n",
    "exog = data[[\"Intercept\", \"Time\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the statsmodels LME fit for a basic model with a random intercept.  We are passing the endog and exog data directly to the LME init function as arrays.  Also note that endog_re is specified explicitly in argument 4 as a random intercept (although this would also be the default if it were not specified)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Mixed Linear Model Regression Results\n",
      "=======================================================\n",
      "Model:             MixedLM Dependent Variable: size    \n",
      "No. Observations:  395     Method:             REML    \n",
      "No. Groups:        79      Scale:              0.0392  \n",
      "Min. group size:   5       Log-Likelihood:     -82.3884\n",
      "Max. group size:   5       Converged:          Yes     \n",
      "Mean group size:   5.0                                 \n",
      "-------------------------------------------------------\n",
      "              Coef. Std.Err.   z    P>|z| [0.025 0.975]\n",
      "-------------------------------------------------------\n",
      "Intercept     2.273    0.088 25.864 0.000  2.101  2.446\n",
      "Time          0.013    0.000 47.796 0.000  0.012  0.013\n",
      "Intercept Var 0.374    0.345                           \n",
      "=======================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "md = sm.MixedLM(endog, exog, groups=data[\"tree\"], exog_re=exog[\"Intercept\"])\n",
    "mdf = md.fit()\n",
    "print(mdf.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the same model fit in R using LMER:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%R\n",
    "data(Sitka, package=\"MASS\")\n",
    "print(summary(lmer(\"size ~ Time + (1 | tree)\", data=Sitka)))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Linear mixed model fit by REML ['lmerMod']\n",
    "Formula: size ~ Time + (1 | tree)\n",
    "   Data: Sitka\n",
    "\n",
    "REML criterion at convergence: 164.8\n",
    "\n",
    "Scaled residuals: \n",
    "    Min      1Q  Median      3Q     Max \n",
    "-2.9979 -0.5169  0.1576  0.5392  4.4012 \n",
    "\n",
    "Random effects:\n",
    " Groups   Name        Variance Std.Dev.\n",
    " tree     (Intercept) 0.37451  0.612   \n",
    " Residual             0.03921  0.198   \n",
    "Number of obs: 395, groups:  tree, 79\n",
    "\n",
    "Fixed effects:\n",
    "             Estimate Std. Error t value\n",
    "(Intercept) 2.2732443  0.0878955   25.86\n",
    "Time        0.0126855  0.0002654   47.80\n",
    "\n",
    "Correlation of Fixed Effects:\n",
    "     (Intr)\n",
    "Time -0.611\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now try to add a random slope.  We start with R this time.  From the code and output below we see that the REML estimate of the variance of the random slope is nearly zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%R print(summary(lmer(\"size ~ Time + (1 + Time | tree)\", data=Sitka)))\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "Linear mixed model fit by REML ['lmerMod']\n",
    "Formula: size ~ Time + (1 + Time | tree)\n",
    "   Data: Sitka\n",
    "\n",
    "REML criterion at convergence: 153.4\n",
    "\n",
    "Scaled residuals: \n",
    "    Min      1Q  Median      3Q     Max \n",
    "-2.7609 -0.5173  0.1188  0.5270  3.5466 \n",
    "\n",
    "Random effects:\n",
    " Groups   Name        Variance  Std.Dev. Corr \n",
    " tree     (Intercept) 2.217e-01 0.470842      \n",
    "          Time        3.288e-06 0.001813 -0.17\n",
    " Residual             3.634e-02 0.190642      \n",
    "Number of obs: 395, groups:  tree, 79\n",
    "\n",
    "Fixed effects:\n",
    "            Estimate Std. Error t value\n",
    "(Intercept) 2.273244   0.074655   30.45\n",
    "Time        0.012686   0.000327   38.80\n",
    "\n",
    "Correlation of Fixed Effects:\n",
    "     (Intr)\n",
    "Time -0.615\n",
    "convergence code: 0\n",
    "Model failed to converge with max|grad| = 0.793203 (tol = 0.002, component 1)\n",
    "Model is nearly unidentifiable: very large eigenvalue\n",
    " - Rescale variables?\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we run this in statsmodels LME with defaults, we see that the variance estimate is indeed very small, which leads to a warning about the solution being on the boundary of the parameter space.  The regression slopes agree very well with R, but the likelihood value is much higher than that returned by R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Mixed Linear Model Regression Results\n",
      "===============================================================\n",
      "Model:               MixedLM    Dependent Variable:    size    \n",
      "No. Observations:    395        Method:                REML    \n",
      "No. Groups:          79         Scale:                 0.0264  \n",
      "Min. group size:     5          Log-Likelihood:        -62.4834\n",
      "Max. group size:     5          Converged:             Yes     \n",
      "Mean group size:     5.0                                       \n",
      "---------------------------------------------------------------\n",
      "                     Coef.  Std.Err.   z    P>|z| [0.025 0.975]\n",
      "---------------------------------------------------------------\n",
      "Intercept             2.273    0.101 22.513 0.000  2.075  2.471\n",
      "Time                  0.013    0.000 33.888 0.000  0.012  0.013\n",
      "Intercept Var         0.646    0.914                           \n",
      "Intercept x Time Cov -0.001    0.003                           \n",
      "Time Var              0.000    0.000                           \n",
      "===============================================================\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3/dist-packages/statsmodels/regression/mixed_linear_model.py:2237: ConvergenceWarning: The MLE may be on the boundary of the parameter space.\n",
      "  warnings.warn(msg, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "exog_re = exog.copy()\n",
    "md = sm.MixedLM(endog, exog, data[\"tree\"], exog_re)\n",
    "mdf = md.fit()\n",
    "print(mdf.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further explore the random effects structure by constructing plots of the profile likelihoods. We start with the random intercept, generating a plot of the profile likelihood from 0.1 units below to 0.1 units above the MLE. Since each optimization inside the profile likelihood generates a warning (due to the random slope variance being close to zero), we turn off the warnings here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    likev = mdf.profile_re(0, \"re\", dist_low=0.1, dist_high=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a plot of the profile likelihood function.  We multiply the log-likelihood difference by 2 to obtain the usual $\\chi^2$ reference distribution with 1 degree of freedom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '-2 times profile log likelihood')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(likev[:, 0], 2 * likev[:, 1])\n",
    "plt.xlabel(\"Variance of random intercept\", size=17)\n",
    "plt.ylabel(\"-2 times profile log likelihood\", size=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a plot of the profile likelihood function. The profile likelihood plot shows that the MLE of the random slope variance parameter is a very small positive number, and that there is low uncertainty in this estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
 
 
 
 
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wh6PUscjMlatrsf1sLn6Jycaxi0UAgMHhHvjykS5QWPGZNCIiU6FrN+CP3oiI7iL+Ugkmfn0EOcWVaO3mgI1P9GIxo2bhoLDCxG7++HlOL6ye0QMKKxn2JOZj7tpTUNdqpI5HREQGxnJGRHQHJ9KL8OC3R1FYVo123kpsfKIXx52TJPqHumP5tO5QWMmwLykfc9bEoKqGBY2IyJywnBER3cb+5HxMWX4MpVW16B7YAj/NiYSbo0LqWGTB+oa4YcX07rC1luFAcgFms6AREZkVljMiolvYEncJs1adRFWNFgPD3LF6Rk8oba2ljkWE3sFuWDG9B+ys5TiUUoBZq0+yoBERmQmWMyKi/7HuWAbm/3QatVoRozv54Nsp3XggMBmVXm1aYuVj3WFvI8fh84WYueokKqtZ0IiITB3LGRHRv3x1IBWv/XYOogg80jMASyd35thyMko9g1pi5WM94GAjx1+phXh81QlUVNdKHYuIiBqB33EQEQEQRRHv7UjEhzuTAQBPDWqDdx64B3KZIHEyotvr0doVq2bUFbQjF65gxkoWNCIiU8ZyRkQWT6MV8cqms/jmYBoA4NX72uL/hrWFILCYkfHrFuiK1Y/3gKPCCkfTijB9xQmUq1nQiIhMEcsZEVk0da0GT68/hZ9OZEEmAB+M74DZ/dtIHYuoQbq2qitoTgorHL9YhOkrjqOMBY2IyOSwnBGRxaqorsXMVSex/WwerOUCvni4CyZ3D5A6FpFeugS0wJqZPeFka4UT6Vcx/YfjKK2qkToWERE1AMsZEVmkkooaPPr9MRw+Xwg7azl+mN4d93XwljoWUaN09nfBupk9obS1wsmMq5j2w3GoWNCIiEwGyxkRWZx8VRUmfxuNU5nFcLazxtqZPdEvxF3qWEQG0dHPBetmRsLZzhqnMosxdTkLGhGRqWA5IyKLklVUgYnfRCMprxTuTgr8PCcSXVu1kDoWkUF18HPGupk94WJvjdisYkxZfhwllSxoRETGjuWMiCxGyuVSjP/vEWRcqYC/qx1+faI32noppY5F1CTu8XXGjzMj0cLeGnFZxZiy/BhKKljQiIiMGcsZEVmE05lXMembaOSXqhHm6YRfnuiNgJb2UscialLtfJT4cVYkXB1scCa7BI8sP4riimqpYxER0W2wnBGR2fs7tRCPfH8MxRU16Ozvgp/nRMJTaSt1LKJmEe6txPpZkWjpYINzOSo8/N0xXC1nQSMiMkYsZ0Rk1naey8NjK06golqDvsFu157DsZE6FlGzCvNywvrZkXBztEFCrgoPf38MRSxoRERGh+WMiMzWxpNZeHJdDKo1Wgxv74Xl07vBQWEldSwiSYR6OmH9rEi4OSqQmKvCw98dxZUytdSxiIjoX1jOiMgsLf/rIv7vlzPQisDErn744uEIKKzkUsciklSIpxN+mh0JdycFkvJK8fB3x1DIgkZEZDRYzojIrIiiiE93J+PtrQkAgJl9W+PDCR1hJedfd0QAEOzhiJ9mR8LDSYHky6V46NujKChlQSMiMgb8boWIzIZWK+KNP+Lx2b5UAMALQ0Px2shwCIIgcTIi49LG3RE/z+kFL6UtzueX4aHvjiK/tErqWEREFo/ljIjMQo1Gi+c2xGJVdAYEAXh7THvMiwphMSO6jdZuDvhpdiS8nW2Rml+Gh749inwVCxoRkZRYzojI5FXVaPDEmhj8HnsJVjIBSyd3xpRegVLHIjJ6gdcKmo+zLS4UlOPBb4/iMgsaEZFkWM6IyKSVVtVg2g/HsTcpHworGb6d2hVjOvtKHYvIZLRq6YCfZveCr4sd0grrClpeCQsaEZEUWM6IyGRdKVPjoe+O4tjFIjgprLB6Rg9EtfWUOhaRyQloaY+fZkfC18UOFwvLMfnbaFwqrpQ6FhGRxWE5IyKTdKm4EhO/ica5HBVaOthg/exI9AxqKXUsIpPl72qPn+dEwt/VDhlXKvDgt0eRw4JGRNSsWM6IyOSkFZRhwn+PIK2gHD7OttjwRC/c4+ssdSwik+fXwh4/ze6FAFd7ZBZV4MFvo5F9tULqWEREFoPljIhMyrmcEkz8OhqXSqoQ5OaAjXN7o427o9SxiMyGr4sdfpodiVYt7ZFVVIkHvz2KrCIWNCKi5sByRkQm4/jFIjz07VFcKa9Gex8lNjxRN8SAiAzLx8UOP8/uhdZuDsi+WlfQMq+woBERNTWWMyIyCfuT8jFl+TGUqmvRo7Ur1s+OhJujQupYRGbLy9kWP82ORJCbA3KKK/Hgt9HIuFIudSwiIrPGckZERm9zbA5mrT4Jda0WUW09sHpGDyhtraWORWT2PJV1Ba2NuwMulVThwW+PIr2QBY2IqKmwnBGRUVtzNAMLfo5FrVbEmM4++GZKV9hay6WORWQxPJS2WD87EsEejsgtqcLkb6NxkQWNiKhJsJwRkVESRRFf7k/Ff34/B1EEpkS2wpJJnWEt519bRM3Nw8kW62dFItTTEZdVakz+JhoXCsqkjkVEZHb4XQ4RGaW1RzPw0a5kAMDTUcF4a0x7yGSCxKmILJe7kwI/zopEmKcT8kvVePDbo0jNZ0EjIjIkljMiMjpXytT48Foxe35IKJ4fGgZBYDEjkpqbowI/zuqJtl5OKLhW0M5fLpU6FhGR2WA5IyKj88mfKSitqkU7byWeHBQsdRwi+peWjnUraOHeShSWqfHQd0eRwoJGRGQQLGdEZFQSLqnw0/FMAMCi0e0g51ZGIqPj6mCDH2f2RHsfJQrLqvHQt0eRlKeSOhYRkcljOSMioyGKIt7aGg+tCIzs6I2eQS2ljkREt9HCwQbrZvZEB19nXCmvxsPfHUNiLgsaEVFjsJwRkdHYeS4PR9OKoLCS4ZURbaWOQ0R34WJvg7WP90RHP2cUlVfj4e+OIv5SidSxiIhMFssZERmFqhoNFm9PBADMGdAGfi3sJU5ERLpwtrfGmsd7opO/C65W1OCR74/hXA4LGhGRPljOiMgofH84DdlXK+HtbIsnBgRJHYeIGsDZzhprHu+Bzv4uKL5W0M5ms6ARETUUyxkRSS6vpApf7r8AAHh5RFvY21hJnIiIGkppW1fQugS4oKSyBo98fxRxWcVSxyIiMiksZ0QkuQ92JqGyRoOurVrg/k4+UschIj052Vpj9eM90a1VC6iqavHo8mOIZUEjItIZyxkRSSom4yp+O50DoG50Pg+bJjJtjgorrJzRA90DW6C0qhZTvj+GU5lXpY5FRGQSWM6ISDJarYi3tsQDACZ29UNHPxdpAxGRQTgqrLDysR7o0doVpepaTF1+HDEZLGhERHfDckZEktl0Ogdx2SVwsJHj/4aHSR2HiAzIQWGFlY91R2SQK8rUtZi6/BhOphdJHYuIyKixnBGRJMrUtfhgZxIA4Ol7Q+DhZCtxIiIyNHsbK6yY3gO927REebUGU384juMXWdCIiG6H5YyIJPHV/lQUlKrRqqU9HusTKHUcImoidjZyLJ/WHX2D3VBRrcH0FcdxLO2K1LGIiIwSyxkRNbvMKxX4/vBFAMDrI9tBYSWXOBERNSU7Gzm+n9YN/UKuF7QTiL7AgkZE9L9Yzoio2S3enoBqjRb9QtwwONxD6jhE1AxsreX4bmo3DAh1R2WNBo+tPI4jqYVSxyIiMiosZ0TUrP5OLcSu+MuQywT8ZxRH5xNZEltrOb6Z0hWDwtxRVaPFYytP4K/zLGhERNexnBFRs6nVaPHWlgQAwJTIVgj1dJI4ERE1N1trOb6e0hVRbT2grtXi8VUncCilQOpYRERGgeWMiJrN+uOZSL5cChd7aywYHCJ1HCKSiMJKjv8+2gWDw+sK2szVJ3EgOV/qWEREkmM5I6JmUVxRjU/+TAEAPDckFC72NhInIiIpKazk+OqRrhjSzhPVtVrMXh2D/UksaERk2VjOiKhZLN1zHsUVNQj1dMTDPQKkjkNERsDGSoYvH+6CYe09Ua3RYs6aGOxNvCx1LCIiybCcEVGTS7lcijVHMwAAi0a3h5Wcf/UQUR0bKxm+eLgL7uvghWqNFk+sjcGeBBY0IrJM/A6JiJqUKIp4e2sCNFoRQ9t5ok+wm9SRiMjIWMtlWPZgBEZ29EaNRsTcdTHYHZ8ndSwiombHckZETWpvYj4Ony+EjVyG10aGSx2HiIyUtVyGZZM7Y3QnH9RoRDy57hR2nmNBIyLLwnJGRE1GXavBO9vqRuc/3q81WrV0kDgRERkzK7kMSyZ1wpjOPqjVipj34ynsOJsrdSwiombDckZETWbl3+lIv1IBdycFnhoULHUcIjIBVnIZPp3UGWMjfOsK2vrT2HrmktSxiIiahZXUAYjIPOWXVuHzfakAgJeGt4Wjgn/dEJFu5DIBH0/sBEEANp3KwTM/xUIrAvd38pE6GhFRk+LKGRE1iY93JaNMXYtOfs4YF+ErdRwiMjFymYCPJnTChK5+0GhFLPjpNDbH5kgdi4ioSbGcEZHBnckuxsaYbADAwtHtIZMJEiciIlMklwn4cHxHTO7mD60IPPtzLH47nS11LCKiJsNyRkQGJYoi3tySAFEExkb4omurFlJHIiITJpMJeG9cBzzUo66gPbchDr/GsKARkXliOSMig/oj7hJiMq7CzlqOl4a3lToOEZkBmUzA4gc64JGeARBF4IVf4rDxZJbUsYiIDI7ljIgMpqK6Fu/vSAIAPDmwDbycbSVORETmQiYT8M4D92BKZCuIIvDir2ew4QQLGhGZF5YzIjKYrw+mIbekCr4udpjVP0jqOERkZgRBwFtj2mNar38K2vrjmVLHIiIyGJYzIjKInOJKfHPwAgDgtZHhsLWWS5yIiMyRIAh44/72eKxPIADglU1nse5YhrShiIgMhOWMiAzive2JUNdq0bO1K0bc4yV1HCIyY4IgYOGodni8b2sAwGu/ncOa6HRpQxERGQDLGRE12vGLRdh6JhcyAVg4uh0EgaPziahpCYKA10eGY1a/uoL2n83xWHUkXdpQRESNxHJGRI2i0Yp4c0s8AODBHgFo7+MscSIishSCIODV+8IxZ0DdM66L/ojHir8vSpyKiEh/LGdE1CgbT2Yh/pIKTrZWeH5IqNRxiMjCCIKAl4e3xZMD2wAA3tySgO8Pp0mciohIPyxnRKQ3VVUNPtqVDABYMDgULR0VEiciIkskCAL+b1gY5g0KBgC8sy0R3x1iQSMi08NyRkR6+3zveVwpr0YbdwdM7dVK6jhEZMEEQcDzQ0Mx/94QAMDi7Yn1E2SJiEwFyxkR6eVCQRlW/J0OAPjPqHawlvOvEyKSliAIeG5IKBYMrito7+1IwlcHUiVORUSkO343RUR6WbwtEbVaEYPC3DEwzEPqOERE9RYMDsVz156B/XBnMr7cz4JGRKaB5YyIGuxAcj72JeXDSibg9VHtpI5DRHST+feG4IWhdQXto13J+GzveYkTERHdnVmVs8WLF6N3796wt7eHi4vLTe/HxcXhoYcegr+/P+zs7BAeHo5ly5bddJ0oivj4448RGhoKhUIBf39/vPvuu83wJyAyfjUaLd7emgAAmN47EG3cHSVORER0a/OiQvDi8DAAwKd/pmDpnhSJExER3ZmVLhdlZmYa5GYBAQEG+Zzbqa6uxsSJE9GrVy8sX778pvdjYmLg7u6OtWvXwt/fH0eOHMHs2bMhl8sxb968+uueeeYZ7N69Gx9//DE6dOiAkpISFBYWNml2IlOxJjoDFwrK0dLBBk9fe/CeiMhYPTkwGDJBwPs7krB0z3loReDZwSEQBEHqaERENxFEURTvdpFcLm/8jQQBtbW1jf4cXaxcuRILFixAcXHxXa996qmnkJiYiH379gEAEhMT0bFjR5w7dw5hYWF6Z1CpVHB2dkZJSQmUSqXen0NkTK6UqTHw4wMorarFe+M64KEeTfsDFyIiQ/nuUBoWb08EAMwbFIznh4ayoBFRs9G1G+i0rVEUxUZ/abVag/3hDKmkpASurq71v96yZQuCgoKwdetWtG7dGoGBgZg5cyaKiookTElkHD79MwWlVbVo563EpG7+UschItLZrP5BeH1kOADgi/2p+GhXMnT4+TQRUbPSqZxptdpbfn3++eewtrbGkCFDsG3bNqSnp6OyshLp6enYtm0bhgwZAmtra3zxxRdGWc6io6OxYcMGzJkzp/61tLQ0ZGRkYOPGjVi9ejVWrlyJmJgYTJgw4Y6fpVaroVKpbvgiMicJl1RYf7xui/Oi0e0gl/EnzkRkWmb2C8Ki0XVDjL46cAHv70xiQSMio6L3QJDt27dj/vz5eOWVV7Br1y6MGDECAQEBUCgUCAgIwIgRI7Br1y68+uqrePrpp7Fjxw697vPGG29AEIQ7fp08ebLBnxsfH48xY8Zg4cKFGDJkSP3rWq0WarUaq1evRr9+/TBw4EAsX74c+/fvR3Jy8m0/77333oOzs3P9l78/VxXIfIiiiLe2xkMrAiM7eqNnUEupIxER6eWxPq3x5v3tAQDfHEzDF/s4Zp+IjIdOz5zdyr333ou4uDhcvnz5js+kaTQaeHp6onPnztizZ0+D71NYWHjXYRyBgYGwtbWt//XdnjlLSEjAoEGDMHPmTCxevPiG9xYtWoR3330XNTU19a9VVlbC3t4eu3fvvqHI/ZtarYZara7/tUqlgr+/P585I7Ow42wu5q47BYWVDHufHwC/FvZSRyIiapTV0elYuDkecpmAjU/0QpeAFlJHIiIzpuszZzpNa7yV06dPIyQk5K7DQuRyOdq0aYNTp07pdR83Nze4ubnp9XtvJT4+HlFRUZg2bdpNxQwA+vTpg9raWly4cAFt2rQBAKSk1I3ebdWq1W0/V6FQQKFQGCwnkbGoqtHUP0Q/Z0AbFjMiMgtTewUiJuMqNsdewnM/x2Lb/H5wUOj9bRERkUHova2xuroa2dnZOl2bnZ2N6upqfW+ls8zMTMTGxiIzMxMajQaxsbGIjY1FWVkZgLpiNmjQIAwZMgTPPfcc8vLykJeXh4KCgvrPGDx4MLp06YIZM2bg9OnTiImJwZw5czBkyBCEhoY2+Z+ByNh8fzgN2Vcr4e1siycGBEkdh4jIYN4acw+8nW2RfqUC72xLlDoOEZH+5axdu3bIy8vDihUr7njdihUrkJubi/bt2+t7K50tXLgQERERWLRoEcrKyhAREYGIiIj6Z9I2btyIgoICrFu3Dt7e3vVf3bt3r/8MmUyGLVu2wM3NDf3798fIkSMRHh6On376qcnzExmbvJIqfLn/AgDg5RFtYW/DnyoTkflwtrPGJxM7AQDWH8/E3sTLEiciIkun9zNnK1euxIwZM2BlZYU5c+bgmWeeQXBwcP37Fy5cwLJly/DNN9+gtrYWy5cvx/Tp0w2V2+jxnDMyB8/9HItNp3PQtVUL/PJEL54JRERm6Z2tCfj+r4twc7TBzgX94ebIxxSIyLB07QZ6lzMAePzxx7FixYr6b9hsbGzg4eGBgoKC+uEYoihi+vTp+OGHH/S9jUliOSNTdyrzKsZ9dQQA8Me8Pujo5yJtICKiJlJVo8H9X/yFlMtlGNLOE99O6cofRhGRQRn0EOrbWb58OZYvX442bdpAFEWo1WpkZWWhqqoKoigiKCgI33//vcUVMyJTp9WKeHNLAgBgYlc/FjMiMmu21nIsnRwBa7mAPxMuY+NJ3Z6pJyIytEatnP1bSkoKkpKSUFpaCicnJ7Rt29aiB2hw5YxM2a8x2Xh+YxwcbOTY/38D4eFke/ffRERk4r4+eAHv70iCg40cO57pj4CWnE5LRIbR5KP0/1doaKhFlzEic1GmrsUHO5MAAE/fG8JiRkQWY1a/IOxLzMfx9CI8uyEWG+b0glzG7Y1E1Hwata3xf6nV6hueNyMi0/PV/lTkl6rRqqU9HusTKHUcIqJmI5cJ+GRSJzgqrBCTcRVfH7wgdSQisjCNLmcZGRmYN28eWrduDXt7e3h5ecHe3h5BQUGYP38+MjIyDJGTiJpB5pUKfH/4IgDg9ZHtoLC68yHzRETmxt/VHm/cX3f8z5I/U3Aup0TiRERkSRpVzrZt24aOHTviv//9LzIyMiCKYv1Xeno6vvzyS3Ts2BHbt283VF4iakKLtyegWqNFvxA3DA73kDoOEZEkxnfxxYh7vFCrFbHg51hU1WikjkREFkLvcpaWloZJkyahtLQUAQEBWLp0Kfbt24fExETs27cPy5YtQ6tWrVBaWopJkyYhLS3NkLmJyMD+Ti3ErvjLkMsE/GdUO46RJiKLJQgCFo/tAHcnBVLzy/D+jiSpIxGRhdC7nH344YeorKzEpEmTcP78ecyfPx8DBw5EWFgYBg4ciKeffhopKSmYPHkyKioq8PHHHxsyNxEZUK1Gi7eujc6fEtkKoZ5OEiciIpKWq4MNPpzQEQCw8kg6Dp8vkDgREVkCvcvZnj17YGdnh2+//RZWVrce+mhlZYVvvvkGdnZ22L17t94hiahprT+eieTLpXCxt8aCwSFSxyEiMgqDwjwwJbIVAOCFjXEorqiWOBERmTu9y1lOTg7atWt31zO8lEol2rVrh5ycHH1vRURNqLiiGp/+mQIAeG5IKFzsbSRORERkPF69LxxBbg64rFLjtd/PwUDHwxIR3ZLe5UyhUKCsrEyna8vKyqBQKPS9FRE1oaV7zuNqRQ1CPR3xcI8AqeMQERkVOxs5lkzuDLlMwLYzudgce0nqSERkxvQuZ23btkVKSgrOnDlzx+tiY2ORnJyMtm3b6nsrImoi5y+XYs3RuuMuFo1uDyu5QY8+JCIyC538XTA/qm7L9382n0NOcaXEiYjIXOn9ndikSZMgiiLGjRuHI0eO3PKaI0eOYPz48RAEAZMnT9Y7JBEZniiKeGtrAjRaEUPbeaJPsJvUkYiIjNZTg9qgs78LSqtq8cKGOGi13N5IRIYniHpunlar1ejZsyfOnDkDQRDQs2dPdOjQAV5eXsjLy8PZs2dx7NgxiKKIzp07Izo62qK2NqpUKjg7O6OkpOSuz+URSWFPwmXMXH0SNnIZ/nyuP1q1dJA6EhGRUbtYWI77lh1GZY0Gr48Mx8x+QVJHIiIToWs30LucAUB+fj4effRR7Nmzp+7D/nUu0vWPHTp0KFavXg0PD8s60JbljIyZulaDYUsOIf1KBeYObIOXhnPbMRGRLtYdy8Brv52DjVyGLU/3RZgXjx4horvTtRvcega+jjw8PLB7925ER0dj69atSEpKgkqlglKpRNu2bTFq1Cj06tWrMbcgoiaw8u90pF+pgLuTAk8NCpY6DhGRyXi4RwD2JuZjX1I+nvnpNDbP6wOFlVzqWERkJhq1cka3x5UzMlb5pVWI+vggytS1+HhiJ0zo6id1JCIik5JfWoXhSw+jqLwacwYE4ZUR4VJHIiIjp2s34Gg2Igvz8a5klKlr0cnPGeMifKWOQ0RkcjycbPHeuA4AgG8PpeFo2hWJExGRuWjUtsbrTp48iW3btt2wrTE8PBwjR45E165dDXELIjKAs9kl2BiTDQBYOLo9ZDLhLr+DiIhuZVh7L0zq5ocNJ7Px/IY47FjQD0pba6ljEZGJa1Q5u3LlCqZPn47t27cD+GcICFA3HOTNN9/EqFGj8MMPP6Bly5aNS0pEjSKKIt7cEg9RBB7o7IOurVpIHYmIyKQtHN0e0WlXkFVUiTf+iMenkzpLHYmITJze5UytVmPYsGE4ffp0/bj8jh07wtvbG7m5uTh79ixOnz6NrVu3Yvjw4fj7779hY2NjyOxE1ABbzuTiZMZV2FnL8dIITmckImosR4UVlkzqjEnfRGPTqRwMDvfEfR28pY5FRCZM73L23//+F6dOnYKvry9++OEHDBky5KZr9uzZg8cffxynTp3Cf//7XzzzzDONCktE+qms1uC97YkAgCcHtoG3s53EiYiIzEO3QFc8MaANvjpwAa/+dhbdWrWAh9JW6lhEZKL0Hgjy888/QxAE/P7777csZgAwePBg/PrrrxBFET/99JPeIYmocb4+eAG5JVXwdbHDrP48NJWIyJAWDA5Fex8liitq8H+/nAEHYRORvvQuZ0lJSQgJCbnrwI9u3bohNDQUiYmJ+t6KiBohp7gSXx+8AAB4bWQ4bK15Hg8RkSHZWMmwdHJnKKxkOJhSgLVHM6SOREQmSu9yVlVVBScnJ52udXJyglqt1vdWRNQI721PhLpWi56tXTHiHi+p4xARmaUQTye8fO153sXbE3GhoEziRERkivQuZ76+vkhMTERZ2Z3/8iktLUVCQgJ8fHz0vRUR6en4xSJsPZMLmQAsHN0OgsDR+URETWVar0D0DXZDVY0Wz/4cixqNVupIRGRi9C5nUVFRqKysxNNPP33bvdWiKGLevHmoqqq67XNpRNQ0NNq60fkA8GCPALT3cZY4ERGReZPJBHw8sROc7axxJrsEn+9LlToSEZkYQdTzqdXU1FR07NgRarUaYWFhWLBgATp06AAvLy/k5eXh7NmzWLJkCVJSUmBra4u4uDgEBwcbOr/RUqlUcHZ2RklJCZRKpdRxyAL9dDwTL286CydbKxx4YSBaOiqkjkREZBG2xF3C0+tPQy4TsPGJXugSwHMliSydrt1A73IGAJs3b8YjjzyCioqKW26XEkUR9vb2WLduHcaMGaPvbUwSyxlJSVVVg0EfHcCV8mr8Z1Q7PN63tdSRiIgsyjM/ncbm2EsIbGmPbfP7wUGh9+lFRGQGdO0Gem9rBIAxY8YgLi4OM2fOhJeXF0RRrP/y8vLCrFmzEBcXZ3HFjEhqX+xLxZXyarRxd8DUXq2kjkNEZHHeGnMPvJ1tkX6lAu9s48RqItJNo1bO/ldpaSlUKhWUSqXOkxzNFVfOSCppBWUYtvQQajQiVjzWHYPCPKSORERkkY6kFuLh748BAJZP64Z7wz0lTkREUmmWlbP/5eTkBF9fX4svZkRSWrwtETUaEYPC3FnMiIgk1DvYDTOvbSt/6dczKCzjsUJEdGcGLWdEJK0DyfnYm5QPK5mA10e1kzoOEZHFe2FYGEI9HVFYVo1XNp297YRrIiIAaPTTqRkZGdi6dSvOnz+PsrIyaLW3PtNDEAQsX768sbcjotuo0Wjx9tYEAMD03oFo4+4ocSIiIrK1lmPp5AiM+fIv/JlwGRtPZmNSd3+pYxGRkWpUOXv11Vfx0Ucf1ReyO/00iOWMqGmtic7AhYJytHSwwdP3hkgdh4iIrmnno8TzQ8Pw/o4kvLklHpFBLRHQ0l7qWERkhPQuZ9988w3ef/99AECHDh3Qq1cveHp6QibjTkmi5nalTI0le1IA1G2hcbazljgRERH926x+QdiXmI/j6UV4dkMsNszpBbns5mOIiMiyNaqcCYKAxYsX4+WXXzZkJiJqoE//TEFpVS3aeSsxqRu3yxARGRu5TMAnkzphxLLDiMm4iq8PXsBTg4KljkVERkbvZa7k5GS0bNmSxYxIYgmXVFh/PBMAsGh0O/4klojISPm72uON+9sDAJb8mYJzOSUSJyIiY6N3OVMoFGjdurUhsxBRA4miiLe2xkMrAiM7eqNnUEupIxER0R2M7+KLEfd4oVYrYsHPsaiq0UgdiYiMiN7lLCIiAhkZGYbMQkQNtCs+D0fTiqCwkuGVEW2ljkNERHchCAIWj+0AdycFUvPL8P6OJKkjEZER0bucPfvss8jPz8e6desMmYeIdFRVo8E72xIBAHMGtIFfC07+IiIyBa4ONvhwQkcAwMoj6Th8vkDiRERkLPQuZ6NGjcLbb7+N2bNn45133kFxcbEBYxHR3Sz/6yKyr1bCS2mLJwYESR2HiIgaYFCYB6ZEtgIAvLAxDsUV1RInIiJjIIg6HFUfFHT7b/xycnJQW1sLAHBzc4ODg8OtbyQIuHDhgp4xTY9KpYKzszNKSkqgVCqljkNmpqBUjQEf7UdFtQbLHuyMMZ19pY5EREQNVFmtwcjPDiOtsBwjO3rji4ciIAgc6kRkjnTtBjqN0k9PT9fppgUFBSgouPXSPP+yITKc305no6Jag45+zri/k4/UcYiISA92NnIsmdwZ4/57BNvO5GJIuCceiOAP24gsmU7lbP/+/U2dg4h0JIoifo3JAQA82D2AP/ggIjJhnfxdMD8qBEv2pOA/m8+he2tX+LrYSR2LiCSiUzkbMGBAU+cgIh0l5KqQfLkUNlYyjOzgLXUcIiJqpKcGtcH+5HzEZhXjhQ1xWDezJ2Q8s5LIIuk9EISIpLHpVN2q2ZBwTzjbW0uchoiIGstKLsOSyZ1hZy1HdNoV/PD3RakjEZFEWM6ITEitRovNsXXlbHxXPpdARGQuWrs54PVR4QCAD3cmIzmvVOJERCQFnbY1rl69GgDg7OyMMWPG3PBaQ0ydOrXBv4eI/nHofAEKy6rh5miDfiHuUschIiIDerhHAPYm5mNfUj6e+ek0Ns/rA4WVXOpYRNSMdBqlL5PJIAgCwsLCkJCQcMNrDaHRaPRLaYI4Sp+awlM/nsK2M7mY0ac1Fo5uJ3UcIiIysPzSKgxfehhF5dWYMyAIr4wIlzoSERmAQUfp9+/fH4IgICAg4KbXiKh5lFTW4M+EywCAcV24pZGIyBx5ONnivXEdMGdNDL49lIZBYR6IDGopdSwiaiY6lbMDBw7o9BoRNZ3tZ3NRXatFmKcT2vtwNZaIyFwNa++FSd38sOFkNp7fEIcdC/pBacsBUESWgANBiEzEplPZAOpWzbhqTURk3haObg9/VzvkFFfijT/ipY5DRM2E5YzIBGRcKceJ9KuQCcADEdzSSERk7hwVVlgyqTNkQt0RKtvP5kodiYiaAcsZkQn47XTd+Pw+wW7wVNpKnIaIiJpDt0BXPDGgDQDg1d/OIl9VJXEiImpqOj1zFhUV1egbCYKAvXv3NvpziCyNKIr1B0+P7+IncRoiImpOCwaH4mBKAeIvqfB/v5zByse6c2s7kRnTeyBIQ/EvEiL9xGRcRWZRBRxs5BjW3kvqOERE1IxsrGRYOrkzRn3+Fw6mFGDt0QxM6RUodSwiaiI6lbMVK1Y0dQ4iuo1frw0Cua+DN+xseBgpEZGlCfF0wssj2uLNLQlYvD0RvYPd0MbdUepYRNQEdCpn06ZNa+ocRHQLVTUabD1T9xD4OG5pJCKyWNN6BWJvYj7+Si3Esz/H4te5vWEt5+gAInPDf6uJjNiexMsoraqFr4sderZ2lToOERFJRCYT8NHEjlDaWuFMdgk+35cqdSQiagIGK2eiKKKwsBCZmZmG+kgii3d9EMjYCF/IZHxuk4jIknk722Hx2A4AgC/3p+JU5lWJExGRoTW6nB0+fBijRo2Ck5MTPD09ERQUdMP7H3zwAWbMmIGioqLG3orIohSUqnEwpQAAMLYLzzYjIiJgdCcfjOnsA41WxHM/x6JcXSt1JCIyoEaVs08++QSDBg3C9u3bUVFRAVEUIYriDdc4OTlh1apV2Lp1a6OCElmaP+IuQaMV0dnfhQ9+ExFRvbfG3ANvZ1ukX6nAO9sSpY5DRAakdzk7fPgwXnzxRdja2uKjjz7CxYsX0bt375uuGzduHERRxJYtWxoVlMjSbLo2pXE8V82IiOhfnO2s8cnETgCA9cczsTfxssSJiMhQ9C5nS5YsAQB8/fXXeP7559GqVatbnmXm5eUFX19fJCUl6Z+SyMIk5akQf0kFa7mAUR19pI5DRERGpnewG2b2bQ0AeOnXMygsU0uciIgMQe9yFh0djRYtWuDRRx+967Xe3t7IycnR91ZEFue3a4NAotp6oIWDjcRpiIjIGL0wLAyhno4oLKvGK5vO3vRoCRGZHr3LWVFREQIDA3W6VhRFVFVV6XsrIotSq9Hit9N15Ww8zzYjIqLbsLWWY+nkCFjLBfyZcBkbT2ZLHYmIGknvcubi4oLs7Lv/JSCKIi5cuAAPDw99b0VkUf6+cAX5pWq0sLfGwDD+e0NERLfXzkeJ54eGAQDe3BKPzCsVEiciosbQu5x16dIFBQUFOHbs2B2v+/3331FcXIzIyEh9b0VkUa4PArm/kw9srHhOPBER3dmsfkHoEeiK8moNnt0QC42W2xuJTJXe3/lNmzYNoihi9uzZt11Bi42Nxdy5cyEIAh577DG9QxJZitKqGuyKzwMAjOOWRiIi0oFcJuCTSZ3gqLBCTMZVfH3wgtSRiEhPepezBx98EMOHD8fZs2fRoUMHPPzww8jKygJQd/7Z2LFj0aNHD+Tn52PcuHEYNmyYwUITmasd5/JQVaNFG3cHdPRzljoOERGZCH9Xe7xxf3sAwJI/U3Aup0TiRESkj0btmdq4cSMefPBBlJSU4KeffkJmZiZEUcSLL76IzZs3o7a2FpMnT8aaNWsMlZfIrF3f0jiui98tj6YgIiK6nfFdfDHiHi/UakUs+DkWVTUaqSMRUQMJogHmrsbExODnn39GXFwcrl69CkdHR3Ts2BGTJk265cHUlkClUsHZ2RklJSVQKpVSxyETkH21An0/2A9BAP56KQq+LnZSRyIiIhNTVF6NYUsPoaBUjem9A+tX04hIWrp2Ayt9b1BUVARXV1cAQNeuXdG1a9c7Xv/333+jT58++t6OyOz9fm18fq+glixmRESkF1cHG3w4oSMeW3ECK4+k495wD/QLcZc6FhHpSO9tjaNHj4Zardtp9MePH8fIkSP1vRWR2RNFEZuuHTzNQSBERNQYg8I8MCWyFQDghY1xKK6oljgREelK73IWHR2Nhx566K6n0cfGxmL48OEoLS3V91ZEZu90VjHSCsthZy3HiHu8pI5DREQm7tX7whHk5oDLKjVe+/3cXb9fIyLjoHc569GjBzZv3oz58+ff9ppz585hyJAhKC4uxrx58/S9FZHZuz4IZMQ9XnBQ6L3bmIiICABgZyPHksmdIZcJ2HYmF5tjL0kdiYh0oHc527ZtG9q0aYOvvvoKH3zwwU3vJyUlYfDgwbhy5QpmzZqFZcuWNSookblS12qwJS4XALc0EhGR4XTyd8H8qBAAwH82n0NOcaXEiYjobvQuZy1btsTOnTvh5uaGV199FT/++GP9excuXMC9996L/Px8TJkyBd98841BwhKZo/1J+SiprIGX0ha92rSUOg4REZmRpwa1QWd/F5RW1eKFDXHQarm9kciYNeqcs6CgIGzbtg12dnaYMWMG9u/fj8zMTERFRSE3NxeTJk3CypUrDRSVyDz9em0QyAMRvpDLeLYZEREZjpVchiWTO8POWo7otCv44e+LUkciojtoVDkDgG7dumHDhg3QarUYO3YsBg4ciKysLNx///1Yu3YtD9IluoOi8mrsT8oHAIzr4itxGiIiMket3Rzw+qhwAMCHO5ORnMchbUTGqtHlDADuu+8+/Pe//4VKpUJGRgaGDx+OjRs3wsqKgw2I7mRL3CXUakV08HVGqKeT1HGIiMhMPdwjAFFtPVCt0eKZn05DXauROhIR3YJO7Wn16tV3vcba2hrdu3fH+fPnMWbMGKxfv/6ma6ZOndrwhERm7PqURq6aERFRUxIEAe+P74DhSw8jKa8Un/6ZgldGhEsdi4j+hyDqcPCFTCbTeXuiKIq3vVajsZyf0qhUKjg7O6OkpARKpVLqOGSEUvNLMfjTQ7CSCTj66r1wc1RIHYmIiMzcrvg8zFkTA0EA1s+KRGQQB1ERNQddu4FOK2f9+/fns2NEBnZ9EMjAMHcWMyIiahbD2nthUjc/bDiZjec3xGHHgn5Q2lpLHYuIrtGpnB04cKCJYxBZFo1WxO+n68rZeJ5tRkREzWjh6PaITruCrKJKvPFHPD6d1FnqSER0jUEGghBRwxxNu4Lckiooba0QFe4hdRwiIrIgjgorLJnUGTIB2HQqB9vP5kodiYiuYTkjksCv1waBjO7kA4WVXOI0RERkaboFuuKJAW0AAK/+dhb5qiqJExERwHJG1OzK1bXYeS4PADCOWxqJiEgiCwaHor2PEsUVNfi/X85AhxlxRNTEdHrmLCgoCAAQHByM3bt33/CargRBwIULFxoYj8j87IrPQ0W1BoEt7dElwEXqOEREZKFsrGRYOrkzRn3+Fw6mFGDt0QxM6RUodSwii6ZTOUtPTwcA2Nra3vSarjjtkajOpmtTGsd18eO/F0REJKkQTye8PKIt3tySgMXbE9E72A1t3B2ljkVksXQqZ/v37wcA2Nvb3/QaEekut6QSf18oBACMjeDB00REJL1pvQKxNzEff6UW4tmfY/Hr3N6wlvPJFyIp6FTOBgwYoNNrRHRnv53OgSgCPVq7wt/V/u6/gYiIqInJZAI+mtgRw5YcwpnsEny+LxXPDQmVOhaRReKPRYiaiSiK9Vsax3fhqhkRERkPb2c7LB7bAQDw5f5UnMq8KnEiIsvEckbUTM7mlCA1vwwKKxnu6+AtdRwiIqIbjO7kgzGdfaDRinju51iUq2uljkRkcXTa1njo0CGD3Kx///4G+RwiU3R91WxYey842VpLnIaIiOhmb425B8cvFiH9SgXe2ZaI98Z1kDoSkUXRqZwNHDiw0VPlBEFAbS1/AkOWqbpWiz/iLgEAxnFLIxERGSlnO2t8MrETHv7+GNYfz8TgcA/cG+4pdSwii6FTOQsICODIb6JGOJhSgKLyarg7KdA32E3qOERERLfVO9gNM/u2xvd/XcRLv57BzgX94eaokDoWkUVo0DlnRKSfTaeyAQAPdPaBFccTExGRkXthWBgOnS9AyuUyvLLpLL6d0pU/qCdqBvwukaiJFVdUY29iPoC6g6eJiIiMna21HEsnR8BaLuDPhMvYeDJb6khEFsGsytnixYvRu3dv2Nvbw8XF5ab34+Li8NBDD8Hf3x92dnYIDw/HsmXLbrjmjTfegCAIN305ODg005+CzM3WM7mo1mgR7q1EuLdS6jhEREQ6aeejxPNDwwAAb26JR+aVCokTEZk/sypn1dXVmDhxIubOnXvL92NiYuDu7o61a9ciPj4er732Gl555RV88cUX9de88MILyM3NveGrXbt2mDhxYnP9McjM/HptSyPPNiMiIlMzq18QegS6orxag2c3xEKjFaWORGTWBFEUze7fspUrV2LBggUoLi6+67VPPfUUEhMTsW/fvlu+HxcXh86dO+PQoUPo16+fzhlUKhWcnZ1RUlICpZKrJZYqraAMUZ8chFwmIPqVKHg42UodiYiIqEGyiiowYtlhlKlr8X/DwvDUoGCpIxGZHF27gVmtnOmjpKQErq6ut33/+++/R2ho6F2LmVqthkqluuGL6LfTdWeb9Q9xYzEjIiKT5O9qjzfubw8AWPJnCs7llEiciMh8WXQ5i46OxoYNGzBnzpxbvq9Wq7Fu3To8/vjjd/2s9957D87OzvVf/v7+ho5LJkarFesPnuYgECIiMmXju/hixD1eqNWKWPBzLKpqNFJHIjJLRl/Objeg499fJ0+ebPDnxsfHY8yYMVi4cCGGDBlyy2s2bdqE0tJSTJ069a6f98orr6CkpKT+Kysrq8GZyLwcTy9CTnElnBRWGNKOB3gSEZHpEgQBi8d2gLuTAqn5ZXh/R5LUkYjMkk7nnElp3rx5ePDBB+94TWBgYIM+MyEhAVFRUZg1axZef/312173/fffY9SoUfDy8rrrZyoUCigUPKCR/nH9bLORHb1hay2XOA0REVHjuDrY4MMJHfHYihNYeSQd94Z7oF+Iu9SxiMyK0ZczNzc3uLm5Gezz4uPjERUVhWnTpmHx4sW3ve7ixYvYv38//vjjD4PdmyxHZbUG28/mAeCWRiIiMh+DwjwwJbIV1hzNwAsb47BrQX+42NtIHYvIbBj9tsaGyMzMRGxsLDIzM6HRaBAbG4vY2FiUlZUBqCtmgwYNwpAhQ/Dcc88hLy8PeXl5KCgouOmzfvjhB3h7e2PEiBHN/ccgM7A7IQ9l6lr4u9qhW6sWUschIiIymFfvC0eQmwMuq9R47fdzMMPB30SS0Xvl7NChQzpfK5fLoVQqERgYCCcnJ31veVcLFy7EqlWr6n8dEREBANi/fz8GDhyIjRs3oqCgAOvWrcO6devqr2vVqhXS09Prf63VarFy5UpMnz4dcjm3o1HDXR8EMjbCDzKZIHEaIiIiw7GzkWPJ5M4Y998j2HYmF0PCPfFABM/yJDIEvc85k8lkEISGfdMpCAIiIiIwf/58TJkyRZ/bmgyec2a5Lquq0Ou9vdCKwIEXBiLQzUHqSERERAa3bM95LNmTAidbK+xc0B++LnZSRyIyWk1+zllAQAACAgJgZWUFURQhiiKcnJzg4+MDJyen+tesra0REBAAV1dXaLVaxMTEYPr06ZgxY4a+tyYyaptjc6AVga6tWrCYERGR2XpqUBt09ndBaVUtXtgQB62W2xuJGkvvcpaeno7JkydDFEW89NJLuHDhAoqLi5GVlYXi4mKkpaXh5ZdfhiiKmDx5MgoKCnD58mW8/fbbsLa2xqpVq/Dbb78Z8s9CJDlRFPFrzPWzzbjFg4iIzJeVXIYlkzvDzlqO6LQr+OHvi1JHIjJ5epezH3/8ER999BG+/fZbvPfee2jduvUN7wcGBuLdd9/Ft99+i48++gjr1q2Du7s7XnvtNXz66acQRRE//PBDo/8ARMYkIVeF5MulsLGSYVQHH6njEBERNanWbg54fVQ4AODDnclIzS+VOBGRadO7nH355Zfw8vLC9OnT73jdtGnT4OXlhS+++KL+tdmzZ8PJyQknTpzQ9/ZERun6IJAh4Z5wtreWOA0REVHTe7hHAAaGuaNao8XbWxOljkNk0vQuZ+fOnYO/v79O1/r7+yMhIaH+11ZWVggNDcXVq1f1vT2R0anVaLE5llsaiYjIsgiCgDdGt4e1XMDBlALsT86XOhKRyWrUOWcXL16869kWWq0WaWlpN11XVVUFBwcOSyDzcfh8IQrLqtHSwQb9Q92ljkNERNRsAt0cML13IABg8bZE1Gi00gYiMlF6l7NOnTqhsLAQH3/88R2v+/TTT1FYWFh/5th1aWlpcHfnN7BkPn49lQ0AuL+zD6zlZnW+OxER0V3NiwqBq4MNUvPL8OOxTKnjEJkkvb+DXLBgAURRxMsvv4xp06bh77//Rnl5OQCgvLwcR44cwWOPPYaXXnoJgiBgwYIF9b/34MGDqKysRGRkZKP/AETGoKSyBrsTLgMAxnfxkzgNERFR83O2s8azQ0IBAEv2pKCkokbiRESmR+9yNm7cOLzyyisQRRFr165F//79oVQqYWVlBaVSiX79+mH16tUQRRGvvvoqxo4dW/97k5KSMHny5LsOEyEyFdvP5qK6VotQT0e09+Gh40REZJke6u6PUE9HFFfUYNne81LHITI5jdp7tXjxYuzcuRP9+/eHTCaDKIrQarUQRREymQwDBgzArl278Pbbb9/w++bMmYP169dj0KBBjQpPZCw2XdvSOK6LHwRBkDgNERGRNKzkMrw+sh0AYHV0OtIKyiRORGRarBr7AUOHDsXQoUNRUVGB1NRUlJaWwsnJCcHBwbC3tzdERiKjlnGlHCfSr0ImAA905pRGIiKybP1D3RHV1gP7kvLx7vZEfD+tu9SRiExGo8vZdfb29ujYsaOhPo7IZPx2um58fp9gN3g520qchoiISHqv3heOQykF2JOYj7/OF6JviJvUkYhMgsFGytXU1CA5ORknTpxAcnIyamr4ECiZP1EU6w+e5iAQIiKiOsEejng0shUA4O2tCajlaH0inTS6nB0/fhyjRo2CUqlEu3btEBkZiXbt2kGpVOL+++/HiRMnDJGTyCjFZFxFZlEFHGzkGNreU+o4RERERmPB4BA421kj+XIpfj6ZJXUcIpPQqHL23XffoW/fvtixYwfUajVEUaz/UqvV2Lp1K/r06YPly5cbKi+RUfn12qrZiA7esLcx2C5hIiIik+dib4MFg0MAAJ/uToGqiruqiO5G73IWGxuLJ598ErW1tejVqxd+//13pKWlobKyEmlpadi8eTN69+6N2tpazJ07F7GxsQaMTSS9qhoNtp65BAAY14WDQIiIiP7Xo5GtEOTugCvl1fhyX6rUcYiMnt7l7JNPPoFGo8H8+fPx119/4f7770dgYCAUCgUCAwMxevRo/PXXX1iwYAFqa2uxZMkSQ+YmktyexMsoraqFj7MtIlu3lDoOERGR0bGWy/D6yHAAwIq/05FxpVziRETGTe9ydujQISiVSnz44Yd3vO69996DUqnEgQMH9L0VkVG6PghkbBdfyGQ824yIiOhWBoV5oF+IG6o1Wry3PUnqOERGTe9ydvnyZYSFhcHGxuaO1ykUCoSFheHy5cv63orI6BSUqnEwpQAAMDaCUxqJiIhuRxAEvD6yHWQCsDM+D0fTrkgdicho6V3OHBwcUFBQoNO1BQUFPJCazMofcZeg0Yro5O+CYA9HqeMQEREZtTAvJzzcMwBA3Wh9jVaUOBGRcdK7nHXo0AEZGRl33a64b98+pKeno1OnTvreisjobDqVDQAYz0EgREREOnl2cCicbK0Qf0mFX2OypY5DZJT0LmdTpkyBKIqYOHEi1q9fD1G88Scgoijixx9/xOTJkyEIAqZMmdLosETGIClPhfhLKljLBYzu6CN1HCIiIpPQ0lGB+VF1o/U/2p2MMnWtxImIjI8g/m+r0pFWq8XQoUOxb98+CIIAHx8ftGvXDl5eXsjLy0NCQgIuXboEURRx7733YteuXZDJGn3mtclQqVRwdnZGSUkJlEql1HHIgN7bnohvDqVhWHtPfDOlm9RxiIiITEZ1rRZDlxxE+pUKPDWoDf5vWFupIxE1C127gd5tSSaT4Y8//sDMmTMhk8mQk5ODP//8E2vWrMGff/6JnJwcyGQyzJ49G5s3b7aoYkbmS6MV8dvpuimN47pwEAgREVFD2FjJ8Mp9daP1vzt8EdlXKyRORGRc9F45+7esrCzs3LkTSUlJUKlUUCqVaNu2LUaMGAE/P8v8BpYrZ+bpUEoBpv5wHC721jj+6mDYWPGHDkRERA0hiiIe/u4YotOuYFRHb3zxcBepIxE1OV27gZUhbubv749Zs2YZ4qOIjNqv1waB3N/Jh8WMiIhID4Ig4PVR4Rj1+V/YeiYXj/UpQtdWrlLHIjIK/O6SSEelVTXYFZ8HgFsaiYiIGqO9jzMmd/MHALy1JQFajtYnAsByRqSzHefyUFWjRZC7Azr5OUsdh4iIyKQ9NzQUDjZyxGWXYHNcjtRxiIyCTtsag4KCGn0jQRBw4cKFRn8OkVT+OdvMD4IgSJyGiIjItHk42eKpqGB8uDMZH+xIxrD2XrC3McgTN0QmS6d/A9LT0xt9I34zS6Ys+2oFjqYVQRCAByJ48DQREZEhzOjTGj8ey0T21Up8czANzw4JlToSkaR0Kmf79+9v6hxERu33a+PzewW1hK+LncRpiIiIzIOttRyvjAjHUz+ewjeHLuDBHv7wduZ/Z8ly6VTOBgwY0NQ5iIyWKIrYdIpnmxERETWF+zp4oXtgC5xIv4oPdyZjyeTOUkcikgwHghDdRWxWMdIKy2FnLcfwe7ykjkNERGRWBEHAf0a1AwD8djoHsVnF0gYikhDLGdFdXF81G36PFxwVfFCZiIjI0Dr6uWD8td0pb29NgChytD5ZJpYzojtQ12rwR9wlAMC4LhwEQkRE1FReHB4GO2s5YjKuYuuZXKnjEEmC5YzoDvYn5aOksgaeSgV6t3GTOg4REZHZ8lTaYu7ANgCA93ckoapGI3EioubHckZ0B79e29L4QIQv5DIeB0FERNSUZvULgo+zLXKKK/H94TSp4xA1O5YzotsoKq/G/qR8AMC4CE5pJCIiamp2NnK8NKItAOCrAxeQr6qSOBFR82I5I7qNLXGXUKsVcY+vEmFeTlLHISIisgj3d/JBRIALKqo1+GhXstRxiJoVyxnRbWw6lQ0A9dOjiIiIqOn9e7T+L6eycS6nROJERM2H5YzoFlLzSxGXXQIrmYDRnXykjkNERGRRugS0wJjOPhBF4C2O1icL0uhyptVq8csvv2Du3LkYNWoU7r333hvej4mJwaFDh6DVaht7K6Jmc/1ss4Fh7nBzVEichoiIyPK8OLwtFFYyHL9YhF3xeVLHIWoWjTpRNyEhARMmTEBycnL9TzQE4caJdmvXrsVnn32GPXv2YNCgQY25HVGz0GpF/Ha6rpyN45ZGIiIiSfi62GFO/yB8ti8Vi7cnYlBbDyis5FLHImpSeq+cFRQUYMiQIUhKSkKnTp2waNEiBAcH33TdI488AlEU8ccffzQqKFFziU67gtySKihtrRDV1kPqOERERBZrzoA28FQqkFVUiRV/p0sdh6jJ6V3OPvzwQ+Tm5mLKlCk4efIkFi1aBE9Pz5uu69atGxwdHXHkyJFGBSVqLr9eGwQyqpMPbK35EzoiIiKpOCis8H/D6kbrf7EvFQWlaokTETUtvcvZ1q1boVAo8Pnnn0Mmu/PHBAUFIScnR99bETWbcnUtdp6r29c+vouvxGmIiIhoXIQvOvg6o0xdi0//TJE6DlGT0rucZWRkICQkBEql8q7X2tnZobCwUN9bETWbXfF5qKjWoFVLe3QJaCF1HCIiIosnk/0zWv/nE5lIzFVJnIio6ehdzqytrVFVpdup7fn5+XBy4iG+ZPyuT2kcF+F303AbIiIikkaP1q4Y2cEbWhF4ZxtH65P50ructWnTBhkZGSgqKrrjdefPn8fFixfRrl07fW9F1CxySyrx94W6Fd5x3NJIRERkVF4e0RY2chn+Tr2CvYn5UschahJ6l7ORI0eipqYGr7322m2v0Wg0mD9/PgRBwAMPPKDvrYiaxe+nL0EU63465+9qL3UcIiIi+hd/V3s83q81AGDx9kRU1/IMXTI/epezZ599Fm5ubvj2228xfvx47N69G2p13QSdoqIibN68GX379sWuXbvg7++P2bNnGyw0kaGJoohN16Y0chAIERGRcXpyYBu4OSpwsbAca45mSB2HyOAEsRGbdo8ePYrRo0fjypUrt3w+RxRFuLm5YdeuXYiIiGhUUFOjUqng7OyMkpISnYamkLTOZpdg9Bd/QWElw4nXB0Npay11JCIiIrqFn45n4uVNZ6G0tcKB/xsEVwcbqSMR3ZWu3UDvlTMAiIyMxJkzZ/Dkk0/C3d0doijWf7m4uGDmzJk4ffq0xRUzMj3XzzYb2t6LxYyIiMiITezmj3BvJVRVtVi6h6P1ybw0auXsf+Xm5uLq1atwdHSEn5/fXc8/M2dcOTMd1bVaRL63F0Xl1VjxWHcMCvOQOhIRERHdwZELhXj4u2OQywTsfKYfQjw5FZyMW7OsnP0vb29vtGvXDgEBARZdzMi0HEwpQFF5NdwcFegX7CZ1HCIiIrqL3m3cMLSdJzRaEe9sS5Q6DpHBsEGRxbs+COSBzj6wkvNfCSIiIlPw6n3hsJYLOJhSgP3JHK1P5sFKl4sOHTpkkJv179/fIJ9DZCjFFdX1Z6WM6+IncRoiIiLSVaCbA6b3DsR3hy9i8bZE9A12gzV/yEomTqdyNnDgwFtOY2wIQRBQW1vbqM8gMrStZ3JRrdGirZcT2vnw2UAiIiJTMi8qBL+eykFqfhnWH8/E1F6BUkciahSdyllAQECjyxmRMbq+pXFCV66aERERmRpnO2s8OyQU//n9HD79MwVjOvnC2Z5Tl8l06VTO0tPTmzgGUfO7WFiOU5nFkAnA/Z19pI5DREREeniouz/WRKcj5XIZlu09j4Wj20kdiUhv3JhLFuu3a6tm/UPd4eFkK3EaIiIi0oeVXIbXR9YVstXR6UgrKJM4EZH+WM7IImm1In49lQOAg0CIiIhMXf9Qd0S19UCtVsS72zlan0wXyxlZpOPpRcgproSTwgpD23lKHYeIiIga6dX7wmElE7AnMR9/nS+UOg6RXnR65mzGjBkA6g6ZXrx48Q2v6UoQBCxfvryB8YiaxvVBIPd18IattVziNERERNRYwR6OeDSyFVYeScc72xKwbX4/yGUcaEemRRBFUbzbRTJZ3QJb27ZtkZCQUP+aIAjQ4bfX3UgQoNFoGhHVtKhUKjg7O6OkpARKJUe0G5PKag26L96DMnUtfp4diZ5BLaWORERERAZQXFGNAR8dQEllDd4d2wEP9wyQOhIRAN27gU4rZwsXLoQgCHBzc6t/bdGiRY1PSSSB3Ql5KFPXwq+FHboHukodh4iIiAzExd4GCwaH4M0tCfhkdzJGdfKG0paj9cl06LRyRg3HlTPjNe2H4ziYUoD5UcF4bmiY1HGIiIjIgGo0WgxbeghpBeWY0z8Ir9wXLnUkIp27gU4DQWbMmIEPPvjAYOGIpJKvqsLh8wUAgLGc0khERGR2rOUyvD6yrpCt+DsdGVfKJU5EpDudytnKlSuxbdu2G16Ty+UYMGBAk4QiaiqbYy9BKwJdW7VAazcHqeMQERFRExgU5oF+IW6o1mjx3vYkqeMQ6UynciaTyVBTU3PDa6Io6jwMhMgYiKKIX69NaRzXxVfiNERERNRUBEHA6yPbQSYAO+PzcDTtitSRiHSiUzlzd3dHamoq1Gp1U+chajIJuSok5ZXCRi7DqA4+UschIiKiJhTm5VQ/rfHtrQnQaLmoQMZPp2mNAwYMwMaNG9G7d28MGTIE9vb2AIDMzEy89dZbOt9s4cKF+qUkMoBNp3IAAIPbecDZnpObiIiIzN2zg0OxOfYS4i+p8OupbEzq5i91JKI70mlaY2pqKvr27Yv8/HwIQt1hfqIo1v/vu7l+Lc85I6nUarSIfG8vCsuq8f3UbhjczlPqSERERNQMvjuUhsXbE+HupMD+FwbCUaHT2gSRQRn0nLPg4GAkJiZi/fr1iI+PR2VlJVauXAkPDw8MHz7cYKGJmsrh84UoLKuGq4MNBoS5Sx2HiIiImsm03oFYdywD6Vcq8PWBC3hhGI/RIeOl9zlnMpkMffv2xaFDhwydySxw5cy4zPvxFLaeycX03oF44/72UschIiKiZrQrPg9z1sTAxkqGfc8PgF8Le6kjkYUx6DlntzJt2jTcd999+v52omZTUlmD3QmXAQDjebYZERGRxRnazhO9glqiulaL93dwtD4ZL73L2YoVK/Dyyy8bMgtRk9hxNhfVtVqEeDjiHl+uYhIREVkaQRDw+qhwCAKw9UwuYjKKpI5EdEsGeSIyNzcXu3fvRlJSElQqFZRKJcLDwzF06FB4eXkZ4hZEers+pXF8Vz+dh9gQERGReWnv44zJ3fzx04ksvLUlAb892QcyGb8vIOPSqHKmVqvxwgsv4LvvvrvpkGoAsLa2xpw5c/Dhhx9CoVA05lZEesm8UoHj6UUQBOCBzjx4moiIyJI9PzQMW8/kIi67BJvjcjA2go87kHHRe1ujVqvFmDFj8NVXX6G6uhpubm7o168fJk2ahH79+sHd3R3V1dX44osvMHbsWOg5d4SoUTadzgYA9A12g5ezrcRpiIiISEruTgo8OagNAOCDHcmoqK6VOBHRjfQuZ6tXr8bu3bvh5OSEb775Bjk5OThw4ADWr1+PAwcOIDs7G99++y2USiV27dqF1atXGzI30V2Joli/pXFcF66aERERETCjT2v4tbBDnqoK3x5KkzoO0Q30LmerVq2CIAjYsGEDZs2aBSurG3dIWllZYebMmfj5558hiiJWrlzZ2KxEDRKTcRWZRRWwt5FjWHs++0hERESArbUcr4wIBwB8ffACcksqJU5E9A+9y9mZM2fQqlUrDB069I7XDR06FIGBgThz5oy+tyLSy6/XVs1G3OMNexuDzL4hIiIiM3BfBy90D2yBqhotPtyZLHUconp6l7Py8nK4u7vrdK27uzvKy8v1vRVRg1XVaLD1zCUAwHhuaSQiIqJ/EQQB/xnVDgDw2+kcxGYVSxuI6Bq9y5mHhwdSUlJuOaXx36qrq5GcnAwPDw99b0XUYHsT81FaVQsfZ1tEBrWUOg4REREZmY5+LhjfpW5a49tbEzi8joyC3uWsf//+UKlUWLRo0R2vW7RoEVQqFQYMGKDvrYgabNOpuimND0T48gwTIiIiuqUXh4fBzlqOmIyr2HomV+o4RPqXs+effx6CIOCDDz7AsGHDsHv3buTm5kKr1dYfSj106FB8+OGHkMvleO655wyZm+i2CsvUOJBSAAAY14XnlxAREdGteSptMXdg3Wj993ckoapGI3EisnR6l7OIiAh8/vnnEAQBe/bswYgRI+Dn5wdra2v4+flhxIgR2LNnDwRBwGeffYaIiAhD5ia6rc2xl6DRiujk74JgD0ep4xAREZERm9UvCD7OtsgprsTyvy5KHYcsnN7lDADmzp2LAwcO4N5774VcLocoivVfcrkcQ4YMwcGDBzF37lxD5SW6q+tbGjkIhIiIiO7GzkaOl0a0BQB8uT8V+aoqiRORJWv0fPG+ffti9+7dqKysRGpqKlQqFZRKJYKDg2FnZ2eIjEQ6S84rRfwlFazlAkZ19JE6DhEREZmA+zv5YOWRdJzOLMbHu5Px4YROUkciC6X3ylmLFi0QGBgItVoNALCzs0OHDh3Qp08fdOjQgcWMJLEvKR8A0C/EHa4ONhKnISIiIlPw79H6G2OycS6nROJEZKn0LmfV1dXw8PCAQqEwZB6iRolOuwIA6BvsJnESIiIiMiVdAlpgTGcfiCLwFkfrk0T0LmcBAQEoKyszZBaiRqnRaHEyvQgA0KsNzzYjIiKihnlpeFvYWstw/GIRdsXnSR2HLJDe5eyBBx5ASkoKUlNTDZmHSG9nsotRUa1BC3trhHk6SR2HiIiITIyPix1m9wsCALy7PQnqWo7Wp+aldzl79dVXERQUhMmTJyMrK8uQmYj0En2hbktjZFBLHjxNREREepkzoA08lQpkFlVg5d/pUschC6P3tMYlS5Zg+PDh+O9//4uQkBAMHjwY7du3h4ODw21/z8KFC/W9HdFdXX/eLDKIWxqJiIhIPw4KK/zfsLZ4YWMcPt+XivFd/eDmyBkL1DwEUc+nHWUyGQRBuOFhSUG49WqFKIoQBAEajeUsDatUKjg7O6OkpARKpVLqOGZPXatBxzd2Q12rxe5n+yOU2xqJiIhIT1qtiDFf/o2zOSV4uGcA3h3bQepIZOJ07QZ6r5xNnTr1tmWMqLnFZhZDXauFm6MNQjwcpY5DREREJkwmqxutP+mbaPx0PBNTIlsh3Js/bKemp3c5W7lypQFjEDXO0bS6KY09g1ryhwZERETUaD1au2JkB29sO5uLd7YlYO3jPfk9BjU5vQeCEBmT6LRCAEAvPm9GREREBvLyiLawsZLh79Qr2JuYL3UcsgBmVc4WL16M3r17w97eHi4uLje9HxcXh4ceegj+/v6ws7NDeHg4li1bdtN1u3btQmRkJJycnODu7o7x48fj4sWLzfAnIH1U1WhwKrMYAM83IyIiIsPxd7XH431bAwAWb09Eda1W4kRk7hpdzlQqFT799FNERUXBy8sLDg4O8PLyQlRUFD799FOoVCpD5NRJdXU1Jk6ciLlz597y/ZiYGLi7u2Pt2rWIj4/Ha6+9hldeeQVffPFF/TVpaWkYM2YMoqKiEBsbi127dqGwsBDjxo1rrj8GNdCpzKuortXCw0mBILfbTwslIiIiaqgnB7aBm6MCFwvLseZohtRxyMzpPa0RAI4cOYJJkyYhNzcXt/oYQRDg5eWFn3/+GX379m1U0IZYuXIlFixYgOLi4rte+9RTTyExMRH79u0DAPzyyy946KGHoFarIZPVddctW7ZgzJgxUKvVsLa21ikDpzU2n093J+OzfakY09kHyx6MkDoOERERmZmfjmfi5U1nobS1wsH/G4QWDjZSRyITo2s30HvlLDU1FcOGDcOlS5fg7OyM559/HitWrMCOHTuwcuVKvPDCC3BxcUFubi5GjBiB8+fP63urJlVSUgJXV9f6X3fr1g1yuRwrVqyARqNBSUkJ1qxZg6FDh96xmKnVaqhUqhu+qHnwfDMiIiJqShO7+SPcWwlVVS2W7kmROg6ZMb3L2Ztvvony8nIMHz4cGRkZ+OijjzBt2jQMGzYMU6dOxYcffoiMjAyMGDEC5eXleOuttwyZ2yCio6OxYcMGzJkzp/61wMBA7N69G6+++ioUCgVcXFyQnZ2Nn3766Y6f9d5778HZ2bn+y9/fv6njE4DKag1is4oBcBgIERERNQ25TMB/RoUDANYey8T5y6USJyJzpXc527NnDxQKBdasWQMnp1sf+Ovo6IhVq1bBxsYGe/bs0es+b7zxBgRBuOPXyZMnG/y58fHxGDNmDBYuXIghQ4bUv56Xl4eZM2di2rRpOHHiBA4ePAgbGxtMmDDhlls3r3vllVdQUlJS/5WVlaXXn5caJibjKmo0IrydbdGqpb3UcYiIiMhM9W7jhqHtPKHRinhnW6LUcchM6X3OWXFxMdq3b4+WLe+8WuHm5oZ77rkH8fHxet1n3rx5ePDBB+94TWBgYIM+MyEhAVFRUZg1axZef/31G9778ssvoVQq8eGHH9a/tnbtWvj7++PYsWOIjIy85WcqFAooFIoG5aDG+/cIfZ49QkRERE3p1fvCsT85HwdTCrA/OR+DwjykjkRmRu9y5uvri8rKSp2urayshK+vr173cXNzg5ubm16/91bi4+MRFRWFadOmYfHixTe9X1FRAblcfsNr13+t1XJ8qrGJvnDteTOO0CciIqImFujmgOm9A/Hd4YtYvC0RfYPdYC03q5OpSGJ6/9M0ZswYJCUl4dy5c3e87ty5c0hMTMQDDzyg7610lpmZidjYWGRmZkKj0SA2NhaxsbEoKysDUFfMBg0ahCFDhuC5555DXl4e8vLyUFBQUP8ZI0eOxIkTJ/DWW2/h/PnzOHXqFB577DG0atUKERGcBGhMytW1OJNdAoDPmxEREVHzmBcVAlcHG6Tml2H98Uyp45CZ0bucLVq0CMHBwXjggQdw5MiRW15z5MgRjB07FqGhoVi4cKHeIXW1cOFCREREYNGiRSgrK0NERAQiIiLqn0nbuHEjCgoKsG7dOnh7e9d/de/evf4zoqKi8OOPP+L3339HREQEhg8fDoVCgZ07d8LOzq7J/wykuxPpRajVivBrYQd/Vz5vRkRERE3P2c4azw4JBQB8+mcKSipqJE5E5kTvc87eeustXL16FV999RVqa2vRrVs33HPPPfDy8sLly5dx7tw5nDhxAtbW1pg7dy5atGhxy89pjtImBZ5z1vTe25GIbw6mYUJXP3w8sZPUcYiIiMhC1Gq0uO+zw0i5XIbH+7bGf0a1kzoSGTldu4He5Uwmk0EQhJsmGN7utf8liiIEQYBGo9Hn9kaP5azpjfniL8Rll+CTiZ0wvquf1HGIiIjIghxKKcDUH47DSiZg97P9EeTuKHUkMmK6dgO9B4JMnTqV0/FIMqqqGpzNufa8GYeBEBERUTPrH+qOqLYe2JeUj3e3J+H7ad2kjkRmQO9ytnLlSgPGIGqYk+lF0IpAq5b28HHhs4BERETU/F69LxyHUgqwJ/Ey/jpfiL4hhpswTpaJsz/JJF0foc8pjURERCSVYA9HPBrZCgDwzrYEaLR6PS1EVI/ljExSdNq1csYtjURERCShBYND4GxnjaS8Uvx8IkvqOGTiWM7I5JRU1CD+kgoAV86IiIhIWi72NlgwOAQA8MnuZKiqOFqf9MdyRibn2MUrEEUgyN0BHkpbqeMQERGRhXs0shWC3B1wpbwaX+5PlToOmTCWMzI517c0RnLVjIiIiIyAtVyG/4ysO+tsxV/pyLxSIXEiMlUsZ2RyOAyEiIiIjM3AMHf0C3FDtUaL93YkSh2HTBTLGZmUovJqJOWVAuDKGRERERkPQRDwn1HtIBOAHefycPTaTh+ihmA5I5Ny/GLdX3QhHo5wd1JInIaIiIjoH6GeTni4ZwAA4O2tHK1PDcdyRialfksjR+gTERGREXp2cCicbK0Qf0mFX09lSx2HTEyTlLNLly7ho48+woIFC7Bu3bqmuAVZqPrzzbilkYiIiIxQS0cF5kfVjdb/aFcyytS1EiciU6J3Ofvhhx/g4+ODzz777IbXz549i44dO+Lll1/G559/jqlTp2LcuHGNDkpUWKZGyuUyAEBPljMiIiIyUtN6ByKwpT0KStX4+sAFqeOQCdG7nO3YsQOXL1/G8OHDb3j9xRdfRFFREQICAnD//ffD3t4emzdvxvr16xsdlizb9Qdr23o5wdXBRuI0RERERLdmYyXDq/eFAwC+PZyG7KscrU+60bucnT17Fi4uLggNDa1/rbCwEH/++Sc8PT0RFxeH3377DRs3boQoili9erVBApPluv68Gac0EhERkbEb0s4TvYJaorpWiw92Jksdh0yE3uUsPz8ffn5+N7x26NAhaLVaTJ48GUqlEgAwfPhweHt7IzY2tlFBieqfN+MwECIiIjJy10frCwKwJe4SYjKKpI5EJkDvclZWVgZra+sbXjt69CgEQcCAAQNueN3Pzw9FRfwHkvSXr6pCWkE5BAGIbM1yRkRERMavnY8Sk7v5AwDe2pIALUfr013oXc7c3NyQlZV1w2sHDhwAAPTu3fuG16urq+Hg4KDvrYjqV83aeSvhbG99l6uJiIiIjMPzQ8PgqLBCXHYJNsflSB2HjJze5axLly4oLCzEL7/8AgA4ceIETp48ibZt28LT0/OGa9PS0m56jaghjnKEPhEREZkgdycFnhzUBgDwwY5kVFRztD7dnt7l7IknnoAoinjkkUfQtWtXREVFQRAEzJw584brYmJiUFpaio4dOzY6LFkuHj5NREREpmpGn9bwa2GHPFUVvj2UJnUcMmJ6l7NRo0bhjTfegCiKOH36NMrLy/Hoo49i/vz5N1y3Zs0aAMCgQYMal5QsVm5JJdKvVEAmAN1bu0odh4iIiKhBbK3l9aP1vz54AbkllRInImOldzkDgIULFyIvLw/R0dG4dOkSVq1aBblcfsM1o0ePxm+//YZJkyY1KihZruurZh18naG05fNmREREZHpG3OOFHoGuqKrR4iOO1qfbaFQ5AwBXV1f07NkTXl5et3z/3nvvxZgxY+DqyhUP0g/PNyMiIiJT9+/R+ptO5yA2q1jqSGSEGl3OrhNFEYWFhcjMzDTURxIB+GdSYySfNyMiIiIT1sHPGeMi6s4JfntrAkSRo/XpRo0uZ4cPH8aoUaPg5OQET09PBAUF3fD+Bx98gBkzZvCcM9JLVlEFsq9WQi4T0D2Qq69ERERk2l4cHgY7azliMq5i65lcqeOQkWlUOfvkk08waNAgbN++HRUVFRBF8aafADg5OWHVqlXYunVro4KSZbo+Qr+jnzMcFVYSpyEiIiJqHE+lLeYOrBut//6OJFTVaCRORMZE73J2+PBhvPjii7C1tcVHH32Eixcv3nT4NACMGzcOoihiy5YtjQpKlima55sRERGRmZnVLwg+zrbIKa7E8r8uSh2HjIje5WzJkiUAgK+//hrPP/88WrVqBUEQbrrOy8sLvr6+SEpK0j8lWSRRFHGU55sRERGRmbGzkeOlEW0BAF/uT0W+qkriRGQs9C5n0dHRaNGiBR599NG7Xuvt7Y2cnBx9b0UWKrOoApdKqmAtF9C1VQup4xAREREZzP2dfBAR4IKKag0+3s3R+lRH73JWVFSEwMBAna4VRRFVVfyJADXM9RH6nfxcYG/D582IiIjIfFwfrQ8AG2OycS6nROJEZAz0LmcuLi7Izs6+63WiKOLChQvw8PDQ91ZkoeqfN+OWRiIiIjJDXQJaYExnH4giR+tTHb3LWZcuXVBQUIBjx47d8brff/8dxcXFiIyM1PdWZIFEUaxfOeMwECIiIjJXLw1vC1trGY5dLMKu+Dyp45DE9C5n06ZNgyiKmD179m1X0GJjYzF37lwIgoDHHntM75BkeS4WliO/VA0buQxd+LwZERERmSkfFzvM7ld3TvC725OgruVofUumdzl78MEHMXz4cJw9exYdOnTAww8/jKysLAB155+NHTsWPXr0QH5+PsaNG4dhw4YZLDSZv+tbGiMCXGBrLZc4DREREVHTmTOgDTyVCmQWVWDl3+lSxyEJNeoQ6o0bN+LBBx9ESUkJfvrpJ2RmZkIURbz44ovYvHkzamtrMXnyZKxZs8ZQeclCRHOEPhEREVkIB4UVXhxWN1r/832pKCxTS5yIpNKoEXgODg748ccf8fzzz+Pnn39GXFwcrl69CkdHR3Ts2BGTJk265cHURHciiiKOphUB4PNmREREZBnGRvhiVXQ6zmSX4NM/U/Du2A5SRyIJGGQ+edeuXdG1a1dDfBQRUvPLUFimhsJKhs4BLlLHISIiImpyMlndaP2JX0fjp+OZmNEnEMEeTlLHombWqG2NRE3h+vNmXVu1gMKKz5sRERGRZege6Iqh7TyhFYHP9qZKHYckwHJGRocj9ImIiMhSLRgcCgDYcuYSzl8ulTgNNbdGbWvUaDRYu3YttmzZgtTUVJSVlUGr1d7yWkEQcOHChcbcjiyAVivi2MVrz5txGAgRERFZmHY+Sgxv74Wd8Xn4bF8qPn8oQupI1Iz0LmfFxcUYOnQoYmJidDrNXBAEfW9FFiQlvxRF5dWws5ajo5+L1HGIiIiImt0zg0OwMz4PW89cwtNRwQj15LNnlkLvcvbqq6/i5MmTcHZ2xqxZs9CtWzd4eHiwhFGjXN/S2C2wBWysuOuWiIiILE+4txIj7vHCjnN5+GzveXzxcBepI1Ez0buc/f7775DL5fjzzz/RrVs3Q2YiC8bzzYiIiIiA+feGYMe5PGw7m4v5l0u5emYh9F6aKC4uRkhICIsZGcwNz5txGAgRERFZsHBvJe7r4AVRBJbtPS91HGomepezgIAAWFtbGzILWbiEXBVKKmvgYCNHB19nqeMQERERSWr+vSEAgO1nc5Gcx8mNlkDvcjZx4kQkJCQgKyvLkHnIgh29dr5Z99ausJLzeTMiIiKybG29/lk9+4yrZxZB7++AX3rpJYSFhWHy5MnIzs42ZCayUDzfjIiIiOhGz9xbd+7ZNq6eWQS9B4I4Ojri0KFDePjhhxEaGorhw4cjJCQEDg4Ot/09Cxcu1Pd2ZOY0WhHHeb4ZERER0Q3CvJwwsoM3tp3NxbK9Kfjqka5SR6Im1KhDqNevX4+jR4+iqqoKmzdvvu11oihCEASWM7qt+EslKFXXwsnWCu19+LwZERER0XXz7w3B9nO52H42D0l5KrT1UkodiZqI3uVs/fr1ePrppwEAPj4+6NixI885I71d39LYs7Ur5DL+M0RERER0XZiXE+7r4I1tZ3KxbM95/PdRrp6ZK73L2SeffAJBEPD6669j4cKFkMvlhsxFFib62jCQSD5vRkRERHSTZ+4NwfazudhxLg+JuSqEe3P1zBzpPRAkKSkJ7u7uePPNN1nMqFFqNFqc4PNmRERERLcV6ln37BkALNvDyY3mSu9y5uDggICAAENmIQt1NqcE5dUaONtZI5x7qImIiIhu6Zl7QyAIwM74PCRcUkkdh5qA3uWsT58+uHDhAmpraw2ZhyzQv583k/F5MyIiIqJbCvF0wqiOPgCAZXtTJE5DTUHvcvb666+jvLwc7777riHzkAW6fvg0tzQSERER3dn8qGAIArAr/jLiL5VIHYcMTO+BIG5ubli6dCkWLFiA48ePY/bs2Xc954zbIOl/VddqcTL9KgCWMyIiIqK7CfF0wuiOPvgj7hI+23se30zpJnUkMiC9y1nr1q3r//eOHTuwY8eOO14vCAK3QNJNzmQXo7JGA1cHG4R6OEkdh4iIiMjozb83GFvOXKpfPeMZseZD722Noig26Eur1RoyN5mJ68+bRQbxeTMiIiIiXQR7OOH+TteePePkRrOidznTarUN/iL6X9fPN+vF882IiIiIdPZ0VAhkArA74TLO5fDZM3Ohdzkjaix1rQYxGXzejIiIiKihgj0c/1k928vVM3PBckaSOZ1ZDHWtFm6OCrRxd5Q6DhEREZFJefreutWzP7l6ZjZYzkgy/37eTBD4vBkRERFRQ7Rxd8SYzr4AgKV89sws6DStccaMGQAAb29vLF68+IbXdCUIApYvX97AeGTOeL4ZERERUePMiwrG5tgc7Em8jLPZJejgx8mNpkwQRVG820UyWd0CW9u2bZGQkFD/miAI0OG3191IEKDRaBoR1bSoVCo4OzujpKQESqVS6jhGp6pGg45v7Ea1Rot9zw9AELc1EhEREenl2Z9j8dvpHAwO98D307pLHYduQdduoNPK2aJFiwDUHTz9v68R6eNUxlVUa7TwVCrQ2u32B5cTERER0Z09Xb96ls/VMxPXoHJ2t9eIdPXvEfp83oyIiIhIf0Hujnigsy82nc7B0j0pWD6dq2emigNBSBLXh4HweTMiIiKixrs+uXFvUj7OZBdLHYf0pHc5mzFjBt5//32drv3ggw8aPECEzFdFdS3irv2l0SvI7c4XExEREdFdtXZzwAMRnNxo6vQuZytXrsT27dt1unbnzp1YtWqVvrciM3My/SpqNCJ8nG3h72ondRwiIiIiszA/KgRymYB9SfmIyyqWOg7poVm2NWq1Wj5XRPWuP28W2YbPmxEREREZSqCbAx6oP/csReI0pI9mKWc5OTlwdOSodKpz9F/DQIiIiIjIcJ6OCoZcJmB/cgFiuXpmcnSa1ggAmZmZSE9Pv+G1kpISHDp06La/p7KyEvv27UNaWhoiIyP1Dknmo0xdizPZJQA4DISIiIjI0ALdHDA2whe/xGRj6Z4UrHysh9SRqAF0LmcrVqzAW2+9dcNr586dw6BBg3T6/XPmzGlYMjJLJ9KLoNGK8He1g18Le6njEBEREZmdp6OC8dvpHBxILsDpzKuICGghdSTSkc7lzMXFBQEBAfW/zszMhI2NDby8vG55vSAIsLe3R3BwMKZOnYpx48Y1Pi2ZvKMXuKWRiIiIqCm1aumAcRG+2BiTjaV7zmPVDK6emQqdy9kzzzyDZ555pv7XMpkM3bt3v+O2RqL/VX/4NLc0EhERETWZeVHB2HQ6BwdTCnAq8yq6cPXMJOg9EGTFihV49dVXDZmFzJyqqgbncuqeN4vkyhkRERFRk2nV0gHju9RNblzGc89Mht7lbNq0aRg+fLghs5CZO55WBK0IBLa0h7czzzcjIiIiakrzBoXASibgYEoBYjKuSh2HdNAso/SJgH+N0OeWRiIiIqImF9DSHuO7+AEAlu3l6pkpYDmjZlN/+DS3NBIRERE1i3lRwbCSCTjE1TOTwHJGzaK4ohoJuSoAnNRIRERE1Fz8Xe0xoWvd6tnSPSkSp6G7YTmjZnHsYhFEEWjj7gAPpa3UcYiIiIgsxlOD6lbPDp8vRExGkdRx6A5YzqhZRF/g82ZEREREUvB3tcfEbtdXz/jsmTFjOaNmUT8MJMhN4iRERERElufJgf+snp1M5+qZsWI5oyZ3pUyNpLxSAEDPIFeJ0xARERFZHq6emQaWM2pyxy7W/XQm1NMRbo4KidMQERERWabrz579lVqIE1w9M0p6l7OCggKUl5frdG1+fj4yMzP1vRWZuH+2NPJ5MyIiIiKp+LWwx8Ru/gA4udFYNaicaTQaLFy4EB4eHvDy8oJSqUSPHj3wxx9/3PH3jR8/HkFBQY0KSqaLw0CIiIiIjMNTg9rAWi7g79QrOH6Rq2fGpkHl7OGHH8bixYtRWFgIURQhiiJOnjyJsWPH4rHHHkN1dfVtf68oio0OS6anoFSN8/llEASgZ2uWMyIiIiIpcfXMuOlczjZv3oyNGzdCFEXMmDEDv/76K9atW4dhw4ZBFEWsXr0ao0ePhlqtbsq8ZGKub2ls66VECwcbidMQERER0VODgmEtF3DkwhUcu/a9GhkHncvZihUrIAgC3n33XXz//fcYO3YsHnroIezYsQPr16+Hvb099uzZg9GjR6OqqqopM5MJiebzZkRERERGxdfFDpPqV884udGY6FzOTpw4AUdHR7zwwgs3vTd58mQcPnwYbm5u2Lt3Lwsa1TvK582IiIiIjM711bPotCv1O51IejqXs8LCQgQHB8PKyuqW73fu3BkHDhyAh4cH9u3bh1GjRrGgWbjLqiqkFZZDEIAegTzfjIiIiMhY+LjYYXJ3PntmbHQuZwqF4o4DPwAgPDwc+/fvh4eHB/bv388VNAt3fUpjex8lnO2tJU5DRERERP/25MBg2MhlOJpWVP99G0lL53IWFBSE8+fP33XgR9u2besL2vUVtMrKykYHJdPD882IiIiIjBdXz4yPzuWsR48eqKmpwa5du+56bdu2bXHgwAF4enpi//79OHXqVKNCkmmqHwbC582IiIiIjNKTg9rARi7DsYtcPTMGOpezIUOGQBRFfP311zpdHxYWhgMHDsDb21vvcGS6LhVXIuNKBeQyAd35vBkRERGRUfJ2tsODPepWz5bsSeHZxBLTuZzdd999eOSRR+Dm5oaiIt1OEw8NDcWhQ4cwZMgQ9O/fX++QZHqu/+TlHl9nONnyeTMiIiIiYzV3YN3q2fGLRfU7n0gatx69eAsODg5Ys2ZNg28QFBSk01ZIMi8834yIiIjINHg72+GhHv5YFZ2BpXvOo1dQSwiCIHUsi6TzyhlRQ0TzfDMiIiIikzF3YDBsrK6tnvHZM8kYvJzt2rULq1evNvTH6mTx4sXo3bs37O3t4eLictP7cXFxeOihh+Dv7w87OzuEh4dj2bJlN123YcMGdO7cGfb29mjVqhU++uijZkhvPrKKKpBTXAkrmYBurVpIHYeIiIiI7sLL2RYP9wgAACzdc57PnknE4OXsnXfewWOPPWboj9VJdXU1Jk6ciLlz597y/ZiYGLi7u2Pt2rWIj4/Ha6+9hldeeQVffPFF/TU7duzAI488gieeeALnzp3DV199hU8//fSGa+jOrm9p7OjnDAeFzjtniYiIiEhCTwxoU7d6ll6EI1w9k4RZfef85ptvAgBWrlx5y/dnzJhxw6+DgoIQHR2NTZs2Yd68eQCANWvW4IEHHsATTzxRf81LL72EDz74AE899RT33+rgKLc0EhEREZmc66tnK4+kY+meFPRuw2fPmpvFP3NWUlICV9d/Rr2r1WrY2trecI2dnR2ys7ORkZHR3PFMjiiK/xoG4iZxGiIiIiJqiLkD61bPTqRfxd+pXD1rbhZdzqKjo7FhwwbMmTOn/rVhw4Zh06ZN2Lt3L7RaLVJSUrB06VIAQG5u7m0/S61WQ6VS3fBliTKuVCC3pArWcgFd+bwZERERkUnxVP772TOee9bcjL6cvfHGGxAE4Y5fJ0+ebPDnxsfHY8yYMVi4cCGGDBlS//qsWbMwb948jBo1CjY2NoiMjMSDDz4IAJDL5bf9vPfeew/Ozs71X/7+/g3/w5qB66tmEf4tYGdz+/97EREREZFxenJgGyisZDiZcRV/pRZKHceiGLyceXl5ISAgwGCfN2/ePCQmJt7x65577mnQZyYkJCAqKgqzZs3C66+/fsN7giDggw8+QFlZGTIyMpCXl4cePXoAAAIDA2/7ma+88gpKSkrqv7Kyshr8ZzUH10evRvJ5MyIiIiKT5KG0xcM9OblRCgYfCLJx40aDfp6bmxvc3Az37FJ8fDyioqIwbdo0LF68+LbXyeVy+Pr6AgDWr1+PXr16wcPD47bXKxQKKBQKg+U0RTc+b8ZyRkRERGSq5g5ogx+PZSLm2upZvxB3qSNZBLOa1piZmYmioiJkZmZCo9EgNjYWABAcHAxHR0fEx8dj0KBBGDp0KJ577jnk5eUBqCti7u51/8AVFhbil19+wcCBA1FVVYUVK1Zg48aNOHjwoFR/LJNxoaAcBaVq2FjJEBHgInUcIiIiItKTh9IWj/RshR/+voglf6agb7AbJzc2A6N/5qwhFi5ciIiICCxatAhlZWWIiIhARERE/TNpGzduREFBAdatWwdvb+/6r+7du9/wOatWrUK3bt3Qp08fxMfH48CBA/VbG+n2jl5bNesS4AJbaz5vRkRERGTKnhgYBIWVDKcyi3H4PJ89aw6CyE2kTUKlUsHZ2RklJSVQKpVSx2kWT/14CtvO5OLZwaF4ZnCI1HGIiIiIqJHe3pqA5X9dRESACzbN7c3VMz3p2g3MauWMpCOKIo6l8fBpIiIiInMyZ0AQbK1lOJ1ZjENcPWtyLGdkEOfzy1BYVg1baxk6+TtLHYeIiIiIDMDDyRaP9mwFAFjyJ889a2osZ2QQ10fod2vlCoUVnzcjIiIiMhdzBrSBrbUMsVnFOJhSIHUcs8ZyRgZxvZxxSyMRERGReXF3UmBK5LXVM5571qRYzqjRtFoRRy9eO3w6yFXiNERERERkaLP7162exWUV4wBXz5oMyxk1WlJeKYoramBvI0dHPxep4xARERGRgbk7KTC1VyAAYCmfPWsyLGfUaNfPN+sW6AprOf+RIiIiIjJHs/sHwc5ajrjsEhxI5upZU+B30tRo0ddH6AfxeTMiIiIic+XmqMDUXnXPni3dw9WzpsByRo2i0fJ8MyIiIiJLMetfq2f7k/OljmN2WM6oURJzVVBV1cJRYYV7fG5/2jkRERERmb4bV884udHQWM6oUa6P0O/R2hVWfN6MiIiIyOxdf/bsTHYJ9iVx9cyQ+N00NQqfNyMiIiKyLC0dFZjam6tnTYHljPRWq9Hi+MUiAEAkyxkRERGRxZjdLwj2NnKczSnB3kSunhkKyxnpLf6SCmXqWihtrdCOz5sRERERWYyWjv8692wvJzcaCssZ6e36lsYerVtCLhMkTkNEREREzWl2/7rVs3M5Kuzh6plBsJyR3q4PA+EIfSIiIiLL4+pgg2m9AwHw3DNDYTkjvdRotDiRXve8GYeBEBEREVmmWf2C4GAjR/wlFf5MuCx1HJPHckZ6OZNdgopqDVrYW6Otl5PUcYiIiIhIAjeunnFyY2OxnJFejl573qxn65aQ8XkzIiIiIot1ffUsIZerZ43FckZ64fNmRERERAQALRxsML1PIACunjUWyxk1mLpWg5MZPN+MiIiIiOrM7BsER4UVEnJV2M3VM72xnFGDnckuQVWNFi0dbBDq6Sh1HCIiIiKSWAsHG0z/17NnWi1Xz/TBckYNdn1LY2RQSwgCnzcjIiIiImBmv9ZwVFghkatnemM5owarL2d83oyIiIiIrnGxt8Fj9c+epXD1TA8sZ9QgVTUaxGReBcDzzYiIiIjoRo/3bQ0nhRWS8kqxOyFP6jgmh+WMGuR0ZjGqa7Vwd1KgjbuD1HGIiOj/27vzuCjLtQ/gvxlmhk0WAZFhEdQS1yMGuOGae68tkktpuWSnND3WMfNYWuppOWWmpml1jormq+KelXosc0tfs0wod1REMSDFBXFhG673D50nRgacQYaZgd/385nPp+Z57ue5nnsecC6u575vIiIHYlo949gzazE5I6vsu7O+WTuONyMiIiIiM0Z2aKBUz7YeYfXMGkzOyCo/lpgMhIiIiIjobj4eWozoUB8Aq2fWYnJGFrtVYEBS+p3xZpwMhIiIiIjKMDLu9tizE3/k4r+snlmMyRlZ7OC5Kyg0CIK83RDh72HvcIiIiIjIQZWsnn3M6pnFmJyRxYxT6LdryPFmRERERFS+kR3qw8vtdvVsy2FWzyzB5IwsVnIyECIiIiKi8vi4a/Fc3J3q2fdc98wSTM7IIjfyi/Br+lUAHG9GRERERJZ57k71LOWP69h8ONPe4Tg8JmdkkQNnr6CoWBDi644wP443IyIiIqJ783HXYiTHnlmMyRlZpOR4MyIiIiIiS42Iqw9vNw1OXriOTYdYPSsPkzOyiHG8Gdc3IyIiIiJr3K6eNQAAzP3+JAysnpWJyRndU25eIQ7/ngOAlTMiIiIist6IDhGsnlmAyRnd04G0KzAUC+r5eSDE193e4RARERGRk/F20+L5jqye3QuTM7onTqFPRERERPdreNzt6tkpVs/KxOSM7omTgRARERHR/fJ20+Kvd6pnH29LYfXMDCZnVK6cW4U4ksHxZkRERER0/4bHRcDHXYvTF2/gm98y7B2Ow2FyRuX66cxlFAvQIMATdb3d7B0OERERETkxLzct/trx9rpnHHtWGpMzKpfxkca2rJoRERERUSUY1j4Cvh6snpnD5IzKxfXNiIiIiKgyeZUce8bqmQkmZ1SmqzcLcDzrGgCgbQM/O0dDRERERNXF0Hbh8PXQIvXiDXz9K6tnRkzOqEw/pl6GCPBAYC0EenG8GRERERFVjpLVs7nfn0SRodjOETkGJmdUph+5vhkRERER2Yhx7Flq9g18zbFnAJicUTm4vhkRERER2UotV02J6tkpVs/A5IzKcOl6Pk78kQuAk4EQERERkW0Max+B2h5anMm+ga849ozJGZn3Y+plAEDjIC/4eersHA0RERERVUe1XDX4a6fb1bN521k9Y3JGZu1LzQbAqhkRERER2dawdn9WzzYm1+zqGZMzMktZfJrJGRERERHZkKerBi90aggAmLe9Zs/cyOSMSrmQm4fTF29ApeL6ZkRERERke0PbhcPPU4e0SzfxZQ2unjE5o1KM482aBHnD14PjzYiIiIjItm5Xz4xjz2pu9YzJGZXCKfSJiIiIqKoZq2dnL93EhqTf7R2OXTA5o1K4+DQRERERVTUPnQYv3qmefbKjZs7cyOSMTGTl5OFM9g2oVUBrjjcjIiIioir0bLtw+N+pnq2vgdUzJmdkwjiFfvMQH3i7ae0cDRERERHVJB46DV7sfKd6tv0UCmtY9YzJGZngFPpEREREZE/PtA1HQC0dzl2+iQ0Ha1b1jMkZmTDO1MjxZkRERERkD7fHnt1Z92zHyRpVPWNyRorfr97Cucs34aJWIbY+x5sRERERkX0MaVsPAbV0SL98q0ZVz5ickcL4SGOLEB/UctXYORoiIiIiqqk8dBqM6lzzqmdMzkjB9c2IiIiIyFEMaROOgFquSL98C+sPnrd3OFWCyRkBAESE65sRERERkcNw17lg1J2ZG+dtP4WCoupfPWNyRgCA9Mu38PvVW9C6qBATUdve4RARERERKdWz81dqRvWMyRkB+HN9s5ahvvDQcbwZEREREdlfTaueMTkjAFzfjIiIiIgc0zNtw1HHyxW/X72FddW8esbkjO6MN7uzvhknAyEiIiIiB+KmdVFmbvykmlfPmJwR0i7dRNa1POhc1IgO53gzIiIiInIsQ9rUU6pna3+pvtUzJmekPNIYVc8XbloXO0dDRERERGTKTeuC0XeqZ/N3VN/qGZMzwj5OoU9EREREDm5wm3oIvFM9W/NLur3DsQkmZzWciHDxaSIiIiJyeG5aF4zucqd6Vk3HnjE5q+FOX7yO7Ov5cNWo0aqer73DISIiIiIq09Otb1fPMnLysPpA9aueMTmr4YxVs+jw2nDVcLwZERERETkuN60LXrpTPVuw4xTyiwx2jqhyMTmr4Yzjzbi+GRERERE5g6da10Ndb2P1rHrN3MjkrAbj+mZERERE5GxuV88eAFD9qmdMzmqwlD+u4/KNArhrXdAy1Nfe4RARERERWWRQbBiCvN2QmZOH1T9Xn7FnTM5qsH2nswEAMRG1odPwViAiIiIi5+CmdcFLXY3rnp2uNtUzfiOvwTjejIiIiIic1cCY29WzrGt5WFVNqmdMzmqo4mLB/jMcb0ZEREREzslN64IxXY0zN55GXqHzV8+YnNVQx7Ku4erNQnjqXNAixMfe4RARERERWW1gbBj0PrerZ9Vh3TMmZzWUcX2zmAg/aF14GxARERGR83HVuOClrrdnbpy/45TTV8/4rbyG4hT6RERERFQdDIwJRbCPG/64lu/0Y8+YnNVAhmLB/jO3K2ftOBkIERERETmxktWzBTudu3rG5KwGOppxDbl5RfBy1aBZsLe9wyEiIiIiui8DSlTPEn86Z+9wKozJWQ20L/X2+mat6/tBw/FmREREROTkTKtnzjtzI7+Z10DGyUA43oyIiIiIqouBMWEI9nHDhdx8rHTS6hmTsxqmyFCMn9OuAODi00RERERUfeg0aox52LmrZ0zOaphDv+fgen4RfNy1aKrneDMiIiIiqj4GRIchxNcdF3PzsWK/81XPmJzVMPtSbz/S2Lq+H9RqlZ2jISIiIiKqPDqNGmPujD37dJfzVc+YnNUwyvpmfKSRiIiIiKqh/tGhSvVsuZNVzzT2DoCqTqGhGAfSuPg0EREREVVfOo0aL3d/ED+fuYzuTQLtHY5VmJzVIL+dv4qbBQbU9tAisq6XvcMhIiIiIrKJgTFhGBgTZu8wrMbHGmsQ4xT6bRv4c7wZEREREZGDYXJWgxgnA+EjjUREREREjofJWQ2RX2TAgTvrm3EyECIiIiIix8PkrIZIPncV+UXFCKilwwOBtewdDhERERER3YXJWQ1hfKSxTQN/qFQcb0ZERERE5GiYnNUQPxrHm/GRRiIiIiIih8TkrAbIKzTg4LmrADgZCBERERGRo2JyVgMcPHcFBUXFCPRyRYMAT3uHQ0REREREZlSr5Ozdd99F+/bt4eHhAV9f31LbL126hN69eyM4OBiurq4ICwvD2LFjce3aNZP9Dh06hM6dO8Pd3R0hISH45z//CRGpoquofD+e/nMKfY43IyIiIiJyTNUqOSsoKMCAAQMwevRos9vVajUef/xxfPXVV0hJScGSJUuwbds2jBo1Stnn2rVr6NGjB4KDg/Hzzz9j3rx5mDlzJmbNmlVVl1Hp9nG8GRERERGRw9PYO4DKNH36dADAkiVLzG6vXbu2SeIWHh6Ol156CR9++KHy3vLly5GXl4clS5bA1dUVzZs3R0pKCmbNmoXx48c7XeXpVoEByelXAXC8GRERERGRI6tWlTNrZWRkYP369ejcubPy3r59+9C5c2e4uroq7/Xq1QsZGRlIS0sr81j5+fm4du2aycsRHDh7GYUGQbCPG+r5edg7HCIiIiIiKkONTM6efvppeHh4ICQkBN7e3li4cKGyLSsrC3Xr1jXZ3/j/WVlZZR7zX//6F3x8fJRXWFiYbYK3kkatRtwD/ujSONDpqn5ERERERDWJwydn06ZNg0qlKvd14MABq445e/ZsHDx4EF9++SVOnz6N8ePHm2y/O4kxTgZSXnLz+uuvIycnR3mlp6dbFZOttGvoj+XPt8V7/VrYOxQiIiIiIiqHw485Gzt2LJ566qly94mIiLDqmEFBQQgKCkLjxo3h7++Pjh074s0334Rer0dQUFCpCtmFCxcAoFRFrSRXV1eTRyGJiIiIiIis4fDJWUBAAAICAmx2fGNVLD8/HwDQrl07vPHGGygoKIBOpwMAfPvttwgODrY6CSQiIiIiIrKUwz/WaI1z584hOTkZ586dg8FgQHJyMpKTk3H9+nUAwObNm5GQkIDDhw8jLS0NmzdvxujRoxEXF6ckXoMHD4arqyuGDx+Ow4cPY8OGDXjvvfeccqZGIiIiIiJyHg5fObPGW2+9haVLlyr/36pVKwDAjh070KVLF7i7u+M///kP/v73vyM/Px9hYWGIj4/HpEmTlDY+Pj747rvvMGbMGMTExKB27doYP358qXFpRERERERElUklxuf6qFJdu3YNPj4+yMnJgbe3t73DISIiIiIiO7E0N6hWjzUSERERERE5KyZnREREREREDoDJGRERERERkQNgckZEREREROQAmJwRERERERE5ACZnREREREREDoDJGRERERERkQNgckZEREREROQAmJwRERERERE5ACZnREREREREDoDJGRERERERkQNgckZEREREROQAmJwRERERERE5ACZnREREREREDoDJGRERERERkQNgckZEREREROQAmJwRERERERE5ACZnREREREREDoDJGRERERERkQNgckZEREREROQANPYOoLoSEQDAtWvX7BwJERERERHZkzEnMOYIZWFyZiO5ubkAgLCwMDtHQkREREREjiA3Nxc+Pj5lblfJvdI3qpDi4mJkZGTAy8sLKpXKJue4du0awsLCkJ6eDm9vb5uco6ZjH9se+9j22Me2xf61Pfax7bGPbY99bHuO3McigtzcXAQHB0OtLntkGStnNqJWqxEaGlol5/L29na4G7C6YR/bHvvY9tjHtsX+tT32se2xj22PfWx7jtrH5VXMjDghCBERERERkQNgckZEREREROQAmJw5MVdXV0ydOhWurq72DqXaYh/bHvvY9tjHtsX+tT32se2xj22PfWx71aGPOSEIERERERGRA2DljIiIiIiIyAEwOSMiIiIiInIATM6IiIiIiIgcAJMzIiIiIiIiB8DkzMEtWLAA9evXh5ubG6Kjo/HDDz9Y1G7v3r3QaDSIioqybYDVgDV9vHPnTqhUqlKv48ePV2HEzsfa+zg/Px+TJ09GeHg4XF1d0bBhQyxevLiKonVO1vTx8OHDzd7HzZo1q8KInYu19/Dy5cvRsmVLeHh4QK/XY8SIEbh06VIVReucrO3j+fPno0mTJnB3d0dkZCS++OKLKorUOe3evRuPPvoogoODoVKp8OWXX96zza5duxAdHQ03Nzc0aNAAn332me0DdVLW9m9mZiYGDx6MyMhIqNVqvPLKK1USpzOzto/Xr1+PHj16oE6dOvD29ka7du2wdevWqgn2PjA5c2CrVq3CK6+8gsmTJyMpKQkdO3ZEnz59cO7cuXLb5eTkYOjQoejWrVsVReq8KtrHJ06cQGZmpvJ68MEHqyhi51ORPh44cCC+//57LFq0CCdOnMDKlSvRuHHjKozauVjbxx9//LHJ/Zueng4/Pz8MGDCgiiN3Dtb27549ezB06FCMHDkSR44cwZo1a/Dzzz/j+eefr+LInYe1ffzpp5/i9ddfx7Rp03DkyBFMnz4dY8aMwddff13FkTuPGzduoGXLlvjkk08s2v/MmTN45JFH0LFjRyQlJeGNN97AuHHjsG7dOhtH6pys7d/8/HzUqVMHkydPRsuWLW0cXfVgbR/v3r0bPXr0wObNm/HLL7+ga9euePTRR5GUlGTjSO+TkMNq3bq1jBo1yuS9xo0by6RJk8ptN2jQIJkyZYpMnTpVWrZsacMInZ+1fbxjxw4BIFeuXKmC6KoHa/t4y5Yt4uPjI5cuXaqK8KqFiv6uMNqwYYOoVCpJS0uzRXhOz9r+/fDDD6VBgwYm782dO1dCQ0NtFqOzs7aP27VrJxMmTDB57+WXX5a4uDibxVidAJANGzaUu8/EiROlcePGJu+9+OKL0rZtWxtGVj1Y0r8lde7cWV5++WWbxVMdWdvHRk2bNpXp06dXfkCViJUzB1VQUIBffvkFPXv2NHm/Z8+e+L//+78y2yUkJOD06dOYOnWqrUN0ehXtYwBo1aoV9Ho9unXrhh07dtgyTKdWkT7+6quvEBMTgxkzZiAkJASNGjXChAkTcOvWraoI2encz31stGjRInTv3h3h4eG2CNGpVaR/27dvj/Pnz2Pz5s0QEfzxxx9Yu3Yt/ud//qcqQnY6Fenj/Px8uLm5mbzn7u6On376CYWFhTaLtSbZt29fqc+kV69eOHDgAPuYnFJxcTFyc3Ph5+dn71DKxeTMQWVnZ8NgMKBu3bom79etWxdZWVlm25w8eRKTJk3C8uXLodFoqiJMp1aRPtbr9fj3v/+NdevWYf369YiMjES3bt2we/fuqgjZ6VSkj1NTU7Fnzx4cPnwYGzZswJw5c7B27VqMGTOmKkJ2OhXp45IyMzOxZcsWPnJXhor0b/v27bF8+XIMGjQIOp0OQUFB8PX1xbx586oiZKdTkT7u1asXFi5ciF9++QUiggMHDmDx4sUoLCxEdnZ2VYRd7WVlZZn9TIqKitjH5JQ++ugj3LhxAwMHDrR3KOXiN3gHp1KpTP5fREq9BwAGgwGDBw/G9OnT0ahRo6oKr1qwtI8BIDIyEpGRkcr/t2vXDunp6Zg5cyY6depk0zidmTV9XFxcDJVKheXLl8PHxwcAMGvWLPTv3x/z58+Hu7u7zeN1Rtb0cUlLliyBr68vnnjiCRtFVj1Y079Hjx7FuHHj8NZbb6FXr17IzMzEa6+9hlGjRmHRokVVEa5TsqaP33zzTWRlZaFt27YQEdStWxfDhw/HjBkz4OLiUhXh1gjmPhNz7xM5upUrV2LatGnYuHEjAgMD7R1OuVg5c1ABAQFwcXEp9VfDCxculPpLFgDk5ubiwIEDGDt2LDQaDTQaDf75z3/i119/hUajwfbt26sqdKdhbR+XpW3btjh58mRlh1ctVKSP9Xo9QkJClMQMAJo0aQIRwfnz520arzO6n/tYRLB48WI8++yz0Ol0tgzTaVWkf//1r38hLi4Or732Gv7yl7+gV69eWLBgARYvXozMzMyqCNupVKSP3d3dsXjxYty8eRNpaWk4d+4cIiIi4OXlhYCAgKoIu9oLCgoy+5loNBr4+/vbKSoi661atQojR47E6tWr0b17d3uHc09MzhyUTqdDdHQ0vvvuO5P3v/vuO7Rv377U/t7e3jh06BCSk5OV16hRoxAZGYnk5GS0adOmqkJ3Gtb2cVmSkpKg1+srO7xqoSJ9HBcXh4yMDFy/fl15LyUlBWq1GqGhoTaN1xndz328a9cunDp1CiNHjrRliE6tIv178+ZNqNWm/7waqznGygP96X7uYa1Wi9DQULi4uCAxMRF9+/Yt1fdUMe3atSv1mXz77beIiYmBVqu1U1RE1lm5ciWGDx+OFStWOM+4X7tMQ0IWSUxMFK1WK4sWLZKjR4/KK6+8Ip6ensqMapMmTZJnn322zPacrfHerO3j2bNny4YNGyQlJUUOHz4skyZNEgCybt06e12Cw7O2j3NzcyU0NFT69+8vR44ckV27dsmDDz4ozz//vL0uweFV9HfFM888I23atKnqcJ2Otf2bkJAgGo1GFixYIKdPn5Y9e/ZITEyMtG7d2l6X4PCs7eMTJ07IsmXLJCUlRfbv3y+DBg0SPz8/OXPmjJ2uwPHl5uZKUlKSJCUlCQCZNWuWJCUlydmzZ0WkdB+npqaKh4eH/P3vf5ejR4/KokWLRKvVytq1a+11CQ7N2v4VEWX/6OhoGTx4sCQlJcmRI0fsEb5TsLaPV6xYIRqNRubPny+ZmZnK6+rVq/a6BIswOXNw8+fPl/DwcNHpdPLQQw/Jrl27lG3Dhg2Tzp07l9mWyZllrOnjDz74QBo2bChubm5Su3Zt6dChg2zatMkOUTsXa+/jY8eOSffu3cXd3V1CQ0Nl/PjxcvPmzSqO2rlY28dXr14Vd3d3+fe//13FkTona/t37ty50rRpU3F3dxe9Xi9DhgyR8+fPV3HUzsWaPj569KhERUWJu7u7eHt7y+OPPy7Hjx+3Q9TOw7gUzN2vYcOGiYj5+3jnzp3SqlUr0el0EhERIZ9++mnVB+4kKtK/5vYPDw+v8tidhbV93Llz53L3d1QqET5jQUREREREZG98MJuIiIiIiMgBMDkjIiIiIiJyAEzOiIiIiIiIHACTMyIiIiIiIgfA5IyIiIiIiMgBMDkjIiIiIiJyAEzOiIiIiIiIHACTMyIiJ7BkyRKoVCp06dLF3qFUO99++y26desGX19fqNVqqFQqLFmyxN5h2VyXLl1qzLWao1KpoFKpkJaWZu9QiMgGdu/ejUcffRTBwcFQqVT48ssvbXq+adOmKb9XjK+goCCrj8PkjIgIwIgRI6BSqRAVFWVxm0GDBkGlUqFTp062C4xs6ocffkCfPn2wfft2eHt7o127doiLi0PdunXtHRoREd2HGzduoGXLlvjkk0+q7JzNmjVDZmam8jp06JDVx2ByRkQEYOTIkQCAX3/9FQcPHrzn/pcvX8bGjRtN2tqSj48PIiMjUa9ePZufqyaZO3cuiouL8eKLL+Ls2bPYu3cv9uzZgz59+tg7NCIiug99+vTBO++8g/j4eLPbCwoKMHHiRISEhMDT0xNt2rTBzp077+ucGo0GQUFByqtOnTpWH4PJGRERgA4dOiAyMhIAkJCQcM/9V6xYgfz8fHh5eWHAgAG2Dg/9+vXD8ePH8cUXX9j8XDXJkSNHAACPPfYYVCqVnaMhIqKqMmLECOzduxeJiYn47bffMGDAAPTu3RsnT56s8DFPnjyJ4OBg1K9fH0899RRSU1OtPgaTMyKiO5577jkAfyZe5Vm8eDEA4Omnn4aHh4fNYyPbuHXrFgDwMyQiqkFOnz6NlStXYs2aNejYsSMaNmyICRMmoEOHDhb9gdacNm3a4IsvvsDWrVvxn//8B1lZWWjfvj0uXbpk1XGYnBER3TFs2DBoNBqTRxbN+fXXX5GUlATgz0cas7OzsXDhQsTHxyMyMhJeXl7w8PBA48aNMW7cOKSnp5s91s6dO6FSqRAREQEAWLVqFbp27Qp/f3+TAczlTQiSkpKCDz74AN26dUNERATc3Nzg4+OD1q1b4/3338eNGzfMnts4eHn48OEwGAz4+OOPERUVBU9PT/j4+KB79+7YsWNHuX2WmpqKl19+Gc2aNUOtWrVQq1YtREZGYujQofjuu+/Mtjl69CheeOEFPPDAA3B3d4e3tzdat26NOXPm3DMpLs+6devQu3dv1KlTBzqdDnq9HvHx8WYfU4mIiDCZDKJr167KAG5LJ10p+ZkUFxfjs88+Q9u2beHr6wuVSoXk5GQAlXdvfPPNN+jWrRtq164NDw8PtGrVCp999lm5Me7ZswePPPIIateuDU9PT0RFRWHOnDkoLi6+5/UdPHgQzzzzDMLCwuDq6go/Pz906dIFCQkJMBgMZtuUnGTj4MGDiI+PR2BgIDw9PREdHY0VK1Yo++bm5uLNN99EZGQk3N3dodfrMXr0aFy5cuWesZnzzTffoG/fvtDr9dBqtahduzYiIyPRv39/LFq0yOrjbd++HfHx8dDr9dDpdKhTpw769OmDDRs2mN0/LS1NuX4A2Lx5M7p37w4/Pz94eHjgoYcewoIFC8rt+8LCQnz++efK7wCdTofQ0FA888wz+O2336y+BiIy7+DBgxARNGrUSPm3q1atWti1axdOnz4NwPRnuqzX2LFjlWP26dMHTz75JFq0aIHu3btj06ZNAIClS5daF5wQEZHiscceEwDSq1evMvcZN26cAJBmzZop782bN08AiFarlbCwMImJiZHIyEhxc3MTAOLn5ycHDx4sdawdO3YIAAkPD5dXXnlFAEhgYKDExsZKaGiobNiwQUREEhISBIB07ty51DGefPJJASCenp7SsGFDiY2Nlfr164tarRYA0rJlS8nJySnVburUqQJAhgwZIr179xYAUr9+fWnVqpV4eHgIAHFxcZGvv/7abD8sW7ZMXF1dBYBoNBpp3ry5REVFia+vr3Leu3322Wei0WgEgHh4eEjz5s2lfv36olKpBIC0b99erl27Vmbfm2MwGGTIkCECQACIXq+X2NhY8ff3V96bMmWKSZv+/ftLXFycEn/z5s0lLi5O4uLiZOzYsRad1/iZdOrUSeLj4wWAhIaGSmxsrAQGBkpSUpKIVM698fbbbwsAqVOnjsTExJhc2z/+8Q+z8S1ZskS5B7y8vCQmJkbCwsIEgPTv3186d+4sACQhIaFU2wULFihtvb29JSYmRsLDw5Vz9uzZU27evFmqnXH7ggULRKfTia+vr0RHR0udOnWUbXPnzpXs7Gxp1qyZqNVqad68uURGRirni46OloKCAos+A6Pp06crxw8ICJBWrVpJ8+bNlXuxbt26ZcZ65syZUtsmTZqkbPf395fY2FgJCgpS3hs2bJgYDAaTNmfOnFG2Gz9zX19fiYmJkZCQEGVbfHy8FBUVlTpnVlaWxMTECABRqVQSHBwsrVq1Ei8vL+X+WblypVX9QkS3AVD+PRURSUxMFBcXFzl+/LicPHnS5JWZmSkiIgUFBXLs2LFyX1lZWeWet3v37jJq1CjrYrX66oiIqrGNGzcKAFGr1ZKenl5qe35+vvLFeNasWcr7+/fvl02bNsmtW7dM9s/JyZHJkycrCcDdjF/AXVxcxNXVVZYuXSrFxcXKduPxykvOvvzyS/nxxx9N2omIpKWlSd++fQWAjBkzplQ7Y3Km1WqlXr16sn//fmVbbm6uPPHEE0rCdvexd+zYIS4uLgJA/vrXv0p2drbJ9qSkJJP+ERHZsmWLqFQq0el0MmfOHJMv4MePH5fo6GgBIM8991ypWMvz7rvvCgBxc3Mz+fJaVFQk77//vvKlePXq1aXaGhOOHTt2WHVOkT8/ExcXF/H19ZUtW7aYnDs/P19E7v/e0Gq14ubmZnJvFBcXK9etVqslNTXVpO2xY8eUxPOFF14wSaTWr18v7u7uotVqzSZnP/zwg5IoTZw4UfLy8pRtW7ZsEW9vbwEgL730UqmYjX2t1WrlrbfeUvqguLhYpkyZoiR7PXv2lDZt2pgkRvv37xcfHx8BIIsXLy6v601kZ2eLRqMRjUYjiYmJpZKmY8eOyezZs8uM9e7kbPny5Uq/zp49W0mkiouLZenSpUq/zZgxw6RdyeRMq9XKpEmTlOsXEVm9erWSkH/wwQcmbQ0Gg8TFxQkA6dChgxw+fFjZVlRUJDNnzhS1Wi1ubm5y4sQJi/uGiG67Ozk7ceKEAJDdu3fb7Jx5eXkSEhIi06dPt6odkzMiohIKCwuVv5C/8847pbavWbNGAIhOp5OLFy9afNz27dsLAPnpp59M3jd+AQcg7777bpnty0vOynP9+nXRarXi5eVV6q/1xuQMgOzcubNU28zMTOWL6G+//WayzfgX/kGDBlkcS/PmzUsltSWlpaWJh4eHuLi4yO+//27RMW/cuKFUR95++22z+zz99NMCQJo2bVpqW2UkZwBk+fLlVrc3suTemDZtmtm2LVq0UKpRJY0cOVKp7t6dWIv8mdCaS8569OghAKRbt25mz/n5558rCcj58+dNthmP2bt371LtCgsLJTg4WACIq6urpKWlldpnwoQJAkD69etn9tzm7Nu3TwBIVFSUxW1Kxnp3cvbggw8KABk5cqTZdq+//rpS8SyZcJdMzh5++GGzbY0VPn9/f5O2xt8r9erVkytXrphtO2bMGAEgo0ePtuo6iWqq3NxcSUpKkqSkJOXfnqSkJDl79qyIiAwZMkQiIiJk3bp1kpqaKj/99JO8//77smnTpgqd79VXX5WdO3dKamqq/Pjjj9K3b1/x8vIy+7uuPEzOiIju8o9//EMAyAMPPFBq2yOPPKI8Fna3vLw8WbVqlYwaNUp69+4tHTt2VB6VCwwMFADy6aefmrQp+QW8vMcj7pWcXbx4UT755BMZOnSo9OjRQzp06KCc21hBOXbsmEkbY3LWokWLMs/bqFEjASDr1q1T3ktLS1NiLvkX/vIcPnxYgNuPP5b32KLxUbsVK1ZYdNytW7cqyfLVq1fN7pOcnKzEe3eFqTKSMy8vLyksLCx33/u9Ny5dumT2uC+88IIAkL/97W8m7+v1egEgn3/+udl2V69eVR4vLZmc3bhxQ3m/ZCWwpIKCAuUxxbsrXMZ4S/6FuiTj47OPP/642e2JiYn3vCfvlpGRIcDtx2RLVn/vxVxyZvxrurmfF6Ps7Gylarx9+3bl/ZLJ2TfffGO27ZUrV5T+LXnP9e/fX4CyH1EV+fN+aNSokcXXSFSTlfwdWvI1bNgwEbn9u+ytt96SiIgI0Wq1EhQUJP369Sv1x0hLDRo0SPR6vWi1WgkODpb4+Hg5cuSI1cfRgIiITDz33HP44IMPcOrUKezevVtZZDojIwNbt24FUHpts5SUFPTt2/eeU/CWNWtTQEBAhRc+3rhxI4YNG4acnJwKndu4hIA5devWRUpKCq5fv668Z5yYwMfHB82aNbMoxl9//RUA4OLiUu4aYikpKQBQ5iQZdzt+/DgAoF69evDx8TG7T7NmzeDi4gKDwYDjx4+jfv36Fh3bUpGRkdBoyv7ntDLuDT8/P7PbjPdMyc8nJycHmZmZAIDmzZubbefj44PQ0FBlQhSjU6dOoaioCADQokULs221Wi2aNGmCixcvKv1/twcffNDs+4GBgRZtL3k996LX6/Hss89i2bJlaNOmDWJiYtClSxe0bdsWnTp1smqdIeP16HQ6NGrUyOw+/v7+CAkJwblz53D8+HF07dq11D5l9buvry9CQkJw9uxZHD16VJl8xvjzsX79euzZs8ds27y8PACW/2wQ1XRdunSBiJS5XavVYvr06Zg+fXqlnC8xMbFSjsPkjIjoLo0aNULHjh3xww8/ICEhQUnOvvjiCxgMBoSGhqJnz57K/sXFxXjyySdx8uRJtGjRAtOmTUPr1q0RGBgInU4HABg6dCiWLVuGwsJCs+f09PSsUKznzp3DU089hby8PPTr10+ZOdHHxwdarRbA7cQlPT29QudWq9XKNRpdu3YNwO0vmpYyzsCXn5+PvXv33nP/mzdvWnRcYyxBQUFl7qPRaBAQEIA//vhD2b8yldd/tr43zH0+ubm5yn+Xl/DXrVu3VHJWsn/Ka6vX60vtb0nMxpkM77XdktkkS1q0aBFatmyJhQsX4sCBAzhw4ACA2/3To0cPzJgxA3/5y1/ueRzj9QQGBip9a45er8e5c+fKvP7y7segoCCcPXvW5HMy/nycPHnynkm8cfkHIqqeOJU+EZEZxjXP1qxZo/wV37j2yfDhw02+uB04cACHDx+Gm5sbtm3bhvj4eISGhipfvoGyqyL3KzExEXl5eYiJicGaNWvQuXNnBAQEKImZiFR4avKyeHt7AwCuXr1qcZtatWoBAJo2bQq5/Uh9ua9p06ZZFUtWVlaZ+xQVFSE7O9tk/6pij3vDy8tL+e8//vijzP3MbSvZP+W1NVbmqro/y6LVavHqq6/i2LFjyMjIwNq1a/G3v/0NAQEB2Lp1K7p06YLz58/f8zjG67lw4UK5CeK9rt+Sfi/5ORl/PlavXm3RzwcRVV9MzoiIzBgwYAC8vLxw48YNrF69Gnv37kVKSgpUKhVGjBhhsq9xTZQmTZooj2WVVFRUhJ9//tkmcRrP3bFjR7i4uJTa/ttvv1n1iJgljBWInJwcHDlyxKo2J06cUBKlytC4cWMAKLeKcfToUWVdriZNmlTauS1hj3vDx8dHqWyV9fnk5OSYTVYeeOAB5RHNQ4cOmW1bWFioPP5X1f1pCb1ejyeffBJz587FiRMnEB4ejitXrmD58uX3bGu8nwoKCpRHbO92+fJl/P777wDKvv7Dhw+bfb9kvzdt2lR53/jzUdYjjURUczA5IyIyw9PTE0899RQAYPHixVi8eDGA24sVN2jQwGRfDw8PALf/Im7ur9oJCQm4ePGiTeI0ntv4l/y7zZgxo9LPGR4ejpiYGADAu+++a1GbqKgoNGrUCAaDweI2lujQoQN8fX1RUFCAuXPnmt3H2AdNmzZVFnSuKva6N4zj+ubOnWv2vAsWLFDGlpXk4eGhjKGaOXOm2WMvXboUFy5cgFarNXm81xH5+voiKioKAJSEqjyNGjVSxpqVdf2zZs2CwWCAv78/2rZta3af2bNnm31/3rx5KCoqgp+fn0nbgQMHArh9P3BMGVHNxuSMiKgMxkk/9u7dixUrVpi8V1L79u2h0+mQkZGByZMnK196RQQrVqzAuHHj4ObmZpMYjRMKrF27Fhs3blTez8vLw8SJE7Fy5UqTR+gqy4wZM6BWq7Fy5UqMHj0aly9fNtmenJyMOXPmKP+vUqkwe/ZsqNVqzJkzBxMmTCjVJj8/H//9738xYMAAi+Pw8PDAxIkTAdxOFEsOyDYYDJg5c6by2VXWoG9r2OvemDBhAnQ6HQ4fPowxY8aYjFP66quv8M477yiPvt7trbfeglqtxvfff49JkyYhPz9f2fbtt99iwoQJAIAXXngBwcHBlR67tbZt24aXX34ZBw4cKJWI/ve//8W2bdsAALGxsRYdz/hIbUJCAj7++GOl6ioi+N///V98+OGHAIBJkyaV+dnt2rULb775psk4wnXr1uG9994DALz22msmbZ9++mm0b98eubm5ePjhh7F9+/ZSxzxz5gxmzJiBhQsXWnQdROSkKjRXJBFRDdGsWTNl+l1fX99SCwkbTZs2TdnP399fYmJilPXSevfuLc8884wAkKlTp5q0M071Gx4eXm4cZU2lbzAY5OGHH1bOHR4eLjExMVKrVi1l7bSypos3TqVvnFbYHOPU9nevhWWMSafTKVPkt2jRQlq1aiW1a9cWANKyZctSbZYsWaIsxOvi4iJNmzaVtm3bSuPGjZU11az9p8lgMMjgwYOVtsHBwdK6dWtlsXAAMmXKFLNtK2Mq/XutPWfLe6O8z3DhwoWiUqmU6f5jYmKkXr16ylIQ5X228+fPVxai9vb2ltjYWImIiFCuo2fPniYLWxsZt9+9dpjRsGHDzF6rNdd8tw0bNijn9fLykpYtW0psbKyynAAAeeyxx0otTl1erMblNABIQECAtG7d2uR4Q4cOLXW8klPpz5s3T/mdERsbK6GhoSaxmFt64eLFi9KpUydlvzp16khsbKxER0cryy2U13dEVD2wckZEVI6SlbLBgweX+ZfyqVOnYsmSJYiOjsb169dx/Phx6PV6fPTRR/jmm2/MjgerDGq1Gps2bcKUKVPQsGFDZGRkIDU1FW3atMHGjRvxxhtv2OS8wO2JUQ4dOoRRo0ahfv36OHXqFE6dOoXAwEAMHz4cH330Uak2w4YNw7Fjx/Dqq6+iWbNmSE9PR1JSEvLy8tCuXTtMmzYNSUlJVsWhVquxfPlyrFmzBj179kR+fj4OHjwIjUaDfv36Yfv27Xj77bcr67KtZq97Y+TIkdi5cyd69+4NtVqNo0ePwtfXF7Nnz8aqVavKbfvSSy/hp59+wuDBg+Hl5YXk5GRcvXoVnTp1wqJFi7B582a4u7vbJG5rdezYEfPnz0f//v2h1+uRlpaGpKQkGAwG9OzZE8uWLcOGDRvKnX3xbu+//z62bduGJ554Ai4uLkhKSkJBQQF69eqFdevWYenSpeUeb+zYsdi0aRMeeughnDx5EtnZ2YiKisK8efOwfv16s0svBAQEYPv27UhMTETfvn3h4uKC5ORkpKWloW7duhg8eDASExMxfvz4CvUTETkHlQin/SEiIiK6H2lpacoaevxqRUQVxcoZERERERGRA2ByRkRERERE5ACYnBERERERETkAJmdEREREREQOgBOCEBEREREROQBWzoiIiIiIiBwAkzMiIiIiIiIHwOSMiIiIiIjIATA5IyIiIiIicgBMzoiIiIiIiBwAkzMiIiIiIiIHwOSMiIiIiIjIATA5IyIiIiIicgBMzoiIiIiIiBzA/wPjIFlfszjO5wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "re = mdf.cov_re.iloc[1, 1]\n",
    "with warnings.catch_warnings():\n",
    "    # Parameter is often on the boundary\n",
    "    warnings.simplefilter(\"ignore\", ConvergenceWarning)\n",
    "    likev = mdf.profile_re(1, \"re\", dist_low=0.5 * re, dist_high=0.8 * re)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "plt.plot(likev[:, 0], 2 * likev[:, 1])\n",
    "plt.xlabel(\"Variance of random slope\", size=17)\n",
    "lbl = plt.ylabel(\"-2 times profile log likelihood\", size=17)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
